WPS5254
Policy Research Working Paper 5254
Can Disaggregated Indicators Identify
Governance Reform Priorities?
Aart Kraay
Norikazu Tawara
The World Bank
Development Research Group
Macroeconomics and Growth Team
March 2010
Policy Research Working Paper 5254
Abstract
Many highly-disaggregated cross-country indicators of and several closely-related outcome variables of interest
institutional quality and the business environment have using two leading datasets: the Global Integrity Index
been developed in recent years. The promise of these and the Doing Business indicators. The authors find
indicators is that they can be used to identify specific major instability across outcomes and across levels of
reform priorities that policymakers and aid donors disaggregation in the set of indicators identified by BMA
can target in their efforts to improve institutional and as important determinants of outcomes. Disaggregated
regulatory quality outcomes. Doing so however requires indicators that are important determinants of one
evidence on the partial effects of these many very detailed outcome are on average not important determinants of
variables on outcomes of interest, for example, investor other very similar outcomes. And for a given outcome
perceptions of corruption or the quality of the regulatory variable, indicators that are important at one level of
environment. The analysis in this paper uses Bayesian disaggregation are on average not important at other
Model Averaging (BMA) to systematically document levels of disaggregation. These findings illustrate the
the partial correlations between disaggregated indicators difficulties in using highly-disaggregated indicators to
identify reform priorities.
This paper--a product of the Macroeconomics and Growth Team, Development Research Group--is part of a larger effort
in the department to study the causes and consequences of governance. Policy Research Working Papers are also posted
on the Web at http://econ.worldbank.org. The author may be contacted at akraay@worldbank.org.
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
Produced by the Research Support Team
Can Disaggregated Indicators Identify Governance Reform Priorities?
Aart Kraay (The World Bank)
Norikazu Tawara (Kanto Gakuen University and Nihon University)
1818 H St. NW, Washington, DC, akraay@worldbank.org, and 200 Fujiakucho, Ota, Gunma, Japan, nori.tawara@gmail.com,
respectively. We would like to thank Nathaniel Heller, Daniel Kaufmann, Eduardo Ley, Chris Papageorgiou, Luis Serven, and
Stefan Zeugner for helpful discussions, and especially Martin Feldkircher and Stefan Zeugner for providing their Rcode for
implementing Bayesian Model Averaging. Financial support from the Japan Consultant Trust Fund and the Knowledge for
Change Program of the World Bank is gratefully acknowledged. The views expressed here are the authors' and do not reflect
those of the World Bank, its Executive Directors, or the countries they represent.
1. Introduction
Strong institutions, including a sound regulatory environment for private sector economic
activity, are widely considered to be crucial to successful economic development. This consensus has
been informed by a vast body of empirical evidence linking various measures of institutional and
regulatory quality to development outcomes. Translating this empirical consensus into concrete policy
advice for countries seeking to improve their institutional and regulatory environment has been much
more difficult, however, as many of the empirical measures used in this literature have been short on
specifics. For example, in one of the most influential papers in the "institutions matter" literature,
Acemoglu, Johnson and Robinson (2001) proxy for institutional quality using the risk of expropriation, as
perceived by analysts at a commercial risk rating agency. Similarly, in a seminal paper, Mauro (1995)
documents the links between perceptions of corruption from a commercial risk rating agency and
investment and growth rates across countries. Absent details on the specific policy interventions that
might affect these perceptions of expropriation risk or corruption, providing policy advice based on such
broad measures is a little bit like telling aspiring golfers that they should play more like Tiger Woods.
Recognizing this, a number of organizations have embarked on major efforts to develop much
more disaggregated measures of specific details of the institutional and regulatory environment. The
promise of such detailed and disaggregated indicators is to pinpoint specific areas in need of reform in
order to improve institutional and regulatory outcomes. As noted by Global Integrity, which produces a
very detailed set of over 300 indicators of public sector accountability mechanisms that we use in this
paper, "we view the [Global Integrity] Indicators' greatest strength as their ability to unpack governance
challenges within a country into discrete, actionable issues rather than just single numbers or rankings.
The richness of the data set more than 300 indicators for each country enables a discussion of how
best to allocate limited political and financial capital when the challenges are many and the resources
few.1". Global Integrity goes on to argue that "The Global Integrity Index and Integrity Indicators assist
[foreign aid] donors by helping to prioritize governance and anticorruption challenges in a country,
region, or globally. By providing an actionable roadmap for reform, donors can begin to sequence key
governance interventions to tackle the most pressing anticorruption weaknesses in a country or help
1
All of the quotations from Global Integrity are taken from their website, at this link accessed on November 12,
2009: http://report.globalintegrity.org/methodology.cfm.
2
bolster those "pillars of integrity" that are functioning well. The Index empowers donors, both bilateral
and multilateral, by offering a platform for evidencebased reform efforts."
Similarly, the World Bank, which produces the very detailed Doing Business indicators of the
business regulatory environment, notes that the goal of this exercise is "to provide an objective basis for
understanding and improving the regulatory environment for business."2 The World Bank has also
developed considerable resources to developing and promoting other such "actionable" governance
indicators (AGIs), of which they include Doing Business and Global Integrity as leading examples.
According to the World Bank, the distinguishing feature of such "actionable" indicators is that they
provide "...convenient and replicable guidance on the features (rules of the game, organizational
capabilities) for which reform interventions are likely to prove most helpful for improving the
performance of particular governance elements".3
Realizing the promise of such detailed indicators to identify and prioritize specific reform efforts
requires an understanding of the relative magnitude of the effects of each of the individual
disaggregated indicators on the corresponding outcomes that policymakers might want to improve. For
example, a policymaker interested in reducing corruption (or even just the perceptions of the
prevalence corruption held by domestic or foreign investors) might want to know which of the over 300
individual measures comprising the Global Integrity Index would have the largest impact on corruption.
Our premise is that a policymaker or an aid donor would not particularly care whether a country scores
well on any specific disaggregated indicator (for example, the existence of an anticorruption
commission), but rather cares whether improving such an indicator (for example, by creating an
anticorruption commission) will actually reduce corruption. Similarly, a policymaker looking for the
most "bang for her buck" in the area of business regulatory reform would want to know the magnitude
of the partial effects of each of the many indicators in the Doing Business dataset before choosing the
few on which she would like to expend her political capital to seek improvements in these areas.4
2
See http://www.doingbusiness.org/Documents/DB10_About.pdf.
3
http://www.agidata.org
4
Of course, the process of developing and implementing governancerelated reforms is much more complex that
simply identifying those areas with the highest impact and acting on them. In reality, policymakers must balance
all of the political and financial costs and benefits of reforms in particular areas. All we attempt to do here is to
shed some light on the difficulty of quantifying a narrow measure of the benefits of reforms, which is their
estimated impact on outcomes.
3
On this crucial question of partial effects of disaggregated indicators on outcomes of interest,
empirical evidence has not kept pace with the proliferation of very detailed indicators of institutional
quality and the regulatory environment. While identifying these partial effects is central to realizing the
promise of disaggregated indicators to identify specific reform priorities, in this paper we argue that it is
also extremely difficult if not impossible to do so convincingly. The problem is simply one of degrees
of freedom: the more disaggregated indicators become, the more partial effects of individual indicators
on outcomes of interest there are to be estimated, and the less precisely each individual partial effect
can be estimated. For example, the overall Ease of Doing Business ranking of the Doing Business
project consists of 41 measures of the business regulatory environment, for 181 countries. This means
that there are on average just 4.5 data points with which to estimate the partial effects of each
individual indicator on some outcome of interest in a crosssectional regression. In the case of Global
Integrity, the degreesoffreedom problem is even more stark, as there are over 300 variables in this
dataset which spans just 92 countries, so that it simply is infeasible to estimate the partial effects of
each of them in a single encompassing crosscountry regression including all indicators.
One possible solution to this degrees of freedom problem is to apply data reduction techniques
of some sort. That is, one might simply reaggregate the highly disaggregated variables by averaging
them together in some way. For example, one could simply average together the 41 indicators of the
business regulatory environment in the Doing Business dataset to obtain the overall "Ease of Doing
Business" ranking, and then estimate a regression of the outcome of interest on this overall ranking.
However, this amounts to imposing the restriction that the partial effects of each of the 41 individual
variables are the same and equal to 1/41 of the impact of the aggregate indicator. And if this really
were true, then it would not matter at all which dimension of the regulatory environment is improved,
since each individual indicator is assumed to have the same effect on outcomes. This seems quite
implausible, and moreover contrary to the entire spirit of developing such disaggregated and
`actionable' indicators. A slightly more sophisticated and commonlyused alternative would be to
extract the first principal component of the 41 indicators and use it as an explanatory variable. But this
would be no improvement. The first principal component is simply a weighted average of all of the
individual indicators, with weights proportional to their intercorrelations. Thus, the few variables that
happen to be highly correlated with each other would receive more weight in the aggregate. But there
4
is no reason to expect that these variables that happen to be highly correlated with each other also are
those that have the largest effects on the outcome of interest.5
A different approach to solving the degreesoffreedom problem would be to simply choose a
subset of the many disaggregated indicators that seem most plausible a priori, and try to estimate a
regression of the outcome of interest on this smaller set of preselected variables. The advantage of this
approach is that it tries to more precisely estimate the effect of a few of the disaggregated indicators on
the outcome of interest by imposing the restriction that the remaining indicators have no effect.
However, the problem of course is that this will give valid estimates of the partial effects of the included
variables only if they are orthogonal to all of the other disaggregated indicators that matter for
outcomes but are not included in the regression. This is the standard problem of omitted variable bias.
Moreover, this approach of preselecting a subset of regressors throws open the door to specification
searching or data mining for a subset of variables that happens to confirm the researcher's or
policymaker's priors.
To avoid these problems of specification searching, we instead use Bayesian Model Averaging
(BMA) to systematically document the partial effects of many disaggregated indicators on outcomes of
interest. We do this using disaggregated indicators from Global Integrity and Doing Business, in three
steps. First, we identify a set of potential outcome variables for each of these two sets of disaggregated
indicators. These variables capture outcomes we think a policymaker might reasonably want to
influence by reforming the areas captured by these indicators. For reasons we elaborate below, we
choose seven closelyrelated subjective measures of corruption as outcome variables linked to the
Global Integrity Index, and seven closelyrelated subjective measures of the quality of the regulatory
environment as outcome variables for Doing Business. Second, for each combination of outcome
variable and disaggregated indicator set, we use BMA to obtain estimates of the partial effects of each
of the disaggregated indicators on the corresponding outcome of interest. Third, once we have
identified the partial effects of the disaggregated variables on each outcome, we compare these results
across different outcome variables and across different levels of disaggregation.
The good news is that we find BMA to be an effective tool for identifying a relatively small
number of disaggregated indicators that display strong partial correlations with a given outcome of
5
Lubotsky and Wittenberg (2006) elaborate on this point, showing that the use of multiple proxies as explanatory
variables in a linear regression generally dominates the use of a single summary of those proxies.
5
interest. In this respect, we join a growing literature in recognizing the value of BMA as a tool for
systematically identifying robust partial correlates of outcomes when the precise empirical specification
is unknown. However this positive message is tempered by two important pieces of bad news. The first
is that there is a great deal of instability across very similar outcomes in terms of which variables the
BMA procedure identifies as important partial correlates of outcomes. To take the most extreme
example, our seven corruption outcome variables have a quite strong average pairwise correlation of
0.62. Yet when we compare across these very similar outcomes, we find that there is virtually no
overlap in the subsets of the 303 disaggregated individual indicators in GII that are identified as
important determinants of these outcomes by the BMA procedure. The second is that there is a very
high degree of instability across levels of aggregation in terms of which individual indicators are
identified as important determinants of outcomes by BMA. In particular, we find that the probability
that an individual disaggregated indicator is identified as an important determinant of outcomes is not
significantly increased by knowledge that the higherlevel aggregate to which it belongs was identified
as an important determinant of outcomes. And conversely, knowing that a higherlevel aggregate is
identified by BMA as an important determinant of an outcome does not mean that the more
disaggregated variables on which it is based are more likely to be identified as important determinants
of the same outcome.
These results suggest that it may be very difficult to use the disaggregated indicators of
institutional and regulatory quality that we examine here to provide policy advice to guide reforms in
these areas. Under the reasonable assumption that policymakers would like to identify highimpact
reforms that that matter for outcomes, it becomes important to identify which those reforms are. Yet
we find that quite small changes in the empirical proxies for outcomes that we consider lead to wild
fluctuations in the set of variables that are identified as important for those outcomes.
The rest of this paper proceeds as follows. In Section 2 we provide details on the Global
Integrity and Doing Business datasets that we use in our empirical analysis, and we justify our selection
of outcome variables corresponding to these datasets. In Section 3 we explain the Bayesian Model
Averaging methodology. Section 4 contains the results and Section 5 discusses the robustness of the
results and caveats. Section 6 offers conclusions.
6
2. A First Look at the Data
We illustrate the challenge of identifying relevant determinants of governance outcomes based
on very disaggregated governance indicators using two leading datasets. The first is the Global Integrity
Index (GII), compiled by Global Integrity, a Washingtonbased advocacy organization. Quoting from its
mission statement, "Global Integrity generates, synthesizes, and disseminates credible, comprehensive
and timely information on governance and corruption trends around the world. As an independent
information provider employing ontheground expertise, we produce original reporting and quantitative
analysis in the global public interest regarding accountable and democratic governance. Our
information is meant to serve simultaneously as a roadmap for engaged citizens, a reform checklist for
policymakers, and a guide to the business climate for investors." The GII reports over 300 individual
variables that score countries on various highlydetailed dimensions of institutions that matter for public
sector integrity and accountability. The individual questions on which the GII is based are scored by
locallyrecruited experts (typically one per country), and are then vetted by an anonymous peerreview
process involving 35 reviewers per country.
The GII can be disaggregated at three levels. The overall index is organized into six main
categories (Civil Society, Public Information and the Media, Elections, Government Accountability,
Administration and Civil Service, Regulation and Oversight, and Anticorruption and Rule of Law). These
six are further disaggregated into 23 subcategories (e.g. Elections is further decomposed into Voting
and Citizen Participation, Election Integrity, and Political Financing). And finally these 23 subcategories
are built up from the 303 individual variables. For example, Election Integrity is an average of 15
separate questions relating to the existence and effectiveness of electoral monitoring bodies. A key and
extremely valuable feature of GII is that it consistently matches up questions about de jure rules and the
de facto implementation of these rules. For example, within Government Accountability, question 12a
assesses whether "In Law, citizens have a right of access to government information and basic
government records (Yes/No)". This is followed by question 13a which asks "In practice, citizens receive
responses to access to information requests within a reasonable time (0100 scale)". In our use of the
GII data we systematically distinguish between the "In Law" and "In Practice" questions in GII. In
particular, we decompose each of the six GII main categories into separate averages of all the "In Law"
and "In Practice" questions, resulting in 12 highlevel aggregates. Similarly, we disaggregate each of the
7
23 subcategories into averages of the corresponding "In Law" and "In Practice" questions, resulting in
45 indicators at the more disaggregated level.6
GII has cumulatively covered 92 countries since its inception in 2004, some for multiple years.
We take data from the 2007 and 2008 waves of GII, covering 50 and 46 countries respectively (available
from the GII website as of February 2009). We take all of the 46 countries covered in 2008, and add to
this 24 countries covered in 2007 but not in 2008, to obtain a crosssection of 70 countries in total. In
some cases our sample size will be slightly smaller depending on the country coverage of the outcome
variables we work with. The 2008 questionnaire contains 320 individual items, while the 2007
questionnaire covers 304 items. The overlap between the two is extremely close, and after merging the
two questionnaires we have a total of 303 questions asked in both years.7 Of these, 184 are "In
Practice" questions and 119 are "In Law" questions.
The second dataset we use is the Doing Business (DB) indicators produced by the World Bank.
The DB indicators cover 10 dimensions of the business regulatory environment (Starting a Business,
Dealing with Construction Permits, Employing Workers, Registering Property, Getting Credit, Protecting
Investors, Paying Taxes, Trading Across Borders, Enforcing Contracts, and Closing a Business). These are
based on 41 individual measures. For example, the "Starting a Business" measure is itself based on four
subindicators, measuring (1) the number of procedures, (2) the number of days, (3) the cost of
associated fees, and (4) the minimal capital requirement, required to start a new business. The DB data
is scenariobased. Respondents are provided with a very detailed scenario about a hypothetical
transaction, for example, registering a firm with particular characteristics in the capital city of the
country. The data collected by DB correspond to what a hypothetical firm described in the scenario
would experience. Since the subcomponents of each of the 10 DB measures are measured in different
units, countries are first ranked on the individual variables. These ranks are then averaged within each
of the 10 broad indicators to arrive at the indicator ranks. Finally, the average ranks on 10 indicators
6
One of the GII subcategories, Anticorruption Law, consists exclusively of "In Law" questions and so there is no
corresponding "In Practice" aggregate for us to construct. This is why we have only 45, and not 46 variables at this
level of disaggregation.
7
We focus on the 2007 and 2008 questionnaires which are most comparable to each other. A few questions were
asked at a more detailed level in the 2008 data when compared with 2007. We therefore average the following
pairs of questions in the 2008 data to make them comparable to their 2007 analogues : questions 20b and 21b;
20e and 21d; 20f and 21e; 20g and 21f; 22a and 23a; 22b and 23b; 22d and 23c; 22e and 23d; 22f and 23e; 24a and
25a; 24b and 25b; and 24c and 25c. We also delete questions 21a, 46a, 46e, and 46i from the 2008 questionnaire
that we not asked in 2007. This reduces the number of individual questions in 2008 to 303.
8
themselves are averaged to arrive at the overall Ease of Doing Business ranking.8 The DB respondents
consist primarily of locallyrecruited attorneys familiar with the relevant laws that form the basis for the
DB summary measures. In contrast with GII, DB is primarily focused on collecting de jure as opposed to
de facto information, and so we cannot distinguish the DB indicators along this dimension as we do for
GII.
Our next step is to identify outcome variables of interest corresponding to these two datasets of
potential policy interventions. Our objective in doing so is to try to identify outcomes sufficiently close
to the indicators themselves that a policymaker might reasonably consider trying to affect this outcome
through policy reforms that would be identified and captured by changes in the individual indicators. In
the case of GII, Global Integrity is very explicit that the goal of the GII is to provide guidance on specific
indicators to be improved (recall quotes in introduction). Global Integrity also encourages users of the
GII to view it as "...a powerful variable with which to explore other key development indicators --
economic growth, income distribution, health and education rates, and other key socioeconomic
indicators." While ultimately there surely are links between the dimensions of governance captured by
GII and these very broad outcomes, we set ourselves the more limited goal of assessing the links
between the many disaggregated variables in GII and more proximate outcomes related to corruption
itself. After all, according to Global Integrity the GII "...represent one of the world's most comprehensive
data sets providing quantitative data and analysis of anticorruption mechanisms and government
accountability in diverse countries around the globe." Global Integrity goes on to very sensibly note
that the relationship between the GII and corruption is unlikely to be perfect, cautioning that: "...users
should not necessarily interpret high scores on the Global Integrity Index as reflective of countries where
there is no corruption. Instead, those results should simply be understood to reflect circumstances where
key anticorruption safeguards exist and have been enforced, which while one would hope reduces
corruption may not eliminate it entirely. In simple terms, corruption can and will occur even where
societies have implemented what are understood to be ideal reforms."
Based on this, we think it is reasonable to investigate the links between the many disaggregated
GII measures and direct proxies for corruption itself. Of course, corruption is very difficult to measure
directly, and the vast majority of empirical measures of corruption are based on the perceptions of
8
There are missing values for some countries on some of the DB variables. We follow DB's practice of assigning
the lowest possible rank to such observations prior to averaging ranks across indicators. The overall DB ranking is a
simple average of the ranking on the 10 subcategories.
9
survey respondents. This is not necessarily a handicap as argued in Kaufmann and Kraay (2008), not
only do subjective assessments of corruption provide valuable information, but also policymakers
should care about these perceptions because respondents act on them.9 We draw on seven different
measures, all of which are taken from the Worldwide Governance Indicators project (see
www.govindicators.org, and Kaufmann, Kraay and Mastruzzi (2009) for descriptions). Five of these are
expert assessments of the prevalence of corruption taken from commercial business information
providers (Economist Intelligence Unit (EIU), Political Risk Services (PRS), Global Insight Global Risk
Service (DRI), Global Insight Business Risk Conditions (WMO), and Cerebus Corporate Intelligence Gray
Area Dynamics (GAD)). One additional expert assessment is the World Bank's Country Policy and
Institutional Assessment (CPIA). Finally, we draw on responses from a large crosscountry survey, the
Global Competitiveness Report (GCS) survey of firms in 134 countries, that asks firm managers a variety
of questions about corruption. Table 1 lists the precise questions about corruption assessed by each of
these sources.
Given DB's emphasis on the business and regulatory environment, we adopt the same strategy
as with GII of using closelyrelated perceptions of the quality of the business environment as
corresponding outcome variables. The DB project provides as evidence of its own relevance strong
correlations of the overall DB measure with other leading indicators of the business environment
produced by OECD and World Economic Forum.10 We follow a similar approach here, relating the DB
indicators to a set of seven outcome variables that are also taken from the Worldwide Governance
Indicators project, and capture a variety of perceptions regarding the quality of the regulatory
environment. We use data from the same six expert assessments as we do for GII, but now focused on
the regulatory environment, as well as data from the Global Competitiveness Survey. Moreover, we
observe that Doing Business is also circumspect about the limited nature of its indicators, sensibly
noting that "Doing Business does not measure all aspects of the business environment that matter to
firms or investors--or all factors that affect competitiveness. It does not, for example, measure security,
macroeconomic stability, corruption, the labor skills of the population, the underlying strength of
9
In some cases policymakers may very well have the immediate objective of influencing these perceptions directly,
either to improve the government's polling results or to improve the country's standing in crosscountry rankings
based on these corruption assessments.
10
See documentation provided at http://www.doingbusiness.org/Documents/DB10_About.pdf
10
institutions or the quality of infrastructure." The precise questions about the regulatory environment
assessed by each of these sources can also be found in Table 1.
We emphasize that our choice of outcome variables for the GII and DB datasets is not intended
to be exclusive in any sense, but rather is purely illustrative. These outcomes are surely not the only
ones that policymakers might want to influence by reforms to the policies and institutions captured by
GII and DB. Rather, we think these particular outcomes might plausibly be among the many considered
by policymakers, and provide a good illustration of the challenges of identifying the partial effects of the
many disaggregated indicators making up these datasets. We recognize also that in the case of GII, the
objective of the GII is broader than simply measuring corruption, but rather seeks to document the
disaggregated ingredients of a wide range of transparency and accountability mechanisms. 11
Nevertheless, one can readily rationalize looking at narrower measures of corruption as a relevant
outcome variable for GII by noting that corruption can be viewed as a symptom of the failure of such
transparency and accountability measures, and so would be a reasonable proxy outcome for
policymakers to consider.
