Research on incarceration has focused on prisons, but jail detention is far more common than imprisonment. Jails are local institutions that detain people before trial or incarcerate them for short sentences for low-level offenses. Research from the 1970s and 1980s viewed jails as “managing the rabble,” a small and deeply disadvantaged segment of urban populations that struggled with problems of addiction, mental illness, and homelessness. The 1990s and 2000s marked a period of mass criminalization in which new styles of policing and court processing produced large numbers of criminal cases for minor crimes, concentrated in low-income communities of color. In a period of widespread criminal justice contact for minor offenses, how common is jail incarceration for minority men, particularly in poor neighborhoods? We estimate cumulative risks of jail incarceration with an administrative data file that records all jail admissions and discharges in New York City from 2008 to 2017. Although New York has a low jail incarceration rate, we find that 26.8% of Black men and 16.2% of Latino men, in contrast to only 3% of White men, in New York have been jailed by age 38 y. We also find evidence of high rates of repeated incarceration among Black men and high incarceration risks in high-poverty neighborhoods. Despite the jail’s great reach in New York, we also find that the incarcerated population declined in the study period, producing a large reduction in the prevalence of jail incarceration for Black and Latino men.

In forced-choice conjoint experiments, respondents choose between two options, each characterized by a set of randomized attributes. Political scientists and sociologists increasingly implement this kind of design, almost always to capture respondents’ preferences. In so doing, they routinely rely on a single quantity of interest—the average marginal component effect (AMCE). The AMCE, however, not only captures preferences, it also captures a compositional effect reflecting the distribution of the pool of options. As a result, when the goal is to infer preferences, as is almost always the case, the quantity estimated is not the quantity of interest. This paper shows why the AMCE does not identify preferences and proposes a novel estimand—the average marginal component effect on preferences (AMCEP)—designed for this purpose. It presents a general method for estimating this quantity and illustrates its use and interpretation by replicating a classic forced-choice conjoint experiment on attitudes toward immigrants.

Scholarship on the consequences of racial/ethnic diversity often claims that diversity undermines trust, participation and cooperation. This work has been criticized for its inability to discern the causal effects of diversity. We draw attention to a more elementary issue: most studies are unable to interpret associations between their outcomes of interest and diversity, as these may be due to associations with non-White or immigrant shares. We make the practical and theoretical case for preserving the distinction between diversity—i.e., mixture—and non-White or immigrant shares—e.g., percentage Black or percentage foreign-born—, and we warn scholars about the dangers of using language and measures associated with diversity, especially in contexts, like North America and Europe, where diversity strongly overlaps with non-White or immigrant shares. On a practical front, the policy recommendations that follow from the claim “greater diversity is associated with less prosociality” are different from those that follow from the claim “greater non-White share is associated with less prosociality among Whites.” On a theoretical front, most studies of diversity rely on theories that are predicated on ingroup/outgroup shares—most commonly intergroup conflict/threat theory—rather than on diversity; the predictions implied by popular theories, however, contradict those implied by claims about diversity. Importantly, two empirical obstacles undermine our capacity to disentangle associations with diversity from associations with non-White or immigrant shares. The first stems from the underrepresentation of predominately non-White (or predominately immigrant) communities in the real world. The second concerns our ability to infer that individual attitudes and behavior are correlated with diversity from correlations between macro-level outcomes and diversity. Finally, we spell out the kinds of data required to draw empirically sound conclusions about associations between diversity and social outcomes. With an appendix by Daniel Lacker.

Forced-choice conjoint experiments have become a standard component of the experimental toolbox in political science and sociology. Yet the literature has largely overlooked the fact that conjoint experiments can be used for two distinct purposes: to uncover respondents’ multidimensional preferences, and to estimate the causal effects of some attributes on a profile’s selection probability in a multidimensional choice setting. This paper makes the argument that this distinction is both analytically and practically relevant, because the quantity of interest is contingent on the purpose of the study. The vast majority of social scientists relying on conjoint analyses, including most scholars interested in studying preferences, have adopted the average marginal component effect (AMCE) as their main quantity of interest. The paper shows that the AMCE is neither conceptually nor practically suited to explore respondents’ preferences. Not only is it essentially a causal quantity conceptually at odds with the goal of describing patterns of preferences, but it also does generally not identify preferences, mixing them with compositional effects unrelated to preferences. This paper proposes a novel estimand—the average component preference—designed to explore patterns of preferences, and it presents a method for estimating it.

We explore what people mean by “diversity” when they use the term to describe real communities. “Diversity” can refer to multiple differences—ethnoracial, economic, and so on. It may also refer to multiple dimensions of the same difference, that is, heterogeneity or group representation. Analyzing a survey of Chicago-area residents, we ask: (1) When people describe a community as diverse, on which kinds of differences are they drawing? (2) Within each relevant difference, are evaluations of diversity predicted by heterogeneity, the share of specific groups, or both? Findings suggest that respondents associate diversity primarily with a community’s ethnoracial attributes and secondarily with its economic attributes. Within ethnoracial attributes, both heterogeneity and the share of disadvantaged ethnoracial groups, especially Blacks, predict assessed diversity. Within economic attributes, income inequality predicts assessed diversity, albeit negatively; the representation of poor people does not. Qualitative responses reveal varied understandings of diversity while confirming the dominance of ethnoracial attributes.