control firms for treated and placebo firms.<sup>40</sup> Larger donor pools and the volatility of the outcome variables substantially slowed the underlying SCM fitting procedure.

We have very large potential donor pools made up of all the Serbian firms that imported between 2010 and 2013. Consistent with other applications of the SCM we limit the donor pools to firms that share some similarities. In particular, firms that are potential donors and/or placebos are limited to being within the same decile of 2010 total import value as the treated firm. The treated firms for which there are continuous imports are all within the 4<sup>th</sup> decile or higher in terms of their 2010 import value.

As noted in Section 3.2 we do not draw placebo firms from the donor pool used to construct the synthetic control firm for the estimated treatment effect of the IHC. Rather, for each treated firm we first randomly allocate untreated firms in the 2010 import value decile to placebo or donor status so that placebo firms are not eligible to be components of the synthetic control firm for the treated firm (nor can they be components of the synthetic control firm for other placebos). The synthetic control units for both treated and placebo firms are constructed by drawing from the same (large) pool of potential donors. For our benchmark (and preferred) specification, with three pretreatment values of the outcome variable as conditioning variables, as well as for the majority of other specifications a potential donor pool of 125 firms was large enough to allow synthetic control firms to be constructed for treated and placebo firms for all outcome variables.<sup>41</sup>

# **5. Results**

## **5.1 Difference-in-Differences Results**

Before presenting our main results from the pooled SCM, as a benchmark we provide results from DiD regressions to estimate the effect of IHC adoption on the five firm-level outcomes discussed in Section 4.1.<sup>42</sup> The DiD estimator compares the differences in an outcome variable

> 40 The restriction that the w* weights all be positive and sum to one forces the synthetic control firm to be a convex combination of the firms that are used to construct it.  Smaller donor pools may not contain a sufficient variety of firms to allow the joint fitting of time series outcomes and a large number of firm characteristics.

> 41 In one of the robustness exercises (log import value with the average lag of log imports included in the characteristic variables), we required a larger set of placebo firms to insure the construction of synthetic control firms for two of the treated firms and their respective placebos.  Since 125 potential donors were not sufficient, we expanded the size of the donor pool to 200.  Because our inference relies on rank order of the treated firm, relative to its placebo firms, it is not directly affected by varying the donor pool size so long as we give the treated firm and its placebo access to the same donor pool.

> 42 While we use logarithms and moving averages to smooth the dependent variables, which as discussed in Section 4, is important for the SCM estimation, the qualitative DiD results we present here follow through if we use the log of monthly variables rather than the three-month moving average of that log.

29