Before turning to the formal analysis of the links between disaggregated indicators and
outcomes, we document two important features of the data. The first is that both the overall aggregate
GII and DB measures are in fact strongly correlated with each of the outcome variables. We show this in
Table 2 , which summarizes the results of regressing each of the outcome variables on its corresponding
aggregate GII or DB measure, both unconditionally (in the top panel), and conditionally controlling for
log per capita GDP (in the bottom panel). A unit increase in the overall measure of GII will increase a
measure of each outcome variables by 0.8 on average statistically significantly. Conditioning on per
capita GDP (in logs) only slightly reduce the effects and significance of the overall GII measure on each
of the 7 outcome variables. The effects of the overall DB measure on each of the 7 outcome variables
are more significant and greater in size, both unconditionally and conditioning on GDP per capita. Of
course, we cannot interpret these correlations in Table 2 as purely reflecting a causal effect from the DB
and GII indicators to the outcomes of interest there are many potentially confounding omitted
11
We note that Global Integrity does place a disclaimer on its website to the effect that the GII do not measure
corruption: "...it is worth emphasizing that the Integrity Indicators do not measure corruption but rather assess its
opposite, that is, anticorruption and good governance institutions, mechanisms, and practices. While corruption
and bribery are difficult if not impossible phenomena to capture empirically, assessing the performance of key
integritypromoting mechanisms such as civil society, the media, and law enforcement provides a much more
concrete access point through which to analyze and monitor government accountability.".
11
variables. However, it seems reasonable to think that they at least in part reflect an effect running from
the specific institutions and regulations measured by these two datasets to the relevant outcomes. To
the extent that this is the case, our goal in this paper is to document the extent to which these
correlations between the aggregate GII and DB measures and outcomes of interest can be unbundled
into differential impacts of the many highlydetailed subcomponents of these broader measures.
This raises the question of a second feature of the data that we want to document before
moving on: the many disaggregated variables underlying these two broad variables have surprisingly (at
least to us) low intercorrelations among themselves. We summarize these in Table 3. The rows of
Table 3 correspond to the GII and DB datasets at varying levels of disaggregation. For each level of
disaggregation, we compute all of the pairwise correlations between the variables at that level of
disaggregation. Then we summarize the distribution of these (many!) estimated correlations by
reporting the 10th, 25th, 50th, 75th and 90th percentiles for each combination of indicators and levels of
disaggregation. For example, the median pairwise correlation among all combinations of GII variables is
0.4 at the GII12 level of disaggregation, 0.25 at the GII45 level of disaggregation, and just 0.09 at the
GII303 level of disaggregation. At this highest level of disaggregation, fully 90 percent of all pairwise
correlations are less than 0.35. In the case of Doing Business, the median pairwise correlations are just
0.32 and 0.18 at the DB10 and DB41 levels of disaggregation. These quite moderate pairwise
correlations between the disaggregated indicators are a key feature of the data because they highlight
their potential to be informative about their corresponding outcomes. Had these individual
disaggregated indicators been very highly correlated with each other, it would have been obvious a
priori that it would be very difficult to identify the partial effects of any one of them due to problems of
strong collinearity. However, this does not appear to be a major problem in the GII and DB data.12
For comparison purposes, we also document the distribution of the pairwise correlations
between the outcome variables of interest. There is a striking contrast here with the individual
indicators. The outcome variables are quite strongly correlated with each other,with a median pairwise
correlation of 0.61 for the GII outcomes measuring corruption, and 0.68 for the DB outcomes measuring
the regulatory environment. We interpret these high correlations as suggesting that these different
candidate dependent variables are measuring broadly similar outcomes.
12
Of course, these pairwise correlations are not sufficient to indicate or rule out problems of collinearity in models
with more than two explanatory variables. We discuss below in Section 5 why our main instability results are likely
not due to problems of collinearity among regressors.
12
3. Bayesian Model Averaging
We now describe in some detail the Bayesian Model Averaging (BMA) procedure that we will
use in the remainder of the paper to document the partial correlations between the disaggregated GII
and DB indicators and their corresponding outcome variables. Over the past several years BMA has
become a widelyused tool for assessing the robustness of regression results to variations in the set of
included control variables. The seminal application to crosscountry growth empirics is Fernandez, Ley
and Steel (2001), followed by SalaiMartin, Doppelhofer and Miller (2004), and then many others.
Brock, Durlauf and West (2003) particularly emphasize the decisiontheoretic aspects of BMA as a useful
tool for guiding policy choices. Recently Ciccone and Jarocinski (forthcoming) have used BMA to
document the nonrobustness of growth empirics to minor data revisions in the dependent variable,
which is closely related to our finding of instability across alternative outcome variables. There is also
an active literature extending the BMA methodology in various dimensions, including collinear
regressors (Durlauf, Kourtellos, and Tan (2009)) panel data applications (Moral (2009)), and instrumental
variables estimation (Eicher, Lenkoski, and Raferty (2009). Finally, several papers including Fernandez,
Ley and Steel (2001), Ley and Steel (2009), Eicher, Papageorgiou and Raferty (2009) and Feldkircher and
Zeugner (2009) all discuss the consequences of alternative prior assumptions for the outcome of BMA.
The basic idea of BMA is simple. Rather than base inferences about parameters of interest on
just one preferred model consisting of one particular set of explanatory variables, BMA combines
inferences about parameters of interest across many candidate models corresponding to different sets
of explanatory variables. To be more precise, let y denote an Nx1 vector of observations on the
dependent variable of interest, and let X denote an NxK matrix of potential explanatory variables for y.
Let j 1,2, ... , 2K index models, distinguished by their included set of regressors. In particular let Xj
denote an NxKj matrix containing a subset of KjK regressors from X. A model j consists of a linear
regression of y on the variables in Xj, i.e.:
(1)
where is an Nx1 vector of ones and j is an Nx1 vector of i.i.d. normal disturbances with zero mean and
variance .The scalars and j and the Kjx1 vector j are the parameters of model j, and following the
bulk of the literature on BMA we use Zellner's gprior for them, i.e.
13
(2) , , ; 0,
where ; , denotes a normal density function for x with mean a and variance b, and f() denotes a
joint density function for variables inside the parenthesis. The prior distribution for the slope
coefficients, conditional on , and model j, is normal and centered on zero, with a variance equal to
that of the OLS estimator, but scaled by g. As the prior parameter g becomes small, the prior variance
expands and so the prior for the slopes becomes more diffuse or agnostic. As is wellknown, when g is
small, Bayesian inference for the parameters of the model mimic frequentist ones. In particular, the
posterior distribution of the slope coefficients for a given model is a multivariatet distribution with
mean and variance equal to that of the conventional OLS estimator, but both scaled by a "shrinkage
factor" of that approaches 1 as the prior becomes more and more diffuse. In contrast, larger values
of g reflect a stronger prior belief that the slope coefficients are in fact zero, and so the posterior mean
shrinks towards zero and the posterior variance is smaller.
The key ingredient in BMA is the assignment of probabilities to different models. Let
| , denote the posterior probability of model j. These are computed using Bayes' Rule, i.e.
(3) | , | ,
where | , is the marginal likelihood of model j, and is the prior probability assigned by the
researcher to model j. Fernandez, Ley and Steel (2001) show that, given the gprior and the assumption
of homoskedastic normal disturbances, the marginal likelihood is given by:
(4) | , 1
1 1
where is the Rsquared associated with model j. This expression tells us that models with better fit,
as measured by a higher Rsquared, have higher likelihood. However the marginal likelihood trades off
improvements in fit against increases in model size, with the model size penalty captured by the first
term. The prior parameter g plays two roles here: the smaller is g, the greater is the model size penalty,
but at the same time the more responsive is the likelihood to improvements in Rsquared.
We will use a very standard and straightforward prior for model j that reflects the assumption
that there is a fixed probability that any one of the variables in X is included in model Mj. Assuming
14
independence of inclusion across the variables in X, this prior implies a mean prior model size of
, and a prior probability for model j given by:
(5)
As long as prior model size /2 then the prior favours more parsimonious models with fewer
regressors.
Putting these ingredients together we have the following expression for the posterior
probability of model j:
(6) | , 1
1 1
Thus BMA can be thought of as a way of assigning probabilities of models with different sets of
regressors, with higher probabilities assigned to models with better fit, subject to a model size penalty.
These posterior model probabilities can then be used to average inferences across different models. For
example, a key quantity we will be considering is the Posterior Inclusion Probability (PIP) of a particular
explanatory variable k. This is defined as the sum of the posterior probabilities of all models including
variable k, and is a useful summary of how "important" a variable is in the sense of being included in
models that are more likely. Similarly, a useful summary of the magnitude of the effect of a particular
regressor is its posteriorprobabilityweighted average effect across all models.
Implementing BMA requires the choice of the two prior parameters, and g. Our choice of
these parameters is driven primarily by the logic of the thought experiment we are performing. We
have in mind a policymaker interested in improving one of the outcome variables, who would like to
identify a "small" subset of individual indicators with "large" impacts on outcomes, on which to focus
reform efforts. We choose prior mean model size to ensure that posterior mean model size is "small".
While the threshold determining "small" is of course arbitrary, we find that by setting 0.25 we
obtain posterior mean model sizes in the range of typically 25 righthandside variables, which seems to
us a plausibly small set that a policymaker might focus on.13 Turning to g, our objective here is simply to
13
The only exception is that we set =10 for a case of GII303. If we set =0.25*303 in this case, then the sampler
chain attempts to visit infeasible models where k>N (more on this below).
15
ensure that the inferences from any given model mimic closely traditional frequentist ones, and
accordingly we set g to be small, i.e. g=0.01, so that the shrinkage factor is very close to one.
We note however that fixing the prior parameters based on these objectives of course does not
avoid the problem of sensitivity of results to prior choices. Of primary concern here is the choice of g,
which as noted above plays two roles in the assignment of posterior probabilities across models: lower
values of g increase the model size penalty for adding additional regressors, and also increase the
sensitivity of the posterior probability to improvements in Rsquared. Together these two forces imply
that when g is small, the posterior probability will tend to concentrate on models with few regressors,
and among these, on models with high Rsquareds. This concentration of posterior model probabilities
on a few models can be extreme, which Feldkircher and Zeugner (2009) label the "supermodel effect".
And this in turn can lead to a strong concentration of high PIPs on just a few variables. Potentially, this
"supermodel effect" can lead to very large changes in posterior model probabilities and PIPs as we move
from one dependent variable to another. However, as we discuss further below, our main finding of
instability across outcomes will not be driven by this effect.14
We note that implementing BMA in principle poses major computational problems, as the
number of models to be estimated and averaged increases in the number of explanatory variables at the
rate 2K. When K=303 as is the case in GII, this is an astronomically large number of models (1.6 followed
by 91 zeros!). Even for the more moderate K=41 in DB, there are still over two trillion (2.2x1012)
potential models to consider. Fortunately, fast and accurate algorithms for identifying and sampling
only those models with the largest posterior probabilities have been developed, greatly reducing the
computational burden, and we rely on them here.15 Following the BMA literature, the posterior
distribution is approximated by simulating a sample from it by applying MC3 sampler (Madigan and York
1995, as described in Fernandez, Ley and Steel (2001)). We also follow Fernandez, Ley and Steel (2001)
in using the correlation between analytical and empirical posterior model probabilities as a criterion for
convergence of the sampling chain. We will report results in the next section from a simulation run with
a burnin of 100,000 discarded drawings and N million recorded drawings, where N is 0.2, 0.5, 1, 0.1, and
14
See also Ciccone and Jarocinski (forthcoming) who find that the set of crosscountry growth determinants
identified as robust using BMA changes drastically as the dependent variable changes using different revisions of
the Penn World Tables. Feldkircher and Zeugner (2010) argue that this sensitivity is largely driven the authors'
choice of a small value for g.
15
We are very grateful to Martin Feldkircher and Stefan Zeugner whose Rcode (available at
http://feldkircher.gzpace.net/links/bma) we used to implement BMA in this paper.
16
0.3 for cases of GII12, GII45, GII303, DB10, and DB41. We choose this number so that a high positive
correlation between posterior model probabilities based on empirical frequencies and the exact
analytical likelihoods is obtained. We also report estimated total posterior model probabilities visited by
the chain using a measure of George and McCulloch (1997).
Note also that in our GII application the number of candidate explanatory variables far exceeds
the number of countries when we work with the GII data at the highest level of disaggregation (K=303
but N=70). This means that the space of models to be considered potentially includes very many models
that cannot feasibly be estimated since there are more regressors than observations. In this paper we
take the shortcut of ignoring this feasibility constraint. We find that, given our choice of prior
parameters, the BMA algorithm is able to cover a very high fraction of the posterior probability without
ever attempting to visit infeasible models with K>N.16 This is because our choice of priors ensures that
the model size penalty is sufficiently high that such large models have extremely small posterior
probabilities.17
Finally we acknowledge at the outset the important caveat that we are combining inferences
from a series of very simple linear OLS regressions. As such, all of our conclusions are subject to the
usual limitations of such a model. In particular, a maintained assumption is that the error term is
independent of the regressors in all models, an assumption that would clearly be violated if there were
reverse causation or omitted variables. We also by assumption rule out any plausible nonlinearities
such as interactive effects between variables. As we discuss further below however, addressing these
very likely important issues we think would only further reinforce our basic point that it is extremely
difficult to identify a small subset of indicators that are robust determinants of outcomes of interest.
16
More formal approaches to the K>N problem are available. One approach is to formally apply a prior weight of
zero to infeasible models. Another example is Eicher, Papageorgiou and Roehn (2007) who propose an iterative
BMA procedure applicable when K>N. For a nonBayesian approach to this problem, see Candes and Tao (2005)
propose an algorithm that can reliably estimate the parameters of a single "true" model with Kj>N even when the subset of the columns of X included in the "true"
model is not known a priori.
17
To see this, note that in the case of GII303, the largest model visited by the BMA algorithm has 16 explanatory
variables when CPIA is an outcome variable, 25 when DRI is an outcome variable, 19 when EIU is used, and so on.
Consider now comparing this with the smallest infeasible model with ki=70. Given K=303 and our choices of =10
and g=0.01 , the model size penalty in the first two terms of Equation (6) would be on the order of 1.7x1099,
suggesting that infeasible models have vanishingly small posterior probabilities and so can safely be ignored. This
dimensionality problem also means that we cannot use alternative model selection techniques based on
encompassing regressions advocated by Hendry and Krolzig (2004, 2005).
17
4. Results
In order to develop familiarity with the methodology, we begin by discussing in some detail the
results of the BMA exercise for GlI, at the leastdisaggregated level, that are reported in Table 4A.
Subsequent tables report the same information, for higher levels of disaggregation of GII (Tables 4B and
4C), and for DB (Tables 5A and 5B). In Table 4A we have K=12 candidate righthandside variables in the
X matrix, and the 7 choices of outcome variables y discussed in Section 2. The rows of the main part of
Table 4A correspond to these righthandside variables, identified in the first column (with the prefixes
"LQ" and "PQ" denoting the "In Law" and "In Practice" questions in GII). The sets of columns of Table 4A
correspond the different outcome variables.
For each outcome variable, Table 4A first reports the Posterior Inclusion Probability (PIP) for
each variable. This is simply the sum of the posterior probabilities across all models in which the
variable appears. A high PIP indicates that the set of models in which the variable appears jointly has a
high posterior probability. Consider for example the CPIA outcome variable, measuring World Bank
country economists' assessments of "Public Sector Transparency and Accountability". The variable GII
variable "PQElections" has a high PIP of 0.934. This means that 93.4 percent of models on a probability
weighted basis include this variable, which captures GII respondents' views of the de facto fairness of
elections. The secondmost important GII variable is PQOversightRegulation, which appears in 21.4
percent of models on a probabilityweighted basis, and the thirdmost important variable for this
outcome is PQ3GovernmentAccountability, which appears in 17.5 percent of models. The remaining GII
variables all appear much less important, in the sense that the models in which they appear have much
smaller posterior inclusion probabilities.
We also report some summary statistics on the distribution of posterior probabilities across
models at the bottom of Table 4A. We first report the posterior probability of the top three models
(ranked by posterior probabilities), and then also the number of models required to cover 50 percent, 75
percent, and 90 percent of the posterior model probabilities. In the case of CPIA, the top three models
have posterior probabilities of 41 percent, 12 percent, and 8 percent respectively. We also see that the
posterior probabilities are quite concentrated across models. The top two models alone account for 50
percent of the posterior probability, and only 9 (27) models are required to account for 75 percent (90
percent) of the posterior probability. This concentration of posterior probabilities is also reflected in the
18
posterior mean model size: the posterior probabilityweighted average number of regressors (across all
possible models) is just 1.73.
While these posterior model probabilities, and associated inclusion probabilities for individual
variables, are a useful way of summarizing the relative importance of particular models and variables,
we do not want to overinterpret the precise magnitude of these probabilities and their concentration
across models. This is because, as noted in the previous section, the concentration of posterior
probability mass across models is sensitive to our choice of prior parameter g. When g is chosen to be
small (which we do in order to mimic standard frequentist inference for a given model), the BMA
algorithm is more sensitive to small differences in model fit when assessing the relative probabilities of
models. As a result, posterior probabilities are more concentrated across models, and similarly,
posterior inclusion probabilities are more strongly concentrated on fewer variables. Instead, we simply
emphasize the ranking of models and variables by their posterior probabilities. In particular, in Table 4A
we have highlighted the top three variables ranked by their PIPs for each outcome variable. This allows
us to identify at a glance the relatively most important determinants of each outcome without reference
to the precise magnitude of the variables' inclusion probabilities, which in some cases is quite small. We
also think that this exercise of picking the top few variables as ranked by PIP is analogous to the kind of
exercise that a policymaker interested in allocating scarce political capital across a few highimpact
reforms might do. In what follows we will refer to these variables with the highest PIPs as the most
"important" or most "significant" for a given outcome variable even though this terminology is
somewhat imprecise.
In the second and third column for each outcome variable we report the posterior mean and
standard deviation of the slope coefficient corresponding to that variable. Note that these are
unconditional means and standard deviations, i.e. averaging across all models including those in which
the variable does not appear and for which the slope coefficient is then by definition zero. To obtain the
posterior mean conditional on inclusion, we need to scale the reported mean by the inclusion
probability. Returning to the CPIA as a specific example, the variable with the highest PIP, PQ2Elections,
has a posterior mean for the slope coefficient equal to 0.44.18 This is the expected impact of this
variable on the CPIA, averaging across all models. Considering only models in which this variable
18
To interpret the magnitude of these coefficients, note all variables are scaled to run from 0 to 1. So a chance in
the value of PQ2Elections from its worst possible value of 0 to its best possible value of 1 would lead to an increase
in the CPIA of 0.44 (also on a scale from 0 to 1
19
appears, the expected impact is slightly larger at 0.44/0.93=0.47. We note also that the ranking of
variables by their PIPs is very similar to the ranking of variables by the posterior means of their
associated slope coefficients. This tells us that variables that are "important" in the sense of having high
PIPs also have high expected impacts on the outcome variable.
Looking across the various outcome variables in Table 4A, we observe a number of consistent
patterns. Posterior mean model size is fairly small, in the vicinity of 2 for all outcome variables, and
posterior model probabilities are concentrated on a fairly small number of models: at most the top 6
models together account for half of the posterior probability. Perhaps most interesting are the patterns
across outcome variables in those variables highlighted as having high posterior inclusion probabilities.
Two variables, PQ2Elections and PQ5OversightRegulation, consistently appear among the top three
explanatory variables ranked by PIP across the choices of outcome variables (for all 7 outcome
variables). In contrast, however, 4 of the 12 variables in Table 3A do not appear in the list of top three
explanatory variables for any of the outcome variables, and a further five have high PIPs for just one or
two outcome variables. Notably, all six of the "In Law" questions are included in this group of eight
variables with low explanatory power.19
An attractive feature of these results is that they suggest a very consistent pattern across the
different and very closelyrelated corruption variables used as outcomes in Table 4A. Variables that
have high PIPs tend to do so consistently across nearly all outcome variables, and similarly, variables
with low PIPs tend to do so fairly consistently as well across all outcome variables. To provide a more
formal method of documenting this feature of the results, we perform the following simple non
parametric test. By construction, 25 percent of the explanatory variables are highlighted for each
outcome variable (since we have highlighted the top three out of 12 explanatory variables). Suppose as
a null hypothesis we assume that the event that an explanatory variable is included in the `top three' list
for outcome variable i is independent of the event that it is in the `top three' for outcome j. This would
correspond to the extreme case of no stability whatsoever across outcome variables in terms of which
explanatory variables are identified as important by the BMA procedure. Under this null hypothesis, the
probability of observing an explanatory variable highlighted as being in the top three for none of the
19
In fact, it is surprising that one of these "In Law" variables that makes a topthree list
(LQGovernmentAccountability, for dependent variable PRS), actually has a negative posterior mean for the slope
coefficient, indicating a negative partial correlation with the corruption outcomes.
20
outcome variables would be (10.25)7=0.13. The fact that we observe 4 out of 12 or 33 percent of
variables in this category is evidence against the null of independence.
More generally, under the null of independence across the seven outcome variables, the
distribution of the number of outcomes for which an explanatory variable is in the top three list is a
binomial random variable with 7 trials and a success probability of 0.25. We can then compare the
predicted proportions from this distribution with the observed proportions, using a standard chi
squared test.20 Performing this test we strongly reject the null of independence across outcome
variables (with a pvalue of 0.00), and so conclude that there is a great deal of stability across outcome
variables in terms of which explanatory variables at this high level of aggregation that are identified as
important by having large PIPs. However, as we shall shortly see, this very desirable feature of stability
across outcome variables quickly breaks down as we move to greater levels of disaggregation.
