Poverty reduction, economic growth and inequality in Africa

Abstract

We study the relationships among economic growth, inequality and poverty. Economists agree that growth is fundamental to reducing poverty. But the links among growth, distribution and poverty is still a subject of debate because the growth elasticity of poverty seems to differ from one country to another. Using a data set for 16 African countries, based on household budget surveys, we find strong support that poverty decreases in response to economic growth, with the estimated elasticity ranging between −0.5 and −1.10. Other variables, albeit important in varying degrees, are much less significant as determinants of poverty.

Notes

¹ The Lorenz curve of income distribution shows the proportion of income earned by any given proportion of households, when the households are arranged from the poorest to the richest.

² Average years of schooling refer to educational attainment of the total population aged 15 and above, from the Barro–Lee data. For more information, see http://post.economics.harvard.edu/faculty/barro/data.html. We also consider a more restricted definition of human capital such as average years of schooling for males or secondary school enrollment rate.

³ Winters et al. (2004) compile evidence to show that trade liberalization is associated with poverty alleviation in the long run.

⁴ While trade openness can be measured several different ways, we take it as the ratio of exports plus imports to GDP. For various measurements of openness, see, among others, Sachs and Warner (1995), Edwards (1998) and Warner (2003). Our data for openness come from World Development Indicators, 2005.

⁵ World Bank, IMF staff report and Poverty Reduction Strategy papers are the main sources of Iradian's data set.

⁶ The countries included are: Algeria, Cameroon, Egypt, Ethiopia, Ghana, Ivory Coast, Lesotho, Madagascar, Mali, Mauritania, Morocco, Nigeria, Senegal, Tunisia, Uganda and Zambia.

⁷ In an alternative specification, we used growth rate of per capita real GDP based on national income accounts to compare with the results for mean income growth rate based on household surveys. As expected, it has a negative relationship with a change in head count poverty, but now the value of growth elasticity of poverty falls dramatically to −0.001⁷ (see a similar observation by Adam, 2003).

⁸ Note that the variable Dev is the inverse of the development level.

⁹ The interaction term between inequality and initial development level is not included in the calculation of inequality elasticity due to its statistical insignificance.

¹⁰ On the contrary, Gibson (2000) finds that distribution component dominates the growth component of the change in urban poverty in Papua New Guinea between 1986 and 1996.

¹¹ The coefficient of average years of schooling is positive and far from the zone of statistical significance.

¹² The results for human capital and openness are available on request.