RESEARCH OF LONG-TERM FORECASTING SYSTEM OF THE …and long-term labor market supply and demand forecasting system, according to which the labor market development policy could be

PROJECT “RESEARCHES OF THE MINISTRY OF WELFARE” OF THE

NATIONAL PROGRAM “LABOR MARKET RESEARCHES” OF THE

EUROPEAN UNION STRUCTURAL FUNDS

No. VPD1/ESF/NVA/04/NP/3.1.5.1/0001/0003

RESEARCH OF LONG-TERM FORECASTING SYSTEM

OF THE LABOR MARKET DEMAND AND ANALYSIS OF

IMPROVEMENT OPTIONS

The research has been performed with a 75% financial support

from the European Union

University of Latvia

Institute of Development Projects

Riga, 2007

University of Latvia Research of long-term forecasting system of the labor market Institute of Development Projects demand and analysis of improvement options, 2005-2007

UDK 331.5(474.3)

Da 620

Research of Long-Term Forecasting System of the Labor Market Demand and Analysis of

Improvement Options. LR LM: Riga, 2007, XII + 176 p.

Project manager: Ludmila Frolova

Researchers: Inta Krūmiņa, Pēteris Rivža, Evija Kopeika, Irina Arhipova, Inguna Ieviņa (Ruža), Jekaterina Meļņikova, Kristīne Rozīte, Ilona Ozoliņa, Baiba Rivža, Grigorijs Oļevskis, Oļegs Barānovs, Ārija Konstantinova, Nataļja Berezina, Ināra Kantāne, Anda Vītiņa, Jānis Hofmanis, Ilona Tole, Elīna Ērmane, Inga Šķendere-Drēģere, Jūlija Stare, Agnese Briška, Ivars Eglājs, Ritvars Briska, Ieva Vērzemniece, Jānis Briedis, Roberts Mencis, Baiba Berovska, Laima Levanoviča, Kristaps Āboliņš, Mārtiņš Šķiņķis, Iluta Bērziņa, Sanita Vetšteina, Agnese Jacino.

Editors: Ināra Mikažāne and Anna Šmite

Computer composed matter: Ludis Neiders

Cover design: Uldis Freimanis

© LR Labklājības ministrija, 2007

ISBN 978-9984-993-05-8


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The Group of Researchers

The research “Research of long-term forecasting system of the labor market demand and analysis of the improvement options” of the project “Researches of the Ministry of Welfare” No. VPD1/ESF/NVA/04/NP/3.1.5.1/0001/0003 of the National program “Labor Market Researches” of the European Union structural funds was performed by the University of Latvia, under supervision of the Dr. habil. oec. Ludmila Frolova, in coordination with the research partner SIA “Institute of Development Projects”. Ludmila Frolova, the leader of the research group, is a professor at the University of Latvia, the director of the Economic bachelor and master degree study programs at the Department of Economics and Management. The most significant prior research experience of Ludmila Frolova is related to mathematic modeling and forecasting of economic and managerial processes. During the research, the University of Latvia was represented by the following researchers: Inta Krūmiņa, Pēteris Rivža, Evija Kopeika, Irina Arhipova, Inguna Ieviņa (Ruža), Jekaterina Meļņikova, Kristīne Rozīte, Ilona Ozoliņa, Baiba Rivža, Grigorijs Oļevskis, Oļegs Barānovs, Ārija Konstantinova, Nataļja Berezina, Ināra Kantāne, Anda Vītiņa, Jānis Hofmanis, Ilona Tole, Elīna Ērmane, Inga Šķendere-Drēģere, Jūlija Stare, and Agnese Briška. SIA “Attīstības projektu institūts” during the research was represented by the following researchers: Ivars Eglājs, Ritvars Briska, Ieva Vērzemniece, Jānis Briedis, Roberts Mencis, Baiba Berovska, Laima Levanoviča, Kristaps Āboliņš, Mārtiņš Šķiņķis, Iluta Bērziņa, Sanita Vetšteina, and Agnese Jacino. Within framework of the research, an employer survey was performed, which was implemented by SIA “InMind” (currently – SIA “GfK Custom Research Baltic”). However the quantitative employer survey was performed by SIA “Latvijas Fakti”.


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Summary

Currently in the Republic of Latvia there is no one uniform and coordinated medium term and long-term labor market supply and demand forecasting system, according to which the labor market development policy could be based. Within the framework of the given research, the necessary statistical information was acknowledged for establishing the labor market supply and demand, as well as analytical and forecasting methods for ensuring conformability of the labor market supply to the demand were elaborated. The researchers elaborated and approbated the statistical models and innovative dynamic optimization model for forecasting the supply and demand and for simulator modeling thereof. Information technology solution and implementation plan of improvements to the institutional system, which is necessary to be implemented for practical application of the forecasting models, was elaborated during the research. Implementation of the obtained results will form an option to use the state budgetary resources more purposefully (for instance, when planning the study positions), to implement new labor market measures and/or complete the existing measures with more justification, to describe the causes of problem origination in the labor market more exhaustively. Key words: labor force, supply and demand, modeling, optimization, imitating modeling,

information technology solution, institutional system.


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Table of Contents

Table Index ...............................................................................................................................vi

Figure Index .............................................................................................................................vii

List of Abbreviations ............................................................................................................. viii

Terminology Definitions............................................................................................................x

Introduction................................................................................................................................1

1. General Conclusions and Proposals.....................................................................................3

2. Literature Review ................................................................................................................7 2.1. Review on Legislation and Policy Documentation........................................................7 2.2. Review on Data and Previously Performed Researches ................................................9 2.3. Development of Research Approaches........................................................................12

3. Research Methodology ......................................................................................................17

4. Research Results ................................................................................................................21 4.1. The Practical Application Spheres of the Labor Market Forecasting

System to be Created ...................................................................................................21 4.2. Evaluation of the Existing Labor Market Forecasting System....................................22 4.3. Data Obtainment Model ..............................................................................................26 4.4. Data Summarization and Processing Model................................................................31 4.5. Model Construction .....................................................................................................35 4.6. Evaluation and Interpretation of the Results ...............................................................46 4.7. Plan of Improvement/Implementation of the Labor Market Forecasting System.......58 4.8. IT Solution Requirements............................................................................................66

5. Conclusions and Recommendations ..................................................................................75 5.1. Most Significant Decisions and Matters, in Consideration of which

the Forecasting System to be Created can be Used .....................................................75 5.2. Optimum Degree of Detailed Elaboration and Sections for Establishing

the Labor Force Supply and Demand..........................................................................76 5.3. Necessary Statistical Information for Determining the Labor Force

Supply and Demand ....................................................................................................81 5.4. Recommended Analytical and Statistical Forecasting Methods .................................85 5.5. Causes of Nonconformance of Labor Force Supply and Demand

and Options of Preclusion thereof ...............................................................................86 5.6. Conformance of the Labor Force Supply to the Demand in Short-Term....................90 5.7. Conformance of the Anticipated Labor Force Supply

to the Demand in Long-Term ......................................................................................92

6. Policy Alternatives.............................................................................................................96 6.1. Selected Alternatives ...................................................................................................96 6.2. Evaluation Methods of Alternatives ............................................................................99 6.3. Evaluation of Alternatives .........................................................................................100

7. Gratitude ..........................................................................................................................120

8. References........................................................................................................................121

Annexes..................................................................................................................................124


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Table Index

Table 1. Modeling Methodology .............................................................................................18

Table 2. Applicable Technological Environments...................................................................19

Table 3. Model dimensions used in MS Excel environment ...................................................41

Table 4. Dimensions used in DOM..........................................................................................46

Table 5. Indicative scale of analysis ........................................................................................49

Table 6. Satisfaction level of labor force demand in profession groups (MS Excel forecasts)..........................................................................................................49

Table 7. Satisfaction level of labor force demand in industries (DOM forecasts) ..................53

Table 8. Satisfaction level of labor force demand in profession groups (DOM forecasts) .....54

Table 9. Options of channeling the labor force surplus ...........................................................56

Table 10. Profession groups with significant deficiency of labor force ..................................57

Table 11. Activities of enforcement of the newly introducible system ...................................61

Table 12. Measures for implementing the IT solution for labor force forecasting..................67

Table 13. Costs of system elaboration .....................................................................................70

Table 14. Sections of Statistical Information for Making Decisions.......................................77

Table 15. Contents of the data array used in the research .......................................................82

Table 16. Applicable analytical and statistical forecasting methods .......................................85

Table 17. Comparison of demographic and economic situation..............................................87

Table 18. Short-term forecasts of labor force supply and demand ..........................................91

Table 19. Macroeconomic parameters forecasted by the Ministry of Finance 2008-2011 ......................................................................................................103

Table 20. Assumptions of creation and maintenance costs of new places of employment ......................................................................................................104

Table 21. Financial calculations of research maintenance alternative...................................105

Table 22. Financial calculations of the alternative of improvement of the existing system ......................................................................................................106

Table 23. Costs of formation and maintenance of new places of employment at the Research Institute ...................................................................................................107

Table 24. Cost assumptions of IT system improvement........................................................108

Table 25. Financial calculations of the alternative of creating an agency and implementation of IT solution...................................................................................111

Table 26. Comparison of cash flows of the alternatives........................................................112

Table 27. Calculations of global priorities.............................................................................119


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Figure Index

Figure 1. Components of the Forecasting Methodology .........................................................17

Figure 2. Data array obtained during the research...................................................................27

Figure 3. Data array used during the research .........................................................................28

Figure 4. Number of the employed in 37 profession groups and in one-year age groups .......34

Figure 5. Number of the employed in industries and in one-year age groups .........................35

Figure 6. Data study, modeling, and forecasting in MS Excel environment ...........................37

Figure 7. Structure of the MS Excel file APRtab_ISTbloks.xls ..............................................39

Figure 8. Schematic illustration of DOM structure .................................................................42

Figure 9. Description of the LR education system in the dynamic optimization model .........43

Figure 10. Trends of possible labor force provision in agriculture and forestry .....................48

Figure 11. Scheme of DOM simulation results analysis..........................................................52

Figure 12. Total labor force supply and demand (DOM forecasts) .........................................53

Figure 13. Structure of the Consultative Council ....................................................................62

Figure 14. Mechanism of cooperation between the Research Institute and ministries............63

Figure 15. Mechanism of institutional cooperation in labor market forecasting.....................66

Figure 16. Logical architecture of the system..........................................................................68

Figure 17. General scheme of the institutional solution ..........................................................69

Figure 18. Data to be used in forecasting ................................................................................84

Figure 19. Demand, supply of teachers ...................................................................................90

Figure 20. Mechanism of cooperation between institutions in case of Research Maintenance Alternative................................................................................97

Figure 21. Mechanism of cooperation between institutions in case of Alternative of Establishing an Agency and Implementation of IT Solution ........................................99

Figure 22. Schematic illustration of levels of the hierarchy analysis methods......................112

Figure 23. Evaluation of research maintenance alternative ...................................................116

Figure 24. Evaluation of the alternative of improving the existing system...........................117

Figure 25. Evaluation of the alternative of agency creation and implementation of IT solution .........................................................................................118


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List of Abbreviations AIP – Higher Education Council

ANL – Analytical tables

APR – Calculation tables

BLS – Bureau of Labor Statistics

COPS – Canadian Occupational Projection System

CPS – Centre of Policy Studies

CSP – Central Bureau of Statistics

DDA – Employer survey conducted within framework of the research

DfES – Department for Education and Skills

DŅA – Employee survey conducted within framework of the research

DOM – dynamic optimization model

EM – Ministry of Economics of the Republic of Latvia

FNPV – financial net present value

HRDC – Human Resources Development Canada

IAB – Institute for Employment Research

IfO – Institute for Economic Research

GDP – gross domestic product

IT – information technologies

IVD – electronic table of input data block

IZM – Ministry of Education and Science of the Republic of Latvia

KM – Ministry of Culture of the Republic of Latvia

LBAS – Latvian Free Labor Union Association

LDDK – Latvian Employer Confederation

LIAA – Latvian Investment and Development Agency

LM – Ministry of Welfare of the Republic of Latvia

LR – Republic of Latvia

LVL – Latvian currency lats

MK – Cabinet of Ministers of the Republic of Latvia

MRP – Mediterranean Regional Project

MS – company Microsoft

NVA – Employment State Agency

NGO – non-governmental organization


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OECD – Organization for Economic Co-operation and Development

PIC – Professional Education Center

PKIVA – Professional Career Selection State Agency

RAPLM – Ministry of Regional Development and Municipal Affairs of LR

ROA – Research Centre for Education and the Labor Market

SPSS – Statistical Package for the Social Sciences

VM – Ministry of Health of LR

VRAA – State Regional Development Agency


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Terminology Definitions

Researches of the 1st component are the researches within framework the project “Researches of the Ministry of Welfare” of the European Union structural fund national program “Labor market researches”: research on “Detailed labor force and labor market research in economic sectors”, research on “Professional mobility of the labor force”, research on “Professional activities of professional and higher education institution graduates upon graduation”, research on “Adequacy of the professional and higher education programs to the labor market requirements”, and “Research of long-term forecasting system of labor market demand and the analysis of improvement opportunities”.

Analytical model is a formula displaying the real functional coherences in economics, according to which the results of the economic processes are dependent from the expenses.

Analysis phase is the stage of the information system elaboration project, during which the requirements are elaborated in detail for the system, the system subdivision in sub-systems, applicable technical solutions, the volume of the introducible activities and the plan of the elaboration terms specified.

Employer (owner) is a person performing business activities, professional practice, or managing a farm with a purpose to gain income or benefits and employs one or more individuals for a valuable consideration.

Employee (hired worker, laborer) is an individual performing certain work (including work in a family far or family enterprise/private practice), from which the individual or its family gains income, profits (monetary payment, compensation with goods, or services).

Labor market is the place and time where and when coordination of the labor supply and demand occurs.

Balancing of the labor market is conformance of the labor force supply described by the quantitative and the qualitative aspect to the labor force demand.

Labor force is the population of the country having capacity for labor.

Labor force supply is the totality of the economically active inhabitants in the age of labor capacity from 15 to 74 years of age.

Labor force demand is the number of the employed necessary in economics, which is established by the total number of the unfilled places of employment and the vacant positions.

Definition phase is the stage of the information system elaboration project, within which high level requirements are gathered for the system, distribution in phases, and the activities to be performed and results achieved during each phase, the guidelines of system architecture.

Expert evaluation method is a quantitative analysis method, which is used in formation of the mathematical model entry information, for verification and correction of the acquired results.

Global priorities are the final values of calculations of the hierarchy analysis method, which are acquired based on expert evaluation, by comparing criteria groups, separate criteria, and alternatives and allow selecting the optimum solution.


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Hierarchy analysis method is an analysis method created by an American mathematician T. Saaty, which is based on arrangement of the system elements, by using decomposition or problem distribution in equal sections and synthesis or numeral evaluation of experts about the level of element interaction intensity in the hierarchy.

Implementation phase is the stage of information system elaboration, where the users are trained to work with the system, the administrative procedures related to use of the system are mastered, as well as various imprecision are sought and eliminated. The result of the implementation phase is commencement of system exploitation for the relevant needs.

Simulative modeling allows not only forecasting the system condition in the future, but also to analyze, how certain policy alterations will affect it.

Information technology solution – guarantee of technical devices and software, as well as informative and administrative provisions for performance of certain functions.

Institutional system is an arranged administration in a uniform hierarchical system. The basic elements of institutional system of state administration are the state administration institutions established within the legal enactments, establishment and operation regulations thereof, and the procedures of decision preparation and making.

Development phase is the project phase of information system development, wherein the technical projecting is formed and the system itself is developed (programmed), and the result of this phase is operating software.

Modeling is an indirect research method of objects-originals, by researching the object-substitute.

A model is the basic principle of the modeling theory. According to the definition of the modeling theory, the model is the object-substitute, which can be an artificial or actual auxiliary system, which is in an objective conformance with the object-original, and as a result of the research the necessary information about it is acquired.

Model adequacy is its objective conformance to the modeling object. The model adequacy is to a certain extent a conditional conception, because complete conformance of the model to the actual object is not possible. Therefore the mathematical model adequacy in the modeling process means objective conformance only to such qualities of the actual object, which in the given research are quintessential.

Model body is a complex of various types of models, where there are direct and reverse links between the models, as well as a complex approach at the base of its composition.

Optimization is the development process of optimal solutions, i.e., selection of the optimum variant from the body of alternative variants. In economics and practical entrepreneurship, this term indicates such improvement and development of the existing activities, which requires significant structural alterations in it for achieving he desired goal. This term may not be used without established limitations and without precisely established optimality criterion.

Optimization model is the central mathematical model type in the modeling theory, the parameters of which are assessed by applying the optimization methods. The optimization models provide with an option to perform analytical calculations according to the alternative variants of economic development, to compare them, to evaluate the benefits and consequences, to formulate the necessary implementation conditions, by selecting the best variant and quantitative justification thereof. These models are useful for analysis of structural modifications and in forecasting of development in economic processes.


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Research institute is an agency formed on the base of the Latvian Statistical Institute, which will further analyze the labor market and elaborate the development scenarios and forecasts of the labor market.

Profession or position is the professions of employees of the economy in compliance with the profession classifier of the LR.

Forecasting methodology is the technological, informative, and administrative provision of the elaborated forecasting system.

Administrative provision of the forecasting system is defining the IT solution requirements and implementation of long-term labor force market forecasting system by using various models and criteria of the IT solution selection.

Informative provision of the forecasting system is the body of analytical methods of information gathering, arrangement, and processing, wherein various information types, sources, and flows are utilized.

Technological provision of the forecasting system is the body of forecasting models, which allows performance of analytical calculation in various mutually interrelated technological environments.

System dynamics is a computerized simulation modeling methodology, which is based on application of systemic thinking principles for analysis of complex system structure and behavior.


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Introduction The research “Research of long-term forecasting system of labor market demand and analysis of improvement opportunities” is included in the National Program of the European Union structural funds “Labor Market Researches” in the set of the project “Researches of the Ministry of Welfare”. The research was performed in compliance with the priority of the Uniform Program-Document 2004-2006 “Development of human resources and promotion of employment”. The general goal of it is to promote creation of employment and social policy programs based on the research results, for program region balance and durable improvement of development, to create justified and effective analytical base of decision making, thus promoting increase of employment, creation of inclusive labor market, and general development of Latvian economy. Implementation of the research occurred during the time frame from October 10, 2005 until May 31, 2007. Currently, in Latvia there is no uniform and coordinated medium-term or long-term labor force demand and supply forecasting system, on which the labor market development policy could be based. Due to lack of such uniform and coordinated system, the reaction time of the relevant state ministries and services to the changes in economic structure is too extensive. The deficiencies and slow policy reaction timing to labor market forecasts, which could be used in labor market decision making, indirectly affects also the following indices in the country:

• Structural unemployment level; • Large number of the employed in spheres not corresponding to the education; • Lack of labor force in specific industries and profession groups; • Employment predominantly in low added value spheres/professions; • Career selection of the existing and potential employees not corresponding to the

economic structure. The topicality of implementing the long-term labor market forecasting system is increasing every year, because it is anticipated that due to the demographical situation during the following decades the number of inhabitants in the country will significantly decrease. That, however, will create new circumstances that are to be considered in education planning, as well as in analyzing the development perspectives of the economic industries. The direct goal of the research, which derives from the established problem, is to ensure adequate statistical information for determining the labor market supply and demand, as well as to create and develop analytical and forecasting methods for ensuring the conformance of the labor force during this and upcoming periods. Prior to the National Program “Labor Market Researches”, several other researches had been performed on forecasting of supply and demand in certain labor market aspects or sectors of Latvia. This research had a broader approach, which analyzed opportunities of implementation of uniform forecasting system in the Latvian labor force development policy. Within framework of the research, for forecasting the Latvian labor market trends the necessary data was acquired, processed, and systematized from the Central Bureau of Statistics (CSP),


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Employment State Agency (NVA), Ministry of Education and Science (IZM), Employer and Employee Surveys, and other sources. The most significant results are as follows:

• Forecasting methodology has been developed, which includes the technological, informative, and administrative provision. The technological provision is based on the complex of the elaborated models, wherein included is the original dynamic optimization model, as well as the statistical and econometric models. The informative provision is based on the elaborated methodology of information gathering, arrangement, and processing, wherein included are various types of information, sources thereof, and flows. The administrative provision is based on defining the requirements of the elaborated information technology (IT) solution and on the elaborated implementation methodology of labor market forecasting system;

• Statistical forecasting models adequate for the Latvian situation in Microsoft (MS) Excel environment and the innovative dynamic optimization model with simulation options in Powersim Studio environment have been elaborated;

• Examination of adequacy of the elaborated models has been performed, they have been approbated with real statistical data, and the acquired forecast results have been economically interpreted;

• Guidelines were prepared for work with the labor market demand long-term forecasting models to extent of 118 pages (User’s reference book for work with the improved Swedish labor market agency forecasting model in MS Excel environment and with the dynamic optimization model in Powersim Studio environment);

• The IT solution requirements were elaborated for practical implementation of the forecasting models into the state administration;

• Policy alternative analysis has been performed and the optimum implementation plan of the labor market supply and demand forecasting system has been determined.

Review of the related literature, research methodology, acquired research results, conclusions, and provisions have been included in the research, as well as the alternatives have been analyzed in order to establish the optimum route for implementation of the forecasting system into the state policy. In the annexes to the research, the list of the interviewed individuals is included, along with the data tables, and a CD, which includes the data arrays acquired and used during the research, the modeling results in MS Excel environment and in Powersim Studio environment, user’s reference book for work with labor market long-term forecasting models. The CD is available at the Labor Department of the Ministry of Welfare (LM).


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1. General Conclusions and Proposals The research allows making conclusions on situation in the Latvian labor market, as well as about the necessary measures and tasks for implementation of a new labor force supply and demand forecasting system. The conclusions and proposals have been elaborated based on the acquired statistics, performed analysis, and modeling results:

(1) By implementing the labor force supply and demand forecasting system, valuable statistical information will be acquired on the existing situation, as well as about the future deve-lopment trends and the effects thereof on the balance of labor force supply and demand. When planning the labor force demand structure in the future, the state administration institutions will be able to perform forecasting calculations more precisely, objectively, and operatively for ensuring adequate labor force supply and demand. By implementing the labor force supply and demand forecasting system, a widely applicable tool will be created for justified and coordinated elaboration of state decisions to be made further on.

(2) The most significant causes of non-conformance of the labor force supply and demand are related to several factors. The results of the research are indicative of the fact that already beginning with year 2008, the total labor force demand in Latvia will begin to significantly exceed the total supply of the labor force. In addition, during the whole forecast period until year 2030, this situation will intensify by every year, which is related to the demographical situation in the country. The non-conformance in the labor market is promoted also by rapid changes in the economic structure, the technological progress, labor force migration, and the delay time in adaptation of the education system.

(3) In order to preclude non-conformance of the labor force supply and demand on the macro-economic level, there are several alternatives available: • Performance of significant changes in the productivity level, by implementing the most

recent technologies and by increasing the load of the labor force and the equipment; • Introduction of labor force of the necessary qualification in Latvia from other

countries, thus quantitatively increasing the extent of the labor force; • Promotion of a more active participation of the Latvian inhabitants in the economic

life of the country (employment of students, young mothers, senior citizens, and individuals with special needs), thus increasing the percentage of the economically active inhabitants.

(4) In order to preclude the main cause of non-conformance of labor force supply and demand on macro-economic level – non-conformance of labor force qualification to the market requirements – one of the core solutions is adaptation of the education system to the current situation, by ensuring appropriate work for new-comers to the labor market . Firstly, by balancing the structure of the education system (number of student spots in professional programs) in such way that it would support the long-term economic demand. Secondly, the commenced work must be continued in development of life-long learning, which includes both – improvement of professional skills of the employer, and training for the unemployed, as well as re-qualification from one profession group to another. Thirdly, in order to achieve faster balancing of labor force supply and demand, it is necessary to decrease the labor market reactions on alterations in economic structure for the total delay period, including by informing the society (employers and potential employees) about the trends existing in the labor market, in order for the participants of the labor market to be able to react more operatively to the occurring changes.


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(5) The acquired forecast results are indicative of the fact that in short-term, excluding several industries, the situation of the labor market is close to balance, besides both – the demand and the supply have a tendency to increase, although not at a steady manner. Thus, without making changes in the previous policy in many spheres, the disbalance in medium-term in labor market will become more distinct. For improvement of the situation, it is recommended to review the distribution of state paid spots both – in professional-secondary and in higher education level institutions, as well as allocate additional means for arranging re-qualification and improvement of the professional skills of the unemployed and of the employed.

(6) It is impossible to completely balance out the labor force supply according to the changes in demand within 20 years only with ensuring conformance of the education system to the labor market, because not only qualitative, but also quantitative disproportion between the labor force supply and demand is expected during the period to be analyzed. If significant improvements will not be introduced on the level of productivity, which could quantitatively decrease the demand for labor force resources, Latvia in future will face the necessity to attract the labor force from other countries.

(7) When analyzing the quantitative disproportions in the labor force market, it must be emphasized that an undesired situation could be observed having the total demand of the economic labor force not being fully satisfied, whilst at the same time, in several profession groups a labor force surplus could be observed. A tendency has been observed that a majority of professions, in which during long-term labor force surplus is maintained, are social and humanitarian professions (“Specialists of humanitarian sciences, representatives of creative professions”, “Other specialists of social sciences”, “Legal professions (judges, attorneys, etc.)”, “Employees working in art, culture, entertainment”, and others). Surplus of the labor force could also be observed in such profession groups as “Agriculturists, other senior specialists of agricultural professions” and “Individual services”. Such results are indicative of the features of structural unemployment, the origination of which can be affected by separate changes of growth trends, development of technological progress, as a result of which the demand of labor force decreases in specific profession groups. The representatives of the above mentioned professions must be ensured with options of re-qualification, continuing education, and life-long learning, in order for it to be possible to change the qualification from the undemanded profession group to the demanded related profession group.

(8) Deficiency of labor force could be observed in the professions of hard sciences, industrial and construction specialist professions, medicine professions, teacher and defense specialist professions. Considering the fact that in these professions currently the re-qualification options are limited, the state must implement measures to promote these professions amongst youth, by stimulating their choice through increasing the number of state paid study spots, through allocating special scholarships, etc. In order to promote a more balanced development of labor market, a well-considered policy must be implemented in respect to distribution of the study places, considering the foreseeable labor market development trends. In general, it must be concluded that the demand is increasing for specialists of medium and lower level qualifications, therefore it is necessary to promote the choice of the youth in favor of acquiring the professional-secondary education, not confining to obtaining only the general education. In the professions, which require highest level of education, timely national planning must be implemented in respect to the supportable education spheres, because it is these profession groups that the balancing of the labor force supply and demand occurs with a greater delay.


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(9) By analyzing the provision of the economic spheres with the human resources, it must be concluded that the most critical situation is forming in those spheres, where qualified labor force in hard sciences is required: C – “Extractive industry and quarry elaboration”, D – “Manufacturing industry”, E – “Electric energy, gas and water supply”, F – “Construction”, and I – “Transportation, storage, and communications”. In these spheres, significant deficiency of labor force could be observed in the medium-term and long-term forecasts, however in two spheres – D and F – the deficiency is in the whole forecasting period, including short-term. The spheres H – “Hotels and restaurants” and J “Financial intermediation” in long-term forecasts is relatively the best supplied with labor force. It must be noted that in these spheres the representatives of social and humanitarian professions are employed, and in long-term in these professions surplus could be observed.

(10) In the analysis of policy alternatives, evaluation and versatile comparative analysis have been performed to the three of the selected alternatives – Research maintenance alternative, Alternative of improving the existing system, and Alternative of establishing an agency and implementation of IT solution. Considering the fact that the implementation of the forecasting system is not related to direct financial gains, then the financial net present value (FNPV) is negative. In respect to this aspect, the most optimum alternative would be Research maintenance alternative, because the costs thereof are significantly lower than the costs of implementation of the other alternatives: FNPV in case of this alternative is – 1 229 861 Latvian lats (LVL), in case of improvement of the existing system alternative, the indicator reaches already 2 179 498 LVL, however the alternative of establishing an agency and implementation of IT solution is the least beneficial in financial respect – 8 647 154 LVL.

(11) The selection of the optimum alternative according to the cost principle is possible only in case of completely equal gains, however the analysis of these alternatives according to the hierarchy analysis method is indicative to significant differences when assessing the usefulness of the alternatives. When assessing the three alternatives according to the criteria, which are summarized in criterion groups “Costs”, “Institutional interests”, “State interests”, “Effect on the policy implementation results”, and “System functionality”, the alternative of establishing an agency and implementing the IT solution was recognized to be significantly superior over the others – its global priority being 0.6 out of 1, which is a significant predominance in comparison with evaluations of the other alternatives. The socio-economic analysis of the alternatives confirm that the social effect of all alternatives to the participant groups – the state institutions, non-governmental organizations (NGO), and the society – will be positive, however the grandest effects is expected from implementing the Alternative of agency establishing and implementation of IT solution.

(12) As a result of the comparative analysis it was concluded that in general the most optimal policy alternative must be selected the Alternative of establishing an agency and implementation of the IT solution. In order to implement the selected alternative in the state policy, the following key measures must be taken:

• Based on the IT solution elaborated during the research to implement new forecasting models in state institutions.

• The Ministry of Economy (EM) must undertake additional functions, which are related to the coordination of the medium and long-term labor market forecasting system. For implementation of these functions, EM must establish a new department.


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• Create a new research institution under subordination of EM on the base of the existing Latvian Statistical Institute, which would perform coordination and improvement of the general forecasting system, as well as maintenance of the new models, and forecasting. The newly established Research Institute to arrange data gathering and processing, as well as use the IT system elaborated during the research for further implementation of forecasts and to perform regular forecast updates.

• In order to ensure the inter-institutional cooperation, system implementation, and successful operations, to create a Consultative Council for the matters of labor market analysis and forecasting. The Consultative Council would be created according to the initiative of EM as the institution coordinating the labor market forecasting process in the country. It should definitely include the institutions involved in creation, analysis, and forecasting of the labor market policy (LM, IZM, EM, Ministry of Regional Development and Municipal Affairs (RAPLM), Ministry of Agriculture (ZM), Ministry of Culture (KM), and Ministry of Health (VM). The staff of the Consultative Council should also include representatives from the Latvian Employer Confederation (LDDK) and the Latvian Free Labor Union Association (LBAS).


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2. Literature Review

2.1. Review on Legislation and Policy Documentation

One of the most significant policy planning documentations of the European Union is the Lisbon Strategy, wherein defined is the strategic goal of the European Union – until year 2010, to make Europe the most competitive and dynamic economy in the world based on knowledge, basing on sustainable development with better employment positions and higher social cohesion. In order to implement the goals of the Lisbon Strategy, the following was established: (1) to achieve employment to a 70% level; (2) to decrease the average unemployment level down to 4%; (3) to achieve the female employment to 60% level (European Commission 2000: 20). The Lisbon Strategy encompasses the following policy spheres: economy and finance; information society; employment and social cohesion; environmental development based on a wide economical reform packet, and a transfer to information economy and society. In order to create information economy, special attention is devoted to the following directions (1) information society; (2) science and innovations; (3) capital of human resources and education.

In implementation of the employment goals of the Lisbon Strategy, the European Employment Strategy has the leading role. The EU Employment Strategy is focused on contribution of the employment policy in creation of new and better employment positions. The implementation thereof must promote the rising of the employment level, increase of employment productiveness, and social unity. The employment strategy establishes general employment goals and priorities for operations of EU and member states. One of the basic approaches of the Employment Strategy sets forth “to implement such employment policy that has a goal of achieving complete employment, improvement of labor quality, and increase of the labor productivity, as well as strengthening social and territorial cohesion” (European Union Council 2005: 3). The long-term forecasting system of the labor market supply and demand elaborated within framework of the research directly promotes achievement of the above-mentioned goal.

For achieving the Lisbon Strategy, the Commission offers concentrating on two basic tasks: (1) to achieve a more rapid and sustainable growth; (2) to create more and better employment positions. The employment policy of Latvia is geared towards the main challenges in the employment sphere. As the most important of Latvian employment policy problems the following are identified: low mobility of the labor force and adaptation ability thereof to the new market requirements, necessity to fortify the labor market institutions in order to ensure flexible labor market development. In elaboration of the employment policy, the relatively high level of unemployment and especially the lasting unemployment problem and structural unemployment is taken into consideration.

Latvia has prepared the Latvian National Lisbon Program for 2005-2008, which is a policy planning document, which is indicative of what Latvia will be executing for reaching medium-term period goals – national growth and employment promotion, and how it will implement the Integrated Basic Approaches adopted by the Council on July of 2005. For achieving the goal, the annual GDP increase of 6-8% must be ensured and the employment level must be increased to 65%, including female employment – to 61% and older individuals –


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to 48%. In the Latvian National Lisbon Strategy it is concluded that “The market economy, by setting forth new requirements for professional qualification, is developing faster than the supply of corresponding professional and higher education programs. Therefore in several professions nonconformance is building between the labor market demand and the existing educational demand. The distribution of the students in study thematic groups does not conform to the economic needs and changes in labor market. The cooperation between the educational institutions and the employers is not sufficient enough.” (The Cabinet of Ministers 2005: 7).

On February 6, 2003, a mutual declaration was signed between the Latvian government and the European Commission on Latvian employment policy priorities (Joint Assessment Paper), wherein the readiness of Latvia to adapt the guidelines of the European Union employment policies is evaluated (improvement of labor capacity, entrepreneurial development, promotion of adaptation abilities of companies and their employees, ensuring policy of equal opportunities) and the further development directions of the Latvian employment policy were set forth. The joint declaration on priorities of Latvian employment policies is geared towards the key challenges in the employment sphere: the labor market must display the dynamic market economy requirements in the single market circumstances, especially depending on whether the labor force is mobile, able to adapt, and professional; the necessity to create a corresponding policy and institutions, in order to ensure development of a flexible labor market. Several priority policy spheres are put forward in the joint declaration, wherein progress must be achieved in the Latvian market and where further monitoring must be implemented. As one of the most significant disadvantages in the system of continuing professional education the following is mentioned: “As long as there are indications specifying that provision of state paid continuing professional education does not conform to the current needs, it is difficult to establish the place for further professional education at a situation of lacking a comprehensive database in respect to education provision level, its financing, and distribution of continuing professional education between industrial training and other programs. Preparation of such database in a capacity of an important contribution in the development of future policy is to be established as one of the priorities.” (Ministry of Welfare, European Commission 2003: 23)

On October 6, 2006, the European Union Council has issued a decree on Community Strategic Guidelines on Cohesion Strategy. Within framework of the strategic guideline “to create more and better employment positions” it is established that in relation to the development of human capital, the following action priority is emphasized in the strategic employment guidelines: “to improve the adaptation ability of employees and companies and to improve flexibility of the labor market” (Council of the European Union 2006:13). Thus the analysis of the labor market supply and demand in long-term and medium-term forecasting system performed within framework of this research provides a contribution to achievement of this goal.

On October 31, 2006, the Cabinet of Ministers (MK) approved the statutes of the Ministry of Economy (EM), providing that beginning with July 1, 2007, the Ministry will be responsible for coordinating the labor market medium-term and long-term forecasting process in the country. For implementation of this function, the Ministry will establish a new department and a new special state agency with EM must be established for the forecasting of medium-term and long-term labor market trends.

In order to ensure preparation of medium-term and long-term labor market forecasts, it is planned that the agency (Research Institute) will be formed on the base of the Latvian


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Statistical Institute, which will from there on analyze the labor market, development scenarios and forecasts of the labor market development.

Within framework of this research, the implementation alternatives of the long-term forecasting systems of the labor market supply and demand are coordinated with the above-mentioned MK decree and will provide assistance in implementation of this decree (MK, 2006).

2.2. Review on Data and Previously Performed Researches

When commencing the work on elaboration of the long-term forecasting system, a research of previously performed researches in this sphere was performed and the criteria for research analysis were established.

For exploration of the researches, mainly the electronic database was utilized. The attention of the researchers was concentrated on those researches that display the situation in the labor market in Latvia and abroad. Particular attention was devoted to a detailed research of the Swedish Labor Market Agency model. The sequence of the works performed:

• The data or conclusions, which could be obtained from the previously performed researches in Latvia, that are useful for the forecasting system were explored and analyzed. More than 15 researches of Latvian labor market were examined and analyzed;

• The data or conclusions, which could be obtained from the previous researches performed abroad, that are useful for the forecasting system were explored and analyzed. Mostly analyzed were labor force supply and demand forecasting models, which have been implemented abroad (please, see Chapter 2.3 for more information);

• The utilization opportunities of the Swedish labor market agency model were analyzed;

• Information that is necessary for elaboration of forecasting models was gathered.

After obtaining the researches, the researchers analyzed the utilization opportunities of the existing researches according to the goal of the given research. The aspects that are necessary in integration into the forecasting system were established: the effects of legislation on employment are displayed in the research; the effect of the amount of salary on employment; macroeconomic regularities; econometric models used in the research; conceptual model; flow diagram models; dynamic rows; implementation of models displayed in the research; necessary data model; credibility of the available data evaluated. The most significant researches according to the long-term labor market forecasting are those, wherein the information displayed is based on scientific approach in the analysis of the research object. In these researches, the econometric models are applied, the variables used are scientifically proven, and the dynamic rows have been used. Attention was devoted also to those researches, where the displayed information is based only on conceptual models.

Several works related to use of dynamic models in labor market forecasting abroad were also examined. The modeling environments are various: Vensim, Powersim, Dynasys, Heraklit, and other. It must be added that in most cases of dynamic model creating, Vensim and Powersim modeling environments are used. Also the goals were examined, which are set


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forth when creating a dynamic micro-imitation model, as well as application opportunities and conditions of such models.

To a large extent and in great detailed analyzed were the most common dynamic micro-imitation models of the world and illustration of the labor market problems in these models.

Within framework of the research, these previously elaborated models and performed researches had the greatest practical importance – the Swedish labor market agency model, the research on “Modeling of the Latvian economy – with a special emphases on the labor market”, the continuous labor force survey performed by CSP, Profession survey, Wage structure survey, NVA survey.

The Model of the Swedish Labor Market Agency

The forecasting methodology of the Swedish labor market was initiated in year 1960 and it has been modified multiple times (the work materials of the specialists of the Swedish Labor Market Agency). The current labor market forecasting model was implemented beginning with year 1990. The labor market forecasting model was elaborated for use in several institutions: Public Employment Offices, County Labor Boards, and National Labor Market Board.

The content and meaning of the forecasting has also changed over the course of time. Initially, the forecasting was not quantitative – the anticipated modifications were described in words, for instance, the employment will increase/decrease etc. The labor force supply and employment was very shortly described. The forecasting methodology was not identical in all regions. Only beginning in year 1990, a uniform labor market forecasting methodology was implemented. The forecasting data are both – on national and on regional level. The following spheres are selected in Sweden: agriculture and forestry, manufacturing, construction, services, retail and public catering, communication and education, social care and health protection.

There is a well-arranged register of companies in Sweden, where sufficient information is available about the employment positions of the companies. It is used for planning the company surveys. The Swedish statistics is accountable for the inhabitant forecast in the country and regions thereof, also considering migration. The regional labor force councils must forecast the inhabitants of the region that are labor capable, considering the education level. The economic situation in the country and its regions is analyzed and forecasted with the goal to specify the forecast of the necessity for the labor force. The economic forecast is the most difficult one of all forecasts. For economic forecasts, macroeconomic indicator linear trends and macroeconomic models are used.

The Swedish labor market agency model consists of three blocks: Model of retirements, Short-term labor market forecasting model, and Long-term labor market forecasting model. The model of retirement provides with input data for the other two labor market forecasting models. The labor market forecasting short-term model is formed to obtain data for the labor market analysis and for short-term (1-2 years) planning. The results of this model can be used planning individual short-term education programs. The long-term model of the labor market forecasting has been formed to obtain data for the labor market analysis and for long-term (10-15 years) forecast. The results of this model can be used for planning long-term education systems and individual study programs.


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Modeling of Latvian Economy – with a special emphasis on the labor market (Oļegs Jemeļjanovs, ERASMUS University, within framework of ACE project, 1999)

The goal of the research was to elaborate an “instrument” for quantitative analysis of the effects of economic measures in the economy sphere, including the sphere of labor market (Jemeļjanovs, 1999). The method, which is used in the research, is the applied general equilibrium model. In this research, traditional approach, which is based on analysis of the labor force supply and demand in the labor market, is combined with a new theory, which observes also the institutional aspects. For elaboration of the modeling scenarios, the effects on the labor market were analyzed: by increasing the retirement age, by introducing investment subsidies, by decreasing the business income tax rate, by decreasing the employer income tax rate, and by increasing the employee social tax rate.

The Continuous Labor Force Survey Performed by CSP

The labor force survey methodology is elaborated based on the methodology of the International Labor Organization. In the labor force survey information is obtained about the economic activity, employment, and search for employment of the inhabitants of Latvia. The information obtained as a result of the survey is summarized in CSP data collection “Main indices of labor force survey”.

Profession Survey

CSP performs profession survey about the month of October every year since year 1997. The data source of the profession survey is data in a form of reports by legal entities – enterprises, companies, and organizations – on the number of employees, paid time of work, and the calculated gross work salary in certain profession classification groups. In the profession survey, the employees are accounted form who during the month of October have a calculated work salary and for whom the work time registration must be performed in compliance with the Labor Law. In the survey, information is gathered about the following employee groups: full time employees, who have worked the month of October in full; part time employees, who have worked the month of October in full; employees, who have worked partially during the month of October or whose salary has been affected by absence. Information is acquired in a section of economic activity type, in professions, and according to the gender; in age groups, in statistical and planning regions. Data is obtained about the number of employees, the number of vacancies, gross salaries. The results of the survey are summarized in a data collection “Results of profession survey in Latvia”.

The Survey of Salary Structure

The survey of companies and enterprises on the salary structure was first executed by CSP in year 2003 (on the previous year). It is planned to implement this survey every four years in Latvia, as well as in other EU member states. In the survey of salary structure, information is acquired on the number of employees and the salaries in professions, according to the gender, age, education level, according to the size of the enterprise and the types of economic activities, in public or private sector, according to the number of full years worked in the specific company, and in other groups in the report year or according to individual indices in October of the report year. The survey did not include budgetary institutions and public


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organizations, agricultural enterprises, and farms, as well as the self-employed. In year 2006, the Profession Survey was included in the Survey of Salary Structure.

CSP. Summary of Activities

The reports are submitted by the enterprises once quarterly. The most significant indicator in the report in respect to modeling is the number of vacant positions.

Labor Market Conjuncture Survey

The labor market conjuncture survey in Latvia, in June of year 2004 was carried out by the Latvian Institute of Statistics. The survey is arranged once in five years by the European Commission and the labor market survey is carried out with the assistance of institutions of EU member states and several candidate states. The survey included three spheres: manufacturing (manufacturing industry), retail, and service sector. Besides, the Latvian Institute of Statistics also performed the labor market survey in the enterprises of the public sectors of agriculture.

NVA Survey

The biannually executed employer survey. More than 4000 employers are included in the selections in such manner that in total data from 4000 respondents would be acquired. In the survey, data about the number of the employed, the number of planned employees after 0.5 years in professions, as well as information about the specialists required by the employees in specific professions, and information about employers ensuring the options of practice in their enterprises are acquired.

2.3. Development of Research Approaches

Since the end of the World War II, ever stronger grew the conviction that besides the capital resources also human resources are an important factor to sustainable economic development Schultz (1961) and Becker (1962) were of the first economists, who declared that education and training are crucial factors for improving productivity. Contributions into various types and levels of education can increase the productivity and volume of various industries. Resulting from this assertion, the labor force planning models began the development.

One of the first labor force planning projects was the Mediterranean Regional Project (MRP), which at the beginning of the 1960-ties was commenced by the Organization for Economic Cooperation and Development (OECD). Within framework of this project the “Necessary Labor Force Approach” was elaborated. The main goal of MRP was to outline the requirements in respect to education for the following 15 years, in order to achieve the expected economic growth ratios in the countries of the Mediterranean region. The goals of MRP were based on the education planning concept. The emphasis was put on establishing what types of education is necessary to satisfy the demand for corresponding education. Thus, the applied models were geared not towards the forecasting of future labor force


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demand and supply, but to calculations of labor force supply, which were geared towards the goal to achieve specific economic growth ratios.

After this approach appeared, it received plenty of critical evaluations. The OECD report prepared by Hollister (1967) displayed the first evaluation of the “Necessary Labor Force Approach” in general and provided an evaluation for the MRP project. Blaug (1967) evaluated several approaches to education planning, including this. Other empirical character evaluations on this topic were performed by Agamad and Blaug (1973) and Youdi and Hinchcliffe (1985). The first question about MRP is: do the requirements in respect to the labor force have any effect on the operations of the education system? If the student selection of where and what to study is an autonomous process, where planning and control does not have any influence, MRP does not have any practical application and significant influence. Another argument by the critics was geared towards the forecasting methods – they were supposed to be so weak that the credibility quotient is very low.

Ahamand and Blaug (1973) mention another important aspect, which has become known as the labor force planning paradox. On one part, there is a necessity for long-term planning, because a certain time period passes between the implementation of the corrections into the education system and the time, when they start actually working. On other part, the biggest is the planning period, the lower is the planning precision. In this context, the more adequate should be considered the medium-term forecasting. This more or less fundamental critic caused reassessment of the forecasts that this forecasting method allowed to obtain. From the leading trend during the 1960-ties to pay more attention to planning, the planning methods started gearing more towards ensuring the lucidity of the labor force market during the 1980-ties. In some countries, the main goal of the labor force forecasting methods turned out to be the necessity of ensuring the decision making institutions with information on the labor market development trends during the following years, thus increasing the functioning of the labor market.

