Aleksei Opacic

Welcome! I am a PhD candidate in Sociology at Harvard University. I study how organizations determine the value of human capital and shape access to skills and credentials. By opening up these organizational processes, my work brings theory from economic sociology to core questions of social stratification: how education structures individual life chances, economic inequality, and mobility across generations. My projects have examined how colleges channel students into firms and jobs, how mismatch between skills and workplace tasks generates earnings inequality, how employers evaluate and recruit talent, and how alternative college admissions policies would affect mobility. To make these processes visible, I develop novel computational and causal inference approaches to measure workers’ skills, organizational effects, and career trajectories, and build administrative data linkages that follow individuals from college into the workplace.

1. Where Do the Returns to Schooling Come From? Educational Transitions and Labor Market Payoffs. [Abstract]
   
   Conventional research on educational effects typically either employs a “years of schooling” measure of education, or dichotomizes attainment as a point-in-time treatment. Nevertheless, such a conceptualization of education is at odds with the sequential process by which individuals make educational transitions. In this paper, I propose a causal mediation framework for the study of educational effects on outcomes such as earnings. The framework considers the effect of a given educational transition as operating indirectly, via progression through subsequent transitions, as well as directly, net of these transitions. I demonstrate that the average treatment effect (ATE) of education can be additively decomposed into mutually exclusive components that capture these direct and indirect effects. The decomposition has several special properties which distinguish it from conventional mediation decompositions of the ATE, properties which facilitate less restrictive identification assumptions as well as identification of all causal paths in the decomposition. An analysis of the returns to high school completion in the NLSY97 cohort suggests that the payoff to a high school degree stems overwhelmingly from its direct labor market returns. Mediation via college attendance, completion and graduate school attendance is small because of individuals' low counterfactual progression rates through these subsequent transitions.
   Accepted at Annals of Applied Statistics
   • Clifford C. Clogg Graduate Student Paper Award, Methodology Section, American Sociological Association
   • Outstanding Graduate Student Paper Award, Mathematical Sociology Section, American Sociological Association
2. Disparity Analysis: A Tale of Two Approaches. (with Lai Wei and Xiang Zhou) Journal of the Royal Statistical Society Series A: Statistics in Society, 2026, 189, 805–833 [Abstract]
   
   To understand the patterns and trends of various forms of inequality, quantitative social science research has typically relied on statistical models linking the conditional mean of an outcome of interest to a range of explanatory factors. A prime example of this approach is the widely used Kitagawa-Oaxaca-Blinder (KOB) method. By fitting two linear models separately for an advantaged group and a disadvantaged group, the KOB method decomposes the between-group outcome disparity into two parts, a part explained by group differences in a set of background characteristics and an unexplained part often dubbed “residual inequality.” In this paper, we explicate and contrast two distinct approaches to studying group disparities, which we term the descriptive approach, as epitomized by the KOB method and its variants, and the prescriptive approach, which focuses on how a disparity of interest would change under a hypothetical intervention to one or more manipulable treatments. For the descriptive approach, we propose a generalized KOB decomposition that considers multiple (sets of) explanatory variables sequentially. For the prescriptive approach, we introduce a variety of stylized interventions, such as lottery-type and affirmative-action-type interventions that close between-group gaps in treatment. We illustrate the two approaches to disparity analysis by assessing the Black-White gap in college completion, how it is statistically explained by racial differences in demographic and socioeconomic background, family structure, ability and behavior, and college selectivity, and the extent to which it would be reduced under hypothetical reallocations of college-goers from different racial/economic backgrounds into different tiers of college --- reallocations that could be targeted by race- or class-conscious admissions policies.  
3. Absolute Income Mobility Obscures Marginalized Children’s Disadvantages (with Deirdre Bloome). Proceedings of the National Academy of Sciences, 121 (25). [Abstract]
   
   Intergenerational mobility captures the distance between the socioeconomic positions of parents versus their adult children. Researchers measure this distance in absolute and relative units, such as absolute dollars and relative ranks. Absolute and relative mobility often diverge. For example, absolute mobility can rise while relative mobility declines. How should scholars and policymakers understand this divergence? We conclude that they should understand it as follows: absolute mobility is less reflective than relative mobility of marginalized children's socioeconomic disadvantages. We base this conclusion on analyses of survey, administrative, and simulated data on income mobility in the contemporary United States. We analyze multiple points of difference in mobility, which facilitates the recognition of several asymmetries. First, high-income children's experiences weigh more heavily in absolute-mobility trends than low-income children's experiences, particularly when economic growth is positive. Second, this asymmetry is more characteristic of absolute- than relative-mobility trends. Third, absolute-mobility differences across demographic groups are more prone than relative-mobility differences to obscure marginalized groups' socioeconomic disadvantages. These asymmetries have policy implications: We caution that focusing on absolute mobility as a policy target can divert attention away from society's most disadvantaged children.  

