Job market research statement
This file is my general research statement, written at a fairly high level of abstraction; this is part of my job market file. The first chapter of my dissertation has received a Revise & Resubmit at the American Journal of Sociology. The second is approaching final submission to The Journal of Economic Inequality, and the third remains in manuscript form at present.
My research
My research re-opens the debate on U.S. income inequality. Despite the overall salience of inequality in academic research and popular discourse, many open questions remain about its extent and nature. My dissertation tackles three closely-related questions.
First, I intervene in the long-standing debate on the determinants of inequality. Using the coefficient of variation decomposition, I demonstrate that the majority contributor to inequality of incomes is capital income — essentially, income from wealth — rather than wage income, the focus of the great majority of sociological research. I show that this surprising result holds only if high-quality data sources, such as tax data or the Survey of Consumer Finances, are used, a fact obscured from the sociolocial vision in part thanks to the common use of survey data such as the Current Population Survey or General Social Survey, which only capture a truncated view of the extent and sources of income of top earners.
This is no mere technical finding: most sociological research on the topic trains its explanatory lens on labor-market mechanisms (e.g. education, skills, exposure to offshoring), leaving it unable to convincingly explain this largely-unkown empirical result: wage income is far from the whole story. I suggest that an important theoretical consequence follows: sociologists should broaden our focus beyond those labor-market mechanisms and (re-)incorporate the role of ownership relations into the analysis. While a small stream of existing research has taken such a “property relations” approach, it has done so by constructing static matrices of social classes, and it has tended to become mired in debate: “which axes should generate our class map?” is a very difficult question. I put forward a convincing alternative — instead of viewing individuals as members of fixed “classes” defined by a crude owner-employee binary, we might instead think of individuals as combining the economic functions of both workers and capitalists, even if very many persons largely spend their lives in only one of those roles. This parsimonious conceptualization of “class” as a spectrum, defined by the extent of asset ownership, allows us to avoid the murky waters of class schemes while preserving the critical insight that the unequal distribution of wealth dominates the role of the unequal distribution of labor-market characteristics in the empirical analysis.
The remaining two chapters further investigate this puzzle, broadly construed: why can we say so little with confidence about the nature of inequality? In my second chapter, I suggest that the definition of “income” itself is at the heart of this lacuna. Though contested among economists, “income” as a theoretical object is rarely addressed by sociologists. In fact, some economists suggest that expanding the definition of income to include capital income as discussed above, but also fringe benefits (such as employer-sponsored health insurance) or even “imputed rent” (the hypothetical rental value of one’s home), significantly decreases measured trends in inequality, issuing a challenge to the consensus that inequality in the United States has risen sharply in recent decades.
My second chapter addresses this challenge in two ways. First, I systematically demonstrate the marginal effects of various common ways of defining income on inequality. My research shows that, relative to the use of wages alone, expanding the definition of income in fact almost invariably produces an increased trend in inequality (over the years 1980 to 2020), in stark contrast to previous research. The paper goes on to make a theoretical contribution: I demonstrate that many of the circulating definitions of income are theoretically ambiguous. For instance, it is not clear whether to include health care benefits or forgone rent in measures of income inequality. In this chapter, I review the benefits and limitations of different measures of income, and suggest a simple, conceptually defensible measure for general use by sociologists.
Finally, in my third chapter, I move from the measurement of income to the measurement of inequality itself. Social scientists have taken a somewhat ad hoc approach to the definition and operationalization of inequality. Many measures in common use, such as the Gini coefficient, have significant limitations: for example, we cannot easily decompose such measures into identifiable contributions between and within social groups. I synthesize a scattered literature that identifies the inequality measures which satisfy axioms meaningful to social scientists in common-sense terms: that they be decomposable into sub-groups and factors, independent of scale or population size, and sensitive to redistribution of incomes. I follow by outlining a novel method for combining factor and sub-group decompositions which avoids the problem of path-dependence common in many existing approaches. I also demonstrate, however, that inequality research is inevitably contentious: even among the small class of measures which satisfy the criteria outlined above, one’s choice of an inequality measure depends on one’s aversion to inequality. I demonstrate that even small tweaks in this inequality aversion parameter can make significant changes in our assessment of which factors are responsible for inequality.
As of this writing, my first chapter is under review at the American Journal of Sociology and my second chapter is under review at the Journal of Economic Inequality. The third chapter will be submitted to Sociological Methodology in the near future. I presented the first chapter at the ASA Regular Session on Inequality, Poverty, and Mobility in 2024, for which I won the Wisconsin Scholarship Hub (WISH) Student Research Grants Competition Travel Award. This paper was also accepted for an oral presentation and won the Jaap Dronkers Travel Award “in recognition of outstanding scholarship” from RC28 in 2026. The second paper was presented at an ASA Roundtable in the Inequality, Poverty, and Mobility section in 2026.
In my future research, I plan to expand on the third chapter of my dissertation. The most interesting finding from this chapter is known to specialists, but it remains surprising and in need of further research: questions such as “what causes inequality” depend strongly on the measure of inequality selected and the question “what does it mean mathematically for $X$ to cause $Y$?” I am particularly interested in developing systematically the link between the unified framework for decomposition analysis based on the Shapley approach, with which my third paper deals, and recent research by scholars working in the potential outcomes framework. Researchers in the latter tradition have developed a robust framework for identifying causal effects in the (ubiquitous) situation where assignment to a treatment is non-random, while scholars in the former tradition have focused their attention on the problem of assigning to a given factor a particular “contribution” to inequality, depending on the inequality measure and model used. These traditions have developed largely in isolation from one another, but both fundamentally address the puzzle of “when can we say that $p$ percent of inequality ‘owes’ to some factor?”
My research focus has shifted over the course of my time in graduate school. I began my career as a researcher focused on international development, and my master’s thesis investigated the “premature deindustrialization” of Brazil and Argentina, that is, the decline in the share of industrial output and employment in the total, relative to the “normal” trajectory of economic development. I discovered in the course of this project that I was particularly interested in (and gifted in the use of) statistical methods: in particular, I realized just how hard it is to get causal questions “right” and how many different angles one might take on this problem. I also realized that I wanted my work to speak to a broader community of scholars, especially those working on the United States, where I am an active participant in public life. While this shift has lengthened the amount of time I have spent in graduate school, it has also given me a strong appreciation for a broad array of statistical methods, as well as a perspective informed by global social thought.
Finally, I should note the connection of my research to my teaching. I am an especially teaching-focused scholar, and my research is closely tied to my pedagogy. As a lecturer, I am particularly interested in helping students understand statistical methods, and methodology more generally, from the ground up: I have been successful in helping students come to see why certain fundamentals are true and why so much debate exists among scholars about essentially everything but those fundamentals. In my own research, I have found my niche by leaning into this uncertainty.