Ideology and Performance in Public Organizations
Bibliographic record
Abstract
We combine personnel records of the United States federal bureaucracy from 1997-2019 with administrative voter registration data to study how ideological alignment between politicians and bureaucrats affects the personnel policies and performance of public organizations. We present four results. (i) Consistent with the use of the spoils system to align ideology at the highest levels of government, we document significant partisan cycles and substantial turnover among political appointees. (ii) By contrast, we find virtually no political cycles in the civil service. The lower levels of the federal government resemble a "Weberian" bureaucracy that appears to be largely protected from political interference. (iii) Democrats make up the plurality of civil servants. Overrepresentation of Democrats increases with seniority, with the difference in career progression being largely explained by positive selection on observables. (iv) Political misalignment carries a sizeable performance penalty. Exploiting presidential transitions as a source of "within-bureaucrat" variation in the political alignment of procurement officers over time, we find that contracts overseen by a misaligned officer exhibit cost overruns that are, on average, 8% higher than the mean overrun. We provide evidence that is consistent with a general "morale effect," whereby misaligned bureaucrats are less motivated.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".