Integrated market and nonmarket strategies: Political campaign contributions around merger and acquisition events in the energy sector
Why this work is in the frame
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Bibliographic record
Abstract
We examine how firms use political strategies to protect economic rents created by mergers and acquisitions against dissipation by regulators. In regulated industries, regulators can impose costly merger conditions, for instance consumer rate reductions in the utilities sector, thereby reducing shareholder gains. We investigate empirically whether and how firms use election campaign contributions to politicians as a method of influencing regulatory merger approvals. In a statistical analysis of campaign contributions by all electric utilities from 1998 to 2006, we find that utilities increased their contributions in the year before they announced a merger and that merging utilities increased their contributions more in states with greater political party competition. Our findings contribute to political strategy research by providing novel evidence that firms integrate market and nonmarket strategies . Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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