The MCC Incentive Effect:
Quantifying Incentives
for Policy Change
in an Ex-Post Rewards System
Prepared for the Millennium Challenge Corporation
By
Ingrid Aune
Yanyan Chen
Christina Miller
Joshua Williams
October 2013
Workshop in International Public Affairs
Spring 2013
©2013 Board of Regents of the University of Wisconsin System
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Table of Contents
List of Figures ....................................................................................................... vii List of Tables ....................................................................................................... viii Foreword ................................................................................................................ ix Acknowledgments................................................................................................... x Executive Summary ............................................................................................... xi Introduction ............................................................................................................. 1 I. The MCC and the Incentive Effect ...................................................................... 2 A. Selection Indicators and Incentive Effects ..................................................... 2 B. Evidence of the MCC Incentive Effect: Status Quo ...................................... 3 1. Qualitative Evidence ................................................................................... 3 2. Quantitative Evidence ................................................................................. 4 II. Data .................................................................................................................... 4 A. Indicator Score: Description of Data ............................................................. 5 1. Economic Freedom ..................................................................................... 5 2. Investing in People ...................................................................................... 6 3. Ruling Justly ............................................................................................... 6 B. Indicator Rates of Reform .............................................................................. 6 C. Time Correlation ............................................................................................ 7 D. Indicator Mobility .......................................................................................... 7 E. Correlation between Indicators ...................................................................... 9 III. Which Countries Have Incentives? ................................................................. 10 A. LICs versus LMICs ...................................................................................... 10 B. Treatment Groups......................................................................................... 11 1. Qualitative MCC Incentive Effect ............................................................ 11 2. Bordering the Eligibility Threshold .......................................................... 11 3. Passing Control of Corruption .................................................................. 12 4. Threshold Partnership ............................................................................... 12 5. Government Expenditures......................................................................... 12 5a. Countries in the Middle Range of Government Expenditures ................ 13 5b. Countries in the Highest Quartile of Government Expenditures ............ 13 5c. Countries in the Lowest Quartile of Government Expenditures ............. 13 6. Bordering the Eligibility Threshold and Passing Control of Corruption .. 13 7. Bordering the Eligibility Threshold or Passing Control of Corruption .... 13 8. Passing or Falling Short of the Eligibility Threshold ............................... 13 C. Additional Control Variables ....................................................................... 13 IV. Bearing Down and Moving Forward .............................................................. 14 V. Preliminary Analyses ....................................................................................... 16 A. Nonparametric Approach ............................................................................. 16 B. Basic Rates of Reform Mean Comparison ................................................... 20 VI. Models ............................................................................................................ 23 A. Rate of Reform: Difference-in-Differences Model 1................................... 23 B. Likelihood of Improvement: Difference-in-Differences Model 2 ............... 25 C. Proportion of Indicators Improved: Difference-in-Differences Model 3 ..... 25 D. Assumption and Caveat ............................................................................... 25 VII. Difference-in-Differences Analysis for All Sample Countries ..................... 26 A. Overview of Results ..................................................................................... 28 B. Economic Freedom ...................................................................................... 29 1. Qualitative MCC Incentive Effect ............................................................ 31 2. Bordering the Eligibility Threshold .......................................................... 31 3. Passing Control of Corruption .................................................................. 31 4. Threshold Partnership ............................................................................... 32 5. Countries in the Middle Range of Government Expenditures .................. 32 C. Investing in People ....................................................................................... 32 1. Qualitative MCC Incentive Effect ............................................................ 33 2. Bordering the Eligibility Threshold .......................................................... 33 3. Passing Control of Corruption .................................................................. 34 4. Threshold Partnership ............................................................................... 34 5. Countries in the Middle Range of Government Expenditures .................. 34 D. Ruling Justly ................................................................................................ 34 1. Qualitative MCC Incentive Effect ............................................................ 36 2. Bordering the Eligibility Threshold .......................................................... 36 3. Passing Control of Corruption .................................................................. 36 4. Threshold Partnership ............................................................................... 36 5. Countries in the Middle Range of Government Expenditure ................... 37 E. Results from Proportion of Indicators Improved Model .............................. 37 VIII. DD Analysis for LICs and LMICs ............................................................... 38 A. LICs Breakdown .......................................................................................... 38 B. LMICs Breakdown ....................................................................................... 39 C. Time Effects ................................................................................................. 42 IX. Conclusions and Recommendations ............................................................... 44 A. Lack of Quantitative Evidence..................................................................... 45 B. How to Improve Analyses:........................................................................... 45 C. How to Improve MCC Incentives ................................................................ 46 1. Create Aspirations ..................................................................................... 46 2. Provide Knowledge and Information ........................................................ 46 3. Separate Country Group—LICs and LMICs ............................................ 47 D. Final Thoughts ............................................................................................. 47 Appendix A: Descriptions of Indicators ............................................................... 49 A. Economic Freedom Indicators ..................................................................... 49 1. Credit Depth .............................................................................................. 49 2. Legal Rights .............................................................................................. 49 3. Cost to Start Business ............................................................................... 49 4. Time to Start Business .............................................................................. 49 5. Fiscal Policy .............................................................................................. 50 6. Inflation ..................................................................................................... 50 7. Time to Register Property ......................................................................... 50 8. Cost to Register Property .......................................................................... 50 9. Regulatory Quality .................................................................................... 50 10. Trade Freedom ........................................................................................ 51 11. Gender in the Economy .......................................................................... 51 12. Access to Land ........................................................................................ 51 B. Investing in People Indicators ...................................................................... 51 1. Girls’ Primary Education .......................................................................... 51 2. Health Expenditures .................................................................................. 52 3. Immunization Rate .................................................................................... 52 4. Education Expenditures ............................................................................ 52 5. Girls’ Secondary Education Enrollment ................................................... 52 6. Child Health .............................................................................................. 53 7. Natural Resource Protection ..................................................................... 53 C. Ruling Justly Indicators................................................................................ 53 1. Civil Liberties ........................................................................................... 53 2. Control of Corruption ............................................................................... 53 3. Government Effectiveness ........................................................................ 53 4. Political Rights .......................................................................................... 54 5. Rule of Law............................................................................................... 54 6. Voice and Accountability ......................................................................... 54 7. Freedom of Information ............................................................................ 54 Appendix B: Indicator Information and Rates of Reform Statistics ..................... 55 Appendix C: Autocorrelation within Indicators ................................................... 57 Appendix D: Correlation between Indicators ....................................................... 60 Appendix E: Average Number of MCC Indicators Passed .................................. 62 Appendix F: Treatment Countries ........................................................................ 64 Appendix G: Full Regression Results ................................................................... 67 Endnotes ................................................................................................................ 73 Works Cited .......................................................................................................... 74 List of Figures
Figure 1: Indicator Mobility: Indicator Rank Change ............................................ 8 Figure 2. Qualitative MCC Incentive Effect: Bordering the Eligibility
Threshold Treatment Rates of Reform for Control of Corruption ................ 17 Figure 3. Mixed or Nonexistent Effect: Qualitative MCC Incentive Effect
Treatment Rates of Reform for Control of Corruption .................................. 18 Figure 4. Negative Effect: Countries in the Middle Range of Government
Expenditures Treatment Rates of Reform for Control of Corruption............ 19 Figure 5. Qualitative MCC Incentive Effect: Bordering the Eligibility
Threshold Treatment Mean Rates of Reform for Control of Corruption ...... 21 Figure 6. Mixed or Nonexistent Effect: Qualitative MCC Incentive Effect
Treatment Mean Rates of Reform of Control of Corruption ......................... 22 Figure 7. Negative Effect: Countries in the Middle Range of Government
Expenditures Treatment Mean Rates of Reform of Control of Corruption ... 23 Figure 8: Number of Positive and Negative Results in LICs................................ 40 Figure 9: Number of Positive and Negative Results in LMICs ............................ 41 Figure 10: Number of Positive and Negative Results in Different Time
Periods ........................................................................................................... 43 Figure C1. Fiscal Policy........................................................................................ 57 Figure C2. Inflation ............................................................................................... 57 Figure C3. Regulatory Quality.............................................................................. 57 Figure C4. Trade Freedom .................................................................................... 57 Figure C5. Girls’ Primary Education .................................................................... 58 Figure C6. Health Expenditures ............................................................................ 58 Figure C7. Immunization Rate .............................................................................. 58 Figure C8. Education Expenditures ...................................................................... 58 Figure C9. Control of Corruption ......................................................................... 58 Figure C10. Government Effectiveness ................................................................ 58 Figure C11. Rule of Law ...................................................................................... 59 Figure C12. Voice and Accountability ................................................................. 59 Figure D1: Correlation between Economic Freedom Indicators .......................... 60 Figure D2: Correlation between Investing in People Indicators ........................... 61 Figure D3: Correlation between Ruling Justly Indicators .................................... 61 Figure E1: Average Number of MCC Indicators Passed by LICs ........................ 62 Figure E2: Average Number of MCC Indicators Passed by LMICs .................... 63 vii
List of Tables
Table 1. Indicator Mobility: Average of Indicator Rank Change ........................... 9 Table 2. Correlation of Qualitative MCC Inventive Effect to Other
Treatments ..................................................................................................... 15 Table 3. Number of Positive and Negative Results of Three Models for Three
Categories of Indicators ................................................................................. 27 Table 4. Statistically Significant Results for Economic Freedom ........................ 30 Table 5. Statistically Significant Results for Investing in People ......................... 33 Table 6. Statistically Significant Results for Ruling Justly .................................. 35 Table 7: Statistically Significant Results for Proportion of Indicators
Improved Model ............................................................................................ 37 Table B1: Indicator and Rates of Reform Information ......................................... 55 Table F1: LICs included in Treatments ................................................................ 64 Table F2: LMICs included in Treatments ............................................................. 66 Table G1: Economic Freedom Indicators Rate of Reform Model ........................ 67 Table G2: Economic Freedom Indicators Likelihood of Improvement Model .... 68 Table G3: Investing in People Indicators.............................................................. 69 Table G4: Ruling Justly Indicators Rate of Reform Model .................................. 70 Table G5: Ruling Justly Indicators Likelihood of Improvement Model .............. 71 Table G6: Proportion of Indicators Improved Model ........................................... 72 viii
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 Public Affairs or 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.
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. It provides
them with practical experience 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) prescriptive
responses 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.
Melanie Frances Manion
Professor of Public Affairs and Political Science
May 2013
ix
Acknowledgments
The journey toward the completion of this report has been arduous but rewarding.
We would like to thank the following people, without whom we could not have
accomplished this task: First and foremost, Professor Melanie Manion, who was
understanding and encouraging, and who went beyond the call of duty to ensure
we could present the best as possible work; Andria Hayes-Birchler and the people
at the Millennium Challenge Corporation, who provided us with the project, gave
us useful insight and helpful feedback; Karen Faster, who assisted us with a
watchful editorial eye; Shirley Smith, who added an artist’s perspective to our
tables and graphs; our families for their understanding and support; finally, all of
the staff and students at the La Follette School of Public Affairs, who consistently
encouraged us as we made the common study space our home.
x
Executive Summary
The Millennium Challenge Corporation (MCC) is an independent U.S. foreign aid
agency that uses an ex-post rewards system to determine potential eligibility for
funding. When countries seek to change their policies specifically to meet MCC
eligibility requirements, the “MCC incentive effect” is taking place. This report
analyzes that effect.
We independently compiled data from the original third-party sources the MCC
uses for its eligibility indicators. We then defined a series of plausible “treatment
groups”—countries we believe are most likely to show signs of the MCC
incentive effect. We conducted preliminary analyses to test our hypotheses
and models. We then used three difference-in-differences regression models
and analyzed the results by indicators, treatment groups, country income groups,
and time periods.
We conclude no compelling evidence suggests an overall MCC incentive effect.
However, we do find subtle evidence of the MCC incentive effect that varies by
how we divide our data. When we separate the indicators by the three MCC
indictor categories of Economic Freedom, Investing in People, and Ruling Justly,
we see differences in our results depending on which of the five of our most
plausible treatments we use. For instance, countries that border the MCC
eligibility threshold appear to have the incentive to improve on indicators oriented
toward good governance. Similarly, when we separate countries by per-capita
gross domestic product, we see differences in results that vary by indicator
category and time period. For instance, low-income countries appear to have
incentives to improve indicators oriented toward good governance. Yet, this
evidence may not be strong enough to truly convince skeptics that the MCC
incentive effect exists.
We further find that two treatment groups that should show the strongest signs of
the MCC incentive effect because of their structural biases at best show weak
evidence of its existence. We therefore finish the report with recommendations on
what the MCC could do to improve research on the topic, as well as what the
MCC might do to stimulate a stronger MCC incentive effect. These steps most
notably include: creating aspirations in countries to pursue MCC funding;
providing more information about MCC criteria; and creating more specified
differences in eligibility requirements based on country groups.
xi
xii
Introduction
The United States government established the Millennium Challenge Corporation
(MCC) in 2004 to reduce poverty and promote sustainable economic growth in
other countries. The agency has contributed directly to this development work by
having allocated more than $7 billion in poverty-reduction compacts with poor,
but well-governed countries as of spring 2013. The MCC selects countries for
assistance based on past policy performance, an ex-post rewards system. The
MCC’s foreign aid system may also contribute to fighting poverty indirectly: the
possibility of obtaining substantial funding from the MCC may create incentives
for governments to change policies based upon MCC criteria. This response is the
“MCC incentive effect.” However, little research demonstrates the quantifiable
existence of the MCC incentive effect. Documentation of a quantifiable incentive
effect would support the claim that the relatively new ex-post rewards system the
MCC applies and promotes is beneficial in the fight against poverty.
As the MCC is one of a handful of worldwide agencies using the ex-post foreign
aid model, evidence of the MCC incentive effect could have two important
implications. First, it could provide a model for change in the international aid
community. Second, it could improve the MCC’s standing in Congress to help it
secure funding to keep fighting poverty around the world.
This report seeks to answer two questions:
1. Does rigorous quantitative analysis support MCC anecdotal evidence
of the MCC effect?
2. What analytical and policy prescriptions do quantitative findings
suggest for the agency?
Section I of this report introduces the MCC and research on the MCC incentive
effect. Section II describes the data we utilize in the analysis. Section III outlines
our conjectures about which countries might show MCC incentive effects. Section
IV details how we focus the analysis. Section V presents preliminary analyses of
our data to justify our choice of treatment groups and regression models. Section
VI details the difference-in-differences regression models we utilize. Section VII
provides general analysis of the results of the regression models for all countries.
Section VIII looks at differences between low-income countries and low-middleincome countries, as well as differences over time. Section IX provides our
conclusions and recommendations to the MCC for action.
1
I. The MCC and the Incentive Effect
The MCC is an independent U.S. foreign aid agency aimed at fighting global
poverty through focus on “good policies, country ownership, and results” (MCC
2013a). The MCC’s board of directors selects countries as eligible for grant
assistance based on how they perform on a series of 20 (as of fiscal year 2013)
quantitative policy indicators derived from independent third-party sources. The
indicators are grouped into three policy categories: Economic Freedom, Investing
in People, and Ruling Justly (MCC 2012, 3).
Rather than base eligibility for aid on need or strategic interest, the MCC bases
awards on past performance—an “ex-post rewards” system. Two main reasons
are given for this approach. First, taxpayer money will be better spent because
MCC funds will only go to the best-governed poor countries. Second, when aid is
based on performance, poor countries should have an incentive to pursue good
governance. This latter reason is known as the “MCC effect” (Johnson and Zajonc
2006, 2-4) or, more precisely, the “MCC incentive effect.”
The MCC Effect is the positive impact that MCC is having on developing
countries beyond its direct investments. To date, the most significant
impact has been the incentive created for countries to adopt legal, policy,
regulatory, and institutional reforms related to the MCC eligibility criteria.
(MCC 2008, 1)
A. Selection Indicators and Incentive Effects
The amount of foreign aid assistance the MCC allocated as of spring 2013 is more
than $7 billion in poverty-reduction compacts. Almost $500 million went for
policy improvement through its “Threshold Programs” (MCC 2013a). Dreher,
Nunnenkamp, and Öhler (2012) find that countries receiving funding from the
MCC are more likely to receive funding from other countries and donor
organizations, which may contribute to strong economic benefits and, hence,
incentives to improve outcomes measured by the MCC. However, Grabbe (2005)
and Öhler, Nunnenkamp, and Dreher (2010) explain that if MCC funding declines
or does not reach the level expected by potential partnership countries, the
financial limits may constrain potential partnership countries’ willingness to
comply with MCC criteria. The funds allocated to the MCC as of spring 2013
were less than the amount first outlined by former President George W. Bush, and
Congress was expected to continue cutting the MCC’s funding through
sequestration requirements under the Budget Control Act of 2011 (Tarnoff 2013).
The MCC only considers distributing aid to a country once that country has
passed a threshold on its indicator criteria list. The MCC empirically determines
each indicator based on data from third-party institutions. The number of
indicators has grown and changed since the MCC’s creation (MCC 2012). The
MCC’s Board of Directors evaluates countries for eligibility by looking at how
they perform compared to countries with similar income levels in one of two
groups: low-income countries (LICs) or lower-middle-income countries (LMICs).
2
For fiscal year 2013, candidate countries must perform better than the median of
its peers.
To be more specific, the candidate countries need to perform “above the median
or absolute threshold on at least half of the indicators” within their income peer
group, as well as “above the median on the Control of Corruption indicator[,] and
above the absolute threshold on either the Civil Liberties or Political Rights
indicators.” For the Political Rights, Civil Liberties, Inflation, and Immunization
Rate indicators, countries’ performance is gauged against an absolute threshold as
opposed to the median score. The board also considers whether a country passes
at least one indicator in each of the three indicator categories—Ruling Justly,
Investing in People, or Economic Freedom (MCC 2012, 4-5).
The selection process for MCC funds is rather complex, which undoubtedly
means countries have difficulty understanding how they can improve their MCC
scores or which indicators they should improve on to meet the MCC eligibility
criteria. Accordingly, the more complex the selection process, the less likely we
expect the incentive effect to occur.
B. Evidence of the MCC Incentive Effect: Status Quo
Previous work finds qualitative and quantitative evidence of the MCC incentive
effect.
1. Qualitative Evidence
The MCC finds qualitative or anecdotal evidence that suggests the MCC
incentive effect exists. In a 2008 report, the MCC explains that third parties,
such as “academics, journalists, NGOs, investors and donor agencies,” have
independently recognized the MCC effect (MCC 2008, 3). However, none
of the information or authoritative citations presented is quantitative in nature.
MCC employees working closely with candidate and potential candidate
countries believe that the policy criteria have positive externalities. They report
that candidate countries “continuously come back to the MCC to see what they
can change in order to improve their MCC score” (Hayes-Birchler 2013).
Governments regularly contact the MCC to ask what kind of reforms and
investments can improve their MCC score. “Some governments subsequently
implement reforms, strengthen institutions and improve data quality” (MCC
2013b, 4).
