My research focuses on topics in labor economics and applied microeconomics. In particular, I study how educational and career dynamics are affected by public policy. Much of my work considers how policies affect the acquisition of human capital and the role of cognitive and non-cognitive skills in the labor market.
My published research includes work on the General Educational Development (GED) test, educational choice, the returns to education, the role of cognitive and non-cognitive skills in later-life outcomes, and the combination of skills measured by grades and achievement tests.
My Job Market Paper studied the determinants and consequences of entry into and exit from self-employment over the life cycle. I find that pre-existing skills and career dynamics are important determinants of what types of businesses individuals start, how much capital they employ, and how long they remain in self-employment. Subsidies that incentivize self-employment are generally ineffective, both in terms of promoting long-lasting firms and in terms of the welfare and earnings of those induced to enter self-employment.
This paper investigates the determinants and consequences of entry into and exit from self-employment over the life cycle. It integrates traditional models of dynamic career choice that feature human capital investment with models of business start-up that feature costly capital investment. Applying machine learning methods to matched worker-firm data from Sweden, I isolate seven distinct patterns of participation in self-employment as part of broader life-cycle employment profiles. These patterns are rationalized using a dynamic Roy model with both human capital and physical capital. Using structural methods, I estimate the model and use it to evaluate policies designed to promote self-employment. Cognitive and non-cognitive skills, education, and past work experience are important determinants of which types of businesses individuals start, how much capital they employ, and how long they remain in self-employment. Subsidies that incentivize self-employment are generally ineffective, both in terms of promoting long-lasting firms and in terms of improving the welfare and earnings of those induced to enter self-employment.
This paper analyzes the non-market benefits of education and ability. Using a dynamic model of educational choice we estimate returns to education that account for selection bias and sorting on gains. We investigate a range of non-market outcomes including incarceration, mental health, voter participation, trust, and participation in welfare. Unlike previous evidence on the monetary benefits of education, the benefits to education for many non-market outcomes appear to be larger for low-ability individuals. College graduation decreases welfare use, lowers depression, and raises self-esteem more for less-able individuals. Accounting for the non-market benefits of education is an important component of any analysis of educational policy.
Across academic sub-fields such as labor, education, and behavioral economics, the measurement and interpretation of non-cognitive skills varies widely. As a result, it is difficult to compare results on the importance of non-cognitive skills across literatures. Drawing from these literatures, this paper systematically relates various prototypical non-cognitive measures within one data set. Specifically, we estimate and compare several different strategies for measuring non-cognitive skills. For each, we compare their relative effectiveness at predicting educational success and decompose what is being measured into underlying personality traits and economic preferences. We demonstrate that the construction of the non-cognitive factor greatly influences what is actually measured and what conclusions are reached about the role of non-cognitive skills in life outcomes such as educational attainment. Furthermore, we demonstrate that, while sometimes difficult to interpret, factors extracted from self-reported behaviors can have predictive power similar to well established taxonomies, such as the Big Five.
Does the college major premium reflect returns to innate abilities, prior skills, or college education? We decompose the college major premium into labor market returns to cognitive and non-cognitive abilities, and skills learned in college. This allows us to quantify how much of the college major premium is due to sorting on abilities and how much is due to the differential labor market value of major-specific skills. We find that sorting on abilities accounts for 10-50% of the college major premium. We also provide novel estimates of complementarities and interaction effects between abilities and skills, since both the returns to abilities and prior skills vary significantly across college majors. We document that 40% of students who enter STEM degrees change major or drop out. We evaluate counterfactual policies to promote STEM degrees, accounting for the the fact that many who start STEM degrees do not finish. (The project is funded by Riksbankens Jubileumsfond through the Stockholm School of Economics).
In Cook County, IL, more than 35,000 eviction cases appear before the circuit court every year, the majority involving tenants from the poorest areas in Chicago. Prior research suggests that eviction may not only be a symptom of poverty but may, in fact, cause or exacerbate poverty by contributing to circumstances that are adverse to economic mobility. Yet those facing eviction are likely to have recently faced negative economic shocks, which makes establishing the proposed causal relationship difficult. This paper proposes the first quasi-experimental design for evaluating the causal impact of eviction on employment, social, and schooling outcomes. Using over 400,000 eviction case histories, our research design leverages Cook County's random assignment of eviction court cases to judges, where some judges are more lenient than others. This provides a source of exogenous variation in eviction outcomes, allowing us to study the effect of eviction on a wide range of short- and long-run household outcomes associated with poverty. This project has been selected as part of the ``Using Linked Data to Advance Evidence-Based Policy making" initiative in partnership with the Census Bureau and the Arnold Foundation to facilitate policy evaluation through linking records to Census Bureau microdata.
