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School Dropout and Working Opportunities in Developing Countries:
A Comparative Analysis
Chiara Mussida*, Dario Sciulli** and Marcello Signorelli***
Preliminary version. Please, do not quote
Abstract
This study contributes to the knowledge regarding the consequences of school dropout in ten
developing economies, distinguished in three “macroregions”, focusing on the working perspectives
of young individuals aged 15 to 29. In particular, we adopt a simultaneous two equations model for
school dropout and working outcomes allowing for correlation between error terms and therefore
accounting for endogeneity of school dropout. Our study shows the existence of a negative
correlation between errors terms of work and dropout equations; this suggests that confounding
factors drive, simultaneously, both dropout and working decisions, but in a reverse way. So, a major
finding of this study is that the underlying mechanisms driving the positive impact of school
dropout on working perspectives differs across outcomes (unpaid work, self-employment and
employment); however, even though confounding factors may affect the probability of being in
unpaid work or self-employment, their role is not crucial to determine a positive effect, as
demonstrated by the specification not accounting for endogeneity; the positive impact of school
dropout is, instead, more explained by the irrelevance of having high educational attainments to
access to unpaid work and self-employment. Our results have a number of policy implications.
JEL Classification codes: I25, J21, J24, O12, O18
Keywords: school dropout, working perspectives, endogeneity, developing countries
* Corresponding author: Assistant Professor, Department of Economic and Social Sciences, Università Cattolica del
Sacro Cuore, via Emilia Parmense, 84, 29122, Piacenza. Tel.: +39 0523 599440. E-mail: [email protected] ** Department of Economic Studies, Università "G. d'Annunzio" di Chieti-Pescara. E-mail: [email protected] *** Department of Economics, Università di Perugia. E-mail: [email protected]
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1. INTRODUCTION AND LITERARTURE REVIEW
The investment in human capital is detected by a large and variegate literature (see, for example, the
well-known “endogenous growth theory”1) as a key factor for favouring economic growth and development
(as for developing countries, see Hanushek, 2013), and the participation to (and completion of) formal
education is usually considered the most important component of that investment (e.g., World Economic
Forum, 2015). In the past decades a number of specific aspects about the economic role of education have
been analysed in many theoretical and empirical researches; for example, Boccanfuso et al. (2015) analysed
the relationship between the quality of education and the labour market in developing countries, with
specific focus on Senegal, while Leuven and Oosterbeek (2011) investigated the relevance of mismatch (or
bad matching or over-education) between the professional contents of (post-primary) formal education and
the skills requested by the productive system.
In particular, while the quantitative and qualitative features of access/enrolment and the completion
of the several school cycles (education attainment) have been largely investigated and often used as proxy of
human capital investment and accumulation (e.g., Mulligan and Sala-I-Martin, 1995), school dropout
phenomena has a growing but still largely insufficient research attention (e.g., Hunt, 2008), partly due to
both lack of data and to the complexities to detect (individual, household, school and community level)
motivations.
It should be noted that school dropout may have strong detrimental effect on the economic
development of countries experiencing a significant increase in the demand for a skilled workforce. In
addition to the general effect on human capital accumulation and potential development, school dropout can
exacerbate urban/rural and gender inequality (e.g., Glick, 2008).2
A crucial point refer to the need to account for endogeneity, as suggested by Campolieti et al. (2010);
there is indeed potential endogeneity of the dropout status with respect to the labour market outcomes. It
should be stressed that – to our knowledge – no studies addressed possible endogeneity problems in a panel
of developing countries. So, the complex relationship between determinants of dropout and employment
opportunities is still largely unexplored in developing countries. In order to extend the existing literature, we
estimate the impact of school dropout on work perspectives of young people living in ten developing
countries, adopting a “comparative perspectives” for three world regions, and we address a specific
endogeneity problem related to the fact that the existing employment opportunities could affect school
dropout decisions. In addition, the analysis of gender and urban vs. rural differences is specifically
investigated for detecting his relevance in the considered countries and world regions. In particular, our
analysis focuses on the following three macroregions: i) ‘Sub Saharan Africa’, ii) ‘South-East and Central
Asia’, iii) ‘Latin America and Caribbean’. The overall countries considered substantially correspond to the
current availability of comparable data regarding young individuals aged 15 to 29. As above mentioned,
1 As for the overall determinants of economic growth, see Barro and Sala-I-Martin (1995). 2 So, school dropout features can also have effects in terms of “equality of opportunity” (on both educational and labour market
sides). Rendall (2013) showed a reduction of gender duality in developing countries experiencing a decrease of labour demand for
physical attributes.
3
because the dropout decision is potentially endogenous in the employment equation, we adopt a
simultaneous two equations approach where the dropout variable is, in turn, a dependent variable in the first
equation and an endogenous variables in the second equation. The simultaneous equation allows the error
terms of both equations to be correlated. The significance of the correlation term would be indicative of
endogeneity of dropout in the employment equation. The issue of endogeneity would be particularly relevant
in those countries characterized by a sizeable demand for low skilled workers; in that case, low educated
individuals are more likely to leave school in case low skilled jobs are available in the labor market.
In short, our main research questions are the following: i) are school dropout decisions endogenous
with respect to employment opportunities?; ii) do significant differences emerge between and within the
three world regions, also considering gender and urban vs. rural features? Our results can shed more light on
a very complex phenomena with relevant policy implications.
The paper has the following structure: section 2 provides a description of the econometric
methodology used to estimate the impact of school dropout on work perspectives. Section 3
describes the data and defines the indicators. Section 4 describes the estimation results for both
work and dropout equations. Section 5 offers a discussion of the findings. Section 6 concludes.
2. ECONOMETRIC ANALYSIS
We are interested in estimating the impact of school dropout on work perspectives of young people
living in some transition and emerging countries. Because employment perspectives possibly guide
school dropout decisions, an endogeneity problem because of simultaneity possibly arises.
With the aim of taking into account this endogeneity issue, we estimate a two-equation model. The
first equation models the school dropout choice suspected of being endogenous, while the second
one models the work decisions, and includes in its right side the school dropout indicator. This
results in a recursive simultaneous equations model (see Altonji et al. 2005 for a similar
application), that can be consistently and efficiently estimated by full information maximum
likelihood.
The outcome of the first equation is a binary variable taking value one if the individual has dropout
from the school system before the individual completed the cycle at which he/she has enrolled and
zero otherwise. The work equation has multinomial outcomes, i.e. not working (j = 1), unpaid work
(j = 2), self-employment (j = 3) and employment (j = 4), respectively. The first equation is
estimated using a probit model, while the second equation is a multinomial probit model both
assuming normally distributed errors. By using a multinomial rather than an ordinal probit model
allows individuals choose from alternatives that are not inherently ordered. This would be the case
4
of our second-stage equations, where the utility associated to unpaid work may prevail on that
associated to self-employment and employment for some individuals.
