paper on 2010...This document should be cited as ii

Discussion paper on “Available and needed skills in Rwanda”

August

2010 First draft

i 

Prepared and published by Labour Market Information System (LMIS) department in Rwanda Development Board (RDB). P.O.BOX 6239, Kigali Gasabo district Kigali, RWANDA E-mail: [email protected] Internet: www.lmis.gov.rw Specialists who worked on the paper: 1. Pacifique KARINDA, LMIS Director (Information Technologist) 2. Steven MUGABI, LMIS Specialist (Economist) 3. Mike FINSCH, LMIS Practitioner (Information Technologist) 4. Jele OPPERMANN, LMIS volunteer (Information Technologist) 5. Alphonse HITAYEZU, gtz representative Consultant who worked with the team: Mr. Erich Maierhofer, gtz consultant in labour market issues © Rwanda Development Board (RDB), June 2010

Different sources of data were used to get to the information contained in this document as indicated on each table.

This document should be cited as <<Rwanda Labour Market Information System, 2010, LMIS discussion paper on "Available and needed skills in Rwanda">>

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Acknowledgement 

Special thanks to Social Security Fund of Rwanda and WDA who provided us with all necessary data for the compilation of this discussion paper. 

Thanks also go to GTZ Eco‐emploi for hiring an International consultant to work with LMIS on the compilation of this discussion paper. 

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Acronyms  CSV: Comma Separated Values

EICV2: Enquête Intégrale sur les Conditions de Vie des Ménages (Integrated Household Living Conditions Survey)

GNU:  is a recursive acronym for "GNU's Not Unix!"

ICT: Information Communication Technology

ISAR: Institut des Sciences Agronomiques du Rwanda

IUCEA: Inter-University Council for East Africa

LAMP: Linux Apache MySQL PHP

MDA: Ministries, Departments and Agencies

MS: Microsoft

MySQL: Database Management System based on Structured Query Language

NISR: National Institute of Statistics of Rwanda

ODBC: Open Data Base Connection

PASNVA: National Agricultural Extension Support Project

PHP: Private Home Page

PSPP: It is a free software application for analysis of sampled data

RRA: Rwanda Revenue Authority

SPSS: Statistical Program for Social Scientist

SSFR: Social Security Fund of Rwanda

TVET: Technical and Vocational Education and Training

WDA: Workforce Development Authority

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Table of Contents Acknowledgement .................................................................................................................................... ii 

Chapter 1: INTRODUCTION ...................................................................................................................... 1 

Chapter 2: ADMINISTRATIVE AND SURVEY DATA USED ........................................................................... 2 

Chapter 2: ADMINISTRATIVE AND SURVEY DATA USED ........................................................................... 2 

2.1. Social Security Fund of Rwanda (SSFR) ......................................................................................... 2 

2.2. Workforce Development Authority (WDA) .................................................................................. 3 

2.3. National Institute of Statistics of Rwanda (NISR) .......................................................................... 3 

Chapter 3: METHOD AND VARIABLES ...................................................................................................... 5 

3.1. Administrative data from SSFR ..................................................................................................... 5 

3.1.1.  Limitations of SSFR data .................................................................................................. 6 

3.1.2.  Entity and variable definitions ......................................................................................... 6 

3.2. Administrative data from WDA about TVET enrolment ............................................................... 7 

3.3. Survey data (EICV 2) from NISR .................................................................................................... 7 

Chapter 4: DESCRIPTION OF TECHNICAL IMPLEMENTATION .................................................................. 8 

4.1. Data flow for the report ............................................................................................................... 8 

Chapter 5: COMPARISONS OF GROWING SECTORS FOR A DEMAND DRIVEN RWANDA ECONOMY AS BASIS OF NEEDED SKILLS ..................................................................................................... 11 

Chapter 6: OVERALL EMPLOYMENT DEVELOPMENT BY EMPLOYEE ..................................................... 13 

6.1. Who is working at growing sectors? ........................................................................................... 16 

6.2. Comparison of growing sectors and available skills ................................................................... 18 

6.3. General influences regarding skills ............................................................................................ 19 

Chapter 7:  CONCLUSION ...................................................................................................................... 23 

1.  Appendix .............................................................................................................................. 24 

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List of tables Table 1: Employment by sector and nationality .................................................................................... 17 

Table 2: Participants of TVET Courses matched to sectors (2010) ......................................................... 18 

Table 3: Distribution of formal employees and of TVET participants per economic sector .................. 18 

Table 4: Literacy distribution in 2005 .................................................................................................... 19 

Table 5: Net enrolment rates in primary schools by gender and year .................................................. 20 

Table 6: Crops production in tons by year ............................................................................................. 20 

Table 7: Exportation ............................................................................................................................... 21 

Table 8: Enrolment in public and governmental aided schools in the field of Agriculture and related (2010) ....................................................................................................................................... 21 

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List of figures Figure 1: Format of the data from SSFR .................................................................................................. 5 

Figure 2: Excel sheet dataset got from SSF .............................................................................................. 8 

Figure 3: Picture showing data flow through the LMIS system ............................................................... 9 

Figure 4: Picture below showing a part of the data warehouse and some fragments of data records 10 

Figure 5:  Picture showing two dimensional table generated by LMIS system on a user's computer .. 10 

Figure 6: Trend in economic sectors ...................................................................................................... 12 

Figure 7: Employment growth ............................................................................................................... 13 

Figure 8: Public and private employment growth dates based on quarter to quarter change ............. 14 

Figure 9: Growth rate of employee by sector ....................................................................................... 15 

Figure 10: Growth rate of employee by sectors .................................................................................... 16 

Figure 11: Number of registered employees by nationalities and sectors of occupation ..................... 17 

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Chapter 1: INTRODUCTION

In December 2009, LMIS conducted a survey targeting to get what the end users of Labour Market Information System need. The main objective of the survey was to answer the following question: “What are the expectations of LMIS users toward Labour Market Information System?”

