# **Appendix 6: Robustness of Firm Performance Results**

Appendix Tables 6.1 to 6.4 examine the robustness of our firm performance results to alternative specifications.

Appendix Table 6.1 conditions on whether firms are operating. In our main specifications, firms which are closed are coded as having zero profits, sales, and employment. Treated firms are slightly more likely to survive, albeit not significantly so (Appendix Table 3.4), and so part of the unconditional estimates capture the extensive margin impact of moving from zero profits or sales to a positive number. In the inverse hyperbolic sine transformation, this gets captured as a very large percentage change at the bottom of the distribution. Conditioning on survival therefore reduces the magnitude of the coefficients, but qualitatively we get similar results to our main specifications in Table 5, and continue to see significant round 2 impacts of outsourcing.

Appendix Table 6.2 attempts to improve power (and correct for any selective attrition) by using post-double selection lasso to control for baseline variables that either strongly predict the outcome variables or treatment status. In practice, only one outcome variable (yearly sales) has a control selected in round 1 (full-time employees) and so these results hardly change. For round 2, yearly sales has one control selected (owner’s age), and the two employment outcomes have one control selected (full-time employees). The consequence is that the results do not change very much from those in Table 5.

Appendix Table 6.3 pools together multiple rounds of data in order to attempt to boost statistical power. The first seven columns pool together data from the first and second follow-up rounds. Since the point estimates were smaller in the first round than the second, the reduction in standard errors from more data is offset by the smaller average effect than is given by the point estimates in round 2, and so we do not detect find significant results from pooling that were not significant in round 2 before pooling. The last two columns use a different approach to pooling data to improve power. In the round 2 survey, firms were asked for the last month’s sales and profits, and then also asked for monthly profits for October, November, and December 2018, and monthly sales for each of the six months from July 2018 through December 2018. We stack this data in a panel, and estimate the pooled average impacts on 4 months of profits and 7 months of sales. The results are again similar to those in Table 5 for round 2 only.

Appendix Table 6.4 considers two additional checks. The first is to examine whether treatments changed the reporting of profits and sales, by examining the number of reporting errors. We measure five such errors, which arise from firms reporting profits larger than sales, monthly totals larger than annual totals, or total wage and salary bills greater than total sales. Since the main impacts of insourcing and outsourcing were on marketing, rather than accounting, practices, we do not expect reporting accuracy to have changed with treatment. Column 1 shows marginally significant evidence of reporting improving with insourcing at the one year follow-up, but column 2 shows there are no significant treatment effects on reporting errors in the two year follow-up. We conclude that our results are unlikely to be driven by changes in reporting. The second check is to examine levels of profits and sales, rather than the inverse hyperbolic sine. As noted in the text, we think a proportional rather than level treatment effect makes more sense, and levels are 72