How Important Is Human Capital? A Quantitative Theory Assessment of World Income Inequality
Bibliographic record
Abstract
We build a model of heterogeneous individuals—who make investments in schooling quantity and quality—to quantify the importance of differences in human capital vs. total factor productivity (TFP) in explaining the variation in per capita income across countries. The production of human capital requires expenditures and time inputs; the relative importance of these inputs determines the predictions of the theory for inequality both within and across countries. We discipline our quantitative assessment with a calibration firmly grounded on US micro evidence. Since in our calibrated model economy human capital production requires a significant amount of expenditures, TFP changes affect disproportionately the benefits and costs of human capital accumulation. Our main finding is that human capital accumulation strongly amplifies TFP differences across countries: to explain a 20-fold difference in the output per worker, the model requires a 5-fold difference in the TFP of the tradable sector, vs. an 18-fold difference if human capital is fixed across countries.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".