ABSTRACT Conventional wisdom states that women are less willing than men to apply for a job for which they feel only partly qualified. Is this due to gender differences in anticipated returns to meeting or exceeding the desired level of qualification for a job? In a series of studies, I investigate whether men and women rate more and less qualified candidates’ chances of being hired differently. In the lab, I elicit beliefs about callback and offer likelihood by having subjects “bet” on the outcomes of other applicants' job searches. In a stylized online labor market experiment, I observe subjects' job application decisions and elicit beliefs regarding how qualified they will appear to a recruiter. Across studies, I find that women anticipate the same or greater returns than men to moving from ``not at all'' to ``somewhat'' qualified for a position, but the same or lower returns to moving from ``somewhat'' to fully or ``highly'' qualified. Controlling for gender differences in willingness to rate one's own or others' resumes as qualified does not change the pattern of results. Consistent with these findings, women in my experiment do not differ from men in how likely they are to apply if they fulfill some, but not all, of the listed qualifications in a job posting.

ABSTRACT Research shows that, holding qualifications equal, women are less willing than men to apply for certain high-paying jobs. I experimentally investigate whether the “gender application gap” for high-paying jobs depends on the presence or magnitude of application costs. I randomly vary the cost of applying for such a job: subjects in an online stylized labor market face no marginal cost, pay a fee, or write a cover letter. Men are significantly more likely than equally qualified women to apply for a job only when the marginal cost of applying is zero. This arises from gender differences in self-selection: women prefer not to apply when unskilled regardless of costs, whereas unskilled men drop out only when tangible costs are introduced. Women face a higher intangible cost from applying for a job they’re likely to perform poorly at, especially if the job is in a stereotypically “male-typed” domain. While fees modestly increase women's share of hires through applicant self-selection, cover letters reduce women's share of hires and appear to bias recruiters against women applicants.

When workers from underrepresented groups choose not to apply to “higher risk, higher reward” jobs, income gaps arise. Workers may rationally avoid jobs where they’d be disproportionately punished for failure, and employer bias informs how employers interpret negative productivity signals from workers of different identities. Socialized group differences in traits such as risk aversion or other-regarding preferences also influence job choice. Our proposed research aims to answer: Do workers from underrepresented groups take disproportionate action to avoid failure on the job, such as not taking on “higher risk, higher reward” work? If so, what proportion of any resultant gaps in job choice are due to anticipated discrimination versus socialized differences in behavioral traits? We propose running an experiment in which we randomly vary the potential consequences for failure on the job, and observe how this affects job choices for workers from different demographic groups.

Employment status classification is core to major U.S. household surveys, including the Current Population Survey (CPS) – the official source of U.S. unemployment statistics –as well as the American Community Survey (ACS) and the Survey of Income and Program Participation (SIPP). Historically, these surveys have produced differing estimates of unemployment, and previous non-experimental work finds the wording of questions on job search contributes to these differences. In particular, approaches differ in the implicit assumption of whether respondents understand what it means to “actively” look for work. We randomly vary the questions used to measure job search activity in a large online survey, and show differences in reported job search (and thereby in unemployment rates) by approach. Our design also allows us to estimate bias in survey-based job search rates, based on within-person inconsistencies in responses. We present differences in observed inconsistencies across treatments and demographic groups, providing insight into one source of heterogeneity in unemployment estimates. Differences in question interpretation across demographic groups may affect the size of measured unemployment gaps.

Early job search outcomes have lasting effects on lifetime earnings trajectory. I gather a new data set of resumes, search activities, and search outcomes of business school undergraduates looking for summer internships, an important pipeline to full-time employment in this setting. I find that business students whose parents also hold business degrees are twice as efficient at internship search as equally qualified "first generation" business students, in terms of offers received per hour spent searching. In a lab study, I test whether "second+ generation" business students are more knowledgeable about the early career labor market. I use data on business students' resumes and the internships they applied for to elicit other students’ job market beliefs, employing a incentivized “crossover mechanism” that has subjects bet on the outcomes of applicants' job searches. I find evidence that second+ generation business students hold more accurate beliefs about the job market compared to other students, in that they are more successful at identifying applicants who received offers. This gap reflects potential intergenerational transmission of labor market knowledge from parents to children.

ABSTRACT Increasingly, international trade policy analysis explores the economic effects of changes in ad-valorem tariffs or equivalent nontariff measures on vertically integrated markets for which high quality data are unavailable. Standard Constant Elasticity of Substitution (CES) Armington models fail to account for either vertical linkages or parameter uncertainty. Here, we introduce a vertically integrated, nested two-sector Armington model that incorporates uncertainty in the estimates of Armington elasticities through Monte Carlo simulation. As an illustrative case, we model the effects of changes in country of origin labeling (COOL) rules on the market shares of cattle in the U.S. beef market. By accounting for parameter uncertainty in this way, we are able to estimate the distribution of potential effects of repealing mandatory COOL. Ultimately, we predict that, in all but the most extreme cases, Mexico and Canada would not gain as much market share from the repeal of mandatory COOL as they claim in their World Trade Organization (WTO) filings against the regulation.