Constructing an Index of Objective Indicators of Good Governance Steve Knack & Mark Kugler

PREM Public Sector Group, World Bank October 2002

Introduction Different indicators of good governance are appropriate for different purposes. Indicators differ across (at least) two important dimensions. First, some indicators measure relatively specific aspects of the quality of governance while others are more highly aggregated. Second, some indicators are more transparently constructed and replicable, whereas others are less so – for example, subjective ratings provided by firms assessing political risks to foreign investors. Relevance for Bank operations requires the use of indicators that are as specific and disaggregated as possible. For other purposes, such as making broad comparisons across countries, or conducting research on the causes and consequences of good governance broadly defined, highly aggregated indicators are often preferred. For many purposes, researchers and donor organizations are free to use subjective assessments of the quality of governance constructed wholly without the cooperation or knowledge of developing country governments. In some cases, however, donors find that “ownership” of indicators by developing country governments is essential. Governments commonly object to use of broad, subjective assessments of corruption, political freedoms, etc. produced by TI, Freedom House and commercial firms assessing political risk. This note describes a methodology for constructing an index of objective indicators of good governance. The indicators were selected primarily with regard to broad cross-country coverage, and acceptability to developing country governments. Indicators available only for a small number of countries were avoided, as were indicators based wholly or in large part on expert opinion of westerners. This exercise is intended to provoke debate regarding the value of an index, and how one should be constructed, rather than to generate a final set of rankings. Although we believe there is merit in the particular set of indicators used here, we recognize that each indicator has its own idiosyncrasies and deficiencies, and we hope to gradually add to this set and replace some of the conceptually weaker indicators as more data become available. Rationale for an index The DAC criteria for indicators of good governance to potentially include in the MDGs specified that the number of indicators should be small. Because any single objective indicator tends to measure only a very small part of the institutional and governance environment, a large number of indicators is needed for a fair and accurate depiction. The only way to attain reasonable accuracy, while maintaining objectivity and keeping the number of indicators low, is to aggregate indicators into a smaller number of indexes.

Aggregating tends to reduce measurement error. Indexes of several variables which all purport to measure a similar concept are in general more accurate than are their component variables. Each component variable reflects not only something about the quality of governance, but also idiosyncratic factors. For example, trade taxes as a share of all government revenues is sometimes sued as a proxy for administrative capacity, but it also may be affected by trade policy.1 As long as the idiosyncratic factors in each component variable are largely independent of each other, their effects on country rankings will be dampened greatly by aggregation. Index components The nine indicators we use are the regulation of entry, contract enforcement, contract intensive money, international trade tax revenue, budgetary volatility, revenue source volatility, telephone wait times, phone faults, and the percentage of revenues paid to public officials in bribes, as reported in surveys of business firms. A brief description of each component indicator follows:

Regulation of Entry: The number of procedures to start new businesses varies dramatically across countries. Some regulation is required on efficiency and equity grounds; however, the number of procedures required to start a new business, and the cost in time and fees, tends to be very low in many countries (such as Canada) in which social and environmental regulations are most stringent. The obstacles that an entrepreneur must surmount to open a new business in many countries far exceed anything that can be justified on efficiency grounds. Djankov et al. (2001) have collected data on the number of procedures that are officially required to obtain all necessary permits and completing all of the required notifications for the company to operate legally. For simplicity, the data collected apply to a “standardized firm” which operates in the largest city, performs general industrial or commercial activities, does not trade across national borders or in goods subject to excise taxes, is domestically owned, does not own land, etc. Contract Enforcement: Sometimes it is necessary administer the relationships between creditors and debtors to ensure equality, but the inability to enforce contracts without exceeding expense is indicative of overregulation. The indicator of contract enforcement refers to the number of formal independent procedures to collect a debt. The data pertaining to contract enforcement are derived from questionnaires answered by attorneys at private law firms. The current set of data refers to January 2002. The questionnaire covers the step-by-step evolution of a debt recovery case before local courts in the country’s largest city. The number of procedures covers all independent procedural actions, mandated by law or court regulation, that demand interaction between the parties or between them and the judge or court officer. Contract Intensive Money: Contract intensive money is the proportion of the money supply that is not held in the form of currency, i.e. the proportion that is held in bank

1 Higher import tariffs will increase trade tax revenues for a given level of imports, but may reduce revenues if they lower import volumes sufficiently.

