Bibliographic record
Abstract
This thesis examines how the interaction between organizations, institutions, and key stakeholders shapes organizational performance. It includes three studies, each of which examines in detail the relationship between actors engaged in promoting the public interest. The first study investigates how elected state officials shape the performance of government-controlled organizations over time. This study argues that the political incentives of state officials create systematic trends in the behavior and performance of government-controlled organizations, which are particularly common in industries such as power distribution, transportation, and water and sanitation. State officials seeking votes manipulate government-controlled organizations to boost employment just before elections. As a result, these organizations exhibit both higher employment levels and lower financial performance in election years. Data from Brazil’s water sector—an industry managing a crucial societal resource—support these predictions. The second study investigates how the engagement of highly productive workers affects the operational performance of organizations. The research isolates these workers’ direct contributions to organizational performance from their indirect contributions—i.e., the influence on the performance of coworkers in the organization. Exploiting the temporary absences of high-volume physicians, I find that in-hospital patient mortality increases during these absences for two reasons: first, because the physician is absent; and second, because of the impact of the absence on the performance of colleagues. This second pathway—the role of indirect contributions—accounts for about 38 percent of the overall increase in patient mortality. This research shows that highly productive workers have sizeable influence on the performance of organizations, but this influence occurs, to a large extent, through peer effects. The third study investigates how the implementation of strong intellectual property rights for pharmaceuticals in emerging markets influences the strategy of pharmaceutical firms. Prior research suggests that patents shape the behavior of firms by affecting prices. This study argues that patents can shape the market strategy of firms through mechanisms other than price. The results show that patent holders modify their product market strategy despite little changes in drug prices.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".