The probit model used to estimate the dropout equation is derived from a latent continuous variable
(y1*) related to a set of explanatory variables x according to a standard linear model that can be
represented as follows:
iii xy *
1 (1)
where, β is a vector of associated parameters to x and υ is an error term drawn from a standardized
normal distribution. While y1* is unobserved, y1 would be observed, and related to y1* by the
following relationship:
otherwise 0
0 if 1 *
1
1
i
i
yy (2)
The multinomial probit model also derives from a latent continuous variable (y2*) related to a set of
explanatory variables z according to a standard linear model, represented as follows:
iiii uzyy 1
*
2 (3)
where, α is the coefficient associated to the endogenous school dropout variable, γ is a vector of
associated parameters to z, including some x-variables, and u is an error term drawn from a
standardized normal distribution.
While y2* is unobserved, y2 would be observed, and related to y2* by the following general
relationship:
otherwise 0
,,max if j if
*
42
*
32
*
22
*
12
*
2*
22
iiiiij
iji
yyyyyyjy (4)
The probability to choose the category j would be written as:
jijiiijjijiiij
iijiijiii
yzuuyzuu
yyyyzyjy
41441111
*
42
*
2
*
12
*
212
'',...,''Pr
,...,Pr,|Pr
(5)
5
The recursive simultaneous equations model allows the error terms of both equation to be
correlated. It follows, we also estimate three correlation terms ρυu2, ρυu3 and ρυu4, measuring the
correlation between residuals related to unpaid work, self-employment and employment with that of
the dropout equation. Particularly a positive correlation would be indicative that an unobserved term
increases both the employment outcome and school dropout, and vice versa in case of negative
correlation.
3. DATA
We use the School to Work Transition Surveys (SWTS),3 conducted in 2012 as part of the
Work4Youth Programme (coordinated by the International Labour Office and Mastercard
Foundation), by the International Labour Office (ILO). The ILO SWTS provide comparable
information for several countries on the reasons why young people did not attend school or left
before completion. They also provide information on employment status and job related
characteristics, as well as other individual and household information. The SWTS were carried out
in more than 30 countries between 2012 and 2015 and currently comparable microdata are available
for 28 countries.4 In almost all countries, the SWTS is being implemented through the National
Statistics Offices, thus offering an important opportunity for building national capacity on the area
of labour market information on youth and at least attempting to ensure some sustainability of the
survey within the national statistical agenda.
The SWTS serves a number of purposes. First, it detects the individual characteristics of young
people that determine labour market disadvantage. This, in turn, is instrumental to the development
of policy response to prevent the emergence of risk factors, i.e., school dropout, social exclusion, as
well as measures to remedy those factors that negatively affect the transition to decent work.
Second, it identifies the features of youth labour demand, which help determine mismatches that
can be addressed by policy interventions. Third, in countries where the labour market information
system is not developed, it serves as an instrument to generate reliable data for policy-making. In
countries with a reasonably developed labour market information system, instead, the survey helps
to shed light on areas usually not captured by household-based surveys, such as youth school
3 For details on the SWTS, see http://www.ilo.org/employment/areas/youth-employment/work-for-
youth/WCMS_191853/lang--en/index.htm. 4 The first/pioneer SWTS questionnaires were developed in 2003 by the ILO to conduct structured surveys on the
question of gender equality in youth employment. The first surveys were implemented in Indonesia, Sri Lanka and Viet
Nam to inform the preparation of youth national action plans. In 2004, the ILO developed the analytical framework
underpinning the concept of transition to decent work and reshaped the data collection instruments. The new framework
was applied between 2004 and 2006 to conduct surveys in ten countries – Azerbaijan, China, Egypt, Islamic Republic
of Iran, Jordan, Kosovo, Kyrgyzstan, Mongolia, Nepal and Syrian Arab Republic.
6
dropouts, labour market outcomes, such as conditions of work, wages and earnings, engagement in
the informal economy, e.g., formal or informal employment contractual arrangements.5
Our analysis focuses on three macroregions including some emerging or transition countries with
different level of development and investigates the impact of school dropout on employment
decision. In detail, the three macroregions are ‘Sub Saharan Africa’, including Benin, Liberia,
Tanzania, and Zambia, ‘South-East and Central Asia’, including Cambodia, Nepal, and Vietnam,
and ‘Latin America and Caribbean’, including Brazil, Jamaica, and El Salvador.
Because the dropout decision is potentially endogenous in the employment equation, we adopt a
simultaneous two equations approach where the dropout variable is, in turn, a dependent variable in
the first equation and an endogenous variables in the second equation. The simultaneous equation
allows the error terms of both equations to be correlated. The significance of the correlation term
would be indicative of endogeneity of dropout in the employment equation. The issue of
endogeneity would be particularly relevant in those countries characterized by a sizeable demand
for low skilled workers. In that case, low educated individuals are more likely to leave school in
case low skilled jobs are available in the labor market.
The descriptive statistics for our dependent variables and for the set of control variables are shown
in Table 1 both for the total sample and by living area (non-rural and rural areas, respectively). The
dependent variable for our first equation is the dropout variable, which equals one if the youth left
education before completion, and zero otherwise. The dropout variable is obtained by using the
information available in the SWTS. In detail, there is a specific question asking if the youth is
currently attending school/training programme and one of the possible answers is the youth left
education/training before completion. The dropout variable is one if the youth left before
completion and also if he/she is not (currently) attending education (not a student).
The dropout rates (Table 1) do not vary considerably between living areas. Those vary instead
across the macroregions analysed. The percentage is 35,9% for Sub Saharan African countries,
37,6% for Latin America and Caribbean and it reaches its highest level, i.e., 59,5%, in South-East
and Central Asian countries.6
5 For methodological details, see Elder (2009). 6 The macroregional dropout rates are averages of single-country rates. For Sub Saharan Africa it is the average of
33,6% for Benin, 54,7% for Liberia, 15,6% for Tanzania, and 48,2% % for Zambia. As per the Latin American (and
Caribbean) countries, the dropout is the average of 25,9% for Jamaica, 32% for Brazil, and 51,9% for El Salvador.
Finally, for Asian countries, the values of dropout range from 35,4% of Vietnam to 65,4% of Nepal and 73,7% of
Cambodia.
7
The control variables for the dropout equation include dummy variables for parental attitudes to
education, i.e., the level of education of father and mother,7 gender, completed educational level,
i.e., primary, secondary or tertiary education. The equation includes a dummy variable equal to one
if the individual was married when leaving education, whether he/she worked while studying, if
he/she lived in a rural area or not, and also whether he/she moved from a rural to urban area or vice
versa. We also add an indicator to approximate the demand side effect, i.e., the unemployment
rates by country and gender when the individual (in the precise year) left the educational system.8
Finally, two country specific indicators of demographic and institutional factors possibly affecting
dropout, i.e., life expectancy at birth (by gender), and the duration of compulsory education (years)9
are included in our set of covariates.
