Using Divide-and-Conquer to Improve Tax Collection
Bibliographic record
Abstract
Tax collection by capacity constrained governments may exhibit multiple equilibria: if delinquency is low, limited enforcement capacity is enough to discipline deviators; if delinquency is high, limited enforcement capacity is overstretched and no longer dissuasive.In principle, divide-and-conquer, a theoretically important but untested principle from mechanism design, can be used to unravel the undesirable high-delinquency equilibrium.We investigate the challenge of doing so in practice.Our preferred mechanism takes the form of Prioritized Iterative Enforcement (PIE).Tax-payers are assigned a rank trading-off expected collection and expected capacity use.Tax-payers are then iteratively threatened in small groups for which collection capacity is sufficient to induce compliance.After repayment occurs, unused collection capacity is released to issue the next round of threats.In partnership with a district of Lima (Peru) we experimentally evaluate the impact of PIE on the collection of property taxes from 13432 tax-payers.Reduced-form evidence both validates and refines the theoretical benchmark.A structural model of tax-payer behavior suggests that, keeping the number of collection actions fixed, PIE would increase tax revenue by 11.3%.
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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 | 0.016 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".