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Gender Pay Gap in American CFOs: Theory and Evidence
Bill Francis, Iftekhar Hasan, Gayane Hovakimian, Zenu Sharma
Journal of Corporate Finance,
June
2023
Abstract
Studies document persistent unexplained gender-based wage gap in labor markets. At the executive level, where skill and education are similar, career interruptions and differences in risk preferences primarily explain the extant gender-based pay gap. This study focuses on CFO compensation contracts of Execucomp firms (1992–2020) and finds no gender-based pay gap. This paper offers several explanations for this phenomenon, such as novel evidence on the risk preferences of females with financial expertise and changes in the social and regulatory climate.
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Economic Preferences for Risk-Taking and Financing Costs
Manthos D. Delis, Iftekhar Hasan, Maria Iosifidi, Chris Tsoumas
Journal of Corporate Finance,
June
2023
Abstract
We hypothesize and empirically establish that economic preferences for risk-taking in different subnational regions affect firm financing costs. We study this hypothesis by hand-matching firms' regions worldwide with the corresponding regional economic risk-taking preferences. We first show that higher regional risk-taking is positively associated with several measures of firm risk and investments. Subsequently, our baseline results show that credit and bond pricing increase when risk-taking preferences increase. For the loan of average size and maturity a one-standard-deviation increase in regional risk-taking increases interest expense by $0.54 million USD. We also find that these results are demand (firm)-driven and stronger for firms with more local shareholders.
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Climate Change Concerns and Information Spillovers from Socially-connected Friends
Maximilian Mayer
IWH Discussion Papers,
Nr. 2,
2023
Abstract
This paper studies the role of social connections in shaping individuals’ concerns about climate change. I combine granular climate data, region-level social network data and survey responses for 24 European countries in order to document large information spillovers. Individuals become more concerned about climate change when their geographically distant friends living in sociallyconnected regions have experienced large increases in temperatures since 1990. Exploring the heterogeneity of the spillover effects, I uncover that the learning via social networks plays a central role. Further, results illustrate the important role of social values and economic preferences for understanding how information spillovers affect individual concerns.
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Where to Go? High-skilled Individuals’ Regional Preferences
Sabrina Jeworrek, Matthias Brachert
IWH Discussion Papers,
Nr. 27,
2022
Abstract
We conduct a discrete choice experiment to investigate how the location of a firm in a rural or urban region affects job attractiveness and contributes to the spatial sorting of university students and graduates. We characterize the attractiveness of a location based on several dimensions (social life, public infrastructure, connectivity) and combine this information with an urban or rural attribution. We also vary job design as well as contractual characteristics of the job. We find that job offers from companies in rural areas are generally considered less attractive. This is true regardless of the attractiveness of the region. The negative perception is particularly pronounced among persons with urban origin and singles. These persons rate job offers from rural regions significantly worse. In contrast, high-skilled individuals who originate from rural areas as well as individuals with partners and kids have no specific preference for jobs in urban or rural areas.
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Carbon Transition Risk and Corporate Loan Securitization
Isabella Müller, Huyen Nguyen, Trang Nguyen
Abstract
We examine how banks manage carbon transition risk by selling loans given to polluting borrowers to less regulated shadow banks in securitization markets. Exploiting the election of Donald Trump as an exogenous shock that reduces carbon risk, we find that banks’ securitization decisions are sensitive to borrowers’ carbon footprints. Banks are more likely to securitize brown loans when carbon risk is high but swiftly change to keep these loans on their balance sheets when carbon risk is reduced after Trump’s election. Importantly, securitization enables banks to offer lower interest rates to polluting borrowers but does not affect the supply of green loans. Our findings are more pronounced among domestic banks and banks that do not display green lending preferences. We discuss how securitization can weaken the effectiveness of bank climate policies through reducing banks’ incentives to price carbon risk.
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Unethical Employee Behavior Against Coworkers Following Unkind Management Treatment: An Experimental Analysis
Sabrina Jeworrek, Joschka Waibel
Managerial and Decision Economics,
Nr. 5,
2021
Abstract
We study unethical behavior toward unrelated coworkers as a response to managerial unkindness with two experiments. In our lab experiment, we do not find that subjects who experienced unkindness are more likely to cheat in a subsequent competition against another coworker who simultaneously experienced mistreatment. A subsequent survey experiment suggests that behavior in the lab can be explained by individuals' preferences for norm adherence, because unkind management behavior does not alter the perceived moral appropriateness of cheating. However, having no shared experience of managerial unkindness opens up some moral wiggle room for employees to misbehave at the costs of others.
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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Macroeconomic Dynamics,
Nr. 1,
2021
Abstract
Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Are Bank Capital Requirements Optimally Set? Evidence from Researchers’ Views
Gene Ambrocio, Iftekhar Hasan, Esa Jokivuolle, Kim Ristolainen
Journal of Financial Stability,
October
2020
Abstract
We survey 149 leading academic researchers on bank capital regulation. The median (average) respondent prefers a 10% (15%) minimum non-risk-weighted equity-to-assets ratio, which is considerably higher than the current requirement. North Americans prefer a significantly higher equity-to-assets ratio than Europeans. We find substantial support for the new forms of regulation introduced in Basel III, such as liquidity requirements. Views are most dispersed regarding the use of hybrid assets and bail-inable debt in capital regulation. 70% of experts would support an additional market-based capital requirement. When investigating factors driving capital requirement preferences, we find that the typical expert believes a five percentage points increase in capital requirements would “probably decrease” both the likelihood and social cost of a crisis with “minimal to no change” to loan volumes and economic activity. The best predictor of capital requirement preference is how strongly an expert believes that higher capital requirements would increase the cost of bank lending.
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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance metric, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly efficiently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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