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.

1 Introduction

The rise of China as a development partner has been one of the most important phenomena in the international development field over the past decade. As a “non-traditional” donor that has, to date, eschewed the OECD’s Development Assistance Committee (DAC), and its classifications of “Official Development Assistance” (ODA), China is forging a new path in development assistance (Kim and Lightfoot 2011; De Haan 2011). While China’s rise creates space for “South-South” or “Triangular” modes of development cooperation, the implications of this emergence are not yet fully understood. The lack of transparency in Chinese aid programs and the apparently uninterested stance towards the governance implications of development lead many to wonder if Chinese engagement will contribute to or undermine the efforts of traditional development partners (Zimmermann and Smith 2011; Abdenur 2014; De Haan 2011; Strange et al. 2015).

These concerns bring the Chinese development ascendancy directly into the ongoing debate over the relationship between good governance and positive development outcomes. As noted by Bräutigam and Knack (2004), the World Bank (1998: 60) has argued that “underlying the litany of Africa’s development problems is a crisis of governance.” Many African states are characterised by high levels of corruption, a lack of accountability, poor institutions and a weak rule of law (Bräutigam 2011). A substantial recent literature has considered how development assistance impacts this governance. Scholars have argued there is a “curse” of development efforts on institutions, that there is no such political curse at all, or that there is a more nuanced, non-linear, relationship between development flows and governance (Djankov et al. 2008; Altincekic and Bearce 2014; Brazys 2016). Some of the ambiguity in this relationship perhaps rests on the fact that the interaction between development projects and good governance may depend not only on which recipient is receiving the development flow, but also on which donor is providing it (Schudel 2008). Particular donors have been explicit in their efforts to use development flows to improve governance. One of the leaders in this effort is the World Bank with its system of “performance-based allocation” (Hout 2007). Indeed, recent evidence suggests that flows from the World Bank are both allocated to, and associated with, countries with better governance (Winters 2010; Okada and Samreth 2012). Therefore, it may be that while development flows from some donors promote good governance (or at least do not hinder it), flows from other donors undermine governance and institutional quality. Yet little work has considered what happens if the impacts of development efforts on governance from different donors are at cross-purposes. Additionally, we are aware of no published work that considers the micro-level relationship between development flows and governance at the local level. Accordingly, in this paper we add to a growing literature that focuses on the emerging development actor China in three ways: first, by asking whether Chinese development flows undermine local good governance; second, by evaluating whether the type of development flow affects the impact of Chinese development efforts on local governance; and third, by asking what the impact is on local governance when donors with different stances towards good governance have projects that are co-located.

In order to examine these questions, this paper takes advantage of recent innovations in development data to conduct a micro-level study relating perceptions and experiences of corruption to proximity to development projects. Corruption is a particularly insidious form of poor governance, with evidence suggesting that increases in corruption both reduce growth and increase income inequality (Gyimah-Brempong 2002; Ugur 2014). Corruption is recognised internationally as a significant problem, with ‘grand’ corruption – the diversion of public funds meant for development – having significant impacts on welfare and political and economic reform and with ‘petty’ corruption placing a greater burden on the most vulnerable in society (Richmond and Alpin 2013). As with broader studies on the relationship between development flows and governance, the evidence on the relationship between aid and corruption is inconclusive, ranging from findings that suggest ODA reduces corruption, to findings that ODA increases corruption, to studies that suggest the result depends on the donor, with multilateral donor efforts associated with reduced corruption and bilateral efforts showing no relationship (Okada and Samreth 2012; Asongu and Nwachukwu 2014; Charron 2011).

In order to evaluate the relationship between development flows and local corruption we combine geo-located data on Chinese and World Bank development projects from the AidData initiative with similarly geo-referenced data from household surveys in Tanzania to employ a spatial identification strategy. These citizen surveys allow for a direct investigation of how proximity to projects is correlated with citizen perceptions of, and experiences with, local corruption. We focus our study on Tanzania for two reasons. First, Tanzania is one of the few countries in Africa where the history and scope of Chinese engagement is roughly comparable to that of the World Bank. Both have been important actors in Tanzania’s development since the early 1960s and as such we can avoid any bias as a result of sequencing or primary or “lead” donorship (Steinwand 2015). Second, focusing on one country allows us to narrow in on issues of donor and project heterogeneity that are crucial to our theoretical expectations and empirical investigation.

We argue that the relationship between development flows and local corruption is nuanced. We do indeed find evidence that Chinese projects are associated with increased experiences and, to a limited extent, perceptions, of corruption when controlling for co-location with World Bank projects, but, crucially, that this finding is predicated on the type of Chinese development flow. Only “other official flows” (OOF) projects are associated with increased corruption, while “ODA-like” projects show no relationship with worsening governance. Finally, while we find that World Bank projects, on their own, are associated with reduced corruption experiences, we find that when Chinese and World Bank projects are co-located, both contribute to higher levels of experienced corruption. These findings suggest that the impact of development flows on local corruption may depend on the donor, flow type and the spatial proximity to other projects.

2 Theorizing development flows and local corruption

We take our theoretical cues from recent scholarship on the determinants of corruption that has focused on the relationship between experiences and perceptions of corruption (Olken 2009; Olken and Pande 2011; Gutmann et al. 2015; Belousova et al. 2016; Donchev and Ujhelyi 2014). This research concludes that perceptions and experiences of corruption are not analogous and that the latter only weakly predict the former. As discussed by Donchev and Ujhelyi (2014) and Gutmann et al. (2015), individual corruption perception is the more encompassing of the two, as it is a function not only of individual corruption experience, but also of awareness of corruption experiences of others or reported corruption in the media, as well as individual and aggregate characteristics such as religion, ethnicity, and economic status that may bias perceptions vis-à-vis actual experience. Accordingly, the pool of individuals who may perceive corruption is substantially broader than those who experience corruption, as the latter may be a sufficient, but not necessary, cause of the former (Belousova et al. 2016).

