The Changing Structure of Wages in the US and Germany: What Explains the Differences?
Bibliographic record
Abstract
Over the last twenty years the wage-education relationships in the US and Germany have evolved very differently, while the education composition of employment has evolved in a surprisingly parallel fashion.In this paper, we propose and test an explanation to these conflicting patterns.The model we present has two important elements: (1) technological change arises in the form of an alternative production process as opposed to being in the factor augmenting form, which renders technological adoption endogenous, (2) aggregate production depends on three factors (physical capital, human capital and labor).Based on this framework, we show why imbalances in the accumulation of human versus physical capital will be especially detrimental to low skill workers when the new technology is skill-biased and exhibits capital-skill complementarity.Using matched files from the PSID (US) and the GSOEP (Germany), we demonstrate how factor movements within these countries are associated with wage changes that are strongly supportive of our endogenous technological adoption model.Our conclusion is that the difference in the US and German experiences appear driven by the US having under-accumulated physical capital relative human capital over the 1979-96 period, while Germany accumulated factors in a more balanced manner.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".