Analyzing the Sensitivity of the Control of Corruption Indicator and

its Sub-Sources to Discrete Corruption-Related Events

Prepared for the Millennium Challenge Corporation

By Katie Cary

Constance Chucholowski Ryan Hohler

Jiaqi Lu Malika Taalbi

May 2014

Workshop in International Public Affairs

Spring 2014

©2014 Board of Regents of the University of Wisconsin System All rights reserved.

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TABLE OF CONTENTS Table of Contents ..................................................................................................... i List of Tables ......................................................................................................... iii List of Figures ........................................................................................................ iv Foreword ................................................................................................................. v Acknowledgments.................................................................................................. vi List of Abbreviations ............................................................................................ vii Executive Summary ............................................................................................. viii I. Introduction ......................................................................................................... 1 

1. Millennium Challenge Corporation ................................................................ 1 2. The Control of Corruption Indicator as a Measurement of Corruption .......... 2 3. Potential Limitations of the Control of Corruption Indicator ......................... 3 

II. Methodology ...................................................................................................... 4 1. Case Selection ................................................................................................. 4 2. Case Study Framework ................................................................................... 5 

IV. Limitations ........................................................................................................ 5 1. Lack of External Validity ................................................................................ 5 2. Difficulty Drawing Causal Relationships ....................................................... 5 3. Sampling Biases .............................................................................................. 6 4. Data Reporting and Access Limitations ......................................................... 6 

V. Case Study Analyses .......................................................................................... 6 1. President Yayi’s Financial Scandal, 2010—Benin ......................................... 6 2. Sponsorship Program Scandal, 2004—Canada ............................................ 10 3. The Rose Revolution, 2003—Georgia .......................................................... 14 4. Credible Anti-Corruption Reforms, 2002—Kenya....................................... 18 5. Anti-Corruption Reforms Following Milosevic’s Ouster, 2000—Serbia ..... 23 

VI. Sub-source Findings ....................................................................................... 28 1. African Development Bank Country Policy and Institutional Assessments 28 2. Afrobarometer ............................................................................................... 29 3. Asian Development Bank ............................................................................. 30 4. Business Enterprise Environment Survey ..................................................... 30 5. Bertelsmann Transformation Index .............................................................. 30 6. Corruption in Asia Survey ............................................................................ 31 7. Countries at the Crossroads .......................................................................... 31 8. Economist Intelligence Unit ......................................................................... 32 9. Freedom House ............................................................................................. 32 10. Gallup World Poll ....................................................................................... 33 11. Global Competitiveness Report .................................................................. 33 12. Global Corruption Barometer Survey ......................................................... 34 13. Global Insight Business Condition and Risk Indicators ............................. 34 14. Global Integrity Index ................................................................................. 35 15. Institutional Profiles Database .................................................................... 35 16. International Institute for Management and Development ......................... 36 17. Latinobarometer .......................................................................................... 36 18. Political Risk Services International Country Risk Guide .......................... 37 

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19. Rural Sector Performance Assessment ....................................................... 37 20. Vanderbilt University Americas Barometer Survey ................................... 37 21. World Bank Country Policy and Institutional Assessments ....................... 38 22. World Justice Project Rule of Law Index ................................................... 38 

VII. General Findings ........................................................................................... 39 VII. Recommendations ......................................................................................... 40 

1. Complete Additional Research on the Responsiveness of the CoC ............. 41 2. Evaluate the Continued Efficacy and Construct Validity of the CoC to MCC’s Objectives ............................................................................................. 41 3. Conduct Further Analysis on the Correlations among the CoC and Other Governance Indicators ...................................................................................... 42 

IX. Conclusion ...................................................................................................... 42 Appendix A: MCC Indicators, 2014 Scorecard .................................................... 43 Appendix B: Proposed Analysis Framework ........................................................ 44 Appendix C: Sub-source Weights ......................................................................... 46 Appendix D: Questions from the African Development Bank Sub-Source Used in the Control of Corruption Indicator ...................................................................... 49 Appendix E: Questions from the Freedom House Sub-Source Used in the Control of Corruption Indicator ......................................................................................... 51 Appendix F: Questions from the Global Corruption Barometer Used in the Control of Corruption Indicator ............................................................................ 53 Appendix G: Questions from the Global Integrity Index Sub-Source Used in the Control of Corruption Indicator ............................................................................ 54 Appendix H: Syria Case Study ............................................................................. 55 Appendix I: The WGI Project Weighting Methodology ...................................... 59 References ............................................................................................................. 61 

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LIST OF TABLES Table 1: Control of Corruption Indicator Sub-Sources ........................................... 3 Table 2: Benin – Control of Corruption and Sub-source Data for CRE ................. 8 Table 3: Canada—Control of Corruption and Sub-source Data for CRE ............ 12 Table 4: Georgia—Control of Corruption and Sub-source Data for CRE ............ 16 Table 5: Kenya—Control of Corruption and Sub-source Data for CRE .............. 20 Table 6: Serbia—Control of Corruption and Sub-source Data for CRE .............. 25 Table 7: Benin’s Sub-source Weights................................................................... 46 Table 8: Canada’s Sub-source Weights ................................................................ 46 Table 9: Georgia’s Sub-source Weights ............................................................... 47 Table 10: Kenya’s Sub-source Weights ................................................................ 47 Table 11: Serbia’s Sub-source Weights ................................................................ 48 Table 12: Syria—Control of Corruption and Sub-source Data for CRE .............. 57 Table 13: Effects of Changed Weights on Georgia's Control of Corruption ........ 59 Table 14: Effect of Average Sub-source Change on Average Weight ................. 60 

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LIST OF FIGURES Figure 1: Benin--Change in Control of Corruption Score ...................................... 9 Figure 2: Canada--Change in Control of Corruption Score .................................. 13 Figure 3: Georgia--Change in Control of Corruption Score ................................. 17 Figure 4: Kenya--Change in Control of Corruption Score ................................... 21 Figure 5: Serbia--Change in Control of Corruption Score .................................... 26 Figure 6: Syria--Change in Control of Corruption Score ..................................... 58 

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FOREWORD The La Follette School of Public Affairs at the University of Wisconsin–Madison offers a two-year graduate program leading to a Master of International Public Affairs degree. In both programs, students develop analytic tools with which to assess policy responses to issues, evaluate implications of policies for efficiency and equity, and interpret and present data relevant to policy considerations. A team of students in the Master of International Public Affairs program produced this report for the Millennium Challenge Corporation. The students are enrolled in the Workshop in International Public Affairs, the capstone course in their graduate program. The workshop challenges the students to improve their analytical skills by applying them to an issue with a substantial international component and to contribute useful knowledge and recommendations to their client in a clear and balanced fashion. It provides them with practical experience by applying the tools of analysis acquired during three semesters of prior coursework to actual problems clients face in the public, non-governmental, and private sectors. Students work in teams to produce carefully crafted policy reports that meet high professional standards. The reports are research-based, analytical, evaluative, and (where relevant) provide prescriptive responses and always recommendations for real-world clients. This culminating experience is the ideal equivalent of the thesis for the La Follette School degrees in public affairs. While the acquisition of a set of analytical skills is important, it is no substitute for learning by doing. The opinions and judgments presented in the report do not represent the views, official or unofficial, of the La Follette School or of the client for which the report was prepared. Timothy Michael Smeeding A&S Distinguished Professor of Public Affairs and Economics May 2014

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ACKNOWLEDGMENTS Our team owes a debt of gratitude to all of those who offered assistance and guidance during the production of this report. We thank Andria Hayes-Birchler and her colleagues in the Millennium Challenge Corporation’s Department of Policy and Evaluation, who solicited this report and provided us with extremely useful information and guidance; Professor Timothy Smeeding for his careful instruction and feedback; Professor Melanie Manion, who provided us information on methodology and went above and beyond to provide her expertise on our project; and finally, the students and staff at the La Follette School of Public Affairs, who have enriched our educational experience immensely.

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LIST OF ABBREVIATIONS ADB African Development Bank Country Policy and Institutional

Assessments AFR Afrobarometer ASD Asian Development Bank Country Policy and Institutional

Assessments BPS Business Enterprise Environment Survey BTI Bertelsmann Transformation Index CBI/CBIP Commercial Business Information CCR Countries at the Crossroads CIESIN Center for International Earth Science Information Network CoC Control of Corruption indicator CRE Corruption-Related Events EIU Economist Intelligence Unit EU European Union FRH Freedom House GCB Global Corruption Barometer Survey GCS Global Competitiveness Report GII Global Integrity Index GOV Public Sector Data GWP Gallup World Poll IFD Rural Sector Performance Assessment IFAD International Fund for Agricultural Development IMD International Institute for Management and Development IMF International Monetary Fund IPD Institutional Profiles Database LBO Latinobarometer LICs Lower Income Countries LMICS Lower Middle Income Countries MCC Millennium Challenge Corporation NGO Nongovernmental Organization PIA Country Policy and Institutional Assessments PRC Corruption in Asia Survey PRS Political Risk Services International Country Risk Guide UCM Unobserved Components Model UNICEF United Nations Children's Fund UNODC United Nations Office on Drugs and Crime VAB Vanderbilt University Americas Barometer Survey WCY World Competitiveness Yearbook WEO World Economic Outlook WGI Worldwide Governance Indicators WJP World Justice Project Rule of Law Index WMO Global Insight Business Condition and Risk Indicators YCELP Yale Center for Environmental Law & Policy

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EXECUTIVE SUMMARY The Millennium Challenge Corporation (MCC) grants large-scale foreign assistance based on ex post criteria. Rather than prescribing conditional aid, they evaluate a country’s past performance on several governance and economic development indicators. Each year, the MCC creates a scorecard that indicates where a country ranks in these indicators relative to the median of its economic peers. At the request of the MCC, our team completed an analysis of the Control of Corruption indicator (CoC)—an aggregate of 22 sub-sources maintained by the Worldwide Governance Indicators (WGI) project of the World Bank—and its responsiveness to discrete corruption-related events (CRE). We define a positive CRE as a significant reform attempt and a negative CRE as a scandal involving corrupt activity. We find the evidence is inconclusive at this stage of analysis, as the CoC and its sub-sources do not respond to a CRE in a uniform manner and our limitations preclude a causal analysis. Corruption has a dichotomous nature in global development; it has become a fundamental supplement to assessing predictors of governance and economic performance, yet it is difficult to accurately measure using a quantitative method. By its very nature, corruption is concealed and resistant to change. Even the CoC represents a measurement of the relative perception of corruption within a country, not the real level of corruption itself. The MCC uses the CoC as a “hard hurdle” measurement, meaning that eligibility for an MCC aid compact is contingent on achieving a successful ranking in this particular indicator. The CoC is therefore subject to more scrutiny and public criticism than most indicators, since it directly impacts a country’s potential to receive foreign development aid from the MCC. Thus, the MCC’s stakeholders have a vested interest in better understanding the responsiveness of the CoC and its sub-sources to CREs to increase their likelihood of receiving foreign aid. In addition, the practice of quantitatively measuring corruption is relatively new; therefore, an in-depth analysis may contribute to identifying historical trends. To answer this research question, we implement a qualitative and quantitative methodology. First, we choose five case studies (CREs in Benin, Canada, Kenya, Georgia, and Serbia) through a purposive selection process. With this method, we explicitly select cases through a non-probability sampling of prioritized statistics. We utilize a qualitative analysis of the CRE and implications resulting from the country’s sociopolitical context. We also conduct a quantitative analysis of the corresponding data changes—or lack thereof—in the CoC and relevant sub-sources. In each case study, we include a table indicating the values of the CoC and its sub-sources for a period of time immediately before and after the CRE. Additionally, we include a graph of the CoC changes relative to the yearly confidence intervals. This graph highlights the difficulties of proving year-over-year changes that are statistically significant, as the yearly confidence intervals are large and overlap one another. Thus, from an empirical standpoint, we cannot

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conclude changes in CoC are statistically different from zero, although clearly not the case from a policy-making perspective. In performing our analysis, we face a number of challenges that restrict the robustness and external validity of our results. The scope of our analysis is limited; with only five case studies, the conclusions that we identify may not be applicable to CREs in other contexts. Sub-source data are inconsistent and difficult to access. This inaccessibility creates many challenges for stakeholders across the globe to appropriately assess data and the data’s implications for their country. Without full access to all sub-source datasets and underlying questionnaires, it is especially difficult to assess how a CRE may affect the CoC score. We do see a variation in the CoC and sub-source data that reflects the hypothesized direction of change for some case studies, but we cannot conclude that the changes are causally connected. The results of our analysis assist the MCC in recommending more targeted policy solutions for stakeholders who need more support to focus on long-term capacity development rather than short-term program fixes. We also contribute to a gap in the literature by evaluating how well an indicator responds to a CRE instead of how effectively it measures the overall level of corruption. Our findings reinforce WGI’s recommendation not to attribute year-over-year changes to CREs or specific policies. By our facilitating a better understanding of the sub-source methodologies and their effect on the CoC, stakeholders will have a clearer picture of the challenges associated with improving country-level corruption. Most significantly, our findings indicate that the CoC does not uniformly respond to CREs. We identify biases in the sub-sources based on the type of assessment and their intended audiences, along with differences in their sensitivity to the prior level of corruption. We find this lack of uniformity is likely due to the nature of the CoC, which measures perceptions of corruption rather than real levels of corruption. In environments with high state capacity and public confidence in institutions, we posit that sub-sources are more responsive to change. Higher sensitivity reflects the impression of a negative CRE as an anomaly and high expectations of a positive CRE as a credible signal. We recommend additional research on this topic, ideally through a large sample statistical analysis. In addition, we recommend the MCC continue internal evaluation to determine the efficacy of the CoC in meeting the assessment needs of the MCC. Finally, we recommend the MCC continue to facilitate communication with stakeholders to promote long-term development over short-term policy approaches.

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I. INTRODUCTION In low-income countries, corruption and economic growth have a mutually reinforcing negative relationship. This correlation creates a cyclical development trap; whereby corruption distorts economic performance, resulting in more entrenched corrupt systems (Mauro 1997). Consequently, external parties are less likely to provide foreign aid or direct investment due to the uncertainty of the fragile economy. For this reason, donor agencies and organizations consider a country’s level of corruption when awarding international development funds. The Millennium Challenge Corporation (MCC), an independent U.S. foreign aid agency, utilizes a measurement of corruption as a “hard hurdle” in the selection of their potential aid recipients. The MCC uses the “Control of Corruption” (CoC) indicator compiled by the World Bank’s Worldwide Governance Indicators project as that corruption measurement. To be considered eligible for aid, a MCC candidate country must “clear the hurdle” by scoring at or above the median CoC score among its income group. Understandably, MCC stakeholders would thus desire positive improvements in the CoC; it is unclear, however, what causes the CoC to change. At the request of the MCC, our team analyzes the sensitivity of the CoC and its sub-sources to discrete corruption-related events (CRE) in five countries. This report provides a rigorous qualitative and quantitative analysis of several independent CREs and any correlated changes in the CoC and sub-source data points. In the first section, we provide a brief review of corruption and the methodology of the CoC. We then discuss the methodology of our case study approach, including potential limitations to our analysis. The next section includes case studies of five CREs and corresponding data changes. We also offer detailed descriptions of each sub-source included in the CoC, along with related inferences from our case studies. Finally, we identify several findings from our analysis and propose recommendations for future MCC policy and research. 1. Millennium Challenge Corporation The MCC began operations approximately 10 years ago under the authority of the Millennium Challenge Act of 2003. Traditionally, strategic interests and conditional requirements dominate the distribution methods of foreign aid. With conditionality, for example, an agency would set requirements for countries to implement certain policies before receiving additional tranches of aid. Conditionality is now considered a less effective and patrimonial way to promote international development. Numerous scholars have maintained the overarching theory of aid promoting economic development in countries with clean governance (Burnside and Dollar 2000, Radelet 2005). The MCC operates with an ex post framework, granting aid to countries who have already performed successfully on a number of governance indicators. The MCC believes that development aid granted through competitive selection and involving country-led solutions and country-led implementation will have the greatest effect on poverty reduction (MCC 2014).

