variation in the presence of exclusionary values is, in part, related to country-specific factors. In contrast, the associations between the socio-economic characteristics of households and individuals’ exclusionary attitudes are weaker than the associations between country-specific factors (history, culture, and other factors that do not vary across households from the same country) and exclusionary attitudes. These results are novel in indicating that three distinct attitude types may drive exclusionary behavior and that country-specific factors are more important in explaining the prevalence of these attitudes than individual-level characteristics. The findings could have important implications for theories about exclusion and for the design of relevant social inclusion policies.

# **Three distinct clusters of intolerant attitudes**

We begin the analysis by examining clustering among the pairwise correlations of attitudes (see Table 2). A distinct pattern emerges among the correlations. If someone says she does not want to have a gypsy (Roma) as neighbor, chances are she will also not want to have persons of another race or language group, religion, and country of origin (e.g. immigrants) as neighbors. That is, there is a fairly high pairwise correlation of 0.2 to 0.4 between not wanting any of these groups as neighbors.<sup>6</sup> A similar finding emerges for other indicators: for example, there are high correlations between not welcoming drug users, drinkers, and homosexuals. Based on the correlations, three broad clusters of ‘intolerance attitudes’ emerge<sup>7</sup> :

- Cluster 1: Intolerance for the poor and for different lifecycle stages: not wanting families with children, the poor and the elderly as neighbors.

- Cluster 2: Intolerance toward stigmatized attributes and behaviors: not wanting drug users, pedophiles, heavy drinkers, homosexuals, and cohabitating unmarried couples as neighbors.

- Cluster 3: Intolerance to different ethnic, religious, and linguistic identity groups: not wanting people of a different race, people speaking a different language, immigrants, people who belong to a different religion, Jewish people, or gypsies (Roma) as neighbors.<sup>8</sup>

Next we use principal component analysis to examine whether the twelve indicators of unwelcome neighbor attitudes load on to one distinct measure or separate components. With one exception, the estimates identify the same three distinct components as the simple bivariate correlations discussed above.<sup>9</sup> Thus, each of the three components – intolerance for the poor or different lifecycle states,

> 6 We cross-checked with other parts of the LiTS survey, including with questions about what kind of people can be trusted. There is a high degree of correspondence in that people who feel that foreigners, people of another religion, or from a different ethnic group cannot be trusted are likely to also not want to have such people as neighbors.

> 7 The distinctions are largely based on patterns of pairwise correlations, but in some cases a judgment was made so as to maintain similarities across the clusters.

> 8 Not surprisingly, given the broad nature of these groups, a high share of respondents have a non-zero value of the three indices of intolerance attitudes: around 10% of respondents have a non-zero value of the index of intolerance for the poor and for different lifecycle stages; the same is true for around 95% of respondents in the case of intolerance toward behavioral traits (91% if intolerance of pedophiles is excluded) and for around 85% in the case of intolerance on ethnic, religious, or linguistic identity lines.

> 9 We dropped attitudes toward unmarried cohabitating couples from our indices, as it did not load on to any of the three factors.

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