Table 1: Descriptive statistics
TOTAL NON-RURAL AREAS RURAL AREAS
Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Dropout equation
Father up to primary education 0,605 0,489 0,549 0,498 0,655 0,476
Mother up to primary education 0,734 0,442 0,690 0,463 0,774 0,418
Female
0,538 0,499 0,541 0,498 0,536 0,499
Up to primary education 0,452 0,498 0,414 0,492 0,487 0,500
Secondary education 0,433 0,495 0,442 0,497 0,425 0,494
Tertiary education 0,113 0,317 0,158 0,365 0,073 0,261
Married before end of education 0,252 0,434 0,216 0,411 0,284 0,451
Working during studies 0,210 0,408 0,187 0,390 0,231 0,421
Rural area during studies 0,632 0,482 0,218 0,413 1,000 0,000
Moved
0,297 0,457 0,374 0,484 0,229 0,420
Unemployment rate at end of education 10,082 10,000 11,434 9,636 8,882 10,163
Compulsory education (years) 7,070 2,786 7,744 2,976 6,473 2,456
Life expectancy at birth) 64,783 8,816 66,114 8,753 63,602 8,702
Employment equation
Dropout
0,459 0,498 0,445 0,497 0,472 0,499
Aged 15-19
0,232 0,422 0,224 0,417 0,239 0,426
Aged 20-24
0,368 0,482 0,369 0,483 0,367 0,482
Aged 25-29
0,400 0,490 0,407 0,491 0,395 0,489
Female
0,538 0,499 0,541 0,498 0,536 0,499
7 In detail we have a dummy variables for father and a dummy variable for mother. These dummies are one if father
and mother have no education or, at maximum, elementary education, and zero for higher educational levels. Therefore
these dummies capture the impacts of low parental education on the probability of dropout. 8 The unemployment rates by country and gender are available in the Internet (World Bank website) at
http://data.worldbank.org/indicator/SL.UEM.1524.MA.ZS. 9 Figures available from the World Bank website: for life expectancy at birth, see
http://data.worldbank.org/indicator/SP.DYN.LE00.IN, for compulsory education (years), see
http://data.worldbank.org/indicator/SE.COM.DURS.
8
Up to primary education 0,452 0,498 0,414 0,492 0,487 0,500
Secondary education 0,433 0,495 0,442 0,497 0,425 0,494
Tertiary education 0,113 0,317 0,158 0,365 0,073 0,261
Married
0,395 0,489 0,340 0,474 0,444 0,497
Children
0,524 0,499 0,507 0,500 0,539 0,498
Poor financial status 0,409 0,492 0,365 0,482 0,449 0,497
Poor health status 0,073 0,260 0,085 0,279 0,063 0,242
Working during studies 0,210 0,408 0,187 0,390 0,231 0,421
Rural area
0,530 0,499
Moved
0,297 0,457 0,374 0,484 0,229 0,420
GDP growth 21,584 24,536 29,008 29,756 14,997 16,071
Corruption free 30,707 5,445 32,515 4,762 29,102 5,511
Political stability -0,027 0,348 -0,020 0,288 -0,034 0,394
Observations 19,215 9,034 10,181
Source: Authors’ elaborations on ILO SWTS data
The dependent variable for the second equation, i.e., the employment equation, is the labour market
status of the youth and we have four outcomes, not employed/inactive, in unpaid work, and in paid
work, by considering self-employed and employed. There are relevant differences across countries
(also) in the relative percentages pertaining to the three outcomes. Figure 1 offers a visual
inspection of the differences across countries also considering the differences between youth
completing education and youth dropping out from education. Paid work as employee is
predominant in Vietnam (around 58%) and Brazil (around 50%), especially in for those completing
education. Self-employment (paid work) is instead more relevant in Sub Saharan Africa countries.
In those countries the percentages of self-employed are higher among youth dropping out from
schooling training compared to youth completing education especially in Liberia (more than 39%
for those leaving education and around 32% for those completing), Tanzania (around 27% versus
23% for those completing education), and Zambia (24.35% for youth dropping out and around 15%
for those completing). In Benin, instead, the percentages of self-employed are higher among youth
completing education. Unpaid work is instead more relevant in Asian countries, especially in
Cambodia for both youth dropping out from education (around 36%) and youth completing
education (approximately 31%). We find high percentages also for youth leaving education before
completion in Vietnam (approximately 36%) and Nepal (around 26%). Percentages are relatively
high also in Jamaica and Benin (around 58% and 57%, respectively). The labour market outcomes
9
considered, i.e., the dependent variable of our second equation, therefore vary not only between
macroregions, but also among countries pertaining to each macroregion.10
Figure 1: Youth completing and dropping out from education by country
Source: Authors’ elaborations on ILO SWTS data
To take into account the heterogeneity of dropout across macroregions and countries pertaining to
the same macroregion, as explained above, and the differences between countries in labour market
outcomes, the control variables for the employment equation include specific dummy interactions
between dropout and each country.
Age is splitted in three dummies corresponding to three age ranges, i.e., dummies for the age ranges
[15, 19], [20, 24], and [25, 29], gender and the interaction between gender and dropout. We also
control for education and its interaction with dropout. For education we consider three educational
levels in each country which can be summarized by primary education, secondary education, and
10 Figure A1 in the Appendix shows the relative percentages of youth both completing and dropping out from education
in each macroregion obtained as averages of the relative percentages for each country (displayed in Figure 1). This
gives a visual inspection of the mean relevance of the labour market outcomes examined in each macroregion.
10
tertiary education,11 and the interactions between primary education and dropout, secondary
education and dropout, and tertiary education and dropout. The interaction is quite useful since
gives details on the timing of the dropout with respect to education. In detail, if the interaction
between primary education and dropout is one, this means the youth left education (dropped out)
after completing primary education, the same applies for the interactions with secondary and
tertiary education.12 This is quite relevant, since a different timing for dropping out might have
different impacts on employment or, more in general, labour market outcomes. It is worthy to stress
that primary education, or the lowest educational level, also includes youth without education (no
education, none, or not completed primary education). This therefore implies that if the interaction
between primary education and dropout is one this may include also youth dropping out from
education before completing the primary educational level (or having none education). However the
numbers of youth not completing primary/with none education are on average low in each country
and in some countries the number of youth without education is zero.13 For these reasons we kept
those youth in our estimates since they do not affect our results.14
We also control for marital status, presence of children, household financial status and individual
health status. We include dummies for living in rural area currently and at the timing of the dropout
(interaction between rural and dropout), and a dummy which is one if the individual moved from a
rural to urban area or viceversa. Finally, the economic and environmental conditions are controlled
for by including a GDP per-capita indicator (GDP growth rates at the year of the survey), an index
measuring the level of corruption, and another one measuring the level of political stability, all at
the country level.15
11 The SWTS gives information on the highest level of completed education and in each country the detail offered
allows the identification of three main educational level, i.e., primary, secondary, and tertiary. 12 If the interaction between secondary education and dropout is one, the youth left education after completing
secondary education, whereas if the interaction between tertiary education and dropout is one, the youth dropped from
education after completing tertiary educational level. 13 The incidence of individuals without education on the interaction between primary education and dropout, i.e., those
youth dropping out from education before completing primary education, is 0.61% in Benin, 0.3% in Brazil, and 3.61%
in Cambodia. In Jamaica, Liberia, and Tanzania the numbers of youth without schooling were 9, 35, and 37 youth,
respectively. Only one youth did not complete primary education in Ukraine, whereas the numbers were 86, 15, and 4 in
Tanzania, Zambia, and El Salvador. Finally, in Nepal the number of youth without education is zero. 14 In a first attempt we tried to consider youth without education as a separate category and to build the interaction
between no education and dropout but given that the size of this specific category was on average low and absent for
some countries (e.g., Nepal) we did not succeed in estimating a model considering them (and the interaction with
dropout) as a separate category. 15 Figures for GDP growth rates, corruption free, and political stability are available in the Internet (World Bank
website) at http://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG.