We now turn to Tables 4B and 4C, which contain the same information for the two more
disaggregated versions of GII. These tables report the same information as Table 4A. The only
difference however is that we have highlighted the top 11 out of 45 explanatory variables for each
outcome variable in Table 4B, and the top 76 out of 303 variables in Table 4C, i.e. we have highlighted
the top 25 percent of explanatory variables for each outcome variable in each of the three tables. We
do this in order to keep our results on the stability of important explanatory variables across outcomes
as comparable as possible as we move to higher levels of disaggregation. Comparing tables 4A4C we
see some important similarities and differences. As before we find that posterior probabilities are quite
highly concentrated across a fairly small number of models, and also across a fairly small number of
variables. We also find only slightly larger posterior mean model sizes, ranging from 3 to 6 for the
different outcomes at the GII45 level of disaggregation, and 4 to 12 for GII303 level. This tells us that,
for a given outcome variable, the BMA procedure discriminates reasonably sharply among the many
potential explanatory variables and isolates a fairly small number of variables that are relatively more
important in explaining that outcome.
The major difference however as we move to more and more disaggregated explanatory
variables is that there is much less stability across outcomes in terms of which explanatory variables are
identified as having large PIPs. In Table 4A for example we found that 2 out of 12, or 17 percent of
20
In particular the sum of the squared deviations between expected and observed proportions, normalized by
expected proportions, will be a chisquared random variable with 6 degrees of freedom.
21
explanatory variables, appeared in the top 25 percent list for all seven indicators. In Table 4B the
corresponding figure is just 1 out of 45 variables, or only 2.5 percent of explanatory variables. And in
Table 4C just one out of 303, or 0.3 percent of variables, falls in the top 25 percent list for all seven
outcome indicators. We also find that far fewer variables also consistently do not appear in the top 25
percent of explanatory variables. In Table 4A for example 33 percent of variables (4 out of 12) appeared
in the top 25 list for none of the outcome variables. In Table 4B and 4C the corresponding rates are 18
percent and 14 percent, respectively. More formally, the pvalue for the chisquared test of the null
hypothesis that inclusion in the top 25 percent of variables by PIP is independent across outcome
variables is 0.12 in Table 4B, and 0.99 in Table 4C, and so we do not reject the null hypothesis of
independence at conventional levels in both cases.
Since we can disaggregate the GII data at three levels, we can also look at the extent to which
variables that appear important at one level of disaggregation are also found to be important at other
levels. To take a specific example, consider the DRI outcome variable. At the most aggregated GII12
level in Table 4A, we saw that PQElections had by far the strongest explanatory power, with a PIP of
0.90. However, the three subcomponents of PQElections in Table 4A have considerably lower
explanatory power for the DRI measure of corruption (ranking 8th, 9th, and 35th out of 45 variables). By
contrast, by far the most significant variable for DRI at the GII45 level of disaggregation is
LQ3.4BudgetProcess, with a PIP of 0.9. However, the corresponding aggregate at the GII12 level of
aggregation to which this variable belongs, LQ3GovtAccountability, has only the thirdhighest PIP of
0.14. We can see a similar pattern of instability at lower levels of aggregation. Continuing with DRI as
an example, when we move to the GII303 level of disaggregation, we find that just 5 of the 23 individual
variables making up PQElections fall in the top 25 percent of variables at this highest level of
disaggregation.
More systematically, we perform the following simple test to document the degree of stability
across levels of aggregation in terms of which variables are identified as important by the BMA
procedure: we ask whether the probability that a variable is in the top 25 percent of all 303 fully
disaggregated GII measures is significantly higher conditional on it belonging to a cluster of variables
that was identified in the top 25 percent at a lower level of disaggregation. We perform this test using
simple contingency tables that document whether a given variable is in the top 25 percent at both levels
of disaggregation, neither level, or the one level but not the other. Across all 7 outcome variables, and
22
comparing the GII303 level with the GII45 level, and the GII303 level with the GII12 level of
disaggregation, we never fail to reject the null hypothesis that falling in the top 25 percent of
explanatory variables at the highest level of disaggregation is statistically independent of falling in the
top 25 percent at lower levels of disaggregation. This suggests that there is little evidence that variables
that are identified as important at one level of disaggregation will be identified as important
determinants of outcomes at another level of disaggregation.
Tables 5A and 5B report the results we obtain from applying the BMA methodology to the Doing
Business dataset. The results are qualitatively very similar to those we obtain for GII. As with GII, we
find that posterior probabilities are fairly strongly concentrated on a relatively small number of models,
and a fairly small set of variables is identified as having large posterior inclusion probabilities. So as not
to overinterpret the precise magnitudes of these probabilities, we again highlight the top 25 percent of
explanatory variables according to their posterior inclusion probabilities.
Most interestingly for our purposes, we see that our main findings on stability across outcomes,
and stability across levels of disaggregation, hold in this dataset as well. In Table 5A, at the least
disaggregated DB10 level, we find a good deal of stability across outcomes. Two out of 10 DB variables
have consistently high PIPs across the seven outcome variables (the CrossingBorders variable which is in
the top three PIPs for all seven outcome variables, and the GettingCredit variable, which is in the top
three PIPS for five of seven outcomes). At the other extreme, 6 out of 10 variables appear in the list of
top three regressors for just 0 or 1 outcomes, suggesting a great deal of stability across outcomes in
terms of variables that appear to matter much for outcomes. More formally, when we test the null
hypothesis that appearing in the topthree list is independent across outcomes we very strongly reject
the null of independence. This very attractive feature of the results breaks down however when we
move to the DB41 level of disaggregation. Here for example we find that just 3 out of 41 indicators
appear in the top 25 percent of regressors for 5 or more outcomes, and the chisquared test of the null
hypothesis of independence across outcomes delivers a pvalue of 0.99.
Looking across levels of aggregation in the DB dataset, we also find the same feature of
instability that we saw in the GII dataset. In particular, an individual indicator at the DB41 level of
disaggregation is not more likely to be included in the top 25 percent of explanatory variables if the
higherlevel aggregate to which it belongs is in the top 25 percent of variables at that level of
disaggregation. To make this concrete, consider for example the DB10 indicator "GettingCredit" which
23
ranks among the top three explanatory variables for 5 of 7 outcomes (DRI, EIU, GAD, WMO, and CPIA).
This indicator consists of four subindicators at the DB41 level of disaggregation. But these sub
indicators turn up in the top 25 percent of explanatory variables at the DB41 level for only 6 of 20
possible cases for these five outcomes. This rate of 0.30 is only marginally higher than the unconditional
probability of appearing in the top25 percent. More systematically, as we did with GII we test the null
hypothesis that falling in the top 25 percent of explanatory variables at the highest level of
disaggregation is statistically independent of falling in the top 25 percent at lower levels of
disaggregation. Across all 7 outcome variables we never fail to reject this null hypothesis (the lowest p
value for the null of independence that we observe is 0.64). This indicates that the DB dataset has the
same problem of instability across levels of disaggregation that we saw with GII.
5. Robustness and Caveats
We began this paper with the observation that realizing the promise of disaggregated
governance indicators to identify reform priorities requires knowledge of the partial effects of these
potentially very many variables on outcomes that policymakers might reasonably care about. For a
given outcome and set of indicators at a given level of disaggregation, we find that the BMA
methodology used here yields useful results by identifying a fairly small subset of indicators that are
robustly partially correlated with the outcomes of interest. However, we have also documented that
these ostensibly sensible results for a given outcome and level of disaggregation break down when we
consider other closelyrelated outcomes and greater levels of disaggregation. Disaggregated indicators
that are important predictors of one outcome are not systematically also important predictors of other
very similar outcomes. Recall for example that the median pairwise correlation across our 7 measures
of corruption was high at 0.61 for 7 outcome variables used with GII, and 0.68 for 7 outcome variables
used with DB, yet we could not reject the null hypothesis that and indicator being included in the top25
percent of explanatory variables ranked by importance for one outcome was independent of appearing
in the same top25 list for other outcomes. And looking across levels of disaggregation, we found no
evidence that being in the top25 list for a given outcome at one level of disaggregation was a good
predictor of being in the top25 list at another level of disaggregation.
One might first reasonably wonder how robust this key instability finding is. For example one
might wonder whether this strong instability is driven by the somewhat arbitrary cutoff value that we
have been using to identify the set of important explanatory variables. After all, in Tables 4A4C, many
24
of the explanatory variables have very low PIPs that are practically indistinguishable from zero or from
each other. To the extent that the list of top25 percent of explanatory variables includes such variables
with very low PIPs, it would not be very surprising to find a lot of instability across outcome variables.
We explore this possibility in Table 6, where we consider how our results on instability across outcomes
vary for different thresholds used to identify the set of important regressors. The first row of Table 6
contains the benchmark results discussed above: we identify regressors as "important" if they fall in the
top quartile of indicators as ranked by their PIPs for each outcome variable. We then report the p
values discussed above for the null hypothesis that the event of inclusion in the set of "important"
regressors is independent across outcomes, and saw that we could not reject the null only for the least
disaggregated GII12 and DB10 levels of disaggregation. The remaining rows of the table consider other
possible rules for identifying "important" regressors. If for example we focus only on the top10 percent
of regressors we see the same pattern of instability. And if we look at just the top five, three, or even
just one indicator as ranked by PIPs, we still see in nearly all cases that we cannot reject the null
hypothesis of independence for the more disaggregated sets of indicators (GII45, GII303, and DB41).
We note also that our instability findings are robust to the choice of the key prior parameter g.
Recall from the discussion in Section 3 that g plays a key role in determining how the posterior
probability mass is distributed across models. When g is chosen to be small as we do, posterior model
probabilities are highly sensitive to differences in model fit as measured by Rsquared. As a result,
posterior model probabilities tend to be very highly concentrated on a few models with good fit, and the
posterior inclusion probabilities of individual righthandside variables are very concentrated on the few
variables included in those models. Small differences in Rsquareds as we move to different choices of
outcome variables can then lead to large changes in PIPs. One might therefore reasonably wonder
whether our findings on instability are driven by this important sensitivity of BMA to the choice of prior
parameter g. It turns out however that this is not the case. First, recall that we have focused
throughout on the ordering of righthandside variables by their PIPs, rather than the magnitudes of the
PIPs themselves which are very sensitive to the choice of g. Second and more important, we have also
reproduced the BMA analysis for all of the datasets considered here, but using a hyperprior distribution
for the prior parameter g, as advocated by Liang et. al. (2008) and Feldkircher and Zeugner (2009). This
effectively averages all inferences across a range of alternative values of g, and so smooths out the
effect of g on inferences. When we do this, we find that the rank ordering of variables by their PIPs is
25
nearly identical, and so we are confident that our instability finding is not driven by the particular choice
of the prior parameter g.
Another possible objection to the instability finding is that it reflects multicollinearity problems
in the individual models considered by BMA. As is wellknown, one consequence of having nearly
collinear regressors in finite samples is that parameter estimates are highlysensitive to small changes in
model specification. Moreover, individual slope coefficients would themselves be very imprecisely
estimated. Together this would potentially be reflected in large posterior variances of the slope
parameters of interest. However it is less clear that this will matter for posterior model probabilities
and the derived posterior inclusion probabilities, since these reflect only differences across models in
model size and goodnessoffit. Indeed, the strong tradeoff between model size and Rsquared due to
our choice of a small value for the prior parameter g helps to ensure that models with nearcollinear
regressors are assigned low posterior probabilities (since adding a nearcollinear regressor to a model
will result in only a very small improvement in Rsquared). In addition, we have also computed (but not
reported) posterior standard deviations conditional upon inclusion for the GII12 and GII45 levels of
disaggregation. If collinearity were important we would expect to find large conditional standard
deviations for the slope coefficients reflecting the low information content of collinear regressors. Yet
we find that these posterior standard deviations are not especially large. At the GII45 level of
disaggregation, we find that roughly the top half of all variables as ranked by their PIPs have ratios of the
posterior mean to the posterior standard deviation in excess of two, suggesting that conventional levels
of statistical significance are attained by these variables.
A final very pedestrian potential explanation for our instability result across outcomes is that it
might simply reflect differences across outcomes in the set of countries included in the analysis. Not all
of the outcome variables are available for all countries, and in order to use as much information as
possible we have performed our analysis using the largest available set of countries for each choice of y
variable. However, if the set of included countries changes systematically from one outcome to the next
in terms of the relevant determinants of outcomes, then this would contribute to our instability finding
(as noted by Feldkircher and Zeugner (2010) in their comment on Ciccone and Jarocinski (forthcoming)).
To investigate this possibility we repeated the analysis of the DB41 dataset, but restricting attention to
26
the (much smaller) set of 70 countries for which all seven outcome variables are available.21 In this
dataset, we find the same pattern of strong instability across outcomes. The pvalues for the null
hypothesis that the event of inclusion in the set of "important" regressors is indepdendent across
outcomes is 0.96. This suggests to us that our instability across outcomes finding is not driven by such
compositional effects.22
Based on all this we argue that our instability finding is indeed a robust feature of the data:
disaggregated indicators that are significant determinants of one outcome are on average not significant
determinants of other very similar outcomes, and for a given outcome variable, indicators that are
significant at one level of disaggregation are on average not significant at other levels of disaggregation.
Given that this finding is so robust, we now ask how it should be interpreted. One possibility is simply
that the world is in fact complicated, in the sense that different outcome variables truly do respond very
differently to the many indicators in DB and GII. For example, it might truly be the case that the notions
of transparency and accountability reflected in the World Bank's CPIA assessments do indeed require a
very different set of policy interventions from those required to improve the notions of clean
government and absence of corruption monitored by commercial risk rating agencies such as DRI or EIU.
However, this interpretation is hard to reconcile with the very strong correlations across outcome
variables, which limit how different can be the notions of corruption, or regulatory quality, that these
different outcome variables are measuring.
Another interpretation is that all of the disaggregated indicators, and all of the outcome
variables, are just imperfect proxies for some broader notion of good governance. Under this
interpretation, the most disaggregated indicators could be thought of as the noisiest proxies for
governance, and averaging them together at successively higher levels of aggregation smooths out
measurement error in the individual indicators, resulting in better and better proxies for governance at
higher levels of aggregation. This interpretation can account for the fact that there is less instability
21
We do not attempt a similar exercise for the GII data simply because the resulting dataset would contain only
44 countries.
22
A median of pairwise correlation coefficients across seven outcome variables for these 70 countries is reduced
to 0.59 (from 0.68). One may argue that this might contribute to instable bma results across seven outcome
variables. When comparing bma outputs across DRI, PRS, WMO and CPIA, four of these are correlated as much as
7 outcome variables are in our analysis above, the similar instability across these 4 outcome variable emerges.
More specifically, only three (Tax Total, EmployFiringCost, and BorderTimeImp) out of 41 variables appear in top
25 percent of indicators for these four outcome variables. This suggests that the slightly smaller correlation across
seven y variables under 70 sample countries is not driving the "instability" outcome.
27
across outcomes at higher levels of aggregation, since the set of candidate regressors has less
measurement error. However this interpretation runs strongly counter to the spirit of collecting
disaggregated indicators, which is that each of these variables is capturing an important and distinct
dimension of policy interventions that matter for improving governance or the regulatory environment.
Unfortunately we cannot distinguish between these two interpretations, and so the best that
we can do is to recommend a healthy dose of caution in using very disaggregated indicators to inform
policy advice in the areas of governance and regulatory quality.
6. Conclusions
Policymakers interested in using highlydisaggregated indicators of governance and the
investment climate to identify reform priorities would like to know what the impacts of reforms in
specific areas will be on outcomes that they care about. The results of this paper suggest that the data
on indicators and outcomes we have do not allow us to sharply discriminate between indicators that do
and do not matter for outcomes, at least using the available crosscountry variation. This should be
worrisome for a policymaker interested in using these indicators to identify reform priorities. To give a
very stark example, the results here suggest that more than 85 percent of the disaggregated GII
indicators (260 out of 303) are important partial correlates of at least one of the seven very closely
related outcome measures of corruption we have considered. While this is a tribute to the likely
relevance of the overall GII exercise, it does little to narrow down the set of measures a policymaker
might want to target for reforms.
Beyond this, we note that there are likely to be even bigger obstacles to using such
disaggregated indicators to identify reform priorities than the ones we have seen here. In this paper we
have relied on the very simple tool of linear OLS regressions as a means of identifying the effects of
disaggregated indicators on outcomes. As we have noted, the standard exogeneity assumptions
required to justify such an empirical approach are unlikely to hold in reality. However it is very unclear
how one might find instruments or other sources of exogenous variation in the very many different
dimensions of governance and regulatory policy measured by GII and DB. The assumption of linearity is
also very restrictive: one could easily imagine that improvements in various combinations of the
individual indicators are required to improve outcomes, implying that we need to consider not only the
28
2K potential combinations of regressors, but the vastly more possible combinations of interactions
between them.
In concluding, we want to be clear that we do not think that the information painstakingly
gathered in the many individual indicators that comprise the GII and DB datasets is irrelevant. To the
contrary, most if not all of them measure things that plausibly are intrinsically good on their own (it is
hard to imagine why it might be a bad idea to simplify business entry regulation from current levels in
most countries, for example), and it also seems intuitive that they matter for outcomes. Rather, we
simply note that it seems extremely difficult to quantify the partial effects of these many indicators on
relevant outcomes. And so it is very difficult to use these indicators as a recipe or a roadmap to reforms
in the real world where policymakers must choose where to spend their political capital.
29
References
Brock, William A., Steven N. Durlauf, Kenneth D. West (2003). "Policy Evaluation in Uncertain Economic
Environments". Brookings Papers of Economic Activity. 2003(1):235301.
Candes, Emmanuel and Terence Tao (2005). "The Dantzig selector: statistical when p is much larger than
n". Manuscript, California Institute of Technology.
Ciccone, Antonio and Marek Jarocinski (forthcoming). "Determinants of Economic Growth: Will the
Data Tell?". American Economic Journal, Macroeconomics.
Durlauf, Steven N., Andros Kourtellos, and Chih Ming Tan (2008). "Are Any Growth Theories Robust?"
The Economic Journal. 118(March):329346.
Eicher, Theo S., Alex Lenkoski, and Adrian E. Raftery. " Bayesian Model Averaging and Endogeneity
Under Model Uncertainty: An Application to Development Determinants". Manuscript, University of
Washington.
Eicher, Theo S., Chris Papageorgiou, and Adrian E. Raftery (2009). "Default Priors and Predictive
Performance in Bayesian Model Averaging, with Application to Growth Determinants". Manuscript, IMF
and University of Washington.
Eicher, Theo S., Chris Papageorgiou, and Oliver Roehn (2007). " Unraveling the Fortunes of the
Fortunate: An Iterative Bayesian Model Averaging (IBMA) Approach". Manuscript, IMF.
Feldkircher, Martin and Stefan Zeugner (2009). "Benchmark Priors Revisited: On Adaptive Shrinkage
and the Supermodel Effect in Bayesian Model Averaging". IMF Working Paper No. 09/202.
Feldkircher, Martin and Stefan Zeugner (2010). "Data Revisions Revisited: A Comment on
`Determinants of Economic Growth: Will the Data Tell?'". Manuscript, Austrian National Bank and
ECARES.
Fernandez, Carmen, Eduardo Ley, and Mark F.J.Steel (2001). "Benchmark prior for Beyesian model
averaging". Journal of Econometrics. 100(2001):381427.
Fernandez, Carmen, Eduardo Ley, and Mark F.J.Steel (2001). "Model Uncertainty in CrossCountry
Growth Regressions". Journal of Applied Econometrics. 16:563576.
George, Edward I. and Robert E. McCulloch (1997). "Approaches for Bayesian Variable Selection".
Statistica Sinica. 7:339373.
Hendry, David F., and HansMartin Krolzig (2004). "We Ran One Regression". Oxford Bulletin of
Economics and Statistics. 66(5):799810.
Hendry, David F., and HansMartin Krolzig (2005). "The Properties of Automatic Gets Modelling". The
Economic Journal. 115(March):C32C61.
30
Ley, Eduardo and Mark F.J. Steel (2008). "On the Effect of Prior Assumptions in Bayesian Model
Averaging with Applications to Growth Regression". MPRA Paper No. 6637.
Liang, F., Paulo, R., Molina, G., Clyde, M.A., and Berger, J.O. (2008). "Mixtures of g Priors for Bayesian
Variable Selection". Journal of the American Statistical Association. 103:410423.
Lubotsky, Darren and Martin Wittenberg (2006). "Interpretation of Regressions with Multiple Proxies".
The Review of Economics and Statistics. 88(3):54962.
Madigan, D. and York, J. (1995). "Bayesian Graphical Models for Discrete data". International Statistical
Review. 63: 215232.
MoralBenito, Enrique (2009). "Determinants of Economic Growth: A Bayesian Panel Data Approach".
World Bank Working Paper No. 4830.
SalaiMartin, Xavier, Gernot Doppelhofer and Ronald Miller (2004). "Determinants of LongTerm
Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach". American Economic Review.
94(4):813835.
31
Table 1: Outcome Variables
Outcome Variables for Global Integrity
DRI Losses and Costs of Corruption
EIU Corruption among public officials
GAD Cronyism
(Average of) Government Efforts to Tackle Corruption
GCS Public trust in financial honesty of politicians
(Average of) Diversion of public funds due to corruption is common
Frequent for firms to make extra payments connected to trade permits
Frequent for firms to make extra payments connected to public utilities
Frequent for firms to make extra payments connected to tax payments
Frequent for firms to make extra payments connected to loan applications
Frequent for firms to make extra payments connected to awarding of public contracts
Frequent for firms to make extra payments to influence laws, policies regulations, decrees
Frequent for firms to make extra payments to get favourable judicial decisions
Extent to which firms' illegal payments to influence government policies impose costs on firms
Extent to which influence of powerful firms with political ties impose costs on other firms
PRS Corruption in the Political System
WMO Corruption: Intrusiveness of bureaucracy and likelihood of encountering corrupt officials
CPIA Transparency, accountability and corruption in public sector
Outcome Variables for Doing Business
DRI Export Regulation
(Average of) Import Regulation
Other Regulatory Burdens
Restrictions on Foreign Business Ownership
Restrictions on Foreign Equity Ownership
EIU Unfair competitive practices
(Average of) Price controls
Discriminatory tariffs
Excessive protections
Discriminatory taxes
GAD Stock Exchange / Capital Markets
(Average of) Foreign Investment
GCS Administrative regulations are burdensome
(Average of) Tax system is distortionary
Import barriers / cost of tariffs as obstacle to growth
Competition in local market is limited
It is easy to start company
Anti monopoly policy is lax and ineffective
Environmental regulations hurt competitiveness
PRS Risk to operations from contract viability, expropriation, repatriation and payment delays.