Recognizing this difference, we build and evaluate theory of development flows on local corruption experiences and perceptions. We argue that different causal mechanisms underlie these two effects. Further, drawing on empirical literature that suggests development flows may both increase or decrease corruption (Okada and Samreth 2012; Charron 2011), we develop theoretical mechanisms with two-sided expectations that are dependent on donor and project characteristics. That the corruption effects of development projects are donor-dependent draws on the literature that examines donor heterogeneity in foreign aid approaches and outcomes (Berthélemy 2006; Kilby and Dreher 2010; Brazys 2013). We first develop our general theoretical perspective before suggesting the implications in Tanzania for the two donors in this study.

2.1 Development flows and local corruption experience

We suggest that development projects may influence corruption experiences via three mechanisms: direct experience, general growth effects, and normative change (Gutmann et al. 2015; Knutsen et al. 2016; Isaksson 2015). In this text, we consider bribes as synonymous with the corruption experience outcome as household surveys indicate it is this type of behaviour which is most universally understood as corruption.¹ Projects may influence citizens’ direct experience with corruption by creating opportunities for bribes via involvement and/or access. First, if citizens are locally involved in the construction or supply of a project as sub-contractors they may experience corruption as they may be required to pay a bribe to secure contracts or provide materials. Second, the project may develop or improve a local private or toll good such as a utility (water and electric supplies, information and communications infrastructure, etc.); or school, hospital, stadium, or transportation facilities, which may facilitate the extraction of bribes for access to these goods. In essence, the presence of these goods potentially increases the “demand” for corruption, similar to the logic in Knutsen et al.’s (2016, p. 4) analysis of local corruption and mines that suggests that the presence of mines may attract a higher number of local officials who are then demanding bribes. A development project may bring a water main or internet connection to a village, or build a school, but this creates an opportunity for a bribe to be demanded for a household to access the good. However, if the project is replacing or improving existing infrastructure it may also have the opposite effect if the donor implements transparency and accountability mechanisms that were not present in the existing infrastructure. While an existing purely local utility may have extracted bribes, officials working on a project developed in conjunction with a donor partner may not be able to extract bribes if donor monitoring is effective.

The effect of development aid on corruption experiences by individuals can also be more indirect, via change in the prevailing corruption norms among officials. As suggested by Isaksson (2015, p. 81), if donors engage in “high level” corrupt practices this may alter the norm for local officials who then become more inclined to engage in corrupt practices of their own. Likewise, if donors simply turn a blind eye to the behaviour of partner officials with whom they work, this may be enough to allow corruption to flourish. Alternatively, if donors actively seek to implement or institutionalize anti-corruption norms through education or conditionality, this may lead to normative feedback that leads to an environment of reduced corruption (Isaksson and Kotsadam 2016).

An alternative indirect effect relates to the supply side of bribes as a result of new resources resulting from development projects. The logic is that increased local resources, for example as a result of mining, can put upward pressure on wages, which creates wealth that officials can tap into through bribing (Knutsen et al. 2016). While Knutsen et al. are skeptical of this mechanism, we think the logic remains applicable to development projects. Increased local resources may increase the ability of citizens to pay bribes. Unlike the previous two mechanisms, this mechanism would serve only to increase corruption experience. Thus, development projects may either increase or decrease the experience of corruption via a direct effect, a norm effect, or increase corruption via a growth effect.

2.2 Development projects and local corruption perception

We suggest that related but conceptually distinct causal mechanisms drive the relationship between development projects and local perceptions of corruption. Indeed, many of the ways by which development projects might be related to corruption are unlikely to involve direct experience for citizens. Actual corruption related to the locating of projects, permit issues for projects, or the use of a preferred contractor are all unlikely to involve general citizen engagement (Dreher et al. 2015). However, as noted by Olken and Pande (2011), perceptions of corruption can also be based on hearsay of someone else’s direct experience of corruption with the project or via a stereotype regarding the donor’s aptitude for corruption irrespective of any actual corruption. Thus, perceptions of corruption may include perceptions both of “high level” maleficence such as nepotism, cronyism, or coercion; or of the prevalence of low-level or personal corruption among a respondent and/or her associates (Afrobarometer 2006).

The likelihood of hearsay of someone else’s experience with corruption should be monotonically related to the amount of actual corruption, although not necessarily homogenous in degree one, as this hearsay of actual corruption can be enhanced or mitigated by the extent of information dissemination. If an instance of corruption is widely reported via formal media or informal information networks, perceptions of corruption may increase by a factor greater than the increase in actual corruption. Conversely, in a low-transparency environment, perceptions may increase by a factor less than experience.

However, projects implemented with low levels of transparency may lead to an impression of corruption, especially when there are preconceptions about a donor. These stereotypes are likely to be rooted in the donor’s history in a particular country. In order for these donor impressions to lead to a change in perceptions of local corruption, citizens would need to be able to accurately identify the donors that are operating in their locality. However, recent work suggests that citizens may not have a high degree of information about the branding of development projects in their vicinity (Dietrich et al. 2015; Baldwin and Winters 2016). Therefore, beyond “direct corruption” experience driving corruption perception, as evidenced in Gutmann et al. (2015), there are two additional causal mechanisms to link development projects to perceptions of corruption: “hearsay” of actual experience and “impressions” based on donor characteristics or stereotypes.

