Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry
Bibliographic record
Abstract
Abstract We measure the distribution of welfare losses from non-competitive behaviour in the U.S. credit card industry during the 1970s and 1980s. The early credit card industry was characterized by regional monopolies. Ensuing legal decisions led to 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. The transition from monopoly to oligopolistic competition yields welfare gains equivalent to a one-time transfer worth $3,600 (in 2016 dollars) for the bottom decile of earners (roughly 50% of their annual income) versus $1,200 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. Greater competition also explains rising bankruptcies, chargeoffs, and credit to income ratios. Lastly, we bound the welfare gains from competition by computing a perfectly competitive benchmark. Aggregate welfare gains are 40% larger from perfect competition but distributed similarly to oligopolistic competition.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".