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accounts and as other financial assets. The percentage of contract intensive money indicates in part how much faith investors have in the government's ability and willingness to enforce financial contracts, and to refrain from expropriating financial assets. It is a measure of trust in government and in banks, which are regulated by government. Contract intensive money is calculated as one minus the ratio of currency outside of banks to the sum of money and quasi-money (one minus line 14a divided by the sum of lines 34 and 35 in the IFS). International Trade Tax Revenue: Reliance on revenue from international trade taxes is widely believed to reflect weak administrative capacity. Economic theory suggests that taxing all transactions at low or moderate levels is more efficient than collecting taxes from only a subset of transactions at high rates. However, effectively collecting income, sales or other taxes on a broad range of transactions requires a certain degree of administrative capability on the part of governments. It is relatively easy for governments to collect tax revenues from cross-border transactions, because they are more easily monitored. Budgetary Volatility: Theory and evidence indicate that volatile and unpredictable government policy reduces private investment. The budget is one key arena in which government policy issues are played out, resulting in executive spending decisions. To the extent that policy decisions are captured in the budget, then stable policy should be reflected in stable budget allocations, and vice versa. Budgetary volatility is calculated using data from the most recent 4-year period on fluctuations in expenditure shares across the 14 functional classifications in the Government Finance Statistics data. Revenue Source Volatility: Volatile and unpredictable government revenue collection policy can discourage adequate long run planning. The manner and degree of revenue collection is an aspect of government policy determined in part by the executive. To the extent that policy decisions are captured in revenue collection policy, then stable policy should be reflected in stable revenue proportions, and vice versa. Revenue volatility is calculated using data from the most recent 4-year period on fluctuations in revenue shares across the 20 revenue classifications in the Government Finance Statistics data. Telephone Faults: The ability to provide and maintain consistent telephone service, or to regulate effectively private telecom industry, is an indicator of administrative capability. Access to telecommunication services helps to promote an environment conducive to business, and is necessary for businesses and households to take advantage of “E-Government” services. Telephone faults per 100 main lines is calculated by dividing the total number of reported faults for the year by the total number of main lines in operation and multiplying by 100. The definition of fault can vary. Some countries include faulty customer equipment. Others distinguish between reported and actual found faults. There is also sometimes a distinction between residential and business lines. Another consideration is the time period as some countries report this indicator on a monthly basis; in these cases data are converted to yearly estimates.

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Telephone Wait Times: See above for rationale. Waiting time is the approximate number of years applicants must wait for a telephone line. Percentage of Firm Revenues Paid as Bribes: Bribery and corruption are both a cause and a consequence of weakened governing institutions. Gauging the level of corruption that businesses face can provide information about the strength of governance in countries. The World Business Environment Survey (WBES) regularly asks businesses, “On average, what percentage of revenues do firms like yours pay in unofficial payments per annum to public officials?” The component indicator is the mean category of response within a country for 2000, the latest year available.

Methodology In constructing an index of objective indicators of good governance, the component indicators should be reasonably well correlated with each other. A standard statistical measure of index reliability, “alpha,” varies from a low of 0 to a maximum possible value of 1. Alpha is a positive function of (1) the mean inter-item correlation of the index components, and of (2) the number of index components. Our index is based on nine indicators, and the average inter-item correlation is about .25, producing a relatively high alpha reliability coefficient of .75. 2 All 36 of the inter-item correlations among the 9 component variables are in the expected direction, with the majority of these relationships being statistically significant at the .05 level. Controlling for per capita GDP tends to reduce the strength of these correlations: although most of them remain in the expected direction, only 7 of them are significant when the common effects of per capita income are statistically removed. Furthermore, it is encouraging that the indicators are correlated with other comprehensive measures of governance. The most encompassing measures of governance to date are the six “KKZ” (Kaufmann, Kraay, and Zoido-Lobaton, 2002) indexes, constructed from subjective assessments of governance. All 9 of the objective indicators are significantly correlated with each of the 6 KKZ indexes. Even after controlling for per capita GDP, 49 of these 54 correlations retain the expected sign, and 27 of them remain statistically significant. A strong relationship between two sets of indicators does not necessarily imply, of course, that either set is necessarily valid; however, the absence of such a relationship would have strongly suggested that one set or the other, or both, were not valid. These findings suggest that it is appropriate to aggregate these 9 objective indicators to construct a broader measure of governance. The first complication in aggregating the component indicators is that values for each of them are on disparate scales. To overcome this obstacle, each indicator is recoded to the standard normal distribution by

2 Factor analysis confirms that these indicators load primarily onto a single factor, indicating that they are measuring something in common. The only exception is the Djankov measure of contract enforcement (the number of independent procedures necessary to collect a bad check). Throughout the analyses reported below, however, there are no substantive changes in the results if this variable is omitted from the index.

