Education and Labour Market Outcomes
in Sub-Saharan West Africa
Mathias Kuepie*
Christophe Nordman**
and
François Roubaud**
March 2006, preliminary
Abstract:
The purpose of this paper is to study the effects of education on urban labour market
participation and earnings in seven West African capitals. Although the effect of education on
earnings differs depending on the employment sector and the type of job held, it is also a
determinant of individual choices made upstream, i.e. when the decision is made to enter the
labour market, and especially sector choices. We show that education clearly increases earnings
on the labour markets in Abidjan, Bamako, Cotonou, Dakar, Lomé, Niamey and Ouagadougou.
Although it does not always guard against unemployment, it does open the door for the most
well-educated to get into the most profitable niches, which are found in the formal private and
public sectors. Our study also makes a contribution by showing that human capital, including
at advanced levels, substantially increases the gains in the informal sector in most of these
WAEMU cities. This finding has considerable political implications: the entry of highly
educated workers into the informal labour market is far from a loss in terms of household and
government investment in education.
JEL Classification: J24, J31, O12
Key words: Returns to education, urban labour markets, earnings, informal sector, Sub-Saharan
West Africa
DIAL, CEPS/INSTEAD. E-mail: [email protected]
IRD, DIAL, 4 rue d’Enghien 75010, Paris, France. E-mail: [email protected] (corresponding author);
[email protected]
*
**
1.
Introduction
Studies of the external efficiency of education systems look at the impact of the education
received by individuals once they have left their school or training establishment to continue
their lives as adults in society1. There are two types of impacts: economic in the narrowest sense
and social in the broader sense. These can be interpreted using two further dimensions,
individual and collective.
This study focuses solely on the economic dimension of the external efficiency of education.
Analyses of the individual effects of education in the economic sphere often study the earnings
differentials associated with different levels of human capital endowment. For example, classic
human capital theory (Mincer, 1974; Becker, 1975) has substantial implications for poor
countries because it interprets income differences between individuals on the labour market.
The Mincer earnings model derives directly from the theory’s assumption that individuals are
paid based on their marginal productivity. This suggests that investment in education is an
explanatory factor in the distribution of earnings. The classic theory effectively amounts to
considering that wage differentials between individuals are due to their differences in
education alone, since the market equalises individuals’ earnings, at equal levels of training.
Under this assumption, a strong economic policy implication is that if inequalities in income
distribution are to be reduced in a given country, the starting point is to reduce inequalities in
access to education, given that income inequality seems to be higher when education is less
equally distributed.
Education policies can help reduce poverty by increasing the earned income of the most highly
educated workers. It is therefore useful to know the returns to education for individuals with
different living standards in different countries. If returns to education are high for the poor,
poverty reduction policies designed to promote equal opportunities in access to schooling
would be appropriate. However, if investment in education is mainly of benefit to the rich, then
improving access to the education system may generate sustained growth, but could well
increase inequalities in income distribution without actually reducing poverty. There have been
numerous objections and criticisms regarding the assumption that education and productivity
are the only determinants of differences in remuneration. The first models were built for
By way of comparison, “Analyses on the internal efficiency of education systems concern the school
processes and the way the teaching establishments operate: generally speaking, they compare the
schools’ activities and organisational methods with the results obtained by pupils whilst they are still in
the system, looking for the most cost-effective situations.” (Mingat and Suchaut, 2000, p. 170).
1
2
industrialised countries (mainly the United States). Yet many authors have demonstrated,
particularly in an African context, that the traditional theories positing the levelling of income
levels among individuals with identical levels of human capital endowments do not fit when
the markets are imperfect or segmented.
Markets in most African countries are not only imperfect, but the nature of employment
contracts also has a significant effect on the relationship between human capital characteristics
and earnings. In particular, it is widely acknowledged that there are four types of labour
markets in developing countries: rural, public, formal private and informal. These markets each
have their own characteristics, such as job seasonality, uncertainty of demand, nature of
contracts and structure of wages and earnings (Adams, 1991; Ray, 1998; Hess and Ross, 1997;
Schultz, 2004).
However, many studies of the external efficiency of education in these countries (particularly
the match between training and employment or the private returns to education) overlook the
fact that the existence of different employment segments, especially in the rural and informal
sectors, can have major implications in terms of the role of education in labour market
integration. Vijverberg (1995) observes that some types of employment, such as selfemployment, cannot be linked to the individuals’ credentials, or to a pay scale, meaning that
education can only play a minor role in explaining individual earnings levels. Bennell (1996)
notes that many studies of developing countries are based on data for formal-sector employees
and do not take into account income in rural and informal sectors where returns to education
are probably very low. Glewwe (1996) also finds that the wage structure in the private sector
reflects the impact of education on worker productivity more than the wage structure in the
public sector.
The purpose of this paper is to study the effects of education on urban labour market
participation and earnings in seven West African countries. Although the effect of education on
earnings differs depending on the employment sector and the type of job held, it is also a
determinant of individual choices made upstream, i.e. when the decision is made to enter the
labour market, and especially sector choices. Hence, it is widely accepted that observable
individual characteristics (such as human capital in general), but also unobservable individual
characteristics, influence both decisions to participate and the level of individual earnings.
Based on first-hand, recent, comparable household surveys in seven capitals in French-speaking
Africa of the WAEMU, we broaden the scope and refine the indicators generally used to assess
the efficiency of education for labour market integration in Africa. We set out in particular to reestimate the determinants of earned income, especially the effect of education, while
3
differentiating individuals by their institutional sector (public/formal private/informal
private). Using our unique household survey data, we can correct for possible sampling-related
estimation biases regarding paid-work participation and sector choices. To our knowledge, this
is the first time that such a comparative study of several African countries has been made based
on surveys using identical methodologies.
The paper is set out as follows. Section 2 describes the data and survey design. Section 3
presents our methodology and the econometric models. Section 4 analyses and discusses the
findings. Section 5 presents our conclusion.
2.
Presentation of the data
Our data are taken from an original series of urban household surveys in West Africa,
the 1-2-3 Surveys conducted in seven major WAEMU2 cities (Abidjan, Bamako, Cotonou,
Dakar, Lomé, Niamey and Ouagadougou) from 2001 to 2002. The surveys were carried
out by the relevant countries’ National Statistics Institutes (NSIs), AFRISTAT and DIAL
as part of the PARSTAT Project.3
The surveys cover the economic city, i.e. the “administrative city” and all the small
towns and villages directly attached to it and with which there are frequent exchanges.
As suggested by its name, the 1-2-3 Survey is a three-phase survey. The first phase
concerns individuals’ sociodemographic characteristics (including education and
literacy) and labour market integration. The second phase covers the informal sector
and its main productive characteristics. The third phase focuses on household
consumption and living conditions. The same methodology and virtually identical
questionnaires were used in each city, making for totally comparable indicators.
Our study uses solely the Phase 1 data. Phase 1 of the 1-2-3 Survey is a statistical
employment survey designed to:
WAEMU: West African Economic and Monetary Union. The survey was not carried out in GuineaBissau
3 Regional Statistical Assistance Programme for multilateral monitoring sponsored by the WAEMU
Commission.
2
4
-
Provide the main indicators to describe the situation of individuals and
households on the labour market. It covers household employment and economic
activities, especially in the informal sector;
-
Serve as a filter survey to identify a representative sample of informal production
units, which are then surveyed in Phase 2.
The following presents a brief description of the sampling plan, the content of the
questionnaires and the handling of the tricky question of income, which plays a key role
in this study.
The sampling plan
The detailed methodology is described in Brilleau, Roubaud and Torelli (2004, 2005).
The sampling plan chosen used the classic technique of two-stage area sampling.
Primary and/or secondary stratification was conducted where possible. The primary
sampling units were small area units: Enumeration Areas (Zones de Dénombrement),
Census Districts (Districts de Recensement), segments or even Enumeration Sections
(Sections d’Enumération), depending on the country. Each area unit contained an average
of 200 households. In general, a full list of these units was available from the last
population census. The survey periods were as follows: 2001 for Cotonou,
Ouagadougou, Bamako and Lomé; and 2002 for Abidjan, Dakar and Niamey.
Following a stratification of the primary units based on socio-economic criteria, 125
primary units were sampled with probabilities proportional to their size. An exhaustive
enumeration of the households in the selected primary units was then conducted.
Following a stratification of the secondary units where possible, systematic random
sampling was applied to sample approximately 20 households with equal probabilities
in each primary unit.
The theoretical household samples were made up of 2,500 households in each of the
seven cities, with the exception of Cotonou where the number was able to be raised to
3,000. A full 17,841 households actually answered the questionnaire. This corresponds
to 93,213 individuals and 69,565 people aged ten and over (which is the potential labour
force) for whom an individual questionnaire was completed. Table 1 below describes
the theoretical and actual samples obtained for each city.
5
Table 1. PARSTAT survey sampling
Cotonou
Total number of primary
units
Number of primary units
in the sample
Initial
number
of
households in the sample
Final
number
of
households in the sample
Number of individuals in
the sample (inc. visitors)
Number of individuals
aged ten and over in the
sample
Ouagado
ugou
Abidjan
Bamako
Niamey
Dakar
Lomé
Total
464
713
2,483
993
368
2,041
129
7,191
125
125
125
125
125
125
125
875
3,000
2,500
2,500
2,500
2,500
2,500
2,500
18,000
3,001
2,458
2,494
2,409
2,500
2,479
2,500
17,841
11,574
13,756
11,352
13,002
14,557
19,065
9,907
93,213
8,967
10,295
8,682
9,061
10,141
14,871
7,548
69,565
Source: 1-2-3 Surveys, Phase 1, 2001-2002 (INS, AFRISTAT and DIAL); seven WAEMU cities.
In general, sample size was much higher than that observed in most of the urban
household surveys with the result that the findings are more reliable. The sampling
strategy used meant that the standard estimator quality indicators could be rigorously
calculated (see Brilleau, Roubaud and Torelli, 2005)4
The questionnaires
The questionnaire was made up of two forms: a household section covering all the
sociodemographic characteristics of each household member, housing conditions and
the household’s durable goods; and an individual questionnaire for each individual aged
ten or over. The individual questionnaire was made up of six modules designed to
define each person’s labour market situation and therefore to measure, among other
things, what is termed the “external” efficiency of education. For example, the following
aspects can be studied: employment status (employed worker, unemployed, out of the
labour force), the characteristics of the main job (job status, seniority, earnings, etc.) and
of the employer businesses (institutional sector, line of business, size, etc.), the
characteristics of the secondary job, and unemployment (length, type of job sought and
mode). Also included were a certain number of career path elements (last job held and
situation of the interviewee’s father when he was 15 years old) and unearned income.
4
Table A1 in the appendix presents some composite labour market indicators for the seven capitals.
6
Although no specific module was included on education, this field was covered by a
series of questions put to each household member5 concerning: school attendance
(current or past), the school level reached, the number of completed years of education,
the qualifications obtained (differentiating between general and vocational education),
the type of school attended in the last year of schooling (state, private denominational
or private non-denominational), and the interviewee’s father’s level of education.
Constructing the income variable
It is not easy to study earnings in an African urban environment since a large majority
of workers work in the informal sector where there are no accounts or pay slips and
individuals are naturally reticent to disclose their incomes (this is not specific to Africa).
Two strategies were adopted for the 1-2-3 Surveys to at least partially overcome these
problems:
-
For non-wage earners (self-employed and employers), the interviewers were
asked to help them reconstitute their earnings by recapping incomings and outgoings
over a reference period to which the interviewee could relate. Following this exercise,
non-wage earners’ incomes were translated into a monthly sum in the questionnaire;
-
The individuals who were unable or unwilling to disclose their exact earnings
were asked to give a bracket, defined by multiples of the minimum wage in force.
This strategy produced the following results:
Table 2. Method of declaring the variable relating to income from the main job (%)
Detailed income
Income bracket
Unpaid worker
Income not disclosed
Total
Cotonou Ouagad Abidjan
ougou
51.2
42.5
53.2
32.3
44.4
34.1
14.6
7
9.8
2.0
6.1
2.9
100.0
100.0
100.0
Bamako Niamey
54.3
35.2
4.1
6.4
100.0
42.1
32.9
11.5
13.4
100.0
Dakar
Lomé
Total
38.3
41.5
11.6
8.7
100.0
55.0
31.2
12.3
1.5
100.0
48.1
36.2
10.3
5.8
100.0
On average, nearly half of all employed workers (48%) declared a precise income figure
and over one-third (36%) gave a bracket. Less than 6% of workers gave no information.
