The cancer of corruption and World Bank project performance: Is there a connection?

Feil, Hauke Article — Published Version The cancer of corruption and World Bank project performance: Is there a connection? Development Policy Review Provided in Cooperation with: John Wiley & Sons Suggested Citation: Feil, Hauke (2021) : The cancer of corruption and World Bank project performance: Is there a connection?, Development Policy Review, ISSN 1467-7679, Wiley, Hoboken, NJ, Vol. 39, Iss. 3, pp. 381-397, https://doi.org/10.1111/dpr.12503 This Version is available at: https://hdl.handle.net/10419/241264 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Dev Policy Rev. 2021;39:381–397. | 381wileyonlinelibrary.com/journal/dpr 1 | INTRODUCTION In 1996, the then president of the World Bank, James D. Wolfensohn, called corruption a cancer which needs to be tackled (Wolfensohn, 2005, p. 5). Yet, after years working at the World Bank, authors like Berkman share a pessimistic view on the prevalence of corruption in development projects in African countries. He has not worked on one project “that did not reek of corruption” (Berkman, 2008, p. 121). Other authors confirm the occurrence of corruption in World Bank projects (Olken, 2007; Winters, 2014). In addition, qualitative studies show that corruption can decrease the performance of development projects (Beekman et al., 2013; Damoah et al., 2018; Olken, 2006). This raises the question Received: December 2019 | Accepted: April 2020 DOI: 10.1111/dpr.12503 ARTICLE The cancer of corruption and World Bank project performance: Is there a connection? HaukeFeil This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2020 The Authors. Development Policy Review published by John Wiley & Sons Ltd on behalf of Overseas Development Institute Faculty of Economics and Social Sciences, University of Potsdam, Germany; Deutsche Bundesbank, DG Statistics, Germany Correspondence Hauke Feil, Deutsche Bundesbank, DG Statistics, Germany. Email: hauke.feil@ bundesbank.de Abstract Motivation: Corruption is often cited as a central reason why development projects fail. The article tests this claim by assessing whether World Bank projects perform worse in implementation environments with a higher corruption level. The article focuses specifically on bribery between public officials and firms during the procurement of needed goods and services. Approach and Methods: I use data from the World Bank’s Enterprise Surveys to avoid the oftencriticized corruption perception indices and to allow for an assessment of effects at the subnational level. The analysis builds on an assessment of the performance ratings of 1,228 World Bank projects and covers 87 different countries. Finding: Overall, the article finds a small but statistically significant correlation between the corruption level and project performance. This result indicates that the corruption level of recipient countries should be considered during the design and implementation of projects. Policy Implications: Nonetheless, the relatively small correlation and the low pseudo R- squareds advise not overestimating the relevance of corruption for project performance. At least for the project level, the article finds no indication that corruption is a primary obstacle to aid effectiveness. KEYWORDS Aid effectiveness, corruption, Enterprise Survey, project performance, World Bank 382 | FEIL whether corruption has a systematic effect on the performance of World Bank projects, particularly as organizations like Transparency International name corruption as one of the main obstacles to aid effectiveness (Transparency International, 2011). Yet, quantitative studies dealing with this question are surprisingly scarce. In a background paper for the World Bank Independent Evaluation Group (IEG), Kilby (1994) assessed the effect of different macroeconomic and sociopolitical factors on the performance of World Bank projects. Among other aspects, his models show a negative effect of corruption on project performance. Two studies by the IEG confirm this result (World Bank OED, 1999a, 1999b). In three other studies, however, the effect of corruption indicators remains mostly statistically insignificant (ADB, 2016; Buntaine & Parks, 2013; Limodio, 2011). Thus, the current state of the research is inconclusive. Furthermore, none of the existing studies differentiate between different forms of corruption, even though it is likely that the effect of corruption varies depending on the type of development project and the form of corruption. Investment projects are likely to be vulnerable to corruption in procurement procedures, while projects in the area of budget support may be more vulnerable to the misallocation of resources. Thus, to better understand the effect of corruption on project performance, it is essential to assess different forms of corruption separately (see also Kenny, 2017). The present article seeks to address the inconclusive state of the research while focusing on one specific