4.

subtracting each value from its mean, and then dividing by its standard deviation. Standardization ensures that the rank and difference between countries is preserved and that each component in the index receives equal weight.3 The second complication in aggregating the component indicators is the potential for bias caused by the use of different data sources. Even if the values for all countries are accurate for a set of indicators, varying country coverage among the indicators could produce an inaccurate index. For example, countries ranked near the bottom on indicators constructed from GFS data (budgetary volatility and revenue source volatility) conceivably are not actually among the most poorly-governed countries in the world, but instead may just be the most poorly-governed among those with a reasonable capacity for statistical reporting. Countries without such minimum capacity could be rewarded, in effect, for their inability or unwillingness to report data. An index that includes some variables covering non-represented samples of countries could therefore contain bias. Our solution to this problem is to identify a subset of indicators that cover a representative sample of countries, and use values for those indicators to adjust the values for indicators with non-representative coverage.4 To determine which indicators cover representative samples of countries, we created a dichotomous variable for each component indicator that takes the value of 1 for any country for which there are data present for the indicator in any of the past five years, or a value of 0 if data are missing for all of the previous five years. Each of the 9 dichotomous variables was then regressed on the log of per capita income, using logit regression.5 Data availability was positively and significantly (using a .10 significance level) related to per capita income for 6 of the 9 indicators, and negatively and significantly related to contract-intensive money. Country coverage was representative (by income level) only for two indicators, telephone faults and telephone wait times. These two representative indicators were combined to form an unweighted index, ignoring missing values for either of the two components. This index, free of bias from non-representative country coverage, was then used to adjust the values for the component indicators with non-representative coverage, to keep from penalizing countries that are ranked poorly among a sample of countries biased toward those with stronger governance. This “percentile-matching” adjustment is done in the following way: (1) countries were ranked by their values on the non-representative indicator; (2) the same set of countries is ranked by their values on the index of two representative indicators, and; (3) each country’s adjusted score on the non-representative indicator is

3 Without standardizing, index components with higher means or variances are implicitly weighted more heavily in the index, even when no explicit weighting procedure is used. 4 We borrowed this percentile matching procedure from other work on governance indicators conducted within the World Bank by Aart Kraay. 5 The assumption is that a sample that is representative with respect to income will likely be representative in terms of the quality of governance. Data availability is regressed on the log of per capita income because it provided a better fit than per capita income. However, there are no substantive changes if per capita income is substituted for the log of per capita income.

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determined by matching its rank with the similar-ranking score on the index of representative indicators.6 This percentile-matching procedure requires that all countries with available data on the non-representative indicator also have available data on the index of representative indicators. Where data on the index of representative indicators were unavailable, values were estimated, from a regression of the index on the non-representative indicator.7 Index Validity Because most of the 9 objective indicators individually measure only very narrow aspects of the quality of governance, their partial correlations (controlling for per capita income) with the KKZ governance indicators tend to be modest. If the index accomplishes its purpose of reducing measurement error – reflecting idiosyncratic factors influencing each of the 9 component indicators – then its correlation with the KKZ governance indicators should be higher (assuming, again, that the KKZ indicators themselves are reasonably valid). Results provide support for this assumption. The average of the 9 correlations between the component indicators and each of the 6 KKZ indexes ranges from a low of .42 (for the KKZ Voice & Accountability index) to a high of .51 (for the KKZ Government Effectiveness index). Correlations of the index of objective indicators with the KKZ variables are much higher, as shown in the first column of figures in Table 1, ranging from a low of .55 for Voice & Accountability to .70 for Government Effectiveness. Controlling for per capita income, the average of the partial correlations of the 9 component indicators with each of the KKZ indexes ranged from only .13 for Voice & Accountability to .21 for Government effectiveness. The partial correlations of the objective index with the KKZ indexes are again higher, as shown in the figures in parentheses in the first column of figures in Table 1, ranging from .19 (for KKZ Political Stability and Control of Corruption) to .33 (for KKZ Regulation Quality).

6 For example, suppose a country is ranked 80th-best of 90 countries on a non-representative indicator. These 90 countries (and only these 90) are then ranked by their values on the representative indicator (ignoring the values and ranks of any other countries with data on the representative indicator for which data were unavailable on the non-representative indicator). The value for the 80th-ranked country on the representative indicator is then identified, and that value is assigned as the adjusted value of the non-representative indicator for the country ranking 80th on the non-representative indicator. 7 There are no substantive differences in the results when imputed values are left out of the analysis, but imputation allows many more countries to be included in the final index.