4. Who Gets to Use Their College Skills? Skill-Job Alignment and Earnings Inequality Among College Graduates. [Abstract]
   
   Graduates who do not work in an occupation typical for their field of study suffer severe earnings penalties. But as labor markets increasingly reward transferable skills and many four-year degrees prepare students for broad rather than occupation-specific careers, these major-to-occupation pathways capture only one way graduates may use their education. To capture broader forms of skill utilization in the labor market, I develop a framework of transferable human capital and measure skill-task match: the alignment between graduates' transcript-derived skill profiles and their job tasks. Empirically, I construct the first individual-level linkage between college course-catalog records and students' jobs using administrative transcript and employment data from a U.S. state. Leveraging this dataset, I document three findings. First, there is substantial variation in the extent to which graduates use their skills at work. Second, graduates who do work in skill-aligned jobs earn significantly more than their peers, even within the same occupation. Third, there are large demographic disparities in access to skill-aligned work; critically, these disparities account for a full quarter of the Black–White earnings gap. Common education-to-work pathways are one route into skill-utilizing work, but much of higher education's economic value—and inequalities in access to that value—depends on skill transfer across occupational boundaries.
   Revise & Resubmit at American Sociological Review
   • Geoffrey Tootell Award, Mathematical Sociology Section, American Sociological Association

5. From Flagship to Firm: Employer Sorting and the Organizational Returns to College. [Abstract]
   
   Why do some colleges generate substantially higher earnings returns for their graduates than others? To explain educational returns, economists and sociologists have focused on education's productivity-enhancing and occupational-sorting components. Both mechanisms center on factors that shift individuals across occupational and pay boundaries within organizations, while treating employers as interchangeable settings. Instead, I argue that much of education's value comes from college-to-employer linkages that shape access to high-paying workplaces. To test this, I assemble a novel US linked employer-employee earnings dataset merged with academic records. Firm placement accounts for much of colleges' value-added. Occupational and skill-based sorting account for approximately half of colleges' firm advantages; repeated college–employer recruitment relationships and spatial access to local employer ecologies explain much of the remaining variation. A college's value-added is not simply an internal feature of the institution, but a relational outcome shaped by its position in the broader organizational structure of the labor market.
   • Ronald Burt Student Paper Award, Economic Sociology Section, American Sociological Association, Honorable Mention
6. Marginal Interventional Effects. (with Xiang Zhou) Resubmitted to Statistical Science [Abstract]
   
   Conventional causal estimands, such as the average treatment effect (ATE), reflect how the mean outcome in a population or subpopulation would change if all units received treatment versus control. Real-world policy changes, however, are often incremental, changing the treatment status for only a small segment of the population who are at or near “the margin of participation.” To capture this notion, two parallel lines of inquiry have developed in economics and in statistics and epidemiology that define, identify, and estimate what we call interventional effects. In this article, we bridge these two strands of literature by defining interventional effect (IE) as the per capita effect of a treatment intervention on an outcome of interest, and marginal interventional effect (MIE) as its limit when the size of the intervention approaches zero. The IE and MIE can be viewed as the unconditional counterparts of the policy-relevant treatment effect (PRTE) and marginal PRTE (MPRTE) proposed in the economics literature. However, different from PRTE and MPRTE, IE and MIE are defined without reference to a latent index model, and, as we show, can be identified either under unconfoundedness or through the use of instrumental variables. For both scenarios, we show that MIEs are typically identified without the strong positivity assumption required of the ATE, highlight several “stylized interventions” that may be of particular interest in policy analysis, discuss several parametric and semiparametric estimation strategies, and illustrate the proposed methods with an empirical example.
7. Assumption Smuggling in Intermediate Outcome Tests of Causal Mechanisms. (with Matthew Blackwell and Ruofan Ma) [Abstract]
   
   Political scientists are increasingly attuned to the promises and pitfalls of establishing causal effects. But the vital question for many is not if a causal effect exists but why and how it exists. Even so, many researchers avoid causal mediation analyses due to the assumptions required, instead opting to explore causal mechanisms through what we call intermediate outcome tests. These tests use the same research design used to estimate the effect of treatment on the outcome to estimate the effect of the treatment on one or more mediators, with authors often concluding that evidence of the latter is evidence of a causal mechanism. We show in this paper that, without further assumptions, this can neither establish nor rule out the existence of a causal mechanism. Instead, such conclusions about the indirect effect of treatment rely on implicit and usually very strong assumptions that are often unmet. Thus, such causal mechanism tests, though very common in political science, should not be viewed as a free lunch but rather should be used judiciously, and researchers should explicitly state and defend the requisite assumptions.
 

8. Matchmakers and Gatekeepers: AI Recommendations and Racial Disparities in Hiring

9. How Social Class Influences Political Choices (with Geoffrey Evans). Cambridge Handbook of Political Psychology, eds. Danny Osborne and Chris G. Sibley. Cambridge University Press. pp. 382-398. [Abstract]
   
   This chapter examines research from political science, sociology and psychology to understand how and why class position is associated with differences in political attitudes and choices. After reviewing influential definitions of social class, we examine research on explanations of class differences in political behavior. These include class differences in identity, values and self-interest, authoritarianism and control. We then review explanations for changes in political choices between classes over time, focusing on supply-side versus demand-side interpretations of declining levels of class voting in Western democracies, and emphasizing the effects of party programmatic convergence on the political relevance of class-based values. Finally, we assess recent interpretations of the rise of working class support for the radical right political support, pointing to the limitations of status threat accounts, and noting that such political choices are more powerfully associated with educational attainment than with class position. We consider whether this suggests a reconfiguration of electoral behavior from a class-based to an educational divide.
    

disparity: an R package for descriptive and prescriptive disparity analysis