Thus, forecasting could be considered as one of the information sources, which is necessary for academic and professional education management (Heijke, 1986, 1993). Besides, labor force forecasting may provide with valuable information in policy making (especially in the spheres of education and employment), serving as an instrument to understand the causal relationship between various decisions and the employment structure in total, as well as to understand the necessity of creating new employment positions (Hughes, 1993, OECD, 1994).

Parnes (1962) was one of the first, who elaborated complete method for forecasting future labor force requirements. He distinguishes three elements in the future labor force mass depending on education level: the number of the employed with the established education level; the influence of the newcomers and those returning to the labor market; influence caused by death of the employed, retirement, and any other type of withdrawal from the labor market, which causes the necessity to substitute the vacant employment positions (the so-called substitution demand). If the labor force supply is divided into professions, then it is necessary to pay attention also to the migration of the employed from one profession to another.

Tinbergen and Bos (1965) elaborated another education planning model, which is one of the few examples of the time, when the planning model was described in a mathematical form.

In the 1970-ties and 1980-ties, almost none of the labor force forecasting models did not take into consideration the demand and substitution demand modeling. First of all, the goals of the


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labor force planning models were geared away from the education planning partially due to the fact that the first labor force planning models were greatly criticized. Secondly, it became ever clearer that it is difficult to explain the forecasted differences between the demand and supply. The traditional models did not take into consideration the possibility that qualified workers could migrate from one profession to another. Therefore, exclusion of such presumption made the model significantly inadequate for the real situation. As a result of these two aspects, the forecasting models were ever less geared towards the education levels, and ever more were oriented towards the profession groups. Most part of the researches performed in the 1990-ties was geared towards tendencies in the employment structure according to the professions.

Currently in the world, the best acknowledged labor force supply and demand systems, which are used in the employment policies of the developed countries, the following systems must be indicated: Hamburg Institute of International Economics, 2004; Maastricht University, 1996).

Centre of Policy Studies – CPS

CPS (Australia) since 1994 provides information every two years about the Australian state institutions, which are responsible for professional education and training. The CPS forecasts are formed by including therein both – the macro-model (in order to establish the total employment) and the general balance model (in order to establish the employment trends in section of industries), and the labor market model (in order to establish the employment trends in section of professions). The Australian approach to the labor market forecasting is very informative, it has practical usability, and it is applied in policy planning. Besides, it is open and the information is openly accessible. The forecasts are available both – in CD-ROM format, as well as easily usable computer programs, which allows the policy planners to use them flexibly.

Human Resources Development Canada – HRDC

HRDC is formed by the Canadian Occupation Projection System – COPS. COPS ensures forecasts on the economic development in section of industries and professions. COPS serves as the base for creation of such integrated system, which allows forecasting the demand and supply in sections of industries and professions. COPS is an integrated supply and demand model, which ensures forecasts in sections of 139 professions and 5 combined profession groups. Initially, COPS was a model for modeling the demand, however since the mid 1990-ties, information of the labor force supply is included about the school graduates, immigrants, as well as about individuals returning to the labor market. COPS forecasts deficiencies and surplus in sections of professions and education levels. It is based on 5 components: 1) Demand model: New employment positions – expansion of labor market + substitution demand; 2) Supply model: Supply = school graduates + immigrants + those returning to the labor market; 3) Balance: combines the supply and demand; 4) Supply surplus = (Supply + Unemployment) – new employment positions; 5) Perspective: fluctuations in surplus of the supply. The forecasting results in Canada are widely used in employment and education planning. Likewise, these data are used also by students and those looking for a job. Accessibility of this information is also ensured in regional section.


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Institute for Employment Research – IAB

IAB (Germany) since 1969 performs labor market researches for the needs of Federal Labor Bureau (Bundesanstalt für Arbeit). Included in the researches is information on labor market and education system trends, as well as the potential labor force forecasts and the forecasts of labor force demand. IAB commenced forecasting the labor force demand in sections of industries, professions, and specialties in year 1985 (Fuchs, Tessaring, 1994). Since then, the methodology has been improved multiple times, by supplementing it with employment forecasts and forecasts in sections of professions and specialties, as well as in labor force supply.

Institute for Economic Research – IfO

Even though IAB has the greatest experience in Germany, there are several other economy institutes that have introduced methods of forecasting labor market deficiency, by submitting these methods in year 2001 at the parliamentary migration commission of Germany. The most sustained of them was elaborated in Munich by IfO.

Research Centre for Education and the Labor Market – ROA

ROA (Maastricht) has developed a forecasting model, where interconnection between the education levels and the situation in the labor market of the Netherlands is researched. Every two years, ROA summarize medium-term forecasts (for a 5 year period) about the expected changes in the labor market, by considering the sections of industries, profession groups, and education obtainment forms. In the most recent forecast, 13 economic industries, 123 profession groups, and 98 education obtainment forms are considered. These researches include also the demand increase and the substitution demand. The total sum of the demand increase and substitution demand forecasts is contrasted with sections of labor force supply, growth thereof, and various education obtainment forms. By comparing the labor force supply and demand, the ROA can calculate the indicator for the future labor market situation in each type of education. This indicator is converted into a 5-point scale, by assessing the conformance of the supply and demand from very good to bad.

Department for Education and Skills – DfES

DfES (Great Britain) began forecasting employment in profession section in year 2000. Employment was forecasted in sections of industries and professions for the time period from year 2005 until year 2010 for Great Britain in general and by separately dividing into 9 geographical regions. However the Institute for Employment Research in Warwick (IER) has vaster experience in this research sphere. The IER made forecasts consisting of 3 steps (Haskel, Holt, 1999): forecasting of employment changes in economy in general; forecasting of relative proportion changes of each industry; forecasting of relative proportion changes in professions of each industry. Currently, IER and DfES are researching, how to improve the surveys of labor force in Great Britain in order to make the labor force supply and demand model more detailed in the section of professions. DfES is planning to create new databases, including the historical database about employment in sections of industries and regions and a similar database about the key economic indices.


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Bureau of Labor Statistics – BLS

The American BLS has been preparing employment position forecasts for over 50 years now. Since the beginning of the 1970-ties, the forecasts have been prepared every two years. The sphere of BLS activities has grown from preparing simple reports on available employment positions to model-based approaches for forecasting macroeconomic indicators, employment by industries, and volumes of manufacturing.

The scientific ideas of individual previously mentioned researches were generated and modified in the given research, by elaborating a body of mathematical models, which is based on the dynamic optimization model with simulation options, as well as on statistical and econometric models. The Swedish labor market agency model in MS Excel environment was used as the basic prototype for improvement of statistical models, for examination of their adequacy, and for adaptation to the Latvian circumstances, by performing a variety of activities. As a result, the Swedish labor market agency model was significantly restructured. Instead of 3 blocks, now 4 modeling and forecasting blocks were introduced: the retirement model, short-term forecasting model, medium-term forecasting model, and long-term forecasting model. The results of retirement, short-term, and medium-term models are provided for a more detailed research and analysis of the current situation, and for comparison of the acquired results with the original version of the Swedish labor market agency model, as well as to further create the service for its users. However the long-term labor force supply and demand forecasts are provided in order to evaluate and analyze the expected situations of the labor market in long-term and to find improvement opportunities to the system.

The forecasts in section of professions and combined profession groups obtained in the COPS model, as well as the components included in the structure of the model were used as an example for elaboration of econometric models, by structuring them in sections of professions, profession groups, industries, and forecast horizon, as well as by systematizing all parameters of these models.

The approach (indicator) used in the ROA forecasting model for evaluation of conformance between the supply and demand was applied within the framework of the research to elaborate an indicative scale for economic interpretation of the quantitative forecasts obtained with the dynamic optimization model (DOM) and by statistical models, and for summarization thereof.


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3. Research Methodology

The research methodology is determined by the main goal of conducting the research, the procedure of use adopted by the results obtained during the research, as well as prerequisites of the forecasting methodology.

The main goal of conducting the research: to ensure with adequate statistical information for establishing the labor market supply and demand, as well as to create and develop the analytical and forecasting methods for ensuring conformance of the labor force supply and demand for the current period, as well as for the upcoming periods.

The prerequisites for the forecasting methodology: they are formed by three elaborated basic principles, the actuality of their implementation, and common functioning of the forecasting methodology of the planned three components.

The basic principles of forecasting methodology: 1) Practical use of the forecasting models as an analytical tool for various directions of

government decision making on national and regional level; 2) Practical use of the forecasting models as an analytical tool for various interconnected

spheres in industrial policy; 3) Practical use of the forecasting models as analytical tools for establishing the most

significant indices of the labor force market development, and for balancing thereof with the industrial policy alternatives in the country and regions.

Figure 1. Components of the Forecasting Methodology

Methodology of gathering, arrangement, processing, and updating of the necessary adequate information

Components of the forecasting methodology

Methodology of defining IT solution requirements and of implementationof labor force market demand long-term forecasting system

Modeling methodology with the corresponding elaboration of analytical calculation performance Technologies

Elaboration methodology of interaction of the forecast results in various

technological environments and forms of the public representation thereof

Elaboration and balancing methodologyof labor market long-term development

policy and of policy development alternatives in economic sectors

Technological provision


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The forecasting methodology elaborated according to the research goal includes three components – technological, informative, and administrative provision of the forecasting system (see Figure 1).

Technological Provision

The initial model prototype in the technological provision is the Swedish labor market agency model, which has been modified and adapted according to the specific circumstances of Latvian labor market.

Table 1. Modeling Methodology

Methodological Base Advantages Disadvantages

The existing approach

Swedish labor market agency model Labor market research models of EU countries

√ Identity of the modeling methodology

√ Time economy on account of the already elaborated and approbated models

√ Comparability of the obtained results

model developers

√ Constant growth pace √ The research of the struc-

tural changes and their interaction in economy and labor market is not feasible

√ Homogeneous techno-logical environment

model users

Innovative approach Elaboration of the original models, disregarding the previously approbated prototypes

√ Better model adequacy to they real situations in Latvian economy and labor market

√ Faster model adaptation to the external and internal economic environment of Latvia

model users

√ Weak comparability of the acquired results

√ Increased risk level due to vast work amount and time spent

model developers

Realistic approach Elaboration of the body of models, which is based on the original dynamic optimization model, foreign approbated models, as well as on econometric models

√ Adequacy to the tasks and goals of the given research

√ More complete use of information flow and ensuring the dynamics

√ More versatile technological environment

√ Comparability of the acquired results and comfortable service of the summarization thereof

model developers and users

√ Formation of reciprocal algorithmic, functional, and information coherence, i.e., creation of common use environment

√ Additional programming works for the input and output information import and export, as well as for displaying the cause-effect connection

model developers

Upon comprehensive research of the labor market research analysis in Latvia and abroad, as well as of the Swedish labor market agency model, three modeling approaches were defined and considered: the existing, innovative, and realistic (see Table 1). According to the given forecasting methodology, the realistic approach is selected from these three modeling approaches, which is based on the elaborated uniform body of models.


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Table 2. Applicable Technological Environments

MS Excel environment

Formation of information flows

Provided for adaptation of the Swedish labor market agency model, as well as for summarization of the input data of the dynamic optimization model (DOM). Import and export of the information flows is occurring between the models, as well as compatibility of their format.

Powersim Studio environment

DOM functioning

Provided for elaboration and functioning of DOM: • For performing analytical calculations, • For analysis of alternative scenarios.

Statistical Package for the Social Sciences (SPSS) environment MS Access environment

Preparation of input information

Ensures processing of primary and secondary statistical data in order to prepare the input information necessary for the statistical models and DOM.

Visual Basic environment

Development of user service

For input and output information table maquette for more comfortable user service: • The main menu and programming of additional menus, • Programming of input and output data appearance, • Programming the setting of the output result diagrams.

Five technological environments were determined for functioning of the model body (see Table 2).

The theoretical base of the modeling methodology is formed by two forecasting method groups:

Basic methods 1) statistical modeling. Based on regression analysis: linear, logarithmical, exponential,

gradual, hyperbolic, and parabolic. It is used in MS Excel environment for forecasting theoretical values of the factors;

2) system-dynamics. It is used for research of the labor market structure and for analysis of reciprocal influence of the system elements;

3) simulative modeling. It is used in Powersim Studio environment for forecasting development alternatives of the modeling situations.

Auxiliary methods 1) market research. It is used for obtaining input data and for comparison of the results in

MS Excel and in Powersim Studio environment; 2) correlation and regression analysis (one and multi factor). It is used for selection of the

factors affecting the DOM market analysis development and for forecasting thereof; 3) econometric models. They are used for establishing the balance of the labor market; 4) expert evaluations. They are used for adjustment of the long-term forecasts and strategic

evaluations, as well as for modeling the DOM scenarios.


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Informative Provision

The forecasting methodology for functioning of the elaborated models includes the methodology of information gathering, arrangement, and processing, wherein included are various types of information, sources thereof, and flows. Various methods were used for formation of the informative provision:

For secondary data – grouping (data preparation according to the needs of the research), graphical figures – for performance of analysis of the obtained data, cross-tabulation (for establishing connections between certain qualitative indices), interpolation and extrapolation methods (in cases of dynamic line padding, if individual indices from the CSP and the employer survey (DDA) performed within framework of the research had not been obtained for the whole requested period), central tendencies and variation indices, adjustment of dynamic rows.

For primary data – grouping (for comparison and reciprocal supplementing of the data obtained in DDA and employee surveys (DŅA)), cluster analysis (it was performed in order for the differences in the groups to be minimal, but between the groups – maximal), cross-tabulation, central tendencies and variation indices, interpolation and extrapolation methods, dynamic row exponential padding, multi-factor correlation and regression analysis.

Administrative Provision

The implementation methodology of defining the IT solution requirements and of the labor market forecasting system is based on the following methods: 1) system-analysis (interviews, analysis of the functional requirements, analysis of options

of integration of the forecasting models into the system, analysis of the institutional environment, logical architecture, data quality guarantee, convenience of use, user management in modeling of possible alternative solutions);

2) economical evaluations (elaboration costs, technical and software costs, system implementation, maintenance, and exploitation costs, costs related to ensuring system exploitation, cost evaluations of several alternatives for the involved state and municipal institutions);

3) comparative analysis of the alternatives (principled scheme of the IT system and alternative solutions thereof, strength and weakness analysis, as well as cost comparison).

According to the methodology criteria of the implementation methodology of the labor market forecasting system, such well-known models were evaluated, as the Classical or the Waterfall model, Iterative method, Chaos model, and Extreme programming. The Classical or the Waterfall model was recognized as the most suitable model in comparison with other models: in the definition phase gathers high level requirements for the system, division stages, works to be completed and results to be achieved, the guidelines of the system architecture; in the analysis phase detailed requirements for the system are summarized, systems about data model, functional model, and data flow model are elaborated; in the design phase detailed system and sub-system specifications are developed; in the elaboration phase the system is elaborated and tested; in the implementation phase the users and technical personnel is trained, system is installed, the necessary improvements are identified; in the exploitation phase support is ensured and the further development is planned.


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4. Research Results

4.1. The Practical Application Spheres of the Labor Market Forecasting System to be Created

During the research, the scope of the issues was defined, acceptance whereof will affect the labor force supply and demand medium-term and long-term forecasting system to be newly developed. For obtaining information, the letters with a questionnaire about these issues were sent to the competent institutions: LM, NVA, Professional Career Selection State Agency (PKIVA), EM, Latvian Institute of Statistics, CSP, Latvian Investment and Development Agency (LIAA), IZM, Education Content and Examination Center, Latvian Academic Information Center, Professional Orientation Information Center, Professional Education Center (PIC), Higher Education Quality Assessment Center, RAPLM, State Regional Development Agency (VRAA). By summarizing the replies received from the institutions, the most significant decisions and matters were established, in consideration of which the newly developable forecasting system could be used:

• Professional training for the unemployed, organization of re-qualification and improvement of professional skills;

• Short-term forecasts of the employment changes, short-term forecasts of unemployment, preparation of the labor market development short-term forecasts;

• Proposals for decreasing unemployment and psychological support issues for the unemployed and those in search for a job;

• Consultations for school-students and adults about selection of future profession; • Decision on attraction of foreign labor force; • Decision on development of active employment measures, implementation of new

active employment measures in the labor market; • Allocation of financial funds of the European Union for improvement of professional

skills of the employed; • Distribution of the number of state paid study positions (necessary for development of

the country) in specialties and levels; • Paying off the student loans from the state budgetary funds; • Analysis of the socio-economic situation in section of the country and the regions; • Description and comparison of the socio-economic development of the territory; • Establishing specifically supportable territories; • Financing of scientific researches in higher educational establishments; • Infrastructures necessary for innovation development and for creation of beneficial tax policy; • Improvement of the financing system of the necessary courses and connection with

the obtainment of the elementary education.

The forecasting system of supply and demand to be elaborated will be applied already for taking coordinated and statistically justified current decisions, as well as for effect modeling of the already made decisions on further labor market development.


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The most significant advantage of the newly developable system and the practical application thereof will be related not as much with increase of the number of decisions to be made, as much with improvement of the information quality to be used in decision making. By simultaneous coordinated operations with maintenance of the forecasting system and with decision making, the reciprocal cooperation of the institutions will improve which will positively effect the general labor market development and will improve the reciprocal coordination of the institutional operations.

In the spheres of education and welfare, the practical application of the system will be closely linked to preparation of the labor force, by forming a focused and labor-market-oriented education system. The forecasting system will allow significant improvement in distribution of the decisions to be made about education programs, by modeling the effects of the decisions to be made to the balance of the labor farce supply and demand in section of profession groups, by evaluating various economic development scenarios and considering demographical trends.

In the economic industry, the practical application of the forecasting system will be closely related to attraction of foreign investments, where significant role would be played by availability of corresponding labor force in a lasting period of time. Thus the issue of preparation of sustainable and planned labor force would be solved, simultaneously evaluating the upcoming labor market tendencies and the necessary human resources.

4.2. Evaluation of the Existing Labor Market Forecasting System

When evaluating the current situation, which is related to forecasting individual elements of the labor market supply and demand, attention was paid generally to two factors – the analysis of the current institutional framework and on the decision making process of the most significant political decisions influencing the labor market.

Current Institutional Framework

Economic Sector

EM performs the economic policy elaboration and develops economic development forecasts on a macro-economic level. The forecasts are used in elaboration of strategic development documents, for instance, the Latvian National Lisbon Program.

The information necessary for work is mainly obtained from the CSP and EUROSTAT. The statistical information of these organizations and the payment statement of the Latvian Bank is an important base for elaboration of the necessary forecasts and documents.

Cooperation with other institutions occurs only when the ministry participates in elaboration of various documents within framework of inter-institutional work groups, however it is not coordinated and systematic. Currently, coordinated and uniform elaboration and application of labor force forecasts is not in effect. Due to the lack of coordination on an inter-ministry level, the information is not delivered also on a regional level.


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Welfare Sector

LM elaborates state policy for decreasing unemployment, participates in elaboration of the employment policy and in improvement of professional orientation system, as well as ensures elaboration of the state ordered research applications, and promotes effective application of the research results in elaboration of sector policies.

NVA organizes active employment events and promotes diversification of these events according to the labor market demand, as well as performs monitoring and cost analysis of the active employment measures, registration and recording of the unemployed and of those in search for a job, creates and systematically updates the relevant database, as well as gathers information about the vacant positions submitted by the employers, and ensures availability of this information to the unemployed and to those in search for a job. Besides, NVA adopts work invitations for foreigners, who are to be employed in Latvia, oversees and maintains the information registration system of the unemployed and of those in search for a job, and of the vacancies submitted by the employers.

PKIVA is a data user and provides information on the labor market development trends and consults the inhabitants on matters of professional career choice.

Educational Sector

The Higher Education Council (AIP) forecasts the number of state-paid students necessary for the country development in each sector. The AIP provides an opinion to the Minister of Education and Science and to the MK on the prepared state budgetary project for financing the higher educational establishments.

State established higher educational establishments receive financing from the state basic budget for education, from the tuition fees, and from the assets that are scheduled for implementation of certain goals.

The IZM, other ministries, and state institutions can conclude agreements with state accredited higher educational establishments established by municipalities and other legal entities and individuals about preparation of certain specialists, by assigning a corresponding financing.

An important phase in arrangement of the system of financing the higher education was the Regulations No. 334 of July 24, 2001 adopted by MK on “Procedure of financing the higher educational establishments from the state budgetary funds”. In compliance with these regulations, a uniform normative system of higher education financing was introduced in the country.

The extent of the study financing is established, based on the number of the state-paid study positions determined for each higher educational establishment, the base costs of the study positions, and the study cost quotients of the thematic educational spheres, and it is provided only for full time studies.

Planning in thematic sectors is implemented by AIP, based on the existing market researches, government accepted political long-term documents, requests by various professional associations, by recognizing the potential of educational establishments, and by regulating the state financing.

The disadvantages of the existing situation are: insufficiently qualitative and timely acquirement of knowledge, skills, and abilities in general and professional educational


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programs. Cooperation between the institutions involved in study position distribution planning, IZM, and EM must be improved.

The education policy is closely linked to labor market policy, however the labor market forecast is only one of the components that is considered when creating educational policy, however it must be noted that it is a very important component.

AIP evaluates the existing situation, i.e., the capacity of the higher educational establishments, the structure of the education programs, the state development policy documents and decisions, requests and demands of various professional associations, requests and forecasts of ministries of various sectors, scientifically calculated forecast data about corresponding science courses, forecast data about employment positions, researches of labor market in certain sectors. AIP makes annual decisions on distribution of the state-paid spots in relevant thematic branches, considering that the accepted decision can bring changes to the labor market only 4-5 years later. Therefore AIP forms an education policy to preserve and maintain a balance in study position provision in all economic sectors.

Current Decision Making Procedure

Decision – Allocating Financing for Specialties in the Higher Education

The decision is made once a year, when elaborating the state budget; two current databases are maintained, which are serviced by 2 people. The proposal for the decision is submitted to AIP, which then considers a project elaborated by an expert at an open meeting and vote with a majority of votes. The AIP experts take into consideration the demands of the employers and professional associations, the state conceptual documentations, wherein the state development trends and priority scientific courses are established, and perform a survey of Latvian higher educational establishments – for establishing the capacity. Based on AIP proposal, IZM makes decisions on distribution of the state-paid positions according to the actual budgetary volume.

Decision – Allocation of Financing in Specialties in Secondary Professional Education

Whilst taking into consideration the implementation of the student admission plan determined in current year of an educational institution, the number of graduates, and the number of students that have dropped out of the educational establishment, in the month of September, with a decree by the IZM, the number of state financed positions in admission at professional educational establishments for the following year, as well as the average quotient of the education program costs at an educational establishment. The average cost quotient at an educational establishment is established by considering the number of students in each program and the program cost quotient of the education programs set forth Regulations No. 850 of MK. By using this data, considering the MK regulations No. 859, the normative enactments for determining scholarships and salary for pedagogues, the finance amount necessary for ensuring operations of professional education establishments is calculated. Upon approval of the state budget for the following year at Saeima (the Parliament), the ministry specifies financing of each educational establishment, by observing the principle – if the approved financing is lower than the standard – the percentage of the decrease for all educational establishments is equal. The educational establishments, by cooperation with the Professional Education and Employment triangular cooperation regional councils, submit


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proposals to IZM about the distribution of the students to be admitted in certain education programs. The professional education and employment triangular cooperation regional councils consist of representatives from the corresponding regional municipality, labor unions, employer organizations, regional development agencies, as well as representatives from IZM. In the above mentioned councils, also leaders of the regional professional education establishments are participating.

Decision – Organization of professional training for the unemployed, as well as re-qualification, and improvement of professional skills

The Law on “Support for the unemployed and those in search for a job” establishes that “IZM shall cooperate with NVA in arrangement of professional training of the unemployed and with PKIVA – in organization of professional orientation measures”.

The MK regulations No. 309 of June 17, 2003 on “Procedure of organization and financing of active employment measures and principles of selection of the implementers of the active employment measures” establish that “The professions and the spheres of professional education, wherein according to the labor market demand and the economic sector development forecasts it is necessary to perform training of the unemployed, are established by a committee formed by NVA. The committee must include representatives from NVA, LM, EM, IZM, Latvian Municipality Union, LDDK, as well as representatives from LBAS”.

The regularly performed 4000 employer survey, which is performed twice a year, is used in making the decision. Based on these short-term (1/2 year) forecasts, decisions are made on organization of professional training of the unemployed, as well as re-qualification and improvement of professional skills.

Decision – Allocation of EU financial funds for improving of the professional skills

Providing support within framework of the relevant grant scheme is regulated by MK regulations No. 114 of February 7, 2006 “Regulations on conditions of providing support for commercial activities for state support program “Support for improvement of professional skills, re-qualification, and continuing education”. It is the main document determining the procedure of allocating the financing. Based on the regulations, LIAA has elaborated internal instructions establishing how the process will be technically arranged.

The decision making about providing support occurs by observing the conditions of the regulations. A commercial enterprise must conform to the criteria given in the II section of the regulations “General conditions of providing support”. These criteria are not related to the sector of operations. The criteria establish that the commercial enterprise must have available steady and sufficient sources of financial assets. Emphasis is put on the ability to ensure successful project implementation during the course of its progress, as well as sustainability.

Final decision about the project acceptance is made by the evaluating committee, which consists of two EM representatives, as well as one representative from LIAA, LM, and IZM. The decisions are made based on conformance to the established criteria.


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4.3. Data Obtainment Model

The task of the research was to ensure the long-term labor market supply and demand forecasting model with the data necessary for its elaboration within framework of the research group “Research of long-term forecasting system of labor market supply and demand and analysis of improvement opportunities”. The introducible labor market supply and demand forecasting system must ensure the analytical base for solving such issues as improvement and development of education system in ensuring current and future labor market requirements, as well as for employment policy management, distribution of budgetary resources, and implementation of new measures in labor force policy. The forecasting system must allow duly identification of problems and their causal relationship. One of the principles of the model creation was use the existing database as effectively as possible, by recommending the necessary changes to the holders of the database for long-term model operations. Currently, the holders of the information that is necessary for long-term forecasting system elaboration are various institutions, which encumbers implementation of the model.

The existing statistical information was not sufficient to satisfy the requirements of elaboration of the long-term forecasting model. Therefore significant importance was allocated to use of information obtained during surveys. Use of the surveys is necessary in order to ensure the data necessary for forecasting and to acquire the missing data in the existing statistics; to ensure the functioning test of the model; for model corrections.

In order to ensure obtainment of information necessary for forecasting, a data model was elaborated. The task of the data model was to arrange (structure the data), as well as to define the operation (activity) body, with assistance of which the data is modified: the necessary data is selected, and gathered, the necessary calculations executed. As a result of data gathering a data base was created.

The database was developed by using computer software MS Excel, SPSS, and MS Access.

MS Excel is provided for arrangement of secondary, already aggregated data, and the SPSS is provided for arrangement of primary, individual data. The database is made in a form of tables. The data model is open, in order to add the necessary missing data to it according to the model requirements: input and calculation data. For elaboration of the model, the necessary parameter list is made, based on the requirements set forth by the group researchers, experts, and model elaborators.

During the implementation of the research, two data arrays were developed according to the needs of the research: “Data array obtained during the research” and “Data array used during the research”.

The data model was elaborated in several stages: • Those economical ratios were determined, which characterize the labor force supply

and demand in such way that they could be compatible according to the above mentioned principles;

• According to the defined economical ratios, the publicly available statistical data and their sources characterizing the ratios were gathered;


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• The data were established, which could be obtained from the data holders to retrieve a more detailed information about the indices with aggregation level that is too high in the publically available data sources and that does not conform to the requirements of the forecasting model elaboration;

• Those statistical parameters were determined, which are necessary for the forecasting models, but which were not available. The missing indices were integrated into the DŅA and DDA surveys in a form of questions;

• A detailed information from CSP was received; • The data arrays obtained as a result of the DŅA and DDA surveys were received. The obtained information was included in the “Data array obtained during the research”.

Figure 2. Data array obtained during the research

DatabaseData array obtained during the research

CSP published

data(CSP_publ)

The initial form

(CSP_publ_sak)

Data received from CSP with the

agreement(CSP_lig)

NVA published data (NVA

_publ)

IZM data

Secondary data Primary data

Employer survey(DDA)

Employee survey(DNA)

Form necessary for the modelers

Analytical (CSP_publ_a

nal)

The initial form(CSP_lig_sak)


(CSP_lig_mod)

Analytical (CSP_lig_anal)

The initial form (NVA_publ_sak)

Form necessary for the modelers(NVA_publ_mod)

Analytical (NVA

_publ_anal)

The initial form (DDA_sak)


Analytical (DDA_anal)

The initial form (DNA_sak)


(DNA_mod)

Analytical (DNA_anal)

Data of the 1st

component researches

The structure of the data array obtained during the research is displayed in Figure 2. The data grouping was performed according to the data feature: primary or secondary data. The secondary data were obtained from the information holders indicated in the figure. The secondary data were prepared in the form necessary for the forecasting models, and in addition, analysis of this data was performed in case of need.

The primary data were obtained from DŅA and DDA.

DDA and DŅA were performed within framework of the research.

In order to obtain the data necessary for long-term forecasting, the DŅA was performed, which was completed on July 31, 2006. An agreement was concluded with SIA “InMind” for performing the survey, which as a subcontractor was responsible for data gathering


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and creation and maintenance of quality control guarantee system. The general totality of the survey were all permanent residents of the Republic of Latvia (LR) aged from 15 to 74 years – 1 735 325 individuals (considering the information acquired during the research on external migration of the residents). Information was received from 20 025 individuals. The quantitative DŅA results provide detailed information about the respondents in various sections: demographical; professional, educational, etc. The respondents answered questions on evaluation of the acquired education, conformity of the education to the selected profession and to the requirements of the labor market. The information acquired during the survey provides with a view on selection of the work in Latvia or abroad, justifies the selection, helps determining the questions on the professional and geographical internal and external migration of the residents of Latvia.

DDA was organized within framework of the research; implementation of the survey was ensured by SIA “Latvijas Fakti”. The survey took place from February 17, 2006 until November 15, 2006. The selection method: multi-level stratified selection, where the first selection level is the number of the employed in a company – the micro-companies, small, medium, and large enterprises (the size of the selection in each level is directly proportionate to the distribution of the number of the employed in the general cluster), the second level – the economic sectors. In the general cluster, 50 849 companies were included. The volume of the selection – 5000 enterprises. The number of answers received from the respondents was – 2517, valid information therefrom was in cases of 2502 respondents.

Figure 3. Data array used during the research

Upon elaboration of the forecasting models, the data were identified, which were used in model calculations, therefore a necessity appeared for improvement of a data model, by creating the “Data array used during the research” (See Figure 3).


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The information used in the data array is grouped according to the information user and then according to the elaborated data model diagram. In order to emphasize the credibility level, it was useful to group the information into three groups. The first group contained data, which directly without any additional processing were used in models as input data. For instance, the forecasts of number of residents of Latvia in one-year age groups. The second contained data, which could not be used in a form that was directly provided by the information source. In order to satisfy the data of model developers, the data had to be processed either by aggregating it or by performing calculations because the methodology of the information obtainment had changed. The third group contained indicators that were obtained indirectly, by using statistical methods for calculation and evaluation thereof. The data credibility of the first two groups was sufficiently high, however the third group has low data credibility level. Use of such data is dictated by the model elaboration method. Information in Latvia becomes more complete by every year, and it is expected that the necessity for indirectly acquired data will not be needed in respect to the upcoming years. However the information on past ratios in the necessary sections will not be possible to obtain through direct methods. The following ratios are included in the “Data array used during the research”: Gross domestic product (GDP):

• GDP according to the economic activity types, mill. LVL; • Detailed labor force demand forecasts in 37 aggregated profession groups from year

2007 until year 2014, year 2020, and year 2030. Residents:

• Forecasts of number of residents in one-year age groups. Employment:

• The economic activity quotients in age groups; • The number of filled positions, the number of the employed in Latvia in sectors and in

37 profession groups from year 1997 until year 2005; • Average gross monthly salary, LVL, from year 1997 until year 2005; • The distribution of the number of the unemployed after according to the last

profession from year 2003 until year 2005; • The number of the employed on January 1, 2006 in 37 profession groups; • The number of the employed on January 1, 2006 in 37 profession groups and age

groups; • The number of vacant positions on January 1, 2006 in 37 profession groups; • The number of vacant positions on January 1, 2006 according to the types of

economic activity; • The number of vacant positions on January 1, 2006 according to the types of

economic activity and in 37 profession groups; • The number of the employed in 37 profession groups and in one-year age groups in

year 2006; • The distribution of the employed according to the type of economic activity and in

one-year age groups in year 2006;


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• The distribution of the number of the employed in 37 profession groups and in one-year age groups from year 1997 until year 2005;

• The distribution of the employed according to the type of the economic activity and one-year age groups from year 1997 until year 2005;

• The distribution of the respondents according to the answers to the question about changes during the last 7 years of life in 37 profession groups from year 1999 until year 2005;

• The distribution of the respondents according to the answer to the question about changes during the last 7 years of life in 37 profession groups in one-year age groups from year 1999 until year 2005;

• Detailed forecasts of labor force demand in 37 aggregated profession groups from year 2007 until year 2014, 2020, and 2030.

Education:

• The number of students in education programs, • The number of graduates from educational establishments.

In the research, data was used, which were obtained in cooperation with other researches of the project “Researches of the Ministry of Welfare” of the European Union structural fund national program “Labor market researches”. The researches of LM are divided into components. The first component “Labor market analysis and forecasting” combines five researches. The following information was obtained as a results of cooperation:

• Research 1 of the 1st component “Detailed research of labor force and labor market according to the economic sectors”. The DDA data array. 2502 companies and organizations, all professions. Statistical data of the existing labor market researches in Latvia. Data to be obtained in research 1.1 – labor market forecasts and methodology of obtainment thereof.

• Research 2 of the 1st component “Professional mobility of labor force”. Provides evaluation of role, type, and influence of professional mobility on employment.

• Research 3 of the 1st component “Professional activities of higher educational establishment and professional educational establishment graduates upon graduation”. Provides information about graduates, who begin working in profession according to the profession initially acquired (professional, higher education), as well as in sections of regions and gender, including information about the number of graduates, who begin working in the selected profession from the total percentage (%) of the graduates, information about the number (%) of the graduates in respect to the number, who began studying. Data of the graduate survey.

• Research 4 of the 1st component “Conformance of the professional and higher education programs to the requirements of labor market” Ensures:

Availability of the data array prepared by SIA “InMind” about 20 000 DŅA;


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Qualitative information obtained in focus groups; Analysis of information obtained in focus groups; Evaluation of information about conformance of profession standards to the profession classifier and to the requirements of labor market; Evaluation of conformance of several professional and higher education programs to the labor market requirements.

• Research of the 2nd component “Geographic mobility of labor force” Provides summary of data, information about factors influencing the geographic mobility of labor force, and econometric analysis, by using the respondent results of the survey

4.4. Data Summarization and Processing Model

During the progress of elaborating the forecasting models, it was established that all of the necessary data cannot be obtained directly and the obtained data must be processed. The publicly available information was summarized and processed according to the research requirements.

Mainly used in the forecasting models was the information provided by CSP. Detailed information was received from CSP, which was obtained through the CSP Profession survey. The received data was processed to prepare it in a form necessary for the forecasting models. Data processing was necessary to preclude influence of changes of methodological character to the dynamic rows. For instance, during the time frame from year 1997 until year 2003, in reports of official statistics of Latvia, the number of employees in an enterprise was divided into those working in principal work and those working in additional work, which in total formed the number of the filled positions. In order to adapt to the requirements of European Union statistical systems, beginning with year 2004, the registration procedure of the number of employees was changed. In the reports, divided were those working full time and those working part time jobs, which in total formed the number of filled positions. Thus, the changes in the registration procedures did not affect the ratios about the number of filled positions and the dynamics rows could be continued.

However, beginning with year 2004, the dynamics row of ratios about the number of employees at principal work had to be terminated. In order to preclude that and to be able to continue the dynamics row, a method was elaborated for calculation of the number of employees working at principal work.

During the course of model elaboration, the list of the necessary professions was altered. Within framework of the research, a new profession grouping was elaborated – 37 profession groups, which were accepted.

The necessary input information from CSP received data for elaboration of the models was as follows: the number of the filled employment positions; the number of the employed with payroll (at principal work); the number of the employed at additional work according to the type of economic activity and 37 profession groups, and according to average salary in 37 profession groups combined in all types of economic activity.


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Three tasks were executed for preparing the data array in a proper form for the forecasting models:

• The data array received from CSP was recalculated in section of 37 profession groups;

• The ratio was calculated about the number of the employed at principal work in year 2004 and 2005;

• The average salary was calculated in a section of 37 profession groups.

The data array received from CSP was recalculated in a section of 37 profession groups

Firstly, data about years 1997, 1998, ..., 2005 were selected from the data array received from CSP (MS Access format) and within framework of each year – according to the types of economic activity.

Secondly, the data selected from MS Access format were transformed into MS Excel format. In this data array, the individual and aggregated profession groups were indicated.

Thirdly, the data array was cleared of the aggregated profession groups. All further calculations were performed with the data array of all professions. It was performed due to the fact that in case if new profession grouping would be formed, repeated calculations would not be necessary.

Fourthly, the data array is transformed into MS Access format to prepare the data array in section of the demanded 37 profession groups.

Fifthly, the data array has been transformed in a MS Excel format.

The ratio about the number of the employed at a principal work place in year 2004 and 2005 was calculated

In framework of all professions in year 2004 and 2005, the ratio was calculated about the number of employees working at principal work place according to the method elaborated during the research “Detailed research of labor force and labor market in economic sectors”.

The number of the employees working at principal work place in year 2004 and 2005 was calculated as follows:

NNK

advj

pdjpdj =

where:

K pdj – the quotient of the number of the employed at principal work against the number of the filled positions in j-th profession in year 2003;

N pdj – the number of the employed in j-th profession in year 2003 according to the CSP profession survey data.


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KKNN pdvkpdjapdljpdlj ××= ,

where:

N pdlj – the number of the employed at principal work in j-th profession in year 2004 or 2005;

N apvlj – the number of the filled employment positions in year 2004 or 2005;

K pdvk – the quotient establishing the number of employees working at principal work place against the proportion of the number of filled positions in the country in total at the end of year 2004 or 2005 against a similar quotient at the end of year 2003.

Quotient K pdvk in year 2004 was 0,988 (0,878:0,889), however in year 2005 0,982 (0,873:0,889).

The average salary in 37 profession group section was calculated

Firstly, from the data array a ratio calculated within framework of all professions – gross salary, by multiplying the average gross monthly salary with the number of the filled positions.

Secondly, the data array is transformed into MS Access format to prepare data array in a section of 37 profession groups.

Thirdly, the prepared data array is transformed into MS Excel format.

Fourthly, the average salary is calculated, by dividing the gross salary with the number of the filled employment positions.

Analysis has been performed in further research process according to the 37 profession grouping.

Analysis of the dynamics ratios – the number of the filled positions, the number of the employed working at principal work, the number of the employed working at additional work in 37 profession groups – was performed by calculating the growth rate by % against the previous year. In order to acquire a concept about the changes during a time period, average growth rate was calculated according to the time period, for which data was received in 37 profession groups (several profession groups did not have data about all years).

The DŅA data array was recalculated in a 37 profession grouping. The data necessary for the forecasting models had to be at first prepared, by using the computer program SPSS and then the results of the calculations were formatted in MS Excel format.

A part of the DDA data array was received encoded in such format that direct aggregation into 37 profession groups could not be performed. Therefore, an algorithm was at first elaborated in order to transform the data array for further work in suitable format. It was performed by using the MS Access computer program. As a result of the transformation, data array was obtained in section of all professions. Only after that it was feasible to perform data


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aggregation in order to group them into sections of 37 profession groups; MS Access computer program was used. During the final data array preparation phase, the data were transformed into the MS Excel format, which was necessary for the model elaborators.

Through calculations a ration was supposed to be obtained about the number of the employed in 37 profession groups and in one-year age groups, as well as about the number of the employed in industries and in one-year age groups. Information in the above mentioned form is not available. Therefore, it was sought to obtain it through calculations. The calculations were based on information, which was received from CSP about the number of the employed in sections of professions and according to the economic activity types during the time frame from year 1997 until year 2005, for the ratios of Labor force survey about the number of the employed from year 1997 until year 2005, and about the DŅA data about the structure of the number of the employed, % in 37 profession groups and one-year age groups, and about the structure of the number of the employed according to the type of economic activity and in one-year age groups. It must be noted that the acquired results are to be assessed with caution when designated as evaluations.

Figure 4. Number of the employed in 37 profession groups and in one-year age groups

The calculated indices in section of 37 profession groups in one-year age groups were calculated, based on data of CSP Labor force survey about the number of the employed, using the data of DŅA and CSP Profession surveys as the structural indices (see Figure 4).


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Figure 5. Number of the employed in industries and in one-year age groups

The calculated ratios according to the types of economic activity in one-year age groups were calculated, based on data of the Labor Force Survey about the number of the employed, using the data of DŅA and CSP Profession Survey as the structural indices (see Figure 5).

Therefore the calculated ratios in one-year age groups are to be considered as approximate values. Hereinafter these detailed rations are scheduled to be received from the CSP Labor Force Survey.

4.5. Model Construction

One of the tasks set forth in the research is development of analytical and forecasting methods for ensuring conformance of the labor force supply and demand at this period, as well as in upcoming period, by performing a model aggregate elaboration, which is based on the original dynamic optimization model, foreign approbated models, as well as statistical and econometric models.

As a result of the research, a model aggregate has been created, which is made of the adapted Swedish labor market agency forecasting model and the DOM elaborated in Powersim Studio environment. The elaborated aggregate of the models is applicable not just for analysis of the labor force supply and demand, but also for modeling the scenarios of development alternatives, by searching the most effective policy tool and selection of operating strategies.

The adapted Swedish labor market agency forecasting model is oriented on analysis of labor market for short-term and medium-term time periods, by using econometric methods. The results


36

thereof, by obtaining the flow of the employed in certain profession groups and industries according to the age, can be used for analysis of the supply and demand nonconformance causes.

By adapting the Swedish labor market agency forecasting model to the Latvian conditions, regardless of the differences in basic elements of forecasting in specific spheres, similar principles are maintained in its structure. However four modeling and forecasting blocks were introduced instead of the models in MS Excel environment:

1st block – Retirement block,

2nd block – Short-term forecasting block,

3rd block – Medium-term forecasting block,

4th block – Long-term forecasting and strategic evaluation block.

The entry data of labor market forecasting blocks are analyzed in accordance with the potential growth or decrease of the number of individuals. The results can be used in processes of various decision-making and will provide with an option to evaluate the general situation in the labor market. The basic data, which are necessary for the decision making process, will be available for the needs of initial situation planning, in order to increase or decrease the labor force supply within framework of specific specialties, if necessary. Thus it is possible to preclude overproduction of labor force (structural unemployment), as well as prevent a situation of distinct deficiency of labor force.

The input data of the labor market forecasting blocks are as follows: • Residents in age groups, • Labor force in age groups, • The employed in age groups, • The employed in economic sectors, • The employed according to the profession groups, • The employed in profession groups according to the sectors.

The input data were prepared as time rows beginning with year 1997, as well as from DŅA and DDA.

The output data of the labor market forecasting blocks are as follows: • Short-term forecast of labor market demand in profession groups, • Short-term forecast of labor market demand in industries, • Short-term forecast of labor market demand in profession groups, in industries, • Short-term forecast of labor market supply in profession groups, • Short-term forecast of labor market supply in industries, • Short-term forecast of labor market supply in profession groups, in industries.

Two electronic tables – calculation tables (APR) and analytical tables (ANL) – were elaborated and approbated in MS Excel environment for each modeling and forecasting block. The model blocks are implemented, using standard options of MS Excel software. The IT solution consists of several mutually related MS Excel files, which ensure implementation and approbation of the theoretical model, however it does not ensure convenient and secure multi-user operating environment.


37

The improved Swedish Labor Market Agency Forecasting Model

In order to perform labor market development forecasts, two types of electronic tables have to be applied: APR and ANL, incl., the electronic table of input data block (IVD).

Thus, the model elaborated in MS Excel environment consists of four mutually connected model blocks:

• The retirement block, which establishes the labor force flow in profession groups and industries according to age, and the results of which are necessary for the remaining model blocks;

• The short-term forecasting block, which establishes the possible development forecasts of labor force supply and demand in profession groups and in industries for the following 2-3 years. The results of these modeling blocks can be used for short-term planning of individual educational programs, as well as for long-term education system and individual study program planning;

• The medium-term forecasting block establishes the possible development forecasts of labor force supply and demand in profession groups and in industries for the upcoming 4-5 years. The results of these modeling blocks can be used for planning of individual short-term educational programs, as well as for planning long-term education systems and individual study programs;

• The long-term forecasting block evaluates the possible development trends of labor force supply and demand (the possible credibility intervals) in profession groups and industries for a time period of up to 13 years.

Figure 6. Data study, modeling, and forecasting in MS Excel environment

Data study, modeling, and forecasting technology in MS Excel environment in general is displayed in Figure 6. In order to commence the work with the improved software of the


38

Swedish labor market forecasting model in MS Excel environment, the sequence of opening files must be observed:

• IVDtab_dati.xls • APRtab_PENSbloks.xls • APRtab_1_PENSbloks.xls • APRtab_ISTbloks.xls • APRtab_VIDbloks.xls • APRtab_ILGbloks.xls • ANLtab_PENSbloks.xls • ANLtab_ISTbloks.xls • ANLtab_VIDbloks.xls • ANLtab_ILGbloks.xls.

