On the other hand however, the famous Malthusian theory postulates a negative effect since the contribution of each additional labor unit decreases due to limited resources (see Peterson ( 2017) for a discussion of the literature). On the one hand, a larger population means more people, i.e., brains that are more likely to develop advanced technology thus leading to more income growth. The relationship between population size and economic growth is the subject of a large body of work that has not concluded yet. The estimates reveal an interesting pattern as they show that democracy has a decreasing effect on the population size in the non-African countries whereas in the African countries, it has an increasing effect on the total size. We explore this theory by estimating the effect of democracy on the size of the total population. When democracy does not support total GDP but it increases GDP per capita in the non-African countries, the denominator of GDP per capita has to play an important role. These estimates show that no countries’ GDP benefits from democracy. This is an important step because it provides insight how democracy affects GDP per capita. We also estimate the effect of democracy on total GDP. The empirical analysis confirms this conclusion. However, the gains are not significant for the SSA countries. As can be seen, both groups of countries experience some growth from a democratization. This restriction on the countries enables to depict the development of income before and after a democratization. It shows the development of income growth around a democratization for countries that had a single transition to democracy without a reversal to autocracy. Although none of the methods is perfect in solving the endogeneity problem, they are helpful because in sum, they draw a clearer picture of the true effect.įigure 1 gives an illustration of the issue. Yet, both techniques confirm no significant positive effect of democracy on GDP per capita in the African countries.
The methods are based on different identification assumptions (we discuss them below). We address endogeneity with two methods: first, we employ the GMM technique developed by Arellano and Bond ( 1991), and second, we apply an instrumental variable (IV) method based on important work by Acemoglu et al. In addition, there is reverse causality since income is also likely to have an effect on democracy (see, e.g., Cervellati et al. Footnote 3 However, the effects of time-variant variables remain and they can bias estimates. Fixed effects models provide only some relief as they control only for the effects of those variables that are time-invariant such as geography, history, and institutions. The absence of significant correlation between democracy and income has to be taken with caution since the relationship is driven by endogeneity. This pattern is supported by different measures for democracy. In the SSA countries, estimates turn out insignificant suggesting that democracy plays no important role for income growth. However, the positive link is significant only in the non-African countries. The baseline estimates show evidence for a positive association. Our estimation begins with fixed effects OLS estimates of the correlation between democracy and GDP per capita. This approach provides deep insights for the development of Africa. As we discuss below, this method is beneficial because it enables comparisons between African and non-African countries estimated under the same econometric conditions. In our analysis though, we consider almost all countries in the world and the empirical model explores information for all of them in one and the same regression. We estimate the within country effect of democracy on economic growth for countries in SSA. In this study, we explore whether democracy provides a solution to this situation. In 1960, the average child mortality rate of the SSA countries was 40 \(\%\) higher than the global average.
Footnote 2 Another issue is the health situation that has worsened over the last half century. In 1960, SSA made up about 7 \(\%\) of the world population. Footnote 1 This development is accompanied by fast population growth.
This share has constantly decreased to only 22 \(\%\) in 2018. Data by the World Bank show that average GDP per capita (PPP-adjusted, in constant 2011 international \(\$\)) in Africa was about 30 \(\%\) of the world average in 1990 (when the earliest data are available). Sub-Saharan Africa (also referred to as SSA or just Africa) leads in several rankings: it is the world’s poorest region, it has the fastest-growing population, and it has the worst health outcomes.
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