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Bad Neighbors? How Co-located Chinese and World Bank Development Projects
Impact Local Corruption in Tanzania
Please Cite as:
Brazys, Samuel; Elkink, Johan A.; Kelly, Gina (2017) 'Bad Neighbors? How co-located Chinese and World Bank Development Projects Impact Local Corruption in Tanzania'. Review of International Organizations, 12 (2):227-253.
Samuel Brazys
Johan A. Elkink
Gina Kelly1
1 Dr. Samuel Brazys is corresponding author ([email protected]) and assistant professor of international relations in the School of Politics and International Relations at University College Dublin. Johan A. Elkink ([email protected]) is assistant professor of social science research methods in the School of Politics and International Relations at University College Dublin. Gina Kelly ([email protected]) is a Policy Analyst at the Commission for Energy Regulation and a graduate of the Trinity College Dublin-University College Dublin Masters in Development Practice Programme.
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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 (1989: 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
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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 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
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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 again 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
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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 phenomena, 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).
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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 (Berthelemy 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.2 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
2 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).
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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 (2016, 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).
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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 sceptical 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 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
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low-level or personal corruption among a respondent and/or her associates (Afrobarometer,
2006)3.
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.
3 Afrobarometer briefing paper (2006) Combating Corruption in Tanzania: perceptions and experience.
Available at: http://www.afrobarometer.org/publications/bp33-combating-corruption-tanzania-
perception-and-experience Accessed April 5, 2016.
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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 (Berthelemy 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
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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
(Xiaobing 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 projects4. Non-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 4 We thank an anonymous reviewer for this insight.
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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).
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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 Tanzania5. Beyond this, Round 6 of the Afrobarometer
survey was analysed by Mwombela (2015)6 to see if Chinese engagement in Tanzania is 5 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.
6 Mwombela, Stephen (2015) What shapes Tanzanians’ image of China? Findings from the Afrobarometer
Round 6 Survey in Tanzania, Repoa, Policy Research for Development. [ONLINE] Available at:
http://www.repoa.or.tz/images/uploads/TAN_R6_dissemination_2_Chinese_Engagement_25Feb2015
.pdf [Accessed 15 August 2015]
National Bureau of Statistics, Statistics for Development. 2015. [ONLINE] Available
at :http://www.nbs.go.tz/. [Accessed 25 August 2015].
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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 leads 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 (2016) 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 (Molenares et al. 2015; Smets and
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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)7. 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
7 However, as Winters (2014) shows, this aim is not always achieved, due to heterogeneous project effects that are further elaborated below.
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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 approach8. 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 2,072 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. 8 Indeed, the World Bank refused to fund the Tanzania-Zimbabwe railroad subsequently financed by China.
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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:
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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
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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.
19
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
and Jimenez (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
20
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 2014.9
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 3,644 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.
9 “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.pdf accessed
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 sceptical 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.
21
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 1,203 households in 44 village locations and
six case councils, asked the question: “Is corruption a serious problem in your council?”10
Results using this data are presented in Section A3 of Online Appendix I.
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
10 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.
22
“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 locations11 in Africa between 2000 and 2013. Their geocoded dataset details
1,673 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 analysis12. Similarly, we utilize
AidData’s “World Bank IBRD-IDA, Level 1” dataset that codes 1,035 project locations from
1995 to 2014, although once again only 275 of these are at a sufficient level of geographic
11 Importantly, this data maps project locations such that the same project may be listed in multiple locations.
We discuss this in more depth below.
12 We only include projects coded with precision code “1” or “2” by AidData.
23
precision for our analysis13. 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 tables below. 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
kilometer (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 Figure 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.14 Additionally, as the main
consequence of multicollinearity is large standard errors, which is a conservative bias in 13 Full lists of both Chinese and World Bank projects used in the analysis can be found in Appendix 1.
24
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 Figures 4 and 5 below.
Figure 1: Chinese Aid and World Bank Aid Projects and Respondent Wards
14 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.
25
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
26
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 tables below.