2.3 Heterogeneous donor effect hypotheses

The two sided expectations regarding donor impact on corruption may stem from whether a donor’s projects are subject to “aid capture” or “donor control” (Milner et al. 2016). With regard to the former, recipient governments and officials that already have a proclivity for corruption can use development projects to maintain power via inefficient use of the aid resources. In respect to the latter, donors may use a higher degree of oversight to both limit fungibility, but also to achieve policy objectives, which can include good governance (Milner et al. 2016, p. 223–224). Milner et al. (2016) indeed suggest heterogeneity in donor approaches and the empirical literature supports this assertion (Berthélemy 2006). Accordingly, the extent to which a particular donor enables “aid capture” or promotes “donor control” will influence the direction of the relationship for the different causal mechanisms linking development projects and corruption discussed above. The implication is that expectations need to be donor-specific.

With respect to the two donors in this study, China and the World Bank, there are strong empirical and theoretical grounds for thinking the former’s projects may facilitate “aid capture” while the latter’s are likely to manifest “donor control.” China’s approach to engagement as a development partner has differed significantly from that of the DAC donors, with efforts often focusing on infrastructure building and providing concessional loans to countries without conditionality (Wang and Elliot 2014). Chinese involvement in development has also largely eschewed “best practice” principles which have been established in the DAC over decades. In particular, China has paid little heed to the principles developed in the High-Level Fora on Aid Effectiveness outcomes in the Paris Declaration and Accra Accords and has been, at best, a tepid participant in the Busan cum Global Partnership for Effective Development Cooperation (Mawdsley et al. 2014). Beyond limited rhetorical support for the ideals of the aid effectiveness movement, evidence suggests that China has not adhered to these principles (Strange et al. 2015), instead advocating for ‘South-South’ cooperation without the limitations imposed by the OECD and others. While this alternative focus has been appreciated by countries who have felt unduly constrained by DAC conditionality, the approach has also met with both local and international backlash (Zhao 2014). Several authors note that Chinese aid may be easier to exploit, i.e. subject to “aid capture”, than that provided by other donors, for example by politicians involved in patronage politics, due to the Chinese principle of non-interference in the domestic affairs of recipient countries (Brautigam 2009; Tull 2006; Dreher et al. 2016; Isaksson and Kotsadam 2016). Many authors have expressed concern that China’s principle of non-interference, providing unconditional aid and investment regardless of human rights or governance considerations, is obstructing reforms to governance and accountability in African countries (Wang and Ozanne 2000; Collier 2007; Pehnelt 2007; Strange et al. 2013). Beyond a principled non-interference stance, Chinese non-interference may also be a practical matter. As China is still perfecting its development efforts, China may have not yet developed the capacity to effectively monitor and oversee its development projects.²Non-interference, for principled or practical reasons, is likely to be key in driving the likelihood of “aid capture” for Chinese projects.

Thus, with respect to corruption experience, even if China does not directly engage in any corruption itself, non-interference means they may not monitor or sanction officials who engage in corrupt practices related to their projects. For instance, a Chinese project may bring a utility to an area, but Chinese officials may then be indifferent to local officials charging a bribe for connection to that utility. Non-interference may also lead to an increase in experienced corruption, as China is unlikely to promote anti-corruption norms and indeed may implicitly sanction corrupt behaviour. Finally, the new resources combined with non-interference facilitate the growth mechanism outlined above.

Our expectation that Chinese projects will contribute to the experience of corruption means that the projects should also contribute to perceived levels of corruption. As noted by Muchapondwa et al. (2014), emerging donors, for the most part, do not take part in aid reporting regimes such as the International Aid Transparency Initiative (IATI) or the OECD’s Creditor Reporting System (CRS). While this opaqueness may reduce the likelihood of hearsay, it leaves open the possibility of an impression that Chinese development projects increase corruption. Indeed, as Zhao (2014, p. 1041) explains, “the lack of transparency in China’s business deals facilitates corruption” and, indeed, China has been accused of engaging with corrupt states and elites in exchange for access to resources (Pehnelt 2007).

Impressions of China in Tanzania are likely to be based on historical interactions that are some of China’s oldest and deepest on the continent. China first established a diplomatic relationship with Tanganyika (now mainland Tanzania) in 1961 (Sigalla 2014). Following independence, under the rule of Julius Nyerere, the country initially aimed to follow a socialist path of development and to lessen its dependence on the West, while developing a closer relationship with China (Moshi et al. 2008). Today Tanzania is one of China’s top ten preferred investment countries on the African continent (Hinga and Yiguan 2013). In 2011, there were over 400 Chinese enterprises and 20,000 individuals from China operating in Tanzania, and trade between China and Tanzania increased by 40% in 2010 (Sigalla 2014).

China has directed significant development flows to Tanzania for over 40 years, providing over two billion dollars for a large number of projects, including the Urafiki Textile Factory, the Benjamin Mkapa National Stadium, the Mwalimu Nyerere International Convention Centre and the Zambia and Tanzania railway which linked Dar es Salaam and Kapiri Moshi, one of the largest overseas projects China has ever undertaken (Bailey 1975; Furukawa 2014). In addition to this infrastructure, China has provided hundreds of medical teams, constructed advanced medical facilities and built large scale state farms and farmer training stations to promote agricultural development. It has built a number of rural primary schools and granted scholarships to hundreds of Tanzanian university students (Furukawa 2014). Chinese military assistance to Tanzania is also significant, through provision of equipment and training to the Tanzanian army and the construction of military bases (Moshi et al. 2008). Tanzania’s importance to China is due not only to its extensive endowment of natural resources but also to the gateway function it serves via the Indian Ocean to the rest of Africa.

