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Do Natural Resources Breed Corruption? Evidence from China

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Abstract

Rampant corruption is often observed in resource-rich countries, especially developing countries with weak political institutions. However, controversies exist regarding whether and how natural resources systematically breed corruption. With empirical evidence from China and through a subnational approach, I shed new light on the impacts of resources on corruption. By qualitative study of corruption cases, I identify the causal channels through which resources contribute to corruption, and using cross-regional and longitudinal statistical analysis on a unique dataset of corruption rates in China, I find that resource dependence significantly increases the propensity for corruption by state employees.

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Notes

  1. The downfalls of former top officials Zhou Yongkang and Ling Jihua as well as their associates are believed to have triggered the investigation of corruption cases in the oil sector and Shanxi Province.

  2. According to the statistics reported in the Procuratorial Yearbook of China 1998–2011.

  3. In 2009, the per capita GDP was 21,522 yuan in Shanxi, 17,335 yuan in Jiangxi, 39,735 yuan in Inner Mongolia, and 19,942 yuan in Xinjiang, while the national average was 25,963 yuan. The share of mineral industrial output in GDP was 28.8 % in Shanxi, 3.15 % in Jiangxi, 12.2 % in Inner Mongolia, and 21.6 % in Xinjiang, while the national average was 5.2 %. The numbers are calculated based on statistics from China Land and Resources Statistical Yearbook and China Data Online.

  4. Author’s interviews in Inner Mongolia, August 2012 and in Shanxi, October 2013.

  5. Author’s interview in Shanxi, May 2012 confirms this phenomenon.

  6. Author’s interviews in Shanxi, May 2012 and Xinjiang, October 2014.

  7. Author’s interview in Shanxi, October 2013.

  8. Thermal coal prices fluctuated between 400 and 1000 yuan/ton between 2005 and 2012 (Sina Finance 2013). The current exchange rate of Chinese yuan to US dollar is around 6.21:1.

  9. Author’s interview in Shanxi, May 2012.

  10. Approximately 32 million US dollars.

  11. Authors’ interviews in Shanxi, May 2012 and October 2013.

  12. Author’s interviews in Jiangxi, June 2011 and June 2014.

  13. The numbers are reported in the annual provincial procuratorial work reports published in Procuratorial Yearbook of China (Zhongguo Jiancha Nianjian). Data of filed and investigated corruption cases are available from as early as 1986. However, the revision of the Criminal Law in 1997 changed the definition of corruption, making the statistics before and after 1998 not comparable.

  14. Besides the corruption cases handled by the provinces, some cases involving top central and provincial-level officials and top managerial personnel in selected central state-owned enterprises are investigated at the central level. According to the statistics reported by Procuratorial Yearbook of China, centrally handled cases take up less than 1 % of all corruption cases in China, while the vast majority of cases are handled locally. As no information is reported about individual centrally handled cases, I exclude them from the data analyses. Given the small percentage of these cases, their omission should not seriously affect the validity of the findings.

  15. Under the Chinese legal system, when a suspected corruption case is reported, the procuratorate conducts an initial review and decides whether to accept it (shou’an). Upon acceptance and after some preliminary investigation, the procuratorate files a formal charge and investigates the case (li’an zhencha). At the conclusion of the formal investigation, the accused may be referred to the court for prosecution (tiqi gongsu) or be exempted. Only some of the accepted cases are filed and investigated, and not all filed and investigated cases are recommended for prosecution, although the second ratio seems to be rising, according to some provincial procuratorial work reports.

  16. There are less than 10 % missing values in the reported numbers of filed and investigated cases, as some provinces did not report the case numbers in certain years. In such circumstances, the provinces usually provided the number of filed and investigated people in lieu of the case number. This allows me to estimate the missing case number of a province in a given year: I first calculate the average people to case ratio in the years before and/or after the year in which the case number is missing; I then divide the reported number of filed and investigated people by the people to case ratio and derive the estimated number of cases. I am able to estimate most of the missing values through this method, with the exception of Shandong Province, where the case numbers are missing for five consecutive years from 2005 to 2009 and thus estimation would be unreliable.

  17. The detailed regression results are presented in Table 2 (Model 1).

  18. A fixed effects model is adopted over a random effects model because the later does not pass the Hausman test in all the regression models, which suggests that it may be inconsistent.