After the analysis of the expectations of the users, one of the applications that emerged at that time was “Available and needed skills”.

To develop this application, they are many different approaches available but all depend on the availability of data. In this discussion paper three (3) approaches were used: 1. Consulting the keynotes of the Minister of Finance which is formulated by experts basing

on available facts. 2. Using available data and try to analyze it to get a deeper insight of the structure of

employment patterns. 3. Using techniques to discuss topics of international importance for mankind and their

consequences for the society of Rwanda.  Using the above mentioned approaches, allowed to have a look on formal employment over 8 years in order to find out trends in employment, to understand how education was in the past, where the government will put more emphasis.

The discussion paper shows two major growing economic sectors that are mentioned in government 2010/2011 budget, and this is also stressed by TVET system that is being implemented by WDA. This discussion paper is the first that connects these two kinds of approaches.

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Chapter 2: ADMINISTRATIVE AND SURVEY DATA USED

Administrative data is the set of units and data derived from an administrative source1. Any observation or investigation of the facts about a situation may be called a survey. But today the word is most often used to describe a method of gathering information from a number of individuals, a "sample," in order to learn something about the larger population from which the sample has been drawn2. Information is collected by means of standardized questions so that every individual surveyed responds to exactly the same question. The survey's intent is not to describe the particular individuals who by chance are part of the sample, but to obtain a statistical profile of the population. Individual respondents are never identified and the survey's results are presented in the form of summaries, such as statistical tables and charts.3 Whereas various types of data are mentioned above and indeed respective attributes characterize each data type, this report is limited to administrative data from SSFR which is collected from employers registered with the institution; it’s collected periodically from the respective employers covering all the employees in all respective legally fitting pension paying units (individuals, firms, organizations, MDAs and the civil society to mention but a few). Specifically data used in this report was given by SSFR of Rwanda: It covers 332,809 employees from 12,165 employers for the period starting from 2002 to 2009. The key variables in this dataset refer to section 3.1 in this document. The relevancy of this dataset is to give an insight into the employment status of the formal sector as per the national records. In addition, data from EICV2 survey was used to supplement the above findings and the variables of special interest from the survey are mentioned in the section 3.3. The integration of these two different types of data was to give a comprehensive foundation insight into the skills availability in the country as explained in the following sections of the discussion paper. A brief profile of the institutions that provided data to this discussion paper is given below in relation to their relevancy towards the cause of this discussion paper.  2.1. Social Security Fund of Rwanda (SSFR) 

The Social Security Fund of Rwanda is a public institution that manages two social security branches of Pension and Occupational hazards. Since its inception in 1962, the mandate for

 1 OECD, IMF, ILO, Interstate Statistical Committee of the Commonwealth of Independent States, "Measuring the Non-Observed Economy: A Handbook", Annex 2, Glossary, Paris, 2002. 2 FEBER Robert, SCHEATSLEY Paul, TURNER Antony, WASKSBERG Joseph, What is a survey?, Washington, p 1 3 FEBER Robert, SCHEATSLEY Paul, TURNER Antony, WASKSBERG Joseph, op.cit. p.3

http://client.norc.org/whatisasurvey/downloads/pamphlet_1980.pdf

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the Fund has been4 : 1. to collect members’ contributions; 2. provide benefits to eligible members and; 3. productively invest any excess funds.

The effective execution of the three principle functions requires keeping accurate statistics, the only strategy for proper decision making. In view of the role of statistics in the administration and management of Fund’s operations, the Department in charge of Planning, Research and Statistics gathers all required statistics and produces periodical statistical reports containing up-to-date data on employers and employees, contributions, benefits, investment portfolio and returns and key economic indicators. 2.2. Workforce Development Authority (WDA)  WDA is a public institution with the mission5 to promote, facilitate and guide the development and upgrading of skills, knowledge and competency of the national workforce through Technical and Vocational Education and Training (TVET), in order to enhance its efficiency, quality, competitiveness and employability.

The mandate of WDA is summarized in the following five (5) strategic pillars:

1. Skills Development through TVET; 2. Business Incubation (Entrepreneurship Development); 3. Labour Market Information System (LMIS); 4. Setting Industry Standards; 5. Regulation and Accreditation. 2.3. National Institute of Statistics of Rwanda (NISR)  The legal status of NISR6 The Institute has the following responsibilities:

1° to define and ensure the respect of standards and methodologies applied by the national statistical system; 2° to conduct national census and other national surveys; 3° to advise and train the personnel of the national statistical activities; 4° to coordinate and gather statistical information and methodologies of particular sectorial departments in charge of statistical activities in the country; 5° to disseminate the official statistical data whether the one publicized by the Institute or the data bearing its visa; 6° to coordinate the activities of the national statistical system; 7° to advise state institutions regarding the development of the national statistical system; 8° to encourage the public to participate in statistical activities and learn how to use the census and surveys results;

 4 Social Security Fund of Rwanda, http://www.csr.gov.rw/pdf/csr_stat2008.pdf 5 http://www.wda.gov.rw/wda_mission.php 6 N° 09/2005 of 14/07/2005 Law establishing the National Institute of Statistics of Rwanda

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9° to prepare the national program of the statistical system; 10° to accomplish any other function relating to the statistics as may be assigned by State Administration.

