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Higher education, human capital and labour market segmentation in the Sudan

42 Higher education, human capital and labour market segmentation in the Sudan

Bikas C . Sanyal and Jan Versluis

П Е Р Occasional Papers

The studies in this series include papers contributed by the Institute's staff, visiting fellows, interns and consultants. S o m e of the studies have originally been prepared as part of the training programme of the Institute; others have previously appeared as working papers for the Institute's seminars and symposia. All of them, in the Institute's views, are of sufficient interest to merit being re-issued and distributed on a wider scale.

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Printed in France by the International Institute for Educational Planning 7-9, rue Eugène-Delacroix, 75016 Paris April 197 6

CONTENTS

Preface 7

Introduction 9

Some background information about the labour market and the

higher education system in the Sudan 14

Sources and method of collection of data 15

Some characteristics of the graduates surveyed 15

Some characteristics of the operation of the labour market 16

The earnings functions 17

Conclusions for policy 21

Appendixes 23

Appendix A 23

Appendix В 29

PREFACE

This paper is a joint product of the International Institute: for Educational Planning and the International

Labour Organization within the framework of the П Е Р Research Project on 'Employment of

Graduates and Admission Policy1 and the I L O - W E P Research Programme on 'Education and

Employment'. As a result, it is being issued both in the form of an H E P Occasional Paper and a

W E P Working Paper.

There is a particularly close and important linkage between the planning of education and

the understanding of the problems of employment, and it is therefore most appropriate that the HEP

and the I L O - W E P have begun to co-operate in this important area. This paper as well as a number

of other joint efforts bear witness to the usefulness of this co-operation.

Hans N . Weiler Director, HEP

7

I. I N T R O D U C T I O N

Before the 1950s, economists were attributing the reasons for disparities in economic well-

being among different countries and different families within a country primarily to differences

in the amount of physical capital possessed by them. The people having more physical capital

had higher incomes. In the 1950s, and more so in the 1960s, it became evident, especially in

the free-market economies, that factors other than physical resources played an important

role in income growth. Such factors are less tangible, like knowledge possessed by individuals,

families or countries. The human capital theory was therefore developed, emphasizing such

intangible resources as education to explain differences in income among people. The money

rates of return to different levels of education were estimated. Formulation of the theory of

investment in human capital was undertaken by several economists. It was believed that the

analysis of human investment could offer ' . . . a unified explanation of a wide range of empirical

phenomena which had either been given ad hoc interpretation or had baffled investigators. 1_/

The principal forms of direct investment in the productivity and well-being were considered to

be improvement in health and learning.

Investment in education was believed to expand and extend knowledge, leading to in

creased productivity and improved health. In theory, the 'benefits' of education were assumed

to be anything that increases utility for the society. Some of these are: increased production

possibilities, such as increased labour productivity; reduced costs thereby making resources

available for more productive uses like increased employment opportunities and which may in

turn release resources from law enforcement by reducing crime rates; or, increased welfare

possibilities, such as development of public spiritedness or social consciousness.

The benefits of education have been classified by Weisbrod _2/ in five forms:

(i ) Financial return.

(ii) Financial option return, involving the value of the opportunity to obtain still further education.

(iii) The non-monetary opportunity options, involving the broadened individual employment choices which education permits.

(iv) The opportunities for 'hedging' against the vicissitudes of

technological change (the increased ability to adjust to changing job opportunities).

(v ) The non-market benefits, such as protection against deception by someone.

1_/ Becker, Gary S. Investment in H u m a n Capital: A Theoretical Analysis, in Journal of Political Economy, Vol. L X X , N o . 5, Part 2, Chicago, 111., October 1962.

2_l Weisbrod, B . A . , Education and Investment in H u m a n Capital in Journal of Political Economy, Vol. L X X , N o . 5, Part 2, Chicago, 111., October 1962, pp. 108-109.

9


There is one more kind of benefit of education, which is external to the student, such as

benefits to family and relatives, benefits to the work environment and especially working partners,

and benefits to the society in general - a residual category of benefits.

The human capital theory of educational planning involves the measurement of the returns

of investment made in education. Measurement of such returns is expressed as the rate of re

turn which consists of calculating separately the social and/or private costs of education, estimating

the discounted social and/or private benefits of education, and comparing the two as a guide to

verify the economic viability of an educational project. This brings in the task of dealing with costs

of education. Social costs of education would involve both direct costs, such as teachers' salaries,

current expenditure on goods and services, expenditure on books, etc., and imputed rent, and

indirect costs, being the earnings foregone as a measure of foregone production. In calculating

the private costs of education, only that part incurred by the student is considered, namely the

fees - adjusted for scholarships, if any - books, etc., and the earnings foregone. A complete

picture of the costs of education would include all foregone opportunities whether or not reflected

by actual expenditures. In the estimation of costs, only the part by which the cost is higher for

students than it would have been if they were not in school is to be charged for the education.

In the process of applying the human capital theory, costs and benefits are taken as

' . . . a stream of expenditures or returns spread over the life of an individual while he is being

educated or afterwards when he is working as a contributor to the production of the economy.

Rate of return calculations initially estimate the costs or earnings over an individual's lifetime

and then, either discount the stream using conventional discounting techniques to obtain the

present value of costs and benefits, or calculate an internal rate of return, defined as that rate

of return for which discounted costs just equal discounted benefits over the individual's life1. 1_/

The widest application of the human capital theory for the developing countries in the field of

educational planning has been through the use of the rate of return analysis, or the cost/benefit

analysis as it is sometimes called. In the theoretical analysis, even G . S . Becker was, however,

not quite sure of its applicability, as he says 'The next few years should provide much stronger

evidence on whether the recent emphasis placed on this concept is just another fad or a development

of great and lasting importance. "_2_/ During the decade which followed, a large number of studies

appeared in the field of educational planning where this theory was applied. 3j

The human capital theory, as developed by the planners, did attempt to take into con

sideration all the detailed elements of benefits or costs but, in practice, data were lacking.

Some aspects of the benefits, namely the non-economic benefits, could not even be quantified.

So, a large number of objections were raised against the applicability of the rate of return

technique to education, especially in developing countries, and these were:

(i ) Earnings differentials, assumed to be purely due to the

1_/ Jolly, A . R . and Colclough, C , African Manpower Plans: A n Evaluation, ILR, Vol. 106, Nos . 2-3, p. 243.

2_/ Becker, Gary S . , op. cit. 3_/ See Psacharopoulos, G . , Returns to Education; A n international

comparison by G . Psacharopoulos assisted by Keith Hinchliffe, Elsevier, Amsterdam, 1973.

10

Introduction

educational differential in the technique, were not so. Natural ability,, motivation, social background, sex, etc., contribute to such differences.

(ii ) Imperfections in the labour market do not allow the differences in earnings to be purely due to productivity, so that earnings do not measure accurately the financial return of education.

(iii) Earnings differentials have not been able to take into account any of the other benefits listed above, except for financial return alone.

(iv ) Adjustment for unemployment or waiting period for a job of an educated worker is very difficult to measure precisely, especially in a developing country.

(v ) Age-earnings profiles, drawn from cross-section data and providing the basis for rate of return calculations, reflect present and past demand and supply conditions, whereas it is future demand and supply that concern the planner reducing much of its reliability.

(vi ) The rate of return analysis has been concerned with the direction of marginal quantitative changes in the existing system. Guidance on the direction and size of non-marginal and qualitative changes in education is not available from this approach.

(vii) Estimation of the earnings foregone, as an element of cost, is very difficult to make in the context of a developing country which experiences large-scale unemployment and underemployment.

Last, but not least, the basic philosophy behind the human capital approach that ' . . . the supply

of human capital is the simple aggregation of the individual choices in embarking upon a course

of investment in personal development exhibiting a particular subjective rate of time preference

and faced with an array of jobs having specific pecuniary and non-pecuniary attractions and

requiring certain skills' ._l/ is being challenged, even in the United States of America. A n analysis

of the dynamics of personal development and of the educational system in the USA shows a sub

stantially different picture and it is much more complex.

According to these challenges, schools indeed 'produce' better workers, but do so, not

by the education that they offer, but primarily through the 'structural correspondence of the social

relations of education with those of capitalist production' 2j, whereas the social relations have

nothing to do with individual choices. According to these authors, the human capital theory has,

of course, made some fundamental improvements on the neo-classical economics. These are in

extending the Ricardian and Marxian idea of 'treating labour as a produced means of production whose

characteristics depend on the total configuration of economic forces', rejecting the simplistic

assumption of homogeneous labour, and centering attention on the different types of labour and in

bringing basic social institutions such as schools and family into the domain of economic analysis.

JL/ Bowles, S. and Gintis, H . , in American Economic Review, Vol. 65, N o . 2, 1975, p. 77.

2/ Ibid.

11


But, they believe that unfortunately 'labour' disappeared as a fundamental explanatory category and

was absorbed into a concept of capital, ignoring completely labour's special character. Every

worker, according to the human capitalists, is now a capitalist. They reject the human capital

theory as 'nothing more than a black box theory of both the firm and the school, . . . forced to

offer explanations which are either superficial (supply and demand) or misleading (the interaction

of tastes, technologies and abilities)', and 'as a poor science for understanding either the workings

of the capitalist economy or the way towards an economic order more conducive to human happi

ness1. _1_/ These challengers believe that: (a) investment in education may increase the 'labour

power' 2_/ of the individual, either through increasing skills and productive capacities or through

supplying credentials which enhance supervisory authority, (b) schooling makes the employer

extract labour from a worker with given labour power more easily by generating or selecting in

dividual motivational patterns more compatible with the class-based power structure and the

incentive mechanisms of the enterprise; (c) the segmentation of workers by income and status

characteristics inhibits the formation of coalition of workers capable of facing the existing

power structure. A n educated worker is, therefore, more valuable to the employer to retain the

status quo.

