employment (Table 10) and the wage index (Table 9) yield different results for the OLS-FE specifications. When the dependent variable is employment or female employment, we find that the coefficient on POST becomes significantly negative but the coefficient on POST interacted with the dummy for labor abundant countries is now significant and positive. For female employment and the wage index, the coefficient on POST interacted with the dictatorship indicator is negative and significant. Thus reforms are associated with higher total employment and constant female employment levels in labor abundant countries, but lower employment levels in labor scarce countries. Female employment and wages also decrease post reform in dictatorships.

To tackle the endogeneity of POST discussed in sections 2 and 3, we run the same fixed-effects specifications for each labor-market outcome variable as IV regressions, using alternative sets of instruments. The variable that is instrumented is POST. The instruments for regressions presented in columns (3) and (4) are external debt-to-GDP ratio, terms of trade and five-year change in democracy score. The one-year lagged value of POST is added as an instrument in columns (5) and (6). Columns (7), (8), (9) and (10) are respectively the same as (3), (4), (5) and (6), except that now the external debtto-GDP ratio as an instrument is replaced with the interaction between the change in the world interest rate (proxied by the U.S. Treasury Bill rate) and external debt-to-GDP.

A valid instrument is one that is correlated with the endogenous regressor yet orthogonal to the errors. To determine the quality of the instruments, three empirical tests are used that provide evidence of the instruments’ validity. First, to assess the correlation of the instruments with the endogenous regressor, it is sufficient to examine the significance of the excluded instruments in the first-stage regression. A commonly used statistic is the R<sup>2</sup> of the first-stage regression, after partialling out the effect of the exogenous left-hand side variables in the second-stage regression, referred to as the partial R<sup>2</sup> (Shea 1997). Although there is no threshold level, the instruments should be sufficiently relevant to explain a significant share of the variance of the endogenous regressor. As a rule of thumb, an estimated equation that yields a first-stage partial R<sup>2</sup> lower than 10% indicates a “weak instruments” problem. Second, we also report the Kleibergen-Paap rk Wald F statistic to test for weak instruments and compare them to critical values tabulated by Stock and Yogo (2005). Instruments are considered weak if the statistic falls below the critical value, unable to reject the null hypothesis that the equation is weakly identified.

Third, whether the instruments are orthogonal to the errors can be tested in an overidentified model where the number of instruments is greater than the number of endogenous regressors using the Hansen J statistic p-value (when allowing for robust and clustered standard errors). The Hansen J statistic is from a test of the hypothesis that the instruments are uncorrelated with the error term or that the overidentification restrictions are valid. A rejection would call this hypothesis into question and cast doubt on the validity of the instruments. The first-stage partial R<sup>2</sup> , the Kleibergen-Paap F statistic and the Hansen J p-value are reported in each of the IV results.

When the unemployment rate is the dependent variable, POST consistently has a negative coefficient sign and is significant in three of the eight regressions. Only in columns (5), (6), (9) and (10), where lagged POST is also included as an instrument, do both the first-stage partial R<sup>2</sup> , the KleibergenPaap F statistic and the Hansen J p-value support that the set of instruments possesses both ideal characteristics, namely a fair degree of correlation with the instrumented variable and the joint 18