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2. The Control of Corruption Indicator as a Measurement of Corruption As defined by the MCC, corruption is “the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as ‘capture’ of the state by elites and private interests” (World Bank. 2014h). This definition matches the common features of others put forth by scholars and policy practitioners; there is vigorous debate, however, regarding the extent and classification of the related terms. By which standards does one define “public power” or “private gain,” for example? It is also challenging to empirically capture a phenomenon that is secretive by nature. As a result, corruption is extremely difficult to measure both quantitatively and qualitatively. In the last twenty years, many organizations have attempted to address this problem by creating indicators of the relative and perceived levels of corruption within a country. Organizations may include hard and soft measurements—i.e. the number of officials prosecuted for corrupt offenses and the perceived corruptness of public officials, respectively. Despite comprehensive surveying and assessment methodologies, these indicators are still limited in their measurement accuracy due to limited resource capacities, geographic foci, and intended audiences of the organization. These limitations create biases that may affect the accuracy and comparability of measurements among sources. The CoC attempts to address these concerns by creating a composite indicator that aggregates 22 independent sub-sources (See Table 1). This approach allows the inclusion of more than 200 countries in the CoC, addresses perceived corruption levels in different sectors like the executive, legislative, and judiciary, and combines both public and expert opinions. In so doing, the CoC comprehensively measures different aspects of corruption through a globally comparable method (World Bank. 2014b). To facilitate uniformity among sub-sources, the WGI project must use a complex methodology to compile its CoC indicator. First, the WGI project must rescale each sub-source on a 0 to 1 range, because the scales employed by individual sub-sources differ widely. A score of “1” naturally represents a different measurement on a 0 to 5 scale than a 0 to 1 scale. Then, the WGI project uses an unobserved components model to determine the appropriate weights assigned to the sub-source data per country. The unobserved components model assumes the “observed data from each source are a linear function of the unobserved level of governance, plus an error term” (World Bank. 2014b). It also assumes that sub-sources with stronger correlation indicate more accurate measurements; more highly correlated sub-sources will thus receive a higher weight in the aggregate indicator. This model returns a weighted average score for each country on a scale of -2.5 to +2.5, with a higher score indicating better governance (World Bank. 2014b).

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Table 1: Control of Corruption Indicator Sub-Sources

Source: World Bank 2014b

3. Potential Limitations of the Control of Corruption Indicator Despite the strengths of the CoC methodology, there are inherent limitations to the CoC’s efficacy for use in evaluation and policymaking decisions. Its primary weakness stems from its use of multiple sub-sources with disparate methodologies and reporting perspectives. Including 22 sub-sources is a benefit to be sure, but it also creates challenges when not every country uses the same sub-sources over the same time periods. The Afrobarometer, for example, only focuses on African countries; whereas Freedom House’s Countries at the Crossroads measures 70 countries across the world that are “strategically important” to the U.S. (Freedom House 2014). In addition, sub-sources report their data annually, monthly, biannually, or triennially. The WGI project will lag data or repeat measurements, as determined to be appropriate. The differences can lead to biases that are not readily transparent and a methodology too complex for practitioners to replicate or analyze. In particular, we consider the weighting method related to the unobserved components model a strong limitation of the CoC. On one hand, the WGI project methodology is generally regarded as statistically sound; WGI-affiliated and unaffiliated researchers have written exhaustively on its approach. Aart Kraay of the World Bank noted the CoC scores do not change in a statistically significant manner even if the weights are completely removed (Kraay 2014). On the other hand, the CoC scores have extremely wide confidence intervals. A wide confidence interval may not correspond with a statistically significant change; however, the same wide confidence interval may correspond with a drastic policy

No. Acronym Control of Corruption Data Sources Type* Organization 1 ADB Country Policy and Institutional Assessments Expert (GOV) African Development Bank

2 AFR Afrobarometer Survey Afrobarometer

3 ASD Country Policy and Institutional Assessments Expert (GOV) Asian Development Bank

4 BPS Business Enterprise Environment Survey Survey World Bank

5 BTI Berelsmann Transformation Index Expert (NGO) Bertelsmann Foundation

6 CCR Countries at the Crossroads Expert (NGO) Freedom House

7 EIU Country Risk Service Expert (CBIP) Economist Intelligence Unit

8 FRH Nations in Transit Expert (NGO) Freedom House

9 GCB Global Corruption Barometer Survey Survey Transparency International

10 GCS Global Competitiveness Survery Survey World Economic Forum

11 GII Global Integrity Index Expert (NGO) Global Integrity

12 GWP Gallup World Poll Survey Gallup

13 IFD Rural Sector Performance Assessments Expert (GOV) International Fund for Agricultural Development

14 IPD Institutional Profiles Database Expert (GOV) French Government

15 LBO Latinobarometro Survey Latinobarometro

16 PIA Policy and Institutional Assessments Expert (GOV) World Bank

17 PRC Corruption in Asia Survey Political Economic Risk Consultancy

18 PRS International Country Risk Guide Expert (CBIP) Political Risk Service

19 VAB Americas Barometer Survey Vanderbilt University

20 WCY World Competitiveness Yearbook Survey Institute for Management and Development

21 WJP Rule of Law Index Survey World Justice Project

22 WMO Business Conditions and Risk Indicators Expert (CBIP) Global Insight

*Types of Expert Assessments: GOV - Public Sector Data, CBI - Commercial Business Information, NGO - Nongovernmental Organization Data

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impact on countries near the cutoff level for MCC aid. In addition, we find that the sub-sources have different perspectives and intentionally capture different aspects of corruption. Therefore, uncorrelated sub-sources may not be inaccurate but instead represent precisely these different measurements. Weighting these sources weakly might mask the importance of their unique data. For more information on the weighting methodology, see Appendix I. Finally, the inaccessibility of some sub-source data, as well as the complexity of the CoC methodology renders it nearly impossible to establish causal connections between changes in CoC or sub-source data and CREs. This has direct implications for the policymaking decisions of MCC stakeholders. The CoC may provide a comprehensive view of corruption within a country, but it does not reveal significant progress or setbacks in a particular sector. As such, countries will not be able to identify which specific policies they should target to improve their performance on the CoC. This challenge complicates the MCC’s efforts to approve Compact and Threshold Program assistance promoting good governance if the MCC can not evaluate the quantitative improvement associated with policy changes. II. METHODOLOGY We analyze the responsiveness of the CoC through five case studies of CREs. For each case, we provide a qualitative analysis and examine the corresponding quantitative changes in the CoC and each sub-source used in that country. Because the WGI uses different sub-sources in each country, our case studies vary in terms of the number and type of indicators assessed. However, we follow a case study framework throughout our report to maximize comparability across cases. 1. Case Selection We begin our analysis with the selection of our five case studies. Our preliminary research identifies four case studies through purposive sampling, a non-probability sampling method in which we explicitly select cases based on certain characteristics. Our approach primarily begins with the identification of a significant CRE from which we would expect to see corresponding changes in the CoC and sub-source indicator data. These case studies evaluate CREs in Benin, Canada, Georgia, and Kenya. We select a fifth case study through a “needle jump” method in which we first identify a significant data change in a sub-source and then attempt to match a CRE to that change in the corresponding time period. This case study evaluates a CRE in Serbia. Our team believes that the combination of “needle jump” and purposive sampling provides insight into the responsiveness of the CoC and its sub-sources. In the purposive case studies, we first identify a CRE that we anticipate to result in data changes within the sub-sources; these corresponding changes may or may not be reflected. We use the “needle jump” case as a type of robustness check, since we

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start with a case where a significant data change has already happened. We then review the applicable time period for a CRE that theoretically should be reflected in the data. This selection process is beneficial because it attempts a causal connection between the CRE and changes in the sub-source data. Although we do make an assumption about the appropriate CRE, we still anticipate some change directly resulting from the CRE selected. 2. Case Study Framework We begin our analysis with the background of the country. This section includes a history of corruption within the country to frame our specific CRE. We report income level using the World Bank’s assessment of gross national income per capita as that is used to determine the MCC’s income level classifications. Next, we identify the CRE. For each case, we analyze a corruption scandal or reform effort. In analyzing reform efforts, we include multiple laws or actions which were part of a singular reform effort within a short, distinct time period. In analyzing scandal events, we consider scandal events that have effects in multiple sectors or involve a diverse set of actors. However, we do not consider reform efforts that span a period of more than two years, as it would be too difficult to establish a causal connection between the CRE and changes in the CoC and sub-source data. We include in all cases a background of the reform or scandal. For reform cases, we also include the type of reform, size of reform, sector affected by the reform, and external reaction. For scandal cases, we include the type of scandal, the magnitude of the scandal in U.S. dollars, the sector affected by the scandal, and the external reaction. Finally, we examine the impacts of each case study on the indicators. We look for jumps in the CoC indicator and any changes in the sub-sources. For a more detailed explanation of our case study framework, see Appendix B. IV. LIMITATIONS In conducting our analysis we encountered the following challenges: 1. Lack of External Validity Purposive sampling allows us to explore CREs with targeted characteristics, but our small sample size limits the robustness of our analysis. Given the low number of cases and deliberate selection bias, we acknowledge the biases that may be present in our findings. Further, as a result of purposive selection, the conclusions at which we arrive lack external validity, and thus may not be directly applicable to other CREs. 2. Difficulty Drawing Causal Relationships We are unable to prove causality between CREs and changes in the CoC or CoC sub-sources. We cannot isolate the CREs’ impact on the CoC or its sub-sources.

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Additionally, we cannot attribute a change in the CoC or its sub-sources solely to a specific CRE. At best, we can prove correlation between the sub-sources and the event. For example, the change of leadership in Syria inspired a positive expectation of clean government that was an illusion (see Appendix H). These factors and the absence of a counter scenario, make causal connections between the CoC sub-sources and CREs difficult. 3. Sampling Biases In addition, surveys and assessments themselves face sampling biases; therefore we are unable to evaluate the full picture of corruption in a specific country at any given time. Last, real corruption is impossible to measure. Neither business assessments nor household-wide perception surveys are direct measures of corruption. 4. Data Reporting and Access Limitations The WGI project does not provide full datasets for all of its CoC sub-sources. Additionally, the vast majority of sub-sources do not allow full public access to their original datasets, survey questionnaires, or reports. Variation among sub-sources further complicates our analysis. Sub-sources cover different sectors, forms of corruption, countries, and time periods. In addition, individual sub-sources have the tendency to change their surveys from year to year. Because we only have access to partial datasets, understanding the data and reasons for specific changes in sub-sources is difficult. The differences among sub-sources make the aggregation process somewhat ambiguous. Some of the sub-source data reported through the WGI project are backdated as a result of a data lag. For instance, the results of a sub-source that conducts a tri-yearly survey are reported for the previous three years. Thus, there is no possible way we can determine a year-over-year change in these sub-sources as a result of a specific CRE. V. CASE STUDY ANALYSES We selected five case studies: Benin, Canada, Georgia, Kenya, and Serbia. 1. President Yayi’s Financial Scandal, 2010—Benin In 2010, the sudden crash of a micro-lending bank shook Benin; the bank was later discovered to be no more than a Ponzi scheme. The scandal affected the lives of approximately 100,000 depositors, the majority of whom made less than $2 a day. The scandal also implicated the Beninese president, who was seen endorsing the bank. This CRE represents a corruption scandal in a highly corrupt, low-income country that is also an MCC compact country.

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History of Corruption in Benin Benin is a small west-African country, but it was once home to a large regional power. Benin gained independence from its French colonizers in 1960 and was subsequently ruled by military regimes (CIA 2014b). In 1972, Mathieu Kerekou seized power and imposed a single party Marxist-Leninist government. By 1990, civil unrest and economic hardship forced Kerekou to begin transitioning toward a democratic system of governance. Elections in 1991 brought to power a new leader, President Soglo. However, Kerekou returned to power in 1996. Kerekou retained the presidency until 2006, when Boni Yayi won election (CIA 2014b). President Yayi ran his first presidential campaign on the promise of anti-corruption reforms (Freedom House 2012). After winning the election, Yayi announced that anti-corruption efforts would be the central theme of his new administration (World Bank 2011). Beginning in 2006, he ordered an audit of the country’s 60 state-owned enterprises (Freedom House 2012). Coordinating with the World Bank Institute, Yayi’s administration launched the Governance and Corruption Survey in 2007 to gain information about how to improve governance and tackle corruption (World Bank 2011). In October 2011, following the CRE, Benin’s National Assembly passed the “Fight against Corruption and Related Offenses in the Republic of Benin” (Department of State 2014). The new law forced government employees to declare their financial assets when they entered and left office (Freedom House 2014). As the U.S. Department of State highlights, “although the law provides criminal penalties for official corruption, the government did not implement the law effectively, and officials sometimes engaged in corrupt practices with impunity” (2014). The MCC categorizes Benin as a low-income country (MCC 2013c). In 2012, its gross national income per capita was approximately $750 (World Bank 2014d). i. President Yayi’s Financial Scandal President Yayi was embroiled in a financial scandal that broke in July 2010. Yayi was connected with an organization called Investment Consultancy and Computing Services, a microcredit lender found to be operating a $327 million Ponzi scheme (McLure and Zoueme 2011). Investors believed Yayi had publicly endorsed Investment Consultancy and Computing Services by showing up in print, television, and radio advertisements with the bank’s executives (Ahissou and Henderson 2010). Victims stated that Yayi gave the bank credibility and sparked interest in investing (Milmo 2010). The Ponzi scheme may have gone undetected by investing some of its funds in the country’s evangelical Christian groups and development efforts, including health clinics, orphanages, and well-digging (Nossiter 2010). Following the collapse of

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the Ponzi scheme, the president removed his interior minister and chief prosecutor from office due to their involvement with Investment Consultancy and Computing Services. The interior minister provided body guards to protect Investment Consultancy and Computing Services executives, and the chief prosecutor allegedly blocked a government investigation into the company’s finances. At least 13 Investment Consultancy and Computing Services executives were jailed on fraud charges (Milmo 2010). In the aftermath, 48 of 83 members of Parliament requested President Yayi submit himself to the country’s court system for a trial investigating his involvement in the scheme. A vote of impeachment against Yayi nearly passed; the attempt cited his involvement in the Ponzi scheme as grounds for removal from office (Freedom House 2012). Despite discontent over Yayi’s role in the scandal, the president won a second term in office on March 14, 2011. The scandal cost Beninese citizens approximately $327 million which amounts to approximately 5 percent of the country’s gross domestic product (McLure and Zoueme 2011). Official reports indicate 50,000 to 70,000 victims, while some press outlets report as many as 150,000 victims. Table 2: Benin – Control of Corruption and Sub-source Data for CRE

Year(-2) Year (-1) Year 0 Year +1 Year +2 2008 2009 2010 2011 2012CoC -0.53 -.0.67 -0.74 -0.63 -0.92ADB 0.6 0.6 0.6 0.6 0.57AFR 0.51 0.51 0.51 0.56 0.56BTI 0.5 0.45 0.45 0.45 0.4EIU 0.25 0 0 0 0GCS 0.28 0.32 0.23 0.33 0.19GII 0.93 GWP 0.37 0.37 0.37 0.3 0.24IFD 0.58 0.6 0.6 0.6 0.5IPD 0.17 0.33 0.33 0.33 0.08PIA 0.5 0.5 0.5 0.5 0.5WMO 0.38 0.38 0.38 0.38 0.38

Source: World Bank 2013b ii. Change in the Control of Corruption Indicator Benin has historically received low CoC scores. To assess changes in Benin’s CoC score associated with this CRE, we use 2010 as “year 0.” Benin’s 2010 CoC score was -0.74. Benin’s CoC score increased to -0.63 in 2011. In 2012, Benin’s CoC score fell significantly to -0.92 close to its 2005 score of -0.97. Benin CoC score included 10 sub-sources from 2009 to 2012.

9

Figure 1: Benin--Change in Control of Corruption Score, Including Standard Error

Source: World Bank 2014b

iii. Changes in the Sub-source Data One sub-source significantly increased between 2010 and 2011: the GCS. The GCS increased from 0.23 to 0.33 between the two years. iv. Conclusion In 2011, Benin’s CoC increased; we expected Benin’s score to decrease in response to the CRE. In 2012, however, the CoC decreased to -0.92, far lower than the previous four years. The GCS increased significantly from 0.23 to 0.33 between 2010 and 2011. In addition, the GCS fell to 0.19 in 2012. The GCS made up 12 percent of Benin’s CoC score in 2010 and 2011, and it made up 11 percent of the score in 2012. The GCS does not publish individual survey questions and respective scores for the years of our analysis. However, we do know that the GCS measures public trust in politicians and diversion of public funds. We did not expect the GCS to increase in the year following the CRE, as public trust in politicians should have been called into question following Yayi’s implication in the scandal. Benin’s AFR score increased from 0.51 to 0.56 in 2010 to 2011. The AFR score used for the 2010 CoC was reported in 2008. For this reason, we cannot make a single-year inference about the effect of this CRE on the 2011 AFR score. The 2012 CoC used the 2011 AFR score as well, which is why the AFR score did not change. The weight associated with the AFR was 12 percent in 2010 and 7

‐1.20

‐1.00

‐0.80

‐0.60

‐0.40

‐0.20

0.00

2008 2008 2009 2009 2010 2010 2011 2011 2012 2012 2013

Benin

10

percent in 2011; thus, the increase in the reported AFR score in 2011 contributed slightly to the CoC increase in that year. The GWP decreased from 0.37 to 0.3 from 2010 to 2011. The GWP further decreased to 0.24 in 2012. Though the GWP seems to reflect the CRE, its change had no influence on Benin’s total CoC score, as it received zero weight in 2001. The GWP surveys households about the perceived prevalence of corruption in government. We postulate that the GWP was responsive to the CRE because the scandal largely affected average households in Benin rather than businesses. The ADB, BTI, EIU, IPD, PIA, and WMO scores did not change between 2010 and 2011. In addition, none of these sub-sources changed from 2009 to 2010. The unresponsiveness of these sub-sources is suspect, as the CRE should have affected sub-source scores in these years. All of these sub-sources rely on expert opinion, which leads us to question the ability of expert opinion sub-sources to respond to corruption that primarily affects average citizens, especially in developing countries. The CoC did not reflect Benin’s CRE in the year following the event. However, it is unclear whether the drop in Benin’s 2012 CoC score can be attributed to a lagged response to this CRE or to other corruption events occurring at the time. The GWP was the only sub-source to respond to the CRE in the way we expected. Because the GWP was the only responsive sub-source, we suspect the CoC is less likely to capture scandals that affect households. 2. Sponsorship Program Scandal, 2004—Canada Canada’s sponsorship scandal is a good example of a CRE in a high-income country. We selected this case study to evaluate whether the CoC and its sub-sources respond differently to corruption depending on country income. The sponsorship scandal also represents a CRE with widespread effects on political power and governance behaviors. i. History of Corruption in Canada Canada’s system of governance is a parliamentary democracy, constitutional monarchy, and federation (CIA 2014c). Canada has strong economic institutions and liberalized, open, and mature markets. As a developed country, Canada has maintained relatively low levels of corruption. The Liberal Party came to power in 1993 with the election of Prime Minister Chretien. Chretien was famous for maneuvering the Liberal Party into a position of political dominance. In 2003, Chretien retired after ten years in power; Liberal Party member Paul Martin succeeded Chretien (Freedom House 2004). In 2004, Quebec’s provincial government announced a referendum on Quebec’s sovereignty (Makarenko 2006).

