Credit limits

The level of financial markets development, accessibility of credit resources. Summary statistics of parameters of interest, split by export status. Typical overlap plot for most of model specifications. Firms that have the potential to start exporting.

,
22.01.2016
89,4 K

. ,

, , , , .

(17)

(18)

(19)

(20)

Variables

MI: Severity

MII: Severity

MIII: Severity_b0

MIII: Severity_b1

Age

-0.00324

-0.0185

-0.287*

0.417*

(0.126)

(0.130)

(0.162)

(0.217)

Export Sales

-0.0411

-0.0386

-0.114

0.103

(0.0911)

(0.0920)

(0.126)

(0.136)

Employment

-0.0588

-0.0616

-0.0508

-0.0301

(0.0805)

(0.0814)

(0.105)

(0.129)

Turnover

-0.0174

-0.00345

0.0311

-0.102

(0.0485)

(0.0500)

(0.0615)

(0.0857)

Ruble costs

-0.390

-0.369

-0.945

0.228

(0.434)

(0.437)

(0.631)

(0.592)

Foreign ownership

-0.0488

-0.0425

-0.875**

0.796*

(0.318)

(0.321)

(0.422)

(0.475)

bank

-0.129

(0.175)

Constant

4.052*

3.952*

7.258**

-0.333

(2.076)

(2.087)

(3.003)

(2.836)

Industry f.e.

yes

yes

yes

yes

Region f.e.

yes

yes

yes

yes

Obs

218

215

137

78

Adj R^2

0.016

0.016

0.060

0.111

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).

Reference List

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Antrs, P. & Caballero R. (2010.) On the Role of Financial Frictions and the Saving Rate during Trade Liberalizations. Journal of the European Economic Association Papers and Proceedings 8, no. 2-3: 442-55

Beck T., Demirguc-Kunt A. & Maksimovic V. (2005.) Financial and Legal Constraints to Growth: Does Firm Size Matter? The Journal of Finance, Vol. LX, No. 1

Berger A. & Udell G. (1995.) Relationship Lending and Lines of Credit in Small Firm Finance. Journal of Business, Volume 68, Issue 3, 1995, pages 351-382.

Bernard, Andrew & J. Jensen, Bradford & Redding, Stephen J. & Schott , Peter K. (2012). "The Empirics of Firm Heterogeneity and International Trade." Annual Review of Economics, Annual Reviews, vol. 4(1), p. 283-313, 07.

Berman N. & Hericourt J. (2008.) Financial Factors and the Margins of Trade: Evidence from Cross-Country Firm-Level Data. Journal of Development Economics, 2009

Campa, J. & Shaver, J. (2002.) Exporting and capital investment: On the strategic behavior of exporters. IESE Research Papers 469, IESE Business School

Chaney, T. (2013). "Liquidity Constrained Exporters." University of Chicago mimeo

Feenstra RC, Li Z, Yu M. 2015. Exports and Credit Constraints under Incomplete Information: Theory and Evidence from China. Rev. Econ. Stat. In press

Ganesh-Kumar A., Sen K. & Vaidya R. (2001.) Outward orientation, investment and fi- nance constraints: A study of indian firms. Journal of Development Studies 37(4), 133-149

Greenaway, D. & Guariglia, A. & Kneller R. (2007). "Financial Factors and Exporting Decisions." Journal of International Economics 73(2), p.377-95.

Halldin, Torbjrn (2012). External finance, collateralizable assets and export market entry. CESIS Electronic Working Paper Series, Paper No.268

Imbens, G. & Wooldridge J. (2009.) Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, Vol. XLVII (March 2009), p. 23-24, 39

La Porta R., Lopez-de-Silanes F., Shleifer A., Vishny R. (1998.) Law and Finance. J. Polit. Econ. 106(6):1113-55

Manova, K. & Wei S. & Zhang Z. (2014.) Firm Exports and Multinational Activity under Credit Constraints. Review of Economics and Statistics (forthcoming)

Manova K. & Foley F. (2014.) International Trade, Multinational Activity, and Corporate Finance, Annual Review of Economics (forthcoming)

Manova, K. (2013.) Credit Constraints, Heterogeneous Firms, and International Trade, Review of Economic Studies 80, p.711-744.

