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.
For an online copy, see
www.lafollette.wisc.edu/publications/workshops.html
[email protected]
The Robert M. La Follette School of Public Affairs is a teaching and research department
of the University of Wisconsin–Madison. The school takes no stand on policy issues;
opinions expressed in these pages reflect the views of the authors.
The University of Wisconsin–Madison is an equal opportunity and affirmative-action educator and employer.
We promote excellence through diversity in all programs.
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 i
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 ii
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 iii
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 iv
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
v
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.
vi
LIST OF ABBREVIATIONS
ADB
AFR
ASD
BPS
BTI
CBI/CBIP
CCR
CIESIN
CoC
CRE
EIU
EU
FRH
GCB
GCS
GII
GOV
GWP
IFD
IFAD
IMD
IMF
IPD
LBO
LICs
LMICS
MCC
NGO
PIA
PRC
PRS
UCM
UNICEF
UNODC
VAB
WCY
WEO
WGI
WJP
WMO
YCELP
African Development Bank Country Policy and Institutional
Assessments
Afrobarometer
Asian Development Bank Country Policy and Institutional
Assessments
Business Enterprise Environment Survey
Bertelsmann Transformation Index
Commercial Business Information
Countries at the Crossroads
Center for International Earth Science Information Network
Control of Corruption indicator
Corruption-Related Events
Economist Intelligence Unit
European Union
Freedom House
Global Corruption Barometer Survey
Global Competitiveness Report
Global Integrity Index
Public Sector Data
Gallup World Poll
Rural Sector Performance Assessment
International Fund for Agricultural Development
International Institute for Management and Development
International Monetary Fund
Institutional Profiles Database
Latinobarometer
Lower Income Countries
Lower Middle Income Countries
Millennium Challenge Corporation
Nongovernmental Organization
Country Policy and Institutional Assessments
Corruption in Asia Survey
Political Risk Services International Country Risk Guide
Unobserved Components Model
United Nations Children's Fund
United Nations Office on Drugs and Crime
Vanderbilt University Americas Barometer Survey
World Competitiveness Yearbook
World Economic Outlook
Worldwide Governance Indicators
World Justice Project Rule of Law Index
Global Insight Business Condition and Risk Indicators
Yale Center for Environmental Law & Policy
vii
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 subsources. 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-overyear changes that are statistically significant, as the yearly confidence intervals
are large and overlap one another. Thus, from an empirical standpoint, we cannot
viii
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 shortterm policy approaches.
ix
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).
1
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 subsources 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 subsource 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 subsources 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).
2
Table 1: Control of Corruption Indicator Sub-Sources
No.
1
Acronym Control of Corruption Data Sources
Type*
Organization
ADB
Country Policy and Institutional Assessments Expert (GOV) African Development Bank
2
AFR
Afrobarometer
3
ASD
Country Policy and Institutional Assessments Expert (GOV) Asian Development Bank
4
BPS
Business Enterprise Environment Survey
Survey
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
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
16
PIA
Policy and Institutional Assessments
Expert (GOV) World Bank
17
PRC
Corruption in Asia
Survey
18
PRS
International Country Risk Guide
Expert (CBIP) Political Risk Service
19
VAB
Americas Barometer
Survey
20
WCY
World Competitiveness Yearbook
Survey
Institute for Management and Development
21
WJP
Rule of Law Index
Survey
World Justice Project
Survey
Afrobarometer
World Bank
Gallup
Latinobarometro
Political Economic Risk Consultancy
Vanderbilt University
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
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
3
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 nonprobability 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
4
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 subsource 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 subsources. 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.
5
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 triyearly 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, lowincome country that is also an MCC compact country.
6
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 MarxistLeninist 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 anticorruption 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
7
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
CoC
ADB
AFR
BTI
EIU
GCS
GII
GWP
IFD
IPD
PIA
WMO
Year(-2)
2008
-0.53
0.6
0.51
0.5
0.25
0.28
0.93
0.37
0.58
0.17
0.5
0.38
Year (-1)
2009
-.0.67
0.6
0.51
0.45
0
0.32
Year 0
2010
-0.74
0.6
0.51
0.45
0
0.23
0.37
0.6
0.33
0.5
0.38
0.37
0.6
0.33
0.5
0.38
Year +1
2011
-0.63
0.6
0.56
0.45
0
0.33
Year +2
2012
-0.92
0.57
0.56
0.4
0
0.19
0.3
0.24
0.6
0.5
0.33
0.08
0.5
0.5
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.
