reported in Table 3<sup>16</sup> . In results not shown, we also found the results are not heterogeneous with respect to size of firm, however, statistical power is limited for this test.

One concern with the outcome data from the follow-up survey is that since the information is self-reported, treatment enterprises could have reported more positive outcomes to please the surveyors (for transparency reasons, enterprises were informed that the survey was linked to the consulting program). To address this concern, we test whether (1) treatment enterprises were more likely to provide alternative contact persons on the survey<sup>17</sup> , and (2) treatment enterprises were less likely to not report sales on the follow-up survey, which should be the case if they wanted to please the interviewer. Appendix Table 8 displays the results for these tests. We find no significant differences in both measures across the treatment and control group, although we recognize that these are not dispositive tests.

Table 4 reports the long run impact on employment. As discussed above, for privacy reasons we do not have individual firm data, but rather just average number of employees and average wage bill for five years, separately for our treatment and control groups. Having administrative records rather than self-reported firm data we consider to be a major advantage and reconfirmation of our results. Given the five years of data, we use a difference-in-difference specification, with the treatment effect being identified by the interaction of treatment and post. We find an increase of 4.4 employees (s.e. = 1.7), which corresponds to 44% (average number of employees in the control group is 10.1 in the three post years), and an increase of US$99 in the daily wage bill (s.e. = $44), which is 57% (average daily wage bill in the control group is $172 in the three post years).

Figures 2 and 3 illustrate these results graphically. They show that both the average number of employees and the daily wage bill were similar across the treatment and control groups before the consulting program was implemented (in 2005 and 2006). Two to three years after the program (in 2011 and 2012), the average number of employees and the average daily wage bill were about 45 and 55 percent higher in the treatment group than in the control group, respectively.

> 16 As an additional check, Appendix Table 7 displays average business outcomes from the follow-up survey in the treatment and control group, as well as in the group of treatment enterprises that took up the program. A simple comparison of follow-up survey means in the treatment and control group shows a positive effect of the consulting services on productivity. Comparing only enterprises that took up the program to control group enterprises shows even larger differences in both productivity and return on assets (this comparison is not causal, and in particular note that at baseline the enterprises that took-up the program already had higher productivity and return on assets than enterprises that did not take-up the program, as shown in Table 2).

> 17 We asked for alternative contact persons in case we needed to get in touch with the enterprises at a later stage for clarifications or additional questions and could not reach the enterprise through our contact information on record.

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