Using Census Data to Measure Wealth Inequality In Nineteenth-Century Detroit (1850-1870)
Bibliographic record
Abstract
During the nineteenth century, the social structure of Detroit included a very high level of wealth inequality.Analysis of data contained in U.S. census schedules for the years 1850, 1860 and 1870 discloses a skewed wealth distribution for nineteenth-century Detroit, in which the top one percent of the city's families controlled over forty percent, and the top ten percent of families controlled over eighty percent, of the city's total wealth.At the same time, the bottom fifty percent owned next to nothing.The census data analysis also indicates that Detroit's wealth patterns matched those of other large nineteenth-century U.S. and Canadian cities, in particular those of its Great Lakes neighbors.The historian Robert Manning has called the need to collect historical data, in particular on inequality, the "single greatest challenge in global social-science research." 1 As such, the discussion that follows seeks to address this data need in a small way by introducing new datasets for one major northern U.S. city.In addition, the overall analysis provides a backdrop for understanding Detroit's larger political, economic, social and cultural history.Wealth inequality has become a hotly debated topic within academic and political communities.The dramatic concentration of wealth over the last forty years increasingly draws comparisons with the reviled Gilded Age.Research conducted by the French political economist Thomas Piketty sits at the center of the ongoing debate.Piketty's thesis rests on the belief that when "the rate of return on capital exceeds the rate of growth of output and income, as it did in the nineteenth century and seems quite likely to do so again in the twenty-first, capitalism automatically generates arbitrary and unsustainable inequalities that radically undermine the meritocratic values on which democratic societies are based." 2 At the same time, Piketty argues 1 Patrick Manning, "Inequality: Historical and Disciplinary Approaches," American Historical Review
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".