Bibliographic record
Abstract
In Chapter I, Xing Guo and I study how the U.S. restrictions on skilled immigration affect the Canadian economy and the welfare of American workers. In 2017, there was a policy that tightened the eligibility criteria for U.S. visas and was immediately followed by a trend break in the number of skilled immigrant admissions to Canada. We use quasi-experimental variation introduced by this policy over time and across immigrant groups, along with U.S. and Canadian visa application data, to show that Canadian applications in 2018 were 30% larger than without the restrictions. We then study how the restrictions affected Canadian firms using comprehensive Canadian administrative databases containing the universe of employer-employee-linked records, immigration records, and international trade data. We find that the restrictions increased firms' production, exports, and employment of Canadian workers. Finally, we study the policy’s impact on American workers by incorporating immigration policy into a multi-sector international trade model. With international trade, the increase in immigration to other countries due to the restrictions affects American wages through U.S. exports and consumption prices. We find that the welfare gains for American workers targeted for protection by the 2017 policy are up to 25% larger in a closed economy than in an open economy with the observed trade levels. In Chapter II, Nicolas Morales and I use a detailed establishment-level dataset from Germany to document that large firms allocate a higher proportion of their wage bill to immigrants compared to small firms. We study both analytically and quantitatively the importance of this heterogeneity across firms in the effects of immigration on the welfare of native-born workers. To achieve this, we set up and estimate a model of international trade and immigration where heterogeneous firms choose their immigrant share. We find that our model with no heterogeneity in immigrant share across firms underestimates the native workers' welfare gains by 11%. In chapter III, Alberto Cavallo, Javier Cravino, Andres Drenik and I study how the price of remote work is determined in a globalized labor market using data from a large web-based job platform, where workers from around the world compete for remote jobs. Despite the global nature of the platform, we find that remote wages are higher for workers in regions with higher income per capita. This correlation is not accounted for by differences in workers' observable characteristics, occupations, or differences in the employers' locations. Instead, data on wage histories indicate that remote wages are partly determined by the conditions that workers face in their local labor markets. We also show that remote wages expressed in local currency move strongly with the dollar exchange rate of the worker's country and are highly sensitive to foreign competition. Finally, we identify occupations at high risk of being offshored based on the prevalence of cross-border contracts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".