Before the discussion of the regression results, this section offers a descriptive perspective on the status of the DWO across the developing world. Because a detailed classification scheme for the DWO phase of a region was lacking, we use a new one that takes a simpler scheme used by the United Nations (UN) Population Division (UN, 2004, p. 70) as a starting point (compare Smits, 2016). The UN scheme distinguishes three DW phases: a pre-window phase with 30 or more percent of the population under 15 years old, a window phase with less than 30 percent under 15 years old and less than 15 percent above 64 years old, and a post-window phase with 15 or more percent above 64 years old. To get a more refined picture of the window, the GDL added a traditional phase, for countries that show hardly any sign of fertility reduction, and further subdivided the first two phases of the UN classification to obtain the following scheme:
1. Traditional phase (> 40% under 15 and < 15% over 64),
2. Pre-window phase (30–40% under 15 and < 15% over 64),
3. Early-window phase (25–30% under 15 and < 15% over 64),
4. Mid-window phase (20–25% under 15 and < 15% over 64),
5. Late-window phase (< 20% under 15 and < 15% over 64),
6. Post-window phase (> 15% over 64).
Figure 1 displays the DWO phase for 1921 subnational regions in 91 LMICs. The map reveals a substantial amount of variation in terms of DW phases both between and within LMICs. China is the only country where some sub-national regions are already in the post-window phase, while its northern neighbor, Mongolia, is mostly in the pre-window phase. India, on the other hand, has a clear north–south divide. While most of the south is in the mid-window phase, the north is still mostly in the pre-window phase. The MENA region shows a diverse pattern, with many regions in Turkey, Tunisia, and Iran already in the early-window phase, while other countries are still in the pre-window or traditional phases. In South America, countries such as Brazil and Argentina are in the lead in terms of the demographic transition.
In Sub-Saharan Africa (SSA), the picture is rather different from the other continents, with much less variation between countries and regions. Although a few areas, mostly in the south, appear to be past the traditional or pre-window phases, the majority of the central African region remains in the traditional phase. In Fig. 2, which zooms in on the SSA region, the picture remains more or less the same as well, with only a limited number of areas in the South, around the Gulf of Guinea and at a few other places that are in a later stage. These areas largely coincide with the countries called ‘vanguard countries’ by Eloundou-Enyegue and Hirschl (2017), i.e. those countries that started the earliest with the DWO as measured by having a Total Fertility Rate (TFR) of less than 3.5.
To have an even more refined view of the demographics in SSA, Table 1 displays the SSA countries that, at the national level, are not in the traditional phase anymore. The tourist destinations Mauritius (Phase 5) and Cape Verde (Phase 3) turn out to be the most developed in this respect, while Gabon and the five most southern countries are in phase 2. Thus, in SSA, only eight countries are not in the traditional phase when countries as a whole are considered.
However, demographic developments are generally not homogeneously spread within countries. As was already clear from Fig. 1, in many countries there is substantial sub-national variation in terms of DW phases. Given that fertility reduction tends to start earlier in urban than in rural areas (Easterlin, 1971), we would expect the first signs of an emerging window to be found in the cities (Williamson, 2013). For the SSA region, variation in fertility has already been observed for countries like Nigeria and the Democratic Republic of Congo, where fertility is substantially lower in more urban and capital areas than in rural and remote areas of the countries (Jimenez & Pate, 2017; Shapiro et al., 2017). To see whether more of such signs can be discerned in the SSA region, Table 1 also displays the DW phases of the SSA countries with an urban DW phase above phase one (excluding Mauritius and Cape Verde for which no sub-national DW data were available). In 24 countries, the urban areas are in phase two, and in two countries, Lesotho, and South Africa, already in phase three.
To look even more in depth, Table 2 displays the urban areas of sub-national regions that have a DW phase of three or over (i.e., the urban areas that have surpassed the pre-window phase and have thus actually entered the window). It shows that the DW is opening in more places than one would expect based on Table 1. For instance, while Botswana as a whole is only in the second phase of the DWO and its urban areas as well, the urban area of one of its regions—South-East—is already in the fourth phase, i.e., the mid-window phase. Lastly, while Ethiopia as a whole is in phase one and its urban areas on average in phase two, the country’s capital Addis Ababa is already in phase four.
The different countries and the time periods used in the analyses are shown in the online Supplementary Information (SI). In terms of the descriptive statistics, Table 3 shows there is substantial variation in terms of all variables. T1 IWI, i.e., the value for IWI at the start of the analyzed period, ranges from 1.82 to 94.99. Thus, there are regions where the average household in the first year owned almost none of the assets that are included in the index, while there are also regions in which households owned almost all assets in the first year. In terms of economic growth, there is also substantial variation, as the lowest average yearly change between T1 and T2 in IWI was −3.96 while the largest average yearly change was 5.72. In terms of male education, average mean years of schooling ranged from 0.23 years to 13.40 years. Regarding gender inequality in education, there are regions where men, on average, went to school 5.77 years longer than women, while there are also regions where women, on average, went to school 2.08 years longer than men.
