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Automation with Heterogeneous Agents: The Effect on Consumption Inequality
Tommaso Santini
IWH Discussion Papers,
No. 28,
2022
Abstract
In this paper, I study technological change as a candidate for the observed increase in consumption inequality in the United States. I build an incomplete market model with educational choice combined with a task-based model on the production side. I consider two channels through which technology affects inequality: the skill that an agent can supply in the labor market and the level of capital she owns. In a quantitative analysis, I show that (i) the model replicates the increase in consumption inequality between 1981 and 2008 in the US (ii) educational choice and the return to wealth are quantitatively important in explaining the increase in consumption inequality.
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Trading away Incentives
Stefano Colonnello, Giuliano Curatola, Shuo Xia
IWH Discussion Papers,
No. 23,
2022
Abstract
Equity pay has been the primary component of managerial compensation packages at US public firms since the early 1990s. Using a comprehensive sample of top executives from 1992-2020, we estimate to what extent they trade firm equity held in their portfolios to neutralize increments in ownership due to annual equity pay. Executives accommodate ownership increases linked to options awards. Conversely, increases in stock holdings linked to option exercises and restricted stock grants are largely neutralized through comparable sales of unrestricted shares. Variation in stock trading responses across executives hardly appears to respond to diversification motives. From a theoretical standpoint, these results challenge (i) the common, generally implicit assumption that managers cannot undo their incentive packages, (ii) the standard modeling practice of treating different equity pay items homogeneously, and (iii) the often taken for granted crucial role of diversification motives in managers’ portfolio choices.
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Consumer Defaults and Social Capital
Brian Clark, Iftekhar Hasan, Helen Lai, Feng Li, Akhtar Siddique
Journal of Financial Stability,
April
2021
Abstract
Using account level data from a credit bureau, we study the role that social capital plays in consumer default decisions. We find that borrowers in communities with greater social capital are significantly less likely to default on loans, even after adjusting for different levels of income and other characteristics such as credit scores. The results are strongest for potentially strategic defaults on mortgages; a one standard deviation increase in social capital reduces such defaults by 12.4 %. These results can be generalized to any mortgage default. Our results also indicate that the effect of social capital is most prominent among more creditworthy borrowers, suggesting that when given a choice, the social cost of defaulting is an important factor affecting default decisions. We find a similar impact of social capital on consumer defaults in other datasets with more detailed information on borrowers as well. Our results are robust to modeling and methodology choices, as well as controlling for other drivers of default such as wealth, income and amenities from homeownership. Our results suggest that increasing social capital via measures to build community cohesion such as promotion of owner-occupied home ownership may be one avenue to deter consumer default.
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International Emigrant Selection on Occupational Skills
Miguel Flores, Alexander Patt, Jens Ruhose, Simon Wiederhold
Journal of the European Economic Association,
No. 2,
2021
Abstract
We present the first evidence on the role of occupational choices and acquired skills for migrant selection. Combining novel data from a representative Mexican task survey with rich individual-level worker data, we find that Mexican migrants to the United States have higher manual skills and lower cognitive skills than nonmigrants. Results hold within narrowly defined region–industry–occupation cells and for all education levels. Consistent with a Roy/Borjas-type selection model, differential returns to occupational skills between the United States and Mexico explain the selection pattern. Occupational skills are more important to capture the economic motives for migration than previously used worker characteristics.
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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Macroeconomic Dynamics,
No. 1,
2021
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (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 simulated and real-world evidence that this simplification results in stable thresholds and improves 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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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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Thou Shalt not Bear False Witness Against Your Customers: Cultural Norms and the Volkswagen Scandal
Iftekhar Hasan, Felix Noth, Lena Tonzer
Abstract
This paper investigates whether cultural norms shaped by religion drive consumer decisions after a corporate scandal. We exploit the unexpected notice of violation by the US Environmental Protection Agency in September 2015, accusing the car producer Volkswagen (VW) to have used software to manipulate car emission values during test phases. Using a difference-in-difference model, we show that new registrations of VW (diesel) cars decline significantly in German counties with a high share of Protestants following the VW scandal. Our results suggest that the enforcement culture rooted in Protestantism affects consumer decisions and penalises corporate fraud.
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