Bureaucrats and Policies in Equilibrium Administrations
Bibliographic record
Abstract
Abstract We develop a model of policy making with an endogenous bureaucracy. Parties choose platforms and ideologically differentiated citizens decide whether to enter the public sector, anticipating the platforms that they may be asked to implement. Bureaucrats prefer to work on policies closer to their ideal, and voters judge the performance of an administration taking both politicians’ and bureaucrats’ actions into account. The model provides an equilibrium framework to study the emergence of partisan or neutral bureaucracies and their consequences for government performance. It shows how bureaucratic partisanship can develop in modern civil service systems; why political polarization and bureaucratic partisanship reinforce each other; why bureaucratic neutrality is associated with competitive elections; and why partisanship lowers government efficiency and increases output fluctuations. Our results yield a number of policy implications regarding political appointments, public sector wages, seniority benefits, and recruiting measures that raise the intrinsic motivation of bureaucrats.
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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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".