Jose Garcia Montalvo

We examine gender differences in compliance with AI-based lending recommendations and propose a novel mechanism that can explain rational gender differences in algorithm-informed decisions. Loan screening increasingly relies on objective, algorithmic measures. Using detailed bank data, we analyze loan officers’ compliance by gender. We find that women comply more with algorithmic recommendations than men, which leads to substantially lower default rates on their portfolios. We show that female loan officers have nearly a 15\% lower delinquency rate than male officers. We identify a novel mechanism that can explain managerial gender differences in the context of algorithm-informed decisions. This pattern is consistent with gender bias operating through a mistake–punishment tradeoff, which may partly explain women’s higher compliance with AI recommendations.

The foreclosure crisis associated with the banking crisis transformed banks in the hardest-hit countries into real estate brokers. The main novelty of this paper is to study banks as sellers of their own foreclosed properties and compare banks’ sales outcomes with those of traditional agents in the real estate market. We compare the list price, selling price, time on market and price discount of traditional real estate companies (TRECs) and bank-owned real estate companies (BRECs). We find evidence of a higher selling price, higher list price and longer time on market (TOM) for BRECs than for TRECs. Our findings are consistent with BRECs displaying greater patience as well as lower risk aversion. However, these explanations are not enough to fully account for the magnitudes of the coefficients. The empirical estimates suggest that information in the housing market may also be a source of distortions. In fact, the main aim of the sale varies depending on the incentives of the company. BREC sellers are banks that own the properties for sale, so their incentives are to maximize selling prices to reduce the loss charged to their annual results, while TRECs seek to minimize the TOM.

Este artículo analiza la evolución de la desigualdad de ingresos en España durante la crisis de la COVID-19 en alta frecuencia. Para ello, utilizamos datos anonimizados de registros bancarios de más de 3 millones de titulares de cuentas bancarias entre febrero de 2020 y julio de 2021. Los resultados muestran el fuerte aumento de la desigualdad experimentado en los primeros meses de la COVID-19, así como la recuperación posterior a medida que la economía se ha reactivado. Asimismo, analizamos el crucial papel que han jugado las ayudas públicas para mitigar el impacto de la crisis.

Pandemics have historically had a significant impact on economic inequality. However, official inequality statistics are only available at low frequency and with considerable delay, which challenges policymakers in their objective to mitigate inequality and fine-tune public policies. We show that using data from bank records it is possible to measure economic inequality at high frequency. The approach proposed in this paper allows measuring, timely and accurately, the impact on inequality of fast-unfolding crises, like the COVID-19 pandemic. Applying this approach to data from a representative sample of over three million residents of Spain we find that, absent government intervention, inequality would have increased by almost 30% in just one month. The granularity of the data allows analyzing with great detail the sources of the increases in inequality. In the Spanish case we find that it is primarily driven by job losses and wage cuts experienced by low-wage earners. Government support, in particular extended unemployment insurance and benefits for furloughed workers, were generally effective at mitigating the increase in inequality, though less so among young people and foreign-born workers. Therefore, our approach provides knowledge on the evolution of inequality at high frequency, the effectiveness of public policies in mitigating the increase of inequality and the subgroups of the population most affected by the changes in inequality. This information is fundamental to fine-tune public policies on the wake of a fast-moving pandemic like the COVID-19.

Women are underrepresented in the top ranks of the scientific career, including the biomedical disciplines. This is not generally the result of explicit and easily recognizable gender biases but the outcome of decisions with many components of unconscious nature that are difficult to assess. Evidence suggests that implicit gender stereotypes influence perceptions as well as decisions. To explore these potential reasons of women’s underrepresentation in life sciences we analyzed the outcome of gender-science and gender-career Implicit Association Tests (IAT) taken by 2,589 scientists working in high profile biomedical research centers. We found that male-science association is less pronounced among researchers than in the general population (34% below the level of the general population). However, this difference is mostly explained by the low level of the IAT score among female researchers. Despite the highly meritocratic view of the academic career, male scientists have a high level of male-science association (261% the level among women scientists), similar to the general population.

The Spanish financial system has lived through a tumultuous period. The delay of the Spanish authorities in recognizing the problems, and the mistakes of the original plan to address the issues, has derived in a long and traumatic process of recapitalization. The new regulation, targeted at eliminating the perverse incentives in the housing finance sector, and the consequences of the financial crisis generate incentives to transform the business model of financial companies in order to adapt to the new competitive and regulatory conditions.