Model input data block

The model input data block is provided to ensure that the user has an option to observe the previous situation in labor market (dynamics of the number of the employed and changes in profession groups and industries, the dynamics of those who have left employment), as well as to alleviate operations with the elaborated statistical model blocks, because the indices present in the input data block are automatically sent to all of the following calculation and analytical tables, according to the selected industry or profession group.

Thus, the electronic tables of the model input data block in file IVDtab_dati.xls ensure maintenance of input data and is located in MS Excel sheets: “Konfiguracija”, “IVD Doto faktoru ievade”, “Palīgtabula 1”, “Palīgtabula 2”, “Vecuma grupas”, “Nozares”, “Profesiju grupas”, “Nodarbinatie pa PG un VG”, “Nodarbinatie pa NOZ un VG”, “Nodarbinatie pa PG”, “Nodarbinatie pa NOZ”, “Profesiju pametusie pa PG”, “Profesiju pametusie pa NOZ”, “Pensionejusies atbrīvoti pa PG”, “Pensionejusies atbrīvoti pa NOZ”, “Sakusi macibas PG”, “Sakusi macibas NOZ”, “Absolventi pa PG”, “Absolventi pa NOZ”, “Profesija stradajosie abs pa PG”, “Profesija stradajosie abs pa NOZ”, “Migracija uz citam prof pa PG”, “Migracija uz citam prof pa NOZ”.

Special function for adding new input data and forecasting periods is not elaborated in the MS Excel models. It is expected that the administering institution will perform annual updates to the model, which will include adding input data containing data of the previous period and shifting the forecasting periods by one year. Since the present periods (for input data – 1996-2005, for short-term forecasts – 2006-2008, etc.) are strictly encoded in the MS Excel model, then adding a new period is connected to significant changes in whole model. In order to perform adding of a new period, fine knowledge is required about the model construction and dependencies between various variables, as well as proficiency in MS Excel options.

The retirement block is created mainly to supply three labor market forecasting blocks with input data. The short-term and medium-term labor market forecasting blocks are created to obtain data for labor market analysis, as well as for short-term and medium-term (or long-term) forecasting.


39

Short-term labor force supply and demand forecasting block

The short-term labor market forecasting model consists of calculation tables of short-term forecasting block and of results of analytical table forecasting. This forecasting block establishes the possible development forecasts in labor force supply and demand in profession groups and industries for the upcoming 2-3 years. The results of the corresponding modeling block can be used for planning of individual short-term education programs, as well as for planning of long-term education systems or individual study programs.

Figure 7. Structure of the MS Excel file APRtab_ISTbloks.xls

Model 1.2. APR

Labor force demand

Labor force supply

Results and annual data

Accumulated volume

A - Number of theemployed in a profession

B - Change in numberof the employed (%)

C - Change in number of the employed in a profession(employees who have left the profession) (%)

P - Retired or laid off due to reduction of staff

E - Commenced studies in specificprofession (number)

F - Percentage of graduates in respect to the total number ofthose who started studies (trained and examined) (%)

F - Percentage of graduates in respect to the total number ofthose who started studies (trained and examined)

U - Working in theprofession (%)

U - Working in theprofession

Remaining to workin the profession

I - Migration to otherprofessions or industries (%)

I - Migration to otherprofessions or industries

D - Total demand (number)

S - Total supply (number)

P - Retired or laid off due to reduction in staff

N - Total supply (number)

O - Total demand (number)

H14:R16 Actual data in yearsT14:Y16 Forecasts in years












H48:R50 Actual data in yearsT48:Y50 Forecasts in yearsH51:R53 Actual data in yearsT51:Y:53 Forecasts in years

H55:R57 Actual data in years

T55:Y57 Forecasts in yearsH60:R62 Actual data in yearsT60:Y62 Forecasts in yearsH63:R65 Actual data in yearsT63:Y65 Forecasts in years

LM(0) (Number of thoseemployed in the profession)

LM(1) (Commenced studies inspecific profession (number))

LM(2) (Percentage of graduates in respect to the total numberof those who started studies (trained and examined) (number))

LM(3) (Working in the profession (%) from the graduates)

LM(4) (Staying to work in the profession (%)(i.e., withstand one year) (1+ migration)

LM (5) (Change in number of theemployed in a profession)

Annual supply and demand diagram 1Accumulated supplyand demand diagram 1

A2:V12 Comparable valuesA14:N18 Best modelA20:H62 Summary of forecastsA64:I66 Number of employed in a profession (in years)A64:I66 Number of employed in a profession (in years)

N65:N66 Data count

Models

A70:K96 Linear modelA110:L135 Lin-log modelA143:O167 Log-lin modelA170:L196 Hyperbolic modelA210:M233 Log-log modelA244:J266 Parabolic

Graph (Change in annual supply, demand)

Graph (Accumulated supply and demand)

The binding structure of the calculation tables can be seen in Figure 7. In order to easier find the results of the obtained forecasts and in order for the results to be clearer, analytical table is created, wherein the possible forecasts are displayed and the dynamic thereof is graphically


40

displayed. The corresponding analytical table ensures research and analysis of short-term forecasting results.

Medium-term labor force supply and demand forecasting block

The medium-term labor market forecasting model consists of the calculation tables of medium-term forecasting block and of the forecasting results of the analytical tables.

Work with medium-term forecasting model for the user is identical as with the short-term forecasting model; the differences are only inside, with which only those researchers must interface, who will perform model updates.

For creating the medium-term forecasts, the short-term forecasts from file APRtab_ISTbloks.xls will be used.

Thus, in short-term forecasting block, the possible development forecasts of labor force supply and demand in profession groups and in industries for the upcoming 4-5 years are established. The results of these modeling blocks can be used for short-term education program planning, as well as for planning long-term education systems and individual study programs, similar to short-term forecasts.

Operations with long-term and strategic evaluation forecasting model

With the assistance of the elaborated analytical tool – the modeling results simulating the labor market in Powersim Studio environment – analysis of nonconformance of labor force supply and demand in long-term can be performed in the following sections:

• Employment analysis, which is characterized by changes in number of the employed and by the changes in number of those looking for a job in individual profession groups;

• Analysis of economic development trends; it is described by the labor force demand to be shaped according to the desired industrial development, changes in vacant employment positions, forecasted development of industries influenced by provision of actual labor force;

• Establishment of state-supportable education spheres in order to ensure formation of the labor force supply according to the demand.

The elaborated analytical tool for econometric modeling of labor market in MS Excel environment for short-term and medium-term time periods is suitable for analysis of causes of nonconformance between labor force supply and demand in the following sections:

• For flow of the employed in profession groups and industries according to age; • For analysis of labor force supply and demand in short-term, medium-term, and

long-term.

Even though each of the above-mentioned environments, according to their options and principles used in modeling, form a uniform complex model system, a separate description of each model must be performed for detailed modeling result description.


41

The labor market long-term and strategic evaluation forecasting model consists of calculation tables of long-term and strategic evaluation block and of ANL forecasting results. Short-term and medium-term forecasts are used therein for making the forecasts.

The MS Excel file ANLtab_ILGbloks.xls ensures analysis of long-term and strategic evaluation forecasting results. However the goal of the long-term labor market analysis is to display the line of events, which is referable to the supply and demand balance of labor resources, within framework of certain specialties (profession groups), within a 10-15 year period. This analysis requires the most recent data and development trends, as well as short-term and medium-term supply and demand forecasts and expert forecasts. Considering the historical development of Latvia, the historical data in any of the previously mentioned spheres are very minimal, as well as considering many other initially unpredictable factors in order to be able to forecast specific long-term labor force demand, for instance, in a certain profession group (it is impossible to establish exactly, for instance, that in year 2015, there will be a demand for 20058 doctors). The results in long-term will be displayed as tendencies (interval provided, which would conform to the corresponding demand or supply, as well as surplus or deficit of labor force), moreover, balance in labor market situation is forecasted within framework of industries, profession groups in the country and profession groups of the regions.

The created long-term labor force demand, supply forecasts are provided for evaluation and analysis of long-term labor market situation and to find improvement opportunities for the system.

In order to forecast the possible situation development in labor market in long-term, considering all short available data rows and basic approaches of the statistical methods, additional data sources (expert evaluations) are necessary for the forecasts to be more realistic.

Therefore, considering the previously mentioned: • In long-term, the calculation blocks elaborated in MS Excel environment are offered

not with forecasts, but with development trends; • In long-term, in addition to the dynamic data for the calculation blocks elaborated in

MS Excel environment, also expert evaluations are used; • The simultaneously elaborated DOM model complex mainly is scheduled for elaboration

of long-term forecasts, therefore, considering the model structure, the results of the long-term forecasts must be more precise than the results of MS Excel. Therefore, the labor force supply and demand forecasts elaborated in these modules are used in MS Excel environment as an expert evaluation-assistance in long-term forecasting.

Table 3. Model dimensions used in MS Excel environment

Dimensions Description Module Age groups Division of the economically active residents in age

of labor capacity: 15; 16; 17; ...; 74. Retirement block

Profession groups Aggregation of the professions included in the profession classifier into 37 profession groups, combining the professions similar in respect to the skills and worker abilities, by formation of the labor force supply in certain education system level and industry.

Retirement block Short-term forecasting block Medium-term forecasting block Long-term forecasting block

Industries Display of economic structure, using Level 1 of NACE classifier

Retirement block Short-term forecasting block Medium-term forecasting block Long-term forecasting block


42

3 different dimensions are used in the model structure of MS Excel environment, which ensures model functionality, making its structure easier to comprehend, besides ensures stability of operations (See Table 3).

DOM elaboration is based on use of principles of system dynamics, by performing analysis of labor market as of a complex system, as well as structuring thereof and simulation of operations, demonstrating the behavior of the system elements under influence of decision making. “System dynamics is a perspective view and a set of conceptual tools, with the help of which complicated system structure, dynamics, and functionality can be comprehended” (Sterman 2000: 5).

Figure 8. Schematic illustration of DOM structure

The main goal of DOM is to perform forecasting alternative development scenarios of labor market as a modeling system in long-term. In order to ensure that, five partially independent system modules were created (see Figure 8):

• Social module, which establishes the quantitative (amount) and qualitative (professional qualification) of labor force supply;

• Economic module, which establishes the quantitative and qualitative labor force demand;

• The labor force market module, which results in ensuring balancing out of the labor force market, by summarizing the forecasts and reciprocal interaction results of both previously mentioned modules;

• Technology module, which mainly influences the dynamic behavior of the economic module and indirectly influences the labor force market;

• Environmental and resource module, which influences the social and economic modules and through them indirectly influences the labor force market.

Each of the balancing modules consists of several partially independent blocks.

The goal of the social module is to establish the supply of labor force in profession and age groups. Since the labor force supply is formed as a result of interaction of two influencing factors – demographics (quantitative aspect) and education (qualitative aspect) – two relevant blocks are included in the social module.

The task of the demographics block is to simulate the distribution of economically active residents in profession groups and age groups. For establishing the number of residents in age groups, ready-made demographic forecasts are used, i.e., the number of inhabitants is made

SOCIAL MODULE

ECONOMIC MODULE

LABOR FORCE MARKET MODEL

[Block: Strategicdevelopment of industry]

[Block: Labor forcedemand]

[Block: Labor forcesupply]

[Block: demographics]

[Block: labor force price][Block: education]

TECHNOLOGY MODULE

ENVIRONMENTAL ANDRESOURCE MODULE

[Block: Actual

development of industry]

[Block: Manufacturingfunction]


43

up of the exogenous input indicator of the module. However development of distribution in profession groups is formed by fixed initial condition and alterations that form by aging of the residents of the relevant profession group and by joining of the youth in age groups of 15-19 and 20-24 after obtainment of education in specific education spheres.

In the education block, qualitative aspect is assigned to the quantitative indicator “number of residents”, by establishing the level of labor force qualification and the sphere. In order to include the most significant interconnections in the Education block in respect to the education system in Latvia, it was structured into 5 education levels: elementary education, secondary general education, secondary professional education, college level education, and higher education (See Figure 9).

Figure 9. Description of the LR education system in the dynamic optimization model

Doctor degree studies

3-4 years

Professional education masters degree studies

1-2 years

Higher professional education 4-6 years

Up to 2 years

Academic education masters degree studies

2 years

Higher professional education 1-2 years

College education 2-3 years

Professional education bachelor

degree studies 4 years

Academic education bachelor degree studies

3-4 years

25242322212019

16151413121110987

21

20

19

18

17

16

242322

2827262524

Speciālā izglītība

Pieaugušo izglītība

General secondary education

3 years

Professional secondary education

4 yearsVocational trainings 3 years

1-2 years Up to 2 years

Vocational elementary education 3 years

Vocational trainings 2-3 years

General elementary education 9 years

OE

Education System of Republic of Latvia

Elementary school education

(length – 9 years)

Secondary general education

(length – 3 years)

Secondary professsional

education (length – 4 years)

College education (length – 2 years)

Higher education (length – 3-4 years depending on the

sphere of education)

Source: Website of IZM


44

In the education block, in accordance with the labor market balancing concept, two types of mechanisms are integrated – the self-regulating and national regulation mechanisms.

By implementing the national regulation mechanism, it is possible to examine the influence of managerial decisions on formation of labor force supply. The levers of national regulation mechanism, which can be used knowing the fact which professions are forecasted with labor force deficit, are – the distribution of state financed spots, study tuition and study spots in education spheres, as well as promoting young people to study the specific education spheres.

The main lever to the self-regulating mechanism of labor force supply – conditional labor force price (salary level) in profession groups, when the inhabitants, upon selecting the education, compare the income that could be obtained, by getting the education of the next level with the income, which they can get while remaining at the present education level.

The goal of the economic module is to establish the necessary labor force, in order to ensure the necessary (desired industry growth) and calculate the possible growth volume of he industry, based on the available labor force.

The economic module consists of 3 blocks: • The block of strategic development of the industry – establishes the labor force

demand; • The block of actual development of the industry – simulates the possible industry

development, considering the available labor force resources; • The manufacturing function block – establishes the importance of labor force in

ensuring the economic development.

In the block of strategic development of the industry the necessary labor force demand is calculated, using the manufacturing function and considering the desired growth rates of the economic industries.

The total economic demand for labor force in profession groups is acquired by summing up labor force demand of separate industries and it serves as the input parameter in Labor Force Market Module.

As the desired does not coincide with the actual, also the actual provision of labor force resources in a certain time period may differ from the demanded labor force resource volume. Therefore the task of the actual development block is to simulate, how big would the achievable manufacturing volume be in the industry, if the capital volume is equal to the value integrated into the strategic development block, however the volume of the labor force is established by the actual labor force volume in profession groups simulated in the Labor Force Market Module.

The labor force price – influences the labor force demand through total manufacturing costs to the labor force, which, if growing very rapidly, may slow down development of a certain industry. In the model it is assumed that the part of manufacturing costs, which is directed as a payment for the labor force resources, may not exceed a determined part of the GDP of the industry. If due to lack of labor force, this part of the manufacturing costs exceeds the maximum level, then the Block of Actual Industry Development sends a signal to the other Economic Module block indicating that the demand for labor force must be decreased.

The goal of the Labor Force Market Module is to compare the volume and quality of the labor force necessary for the economy with the supply available in the society, and in case of


45

nonconformity – implementing a balancing activity with assistance of a self-regulating mechanism.

The labor force market module consists of three blocks – Labor force supply block, Labor force demand block, and Labor force price block.

The most important indices of the Labor force supply block are related to the alterations in number of those looking for a job and of the employed. By using the quantitative and qualitative volume of labor force obtained in the social block, changes to the number of those looking for a job are implemented:

• The number of those looking for a job is increased in profession groups, by obtaining the qualitative feature – profession group – of the labor force supply in the Education block;

• In cases of negative changes of demographic forecasts, the number of those looking for a job and the employed is modified according to the calculated proportion of the number of those looking for a job from the total number of economically active inhabitants.

Moreover, the number of those looking for a job is increased by the part of the employed, who lose their job, and decreased by finding a new employment position.

However the changes in the number of the employed are influenced both – by modification of the employee status and by the results of corrections made to the demographic forecasts.

The most significant indices of the labor force demand block are related to the changes in number of filled and vacant employment positions, which are formed according to the economic industry development scenarios.

The number of vacancies is increased by the flow of new jobs and as a result of demographical modifications the decrease of the number of the employed, and the number of vacancies is decreased by closing job positions, filling in the position with a suitable candidate. During simulation, a situation of a negative number of total vacancies is possible, which shows that as a result of development of economic industries, demand for a certain profession group has decreased, however due to the fact that for closing a job position and for laying off the employed time is required, balancing of the demand and actual situation occurs with a time delay.

The labor force price block provides with a signal in a form of relation quotient to the Education block, thus affecting the selection of the youth in respect to education, which to obtain to work in the specific profession group and thus implementing the self-regulating mechanism of labor force supply.

Technological and environmental and resource modules do not directly affect the balancing of labor force supply and demand. The goal of the technological module is to simulate the development of the technological level and influence thereof on the volumes of economic industry output, by elaborating in its structure factors affecting the technological progress.

The goal of the environmental and resource module is to simulate the consumption of natural and energy resources in certain economic industries, as well as to evaluate the influence of manufacturing and society on the alterations of pollution level.


46

In order for the created model to be as flexible as possible, easily understandable and improvable upon delivering to the user, an approach is selected, which permits all of the above mentioned modules to modify separately, without influencing the remaining modules. The main criterion is to maintain the reciprocal connecting variables of the modules.

Table 4. Dimensions used in DOM

Dimensions Description Module Age groups Division of the economically active residents in age

of labor capacity into 7 age groups: 15-19; 20-24; 25-34; 35-44; 45-54; 55-64; 65-74.

Social module

Profession groups Aggregation of the professions included in the profession classifier into 37 profession groups, combining the professions similar in respect to the skills and worker abilities, by formation of the labor force supply in certain education system level and industry.

Social module Labor market module Economic module

Economic sectors Illustration of economic structure, using Level 1 of NACE classifier

Economic module Technological module Environmental and resource module

3 different dimensions are used in the model structure (see Table 4), which ensure model functionality, making its structure easier to understand, besides ensuring stability of operations. The elements of each dimension of one array repeat the model structure, by assuming different values of initial or variable parameters.

4.6. Evaluation and Interpretation of the Results

The elaborated labor market forecasting and analysis models provide with a versatile material for research of the labor force situation, as well as for evaluation of development trends and for analysis of effects of influencing decisions.

Attention must be paid to the fact that relatively precise forecasts about the potential labor force market development can be obtained from the MS Excel environment based short-term forecasting block. Considering the insufficiently long dynamics row currently in Latvia, the medium-term forecasts of MS Excel model include a significant forecasting error, however by every year, as the data dynamics row keeps increasing, also the precision of the medium-term forecasts will increase, therefore the elaborated tool will in future become ever more precise. In the statistical blocks elaborated in MS Excel environment, more precise are the short-term and medium-term forecasts, however in long-term, only trends are offered on up to what extent the labor force will be or will not be satisfied. However the long-term DOM do not provide for forecasts of specific supply or demand value intervals; they provide analysis of labor force market trends under the influence of different factors and decisions and allows creation of various development scenarios.

In order to describe the logical activity in MS Excel environment, and the result interpretation options, data about the labor force supply and demand were selected in spheres of agriculture and forestry. Data array was formed from the following factors:


47

Labor force demand: • The number of the employed in the industry (L) during the time period from 1997

until 2005; • Changes in number of the employed (�L) during the time period from 1997 until

2005; • The employees who have left the industry (�LF) during the time frame from 1997

until 2005; • Retired or laid off from job (P) during the time period from 1997 until 2005;

However the labor supply:

The part of graduates graduating with the specific specialty that have started working in the corresponding sphere (U) during the time period from 1997 until 2005;

• The number of the employed in the industry (L) during the time period from 1997 until 2005;

• The number of the employed in the profession group (L) during the time period from 1997 until 2005;

• The graduates from educational institutions, who have started working in the corresponding industry;

• The graduates from educational institutions, who have started working in the corresponding profession group;

• Migration to other industries (I) during the time period from 1997 until 2005; • Migration to other profession groups (I) during the time period from 1997 until 2005.

Due to the fact that as the prototype for the elaborated statistical models, the Swedish elaborated labor market research model was used, the main results that were calculated are the labor force supply and demand, moreover, within framework of this project, the forecasts were calculated separately in short-term, medium-term, and long-term, and each of the forecasts is offered in separate analytical table, where study and analysis could be performed both – in the dynamic rows and in the graphical images.

In addition to the main results, within framework of the model, forecasted, therefore also applicable in case of need, were several other indices:

• The number of the employed in one-year age groups according to industries; • The retired or laid off in profession groups due to reduction of the staff; • The retired or laid off in industries due to reduction of the staff; • Possible development trends of the number of the employed in profession groups; • Possible development trends of the number of the employed in industries; • Possible trends of the amount of graduates from educational establishments; • Conformance of labor force supply and demand:

− Surplus of labor force (unemployment), − Deficit of labor force.

For instance, considering the actual situation in Latvian economy, a hypothesis was set forth that the demand for labor force in agriculture and forestry is decreasing, however the short-


48

term supply is increasing and in long-term it is decreasing according to the development plan of Latvia.

If the forecasting model operates correctly, i.e., according to the development trends of Latvia, then the forecast results must be corresponding to the hypothesis.

In short-term the total labor force demand in agriculture and forestry sectors is increasing, but the supply is decreasing, until balance is reached within two years period. One of the explanations could be the fact that a significant part of labor force working in agriculture and forestry spheres might emigrate abroad. However, considering the general tendencies of European Union in agricultural policy (for instance, shutdown of sugar production in Latvia, and other), the demand will be decreased and accordingly the supply of the labor force will be partially dissatisfied. Decrease of the labor force supply will be promoted also by re-qualification of the laborers, as well as other measures of agricultural policy.

It can be concluded from the medium and long-term forecasts that the labor force supply and demand is decreasing, which is indicative of correctness of the hypothesis set forth. When executing the first forecast approximation, sufficiently credible result was obtained, which allows specifying the obtained forecasts for further interpretation.

Figure 10. Trends of possible labor force provision in agriculture and forestry

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

1997.1998. 1999.2000.2001.2002.2003. 2004.2005.2006.2007.2008. 2009.2010.2011.2012.2013. 2014.2015.2016.2017.2018. 2019.2020.

By summarizing the previously analyzed situation, trends were established approximately, to what extent the demand for agricultural and forestry laborers could be satisfied (See Figure 10).

By evaluating the initial obtained results, it can be verified that the results of short-term and medium-term forecasting blocks are provided for a more detailed research and analysis of the current situation, for comparison of the acquired data with the original version of the Swedish labor market agency, as well as to make the service easier for the users.

However the long-term forecasts of labor force demand, supply are provided to assess and analyze in long-term the possible situation of labor market and to seek options of improvement of the system.


49

Both – the forecasts obtained with the statistical models of MS Excel environment and the DOM generated results cannot be interpreted as precise statistical indices (forecasts); instead – as possible development and system behavior indices, therefore the supply/demand conformance analysis of the provision of industries and profession groups is performed in a three-step gradation system in forecasting periods (see Table 5).

Table 5. Indicative scale of analysis

Section of the results to be analyzed Indicator to be analyzed Evaluation of the condition

Provision of the demanded labor force in section of industries

Labor force deficiency Good (deficiency less than 5%) Average (deficiency from 5% to 25%) Poor (deficiency exceeding 25%)

Conformance of supply and demand in section of professions

Deviations from the balance (conformance of supply and demand)

Good (deviation within limits of +/- 5%) Average (deviation exceeds the limits of +/-5%, but does not exceed the limits of +/- 25%) Poor (deviation over +/-25%)

Table 6. Satisfaction level of labor force demand in profession groups (MS Excel forecasts)

Satisfaction level of labor force demand Short-term Medium-term Long-term

Profession group code Until y. 2009 Until y. 2014 Until y. 2021

A_0 Officials, legislators deficiency deficiency deficiency A_0_zin Scientific workers, academic

positions in higher educational establishments

deficiency deficiency deficiency

A_1 Teachers deficiency deficiency deficiency A_2 Specialists of humanitarian

sciences, representatives of creative professions

surplus surplus surplus

A_3 Other specialists of social sciences


A_34 Economists, accountants, other senior specialists


A_34_v Managers balance surplus surplus A_38 Legal professions (judges,

lawyers, etc.) surplus surplus surplus

A_4 Specialists of natural sciences deficiency deficiency deficiency A_48 Specialists of computer

sciences balance surplus surplus

A_52 Engineers deficiency deficiency deficiency A_58 Architects surplus surplus surplus A_6 Agronomists, other senior

specialists of agricultural professions


A_7 Doctors surplus surplus surplus A_76 Care and social workers deficiency deficiency deficiency A_81 Service specialists surplus deficiency balance A_84 Ship and aircraft specialists deficiency deficiency deficiency


50

Short-term Medium-term Long-term Profession group code Until y. 2009 Until y. 2014 Until y. 2021

A_86 Civil safety, defense, and other specialists

surplus surplus deficiency

K_3 Librarians, post service workers

deficiency deficiency deficiency

K_34 Specialists of commercial transactions

balance surplus surplus

K_38 Legal specialists (paralegals, etc,)


K_52 Specialists of electric technologies and other

balance deficiency deficiency

K_58 Construction specialists deficiency deficiency deficiency K_7 Intermediate medical

personnel, nurses deficiency deficiency deficiency

V_2 Art, culture, recreational workers


V_34 Secretaries, registrars surplus balance deficiency V_48 Specialists of computation

technologies deficiency surplus surplus

V_52 Mechanics, locksmiths balance deficiency deficiency V_54 Qualified workers in

manufacturing, construction balance deficiency deficiency

V_58 Construction professions balance deficiency deficiency V_6 Agricultural workers surplus surplus surplus V_76 Care workers surplus balance surplus V_81 Individual services surplus surplus surplus V_84 Drivers of transportation

means surplus deficiency deficiency

V_86 Security staff surplus balance deficiency V_0 Ordinary professions surplus surplus surplus P_0 Low-qualified professions surplus surplus surplus

Nomenclature in the table:

Deviation of the supply from the demand

good +/- 5%

average from +/- 5 to +/- 25%

poor more than +/- 25%

~ 0 balance

> 0 surplus

< 0 deficiency

In order to briefly study the labor market situation, for instance, in profession group section, the conformances of supply or demand of labor force, or on the contrary – nonconformance of supply and demand is displayed in Table 6.

It can be seen from the conformance of labor force supply and demand in section of professions that as the forecast horizons are increasing, also imbalance of the labor force market is increasing. The describing statistics is observable in uniform context with the DOM obtained forecasts.


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Unlike the MS Excel forecasting model, the basic task of the DOM is display of interaction results between labor force market factors in long-term and the analysis of optimization opportunities of the labor force market, Thus creation of DOM structure and selection of the variables were established by factors that are significant in long-term behavior detection. As it has been mentioned above, it must be noted that the DOM generated results cannot be interpreted as precise statistical indices (forecasts); they are the indices of system behavior and possible development.

In order to ensure the user with an option to check several simulation scenarios, by performing analysis of consequences of a certain decision making, several alterable parameters are included in the dynamic optimization model.

The types of alterable parameters used in DOM: • Parameters, with the assistance of which the national labor force supply regulating

mechanism could be implemented, for instance – student positions; • Parameters, the values of which are changing when certain activity is performed, for

instance – the quotient of drop-outs in universities; • Various limitations, which ensure correct operation of the model and the values of

which can be altered, by simply establishing a different numeral value, for instance, the critical volume of labor force costs.

An option to choose various scenarios for use of external forecasts in the model operations is integrated into DOM:

• For the economic development – two various scenarios of GDP and capital development (convergence and slow convergence development alternatives);

• Fore demographical forecasts – five different variants of demographical alternatives, using EUROSTAT database information.

Alternative scenarios are also envisaged for development of technological level and for the labor force market reaction time, with the assistance of which an expert can examine the hypotheses about the options of labor force market balancing. The basic task of DOM is displaying the results of interaction of labor force market factors in long-term in order to perform supply and demand analysis and interpretation of balancing or nonconformance causes within context of economy, employment, and education:

• Employment analysis: − Alterations in the number of the employed and those seeking a job in individual

profession groups; • Determining the options of economic industry development trends:

− Formation of labor force demand as a result of the desired industry development;

− Alterations of the vacant employment positions; − Influence of actual labor force provision on the forecasted desired industry

development; − Development options of strategically significant industries.

• Determining the supportable education sectors: − Formation of the labor force supply according to the demand; − Analysis of educational spheres necessary for economic development; − Evaluation of satisfied labor force demand.


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The elaborated DOM provides with an option to analyze data in various sections (both – graphical and tabular form), by combining parameters of several module indices for needs of analysis:

• Analysis of development scenarios for general trends; • For a more detailed analysis of labor force market development; • For analysis of education sector development; • For development analysis of economic industries trends.

Figure 11. Scheme of DOM simulation results analysis

Schematic illustration of DOM result analysis is displayed in Figure 11. By performing analysis of general data of the DOM simulation results, the basic regularities of the long-term behavior of the researchable system are comprehended, by evaluating at first the development trends of the selected scenario: general desired economic growth, total labor force demand, total labor force supply (dynamics of economically active residents). This information provides with a general concept of the potential quantitative balance of the labor force market.

As DOM is provided for testing various development scenarios, then the achievable results are directly dependent on values or scenarios of the selected exogenous variables. In order to describe the possible forecast variants, a base analysis scenario was created, where the base scenario of economic development convergence and demographic forecasts was selected for

Simulation results

General data analysis The goal: comprehension of basic regularities of the long-term behavior of the researchable system Indices to be analyzed:

Total economic growth

Development trends of labor force demand

Conformity of labor force supply and demand

Detailed data analysis The goal: examination of the researchable system behavior in long-term in certain sections Indices to be analyzed:

Relevance of deviations of actual development in certain industry from the desired

Development of desired manufacturing volume and labor force demand of certain sectors

Analysis of the actual volume of sectors of the available labor force and its production

Analysis of the labor force supply changes in certain profession group

Changes in volume of number of certain education level and sphere graduates

Scenario analysis The goal: analysis of the researchable system reaction on the value of the change of altering parameters for achievement of optimum labor force balance in long-term Indices to be analyzed: Variable decision making parameter values, general and detailed analysis of the obtained alternative scenarios


53

ensuring comparability of the scenario results through use of the distribution of number of the student positions throughout the whole simulation period.

Figure 12. Total labor force supply and demand (DOM forecasts)

Kopējais darbaspēka (strādājošo) pieprasījums un kopējais darbaspēkapiedāvājums (ekonomiski aktīvie iedzīvotāji)

01.01.2005 01.01.2010 01.01.2015 01.01.2020 01.01.2025 01.01.2030800000

1000000

1200000

1400000

cilv

Stradajos_piepras_kopa

iedz_EA

Non commercial use only!

Kopējais darbaspēka (strādājošo) pieprasījums un kopējais darbaspēkapiedāvājums (ekonomiski aktīvie iedzīvotāji)

01.01.2005 01.01.2010 01.01.2015 01.01.2020 01.01.2025 01.01.2030800000

1000000

1200000

1400000

cilv

Stradajos_piepras_kopa

iedz_EA

Non commercial use only!

Total labor force (the employed) demand and total labor force supply(economically active inhabitants)individuals

Disproportion of the total labor force market could be seen in Figure 12. The number of economically active inhabitants in short-term exceeds the amount of the labor force necessary in the economy, however in further simulation period it is significantly lower than the volume of the resources demanded in the economy.

It can be concluded from the previous graph that deficiency of labor force should have an effect on practically all economic sectors. It is also indicated by the simulation results displayed in Table 7. As during the initial period, the labor force supply exceed the labor force demand, the provision of the industries with the necessary labor force in short-term is on good level. As it can be seen in Figure 12, the labor force supply and demand numerically draw closer to each other in medium term.

Table 7. Satisfaction level of labor force demand in industries (DOM forecasts)

1st scenario Level of satisfaction of labor force demand (on average in a period)

Short-term Medium-term Long-term Strategic evaluations

Industries

Until y. 2009 Until y. 2014 Until y. 2021 Until y. 2030

A Agriculture, hunting, and forestry + +/- +/- -- B Fishery + +/- +/- -- C Extractive industry and quarry production + +/- -- -- D Manufacturing industry +/- +/- -- -- E Electric energy, gas, and water supply + +/- -- -- F Construction +/- +/- -- --

G Wholesale and retail; repairs of motor vehicles, motor bikes, individual use items, home appliances and devices + +/- +/- --

H Hotels and restaurants + +/- +/- +/- I Transportation, storage, and communication + +/- -- --


54

Short-term Medium-term Long-term Strategic evaluations

Industries


J Financial intermediation + +/- +/- +/-

K Transactions with real estate, lease, computer services, science, and other commercial services + +/- +/- --

L State administration and defense; mandatory social insurance + +/- +/- --

M Education + + +/- -- N Health and social care + +/- +/- +/- O Public, social, and individual services + +/- +/- --

good 13 1 0 0 average 2 14 10 3

poor 0 0 5 12

Nomenclature in the table: deficiency of labor force less than 5% - „+” - good deficiency is from 5- 25% - „+/-„ – average deficiency is greater than 25% - „--„ – „poor”

Also in Table 7, it can be observed that in short-term only two industries are subject to average deficiency, in medium-term – almost all industries, except M “Education”, however as the forecast horizons decrease, the provision with the labor force deteriorates and most part of the industries are subject to significant deficiency of labor force.

In addition, in respect to conformance of labor force supply and demand in profession section, it can be seen that as the forecast horizons increase, disbalance of the labor force market also is decreasing. Moreover, regardless of the different forecasting methodology in statistical and DOM models, common features could be observed in this trend (see Table 7 and 8). Thus, if at the beginning of the period for a large number of the professions either balance (7-17 profession groups) or surplus of the labor force (18-19 profession groups) situations could be observed, then at the end of the period, in most part of the profession groups labor force deficiency (18-30 profession groups) is observed, moreover, in 16 profession groups this deficiency could be characterized as significant.

Table 8. Satisfaction level of labor force demand in profession groups (DOM forecasts)

1st scenario

Satisfaction level of labor force demand (overall during the period)

Short-term Medium-term Long-term Strategic evaluations Profession group

Until y. 2009 Until y. 2014 Until y. 2021 Until y. 2030 A_0 Officials, legislators balance deficiency deficiency deficiency

A_0_zin Scientific workers, academic positions in higher educational establishments balance balance deficiency deficiency

A_1 Teachers balance balance deficiency deficiency

A_2 Specialists of humanitarian sciences, representatives of creative professions surplus surplus surplus surplus

A_3 Other specialists of social sciences surplus surplus surplus surplus

A_34 Economists, accountants, other senior specialists balance balance deficiency deficiency

A_34_v Managers balance balance deficiency deficiency


55

Short-term Medium-term Long-term Strategic evaluations Profession group


A_38 Legal professions (judges, lawyers, etc.) surplus surplus surplus surplus

A_4 Specialists of natural sciences surplus surplus balance deficiency A_48 Specialists of computer sciences surplus surplus surplus balance A_52 Engineers surplus balance deficiency deficiency A_58 Architects balance balance deficiency deficiency

A_6 Agronomists, other senior specialists of agricultural professions surplus surplus surplus surplus

A_7 Doctors balance surplus surplus deficiency A_76 Care and social workers surplus surplus balance deficiency A_81 Service specialists balance deficiency deficiency deficiency A_84 Ship and aircraft specialists balance balance deficiency deficiency A_86 Civil safety, defense, and

other specialists balance balance deficiency deficiency K_3 Librarians, post service

workers surplus balance deficiency deficiency K_34 Specialists of commercial

transactions surplus balance deficiency deficiency K_38 Legal specialists (paralegals, etc,) surplus surplus balance deficiency K_52 Specialists of electric

technologies and other balance deficiency deficiency deficiency K_58 Construction specialists deficiency deficiency deficiency deficiency K_7 Intermediate medical

personnel, nurses balance balance deficiency deficiency V_2 Art, culture, recreational

workers surplus surplus surplus surplus V_34 Secretaries, registrars balance deficiency deficiency deficiency V_48 Specialists of computation

technologies surplus surplus balance deficiency V_52 Mechanics, locksmiths balance deficiency deficiency deficiency V_54 Qualified workers in

manufacturing, construction balance deficiency deficiency deficiency V_58 Construction professions deficiency deficiency deficiency deficiency V_6 Agricultural workers surplus surplus deficiency deficiency V_76 Care workers surplus balance deficiency deficiency V_81 Individual services surplus surplus surplus surplus V_84 Drivers of transportation

means balance deficiency deficiency deficiency V_86 Security staff surplus deficiency deficiency deficiency V_0 Ordinary professions balance balance deficiency deficiency P_0 Low-qualified professions surplus deficiency deficiency deficiency good 17 13 4 1 average 14 14 16 15 poor 6 10 17 21 balance 17 13 4 1 surplus 18 13 8 6 deficiency 2 11 25 30

Nomenclature in the table:

Deviation of the supply

from the demand good +/- 5% average from 5 to +/- 25% poor More than +/- 25% ~ 0 Balance > 0 Surplus < 0 Deficiency


56

However when analyzing the significance of reciprocal deviations of the supply and demand, it could be observed, that in short term period, only in three to six profession groups the situation with labor force demand satisfaction could be characterized as poor (either large labor force surplus or deficiency is characteristic), however in 15-17 profession groups, the situation could be described as good. The situation is changing in long-term period, when dissatisfaction of the labor force demand is increasing. The situation is good only in one or two profession groups, however in 15 profession groups it is average, and in 20-21 profession groups – it is poor.

In order to ensure the model user with options of labor force supply and demand optimization, the number of student positions in levels and spheres of education could be changed throughout the whole forecasting horizon, by limiting the student flow to those professions, where surplus of the labor force is predicted, and by increasing the student positions to those professions, where deficiency is expected.

It must be noted that this optimization in the Education block is performed manually, by establishing the number of student positions for each profession group in each simulation period, thus the obtained result cannot be interpreted as an optimal distribution of student positions.

Nevertheless, in some professions, even after optimization of the student positions, a significant labor force surplus can still remain, and in others – deficiency. It can be explained with the fact that there is no automatic migration of labor force between the professions elaborated into the model. As a result, if a person has obtained certain education, however at a later time the demand for this profession will decrease, the person will be in a status of a person seeking for a job and will be a component to the labor force surplus in this profession. By analyzing the professions, in which labor force surplus remains upon optimization of student positions, it must be noted that most of the professions are humanitarian, which can relatively easily be channeled to work in related profession group with assistance of re-qualification, continuing education, and lifelong learning.

Table 9. Options of channeling the labor force surplus

Code of the

profession group

The profession group, where labor force surplus is observed and that

could be retrained

Code of the profession

group

The profession group, where labor force deficiency is observed and to which retrained individuals from other professions could be

channeled A_1 Teachers

A_2 Specialists of humanitarian sciences, representatives of creative professions A_76 Care and social workers

A_1 Teachers A_34 Economists, accountants, and other senior specialists A_34_v Managers

A_3 Other specialists of social sciences

A_76 Care and social workers

A_0_zin Scientific workers, academic positions of higher educational establishments A_6 Agronomists, other senior specialists of

agricultural professions V_6 Agricultural workers A_0 Officials, legislators

A_38 Legal professions (judges, lawyers, etc.) A_34_v Managers

A_1 Teachers A_76 Care and social workers V_2 Art, culture, recreational workers V_76 Care workers A_81 Service specialists V_84 Drivers of transportation means V_81 Individual services V_0 Ordinary professions


57

In Table 9, the possible variants of channeling labor force surplus are provided.

However labor force deficiency is observed in professions of hard sciences, in specialist professions of manufacturing and construction, in medical professions, teacher and defense specialist professions (See Table 10).

Table 10. Profession groups with significant deficiency of labor force

Professions of hard sciences, qualified specialists of manufacturing and construction A_4 Specialists in natural sciences A_52 Engineers A_58 Architects K_52 Specialists of electric technologies and other K_58 Construction specialists V_48 Specialists of computing technologies V_52 Mechanics, locksmiths V_54 Qualified workers in manufacturing, construction V_58 Construction professions

Medical specialists A_7 Doctors K_7 Intermediate medical personnel, nurses

Teachers and lecturers A_0_zin Science workers, academic positions of higher educational establishments A_1 Teachers

Defense specialists A_86 Civil safety, defense, and other specialists V_86 Security staff

Considering the fact that in these professions the re-qualification options are limited, the state must perform measures to promote the popularity of these professions amongst the youth, by stimulating their choice through increasing the number of state financed study positions, by assigning special scholarships, etc.

The professions, which require higher education level, must perform a timelier national planning in respect to the supportable education spheres, because balancing of labor force supply and demand in these profession groups occurs with a greater delay time.

By evaluating the base scenario according to the above-described steps, the model user may select alternative scenarios, by using the alterable parameters of the model and by setting forth hypotheses, as well as the exchange of the corresponding parameters may affect the dynamic behavior of the system. When implementing simulation of the new model, the user has an option to compare the results generated in the base scenario with the results of the alternative and to examine the set forth hypothesis. Thus, provided various parameter values, there is an option to seek for the optimum solutions in approximation to the long-term balance situation.

When evaluating the obtained results, it can be concluded that the results of forecasting and analysis of labor force market in the statistical models and in the DOM models provide with a versatile material for examination of situation, as well as for evaluation of the development trends and analysis of effects of decisions influencing the development.


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4.7. Plan of Improvement/Implementation of the Labor Market Forecasting System

Elaboration of system implementation plan

The following activities are planned for ensuring system implementation: • To consolidate the EM capacity; • To create a new agency (Research Institute) under subordination of EM; • To create a consultative council of inter-institutional management (Consultative

Council); • To consolidate the IZM capacity; • To consolidate the LM, NVA, and PKIVA capacity; • To consolidate the RAPLM and VRAA capacity.

In order to perform the additionally assigned functions, a new department is scheduled to be established for consolidating the EM capacity in respect to functions related to coordination of medium-term and long-term labor market forecasting system.

Amendments will be necessary in the following normative enactments for creation of the new department:

• EM statutes; • Department regulations; • EM operational strategy.

In order to ensure implementation of the functions, 7 additional staff units are planned to be created in the new department. The functions of EM department include:

• Analyzing the labor market development and elaborating proposals, based on the analysis, for issues of economic policy;

• Elaboration of labor market development scenarios and relevant medium and long-term labor market forecasts;

• Preparation of reports on labor market development trends and perspectives; • To maintain and improve the sets of instruments necessary for the labor market

analyses and forecasting; • To maintain and systematically update the informative database necessary for the

labor market analyses, modeling, and forecasting; • Coordination of activities by the involved ministries in the preparation and usage

process of the medium and long-term labor market forecasts; • Within relevant authority, to participate in elaboration process of various strategic

development documents and legal enactment projects related to the labor market policy; • Organizing expertise and surveys on labor market development issues;


59

• Cooperation with Latvian, European Union (EU), and other international and foreign institutions in respect to matters of labor market forecasting.

The following normative enactments are necessary to be elaborated for regulating the operations of the Research Institute:

• MK decree on establishment of the agency, • MK regulations about the agency statutes, • Operational strategy of the Research Institute, • Amendments to the EM operational strategy, • Amendments to the EM statutes.

In order to ensure initiation of activities at the newly established Research Institute, technical and human resources provision is required. 15 employees are planned to be employed at the Research Institute.

The Research Institute will be in charge of the following functions: • To gather, systematize, analyze, and explain facts about the labor market development

processes; • To analyze the labor market development policy of European Union and Latvia; • To perform scientific researches in labor market sphere; • To create, maintain, and update the databases about labor market development; • To provide with consultations (for free and for a fee) to individuals and legal entities

about the labor market development and about perspectives; • To prepare and publish scientific articles about research results of the Institute; • To prepare and submit at state institutions reports, reviews, and other materials with

proposals, deriving from the results of researches performed by the Institute; • To introduce with projects of scientific researches or to participate in projects of other

organizations; • To promote awareness in the society about the economic processes and development

opportunities especially in labor market sphere.

In order to ensure system implementation and successful operations, the Consultative Council is necessary to be established for issues of labor market analysis and forecasting. The Consultative Council would be formed at the initiative of EM as the institution coordinating the labor market forecasting processes in the country.

Participating in the council should definitely be representatives of the leading involved ministries. The main involved institutions in policy making, analysis, and forecasting of labor market are LM, IZM, EM RAPLM, ZM, KM, and VM. Representatives from LDDK and LBAS should also be participating in the Consultative Council.

The necessary normative enactments: • MK decree about establishment of the Consultative Council; • Statutes of the Consultative Council.

The functions of secretariat of the Consultative Council would be performed by the new EM department. Additional financing for establishing the Consultative Council is not required.


60

The Consultative Council would be the consultative-informational institution in processes of labor market analysis and forecasting. The Consultative Council would recognize the needs of the involved parties in elaboration and use of the labor market forecasts, through discussions and mutual decision-making about the necessary changes. The key functions of the Consultative Council are:

• Evaluating the prepared labor market development scenarios and forecasts, providing with own opinion and forecasts;

• Discussing the topical labor market problems, based on the prepared researches, scenarios, and forecasts;

• Evaluating the strategy of labor market researches and forecasting system operations; • Evaluating the possible alterations in the labor market analysis and forecasting

system.

For consolidation of IZM capacity, Higher Education Departmental Division (consisting of 2 employees) to be created, with the following functions:

• To gather information on long-term forecasts of specialist demand; • To plan initial qualification obtainment and re-qualification necessary for the labor

market; • To forecast development of continuing education; • To evaluate the models of cooperation of science and education; • To forecast the influence of financing mechanisms on opportunities of education

system to satisfy the labor market requirements.

