I use employment as the matching variable since it is the outcome variable of interest and previous research finds that past values of the outcome of interest are the ones that are most strongly correlated with the future outcome (Bruhn and McKenzie 2009). That is, since the outcome of interest is employment, matching on past employment is likely to yield the most comparable groups of industries in the post‐program period.<sup>7</sup> I use averages over increasingly shorter time periods to better capture the growth path of employment in the past few years before the program and to capture the employment response to the crisis, which started to hit Mexico in the second half of 2008.

After obtaining the propensity score by running a probit regression of an indicator variable for being eligible for the program on the employment variables listed above, I examine three different alternatives for selecting comparable industries a) the common support of the propensity score across eligible and ineligible industries, b) keeping industries with a propensity score that falls between the 20th and 80<sup>th</sup> percentile of the score, and (c) nearest neighbor caliper matching without replacement using the logit of the propensity score and calipers of width equal to 0.2 of the standard deviations of this variable, as recommended in Austin (2011 and 2014).

Tables 1 and 2 test whether the above methods yield a sample of eligible and ineligible industries that is comparable in terms of employment levels, and, most importantly, in terms of employment trends in the pre‐program period (from Jan 2004 to December 2008). If employment trends were the same in the pre‐program period, we can assume that they would have continued being the same in the post‐ program period in the absence of the program.

The results in tables 1 and 2 show that, in the full sample of durable goods industries, eligible industries had higher employment levels in the pre‐program period (this difference is large, 47 percent, but not statistically significant), and they displayed a steeper trend in employment than ineligible industries. These differences remain when considering only industries on the common support of the propensity score and also when keeping only industries with a propensity score between the 20 and 80<sup>th</sup> percentiles of the score. However, the sample obtained through caliper matching looks more comparable. In this sample of 34 industries, average employment across eligible and ineligible industries differed by 24.5 percent (not statistically significant) and, most importantly, the trends in employment were virtually the same.

Figures 1 through 4 illustrate the comparability of eligible and ineligible industries graphically. Figure 1 plots average employment over time in eligible and ineligible industries for all durable goods industries, and figures 2 through 4 plot average employment for the samples defined using methods a) through c). The vertical lines in each figure show the start and end dates of the wage subsidy program (January through August 2009). In figure 4, which shows the sample obtained through caliper matching, average

> 7 As described above, the program also intended to reach sectors producing goods for export. However, I do not have accurate data on exports for all IMSS industries producing durable goods since import/export data is recorded under a different industry classification that does not allow for a direct mapping to the IMSS industry classification. The empirical analysis in section 4 includes a specification that controls for imports by Mexico’s largest trading partner (the US) using only the industries that could unambiguously be mapped to IMSS industries.

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