Abstract
This study utilizes the FDIC’s Historical Statistics on Banking from 1966 to 2013 to analyze the long-run relationship between federal funds rate policy, bank capital, and lending at U.S. commercial banks. The empirical analysis uses an autoregressive model to examine loan activity between two different groups of banks with different bank capital structures (high and low capital banks). The results of the study show that commercial loans at high capital banks have a stronger relationship to changes in the federal funds rates than low capital banks. Real estate loans show a slightly stronger negative correlation with federal funds for high capital banks than banks with low capital levels. The change in the portfolio ratio between real estate to commercial loans is negatively related to monetary policy changes.
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Notes
However, asset quality and bank capital adequacy were always a concern of bank regulators (see Mitchell (1909)) and the National Banking Act of 1863 mandated bank solvency.
See Friedman and Schwartz (1963) for detail history on banking runs and crises.
The pooled GLS regression approach used in this study should be appropriate. Panel regression for fixed effects or random effects may be inappropriate since testing by F-Test (or Wald tests on the significance of the alternative intercepts) and the Hausman test indicate that pooled approach is more appropriate.
Specifically, the ratio of total equity capital to total assets (or BKTA ratio).
The FFR is obtained from the Federal Reserve Bank’s website listed within the H.15 data series.
The study did experiment with using the Livingston Survey’s forecast of GDP provided by the Philadelphia Federal Reserve, but this variable offered no improvement over the actual GDP and may have introduced higher order serial correlation.
The study was not able to use the Federal Reserve’s Survey of Senior Loan Officers because of a gap between 1984 and 1990. This survey would be useful in controlling for changing credit standards at banks. The study also tested the first difference between the 3-month U.S. Treasury Bill rate and the Moody’s BAA corporate bond yield as a proxy for credit risk, but the variable was insignificant and not used.
The autoregressive technique transforms the baseline linear model into a nonlinear model. The transformed nonlinear model’s coefficients are then estimated using a Marquardt nonlinear least squares algorithm. This autoregressive approach seeks to minimize the direct usage of the lagged dependent variable. See: Davison and MacKinnon (Davidson and MacKinnon 2004, pp. 214–215).
It is not possible to separate domestic loan activity from loans after 1978 and no separate foreign loan activity is available prior to 1978.
A time trend dummy was test and subsequently dropped since it was insignificant. National Bureau of Economic Research (NBER) recession dummy years were test, but were insignificant and not included.
Full results for all the independent variables like GDP are available upon request and were left out to save space. GDP was highly significant and used as a control variable.
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This paper was part of my dissertation work at the New School for Social Research.
Data Appendix
Data Appendix
1.1 U.S. Bank Capital Groups
This appendix provides details on the construction of the two capital banking groups. Data was obtained from the FDIC’s Historical Statistics on Banking (FDIC HS) from 1966 to 2013. The capital to asset ratio is the total equity capital divided into total assets (or BKTA ratio). The U.S. banking industry average for the capital to asset ratio for all U.S. FDIC commercial banks over this time period was 8.39 %. Each state is calculated to determine its own capital to asset ratio that is then placed into one of two groups (or high and low capital banks) that is either above or below the U.S. banking industry historical average BKTA ratio. U.S. states plus the District of Columbia are given below by their common U.S. Postal abbreviation followed by their respective historical capital to asset ratio average stated as a percentage.
1.2 Capital to asset ratio
States in the ‘high capital’ bank group (states above the U.S. capital to asset ratio) are: AK 11.05 %, DE 10.94 %, NV 10.41 %, NH 9.46 %, WV 9.18 %, SD 9.16 %, AL 9.06 %, GA 9.02 %, KS 8.94 %, AR 8.78 %, IA 8.68 %, ME 8.67 %, NE 8.60 %, RI 8.57 %, ND 8.55 %, LA 8.54 %, WY 8.53 %, MS 8.50 %, TN 8.45 %, KY 8.43 %, UT 8.43 %.
States in the ‘low capital’ bank group (states below the U.S. capital to asset ratio) are: VA 8.38 %, OR 8.37 %, HI 8.37 %, OK 8.29 %, MT 8.28 %, SC 8.25 %, DC 8.24 %, IN 8.15 %, MD 8.13 %, ID 8.13 %, CT 8.12 %, MO 8.11 %, WI 8.11 %, FL 8.08 %, WA 8.02 %, NM 7.98 %, PA 7.92 %, TX 7.91 %, MI 7.83 %, OH 7.82 %, CO 7.82 %, CA 7.75 %, MN 7.68 %, IL 7.57 %, NC 7.56 %, VT 7.53 %, AZ 7.49 %, NJ 7.42 %, NY 7.31 %, MA 7.09 %.
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Orzechowski, P.E. Bank capital, loan activity, and monetary policy: evidence from the FDIC’s Historical Statistics on Banking. J Econ Finan 41, 392–407 (2017). https://doi.org/10.1007/s12197-016-9359-5
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DOI: https://doi.org/10.1007/s12197-016-9359-5