Post-Crisis Bank Regulation: Essays

This section examines the relationship between bank size and the likelihood of receiving government support, focusing on the 'too-big-to-fail' (TBTF) hypothesis. The research uses Fitch Support Ratings (FSR) for US banks as a proxy for the perceived likelihood of government intervention. The initial findings reveal a positive correlation between bank size and FSR; larger banks are assigned higher ratings, indicating a greater perceived probability of receiving support. This supports the notion that regulators are more apprehensive about the systemic consequences of large bank failures. The study further delves into the non-linearity of this relationship. The initial positive relationship shows diminishing returns as bank size increases. At a certain point, the “too-big-to-fail” effect on the support rating decreases, suggesting that even the largest banks may not be guaranteed a bailout. This non-linearity refines the traditional TBTF theory, indicating a tipping point beyond which the size advantage diminishes. The researchers estimate this tipping point using two measures: as a percentage of GDP and as a percentage of total bank assets. This nuanced understanding of the size-support relationship offers valuable insights for policymakers aiming to mitigate systemic risk and manage expectations around government intervention.

This section investigates whether the Dodd-Frank Act, enacted in the aftermath of the 2008 financial crisis, has impacted the relationship between bank size and the expectation of government support. The Act introduced significant reforms aimed at reducing moral hazard and the TBTF perception. Key provisions include new resolution mechanisms designed to impose losses on uninsured creditors, shareholders, and managers in the event of bank failure. This approach seeks to increase the ex-ante belief that creditors will bear losses, thereby decreasing the likelihood of future bailouts. The establishment of the Financial Stability Oversight Council (FSOC) further contributes to these efforts. The FSOC actively works to promote market discipline, explicitly aiming to eliminate expectations of government protection for large financial companies. The study explores the impact of these legislative changes on credit rating agencies' assessments of the likelihood of government support, considering whether the agencies have re-evaluated their support ratings in light of Dodd-Frank. While acknowledging a lack of extensive research specifically on Dodd-Frank's impact on TBTF expectations, this section provides a critical assessment of the legislation's potential to shift the landscape of government support for failing banks.

This section details the data sources and analytical methods employed to study the relationship between bank size and government support. Bank balance sheet and income statement data are drawn from the Federal Reserve's Reports on Condition and Income (Call Reports) and Y-9C Reports, providing comprehensive financial information for US commercial banks and bank holding companies with total assets exceeding $500 million. Data from US insular areas are excluded, resulting in a sample encompassing 51 states. Importantly, no thresholds are applied to select banks for the sample. Regression analysis is conducted using various measures of bank size—relative to GDP and relative to total bank assets in the U.S.—to assess the robustness of the findings. The results reveal a negative correlation between the relative size variable and Fitch support ratings, indicating that larger banks are more likely to receive support in times of trouble. The introduction of a squared-size term highlights the non-linear relationship, confirming that beyond a certain point, the likelihood of government support decreases as bank size continues to increase. The tipping points for this non-linearity are calculated based on the average across several regression specifications and serve as key empirical findings for the study.

This section presents empirical evidence on moral hazard induced by implicit government guarantees. The study analyzes a panel of 781 banks from 90 countries to examine the relationship between the expectation of government support and banks' risk-taking behavior. The key finding is that banks perceived as more likely to receive government assistance (either individually or because their competitors are also perceived as likely to receive support) exhibit significantly higher levels of leverage, lower capital quality, increased investment in risky assets, and more severe liquidity mismatches. This demonstrates a clear link between the expectation of a bailout and increased risk-taking behavior, confirming the existence of moral hazard. The large sample size and broad geographical coverage allow for a robust examination of this phenomenon across diverse regulatory environments and institutional contexts. The research methodology employed includes the use of instrumental variables and difference-in-difference estimators, strengthening the causal interpretation of the observed relationships.

This subsection focuses on the impact of the Lehman Brothers' default in 2008 on the observed moral hazard. The analysis shows that the short-term effect of Lehman's failure was a reduction in moral hazard. However, this effect proved to be temporary. In the long run, the systemic consequences of Lehman's collapse, particularly the fear of another systemic bank failure, led to a resurgence of adverse incentives and a return to higher risk-taking behaviors. This highlights the limitations of using a single event (like the Lehman bankruptcy) to permanently correct moral hazard. The study observes that the systemic nature of bailout expectations also affects risk-taking, showing that banks are more likely to engage in riskier behaviors if they believe their competitors are also expected to receive government support. The short-term decrease in moral hazard immediately following the Lehman Brothers collapse is noteworthy but emphasizes the need for more robust and long-term solutions to address the issue of moral hazard in the banking sector.