We evaluate perceptions of corruption in Tables 1 and 2, and experiences of corruption in
Tables 3 and 4.
TABLES 1 AND 2 ABOUT HERE
FIGURE 1 ABOUT HERE
Table 1: Perceptions of Corruption (Binary)
Model 1 Model 2 Model 3China 0.0190
(1.16)World Bank -0.0168
(1.40)China Infrastructure 0.0285
(0.86)0.0114(0.21)
World Bank Infrastructure -0.0157(0.80)
-0.0205(0.90)
China*World Bank 0.0017(0.41)
Baseline Controls Yes Yes YesWard Mixed Effects Yes Yes YesN 1836 1836 1836Prob > χ2 0.0000 0.0000 0.0000
Absolute value of z-scores in parentheses. † p>0.10, *5% p>0.05, **1% p>0.01
27
0.5
11.
5H
isto
gram
of C
ount
Wor
ld B
ank
-.1-.0
50
.05
.1C
oeffi
cien
ts a
nd 9
5% C
Is
0 2 4 6 8 10 12 14 16Count World Bank Aid
Corruption PerceptionsFigure 2: Coefficient on China by World Bank
0.5
11.
52
His
togr
am o
f Cou
nt C
hina
-.1-.0
50
.05
.1C
oeffi
cien
ts a
nd 9
5% C
Is
0 1 2 3 4 5 6 7 8 9 10 11 12Count Chinese Aid
Corruption ExperienceFigure 3: Coefficient on World Bank by China
28
Table 2: Perceptions of Corruption (Ordered)
Model 1Local
Model 2President
Model 3Parliament
Model 4Bureaucrat
Model 5Judge
Model 6Police
Model 7Tax
Model 7.1Tax
Model 8Traditional
Model 8.1Traditional
China Infrastructure
0.0232(1.08)
-0.0024(0.09)
-0.0011(0.05)
0.0026(0.12)
-0.0006(0.03)
-0.0059(0.29)
0.0530*(2.40)
0.0474†(1.84)
World Bank Infrastructure
0.0031(0.24)
-0.0040(0.24)
0.0061(0.48)
0.0090(0.70)
-0.0031(0.25)
0.0084(0.69)
-0.0296*(2.23)
-0.0337*(2.51)
-0.0415**(2.66)
-0.0482**(3.10)
China ODA Infrastructure
-0.0079(0.19)
-0.0680(1.35)
China OOF Infrastructure
0.0976**(2.91)
0.1283**(3.37)
Baseline Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes YesWard Mixed Effects Yes Yes Yes Yes Yes Yes Yes Yes Yes YesN 1749 1612 1664 1727 1741 1805 1664 1664 1494 1494Prob > χ2 0.0000 0.0000 0.0006 0.0004 0.0039 0.0000 0.0014 0.0008 0.0000 0.0000
Absolute value of z-scores in parentheses. † p>0.10, *5% p>0.05, **1% p>0.01
Table 4: Experiences of Corruption (Ordered)
Model 1(Utility)
Model 2(Utility)
Model 3(Permit)
Model 4(Police)
Model 5(Court)
Model 6(Hospital)
Model 7(School)
China Infrastructure 0.1684*(2.54)
0.0423(0.63)
0.2042**(3.12)
0.1551*(2.01)
0.0587†(1.71)
0.0832*(2.07)
World Bank Infrastructure -0.0671(1.63)
-0.0327(0.91)
-0.0043(0.11)
-0.1361**(3.23)
-0.1080*(2.13)
-0.0188(0.91)
-0.0066(0.26)
China Fiber (OOF) Infrastructure 0.3322*(2.37)
China Water (ODA) Infrastructure -0.1327(0.47)
Baseline Controls Yes Yes Yes Yes Yes Yes YesWard Mixed Effects Yes Yes Yes Yes Yes Yes YesN 353 353 464 396 265 1668 1137Prob > χ2 0.0005 0.0013 0.0001 0.0001 0.0249 0.0000 0.0000
29
Absolute value of z-scores in parentheses. † p>0.10, *5% p>0.05, **1% p>0.01
30
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.15 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.
