development process involve a large reallocation of activity from the agricultural sector to the nonagricultural sector, and that the nature of labor supply and demand may vary across sectors. Individuals living in rural areas may have different levels of labor supply across sectors, and firms in different sectors may demand different types of workers. If this is the case, then the dynamics of structural transformation will potentially influence the dynamics of equilibrium in the labor market.

It is also plausible to think that the effect of various institutions and regulations have differential impacts on both the demand and supply of labor across sectors. Regulations that disproportionately affect large establishments will likely have very different effects in agricultural versus nonagriculture. Policies that increase the cost of capital or access to credit will have differential impact across sectors if sectors differ in the importance of capital or credit. The implication of this observation is that the dynamics of labor market equilibrium may vary across countries with different labor market institutions. Moreover, the overall impact of a particular institution or regulation may differ along the development path: institutions that negatively affect labor demand in the non-agricultural sector will have a smaller aggregate impact in an economy in which almost everyone works in agriculture.

## **2.3 Data**

The dataset includes 160 countries over 1960–2019. However, the baseline sample focuses on 103 EMDEs that are not small states for 2000–19, a period in which there is good data coverage for most EMDEs and which excludes the outlier years during the COVID-19 pandemic.

The main data sources include the World Bank’s _World Development Indicators_ (WDI) database, the International Labour Organization (ILO)’s _ILOSTAT_ database, and the _Penn World Tables_ .

Data for employment comes from the _Penn World Tables_ , and includes both formal and informal (including subsistence) forms of work.<sup>3</sup> The data for baseline real output are from the World Bank’s _Global Economic Prospects_ database, supplemented with data from WDI for earlier years, spliced by sector using WDI’s sectoral gross value-added data. Productivity is calculated as the ratio of real output to the number of workers.

Total and working-age population come from WDI. The working-age population is defined as the number of people aged 15–64 years. Data on employment, real output, and population spans 1960– 2022 for 145 EMDEs. However, the analysis focuses on developments since 2000 because of limited data availability of policy variables for earlier years.

Data for the policy variables come from WDI, the World Bank’s _Women, Business and the Law_ database, the International Monetary Fund’s _Financial Access Survey_ , the World Bank’s _Enterprise Survey_ s, Bento and Restuccia (2021), and the Fraser Institute’s _Economic Freedom of the World_ (EFW) database.

# **3. First-stage regression results: Steady-state employment ratios**

The first-stage regression results in Table 1 provide clear evidence that EWAPs in EMDEs converge toward country-specific steady-state levels. The baseline regression results also suggest that slower productivity growth and faster working-age population growth have been associated with significantly faster increases in EWAPs in EMDEs. This is the case for aggregate EWAPs

> 3 For Nepal, adjusted employment data is drawn from Ruppert Bulmer, Shrestha, and Marshalian (2020), which also includes subsistence employment, consistent with the baseline employment measure.

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