The Cox results are used to predict the relative hazard rates for the treatment and control groups. As the propensity score for each firm, we use the mean of their hazard rates for all years in which they are available.<sup>14</sup> The hazard rate averaged 0.124 for the treatment group and 0.099 for the control group, consistent with the treatment group as a whole having a higher relative probability of program participation. Figure 2 graphs the distributions of these relative hazard rates for the treatment and control groups, and the region of common support.<sup>15</sup> The graph shows the treatment group to have greater density in the upper tail of the distribution of propensity scores than the control group. Nonetheless, within the region of common support which is quite wide, every firm in each group has a positive probability of participating in SME programs though some may have higher probabilities than others.

# **C. Estimating Program Impacts in 2004**

With propensity scores in hand, we turn to an investigation of the impacts of program participation on levels of intermediate and final outcomes in 2004. The definitions and summary statistics of these outcome measures were reported previously in Table 4, separately for the treatment and control groups. That table highlighted the fact that the treatment group generally had higher mean values of intermediate outcomes as compared to the control group, but generally lower levels of most final outcomes measures.

We estimate the average treatment effect of program participation using the nearest neighbor matching estimator. The nearest neighbor estimator essentially compares the treated firm to an untreated firm from the control group with the most similar propensity score, and is an estimator that is commonly used when sample sizes are small.<sup>16</sup> The analysis is restricted to the treatment and control group firms in the region of common support. Table 6 reports the means of each outcome for the matched treatment and control groups, the differences in means, and t-tests of the statistical significance of these differences. Panel A focuses on the intermediate outcomes elicited by the 2004 ICS, Panel B on selected final outcomes computed from the 2004 ENIA.

Panel A of Table 6 reveals that program participation is associated with higher means values of the intermediate outcomes, though these differences in means are statistically significant only for some outcomes. Note that we cannot infer a causal relationship from program participation to outcomes since no pre-participation measures are available. Nonetheless, the patterns observed here are consistent with those found in other impact evaluations of Chilean programs that did have pre- and post-participation data on these variables. Compared to their matched control group, treated firms are significantly more likely to be involved in R&D, to have introduced new product lines and production

> 14 For the treatment group, the means are computed for all years up until the year of program participation, after which relative hazards rates are not defined because the failure event has occurred.

> 15 The region defined by the maximum and minimum values of the propensity scores of the other group. Since the minimum and maximum values for the treatment group was 0.0488 and 0.2932, and 0.0296 and 0.2740 for the control group, the region of common support lies between 0.0488 and 0.274. Some firms in each group fall outside the region of common support – 3 treatment firms with high hazard rates, and 16 control group firms with low hazard rates.

> 16 Other PSM estimators include caliper and kernel matching, but these were not explored in the paper.

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