Bibliographic record
Abstract
This thesis contains three chapters in empirical economics. In Chapter 1, I use administrative data from British Columbia, Canada, to study the short-term effects of a parental layoff on children's academic performance in grades 4, 7, and 10. I find that households where a parent suffers a layoff, earn approximately $8,000 - $10,000 less in after-tax income in the year after the layoff. In spite of such a large loss in financial resources, I find no significant short-term effects on children’s test scores due to parental job loss. My estimates for grade 4 and grade 10 rule out negative treatment effects larger than 3.5% of a standard deviation at the 95% confidence level, and the estimates for grade 7 rule out negative treatment effects larger than 5.3% of a standard deviation. In Chapter 2, I exploit close city council elections in California from 1996 to 2017 to implement a regression discontinuity design and study the causal effects of a nonwhite candidate's victory against a white candidate. I find that in cities where the nonwhite candidate won (“treatment”), compared to cities where the nonwhite candidate lost (“control”), more new white candidates run in the next election. Heterogeneity analysis shows that this effect is driven by cities that have gone through bigger demographic changes over the past few decades, which suggests that changes in the racial composition of the city and the associated perception of threat to the dominant status of whites within the city are an important factor in driving the main result. Chapter 3 is based on my joint work with Louis Bélisle and Vivek Nandur. We compare two prominent methods used to estimate production functions in the literature. We estimate total factor productivity for firms in four manufacturing industries in India for the time period 2005-2012 using both estimation methods and using two different intermediate inputs as proxies. We find large discrepancies in the distribution of estimated total factor productivity for firms depending on the estimation method and the proxies used.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.013 |
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".