Our perspective on EWAP dynamics in EMDEs is motivated by the literature on structural change. A key dynamic among EMDEs is the movement of economic activity out of agriculture and into non-agriculture. Labor markets in these two sectors have very different operating features: agricultural labor markets are dominated by self-employment in low-density rural areas, whereas non-agricultural labor markets are dominated by formal or informal employment in higher-density urban areas. Additionally, many policy and institutional differences are likely to have very different effects on labor markets in these two sectors. For these reasons, the process of structural change will plausibly give rise to dynamics in aggregate labor market outcomes, as activity switches from largely self-employment in rural areas to formal or informal employment in urban areas. Long-run labor market outcomes will thus reflect the steady state outcomes in the nonagricultural sector.

Our methodology for studying EWAP dynamics draws heavily on the growth literature that studies convergence patterns in cross-country panel data sets. Specifically, following the literature on conditional convergence, we include country fixed effects in our convergence regressions to allow for the possibility that each country is converging to its own steady-state level of EWAP. When implementing this procedure, we also control for two time-varying driving forces: productivity and population.

Our analysis of EWAP dynamics proceeds in two steps. The first step runs convergence regressions for EWAP. A by-product of this first step are country-specific steady-state values for the EWAP. In the second step, we examine the correlation between these steady-state levels and various indicators.

Our first step delivers three key results. First, while there are many countries with very similar steady-state EWAP levels, there are many countries with steady-state EWAP levels that display large deviations from the mean. Second, higher population growth is associated with significantly lower EWAP. Third, higher productivity growth is also associated with significantly lower EWAP. This third finding suggests that EMDEs may face a tradeoff between employment and productivity. This would be the case if these countries tend to have many individuals employed in low-productivity activities, as eliminating these jobs would increase productivity growth via composition effects.

Our second step identifies several factors that display significant correlations with long run levels of EWAP. These include greater openness to international trade; more efficient labor, land, and product markets; larger firm size; and better education outcomes. We emphasize that these results only reflect correlations and so do not necessarily imply causation. But we think these correlations are informative as a first step in thinking about the potential effects of various policies.

Our benchmark results are for the aggregate level of EWAP. We also repeat our analysis for EWAP levels by gender. We find that differences in steady-state EWAP levels for women are much more substantial than differences for men.

In this paper, we deliberately focus on the aggregate quantity of employment, assuming that employment is a policy goal in its own right. Individuals value jobs for the earnings, as well as for their contributions to self-esteem and happiness and jobs influence living standards and social cohesion (World Bank 2013). Switching jobs, especially into non-agriculture, is one of the most commonly used forms of climate adaptation by households. The quality of employment in terms of its various characteristics—labor productivity, labor income shares, wage rates, or contractual arrangements—is no less critical for the development process but is well-covered in the literature and beyond the scope of this paper.

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