Credit limits

The model is estimated using OLS method

In the second set-up, where we don't limit the firms to those who took ruble-denominated loans for domestic purposes but instead use dummies for currency and use, coefficients remain very similar with some only becoming slightly less or more economically and statistically significant.

The OLS coefficients on loan use variable turn out to be not significant in this set-up. If we look at simple means it turns out that those firms who indicate export activity as the reason why they need the loan have on average higher interest rates, virtually the same extent, lower collateral to loan ratio, longer maturity and lower self-reported severity of financial constraints than those who indicate domestic activity as their goal.

At the same time, the coefficient on currency is negative and significant in regressions where interest rate and extent are the dependent variables for all the three model specifications (the only exception being model III specification for government banks). This implies that foreign currency loans have lower interest rates - loans in USD and EUR are cheaper because interest rates in US and Europe are lower than those in Russia - but banks give them less generously, as measured by the lower extent. Results are provided in Tables A9-A12.

Finally, it is also worth to look at the results obtained by using the propensity score matching method described above only on the subsample of exporters who took ruble loans with dummy for loan use acting as the treatment and with the same controls we had in the models above. My results show that ruble-denominated loans of Russian exporters have on average higher rates if the loan's purpose is for export-related as opposed to domestic activities, but once a control for maturity is introduced into the model the coefficient on interest rates loses significance. So higher rates can probably be explained by longer maturities in this case. This result is in line with the result obtained by Feenstra, Li and Yu (2015) who argue that bank do not distinguish between domestic and export related loans as long as the borrower is an exporter. Results can be found in Tables A13-A17.

Conclusion

In this work I have studied, first, the average treatment effect of export status on credit constraints of Russian exporting and non-exporting firms and, second, the determinants of credit constraints for exporters separately. I believe I have contributed to the existing literature by showing that export status results in lower interest rates on loans, i.e. less credit constraints, for Russian exporters versus non-exporters. I have also shown that exporters have virtually the same interest rates, i.e. face similar levels of credit constraints, on their export related loans as on domestic ones.

It is also worth to note that I have studied the differences in credit constraints using the metrics for credit rationing that have not been used in literature before (interest rate, collateral, extent and maturity). Most existing studies lacking such detailed loan information use firm-level balance sheet data or credit scores. My result that export status leads to lower interest rates is in line with some of the studies that also argue that exports status is associated with less credit constraints, such as Greenaway, Guariglia and Kneller (2007), Campa and Shaver (2002), Tornell and Westermann (2003), Ganesh-Kumar, Sen and Vaidya (2001). My result that there is no difference in credit constraints for export and domestic purpose loans of exporters is in line with that of Feenstra, Li and Yu (2011).

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Appendix

1. Results for Part I (Exporters vs. Non-Exporters), for only domestic and ruble-denominated loans, with Maturity as control:

Table A1: Model I

The model is estimated using propensity score matching method

Table A2: Model II

The model is estimated using propensity score matching method

Table A3: Model III

The model is estimated using propensity score matching method

Table A4: Results on Severity for Models I, II, III

2. Results for Part I (Exporters vs. Non-Exporters), for all loans, with loan currency and loan use as dummy controls:

Table A5: Model I

The model is estimated using propensity score matching method

Table A6: Model II

The model is estimated using propensity score matching method

Table A7: Model III

The model is estimated using propensity score matching method

Table A8: Results on Severity for Models I, II, III

3. Results for Part II (Exporters only), for all loans, with loan currency and loan use as dummy controls:

Table A9: Model I

The model is estimated using OLS method

Table A10: Model II

The model is estimated using OLS method

Table A11: Model III

The model is estimated using OLS method

Table A12: Results on Severity for Models I, II, III

The model is estimated using OLS method

4. Results for Part II (Exporters only), for ruble loans, for propensity-score matching with loan use as treatment:

Table A13: Model I

The model is estimated using propensity score matching method

Table A15: Model II

The model is estimated using propensity score matching method

Table A16: Model III

The model is estimated using propensity score matching method

Table A17: Results on Severity for Models I, II, III

The model is estimated using propensity score matching method

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