For both the workers who refused to disclose their earnings and those who gave only
5
See Brilleau, Roubaud and Torelli (2005) for a detailed description of the questionnaires.
7
income brackets, earnings were imputed by a econometric estimation based on an
income equation.
An income model was first of all estimated for the employed workers who disclosed
their precise earnings based on the individuals’ characteristics. The explanatory
variables for income are as follows: age, gender, schooling, socio-economic group,
institutional sector, seniority, location, type of contract, number of hours worked,
steady or irregular job, and type of payment. The values predicted from this model
were imputed for all individuals who did not disclose their earnings and those who
gave an income bracket. Random sampling was conducted for these latter individuals
and the result added to the estimated income until the sum obtained came within the
bracket declared by the interviewee.
3.
Methodological approach
Our methodological approach consisted of estimating different models to evaluate the
impact of education in its different forms (years of education, type of school attended,
level reached, and qualifications obtained) on (i) the conditions for labour market
integration (participation and sector choices) and (ii) earned income. Our surveys
enabled us to estimate Mincer earnings models taking account of the selection effects
associated with the individuals’ participation and sector choices. Moreover, the wealth
of the variables available meant that we were able to take into account part of the
income heterogeneity not associated with education in the earnings function. Hence the
following information was included in the different models to better pinpoint the effect
of education: age, gender, potential work experience, seniority in the current job,
migratory status, marital status, dummy variables for religion, per capita income in the
individual’s household, the individual’s relationship to the head of household, and the
household’s dependency ratio.
Bear in mind that the comparative nature of our data on seven capitals gives our study
a unique slant in that the effects of education can be studied in a uniform manner for all
8
the countries. This is because the variables are measured in the same way since they are
taken from surveys using identical sampling plans and questionnaires.
Two types of econometric models are used for this study: discrete choice models (binary
and multinomial logit) to estimate the effect of education on the probability of being
unemployed and of holding a job in the informal sector and the effect of education on
sector choices (out of the labour force, public sector, formal sector or informal sector),
and the log-linear earnings model with correction of any selection biases to estimate the
returns to education.
Modelling the sector choice
Let j be the different occupational situations (j = 0 to 3): 0 = no work, 1 = public sector, 2
= formal sector, 3 = informal sector. Each individual i will normally compare the
different “levels of utility” associated with the different “choices”6 and opt for the
alternative that maximises his utility Uij. We posit that the utility of choice j is Uij =βj’Xi
+ εij where Xi is a vector of observed individual characteristics, βj is a vector of
parameters to be estimated and εij is a random error term. The probability of individual
i participating in sector j is equal to the probability of the utility of sector j being greater
than that associated with the other sectors:
Prob(Uij > Uik) for k ≠ j; k = 0, 1, 2, 3
(1)
By replacing Uij and Uik with their expression, we obtain:
Prob(βj’Xi + εij > βk’Xi + εik ) = Prob(βj’Xi - βk’Xi > εik -εij) for k ≠ j; k = 0, 1, 2, 3
(2)
The form of the participation equation will depend on the assumption adopted as
regards the distribution of error terms. If we assume that the errors are independently
and identically distributed with a Weibull distribution, then the difference between the
errors follows a logistic distribution and the probability of individual i choosing sector j
The notion of choice needs to put into perspective here, especially when the labour market is segmented
as is the case in Africa: although certain individuals may be able to choose from different alternatives, a
large majority are faced with the impossibility of gaining access to certain highly rationed jobs (public
and modern private) and therefore have to enter the informal sector. In this case, they do not really make
a choice. The assumption could be made that, for these individuals, the utility associated with the
inaccessible jobs is zero and that they therefore always “prefer” the informal sector to the modern sector.
6
9
is expressed by:
Prob(Yi=j)=exp(βj’Xi)/Σkexp(βk’Xi) with k ranging from 0 to 3.
(3)
For the model to be identifiable, we posit by convention β0=0. The parameters of the
estimates hence represent the effect of a given characteristic on the chances of being in a
segment rather than not working7. The binary logit is deduced from the multinomial
based on the assumption of two exclusive choices (k=0 or k=1).
Modelling the earnings equations with selection bias correction
Modelling the earnings functions follows on from the estimation of the sector choice
equations and is therefore the next step.
Let’s say, as above:
Uij =βj’Xi + εij
(4)
and
Yij= ζj’Zi + ηij
(5)
Yij denotes the income that individual i earns by working in sector j where, as above, j=1
(public sector), 2 (formal sector) and 3 (informal sector). Zi is the vector of observable
individual characteristics (including education), ζj is a vector of parameters to be
estimated and ηij is an error term. The aim is then to estimate the coefficients ζj for each
sector. Yj is only observed if sector j is chosen and, therefore, ηj and εj are not
independent. In this case, the OLS estimator is potentially biased.
One of the ways of correcting this bias is to add a correction term to the earnings
equation using Lee’s method (1983). This technique is a generalisation of the Heckman
correction (1979) when the first-stage choice equation has several modalities. The
inverse Mills ratio introduced into the earnings equation for each sector sub-sample
yields consistent estimators and, in our case in particular, estimators of the effect of the
education variable on the levels of individual earnings. However, this Lee correction
In our case, this category corresponds to the individuals who did not declare positive earnings for the
reference month.
7
10
method was questioned because it is based on a strong assumption regarding the joint
distribution of error terms in the equations of interest (Bourguignon, Fournier and
Gurgand, 2004). Nevertheless, the alternative methods proposed by these authors were
inconclusive in the case of our data. The Lee method often performed better considering
the small size of our sector sub-samples.
4.
Analysis of the findings
We start by looking at the distribution of the stock of education in the seven cities
studied. We then analyse the efficiency of education in terms of exits from
unemployment,
integration
into
the
different
labour
market
segments
(formal/informal), and remuneration.
4.0
Overview of the level of education in the seven cities
Education remains a rare factor
The performance of the education system is found to be mediocre in all seven cities:
over half (55%) of the individuals aged 15 and over either never attended school or
attended school but did not complete primary school. Yet people are only considered to
be literate as adults when they have completed primary school. On this basis, we
estimate the proportion of literate individuals aged 15 and over in the WAEMU cities in
the early 2000s at 45%. Moreover, these literate individuals’ level of education was
extremely modest since nearly half of them did not complete fourth year/ninth grade
(the end of middle school) and less than one quarter had completed secondary school
and might have gone on to higher education.
The distribution of individuals aged 15 and over by level of education in each of the
cities taken individually is pyramid-shaped with a broad base and a very tapered tip.
This is indicative of a high level of illiteracy (at least 44%) and high drop-out rates
between and within education system levels.
11
Graph 1. Distribution of individuals aged 15 and over by education
level and city
Higher education
Lomé
Dakar
Niamey
Bamako
Abidjan
Ouagadougou
Cotonou
Total
Secondary complete
Vocational secondary
Middle school complete or secondary
incomplete*
Primary complete or middle school
incomplete*
Primary incomplete*
No schooling
0
10
20
30
%
40
50
60
70
* Did not reach the last year of that level of schooling
Although the cities have a common curve, they also display differences. If we look at
the base of the schooling pyramid, i.e. the individuals who did not start or complete
primary school, Bamako, Niamey and Dakar are found to be the most disadvantaged
from this point of view (Table 3 and Graph 1). Approximately 60% of the over-15s in
these cities do not have the minimum level of schooling in terms of having completed
primary school. Conversely, “only” 45% of the population lack this basic level in
Cotonou and Lomé. Abidjan and Ouagadougou are in intermediate positions with
respectively 56% and 51% of the population who did not start or complete primary
school. Abidjan has the highest proportion (12.5%) of individuals at the top of the
educational pyramid (secondary school completed or higher education), ahead of
Cotonou (11%). The other cities post percentages ranging from 6.5% to 8.5%.
Possession of the minimum human capital (i.e. at least completed primary schooling)
also varies markedly by the two demographic identification variables of generation and
gender (Table 3). Women are largely disadvantaged by gender in that nearly two-thirds
(64%) did not complete primary school (as opposed to 45% of men). This rate rises to
68% in Dakar, Niamey and Bamako. Even in the cities with the longest-standing and
most developed schooling (Cotonou and Lomé), women remain largely on the fringes:
59% did not complete primary school.
12
Table 3. Proportion (%) of individuals aged 15 and over who at least completed primary school
Cotonou Ouagadougou
Total
15-24 years
25-34 years
35-44 years
45-59 years
60 years &+
Total
Men
15-24 years
25-34 years
35-44 years
45-59 years
60 years &+
Total
Women
15-24 years
25-34 years
35-44 years
45-59 years
60 years &+
Total
Abidjan
Bamako Niamey
Dakar
Lomé
Total
58.8
49.7
55.0
44.5
34.5
52.6
57.1
46.2
35.0
24.8
8.5
43.9
49.0
51.3
48.1
46.4
21.6
48.5
49.9
35.2
38.2
33.2
15.7
39.7
43.5
40.8
36.2
23.5
8.1
37.0
38.3
39.8
38.2
38.0
16.0
37.1
57.1
58.7
59.7
46.7
30.5
55.7
48.8
47.0
44.6
39.0
18.8
44.9
70.8
62.1
67.7
60.6
49.1
65.4
64.8
54.9
41.4
31.2
14.0
50.8
63.1
60.1
57.6
56.1
32.6
59.3
61.1
43.7
45.8
41.1
19.7
48.0
47.4
49.0
45.1
30.1
12.6
42.5
43.0
45.7
48.5
47.5
26.3
43.9
71.1
74.6
77.2
63.6
55.2
71.9
59.1
56.4
54.9
48.9
28.1
54.6
48.2
38.4
42.8
29.8
20.1
40.9
50.0
36.1
28.6
17.7
2.2
36.8
38.4
41.4
36.4
32.9
9.2
37.7
40.1
28.0
29.8
23.2
9.4
31.6
40.4
33.4
27.6
14.7
2.0
31.7
34.4
34.2
29.5
28.7
4.8
31.0
45.8
42.7
41.7
29.8
14.1
40.9
40.4
37.4
33.8
27.4
8.4
35.7
When studied by generation, more under-35s (48%) have the minimum level of
schooling than their elders aged 35 to 44 (44%) and especially those aged 45 and over
(34%). This configuration reflects the steady development of the education system in the
African countries. Yet the schooling dynamic is not the same everywhere. At one end of
the scale, there are the cities with a long tradition of schooling. At the other end of the
scale are those where the development of schooling has been stepped up more recently.
The first group comprises Lomé, Abidjan and Cotonou where, even among the
individuals aged 45 to 59, a not-inconsiderable proportion (at least 45%) has the
minimum level of schooling. In the second group (Bamako, Niamey and, to a certain
extent, Ouagadougou), over 60% of the over-35s do not have the minimum level of
schooling. Dakar stands out for its stagnation (at around 60%) in the proportion of
individuals without the minimum grounding in education across all generations (15 to
59 years old).
A last point worth mentioning about the educational landscape of the major WAEMU
cities is the low weight of vocational education, which never exceeds 2% of the over-15s
with the notable exception of Mali where it comes to 6%. This is characteristic of an
education system in which vocational training is left by the wayside.
13
4.1 Labour market integration of the “products” of the education and training system:
unemployment
Definition of unemployment
The ILO defines as unemployed any person who has not worked in the week preceding
the survey and who is actively seeking work. The 1-2-3 Surveys add a second definition
to this strict notion of unemployment: a broader concept of unemployment that takes
the ILO definition and adds in all those who are not actively seeking a job, but are
prepared to work should the opportunity arise. This broader definition of
unemployment raises the number of unemployed from 460,500 to 673,000 individuals
for all the WAEMU cities together. We feel it better reflects the real situation on the
African urban labour markets, which typically have low rates of wage earners (only
36% of employed workers in all the cities considered are salaried employees) and no
operational institutions to register job seekers and help them to find a job.
A mixed bag of correlations between unemployment and level of education
Taking all the WAEMU cities together (Table 4), the unemployment rate is the lowest
(14.6%) among those individuals without the minimum level of schooling. It rises to
20%-21% for those with levels ranging from completed primary schooling to completed
secondary schooling. It then drops slightly (19%) among those individuals who have
completed at least one year of higher education. Lastly, the fact that human capital is
thin on the ground does not appear to protect those who have it against unemployment.