11

The MCC classifies Canada as a high income country (World Bank 2014f). Canada’s gross national income per capita is estimated at around $51,570. Sponsorship Scandal From 1996 to 2004, Liberal Party members used the sponsorship program, the federal advertising campaign agency, to provide massive payoffs and kickbacks to party supporters. The Canadian government established the sponsorship program to promote national unity and the profile of the federal government to counter Quebec’s independence movement (Makarenko 2006). A total of U.S. $281 million was spent on the program, millions of which was misspent (Facts on File 2005). A 2004 investigative report by Auditor General Sheila Fraser found that public works officials broke “every rule in the book” in awarding the sponsorship contracts (Warn 2004). Liberal Party members accepted bribes from advertising firms in exchange for the receipt of government contracts (Freedom House 2004). Contracts were also frequently awarded to firms with close ties to the Liberal Party. Fraser said that hundreds of millions of dollars were mismanaged during the program, and that the scandal involved several Canadian government agencies (CBC 2006). Two weeks after Fraser’s report was released, Prime Minister Martin created the Commission of Inquiry into the Sponsorship Program and Advertising Activities and selected Quebec Superior Court Judge John Gomery to head the commission (Ottawa Citizen 2006). Gomery’s report, released on November 1, 2005, revealed clear evidence of political involvement in the administration of the sponsorship program (CBC 2006). The report implicated several senior members of the Liberal Party serving under Prime Ministers Chretien and Martin (Freedom House 2005). Chretien was not accused of direct involvement in the scandal, but he was accused of not properly supervising the sponsorship program. Less than a month after Gomery’s first report was released, the opposition parties united to topple Prime Minister Martin and his administration with a vote of no confidence (CBC 2006). A parliamentary election, held January 23, 2006, resulted in the Liberal Party losing control over the House of Commons for the first time in 12 years (CBC 2006). In response to the corruption scandal, the newly elected Prime Minister Stephen Harper passed the Accountability Act to improve governance in the aftermath of the scandal (CBC 2006). Tens of millions of dollars were allegedly stolen through the sponsorship program. As a result, criminal charges were filed against the major players in the scandal. Jean Lafleur, former president of Lafleur Communications Marketing reportedly scammed $36.5 million in commissions, fees, and costs (Vancouver Province 2005). Paul Coffin, head of Coffin Communications advertising, pleaded guilty to 15 counts of fraud for stealing $1.5 million Canadian, and served 18 months in jail. Charles Guite, a senior federal bureaucrat, and Jean Brault, an

12

advertising executive, were both found guilty on five counts of fraud for taking $1.5 million Canadian and, respectively, served 3.5 years and five months in prison (CBC 2006). The Liberal Party set up a $750,000 Canadian trust fund to repay the government for illicit funds it received. The sponsorship scandal is characterized as an institutional CRE that affected the executive and legislative members of the Liberal Party. The scandal caused the public to question the Liberal Party in general. Table 3: Canada—Control of Corruption and Sub-source Data for CRE

Year (-2)

Year (-1)

Year 0 Year +1 Year +2 Year +3 Year +4 Year +5

2000 2002 2003 2004 2005 2006 2007 2008CoC 2.23 2.11 2.04 1.85 1.86 1.96 1.98 1.99EIU 1 1 1 1 1 1 1 1GCB 0.71 0.71 0.74 0.72 0.73GCS 0.8 0.83 0.72 0.77 0.75 0.77 0.8 0.82GII 0.53 0.55GWP 0.43 0.56 0.59IPD 1 1 1PRS 1 0.67 0.83 0.67 0.83 0.83 0.83 0.83VAB 0.47 0.47 0.43WCY 0.83 0.75 0.78 0.75 0.7 0.73 0.67 0.66WMO 0.88 1 1 0.85 1 1 1 1

Source: World Bank 2013b ii. Change in the Control of Corruption Indicator We chose 2003 as year zero for our analysis because the Fraser report broke in early 2004. Thus, Canada’s 2004 CoC score should reflect the scandal. Canada’s CoC score reached an all-time low of 1.85 in 2004, down from 2.04 in 2003. The score inched up to 1.86 in 2005 and began to gradually increase from 2006 to 2008. Canada’s scores finally climbed back above the 2.0 mark in 2009.

13

Figure 2: Canada--Change in Control of Corruption Score, Including Standard Error

Source: World Bank 2014b

iii. Changes in the Sub-source Data There were two sub-sources that had significant jumps from 2003 to 2004: the PRS and WMO. The PRS decreased from 0.83 to 0.67, and the WMO decreased from 1 to 0.85. iv. Conclusion As expected, Canada’s CoC score dropped substantially in 2004 following the sponsorship scandal. The score barely increased in 2005, then started to recover from 2005 to 2008. The PRS and WMO were the only two sub-sources to change significantly during that year, and original data are not available for either source. The decrease in the WMO score had the largest effect on the CoC, as the WMO was weighted at 25 percent in 2003 and 20 percent in 2004. By comparison, the PRS score change had a smaller effect, as PRS was weighted at 5 percent and 7 percent, respectively. In 2005, the scaled PRS score jumped back to 0.83, and the scaled WMO score jumped back to 1. The 2005 PRS increase is inconsistent with some indicators that showed a further decrease in 2005. The WCY, a survey of business executives combined with hard statistics, broadly measures the existence of bribery and corruption in the economy. Question-level survey data are not available for the WCY; however, average score data are publicly available. Canada’s average score decreased from 7.8 to 7.47 on a 1 to 10 scale between 2003 and 2004. In 2005, the WCY score decreased further to 7.3.

1.50

1.60

1.70

1.80

1.90

2.00

2.10

2.20

2.30

2.40

2003 2003 2004 2004 2005 2005 2006 2006 2007 2007 2008

Canada

14

Canada’s GCS scaled score increased from 0.72 to 0.77 from 2003 to 2004. This is not a large increase, but the GCS makes up 15 percent of Canada’s CoC score in both years, so the change may have had an effect. Because the original sub-source data are not accessible to the public, we cannot analyze the sub-source changes that took place. However, we did expect Canada’s GCS score to decrease after the sponsorship scandal, as the GCS includes evaluations of public trust in politicians, diversion of public funds, and irregular payments in public contracts. Canada’s GCS score did decrease in 2005, which may be as a result of a lag in data, or because further detail about the sponsorship scandal was revealed in 2005. Canada’s EIU score has remained at 1 on the CoC’s scaled data since 1996. The unresponsiveness of the EIU is suspect, as the revelation of the sponsorship scandal indicated real corruption in Canada. EIU makes up a large portion of Canada’s CoC score; it is weighted at 28 percent in 2003, 22 percent in 2004, and 19 percent in 2005. Because Canada received a perfect score on the EIU, the decrease in its weight results in a decrease in Canada’s overall CoC. The GCB, a measure that reflects the level of corruption experienced by an average citizen in a given country, was not used to compile Canada’s CoC score in 2003. In 2004, Canada’s scaled GCB score was 0.71. GCB’s telephone survey, conducted from July 12 to July 22, 2004, asked 1,000 Canadians about their perceptions of corruption in a number of sectors. Respondents ranked each sector on a scale of 1 to 5, where 1 is not all corrupt and 5 is extremely corrupt. Canada’s political parties received a score of 3.8, which was the highest score across all sectors. The parliament was perceived as the second most corrupt sector, with a score of 3.5. These scores accurately reflect perceptions of widespread corruption within political parties and the parliament following the sponsorship scandal. Canada’s scaled GCB score remained the same in 2004. However, the original GCB data showed a slight increase in perceived corruption in political parties and the parliament to 3.9 and 3.6 respectively. The GCB increase may be a response to the publication of the Gomery report (Hodess 2004). We anticipated the large decrease in Canada’s CoC score following the sponsorship scandal. Canada’s CoC score rebounded quickly post-scandal; the rebound was unsurprising given strong domestic and international confidence in Canada’s institutions. Sub-source data, however, responded inconsistently to the CRE, even among those that measure corruption in the same sector. The GCS response was the most surprising, which increased in the year following the CRE despite assessing corruption in the exact sectors in which it occurred. 3. The Rose Revolution, 2003—Georgia The Rose Revolution is an excellent example of widespread and effective anti-corruption reform efforts. We include the Rose Revolution as a case study, as we know that the reforms have had massive effects on the everyday lives of citizens

15

in Georgia. If the CoC and its sub-sources do not respond to the reforms following the Rose Revolution, we can conclude that the CoC may not respond well to even the largest and most effective reform efforts. i. History of Corruption in Georgia After the Russian Revolution, Georgia declared its independence. Georgia was forcibly absorbed by the Union of Soviet Socialist Republics in 1921, and then regained independence in 1991. Georgia underwent a bloody civil war beginning in 1992; in 1995, the war ended with Eduard Shevardnadze elected president. Shevardnadze was re-elected in 2000 (CIA 2014d). His presidency was plagued with difficulties, the largest of which was rampant public sector corruption (Economist 2000). President Shevardnadze consistently said anti-corruption reform was a priority in his administration, despite the fact that he was a main instigator of corruption (Economist 2000). Shevardnadze’s supporters and family held an inordinate amount of power within Georgia. Widespread corruption led to tax shortages; the state responded by refusing to pay salaries and pensions to public servants. The public servants were expected to earn their living through bribery and to share their spoils with their superiors. The executive would then retain information about the illegal activities of other elites and businesses to ensure loyalty. As soon as the groups or individuals were not needed, they would be charged with corruption. Corruption became such a large problem that in 2003, the International Monetary Fund suspended its programs in Georgia (Kandelacki 2006). Georgia is considered a lower middle income nation, with a gross national income per capita of $3,290 (World Bank 2014h). ii. The Rose Revolution In the fall of 2003, President Shevardnadze was forced to resign (CIA 2014d). Public discontent over high levels of corruption and blatant electoral fraud led to widespread political protests (Kandelacki 2006). Though the public’s demands were strongly felt, the Rose Revolution is noted as “the first bloodless change of power in the region’s history” (Kandelacki 2006). Shevardnadze resigned, and in 2004, Mikhael Saakashvili was elected president (CIA 2014d). Saakashvili immediately began massive institutional reforms, including a complete overhaul of the police system. Saakashvili’s government fired all 16,000 traffic police members in a single day. He then created a new police unit, which was smaller but provided higher pay to reduce incentives for graft. In 2005, a majority of Georgians said that they trusted the police for the first time in the country’s history (Nasuti 2014). Corruption reform was not limited to the police. In 2002, 38 percent of firms said that they frequently made unofficial payments to governmental officials (World

16

Bank 2006). The number fell to 10 percent in 2005 and to 4 percent in 2008 (World Bank 2010). The government implemented a national standardized exam to be used by university admissions officers instead of individual interviews; the examcut out the opportunity for the officers to ask for bribes in exchange for admission (Nasuti 2014). Petty bribery, which was once a part of the everyday lives of Georgian citizens, has virtually disappeared since 2004. Reforms have increased government transparency through methods such as e-government tools and an asset declarations database (Transparency International 2012). Georgia’s peaceful revolution and successful reforms gained international attention. The World Bank has called Georgia an anti-corruption success story, and Georgia’s scores on a variety of corruption indexes have increased substantially since the revolution (OCCRP 2012). Table 4: Georgia—Control of Corruption and Sub-source Data for CRE

Year (-2)

Year (-1)

Year 0 Year +1 Year +2 Year +3 Year +4 Year +5

2002 2003 2004 2005 2006 2007 2008 2009CoC -1.14 -0.65 -0.61 -0.36 -0.04 -0.25 -0.22 -0.22BPS 0.57 0.57 0.57 0.8 0.8 0.8 0.76 0.76FRH 0.21 0.21 0.21 0.25 0.33 0.33 0.33 0.33PIA NP NP NP 0.5 0.5 0.4 0.4 0.4WMO 0.13 0.38 0.4 0.38 0.38 0.25 0.25 0.25BTI 0.7 0.7 0.75 0.75 0.65 0.65 0.60GCB 0.67 0.66 0.66 0.66 0.73 0.73GCS 0.31 0.42 0.45 0.57 0.58 0.55IFD 0.56 0.56 0.56 0.51 0.6 0.65EIU 0.5 0.25 0.25 0.25GII 0.7 0.03 0 0.35GWP 0.44 0.48 0.64 0.61ASD 0.6 0.6 0.5

Source: World Bank 2013b iii. Change in the Control of Corruption Indicator In 2002, the CoC for Georgia was -1.14. The CoC score jumped to -0.65 in 2003, and the score continued to rise for the next three years up to -0.04 in 2006.

17

Figure 3: Georgia--Change in Control of Corruption Score, Including Standard Error

Source: World Bank 2014b

iv. Changes in the Sub-source Data Two indicators jump significantly between 2004 and 2005: the BPS and GCS. The BPS score increased from 0.57 to 0.8, and the GCS score increased from 0.32 to 0.42. v. Conclusion The most substantial CoC increase occurred from 2002 to 2003, prior to the reform efforts. The only sub-source increase during that period was in the WMO. The WMO data were not available, but we do know that the WMO measures banking and investment risk. We find it interesting that the CoC jumped even prior to the implementation of anti-corruption reforms. We postulate that the CoC may be reflecting changing expectations about Georgia’s political and economic path. After the 2004 reform efforts, the CoC increased substantially. This jump reflects large changes in the sub-sources, specifically the BPS and GCS. While the BPS score increased significantly, it only makes up 4 to 5 percent of the overall CoC score in those two years. The GCS score also increased significantly in that year, and it makes up 11 to 12 percent of the overall CoC score. The Freedom House indicator is weighted most heavily in these years, but sees very little change (for details on the specific weights, see Appendix C). Freedom House, a non-governmental organization, compiles rankings by country experts; thus, there are no individual survey questions to analyze. We anticipated a larger change in the Freedom House indicator, as it assesses the implementation of anticorruption initiatives.

