Election Cycles and Organizations: How Politics Shapes the Performance of State-owned Enterprises over Time
Bibliographic record
Abstract
This study develops a dynamic perspective on how elected state officials’ political incentives shape the behavior and performance of organizations, particularly state-owned enterprises (SOEs). Drawing on theoretical views about the relationship between politicians and firms, I argue that state officials seeking votes manipulate SOEs to boost employment before elections. As a result, SOEs exhibit both higher employment levels and lower financial performance in election years. The positive relationship between elections and SOE employment, however, is not uniform across firms and geographic communities: it is likely to be stronger in economically disadvantaged communities and weaker for SOEs with private investors. Data from Brazil’s water sector—an industry managing a crucial societal resource—support these predictions. These results shed light on the mechanisms linking officials’ political incentives and SOE behavior and show that SOE performance is politically contingent and thus varies systematically over time. More broadly, this study reveals how firms’ responses to political pressures depend on both organizational and community attributes and highlights how the interplay of election cycles, organizations, and communities shapes the performance of organizations in state capitalism.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".