Bibliographic record
Abstract
We study how individual political views shape firm behavior and labor market outcomes. Using new micro-data on the political affiliation of business owners and private-sector workers in Brazil over the 2002-2019 period, we first document the presence of political assortative matching: business owners are significantly more likely to employ copartisan workers. Political assortative matching is larger in magnitude than assortative matching along gender and racial lines. We then provide three sets of results consistent with the presence of employers' political discrimination. First, several patterns in the micro-data and an event study are consistent with a discrimination channel. Second, we conduct an incentivized resume rating field experiment showing that owners have a direct preference for copartisan workers opposed to workers from a different party. Third, we conduct representative large-scale surveys of owners and workers revealing that labor market participants view employers' discrimination as the leading explanation behind our findings. We conclude by presenting evidence suggesting that political discrimination in the workplace has additional real consequences: copartisan workers are paid more and are promoted faster within the firm, despite being less qualified; firms displaying stronger degrees of political assortative matching grow less than comparable firms.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".