Ideology and Performance in Public Organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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 it