11
4. ESTIMATION RESULTS
Table 2 reports estimated average marginal effects (AME) for the work equation, which allows for
four possible outcomes, i.e. inactivity (our base-category), unpaid work, self-employment and
employment. The impact of school-dropout is measured by introducing a dichotomous variable
taking value one if the individual has dropped out from school. In addition, we identify
heterogeneous country-specific effects by introducing dummy interaction country variables. In this
respect, Brazil is the (country) base-category and, therefore, the AME related to dummy interaction-
country variables, would be interpreted as the change of the impact of school dropout on work
perspective for each country with respect to Brazil.
Because school dropout is potentially endogenous in the work equation our empirical strategy
consists in estimating a recursive simultaneous equations model, which require to estimate
simultaneously a dropout equation. Table 4 reports related AME. The model provides for the
estimation of a number of rho-parameters, measuring the correlation between error terms of
outcomes of the work equation and the error term of the dropout equation. The significance of the
correlation term would be indicative of endogeneity of dropout in the work equation. Table A1
shows results without accounting for endogeneity. Finally, because work and school dropout
behaviors is likely to be different in rural and non-rural areas, we provide a supplementary analysis
based on sub-samples splitting individuals according they live in rural and non-rural areas (Tables
ZZZ and WWW for employment and dropout equation, respectively).
4.1 The work equation
Looking at Table 2, our estimation results suggest the impact of school dropout is heterogeneous
across analyzed countries (as shown by the descriptive analysis in Section 3), but it is essentially
positive for working perspectives of individuals.
The first row suggests that dropping-out from school in Brazil (our country base-category),
increases (with respect to the inactivity status), by 15.1% the probability of being an unpaid worker,
by 20.6% the probability of being self-employed and by 12.1% the probability of being employed.
By considering the dummy interaction variables we are able to disentangle the change in the impact
of school dropout in other countries. When significant, the impact of school dropout on the
probability of being an unpaid worker ranges between -2.4% for Benin (i.e. 15.1% minus 17.5%)
and 26.3% for Vietnam (i.e. 15.1% plus 11.2%). Other countries show intermediate but positive
effects of school dropout. Specifically, it is 3.9% for Jamaica, about 26.2% for Liberia and about
0.8% for Tanzania. When focusing on self-employment, the impact of school dropout ranges
12
between -1.5% for Nepal and 37.3% for Liberia. Other countries show a positive, but relatively
smaller impact for school dropout. In particular, it is 5.3% for El Salvador, 7.6% for Jamaica and
10.6% for Benin. Finally, individuals dropping out from school are relatively more likely to be
employed rather than inactive. This positive impact ranges between 4.2% for Benin and 40% for
Vietnam. When significant, the positive effect is 30.8% for Benin, 18.8% for Nepal, 24% for
Tanzania and 17.9% for Zambia.
Although these findings, indicating a positive impact of school dropout on working perspectives,
and especially on the probability of being employed may be quite counterintuitive, they should be
interpreted in the light of the specific control we adopt for endogeneity issues. Focusing on the rho
estimated parameters, our results reveal they are always negative; however the correlation
parameter is negative and significant when considering the error terms of the employment outcome
and the dropout equation (-0.372). This suggests that school dropout decision and employment are
guided by common unobserved factors affecting positively one outcome and negatively the other
one. For example, scarce motivation, preferences for leisure, and/or economic shocks, may be seen
as potential factors increasing the probability of dropping out from school and, at the same time,
decreasing the probability of being employed. Once the role of these unobserved factors have been
disentangled and put aside, the effect of school dropout on the probability of being employed is,
therefore, actually positive16.
We control residual observable heterogeneity by including a number of explanatory variables. Some
of them (i.e. gender, education and living in rural areas), are interacted with the dropout indicator to
highlight possible sources of heterogeneities. Because, we do not control for country-specific
effects with respect to these control variables, the related AMEs may be interpreted as average
values across the analyzed countries.
According to our estimations, the probability of being employed and, especially of being self-
employed increases with age, while it is irrelevant for being an unpaid worker. Being female
decreases the probability of working, suggesting the existence of a gender effect against females. In
particular, with respect to the inactivity status, being female decreases by 2.5% the probability of
being an unpaid worker, by 12.3% the probability of being self-employed and by 12.8% the
probability of being employed. In addition, while the overall impact of school dropout is positive,
we find that females dropping out from school experience a further deterioration of their working
perspectives. Specifically, when females leave school before their completion, the probability of
being an unpaid worker decreases by a further 10.1%, while self-employment and employment
16 Table A1 shows estimation results from a standard multinomial probit specification. It results that when not
accounting for endogeneity the impact of school dropout on employment probabilities is negative.
13
decrease by further 9% and 12%, respectively. Our empirical specifications, importantly control for
educational level and their interactions with dropout. This allows us to disentangle the role of the
timing of dropout and its influence on working perspectives. Our results suggest that educational
level and eventual dropout do not change the probability of being an unpaid worker with respect to
the probability of being inactive. Conversely, being low educated increases by 8.3% the probability
of being self-employed, and in case the individual reached a low educational level, but his/her
leaves school in the successive educational cycle, the probability of being self-employed is positive
but relatively smaller. Having a lower educational level decreases the probability of being
employed. In particular, being low educated decreases by 27.9% the probability of being employed
(with respect to highly educated), while having a medium educational level decreases by 11.9% the
probability of being employed. In both cases, those dropping out from school in the successive
educational cycle, see mitigated the mentioned negative effects. Being married increases the
probability of being self-employed by 5.3%, and a similar positive effect is found in presence of
children (+5.2%). Conversely, the presence of children decreases the probability of being an unpaid
worker and the probability of being employed. We find a negative association between poor
financial status and working perspectives, while poor health status decrease both the probability of
being an unpaid worker (by 4.1%) and especially the probability of being employed (by 9%).
Having worked during studies increases the probability of working, suggesting a positive role of job
experience accumulation. One considering the impact of living in rural areas, we find a positive
effect on both the probability of being an unpaid worker (+ 8.1%) and the probability of being self-
employed (+ 3.9%). However, when taking into account the role of dropout, we find these positive
effects are mitigated and almost set-off for self-employment. The impact for employment is
negative, especially for those having experienced school dropout, but not significant. In addition,
we find that moving from the original living area decreases the probability of being an unpaid
worker and increases the probability of being self-employed.
Finally, we control for a set of variables able to take into account country differences of economic,
institutional and developing conditions. In this respect, we include three indicators, the GDP per-
capita, an index of corruption and an index of political stability. When significant, the indicators
show the expected signs. In particular, higher GDP per capita is associated to higher probability of
being employed and lower probability of unpaid work, suggesting higher levels of development
decreases unpaid working activities and possibly increase the size of labour market. Higher levels
of corruption decrease the probability of working; in particular it decreases by 2.5% the probability
of being an unpaid worker, by 1.7% the probability of self-employment and by 3.4% the probability
of being employed. Conversely, higher levels of political stability improve working perspectives,
14
especially unpaid work (by 8.3%) and employment (by 12.7%), while the probability of self-
employment just increases by 1.7%.
