When spatial equilibrium fails: is place-based policy second best?
Bibliographic record
Abstract
Place-based or geographically-targeted policy has been promoted as a way to help poor regions and the poor people who live there. Yet, such policy has often been attacked by economists as slowing needed economic adjustments, redirecting resources to lower productivity regions, and supporting political agendas rather than economic prosperity. The spatial equilibrium model in particular predicts that people readily move to the locations providing the highest expected utility, suggesting that policy interventions only impede needed adjustments. Given the high mobility of Americans, the spatial equilibrium model should then be most applicable to the US. We review the empirical evidence on whether the spatial equilibrium model applies and find that, even in the United States, people are not as mobile as the model suggests and that economic shocks have rather persistent effects. Although this suggests the potential need for place-based policy, we describe the informational and political economy conditions that need to be met before place-based policy can be effective.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".