This is particularly true in Lomé where unemployment increases strictly with the level
of education (from 8% for those with no education to 22.5% for those with higher
education). The trends are less linear in the other cities. In most cases, unemployment
tends first to increase with the level of education, but then decrease with the completion
of secondary school and entry into higher education studies. This is particularly the
case in Cotonou, Dakar and Ouagadougou where higher education somewhat reduces
the extent of unemployment.
14
Table 4. Unemployment rates by level of education (%)
Cotonou
Primary incomplete or
no schooling
Primary complete or
middle school
incomplete
Vocational secondary
Middle school complete
or secondary incomplete
Secondary complete
Higher education
Total
Ouaga- Abidja
dougou
n
Bamak
o
Niame
y
Dakar
Lomé
Total
4.4
19.6
12.6
11.1
22.9
18.7
8.5
14.1
7.8
27.3
21.4
16.1
33.1
24.5
11.3
19.9
5.6
23.1
26.4
16.6
20.6
21.2
15.3
20.5
10.4
33.0
20.2
21.0
24.9
21.6
15.3
20.4
22.3
14.2
6.8
34.0
14.2
22.6
19.7
22.8
16.4
6.3
9.2
12.6
14.3
11.0
23.6
22.4
15.0
19.8
22.7
22.4
11.3
21.0
18.5
16.4
The findings of the econometric analysis (Table 5) are similar to those of the descriptive
analysis. Other things being equal, individuals without the minimum level of schooling
appear to be less exposed to unemployment that those who have at least completed
primary school. Lomé shows a strong positive relation between unemployment and
education. Cotonou and Abidjan also follow this trend. In the other cities, the link
between unemployment and level of education takes the bell shape observed
previously.
Table 5. Logistic modelling of unemployment probabilities for individuals aged 15 to
59 (all capitals)
Relative risk of
unemployment (odds ratio)
Marginal
probability
0.93***
1.00***
1.42***
-0.004***
0.000***
0.021***
Primary complete or middle incomplete
Vocational secondary
Middle complete or secondary incomplete
Secondary complete
General higher education
1.55***
2.17***
1.89***
2.15***
2.48***
0.029***
0.064***
0.048***
0.063***
0.077***
Pseudo R2
Log pseudo-likelihood
Overall significance (Wald Chi2(32))
Observations
0.23
-12,328
2,384
37,309
Individual characteristics
Age
(Age)2
Woman
Level of education
(Reference = None or primary incomplete)
15
*, ** and *** signify respectively significant coefficient at the 10%, 5% and 1% level. The
additional explanatory variables in the model are migratory status, marital status, the
household’s per capita income, how the individual is related to the head of household, the
household’s dependency ratio, dummy variables for the country, and a dummy variable for
an undeclared level of education (not significant).
The fact that investment in human capital does not always open the door to
employment reflects the state of deterioration on the African urban labour markets. This
deterioration is due to the failure (or absence) of urbanisation policies unable, for
whatever reason, to set in motion a drive to create skilled jobs. It is also a consequence
of the structural adjustment policies whose credo was, among other things, to reduce
staff in the civil service.
This explanation is all the more plausible in that among the individuals aged 45 to 59,
who entered the labour market before the urban boom and before the full force of the
structural adjustment plans was felt, higher education is synonymous with a low risk of
unemployment across all the countries (Table 6).
Table 6. Unemployment rates by level of education among 45-59 year olds
Primary incomplete or
no schooling
Primary complete or
middle school
incomplete
Vocational secondary
Middle school complete
or secondary incomplete
Secondary complete
Higher education
Total
Cotono
u
Ouagadougou
Abidja
n
Bamak
o
Niamey
Dakar
Lomé
Total
4.4
12.1
7.7
5.5
13.9
11.5
8.5
9.1
4.1
9.8
12.9
12.6
22.4
19.0
8.4
12.8
3.5
10.1
18.3
4.4
23.7
10.2
16.5
12.1
7.6
12.7
2.1
12.1
27.3
10.1
2.8
7.4
0.0
0.0
4.0
0.0
1.6
11.0
0.0
6.2
8.2
14.5
3.0
6.1
17.8
9.2
15.0
6.9
4.2
11.6
7.7
9.5
8.3
3.4
4.9
9.0
Although being unemployed is an indicator of exclusion from the labour market,
having a job does not always guard well against precarity. Before drawing any
conclusions about the efficiency of human capital in the labour market integration
process, we need to look at its effect on the quality of the job held in addition to its
impact on unemployment.
16
4.2 The match between level of education and job: the “qualitative” balance on the
urban labour market
A quantitative analysis of the balance on the labour market reveals the existence of notinconsiderable unemployment against which human capital accumulation is no shield,
especially among young people. An analysis of external efficiency should also consider
the correspondence between level of education and job quality. Job quality is studied
here in terms of the employment sector: public formal, private formal and informal.
Close correspondence between level of education and institutional sector
There is a very close link between level of education and employment sector. In all of
the cities, virtually all of the employed workers (91%) who had not started or completed
primary school worked in the informal sector (Graph 2). Complete primary schooling
brings the proportion in the informal sector down to 75% and the fact of having
completed middle school further reduces it to 50%. Only 19% of the individuals who
had entered higher education worked in the informal sector. Give or take a few
fluctuations, this configuration holds for all the cities except Lomé. Although, in the
Togolese capital, the formal sector clearly supplants the informal sector as the level of
education rises, this trend is slower than in the other cities and a not-inconsiderable
proportion (39%) of people with higher education work in the informal sector.
However, it is worth noting that this city also displays a phenomenon whereby 95% of
individuals who did not start or complete primary school work in the informal sector.
Even when controlling for a certain number of factors (age, gender, migratory status,
marital status, household’s per capita income, how the individual is related to the head
of household, and the household’s dependency ratio), the link between level of
education and employment sector barely changes, regardless of the city considered8.
These findings derived from a multinomial logit model of sector participation are not presented, but are
available on request from the authors.
8
17
Graph 2. Share of workers in the informal sector by education level
1
0.9
0.8
Cotonou
0.7
Ouagadougou
0.6
Abidjan
0.5
Bamako
0.4
Niamey
0.3
Dakar
0.2
Lomé
Total
0.1
0
Primary
incomplete or
no schooling
Primary
complete or
middle
incomplete
Middle
complete or
secondary
incomplete
Vocational
secondary
Secondary
complete
Higher edu.
Although the level of education plays an important role in access to the modern sector,
the type of education also has a not-inconsiderable effect. For example, only 37% of the
individuals with vocational training9 work in the informal sectors as opposed to nearly
50% of their counterparts who reached an equivalent level in the secondary system
(having completed at least middle school without reaching secondary school). When the
cities are taken separately, vocational education is found to be a better instrument for
integration into the modern sector than general education in Niamey, Dakar, Bamako,
Cotonou and Lomé. Approximately 82% of the Nigerian capital’s workers with
vocational training work in the formal sector, as opposed to 71% in Dakar and Bamako,
58% in Cotonou and 50% in Lomé. By way of comparison, the proportion of people who
had completed general studies at middle school and worked in the formal sector stood
at 68% in Niamey, 55% in Dakar, 41% in Bamako, 44% in Cotonou and 30% in Lomé.
However, in Abidjan and Ouagadougou, vocational education shows no advantage
over general education in terms of the chances of entering the formal sector.
Individuals who completed at least four years of vocational education and who therefore obtained at
least the Occupational Proficiency Certificate (CAP).
9
18
4.3 The effect of education on earnings
4.3.1 Intercity earnings vary by level of education and employment sector
The average earned income for all the WAEMU cities is 62,000 CFA francs10. There are
some substantial differences between cities (Table 7). A worker in Abidjan earns an
average 76,000 CFA francs per month whereas a worker in Dakar earns 66,000 CFA
francs per month and a worker in Lomé earns a mere 34,000 CFA francs per month. The
other cities are in intermediate positions with earned incomes of 48,000 to 57,000 CFA
francs. At this aggregate level, there appears to be no clear link between the level of
earnings and the level of human capital as measured by education.
Table 7. Average income and average number of years of education
Cotonou
Ouagadougou
Abidjan
Bamako
Niamey
Dakar
Lomé
Total
Average earned income per worker
(in thousands of CFA francs)
49.2
48.2
76.2
56.9
55.5
65.5
33.9
61.7
Average number of years of
education per employed worker
5.9
4.4
5.2
4.1
4.7
4.7
6.0
5.0
Lomé paradoxically posts the lowest earnings and the highest average number of years
of education. Conversely, workers are much better paid in Abidjan and Dakar where
average levels of education are lower than in Lomé. However, there is a very close link
at the individual level between level of education and earned income (Graph 3). For
example, across all the cities, incomes range from 38,000 CFA francs for those without
the basic level of education (no schooling or primary incomplete) to 122,000 CFA francs
for those who completed secondary school. Entry into higher education prompts a huge
quantitative leap with earnings virtually doubling (from 122,000 to 228,000 CFA francs).
Taken separately, each city follows this same earned income curve: steady growth
through to the end of secondary school followed by a surge at higher education level.
10
Income in terms of Purchasing Power Parity (PPP). Dakar’s PPP factor was taken as the reference.
19
Graph 3. Earned incomes by level of education
Earned income (in 1000 CFAF)
280
240
200
160
120
Cotonou
Ouagadougou
80
Abidjan
Bamako
40
Niamey
0
Dakar
Primary
incomplete or
no schooling
Primary
complete or
middle
incomplete
Middle
complete or
Secondary
incomplete
Vocational
Secondary
Secondary
complete
Higher edu.
Lomé
Total
Earned income (in 1000 CFAF)
Graph 4. Earned income by employment sector
and city
250,0
Public sector
Formal private sector
200,0
Informal sector
150,0
100,0
50,0
l
To
ta
Lo
m
é
ak
ar
N
ia
ak
am
B
D
m
ey
o
n
ja
bi
d
A
ug
ou
ad
o
ua
g
O
C
ot
on
ou
0,0
The breakdown by sector also reveals substantial earnings inequalities (Graph 4). For
example, public-sector workers earn an average of 145,000 CFA francs per month,
which is approximately three and a half times more than informal sector workers who
scrape by with just 39,000 CFA francs per month. Formal private sector workers are also
winners on the labour market with 112,000 CFA francs per month. This bipolar
configuration is found in all the cities studied: high earnings in the public sector,
20
followed closely by the formal private sector (except in Abidjan where private-sector
earnings stand apart at 75,000 CFA francs), while the informal sector lags far behind
these high yields.
4.3.2 Private returns to education
The descriptive analyses above show the huge variability in earned incomes in the
major West African cities: variability by city, level of education and employment sector.
Yet although these analyses find a close link between investment in education and
earnings, it is hard and tricky to deduce the intrinsic efficiency of investment in human
capital on the labour markets considered. Isolating this efficiency entails first
controlling for a certain number of factors that could simultaneously affect education
and productivity or remuneration (migratory status, marital status, religion, job
seniority, potential experience, gender and employment sector). Moreover, a notinconsiderable proportion (approximately 40% in all the cities) of the potentially
working population aged 15 and over is either out of the labour force or unemployed.
Although this decision to not work is not random, any estimate of returns to education
not taking into account non-participation is potentially biased. We estimated selectioncorrection models using the method advocated in Section 3 in order to correct this
selection bias. In addition, the estimates were made on the log of hourly rather than
monthly earnings to take account of the heterogeneity of working hours in different
sectors. Lastly, the probable segmentation of the labour market calls for an estimation of
models by employment sector. The findings of all these exercises are presented in tables
A2 to A12 in the appendix.
Marginal returns to education increase in line with years of education
The findings confirm one of the facts revealed by the descriptive analyses, that of a nonconstant rate of returns to education in each city. Regardless of the city considered, the
quadratic term (level of education squared) is always significant and positive (Table A2
in the appendix and Graph 5a below). This means that education has an accelerator
effect on labour market remuneration and hence that the marginal returns increase as
human capital is accumulated. This growth is due largely to the surge in earnings
observed in the descriptive analyses when individuals make the transition from
secondary to higher education.