8
Figure 1: Benin--Change in Control of Corruption Score, Including
Standard Error
Benin
0.00
‐0.20
‐0.40
‐0.60
‐0.80
‐1.00
‐1.20
2008
2008
2009
2009
2010
2010
2011
2011
2012
2012
2013
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
9
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 subsources 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).
10
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
11
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
CoC
EIU
GCB
GCS
GII
GWP
IPD
PRS
VAB
WCY
WMO
Year
(-2)
2000
2.23
1
Year
(-1)
2002
2.11
1
Year 0 Year +1 Year +2 Year +3 Year +4 Year +5
0.8
0.83
0.72
2004
1.85
1
0.71
0.77
1
0.67
0.83
0.67
0.83
0.88
0.75
1
0.78
1
0.75
0.85
2003
2.04
1
2005
1.86
1
0.71
0.75
2006
1.96
1
0.74
0.77
2007
2008
1.98
1.99
1
1
0.72
0.73
0.8
0.82
0.53
0.55
0.43
0.56
0.59
1
1
1
0.83
0.83
0.83
0.83
0.47
0.47
0.43
0.7
0.73
0.67
0.66
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.
12
Figure 2: Canada--Change in Control of Corruption Score, Including
Standard Error
Canada
2.40
2.30
2.20
2.10
2.00
1.90
1.80
1.70
1.60
1.50
2003
2003
2004
2004
2005
2005
2006
2006
2007
2007
2008
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.
13
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 subsource 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 anticorruption 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
14
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
15
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
CoC
BPS
FRH
PIA
WMO
BTI
GCB
GCS
IFD
EIU
GII
GWP
ASD
Year
Year Year 0 Year +1 Year +2 Year +3 Year +4 Year +5
(-2)
(-1)
2002
2003
2004
2005
2006
2007
2008
2009
-1.14
-0.65
-0.61
-0.36
-0.04
-0.25
-0.22 -0.22
0.57
0.57
0.57
0.8
0.8
0.8
0.76
0.76
0.21
0.21
0.21
0.25
0.33
0.33
0.33
0.33
NP
NP
NP
0.5
0.5
0.4
0.4
0.4
0.13
0.38
0.4
0.38
0.38
0.25
0.25
0.25
0.7
0.7
0.75
0.75
0.65
0.65
0.60
0.67
0.66
0.66
0.66
0.73
0.73
0.31
0.42
0.45
0.57
0.58
0.55
0.56
0.56
0.56
0.51
0.6
0.65
0.5
0.25
0.25
0.25
0.7
0.03
0
0.35
0.44
0.48
0.64
0.61
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.
16
Figure 3: Georgia--Change in Control of Corruption Score, Including
Standard Error
Georgia
0.00
‐0.20
‐0.40
‐0.60
‐0.80
‐1.00
‐1.20
‐1.40
‐1.60
1999
2000
2001
2002
2003
2004
2005
2006
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 nongovernmental 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.
17
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
18
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
19
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
CoC
ADB
AFR
BTI
CCR
EIU
GCB
GCS
GII
GWP
IFD
IPD
PIA
PRS
WMO
Year
(-2)
2000
-0.7
Year Year 0 Year +1 Year +2 Year +3 Year +4 Year +5
(-1)
2002
2003
2004
2005
2006
2007
2008
-0.99
-0.83
-0.8
-0.97
-0.58
NP
NP
NP
0.4
0.4
0.4
0.4
0.4
0.66
0.66
0.66
0.6
0.6
0.6
0.53
0.35
0.35
0.35
0.35
0.3
0.3
0.54
0.46
0.46
0.44
0.44
0.44
0
0
0
0
0
0
0
0
0.48
0.59
0.63
0.63
0.53
0.28
0.45
0.36
0.37
0.31
0.36
0.8
0.85
0.85
0.13
0.22
0.09
0.6
0.6
0.6
0.6
0.5
0.17
0.17
0.17
NP
NP
NP
NP
0.4
0.4
0.4
0.4
0.33
0.33
0.58
0.25
0.08
0.08
0.08
0.08
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 subsources 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.
