Three Essays in North American Economic History
Bibliographic record
Abstract
This thesis studies two marginalized groups in North American history--French Canadians in the early 20th century and African Americans in the mid 20th century. Chapter 2, co-authored with Bill Dorval, investigates the impact that the arrival of access to financial services had on economic and demographic outcomes for rural French-Canadians in unbanked areas of Quebec. Using propensity score matching, I find that the establishment of a caisse populaire (a type of credit cooperative) accelerated rural population growth in treated parishes over 1910 and 1920. These effects persisted into the following decade. Chapters 3 and 4, co-authored with Lisa D. Cook, Maggie E.C. Jones, and Trevon D. Logan, use the Negro Motorist Green Books to quantify and study discrimination in public accommodations against Black Americans before the passage of the 1964 Civil Rights Act. These guides were a tool used by Black motorists to locate hotels, motels, and other businesses that would treat them with dignity. Chapter 3 describes the digitization of the guides and the spatial distribution and temporal trends of public-facing businesses that treated African Americans consumers on a non-discriminatory basis. Chapter 4, considers the relationship between the racial composition of markets and the number of Green Book listings across different industries. The exogenous change in the racial composition of a county brought on by WWII mortality of White soldiers is used to identify the relationship. WWII deaths led to an increase in the number of consumer-facing businesses that served African Americans. Next, I use a model to explore a potential mechanism: non-prejudiced firms may decide to discriminate when they face a subset of White consumers who have a distaste for consuming alongside Black consumers. An implication of the model is that in equilibrium the ratio of non-discriminatory to discriminatory firms should respond to the Black-White population ratio. This is tested empirically with data about hotels and motels. Using WWII White deaths as an instrument for the Black-White population ratio, I find that a 1 percent increase in the Black-White population ratio is associated with a 2 percent increase in the ratio of non-discriminatory to discriminatory firms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".