15 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.
31
Table 3: Experiences of Corruption (Binary)
Model 1 Model 2 Model 3 Model 4China 0.0378*
(2.51)World Bank -0.0268*
(2.32)China Infrastructure 0.0672*
(2.23)-0.0082(0.16)
World Bank Infrastructure -0.0309†(1.68)
-0.0343†(1.78)
-0.0522*(2.41)
China ODA Infrastructure -0.0369(0.50)
China OOF Infrastructure 0.1311**(2.79)
China*World Bank 0.0074†(2.89)
Baseline Controls Yes Yes Yes YesWard Mixed Effects Yes Yes Yes YesN 1902 1902 1902 1902Prob > χ2 0.0000 0.0000 0.0000 0.0000
Absolute value of z-scores in parentheses. † p>0.10, *5% p>0.05, **1% p>0.01
TABLES 3 AND 4 ABOUT HERE.
FIGURE 2 ABOUT HERE
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
32
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 Figure 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 projects16.
Table 4 similarly shows broad support for our hypotheses, with the sign and significance
holding across a number of different institutions.17 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.18 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 16 All interactions plots were generated modifying code used to produce similar figures in Copelovitch and Ohls
(2012).
17 Interaction models of those presented in Table 4 also show general support for Hypothesis 4, as shown in
Figure A2 in Online Appendix I.
18 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.
33
arrival of the service.19 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.20 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
19 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.
20 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.
34
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.21 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.
Figure 4: Coefficients by Radius for Corruption Perceptions
21 Where Knutsen et al (2016) find that the method used to geo-code the Afrobarometer respondents is precise to
roughly 15km, while AidData projects at precision code “2” are precise to 25km.
35
Figure 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 25km, 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 25km with relatively smooth decays as the distance increases. The
confidence intervals in Figure 5 suggest that the corruption experience results have the
highest levels of statistical significance when utilizing radii between roughly 20 and 40km.
36
Finally, it is worthwhile to emphasize that all of the results above account for the
confounding influence of the co-location of natural resources22, 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
22 Our results available in Online Appendix I also suggest a weakly significant (α=0.10) positive relationship
between resources and local experiences of corruption.
37
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).23 It should be noted that while Isaksson and
23 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.
38
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
make it difficult to monitor and sanction corruption. While there may be important reasons
39
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.
40
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48
APPENDIX I
A1 Data Sources and Descriptive Statistics
Data on our main independent variables came from the AidData project, with the base data for the Chinese Counts from china.aiddata.org version 1.2 available at http://china.aiddata.org/datasets/1.2 Williamsburg, VA and Washington, DC: AidData. Accessed on [18-04-2016]. http://aiddata.org/research-datasets. and the base data for World Bank counts from AidData. 2016. WorldBank_GeocodedResearchRelease_Level1_v1.4.1 geocoded dataset. Williamsburg, VA and Washington, DC: AidData. Accessed on [18-04-2016]. http://aiddata.org/research-datasets.
Our outcome variables, and many of our control variables including age, education, female, urban and occupation come from the AfroBarometer 6 Survey. Most of our variables came from the publicly available version of this data available at http://www.afrobarometer.org/data/tanzania-round-6-data-2015 Accessed on 15-09-2016. However, in order to get access to the respondent Ward data we needed to file an application to AfroBarometer for restricted data. This request was granted on 10-10-2016 and we used the Ward information to geo-locate the respondents in order to formulate the count variables of Chinese and World Bank Projects.
As mentioned in the text, we also use survey data based on research carried out by Repoa (http://www.repoa.or.tz/) which initially focused on citizens’ views of local government reform in Tanzania. The Local Government Reform Programme (LGRP) began in 1997 as a process by which to devolve powers from the central government to the local level, and improve citizen participation, service delivery and governance (Fjeldstad et al, 200524). REPOA (Research for Poverty Alleviation), an independent research-based NGO, followed the process of reform over a ten-year period with the Citizen’s Survey (Fjeldstad et al, 2005). This study uses a number of data points from REPOA’s Citizen’s Survey, which was carried out across six case councils in and 2013.
