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Anthony Orji_2023 AGRODEP Annual Conference

  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