Robust identities or nonentities? Typecasting in the feature-film labor market
Citation: Ezra W. Zuckerman, Tai-Young Kim, Kalinda Ukanwa, James von Rittmann (2003) Robust identities or nonentities? Typecasting in the feature-film labor market. The American Journal of Sociology (RSS)
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Tagged: Sociology (RSS) typecasting (RSS), categories (RSS), cateogry pressures (RSS)
Zuckerman et al. can be seen as an explicit attempt to pit two contradictory findings from the sociological literature on category pressures. The first finding is the result, shown for example by Padgett and Ansell (1993) in Robust action and the rise of the Medici, 1400-1434, that complex and multivalent or multi-vocal identities are useful in that they add greater flexibility. The second finding is, for example shown by Zuckerman (1999) in The categorical imperative: Securities analysts and the illegitimacy discount, that actors with clear simple, focused identities that are easy to categorize will do better. The authors suggest that clear identities will be useful in gaining entry into a environment but will, over time, result in a limitations.
The authors use to test this basic theory using data on typecasting in the labor market for Hollywood films. The suggest that the positive effect of being typecast (i.e., folks will be more likely to get jobs in the same genre) will offset the negative benefit (i.e., they will be less likely get jobs in other genres). They also suggest several contingency hypotheses. For example, they will suggest that the overall positive benefit of typecasting (described similarly) is lower among veterans.
The authors present two types of data. The first was an extensive set of interviews of agents, actors, casting directors, and student actors. The interviews seemed to confirm the basic hypotheses that typecasting was good in that made actors "a known quantity" but ultimately imposed limitations that would keep actors from reading the highest levels. They suggested that typecasting was a "double edged sword."
The second dataset is one that the authors assembled from the Internet Movie Database (IMDB) and essentially measure the degree to which being typecast is associated with career success. Typecasting was measured simply by genre (i.e., did a person act in the same genre film). They ask if authors are more or less likely to get jobs in different or the same genres and pay particular attention to the interaction between their measure of typecasting and the actors tenure in the market.
The authors found that the results from a series of multinomial logistic regression models were broadly consistent with the hypotheses laid about above.