Effect of quantity and quality of education on per capita real-GDP growth: evidence from low- and middle-income African countries

ABSTRACT

Although several empirical studies have investigated the growth impact of the quantity of schooling in African countries, research on the impact of schooling quality (the degree to which schooling nurtures professional and cognitive skills) is rare. To fill this research gap, this study examines the effects of the quantity and quality of education on per capita real-GDP growth in low-income and a combination of low- and middle-income African countries using dynamic generalized method of moment estimation on panel data from 2003 to 2016. The results suggest the quantities of primary and secondary education have a positive and statistically significant impact on per capita real-GDP growth; however, the correlation between the quality of education and economic growth is weak and not robust. Furthermore, standardized coefficients indicate the quantity of primary education has a stronger impact on growth than the quantity of secondary education and quality of education, and the quantity of secondary education has a stronger impact on growth than the quality of education. Overall, these results suggest policy authorities should target both the quantity and quality of education to achieve per capita real-GDP growth, with a particular focus on ensuring universal primary education.

KEYWORDS:

• Quantity and quality of education
• low- and middle-income countries
• Africa
• real GDP
• per capita growth
• dynamic panel data

JEL CLASSIFICATION:

• C33
• I21
• J01
• J24
• N37
• O55

Disclosure statement

No potential conflicts of interest are reported by the authors.

Notes

¹ See: United Nations, Population (webpage). Available at: http://www.un.org/en/sections/issues-depth/population/(Accessed 24 February 2019).

² Data limitations preclude a separate regression on middle-income African countries.

³ The model’s instrumental variables are exhibited in Table 2.

⁴ Table 3 exhibits the control and instrumental variables.

⁵ Since the widely used proxies, Progress in International Reading Literacy Study (PIRLS), Programme for International Student Assessment (PISA), Trends in International Mathematics and Science Study (TIMSS), and the Southern and Eastern Africa Consortium for Monitoring Educational Quality (SACMEQ) do not cover many of the African countries this study includes, they are not used in this study.

⁶ Table 4 exhibits the control and instrumental variables associated with each model.

⁷ Data constraints preclude regressions using GRE scores as a proxy for education quality.

⁸ The control and instrumental variables associated with each model are reported in Table 6.