Manova K. & Chor D. (2012.) Off the Cliff and Back: Credit Conditions and International Trade during the Global Financial Crisis, Journal of International Economics 87, p.117-133

Manova K., Aghion P., Angeletos M., Banerjee A. (2010.) Volatility and Growth: Credit Constraints and the Composition of Investment, Journal of Monetary Economics 57 (2010), p.246-265.

Manova K. (2009.) Credit Constraints and the Adjustment to Trade Reform In G. Porto and B. Hoekman ed.: Trade Adjustment Costs in Developing Countries: Impacts, Determinants and Policy Responses, The World Bank and CEPR

Manova K. (2008.) Credit Constraints, Equity Market Liberalizations and International Trade, Journal of International Economics 76, p.33-47

Melitz, Marc J. (2003.) International Trade and Heterogeneous Firms.

Melitz, Marc J & Stephen J Redding. (2014). Heterogeneous Firms and Trade. Handbook of International Economics, 4th ed, 4, p.1-54.

Minetti, R. & Zhu S.C. (2010.) Credit constraints and firm export: Microeconomic evidence from Italy. Journal of International Economics, p.109-125.

Muuls, M. (2008). "Exporters and Credit Constraints. A Firm Level Approach." London School of Economics mimeo.

Paravisini D., Rappoport V., Schnabl P., & Wolfenzon D. Dissecting the Effect of Credit Supply on Trade. Working Paper, 2011

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Tornell A. & Westermann F. (2003.) Credit market imperfections in middle income countries. The paper was prepared for the conference Financial Market Development in Latin America, at the Center for Research on Economic Development and Policy Reform at Stanford University.

Wagner J. (2013.) Credit Constraints and exports: A survey of empirical studies using firm level data. Working Paper Series in Economics, 287, University of Lneburg

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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

(1)

(2)

(3)

(4)

Variables

Extent

Interest rate

Collateral

Maturity

r1vs0.Exporter

0.154

-0.0976***

0.138

-0.143***

(0.106)

(0.0327)

(0.109)

(0.0463)

Observations

318

254

196

333

The model is estimated using propensity score matching method

Table A2: Model II

(5)

(6)

(7)

(8)

Variables

Extent

Interest rate

Collateral

Maturity

r1vs0.Exporter

0.0415

-0.125***

0.179

-0.0893*

(0.0717)

(0.0300)

(0.159)

(0.0466)

Observations

316

253

196

330

The model is estimated using propensity score matching method

Table A3: Model III

(9)

(10)

(11)

(12)

(13)

Variables

Extent_b1

Extent_b0

Interest rate_b1

Interest rate_b0

Maturity_b0

r1vs0.Exporter

-0.0893*

-0.0165

-0.0165

-0.162***

-0.317***

(0.0466)

(0.0222)

(0.0222)

(0.0342)

(0.0488)

Observations

330

189

189

163

200

The model is estimated using propensity score matching method

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

(14)

(15)

(16)

MI: Severity

MII: Severity

MIII: Severity_b0

0.366**

0.246

0.722***

(0.172)

(0.152)

(0.156)

328

325

198

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

(1)

(2)

(3)

(4)

(5)

Variables

Extent

Interest rate

Collateral

Severity

Maturity

r1vs0.Exporter

0.0256

-0.113***

0.0486

0.0428

-0.319***

(0.0225)

(0.0159)

(0.103)

(0.163)

(0.0554)

Observations

456

334

259

491

433

The model is estimated using propensity score matching method

Table A6: Model II

(6)

(7)

(8)

(9)

(10)

Variables

Extent

Interest rate

Collateral

Severity

Maturity

r1vs0.Exporter

0.0865

-0.114*

0.0823

0.157

(0.0610)

(0.0651)

(0.114)

(0.167)

Observations

452

332

258

484

The model is estimated using propensity score matching method

Table A7: Model III

(11)

(12)

(13)

(14)

Variables

Extent_b0

Interest rate_b0

Severity_b0

Maturity_b0

r1vs0.Exporter

-0.00818

-0.173

0.516***

-0.309***

(0.0216)

(0.122)

(0.134)

(0.120)

Observations

262

214

289

256

The model is estimated using propensity score matching method

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

(15)

(16)

(17)

MI: Severity

MII: Severity

MIII: Severity_b0

0.0428

0.157

0.516***

(0.163)

(0.167)

(0.134)

491

484

289

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

Table A9: Model I

(1)

(2)

(3)

(4)

Variables

Extent

Interest rate

Collateral

Maturity

Age

-0.0102

-0.0739**

-0.138

0.0486

(0.0177)

