Bibliographic record
Abstract
A large body of evidence suggests that social interactions causally influence individuals' economic decisions (e.g. Duo and Saez, 2003; Bayer, Ross and Topa, 2008; Dahl, Loken and Mogstad, 2014). This finding implies that differences in the social environment faced by members of different races â in particular, differences in social norms and in the characteristics of social networks - may help perpetuate racial inequality. In this dissertation, I present two papers that attempt to understand how these differences in the social environment are created and reinforced. In the first chapter, I assess the influence of media role models on black educational attainment by examining the impact of a popular 1980's sitcom: The Cosby Show. The show portrayed an upper-middle class black family, and frequently emphasized the importance of a college education. If role model effects exist, young black people should have responded more strongly to this message. I test this hypothesis by relating educational attainment to city-level Cosby ratings, using Thursday NBA games and very warm Thursdays as instruments. I find that Cosby increased years of education by 0.2-5.0% among black youth, but had no effect on white youth. In the second chapter, I examine a determinant of social segregation by race in the United States: physical distance. Because U.S. cities are highly segregated, the time cost of interacting with a member of another race is typically higher than the cost of interacting with a same-race friend. My goal in this chapter is to quantitatively assess the importance of this channel in explaining why people typically interact with members of their own race. I argue that the causal effect of distance on social interactions is captured by consumers' distaste for travel. Based on external estimates of this parameter, I simulate the frequency of cross-racial interactions that would occur if only distance mattered in determining individuals' choice of interaction partners. I compare the simulation results to a new measure of the actual frequency of inter-racial interactions based on Flickr photographs. I estimate that 25-30% of social segregation for whites in the U.S. is attributable to physical distance alone.
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 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 | 0.001 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".