Net Investment and Stocks of Human Capital in the United States, 1975-2013
Bibliographic record
Abstract
This article continues the research initiated in Christian (2010, 2014) on measurement of human capital stocks and investment in the United States. It develops estimates of a series of human capital stock and net investment from 1975 to 2013, using the lifetime earnings approach of Jorgenson and Fraumeni (1989, 1992). The series decomposes net investment into investment from births, investment in education net of aging of persons enrolled in school, depreciation from aging of persons not enrolled in school, depreciation from deaths, and a residual term that includes net migration and measurement error. The study also discusses the cost-based approach of measurement in human capital of Kendrick (1976) and compares investment in education between the cost and income approaches. The stock of human capital rose at an annual rate of 1.0 per cent between 1977 and 2013, with population growth as the primary driver of human capital growth. Per capita human capital remained much the same over this period, with the effect of greater levels of education being offset by the effect of an aging population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".