WMO Efficiency of Tax Collection
(Average of) Business Legislation Complete and Compatible
CPIA Business regulatory environment
(Average of) Trade policy
32
Table 2: Aggregate Indicators and Outcomes
Global Integrity
Perceptions of Corruption from:
DRI EIU GAD GCS PRS WMO CPIA
Unconditionally
Slope for Overall GII 1.11 0.98 0.71 0.65 0.53 0.96 0.66
Standard error 0.23 0.24 0.17 0.16 0.17 0.19 0.14
t value 4.91 4.06 4.23 4.15 3.17 5.00 4.76
Rsquared 0.26 0.20 0.20 0.21 0.14 0.26 0.27
N 65 63 67 61 58 70 61
Conditional on Log GDP Per Capita
Slope for Overall GII 0.45 0.40 0.50 0.23 0.15 0.33 0.48
Standard error 0.21 0.25 0.20 0.17 0.19 0.20 0.13
t value 2.11 1.61 2.55 1.35 0.83 1.66 3.56
Rsquared 0.54 0.38 0.25 0.43 0.25 0.48 0.43
N 63 62 65 60 56 67 60
Doing Business
Perceptions of Regulatory Quality from:
DRI EIU GAD GCS PRS WMO CPIA
Unconditionally
Slope for Overall DB 0.49 1.11 1.17 0.60 0.98 1.33 0.90
Standard error 0.06 0.09 0.08 0.05 0.10 0.08 0.08
t value 7.82 12.54 15.07 12.63 10.28 16.71 11.10
Rsquared 0.31 0.52 0.59 0.55 0.44 0.61 0.47
N 137 144 158 130 133 178 139
Conditional on Log GDP Per Capita
Slope for Overall DB 0.37 0.72 0.79 0.46 0.56 0.57 0.78
Standard error 0.09 0.13 0.12 0.07 0.14 0.10 0.10
t value 3.96 5.73 6.85 6.58 3.93 5.79 7.61
Rsquared 0.30 0.57 0.63 0.57 0.50 0.75 0.48
N 133 141 156 128 131 173 137
33
Table 3: Correlations Among SubIndicators
and Outcomes
Percentiles of Distribution of
Estimated Pairwise Correlations
10th 25th 50th 75th 90th
GII12 0.27 0.34 0.40 0.57 0.74
GII45 0.02 0.13 0.25 0.40 0.56
GII303 0.11 0.03 0.09 0.23 0.35
DB10 0.15 0.25 0.32 0.45 0.50
DB41 0.01 0.08 0.18 0.29 0.39
GII Outcomes
GII Sample 0.46 0.51 0.61 0.67 0.74
All Countries 0.56 0.68 0.73 0.77 0.84
DB Outcomes
DB Sample 0.57 0.64 0.68 0.74 0.79
All Countries 0.56 0.64 0.69 0.74 0.80
34
Table 4a: BMA Results for GII12 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ1.CivilSoc.PubInfo.Media 0.045 0.007 0.058 0.090 0.034 0.136 0.310 0.117 0.195 0.059 0.009 0.050 0.044 0.001 0.043 0.044 0.005 0.050 0.033 0.000 0.022
PQ2.Elections 0.904 0.686 0.294 0.767 0.531 0.334 0.125 0.038 0.117 0.320 0.101 0.165 0.684 0.271 0.208 0.705 0.389 0.293 0.934 0.439 0.165
PQ3.GovtAccountability 0.099 0.053 0.208 0.124 0.074 0.238 0.056 0.009 0.068 0.159 0.060 0.161 0.087 0.024 0.113 0.053 0.011 0.091 0.175 0.052 0.136
PQ4.Admin.CivilSer 0.191 0.068 0.163 0.066 0.015 0.080 0.049 0.001 0.047 0.201 0.059 0.135 0.056 0.009 0.055 0.399 0.166 0.234 0.065 0.007 0.039
PQ5.OverSight.Regulation 0.271 0.152 0.291 0.219 0.136 0.293 0.673 0.337 0.269 0.534 0.243 0.250 0.236 0.086 0.173 0.389 0.246 0.342 0.214 0.060 0.133
PQ6.AntiCorr.RuleLaw 0.058 0.008 0.062 0.044 0.001 0.056 0.068 0.012 0.075 0.082 0.018 0.080 0.060 0.010 0.059 0.079 0.020 0.093 0.046 0.003 0.030
LQ1.CivilSoc.PubInfo.Media 0.076 0.017 0.075 0.052 0.010 0.061 0.036 0.002 0.030 0.055 0.006 0.039 0.041 0.004 0.036 0.037 0.003 0.031 0.036 0.001 0.019
LQ2.Elections 0.040 0.003 0.040 0.037 0.001 0.040 0.047 0.005 0.038 0.047 0.005 0.036 0.053 0.007 0.047 0.078 0.016 0.073 0.064 0.008 0.039
LQ3.GovtAccountability 0.136 0.047 0.145 0.097 0.034 0.129 0.035 0.001 0.026 0.100 0.023 0.085 0.146 0.037 0.107 0.058 0.010 0.057 0.047 0.004 0.035
LQ4.Admin.CivilSer 0.060 0.011 0.065 0.051 0.007 0.048 0.075 0.011 0.053 0.054 0.006 0.036 0.038 0.002 0.027 0.089 0.019 0.084 0.039 0.001 0.021
LQ5.OverSight.Regulation 0.049 0.009 0.065 0.076 0.024 0.108 0.096 0.026 0.098 0.054 0.009 0.058 0.044 0.004 0.042 0.042 0.004 0.048 0.041 0.002 0.027
LQ6.AntiCorr.RuleLaw 0.037 0.004 0.050 0.042 0.005 0.057 0.113 0.034 0.112 0.118 0.033 0.108 0.032 0.000 0.032 0.149 0.055 0.153 0.033 0.001 0.025
Posterior Probability of:
Firstbest model 0.329 0.410 0.345 0.264 0.414 0.158 0.411
Secondbest model 0.096 0.072 0.095 0.086 0.118 0.138 0.123
Thirdbest model 0.095 0.045 0.052 0.049 0.057 0.128 0.082
Posterior Mean Model Size 1.967 1.663 1.683 1.781 1.520 2.123 1.726
Number of Models Visited 736 716 811 886 677 859 535
Number of Models Covering
x% of Posterior Probability
x=50% 3 3 4 6 2 5 2
x=75% 15 14 16 24 13 22 9
x=90% 46 47 53 81 36 63 27
Corr(PMP) 1.000 1.000 1.000 1.000 1.000 1.000 1.000
G&M Measure of Probability
Mass Visited 0.996 0.993 0.994 0.998 0.984 0.999 0.999
Number of Observations 65 63 67 61 58 70 61
Table 4b: BMA Results for GII45 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ1.1.CivilSocOrg 0.038 0.002 0.033 0.040 0.005 0.046 0.059 0.009 0.048 0.035 0.002 0.022 0.039 0.004 0.034 0.035 0.000 0.028 0.047 0.003 0.025
PQ1.2.Media 0.039 0.001 0.039 0.216 0.087 0.190 0.481 0.169 0.198 0.031 0.001 0.021 0.056 0.009 0.052 0.084 0.018 0.075 0.042 0.001 0.031
PQ1.3.PubAccInfo 0.049 0.005 0.033 0.038 0.003 0.031 0.037 0.002 0.021 0.072 0.008 0.037 0.031 0.001 0.020 0.033 0.000 0.018 0.033 0.000 0.013
PQ2.1.VoteCitiPar 0.268 0.085 0.160 0.163 0.056 0.146 0.108 0.025 0.090 0.063 0.008 0.041 0.078 0.014 0.062 0.426 0.141 0.184 0.843 0.263 0.142
PQ2.2.ElecInteg 0.037 0.000 0.036 0.070 0.015 0.073 0.045 0.004 0.036 0.039 0.001 0.025 0.095 0.018 0.068 0.069 0.011 0.058 0.084 0.014 0.060
PQ2.3.PolitFinanc 0.262 0.079 0.149 0.070 0.014 0.064 0.038 0.003 0.026 0.200 0.037 0.084 0.298 0.078 0.134 0.052 0.006 0.039 0.045 0.003 0.022
PQ3.1.ExecAcc 0.092 0.024 0.095 0.081 0.023 0.098 0.043 0.004 0.039 0.047 0.004 0.041 0.104 0.027 0.096 0.036 0.001 0.038 0.211 0.054 0.119
PQ3.2.LegisAcc 0.127 0.044 0.138 0.047 0.010 0.067 0.046 0.006 0.042 0.048 0.006 0.040 0.036 0.000 0.040 0.036 0.001 0.040 0.303 0.078 0.133
PQ3.3.JudicAcc 0.038 0.004 0.044 0.048 0.011 0.083 0.037 0.003 0.036 0.536 0.279 0.290 0.049 0.009 0.064 0.035 0.003 0.050 0.049 0.005 0.037
PQ3.4.BudgetProc 0.060 0.012 0.067 0.061 0.015 0.077 0.037 0.003 0.032 0.044 0.002 0.033 0.031 0.002 0.030 0.031 0.001 0.030 0.032 0.001 0.022
PQ4.1.CivilSerReg 0.255 0.128 0.249 0.166 0.092 0.238 0.065 0.015 0.079 0.050 0.008 0.056 0.126 0.044 0.138 0.941 0.713 0.270 0.173 0.047 0.119
PQ4.2.Whistle 0.035 0.002 0.022 0.074 0.013 0.058 0.050 0.004 0.036 0.039 0.001 0.018 0.075 0.010 0.044 0.073 0.011 0.049 0.057 0.005 0.027
PQ4.3.Procure 0.055 0.008 0.049 0.040 0.003 0.037 0.034 0.001 0.024 0.060 0.009 0.051 0.069 0.011 0.051 0.094 0.020 0.078 0.045 0.003 0.023
PQ4.4.Privitize 0.042 0.003 0.032 0.064 0.010 0.052 0.035 0.001 0.018 0.059 0.005 0.029 0.054 0.006 0.036 0.057 0.008 0.046 0.150 0.019 0.053
PQ5.1.NatOmbuds 0.034 0.001 0.018 0.033 0.000 0.020 0.048 0.004 0.025 0.095 0.010 0.036 0.029 0.000 0.016 0.041 0.002 0.022 0.123 0.012 0.038
PQ5.2.SupAudInst 0.836 0.411 0.230 0.086 0.022 0.090 0.036 0.001 0.024 0.073 0.008 0.038 0.051 0.006 0.040 0.116 0.025 0.084 0.297 0.055 0.095
PQ5.3.TaxCust 0.125 0.041 0.130 0.031 0.001 0.036 0.034 0.002 0.030 0.073 0.013 0.058 0.045 0.006 0.039 0.030 0.000 0.026 0.039 0.003 0.026
PQ5.4.SOE 0.037 0.002 0.028 0.048 0.007 0.045 0.121 0.022 0.070 0.032 0.001 0.016 0.177 0.039 0.095 0.033 0.001 0.023 0.033 0.001 0.015
PQ5.5.BusLicReg 0.555 0.236 0.241 0.831 0.513 0.291 0.610 0.223 0.204 0.519 0.150 0.165 0.233 0.066 0.134 0.146 0.038 0.109 0.099 0.014 0.052
PQ6.2.AntiCorrAgency 0.122 0.026 0.084 0.193 0.063 0.149 0.090 0.015 0.059 0.048 0.003 0.029 0.036 0.001 0.025 0.030 0.000 0.021 0.040 0.003 0.020
PQ6.3.Rulelaw 0.309 0.130 0.220 0.048 0.009 0.064 0.040 0.003 0.041 0.053 0.008 0.052 0.030 0.001 0.031 0.036 0.003 0.035 0.056 0.007 0.040
PQ6.4.LawEnf 0.052 0.009 0.061 0.039 0.001 0.052 0.044 0.003 0.041 0.112 0.027 0.092 0.130 0.034 0.103 0.036 0.003 0.043 0.047 0.005 0.034
LQ1.1.CivilSocOrg 0.351 0.128 0.196 0.099 0.029 0.105 0.057 0.009 0.050 0.091 0.016 0.062 0.112 0.026 0.087 0.064 0.012 0.058 0.051 0.006 0.033
LQ1.2.Media 0.335 0.096 0.152 0.168 0.050 0.127 0.038 0.002 0.026 0.038 0.002 0.020 0.034 0.002 0.028 0.027 0.000 0.019 0.032 0.001 0.015
LQ1.3.PubAccInfo 0.037 0.001 0.013 0.052 0.005 0.026 0.064 0.004 0.020 0.501 0.062 0.070 0.082 0.007 0.027 0.035 0.000 0.010 0.037 0.001 0.009
LQ2.1.VoteCitiPar 0.034 0.001 0.027 0.058 0.011 0.056 0.029 0.000 0.019 0.027 0.001 0.016 0.038 0.003 0.030 0.040 0.004 0.033 0.038 0.002 0.021
LQ2.2.ElecInteg 0.050 0.005 0.038 0.030 0.000 0.022 0.060 0.007 0.035 0.121 0.015 0.047 0.030 0.000 0.016 0.180 0.035 0.085 0.040 0.002 0.019
LQ2.3.PolitFinanc 0.423 0.074 0.096 0.040 0.002 0.021 0.044 0.002 0.017 0.043 0.001 0.014 0.068 0.006 0.028 0.030 0.001 0.012 0.032 0.001 0.009
LQ3.1.ExecAcc 0.100 0.020 0.073 0.061 0.013 0.070 0.084 0.013 0.052 0.033 0.002 0.021 0.034 0.001 0.024 0.034 0.002 0.028 0.046 0.002 0.024
LQ3.2.LegisAcc 0.048 0.005 0.052 0.049 0.003 0.054 0.042 0.000 0.030 0.038 0.002 0.024 0.047 0.006 0.040 0.044 0.005 0.039 0.047 0.003 0.025
LQ3.3.JudicAcc 0.044 0.004 0.036 0.158 0.046 0.125 0.049 0.006 0.037 0.574 0.228 0.223 0.112 0.021 0.072 0.170 0.037 0.095 0.037 0.002 0.021
LQ3.4.BudgetProc 0.904 0.272 0.120 0.122 0.023 0.074 0.027 0.000 0.014 0.052 0.004 0.024 0.073 0.009 0.038 0.130 0.020 0.062 0.359 0.047 0.070
Table 4b Continues on Next Page
36
Table 4b, Cont'd: BMA Results for GII45 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
LQ4.1.CivilSerReg 0.030 0.000 0.027 0.035 0.002 0.038 0.028 0.000 0.023 0.050 0.005 0.034 0.031 0.001 0.026 0.034 0.000 0.031 0.097 0.015 0.054
LQ4.2.Whistle 0.056 0.004 0.021 0.034 0.001 0.016 0.223 0.027 0.057 0.135 0.011 0.033 0.038 0.001 0.013 0.129 0.014 0.042 0.064 0.003 0.017
LQ4.3.Procure 0.067 0.011 0.058 0.075 0.017 0.079 0.042 0.003 0.030 0.091 0.017 0.068 0.031 0.000 0.022 0.098 0.019 0.072 0.038 0.002 0.019
LQ4.4.Privatize 0.063 0.008 0.040 0.042 0.004 0.031 0.035 0.001 0.017 0.039 0.002 0.016 0.103 0.016 0.057 0.041 0.003 0.023 0.043 0.002 0.016
LQ5.1.NatOmbuds 0.052 0.004 0.022 0.034 0.001 0.017 0.054 0.004 0.022 0.084 0.007 0.029 0.035 0.002 0.015 0.031 0.001 0.013 0.056 0.003 0.016
LQ5.2.SupAudInst 0.036 0.003 0.029 0.053 0.009 0.054 0.058 0.008 0.040 0.037 0.001 0.021 0.032 0.001 0.022 0.035 0.001 0.025 0.035 0.001 0.017
LQ5.3.TaxCust 0.056 0.020 0.116 0.042 0.014 0.104 0.030 0.002 0.055 0.062 0.018 0.095 0.034 0.006 0.066 0.030 0.000 0.063 0.033 0.002 0.043
LQ5.4.SOE 0.056 0.005 0.029 0.033 0.001 0.021 0.219 0.034 0.072 0.029 0.000 0.011 0.047 0.004 0.024 0.042 0.003 0.021 0.058 0.004 0.019
LQ5.5.BusLicReg 0.033 0.002 0.022 0.035 0.003 0.034 0.030 0.001 0.020 0.032 0.001 0.017 0.031 0.001 0.021 0.052 0.006 0.035 0.045 0.003 0.020
LQ6.1.AntiCorrLaw 0.109 0.040 0.137 0.342 0.261 0.406 0.048 0.011 0.066 0.030 0.000 0.030 0.057 0.017 0.093 0.030 0.001 0.036 0.034 0.002 0.031
LQ6.2.AntiCorrAgency 0.047 0.003 0.023 0.035 0.001 0.021 0.040 0.003 0.020 0.064 0.005 0.026 0.047 0.003 0.023 0.053 0.004 0.026 0.031 0.000 0.010
LQ6.3.RuleLaw 0.033 0.000 0.034 0.029 0.000 0.035 0.061 0.012 0.062 0.046 0.004 0.037 0.035 0.003 0.036 0.034 0.002 0.035 0.051 0.006 0.036
LQ6.4.LawEnf 0.036 0.002 0.023 0.115 0.024 0.078 0.226 0.045 0.093 0.078 0.008 0.036 0.045 0.004 0.028 0.258 0.057 0.108 0.049 0.004 0.022
Posterior Probability of:
Firstbest model 0.017 0.038 0.044 0.039 0.042 0.045 0.036
Secondbest model 0.013 0.029 0.024 0.030 0.035 0.036 0.022
Thirdbest model 0.010 0.020 0.021 0.029 0.017 0.021 0.021
Posterior Mean Model Size 6.466 4.231 3.726 4.626 3.070 4.088 4.203
Number of Models Visited 49491 38357 32134 35494 29182 30118 34666
Number of Models Covering
x% of Posterior Probability
x=50% 715 368 268 294 252 231 316
x=75% 3260 2124 1614 1779 1565 1434 1858
x=90% 8194 5867 4814 4992 4423 4404 5238
Corr(PMP) 0.973 0.991 0.993 0.992 0.992 0.994 0.991
G&M Measure of Probability
Mass Visited 0.571 0.635 0.685 0.645 0.691 0.699 0.664
Number of Observations 65 63 67 61 58 70 61
37
Table 4c: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ1.1.2a.NoCSOBarriers 0.093 0.021 0.071 0.032 0.007 0.046 0.036 0.006 0.037 0.005 0.000 0.005 0.005 0.000 0.005 0.003 0.000 0.005 0.010 0.001 0.010
PQ1.1.2b.CSOEngagePolicy 0.008 0.000 0.009 0.019 0.004 0.037 0.077 0.019 0.070 0.005 0.000 0.008 0.006 0.000 0.006 0.009 0.001 0.013 0.006 0.000 0.008
PQ1.1.2c.NoCSOShutdown 0.023 0.003 0.020 0.011 0.001 0.016 0.004 0.000 0.008 0.005 0.000 0.006 0.007 0.000 0.006 0.009 0.001 0.011 0.005 0.000 0.003
PQ1.1.3a.NoActivistImprisoned 0.005 0.000 0.004 0.006 0.000 0.007 0.004 0.000 0.004 0.004 0.000 0.004 0.010 0.000 0.006 0.011 0.001 0.008 0.005 0.000 0.002
PQ1.1.3b.NoActivistHarmed 0.019 0.001 0.011 0.007 0.000 0.007 0.006 0.000 0.005 0.006 0.000 0.004 0.062 0.005 0.020 0.005 0.000 0.005 0.004 0.000 0.003
PQ1.1.3c.NoActivistKilled 0.005 0.000 0.005 0.004 0.000 0.008 0.027 0.004 0.024 0.005 0.000 0.006 0.007 0.000 0.006 0.008 0.000 0.006 0.008 0.000 0.005
PQ1.1.4b.OrganizeUnions 0.014 0.001 0.014 0.009 0.002 0.025 0.027 0.005 0.036 0.008 0.001 0.012 0.012 0.001 0.013 0.008 0.001 0.010 0.005 0.000 0.005
PQ1.2.10c.JournalistsEditorsProfessional 0.007 0.000 0.012 0.005 0.001 0.019 0.004 0.000 0.008 0.006 0.000 0.011 0.086 0.022 0.079 0.006 0.001 0.011 0.010 0.001 0.009
PQ1.2.10d.FairMediaCoverage 0.011 0.002 0.020 0.123 0.046 0.132 0.023 0.004 0.033 0.006 0.000 0.010 0.023 0.004 0.029 0.010 0.001 0.015 0.010 0.000 0.007
PQ1.2.10e.EquitableAccessOutlets 0.051 0.009 0.041 0.069 0.019 0.075 0.040 0.007 0.040 0.013 0.001 0.014 0.065 0.013 0.054 0.035 0.006 0.037 0.008 0.001 0.008
PQ1.2.11a.NoJournalistsImprinsoned 0.022 0.002 0.014 0.027 0.004 0.024 0.009 0.000 0.007 0.014 0.001 0.008 0.004 0.000 0.002 0.006 0.000 0.005 0.004 0.000 0.002
PQ1.2.11b.NoJournalistsHarmed 0.013 0.000 0.006 0.015 0.001 0.014 0.008 0.001 0.007 0.008 0.000 0.004 0.015 0.000 0.005 0.004 0.000 0.003 0.008 0.000 0.004
PQ1.2.11c.NoJournalistsKilled 0.013 0.001 0.011 0.022 0.003 0.025 0.020 0.002 0.017 0.018 0.001 0.011 0.006 0.000 0.004 0.015 0.001 0.012 0.006 0.000 0.004
PQ1.2.6a.NoPrintBarriers 0.008 0.000 0.012 0.015 0.002 0.025 0.076 0.016 0.060 0.007 0.000 0.008 0.009 0.001 0.008 0.018 0.003 0.023 0.015 0.001 0.012
PQ1.2.6c.ObtainPrintLicenseTime 0.548 0.238 0.229 0.014 0.002 0.025 0.100 0.024 0.078 0.004 0.000 0.006 0.006 0.000 0.007 0.026 0.004 0.028 0.214 0.030 0.061
PQ1.2.6d.ObtainPrintLicenseCost 0.009 0.001 0.027 0.018 0.004 0.034 0.180 0.047 0.106 0.003 0.000 0.005 0.014 0.001 0.015 0.016 0.002 0.021 0.065 0.008 0.032
PQ1.2.7a.NoBroadcastBarriers 0.008 0.000 0.008 0.003 0.000 0.008 0.003 0.000 0.006 0.031 0.004 0.025 0.007 0.000 0.010 0.008 0.000 0.010 0.013 0.001 0.011
PQ1.2.7c.ObtainBroadcastLicenseTime 0.007 0.000 0.006 0.003 0.000 0.007 0.006 0.000 0.007 0.009 0.001 0.009 0.011 0.000 0.009 0.007 0.000 0.008 0.006 0.000 0.004