Data on the location of natural resources utilizes the United States Geological Survey Mineral Resources Online Spatial Data at http://mrdata.usgs.gov/minfac/select.php?place=fTZ&div=fips accessed 22-11-2015.
Data on the party of the local parliamentarian as of the 2015 election was sourced from https://web.archive.org/web/20151126095523/http://www.nec.go.tz/uploads/documents/
24 Fjeldstad et al (2005), Local governance, finances and service delivery in Tanzania, a summary of findings from six councils, CHR. Michelsen Institute.
49
1448025692-MALIMA-WABUNGE.pdf accessed 10-10-2016 and matched to the wards in the Afrobarometer 6 data.
Regressions in the main text were run in STATA 13. Regressions in Appendix I were run with STATA 13 and Matlab R2014a. Do files available upon request.
Summary statistics are presented available in table A1.1 below.
50
Table A1.1: Summary Statistics
Mean Variance Min Max NCorruption Perception 0.79 0.41 0 1 2304Corruption Experience 0.23 0.42 0 1 1956
Corruption Perception Bureaucrat 1.19 0.64 0 3 2178Corruption Perception Judge 1.31 0.73 0 3 2195Corruption Perception Local 1.17 0.67 0 3 2202
Corruption Perception Parliament 1.13 0.67 0 3 2110Corruption Perception Police 1.55 0.75 0 3 2267
Corruption Perception President 0.93 0.71 0 3 2051Corruption Perception Tax 1.36 0.74 0 3 2102
Corruption Perception Traditional 0.71 0.80 0 3 1832Corruption Experience Utility 0.32 0.72 0 3 359Corruption Experience Permit 0.20 0.54 0 3 471Corruption Experience Police 0.56 0.93 0 3 404Corruption Experience Court 0.52 0.85 0 3 272
Corruption Experience Hospital 0.31 0.73 0 3 1715Corruption Experience School 0.16 0.51 0 3 1165
China 3.78 6.60 0 22 2386China Infrastructure 1.93 3.20 0 12 2386
China ODA Infrastructure 0.50 1.20 0 12 2386China OOF Infrastructure 1.42 2.20 0 8 2386China Fiber Infrastructure 0.96 1.30 0 4 2386
China Water Infrastructure 0.13 0.75 0 12 2386World Bank 6.43 8.44 0 24 2386
World Bank Infrastructure 3.40 4.99 0 16 2386Age 38.39 14.33 18 93 2373
govtParty 0.74 0.44 0 1 2340Female 0.50 0.50 0 1 2386Urban 0.83 0.37 0 1 2386
Resources 0.28 0.45 0 1 2386Education
No Formal Schooling 254Primary 1192
Secondary 524Some Schooling 262
Tertiary 154Occupation
Private 146Public 131
Self-Employed 1811Unemployed 284
Income (Gone without cash Income)0 (Never)
1 (Just Once or Twice)2 (Several Times)3 (Many Times)
4 (Always)
45342560983067
51
52
A2 Full Regression Results
In the tables below we present the full regression results for the tables in the main text.
