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A Censored Maximum Likelihood Approach to Quantifying Manipulation in China’s Air Pollution Data

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  • Dalia Ghanem
  • Shu Shen
  • Junjie Zhang

Abstract

Data manipulation around cutoff points is observed in economics broadly and in environmental and resource economics in particular. This paper develops a simple and tractable censored maximum likelihood approach to quantify the degree of manipulation in China’s air pollution data around the “blue-sky day” cutoff. We construct annual measures of manipulation for 111 Chinese cities. For Beijing, we estimate 4%–16.8% of manipulation among reported blue-sky days annually, which translate to an estimated total of 208.1 manipulated blue-sky days between 2001 and 2010. For the remaining cities reporting pollution data over the 10-year period, we estimate a 93.9 average for the total number of manipulated blue-sky days with a 395.9 maximum. Using LASSO shrinkage, we examine the relationship between manipulation and local official characteristics, and find a positive correlation between manipulation and having an elite-educated party secretary, robust to numerous checks. Further empirical analysis suggests that promotion considerations may help explain this finding.

Suggested Citation

  • Dalia Ghanem & Shu Shen & Junjie Zhang, 2020. "A Censored Maximum Likelihood Approach to Quantifying Manipulation in China’s Air Pollution Data," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(5), pages 965-1003.
    • Handle: RePEc:ucp:jaerec:doi:10.1086/709649
    DOI: 10.1086/709649
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    Cited by:

    1. Dong, Yan & Tian, Jinhuan & Wen, Qiang, 2022. "Environmental regulation and outward foreign direct investment: Evidence from China," China Economic Review, Elsevier, vol. 76(C).
    2. He, Xiaobo & Luo, Zijun & Zhang, Junjie, 2022. "The impact of air pollution on movie theater admissions," Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
    3. Shihe Fu & V. Brian Viard, 2022. "A mayors perspective on tackling air pollution," Chapters, in: Charles K.Y. Leung (ed.), Handbook of Real Estate and Macroeconomics, chapter 16, pages 413-437, Edward Elgar Publishing.
    4. Wang, Zhenxuan & Zhang, Junjie, 2023. "The value of information disclosure: Evidence from mask consumption in China," Journal of Environmental Economics and Management, Elsevier, vol. 122(C).
    5. Jiang, Qisheng & Tang, Pengcheng, 2023. "All roads lead to Rome? Carbon emissions, pollutant emissions and local officials’ political promotion in China," Energy Policy, Elsevier, vol. 181(C).
    6. Myunghyun Song, 2024. "Identification and Inference in General Bunching Designs," Papers 2411.03625, arXiv.org, revised Nov 2024.
    7. Ahmad, Husnain F. & Gibson, Matthew & Nadeem, Fatiq & Nasim, Sanval & Rezaee, Arman, 2022. "Forecasts: Consumption, Production, and Behavioral Responses," IZA Discussion Papers 15831, Institute of Labor Economics (IZA).
    8. Bi, Ruigang & Kou, Zonglai & Zhao, Cheng & Zhong, Yiwen & Zhou, Min, 2024. "Information disclosure and pollution reduction: evidence from environmental NGO monitoring in China," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 1459-1473.
    9. Huang, Jialin & Xing, Jianwei & Yongchen Zou, Eric, 2023. "(Re)scheduling pollution exposure: The case of surgery schedules," Journal of Public Economics, Elsevier, vol. 219(C).
    10. Liu, Chenhao & Kong, Dongmin, 2021. "Does political incentive shape governments' disclosure of air pollution information?," China Economic Review, Elsevier, vol. 69(C).

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