The theory of labour market segmentation assumes that groups of workers or classes

of people face objectively different labour market situations which systematically condition their

'tastes' and restrict their range of effective choices. The behaviour of these groups or classes

then conditions the subsequent development of technology and job structures. The segmentation

theory attempts to explain the development of the institutions themselves as the result of inter

actions of groups or classes of individuals with objectively different interests determined by

prior developments of the institutions. The theory, therefore, claims that the labour market

is fragmented into persisting groups with their own characteristics with different life patterns

which emerge not from individual choice or individualized employer evaluation but from the

structure of the labour market for particular sets of jobs. Each segment of the labour market

is rewarded according to a certain scale, not because of marginal productivity but for political

and social reasons. Earnings distributions will not, according to the theory, change with

the change in the distribution of education and other personal characteristics without changes

affecting the political power of the various segments. The segmentation is based on criteria

for hiring and advancements, supervisory procedures, working conditions, and wage levels.

'The "primary independent" segment includes jobs requiring creative, s elf-initiating action

on the part of the worker; the "primary subordinate" segment includes jobs requiring con

formity to externally imposed norms (in contrast to internalization of norms required by

primary independent jobs); and the "secondary" segment comprises jobs requiring the least

on-the-job training, the min imum of skills and response to simple direct orders. ' 3_/

1_/ Bowles, S. and Gintis, H . , op. cit. 2/ Ibid. 3_/ Carnoy, M . , Can Educational Policy Equalize Income

Distribution in Latin America? , World Employment Programme, Research Working Papers, W E P 2 - 1 8 / W P 6 , ILO, Geneva, 1975.

12

Introduction

To the potential users of these theories, i. e. the human capital and labour market seg

mentation, especially in the context of a developing country, problems of measurement are crucial.

W e have already indicated the problems for the human capital theory, and the labour market seg

mentation theory also poses a list of serious problems in respect of quantification and measurement.

The basic problem; is to identify the different segments of the labour market, as defined above,

from the job description of workers and employees, and a description of the operation of the labour

market. Identification of the groups with economic and political power is also a difficult task and

can be very subjective. The international standard classification of occupation, according to the

segmentation theorists, may also be faulty because it may also be used to perpetuate the social

power structure.

Some characteristics of social systems are not measurable and therefore statistical

analysis becomes a complex task. Fortunately, recent advances in application of statistical

techniques to social sciences help us to confront this problem. These techniques use d u m m y

variables for characteristics which cannot be measured quantitatively. A pre-analysis of corre

lation or degree of association also helps us to use one proxy variable for two highly inter-

correlated or associated variables.

W e also assume that a generalized model, which will combine both the human capital

theory and the theory of labour market segmentation, can be more applicable in the context of a

developing country on which precise information on the actual operation of the socio-political

structure is not known. This model will not accept the capitalist theory of social operation as such,

nor will it accept the Marxian theory, but will put the actual operation of the education system and

labour market to test. To us, it appears that both these theories are constantly under revision

and it is extremely difficult to identify a country's social system precisely by following a particular

model of development. Whatever model for development a country accepts, it adapts the model

according to its socio-economic-cultural environment. It is with these ideas in mind that we wish

to test the applicability of both the human capital theory and the theory of labour market segmen

tation in the case of the Democratic Republic of the Sudan. In our analysis 1_/, we restrict ourselves

only to the system of the post-secondary education and the labour market that it is 'supposed' to

serve. This means that we are concerned only with the two segments of the labour market, i. e.

the primary independent segment and the primary subordinate segment, as defined above. W e

shall, therefore, restrict ourselves to the investigation of any characteristics which are either

of human capital type or of labour market segmentation type and that have any influence on the

earnings. W e are also restricting ourselves to the analysis of the effects of school on earnings

for the post-secondary education alone, a duration varying from one to eight years.

l_l The authors are indebted to M r , David Dviry for his contributions to the data processing phase of this paper.

13


II. S O M E B A C K G R O U N D INFORMATION A B O U T T H E L A B O U R - M A R K E T . A N D T H E HIGHER EDUCATION SYSTEM IN T H E SUDAN

The Sudan's primary segment of the labour market is dominated by the Government sector, with

84 per cent of the people being employed in this sector. Another 7 per cent are employed by the

public enterprises. 1_/ So, it appears comparatively easy for the government to regulate the

operation of its primary segment of the labour market. Women, however, have a very insignifi

cant role in this segment, occupying only 5 per cent of the positions. The majority of the primary

segment workers is employed.in the Services sector (68 per cent)̂ followed by the Agriculture

sector (13 per cent).

The higher education system in the Sudan had produced, as of 1973, approximately

15 000 graduates, 60 per cent of whom had an arts-based degree. In 1973-74, the complex of

the system included three universities and 18 higher education institutions and colleges, under

the Ministry of Education, and 17 other post-secondary institutions under the other ministries.

The control of the national authorities over the system of higher education, including studies

abroad, is limited to the extent that only 39 per cent of the student population in 1973-74 was under

the direct control of the Sudanese authorities - the University of Cairo in Khartoum was at that

time under the control of the Senate of the University of Cairo in the A R E and a significant

number of Sudanese (more than 4 000) were studying abroad. Under these circumstances, it is

difficult to have a national policy of higher education to meet the social and economic needs of

the country. The whole complex of higher education is under a National Council of Higher Edu

cation, which attempts to relate the development of higher education as much as possible to the

national priorities.

In 197 3-74, only one out of eight students was female. The percentage of arts-based

students admitted to the institutions of higher education reduced from 66 per cent in 1968-69 to 53

per cent in 1973-74. There is a wide disparity in the participation in higher education among

different regions; the two provinces of the South, i, e. Bahr-El-Ghazal and Upper Nile, being

the least privileged and the Northern province and Khartoum province having the largest parti

cipation in comparison with their population. All the institutions of higher education, except one,

are located in the Greater Khartoum area.

With the above background information on the system of higher education and the labour

market of the Sudan, we can proceed to discuss the basis of the role of the human capital theory and

labour market segmentation characteristics in the distribution of earnings in the Sudan.

1_/ See Sanyal, B . C . and Yacoub, ElSammaniA., Higher Education and Employment of Graduates in the Sudan, H E P , Paris, 1975, p.121.

14

S o m e characteristics of the graduates surveyed

III. SOURCES A N D M E T H O D OF COLLECTION OF D A T A

As a part of the IIEP Research Project on 'Employment of Graduates and Admission Policy in the

Higher Education System in the Sudan1, 376 graduates of the post-secondary education system were

surveyed in respect of their professions, employment sectors, specializations, duration of studies,

age, earnings, type of job, waiting period to get their first job, social and economic background,

defined by sex, father's occupation and h o m e region.

The sample of graduates was taken from the graduate population who had obtained their

degree/diploma since 1968. The sample size of the graduates was fixed at 410, and 37 6 question

naires were returned - a response rate of 94 per cent. The sampling ratio was approximately 4

per cent of the total graduate population. The method of sampling was stratified random sampling

and stratified by employers - government departments, public sector corporations, institutions of

education and private sector firms. The survey was administered by the National Council for R e

search in the Sudan.

IV. S O M E C H A R A C T E R I S T I C S O F T H E G R A D U A T E S S U R V E Y E D

The largest number of graduates surveyed (75 per cent) had an arts-based specialization, which

corresponds to the earlier development of higher education in the country. About one out of five

has a present occupation as social scientist, one out of four serves the manufacturing, mining

and industrial sector. A slightly lesser proportion serves the Ministry of Education or institutions

of education. One out of every five of the graduates comes from a peasant background, and the

majority consider their home region to be in the Greater Khartoum area, which is also the lo

cation of most of the employing units. T w o out of five have an undergraduate degree, one out of

five has a graduate diploma and one out of six has a professional degree such as Engineering,

Agriculture, Medicine or L a w . Only about 5 per cent have a post-graduate degree.

The mean annual income at the time of first employment was S£. 513 (the median was

S£. 514) and the income at the time of the survey was S£. 774 (the median was S£. 696) - an increase

of nearly 51 per cent during the period of working life. The average age of the graduates surveyed

was 29 years, with a standard deviation of 4. 5 years. The proportion of female graduates was 3. 5

per cent, which was very close to the population proportion mentioned earlier. The average du

ration of studies of the graduate was 4 . 39 years with a standard deviation of 1. 03 years. The

distribution of present income is positively skewed with a skewness coefficient of 2. 383. The

distribution of initial income is more or less symmetrical (skewness coefficient is 0. 172). Only

two graduates replied that they were working on a part-time basis. Although the labour market

of the Sudan shows a surplus of the primary segment workers, 78 per cent of the graduates found

their first job within six months after graduation, 14 per cent had to wait for less than a year, and

7 per cent had to wait for more than a year.

15


V . S O M E CHARACTERISTICS OF T H E OPERATION OF THE LABOUR MARKET

In this section we present some findings which appeared to be interesting in order to understand

the functioning of the labour market. Tables are presented only for those characteristics where the

data strongly suggested some degree of association or some striking results. The information on the

duration of unemployment experienced by educated people in less developed countries is very frag

mentary. In the Sudan, the waiting period for graduates to get their first job was estimated from

the survey, as mentioned above. When classified by home region, those graduates with a special

ization in social sciences and their home outside the Greater Khartoum area have to wait longer for

a job (see Appendix Table A . 1). This shows that region of residence has an effect on the waiting

period to get a job for social scientists. It is interesting to note that sex does not have any influence

on the waiting period. The data suggest that graduates in the humanities field, whose father had an

occupation as a nomad or peasant, had to wait also a longer period before getting a job (see Appendix

Table A . 2. ). This also leads us to fear that some of the graduates, whose parents are in the second

ary segment of the labour market, are less privileged.

When the initial earnings are cross-tabulated with the length of the waiting period, no

significant association is observed (Appendix Table A . 3.) . This would suggest that people who wait

longer in general would not get a higher or less well paid job. When analysed with respect to initial

earnings and specialization of the graduates, only those with specialization in commerce appear to

have a degree of association between the waiting period and the initial income. Those with higher

initial income have to wait for a shorter period. This shows that the graduates of commerce do not

wait for a higher initial income - they wait simply for a job. It m a y also mean that those who waited

for a longer period found that all the better jobs had been taken and they ended up with a lower paid

job (Appendix Table A . 4. ). It is also interesting to note that this does not occur in any other

specialization.

In the Sudan, sex, which is a segmentation variable, does not have any influence on the

initial earnings of graduates. The higher the duration of studies, the higher the initial income

(Appendix Table A . 5. ). This is also true for the growth of income for the year, but this will be

analysed in detail in Section VI. Duration of studies is a human capital variable giving the amount

of schooling received.

