Statistical Discrimination and Duration Dependence in the Job Finding Rate (w/ Gregor Jarosch) [Paper] - Review of Economic Studies
This paper models a frictional labor market where employers endogenously discriminate against the long term unemployed. The estimated model replicates recent experimental evidence which documents that interview invitations for observationally equivalent workers fall sharply as unemployment duration progresses. We use the model to quantitatively assess the consequences of such employer behavior for job finding rates and long term unemployment and find only modest effects given the large decline in callbacks. Interviews lost to duration impact individual job-finding rates solely if they would have led to jobs. We show that such instances are rare when firms discriminate in anticipation of an ultimately unsuccessful application. Discrimination in callbacks is thus largely a response to dynamic selection, with limited consequences for structural duration dependence and long term unemployment.
We develop a model where selection into marriage and household search generate a marital wage premium. Beyond selection, married individuals earn higher wages for two reasons. First, income pooling within a joint household raises risk-averse individuals’ reservation wages. Second, married individuals climb the job ladder faster, as they internalize that higher wages increase their partner’s selectivity over offers. Specialization according to comparative advantage in search generates a premium that increases in spousal education, as in the data. Quantitatively, household search explains 16-41% and 20-68% of the premium for males and females respectively, and accounts for its increase with spousal education.
What are the characteristics of workers in jobs likely to be initially affected by broad social distancing and later by narrower policy tailored to jobs with low risk of disease transmission? We use O*NET to construct a measure of the likelihood that jobs can be conducted from home (a variant of Dingel and Neiman (2020)) and a measure of low physical proximity to others at work. We validate the measures by showing how they relate to similar measures constructed using time use data from ATUS. Our main finding is that workers in low-work-from-home or high-physical-proximity jobs are more economically vulnerable across various measures constructed from the CPS and PSID: they are less educated, of lower income, have fewer liquid assets relative to income, and are more likely renters. We further substantiate the measures with behavior during the epidemic. First, we show that MSAs with less pre-virus employment in work-from-home jobs experienced smaller declines in the incidence of `staying-at-home', as measured using SafeGraph cell phone data. Second, we show that both occupations and types of workers predicted to be employed in low work-from-home jobs experienced greater declines in employment according to the March 2020 CPS. For example, non-college educated workers experienced a 4ppt larger decline in employment relative to those with a college degree.
We estimate the unconditional distribution of marginal propensities to consume (MPC) using clustering regression applied to the 2008 economic stimulus payments. By deviating from the standard approach of estimating MPC heterogeneity using interactions with observables, we can recover the full distribution of MPCs. We find households spent at least 8% of the rebate, and individual households used rebates for different goods. While many observables correlate individually with our estimated MPCs, these relationships disappear when tested jointly, except for income and the average propensity to consume. Household observables explain only 8% of MPC variation, highlighting the role of latent heterogeneity.
Gender Differences in Job Search Behavior and the Gender Earnings Gap: Evidence from the Field and the Lab (with Patricia Cortes, Jessica Pan, and Basit Zafar) [Draft] Revise & Resubmit QJE
This paper investigates gender differences in the job search process, both in the field and lab. First, we collect rich information on job offers and acceptances from undergraduates of Boston University’s Questrom School of Business. We document two novel empirical facts: (1) there is a clear gender difference in the timing of job offer acceptance, with women accepting jobs substantially earlier than men, and (2) the gender earnings gap in accepted offers narrows in favor of women over the course of the job search period. To rationalize these patterns, we develop a job search model that incorporates gender differences in risk aversion and overoptimism about prospective offers. We validate the model assumptions and predictions using the survey data, and present empirical evidence that the job search patterns in the field can be partly explained by greater risk aversion displayed by women and the higher levels of overoptimism (and slower belief updating) displayed by men. Next, we replicate the findings from the field in a specially-designed laboratory experiment that features sequential job search, and provide direct evidence on the purported mechanisms. Our findings highlight the importance of risk preferences and beliefs for gender differences in job-finding behavior, and consequently, early career wage gaps among the highly-skilled.
We develop a model of educational investment, marriage, and household labor market search to quantify how changes in incentives to positively sort in marriage - summarized by changes in marital surplus - contributed to the rise in U.S. household income inequality. We estimate that changes in economic marital surplus increased incentives to positively sort, while the non-economic returns to marriage did the opposite. The former movements significantly dampened the rise in inequality by increasing educational attainment and marriage propensities. However, changes in the non-economic incentives counteracted these forces, raising the Gini coefficient by 2%, about 20% of the total rise.
Sectoral Shocks and Move Unemployment [new draft coming soon] (previously circulated under "A Multisector Equilibrium Search Model of Labor Reallocation")
This paper develops a multisector search model in which workers choose their sectors in response to sector-specific and idiosyncratic shocks. Unemployed workers can search for work in their sector of last employment, or become ``move unemployed,'' spending extra time in unemployment to reach a new sector. Sectoral shocks induce net movements of labor into relatively productive sectors, while idiosyncratic worker shocks ensure that gross intersectoral flows through unemployment are always positive, a prediction consistent with the data. I use the model to test the sectoral shifts hypothesis (Lilien (1982)) in the context of the Great Recession in which the construction sector experienced a relatively large shock. In a two-sector calibration of the model to construction and non-construction, I find that sectoral dispersion shocks have no impact on aggregate unemployment. While they increase net mobility, this increase is accomplished through a change in the composition of gross flows rather than their level. The results suggest that, a priori, we should not expect sectoral shocks to generate unemployment fluctuations.
Work In Progress:
Partial Insurance with Advanced Information: Evidence from Income and Consumption Expectations (with Noah Kwicklis and Fatih Karahan)
Discussion of "On Worker and Firm Heterogeneity in Wages and Employment Mobility: Evidence from Danish Register Data," by Lentz, Piyapromdee, and Robin
Discussion of "The Consequences of Long-Term Unemployment: Evidence from Matched Employer-Employee Data," by Abraham, Haltiwanger, Sandusky, and Spletzer
Discussion of "Secular Labor Reallocation and Business Cycles," by Chodorow-Reich and Wieland
Discussion of "Search, Matching, and Training" by Flinn, Gemici, and Laufer
Discussion of "Marriage, Labor Supply, and Home Production" by Gousse, Jacquemet, and Robin
Discussion of "How Sticky Wages in Existing Jobs Can Affect Hiring," by Bils, Chang, and Kim
Manhattan College 2/2/2022
UT Austin 2/17/2022
Yale University 3/29/2022
U Penn 3/30/2022
MIT Sloan 4/27/2022
University of Madison 05/02/2022
Danmarks Nationalbank 05/24/2022
SIPP one-click download on GitHub: code which scrapes all the SIPP files from NBER and merges 1990-present panels
- for public use: please email us with any comments or issues
Sample code for Parallel computation in Matlab which calls Dynare
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