The following collection of research papers studies a variety of inter-related topics in labor economics. Coupling such a scope of the field of labor economics with the broad set of empirical tools used herein provides a solid foundation for conducting high-quality economic analyses of policy-relevant issues.
A large literature has shown that economics has a pervasive diversity problem; women and minorities are underrepresented throughout all areas of the economics profession as college graduates, graduate students, and professors; as representatives on governing bodies; at conferences as authors, chairs, and discussants; and in publications as authors, editors, and representative examples. An existing literature elucidates the importance of diversity and attempts to quantify the benefits of inclusion. We focus on measuring and describing the dynamics of one area: employment of PhD economists. Whereas much of the existing literature on diversity of economists focuses on the academic market, we bring a broader perspective and focus on multiple labor markets, including the federal government, in this descriptive paper. We combine information on PhD economists from multiple sources with detailed labor market information from the Longitudinal Employer-Household Dynamics (LEHD) database to provide a more formal accounting of the gender and racial distribution, pay, and career trajectories of economists.
Most applied research in labor economics that examines returns to worker skills or differences in earnings across subgroups of workers typically accounts for the role of occupations by controlling for occupational categories. Researchers often aggregate detailed occupations into categories based on the Standard Occupation Classification (SOC) coding scheme, which is based largely on narratives or qualitative measures of workers’ tasks. Alternatively, we propose two quantitative task-based approaches to constructing occupational categories by using factor analysis with O*NET job descriptors that provide a rich set of continuous measures of job tasks across all occupations. We find that our task-based approach outperforms the SOC-based approach in terms of lower occupation distance measures. We show that our task-based approach provides an intuitive, nuanced interpretation for grouping occupations and permits quantitative assessments of similarities in task compositions across occupations. We also replicate a recent analysis and find that our task-based occupational categories explain more of the gender wage gap than the SOC-based approaches explain. Our study enhances the Federal Statistical System’s understanding of the SOC codes, investigates ways to use third-party data to construct useful research variables that can potentially be added to Census Bureau data products to improve their quality and versatility, and sheds light on how the use of alternative occupational categories in economics research may lead to different empirical results and deeper understanding in the analysis of labor market outcomes.
This paper involves a field experiment wherein we use a random encouragement design in conjunction with administrative data to estimate the causal effects of student participation in a globally used, yet poorly evaluated, academic support program in higher education, namely Supplemental Instruction (SI). Universities frequently provide SI in college courses with high D/F/W rates, including economics, as a means of addressing college accessibility, retention and completion. Proponents make claims that: (1) participation substantively raises students' grades; and (2) students sub-optimally participate because of behavioral biases. Consistent with a model of limited attention, we find that well-timed informational reminders increase participation. Reminders increased participation at a cost less than $1 USD per additional session attended. Attending an additional SI session is predicted to increase grade by about 0.03 quality-points or 0.03 standard deviations; a smaller effect than often claimed by proponents. Nevertheless, SI is relatively inexpensive and students can attend multiple SI sessions, thus implying it can be a cost-effective intervention for improving academic performance in difficult introductory courses. Moreover, we find no differential treatment effect across racial or ethnic subgroups, suggesting that SI can help all students.
Bullying is a persistent and prevalent problem. Researchers have studied correlates of bullying and have provided legislative recommendations to address bullying in schools. Examining whether, and to what extent, state laws reduce in-school bullying is important for several reasons beyond providing guidance for federal legislation that would unify state laws. Bullying can be financially problematic for schools. An economics literature suggests bullying may have serious negative labor market consequences by disrupting cognitive and non-cognitive skill development. Bullying during youth may carry over into adulthood and have longer-run impacts within households, labor markets, and the larger economy. I conduct student- and school-level policy evaluations using variation across states in the timing and type of law adopted in conjunction with data from large, nationally representative surveys. Quasi-experimental results indicate that state legislation may have no effect on bullying in high schools, however, bullying occurs most often in middle schools. Estimated policy impacts also differ by school type and by legislation type. Lastly, current challenges facing research on bullying are revealed.
This paper examines the role of wage penalties for caring work in explaining the gender wage gap. Simply put, caring work involves helping and caring for others and is done primarily by women. Our preferred estimates suggest caring wage penalties of about 2% for a one standard deviation increase in our caring index. We find little evidence for systematic differences in caring penalties across the wage distribution. Even were caring penalties erased from the labor market, there would be minimal closing of the gender wage gap.