The LM functions will be increased with participation in the Consultative Council and involvement into creation and development of the long-term labor market supply and demand forecasting system. For execution of these functions, one additional employment position is necessary at the ministry.

The functions of NVA will be supplemented with the following matters: • Providing statistical information for research institution; • Participation in creation of the labor market supply and demand planning system.

For execution of these functions, one additional employment position is necessary at NVA.

The authority of PKIVA will include utilization of labor market supply and demand forecasts in daily operations, by performing student consulting in selection of future profession.

For consolidation of RAPLM capacity, additional employment positions will be created from RAPLM Development Planning Department, having the following tasks:

• Providing the necessary information and provision of information exchange with the research institution;

• Preparation/exchange of necessary information with cooperation partners of regional level;

• Evaluation of influence by the labor market supply and demand system results on the regional policy and planning system;

• Ensuring participation in the Consultative Council.


61

The functions of VRAA will be improved with the following issues: • Ensuring cooperation with the Research Institute and ensuring reciprocal interactivity

of the forecasting systems, regular exchange of results and data; • Participation in elaboration of labor force supply and demand planning system.

Table 11. Activities of enforcement of the newly introducible system

No. Institution Activity Time period 1. LM Delivery of research results to EM 2nd half of year 2007 2. LM Nominating a representative at the Consultative

Council 1st half of year 2008

3. NVA Cooperation in provision of statistical information for the Research Institute

1st half of year 2008

4. EM Acceptance of research results from LM 2nd half of year 2007 5. EM Storage of research results From 2007 further on 6. EM Creation of a new department:

− Ensuring technical equipment and premises (performing purchases);

− Ensuring human resources.

2nd half of year 2007 and 1st half of year 2008

7. EM Preparation of necessary normative enactments for establishment of Research Institute


8. EM Establishment of the Research Institute: − Ensuring technical equipment and premises

(performing purchases); − Ensuring human resources.

2nd half of year 2007 and 1st half of year 2008

9. EM Preparation of the necessary normative enactments for establishment of Consultative Council

2nd half of year 2007

10. EM Elaboration of budgetary request for ensuring implementation of the alternative


11. EM Initiation of operations at the Consultative Council


12. IZM Ensuring human resources 1st half of year 2008 13. IZM Gather information about long-term specialist

demand forecasts 1st half of year 2008

14. IZM Plan the initial qualification and re-qualification necessary for the labor market


15. IZM Forecast development of continuing education Year 2008 16. IZM Evaluate the education and science cooperation

models with EM Year 2008

17. IZM Forecast the influence of the financing mechanisms on the options of education system to satisfy the labor market requirements

Year 2008

18. RAPLM Nominating a representative for participation at the Consultative Council


19. RAPLM Ensuring human resources Year 2008 20. RAPLM Distribution of information on forecasting

results to regional cooperation partners 2nd half of year 2008

21. VRAA Cooperation in creation of forecasting system and initiation of system data exchange

Year 2008

22. VRAA Analysis of forecasting system results 2nd half of year 2008

The time schedule of the newly introducible system is provided in Table 11.


62

Elaboration of optimum cooperation mechanism between institutions and organizations

Being the responsible institution, EM coordinates uniform labor market forecast elaboration and application. Consultative Council is created for matters of labor market analysis and forecasting.

Cooperation between the involved ministries occurs in two ways: • Mutual participation in the Consultative Council, • Direct cooperation.

Cooperation on the ministerial level occurs within framework of the Consultative Council.

Figure 13. Structure of the Consultative Council

EM in a capacity of secretariat of the Consultative Council arranges the council meetings. Participating in the Consultative Council are representatives from the main ministries forming and influencing the labor market policies, as well as LDDK and LBAS (See Figure 13). Regular meetings of the Consultative Council are planned to be held once in a quarter or as needed for reviewing certain issues.

The decisions of the Consultative Council in respect to labor market analysis and forecasting issues, as well as the institutions involved, will have an informative/recommendation character. The decisions of the Consultative Council will be an additional source of information and a justification in taking decisions influencing the labor market. Other parties involved will be informed about the decisions of the Consultative Council through intermediation of EM.

Any member of the Consultative Council may initiate discussion of a topical matter, by registering it at the Ministry of Economy – as the secretariat of the Consultative Council.

CONSULTATIVE COUNCIL

VM

RAPLM

IZM

EM

LDDK LBAS

LM

Other

ZM

KM


63

Figure 14. Mechanism of cooperation between the Research Institute and ministries

The Research Institute ensures the necessary information to EM as the coordinating ministry and to other ministries involved (See Figure 14).

The Research Institute is an agency in subordination of EM. According to the decisions of the Consultative Council, EM establishes the basic directions of operations at the Research Institute.

The Research Institute, being a subordinate institution, coordinates the operational strategies and annual operation plans with EM. The EM can instruct the Research Institute to research the topical labor market problems and options of solution thereof.

EM ensures connection of the Consultative Council with the Research Institute, which is under subordination of the Ministry. The representatives of the Research Institutes may be directly involved in the Consultative Council, by invitation to participate at the meeting.

Preparation of plan of system use for taking the policy development decisions

The medium-term and long-term labor force supply and demand forecasts prepared by the Research Institute will be one of the information sources when discussing about the labor market policy matters at the Consultative Council. The Consultative Council will prepare recommendations on topical matters of the labor market policy. These recommendations will be offered for decisions of relevant policies to the responsible institution, which will then perform the policy development decision making, by evaluating the recommendations of the Consultative Council, as well as other information sources. Thus the Consultative Council will not have direct influence in policy decision making, but it will prepare well-justified proposals. Considering the significance of recommendations to the labor market policy made by the Consultative Council, an important issue will be the matter of justification of the decisions prepared by this institution in convincing the relevant policy decision makers.

EM

RAPLM

IZM

LM

RESEARCH INSTITUTE


64

AIP operates in coordination with the newly established Research Institute, orders certain information. Inter-ministerial work groups are formed, which evaluate the proposed forecasts. The obtained information is used in education policy making, by taking annual decisions about the number of state financed student positions, considering the database about the total number of students in the corresponding academic year in all thematic spheres of education. By implementing this system into the educational policy, a question arises about courses and modules proper for life-long learning. Structuring of university level study programs must be formed and used in modules according to the suggestions elaborated in the Bologna process. The modules allow the specialists to adapt to the labor market requirements and to obtain the necessary new skills and abilities at the same level as the graduates. The modules can be used also separately - for mastering specific questions. The universities and other education providers must supply with an option to master also the missing base knowledge, which allows obtaining higher level qualification during the later career stages. A course and separately learnable module system coordinated with the higher education would allow inhabitants to perform re-qualification on various education levels according to the labor market needs and requirements.

The needs and opportunities of acquiring qualification and re-qualification in the country must be coordinated with forecasts of various economic sectors and with possibilities of higher educational institutions that are under control of ministries in preparation and re-qualification of specialists. The EM prepared economic development forecasts are the determinative for forecasting the directions of higher education development, however the economic forecasts are becoming ever more dependent on the human resource formation and use forecasts. IZM uses the data offered during the forecasting progress for allocating study and student credits with the state assistance and in cancelling of the credits according to the needs of the national economic development policy. The forecasts have an important role in the following spheres:

• Establishing the state financed study positions in various study directions in various universities,

• Re-qualification (incl. of the unemployed), • Improvement of the financing system of the necessary courses and connection with

obtainment of the initial education.

NVA will have continuous access to the information source on medium and long-term forecasts of labor force supply and demand, which will be used simultaneously with the short-term forecasts, which will be used when organizing re-qualification of the unemployed. The decision will be made co-coordinately and will not be removed from the rest of the education system, thus it will integrate into the balancing of the common labor force supply both – in sections of professions and regions.

PKIVA will have access to information source of medium and long-term development forecasts of labor market supply and demand. Thus information about the most demanded and perspective future professions in the labor market will be provided, which will establish the influence on deciding in favor of one or another training program, according to the skills and interests of each student.

VRAA will have access to the information source on medium and long-term labor market supply and demand development forecasts. This information will be used in creation of regional development forecasting system and in ensuring of the system operations. Both – the


65

labor market forecasting and the regional development monitoring systems will be with complementing effects, i.e., mutually complementing.

Elaboration and data gathering and processing scheme

The Research Institute will obtain the information necessary for work from various data source. The statistical data mainly is obtained from CSP, NVA, and IZM. During the transition period, until the moment when the necessary statistical information will be gathered for the sections necessary for input data of the IT solutions elaborated during the research, the statistical information is scheduled to be obtained by using the currently available statistical data for calculations.

The data necessary for IT solution will be received from the following data sources: • labor force demand:

− CSP “Profession survey”; − CSP “Salary structure survey”; − CSP “Employment report (quarterly), 2nd work”; − NVA “Employer survey”.

• labor force supply: − CSP “Labor force survey”; − IZM data about the number of students.

The main source of statistical information for ensuring forecasts of labor force supply and demand is CSP (approximately 85%). Thus, CSP will take an important role in operation of labor force supply and demand forecasting system, because the quality of input data will have crucial importance in guaranteeing credibility of the further forecasts.

Missing statistical information, which cannot be obtained from the existing information sources, is scheduled to be obtained by supplementing the amount of questions and increasing the volume of selection in surveys performed by CSP. Increasing the selection volume will allow receiving more detailed information where necessary. For instance, distribution of the employed inhabitants in one-year age groups, industries, and professions (CSP “Labor force survey”), the number of employment positions in all professions and according to the main age groups (CSP “Profession survey”).

The Research Institute will use the IT solution elaborated within framework of the research for data processing, which can be adopted and supplemented according to the needs of the Research Institute and other forecasting result users.

Preparation of plan of system maintenance and optimum institutional cooperation

The IT system elaborated during the research will be delivered to the established Research Institute. The Research Institute, according to the requirements of the technical solution defined during the research, will ensure purchase of technical equipment and software. Simultaneously with the purchase of the technical equipment, employee training with the IT system will be performed. Daily maintenance of the newly established system - inserting input data, system supplementation, processing of the results, and interpretation of the


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obtained data will be ensured by the Research Institute under subordination of EM, by attracting external service performers for modernization of the IT system in case of necessity.

Prior to further distribution of the obtained forecasts, the experts employed at the Research Institute will perform examination thereof, which will ensure control of the obtained forecasts and interpretation of the obtained results. Thus, the data received from the Research Institute will be correct and with high level of credibility. Further, the acquired forecasts will be delivered to EM, which will perform distribution of data and will ensure the Consultative Council with the necessary information.

Figure 15. Mechanism of institutional cooperation in labor market forecasting

The reciprocal coordination operations between the involved institutions will be ensured by EM, by organizing the operations of the Consultative Council and by performing distribution of the forecasts to the state administration institutions, as well as informing the rest of the society. The Research Institute and EM will control the correctness of the forecasts prior to delivering information to broader society (See Figure 15).

Information to the society will be provided by EM, through arranging publications of the materials prepared by the Research Institute and by publications of the decisions made by the Consultative Council.

4.8. IT Solution Requirements

The goal of IT solution is creation of such information system, which would be available and conveniently usable for application of mathematical model for labor market supply and demand comparison, forecasting, and optimization in decision making and planning.

EM

RESEARCH INSTITUTE

CONSULTATIVE

COUNCIL

INVOLVED STATE ADMINISTRATION

INSTITUTIONS

SOCIETY


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Within framework of the research “Research of labor market long-term forecasting system and analysis of improvement opportunities”, definition of IT solution requirements was performed, the result of which is a description of system requirements. The goal of it is to provide with a concept to the system users and potential elaborators about the functionality of the system, technical architecture, performance, and user management.

Table 12. Measures for implementing the IT solution for labor force forecasting

Labor force forecasting components created as a result of the research

Labor force forecasting components, which are yet to be

elaborated/implemented in state administration

• Operating prototype of the system, • Forecasts in test mode and with real data, • Tested forecasting models, • Description of optimum IT solution, • Specification of IT system

requirements.

• Administrative provision, • Technical provision, • Purchase of standard software, • IT system according to specification of

requirements.

The achievements of the research and additionally necessary measures for implementation of labor force forecasting IT solution are compared in Table 12.

An option to acquire actual labor force forecasts in test mode is possible with the technological solutions used during the researches. During the research, an operating system prototype was created, which provides with an option to test the forecasting models. Programming operations were performed and technical support was provided for ensuring the model functionality and adaptation to work in actual exploitation environment. According to the functional, data, and institutional solution requirements, also a description of roles and access rights, description of interface with other information systems, as well as the scheduled technical architecture and performance requirements were prepared.

3 alternatives of IT solution were considered – various technological solutions, wherein functionality is ensured, starting from the minimum necessary for performance of forecasting work to such that would ensure the broadest opportunities for data processing, analysis, and effective work. For evaluation of IT solution alternatives, an indicative cost calculation for system elaboration, purchase of software and technologies, and annual maintenance was prepared. For further analysis and elaboration of specification of requirements, the optimum alternative was selected - a solution, which ensured the broadest functionality and wherein the existing solutions of available standard software could be used the most effectively. The Powersim Studio software was used during the course of the research, as well as it ensures additional options, which in accordance to the descriptions conform to the system creation requirements (Powersim Software, Products, 2006).

Included in the list of the requirements are: general description of the system, requirements from the functional and user viewpoint, list of entry data, which are necessary for forecasting, requirements (coordinated with legislation regulating the corresponding sphere and with interests of the involved state institutions), description of the institutional IT solutions (for data entry, storage, analyzing, and preparation of reports), proposals for methodology of elaboration and implementation of the IT solution, and for distribution of functions in phases and for the managerial structure of the project.


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Detailed system specification was elaborated, which includes system data model, system function description, description of roles and access rights, description of interface with other software, requirements of performance and technical architecture.

In cost assessment, the main expense positions are distinguished: costs of elaboration works, purchase of software, purchase of technical equipment provision, and annual maintenance of the system. Specifications of IT system requirements will be possible to use as an annex to the tender regulations, if a decision is made to elaborate the labor market forecasting system.

Logical architecture of the system

The system consists of entry data sub-system (ensures maintenance and updating of information necessary for the system operations), calculation sub-system (ensures maintenance and updating of the mathematical model necessary for forecasting), analytical sub-system (ensures setting of forecasting parameters and obtainment of results in form of tables, diagrams, and charts), and administering sub-system (ensures user maintenance, definition of access rights and other administrative functions of the information system). (See Figure 16).

Figure 16. Logical architecture of the system


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Institutional solution

When working with definition of requirements of IT solution of the labor market forecasting, it was important that currently there is no established process (document flow, institutional cooperation model and decision making mechanism), which could be automated. The institutional description of IT solution is formed through modeling of this process with cooperation of the project researchers with the representatives of the state institutions. Considered was the alternating situation, therefore one of the conditions elaborated into the system requirements was its flexibility, adjustment properties, and extendibility. The requirements of the potential users were considered in the general description of IT system, by specifying the data to be stored and analyzed, as well as the reports to be created in the system. The requirements formulated in the institutional solution description would ensure functioning of forecasting models, as well as requirements of the institutions in respect to usage convenience and work process (See Figure 17).

Figure 17. General scheme of the institutional solution

The end users, in compliance with their functions, will be ensured with access of various levels to system functionality: for updating or processing of input data, for preparation of analytical reviews, for performing analysis, for viewing analytical reviews; other institutions and the society must be ensured with public access to the forecasts, which are simply and clearly prepared and designed specifically for this purpose.


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Terms and costs

The costs are described in division of projects phases and of sub-systems. Separately distinguished are the costs of software and technical provision and system maintenance costs. The calculations are based on evaluations of the researches on volume and complexity of the system to be elaborated, necessary qualifications of the attracted specialists, the number of the necessary man-hours, and current average man-hour rates in the industry. The costs are provided within limits that are established with the precision possible in the definition phase, however the actual costs depend on proposals provided by the potential elaborators. The costs of technical resources are to be evaluated in a broader range, because they are dependent on decisions on number of users in the system and a desire to use additional functions (for instance, perform also analysis of the existing situation in the forecasting system). The costs of the software are evaluated at a broader range than the costs of technical resources, because it is possible to select high-level licensed products as well as corresponding free code software programs. The maintenance costs included expenses for employing an administrator for maintaining the technical infrastructure (or selection of a corresponding external service provider) and the charge to the elaborator for maintenance of the system, as well as error correction, performance of necessary corrections and minor additions, which are calculated at 10-15% of the system elaboration costs (See Table 13).

Table 13. Costs of system elaboration

Sub-systems LVL (without VAT) Input data sub-system 24020-26700 Calculation sub-system 30120-36080 Analytical sub-system 25260-28960 Administering sub-system 10760-11940 Total costs 90160-103680 Annual maintenance costs 13240-21960

The total number of man-hours in the project is planned to be from 635 to 730. In time mode – from the project initiation, when the tender documentation is being prepared, until an operating, implemented system one full year would be necessary.

The costs for the technical resources and standard software are calculated, based on the prices published during the research performance progress by the official distributors (Price sheets of computer technologies and standard software, 2006). The costs of the technical resources (database server, application server, backup copying devices): LVL 6000-16000 (without value added tax). The costs of software (database management, application server software and modeling software) are within limits of LVL 2500-30000 (without value added tax).

Functional requirements

All sub-systems must ensure user authentication, authorization, and access rights control. The documentation to be developed for each sub-system – user’s reference book.


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Input data sub-system

The input data sub-system ensures maintenance and updating of the information necessary for model operation.

It is scheduled that the following user groups will be working with the input data sub-system: • Administering institution, • Data providers.

The following functionality must be ensured for the input data sub-system:

Firstly, manual maintenance of the classifiers, by using the input forms (types of classifiers with manual input), list viewer, classifier value adding, correction, and deletion. The necessary classifiers are as follows:

• professions, • profession groups (1-37), • conformance of profession groups and professions, • industries (A-O..P), • administrative units (regions), • education levels, • education spheres, • forecasting periods (years), • one-year age groups, • five-year and ten-year age groups.

Secondly, manual maintenance of input data, by using the input forms (types of input data, which can be manually entered), list viewing, correction, and deletion, as well as ensuring integrity of the input data. By using the input forms, it is possible to manually input the following data:

• GDP in industries, • Long-term investments in industries, • Forecasts of number of inhabitants in one-year age groups, • Number of inhabitants at the end of year, • Percentage of the economically active inhabitants in the age group, • Average monthly gross salary in industries, • Average monthly gross salary in profession group, • The distribution the employed in principle jobs in industries, • The employees, who have quit the profession, in profession groups, • Changes in employment in industries, • Retired or laid off due to reduction of staff in profession groups, • Retired or laid off due to reduction of staff in industries, • Students beginning studies in a particular profession group, • Number of students in a particular education level, education sphere, • Students beginning studies in a particular profession group,


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• Part of the graduates from those, who started studies (trained and examined) in industries, • Graduates working in the trained profession according to profession groups, • Graduates working in the industry, • Migration to other professions in profession groups, • Migration to other professions in industries, • Percentage of the employed in profession groups, • Number of the employed in profession groups and according to age, • Number of the employed in industries and according to age, • Number of the employed in profession groups, • Number of the employed and employment positions in industries and profession groups, • Pollution quotient, volume of emissions against GDP (tons/mill. LVL), • Investment quotient (investments against GDP), • Energy-intensity (energy consumption against GDP) (tons/mill. LVL), • Energy consumption for one inhabitant.

Thirdly, automatic input data import for data, which are available in corresponding format. These formats are MS Excel, MS Access, and SPSS files. As much as possible, all types of input data parallel to manual maintenance, also automatic data receipt from the data provider (interface) must be ensured, if the data provider has such technical potentialities. The system must perform automatic processing, correction, and data integrity check.

Calculation sub-system

The calculation sub-system ensures maintenance and updating of the mathematical model necessary for the forecasting process.

For the calculation sub-system, it is necessary to ensure model maintenance and configuration (calculations, formulas, parameters, etc.), model updating, setting and modification of the configuration parameters, and version control.

The main function, which must be ensured for the calculation sub-system is model operation – receipt of input data and classifiers necessary for calculations from a centralized database, automatic input data processing, receipt of performance parameters from the analytical sub-system, automatic selection of the most suitable model, storage of calculation interim results and final results in a centralized database. The results of the calculation sub-system (forecasts, analytical reviews) must be delivered to analytical sub-system.

Analytical sub-system

The analytical sub-system ensures setting of the forecasting parameters and obtainment of the results in a form of tables, diagrams, and charts.

The analytical sub-system must be ensured with the following functionality:

Firstly, preparation of analytical reviews – list viewing of available analytical report types, selection of the necessary analytical report type, specification of parameters, delivery of the analytical report parameters to the calculation sub-system, receipt of the prepared analytical


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report from the calculation sub-system, an option to store the prepared analytical report in the centralized database. The following analytical reports, which are to be acquired with the assistance of the system, are defined:

• Forecast of labor force demand in the country in general, • Forecast of labor force supply in the country in general, • Forecast of labor force demand in industries, • The ensured labor force in industries, • Forecast of labor force demand in profession groups, • Forecast of labor force supply in profession groups, • Forecast of labor force demand in profession groups and industries.

Secondly, management of the stored analytical reports – list viewing, selection, viewing, and deletion of the necessary report (according to the determined access rights), comparison with other report, export, publishing on the Internet (allocating public access).

Thirdly, the operations with the stored reports – viewing thereof (in a form of tables, diagrams, and charts) and export (in MS Excel, PDF, and XML format). Storage of the already created analytical reports and their parameters must be ensured as preparation for creation of new reports (for instance, recalculate a once defined report, when new input data are received).

Fourthly – public access to the Internet – viewing of publically available analytical reports.

Administering sub-system

The administering sub-system ensures user maintenance, definition of access rights, and other administrative functions of an information system.

The following functionality must be ensured for the administering sub-system: • User (administrator) authentication and authorization, control of access rights, • User maintenance – list viewing, user addition, correction, deletion, blocking, and

password change, • Role maintenance – list viewing, role addition, correction, deletion, and establishing

role access rights, • Maintenance of user’s access rights – allocation and deprivation of user roles, • Data auditing – recordkeeping of all changes made in the database (addition,

correction, deletion), viewing and selection of this information, • Events auditing – recordkeeping of operations performed by each user, viewing and

selection of this information, • Maintenance of system settings.

Data model

The result of the system analysis of the forecasting models is documented in the data model and data field description, in the description of functional requirements, as well as in the description of the system and the sub-system model. The data model describes the data structure for storage and processing of information system data, and the entity-relation modeling method is used for description thereof.


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Conclusions

As a result of the research, the requirement description for the IT solution has been created, which is the main tool necessary for implementation of the labor market forecasting system into the state administration. The necessary standard software, technologies, and costs for elaboration, implementation, and maintenance of the IT solution have been described.


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5. Conclusions and Recommendations

In the conclusions and recommendations, the newly developable labor market forecasting system application spheres are considered, as well as the key parameters of the system, the data to be gathered hereinafter for maintenance of the system, and the main forecasting results, which describe the Latvian labor market in short-term and long-term. Recommendations by the researchers have been provided for improving the situation, which would allow the state institutions to implement the balancing goals of the labor market supply and demand established in the policy planning documentation of the European Union and Latvia.

5.1. Most Significant Decisions and Matters, in Consideration of which the Forecasting System to be Created can be Used

During the research, when evaluating the wide spectrum of matters, which are related to labor market regulation, it was decreased to those matters, which directly can affect the labor force supply or demand. Thus, the grandest contribution of the forecasting system to be developed is going to be in solution of the following matters:

• State budgetary resource allocation, • Implementation of new labor market policy measures, • Establishment of problem causes in the labor market.

Researcher opinion in the most significant practical application spheres in the sectoral policy of the forecasting models

Education sector • Resource allocation to secondary and higher professional education programs

(maintenance costs and investments); • Distribution of the state paid student positions in specialties, education levels, and in

future – also according to regions.

Welfare sector • Allocation of resources for re-qualification of the unemployed and for programs of

improvement of professional skills (maintenance costs and investments), • Attraction of foreign labor force, • Consultations on selection of future profession.

Economic sector • Allocation of resources for re-qualification of the employed and for programs of

improvement of professional skills (cooperation with the entrepreneurs), • Information for attraction of foreign and local investments.


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During the research, it was established that when implementing the labor force supply and demand forecasting system, a valuable statistical information will be obtained about the current situation, as well as bout the future development trends and influence thereof on the balance of labor force supply and demand. When examining the spectrum of the matters, in consideration whereof the forecasting system to be developed can be used, it can be observed that generally the matters are related to changes of the labor force supply structure in the labor market. It is to a great extent related to the responsibility of state administration institutions about ensuring with proper labor force supply in the labor market. Thus, by using the forecasting system and timely planning of the labor force demand structure in the future, the state administration institutions will be able to perform this function – ensuring proper labor force supply – more precisely. By implementing the labor force supply and demand forecasting system, a widely applicable tool will be created for well based and coordinated labor market policy decision making.

Thus, we would like to emphasize that the forecasting system by itself will not improve the structure of labor force supply and demand and will not provide with distinct indications about the made decisions, however it will alleviate the work, in order to make political decisions according to the planned labor market development trends. The introducible forecasting system will ensure with an option for examination of the set forth political decisions in respect to their influence on the structure of labor market supply and demand, which will be ensured by the forecasting model, which would be created at the Research Institute, and correspondingly attracted team of experts.

As one of the significant aspects in the labor force supply and demand forecasting system is decreasing of the reaction time between the time when non-conformance of labor force supply and demand occurs and the moment of policy decision making. Therefore, we consider that the introducible forecasting system will provide with a grand contribution to timely and proper provision of labor force supply, by implementing significant improvements to policy decision making in the education sphere.

5.2. Optimum Degree of Detailed Elaboration and Sections for Establishing the Labor Force Supply and Demand

When elaborating the labor force supply and demand forecasting system, an analysis was performed about the decisions, which are made in state administration institutions in respect to use of labor force supply and demand forecasts, as well as the type of statistical information that would be necessary for making these decisions was established. The main goal of obtainment of this information is to establish the spectrum of the questions, for solution whereof the created forecasting model is used and so that the obtained statistical information would be corresponding for making the necessary decisions. Simultaneously, a solution for further development of the current forecasting model is offered so that it could provide with all the information, which is necessary for making political decisions in respect to the labor market issues.


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Table 14. Sections of Statistical Information for Making Decisions

The matter in acceptance whereof the information provided by the labor market supply and demand

long-term forecasting model will be used

What type of statistical information is necessary

for decision making

To what degree of detailed

elaboration the statistical

information is necessary

Option offered from the current forecasting model

Operations to be performed for obtaining the

necessary sections

Arrangement of professional training, re-qualification, and improvement of professional skills of the unemployed

Planned increase and decrease of the number of employees at the permanent jobs

Divisions of professions, regions, districts, and cities, economic sectors

Alterations in the number of the employed in profession groups and economic sectors

Improvement of DOM dimensions, obtainment of the statistical information in regional sections

Changes of demand in products/services in companies

Division in regions and economic sectors

None Elaborate corresponding module

GDP alterations In divisions of regions and economic sectors

The forecasted GDP alterations in economic sectors

Supplementing the DOM dimensions, obtainment of statistical information in regional sections

Alterations in the number of the employed

In division of regions and economic sectors

Alterations in the number of the employed in economic sectors


Planned increase and decrease of the number of the employed in at permanent jobs

In division of professions, regions, and economic sectors

Alterations in the number of the employed in profession groups and economic sectors


Preparation of short-term forecasts of the employment alterations, short-term unemployment forecasts, and short-term labor market development forecasts

Alterations in number of the registered unemployed individuals

In division of professions, regions, and economic sectors

The number of those looking for a job in section of profession groups


Distribution of the number of state financed study positions in specialties and levels

Labor force demand in sectors and regions, by indicating the alterations in time progress Detailed research on work experience of the graduates within 1-3 years after graduating from a college or higher educational establishment: working according to the obtained specialty continues studies working in another sector – why? not working – why?

In the country in general In regions In sectors and levels of higher education

Optimization of the study positions in the spheres of education and levels, which conform to a certain profession group Labor force demand in division of profession groups and sectors



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The matter in

acceptance whereof the information provided by the labor market supply and demand long-term forecasting model will

be used


for decision making






necessary sections

Cancelling the student loans and credits from the state budgetary funds

Labor force demand in sectors and regions, by indicating the alteration trends in time progress Salary of specialists, who have obtained all level higher education in sectors and regions, salary fluctuation forecasts

In the country in general In regions In sectors and levels of higher education

The number of the graduates in higher level studies, in divisions of the education spheres


Forecasting the necessary number of students in the country in general and elaboration of proposals for state financed number of students in each sector

About those employed in the given sector. Division of sectors in regions Division of the graduates in sectors

Age structure, gender structure

The number of the employed in division of economic sectors and profession groups. The number of graduates in a specific profession group


Consulting the school-students and adults in selection of future profession

Existing labor force demand and supply in specific professions. Planned labor force supply and demand upon obtainment of education in specific professions

In regional section (2nd level division in 33 cities and districts) In section of the professions

Alterations in labor force supply and demand in division of profession groups


The information will be available for use by establishing the priorities and providing with support to the professional educational establishments: (in elaboration of profession standard; in elaboration of the education programs; in professional improvement of the professional education pedagogues)

Denomination of the professional qualification

Not applicable for the statistical models

Evaluation and accreditation of the study program quality

Demand for the graduates in labor market for each study program

Regional section Qualifications and/or study programs

Not applicable for the statistical models

Analysis of socio-economic situation in the section of the state and the regions, including the employment issues

GDP, unemployment, non-financial investment, residents’ income tax, demographic load, number of economically active enterprises and businesses, density of the inhabitants, average cadastre value of land, alterations in the number of inhabitants

Planning regions, districts, cities, territories, parishes

GDP, DOM input data: capital forecasts in division of sectors, demographic forecasts



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The matter in acceptance whereof the

information provided by the labor market supply and demand long-term forecasting model will

be used


for decision making






necessary sections

For establishing priority regional development directions, which will promote economic growth of the region, competitiveness, welfare growth of the inhabitants, preservation of healthy and safe environment, creating prerequisites for sustainable and balanced development of the region

Alterations in employment (%), the number of the employed in professions, investment volume in manufacturing technologies and in human resources, demographical situation, alterations and migration of the unemployed and the employed


Alterations in employment (%), in profession groups – the number of the employed, alterations in change of the employed, migration, demographic forecasts


For performing researches on regional development in Latvia



Not available in regional section


Absorption of financial resources of EU, national, and other support tools





System of monitoring and evaluation of regional development





Connection of study programs at regional higher educational establishments (branches of higher educational establishments) with the labor market requirements in the region

Alteration in employment (%), in professions, the number of the employed, the volume of investments in manufacturing technologies and in human resources, the demographical situation, alterations of the unemployed and the employed, migration, number of graduates, number of unemployed in the obtained profession

Planning regions, districts




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The matter in acceptance whereof the

information provided by the labor market supply and demand long-term forecasting model will

be used


for decision making






necessary sections

For description and comparison of the socio-economic development of the territory





Availability and disposition (density) of the labor force in certain territories

Number of workers in certain territories (employed and unemployed) Division of territories (cities, districts, regions) Qualification of the employees



Qualification of the labor force (education, experience)

Education (higher, special, secondary) Experience in certain sphere (years worked in the sphere) Language knowledge

The number of economically active residents in a certain profession group

Students studying in certain study programs, graduates (various level educational establishments)

Number in years (admitted, graduated, to be admitted), qualification to be obtained, education (those, who have obtained qualification, education). Disposition of the higher educational establishments and specialized schools (districts)

The number of the admitted, the students, and the graduates in a certain education sphere/group. The geographical disposition is not available

Labor force costs (monthly salaries + bonuses + other benefits)

According to quali-fication (education, experience) According to profession (profession group) According to disposition (cities, districts, regions) According to the employment sector. LVL or EUR monthly

The forecasted labor force cost relation available in DOM between the profession groups, which are influenced by the supply and demand correlation


Attraction of foreign investments

Labor force age structure

Age (levels) Qualification (edu-cation, experience) Disposition (cities, districts, regions)

Age structure of the labor force, incl., in profession group section



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The labor force supply and demand forecasting system to be elaborated within limits of possibilities has strived to provide with all of the necessary statistical information for analysis of the necessary political decisions and for justified decision making (see Table 14). Even though the offered forecasting system provides a wide spectrum of statistical information, it cannot fully offer all desired sections and degrees of detailed elaboration for making the above mentioned decisions. While elaborating the current forecasting model, the main attention was geared towards acquiring correct basic data in the key sections and thus the most significant political decisions in respect to the labor market could be made. The main difference of the statistical information offered by the current model from the desired is related to not having access to data in regional section and in respect to certain professions. The most significant deficiency in obtaining the necessary data in regional section is lack of degree of detailed elaboration of the input data. For solving this problem, obtainment of input data in regional section should be ensured, by increasing the labor force survey selection and correspondently increasing the input data – thus, the desired degree of detailed elaboration will be achieved. In respect to the necessary data in profession section, the existing forecasting model offers a section of 37 professions. The key advantages of this distinction are that the profession groups are stable in a long period of time, it is also numerically significant, and envelopes all labor market, which was also the determinant factor for transition from the profession section to the section of profession groups. Therefore, we can conclude that the key question in obtaining statistical information in the necessary sections is related to qualitative and quantitative amplification of the input data.

5.3. Necessary Statistical Information for Determining the Labor Force Supply and Demand

The information necessary for model elaboration is summarized in the data array used in the research:

• Data obtained directly from the information sources, • Data obtained trough calculations:

− Aggregated data and data recalculated according to the methodological modifications,

− Evaluations.

The direct data was received from the data source without additional processing, for instance, “Number of inhabitants and forecasts thereof in one-year age groups”. The aggregated data were calculated, by summarizing the initial information, for instance, “Number of filled positions in all professions at 37 profession groups”. According to the methodological alterations, calculated were, for instance, “those employed at principal and additional work” and “those employed at full and partial time”. Evaluations were necessary for calculating such indices, about which information is not available in such degree of detailed elaboration that is necessary for the forecasting models. For instance, data about distribution of the employed in on–year age groups and in 37 profession groups during the time frame from 1997 until 2005. The evaluations were isolated as a separate data group with a purpose to provide with an option to evaluate the data credibility.


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Table 15. Contents of the data array used in the research

Title of the parameter Data sourceManner of

information obtainment

Information users

Inhabitants Forecasts of inhabitant count in one-year age groups

Eurostat Data obtained directly from the information source

Forecasting models in MS Excel environment DOM in Powersim Studio environment

Employment Quotients of economic activity in age groups

Eurostat CSP

Data obtained directly from the information source

DOM in Powersim Studio environment

Number of the filled positions, number of the employed in Latvia in sectors and in 37 profession groups from year 1997 until year 2005

CSP Calculation data DOM in Powersim Studio environment

Average gross monthly salary, LVL from 1997 until 2005

CSP Calculation data DOM in Powersim Studio environment

Division of the number of the unemployed according to the last profession from year 2003 until year 2005

NVA Data obtained directly from the information source


Number of the employed on January 1, 2006 in 37 profession groups

DDA Calculation data DOM in Powersim Studio environment

Number of the employed on January 1, 2006 in 37 profession groups and age groups


Number of vacant positions on January 1, 2006 in 37 profession groups


Number of vacant positions on January 1, 2006 in types of economic activity (sectors)


Number of vacant positions on January 1, 2006 in types of economic activity and 37 profession groups


Distribution of the employed in 37 profession groups and one-year age groups in year 2006

DŅA Calculation data Forecasting models in MS Excel environment DOM in Powersim Studio environment

Distribution of the employed according to types of economic activity and in one-year age groups in year 2006

DŅA Calculation data Forecasting models in MS Excel environment

Distribution of the employed in 37 profession groups and in one-year age groups from year 1997 until year 2005

DŅA, CSP – ”Labor force survey”

Evaluation Forecasting models in MS Excel environment

Distribution of the employed according to the types of economic activity and in one-year age groups from year 1997 until year 2005

DŅA, CSP – ”Labor force survey”

Evaluation Forecasting models in MS Excel environment

Distribution of the respondents according to the answer on a question about changes during the last 7 years of life in 37 profession groups from year 1999 until year 2005

DŅA Calculation data Forecasting models in MS Excel environment

Distribution of the respondents according to the answer on a question about changes during the last 7 years of life in 37 profession groups and in one-year age groups from year 1999 until year 2005

DŅA Evaluation Forecasting models in MS Excel environment


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Title of the parameter Data sourceManner of

information obtainment

Information users

Education Number of students according to the education levels and programs

IZM Data obtained directly from the information source

Forecasting models in MS Excel environment DOM in Powersim Studio environment

Number of admitted students according to the education levels and programs



Data about prior education of those admitted to certain education level



Number of graduates from educational establishments according to the education levels and programs

IZM Forecasting models in MS Excel environment DOM in Powersim Studio environment

Number of state financed study positions in education levels and study programs

IZM DOM in Powersim Studio environment

Number of study positions in education levels and study programs

IZM DOM in Powersim Studio environment

Tuition in education levels and study programs

GDP GDP according to the types of economic activity, mill. LVL

CSP Data obtained directly from the information source


GDP growth forecasts Researches of the 1st component



Capital growth forecasts Researches of the 1st component



Detailed labor force demand forecasts in 37 aggregated profession groups from year 2007 until year 2014, year 2020, and year 2030

Researches of the 1st component



Environment and resources Investments in environmental protection according to industries Volume of emissions (tons) in industries Energy consumption (petroleum equivalent of a ton) in industries

CSP Data obtained directly from the information source


For creation of data array used in the research (See Table 15), past data were arranged according to the model elaboration requirements and data available in the future, the existing data were recognized, and missing data were identified, additionally necessary data were received from DDA and DŅA.

For obtaining past data the following was performed: − Calculation of the dynamic row parameters, according to the changes of methodical

character, − In cases when more detailed data is necessary, the parameter evaluation was ensured,

by applying statistical methods, based on information of various (compatible) data (secondary and primary) sources.


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Further special surveys are not necessary for functioning of forecasting models. Information, which is to additionally acquired is obtained during the already occurring surveys, for instance, in CSP Labor force survey, Profession survey, additionally about work, 2nd work (quarterly), by supplementing to the existing questionnaires and by increasing the number of respondents. It is important to further obtain data about distribution of the employed according to types of economic activities, professions, and one-year age groups, as well as according to the profession of the employed one year before.

Figure 18. Data to be used in forecasting

Obtainment of future parameters necessary for model elaboration is scheduled from the following information sources (See Figure 18):

• Labor force demand: − CSP “Profession survey”; − NVA “Employer survey”;

• Labor force supply: − CSP “Labor force survey”.

Information about labor force supply

Information about labor force supply hereinafter is planned to be received from the data of Labor force survey. Information for forecasting models in profession section is necessary to a four sign code degree of detailed elaboration, according to the LR Profession classifier code in types of economic activity and one-year age groups. The profession section in four sign code is necessary, in order to calculate the parameters for the forecasting models in division of the elaborated 37 profession groups. Therefore, in order to obtain representative information during the Labor force surveys, which is necessary for the forecasting models, it is desirable to ensure the volume of selection to be around 50-60 thousand inhabitants. The volume of selection of medium-term forecasts could be additionally increased (total selection reaching 80 thousand), in order to obtain even more detailed forecasts in regional section.


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Information about labor force demand

Hereinafter, the information necessary for forecasting models is scheduled to be received from the Profession survey data performed by CSP. In year 2006, the questions of the Profession survey were included in the Salary structure survey. The questionnaire of the survey (Report on salary structure in year 2006, 5th work. Part B. Data about employees) is necessary to be supplemented with a feature characterizing the employees: principal work – 1; additional work – 2.

The parameters are necessary in order to preclude interruption in the employee count dynamics, because, beginning with reports of year 2004, the number of employees in an enterprise (institution, organization) is divided into those working full time and those working part time. In previous years, the number of employees in CSP reports was divided into those working at a principal job and at an additional job.

Report about job (quarterly), 2nd work (chapter 4). The number of the employees and vacant positions in professions is preferable to be obtained at a greater degree of detailed elaboration.

The volume of the selections in Profession surveys would have to be maintained at least to the existing extent.

CSP and NVA have accumulated significant experience in arranging selection surveys. Experience shows that data obtained are becoming more complete by every year, furthermore, by acquiring additional financing, the volume of selection and the number of the survey questions could be increased. Thus, the customer can receive the necessary information on a regular basis.

5.4. Recommended Analytical and Statistical Forecasting Methods

For determining the conformance of labor force supply and demand as a result of a long-term research, various types of analytical and statistical forecasting methods are recommended, each of them performing a certain task in the common system (See Table 16).

Table 16. Applicable analytical and statistical forecasting methods

Applicable forecasting

methods In MS Excel environment In Powersim Studio environment

Basic methods

1. Statistical modeling It is based on regression analysis: linear, logarithmic, exponential, degree, hyperbolic, parabolic. It is used for forecasting theoretical values of factors. As the labor force market development dynamics in each industry, profession group, etc., is individual and has its own specifics, the further growth trends thereof cannot be forecasting according to one template. Six alternatives regression analysis models are used, of which each forecasting sphere and term (short-term, medium-term, and long-term) is fit with the

1. System dynamics It is used for examination of labor market structure and for analysis of reciprocal influence of the system elements. 2. Simulating modeling It is used for forecasting development alternatives of the situations to be modeled.


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most adequate, i.e., most credible model. Data interpolation is used to evaluate the missing data in the dynamics of the number of the employed in one-year age groups.

Auxiliary methods

2. Market research It is used for obtainment of input data and for comparison of the results. 3. Expert evaluation Used for logical examination and corrections of long-term forecasts and strategic evaluations 4. Econometric models Used for establishing the labor force market balance

3. Market research It is used for obtainment of input data and for comparison of the results 4. Correlation and regression analysis (one and multi-factor). It is used for selection of factors influencing labor market development and for forecasting thereof. 5. Econometric models Used for establishing the labor force market balance. 6. Expert evaluation Used for logical examination and corrections of long-term forecasts and strategic evaluations, as well as for modeling of DOM scenarios

5.5. Causes of Nonconformance of Labor Force Supply and Demand and Options of Preclusion thereof

The labor force demand can be divided into expansion demand, which is caused due to economic growth and which manifests itself in creation of new employment positions, and substitution demand, which is caused due to aging of the labor force and vacating the existing employment positions. In both cases, vacancies are created and filling thereof is dependent on the labor force supply. The labor force supply is described by two aspects – the quantitative and qualitative. The quantitative aspect of labor force is determined by the demographical situation in the country and the migration flows, as well as level of economic activity. However the qualitative aspect of the labor force is established by the level of labor force qualification.

Nonconformance of supply and demand in labor force market may arise due to unbalance development of any of its components. Quantitative disproportion in labor force market can originate if, for instance, the economy is developing at decelerated pace, creating a relatively smaller labor force demand, however the demographical situation develops explosively, as a result of which the inhabitants are not employed to the fullest. Such situation could be observed in underdeveloped countries, for instance, in the poor countries in Africa.

If explosive growth demographical situation occurs simultaneously with rapid economic growth pace, a more balanced labor force market development is guaranteed. A good example for this situation currently is China.

In the developed countries of Europe, a process could be observed of decreasing demographical situation and the pace of economic growth is also not very high, and there the labor force is ensured by importing it (through immigration) and by developing technologies.

In Latvia, a rather peculiar process could be observed – similar to the developed European countries, the demographical situation is deteriorating and similarly to the developed countries –


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the economic growth pace is high. In short-term, such growth is ensured by a more active participation of the inhabitants or employment and higher load of the devices, however in long-term, in order to maintain high development pace, goal-oriented measures should be performed for building productivity. Simplified occasions are displayed in Table 17.

Table 17. Comparison of demographic and economic situation

Economic development

Demographics

Good Poor

Good China + + Africa + -

Poor Latvia - + Europe - -

It could be stated that provided the given conditions both in the developed European countries and in China, a balanced labor force market development could be observed, which cannot be said about the cases of poor countries in Africa and of Latvia.

In both cases, these are lost opportunities – the development of the country would be more rapid if it would employ all available labor force resources or if the resources of labor force would be available in significant amount. Therefore it is very important to ensure balanced development of labor force market.

Previously considered were the conformance aspects of quantitative labor force demand and supply, however not less important is to ensure balance of labor force market in the qualitative aspect, when the qualification of the labor force supply conforms to the qualification required by the employers.

Considering the above mentioned, the most significant causes of nonconformance between supply and demand, which are elaborated into the dynamic optimization model, are connected with the following factors:

• Demographic situation of the country

In case of Latvia, considering the pessimistic demographical forecasts, the economy must achieve the optimistic development goals.

• Economic development scenarios

The goals of economic development must be balanced with the availability of labor force in the country.

• Technological progress

Provided the slow technological development, the economic sectors require a bigger volume of labor force, in order to ensure the economic growth. And vice versa – growth is ensured if the economic production volume increases faster than the increase of the employed, i.e., labor productivity is increasing.

• Factor of labor force migration

It is used as an additional factor, with assistance of which additional labor force quantitative provision scenarios can be created.


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Reciprocity of the first four factors ensures balance or disproportion of the quantitative labor force supply and demand. In order to ensure balanced labor market development in a quantitative sense, it must be ensured that the demographic trends, economic growth trends, and productivity growth trends would promote sensible conformance of labor force market supply and demand. An option has been elaborated in the dynamic optimization model, which allows varying with the numerical features of these factors, in order to set forth hypotheses about optimum development scenario.

• Structure of the education system

It must ensure formation of labor force supply corresponding to the economic requirements. This is the key factor, with assistance of which the model users may modify the qualitative conformance of the labor force supply to the demand, thus creating the optimum distribution of labor force in profession groups, thus in situation, when in the labor force market the quantitative feature is indicative of balance also at a more detailed level (economic sectors or profession groups), a state of balance or nearing the state of balance could be observed.