21
The non-linear nature of the relationship between years of education and remuneration
means that it is impossible to estimate a single marginal return. Instead, we have to
estimate an average marginal return, i.e. a marginal return corresponding to the
average number of years of education. This estimate finds that Ouagadougou has the
highest returns to education, at nearly 10%. Next in line are Lomé (8.3%), and Cotonou,
Abidjan and Niamey (7.5%). At the bottom of the scale, the average returns in Bamako
and Dakar are just 6% and 6.5% respectively. Our estimates also confirm a certain
number of findings noted by other papers (Table A2 in the appendix). For example,
women in all the cities earn, other things being equal, from 18% (in Niamey) to 46% (in
Bamako) less than the men. Likewise, the informal sector pays a lot less11 than the
private sector, which pays slightly less than the public sector12 (Tables A7 to A9).
We estimated the returns to qualifications to better explain the non-linearity of the link
between level of education and remuneration and also to address both the quality and
quantity of education.
Returns to qualifications
Returns to qualifications can be studied in two ways. One way is to directly consider
the regression model coefficients (Tables A4 to A6 in the appendix). In this case, the
coefficient associated with each qualification is interpreted as the rate of increase in
earnings between individuals with no qualifications (the reference in the regressions)
and individuals with the qualification considered. Another way is to calculate the
marginal returns obtained by subtracting from the considered qualification’s coefficient
(qualification d) the value of the coefficient for the qualification immediately below it
(qualification d-1). For example, the marginal returns to a baccalauréat plus two years of
higher education (Bac+2) are calculated by finding the difference between the
coefficient for the Bac+2 and the coefficient for just the baccalauréat alone. For the
occupational proficiency certificate (CAP), qualification d-1 is the primary certificate
(CEP) and not the middle school certificate (BEPC). The marginal returns hence
correspond to the increases in earnings generated by the acquisition of the successive
11
12
The differences between the two sectors range from 23% in Cotonou to 62% in Ouagadougou.
With deviations varying from some 4% in Ouagadougou to 20% in Lomé and Cotonou.
22
qualifications. In this paper, we choose to interpret the marginal returns since they
measure the additional value of each qualification rather than the value compared with
“no qualifications”, which can almost always only ever be positive.
Not surprisingly, the effect of each qualification on remuneration is positive overall
with a huge quantitative leap for higher education, as already shown by the descriptive
analyses. Moreover, vocational education qualifications are often found to be more
profitable than general education qualifications when compared with the number of
years required to obtain them (Graph 5). For example, although it generally takes one
year less to obtain the vocational certificate (BEP) than to obtain the baccalauréat, the
BEP is much more profitable than the baccalauréat in all the cities except Abidjan and
Niamey. The returns to the BEP are even found to be over 30% higher than the returns
to the baccalauréat in Cotonou, Ouagadougou, Bamako and Lomé.
Likewise, although it takes the same number of years to study for both the BEPC and
the CAP, this occupational proficiency certificate drives hourly wages higher than the
BEPC in all the cities except Ouagadougou and Lomé, where it is less profitable. The
returns to the CAP are hence over 20% higher than the returns to the BEPC in Cotonou,
Niamey and Dakar and approximately 5% higher in the other cities.
The two lowest educational qualifications, i.e. the CEP and the BEPC, are two of the
most profitable qualifications with marginal returns of 35% each overall and returns
varying from 25% in Lomé to 54% in Ouagadougou for the CEP and from 12% in Dakar
to 55% in Ouagadougou for the BEPC. The decisive effect of these two basic
qualifications, especially the CEP, on the increase in earnings constitutes a weighty
argument in favour of the Millennium Development Goal of “primary education for
all”.
In addition to the most basic level of qualifications, the Bac+2 qualifications obtained
from two years of higher education are also highly profitable with marginal rates of
around 35% for all the cities and a rate over 60% higher than for the baccalauréat in
Lomé. Niamey is the only city where a Bac+2 qualification brings in little (merely 5%). It
is striking to see that the baccalauréat, taken at the end of secondary school and directly
23
or indirectly (via a competitive exam) opening the door to university, earns little in
virtually all the cities except Abidjan where it brings in as much as Bac+2 qualifications.
Graph 5. Marginal returns to qualifications in all employment sectors
0,8
0,6
0,4
0,2
0
Cotonou
Ouagadougou Abidjan
Bamako
Niamey
Dakar
Lomé
Total
-0,2
-0,4
Primary certificate (CEP)
Middle school certificate (BEPC)
Occupational proficiency certificate (CAP)
Vocational certificate (BEP)
Baccalauréat (BAC)
Qualifications requiring two years of higher education
(DEUG/DUT/BTS)
Qualifications requiring over two years of higher education
Estimation of the returns to qualifications by employment sector
The application of a single model to all gainfully employed individuals can only
observe the average effect of education on earnings owing to specific effects found in
each employment sector. In the case in which these specific effects differ little from one
sector to the next (i.e. education acts in the same way in the informal, formal private
and public sectors), an overall model suffices to be able to draw conclusions applicable
to each of the labour market segments. Where these effects vary a great deal, it is also
vital to estimate the returns to education separately for each sector to be able to observe
their specific effects. These estimates find highly contrasting configurations (Tables A7
to A12 in the appendix).
The explanatory power of the models corrected for the selection bias used in tables A6
to A11 explains on average 50%, 43% and 33.5% respectively of the individual earnings
deviations in the public sector, the formal private sector and the informal sector.
24
Graph 6. Marginal returns to qualifications in the public sector
0,8
0,6
0,4
0,2
0
Cotonou
Ouagadougou
Abidjan
Bamako
Niamey
Dakar
Lomé
Total
-0,2
-0,4
Primary certificate (CEP)
Middle school certificate (BEPC)
Occupational proficiency certificate (CAP)
Vocational certificate (BEP)
Baccalauréat (BAC)
Two years of higher education (DEUG/DUT/BTS)
Over two years of higher education
In the public sector (Graph 6), as in the overall models, the disparities in levels of human
capital measured by qualification level are highly significant in the explanation of
earnings differentials. Regardless of the city considered, the two middle school
qualifications, i.e. the BEPC and the CAP, have a substantial marginal profitability. Yet
aside from this constant, the additional profitability of the other qualifications fluctuates
from one city to the next. For example, compared with individuals with no
qualifications, individuals holding solely the primary certificate (CEP) secure no
additional remuneration gain in Lomé, Cotonou and Bamako. Although the absence of
profitability in Cotonou and Lomé might be explained by the extensive development of
schooling in these cities, the case of Bamako is hard to explain. Contrary to the
observation in these three cities, holding the CEP gives a substantial boost (25% to 42%)
to the wages of individuals in the public sector in Ouagadougou, Niamey and Dakar.
Although the baccalauréat is highly profitable in Abidjan (65%), Bamako (39%) and, to a
lesser extent, Lomé (22%), it has a limited if not no effect in Cotonou, Ouagadougou and
Dakar. Qualifications from higher education courses lasting more than two years make
a substantial marginal contribution to earnings (over 20%) in all the cities except Lomé,
25
where returns are nonexistent compared with the returns to qualifications from twoyear higher education courses (Bac+2).
In the formal private sector, human capital measured by qualifications does very little to
explain the earnings differences in hardly any of the cities. With the notable exception
of Lomé, where qualifications higher than the primary certificate (CEP) generate a gain
on the labour market, the analyses (Table A11) show that only higher education
qualifications clearly and significantly increase earned income in the other cities. The
analyses to date do not provide any clear explanations as to why this might be. One
hypothesis, however, could be that unobservable characteristics affect education and
income in different ways13. In any case, although these unobservables are at work via
the selection factor, this selection factor appears to be negatively correlated with income
and the analyses would have to be developed in more depth to find out whether it is
correlated positively with the level of education.
In the informal sector (Graph 7), contrary to the formal private sector, the returns to
education are much more significant. Save in Dakar, where only qualifications from
higher education courses lasting more than two years have a significant effect on
earnings, the returns to each qualification in the other six cities are significantly positive
and increase overall with the qualification level attained compared with individuals
with no qualifications. Here, as in the public sector, the marginal profitability of
qualifications varies from one city to the next. For example, although the vocational
certificate is highly profitable in Ouagadougou, Bamako, Niamey and Lomé, it is not
found to be significant for Cotonou and is less profitable than the occupational
proficiency certificate (CAP) in Abidjan. In these latter two cities, the baccalauréat
generates a substantial gain (of over 40%) compared with the vocational certificate,
whereas it is much less profitable in all the other cities.
13
For example, positively with income and negatively with the level of education, or vice versa.
26
Graph 7. Marginal returns to qualifications in the informal sector
0,8
0,6
0,4
0,2
0
Cotono
Ouagadoug
-0,2
Abidja
Bamak
Niamey
Dakar
Lomé
Total
-0,4
Primary certificate (CEP)
Middle school certificate (BEPC)
Occupational proficiency certificate (CAP)
Vocational certificate (BEP)
Baccalauréat (BAC)
Two years of higher education (DEUG/DUT/BTS)
Over two years of higher education
5. Conclusion
Education appears to be a definite factor for increased earnings on the urban
labour markets in West Africa. Although it does not always guard against
unemployment, it does open the door for the most well-educated to get into the most
profitable niches, which are found in the formal private and public sectors. These
findings have already been relatively well covered by other papers on the subject. The
major contribution of our study is that we show that educational capital, including at
advanced levels, substantially increases the gains in the informal sector in most of the
WAEMU cities. This finding has considerable political implications: we are seeing a
boom in the number of highly educated young people in the African cities who are not
finding work compatible with their skills. The findings of this study suggest that their
integration into the informal sector is far from a loss in terms of household and
27
government investment in education. In fact, their intellectual grounding should enable
them, doubtless by means of innovation and adaptation, to be more productive than
their colleagues with little or no qualifications. This constitutes a weighty argument
against the idea that investment in education should concentrate more on primary
education to the detriment of the higher levels of education. Given that the informal
sector creates over 80% of the jobs in the WAEMU cities (Brilleau et al., 2005),
concentrating public employment action in this sector by introducing policies that are
really attractive for the most productive workers (i.e. all those with qualifications)
would, at least in the short term, provide a serious alternative to the lack of
employment observed in the public and formal private sectors. Such a policy, coupled
with continued support to primary and post-primary education, could also pay off in
the medium to long term by generating the accumulation required for the modern
economy to take off in the African cities.
28
APPENDIX
Table A1. Overview of certain labour market indicators in the seven capitals (2001-2002)
Indicators
Cotonou
Benin
Ouagadougou
Burkina
Abidjan
Côte
d’Ivoire
49.6
49.3
70.9%
Bamako
Mali
Niamey
Niger
Dakar
Senegal
Lomé
Togo
Total
78.4
59.4
57.3
67.1
34.9
63.4
58.9%
66.7%
46.3%
57%
59.9%
73.5%
63.1%
6.4%
5.4%
5.6%
7.5%
8.7%
10.2%
30.3%
9.3%
27%
40.2%
29.6%
48.7%
38.2%
35.9%
21.7%
30.2%
(% of the female labour force
aged 15-59)
83.8%
77.9%
87.0%
67.4%
56.8%
63.3%
88.3%
74.6%
- No schooling
- Higher education
84.4%
87.1%
73.8%
96.6%
76.4%
94.3%
64.2%
83.7%
55%
91.1%
62%
76.6%
87.9%
91.1%
70.6%
90.0%
Proportion of the male
labour force in the informal
sector
70.0%
74.9%
63.1%
71.0%
67.6%
69.4%
74.7%
68.2%
91.2%
75.6%
19.6%
90.9%
71.6%
20.0%
85.3%
58.7%
23.3%
84.7%
74.6%
17.3%
85.0%
60.9%
18.6%
85.8%
54.4%
19.6%
88.1%
76.0%
39.2%
86.2%
65.1%
22.4%
41.8%
37.3%
34.1%
32.4%
36.3%
33.8%
42.2%
35.2%
20.5%
17.8%
25.5%
21.9%
16.7%
16.4%
22.0%
21.9%
7.3%
2.3%
5.9%
7.5%
1.0%
5.2%
3.6%
5.4%
Average monthly income
from main job
(1,000 CFAF)
Literacy
Reads and
French
writes
in
(% of the adult population
aged 20-45)
Reads and writes in a
national language
(% of the adult population
aged 20-45)
Cannot read or write in
any language
(% of the adult population
aged 20-45)
Female participation rate
(% of the male labour force
aged 15-59)
- No schooling
- General middle school
- Higher education
Proportion
households
of
poor
(1st quartile of per capita
income)
- Head of household with
no schooling
- Head of household with
general middle school
edu.
