Bibliographic record
Abstract
This thesis studies a range of issues in spatial and international economics. The first two chapters focus on spatial earnings inequality across cities; the last chapter studies international profit shifting. In the first chapter, I examine how the two-sided sorting of workers and firms affects spatial earnings inequality, efficiency of the allocation of workers and firms across cities, and the welfare consequences of place-based policies. I build a general equilibrium model in which heterogeneous workers and firms sort across cities and match within cities. I structurally estimate the model using Canadian matched employer-employee data and decompose the urban earnings premium, finding that worker and firm sorting account for 67% and 27% of this premium, respectively. The decentralized equilibrium is inefficient as low-productivity firms overvalue locating in high-skilled cities. The optimal spatial policy would incentivize high-skilled workers and high-productivity firms to co-locate to a greater extent while redistributing income towards low-earning cities, leading to a 6% increase in social welfare. Model counterfactuals underscore the importance of two-sided sorting when evaluating distributional and aggregate outcomes of place-based policies. It has been documented that larger cities foster faster earnings growth, which is an important driver for cross-sectional spatial earnings inequality. In the second chapter, I empirically investigate the sources of the urban earnings growth premium. I find that the between-firm and within-firm growth components each explain 66% and 34% of the greater returns to big city experience, respectively. Workers do not move between jobs more frequently but enjoy a bigger earnings increase on average per movement in larger cities. Faster within-firm learning in larger cities is mostly explained by better learning environments at the firm level. The empirical results of this chapter highlight the important role of firm heterogeneity across cities in explaining the dynamic gains from working in bigger cities. In the jointly authored third chapter, we study the macroeconomic consequences of tax policies designed to reduce international profit shifting by multinational enterprises (MNEs) using a model that emphasizes the transfer pricing of intangible capital. We prove analytically that such policies would reduce MNEs’ intangible investment, reducing outputboth at home and abroad. We then quantify the effects of the OECD’s proposed reforms: reallocating the rights to tax MNEs’ profits to the countries where they sell their products; and a minimum global corporate income tax. These policies would reduce profit shifting by more than two-thirds but would also reduce output in all regions of the global economy.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".