Aleksei Opacic

Welcome! I am a PhD candidate in Sociology at Harvard University. My research examines how formal and informal linkages between higher education and the labor market shape inequalities among college graduates. My projects have studied how colleges channel students into firms and jobs, how employers evaluate and recruit talent, and how the fit between graduates’ skills and workplace tasks affects earnings. This work combines computational methods with administrative and proprietary data, often through collaborations with state agencies and firms. In related work, I develop computational and causal inference methods to examine the mechanisms through which education and work shape economic inequality.

1. Disparity Analysis: A Tale of Two Approaches. (with Lai Wei and Xiang Zhou) Journal of the Royal Statistical Society, Series A: Statistics in Society, 00, 1–29 [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 in- tervention to one or more manipulable treatments. For the descriptive approach, we propose a generalized KOB decomposition that considers multiple (sets of) explanatory variables sequen- tially. 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 treat- ment. 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.  
2. 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.  

3. The Skill-Task Gap: Skill Transferability and Labor Market Alignment
   • Geoffrey Tootell Outstanding Dissertation-in-Progress Award, Mathematical Sociology Section, American Sociological Association
4. From Flagship to Firm: Gatekeeping, Employer Sorting, and the Returns to College. [Abstract]
   
   Firms increasingly drive college graduates' labor market earnings, yet the vast literature on the value-added of college has largely overlooked their role. Drawing on human capital theory and institutional theories of credentialism, closure, and organizational matching, I argue that college-to-workplace pipelines are a critical driver of college value-added. To test this, I assemble a novel US employer-employee matched dataset merged with postsecondary and high school academic records. Firm placement explains much of the variation in earnings premiums between colleges. Absent firm sorting, the range of counterfactual earnings differences across colleges would fall by 56%, and over half of the earnings advantage to attending the state flagship comes from access to higher-paying employers. These sorting effects extend broadly across the distribution of high-wage firms, and not merely a handful of elite employers. Crucially, sorting effects do not simply reflect skill-based advantages, implying that college quality derives as much from institutional linkages to the labor market as from human capital development. Policies aimed at broadening recruitment pipelines, rather than solely improving instructional inputs, are therefore essential to reducing inequalities in the economic returns to higher education.
    
5. Monotonic Path-Specific Effects: Application to Estimating Educational Returns. [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.
   Revise & Resubmit (2nd) 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
6. Marginal Interventional Effects. (with Xiang Zhou) Reject & Resubmit at 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