Poverty mitigation and anti-corruption campaigns: evidence from Chinese cities
Bibliographic record
Abstract
In China, firms actively participate in poverty alleviation to comply with the national policy and to build political connections. Whether firms curry favor with the government by increasing spending on poverty alleviation is an interesting research question under the context of anti-corruption campaigns. Using hand-collected data from the period 2016–2018, we examine how anti-corruption campaigns have influenced corporate poverty alleviation spending at the city level. Our results show that anti-corruption campaigns are positively related to corporate poverty alleviation spending. We further identify two possible channels through which the anti-corruption campaign increases corporate poverty alleviation spending: (1) political connections and (2) stock price crash risk. Finally, we find that the effects of the anti-corruption campaign on corporate poverty alleviation spending are stronger in firms located in cities with lower degrees of marketization, a lower media index, and a higher poverty rate, as well as in firms receiving fewer government subsidies.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".