Chapter 3: METHOD AND VARIABLES  Whereas a brief insight has been given to the effect, this section gives a detailed description of the methods, variables and data sources that have been used in this report. The relevancy of this is to facilitate the understanding of environment against which any interpretation of the findings of this report should be limited. The methods used in this report: • Collecting and analyzing available data i.e. EICV (2005) and SSFR data (2002-2009) to

get a deeper insight of literacy and the structure of employment patterns.

• Reviewable by respective experts from the political, academic and policy formulation community such as keynotes by governmental officials, which are formulated by experts.

• Using techniques to discuss topics of international importance for mankind and their consequences for the society of Rwanda.

The output from all this is limited to descriptive findings such as proportions and averages respectively with a clear insight on the gaps that should be filled by future studies. The used dataset in this report are limited to:

A) Administrative data from SSFR; B) Administrative data about TVET enrolment from WDA; C) Survey data from NISR. 

 3.1. Administrative data from SSFR  The data was given in two excel sheets from SSFR without description (metadata) as shown below. Figure 1: Format of the data from SSFR

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Both excel sheets contained anonymous data, meaning that no names of employers or employees were given but only unique numbers to identify an employer or an employee. LMIS does neither need nor want personal data of employers or employees. Employer's data file given contained a list of employers registered in SSFR disaggregated by the following variables: - Unique number for identification; - Economic sector; - Geographical data (province and district); - Legal status (private or public); - The quarter (from I / 2002) and the total number of employees fees are paid for. Employee's data file given contained a list of employees registered in SSFR disaggregated by the following variables: - Unique number for identification; - Gender; - Date of birth (as an absolute number); - Type of identification (e.g. identity card); - Province and district of birth ; - Nationality ; - Registration date (as an absolute number); - Unique identification number of Employer who registered the employee.

3.1.1. Limitations of SSFR data  The limitations encountered while working on SSFR data are the following: - Employers were counted for a quarter as active only if they paid for at least one employee. - It could not be determined which employee was paid for in which quarter and if he still

"active" or left the SSFR as there is nothing like a "quit-date", so all information about employees (except how many an employer declared in a quarter) is "in general". This also means that there is no possibility to say if an individual employee still works for a certain employer.

- In the dataset no professions are collected from employers in order to help in determining which professions are working in which economic sectors.

- As the data contains no registration and no quit date of the employer it can't be determined how long an enterprise exists.

3.1.2. Entity and variable definitions  

Employee: In SSFR data collection, someone is considered as employee if and only if he/she has been registered by an employer as an employee. All employees’ data is given by the employer. Employer: In SSFR data collection, someone is considered as employer if he/she or a deputy or person in charge affiliates at least one employee to SSFR. An employer can be a private household, a self employed person, a legal body or a corporation.

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Ownership status (legal status): The owner of the enterprise is either government or private individual, group of individuals or charity organization. But in SSFR data, it is either public or private only. Economic sectors: The economic activity of an employer is classified by SSFR. There are nine (9) categories: 1. Suppliers, collection & social and human resource services 2. Agriculture, hunting, forestry & fishing 3. Banking, assurances, real estate, financial services 4. Construction and public work 5. Industrial manufacturing 6. Extracting industries 7. Wholesale and retail trade, restaurants & hotels 8. Transport, storage and communications 9. Electricity, gas and water Quarter: This is considered as periodicity of declaration of employees. The declaration and fee paying must be done personally every quarter at a SSFR branch. 3.2. Administrative data from WDA about TVET enrolment   The dataset provided to LMIS by WDA inspectors as a result of their activity to inspect all public and government aided TVET schools contained 76 out of 80 schools. The four remaining schools are not yet inspected. For this report, July 2010 enrolment data were used to show the number of students in each course. The matching of TVET administrative data was accomplished by outlining the nine (9) economic sectors used by SSFR and allocating the professions (“courses”, “options”) from TVET schools to the sector they mainly belong to. 3.3. Survey data (EICV 2) from NISR  EICV 2 provides a dataset about education and the file name is eng_eicv2_s2_education.sav. The following variables were used: - AGE – Age - SEX - Sex - S2AQ2 - Ever been to school - S2CQ6 - Attended literacy courses - HH_WT - Household weight  

Chapter 4: DESCRIPTION OF TECHNICAL IMPLEMENTATION  The LMIS system is implemented as a typical web based open source system (LAMP; Linux, Apache Web server, MySQL, PHP). It means that several parts of the system (database management system: MySQL, and programming language: PHP) are under the GNU license and without any costs. Apart from money there are three more reasons. - First PHP and SQL are the most spread languages, thus the further development and

maintenance of the applications will be easy. - Second they are very well documented and literature is available for free in the internet. - Third they are not possessed by an enterprise but by a community of developers. This

makes sure that they will have a long life cycle and that security lacks will be fixed quickly.

To connect to the server the user only needs a browser (Internet Explorer, Firefox, and Opera etc.). There are no costs for special program licenses on the user side. No special knowledge is needed and the user gets in touch with the data only through a graphical user interface. So the system is a typical configuration for internet and intranet. To analyze EICV2 data PSPP was used. PSPP is an open source application made for statistical analysis of sampled data. It is a free replacement for the proprietary program SPSS. PSPP can perform descriptive statistics, T-tests, linear regression and non-parametric tests.  

