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Alexander Popov European Central Bank Gregory F. Udell Indiana University

Cross-border Banking and the International Transmission of Financial Distress During the Crisis of 2007-2008. Alexander Popov European Central Bank Gregory F. Udell Indiana University. On the Credit Crunch in Central and Eastern Europe

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Alexander Popov European Central Bank Gregory F. Udell Indiana University

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  1. Cross-border Banking and the International Transmission of Financial Distress During the Crisis of 2007-2008 Alexander Popov European Central Bank Gregory F. Udell Indiana University

  2. On the Credit Crunch in Central and Eastern Europe • “There is no credit crunch in Europe and the IMF has been too pessimistic in its growth forecasts for the region.” • Jean-Pierre Landau, deputy governor of Banque de France, • 8 April 2008

  3. On the Transmission of Financial Distress by Foreign Banks in Central and Eastern Europe “[…] foreign banks have so far exerted a stabilizing influence, as witnessed by the contrast in gradual slowdown in credit in the Baltics and the much sharper contraction in Kazakhstan. ” Eric Berglof, EBRD Chief Economist, 19 September 2008

  4. Motivation • Question 1: Was there a credit crunch in central and eastern Europe in the early stages of the crisis? • We focus on period between August 2007 and September 2008 • Look at one particular channel – bank lending to SMEs • Question 2: If yes, were foreign banks a stabilizing influence? • - Or, were foreign banks a channel through which this crisis was propagated? • Question 3: Can Q1 and Q2 can be answered in a satisfactory fashion?

  5. Our Contribution • We are only paper that simultaneously: • Analyses international transmission of the effects of bank financial distress and • Accounts for changes in demand and • Accounts for contamination due to changing composition of firms demanding credit

  6. The Literature on Cross Border Bank Lending • Evidence is ambiguous • Some studies find increased access • More credit (Clarke, Cull and Peria 2006) • Higher sales (Giannetti and Ongena 2009) • Lower rates (Ongena and Popov 2009) • Some studies find foreign banks cherry pick • Berger, Klapper and Udell (2001) • Mian (2006) • Gormley (2009)

  7. The Literature on the Credit Crunch • Historical Crises: • US: e.g., Bernanke and Lown (1991), Berger and Udell (1994), Hancock and Wilcox (1998) • Japan: e.g., Peek and Rosengren (1997), Woo (1999), Kang and Stulz (2000), Hayashi and Prescott (2002), Watanabe (2006), Taketa and Udell (2007) • Other Crises: e.g. Bae, Kang and Lim (2002), Jiangli, Unal and Yom (2009), Park, Shin and Udell (2009), Chava and Purnanadam (JFE 2009), Khwaja and Mian (AER 2008) • Current Crisis: • Ivashina and Scharfstein (2009), Puri, Rocholl, and Steffen (2009), de Haas and van Horen (2009), Jimenez, Ongena, Peydro, and Saurina (2009)

  8. Key Challenges in Analyzing Credit Crunches:(The Problems Related to Question 3) • Credit crunches notoriously difficult to identify • Simultaneity issue at macro level • Supply and demand for credit can both be affected – and usually are • Simultaneity issue at micro level • Demand at worst hit banks can be relatively more affected • Composition of applicants and non-applicants may be different for different banks

  9. Identifying Demand vs. Supply: Micro Data • Approach 1. Select a setting where demand didn't change • Peek and Rosengren (AER 1997) – Japanese banks and US households after Nikkei collapse • Domestic event - no change in US firms‘ demand • Not applicable for 2007-2008 – global recession

  10. Identifying Demand vs. Supply: Micro Data • Approach 2. Use application data, make sure demand changes throughout • Puri, Rocholl, and Steffen (2009) - US banks and German firms after August 2007 • However, key problem • Doesn‘t account for composition of firms that self-select out of the application process because they get discouraged • Approach 3. Our data allows to overcome this problem by using application data that controls for discouraged applicants

  11. Empirical Approach • Calculate financial distress by bank, map into incidence of credit constraint to identify transmission • Adjust for discouraged applicants • Control for common macro factors, common industry factors, local macro factors, and account for soft information • Compare transmission by foreign and domestic banks • Use industry characteristics to study differential effect • Hypothesis 1: Distressed banks have higher probability of rejecting a loan application by an identical firm • Hypothesis 2: For the same level of distress, foreign banks have higher probability of rejecting a loan application by an identical firm

  12. Data on Firms • 2005 and 2008 Business Environment and Enterprise Performance Survey (BEEPS) by the World Bank and the EBRD. • 2008 wave interviewed in April 2008, asked about experience with banks during “fiscal year 2007” • For all countries, firms extend fiscal year to end of March • 1.5 non-crisis and 2.5 crisis quarters (bias goes against finding anything) • No match to specific bank