- Head of household with
higher education
Source: PARSTAT, 1-2-3 Survey, Phase 1 (2001-2002).
29
Table A2. Estimation of the marginal returns to education from an earnings function (both genders)
Dependent variable: log of hourly earnings
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.052***
(6.11)
0.002***
0.080***
(8.78)
0.002***
0.012
(1.50)
0.006***
0.028**
(2.42)
0.004***
0.047***
(4.88)
0.003***
0.047***
(5.61)
0.002***
0.023**
(2.18)
0.005***
0.032***
(8.19)
0.004***
(3.95)
(4.57)
(11.10)
(4.49)
(5.64)
(4.35)
(6.67)
(15.93)
Potential experience
0.004
0.031***
0.020***
0.041***
0.017***
0.025***
0.018***
0.019***
(age – years of education – 6)
(0.67)
(5.01)
(3.81)
(6.35)
(2.70)
(4.26)
(2.82)
(7.29)
Completed years of education
(Completed years of education)2
(Potential experience)2/100
Seniority in current job
(Seniority in current job) 2/100
Woman
Public sector
Formal private sector
(8)
0.010
-0.029***
-0.010
-0.046***
-0.013
-0.023***
-0.013
-0.013***
(0.98)
(3.17)
(1.20)
(4.93)
(1.57)
(2.71)
(1.22)
(3.26)
0.026***
0.031***
0.029***
0.030***
0.032***
0.025***
0.034***
0.029***
(4.31)
(5.06)
(4.79)
(4.73)
(5.73)
(4.80)
(5.77)
(11.69)
-0.049**
-0.047**
-0.066***
-0.051***
-0.051***
-0.039***
-0.063***
-0.055***
(2.44)
(2.29)
(3.31)
(2.72)
(3.09)
(2.62)
(3.21)
(7.26)
-0.436***
-0.354***
-0.359***
-0.456***
-0.179***
-0.285***
-0.314***
-0.436***
(12.98)
(9.96)
(11.24)
(11.22)
(4.11)
(7.92)
(8.82)
(12.98)
0.402***
0.664***
0.648***
0.326***
0.446***
0.494***
0.628***
0.402***
(7.39)
(13.59)
(12.81)
(5.28)
(10.16)
(10.73)
(11.17)
(7.39)
0.232***
0.621***
0.469***
0.237***
0.404***
0.403***
0.438***
0.232***
(4.99)
(12.77)
(13.54)
(4.28)
(8.19)
(10.60)
(7.59)
(4.99)
-0.416***
(7.75)
-2.112***
(17.25)
-0.329***
(6.64)
-2.962***
(26.40)
-0.313***
(6.99)
-2.086***
(22.01)
-0.002
(0.04)
-2.086***
(22.01)
-0.272***
(5.33)
-2.563***
(21.77)
-0.246***
(4.59)
-2.434***
(19.76)
-0.325***
(5.00)
-2.799***
(23.47)
-0.333***
(14.22)
-2.083***
(39.08)
4182
0.43
3663
0.56
4011
0.49
4011
0.36
3817
0.44
3068
0.39
3491
0.37
26561
0.47
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for column (8). The inverse
Mills ratio is derived from a probit estimation of labour market participation for each country (with, as dependent variable, a dummy variable of strictly positive income) comprising age and age squared, gender,
years of education, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita income, how the individual is related to the head of household and the
dependency ratio. The Student statistics are given in brackets. The standard deviations are corrected using White’s method. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1%
level. The reference category is a man (Nigerian in Model (8)) employed in the informal sector.
Table A3. Estimation of the marginal returns to education by level of education based on an earnings function (both genders)
Dependent variable: log of hourly earnings
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.072***
0.079***
0.038***
0.043***
0.039***
0.065***
0.050***
0.052***
(8.79)
(9.54)
(4.92)
(3.94)
(4.27)
(9.24)
(5.66)
(14.70)
0.041***
0.147***
0.106***
0.082***
0.144***
0.065***
0.083***
0.090***
(3.02)
(9.35)
(6.90)
(3.15)
(7.52)
(4.35)
(5.91)
(12.99)
0.168***
0.180***
0.194***
0.170***
0.153***
0.110***
0.192***
0.175***
(6.60)
(6.80)
(7.52)
(3.72)
(5.50)
(4.35)
(6.90)
(14.10)
0.245***
0.192***
0.145***
0.136***
0.166***
0.117***
0.160***
(8.23)
0.102***
(8.07)
0.102***
(5.90)
0.157***
(6.33)
0.081**
(8.13)
0.086***
(3.90)
0.131***
(5.70)
0.135***
0.130***
(11.71)
(5.84)
(5.62)
(8.39)
(2.30)
(4.95)
(6.77)
(4.74)
(13.64)
Potential experience
0.003
0.033***
0.020***
0.041***
0.017***
0.025***
0.018***
0.003
(age – years of education – 6)
(0.45)
(5.29)
(3.78)
(6.29)
(2.80)
(4.22)
(2.77)
(0.45)
0.012
-0.032***
-0.011
-0.046***
-0.015*
-0.022***
-0.012
0.012
(1.18)
(3.44)
(1.23)
(4.84)
(1.70)
(2.63)
(1.18)
(1.18)
0.026***
0.031***
0.029***
0.030***
0.031***
0.025***
0.034***
0.026***
(4.25)
(5.08)
(4.83)
(4.72)
(5.56)
(4.76)
(5.74)
(4.25)
-0.049**
-0.048**
-0.066***
-0.051***
-0.050***
-0.038**
-0.062***
-0.049**
(2.43)
(2.32)
(3.30)
(2.74)
(2.99)
(2.57)
(3.16)
(2.43)
-0.433***
-0.368***
-0.364***
-0.459***
-0.194***
-0.283***
(12.95)
0.388***
(10.29)
0.645***
(11.38)
0.641***
(11.19)
0.301***
(4.43)
0.409***
(7.89)
0.495***
-0.317***
(8.92)
-0.433***
(12.95)
0.616***
0.388***
(7.34)
(13.13)
(12.55)
(4.92)
(9.33)
(10.75)
(11.11)
(7.34)
0.213***
0.602***
0.467***
0.224***
0.383***
0.404***
0.423***
0.213***
(4.51)
(12.29)
(13.44)
(4.01)
(7.80)
(10.58)
(7.36)
(4.51)
-0.396***
-0.288***
-0.276***
0.009
-0.251***
-0.240***
-0.286***
-0.300***
Years in primary education
Years in general middle school
Years in general secondary school
Years in vocational education
Years in higher education
(Potential experience)2/100
Seniority in current job
(Seniority in current job) 2/100
Woman
Public sector
Formal private sector
Selection correction
Inverse Mills ratio
(8)
0.126***
31
(7.29)
-2.097***
(17.08)
4183
0.43
Constant
Observations
R-squared
(5.81)
-2.963***
(26.54)
3663
0.56
(6.19)
-2.080***
(21.51)
4011
0.51
(0.17)
(4.86)
(4.46)
(4.40)
(12.84)
-2.549***
(21.73)
3817
0.36
-2.391***
(19.45)
3068
0.45
-2.158***
(17.72)
4354
0.39
-2.800***
(23.37)
-2.802***
(54.51)
3493
0.38
26589
0.48
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for column (8). The inverse
Mills ratio is derived from a probit estimation of labour market participation for each country (with, as dependent variable, a dummy variable of strictly positive income) comprising age and age squared, gender,
years of education, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita income, how the individual is related to the head of household and the
dependency ratio. The Student statistics are given in brackets. The standard deviations are corrected using White’s method. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1%
level. The reference category is a man (Nigerian in Model (8)) employed in the informal sector.
Table A4. Estimation of the returns to qualifications based on an earnings function (both genders)
Dependent variable: log of hourly earnings
CEP (primary certificate)
BEPC (middle school certificate)
Occupational proficiency certificate
(CAP)
Vocational certificate (BEP)
Baccalauréat
DEUG/DUT/BTS (Two years of
higher education)
Qualification obtained after more
than two years of higher education
Other qualifications
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
0.325***
(8.68)
0.592***
(11.80)
0.863***
(9.95)
1.291***
(7.09)
1.035***
(12.50)
1.286***
(9.31)
1.310***
(17.46)
1.183***
(7.20)
0.550***
(12.50)
1.076***
(17.46)
0.973***
(10.58)
1.425***
(12.18)
1.456***
(17.60)
1.725***
(14.71)
1.933***
(21.59)
1.501***
(12.55)
0.292***
(7.94)
0.658***
(12.18)
0.705***
(5.30)
0.729***
(7.13)
1.056***
(13.23)
1.378***
(17.66)
1.657***
(20.75)
1.336***
(10.91)
0.381***
(6.22)
0.636***
(7.68)
0.681***
(7.44)
0.999***
(12.74)
0.848***
(4.71)
1.055***
(9.98)
1.340***
(15.20)
1.178***
(4.71)
0.401***
(7.98)
0.716***
(9.08)
1.039***
(10.13)
1.005***
(10.63)
1.155***
(11.12)
1.208***
(9.71)
1.491***
(21.81)
1.367***
(14.24)
0.370***
(8.56)
0.497***
(8.18)
0.728***
(5.97)
0.814***
(5.92)
0.765***
(10.77)
0.986***
(8.55)
1.222***
(13.82)
1.330***
(12.90)
0.241***
(6.20)
0.636***
(10.91)
0.555***
(3.74)
0.967***
(7.37)
0.943***
(9.24)
1.523***
(8.72)
1.610***
(16.02)
1.199***
(7.99)
Total
(8)
0.332***
(17.93)
0.666***
(25.50)
0.713***
(12.82)
0.857***
(17.93)
1.019***
(24.31)
1.350***
(25.80)
1.527***
(37.34)
1.301***
(22.10)
32
Potential experience
(Potential experience)2/100
Seniority in current job
(Seniority in job) 2/100
Woman
Public sector
Formal private sector
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
-0.001
(0.23)
0.014
(1.42)
0.027***
(4.52)
-0.053***
(2.62)
-0.486***
(14.89)
0.398***
(7.13)
0.227***
(4.78)
0.024***
(3.99)
-0.024***
(2.59)
0.032***
(5.26)
-0.050**
(2.42)
-0.380***
(10.57)
0.658***
(13.19)
0.606***
(12.26)
0.013**
(2.47)
-0.004
(0.44)
0.031***
(5.16)
-0.070***
(3.47)
-0.392***
(12.25)
0.674***
(12.78)
0.494***
(14.10)
0.040***
(6.22)
-0.046***
(4.88)
0.029***
(4.65)
-0.050***
(2.67)
-0.473***
(11.48)
0.328***
(5.16)
0.235***
(4.15)
0.008
(1.39)
-0.006
(0.77)
0.032***
(5.86)
-0.052***
(3.16)
-0.194***
(4.37)
0.435***
(9.87)
0.404***
(8.19)
0.013**
(2.21)
-0.012
(1.44)
0.026***
(4.89)
-0.036**
(2.44)
-0.308***
(8.48)
0.531***
(11.67)
0.444***
(11.37)
0.011*
(1.74)
-0.008
(0.75)
0.037***
(6.22)
-0.068***
(3.50)
-0.349***
(9.91)
0.651***
(11.89)
0.458***
(8.03)
0.012***
(4.54)
-0.007*
(1.68)
0.031***
(12.41)
-0.058***
(7.64)
-0.358***
(22.40)
0.573***
(25.70)
0.439***
(22.16)
-0.420***
(7.71)
-1.834***
(15.69)
4182
0.42
-0.298***
(5.98)
-2.723***
(25.92)
3663
0.56
-0.281***
(6.28)
-1.885***
(20.57)
4011
0.50
0.018
(0.34)
-2.480***
(21.84)
3817
0.36
-0.266***
(5.09)
-2.149***
(19.03)
3068
0.45
-0.272***
(5.00)
-1.770***
(14.62)
4329
0.38
-0.311***
(4.92)
-2.527***
(22.56)
3491
0.38
-0.314***
(13.40)
-2.128***
(42.73)
26561
0.47
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for column (8). The inverse
Mills ratio is derived from a probit estimation of labour market participation for each country (with, as dependent variable, a dummy variable of strictly positive income) comprising age and age squared, gender,
qualifications obtained, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita income, how the individual is related to the head of household and the
dependency ratio. The Student statistics are given in brackets. The standard deviations are corrected using White’s method. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1%
level. The reference category is a man (Nigerian in Model (8)) with no qualifications employed in the informal sector.