Although few studies specifically document the MCC incentive effect, studies
of incentive effects examine ex-post policy criteria. The literature on European
Union membership criteria widely acknowledges that ex-post conditionality
brings about substantial change in domestic policy (Grabbe 2005;
Schimmelfennig, Engert, and Knobel 2006; Schimmelfennig and Sedelmeier
2005).1 Freyburg and Richter (2010) stipulate that incentive-based instruments are
only suitable for triggering policy change under certain domestic conditions.2
Similarly, Öhler, Nunnenkamp, and Dreher (2010, 18-21) anticipate that the MCC
3
incentive effect will get weaker with time because aid rewards will not be
perceived as sufficiently compensating the costs of reform efforts (i.e., the costs
of compliance with criteria that are not primary national interests).
2. Quantitative Evidence
Two empirical studies document the MCC incentive effect.
The first systematic and quantitative attempt to identify the MCC incentive effect
was published less than two years after the MCC was created. Johnson and Zajonc
(2006) employ two statistical methods, a difference-in-differences model and a
regression discontinuity model.3 They (2006, 22) find “substantial evidence that
countries respond to MCC incentives by improving their indicators.”
However, Johnson and Zajonc’s evidence is neither consistent nor overpowering.
The findings include few data points and center around the two years prior to and
after the announcement of the MCC in 2002, rather than its establishment in 2004.
If the MCC incentive effect in fact exists, we expect that some time would be
required for it to show up in the data.
The second and more recent study, by Öhler, Nunnenkamp, and Dreher (2010),
also documents the existence of the MCC incentive effect; however, the authors
conclude that the effect gets weaker over time. The study uses a difference-indifference method similar to that of Johnson and Zajonc (2006), but it focuses on
only one of the criteria for eligibility: the Control of Corruption indicator.
Because of this limited scope of analysis, Öhler, Nunnenkamp, and Dreher do not
provide a strong empirical analysis of the MCC incentive effect for all MCC
indicators. Rather, the study provides another stepping stone for use of a
difference-in-differences model in an expanded analysis. It also indicates a need
to analyze effects over different time periods.
II. Data
For our analysis we started with a list of 26 indicators based on the MCC’s
current and past indicators; a majority of the indicators are the same as the
indicators the MCC uses in fiscal year 2013 (MCC 2012, 4-5). However, we
adapted some and eliminated others as described in appendix A to come up with
20 indicators for our analysis. Appendix B summarizes the data sources and
availability. Drawing directly from the third-party sources the MCC uses, we
compiled a single large panel database. Note that when we use the term
“indicator,” we are from this point forth referring to the data indicators we have
gathered, unless otherwise specified. Availability of data for each of the 20
indicators is not consistent across countries and years, but overall the panel is
mostly balanced. The reason we use these data—instead of MCC data—is
because the third-party sources revise their historical data as new information
becomes available, while the MCC does not. Moreover, we have found
inconsistences in MCC data.4 Therefore, our data are more up-to-date and
4
consistent than data used in previous research on the MCC incentive effect, which
should help our analysis yield more reliable results.
We examine indicators in four ways: indicator score, rates of reform, time
correlation, and indicator mobility. Our conclusions are based on our data.
A. Indicator Score: Description of Data
Our list of indicators differs from the indicators the MCC uses in fiscal year 2013
for three reasons:
1. MCC policy inconsistencies over time. We exclude the Freedom of
Information indicator, which the MCC added in 2012. Instead, we use Voice
and Accountability, the predecessor to Freedom of Information, to ensure
comparable data over the time periods we analyze. We exclude Girls’
Secondary Education Enrollment and Gender in Economy indicators, which
the MCC also added in fiscal year 2012.
2. Lack of data. We drop Child Health and Natural Resource Protection
indicators because our models require pre-MCC and post-MCC period data,
which these indicators lack.
3. Gaps between the MCC and third-party indicators. The MCC combines thirdparty data to create its Access to Credit, Business Start-Up, and Land Rights
and Access indicators (not listed in appendix A). Because we obtain our data
from the MCC’s third-party sources, we keep the original data as separate
indicators in our analysis: Credit Depth, Legal Rights, Cost to Start Business,
Time to Start Business, Cost to Register Property, and Time to Register
Property, respectively.5
From this point we only discuss our 20 indicators, which we divide using the
MCC’s three indicator categories: Economic Freedom, Investing in People, and
Ruling Justly (MCC 2012, 4-5).
1. Economic Freedom
The Economic Freedom indicators assess a government’s commitment to strong
economic policies and economic opportunities for its people through market
forces (MCC 2012, 29). The indicators in this category we use are: Credit Depth,
Legal Rights, Cost to Start Business, Time to Start Business, Fiscal Policy,
Inflation, Time to Register Property, Cost to Register Property, Regulatory
Quality, and Trade Freedom. An environment where people can access credit
(MCC 2012, 33-35), start a business and register property with little time and cost
(MCC 2012; World Bank Group 2013c), own property (World Bank Group
2013c), have a government whose policies reduce barriers to trade (Miller,
Holmes, and Feulner 2013), and have sound monetary and fiscal policy will
promote economic growth through lower consumer prices and increased
consumption, production, savings, and investment (MCC 2012).
5
2. Investing in People
The Investing in People indicators assess the government’s commitment to
improving the standard of living of its people (MCC 2012, 23). The indicators in
this category we use are: Girls’ Primary Education, Health Expenditures,
Immunization Rate, and Education Expenditures. Studies show that investments
in health result in increased economic activity because healthier people are more
productive (MCC 2012, 24-25). The protection and preservation of the
environment is also important to sustain increased economic development without
depletion of natural resources (MCC 2012, 29). Additionally, research shows that
investment in education contributes to economic growth (MCC 2012, 26).
3. Ruling Justly
The Ruling Justly indicators assess a country’s level of democratic governance
(MCC 2012, 13). The indicators in this category we use are: Civil Liberties,
Control of Corruption, Government Effectiveness, Political Rights, Rule of Law,
and Voice and Accountability. Countries with the following characteristics have
higher rates of economic growth: citizen participation in the selection of
government (MCC 2012, 13-14); freedom of expression (World Bank Group
2013g); a perception of low corruption and high ability to combat corruption
(MCC 2012, 16-18); a high quality of public and civil services (MCC 2012, 18-20;
World Bank Group 2013d); a legitimate legal system (MCC 2012, 20-21; World
Bank Group 2013f); democratic governance (MCC 2012, 13-14); and expanded
organizational, human, and economic rights (MCC 2012, 15-16).
B. Indicator Rates of Reform
For each country, we devise the rates of reform in each policy area by calculating
two-year differences in indicator scores. A two-year difference helps to remove
yearly fluctuations that might be present in single-year differences. Using
differences at intervals of three or more years decreases the level of detail by
which we can do the analysis, particularly in regard to changes over time.
For 15 of our 20 indicators, a positive rate of reform means a country is
improving its policies and getting closer to (or is passing) the threshold to become
eligible for an MCC poverty-reduction compact. For example, a rate of reform of
0.5 for the Control of Corruption indicator for 2004-2006 indicates a positive
policy change. For the other five indicators, Cost to Start Business, Time to Start
Business, Inflation, Cost to Register Property, and Time to Register Property,
reform occurs when the original scores are negative. For instance, a lower number
of days to start a business is regarded as better than a higher number. So, we
multiply the rates of reform of those five indicators by negative one (-1) for
consistent interpretation: the greater the value, the better that country performs on
the indicator.
Appendix B summarizes statistics of the 20 indicator rates of reform in this study.
6
C. Time Correlation
Changing and implementing national policies takes time. In our data we look at
98 countries in four time periods (2002-2004, 2004-2006, 2006-2008, and 20082010). The presence of autocorrelation within each country must be expected.
That is, the policy a country has in one time period is likely to influence policy in
the following periods. We examine autocorrelation by calculating the correlation
of residuals from the regression of the dependent variable of interest to the time
periods. We do this to help understand what exactly is affecting changes in the
rates of reform of indicators over time.
Appendix C gives an overview of the autocorrelation within 12 of the indicators.
The autocorrelation does not seem to be very strong as a whole, meaning a
country’s policy does not have much influence on later policy. First, for most
indicators, we observe that the correlation of the rates of reform of any two
periods for a given indicator decreases the further apart in time the two periods
are from each other. This decrease in correlation is consistent with real-world
experience that impacts of policy reform generally tend to fade with time. Second,
we observe a high correlation for a few indicators. For example, for Fiscal Policy
(figure C1) we see that the periods 2004-2006 and 2006-2008 have a correlation
as high as 0.85 (where “1” is perfect correlation). This high correlation may be
due to a connection between the time needed for policy reform to be implemented
and enforced. Yet the high correlation usually lasts no more than three time
periods.
D. Indicator Mobility
To determine how quickly one policy indicator can change compared to others,
we examine indicator mobility by calculating indicator rank change. Since each
indicator score has its own scale, we standardize by ranking the indicator scores
for the 98 countries every two years. For example, for Control of Corruption, we
rank the countries according to their score when the MCC was established, 2004.
We then re-rank all countries every two years thereafter according to how much
their ranking has changed. A country that has a relatively larger increase (or
decrease) in its Control of Corruption score is ranked higher (or lower) than a
country that has not changed its Control of Corruption score much from one year
to the next.
In figure 1 we present the 10 countries that change the most for each of the 20
indicators. For example, the 10 countries with the highest change in ranking for
Control of Corruption are represented, as are the 10 countries that change most in
rank for Inflation, and for Education Expenditures, and so forth. These countries
are not necessarily the same for each indicator.
7
Figure 1: Indicator Mobility: Indicator Rank Change
10 Countries that Change the Most for Each of the 20 Indicator Scores
Note: The lighter the color, the faster the indicator score changes
Source: Authors’ calculations
The shades in the figure reflects an indicator’s mobility, so the lighter the color
the more mobile the indicator. The Economic Freedom indicators are the lightest
in color, followed by the Investing in People and Ruling Justly indicators, which
implies that the Economic Freedom indicators generally have more mobility. This
pattern might also mean indicators in the Economic Freedom category are less
stable and easier to change. Inflation shows the fastest rate of change, followed by
Time to Start Business and Credit Depth. Indicators in the Investing in People and
Ruling Justly categories are darker in color, which suggests that these indicators
are more difficult to change.
Another way of presenting indicator mobility is to average all the changes in
ranking for each indicator. An indicator with a higher average score reflects
8
greater mobility across our data. These average scores are presented in table 1.
The expectations of change in ranking for indicators in the Economic Freedom,
Investing in People, and Ruling Justly categories are 13.55, 9.60, and 5.97
respectively. This result confirms the impression that the Economic Freedom
indicators are more mobile in our data than the other two indicator categories.
Ruling Justly
Investing
in People
Economic Freedom
Table 1. Indicator Mobility: Average of Indicator Rank Change
Time to Start
Business
17.45
Average Credit Depth
Legal Rights
Cost to Start Business
13.55
12.16
9.70
Fiscal Policy
Inflation
5.76
24.82
12.3
Time to Register
Property
15.03
Cost to Register
Property
Regulatory Quality
Trade Freedom
7.17
20.12
Health Expenditures
Immunization Rate
Education
Expenditures
8.44
5.03
Average 11.03
Girls’ Primary
Education
9.60
14.08
10.84
Average Civil Liberties
Control of Corruption
5.97
4.35
4.59
Political Rights
Rule of Law
6.34
Source: Authors’ calculations
5.97
Government
Effectiveness
8.76
Voice and
Accountability
5.80
E. Correlation between Indicators
In appendix D, we plot correlations between indicator scores and between rates of
reform for each of our 98 countries. Low correlations would suggest that indicators
are independent from each other and are not closely related, and vice versa.
The Economic Freedom indicators have low correlations in indicator scores and
rates of reform. For the rates of reform, only one correlation of indicator scores is
above 0.3; the correlation between Cost to Start Business and Regulatory Quality
is 0.61.
For the Investing in People category, the correlations of the indicator scores are
above 0.3 for three of the six indicators; however, only the correlation between the
rates of reform for Girls’ Primary Education and Health Expenditures is above 0.3.
The Ruling Justly indicators have the highest correlations in terms of both
indicator scores and rates of reform. The indicator score correlations for 10
out of 15 indicator combinations are close to, or above 0.3, implying that these
indicators have strong linear relationships and are somewhat interdependent. The
correlations of the rates of reform for seven out of 15 indicator combinations are
also close to, or above 0.3.
9
These different correlation structures of the indicator categories have three
implications. The first is that the movement of an indicator is not always isolated;
some indicators move as a cluster. For Ruling Justly, high correlations indicate
that the indicators move or change together, meaning improving one indicator
results in an improvement in others. Economic Freedom is the opposite. For
example, Time to Register Property has an extremely low correlation with
Inflation; this relationship is reasonable because we would not expect the time it
takes to register property to increase if the inflation rate increases.
Second, moving one indicator appears easier than moving a bundle of indicators.
This movement of individual indicators is another possible reason for the active
ranking change in Economic Freedom indicators we observe. At the same time,
this pattern of movement suggests the existence of a potential to manipulate the
scores of the indicators in the Economic Freedom category with greater ease, at
least in the short run. On the other hand, thorough political reform should create
better results in the Ruling Justly category in the long run.
The third implication is that the MCC’s indicators in the Ruling Justly category
may be redundant because of the high correlations across indicators. The Ruling
Justly indicators are not likely to change; the high correlations reinforce their
immutability.
III. Which Countries Have Incentives?
In this section we briefly discuss groups of countries. First, we take an initial look
at whether an analytical division should be made based on country income levels.
Then, we discuss conjectures on reasonable treatment groups to explore the MCC
incentive effect. While we are calling these groups of countries “treatment groups,”
we do not believe we have a “natural experiment.” Rather, we consider our
analysis to be a “quasi-experiment” because we select the treatment groups
specifically to test for the MCC incentive effect; the groups are not randomly
assigned (Remler and Van Ryzin 2011). Finally, we look at potential control
variables.
A. LICs versus LMICs
We initially planned to analyze LICs and LMICs as one group.6 However,
because the MCC distinguishes between LICs and LMICs, we investigate whether
a distinction between the two gives supplementary information about the MCC
incentive effect. Based on analysis of the number of individual indicators for
which each country meets minimum required scores, we conclude that enough of
a difference exists between LICs and LMICs to justify separate analyses of each
group of country, in addition to analyzing all countries as one group. See
appendix E for graphs showing how the 69 LICs and 29 LMICs performed.
10
B. Treatment Groups
We investigate the MCC incentive effect in multiple ways. We find it reasonable
to assume that all countries do not respond the same way to the same incentives.
We posit that some countries have greater incentive than others to change policies
in accordance with MCC criteria and selection indicators. Countries that we
believe to be more likely to exhibit the MCC incentive effect constitute our
treatment groups, and the remaining countries constitute the corresponding
control groups.
No clear theoretical or empirical guidance favors one treatment as more realistic
than any other. We present the following 10 treatments as reasonable conjectures,
then analyze five of them in detail.
1. Qualitative MCC Incentive Effect
We test the countries that show qualitative signs of the MCC incentive effect to
determine if those countries also show quantitative evidence of such an effect. We
identify 31 countries in this treatment group (as noted in appendix F) based upon
our review of MCC literature and discussions with MCC employees (MCC 2008;
Hayes-Birchler 2013).
2. Bordering the Eligibility Threshold
We postulate that countries that are bordering the MCC eligibility threshold have
stronger incentives to change policies than do countries that are not close. We
arbitrarily define this incentivized group as countries that are +/- 3 passing
indicators within the threshold. For instance, in 2004, countries must pass
minimum required scores on eight indicators to meet the eligibility threshold;
therefore, our treatment group consists of countries that pass the required scores
on five to 11 indicators. We conjecture that countries reaching required scores for
five to seven indicators may have greater incentive to change their policies
(compared to other LICs and LMICs) because they have fewer improvements to
make to be considered eligible to enter into poverty-reduction compacts with the
MCC. Countries that meet required scores on eight to 11 indicators may have
greater incentives to change their policies (compared to other LICs and LMICs)
because they need to maintain their eligibility.
We created this treatment group by compiling a database with the number of
MCC indicators meeting required scores (obtained from MCC scorecards that
summarize how each country performed on the indicators for a given fiscal year)
for each country that the MCC evaluated each year since it began operations in
2004. We then used computational software to determine which countries fell
within +/- 3 of the eligibility threshold during each year. We also note in our
database whether each country passed Control of Corruption each year, which is
mandatory to become compact eligible, as we discuss below.
11
3. Passing Control of Corruption
In addition to passing the eligibility threshold, countries need to fulfill some other
compulsory requirements to be truly eligible for MCC aid. Meeting the minimum
required score for the Control of Corruption was the only compulsory requirement
until 2013, when meeting required scores on either Political Rights or Civil
Liberties became compulsory as well (MCC 2012). The MCC requires
performance above the median of all countries within a given income group for
Control of Corruption for a country to be considered eligible for a compact.
Several countries meet required scores on enough indicators, but are ineligible for
funds because they fail to meet the MCC’s standard for Control of Corruption.
We conjecture that a country may lack an incentive to pass other standards if it
has not (and potentially cannot) pass the Control of Corruption benchmark.
Additionally, Öhler, Nunnenkamp, and Dreher (2010) focus on Control of
Corruption in their study of the MCC incentive effect, so we include this
treatment to allow for comparisons between our conclusions and theirs.
4. Threshold Partnership
Another treatment group we define is countries that have received funding from
the MCC as threshold partners. These countries receive MCC funding and
assistance to help surmount the final hurdles to become eligible to enter into full
poverty-reduction compact. Upon advice from MCC staff, we conjectured that
if these partnership programs were efficient, one would expect the countries in
a threshold partnership with the MCC to have greater incentives to change
policies than other LICs and LMICs.
These countries are subject to economic incentives driven by direct cooperation
with the MCC (ex-ante rewards) in addition to the MCC incentive effect (ex-post
rewards). Since the threshold partnership countries are receiving financial and
direct support to improve policies and become eligible for MCC aid, the
partnership may contribute to a positive bias in our quantitative evidence of
the MCC incentive effect. However, quantitatively distinguishing between the
ex-ante rewards incentives and the MCC incentive effect is difficult. To this
extent, our analysis considers these countries similar to the Bordering the
Eligibility Threshold treatment, and therefore worthy of scrutiny to see
if they portray similar signs of the MCC incentive effect.
5. Government Expenditures
We can think of government behavior as similar to a human agent whose
expenditures have certain financial constraints. Thus we would expect that
governments generally want to increase revenue. However, the desire to increase
revenue through MCC aid is in turn dictated by a government’s overall
expenditure and ability to generate revenue through other means. Based on this
line of thought, we divided countries into quartiles according to General
Government Consumption Expenditure (U.S. Dollars) from World Bank Group
(2013a) data and created three treatment groups, listed below.
12
5a. Countries in the Middle Range of Government Expenditures
We expect the countries in this group, countries in the second and third quartiles,
to have incentive to pursue MCC funding.
5b. Countries in the Highest Quartile of Government Expenditures
We expect the countries in this group may have so much invested domestically
that they are unwilling to seek or unlikely to care about MCC funding.
5c. Countries in the Lowest Quartile of Government Expenditures
We expect the countries in this group do not have the ability or do not care to
engage in activity that would likely lead to the policy reforms needed to become
eligible for MCC aid.
6. Bordering the Eligibility Threshold and Passing Control of Corruption
This treatment group includes countries which are bordering the eligibility
threshold (treatment 2 group) and meet required scores for Control of Corruption
(treatment 3 group).
7. Bordering the Eligibility Threshold or Passing Control of Corruption
This treatment group includes countries which are bordering the eligibility
threshold (treatment 2 group) or meet required scores for Control of Corruption
(treatment 3 group).