Table 2: Working outcomes equation: multinomial probit model accounting for endogeneity
UNPAID WORK SELF-EMPLOYMENT EMPLOYMENT
AME s.e. AME s.e. AME s.e.
Dropout 0.151 0.062 * 0.206 0.061 *** 0.121 0.051 *
Dropout x El Salvador 0.066 0.041
-0.153 0.035 *** 0.187 0.028 ***
Dropout x Jamaica -0.112 0.050 * -0.130 0.041 *** 0.019 0.034
Dropout x Cambodia -0.004 0.046
-0.037 0.043
0.050 0.036
Dropout x Nepal 0.029 0.048
-0.221 0.046 *** 0.067 0.039 *
Dropout x Vietnam 0.112 0.046 * -0.037 0.044
0.279 0.035 ***
Dropout x Benin -0.175 0.046 *** -0.100 0.037 *** -0.079 0.031 *
Dropout x Liberia 0.111 0.054 * 0.167 0.050 *** 0.074 0.055
Dropout x Tanzania -0.143 0.059 * 0.025 0.051
0.119 0.046 *
Dropout x Zambia -0.021 0.046 0.008 0.039 0.058 0.033 *
Aged 15-19 base-category
Aged 20-24 -0.009 0.009
0.160 0.014 *** 0.087 0.011 ***
Aged 25-29 -0.017 0.011
0.284 0.014 *** 0.165 0.012 ***
Female -0.025 0.010 * -0.123 0.013 *** -0.128 0.011 ***
Female x Dropout -0.101 0.015 *** -0.090 0.018 *** -0.120 0.016 ***
Up to primary education -0.024 0.021
0.083 0.025 *** -0.279 0.019 ***
Dropout during secondary education 0.007 0.026
-0.064 0.033 * 0.148 0.031 ***
Secondary education -0.010 0.018
0.014 0.021
-0.119 0.016 ***
Dropout during tertiary education -0.035 0.026
-0.010 0.033
0.076 0.030 *
Tertiary education base-category
Married -0.003 0.010
0.053 0.012 *** -0.018 0.011
Children -0.042 0.010 *** 0.052 0.012 *** -0.082 0.010 ***
Poor financial status -0.030 0.008 *** -0.055 0.010 *** -0.052 0.009 ***
Poor health status -0.041 0.016 * -0.024 0.018
-0.090 0.016 ***
Working during studies 0.159 0.013 *** 0.168 0.015 *** 0.122 0.013 ***
Rural area 0.081 0.010 *** 0.039 0.013 *** -0.007 0.011
Rural area x Dropout -0.035 0.016 * -0.036 0.019 * -0.026 0.017
Moved -0.029 0.009 *** 0.026 0.011 * -0.011 0.010
GDP per-capita -0.001 0.000 * 0.000 0.000
0.006 0.000 ***
Corruption index -0.025 0.002 *** -0.017 0.002 *** -0.034 0.002 ***
Political stability index 0.083 0.018 *** 0.017 0.023 * 0.127 0.020 ***
Rho -0.115 0.070
-0.083 0.059
-0.372 0.047 ***
Source: Authors’ elaborations on ILO SWTS data
A supplementary analysis, which results are provided in the Table 3, is based on sub-samples
obtained by splitting the original sample according to the rural variable. This would allow us to
identify possible heterogeneity in dropout and working behaviors according to the area of living.
Generally, the sub-sample analysis confirms previous findings. The rho parameters are always
negative, but they are greater in magnitude when focusing on individuals living in non-rural areas.
The impact of school dropout is positive with very few exceptions, which especially regard unpaid
work and self-employment in rural areas. In addition, the impact of school dropout on working
15
perspectives tends to be greater in non-rural areas rather than in rural ones, especially for
employment positions.
The impact of school dropout on the probability of being an unpaid worker ranges between 23.3%
(Brazil) and 48.1% (Vietnam) in non-rural areas and between -11.7% (Benin) and 48.6% (Brazil) in
rural areas. When focusing on the probability of being self-employed, the impact of school dropout
varies between 11% (Nepal) and 34.5% (Brazil) in non-rural areas, and between -6.6% (Nepal) and
25.2% (Brazil). Finally, individuals dropping out from school experience a greater probability of
being employed, when compared to inactivity. The impact ranges between 16.2% (Benin) and
59.7% (Vietnam) in non-rural areas and between 1.9% (Jamaica) and 26.4% (Vietnam) in rural
areas. The impact of school dropout on the probability of being employed for rural Brazilian is
negative but not significant.
The sub-samples analysis reveals some other relevant findings. First, the poor working perspectives
of females is heavier in non-rural areas than in rural ones. In addition, females dropping out from
school see reduced their probability of being employed by 14.8% with respect to those not dropping
out. Second, educational achievements are irrelevant for self-employment perspective in non-rural
areas, while self-employment is more likely for low educated individuals in rural areas. Third, poor
health status is particular disadvantaging for working perspectives in rural areas. Finally, economic
and environmental indicators seem to be more relevant for working perspectives of individuals
living in non-rural areas. In particular, when looking at the probability of being employed, the AME
related to the political stability variable is three times greater for individuals living in non-rural
areas rather than in rural ones.
16
Table 3: Working outcomes equation by rural area: multinomial probit model accounting for endogeneity
NON-RURAL AREAS RURAL AREAS
UNPAID WORK SELF-EMPLOYMENT EMPLOYMENT UNPAID WORK SELF-EMPLOYMENT EMPLOYMENT
AME s.e. AME s.e. AME s.e. AME s.e. AME s.e. AME s.e.