This part explores potential policy interventions and regulatory mechanisms to mitigate moral hazard. The findings suggest that stricter regulation of the permissible range of bank activities can help reduce the adverse effects of bailout expectations on bank leverage and capital quality. Furthermore, the presence of multiple supervisory agencies proves effective in reducing the liquidity mismatch associated with moral hazard. In environments with effective governments and strong corruption control, risky investments are reduced. These results suggest that a combination of stricter activity restrictions and enhanced oversight mechanisms can help curb moral hazard-induced risk-taking. The efficacy of different regulatory environments is assessed by analyzing interaction terms between measures of government effectiveness, corruption control, supervisory pluralism, and activity restrictions with indicators of bailout expectations. The study also considers the impact of access to the Lender of Last Resort (LOLR) facilities, suggesting that more facilitated access can potentially reduce liquidity risk induced by bailout expectations. This analysis contributes significantly to the discussion of effective regulatory policies to manage moral hazard and enhance financial stability.

This section positions the research within the existing literature on moral hazard and implicit government guarantees. The authors discuss several closely related studies, including the work of Nier and Baumann (2006), Gropp et al. (2011a), and Dam and Koetter (2012). They highlight how their research complements and extends these previous studies by adding a time dimension, encompassing the Lehman Brothers' failure. This inclusion allows the authors to observe changes in moral hazard over time, providing valuable insights into the evolving impact of bailout expectations. The study's contribution includes testing the moral hazard hypothesis across investment, funding, and liquidity risks using a large cross-sectional panel dataset. It provides valuable information for financial regulation by examining the varying effects of moral hazard across different institutional and regulatory settings and over time. A key contribution is empirically verifying and refining the long-standing hypothesis that public bailouts negatively impact banks' risk-taking behaviour ex ante.

This section investigates the impact of bank lobbying activities and political connections on regulatory treatment, particularly focusing on the US context. The research uses a unique dataset combining bank lobbying data, financial information, and details on board members' prior affiliations with regulatory or government bodies. The study employs the Prompt Corrective Action (PCA) framework as a primary lens for analyzing regulatory treatment. Under this framework, banks are subject to mandatory actions and provisions when they fall below predefined capital ratio thresholds. However, regulators have discretion to impose additional measures. This discretionary element is the focus of the analysis. The study examines whether lobbying activities and political connections affect the probability of a bank receiving these additional, discretionary actions. The findings indicate that lobbying activities and political connections—both through proximity to relevant legislative committees and prior board affiliations with regulatory or government institutions—significantly decrease the probability of a bank receiving additional regulatory actions beyond the mandatory ones. This suggests that banks can effectively use lobbying and political connections to influence their regulatory treatment, even when they are financially distressed. The study further explores the robustness of its findings across different model specifications and potential sources of bias.

This section delves deeper into the effectiveness of different sources of bank influence. The study finds that the mere existence of a lobbying channel between the bank and regulatory institutions is crucial. Even modest lobbying expenditures can prove effective in influencing regulatory decisions. This highlights that established relationships, rather than the sheer amount spent on lobbying, are key in gaining favorable treatment. Furthermore, lobbying becomes more effective when former politicians are employed as lobbyists, implying that personal networks and connections can significantly amplify the effects of lobbying. The study also explores the influence of campaign contributions. The analysis suggests that proximity to legislators becomes more influential as financial industry contributions received by those legislators increase. This indicates a potential link between campaign contributions and the effectiveness of political connections. This subsection presents empirical evidence highlighting the effectiveness of lobbying and political connections and further tests its findings through robustness checks, including examining if the findings are different during and before the financial crisis.

While lobbying and political connections appear effective in shaping regulatory treatment, this section examines whether this influence extends to decisions concerning bank closures. Specifically, the study investigates whether banks can leverage their influence to reduce the likelihood of closure, particularly focusing on cases where banks are critically undercapitalized. Surprisingly, the analysis reveals no significant negative effect of lobbying or political connections on the probability of closure decisions. This suggests that the effectiveness of bank influence is limited. While banks may succeed in delaying closure decisions, evidence suggests that lobbying does not provide a significant influence on regulators' ultimate decision to close a severely distressed bank. However, a separate analysis of the duration until closure—measuring the time a critically undercapitalized bank remains in distress—suggests that lobbying activities may prolong this duration, even if the probability of closure is not significantly impacted. This provides additional nuanced insights into the scope of bank influence on regulatory outcomes.