20
Figure 4: Kenya--Change in Control of Corruption Score, Including
Standard Error
Kenya
‐0.60
‐0.70
‐0.80
‐0.90
‐1.00
‐1.10
‐1.20
‐1.30
1999
2000
2001
2002
2003
2004
2005
2006
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
21
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
22
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.
23
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” anticorruption 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”
24
(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 onetime 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
CoC
BPS
BTI
EIU
FRH
GCB
GCS
GII
GWP
PIA
PRS
WMO
Year(-2)* Year (0)* Year +2* Year +3 Year +4 Year +5
1998
2000
2002
2003
2004
2005
-1.08
-1.12
-0.91
-0.47
-0.48
-0.38
0.33
0.72
0.72
0.72
0.68
0.55
0.55
0.65
0
0
0
0
0
0
0
0.13
0.33
0.33
0.33
0.38
0.54
0.4
0.44
0.5
NP
0.17
0.25
NP
0.17
0.25
0.17
0.25
NP
NP
0.33
0.33
0.33
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.
25
Figure 5: Serbia--Change in Control of Corruption Score, Including
Standard Error
Serbia
0.00
‐0.20
‐0.40
‐0.60
‐0.80
‐1.00
‐1.20
‐1.40
‐1.60
1997
1998
1999
2000
2001
2002
2003
2004
2005
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).
26
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 anticorruption 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 Westernstyle 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 anticorruption efforts. In hindsight, research identifies that Serbia’s series of anticorruption 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
27
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,
28
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.
29
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 subsources 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
30
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).
31
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
32
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).
33
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 anti34
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
35
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).
36
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 subsource 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,
37
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 sub38
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
39
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, longterm 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
41
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)
43
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, eventlevel, 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
44
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.
45
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 subsources 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)
2008
0.06
0.06
0.10
0.14
0.12
0.02
0.06
0.22
0.08
0.12
ADB
AFR
BTI
EIU
GCS
GWP
IFD
IPD
PIA
WMO
Year (-1)
2009
0.04
0.13
0.09
0.17
0.13
0.02
0.07
0.13
0.09
0.13
Year 0
2010
0.05
0.12
0.10
0.20
0.12
0.02
0.05
0.12
0.10
0.12
Year 1
2011
0.05
0.07
0.14
0.19
0.12
0.00
0.05
0.14
0.09
0.16
Year 2
2012
0.03
0.08
0.14
0.19
0.11
0.00
0.08
0.11
0.11
0.16
Table 8: Canada’s Sub-source Weights
EIU
GCB
GCS
GII
GWP
IPD
PRS
VAB
WCY
WMO
Year
(-2)
2000
Year
(-1)
2002
Year 0
0.35
0.56
0.28
0.20
0.12
0.15
2003
Year
+1
2004
