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 PopovEuropean Central Bank

Gregory F. UdellIndiana University

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

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

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?

Our Contribution

• We are only paper that simultaneously:

1. Analyses international transmission of the effects of bank financial distress and

2. Accounts for changes in demand and

3. Accounts for contamination due to changing composition of firms demanding credit

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)

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)

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

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

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

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

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

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

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

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)

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!

Fraction Foreign Bank Ownership (2008)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Albania

Bulgar

ia

Croat

ia

Czech

Rep

ublic

Estonia

Hunga

ry

Latvi

a

Lithu

ania

Mac

edon

ia

Mon

tene

gro

Poland

Roman

ia

Slovak

ia

Sloven

ia

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)

Basic Empirical Model

• Express probability of constraint as a two-equation

Y* = f(bank locality-specific distress, 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

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

(Affected = Tier 1 capital decreased)

Rejectionsincreased

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

Small firms very vulnerable!

Also, transparency important

Only for Tier 1 related financial distress affects financial constraints

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

Pooling firms applying in both periods also controls

for demand

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

Foreign-bank domination matters

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

Foreign effect almost always negative and often significant

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

a. Access to external finance

b. Asset tangibility

c. Capital intensity

Opaque firms clearly hurt most

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