form of corruption, namely bribery between private firms and public officials. The level of corruption is measured by the “corr2” indicator of the Enterprise Surveys, which identifies the “percent of firms expected to give gifts to secure a government contract” in a country (World Bank, 2017b, p. 36). Based on a sample of 1228 World Bank projects, the article tests the following research question: Do World Bank projects perform worse in implementation environments with a higher corruption level? In so doing, the study provides important theoretical, methodological, and practical contributions. From a theoretical point of view, the article contributes to the small but growing discussion on the determinants of the performance of development projects. This discussion was started by Kaufmann and Wang (1995) and Isham et al. (1995), who assess the effect of macroeconomic policies and civil liberties on the performance of World Bank projects. Later studies include additional determinants at the level of the project, aid agency and recipient country (Bulman at al., 2017; Deininger et al., 1998; Denizer et al., 2013; Dollar & Levin, 2005; Honig, 2018; Vawda et al., 2003). Corruption, however, is one of the few potential determinants which has been largely left out of quantitative studies (see also Langley et al., 2018; World Bank IEG, 2017, pp. 171– 184). The present article addresses this research gap. In addition, the results are also relevant for the methodological discussion on the measurement of corruption, particularly because the article compares the results of different corruption indicators. Furthermore, corruption indicators are heavily used by aid agencies. Multilateral Development Banks and the UK’s Department for International Development use corruption indicators in their allocation formula to steer more resources to places where they are most effective (DFID, 2011; World Bank, 2001a, p. 1). In addition, the German Federal Ministry for Economic Cooperation and Development argues that good governance, measured by corruption indicators, among others, is “a precondition for effective development work” (BMZ, 2018). Thus, the article also gives insights as to whether the strong reliance of aid agencies on corruption indicators is reasonable. The analysis reveals a small but statistically significant effect of corruption on project performance. On average, an increase of 10 percentage points in the number of firms that expect to give gifts to secure government contracts decreases the likelihood of a satisfactory project performance by 3 percentage points. Different reliability tests confirm the robustness of this result. The result is also robust when the countrylevel corruption values are replaced by subnational values that are calculated based on the actual implementation locations of projects. Furthermore, comparable results are obtained if the corruption indicator of the Enterprise Surveys is replaced by the Corruption Perceptions Index (CPI) given by Transparency International or the Control of Corruption index (CC) from the | 383 FEIL Worldwide Governance Indicators. Yet, a glance at the values of the three indicators reveals that the comparable results are more a coincidence than a confirmation for a comparable measurement of corruption. The article proceeds by presenting the state of the research, the selection process of the independent variable and the analytical model. This is followed by the analysis and a short conclusion discussing the implications of the results. 2 | STATE OF THE RESEARCH AND THE MEASUREMENT OF CORRUPTION Corruption is a central topic in multiple disciplines. However, despite “extensive research devoted to the subject, there is limited research on the potential impact of corruption on project failure” (Damoah et al., 2018, p. 17). One of the few contributions to the literature stems from Tanzi and Davoodi (1998). The authors show that countries with higher corruption levels tend to have a lower quality of infrastructure. Galilea and Medda (2010) and Jiménez et al. (2017) also find a negative correlation between the level of corruption and the performance of public– private partnership projects. A few studies also focus specifically on development projects. Based on interviews with project practitioners in Ghana, Damoah et al. (2018) find that corruption negatively affects the performance of development projects. A quantitative study done by Kilby (1994) also shows a negative correlation between the corruption level in the recipient country and the performance of World Bank projects. Two studies by the World Bank Operations Evaluation Department obtain a similar result (World Bank OED, 1999a, 1999b). However, there are also studies with divergent findings. In their paper on World Bank projects, both Buntaine and Parks (2013) and Limodio (2011) include corruption indicators as control variables. In most of their models, the effect of the corruption indicators remains statistically insignificant. An assessment