Geography, population density, and per-capita income gaps across US states and Canadian provinces
Bibliographic record
Abstract
We explain per-capita income gaps across US states and Canadian provinces by the following chain of causation. Geography determined where Europeans originally settled: in Northeastern USA, along those segments of the Atlantic coast where the climate was neither too hot (the US South), nor too cold (Canada). Higher population densities in this early settled region have prevailed to this day. This has in turn affected per-capita incomes because densely populated areas are conducive to skill accumulation; indicatively, many of the world’s top universities lie in this region. Our ordinary least-squares regressions show university education having a robust positive and significant effect on per-capita incomes. To control for endogeneity we run various instrumental-variable regressions: some where education today is instrumented with e.g. population density in 1900; and some where different sets of geography variables (e.g. temperature) are used as instruments. Our findings are consistent with the type of causal chain described.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".