53
Table A2.1 “Table 1: Perceptions of Corruption (Binary)”Model 0 Model 1 Model 2 Model 3
bribe 0.134 0.128 0.131 0.129(1.40) (1.34) (1.37) (1.35)
age -0.004 -0.004 -0.004 -0.004(1.43) (1.33) (1.40) (1.38)
No schooling 0.000 0.000 0.000 0.000Primary 0.022 0.009 0.017 0.014
(0.15) (0.06) (0.11) (0.10)
Secondary -0.010 0.009 -0.001 0.001(0.06) (0.06) (0.00) (0.01)
Some schooling 0.107 0.093 0.104 0.101(0.60) (0.52) (0.58) (0.56)
Tertiary -0.070 -0.070 -0.072 -0.075(0.31) (0.31) (0.32) (0.34)
Private 0.000 0.000 0.000 0.000Public 0.193 0.197 0.203 0.204
(0.77) (0.79) (0.81) (0.81)
Self-Employed -0.394 -0.394 -0.388 -0.389(2.28)* (2.28)* (2.24)* (2.25)*
Unemployed -0.174 -0.173 -0.167 -0.168(0.84) (0.84) (0.81) (0.81)
govtParty 0.030 0.019 0.022 0.027(0.26) (0.16) (0.19) (0.23)
female -0.068 -0.066 -0.067 -0.067(0.90) (0.88) (0.90) (0.89)
urban 0.868 0.855 0.863 0.862(8.63)** (8.47)** (8.56)** (8.54)**
Resources -0.013 -0.040 -0.053 -0.067(0.11) (0.29) (0.38) (0.47)
0b.income 0.000 0.000 0.000 0.0001.income 0.339 0.338 0.340 0.342
(2.69)** (2.67)** (2.69)** (2.70)**
2.income 0.076 0.076 0.078 0.078(0.65) (0.65) (0.67) (0.67)
3.income 0.445 0.443 0.449 0.449(3.73)** (3.69)** (3.74)** (3.74)**
4.income 0.104 0.097 0.106 0.104(0.41) (0.39) (0.42) (0.42)
China 0.019(1.16)
World Bank -0.017(1.40)
China Infrastructure 0.028 0.011(0.86) (0.21)
World Bank Infrastructure -0.016 -0.021(0.80) (0.90)
China*World Bank 0.002(0.41)
_cons 0.241 0.240 0.240 0.241(4.19)** (4.19)** (4.18)** (4.19)**
1,836 1,836 1,836 1,836* p<0.05; ** p<0.01
54
Table A2.2 “Table 3: Experiences of Corruption (Binary)”Model 0 Model 1 Model 2 Model 3 Model 4
Age -0.006 -0.005 -0.005 -0.005 -0.005(2.00)* (1.79) (1.92) (1.93) (1.82)
No schooling 0.000 0.000 0.000 0.000 0.000Primary 0.102 0.085 0.095 0.102 0.086
(0.77) (0.65) (0.72) (0.77) (0.65)
Secondary 0.085 0.096 0.093 0.111 0.094(0.56) (0.63) (0.61) (0.72) (0.62)
Some schooling 0.242 0.222 0.238 0.242 0.223(1.52) (1.39) (1.49) (1.52) (1.40)
Tertiary 0.050 0.034 0.034 0.044 0.020(0.24) (0.16) (0.16) (0.21) (0.09)
Private 0.000 0.000 0.000 0.000 0.000Public 0.155 0.180 0.190 0.207 0.194
(0.77) (0.89) (0.94) (1.02) (0.96)
Self-Employed -0.329 -0.321 -0.308 -0.294 -0.309(2.39)* (2.33)* (2.23)* (2.13)* (2.24)*
Unemployed -0.328 -0.318 -0.306 -0.293 -0.305(1.90) (1.84) (1.78) (1.70) (1.77)
govtParty 0.117 0.088 0.092 0.092 0.112(1.09) (0.83) (0.86) (0.86) (1.05)
female -0.184 -0.181 -0.183 -0.183 -0.181(2.67)** (2.63)** (2.66)** (2.66)** (2.63)**
urban -0.247 -0.260 -0.253 -0.251 -0.257(2.47)* (2.58)** (2.52)* (2.50)* (2.56)*
Resources 0.343 0.232 0.214 0.154 0.158(3.39)** (1.86) (1.71) (1.19) (1.24)
0b.income 0.000 0.000 0.000 0.000 0.0001.income 0.097 0.095 0.099 0.102 0.103
(0.80) (0.79) (0.82) (0.84) (0.85)
2.income 0.187 0.189 0.192 0.193 0.191(1.66) (1.67) (1.70) (1.71) (1.69)
