Preliminary Draft (Please do not cite) The Value of Climate Amenities: Evidence from U.S. Migration Decisions
Bibliographic record
Abstract
There is a large literature that attempts to value climate amenities in the US and elsewhere using the fact that climate amenities are capitalized into wages and property values. Many of these estimates, which were produced in the 1970s and 1980s, assume that people are perfectly mobile and are based on estimates of national hedonic wage and property value functions. These functions will yield biased estimates of consumers’ willingness-to-pay for climate amenities if consumers are not in locational equilibrium, as may occur due to information or other moving costs. We value climate amenities by estimating a discrete model of residential location choice for households who changed metropolitan statistical areas (MSAs) between 1995 and 2000. We assume that the utility that a household derives from living in an MSA depends on climate amenities along with earnings potential, housing costs and locationspecific amenities. To avoid assuming a national labor market we estimate separate hedonic wage functions for each MSA to predict earnings opportunities in each city. Households choose the MSA where they derive maximum utility. The model is estimated using a two step procedure (Bayer, Keohane and Timmins, 2006). In the first stage, location-specific constants are estimated together with other parameters of the utility function. In the second stage, location-specific intercepts are regressed on locationspecific amenities and housing costs to estimate the average utility attached to these amenities. We find winter temperature and summer precipitation to be amenities, but summer temperature to have no statistically significant effect on migration decisions. Models estimated using “stayers ” as well as movers suggest that the former are not in equilibrium; and hence that their location decisions cannot be used to estimate the value they attach to climate amenities.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.295 | 0.073 |
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".