Markets Versus Governments: Political Economy of Mechanisms
Bibliographic record
Abstract
We study the optimal Mirrlees taxation problem in a dynamic economy with idiosyncratic (productivity or preference) shocks.In contrast to the standard approach, which implicitly assumes that the mechanism is operated by a benevolent planner with full commitment power, we assume that any centralized mechanism can only be operated by a self-interested ruler/government without commitment power, who can therefore misuse the resources and the information it collects.An important result of our analysis is that there will be truthful revelation along the equilibrium path (for all positive discount factors), which shows that truth-telling mechanisms can be used despite the commitment problems and the different interests of the government.Using this tool, we show that if the government is as patient as the agents, the best sustainable mechanism leads to an asymptotic allocation where the aggregate distortions arising from political economy disappear.In contrast, when the government is less patient than the citizens, there are positive aggregate distortions and positive aggregate capital taxes even asymptotically.Under some additional assumptions on preferences, these results generalize to the case when the government is benevolent but unable to commit to future tax policies.We conclude by providing a brief comparison of centralized mechanisms operated by self-interested rulers to anonymous markets.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".