The U.S. labor market literature relies, for the most part, on government survey data, which is invaluable for identifying economywide trends and differentials but lacks the detail needed about tasks and occupations to detect changes in how work is organized. This project will use two datasets assembled by Burning Glass Technologies containing millions of job postings and millions of resumes. The data offer the scope and scale to make fine distinctions and explore the array of skills embodied in workers and the tasks called out by employers.
This project has three aims. First, it will examine variation within occupational categories and connections across categories. Given the growth of within-occupational inequality, understanding within-occupation heterogeneity in skills and other attributes is extremely important. Second, it will use resume data to examine worker mobility. This is a novel approach and is likely to offer important insights about labor market mobility. Third, it will investigate job quality, including nonwage benefits and employment relations, and the incidence of nonstandard work (temporary, on call, and contract based). Nonstandard work is not regularly measured in government surveys, so exploring whether job posting data can offer insight into the prevalence of nonstandard work arrangements and whether such arrangements are associated with job mobility and access to nonwage benefits, as well as differences in wage levels between similar occupations with different employment relations, could lay the groundwork for future research.
There is an enormous amount of interest recently in the heterogeneous effects of monetary policy, with an eye on questions such as whether monetary policy contributes to inequality. This research will examine two significant research questions within this literature: the effect of monetary policy on inequality and differential effects of the credit transmission channel.
This project will use Portuguese administrative data that matches employer-employee data with credit registry data. The project is likely to lead to fine-grained estimates of the effects of monetary policy on the distribution of labor income, as well as on the effects of the distribution of firm credit outcomes.
This project will explore the problem of implementing federal tax policy aimed at encouraging work when U.S. labor markets are imperfectly competitive. More specifically, this researcher will study the effect and optimal design of wage subsidies through the Earned Income Tax Credit in monopsonistic labor markets. In estimating the incidence of the EITC on both workers and firms, this research seeks to understand whether the purported inequality-combatting benefits of this federal tax credit are undermined by the capture of a large portion of the subsidy by employers. Because labor market power is known to vary considerably across place, and because exposure to concentrated markets varies by race and gender, this research will help to benchmark the effects of labor market power on places and demographic groups.
This project examines the level of alignment between employer and employee beliefs about the accessibility of paid leave. It takes advantage of a unique data source, The Shift Project, which samples low-wage, service-sector workers from within a set of large retail and food service employers across the United States and allows the team to match employee responses with individual employers. The research will pair quantitative analyses of Shift Project data and in-depth interview data from interviews with twenty human resources staff members at firms in the sample to provide important information about how employer practices may mediate awareness and take-up of paid leave benefits.
This project will examine the effect of both state-provided paid medical leave and city- and state-level sick pay mandates on the provision of paid leave. The proposed project will use restricted-use National Compensation Survey data with geographic identifiers and a difference-in-differences approach to determine whether employers react to the mandated provision of sick leave and state paid leave social insurance programs by reducing their voluntary provision of medical leave, private group disability insurance, and other forms of paid leave such as family leave. No other study has comprehensively studied the interactions and interdependencies of state-level sick pay mandates, employer provisions of paid leave, and state-run medical leave systems.
This project seeks to understand how access to paid family leave influences the provision of eldercare and labor market outcomes among individuals in midlife and whether the effects vary by individual and care recipient characteristics. To examine these issues, the research team will pool data from 11 waves of the Health and Retirement Survey to examine the experiences of respondents aged 51 to 65 with at least one living parent. They will survey responses to determine the intensity of caregiving provided, as well as the intensity of labor force participation, and use a difference-in-differences approach to compare the experiences of individuals residing in states with operational paid leave social insurance programs (California, New Jersey, and Rhode Island) to those who reside elsewhere.
This research projects aims to identify the characteristics of individuals who have a family member who experiences the onset of disability or health shock but lack access to paid caregiving leave. The investigators will also estimate the impact of access to paid caregiving leave on financial security and employment for this group of individuals. The research team will use data from the National Compensation Survey to develop a machine-learning classification model that will be used to determine the likelihood that individuals observed in the Survey of Income and Program Participation have access to paid leave. This novel technique overcomes limitations of existing data sources that have hamstrung previous research efforts and poises the project to make a significant contribution to the small but growing body of research on caregiving leave.
This project seeks to determine the feasibility of a paid family and medical leave insurance (PFMLI) program with an opt-out mechanism. While state PFMLI programs in the United States vary somewhat in program parameters, all essentially require eligible workers to participate and are funded through payroll contributions. Some allow employers to self-insure and opt out of the public program. Generally, employees do not have that option. But proposed legislation in New Hampshire would create a program that contains an employee opt-out mechanism. There is a large gap in understanding of this design choice. Which individuals would opt out? And which would opt out initially and then opt in later? And how would that interact with the employer self-insurance opt-out? The answers have lasting implications for the cost of the program, the level of benefits that can be offered, the possibility of implicit or explicit bias against low-wage women workers of reproductive age, and the ultimate success of the program. The researcher will create, distribute, and analyze a survey of New Hampshire workers in an effort to predict their behavior if such a program were to be implemented.
This project seeks to identify the impact of workforce characteristics on the employer costs of implementing paid leave policies. The researchers will perform an empirical examination of the way New York City’s paid sick days law has affected covered businesses. They will undertake an employer survey of 350 firms, analyze the impact of workforce characteristics on costs, firm profitability, the share of the workforce that has access to paid sick days, and on the number of paid sick days available to specific categories of workers before and after the law.
This research will analyze how state-level paid family and medical leave and Medicaid expansions influence drug-related outcomes. The research team will draw from a variety of administrative data sources, including restricted individual-level mortality data from the Centers for Disease Control; data from the Healthcare Cost and Utilization Project’s National Inpatient
Sample and Nationwide Emergency Department Sample; and the Treatment Episode Data Set-Admissions. Drawing from these rich data sources in combination with information on population demographic characteristics and state policies, the team will employ a variety of causal inference techniques to examine whether access to paid family and medical leave can help reduce the abuse of opioids. While a large literature has examined the impacts of paid family and medical leave on parental leave-taking, labor market outcomes, and child health, there is no research to date on whether it can influence drug abuse and treatment. Similarly, there is little research on the effect of Medicaid coverage on drug-related deaths or measures of drug-related morbidity.