8. Passing or Falling Short of the Eligibility Threshold
Although the theoretical basis for inclusion as a treatment seems weaker to us, we
test treatment groups defined by whether countries have or have not passed the
eligibility threshold of meeting required scores on at least eight indicators.
Treatment 8a consists of countries with eight or more indicators that meet or
exceed required scores. Treatment 8b consists of countries with fewer than eight
indicators that meet or exceed required scores. Countries that have not passed the
eligibility threshold have different incentives than those that have. As these are
essentially opposite groups, we would expect to find essentially opposite results.
C. Additional Control Variables
We created a short list of control variables to account for factors that may affect a
country’s performance.
The country-level control variables we gathered from the World Bank Group
(2013a) include: gross domestic product (GDP), per–capita GDP, general
government final consumption expenditures, population, and tax revenue. We
additionally control for the year for each of these control variables.
Through statistical variable selection methods (backward selection and Akaike
information criterion), we ultimately decided to control for GDP and population
as exogenous variables. We scaled down these two variables to make their effects
discernible. Also, we averaged values across the three consecutive years covered
13
in each of our rates of reform periods (i.e., for the 2004-2006 period we use the
average of the values in 2004, 2005, and 2006). We believe this approach better
reflects the period’s trends than would a snapshot of GDP and population at a
certain time point. We chose to control only for GDP and population in our
analyses due to concerns of endogeneity: some MCC indicators are built by
incorporating variables we considered for potential controls.
IV. Bearing Down and Moving Forward
Due to MCC’s internal information and staff members’ abundance of experience
in dealing with international development, we have sound reason to believe that
the countries identified in the Qualitative MCC Incentive Effect treatment
(treatment 1) are the most likely to display a quantifiable MCC incentive effect, if
it exists. Thus the treatment is an ideal approximation of which countries MCC
policy actually incentivizes. This approximation allows us to test our other
treatment groups, which we believe might exhibit the MCC incentive effect. We
test the validity of our treatment group conjectures by estimating the probability
that a country under each treatment is actually being incentivized to change its
policy, i.e., falling into treatment 1. The model is as below:
1 BeingIncentivized: BeinginTreatment1
α β ∗ 1 BeinginTreatmentGroup j
c∗X
(j ≠1)
(1)
Where 1(∙ is a function of whether the expression in the parentheses is true.
a country time specific variable. The results are shown in table 2:
14
is
Table 2. Correlation of Qualitative MCC Inventive Effect to Other Treatments
Dependent Variable: Qualitative MCC Incentive Effect
Covariates
(2)
Treatment
Population
GDP
Intercept
(3)
(4)
(5a)
(5b)
(5c)
(6)
(7)
(8)
-0.09
0.49***
0.72***
0.54***
-0.02
-0.61***
-0.10
0.62***
0.62***
(0.14)
0.11
(0.13)
-2.13*
(1.29)
-0.37***
(0.10)
(0.14)
0.13
(0.12)
-2.28*
(1.26)
-0.71***
(0.11)
(0.16)
0.15
(0.15)
-2.69*
(1.47)
-0.58***
(0.08)
-0.14
-0.09
-0.07
(0.17)
-0.12
(0.07)
(0.15)
-0.06
(0.06)
-0.68***
-0.1
-0.43***
(0.08)
-0.25***
(0.08)
(0.15)
0.11
(0.14)
-2.17*
(1.31)
-0.39***
(0.08)
(0.17)
0.11
(0.14)
-2.33*
(1.33)
-0.90***
(0.16)
(0.14)
0.11
(0.14)
-2.38*
(1.33)
-0.80***
(0.12)
390
390
390
Observations 390
386
390
392
392
392
Note: Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Source: Authors’ calculations
Treatment 2: Bordering the Eligibility Threshold
Treatment 3: Passing Control of Corruption
Treatment 4: Threshold Partnership
Treatment 5a: Countries in the Middle Range of Government Expenditures
Treatment 5b: Countries in the Highest Quartile of Government Expenditures
Treatment 5c: Countries in the Lowest Quartile of Government Expenditures
Treatment 6: Bordering the Eligibility Threshold and Passing Control of Corruption
Treatment 7: Bordering the Eligibility Threshold or Passing Control of Corruption
Treatment 8: Passing or Falling Short of the Eligibility Threshold
The countries that display qualitative evidence of the MCC incentive effect are
statistically positively correlated with five treatments: Passing Control of
Corruption, Threshold Partnership, Countries in the Middle Range of Government
Expenditures, Bordering the Eligibility Threshold or Passing Control of
Corruption, and Passing or Falling Short of the Eligibility Threshold. The
countries that display qualitative evidence of MCC incentive effects are
statistically negatively correlated with one treatment: Countries in the Lowest
Quartile of Government Expenditures.
Countries in the Middle Range of Government Expenditures and Countries in the
Lowest Quartile of Government Expenditures both statistically correlate with the
countries that display qualitative evidence of the MCC incentive effect, but with
opposite signs, and Countries in the Highest Quartile of Government
Expenditures has no statistical correlation. These results coincide with our
conjecture that countries with large expenditures are not incentivized, and
countries with low government expenditures lack the power or political capacity
to comply. Countries with government expenditures in the middle range react to
MCC actively.
Two other treatment groups have no statistical correlation with the countries that
display qualitative evidence: countries in the Bordering the Eligibility Threshold,
and countries in the Bordering the Eligibility Threshold and Passing Control of
Corruption.
15
Partially due to this initial comparison, we selected five treatments for further
investigation: Qualitative MCC Incentive Effect, Bordering the Eligibility
Threshold, Passing Control of Corruption, Threshold Partnership, and Countries
in the Middle Range of Government Expenditures. We keep the Bordering the
Eligibility Threshold treatment based on its highly plausible assumptions and
client interest. Although we present the Qualitative MCC Incentive Effect group
here as an approximation of incentivized countries, we use this group as a
treatment (like the others) in further analysis.
Appendix F lists the LICs included in each of these five treatments (table F1) and
LMICs (table F2).
V. Preliminary Analyses
In this section, using two nonparametrc estimation approaches for preliminary
analysis of our initial sample with all LICs and LMICs, we find some differences
among our five treatment groups and their corresponding control groups. All of
the treatment groups show distinctively different trends compared to the control
groups for the vast majority of the indicators. These results provide further ground
for a quantitative analysis of the MCC incentive effect based on our treatment
groups using more sophisticated methodology, which we do in Section VII.
A. Nonparametric Approach
Plotting the data for all 98 countries in our sample by indicator and treatment
using locally weighted scatterplot smoothing (LOWESS), we initially compare
the treatment and control groups. A merit of LOWESS is that it does not depend
heavily on any model assumptions; rather, it allows us to provide a visual
presentation of the data (Cleveland 1979). The rates of reform of countries show
how much their indicator scores change from one time period to another. If no
extraordinary events cause the rates of reform of our treatment and control groups
to change from 2002 to 2010, holding other factors equal, one would expect the
rates of reform for both groups to follow the same trend. Visually, the trend lines
in the LOWESS scatterplot would be parallel to one another.
By plotting the indicator rates of reform for all treatment and control groups, we
find three trends. Some indicators in some treatment groups show trends that
suggest the MCC incentive effect exists and is influencing countries in the
treatment groups to perform better on indicators over time than the control groups.
For other indicator scatterplots, we find mixed or no evidence of the MCC
incentive effect. And yet for others, we find what seems to be a “negative” effect,
where countries in our treatment groups appear to perform worse over time on the
indicator than the countries in the control groups.
We plotted rates of reform of each country, for all 20 indicators, and for all five
treatment groups that display qualitative evidence of the MCC incentive effect
16
(100 scatterplots). In figures 2, 3, and 4, we present three scatterplots for Control
of Corruption to illustrate the three general trends we see for the treatment groups:
the MCC incentive effect, a mixed or nonexistent effect, and a negative effect.
In figure 2, the Bordering the Eligibility Threshold treatment shows distinctively
different rates of reform for Control of Corruption when compared to its control
group.
Indicator Rates of Reform Score
Figure 2. Qualitative MCC Incentive Effect:
Bordering the Eligibility Threshold Treatment
Rates of Reform for Control of Corruption
Time Period
Source: Authors’ calculations
The treatment group (solid diamonds and dashed line) has a lower score on
Control of Corruption than does the control group (circles and solid line) prior to
the establishment of the MCC. As time passes, treatment group countries seem to
improve faster and better on their Control of Corruption indicator rates of reform
score than do the control group countries, as indicated by the dashed line
17
surpassing the solid line for 2004-2006 and diverging further from the solid line
for 2006-2008.
This improvement suggests the MCC incentive effect can be found in countries in
the Bordering the Eligibility Threshold treatment group for Control of Corruption.
Our data also suggest in this example that prior to the establishment of the MCC,
the control group had higher rates of reform on policies related to Control of
Corruption than did treatment countries, as shown by the solid line appearing
above the dotted line for 2002-2004. This initial difference in the rates of reform
indicates that policy reform has slowed or worsened in the countries in the control
group. In other words, the indicator scores of countries that we do not believe
might be subject to the MCC incentive effect are improving slower than are
scores of the counterpart countries that border the MCC eligibility threshold.
In figure 3, the Qualitative MCC Incentive Effect treatment displays a mixed or
nonexistent effect for Control of Corruption when compared to the control group.
Indicator Rate of Reform Score
Figure 3. Mixed or Nonexistent Effect:
Qualitative MCC Incentive Effect Treatment
Rates of Reform for Control of Corruption
Time Period
Source: Authors’ calculations
18
The rates of reform for the countries for the Qualitative MCC Incentive Effect
treatment group are higher than the rates of reform for the control group countries
in 2002-2004. However, the rates of reform for the treatment group decline in
2004-2006, rise above the control group in 2006-2008, and decline again in 20082010. The overall trend for the treatment countries does not show any consistent
direction of change for Control of Corruption, while the control group’s consistent
near zero scores throughout 2002-2010 implies no change.
In figure 4, the Countries in the Middle Range of Government Expenditures
treatment displays a negative effect for Control of Corruption when compared to
the control group.
Indicator Rates of Reform Score
Figure 4. Negative Effect:
Countries in the Middle Range of Government Expenditures Treatment
Rates of Reform for Control of Corruption
Time Period
Source: Authors’ calculations
19
The rates of reform for the Countries in the Middle Range of Government
Expenditures treatment group is at first significantly higher than control group
countries for Control of Corruption in 2002-2004. The rates of reform then
decline in the later time periods as they converge with the control group’s rates.
The different starting points for the lines in figures 2, 3, and 4 suggest that the
natural rates of reform, those that would occur without MCC incentives, might
differ for the treatment groups compared to their control groups. If we assume that
treatment group countries are subject to the MCC incentive effect and that control
group countries are not, and the two lines diverge in later time periods, the MCC
incentive effect likely exists (as seen in figure 2). If the dashed line and the solid
line in later time periods, on average, move together or not at all, there is likely no
MCC incentive effect (as seen in figure 3). When the two lines converge (as seen
in figure 4), the MCC incentive effect is negative for the treatment group
countries in the sense that their rates of reform are slowing down.
B. Basic Rates of Reform Mean Comparison
In our second nonparametric approach, we compare the expectation of change
for the rates of reform of the treatment groups to the control groups through the
mean of the rates of reform of all countries for each treatment, respective control,
indicator, and time period. If the MCC incentive effect exists for the treatment
group, the average rates of reform should increase for treatment groups and
remain the same for the control groups. On the contrary, we find that the average
rates of reform for the control groups differ in the three periods. In other words,
the control groups we established also exhibit different trends for the various
indicators. At the same time, however, the variation for the control groups is
smaller than the variation for the treatment groups.
For the basic rates of reform mean comparison we also find three trends—
the MCC incentive effect, a nonexistent or mixed effect, and a negative effect
(where countries perform worse). Again, we calculate but do not present all 100
comparisons for the 20 indicators and the five treatment groups; instead, we
choose the same indicator and treatment group as presented in figures 2, 3, and 4.
20
In figure 5, the mean rates of reform of Control of Corruption for the Bordering
the Eligibility Threshold treatment show an example of the MCC incentive effect.
For the treatment group, the difference in average rates of reform for the second
time period after the MCC was established (2006-2008) and the baseline period
(2002-2004) is +0.04. For the control group, the change from 2002-2004 to 20062008 is -0.06. The difference in these two differences is +0.1. The positive
number implies the MCC incentive effect exists. By similar calculation, the MCC
does not provide an incentive in 2004-2006, but does in 2008-2010, as compared
to 2002-2004.
Time Period
Figure 5. Qualitative MCC Incentive Effect:
Bordering the Eligibility Threshold Treatment
Mean Rates of Reform for Control of Corruption
Mean Rates of Reform Score
Note: Values presented next to each bar in the figure are rounded to the nearest second decimal.
Source: Authors’ calculations
21
In figure 6, the mean rates of reform of Control of Corruption for the Qualitative
MCC Incentive Effect treatment show an example of a mixed or nonexistent
effect. The treatment group illustrates a change of 0.00 from 2002-2004 to 20062008, while the control group’s change is -0.01. The difference between the two is
+0.01, which weakly indicates existence of the MCC incentive effect. However,
for 2008-2010 the control group’s change is -0.03 from 2002-2004, while the
treatment group’s change is -0.07, making the difference-in-difference estimate 0.04 and suggesting the existence of a negative effect. The oscillation between
positive and negative results indicates a mixed or nonexistent MCC incentive
effect.
Time Period
Figure 6. Mixed or Nonexistent Effect:
Qualitative MCC Incentive Effect Treatment
Mean Rates of Reform of Control of Corruption
Mean Rates of Reform
Note: Values presented next to each bar in the figure are rounded to the nearest second decimal.
Source: Authors’ calculations
22
In figure 7, the mean rates of reform of Control of Corruption for the Countries in
the Middle Range of Government Expenditures treatment show an example of a
negative effect. For the treatment group, the difference between 2006-2008 and
2002-2004 is -0.04. The control group experiences a change of +0.02. The
difference in these two differences is -0.06, which implies a negative MCC
incentive effect. Similar calculations show the effect to be negative for the other
two time periods.
Time Period
Figure 7. Negative Effect: Countries in the Middle Range
of Government Expenditures Treatment
Mean Rates of Reform of Control of Corruption
Mean Rates of Reform
Note: Values presented next to each bar in the figure are rounded to the nearest second decimal.
Source: Authors’ calculations
VI. Models
In this section we describe three variations of the difference-in-differences (DD)
model we use to investigate the MCC incentive effect.
A. Rate of Reform: Difference-in-Differences Model 1
We adopt the DD model as applied by Johnson and Zajonc (2006) but with a
longer time horizon; we use data from 2002-2010 rather than 2000-2004. We
choose the DD model because of its relative ease of implementation and its ability
to control for fixed effects. The DD model is also widely used in estimating
causal relationships (Bertrand, Duflo, and Mullainathan 2002).
23
This DD model is written as follows:
Y
Y
α β ∗ period γ ∗ treatment
treatment ∗ period
c∗X
δ∗
(2a)
Or:
δ ∗ treatment ∗
R α β ∗ period γ ∗ treatment c ∗ X
period (2b)
where Y is the score of a particular indicator of a specific country in a given year
t, and R is the rate of change (i.e., rate of reform) of a country from year t to
year t+2. Period is a categorical variable indicating into which two-year period
(between 2002 and 2010) the data fall. Treatment is a categorical variable
indicating whether a country is in the treatment group during period t. X stands
for country-specific characteristics we believe may influence a country’s rate of
reform. The coefficientδ, also called the DD estimator, measures the MCC
incentive effect.
The DD estimator is expressed as:
DD
E Y
Y
Y
Y
Y
Y
Y
Y
(3)
For our analysis, we set the baseline as year 2002; other years used are 2004,
2006, 2008, and 2010. We examine four time periods. The MCC was announced
in 2002 but did not begin operations until 2004; therefore, 2002-2004 is an
appropriate baseline or control period. The other three periods we use are: 20042006, 2006-2008, and 2008-2010.
The differences subtract all fixed effects. The first subtraction of indicator scores
of a given country leaves us with the rate of reform ~
Y
Y . Our
model uses a two-year difference of the indicator scores to reduce the possibility
of a single-year endogenous shock affecting our results; for example, a bad
growing season may cause the inflation rate to increase and then recover in the
following year. The second subtraction removes fixed time effects of the rate of
R
. This second subtraction is also known
reform for each groupR
as the natural rate of reform; in other words, it is the rate of reform in the absence
of the MCC. The third difference is the subtraction of the change of natural rate of
reform of the control group from the natural rate of reform change in the
treatment group. This subtraction accounts for the possibility that countries in the
treatment groups might have differences in their rates of reform compared to
countries in the respective control groups. The third difference, if it statistically
exists, is where we would find the MCC incentive effect, as seen through the
DD ~ estimator.
24
B. Likelihood of Improvement: Difference-in-Differences Model 2
We use a probit regression model to estimate the likelihood of a given country to
improve on a given indicator. We refer to the results from this model as Likelihood
of Improvement. This model’s output is originally in terms of the change in the
indicator’s standard deviation. Our statistical software converts the standard
deviation into a probability for easier interpretation. For this model, we adopt an
approach similar to Johnson and Zajonc (2006) by defining a DD estimator as:
DD
E1 Y
Y
1 Y
Y
1 Y
Y
1 Y
Y
(4)
where 1{∙} is a function of whether the expression in the parenthesis is true.
Given a particular period, when a country shows a positive change, i.e.,
improvement in performance on an indicator, a 1 is assigned. A zero is assigned
otherwise. Other variables are the same as described in sub-section A.
C. Proportion of Indicators Improved: Difference-in-Differences Model 3
In our third model we calculate the number of improved indicators as a proportion
of all indicators in a given period (Johnson and Zajonc 2006). If the MCC
incentive effect exists, the treatment group should have a larger proportion of
improved indicators compared to the control group.
By employing the following calculation (equation 5), we combine all of the MCC
indicators into one variable.
P
∑ 1 Y,
Y,
(5)
The DD estimator for this outcome is:
DD
E P
P
P
P
(6)
D. Assumption and Caveat
We assume that the rates of reform of indicators for one country are independent
of the rates of reform for other countries within the same treatment or control
group. This assumption is intuitively true; one impoverished country’s domestic
policy is not likely to be correlated with that of another impoverished country,
especially for the good governance indicators. For example, policies to control
corruption in China have little or no effect on the same policy in Indonesia. A
potential problem would be that some economic indicators, such as inflation rates,
might not satisfy this assumption, due to economic globalization. The magnitude
and direction of such effects are unknown; therefore, as a simplifying assumption
we ignore this possibility in our analysis.
A major caveat to our analysis is that, as already stated, we do not have a “natural
experiment.” We consider this a “quasi-experiment,” because we select the
treatment groups to investigate whether the MCC incentive effect exists; our
results may contain selection bias (Remler and Van Ryzin 2011).
25
VII. Difference-in-Differences Analysis for All Sample Countries
In this section we present the results from our three DD models using our initial
sample with all LICs and LMICs. In each sub-section we present and discuss
statistically significant results and briefly summarize other results. Tables
presenting all the results can be found in appendix G.
