Targeting Corporate Political Strategy: Theory and Evidence from the U.S. Accounting Industry
Why this work is in the frame
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Bibliographic record
Abstract
By analyzing the interaction between a business firm and multiple government institutions (including a regulatory agency, an executive and a bicameral legislature), we develop predictions about how firms target their political strategies at different branches of government when seeking more favorable public policies. The core of our argument is that firms will target their resources at the institution that is ‘pivotal’ in the policy-making process. We develop a simple framework, drawing on the political science literature, which identifies pivotal institutions in different types of political environments. We find empirical support for our thesis in an analysis of how U.S. accounting firms shifted their political campaign contributions between the House and Senate in response to the threat of new regulations governing auditor independence during the 1990s.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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