Year
+2
2005
Year
+3
2006
Year
+4
2007
Year
+5
2008
0.22
0.07
0.15
0.19
0.07
0.14
0.12
0.06
0.16
0.14
0.04
0.12
0.00
0.02
0.24
0.06
0.02
0.22
0.14
0.16
0.02
0.14
0.00
0.02
0.25
0.05
0.02
0.20
0.14
0.05
0.06
0.05
0.07
0.07
0.23
0.18
0.15
0.12
0.28
0.25
0.29
0.20
0.26
0.28
46
0.02
0.22
0.06
0.02
0.22
0.12
Table 9: Georgia’s Sub-source Weights
ASD
BPS
BTI
EIU
FRH
GCB
GCS
GII
GWP
IFD
PIA
WMO
Year
(-3)
2000
0.00
0.00
0.00
0.00
0.48
0.00
0.00
0.00
0.00
0.00
0.29
0.23
Year
(-1)
2002
0.00
0.07
0.00
0.00
0.52
0.00
0.00
0.00
0.00
0.00
0.19
0.22
Year 0
2003
0.00
0.04
0.13
0.00
0.50
0.00
0.00
0.00
0.00
0.00
0.13
0.21
Year
+1
2004
0.00
0.04
0.10
0.00
0.39
0.06
0.12
0.00
0.00
0.02
0.12
0.16
Year
+2
2005
0.00
0.05
0.07
0.00
0.40
0.05
0.11
0.00
0.00
0.02
0.07
0.22
Year
+3
2006
0.00
0.11
0.11
0.11
0.19
0.06
0.15
0.00
0.02
0.06
0.09
0.11
Year
+4
2007
0.02
0.11
0.11
0.12
0.25
0.04
0.11
0.00
0.02
0.04
0.09
0.12
Year
+5
2008
0.02
0.10
0.08
0.12
0.32
0.02
0.10
0.00
0.02
0.05
0.07
0.10
Year
+1
2004
0.08
0.06
0.10
0.00
0.19
0.06
0.13
Year
+2
2005
0.04
0.04
0.09
0.00
0.18
0.07
0.13
0.02
0.02
0.13
0.06
0.17
0.09
0.07
0.27
Year
+3
2006
0.08
0.05
0.10
0.00
0.10
0.05
0.13
0.00
0.02
0.05
0.18
0.08
0.05
0.10
Year
+4
2007
0.09
0.03
0.10
0.00
0.12
0.03
0.10
0.00
0.02
0.03
0.21
0.09
0.05
0.12
Year
+5
2008
0.06
0.06
0.10
0.00
0.13
0.02
0.12
0.00
0.02
0.06
0.21
0.08
0.04
0.12
Table 10: Kenya’s Sub-source Weights
ADB
AFR
BTI
CCR
EIU
GCB
GCS
GII
GWP
IFD
IPD
PIA
PRS
WMO
Year
(-3)
2000
0.09
Year
(-1)
2002
0.04
0.04
0.40
0.62
Year 0
2003
0.02
0.02
0.14
0.00
0.26
0.14
0.26
0.06
0.20
0.11
0.06
0.13
0.14
0.05
0.23
47
Table 11: Serbia’s Sub-source Weights
BPS
BTI
EIU
FRH
GCB
GCS
GII
GWP
PIA
PRS
WMO
Year (-2)
1998
Year 0
2000
0.00
Year +2
2002
0.03
0.64
0.21
0.37
0.39
0.49
0.24
0.03
0.12
0.05
0.18
0.08
0.05
0.10
48
Year +3
2003
0.03
0.09
0.16
0.36
Year +4
2004
0.03
0.08
0.15
0.34
0.09
0.10
Year +5
2005
0.05
0.06
0.12
0.34
0.05
0.09
0.09
0.03
0.15
0.10
0.05
0.14
0.06
0.05
0.18
APPENDIX D: QUESTIONS FROM THE AFRICAN DEVELOPMENT BANK SUBSOURCE USED IN THE CONTROL OF CORRUPTION INDICATOR
…Rating in Transparency, Accountability, and Corruption in the Public
Sector
Score Guidelines
There are no checks and balances on executive power. Public officials use
a their positions for personal gain and take bribes openly. Seats in the
legislature and positions in the civil service are often bought and sold.
1
Government decision-making is secretive. The public is prevented from
b
participating in or learning about decisions and their implications.
The state has been captured by narrow interests (economic, political, ethnic,
c
and/or military). Administrative corruption is rampant.
There are only ineffective audits and other checks and balances on
a executive power. Public officials are not sanctioned for failures in service
delivery or for receiving bribes.
Decision-making is not transparent, and government withholds information
needed by the public and civil society organizations to judge its
b
performance. The media are not independent of government or powerful
2
business interests.
Boundaries between the public and private sector are ill-defined, and
conflicts of interest abound. Laws and policies are biased towards narrow
c private interests. Implementation of laws and policies is distorted by
corruption, and resources budgeted for public services are diverted to
private gain.
