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Categorization processes are generally treated as consistent mappings of the underlying characteristics that they group. Yet, in many cases, the identities of actors influence these processes. When identity matters, high-status actors often obtain more favorable classifications. We examine these processes in the context of the Motion Picture Association of America's parental guidance classifications of movies (G, PG, R, NC-17). We find that, conditional on a given level of content, films distributed by the Association's members and those that involve more central producers and directors receive more lenient classifications than those carried by independent distributors and involving more peripheral personnel. Conversely, and again conditional on content, films involving directors with a history of producing R-rated features receive more restrictive ratings. We discuss the mechanisms that might account for these effects. Regardless of the mechanism, however, because ratings influence revenue and consequently profitability, the movie certification system in the United States places independent distributors and peripheral individuals at a disadvantage, relative to their larger and more central rivals.

What types of human and social capital identify the emergence of leaders of open innovation communities? Consistent with the norms of an engineering culture, we find that future leaders must first make strong technical contributions. Beyond technical contributions, they must then integrate their communities in order to mobilize volunteers and avoid the ever-present danger of forking and balkanization. This is enabled by two correlated but distinct social positions: social brokerage and boundary spanning between technological areas. An inherent lack of trust associated with brokerage positions can be overcome through physical interaction. Boundary spanners do not suffer this handicap and are much more likely than brokers to advance to leadership. The research separates the influence of human and social capital on promotion, and highlights previously unexamined differences between brokerage- and boundary-spanning positions. Longitudinal analyses of careers within the Internet Engineering Task Force community from 1986–2002 support the arguments.

This study uses data on the U.S. film industry from 1982 to 2001 to analyze the effects on box office performance of prior relationships between film producers and distributors. In contrast to prior studies, which have appeared to find performance benefits to both buyers and sellers when exchange occurs embedded within existing social relations, we propose that the apparent mutual advantages of embedded exchange can also emerge from endogenous behavior that benefits one party at the expense of the other: actors offer better terms of trade and allocate more resources to transactions embedded within existing social relations, thereby contributing to the ostensible advantages of such exchange patterns. Findings show that not only do distributors exhibit a preference for carrying films involving key personnel with whom they had prior exchange relations, but also they tend to favor these films when allocating scarce resources (opening dates and promotion effort). After controlling for the effects of these decisions, films with deeper prior relations to the distributor perform worse at the box office. The results suggest that, rather than benefiting from repeated exchange, distributors overallocate scarce resources to these prior exchange partners, enacting a self-confirming dynamic.

Is ethnic social diversity relevant to cross-national variation in economic development, or is the inclusion or exclusion of said groups in political decision-making the more salient factor? We argue that deleterious policy effects resulting in diminished economic growth are caused by exclusion of mobilized ethnic groups from the policy process and not just ethnic social diversity per se. Conversely, a positive impact of ethnicity as more groups are included in the policy process with increasing access to cabinet is due, first, to the fact that a population finding its preferences represented in the policy process likely supports implementation of resultant policy. Second, the policy quality likely improves with greater variety in input. Third, a greater number of included ethnic groups in cabinet increases the number of ethnic partisan veto players in the policy process — thereby generating increased policy stability in the long term. We test this idea first on long-run growth in democracy and, second, on annual indicators of growth. We find that increasing ethnic social fragmentation still negatively impacts on the economy. However, cabinet diversity offsets some of these negative effects as it improves growth of GDP per capita.

The article introduces the All Minorities at Risk (AMAR) data, a sample of socially recognized and salient ethnic groups. Fully coded for the forty core Minorities at Risk variables, this AMAR sample provides researchers with data for empirical analysis free from the selection issues known in the study of ethnic politics to date. We describe the distinct selection issues motivating the coding of the data with an emphasis on underexplored selection issues arising with truncation of ethnic group data, especially when moving between levels of data. We then describe our sampling technique and the resulting coded data. Next, we suggest some directions for the future study of ethnicity and conflict using our bias-corrected data. Our preliminary correlations suggest selection bias may have distorted our understanding about both group and country correlates of ethnic violence.

The article introduces the All Minorities at Risk (AMAR) data, a sample of socially recognized and salient ethnic groups. Fully coded for the forty core Minorities at Risk variables, this AMAR sample provides researchers with data for empirical analysis free from the selection issues known in the study of ethnic politics to date. We describe the distinct selection issues motivating the coding of the data with an emphasis on underexplored selection issues arising with truncation of ethnic group data, especially when moving between levels of data. We then describe our sampling technique and the resulting coded data. Next, we suggest some directions for the future study of ethnicity and conflict using our bias-corrected data. Our preliminary correlations suggest selection bias may have distorted our understanding about both group and country correlates of ethnic violence.