Impressions of China will be influenced by the national and international media reporting of corrupt practices and bribes related to procurement and natural resources in relation to Chinese officials and projects in Tanzania.³ Beyond this, Round 6 of the Afrobarometer survey was analysed by Mwombela (2015) to see if Chinese engagement in Tanzania is considered as positive or negative by Tanzanians. He finds that China’s economic and political influence in Tanzania is viewed mostly positively, and China is perceived as having more influence on Tanzania than the USA, UK, India, South Africa, the UN or the World Bank (Mwombela 2015). The factors that contribute to a positive impression of Chinese engagement in Tanzania are its investment in infrastructure and the low cost of Chinese products, while Chinese economic activities taking jobs and business from local people and a perceived low quality of Chinese products lead to negative perceptions (Mwombela 2015). Likewise, a recent survey experiment in Uganda found that opinions of Chinese development projects are no worse than those of World Bank and United States projects (Milner et al. 2016). Beyond this, there is little quantitative evidence that China and other emerging partners have worsened governance in Africa (Collier 2007; Alves 2013). Thus, while we expect Chinese development projects to increase perceptions of corruption, we suspect that this happens more via the mechanisms of experience (both direct and hearsay), than by inference based on existing perceptions or stereotypes of Chinese engagement.

• Hypothesis 1: A higher number of local Chinese aid projects will correlate with a higher likelihood of local corruption perceptions and experiences in Tanzania.

Conversely, we expect that the World Bank is more reflective of “donor control” theory (Milner et al. 2016), and indeed Annen and Knack (2015) formally lay out the mechanism by which this might take place. As Charron (2011) explains, since 1997, a number of multilateral aid donors, including the World Bank, shifted the focus of their aid to encourage good governance practices. The World Bank has explicitly allocated its aid based on “Country Policy and Institutional Assessment” (CPIA) scores that consider, among other dimensions, transparency, accountability and corruption (Molenaers et al. 2015; Smets and Knack 2016). Rather than non-interference, these efforts indicate an explicit focus by the World Bank to actively engage with partner countries to reduce corruption. As such, the World Bank is likely to condemn and sanction any corruption it finds within its partner projects. Indeed, the World Bank has been explicitly involved in a “fight against corruption” in Tanzania since 1995 (World Bank 1998, p. iv; Leeuw et al. 1999). These efforts have consisted of building national integrity systems that included engagement across a range of actors and issues to directly stem corrupt practices, but also to alter prevailing corruption norms (Leeuw et al. 1999). More broadly, previous research has shown that aid projects from multilateral donors, and in particular the World Bank, correlate with reduced levels of corruption, particularly since 1997 (Okada and Samreth 2012; Charron 2011).⁴ The World Bank’s project oversight should avoid the increase in experienced corruption that can result from development without such oversight and indeed could reduce experienced corruption if the project substitutes for existing structures that facilitated corruption. Furthermore, World Bank policies may stimulate a local normative environment that promotes good governance, further reducing experienced corruption.

As with China above, once again the relationship between World Bank projects and perceptions of corruption is nuanced. We expect that the World Bank’s hypothesized impact on corruption experience will lead to lower perceptions of corruption based on direct or hearsay experience. However, it remains difficult to assess the extent to which existing views about the World Bank will lead to a positive impression effect. Despite the importance of the World Bank as an aid donor there is surprisingly little research on perceptions of the World Bank in the developing world. One exception is Breen and Gillanders (2015), who find that those who had experienced corruption held less positive views of the World Bank. While this finding poses an endogeneity challenge, one interpretation is that perceptions of the World Bank are associated with corruption.

With regard to the historical experience, the World Bank’s breadth and depth of engagement in Tanzania is similar to that of China, as it has been active in what is now Tanzania since 1959 (Payer 1983). As Payer notes, the early relationship was focused largely on agricultural projects and was tepid due to Tanzania’s socialist leanings, which were at times at odds with the World Bank’s preferred development approach.⁵ This low-level antagonism culminated with a struggle beginning in 1979–1980 over the implementation of a World Bank/International Monetary Fund (IMF) structural adjustment program that ultimately ended with a Tanzanian “capitulation” in 1985 as a result of World Bank “coercion” (Holtom 2005, p. 549). While the relationship has smoothed in more recent years, this somewhat rocky history may suggest mixed attitudes towards the World Bank’s presence. Looking at descriptive data from Round 6 of the Afrobarometer, the World Bank, explicitly mentioned but only as an example (along with the UN) of an international organization, was rated as the “most influential” actor by only 31 of the 2072 respondents (1.5%) who had an opinion (Afrobarometer 6). This suggests that regardless of the direction of the impression about the World Bank and corruption, the salience of the World Bank in defining a respondent’s overall perception of corruption is likely to be low. Finally, as discussed above, respondents in Uganda had impressions of World Bank aid indistinguishable from Chinese aid (Milner et al. 2016). Thus, as with China, we expect that the direct experience and hearsay mechanisms will dominate the impression mechanism for the relationship between World Bank projects and perceptions of corruption and we hypothesize that:

• Hypothesis 2: A higher number of local World Bank aid projects will correlate with a decreased likelihood of local corruption experiences and perceptions in Tanzania.

2.4 Heterogeneous project effects and co-location hypotheses

The theory above suggests that some types of projects may be more or less prone to act as vehicles for linking development flows and corruption. Brazys (2016, p. 298) notes that aid can be used to provide either “public goods” or “selective benefits” and that these differing provisions in aid will have opposite effects on governance. Milner et al. (2016) link this variation in types of benefits to that in aid capture. These findings imply there are likely to be heterogeneous project effects in the relationship between development projects and local corruption. We would expect projects that supply private or toll goods (i.e., goods that are excludable, or provide “selective benefits”) are more likely to be related to changes in corruption perception and experience vis-à-vis projects that supply intangible or non-excludable goods. Excludability is necessary in order to provide the opportunity for extra-legal exchange. As such, pure public goods projects, such as national debt forgiveness or technical assistance based policy projects are unlikely to provide an opportunity for an exacerbation or mitigation of individual-level corruption experience. Conversely, projects that provide direct cash or in-kind assistance, or those that provide excludable infrastructure, are more likely to be linked to corruption, as individuals may have to engage in extra-legal exchange to gain or maintain access to the good. Beyond this, Winters (2014, p. 394) shows how more “delimited” aid projects, or those that are more precisely targeted, are less likely to lead to corruption. A targeted project, say, to build a single school in a village, is likely to have greater transparency and accountability mechanisms in place than a broad national roads or electrification project, spread over a number of sites, that is necessarily more difficult to oversee and thus may be prone to facilitating local corruption. Accordingly, we hypothesize:

• Hypothesis 3: “Selective benefit” development projects are more likely to have an association with experienced and perceived corruption than “public good” development projects.