‐1.60

‐1.40

‐1.20

‐1.00

‐0.80

‐0.60

‐0.40

‐0.20

0.00

1999 2000 2001 2002 2003 2004 2005 2006

Georgia

18

The BPS was measured in 2002 and 2005; the 2003 and 2004 CoC used the 2002 BPS scores. The BPS reported a 42.7 percent decrease in the percentage of firms expected to give gifts in meetings with tax officials. In addition, the BPS reported a 35.8 percent decrease in the number of firms expected to give gifts to secure government contracts. Finally, the BPS showed a 63.7 percent decrease in the number of firms expected to give gifts to public officials “to get things done” (International Finance Corporation 2014). The GCS does not publish individual survey questions and respective scores for the years of our analysis. However, we do know that the GCS measures public trust in politicians, diversion of public funds, state capture, and irregular payments in exports, imports, public utilities, tax collection, public contracts, and judicial decisions. The BPS and GCS measure irregular payments by businesses in various public sector activities. Bribes were rampant prior to the change in Georgian leadership, but fell significantly after Saakashvili’s reforms. Therefore, the change in the BPS and GCS scores likely reflect the decreased expectation of bribery by the business sector post-reform. The CoC scores continued to increase in the five years after the reforms were implemented. The sustained improvement of the CoC may reflect growing confidence in the permanence of Georgia’s new liberalization policies, economic freedom, and anti-corruption reforms. While Georgia’s anti-corruption reforms efforts were widely successful in decreasing real corruption, their success may not be the only factor that influenced the drastic increase in the CoC score. Georgia underwent major political upheaval, resulting in changes in the governmental structure and economic policies. CoC sub-sources are likely to reflect these changes. 4. Credible Anti-Corruption Reforms, 2002—Kenya Kenya’s 2002 and early 2003 commitment to anti-corruption policies was selected as a CRE for analysis because of the kleptocratic nature of its corruption. Additionally, Kenya’s low-income country status and regional location are important characteristics for case analysis. i. History of Corruption in Kenya The Republic of Kenya is located in East Africa and has a diverse ethnic population of approximately 45 million people (CIA 2014e). Ethnic division has characterized Kenya’s governance since independence. Unequal treatment of ethnic groups by Kenyan administrations has resulted in cyclical violence. Kenya has long been characterized as a country at a crossroads. Since its independence in 1963, Kenya has attempted political and economic reforms with

19

little lasting impact. For decades, Kenya has been hampered by corruption at the highest echelons of government and throughout its civil service sector. Mwai Kibaki was elected following what is said to be Kenya’s most corrupt rule at the hands of Daniel arap Moi (BBC News 2006). Kenya has been plagued with large-scale corruption scandals, including the Goldenberg Affair and the Anglo-Leasing scandal, the latter of which was publicized following Mwai Kibaki’s election and implementation of what Kenyans felt were sincere anti-corruption reforms. As of 2012, the World Bank classifies Kenya as a low-income country, with a gross national income per capita of $3,420 (World Bank 2014g). ii. Credible Anti-Corruption Reforms The National Rainbow Coalition pledged to fight corruption in every way possible when its leader, Mwai Kibaki, was elected president in December 2002. Kibaki ran for president with a platform to rid Kenya of corruption. Upon his election, Kibaki proclaimed in a speech:

“[C]orruption will now cease to be a way of life in Kenya and I call upon all those members of my government and public officers accustomed to corrupt practice to know and clearly understand that there will be no sacred cows under my government” (Odero 2011).

Kibaki made this signal of anti-corruption reforms credible when he appointed John Githongo as Permanent Secretary for Governance and Ethics in the Office of the President. Githongo was an experienced anti-corruption reformer who previously worked for Transparency International’s Kenya office. Kibaki also announced a commission of inquiry into the 1990-1993 Goldenberg Affair, a crippling corruption incident that had gone without investigation or punishment (Kaona 2003). The appointment of a Permanent Secretary for Governance and Ethics is an institutional reform. Kibaki’s announcement of an independent Commission of Inquiry into the Goldenberg sAffair is also institutional, as a credible threat of punishment did not accompany the investigation. Kibaki’s anti-corruption reform efforts targeted the executive sector. Kibaki deliberately placed the Permanent Secretary for Governance and Ethics close to the Presidency, physically and symbolically, to emphasize that reforms would be taking effect where they were needed most. Scandals under former President Daniel arap Moi took place at the highest levels of government; those incidents are precisely what Kibaki promised would be prevented under his administration. John Githongo’s appointment and the investigation into the Goldenberg scandal signaled a high commitment to root out graft and kleptocracy by government executives. The Kenyan people heralded the anti-corruption reforms implemented early in Kibaki’s presidency and strongly supported John Githongo’s appointment as

20

Permanent Secretary for Governance and Ethics (Odero 2011).nternational actors saw the appointment as an important signal by Mwai Kibaki as well, as Githongo was seen as an anti-corruption crusader in his journalistic and Transparency International work. Following Kibaki’s reforms, international donors resumed giving Kenya grants and loans (Odero 2011). The investigation into the Goldenberg Affair was widely supported because the affair, which was a direct cause of Kenya’s failing economy in 1992, had gone unpunished (Karanja 2003). Table 5: Kenya—Control of Corruption and Sub-source Data for CRE

Year (-2)

Year (-1)

Year 0 Year +1 Year +2 Year +3 Year +4 Year +5

2000 2002 2003 2004 2005 2006 2007 2008CoC -0.7 -0.99 -0.83 -0.8 -0.97 -0.58ADB NP NP NP 0.4 0.4 0.4 0.4 0.4AFR 0.66 0.66 0.66 0.6 0.6 0.6 0.53BTI 0.35 0.35 0.35 0.35 0.3 0.3CCR 0.54 0.46 0.46 0.44 0.44 0.44EIU 0 0 0 0 0 0 0 0GCB 0.48 0.59 0.63 0.63 0.53GCS 0.28 0.45 0.36 0.37 0.31 0.36GII 0.8 0.85 0.85GWP 0.13 0.22 0.09IFD 0.6 0.6 0.6 0.6 0.5IPD 0.17 0.17 0.17PIA NP NP NP NP 0.4 0.4 0.4 0.4PRS 0.33 0.33 0.58 0.25 0.08 0.08 0.08 0.08WMO 0.25 0.25 0.25 0.32 0.25 0.25 0.25 0.13

Source: World Bank 2013b iii. Change in the Control of Corruption Indicator We use 2003 as year 0 to evaluate the CRE, as anti-corruption reforms were signaled in early 2003 and implemented throughout the year. From 2003 to 2004 the CoC indicator increased from -0.83 to -0.8. The CoC included nine sub-sources in 2003 and 11 sub-sources in 2004. Between 2003 and 2004, Kenya moved from the 21.46 percentile to the 22.44 percentile of measured countries.

21

Figure 4: Kenya--Change in Control of Corruption Score, Including Standard Error

Source: World Bank 2014b

iv. Changes in Sub-source Data There are three significant jumps from 2003 to 2004: the CCR, GCS and PRS. The GCS shows the only significant increase, jumping from 0.28 to 0.45. The CCR fell from 0.54 to 0.46, and the PRS score fell from 0.58 to 0.25. v. Conclusion In 2002, Kenya’s CoC score was -0.99, which reflects the perceived level of corruption at the end of Daniel arap Moi’s presidency. The CoC score jumped to -0.83 in 2003, following Mwai Kibaki’s election in December 2002 and subsequent anti-corruption reforms. Kenya’s CoC increased very slightly in 2004, reflecting sustained faith in the anti-corruption efforts. In 2005, Kenya’s CoC fell drastically to -0.97, likely as a response to John Githongo’s resignation from his post. The Kenyan people and the international community interpreted this action as a red flag that the administration was still engaging in corruption. The GCS did not report on Kenya in 2002, but it was the only indicator to significantly increase from 2003 to 2004. While the GCS does not publish individual survey questions or the corresponding data, we do know its survey includes measures of public trust in politicians, diversion of public funds, and irregular payments in public contracts. We expected the GCS increase from 2003 to 2004, as public trust in politicians increased following Kibaki’s election. The GCS increase likely influenced the overall CoC score, as the GCS was weighted at 13 to 14 percent during these two years. The GCS fell to 0.36 in 2005, which corresponds with John Githongo’s resignation and falling public opinion about the

‐1.30

‐1.20

‐1.10

‐1.00

‐0.90

‐0.80

‐0.70

‐0.60

1999 2000 2001 2002 2003 2004 2005 2006

Kenya

22

commitment of the Kenyan administration to root out corruption. We expected the GCS to continue to fall after the Anglo-Leasing scandal was revealed, as the scandal involved diversion of public funds and corruption in public contracts. The GCS score stayed fairly constant through 2008. The PRS decreased significantly from 0.58 to 0.25 from 2003 to 2004. This decrease does not reflect the CRE, but it may measure real levels of corruption. The PRS measures business risk; as mentioned, corruption occurred in business at the same time that reform efforts were being implemented. In 2005, the PRS decreased again to 0.08, which may reflect John Githongo’s resignation and the publicity of the Anglo-Leasing scandal. The PRS received little weight from 2003 to 2005, so these changes had a relatively small impact on the CoC. The World Bank reports a CCR score of 0.54 for Kenya in 2003 that was taken from the 2004 CCR report. The WGI project states that Kenya’s 2004 CCR score was taken from the 2006 CCR report. However, the 2006 CCR report did not include a sub-score for corruption, which is suspect, as the CCR score for Kenya decreased to 0.46 in 2004. We are uncertain about where the 2004 score was ascertained, as neither the World Bank nor Freedom House provides an original score for the CCR in 2004. Despite the concerns, the CCR did not affect Kenya’s overall CoC score, as the CCR was given zero weight every year. The AFR reported data for Kenya in 2003, 2005, and 2008. Though the WGI project reports a score of 0.66 for Kenya from 2002 to 2004, neither the World Bank nor AFR provide original scores for those years. The 2005 report indicates a 0.55 score on a 0 to 1 scale (where 0 indicates high corruption) for corruption in elected officials. Elected officials received the lowest score, indicating the highest level of perceived corruption. The 2008 report was very similar; Kenya received a 0.55 score for perceived corruption of elected officials (World Bank 2014b). However, perceived corruption for tax/customs officials and elected officials decreased substantially from 2005 to 2008, which caused the AFR score to decrease. We expected high levels of perceived corruption of elected officials in 2005, as a result of Githongo’s resignation and decreased faith in the credibility of anti-corruption reforms. The changes between 2005 and 2008 likely reflect other corruption events and wide-scale governance challenges. The BTI did not change between 2003 and 2006. While original data were not available, we know the BTI measures anti-corruption policies and prosecution of public office abuse. Credible anti-corruption reforms should have caused a change in the BTI score. Kenya received a scaled score of 0 from the EIU from 2000 to 2008. We expect to see an increase in Kenya’s EIU score as a result of the CRE. In addition, the unchanging score is suspect, given considerable corruption scandals, reform efforts, and changes in political leadership from 2000 to 2008. The EIU is heavily weighted in Kenya’s compiled CoC score. In 2003, the EIU was weighted at 26

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percent; in 2004, it was weighted at 19 percent. Because Kenya’s EIU score is 0, a decrease in the EIU weight acts as an increase in the overall CoC score. The WMO increased from 0.25 to 0.32 between 2003 and 2004. The WMO does not publish original data. It broadly measures business and investment risk. The lack of reported data is problematic, as the WMO consistently receives high weights in Kenya’s CoC score. In 2005, the WMO decreased back to 0.25, where it stayed for three years. Without original data, we cannot accurately analyze these changes. Neither the PIA nor ADB provide average score data for 2003. In 2004, ADB received a scaled score 0.4; this score remained unchanged through 2008. PIA does not report data until 2005. Without 2003 data, we cannot assess the effect of the CRE on these sub-sources. ADB was not weighted heavily in 2003 or 2004, so changes in ADB’s score would not have had a large effect on the CoC. However, the PIA received fairly substantial weights in 2003 and 2004, so unobserved changes in the PIA data may have influenced the change in CoC score. Finally, the GCB and IFD were added to Kenya’s CoC in 2004. The combined weight of GCB and IFD is 8 percent in 2004; therefore, the addition of these two sub-sources was unlikely to have a great impact on the overall CoC score. Kenya’s CoC increased substantially in anticipation of a regime change and a signal of credible anti-corruption reforms. Although real corruption was occurring in conjunction with reforms, it was not widely known by the public. Business indicators likely reflected the real corruption that was occurring at the time, while indicators that measure public perceptions were responding to faith in the new regime’s ability to implement anti-corruption policies. 5. Anti-Corruption Reforms Following Milosevic’s Ouster, 2000—Serbia The needle-jump case study is based on a large magnitude jump in Serbia’s FRH score. From 2000 to 2002, Serbia and Montenegro’s score on the Freedom House sub-source rose by about 21 percent, indicating a potential reform event.1 We researched reform efforts in the time period and assessed the likelihood that they contributed to the indicator jump. i. History of Corruption in Serbia The Republic of Serbia is in southeast Europe and has a diverse ethnic population of 7.2 million people, excluding Kosovo (CIA 2014a). Ethnic conflict has been a factor in shaping Serbia’s government and defining its borders during the last 20

1 In 2000, Freedom House reported data for then Serbia and Montenegro; the World Bank’s WGI project has placed these data in Serbia’s current country data file. For this reason, the CRE will be described to have taken place in Serbia, even though it was Serbia and Montenegro at the time.

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years. The United States characterizes Serbia as a transition economy largely dominated by market forces, yet with substantial state involvement in industry. Serbia was the last Eastern European country to adopt western-style reforms in 2000 (Duinkerken 2012). In 2006, The Republic of Serbia became independent of Serbia and Montenegro, and adopted a new constitution. It is governed as a republic with a civil law system. In 2012 Serbia gained European Union candidate country status; it continues to implement necessary governance and economic reforms to move closer to member country status (CIA 2014a). In September 2000, former Federal Yugoslav President Milosevic was removed from office by the Democratic Opposition of Serbia and Prime Minister Zoran Djinjic was elected in October 2000. Djinjic was elected based on a platform espousing widespread anti-corruption reforms (Duinkerken 2012). The “Bulldozer Revolution” was the spontaneous culmination of a yearlong fight by the Serbian people to strip Slobodan Milosevic of his ruling legitimacy. His rule included corruption, oppression, and information censorship. Milosevic’s cabinet members and Milosevic himself were at the center of large-scale revenue looting scandals (Cohen 2004). During Milosevic’s rule the Serbian populace identified corruption as the greatest challenge facing Serbia, and later data show (UNODC 2011). The MCC classifies Serbia as an upper-middle income country. Serbia has a gross national income per capita of $5,280 (World Bank 2014c). ii. Widespread Anti-corruption Reforms Following Milosevic’s Ouster Following the removal of President Milosevic from office in September 2000, the new administration, led by Prime Minister Zoran Djinjic, implemented a series of large-scale political, economic, and specifically anti-corruption reforms at the highest echelons of the Serbian government. The Serbian populace elected Djinjic in anticipation of anti-corruption and justice system reforms (Cohen 2004). Following his election, Djinjic embarked on an anti-corruption crusade that spanned the entire Serbian government. The reforms evaluated here are only the start of post-Milosevic anti-corruption reforms. Three reforms from late 2000 to 2002 can be defined as “credible signal” anti-corruption policy reforms. First, Serbia ratified the Council of Europe’s Criminal Law Convention against Corruption. Second, Serbia reformed the court system and replaced 187 judges. Third, the Interior Ministry made 30 arrests for corruption in 2001-2002. Two less credible reforms were also implemented: the adoption of a law introducing a one-time tax on extra profit gained and property acquired by use of special privileges from 1989 until the summer of 2001, and Serbia joining the Stability Pact’s Anti-corruption Initiative (Matic 2002). Serbia ratified the Criminal Law Convention on Corruption in December 2002. The convention calls on member states to “pursue, as a matter of priority, a common criminal policy aimed at the protection of society against corruption, including the adoption of appropriate legislation and preventive measures”

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(Criminal Law Convention on Corruption 1999). The convention is an international legal instrument that Serbia used to combat domestic corruption. The ratification should have had a significant effect on all sectors of Serbian life. Serbia’s replacement of judges and arrests of individuals charged with corruption are institutional and punishment reforms respectively. Reform of the court system targeted the judiciary, while corruption arrests should target all sectors. The one-time tax constitutes a public reform as well as a punishment. Joining the Stability Pact’s Anti-corruption Initiative is a public reform. The five aforementioned anti-corruption reforms received strong support from international actors (Duinkerken 2012). Because reforms were largely being implemented in an attempt to appeal to the European Union’s demands for EU membership and economic partnerships, EU countries and the West endorsed these reforms and offered technical assistance for their implementation (Duinkerken 2012). The Serbian public was highly skeptical of its ability to alter the political patronage, cronyism, and corruption that were part of the Serbian way of life for decades (Pesic2007). Retrospective literature has identified the 2000 to 2002 reforms as unsuccessful and identifies corruption as one of the three most pressing challenges for Serbia. Table 6: Serbia—Control of Corruption and Sub-source Data for CRE

Year(-2)* Year (0)* Year +2* Year +3 Year +4 Year +5 1998 2000 2002 2003 2004 2005

CoC -1.08 -1.12 -0.91 -0.47 -0.48 -0.38BPS 0.33 0.72 0.72 0.72 0.68BTI 0.55 0.55 0.65EIU 0 0 0 0 0 0FRH 0 0.13 0.33 0.33 0.33 0.38GCB 0.54GCS 0.4 0.44 0.5GII GWP PIA NP NP NP NP PRS 0.17 0.17 0.17 0.33 0.33 0.33WMO 0.25 0.25 0.25 0.5 0.53 0.5

Source: World Bank 2013b iii. Change in the Control of Corruption Indicator Serbia’s reform efforts began in 2001; however the CoC does not report for 2001, so we used the 2000 dataset for our first year of analysis. Between the 2000 and 2002 reported data, Serbia’s score on the CoC increased from -1.12 to -0.91. Compared to the other measured countries, Serbia moved from the 6.83 percentile to the 22.93 percentile rank.