3.income 0.350 0.352 0.361 0.360 0.359(3.19)** (3.19)** (3.28)** (3.26)** (3.26)**
4.income 0.510 0.500 0.512 0.510 0.503(2.26)* (2.21)* (2.26)* (2.26)* (2.23)*
China 0.038(2.51)*
World Bank -0.027(2.32)*
China Infrastructure 0.067 -0.008(2.23)* (0.16)
World Bank Infrastructure -0.031 -0.034 -0.052(1.68) (1.78) (2.41)*
China ODA Infrastructure -0.037(0.50)
China OOF Infrastructure 0.131(2.79)**
China*World Bank 0.007(1.89)
_cons 0.199 0.187 0.190 0.188 0.182(3.92)** (3.81)** (3.85)** (3.85)** (3.77)**
1,902 1,902 1,902 1,902 1,902* p<0.05; ** p<0.01
55
Table A2.2 “Table 2: Perceptions of Corruption (Ordered)”Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 7.1 Model 8 Model 8.1
Local President Parliament Bureaucrat Judge Police Tax Tax Traditional TraditionalChina Infrastructure 0.023 -0.002 -0.001 0.003 -0.001 -0.006 0.051 0.050
(1.08) (0.09) (0.05) (0.12) (0.03) (0.29) (2.29)* (1.92)
World Bank Infrastructure 0.003 -0.004 0.006 0.009 -0.003 0.008 -0.027 -0.031 -0.041 -0.048(0.24) (0.24) (0.48) (0.70) (0.25) (0.69) (2.05)* (2.33)* (2.60)** (3.03)**
bribe 0.213 0.254 0.089 0.191 0.117 0.277 0.186 0.180 0.172 0.165(3.20)** (3.55)** (1.32) (2.85)** (1.80) (4.33)** (2.80)** (2.72)** (2.28)* (2.20)*
age -0.003 -0.001 -0.002 -0.003 -0.003 -0.003 -0.004 -0.004 -0.000 -0.000(1.37) (0.44) (0.88) (1.38) (1.18) (1.57) (1.72) (1.74) (0.15) (0.19)
No schooling 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Primary -0.113 0.060 -0.099 -0.114 0.017 0.199 -0.158 -0.156 -0.069 -0.065
(1.09) (0.52) (0.93) (1.07) (0.17) (2.00)* (1.45) (1.44) (0.60) (0.56)
Secondary -0.047 0.129 0.076 -0.026 0.046 0.301 -0.203 -0.194 -0.210 -0.191(0.40) (1.00) (0.64) (0.22) (0.40) (2.67)** (1.67) (1.60) (1.58) (1.44)
Some schooling -0.164 -0.120 -0.141 -0.160 0.043 0.243 -0.150 -0.151 -0.176 -0.180(1.28) (0.84) (1.05) (1.23) (0.34) (1.98)* (1.13) (1.14) (1.19) (1.23)
Tertiary -0.039 0.135 -0.017 -0.156 0.112 0.431 -0.186 -0.184 -0.211 -0.207(0.24) (0.76) (0.11) (0.96) (0.71) (2.79)** (1.13) (1.12) (1.14) (1.13)
Private 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000Public -0.191 -0.000 -0.068 -0.007 -0.090 -0.188 0.022 0.038 -0.375 -0.343
(1.15) (0.00) (0.40) (0.04) (0.55) (1.17) (0.13) (0.23) (1.95) (1.79)
Self-Employed -0.179 -0.035 -0.228 -0.199 -0.233 -0.277 -0.033 -0.021 -0.406 -0.378(1.59) (0.28) (1.97)* (1.75) (2.09)* (2.50)* (0.29) (0.18) (3.08)** (2.87)**
Unemployed -0.073 0.141 -0.050 -0.143 0.005 -0.361 0.008 0.018 -0.074 -0.043(0.52) (0.94) (0.36) (1.01) (0.03) (2.66)** (0.06) (0.13) (0.46) (0.27)
govtParty -0.183 -0.265 -0.183 -0.156 -0.103 -0.057 -0.136 -0.136 -0.062 -0.061(2.39)* (2.74)** (2.40)* (2.05)* (1.38) (0.78) (1.74) (1.75) (0.67) (0.68)
female -0.100 -0.108 -0.120 -0.136 -0.140 -0.075 -0.191 -0.191 0.026 0.026(1.80) (1.82) (2.11)* (2.41)* (2.58)** (1.42) (3.44)** (3.44)** (0.42) (0.42)