Table A5. Estimation of the returns to qualifications based on an earnings function for men
Dependent variable: log of hourly earnings
CEP (primary certificate)
BEPC (middle school certificate)
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.323***
(6.47)
0.618***
(9.70)
0.526***
(10.68)
0.996***
(14.00)
0.280***
(5.86)
0.611***
(9.22)
0.460***
(5.61)
0.799***
(7.37)
0.379***
(6.14)
0.740***
(8.19)
0.356***
(6.74)
0.612***
(8.52)
0.193***
(3.30)
0.668***
(9.44)
0.326***
(13.73)
0.686***
(21.37)
(8)
33
Occupational proficiency certificate
(CAP)
Vocational certificate (BEP)
Baccalauréat
DEUG/DUT/BTS (Two years of
higher education)
Qualification obtained after more
than two years of higher education
Other qualifications
Potential experience
(Potential experience)2/100
Seniority in current job
(Seniority in job) 2/100
Public sector
Formal private sector
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
0.938***
(10.67)
1.440***
(5.50)
1.044***
(11.53)
1.408***
(8.31)
1.366***
(15.94)
1.347***
(7.70)
-0.008
(0.88)
0.038**
(2.48)
0.029***
(3.25)
-0.044
(1.43)
0.204***
(3.16)
0.064
(1.14)
1.071***
(9.12)
1.235***
(8.48)
1.444***
(15.21)
1.744***
(12.55)
1.866***
(18.09)
1.453***
(11.46)
0.020**
(2.44)
-0.011
(0.88)
0.028***
(3.85)
-0.041*
(1.70)
0.475***
(9.61)
0.424***
(8.17)
0.792***
(4.41)
0.809***
(5.71)
1.044***
(11.25)
1.296***
(13.57)
1.619***
(17.60)
1.421***
(9.84)
0.014*
(1.85)
0.005
(0.40)
0.024***
(3.15)
-0.049**
(2.05)
0.572***
(9.28)
0.385***
(9.61)
0.631***
(4.84)
0.925***
(10.42)
0.977***
(4.54)
1.064***
(8.15)
1.333***
(14.27)
1.407***
(6.83)
0.014
(1.50)
0.002
(0.16)
0.030***
(3.88)
-0.061***
(2.77)
0.242***
(3.26)
0.207***
(3.42)
1.018***
(6.95)
0.970***
(7.23)
1.086***
(9.52)
1.191***
(8.25)
1.490***
(18.69)
1.433***
(12.35)
-0.007
(0.92)
0.021**
(1.98)
0.026***
(3.80)
-0.047**
(2.30)
0.370***
(7.55)
0.367***
(6.78)
0.774***
(6.68)
0.837***
(3.88)
0.811***
(10.17)
1.096***
(8.69)
1.260***
(14.00)
1.553***
(13.61)
-0.019**
(2.34)
0.048***
(3.78)
0.025***
(3.74)
-0.038**
(2.11)
0.459***
(8.89)
0.360***
(8.31)
0.714***
(3.66)
0.913***
(6.45)
0.888***
(7.35)
1.338***
(7.18)
1.610***
(14.63)
1.207***
(7.01)
0.006
(0.65)
0.012
(0.78)
0.032***
(3.83)
-0.066**
(2.34)
0.489***
(7.69)
0.323***
(5.12)
0.771***
(9.89)
0.886***
(15.13)
1.035***
(21.48)
1.314***
(22.54)
1.519***
(33.51)
1.420***
(21.57)
0.002
(0.43)
0.019***
(3.50)
0.028***
(8.85)
-0.053***
(5.61)
0.438***
(17.08)
0.328***
(14.76)
-0.579***
(7.70)
-1.733***
(11.43)
2064
0.39
-0.680***
(9.49)
-2.527***
(19.24)
2135
0.54
-0.412***
(5.88)
-1.887***
(15.47)
2244
0.46
-0.575***
(6.05)
-2.096***
(13.04)
2129
0.34
-0.479***
(6.13)
-1.870***
(13.28)
1941
0.45
-0.811***
(9.85)
-1.264***
(8.22)
2448
0.40
-0.446***
(5.55)
-2.351***
(15.34)
1687
0.34
-0.613***
(16.65)
-2.412***
(37.59)
14648
0.44
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for
column (8). The inverse Mills ratio is derived from a probit estimation of labour market participation for each country (with, as dependent variable, a dummy variable of strictly positive
income) comprising age and age squared, gender, qualifications obtained, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita
income, how the individual is related to the head of household and the dependency ratio. The Student statistics are given in brackets. The standard deviations are corrected using White’s
method. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1% level. The reference category is a man (Togolese in Model (8)) with no qualifications
employed in the informal sector.
34
Table A6. Estimation of the returns to qualifications based on an earnings function for women
Dependent variable: log of hourly earnings
CEP (primary certificate)
BEPC (middle school certificate)
Occupational proficiency certificate
(CAP)
Vocational certificate (BEP)
Baccalauréat
DEUG/DUT/BTS (Two years of
higher education)
Qualification obtained after more
than two years of higher education
Other qualifications
Potential experience
(Potential experience)2/100
Seniority in current job
(Seniority in job) 2/100
Public sector
Formal private sector
Selection correction
Inverse Mills ratio
Constant
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.322***
(5.73)
0.465***
(5.64)
0.625***
(3.56)
0.943***
(5.20)
1.021***
(6.04)
0.845***
(6.38)
1.279***
(9.45)
0.811***
(2.89)
0.007
(0.81)
-0.007
(0.55)
0.027***
(3.77)
-0.068***
(3.18)
0.820***
(8.37)
0.658***
(8.42)
0.574***
(7.21)
1.215***
(10.39)
0.925***
(6.58)
1.597***
(9.14)
1.531***
(10.78)
1.687***
(9.94)
2.190***
(13.46)
1.666***
(7.07)
0.025***
(2.82)
-0.029**
(2.19)
0.037***
(3.28)
-0.087**
(2.03)
0.995***
(9.14)
0.936***
(8.49)
0.271***
(4.65)
0.766***
(8.55)
0.485***
(3.10)
0.552***
(3.73)
1.040***
(7.14)
1.335***
(10.20)
1.846***
(12.31)
1.191***
(4.94)
0.008
(1.02)
-0.003
(0.23)
0.044***
(4.38)
-0.110***
(3.06)
0.846***
(8.52)
0.756***
(10.35)
0.335***
(3.61)
0.477***
(3.30)
0.763***
(5.37)
1.168***
(8.26)
1.323***
(3.50)
1.193***
(5.90)
1.562***
(6.10)
0.395
(0.49)
0.064***
(7.23)
-0.087***
(6.77)
0.029***
(2.67)
-0.066*
(1.69)
0.561***
(4.74)
0.289**
(2.37)
0.519***
(5.63)
0.795***
(5.08)
1.245***
(7.28)
1.235***
(8.83)
1.685***
(7.05)
1.303***
(6.03)
1.719***
(12.11)
1.417***
(7.96)
0.039***
(4.00)
-0.050***
(3.66)
0.038***
(4.24)
-0.066**
(2.37)
0.558***
(5.45)
0.465***
(4.13)
0.456***
(6.13)
0.429***
(4.03)
0.897***
(3.82)
0.789***
(4.22)
0.778***
(5.38)
0.911***
(4.00)
1.321***
(5.41)
1.002***
(4.89)
0.027***
(3.30)
-0.038***
(3.24)
0.026***
(3.12)
-0.038
(1.55)
0.668***
(7.17)
0.587***
(6.78)
0.268***
(5.04)
0.474***
(4.22)
0.201
(0.97)
0.802***
(3.13)
1.102***
(6.47)
2.264***
(10.89)
1.712***
(6.08)
1.398***
(6.94)
0.014*
(1.65)
-0.020
(1.53)
0.038***
(4.61)
-0.065**
(2.42)
1.113***
(11.85)
0.822***
(6.64)
0.329***
(11.26)
0.616***
(13.69)
0.584***
(8.42)
0.771***
(9.89)
1.056***
(13.55)
1.360***
(14.36)
1.692***
(19.05)
1.081***
(8.61)
0.020***
(5.19)
-0.025***
(4.43)
0.036***
(9.10)
-0.077***
(6.25)
0.863***
(19.97)
0.697***
(16.55)
-0.224***
(2.92)
-2.582***
(15.15)
0.056
(0.83)
-3.436***
(21.25)
-0.184***
(3.11)
-2.259***
(17.28)
0.397***
(5.72)
-3.378***
(23.32)
0.086
(1.11)
-3.157***
(15.14)
0.036
(0.51)
-2.474***
(12.98)
-0.197**
(2.18)
-3.049***
(19.10)
-0.060*
(1.84)
-2.884***
(39.39)
(8)
35
Observations
R-squared
2118
0.29
1528
0.55
1767
0.44
1688
0.32
1127
0.41
1881
0.28
1804
0.27
11913
0.39
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for
column (8). The inverse Mills ratio is derived from a probit estimation of labour market participation for each country (with, as dependent variable, a dummy variable of strictly positive
income) comprising age and age squared, gender, qualifications obtained, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita
income, how the individual is related to the head of household and the dependency ratio. The Student statistics are given in brackets. The standard deviations are corrected using White’s
method. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1% level. The reference category is a woman (Beninese in Model (8)) with no qualifications
employed in the informal sector.
36
Table A7. Estimation of the marginal returns to education in the public sector (both genders)
Dependent variable: log of hourly earnings
Total completed years of education
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.122***
(15.89)
0.029**
(2.26)
0.005
(0.19)
-0.002
(0.03)
0.111***
(18.25)
0.046***
(5.06)
-0.041**
(2.52)
0.038
(0.67)
0.122***
(13.41)
0.011
(0.70)
0.034
(1.02)
0.094
(1.15)
0.093***
(13.56)
0.046***
(4.08)
-0.051**
(2.51)
-0.062
(0.85)
0.104***
(16.28)
0.045***
(5.14)
-0.051***
(3.55)
0.105
(1.36)
0.088***
(12.75)
0.016
(1.45)
0.009
(0.47)
-0.049
(0.69)
0.138***
(12.78)
0.008
(0.45)
0.030
(0.89)
0.231**
(2.23)
0.108***
(39.89)
0.036***
(8.52)
-0.026***
(3.40)
0.051*
(1.75)
0.025
(0.32)
-2.648***
(11.18)
410
0.48
0.174***
(3.19)
-2.871***
(18.16)
594
0.55
0.342***
(5.48)
-2.565***
(10.45)
306
0.51
0.038
(0.69)
-2.619***
(12.95)
459
0.40
0.220***
(3.75)
-2.913***
(17.04)
597
0.48
0.296***
(4.20)
-2.156***
(11.52)
477
0.42
0.250**
(2.29)
-3.111***
(9.85)
310
0.46
0.193***
(7.35)
-2.268***
(25.80)
3153
0.50
(8)
The Student statistics are given in brackets. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1% level.