Tables 4 and 5 show the results of our multivariate analyses. Model 1 in Table 4 includes all sub-national determinants of the change in IWI. The demographic window effect is clearly present, as the results show that both a lower T1 dependency ratio and a lower growth rate of the dependency ratio are associated with a significant increase in IWI growth.
Regarding the other factors in the model, we observe the expected negative effect of IWI at T1 on the growth in IWI. IWI growth is also higher in urban regions, in regions with higher levels of male schooling, in regions with a higher growth in male schooling, in regions with a smaller gender difference in schooling and in regions where the gender difference in schooling is decreasing.
Model 2 in Table 4 introduces all significant interaction terms between the independent variables and the dependency ratios and between the subnational control factors themselves. Regarding the main effects of the independent variable, we observe little change. The coefficients of the T1 dependency ratio and of the growth of the dependency ratio remain highly significant and negative, thus again confirming that both a lower dependency ratio and a decreasing dependency ratio are associated with significantly more economic growth.
The interaction coefficients show that the effect of the dependency ratio is conditional on the level of urbanization of the region. Both the T1 dependency ratio and the growth rate of the dependency ratio have a significantly weaker negative effect in urban regions. Hence the DWO effect is, on average, stronger in rural areas. Regarding education, we observe that declines in the T1 dependency ratio are more effective in increasing growth when the region has high levels of initial male education (human capital stock). In terms of the growth rate of the dependency ratio, we find that its effect is larger in countries with a stronger institutional environment. In unreported analyses, we find that the governance effect predominantly occurs due to variation in the control of corruption component of the Worldwide Governance Indicators and that no interaction of the other components is significant if control of corruption is included. The interaction coefficient of control of corruption is −0.165 and has a t-value of −4.02. Somewhat surprisingly, we find that the DWO is more effective in regions with less financial development. This suggests that the DWO might be able to create growth even in regions where factors usually associated with job creation (inflation reduction, financial market development, economic openness) are not (yet) well developed.
Our effects are quantitatively important. For instance, a one standard deviation decrease in the T1 dependency ratio or the growth in the dependency ratio, leads to a 0.36 or 0.15 standard deviation increase in IWI growth for an average region, respectively. The effect of the T1 dependency ratio is 50% stronger than the average effect in regions with one standard deviation of T1 male education above the average, and 28% weaker than the average effect in urban regions. Further, the effect of growth in the dependency ratio is 47% stronger in regions with one standard deviation of governance above the average.
The negative effect of IWI at T1 remains in the interaction model, but it is now non-linear, and conditional on the level and change in (male) schooling. The larger the level of or increase in schooling, the stronger the T1 IWI effect. Conversely, one could argue that (faster) increases in the level of schooling have a stronger effect in regions with an initially lower level of development.
To get a more detailed picture of the relationship between the DWO and economic growth, in Table 5 the dependency ratios are replaced by the T1 DW phases. With regard to the other independent variables, the model is equal to Model 1 of Table 4. The table shows that regions in the second, third and fourth DW phase have significant higher growth rates than the traditional DW phase. This effect is still positive but not significant in the late window phase. In the post-window phase, it has disappeared completely. As such, the results are in line with the idea that the DWO is a temporary period of higher growth. However, given that the effect is already positive in the pre-window phase and not significant anymore in the late window phase, it seems that the positive effects of a decrease of the dependency ratio can already be felt earlier than has been assumed before (e.g., UN, 2004).
The models presented in Table S1 in the Supplementary Materials use the same strategy as those in Table 4, but with different data, estimation techniques or variables. The first column shows the coefficients of the model that controls for migration flows within and between regions. Although the coefficient of the migration variable has the expected positive sign, it is not significant at the conventional confidence level of 95%. All other effects are robust to the inclusion of the proxy for migratory flows. In the T0–T1 column, we use the same strategy as before, but we go back one period in time for all countries where another period of at least four years is available. Thus, instead of explaining the variation in changes in the period between times T1 and T2, we now analyze the variation in changes in the period between times T0 and T1. This is possible for 1254 regions in 55 countries. The effects of the main variables—dependency ratio and change in dependency ratio—remain significant. We also note that the main effects of the DWO increase in size, while only the interaction between the change in the dependency ratio and T1 governance remains significant. Of the other variables, we observe that the T1 difference between male and female schooling, and some interactions, lose their significance. The third column shows the results we would have obtained if we only had data at the national level, but with a split between urban/rural regions. This analysis is based on data for 91 countries and 182 observations in total. In this setup, the T1 dependency ratio and the change in the dependency ratio are no longer significant. In addition, many of the control factors and all interactions lose their significance. This highlights the added value of our sub-national approach.
Overall, we conclude that our main findings regarding DWO effectiveness are robust to the inclusion of migratory flows and the use of older survey data. Nevertheless, T1 Governance is the only significant determinants of DWO effectiveness in the T0-T1 setup. Further, we highlight that sub-national data is better able to capture context-specific effects than national data.