Election Cycles and Organizations: How Politics Shapes the Performance of State-owned Enterprises over Time
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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 it