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Figure 5: Serbia--Change in Control of Corruption Score, Including Standard Error

Source: World Bank 2014b

iv. Changes in the Sub-source Data The sub-sources used to compile Serbia’s CoC score changed from 2000 to 2002. In 2000, the CoC used five sub-sources for Serbia; in 2002, the PIA was added. Two sub-sources significantly increased from 2000 to 2002: the FRS and BPS. In 2000, Serbia received a FRH score of 0.13; by 2002, Serbia’s score had improved to 0.33. The BPS score jumped from 0.33 to 0.72 between 2000 and 2002. v. Conclusion Serbia’s CoC score jumped substantially from 2000 to 2002 Serbia’s BPS score saw an enormous increase from 0.33 to 0.72. We anticipated that this score change influenced the substantial CoC increase; however, the BPS score was weighted at 0 percent of the overall CoC score in 2000 and at 3 percent in 2002. The BPS publishes the percentage of firms that reported paying bribes for public sector activities in a given year. While the BPS does typically publish survey question data, it did not publish Serbia’s data for 2000. For this reason, we cannot identify percent changes in bribery within specific sectors. However, we are able to view select BPS data through the World Bank. In 2002, Serbia’s “corruption frequency” score jumped by 2.89 on a 1 to 6 scale. Serbia’s “bribe share” score jumped 1.02 and their “bribes to get things done” score jumped 1.37 (World Bank 2014b).

‐1.60

‐1.40

‐1.20

‐1.00

‐0.80

‐0.60

‐0.40

‐0.20

0.00

1997 1998 1999 2000 2001 2002 2003 2004 2005

Serbia

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The FRH assessment compiles expert essays converted into rankings; therefore, there are no individual survey questions to analyze. The Freedom House’s scaled CoC score increased from 0.13 in 2000 to 0.33 in 2002. In addition, Freedom House was heavily weighted in Serbia’s compiled CoC score; it was weighted at 39 percent in 2000 and 25 percent in 2002. For this reason, we conclude that the jump in the Freedom House score strongly influenced the jump in Serbia’s CoC score. In 2002, the PIA was added as a sub-source to Serbia’s CoC. The scores for PIA are not publicly available, so we cannot assess whether the inclusion of PIA influenced Serbia’s 2002 CoC score. Serbia’s EIU, PRS, and WMO scores did not change from 2000 to 2002. The original data are not available for any of these sub-sources. Serbia’s EIU score remained at 0 from 1998 to 2005, even though EIU updates country scores yearly. The EIU score is either inconsistent with the constant implementation of anti-corruption reforms in Serbia or reflects the reforms’ ineffectiveness. The PRS and WMO sub-sources measure business risk. The anti-corruption reforms implemented in Serbia are unlikely to be seen in business risk assessments. Serbia’s CoC score also increased significantly from 2002 to 2003, which may reflect sustained reform efforts or a lag in changed perceptions of corruption. The largest sub-source increases were in the WMO and PRS scores. As discussed, Serbia’s reforms were not targeted toward the business sector, so this response was surprising. The WMO and PRS were likely responding to Serbia’s Western-style economic reforms, especially efforts to comply with EU-guided policies, instead of changes in real levels of corruption. EIU’s weight in Serbia’s overall CoC score decreased substantially from 2002 to 2003, from approximately 50 percent to 16 percent. Given that Serbia’s EIU score was 0 for both years, its decrease in weight would greatly increase Serbia’s CoC score. The sustained change Serbia’s CoC score reflects a drastic decrease in corruption in the country, which should point at the implementation of effective anti-corruption efforts. In hindsight, research identifies that Serbia’s series of anti-corruption efforts were highly ineffective (Pesic 2007, UNODC 2011). Thus, we know the change in Serbia’s CoC score from 2000 to 2002 does not reflect changes in real corruption levels. Instead, the change likely reflects movements toward Western-style governance and economics. The BPS and FRH sub-sources are the only two that saw changes from 2000 to 2002. Because they did not reflect real changes in corruption, we can ascertain they reflected changes in the perception of corruption or other governance characteristics. These sub-sources likely reflected economic liberalization and

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sweeping justice reforms taking place in Serbia, both of which are highly correlated with less corruption. In 2011, the United Nations Office on Drugs and Crime published a report about corruption in Serbia, specifically the impact of bribery on the Serbian populace. This report indicates that the Serbian people identify corruption as the third most important problem facing their country after unemployment and poverty (UNODC 2011). Serbia’s CoC score has continued to increase since 2002, despite consensus among corruption experts that real levels of corruption have not improved. VI. SUB-SOURCE FINDINGS In this section, we provide information on each sub-source and our particular findings for that source. We draw conclusions from across all of our case studies and our research to evaluate each of the sub-sources. 1. African Development Bank Country Policy and Institutional Assessments The African Development Bank Country Policy and Institutional Assessments (ADB) survey is an annual expert assessment performed by economists at the African Development Bank. The ADB assesses the policy and institutional capacity of member countries’ governments to ensure the “efficient utilization of scarce resources for achieving sustainable and inclusive growth” (ADB Group 2014). The multilateral development bank with the mission of promoting sustainable economic development with positive social outcomes, resulting in the reduction of poverty. The African Development Bank began reporting data in 1998 for 51 countries and expanded to 54 countries by 2012. However, data from surveys prior to 2004 are not publicly available. From 2004 to 2011, the data are reported on a scale of 1 to 6, and scores are measured in increments of 0.5. In 2012, the increments change to approximately 0.17. The ADB data included in the CoC measure the following indicators: “the accountability of the executive to oversight institutions and of public employees for their performance; access of civil society to information on public affairs; and state capture by narrow vested interests” (ADB Group 2014). The ADB data used by the WGI only include the assessment section marked Transparency, Accountability, and Corruption in the Private Sector, although corruption is referenced in three other sections: Efficiency of Revenue Mobilization, Trade Policy, and Quality of Public Administration. For the ADB’s survey questions, see Appendix D. The ADB has some limitations in responding to CREs, and its use in the CoC poses a few methodological issues. First, the ADB specifically evaluates countries based on the implementation of “policies over a sustained period of time rather than on promises and/or intentions” (ADB Group 2014). Consequently, we would expect that CREs are not likely to be reflected in the ADB data, especially if the CRE does not include sustained reforms. The ADB’s emphasis on sustained reforms also means that the ADB is less likely to quickly respond to a CRE,

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which makes a single-year analysis of a CRE difficult. In addition, the African Development Bank uses a methodology that compares other sources as “guideposts” to inform its analysis. Typically, this methodology would not be a concern; however, since several of the sources used as a guidepost for corruption are also used in the CoC (including the Corruption Perceptions Index and the Afrobarometer), there is a distinct problem of endogeneity. The CoC methodology further complicates this problem, as the WGI project weights correlated sub-sources more heavily in compiling the CoC. If the African Development Bank uses other sub-sources to check the ADB data, then the ADB is more likely to be correlated with those sources and thus receive a higher weight. Finally, the African Development Bank also requires a “stronger level of justification and evidence” for improving scores at higher levels of the scale and for maintaining scores (ADB Group 2014). Even though the African Development Bank says that countries need additional evidence to receive the same score in consecutive years, we found that the ADB scores were unchanged after the CREs in Kenya and Benin. We found the unresponsiveness of the ADB suspect, given that ADB should be more likely to change than to stay the same, even in the absence of a CRE. 2. Afrobarometer Researchers at Michigan State University and two African NGOs compile the Afrobarometer (AFR), a survey of households that evaluates public perceptions toward democracy and governance within African countries (Institute for Democracy, South Africa, and the Centre for Democracy and Development, Ghana). Beginning in 1999, the AFR was compiled approximately every three years, though the WGI project omits the 1999 survey data since the “questionnaire from this year differs substantially from subsequent years” (World Bank 2014b). The AFR analyzed 16 countries in 2002 and expanded to 22 countries by 2013. Overall, this sub-source represents “an independent, nonpartisan research project that measures the social, political, and economic atmosphere in Africa” (Afrobarometer 2014). Regarding corruption, the AFR includes survey questions asking individuals to estimate how many officials in certain categories engage in corruption. The categories include elected leaders, judges and magistrates, government officials, and border/tax officials. Possible responses are “none,” “some of them,” “most of them,” “all of them,” or “don’t know/haven’t heard enough.” The AFR interviewer codes the responses on a 0 to 1 (good) scale (Afrobarometer 2014). The AFR survey was included in two of our case studies: Benin and Kenya. However, we were not able to draw conclusions about the effect of either CRE on the AFR. The AFR data were not publicly available for all of the years of our analysis. In addition, the AFR is not compiled annually, which poses a problem for assessing the single-year effects of a CRE.

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3. Asian Development Bank The Asian Development Bank Country Policy and Institutional Assessments (ASD) is a yearly expert assessment of country performance on 16 indicators. The Asian Development Bank country economist compile the ASD scores, which are then subject to a centralized review process to ensure comparability. The ASD has been measured every year since 2000, but data are only publicly available for assessments that occurred in or after 2006. ASD scores range from 1 to 6 (good). The scores are then used to allocate concessional loans from the Asian Development Bank; therefore, the ASD only covers Asian Development Bank client countries who are eligible for these loans (Asian Development Bank 2014). The WGI uses ASD scores for “transparency, accountability, and corruption in the public sector” to compile the CoC (World Bank 2014b). The ASD was not included in any of our case studies, so we cannot draw broad conclusions about the efficacy of these data. However, we do know that the ASD is an expert assessment, so it is subject to the issues we found with all of the expert assessments. ASD also only publishes average scores for the “transparency, accountability, and corruption in the public sector” question, so it is not possible for us to assess which events may affect the ASD corruption score. 4. Business Enterprise Environment Survey The Business Enterprise Environment Survey (BPS) is compiled by the World Bank and the European Bank for Reconstruction and Development. The BPS assesses transitional economies in Eastern Europe and the former Soviet Union. The BPS is a part of the World Bank’s Investment Climate Survey project, and it collects information on a firm’s financial performance and perceptions of the business climate. The questions included in the CoC score ask firms about the frequency of irregular payments in various public sector activities and how problematic corruption is for the growth of their business. The BPS is compiled every three years, beginning in 1999. The 2011 survey was delayed to 2013, so the 2008 survey data were used for the 2012 CoC (International Finance Corporation 2014). The BPS data were very interesting to analyze. The BPS is one of the few sub-sources with publicly-availablequestion data , making analysis of the factors that influence the BPS score easier. Although the BPS appeared to be good at measuring changes in real corruption in Georgia, it may have been responding to the economic reforms rather than the anti-corruption reforms. The results of the Serbian case study reinforce this possibility; the BPS increased after the CRE, even though real levels of corruption did not change. The BPS may be more responsive to economic reforms than to anti-corruption efforts, which is problematic for an indicator that is supposed to measure real levels of corruption. 5. Bertelsmann Transformation Index The Bertelsmann Foundation uses expert assessments to compile the Bertelsmann Transformation Index (BTI) every two to three years. The Bertelsmann

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Foundation is a nongovernmental organization headquartered in Berlin, Germany; its mission is to study social challenges and problems and propose solutions. The BTI began in 2003, but the corruption variable is drawn from a sub-component of resource efficiency, which is only available from 2005 onward. To compile BTI scores, staff at the Bertelsmann Foundation evaluate “anti-corruption policy” and “prosecution of office abuse.” The evaluations are then translated into a corruption score for each country, measured on a scale of 0 (bad) to 10 (World Bank 2014b). The BTI was included in the Benin and Kenya CRE case studies. The BTI did not change after either CRE, indicating that it may be sensitive to corruption reforms or scandals. Because the BTI is assessed every two to three years, it is not possible to assess single-year effects of a CRE on the BTI. In addition, the BTI only publishes a final corruption score for each country, which makes insight into the factors that affect the BTI extremely difficult. 6. Corruption in Asia Survey The Corruption in Asia Survey (PRC) is a report published by the Political and Economic Risk Consultancy every March as part of the Asian intelligence service. The 2014 report is based on a survey of 1,833 middle and senior foreign business managers across all 16 countries in Asia. The full Asian Intelligence service and individual country reports are restricted from view by the general public, but the Regional Overview of Corruption Report is available. Although the survey has 20 questions that cover different fields, the WGI project uses the question, “To what extent does corruption exist in a way that detracts from the overall business environment for foreign companies?” (World Bank 2014b). These characteristics cause the PRC to be sensitive toward business-related corruption events and corruption in major cities. Therefore, an Organisation for Economic Cooperation and Development (OECD)-style anti-corruption effort might have a bigger effect on increases in a country’s PRC sub-source score. Because the PRC does not publish question-specific data assessing the impact of a CRE on the PRC is difficult. The PRC was not a sub-source for any of our case studies. 7. Countries at the Crossroads Freedom House compiles the Countries at the Crossroads (CCR) survey, which is treated as a separate sub-source from the FRH assessment. Freedom House designed the CCR assessment to complement the goals and aims of the Bureau of Democracy, Human Rights, and Labor at the U.S. Department of State. Freedom House staff and consultants draft analytical narratives using a ratings system and questionnaire, which are then combined into a country report. The reports include an “anti-corruption and transparency” score on a 0 to 7 scale, which the WGI project uses to compile the CoC. The CCR also backdates its data; therefore, the 2004 CCR report would be used for the 2003 CoC score (Freedom House 2014a).

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The CCR’s methodology is extremely problematic. By backdating the data, the CCR does not accurately reflect level of corruption at the time of the CRE. In addition, the CCR data are not easily analyzed, as the country reports do not provide insight into why a score changed. We also found inconsistencies and gaps in the CCR data while assessing the CRE in Kenya. The CCR has received zero weight for every year since it was added in 2003; the consistent zero weight raises the question of why the World Bank continues to use the CCR in compiling the CoC. 8. Economist Intelligence Unit The Economist Intelligence Unit (EIU) is the research and analysis division of The Economist Group. The Country Risk Service is the name of the dataset that the EIU constructs. The EIU relies on expert assessments of more than 500 correspondents. Panels of regional experts then review the EIU expert assessment data for consistency. The EIU does provide a global metric; for example, the EIU assessed 183 countries in 2012. The data provide an average score on a 1-4 scale, where 4 signifies the highest level of corruption. The WGI project lists the EIU’s CoC relevant data points simply as “corruption among public officials.” No original EIU data used in the CoC composite are available to the public via The Economist Group (The Economist 2014). The EIU is highly weighted in almost every year since its publication (World Bank 2014b). This weighting has important implications for countries whose CoC score is composed of few sub-sources in a given year. Across the five case studies we conducted, the EIU sub-source remained unchanged for most years. Canada’s EIU score saw a distinct change from 1 to 0.75 in its scaled CoC score in the year following the CRE (2005) and immediately returned to 1 in 2006. Benin’s EIU score did not change in the year following the CRE (2011). Its 2010 score of 0 was not able to reflect a corruption scandal, as the score could not decrease further. The nature of the EIU supports our hypothesis that the business risk indicators will show less sensitivity to CREs in low-income countries. The experts and individuals within these countries may assume a default level of corruption. Scandals would thus be viewed as the status quo, and anti-corruption reforms would not be considered to be credible signals. Therefore, low-income countries must go beyond signaling a credible commitment to anti-corruption reforms for their EIU (and perhaps total CoC) scores to increase. In addition, the EIU illuminates the problems with using extremely course indicators, which only have a few possible values. Since the EIU can only change by a relatively large amount (0.25 out of 1), it is unlikely to respond to smaller CREs; a large change would be required to affect the EIU score. 9. Freedom House The WGI project uses Freedom House data from the Nations in Transit report for the CoC aggregate score (World Bank 2014b). Freedom House consulted with the

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U.S. Agency for International Development in developing the assessments used in the report. Nations in Transit measures the success of political and economic reforms in the former Soviet Union countries of Central and Eastern Europe. The 1995 report covered 27 countries; the 2012 report assesses 29 countries. The Nations in Transit reports are accessible to the public only for the years 2003 to the present. Freedom House methodology includes assessing “public perceptions of corruption, the business interests of top policy makers, laws on financial disclosure and conflict of interest, and the efficacy of anticorruption initiatives” (Freedom House 2014. Teams of experts write narratives and give countries scores on a 1 to 7 scale, where 1 is the best. The Freedom House score used for the CoC is simply labelled “corruption” (Freedom House 2014b). For Freedom House’s expert questions, see Appendix F. The Freedom House indicator is typically weighted very heavily in the CoC compilation; this weighting can be very important for countries only covered by a few indicators (World Bank 2014b). While Freedom House did respond to the CREs in Georgia and Serbia, we found that the expert narratives tended to reference economic reforms rather than anti-corruption efforts; again, this response is problematic for a sub-source that intends to measure real corruption. 10. Gallup World Poll The Gallup Organization, headquartered in Washington, D.C., constructs the CoC sub-source Gallup World Poll (Gallup 2014). In 2012, Gallup conducted representative sample household surveys in 161 countries. Gallup states that it follows the same methodology in every country in which it operates. Gallup evaluates its data with a 95 percent confidence interval. Although the complete GWP dataset is not publicly available, Gallup provides detailed information about its survey, including country level sample sizes, dates, languages used, and the margin of error in Country Data Set Details (Gallup 2014). Helpfully, Gallup does provide specific details about the exclusion of certain areas due to conflict or instability. The northeastern region of Kenya, for example, was excluded from the data due to insecurity (Gallup 2014). GWP excluded the regions of South Ossetia and Abkhazia in Georgia in its 2005-2006 survey, as they were not safe for interviewers (Gallup 2014). The GWP was very responsive to the CRE analyzed in Benin. We expect that the GWP reflected corruption levels in Benin because the scandal primarily affected individual households. However, our hypothesis cannot be fully tested without the complete GWP dataset. 11. Global Competitiveness Report The Global Competitiveness Report (GCS) is an annual survey of firms conducted by the World Economic Forum, a nongovernmental organization whose mission is “bringing together business, government, academic, and media leaders to address economic, social and political issues” (World Bank 2014b).

