Table 4: Rule of Law and Margins of Export: Destination-Based Definitions

|**_Dep. Variable_**:|**_ln(Exp_**|**_ort)jst_**|**_ln(Export/_**|**_Destination)jst_**|**_ln(No. of _**|**_Destinations)jst_**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Rule of law|-0.261<sup>_∗∗∗_</sup>|-0.278<sup>_∗∗∗_</sup>|-0.143<sup>_∗_</sup>|-0.184<sup>_∗∗_</sup>|-0.118<sup>_∗∗∗_</sup>|-0.095<sup>_∗∗∗_</sup>|
|Rule of law x Inst-Int|0.409<sup>_∗∗_</sup>|0.402<sup>_∗∗∗_</sup>|0.350<sup>_∗∗_</sup>|0.285<sup>_∗∗_</sup>|0.059|0.116<sup>_∗∗∗_</sup>|
|lnH|-0.159|-0.201|0.081|-0.029|-0.241<sup>_∗∗∗_</sup>|-0.172<sup>_∗∗∗_</sup>|
|lnH x H-Int|0.034|0.033|-0.005|-0.014|0.039<sup>_∗∗∗_</sup>|0.047<sup>_∗∗∗_</sup>|
|lnK|-0.273|-0.308|-0.150|-0.277|-0.123|-0.031|
|lnK x K-Int|-0.010|-0.059|0.166|0.179|-0.176<sup>_∗∗_</sup>|-0.238<sup>_∗∗∗_</sup>|
|NR|-0.003|-0.001|-0.002|0.005|-0.001|-0.006<sup>_∗∗_</sup>|
|NR x N-Int|-0.001|-0.003|-0.004|-0.004|0.002|0.001|
|lnESTABLISH|0.050<sup>_∗_</sup>|0.047<sup>_∗_</sup>|0.051<sup>_∗∗_</sup>|0.047<sup>_∗∗_</sup>|-0.001|-0.000|
|lnGDPE|2.142<sup>_∗∗∗_</sup>|2.204<sup>_∗∗∗_</sup>|1.373<sup>_∗∗∗_</sup>|1.580<sup>_∗∗∗_</sup>|0.769<sup>_∗∗∗_</sup>|0.624<sup>_∗∗∗_</sup>|
|No. of Observations|9377|9377|9377|9377|9377|9377|
|F-Statistic|82.125|81.111|42.241|43.066|45.583|45.420|
|Prob _>_F-Statistic|0.000|0.000|0.000|0.000|0.000|0.000|
|R<sup>2</sup>|0.921|0.922|0.899|0.901|0.914|0.916|
| ̄_R_<sup>2</sup>|0.920|0.921|0.898|0.900|0.914|0.915|

_∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01. All regressions include (unreported) exporter, industry and year fixed effects. Standard errors are clustered by exporter-industry pairs. Regressions in Columns 1, 3 and 5 correct for Heckmantype selection bias only while those in Columns 2, 4 and 6 control for firm selection into exporting as well, using the 50-bins dummy variable approach.

to Lee (2017) who finds the extensive margin to be the more robust channel of influence (statistical significance) despite the intensive margin having greater contribution (magnitude).

In sum, there is a robust effect of institutional quality on the pattern of trade, and this effect materializes through both intensive and extensive margins. These results generally hold regardless of whether one uses variety-based or destination-based definitions of the margins. Again, regardless of the definition used, the intensive margin accounts for the larger share of the influence of institutions on the pattern of trade. Moreover, it represents the (statistically) more robust channel of influence as per all

**Institutions versus Factor Endowments** The results so far establish that institutional quality is an important source of comparative advantage, and that the influence operates through affecting both intensive and extensive margins. Next, we assess how this influence of institutional quality fares in comparison with factor endowments – the traditional sources of comparative advantage.

To be able to compare the relative importance of the two sources of comparative advantage (institutions and factor endowments), we need to look at the standardized beta coefficients. To this end, we rerun the models estimated earlier (in Tables 3 and 4). The results are summarized in Table 5. The estimates under each column are from the estimation technique that corrects for selection bias using the dummy variable approach.

As can be seen from the estimates in the first three columns, human capital plays a much greater role than institutional quality in shaping the pattern of trade in exports overall (Column 1) as well as in average export per variety (Column 2) and number of varieties exported (Column 3). However, as the second half of Table 5 shows, institutional quality has a robustly greater influence on comparative advantage than factor endowments with the use of destination-based definitions. Even here, human 17