Access to Higher Education and Human Development in Sub-Saharan Africa: A Life-Cycle Perspective from Ethiopia

1. Access to Higher Education and Human
Development in Sub-Saharan Africa: A Life-Cycle
Perspective from Ethiopia
Godstime Eigbiremolen
Department of Economics, University of Nigeria
and Anthony Orji

2. ADRODEP Conference, Kigali, Rwanda
Access to Higher Education and Human Development in Sub-Saharan
Africa: A Life-Cycle Perspective from Ethiopia
21st -23rd March, 2023
Godstime Eigbiremolen
Department of Economics, University of Nigeria
Anthony Orji
Department of Economics, University of Nigeria

3. Background and Motivation
• Education is generally seen as an important instrument for promoting economic
growth and development.
• Education is particularly important for the Sub-Saharan Africa region, where
growth and development are essential if the region is to overcome the many
challenges facing it (Bloom et. al., 2014).
• For many years, development agencies and other relevant stakeholders have
promoted primary education and, more recently, secondary education, as means
of improving economic growth and mitigating poverty in Sub-Saharan Africa.
• As a result, the discussions about access to education were mostly focused on
primary and secondary education. For example, the Education for All Summit
held in Dakar in 2002 promoted only primary education as a driver of broad
social welfare, leaving secondary and tertiary education in the background
(Bloom et. al., 2014).

4. Background and Motivation
• Currently, Sub-Saharan Africa has the lowest enrolment rates for higher
education as well as the lowest tertiary gross enrolment ratio in the world
(World Bank, 2020).
• A major reason for relegating higher education to the background in
development initiatives was the paucity of empirical evidence that it
enhances economic growth and development (Tilak, 2005).
• However, substantial empirical evidence now exists in the literature that
shows that higher education is a determinant of income, can produce both
private and societal benefits, facilitates economic growth, and improves
technological catch-up (Gyimah-Brempong, Paddison and Mitiku, 2006;
Seetanah and Teeroovengadum, 2019).

5. Background and Motivation
• In this study, our primary aim is to analyse the determinants of access to
higher education from a life-cycle perspective in Sub-Saharan Africa, using
Ethiopia as a case study.
• The education sector in Ethiopia has gone through different development
experiences and it is believed that Ethiopia has made some progress in
extending access to higher education during the last two decades (Yallew,
2020)
• While the enrolment rate in higher education grew by only 0.6% between
1970 and 2002, general enrolment has risen to about 8.13% since the turn of
the 21st century and more than 800,000 students are enrolled in Ethiopian
higher education institutions ((Saint, 2004; Ministry of Education, 2017).

6. Background and Motivation
• Despite the rapid expansion in access to higher education in Ethiopia,
more expansion initiatives need to take place to further expand access as
the current gross enrolment, which stands at 8.13%, is less than the gross
enrolment ratio of both sub-Saharan Africa and low-income countries
(UNESCO, 2015).

7. Background and Motivation
Source: UNESCO Institute of Statistics (2018)
Figure 2.1: Gross enrolment ratio in Ethiopian higher education
(2000-2014)

8. Background and Motivation
• Figure 2.2 depicts the relationship between government expenditure on
education as a percentage of GDP (%) and the gross enrolment ratio for
tertiary education (%) in Ethiopia.
• It shows that between 2000 and 2002, the average enrolment rate was
1.43%, while government expenditure as a percentage of GDP averaged
around 3.77%.
• According to published data, gross enrolment in tertiary education was
around 8.1% in 2014. This is inadequate in light of Ethiopia's need for
higher education.

9. Background and Motivation

10. Background and Motivation
• Aside from the dilemma of low enrolment in higher education in Ethiopia, the
higher education system is also riddled with other obstacles.
• First, growing student enrolment has failed to account for universities' finite
human capital, resulting in the overburdening of academic personnel (Akalu,
2014; Ayalew, 2017).
• Second, the expansion of higher education in Ethiopia has been marked by the
under-representation of women, geopolitically marginalized ethnic groups, those
from low socioeconomic origins, and people from minorities and rural areas
(Molla and Gale, 2015).
• Finally, as a result of excessive state intrusion, university autonomy , has not been
fully implemented to aid higher education enrolment (Areaya 2010).

11. Research problem and research objectives
• Although there is an acknowledgement of the importance of higher
education in SSA, little or nothing is known about the life-cycle
determinants of access to higher education in Sub-Saharan Africa and the
role of selection (i.e., individual-specific unobserved heterogeneity).
• The few empirical evidence on the determinants of access to higher
education in the region have been based on cross-sectional data with
limited set of socio-economic factors and also ignore the issue of selection.
• To fill this gap, we use a unique longitudinal data (Young Lives Survey in
Ethiopia) to answer the following questions: (i) to analyse the patterns and
inequalities in access to higher education; and (ii) to estimate the life-cycle
determinants of access to higher education in Ethiopia.

12. Data
• The Young Lives Survey follows the lives of 12, 000 children in four low-
income and middle-income countries: Ethiopia, Peru, Vietnam and India.
• The sample in each country consists of two cohorts of children: a younger
cohort of about 2000 children and an older cohort of about 1000 children.
• Round 1 of the Young Lives survey was carried out in 2002 when the
younger and older cohorts were aged 1 and 8, respectively.
• Follow-up surveys were carried out in 2006 (Round 2), 2009 (Round 3),
2013 (Round 4), and 2016 (Round 5) when the younger cohort children
were 5, 8, 12, and 15 years and the older cohort children were 8, 12, 15, 19,
and 22 years, respectively. We used Education History module from
Round 5 of the survey, which the latest.