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The GCS is assessed annually, beginning in 1996. While the full dataset is commercially available, only averages of the sub-sources are publicly available. The GCS includes questions relating to: public trust in politicians, diversion of public funds, state capture, and irregular payments in exports and imports, public utilities, tax collection, public contracts, and judicial decisions. Most of the survey questions are scored on a seven-point scale (World Economic Forum 2014). The GCS was included in four out of our five CRE case studies, with inconsistent results. The GCS seemed to respond well to the CREs in Georgia and in Kenya. However, the GCS actually changed in the opposite direction from what was expected following the CREs in Benin and Canada. This change may be as a result of a data lag in these cases. In each case, the GCS responded as expected two years after the event. It is surprising, however, that the GCS appeared to respond more quickly to anti-corruption reforms than to scandals. 12. Global Corruption Barometer Survey The Global Corruption Barometer Survey (GCB) is a household survey of corruption compiled by Transparency International, an NGO based in Berlin, Germany. The GCB is a global metric; in 2013, the GCB included 115 countries. The sampling population consists of 500 or 1,000 respondents depending on country size. The questions asked as part of the survey speak to households’ experiences with petty corruption and their perceptions of the overall incidence of corruption in the given country (Transparency International 2014). For country level questions, see Appendix G. As expected, we found the GCB to be highly correlated with the AFR and the LBO, as all three sub-sources capture the average citizens’ experiences with corruption. The GCB and AFR, however, do not survey all countries on a consistent basis. The GCB appeared to accurately capture the CRE in Canada; however, without additional case studies, we cannot draw conclusions about the GCB’s overall effectiveness at capturing CREs. 13. Global Insight Business Condition and Risk Indicators Global Insight Business Condition and Risk Indicators (WMO) is an expert assessment of more than 250 economists and analysts from more than 170 industries. The data is published every year since 1998, originally covering 181 countries and expanding to 203 countries in 2009. The WMO corruption data measure the likelihood of encountering corrupt officials and other groups as a sub-indicator (“Losses and Costs of Corruption”) on a scale from 0 to 10. The full WMO dataset and questionnaire are only commercially available (IHS 2014). The WMO did not reflect anti-corruption reforms in the immediate aftermath of the CRE, however it did increase in the following year. The reforms evaluated in Serbia were later acknowledged to be unsuccessful in controlling corruption, which leads us to believe the later change WMO score resulted from large-scale economic liberalization that occurred in conjunction with unsuccessful anti-

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corruption reforms. Georgia’s CRE was not reflected in the WMO as expected. Instead of increasing following reforms, the WMO decreased slightly. We are not able to identify a plausible explanation for this. In the case of Benin’s corruption scandal, the WMO failed to respond. However, following Canada’s corruption scandal, the WMO saw a significant decrease. These two cases suggest the WMO is more sensitive to corruption scandals in high-income countries than in low-income countries. The WMO consistently receives high weights in aggregate CoC. As a result, it can greatly influence a country’s CoC score. Since the WMO has been relatively unresponsive to the CREs selected, it creates an additional barrier to assessing any yearly changes in the CoC. 14. Global Integrity Index The Global Integrity Index (GII) is produced by Global Integrity, a non-profit corporation headquartered in Washington, D.C. A team of experts—including local researchers, lawyers, journalists, and academics—evaluates anti-corruption legal frameworks in their existence, implementation, and enforcement. Currently, Global Integrity uses a double-blind peer review to create the official GII dataset within an annual report. The most recent report, published in 2011, includes data from 62 countries. The CoC utilizes GII data from 2006 to the present and has carried the 2011 data over to 2012 (Global Integrity 2014). We do not assess the responsiveness of the GII through our case studies, as it did not cover any of the countries we analyzed. Nevertheless, we would hypothesize that the GII may display a higher degree of responsiveness to CREs due to the comprehensive scope of its measurements. On one hand, the GII includes data from more than 300 individual measurements, ten of which are included in the CoC. Using many different measurements would allow the data to respond to changes in very specific sectors. On the other hand, a CRE not covered by the CoC-specific questions would not be associated with any data change. Specifically, the CoC uses GII questions that refer to an anti-corruption agency, so countries without anti-corruption agencies would not be covered. See Appendix G for more details. 15. Institutional Profiles Database The Institutional Profiles Database (IPD) is compiled by the French Ministry of the Economy, Industry and Employment, and the Agence Francais de Developement. The IPD survey is an expert assessment compiled by office staff of the two ministries. It reports information on more than 120 countries every three years. The IPD provides an indicator entitled “Level of Corruption” by averaging scores in four areas: petty corruption, political corruption, corruption between administration and local businesses, and corruption between a country’s administration and foreign businesses. The IPD indicator thus has strong analytical power, as it covers different aspects of corruption spanning various sectors. However, the WGI project uses the same IPD score for the three-year

36

period following IPD’s publishing. For this reason, the IPD sub-source does not immediately respond to CREs that take place at the beginning of the three-year period (Ministere de L’Economie et de Finances 2014). The IPD only showed changes in the case of Benin’s CRE. Although the scandal was reflected in the IPD’s next published report, the lag in IPD reporting frequency prevents an immediate change in Benin’s IPD score. The IPD was not reported in the years relevant to our case studies. No conclusions can be made about the IPD on the basis of one case study. 16. International Institute for Management and Development The International Institute for Management and Development, headquartered in Luzerne, Switzerland, began publication of the World Competitiveness Yearbook (WCY) in 1989. WCY covered 46 countries in 1996 and increased its scope to 59 countries as of 2012. The WCY ranks countries based on a large number of factual and subjective indicators relating to a country’s business environment. The WGI project uses responses drawn from WCY Executive Opinion Survey capturing the views of approximately 4,000 business professionals in the targeted countries. Although the full data and questionnaire are not publicly available, the WGI program describes the survey question as a measure of the existence of bribery and corruption in the economy (IMD 2014). Given the identity of the respondents and the nature of survey data, we believe the WCY score is similar to the PRC and RPS score. Because the WCY is published in May, any CRE that occurs after May might not affect the WCY score immediately. For instance, following a corruption scandal in Brazil in June 2005, the WCY score actually increased by 0.02 from 2004 to 2005. Brazil’s WCY score then decreased by about 50 percent from 2005 to 2006. The delayed response might affect analysis due to the WGI project’s weighting mechanism. Across our case studies, the WCY was only used to compile Canada’s CoC score. Canada’s WCY decreased slightly in 2004, following a negative CRE. 17. Latinobarometer The Latinobarometer (LBO) is an annual opinion survey of 3,000 randomly selected households in 18 Latin American countries. The LBO indicator is compiled by the Latinobarómetro Corporation, a non-profit NGO based in Chile. The survey assesses corruption in various ways. For example, one question asks: “How much progress do you think there has been in reducing corruption in state institutions over the past 2 years?” However, complete responses to the survey are not publicly available. The extensive coverage and randomness of the LBO survey contribute to the LBO’s ability to capture ordinary citizens’ perceptions of corruption. We believe that the LBO is not sensitive to reforms or scandals in any one particular sector, but instead to events with high media coverage and public attention. For instance, a negative CRE in Brazil that had captured widespread media attention in 2005 resulted in a 20 percent decrease of its score the prior year (Corporacion Latinobarometro 2014).

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18. Political Risk Services International Country Risk Guide The Political Risk Services International Country Risk Guide (PRS) has been published on a yearly basis since 1984. The survey is a compilation of expert assessments by PRS staff, measuring the level of political, financial, and economic risk to international business operations in 139 countries across the globe. These sources provide measures of political stability in a country as well as institutional quality, particularly levels of corruption within institutions. Although the report and its questionnaire are not publicly available, we expect this sub-source to be especially responsive to business and investment CREs (PRS Group 2014). The PRS changed in response to some of the CREs; however, it changed in the opposite direction anticipated in conjunction with the CRE. We do note, however, that the PRS did reverse direction in the following year for the Serbia case study. Both reforms followed changes in administration, thus the PRS may reflect caution on the part of the business community. 19. Rural Sector Performance Assessment The Rural Sector Performance Assessment indicator (IFD) is compiled by the International Fund for Agricultural Development on a yearly basis. The IFD began publishing data in 2004 and has covered all its client countries on a yearly basis since then (today the IFD covers 80 countries). The IFD is an expert assessment made by International Fund for Agricultural Development country economists about corruption in rural sectors. The WGI project uses one question (scaled from 1 to 6) from the IFD survey for the CoC indicator, measuring the extent to which corruption in rural areas is being dealt with through preventive mechanisms and sanctions (IFAD 2014). The IFD was used in the case studies for Georgia and Kenya. In both cases, the IFD score was unchanged for multiple years, even though the CREs were occurring in the countries. The IFD appears to be unresponsive to CREs, which is likely due to its focus on rural areas. The IFD also has a potential bias toward some particular forms of corruption (such as petty corruption); this bias may weaken the IFD’s capacity to register CREs that happen in urban areas and on a national level. For instance, following Brazil’s national congress corruption scandal in 2005, Brazil’s IFD score saw no change. 20. Vanderbilt University Americas Barometer Survey The Vanderbilt University Americas Barometer Survey (VAB) is a general survey of democratic values and behaviors. The survey is conducted bi-annually. Most recently, more than 43,000 individuals in 24 American countries were surveyed. Some country-level aggregate data are available; however, the rest of the dataset is available via subscription. The VAB measures corruption in a variety of ways. The WGI project selects one question to represent the VAB’s CoC sub-source score: “taking into account your own experience or what you have heard,

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corruption among public officials is (1) Very common (2) Common (3) Uncommon or (4) Very uncommon?” This question attempts to measure the frequency of corruption among government officials. The VAB appears similar to the LBO and other public opinion surveys in its ability to reflect corruption events that captured high public attention (Vanderbilt University 2014). The VAB did not appear in our case studies, so we cannot draw any broad conclusion about its ability to reflect CREs. However, the VAB does not report annually, posing a problem for single-year analysis of CRE effects. In addition, the dataset is not publicly available, so analysis on the reasons for VAB changes would be difficult. 21. World Bank Country Policy and Institutional Assessments The World Bank Country Policy and Institutional Assessments (PIA) is an expert survey of World Bank country economists in 136 counties. The PIA began publication in 1978. The aggregate source covers 16 dimensions of policy and institutional performance. Thus, the PIA should capture corruption in the public sector. The PIA score is highly correlated with ASB and ADB because the Asian Development Bank and African Development Bank use the PIA questionnaire in their assessment of country performance. Although some differences are to be expected, the WGI methodology weights similar (more correlated) scores more heavily. Thus, any biases have larger effects on the results if the correlation among all PIA, ADB, and ASB is high in a given year (World Bank 2014b). The PIA data posed one of the largest problems in our analysis. Even the average PIA country scores were not publicly available for many of the years in our analyses; the inaccessibility makes it impossible for us to analyze the potential effects of the PIA on the overall CoC score. When we were able to assess the average PIA scores, we found that the scores were not responsive to the CREs. The individual question data are not available for any year, so we were not able to analyze which factors affect a country’s PIA score. 22. World Justice Project Rule of Law Index World Justice Project Rule of Law Index (WJP), which began in 2011, is conducted by a nonprofit organization headquartered in Washington, D.C. The WJP is a combination of expert assessment and general public polling. More than 300 local experts per country participated in the survey. WJP compiles the household surveys using the results from local polling companies. The WGI program uses the absence of corruption index for the CoC. The WJP absence of corruption index is an aggregation of four sub-factors that cover corruption in the executive branch, judicial branch, legislature, police, and the military. The index responds to various forms of corruption, including bribery, extortion, improper influence by public or private interests, and misappropriation of public funds or other resources. Given the characteristics of the index, the WJP has the advantages of expert assessments and public opinion surveys. Although the WJP data are publicly accessible, we can hardly draw any conclusions about the sub-

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source because of its short history. In addition, the WJP was not included in any of our case studies; again, this exclusion is due to the relatively short history of the WJP (WJP 2014). VII. GENERAL FINDINGS Overall, we find the data within the CoC and its sub-sources do not uniformly respond to CREs. Our case studies were selected deliberately because we thought that they would be examples where changes should have been present. Our methodology favors finding these jumps, but the results do not support this hypothesis. In addition, due to the limited scope of our case studies, including some instances of major political upheaval, we are not confident that the CoC will accurately respond to CREs in countries not evaluated by this study. It is difficult to obtain externally valid results on a global indicator such as this one without a larger and more randomly selected sample. Nevertheless, our preliminary results indicate that the responsiveness of the CoC will depend on the specific country context and the type of sub-source data collection conducted within that country. Second, we find that the unobserved components model methodology and its corresponding weighting may not accurately reflect the comprehensive measure of corruption perceptions within a country. First, many of the sub-sources are focused on the business perspective of domestic corruption. Since there are more sub-sources in this area, they end up being more highly correlated, thus more heavily weighted. In addition, we posit the business-focused sub-sources are not as sensitive to CREs in developing countries. This unresponsiveness may be because an assumption exists within the international business community of a constant level of corruption; consequently, businesses do not view reform efforts as credible. This reaction is in contrast to Canada’s example where the business sub-sources displayed a large jump after the CRE. Confidence and the perception of the efficacy of institutions to respond to CREs would be higher in developed countries. A negative CRE is viewed as an anomaly, rather than a regular occurrence. In addition, regional surveys and household surveys tend to have significantly lower weights than the other sub-sources. First, regional surveys target types of corruption particular to those countries, thus they would not be picking up the same data on corruption as the more widespread surveys and assessments. Since the regional surveys are picking up different data, they are less correlated with the other sub-sources and thus receive a lower weight, even though they may be reflecting real corruption levels. We also find that in the Benin case study, the GWP sub-source, a household survey, was the only sub-source that responded to the CRE in the direction we would have expected in the year following the CRE. Despite this, the GWP received a 0 weight for the CoC. Additionally, there is a distinct bias toward Freedom House and EIU data. If every sub-source was used to compile a country’s CoC score, FRH would receive an average weight of 18 percent for every year and EIU would receive an average weight of 12 percent for

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every year. Without additional data, we cannot draw conclusions about the effects of weighting the EIU and FRH sub-sources more heavily; we, suggest further research into those effects. Given the limited responsiveness of the business-focused sub-sources, the CoC may be prone to a systematic bias against capturing real changes in the perception of corruption in developing countries if the household surveys continue to be weakly weighted. We also find that some of the lack of CoC responsiveness is likely due to a bias in capturing long-term sustainable change over short-term events. In many cases, it is not enough to commit resources to a single CRE reform effort; a credible signal requires continued policymaking and implementation. Two of our case studies involved extensive political changes that corresponded with significant one-year changes in the CoC, but the increases did not remain over time. Kenya, for example, reversed its improvement. Since then, a lack of credible anti-corruption reforms and additional political crises have influenced many smaller fluctuations in the CoC data. In the case of Serbia, the transition from a post-Soviet country corresponded with a one-time spike in the CoC, but the score has remained essentially constant since that time due to a lack of sustained reforms. These cases demonstrate that consistent growth in the CoC is connected to sustainable, long-term anti-corruption governance. Furthermore, we find that the connection between CREs and other governance and economic reform—including other simultaneous, unobserved CREs—is too difficult to separate when analyzing changes in CoC and/or sub-source data. Any change in the CoC is endogenous to other events, including other CREs, which are occurring already within the country. In Kenya and Serbia, our research indicates that CRE scandals were occurring in the same sectors in which reforms were implemented; these scandals were later revealed. Thus, positive changes in the sub-source scores indicated improved public perceptions of corruption levels but obscured a more accurate measurement of corruption in those countries. Georgia did have continued CoC growth rates, but we believe that was a combination of economic reforms, anti-corruption reforms, and governance changes following a revolution that contributed to this trend. Further evidence for this connection can be found by the correlation of the CoC with the other Ruling Justly indicators used in the MCC scorecard. VII. RECOMMENDATIONS Based on our findings from the specific case studies and overall research, we recommend the following three actions for the MCC: (1) conduct additional qualitative and quantitative research on the responsiveness of the CoC; (2) evaluate the continued efficacy and construct validity of the CoC to the MCC’s needs; and (3) conduct further analysis on the CoC and its correlation to other governance indicators.

