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\nCanadian provinces by the following chain of causation. Geography determined\nwhere Europeans originally settled: in Northeastern USA, along those\nsegments of the Atlantic coast where the climate was neither too hot (the\nUS South), nor too cold (Canada). Higher population densities in this early\nsettled region have prevailed to this day. This has in turn affected per-capita\nincomes because densely populated areas are conducive to skill accumulation;\nindicatively, many of the world’s top universities lie in this region.\nOur ordinary least-squares regressions show university education having a\nrobust positive and significant effect on per-capita incomes. To control for\nendogeneity we run various instrumental-variable regressions: some where\neducation today is instrumented with e.g. population density in 1900; and\nsome where different sets of geography variables (e.g. temperature) are used\nas instruments. Our findings are consistent with the type of causal chain\ndescribed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".