The Evolution of Access to Public Accommodations in the United States
Bibliographic record
Abstract
Abstract The economic analysis of racial discrimination in public accommodations is remarkably limited. To study this issue, we construct a national data set of nondiscriminatory establishments from the Negro Motorist Green Books, a travel guide published from 1936 to 1966 to aid Black Americans in finding nondiscriminatory retail and service establishments. We document patterns in the geographic spread and evolution of Green Book establishments, as well as the correlates of Green Book presence. We find that economic and social measures, as well as state laws relating to racial discrimination and antidiscrimination, were correlated with the provision of nondiscriminatory services. We then use the Green Book data to test whether market conditions and white consumer discrimination led businesses to bar Black customers prior to the Civil Rights Act of 1964. We use plausibly exogenous variation from white World War II casualties and Black migration patterns to isolate the effect of a change in the racial composition of consumers on the growth of nondiscriminatory businesses. We find that the share of nondiscriminatory establishments grew faster in locations with larger increases in the share of the Black population, but the magnitudes were small. These results highlight the importance of federal legislation in ending racial discrimination in public accommodations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".