4.1. Data flow for the report  The datasets used from SSFR and WDA were delivered as MS-Excel® files.

Figure 2: Excel sheet dataset got from SSF 

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Figure 3: Picture showing data flow through the LMIS system 

Step 1: The raw data material for the report is stored in MS-Excel and SPSS format.

Step 2: The integration into the MySQL database is managed via csv format (comma separated values) and ODBC (open database connectivity). These are special methods to integrate the Excel files into the SQL-Standard database.

Step 3: The database server (MySQL) holds three parts: The “orig”, the “change” and the “datawarehouse” section. After import, the data is stored in the “orig”-database. An exact copy of the data is stored in the “change” database. The data stored here will be worked on (normalizing, combining) so that it is ready for analysis. After plausibility tests the data resulting will be copied into the “datawarehouse”. This ensures that all steps taken can be monitored and controlled.

Step 4: To create the so called fact tables and the tables for further analysis the special cases for the content will be developed and programmed. The algorithms and the SQL logic for the requests are implemented in PHP programs. Every PHP program generates tables for a special use case or request.

Step 5: These tables can be exported in several formats e.g. for MS-Excel or OpenOffice Calc for further analysis.

Step 6: EICV data is exported to PSPP in order to weight it and extract the data fitting to the needed cases.

Step 7: The end products are export tables (either MS Excel or OpenOffice calc) that can be edited and are used a basis for charts and diagrams.

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Figure 4: Picture below showing a part of the data warehouse and some fragments of data records

Figure 5:  Picture showing two dimensional table generated by LMIS system on a user's computer

Simultaneously the system creates the table for export.

Why do we use such a mighty system like MySQL and not a spreadsheet like Excel? The datawarehouse is the base for all analysis and LMIS reports. We want to separate strictly the data collection system from the data analysis part. This minimizes the risk of (willingly or unwillingly) manipulation, makes data usable several times without editing the base, avoids confusing double entries and makes backup easier. The interfaces for Excel and OpenOffice are implemented, so that the system is independent from any analysis software. Preparing data records for analysis needs a huge amount of time and concentrated work.

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Chapter 5: COMPARISONS OF GROWING SECTORS FOR A DEMAND DRIVEN RWANDA ECONOMY AS BASIS OF NEEDED SKILLS 

According to the speech of the Minister of Finance and Economic Planning7 the key priority areas of the government of Rwanda for investment in the financial year 2010/11 are the following: - Education; - Health; - Agriculture; - Trade and industry; - Energy; - Transport; - ICT; - Social protection and governance. Most of these sectors are also found in SSFR dataset as promising sectors because their growth is almost constant. Data from SSFR (see appendix) show that these formal sectors grew in numbers of employees from 2002 to 2009. The bold printed even grew constantly. - Agriculture, hunting, forestry & fishing - Banking, assurances, real estate, financial services - Construction and public work - Industrial manufacturing - Extracting industries - Wholesale and retail trade, restaurants and hotels - Transport, storage and communication - Electricity, gas and water These formal sectors grew in numbers of enterprises from 2002 to 2009. The bold printed even grew constantly. - Suppliers, collection & social and human resource services - Agriculture, hunting, forestry & fishing - Banking, assurances, real estate, financial services - Construction and public work - Industrial manufacturing - Wholesale and retail trade, restaurants and hotels - Transport, storage and communication - Electricity, gas and water Both kinds of economic sectors (those that grow over a long period and those that are in focus of government investment) will need more employees. The demand is described mainly by the growth of employees and the growth of enterprises may stress this.  

7 MINECOFIN – www.minecofin.gov.rw/spip.php?article45, Removing bottlenecks to exports of goods and services

  Figure 6: Trend in economic sectors 

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2003 2004 2005 2006 2007 2008 20090

50

100

150

200

250

300

Enterprises

Year

Per

cent

age,

200

2 =

100

%

Wholes ale and retail trade, res taurants & hotels

2003 2004 2005 2006 2007 2008 20090

50

100

150

200

250

Employees

YearP

erce

ntag

e, 2

002

= 10

0 %

Wholes ale and retail trade, res taurants & hotels

The trend in the sector “Wholesale and retail trade, restaurants & hotels” is homogenous for both employees (4,930 in 2002 to 10,603 in 2009) and employers or enterprises (452 in 2002 to 1,130 in 2009).

2003 2004 2005 2006 2007 2008 20090

50

100

150

200

250

Employees

Year

Per

cent

age,

200

2 =

100

%

Banking, as s urances , real es tate,financial s ervices

2003 2004 2005 2006 2007 2008 20090

204060

80100120140

160180200

Enterprises

Year

Per

cent

age,

200

2 =

100

%

Banking, as s urances , real es tate, financial s ervices

In the sector “Banking, assurances, real estate, financial services” the number of enterprises grew until 2008 (from 255 in 2002 to 480 in 2008) to fall in 2009 to 421, while the number of employees grew constantly from 2003 (4,456) to 9,038 in 2009. This may be an example for the thesis that the growth of the number of enterprises is less important to describe the demand of skills.