PQ1.2.7d.ObtainBroadcastLicenseCost 0.013 0.001 0.011 0.009 0.000 0.010 0.021 0.003 0.022 0.004 0.000 0.004 0.007 0.000 0.006 0.006 0.000 0.006 0.007 0.000 0.004
PQ1.2.8a.NoPreventAccess 0.018 0.002 0.023 0.006 0.001 0.014 0.008 0.001 0.016 0.006 0.001 0.009 0.008 0.000 0.007 0.011 0.001 0.018 0.003 0.000 0.004
PQ1.2.8b.NoCensorCreating 0.096 0.023 0.077 0.007 0.001 0.015 0.014 0.001 0.017 0.006 0.000 0.006 0.005 0.000 0.005 0.004 0.000 0.008 0.007 0.000 0.006
PQ1.2.9b.NoEncourageSelfCensor 0.008 0.001 0.010 0.007 0.001 0.015 0.009 0.001 0.013 0.012 0.001 0.010 0.009 0.000 0.007 0.006 0.000 0.010 0.099 0.016 0.052
PQ1.2.9c.NoPrePublicationCensor 0.016 0.002 0.021 0.002 0.000 0.007 0.004 0.000 0.009 0.007 0.001 0.009 0.014 0.000 0.009 0.006 0.000 0.009 0.012 0.000 0.008
PQ1.3.13a.ResponseRequestsTime 0.005 0.000 0.006 0.005 0.000 0.010 0.004 0.000 0.008 0.005 0.000 0.007 0.034 0.004 0.022 0.010 0.001 0.013 0.019 0.002 0.017
PQ1.3.13b.ResponseMechanismCost 0.006 0.000 0.006 0.009 0.000 0.009 0.014 0.001 0.014 0.028 0.003 0.018 0.005 0.000 0.004 0.004 0.000 0.004 0.003 0.000 0.002
PQ1.3.13c.AppealsRequestsTime 0.005 0.000 0.006 0.003 0.000 0.009 0.009 0.000 0.009 0.005 0.000 0.008 0.005 0.000 0.006 0.008 0.001 0.014 0.009 0.000 0.006
PQ1.3.13d.AppealsRequestsCost 0.015 0.001 0.013 0.005 0.000 0.007 0.006 0.000 0.005 0.006 0.000 0.005 0.010 0.000 0.009 0.010 0.001 0.011 0.003 0.000 0.003
PQ1.3.13e.GovtReasonsDeny 0.021 0.002 0.021 0.007 0.000 0.012 0.006 0.000 0.008 0.010 0.001 0.010 0.008 0.000 0.008 0.012 0.001 0.015 0.003 0.000 0.004
PQ2.1.15a.CanVote 0.005 0.000 0.007 0.008 0.001 0.015 0.008 0.001 0.013 0.007 0.000 0.008 0.011 0.001 0.011 0.007 0.000 0.008 0.007 0.000 0.007
PQ2.1.15b.BallotsSecret 0.009 0.000 0.011 0.010 0.002 0.022 0.014 0.002 0.018 0.016 0.002 0.017 0.006 0.000 0.008 0.006 0.000 0.008 0.007 0.000 0.007
PQ2.1.15c.ElectionsRegularSchedule 0.031 0.004 0.029 0.017 0.004 0.035 0.004 0.000 0.007 0.005 0.000 0.005 0.009 0.001 0.009 0.045 0.008 0.043 0.024 0.003 0.021
PQ2.1.16c.FormParties 0.450 0.123 0.143 0.017 0.003 0.025 0.012 0.002 0.017 0.005 0.000 0.005 0.007 0.000 0.007 0.003 0.000 0.004 0.009 0.001 0.010
Table 4c Continues on Next Page
38
Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ2.1.16d.RunOffice 0.052 0.010 0.045 0.012 0.002 0.020 0.022 0.003 0.025 0.008 0.001 0.009 0.004 0.000 0.005 0.005 0.000 0.007 0.014 0.001 0.011
PQ2.1.16e.OppositionPartyLegislature 0.022 0.002 0.016 0.011 0.001 0.014 0.005 0.000 0.006 0.008 0.000 0.006 0.007 0.000 0.006 0.029 0.004 0.029 0.042 0.004 0.019
PQ2.2.18b.AgencyIndependence 0.092 0.012 0.042 0.028 0.006 0.038 0.006 0.000 0.008 0.004 0.000 0.004 0.008 0.000 0.005 0.004 0.000 0.005 0.011 0.001 0.008
PQ2.2.18c.AgencyFulltimeStaff 0.019 0.002 0.020 0.010 0.001 0.017 0.008 0.000 0.008 0.008 0.001 0.008 0.009 0.001 0.009 0.006 0.000 0.005 0.034 0.004 0.021
PQ2.2.18d.AgencyReports 0.016 0.002 0.019 0.015 0.002 0.022 0.005 0.000 0.008 0.013 0.002 0.016 0.009 0.000 0.007 0.005 0.000 0.005 0.008 0.000 0.006
PQ2.2.18e.AgencyPenaltiesOffenders 0.014 0.001 0.014 0.012 0.002 0.020 0.006 0.000 0.006 0.002 0.000 0.003 0.050 0.010 0.046 0.006 0.000 0.007 0.003 0.000 0.003
PQ2.2.19a.TransparentVoterRegistration 0.010 0.001 0.011 0.012 0.002 0.024 0.004 0.001 0.010 0.004 0.000 0.006 0.006 0.000 0.006 0.005 0.000 0.005 0.054 0.007 0.033
PQ2.2.19c.ElectionResultsAppealed 0.013 0.001 0.012 0.005 0.000 0.007 0.007 0.001 0.008 0.007 0.000 0.005 0.023 0.002 0.017 0.005 0.000 0.005 0.002 0.000 0.002
PQ2.2.19d.MilitaryNeutralElections 0.009 0.000 0.005 0.007 0.000 0.010 0.009 0.001 0.010 0.008 0.001 0.009 0.016 0.001 0.012 0.020 0.003 0.020 0.010 0.001 0.007
PQ2.2.19f.MonitorElections 0.007 0.001 0.012 0.004 0.000 0.010 0.037 0.006 0.034 0.005 0.000 0.005 0.010 0.001 0.013 0.004 0.000 0.005 0.008 0.000 0.006
PQ2.3.22a23a.LimitsIndividualDonations 0.008 0.001 0.016 0.006 0.001 0.014 0.017 0.003 0.024 0.075 0.016 0.060 0.043 0.010 0.056 0.011 0.001 0.016 0.005 0.000 0.005
PQ2.3.22b23b.LimitsCorporateDonations 0.008 0.001 0.014 0.009 0.002 0.023 0.009 0.001 0.017 0.022 0.004 0.029 0.130 0.035 0.099 0.014 0.002 0.020 0.007 0.000 0.009
PQ2.3.22c.LimitsPartyExpenditures 0.006 0.000 0.008 0.007 0.001 0.016 0.002 0.000 0.004 0.006 0.000 0.009 0.006 0.001 0.011 0.035 0.007 0.040 0.010 0.001 0.011
PQ2.3.22d23c.InitiateInvestigations 0.008 0.000 0.007 0.013 0.002 0.022 0.005 0.000 0.006 0.009 0.001 0.011 0.051 0.007 0.035 0.008 0.001 0.011 0.009 0.001 0.008
PQ2.3.22e23d.PenaltiesOffenders 0.008 0.001 0.010 0.014 0.002 0.023 0.004 0.000 0.006 0.006 0.000 0.008 0.037 0.004 0.028 0.012 0.001 0.013 0.006 0.000 0.006
PQ2.3.22f23e.ContributionsAudited 0.005 0.000 0.008 0.004 0.000 0.009 0.021 0.003 0.026 0.012 0.001 0.014 0.027 0.004 0.027 0.010 0.001 0.013 0.003 0.000 0.003
PQ2.3.24a25a.DiscloseData 0.010 0.001 0.015 0.022 0.005 0.040 0.022 0.004 0.027 0.023 0.003 0.022 0.009 0.001 0.014 0.007 0.001 0.011 0.032 0.004 0.027
PQ2.3.24b25b.AccessRecordsTime 0.011 0.001 0.013 0.017 0.003 0.023 0.010 0.001 0.013 0.009 0.001 0.010 0.005 0.000 0.006 0.054 0.008 0.039 0.526 0.079 0.081
PQ2.3.24c25c.AccessRecordsCost 0.014 0.002 0.019 0.036 0.006 0.034 0.050 0.007 0.034 0.026 0.003 0.017 0.007 0.000 0.008 0.072 0.010 0.037 0.205 0.025 0.053
PQ3.1.27a.ExecutiveGivesReasonsPolicy 0.042 0.008 0.042 0.008 0.001 0.019 0.004 0.000 0.008 0.003 0.000 0.006 0.008 0.000 0.008 0.005 0.000 0.009 0.010 0.001 0.010
PQ3.1.27c.JudiciaryReviewsExecutive 0.048 0.007 0.035 0.009 0.001 0.017 0.005 0.000 0.008 0.005 0.000 0.005 0.240 0.051 0.099 0.005 0.000 0.007 0.012 0.001 0.010
PQ3.1.27d.ExecutiveLimitsOrders 0.007 0.001 0.010 0.004 0.000 0.009 0.003 0.000 0.006 0.005 0.000 0.006 0.046 0.007 0.037 0.004 0.000 0.008 0.006 0.000 0.006
PQ3.1.29f.RestrictPostGovtEmploymentExecutive 0.454 0.163 0.189 0.050 0.014 0.068 0.006 0.000 0.012 0.007 0.000 0.007 0.012 0.001 0.010 0.007 0.000 0.011 0.011 0.001 0.012
PQ3.1.29g.RegulationsGiftsHospitalityToExec 0.013 0.001 0.012 0.007 0.001 0.014 0.016 0.002 0.021 0.012 0.001 0.016 0.296 0.063 0.102 0.008 0.001 0.010 0.005 0.000 0.005
PQ3.1.29h.ExecutiveAssetAudited 0.006 0.000 0.008 0.062 0.017 0.073 0.008 0.001 0.013 0.037 0.005 0.029 0.529 0.170 0.169 0.320 0.086 0.133 0.005 0.000 0.005
PQ3.1.30b.AccessExecAssetDisclosureTime 0.087 0.010 0.036 0.016 0.002 0.020 0.004 0.000 0.004 0.004 0.000 0.003 0.021 0.002 0.019 0.017 0.001 0.013 0.017 0.001 0.010
PQ3.1.30c.AccessExecAssetDisclosureCost 0.077 0.008 0.029 0.020 0.002 0.020 0.011 0.001 0.009 0.009 0.001 0.007 0.039 0.004 0.021 0.022 0.002 0.014 0.005 0.000 0.004
PQ3.1.31.GovtSeparateRulingParty 0.010 0.000 0.008 0.003 0.001 0.011 0.004 0.000 0.005 0.011 0.001 0.012 0.019 0.002 0.015 0.005 0.000 0.005 0.006 0.000 0.006
PQ3.2.32b.JudiciaryReviewsLaws 0.007 0.000 0.007 0.006 0.000 0.009 0.006 0.000 0.007 0.006 0.000 0.007 0.071 0.011 0.044 0.005 0.000 0.007 0.006 0.000 0.005
PQ3.2.33e.RestrictPostGovtEmploymentLegislators 0.017 0.003 0.036 0.042 0.013 0.070 0.012 0.002 0.023 0.008 0.001 0.012 0.018 0.001 0.012 0.007 0.000 0.012 0.013 0.002 0.019
PQ3.2.33f.RegulationsGiftsHospitalityToLegislators 0.024 0.003 0.025 0.007 0.001 0.015 0.008 0.001 0.015 0.012 0.001 0.016 0.007 0.001 0.010 0.046 0.010 0.052 0.007 0.000 0.008
PQ3.2.33g.LegislativeAssetAudited 0.138 0.032 0.085 0.087 0.027 0.094 0.005 0.000 0.009 0.002 0.000 0.004 0.266 0.050 0.088 0.005 0.000 0.010 0.007 0.000 0.006
Table 4c Continues on Next Page
39
Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ3.2.34b.AccessLegislativeAssetTime 0.030 0.002 0.017 0.005 0.000 0.008 0.004 0.000 0.004 0.004 0.000 0.003 0.012 0.001 0.011 0.003 0.000 0.005 0.041 0.003 0.018
PQ3.2.34c.AccessLegislativeAssetCost 0.026 0.002 0.013 0.003 0.000 0.005 0.005 0.000 0.005 0.004 0.000 0.003 0.008 0.000 0.006 0.008 0.000 0.007 0.021 0.002 0.012
PQ3.2.35b.AccessLegislativeDocTime 0.026 0.003 0.025 0.035 0.007 0.042 0.011 0.001 0.015 0.009 0.001 0.009 0.007 0.000 0.004 0.009 0.000 0.011 0.005 0.000 0.005
PQ3.2.35c.AccessLegislativeDocCost 0.053 0.006 0.028 0.009 0.001 0.013 0.013 0.001 0.015 0.008 0.000 0.008 0.012 0.000 0.007 0.010 0.001 0.014 0.005 0.000 0.004
PQ3.3.36b.CriteriaSelectingJudges 0.028 0.004 0.028 0.003 0.000 0.010 0.004 0.000 0.006 0.002 0.000 0.004 0.008 0.000 0.008 0.011 0.002 0.019 0.126 0.022 0.063
PQ3.3.37b.JudicaryGiveReasonsDecisions 0.009 0.000 0.009 0.004 0.000 0.009 0.005 0.000 0.007 0.004 0.000 0.006 0.008 0.000 0.006 0.004 0.000 0.007 0.006 0.000 0.006
PQ3.3.37e.JudiciaryInitiateInvestigations 0.015 0.001 0.013 0.008 0.001 0.016 0.008 0.001 0.011 0.003 0.000 0.005 0.012 0.001 0.012 0.006 0.001 0.011 0.001 0.000 0.002
PQ3.3.37f.JudiciaryImposePenaltiesOffenders 0.014 0.001 0.014 0.004 0.000 0.010 0.005 0.000 0.006 0.006 0.000 0.007 0.034 0.004 0.020 0.008 0.001 0.011 0.018 0.002 0.014
PQ3.3.38e.RestrictPostGovtPrivateEmployment 0.014 0.001 0.010 0.005 0.000 0.008 0.007 0.000 0.007 0.017 0.002 0.014 0.068 0.005 0.018 0.014 0.001 0.013 0.004 0.000 0.003
PQ3.3.38f.RegulationsGiftsHospitalityToJudiciary 0.054 0.007 0.033 0.011 0.001 0.015 0.004 0.000 0.005 0.007 0.001 0.010 0.007 0.000 0.005 0.007 0.000 0.009 0.007 0.000 0.004
PQ3.3.38g.JudiciaryAssetAudited 0.007 0.000 0.007 0.093 0.021 0.072 0.006 0.001 0.011 0.010 0.001 0.011 0.120 0.024 0.071 0.120 0.023 0.065 0.006 0.000 0.006
PQ3.3.39b.AccessJudicialAssetTime 0.049 0.006 0.030 0.004 0.000 0.008 0.010 0.001 0.011 0.008 0.000 0.006 0.013 0.001 0.015 0.009 0.001 0.008 0.055 0.005 0.023
PQ3.3.39c.AccessAssetCost 0.018 0.001 0.013 0.007 0.000 0.009 0.011 0.001 0.010 0.004 0.000 0.003 0.044 0.005 0.026 0.014 0.001 0.010 0.044 0.003 0.017
PQ3.4.40b.PublicExpenditureLegislativeApproval 0.926 0.258 0.095 0.011 0.001 0.018 0.012 0.002 0.017 0.003 0.000 0.005 0.334 0.060 0.089 0.003 0.000 0.004 0.049 0.006 0.027
PQ3.4.40c.LegislatureMonitorBudgetProcess 0.053 0.008 0.036 0.003 0.000 0.006 0.005 0.000 0.007 0.002 0.000 0.004 0.004 0.000 0.005 0.006 0.000 0.006 0.007 0.001 0.009
PQ3.4.41a.BugetProcessTrasparent 0.028 0.003 0.020 0.168 0.049 0.118 0.023 0.003 0.024 0.003 0.000 0.004 0.009 0.000 0.007 0.007 0.001 0.011 0.004 0.000 0.004
PQ3.4.41b.CitizensInputBudgetHearings 0.003 0.000 0.005 0.007 0.001 0.013 0.070 0.014 0.056 0.004 0.000 0.005 0.015 0.001 0.008 0.003 0.000 0.005 0.005 0.000 0.004
PQ3.4.41c.CitizensAccessBudgetAllocations 0.010 0.000 0.007 0.010 0.002 0.019 0.021 0.002 0.020 0.004 0.000 0.005 0.007 0.000 0.004 0.010 0.000 0.007 0.004 0.000 0.003
PQ3.4.43a.DeptHeadsReportsCommittee 0.034 0.004 0.026 0.005 0.000 0.009 0.024 0.004 0.027 0.003 0.000 0.004 0.088 0.015 0.051 0.005 0.000 0.007 0.003 0.000 0.003
PQ3.4.43b.CommitteeNonPartisan 0.006 0.000 0.006 0.009 0.001 0.012 0.004 0.000 0.005 0.005 0.000 0.005 0.217 0.031 0.061 0.008 0.000 0.006 0.006 0.000 0.005
PQ3.4.43c.CommitteeInitiatesInvestigations 0.008 0.001 0.010 0.003 0.000 0.006 0.007 0.000 0.009 0.009 0.001 0.009 0.004 0.000 0.004 0.004 0.000 0.006 0.007 0.000 0.006
PQ4.1.45a.CivilServantsProtectedPolitical 0.029 0.004 0.025 0.121 0.050 0.143 0.006 0.000 0.010 0.006 0.000 0.007 0.009 0.001 0.015 0.027 0.006 0.041 0.007 0.000 0.007
PQ4.1.45b.CivilServantsAppointedCriteria 0.020 0.003 0.024 0.037 0.016 0.090 0.009 0.001 0.017 0.003 0.000 0.007 0.028 0.006 0.039 0.024 0.006 0.041 0.008 0.000 0.008
PQ4.1.45c.CivilServiceNoNepotismCronyism 0.004 0.000 0.010 0.129 0.063 0.176 0.006 0.000 0.011 0.011 0.001 0.017 0.041 0.006 0.031 0.091 0.025 0.087 0.002 0.000 0.003
PQ4.1.45d.CivilServantsClearJobDescriptions 0.032 0.005 0.028 0.013 0.001 0.019 0.007 0.001 0.010 0.004 0.000 0.007 0.006 0.000 0.006 0.007 0.000 0.009 0.017 0.002 0.015
PQ4.1.45e.CivilServantBonusSmall 0.014 0.001 0.010 0.009 0.001 0.014 0.003 0.000 0.005 0.004 0.000 0.003 0.009 0.000 0.006 0.031 0.004 0.025 0.004 0.000 0.003
PQ4.1.45f.GovtPublishNumCivilServicePositions 0.011 0.001 0.010 0.029 0.005 0.031 0.113 0.018 0.054 0.012 0.001 0.010 0.006 0.000 0.005 0.230 0.043 0.084 0.003 0.000 0.003
PQ4.1.45g.RedressCivilService 0.003 0.000 0.004 0.005 0.000 0.009 0.012 0.001 0.015 0.011 0.001 0.012 0.003 0.000 0.005 0.004 0.000 0.006 0.007 0.000 0.006
PQ4.1.45h.CivilServantsPaidOnTime 0.956 0.364 0.135 0.009 0.002 0.022 0.002 0.000 0.005 0.023 0.004 0.026 0.021 0.002 0.019 0.017 0.003 0.027 0.005 0.000 0.008
PQ4.1.45i.CivilServantsCorruptionProhobitedEmployment 0.008 0.000 0.007 0.003 0.000 0.007 0.002 0.000 0.004 0.006 0.000 0.005 0.008 0.000 0.006 0.011 0.001 0.012 0.033 0.003 0.016
PQ4.1.46f.RestrictPostGovtPrivateEmploymentCivilServants 0.097 0.024 0.077 0.059 0.024 0.103 0.006 0.001 0.015 0.175 0.049 0.114 0.024 0.005 0.036 0.046 0.012 0.059 0.028 0.005 0.034
PQ4.1.46g.RegulationsGiftsHospitalityToCivilServants 0.017 0.002 0.019 0.012 0.002 0.025 0.007 0.001 0.010 0.005 0.001 0.010 0.171 0.038 0.091 0.025 0.004 0.030 0.007 0.000 0.006
Table 4c Continues on Next Page
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Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ4.1.46h.CivilServiceRecusal 0.042 0.008 0.039 0.009 0.002 0.023 0.020 0.004 0.029 0.016 0.002 0.022 0.005 0.000 0.011 0.011 0.001 0.016 0.003 0.000 0.004
PQ4.1.47b.AccessAssetCivilServantsTime 0.013 0.002 0.029 0.012 0.002 0.018 0.003 0.000 0.003 0.005 0.000 0.005 0.008 0.000 0.008 0.004 0.000 0.005 0.007 0.001 0.016
PQ4.1.47c.AccessAssetCivilServantsCost 0.015 0.001 0.022 0.010 0.001 0.014 0.003 0.000 0.005 0.003 0.000 0.005 0.011 0.000 0.008 0.003 0.000 0.005 0.007 0.001 0.013
PQ4.2.48b.CivilServantsReportCorruptionProtected 0.012 0.001 0.014 0.005 0.000 0.013 0.028 0.005 0.031 0.007 0.000 0.008 0.011 0.000 0.011 0.006 0.000 0.009 0.005 0.000 0.004
PQ4.2.48d.PrivateEmployeeReportCorruptionProtected 0.007 0.001 0.010 0.004 0.000 0.009 0.060 0.012 0.050 0.025 0.003 0.021 0.016 0.001 0.012 0.007 0.000 0.008 0.009 0.000 0.006
PQ4.2.50a.ReportingMechanismCorruptionStaff 0.005 0.000 0.006 0.009 0.001 0.015 0.005 0.000 0.005 0.005 0.000 0.005 0.008 0.000 0.006 0.006 0.000 0.005 0.011 0.001 0.009
PQ4.2.50b.ReportingMechanismCorruptionFunding 0.005 0.000 0.005 0.004 0.000 0.006 0.008 0.000 0.007 0.006 0.000 0.004 0.007 0.000 0.006 0.006 0.000 0.005 0.012 0.000 0.007
PQ4.2.50c.ReportingMechanismCorruptionTime 0.008 0.000 0.007 0.004 0.000 0.007 0.006 0.000 0.007 0.010 0.001 0.011 0.009 0.000 0.009 0.004 0.000 0.005 0.043 0.005 0.027
