2019). Such laws are proxied using various indices from the World Bank’s _Women, Business and the Law_ surveys (Hyland, Djankov, and Goldberg 2020).

# **4.2 Second-stage results for aggregate steady-state employment ratios**

Table 6 shows the coefficient estimates on each correlate from a series of linear regressions of the country fixed effects from the first-stage regressions in Table 1. For these regressions, all correlates are country averages for 2000-19. Few of the correlates identified in the literature are significantly correlated with our estimated steady-state EWAPs for _aggregate_ employment.

**Policy uncertainty.** We find, as expected, that greater political instability or corruption have been associated with significantly lower steady-state EWAPs. These effects appear to have been associated less with sectoral shifts and more with economy-wide employment losses, especially among women.

**Tax regimes.** The econometric analysis conducted here supports the earlier findings in the literature. Tax-related complaints by firms, including about tax rates and tax administration, have been associated with significantly lower steady-state EWAPs. These constraints manifested themselves in lower economy-wide EWAPs rather than as shifts of workers from one sector to another.

# **4.3 Second-stage results for non-agricultural steady-state employment ratios**

In contrast to steady-state EWAPs for aggregate employment, those for non-agricultural employment account are correlated with many of the factors identified in the literature. Table 5 shows the estimate regression coefficients or these correlates.

**Increased exports** have also been associated with significantly higher steady-state EWAPs in nonagriculture. These differences have been not only statistically significant but also economically meaningful. EMDEs in the bottom quartile of export-to-GDP ratios have, on average, had lower steady-state non-agricultural EWAPs than those in the top quartile, by 11 percentage points.

**Better access to finance** —as proxied by the number of commercial bank branches per capita, the proportion of firms’ working capital that is financed by banks, or the percent of firms citing access to finance as a major constraint—has been associated with a significantly higher steady-state EWAP in non-agriculture. Again, the differences are economically meaningful. Compared with EMDEs in the bottom quartile by number of commercial bank branches per capita, those in the top quartile have had, on average, 5 percentage points higher steady-state EWAPs in non-agriculture. Compared with EMDEs in the top quartile by the percent of firms reporting major constraints in access to finance, those in the bottom quartile have had, on average, 10 percentage points higher steady-state EWAPs in non-agriculture.

**More flexible product and labor markets** have been associated statistically significantly with higher non-agricultural EWAPs among EMDEs. The regression coefficients suggest that EMDEs in the top quartile of product or labor market flexibility have had 7-10 percentage points higher non-agricultural EWAPs than EMDEs in the bottom quartile.

**Land constraints** have been associated with lower steady-state EWAPs. The steady-state nonagricultural EWAP has been significantly lower in EMDEs where a larger share of firms reported major constraints in accessing land. Compared with the quartile of EMDEs in which firms most often cited access to land as a constraint to doing business, the average EMDE in the quartile with

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