(0.0373)

(0.114)

(0.0773)

Export Sales

0.00145

-0.0124

-0.191***

0.00922

(0.0129)

(0.0249)

(0.0720)

(0.0542)

Employment

0.00904

-0.00913

0.107

-0.105**

(0.0109)

(0.0237)

(0.0764)

(0.0484)

Turnover

0.00122

-0.0248*

-0.113***

0.0214

(0.00648)

(0.0142)

(0.0422)

(0.0279)

Ruble costs

0.00846

-0.0662

-0.0985

0.242

(0.0405)

(0.0774)

(0.219)

(0.170)

Foreign ownership

0.0379

-0.191**

-0.00590

0.301*

(0.0400)

(0.0861)

(0.250)

(0.177)

cy

-0.0840*

-0.236**

-0.417

0.188

(0.0467)

(0.107)

(0.285)

(0.193)

use

0.0125

0.0469

0.274

0.169

(0.0288)

(0.0589)

(0.167)

(0.120)

Constant

4.487***

3.756***

4.842***

2.397***

(0.210)

(0.405)

(1.155)

(0.884)

Industry f.e.

yes

yes

yes

yes

Region f.e.

yes

yes

yes

yes

Observations

272

204

162

268

Adj R^2

-0.008

0.214

0.265

0.083

The model is estimated using OLS method

Table A10: Model II

(5)

(6)

(7)

(8)

Variables

Extent

Interest rate

Collateral

Maturity

Age

-0.0114

-0.0635*

-0.142

0.0552

(0.0182)

(0.0373)

(0.113)

(0.0782)

Export Sales

0.00140

-0.0136

-0.193***

0.00850

(0.0130)

(0.0247)

(0.0718)

(0.0544)

Employment

0.00929

-0.0125

0.105

-0.106**

(0.0111)

(0.0236)

(0.0763)

(0.0485)

Turnover

0.00123

-0.0242*

-0.113***

0.0204

(0.00663)

(0.0141)

(0.0421)

(0.0283)

Ruble costs

0.00748

-0.0587

-0.0993

0.237

(0.0408)

(0.0769)

(0.218)

(0.170)

Foreign ownership

0.0387

-0.196**

0.0336

0.292

(0.0404)

(0.0854)

(0.252)

(0.177)

0.0152

-0.0870*

0.179

-0.139

(0.0241)

(0.0482)

(0.142)

(0.102)

cy

-0.0817*

-0.247**

-0.416

0.171

(0.0472)

(0.106)

(0.285)

(0.193)

use

0.00980

0.0618

0.250

0.183

(0.0295)

(0.0588)

(0.167)

(0.121)

Constant

4.491***

3.720***

4.854***

2.418***

(0.212)

(0.402)

(1.153)

(0.883)

Industry f.e.

yes

yes

yes

yes

Region f.e.

yes

yes

yes

yes

Observations

269

203

162

265

Adj R^2

-0.012

0.225

0.267

0.081

The model is estimated using OLS method

Table A11: Model III

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

Variables

Extent_b1

Extent_b0

Interest rate_b1

Interest rate_b0

Collateral_b1

Collateral_b0

Maturity_b1

Maturity_b0

Age

-0.0111

-0.00324

-0.120

-0.0273

0.0614

-0.215

-0.0808

0.104

(0.0346)

(0.0214)

(0.0860)

(0.0385)

(0.181)

(0.153)

(0.128)

(0.102)

Export Sales

-0.00682

-0.00379

-0.108*

0.0338

-0.120

-0.230**

-0.0699

0.0318

(0.0235)

(0.0158)

(0.0558)

(0.0265)

(0.114)

(0.108)

(0.0864)

(0.0738)

Employment

0.0104

-0.000241

-0.0159

-0.00776

-0.0578

0.138

-0.0226

-0.145**

(0.0211)

(0.0132)

(0.0528)

(0.0246)

(0.124)

(0.104)

(0.0778)

(0.0665)

Turnover

-0.0151

0.0123

-0.0272

-0.0238*

-0.0129

-0.144**

0.0523

0.0121

(0.0128)

(0.00756)

(0.0324)

(0.0143)

(0.0693)

(0.0548)

(0.0465)

(0.0368)

Ruble costs

0.0152

-0.00271

-0.214

0.0301

-0.223

-0.0710

0.175

0.275

(0.0705)

(0.0503)

(0.157)