PQ4.2.50d.ReportingMechanismCorruptionInitiatesInvestiga0.005 0.000 0.005 0.004 0.000 0.007 0.007 0.000 0.008 0.004 0.000 0.005 0.008 0.000 0.006 0.003 0.000 0.005 0.181 0.022 0.050
PQ4.3.51c.ConflictsInterestPublicProcurement 0.243 0.048 0.092 0.006 0.000 0.012 0.005 0.000 0.009 0.008 0.001 0.010 0.018 0.002 0.018 0.030 0.004 0.028 0.010 0.001 0.008
PQ4.3.51j.CompaniesViolationsProhibitedBids 0.006 0.000 0.005 0.013 0.002 0.018 0.004 0.000 0.005 0.004 0.000 0.004 0.005 0.000 0.004 0.003 0.000 0.004 0.004 0.000 0.004
PQ4.3.52c.CitizensAccessRegulationsTime 0.009 0.000 0.010 0.010 0.001 0.017 0.012 0.001 0.011 0.004 0.000 0.004 0.007 0.000 0.008 0.008 0.001 0.011 0.003 0.000 0.003
PQ4.3.52d.CtizensAccessRegulationsCost 0.013 0.001 0.011 0.017 0.003 0.024 0.007 0.000 0.009 0.008 0.000 0.009 0.006 0.000 0.005 0.013 0.002 0.015 0.005 0.000 0.004
PQ4.3.52e.PublicProcurementAdvertized 0.006 0.000 0.007 0.009 0.001 0.016 0.009 0.001 0.013 0.004 0.000 0.006 0.006 0.000 0.006 0.021 0.003 0.021 0.005 0.000 0.004
PQ4.3.52f.CitizensAccessResultsBids 0.007 0.001 0.011 0.006 0.000 0.009 0.006 0.000 0.008 0.010 0.001 0.012 0.009 0.000 0.008 0.005 0.000 0.006 0.003 0.000 0.003
PQ4.4.53c.ConflictsInterestRegulationsPrivatization 0.160 0.028 0.068 0.005 0.000 0.008 0.005 0.000 0.005 0.003 0.000 0.005 0.052 0.007 0.034 0.009 0.000 0.008 0.019 0.002 0.013
PQ4.4.54b.PrivatizationsAdvertized 0.042 0.007 0.037 0.017 0.003 0.026 0.013 0.001 0.014 0.267 0.044 0.078 0.008 0.001 0.009 0.019 0.002 0.020 0.021 0.002 0.014
PQ4.4.54d.CtizensAccessPrivatizeRegTime 0.025 0.004 0.027 0.009 0.001 0.014 0.013 0.001 0.013 0.005 0.000 0.006 0.009 0.000 0.006 0.010 0.001 0.014 0.017 0.001 0.008
PQ4.4.54e.CitizensAccessPrivatizeRegCost 0.343 0.107 0.159 0.008 0.001 0.017 0.007 0.000 0.009 0.005 0.000 0.006 0.013 0.001 0.013 0.006 0.000 0.010 0.002 0.000 0.002
PQ5.1.56b.OmbudsmanProtectedPolInterference 0.008 0.000 0.007 0.002 0.000 0.004 0.004 0.000 0.005 0.002 0.000 0.004 0.011 0.000 0.007 0.005 0.000 0.006 0.006 0.000 0.004
PQ5.1.56c.HeadOmbudsmanProtected 0.010 0.000 0.007 0.008 0.000 0.008 0.006 0.000 0.005 0.003 0.000 0.004 0.016 0.000 0.007 0.004 0.000 0.004 0.004 0.000 0.003
PQ5.1.56d.OmbudsmanFullTimeStaff 0.003 0.000 0.003 0.005 0.000 0.007 0.010 0.001 0.010 0.020 0.002 0.016 0.035 0.004 0.027 0.005 0.000 0.004 0.008 0.000 0.006
PQ5.1.56e.IndependenceOmbudsman 0.009 0.000 0.007 0.008 0.000 0.009 0.005 0.000 0.006 0.006 0.000 0.006 0.008 0.000 0.007 0.020 0.004 0.029 0.006 0.000 0.005
PQ5.1.56f.Ombudmanfunding 0.018 0.001 0.014 0.009 0.001 0.011 0.003 0.000 0.005 0.008 0.000 0.009 0.391 0.088 0.114 0.003 0.000 0.004 0.004 0.000 0.003
PQ5.1.56g.OmbudsmanMakesReports 0.015 0.002 0.025 0.006 0.000 0.009 0.008 0.000 0.007 0.008 0.000 0.007 0.007 0.000 0.006 0.005 0.000 0.005 0.006 0.000 0.003
PQ5.1.56h.OmbudsmanInitiatesInvestigations 0.006 0.000 0.005 0.004 0.000 0.007 0.002 0.000 0.004 0.008 0.000 0.006 0.008 0.000 0.006 0.003 0.000 0.005 0.009 0.001 0.011
PQ5.1.56i.OmbudsmanPenaltiesOffenders 0.007 0.000 0.006 0.010 0.001 0.013 0.003 0.000 0.004 0.001 0.000 0.003 0.007 0.000 0.006 0.005 0.000 0.006 0.004 0.000 0.003
PQ5.1.56j.GovtActsonFindingOmbudsman 0.007 0.000 0.007 0.008 0.001 0.015 0.004 0.000 0.006 0.004 0.000 0.006 0.007 0.000 0.006 0.003 0.000 0.006 0.004 0.000 0.004
PQ5.1.56k.OmbudsmanActsCitizenComplaints 0.017 0.003 0.025 0.006 0.001 0.012 0.009 0.001 0.009 0.009 0.001 0.009 0.079 0.018 0.066 0.006 0.000 0.007 0.018 0.002 0.016
PQ5.1.57b.CitizensAccessOmbudsmanReportsTime 0.011 0.001 0.012 0.008 0.001 0.010 0.011 0.001 0.010 0.030 0.003 0.017 0.015 0.001 0.020 0.006 0.000 0.007 0.004 0.000 0.003
PQ5.1.57c.CitizensAccessOmbudsmanReportsCost 0.031 0.004 0.031 0.012 0.001 0.017 0.062 0.008 0.034 0.116 0.014 0.042 0.340 0.069 0.100 0.026 0.004 0.027 0.009 0.000 0.005
PQ5.2.59b.HeadAuditProrected 0.006 0.000 0.008 0.005 0.000 0.007 0.005 0.000 0.006 0.006 0.000 0.005 0.035 0.003 0.019 0.011 0.000 0.009 0.008 0.000 0.006
Table 4c Continues on Next Page
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Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ5.2.59c.AuditAgencyFulltimeStaff 0.058 0.015 0.065 0.005 0.000 0.010 0.005 0.000 0.006 0.003 0.000 0.004 0.008 0.000 0.008 0.007 0.000 0.009 0.014 0.001 0.010
PQ5.2.59d.AuditAgencyAppointmentIndependence 0.019 0.002 0.015 0.010 0.002 0.020 0.019 0.002 0.020 0.007 0.000 0.007 0.006 0.000 0.006 0.006 0.000 0.008 0.013 0.001 0.012
PQ5.2.59e.AuditAgencyFunding 0.122 0.033 0.095 0.008 0.001 0.020 0.002 0.000 0.004 0.004 0.000 0.005 0.008 0.000 0.008 0.004 0.000 0.008 0.008 0.000 0.007
PQ5.2.59f.AuditAgencyMakesReports 0.009 0.000 0.009 0.005 0.000 0.009 0.005 0.000 0.008 0.005 0.000 0.005 0.015 0.001 0.013 0.011 0.001 0.014 0.015 0.001 0.009
PQ5.2.59g.GovtActsOnAuditAgency 0.067 0.010 0.039 0.012 0.002 0.028 0.006 0.001 0.010 0.013 0.001 0.014 0.004 0.000 0.005 0.009 0.001 0.015 0.010 0.001 0.010
PQ5.2.59h.AuditAgencyInitiateInvestigations 0.004 0.000 0.005 0.002 0.000 0.006 0.007 0.001 0.008 0.005 0.000 0.005 0.005 0.000 0.006 0.402 0.088 0.115 0.017 0.001 0.010
PQ5.2.60b.CitizensAccessAuditReportsTime 0.011 0.001 0.012 0.021 0.003 0.021 0.022 0.003 0.019 0.006 0.000 0.004 0.011 0.000 0.005 0.028 0.003 0.039 0.012 0.001 0.008
PQ5.2.60c.CitizensAccessAuditReportsCost 0.468 0.115 0.129 0.016 0.002 0.016 0.019 0.002 0.016 0.003 0.000 0.003 0.025 0.001 0.008 0.299 0.052 0.090 0.056 0.005 0.023
PQ5.3.62a.TaxAgencyFulltimeStaff 0.036 0.007 0.039 0.007 0.001 0.015 0.006 0.000 0.010 0.005 0.000 0.008 0.007 0.000 0.006 0.007 0.000 0.008 0.008 0.001 0.009
PQ5.3.62b.TaxAgencyFunding 0.037 0.006 0.036 0.007 0.001 0.025 0.009 0.001 0.018 0.008 0.001 0.017 0.010 0.001 0.012 0.008 0.001 0.014 0.058 0.010 0.046
PQ5.3.63.TaxLawsNoDiscrimination 0.015 0.002 0.017 0.092 0.024 0.082 0.017 0.002 0.020 0.007 0.001 0.010 0.019 0.002 0.017 0.010 0.001 0.011 0.098 0.013 0.044
PQ5.3.65a.CustomsFulltimeStaff 0.005 0.000 0.006 0.004 0.000 0.007 0.001 0.000 0.003 0.006 0.000 0.006 0.007 0.000 0.007 0.003 0.000 0.005 0.014 0.001 0.011
PQ5.3.65b.CustomsFunding 0.018 0.003 0.024 0.003 0.000 0.016 0.005 0.000 0.010 0.006 0.000 0.009 0.008 0.001 0.009 0.007 0.000 0.008 0.036 0.005 0.031
PQ5.3.66.CustomsNoDiscrimination 0.014 0.001 0.014 0.016 0.003 0.025 0.005 0.000 0.007 0.068 0.012 0.048 0.033 0.004 0.026 0.005 0.000 0.006 0.089 0.011 0.037
PQ5.4.68b.AgencyOverseeingSOEFulltimeStaff 0.004 0.000 0.004 0.004 0.000 0.007 0.006 0.000 0.007 0.005 0.000 0.004 0.006 0.000 0.007 0.009 0.000 0.008 0.007 0.000 0.005
PQ5.4.68c.AgencyOverseeingSOEFunding 0.005 0.000 0.005 0.013 0.002 0.021 0.004 0.000 0.004 0.003 0.000 0.003 0.005 0.000 0.005 0.006 0.000 0.005 0.010 0.001 0.006
PQ5.4.68d.AgencyOverseeingSOEInitiateInvestigations 0.008 0.000 0.007 0.003 0.000 0.006 0.075 0.013 0.049 0.005 0.000 0.005 0.006 0.000 0.005 0.006 0.000 0.006 0.009 0.000 0.006
PQ5.4.68e.AgencyOverseeingSOEPenaltiesOffenders 0.006 0.000 0.006 0.005 0.000 0.009 0.013 0.001 0.014 0.012 0.001 0.011 0.008 0.000 0.009 0.003 0.000 0.004 0.007 0.000 0.005
PQ5.4.69b.RecordsSOEUpdated 0.005 0.000 0.007 0.012 0.001 0.016 0.007 0.001 0.009 0.004 0.000 0.003 0.061 0.004 0.020 0.021 0.003 0.020 0.004 0.000 0.003
PQ5.4.69c.RecordsSOEAudited 0.005 0.000 0.003 0.014 0.001 0.016 0.019 0.002 0.018 0.004 0.000 0.004 0.014 0.001 0.008 0.004 0.000 0.004 0.002 0.000 0.002
PQ5.4.69d.CitizensAccessRecordsSOETime 0.010 0.000 0.011 0.025 0.004 0.028 0.016 0.002 0.016 0.003 0.000 0.003 0.007 0.000 0.005 0.004 0.000 0.007 0.005 0.000 0.003
PQ5.4.69e.CitizensAccessRecordsSOECost 0.079 0.015 0.054 0.074 0.014 0.055 0.025 0.003 0.022 0.006 0.000 0.004 0.018 0.001 0.013 0.042 0.006 0.032 0.009 0.000 0.006
PQ5.5.70c.CitizensBusLicenseTime 0.015 0.002 0.020 0.011 0.002 0.019 0.011 0.002 0.029 0.003 0.000 0.004 0.007 0.000 0.008 0.002 0.000 0.004 0.003 0.000 0.003
PQ5.5.70d.CitizensBusLicenseCost 0.013 0.002 0.018 0.040 0.012 0.064 0.240 0.074 0.141 0.014 0.001 0.015 0.010 0.000 0.010 0.004 0.000 0.007 0.008 0.000 0.006
PQ5.5.72a.InspectionsPublicHealth 0.070 0.013 0.053 0.310 0.135 0.214 0.025 0.005 0.035 0.612 0.175 0.151 0.107 0.028 0.088 0.015 0.002 0.023 0.012 0.001 0.011
PQ5.5.72b.InspectionsPublicEnvironment 0.045 0.007 0.036 0.208 0.084 0.175 0.012 0.001 0.018 0.060 0.014 0.059 0.062 0.013 0.058 0.024 0.004 0.030 0.048 0.006 0.028
PQ5.5.72c.InspectionsPublicSafety 0.044 0.007 0.037 0.098 0.037 0.120 0.086 0.021 0.074 0.059 0.014 0.059 0.491 0.142 0.152 0.034 0.008 0.047 0.009 0.001 0.009
PQ6.2.75b.AntiCorrAgencyProtectedPolitics 0.010 0.000 0.008 0.005 0.000 0.016 0.006 0.000 0.011 0.005 0.000 0.006 0.005 0.000 0.008 0.010 0.000 0.012 0.006 0.000 0.008
PQ6.2.75c.HeadAntiCorrAgencyProtected 0.015 0.001 0.010 0.004 0.000 0.009 0.003 0.000 0.004 0.009 0.000 0.006 0.057 0.009 0.037 0.013 0.001 0.012 0.005 0.000 0.003
PQ6.2.75d.AppointmentAntiCorrAgency 0.004 0.000 0.007 0.010 0.001 0.018 0.008 0.000 0.008 0.003 0.000 0.005 0.008 0.000 0.006 0.008 0.001 0.011 0.005 0.000 0.005
PQ6.2.75e.AntiCorrAgencyFulltimeStaff 0.013 0.001 0.013 0.008 0.001 0.014 0.004 0.000 0.005 0.009 0.000 0.007 0.012 0.000 0.007 0.005 0.000 0.006 0.008 0.000 0.004
PQ6.2.75f.AntiCorrAgencyFunding 0.043 0.005 0.025 0.018 0.003 0.023 0.007 0.000 0.006 0.008 0.001 0.008 0.007 0.000 0.006 0.007 0.001 0.008 0.005 0.000 0.004
Table 4c Continues on Next Page
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Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
PQ6.2.75g.AntiCorrAgencyMakesReports 0.029 0.003 0.018 0.030 0.008 0.047 0.007 0.001 0.009 0.012 0.001 0.008 0.012 0.001 0.009 0.012 0.001 0.014 0.040 0.004 0.022
PQ6.2.75h.AntiCorrAgencyPowersMandate 0.130 0.045 0.125 0.146 0.047 0.122 0.017 0.003 0.022 0.024 0.002 0.018 0.009 0.000 0.010 0.007 0.000 0.008 0.010 0.000 0.006
PQ6.2.75i.AntiCorrAgencyInitiateInvestigations 0.006 0.000 0.005 0.045 0.010 0.053 0.005 0.000 0.006 0.006 0.000 0.005 0.006 0.000 0.004 0.006 0.000 0.007 0.004 0.000 0.004
PQ6.2.76a.AntiCorrAgencyActsComplaints 0.099 0.024 0.076 0.004 0.000 0.011 0.007 0.000 0.009 0.007 0.000 0.008 0.032 0.004 0.024 0.003 0.000 0.007 0.005 0.000 0.004
PQ6.2.76b.CitizensComplianAntiCorrAgency 0.006 0.001 0.013 0.014 0.002 0.020 0.002 0.000 0.005 0.008 0.000 0.008 0.013 0.001 0.015 0.005 0.000 0.005 0.008 0.000 0.005
PQ6.3.77b.AppealsResolvedTime 0.429 0.158 0.190 0.007 0.001 0.014 0.020 0.004 0.030 0.030 0.004 0.026 0.007 0.000 0.007 0.009 0.001 0.013 0.005 0.000 0.004
PQ6.3.77c.CitizensUseAppealsCost 0.022 0.003 0.026 0.006 0.000 0.010 0.016 0.003 0.023 0.004 0.000 0.006 0.021 0.002 0.013 0.008 0.000 0.008 0.058 0.007 0.031
PQ6.3.78.JudgementsCriminalFollowLaw 0.023 0.003 0.024 0.006 0.000 0.011 0.004 0.000 0.007 0.005 0.000 0.007 0.002 0.000 0.004 0.005 0.000 0.007 0.014 0.001 0.011
PQ6.3.79.JudicialDecisionsEnforcedState 0.107 0.029 0.088 0.006 0.000 0.012 0.010 0.001 0.017 0.010 0.000 0.010 0.005 0.000 0.007 0.006 0.000 0.008 0.005 0.000 0.006
PQ6.3.80b.JudgesProtectedPolitics 0.028 0.004 0.025 0.089 0.028 0.097 0.026 0.004 0.028 0.142 0.028 0.074 0.010 0.001 0.011 0.970 0.413 0.113 0.998 0.293 0.066
PQ6.3.81a.NoJudgesHarmed 0.016 0.002 0.018 0.003 0.000 0.006 0.006 0.000 0.006 0.003 0.000 0.004 0.012 0.000 0.008 0.010 0.000 0.008 0.006 0.000 0.006
PQ6.3.81b.NoJudgesKilled 0.010 0.001 0.012 0.007 0.001 0.015 0.007 0.000 0.010 0.006 0.001 0.010 0.011 0.001 0.011 0.002 0.000 0.005 0.006 0.000 0.006
PQ6.3.82a.JudicialDecisionsNoRacialBias 0.009 0.001 0.012 0.007 0.001 0.015 0.004 0.000 0.007 0.004 0.000 0.005 0.008 0.001 0.012 0.003 0.000 0.004 0.010 0.001 0.009
PQ6.3.82b.WomenAccessJudicialSystem 0.007 0.001 0.013 0.008 0.001 0.014 0.004 0.000 0.008 0.004 0.000 0.007 0.016 0.001 0.012 0.003 0.000 0.006 0.008 0.000 0.005
PQ6.3.82d.LegalCoundelDefendants 0.010 0.001 0.012 0.005 0.000 0.011 0.033 0.007 0.042 0.007 0.001 0.009 0.010 0.000 0.011 0.004 0.000 0.006 0.008 0.000 0.007
PQ6.3.82e.CitizensMedianIncomeAffordSuit 0.006 0.000 0.009 0.004 0.000 0.007 0.076 0.018 0.069 0.008 0.000 0.007 0.287 0.048 0.085 0.033 0.006 0.034 0.019 0.002 0.017
PQ6.3.82f.SmallBusinessAffordSuit 0.021 0.002 0.020 0.003 0.000 0.011 0.002 0.000 0.007 0.005 0.000 0.006 0.030 0.003 0.017 0.015 0.002 0.020 0.021 0.002 0.015
PQ6.3.82g.CitizensAccessCourtLaw 0.011 0.001 0.011 0.002 0.000 0.005 0.006 0.000 0.008 0.004 0.000 0.004 0.013 0.001 0.010 0.009 0.000 0.010 0.010 0.000 0.006
PQ6.4.83a.AppointmentsLawAgencyCriteria 0.011 0.001 0.012 0.011 0.002 0.023 0.008 0.001 0.010 0.012 0.001 0.016 0.025 0.003 0.020 0.007 0.001 0.012 0.006 0.000 0.005
PQ6.4.83b.LawAgencySufficientBudget 0.006 0.000 0.008 0.009 0.001 0.019 0.008 0.001 0.013 0.055 0.010 0.043 0.019 0.002 0.023 0.008 0.001 0.014 0.003 0.000 0.005
PQ6.4.83c.LawAgencyProtectedPolitics 0.005 0.000 0.005 0.059 0.020 0.085 0.010 0.001 0.014 0.009 0.001 0.012 0.106 0.023 0.070 0.017 0.003 0.027 0.019 0.002 0.019
PQ6.4.84b.LawReportingMechRespondsComplaints 0.005 0.000 0.005 0.012 0.002 0.024 0.004 0.000 0.006 0.005 0.000 0.006 0.008 0.000 0.007 0.006 0.001 0.011 0.192 0.029 0.063
PQ6.4.84d.LawOfficialsInvestigationsCorruption 0.020 0.003 0.023 0.008 0.001 0.016 0.009 0.001 0.010 0.005 0.000 0.006 0.009 0.000 0.008 0.004 0.000 0.006 0.006 0.000 0.006
PQ6.4.84f.LawOfficialsNotImmuneCriminal 0.017 0.001 0.013 0.009 0.000 0.016 0.010 0.001 0.011 0.009 0.000 0.011 0.008 0.001 0.011 0.009 0.000 0.010 0.002 0.000 0.002
LQ1.1.1a.CitizensFormCSO 0.020 0.002 0.026 0.005 0.000 0.012 0.002 0.000 0.007 0.005 0.000 0.008 0.005 0.000 0.007 0.006 0.001 0.011 0.006 0.000 0.006
LQ1.1.1b.CSOAcceptFunding 0.009 0.000 0.009 0.004 0.000 0.008 0.006 0.000 0.008 0.005 0.000 0.009 0.007 0.000 0.005 0.005 0.000 0.006 0.002 0.000 0.003
LQ1.1.1c.CSODiscloseFundingSources 0.031 0.002 0.011 0.058 0.007 0.032 0.039 0.003 0.018 0.009 0.001 0.006 0.017 0.001 0.009 0.011 0.001 0.007 0.005 0.000 0.002
LQ1.1.4a.CitizensTradeUnions 0.019 0.006 0.049 0.021 0.013 0.103 0.006 0.002 0.035 0.032 0.014 0.088 0.003 0.000 0.013 0.018 0.008 0.071 0.002 0.000 0.008
LQ1.2.10a.PrintMediaDiscloseOwnership 0.010 0.000 0.006 0.008 0.000 0.008 0.005 0.000 0.004 0.006 0.000 0.004 0.013 0.001 0.007 0.011 0.000 0.006 0.016 0.001 0.007
LQ1.2.10b.BroadcastMediaDiscloseOwnership 0.006 0.000 0.006 0.012 0.001 0.012 0.007 0.000 0.004 0.006 0.000 0.004 0.015 0.001 0.008 0.005 0.000 0.003 0.008 0.000 0.004
LQ1.2.5a.FreedomMedia 0.100 0.028 0.089 0.006 0.001 0.018 0.004 0.000 0.007 0.007 0.001 0.009 0.006 0.000 0.006 0.007 0.001 0.012 0.007 0.000 0.008