Specialization is also associated with the initial income (see Appendix Table A . 6. ). W e

are in slight confusion in classifying specialization as a human capital or a segmentation variable.

Insofar as specialization is associated with the duration of studies or the ability of a student (a

brighter student will go for medicine or engineering, a less bright one will go for liberal arts), it

can be regarded as a human capital variable. But, if we assume that family background, information

availability, etc. influence a student to go for a high paying specialization, then it has to be con

sidered as a segmentation variable.

Another striking result is that the father's occupation does not have any significant degree

of association with the initial income of the graduates, nor does the region of home residence. These

two variables are segmentation variables. Type of diploma, which is a human capital variable

associated to the duration of studies, has some degree of association with the initial income

(Appendix Table A . 7.).

16

The earnings functions

VI. T H E E A R N I N G S F U N C T I O N S

A s a first step in an attempt to explain earnings, a rather large number of variables was intro

duced. Several of the explanatory variables are of a qualitative rather than a quantitative nature:

sex, type of diploma, specialization, occupation, father's occupation, home address. For these

variables, dummies were introduced. In actual fact, only age and duration of studies are quanti

tative variables, the waiting period for the first job also being transformed into a d u m m y in the

first set of equations.

One first problem encountered is that occupation and secialization are grouped in such a

way that a very strong intercorrelation between the two is unavoidable. For that reason, alternative

equations have been tried using either occupations or specializations as the explanatory variables.

In addition, a special d u m m y variable is introduced which is put equal to zero when occupation and

specialization are the same, or when the occupation is teaching, otherwise - i.e., when the person

in question is working in a field clearly different from the one he was educated in - the d u m m y

takes the value 1.

T w o alternatives have been tried for the dependent variables: present earnings and log

present earnings.

A s shown in Appendix Tables B . 1 to B . 10, not much difference can be found between the

alternative specifications of the earnings function. Variables that appear to have a significant

coefficient in all the equations are age, short waiting period, father's occupation in civil service,

sector of activity in agriculture and specialization in humanities in one set, and occupation in

agriculture in the other version. Also, coefficients of determination are found to be rather similar,

around 60 per cent being explained in all cases. A s shown in those cases, where a stepwise re

gression procedure was used, the contribution of the variable 'age' to the determination coefficient

of 0. 60 was no less than 0.40, i. e. two-thirds of the explanation comes from one single variable.

The interpretation of this phenomenon could run two ways. One would be to say that

salary scales are largely determined by institutional factors; the other way of interpreting the

findings would be to say that experience - human capital type of variable - is the main determinant

of productivity and hence of earnings. It must be admitted, that whatever the interpretation the

result found is not very interesting and certainly not new. There is no reason, however, to stop the

analysis at this stage. Several further steps may be tried.

One way of describing a person's performance in his work is by his earnings at a given

moment in time. Another way is by looking at the development of his earnings over time. It m a y

be hypothesized that someone who does better in his job experiences a higher rate of growth of his

earnings, even though the difference in performance was not expressed in initial earnings.

There is some noise in the data when we want to use them for the purpose of determining

the rate of growth of earnings over time. The method proposed is to estimate the period of work

experience that a person has by his age minus the sum of his age when starting his higher education

and the dtiration of his studies. For convenience - and for lack of more detailed information - we

have assumed that each person begins his higher education at the same age, which for the Sudan would

be at 18 years. This gives us:

17'

Higher education, h u m a n capital and labour market segmentation in the Sudan

work experience = age - (18 + duration of studies)

where the term in brackets would estimate the age at which the person finished his studies. The

disturbing factor, which is not taken into consideration, is that the sample contains a number of

people who undertook their studies - or part of their studies - at a later age. 1_/ Implicitly, w e

are assuming that, say, 10 years of experience while having two years of higher education completed

plus five years while having four years completed.is equivalent to 15 years experience with four

years of education completed. The relatively small number of cases where this phenomenon seems

to have occurred m a y be used as an argument for the simplifying assumption made«,

T w o estimates were m a d e of an equation relating the logarithm of the ratio between present

and initial earnings to work experience. First, an equation was used with a constant term leading

to the following result:

log (present earnings) - log (initial earnings) = - 0 .0200+ 0. 0278 x work experience ( i)

(0.0143) (0.0018)

R 2 = 0.4459, 294 obs.

A s the constant term was found not to be significant 2_/ an estimate was also m a d e of an equation

without a constant:

log (present earnings) - log (initial earnings) = 0. 0257 x work experience (ii)

(0.0010) R 2 = 0.4422, 294 obs.

Noticing that over 40 per cent of the development of earnings are conveniently explained by our

concept of work experience, w e m a y now try to find any variables that could contribute to an

explanation of the unexplained part. For that purpose, w e define a new dependent variable: log (present earnings) - log (initial earnings)

work experience

and run regressions on sets of variables similar to those of the earnings functions used above. _3_/

The results are shown in Appendix Tables B . 5 and B . 6.

One of the striking elements is the significance of the variable 'waiting period1 in the

equation with 'occupation' as a variable while it is not significant in the equation with 'special

ization'. The reason for this being that while waiting period is associated with specialization, it

is less so with occupation.

One aspect seems to be hidden in the coefficient of 'waiting period' that has been over

looked in the definition of 'work experience' and that stands in the way of a proper interpretation.

Persons who have passed through a longer waiting period have obviously a shorter work experience

than those who had a shorter waiting period. In order to m a k e sure that we are not mixing up two

different influences, a correction will have to be m a d e for the influence of the waiting period on the

1_/ Graduates have been selected who completed their higher education not m o r e than five years earlier, but 4 per cent of them appear to be over 40 years of age.

2 / The reader m a y notice that the tables with the earnings functions regressions do not specify the standard error of the constant term, the reason being that different programmes were used: SPSS for the earnings functions, Omnitab for the smaller regressions. Unfortunately the SPSS regression p rogramme does not specify the level of significance of the constant.

3j The only difference is that age is no longer among the explanatory variables.

18

The earnings functions

work experience. A problem is that the variable 'waiting period1 is not cardinal. In order to

transform it into such a variable (from n o w on to be indicated by P ) , two alternative ways have been

used as shown in Table 1. T h e basic idea of the transformation is that 'less than half a year' would

be either 'none' (Alternative 1) or three months (Alternative 2), etc.

Table 1. Transformation of 'waiting period' into a cardinal n u m b e r

Length of waiting period Alternative 1 Alternative 2

Less than half a year Between half a year and a year M o r e than one year

0 .5 1

0..25

0.75 1. 25

O u r n e w concept of years of experience would n o w be measured as:

work experience51 = age - (18 + duration of studies + P ) ;

using this concept, regressions have been run with the following results:

Alternative 1

log (present earnings) - log (initial earnings) =

- 0 . 0162 + 0 . 0279 x work experience31

(0.0142) (0.0018) (iii) R 2 = 0 .4536, 288 obs.

log (present earnings) - log (initial earnings) = 0.0261 x work experience51

(0.0011) (iv) R 2 = 0 .4511, 288 obs.

Alternative 2

log (present earnings) - log (initial earnings) = - 0. 0093 + 0. 0279 x work experience31

(0.0138) (0.0018) (v ) R 2 = 0.4536, 288 obs.

log (present earnings) - log (initial earnings) = 0.0268 x work experience31

(0.0010) (vi) R 2 = 0.4528, 288 obs.

The version (vi) is adopted to arrive at an analysis of the growth of earnings over time. For that

purpose, w e again define our variable:

log (present earnings) - log (initial earnings) work experience31

in which work experience is measured in the same way as in equations (v) and (vi). Again, we

run a series of regressions on a relatively large set of variables. The differences in specifi

cations of the model for the four alternative estimates presented in Appendix Tables B . 7 to B . 10

are again in 'occupation' versus 'specialization' and in the use of a d u m m y for 'waiting period' or

the variable P (second alternative).

First among the findings must be mentioned that also after the correction for the in

fluence of the waiting period on work experience, the variable 'waiting period before finding the

first employment1 appears to influence significantly the rate of growth of earnings over time.

19

i


In other words, the persons who experience a longer period of unemployment before finding their

first job tend to end up in jobs which do not have lower initial earnings _l/, but which show a lower

rate of growth of earnings.

A s it is apparent from earlier analysis _2/ that employers select candidates for a post

mainly according to academic performance, it may be assumed that the persons who have more

difficulty in finding a job, i. e. the persons who go through a longer waiting period, tend to be those

who have a less good academic record.

W e m a y now rephrase somewhat the conclusion from the findings of the regression analysis,

interpreting the rate of growth of earnings as determined by the performance of the individual rather

than as a characteristic of the post he occupies. Thus, we might conclude that persons with better

academic records tend to perform better in their work as measured by their earnings.

The first interpretation of the significance of the waiting period as a variable, using the

rate of growth of earnings as a characteristic of a job rather than of a person, might be seen as

incompatible with human capital theory. The second interpretation, however, would be in perfect

conformity with the human capital approach.

A second variable that is found to have a significant influence on the rate of growth of

earnings is the duration of studies. This is not the case in the equations using specialization as a

variable which is due to an association between specialization and our variable 'duration of studies'

(see pages 15 and 16).

It is clear, however, in the occupation type of equation, that the longer the period of

studies, the higher the rate of growth of earnings over time. This result would seem to point in the

direction of a human capital type of interpretation. Especially because of the fact that occupation as

a variable is not found to be of significant influence, the interpretation would seem to be justified

that longer education tends to have a positive influence on performance on the job. The significant

influence of certain types of specializations does not seem to invalidate such a statement because

of the association mentioned earlier.

None of the other variables is found to have any significant influence on the rate of growth

of earnings. Typically, these other variables are all of the type that would have led to a labour

market segmentation type of interpretation: sex, father's occupation, type of diploma, sector of

activity, occupational group. The conclusion would then seem to be justified that on the basis of

the available material no reason is found to reject human capital theory and to accept a segmentation

approach to the analysis of the labour market for higher educated manpower in the Sudan. The most

that can be said in favour of labour market segmentation theory is that some of the results obtained

could be seen as not to contradict either of the two approaches, depending on the way the result is

interpreted.

