At the end of last year, research organization Aid Data published the first extensive data set documenting Chinese aid flows around the world. The Chinese government is notoriously secretive about its development aid outflows: it does not publish any project-level or country-specific data on its own nor does it work with international organizations that attempt to quantify and release this information. To create the data set, researchers at Aid Data scoured publicly available news reports, official embassy documents, and aid/debt information from other countries for the past five years. The Tracking Underreported Financial Flows methodology that they rely on is detailed here.
China's lack of transparency has been cited as a growing issue given its increasing role on the international stage. In the past few years, China surpassed the U.S. in terms of annual spend on development aid and has established itself as one of the key development players in Africa. Critics - such as Moises Naim in this opinion post years ago in the New York Times - have raised concerns that in competing with Western donors and international organizations such as the World Bank, China is seen as the "no strings attached" donor likely to give to undemocratic regimes and countries with poor institutions that would be subject to higher scrutiny under traditional Western giving and lending practices.
It is unsurprising, then, that researchers have already taken to the new Aid Data data set to answer a myriad of questions about the impact of Chinese aid on local economies. In this post I look specifically at the paper, "Chinese aid and local corruption" published last month in the Journal of Public Economics. Authors Isakkson and Kotsadam (2018) employed Aid Data's Chinese Official Finance to Africa data set to identify locations with: (1) ongoing Chinese aid projects, and (2) those selected for future Chinese aid projects. They then connected this data with Afrobarometer survey data eliciting survey respondents' experiences with corruption (whether they "had to pay a bribe, give a gift, or do a favor to government officials") in order to estimate the effect of an ongoing Chinese aid project in a given location on the level of local corruption. Because they geocode both data sets and restrict their sample to only those aid projects for which they can identify a granular location, the authors are able to identify respondents within 50 or 25 km of aid project locations to analyze their experiences with localized corruption.
The authors find that Chinese aid projects have a statistically significant effect on local corruption with point estimates of a 3.5% (bribes given to "avoid a problem with the police") or 2.7% (bribes given to "get a document or permit") increase in bribery in locations with ongoing Chinese projects relative to locations selected for future Chinese projects. They speculate that Chinese aid increases local corruption through two potential mechanisms: first, that presence of the donor changes the cost-benefit structure of engaging in corruption (i.e. if the donor is indifferent as to the "means" by which a project is completed and is willing to reward for the "ends" of completing it then this raises the benefits associated with corruption). Second, they posit that the donor is in a position of power to influence social norms and create institutional change. A donor's acceptance or propagation of corrupt activity could worsen norms (noting that norms are easier to change for the worse than the better). Finally, they employ the same strategy around World Bank aid project locations and do not find any effect of these projects on local corruption.
Estimation strategy
The paper employs a model similar to a difference-in-differences model wherein the responses of individuals who live near a site that is currently developed by the Chinese are compared to the responses of individuals who live near a site that will be developed by the Chinese in the future. In the following regression model, the authors employ the difference between the coefficients on "active" and "inactive" as the key parameter of interest. Individuals located within the radius of an ongoing Chinese aid project are "active" ("active" = 1). Those located within the radius of a future Chinese aid project are "inactive" ("inactive" = 1). And those that are outside the radii of any current or future Chinese aid projects are neither active nor inactive ("active" = 0; "inactive" = 0).
(1) Yit = β1 activeit + β2 inactiveit + αs + δt +y Xit +εivt
Isakkson and Kotsadam employ this method rather than interpreting the coefficient on the "active" dummy to avoid the ex-ante assumption that "there is no relationship between project localization and the pre-existing institutional characteristics of project sites." In other words, if the locations for Chinese aid projects were selected based on certain institutional characteristics it is very possible that those institutional characteristics are correlated with corruption levels and that, as a result, interpreting the coefficient on "active" alone erroneously captures pre-existing differences in corruption levels between locations with Chinese aid projects and those without.