Dropout 0.233 0.091 *** 0.345 0.093 *** 0.228 0.077 *** 0.486 0.109 *** 0.252 0.100 ** -0.093 0.083
Dropout x El Salvador 0.135 0.038 *** -0.127 0.037 *** 0.167 0.033 *** -0.335 0.087 *** -0.244 0.075 *** 0.346 0.058 ***
Dropout x Jamaica -0.031 0.052
-0.081 0.047 * 0.025 0.043 -0.422 0.094 *** -0.238 0.078 *** 0.112 0.064 *
Dropout x Cambodia 0.040 0.048
0.006 0.057
-0.002 0.053 -0.358 0.093 *** -0.156 0.079 ** 0.195 0.063 ***
Dropout x Nepal 0.059 0.049
-0.235 0.058 *** 0.022 0.056 -0.328 0.097 *** -0.318 0.085 *** 0.169 0.068 **
Dropout x Vietnam 0.248 0.046 *** 0.004 0.062
0.369 0.053 *** -0.278 0.093 *** -0.165 0.080 ** 0.357 0.063 ***
Dropout x Benin -0.045 0.043
-0.026 0.039
-0.066 0.036 * -0.603 0.099 *** -0.273 0.082 *** -0.006 0.067
Dropout x Liberia 0.160 0.051 *** 0.087 0.058
0.148 0.069 ** -0.310 0.112 *** 0.112 0.097
0.060 0.106
Dropout x Tanzania -0.035 0.056
0.068 0.053
0.132 0.053 ** -0.532 0.118 *** -0.167 0.110
0.198 0.092 **
Dropout x Zambia 0.018 0.047 0.010 0.047 -0.007 0.045 -0.377 0.096 *** -0.109 0.081 0.195 0.064 ***
Aged 15-19 base-category
Aged 20-24 -0.012 0.012
0.156 0.019 *** 0.107 0.016 *** -0.012 0.014
0.150 0.019 *** 0.068 0.015 ***
Aged 25-29 -0.017 0.013
0.244 0.022 *** 0.178 0.018 *** -0.019 0.016
0.302 0.019 *** 0.152 0.017 ***
Female -0.028 0.013 ** -0.103 0.018 *** -0.137 0.016 *** -0.009 0.016
-0.128 0.018 *** -0.108 0.016 ***
Female x Dropout -0.087 0.018 *** -0.095 0.025 *** -0.148 0.023 *** -0.095 0.022 *** -0.069 0.026 *** -0.092 0.023 ***
Up to primary education -0.105 0.034 *** -0.024 0.045
-0.275 0.032 *** -0.015 0.028
0.102 0.032 *** -0.272 0.025 ***
Dropout during secondary education -0.069 0.036 * -0.070 0.046
0.063 0.045 0.027 0.038
-0.060 0.046
0.203 0.042 ***
Secondary education -0.044 0.020 ** -0.040 0.027
-0.140 0.021 *** 0.019 0.027
0.056 0.031 * -0.079 0.024 ***
Dropout during tertiary education -0.097 0.033 *** -0.021 0.045
0.052 0.043 -0.043 0.039
-0.031 0.048
0.061 0.042
Tertiary education base-category
Married 0.007 0.013
0.056 0.015 *** 0.010 0.015 -0.035 0.016 ** 0.034 0.018 * -0.054 0.016 ***
Children -0.043 0.012 *** 0.036 0.015 ** -0.087 0.014 *** -0.025 0.015
0.080 0.018 *** -0.075 0.015 ***
Poor financial status -0.007 0.010
-0.041 0.013 *** -0.053 0.013 *** -0.054 0.012 *** -0.068 0.014 *** -0.050 0.012 ***
Poor health status -0.001 0.020
0.016 0.023
-0.067 0.021 *** -0.082 0.024 *** -0.073 0.027 *** -0.116 0.024 ***
Working during studies 0.144 0.015 *** 0.169 0.023 *** 0.154 0.018 *** 0.126 0.019 *** 0.135 0.022 *** 0.093 0.019 ***
Moved -0.035 0.011 *** 0.005 0.014
-0.011 0.013 0.000 0.015
0.049 0.017 *** -0.005 0.015
GDP per-capita 0.000 0.000
0.000 0.001
0.007 0.001 *** -0.006 0.001 *** -0.003 0.001 *** 0.006 0.001 ***
Corruption index -0.015 0.002 *** -0.014 0.004 *** -0.039 0.003 *** -0.030 0.003 *** -0.019 0.003 *** -0.031 0.003 ***
Political stability index 0.049 0.023 ** -0.047 0.031 0.197 0.030 *** 0.109 0.027 *** 0.062 0.032 * 0.068 0.027 **
Rho -0.421 0.162 ** -0.360 0.127 *** -0.451 0.085 *** -0.127 0.069 * -0.020 0.064
-0.298 0.057 ***
Source: Authors’ elaborations on ILO SWTS data
17
4.2 Dropout equation
The AME of the probit model for the dropout equation are reported in Table 4. The use of the ILO
SWTS data allow including both current individual and household characteristics of the youth, and
some information on the characteristics of the youth at the time when they dropout from education.
The former group includes controls for parental attitudes to education, i.e., the level of education of
father and mother,17 gender, completed educational level. The latter group includes controls
referring to the time of education (before dropping out), precisely a dummy variable equal to one if
the individual was married before leaving education, whether he/she worked while studying, if
he/she lived in a rural area or not and also whether he/she moved from a rural to urban area or vice
versa. We add an indicator to approximate the demand side effect, i.e., the unemployment rate
when the individual (in the precise year) left the educational system. Finally, two country specific
indicators of demographic and institutional factors possibly affecting dropout, i.e., life expectancy
at birth (by gender), and the duration of compulsory education (years)18 are included in our set of
covariates.
Table 4: Dropout equation: probit model estimates
AME s.e.
Female 0.009 0.007
Up to primary education 0.531 0.010 ***
Secondary education 0.289 0.011 ***
Working during studies 0.160 0.008 ***
Moved 0.018 0.007 *
Father up to primary education -0.044 0.008 ***
Mother up to primary education 0.079 0.009 ***
Married before the end of
education -0.266 0.008 ***
Rural area during studies 0.019 0.007 ***
Unemployment rate 0.001 0.000 *
Compulsory education -0.012 0.001 ***
Life expectancy 0.002 0.000 ***
Source: Authors’ elaborations on ILO SWTS data
Our results suggest that gender does not play a significant role in explaining the dropout probability.
Education, instead, plays a relevant role. Low educated show significantly higher probabilities of
dropping out from schooling with respect to highly educated (+53.1%). The difference between
17 In detail we have a dummy variables for father and a dummy variable for mother. These dummies are one if father
and mother have no education or, at maximum, elementary education, and zero for higher educational level. Therefore
these dummies capture the impacts of low parental education on the probability of dropout. Unfortunately information
on household income at the time of dropout is not available in the SWTS survey. 18 Figures available from the World Bank website: for life expectancy at birth, see
http://data.worldbank.org/indicator/SP.DYN.LE00.IN, for compulsory education (years), see
http://data.worldbank.org/indicator/SE.COM.DURS.
18
secondary educational attainment level and tertiary is lower (+28.9%) but positive and significant.
This is in line with expectations.
As far as parental education is concerned, we find that the impacts of the education of the father and
mother on the probability of leaving education before completion work in opposite directions.
Maternal education exerts a positive effect of around +7.9% on the probability of leaving education
before completion, whereas the impact of paternal education is negative and of about -4.4%. Our
results of a positive impact of mother’s education, or more in detail, of a stronger impact of
maternal with respect to paternal education, is in line with the findings suggested in the literature
(Chevalier et al., 2013).19
Being married before leaving education reduces the risk of dropping out from education by around -
26.6%. This finding might be partly due to the fact that being married before, i.e., get married at a
very young age, likely gives to the youth the possibility to reconcile its educational activity and its
household activity and therefore reduces the risk of dropping out from education. The literature
(e.g., Fox et al., 2016, and Llyod, 2009), indeed, shows that a relevant share of young women in
developing countries, get married and have child during education.20 Consequently many young
(especially women) in developing countries, have to decide whether to continue or dropout from
school, whether and how to enter the labour force at a very young age.
Working while studying increases the likelihood of leaving education before completion by 16%.
This is probably due to the fact that the wage of the youth start working before ending education
may be a fundamental economic support for their family, especially in South and Central Asian
countries. The majority of youth living in those countries, indeed, drop from schooling for
economic reasons, i.e., the family cannot afford the costs of schooling (Krogh et al, 2009; Elder,
2014).
