Racial Inequality in the U.S. Unemployment Insurance System

The article of record as published may be found at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4165324The U.S. unemployment insurance (UI) system operates as a federal-state partnership, where states have considerable autonomy to decide on specific UI rules. This has allowed for systematically stricter rules in states with a larger Black population. We study how these differences in state rules create a gap in the unemployment insurance that Black and White unemployed workers receive. Using administrative data from random audits on UI claims in all states, we first document a large racial gap in the UI that unemployed workers receive after filing a new claim. Black claimants receive an 18% lower replacement rate (i.e., benefits relative to prior wage, including denials) than White claimants. In principle, the replacement rate of each claimant mechanically depends on the rules prevailing in her state and on her work history (e.g., the earnings before job loss and the reason for separation from prior employer). Since we observe claimants' UI-relevant work history and state, we are in a unique position to identify the role of each factor. After accounting for Black-White differences in work history, differences in rules across states create an 8% Black-White gap in replacement rate (i.e., slightly less than half of the overall gap). Using a standard welfare calculation, we show that states with the largest shares of Black workers would gain the most from having more generous UI rules. Altogether, our results highlight that disparate state rules in the UI institution create racial inequality without maximizing overall welfare.This work was funded by a Washington State University Faculty Seed grant awarded to CJD and a Congressionally Directed Medical Research Program grant W81XWH-16-1- 0319 awarded to Hans Van Dongen.This work was funded by a Washington State University Faculty Seed grant awarded to CJD and a Congressionally Directed Medical Research Program grant W81XWH-16-1- 0319 awarded to Hans Van Dongen

The Determinants of Disparities in Reservation Wages

In this project, we will use uniquely rich data on reservation wages for a large, representative sample of US unemployment insurance recipients, collected through the Benefit Accuracy Management (BAM) program since 1987. It allows us to precisely describe the heterogeneity of the reservation wage to prior wage ratio, and analyze its changes over time as well as how it relates to labor market conditions. In a first part, this project will document cross-sectional differences in reservation wages across demographic groups. We will particularly explore the potential drivers of differences across races. In a second part, this project will investigate the influence of labor market conditions. For identification, we will leverage variation across sectors and states

Motherhood and the Cost of Job Search

Why do women experience a persistent drop in labor earnings upon becoming mothers, i.e. a "child penalty"? We study a new mechanism: search frictions. We analyze data on job applications sent on a popular online platform linked with administrative data for 350,000 involuntarily unemployed workers in France. First, we highlight differences in job search behavior between mothers and similar women with no children. Mothers send 12.2% fewer job applications and are more selective regarding wage and non-wage amenities. Consistently, they have a lower job finding rate. Second, we analyze the exact time when applications are sent and highlight differences in the timing of job search. We find that mothers' rate of applications decreases by 20.3% in the hours and days when there is no school. We also show that mothers responded to a reform that introduced school on Wednesday by smoothing their search across weekdays and narrowing their search timing gap with other women. In a simple search model, we show that our results imply that mothers both face lower incentives and higher costs to search. We conclude that search frictions disproportionately prevent mothers from improving their labor market situation and contribute to the child penalty

The Impact of the Federal Pandemic Unemployment Compensation on Job Search and Vacancy Creation

During the COVID-19 pandemic, the Federal Pandemic Unemployment Compensation (FPUC) increased US unemployment benefits by $600 a week. Theory predicts that FPUC should decrease job applications, while the effect on vacancy creation is ambiguous. We estimate the effect of FPUC on job applications and vacancy creation week by week, from March to July 2020, using granular data from the online jobs platform Glassdoor. We exploit variation in the proportional increase in benefits across local labor markets. To isolate the effect of FPUC, we flexibly allow for different trends in local labor markets differentially exposed to the COVID-19 crisis. We verify that trends in outcomes prior to the FPUC do not correlate with future increases in benefits, which supports our identification assumption. First, we find that a 10% increase in unemployment benefits caused a 3.6% decline in applications, but did not decrease vacancy creation; hence, FPUC increased labor market tightness (vacancies/applications). Second, we document that tightness was unusually depressed during the FPUC period. Altogether, our results imply that the positive effect of FPUC on tightness was likely welfare improving: FPUC decreased competition among applicants at a time when jobs were unusually scarce. Our results also help explain prior findings that FPUC did not decrease employment

Política monetaria y estabilidad financiera en economías pequeñas y abiertas

Descripción de un modelo de alerta temprana para la predicción del auge de crédito usando los agregados macroeconómicos. Se hace un estudio comparado de las probabilidades de crédito en diferentes países y se evalúan los resultados

Un modelo de alerta temprana para la predicción de booms de crédito usando los agregados macroeconómicos

En este documento se propone una novedosa metodología para determinar la existencia de booms de crédito, el cual es un tema bastante complejo y de crucial importancia para las autoridades económicas. En particular, se explota la idea de Mendoza y Terrones (2008) que señala que los agregados macroeconómicos contienen información valiosa para predecir los episodios de boom. El ejercicio econométrico realiza la estimación y predicción de la probabilidad de estar en un boom de crédito. El trabajo empírico se lleva a cabo a partir de datos trimestrales de seis países latinoamericanos entre 1996 y 2011. Para capturar simultáneamente la incertidumbre en la elección del modelo y el valor de los parámetros, se emplea la técnica Bayesian Model Averaging. Como se hace uso de datos panel, los resultados econométricos podrían ser empleados para predecir booms de países que no se consideran en la estimación. En conjunto, los resultados muestran que las variables macroeconómicas contienen información importante para identificar y predecir los booms de crédito. De hecho, con nuestro método la probabilidad de detectar un boom de crédito es 80% mientras la probabilidad de no tener falsas alarmas es mayor al 92%.In this paper, we propose an alternative methodology to determine the existence of credit booms, which is a complex and crucial issue for policymakers. In particular, we exploit the Mendoza and Terrones's (2008) idea that macroeconomic aggregates contain valuable information to predict lending boom episodes. Specifically, our econometric method is used to estimate and predict the probability of being in a credit boom. We run empirical exercises on quarterly data for six Latin American countries between 1996 and 2011. In order to capture simultaneously model and parameter uncertainty, we implement the Bayesian model averaging method. As we employ panel data, the estimates may be used to predict booms of countries which are not considered in the estimation. Overall, our findings show that macroeconomic variables contain relevant information to identify and to predict credit booms. In fact, with our method the probability of detecting a credit boom is 80%, while the probability of not having false alarms is greater than 92%

An Early Warning Model for Predicting Credit Booms Using Macroeconomic Aggregates

In this paper, we propose an alternative methodology to determine the existence of credit booms, which is a complex and crucial issue for policymakers. In particular, we exploit the Mendoza and Terrones (2008)’s idea that macroeconomic aggregates other t

How Economic Crises Affect Inflation Beliefs: Evidence from the Covid-19 Pandemic

This paper studies how inflation beliefs reported in the New York Fed's Survey of Consumer Expectations have evolved since the start of the COVID-19 pandemic. We find that household inflation expectations responded slowly and mostly at the short-term horizon. In contrast, the data reveal immediate and unprecedented increases in individual inflation uncertainty and in inflation disagreement across respondents. We find evidence of a strong polarization in inflation beliefs and we show differences across demographic groups. Finally, we document a strong link, consistent with precautionary saving, between inflation uncertainty and how respondents used the stimulus checks they received as part of the 2020 CARES Act