Table 3 summarizes the number of indicators with statistically significant results
identifying the MCC incentive effect for all the countries. We disaggregate results
for each model by the three indicator categories, the five main treatment groups,
and whether the analysis suggests the existence of a positive (the MCC incentive)
effect or a negative effect. The total number of positive and negative results by
indicator category for each of the models appears at the bottom of the table.
26
Table 3. Number of Positive and Negative Results of Three Models for Three Categories of Indicators
Model 1
Rate of Reform
Economic
Freedom
Investing
in People
+
–
+
–
+
1: Qualitative MCC Incentive
Effect
2
1
1
1
2: Bordering the Eligibility
Threshold
1
0
0
3: Passing Control of
Corruption
2
3
4: Threshold Partnership
0
5a: Countries in the Middle
Range of Government
Expenditures
Totals of Positive
and Negative Results
Treatment
Model 3
Proportion of
Indicators Improved
Model 2
Likelihood of Improvement
Ruling
Justly
Economic
Freedom
Investing
in People
Ruling
Justly
Economic
Freedom
Investing
in People
–
+
–
+
–
+
2
3
2
0
3
0
0
2
0
0
0
0
0
0
2
0
1
2
2
1
3
2
1
0
3
2
2
1
0
2
8
8
4
5
8
6
Ruling
Justly
–
+
–
+
–
+
–
2
3
0
0
0
0
0
1
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
2
1
0
1
0
1
0
0
1
0
3
0
0
1
0
0
1
0
0
0
4
2
6
2
7
5
0
1
1
1
0
1
All results: 38+ / 31+
–
+
–
+
–
+
–
+
–
+
–
+
–
+
–
+
–
Source: Authors’ Calculations
Economic Freedom Indicators: Credit Depth, Legal Rights, Cost to Start Business, Time to Start Business, Fiscal Policy, Inflation, Time to Register Property, Cost to Register Property, Regulatory
Quality, Trade Freedom
Investing in People Indicators: Girls’ Primary Education, Health Expenditures, Immunization Rate, Education Expenditures
Ruling Justly Indicators: Civil Liberties, Control of Corruption, Government Effectiveness, Political Rights, Rule of Law, Voice and Accountability
27
A. Overview of Results
We recall here the two main results from the previous quantitative studies of the
MCC incentive effect. Johnson and Zajonc (2006) found the MCC incentive
effect, but it was not statistically significant. Öhler, Nunnenkamp, and Dreher
(2010) find a statistically significant effect on the one indicator they investigated
(Control of Corruption), but it diminished over time.
The overall results from our quantitative analysis do not provide strong support
for the existence of the MCC incentive effect. Of 645 possible instances of
statistical significance listed in appendix G, 38 (or 6 percent) exhibit a positive
and statistically significant MCC incentive effect. At the same time, there are
almost as many statistically significant negative results (32 results, or 5 percent).
Negative results (i.e., a negative effect) suggest that the rates of reform are
worsening in the treatment group countries compared to the respective control
group countries.
The negative results are not isolated to a particular model or treatment group in
this analysis. In relation to this pattern, three points are worth noting. First, even
though we present only the treatment groups that have the strongest qualitative
evidence and preliminary support for being affected by MCC incentives, we find
statistically significant results that suggest MCC incentives do not always prompt
countries to improve their performance on the various indicators. Second,
observers should ask whether a country’s receipt of MCC funds may affect the
presence of the MCC incentive effect. Third, we find an interesting phenomenon
in our models: when we exchange the treatment and control groups, the effect
often, though not always, switches sign accordingly in a statistically significant
way. For example, when we switch treatment and control groups in the Passing or
Falling Short of the Eligibility Threshold treatment (that is, observing results from
treatment 8a and 8b), almost every DD estimator changed signs. The near
symmetry of our model implies that the MCC has a trade-off between its policies.
Furthermore, the model can determine which countries the MCC incentive effect
will influence.
The Qualitative MCC Incentive Effect treatment group shows the most
statistically significant results (about one-third of all the significant results we
present). Fifty-seven percent of these quantitative results identify the MCC
incentive effect. The Countries in the Middle Range of Government Expenditures
treatment group has fewer statistically significant results, but of the 16 we find, 62
percent show the MCC incentive effect. The Bordering the Eligibility Threshold
treatment group has six significant results (5 percent of all possible results for that
treatment), but all of those results show the MCC incentive effect. Countries in
the Passing Control of Corruption treatment group do not really show better
results on improving policies than countries that are in the Passing Control of
Corruption control group. On one hand, the Control of Corruption indicator
correlates with other Ruling Justly indicators, so the treatment group might not
28
show results that differ from the control group because the treatment group has
met minimum requirements on a number of indicators. One could ask why a
country should improve a Ruling Justly indicator to get MCC funds if it has
already met requirements for Control of Corruption. On the other hand, since
Control of Corruption correlates with the other five Ruling Justly indicators, one
would assume that it would be easier for this group of countries to improve on
these indicators. That is, these countries have already passed the highest hurdle,
and the cost of improving the other scores, given that they might not be too far
away from the required scores, should therefore be relatively lower. In turn, one
might expect these countries to show stronger evidence of the MCC incentive
effect in our data, but they do not. The countries in the Threshold Partnership
treatment group show the lowest rate of result of the MCC incentive effect,
despite these countries being subject to economic incentives as well as the MCC
incentive effect. Twenty-nine percent of the statistically significant results we
present for Threshold Partnership suggest the MCC incentive effect.
Our indicator mobility analysis suggested that the Economic Freedom indicators
are the most volatile in our dataset. Table 3 only partially confirms those results.
The Economic Freedom indicators provide 33 percent of all significant results,
and 32 percent of the MCC incentive effect results. The Investing in People
indicators provide 28 percent of all significant results, and 29 percent of the MCC
incentive effect results. The Ruling Justly indicators provide 39 percent of all
significant results, and 39 percent of the MCC incentive effect results.7
In the following section we present the results for each indicator category, as well
as plausible conjectures for those results when possible. Although the overall
results are very mixed, with almost half the regressions with statistically
significant results finding a negative effect, we focus more on those results that
identify the MCC incentive effect.
B. Economic Freedom
The Economic Freedom category contains 22 statistically significant results in the
Rate of Reform and Likelihood of Improvement models, the most of the three
indicator categories. Table 4 displays statistically significant results for each of
the five treatment groups separated by the two models. The results are further
disaggregated by time period. Appendix G displays all results. (Table G1 shows
results for the Rate of Reform model, and table G2 shows results for the
Likelihood of Improvement model for the Economic Freedom category.)
29
Table 4. Statistically Significant Results for Economic Freedom
Treatment
Model
Rate of Reform
1: Qualitative
MCC
Incentive
Effect
2: Bordering
the Eligibility
Threshold
Indicator
2004-2006
2006-2008
Cost to Start Business
53.3**
(26.94)
Cost to Register Property
1.205**
2008-2010
(0.601)
Trade Freedom
-5.690*
(3.188)
Likelihood
of Improvement
Regulatory Quality
Rate of Reform
Trade Freedom
0.429***
0.456***
(0.146)
(0.144)
5.808*
(3.145)
Rate of Reform
0.509**
Legal Rights
(0.235)
Inflation
3.714*
(2.251)
Cost to Start Business
-50.2*
(26.13)
3: Passing
Control of
Corruption
Trade Freedom
-5.988*
-7.790**
(3.172)
Likelihood
of Improvement
(3.180)
0.208**
Cost to Register Property
(0.0981)
Fiscal Policy
-0.439**
(0.215)
Regulatory Quality
-0.288**
Time to Register Property
-24.41*
(0.141)
Rate of Reform
(14.45)
4: Threshold
Partnership
Credit Depth
-0.657**
(0.330)
Rate of Reform
Time to Start Business
Trade Freedom
5a:
Countries in
the Middle
Range of
Government
Expenditures
10.73*
11.75**
(5.640)
(5.508)
5.291*
(3.138)
Inflation
-4.346*
(2.447)
Regulatory Quality
-0.106*
(0.0592)
Likelihood
of Improvement
Cost to Start Business
0.229**
(0.109)
Source: Authors’ calculations
Standard errors in parentheses
***p<0.01, **p<0.05, *p<0.10
30
1. Qualitative MCC Incentive Effect
Qualitative MCC Incentive Effect treatment countries show a statistically
significant decrease for Cost to Start Business and Cost to Register Property in the
Rate of Reform model. Trade Freedom shows a negative effect, meaning trade
barriers have increased or reforms to loosen them have slowed in comparison to
the control group. One plausible reason is that the increase of private business
investments might have resulted in tightening trade barriers to protect the new
“producers” in the economy. Although consumers enjoy lower prices when trade
barriers are lower, many producers are not able to compete with the new world
prices of goods, and a decline in business investment is likely. Therefore, this
negative result for Trade Freedom is reasonable. The magnitude of the rate of
reform for Trade Freedom is notable. The coefficient suggests that countries in
this treatment will, on average, have a decrease of 5.7 on their scores compared to
countries in the group; this indicator is measured on a scale of 0 to 100. The
Likelihood of Improvement model shows statistical significance for the
Regulatory Quality indicator in 2006-2008 and 2008-2010 with coefficients of
0.429 and 0.456 respectively, which implies that the countries in this treatment
are on average 42.9 percent (45.6 percent for 2008-2010) more likely to improve
their Regulatory Quality score compared to countries in the control group during
the respective time periods.
2. Bordering the Eligibility Threshold
Bordering the Eligibility Threshold treatment countries show one result that is
statistically significant and positive in the Rate of Reform model: Trade Freedom
in 2008-2010, with an estimator of 5.8. The result implies that countries in this
treatment group, on average, would have an increase of 5.8 on their Trade
Freedom score. Also, we notice that the estimator for Trade Freedom has close to
the same magnitude as in the Qualitative MCC Incentive Effect treatment, but in
the opposite direction: reform is taking place to increase free trade. This change of
direction might occur because the countries in the Qualitative MCC Incentive
Effect treatment group could be in an earlier stage of economic development and
be seeking trade barriers to protect domestic business, while countries in the
Bordering the Eligibility Threshold treatment group could be in the process of
lowering trade barriers because they are in a later stage of development. Overall,
we find few statistically significant results for countries in this treatment group.
Our indicator mobility analysis suggested Economic Freedom indicators may be
easier to reform, so this lack of results could occur because countries that are
bordering the eligibility threshold have, on average, already met more minimum
requirements for in the Economic Freedom indicators than indicators in Investing
in People or Ruling Justly.
3. Passing Control of Corruption
Passing Control of Corruption countries have the greatest number of statistically
significant results, but they are mixed. Three indicators identify the MCC
incentive effect. Four indicators show a negative effect. Trade Freedom shows a
negative result in 2004-2006 and 2008-2010, but not 2006-2008; the rate of
31
reform for this indicator is not consistent over time. (The effect of time is
discussed in section VIII.B.) The combination of two possible reasons could
explain the mix of positive and negative results. First, we expect many reforms to
occur in the Economic Freedom category because these indicators are easier to
change than indicators in the Ruling Justly and Investing in People categories.
The countries in the Passing Control of Corruption treatment group passed the
minimum requirements for the Control of Corruption indicator and need to meet
the MCC’s overall eligibility threshold (at least half of indicators should meet
minimum requirements) to be considered compact-eligible. Because Economic
Freedom indicators are relatively easy to improve upon, we expect more changes
to take place in this category. Second, we believe a trade-off exists between
indicators within this category. For example, to encourage domestic business
investments, a country may increase trade barriers, which would show up as a
negative effect. These two reasons combined may explain the mixed evidence of
the MCC incentive effect in the Passing Control of Corruption treatment group.
4. Threshold Partnership
Threshold Partnership treatment countries have two statistically significant
negative results for Economic Freedom indicators in the Rate of Reform model;
Time to Register Property, on average, increases by 24.4 days and the score for
Credit Depth, on average, declines by 0.7 points (on a scale of 0 to 6) compared to
countries in the control group. No statistically significant results exist for the
Likelihood of Improvement model for this treatment. Overall, the MCC incentive
effect is not evident for the countries that have been receiving extra assistance
from the MCC to become eligible to enter into a poverty-reduction compact.
5. Countries in the Middle Range of Government Expenditures
The Countries in the Middle Range of Government Expenditures treatment shows
six statistically significant results. The Rate of Reform model shows positive,
statistically significant results for Time to Start Business for 2004-2006 and 20062008, implying that the time to start a business is declining. Similarly, Trade
Freedom shows a positive, statistically significant result for 2004-2006, implying
trade barriers are decreasing. The other positive, statistically significant result is
for the Cost to Start Business in the Likelihood of Improvement model with an
estimator of 0.229. This estimator means that countries in this treatment are 22.9
percent more likely to reduce the cost to start a business compared to the control
group countries.
C. Investing in People
The Investing in People category has 17 statistically significant results, almost as
many as Economic Freedom. Table 5 presents results for the three treatment
groups with statistically significant results separated by the Rate of Reform model
and Likelihood of Improvement model; the results are further disaggregated by
time period. Appendix G’s table G3 displays all results for Investing in People for
both models.
32
Table 5. Statistically Significant Results for Investing in People
Treatment
Model
Rate of Reform
Indicator
Girls' Primary Education
2004-2006
2006-2008
7.215***
2008-2010
(2.762)
1: Qualitative
MCC Incentive
Effect
Immunization Rate
-4.504**
(2.224)
Likelihood of
Improvement
Girls' Primary Education
Rate of Reform
Education Expenditures
0.353**
0.487***
0.366**
(0.166)
(0.164)
(0.178)
0.911**
(0.373)
Health Expenditures
-4.678**
-5.614***
(2.127)
4: Threshold
Partnership
(2.134)
Immunization Rate
-5.453**
(2.540)
Likelihood of
Improvement
Rate of Reform
5a: Countries in
the Middle Range
of Government
Expenditures
Health Expenditures
-0.459***
-0.435**
(0.165)
(0.166)
Girls' Primary Education
7.085**
5.815**
(2.733)
(2.752)
Immunization Rate
-3.880*
(2.125)
Likelihood of
Improvement
Girls' Primary Education
0.322*
0.354**
0.454**
(0.169)
(0.167)
(0.174)
Standard errors in parentheses
***p<0.01, **p<0.05, *p<0.10
Source: Authors’ calculations
1. Qualitative MCC Incentive Effect
Qualitative MCC Incentive Effect treatment countries show a statistically
significant, positive result for Girls’ Primary Education. The Rate of Reform
model shows an average increase in girls’ primary education of 7.2 percent for
countries in the Qualitative MCC Incentive Effect treatment group compared to
the countries in the control group. The Likelihood of Improvement model has
positive, statistically significant results for all three time periods for Girls’
Primary Education: a 35.3 percent likelihood of improvement in 2004-2006, a
48.7 percent likelihood of improvement for 2006-2008, and a 36.6 percent
likelihood of improvement for 2008-2010 for countries in the treatment group
when compared to countries in the control group.
2. Bordering the Eligibility Threshold
Bordering the Eligibility Threshold treatment countries have no statistically
significant results for the Rate of Reform or Likelihood of Improvement models.
We do not have a plausible conjecture as to why we find no results.
33
3. Passing Control of Corruption
Passing Control of Corruption treatment countries do not display any statistically
significant results in the Rate of Reform or Likelihood of Improvement models.
Again, we do not have a plausible conjecture as to why we find no results.
4. Threshold Partnership
Threshold Partnership treatment countries have statistically significant results;
however, all but one implies a negative effect. The only positive result is Girls’
Primary Education for 2006-2008 in the Rate of Reform model.
5. Countries in the Middle Range of Government Expenditures
The Countries in the Middle Range of Government Expenditures treatment group
has positive, statistically significant results for Girls’ Primary Education in the
Rate of Reform model for 2006-2008 and 2008-2010, and in the Likelihood of
Improvement model for all three time periods. These results imply that the
Countries in the Middle Range of Government Expenditure group are improving
the primary education completion rates for girls more than the control group
countries. The Immunization Rate indicator is statistically significant and negative
for 2006-2008. The estimator of -3.88 suggests that countries in the middle range
of government expenditure, on average, decrease immunization coverage by 3.88
percent compared to the countries in the control group. A 3.88 percent drop in
immunization rate coverage may not be much of a change in practical terms.
D. Ruling Justly
The Ruling Justly category has 26 statistically significant results for the Rate of
Reform and Likelihood of Improvement models. Table 6 displays the statistically
significant results for each of the five treatment groups separated by the two
models; the results are further disaggregated by time period. Appendix G displays
all results. (Table G4 shows results for the Rate of Reform model, and table G5
shows results for the Likelihood of Improvement model for the Ruling Justly
category.)
34
Table 6. Statistically Significant Results for Ruling Justly
Treatment
Model
Rate of Reform
Indicator
2004-2006
2006-2008
2008-2010
-0.117**
-0.213***
-0.181***
(0.0575)
(0.0579)
(0.0579)
Civil Liberties
1.566*
Political Rights
2.398**
Voice and Accountability
(0.878)
(1.146)
1: Qualitative
MCC
Incentive
Effect
Likelihood of
Improvement
Political Rights
0.366**
(0.139)
Control of Corruption
0.278*
(0.148)
Voice and Accountability
Rate of Reform
-0.295**
-0.442***
-0.324**
(0.145)
(0.142)
(0.145)
Control of Corruption
0.112*
(0.059)
2: Bordering
the Eligibility
Threshold
Voice and Accountability
Likelihood of
Improvement
3: Control of
Corruption
0.102*
(0.0553)
Rate of Reform
Rate of Reform
Control of Corruption
Government
Effectiveness
Civil Liberties
0.255*
0.322**
0.374***
(0.139)
(0.138)
(0.138)
0.138**
0.103*
(0.0553)
(0.0556)
2.307**
(1.006)
Political Rights
2.443*
(1.335)
Rule of Law
4: Threshold
Partnership
-0.124**
(0.0611)
Likelihood of
Improvement
0.427**
Control of Corruption
(0.170)
Political Rights
0.284*
(0.168)
Rule of Law
-0.357**
Rule of Law
-0.099*
(0.177)
Rate of Reform
5a:
Countries in
the Middle
Range of
Government
Expenditures
(0.0519)
Voice and Accountability
-0.174***
(0.0559)
Likelihood of
Improvement
Voice and Accountability
-0.788**
(0.374)
Standard errors in parentheses
***p<0.01, **p<0.05, *p<0.10
Source: Authors’ calculations
35
1. Qualitative MCC Incentive Effect
Qualitative MCC Incentive Effect treatment countries have positive, statistically
significant results for Civil Liberties and Political Rights for the Rate of Reform
model in 2004-2006. While these results are statistically significant, the
magnitude of the change in rate of reform may not be practically significant
because Civil Liberties is measured on a scale of 0 to 60, and Political Rights is
measured on a scale of 0 to 40. The estimators on these are 1.556 and 2.398,
implying that countries in this treatment group have, on average, an increase in
their scores of 1.556 and 2.398, compared to the countries in the control group.
2. Bordering the Eligibility Threshold
Bordering the Eligibility Threshold treatment countries have three statistically
significant results, all of which are positive. Voice and Accountability, and
Control of Corruption are statistically significant and positive for 2006-2008 in
the Rate of Reform model. Control of Corruption is statistically significant and
positive for all three time periods in the Likelihood of Improvement model. This
last result could be because the countries in this treatment group are close to or
have met the MCC’s eligibility threshold and may need to only meet the
minimum requirements for Control of Corruption to be considered eligible for a
poverty-reduction compact.
