Labor policy and multinational firms: The “race to the bottom” revisited
Bibliographic record
Abstract
Abstract This paper revisits the “race to the bottom” phenomenon in a simple game theoretic framework. We consider one multinational firm, which requires two inputs that are imperfect substitutes, and two countries. In the benchmark model the labor of each country specializes in a distinct input. Seeking to maximize their labor incomes, countries simultaneously announce wages after which the firm chooses its labor employment in each country. We show that “race to the bottom” (countries setting minimum possible wages) is never an equilibrium. Moreover there are equilibria with “race to the top,” that is, countries set maximum possible wages. This result is robust in an extended model where prior to competing in wages, each country can make input‐specific investments to make its labor available for one or both inputs. Provided the production function of the firm is not asymmetrically intensive in either one of the two inputs, there are equilibria of the extended game with specialization (i.e., countries invest in distinct inputs) as well as “race to the top.”
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".