Table 2: Institutional Quality and the Probability of Exporting

|**_Dependent Variable:_**|||_Pr_(_Exp_|_ortjist >_0)|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Days|-0.002<sup>_∗∗∗_</sup>|||-0.002<sup>_∗∗∗_</sup>|||
|Procedure||0.001|||0.000||
|Cost|||-0.012<sup>_∗∗∗_</sup>|||-0.012<sup>_∗∗∗_</sup>|
|Rule of law|-0.268<sup>_∗∗∗_</sup>|-0.273<sup>_∗∗∗_</sup>|-0.144<sup>_∗∗∗_</sup>||||
|Rule of law x Inst-Int|0.357<sup>_∗∗∗_</sup>|0.358<sup>_∗∗∗_</sup>|0.358<sup>_∗∗∗_</sup>||||
|Contract||||-0.019<sup>_∗∗_</sup>|-0.017<sup>_∗∗_</sup>|-0.044<sup>_∗∗∗_</sup>|
|Contract x Inst-Int||||0.109<sup>_∗∗∗_</sup>|0.109<sup>_∗∗∗_</sup>|0.108<sup>_∗∗∗_</sup>|
|lnH|-0.173<sup>_∗∗_</sup>|-0.232<sup>_∗∗∗_</sup>|0.064|-0.235<sup>_∗∗∗_</sup>|-0.295<sup>_∗∗∗_</sup>|0.038|
|lnH x H-Int|0.065<sup>_∗∗∗_</sup>|0.065<sup>_∗∗∗_</sup>|0.065<sup>_∗∗∗_</sup>|0.079<sup>_∗∗∗_</sup>|0.079<sup>_∗∗∗_</sup>|0.079<sup>_∗∗∗_</sup>|
|lnK|-0.186<sup>_∗_</sup>|-0.006|-0.602<sup>_∗∗∗_</sup>|-0.161|0.013|-0.608<sup>_∗∗∗_</sup>|
|lnK x K-Int|-0.437<sup>_∗∗∗_</sup>|-0.437<sup>_∗∗∗_</sup>|-0.438<sup>_∗∗∗_</sup>|-0.547<sup>_∗∗∗_</sup>|-0.547<sup>_∗∗∗_</sup>|-0.548<sup>_∗∗∗_</sup>|
|NR|-0.013<sup>_∗∗∗_</sup>|-0.013<sup>_∗∗∗_</sup>|-0.007<sup>_∗∗∗_</sup>|-0.014<sup>_∗∗∗_</sup>|-0.014<sup>_∗∗∗_</sup>|-0.009<sup>_∗∗∗_</sup>|
|NR x N-Int|0.001|0.001|0.001|0.008<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|0.008<sup>_∗∗∗_</sup>|
|lnESTABLISH|0.029<sup>_∗∗∗_</sup>|0.028<sup>_∗∗∗_</sup>|0.028<sup>_∗∗∗_</sup>|0.038<sup>_∗∗∗_</sup>|0.037<sup>_∗∗∗_</sup>|0.037<sup>_∗∗∗_</sup>|
|lnGDPE|1.101<sup>_∗∗∗_</sup>|1.058<sup>_∗∗∗_</sup>|0.899<sup>_∗∗∗_</sup>|1.101<sup>_∗∗∗_</sup>|1.060<sup>_∗∗∗_</sup>|0.945<sup>_∗∗∗_</sup>|
|lnGDPI|0.027|0.024|0.023|0.027|0.024|0.024|
|lnDIST|-0.151<sup>_∗∗∗_</sup>|-0.151<sup>_∗∗∗_</sup>|-0.151<sup>_∗∗∗_</sup>|-0.151<sup>_∗∗∗_</sup>|-0.151<sup>_∗∗∗_</sup>|-0.151<sup>_∗∗∗_</sup>|
|No. of Observations|1662720|1662720|1662720|1662720|1662720|1662720|
|No. of Clusters|9714|9714|9714|9714|9714|9714|
|_χ_<sup>2</sup><br>|37178.764|37212.19|37159.165|37067.941|37114.552|37030.968|
|Prob _> χ_<sup>2</sup>|0.000|0.000|0.000|0.000|0.000|0.000|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. All regressions include exporter, importer, industry and year fixed effects and a constant, which are not reported. Standard errors are clustered by exporter-importer pair.

More importantly, the variable of interest – institutions – retains its sign and significance across all specifications, with little to no change in magnitude.

**Trade Volume** Next, we turn to the effects of institutions on the intensive margin of export. The results are in Columns 1-3 of Table 3. Column 1 provides estimates after correcting for the conventional selection-bias by incorporating information on the likelihood of exporting from the results in Table 2. It specifically saves the Inverse-Mills-Ratio (Heckman’s correction term) from Column 1 of Table 2 and includes it as an additional regressor. The coefficient estimate of the effect of institutions on comparative advantage is larger here than the corresponding OLS estimate (compare 0.683 in Column 1 of Table 3 to 0.433 in Column 6 of Table 1). This suggests that estimating the effect of institutions on the trade pattern using data from those country-pairs that are actually trading – and ignoring the zero-trade – undermines the true effect of institutions. That is, the role of institutions is more pronounced in the population than in the trading sample of countries.

In Columns 2 and 3, we subsequently correct for bias due to selection into exporting emanating from underlying firm-heterogeneity. We do so using two of the techniques proposed by Manova (2013). The techniques we chose are the ones that rely on minimal distributional assumptions. We proceed as follows. Common to both approaches, we first save the probability of exporting from the Probit model (Column 1 of Table 2), and then winsorize the top 1 percent and the bottom 1 percent of the probabilities in order to reduce the influence of extreme values.<sup>5</sup> In accordance with the first approach, we generate

> 5 That is, values of _ρ_ ˆjist below 0.0001129 are set to 0.0001129 and values of _ρ_ ˆjist above 0.999703 are set equal to 0.999703. Qualitatively, the results are robust to the use of different cutoff points – such as 0.0000001 and 0.9999999 as used in

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