Research shows how important college is to upward economic mobility. Yet there are many barriers to getting into and completing college, most notably cost. Community colleges are frequently touted as a cost-effective path, whereby students begin at a community college and then transfer to a 4-year university. This research focuses on transfer students and Wisconsin’s Promise Tuition grants, a place-based scholarship which offers debt-free tuition assistance. Over the past decade, more and more states and postsecondary institutions are offering such grants, yet there is virtually no research that focuses on their impact on transfer students, particularly transfer students’ degree completion. This project explores the intersection between transfer students, their perceptions related to college finances, and the design of Promise Tuition scholarships and grants by using a mixed methods study. The first part utilizes student-level administrative data from the University of Wisconsin to examine course-taking patterns, credits attempted and completed, Grade Point Average, persistence rates, financial aid eligibility and receipt, and degrees conferred. The second part is a survey of a random sample of transfer students in order to elicit information regarding college experiences and finances. This rich case study promises to inform not only policy debates around college affordability and completion, but also our understanding of how the institutional structures of postsecondary education in the United States are supporting or inhibiting intergenerational mobility.

Previous research indicates that wealth inequality in the United States has increased since the mid-20th century and is much higher than income inequality. Wealth inequality is particularly worrisome since wealth provides many advantages, including securing against shocks and transferability to the next generation. Yet despite the relevance of wealth for our understanding of inequality and mobility, available data on wealth inequality is limited. This project will make an important contribution by drawing on tax data linked to external data on housing equity to overcome the limitations of survey data and by linking these data across generations within families and by generating geographic aggregates at small-scale geographical levels. This will allow the author to answer pressing questions, such as how concentrated wealth is locally and the stickiness of the wealth distribution across generations.

A clearer picture of U.S. intergenerational mobility is emerging for the latter part of the 20th century, but the same is not true for earlier in the century. This project is a massive data undertaking that will produce a database of mobility rates going back to 1900. Previous work, most notably the American Opportunity Study, links U.S. Census Decennials from 1940–2000. This project makes some important extensions. It will expand the feasible linkages back to 1900 so that the panel spans the entire 20th century. Perhaps the most important contribution is the use of the Social Security Numerical Identification Files, or SS-5s, which contain information obtained from the application for a Social Security card for more than 40 million individuals who died prior to 2007 and include substantially more information on individuals than U.S. Census records, increasing the number of linkages and the quality of those linkages. In particular, the wealth of information in a single record is vastly superior to a Census-to-Census linking process and will better facilitate linkages within families, including for married women who have changed their names, improving the representation of women and racial and ethnic minorities. This will allow the researchers to study differences across space (states), as well as differences by race and gender.

This project will examine how Unemployment Insurance policies interact with job search behavior in dual-earner households in the United States. More specifically, the researcher will explore the impacts of an expansion of Unemployment Insurance to include workers who leave their jobs due to their spouse getting a job that requires relocation. Using the National Longitudinal Survey of Youth from 1979 and 1997, and the Survey of Income and Program Participation from 2008, this study will seek to explain how and if access to Unemployment Insurance influences whether households will migrate long distances and attempts to measure if access to Unemployment Insurance is associated with higher wages after the household moves. The findings have the potential to inform our understanding of the gender wealth gap and women’s labor force participation, as well as geographic mobility, which has been declining in recent decades.

This study will seek to explore the consequences of diversity programs on recipients’ individual-level labor market outcomes. Using an audit study, the researcher will examine how Black university diversity scholarship recipients fare when seeking entry-level jobs after graduation in comparison to other Black job applicants. The author then will link these results to a survey of hiring professionals to understand the social and psychological phenomena that may explain the differential treatment of diversity scholarship recipients. The author also will use administrative data from a California postdoctoral program to compare post-Ph.D. outcomes of diversity scholarship recipients to applicants who were closely considered but did not receive the scholarship. The study could add to our understanding of the unintended consequences of diversity initiatives, which may inadvertently stigmatize recipients, and the broader implications of efforts to create more access and more means of support for scholarship recipients of color.

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.