Chapter 6: OVERALL EMPLOYMENT DEVELOPMENT BY EMPLOYEE 

Thesis: Stability in employment growth is an indicator for labor market demand. Stability of employment means continuous jobs for the workers. In order to show increase or decrease in relative view, percentage was used. SSFR data describes the development over 8 years (2002 to 2009). Percentages show trends in this time period distinctly and demand in a lively economy can never be figured out in absolute numbers as the concrete numbers may change from day to day. Thus trends are a more reliable source. Figure 7: Employment growth 

0

50

100

150

200

250

percentage

Employment growth base: 2002/I is 100% 

Employees public growth

Employees private growth

Employees total growth

The chart above shows the development of the number of employees in the formal sector from the first quarter 2002 to the last quarter 2009. Considering the figures of I / 2002 as 100%, the growth of public sector keeps on going up and down (because most employees in the formal sector work here) while the private sector experience a continuous growth. The overall development is influenced by the public sector shape because government is the biggest employer in the country. If a stable trend of employment growth can be found in some economic sectors, the demand driven need of skills will also grow. It can be assumed that these sectors will need skilled workers in future. If necessary skills are not available the labour market will attract workers from outside.

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With respect to the above graph, the public sector is decreasing, but the downs are not distributed continuously. The graph shows three major down peaks (I/2004, I/08, IV/09) and a phase of strong increase (III/04). The following diagram shows the development of public and private sectors as percentage development from quarter to quarter. The data itself does not allow an explanation of the trends. Figure 8: Public and private employment growth dates based on quarter to quarter change 

-0,5

-0,4

-0,3

-0,2

-0,1

0

0,1

0,2

0,3

0,4

0,5

0,6

Growth rate in %

Quartal

Public and private employment growth rates based on quartal to quartal change. The previous quartal is always 100%

% public growth rate

% private growth rate

 With respect to the above graph, the private sector varies in the 10% range, while the public sector shows large ups and downs. It is not possible to say that reforms or restructuring are the cause for this behavior because the peaks do not occur only in the years where reforms or restructuring took place. More studies are needed to clarify this. What can be said is that the decrease of employment in the public sector is not observed in the private enterprises as they did not grow in the same period. It may therefore be assumed that the employees did not quit voluntarily to find a job in the private sector but that the decrease may be initiated by the employers. The employees set free by public sector can shift either to the formal private sector (what they did not as the figures show), retire, go to university or shift to the informal private sector.

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Figure 9: Growth rate of employee by sector 

0,00

50,00

100,00

150,00

200,00

250,00

300,00

350,00

I / 2002

II / 2002

III / 2002

IV / 2002

I / 2003

II / 2003

III / 2003

IV / 2003

I / 2004

II / 2004

III / 2004

IV / 2004

I / 2005

II / 2005

III / 2005

IV / 2005

I / 2006

II / 2006

III / 2006

IV / 2006

I / 2007

II / 2007

III / 2007

IV / 2007

I / 2008

II / 2008

III / 2008

IV / 2008

I / 2009

II / 2009

III / 2009

IV / 2009

Achsentitel

growthrate of employee by sector ‐‐

Agriculture, hunting, forestry & fishing

Banking, assurances, real estate, financial services

Construction and public work

Industrial manufacturing

Extracting industries

Wholesale and retail trade, restaurants & hotels

Transport, storage and communications

Electricity, gas and water

suppliers, collection & social and human ressource services

With respect to the above graph, economic stability in a sector can be justified if the sector grows in over 50% of the monitored time period. To look only at the start (I/2002) end (IV/2009) point to identify growing is not enough. The employment in construction sector both in public and private reached (I/2002 = 100%) a peak of 324 percent after three years, which means it was growing by more than two hundred percent, decreased within one year (I/2006) to 195 percent, and reached in III/2007 the level of IV/2004 again. At the end of the monitored time period it reached 215 percent, similar to the level of I/2006.

In the construction sector the level of employment at the end of the monitored period is higher than at the beginning, but if the whole period is monitored, this good development cannot be taken as reliable and positive since its reasons are not known.

Even if the graphs show a continuous growth, this does not mean that individual employees are working there continuously as the data does not allow talking about fluctuation as there is no date of leaving a company. This means that even if a sector shows no variety in number of employees from one time period to another it is possible that they all shifted.

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Figure 10: Growth rate of employee by sectors 

0,00

50,00

100,00

150,00

200,00

250,00

I / 2002

II / 2002

III / 2002

IV / 2002

I / 2003

II / 2003

III / 2003

IV / 2003

I / 2004

II / 2004

III / 2004

IV / 2004

I / 2005

II / 2005

III / 2005

IV / 2005

I / 2006

II / 2006

III / 2006

IV / 2006

I / 2007

II / 2007

III / 2007

IV / 2007

I / 2008

II / 2008

III / 2008

IV / 2008

I / 2009

II / 2009

III / 2009

IV / 2009

Growth in percentage

Growth of employee by sectorsbase (=100%): IV/2004

Agriculture, hunting, forestry & fishing

Banking, assurances, real estate, financial services

Construction and public work

Industrial manufacturing

Extracting industries

Wholesale and retail trade, restaurants & hotels

Transport, storage and communications

Electricity, gas and water

suppliers, collection & social and human ressource services

In reference to the table above, as a result of different starting points and considering time series, different pictures of growing employment patterns are indicated. For example if the last quarter of 2004 is taken as the base (100%=IV/2004), the growth of employees by sectors differs with banking, assurances, real estate and financial services recording a steady growth over the years as well as wholesale and retail trade, restaurants and hotels. However other sectors indicate dynamic situations at one point growing rapidly while on another point reducing gradually. For instance it is shown that electricity, gas and water and suppliers, collection and social and human resource services as well as construction and public work recorded the highest rates of dynamisms in growth rates both before and after the base year. Though other sectors show some dynamic situations, they were not pounced compared to other sectors.