3. Passing Control of Corruption
Passing Control of Corruption treatment countries have only one indicator that is
statistically significant, Government Effectiveness. It is positive in two time
periods, 2004-2006 and 2008-2010. Countries in this treatment group should have
a higher score on the Ruling Justly indicators than the control countries because
Control of Corruption correlates strongly with most of the other Ruling Justly
indicators. Also, because of the high correlation among the Ruling Justly
indicators, one would assume that it would be easier, or less costly, for a country
that has already passed the Control of Corruption benchmark to improve and pass
other Ruling Justly thresholds. So, to see such limited results is somewhat
surprising. Yet, due to the correlation, some problems with endogeneity exist
within this treatment group.
4. Threshold Partnership
Threshold Partnership treatment countries have very similar results to countries in
the Qualitative MCC Incentive Effect treatment group. Threshold Partnership
countries have received economic support from the MCC to help surmount the
final hurdles to be considered eligible for a poverty-reduction compact. Most of
these programs are targeted toward improving Ruling Justly indicators (MCC
2013c), most notably Control of Corruption, which, again, is highly correlated
with other Ruling Justly indicators. In addition to any MCC incentives, these
countries receive economic incentives and receive direct “moral support” and help
from the MCC to improve their policy indicators. However, our data do not
provide quantitative documentation of the effects of these programs. Threshold
Partnership countries are exposed to several incentive effects to improve their
36
MCC selection indicators and are relatively close to passing the MCC eligibility
threshold; therefore, they should show evidence of the MCC incentive effect if
the effect exists. As we are not quantitatively able in our analysis to subtract the
economic incentives this group of countries is subject to, our numbers might be
positively biased. Yet, statistically these countries are not different from the
control countries.
5. Countries in the Middle Range of Government Expenditure
Countries in the Middle Range of Government Expenditures treatment group
have statistically significant results, however they are all negative. We do not
conjecture as to why here.
E. Results from Proportion of Indicators Improved Model
The Proportion of Indicators Improved model measures whether the MCC
incentive effect influences the proportion of indicators improved. Statistically
significant results are presented in table 7. A full summary of the results is listed
in appendix G, table G6. The merit of this method is that aggregation of indicators
allows us to locate the MCC incentive effect in a bigger framework,
not only indicator by indicator as in the Rate of Reform, or Likelihood to
Improve models.
In this section, we delve into the Proportion of Indicators Improved model for
four groups of indicators: all indicators together and the three indicator categories:
Economic Freedom, Investing in People, and Ruling Justly. Because aggregating
20 indicators results in the largest number of missing values, a missing value for
any one of the 20 indicators would contaminate the aggregated results by causing
a loss of a fraction of information. Thus we treat missing values conservatively:
whenever a missing value appears, we assume the indicator has not improved.
This procedure comes at the cost of a possible downwardly biased MCC incentive
effect estimator. Also, for the dependent variable, we apply a logarithmic
transformation to get a better linear relationship. (The interpretation of coefficient
is, therefore, the percentage change of the proportion of indicators improved.)
Table 7: Statistically Significant Results for Proportion of Indicators Improved Model
Treatment
Model
Indicator Category
1: Qualitative
MCC Incentive
Effect
Proportion of Indicators
Improved
Ruling Justly
4: Threshold
Partnership
Proportion of Indicators
Improved
All Indicators
Economic Freedom
Investing in People
5a: Countries in
the Middle
Range of
Government
Expenditures
Proportion of Indicators
Improved
Investing in People
Source: Authors’ calculations
2004-2006
2006-2008
2008-2010
-0.101*
-0.0776*
-0.0548**
-0.00854**
-0.134***
0.0906**
Standard errors in parentheses
***p<0.01, **p<0.05, *p<0.10
37
The results are concentrated in two groups: negative results for the Threshold
Partnership treatment group and a positive result for Countries in the Middle
Range of Government Expenditures. For the Threshold Partnership treatment, all
the statistical significant results we find are negative. For all indicators in 20082010 there is a decrease of 5 percent, for the Economic Freedom indicators there
is a decrease of 0.8 percent, and for the Investing in People indicators there is a
decrease of 13 percent. For the Countries in the Middle Range of Government
Expenditures treatment, although there are no statistically significant results for
any of the time periods, the joint F test yields positive results that are significant
at the 0.1 level, suggesting the MCC incentive effect exists. Although the
direction of the MCC incentive effect is hard to discern without significance at the
individual indicator level, considering that we have awarded all the missing
values “no indicator performance improvement,” our data is likely to be
negatively biased: the MCC incentive effect may be positive for the Countries in
the Middle Range of Government Expenditures treatment group.
VIII. DD Analysis for LICs and LMICs
Up until this point we have treated LICs and LMICs as essentially the same. Yet,
it is more intuitive that countries with different levels of economic activity will
act in different ways. Preliminary evidence also indicated the possibility of a
difference. For these reasons, we decide to investigate whether statistically
evident differences exist between LICs and LMICs.
A. LICs Breakdown
In figure 8 we break down statistically significant results by individual indicator
and indicator category (as well as model) for LICs. Most evident is that the
statistical significance is not distributed evenly among the 20 indicators or among
the three indicator categories.
In the Economic Freedom category, we can see relatively little activity. We find
an average of 2.0 statistically significant results per indicator, with 40 percent of
those results positive in orientation. In comparison, Investing in People shows an
average of 5.8 statistically significant results per indicator, with 48 percent of
those results positive in orientation, while Ruling Justly shows an average of 3.3
statistically significant results per indicator, with 70 percent positive in orientation.
The strongest individual indicator to show the MCC incentive effect in the
Economic Freedom category is Cost to Start Business, with three positive and one
negative result. The Investing in People category shows two stronger indicators:
Girls’ Primary Education and Education Expenditures. The Ruling Justly category
shows four strong indicators: Civil Liberties, Control of Corruption, Government
Effectiveness, and Political Rights.
38
These results all suggest that LICs may tend to focus more on policies that affect
the general governance of people, rather than policies that affect economic
activity; this finding is intuitive given that these countries have little wealth and
are under pressure from the wealthier countries to improve policies toward
citizens. Regardless, 9.8 percent of all possible results for the MCC incentive
effect in these countries show statistically significant results; of those, 52 percent
is positive.
B. LMICs Breakdown
In figure 9, we break down statistically significant results by individual indicator
and indicator category (as well as model) for LMICs. We see a different pattern
than with LICs, most notably the lack of statistically significant positive results.
Overall, 7.4 percent of all results are statistically significant, with 40 percent of
them positive.
More specifically, the Economic Freedom category averages 1.8 statistically
significant results per indicator, with 44 percent positive. The Investing in People
category averages 3.3 results per indicator, with 54 percent positive. The Ruling
Justly category averages 2.8 results per indicator, with 24 percent positive.
Of the individual indicators, we find the most evidence for the MCC incentive
effect for Inflation, Girls’ Primary Education, and Control of Corruption—one
indicator from each of the three indicator groups.
In comparison to the results from LICs, these results for LMICs could imply that
LICs have stronger incentives to improve their policies to become eligible for
MCC funding. One could expect a poorer country with lower GDP to have
stronger incentives to attract aid.
The results in part support the MCC distinction between the two income levels
because they have different incentives. The major qualification, however, is that
the LMIC group sample size of 29 countries is small, which could adversely
affect the potential for statistically significant results.
39
Figure 8: Number of Positive and Negative Results in LICs
Figure 8 depicts the cumulative number of statistically significant positive and negative results from the three models for LICs. We break down the results by indicators and group
the indicators under their respective MCC category—Economic Freedom, Investing in People, and Ruling Justly. The orientation of the bars is purely for visualization and has no
other intended meaning.
Number of Results
15
10
5
Statistically Significant Positive Results
0
Rate of Reform
Likelihood of Improvement
-5
Proportion of Indicators Improved
-10
-15
Statistically Significant Negative Results
Rate of Reform
Likelihood of Improvement
Proportion of Indicators Improved
Economic Freedom
Investing in People
Ruling Justly
Indicators
Source: Authors’ calculations
40
Figure 9: Number of Positive and Negative Results in LMICs
Figure 9 depicts the cumulative number of statistically significant positive and negative results from the three models for LMICs. We break down the results by indicators and
group the indicators under their respective MCC category—Economic Freedom, Investing in People, and Ruling Justly. The orientation of the bars is purely for visualization and
has no other intended meaning.
Number of Results
15
10
5
Statistically Significant Positive Results
0
Rate of Reform
Likelihood of Improvement
-5
Proportion of Indicators Improved
-10
-15
Statistically Significant Negative Results
Rate of Reform
Likelihood of Improvement
Proportion of Indicators Improved
Economic Freedom
Investing
in People
Indicators
Ruling Justly
Indicators
Source: Authors’ Calculations
41
C. Time Effects
We turn briefly to the issue of time periods. Given that time is needed for policies
to be implemented and for results to be recorded, it is reasonable to assume that
the MCC incentive effect will show up stronger in our data in time periods after
the initial period from when the MCC was established. At the same time, Öhler,
Nunnenkamp, and Dreher (2010) suggest diminishing returns as time passes. Our
results are not conclusive about either argument, as seen in figures 10a, 10b, and
10c.
Figures 10a, 10b, and 10c depict the cumulative number of statistically significant
positive and negative results from the three indicator categories (Economic
Freedom, Investing in People, and Ruling Justly) separated by LICs and LMICs.
The orientation of the bars is purely for visualization and has no other intended
meaning.
Looking strictly at positive results in LICs, we see results somewhat similar to
Öhler, Nunnenkamp, and Dreher (2010) in Economic Freedom (figure 10a) and
Ruling Justly (figure 10c) —an increase in results from the 2004-2006 to 20062008, then a decrease in 2008-2010. In Investing in People (figure 10b), we see an
increase in results from 2004-2006 to 2006-2008, followed by another increase in
2008-2010.
Positive results for LMICs show different patterns. In Economic Freedom (figure
10a), we see an increase in results from 2004-2006 to 2006-2008, followed by
stagnation in 2008-2010. Investing in People (figure 10b) shows stagnation from
2004-2006 to 2006-2008, followed by an increase in 2008-2010. Ruling Justly
(figure 10c) shows continuing decreases as time progresses, with no results for
2008-2010.
These results are mixed. In the best scenario, we see an increase in results over
time. In the worst, the results started to decrease after the initial surge. This latter
scenario could be due, for instance, to a general cooling off of interest by
countries in the potential for MCC aid. Limited results from the current models
restrict the possibility for definite conclusions.
42
Figure 10: Number of Positive and Negative Results in Different Time Periods
(a) Economic Freedom
8
6
Statistically Significant Positive
Results
Number of Results
4
Low-Income Countries
2
Lower-Middle-Income Countries
0
-2
Statistically Significant Negative
Results
-4
Low-Income Countries
-6
Lower-Middle-Income Countries
-8
2004 to 2006
2006 to 2008
Time Period
2008 to 2010
Source: Authors’ calculations
(b) Investing in People
8
6
Statistically Significant Positive
Results
Number of Results
4
Low-Income Countries
2
Lower-Middle-Income Countries
0
-2
Statistically Significant Negative
Results
-4
Low-Income Countries
-6
Lower-Middle-Income Countries
-8
2004 to 2006
2006 to 2008
Time Period
2008 to 2010
Source: Authors’ calculations
43
(c) Ruling Justly
8
6
Statistically Significant Positive
Results
Number of Results
4
Low-Income Countries
2
Lower-Middle-Income Countries
0
-2
Statistically Significant Negative
Results
-4
Low-Income Countries
-6
Lower-Middle-Income Countries
-8
2004 to 2006
2006 to 2008
Time Period
2008 to 2010
Source: Authors’ calculations
IX. Conclusions and Recommendations
We have quantitatively assessed the MCC incentive effect. Building on limited
documentation, we identified five groups of countries that are the most likely to
be incentivized to work to meet MCC eligibility criteria. We find the five country
groups do not exhibit a strong, statistically significant MCC incentive effect and
conclude that not enough quantitative evidence supports the argument that an
overall MCC incentive effect exists.
Our conclusion should come as no surprise. Explaining that changes in world
politics and countries’ rationales for policies as being due to the existence of a
foreign aid agency is a courageous task. What is interesting then is that we do find
some positive and statistically significant results throughout our array of analyses
in support of the MCC incentive effect. When we break down our analysis by
country income level, we find statistically significant (albeit limited) quantitative
evidence of the MCC incentive effect. In particular, LICs show evidence of the
MCC incentive effect for indicators in the Ruling Justly category. Accordingly,
we cannot rule out that the MCC incentive effect indeed exists at some level.
We finish by discussing two of the treatment groups, by recommending ways to
improve similar analyses, and by providing ways the MCC could improve its
policies to foster the MCC incentive effect.
44
A. Lack of Quantitative Evidence
Two of the treatment groups presented in this study, the Qualitative MCC
Incentive Effect and the Threshold Partnership, should by the nature of their
composition represent the countries that are most likely to be incentivized by
the MCC. Therefore, these treatment groups should be the most likely to show
a quantifiable MCC incentive effect. In this context, we are disappointed that
we did not find a stronger MCC incentive effect for these two treatment groups
when analyzing all countries in our sample together.
The Qualitative MCC Incentive Effect treatment countries are the countries that
the MCC itself believes it incentivizes to change their policies. In our study this
treatment group is also the group that reveals the most statistically significant
results throughout our models, with 21 significant results. However, just 57
percent of those rather limited results showed a positive effect. For the other
43 percent, the control countries did better than the countries that MCC staff
assumes would show the MCC incentive effect.
The Threshold Partnership countries are countries that, in addition to being
subject to any MCC incentive effect, receive direct MCC support and ex-ante
rewards to improve their MCC policy indicator scores. Since we cannot
quantitatively distinguish these various incentives for this group of countries in
our models, the additional Threshold Partnership incentives should add a positive
bias to the MCC incentive effect. Therefore, the expectation should be that if the
MCC incentive effect can be quantified, it should show up in our data in a more
pronounced manner for this treatment group. However, in our overall results the
Threshold Partnership treatment group is where we find the highest ratio of
negative MCC incentive effects—indicating that countries that do not belong to
this treatment group in fact improve their policies more than those countries
which do.
In sum, we do not find strong evidence of a quantifiable MCC incentive effect.
If one existed, we would have liked to see more positive results for these two
treatment groups. Nonetheless, certain modifications to the models and the
MCC selection process may allow these results to appear if they do exist.
B. How to Improve Analyses:
For future studies, we recommend the following:
1. Run randomized samples in treatment groups to get an empirical distribution
to check for false positives. Although we believe we have analyzed the
strongest possibilities for the MCC incentive effect, given the low number
of positive results, we may be seeing false positives in our results.
2. Reduce the endogeneity inherent in the data and models. We recommend,
for example, employing an instrument variable, or combining a regression
discontinuity design with the DD model (fixed around countries with
similar per-capita GDP).
45
3. Use multivariate longitudinal data analysis, which allows for more than
one response variable to be analyzed simultaneously over time.
4. Add random effects for each country to adjust for some of the inherent
heterogeneity that exists in countries’ decision-making structures related
to policy change. A mixed effects model also would help expand the
predictive ability of the model to each country.
C. How to Improve MCC Incentives
As explained in this report, recent research shows that countries that meet the
MCC eligibility criteria are also more likely to receive foreign aid from other
agencies (Dreher, Nunnenkamp, and Öhler 2012). Hence, there seems to be “a
tempting apple” hanging in the tree. But are countries interested in or capable of
harvesting the fruit? Or do they even know that the golden opportunity of MCC
funding exists? Three recommendations that the MCC should take into
consideration when working to improve the strength of the MCC incentive effect
are:
1. Create Aspirations
The first, and most obvious, is that the MCC must encourage countries’
aspirations to get MCC funding. Countries in need of or with a national interest in
increasing the aid they receive should be encouraged more to comply with MCC
eligibility criteria. For instance, countries where aid is a relatively large part of
their GDP might be easier to incentivize and may provide a target group for the
MCC, should the organization seek to strengthen the MCC incentive effect.
2. Provide Knowledge and Information
The second criteria for an efficient MCC incentive is that countries with some
interest in receiving aid must have better knowledge and information about MCC
funding opportunities and, more importantly, about how they can comply with the
criteria to meet the eligibility threshold. This point is perhaps the biggest Achilles’
heel of the MCC today.
Throughout the MCC’s nine years of existence, the number of policy indicators a
country has to improve to achieve eligibility for MCC poverty-reduction
compacts has changed on multiple occasions. Moreover, the requirements for
complying with criteria have changed. (For example, in addition to the absolute
threshold for Control of Corruption, after 2012, countries need to get a certain
score on the Political Rights or Civil Liberties). Indeed, the indicator criteria are
quite a jungle for a third party, such as the authors of this report, to navigate.
These issues with criteria have forced certain restrictions on this quantitative
analysis; more importantly, the issues most certainly impede countries interested
in obtaining eligibility for MCC support in learning what to do or which policies
to change. Actually knowing how one needs to improve on a policy to pass an
indicator requirement is difficult. In sum, the road to meeting the MCC eligibility
criteria is filled with unpredictable turns, and the map the countries are equipped
46
with does not provide them with the necessary motivation to get to the final
destination.
Although the annual MCC scorecards for each country provide some useful
information for potential applicants, up until now there has been no official “tote
board” for MCC scores. Since the MCC eligibility criteria are relative (for
instance, the MCC board considers how countries perform relative to the median
score of their income peer group), the actual status a country has relative to
meeting the eligibility criteria is not transparent.
We suggest three ways to deal with this problem:
1. Publish the ranking each country has for each indicator. “Tote board
diplomacy” can be a cheap and effective way to promote interest,
knowledge, and competition.
2. Reduce the number of indicators, particularly in the Ruling Justly
category. Since the Ruling Justly indicators are highly correlated and
change very slowly (both for treatment and control countries), cutting the
number of these indicators has some incentivizing advantages, such as
making the way forward for countries more clear cut regarding exactly
which policies to change.8
3. Set five-year plans for selection indicators. Maintaining consistency with
criteria over a longer period of time will help improve transparency.
If these three recommendations are combined, despite some potential problems,9
the results will allow each country to better know how much is demanded to reach
a certain MCC score. In addition, these actions may help certain countries that
today do not see MCC as a potential source of aid to realize that they are actually
closer to the eligibility threshold than they thought, thus incentivizing them to
pursue eligibility. The result will strengthen the MCC incentive effect.
3. Separate Country Group—LICs and LMICs
Through our various analyses, we have found that some groups of countries
appear to have more difficulty complying with some selection indicators than do
others. Those countries might be limited by the current selection system.
Although slight differences exist between the LICs and LMICs in terms of
eligibility requirements, setting more distinct individual goals for the two income
groups toward indicators they respectively show ability to improve on may help
with overall performance.
At the same time, this recommendation would come at a cost of some consistency
and transparency, that cost is likely to be minimal if done in combination with the
other recommendations.
D. Final Thoughts
Our report is at best inconclusive as to determining the existence of the MCC
incentive effect. In comparison, Johnson and Zajonc (2006) conclude that the
47
MCC incentive effect exists, but they did not provide statistically significant
data. Öhler, Nunnenkamp, and Dreher (2010) find a statistical significant effect
on the Control of Corruption indicator, but said that it diminished over time.