If our expectations above are correct, then Chinese and World Bank development projects will have opposite effects on corruption. What then happens when projects with inducing and mitigating effects on corruption are co-located? The logic in Winters (2014, p. 393) is again instructive when considering the consequences of development project co-location. Co-located projects may have overlapping implementers, stakeholders, responsibilities and, indeed, outcomes, which contribute to an increasingly complex information environment. This may make monitoring efforts increasingly difficult as individuals may be implementers, officials and/or beneficiaries of multiple projects, but a donor may only have remit for oversight on its particular project.

Therefore, the World Bank will be less able to implement its good governance policies around aid. Note that for the mechanisms outlined above, all development projects create new opportunities for corruption, regardless of the donor, but that the effects for the World Bank differ through conditionality on local conditions, monitoring, and establishing good governance norms. However, where projects from China and the World Bank co-locate, the World Bank will have decreased ability to implement and enforce these policies. As the prospect of a sanction becomes less likely, the cost of corruption decreases and corruption is more likely to occur. Indeed, recent cross-country empirical work by Hernandez (2015) finds that World Bank conditionality is less stringent in countries that receive assistance from China, Kuwait or the United Arab Emirates because the World Bank must “compete” with these donors to maintain a presence in the countries. This mechanism may also operate at a local level where local World Bank projects have less oversight so they can remain attractive to participation from local stakeholders in locations that also have Chinese projects. Accordingly, our expectation with respect to the co-location of development projects and corruption is that:

• Hypothesis 4: An increasing number of co-located development projects between China and the World Bank will increase the likelihood of corruption experience and perceptions as a result of either donor’s projects.

One final consideration of project heterogeneity rests on Dreher et al.’s (2015) important delineation of the types of development flows. As China eschews DAC principles on classification and reporting, the distinction between different types of Chinese development flows is often murky. Using the classification codes from the AidData database, Dreher et al. (2015) find that Chinese “ODA-like” flows are driven more by foreign policy goals, and indeed are linked to recipient needs, while “OOF-like” flows are more closely linked to commercial interests, in particular resource endowments. Reflecting on these findings, it seems plausible that “ODA-like” flows may be less influential on corruption perceptions and experience vis-à-vis their “OOF-like” counterparts, as much of the evidence surrounding Chinese corrupt practices is linked to commercial endeavours. Zhu’s (2016) recent analysis finds that the presence of multinational corporations (MNCs) in China increases corruption. Central to the argument is that the presence of MNCs creates (local) rents, the capture of which provide incentives for corruption. By extension, the commercial and rent-creating Chinese “MNC-like” activity abroad (i.e. OOF) may operate in a similar fashion, leading to an increased impact on corruption vis-à-vis ODA-like flows.

• Hypothesis 5: Chinese “ODA-like” flows are less likely to be associated with corruption experiences and perceptions than Chinese “OOF-like” flows.

3 Evaluating development projects and local corruption

In order to identify a relationship between citizen perceptions and experiences of corruption and Chinese and World Bank development projects we employ a spatial strategy. This spatial identification approach has been used in several recent studies on aid (Dreher et al. 2016; Manda et al. 2014) and allows for a micro-level evaluation of aid impact. Proximity and distance are often utilized in the broader social sciences as a proxy measure to represent different social processes based on Tobler’s “First Law of Geography” (Manda et al. 2014; Tobler 1970). We expect that perceptions and experiences of corruption from particular projects or endeavors will be stronger for those nearer to the events. Indeed, Lopez-Valcarcel et al. (2014) recently found that local corruption contagion is inversely related to distance.

3.1 Dependent variables

To gain an understanding of the impact of proximity to aid projects on perceptions of and experiences with corruption, we make use of individual level survey data. In particular, we make use of the fact that the most well-known survey in Africa, the Afrobarometer, allows for geo-locating the respondents. This provides an opportunity to link this survey data to the geo-referenced and project-level AidData database – similar to, for example, Milner et al. (2016), Findley et al. (2015) or Isaksson and Kotsadam (2016). In other words, we have ward-level measures of proximity to aid projects – as well as a number of ward-level control variables – which we then link to individual-level data on perceptions and reported experience – as well as a number of individual-level control variables.

Our primary outcome variables are measures of perceptions and experiences of corruption from Round 6 of the Afrobarometer survey, conducted in August and September of 2014.⁶ Afrobarometer applies a clustered sampling strategy, typically sampling eight individual respondents within each town or village visited. We geo-code this data in a manner similar to that used in Knutsen et al. (2016), by taking the centroid of the ward reported in the Afrobarometer data as the geo-location of the respondent. Wards are a relatively small spatial delineation in the Tanzanian structure, often referring to segments of larger cities or rural parts of larger regions, with a total of 3644 wards in the country. The ward thus provides a relatively fine-grained measure of geo-location. Afrobarometer 6 covers 209 wards with typically one or two villages per ward.

