Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry
Bibliographic record
Abstract
We measure the distribution of welfare losses from non-competitive behavior in the U.S. credit card industry during the 1970s and 1980s. The early credit card industry was characterized by regional monopolies that excluded competition. Several landmark court cases led the industry to adopt competitive reforms that resulted in greater, but still limited, oligopolistic competition. We measure the distributional consequences of these reforms by developing and estimating a heterogeneous agent, defaultable debt framework with oligopolistic lenders. Welfare gains from greater lender entry in the late 1970s are equivalent to a one-time transfer worth $3,400 (in 2016 dollars) for the bottom decile of earners (roughly 50% of their annual income) versus $900 for the top decile of earners. As the credit market expands, low-income households benefit more since they rely disproportionately on credit to smooth consumption. We find that greater lender entry resulting from these reforms delivers 65% of the potential gains from competitive pricing.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".