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1. Complete Additional Research on the Responsiveness of the CoC We strongly recommend that the MCC continue to pursue this research question by conducting additional qualitative and quantitative research on the responsiveness of the CoC. There are restrictions to our analysis, not least of which is the limited depth of the case studies. Completing additional case studies in a wide variety of corruption sectors and socioeconomic indicators would yield information for more comprehensive and robust conclusions. We also recommend a quantitative, statistical analysis, which would provide the benefit of being able to statistically test many of the conclusions for which we have found preliminary evidence. In addition, a large-n regression would increase the probability of finding a causal connection between the changes in the CoC, and sub-source data and CREs. We recommend the following procedure for this analysis: First, create a large database of CREs through a methodological search using an academic search engine such as LexisNexis. Consistent search terms, such as “CountryName” and “corruption,” and a set time-frame should be used. We recommend a 10- to 15-year timeframe to incorporate data lags. The list of countries to include search terms for should match the WGI project’s dataset. Some countries may not return many results for geographic, linguistic, or sociopolitical reasons; the researcher can code results as a high, medium, or low visibility to control for this bias. Each case example should then be coded based on our case study framework to maximize the comparability of findings. Researchers should code for CRE variables like the size and sector of the CRE, the level of media coverage, and the public reaction to the CRE. Researchers will determine how best to classify this information, much of which will be qualitative. Finally, several regression analyses should be performed with the respective level of change in the CoC and sub-sources as the dependent variable. 2. Evaluate the Continued Efficacy and Construct Validity of the CoC to MCC’s Objectives We recommend that the MCC consider further internal evaluation and discussion to determine the best way to proceed with (a) stakeholders asking for specific policy feedback and (b) the CoC in its current MCC usage. Despite our findings, we do not believe the CoC’s unresponsiveness to CREs is necessarily a problem. As discussed, having a country-level indicator that is too “jumpy” may encourage artificial manipulation or discourage confidence in institutions through its instability. However, the CoC’s unresponsiveness does create complications for the MCC’s ability to appropriately respond to stakeholders with policy feedback. We recommend increased communication with stakeholders to provide these preliminary results and to emphasize the importance of sustained policy reform. Additionally, we recommend that the MCC evaluate whether the limitations identified with the CoC—including its lack of responsiveness—merit its continued priority placement in the scorecard, or whether another approach would better measure the aspects of corruption relevant to the MCC’s mission. For example, the MCC may consider creating a joint corruption indicator group that includes the CoC and a different corruption indicator (such as the Corruption

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Perceptions Index); the MCC could then require passing at least one of the corruption indicators as the new hard hurdle. We do not recommend that the MCC make this change immediately; rather, we recommend further research into potential changes in the use of the CoC. 3. Conduct Further Analysis on the Correlations among the CoC and Other Governance Indicators We also recommend that the correlations among the CoC and other governance indicators be further analyzed to provide more support for this research question. Control of corruption within a country is highly correlated with institutional capacity, economic development, and rule of law. Many of the CREs reviewed in our initial analysis also affected sectors in governance and rule of law. CREs may affect data for indicators other than the CoC, including those captured by the MCC scorecard. Our report does not include data from those sources; further research that includes governance and rule of law indicators would likely assist in determining the impact of CREs on country-level indicators. IX. CONCLUSION The CoC receives an increased amount of attention from MCC stakeholders due to its use as a hard hurdle for aid compact eligibility. In this report, we evaluated the CoC, its sub-sources, and their responsiveness to CREs. Overall, we find causal connections between changes in the CoC sub-sources and the effects of a CRE extremely difficult. This difficulty is directly tied to three factors: the weighting used in the unobserved components model methodology, the inaccessibility of data and questionnaires used to compile each sub-source, and that CREs do not occur in isolation of other governance and policy changes. Following our conclusions, we recommend that the MCC complete additional quantitative and qualitative research following the frameworks described in this report, including an analysis of the CoC and its correlation to other governance indicators. We also recommend that the MCC internally evaluate the continued efficacy of using the CoC to fulfill its mission and policy objectives

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APPENDICES APPENDIX A: MCC INDICATORS, 2014 SCORECARD The MCC uses a set of 20 indicators to evaluate candidate countries. These indicators are categorized into Ruling Justly, Investing in People, and Encouraging Economic Freedom. Ruling Justly

Civil liberties (Freedom House) Political rights (Freedom House) Control of corruption (World Bank/Brookings Institution Worldwide

Governance Indicators) Government effectiveness (World Bank/Brookings Institution Worldwide

Governance Indicators) Rule of law (World Bank/Brookings Institution Worldwide Governance

Indicators) Freedom of information (Freedom House/FRINGE Special/Open Net

Initiative)

Investing in People Immunization rates (World Health Organization and UNICEF) Public expenditure on health (World Health Organization) Girls’ education (UNESCO)

o Primary education completion (Scorecard LICs) o Secondary education enrollment (Scorecard LMICs)

Public expenditure on primary education (UNESCO and national sources) Child health (CIESIN and YCELP) Natural resource protection (CIESIN and YCELP)

Encouraging Economic Freedom

Business start-up (IFC) Land rights and access (International Fund for Agricultural Development

and IFC) Trade policy (Heritage Foundation) Regulatory quality (World Bank/Brookings Institution Worldwide

Governance Indicators) Inflation (IMF WEO) Fiscal policy (IMF WEO) Access to credit (IFC) Gender in the economy (IFC)

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APPENDIX B: PROPOSED ANALYSIS FRAMEWORK ANALYSIS FRAMEWORK Our team developed a common framework for evaluating each of the case studies. In each of the five case studies, we consider variables on a country-level, event-level, and indicator-level. Country We begin each case study with background information on the country. The background section includes the following subsections: Country History We begin our analysis with a historical background of the country as context for the culture and legacy of corruption within the country. The section also includes the country’s geographical location and relation with neighboring countries. Income We report income level using the World Bank’s assessment of gross national income per capita, since that is used to determine the MCC’s income level classifications. Past Behavior of Country In this section, we focus on the recent history of the country. Specifically, we focus on the past reform efforts or scandals within the country, and their public reception. We also consider the number of reforms or scandals in recent history. Event Next, we identify the specific corruption event that we are investigating. We separate our cases by “reform” or “scandal.” The event section includes the following details in the description of the event, which differ based on whether it is a reform or scandal: Reform Cases The reform cases include the following details, in addition to a thorough background and explanation of the history and efficacy of the reform effort.

1. Type of Reform We break up type of reform into five categories: international, punishment, financial, institutional reform, and public reform.

2. Size of Reform In this section, we analyze the size of the reform. We look at the monetary commitment to the reform (in U.S. dollars), the institutional impact of the reform, and the level of change in the daily lives of citizens.

3. Sector of Reform. In this section, we look at the sector targeted by the reform, as well as any other sectors that the reform may have affected. We divide the cases into

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the following sectors: business, executive, bureaucratic/legislative, police, judiciary, tax/customs.

4. Level of Support In this section, we look at the level of support from the following actors: public, institutions, and external actors.

5. Media Coverage In this section, we look at the amount of media coverage garnered by the reform effort. We complete this section by searching LexisNexis for news articles related to the event.

Scandal Cases The scandal cases include the following details, in addition to a thorough background and explanation of the scandal.

1. Type of Scandal We break up type of scandal into six categories: international, punishment, financial, institutional, electoral, and public scandal.

2. Magnitude of the Scandal In this section, we look at the magnitude of the scandal in U.S. dollars.

3. Sector In this section, we look at what sectors were affected by the scandal. We divide the cases into the following sectors: business, executive, bureaucratic/legislative, police, judiciary, tax/customs.

4. Level of Support In this section, we look at the level of support for the scandal from the following actors: public, institutions, and external actors.

6. Media Coverage In this section, we look at the amount of media coverage garnered by the reform effort. We complete this section by searching LexisNexis for news articles related to the event.

Variables Finally, we examine the impacts of each of the case studies on the indicators. Change in the CoC Indicator In this section, we look at the change in the CoC indicator associated with the event. We look first to see if a change is noticeable in the year after the event. We then look to see if the change is noticeable in the five years following the event. Change in the Sub-sources In this section, we look at the change in the CoC sub-sources associated with the event. We look first to see if a change is noticeable in the year after the event. We then look to see if the change is noticeable in the five years following the event.

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APPENDIX C: SUB-SOURCE WEIGHTS For each CRE, we look both at sub-source scores and their respective weights. The World Bank weights each sub-source by its correlation to the other sub-sources across all countries measured. The World Bank publishes the weights as though all sub-sources were used in a given year for a fictional country. Because the sub-sources do not cover all of the case study countries equally, we rescale the World Bank’s published weights to scale to one. These values reflect the actual weight each sub-source received when calculating a country’s CoC for a particular year. Table 7: Benin’s Sub-source Weights

Year (-2) Year (-1) Year 0 Year 1 Year 2 2008 2009 2010 2011 2012

ADB 0.06 0.04 0.05 0.05 0.03AFR 0.06 0.13 0.12 0.07 0.08BTI 0.10 0.09 0.10 0.14 0.14EIU 0.14 0.17 0.20 0.19 0.19GCS 0.12 0.13 0.12 0.12 0.11GWP 0.02 0.02 0.02 0.00 0.00IFD 0.06 0.07 0.05 0.05 0.08IPD 0.22 0.13 0.12 0.14 0.11PIA 0.08 0.09 0.10 0.09 0.11WMO 0.12 0.13 0.12 0.16 0.16

Table 8: Canada’s Sub-source Weights

Year (-2)

Year (-1)

Year 0 Year +1

Year +2

Year +3

Year +4

Year +5

2000 2002 2003 2004 2005 2006 2007 2008

EIU 0.35 0.56 0.28 0.22 0.19 0.12 0.14 0.16GCB 0.07 0.07 0.06 0.04 0.02GCS 0.20 0.12 0.15 0.15 0.14 0.16 0.12 0.14GII 0.00 0.00GWP 0.02 0.02 0.02IPD 0.22 0.24 0.25PRS 0.05 0.06 0.05 0.07 0.07 0.06 0.06 0.05VAB 0.02 0.02 0.02WCY 0.23 0.15 0.28 0.29 0.26 0.22 0.22 0.20WMO 0.18 0.12 0.25 0.20 0.28 0.12 0.14 0.14

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Table 9: Georgia’s Sub-source Weights

Year (-3)

Year (-1)

Year 0 Year +1

Year +2

Year +3

Year +4

Year +5

2000 2002 2003 2004 2005 2006 2007 2008

ASD 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.02BPS 0.00 0.07 0.04 0.04 0.05 0.11 0.11 0.10BTI 0.00 0.00 0.13 0.10 0.07 0.11 0.11 0.08EIU 0.00 0.00 0.00 0.00 0.00 0.11 0.12 0.12FRH 0.48 0.52 0.50 0.39 0.40 0.19 0.25 0.32GCB 0.00 0.00 0.00 0.06 0.05 0.06 0.04 0.02GCS 0.00 0.00 0.00 0.12 0.11 0.15 0.11 0.10GII 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00GWP 0.00 0.00 0.00 0.00 0.00 0.02 0.02 0.02IFD 0.00 0.00 0.00 0.02 0.02 0.06 0.04 0.05PIA 0.29 0.19 0.13 0.12 0.07 0.09 0.09 0.07WMO 0.23 0.22 0.21 0.16 0.22 0.11 0.12 0.10

Table 10: Kenya’s Sub-source Weights

Year (-3)

Year (-1)

Year 0 Year +1

Year +2

Year +3

Year +4

Year +5

2000 2002 2003 2004 2005 2006 2007 2008

ADB 0.09 0.04 0.02 0.08 0.04 0.08 0.09 0.06AFR 0.04 0.02 0.06 0.04 0.05 0.03 0.06BTI 0.14 0.10 0.09 0.10 0.10 0.10CCR 0.00 0.00 0.00 0.00 0.00 0.00EIU 0.40 0.62 0.26 0.19 0.18 0.10 0.12 0.13GCB 0.06 0.07 0.05 0.03 0.02GCS 0.14 0.13 0.13 0.13 0.10 0.12GII 0.00 0.00 0.00GWP 0.02 0.02 0.02IFD 0.02 0.02 0.05 0.03 0.06IPD 0.18 0.21 0.21PIA 0.26 0.11 0.14 0.13 0.09 0.08 0.09 0.08PRS 0.06 0.06 0.05 0.06 0.07 0.05 0.05 0.04WMO 0.20 0.13 0.23 0.17 0.27 0.10 0.12 0.12

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Table 11: Serbia’s Sub-source Weights

Year (-2) Year 0 Year +2 Year +3 Year +4 Year +5 1998 2000 2002 2003 2004 2005

BPS 0.00 0.03 0.03 0.03 0.05BTI 0.09 0.08 0.06EIU 0.64 0.37 0.49 0.16 0.15 0.12FRH 0.21 0.39 0.24 0.36 0.34 0.34GCB 0.05GCS 0.09 0.10 0.09GII GWP PIA 0.08 0.09 0.10 0.06PRS 0.03 0.05 0.05 0.03 0.05 0.05WMO 0.12 0.18 0.10 0.15 0.14 0.18

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APPENDIX D: QUESTIONS FROM THE AFRICAN DEVELOPMENT BANK SUB-SOURCE USED IN THE CONTROL OF CORRUPTION INDICATOR …Rating in Transparency, Accountability, and Corruption in the Public Sector Score Guidelines

1

a There are no checks and balances on executive power. Public officials use their positions for personal gain and take bribes openly. Seats in the legislature and positions in the civil service are often bought and sold.

b Government decision-making is secretive. The public is prevented from participating in or learning about decisions and their implications.

c The state has been captured by narrow interests (economic, political, ethnic, and/or military). Administrative corruption is rampant.

2

a There are only ineffective audits and other checks and balances on executive power. Public officials are not sanctioned for failures in service delivery or for receiving bribes.

b

Decision-making is not transparent, and government withholds information needed by the public and civil society organizations to judge its performance. The media are not independent of government or powerful business interests.

c

Boundaries between the public and private sector are ill-defined, and conflicts of interest abound. Laws and policies are biased towards narrow private interests. Implementation of laws and policies is distorted by corruption, and resources budgeted for public services are diverted to private gain.

3

a External accountability mechanisms such as inspector-general, ombudsman, or independent audit may exist but have inadequate resources or authority

b

Decision-making is generally not transparent, and public dissemination of information on government policies and outcomes is a low priority. Restrictions on the media limit its potential for information-gathering and scrutiny.

c Elected and other public officials often have private interests that conflict with their professional duties.

4

a

External accountability mechanisms limit somewhat the degree to which special interests can divert resources or influence policymaking through illicit and non-transparent means. Risks and opportunities for corruption within the executive are reduced through adequate monitoring and reporting lines.

b

Decision-making is generally transparent. Government actively attempts to distribute relevant information to the public, although capacity may be a constraint. Significant parts of the media operate outside the influence of government or powerful business interests, and media publicity provides some deterrent against unethical behaviour.

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c Conflict of interest and ethics rules exist and the prospect of sanctions has some effect on the extent to which public officials shape policies to further their own private interests.

5

a

Accountability for decisions is ensured through a strong public service ethic reinforced by audits, inspections, and adverse publicity for performance failures. The judiciary is impartial and independent of other branches of government. Authorities monitor the prevalence of corruption and implement sanctions transparently.

b

The reasons for decisions, and their results and costs, are clear and communicated to the general public. Citizens can obtain government documents at nominal cost. Both state-owned (if any) and private media are independent of government influence and fulfil critical oversight roles.

c Conflict of interest and ethics rules for public servants are observed and enforced. Top government officials are required to disclose income and assets, and are not immune from prosecution under the law for malfeasance.

6 Criteria for “5” on all three sub-ratings are fully met. There are no warning signs of possible deterioration, and there is widespread expectation of continued strong or improving performance.

Source: African Development Bank 2014

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APPENDIX E: QUESTIONS FROM THE FREEDOM HOUSE SUB-SOURCE USED IN

THE CONTROL OF CORRUPTION INDICATOR

1. Are significant limitations enforced on the participation of government officials in economic life? What are the legal and ethical standards and boundaries between public and private sector activity? Are they observed in practice? Do top policymakers (the president, ministers, vice ministers, top court justices, and heads of agencies and commissions) have direct ties to businesses?

2. Are there laws requiring financial disclosure and disallowing conflict of interest? Are such laws enforced? Have publicized anticorruption cases been pursued? To what conclusion? Are there laws against racketeering? Do executive and legislative bodies operate under audit and investigative rules?

