In Table 10, Column 1, we present OLS estimates of Equation (3), which allows the relationship between enforcement and shocks to vary across firms above and below this threshold. It does so by including interactions of _Large_ , an indicator for a firm being above the size threshold of 50, with _Surge*Enforcement_ and _Slump*Enforcement_ .

Consistent with our hypothesis, the coefficient on _Surge*Enforcement*Large_ is negative. It indicates that stronger enforcement attenuates hiring during surges to a greater extent among _Large_ firms. The point estimate is sizable: specifically, it implies that the coefficient on _Surge*Enforcement_ expands from ‐0.06 for firms below the size threshold to ‐0.22 for those above it.

Analogously, the positive coefficient on _Slump*Enforcement*Large_ indicates that stronger enforcement attenuates firing during slumps to a greater extent among _Large_ firms. Though the coefficient is not significant—it is marginally below the 10% level—the point estimate is large: it implies that the coefficient on _Slump*Enforcement_ goes up from 0.007 for firms below the size threshold to 0.150 for those above it. This too suggests that the observed relationship between enforcement and labor adjustment during shocks is driven by the _Large_ firms in our sample.

Column 2 includes triple interactions of a continuous measure of firm size (lagged employment level) with _Surge*Enforcement_ and _Slump*Enforcement._ This makes it, in effect, a test for a ‘break’ at size 50. This modification does not make a qualitative difference to the estimated coefficients on _Surge*Enforcement*Large_ and _Slump*Enforcement*Large_ , although their statistical significance drops away. This result is therefore weakly consistent with a threshold effect of enforcement at size 50. The weak result could be because the fuzzy definition of threshold lowers the power of this test. Column 3 adds triple interactions of _Surge*Enforcement_ and _Slump*Enforcement_ with other firm attributes (age and capital intensity) as there is a concern that the employment measure could be correlated with other firm attributes. The main results are the same. Overall, the results in Table 10 suggest that the relationship between enforcement and employment adjustment is indeed stronger among those firms that are the focus of labor inspectorates.

# **5.3 Effect on firm exit**

Table 11 provides evidence of the impact of shocks and labor regulation enforcement on firm exit, estimating Equation (5) through OLS. The baseline specification is shown in column 1; column 2 adds sectors‐year FEs and columns 3 and 4 add interactions of firm employment, age and capital‐labor ratio with shocks as additional controls.

The significant negative coefficient on _Slump_ indicates that on average, firms are more likely to exit during slumps than during normal and surge years. At ‐0.01, it is sizable compared to the baseline annual exit rate in the RUSLANA panel, which ranges between 0.04 and 0.07 during the study period. However, the estimated interaction between _Slump_ and _Enforcement_ is small (‐0.001) and statically not significant; and the same holds for the estimated interaction between _Surge_ and _Enforcement_ . Thus, enforcement is not associated with exit rates during shocks, and ignoring or including employment loss due to firm exit is immaterial to our main results.

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