(0.0828)

(0.307)

(0.313)

(0.254)

(0.235)

Foreign ownership

0.0360

0.0499

-0.103

-0.197**

0.401

0.0199

0.634**

0.209

(0.0813)

(0.0476)

(0.208)

(0.0897)

(0.531)

(0.333)

(0.312)

(0.232)

bank

cy

0.0441

-0.136***

-0.740*

-0.203**

0.378

-0.601

0.455

0.112

(0.113)

(0.0501)

(0.411)

(0.0975)

(0.549)

(0.363)

(0.404)

(0.232)

use

0.0276

-0.00159

0.202

0.0241

0.0736

0.325

0.214

0.243

(0.0546)

(0.0354)

(0.133)

(0.0617)

(0.270)

(0.228)

(0.195)

(0.165)

Constant

4.420***

4.567***

4.342***

3.122***

5.305***

5.138***

2.696**

2.303*

(0.365)

(0.261)

(0.850)

(0.432)

(1.620)

(1.669)

(1.320)

(1.210)

Industry f.e.

yes

yes

yes

yes

yes

yes

yes

yes

Region f.e.

yes

yes

yes

yes

yes

yes

yes

yes

Observations

107

162

70

133

56

106

100

165

Adj R^2

-0.060

0.046

0.179

0.310

-0.132

0.301

0.047

0.092

The model is estimated using OLS method

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

(17)

(18)

(19)

(20)

MI: Severity

MII: Severity

MIII: Severity_b1

MIII: Severity_b0

-0.0228

-0.0466

0.304

-0.239*

(0.111)

(0.113)

(0.194)

(0.141)

-0.0448

-0.0421

0.105

-0.137

(0.0796)

(0.0804)

(0.135)

(0.103)

-0.460*

-0.461*

-0.589

-0.361

(0.244)

(0.249)

(0.459)

(0.296)

-0.355**

-0.390**

-0.603**

-0.299

(0.178)

(0.181)

(0.301)

(0.226)

-0.168

-0.178

-0.232

-0.228

(0.213)

(0.216)

(0.382)

(0.260)

-0.336

-0.343

0.0483

-0.493

(0.290)

(0.292)

(0.582)

(0.333)

0.356*

0.356*

0.515

0.331

(0.202)

(0.206)

(0.465)

(0.229)

0.0346

0.0386

-0.0414

0.0773

(0.0681)

(0.0689)

(0.118)

(0.0880)

-0.0617

-0.0544

-0.0930

-0.0376

(0.0407)

(0.0416)

(0.0705)

(0.0517)

-0.388

-0.381

0.172

-0.857**

(0.252)

(0.254)

(0.404)

(0.329)

-0.0208

-0.0283

0.837*

-0.378

(0.248)

(0.250)

(0.464)

(0.310)

0.0515

(0.150)

0.104

0.104

0.746

-0.0472

(0.290)

(0.292)

(0.644)

(0.332)

-0.195

-0.195

0.225

-0.400*

(0.178)

(0.181)

(0.307)

(0.231)

3.882***

3.852***

0.324

6.597***

(1.292)

(1.301)

(2.087)

(1.673)

299

295

108

187

0.016

0.012

0.051

0.034

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

(1)

(2)

(3)

(5)

Variables

Extent

Interest rate

Collateral

Maturity

r1vs0.use

0.0135

0.0697

0.0437

0.324**

-0.022

-0.0425

-0.126

-0.137

Observations

297

229

175

289

The model is estimated using propensity score matching method

Table A15: Model II

(6)

(7)

(8)

(10)

Variables

Extent

Interest rate

Collateral

Maturity

r1vs0.use

0.0279*

0.0627*

0.135

0.294***

-0.0165

-0.0347

-0.124

-0.101

Observations

294

228

175

286

The model is estimated using propensity score matching method

Table A16: Model III

(11)

(12)

(13)

(15)

Variables

Extent_b0

Interest rate_b0

Collateral_b0

Maturity_b0

r1vs0.use

0.0289

0.000664

-0.125

0.600***

-0.0209

-0.0406

-0.218

-0.0787

Observations

169

145

108

170

The model is estimated using propensity score matching method

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

(4)

(9)

(14)

Variables

Severity

Severity

Severity_b0

r1vs0.use

-0.347*

-0.148

-0.418*

-0.188

-0.182

-0.244

Observations

329

324

194

The model is estimated using propensity score matching method

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