Living in rural areas during studies and/or a move from a rural to urban area or vice versa both exert
a positive and significant impact on the dropout probability of almost the same magnitude, by 1.9%
and 1.8%, respectively.
As far as demand-side factors are concerned, the unemployment rate exert a positive impact on
dropout even if of a low magnitude (+0.1%). This might be partially due to the fact that, even if
unemployment rates are high, those youth with financial and economic limitations, i.e., family
cannot afford the costs of schooling, are constrained to dropout from schooling before completion
19 The literature (e.g., Astone and Lanahan, 1994, McNeal, 1999, Pirog and Magee, 1997, Pong and Ju, 2000) suggests
that parental education is a powerful predictor of school achievement and dropout behavior. Students with highly
educated parents are more likely to achieve higher educational level compared to students with low educated parents. In
addition, maternal education seems to have a stronger effect on children education compared to paternal education
(Chevalier et al., 2013). 20 In 2009, for instance, around one-fourth of females aged 15-19 got married and had children in Sub Saharan Africa.
19
and to work as family unpaid workers (Krogh et al, 2009; Elder, 2014). Financial constraints are
more widespread in Asian countries and in those countries unpaid work is quite relevant (see
Section 3).
The indicators summarizing demographic and institutional country specific factors suggest that
increasing years of compulsory education, as expected, reduces the risk of dropping out before
completion (-1.2%), whilst surprisingly higher life expectancy at birth increases the dropout
probability even if with a low magnitude (+0.2%). One would expect that higher life expectancy as
a proxy of good health conditions would increase education and therefore reduce the risk of dropout
from schooling before completion (Freudenberg and Ruglis, 2007). Our findings might be partly
due to the kind of work available for youth in developing countries, i.e., job requiring physical
effort and therefore healthy youth might be more oriented to work in these jobs, and therefore
increase their dropout rates, instead of completing their education.
The supplementary analysis by area of living (rural and non-rural), displayed in Table 5, allow
capturing heterogeneity based on the area of leaving and offers additional and interesting findings
with respect to our benchmark (and aggregate/not disaggregated) model. First, gender is an
important determinant of dropout in rural areas, emphasizing the disadvantage of female living in
rural areas. In other words, females in rural areas suffer of higher probability of dropping out from
education before completion compared to females living in non-rural areas. Working while studying
maintains its positive and significant impact on both living areas, but it shows a significantly higher
effect on dropout probability of youth living in rural compared to non-rural areas (+20.6% in rural
areas compared to +7.2% in non-rural areas). A marriage before completing education most
importantly and negatively affect the probability of dropping out from education before completion
in rural areas (-34.5%), with respect to non-rural areas (-14.2%). Finally, high life expectancy
slightly increases (+0.1%) the risk of dropout of youth living in non-rural areas.
20
Table 5: Dropout equation by rural area: probit model estimates
NON-RURAL AREAS RURAL AREAS
AME s.e. AME s.e.
Female -0,011 0,010
0,027 0,009 ***
Up to primary education 0,535 0,014 *** 0,518 0,014 ***
Secondary education 0,236 0,016 *** 0,334 0,014 ***
Working during studies 0,072 0,013 *** 0,206 0,010 ***
Moved -0,016 0,013
0,014 0,011
Father up to primary education -0,037 0,011 *** -0,043 0,011 ***
Mother up to primary education 0,076 0,012 *** 0,078 0,012 ***
Married before the end of education -0,142 0,012 *** -0,345 0,010 ***
Rural area during studies 0,089 0,015 ***
Unemployment rate 0,002 0,001 *** 0,000 0,001
Compulsory education -0,010 0,002 *** -0,018 0,002 ***
Life expectancy 0,001 0,001 * 0,000 0,001
Source: Authors’ elaborations on ILO SWTS data
5. DISCUSSION
School dropout is a widespread and heterogeneous phenomenon in developing countries.
When focusing on countries analyzed in this study, it ranges between about 15% (Tanzania) and
about 70% (Cambodia).21 Whatever reason guides school dropout (from economic reason to fertility
choices, or from scarce motivation to low returns to education, e.g. Levy 1971, Elder 2014), it
prevents the accumulation of human capital, which has been identified as a major driver of
development and economic growth (Hanushek, 2013). In addition, one can expect that poor labor
market conditions and employment perspectives of youth in developing countries may be associated
to high dropout rates. The decision to leave school may be critical in shaping later life experience,
fertility included; the same may apply to the nature of the first job. These are important issues in the
emerging and transition countries, especially for poorer women (Krogh et al, 2009).
Nevertheless, our empirical findings reveal that, with few exceptions, the effect of school dropout
on working perspectives is not negative per se. Dropping out from school, instead, increases both
the probability of being in unpaid work and being self-employed and, once endogeneity is taken
into account, it also increase the probability of being employed.
Accounting for endogeneity is, therefore, crucial to avoid biased estimates (see Campolieti et al.
2010). There is indeed potential endogeneity of the dropout status with respect to the labour market
outcomes we analyse. In particular, our study, shows the existence of a negative correlation
between errors terms of work and dropout equations; this suggests that confounding factors drive,
21 In this work we analyse dropout at all school attainment levels. Nonetheless in developing countries increasing
dropout rates are found also from primary school (Sabates et al., 2010)
21
simultaneously, both dropout and working decisions, but in a reverse way. In other words,
unobserved factors increasing the risk of dropping out from school simultaneously decreases
working perspectives and vice versa. Among others, confounding factors which are possibly at
work may include scarce motivation, preference for leisure and/or economic shocks. At empirical
level, by accounting for their role reinforces the positive impact of school dropout on unpaid work
and self-employment outcomes, and reveals the true effect on employment is actually positive. In
addition, when splitting the sample according to living or not in rural areas, it emerges the role of
confounding factors are much more relevant in non-rural areas.
Given these premises, a major finding of this study is that the underlying mechanisms driving the
positive impact of school dropout on working perspectives differs across outcomes. This confirms
the relevance of distinguishing among different working outcomes, i.e. un paid work, self-
employment and employment.
In particular, even though confounding factors may affect the probability of being in unpaid work or
self-employment, their role is not crucial to determine a positive effect, as demonstrated by the
specification not accounting for endogeneity.
The positive impact of school dropout is, instead, more explained by the irrelevance of having high
educational attainments to access to unpaid work and self-employment. This is not surprising when
focusing on unpaid work, especially in rural areas, for which we can expect that young individuals
voluntarily leave school before its completion to be employed, for example, in unpaid familiar
activities (Krogh et al, 2009; Elder, 2014). At the same time, lower educational attainments are
likely to be associated to self-employment in developing countries. This agrees with Pietrobelli et
al. (2004), which found a positive relation between primary education and self-employment rates,
supporting the hypothesis that just some basic skills are required in order to start a business. In this
context, it is not surprising that school dropout would be associated with higher propensity of being
an unpaid worker or self-employed, as it accompanies the acquisition of low educational
attainments and, at the same time, anticipate the entering in the unskilled labor market.
When focusing on the employment outcome, the role of confounding factors seems to be, instead,
essential, especially for non-rural areas. Differently from working outcomes discussed above, the
educational explanation does not hold, as having a low educational attainment is strongly penalizing
in terms of employment perspectives. This suggests, in line with Boccanfuso et al. (2015), that
having higher educational levels is important to obtain employment gains in developing countries.
