Emulate or differentiate?

Chinese development finance, competition, and World Bank infrastructure funding


Foreign aid relationships are valuable to donors as a means of improving development outcomes and influencing recipient country policy. The emergence of new donors can lead to competition as donors vie for influence over recipient government policy and attention. How does such competition affect the behavior of traditional donors? I draw attention to how the rise of China as a provider of development finance is changing the type of development that traditional donors support. Chinese development finance is particularly targeted at large infrastructure projects, and this focus can exert pressure on traditional donors. I suggest traditional donors can either emulate China’s approach to development, i.e. offer projects in infrastructure-intensive sectors, or differentiate themselves and specialize in alternative approaches to development, e.g. focus on governance and social sector interventions. I test this using data on the terms of World Bank and Chinese development finance in over 100 countries. I find the World Bank responds to competitive pressure from China by emulating the Chinese emphasis on infrastructure, allocating a greater share of its development projects in infrastructure-intensive sectors when recipient countries receive more Chinese development finance. Furthermore, subnational data shows that the World Bank also emulates China’s approach to development in response to competition at the regional level. China’s growing role as a provider of development finance affects traditional donor behavior, shaping the type of development donors support by introducing bottom-up competitive pressure.

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  1. 1.

    By comparison, the EU had previously allocated roughly EUR 280 million to the entire road sector in Ethiopia over a period of 6 years, 2008-2013.

  2. 2.

    Note that in order to encompass both the concessional and non-concessional finance provided by China I refer to development finance or overseas finance rather than “aid,” since aid or overseas development assistance (ODA) has clear requirements with respect to concessionality. As defined by the OECD, in order to qualify as ODA, a project must have development as its purpose and at least a 25% grant element. Much of China’s development finance does not meet this definition.

  3. 3.

    In both the descriptive statistics that follow and the analysis in Section 4, I use data on donors’ financing commitments, rather than disbursements. In part, this is due to data availability. The AidData Global Chinese Official Finance dataset uses the “Tracking Underreported Financial Flows” (TUFF) methodology, which provides data only on commitments, not disbursements of Chinese finance. For direct comparison, I therefore use data on World Bank commitments, rather than disbursements, though the amount disbursed can differ from the amount committed. While studies of the effects of aid and finance require data on the amount of finance actually received, commitments data is suitable for studying the intentions of donors and therefore appropriate for studying competition. Disbursements can sometimes vary for reasons that are out of the control of donors, while commitments are fully determined by donors.

  4. 4.

    By drawing attention to these differences in the sectoral allocation of different donors’ development finance, I follow the advice of Nielson et al. (2017), who encourage researchers to further disaggregate analyses of development finance by sector and modality.

  5. 5.

    In fact, the World Bank’s perceived neglect of infrastructure projects and productive sectors has been a long-standing concern for some recipient countries, as Kellerman (2019) describes with respect to Asian countries’ criticisms of the World Bank in the 1960s.

  6. 6.

    Projects are recorded as being in hard sectors if they fall into one of the following sectors in the OECD’s Creditor Reporting System (CRS) sector classification scheme: (1) water supply and sanitation, (2) transport and storage, (3) communications, (4) energy, (5) agriculture, forestry & fishing, and (6) industry, mining, and construction.

  7. 7.

    Humphrey notes that the increase in infrastructure investment in the late 2000s is part of counter-cyclical lending by multilateral development banks in the period after the Global Financial Crisis. See Humphrey (2015: 3-4).

  8. 8.

    In the 2005 Paris Declaration, donors in the OECD Development Assistance Committee (DAC) committed to “harmonizing” their aid programs. However, coordination has in practice not been as extensive as promised. See Aldasoro et al. (2009) and Nunnenkamp et al. (2013).

  9. 9.

    See http://pubdocs.worldbank.org/en/795101541106471736/IBRDCountryVotingTable.pdf and http://pubdocs.worldbank.org/en/845861541106477171/IDACountryVotingTable.pdf

  10. 10.

