Abstract
This paper tests whether the political connections of banks were important in explaining participation in the Federal Reserve’s emergency lending programs during the recent financial crisis. Our multivariate tests show that banks that were politically connected—either through lobbying efforts or employment of politically connected individuals—were substantially more likely to participate in the Federal Reserve’s emergency loan programs. In economic terms, participation in these programs was 28–36% more likely for banks that were politically connected than for banks that were not politically connected. In our final set of tests, we attempt to identify a proper explanation for this peculiar relationship. While a broad literature speaks of the moral hazard associated with receiving bailouts, we test whether another type of moral hazard exists in the period preceding the bailout. In particular, we argue that, to the extent that political connections act as synthetic insurance, banks may have engaged in more risky behavior that lead them to the Fed’s emergency lending facilities. Tests seem to confirm this explanation.
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Notes
As mentioned above, O’Hara and Shaw (1990), among others, suggest that “Too-Big-To-Fail” banks are likely the largest banks. However, we recognize the possibility that market capitalization and assets might not fully capture banks deemed TBTF.
While the Fed did announce eligibility requirements for participation, most loans were provided through an auction process, which provided a way for institutions to purchase loanable funds in an open market. In the first auction, the Fed reported that the total dollar volume of bids was three times the total amount of dollar volume actually borrowed, suggesting an unusual institutional demand for these emergency loans (https://www.stlouisfed.org/publications/re/articles/?id=2062).
La Porta et al. (2003) find that while earlier research suggests that relations between banks and borrowers can increase credit efficiency, much higher default rates exist in related lending than in unrelated lending. They argue for the existence of a new type of moral hazard, such that loans between borrowers and lenders that have strong relationships might be riskier, ceteris paribus, because borrowers engage in riskier activity. In particular, they show that before the 1995 credit crisis in Mexico, “well-connected” Mexican banks engaged in riskier lending that presumably contributed to the severity of the crisis.
The full report of the GAO audit is located at http://www.gao.gov/new.items/d11696.pdf.
For instance, the GAO report suggests that lending from the MMMF was negligible. TALF lending comprised of $71.1 billion. The GAO reports lending totals for TAF, TSLF, PDCF and CPFF of $3818 billion, $2319 billion, $7389 billion and $738 billion, respectively. Lending from the DSL was indeed the highest: $10,057 billion; however, this facility interacted with 10 foreign central banks.
We recognize an important piece of data missing from our tests. While many of the emergency lending programs were conducted by auctions, we do not observe the outcome of the application process. It is possible that many of the application bids were rejected, which will no doubt affect the conclusions we are trying to draw. Given that those data are unavailable, we are left to assume that the distribution of banks that applied for emergency assistance was similar to the distribution of banks that eventually received emergency assistance. This assumption certainly raises some caution about the strength of the conclusions that we are able to draw.
As a measure of robustness, we use balance sheet and income statement information from the quarter before the loan was received. Results from these unreported tests are qualitatively similar to those reported in this study.
As mentioned in Sect. 2, the variable LobbyAmt is the amount of lobbying expenditures to the nearest $20,000. However, firms that spent close to $10,000 in lobbying were not required to disclose their expenditures. Therefore, any inferences we make regarding the amounts of lobbying must be made with caution given that some firms may have undisclosed lobbying expenditures. In the tests that follow, we focus only on our two indicator variables owing to this potential bias.
We do not enter both LOBBY and EMPLOY in the same regression because of severe multicollinearity issues. Said differently, both of these indicator variables are highly correlated, thus affecting our ability to infer the significances of the coefficients. The variance inflation factors for LOBBY and EMPLOY, when including both indicator variables in the same regression, are well above 10.
While there is not a specific test-statistic determining a critical value for variance inflation factors, the idea is that standard errors might be inflated by the square root of the variance inflation factor. Therefore, for a variance inflation factor of 10, standard errors might be 3.16 times the given standard error.
A natural extension of these tests is to determine whether lobbying is a more important determinant in the receipt of emergency loans than bank size. In unreported tests, we scale the amount of lobbying (as reported by the CPR) by the total assets of the bank. We repeat that the CPR data about the amount of lobbying is a crude measure. That is, some banks that may have spent slightly less than $10,000 on lobbying in a particular year would have not been included in the dataset. Further, the lobbying amounts reported by the CPR are in $10,000 increments, so the lobbying amount variable does not contain the exact amount of lobbying expenditures. Therefore, we exercise caution when making inferences regarding these unreported tests. Nevertheless, the results show that the estimate for the ratio of lobbying expenditures relative to the total assets produces a coefficient that is statistically close to zero, suggesting that the amount of lobbying expenditures and total bank assets are equally important factors in explaining the likelihood of receiving emergency support.
Chi-squared statistics are large enough to reject the null hypothesis of no heteroscedasticity. Therefore, we use White’s (1980) method for robust standard errors.
Recall that all bank observations are included in the analysis. Therefore, many of the banks did not receive emergency loans from the Fed and therefore, they have a dependent variable that is equal to zero.
Of the banks included in this analysis, five banks are on the Financial Stability Board’s list of TBTF banks. These banks are the Bank of America, Deutsche Bank, JP Morgan, State Street, and Wells Fargo.
To measure market-based measures of risk, we used daily returns to estimate a Capital Asset Pricing Model (CAPM) and obtained the level of systematic risk (beta) for each bank. From the residuals of the CAPM, we calculate the standard deviation to obtain a measure of idiosyncratic risk. We then entered those two measures of risk as additional control variables in our multivariate tests.
While we do not report the results in this version of the paper, these unreported tests (and those that follow) are available from the authors by request.
While the tests in Table 7, as well as the process through which lending from various facilities was initiated, question the possibility that favoritism may have been involved, we recognize that the Fed is not immune to providing political favors through other indirect channels. However, determining these channels is difficult given that the motives behind monetary policies are unobserved.
We recognize that in prior research, some studies treat debt levels as a measure of riskiness. In unreported tests, we exclude the level of liabilities from the specification in Eq. (4) and find results that are very similar to those reported in this study.
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This research was partially funded by the Mercatus Center at George Mason University.
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Blau, B.M. Lobbying, political connections and emergency lending by the Federal Reserve. Public Choice 172, 333–358 (2017). https://doi.org/10.1007/s11127-017-0446-8
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DOI: https://doi.org/10.1007/s11127-017-0446-8