Professor Humphries is recruiting one or more Fellows to work on the following projects:
Eviction, Rental Markets, and Housing Instability of the Working Poor
This project consists of a set of projects on the boundary between labor economics and the public economics aimed at studying evictions and housing instability among the urban poor. The predoctoral fellow would be working on a set of projects building on Humphries, Mader, Tannenbaum, and van Dijk (2019), which studies the effects of eviction in Cook County Illinois.
First, the research would involve working on a new project studying the impacts of eviction across several major US cities, and studying how recent policy changes around the eviction process affect the number and impact of evictions. This research is highly policy relevant given the on-going debate around eviction policy and housing instability.
Second, the researcher would contribute to a new project studying the increased concentration of ownership in rental markets in many US cities. Since the great recession, the ownership of rental properties in many cities has become more concentrated. Such concentration may lead to reduced operation costs for the owners, but may also increase market power. The recent concentration may be particularly salient for low-income renters who may be riskier tenants, but who may also have few other rental options.
The predoc hired for this job would work closely with my coauthors and me to answer a broad set of questions related to urban rental markets. As part of this project, the predoctoral fellow would have the opportunity to develop a broad array of research-related skills through working with a number of large administrative and proprietary data sets, documenting institutional details, and conducting empirical analyses.
The Role of Noncognitive Skills in Education and Labor Market Trajectories
This project consists of a set of studies in labor economics and the economics of education focused on the measurement and importance of non-cognitive skills in education and labor market outcomes. This research agenda broadly aims to understand how education career experience fosters non-cognitive skills such as conscientiousness and grit, and how those skills go on to affect labor market success. The predoctoral fellow would work closely with me and my coauthors to contribute to three new and on-going projects.
First, the predoc would work on a new project aimed at understanding how relative importance of cognitive and non-cognitive skills varies over workers’ careers. Evidence suggests that some non-cognitive skills may be less important for earnings early in a worker’s career, but that their relative importance increases with experience. This project aims at understanding this phenomenon, and to evaluate the importance of non-cognitive skills in promotion and career switching.
Second, the predoc would be integrated into a new project working with a company that prescreens highly skilled workers looking for jobs. The project aims to evaluate the effectiveness of modern psychometric methods to measure non-cognitive skills in a high-stakes environment, and to then evaluate the importance of these skills in the placement, retention, and promotion of workers. Based on the initial evidence, the project then plans to study which non-cognitive skills companies appear to screen on, and if those skills are indeed related to retention and promotion of hired workers. Third, the predoc would be working on a number of ongoing projects aimed estimating the returns to education among highly educated workers.
If hired, the predoctoral fellow would have the opportunity to develop a broad array of research-related skills through working with large data sets, applying econometric and machine learning tools, working with collaborators from other disciplines, and writing up and presenting results.
A love of working with data—cleaning it, understanding it, and presenting it in enlightening ways—is essential for this position. Methodological interests in labor economics, public economics, econometrics, machine learning, and statistics are also a big plus.
Candidates should have quantitative and coding skills, especially experience in general purpose languages like Python and statistical languages like Matlab, R, or Stata. Candidates experienced working with R, Stata, and Latex are preferred. Candidates need not be economics majors, though they should have experience with economics. We welcome applicants from other fields such as, but not limited to, computer science, engineering, mathematics, physics, political science, psychology, and statistics.