LQ1.2.5b.FreedomSpeech 0.450 0.124 0.143 0.005 0.000 0.007 0.005 0.000 0.007 0.008 0.000 0.005 0.006 0.000 0.005 0.006 0.000 0.008 0.005 0.000 0.003
Table 4c Continues on Next Page
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Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
LQ1.2.6b.PrintMediaLicenseAppeal 0.009 0.000 0.006 0.054 0.009 0.043 0.007 0.000 0.006 0.007 0.000 0.007 0.034 0.003 0.016 0.008 0.000 0.006 0.009 0.000 0.003
LQ1.2.7b.BroadcastMediaLicenseAppeal 0.030 0.003 0.018 0.005 0.000 0.010 0.006 0.000 0.006 0.004 0.000 0.005 0.006 0.000 0.005 0.006 0.000 0.005 0.005 0.000 0.003
LQ1.2.9a.ReportNewsDamagingPublicFigure 0.154 0.019 0.047 0.003 0.000 0.005 0.008 0.001 0.008 0.008 0.001 0.009 0.030 0.002 0.012 0.005 0.000 0.006 0.004 0.000 0.004
LQ1.3.12a.CitizensAccessGovtInfo 0.009 0.000 0.006 0.006 0.000 0.007 0.007 0.000 0.006 0.009 0.001 0.007 0.009 0.000 0.007 0.009 0.000 0.006 0.005 0.000 0.003
LQ1.3.12b.CitizensAppealIfAccessGovtInfoDenied 0.012 0.000 0.007 0.005 0.000 0.005 0.010 0.001 0.009 0.020 0.001 0.011 0.031 0.003 0.017 0.008 0.001 0.007 0.006 0.000 0.003
LQ1.3.12c.CitizensRequestGovtRecord 0.005 0.000 0.003 0.055 0.008 0.038 0.019 0.001 0.012 0.031 0.002 0.014 0.031 0.001 0.008 0.005 0.000 0.003 0.005 0.000 0.002
LQ2.1.14a.UniversalAdultSuffrage 0.038 0.011 0.064 0.007 0.002 0.032 0.003 0.000 0.010 0.005 0.000 0.009 0.005 0.000 0.013 0.006 0.000 0.012 0.004 0.000 0.007
LQ2.1.14b.ElectionsRegularIntervals 0.005 0.000 0.014 0.000 0.000 0.000 0.004 0.000 0.012 0.004 0.000 0.007 0.000 0.000 0.000 0.005 0.000 0.011 0.005 0.000 0.007
LQ2.1.16a.CitizensFormParties 0.006 0.000 0.005 0.006 0.001 0.015 0.007 0.000 0.008 0.008 0.000 0.006 0.047 0.004 0.019 0.004 0.000 0.005 0.004 0.000 0.003
LQ2.1.16b.CitizensRunPoliticalOffice 0.007 0.000 0.006 0.006 0.000 0.008 0.010 0.001 0.008 0.002 0.000 0.003 0.010 0.000 0.005 0.006 0.000 0.005 0.008 0.000 0.004
LQ2.2.17.ElectionMonitoringAgency 0.027 0.004 0.029 0.005 0.000 0.009 0.009 0.001 0.011 0.004 0.000 0.005 0.013 0.001 0.008 0.005 0.000 0.005 0.005 0.000 0.003
LQ2.2.18a.ElectionAgencyProtectedPolitics 0.015 0.001 0.013 0.004 0.000 0.004 0.005 0.000 0.003 0.018 0.001 0.009 0.006 0.000 0.003 0.009 0.001 0.008 0.004 0.000 0.002
LQ2.2.19b.ElectionResultsContested 0.006 0.000 0.007 0.004 0.000 0.008 0.008 0.000 0.008 0.005 0.000 0.004 0.002 0.000 0.002 0.005 0.000 0.006 0.003 0.000 0.003
LQ2.2.19e.ElectionObserversMonitor 0.006 0.000 0.006 0.005 0.000 0.007 0.004 0.000 0.005 0.013 0.001 0.008 0.003 0.000 0.003 0.015 0.001 0.014 0.009 0.000 0.004
LQ2.3.20a.RegPrivateContributionsParties 0.053 0.006 0.030 0.024 0.004 0.026 0.008 0.000 0.007 0.007 0.000 0.004 0.010 0.000 0.007 0.010 0.000 0.007 0.003 0.000 0.002
LQ2.3.20b21b.LimitsIndividualDonations 0.010 0.000 0.005 0.004 0.000 0.005 0.008 0.000 0.007 0.018 0.001 0.010 0.034 0.002 0.013 0.004 0.000 0.004 0.004 0.000 0.002
LQ2.3.20c21c.LimitsCorporateDonations 0.012 0.000 0.006 0.006 0.000 0.007 0.008 0.000 0.006 0.015 0.001 0.008 0.014 0.001 0.008 0.015 0.001 0.010 0.006 0.000 0.003
LQ2.3.20d.LimitsPartyExpenditures 0.010 0.001 0.006 0.008 0.000 0.007 0.005 0.000 0.003 0.013 0.001 0.007 0.025 0.001 0.011 0.006 0.000 0.005 0.011 0.000 0.005
LQ2.3.20e21d.DiscloseDonationsCandidatesParties 0.024 0.002 0.011 0.005 0.000 0.005 0.004 0.000 0.004 0.005 0.000 0.003 0.009 0.001 0.008 0.008 0.000 0.005 0.055 0.004 0.016
LQ2.3.20f21e.AuditFinanceCandidatesParties 0.008 0.000 0.005 0.011 0.001 0.010 0.016 0.001 0.010 0.008 0.000 0.005 0.029 0.002 0.016 0.007 0.000 0.005 0.008 0.000 0.003
LQ2.3.20g21f.AgencyMonitorPolitiFinance 0.004 0.000 0.003 0.009 0.001 0.010 0.005 0.000 0.004 0.006 0.000 0.003 0.370 0.052 0.071 0.004 0.000 0.003 0.013 0.000 0.005
LQ3.1.26.CitizensSueGovt 0.010 0.001 0.022 0.007 0.000 0.013 0.004 0.000 0.009 0.008 0.000 0.011 0.005 0.000 0.006 0.003 0.000 0.005 0.003 0.000 0.004
LQ3.1.27b.JudiciaryReviewExecutive 0.028 0.008 0.053 0.006 0.001 0.016 0.006 0.000 0.009 0.000 0.000 0.000 0.002 0.000 0.006 0.007 0.000 0.015 0.003 0.000 0.004
LQ3.1.28a.HeadsGovtProsecuted 0.003 0.000 0.001 0.013 0.001 0.012 0.024 0.002 0.013 0.005 0.000 0.003 0.009 0.000 0.004 0.156 0.018 0.044 0.098 0.006 0.020
LQ3.1.28b.TopGovtOfficialsProsecuted 0.006 0.000 0.006 0.007 0.001 0.013 0.018 0.002 0.015 0.003 0.000 0.003 0.028 0.002 0.013 0.013 0.001 0.012 0.010 0.000 0.006
LQ3.1.29a.HeadsGovtAssetDisclosure 0.004 0.000 0.005 0.009 0.001 0.011 0.008 0.000 0.007 0.005 0.000 0.004 0.008 0.000 0.005 0.006 0.000 0.005 0.004 0.000 0.002
LQ3.1.29b.TopGovtOfficialsAssetDisclosure 0.004 0.000 0.004 0.011 0.001 0.011 0.012 0.001 0.010 0.002 0.000 0.002 0.006 0.000 0.004 0.005 0.000 0.006 0.004 0.000 0.002
LQ3.1.29c.RegGiftsHospitalityExecutive 0.019 0.001 0.010 0.005 0.000 0.005 0.004 0.000 0.003 0.024 0.002 0.012 0.015 0.001 0.008 0.004 0.000 0.003 0.007 0.000 0.002
LQ3.1.29d.AuditExecutiveAssetDisclosure 0.022 0.001 0.009 0.005 0.000 0.005 0.004 0.000 0.003 0.006 0.000 0.004 0.055 0.004 0.020 0.013 0.001 0.008 0.008 0.000 0.003
LQ3.1.29e.HeadsGovtRestrictedEnterPrivate 0.361 0.057 0.080 0.084 0.013 0.046 0.016 0.001 0.012 0.009 0.000 0.006 0.005 0.000 0.004 0.020 0.002 0.013 0.007 0.000 0.004
LQ3.1.30a.CitizensAccessAssetGovtHeads 0.007 0.000 0.004 0.006 0.000 0.006 0.005 0.000 0.003 0.003 0.000 0.002 0.013 0.000 0.006 0.010 0.000 0.006 0.008 0.000 0.004
LQ3.2.32a.JudiciaryReviewLaws 0.047 0.008 0.041 0.004 0.000 0.006 0.003 0.000 0.004 0.007 0.001 0.008 0.011 0.000 0.007 0.004 0.000 0.005 0.015 0.001 0.009
Table 4c Continues on Next Page
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Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
LQ3.2.32c.LegislatureMembersCriminalProceedings 0.008 0.000 0.004 0.018 0.002 0.017 0.004 0.000 0.003 0.017 0.001 0.009 0.006 0.000 0.004 0.003 0.000 0.003 0.004 0.000 0.002
LQ3.2.33a.LegislatureMembersAssetDisclosure 0.007 0.000 0.005 0.005 0.000 0.006 0.004 0.000 0.004 0.008 0.000 0.005 0.018 0.000 0.008 0.006 0.000 0.006 0.014 0.001 0.008
LQ3.2.33b.LegislatureRestrictedPrivateEmployment 0.031 0.006 0.037 0.008 0.001 0.016 0.004 0.000 0.006 0.003 0.000 0.004 0.006 0.000 0.005 0.008 0.000 0.008 0.006 0.000 0.005
LQ3.2.33c.RegGiftsHospitalityLegislature 0.005 0.000 0.003 0.003 0.000 0.003 0.016 0.001 0.014 0.010 0.001 0.006 0.007 0.000 0.005 0.003 0.000 0.004 0.009 0.000 0.003
LQ3.2.33d.AuditAssetDisclosureLegislature 0.014 0.001 0.006 0.013 0.001 0.014 0.016 0.001 0.010 0.003 0.000 0.003 0.039 0.005 0.024 0.020 0.001 0.014 0.008 0.000 0.004
LQ3.2.34a.CitizensAccessAssetRecordsLegislature 0.009 0.000 0.006 0.005 0.000 0.005 0.005 0.000 0.004 0.004 0.000 0.003 0.008 0.000 0.005 0.005 0.000 0.003 0.011 0.000 0.005
LQ3.2.35a.CitizensAccessLegislativeDoc 0.117 0.016 0.048 0.007 0.001 0.011 0.003 0.000 0.004 0.009 0.000 0.007 0.015 0.001 0.009 0.007 0.000 0.006 0.005 0.000 0.002
LQ3.3.36a.JudgesSelectedTransparent 0.012 0.001 0.009 0.005 0.000 0.006 0.004 0.000 0.003 0.012 0.001 0.009 0.007 0.000 0.006 0.008 0.000 0.006 0.023 0.001 0.009
LQ3.3.36c.JudgesConfirmation 0.009 0.000 0.006 0.002 0.000 0.003 0.006 0.000 0.004 0.007 0.000 0.005 0.022 0.001 0.008 0.006 0.000 0.004 0.005 0.000 0.002
LQ3.3.37a.JudiciaryMustGiveReasonsDecisions 0.020 0.000 0.011 0.007 0.001 0.010 0.003 0.000 0.003 0.004 0.000 0.004 0.004 0.000 0.003 0.010 0.001 0.010 0.007 0.000 0.005
LQ3.3.37c.DisciplinaryAgencyJudiciarySystem 0.010 0.001 0.009 0.006 0.000 0.009 0.008 0.000 0.008 0.005 0.000 0.005 0.031 0.002 0.015 0.003 0.000 0.004 0.093 0.011 0.036
LQ3.3.37d.JudiciaryAgencyProtectedPolitics 0.006 0.000 0.003 0.007 0.000 0.007 0.007 0.000 0.005 0.008 0.000 0.003 0.005 0.000 0.003 0.009 0.000 0.006 0.007 0.000 0.004
LQ3.3.38a.JudiciaryAssetDisclosure 0.004 0.000 0.002 0.007 0.001 0.008 0.014 0.001 0.009 0.014 0.001 0.008 0.011 0.000 0.004 0.004 0.000 0.003 0.020 0.001 0.009
LQ3.3.38b.RegGiftsHospitalityToJudiciary 0.013 0.000 0.006 0.013 0.001 0.011 0.026 0.002 0.016 0.005 0.000 0.004 0.011 0.000 0.008 0.036 0.003 0.016 0.005 0.000 0.002
LQ3.3.38c.AuditAssetDisclosureJudiciary 0.035 0.002 0.014 0.019 0.002 0.015 0.006 0.000 0.005 0.012 0.001 0.007 0.045 0.004 0.022 0.083 0.009 0.033 0.009 0.000 0.004
LQ3.3.38d.JudgesRestrictedPrivateEmployment 0.007 0.000 0.005 0.004 0.000 0.004 0.003 0.000 0.003 0.009 0.000 0.005 0.023 0.001 0.008 0.006 0.000 0.005 0.006 0.000 0.003
LQ3.3.39a.CitizensAccessAssetDisclosureJudiciary 0.007 0.000 0.003 0.005 0.000 0.005 0.003 0.000 0.003 0.004 0.000 0.003 0.004 0.000 0.003 0.002 0.000 0.002 0.029 0.002 0.011
LQ3.4.40a.LegislatureAmendBudget 0.133 0.015 0.042 0.027 0.003 0.023 0.009 0.000 0.006 0.005 0.000 0.004 0.010 0.000 0.007 0.002 0.000 0.002 0.017 0.001 0.007
LQ3.4.42.LegislativeCommitteeOversightPublicFunds 0.007 0.000 0.010 0.004 0.000 0.010 0.006 0.001 0.013 0.005 0.000 0.008 0.006 0.001 0.010 0.004 0.000 0.009 0.002 0.000 0.003
LQ4.1.44a.RegulationsFairlyManagedCivilService 0.007 0.000 0.008 0.007 0.001 0.014 0.001 0.000 0.003 0.008 0.001 0.009 0.026 0.004 0.032 0.014 0.002 0.019 0.005 0.000 0.006
LQ4.1.44b.RegulationsNoNepotismCronyismCivilService 0.023 0.003 0.027 0.002 0.000 0.006 0.005 0.000 0.011 0.000 0.000 0.000 0.005 0.000 0.005 0.003 0.000 0.006 0.004 0.000 0.004
LQ4.1.44c.RedressMechCivilService 0.009 0.000 0.005 0.013 0.001 0.014 0.008 0.000 0.005 0.010 0.000 0.005 0.006 0.000 0.003 0.006 0.000 0.004 0.007 0.000 0.003
LQ4.1.44d.CivilServantsCorruptionNoGovtEmployment 0.007 0.000 0.003 0.012 0.001 0.011 0.027 0.003 0.017 0.008 0.000 0.005 0.007 0.000 0.003 0.006 0.000 0.004 0.022 0.001 0.008
LQ4.1.46b.CivilServantsAwayPolicyDecisionsPersonal 0.004 0.000 0.004 0.005 0.000 0.005 0.026 0.003 0.019 0.006 0.000 0.005 0.048 0.004 0.021 0.005 0.000 0.005 0.005 0.000 0.003
LQ4.1.46c.CivilServantsNoPrivateEmployment 0.059 0.004 0.018 0.014 0.001 0.013 0.003 0.000 0.003 0.039 0.003 0.017 0.008 0.000 0.005 0.054 0.005 0.022 0.110 0.010 0.030
LQ4.1.46d.NoGiftsHospitalityToCivilServants 0.036 0.003 0.018 0.006 0.000 0.007 0.006 0.000 0.005 0.006 0.000 0.004 0.009 0.000 0.007 0.005 0.000 0.004 0.004 0.000 0.003
LQ4.1.47a.CitizensAccessAssetRecordsCivilServants 0.008 0.000 0.003 0.004 0.000 0.003 0.002 0.000 0.002 0.003 0.000 0.003 0.011 0.000 0.004 0.005 0.000 0.004 0.010 0.000 0.003
LQ4.2.48a.CivilServantsReportingCorruptionProtected 0.005 0.000 0.002 0.005 0.000 0.006 0.186 0.021 0.047 0.106 0.008 0.025 0.013 0.000 0.006 0.004 0.000 0.003 0.003 0.000 0.001
LQ4.2.48c.PrivateEmployeeReportingCorruptionProtected 0.006 0.000 0.003 0.004 0.000 0.005 0.040 0.004 0.022 0.013 0.001 0.006 0.005 0.000 0.002 0.003 0.000 0.002 0.004 0.000 0.002
LQ4.2.49.InternalMechCivilServantsReportCorruption 0.005 0.000 0.003 0.002 0.000 0.003 0.005 0.000 0.003 0.008 0.000 0.005 0.014 0.000 0.005 0.009 0.000 0.005 0.011 0.000 0.004
LQ4.3.51a.RegConflictsPublicProcurementOfficials 0.037 0.004 0.025 0.003 0.000 0.004 0.006 0.000 0.005 0.004 0.000 0.003 0.011 0.001 0.010 0.007 0.000 0.007 0.005 0.000 0.003
LQ4.3.51b.TraningPublicProcurementOfficials 0.010 0.000 0.005 0.008 0.000 0.007 0.002 0.000 0.002 0.010 0.000 0.006 0.105 0.011 0.034 0.577 0.092 0.086 0.014 0.001 0.007
Table 4c Continues on Next Page
45
Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
LQ4.3.51d.MonitorPublicProcurementOfficials 0.013 0.000 0.006 0.005 0.000 0.006 0.009 0.000 0.006 0.221 0.023 0.045 0.006 0.000 0.005 0.030 0.002 0.016 0.060 0.004 0.015
LQ4.3.51e.ProcurementCompetitivebidding 0.021 0.003 0.021 0.007 0.001 0.015 0.003 0.000 0.006 0.004 0.000 0.005 0.007 0.000 0.009 0.002 0.000 0.005 0.009 0.000 0.008
LQ4.3.51f.SoleSourcingLimited 0.006 0.000 0.005 0.010 0.001 0.014 0.006 0.000 0.005 0.004 0.000 0.003 0.016 0.001 0.011 0.010 0.001 0.008 0.010 0.000 0.005
LQ4.3.51g.UnsuccessfulBiddersReviewProcurement 0.022 0.002 0.013 0.003 0.000 0.006 0.017 0.002 0.015 0.003 0.000 0.003 0.009 0.000 0.004 0.006 0.000 0.005 0.006 0.000 0.003
LQ4.3.51h.UnsuccessfulBiddersChallengeDecisions 0.017 0.001 0.013 0.011 0.001 0.013 0.031 0.004 0.026 0.004 0.000 0.006 0.074 0.008 0.032 0.003 0.000 0.004 0.003 0.000 0.002
LQ4.3.51i.CompaniesViolatingProhibitedBidding 0.473 0.063 0.071 0.010 0.001 0.012 0.009 0.000 0.007 0.007 0.000 0.005 0.008 0.000 0.004 0.275 0.036 0.062 0.007 0.000 0.004
LQ4.3.52a.CitizensAccessProcurementRegulations 0.010 0.001 0.015 0.006 0.000 0.010 0.006 0.001 0.011 0.009 0.000 0.009 0.027 0.004 0.027 0.004 0.000 0.006 0.003 0.000 0.003
LQ4.3.52b.GovtMustAnnounceProcurementResults 0.009 0.000 0.010 0.005 0.000 0.008 0.004 0.000 0.004 0.001 0.000 0.003 0.008 0.000 0.006 0.007 0.001 0.008 0.010 0.000 0.005
LQ4.4.53a.BusinessesCompeteStateAssets 0.004 0.000 0.005 0.002 0.000 0.006 0.003 0.000 0.005 0.008 0.000 0.007 0.006 0.000 0.006 0.002 0.000 0.003 0.004 0.000 0.004
LQ4.4.53b.ConflictGovtOfficialsPrivatization 0.004 0.000 0.004 0.004 0.000 0.004 0.003 0.000 0.003 0.003 0.000 0.002 0.014 0.001 0.010 0.007 0.000 0.004 0.005 0.000 0.002
LQ4.4.54a.CitizensAccessPrivatizationRegulations 0.011 0.000 0.007 0.004 0.000 0.005 0.003 0.000 0.004 0.014 0.001 0.009 0.006 0.000 0.003 0.004 0.000 0.003 0.006 0.000 0.003
LQ4.4.54c.GovtMustAnnounceResultsPrivatization 0.039 0.004 0.021 0.005 0.000 0.006 0.003 0.000 0.004 0.002 0.000 0.002 0.007 0.000 0.004 0.006 0.000 0.003 0.006 0.000 0.003
LQ5.1.55.OmbudsmanCoveringAllPublicSector 0.180 0.024 0.055 0.005 0.000 0.006 0.004 0.000 0.004 0.014 0.001 0.010 0.009 0.000 0.006 0.004 0.000 0.003 0.004 0.000 0.003
LQ5.1.56a.OmbudsmanProtectedPolitics 0.043 0.004 0.024 0.005 0.000 0.006 0.003 0.000 0.003 0.007 0.000 0.006 0.004 0.000 0.003 0.003 0.000 0.003 0.005 0.000 0.002
LQ5.1.57a.CitizensAccessReportsOmbudsman 0.052 0.006 0.027 0.003 0.000 0.004 0.005 0.000 0.007 0.007 0.000 0.005 0.007 0.000 0.003 0.018 0.001 0.012 0.002 0.000 0.001
LQ5.2.58.SupAuditInstCoveringAllPubSector 0.000 0.000 0.000 0.000 0.000 0.000 0.006 0.001 0.016 0.007 0.001 0.013 0.000 0.000 0.000 0.011 0.002 0.028 0.007 0.001 0.013
LQ5.2.59a.SupAuditInstProtectedPolitics 0.008 0.001 0.009 0.009 0.001 0.016 0.010 0.001 0.015 0.006 0.000 0.009 0.018 0.001 0.011 0.011 0.001 0.016 0.005 0.000 0.005
LQ5.2.60a.CitizensAccessReportsAuditAgency 0.013 0.001 0.009 0.003 0.000 0.004 0.002 0.000 0.002 0.003 0.000 0.002 0.011 0.000 0.006 0.004 0.000 0.003 0.010 0.000 0.004
LQ5.3.61.NationalTaxCollectionAgency 0.015 0.001 0.044 0.009 0.002 0.026 0.004 0.000 0.011 0.012 0.002 0.022 0.005 0.000 0.009 0.008 0.001 0.019 0.005 0.000 0.008
