Legitimacy is widely invoked as a condition, cause, and outcome of other social phenomena, yet measuring legitimacy is a persistent challenge. In this article, I synthesize existing approaches to conceptualizing legitimacy across the social sciences to identify widely agreed upon definitional properties. I then build on these points of consensus to develop a generalizable approach to operationalization. Legitimacy implies specific relationships among three empirical elements: an object of legitimacy, an audience that confers legitimacy, and a relationship between the two. Together, these empirical elements constitute a dyad (i.e., a single unit consisting of two nodes and a tie). I identify three necessary conditions for legitimacy— expectations, assent, and conformity—that specify how elements of the dyad interact. I detail how these conditions can be used to empirically establish legitimacy (and illegitimacy), distinguishing it from dissimilar phenomena that often appear similar empirically. Followed to its logical conclusion, this operationalization has novel implications for understanding the effects of legitimacy. I discuss these implications, and how they inform debates over the relevance of legitimacy as an explanation for socially significant outcomes.

No abstract

This article examines the consequences of prison overcrowding litigation for U.S. prisons. We use insights derived from the endogeneity of law perspective to develop expectations about the likely impact of overcrowding litigation on five outcomes: prison admissions, prison releases, spending on prison capacity, prison crowding, and incarceration rates. Using newly available data on prison overcrowding litigation cases joined with panel data on U.S. states from 1971 to 1996, we offer a novel and comprehensive analysis of the impact that overcrowding litigation has had on U.S. prisons. We find that it had no impact on admissions or release rates and did not lead to any reduction in prison crowding. Litigation did, however, lead to an increase in spending on prison capacity and incarceration rates. We discuss the implications of these results for endogeneity of law theory, attempts to achieve reform through litigation, and the politics of prison construction.The unprecedented and unparalleled size of the U.S. prison population has received an enormous amount of scholarly attention. Incarceration has become so common and widespread, especially among African Americans, that it has left significant marks on racial inequality in labor markets, wages and health, and has reshaped citizenship and race relations in contemporary America (Alexander

The intersection of terrorism and organized crime is a central global security concern. However, the conditions that contribute to this intersection or hinder its development are widely debated. Drawing on prominent cases of ideologically driven violent nonstate actors engaged in illicit economies, some scholars argue that this intersection is a logical evolution. Other scholars, focusing on the fact that relatively few groups engage in both organized crime and terrorism, argue that ideological differences hinder this intersection. We use data on 395 terrorist organizations to analyze how organizational and environmental factors affect the likelihood of terrorist involvement in illicit drug trafficking. Our analysis shows that the degree of connectivity within networks of terrorist groups is the most significant predictor of a group engaging in drug trafficking. Further, contrary to the theorized effects of ideology, an explicit religious ideology has no significant effect while an ethnopolitical ideology actually increases the likelihood of drug trafficking.

No abstract

No abstract

No abstract

We contend that clusters of cases co-constitute statistical interactions among variables. Interactions among variables imply clusters of cases within which statistical effects differ. Regression coefficients may be productively viewed as sums across clusters of cases, and in this sense regression coefficients may be said to be ''composed'' of clusters of cases. We explicate a four-step procedure that discovers interaction effects based on clusters of cases in the data matrix, hence aiding in inductive model specification. We illustrate with two examples. One is a reanalysis of data from a published study of the effect of social welfare policy extensiveness on poverty rates across 15 countries. The second uses General Social Survey data to predict four different dimensions of ego-network homophily. We find support for our contention that clusters of the rows of a data matrix may be exploited to discover statistical interactions among variables that improve model fit.