by the Asian Development Bank also yields no statistically significant effect between corruption and project performance (ADB, 2016). Thus, at least when it comes to development projects, the current state of the research is inconclusive. Furthermore, none of the existing studies differentiate between different forms of corruption, even though it is likely that different types of development projects are affected by different forms of corruption. Investment projects, for example, are likely to be vulnerable to corruption in procurement procedures. In contrast, projects in the area of budget support usually do rely on the procurement of goods and services. However, they may be more vulnerable to corruption in the form of misallocation of resources. To better understand the effect of corruption on project performance, it is therefore essential to assess different forms of corruption separately. The present article addresses the conclusive state of the research by testing whether World Bank projects perform worse in implementation environments with a higher level of corruption. In so doing, it focuses on one specific form of corruption, namely bribery between private firms and public officials. Bribery in this context is defined as the “explicit exchange of money, gifts in kind, or favours for rule breaking or as payment for benefits that should legally be costless or be allocated on terms other than willingness to pay” (Rose- Ackerman & Palifka, 2016, p. 8). The corruption form of bribery is selected, as it is the one discussed most in the literature. The main theoretical argument stems from Tanzi and Davoodi (1998), who argue that corruption decreases the performance of infrastructure projects because the payers of bribes will try to recover their expenses during the contract execution (see also Collier et al., 2016; Compte et al., 2005). This can happen in three different ways. First, the payer can include the bribe in the official offer or can overprice certain services, which raises the costs of projects. Second, in cooperation with public officials, the payer can try to adjust the design of the project during the implementation to increase earning opportunities, which causes more expensive and 384 | FEIL more complex projects. Third, the payer can reduce the implementation costs by lowering the quality of work or by using lowquality materials. This leads to inferior project implementation. Furthermore, corrupt public officials may have incentives to approve projects with deficits or to reduce maintenance of the built infrastructure because they could then take a second bribe for the repairs (Tanzi & Davoodi, 1998, pp. 42– 47). Thus, corruption can lead to inadequate project implementation, which reduces the performance of projects. Damoah et al. (2018) identify an additional negative mechanism caused by corruption, namely time overrun. They argue that corrupt public officials will do everything they can to acquire bribes. If contractors are unwilling to pay these, they may try to force them. One way to do so is to refuse to approve necessary documents until bribes are paid. This can cause delays or even the abandonment of projects (Damoah et al., 2018, pp. 22– 24). Davis (2004) and Kenny (2010) highlight that it is not only the procurement of contracts that is susceptible to corruption. During the execution of a contract, officials may demand bribes for their services, which increases the cost of projects. At the same time, contractors may bribe public officials to look the other way when they lower the quality of work or overprice their services. Thus, according to the theoretical arguments in the literature, development projects perform worse in more corrupt environments due to negative effects caused by the payment of bribes by firms to public project officials during the implementation of projects. In particular, the theoretical arguments relate to the procurement of goods and services that are needed for the project. This requires a measurement of corruption that focuses on this specific form of corruption. The three standard corruption indicators used in the literature are unable to fulfil this criterion. The CPI and CC are both composite indices that cover multiple different forms of corruption and are based on a broad range of different sources. Thus, while the two indices may give an overview of the general corruption level in a country, they say little on the level of one specific form of corruption (see also Andersson & Heywood, 2009; Rose- Ackerman & Palifka, 2016, pp. 19– 21). In other words, a high corruption value on the CPI does not imply that there is a high level of corruption in the procurement process of the respective country. The third corruption indicator often used in the literature, the Corruption Index of the International Country Risk Guide, focuses on the risk of corruption for foreign investment. Yet, even this more focused measurement of corruption includes forms of corruption that are not relevant for