Bibliographic record
Abstract
This dissertation consists of three chapters. Chapter 1 is solo-authored, while Chapters 2 and 3 are coauthored with researchers at the University of Toronto and other institutions. In “Wiring Growth” I investigate the effect of communications technology improvements on the level and composition of economic activity. Between 1840 and 1860 the United States experienced a dramatic shift in economic geography, as population and output moved west and Northeastern counties shifted production away from agriculture. At the same time, the country saw the rapid growth of a new communications technology, the electric telegraph. I investigate how the telegraph facilitated the geographical transformation, using an instrumental variables approach and cross- county variation in telegraph growth from 1840 to 1850. I find persistent positive effects of the telegraph on the growth of county-level goods output per capita. This finding is consistent with historical accounts of the telegraph’s effects on production and distribution. I also find evidence that the telegraph facilitated the transition away from home production in nonagricultural sectors. In “VancUber,” we investigate the long-run effect of ride-hailing on public transit ridership, traffic congestion, and traffic fatalities. We estimate the long-run effect of ride-hailing by exploiting British Columbia’s use of a pre-existing regulation in 2013 to ban ride-hailing from Vancouver and using the synthetic control method to construct a counterfactual Vancouver. We do not find a statistically significant effect of ride-hailing on our outcomes. Our results for fatalities are imprecise, but our confidence intervals for the effect on transit ridership and congestion provide bounds on the likely effect that are smaller in magnitude than many existing estimates. In “Multi-Product Markups,” we examine the identification of firm-product markups in multi- product firms using production-side data. Identifying within-firm markup differences relies on identifying the marginal rates of transformation across goods. Since marginal rates of trans- formation are generally functions of (i) the degree of joint production and (ii) the magnitude of within-firm productivity differences, we explore whether markup estimates are sensitive to misspecification in these factors. Monte Carlo exercises indicate that misspecification of within-firm productivity differences is particularly likely to generate bias. However, a weighted average of 2 firm-product level markups (the “firm markup”) can be identified without information on these factors, and nests the firm markup of De Loecker and Warzynski (2012). The firm markup can be estimated using standard empirical methods. Standard parametric restrictions on the marginal rates of transformation across goods often deliver implausible estimates of plant-product markups. Firm markups, which do not require these restrictions, are more well behaved. We also discuss the welfare properties of the firm markup.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".