In a situation, when the supply on a general level of the labor force market does not satisfy the demand, with corrections to the education system policy it could be achieved that primarily such sectors are ensured with proper labor force, which are considered as strategically significant. Thus, the labor force division between various profession groups is being optimized.

• Re-qualification of the labor force

This is another significant way of organizing optimum distribution of labor force market. There is no automatic mechanism elaborated into the dynamic optimization model, which would ensure migration between various profession groups, however it is designed that the user, by analyzing the data tables, can make conclusions about the possible re-qualification scenarios depending on the profession groups, which have formed surplus and which have deficiency of labor force supply.

• Slow reaction time of the labor market

Slow reaction time of the involved participants (employers, employees, and state institutions) affects the labor market significantly, firstly, it is related to the fact that time is necessary to create a vacancy, when a necessity (demand) is recognized for workers of the specific profession, secondly, in order to find a suitable candidate and fill the vacancy, and thirdly, if there is no labor force supply for the specific profession in the labor market or if the existing supply does not conform to the demand and the vacancy is not filled for a long time, time is necessary for the employers to recognized this situation and react accordingly, by increasing the salary to attract the necessary labor force, fourthly, the inhabitants require time to recognize the economic demand for labor force of specific profession, fifthly, time is required for obtaining corresponding education. Thus the labor force supply is attempting to adjust itself to the labor force demand, which in turn is fluctuating again. As a result, the total delay time may reach even up to 10 years, during which the initial labor force demand has changed. Therefore, the shorter is the delay time, the more flexible is the labor force supply, and the faster a state of balance can occur in the labor force market.

In the dynamic optimization model, there is an option to examine the results of measures taken to preclude disbalance in the labor force market, by reviewing various scenarios influenced by delay time.


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The ways of solving nonconformance of labor force supply and demand can be multiple and they derive from the identified causes of nonconformance.

In order to preclude nonconformance of labor force supply and demand on a macroeconomic level, several alternatives are possible:

a. In cases when the increase of production volume is not possible by quantitative increase of labor force volume, given ever decreasing labor force volume significant changes are necessary in the level of productivity, by implementing more modern technologies and by increasing the load of the labor force and equipment;

b. Importing labor force of the required quality into Latvia from other countries, thus increasing the labor force volume both – quantitatively and qualitatively;

c. Promotion of Latvian residents to more active participation in the economic life (students, young mothers, senior citizens employed in part time jobs), thus increasing the percentage of the economically active residents.

In order to preclude the key cause of nonconformance of labor force supply and demand on a macroeconomic level – nonconformance of the labor force qualifications to the market demands – one of the main solutions is adaptation of the education system to the new situation, ensuring that the newcomers to the labor market could find a suitable job for themselves. Firstly, by balancing the structure of education system (number of student positions in the professional programs) in such way that it would support long-term economic demand.

Secondly, the already commenced work must be carried on in development of lifelong learning, which includes improvement of professional skills of the employed, as well as training of the unemployed, and re-qualification from one profession group to another. There is no lifelong learning structure elaborated into the DOM model, however the results of the model provide with exhaustive information, therefore the users could conclude by analyzing the data on nonconformance of supply/demand, as to which profession groups are available for re-qualification to the deficient professions.

Thirdly, in order to achieve a faster balancing of labor force supply and demand, it is necessary to decrease the total delay time of labor market reaction to the changes in economic structure, by performing complex measures:

• In order to facilitate the employers in search for employees, detailed information about those seeking for a job, their level of qualification could be provided (also promotion of use of services by human resource management companies),

• Informing the society (employers and potential employees) about the trends existent in the market, in order for the labor market participants to be able to react faster to the occurring changes.

The offered models allow examination of influence by various managerial decisions on the labor market development, which in turn provides with an option to select the optimum strategy.


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5.6. Conformance of the Labor Force Supply to the Demand in Short-Term

The short-term forecasting model elaborated in MS Excel environment establishes the possible labor force supply and demand development forecasts in profession groups and industries for the upcoming 2-3 years (planned year + two forecasted years). The results of this modeling block can be used for planning of certain short-term education programs, for sustainable planning of educational system and for separate study programs.

The results of short-term forecasting are provided for a more detailed research and analysis of the current situation, as well as for comparison of the obtained results with the original version of the Swedish Labor Market Agency model.

The overview on the forecasting relations of the obtained supply and demand is already displayed in Chapter 4.6, however for better visibility only several profession groups and industries are displayed. Currently, it is important, for instance, to observe the trends of fluctuations in numerical supply and demand in professions of the teachers, based on demographical forecasts, prior dynamics, as well as several other factors.

Figure 19. Demand, supply of teachers

As it could be seen from Figure 19, the supply and demand in this profession is almost at balance during the following couple of years and it is planned to remain at such level (with interrupted lines in Figure 19 the possible deviation intervals are displayed “from” and “until”, within limits of which the forecasts could fluctuate, if the current labor and/or general

Annual fluctuations of supply and demand

0

20000

40000

60000

80000

100000

120000

1996. 1997. 1998. 1999. 2000. 2001. 2002. 2003. 2004. 2005. 2006. 2007. 2008.

Years

Num

ber o

f ind

ivid

uals

Total number of labor force demand Total labor force demand from Total labor force supply until Total number of f labor force demand Total labor force supply from Total labor force supply until


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economic conditions would slightly change in positive or on the contrary – in negative direction). Such situation also has an explanation – in the surveys generally the number of filled positions are researched, which currently on such level is only due to selflessness and other features of the existing pedagogies, and the employed pedagogues work 1 ½, 2, and even more shifts. Therefore it is necessary to seek urgently for solutions to this problem, for instance, by subsidizing teachers in training spheres, where there is the biggest deficit of specialists (for instance, in math, physics, foreign languages, etc.). Of course, this activity would not solve the general problem. For improvement of the situation, a radical decision must be made by the government in salary policy.

Table 18. Short-term forecasts of labor force supply and demand

Labor force supply and demand forecasts in industries, short-term

2006. 2007. 2008.

Total labor force demand

Total labor force supply





Industries number from to number from to number from to number from to number from to number from to

A 13580 10398 35880 20558 11278 40196 18123 9365 34980 19253 10180 39903 16973 8404 34173 18032 9160 40047

B 2089 1209 2850 2087 1190 9812 2038 1166 2911 2087 1142 11867 2032 1105 2961 2087 1090 14834

C 3752 2743 3925 3579 2605 5315 3559 2930 4189 3740 2736 5565 3792 3123 4462 3927 2887 5859

D 196747 165436 240749 201771 161107 254748 202956 163028 243111 201771 158730 259456 202882 160453 245600 201771 156156 264983

E 25155 13115 36036 25083 13237 38228 24582 12388 36781 25092 12574 39151 24577 11601 37558 25101 11856 40168

F 48914 40469 63183 52295 39888 65987 51828 39750 63922 52016 39002 66447 51823 38971 64693 51706 38187 66920

G 207579 154400 247992 194056 145910 246388 206956 157746 256197 199673 149148 254799 212718 160895 264570 205299 152158 263533

H 24662 16935 33129 24200 15982 37959 24819 17360 32291 24975 16407 40042 25598 11742 33467 25750 16797 42371

I 88322 77201 110693 93370 75838 111802 93933 76138 111779 93370 74786 112976 93928 74993 112918 93370 73647 11459

J 23895 11975 25282 21781 18244 27640 23910 21085 26729 22893 19144 29363 25314 22307 28310 24141 20158 31330

K 75032 58993 84203 69490 55344 85151 73375 60120 86639 71233 56346 87837 75142 61183 89110 72977 57279 90627

L 81722 79588 94834 87036 76951 98433 87095 79116 95362 86769 76270 98828 87090 78600 95913 86484 75681 99258

M 114305 92425 141337 114686 87422 144697 116881 90865 142914 114609 85811 146430 116876 89192 144575 114542 84078 148350

N 67391 49073 86007 66995 44299 93987 67532 47898 87202 66995 43031 95835 67527 46634 88458 66995 41669 97870

O 53565 49184 55271 50275 46515 54273 54126 50898 57376 51920 47927 56193 56237 52802 59697 53781 49527 58360

In order to display the current situation and its proximate time perspectives in the labor market more visibly, in Table 18 the possible development trends of labor market supply and demand until year 2008 are displayed.

By analyzing the acquired results, it can be seen that with an exception of several industries, the labor market situation is close to a balance, besides both – the demand and supply have a tendency to increase, however not steadily, therefore if the current policy is not changed in many spheres, then unemployment will rise and at the same time there will surplus of the labor force.

Special situation has developed in construction, where there already is a significant deficiency of specialists, considering the great demand for constructions, repairs and similar works in residential houses and office buildings. The current number of construction specialists cannot satisfy the fast growing demand. An important cause for such situation is also the fact that many qualified workers have left Latvia and gone to earn money in other countries of Europe. For improvement of situation, it would be recommended to review the distribution of state financed positions in secondary professional and in higher educational establishment level, as well as allocate additional measures for re-qualification of the unemployed and for arrangement of improvement of professional skills.


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5.7. Conformance of the Anticipated Labor Force Supply to the Demand in Long-Term

The statistical modeling trend forecasts are elaborated based on dynamics data, in addition using expert evaluations for obtaining long-term forecasts. Thus, the obtained forecasts are elaborated and analyzed according to one scenario. However the DOM is designed for examination of various development scenarios and the obtainable results are directly dependent on the values of the selected exogenous variables or the applied alternative scenarios.

When analyzing the forecasts obtained in MS Excel environment with the statistical modeling, the following conclusions could be made about the labor force market development:

• When researching the obtained long-term forecasts, their development trends are similar to the short-term forecasts, however the disbalance tends to become more expressed further away in the future.

Generally in the labor market, deficit of qualified labor force could be observed, in addition the tendency of the deficiency is increasing. One of the main causes of such development scenario is the demographical situation in Latvia.

• When researching the possible development trends in section of Profession groups (for more details, please, see Annex 4):

− In long-term, without changing the current policy in labor market and in education system, large deficit of the employed will be observed in the following profession groups: architects (111%), construction professions (93%), specialists of natural sciences (64%), scientists (52%), and engineers (50%).

In order for these forecasts not to become true, it is recommended to perform promotion to these study spheres and attraction of entrants to the corresponding studies, paying particular attention to maximization of the new specialist qualification (maximum development of the skills of individuals already in school, as well as in higher educational establishments and professional schools, by evaluating the study length and the necessity for state exams, evaluation of the obtained qualification, etc.).

− Opposite situation in long-term could be observed in professions related to jurisprudence and economy (surplus 64-69%) and in other spheres of social sciences, as well as in agricultural specialties.

The causes of such situation can be sought already during the transition economy period, when the entrants were trying to obtain one of the above mentioned specialties in large quantities (except for agricultural specialists, whose surplus is related to consequences of restructuring of the agricultural industry) with a view to earn good money. The reasons for surplus in specialists of professions related to agriculture are connected with regulations of the European Union, the rapid technological development, as a result of which the necessity for agricultural laborers and representatives of other low-qualified professions has decreased. The highly qualified specialists, owing to knowledge and new innovations, are able to


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envelope ever broader field of activities, therefore the demand for additional specialists is also decreasing.

As it has been already mentioned, depending on the purpose of use of DOM and on the alternative scenarios to be examined, the user can acquire various long-term forecast results, especially in division of separate profession groups. However regardless of the selected scenarios, common conclusions can be made about the long-term labor market development trends:

• The EUROSTAT demographic forecasts of the number of inhabitants used in DOM show that the decreasing trends of all labor capacitated inhabitants in all types of alternative scenarios (see Annex 4) and therefore also in the future, unless the level of economic activity increases, decrease of common labor force supply is expected;

• The demand for labor force in short-term is indicative of increasing trends in all types of economic development alternative scenarios. Demand for labor force in long-term can decrease, given a slow economic convergence scenario and effective application of technological progress development;

• The obtained DOM results are indicative of that in short-term an insignificant surplus of labor force is maintained. However in long-term, labor force supply deficiency is expected even at the most beneficial demographic scenario and at slow economic convergence development. This conclusion is valid provided a condition that the economic conjuncture will not change dramatically and no significant “breakthrough” is going to occur in technological development, or there will be no radical changes to the immigration policy;

• Practically, even given the optimum balancing scenario, it is not possible to fully ensure the qualitative labor force supply according to the demand in such short time (~20 years) only with ensuring conformance of the education system to the requirements of labor market. The education system provides with the initial professional direction, following the labor market trends with a certain delay time and only for two age groups (15-19 and 20-24 years) from all totality of the economically active inhabitants. In order to exchange the qualitative (professional) labor force supply structure in such manner, a cycle of at least one generation exchange would be necessary, however the economic conjuncture meanwhile would have changes and accordingly – so would the labor force demand;

• In order to ensure the labor market with a supply corresponding to the demand, a great importance is to the exchange of the qualitative labor force structure in all inhabitant age groups, i.e., by careful planning not just of the basic professional education, but also the continuous education and lifelong learning according to the long-term requirements of the labor market;

• As it is expected during the period of analysis to have not just qualitative but also quantitative disproportion between the labor force supply and demand, unless significant improvements are made on the level of productivity, which could quantitatively decrease the demand for the labor force resource, in future Latvia will encounter the necessity to attract labor force from other countries;

• When analyzing the quantitative disproportions in the labor force market, it must be noted that undesirable situation could be observed when the total economic labor force demand is not fully satisfied while at the same time in certain profession groups labor force surplus is observed. A tendency has been established where most of the


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professions, which in long-term maintain labor force surplus, are the social and humanitarian professions, for instance:

A_2 “Specialists of humanitarian sciences and representatives of the creative professions”

A_3 “Other specialists of social sciences” A_38 “Legal professions (judges, attorneys, etc.) V_2 “Art, culture, recreational specialists”

Labor force surplus could be observed also in such profession groups as A_6 “Agronomists, senior specialists of other agricultural professions” and V_81 “Individual services”.

Such results are indicative of the features of structural unemployment, the cause of which could be effected by changes in certain growth trends, as well as by development of technological progress, due to which the labor force demand for certain profession groups is decreasing.

Representatives of the above mentioned professions must be ensured with options of re-qualification, continuous education, and lifelong learning, in order to change the qualification from the undemanded profession groups to a demanded related profession;

• However the labor force deficiency is observed in the professions of exact sciences, in professions of industrial and construction specialists, in medical professions, in teacher and defense specialist professions.

Considering the fact that these professions are with limited options of re-qualifications, the state must take measures to promote these professions amongst the youth, by stimulating their selection with increased number of state financed study positions, by allocating special scholarships, etc.

In order to promote a more balanced labor market development, a well thought through policy in regards to the distribution of study positions must be implemented, considering the forecasted labor market development tendencies.

In general, it must be concluded that demand from the medium and lower level qualified specialists is increasing, therefore it is necessary to promote selection of the youth in favor of obtaining the secondary professional education, not limiting to obtaining general education.

Professions, which require higher education level, must be applied with a more timely national planning in respect to the supportable education spheres, because in these profession groups the balancing of labor force supply and demand is occurring with a greater delay time;

• When analyzing the sectoral provision with labor force resources, it must be concluded that the most critical situation is forming in those sectors, where qualified labor force with exact knowledge is necessary:

C “Extractive industry and quarry production”, D “Manufacturing industry”, E “Electric energy, gas and water supply”, F “Construction”, I “Transportation, storage, and communications”.


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In these sectors, a significant deficiency of labor force is observed beginning with the medium-term forecasting horizon, however for two sectors – D and F – the deficiency could be observed during the whole forecasting period.

The sectors H – “Hotels and restaurants” and J – “Financial intermediation” also in the strategic forecasting horizon are relatively better supplied with the labor force. It must be noted that it is these industries, where the representatives of social and humanitarian professions are employed, the supply whereof in long-term is scheduled to have surplus.


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6. Policy Alternatives

6.1. Selected Alternatives

Initially for defining the alternative, the experts defined the problem: “There is no uniform and coordinated medium-term and long-term labor force supply and demand forecasting system present in the country, upon which the labor market policy could be based, thus there is a high structural unemployment level in Latvia and many of the employed are not working according to their education”.

Due to lack of such uniform and coordinated system, the reaction time of the state responsible ministries and services onto the changes in economic structure is too long. The gaps in labor market forecasts, which should be used for making decisions related to labor market, as well as the slow policy reaction time indirectly affects also the following indices of the state:

• Level of structural unemployment;

• High number of those working not corresponding to the obtained education;

• Deficiency of labor force in specific industries and profession groups;

• Employment mostly in low added value industries/professions;

• Career choice of the existing and potential employees not corresponding to the economic structure.

Further on, LM selected the following alternatives for problem solution and further analysis.

Alternative – Research Maintenance Alternative

Within framework of this alternative, it is assumed that results of this research will be used for problem solving. The institutions involved in forecasting of labor market supply and demand will use the medium-term and long-term labor force supply and demand forecasts performed within framework of this research.

LM will be performing updates to the forecasts obtained within framework of research once a year (See Figure 20). Thus, the involved institutions will have access to frequently updated forecasts with expert commentaries.


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Figure 20. Mechanism of cooperation between institutions in case of Research Maintenance Alternative

Solution Level

The institutions involved are using the regularily prepared medium-term and long-term forecasts in their operations and in making decisions. The forecasts are updated on a regular basis, thus ensuring continuity of the input data. Also ensured are researcher recommendations and commentaries for further actions to be made in problem solution. Deficiency is that the forecasts are used in operations of each institution at a non-coordinated manner and separately from the operations of other institutions.

Mainly, the implementation of the alternative would solve issues that are related to educational sector (distribution of state financed study positions, arrangement of re-qualification of the unemployed, in selection of further education, etc.).

Alternative – Alternative of Improvement of the Existing System

Within framework of this alternative, it is assumed that the data and methods used in forecasting are coordinated between all institutions, which currently perform partial forecasting of labor force supply and demand. EM in this alternative would ensure the coordination functions between the institutions involved.

It is scheduled to create a new structural unit at EM. Significant amendments to the legislation are not scheduled, however amendments are due in the statutes of EM.

Information Data

EM

RAPLM

IZM

LM Research

renovator

CSP

NVA EUROSTAT LB PKIVA

VRAA

Research results


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Analysis and forecasting of labor market is performed separately within limits of each institution, by using the results of the labor market research, to perform elaboration of policy planning documentation, legal enactments, and other documents. EM is appointed as the responsible institution, which coordinates uniform elaboration and application of the labor market forecasts. It establishes the main tasks to be executed by the institutions, but in general the forecasting elements would be managed by several institutions. Elaboration mechanism on inter-ministerial and regional level is being elaborated.

Solution Level

The institutions involved use the prepared forecasts in their operations and in decision making. Forecasts are updated on a regular basis, thus continuously ensuring with credible information and succession. Researcher recommendations and commentaries are also ensured for further activities to be performed in problem solving. Positive trait is that use of the forecasts is coordinated.

Partially solved is the institutional coordination problem. Without concentrating the responsibility of forecasting in general in one institution, uniform medium-term and long-term forecasting system is not created. Each institution continues operations with matters of their authority. The matter of further use and maintenance of the elaborated IT system is not solved (institutional responsibility).

This alternative partially solves the problem set forth by the experts. Implementation of the alternative, in comparison with the first alternative, improves the quality and credibility of the available data, which allows broader use of the available data, because the forecast credibility is a crucial element in further implementation of the system. Thus the influence of the alternative on problem solution is greater than the previous alternative and it ensures coordinated use of the available data in policy decision making.

Alternative – Alternative of Establishing an Agency and Implementation of IT Solution

Within framework of this alternative, it is assumed that based on the IT system elaborated during the research, new forecasting modules are created and they are connected with the already existing forecasting elements in the corresponding institutions. A new research institute is created under subordination of EM. The Research Institute to be established performs the general coordination and improvement of the forecasting system, as well as maintenance of the new modules and ensuring the analytical part of the forecasting (See Figure 21).

A new mechanism of cooperation is created. EM is assigned with additional functions, which are related to coordination of the medium-term and long-term labor market forecasting system. A new department at the EM is created for performance of these functions and the Research Institute under subordination of EM is established for labor market forecasting. The requirements of the involved parties are acknowledged. The newly established Research Institute arranges data gathering and processing. It uses the IT system elaborated during the research for further performance of the forecasts, as well as performs regular updating of the forecasts.


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Figure 21. Mechanism of cooperation between institutions in case of Alternative of Establishing an Agency and Implementation of IT Solution

Solution level The institution responsible for coordination of the cooperation is assigned and a specialized Research Institute is established for elaboration of labor market forecasts. The forecasts are elaborated, based upon the developed IT solution. In this process, the requirements of the involved institutions are considered, in order to be able to use the forecasts in further operations and in decision making.

The main difference of implementation of this alternative from the previously mentioned alternatives is that the labor market supply and demand forecasts are available at any given moment, as well as scientific researching staff is available for data interpretation, which significantly improves the usage of the available data. In case of implementation of such alternative, the usage of the forecast results increases significantly and the forecasts can be used also for taking the following decisions: attraction of foreign investors in a specific region and specific industry; in allocation of financial means of European Social Fund, timely re-qualification of labor force, when performing structural reforms, etc.

6.2. Evaluation Methods of Alternatives

Evaluation of the project alternatives is based on practice and methodologies of countries of the world and of the European Union in analysis of similar investment projects. As the main sources for preparation of the report, the following documents were used:

• Methodological material of the World Bank “Guidebook of economic analysis of investment programs” (Belli, Anderson, Barnum, Dixon, Tan, 1998);

Coordination Data Information

EM

RAPLM

IZM

LM

RESEARCH INSTITUTE

EUROSTAT

CSP LB

VRAA NVA

Regions


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• “Guidelines in cost-benefit analysis of investment projects” (Evaluation Unit DG Regional Policy European Commission, 1997);

• “Methodological guidelines in performance of cost and benefit analysis” (General directorate of Regional Policy of European Commission, 2006);

• Descriptions of hierarchy analysis method (Saaty); • LM recommended methodology “Guidelines in evaluation of labor market research

policy alternatives” („Vadlīnijas darba tirgus pētījumu politikas alternatīvu izvērtēšanai”) (Brigsa 2006);

• Policy influence evaluation in policy making system (Politikas ietekmes vērtēšana politikas veidošanas sistēmā) (Kļaviņa, Klapkalne, Pētersone, 2005).

For evaluation of the alternatives, an option to use the cost-effectiveness, cost-benefit, multi-criteria, and hierarchy analysis methods was considered. During the work meeting on February 12, 2007, representatives from LM, EM, and IZM were presented with the feasibility of usage of the above mentioned methods, comparison of methods, and method selection algorithm, as a result of which the alternative analysis was mutually coordinated, based upon use of the hierarchy analysis method, as well as other methods, if it is necessary and feasible.

The hierarchy analysis method in a capacity of multi-criteria analysis form envisages evaluation of alternatives according to comparison of selected criteria and alternatives and as the optimum alternative is determined that particular alternative, which has the highest evaluation. Unlike the multi-criteria base analysis, this method envisages also use of the mathematical models and evaluation of expert assessment coordination, thus providing with a more qualitative result. The method has the following comparative advantages:

• Aspects that cannot be evaluated quantitatively and monetarily are taken into consideration,

• The experts do not assess the alternative, but instead compare the significance of the criteria and alternatives,

• Wide range of criteria, complex access.

Besides the hierarchy analysis method, as the qualitative expert method, in alternative analysis also the quantitative financial analysis is used.

6.3. Evaluation of Alternatives

Implementation plans of the three selected alternatives are elaborated and evaluation of influence of these alternatives is performed.

Implementation plans of the alternatives

The institution responsible for implementation of the Research Maintenance Alternative is LM, which upon acceptance of the research results ensure further maintenance of the Research. Other institutions involved (ministries, subordination institutions, etc.) in medium-term and long-term labor market supply and demand forecasting are not actively


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participating in implementation of the alternative. Implementation of the alternative requires the following activities:

• Ensure financing for research maintenance – annually, when creating the budget of LM for the upcoming year, the costs are included, which are necessary for implementation of the alternative;

• Preparation of purchase documentation – annually, the LM must prepare purchase documentation, which is necessary for involvement of external service providers;

• Involving external service provider – annually, LM must ensure involvement of an external service provider according to the requirements of legislation regulation purchases, in order to involve researchers, who would then update the forecasts;

• Monitor research performance – during the time period, when the involved service provider is performing the research, LM must ensure monitoring of the research performance;

• Distribution of the final results – LM delivers the updated forecasts to other institutions, which are related to labor market supply and demand;

• Distribution of research data – in order to ensure use of the prepared forecasts in decision making, LM must ensure data accessibility to all linked institutions not only upon update of the forecasts, but at any time when the institutions require it;

• Store the research data – in order to maintaining the IT solution and ensure data accessibility for researches related to research updating, LM must ensure that the research data are stored.

EM is the responsible institution for implementation of Alternative of Improvement of the Existing System, which is scheduled to be entrusted with the labor market forecasting function and thus take over the labor market research results from LM. Thus, in implementation plan of the alternative all of the above mentioned activities are to be included, upon change of the responsible institution, and additionally – the following activities are to be performed:

• To take over the labor market research results – upon the end of the research, LM delivers the labor market research results, including results of this research to EM;

• To strengthen EM capacity – a new department is established at EM, wherein the head of the department, assistant thereof, and five employees would be employed. In order to ensure that, amendments have already been made to the statutes of EM, providing for the new functions, however the regulations of the department are additionally necessary to be prepared and amendments must be implemented to the EM operational strategy, as well as ensure work places of the new department, including arrangement of purchases;

• Creation of management/consultative council – in order to ensure coordinated work between the institutions, which are related to labor market supply and demand, upon initiative of EM, the management/consultative council would be created, which would include representatives of the main involved ministries (LM, IZM, EM, RAPLM, VM, KM, ZM) and subordination institutions thereof, as well as representatives of the involved parties (NVA, Adult education and development agency (PIAA), CSP, VRAA, etc., for instance, NGO representatives). For implementation of the activity, it is necessary to prepare a MK decree about establishment of the council and elaborate the statutes of the council;


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• Elaboration and implementation of the optimum cooperation mechanism – according to the created consultative council and statutes thereof, the cooperation mechanism is completed and implemented;

• Participation in the consultative council of the labor market supply and demand system – involvement of IZM, LM, RAPLM, VM, KM, ZM, NVA, AIP, PIC, PIAA, and others in work group of labor force supply and demand system evaluation, elaboration of proposals for improvement of the system, participation in decision making, nominating the responsible employee;

• Perform labor market development analysis and elaborate proposals in matters of economic policy.

EM is also the responsible institution in implementation of the Alternative of Establishing an Agency and Implementation of IT Solution. As this alternative is more extensive than the previous, in framework thereof it is necessary to perform the following activities:

• Establish the Research Institute under subordination of EM – a new agency is established, where 15 individuals are employed. In order to implement it, the following regulatory normative acts are elaborated: MK decree on establishment of an agency, MK regulations on agency statutes, operational strategy of the agency, amendments are made to the EM operational strategy and to the statutes of EM. It is necessary to ensure work places of the new agency, including arrangement of purchases;

• Consolidate the capacity of other institutions involved – it is necessary to recruit additional employees at IZM (two employees), LM, NVA, PIC, Center of quality evaluation of the higher education, RAPLM, VRAA, in order to ensure participation in cooperation, information provision, forecasting, etc.;

• Elaboration and implementation of data gathering and processing scheme – EM and the Research Institute defines the information sources and cooperation between the Research Institute and the institutions, which are the data owners/keepers, as well as defines the data processing scheme at the Research Institute and in cooperation with the Consultative Council.

Financial analysis (cash flow) of the alternatives

The financial analysis is based on the following general assumptions: • The costs of creation of one level new work place are equal in all ministries or

agencies; • The costs of creation of a work place are calculated, based on calculations on work

place creation costs prepared by EM, and they are calculated only for creation of full time work place – in case of additional part time load, it is assumed that the alternative maintenance costs are formed (i.e., an existing employee is involved in implementation of the alternative); when forecasting a creation of a new work place without establishing a new department or agency, it is assumed that the new employee conforms to the level of senior referent;

• The sum of creation of information system is defined according to the calculations of the IT specialists. As the IT specialists of the research have defined the limits of the sums of costs, the average parameters are used in the cost calculations of the alternatives;


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• Costs of annual researches are derived from the initial research costs according to the opinions of the researchers involved in the research about the necessary work extent in case of a repeated research; all of the necessary survey data are obtained from CSP, NVA, and IZM;

• Costs of amendments to the statutes, part time loads, etc. are considered as the alternative costs with the cash flow, which is already included in the state budget, therefore are not considered in financial analysis of these alternatives, and therefore are referable to the economic analysis, not the financial analysis, because in the financial analysis, according to the guidelines of European Community about analysis of investment projects, only investment costs and maintenance expenses are to be included in the costs of the project, however the expenses, which are covered by other participant groups, are displayed in the economic analysis (“Evaluation of policy influence in policy making system” („Politikas ietekmes vērtēšana politikas veidošanas sistēmā”), State Chancellery, p. 27);

• The investment year or the “0” year (base year) provisionally conform to year 2008; a part of the activities could be executed already during the second half of year 2007, however due to the fact that financing is not allocated for this, in the calculations it is scheduled to occur in year 2008;

Table 19. Macroeconomic parameters forecasted by the Ministry of Finance 2008-2011

Parameters 2008 2009 2010 2011

Consumer price inflation, annual average, % 4,30% 3,40% 2,70% 2,50%

Salary, increase in comparative prices, % 6,40% 6,90% 7,10% 6,90%

• In the calculations of financial flow, applied is the consumer price inflation and the salary growth forecasted by the Ministry of Finance (See Table 19);

• The remaining value of investments at the end of the period – 0 LVL; • The selected analysis time period – 15 years; • Applied discount rate – 5%, according to the Methodological regulations of the

General Directorate of the European Regional Policy on performance of cost and benefit analysis (General Directorate of the Regional Policy of European Commission, 2006: 7).

Simultaneously with the general assumptions, all of the necessary assumptions were made in case of each alternative.

Within framework of the Research Maintenance Alternative: • Two new employment positions are created at LM (investment costs 3 254 LVL

during the year 0, maintenance costs 20 586 LVL in year 0), as well as additional ½ time load volume is formed for the IT specialist of IZM, RAPLM, and LM; the costs in relation to creating the new employment positions are based on detailed calculations about creation and maintenance of work places, which has been elaborated by EM in prices of 2006/2007 and which are increased according to the inflation quotient, i.e.: − The material costs for arrangement of a new work place – costs for purchase of a

new office chair, visitor's chair, calculator, equipment of office premises, table lamps, purchase of a telephone for each employee;


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− Capital costs for arrangement of a new work place – costs of computer purchase, costs of office furniture, i.e., desk and file cabinet purchase; investments into purchase of computers are repeated every 5 years, considering the wear of these devices;

− Salary – 490 LVL monthly for an officer of senior referent level, including a bonus of one monthly salary;

− State social security expenses in amount of 24.09%; − Costs of business trips – 1000 LVL annually per one employee; − Payment for services included payment for telecommunications (LVL 35

monthly), internet, NAIS database, and payment for other connections (LVL 20 monthly), costs of mailing (35 LVL annually);

− The material costs include also costs for office supplies (5 LVL monthly) and administrative expenses (50 LVL monthly).

Table 20. Assumptions of creation and maintenance costs of new places of employment

Arrangement of a new work place (at all institutions, except the Research Institute)

Costs in year 0, LVL

Investment costs 1 627 Material costs 198

Office chairs 47 Visitors’ chairs 31 Calculators 5 Equipment of the office premises 52 Table lamps 21 Telephone sets 42

Capital expenses 1 429 Computers with software 991 Office furniture 438

Desk 250 File cabinet 188

Maintenance costs 10 293 Salary: 6 778 Costs of state social insurance 1 633 Costs of business trips 1 043 Payments for services 725

Payment for telecommunications 438 Internet, NAIS database, and other connections 250 Mailing expenses 37

Material costs 115 Office supplies 63 Administrative costs 52

The mentioned costs are displayed in Table 20, considering inflation. • Investments into computer system are related only to purchase of the server necessary

for data storage and alternative costs for salary of IT specialist (investment costs 4 250 LVL in year 0);

• For updating the forecasts, the costs of the necessary order are evaluated at 50 000 LVL in year 0. The assumption is based on the expense item analyses performed within framework of the research and on the rearcher opinion on the necessary extent of work in case of repeated data analysis.


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Table 21. Financial calculations of research maintenance alternative

Year of investment Years of operations

Position 0 1 2 3 4 5 6 7

Investment costs: 7504 0 0 0 0 2336 0 0Arrangement of new employment positions at LM 3254 0 0 0 0 2336 0 0

IT solution costs 4250

Maintenance costs: 70586 75325 80501 85880 91626 97763 104319 111323New work loads at LM 20586 21875 23257 24685 26208 27832 29563 31408

Order costs 50000 53450 57245 61195 65417 69931 74756 79915

IT solution costs 0 0 0 0 0 0 0 0

Income: 0 0 0 0 0 0 0 0

Outgoing cash flow: 78090 75325 80501 85880 91626 100099 104319 111323

Incoming cash flow: 0 0 0 0 0 0 0 0

Cash flow: -78090 -75325 -80501 -85880 -91626 -100099 -104319 -111323

Consolidated cash flow: -78090 -153415 -233916 -319797 -411422 -511521 -615841 -727163

Years of operations

Position 8 9 10 11 12 13 14 15

Investment costs: 0 0 2687 0 0 0 0 0Arrangement of new employment positions at LM 0 0 2687 0 0 0 0 0

IT solution costs

Maintenance costs: 118805 126799 135339 144463 154211 164626 175755 187645New work loads at LM 33377 35475 37714 40102 42649 45367 48267 51360

Order costs 85429 91323 97624 104361 111561 119259 127488 136285

IT solution costs 0 0 0 0 0 0 0 0

Income: 0 0 0 0 0 0 0 0



Cash flow: -118805 -126799 -138026 -144463 -154211 -164626 -175755 -187645



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Table 22. Financial calculations of the alternative of improvement of the existing system

Year of investment Years of operations

Position 0 1 2 3 4 5 6 7

Investment costs: 15640 0 0 0 0 8177 0 0Arrangement of new employment positions at EM 11390 0 0 0 0 8177 0 0

IT solution costs 4250

Maintenance costs: 126342 134599 143556 152845 162760 173344 184642 196702New work loads at EM 76342 81149 86311 91650 97343 103413 109885 116788

Order costs 50000 53450 57245 61195 65417 69931 74756 79915

IT solution costs

Income: 0 0 0 0 0 0 0 0



Cash flow: -141982 -134599 -143556 -152845 -162760 -181521 -184642 -196702


Years of operations Position

8 9 10 11 12 13 14 15

Investment costs: 0 0 9404 0 0 0 0 0Arrangement of new employment positions at EM 0 0 9404 0 0 0 0 0

IT solution costs

Maintenance costs: 209578 223325 238002 253674 270409 288279 307362 327742New work loads at EM 124149 132002 140378 149314 158847 169019 179874 191457

Order costs 85429 91323 97624 104361 111561 119259 127488 136285

IT solution costs

Income: 0 0 0 0 0 0 0 0



Cash flow: -209578 -223325 -247406 -253674 -270409 -288279 -307362 -327742


To sum up, the costs of investments of research maintenance alternative in year 0 constitutes 7 504 LVL, however the maintenance costs in year 0 add up to 70 586 LVL (See Table 21). Due to the fact that the potential income is not identified in the implementation of the alternative, both – the cash flow and the FNPV are negative. The FNPV calculated with at 5% discount rate conforms to – 1 229 861 LVL.


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The necessary additional assumptions for financial analysis of System Improvement Alternative: • 7 new employment positions are created at EM (head of the department, assistant

director, and 5 senior referents), as well as additional ½ load at LM, NVA, IZM, AIP, PIAA, RAPLM, VRAA. The costs for arrangement and maintenance of work places of the senior referents are analogues to the previously described expenses, when forming new work places at LM, however the head of the department and its assistant have higher salary – 640 LVL monthly salary for the head of the department, and 590 LVL monthly to the assistant of the head of the department; the costs for half times are calculated as one half of the costs in cases of senior referents and these expenses are considered as alternative costs;

• Investments for development of computer system remain the same as for the previous alternative – only for purchase of a server necessary for data storage; also the research update is entrusted to an external service provider and the necessary research data are obtained from CSP, NVA, and IZM for a monetary consideration, which is analogous to the research maintenance alternative.

Hence, the expense and income sections of this alternative provide investment costs of 15 640 LVL in year 0, which adds up of arrangement of the new employment positions at EM (11 390 LVL in year 0) and costs of purchase of the IT solution necessary for data storage (4250 LVL in year 0), and maintenance costs (126 342 LVL in year 0), which includes the costs of new work places (76 342 LVL in year 0) and the order costs (50 000 LVL in year 0) (See Table 22). In implementation of this alternative, the potential income is also not identified, and the FNPV calculated with the five percent discount rate conforms to – 2 179 498 LVL.

In case of Alternative of Establishing an Agency and Implementation of IT Solution: • Similar to the previous alternative, 7 new employment positions are created at EM,

however in addition to that 15 new employment positions are created at the newly established Research Institute, 2 new employment positions at IZM, and one new employment positions in each of the following: LM, NVA, IZM, PIC, Center of quality evaluation of higher education (AIKNC), RAPLM, VRAA, as well as additional ½ work load at AIP, PIAA; the costs for establishment of the new department and separate employment positions are explained in detail in descriptions of the previous alternatives, therefore a more detailed explanation is necessary for the 15 new employment positions at the Research Institute. It is assumed, that the following employees will be required: director of the institute, 4 administrative workers, and 10 researchers; the salary is defined for each level: director of the institute – 1250 LVL monthly, administrative workers – 750 LVL monthly, and the researchers – 1025 LVL monthly.

Table 23. Costs of formation and maintenance of new places of employment at the Research Institute

Arrangement of a new employment position (at the Research Institute)

Expenses in year 0, LVL

Investment expenses 33 220 Material costs 3 285

Office chairs 704 Visitors’ chairs 469 Calculators 78 Equipment of office premises 782 Table lamps 313 Telephone sets 626 Cellular telephone sets 313


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Arrangement of a new employment position (at the Research Institute)

Expenses in year 0, LVL

Capital expenses 29 934 Computers with software 17 210 Server with software 2 608 Office furniture 6 258 Desks 3 755 File cabinets 2 503 Photocopiers 1 877 Printers 1 565 Scanner, fax, etc. 417

Maintenance costs 327 566 Salary for the director 17 290 Salary for the administrative workers 41 496 Salary for the researchers 141 778 Costs of state social insurance 48 316 Costs of business trips 15 960 Payments for services 29 287

Rent of the premises 11 264 Utilities 6 571 Payment for telecommunications 6 571 Payment for mobile communications 3 379 Internet, NAIS database, and other connections 250 Mailing expenses 1 252

Books and press 1 043 Payments for legal services 32 333 Material costs 63

Office supplies 63

Majority of the remaining costs is analogous to calculations of other employment positions, however in case of the institute, additional scheduled are costs for premises with total floor-space of 150 m2 (6 LVL per m2 monthly rent, utilities – 250 LVL per month), purchase and maintenance costs of cellular telephones (6 cellular phones, 50 LVL each, current costs – 45 LVL monthly per mobile phone), other capital investments are also scheduled – a server with software (2500 LVL), 2 photocopiers (900 LVL each), 10 printers (150 LVL each), scanner and other devices (400 LVL), costs for books and press (1000 LVL annually), as well as for legal services (31 000 LVL annually). The mentioned costs, considering inflation, are displayed in Table 23.

• Within framework of this alternative, investments are scheduled in improvement of IT system and they include costs of elaboration of the information system, technological resources, and software, as well as maintenance costs. The average costs are included in the financial calculations (See Table 24);

Table 24. Cost assumptions of IT system improvement

Creation of information system Costs (min.), LVL

Costs (max), LVL

Average costs, LVL

Input data sub-system 25 360 Project management 2 400 2 600 2 500 Analysis/designing 6 560 7 360 6 960 Elaboration 7 980 8 820 8 400 Testing 3 600 3 960 3 780 Documentation 960 1 080 1 020 Training 840 960 900 Implementation 1 680 1 920 1 800


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Creation of information system Costs (min.), LVL

Costs (max), LVL

Average costs, LVL

Calculations sub-system 33 100

Project management 3 000 3 600 3 300 Analysis/designing 8 160 9 760 8 960 Elaboration 10 080 12 040 11 060 Testing 4 560 5 520 5 040 Documentation 1 320 1 560 1 440 Training 960 1 200 1 080 Implementation 2 040 2 400 2 220

Analytical sub-system 27 110

Project management 2 400 2 800 2 600 Analysis/designing 6 880 7 840 7 360 Elaboration 8 540 9 800 9 170 Testing 3 840 4 440 4 140 Documentation 1 080 1 200 1 140 Training 840 960 900 Implementation 1 680 1 920 1 800

Administering sub-system 11 350

Project management 1 000 1 200 1 100 Analysis/designing 2 880 3 200 3 040 Elaboration 3 640 4 060 3 850 Testing 1 680 1 800 1 740 Documentation 480 480 480 Training 360 360 Implementation 720 840 780

Total: 96 920 Technological resources 11 000

Database server computer 2 500 6 000 4 250 Application/web server computer 2 500 6 000 4 250 Backup copying device 1 000 4 000 2 500

Software 16 250

Database management system (free code software or paid product) 0 10 000 5 000 Application server software (free code software or paid product) 0 10 000 5 000 Modeling software (for instance, Powersim) 2 500 10 000 6 250

Total costs (LVL without VAT): 124 170 Maintenance

Maintenance of technical infrastructure (IT system administrator) 4 224 6 408 5 316 Maintenance of information systems (Elaborator) 9 016 15 552 12 284

Annual maintenance costs (LVL without VAT): 17 600


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• Updating of the research is performed by the Research Institute and the necessary

survey data are obtained from CSP, NVA, and IZM, thus additional research order costs are not scheduled in this alternative.

To sum up, the income and expense parts of this alternative are related to the following costs: investment costs (181 796 LVL in year 0), which are made of arrangement of new employment positions at the Ministry of Economy (11 390 LVL in year 0), arrangement of new employment positions at the Research Institute (33 220 in year 0), and in other institutions (13 017 LVL in year 0) and purchase costs of the necessary IT solution for data storage (124 170 LVL in year 0); maintenance costs (503 852 LVL in year 0) include costs of the new employment positions at EM (76 342 LVL in year 0), at the Research Institute (327 566 LVL in year 0), and in other institutions (82 344 LVL in year 0), as well as the maintenance costs of IT solution (17 600 LVL in year 0) (See Table 25).

In implementation of this alternative, potential income could be obtained, however the extent thereof is relatively insignificant, therefore the income position is not considered in the cash flow analysis. Thus, both – the cash flow and the FNPV are negative and conform to – 8 647 154 LVL.

To compare the selected alternatives according to the results of financial analysis (see Table 26): • Implementation of the research maintenance alternative is connected with the lowest

expenses, even though annual expenses are necessary for updating of the ordered research. Only several employment positions are created and it is not associated with significant contributions into development of the IT system,

• The costs of the system improvement alternative are assessed as average, because in case of this alternative, the costs of creating new employment positions are higher than in the alternative of research maintenance, however they, just as the contribution into the IT system, are significantly lower than the costs of the case of establishing the Research Institute,

• Implementation of the alternative of establishing an agency and implementing the IT system is the most expensive, because it provides for the greatest number of new employment positions, as well as significant contributions into the IT system.

Thus, selecting the alternatives according to the results of financial analysis, the research maintenance alternative could be considered as the most beneficial. However, it must be taken into consideration that the alternatives did not result in monetary income therefore the usefulness of the alternatives from the social and economic aspect must be additionally evaluated.


