What Has Been Happening to Aggregate Concentration in the U.S. Economy in the 21st Century
Bibliographic record
Abstract
Fifteen years ago White (2002) provided estimates of aggregate concentration in the U.S. economy – the percentage of aggregate economic activity that could be attributed to the largest “X” companies – that covered primarily the last quarter of the 20th century. Those data sets showed that despite major merger waves during that period, aggregate concentration at the end of the 1990s was lower than it had been in the early 1980s, although there had been some upward movement after the mid 1990s. Since then (to our knowledge) there have been no studies that have updated/extended those data. Major mergers are again prominent during the period since the millennium, which has also been marked by historic business cycles as well as by frequent mentions of phrases such as “Big Oil”, “Big Pharma”, “Big Food”, “Big Tech”, etc., in popular descriptions of the U.S. economy. This paper extends the earlier data series into the first two decades of the 21st century. We find that there has indeed been a moderate but continued increase in aggregate concentration since the mid 1990s. This increase appears in data on employment and payroll that have been compiled by the U.S. Bureau of the Census, as well as employment and profits data that are drawn from the annual “Fortune 500” lists. This increase does not, however, appear to have raised aggregate concentration above the levels of the early 1980s. This paper also computes annual Gini coefficients for 1988-2014 for employment by firm size and payroll by firm size. We find gradual annual increases in both sets of Gini coefficients for this time period. These increases appear to be due to increases in the sizes of larger firms generally and not just increases by the largest firms.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".