the research question (see PRS Group, 2018, pp. 4– 5). Furthermore, the country coverage of the indicator is limited when it comes to developing countries. This makes it necessary to select another corruption measurement for the analysis. The most suitable ones are the corruption indicators of the Enterprise Surveys. In these firmlevel surveys conducted by the World Bank and other organizations, local business owners and managers are asked to assess the business environment in which they are active. The surveyed firms are selected according to a stratified random sampling process and each survey covers between 150 and 1800 firms in a country, depending on the size of the economy. Among other aspects, the questionnaire includes a section on corruption. For the analysis, the paper uses the “corr2” indicator, which identifies the “percent of firms expected to give gifts to secure a government contract” (World Bank, 2017b, p. 36). This indicator is selected because it corresponds perfectly with the research question and the main causal mechanism described in the literature. The indicator also addresses two concerns that authors have raised regarding the validity and reliability of the CPI and CC.1 The first concern is that both indicators build on assessments of experts and business leaders. These can be highly subjective, given that the corruption level may be assessed 1Corruption indicators based on surveys are also not without their critics. Kenny, for example, shows that “survey evidence is subject to considerably uncertainty.” However, he also highlights that “perception measures appear to perform even worse in measuring the extent or impact of corruption in sector outcomes” (Kenny, 2009, p. 315). | 385 FEIL based on different standards and definitions of corruption (see Andersson & Heywood, 2009; Donchev & Ujhelyi, 2014; León et al., 2013; Rose- Ackerman & Palifka, 2016, p. 20). Other authors criticize the fact that the CPI and CC rely heavily on the perception of Western or remote business leaders and experts, which could lead to biased estimates due to “consistent but erroneous” judgements on how countries work (Razafindrakoto & Roubaud, 2010, p. 1067). The selected “corr2” indicator addresses this concern by asking respondents a precisely defined question on one specific form of corruption.2 This procedure provides respondents with common definitions and standards for their answers and helps to reduce the level of subjectivity in the responses. Furthermore, the respondents are local business owners and managers from the country in question and, since 2008, only firms that actually have attempted to secure a government contract are asked to answer the respective question. Thus, the responses to the surveys stem from relevant local actors who are able to assess how the local procurement system works in practice. The second concern is that countrylevel indices like the CPI or CC “cannot satisfactory [sic] explain the withincountry variation of corruption” (Reinikka & Svensson, 2002, p. 135). Multiple studies find strong differences in the level of corruption within countries (see Goel & Nelson, 2011; Langbein & Sanabria, 2013; Nguyen & van Dijk, 2012; Olken, 2009). This aspect is essential, as World Bank projects are often only implemented in certain areas of a country. Thus, countrylevel indicators may represent an imprecise measurement of the corruption level in a project’s implementation environment. However, contrary to the CPI and CC, the Enterprise Surveys provide data for subnational levels. This makes it possible to run a reliability test based on the data for the subnational areas in which a World Bank project is implemented. 3 | RESEARCH DESIGN Like most studies on the determinants of the performance of World Bank projects, the article uses the official project “outcome rating” of the IEG as a dependent variable. This rating assesses “the extent to which the operation's major relevant objectives were achieved, or are expected to be achieved, efficiently” (World Bank IEG, 2014, p. 5). Each project is rated on a sixpoint scale, which goes from “Highly Satisfactory” to “Highly Unsatisfactory”. Following Bulman et al. (2017), Feeny and Vuong (2017), and Assefa et al. (2014), all ratings are recoded into a binary format based on the underlying World Bank classification into satisfactory (ratings 1 to 3) and unsatisfactory projects (ratings 4 to 6).3 As highlighted above, the independent variable builds on the “corr2” indicator of the Enterprise Surveys. Contrary to other corruption indicators, data for the Enterprise Surveys are not available on an annual basis. Between 2006 and 2017, the Enterprise Surveys covered 20 countries each year on average.4 In addition, while some countries have been covered multiple times, others were surveyed just once or not all. This reduced coverage makes it impossible to include the entire population of World Bank projects in the analysis. Instead, the analysis is restricted to projects that were implemented while an Enterprise Survey took place in the respective country. Thus, projects are only included in the sample if values from the Enterprise Surveys are available for at least one year of the project’s implementation period.5 Each project receives the corruption value of the Enterprise Survey that was conducted during 2See World Bank (2017b) for the formulation of the survey question. 