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Table 25. Financial calculations of the alternative of creating an agency and implementation of IT solution

Year of

investment Years of operation Position

0 1 2 3 4 5 6 7 Investment costs: 181796 0 0 0 0 45437 0 0Arrangement of new employment positions at EM 11390 0 0 0 0 8177 0 0Arrangement of new employment positions at the Research Institute 33220 0 0 0 0 27914 0 0Arrangement of new employment positions in other institutions (LM, NVA, IZM (2), PIC, AIKNC, RAPLM, VRAA) 13017 0 0 0 0 9346 0 0IT solution costs 124170 Maintenance costs: 503852 535435 569312 604344 641687 681498 723944 769205New work loads at EM 76342 81149 86311 91650 97343 103413 109885 116788New work loads at the Research Institute 327566 347973 369825 392412 416484 442142 469494 498654New work loads in other institutions (LM, NVA, IZM (2), PIC, AIKNC, RAPLM, VRAA) 82344 87499 93026 98741 104833 111327 118251 125634Order costs 0 0 0 0 0 0 0 0IT solution costs 17600 18814 20150 21541 23027 24616 26314 28130

Income: 0 0 0 0 0 0 0 0

Outgoing cash flow: 685648 535435 569312 604344 641687 726935 723944 769205Incoming cash flow: 0 0 0 0 0 0 0 0Cash flow: -685648 -535435 -569312 -604344 -641687 -726935 -723944 -769205Consolidated cash flow: -685648 -1221083 -1790395 -2394739 -3036426 -3763361 -4487306 -5256511

Years of operation Position

8 9 10 11 12 13 14 15 Investment costs: 0 0 52253 0 0 0 0 0Arrangement of new employment positions at EM 0 0 9404 0 0 0 0 0Arrangement of new employment positions at the Research Institute 0 0 32101 0 0 0 0 0Arrangement of new employ-ment positions in other institu-tions (LM, NVA, IZM (2), PIC, AIKNC, RAPLM, VRAA) 0 0 10748 0 0 0 0 0IT solution costs Maintenance costs: 817471 868948 923853 982420 1044899 1111557 1182677 1258566New work loads at EM 124149 132002 140378 149314 158847 169019 179874 191457New work loads at the Research Institute 529745 562899 598255 635964 676185 719090 764862 813695New work loads in other institutions (LM, NVA, IZM (2), PIC, AIKNC, RAPLM, VRAA) 133506 141902 150857 160408 170598 181468 193066 205441Order costs 0 0 0 0 0 0 0 0IT solution costs 30071 32146 34364 36735 39270 41979 44876 47972

Income: 0 0 0 0 0 0 0 0

Outgoing cash flow: 817471 868948 976106 982420 1044899 1111557 1182677 1258566Incoming cash flow: 0 0 0 0 0 0 0 0Cash flow: -817471 -868948 -976106 -982420 -1044899 -1111557 -1182677 -1258566Consolidated cash flow: -6073982 -6942930 -7919036 -8901457 -9946356 -11057912 -12240590 -13499156


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Table 26. Comparison of cash flows of the alternatives

Years

Alternatives 0 1 2 3 4 5 6 7

Financial calculations of research maintenance alternative -78090 -75325 -80501 -85880 -91626 -100099 -104319 -111323Financial calculations of the alternative of improvement of the existing system -141982 -134599 -143556 -152845 -162760 -181521 -184642 -196702Financial calculations of alternative of creating an agency and implementation of the IT solution -685648 -535435 -569312 -604344 -641687 -726935 -723944 -769205

Years

Alternatives 8 9 10 11 12 13 14 15

Financial calculations of research maintenance alternative -118805 -126799 -138026 -144463 -154211 -164626 -175755 -187645Financial calculations of the alternative of improvement of the existing system -209578 -223325 -247406 -253674 -270409 -288279 -307362 -327742Financial calculations of alternative of creating an agency and implementation of the IT solution -817471 -868948 -976106 -982420 -1044899 -1111557 -1182677 -1258566

Analysis of the Alternatives by Using the Hierarchy Analysis Method

Evaluation of the alternatives and selection of the optimum alternative is performed by acknowledging the opinions of five researchers and potential users of the labor market forecasts (four experts of LM, EM, RAPLM, and IZM) on criteria groups and criteria included therein.

Figure 22. Schematic illustration of levels of the hierarchy analysis methods

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The criteria and criteria groups were defined by researchers, and experts had an option to evaluate only the offered variants (See Table 22).

The highest goal of the analysis (level 1) is “Evaluation of the Alternatives”, and the second level contains five criteria groups:

• Costs; • Institutional interests; • State interests; • Influence on policy implementation results; • System functionality.

The third level contains evaluation criteria, which were defined by the researchers just as the criteria groups and that experts had an option to evaluate only the offered variants. The fourth level includes three alternatives, which had to be evaluated in regards to the third level criteria.

The first criteria group – costs, was included in order to consider the costs, when selecting the optimum alternative. As the evaluation criteria, various income and expense types were established, which were found or are expected to occur during the implementation progress: investment costs, maintenance costs, potential income, and alternative costs. During the evaluation, the experts had access to the calculations performed within framework of the financial analysis.

The second criteria group – institutional interests, is included because when elaborating the long-term labor market forecasts, use thereof in decision making phase is dependent on conformance of the forecasts to the institutional interests. The criteria included in the group are selected to evaluate this conformance to utmost extent:

• Conformance of data and forecasts to the needs of institutions as direct conformance to the needs of the interested institutions;

• Variety of forecasts, i.e., how broad will the range of forecasts be available to be obtained by the system in each alternative;

• Forecast data in regional section, i.e., whether the models will offer the forecast data in regional section, which is significant for RAPLM and institutions located in regions to make decisions on regional level;

• Use and usability of the forecasts, i.e., whether the obtained forecasts can be applicable and made use of in operations of institutions and in decision making;

• The frequency of forecast obtainment, i.e., how often will the forecasts be updated; • Credibility and quality of data and forecasts, i.e., how credible and qualitative are the

prepared forecasts. This factor was emphasized in comparison of the alternatives and of various methods of analysis obtainment – purchase of external service provider and forecasts prepared by the employees of the agency employees.

The criteria group “state interests” is included to ensure conformance of the forecasts not only to requirements of separate institutions, but also to national goals and priorities. The selected criteria in this group are:

• Coordinated and complementary operation of institutions – as the alternatives are not nominated only for obtainment of data and forecast, but also for effective use thereof, then this criterion will allow evaluation of the alternatives from this aspect;


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• Mutually complementary decisions, i.e., whether the implementation of the alternatives will promote making mutually completing decisions in the aspect of labor market;

• Effective information flow, i.e., whether the alternative provides for creation of effective information flow;

• Preclusion of doubling, i.e., whether implementation of the alternatives will preclude doubling of operational or existing databases in various institutions;

• Achievement of goals established in long-term planning documentation of the European Union and Latvia, i.e., whether the obtained forecasts will influence achievement of long-term goals;

• Inclusion of future in the forecasting system of European Union labor market, i.e., whether the created forecasting system in future could integrate in to the general system of labor force forecasting of the European Union without significant corrections.

In evaluation of the alternatives, it is always important to evaluate the macroeconomic gains, which are resulted from policy implementation, therefore the criteria group “Influence on policy evaluation results” was created, wherein criteria were included, which could be influenced by implementation of alternatives:

• Structural unemployment – the long-term labor market forecasts will provide with information about the forecasted demand of labor force, thus with assistance of education measures it is possible to correct the labor force supply and in case of better conformance, the structural unemployment will decrease;

• The number of the employed not corresponding to the education – DŅA is indicative of the fact that people, who work in a profession corresponding to their education, receive higher salary than those working not according to the education, thus also this factor is important;

• Lack of labor force – analogous to the structural unemployment, better conformance of the labor force supply and demand will decrease the actual deficiency of labor force;

• Employment in generally low added value sectors/professions – an option to forecast the situation in long-term allows making more justified decisions that are important to the national development, incl., about promotion of employment in higher added value sectors or professions;

• The reaction time of labor market policy to the changes in economic structure – in situation of lack of the cooperation system and effective information flow, the reaction time of labor market policy to the changes in economic structure is greater, for instance, studies in technical sectors are promoted by the educational establishments much later than the increase of the operations started in the technical sectors, thus a significant deficiency of labor force had formed.

The last group of criteria “System functionality” allows evaluation of the practical level of operations, which distinguishes the following alternatives:

• Use and improvement of the IT solution, i.e., up to what extent the IT solution elaborated within framework of the research is used and improved and how the researchers’ recommendations for improvement thereof are considered;


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• Ability of the models to create various scenarios, i.e., whether the models can offer different scenarios to the users and thus to evaluate the consequences of various decisions more effectively;

• Maintenance of the research results and viability of the alternative, i.e., to what extent the maintenance of research results is clear in case of each alternative and whether the alternatives could be considered as viable;

• Forecasts in sections of professions/profession groups, i.e., whether it will be possible to obtain the forecasts in profession or profession group sections;

• Forecasts in sections of economic sectors, i.e., whether it will be possible to obtain labor market forecasts in section of economic sectors;

• Risks of implementing the alternatives, i.e., to what extent is the implementation of the alternatives linked to risks.

According to the evaluation of criteria groups, as the most important evaluated was the criteria group “State interests” (value of the priority vector is 0.38 of 1, dispersion from 0.13 to 0.59), because these criteria groups will ensure achievement of common goals; following is the criteria group “System functionality” (0.23 of 1, dispersion from 0.05 to 0.42), because a developed technological solution will ensure the criteria included in other criteria groups, for instance, data in various sections. As the least important, evaluated was the criteria group “Costs” – value of the priority vector being only 0.07 of 1 with dispersion from 0.03 to 0.09, because the results and gains are significantly more important than the costs, which are necessary for acquisition thereof.

When examining the criteria scale in the criteria groups, in the criteria group “Costs” the most significant criteria was “Maintenance costs” (0.36 of 1 with dispersion from 0.11 to 0.61). These costs have the longest influence on implementation of alternatives and it they are comparatively large, therefore they are to be considered as important. The least important criteria is “Alternative costs” (0.13 of 1 with dispersion from 0.06 to 0.30), because these costs are scheduled by the state budged regardless of implementation of the alternatives. In the criteria group “Institutional interests”, the greatest importance is allocated to the criteria “Credibility and quality of data and forecasts” – 0.38 of 1 with dispersion of 0.16 to 0.55, because only such data and forecasts can be used in decision making. The next criteria in importance is “Applicability and use of the forecasts” (0.25 of 1 with dispersion of 0.18 to 0.34), because implementation of this criteria is also significant in decision making process. Only the third in importance is the criteria “Conformance of data and forecasts to the institutional needs” (0.14 of 1 with dispersion of 0.02 to 0.34), because it prescribes establishment of analysis of institutional needs and use during the forecast realization stage. The remaining criteria are evaluated comparatively low, because they include only certain parameters characterizing the forecasts, which are already included in criteria acknowledged as important.

In the criteria group “State interests”, several criteria were evaluated rather equally – within limits from 0.19 to 0.21 – “Coordinated and complementary institutional operation”, “Effective information flow” (0.2 of 1, dispersion 0.1 to 0.42), and the criteria “Mutually complementary decisions” (0.19 of 1, dispersion 0.04 to 0.39). All of these criteria are significant, because they ensure completing the operations of those institutions, which influence the labor market supply and demand, and thus maximum gains are ensured. As less important considered are criteria “Inclusion of future in the EU labor market forecasting system” and “Precluding doubling”, because common forecasting of the EU labor market is


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only a futuristic vision, however preclusion of doubling, according to the opinion of researchers and experts, is not possible until complete mastering of the forecasting system.

In the criteria group “Influence on policy planning results”, with a great predominance, as the main criteria considered is “Reaction time of labor market policy to changes in economic structure” (0.48 of 1 with dispersion of 0.19 to 0.69), because this gain is evaluated as the most actual and topical for the current situation, when timely decision making would preclude origination of other problems. Neither of the criteria has gained significant predominance over others in the criteria group “System functionality” – five of the criteria were within limits of 0.16 to 0.19, thus they are considered to be equally important.

When comparing the alternatives according to the defined criteria, in all criteria, except for investment costs, maintenance costs, and alternative costs, the optimum alternative is the alternative of establishing an agency and implementation of the IT solution (average evaluations were from 0.51 to 0.70), however the lowest evaluation was obtained in the Research maintenance alternative (0.11 to 0.21), which was financially more beneficial (average evaluations of costs were within limits of 0.43 to 0.55). The alternative of system improvement, however, was evaluated as average.

Figure 23. Evaluation of research maintenance alternative

Prio

rity

vec

tor

co-o

rdin

ate

0.1640.1590.1640.1350.133

0.433

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

0,45

0,50

Costs Institutionalinterests

State interests Influence onpolicy

eveluationresults

Systemfunctionality

Global priorities

Criteria groups

0.4

0.4

0.4

0.3

0.30.2

0.2

0.1

0.1

0.0

0.0

The value of the priority vector of the research maintenance alternative is 0.16 of 1 with dispersion of 0.08 to 0.33 and its evaluation is the lowest. When examining certain values of priority vectors of alternatives in certain criteria groups, it could be seen that the evaluation of the alternative is considerably low in all groups, however the strongest part of this alternative is the low costs (See Figure 23). In evaluation of the improvement of the existing system alternative, amongst all three alternatives, the value of vector priority of 0.23 of 1 in dispersion of 0.15 to 0.32, was evaluated as the second superior alternative, which means that implementation of this alternative will partially achieve the results and will provide the key participant groups with partial gains. In compliance with the allocated scales to the criteria


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groups and criteria (See Figure 24), by implementing this alternative, the state interests would be achieved the best (0.244) and influence on policy evaluation results will be realized (0.240).

Figure 24. Evaluation of the alternative of improving the existing system

Prio

rity

vec

tor

co-o

rdin

ate

0.232

0.215

0.2400.244

0.222

0.238

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eveluationresults

Systemfunctionality

Global priorities

Criteria groups

0.240

0.24

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0.2350.23

0.22

0.22

0.21

0.21

0.20

0.20

In evaluation of the alternative of improving the existing system, the value of the priority vector amongst all three alternatives is 0.23 of 1 within limits of 0.15 to 0.32 and it is evaluated as the second superior alternative, which means that implementation of this alternative will partially achieve the result and will provide the key participant groups with partial gains. In compliance with the allocated scales to the criteria groups and criteria (See Figure 24), by implementing this alternative, the state interests would be achieved the best (0.244) and influence on policy evaluation results will be realized (0.240).

In evaluation of the alternative of establishing an agency and implementation of IT solution the value of the priority vector amongst all three alternatives is 0.6 of 1 within limits of 0.35 to 0.76 and it is evaluated as the superior alternative, which means that implementation of this alternative would provide with the most complete result and will provide the key participant groups with the best gains and this alternative is to be considered as the optimum.


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Figure 25. Evaluation of the alternative of agency creation and implementation of IT solution

Prio

rity

vec

tor

co-o

rdin

ate

0.6030.6260.5970.6190.646

0.330

0,000

0,100

0,200

0,300

0,400

0,500

0,600

0,700



eveluationresults

Systemfunctionality

Global priorities

Criteria groups

0.00

0.10

0.20

0.30

0.40

0.500

0.600

0.70

In compliance with the allocated scales in criteria groups and criteria (See Figure 25), by implementing this alternative, the institutional interests would be achieved the best (0.646), system functionality will be ensured (0.626), and the state interests will be satisfied (0.619).

Selection of the Optimum Alternative

In analysis of the alternatives, evaluation of influence of the three alternatives – Research maintenance alternative, Alternative of improvement of the existing system, and the Alternative of establishing an agency and implementation of the IT solution – was performed in order to select the optimum alternative for solving the problem “There is no uniform and coordinated medium-term and long-term labor force supply and demand forecasting system present in the country, onto which the labor market policy could be based”.

Financial or cash flow analysis of the alternatives indicates the research maintenance alternative as the optimum selection, because the costs thereof are significantly lower than costs of implementation of other alternatives: FNPV in case of this alternative conforms to –1 229 861 LVL, in case of alternative of improving the existing alternative, the parameter reaches already – 2 179 498 LVL, however the alternative of establishing an agency in financial sense is the least beneficial and its FNPV corresponds to – 8 647 154 LVL.

However selection of the optimum alternative only according to the cost principle is possible only provided that the gains are equal; analysis of these alternatives according to the hierarchy analysis method is indicative of significant differences in evaluating the usefulness of the alternatives.


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When evaluating all alternatives according to the criteria, which are summarized in criteria groups “Costs”, “Institutional interests”, “State interests”, “Influence on policy implementation results”, and “System functionality”, as significantly predominant acknowledged was the alternative of establishing an agency – the global priority thereof is 0.6 of 1, which is a significant superiority over the evaluations of the remaining alternatives: only 0.16 of 1 in case of Research maintenance alternative and 0.23 of 1 in case of alternative of improvement of the system (See Table 27).

Table 27. Calculations of global priorities

Alternatives Costs Interests

of institutions

State interests

Influence on policy

evaluation results

System functionality

Average values of priority vectors

0,065 0,172 0,381 0,151 0,231

Global priorities

Research maintenance 0,43 0,13 0,13 0,16 0,16 0,16 Improvement of the existing system

0,24 0,22 0,24 0,24 0,21 0,23

Establishing an agency and implementing the IT solution

0,33 0,65 0,62 0,60 0,63 0,60

Thus, according to this analysis, the optimal choice is considered the alternative of establishing an agency and implementation of the IT solution, moreover, the researchers and experts involved in the evaluation process have unequivocally acknowledged that factor of costs is the least important criteria group, when selecting the optimum alternative.

Also, when evaluating the political and economical feasibility of the alternatives, the optimal choice was the alternative of establishing an agency and implementation of IT solution, because, regardless of the fact that it requires the most significant amendments to the legal enactments, it could provide with the best influence on the basic approaches of the state policies, as well as the politically-economic environment, and its jurisdiction groups. The technical feasibility of all alternatives requires additional contributions to creation of additional work loads and in improvement of IT solution.

The socio-economic analysis of the alternatives confirm that the social influence of all alternatives on the participant groups: state institutions, NGOs, and the society will be positive, however the most significant influence is expected from implementation of the alternative of establishing an agency and implementation of the IT solution. In evaluation of the alternatives, mainly state institutions are involved, however it could be expected to receive support of the other participants in favor of implementation of the labor market forecasting system. It is expected that all alternatives would completely preclude the causes of the problem, however only implementation of the alternative of establishing an agency and implementation of the IT solution is related to long-term solution, which can develop and alter in time, as well as beneficially ensure integration of regional aspects to the forecasts.

To sum up all of the above mentioned, implementation of all alternatives is to be considered as useful, however the optimum alternative is the Alternative of Establishing an Agency and Implementation of the IT Solution.


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7. Gratitude

The researcher group would like to express appreciation to the coordinator of the 1st component of Implementation Department project of the “Researches of Ministry of Welfare” project – Vita Skuja, to the manager of the Implementation Department of the project “Researches of the Ministry of Welfare” – Edvīns Drigins, to the assistant director of the LM Labor Department, the head of the Labor Market Policy and Monitoring Department – Imants Lipskis, to the assistant director of this department – Ilze Nesaule, to the assistant director of the Economic Structure Policy Department of EM – Gunta Piņķe, to the head of the Financial Planning and Crediting Division of the Higher Education and Science Department of IZM – Marina Mekša for the timely provided consultations, as well as for objective and qualitatively prepared commentaries and objections in matters, which were in competence of the given research.

We would like to express our thanks to the director of Strategic Planning and Analysis Department of NVA – Grieta Tentere for great cooperation and active participation during the course of the research. We are thankful to the director of the Social Statistical Department of CSP – Maranda Behmane and to the head of the Employment Statistical division of the same – Zaiga Priede for involvement and readiness to discuss the interim results obtained during the research.

We are grateful to the leaders of the University of Latvia – the science prorector prof. Indriķis Muižnieks, chancellor – Pāvels Fricbergs, To the director of the Development and Planning Department – Dace Gertnere for assistance in solving the topical problems and resultative support during the research.

Many thanks to our colleagues in other research groups of the 1st component for interesting and fruitful professional cooperation when performing jointly the rather difficult and lasting researches of the project “Researches of the Ministry of Welfare” of the National Program “Labor Market Researches” of the European Union structural funds. The obtained experience of scientific cooperation cannot be overrated and it will in future be definitely useful, when applying for scientific projects financed by the Latvian Science Board and other international science projects.

We would like to express our appreciation to experts of the Swedish National Labor Board – Tord Strannefor and Torbjörn Israelsson for high evaluation of the analytical tool elaborated during the given research, as well as for discussion thereof.


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8. References

Journal Articles Blaug, M. (1967), 'Approaches to Educational Planning', The Economic Journal, Vol. 76, June, pp. 262-287. Becker, G.S. (1962), 'Investment in Human Capital: A Theoretical Analysis', Journal of Political Economy, pp. 9-49. Schultz, T.W. (1961), 'Investment in Human Capital', American Economic Review, Vol. 51, pp. 1-17. Books and Researches Ahamad, B. and M. Blaug (eds) (1973), The Practice of Manpower Forecasting, Elsevier, Amsterdam. Belli P., Anderson J., Barnum H., Dixon J., Tan J.P. (1998). Handbook on Economical Analysis of Investment Operations. Operational Core services network Lerning and Leadership Center. Brigsa S. (2006). Vadlīnijas darba tirgus pētījumu politikas alternatīvu izvērtēšanai. Riga. Central Bureau of Statistics (2006) Profesiju apsekojuma rezultātu Latvijā October, 2005, data collection. Evaluation Unit DG Regional Policy European Commision (1997). Guide to Cost-benefit analysis of investment projects. Forecasting Labour and Skills Shortages: How Can Projections Better Inform Labour Migration Policies? by Christina Boswell, Silvia Stiller and Thomas Straubhaar, Hamburg Institute of International Economics (HWWA), Migration Research Group. Paper Prepared for the European Commission, DG Employment and Social Affairs, July 2004. Fuchs, Johann and Manfred Tessaring (1994), “Medium and Long-term Forecasting of Employment in Germany”, in Hans Heijke (ed.), Forecasting the Labour Market by Occupation and Education: The Forecasting Activities of Three European Labour Market Research Institutes (Boston, Dordrecht and London: Kluwer Academic Publishers), pp. 37-53. Haskel, Jonathan and Richard Holt (1999), “Anticipating Future Skill Needs: Can it be Done? Does it Need to be Done?”, Skills Task Force, Research Paper 1 (September). Heijke, J.A.M. (1986), The Research Centre for Education and Labour Market, ROA-R-1986/1E, Maastricht. Heijke, J.A.M. (1993), Towards a Transparent Labour Market for Training Decisions: in Europe's Human Resources in the 1990's. Report from the 1993 Cumberland Lodge


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Conference, prepared by TARGET with support from the European Commission, Task Force Human Resources, Education, Training and Youth, pp. 60-75. Hollister, R. (1967), A Technical Evaluation of the First Stage of the Mediterranean Regional Project, OECD, Paris. Hughes, G. (1993), Projecting the Occupational Structure of Employment in OECD Countries, OECD, Paris, Labour Market and Social Policy Occasional Papers. No. 10. Hughes, G. (1994), An Overview of Occupational Forecasting in OECD Countries, Paper for the ECE/Eurostat Joint Work Session on Demographic Projections, Monsdorf-les-Bains, Luxembourg, June 1-4. Jemeļjanovs, O. (1999), Modelling the Latvian Economy with Special Emphasis on the Labour Market, ERASMUS Universitāte, ACE projekts. Kļaviņa S., Klapkalne U., Pētersone B. (2005). Politikas ietekmes vērtēšana politikas veidošanas sistēmā. Riga: State Secretariat. Manpower Forecasting and Modelling Replacement Demand: An Overview ROA-W-1996/4E. Ed Willems. Research Centre for Education and the Labour Market. Faculty of Economics and Business Administration. Maastricht University.- Maastricht, September 1996. Parnes, H.S. (1962), Forecasting Educational Needs for Economic and Social Development, OECD, Paris. Sterman, J.D. “Business Dynamics: System Thinking and Modeling for a Complex World”, (2000): Irwin McGraw-Hill. Tinbergen, J. and H.C. Bos (1965), 'A Planning Model for the Educational Requirements of Economic Development', in: Organisation for Economic Co-operation and Development, The Residual Factor and Economic Growth, OECD, Paris. Youdi, R.V. and K. Hinchcliffe (eds.) (1985), Forecasting Skilled Manpower Needs. The Experience of Eleven Countries, Paris, UNESCO International Institute for Educational Planning. Policy Documentation European Commission (28 February, 2000). The Lisbon European Council – and Agenda of Economic and Social Renewal for Europe. Contribution of the European Commission to the Special European Council in Lisbon, 23-24 – th March , 2000. Brussels, DOC/00/7. European Union Council (July 12, 2005). Decision of the Council about the basic approaches to employment policy of member states. Brussels, 2006/600/EK. European Union Council (October 6, 2006). Decision of the Council about basic approaches to Community Cohesion Strategy. 2006/702/EK.


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Meeting decision and meeting materials on labor force supply and demand forecasts of July 25, 2006 of the Cabinet of Ministers of the Republic of Latvia. Cabinet of Ministers of the Republic of Latvia (Decree No. 684 of October 19, 2005). Latvian national Lisbon program for 2005-2008. Cabinet of Ministers of the Republic of Latvia (October 31, 2006). Amendments to Regulations No. 238 of April 29, 2003 of the Cabinet of Ministers “Regulations of the Ministry of Economy”. Ministry of Welfare of the Republic of Latvia and the General Secretariat of the Employment and Social Affairs of the European Commission (February 6, 2003). Common declaration of Latvian government and European Commission on priorities of employment policy in Latvia. Work Materials Work materials (MS Word files) of the specialists from the Swedish Labor Market Agency:

a. Forecasting Handbook, 13 pp, b. Forecasting Project in Latvia, 13 pp, c. The Swedish forecast method, 4 pp, d. Needs of statistics for long term forecasts, 2 pp, e. Instruction of using long term model, 5pp.

Internet Sources Price sheets of computer technologies and standard software (2006). Viewed on 20.12.2006. http://www.ibm.com, http://www.ms.lv, http://www.oracle.com/, http://www.microsoft.com General directorate of Regional Policy of the European Commission (august 2006). Document of part 4. Methodic instructions in performance of cost and benefit analysis. Viewed on 20.02.2007. http://ec.europa.eu/regional_policy/sources/docoffic/2007/working/wd4_cost_lv.pdf Powersim Software, Products. (2006). Powersim Studio software descriptions and prices. Viewed on 20.12.2006. http://www.powersim.com/main/products___services/powersim_products/


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Annexes


125

Table of Contents of the Annexes

Annex 1. List of Persons Interviewed....................................................................................126

Annex 2. Application Samples ..............................................................................................127

Annex 3. Formalization of Models used in Forecasting........................................................138

Annex 4. Data Tables.............................................................................................................152

Annex 5 (CD).........................................................................................................................176


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Annex 1. List of Persons Interviewed

Anatolijs Melnis, Assistant director of the Higher Education and Science department of the Ministry of Education and Science

Andrejs Bessonovs, Senior referent of the Macroeconomic Analysis and Forecasts division of the Economic Structure Policy Department of the Ministry of Economy

Artis Grīnbergs, Director of the Development Planning Monitoring department of the Ministry of Regional Development and Municipal Affairs

Grieta Tentere, Director of the Strategic Planning and Analysis department of the Employment State Agency

Gunta Piņķe, Assistant director of the Economic Structure Policy department of the Ministry of Economy

Ieva Šnīdere, Assistant director of the Macroeconomic Analysis and Forecast division of the Economic Structural Policy department of the Ministry of Economy

Ilona Raugze – Director of the Medium-Term Development Planning department of the Ministry of Regional Development and Municipal Affairs

Imants Krupenkovs, head of the Economic Program Coordination Division of the Economic Structural Policy Department of the Ministry of Economy

Imants Lipskis, head of the Labor Market Policy and Monitoring Division, assistant director of the Labor Department of the Ministry of Welfare

Inta Vadone, head of the Professional Education Development division of the Professional Education and Continuing Education Department of the Ministry of Education and Science

Irina Šmatkova, senior referent of the Professional Education and Continuing Education Department of the Ministry of Education and Science

Jānis Salmiņš, senior referent of the Macroeconomic Analysis and Forecasts division of the Economic Structural Policy Department of the Ministry of Economy

Jānis Ušpelis, assistant director of the Economic Program Coordination Division of the Economic Structural Policy Department of the Ministry of Economy

Kristaps Prēdelis, project analyst of the Monitoring and Control Division of the State Regional Development Agency

Marina Mekša, head of the Financial Planning and Crediting division of the Higher Education Department of the Ministry of Education and Science

Torbjorns Israelsons, expert of the Swedish National Labor Board

Tords Strannefors, expert of the Swedish National Labor Board

Valentīna Locāne, project analyst of the Monitoring and Control Division of the State Regional Development Agency

Zinta Daija, acting director of the Professional Career Choice State Agency


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Annex 2. Application Samples

The primary data necessary for the research were obtained from DDA and DŅA. The questionnaires were elaborated in cooperation with the research groups of the first component “Labor market analysis and forecasting”. It must be added that part of information, which was acquired in the surveys was not used within framework of this research, because the questionnaire was created for achieving all goals of the 1st component researches. The obtained information was used for the necessary calculations in order to satisfy the requirements of the forecasting models. It was important to obtain data for forecasting about the alterations of labor force demand (in certain profession, certain territory, certain country):

• Net changes in the number of employment positions: Number of the employment positions created anew; Number of closed employment positions.

• Number of vacated employment positions: Transferring from employment to job search; Transferring from employment to performing family duties; Transferring from employment to training; Transferring from employment to age retirement; Transferring from employment to a different status (disability, death, imprisonment, informal employment, etc.); Emigration; Transferring from employment to a different profession.

• Price of an employment position

The mentioned information was obtained from DDA. However, the alterations in labor force supply in specific profession, specific territory, and specific country were acquired from DŅA. Information was obtained, which described transition from training to employment, from search of a job to employment, transition from performing family duties to employment, other transition (disablement, imprisonment, informal employment, etc.) to employment, transition from employment to search for a job, transition from training to search for a job, transition from performing family duties to search for a job, other transition (disablement, imprisonment, informal employment, etc.) to search for a job, professional mobility, geographical mobility, desired profession, as well as descriptive indices about the profession, gender, nationality, territory, etc. during the time period from year 1999 until year 2004.

Information about labor force demand Within framework of the research, information about labor force demand was obtained from DDA. Information necessary for elaboration of the forecasting models is displayed in the section of DDA questionnaire.


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Table P1. Section of the questionnaire of labor market demand (Employer) survey

Identification block 1. Name of the respondent (company, organization, institutions)

___________________________________________________________________________

2.1. Registered office _________________________________________________________ 2.2. Actual address of the office ________________________________________________ 3. Telephone __________________________ Fax __________________________ 4. E-mail ___________________________________________________________________ 5. Code of Commercial Register/Tax Payer Register * 6. Contacts _________________________________________________________________ 7.1. Type of ownership (select one answer)

Private sector 1

Public sector 2

7.2. Detailed code of the form of ownership and entrepreneurship 8.1. Key type of economic activity (select one – the key type of economic activity)

A Agriculture, hunting, and forestry 1 B Fishing industry 2 C Extractive industry and quarry processing 3 D Manufacturing industry 4 E Electric energy, gas, and water supply 5 F Construction 6

G Retail sales, wholesale, repairs of automobiles, motorbikes, individual use items, household appliances and devices 7

H Hotels and restaurants 8 I Transportation, storage, and communications 9 J Financial intermediation 10 K Transactions with real estate, lease, computer services, science, and other commercial services 11 L State administration and defense, mandatory social insurance 12 M Education 13 N Health and social care 14 O Public, social, and individual services 15

8.2. Detailed code of the key type of economic activity

9. Code of the administrative territorial unit (ATVK)

10. Date of filling in the questionnaire (dd.mm) 11. Time of beginning the interview (hh.mm)

* 11 signs in tax payer register, 9 – in commercial register


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Name of the respondent (company, organization, institution): Code of the respondent:

Part B Data of professions

Number of the em-

ployees

Including from

the 2nd column

From column 2 age groups

(years)

From column 2 according to the education levels

From column 2 in statistical regions

Expected number of employees (at the end

of year)

Expected number of employees

will increase greatly (++), will increase slightly (+),

will not change (=),

will decrease

slightly (-), will

decrease significantly

(--)

Professions

Prof

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3

A B 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

In total in a company (organization, institution)* incl.

Profession 1

Profession 2

Profession...

Profession 120

* In total in a company (1st row) must be the total number of the employees and distribution thereof, which generally is greater


130

Information about labor force supply

Parameters descriptive of the labor force supply were obtained from DŅA. Information used for forecasting model elaboration is summarized in a section of DŅA questionnaire.

Table P2. Section of DŅA survey

Tested Entered Coded

/_______/_______/________/

Interviewer ID Code of questionnaire/address

Date: /____/____/ (day, month)

Beginning of interview: /____:____/ (hour : minute)

1 No substitution of the respondent During the interview 2 Substitution of the respondent

Up to 15 years Finish the interview! 15-74 years Q01

Q0. Please, could you tell me, how old did you become on your last birthday?

_____________ (ENTER FULL AGE) More than 74 years Finish the interview!

Q01. Please, could you tell me, how many persons of those living at your residence, including yourself are aged from 15 to 74 years of age?

______________ (ENTER THE NUMBER)

Q1

1 Attended elementary school course/lower than elementary education

2 Elementary education

3 Incomplete secondary/secondary professional education

4 Secondary/general education

Q7

5 Incomplete higher education (successfully completed at least half of the time of basic study course) Q5

6 Secondary professional education

7 1st level professional higher education (college) and 4th level professional qualification (at least 2 years after the secondary school)

8 Higher education (at least 4 years)/professional higher education, and 5th level professional qualification

9 Academic bachelor degree (3 or 4 years)

10 Professional bachelor degree (at least 4 years) and 5th level professional qualification

11 Academic masters degree

12 Professional masters degree (at least 5 years) and 5th level professional qualification

13 Doctors degree

Q2

Q1. Please, could you tell me, what is the highest level of education you have obtained thus far? READ THE ANSWER OPTIONS! ONE ANSWER ONLY!

98 Does not know/does not remember Q11


131

Q2 And now – several questions about the last educational establishment that you have graduated from (successfully graduated professional or higher). K1: WRITE CODE 98 TO QUESTIONS (Q2.1-Q2.6) TO WHICH THE RESPONDENT CANNOT REMEMBER THE ANSWERS

1 Latvia Q2.2 2 Other EU country 3 CIS country

Q2.1 Please, could you tell me, which country did you graduate in?

4 Other country Q5

Q2.2 What was the title of the educational establishment? ________________________

Q2.3 What faculty and program did you study in? ________________________

Q2.4. And which specialty/qualification did you train in? ________________________

Q2.5. Please, could you tell me the year, when you graduated from the last educational establishment? In year _ _ _ _ Q11. What is your current professional status? You are... K4: (SEE INSTRUCTION) READ THE ANSWER OPTIONS FIRST, WHICH ARE HIGHLIGHTED IN BOLD FONT, AFTERWARDS ASK THE RESPONDENT ABOUT THE ADDITIONAL OPTIONS. MARK ONLY ONE ANSWER! Q11.1. Please, could you tell me, for how long have you had this status? (uninterrupted)! WRITE IT IN!

Q11.1 Q11 Years Months

Employee working in a profession related/rather related to the last obtained education 1 ____ ____ Q13

Employee working in a profession, which is not related to the last obtained education 2 ____ ____ Q12

Employer working in a profession related/rather related to the last obtained education 3 ____ ____ Q13

Employer working in a profession, which is not related to the last obtained education 4 ____ ____ Q12

Student, trainee, school student working in paid employment and is not an entrepreneur/self-employed 5 ____ ____ Q14

In search for a job (considering job offers), currently not working, but before worked in a profession related/rather related to the last obtained education

6 ____ ____

Q13

In search for a job (considering job offers), currently not working, but before worked in a profession not related to the last obtained education

7 ____ ____

Q12

Self-employed employed at a profession related/rather related to the last obtained education 8 ____ ____ Q13

Self-employed working in a profession, which is not related to the last obtained education 9 ____ ____ Q12

Currently on a maternity leave, however previously worked in a profession related/rather related to the last obtained education 10 ____ ____ Q13

Currently on a maternity leave, however previously worked in a profession unrelated to the last obtained education 11 ____ ____ Q12

On a long-term sick leave (including disablement) 12 ____ ____ Q13

Working retiree (working in a profession related/rather related with the last obtained education) 13 ____ ____ Q13


132

Working retiree (working in a profession unrelated with the last obtained education) 14 ____ ____ Q12

Unemployed retiree, earning extra money 15 ____ ____ Q21 Unemployed retiree 16 Currently unemployed and not looking for a job 17 Has never been employed 18

D1

Q13. Please, could you tell me, what is your current (or last) profession (position)? WRITE IN THE PROFESSION GIVEN BY THE RESPONDENT!

_____________________________________________________________________________________________________ Q14. Please, could you tell me, whether any of the following changes have occurred in your employment life during the last seven years? SEVERAL ANSWERS POSSIBLE! MAKE A CHECK IN THE CORRESPONDING BOX.

1999

2000

2001

2002

2003

2004

2005

1 Transferred from training to employment (completed training and started working)

2 Transferred from training to search for a job (you have completed training and are looking fore a job)

3 Transferred from job search to employment

4 Transferred from “other” (disablement, imprisonment, informal employment, etc.) to employment

5 Transferred from employment to job search (terminated employment relationships)

6 Transferred from performance of family duties to employment

7 Transferred from performance of family duties to job search

8 Transferred from “other” (disablement, imprisonment, informal employment, etc.) to job search

Q19

9

Transferred to another profession from the previous profession (re-qualification, position mobility, position change)

Q15

10 Change of employment (employer) within limits of the same district in the same profession

11 Change of employment (employer) in the same profession to another district of Latvia

12 Change of employment (employer) in the same profession outside Latvia (within borders of EU)

13 Change of employment (employer) in the same profession outside Latvia (outside of EU)

14 Entering the country 15 None of the above-mentioned

Q19


133

16 Changes have not occurred during the indicated period

Q18

Q15 Q16 Years Months

Q15. What was your pre-vious profession? FILL IN! Q16. How long did you work in it? FILL IN!

Profession ______________ ___

____

Q17

I will definitely choose a job in Latvia

1

I will rather choose a job in Latvia 2 Q20.1

I will rather choose a job abroad 3

Q20. Please, evaluate your future intention to work in Latvia or abroad during the upcoming two years!

I will definitely choose a job abroad 4 Q20.2

Q20.1. SHOW CARD 3.A TO THE RESPONDENT! I will indicate several reasons why people choose to work in Latvia and not leave for work abroad. Please, list the reasons in such sequence that the first (mark with number 1) would be the most important for you in choice of working in Latvia and the last (mark with number 5) the least important!

Sufficiently high salary level 1 ______ The job corresponds to your education and qualification 2 ______

There are options for career growth 3 ______ Cannot leave Latvia 4 ______

Do not want to leave Latvia 5 ______ It would be difficult to adapt abroad 6 ______

Q20.2. Please, could you tell me, in which country would you like to work in future? MULTIPLE ANSWERS ARE POSSIBLE!

Ireland 1 Other economically well-developed country 7 Great Britain 2 Russia 8

Germany 3 Belarus 9 Non-EU country (not Latvia) 4 Ukraine 10

Norway 5 Other 11 USA 6 Does not know 98

1 Discussions with colleagues about options to draw the employer’s attention to the problem of social payments

2 I have addressed state institutions (SRS, Labor Inspection, VSAA) 3 I have addressed a lawyer or court 4 I have asked for assistance in labor union 5 The management of the company ignored my request 6 The company started paying the taxes partially 7 The company started paying the taxes completely

Q24.1. What was your and/or your business conduct after you discussed paying social payments from the whole sum? MULTIPLE ANSWERS ARE POSSIBLE!

8 Other

1 Because I understand that the company could not exist if they would execute the social payments for all employees

2 Because I am afraid of losing my job 3 Because I do not care about the social security in future

Q24.2. Why have you not requested that social pay-ments are executed for your job? MULTIPLE ANSWERS ARE POSSIBLE! 4 Other Q25. Please, could you tell me, why have you engaged into “informal” labor relationships? Please, evaluate, to what extent each of the below mentioned reasons can be referable to your situation! If a reason cannot be referred to you not to the slightest extent, please, mark 1, if it can be referred to you to the utmost extent, please, mark 4! Not at all To the utmost

extent1 It was not possible to find another job near

my place of residence 1 2 3 4


134

2 An option to earn more 1 2 3 4 3 I do not have trust in the system of social

payments 1 2 3 4

4 There are no options to work in my specialty 1 2 3 4 5 I do not have corresponding education 1 2 3 4 6 Health condition 1 2 3 4 7 Family situation 1 2 3 4 8 Age restrictions 1 2 3 4 9 Gender restrictions/prejudice 1 2 3 4

10 Nationality 1 2 3 4 11 Native language 1 2 3 4 12 Religious affiliation 1 2 3 4 13 Criminal record 1 2 3 4 14 Difficult procedure of labor relationship execution 1 2 3 4

Q26 Q26. In what situation would you be willing to enter legal labor relationships? MULTIPLE ANSWERS ARE POSSIBLE! 1 If I could find a legal job 2 If I could find a legal job in my profession 3 If the net salary at the legal job would be approximately at the same level as at the illegal job 4 If I would require social securities (benefits, pension) 5 If the social securities would improve 6 If penal actions would be implemented against employees for working illegally 7 Other

K8 K8: IF Q21 IS MARKED WITH CODE “5” Q29

IF Q21 IS MARKED WITH CODES “6” AND “7” Q27 Q27. SHOW CARD 5 TO THE RESPONDENT! Please, could you tell me in which sphere do you earn extra money? Q27.1. How often? Q27.2. And what is the average monthly salary (in each of the spheres of extra work, if there are multiple)?

Q27.1 On average how often

Very often Sometimes Very rarely or seasonal

Spheres of additional work Q

27

Mor

e th

an 3

2 ho

urs a

wee

k (4

an

d m

ore

wor

k da

ys a

wee

k)

9-32

h p

er w

eek

(fro

m 1

to 4

w

ork

days

a w

eek)

2-8

h pe

r wee

k (1

and

less

w

ork

days

per

wee

k)

4-10

h p

er m

onth

3 m

onth

s or a

seas

on

(wor

king

full

wor

k da

ys)

Mor

e th

an 3

mon

ths b

ut le

ss

than

100

hou

rs p

er m

onth

Less

than

3 m

onth

s per

yea

r

Q27.2 Ls/monthly

(WRITE THE SUM IN FRONT OF EACH TYPE

OF ADDITIONAL EARNINGS)

Construction and repair works 1 1 2 3 4 5 6 7 ___ LVL/monthly


135

Forestry 2 1 2 3 4 5 6 7 __ LVL/ monthly Agricultural and/or garden works 3 1 2 3 4 5 6 7 __ LVL/ monthly Household help (babysitting, housekeeping)

4 1 2 3 4 5 6 7 __ LVL/ monthly

Private lessons 5 1 2 3 4 5 6 7 __ LVL/ monthly Services of infor-mation technologies 6 1 2 3 4 5 6 7 __ LVL/ monthly Preparation of business documents and cost estimates

7 1 2 3 4 5 6 7

__ LVL/ monthly

Health care 8 1 2 3 4 5 6 7 __ LVL/ monthly

Security staff 9 1 2 3 4 5 6 7 __ LVL/ monthly Tailor and shoemaker services

10 1 2 3 4 5 6 7 __ LVL/ monthly

Trading 11 1 2 3 4 5 6 7 __ LVL/ monthly Other type of employment 12 1 2 3 4 5 6 7 __ LVL/ monthly

Q28 Q28. I will read you several reasons why people have extra jobs. Please, evaluate the extent to which each of them can be referable to you! Please, use the scale from 1 to 4 to evaluate, where 1 indicates that it cannot be referred to you at all and 4 means that it is referable to you to the utmost extent. Not at

all To the utmost extent

1 Low income, need to have extra earnings 1 2 3 4 2 I don’t have a full time principal job, therefore I can work

additionally 1 2 3 4

3 I want to obtain additional skills and experience in other professions 1 2 3 4 4 I cannot express my creativity at the principal job 1 2 3 4

Q29 1 Agriculture and hunting, and forestry 2 Fishing industry 3 Extraction industry and quarry production

4 Manufacturing industry: Production of foodstuffs and beverages

4.1 Manufacturing of tobacco products 4.2 Manufacturing of textiles

4.3 Production of lumber, wood, and cork products, except furniture; production of straw and weaved products

4.4 Publishing, printing 4.5 Furniture production 4.6 Engineering industry and metal processing 4.7 Other processing industries 12 Electric energy, gas, and water supply

Q29. Please, indicate the industry, in which you are currently employed! (Industry, to which the company belongs, where the respondent is employed) IF THE RESPONDENT CANNOT NAME THE INDUSTRY OF EMPLOYMENT, READ THE ANSWER ALTERNATIVES! MARK ONLY ONE ANSWER!

13 Construction


136

14 Wholesale, retail sales, repairs of automobiles, motorbikes, individual use items, household appliances and devices

15 Hotels and restaurants 16 Transportation, storage, and communication 17 Financial intermediation 18 Transactions with real estate:

18.1 Lease of vehicles and devices, and individual use items, and household appliances

18.2 Computers and operations related thereto 18.3 Work of scientific research 22 State administration and defense, mandatory social insurance 23 Education 24 Health and social care 25 Public, social, individual services

Other (FILL IN THE RESPONDENT’S ANSWER!) ____________________________________________

Q29.1. 1 Public sector 2 Non-governmental organizations

Q29.1. Please, could you indicate the sector, in which you currently are employed? MULTIPLE ANSWERS ARE POSSIBLE! 3 Private sector

Q30

Q30 1 I attended course 2 I trained through correspondence course 3 In professional or higher educational establishment 4 Self-taught 6 Training at the job 7 Training was not necessary

Q30. How did you satisfy the requirements necessary for execution of your current employment duties (for filling the position)? MULTIPLE ANSWERS ARE POSSIBLE! 8 Other

Q31

Yes 1 Q32 Q31. Besides your basic duties, do you perform additional

duties/tasks, which are not paid extra? No 2 Q33 Q32. What additional duties (functions/tasks of other professions) do you have? (MULTIPLE ANSWERS ARE POSSIBLE!) 1 In management 9 Driving 2 In accounting 10 In matters of labor security 3 In law 11 Security staff 4 In record keeping 12 In designing 5 In economy 13 In tailoring 6 In marketing 14 In construction 7 Translations 15 In cleaning premises, improving the territory 8 Computer administration, service 16 Other

Q33

1 Yes D2 D1. Is the education you obtained the last your higher successfully obtained education level thus far? 2 No D1.1


137

K1: TO QUESTIONS (D1.1-D1.5), WHICH THE RESPONDENT DOES NOT REMEMBER THE ANSWERS FOR, FILL IN CODE 98!

1 Latvia 2 Other EU country 3 CIS

D1.1 Please, tell me, in which country did you obtain this education?

4 Other country D1.2 What was the name of this educational establishment? ________________________

D1.3 In what faculty and which program did you study? ________________________

D1.4. Which specialty/qualification did you obtain? ________________________

D1.5. Please, tell me the year, when you graduated this educational establishment! In year_ _ _ _

D4. Please, tell me your citizenship (national origin)! ___________________

D5 1 Male D5. DO NOT ASK, ENTER THE

GENDER OF THE RESPONDENT! 2 Female D6


138

Annex 3. Formalization of Models used in Forecasting

Description of the statistical model software The statistical model has been implemented in MS Excel environment. The solution consists of several mutually related files, which ensure implementation of the necessary functionality of the scientific model (please, see the technical architecture of the model in Figure P1). All of the used files and short description thereof are listed in Table P3.