In this respect, the negative impact of school dropout on employment probabilities (except that for
Nepal) emerging from specifications not accounting for endogeneity is, therefore, actually spurious.
This is consistent with evidence emerging from Campolieti et al. (2010) for Canada, for which the
22
negative impact of school dropout on employment probabilities becomes not significant when
accounting for endogeneity.
Our study, show that the negative impact of school dropout on employment probabilities in
developing countries is crucially driven by confounding factors, which may include scarce
motivation, preference for leisure or economic shocks. Once disentangled their contribution, the
impact of school dropout on employment probabilities is actually positive (but it is not significant
for Brazil). This possibly suggests that, apart motivations ascribable to unobserved factors, school
dropout per se might be seen as a step aimed at entering in employment activities. Among others,
this would be the case of individuals having previous job experiences, because employed during
studies, and/or having more direct links with the labor market.
On the contrary, when school dropout is driven by confounding factors, these confounding factors
would be also detrimental for employment opportunities. In that case, measures contrasting school
dropout should be possibly aimed at improving labor market conditions and returns to education
where particularly low (e.g. Oyelere, 2010), to incentive high-skills acquisition. At the same time, if
school dropout is guided by economic shocks, macroeconomic policies would be called upon.
Our results also show an important regularity in terms of gender differences. Being female
decreases the probability of working, with the only exception for those living in rural areas and
being employed in unpaid activities. In addition, while the average impact of school dropout on
working perspectives is generally positive, females dropping out from school are, instead, strongly
penalized in their working performances, and in particular in terms of employment probabilities.
This remark the importance of promoting policies aimed at mitigating female school dropout and/or
girls’ enrollments as suggested by Glick (2008). In general our results confirms strong gender
duality still persists in developing countries, even though recent findings show that gender
inequality is lowering in developing countries experiencing a decrease of labor demand for physical
attributes (Rendall, 2013).
6. CONCLUSIONS
The phenomenon of dropout from schooling before completions is widespread and heterogeneous
across developing countries. This paper studies the impact of dropout on working perspectives of
young people living in some transitions and emerging countries.
The analysis focus on three macroregions, i.e., Sub Saharan Africa, South-East and Central Asia,
and Latina America and Caribbean and on three employment outcomes, i.e. un paid work, self-
employment and employment by using data from the ILO SWTS.
23
The contribution of this paper to the empirical literature is at least twofold. First, we distinguish
among three working outcomes and especially between self-employment and employment. This is
quite important, since a major finding of this study is that the underlying mechanisms driving the
impact of school dropout on working perspectives differs across outcomes. Second, we model the
possible endogeneity of the dropout decision in the employment equation by adopting a
simultaneous two equations approach. We find that endogeneity is an important issue to avoid
biased estimates (Campolieti et al., 2010).
In general, one would expect that poor labour market conditions and employment perspectives of
youth in developing countries may be associated to high dropout rates and therefore a negative
impact of dropout from schooling on employment perspectives. However, our empirical findings,
especially those accounting for endogeneity, reveal that the impact of dropout from school on
employment perspectives is not negative per se and varies across different working outcomes, i.e.,
unpaid work, self-employment, and employment.
For unpaid and self-employment working opportunities there is a positive effect of dropout, even
when endogeneity is not taken into consideration. The role of endogeneity is instead essential for
the effect of dropout on employment, which comes out to be positive when the issue is modelled.
This finding suggest that confounding factors, as scarce motivation, preference for leisure and/or
economic shocks, exert a relevant role on employment outcome, especially for rural areas.
The conclusions to be drawn from this research are that the underlying mechanisms driving the
impact of school dropout on working perspectives in developing differ across outcomes and in
particular it is important to distinguish among unpaid work and, within employment, between self-
employed and employee. In addition, confounding factors are quite important and their contribution
must be disentangled especially when analyzing employment probabilities.
24
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APPENDIX
Figure A1. Youth completing and dropping out from education by macroregion
Source: Authors’ elaborations on ILO SWTS data
27
Table A1: Working outcomes equation: multinomial probit model not accounting for endogeneity
UNPAID WORK SELF-EMPLOYMENT EMPLOYMENT
AME s.e. AME s.e. AME s.e.
Dropout 0.076 0.032 ** 0.122 0.026 *** -0.201 0.028 ***
Dropout x El Salvador 0.028 0.028
-0.165 0.022 *** 0.203 0.023 ***
Dropout x Jamaica -0.070 0.035 ** -0.077 0.026 *** 0.100 0.029 ***
Dropout x Cambodia -0.010 0.031
-0.039 0.025
0.071 0.027 ***
Dropout x Nepal 0.037 0.032
-0.171 0.028 *** 0.091 0.031 ***
Dropout x Vietnam 0.027 0.030
-0.119 0.026 *** 0.252 0.026 ***
Dropout x Benin -0.099 0.032 *** -0.020 0.023
0.019 0.026
Dropout x Liberia 0.044 0.037
0.076 0.031 ** -0.006 0.046
Dropout x Tanzania -0.131 0.040 *** 0.013 0.031
0.135 0.037 ***
Dropout x Zambia -0.030 0.032
-0.007 0.024
0.072 0.027 ***
Aged 15-19 base-category
Aged 20-24 -0.046 0.006 *** 0.089 0.008 *** 0.043 0.008 ***
Aged 25-29 -0.083 0.007 *** 0.155 0.008 *** 0.085 0.009 ***
Female 0.024 0.007 *** -0.048 0.007 *** -0.074 0.008 ***
Female x Dropout -0.037 0.009 *** -0.014 0.011
-0.059 0.012 ***
Up to primary education 0.022 0.010 ** 0.116 0.011 *** -0.183 0.012 ***
Dropout during secondary education -0.020 0.016
-0.089 0.019 *** 0.190 0.022 ***
Secondary education 0.012 0.009
0.033 0.010 *** -0.056 0.011 ***
Dropout during tertiary education -0.042 0.016 *** -0.023 0.020
0.098 0.022 ***
Tertiary education base-category
Married -0.005 0.007
0.043 0.007 *** -0.039 0.008 ***
Children -0.021 0.006 *** 0.063 0.007 *** -0.073 0.008 ***
Poor financial status -0.004 0.005
-0.019 0.006 *** -0.026 0.007 ***
Poor health status -0.008 0.010
0.013 0.011
-0.065 0.012 ***
Working during studies 0.066 0.006 *** 0.051 0.008 *** 0.054 0.008 ***
Rural area 0.054 0.007 *** 0.014 0.008 * -0.036 0.009 ***
Rural area x Dropout -0.015 0.010
-0.012 0.012
-0.004 0.013
Moved -0.022 0.006 *** 0.025 0.006 *** -0.003 0.007
GDP per-capita -0.002 0.000 *** -0.002 0.000 *** 0.006 0.000 ***
Corruption index -0.009 0.001 *** 0.001 0.001
-0.020 0.001 ***
Political stability index 0.033 0.011 *** -0.033 0.013 ** 0.072 0.015 ***
Source: Authors’ elaborations on ILO SWTS data