\j See page 16. 2_/ Sanyal, B . C . and Yacoub, El Sammani A . , op. cit., p. 176

20

VII. CONCLUSIONS F O R POLICY

1. Broadly speaking, there does not seem to be a great problem of graduate unemployment in

the Sudan, with four out of five graduates obtaining a job within six months after graduation. However,

for certain kinds of specialization, the problem does exist. The specializations in which it takes a

graduate a longer time to get a job are all the arts-based ones, and, in such cases, substitution bet

ween education and occupation occurs more frequently. The government is in the right direction in

controlling intake to arts-based fields of study. The restriction of admission to the arts faculties

of the University of Khartoum and the University of Cairo is also a corrective action.

Within certain groups of graduates, segmentation types of variables _l/ have been found to

influence either the waiting period or the initial earnings. These variables are 'region of home1

for social scientists, 'father's occupation1 for humanities graduates and possibly 'specialization' „

This m a y call for specific policy measures for equalizing opportunities for earnings.

2. It is apparent from the above analysis that in the Sudan age or experience plays a very

important role in determining earnings of graduates. In fact, the public sector salary scales of

graduates are based on the length of studies that a graduate has undergone - as has been noted else

where. _2_/ Again, as we have noted before, most of the graduates in the Sudan work for the govern

ment and for the public enterprises. Although ' . . . administratively determined government pay

scales are nevertheless a more or less accurate reflection of the relative scarcities of different

kinds of labour in an economy . . . ' , as has been argued by Mark Blaug 3_/, the situation in the Sudan

does not permit us to accept that argument because of the government-established securities for

unemployed graduates in the form of the 'Unemployed Reserve Fund' which existed until recently.

Earnings, therefore, do not give a measure of productivity - a basic assumption of the human

capital theory. The fixation of pay scales is an institutional phenomenon which m a y reflect seg

mentation of the labour market, but not much contribution can be made through the higher education

system to the cause of income redistribution without changing the institutional framework of re

muneration.

Those who do not have to wait for a job have their growth of earnings higher than those who

do have to wait, although the initial earnings m a y not differ significantly. In this connection, it may

be mentioned that academic performance being an important criterion for selection used by the e m

ployers, graduates with better academic performance get a job more quickly and it is these graduates

again who appear to perform better in the job as measured by their growth of earnings. This means

that selection by the criterion of academic performance is rational although the employers hold the

view that there is no correspondence between job performance and academic performance. 4/

1_/ Characteristics which divide the labour market into primary and secondary segments and define the economic and political power groups in the society.

2_/ Sanyal, B . C . and Yacoub, El Sammani A . , op. cit. , p. 91 3_/ Blaug, Mark 'Manpower Forecasting as a Technique not an Approach to

Planning' in Prospects, Vol. Ill, N o . 4, Winter, 1973 4_/ Sanyal, B . C . and Yacoub, El Sammani A . , op. cit. , p. 169

21

Appendix A

ARLE Л Л : SPECIALISATION SOCIAL SCIENCES

О S S ï Л В IJ L A T J O N О

W Г1 WAITING PERIOD TO GET 1ST JOB

P. Y OKI HONE ADDRESS <GKI ! - 0> : * r\- - , ; ; >\i * * •;',: * * * *

GKH

V/P

LESS

6 TO

MORE

COUNT ROW POT COL PCT TOT PCT

1. TOAN 6 MTNS

2C 12 ETI IS

3 t. THAN 1 YR

C O L U M N TOTAL

I ÏG IK

I - I -

I ï I I

- I -I I I

] „ ! _

I 1 1 ï

- I-

R E A 1 E R H A R "Kl M

0

3 2 64 t O 88*9 50, Я

4 40.0 11 Л

6 „ 3

о о

о о

о о

о

36 57 Л

OTHERS

I ].

1 1В ï 3 6 , 0 I 66 „7 I 2 R , 6

] 6 I 60 с 0 ï 22 Л 1 9 с. 5

I 3 I 1 0 0 . 0 I 11 « 1 I 4 , 8

2 7 42 с-9

о I

I I I Т 1

-I 1 I I I

-1 I I î I

~1

ROW TOTAL

50 7 9 .4

10 15,9

3 4 ,8

6 3 100,0

NUMBER CE MISSING ORSERVATIONS 12

TARLE A.2 : S PFC IALI SAT ION HUMANITIES

* * ' • ' • * * * * C R O S S T A R U L A T I O N WAITING PFRIOD TO GET 1ST JOB

O F . * * * * * *

FATHER'S OCCUP; * Л ф £ :̂ £ ф ¡j; -•;

* * * * * TiON :r r * Ф *

LESS

6 TO

COUNT ROW PCT COL PCT ТГЛ PCT

1. THAN b M THF,

i

12 KT HS

3 . THAN 1 YR

FOG

PEASANT OR КОМAD

2 .

9 2 5 . 0 5 2 . 9 14.1

« A 0 . 0 'i 7 . 1

1 2 . 5

0 0 . 0 0 . 0 0 . 0

MFR С НАMT

ч ,

5 1 3 . Ч 50 *0

7 . Р

2 1 0 . 0 2 0 . С

3.1

3 3 7 . 5 3 0 . 0

^ . 7

иг S К

WORKER /hc

6 1 6 . 7 Я 5 . 7

с ' . 4

0 0 , 0 0 . 0 0 . 0

1 1 2 . 5 1 4 . 3

1.6

CI V SRV OR CLERC

6 .

л 11.1 4 4 . ¿

6 . ?

4 20.0 44.4

6 . 3

1 1 2 . 5 11.1

1.6

SKILLED WORKER

7 .

c3

1 3 , 4 5 5 . 6

7 . 0

1 5 . 0

11 .1 1.6

3 3 7 . 5 3 3 . 3

¿ - . 7

OTHERS

8 .

7 1 4 . 4 5P . 3 1 0 . 9

5

2 5 . 0 4 1 . 7

7 . P

0 0 . 0 0 . 0

I 0 . 0

COLUMN TOTAL

17 26.6

10 7 10.9

9 14.1

9 14.1

12 ie,8

ROW TOTAL

56.:

20 31.3

12.5

64 100.0

NUi-'RfP. OF MISSING OBSERVATIONS =

23

Appendixes

Table А . З.

* * * * * * * * - . : Y 1С, I N I T I A L

C R O S S T A B U L A I I O N

BY WP * * * * * * * * * * OF * * * * * * * * * * * * * *

WAITING P f u n n TO OFT 1ST JOB * * * * * * * * * * * * * * * * * * * * * crur.'T i ROW PCT ILESS THA fc TO 1? МОРЕ ТЧД COL PCT II'.' 6 MTHS KTHS M 1 YP TOT PCT I Í.I 2.1 3.

I . I 6 1 0 1 0 I 100 .0 I 0 . 0 I O . C I 2 .3 1 0 . 0 I 0 .0 1 1. P I 0 . 0 I 0 . 0

2. I 15 I I I 0 I 9?.я i 6.3 i o.o I 5.7 I l.Q I 0.0 I 4.5 I C.3 ! 0.0

3 . I 20 I 5 1 0 I P O . O I 2 0 . 0 I O . C I 7 .5 I 9 .6 I 0 . 0 I 6 . 0 I 1.5 I 0 . 0

A . I PI I 20 I B I 74 .3 I 1Я .З I 7 .3 I 3 C . 6 I 33 .5 I 53 .3 I 24.'i I 6 .0 I 2 . 4

5 . I 4 Я I 1 7 I 6 I 6 7 . 6 I 2 3 . 9 I P . 5 I IP.1 I ?? .7 I 4 0 . 0 I 14 .5 I 5.1 I 1.8

6 . I 53 I 5 1 1 I R ° . P I P . 5 I 1.7 I 2 0 . 0 I 9 .6 I 6 . 7 I 16 .0 I 1.5 I 0 .3

7 . I 33 I <> I 0 I P " . 2 I 10.fi I 0 . 0 I 12 .5 I 7 .7 I 0 . 0 1 о .9 I 1.2 I 0 . 0

COLUMN TOTAL

POW TOTAL

109 32.8

15 3 32 4.5 100.0

WP Cni.iNT I

FOW PCT ILËSS Тнд 6 TO 12 MORE THA COL PCT F N 6 MTHS MTHS I; 1 YP ТГП PCT I I.]

Y IG

Я 0 0 - 8 9 с R .

Q .

I 6 I 1 0 0 . 0 ! 2 . 3 I 1 , R

I 3 I 1 0 0 . 0 I 1.1 I 0 . 9

0 0 . 0 0 . 0 0 . 0

0 0 . 0 0 . 0 0 . 0

0 0 . 0 0 . 0 0 . 0

0 0 . 0 0 . 0 0 . 0

6 l.p

3 0 . 9

CnLUHN TOTAL

265 79.p

NUMBER OF MISSING OBSERVATIONS =

ROW TOTAL

332 100.0

IC0N1INUED)

24

http://10.fi

Appendixes

TABLE A . 4 : SPECIALISATION C O M M E R C E

CROSS TABULATION OF

Y l G I N I T I A L INCni-11

BY WP WAITING PFRÏOD TO GP

л'Р

Y1G

2 00-

400-

500-

600-

700-

P. 0 0 -

о 0 0 -

2 9 9

-'-¡ 9 9

- 5 9 9

-69 9

-7 99

- П 9 9

- G C ) •-.)

C O U N T R0VÍ PCI CfiL PCT TOT PCT

?..

4 .

5 c

6 .

7 ,

R .

C)

С П 1 . 1 Т Г ' "I0Í Ai.

ï î 1. ir* I

~ J ~

I I I I

I

I Î Î

Ï

] T I

- I -

I

I J 1

I

1 T I

1 î 1 I 1

ï 1 I I

J

ESS 11-!Л 6 M TMS

1 г

0 ОсО ОоО 0 о 0

6 8 5 . 7 1 7 . 1 1 4 с 0

10 6 2 с 5 2 Я е 6 2 3 . ^

13 100 с 0

37« 1 ?0с2

4 1 0 0 . 0

1! , 4 9 с 3

1 3 С О «0

2 . 9 2Г3

1 1 0 0 . 0

2 , 9 ? * 3

'А 5

Я] «4

6 ТО 1 H T H S

1 • юс „о [ 1 2 с !5

2 . 3

1 1 4 „ 3

Í 1 2 . 5

2 „ 3

1 6 '2 I Г.