3All models are recalculated based on an ordered logistic regression using the original sixpoint scale of the IEG. In so doing, comparable results are achieved (results not reported). 4Earlier Enterprise Surveys are excluded due to an incomparable methodology. 5The implementation period is defined as the time frame from the effectiveness date to the closing date of a project. 386 | FEIL its implementation period. In the few cases where two Enterprise Surveys were conducted during an implementation period, the mean of the values for both surveys is assigned to a project. The study sample is limited to projects in the area of Investment Project Financing (IPF). Development Policy Financing projects are excluded, as they focus on budget support and do not usually rely on the procurement of products and services or on the implementation of physical and social infrastructure. Thus, the theoretical arguments outlined above may not apply to this financing instrument. For better comparability among the included projects, the sample also excludes projects that became effective before the year 2000. Overall, the sample contains 1,228 projects that were implemented in 87 different countries. This number implies that the sample covers 49% of all the projects in the population.6 A comparison with IEG project data shows that the project distribution among countries, project sectors and project types hardly differs between the sample and the population. This makes it appropriate to use statistical inference to generalize the results of the sample to the entire population of IPF projects of the World Bank. All regression models also include different control variables,7 which are described in detail in Table A4 in the Online Supplementary Material. Like in most studies on project performance, regional dummy variables are included to account for potential unobserved heterogeneity between regions. At the country level, the models include gross domestic product (GDP) per capita and GDP growth per capita as control variables. The first variable captures effects caused by a different level of development, while the second variable controls for potential macroeconomic shocks. In addition, it is also necessary to control for the level of civil liberties, political rights and accountability in the recipient country. Multiple factors like a free press or participatory practices can reduce the level of corruption in a country (see Lederman et al., 2005; Lyrio et al., 2018; Rose- Ackerman & Palifka, 2016, pp. 374– 411). Furthermore, these factors can also affect project performance (see Blum, 2014; Dollar & Levin, 2005; Isham et al., 1997). The different models therefore include the voice and accountability indicator from the Worldwide Governance Indicators as a control variable. Furthermore, controls for four project characteristics are included. The first one is the sector of the project, as projects in some sectors may be more prone to corruption than in others. The second one is the logarithm of the financial volume of the project. The main reason for including this variable is that parts of the World Bank’s financial resources are allocated according to characteristics of recipient countries. Postconflict countries, for example, receive more resources and, thus, potentially larger projects (see World Bank, 2017a). At the same time, larger projects also may attract more corruption. In addition, all models control for the lending instrument, given that effects may differ between different types of lending instruments (see also World Bank, 2001b). The fourth projectlevel control variable is based on Noltze et al. (2018) and controls for the evaluation delay, which is the time between the closing date and the evaluation date of a project. The argument behind this control variable is that the later an evaluation is conducted, the more shortcomings in the implementation may become visible. The last included control variable is a dummy variable for the evaluation year of the project. The variable controls for potential changes over the period under analysis. This includes potential changes in the methods and standards of project evaluations.8 6The population is defined as all World Bank IPF projects that were closed and evaluated after 2005. The threshold of 2005 was selected as there are no comparable Enterprise Surveys for years prior to 2006. 7All countrylevel control variables are included by calculating projectlife averages based on the effectiveness date and the closing date of projects. Thus, for each project, countrylevel controls indicate the mean of the values of the respective variable for the years in which the project was implemented. 