6.1. Who is working at growing sectors?  Regarding the table below, the distribution of employees by nationality is clearly indicated hence showing employees distribution among different growing sectors. Therefore as we closely look into this table, a new picture of skills emerges. For example in the major consistent growing sectors like Banking, assurance , real estate, financial services and whole sale and retail trade, restaurants and hotels, Rwandese are underrepresented hence an indication that foreigners with more skills occupy these sectors. This analysis is made without

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considering suppliers, collection and social and human resource services sector that happens to occupy over 60% of the employees compared to other sectors.

The fraction of foreign employees is 15%. Half of the foreign workers come from 4 countries: Uganda, Burundi, Tanzania and Kenya.

Among these growing sectors, Burundians have the biggest percentage of employees in the Banking sector while Kenyans dominate the wholesale sector with all these foreigners having the biggest numbers compared to 8% and 18% Rwandese respectively in the same sectors. This therefore clearly indicates the skills gap on the side of Rwandese.

Table 1: Employment by sector and nationality 

 without suppliersSector Rwandan Burundian Kenyan Tanzanian Uganda Other TOTALAgriculture, hunting, forestry & f 6% 3% 1% 3% 3% 3% 6%Banking, assurances, real estat 8% 25% 22% 12% 24% 20% 10%

Wholesale and retail trade, rest

Construction and public work 28% 12% 15% 25% 12% 18% 27%Industrial manufacturing 13% 9% 5% 13% 9% 10% 12%Extracting industries 9% 1% 0% 1% 1% 3% 8%

a 18% 27% 35% 29% 30% 28% 20%Transport, storage and commun 4% 15% 14% 11% 13% 9% 5%Electricity, gas and water 2% 3% 1% 1% 1% 2% 2%Unspecified 12% 6% 5% 6% 7% 8% 11%TOTAL 100% 100% 100% 100% 100% 100% 100%  

Figure 11: Number of registered employees by nationalities and sectors of occupation 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Rwandan Burundian Kenyan Tanzanian Uganda Other

% in the sector

Nationalities

Number of registered employees by nationalities and sectors of occupation

Unspecified

Construction and public work

Wholesale and retail trade, restaurants & hotels

Industrial manufacturing

Extracting industries

Banking, assurances, real estate, financial services

Agriculture, hunting, forestry & fishing

Transport, storage and communications

Electricity, gas and water

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6.2. Comparison of growing sectors and available skills   While joining WDA and SSFR data, the economic sectors of SSFR were used to make the matching possible. In addition, TVET professions were matched with sectors of SSFR. This was done by qualified expert based on where TVET graduates have the highest possibility to enter in a specific sector. 

Table 2: Participants of TVET Courses matched to sectors (2010) 

Year and Gender Year 1 boys Year 1 girls Year 2 boys Year 2 girls Year 3 boys Year 3 girls Summe ratio TVET10Suppliers, collection & social  and human ressource services 647 866 422 536 379 253 3103 21%Agriculture, hunting, forestry & fishing 550 397 530 224 653 142 2496 17%Banking, assurances, real  estate, financial  services 466 578 527 711 726 602 3610 25%

Wholesale and retail  trade, restaurants & hotels 109 276 33 161 0 8 587 4%

Construction and public work 1524 227 965 163 553 58 3490 24%Industrial  manufacturing 87 8 29 0 0 0 124 1%Extracting industries 0 0 0 0 0 0 0 0%

Transport, storage and communications 160 158 195 126 136 131 906 6%Electricity, gas  and water 98 13 59 2 0 0 172 1%

14488  

The table above shows the distribution of TVET participants per economic sector. Banking, insurances, real estate, financial services and construction and public work are the leading subjects, followed by collection & social and human resource services and agriculture, hunting, forestry & fishing. Banking, insurances, real estate, financial services was identified above as a sector growing constantly from 2003 to today. A large number of skilled workers will be needed here. Wholesale and retail trade, restaurants and hotels seems to be underrepresented in public schools compared to the growth identified above.

Table 3: Distribution of formal employees and of TVET participants per economic sector 

Employee registerd since 2002 by nationality/sector Burundian Kenyan Rwandan Tanzanian Uganda Other TOTAL ratio TVET10Suppliers, collection & social and human ressource servi 56,3% 43,3% 55,0% 68,1% 61,5% 67,2% 56,2% 21,4%Agriculture, hunting, forestry & fishing 1,4% 0,7% 2,7% 1,0% 1,1% 0,9% 2,5% 17,2%Banking, assurances, real estate, financial services 10,9% 12,4% 3,6% 3,7% 9,2% 6,5% 4,3% 24,9%

Wholesale and retail trade, restaurants & hotels 11,7% 20,0% 8,2% 9,3% 11,4% 9,3% 8,6% 4,1%

Construction and public work 5,2% 8,7% 12,8% 7,8% 4,7% 5,9% 11,7% 24,1%Industrial manufacturing 3,8% 2,9% 5,7% 4,1% 3,6% 3,1% 5,4% 0,9%Extracting industries 0,3% 0,1% 3,9% 0,3% 0,3% 0,8% 3,4% 0,0%

Transport, storage and communications 6,4% 8,1% 1,9% 3,4% 5,0% 3,0% 2,3% 6,3%Electricity, gas and water 1,4% 0,7% 0,8% 0,4% 0,6% 0,7% 0,8% 1,2%Unspecified 2,5% 3,0% 5,3% 1,8% 2,6% 2,5% 4,9%  

The table above shows the distribution of formal employees (second last column) and of TVET participants (last column) per economic sector. “Suppliers, collection & social and human resource services” holds more than a half of all formal employees but only the fifth part of TVET enrolment. Although only the twenty five percent (25%) of employees worked in banking, assurances, real estate, financial services, a lot of workers were from outside Rwanda. TVET education may be able to fill this domestic skills gap. The percentage of foreign workers in the second constantly growing sector, wholesale and retail trade, restaurants and hotels, also is significant high, but public TVET education seems not to be able to fill this in the near future.