This part explores the impact of lobbying and political connections on market-inferred expectations of government support for banks. The analysis uses Fitch Support Ratings (FSR) as a measure of the market's perception of the probability that a bank will receive government assistance in times of distress. A lower Fitch rating indicates a higher likelihood of support. The results consistently show that past lobbying activities significantly reduce the Fitch support rating. This is an important finding as better support ratings are associated with cheaper funding costs for banks. This implies that lobbying and political connections can indirectly translate into lower funding costs through their influence on market perceptions of government support. This effect is also examined against different control variables. The robustness of these findings is tested using various specifications and by analyzing data from pre- and post-crisis periods to determine if the impact of lobbying on the likelihood of support has changed over time. The study's data primarily includes banks with either a very high (1) or very low (5) FSR. This point needs to be taken into consideration.

This section outlines the data sources and methods used to measure bank lobbying and political connections. Lobbying data is obtained from reports filed to the US Senate Office of Public Records under the Lobbying Disclosure Act. The data covers lobbying activities between 1998-2012 but the research focuses primarily on the period from 2003-2012, which has more complete coverage. The analysis encompasses both in-house lobbying and lobbying conducted by external firms. The research uses bank financial data (Call Reports) to connect banks to their holding companies to compute total lobbying expenditures for each bank and related entities. Data on board members' prior affiliations is gathered from BoardEx, focusing on publicly listed bank holding companies due to data availability. This combination of data sources allows for a comprehensive study that also explores the effects of three variables: lobbying activities, proximity to relevant legislative committees, and prior regulatory or government affiliation. This detailed information allows the study to carefully consider the impact of multiple sources of influence on both regulatory decisions and market expectations of government support.

This section investigates the factors driving banks' engagement in lobbying activities. The analysis reveals a negative correlation between bank profitability and the likelihood of lobbying. This suggests that banks experiencing lower profitability are more inclined to lobby, potentially seeking to influence regulations in their favor to improve their financial situation. Alternatively, it could be argued that lobbying itself reduces profitability due to resource misallocation. The study also finds a positive correlation between lobbying and the presence of board members with prior affiliations to regulatory or legislative bodies. This suggests that banks utilize pre-existing political connections to facilitate their lobbying efforts. However, it's important to note that this correlation doesn't necessarily imply causation; it's plausible that banks actively recruit politically connected board members to enhance their lobbying capabilities. The research employs various econometric techniques, including Probit and OLS regression, with different lag structures for variables to ensure robustness of the findings. These analyses help to understand the motivations behind banks' choices to engage in lobbying, identifying both financial incentives and the exploitation of existing political networks.

This section examines the relationship between bank risk-taking and lobbying activity. The initial hypothesis was that riskier banks would be more likely to lobby, anticipating potential bailouts or regulatory interventions. However, the empirical findings show a different pattern. The majority of risk indicators examined—including non-performing loans, return on assets (ROA) volatility, risk-weighted assets (RWA), Z-scores, and Tier 1 equity ratios—demonstrate little to no relationship with bank lobbying. In fact, one significant finding shows a negative correlation between risk weights (a measure of riskier assets on a bank's balance sheet) and the likelihood of lobbying. This suggests that less risky banks are more likely to engage in lobbying. While a correlation with profitability is observed, control variables are used to account for potential confounding effects. Various econometric methods (Probit, OLS) are used in the analysis to ensure the robustness of the results, confirming that the observed negative relationship between risk weights and lobbying is not simply driven by an underlying correlation with profitability. The results challenge the conventional wisdom that banks strategically lobby to mitigate the consequences of high-risk behavior, indicating a more nuanced relationship.

This section describes the data used and methodology employed in analyzing the relationship between lobbying and risk-taking. The data on lobbying activities is sourced from reports filed with the US Senate Office of Public Records, focusing on activities related to the financial industry (2003-2012). Bank financial data, including various measures of risk (non-performing loans, ROA volatility, risk weights from RWA, Z-score, and Tier 1 equity ratio), is gathered from Consolidated Reports of Condition and Income (“call reports”). The analysis uses a combination of Probit and OLS regression models to examine the relationship between lobbying indicators and different risk measures. The study accounts for several potential confounding factors and introduces robust standard errors and alternative estimators to increase the reliability of its findings. This rigorous methodological approach ensures that the findings on the relationship between bank lobbying, risk-taking, and profitability are robust and reliable. Different econometric approaches like fixed effects regressions, logit, and probit are employed in several robustness tests.