No magic bullet: estimating anti-immigrant sentiment and social desirability bias with the item-count technique

Abstract

Extant scholarship on attitudes toward immigration and immigrants relies mostly on direct survey items. Thus, little is known about the scope of social desirability bias, and even less about its covariates. In this paper, we use probability-based mixed-modes panel data collected in the Southern Spanish region of Andalusia to estimate anti-immigrant sentiment with both the item-count technique, also known as list experiment, and a direct question. Based on these measures, we gauge the size of social desirability bias, compute predictor models for both estimators of anti-immigrant sentiment, and pinpoint covariates of bias. For most respondent profiles, the item-count technique produces higher estimates of anti-immigrant sentiment than the direct question, suggesting that self-presentational concerns are far more ubiquitous than previously assumed. However, we also find evidence that among people keen to position themselves as all-out xenophiles, social desirability pressures persist in the list-experiment: the full scope of anti-immigrant sentiment remains elusive even in non-obtrusive measurement.