I 7 5 , 0 I 1 4 . 0

1 0 ï 0 , 0 I ОсО ï 0 е- 0

I 0 I О с О I 0 с 0 [ 0 . 0

1 0 ОсО 0 с, 0

1 О с О

I 0 I 0 , 0 I С . 0 I 0 • 0

в 1 г< 16

Г.; UI-Mi И» [íf f-]SSj;^¡; pp. S P P V / Ч ] O N S

25

Appendixes

Table A. 5

Ф * £ >;• * £ $ --!

INITIAL INCOME

C R O S S T A B U I. A T I 0 M

BY DOS >;.- ф Ф « *

DURATION OF STUDY <YFARS>

no;

Y1G

100-199

200-299

300--399

400-499

500--599

600-699

700-799

300-Р9Ч

900-999

COUNT I ROW PCT I COL PCT ] TOT PCT I

1. I

2.

3,

4 . •

5 .

6.

7.

Я«

О ,

COLUMN TOTAL

1 .1

0 I 0.0 I 0.0 I 0„C I

0 0„0 I 0.0 ] 0,0

0 0.0 0.0

- 0.0

1 1.0

a oo.o 0.3

0 0.0 0.0 0.0

0 0.0 0.0 0.0

I 0 I 0.0 I C.C I 0.0

I 0 I 0,0 1 0.0 I 0.0

I 0 I 0.0 I 0.0 I 0.0

1 0 .?

2. I

0 I 0.0 I 0,0 I 0.0 I

2 I 1 6.7 I 22.2 I 0,7

3 11.5 33.3 I .0

1 1.0

11.1 0.3

1 1.5

11.1 0.3

0 0.0 0.0

! 0.0

I 2 I 5.4 Î 22.2 I 3.7

I 0 1 0.0 1 0.0 I 0.0

I 0 1 0.0 I 0.0 I 0.0

p

3.0

3, I

1 I

3 6.7 I 4.2 I 0,3 I

3 25.0 ] 12.5 I 1.0

3 11 .5 I ?.. 5

1 .0

S P.2

ЭЗ. 3 2.6

6 9.2

25.0 2.0

2 3.8 Я.З 0.7

I 0 0.0

I 0.0 I 0.0

I 1 I 16.7 I л.2 I 0.3

I 0 I 0.0 I 0.0 I 0.0

24 7.9

4. '

5 I ЯЗ.?. I 3.3 I 1.6 I

6 1 50.0 I 3.9 1 2.0

14 5 3 » Я 9.2 4.6

53 54.6 34.6 17.4

36 55.4 23.5 11. P.

26 50.0 17.0

I Я.6

I 12 3 2.4

I 7, Я I 3 9

1 1 I 16.7 I 0. 7 3 0.3

I 0 I 0.0 I 0,0 I 0.0

153 50.3

5. )

0 I 0. 0 1 0.0 1 0.0 I

1 1 0.3 1.2 1 0.3

3 11.5 3.7 I .0

2 6 26.8 31.7 fi.6

17 26.2 2 0.7 5. 6

14 26.9 17.3 4 .6

I 15 40. 5

1 18.3 4.9

! 4-I 66.7 I 4.9 I 1.3

1 2 I £.6.7 I 2.4

I 0.7

P2 27.0

6. ]

0 I 0.0 I 0.0 3 0.0 I

0 I 0,0 3

0.0 3 0.0

0 0.0 0.0 0.0

6 6.2

2A.0 I 2.0

3

4.6 12.0 3.0

9 17.3 36.0

I 3.0

I 6 I 16,2 3 2 4.0 I ?.0

3 0 3 0.0 I 0.0 I 0.0

I 1 I 33.3 I 4. С I 0.3

25 P. 2

7.1

0 I 0.0 I 0.0 I 0.0 I

0 I 0.0 3 0.0 3 0.0

0 0.0 0.0 0. 0

2 2.1

33.3 0.7

2 3. 1

33.3 0.7

1 1.9

16.7 0.3

] 2.7

1 16.7 I 0.3

1 0 I 0.0 I 0.0 I 0.0

I 0 I 0.0 I 0.0 I 0,0

6 2.0

8. I

0 1 0.0 I 0.0 I 0.0 I

0 I 0.0 3 0.0 3 0.0

3 13.5 75,0 3 .0

0 0.0 0.0 0.0

о 0.0 0.0 0.0

0 0.0 0.0

I 0.0

I 1 2.7

1 25.0 1 0.3

I 0 3 0.0 I 0.0 3 0,0

I 0 3 0.0 I 0.0 I 0.0

4 1.3

ROW TOTAL

6 2.0

12 3.9

26 0.6

97 31 .9

6 5 21.4

52 17.1

I 37 12.2

I 6 I 2.0

T 3 ! 1.0

304 100.0

NUMB F R OF MISSlMn C)r<SE°.VATIOMS = 72

26

Appendixes

Table A . 6

« 4 4 4 t * * *

I N I T I A L INCriKE C R O S S T A P U L A r i O N : ¡f * 4 » J 4

S P E C I A L I SA T 1 П М * * * * . • ) * * * * * £ i: £ •(( »>t * *

Y1C

100-

SPN C O U N T I

Rill» PCT lADI' + O T H E S C I E N C E S E N M N E E » S n C . S C Hl.'M*NITI H E A L T H E P U C A T J U C C . M M Ç R C E AOR L A W COL PC T 1RS INC, ES N ТП1 PCT I O . I 1.1 2 . 1 3.1 ' - . ! 5 .1 ft. I 7 . 1 P . I 9

1 . 1 0 1 0 1 0 1 0 1 ? I 0 1 3 1 0 1 0 1 0 I O.C I C.C I 0.0 I C.C I 50.0 J 0.0 I БО. Г. I 0.0 I 0.0 I 0.0 I C.C I 0.0 I 0.0 1 0.0 ¡ 4 .С ! 0.0 I 13.0 1 0.0 I 0.0 I 0.0 i o.c i c.o i o.o i o.c i ci ; o.c i 0.9 ! 0.0 1 0.0 1 0.0

2. I 1 ! I I ? ! ? I I I 1 1 7 1 I I 0 1 0 I 5. ° I 5.9 I ll.P I 17. 6 I 5. 9 I 5.9 I ¿1.2 I 5.9 1 0.0 I 0.0 I 1?.? I 5.0 I 4.7 I ¿.3 I 1.? I 5.3 1 30.4 I ?.? I 0.0 I 0.0 1 0.? I C ? I O.t- I 0.9 I 0.? 1 0.? I ?.l I 0.3 ] 0.0 I 0.0

3 . 1 I I 2 1 3 1 6 1 6 1 A 1 0 1 C I 3 1 2 I 3.-7 1 7.A I 11.i I 22.2 I 22.2 ! 14.P I C.C I 0.0 I 11.1 I 7.i ] 12.5 I 10.0 I 7.0 I F.f. I P.С I 21.1 I 0.0 I 0.0 I 10.3 I 18.2 I 0.3 1 0.6 I 0.9 i 1.P I l.P I 1.2 I O.C I 0.0 I 0.9 I 0.6

4 . I 2 1 « I 7 1 2 1 1 34 I P I 1 0 1 7 ! 10 I 3 I 1.8. I 8.1 I 6.3 ! 1Р.Ч I 30.6 I 7.2 I 9.0 I 6.3 I 9.0 I 2 . 7 I 25 .0 I 4 5 . 0 I 16.3 I 3 0 . 0 I ' . 5 .3 1 42.1 I «• ? . 5 I 16 .3 I 3 ' . 5 I 27 .3 I 0 . 6 I 2 . 6 I 2 .1 I 6 .2 I 10 .0 ! 2 . 3 I 2 . 9 I 2 .1 I 2 . 9 I 0 .9

5. I I I 4 1 0 1 lb I 20 I 4 1 I I 16 I 6 1 3 I 1.4 I 5.6 I C.O I 22.5 I 2P.2 Ï 5.'- I 1.4 ¡ ;-Г.5 I =,5 ¡ 4.2 I 12.5 I 20.0 I 0.0 I 22.9 ] 26.7 I ?1.1 J 4.3 I 37.? ! 20.7 I 27.3 I 0.3 I 1.2 I O.C ] 4.7 I 5.9 I 1.2 I 0.3 I 4.7 1 1.8 I 0.9

6. I I I 2 1 12 I R I 1С I 2 1 I I 13 I B I 3 I 1.7 I 3.3 I 20.0 I 13.3 I 16.7 1 3.3 I 1.7 I 2 1 . 7 I 13.3 I 5 .0 I 12.5 I 1 C . C I 2 7 . 9 I 11 .4 I 13 .3 I 10,5 I 4 .3 I Э 0 . 2 I 27 .6 I 27 .3 I 0 . 3 I 0 . 6 I 3 .5 I 2 .3 I 2 . 9 I 0 .6 I 0 . 3 I ? . П 1 2 . 3 I 0 . 9

7. I 2 1 2 1 14 I 13 I I I 0 1 I I 4 1 ? I 0 I 5.1 I 5.1 1 35.9 I 33.3 I 2.6 I 0.0 I ?. (- I 10.3' ! 5.1 I 0.0 I 25.0 I 10.С 1 ?2.6 I IP.6 I 1.3 I 0.0 I 4.3 ! 9.3 I 6.9 I 0.0 I 0.6 I 0.6 1 4.1 I 3.f> I 0 .3 1 0 , 0 I 0 .3 I I . ? I 0 ,6 I 0 . 0

8. I 0 1 0 ] 2 1 3 1 0 1 0 1 0 1 I I 0 1 0 I 0.0 I 0.0 I 33.3 I 50.0 I 0.0 I 0.0 I 0.0 I 16,7 I 0.0 I 0.0 I 0.0 I 0.0 I 4.7 I 4.3 I 0.0 1 0.0 I 0,0 1 2.3 ! 0.0 I 0.0 I 0.0 I 0,0 I 0.6 I 0,9 I 0,0 I 0,0 I 0.0 I P . ч 1 0 . 0 I 0 . 0

9. I 0 1 0 1 3 1 0 1 0 1 0 1 P I I I P I 0 I 0.0 I 0.0 I 75.0 I 0.0 I 0.0 ! 0.0 I 0.0 I 75.P I 0.0 I 0.0 I 0.0 I 0.0 I 7.0 I 0.0 I 0.0 I 0.0 I 0.0 I 2,3 I 0.0 I 0,0 I 0.0 I 0.0 1 0.9 I 0.0 I 0.0 1 0.0 I 0.0 ! 0*? I 0.0 I 0.0

ROW TOTAL

17 5.0

111 32.6

COLUMN TOTAL

341 100.0

M U M R F R CF" M I S S INC, llftS E R . V A T I U N S =

27

Appendixes

Table A . 7

Y1G INITIAL INCOME £ * Ф Ф ?(• Ф # й

C R O S S T A B LI L A T I O N

RY DIP * * * *

TYPE OF D I P L O M A t ii í¡: * * ф Ф Ф í¡

200-299

300-399

500-599

700-799

COUNT I ROW PCT I С TL PCI ) TOT PCT 1

1« 1

2.

o,

4.

