Abstract
Several studies have found a relationship between trust and attitudes toward immigrants and refugees. Following Uslaner and others, we make a distinction between different kinds of trust and reject the idea that it must be analyzed solely as an element of social capital. It is predicted that generalized trust or trust in people who are different from us is a predictor of attitudes toward refugees. Generalized trust (as well as racist values) is established early in life. It is expected that variables that do not depend on experience will be better predictors of attitudes toward refugees than those that are influenced by experience. We explore these hypotheses with data from Portugal and Spain collected in the frame of the seventh wave of the European Social Survey. Regional particularities than can be masked in studies with heterogeneous samples can make a difference in the explanation of attitudes. Data analyses rely on standard regression tools.
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Notes
- 1.
Though validity is a theoretical concept, it can be explored empirically if the three items about whether most people can be trusted, try to take advantage of you and are helpful are indicators of the same latent construct. Unfortunately, we only can use exploratory tools here because a confirmatory factor analysis with only three observed indicators is just identified. As suggested by Crepaz and others (2014, p. 8), a two-factor CFA with six additional items of institutional trust as indicators of factor 2 does not fit the data (CFI = 0.906; TLI = 0.87; N = 2770), but this is not a decisive proof. Cronbach’s alpha for the three items is a modest 0.647, but this statistic is strongly influenced by the number of items included. An exploratory principal components analysis clearly identifies a single latent factor that explains 58,756% of the variance, but the KMO is again a modest 0.646. Our exploratory approach does not allow to test the decisive hypothesis about the internal structure of the tree items about trust.
- 2.
A strong relationship between attitudes towards immigrants in general and refugees in particular is expected. The ESS questionnaire includes in its seventh wave the following questions about immigrants: “Immigration bad or good for country’s economy”; “Country’s cultural life undermined or enriched by immigrants”; “Immigrants make country worse or better place to live”; “Immigrants take jobs away in country or create new jobs”; “Taxes and services: immigrants take out more than they put in or less”; and “Immigrants make country’s crime problems worse or better”. All items can be answered using 11-point ordinal scale of substantive answers. Information in these items can be reduced to one clear single factor using principal components analysis (KMO = 0.85; p for Bartlett’s test <0.0005; variance explained = 54.547%). The questionnaire includes the following item too: “Allow many/few immigrants of different race/ethnic group from majority”, which is measured at the ordinal level with just four potential substantive answers. As expected, there is a strong correlation between our measure of attitudes towards refugees on the one hand and attitudes towards immigrants using the latent principal component based on six observed items (r = 0.355; p < 0.0005; N = 2602) and the item about out-group immigrants (r = −0.287; p < 0.0005; N = 2894). This exercise adds confidence in the validity of our dependent variable.
- 3.
Some assumptions of the ordinary least squares model seem to have been violated (Greene 2012), though they have no impact regarding our hypothesis. There is proof of a certain degree of heterocedasticity (p for Breusch-Pagan test <0.05), but heterocedasticity-consistent standard errors (HC2 and HC3) are almost the same as in the original estimates in Model 3. When analyses are repeated removing, with the sole idea of checking their effects, potential problematic observations, for example those exceeding a value of ±2 in standardized residuals; or ±2√p/n in DFFIT, findings do not significantly depart from the ones described in the text.
I have relied on ordinary least squares (OLS) or linear regression. Our dependent variable, though, is not measured at the continuous level and only has five categories of response. In these cases it is common to use OLS when its assumptions are reasonably met. There are alternative analytical tools to regress limited dependent variables measured at the ordinal level, though (O’Connell 2006). Analysis has been repeated via ordinal regression for Model 3 with findings equivalent to those of OLS.
- 4.
The fact that we have not found statistically significant differences in the coefficients of institutional and generalized trust when joining together all independent variables in Model 3 suggests that there is no interaction between them. The inclusion of a multiplicative term in Model 3, though, is marginally significant (b = −0.036; p = 0.077; N = 1783). With the additional term, though, the Model does not show any significant improvement (R 2 adj = 0.107; AIC = −475,947; BIC = −344,269).
- 5.
Differences in AIC are not significant from a statistical point of view.
- 6.
This result is not due to the different number of observations used in each model. When analyses are repeated for the same, unique subsample of observations with complete information, Model 2 (R 2 adj = 0.088; AIC = −463,398; BIC = −386,556; N = 1787) still significantly outperforms Model 1 (R 2 adj = 0.083; AIC = −435,942; BIC = −326,168; N = 1787).
- 7.
In fact, watching news on TV (b = 0.042; p < 0.0005) and interaction (b = 0.037; p < 0.0005) (but not watching general TV or satisfaction with economy) correlate with attitudes in bivariate analysis, which suggests that their effects are mediated by other variables.
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Serrano-Maillo, A. (2018). Generalized Trust and Attitudes Toward Refugees in Portugal and Spain. In: Kury, H., Redo, S. (eds) Refugees and Migrants in Law and Policy. Springer, Cham. https://doi.org/10.1007/978-3-319-72159-0_18
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