8Given that data for the independent variable are only available for a limited number of years, the study is unable to use fiveyear period fixed effects as is the case in other studies with a longer time frame (Denizer et al., 2013). However, the results also remain robust if one additionally controls for the approval, effectiveness and closing year of a project. | 387 FEIL 4 | ANALYSIS The analysis is comprised of three parts. The first part presents the main model and its result, while the second part contains six reliability tests regarding the risk of endogeneity. The last part assesses the sensitivity of the results with respect to the selected measurement of corruption. The results of the different parts are displayed in Tables 1 to 3. The respective tables hide all control variables to provide a better overview of the coefficients of the independent variables. The entire regression results are displayed in Tables A2 and A3. Table 1 highlights the results for the main model based on different model specifications. The first four models constitute bivariate regressions between project performance and the corruption indicator, while the latter four include all control variables discussed above. All eight models highlight a statistically and economically significant correlation between project performance and the corruption level in a project’s implementation environment. According to Models 1 and 2, a onepercentage point increase in the number of firms expected to give gifts to secure a government contract decreases the likelihood of a satisfactory project performance by around 0.3 percentage points. In a similar way, a 10- point decrease in the independent variable increases the likelihood of a satisfactory project performance by 3.5 percentage points. When using odds ratios instead of average marginal effects (AME), a comparable picture is obtained, regardless of whether the dependent variable is measured on a binary or sixpoint scale (Models 3 and 4). A onepoint increase of the independent variable decreases the odds of a successful project (Model 3) or of being in a higher category of the dependent variables (Model 4) by about 1.5%. Models 5 to 8 demonstrate that the coefficient, standard error and P- value of the main independent variable only change slightly if control variables are included in the model. A look at the penultimate column of Table 1 also reveals that the corruption indicator alone explains around 1% to 2% of the variation in the dependent variable. Including the control variables in the model increases this value to around 5% to 9%, depending on the model specification. Thus, even though the effect of corruption on project performance is economically meaningful, the corruption variable alone only explains a small amount of variation in the dependent variable. TABLE 1 Regression results for the main model Model Variables Coefficient SE P- value n R2 Model specification (1) Corruption only −0.0031 0.0008 0.000 1228 0.016 Logit, AME (2) Corruption only −0.0032 0.0008 0.000 1228 0.019 Linear probability model (3) Corruption only 0.9841 0.0041 0.000 1228 0.016 Logit, odds ratio (4) Corruption only 0.9851 0.0036 0.000 1228 0.009 Ordered logit, odds ratio (5) Corruption + controls −0.0035 0.0008 0.000 1228 0.081 Logit, AME (6) Corruption + controls −0.0036 0.0008 0.000 1228 0.090 Linear probability model (7) Corruption + controls 0.9808 0.0043 0.000 1228 0.081 Logit, odds ratio (8) Corruption + controls 0.9825 0.0040 0.000 1228 0.050 Ordered logit, odds ratio Notes: SE = standard error, R2 = (pseudo) R- squared, AME = average marginal effect. Standard errors are clustered at the country level. For easier interpretation of Models 4 and 8, the direction of the sixpoint IEG performance rating is reversed (one = highly unsatisfactory, six = highly satisfactory). 388 | FEIL TABLE 2 Regression results for the endogeneity tests Model Indicator Coefficient SE P- value n R2 Model specification (9) Corruption if less targeted project more targeted project −0.0033 −0.0043 0.0017 0.0011 0.055 0.000 701 0.118 Logit, AME, interaction term (10) Corruption if low project volume high project volume −0.0036 −0.0031 0.0010 0.0011 0.001 0.003 1228 0.079 Logit, AME, interaction term (11) Corruption if low financial share of major sector high financial share of major sector −0.0028 −0.0039 0.0010 0.0009 0.007 0.000 1226 0.091 Logit, AME, interaction term (12) Corruption if low project length high project length −0.0041 −0.0029 0.001 0.001 0.000 0.006 1228 0.082 Logit, AME, interaction term (13) Corruption if funding source is IBRD funding source is IDA funding source is external −0.0059 −0.0027 0.0035 0.0016 0.0007 0.0016 0.000 0.000 0.029 1202 0.083 Logit, AME, interaction term (14) Corruption −0.0038 0.0008 0.000 1107 0.080 Logit, AME Notes: SE = standard error, R2 = pseudo R- squared, AME = average marginal effect. 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