Has Chinese aid benefited recipient countries?

The rapid rise of China as a major source of development finance is the subject of much speculation and debate, partly due to the lack of comprehensive data on Beijing’s foreign aid activities. Unlike traditional donors organized in the OECD Development Assistance Committee (DAC), Beijing does not publish detailed country- and project-level information about its foreign aid activities. But the release of AidData’s Global Chinese Official Finance Dataset,  which captures 13,427 Chinese government-financed development projects worth $843 billion across 165 countries over 2000-17, has spurred a growing body of research relying on rigorous empirical analysis to understand the nature and consequences of Chinese foreign assistance.

A nascent literature with mixed evidence

The empirical literature on Chinese aid effectiveness has investigated the effect of Beijing’s foreign assistance on a broad range of outcomes in recipient countries, including economic and social development, governance, conflict, and deforestation (Dreher et al. 2016, 2017; Martorano et al., 2020; Isaksson and Kotsadam, 2018a; Gehring et al., 2022; Ben Yishay et al. 2016, to cite a few). Some researchers have explored whether Chinese aid inflows crowd out development finance from other bilateral or multilateral donors (e.g., Kilama, 2016; Humphrey and Michaelowa, 2019; Zeitz, 2021), while others have examined how they contribute to expanding Beijing’s soft power, including through a change in citizens’ attitude towards China in aid-recipient countries, and foreign policy alignment with Beijing at the United Nations’ General Assembly (e.g., Xu et al., 2020; Struver, 2016). To date, the empirical evidence on the effectiveness of Chinese official finance has been mixed, with studies finding positive, negative, or even no impact of Beijing’s aid on recipient countries. In our recent article published in World Development, we employ a meta-regression analysis to take stock of this controversial literature.

Taking stock of the empirical literature on Chinese aid effectiveness

Meta-regression analysis is a statistical method for systematically reviewing, summarizing, and evaluating the diverse findings from empirical studies conducted on a given topic using different methods and research designs (Stanley, 2001). We implement a meta-regression analysis on the Chinese aid effectiveness literature using 1,149 estimates taken from 29 studies. We find that, on average, Chinese official assistance has had some bearing on development outcomes in recipient countries, but its effect has been heterogeneous and very small in size. Beijing’s aid is associated with a positive – albeit negligible – effect on economic outcomes, somewhat consistent with the claim that Chinese government-financed transport projects contribute to closing developing countries’ infrastructure gaps. It also correlates with deforestation and negative perceptions of China among citizens in recipient countries, although the estimates are very small in size. We find no robust evidence that Beijing’s aid affects social outcomes, stability, governance, or the inflow of aid from other donors. We also show that differences in the type of development outcome considered, how the Chinese aid variable is measured, the estimation method used, the geographic region under study, and author institutional affiliation explain the large variations among Chinese aid effectiveness estimates reported in the empirical literature.

Is Chinese aid different from traditional aid?

Our meta-regression analysis suggests that the impact of Chinese foreign assistance on recipient countries’ development outcomes bears similarities and differences with that of traditional aid from OECD DAC donors. For instance, the positive but negligible effect of Chinese aid on economic outcomes is consistent with previous meta-analyses on traditional aid (Doucouliagos and Paldam, 2013). Similarly, the absence of a robust average effect of Chinese official assistance on governance outcomes appears to echo the mixed results from the Western aid literature, with some studies showing that aid increases corruption (Svensson, 2000), undermines democracy (Djankov et al., 2008), and disincentivizes domestic reforms (Bräutigam and Knack, 2004), while others find beneficial effects on governance (Okada and Samreth, 2012). However, our results for Chinese official development assistance depart from the empirical literature on the impact of traditional aid on Western donors’ soft power, which mostly points to positive effects. For instance, Andrabi and Das (2021) find that Western aid to Pakistan following the 2005 earthquake improved the local population’s trust in Europeans and Americans. Dell and Querubin (2018) show that during the Vietnam War, citizens in regions where the U.S. military implemented development programs reported more positive attitudes towards Americans. Our results for China also contrast with studies that identified the conflict-fueling effect of aid from Western donors (Besley and Persson, 2011), and the overwhelming evidence of positive contributions of OECD DAC aid to education, as summarized by Riddell and Niño-Zarazúa (2016), and health. As for the environmental implications of foreign assistance, our meta-regression analysis suggests an adverse average effect for Beijing, while the results from the traditional aid literature are mixed.

Avenues for future research on Chinese aid

China’s recent pledge to develop a modern statistical information system for foreign assistance is a welcome step toward transparency that could provide fertile ground for further research. With China poised to remain a key provider of development finance in the foreseeable future, a meta-regression analysis could provide useful insight into the debated literature on the determinants of Beijing’s aid allocation. Beyond foreign aid, taking stock of the development effects of other Chinese flows such as trade and foreign direct investment could also be of interest given the considerable interest in China’s footprint in developing countries.