Our primary indicator of corruption perceptions is based on Question 53 of the survey: “How many of the following people do you think are involved in corruption, or haven’t you heard enough about them to say?” across a number of types of officials. In the first instance, in Table 1, we create a binary indicator that equals “1” if the respondent thought any amount of people were involved in corruption across any of the categories and equals “0” otherwise. We only code this variable for respondents who did not respond “Don’t know/Haven’t heard” for all categories. However, some categories are a better operationalization of perceptions of local corruption than others, which may be more strongly influenced by perceptions of national or global corruption. In particular, response category “D,” which asks the question in the context of “local government councillors”, response category “F”, which relates to “local government tax collectors” and category “H” for “traditional leaders”, who tend to be local, have explicit local orientation. Accordingly, we consider ordered responses to these and other categories in Table 2. As a robustness check, we also take advantage of an independent data source to formulate the dependent variable for corruption perceptions. The 2013 REPOA Citizen’s Survey, which was carried out across 1203 households in 44 village locations and six case councils, asked the question: “Is corruption a serious problem in your council?”⁷ Results using this data are presented in Section A3 of Online Appendix I.

Table 1 Perceptions of corruption (Binary)

Table 2 Perceptions of corruption (Ordered)

Our preferred metric of corruption experience is based on Question 55, which is asked in two parts. In the first part, the question asks if the respondent had an interaction with a number of different government institutions. The second part then asks: “And how often, if ever, did you have to pay a bribe, give a gift, or do a favour…?” As paying a bribe is always local, if the respondent answers “yes” we construct a binary indicator across seven institutions that equals “1” if the respondent had paid any bribe to any institution and equals “0” otherwise. We only code this variable for respondents who had an interaction with at least one of the institutions. As with our perceptions question, we also take advantage of the fact that we can disaggregate across the different institutions, which include utilities, permits, police, courts, hospitals and schools, to check our results on each subset of corruption experience in Table 4.

3.2 Independent variables

Our primary indicator of Chinese and World Bank involvement in Tanzania are instances of Chinese and World Bank Development projects as captured by the AidData datasets. Historically it has been difficult to assess the extent of Chinese aid efforts (in Africa and elsewhere) due both to a lack of transparency and of conceptual clarity over the types of activities that may be constituted as aid, trade or investment (Bräutigam 2009; Alves 2013; Sun 2014; Dreher et al. 2016). However, this difficulty is being overcome via AidData’s Tracking Under-reported Financial Flows (TUFF) effort that has compiled a database on Chinese project locations⁸ in Africa between 2000 and 2013. Their geocoded dataset details 1673 project locations across 50 African countries for this time period and includes a classification scheme of Chinese official and unofficial finance (Strange et al. 2015). We code 297 total Chinese project locations over the period, however, only 139 are coded at a sufficient level of precision to be used in our spatial analysis.⁹ Similarly, we utilize AidData’s “World Bank IBRD-IDA, Level 1” dataset that codes 1035 project locations from 1995 to 2014, although once again only 275 of these are at a sufficient level of geographic precision for our analysis.¹⁰ For both donors, we utilize the “total projects” measures, as some projects that were not completed may have been halted due to corruption issues, which would contaminate our analysis. We employ these total counts as the variables China and World Bank in the regression tables. To evaluate Hypothesis 3, we code a subset of these projects as the variables China Infrastructure and World Bank Infrastructure based on whether the record refers to an excludable infrastructure project. Finally, to evaluate Hypothesis 5, we take advantage of the granularity in the AidData database and separate Chinese infrastructure projects that are ODA-like, China ODA Infrastructure, from those that are OOF-like, China OOF Infrastructure.

We calculate the Euclidean distance from each ward to the nearest respective location of each development project and subsequently count the number of projects within a specified distance. We have no a priori theoretical rationale for a specific radius other than the notion that projects must be sufficiently close for villagers to be aware of their existence and any surrounding issues of corruption. The results presented in Table 1 below utilize a 40 km radius, but we consider the model at other radii as discussed further below, expecting that increased proximity to the project increases the likelihood of the project influencing experience and perception of corruption. Utilizing ArcGIS we represent the projects graphically in Fig. 1 below. The map shows considerable co-location between Chinese and World Bank projects. While this artifact is useful for our hypothesis test, it does raise a concern about multicollinearity, and indeed the simple correlation coefficient between our China and World Bank measures is 0.85. However, checking the variance inflation factors (VIFs) across these two and the other geographically-based measures in our models suggests multicollinearity is unlikely to be a problem.¹¹ Additionally, as the main consequence of multicollinearity is large standard errors, which is a conservative bias in terms of confirming our expectations, we make no further corrections. As a robustness check we also provide estimates for the impact of Chinese and World Bank projects on both perceptions and bribes for a range of different radii, from 10 to 150 km distance from the survey respondents. These results, based on Model 2 in Tables 1 and 3, for perceptions and experiences respectively, are presented graphically in Figs. 4 and 5 below.

Fig. 1

Chinese aid and World Bank aid projects and respondent wards

Table 3 Experiences of Corruption (Binary)

3.3 Control variables

We incorporate a number of baseline control variables, largely drawing from Attila (2009) and Gutmann et al. (2015). In the corruption perception models we control for those who indicated they had paid a bribe. In all models we use measures of the respondent’s age, sex, education, income, occupation as well as indicators of whether the respondent’s location is urban or rural, in a constituency whose parliamentarian is from the ruling party Chama Cha Mapinzundi (CCM) as of the 2015 election, or near to natural resources. The latter two variables are not found in Gutmann et al. (2015) or Attila (2009), but recent empirical findings suggest these are important control variables. Knutsen et al. (2016) find significant evidence that the presence of mining increases local corruption in Africa, and Dettman and Pepinsky (2016, p. 2) find that local resources are associated with decreased “accountability of politicians and bureaucrats”. With regard to local political control, Dreher et al. (2015) find that Chinese development projects are more likely to be allocated to a leader’s birth regions. If we fail to control for either the presence of resources or the local political affinity, we may omit potential confounding factors on the relation between development aid and local corruption. Source information and summary statistics on all data can be found in online Appendix I.