To control for the geographic and time-based variation in the data set - which includes data from across the African continent and spanning 2000-2013 - the authors include spatial fixed effects, year fixed effects, and a set of individual controls. While the baseline results indicate that Chinese aid projects led to an increase in local corruption, the various iterations do lead to questions:
- The authors point to two statistics from the regression output to determine whether the parameter of interest is significant: coefficient on the "active" dummy variable (if there is an effect this should be positive and statistically significant) and the statistic for an F-test testing the hypothesis "active - inactive = 0" (if the effect on corruption is a result of a Chinese aid project this hypothesis should be rejected). The baseline results indicate that both with respect to police bribes and permit bribes the coefficient on "active" is positive and highly statistically significant and the F-test hypothesis can be rejected at the 5 percent level. See Table 1 for details.
- However, the sensitivities indicate that the coefficient on "active" is statistically significant across most but not all iterations and the F-test cannot be rejected at the 5 percent level in at least one of the iterations. The results indicate that the effects are stronger for police bribes than they are for permit bribes which leads to questions about the mechanism that leads to increased corruption and why it impacts police bribes more so than permit bribes. See Table 2 for details.
- Furthermore, the authors conclude that World Bank aid projects do not similarly lead to an increase in local corruption not because the coefficient on "active" in that sample set is not positive or statistically significant (in fact it is significant in several iterations) but because the F-test results indicate that it cannot be rejected that the "active" and "inactive" coefficients are equal. But why is it that both "active" and "inactive" locations with World Bank projects see a higher level of local corruption than those without World Bank projects (where Chinese aid projects don't, because the "inactive" coefficient is not significant in most Chinese projects)? It's not a question that this paper seeks to answer but there should be a reasonable hypothesis for why locations selected for World Bank vs. Chinese aid projects differ in this way.
- My main question reading this paper was whether the implementation of an aid project leads to a change in the demographic population of a locality. Given that the Afrobarometer survey is not a panel data set, the same individuals are not necessarily interviewed pre- and post-implementation of an aid project.
- While it is possible that the implementation of a project leads to the corruption of existing actors it also seemed possible that it led to inflows of new actors into the locality due to a possible increase in local economic growth and activity. If the increase in local corruption is due to the influx and changing composition of the locality this is distinct from an increase due to corruption of the existing population.
- The authors attempt to address this question by analyzing whether there are more police stations in active aid areas vs. inactive aid areas (to address the claim that more bribery is a result of more police stations rather than more corruption), stating: "Neither do we find any evidence that the results are driven by increased resource flows making the project areas into 'honey pots' attracting corrupt actors." However, the empirical investigation does not seem to answer the original question of whether aid projects lead to an influx of corrupt actors.
- It boils down to how the effect is interpreted: in the case of this paper, the parameter of interest does not distinguish or isolate the two effects presumably because both lead to an increase in local corruption whether by migration or by impacting existing populations.
Implications for the future of development aid
Overall, the implications of the paper that Chinese aid projects lead to local corruption are the first step in understanding how different forms of aid (and specifically "no strings attached" aid) can create institutional change and impact social norms. While the quantification of this impact is important, the paper does not explicate the mechanism by which these projects increase corruption and without that linkage it is difficult to prescribe appropriate policy solutions to improve local governance and reduce corruption. But the paper reaffirms questions about China's development strategy to work within existing entrenched systems to create economic growth vs. the traditional Western approach to attempt to improve governance and create institutional change at the same time. The broader takeaways for the field of development aid:
- It is clear that conceptualizing Chinese vs. Western aid as a competition is not the most effective way to improve growth and development in these economies, rather, assuming Chinese aid will continue at its current rate how can each set of aid practices complement and supplement one another? Transparency and data availability make it easier to answer these questions.
- Given the importance of international coordination in aid, this new availability of data on Chinese aid offers a novel opportunity for other donors to provide a value-add to these economies in sectors and projects that the Chinese are not investing in and to advocate more strongly for better governance and effective democratic institutions given the apparent worsening of certain aspects of local governance as a result of Chinese aid projects.
- China's increasing role in development and aid places places a need for further introspection on the part of international organizations such as the World Bank, specifically: how should the organization continue to advocate for good governance and effective democratic institutions while simultaneously recognizing the need to work with one of the largest unilateral donors that may not be interested in propagating those norms? Will the World Bank's role and priorities change as funds from China and from private investors play an increasing role in the growth of developing economies? How can it position itself most effectively in this rapidly changing space and provide a distinct value-add?
- Isaksson, A. and Kostadam, A. (2018). Chinese aid and local corruption. Journal of Public Economics.
