Ghbors. The survey used a split-ballot design in Boston and Los Angeles, such that each respondent had a 1/3 probability of being shown a particular vignette out-group. The data include three measures of racial residential preferences. First, for each of the five neighborhood vignettes, each respondent is asked whether he or she would move into that neighborhood. (Whites were asked if they would move out of the neighborhood.) The data consist of five binary responses, each one corresponding to a different proportion own-group and featured out-group. Second, respondents were asked to rank the five vignettes in order of attractiveness. Finally, respondents were given another card with the same configuration of 14 empty houses, but asked to assign each house to one of the four race-ethnic groups according to his or her “ideal” neighborhood composition. Exact wording of these three types of questions is shown in Appendix A. The second of these three types of response provide a full ranking of alternatives. The binary responses to the “would you move in/out” question, provide a partial ranking of the five neighborhoods. The neighborhoods that the respondent would move into are ranked higher than the ones that the respondent would not move into, but the relative desirability beyond this dichotomy is unknown to the analyst. The ideal neighborhood ethnic configuration response indicates that the chosen configuration is preferred to all other possible configurations, but the relative desirability of the configurations that were not chosen is unknown to the analyst. These data have been analyzed using a variety of approaches, including descriptive statistics, OLS regression, and categorical response models of various types (e.g., Farley 1978; Farley et al. 1993; Farley et al. 1994; Charles 2000, 2005; Krysan and Farley 2002). Although these analyses have been informative, they typically do not make full use of information available in the data. In contrast to these approaches, the discrete choice models proposed in this paper make full use of the quantitative information about race-ethnic composition in these data, allow for full examination of complex interactions among raceethnic groups, generalize to data that include more dimensions of neighborhood variationSociol Methodol. Author manuscript; available in PMC 2013 March 08.Bruch and MarePagethan just race-ethnic makeup, and provide a natural Beclabuvir web comparison to analyses of actual residential choices..NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptThe MCSUI vignettes only contain information on neighborhood racial composition; all other neighborhood characteristics are ignored. Thus, it is difficult to know whether to interpret these data as representing the degree of an individual’s true ethnic “tolerance” or a response to other neighborhood characteristics associated with race (e.g., crime, school quality, and housing costs) (Quillian 1995; Harris 1999). Emerson et al. (2001) use vignette neighborhoods that vary along a number of dimensions: school quality, ethnic composition, Chloroquine (diphosphate) web property values, and crime rate. They find that, after controlling for non-race/ethnic neighborhood characteristics, whites’ aversion to predominantly a Hispanic or Asian neighborhood is no longer statistically significant in their sample, but whites’ apparent aversion to black neighborhoods remains. Krysan et al. (2009) construct video vignettes that vary the race of actors portraying neighborhood.Ghbors. The survey used a split-ballot design in Boston and Los Angeles, such that each respondent had a 1/3 probability of being shown a particular vignette out-group. The data include three measures of racial residential preferences. First, for each of the five neighborhood vignettes, each respondent is asked whether he or she would move into that neighborhood. (Whites were asked if they would move out of the neighborhood.) The data consist of five binary responses, each one corresponding to a different proportion own-group and featured out-group. Second, respondents were asked to rank the five vignettes in order of attractiveness. Finally, respondents were given another card with the same configuration of 14 empty houses, but asked to assign each house to one of the four race-ethnic groups according to his or her “ideal” neighborhood composition. Exact wording of these three types of questions is shown in Appendix A. The second of these three types of response provide a full ranking of alternatives. The binary responses to the “would you move in/out” question, provide a partial ranking of the five neighborhoods. The neighborhoods that the respondent would move into are ranked higher than the ones that the respondent would not move into, but the relative desirability beyond this dichotomy is unknown to the analyst. The ideal neighborhood ethnic configuration response indicates that the chosen configuration is preferred to all other possible configurations, but the relative desirability of the configurations that were not chosen is unknown to the analyst. These data have been analyzed using a variety of approaches, including descriptive statistics, OLS regression, and categorical response models of various types (e.g., Farley 1978; Farley et al. 1993; Farley et al. 1994; Charles 2000, 2005; Krysan and Farley 2002). Although these analyses have been informative, they typically do not make full use of information available in the data. In contrast to these approaches, the discrete choice models proposed in this paper make full use of the quantitative information about race-ethnic composition in these data, allow for full examination of complex interactions among raceethnic groups, generalize to data that include more dimensions of neighborhood variationSociol Methodol. Author manuscript; available in PMC 2013 March 08.Bruch and MarePagethan just race-ethnic makeup, and provide a natural comparison to analyses of actual residential choices..NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptThe MCSUI vignettes only contain information on neighborhood racial composition; all other neighborhood characteristics are ignored. Thus, it is difficult to know whether to interpret these data as representing the degree of an individual’s true ethnic “tolerance” or a response to other neighborhood characteristics associated with race (e.g., crime, school quality, and housing costs) (Quillian 1995; Harris 1999). Emerson et al. (2001) use vignette neighborhoods that vary along a number of dimensions: school quality, ethnic composition, property values, and crime rate. They find that, after controlling for non-race/ethnic neighborhood characteristics, whites’ aversion to predominantly a Hispanic or Asian neighborhood is no longer statistically significant in their sample, but whites’ apparent aversion to black neighborhoods remains. Krysan et al. (2009) construct video vignettes that vary the race of actors portraying neighborhood.