Figure P1. Technical architecture of the model in MS Excel environment

Such approach allows for fast and simple realization and approbation of the theoretical model, but it does not ensure convenient and safe multi-user environment.

Table P3. MS Excel files used in the model

Name of the file Model block Description IVDtab_dati.xls Input data block

(IVD table) Ensures maintenance of input data

APRtab_1_PENSbloks.xls Retirement block (1.1. APR table)

Ensures selection of the best model from 4 different models (level, exponential, hyperbolic, logarithmic)

APRtab_PENSbloks.xls Newcomers to the labor market (1.1-a. APR table); Leaving the labor market (1.1-b. APR table); Summary of the results (1.1-c. APR table)

Ensures summary about the labor force incoming into the labor market and leaving the labor market

APRtab_ISTbloks.xls Short-term forecasting block (1.2. APR table)

Ensures short-term forecasting in profession section

APRtab_VIDbloks.xls Medium-term forecasting block (1.3. APR table)

Ensures medium-term forecasting in profession section


139

Name of the file Model block Description APRtab_ILGbloks.xls Long-term forecasting

block (1.4. APR table) Ensures long-term forecasting in profession section

ANLtab_ISTbloks.xls Short-term forecasting results (1.2.1. ANL table)

Ensures analysis of short-term forecasting results

ANLtab_VIDbloks.xls Medium-term forecasting results (1.3.1. ANL table)

Ensures analysis of medium-term forecasting results

ANLtab_ILGbloks.xls Long-term forecasting results (1.4.1. ANL table)

Ensures analysis of long-term forecasting results

ANLtab_PENSbloks.xls Retirement forecasting results (1.1.1. ANL table)

Ensures analysis of retirement forecasting results

The following software is necessary for operating the solution:

• Operating system – MS Windows 2000 or XP; • Office software – MS Excel 2000 or 2003.

In order to work with the statistical model, it is necessary to open the input data and retirement block files in the MS Excel environment (IVDtab_dati.xls, APRtab_1_PENSbloks.xls, APRtab_PENSbloks.xls), as well as the corresponding short-term, medium-term, or long-term forecasting block files. DOM software description DOM is implemented in the Powesim Studio environment. Additionally used are MS Excel files for maintenance of input data and for storage of results.

Figure P2. Technical architecture of the model in Powersim Studio environment

MS Windows

Powersim Studiosoftware

MS ExcelFile (input data)

Powersim Studio file (model))

Scientific personnel End users

Model maintenance Model application

MS Excelsoftware

Model maintenance

All of the Powersim Studio and MS Excel files used in the model are listed in Table P4.


140

Table P4. Files used in DOM model

File name Description DOM_plusma.sip Dynamic optimization model (Powersim Studio) excel_DOM_ieeja.xls Interim results of input data (MS Excel) excel_DOM_izeja.xls Forecasting results (MS Excel) For operating the solution, the following software is necessary:

• Operating system – MS Windows 2000 or XP; • Office software – MS Excel 2000 or 2003; • Modeling software – Powersim Studio 7.

In order to operate the dynamic optimization model, it is necessary to open the main file (DOM_plusma.sip) in Powersim Studio environment. Files of input data and forecasting results (excel_DOM_ieeja.xls, excel_DOM_izeja.xls) will open automatically.

Formulary structure of the elaborated statistical models When adapting the Swedish Labor Market forecasting model to the situation of Latvia, regardless of the differences in the basic elements of sphere forecasting, similar principles are maintained in its structure. However, instead of the models, four modeling and forecasting blocks were implemented:

1st block – Retirement block,

2nd block – Short-term forecasting block,

3rd block – Medium-term forecasting block,

4th block – Long-term forecasting and strategic evaluation block.

The short-term and medium-term labor market forecasting blocks were created to obtain data for market analysis and short-term (1-3 years – y. 2007-2008), medium-term (or long-term) (up to 5 years, up to 14 years) forecasting. The results of these modeling blocks can be used for short term education program planning, as well as for lasting education system planning and planning of certain study course programs.


141

Figure P3. Labor force supply and demand forecasting models in MS Excel environment


142

Labor force market development forecast in short-term The forecast is performed for the planned t = year 2006 and short-term time period

t = 2007, 2008 based on actual data t = 1996, ..., 2005 (see figure P3).

Forecast of short-term time period of labor force demand Let us assume that Di (IST) is the annual demand of labor force demand in short-term i and

Li – the number of the employed in year i;

∆Li – changes of the analytical number of the employed in year i;

∆LFi – changes of the average number of the employed during an actual time period in year i;

Pi – retired or laid off due to reduction of staff in year i;

∆Lij – change in number of the employed in year i and in age j;

Lij – number of the employed in year i and in age j;

Nij – number of the inhabitants in year i and in age j.

intijG – interpolated percentage (%) of the employed in year i and in age j;

Gik – percentage of the employed (%) in year i in age groups k.

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ii

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ii

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2005...,,1996tet

IST_Modelis

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2008...,,1996tet

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Short-term labor force supply forecast

Let us assume that Si (IST) is short term labor force supply in year i and

Ui – the number of graduates starting employment in year i;

Ii – migration of the employed in year i;

Li – number of the employed in year i.

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1

By use of such formula and model generalization the short-term labor force supply and demand forecasts were made in profession groups and in industries.


144

Forecast of the labor market development in a medium-term period

The forecast is performed for a medium-term time period t = 2009, ..., 2013, based on actual data t = 1996, ..., 2005, in the planned t = year 2006 and short-term forecast data t = 2007, ..., 2008 (See Table P1).

Labor force demand forecast in medium-term period

Let us assume that Di(VID) is the labor force demand in medium-term time period in year i and

Li – the number of the employed in year i;

∆Li – analytical change in the number of the employed in year i;

∆LFi – average change in number of the employed in actual time frame in year i;

Pi – the retired and laid off due to reduction of staff in year i;

∆Lij – change in number of the employed in year i and age j;

Lij – number of the employed in year i and age j;

Nij – number of inhabitants in year i and age j.

intijG – interpolated percentage (%) of the employed in year i and age j;

Gik – percentage (%) of the employed in year i in age groups k.

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;2013...,,1996tet

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Medium-term labor force supply forecast

Let us assume that Si(VID) is medium-term labor force supply in year i and

Ui – the number of graduates who have started employment in year i;

Ii – migration of the employed in year i;

Li – number of the employed in year i.

⎪⎪⎪

⎩

⎪⎪⎪

⎨

⎧

+⋅+++

+⋅+

⋅⋅

=⋅⋅

=

======

++=

⋅

ii

ii

ii

ti

i

ii

ii

ii

iiii

tt/

)tln(

ee

,...,tet

VID_Modelis

,...,i,VID_ModelisL,...,i,VID_ModelisI,...,i,VID_ModelisU

LIU)VID(S

ii2

i

εβαεβα

εβα

α

αεβ

εβ

55

44

33

2

1 20081996

131131131

1

By use of such formula and model generalization the medium-term labor force supply and demand forecasts were made in profession groups and in industries.


146

Long-term labor market development forecast

The forecast was performed for the time period t = 2014, ..., 2020, based on actual data t = 1996, ..., 2005, in the scheduled t = year 2006, for short-term forecast data t = 2007, ..., 2008 and fore medium-term forecast data t = 2009, ..., 2013.

The long-term labor force demand forecast

Let us assume that Di(ILG) is the long-term labor force demand in year i:

i,t)ILG(D iii 1111 +⋅+= εβα i = 1, ..., 18; t = 1996, ..., 2013.

Long-term labor market supply forecast

Let us assume that Si(ILG) is long-term labor force supply in year i:

,t)ILG(S iii 77 +⋅+= εβα i = 1, ..., 18; t = 1996, ..., 2013.

Balance point of labor force supply and demand

The labor market forecasts are necessary for establishing the labor force supply and demand and for ensuring reciprocal conformance thereof, thus creating balance in the labor market.

Establish the following of the labor force supply Si(ILG) and demand Di (ILG) in long-term time period:

• Balance point E, when Si(ILG) = Di(ILG); • Labor force deficit, when Di(ILG) > Si(ILG); • Labor force unemployment, when Di(ILG) < Si(ILG).

Formalization of the most significant connections used in DOM structure

DOM elaboration is based on application of system-dynamics principles, performing analysis of the labor market as a complex system, along with structuring and operation simulation, indicating behavior of the system elements in a form of decision making regulations.


147

Formalization of the variables of the labor market module

Variable of the level “Number of vacancies”:

)//( ___)(

slegtVslegtat

atbrivat

aiznslegtat

jaunVjaunatdttaat TVLLTVdtVV −++= +−

Variable of the level “Number of filled positions”:

)( _)(

atbrivat

aiznslegtat

aizpildatdttaat LLVdtLL −−= +−

Process variable “Filling the vacancy”:

aizpildVkontveiksmat

aizpildat TkUV __ /*=

Ensuring conformance between the labor force demand (expressed as the number of positions) and labor force supply (expressed as the number of individuals):

noslodznodarbat

aizpildat kUV *=

noslodzatbrivat

atbrivat kNL *=

Variable of the level “Number of those seeking a job”:

)()(atbriv

atnodarb

atsamaz

atpieaug

atdttaat NUUUdtUU ++−= +−

Variable of the level “Number of the employed”:

)()(atbriv

atsamaz

atnodarb

atdttaat NNUdtNN −−= +−

Balance salary calculation:

Wat*=( vat; ; uat)

Actual salary level calculation:

)/)(( )(*

)(w

dttaatdttaat TWWdtWW −−+−=


148

Formalization of the variables of the society module

Variable of the level “studying at an educational establishment”:

)()( lutlutlutdttlulut FJEdtHH −= −+−

Process variable “started studying at an educational establishment”:

utlturp

ullut FkE )1(*)1( −−=

Process variable “drops out of the educational establishment”: mac

luatbir

lulutlut TkHJ /*=

Process variable “graduates from the certain educational establishment”:

)/)1(*mac

luatbir

lulutlut TkHF −=

Process variable “the graduates of the training level becoming economically active”:

)( )1()1( utlutllutlut JEFS +++−=

Formalization of the variables of the economic module Calculation of the economic output volume, by using the Cobb-Douglass manufacturing

function: αα −= 1

tttt LKAY

ttt AeA εβ +=

Calculation of the labor force demand – desired number of employment positions:

α

α

−

⎟⎟⎠

⎞⎜⎜⎝

⎛=

11

)()(ntnt

ntnt KA

velYvelL

Calculation of the desired labor force in profession groups and industries:

)(*)( velLbvelL ntanant =


149

Total economic demand for labor force in profession groups:

∑=

=15

1)()(

nantat velLvelL

Actually available labor force volume in profession groups and industries:

naatant bLfaktL *)( =

Total actual labor force volume of the industry:

∑=

=37

1)()(

aantnt faktLfaktL

Total actual output volume:

)()( faktLKAfaktY ntntntntα=

Denominations:

Elut – number of entrants to the l education level in the u education sphere in year t

Flut – number of graduates from the l education level in the u education sphere in year t

Jlut – number of dropouts from the l education level in the u education sphere in year t

klu atbir – drop-out quotient in the l education level in the u education sphere in year t

Hlut –number of students in the l education level in the u education sphere in year t

klu turpin – quotient of those continuing the studies in the l education level in the u education sphere

Tlu mac – length of training in the l education level in the u education sphere

Slu – the graduates from the l education level in the u education sphere becoming

economically active in year t

Yt – GDP in comparative prices in period t

At – total factor productivity or the technological progress in period t

Kt – capital in period t

Lt – number of the employed in period t

Α – flexibility of the productivity factors, reciprocal substation possibilities

A0 – initial technological level

e – exponent


150

t – productivity trend dependent on time

β – influence of the productivity trend on GDP

ε – evaluation error

Ynt(vēl) – desired GDP in industry n in period t

Ynt(fakt) – actual GDP in industry n in period t

Ant – total factor productivity or the technological progress in industry n in period t

Knt – capital in industry n in period t

L nt(vēl) – desired labor force volume in industry n in period t

L ant (vēl) – desired labor force volume of profession group a in industry n in period t

ban – percentage of a profession group in total volume of the labor force resources used in industry n

bna – percentage of u industry and a profession group in total volume of labor force resources

used in profession group a

L at (vēl) –desired volume of labor force of profession group a in period t

L ant (fakt) –actual volume of labor force of profession group a in industry n in period t

L at (fakt) –actual volume of labor force in profession group a in period t

Wat * – balance salary in profession group a in period t

vat – percentage of vacant positions in total labor force demand of profession group a in period t

uat – percentage of those in search for a job in total labor force demand of profession group a

in period t

Wat – actual salary in profession group a in period t

Tw – the time necessary to recognize the relation of labor force supply and demand and to

react accordingly

atV – number of vacancies in profession group a in period t

jaunatV – new employment positions in profession group a in period t

slegtatV – closed employment positions in profession group a in period t

aizpildatV – a vacant position is filled in profession group a in period t

jaunVT _– time of creating a new employment position

slegtVT _– time of closing an employment position

aizpildVT _ – time to fill the vacant position

atL – number of the filled vacancies in profession group a in period t aiznslegt

atL _ – the filled position is closed in profession group a in period t atbriv

atL – a position is vacated in profession group a in period t


151

atU – number of those in search for a job in profession group a in period t pieaug

atU – increase in number of those in search of a job in profession group a in period t samaz

atU – decrease in number of those in search of a job in profession group a in period t nodarb

atU – those looking for a job transferring from search to employment in profession group a in period t

atN – the number of the employed in profession group a in period t atbriv

atN – the employed transferring from employment to looking for a job in profession group a in period t

samazatN – decrease of the employed in profession group a in period t

noslodzk – proportion of the filled positions and the employed kontveiksmk _

– successful contact quotient


152

Annex 4. Data Tables

Table P5. List of industries according to the NACE classifier

Code Industry

A AGRICULTURE, HUNTING, AND FORESTRY

B FISHING INDUSTRY

C EXTRACTIVE INDUSTRY AND QUARRY PROCESSING

D MANUFACTURING INDUSTRY

E ELECTRIC ENERGY, GAS, AND WATER SUPPLY

F CONSTRUCTION

G WHOLESALE AND RETAIL SALES; REPAIRS OF AUTOMOBILES, MOTORBIKES, INDIVIDUAL USE ITEMS, HOUSEHOLD APPLIANCES AND DEVICES

H HOTELS AND RESTAURANTS

I TRANSPORTATION, STORAGE, AND COMMUNICATIONS

J FINANCIAL INTERMEDIATION

K TRANSACTIONS WITH REAL ESTATE, LEASE, COMPUTER SERVICES, SCIENCE, AND OTHER COMMERCIAL SERVICES

L STATE ADMINISTRATION AND DEFENSE; MANDATORY SOCIAL INSURANCE

M EDUCATION

N HEALTH AND SOCIAL CARE

O PUBLIC, SOCIAL, AND INDIVIDUAL SERVICES

Table P6. List of profession groups

Group code Denomination A_0 Officials, legislators A_0_zin Scientific workers, academic positions in higher educational establishments A_1 Teachers A_2 Specialists of humanitarian sciences, representatives of creative professions A_3 Other specialists of social sciences A_34 Economists, accountants, other senior specialists A_34_v Managers A_38 Legal professions (judges, lawyers, etc.) A_4 Specialists of natural sciences A_48 Specialists of computer sciences A_52 Engineers A_58 Architects A_6 Agronomists, other senior specialists of agricultural professions A_7 Doctors A_76 Care and social workers A_81 Service specialists A_84 Ship and aircraft specialists A_86 Civil safety, defense, and other specialists K_3 Librarians, post service workers K_34 Specialists of commercial transactions K_38 Legal specialists (paralegals, etc,) K_52 Specialists of electric technologies and other K_58 Construction specialists K_7 Intermediate medical personnel, nurses V_2 Art, culture, recreational workers V_34 Secretaries, registrars V_48 Specialists of computation technologies


153

Group code Denomination V_52 Mechanics, locksmiths V_54 Qualified workers in manufacturing, construction V_58 Construction professions V_6 Agricultural workers V_76 Care workers V_81 Individual services V_84 Drivers of transportation means V_86 Security staff V_0 Ordinary professions P_0 Low-qualified professions

Results obtained with assistance of statistical models Table P7. Degree of satisfaction of labor force demand expressed in percent in

profession groups

Short-term Medium-term Long-term Code of profession group Until y. 2008 Until y. 2013 Until y. 2020

A_0 -7 -22 -31 A_0_zin -3 -22 -52 A_1 -5 -20 -34 A_2 23 47 5 A_3 19 25 38 A_34 4 30 64 A_34_v 2 19 16 A_38 13 28 69 A_4 -13 -20 -64 A_48 1 7 15 A_52 -4 -12 -50 A_58 3 16 12 A_6 57 24 36 A_7 16 23 21 A_76 -32 -36 -35 A_81 4 -14 -2 A_84 -4 -17 -27 A_86 17 19 -12 K_3 -5 -4 -23 K_34 0 29 31 K_38 8 30 28 K_52 1 -6 -34 K_58 -12 -31 -111 K_7 -5 -9 -14 V_2 17 20 16 V_34 5 -2 -5 V_48 -18 36 44 V_52 2 -37 -15 V_54 2 -7 -15 V_58 -2 -16 -93 V_6 14 20 63 V_76 8 2 6 V_81 45 57 50 V_84 4 -26 -37 V_86 8 -2 -6 V_0 15 17 30 P_0 4 6 7

Source: Statistical models in MS Excel environment


154

Table P8. Satisfaction degree of labor force demand in profession groups Short-term Medium-term Long-term Code of the

profession group Until y. 2008 Until y. 2013 Until y. 2020

A_0 +/- +/- -- A_0_zin + +/- -- A_1 +/- +/- -- A_2 +/- -- + A_3 +/- -- -- A_34 + -- -- A_34_v + +/- +/- A_38 +/- -- -- A_4 +/- +/- -- A_48 + +/- +/- A_52 + +/- -- A_58 + +/- +/- A_6 -- +/- -- A_7 +/- +/- +/- A_76 -- -- -- A_81 + +/- + A_84 + +/- -- A_86 +/- +/- +/- K_3 +/- + +/- K_34 + -- -- K_38 +/- -- -- K_52 + +/- -- K_58 +/- -- -- K_7 +/- +/- +/- V_2 +/- +/- +/- V_34 +/- + +/- V_48 +/- -- -- V_52 + -- +/- V_54 + +/- +/- V_58 + +/- -- V_6 +/- +/- -- V_76 +/- + +/- V_81 -- -- -- V_84 + -- -- V_86 +/- + +/- V_0 +/- +/- -- P_0 + +/- +/-



155

Table P9. Satisfaction degree of labor force demand in profession groups Short-term Medium-term Long-term Code of the

profession group Until y. 2008 Until y. 2013 Until y. 2020

A_0 deficiency deficiency deficiency

A_0_zin deficiency deficiency deficiency


A_2 surplus surplus surplus



A_34_v balance surplus surplus



A_48 balance surplus surplus






A_81 surplus deficiency balance



K_3 deficiency deficiency deficiency

K_34 balance surplus surplus

K_38 surplus surplus surplus

K_52 balance deficiency deficiency



V_2 surplus surplus surplus

V_34 surplus balance deficiency

V_48 deficiency surplus surplus

V_52 balance deficiency deficiency




V_76 surplus balance surplus


V_84 surplus deficiency deficiency

V_86 surplus balance deficiency


P_0 surplus surplus surplus



156

Table P10. Satisfied labor force demand in long-term according to profession groups in increasing sequence (%)

K_58 -111 V_58 -93 A_4 -64 A_0_zin -52 A_52 -50 V_84 -37 A_76 -35 A_1 -34 K_52 -34 A_0 -31 A_84 -27 K_3 -23 V_52 -15 V_54 -15 K_7 -14 A_86 -12 V_86 -6 V_34 -5 A_81 -2 A_2 5 V_76 6 P_0 7 A_58 12 A_48 15 A_34_v 16 V_2 16 A_7 21 K_38 28 V_0 30 K_34 31 A_6 36 A_3 38 V_48 44 V_81 50 V_6 63 A_34 64 A_38 69



157

Examples

Figure P4. Specialists of engineering sciences

Ikgada pieprasījuma, piedāvājuma izmaiņas ilgtermiņā

0

10000

20000

30000

40000

50000

60000

1997

.19

98.19

99.20

00.20

01.20

02.20

03.20

04.20

05.20

06.20

07.20

08.20

09.20

10.20

11.20

12.20

13.20

14.20

15.20

16.20

17.20

18.20

19.20

20.

Gadi

Pers

onu

skai

ts

Kopejais darbaspeka piepras ijums D 11728Kopejais darbaspeka piepras ijums D noKopejais darbaspeka piepras ijums D lidzKopejais darbaspeka piedavajums S 15290


0

10000

20000

30000

40000

50000

60000

1997

.19

98.19

99.20

00.20

01.20

02.20

03.20

04.20

05.20

06.20

07.20

08.20

09.20

10.20

11.20

12.20

13.20

14.20

15.20

16.20

17.20

18.20

19.20

20.

Gadi

Pers

onu

skai

ts

Kopejais darbaspeka piepras ijums D 11728Kopejais darbaspeka piepras ijums D noKopejais darbaspeka piepras ijums D lidzKopejais darbaspeka piedavajums S 15290


Figure P5. Doctors


0

5000

10000

15000

20000

25000

30000

1997

.19

98.19

99.20

00.20

01.20

02.20

03.20

04.20

05.20

06.20

07.20

08.20

09.20

10.20

11.20

12.20

13.20

14.20

15.20

16.20

17.20

18.20

19.20

20.

Gadi

Pers

onu

skai

ts

Kopejais darbaspeka pieprasijums D 9467 Kopejais darbaspeka pieprasijums D no

Kopejais darbaspeka pieprasijums D lidz Kopejais darbaspeka piedavajums S 11133


0

5000

10000

15000

20000

25000

30000

1997

.19

98.19

99.20

00.20

01.20

02.20

03.20

04.20

05.20

06.20

07.20

08.20

09.20

10.20

11.20

12.20

13.20

14.20

15.20

16.20

17.20

18.20

19.20

20.

Gadi

Pers

onu

skai

ts

Kopejais darbaspeka pieprasijums D 9467 Kopejais darbaspeka pieprasijums D no

Kopejais darbaspeka pieprasijums D lidz Kopejais darbaspeka piedavajums S 11133

Annual changes in long-term supply and demand

Years 1997

.

Total labor force demand D 9467

Total labor force demand D until

Total labor force demand D from

Total labor force supply S 11133


Table P11. Labor market demand, supply forecasts in profession groups

2006 2007 2008 Total labor force

demand Total labor force

supply Total labor force


supply Total labor force


supply Profession

groups number from to number from to number from to number from to number from to number from to

A_0 47314,032 40302 58436 48627 39116 58522 50022 40489 59544 49332 39351 59739 50620 40581 60622 49978 39488 60945 A_0_zin 5880 5453 6784 6278 5467 7144 6091 5391 6790 6249 5393 7171 6052 5315 6786 6226 5321 7212 A_1 57576 34458 82249 57471 35123 84316 57012 33020 84188 57395 33776 85994 57007 31477 86284 57330 32323 87908 A_2 5319 2561 9447 6344 2825 20597 4582 2290 9156 5966 2524 21885 4254 2032 8883 5615 2247 23643 A_3 4070 3478 5179 4965 3375 11019 4427 3534 5321 5056 3424 11961 4513 3572 5451 5147 3468 13103 A_34 73353 66349 91880 75227 62278 118214 82548 69420 98104 79452 65142 130805 87256 72570 104800 83919 68074 146095 A_34_v 76720 72792 81573 76777 68821 94285 80299 75686 84886 79897 71610 99387 83407 78549 88195 83017 74367 104874 A_38 5682 4587 6097 6106 4071 8292 5876 5073 6676 6571 4471 20766 6455 5601 7303 7095 4922 23818 A_4 15494 2320 87627 11309 7650 83590 11062 729 99897 11309 578 94503 11057 6724 114852 11309 5784 107975 A_48 6650 1590 13136 6800 1798 17122 6337 1295 13768 6800 1523 18306 6332 967 14459 6800 1225 19721 A_52 23979 15654 32889 23063 14710 39655 23019 15504 34142 23404 14555 41850 23309 15303 35446 23722 14352 44331 A_58 5379 3220 25953 5687 222 27047 5124 1129 28784 5832 1281 30023 5118 2624 32126 5996 1239 33609 A_6 893 720 6467 556 120 652769 608 129 4680 504 124 5207 603 224 4367 446 342 2845 A_7 5786 4783 8819 8957 6287 12356 5502 3357 7645 7898 5078 11563 4041 1759 6317 6603 3619 10570 A_76 5251 3815 5774 3972 2869 5822 5565 4524 6604 4485 3383 6512 6411 5303 7512 5066 3957 7295 A_81 10231 6709 10035 6724 4992 9324 10674 8905 12441 8754 6896 11647 13236 11353 15108 11059 9060 14304 A_84 4225 2412 4645 3110 1947 14776 4169 2982 5357 3649 2405 19350 4888 3624 6149 4281 2944 6045 A_86 10618 720 17994 21116 8751 18753 20628 1429 55198 21116 1940 67071 20623 7424 78448 21116 7685 44212 K_3 4711 920 9263 3286 769 3474 6419 1429 13601 3374 542 5682 10375 5724 17374 6897 6480 14324 K_34 10586 820 42796 12779 968 41557 12669 929 46233 12786 13 44689 12664 2458 50179 12794 7818 48358 K_38 1407 820 4653 1433 725 8484 1369 294 5485435 1433 974 2173 1364 924 9856 1433 675 8785 K_52 3539 2420 9665 4718 91 13871 4302 289 11362 4827 124 7470 4297 624 7960 4950 153 8817 K_58 1508 920 10796 2318 1754 20358 2321 29 10595 2318 1425 9042 2316 924 6289 2318 876 9208 K_7 24785 9586 39110 23506 10311 41315 22466 8769 40511 23771 9667 43192 22461 7885 42039 24073 8961 45336 V_2 179439 138975 201712 201959 139036 287552 174344 141362 207307 211477 143071 305861 178058 143325 212715 221040 146445 325570 V_34 14712 8366 14307 9326 5940 22303 14664 11503 17821 12277 8632 52751 18390 15027 21739 15645 11704 21693 V_48 12130 5100 15463 9250 3898 19075 11357 5909 16804 10321 4636 21766 12421 6684 18150 11392 5342 25042 V_52 96676 76120 197848 101931 24512 189554 101984 8729 550502 101931 0 239990 101979 9624 612707 101931 89752 600982 V_54 43264 620 51470 12847 526 85063 51120 1529 59349 18305 1457 58131 92666 52324 176710 54637 32545 66335 V_58 41655 36115 50025 43132 34715 63844 44009 36694 51301 44160 35376 67646 44868 37165 52520 45106 35920 72052 V_6 10298 1120 35499 8919 1457 38358 8599 29 38862 8919 457 22103 8594 2524 24779 8919 6854 16673 V_76 13710 9257 25125 16367 9298 33613 14997 8815 25477 16062 8831 34839 14746 8375 25904 15786 8377 36375 V_81 1887 1228 7016 4155 1267 17784 2826 1125 7133 4016 1144 19379 2718 1018 7282 3893 1033 21433 V_84 31663 10832 69779 41680 11055 83180 39287 8284 70293 40528 8853 84306 38329 5680 70983 39469 6660 85800 V_86 7973 6243 28244 14210 6802 30409 12707 5689 28285 13369 6171 30036 12169 5173 28486 12605 5601 29862 V_0 179439 138975 201712 201959 139036 287552 174344 141362 207307 211477 143071 305861 178058 143325 212715 221040 146445 325570 P_0 416111 12093 7055039 266862 10625 3085058 331024 10874 581652 299008 9763 558510 364958 9559 392689 332002 8737 388123



Research of long-term forecasting system

of the labor market

Institute of Developm

ent Projects dem

and and analysis of improvem

ent options, 2005-2007

158

Table P12. Forecasts in economic sectors in medium-term (short-term forecasts in chapter 5.6)

Labor force supply and demand forecasts in industries, short-term 2009. 2010. 2011. 2012. 2013.











Industries

from to from to from to from to from to from to from to from to from to from to

A 8576 35340 796 53262 7284 33624 399 52926 6489 33330 362 52745 5777 33071 381 52697 4839 34630 486 54139

B 1372 6008 126 5492 1440 11023 69 5635 1403 22633 91 5797 1369 50286 80 5977 1340 238730 185 6175

C 2188 4593 205 428391 2424 5326 117 493049 2430 5650 322 573119 2438 5999 219 672116 2383 6548 324 794571

D 178519 327870 15552 358273 178259 421256 8697 361450 177254 599542 7529 365331 176196 944555 8113 369868 175078 2600882 8218 375017

E 15327 27332 1484 60113 16310 28705 817 61324 15867 28685 1146 62725 15414 28682 981 64310 14953 28698 1086 66070

F 48996 62337 4079 137729 47352 61128 2294 141428 47354 61600 2405 145567 47343 62091 2349 150133 47324 62602 2454 155113

G 183634 248629 15672 844829 189363 256483 9146 919126 193881 263297 10863 107679 198324 270191 10004 1113088 202704 277163 10109 1238526

H 21264 31047 1805 74860 21987 32090 1056 78350 22584 33032 1213 82123 23172 33988 1135 86175 23756 34962 1240 90504

I 18311 26832 7245 162996 18819 27618 4065 164204 18847 27947 3743 165720 18859 28280 3904 167523 18858 28618 4009 169591

J 16645 28167 1527 61099 18136 31710 857 65234 18219 33133 1141 69966 18300 34635 999 75371 18025 36908 1104 82062

K 66593 84673 5701 142297 68618 87289 3311 146861 70012 89322 3668 151775 71388 91379 3490 157030 72750 93463 3595 162619

L 82855 95298 7126 192633 81840 94689 4039 196354 81698 94985 4786 200603 81545 95299 4412 205358 81384 95632 4517 210599

M 98957 126248 8839 498998 100004 128183 5061 542175 99184 128323 7126 595116 98334 128499 6093 659728 97462 128710 6198 738363

N 53351 73258 4836 141528 54543 75097 2776 143889 53884 75138 2644 146629 53204 75206 2710 149733 52509 75301 2815 153184

O 43389 58818 3974 194669 45845 63404 2266 212826 45962 65026 2448 234890 46074 66706 2357 261681 45662 69209 2462 294811




of the labor market


ent Projects dem



159


160

Table P13. Satisfaction degree of labor force demand in economic sectors (%)

Satisfaction degree of labor force demand

Short-term Medium-term Long-term Profession group code Until y. 2008 Until y. 2013 Until y. 2020

A 112% 118% 121%

B 97% 94% 82%

C 132% 142% 121%

D 94% 77% 73%

E 95% 91% 80%

F 91% 85% 75%

G 92% 79% 76%

H 99% 90% 80%

I 95% 78% 75%

J 88% 77% 75%

K 91% 78% 75%

L 97% 82% 82%

M 93% 81% 78%

N 94% 84% 82%

O 90% 79% 75% Source: Statistical models in MS Excel environment


161

Obtained simulation modeling results with the dynamic optimization model

Table P14. Selected alternative scenarios for analysis of the results

Parameters of alternative scenarios: Selected scenario

Demographic forecast scenario Base scenario

GDP growth forecast scenario Convergence

Capital forecast scenario Convergence

Technological progress development scenario Convergence

Labor force market reaction scenario Normal market reaction

Labor force demand calculation alternative Production function – classical

Balance salary calculation alternative Assumptions – relative

Source: excel_DOM_izeja.xls, Scenarijs

Table P15. Demographic forecast alternative scenario: base scenario

Alternative: 1

d 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 15-19 187305 186565 184516 178345 168603 157103 145649 136055 130129 126447 122491 118413 114373 110526 107030 104044 101725 100225 99707 100294 101848 104125 106885 109884 112879 115633 117897 119434

20-24 164717 170411 175086 176670 178265 179632 180524 180705 179929 176382 169058 158934 146988 134199 121542 109996 100539 94148 91801 92340 93815 96009 98710 101701 104770 107700 110274 112281

25-34 321550 321260 320969 321386 322864 324893 326964 328580 329231 328459 326335 323151 319203 314784 310183 305696 301615 298234 295844 291522 282897 271096 257244 242459 227871 214604 203781 196525

35-44 338642 333526 328345 325667 322316 318746 315408 312758 311246 310691 310544 310727 311153 311729 312375 313003 313525 313858 313915 313359 312017 310058 307637 304928 302085 299276 296664 294412

45-54 309771 313431 315489 317244 318938 320411 321504 322064 321933 320603 317905 314191 309815 305126 300477 296221 292706 290286 289316 289542 290381 291686 293299 295077 296867 298517 299881 300808

55-64 267306 262117 259438 259030 258753 258700 258964 259631 260795 262541 264789 267376 270140 272917 275544 277861 279701 280906 281310 280719 279200 276995 274336 271466 268610 266016 263916 262545

65-74 392423 227762 229376 226172 222599 219117 217308 217150 216882 217495 215097 210875 205934 200667 196224 195618 197800 202260 206058 209575 211577 212220 213415 216506 219131 222847 227482 230501

Source: Eurostat, excel_DOM_ieeja.xls, Dem_blokam

Table P16. Alternative scenario of demographic forecasts: optimistic scenario

Alternative: 2

d 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 15-19 187305 186565 184711 182193 176174 168538 157147 144907 131527 118738 106776 99237 95250 94155 94224 95688 98585 102123 105865 111272 117202 123062 128198 133076 137654 141875 145657 148915

20-24 164717 170411 175189 179016 183323 186271 185682 183819 181300 175194 167466 156034 143821 130503 117863 106080 98737 95004 94257 94778 96793 100301 104451 108758 114702 121114 127384 132842

25-34 321550 321260 321266 321119 320873 322496 327529 331634 335740 340734 345865 349971 352168 352905 350941 346361 334981 321883 307330 290222 272455 255350 240888 228118 217068 208188 205142 206297

35-44 338642 333526 328268 323686 319864 316565 313839 312766 313347 312885 312283 311550 311421 311164 310900 312432 317250 321306 325473 331252 337607 343243 347097 349635 349475 346588 336666 324708

45-54 309771 313431 315376 319697 323322 326165 328487 327600 324339 321705 317299 312335 307399 303186 299811 296964 294691 294068 295094 295389 295700 295986 296884 297724 298603 301240 307029 312023

55-64 267306 262117 259459 255957 252655 251662 252137 256159 262398 267375 271422 274897 276829 280848 284325 287138 289510 289177 286804 285246 282337 279044 275734 273152 271330 269882 268851 269234

65-74 392423 227762 229648 229744 230885 229367 227130 224325 218603 212814 208233 204934 203620 201483 199718 199679 200722 204669 210357 215221 219612 223703 226652 231276 235451 239012 242105 242920


Table P17. Alternative scenario of demographic forecasts: pessimistic scenario

Alternative: 3

d 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 15-19 187305 186565 184346 181416 174937 166802 154960 142351 128741 115833 103887 96365 92340 91125 91020 92212 94702 94789 94089 94080 93766 92691 93220 93875 94609 95355 96035 96567

20-24 164717 170411 174760 178184 182093 184649 183709 181478 178555 172023 163857 152074 139609 126193 113565 101920 94619 90759 89685 89710 91028 93631 93854 93282 93367 93126 92110 92673

25-34 321550 321260 319545 317594 315456 315136 318210 320433 322821 326219 329910 332745 333771 333384 330346 324751 312729 299165 284377 267365 249932 233292 219231 206816 195989 187125 183422 180494

35-44 338642 333526 327637 322495 318198 314447 311207 309470 309149 307494 305475 303160 301295 299194 296991 296448 298996 300836 302854 306477 310690 314254 316169 316836 315049 310818 300377 288273

45-54 309771 313431 314770 318458 321404 323533 325117 323496 319528 316134 310997 305360 299801 295020 291123 287746 284870 283472 283446 282480 281317 279965 279053 277982 276839 277257 280529 283073

55-64 267306 262117 259073 255156 251434 249987 249995 253505 259188 263555 266959 269737 270933 274137 276752 278672 280122 278924 275748 273350 269677 265679 261734 258542 256150 254129 252451 251975

65-74 392423 227762 229181 228778 229384 227349 224607 221311 215160 208961 203986 200314 198577 195990 193763 193155 193559 196754 201587 205602 209153 212381 214476 218085 221200 223657 225592 225359




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162

Table P18. Alternative scenario of demographic forecasts: without migration

Alternative: 4 d 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

15-19 187305 186565 184669 182104 176055 168386 156967 144718 131341 118634 106774 99344 95464 94477 94633 96146 99000 100822 102214 104677 107119 109009 111303 113471 115490 117316 118883 120123 20-24 164717 170411 175142 178920 183212 186136 185524 183656 181123 175120 167508 156161 143986 130690 118054 106266 98882 95033 94064 94230 95744 98597 100420 101815 104274 106714 108605 110898 25-34 321550 321260 321078 320736 320385 321906 326838 330929 335042 340600 346521 351444 354292 355543 353862 349237 337430 323555 307922 289526 270387 251942 236188 222185 209951 199893 195489 193551 35-44 338642 333526 328200 323555 319715 316393 313634 312536 313063 312685 312192 311536 311437 311206 310989 312612 317557 321706 325853 331435 337342 342270 345164 346515 344986 340585 329171 315717 45-54 309771 313431 315304 319546 323123 325907 328168 327242 323940 321431 317196 312413 307606 303510 300218 297391 295064 294292 295044 294932 294707 294359 294538 294624 294750 296647 301703 306004 55-64 267306 262117 259416 255869 252541 251516 251956 255957 262166 267208 271364 274946 276957 281023 284505 287259 289514 288993 286364 284495 281227 277536 273809 270792 268533 266624 265110 264947 65-74 392423 227762 229577 229591 230670 229091 226787 223924 218147 212403 207899 204689 203430 201311 199530 199415 200317 204063 209487 214031 218066 221766 224302 228438 232087 235077 237567 237792


Table P19. Alternative scenario of demographic forecasts: high fertiligy variant

Alternative: 5 d 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

15-19 187305 186565 184529 181806 175556 167672 156053 143627 130129 117283 105327 97796 93788 92632 92613 93938 96613 99860 103225 108149 113565 118899 123485 127801 131779 135357 138457 141012 20-24 164717 170411 174975 178600 182709 185460 184698 182651 179928 173608 165660 154051 141706 128338 115703 103988 96647 92803 91801 91963 93533 96508 100096 103808 109062 114739 120259 124992 25-34 321550 321260 320406 319354 318157 318800 322846 325997 329229 333414 337810 341263 342858 343020 340502 335401 323705 310422 295844 278903 261385 244571 230306 217645 206570 197508 193922 194253 35-44 338642 333526 327953 323093 319031 315508 312523 311114 311245 310183 308863 307329 306321 305125 303876 304351 308003 310902 313917 318498 323650 328128 330931 332509 331584 328135 318088 306190 45-54 309771 313431 315073 319079 322364 324849 326802 325549 321931 318918 314141 308839 303584 299083 295443 292328 289760 288775 289316 289032 288631 288115 288106 287977 287821 289308 293779 297471 55-64 267306 262117 259268 255559 252047 250824 251064 254833 260795 265465 269189 272311 273875 277481 280525 282887 284802 284052 281312 279384 276140 272532 268939 266075 263989 262275 260936 260908 65-74 392423 227762 229413 229261 230132 228359 225869 222818 216882 210888 206107 202621 201094 198732 196735 196410 197140 200738 206057 210569 214604 218302 220850 224979 228622 231627 234137 234424


Figure P6. Comparison of demographic forecasts

Demographic forecasts

1400000

1500000

1600000

1700000

1800000

1900000

2000000

2100000

2200000

2005 2010 2015 2020 2025 2030

Base variant Optimistic variantPessimistic variant Without migrationHigh fertility variant

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Table P20. Satisfaction degree of labor market demand in periods and sectors

Satisfaction degree of labor force demand (at the end of period)

Satisfaction degree of labor force demand (in mid period)

Short-term Medium-term Long-term Strategic evaluations Short-term Medium-term Long-term Strategic

evaluations Profession group code Until 2008 Until 2013 Until 2020 Until 2030

Profession group code Until 2008 Until 2013 Until 2020 Until 2030

A 96% 83% 71% 60% A 97% 89% 76% 65% B 94% 84% 72% 60% B 96% 88% 77% 65% C 94% 74% 57% 47% C 96% 83% 62% 52% D 91% 72% 60% 52% D 94% 80% 63% 56% E 93% 78% 64% 55% E 96% 83% 69% 60% F 88% 70% 59% 51% F 93% 77% 64% 55% G 95% 84% 77% 71% G 97% 89% 80% 74% H 96% 86% 79% 74% H 97% 90% 81% 77% I 94% 79% 67% 57% I 97% 85% 72% 61% J 96% 88% 82% 76% J 98% 92% 84% 79% K 94% 84% 74% 65% K 97% 88% 78% 69% L 97% 90% 78% 67% L 99% 93% 83% 72% M 99% 93% 78% 64% M 99% 95% 85% 70% N 97% 92% 82% 74% N 98% 94% 86% 78% O 96% 86% 76% 68% O 98% 90% 80% 72%

Satisfaction degree of labor force demand (at the beginning of period)

Satisfaction degree of labor force demand (in mid period)




A + +/- -- -- A + +/- +/- -- B +/- +/- -- -- B + +/- +/- -- C +/- -- -- -- C + +/- -- -- D +/- -- -- -- D +/- +/- -- -- E +/- +/- -- -- E + +/- -- -- F +/- -- -- -- F +/- +/- -- -- G +/- +/- +/- -- G + +/- +/- -- H + +/- +/- -- H + +/- +/- +/- I +/- +/- -- -- I + +/- -- -- J + +/- +/- +/- J + +/- +/- +/- K +/- +/- -- -- K + +/- +/- -- L + +/- +/- -- L + +/- +/- -- M + +/- +/- -- M + + +/- -- N + +/- +/- -- N + +/- +/- +/- O + +/- +/- -- O + +/- +/- -- + 13 1 0 0

95% 105% good +/- 2 14 10 3 75% 125% average -- 0 0 5 12 47% 150% poor

Source: excel_DOM_izeja.xls, Tab_DSP nodrošinājums_N

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Table P21. Satisfaction of labor force demand in periods and profession groups

Satisfaction degree of the labor force demand (at end of the period)

Satisfaction degree of the labor force demand (in mid-period)




A_0 -2 -19 -35 -51 A_0 0 -12 -29 -44 A_0_zin 3 -2 -17 -32 A_0_zin 3 0 -11 -25 A_1 5 3 -15 -32 A_1 4 5 -7 -25 A_2 75 157 181 166 A_2 35 132 175 178 A_3 65 128 128 100 A_3 35 108 130 116 A_34 1 -9 -15 -19 A_34 1 -5 -13 -16 A_34_v 5 -6 -15 -23 A_34_v 5 -1 -12 -19 A_38 96 148 140 106 A_38 51 138 145 125 A_4 12 9 -7 -22 A_4 9 12 0 -16 A_48 54 55 32 1 A_48 28 61 41 15 A_52 7 -1 -13 -23 A_52 6 4 -8 -19 A_58 7 -4 -20 -37 A_58 1 3 -14 -30 A_6 57 58 36 7 A_6 41 62 44 21 A_7 5 16 6 -12 A_7 0 13 11 -3 A_76 19 16 -7 -23 A_76 13 19 3 -18 A_81 -1 -13 -27 -32 A_81 0 -7 -22 -30 A_84 -4 -8 -21 -40 A_84 -5 -5 -15 -31 A_86 3 1 -17 -35 A_86 2 2 -9 -27 K_3 10 -13 -33 -49 K_3 15 -4 -25 -42 K_34 7 -5 -15 -19 K_34 9 -1 -11 -17 K_38 6 8 -3 -17 K_38 5 8 2 -11 K_52 -4 -24 -32 -29 K_52 -1 -16 -30 -30 K_58 -47 -58 -58 -54 K_58 -45 -54 -58 -56 K_7 -2 -7 -17 -19 K_7 -1 -5 -13 -18 V_2 30 69 92 91 V_2 19 53 84 97 V_34 -1 -15 -23 -26 V_34 1 -10 -20 -24 V_48 13 7 -2 -18 V_48 12 10 2 -10 V_52 -3 -26 -41 -53 V_52 3 -17 -35 -48 V_54 -11 -35 -48 -58 V_54 -5 -26 -42 -54 V_58 -15 -44 -54 -58 V_58 -7 -33 -51 -56 V_6 19 1 -11 -18 V_6 23 8 -7 -15 V_76 7 -1 -15 -24 V_76 9 2 -9 -20 V_81 46 120 208 303 V_81 24 90 171 271 V_84 -7 -30 -48 -62 V_84 -1 -21 -41 -56 V_86 0 -25 -42 -54 V_86 6 -15 -35 -49 V_0 2 -10 -15 -15 V_0 5 -6 -14 -14 P_0 6 -20 -42 -62 P_0 13 -10 -34 -54

good ~ 0 balance average < 0 deficiency poor > 0 surplus

Source: excel_DOM_izeja.xls Tab_DSP nodrosinajums_P

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Satisfaction degree of labor force demand (at the end of period)

Satisfaction degree of labor force demand (in mid-period)




