The Decline Of Manufacturing In The United States And Its Impact On Income Inequality
Bibliographic record
Abstract
The decline of manufacturing in the United States has been a perceptible trend, starting in the aftermath of World War II when manufacturing represented over one quarter of our Gross Domestic Product, to today, when it is less than 12%. The unemployment of the Great Recession, and the most recent State of the Union Address by President Obama, have now made this front page news. The declining trend has been masked by the facts that the U.S. remains, in total, the worlds largest manufacturer, and, along with China, the top value added producers. A second trend has been the decline of manufacturing employment as a percentage of the total labor force, running from just under one quarter post WWII, to less than 8% today. And finally the third trend has been the premium of manufacturing compensation versus all industries, from 11% in 1950 to 23% in 2010. Together these three trends are the major components of the increasingly palpable trend of income inequality from 1950 to 2010. In 1950 the top 20% had 17.3% of family income, whereas in 2010 it was 20%. The Gini coefficient, another measure of negative income distribution, moved from .379 to .440 over the same time frame.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".