( * indicates student coauthor)
Like mother, like child? The rise of women's intergenerational income persistence in Sweden and the United States. (with Gunnar Brandén and Martin Nybom) (Accepted at the Journal of Labor Economics.)
How veteran-nonveteran wage gaps across the wage distribution have evolved over time. (with James Fuller*) (Accepted at Contemporary Economic Policy.)
Spillover bias in multigenerational income regressions. (with Jørgen Modalsli), 2024, Journal of Human Resources, 59(3), 743-776.
Understanding and evaluating the SAS® EVAAS® Univariate Response Model (URM) for measuring teacher effectiveness. (with Cassandra Guarino and Jeffrey Wooldridge), 2018, Economics of Education Review, 66: 191-205.
Is the simple Law of Mobility really a law? Testing Clark's hypothesis. 2018, Economic Journal, 128(612):F404-F421.
Intergenerational persistence in latent socioeconomic status: Evidence from Sweden. (with Martin Nybom), 2017, Journal of Labor Economics, 35(3): 869-901.
Media: Vox column
Other publications
Vosters, Kelly. 2023. "Fuhrer, Jeff. The Myth That Made Us: How False Beliefs about Racism and Meritocracy Broke Our Economy (and How to Fix It)." Journal of Economic Literature, 61 (4): 1583-84.
Single-Family Rentals as a Pathway for Access to High-Performing Public Schools. (with Tom Mayock)
(Revise and Resubmit at Real Estate Economics.)
Abstract: The stock of single-family rental (SFR) units zoned for high-performing public schools surged following the Great Recession. Using a unique dataset of linked student- housing-unit records from North Carolina and a fixed effects research design, we show that increasing the supply of SFRs zoned for high-performing public schools provides a pathway for economically disadvantaged children to attend such schools. Exploiting the exogenous variation in the allocation of SFRs across neighborhoods, we find that children in renter households realized improvements in school quality when relocating to areas with an ample supply of SFRs zoned for high-performing schools. Using school- level housing stock and performance data in conjunction with a shift-share identification strategy, we find that growth in the SFR stock in several markets throughout the U.S. increased the share of economically disadvantaged children attending top-performing schools.
Previous version: Affordable Housing, Household Sorting, and Academic Achievement
Educational Achievement Gains Afforded by Moving to Single-Family Rentals. (with Tom Mayock)
(Revise and Resubmit at Journal of Housing Economics)
Abstract: The rapidly rising supply of single-family rental homes in the US shows to be a promising avenue for low-income families to gain access to higher-quality schools. We show that not only are low-income parents taking advantage of these newly available rental units, but also that their children are experiencing substantial achievement gains from attending higher-performing schools. With our unique database of statewide student-level education records linked to housing units, we find that a one standard deviation increase in school quality leads to over a 0.5 standard deviation increase in achievement. Further, we find that the gains are not limited to certain groups of students, as Black students and previously low-achieving students also benefit. Finally, there appear to be no meaningful negative net effects on non-moving students.
The Expansion of Single-Family Rentals and Student Churn in Public Schools.
(with Tom Mayock)
Parental Responses to Changes in the Characteristics, Outcomes, and Behavior of their Children's Peers.
(with Tom Mayock)
Trends and Racial Disparities in Intergenerational Wealth Persistence in the US.
(with Lisa Schulkind.)
Measurement-Related Biases in Intergenerational Wealth Mobility Estimates.
The State of the Art in the Research on Value-Added Models of Teacher Performance: Taking Stock of What We Know and Don’t Know
(with Cassandra Guarino, Mark Reckase, Jeffrey Wooldridge, Eun Hye Ham, Michelle Maxfield, Brian Stacy, and Paul Thompson)
Abstract: In line with the general push for accountability in education reform, the federal government’s Race to the Top competition (U.S. Department of Education, 2009) promoted the adoption of test-based performance measures as a necessary component of teacher evaluations throughout many states. Currently, states are actively engaging in the development of these measures, and districts are moving toward implementing them in teacher performance evaluation, often weighting them fairly highly as a criterion in an overall evaluation scheme. Value added models (VAMs)—i.e., statistical models that take prior test scores into account—have achieved prominence in these efforts, being generally recognized as superior to models based on student scores at a single point in time. VAMs attempt to isolate the contribution of specific types of “inputs” to student learning growth, typically over the course of one year. They can focus on estimating the impact of any number of inputs—family behaviors, school resources, specific educational interventions, etc.; however, the focus of recent debate—and the focus in this paper—is on models used to determine the effectiveness of individual teachers.This paper summarizes and explains what is currently known and unknown about the ability of value-added models (VAMs) to quantify a teacher’s contribution to learning. It presents a synthesis of findings from a large project regarding methodological problems and solutions in constructing value-added measures of teacher performance using standardized test scores. We outline sources of bias and imprecision in estimating teacher effects, survey the various responses of the research community to addressing these challenges, and illustrate the consequences of making different choices with data from a large urban district.
Precision for Policy: Calculating Standard Errors in Value-Added Models
(with Andrew Bibler, Cassandra Guarino, Mark Reckase, and Jeffrey Wooldridge)
Abstract: The use of teacher value-added models to measure teacher effectiveness is expanding rapidly, with teacher value-added estimates being incorporated into teacher evaluation systems and potentially high-stakes decisions. In some settings, attempts are made to account for precision, such as when the standard errors are explicitly incorporated in constructing performance quantiles. However, we still know little about the precision of these value-added estimates, and whether the standard errors are calculated correctly when precision is addressed. The nested nature of administrative education data leads to several clustering options when computing the standard errors, and there is little or no research providing guidance as to which method is most appropriate in this particular setting. Our study aims to fill this gap in the literature. We first use simulated student achievement data to study the behavior of standard errors for teacher value-added estimates under various types of student, teacher, and school settings. We then use student-level administrative data to shed light on real world applications. Results show that the standard errors can be quite sensitive to the formula one chooses to calculate them, meaning the policy conclusions drawn from using the value-added estimates themselves may depend on that decision. Hence, knowledge of the reliability of value-added models could be a critical part of the decision making process by administrators and policy makers as well as shape future research on teacher value-added.