2.A Extensions
Bank Loan Supply and Capital Buffers
In the following, we show how the response of a bank’s loan supply (relative to assets int−1) to higher uncertainty in banking depends on its capital buffer in t−1.
If we consider φ to be time-varying, we obtain from Equation (2.8):
∂ta∆lt The relevant cross derivative is given by:
∂ta∆lt
We estimate bank productivity using an empirical methodology in the spirit of Levin-sohn and Petrin (2003) and applied to banks by Nakane and Weintraub (2005):
logyit =β0+βlxit+βkkit+βmmit+ωit+ηit
Bank output is given byyit, xit denotes the free input variables, kit the fixed input and mit the intermediate input. The error consists of an unobserved productivity term ωit and a random term ηit. The approach accounts for simultaneity between productivity and the factor input choices of banks. This is achieved by introducing the intermediate input which correlates with productivity. Productivity shocks thus primarily account for supply-side factors. The output of banks is defined as the total lending volume. We choose two free input variables. The first is total long-term funding. The second accounts for bank staff and is proxied by personnel expenses.
Banks have to maintain branches or subsidiaries to provide loans. These cannot be adjusted rapidly and we capture the fixed input by fixed assets. For the intermediate input good, we choose total equity.
Chapter 2: Uncertainty, Bank Lending, and Bank-Level Heterogeneity
2.B Data
The results in this paper are based on various data sources. Data at the bank level are obtained from Bankscope. Information on foreign ownership of banks comes from the database provided by Claessens and Van Horen (2014). Country-level data are obtained from Bloom (2014), Datastream, the International Monetary Fund (IMF), and the Bank for International Settlements (BIS).
List of Countries
Non-OECD Cyprus, Latvia, Malta Argentina, Brazil, Bulgaria, China, Croatia, India, Indone-sia, Lithuania, Romania, RusIndone-sia, Saudi Arabia, South Africa
Bank-Level Variables
Bank lending: Our measure for bank lending is the difference in total loans relative to total assets in t−1 (in percent). The data come from Bankscope and the variable total loans is defined as gross loans minus impaired loans.
Capital/assets: To measure capitalization, we use the Tier 1 regulatory capital rela-tive to total assets (in percent) as obtained from Bankscope.
Committed loans/(committed loans + assets): To control for committed loan obligations, we use committed loans relative to the sum of committed loans and total assets (in percent). Data are provided by Bankscope.
Deposits/assets: The variable deposits/assets denotes the share of customer deposits to balance sheet total (in percent) as obtained from Bankscope.
Liquid assets/assets: The liquidity ratio is defined as the ratio of banks’ liquid assets, that is, the sum of trading securities, loans and advances to banks, reverse repos and cash collateral, cash and due from banks minus mandatory reserves included in these positions, relative to total assets (in percent). Data are taken from Bankscope.
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Log total assets: To obtain a measure for bank size we use the logarithm of banks’
total assets (in thousands of USD) as obtained from Bankscope.
Uncertainty in Banking Measures
Total assets: We use total assets in thousands of USD as provided by Bankscope.
Productivity: Productivity is estimated as proposed by Levinsohn and Petrin (2003).
For the free input variables, we choose total long-term funding and personnel expenses.
The intermediate input good is proxied by total equity and the fixed input is given by fixed assets. For the output variable, we use total loans defined as gross loans minus impaired loans. Data are in thousands of USD and obtained from Bankscope.
Profitability (RoA):Return on assets (RoA) is the ratio of operating profits to total assets (in percent) and calculated from data available in Bankscope.
Short-term funding: The variable short-term funding (in thousands of USD) is obtained by taking the sum of deposits from banks, repos and cash collateral, and other deposits and short-term borrowings as provided by Bankscope.
Alternative Uncertainty Measures
Bank stock return volatility: To construct a measure for bank stock return volatil-ity, we use weekly bank price indices from Datastream. As they are not available for some countries, we resort to aggregates for the particular region. The measure for bank stock return volatility is computed as the volatility of weekly bank index returns for each year (in percent).
Firm return dispersion: From Bloom (2014), we take a measure to control for un-certainty in the real sector. Data come from the WRDS international equity database and are used to construct the standard deviation of quarterly returns across firms. For our analysis, we use the value for the last quarter of the respective year (in percent).
GDP volatility: GDP volatility is computed as the three-year rolling volatility of quarterly (year-over-year) real GDP growth taken from the IMF International Finan-cial Statistics (in percent).
Stock market volatility: To construct a measure for stock market volatility, we use monthly stock price indices from Datastream. We resort to monthly frequency as this is available for all countries. The stock market volatility measure is computed as the volatility of monthly stock market index returns for each year (in percent).
Chapter 2: Uncertainty, Bank Lending, and Bank-Level Heterogeneity
Internationalization
Foreign ownership: Data on foreign ownership are taken from Claessens and Van Horen (2014) and matched with bank-level information from Bankscope. The data are avail-able for 5,324 banks in 137 countries for the period 1995-2009. We keep all banks which are located in one of our sample countries. For the years 2010-12, we project the ownership status of the year 2009 forward.
Openness: The variable openness is defined as total assets held by the banking system in reporting countryj toward the rest of the world relative to nominal GDP in country j (in percent). Data on a country’s banking system’s cross-border activities come from the Locational Banking Statistics of the BIS.
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