- 5.

6.

7.

B,

9.

~

DIP

P,A,P.SC, В СОИ

1 . I

2 I 40,0 I 1.4 1 0.6 1

1 1 6 „ 3 I 0.7 1 0.3

R 29.6

I 5.7 2.5

I 47 I 44.3

"4 3 ч 14. 5

«?. I f.4.2

30.5 13.2

1 20 I 3 f>. 4 I 14.2 I ft.2

I 15 I ? a. 5 I 10.6 ] 4 .(,

[ 4 I 66. 7 I 2. P. I 1.2 I 1 I 2 5.0 I 0.7 I 0.3

M A,MSС,М COM

?. I 0 1

0.0 1 0.0 I 0.0

0 0.0 1 0.0 0.0

3 11 Л I 8. В С.9

8 7.5

50.0 2.5 ;

1.5 6.3 0.3

I 1 1.8

I 6.3 I 0.3

I 2 I 5.1 I 12.5 I 0.6 I 0 I 0.0 I O.C T 0.0 I 1 ! 25.0 I 6.3 I 0,3

RE,PL,BS С AG,ETC

3. I

0 1 0,0 ] 0.0 I 0.0

2 12.5 3.1 0.6

6 22.2 9.4 1 .8

I 18 17.0

I 28.1 5.5

3 I 4.5

4.7 0.9

I 18 32.7 2 f i. 1

I 5.5 I 14 ! 3 5.9 ! 21.9 1 ¿ .3

I 2 I 3 3.3 1 3.1 ! 0.6

! I 25.0 1 1.6 i 0.3

PHD

4, ;

0 1 0.0 ] 0.0 1 0.0 1

0 0.0 0.0 0.0

0 [ 0.0

0.0 I 0.0

I 2 1 .9

1 6 6.7 0.6

С I 0.0

0.0 0.0

I 1 l.R

33.3 I 0.3

I 0 1 0.0 I 0.0 1 0.0

I 0 I C O I 0.0 I 0.0

; о ] 0.0 I 0,0 I 0.0

GPADUA1E DIPLOMA

5. I

2 I 40.0 3.3 1 0.6

11 68.0 18.0 3.4

С

18.5 8.2 1.5 15

14.2 24. 6

Г 4. 6

I ? 17.9 19.7 3.7

I 11 I 20.0

18.0 I 3. 4

I 4 I 10.3 I 6. 6 I 1.2

I 0 I 0.0 1 0.0 I 0.0 1 1 I 25.0 I 1.6 I 0.3

POSTGRAD UATF 01P

6. I

0 ] 0.0 0.0 I 0.0

1 6.3 6« 7 0.3

1 I 3.7

6.7 0,3

5 4.7

3 3.3 ) .5

2 I 3.0

13.3 0.6

I 3 5. 5

I 2 0.0 0.9

3 I 7.7 I 20.0 I 0.9

1 0 I 0.0 I 0.0 I 0.0

I 0 I 0.0 I 0.0 I 0,0

OTHER CE RTJF1CAT

7. I

1 I 20.0 I 4.0 I 0.3 I

1 I 6.3 I 4.0 I 0.3 I

4 I I 14.8 I

16.0 I 1.2 I

I 1 1 1 10.4 I

I 44.0 I 3.4 I

6 1 9.0 I

?4.0 I 1.8 I

I 1 I 1.8 I

I 4.0 1 I 0.3 I

I 1 I 1 2.6 1 I 4.0 1 I 0.3 I

1 0 I I 0.0 I 1 C O 1 I 0.0 I

I 0 1 I 0.0 I I C O 1 I 0.0 1

ROW TOTAL

1.5

16 4.9

27 8.3

106 Í2.6

67 20.6

55 16.9

12 .0

I .8

COLUMN TCTAL

141 43.4

64 19.7 0.9

61 i e . 8

15 - . 6

25

7 . 7 325

100.0

0 F M ] S S I M G 0 ti S ER V Л 1 I С! N! S 51

28

Appendix В

Explanatory variable

SEX

HOME ADDRESS

FATHER'S OCCUPATION

ТУРЕ OF DIPLOMA

dependent variable '

-^^^ ~—-̂ _

Аде Male Female

Greater Khartoum Other

Peasant or Nomad Merchant Unskilled worker Civil Servant or

Clerical worker Skilled Worker Other

B.A., B.Sc., B.Com. M.A., M.Sc., M.Com. B.E., B.L., B.Sc.Ag. etc Ph.D. Graduate diploma Post-graduate diploma Other certificate

PRESENT EARNINGS (S£ per year)

Coefficient

39.4517

- 1.9180

(2)

- 37.9340

(2)

- S3.793d

- 38.220B

-133.7259

- 61.3628

18.3880

(2)

30.0616

- 54.8244

- 8.7340

90:0122

- 34.5001

101.0915

(2)

Standard Error

4.3568

104.2893

34.9254

54.2938

58.4557

67.0283

56.6708

64.5047

74.6724

98.2422

88.2908

189.5У84 82.7755 106.3323

F 82.008

O.OOO

1.180

u.982 0.428 3.980 1.172

0.081

0.162 0.311 ù'.ÙW

Ü.225

0.174

0.9Ü4

SPECIALIZATION

OCCUPATION

Difference Occup.

SECTOR OF ACTIVITY

WAITING PERIOD TO

FIND FIRST JOB

Admin, and 'Others'

Sciences

Engineerinp.

Soc. Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

Civil Service and 1 Others'

Sciences

Engineering

Social Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

,- Specialization(l)

Duration of studies

Agriculture

Industry

Transport

Public Util, and Health

Education

Housing

General Admin.

Other

Less than 6 months

6 to 12 months

More than 1 vear

P

Constant

R2

- 55.5243

23.9878

- 97.4470

-123.7238

- 9.7760

-190.8811

-109.6449

-249.9343

-160.5215

(2)

-16.2707

0.1313

205.9416

97.6837

-87.5649

100.0125

-15.2760

233.7619

9.8395

(2)

204.3610

130.1698

(2)

-456.3805

0.6054

116.6665

96.7350

81.0071

90.38W1

129.7412

105.7037

98.7488

115.9841

141.1249

47.8742

16.6336

88.01U3

65.2500

82.6526

139.6753

68.3180

121.4429

120.2406

B9.3484

93.7298

0.227

0.U61

1.447

1.871

0.006

3.261

1.233

4.644

1.294

0.116

0.000

5.475

2.241 .

1.122

U.513

0.050

3.705

0.007

5.231

1.929

(1) Dujirnv = 0 if Occupation = Specialization or if Occupation is Teachinp, Otherwise = 1.

(2) Dummy category left out.

29

Appendixes

Explanatorv variable

SEX

HOME ADDRESS

FATHER'S OCCUPATION

TYPE OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup.

SECTOR OF ACTIVITY

WAITING PERIOD TO FIND FIRST JOB

dependent variable

Age

Male

Female

Greater Khartoum

Other

Peasant or Nomad

Merchant

Unskilled worker

Civil Servant or Clerical worker

Skilled Worker

Other

B.A., B.Sc., B.Com.

M.A., M.Sc., M.Com.

B.E., B.L., B.Sc.Ag. etc

Ph.D.

Graduate diüloma

Post-graduate diploma

Other certificate

Admin, and 'Others'

Sciences

Engineering

See. Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

Civil Service and 'Others'

Sciences

Engineering

Social Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

- Specialization 1)

Duration of studies

Agriculture

Industry

Transport


Education

Housing

General Admin.

Other

Less than 6 months

6 to 12 months

More than 1 vear

P

Constant

R2

PRESENT EARNINGS CS£. Der

Coefficient

MO.9902

- 19.9338

(2)

- 43.5136

(2)

- 49.6118

- 46.9986

-147.7751

- 68.3503

30.0001

(2)

- 10.8330

- 73.1730

- 76.9578

84.6971

-105.4127

64.5687

(2)

-162.5621

19.7075

-119.5560

-227.3076

- 8.2157

-168.5108

-173.4957

-205.9156

-224.2990

(2)

30.2555

1.9725

167.8206

116.5809

-119.0706

158.3163

- 18.3708

219.0076

- 70.8236

(2)

181.5108

129.2125

(2)

-382.8127

0.6035

Standard Error

4.2995

103.8136

34.8759

53.4275

58.4691

66.4557

56.6456

64.0499

74.6150

100.7827

95.5698

190.6567

92.5151

107.5221

129.2295

125.1155

110.7213

113.1063

145.6277

141.6841

124.7833

140.6778

148.2667

45.0917

16.7705

81.1080

63.3892

82.1931

136.5380

62.7078

118.2047

119.6404

89.2971

93.4346

vear)

F

90.892

0.037

1.557

0.862

0.646

4.945

1.456

0.219

0.021

0.527

0.648

0.197

1.298

0.361

1.5B2

0.025

1.166

4.039

0.003

1.415

1.933

2.143

2.289

0.450

0.014

4.281

3.382

2.099

1.344

0.086

3.433

0.350

4.132

1.912

(1) Dummy = 0 if Occupation = Specialization or if OccuDation is Teaching, Otherwise = 1.

(2) Dummy category left out.