3.4 Estimation strategy and results

Our estimation strategy uses discrete choice models because of the binary and ordinal nature of our outcome variables. However, there are also important reasons to expect strong spatial autocorrelation in our observations. Corruption will be affected by aid projects and socio-economic factors, but also by corruption in geographically proximate areas. One possibility to address this spatial autocorrelation is to allow for a spatial autoregressive component in the error term, the so-called Spatial Error Model (SEM) (McMillen 1992; LeSage 2000). For models with a binary dependent variable there are significant complications in estimating the model, although recent implementations allow for the estimation of the SEM probit (Fleming 2004; Calabrese and Elkink 2014; Wilhelm and Godinho de Matos 2013). In our analysis, however, the effect of the clustering will swamp the impact of a potential diffusion effect of corruption such that a more straightforward multilevel modelling will more appropriately address the concern of a lack of independence in the observations (Case 1991; Case 1992). We therefore add random intercepts for each ward within our sample and use a mixed-effects (ordered) probit model, which is our preferred specification and is used in the regression tables. We evaluate perceptions of corruption in Tables 1 and 2, and experiences of corruption in Tables 3 and 4.

Table 4 Experiences of corruption (Ordered)

3.4.1 Results on perceptions of corruption

We present the results on our key variables in Tables 1 and 2 which show qualified support for our hypotheses. Full model results, including a baseline model of the controls, can be found in Section A2 of Online Appendix I. In Model 1, the aggregated binary specification, there is no statistically significant relationship between proximity to either Chinese or World Bank projects and perceptions of corruption, either when considering all projects (Model 1) or only infrastructure projects (Model 2). As neither World Bank nor Chinese projects have a significant effect, it is unsurprising that the co-location interaction (Model 3) is also insignificant.

There is, however, some support for Hypotheses 1 and 2 in Table 2. While the coefficients for the local officials (Model 1) are insignificant, the coefficients on tax officials (Model 7) and traditional leaders (Model 8) are in the expected direction and statistically significant. This finding is worth further exploration, but suggests that different types of officials may be differently prone to being seen as engaging in corruption as a result of local development projects.¹² Moreover, there is considerable support for Hypothesis 5, the relative effect of Chinese ODA-like versus OOF-like projects, in Models 7.1 and 8.1, which both find a strong and statistically significant effect of OOF-like projects increasing perceptions of corruption while finding no statistically significant relationship between ODA-like projects and perceptions of corruption (Fig. 2).

Fig. 2

Coefficient on China by World Bank corruption perceptions

3.4.2 Results on experiences of corruption

Turning to corruption experiences, we find more consistent support for our hypotheses. In the binary models in Table 3 all of our hypotheses are supported, Chinese projects are associated with more experiences of corruption (Model 1), supporting Hypothesis 1, and this effect is more pronounced when considering only infrastructure projects (Model 2), supporting Hypothesis 3. This relationship only holds, however, for OOF-like projects (Model 3), as Chinese ODA-projects show no statistically significant relationship with increased experiences of corruption (Hypothesis 5). Conversely, World Bank projects are associated with fewer experiences of corruption (Models 1–3) (Hypothesis 2) and, in particular, have their strongest impact on corruption in the absence of any co-located Chinese projects (Model 4). Indeed, co-location of projects increases the impact on corruption for either World Bank or Chinese projects (Model 4) (Hypothesis 4). Plotting this relationship in Fig. 3 shows that World Bank projects are negatively associated with experiences of corruption at a statistically significant level for 0 or 1 co-located Chinese projects, but this relationship becomes statistically insignificant at higher numbers of Chinese projects.¹³

Fig. 3

Coefficient on World Bank by China corruption experience

Table 4 similarly shows broad support for our hypotheses, with the sign and significance holding across a number of different institutions.¹⁴ Model 2 is particularly noteworthy as we are able to further exploit the detail of the AidData dataset to identify specific Chinese development projects that closely map to one of the Afrobarometer corruption experience questions, 55H, which asks if respondents have had to pay a bribe in order to get utilities from the government. Two major Chinese development efforts in our dataset include a national fiberoptic project that was implemented in two phases at 55 project locations and regional water-supply projects, implemented in 35 project locations. Interestingly for our purposes, the fiber project is classified by AidData as “Vague (Official Finance).” The project is a 200 million USD endeavour between the Chinese International Telecommunication Construction Corporation (CITCC) and Chinese telecommunications giant Huawei Technologies and the Tanzania Telecommunications Company TTCL.¹⁵ The project is clearly commercial in nature and has already come under criticism both as a result of contract disputes with subcontractors and for the absence of price decreases with the arrival of the service.¹⁶ This absence of price decreases suggests a capturing of local rents, which may be facilitating the corruption experience, as suggested by our theory above. Model 2 in Table 4 shows a large positive and statistically significant effect between the location of these fiber projects and increased experience of paying bribes to access government utilities.

Conversely, the water projects are all classified as “ODA-like.” Chinese contributions include support to water projects, including the Chalinze Water Supply Project (CWSP) and the Dodoma City Water project that are part of the broader Water Sector Development Program that is supported by the Bank of Arab Development in Africa, the Japanese International Cooperation Agency (JICA), the African Development Bank, the UK Department for International Development, and the World Bank, among others.¹⁷ As shown in Table 4, Model 2, there is no statistically significant relationship between the presence of these Chinese water projects and an increase in experience of paying bribes for utilities. This result, combined with the fiber result above, is strong micro-level evidence in support of Hypothesis 5 that Chinese ODA-like projects are less likely to be associated with increased corruption compared to OOF-like projects. This result is consistent with the theory as the water project is much more of a public utility rather than a commercial good.

