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Bad neighbors? How co-located Chinese and World Bank development projects impact local corruption in Tanzania

Abstract

The rise of China as a "non-traditional" development partner has been one of the most important phenomena in the field over the past decade. The lack of transparency in Chinese development projects, coupled with an uninterested stance towards governance, lead many to wonder if Chinese engagement will contribute to or undermine existing development efforts. This paper adds to the debate by inquiring as to the relationship of Chinese development efforts with perceptions of, and experiences with, corruption when projects are closely-located to those from a traditional donor, the World Bank. Taking advantage of spatial data, the paper evidences an association between the location of a larger number of Chinese projects and higher experiences with and, to some extent, perceptions of corruption when accounting for co-located World Bank projects. Likewise, while World Bank projects are associated with lower levels of corruption in the absence of Chinese projects, this relationship disappears when Chinese projects are nearby. However, these relationships only hold for Chinese projects which are not "aid-like," suggesting that the differentiation of Chinese overseas flows is an important consideration when studying China as a development partner.

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Notes

  1. 1.

    A survey conducted by the Front Against Corrupt Elements in Tanzania (FACEIT) and the Tanzanian Prevention and Combating of Corruption Bureau (PCCB) in 2009 found that most citizens understand corruption as demand for unofficial payment (92.5%), as opposed to demand for sex (29.4%) or abuse of power (25.9%). Perception beyond these facets of corruption was limited, with respondents failing to perceive informal payments for services or embezzlement and fraud as corrupt practices (PCCB, 2009).

  2. 2.

    We thank an anonymous reviewer for this insight.

  3. 3.

    E.g. LaFraniere, S. and J. Grobler “China Spreads Aid in Africa, With a Catch” New York Times, September 21, 2009; “Report: Chinese smuggled ivory out of Tanzania during state visit” Al Jazeera, November 6, 2014; “Chinese bribes in Dar, admits China Envoy”, The Citizen, July 15, 2014.

  4. 4.

    However, as Winters (2014) shows, this aim is not always achieved, due to heterogeneous project effects that are further elaborated below.

  5. 5.

    Indeed, the World Bank refused to fund the Tanzania-Zimbabwe railroad subsequently financed by China.

  6. 6.

    “Summary of Results. Afrobarometer Round 6 Survey in Tanzania, 2014” available at : http://afrobarometer.org/sites/default/files/publications/Summary%20of%20results/tan_r6_sor_en.pdfaccessed 22/10/2016. We utilize only the 6th round of the Afrobarometer for several reasons. Practically, many projects, including the 2nd phase of the national fiberoptic project (discussed further below) have completion dates after earlier rounds of the survey, including the 5th round. Additionally, while AidData has made excellent strides towards capturing the timing of the projects, we remain skeptical about the potential for close temporal identification due to the fact that the data often include the same start or end dates at all project locations for a project at multiple locations. Moreover, it is unclear to us that the timing for corruption would necessarily immediately follow a start or end date. Accordingly, we are most comfortable using the latest round of surveys and taking the cumulative projects over the period, with the idea that our theoretical mechanisms are likely to lead to changes that endure or repeat over time. A project that could convincingly tackle issues of temporal identification would be a significant step forward in this literature.

  7. 7.

    The response categories to this question were ordinal, with “yes,” “no,” “maybe” and “don’t know” as the permissible options. We recode the data as a binary variable coded as 1 for “yes” answers and as 0 for answers of “maybe” and “no”, dropping the “don’t know” responses.

  8. 8.

    Importantly, this data maps project locations such that the same project may be listed in multiple locations. We discuss this in more depth below.

  9. 9.

    We only include projects coded with precision code “1” or “2” by AidData.

  10. 10.

    Full lists of both Chinese and World Bank projects used in the analysis can be found in Appendix I.

  11. 11.

    The mean VIF between China, World Bank, Resources, the Urban indicator and the Government Party indicator is 2.40 with a maximum VIF of 4.55. The condition number for these variables is 7.88.

  12. 12.

    There is also support for Hypotheses 1, 2 and 4 for corruption perceptions when using the REPOA data. These results are presented and discussed in Section A3 of Online Appendix I. There remains no support, however, for Hypothesis 4 in the Afrobarometer data as shown in Figure A1 in Online Appendix I.

  13. 13.

    All interactions plots were generated modifying code used to produce similar figures in Copelovitch and Ohls (2012).

  14. 14.

    Interaction models of those presented in Table 4 also show general support for Hypothesis 4, as shown in Figure A2 in Online Appendix I.

  15. 15.

    http://www.ictworks.org/2011/05/09/why-tanzanian-internet-access-prices-havent-decreased-arrival-seacom/ accessed 28–10-2016

    http://allafrica.com/stories/201411040466.html accessed 28–10-2016.

  16. 16.

    http://www.ictworks.org/2011/05/09/why-tanzanian-internet-access-prices-havent-decreased-arrival-seacom/ accessed 28–10-2016

    http://allafrica.com/stories/201608290091.html accessed 28–20-2016.

  17. 17.

    http://www.chaliwasa.go.tz/index.php/en/about-us/overview accessed 28-10-2016

    http://tanzaniainvest.com/construction/world-bank-boosts-tanzania-water-sector-development-project-with-usd-449-million accessed 28–10-2016.

  18. 18.

    Where Knutsen et al. (2016) find that the method used to geo-code the Afrobarometer respondents is precise to roughly 15 km, while AidData projects at precision code “2” are precise to 25 km.

  19. 19.

    Our results available in Online Appendix I also suggest a weakly significant (α = 0.10) positive relationship between resources and local experiences of corruption.

  20. 20.

    Where BenYishay et al. (2016) are able to incorporate temporal identification by combining “active years” of a project with a panel of forestry satellite data, allowing for observation of changes in the same forest area over a number of years.

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Brazys, S., Elkink, J.A. & Kelly, G. Bad neighbors? How co-located Chinese and World Bank development projects impact local corruption in Tanzania. Rev Int Organ 12, 227–253 (2017) doi:10.1007/s11558-017-9273-4

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Keywords

  • China
  • World Bank
  • ODA
  • OOF
  • Development
  • Corruption
  • Governance
  • AidData
  • Tanzania
  • Africa

JEL Classification

  • F35
  • F63
  • O19
  • O55
  • C21
  • D73