LQ5.3.64.NationalCostomsExerciseAgency 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
LQ5.4.67.AgencyOverseeingSOE 0.012 0.001 0.011 0.006 0.000 0.008 0.013 0.001 0.014 0.005 0.000 0.004 0.009 0.001 0.010 0.003 0.000 0.004 0.005 0.000 0.004
LQ5.4.68a.AgencyOverseeingSOEProtectedPolitics 0.017 0.001 0.008 0.005 0.000 0.005 0.009 0.000 0.006 0.001 0.000 0.002 0.106 0.010 0.032 0.048 0.004 0.019 0.071 0.005 0.018
LQ5.4.69a.CitizensAccessFinanceSOE 0.054 0.006 0.027 0.006 0.000 0.005 0.003 0.000 0.003 0.003 0.000 0.002 0.006 0.000 0.003 0.004 0.000 0.004 0.005 0.000 0.002
LQ5.5.70a.AnyoneApplyBusLicense 0.003 0.000 0.006 0.005 0.000 0.011 0.005 0.000 0.008 0.006 0.000 0.007 0.009 0.001 0.015 0.007 0.001 0.014 0.009 0.001 0.011
LQ5.5.70b.ComplaintMechBusLisenceDenied 0.005 0.000 0.005 0.009 0.001 0.013 0.005 0.001 0.009 0.059 0.009 0.037 0.004 0.000 0.005 0.007 0.000 0.007 0.007 0.000 0.005
LQ5.5.71a.BusRegPublicHealthStandards 0.033 0.004 0.028 0.003 0.000 0.007 0.004 0.000 0.005 0.005 0.000 0.006 0.054 0.006 0.028 0.025 0.003 0.019 0.014 0.001 0.008
LQ5.5.71b.BusRegPublicEnvironmentalStandards 0.049 0.007 0.034 0.006 0.001 0.015 0.006 0.000 0.005 0.006 0.000 0.005 0.012 0.001 0.009 0.022 0.002 0.017 0.012 0.001 0.006
LQ5.5.71c.BusRegPublicSafetyStandards 0.047 0.007 0.035 0.004 0.000 0.010 0.003 0.000 0.005 0.004 0.000 0.007 0.015 0.001 0.011 0.004 0.000 0.006 0.008 0.000 0.005
LQ6.1.73a.AttemptedCorruptionIllegal 0.005 0.000 0.004 0.004 0.000 0.007 0.015 0.002 0.019 0.006 0.000 0.006 0.312 0.037 0.059 0.005 0.000 0.006 0.011 0.001 0.008
LQ6.1.73b.ExtortionIllegal 0.036 0.008 0.049 0.010 0.004 0.045 0.006 0.001 0.013 0.004 0.000 0.009 0.005 0.000 0.018 0.007 0.000 0.012 0.011 0.001 0.012
LQ6.1.73c.OfferingBribeIllegal 0.006 0.000 0.011 0.009 0.002 0.028 0.006 0.001 0.015 0.008 0.001 0.014 0.010 0.003 0.034 0.005 0.000 0.011 0.005 0.000 0.008
LQ6.1.73d.ReceivingBribeIllegal 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Table 4c Continues on Next Page
46
Table 4c, Cont'd: BMA Results for GII303 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
LQ6.1.73e.BribingForeignOfficialIllegal 0.020 0.002 0.015 0.364 0.078 0.111 0.036 0.004 0.021 0.005 0.000 0.003 0.006 0.000 0.004 0.006 0.000 0.006 0.004 0.000 0.002
LQ6.1.73f.UsingPubResourcePrivatelyIllegal 0.004 0.000 0.010 0.009 0.002 0.034 0.004 0.000 0.010 0.007 0.001 0.012 0.226 0.068 0.131 0.007 0.001 0.019 0.017 0.003 0.024
LQ6.1.73g.UsingConfidentialStateInfoPrivatelyIllegal 0.003 0.000 0.004 0.006 0.001 0.021 0.006 0.001 0.012 0.007 0.000 0.008 0.041 0.007 0.036 0.004 0.000 0.005 0.006 0.000 0.005
LQ6.1.73h.MoneyLaunderingIllegal 0.007 0.001 0.019 0.008 0.001 0.026 0.005 0.001 0.014 0.005 0.000 0.017 0.008 0.000 0.010 0.011 0.002 0.020 0.012 0.001 0.012
LQ6.1.73i.OrganizedCrimeIllegal 0.004 0.000 0.008 0.006 0.001 0.024 0.002 0.000 0.007 0.008 0.001 0.017 0.073 0.010 0.038 0.005 0.000 0.013 0.000 0.000 0.000
LQ6.2.74.ThereIsAntiCorrAgency 0.042 0.009 0.047 0.005 0.001 0.014 0.002 0.000 0.006 0.038 0.008 0.046 0.017 0.002 0.022 0.006 0.000 0.009 0.006 0.000 0.007
LQ6.2.75a.AntiCorrAgencyProtectedPolitics 0.005 0.000 0.004 0.004 0.000 0.004 0.005 0.000 0.003 0.011 0.001 0.007 0.005 0.000 0.003 0.005 0.000 0.004 0.005 0.000 0.002
LQ6.3.77a.GeneralRightAppeal 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
LQ6.3.80a.IndependenceJudiciary 0.013 0.003 0.034 0.004 0.000 0.013 0.007 0.000 0.012 0.003 0.000 0.008 0.007 0.001 0.014 0.006 0.001 0.015 0.010 0.001 0.010
LQ6.3.80c.DistributingCasesToNationalJudges 0.011 0.000 0.005 0.004 0.000 0.004 0.005 0.000 0.004 0.006 0.000 0.003 0.014 0.001 0.007 0.006 0.000 0.004 0.010 0.000 0.005
LQ6.3.80d.NationalJudgesProtected 0.005 0.000 0.005 0.003 0.000 0.006 0.004 0.000 0.006 0.006 0.000 0.006 0.520 0.126 0.134 0.004 0.000 0.005 0.005 0.000 0.005
LQ6.3.82c.StateGivesCounselDefendants 0.033 0.006 0.034 0.025 0.008 0.057 0.005 0.001 0.013 0.005 0.000 0.008 0.031 0.008 0.051 0.402 0.183 0.241 0.010 0.001 0.014
LQ6.4.84a.CitizensComplainPolice 0.005 0.000 0.003 0.008 0.001 0.010 0.041 0.005 0.026 0.005 0.000 0.004 0.009 0.000 0.005 0.017 0.002 0.015 0.028 0.002 0.013
LQ6.4.84c.CorruptionByLawEnfProsecuted 0.004 0.000 0.003 0.003 0.000 0.006 0.005 0.000 0.007 0.005 0.000 0.006 0.020 0.001 0.012 0.005 0.000 0.007 0.008 0.000 0.005
LQ6.4.84e.LawEnfOfficialsNotImmuneCriminalProceedings0.007 0.000 0.009 0.024 0.005 0.035 0.019 0.003 0.025 0.024 0.003 0.019 0.014 0.001 0.014 0.109 0.022 0.068 0.006 0.000 0.006
Posterior Probability of:
Firstbest model 0.061 0.051 0.040 0.133 0.121 0.044 0.137
Secondbest model 0.049 0.032 0.025 0.082 0.035 0.032 0.046
Thirdbest model 0.040 0.019 0.018 0.021 0.029 0.032 0.027
Posterior Mean Model Size 12.49 5.133 4.051 4.309 10.06 6.867 6.121
Number of Models Visited 25518 22608 20473 17298 21222 22102 21599
Number of Models Covering
x% of Posterior Probability
x=50% 65 93 99 56 30 89 62
x=75% 250 371 448 319 147 418 316
x=90% 643 1011 1211 750 463 1087 812
Corr(PMP) 0.538 0.818 0.788 0.973 0.380 0.844 0.939
G&M Measure of Probability
Mass Visited 0.085 0.167 0.198 0.256 0.109 0.183 0.181
Number of Observations 65 63 67 61 58 70 61
47
Table 5a: BMA Results for DB10 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
StartBus 0.063 0.002 0.017 0.137 0.016 0.048 0.967 0.218 0.073 0.326 0.030 0.050 0.366 0.083 0.124 0.918 0.208 0.091 0.796 0.114 0.071
ConstPerm 0.043 0.001 0.011 0.308 0.045 0.076 0.082 0.006 0.026 0.207 0.015 0.033 0.642 0.154 0.136 0.778 0.137 0.090 0.125 0.008 0.027
Employ 0.458 0.047 0.058 0.076 0.005 0.026 0.048 0.001 0.013 0.674 0.064 0.053 0.274 0.043 0.081 0.046 0.000 0.013 0.047 0.000 0.011
RegProperty 0.047 0.001 0.010 0.092 0.008 0.033 0.073 0.005 0.023 0.438 0.033 0.043 0.049 0.002 0.018 0.059 0.002 0.018 0.907 0.133 0.060
Credit 0.307 0.045 0.077 0.536 0.134 0.143 0.991 0.365 0.097 0.138 0.012 0.037 0.428 0.128 0.169 0.942 0.319 0.124 0.976 0.284 0.089
Investor 0.181 0.015 0.036 0.051 0.000 0.017 0.039 0.001 0.014 0.107 0.006 0.021 0.248 0.042 0.086 0.054 0.002 0.020 0.063 0.003 0.020
Tax 0.095 0.007 0.026 0.440 0.078 0.101 0.096 0.009 0.035 0.892 0.120 0.057 0.556 0.133 0.137 0.153 0.018 0.050 0.054 0.003 0.018
Border 1.000 0.288 0.038 1.000 0.392 0.062 0.994 0.213 0.055 1.000 0.192 0.033 0.840 0.205 0.115 1.000 0.322 0.059 1.000 0.257 0.041
Contract 0.253 0.023 0.046 0.087 0.007 0.031 0.084 0.006 0.028 0.070 0.003 0.015 0.049 0.000 0.019 0.462 0.072 0.089 0.055 0.002 0.016
ClosingBus 0.072 0.003 0.015 0.982 0.216 0.065 0.982 0.195 0.058 0.978 0.110 0.034 0.487 0.087 0.102 0.713 0.102 0.078 0.354 0.030 0.046
Posterior Probability of:
Firstbest model 0.209 0.137 0.617 0.159 0.038 0.234 0.367
Secondbest model 0.111 0.126 0.061 0.117 0.035 0.122 0.155
Thirdbest model 0.092 0.094 0.053 0.069 0.031 0.114 0.054
Posterior Mean Model Size 2.519 3.709 4.357 4.830 3.938 5.124 4.377
Number of Models Visited 298 267 173 338 650 261 220
Number of Models Covering
x% of Posterior Probability
x=50% 5 6 1 7 26 4 2
x=75% 16 17 4 21 62 13 9
x=90% 38 38 9 48 138 32 23
Corr(PMP) 1.000 0.999 1.000 0.999 0.998 1.000 1.000
G&M Measure of Probability
Mass Visited 0.999 1.000 0.999 1.004 0.994 0.994 0.999
Number of Observations 137 144 158 130 133 178 139
48
Table 5b: BMA Results for DB41 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
StartBusProc 0.042 0.001 0.007 0.255 0.026 0.050 0.346 0.033 0.050 0.431 0.025 0.032 0.113 0.010 0.034 0.035 0.001 0.007 0.111 0.007 0.025
StartBusTime 0.088 0.004 0.018 0.044 0.000 0.013 0.360 0.036 0.055 0.286 0.016 0.029 0.040 0.001 0.014 0.063 0.003 0.014 0.840 0.096 0.053
StartBusCost 0.089 0.005 0.020 0.117 0.012 0.040 0.105 0.009 0.033 0.048 0.001 0.008 0.379 0.068 0.099 0.835 0.126 0.072 0.194 0.016 0.038
StartBusCapital 0.095 0.003 0.012 0.047 0.001 0.010 0.040 0.000 0.007 0.045 0.001 0.005 0.040 0.001 0.009 0.043 0.001 0.007 0.062 0.002 0.009
ConstPermProc 0.210 0.012 0.027 0.047 0.002 0.013 0.076 0.003 0.016 0.040 0.000 0.005 0.044 0.002 0.013 0.039 0.001 0.007 0.060 0.002 0.011
ConstPermTime 0.567 0.051 0.051 0.038 0.001 0.012 0.046 0.001 0.011 0.597 0.039 0.037 0.131 0.013 0.038 0.033 0.000 0.007 0.041 0.001 0.008
ConstPermCost 0.038 0.000 0.007 0.304 0.037 0.064 0.129 0.010 0.030 0.209 0.010 0.022 0.213 0.027 0.060 0.971 0.141 0.045 0.228 0.016 0.034
EmployHiringDiff 0.122 0.005 0.016 0.041 0.001 0.011 0.045 0.001 0.009 0.092 0.002 0.010 0.042 0.001 0.011 0.078 0.003 0.013 0.109 0.005 0.017
EmployHoursRig 0.042 0.000 0.009 0.060 0.003 0.018 0.069 0.003 0.016 0.160 0.007 0.019 0.035 0.001 0.010 0.067 0.003 0.015 0.048 0.001 0.010
EmployFiringDiff 0.142 0.008 0.024 0.235 0.021 0.044 0.040 0.001 0.008 0.042 0.000 0.005 0.042 0.001 0.012 0.039 0.001 0.008 0.131 0.007 0.023
EmployEmployRig 0.084 0.005 0.023 0.045 0.001 0.018 0.056 0.002 0.015 0.153 0.007 0.019 0.046 0.002 0.015 0.108 0.006 0.022 0.063 0.003 0.017
EmployFiringCost 0.794 0.067 0.042 0.306 0.031 0.052 0.120 0.007 0.024 0.095 0.003 0.011 0.768 0.119 0.079 0.475 0.040 0.048 0.219 0.015 0.032
RegPropertyProc 0.026 0.000 0.005 0.060 0.003 0.015 0.125 0.007 0.023 0.716 0.043 0.032 0.036 0.001 0.009 0.112 0.005 0.018 0.400 0.028 0.039
RegPropertyTime 0.050 0.001 0.008 0.038 0.001 0.010 0.113 0.007 0.023 0.076 0.002 0.010 0.046 0.002 0.014 0.064 0.002 0.013 0.047 0.001 0.009
RegPropertyCost 0.045 0.001 0.008 0.045 0.002 0.013 0.030 0.000 0.007 0.041 0.001 0.005 0.040 0.001 0.012 0.040 0.001 0.009 0.304 0.023 0.040
CreditLegal 0.828 0.090 0.051 0.088 0.006 0.026 1.000 0.211 0.041 0.268 0.014 0.026 0.086 0.007 0.029 0.971 0.138 0.044 0.892 0.098 0.046
CreditInfo 0.041 0.001 0.007 0.105 0.007 0.024 0.065 0.003 0.014 0.167 0.006 0.016 0.091 0.006 0.025 0.032 0.000 0.006 0.798 0.078 0.048
CreditPubReg 0.044 0.001 0.012 0.038 0.001 0.017 0.040 0.002 0.017 0.038 0.000 0.007 0.035 0.000 0.017 0.032 0.000 0.011 0.043 0.001 0.017
CreditPrivBureau 0.037 0.001 0.012 0.130 0.020 0.061 0.387 0.071 0.101 0.047 0.002 0.012 0.033 0.001 0.018 0.096 0.010 0.038 0.093 0.012 0.047
InvestorDisclosure 0.036 0.000 0.005 0.032 0.000 0.008 0.035 0.000 0.007 0.055 0.001 0.007 0.046 0.001 0.016 0.038 0.000 0.007 0.042 0.001 0.008
InvestorLiability 0.055 0.001 0.009 0.040 0.001 0.010 0.036 0.001 0.008 0.045 0.001 0.006 0.049 0.002 0.017 0.038 0.000 0.007 0.048 0.001 0.010
InvestorShareholder 0.049 0.001 0.009 0.039 0.001 0.011 0.037 0.001 0.008 0.096 0.003 0.013 0.189 0.021 0.051 0.038 0.001 0.008 0.035 0.000 0.006
InvestorProtect 0.051 0.002 0.010 0.036 0.001 0.012 0.039 0.001 0.010 0.089 0.003 0.014 0.363 0.053 0.079 0.038 0.001 0.010 0.104 0.006 0.024
TaxPayment 0.041 0.000 0.006 0.582 0.071 0.070 0.963 0.144 0.049 0.071 0.002 0.009 0.473 0.066 0.079 0.950 0.123 0.045 0.039 0.000 0.007
TaxTime 0.043 0.001 0.007 0.191 0.017 0.040 0.043 0.001 0.009 0.989 0.091 0.024 0.168 0.018 0.046 0.144 0.009 0.027 0.041 0.001 0.008
TaxProfit 0.740 0.068 0.050 0.058 0.003 0.015 0.032 0.000 0.007 0.057 0.001 0.008 0.035 0.001 0.010 0.037 0.001 0.007 0.037 0.000 0.007
TaxLabor 0.221 0.020 0.043 0.038 0.000 0.010 0.444 0.042 0.054 0.047 0.001 0.007 0.043 0.000 0.014 0.088 0.005 0.020 0.072 0.004 0.018
TaxOther 0.372 0.025 0.037 0.079 0.005 0.022 0.035 0.000 0.008 0.037 0.000 0.005 0.283 0.033 0.059 0.047 0.001 0.009 0.038 0.001 0.008
TaxTotal 0.316 0.029 0.048 0.077 0.004 0.020 0.041 0.000 0.011 0.038 0.000 0.005 0.077 0.006 0.025 0.059 0.002 0.012 0.134 0.008 0.026
Table 5b Continues on Next Page
49
Table 5b, Continued: BMA Results for DB41 Disaggregation
Dependent Variable= DRI EIU GAD GCS PRS WMO CPIA
PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD PIP Mean SD
BorderDocExp 0.161 0.011 0.029 0.411 0.054 0.073 0.153 0.013 0.036 0.031 0.000 0.005 0.080 0.007 0.029 0.918 0.125 0.058 0.518 0.047 0.051
BorderTimeExp 0.174 0.015 0.042 0.089 0.006 0.047 0.469 0.071 0.084 0.102 0.005 0.018 0.122 0.014 0.056 0.947 0.199 0.073 0.115 0.012 0.041
BorderCostExp 0.252 0.023 0.048 0.101 0.004 0.052 0.048 0.002 0.013 0.750 0.092 0.058 0.080 0.006 0.029 0.192 0.014 0.034 0.067 0.003 0.017
BorderDocImp 0.065 0.003 0.019 0.054 0.002 0.023 0.312 0.037 0.060 0.038 0.000 0.006 0.037 0.001 0.017 0.180 0.019 0.047 0.248 0.024 0.048
BorderTimeImp 0.690 0.098 0.078 0.777 0.199 0.130 0.189 0.023 0.056 0.056 0.002 0.013 0.611 0.134 0.122 0.071 0.008 0.040 0.734 0.114 0.081
BorderCostImp 0.646 0.078 0.067 0.461 0.077 0.101 0.041 0.001 0.012 0.279 0.031 0.054 0.138 0.015 0.045 0.189 0.015 0.035 0.185 0.015 0.036
ContractProc 0.084 0.004 0.017 0.376 0.043 0.062 0.159 0.012 0.032 0.103 0.004 0.013 0.043 0.002 0.014 0.037 0.001 0.008 0.077 0.004 0.016
ContractTime 0.826 0.080 0.046 0.033 0.000 0.009 0.049 0.002 0.012 0.063 0.002 0.009 0.084 0.006 0.025 0.042 0.000 0.008 0.044 0.001 0.008
ContractCost 0.035 0.000 0.006 0.112 0.009 0.031 0.031 0.000 0.007 0.031 0.000 0.005 0.047 0.002 0.016 0.072 0.004 0.017 0.045 0.001 0.009
ClosingBusTime 0.038 0.000 0.006 0.296 0.039 0.068 0.043 0.001 0.015 0.044 0.000 0.010 0.122 0.015 0.052 0.108 0.008 0.030 0.064 0.003 0.020
ClosingBusCost 0.035 0.000 0.006 0.094 0.008 0.030 0.074 0.004 0.019 0.068 0.002 0.011 0.040 0.001 0.015 0.057 0.002 0.015 0.243 0.017 0.033
ClosingBusRecovery 0.040 0.001 0.009 0.630 0.118 0.103 0.994 0.227 0.056 0.987 0.125 0.031 0.577 0.127 0.132 0.899 0.126 0.063 0.259 0.024 0.049
Posterior Probability of:
Firstbest model 0.013 0.012 0.014 0.018 0.022 0.042 0.022
Secondbest model 0.010 0.006 0.013 0.017 0.012 0.035 0.018
Thirdbest model 0.009 0.006 0.013 0.015 0.011 0.020 0.008
Posterior Mean Model Size 8.352 6.646 7.460 7.625 5.966 9.320 9.320
Number of Models Visited 33551 44824 28256 30607 36099 22212 42196
Number of Models Covering
x% of Posterior Probability
x=50% 554 923 456 452 585 237 656
x=75% 2130 3407 2004 2017 2476 1319 2655
x=90% 5438 7892 5171 5175 6123 3643 6617
Corr(PMP) 0.951 0.910 0.971 0.967 0.961 0.990 0.955
G&M Measure of Probability
Mass Visited 0.540 0.484 0.620 0.585 0.539 0.681 0.481
Number of Observations 137 144 158 130 133 178 139
50
Table 6: Robustness of Instability Across Outcomes
pvalue for Null of Independence Across Outcome Variables
in the Set of "Important" Regressors
GII12 GII45 GII303 DB10 DB41
"Important" regressors identified as:
Top 25% of Variables 0.00 0.12 0.99 0.00 0.99
Top 10% of Variables 0.00 0.94 0.99 0.00 0.89
Top 5 Variables 0.04 0.94 0.66 0.90 0.94
Top 3 Variables 0.00 0.00 0.59 0.00 0.80
Top 1 Variable 0.00 0.99 0.52 0.00 0.00