A_0 + +/- -- -- A_0 + +/- -- -- A_0_zin + + +/- -- A_0_zin + + +/- -- A_1 +/- + +/- -- A_1 + + +/- +/- A_2 -- -- -- -- A_2 -- -- -- -- A_3 -- -- -- -- A_3 -- -- -- -- A_34 + +/- +/- +/- A_34 + + +/- +/- A_34_v +/- +/- +/- +/- A_34_v + + +/- +/- A_38 -- -- -- -- A_38 -- -- -- -- A_4 +/- +/- +/- +/- A_4 +/- +/- + +/- A_48 -- -- -- + A_48 -- -- -- +/- A_52 +/- + +/- +/- A_52 +/- + +/- +/- A_58 +/- + +/- -- A_58 + + +/- -- A_6 -- -- -- +/- A_6 -- -- -- +/- A_7 +/- +/- +/- +/- A_7 + +/- +/- + A_76 +/- +/- +/- +/- A_76 +/- +/- + +/- A_81 + +/- -- -- A_81 + +/- +/- -- A_84 + +/- +/- -- A_84 + +/- +/- -- A_86 + + +/- -- A_86 + + +/- -- K_3 +/- +/- -- -- K_3 +/- + -- -- K_34 +/- +/- +/- +/- K_34 +/- + K_38 +/- +/- + +/- K_38 +/- +/- + K_52 + +/- -- -- K_52 + +/- -- -- K_58 -- -- -- -- K_58 -- -- -- -- K_7 + +/- +/- +/- K_7 + +/- +/- +/- V_2 -- -- -- -- V_2 +/- -- -- -- V_34 + +/- +/- -- V_34 + +/- +/- +/- V_48 +/- +/- + +/- V_48 +/- +/- + +/- V_52 + -- -- -- V_52 + +/- -- -- V_54 +/- -- -- -- V_54 + -- -- -- V_58 +/- -- -- -- V_58 +/- -- -- -- V_6 +/- + +/- +/- V_6 +/- +/- +/- +/- V_76 +/- + +/- +/- V_76 +/- + +/- +/- V_81 -- -- -- -- V_81 +/- -- -- -- V_84 +/- -- -- -- V_84 + +/- -- -- V_86 + +/- -- -- V_86 +/- +/- -- -- V_0 + +/- +/- +/- V_0 + +/- +/- +/- P_0 +/- +/- -- -- P_0 +/- +/- -- --

+ 17 10 4 1 good ~ 0 balance +/- 14 17 16 16 average < 0 deficiency -- 6 10 17 20 poor > 0 surplus


166



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Satisfaction degree of labor market demand (at the end of period)

Satisfaction degree of labor market demand (in mid-period)




A_0 balance deficiency deficiency deficiency A_0 balance deficiency deficiency deficiency A_0_zin balance balance deficiency deficiency A_0_zin balance balance deficiency deficiency A_1 surplus balance deficiency deficiency A_1 balance balance deficiency deficiency A_2 surplus surplus surplus surplus A_2 surplus surplus surplus surplus A_3 surplus surplus surplus surplus A_3 surplus surplus surplus surplus A_34 balance deficiency deficiency deficiency A_34 balance balance deficiency deficiency A_34_v surplus deficiency deficiency deficiency A_34_v balance balance deficiency deficiency A_38 surplus surplus surplus surplus A_38 surplus surplus surplus surplus A_4 surplus surplus deficiency deficiency A_4 surplus surplus balance deficiency A_48 surplus surplus surplus balance A_48 surplus surplus surplus surplus A_52 surplus balance deficiency deficiency A_52 surplus balance deficiency deficiency A_58 surplus balance deficiency deficiency A_58 balance balance deficiency deficiency A_6 surplus surplus surplus surplus A_6 surplus surplus surplus surplus A_7 surplus surplus surplus deficiency A_7 balance surplus surplus balance A_76 surplus surplus deficiency deficiency A_76 surplus surplus balance deficiency A_81 balance deficiency deficiency deficiency A_81 balance deficiency deficiency deficiency A_84 balance deficiency deficiency deficiency A_84 balance deficiency deficiency deficiency A_86 balance balance deficiency deficiency A_86 balance balance deficiency deficiency K_3 surplus deficiency deficiency deficiency K_3 surplus balance deficiency deficiency K_34 surplus deficiency deficiency deficiency K_34 surplus balance deficiency deficiency K_38 surplus surplus balance deficiency K_38 surplus surplus balance deficiency K_52 balance deficiency deficiency deficiency K_52 balance deficiency deficiency deficiency K_58 deficiency deficiency deficiency deficiency K_58 deficiency deficiency deficiency deficiency K_7 balance deficiency deficiency deficiency K_7 balance deficiency deficiency deficiency V_2 surplus surplus surplus surplus V_2 surplus surplus surplus surplus V_34 balance deficiency deficiency deficiency V_34 balance deficiency deficiency deficiency V_48 surplus surplus balance deficiency V_48 surplus surplus balance deficiency V_52 balance deficiency deficiency deficiency V_52 balance deficiency deficiency deficiency V_54 deficiency deficiency deficiency deficiency V_54 balance deficiency deficiency deficiency V_58 deficiency deficiency deficiency deficiency V_58 deficiency deficiency deficiency deficiency V_6 surplus balance deficiency deficiency V_6 surplus surplus deficiency deficiency V_76 surplus balance deficiency deficiency V_76 surplus balance deficiency deficiency V_81 surplus surplus surplus surplus V_81 surplus surplus surplus surplus V_84 deficiency deficiency deficiency deficiency V_84 balance deficiency deficiency deficiency V_86 balance deficiency deficiency deficiency V_86 surplus deficiency deficiency deficiency V_0 balance deficiency deficiency deficiency V_0 balance deficiency deficiency deficiency P_0 surplus deficiency deficiency deficiency P_0 surplus deficiency deficiency deficiency

balance 17 10 4 1 surplus 18 13 8 7 deficiency 2 14 25 29


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Table P24. Number of economically active inhabitants

2005.1.1 2006.1.1 2007.1.1 2008.1.1 2009.1.1 2010.1.1 2011.1.1 2012.1.1 2013.1.1 2014.1.1 2015.1.1 2016.1.1 2017.1.1 2018.1.1 2019.1.1 2020.1.1 2021.1.1 2022.1.1 2023.1.1 2024.1.1 2025.1.1 2026.1.1 2027.1.1 2028.1.1 2029.1.1 A_0 46046 45890 45847 45775 45643 45434 45127 44652 43949 43090 42136 41137 40143 39208 38353 37601 36954 36289 35490 34607 33691 32788 31917 31116 30405 A_0_zin 4961 4948 4947 4942 4931 4911 4880 4831 4757 4666 4564 4458 4352 4252 4160 4080 4012 3941 3856 3761 3663 3566 3473 3387 3311 A_1 45675 45753 45859 45877 45810 45633 45314 44775 43961 42956 41831 40650 39467 38337 37282 36318 35438 34536 33526 32463 31404 30399 29462 28630 27917 A_2 6088 7835 9618 11364 13053 14640 16061 17225 18085 18701 19134 19447 19695 19928 20182 20488 20866 21187 21331 21334 21237 21082 20901 20734 20605 A_3 4929 5888 6809 7664 8442 9117 9677 10088 10334 10453 10480 10448 10386 10316 10252 10205 10177 10113 9971 9772 9537 9288 9036 8796 8577 A_34 66358 66915 67762 68660 69558 70440 71279 71915 72197 72206 72015 71703 71365 71118 71044 71237 71759 72298 72506 72450 72217 71898 71553 71289 71168 A_34_v 63930 65426 66969 68358 69589 70689 71659 72354 72636 72593 72310 71881 71410 71015 70777 70782 71084 71372 71317 71001 70520 69971 69417 68965 68673 A_38 3744 4875 6017 7082 7992 8718 9277 9662 9880 9980 10003 9981 9940 9898 9868 9857 9868 9845 9744 9585 9388 9173 8952 8740 8546 A_4 6031 6135 6292 6473 6664 6839 6979 7058 7060 7005 6911 6797 6675 6557 6448 6354 6273 6181 6060 5923 5785 5661 5559 5492 5467 A_48 4739 5733 6741 7620 8270 8694 8951 9061 9045 8953 8819 8668 8514 8371 8241 8130 8034 7917 7750 7550 7332 7110 6891 6685 6495 A_52 20802 21302 21951 22655 23374 24048 24632 25044 25231 25240 25125 24934 24714 24514 24362 24287 24305 24312 24205 24020 23801 23587 23405 23293 23274 A_58 4414 4760 5159 5575 5965 6296 6550 6709 6774 6773 6729 6660 6581 6504 6436 6382 6341 6284 6184 6055 5912 5773 5650 5558 5505 A_6 1450 1604 1754 1888 1989 2052 2085 2091 2073 2040 2000 1957 1914 1873 1836 1803 1773 1739 1697 1648 1596 1543 1492 1444 1399 A_7 9372 9618 9932 10272 10625 10972 11276 11485 11569 11557 11480 11364 11230 11100 10985 10896 10834 10750 10599 10398 10165 9916 9662 9417 9190 A_76 3227 3344 3449 3524 3557 3558 3534 3486 3415 3331 3239 3146 3054 2968 2887 2813 2744 2672 2590 2509 2441 2391 2356 2336 2329 A_81 3713 3690 3672 3638 3589 3528 3459 3378 3285 3184 3083 2996 2920 2856 2802 2760 2731 2708 2681 2651 2622 2594 2567 2546 2529 A_84 2332 2365 2421 2488 2562 2641 2720 2771 2786 2774 2746 2707 2665 2623 2585 2552 2525 2493 2446 2388 2323 2257 2190 2125 2066 A_86 27427 27174 27017 26873 26727 26545 26295 25924 25394 24749 24041 23308 22581 21889 21241 20641 20084 19509 18869 18199 17535 16905 16322 15805 15363 K_3 5154 5051 4953 4853 4753 4654 4555 4451 4336 4214 4089 3965 3844 3731 3627 3533 3450 3368 3279 3185 3092 3001 2913 2831 2756 K_34 14739 14747 14683 14608 14549 14513 14500 14470 14381 14244 14072 13881 13688 13517 13382 13298 13274 13261 13205 13118 13019 12921 12834 12774 12749 K_38 1161 1168 1170 1170 1173 1179 1188 1192 1187 1175 1158 1137 1115 1094 1074 1058 1044 1029 1008 983 956 928 900 877 863 K_52 5205 5248 5249 5233 5209 5184 5163 5138 5101 5061 5024 4993 4971 4964 4977 5017 5091 5182 5263 5337 5407 5476 5545 5618 5699 K_58 3507 3578 3619 3659 3709 3776 3863 3958 4044 4121 4191 4258 4326 4404 4500 4625 4786 4964 5127 5277 5415 5545 5671 5798 5929 K_7 19534 19382 19187 18984 18797 18640 18516 18389 18217 18012 17787 17555 17331 17140 16996 16922 16930 16962 16947 16897 16829 16760 16696 16655 16644 V_2 3392 3712 3942 4166 4411 4688 4996 5297 5553 5755 5908 6022 6112 6194 6281 6386 6516 6630 6687 6693 6658 6594 6513 6426 6344 V_34 34128 34196 34106 34000 33944 33974 34092 34210 34223 34148 34008 33830 33653 33531 33502 33613 33897 34218 34407 34486 34485 34439 34368 34317 34310 V_48 5718 5936 6071 6188 6306 6431 6561 6668 6724 6737 6715 6669 6611 6555 6509 6481 6472 6452 6390 6294 6174 6041 5900 5763 5634 V_52 113484 111973 110401 108814 107297 105905 104637 103326 101766 100047 98248 96439 94704 93168 91895 90975 90458 90053 89415 88609 87721 86834 85974 85241 84671 V_54 64580 63721 62826 61919 61054 60269 59567 58850 57999 57063 56084 55103 54169 53355 52702 52266 52079 51977 51758 51455 51116 50783 50468 50225 50070 V_58 49421 48877 48299 47724 47205 46776 46442 46120 45703 45221 44705 44185 43704 43324 43087 43048 43249 43543 43740 43862 43944 44018 44099 44232 44435 V_6 8887 8799 8695 8588 8487 8396 8312 8218 8097 7953 7792 7622 7449 7285 7133 7000 6885 6766 6618 6449 6268 6084 5901 5727 5566 V_76 17673 17355 17063 16777 16505 16252 16020 15784 15514 15220 14914 14603 14300 14024 13783 13591 13453 13328 13170 12989 12800 12617 12443 12293 12173 V_81 4955 6032 6856 7685 8603 9655 10849 12089 13255 14322 15287 16166 16994 17818 18688 19660 20785 21925 22903 23723 24404 24973 25465 25926 26387 V_84 54106 52965 51892 50811 49731 48658 47584 46454 45208 43896 42558 41226 39931 38718 37596 36583 35674 34777 33805 32793 31782 30803 29859 28979 28172 V_86 18419 18044 17695 17346 17002 16667 16340 16000 15625 15229 14826 14427 14041 13686 13367 13093 12867 12652 12413 12158 11902 11655 11418 11203 11013 V_0 197928 197247 196128 195092 194456 194451 195116 195999 196435 196471 196188 195710 195245 195101 195506 196752 199053 201587 203301 204255 204586 204469 204002 203365 202696 P_0 187769 182722 177755 173076 168520 163846 158907 153631 147982 142217 136481 130867 125451 120330 115483 110910 106540 102190 97721 93259 88939 84864 81018 77467 74517 Total 1135000 1134008 1132805 1131419 1130049 1128671 1126960 1122754 1113781 1101356 1086683 1070899 1055245 1041264 1029831 1021996 1018307 1015011 1007977 998135 986666 974704 962791 952073 943446

Source: excel_DOM_izeja .xls, Dem_bloks

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Table P25. Number of individuals in search for a job


Source: excel_DOM_izeja.xls, DSP_piedavaj

169



of the labor market


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Table P26. Number of the employed


Source: excel_DOM_izeja.xls, DSP_piedavaj

170



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Table P27. Number of vacant positions

2005.1.1 2006.1.1 2007.1.1 2008.1.1 2009.1.1 2010.1.1 2011.1.1 2012.1.1 2013.1.1 2014.1.1 2015.1.1 2016.1.1 2017.1.1 2018.1.1 2019.1.1 2020.1.1 2021.1.1 2022.1.1 2023.1.1 2024.1.1 2025.1.1 2026.1.1 2027.1.1 2028.1.1 2029.1.1 A_0 2 (61) 142 734 2316 4199 6333 8735 11127 12963 14424 16004 17752 19558 21275 22836 24227 25651 27264 29005 30755 32428 33999 35451 36784 A_0_zin 4 (13) (33) (16) 9 17 20 45 168 338 533 735 921 1092 1259 1416 1530 1644 1780 1927 2071 2220 2356 2480 2589 A_1 194 (143) (353) (369) (261) (91) 30 132 188 323 834 2332 3818 5215 6588 7920 8978 10020 11179 12370 13499 14612 15576 16400 17086 A_2 751 17 (3) 19 25 32 35 33 11 (2) (9) 4 12 19 21 21 21 23 25 28 37 39 37 34 30 A_3 13 (15) (35) (37) (37) (33) (30) (24) (29) (26) (18) (3) 5 8 8 6 1 5 14 21 27 32 31 30 28 A_34 2384 (75) 213 582 730 1938 3784 5961 8214 9690 10707 11829 13025 14143 15001 15520 15606 15554 15820 16493 17440 18449 19441 20304 20976 A_34_v 538 (143) 273 651 802 876 930 2435 4907 6722 8122 9678 11350 12964 14322 15300 15936 16479 17393 18780 20352 21945 23473 24840 25995 A_38 18 (11) (6) 15 21 24 27 28 22 10 5 9 12 13 12 8 4 9 20 28 35 38 40 39 36 A_4 310 (1) 9 32 41 45 49 50 41 39 43 51 55 110 284 450 594 752 948 1164 1378 1573 1733 1848 1914 A_48 220 (1) 17 40 51 56 60 61 56 50 47 49 49 47 44 40 36 43 57 67 73 76 76 74 71 A_52 1043 (2) 92 203 249 275 297 297 238 250 541 990 1601 2278 2903 3420 3824 4247 4836 5547 6286 6975 7586 8087 8468 A_58 784 188 58 98 116 127 138 148 257 518 711 933 1199 1480 1735 1949 2130 2327 2578 2868 3175 3475 3752 3988 4175 A_6 185 2 2 8 10 11 11 11 16 15 14 15 14 14 13 12 10 11 15 18 19 20 20 19 18 A_7 940 399 9 9 15 5 21 29 13 17 48 72 85 87 75 70 40 51 84 102 364 814 1258 1655 2040 A_76 5 (8) (16) (13) (8) (9) 6 15 19 24 28 31 33 35 119 223 309 395 495 589 671 733 774 791 795 A_81 138 30 21 66 144 252 378 527 682 845 996 1124 1231 1331 1418 1484 1524 1551 1577 1602 1628 1655 1678 1692 1700 A_84 213 140 106 103 113 124 116 137 202 264 316 372 441 516 591 660 722 789 871 962 1039 1123 1218 1315 1407 A_86 18 7 (73) (92) (73) (46) 26 63 96 404 1030 1711 2354 2956 3551 4104 4584 5064 5582 6087 6573 7047 7479 7851 8141 K_3 - 32 63 96 112 120 332 768 1213 1609 1925 2208 2507 2819 3110 3365 3586 3791 4002 4217 4432 4641 4841 5029 5203 K_34 628 (4) 35 92 109 112 116 413 858 1197 1463 1742 2027 2298 2527 2686 2759 2816 2928 3075 3238 3395 3530 3627 3680 K_38 - (4) (8) (7) (4) (3) (1) (0) 1 3 5 6 6 6 11 27 39 53 71 92 116 141 166 185 196 K_52 162 (4) 27 188 446 733 1035 1357 1676 1885 1994 2097 2220 2349 2454 2520 2542 2537 2535 2544 2558 2571 2580 2581 2572 K_58 3382 3205 3313 3673 4128 4620 5137 5666 6107 6274 6281 6346 6503 6706 6888 7025 7116 7192 7296 7435 7577 7693 7790 7874 7948 K_7 565 105 219 460 742 936 1140 1420 1682 1948 2264 2635 3040 3434 3736 3989 4061 4084 4171 4278 4440 4595 4712 4753 4781 V_2 138 (10) (24) (6) 4 10 11 13 4 1 (1) 1 0 4 5 5 3 2 1 2 13 21 24 25 25 V_34 1416 (16) 159 430 1661 3069 4476 5893 7220 8132 8717 9341 10053 10764 11344 11753 11932 12013 12216 12586 13052 13561 14060 14510 14900 V_48 66 (11) 11 37 47 51 54 54 44 23 20 28 32 33 29 55 106 172 290 449 633 827 1025 1216 1397 V_52 5699 442 1007 2315 8259 15075 22178 29361 35448 39161 41598 44703 48749 53221 57530 61434 64947 68538 72700 77345 81930 86046 89752 93098 96171 V_54 8343 1467 3331 7317 12136 17307 22707 28059 32103 34151 35427 37445 40310 43566 46806 49857 52743 55773 59280 63194 66971 70221 73054 75599 77964 V_58 5394 1785 3887 8249 13411 18915 24766 31052 37360 41230 43190 45268 47893 50744 53225 55164 56696 58012 59378 60865 62390 63877 65316 66693 67997 V_6 296 22 43 69 78 79 77 82 82 92 274 483 707 930 1143 1340 1517 1698 1912 2155 2394 2632 2869 3096 3309 V_76 63 66 62 76 79 65 69 84 161 473 850 1267 1694 2108 2463 2788 2978 3133 3342 3553 3788 4021 4220 4362 4480 V_81 641 10 12 32 41 45 47 46 41 28 11 8 13 17 15 13 10 7 6 5 5 5 5 5 5 V_84 1106 277 1018 3623 6751 10087 13552 17116 20591 23342 25393 27349 29494 31746 33863 35764 37476 39126 40850 42628 44371 46029 47610 49101 50507 V_86 351 93 181 272 859 1985 3176 4420 5628 6564 7286 7973 8648 9298 9897 10431 10898 11351 11840 12351 12859 13344 13802 14229 14625 V_0 7064 153 898 1686 1999 7223 13247 19020 24531 28541 31523 34353 36775 38534 39624 39804 38694 36951 35814 35675 36316 37560 39200 41012 42890 P_0 6777 1359 1956 2604 2936 8274 19842 32220 44160 53833 61840 69737 77782 85740 93387 100669 107568 114428 121502 128683 135589 142003 147891 153332 158064 Total 49851 9279 16615 33241 58059 96505 144191 195728 245135 280929 308427 338923 372411 406184 437275 464127 485749 506294 530668 558794 588096 616435 642970 667227 688969

Source: excel_DOM_izeja.xls, DSP_piepras

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Table P28. Actual GDP (mill. LVL)

2005.1.1 2006.1.1 2007.1.1 2008.1.1 2009.1.1 2010.1.1 2011.1.1 2012.1.1 2013.1.1 2014.1.1 2015.1.1 2016.1.1 2017.1.1 2018.1.1 2019.1.1 2020.1.1 2021.1.1 2022.1.1 2023.1.1 2024.1.1 2025.1.1 2026.1.1 2027.1.1 2028.1.1 2029.1.1

A 223,88 231,96 241,59 252,79 265,04 276,34 286,81 298,01 307,63 315,14 322,21 329,10 336,19 343,62 351,47 359,87 368,55 377,17 385,66 394,07 402,60 411,29 420,14 429,22 438,83 B 7,60 7,86 8,18 8,54 8,92 9,31 9,72 10,13 10,49 10,78 11,04 11,27 11,51 11,75 12,01 12,29 12,57 12,84 13,10 13,37 13,63 13,89 14,15 14,43 14,73 C 19,50 20,69 22,63 24,84 27,23 29,74 32,37 34,97 37,18 39,13 41,33 43,69 46,21 48,91 51,83 54,99 58,49 62,21 66,10 70,30 74,58 78,96 83,54 88,41 93,64 D 842,22 897,97 979,34 1071,15 1170,14 1276,44 1390,70 1503,84 1603,44 1693,89 1795,54 1904,98 2019,40 2142,60 2275,45 2420,16 2578,94 2751,09 2934,25 3125,60 3319,94 3522,56 3736,61 3964,42 4208,93 E 187,21 192,79 195,66 199,62 202,87 205,87 208,71 212,24 218,50 225,58 232,82 241,15 250,35 260,24 270,90 282,64 295,72 309,91 324,73 340,09 356,18 373,01 389,89 407,20 425,52 F 457,78 502,69 587,68 681,99 783,71 899,81 1032,22 1185,69 1318,38 1414,99 1520,70 1630,77 1742,85 1860,92 1985,74 2119,77 2264,37 2418,98 2582,18 2754,96 2939,80 3137,58 3349,31 3576,54 3821,67 G 1290,44 1349,90 1442,99 1547,89 1659,74 1770,60 1885,17 2008,80 2135,31 2262,73 2393,80 2517,70 2634,83 2758,77 2890,73 3031,90 3183,43 3343,48 3508,96 3679,68 3857,40 4042,76 4236,54 4439,71 4654,28 H 92,70 97,79 105,28 113,95 123,66 133,48 143,80 154,71 163,74 170,70 177,62 184,21 190,62 197,40 204,62 212,40 220,85 229,77 238,81 247,90 257,18 266,68 276,44 286,52 297,09 I 943,83 990,50 1061,53 1140,16 1224,11 1312,69 1402,59 1495,84 1586,40 1669,71 1750,38 1832,86 1917,75 2006,81 2100,25 2199,43 2303,04 2409,23 2518,08 2630,24 2747,48 2870,17 2998,82 3134,18 3277,85 J 291,28 305,10 330,56 358,99 390,25 423,77 459,89 497,87 529,16 553,13 577,52 601,14 624,13 648,15 673,43 700,07 728,17 757,48 787,57 818,43 850,30 883,34 917,67 953,46 990,99 K 985,19 1036,97 1117,40 1209,35 1310,30 1416,15 1527,77 1645,30 1742,71 1817,33 1890,18 1958,20 2023,32 2091,67 2164,14 2241,49 2323,90 2409,41 2495,70 2583,00 2672,93 2766,23 2863,37 2965,05 3073,04 L 407,60 414,78 423,46 433,05 443,08 452,87 462,43 472,56 484,59 498,11 511,11 524,14 537,44 551,10 565,33 580,37 594,99 608,67 622,35 636,00 649,74 663,84 678,41 693,56 709,62 M 261,68 266,34 272,31 278,66 285,18 291,69 297,91 304,53 312,15 320,57 328,97 336,80 344,80 352,93 361,26 370,26 379,07 387,10 395,11 403,12 411,12 419,41 428,07 436,96 446,44 N 152,90 156,00 159,39 162,86 166,64 170,34 173,77 177,58 182,09 187,01 191,83 196,65 201,58 206,82 212,16 217,93 223,77 229,08 234,52 239,92 245,16 250,50 256,09 261,84 267,80 O 230,84 235,83 240,11 245,17 250,50 255,37 259,89 264,75 270,92 278,29 285,45 292,49 299,69 307,20 315,12 323,55 331,84 339,67 347,50 355,30 363,22 371,36 379,72 388,29 397,27

Total 6394,67 6707,15 7188,11 7729,03 8311,38 8924,47 9573,73 10266,81 10902,71 11457,08 12030,50 12605,16 13180,67 13788,90 14434,44 15127,10 15867,70 16646,09 17454,62 18291,98 19161,27 20071,56 21028,77 22039,79 23117,71

Source: excel_DOM_izeja.xls, Tautsaimniec_Y_N

Table P29. Desired GDP (mill. LVL)

2005.1.1 2006.1.1 2007.1.1 2008.1.1 2009.1.1 2010.1.1 2011.1.1 2012.1.1 2013.1.1 2014.1.1 2015.1.1 2016.1.1 2017.1.1 2018.1.1 2019.1.1 2020.1.1 2021.1.1 2022.1.1 2023.1.1 2024.1.1 2025.1.1 2026.1.1 2027.1.1 2028.1.1 2029.1.1 A 226,49 232,11 244,01 256,51 269,66 283,47 298,00 313,27 326,11 336,04 346,28 356,82 367,68 378,88 390,41 402,30 414,14 425,90 437,99 450,42 463,21 476,36 489,88 503,78 518,09 B 7,70 7,89 8,29 8,72 9,16 9,63 10,13 10,65 11,08 11,42 11,77 12,13 12,49 12,87 13,27 13,67 14,07 14,47 14,88 15,31 15,74 16,19 16,65 17,12 17,61 C 19,74 20,73 22,90 25,31 27,97 30,91 34,16 37,74 41,10 44,08 47,27 50,57 53,97 57,59 61,46 65,58 69,98 74,68 79,69 85,04 90,75 96,84 103,34 110,27 117,67 D 859,71 902,89 997,74 1102,57 1218,41 1346,42 1487,88 1644,20 1790,46 1920,20 2059,33 2203,14 2351,01 2508,80 2677,18 2856,86 3048,59 3253,20 3471,54 3704,53 3953,17 4218,48 4501,61 4803,73 5126,14 E 190,45 193,27 199,16 205,22 211,47 217,91 224,54 231,38 240,77 253,11 266,08 279,71 294,05 309,12 324,96 341,61 359,12 377,52 396,87 417,20 438,58 461,06 484,69 509,52 535,63 F 465,99 506,45 600,12 704,21 818,00 950,17 1103,70 1282,03 1439,06 1558,83 1688,55 1820,14 1952,02 2093,46 2245,15 2407,82 2582,29 2769,39 2970,06 3185,26 3416,06 3663,57 3929,03 4213,71 4519,02 G 1301,62 1350,35 1455,44 1568,71 1688,31 1812,48 1943,81 2084,65 2224,78 2362,27 2508,26 2650,25 2786,07 2928,84 3078,93 3236,72 3402,58 3576,95 3760,25 3952,95 4155,52 4368,47 4592,34 4827,68 5075,07 H 93,80 97,79 106,45 115,89 126,16 137,35 149,52 162,38 172,90 180,86 189,18 197,40 205,45 213,83 222,55 231,63 241,08 250,92 261,15 271,81 282,89 294,44 306,45 318,95 331,96 I 953,16 991,27 1073,76 1163,12 1259,91 1364,76 1474,72 1589,49 1698,16 1797,71 1903,10 2009,74 2116,95 2229,89 2348,85 2474,16 2603,60 2737,02 2877,28 3024,73 3179,73 3342,68 3513,98 3694,05 3883,36 J 292,49 304,93 331,96 361,38 393,41 428,29 466,25 506,34 539,16 563,96 589,91 615,54 640,65 666,79 693,99 722,30 751,76 782,43 814,35 847,57 882,15 918,14 955,59 994,57 1035,15 K 996,24 1038,62 1130,68 1230,90 1340,01 1458,79 1588,09 1724,64 1836,42 1920,91 2009,29 2096,59 2182,12 2271,13 2363,78 2460,21 2560,58 2665,04 2773,75 2886,91 3004,68 3127,25 3254,83 3387,61 3525,80 L 409,27 414,32 424,81 435,56 446,58 457,88 469,47 481,35 494,75 509,81 525,33 541,32 557,81 574,79 592,29 610,32 627,67 644,20 661,16 678,58 696,44 714,79 733,61 752,93 772,76 M 262,59 265,83 272,55 279,45 286,52 293,78 301,21 308,83 317,43 327,09 337,05 347,31 357,89 368,78 380,01 391,58 402,71 413,32 424,20 435,37 446,84 458,60 470,68 483,08 495,80 N 154,11 156,01 159,95 164,00 168,15 172,41 176,77 181,25 186,29 191,96 197,80 203,83 210,03 216,43 223,02 229,81 236,34 242,56 248,95 255,51 262,24 269,14 276,23 283,50 290,97 O 232,93 235,80 241,77 247,89 254,16 260,59 267,19 273,95 281,57 290,15 298,98 308,08 317,46 327,13 337,09 347,35 357,22 366,63 376,29 386,20 396,37 406,81 417,52 428,51 439,80

Total 6466,28 6718,26 7269,59 7869,43 8517,89 9224,84 9995,44 10832,15 11600,04 12268,40 12978,18 13692,58 14405,65 15158,33 15952,93 16791,91 17671,74 18594,23 19568,42 20597,38 21684,36 22832,81 24046,40 25329,03 26684,82

Source: excel_DOM_izeja.xls, Tautsaimniec_Y_N

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Table P30. Labor force demand (number of employment positions)

2005.1.1 2006.1.1 2007.1.1 2008.1.1 2009.1.1 2010.1.1 2011.1.1 2012.1.1 2013.1.1 2014.1.1 2015.1.1 2016.1.1 2017.1.1 2018.1.1 2019.1.1 2020.1.1 2021.1.1 2022.1.1 2023.1.1 2024.1.1 2025.1.1 2026.1.1 2027.1.1 2028.1.1 2029.1.1 A_0 52555 52235 52806 54077 55618 57303 59127 60960 62389 63001 63311 63796 64437 65180 65895 66569 67203 67869 68571 69294 69966 70575 71118 71636 72138 A_0_zin 8174 8117 8081 8078 8097 8124 8140 8172 8176 8199 8222 8243 8246 8251 8268 8287 8278 8275 8265 8250 8227 8216 8189 8172 8152 A_1 57335 56886 56417 56076 55828 55616 55318 55109 54714 54644 54655 54685 54612 54570 54623 54695 54546 54439 54275 54077 53833 53672 53381 53156 52916 A_2 7406 7352 7366 7438 7528 7641 7765 7879 7913 7902 7890 7912 7954 8022 8094 8169 8243 8325 8413 8508 8595 8664 8716 8762 8808 A_3 5476 5430 5384 5350 5313 5285 5258 5240 5203 5186 5174 5163 5146 5140 5135 5130 5112 5094 5068 5039 5013 4991 4962 4933 4906 A_34 85420 84875 85834 87968 90561 93370 96389 99452 101986 103208 103884 104632 105398 106198 106937 107609 108231 108876 109545 110228 110864 111448 111979 112498 113005 A_34_v 80830 80384 81547 83922 86768 89855 93123 96453 99255 100749 101689 102730 103802 104907 105930 106885 107789 108717 109680 110673 111607 112472 113268 114045 114817 A_38 4633 4599 4614 4673 4749 4833 4930 5027 5102 5134 5149 5166 5184 5205 5227 5246 5260 5275 5289 5300 5311 5322 5330 5338 5344 A_4 6820 6780 6827 6945 7091 7248 7421 7596 7729 7794 7833 7879 7927 7978 8031 8083 8129 8178 8230 8281 8329 8373 8409 8443 8476 A_48 5583 5553 5624 5772 5953 6150 6361 6574 6751 6847 6904 6956 7002 7046 7089 7130 7171 7213 7257 7304 7347 7388 7424 7459 7494 A_52 23532 23407 23775 24514 25399 26368 27413 28453 29269 29601 29753 30023 30413 30870 31312 31735 32154 32598 33076 33581 34053 34480 34867 35231 35585 A_58 6171 6157 6358 6712 7122 7569 8056 8575 9048 9274 9393 9536 9709 9892 10051 10189 10315 10438 10562 10687 10810 10931 11048 11165 11282 A_6 2027 2015 2026 2056 2091 2128 2167 2207 2241 2256 2260 2262 2262 2261 2260 2259 2257 2253 2249 2244 2240 2235 2229 2224 2219 A_7 16072 15972 15938 15979 16033 16047 16124 16226 16267 16298 16351 16431 16532 16639 16720 16825 16856 16899 16950 16979 17039 17081 17100 17091 17111 A_76 3531 3508 3491 3484 3478 3463 3462 3468 3461 3457 3461 3470 3485 3502 3514 3532 3532 3535 3538 3535 3540 3542 3539 3530 3528 A_81 5463 5419 5399 5405 5413 5436 5461 5493 5507 5521 5521 5519 5515 5524 5531 5534 5529 5519 5501 5482 5467 5454 5436 5418 5402 A_84 2555 2543 2579 2650 2738 2827 2897 2986 3073 3115 3131 3150 3178 3212 3248 3283 3318 3352 3386 3414 3421 3440 3469 3502 3530 A_86 26874 26635 26356 26130 25932 25722 25587 25428 25221 25104 25061 25001 24901 24823 24784 24727 24634 24534 24393 24209 24036 23890 23736 23594 23430 K_3 8545 8484 8553 8724 8937 9172 9415 9650 9854 9986 10032 10072 10141 10233 10313 10379 10431 10470 10503 10533 10562 10590 10616 10641 10666 K_34 16586 16460 16540 16797 17121 17477 17871 18268 18574 18694 18733 18781 18832 18896 18957 19008 19047 19090 19133 19175 19215 19250 19275 19298 19318 K_38 1229 1219 1210 1205 1203 1200 1201 1201 1199 1196 1195 1194 1190 1188 1186 1185 1181 1178 1173 1166 1160 1154 1149 1144 1137 K_52 5763 5733 5838 6044 6286 6552 6836 7132 7393 7516 7572 7649 7756 7879 7989 8087 8178 8267 8359 8454 8546 8634 8718 8798 8876 K_58 7317 7298 7555 8008 8536 9113 9738 10366 10853 11023 11103 11277 11535 11831 12112 12378 12643 12923 13226 13546 13841 14101 14338 14565 14789 K_7 23564 23421 23359 23395 23449 23441 23527 23654 23683 23705 23762 23868 24014 24174 24299 24461 24506 24570 24649 24693 24784 24846 24872 24855 24883 V_2 6625 6574 6555 6569 6585 6621 6662 6708 6719 6721 6719 6722 6724 6741 6757 6774 6782 6791 6794 6798 6804 6807 6801 6795 6790 V_34 42197 41945 42466 43580 44927 46388 47937 49490 50764 51410 51763 52193 52705 53269 53799 54295 54760 55237 55735 56251 56732 57172 57571 57953 58330 V_48 6046 6006 6063 6198 6364 6544 6734 6924 7074 7148 7188 7234 7281 7331 7378 7424 7466 7512 7560 7611 7658 7700 7737 7772 7806 V_52 115685 115090 117218 121371 126343 131792 137634 143189 146980 148088 148527 150005 152418 155325 158239 161135 164115 167351 170927 174749 178241 181260 183940 186442 188876 V_54 74606 74288 76082 79415 83413 87788 92475 96758 99254 99689 99895 101098 103103 105520 108027 110594 113315 116321 119686 123304 126548 129269 131652 133878 136052 V_58 56290 56206 58599 62746 67476 72630 78251 84285 89789 92216 93390 95072 97313 99751 101860 103669 105315 106920 108546 110202 111816 113374 114891 116398 117911 V_6 8172 8129 8193 8339 8517 8704 8886 9074 9223 9273 9283 9299 9327 9361 9400 9444 9488 9530 9576 9623 9647 9673 9698 9725 9750 V_76 18792 18670 18580 18548 18529 18467 18463 18494 18458 18452 18480 18537 18613 18700 18770 18866 18867 18883 18903 18894 18916 18927 18908 18865 18851 V_81 5302 5264 5314 5432 5576 5736 5901 6061 6204 6297 6329 6356 6403 6465 6519 6562 6596 6621 6641 6659 6677 6695 6712 6729 6746 V_84 57523 57176 57991 59684 61720 63932 66254 68568 70537 71580 72038 72594 73397 74345 75205 75971 76669 77332 77998 78671 79291 79866 80406 80931 81451 V_86 19112 19022 19321 19909 20617 21394 22226 23079 23800 24184 24411 24641 24873 25115 25342 25556 25760 25968 26183 26404 26613 26809 26991 27169 27345 V_0 228210 226878 229121 234090 240090 246531 253235 259988 265823 269381 271803 273977 275701 277223 278636 280009 281294 282554 283823 285154 286413 287587 288634 289678 290741 P_0 190792 189749 191693 195929 201022 206593 212568 218545 223203 225197 226125 227407 229042 230885 232697 234456 236071 237727 239466 241304 242955 244406 245607 246776 247937 Total 1292813 1285478 1300675 1333209 1372422 1415061 1460819 1506734 1542686 1559045 1567992 1580530 1596066 1613449 1630135 1646140 1661042 1676632 1693129 1710277 1726117 1740294 1752715 1764609 1776398 Source: excel_DOM_izeja.xls, Tautsaimniec_D_P

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Table P31. Number of filled positions


Source: excel_DOM_izeja.xls, Tautsaimniec_D_P

174



of the labor market


ent Projects dem



Table P32. Available labor force (employment positions)

2005.1.1 2006.1.1 2007.1.1 2008.1.1 2009.1.1 2010.1.1 2011.1.1 2012.1.1 2013.1.1 2014.1.1 2015.1.1 2016.1.1 2017.1.1 2018.1.1 2019.1.1 2020.1.1 2021.1.1 2022.1.1 2023.1.1 2024.1.1 2025.1.1 2026.1.1 2027.1.1 2028.1.1 2029.1.1

A 138719 142453 140960 141441 142653 141477 138262 135557 133093 130280 126696 122905 119250 115862 112926 110402 108201 105993 103629 101082 98534 96022 93556 91250 89155 B 4341 4496 4534 4618 4726 4661 4597 4746 4810 4769 4688 4584 4483 4398 4334 4290 4254 4209 4152 3994 3843 3795 3754 3719 3624 C 3250 3354 3378 3433 3496 3556 3557 3467 3333 3213 3138 3080 3033 3005 2993 2983 2990 3027 3050 3061 3049 3018 2985 2958 2937 D 177925 187490 189272 192981 195988 198049 199383 195768 189768 184830 182040 180856 180244 180168 180675 182072 184597 187847 191069 193486 194107 194042 193802 193695 194022 E 23277 23662 22645 21961 21090 20176 19256 18521 18103 17547 16993 16608 16324 16098 15934 15860 15900 16007 16108 16189 16271 16351 16351 16319 16309 F 103028 110333 117101 124260 130700 136997 143105 150282 153555 151553 150377 150118 150103 149534 148595 147897 147598 147336 146625 145613 144709 143908 143224 142736 142620 G 201513 206134 205378 207426 210058 210041 209042 208720 209370 210571 210420 208473 205572 203104 201259 200174 199946 199937 199348 198142 196681 195055 193354 191687 190297 H 42263 44213 45392 47293 49610 51303 52777 54318 55300 55713 55848 55717 55497 55378 55413 55656 56157 56755 57199 57456 57618 57708 57745 57764 57844 I 112400 114977 114687 115235 115629 115567 114772 113947 113353 112316 109812 107573 105737 103974 102278 100824 99583 98278 96701 94956 93253 91604 90031 88576 87340 J 21224 21333 21104 21187 21400 21467 21454 21391 21080 20555 19900 19145 18371 17654 17017 16459 15976 15517 15023 14498 13970 13455 12959 12492 12068 K 65612 68199 69469 71761 74392 76505 78219 79956 80936 81057 80499 79366 78027 76835 75865 75162 74702 74252 73580 72726 71846 70996 70202 69502 69003 L 89243 89511 86873 85080 83376 81478 79455 77340 75867 74916 73342 71325 69320 67616 66081 64621 63345 62024 60387 58628 56976 55393 53902 52547 51301 M 104079 104624 102813 101507 100459 98829 96776 94866 93281 92273 90767 88231 85583 83213 81145 79132 77297 75513 73477 71269 69132 67074 65108 63385 61869 N 71086 72210 71362 70600 69772 68803 67991 67216 66454 65741 64925 64121 63445 62749 62147 61657 61190 60875 60353 59771 59060 58147 57200 56373 55743 O 85002 86127 83273 81008 79118 76777 74130 71617 69793 68827 67347 65504 63858 62515 61246 60121 59124 58078 56820 55578 54427 53238 52012 50917 49889

Total 1242962 1279114 1278240 1289790 1302467 1305686 1302777 1297709 1288097 1274159 1256791 1237605 1218847 1202102 1187907 1177311 1170860 1165648 1157522 1146450 1133477 1119806 1106184 1093920 1084020

Source: excel_DOM_izeja.xls, Tautsaimniec_L_N

Table P33. Necessary labor force (employment positions)

2005.1.1 2006.1.1 2007.1.1 2008.1.1 2009.1.1 2010.1.1 2011.1.1 2012.1.1 2013.1.1 2014.1.1 2015.1.1 2016.1.1 2017.1.1 2018.1.1 2019.1.1 2020.1.1 2021.1.1 2022.1.1 2023.1.1 2024.1.1 2025.1.1 2026.1.1 2027.1.1 2028.1.1 2029.1.1 A 143843 142749 145425 148057 150569 153225 155869 158507 159766 159294 158746 158313 157837 157307 156918 156500 155883 155055 154338 153603 152842 152081 151310 150672 149926 B 4512 4547 4722 4915 5136 5170 5221 5526 5689 5691 5707 5734 5773 5824 5885 5958 6023 6078 6143 6061 5986 6080 6184 6296 6276 C 3383 3372 3518 3654 3826 4046 4261 4482 4669 4794 4928 5036 5110 5201 5304 5393 5465 5594 5717 5805 5896 5993 6099 6217 6327 D 190462 190918 201317 212380 224077 236359 249393 263112 273508 280041 286688 292802 298292 303892 309634 315463 321339 327366 333536 339760 346128 352631 359228 365969 372846 E 24311 23812 23683 23554 23426 23298 23172 23046 23146 23485 23829 24178 24532 24891 25256 25625 26001 26381 26767 27159 27557 27960 28370 28785 29207 F 110629 113677 127354 141291 155169 170409 187146 205527 218116 223380 228771 233147 236400 239699 243044 246436 249875 253362 256898 260483 264118 267804 271541 275330 279172 G 208408 206405 212377 218523 224517 230098 235578 241188 245726 249078 252475 254669 255577 256488 257403 258321 259242 260166 261094 262024 262959 263896 264837 265781 266729 H 43625 44207 46778 49499 52378 55424 58647 61907 64073 65145 66234 67177 67960 68752 69553 70363 71183 72013 72852 73701 74560 75429 76308 77197 78097 I 116157 115279 119163 123178 127329 131619 135722 139597 142322 143777 145247 146373 147133 147897 148664 149436 150064 150542 151021 151502 151985 152469 152954 153441 153930 J 21781 21260 21669 22086 22511 22945 23386 23778 23705 23215 22735 22211 21643 21090 20551 20026 19515 19016 18530 18057 17595 17146 16707 16281 15865 K 68003 68547 72151 75945 79939 84142 88567 92996 95744 96832 97933 98803 99428 100057 100689 101326 101967 102611 103260 103913 104570 105232 105897 106567 107240 L 90997 89043 88191 87447 86558 85861 85385 84439 83744 83673 83576 83165 82747 82610 82484 82112 81709 81257 80543 79810 79287 78760 78221 77688 76973 M 105648 103766 103209 102756 102508 101904 101476 100764 100257 100618 100741 100686 100445 100507 100855 100654 100247 100077 99714 99203 98890 98468 97887 97550 97090 N 73126 72221 72286 72406 72092 71874 72327 72371 72156 72255 72535 72987 73602 73943 74425 74689 74544 74849 74892 75039 75335 75372 75193 75133 75230 O 87928 86095 85454 84430 83542 82837 82246 81417 80667 80495 80124 79605 79266 79143 78863 78468 77963 77355 76599 75994 75530 74952 74257 73707 73068

Total 1292813 1285900 1327297 1370122 1413577 1459212 1508396 1558655 1593288 1611773 1630270 1644885 1655746 1667301 1679529 1690771 1701019 1711723 1721904 1732114 1743238 1754273 1764994 1776615 1787977

Source: excel_DOM_izeja.xls, Tautsaimniec_L_N

175



of the labor market


ent Projects dem




176

Annex 5. (CD)

Data array obtained during the research

Data array used during the research

Modeling results: MS Excel Powersim Studio

User’s Reference Book for work with long-term labor market demand forecasting models

RESEARCH OF LONG-TERM FORECASTING SYSTEM OF THE …and long-term labor market supply and demand forecasting system, according to which the labor market development policy could be

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