How Important is Human Capital? A Quantitative Theory Assessment of World Income Inequality
Bibliographic record
Abstract
We develop a quantitative theory of human capital investment in order to evaluate the magnitude of cross-country differences in total factor productivity (TFP) that explains the variation in per-capita incomes across countries. We build a heterogeneous-agent economy with cross-sectional variation in ability, schooling, and expenditures on schooling quality. In our theory, the parameters governing human capital production and random ability process have important implications for a set of cross-sectional statistics - Mincer return, variance of earnings, variance of schooling, and intergenerational correlation of earnings. These restrictions of the theory and U.S. household data are used to pin down the key parameters driving the quantitative implications of the theory. Our main finding is that human capital accumulation strongly amplifies TFP differences across countries. In particular, we find an elasticity of output per worker with respect to TFP of 2.8: a 3-fold difference in TFP explains a 20-fold difference in output per worker. We argue that the cross-country differences in human capital implied by the theory are consistent with a wide array of evidence including earnings of immigrants in the United States, average mincer returns across countries, and the relationship between average years of schooling and per-capita income across countries. The theory implies that using Mincer returns to measure human capital understates differences across countries by a factor of 2.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".