External accountability mechanisms such as inspector-general,
a ombudsman, or independent audit may exist but have inadequate resources
or authority
Decision-making is generally not transparent, and public dissemination of
3
information on government policies and outcomes is a low priority.
b
Restrictions on the media limit its potential for information-gathering and
scrutiny.
Elected and other public officials often have private interests that conflict
c
with their professional duties.
External accountability mechanisms limit somewhat the degree to which
special interests can divert resources or influence policymaking through
a illicit and non-transparent means. Risks and opportunities for corruption
within the executive are reduced through adequate monitoring and reporting
lines.
4
Decision-making is generally transparent. Government actively attempts to
distribute relevant information to the public, although capacity may be a
b 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.
49
Conflict of interest and ethics rules exist and the prospect of sanctions has
c some effect on the extent to which public officials shape policies to further
their own private interests.
Accountability for decisions is ensured through a strong public service ethic
reinforced by audits, inspections, and adverse publicity for performance
a failures. The judiciary is impartial and independent of other branches of
government. Authorities monitor the prevalence of corruption and
implement sanctions transparently.
The reasons for decisions, and their results and costs, are clear and
5
communicated to the general public. Citizens can obtain government
b
documents at nominal cost. Both state-owned (if any) and private media are
independent of government influence and fulfil critical oversight roles.
Conflict of interest and ethics rules for public servants are observed and
c enforced. Top government officials are required to disclose income and
assets, and are not immune from prosecution under the law for malfeasance.
Criteria for “5” on all three sub-ratings are fully met. There are no warning
6
signs of possible deterioration, and there is widespread expectation of
continued strong or improving performance.
Source: African Development Bank 2014
50
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:
51
RATIN
G
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 many policies
that adhere to basic human
rights standards,
democratic norms, and the
rule of law.
Existence of many policies
that adhere to basic human
rights standards,
democratic norms, and the
rule of law.
Existence of many policies
that adhere to basic human
rights standards,
democratic norms, and the
rule of law.
Existence of some policies
that adhere to basic human
rights standards,
democratic norms, and the
rule of law.
Absence 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.
Existence of many
practices 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.
Absence of many practices
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.
Absence of practices that
adhere to basic human
rights standards,
democratic norms, and the
rule of law.
Source: Freedom House 2014b
3
4
5
6
7
52
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
53
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, fulltime 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-questions
76a: 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
54
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 alAssad, age 34. Bashar al-Assad was widely considered to be more reformoriented 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).
55
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
56
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
-1.01
BTI
0.3
0.3
0.15
0.15
0.25
CCR
0.26
0.26
0.28
0.18
EIU
0
0.25
0
0
0
0.25
0.25
GCS
0.31
IFD
0.51
0.51
0.51
0.6
IPD
0
0
PIA
NP
NP
PRS
0.33
0.33
0.33
0.33
0.33
0.33
0.33
WMO
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.
57
Figure 6: Syria--Change in Control of Corruption Score, Including Standard
Error
Syria
0.00
‐0.20
‐0.40
‐0.60
‐0.80
‐1.00
‐1.20
‐1.40
1997
1998
1999
2000
2001
2002
2003
2004
2005
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.
58
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 subsources 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 subsources. 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
BPS
FRH
PIA
WMO
BTI
GCB
GCS
IFD
Total
0.80
0.25
0.50
0.38
0.75
0.66
0.42
0.56
WGI
Weights
0.03
0.22
0.04
0.12
0.04
0.03
0.06
0.01
0.56
Adjusted
Weight
0.05
0.39
0.08
0.22
0.08
0.05
0.12
0.02
Weighted
Scores
0.04
0.10
0.04
0.08
0.06
0.03
0.05
0.01
Averaged
Scores
0.10
0.03
0.06
0.05
0.09
0.08
0.05
0.07
1.00
0.41
0.54
Source: World Bank 2014b and Author Produced
We also hypothesize that the emphasis on correlation could promote stagnant subsource 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
59
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
-5.26
95% Confidence Interval
– Low
-10.88
95% Confidence Interval
– High
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.
60
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