30

Appendixes

Table B.3


SEX

HOME ADDRESS

FATHER'S OCCUPATION

/ dependent variable

Age mie Female




log.(PRESENT EARNINGS)

Coefficient 0.0183

0.0284 (2)

- 0.0125 (2)

- 0.0192 0.0026

- 0.090Б

- 0.0321 0.0055 (2)

Standard Error 0.0022

0.0528

0.0177

0.0275 0.0296 0.0339

0.0287 0.0326

F 68.729

0.290

0.503

0.490 0.008 7.138

1.257 0.028

TYPE OF DIPLOMA

SPECIALIZATION

B.A. , B.Sc., B.Com. M.A. , H.Sc. , M.Com. B.E., B.L. , B.SC.AP,. etc Ph.D. Graduate diploma Post-graduate diploma Other certificate

Admin. and ' Others ' Sciences Engineering Soc. Sciences Humanities Health Education Commerce Agriculture Law

0.0198 0.0262 0.0103 0.0461 0.0240 0.0652 (2)

0.0378 0.0497 0.0447 0.0959 0.0419 0.0538

0.274 0.279 0.053 0.231 0.328 1.470

Difference Occup.


Sciences Engineering Social Sciences Humanities Health Education Commerce Agriculture Law

• Specialization(l)

0.0362 0.0186 0.0692 0.0770 0.0676 0.10B4 0.0703 0.1618 0.0676 (2)

0.0096

0.0590 0.0489 0.0410 0.0457 0.0657 0.0535 0.0500 0.0587 0.0714

0.375 0.145 2.B54 2.835 1.058 4.110 1.982 7.598 0.897

Duration of studies 0.0070 0.0084 0.696

SECTOR OF ACTIVITY Agriculture Industry Transport Public Util, a Health

Education Housing General Admin. Other

0.1127 0.0445 0.0637 0.0330 0.0342 0.0418

0.0073 0.0176 0.0686 0.0352 (2)

0.0707 0.0346 0.0615 0.0608

6.407

3.725

0.670

0.011

0.260

1.246

0.335

MUTING PERIOD TO FIND FIRST JOB Less than 6 months

6 to 12 months

More than 1 year

0.1141

0.0698

(2)

0.0452

0.0474

6.371

2.167

Constant

R2

2.2220

0.5882

(1) Dummy = 0 if Occupation = Specialization or if Occiroation is Teaching, Otherwise = 1.

(2) Dumny category left out.

31

Appendixes

^ \ _


SEX

HOME ADDRESS

FATHER'S OCCUPATION

TYPE OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup

SECTOR OF ACTIVITY


dependent variable

Age Hale Female




B.A., B.Sc., B.Com. M.A. , M.Sc., M.Com. B.E., B.L., B.Sc.Ag. etc Ph.D. Graduate dioloma Post-graduate diploma Other certificate

Admin. and 'Others' Sciences Engineering Soc. Sciences Humanities Health Education Commerce Agriculture Law

Civil Service and 1Others'


.- Specialization 1)

Duration of studies

Agriculture Industry Transport Public Util, and Health


Less than Б months 6 to 12 months Ноте than 1 year

P Constant

R2

log.(PRESENT EARNINGS)

Coefficient 0.0191

0.0192 (2)

- 0.0162 . (2)

- 0.0166 (3)

- 0.0979

- 0.0337 0.0134 (2) (3)

- 0.0359 - 0.0197

0.0508 - 0.0541

0.0490 (2)

- 0.0870 - 0.0170 - 0.0706 - 0.1179 - 0.0574 - 0.0964 - 0.0917 - 0.1296 - 0.0944

(2)

0.0130

0.0084

0.0875 0.0695

- 0.0523

0.0217 - 0.0197

0.0568 (3) (2)

0.1066 0.0733 (2)

2.2445 0.5782

Standard Error 0.0022

0.0524

0.0176

0.0232

0.0306

0.0251 0.0290

0.0384 0.0342 0.0905 0.0315 0.0425

0.0653 0.0626 0.0558 0.0570 0.0726 0.0717 0.0630 0.0708 0.0729

0.0228

0.0085

0.0391 0.0305 0.0392

0.0687 0.0307 0.0583

0.0436 0.0453

F 76.599

0.134

0.846

0.515

10.269

1.793 0.212

0.870 0.333 0.315 2.942 1.328

1.777 0.074 1.600 4.279 0.625 1.806 2.122 3.350 1.675

0.326

0.987

5.017 5.171 1.778

0.100 0.412 0.950

5.977 2.614

Dumny = 0 if Occupation Otherwise = 1.

Specialization or if Occutation is Teaching,

(2) Dummy category left out. (3) SteDwise regression heinn user!, this variable is not included because the tolerance

level was insufficient for further computation.

32

Appendixes

' -^^^

^ ^ Explanatory variable

SEX

HOME ADDRESS

F A T H E R ' S OCCUPATION

TYPE OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup.