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Table 1: Correlation of Objective Governance Indicators with KKZ Governance Variables

(correlation values in parentheses control for per capita GDP)

Second Generation Indicators Percentile

Matching Index Unadjusted

Standardized Index Voice &

Accountability0.55*

(0.23*) 0.60*

(0.27*) Political Stability

0.63* (0.19*)

0.69* (0.24*)

Government Effectiveness

0.70* (0.32*)

0.77* (0.41*)

Regulatory Quality

0.61* (0.33*)

0.67* (0.38*)

Rule of Law

0.69* (0.31*)

0.76* (0.40*)

KK

Z In

dica

tors

Control of Corruption

0.62* (0.19*)

0.72* (0.36*)

Note: * Indicates correlation coefficient is statistically significant at the .05 level.

The right-hand column in Table 1 lists correlations between the KKZ indexes and an unadjusted version of the index of objective indicators, which standardizes and equally weights the 9 components but does not adjust for non-representative samples. These are higher in every case than the correlations with the adjusted index. The percentile matching procedure necessarily discards some information, which may weaken the associations with other variables somewhat. The problem is that the procedure does not preserve the relative distances between the scores of the non-representative component indicators, but preserves only the rankings and converts the relative distances to those represented in the representative components.

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Figure 1: Relationship Between Objective Indicators Index and KKZ Index

(controlling for per capita GDP)

Second Generation Index & KKZ Government Effectiveness(Controlling for Per Capita GDP)

KKZ Gov't Effective, resid-1.5 -1 -.5 0 .5 1 1.5

-1.5

-1

-.5

0

.5

1

BLR

PRY

TKM

RUS

VEN

DZA

AGO

KAZ

ROM

SDN

ALB

ECUMKDKWT

ZWE

AZE

GAB

ARMSYR

UKR

BRA

SAUMDACOL

HTI

TGO

GTM

ARG

COG

UZBKOR

TUR

DOM

TJK

BGR

ITA

PERIRN

GEO

SVK

PANARE

GNB

NIC

MLI

LKA

HRV

KGZ

SLVIDN

JPN

NPLZAF

PNG

GRC

POL

SVN

HNDMEX

CZEJAMMLTTWN

SLE

CIVTHAISRCYPLVAHUN

BOL

LTUHKG

MRT

LBN

NER

PRT

NGA

MYSPAK

GUY

PHL

NORBELFRABGDURYTTO

BHS

BDI

SUR

KEN

CHN

VNM

MUSCMRETHUSA

MAR

IND

LUXNZL

LAO

YEMCRI

ESTAUT

FJINAM

SWE

ZMB

EGYBLZ

DNKAUS

GHA

JORCAN

BWAMWI

FINDEU

UGAIRL

MOZ

ESP

CHL

GBRNLDISLCHE

MDG

SEN

GIN

TZA

BFA

MNG

GMB

TUNKHM

SGP

BEN

N: 142 t-statistic: 4.06 R-Squared: 0.541 Figure 1 depicts the relationship between the index of objective indicators and the KKZ indicator of governance effectiveness (again, controlling for per capita GDP). Appendix I provides similar graphs using the other 5 KKZ governance indicators. Collectively, the findings reported in this paper suggest that one can be reasonably confident that there is a good deal of validity in the index of objective governance indicators.

8.

References de Soto, Hernando. 1989. The Other Path: The Invisible Revolution in the Third World.

New York: Harper & Row. Djankov, Simeon, Rafael La Porta, Andrei Shleifer, and Florencio Lopez de Silanes,

“The Regulation of Entry,” World Bank Working Paper, June 2001. Djankov, Simeon, Rafael La Porta, Florencio Lopez de Silanes, and Andrei Shleifer,

“Legal Structure and Judicial Efficiency: The Lex Mundi Project,” World Bank Working Paper, October 2001.

Kaufmann, Daniel, Aart Kraay, and Pablo Zoido-Lobaton. 2002. “Governance Matters

II: Updated Indicators for 2000/01.” World Bank Policy Research Working Paper 2772.