3. What major anticorruption initiatives have been implemented? How often are anticorruption laws and decrees adopted? Have leading government officials at the national and local levels been investigated and prosecuted in the past year? Have such prosecutions been conducted without prejudice, or have they targeted political opponents?

4. Does the country suffer from excessive bureaucratic regulations, registration requirements, and other controls that increase opportunities for corruption?

5. What is the magnitude of official corruption in the civil service? Must an average citizen pay a bribe to a bureaucrat in order to receive a service? What services are subject to bribe requests—for example, university entrance, hospital admission, telephone installation, obtaining a license to operate a business, applying for a passport or other official documents? What is the average salary of civil servants at various levels?

6. Have surveys of the perception of public sector corruption been

conducted with the support of reputable monitoring organizations? What are the principal findings and year-to-year trends? Do trends suggest growing public intolerance of official corruption as measured in polls? Are there effective anticorruption public education efforts? How do major corruption-ranking organizations like Transparency International rate this country? (Freedom House 2014b)

Scale for Country Ratings:

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RATING

POLICY CRITERIA PRACTICE CRITERIA

1 Existence of policies that adhere to basic human rights standards, democratic norms, and the rule of law.

Existence of best practices that adhere to basic human rights standards, democratic norms, and the rule of law.

2 Existence of policies that adhere to basic human rights standards, democratic norms, and the rule of law.

Existence of most practices that adhere to basic human rights standards, democratic norms, and the rule of law.

3 Existence of many policies that adhere to basic human rights standards, democratic norms, and the rule of law.

Existence of many practices that adhere to basic human rights standards, democratic norms, and the rule of law.

4 Existence of many policies that adhere to basic human rights standards, democratic norms, and the rule of law.

Existence of some practices that adhere to basic human rights standards, democratic norms, and the rule of law.

5 Existence of many policies that adhere to basic human rights standards, democratic norms, and the rule of law.

Absence of many practices that adhere to basic human rights standards, democratic norms, and the rule of law.

6 Existence of some policies that adhere to basic human rights standards, democratic norms, and the rule of law.

Absence of most practices that adhere to basic human rights standards, democratic norms, and the rule of law.

7 Absence of policies that adhere to basic human rights standards, democratic norms, and the rule of law.

Absence of practices that adhere to basic human rights standards, democratic norms, and the rule of law.

Source: Freedom House 2014b

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APPENDIX F: QUESTIONS FROM THE GLOBAL CORRUPTION BAROMETER USED

IN THE CONTROL OF CORRUPTION INDICATOR Global Corruption Barometer 2013 Q1 Over the past 2 years, how has the level of corruption in this country

changed? Q2 To what extent do you think that corruption is a problem in the public

sector in this country? Q3 In your dealings with the public sector, how important are personal

contacts to get things done Q4 To what extent is this country’s government run by a few big entities

acting in their own best interests Q5 How effective do you think your government’s actions are in the fight

against corruption Q6 Perceptions of corruption, by institution Q7 Have you paid a bribe to any one of eight services listed in the past 12 months? Q8 What was the most common reason for paying the bribe/bribes? Q9 Can ordinary people make a difference in the fight against corruption? Q10 Are you willing to get involved in the fight against corruption? Q11 Reporting corruption Q12 Refusing to pay a bribe Experiential question: Have you paid a bribe to any of the nine sectors (education, judiciary, medical, police, permit services, utilities, tax, land, customs)?

Source: Transparency International 2014

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APPENDIX G: QUESTIONS FROM THE GLOBAL INTEGRITY INDEX SUB-SOURCE

USED IN THE CONTROL OF CORRUPTION INDICATOR Indicator 75 sub-questions 75b: In practice, the anti-corruption agency (or agencies) is protected from political interference. 75c: In practice, the head of the anti-corruption agency (or agencies) is protected from removal without relevant justification. 75d: In practice, appointments to the anti-corruption agency (or agencies) are based on professional criteria. 75e: In practice, the anti-corruption agency (or agencies) has a professional, full-time staff. 75f: In practice, the anti-corruption agency (or agencies) receives regular funding. 75g: In practice, the anti-corruption agency (or agencies) makes regular public reports. 75h: In practice, the anti-corruption agency (or agencies) has sufficient powers to carry out its mandate. 75i: In practice, when necessary, the anti-corruption agency (or agencies) independently initiates investigations. Indicator 76 sub-questions76a: In practice, the anti-corruption agency (or agencies) acts on complaints within a reasonable time period.76b: In practice, citizens can complain to the anti-corruption agency (or agencies) without fear of recrimination.

Source: Global Integrity 2014

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APPENDIX H: SYRIA CASE STUDY Given the situation of the conflict in Syria and the complicated strategic and human rights interests of the United States in the region, we chose not to include Syria in our main analysis; however, we felt it was important to provide this example as a supplemental case study. It initially represented a “needle jump” case, revealing a 100 percent increase in the indicator for the Bertelsmann Transformation Index during 2002-2003 and 2004-2005. In addition, this case study represents a notable example of how an attempted, but unsuccessful, CRE reform corresponds with a negative change in the CoC and sub-source data. As such, we encourage the reader to consider the lessons from this case study, even if in retrospect we can see how the country’s governance has evolved. Reform Attempts of Bashar al-Assad—Syria History of Syria Syria has had a tumultuous history over the past century. Under the San Remo Mandate, France assumed control of Syria and its neighbor Lebanon in 1920. Syria gained its independence in 1946 but did not obtain any semblance of domestic stability until then-Defense Minister Hafez Assad seized power in 1970 For the next 40 years, the Assads led Syria, “ruling with a mix of socialist politics, authoritarian tactics and a roguish foreign policy (Osnos 2005, pg. 3). For example, Syria has long considered Lebanon a subordinate nation and attempted to exert significant control within its territory, economy, and government. In 1976, Syria sent troops to intervene in Lebanon’s civil war; those troops would remain for the next few decades (Osnos 2005, pg. 4). Furthermore, Syria is generally believed to have been responsible for the assassination of Lebanese Prime Minister Rafic Hariri in 2005. In 2000, President Hafez Assad died and was replaced by his son Bashar al-Assad, age 34. Bashar al-Assad was widely considered to be more reform-oriented and open-minded than his father, yet he faced a state increasingly plagued by a poor economy and a tenuous global standing. The global community has criticized Syria for decades due to its human rights abuses, corrupt practices, and absence of civil liberties. In addition, Syria received increasing pressure from the United States to disavow any ties to terrorism and weapons of mass destruction in the resulting aftermath of 9/11. When Syria came out in 2003 against the United States in the Iraq War and refused to remove its troops from Lebanon, the United States responded by placing Syria on its so-called “Axis of Evil” and passing the Syria Accountability and Lebanese Sovereignty Restoration Act (U.S. Government Information Office 2003). Income Syria is classified as a lower-middle income country by the World Bank, with a gross national income of $1,850 (World Bank 2014).

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Recent Governance Behavior Since the time period focused on by this case study is from 2003-2005, we have chosen not to include the recent civil conflict in the “History” section. We will, instead, discuss the conflict in a concluding section to this particular case study. It will include an overview of the conflict, its potential (or actual) relationship to the reforms discussed, and applicability to the MCC. We feel this section will help explain how this particular case is relevant to our client, despite the ineligibility of Syria for MCC aid amidst the larger problems of conflict, human rights abuses, and governance failure. Typology of Corruption in Syria This is representative in Syria’s burgeoning economic sector, contrasted with lower levels of participation and poor institutional capacity. Official Mogul countries are usually characterized by kleptocracy and rampant corruption, also true in Syria. Attempted Anti-Corruption Reforms Since the change in indicator indicates a worsening situation in Syria, we would have expected to find a corruption scandal or negative corruption-related event. Within this time period, former President Bashar al-Assad instead attempted to renew efforts to engage in anti-corruption reforms. Reform Type, Size, Sector At the beginning of his presidency, Bashar al-Assad prioritized the following strategies: increase the strength of the public sector; increase private sector investment; increase national savings to facilitate social and political stability; and increase foreign capital flows. Previous anti-corruption campaigns had been used under Hafez Assad’s regime as instruments to reinforce power and stifle dissent (Oxford 2002) In contrast, Bashar al-Assad’s efforts were intended to focus on reforms in order to improve the overall economy and financial stability for Syria and its citizens. His reforms included installing new ministers in the government. This was a significant act, considering the old government had been in place since 1987, excepting small changes in 1993 (Oxford 2000,). Additionally, Assad dismissed a number of key officials in 2002 who had been accused of corrupt behavior—including the directors of the Syrian Arab Airlines, Syria’s Commercial Bank, and the Syrian Arab News Agency, among others. (Oxford 2002). In doing so, Assad created a new wave of officials who were “predominantly technocrats, as well as experienced diplomats, politicians, and academics with international exposure” (Oxford 2000). He also helped push through policies that eased currency restrictions and provisions for foreign ownership of land. His reforms did not include changes to the political or justice systems, but he did permit greater public dissent (Osnos 2005). Level of Support Initially, many of Assad’s attempted reforms were met with popular support by foreign and domestic investors (Oxford 2002, p.1). Syria was beginning to

57

experience significant economic challenges, and Assad sought to maintain its place against the demands of globalization and the technology-driven international economy. Focusing on economic reforms allowed Assad to dissociate from the negative history of his father’s previous “reforms” and increased optimism in the international community that positive change in Syria was indeed possible. Domestically, however, Assad ran into significant challenges from the entrenched military and mercantile interests that had benefitted from the corrupt status quo and stood to significantly lose from any new reform policies. This ultimately stalled all potential reforms and prevented any meaningful improvement. Globally, Syria’s defiance regarding its potential role in terrorism, support for Iraqi insurgents, and interference with the crisis in Lebanon alienated it from most of the world. This prevented Syria from obtaining international support, which may have proved crucial in managing opposition to reforms (Oxford 2003). When the tensions between Lebanon and Syria came to blows with the Hariri assassination in 2005, culminating in a Syrian withdrawal and the loss of thousands of Lebanese temporary jobs for Syrian workers, the popular opinion on stalled reforms shifted from blaming Assad’s advisors to Assad himself. Media Coverage Media coverage of Syrian politics and foreign engagement has been international and widespread for decades. We were able to find many news articles on the attempted reforms in Syria and analysis of its corruption. There has been less coverage of the specifics of the actual reforms policies, but enough explained to assess the scope of and reaction to this event. Table 12: Syria—Control of Corruption and Sub-source Data for CRE

Year(-2) Year 0 Year +1 Year +2 Year +3 Year +4 Year +5 2000 2002 2003 2004 2005 2006 2007

CoC -0.90 -0.24 -0.69 -0.71 -0.71 -0.99 -1.01BTI 0.3 0.3 0.15 0.15 0.25CCR 0.26 0.26 0.28 0.18EIU 0 0.25 0 0 0 0.25 0.25GCS 0.31IFD 0.51 0.51 0.51 0.6IPD 0 0PIA NP NP PRS 0.33 0.33 0.33 0.33 0.33 0.33 0.33WMO 0.38 0.5 0.5 0.49 0.5 0.5 0.38

Source: World Bank 2013b Change in the Control of Corruption Indicator In 2000, Syria received a -.90 and in 2000, a -.24. This is considered a positive improvement based on the WGI scale. In 2003, however, Syria dropped back to -.69 and remained at -.71 through 2005.

58

Figure 6: Syria--Change in Control of Corruption Score, Including Standard Error

Source: World Bank 2014b

Change in the Sub-Source Indicator Data This case was selected because it had one of the highest jumps in a sub-source indicator between years of measurement. According to the BTI, during the reports of 2003-2004 to 2005-2006, Syria’s metric of corruption decreased by 50 percent, or 15 percent of its total possible value.2 Notably, this value actually moves in a negative direction, which would generally have led our research analysis to look for a corresponding scandal. It is clearly evident, however, that Syria was attempting to engage in significant reform during this time. We believe the BTI change is consistent and accurately portrays the level of corruption in Syria, as based on the sub-source’s methodology of data collection. As discussed previously, BTI uses an expert analysis to judge the government’s success at implementing and monitoring a variety of integrity mechanisms (BTI Methodology, p.40). Assad had an initial mandate upon his presidential ascension in 2000 and generated a wave of popularity based on his commitment to reform and improve Syria’s economy. When he attempted and failed to make significant progress, which prompted a dramatic reversal of public opinion and weakened the capacity of the government—particularly regarding its authoritarian influence.

2 The BTI scale is from 0 to 10 on a continuous scale to one significant digit. Scores approaching 10 are considered increasingly positive for assessing corruption levels. In the 2003-2004 report, Syria scored a 3.0 on the BTI scale. In 2005-2006, it scored 1.5.

‐1.40

‐1.20

‐1.00

‐0.80

‐0.60

‐0.40

‐0.20

0.00

1997 1998 1999 2000 2001 2002 2003 2004 2005

Syria

59

APPENDIX I: THE WGI PROJECT WEIGHTING METHODOLOGY To create the compiled CoC score, the WGI weights each sub-source by its correlation with all of the other sub-sources. As mentioned, we consider correlative weighting a limitation of the CoC. As mentioned, changing the sub-sources weights does not result in a statistically significant change in the CoC score. However, the CoC’s confidence intervals are wide, meaning the score could change dramatically without experiencing a statistically significant change. To illustrate, we calculate Georgia’s 2005 CoC score using the WGI weights. For ease of analysis, we keep the CoC score in the 0 to 1 scale used by the sub-sources. With the WGI weights, Georgia’s 2005 CoC score is 0.41. Next, we calculate what Georgia’s 2005 score would be if all sub-source received equal weight; using this methodology, Georgia’s score is 0.54. While the increase is likely not statistically significant, it does represent a 13 percentage point difference, which is relatively large. Table 13: Effects of Changed Weights on Georgia's Control of Corruption indicator

2005 WGI Weights

Adjusted Weight

Weighted Scores

Averaged Scores

BPS 0.80 0.03 0.05 0.04 0.10FRH 0.25 0.22 0.39 0.10 0.03PIA 0.50 0.04 0.08 0.04 0.06WMO 0.38 0.12 0.22 0.08 0.05BTI 0.75 0.04 0.08 0.06 0.09GCB 0.66 0.03 0.05 0.03 0.08GCS 0.42 0.06 0.12 0.05 0.05IFD 0.56 0.01 0.02 0.01 0.07

Total 0.56 1.00 0.41 0.54

Source: World Bank 2014b and Author Produced We also hypothesize that the emphasis on correlation could promote stagnant sub-source scores. If a sub-source consistently gives the same score to a country, then the relative order of its countries will remain the same. If many sub-sources engage in this behavior, then all of those sub-sources are highly correlated with each other. Anecdotally, we found that sub-sources which did not change after our CREs tended to have higher weights. For example, the EIU scores did not change for most years in our case studies, and the EIU consistently receives a high CoC weight. To test our hypothesis, we perform a statistical analysis of the effects of stagnant scores on the average weight of an indicator. First, we calculate the absolute value

60

of the yearly changes in each available sub-source for every country. We then find the average change for each sub-source over all countries and reported years. We also calculate the average weight received by each sub-source over every year. Finally, we run a regression in Stata, a statistical software program, to see the effects of the average sub-source score changes on the average weight received by that sub-source. We control for a number of other factors in our regression analysis to isolate the effect of average score changes. We include a dummy variable for each of the following factors to measure if the sub-source is:

U.S. based Focused on economics or business Expert assessment (vs. a survey) Household survey (vs. a survey of firms) Regional assessment (vs. global)

We find that the average sub-source score change has a coefficient of -5.26, meaning that if a sub-source’s average change increased by 0.1, the weight of that sub-source should decrease by 0.526. While most sub-sources have very small average changes, the coefficient for average score change is huge, so any increase will have a large effect on the sub-source’s weight. The coefficient for average changes is statistically significant; none of the other variables have a statistically significant effect on the sub-source’s average weight. Table 14: Effect of Average Sub-source Change on Average Sub-source Weight

Coefficient 95% Confidence Interval – Low

95% Confidence Interval – High

-5.26 -10.88 0.36 Source: Author Produced

Our results are consistent with our hypothesis: the WGI project’s weighting methodology appears to favor indicators that do not frequently change. Therefore, we believe that the weighting methodology limits the ability of the CoC to respond to a CRE, as sub-sources that change in response to the CRE may receive a lower weight. Despite the statistical strength of our findings, our analysis has many limitations. We only include data sources that are publicly available through the WGI; for a more robust analysis, we would need to include all sub-sources. In addition, we only look at the effect of average sub-source changes on the average weight. We recommend the MCC continue this analysis by looking at the effect of average yearly changes on a sub-source’s yearly weights. We cannot isolate the effect of average sub-source changes from all of the other factors that may influence correlation between sub-sources, so we cannot prove causation. However, the results reinforce our belief that the weighting methodology should be analyzed further.

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