urban -0.194 -0.392 -0.237 -0.187 -0.076 0.167 -0.047 -0.049 -0.374 -0.376(2.40)* (4.62)** (2.91)** (2.28)* (0.97) (2.14)* (0.58) (0.61) (4.38)** (4.41)**
Resources 0.017 0.097 -0.021 0.030 0.149 -0.019 0.031 -0.013 0.237 0.150(0.19) (0.84) (0.23) (0.33) (1.71) (0.22) (0.33) (0.14) (2.21)* (1.38)
0b.income 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0001.income -0.050 0.095 0.163 0.047 -0.070 0.014 0.029 0.033 0.025 0.032
(0.54) (0.96) (1.73) (0.50) (0.77) (0.15) (0.32) (0.36) (0.25) (0.31)
2.income -0.017 0.065 0.131 0.023 -0.016 0.001 -0.058 -0.056 0.025 0.029(0.19) (0.68) (1.46) (0.26) (0.19) (0.01) (0.67) (0.64) (0.25) (0.29)
3.income 0.041 0.038 0.132 0.146 -0.023 0.279 -0.137 -0.136 -0.207 -0.205(0.47) (0.40) (1.48) (1.64) (0.26) (3.34)** (1.57) (1.56) (2.10)* (2.09)*
4.income 0.214 -0.256 0.038 0.106 0.120 0.037 -0.205 -0.204 -0.334 -0.332(1.13) (1.20) (0.19) (0.53) (0.66) (0.20) (1.03) (1.03) (1.60) (1.59)
China ODA Infrastructure -0.008 -0.070(0.20) (1.35)
China OOF Infrastructure 0.093 0.133(2.78)** (3.47)**
1,749 1,612 1,664 1,727 1,741 1,805 1,663 1,663 1,493 1,493* p<0.05; ** p<0.01
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Table A2.4 “Table 4: Experiences of Corruption (Ordered)”Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Utility Utility Permit Police Court Hospital SchoolChina Infrastructure 0.168 0.042 0.204 0.155 0.059 0.083
(2.54)* (0.63) (3.12)** (2.01)* (1.71) (2.07)*
World Bank Infrastructure -0.067 -0.033 -0.004 -0.136 -0.108 -0.019 -0.007(1.63) (0.91) (0.11) (3.23)** (2.13)* (0.91) (0.26)
age 0.013 0.012 0.014 -0.006 -0.001 -0.004 0.004(1.73) (1.60) (1.95) (0.94) (0.15) (1.42) (0.74)
No schooling 0.000 0.000 0.000 0.000 0.000 0.000 0.000Primary 0.391 0.403 0.166 0.383 0.504 0.114 0.004
(1.23) (1.25) (0.51) (1.46) (1.58) (0.79) (0.02)
Secondary 0.314 0.369 -0.133 0.303 0.312 0.002 -0.246(0.84) (0.97) (0.36) (1.02) (0.86) (0.01) (1.00)
Some schooling 0.130 0.127 0.290 0.294 0.173 0.307 -0.105(0.26) (0.25) (0.67) (0.86) (0.45) (1.78) (0.38)
Tertiary 0.173 0.217 -0.325 -0.033 0.331 -0.046 -0.584(0.34) (0.42) (0.69) (0.08) (0.64) (0.20) (1.65)
Private 0.000 0.000 0.000 0.000 0.000 0.000 0.000Public -0.205 -0.201 0.586 0.175 0.729 0.254 0.737
(0.44) (0.42) (1.45) (0.46) (1.41) (1.13) (2.35)*
Self-Employed -0.271 -0.254 -0.424 -0.237 0.390 -0.255 -0.296(0.90) (0.84) (1.39) (0.87) (1.15) (1.69) (1.40)
Unemployed 0.653 0.647 0.091 -0.096 0.633 -0.121 0.536(1.82) (1.80) (0.26) (0.31) (1.70) (0.64) (2.14)*
govtParty 0.407 0.431 0.394 -0.405 -0.200 0.130 0.203(1.58) (1.64) (1.89) (2.54)* (1.01) (1.06) (1.26)
female -0.156 -0.156 -0.504 -0.221 0.086 -0.162 -0.153(0.86) (0.85) (2.73)** (1.61) (0.51) (2.14)* (1.35)
urban -0.444 -0.477 -0.690 -0.663 -0.514 -0.380 -0.372(1.88) (2.00)* (3.27)** (3.54)** (2.41)* (3.59)** (2.61)**