  19. The instrumental variable passes the first stage F-test for weak instruments with f-statistic 446.4 and p value 0.000. The estimation with the instrumental variable is done with Balestra and Varadharajan–Krishnakumar’s method built in the R program.

  20. Both measures of resource dependence are skewed to the right. Xinjiang, Heilongjiang, Shanxi, Shandong and Shaanxi are the outliers for the size of resource rents; Xinjiang, Heilongjiang, Shanxi and Qinghai are the outliers for resource dependence level. I thank one anonymous reviewer for suggesting this robustness check.

  21. The models excluding the outliers yield different results from the original models for only the weight of the state sector. While it appears positively correlated with corruption rates in the original models, the models excluding the outliers yield negative correlation. The difference suggests that the effect of this variable is sensitive to outliers. This intriguing finding deserves more careful examination, but it is beyond the scope of this project to investigate at this time.

  22. Resources and environment (nengyuan ziyuan he shengtai huanjing) is a new category created by the Chinese central government in 2008, an aggregate of corrupt activities that cause damage to China’s natural resources or the environment. A large part of corruption in this area concerns the environment, such as pollution, rather than mineral resources.

    Fig. 3
    figure 3

    Frequency of targeted anti-corruption areas (1999–2009). Note: Figure compiled based on the provincial work reports in Procuratorial Yearbook of China. The vertical axis is the frequency of the anti-corruption areas being mentioned in the procuratorial work reports of the 31 provinces between 1999 and 2009. The figure only presents the areas that are mentioned more than ten times in total. The other minor areas that are mentioned no more than ten times are lumped together as the “Others”

    Full size image

  23. At least 50,000 yuan for bribery and embezzlement cases and 100,000 yuan for misappropriation cases.

  24. County (xian) or office (chu) level and above.

  25. If data were available on the number of higher-level officials involved in the investigated corruption cases, we could also measure the political significance of the cases. Unfortunately the Chinese procuratorate provides limited data regarding this.

  26. As some provinces in certain years failed to report the sum of recovered economic losses, this variable incurs 59 missing values, about 17 % of the total cases. As the missing values do not concentrate in certain provinces or specific periods of time, I do not think they follow a systematic pattern that may distort the data analyses.

  27. To address potential endogeneity problems for law enforcement and economic development, as discussed earlier, lagged law enforcement strength (both 1- and 3-year), lagged per capita GDP (both 1- and 3-year) and the instrumental variable per capita electricity consumption are used in the estimation. The estimation with the instrumental variable is done with Balestra and Varadharajan–Krishnakumar’s method built in the R program. To ensure the estimation is not distorted by the existence of outliners, I have also run the same sets of regressions by excluding the most resource dependent provinces of Xinjiang, Heilongjiang, Shanxi, Shandong, Shaanxi, and Qinghai, one by one and all together. The models excluding the outliers yield results highly consistent with those of the original models.

    Table 3 Resources and average economic loss of corruption cases (1999–2009)
    Full size table

  28. Why law enforcement strength as measured by the ratio of government expenditure on judicial departments does not affect recovered economic losses from corruption deserves to be thoroughly investigated. But it is beyond the scope of this project.

  29. Another possible explanation is that citizens in wealthier regions, better educated and more legally informed, may be more concerned and vocal about corruption, and it leads to higher rates of detection. I thank an anonymous reviewer for suggesting this point.

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Acknowledgments

The author acknowledges the financial support by the Hong Kong Research Grants Council (Project ID 456712) and National Social Science Foundation of China (Project ID 12CZZ018). She is grateful for the valuable comments on the earlier versions of this paper by Ting Gong, Samuel Greene, Margret Levi, Alexander Libman, Daniel Treisman, Jing Ye, Ning Zhang, Jiangnan Zhu, the participants of the ‘Local Governance in China’ workshop and two anonymous reviewers.

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  1. Department of Government and Public Administration, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong

    Jing Vivian Zhan

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Zhan, J.V. Do Natural Resources Breed Corruption? Evidence from China. Environ Resource Econ 66, 237–259 (2017). https://doi.org/10.1007/s10640-015-9947-4

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