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6.3. General influences regarding skills  The global climate change affects several fields and causes chain reactions as all living areas are connected.

The Intergovernmental Panel on Climate Change, where Rwanda is a member, predicts8 an increase of droughts for East Africa. Although scientists, due to different variables and models, still are in discussion about the percentages, the situation must be faced know.

An increase of droughts will of course also affect agriculture and new seeds will be needed. Although the international community can support this procedure, domestic farmers' knowledge is most important here. They already know drought-resistant seeds, like sorghum, that can be a base for developing new productive varieties. This will keep Rwanda's agriculture independent of monopolist seed manufacturers and their often infertile hybrid species or patent protected seeds. It is important to have the freedom to improve seed and replant one’s own harvest.

New knowledge must be spread in agriculture (e. g. by PASNVA, the national agricultural extension support project). To keep information in the villages, a printed form assists multipliers in a sustainable way. But this requires a high literacy rate among the farmers, especially among those who live under the poverty line.

Of the agricultural population aged 15 years and above 29.3% can’t read nor write, 6.9% can read only, 63.7% can read and write9.

From the adult population in 2005 over 23% neither attended school nor literacy courses. While the percentage of women is double of the men, 13,26 % of all adults were reached by literacy programs. To make them able to get part of the information age and benefit from the advantages this rate must be increased dramatically.

All Rwandans 18 years and older in 2005 Table 4: Literacy distribution in 2005

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Attended school Never attended schoolFemale Male Total Female Male Total

Attended literacy courses 3.39% 2.63% 6.02% 4.31% 2.93% 7.24%Never attend literacy courses 32.74% 30.94% 63.68% 15.67% 7.38% 23.06%

Total 36.13% 33.57% 69.70% 19.98% 10.32% 30.30%Source: EICV 2

Of course it is much easier to make people literate in school when they are children. Thus the enrolment rates in primary schools are of interest.

 8 Boko, M., I. Niang, A. Nyong, C. Vogel, A. Githeko, M. Medany, B. Osman-Elasha, R. Tabo and P. Yanda, 2007: Africa.

Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge UK, 433-467.

9 National Institute of Statistics of Rwanda (2008). NATIONAL AGRICULTURAL SURVEY 2008

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Table 5: Net enrolment rates in primary schools by gender and year10

2003-2004 2005 2006Girls 94.5 % 94.7 % 97.0 %Boys 91.5 % 92.2 % 92.7 %Total 93,00% 93.5 % 94.8 %

Source: MINUEDUC

The above table shows that the enrolment rate of girls was increasing strongly while those of boys grew slowly. If men usually are heads of households this rate must be increased, as they will be the main decision makers in future rural and farming households.

If a high literacy rate can be reached, and this means attracting all children to at least primary school and minimize the dropout rate, several 2020 aims can be met at once: Education, reduction of poverty, extension of farming.

Globalization and climate change also mean that plants will be affected by diseases that were unknown in this region before. Institutions like ISAR (Institut des Sciences Agronomiques du Rwanda) will have to deal with these challenges.

It is not by chance that one of the main issues of IUCEA is agriculture11. Initiatives like BIO-EARN12 show that this purpose is understood very well in East Africa.

Nevertheless an increase of education on all levels (Primary, Secondary, TVET and Tertiary) in the several fields of agriculture is indispensable.

Table 6: Crops production in tons by year13 

Crop 2006 2007 Crop 2006 2007

Sorghum 187,380 166,769 Bananas 2,653,548 2,689,176

Maize 91,813 102,447 Irish potatoes 1,136,489 967,283

Wheat 19,548 24,633 Sweet potatoes 777,033 845,133

Rice 62,932 61,701 Yam 125,387 151,513

Beans 283,387 331,107 Cassava 742,525 776,943

Peas 14,199 19,656 Vegetables 518,539 534,428

Peanuts 9,021 9,899 Legumes & Fruits 339,472 369,005

Soybean 27,137 44,165 Source: MINAGRI

The above table shows the main crops production for two years. The table below shows the main exported agricultural goods.

