Addressing Risk by Doing Good: Business Response to Government Policy Initiative
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
Why do some firms in authoritarian regimes respond actively to government policy initiatives while others resist? This article highlights firm-level political risk as the primary motivator of responses, allowing firms to defend their property rights in weak institutional environments. Supporting evidence derives from the responses of listed firms to the Targeted Poverty Alleviation campaign launched by China in 2015. Empirically, firm-level political risk is measured with a text-as-data approach involving 418,480 Q&As in the meetings between institutional investors and listed companies, not only capturing within-firm variations but also providing a substantial understanding of the political risk firms face. Difference-in-differences models show that political risks increase firms’ expenditure on poverty-reduction programs, especially for those without preexisting political connections. Evidence of regulation decisions suggests that firms actively responding to poverty-alleviation initiatives received preferential treatment in terms of the size of fines and the likelihood of punishment for violation of regulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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