Our results do not confirm or refute either argument. At the same time, we have
conducted a much more comprehensive study of the MCC incentive effect.
We also transparently present both the positive and negative statistically
significant results in the data.
This report provides new perspectives on how the MCC incentive effect should
be studied. We hope our work will lead to research and actions by the MCC that
will more solidly point to and foster the MCC incentive effect. Such research and
actions will, at the same time, be important in providing quantitative evidence of
the MCC incentive effect and quantitative evidence of incentive effects in ex-post
rewards systems.
48
Appendix A: Descriptions of Indicators
This appendix describes 26 MCC indicators we initially considered and notes
which six we eliminated for our analysis.
A. Economic Freedom Indicators
Indicators in the Economic Freedom category assess a government’s commitment
to strong economic policies and economic opportunities for its people through
market forces (MCC 2012, 29). We draw on 10 out of 12 indicators described
below in our analysis.
1. Credit Depth
Credit Depth measures “rules and practices affecting the coverage, scope and
accessibility of credit information available through either a public credit registry
or a private credit bureau.” A country receives a score of 0 or 1 for each of six
features available in those credit institutions. The final score is the summation of
the six scores. The data come from the International Finance Corporation’s Doing
Business Survey. The MCC averages Credit Depth with Legal Rights to craft its
Access to Credit indicator (MCC 2012, 33-35). We keep Credit Depth and Legal
Rights separate in our analysis.
2. Legal Rights
Legal Rights measures “the extent to which bankruptcy and collateral laws protect
the rights of borrowers and lenders to facilitate lending.” It contains 10 aspects in
collateral and bankruptcy law. A score of 0 or 1 for each of the 10 features of the
laws, and the final score is the sum. The data come from the International Finance
Corporation’s Doing Business Survey. The MCC averages Legal Rights with
Credit Depth to create its Access to Credit indicator (MCC 2012, 33-35). We keep
Legal Rights and Credit Depth separate for our analysis.
3. Cost to Start Business
Cost to Start Business measures how much it costs to start an industrial or
commercial business while following all the required procedures. This indicator is
measured as a percentage of the country’s per-capita income. A smaller number
for this indicator implies less cost to start a business, which is more desirable. The
data come from the International Finance Corporation’s Doing Business Survey.
The MCC normalizes and averages Time to Start Business with Cost to Start
Business to calculate its Business Start-Up indicator (MCC 2012, 35-37). We
keep Time to Start Business and Cost to Start Business separate for our analysis.
4. Time to Start Business
Time to Start Business measures the number of days it takes to start an industrial
or commercial business while following all the required procedures. The data
come from the International Finance Corporation’s Doing Business Survey. The
49
MCC normalizes and averages Time to Start Business with Cost to Start Business
to calculate its Business Start-Up indicator (MCC 2012, 35-37). We keep Time to
Start Business and Cost to Start Business separate for our analysis.
5. Fiscal Policy
The Fiscal Policy indicator provides insight to how the government uses
“spending and taxation to influence the economy” (Horton and El-Ganainy 2012).
The International Monetary Fund compiles data that measure general government
net lending and borrowing (revenue minus total expenditure) as a percentage of
GDP averaged over the three previous years (MCC 2012).
6. Inflation
Inflation harms a country’s economic growth (De Gregorio 1993). The Inflation
indicator provides insight to a country’s monetary policy because these policies
influence the rate of inflation. The data come from the International Monetary
Fund’s World Economic Outlook Database and are calculated as the percentage
change in consumer prices averaged throughout the year. The MCC requires that
countries perform below the absolute threshold of 15 percent to pass this
indicator’s median (MCC 2012, 38-39).
7. Time to Register Property
Time to Register Property indicates the number of days that are required for a
business to purchase a property from another business and to formally transfer the
property title to the buyer’s name. Smaller Time to Register Property indicates
less time to register properties, which is more desirable. The data come from the
International Finance Corporation’s Doing Business Survey. The MCC uses Time
to Register Property as one of three parts of its Land Rights and Access indicator,
the other two parts being Cost to Register Property and Access to Land (MCC
2012, 31-33). We keep the three parts separate for our analysis.
8. Cost to Register Property
Cost of Register Property records the cost required to register property measured
as a percentage of the value of the property necessary for a business to purchase a
property from another business and to formally transfer the property title to the
buyer’s name. This indicator draws on data from the International Finance
Corporation. The MCC uses Cost to Register Property as one of three parts of its
Land Rights and Access indicator, the other two parts being Time to Register
Property and Access to Land (MCC 2012, 31-33). We keep the three parts
separate for our analysis.
9. Regulatory Quality
Regulatory Quality measures the perception of policy formation and
implementation in regard to promoting private sector development (MCC 2012,
30-31). The data come in the form of an index compiled by the World Bank that
uses a scoring range from -2.5 to +2.5, where -2.5 represents poor regulatory
50
quality and +2.5 represents excellent regulatory quality (World Bank Group
2013e).
10. Trade Freedom
Trade Freedom measures the barriers to trade that affect imports and exports. The
Heritage Foundation compiles the data as part of its Index of Economic Freedom.
Trade Freedom scores range between 0 and 100, where 0 represents high barriers
to trade and 100 represents low barriers to trade. The calculation takes the
weighted average of the tariff rates. Then a penalty can be imposed for non-tariff
barriers determined by the pervasiveness of quantity, price, regulatory, investment,
and customs restrictions, as well as direct government intervention. Depending on
the pervasiveness, the score is reduced 5, 10, 15, or 20 percentage points (MCC
2012, 37-38).
11. Gender in the Economy
Gender in the Economy measures the legal ability of women to “interact with the
private and public sector.” Research suggests that the transition of women from
the primary caretakers of the home to members of the workforce increases
household income and GDP. Data for the indicator come from the World Bank
and the International Finance Corporation (MCC 2012, 39-40). The indicator was
added in year 2012 (Butts 2011); data are only available for 2010 and 2012, and
the format has not been indexed (World Bank Group 2013b). In that regard, the
MCC indicates it combines 20 data points to make its indicator, but it is not
absolutely clear which data points are used and how the agency compiled them
(MCC 2012, 39-40). Because of these limitations, we do not use this indicator in
our analysis.
12. Access to Land
Access to Land is one of three parts of the MCC’s Land Rights and Access
indicator, the other two parts being Time to Register Property and Cost to
Register Property. The MCC collects the data from the International Fund for
Agricultural Development (MCC 2012, 31-33). We were not able to collect data
on this indicator; therefore, we exclude it from our analysis.
B. Investing in People Indicators
The Investing in People indicators assess the government’s commitment to
improving the standard of living of its people (MCC 2012, 23). We draw on four
out of seven indicators as described below in our analysis.
1. Girls’ Primary Education
Girls’ Primary Education uses the gross intake ratio in the last grade of primary
school. It is calculated as the total number of girls enrolled in the last grade of
primary school minus the number of female students repeating the last grade of
primary, school divided by the total population of females that are of theoretical
entrance age of the last grade of primary school. The data come from UNESCO’s
51
Institute of Statistics to measure girls’ primary education completion. The MCC
only uses the Girls’ Primary Education indicator for LICs (MCC 2012, 26-27).
2. Health Expenditures
Health Expenditures measures a government’s “commitment to investing in the
health and well-being of its people.” Increased spending on health, when
combined with “good policies and good governance,” can “promote growth,
reduce poverty, and trigger declines in infant, child, and maternal mortality.” The
MCC has chosen an indicator that is an input for the well-being of citizens
because it may take time to observe the outcome of healthcare interventions. The
data come from the World Health Organization and measure general government
health expenditures as a percentage of GDP (MCC 2012, 24-25).
3. Immunization Rate
The Immunization Rate of a country is used as a proxy to measure overall public
health care. Immunization Rate is the rate of vaccinations in 1-year-olds,
specifically for diphtheria-pertussis-tetanus and for measles. The indicator uses
the simple average of the two vaccination rates based on a combination of health
administrative data and coverage surveys collected by the World Health
Organization and the United Nations Children’s Fund. For fiscal year 2013, the
MCC’s requirements differ between LICs and LMICs. LICs need to perform at or
above the median of the rate of their peers, while LMICs must obtain an average
of at least 90 percent (MCC 2012, 23-24).
4. Education Expenditures
Education Expenditures is the amount a government spends on primary education
as a percentage of GDP. It is based on UNESCO’s Institute of Statistics as its
primary source of data. The MCC uses self-reported data from national
governments as a secondary source. The country GDP is crosschecked with
World Bank and International Monetary Fund’s GDP estimates (MCC 2012, 2526). We use only data from UNESCO in our analysis. Additionally, the MCC uses
a measure of primary education expenditures as a percentage of GDP. We could
not find this exact measure directly from UNESCO, so we calculated the indicator
by taking UNESCO data measuring the total expenditures on education as a
percentage of GDP and multiplying it by the percentage of expenditures on
primary education.
5. Girls’ Secondary Education Enrollment
Girls’ Secondary Education Enrollment measures the female lower secondary
education enrollment ratio based on data from UNESCO’s Institute of Statistics.
The ratio is calculated as the number of females enrolled in lower secondary
divided by the total population of females that are the standard age of lower
secondary enrollment. The MCC uses the Girls’ Secondary Education Enrollment
indicator only for LMICs. (MCC 2012, 27-28). However, due to lack of data
availability, we do not include this indicator in our analysis.
52
6. Child Health
Child Health is based on data from the Center for International Earth Science
Information Network and calculates child health as the average of three equally
weighted indicators that improve child health. The first, Access to Improved
Sanitation, is measured as the percentage of the population with access to
adequate sanitation. The second, Access to Improved Water, is measured as the
percentage of the population with access to improved water supply. The third,
Child Mortality, is measured as the probability of children dying between the ages
of 1 and 5 years (MCC 2012, 28-29). However, due to lack of available data, we
do not include this indicator in our analysis.
7. Natural Resource Protection
The Natural Resource Protection indicator is calculated as the percentage of
biome area protected within a country’s land area based on data from the Center
for International Earth Science Information. The indicator first appeared on MCC
scorecards in 2006 as an index on a scale of 0 to 100, with 0 being no protection
and 100 being fully protected. The number is weighted by the share of the
biome’s area and then multiplied by 10 (MCC 2012, 29). However, due to lack of
data, we do not include this indicator in our analysis.
C. Ruling Justly Indicators
The Ruling Justly indicators assess a country’s level of democratic governance
(MCC 2012, 13). We draw on six out of seven of these indicators as described
below in our analysis.
1. Civil Liberties
Civil Liberties measures a variety of factors relating to personal freedoms. The
data come from the Freedom House in the form of an index. Country evaluation is
on a scale of 0 to 60, where 0 represents the least amount of freedom and 60
represents the most amount of freedom (MCC 2012, 15-16).
2. Control of Corruption
Control of Corruption measures the perception of corruption in government in
both “petty and grand forms,” as well as a country’s ability to combat corruption.
The MCC considers corruption to impede the fight against poverty. As a result,
the MCC requires that countries’ indicators must meet the minimum requirements,
regardless of their overall scores on other indicators, to be considered eligible to
enter into a poverty-reduction compact (MCC 2012, 16-18). The data come in the
form of an index score compiled by the World Bank and range in score from -2.5
to +2.5, where -2.5 represents weak control of corruption and +2.5 represents
strong control of corruption (World Bank Group 2013h).
3. Government Effectiveness
Government Effectiveness largely measures the quality of public service, civil
service, and policy formation (MCC 2012, 18-19; World Bank Group 2013d). The
data come in the form of an index score compiled by the World Bank and range in
53
score from -2.5 to +2.5, where -2.5 represents a government that has low
effectiveness and +2.5 represents a government that has high effectiveness (World
Bank Group 2013h).
4. Political Rights
Political Rights measures a variety of factors relating to the political system. The
Political Rights indicator comes in the form of an index compiled by Freedom
House and is based on 10 questions from three subcategories: Electoral Process,
Political Pluralism and Participation, and Functioning of Government. Points are
awarded to each question on a scale of 0 to 4, where 0 points represents the fewest
rights and 4 represents the most rights. It includes two additional questions related
to political rights, for which 1 to 4 points are subtracted depending on the severity
of the situation. The overall scale is 0 to 40, with 0 being “least free” and 40 being
“most free” (MCC 2012, 13-14; Freedom House 2013b).
5. Rule of Law
Rule of Law measures the perception of the legitimacy of national law by its
citizens, focusing on the justice system (MCC 2012, 20-21; World Bank Group
2013f). The data come in the form of an index score compiled by the World Bank
and range in score from -2.5 to +2.5, where -2.5 represents an illegitimate
perception and +2.5 represents a legitimate perception (World Bank Group
2013h).
6. Voice and Accountability
Voice and Accountability measures “the extent to which a country’s citizens are
able to participate in selecting their government, as well as freedom of expression,
freedom of association, and media independence” (World Bank Group 2013g).
The data come in the form of an index score compiled by the World Bank and
range in score from -2.5 to +2.5, where -2.5 represents the weakest and +2.5
represents the strongest respectively in this policy area (World Bank Group2013h).
The MCC dropped this indicator in 2013 and added a new indicator, Freedom of
Information.
7. Freedom of Information
We do not use the Freedom of Information indicator in our analysis due to data
limitations. Instead, we substitute the older Voice and Accountability indicator.
54
Appendix B: Indicator Information and Rates of Reform Statistics
Table B1 presents initial data for the 20 indicators that we use in our analysis, along with summary statistics for rates of reform values.
Table B1: Indicator and Rates of Reform Information
Indicator Information
Economic Freedom
Category
Indicator
Equivalent MCC
Indicator Category
Credit Depth
Access to Credit
Legal Rights
Access to Credit
Cost to Start
Business
Time to Start
Business
Business Start-Up
Business Start-Up
Fiscal Policy
Fiscal Policy
Inflation
Inflation
Time to Register
Property
Land Rights and
Access
Cost to Register
Property
Land Rights and
Access
Regulatory Quality Regulatory Quality
Trade Freedom
Trade Policy
Rates of Reform Summary Statistics
Years
Available
Score
Bounds
Direction of
Improvement*
Number of
Observations
Mean
Standard
Deviation
Min
Max
2004–2012
[0,6]
+
278
0.4
0.94
-1
5
2004–2012
[0,10]
+
279
0.21
0.8
-1
5
2003–2013
[0, infinite]
-
279
-25.56
89.37
-1168.8
170
2002–2013
[0,infinite]
-
279
-8.22
18.87
-90
124
2002–2011
[0,100]
+
154
0.17
8.3
-31.76
66.55
2002–2013
[-infinite,
infinite]
-
372
-0.52
9.11
-65.33
46.24
2004–2013
[0,infinite]
-
266
-10.46
40.67
-382
43
2005–2013
[0,infinite]
-
183
-0.63
1.92
-16.2
5.5
World Bank Group (2013h)
2002–2011
[-2.5,2.5]
+
392
0.01
0.2
-0.84
0.74
Heritage Foundation (2013)
2002–2011
[0,100]
+
313
3.19
9.8
-48.2
60
Data Source
International Finance
Corporation (2013)
International Finance
Corporation (2013)
International Finance
Corporation (2013)
International Finance
Corporation (2013)
International Monetary Fund
(2013)
International Monetary Fund
(2013)
International Finance
Corporation (2013)
International Fund for
Agricultural Development
(2013)
55
Indicator Information
Ruling Justly
Investing in People
Category
Rates of Reform Summary Statistics
Indicator
Equivalent MCC
Indicator Category
Data Source
Years
Available
Score
Bounds
Direction of
Improvement*
Number of
Observations
Mean
Standard
Deviation
Min
Max
Girls’ Primary
Education
Girls’ Primary
Education
Completion Rate
UNESCO (2013)
2002–2011
[0,>100]
+
218
2.61
7.33
-30.18
27.07
Health
Expenditures
Health Expenditures
World Health Organization
(2013)
2002–2011
0-100
+
390
0.98
7.03
-28.16
69.32
Immunization Rate Immunization Rates
World Health Organization
(2013)
2002–2011
[0,100]
+
392
1.84
7.3
-51
34
Education
Expenditures
Primary Education
Expenditures
UNESCO (2013) / Authors’
Calculations
2002–2011
[0,100]
+
80
0.01
0.44
-1.85
0.97
Civil Liberties
Civil Liberties
Freedom House (2013a)
2002–2011
[0,60]
+
294
-0.08
2.95
-10
17
Control of
Corruption
Control of
Corruption
World Bank Group(2013h)
2003–2013
[-2.5,2.5]
+
392
0.01
0.21
-0.9
0.79
Government
Effectiveness
Government
Effectiveness
World Bank Group (2013h)
2003–2013
[-2.5,2.5]
+
392
0
0.19
-0.87
0.87
Political Rights
Political Rights
Freedom House (2013a)
2002–2012
[-4,40]
+
294
0.01
3.82
-21
19
Rule of Law
Rule of Law
World Bank Group (2013h)
2002–2011
[-2.5,2.5]
+
392
0
0.18
-0.51
0.84
Voice and
Voice and
[-2.5,+2.5]
+
392
0
0.2
-0.83
1.11
World Bank Group (2013a)
2002–2011
Accountability
Accountability
*Notes: In our analyses, we multiply the five negative direction of improvement by negative one (-1) for consistent interpretation: the greater the value, the better that country performs on the
indicator. The Rates of Reform summary statistics reflect this change.
Sources: As noted and authors’ calculations
56
Appendix C: Autocorrelation within Indicators
Figures C1 to C12 show the autocorrelation for the 12 individual indicators for
which data were available for all four time periods. The plot is the correlation of
residuals from the rates of reform among of any two periods. The lower-left
panels are scatterplots, the top-left to bottom-right diagonal panels are histograms,
and the upper-right panels are the correlation coefficients with proportional font
size.
