New BEA-BLS Estimates of the Sources of U.S. Economic Growth between 1987 and 2016
Bibliographic record
Abstract
This article describes new historical statistics for the BEA-BLS integrated industry-level production account. The dataset includes KLEMS and integrated MFP measures that are consistent with the official BEA GDP by Industry statistics and now covers 1987-2016. The most important source of economic growth over the period was the accumulation of capital input. More than three quarters of the contribution of capital was driven by the accumulation of capital inputs in the service sector. The next most important source of economic growth over the period was the accumulation of labour input. Growth in labour input in the services sectors accounted for almost all the economy-wide contribution of labor input. MFP growth accounted for about twenty percent of aggregate economic growth. Of this, the manufacturing sector contributed more than half of this growth, but almost all of this was due to growth in MFP of the computer electronic products industry. Finally, the new dataset shows that the decline in the aggregate income share paid to labour in the manufacturing sector was mostly due to a decrease in the share of income paid to workers without a college degree. In contrast, workers with a college degree accounted for most of the increase in the income share of labour in the service sectors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".