Occupy Government: Democracy and the Dynamics of Personnel Decisions and Public Sector Performance
Bibliographic record
Abstract
We study the causes and consequences of patronage in Brazilian cities since the country’s re-democratization. Our data consist of the universe of local public sector employees merged with their party affiliations, and a dynamic regression discontinuity design is applied to deal with the endogeneity of patronage. Elections have consequences for patronage, with winning political coalitions increasing their shares of public sector workers and wages by 3-4 percentage points during a mayoral term, and also occupying civil servant jobs to perform key service-oriented tasks in education and public health. This type of patronage accounts for more than half of the dramatic increase in public sector political employment since the Brazilian re-democratization. The political occupation of government jobs is not associated with ideology, though. Instead, lack of accountability and rent-seeking are the primary driving forces, while reliance on intergovernmental transfers only increases patronage for smaller cities. Finally, we estimate the long-term consequences of this political occupation for fiscal outcomes conditions and for the quality of education and health care services. More political occupation does not affect the size of local governments, but it changes the composition of expenditures and public workers: the hiring of politically connected workers crowds out, practically one-to-one, non-affiliated teachers and doctors. The increased political occupation in Brazilian cities resulted in negative long term outcomes for local citizens in the form of less years of formal schooling and higher mortality rates.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".