  13. Data on Firms (cont.) • 4,421 firms from 14 central and eastern European countries • Albania, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Macedonia, Montenegro, Poland, Romania, Slovakia, and Slovenia • 1,266 localities • Firm level characteristics • Size (74% <100 workers, 3% >500 workers), Age • Ownership (private/state/foreign), competition, exporter, subsidized, audited • 18 Industries

  14. Data on Financial Distress • Balance sheet data from Bankscope for 2005-2008 • 1) Equity capital / total assets ratio • 2) Tier 1 capital ratio • 3) Gain (loss) on financial assets • Also: • Mortgage lending, deposits, MM funding, profits, securities, problem loans, etc. • 141 banks present in the 1,266 localities • 27 domestic, 117 subsidiaries and branches of foreign banks • 291 localities with more than 1 firms, rest matched manually to closest locality

  15. Data on Financial Distress (cont.) • Don’t have direct matching between bank and borrower • Calculate a locality-specific measure of “financial distress” by weighting balance sheet data for all banks present • 1) equally • 2) by number of branches • For foreign-owned, use the balance sheet data on the mother (group)

  16. The Ideal Data to Study the Credit Crunch • Application data including discouraged applicants • Firm characteristics • Bank characteristics • Loan characteristics • Identification of firm’s bank • Firm’s banking relationship • Panel data • Cross-country data • Third party mercantile data • Lending technology deployed, e.g.: • Financial statement lending • Relationship lending • Real estate-based lending • Equipment lending • Leasing • Factoring • Asset-based lending • Trade credit No one has all this!

  17. Key Survey Questions • K16: “Did the establishment apply for any loans or lines of credit in the fiscal year 2007?” • If “No” to K16, go to K17: “What was the main reason?” • If “No need for a loan”, classify firm as not desiring credit • If “Interest rates too high” or “Collateral requirements too strict” or “Did not think it would be approved”, classify firm as constrained • If “Yes” to K16, go to K18a: “Was any loan or line of credit rejected?” • If “Yes”, classify firm as constrained • Grouping of rejected and discouraged firms standard • Cox and Japelli (JMCB 1993) • Accounting for discouraged firms crucial in the CEE context • Up to 2/3 of constrained firms are discouraged – Brown, Ongena, Popov, and Yesin (2009)

  18. Basic Empirical Model • Express probability of constraint as a two-equation • Y* = f(bank locality-specificdistress, firm characteristics, other controls) • Y* = 1 if the firms is constrained • Estimate probability firm desires credit and employ a Heckman selection procedure • Prob(Desire Credit) = f(W) • where W contains a vector of firm-specific characteristics and locality-specific bank distress characteritics • Probit equation contains at least one more variable than main model

  19. International Transmission of Financial Distress • Add international dimension to basic model • Two approaches • 1. Look at foreign dominated markets – i.e., repeat tests on just foreign dominated markets where 2/3 of branches are foreign • 2. Examine foreign effect by interacting Foreign bank variable with Finance • - where Foreign is the share of foreign branches

  20. Rejections increased (Affected = Tier 1 capital decreased)

  21. Key Results I • Was there a credit crunch related to bank distress? • Evidence that the probability of being credit constrained affected by Tier 1 capital ratio • Also interesting: • Small firms and unaudited more constrained

  22. Small firms very vulnerable!Also, transparency important

  23. Only for Tier 1 related financial distress affects financial constraints

  24. Still only for Tier 1 related financial distress affects financial constraints, although Equity negative

  25. Pooling firms applying in both periods also controls for demand

  26. Key Results II • Was there cross-border transmission of the credit crunch? • Foreign-dominated markets: Evidence of bank distress affecting credit stronger, i.e., more robust to alternative measures of bank distress • Interaction between distress and foreign: Some evidence that effect of Tier 1 capital and leverage exacerbated by foreign bank presence • i.e., some evidence that foreign banks contracted more

  27. Foreign-bank domination matters

  28. Economic Significance • A two standard deviation decrease in equity capital, Tier 1 capital or losses on financial assets leads respectively to: • 30% increase in rejection rate • 55% increase in rejection rate • 32% increase in rejection rate

  29. Foreign effect almost always negative and often significant

  30. Other Results • Some evidence that the interaction of foreign banks presence and bank distress affects opaque firms more • Firms in opaque industries more likely to have loan applications rejected • i.e., firms in most (Rajan/Zingales) opaque industries based on • Access to external finance • Asset tangibility • Capital intensity

  31. Opaque firms clearly hurt most

  32. Conclusion • Firms in localities dominated by distressed banks have higher probability of being rejected • After accounting for self-selection • After eliminating common macro, local, and sector unobservables • Strongest evidence for Tier 1 capital ratio • Foreign banks transmit to the real sector more of the same financial shock than domestic banks • Transmission stronger when more opaque firms and firms with less tangible assets involved

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