As an additional way of seeing whether these impacts are concentrated in a few firms or more widespread, we estimate quantile treatment effects. Figure 1 plots the impacts and compares them to the ITT impact shown in Table 2. Quantile treatment effects for the level of monthly sales are well below the ITT for all but the top decile, showing that the large magnitude of the average improvement is indeed driven by the top of the distribution. However, we would expect training to be result in a constant percentage increase in sales, than the same level increase in sales regardless of initial firm size. This is the case, with the quantile impacts on log sales relatively constant across all quantiles and similar to the estimated ITT average impact. Likewise, we see the quantile treatment impacts on business practices are positive and significant and of similar magnitude across most of the distribution.

# **3.4 Do these impacts last?**

Columns 4-7 of Table 2 show that none of these impacts persist at 6 months. The estimated impact on business practices has fallen to 0.8 percentage points, which is not statistically different from zero, and is statistically different from the 2-month impact. The estimated impacts on sales and profits are all negative in sign, and not statistically different from zero, and likewise are statistically different from the 2-month impacts. This difference is not a result of changing sample composition: Appendix Table B3 shows the results are similar if we restrict analysis to the balanced panel of firms.

We investigated treatment effect heterogeneity to examine whether training had sustained impacts for some subgroup of the sample, even if the overall impact disappeared. We explored two approaches to examining heterogeneity in Appendix D. The first is to examine treatment interactions with firm and owner characteristics. We find the initial impacts of training appear to be higher for those owners with more personal initiative, but even this subgroup does not have lasting impacts. Second, we use the generic machine learning approach of Chernozhukov et al. (2020) to test whether there is predictable heterogeneity in treatment effect based on a set of baseline covariates, and cannot reject that there is no predictable heterogeneity. The drop-off in treatment effect therefore seems widespread.

Figure 2 graphically shows this reversal by showing the distribution of changes in business practices, sales, and profits by treatment status between the baseline and 2-month survey, and then between the 2-month survey and 6-month survey. Three results are apparent. First, not only is there a lot of volatility in sales and profits, but we also show that there is considerable churn in business practices. This is not something that has been documented in previous literature. Even in the control group, many firms are starting and stopping practices between survey rounds. Second, between the baseline and two months, we see relatively more treated firms adding business practices and fewer ones dropping them, and relatively more treated firms growing profits and sales than the control group. Third, in contrast, between 2 and 6 months we see relatively more of the control group adopting new practices, whereas more of the treatment group are dropping business practices, and more of the treated experience a drop in sales and profits. This figure also helps show that the difference in 2- and 6-month results is not being driven by a few observations, but is instead visually apparent in the distributions.

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