SECTOR O F ACTIVITY

WAITING PERIOD T O

FIND FIRST JOB

dependent variable

~~~'--~-̂ Age

tele

Female

Greater Khartoum

Other

Peasant or (Jomad

Merchant

Unskilled worker

Civil Servant or

Clerical worker

Skilled Worker

Other

B . A . , B . S c . , B . C o m .

M . A . , M . S c . , M . C o m .

B . E . , B . L . , B . S c . A g . etc

P h . D .

Graduate dÍDloma


Other certificate

A d m i n , and 'Others '

Sciences

Engineering

Soc . Sciences

Humanities

Health

Education

Commerce

Agriculture

Law


Sciences

Engineering

Social Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

- Specialization(l)

Duration of studies

Agriculture

Industry

Transport


Education

Housing

General Admin.

Other

Less than Б months

Б to 12 months

Мэге than 1 vear

P

Constant

R2

(PKÍ.5LI4I )

log(EARNINGS)

Coefficient

- 0.0043

(2)

- 0.0013

(2)

- 0.0051

- 0.0021

- 0.0150

- 0.0039

- 0.0106

(2)

(Y) - 0.0022

- 0.0049

- 0.0051

0.0030

0.0047

(2)

- 0.0144

- 0.0255

- 0.014B

- 0.0127

- 0.0166

- 0.0121

- 0.0226

- 0.0385

- 0.0117

(2)

0.0027

0.0039

0.0220

0.0049

0.010B

- 0.0048

0.0132

0.0087

- 0.0016

(2)

0.0234

0 .0121

(2)

0 .0044

0 .1956

(IrililrtL j

- log (EARNINGS)

Standard

Error

0 .0120

0 .0041

0 .0063

0.00Б9

0.0078

0 .0067

0.0078

0 .00B8

0.0071

0 .020B

0.0063

0.0098

0 .0140

0 .0122

0 .0100

0.0110

0 .0156

0 .012B

0 .0121

0.0140

0.0160

0 .0056

0.0019

0 .0101

0.0075

0.0010

0.016Э

0 .0078

0.0136

0 .0141

0 .0101

0 .0107

г

"о.Ш

0.103

0 .663

0 .096

3.703

0 .340

1.875

0 .062

0 . 4 B 1

0 . 0 6 0

0 .231

0 .232

1.062

4 .372

2 .212

1.349

1.133

0 .901

3 .465

7 .525

0 .529

0 . 2 4 1

4 .071

4 .749

0 .426

1.174

0 . 0 B 6

2 .61B

0 .405

0 .014

5.358

1.262

(1) Dumny = 0 if Occupation = Specialization or if Occupation is Teaches, Otherwise = 1.

(2) Dumny category left out. (3) Stepwise regression being used, this variable is not included because the tolerance

level was insufficient far further computation.

33

Appendixes

Table В.6

-~-̂__̂


SEX

HOME ADDRESS

FATHER'S OCCUPATION

ТУРЕ OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup

SECTOR OF ACTIVITY


Í

dependent variable

Аде Kale Female


Peasant or Nomad Merchant Unskilled worker Civil Servant or Clerical worker

Skilled Worker Other

B.A. , B.Sc., B.Com. M.A. , M.Sc., M.Com. B.E., B.L., B.Sc.AR. etc Ph.D. Graduate diüloma Post-Graduate diploma

Other certificate

Admin. and 'Others'

Sciences

Eneineerinp.

See. Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

Civil Service and ' Others '

Sciences

Engineering

Social Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

.- Specialization(l)

Duration of studies

Agriculture

Industry

Transport


Education

Housing

General Admin.

Other

Less than 6 months

6 to 12 months

More than 1 year

(PRESENT ) (INITIAL ) loe(EARNINGS) - log(EARNINGS)

WORK EXPERIENCE

Coefficient

- 0.0106

(2)

- 0.0096

(2)

- 0.0006

0.0029

- 0.0113

0.0011

- 0.0157

(2)

- 0.0106

- 0.0192

- 0.0289

- 0.0114

- 0.0285

0.0072

(2)

0.0674

0.0835

0.0691

0.0570

0.0782

0.1020

0.0753

0.0656

0.0568

(2)

0.0064

0.0035

(3)

- 0.0118

- 0.0261

- 0.0073

- 0.0018

- 0.0022

- 0.0193

(2)

0.0204

0.0016

(2)

Standard Error

0.0176

0.0060

0.0092

0.0101

0.0115

0.0098

0.0111

0.0128

0.0173

0.0016

0.0326

0.0159

0.0185

o7o222

0.0215

0.0190

0.0194

0.0250

0.0244

0.0214

0.0242

0.0254

0.0078

0.0029

0.0107

0.0141

0.0235

0.0108

0.0195

0.0206

0.0153

0.0161

F

0.365

2.627

0.005

0.081

0.965

0.012

2.000

0.683

1.226

3.091

0.123

3.221

0.153

9.204

14.995

13.183

8.598

9.788

17.532

12.395

7.357

5.006

0.687

1.477

1.212

3.427

0.098

0.028

0.013

0.882

1.780

0.010

Constant

R2

0.0415

0.2107

(1) Dummy = 0 if Occupation Otherwise = 1.

(2) (3)

Specialization or if OccuDation is Teaching,

Dummy%category left out. Stepwise regression being used, this variable is not included because the tolerance level was insufficient far further computation.

34

Appendixes


SEX

HOME ADDRESS

FATHER'S OCCUPATION

TYPE OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup.

SECTOR OF ACTIVITY


dependent variablp

Лее Male Female




B.A., B.Sc., B.Com. M.A., M.Sc., M.Com. B.E. , B.L. , B.Sc.Ap,. etc Ph.D. Graduate diDloma Post-Graduate diploma Other certificate

Admin. and 'Others' Sciences Enp,ineerinp. Soc. Sciences Humanities Health Education Commerce Agriculture Law


Sciences Engineering Social Sciences Humanities Health Education Commerce Agriculture LâW

- Specialization(l)

Duration of studies

Agriculture

Industry

Transport


Education

Housing

General Admin.

Other

Less than 6 months

6 to 12 months

More than 1 year

P

Constant

R2

(PRESENT ) (INITIAL ) loe(EARNINGS) - loe(EARNINGS)

WORK EXPERIENCE *

Coefficient

0.0382

(2)

- 0.0105

(2)

- 0.0060

- 0.0105

- 0.0096

0.0121

- 0.0097

(2)

- 0.00ВЧ - 0.0080 - 0.0172 - 0.0091 - 0.Q101

0.0141 (2)

0.0086 0.0184 0.0006 0.0203 0.0116 0,0279 0.0241

- 0.0057 0.0075 (2)

0.0153

0.0084

0.0080 - 0.0152 - 0.0255

(3) o.oooa (3)

- 0.0119 (2)

0.0256 (3) (2)

- 0.06B3 0.1295

Standard Error

0.0252

0.0085

0.0130 0.0142 0.0162

0.0137 0.0154

0.0177 0.0232 0.0211 0.0458 0.0198 0.0256

0.Ü265

0.0234

0.0196

0.0219

0.0285

0.0257

0.0239

0.0270

0.0343

0.0117

0.0041

0.0180

0.0146

0.0193

0.0160

0.0286

0.0113

F

2.297

1.538

0.213

0.551

0.349

0.775

0.398

0.224

0.119

0.664

0.040

0.258

0.302

0.104

0.619

0.001

0.855

0.165

1.178

1.014

0.044

0.047

1.721

4.337

0.198

1.074

1.740

0.003

0.173

5.127

(1) Dummy = 0 if Occupation = Specialization or if Occtroation is Teaching, Otherwise = 1.

(2) Dummy category left out. (3) Stepwise regression being used, this variable is not included because the tolerance


35

Appendixes

Table В.В


SEX

HOME ADDRESS

FATHER'S OCCUPATION

TYPE OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup,

SECTOR OF ACTIVITY


dependent variable

Аде Male Female




B.A., B.Sc., B.Com. M.A. , M.Sc. , M.Com. B.E., B.L. , B.Sc.Ag. etc Ph.D. Graduate diploma Post-graduate diploma Other certificate

Admin. and ' Others ' Sciences Engineering See. Sciences Humanities Health Education Commerce Agriculture Law



- Specialization(l)

Duration of studies



Less than 6 months 6 to 12 months Мэге than 1 year

P Constant R2

(PRESENT ) (INITIAL ) log(EARNINGS) - log(EARNINGS)

WORK EXPERIENCE*

Coefficient

0.0427 (2)

- Q.0087 • (2)

- 0.0073 - 0.0099 - 0.0113

0.0069 - 0.0117

(2) - 0.0103 - 0.0119 - 0.0253 - 0.01Ш - 0.0190

0.0235 (2)

0.0600 0.0718 0.0523 0.0589 0.0Б55 0.0849 0.0765 0.0563 0.0518 (2)

0.0086

0.0077

- 0.0019 - 0.0220 - 0.0286

- 0.0075 (3)

- 0.0053 - 0.0206

(2)

0.0290 0.0026 (2)

- 0.1112 0.1452

Standard Error

0.0239

0.0OB4

0.0131 0.0144 0.0162

0.0138 0.0157

""оТоТтэ" 0.0245 0.0233 0.0460 0.0225 0.0261

0.0306 0.0303 0.0266 0.0263 0.0351 0.0325 0.0300 0.0340 0.0359

0.0106

0.0041

0.0184 0.0140 0.0192

0.0319

0.0268 0.0286

0.0217 0.0227

г

3.199

1.059

0.312 0.479 0.484

0.251 0.55В

' 0 .3"32" 0.237

1.185 0.093 0.713 0.807

3.855

5.602 3.880 5.02В 3.483 6.809 6.487 2.743 2.OBI

0.646

3.538

0.010 2.451 2.225

0.055

0.039 0.51В

1.791 0.013

(1) Dunnr/ = 0 if Occupation = Specialization or if OccuDation is Teaching, Othenri.se = 1.

(2) Шипу category left out. (3) Stepwise regression being used, this variable is not included because the tolerance


36

http://Othenri.se

Appendixes

dependent variable (PRESENT ) (INITIAL )

log (EARNINGS) - log(EARNINGS) W3RK EXPERIENCE«


SEX

Age

Male

Female

Coefficient Standard

F.rror

HOME ADDRESS

FATHER'S OCCUPATION

TYPE OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup.

SECTOR OF ACTIVITY

Greater Khartoum

Other

Peasant or Nomad

Merchant

Unskilled worker

Civil Servant or Clerical worker

Skilled Worker

Other

B.A. , B.Sc. , B.Com.

M.A. , M.Sc., M.Com.

B.E., B.L., B.Sc.AR. etc

Ph.D.

Graduate diploma


Other certificate

Admin, and 'Others'

Sciences

Engineering.

Soc. Sciences

Humanities

Health

Education

Commerce

Agriculture

Law


Sciences

Engineering

Social Sciences

Humanities

Health

Education

Commerce

Agriculture

Law

- Specialization(l)

Duration of studies

Agriculture

Industry

Transport


Education

Housing

General Admin.

Other

- 0.0100

(2)

- 0.0064

- 0.0083

- 0.0088

0.0120

- O.00B0

(2)

- 0.0113

- 0.0122

- 0.0203

- 0.0130

- 0.0122

0.0113

(2)

0.0121

0.0201

0.0023

0.0214

0.0125

0.0299

0.0253

- 0.0035

0.0064

(2)

0.0160

0.0086

0.0084

- 0.0157

- 0.0255

- 0.0068

0.0003

0.0041

- 0.0133

(2)

0.0086

0.0135

0.0143

0.0166

0.0141

0.0159

0.01B1

0.0238

0.0215

0.0463

0.0203

0.0260

0.02B5

0.0238

0.0199

0.0223

0.0319

0.0260

0.0243

0.02B3

0.0347

0.011B

0.0041

0.0214

0.0159

0.0204

0.0341

0.0168

0.0287

0.0296

1.363

0.228

0.334

0.283

0.726

0.251

0.3B8

0.264

0.B94

0.079

0.363

0.187

0.181

0.709

0.013

0.923

0.152

1.321

1.0B2

0.016

0.034

1.826

4.342

0.155

0.984

1.565

0.040

0.000

0.020

0.201

WATTING PERIOD TO FIND FIRST JOB Less than 6 months

6 to 12 months

More than 1 vear

Constant

R2

- 0.0346

0.1265

(1) Dunray = 0 if Occupation = Specialization or if Occupation is Teaching, Otherwise = 1.

(2) Dummy category left out. (3) Stepwise regression being used» this variable is not included because the tolerance


37

Appendixes

^


SEX

HOHE ADDRESS

FATHER'S OCCUPATION

TYPE OF DIPLOMA

SPECIALIZATION

OCCUPATION

Difference Occup,

SECTOR OF ACTIVITY


dependent variable

Аде Hale Female




B.A., B.Sc., B.Com. H.A., M.Sc., M.Com. B.E. , B.L. , R.Sc.Ag. etc Ph.D. Graduate diDloma Post-Graduate diploma Other certificate

Admin. and 'Others' Sciences Engineering Soc. Sciences Humanities Health Education Commerce Agriculture Law



,- Specialization(l)

Duration of studies



Less than Б months 6 to 12 months More than 1 year

P Constant R2

(PRESENT ) (INITIAL ) log(EARNDIGS) - log(EARNINGS)

WORK EXPERIENCE*

Coefficient

0.0435 (2)

- 0.0083 (2)

- 0.0071 - 0.0078 - 0.0103

0.0072 - 0.0097

(2)

- 0.0128 - 0.0151 - 0.027Б - 0.0170 - 0.0207

0.0207 (2)

0.Q600 0.0707 0.0510 0.0579 0.0641 0.0841 0.0747 0.0551 0.0499 (2)

0.0091

0.0077

- 0.0005 - 0.021Э - 0.0277

- 0.0102 0.0008

- 0.0022 - 0.0207

(2)

- 0.0Э95 - 0.0722

0.1421

Standard F.rror

0.025�"

0.0084

0.0131 0.01Ш 0.0162

0.0139 0.0155

0.0179 0.0242 0.0231 0.0460 0.0225 0.0260

0.0315 0.0306 0.0270 0.0276 0.0355 0.0346 0.0303 0.0343 0.0359

0.0110

0.0041

0.0196 0.0153 0.0200

0.0330 0.0153 0.0275 0.0292

0.0176

г

3.015

0.955

0.298 0.301 0.401

0.271 0.392

0.514 0.391 1.425 0.136 0.848 0.634

3.620 5.341 3.567 4.401 3,265 5.909 6.071 2.577 1.926

0.685

3.549

0.001 1.947 1.920

0.095 0.003 0.006 0.503

5.025

(1) Dummy = 0 if Occupation = Specialization or if Occutiation is Teaching, Otherwise = 1.

(2) Dummy category left out. (3) Stepwise regression being used, this variable is not included because the tolerance


38

OCCASIONAL P A P E R No. 42: Using the data collected for the H E P study on Higher Education and Employment of Graduates in the Sudan, analysis is made of the relationships between earnings and human capital types of variables as well as labour market segmentation types of variables. Both initial earnings and present earnings of persons with post-secondary educational attainment are analysed. An assessment is made of the influence on earnings of such variables as the waiting period to find the first job, socio-economic background, sex, duration of studies, etc. Finally, a similar analysis is carried out on the rate of growth of earnings over time.

BIKAS C . S A N Y A L , P h . D . (India) is a staff member of the International Institute for Educational Planning and in charge of the Research Project on Employment of Graduates and Admission Policy in selected higher education systems.

JAN VERSLUIS is a staff member of the International Labour Organization and in charge of the Education and Employment Research Project for Developing Countries of the World Employment Programme.

Higher education, human capital and labour market segmentation …unesdoc.unesco.org/images/0007/000751/075190eo.pdf · · 2014-09-25Higher education, human capital and labour market

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