Economy Wide Spillovers From Booms: Long Distance Commuting and the Spread of Wage Effects
Bibliographic record
Abstract
Since 2000, US real average wages have either stagnated or declined while Canadian average wages increased by almost 10%. We investigate the role of the Canadian resource boom in explaining this difference. We construct a model of wage setting that allows for spillover effects of a resource boom on wages in non-resource intensive locations and formulate an empirical specification based on that model. A key feature of this (and other) resource booms was the prevalence of long distance commuting - working in a resource location but residing in another community. The core idea in our model is that the expansion of the value of the commuting option during the boom allowed non-commuters to bargain higher wages. We find that wages do rise in areas with more long distance commuting. Combining these spillover effects with bargaining spillover effects in resource boom locations, we can account for 49% of the increase in the real mean wage in Canada between 2000 and 2012. We find similar effects of long distance commuting on wages in the US but the resource boom was less salient in the US and the effect on wages was one-tenth of that in Canada. Our results have implications for other papers measuring the impacts of resource booms on wages in surrounding areas. Our main finding is that long-distance commuting can integrate regions in a way that spreads the benefits and costs of a boom across the economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".