The results in Model 1 demonstrate the effects of some of the key conventional growth determinants on GDP per capita growth within the African context. It is useful for demonstrating how some of these effects play out independently without accounting for demographic dynamics. FDI inflows and natural resource rents have played a significant role in the development story of every country in this sample; a role that is more significance in countries like Nigeria and Ghana, relative to others. Urban population growth often positively associated with economic growth across most of the literature also shows a similar effect here. All the countries in this sample have experienced some form of conflict at various points in time. Some countries like Ethiopia and South Africa were plagued by long periods of sustained strife, while others like experienced relatively short-lived albeit violent conflict. The negative impact of conflict of various forms is well documented and it is no surprise that the same effect emerges across all four models.
One of the primary motivations of this chapter was to test the effect of decelerating fertility decline on GDP per capita. Models 2 through 4 test this relationship, and results clearly
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demonstrate that stalling fertility has a negative, statistically significant effect on GDP per capita growth. It is however also interesting to note that the results from Model 2 similar to Model 1, show that GDP per capita growth in each period increases relative to the reference period (1990) even after accounting for stalling fertility decline. However, also as with Model 1, this increase does not persist through the last sample period (2015-2020) during which the positive effect on GDP growth relative to 1990 is about 50 percent lower than in the previous period. In fact the increase in GDP per capita growth over time in Model 2 (which controls for stalling fertility) exceeds the increase in Model 1 in all time periods relative to 1990-1995, except in the 2015- 2010 period. In Model 2, the positive effect GDP per capita growth in 2015-2020 relative to 1990- 1995 is over 1 percent lower than in Model 1. These patterns suggest two things. First, that GDP per capita growth may be sustained in the short term in the face of stalling fertility decline (as evidenced by increasing GDP per capita growth in each subsequent period in Models 1 and 2). They also suggest that while positive GDP per capita growth over time may not initially be mediated by the negative effects of stalling fertility decline, it is only a matter of time. Also, as evidenced by Models 1 and 2, the effects of a decelerating fertility transition will eventually over time, exert downward pressure on economic growth.
The results from Model 3 demonstrate the independent effects of education levels among male and females of different cohorts. It is surprising to see some of the negative effects that emerge among male cohorts. However, focusing primarily on the female cohort education variables, in Model 3, the results do show an expected positive, significant effect of education on GDP per capita growth, among all female cohorts with the exception of primary educated females age 40-64. While, the negative effect of stalling fertility increases in this model, this indicates that this negative effect is driven by variables other than female education. That being said, the results do show a positive and highly statistically significant relationship between GDP per capita growth and the percentage of secondary educated females age 40-64, and among primary and secondary educated women age 20-39. These results are very much in line with the findings from Lutz et al. (2008) where the direct productivity effect of education on growth were particularly
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strong for older workers with secondary education. These results are therefore in line with literature on the topic which highlights the benefits of secondary education especially among women. These results also indicate rising returns to education among younger cohorts across all levels compared to returns experienced by older cohorts. It is also interesting to note here that the positive effects on GDP per capita growth in the 2015-2010 period relative to 1990-1995 are even smaller in this iteration and no longer statistically significant indicating that a good deal of the growth in this period is driven by education.
The most significant takeaway from Model 4 is that with the inclusion of demographic variables accounting for the effects of a changing age structure and life expectancy, the negative effect of stall-level declines even though it remains statistically significant. This indicates that gains derived from a rising support ratio i.e. growth in effective producers relative to consumers, and mortality gains mediate some of the negative effects of stalling fertility decline. This model also shows that changing demographic dynamics are also responsible for some of the positive period effects from prior models—which while still highly significant, decline in Model 4. Furthermore, in some instances, the positive impacts of the demographic variables (support ratio for example) exceed those of the education variables. In essence this iteration of the model confirms that demographic dynamics still have an important role to play in the dividend story even after accounting for “competing factors” like education, and “mediating factors” like stalling fertility decline.