13. Methodology – empirical strategy
• In order to estimate the determinants of higher education enrolment, we
specify a stepwise probability model as follows:
• Where 𝑌𝑖𝑗,21, the dependent variable, is a binary variable that takes on the
value of 1 if an individual i in cluster j was enrolled in higher education
between 2013 and 2016 and otherwise.
𝑌𝑖𝑗,21 = 𝜑 + 𝑋𝑖𝑗 .𝛽 (1)
+𝛼1.𝑃𝑃𝑉𝑇𝑖𝑗,12 + 𝛼2.𝑀𝑎𝑡ℎ𝑖𝑗 ,12 (2)
+𝛾𝑗 + 𝜇𝑖𝑗 (3)

14. Methodology – empirical strategy
In Equation (1), which is our first specification, we regress higher education enrolment on a
vector of household and individual characteristics X_ij,
which include: mother’s and father’s education (no education is the reference group),
family wealth index, household size, location of residence (urban is the reference group),
height for age, sex (male is the reference group), age, and aspirations (aspiring to a career
that does not require higher education is the reference group).
In Equation (2), we add two additional variables – test scores on Peabody picture
vocabulary tests (PPVT) and mathematics at the age of 12. These two variables serve two
purposes. First, they allow us to determine the extent to which human capital or learning
accumulated by the age of 12 predicts higher education enrolment at age 22. Second, the
test scores, which are lag achievements in Equation (2), acts as summary statistics for fixed
ability and past investments in human capital.
Finally, in Equation (3), and following Sanchez and Singh (2018), we add we add a vector of
fixed effects for the initial communities in which the individuals were sampled in,
effectively restricting comparisons to those between individuals in the same initially
sampled cluster.

15. Findings
Table 4: Determinants of higher education enrolment
(1) (2) (3)
Mother’s education 0.041 0.055 0.056
(0.042) (0.042) (0.043)
Father’s education 0.031 -0.005 0.009
(0.056) (0.058) (0.057)
Wealth (2002)
Richest half 0.173*** 0.110** 0.103*
(0.050) (0.051) (0.054)
Household size (2002) -0.008 -0.006 -0.002
(0.010) (0.010) (0.010)
Rural (2002) -0.309*** -0.233*** -0.365***
(0.061) (0.062) (0.086)
Height-for-age (2002) 0.021 0.009 0.002
(0.015) (0.015) (0.015)
Age (2016) 0.050* 0.031 0.016
(0.030) (0.030) (0.030)
Female 0.056 0.068* 0.083**
(0.038) (0.039) (0.039)
Aspiration (2002) 0.005 -0.030 -0.036
(0.045) (0.046) (0.046)
Test scores (2006)
Vocabulary test 0.002** 0.002*
(0.001) (0.001)
Mathematics 0.045*** 0.047***
(0.009) (0.009)
Constant -0.619 -0.559 -0.203
Cluster fixed effects
(0.653)
No
(0.655)
No
(0.660)
Yes
Observations 459 434 433
R-squared 0.232 0.294 0.394
Notes: The dependent variable takes the value of 1 if an individual was enrolled in higher
education between 2013 and 2016. ***p<0.01, **p<0.05, *p<0.1.

16. Findings
1. Substantial inequalities in access to higher education arising from location,
household wealth, prior academic performance, and gender.
Being in the richest half of the wealth quantile increases the likelihood of higher education
enrolment by about 17 percentage points on the average,
The gender effect is that boys are less likely to access higher education than girls in Ethiopia.
2. Conditional on household and individual characteristics as well as lagged
achievements, rural dwellers are about 31 percentage points less likely to
enrol in higher education.
3. An academically strong but poor individual at the age of 12 is less likely to
access higher education between ages 19 and 22 compared to an
academically weak but rich individual at the age of 12.
This is not likely the case of the poor being rationed out by price since the best higher
institutions in Ethiopia are public. It is more likely that individuals are rationed out by not
having the resources to continuously invest in tutoring and study to do well in entrance
examinations.

17. Findings
5. The inclusion of both PPVT and mathematics test scores, indicate that test
scores at age 12, especially mathematics, predicts higher education enrolment.
6. An estimation of the determinants of access to higher education that ignores
the role of a life-cycle accumulation of human capital may overstate the effect of
socioeconomic characteristics on the likelihood of higher education enrolment
6 Differences in household wealth and educational attainment are as important
within communities in their relationship with higher education attendance as
the variation across communities

18. Policy recommendations
1. Early intervention that provide direct financial support to academically
strong but poor children could significantly increase higher education
enrolment in the future.
2. There is need to engage in more rural education development programmes
and also employ more teachers to train students in order to boost their
cognitive competences and mathematical abilities.
3. Failure to provide financial support to academically strong children with
shaky economic foundations could have both individual and societal
implications.
At the individual level, such young people who could not access higher education could be
stuck in poverty over time. Again, there could be loss of productivity at the society level in
the long run due to untapped human capital.

19. THANK YOU
Acknowledgements
We wish to express our deep appreciation to African Economic
Research Consortium (AERC) for the financial support to carry
out this research