Skin Tones and Polarized Politics: How Skin Color Differences Between Interviewers and Respondents Influence Survey Answers in Bolivia
Bibliographic record
Abstract
Abstract American scholarship claims that the racial make-up of interviews influences the attitudes disclosed in public opinion surveys. It remains unclear whether such an effect travels to other cases where racial cleavages are less salient, and whether it affects all respondents. We address these gaps by using a flexible approach focusing on skin tone rather than race. Relying on survey data from Bolivia, where polarization maps onto ethnic lines, we investigate whether the skin color difference between an interviewer and a respondent influences the latter’s answers. Building on the race-of-interviewer effect and colorism literatures, this article investigates the effect of the skin color dynamic of interviews by leveraging the random interviewer-to-respondent assignment process of LAPOP surveys. The results suggest that nonresponses are more likely in cross-skin tone interviews and that respondents questioned by interviewers of lighter skin tone than them will express opinions that more closely align with the stereotypical opinions of the interviewer than if their interviewer shared their skin tone. This article contributes to the interviewer effect literature by testing the competing claims of the social distance and social acquiescence theories, along with providing an adaptation of the race-of-interviewer effect to cases that are not polarized along racial lines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".