As noted above, our selection of a 40 km radius for determining proximity to projects and resources is based on a rationale that proximity to projects influences local perceptions and experiences of corruption. Our data allows us to be reasonably certain of our respondent and project locations to a precision of no worse than 25 km, and often at a higher level of precision.¹⁸ Accordingly, while we are confident that we can co-locate respondents and projects at 40 km, we would not be confident in our geo-locating respondents and projects at very small radii. However, we would also expect that as the radius increases, the effects of development projects on local perceptions and experiences of corruption will become increasingly diffuse until they become negligible. To evaluate the robustness of our results to different radii we run Model 2 from Tables 1 and 3 across radii from 10 km to 150 km at 1 km intervals. Figures 4 and 5 below show the coefficients on China and the World Bank, with their 95% confidence intervals, for corruptions perceptions and experiences, respectively.

Fig. 4

Coefficients by radius for corruption perceptions

Fig. 5

Coefficients by radius for corruption experiences

Figures 4 and 5 show that while the impact of local projects on corruption does indeed depend on the distance to the project, this relationship behaves in an orderly way, essentially showing the strongest relationships when projects are nearby, with the effect diminishing in increasing distance. While the impacts are smaller at radii under 25 km, this could well be driven by the lack of geographical precision in our data as discussed above. For both the World Bank and China, and for both perceptions and experience, the impacts are largest at a radius of roughly 25 km with relatively smooth decays as the distance increases. The confidence intervals in Fig. 5 suggest that the corruption experience results have the highest levels of statistical significance when utilizing radii between roughly 20 and 40 km.

Finally, it is worthwhile to emphasize that all of the results above account for the confounding influence of the co-location of natural resources,¹⁹ which were shown to strongly correlate with local corruption in Knutsen et al. (2016). This suggests that the institutional “resource curse” and “aid curse” (or “blessing”) may be separate phenomena. In other words, development projects may induce or mitigate local corruption even in the absence of local resources.

4 Limitations and avenues for further research

There are two major limitations with our research approach that, if resolved, would be important steps forward from this work. The first concerns the external validity of our results. While focusing on Tanzania has allowed us to avoid heterogeneous country-level effects and focus on micro-evaluations such as the fiber and water cases, it means that our results may not be generalizable across the experience of developing countries. An important next step would be to conduct a cross-national evaluation of development projects and local corruption. A promising step in this direction is the recent working paper by Isaksson and Kotsadam (2016) which considers questions similar to ours across almost 100,000 respondents in 29 African countries, although at the expense of more detailed analysis of different types of projects and of co-location of projects from different donors.

The second major limitation with our study is that it is a cross-sectional instead of longitudinal design. In the absence of being able to randomly assign the treatment – the presence of development projects within the spatial proximity of the respondent – the best observational research design would be to have pre- and post-treatment observations on the key dependent variables for all individual respondents, some proximate to the projects and some not. While the Afrobarometer data consists of several waves over time, it is not panel data, which makes it impossible to measure the dependent variable both pre- and post-treatment for each individual survey respondent. Thus, we cannot exclude the possibility that non-randomly distributed exogenous factors affect both the treatment assignment and the outcome variable, or indeed, that there is a reverse effect. It is likely that project locations are selected for any number of socio-political-economic reasons, as suggested by Dreher et al. (2015). Thus, there is likely to be some endogeneity where Chinese and World Bank projects locate to areas that already have higher or lower perceptions and experiences of corruption. From a causal inference perspective, our research design is therefore limited, and the possibility of confounding factors or endogeneity affecting our results cannot be denied, both in terms of selection bias and in terms of differential treatment effect bias (Pearl 2000; Morgan and Winship 2007).

Our findings are therefore based on strong theoretical expectations, from which observable implications in a cross-sectional study can be derived, which are confirmed by our statistical analysis. From an empirical perspective, a quasi-experimental design could be a useful avenue forward for this research, one that engages in both pre- and post-treatment surveys of corruption with the same respondents, where the carefully measured timing of additional development projects are the treatment. (Quasi-) experimental research designs have been gaining increased use in studies of development flows and could well be applicable here (Dietrich et al. 2015; BenYishay et al. 2016).²⁰ It should be noted that while Isaksson and Kotsadam (2016) address the first caveat, these empirical design challenges are also present in their study.

A final interesting avenue for further research would be to compare the results of this study to a similar study using geo-located DAC donor aid or foreign direct investment (FDI) flows, or aid projects or FDI from other emerging donors in Tanzania or across a broader range of developing countries.

5 Conclusions

Chinese development flows bring both opportunities and challenges to Tanzania, the broader African continent, and the entire developing world, directly but also via interaction with other development actors. Yet our results suggest that there are no blanket generalizations to be made about the relationship between Chinese engagement and local corruption in Tanzania. While there is evidence that proximity to Chinese development projects is associated with increased local corruption experience, and to some extent perceptions, this finding appears to be limited to non-ODA-like Chinese projects. Chinese OOF-like projects are much more similar in nature to pure commercial flows like FDI, and as such, an “apples to apples” comparison would need to consider how FDI relates to local corruption. Indeed, such heterogeneity may even extend to the project-level, as we found substantially different effects for an OOF-like fiber-optic project compared to an ODA-like water project. Moreover, we provide evidence that co-location increases the association of local corruption experience with both Chinese and World Bank projects. Our theory suggests that this may be due to something akin to a “fog of aid,” wherein an increasing number of local development projects makes it difficult to monitor and sanction corruption. While there may be important reasons for co-locating development projects, our finding may serve as a warning that there could be negative externalities from this type of clustering.

Much of the literature on Chinese development finance, in particular, is founded on untested assumptions, case studies and incomplete data (Strange et al. 2013). Our study has contributed to an increasingly nuanced understanding of this large, complex and important development actor. As China continues to increase its development presence it is crucial to understand how its efforts and its policy of non-interference complement or hinder the development and governance efforts of other development actors.