                                                            10 National Institute of Statistics of Rwanda (NISR) (2008). Rwanda Development Indicators 2006 11 Core activities are: Education, Agriculture and Forestry, Environmental Management, Arts and Social Sciences,

Business Studies, ICT and Library Sciences, Engineering and Technology, Health Sciences, Gender and Youth, HIV and AIDS, Physical and Biological Sciences, Veterinary/Animal Science

12http://www.bio-earn.org/ 13National Institute of Statistics of Rwanda (NISR) (2008). Rwanda Development Indicators 2006

Table 7: Exportation

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Export good14 Quantity in tons 2005

Coffee 18,399

Tea 15,481

Desert Banana 22

Passion Fruit 11

Roses 9.5

French Beans 100

Snap & Snow peas 55

Spices 25Source: RRA

Proposal: Researches must show if there are seeds among this that are a base for improving seeds in means of drought-resistance. Studies should also be made to show which plants are most affected by climate change so that one can think about alternatives for export or domestic nutrition. Water management is an issue in Rwanda since agriculture on hills makes it difficult to use the water of rivers and brooks in the valleys. Climate change makes this issue even more urgent. The list below shows how farmland is watered nowadays and their proportional15. - Rain 98.0% - Irrigation 0.6%, - Drainage 1.0% - Watering 0.4% Table 8: Enrolment in public and governmental aided schools in the field of Agriculture and related (2010)  

Year 1 boys 550Year 1 girls 397Year 1 total 947Year 2 boys 530Year 2 girls 224Year 2 total 754Year 3 boys 653Year 3 girls 142Year 3 total 795Source: Workforce Development Authority The above table shows that in the next three years 2496 graduates in agriculture and related subjects can be expected from governmental and governmental aided schools. Considering

                                                            14 National Institute of Statistics of Rwanda (NISR) (2008) RWANDA IN STATISTICS AND FIGURES 2008 15     National Institute of Statistics of Rwanda (2008). NATIONAL AGRICULTURAL SURVEY 2008 

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that agriculture still is the largest (informal) employing sector it is evident that most farms are run by uneducated people. How will they take up the gauntlet of climate change?

Proposals:

TVET Curricula in the most sensitive areas (that are related to agriculture, health and renewable energy) shall be harmonized and updated accordingly.

As WDA collects data only from public and governmental aided schools, the collection and analysis software that LMIS is developing, should also be spread among the private TVET providers.

Institutes of higher education shall be asked to establish subjects that correspond with the challenges in engineering, biology, agriculture and so on.

As no reliable data about students in these fields is available, LMIS will also provide a collection and analysis software for institutes of higher education.

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Chapter 7:  CONCLUSION 

The behavior of the formal sector and the public TVET education must be followed up in future. LMIS decided to work on this data, because it is reliable and the TVET education as well as formal employment can be influenced by government through investment and other efforts.

The SSFR data made it possible to monitor the development of employment in the formal sector over 8 years. LMIS will follow the same step with its standing system.

WDA’s TVET administrative data will allow having views on impact of TVET courses on the labour market over time as the LMIS system will hold all related data that is updated regularly.

The combination of all data will make possible to analyze periodically the relation between TVET education and the formal employment in the economic sectors.

This discussion paper is the first pilot to combine TVET data and SSFR formal employment data. Further discussions with all stakeholders, especially about categorization of economic sectors and matching of professionals to the economic sectors are urgently needed.

1. Appendix 

SSFR administrative data Development of Employees 2002 to 2009 per Economic Sector (2002 = 100 %)

2002 2003 2004 2005 2006 2007 2008 2009

96.47 73.16 76.96 94.55 98.85 79.42 88.95

107.12 113.69 124.70 124.41 111.82 120.45 140.33

99.20 113.52 140.42 145.94 157.57 172.91 201.21

142.44 228.82 201.77 209.31 227.92 180.13 179.67

107.82 119.16 119.07 122.18 121.66 133.90 128.53

109.28 115.48 116.76 124.61 112.77 158.95 177.55

109.78 123.57 136.86 151.62 168.46 189.71 215.06

109.65 121.98 123.33 117.67 127.70 154.93 148.58

98.27 115.60 94.94 123.34 174.87 188.13 121.75Unspecified 118.48 139.13 230.43 1305.98 3028.80 9344.57 22895.11

Suppliers, collection& social and human ressource services 124,587 ↔ 100%Agriculture, hunting,forestry & fishing 2,370 ↔ 100%Banking, assurances, real estate,financial services 4,492 ↔ 100%Construction and public work 3,434 ↔ 100%Industrial manufacturing 5,191 ↔ 100%Extracting industries 1,808 ↔ 100%Wholesale and retail trade, restaurants & hotels 4,930 ↔ 100%Transport, storage and communications 1,814 ↔ 100%Electricity, gas and water 1,072 ↔ 100%

46 ↔ 100%

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Development of Enterprises 2002 to 2009 per Economic Sector (2002 = 100 %)2002 2003 2004 2005 2006 2007 2008 2009

107,32 110,77 116,71 119,37 126,87 157,65 168,56

115,46 118,3 122,08 135,65 154,89 193,38 229,97

99,8 108,25 120,53 128,68 147,45 188,7 165,32

126,57 157,43 199,14 230 248,43 257 231,71

107,74 122,61 124,85 125,66 144,81 170,67 173,93

100,4 110 107,4 102,2 66 75 61,4

111,61 132,84 141,74 151,08 170,59 205,75 249,86

113,39 125,52 125,52 136,4 183,26 246,03 264,02

105,51 114,17 134,65 139,37 106,3 138,58 128,35Unspecified 151,35 227,03 321,62 859,46 2478,38 5381,08 8591,89

Suppliers, collection & social and human ressource services 2166 ↔ 100%Agriculture, hunting, forestry & fishing 79 ↔ 100%Banking, assurances, real estate, financial services 255 ↔ 100%Construction and public work 175 ↔ 100%Industrial manufacturing 123 ↔ 100%Extracting industries 125 ↔ 100%Wholesale and retail trade, restaurants & hotels 452 ↔ 100%Transport, storage and communications 60 ↔ 100%Electricity, gas and water 32 ↔ 100%

9 ↔ 100%

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