Anticorruption Enforcement and Sale Mechanism Choice in China's Land Market
中国土地市场中的反腐执法与出让机制选择AI 解读 ↓
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中国土地市场中的反腐执法与出让机制选择
- 研究问题
- 反腐行动中的地方起诉是否改变土地挂牌与拍卖的选择，并进一步影响土地出让价格及资源配置方式？
- 方法与思路
- 作者使用边际结构模型，在逆概率处理加权模型中加入固定效应和时变协变量，并另建模型分析出让价格。
- 材料陈述的贡献
- 摘要称，起诉使挂牌概率下降，连续月份起诉进一步强化该效应，同时土地价格的均值和中位数上升。
- 阅读关注点
- 应核对起诉的处理定义与时序、固定效应和权重各自处理的混杂，以及敏感性分析为何得到向零偏误结论。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未列明完整识别假设和效应大小，敏感性结论不能泛化至任意违背。
原文摘要
Upon taking office in late 2012, Chinese President Xi Jinping launched one of the most intensive anticorruption campaigns in the history of the People's Republic of China. Prior to the campaign, China's land market suffered from corruption, particularly surrounding sale method selection (auction versus listing). Listing is a two-stage sale mechanism that prior research has identified as more susceptible to corruption, leading to lower prices. This paper examines the campaign's impact on land allocation, focusing on whether corruption influences the choice of sale method and, in turn, land sale prices. This paper is the first to utilize Blackwell and Yamauchi (2021, 2024)'s marginal structural model with fixed effects in the inverse probability of treatment weighting model; absorbing time-invariant unobserved confounding and utilizing a set of time-varying covariates as controls, this model can estimate causal effects in the land sale case. I find that indictments in a prefecture cause a statistically significant drop in the probability that land is sold via listing$\unicode{x2014}$an effect that is further compounded when indictments occur in consecutive months. Sensitivity analyses indicate that any violations of the identification assumptions would bias estimates towards zero, confirming the negative effect. A second marginal structural model shows that both mean and median land sale prices increase in the presence of indictments. Together, these results suggest that the anticorruption campaign not only deterred actual corrupt allocation practices, but also impacted the discretionary use of listings.