Essays on Banking, Credit, and Money, and their relationship to Output, Population, and Productivity
Bibliographic record
Abstract
This thesis studies instances of credit constraints in Canadian history. In Chapter 2, I study the productivity of Canadian industrial establishments in late nineteenth century Canada. In particular, I look at the relationship between the perceived credit worthiness of proprietors of industrial establishments, capital accumulation, and productivity. I show that the productivity gap of 22-38%, between Ontario and Quebec, that existed in 1871, could have been reduced by between a quarter and three quarters if francophone-Catholics had received the same credit ratings as anglophone-non-Catholics. In Chapter 3, co-authored with David Rosé, we study the entry of French Canadian credit unions, caisses populaires, into rural Quebec over the 1911 to 1931 period. Using propensity score matching methods, we showed that the caisses populaires slowed down rural exodus. Sub-districts where a caisse was established exhibited 7-9% higher population growth--total, rural, and French--compared to sub-districts where no caisse was established. In Chapter 4, co-authored with Gregor W. Smith, we look for evidence of a correlation between output growth and inflation, or unexpected inflation, during the interwar period, which featured an abundance of credit, followed by a credit crunch. Using time-series and panel data methods for more than 20 countries, including Canada, we find little evidence of a correlation between unexpected inflation and output growth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".