Figure C1. Fiscal Policy
10
20
30
-10
-5
0
5
-10 0 10
30
0
10
20
30
lmres.2004
2
4
lmres.2004
20 40 60
6
0
Figure C2. Inflation
0.36
0.0 9 1
0.14
0
0.0 6 4
30
-40
-4 -2
0 . 0 4 4
0
0.10
lmres.2006
20
30
lmres.2006
-10 0 10
lmres.2008
0.75
30
-30
-20
0.21
lmres.2010
-10
0
-5
10
0
20
5
lmres.2010
-30 -20 -10
-10
0
0
lmres.2008
0.21
0. 0 06 8
10
0.11
0
10
0.85
-4 -2
0
2
4
6
-30
-20
-10
-40
0
0
Figure C3. Regulatory Quality
-0.2
0.2
-0.4
-0.2
0.0
-30 -20 -10
0
10
Figure C4. Trade Freedom
0.2
-10
0
10
20
-20
0
20
40
0.11
0.099
0 .0 8 6
0.12
40
0.31
0.28
0. 03 3
20
-20
0
-0.5
0 . 0 08 7
lmres.2004
0.0
lmres.2004
20
0.5
60
-0.6
20 40 60
10
lmres.2006
0
0.15
0 . 0 46
0.56
-40
-0.8
40
lmres.2010
-0.4
-20
0
-0.2
20
0.0
0.2
lmres.2010
-0.5
0.0
0.5
-0.8
-0.4
0.0
-20
0
lmres.2008
-0.4
0.36
0.0
lmres.2008
20
0.4
-0.6
-10
-0.2
0.2
lmres.2006
-20
0.4
57
0
20
40
60
-40
-20
0
20
Figure C5. Girls’ Primary Education
5
10
-5
0
5
10
15
-15
-5 0
5 10
-20 -10
0
10
20
60
0
lmres.2004
5
lmres.2004
20 40
-5
10 15
-10
Figure C6. Health Expenditures
0.18
0.21
0.15
0
0 . 0 11
10
-20
-10 -5
0 . 0 0 41
0
0.38
lmres.2006
5 10
0.31
0.20
0 . 0 3 9
20
-10
-15
-5
0 .0 6 6
-5 0
0
5
lmres.2006
20
15
-15
-20
-10
0
0.43
-5 0
0.20
10
lmres.2008
5 10
lmres.2008
10
lmres.2010
-5
-20 -10
0
0
5
10
lmres.2010
0
5
10 15
-15
-5 0
5 10
-20
Figure C7. Immunization Rate
0
10
20
60
-20
30
-8 -6 -4 -2 0
0
10
20
2
-5
0
5
10
lmres.2004
0.14
0.77
0 .0 7 2
0.37
2
-40
-5
0
0.0 64
-10
5
lmres.2004
0.0 91
40
10
30
20
Figure C8. Education Expenditures
20 40 60
-10 0 10
0
0
-10 -5
0.21
0.41
10
lmres.2008
0
lmres.2008
0.11
5
0. 0 0 6 8
lmres.2006
-8 -6 -4 -2 0
-10 0 10
30
lmres.2006
-30 -20 -10
-5
0
0 .0 9 3
10
30
0.75
lmres.2010
-5
0
0
10
5
20
lmres.2010
0
20 40 60
-30 -20 -10
0
10
-5
Figure C9. Control of Corruption
-5
0
5
10
-5
0
5
10
5
10
-5
0
5
Figure C10. Government Effectiveness
15
-0.6
10 15
-10
-0.2
0.2 0.4
-0.3
-0.1
0.1
0.3
lmres.2004
0.18
0.28
0.29
0 .0 6 8
10
0.2 0.4
-0.5
-10 -5
0 . 0 0 41
0
0.38
0.31
lmres.2006
0.19
0 . 0 12
-10
-0.6
-5
-0.2
0
5
lmres.2006
0 .0 6 6
0.0
5
lmres.2004
0
0.5
-40
-0.6
-0.2
-5 0
0 .0 7 9
0.3
15
-15
0.20
0.2
lmres.2008
5 10
lmres.2008
lmres.2010
-0.3
-5
0
-0.1
5
0.1
10
lmres.2010
-10 -5
0
5
10 15
-15
-5 0
5 10
-0.5
58
0.0
0.5
-0.6
-0.2
0.2
Figure C11. Rule of Law
0.8
-0.4 -0.2
0.0
0.2
0.4
0.16
0 . 0 34
0.0
0.5
-0.4 -0.2
0.0
0.2
lmres.2004
0.08
0 . 0 0 42
0 . 0 00 1 3
0.13
0 .0 6 8
0.8
-0.6
-0.4
-0.2
0 . 0 24
-0.5
0.6
0.4
0.2
0.0
lmres.2004
0.0 0.2 0.4
-0.4
Figure C12. Voice and Accountability
lmres.2006
0.21
0.0
0.4
0.5
lmres.2006
lmres.2008
0.0
lmres.2008
0 .0 6 9
0.4
-0.4
-0.2
-0.2
0 . 0 04 7
0.2 0.4 0.6
0.2
-0.4
-0.5
0.0
0 .0 7 3
0.2
lmres.2010
-0.4 -0.2 0.0
-0.4 -0.2 0.0
0.2
lmres.2010
-0.4
0.0 0.2 0.4
-0.4
-0.2
0.0
0.2
-0.6
Source: Authors’ calculations
59
-0.2
0.2
0.6
-0.2
0.2 0.4 0.6
Appendix D: Correlation between Indicators
In the three figures below, we plot the correlation between indicators in similar
categories. The upper-right half of the figure presents the correlation of indicator
scores between each two indicators; the lower-left half presents the correlation
of the rates of reform of the indicators. The type size is proportioned to the
correlation—if a correlation is legible, then it is high. As a rule of thumb, a
regression between two variables whose correlation is higher than 0.3 would
result in a very significant linear regression coefficient.
Figure D1 depicts the correlation between each of six Economic Freedom
indicators; the other four Economic Freedom indicators are excluded due to
missing values. Figure D2 depicts the correlation between each of the four
Investing in People indicators. Figure D3 depicts the correlation between
each of the six Ruling Justly indicators.
Figure D1: Correlation between Economic Freedom Indicators
Cost to
Start
Business
0
0
0.095
0.61
0
0.094
Time to
Start
Business
0.15
0
0
0.15
0
0
Fiscal
Policy
0
0
0.1
0
0
0
Inflation
0.14
0.11
0.46
0.43
0
0
Time to
Register
Property
0.25
0.43
0.22
0.12
0.16
0.4
Regulation
Source: Authors’ calculations
60
Figure D2: Correlation
between Investing in People Indicators
Girl's
Primary
Education
0.33
0.1
0.11
0.33
Health
Expenditures
0.11
0
0.64
0.35
Immunization
Rate
0.12
0
0.24
0.062
Education
Expenditures
Source: Authors’ calculations
Figure D3: Correlation between Ruling Justly Indicators
Civil
Liberties
0.19
0.12
0.65
0.14
0.33
0.45
Control of
Corruption
0.29
0.13
0.21
0.24
0.35
0.68
Government
Effectiveness
0.13
0.38
0.1
0.93
0.38
0.29
Political
Rights
0.01
0.28
0.41
Rule of Law
0.26
0.01
0.14
Voice and
Accountability
0.53
0.01
0.8 0.73
0.01
0.11
Source: Authors’ calculations
61
Appendix E: Average Number of MCC Indicators Passed
The figures below depict the trends in the number of MCC indicator that
individual countries have passed (met minimum MCC requirements) over time.
The thick solid black line at the top of the figures indicates the maximum possible
number of MCC indicators a country could pass. The thin solid black line in the
middle is the average over number of indicators passed by all the countries. The
other lines represent individual countries. Figure E1 depicts the results for the 69
LICs. Figure E2 depicts the results for the 29 LMICs.
Figure E1: Average Number of MCC Indicators Passed by LICs
62
Figure E2: Average Number of MCC Indicators Passed by LMICs
63
Appendix F: Treatment Countries
Table F1 displays the LICs included in the five main treatment groups, and table
F2 displays the LMICs in the five main treatment groups.
Table F1: LICs included in Treatments
Country
Code
Afghanistan
AFG
Angola
AGO
Armenia
ARM
Azerbaijan
AZE
Benin
BEN
(1)
(2)
*
*
BFA
*
*
*
*
Bolivia
BOL
Bhutan
BTN
CHN
Comoros
COM
Djibouti
DJI
Congo, Dem
DRC
Egypt
EGY
*
*
*
*
*
Georgia
GEO
*
Ghana
GHA
*
Guinea-Bissau
GNB
Guyana
GUY
Honduras
HND
Haiti
HTI
Indonesia
IDN
India
IND
Iraq
IRQ
Kenya
KEN
Kyrgyzstan
KGZ
Kiribati
KIR
Laos
LAO
Liberia
LBR
Sri Lanka
LKA
Lesotho
LSO
Morocco
MAR
Moldova
MDA
Madagascar
MDG
*
*
ERI
GIN
*
*
ETH
GMB
*
*
*
*
Eritrea
Guinea
*
*
Ethiopia
Gambia
*
*
BGD
CMR
(5a)
*
Bangladesh
China
(4)
*
*
Burkina Faso
Cameroon
Treatments
(3)
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
64
Country
Code
(1)
Mali
MLI
Mongolia
MNG
*
Mozambique
MOZ
*
Mauritania
MRT
(2)
Treatments
(3)
MWI
*
Niger
NER
*
Nigeria
NGA
Nicaragua
NIC
*
*
*
*
*
*
*
Nepal
NPL
*
PAK
*
Philippines
PHL
Papua New Guinea
PNG
*
*
*
*
*
*
*
*
PRY
ROC
Rwanda
RWA
*
*
Senegal
SEN
*
*
Solomon Islands
SLB
Sierra Leone
SLE
Togo
TGO
Tajikistan
TJK
*
*
Paraguay
STP
*
*
Congo, Rep
TCD
*
*
*
Pakistan
Chad
(5a)
*
Malawi
Sao Tome And Principe
(4)
*
*
*
*
*
*
*
*
*
*
*
*
Turkmenistan
TKM
East Timor
TMP
*
Tanzania
TZA
*
*
*
*
Uganda
UGA
*
*
*
*
Ukraine
UKR
Vietnam
VNM
Vanuatu
VUT
Yemen
YEM
Zambia
ZMB
*
*
*
*
*
*
*
*
*
Total Number of Treatments
25
31
Total Number of Controls
44
38
Source: Authors’ calculations
Treatment 1: Qualitative MCC Incentive Effect
Treatment 2: Bordering the Eligibility Threshold
Treatment 3: Passing Control of Corruption
Treatment 4: Threshold Partnership
Treatment 5a: Countries in the Middle Range of Government Expenditures
*
*
*
37
32
18
51
25
44
Note: We also identified Burundi as showing qualitative evidence of the MCC incentive effect, but we excluded Burundi
from the Qualitative MCC Incentive Effect treatment group because it was not one of the 98 countries we use in our
analysis.
65
Table F2: LMICs included in Treatments
(1)
Country
Code
Albania
ALB
Bulgaria
BGR
Belarus
BLR
Brazil
BRA
*
Colombia
COL
*
CPV
*
DOM
*
DZA
ECU
*
*
FJI
*
*
*
*
Guatemala
GTM
Jamaica
JAM
*
*
*
*
*
*
Jordan
JOR
Kazakhstan
KAZ
*
Maldives
MDV
*
Marshall Islands
MHL
*
MKD
*
*
FSM
NAM
*
N/A
Fiji Islands
Namibia
*
*
Micronesia
Macedonia
(5a)
*
*
Cape Verde
Algeria
(4)
*
Dominican Republic
Ecuador
(2)
Treatments
(3)
*
*
*
*
*
*
*
*
*
Peru
PER
*
*
Romania
ROM
*
*
El Salvador
SLV
Suriname
SUR
*
Swaziland
SWZ
Thailand
THA
Tonga
TON
*
Tunisia
TUN
*
Tuvalu
TUV
*
Samoa
WSM
*
*
*
*
*
*
*
*
*
*
*
*
Total Number of Treatments
6
19
Total Number of Controls
23
10
Source: Authors’ calculations
Treatment 1: Qualitative MCC Incentive Effect
Treatment 2: Bordering the Eligibility Threshold
Treatment 3: Passing Control of Corruption
Treatment 4: Threshold Partnership
Treatment 5a: Countries in the Middle Range of Government Expenditures
66
16
12
3
26
12
17
Appendix G: Full Regression Results
Tables G1 to G6 on the following pages present a condensed summary of our
regression results for the MCC incentive effect, excluding basic treatment, time,
and control variables.
278
0.081
0.922
-0.089
(0.233)
278
3.030
0.050
8.055
(25.97)
277
0.028
0.973
-0.314
(0.283)
-0.347
(0.275)
-0.232
(0.239)
278
0.475
0.622
0.367
(0.242)
0.509**
(0.235)
-23.63
(26.66)
278
0.758
0.469
-37.19
(26.88)
-50.20*
(26.13)
274
0.956
0.386
-0.657**
(0.330)
0.009
(0.324)
275
2.477
0.086
0.343
(0.283)
-0.321
(0.279)
275
1.973
0.141
-10.33
(31.86)
25.60
(31.34)
277
2.659
0.072
-0.097
(0.280)
-0.314
(0.273)
278
2.754
0.066
-0.089
(0.240)
0.098
(0.235)
278
0.682
0.506
5.567
(26.69)
-15.32
(26.07)
Observations
278
279
Ftest/chi2-test
0.692
0.304
Prob > F/ chi2
0.501
0.738
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
279
0.337
0.714
1. Qualitative MCC Incentive
Effect
2. Bordering the Eligibility
Threshold
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
5a. Countries in the Middle
Range of Government
Expenditures
4. Threshold Partnership
3. Passing Control
of Corruption
2004 - 2006
2006 - 2008
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
2008 - 2010
-2.698
1.426
0.048
(4.257) (2.549)
(0.061)
-2.424
1.164
3.972
1.205**
0.095
(4.160) (2.548) (12.94)
(0.601)
(0.061)
0.668
1.258
-12.65
0.092
(4.183) (2.631) (13.02)
(0.062)
278
154
371
265
182
390
0.809
0.328
0.129
0.939
4.024
1.056
0.446
0.805
0.943
0.393
0.046
0.368
4.981
3.101
-1.454
7.091
0.288
0.005
(5.492) (3.555) (2.426) (12.33)
(0.576)
（0.058）
1.645
0.682
7.514
-0.002
(3.598) (2.498) (12.37)
（0..057）
6.251
-5.977
-0.672
0.064
(5.637) (3.619) (2.432)
（0.058）
278
154
371
265
182
390
0.702
2.545
0.274
0.229
0.249
0.595
0.497
0.059
0.844
0.795
0.618
0.619
1.052
-5.531
2.513
0.782
0.030
(5.725) (4.043) (2.257) (12.56)
（0.059）
2.554
-1.587
3.714*
-15.84
-0.070
(5.566) (3.939) (2.251) (12.16)
（0.059）
1.516
1.843
-0.590
0.017
(3.972) (2.320)
(0.586)
(0.059)
275
150
367
262
180
386
0.106
1.191
0.948
1.170
1.013
1.168
0.899
0.315
0.417
0.312
0.316
0.322
-2.263
1.514
-1.228
-18.86
-0.011
(6.769) (5.035) (2.918) (14.86)
(0.071)
3.830
0.847
0.142
-24.41*
0.079
(6.658) (4.872) (2.917) (14.45)
(0.071)
4.455
3.063
-0.251
0.041
(4.886) (2.943)
(0.696)
(0.071)
278
154
371
265
182
390
0.418
0.326
0.762
1.557
0.130
0.677
0.659
0.807
0.516
0.213
0.719
0.567
10.73*
1.279
-2.034
13.40
-0.106*
(5.640) (3.913) (2.462) (12.32)
(0.059)
11.75**
1.863
-4.346*
8.683
0.015
(5.508) (3.844) (2.447) (11.98)
(0.059)
0.898
-1.753
0.407
-0.021
(3.941) (2.526)
(0.575)
(0.059)
279
154
372
266
183
392
2.753
0.082
1.068
0.616
0.501
1.694
0.066
0.970
0.363
0.541
0.480
0.168
Note: Italics indicate statistically significant results. Source: Authors’ calculations
67
Trade Freedom
277
0.142
0.867
-0.025
(0.279)
-0.065
(0.279)
2006 - 2008
Regulatory
Quality
-0.716
(5.962)
6.040
(5.786)
Cost to Register
Property
Time to Start
Business
-8.407
(27.76)
53.30**
(26.94)
Time to Register
Property
Cost to Start
Business
0.021
(0.254)
0.095
(0.246)
2004 - 2006
Inflation
Legal Rights
0.086
(0.296)
-0.071
(0.286)
MCC Incentive
Effect
Fiscal Policy
Credit Depth
Table G1: Economic Freedom Indicators Rate of Reform Model
0.108
(3.191)
-5.690*
(3.188)
2.918
(3.212)
312
2.591
0.053
2.000
(3.133)
5.808*
(3.145)
2.909
(3.142)
312
1.180
0.317
-5.988*
(3.172)
-2.697
(3.160)
-7.790
(3.180)
308
2.380
0.070
0.480
(3.738)
-0.516
(3.736)
-0.621
(3.742)
312
0.037
0.990
5.291*
(3.138)
3.679
(3.126)
2.080
(3.129)
313
1.040
0.375
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
278
0.794
0.672
-0.064
（0.106）
0.057
(0.105)
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
278
1.332
0.514
-0.178
(0.109)
-0.069
(0.103)
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
4. Threshold Partnership
2006 - 2008
275
2.661
0.264
0.02
(0.138)
-0.03
(0.131)
2008 - 2010
5a. Countries in the Middle
Range of Government
Expenditures
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
278
0.162
0.922
0.229**
(0.109)
0.163
(0.104)
2008 - 2010
Observations
278
Ftest/chi2-test
3.888
Prob > F/ chi2
0.143
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Cost to Register
Property
Time to Register
Property
Inflation
Fiscal Policy
0.003
(0.0742)
0.002
（0.074）
-0.159
-0.134
-0.023
(0.233)
(0.110) （0.078）
-0.256
0.021
0.078
(0.237)
(0.131)
(0.099)
-0.041
-0.026
(0.244)
(0.135)
278
154
371
233
182
0.002
1.433
2.044
0.086
0.621
0.999
0.698
0.563
0.769
0.431
0.027
-0.225
0.02
-0.027
（0.200） (0.105)
(0.072)
(0.063)
0.035
0.041
-0.105
0.037
-0.072
(0.071)
(0.196)
(0.129)
(0.069)
(0.096)
0.108
-0.059
(0.211)
(0.128)
278
154
371
265
182
0.256
3.098
1.146
0.756
0.561
0.880
0.377
0.766
0.685
0.454
-0.058
-0.279
0.046
0.030
(0.207)
(0.107)
(0.06)
（0.077）
-0.241
-0.015
-0.375
0.208**
(0.206)
(0.128)
(21.70)
(0.098)
-0.439**
0.014
(0.215)
(0.128)
222
150
367
262
180
0.577
4.010
0.291
0.254
4.486
0.447
0.260
0.962
0.881
0.034
0.001
0.136
-0.170
0.014
(0.082)
(0.271)
(0.127)
(0.061)
0.037
0.074
0.01
-0.137
(0.265)
(0.144)
(0.070)
(0.110)
0.039
0.136
(0.287)
(0.169)
257
154
371
265
182
0.000
0.276
4.536
0.058
1.550
0.987
0.965
0.209
0.971
0.213
-0.026
0.104
-0.037
-0.007
(0.072)
(0.211)
(0.107)
(0.071)
-0.031
0.048
0.105
0.132
(0.071)
(0.210)
(0.128)
(0.096)
0.247
-0.038
(0.222)
(0.128)
278
154
371
223
182
0.182
1.381
1.592
0.121
1.880
0.913
0.710
0.661
0.728
0.170
Note: Italics indicate statistically significant results.