Table A7b) Estimation of the marginal returns to education in the public sector (both genders)
Dependent variable: log of hourly earnings
Years in primary education
Years in general middle school
Years in general secondary school
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.046
0.089***
0.132***
0.058**
0.040**
0.082***
0.082*
0.078***
(1.33)
(5.15)
(3.05)
(2.16)
(2.08)
(3.72)
(1.89)
(8.54)
0.088***
0.128***
0.099**
0.083**
0.141***
0.049**
0.077**
0.088***
(2.85)
(6.21)
(2.40)
(2.08)
(5.20)
(2.11)
(2.13)
(8.26)
0.180***
0.130***
0.158***
0.145***
0.144***
0.130***
0.258***
0.158***
(8)
37
Years in vocational education
Years in higher education
Potential experience
(Potential experience)2/100
Woman
(4.98)
(4.84)
(3.82)
(3.01)
(4.59)
(4.19)
(5.92)
(12.05)
0.246***
0.154***
0.058
0.098***
0.153***
0.113***
0.165***
0.127***
(4.12)
(4.93)
(1.14)
(4.10)
(5.90)
(3.33)
(3.83)
(10.98)
0.131***
0.106***
0.111***
0.095***
0.100***
0.102***
0.118***
0.111***
(6.90)
(6.25)
(4.94)
(3.03)
(5.68)
(4.98)
(4.11)
(14.14)
0.037***
0.046***
0.011
0.054***
0.051***
0.019
0.023
0.041***
(2.84)
(4.70)
(0.63)
(4.55)
(5.60)
(1.59)
(1.17)
(9.28)
-0.013
-0.042**
0.035
-0.068***
-0.064***
0.003
-0.002
-0.038***
(0.51)
(2.35)
(0.95)
(3.13)
(4.23)
(0.16)
(0.04)
-0.030
(0.39)
0.026
(0.44)
0.116
(1.39)
-0.037
(0.50)
0.079
(1.01)
-0.034
(0.48)
0.232**
(2.25)
(4.67)
0.049*
-0.056
0.181***
0.355***
0.023
(0.68)
(3.09)
(5.36)
(0.41)
0.201***
(3.28)
0.298***
(4.06)
0.169
(1.50)
0.172***
(6.32)
-2.162***
-2.832***
-2.590***
-2.514***
-2.751***
-2.119***
-2.798***
-2.134***
(7.59)
(17.11)
(8.79)
(11.92)
(15.41)
(10.48)
(7.78)
(23.06)
(1.69)
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
411
594
306
459
597
483
312
3162
0.49
0.55
0.52
0.41
0.50
0.43
0.48
0.51
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for
column (8). The inverse Mills ratio is derived from an estimation of a multinomial logit model of sector choices for each country (with the reference category being individuals with zero
earnings) comprising age and age squared, gender, years of education, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita
income, how the individual is related to the head of household and the dependency ratio. The model is estimated using Lee’s method (1983) and the procedure advocated by Bourguignon,
Martin and Gurgand (2004). The Student statistics are given in brackets. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1% level.
38
Table A8. Estimation of the marginal returns to education in the formal private sector (both genders)
Dependent variable: log of hourly earnings
Completed years of education
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.066***
(5.57)
-0.010
(0.85)
0.060***
(3.00)
0.021
(0.30)
0.032
(1.62)
-0.020
(1.20)
0.056**
(2.20)
0.025
(0.29)
0.064***
(4.80)
-0.015
(1.18)
0.060***
(2.80)
-0.058
(0.88)
0.045***
(3.10)
-0.011
(0.61)
0.049*
(1.81)
-0.124
(0.98)
0.068***
(3.11)
-0.003
(0.17)
0.043
(1.48)
-0.121
(1.11)
0.026**
(2.40)
-0.019*
(1.76)
0.050***
(3.08)
-0.043
(0.70)
0.137***
(8.68)
0.024
(1.22)
0.008
(0.22)
0.161
(1.25)
0.025***
(3.70)
-0.031***
(5.09)
0.080***
(8.37)
0.016
(0.51)
-0.334***
(4.46)
-0.929**
(2.49)
529
0.39
-0.813***
(5.26)
0.038
(0.07)
346
0.52
-0.471***
(5.67)
-0.216
(0.53)
854
0.44
-0.403***
(5.03)
-0.847*
(1.83)
454
0.36
-0.616***
(3.43)
-0.913
(1.44)
414
0.49
-0.634***
(7.49)
0.228
(0.64)
946
0.36
-0.066
(1.02)
-3.036***
(6.97)
305
0.37
-0.791***
(14.67)
-0.120
(0.55)
3848
0.42
(8)
The Student statistics are given in brackets. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1% level.
Table A8b) Estimation of the marginal returns to education in the formal private sector (both genders)
Dependent variable: log of hourly earnings
Years in primary education
Years in general middle school
Years in general secondary school
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
-0.019
-0.013
-0.003
-0.005
0.035
-0.008
0.062
-0.004
(0.67)
(0.49)
(0.17)
(0.16)
(1.12)
(0.47)
(1.26)
(0.41)
0.067**
-0.128***
0.025
0.072
0.068
-0.028
0.076
-0.032**
(2.14)
(3.05)
(0.84)
(1.29)
(1.29)
(1.18)
(1.62)
(2.20)
0.062
0.166***
0.141***
0.114
0.125**
0.046
0.184***
0.084***
(8)
39
(1.51)
(2.99)
(3.58)
(1.27)
(1.98)
(1.40)
(2.81)
(4.52)
0.172***
0.097**
0.099***
-0.015
-0.011
-0.043
0.169***
-0.006
(3.32)
(2.23)
(2.87)
(0.32)
(0.16)
(0.87)
(2.72)
(0.31)
0.133***
0.118***
0.183***
0.082
0.070*
0.105***
0.219***
0.105***
(5.97)
(3.16)
(6.64)
(1.19)
(1.78)
(4.47)
(4.69)
(8.55)
-0.006
-0.033**
0.001
-0.014
0.001
-0.023**
0.024
-0.027***
(0.50)
(1.99)
(0.09)
(0.77)
(0.05)
(2.13)
(1.25)
(4.42)
0.046**
0.069***
0.026
0.052*
0.032
0.056***
0.000
0.071***
(2.38)
(2.75)
(1.24)
(1.95)
(1.06)
(3.33)
(0.01)
(7.35)
0.002
0.090
-0.073
-0.024
-0.093
-0.017
0.112
0.034
(0.03)
(1.05)
(1.14)
(0.19)
(0.82)
(0.29)
(0.86)
(1.06)
-0.281***
-0.940***
-0.389***
-0.417***
-0.603***
-0.732***
-0.063
-0.776***
(4.90)
(6.24)
(4.88)
(5.40)
(3.21)
(8.39)
(0.97)
(14.66)
-0.702**
0.773
-0.268
-0.635
-0.829
0.685*
-2.607***
0.015
(2.07)
(1.32)
(0.68)
(1.40)
(1.25)
(1.87)
(5.62)
(0.07)
Observations
529
346
854
454
414
957
305
3859
R-squared
0.41
0.57
0.48
0.38
0.49
0.38
0.39
0.44
Years in vocational education
Years in higher education
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for column (8). The inverse
Mills ratio is derived from an estimation of a multinomial logit model of sector choices for each country (with the reference category being individuals with zero earnings) comprising age and age squared, gender,
years of education, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita income, how the individual is related to the head of household and the
dependency ratio. The model is estimated using Lee’s method (1983) and the procedure advocated by Bourguignon, Martin and Gurgand (2004). The Student statistics are given in brackets. *, ** and *** indicate
respectively that the coefficient is significant at the 10%, 5% and 1% level.
40
Table A9. Estimation of the marginal returns to education in the informal sector (both genders)
Dependent variable: log of hourly earnings
Completed years of education
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.036***
(7.00)
-0.001
(0.22)
0.017**
(2.19)
-0.468***
(13.88)
0.102***
(18.04)
0.045***
(8.08)
-0.042***
(5.50)
-0.516***
(13.54)
0.047***
(9.13)
0.002
(0.42)
0.020**
(2.36)
-0.281***
(6.80)
0.050***
(9.43)
0.029***
(5.15)
-0.022***
(2.76)
-0.395***
(8.76)
0.071***
(11.53)
0.028***
(4.79)
-0.015**
(2.08)
-0.315***
(6.98)
-0.002
(0.32)
-0.023***
(3.53)
0.044***
(4.86)
0.145***
(3.19)
0.067***
(12.49)
0.031***
(6.02)
-0.029***
(3.74)
-0.399***
(10.09)
-0.007***
(2.80)
-0.043***
(17.35)
0.079***
(22.53)
0.098***
(5.46)
-0.389***
(10.73)
-0.990***
(6.59)
3249
0.28
-0.109***
(6.22)
-2.852***
(23.56)
2770
0.30
-0.365***
(11.51)
-1.142***
(8.73)
2859
0.27
-0.245***
(6.66)
-1.773***
(13.04)
2928
0.23
-0.129***
(4.79)
-2.350***
(16.97)
2232
0.17
-1.072***
(16.21)
0.700***
(3.26)
3404
0.25
-0.114***
(6.06)
-2.678***
(22.09)
2930
0.21
-1.187***
(48.61)
0.860***
(10.47)
20372
0.33
(8)
The Student statistics are given in brackets. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1% level.
Table A9b) Estimation of the marginal returns to education in the informal sector (both genders)
Dependent variable: log of hourly earnings
Years in primary education
Years in general middle school
Years in general secondary school
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.043***
0.057***
0.006
0.033***
0.023**
-0.018**
0.028***
-0.032***
(5.66)
(6.38)
(0.64)
(3.45)
(2.15)
(2.01)
(3.07)
(9.13)
0.017
0.128***
0.077***
0.068**
0.103***
-0.008
0.081***
-0.001
(1.10)
(6.09)
(3.76)
(2.57)
(3.85)
(0.43)
(5.36)
(0.11)
0.154***
0.214***
0.124***
0.052
0.153***
0.064
0.144***
0.060***
(8)
41
(4.51)
(4.09)
(2.79)
(0.74)
(2.63)
(1.61)
(4.20)
(3.80)
0.106**
0.229***
0.103***
0.131***
0.154**
-0.033
0.137***
0.019
(2.01)
(4.63)
(2.65)
(3.95)
(2.47)
(0.54)
(3.10)
(1.15)
0.088**
0.187***
0.178***
0.118
0.118**
0.061
0.132***
0.089***
(2.55)
(3.77)
(3.49)
(1.57)
(2.21)
(1.29)
(3.63)
(5.38)
0.008
0.042***
0.006
0.030***
0.020***
-0.023***
0.032***
-0.043***
(1.56)
(7.55)
(1.04)
(5.46)
(3.42)
(3.65)
(6.25)
(17.60)
0.002
-0.040***
0.013
-0.024***
-0.006
0.044***
-0.033***
0.078***
(0.32)
(5.23)
(1.57)
(3.07)
(0.85)
(4.94)
(4.33)
(22.49)
-0.513***
-0.511***
-0.301***
-0.398***
-0.260***
0.153***
-0.412***
0.105***
(15.50)
(13.44)
(7.41)
(8.82)
(5.60)
(3.36)
(10.45)
(5.85)
-0.252***
-0.124***
-0.346***
-0.231***
-0.213***
-1.075***
-0.119***
-1.195***
(8.75)
(6.70)
(11.44)
(6.45)
(6.34)
(16.35)
(6.21)
(48.80)
-1.416***
-2.687***
-1.119***
-1.801***
-1.975***
0.750***
-2.548***
0.954***
(10.76)
(21.72)
(8.70)
(13.56)
(13.03)
(3.50)
(20.76)
(11.58)
Observations
3249
2770
2859
2928
2232
3413
2930
20381
R-squared
0.27
0.31
0.28
0.23
0.19
0.25
0.22
0.33
Years in vocational education
Years in higher education
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Note: The additional explanatory variables in the models are migratory status, marital status and two dummy variables for religion. Fixed country effects have been added to the model for
column (8). The inverse Mills ratio is derived from an estimation of a multinomial logit model of sector choices for each country (with the reference category being individuals with zero
earnings) comprising age and age squared, gender, years of education, migratory status, marital status, religion and three identifying variables in the form of the household’s per capita
income, how the individual is related to the head of household and the dependency ratio. The model is estimated using Lee’s method (1983) and the procedure advocated by Bourguignon,
Martin and Gurgand (2004). The Student statistics are given in brackets. *, ** and *** indicate respectively that the coefficient is significant at the 10%, 5% and 1% level.