    This is often discussed as an impediment to conditionality, since the Bank cannot credibly threaten to withhold funds in the case of non-compliance with conditions. See Svensson (2003)

  11. 11.

    Bunte (2019) makes a similar point about the impact of debt constraints, highlighting that these constraints mean that a choice for one lender is a choice against another lender.

  12. 12.

    These constraints on recipient countries’ absorptive capacity for grant-funded projects are one reason that budget support is particularly attractive for recipient countries, since budget support releases donor funds directly into the government’s coffers. This suggests that increasing budget support could be a possible response by traditional donors to competitive pressure from China. However, many donors have become more wary of budget support in the years since the global financial crisis, as aid budgets were reduced and recipient countries made less progress in achieving anticipated reforms than expected (Swedlund 2017a).

  13. 13.

    A value of one is added to Chinese commitments prior to taking the log to avoid the undefined log of zero.

  14. 14.

    Internally the World Bank uses a different sector classification scheme than the OECD CRS, but AidData’s dataset of World Bank projects has assigned sector codes from the OECD CRS coding scheme to World Bank projects to make these comparable. While OECD members assign their projects to only one sector and the AidData dataset similarly assigns Chinese projects to only one sector, the World Bank assigns its projects to up to five sectors. To make the World Bank’s share of hard sector projects comparable, I therefore code a World Bank project as being in a hard sector if the largest sector of a project is a “hard” sector.

  15. 15.

    Additional material for this article is available on the website of the Review of International Organizations.

  16. 16.

    A value of one is added to World Bank commitments prior to taking the log to avoid the undefined log of zero.

  17. 17.

    Including all units in countries that have either a Chinese or World Bank project or both in at least one year 2000-2014 leaves 2,851 ADM1 units and 36,329 ADM2 units.

  18. 18.

    Note that with the much larger number of units, the median probability of receiving Chinese finance at the ADM1 and ADM2 level is zero. The plots in Fig. A2 for the subnational units are therefore calculated by splitting units into the bottom 90% and top 10% probability of receiving Chinese finance, unlike the country-level parallel trends plots, which divide observations by the median probability of receiving Chinese finance.

  19. 19.

    The significant coefficient on the residuals in the second stage of the control function in column 4 indicates that we can reject the null of exogeneity. Chinese financing commitments are endogenous to the share of World Bank projects allocated to infrastructure-intensive sectors.

  20. 20.

    Mummolo and Peterson (2018)

  21. 21.

    In a robustness check, logged population is included as a control, since larger countries may be more likely to receive both Chinese financing and World Bank infrastructure projects. Given the high multicollinearity between population and World Bank commitments I choose not to include both controls in the same model. The positive effect of Chinese commitments on the share of World Bank projects in hard sectors is unchanged when controlling for population and population is not a significant predictor of the share of World Bank projects in infrastructure-intensive sectors.

  22. 22.

    I am grateful to one of the anonymous reviewers for this suggestion.

  23. 23.

    I am grateful to two of the anonymous reviewers for these suggestions of extensions to the finding in this paper.


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I thank Ben Ansell, Gerda Asmus, Daniela Campello de Costa Ribeiro, Surupa Gupta, Juliet Johnson, Emily Jones, Peter Knaack, Silvia Marchesi, Sam Rowan, Ricardo Soares de Oliveira, and Folashadé Soulé-Kohndou for helpful comments on earlier drafts of this paper. Participants at the 2017 “Tracking International Aid and Investment from Developing and Emerging Economies” workshop, the 2017 meeting of the International Political Economy Society, and the 2018 meeting of the International Studies Association provided valuable feedback. I gratefully acknowledge support from the UK Economic and Social Research Council (grant number ES/J500112/1). Any errors are my own.

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Zeitz, A.O. Emulate or differentiate?. Rev Int Organ 16, 265–292 (2021). https://doi.org/10.1007/s11558-020-09377-y

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  • Development finance
  • Aid
  • China
  • World bank
  • Competition
  • International political economy
  • Infrastructure