Challenge or chance? Understanding the impact of anti-corruption campaign on China’s hotel industry
Bibliographic record
Abstract
Anti-corruption has garnered increasing attention, especially in China, where President Xi launched an influential and far-reaching anti-corruption campaign in late 2012. A better understanding of the effects of anti-corruption efforts on the hotel sector can reveal insights into the development of the Chinese hotel industry. Based on the quarterly data on China’s hotel industry in 49 cities from quarter 2 of 2010 to quarter 4 of 2015, this study investigates how the anti-corruption campaign (measured by anti-corruption inspections and the number of corruption lawsuits) has influenced hotel industry demand in China. Hypotheses are developed from China’s unique cultural environment of guanxi combined with rent-seeking theory and the crowding-out principle. Empirical results confirm a significant and negative effect of the anti-corruption campaign on hotel lodging and food and beverage demand. Several factors, including a city’s administrative position as a provincial capital, hotel class, level of tourism dependence, and local residents’ entertainment expenditure, are found to moderate the effect of the anti-corruption campaign on hotels’ lodging demand significantly. Theoretical and practical implications are discussed in light of these findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".