The Green Books and the Geography of Segregation in Public Accommodations
Bibliographic record
Abstract
Jim Crow segregated African Americans and whites by law and practice. The causes and implications of the associated de jure and de facto residential segregation have received substantial attention from scholars, but there has been little empirical research on racial discrimination in public accommodations during this time period. We digitize the Negro Motorist Green Books, important historical travel guides aimed at helping African Americans navigate segregation in the pre-Civil Rights Act United States. We create a novel panel dataset that contains precise geocoded locations of over 4,000 unique businesses that provided non-discriminatory service to African American patrons between 1938 and 1966. Our analysis reveals several new facts about discrimination in public accommodations that contribute to the broader literature on racial segregation. First, the largest number of Green Book establishments were found in the Northeast, while the lowest number were found in the West. The Midwest had the highest number of Green Book establishments per black resident and the South had the lowest. Second, we combine our Green Book estimates with newly digitized county-level estimates of hotels to generate the share of non-discriminatory formal accommodations. Again, the Northeast had the highest share of non-discriminatory accommodations, with the South following closely behind. Third, for Green Book establishments located in cities for which the Home Owner’s Loan Corporation (HOLC) drew residential security maps, the vast majority (nearly 70 percent) are located in the lowest-grade, redlined neighborhoods. Finally, Green Book presence tends to correlate positively with measures of material well-being and economic activity. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".