This paper studies the reallocation of highly skilled labor following the 2001 ``dot-com’’ crash in Sweden. The crash resulted in bankruptcy or substantial downsizing in many technology and communication related businesses. Many of these businesses were also subject to ``last-in-first-out'' hiring policies under Swedish law, requiring businesses to fire the most recently hired workers first. The companies affected by the dot-com crash disproportionately employed young high-skill individuals with STEM or engineering degrees. By studying the mass lay-offs and subsequent reallocation of young highly skilled workers, this paper jointly evaluates how labor market shocks impact the careers of high skill workers and how the reallocation of highly skilled labor affects industry growth and the creation of new businesses. The data allows this research to be conducted at the population level and provides rich information on education, IQ, non-cognitive skills, and leadership ability.
This paper estimates returns to education using a dynamic model of educational choice that synthesizes approaches in the structural dynamic discrete choice literature with approaches used in the reduced form treatment effect literature. It is an empirically robust middle ground between the two approaches which estimates economically interpretable and policy-relevant dynamic treatment effects that account for heterogeneity in cognitive and non-cognitive skills and the continuation values of educational choices. Graduating college is not a wise choice for all. Ability bias is a major component of observed educational differentials. For some, there are substantial causal effects of education at all stages of schooling.
This paper develops robust models for estimating and interpreting treatment effects arising from both ordered and unordered multi-stage decision problems. Identification is secured through instrumental variables and/or conditional independence (matching) assumptions. We decompose treatment effects into direct effects and continuation values associated with moving to the next stage of a decision problem. Using our framework, we decompose the IV estimator, showing that IV generally does not estimate economically interpretable or policy-relevant parameters in prototypical dynamic discrete choice models, unless policy variables are instruments. Continuation values are an empirically important component of estimated total treatment effects of education. We use our analysis to estimate the components of what LATE estimates in a dynamic discrete choice model.
The option to obtain a General Educational Development (GED) certificate changes the incentives facing high school students. This article evaluates the effect of three different GED policy innovations on high school graduation rates. A 6-point decrease in the GED pass rate produced a 1.3-point decline in high school dropout rates. The introduction of a GED certification program in high schools in Oregon produced a 4% decrease in high school graduation rates. Introduction of GED certificates for civilians in California increased the dropout rate by 3 points. The GED program induces students to drop out of high school.
This paper discusses and illustrates identification problems in personality psychology. The measures used by psychologists to infer traits are based on behaviors, broadly defined. These behaviors are produced from multiple traits interacting with incentives in situations. In general, measures are determined by these multiple traits and do not identify any particular trait unless incentives and other traits are controlled for. Using two data sets, we show, as an example, that substantial portions of the variance in achievement test scores and grades, which are often used as measures of cognition, are explained by personality variables.
Intelligence quotient (IQ), grades, and scores on achievement tests are widely used as measures of cognition, but the correlations among them are far from perfect. This paper uses a variety of datasets to show that personality and IQ predict grades and scores on achievement tests. Personality is relatively more important in predicting grades than scores on achievement tests. IQ is relatively more important in predicting scores on achievement tests. Personality is generally more predictive than IQ on a variety of important life outcomes. Both grades and achievement tests are substantially better predictors of important life outcomes than IQ. The reason is that both capture personality traits that have independent predictive power beyond that of IQ.
The General Educational Development (GED) credential is issued on the basis of an eight hour subject-based test. The test claims to establish equivalence between dropouts and traditional high school graduates, opening the door to college and positions in the labor market. In 2008 alone, almost 500,000 dropouts passed the test, amounting to 12% of all high school credentials issued in that year. This chapter reviews the academic literature on the GED, which finds minimal value of the certificate in terms of labor market outcomes and that only a few individuals successfully use it as a path to obtain post-secondary credentials. Although the GED establishes cognitive equivalence on one measure of scholastic aptitude, recipients still face limited opportunity due to deficits in noncognitive skills such as persistence, motivation and reliability. The literature finds that the GED testing program distorts social statistics on high school completion rates, minority graduation gaps, and sources of wage growth. Recent work demonstrates that, through its availability and low cost, the GED also induces some students to drop out of school. The GED program is unique to the United States and Canada, but provides policy insight relevant to any nation's educational context.
Objective: To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research.
Methods: The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches.
Results: The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%.
Conclusions: Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.