Trade Freedom
2006 - 2008
0.104
(0.116)
0.045
(0.109)
Regulatory
Quality
3. Passing Control of
Corruption
2. Bordering the Eligibility
Threshold
1. Qualitative MCC
Incentive Effect
2004 - 2006
Time to Start
Business
MCC Incentive
Effect
Cost to Start
Business
Table G2: Economic Freedom Indicators Likelihood of Improvement Model
0.168
(0.144)
0.429***
(0.146)
0.456***
(0.144)
390
12.76
0.005
0.047
(0.141)
0.006
(0.142)
-0.124
(0.141)
390
1.635
0.652
-0.068
(0.141)
-0.288**
(0.141)
-0.134
(0.141)
386
4.381
0.223
0.062
(0.173)
0.102
(0.175)
-0.004
(0.171)
390
0.512
0.916
-0.215
(0.141)
0.052
(0.143)
0.104
(0.143)
390
6.155
0.104
-0.164
(0.139)
-0.168
(0.142)
0.069
(0.138)
312
4.109
0.250
0.190
(0.137)
0.185
(0.141)
0.191
(0.133)
312
2.929
0.403
-0.107
(0.142)
0.117
(0.144)
-0.040
(0.137)
308
2.308
0.511
-0.064
(0.161)
-0.155
(0.161)
0.048
(0.159)
312
1.707
0.635
-0.007
(0.137)
0.222
(0.143)
-0.030
(0.133)
312
3.497
0.321
Source: Authors’ calculations
Note: The DD estimators for Credit Depth and Legal Rights were not obtained due convergence issues.
68
Table G3: Investing in People Indicators
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
5a. Countries in the Middle
Range of Government
Expenditures
4. Threshold Partnership
3. Passing Control
of Corruption
2004 - 2006
2006 - 2008
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
Education Expenditures
2006 - 2008
Immunization Rate
2004 - 2006
Health Expenditures
Observations
Ftest/chi2-test
Prob > F/ chi2
Girls’ Primary Education
2008 - 2010
Education Expenditures
2006 - 2008
Immunization Rate
2. Bordering the Eligibility
Threshold
1. Qualitative MCC
Incentive Effect
2004 - 2006
Health Expenditures
MCC Incentive
Effect
3.080
(2.725)
7.215***
(2.762)
1.565
(2.805)
218
2.449
0.0647
1.432
(1.863)
2.196
(1.864)
-0.639
(1.897)
388
0.949
0.417
-1.882
(2.208)
-1.951
(2.210)
-4.50**
(2.224)
390
1.381
0.248
-0.181
(0.312)
-0.366
(0.281)
-0.374
(0.269)
80
0.819
0.488
0.353**
(0.166)
0.487***
(0.164)
0.366**
(0.178)
218
8.900
0.0306
-0.01
(0.147)
0.104
(0.146)
0.0316
(0.149)
388
0.744
0.863
-0.024
(0.140)
-0.188
(0.136)
-0.176
(0.139)
390
3.094
0.377
0.502
(0.326)
-0.195
(0.291)
-0.243
(0.282)
80
6.084
0.108
2.365
(2.766)
1.571
(2.778)
-1.227
(2.739)
218
0.638
0.591
0.147
(1.769)
1.808
(1.784)
0.928
(1.768)
388
0.435
0.728
-1.078
(2.102)
-1.025
(2.109)
-1.967
(2.101)
390
0.293
0.831
0.311
(0.268)
0.268
(0.254)
0.322
(0.298)
80
0.609
0.611
-0.230
(0.172)
0.01
(0.174)
-0.094
(0.180)
218
2.403
0.493
0.189
(0.139)
0.068
(0.139)
0.217
(0.139)
388
3.193
0.363
-0.127
(0.129)
0.046
(0.129)
-0.159
(0.130)
390
3.488
0.322
0.016
(0.327)
0.112
(0.293)
0.101
(0.288)
80
0.225
0.974
-0.047
(2.834)
-0.108
(2.841)
-0.502
(2.847)
216
0.0125
0.998
1.711
(1.788)
2.121
(1.790)
1.560
(1.807)
384
0.537
0.657
-0.247
(2.126)
-0.776
(2.128)
1.498
(2.138)
386
0.419
0.740
-0.188
(0.329)
-0.161
(0.307)
-0.106
(0.276)
80
0.140
0.936
0.158
(0.178)
-0.043
(0.178)
0.136
(0.183)
216
1.809
0.613
0.085
(0.141)
0.143
(0.140)
0.083
(0.142)
384
1.055
0.788
-0.021
(0.131)
-0.098
(0.131)
0.001
(0.132)
386
0.780
0.854
-0.349
(0.357)
-0.461
(0.331)
-0.194
(0.316)
80
2.032
0.566
3.472
(3.255)
-0.808
(3.213)
-0.702
(3.182)
218
0.732
0.534
-4.678**
(2.127)
-1.553
(2.128)
-5.61***
(2.134)
388
3.044
0.029
-0.732
(2.536)
-0.802
(2.537)
-5.45**
(2.540)
390
1.935
0.123
0.389
(0.344)
0.911**
(0.373)
0.321
(0.301)
80
1.998
0.122
0.157
(0.207)
0.0500
(0.200)
-0.189
(0.201)
218
2.833
0.418
-0.46***
(0.165)
-0.212
(0.169)
-0.435**
(0.166)
388
9.581
0.023
-0.100
(0.158)
-0.147
(0.156)
-0.194
(0.157)
390
1.644
0.649
-0.257
(0.340)
75
0.559
0.455
3.375
(2.067)
0.285
(2.058)
2.315
(2.083)
390
1.242
0.294
-2.204
-0.281
0.322*
0.018
0.013
(2.134)
(0.359)
(0.169)
(0.143)
(0.131)
-3.880*
0.140
0.354**
0.004
-0.161
(2.125)
(0.300)
(0.167)
(0.141)
(0.129)
-0.888
0.156
0.454**
0.176
0.150
(2.135)
(0.287)
(0.174)
(0.143)
(0.134)
392
80
218
388
390
1.269
0.728
6.363
2.475
5.853
0.285
0.538
0.095
0.480
0.119
Note: Italics indicate statistically significant results.
3.597
(2.722)
7.085**
2006 - 2008
(2.733)
5.815**
2008 - 2010
(2.752)
Observations
218
Ftest/chi2-test
2.615
Prob > F/ chi2
0.052
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
2004 - 2006
Likelihood of Improvement Model
Girls’ Primary Education
Rate of Reform Model
Source: Authors’ calculations
69
1.566
(68.19)
1.548
(68.19)
1.753
(68.19)
80
0.536
0.911
5a. Countries in the Middle
Range of Government
Expenditures
4. Threshold Partnership
3. Passing Control of
Corruption
2. Bordering the Eligibility
Threshold
1. Qualitative MCC Incentive
Effect
2004 - 2006
2006 - 2008
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
-1.566*
(0.878)
-1.345
(0.884)
293
1.859
0.158
-0.139
(0.836)
2006 - 2008
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
-0.548
(0.837)
293
0.232
0.793
0.164
(0.847)
0.802
(0.846)
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
290
0.503
0.605
2.307**
(1.006)
0.169
(1.005)
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
293
3.272
0.04
-0.587
(0.840)
0.494
(0.836)
2008 - 2010
Observations
294
Ftest/chi2-test
0.839
Prob > F/ chi2
0.433
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Voice and
Accountability
Rule of Law
Political Rights
Government
Effectiveness
Control of Corruption
MCC Incentive
Effect
Civil Liberties
Table G4: Ruling Justly Indicators Rate of Reform Model
0.05
-0.03
-0.06
-0.117**
(0.063)
(0.058)
(0.053)
(0.058)
0.03
-0.01
-2.398**
-0.02
-0.21***
(0.063)
(0.058)
(1.146)
(0.054)
(0.058)
-0.04
-0.07
-3.47***
-0.06
-0.18***
(0.063)
(0.059)
(1.153)
(0.054)
(0.058)
390
390
293
390
390
0.729
0.549
4.765
0.538
5.315
0.535
0.649
0.01
0.656
0.00
0.112*
-0.09
-1.328
-0.04
0.102*
(0.059)
(0.055)
(1.101)
(0.051)
(0.055)
0.07
-0.06
-0.06
0.05
(0.059)
(0.055)
(0.051)
(0.056)
-0.03
-0.02
-1.258
-0.05
0.000
(0.059)
(0.055)
(1.101)
(0.051)
(0.055)
390
390
293
390
390
2.353
1.059
0.919
0.544
1.560
0.07
0.366
0.400
0.652
0.199
-0.02
0.138**
-0.495
-0.01
-0.07
(0.06)
(0.055)
(1.115)
(0.051)
(0.056)
0.00
0.09
-0.110
-0.02
0.05
(0.0600)
(0.055)
(1.113)
(0.051)
(0.056)
0.01
0.103*
-0.03
0.00
(0.06)
(0.056)
(0.051)
(0.056)
386
386
290
386
386
0.10
2.247
0.109
0.09
1.615
0.961
0.08
0.897
0.967
0.185
0.03
-0.08
2.443*
-0.124**
0.07
(0.072)
(0.067)
(1.335)
(0.061)
(0.067)
0.00
-0.03
1.202
-0.07
-0.08
(0.072)
(0.067)
(1.335)
(0.061)
(0.067)
-0.05
-0.05
-0.04
-0.05
(0.072)
(0.067)
(0.061)
(0.067)
390
390
293
390
390
0.442
0.518
1.674
1.463
1.858
0.723
0.670
0.189
0.224
0.136
-0.01
-0.04
1.390
-0.100*
-0.06
(0.061)
(0.057)
(1.101)
(0.052)
(0.056)
-0.06
-0.04
1.750
-0.02
-0.17***
(0.06)
(0.056)
(1.095)
(0.052)
(0.056)
-0.04
-0.04
-0.02
-0.06
(0.061)
(0.057)
(0.052)
(0.056)
392
392
294
392
392
0.409
0.244
1.418
1.477
3.383
0.747
0.866
0.244
0.220
0.02
Note: Italics indicate statistically significant results.
Source: Authors’ calculations
70
1. Qualitative MCC Incentive
Effect
2004 - 2006
2006 - 2008
0.226
(0.142)
0.031
(0.140)
2008 - 2010
2. Bordering the Eligibility
Threshold
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
293
2.867
0.238
-0.053
(0.136)
-0.034
(0.135)
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
3. Passing Control of
Corruption
2004 - 2006
2006 - 2008
293
0.157
0.925
0.035
(0.137)
0.143
(0.135)
2008 - 2010
Observations
Ftest/chi2-test
Prob > F/ chi2
4. Threshold Partnership
2004 - 2006
2006 - 2008
290
1.204
0.548
0.244
(0.168)
-0.008
(0.160)
2008 - 2010
5a. Countries in the Middle
Range of Government
Expenditures
Observations
Ftest/chi2-test
Prob > F/ chi2
2004 - 2006
2006 - 2008
293
2.769
0.251
-0.024
(0.137)
-0.09
(0.135)
2008 - 2010
Observations
293
Ftest/chi2-test
0.536
Prob > F/ chi2
0.765
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Voice and Accountability
Rule of Law
Political Rights
Government Effectiveness
Control of Corruption
MCC Incentive
Effect
Civil Liberties
Table G5: Ruling Justly Indicators Likelihood of Improvement Model
0.082
0.024
0.366**
-0.05
-0.295**
(0.149)
(0.146)
(0.139)
(0.150)
(0.145)
0.278*
0.003
0.173
0.001
-0.44***
(0.148)
(0.148)
(0.140)
(0.151)
(0.142)
0.045
-0.244
-0.101
-0.324**
(0.150)
(0.147)
(0.151)
(0.145)
390
390
293
390
390
3.99
4.333
6.509
0.627
9.686
0.262
0.228
0.039
0.890
0.021
0.255*
-0.05
-0.083
0.008
-0.061
(0.139)
(0.139)
(0.135)
(0.141)
(0.139)
0.322**
0.038
0.019
-0.035
0.130
(0.138)
(0.141)
(0.136)
(0.141)
(0.139)
0.374***
-0.157
-0.068
-0.108
(0.138)
(0.138)
(0.142)
(0.140)
390
390
293
390
390
8.231
2.206
0.641
0.359
3.228
0.042
0.531
0.726
0.949
0.358
-0.190
0.110
-0.029
-0.023
-0.191
(0.142)
(0.140)
(0.137)
(0.140)
(0.141)
-0.154
-0.026
0.035
-0.040
0.050
(0.143)
(0.143)
(0.138)
(0.141)
(0.141)
-0.169
0.003
-0.052
-0.120
(0.143)
(0.141)
(0.141)
(0.141)
386
386
290
386
386
2.189
1.090
0.207
0.153
3.605
0.534
0.780
0.902
0.985
0.307
0.0311
-0.267
0.065
-0.357**
0.061
(0.170)
(0.170)
(0.161)
(0.177)
(0.178)
0.427**
-0.042
0.284*
-0.159
-0.170
(0.170)
(0.178)
(0.168)
(0.180)
(0.167)
-0.001
-0.101
-0.170
-0.182
(0.170)
(0.171)
(0.179)
(0.167)
390
390
293
390
390
8.430
2.821
2.964
4.048
2.924
0.038
0.420
0.227
0.256
0.404
-0.033
0.152
0.216
-0.167
0.007
(0.146)
(0.142)
(0.134)
(0.145)
(0.141)
-0.093
0.135
0.120
-0.002
-0.299**
(0.145)
(0.143)
(0.135)
(0.145)
(0.139)
-0.023
-0.005
-0.037
0.118
(0.146)
(0.142)
(0.146)
(0.140)
390
390
293
390
390
0.433
2.299
2.728
1.784
9.380
0.933
0.513
0.256
0.618
0.025
Note: Italics indicate statistically significant results. Source: Authors’ calculations
71
Table G6: Proportion of Indicators Improved Model
Dependent Variable
Treatment
Log of Fraction of Indicators Improved
(1)
(2)
(3)
(4)
(5a)
0.009
(0.021)
-0.024
(0.021)
-0.015
(0.021)
0.257
1.032
0.378
0.001
(0.003)
-0.004
(0.003)
-0.002
(0.003)
0.261
0.995
0.395
0.046
(0.039)
0.009
(0.039)
0.022
(0.039)
0.036
0.538
0.656
-0.001
(0.051)
-0.036
(0.051)
-0.101*
(0.052)
0.082
1.681
0.171
0.003
(0.019)
0.017
(0.019)
0.009
(0.020)
0.252
0.308
0.820
0.001
(0.003)
0.002
(0.003)
0.001
(0.003)
0.256
0.236
0.871
-0.017
(0.036)
0.009
(0.036)
-0.007
(0.036)
0.034
0.188
0.904
0.010
(0.048)
0.060
(0.048)
0.023
(0.048)
0.082
0.597
0.618
-0.020
(0.019)
-0.019
(0.019)
-0.015
(0.020)
0.273
0.447
0.720
-0.003
(0.003)
-0.003
(0.003)
-0.002
(0.003)
0.277
0.408
0.747
0.012
(0.036)
-0.025
(0.036)
0.026
(0.037)
0.047
0.713
0.545
-0.045
(0.048)
-0.013
(0.048)
-0.050
(0.048)
0.087
0.525
0.666
-0.023
(0.024)
-0.014
(0.024)
-0.055**
(0.024)
0.260
1.951
0.121
-0.004
(0.004)
-0.002
(0.004)
-0.009**
(0.004)
0.264
1.940
0.123
-0.078*
(0.043)
-0.033
(0.043)
-0.134***
(0.043)
0.061
3.585
0.014
-0.032
(0.058)
0.022
(0.058)
-0.052
(0.058)
0.089
0.654
0.581
-0.009
(0.020)
-0.023
(0.020)
0.028
(0.020)
0.266
2.494
0.060
-0.002
(0.003)
-0.003
(0.003)
0.004
(0.003)
0.270
2.341
0.073
0.042
(0.036)
0.003
(0.036)
0.091**
(0.036)
0.051
2.733
0.044
-0.002
(0.049)
-0.053
(0.049)
-0.025
(0.049)
0.075
0.523
0.667
390
390
386
390
392
MCC Incentive Effect
All Indicators
2004 - 2006
2006 - 2008
2008 - 2010
R-squared
F-test
Prob > F
Economic Freedom
2004 - 2006
2006 - 2008
2008 - 2010
R-squared
F-test
Prob > F
Investing in People
2004 - 2006
2006 - 2008
2008 - 2010
R-squared
F-test
Prob > F
Ruling Justly
2004 - 2006
2006 - 2008
2008 - 2010
R-squared
F-test
Prob > F
Observations
Note: Italics indicate statistically significant results.
Source: Authors’ calculations
Standard errors in parentheses
Treatment (1) Qualitative MCC Incentive Effect *** p<0.01, ** p<0.05, * p<0.1
(2) Bordering the Eligibility Threshold
(3) Passing Control of Corruption (4) Threshold Partnership (5a) Countries in the Middle Range of Government Expenditures 72
Endnotes
1
Examples of previous ex-post-rewards systems include the Copenhagen criteria, which need to
be met before a country can be eligible for European Union membership (Öhler, Nunnenkamp,
and Dreher 2010, 4-5). Accession to the European Union takes place as soon as a state is able to
“assume the obligations of membership,” similar to the MCC (European Council in Copenhagen
1993). The European Neighborhood Policy is another example. These criteria are also, like most
of the MCC criteria, time-consuming to comply with (Öhler, Nunnenkamp, and Dreher 2010, 4-5).
2
Freyburg and Richter (2010) show that when a state’s national identity contradicts the policy
conditions for membership, the state will inconsistently or not at all comply with the conditions.
3
Johnson and Zajonc (2006, 11-12) also refer to the difference-in-differences (DD) model as a
“Difference-in-Difference-in-Difference” (DDD), but note that the technical difference between a
DD and a DDD is that the DD looks at the indicator’s rate of reform, which already includes a
difference, while the DDD looks only at the indicator’s original score. To avoid confusion, they
use the term DD in their analysis; we follow suit.
4
To cite an extreme example, in 2005, the MCC did not realize staff had compiled two scorecards
for the same country, East Timor, also known as Timor-Leste. The country had different scores for
the two scorecards (MCC [2005]b).
5
The Access to Credit indicator is an average of the Credit Depth and the Legal Rights indicators
(MCC 2012, 33-35). The Business Start-Up indicator is an average of the Cost to Start Business
and Time to Start Business indicators (MCC 2012, 35-37). Finally, the Land Rights and Access
indicator is a weighted sum of three indicators; due to data limitations we only include two of
these in our analysis, Cost to Register Property and Time to Register Property, while dropping the
third, Access to Land (MCC 2012, 31-33).
6
The definition of LICs and LMICs has varied throughout the MCC’s existence. In fiscal year
2006, the MCC defined a LIC as a country with a per-capita GDP of less than $1,575 and LMICs
as countries with a per-capita GDP between $1,575 and $3,255 (MCC [2005]a, 3-4). In fiscal year
2013, however, the MCC defined LICs as being one of the 75 poorest countries in the world in
terms of GDP per capita, while a LMIC is any country above that threshold but under $4,035
(MCC 2012, 3). Our groups are adopted partially based on fiscal year 2006 results.
7
The Rate of Reform model yields the most statistically significant results, 51 percent of which
are positive. The Likelihood of Improvement model gives almost half as many statistical
significant results, but of these, 65 percent are positive. The Proportion of Indicators Improved
model gives five significant results, one of which is positive.
8
Because of the high correlation between Ruling Justly indicators, placing weight on some Ruling
Justly indicators (such as Control of Corruption) is likely to be followed with better scores on the
other Ruling Justly indicators, even if those other indicators are removed from the MCC indicator
list. Since these indicators change slowly, a smaller number of these indicators would increase the
overall mobility of the MCC indicators.
9
In sum, increasing the transparency of the selection process and requirements might lower the bar
for countries in need of aid and provide them with extra incentives to improve their MCC
indicator scores. One downside is that LICs seem to have a more positive movement in the Ruling
Justly indicators than LMICs.
73
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