Are immigrants more likely to claim benefits, or is this a stereotype?
A stereotype is a preliminary insight. A stereotype can be true, the first step in noticing differences. For conceptual economy, stereotypes encapsulate the characteristics most people have noticed. Not all heuristics are false.
Here is a relevant paper from Denmark.
Emil O. W. Kirkegaard and Julius Daugbjerg Bjerrekær. Country of origin and use of social benefits: A large, preregistered study of stereotype accuracy in Denmark. Open Differential Psychology.
This study is interesting, in that it was pre-registered, so its absence would have been noticed. It compares stereotypes against actual data to get a test of accuracy. I was particularly struck by how the authors studied the answers at each wave of data collection, and tracked down those who gave perplexing answers, then refining their survey questions to reduce misunderstandings.
The paper also points out an unremarked aspect of stereotypes: they may be too weak. Stereotypes have to show a correlation with the facts, and be good predictors. You have to get the slope right, and also the intercept. It is not enough to have a vague notion that immigrants are on benefits, you ought to be able to estimate how many are on benefits. A stronger stereotype would be a more accurate perception of reality.
A nationally representative Danish sample was asked to estimate the percentage of persons aged 30-39 living in Denmark receiving social benefits for 70 countries of origin (N = 766). After extensive quality control procedures, a sample of 484 persons were available for analysis. Stereotypes were scored by accuracy by comparing the estimates values to values obtained from an official source. Individual stereotypes were found to be fairly accurate (median/mean correlation with criterion values = .48/.43), while the aggregate stereotype was found to be very accurate (r = .70). Both individual and aggregate-level stereotypes tended to underestimate the percentages of persons receiving social benefits and underestimate real group differences.
In bivariate analysis, stereotype correlational accuracy was found to be predicted by a variety of predictors at above chance levels, including conservatism (r = .13), nationalism (r = .11), some immigration critical beliefs/preferences, agreement with a few political parties, educational attainment (r = .20), being male (d = .19) and cognitive ability (r = .22). Agreement with most political parties, experience with ghettos, age, and policy positions on immigrant questions had little or no predictive validity.
In multivariate predictive analysis using LASSO regression, correlational accuracy was found to be predicted only by cognitive ability and educational attainment with even moderate level of reliability. In general, stereotype accuracy was not easy to predict, even using 24 predictors (k-fold cross-validated R2 = 4%).
We examined whether stereotype accuracy was related to the proportion of Muslims in the groups. Stereotypes were found to be less accurate for the groups with higher proportions of Muslims in that participants underestimated the percentages of persons receiving social benefits (mean estimation error for Muslim groups relative to overall elevation error = -8.09 %points).
The study was preregistered with most analyses being specified before data collection began
The observed correlation of .7 is big, and useful. A majority of immigrants from Syria, Somalia and Kuwait are on benefits, as are those from Iraq and Lebanon. Even more to the point, if the benchmark is 25% for Danish citizens, then there are 19 countries with higher benefit rates. More positively, there are countries with lower rates, presumably because they are younger and employed. The data plot does not give us any guide to numbers from each country. However, later in the paper it is shown that immigrant population size is not relevant in judging benefit rates accurately.
The best predictor of having accurate stereotypes was cognitive ability (81% of simulations), followed by educational attainment (74% of simulations). Respondents underestimate the number of Muslims on benefits.
This is a very good paper. Data handling is exceptional, and well explained. There are lots of Figures and Tables. The sample is large and representative. The results have been looked at carefully, to identify those who participated without paying much attention to the questions. The data are available for re-analysis.
The high accuracy of aggregate stereotypes is confirmed. If anything, the stereotypes held by Danish people about immigrants underestimates those immigrants’ reliance on Danish benefits.