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How do bank-specific characteristics affect lending? New evidence based on credit registry data from Latin America Carlos Cant´ u 1 Stijn Claessens 2 Leonardo Gambacorta 2 1 Bank for International Settlements 2 Bank for International Settlements and CEPR 7 September, 2018 Cant´ u Claessens Gambacorta (2018) How do bank-specific characteristics affect lending? 7 September, 2018 1 / 21

How do bank-specific characteristics affect lending? New ... · the main Latin America economies, including their responses to shocks. Our main results are the following: I Within

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Page 1: How do bank-specific characteristics affect lending? New ... · the main Latin America economies, including their responses to shocks. Our main results are the following: I Within

How do bank-specific characteristics affect lending?New evidence based on credit registry data from Latin

America

Carlos Cantu 1 Stijn Claessens 2 Leonardo Gambacorta 2

1Bank for International Settlements

2Bank for International Settlements and CEPR

7 September, 2018

Cantu Claessens Gambacorta (2018) How do bank-specific characteristics affect lending? 7 September, 2018 1 / 21

Page 2: How do bank-specific characteristics affect lending? New ... · the main Latin America economies, including their responses to shocks. Our main results are the following: I Within

Motivation

Banks’ business models are changing.

Is lending more or less resilient to shocks?

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Non-technical Summary

FocusChanges in banks’ activities and business models after the GFC have had profoundeffects on how banks grant credit and how banks react to monetary policy andexternal shocks.

ContributionWe analyse in a comprehensive way the role of bank characteristics in influencingthe supply of credit and how these characteristics affect the reaction of banklending to shocks.

Findings

1 Banks that are large, well-capitalised, and with a ”commercial” businessmodel (i.e. high deposit funding) supply more credit. On the contrary, banksthat take more risk, have volatile sources of funding and a ”universal”business model are associated with a lower supply of loans.

2 The loan supply of well-capitalised banks is less affected by MP shocks, whilethose with ”commercial” business models are more affected by MP shocks.

3 Banks with different characteristics react differently to global shocks.

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Common Methodology

The dependent variable in all specifications is the log-change in the total amount(sum of loans) owed by creditor i to bank b at time t. We focus on firms withmultiple banking relationships (Khwaja and Mian (2008)).

∆log(Crediti,b,t) = βXb,t−1 + αb + γi,t + εi,b,t (1)

∆log(Crediti,b,t) = (β + β∗∆it−1)Xbt−1 + αb + γi,t + εibt (2)

∆log(Crediti,b,t) = (β + β∗C )Xb,t−1 + αb + γi,t + εibt (3)

Xb,t−1 a vector of bank specific characteristics.

αb time invariant bank fixed effects.

γi,t firm*time fixed effects

∆it−1 corresponds to the quarterly change in the monetary policy rate.

C alternates between risk, liquidity, economic policy uncertainty andcommodity price shocks

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Meta-analysis Techniques

The essence of meta-analysis is to obtain a single estimate of the effect bycomputing a weighted average of the studies’ individual estimates.

We conduct our meta-analysis under the assumption of a random effectsmodel.

I We assume that the true effect of bank-specific characteristics on credit θi hasa normal distribution around a mean effect θ with standard deviation τ .

yi |θi N(θi , σ2i ) with θi ∼ N(θ, τ 2)→ yi ∼ N(θi , σ

2i + τ 2) (4)

Two sources of variance:I the variance around the mean of the estimated effect τ 2

I the between-study variance σ2i

The random effects mean corresponds to the weighted average of coefficientsreported in the different estimations. The weights are calculated consideringthe sampling fluctuation of each effect size (standard error per reportedcoefficient) and estimated population variance of effect sizes.

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Bank-specific characteristics classification

1 Main indicators: size (log of total assets), bank capital ratio (equity-to-totalassets), and bank liquidity ratio (cash and securities over total assets).

2 Risk indicators: incidence of loan-loss provisions, share of non-performingloans, share of doubtful loans, securitisation activity, and share of write-offs.

3 Revenue mix (commercial business model): net fees and commissions tooperating income, retail loans as a share of total loans, and broad credit.

4 Revenue mix (universal business model): diversification ratio(non-interest income to total income), trading income to operating income,and assets held for trading as a share of total assets.

5 Stable sources of funding: share of deposits over total liabilities, share ofshort-term funding, share of long-term funding.

6 Volatile sources of funding: wholesale funding ratio, funding in foreigncurrency, and funding from foreign sources.

7 Profitability: return on assets, return on equity, efficiency ratio (operatingcosts to total income), number of employees per total assets, number ofbranches per total assets.

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Effect of bank capital on credit

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Effect of banks’ risk profile on credit

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Effect of revenue indicators in commercial business modelson credit

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Effect of revenue indicators in universal business models oncredit

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Effect of volatile sources of funding on credit

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Interaction effect between bank capital and monetarypolicy shock

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Interaction effect between revenue indicators in commercialbusiness models and monetary policy shock

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Interaction effect between BSC and high risk dummy

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Interaction effect between BSC and FFR (shadow rate)

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Interaction effect between BSC and high economic policyuncertainty dummy

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Interaction effect between BSC and low commodity pricedummy

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Conclusions

This paper studies how bank-specific characteristics influence loan supply inthe main Latin America economies, including their responses to shocks.

Our main results are the following:I Within banks, those that are large, well-capitalised, and with a commercial

business model (i.e. more deposits) supply more credit. On the contrary,banks that have high-risk indicators, volatile sources of funding and a universalbusiness model, other things being equal, are associated with a lower supply ofloans.

I The lending supply of well-capitalised banks is less affected by MP shocks,while those with commercial business models are more affected by MP shocks.

I Banks with high capital, high liquidity and more stable sources of funding areless affected by VIX shocks. In high liquidity periods, banks that are small, lessprofitable and with a universal business model increase more their creditsupply. Banks with high levels of capital and stable sources of funding aremore sheltered against economic policy uncertainty shocks. Finally, banks thatare large and well capitalised reduce less their credit supply when faced with acommodity price shock.

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Thank you!

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Appendix: Tables

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Appendix: Tables

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