Resources 0.244 0.179 0.623 0.231 0.481 0.200 0.195(0.81) (0.56) (2.41)* (1.30) (2.29)* (1.41) (1.08)
0b.income 0.000 0.000 0.000 0.000 0.000 0.000 0.0001.income -0.265 -0.271 -0.342 -0.112 0.139 0.037 -0.176
(0.83) (0.83) (1.26) (0.47) (0.48) (0.28) (0.89)
2.income 0.676 0.647 0.061 0.134 -0.175 0.022 0.029(2.50)* (2.36)* (0.26) (0.58) (0.64) (0.18) (0.16)
3.income 0.553 0.525 0.280 0.171 0.429 0.454 0.373(1.95) (1.82) (1.09) (0.77) (1.63) (3.78)** (2.15)*
4.income 0.847 0.846 0.725 0.407 0.250 0.475 -0.168(1.68) (1.66) (1.55) (1.03) (0.52) (1.95) (0.45)
China Fiber Infrastructure 0.332(2.37)*
China Water Infrastructure -0.133(0.47)
N 353 353 464 396 265 1,668 1,137* p<0.05; ** p<0.01
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Figure A1 – Two-way interactions (China*World Bank) for Table 2 “Perceptions of Corruption Ordered”
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Figure A2 – Two-way interactions (China*World Bank) for Table 3 “Perceptions of Corruption Experience (Ordered)”
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A3 REPOA Regression Results and Discussion
Table A.3: Perceptions of Corruption (Binary) REPOA
Model 1 Model 2 Model 3China 0.0384**
(3.33)World Bank -0.0128
(0.69)China Infrastructure 0.0314*
(2.11)0.0111(0.70)
World Bank Infrastructure 0.0182(0.60)
-0.0837†(1.72)
China*World Bank 0.0092*(2.52)
Age 0.0050(1.54)
0.0052(1.60)
0.0056†(1.74)
Resources -0.2348(0.68)
-0.1817(0.48)
-0.7902†(1.86)
Education 0.0236(0.52)
0.0288(0.63)
0.0305(0.66)
Occupation Factor Variables Yes Yes YesWard Mixed Effects Yes Yes Yes
N 1233 1233 1233Prob > χ2 0.0028 0.0290 0.0003
Absolute vale of Z score in parentheses. † p>0.10, *5% p>0.05, **1% p>0.01
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Table A.3 and Figure A.3 suggest support for hypotheses 1, 2 and 4 for corruption perceptions.
Chinese projects are associated with higher levels of corruption perceptions for all projects (Model
1) and restricting to infrastructure projects (Model 2), although this result suggests a rejection of
Hypothesis 3 as the coefficient in Model 1 is both larger and of greater statistical significance than
the coefficient when just considering infrastructure projects in Model 2. Hypothesis 2 has weak
support in Model 3, suggesting a negative relationship at the 10% level of significance between
World Bank projects and perceptions of corruption when there are no co-located Chinese projects.
However, Model 3 suggests support for Hypothesis 4 as suggested both by the positive and
significant interaction term, but also by Figure A.3 which shows that at high levels of Chinese
projects the impact of World Bank goes from a relationship with perceptions of corruption that is
negative and weakly significant to one that is positive and significant.
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