Per Capita Income and the Demand for Skills
Bibliographic record
Abstract
Almost all of the literature about the growth of income inequality and the relationship between skilled and unskilled wages approaches the issue from the production side of general equilibrium (skill-biased technical change, international trade).Here, we add a role for income-dependent demand interacted with factor intensities in production.We explore how income growth and trade liberalization influence the demand for skilled labor when preferences are non-homothetic and income-elastic goods are more intensive in skilled labor, an empirical regularity documented in Caron, Fally and Markusen (2014).In one experiment, counterfactual simulations show that sector neutral productivity growth, which generates shifts in consumption towards skill-intensive goods, leads to significant increases in the skill premium: in developing countries, a one percent increase in productivity leads to a 0.1 to 0.25 percent increase in the skill premium.In several countries, including China and India, simulations suggest that the historical growth experienced in the last 25 years may have led to an increase in the skill premium of more than 10%.In a second experiment, we show that trade cost reductions generate quantitatively very different outcomes once we account for non-homothetic preferences.These imply substantially less predicted net factor content of trade and allow for a shift in consumption patterns caused by trade-induced income growth.Overall, the negative effect of trade cost reductions on the skill premium predicted for developing countries under homothetic preferences (Stolper-Samuelson) is strongly mitigated, and sometimes reversed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".