INTERNATIONAL INCOME COMPARISONS AND LOCATION CHOICE: METHODOLOGY, ANALYSIS, AND IMPLICATIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper contributes to ongoing debates on international \nincome comparisons by deploying a novel methodology \nfor constructing empirical distribution functions for the United \nStates and Canada over the period 1993 - 2000. We also conduct \ntests for first, second, third order stochastic dominance and of \nintersection of distributions, to determine which,if either, country \nmight be a preferred destination for migration. Our findings \nare for that all of the years for which there is comparable data, \nthe Canadian income distribution second order stochastically \ndominates the US income distribution. We provide an interpretation \nin terms of expected utility theory, considering the case \nof log utility, and relate our findings to an argument by Joseph \nStiglitz, that in the face of skewness of income distributions a \npotential migrant should look at the median rather than the \nmean. It turns out that Stiglitz's intuition is correct, at least \nin the context of our study.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it