42
Table A10. Estimation of the returns to qualifications in the public sector (both genders)
Dependent variable: log of hourly earnings
CEP (primary certificate)
BEPC (middle school certificate)
Occupational proficiency certificate
(CAP)
Vocational certificate (BEP)
Baccalauréat
DEUG/DUT/BTS (two years of
higher education)
Qualification obtained after more
than two years of higher education
Other qualifications
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.147
(1.13)
0.590***
(4.63)
0.733***
(3.95)
1.295***
(3.50)
0.912***
(6.06)
1.311***
(6.70)
1.521***
(11.61)
1.433***
(9.22)
0.040***
(2.99)
-0.025
(0.98)
-0.045
(0.58)
0.423***
(4.98)
0.916***
(11.14)
0.761***
(6.16)
1.231***
(7.69)
1.345***
(11.56)
1.414***
(11.55)
1.730***
(17.38)
1.404***
(11.26)
0.046***
(4.76)
-0.048***
(2.70)
-0.000
(0.01)
0.319*
(1.71)
0.734***
(4.03)
0.894***
(3.67)
0.362
(1.32)
1.025***
(5.13)
1.217***
(5.81)
1.494***
(8.39)
1.130***
(5.60)
0.009
(0.49)
0.025
(0.67)
0.025
(0.28)
0.173
(1.51)
0.577***
(4.25)
0.604***
(5.13)
0.773***
(7.39)
1.158***
(5.82)
0.845***
(6.74)
1.267***
(11.55)
1.095***
(6.96)
0.058***
(4.95)
-0.082***
(3.89)
-0.046
(0.60)
0.348***
(3.89)
0.623***
(6.17)
0.975***
(7.47)
0.900***
(7.31)
1.078***
(8.36)
1.046***
(5.37)
1.407***
(14.79)
1.214***
(10.87)
0.041***
(4.49)
-0.059***
(3.91)
0.068
(0.84)
0.332***
(3.56)
0.561***
(6.34)
0.970***
(5.65)
0.824***
(5.23)
0.850***
(7.75)
0.895***
(5.18)
1.234***
(11.83)
1.281***
(11.26)
0.014
(1.24)
0.002
(0.08)
-0.105
(1.47)
0.138
(1.00)
0.664***
(4.56)
0.765***
(3.22)
0.763***
(3.59)
1.015***
(5.73)
1.607***
(6.40)
1.574***
(9.57)
1.472***
(7.03)
0.018
(0.92)
-0.008
(0.22)
0.257**
(2.45)
0.275***
(6.73)
0.669***
(16.40)
0.800***
(14.09)
0.900***
(16.18)
1.036***
(19.92)
1.157***
(19.29)
1.469***
(34.15)
1.293***
(24.60)
0.039***
(8.90)
-0.045***
(5.63)
0.014
(0.46)
-0.048
(0.58)
-1.906***
(7.75)
410
0.48
0.171***
(2.92)
-2.599***
(16.31)
594
0.55
0.314***
(4.44)
-1.895***
(6.90)
306
0.47
0.044
(0.76)
-2.336***
(11.51)
459
0.40
0.231***
(3.72)
-2.426***
(13.96)
597
0.48
0.272***
(3.78)
-1.695***
(9.74)
477
0.45
0.197*
(1.73)
-2.254***
(7.43)
310
0.47
0.170***
(6.14)
-1.796***
(20.28)
3153
0.49
(8)
43
Table A11. Estimation of the returns to qualifications in the formal private sector (both genders)
Dependent variable: log of hourly earnings
CEP (primary certificate)
BEPC (middle school certificate)
Occupational proficiency certificate
(CAP)
Vocational certificate (BEP)
Baccalauréat
DEUG/DUT/BTS (two years of
higher education)
Qualification obtained after more
than two years of higher education
Other qualifications
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.056
(0.52)
0.190
(1.38)
0.477***
(2.94)
0.591
(1.47)
0.430**
(2.52)
0.330*
(1.65)
0.820***
(4.72)
0.754***
(4.01)
-0.015
(1.32)
0.059***
(3.00)
0.022
(0.32)
-0.193
(1.38)
-0.565**
(2.32)
-0.184
(0.83)
0.048
(0.18)
-0.087
(0.31)
0.012
(0.04)
0.507*
(1.74)
0.015
(0.05)
-0.034**
(2.11)
0.069***
(2.73)
0.126
(1.46)
-0.074
(0.84)
-0.088
(0.62)
-0.071
(0.35)
0.291*
(1.79)
0.252
(1.36)
0.590***
(3.33)
0.872***
(4.59)
0.622***
(2.71)
-0.016
(1.35)
0.048**
(2.27)
-0.078
(1.22)
0.032
(0.22)
0.144
(0.71)
0.006
(0.03)
0.027
(0.13)
0.209
(0.54)
0.669**
(2.43)
0.749***
(3.41)
1.126**
(2.38)
-0.024
(1.30)
0.066**
(2.42)
0.001
(0.01)
0.093
(0.57)
-0.014
(0.05)
0.140
(0.33)
-0.141
(0.40)
0.814**
(2.45)
0.436
(1.34)
0.761**
(2.38)
0.768**
(2.38)
-0.012
(0.62)
0.042
(1.39)
-0.009
(0.09)
-0.116
(1.42)
-0.194*
(1.76)
-0.159
(0.81)
-0.269
(1.12)
-0.109
(0.79)
0.086
(0.46)
0.316**
(2.23)
0.638***
(3.54)
-0.030***
(3.03)
0.061***
(3.91)
-0.031
(0.51)
0.035
(0.23)
0.470***
(2.68)
0.825***
(2.96)
0.670**
(2.28)
0.889***
(3.55)
1.997***
(6.19)
1.498***
(6.58)
1.630***
(3.95)
0.025
(1.26)
-0.011
(0.31)
0.019
(0.15)
-0.175***
(3.83)
-0.318***
(4.44)
-0.240***
(2.64)
-0.320***
(3.24)
-0.084
(0.94)
0.117
(1.16)
0.274***
(2.89)
0.277***
(2.62)
-0.039***
(6.67)
0.084***
(8.95)
0.047
(1.48)
-0.389***
(5.43)
-0.331
(0.99)
529
0.42
-0.956***
(6.45)
0.836
(1.53)
346
0.56
-0.522***
(6.41)
0.404
(1.10)
854
0.48
-0.477***
(5.98)
-0.324
(0.74)
454
0.39
-0.756***
(4.08)
-0.140
(0.23)
414
0.51
-0.724***
(9.30)
0.815***
(2.74)
946
0.38
-0.071
(1.05)
-2.214***
(5.45)
305
0.39
-0.881***
(16.94)
0.520***
(2.67)
3848
0.44
(8)
44
Table A12. Estimation of the returns to qualifications in the informal sector (both genders)
Dependent variable: log of hourly earnings
CEP (primary certificate)
BEPC (middle school certificate)
Occupational proficiency certificate
(CAP)
Vocational certificate (BEP)
Baccalauréat
DEUG/DUT/BTS (two years of
higher education)
Qualification obtained after more
than two years of higher education
Other qualifications
Potential experience
(Potential experience)2/100
Woman
Selection correction
Inverse Mills ratio
Constant
Observations
R-squared
Cotonou
(Benin)
(1)
Ouagadougou
(Burkina Faso)
(2)
Abidjan
(Côte d’Ivoire)
(3)
Bamako
(Mali)
(4)
Niamey
(Niger)
(5)
Dakar
(Senegal)
(6)
Lomé
(Togo)
(7)
Total
0.119***
(3.09)
0.250***
(4.12)
0.290**
(2.42)
0.288
(0.63)
0.585***
(4.57)
1.002***
(4.58)
0.723***
(5.26)
0.673***
(3.11)
-0.005
(0.95)
0.020**
(2.49)
-0.496***
(14.66)
0.425***
(8.54)
0.942***
(10.76)
0.962***
(5.75)
1.326***
(5.30)
1.318***
(6.95)
1.808***
(7.21)
2.157***
(13.02)
1.206***
(2.82)
0.034***
(6.24)
-0.031***
(4.11)
-0.516***
(13.55)
0.102**
(2.27)
0.359***
(4.34)
0.453***
(2.78)
0.363**
(2.08)
0.838***
(6.30)
0.650***
(4.03)
1.221***
(8.93)
0.645**
(2.22)
-0.004
(0.67)
0.026***
(3.03)
-0.254***
(6.05)
0.333***
(6.53)
0.460***
(4.49)
0.337**
(2.26)
0.949***
(7.19)
0.520**
(2.51)
0.549**
(2.31)
1.080***
(6.15)
0.175
(0.43)
0.027***
(4.91)
-0.020**
(2.55)
-0.367***
(7.94)
0.329***
(5.29)
0.609***
(4.50)
0.426
(1.35)
1.025***
(3.45)
0.849***
(4.27)
1.501***
(4.76)
1.530***
(9.68)
1.114***
(3.93)
0.019***
(3.34)
-0.007
(0.95)
-0.303***
(6.69)
0.022
(0.46)
-0.104
(1.32)
-0.081
(0.34)
-0.274
(0.96)
0.132
(1.02)
0.496
(1.17)
0.308**
(1.98)
0.312
(1.46)
-0.028***
(4.75)
0.049***
(5.84)
0.171***
(3.80)
0.181***
(4.42)
0.517***
(8.27)
0.174
(0.99)
1.024***
(4.89)
0.607***
(4.38)
0.877***
(3.54)
1.623***
(11.88)
0.898***
(4.39)
0.026***
(5.13)
-0.028***
(3.65)
-0.422***
(10.80)
-0.093***
(5.10)
-0.073**
(2.37)
-0.194***
(3.09)
0.031
(0.40)
0.150***
(2.65)
0.035
(0.39)
0.473***
(8.20)
0.164*
(1.71)
-0.045***
(19.31)
0.081***
(23.97)
0.112***
(6.28)
-0.399***
(11.27)
-0.802***
(5.79)
3249
0.27
-0.144***
(7.45)
-2.427***
(21.03)
2770
0.31
-0.420***
(12.84)
-0.853***
(6.90)
2859
0.28
-0.281***
(7.37)
-1.640***
(12.50)
2928
0.23
-0.151***
(5.35)
-2.005***
(15.61)
2232
0.18
-1.095***
(18.11)
0.795***
(4.45)
3404
0.25
-0.138***
(7.10)
-2.325***
(20.78)
2930
0.23
-1.197***
(51.06)
0.907***
(12.31)
20372
0.34
(8)
45
References
Adams J. (1991), “The Rural Labour Market in Zimbabwe”, Development and Change, 22
(2), pp. 297-320.
Becker G. (1975), Human Capital, 2nd edition, Chicago: University of Chicago Press,
1975. 1st edition, Chicago: University of Chicago Press, 1964.
Bennell P. (1996), “Rates of Return on Education: Does the Conventional Pattern Prevail
in Sub-Saharan Africa?”, World Development, 24 (1), pp. 183-199.
Bourguignon F., Fournier M. and Gurgand M. (2004), “Selection Bias Corrections Based
on the Multinomial Logit Model: Monte-Carlo Comparisons”, DELTA working
paper, September.
Brilleau A., Roubaud F. and Torelli C. (2005), “L’emploi, le chômage et les conditions
d’activité, enquête 1-2-3 Phase 1”, Stateco, No. 99, pp. 43-64. DIAL working paper,
DT/2004/06: www.dial.prd.fr/dial_publications/PDF/Doc_travail/2004-06.pdf
Glewwe P. (1996), “The Relevance of Standard Estimates of Rates of Return to Schooling
for Education Policy: A Critical Assessment”, Journal of Development Economics, 51,
pp. 267-290.
Heckman J. J. (1979), “Sample Selection Bias as a Specification Error”, Econometrica, 47,
pp. 153-161.
Hess P. and Ross C. (1997), Economic Development: Theories, Evidence and Policies,
Fortworth: Dryden Press/Harcourt Brace Publishers.
Hugon P. (2005), « Que produit l’éducation ? »
http://institut.fsu.fr/chantiers/eco_connaissances/hugon.htm
Lee L.-F. (1983), “Generalized Econometric Models with Selectivity”, Econometrica, 51(2),
pp. 507-512.
Mincer J. (1974), Schooling, Experience and Earnings, New York: National Bureau of
Economic Research.
Mingat A. and Tan J-P, (1996), “Les taux de rendement sociaux “complets” de
l’éducation. Estimation à partir de la performance des pays en termes de croissance
économique”, Les notes de l’IREDU, 96/6.
Ray D. (1998), Development Economics, New Jersey: Princeton University Press.
Schultz T. P. (2004), “Evidence of Returns to Schooling in Africa from Household
Surveys: Monitoring and Restructuring the Market for Education”, Journal of African
Economies, 13, AERC Supplement, pp. ii95-ii148.
Vijverberg W.P. (1995), “Returns to Schooling in Non-Farm Self-Employment: An
Econometric Case Study of Ghana”, World Development, 23(7), pp. 1215-1227.