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Appendix I: Graphs of the relationship between the objective indicators index and KKZ governance indexes (all graphs control for per capita GDP)

Second Generation Index & KKZ Voice and Accountability(Controlling for Per Capita GDP)

KKZ Voice & Account., resid-2 -1.5 -1 -.5 0 .5 1 1.5

-1.5

-1

-.5

0

.5

1

GNQ

SAU

TKM

BLR

HKG

DZA

ARE

SYR

CHN

SDN

PAK

KAZ

SWZ

AGO

UZB

VNMMDV

SGP

TUN

TUR

PRY

RUS

BTN

CIVKWT

ZWE

COL

GAB

RWAEGY

GIN

MYSIRN

VEN

LAO

AZE

COG

TGOLBN

KGZ

CMR

MEX

GTMBDI

UKR

IDN

HTI

GMB

KHM

SLE

ERI

MARLKA

TWNUGA

MRT

MKD

ARGTCDFJIJPNECU

TJKLUXARMUSA

PER

GNB

NAMKENISR

ALB

CAF

HRV

JORTHA

FRAITA

SLV

ETH

GEO

CHL

COM

TTO

KORBRABEL

DOM

YEM

LSO

NIC

HNDROMCANSVN

ESPAUT

PNG

BGD

GRC

CZE

BHS

CYP

BGR

NGA

DEU

GHASVKISLESTIRL

NOR

BWA

GBR

MDA

LVA

BRBBFA

DNK

PHL

PAN

NPL

BOL

HUN

NLDPRTMLTURY

MOZ

CHESWE

SLB

SUR

SEN

AUSFINLTU

ZAF

NZL

ZMB

POL

MUS

CPVBLZ

JAM

MWI

GUY

CRIINDNERTZA

MDG

MNG

MLI

BEN

N: 155 t-statistic: 2.938 R-Squared: 0.478

10.

Second Generation Index & KKZ Political Stability(Controlling for Per Capita GDP)

KKZ Pol. Stability, resid-1.5 -1 -.5 0 .5 1 1.5

-1.5

-1

-.5

0

.5

AGO

COL

MKD

LKA

SDN

ISR

DZA

IDNTUR

TJK

ZWE

PRY

UZB

RUS

NAM

GEO

GTM

GUY

GAB

ECUUGA

LBN

GIN

ARM

UKR

VENALB

AZE

NGA

CYP

COG

BDI

CIVZAF

MEX

MRT

ROMKOR

PERRWA

BLR

BOL

TWN

GNB

HRV

BELSYR

YEMPHLIRN

ITA

TTOMYS

PNG

BHS

KWT

SEN

ARG

KGZ

SAU

THA

SLE

USAKEN

BGDLTU

TGOGRCFRACZESVNPAKSVK

LUX

KAZ

BENHKG

MDA

GBRIRLHUN

BRAESPJPN

TKM

BLZ

JORCAN

SUR

DEU

BGR

NORLVAAUT

HTI

AUSESTMLTPOLMAR

DOM

AREDNK

INDBFA

EGY

NZL

GHA

FJIPAN

SWE

NER

CMR

NPLBWACHLCHN

JAMNLDISL

SGP

KHM

ETH

CHE

ERI

ZMB

HND

LAO

TUN

MDG

SLV

FIN

PRTURY

MUSNICCRI

MLI

TZA

VNM

GMB

MOZMWI

MNG

N: 144 t-statistic: 2.31 R-Squared: 0.506

11.

Second Generation Index & KKZ Regulatory Quality(Controlling for Per Capita GDP)

KKZ Reg. Quality, resid-2.5 -2 -1.5 -1 -.5 0 .5 1 1.5

-1.5

-1

-.5

0

.5

1

BLR

TKM

RUS

ZWE

GNQ

IRN

LAO

AGOUKR

UZBTJK

DZA

HTI

MDA

SUR

KWT

KAZ

SYR

GEO

TGOSAU

CPV

FJI

ROM

PRY

KGZVEN

ARM

GNB

MKD

SLE

KOR

IDN

GAB

ZAFMLT

ARE

ARGBEL

MRT

VNMTURBRBSVK

COLFRAJPN

ALB

ITA

PNG

BRA

CHNRWA

MYS

NOR

BLZ

SVN

HRV

COG

SDN

PAK

AZE

TWN

BGR

CZEMUS

SEN

ETHHND

GUY

NPL

ISRLTUNICIND

LVATCD

POLCIVECU

LSO

GRC

BHS

CYP

EGYGTM

SWZ

PRT

BDI

PHL

MEX

ISL

NGA

CANLBNDNK

PER

USA

UGA

DEUKENSWEYEM

NAM

GMB

THA

CHEAUTBGD

AUS

HUN

NER

CMR

ESP

LUX

DOM

LKA

NZL

GIN

JAMIRL

MNG

TTOGHACRIGBR

MAR

URY

MDG

TUN

BFA

FINBENHKG

KHM

ESTNLDJOR

CHL

PAN

BWAMOZTZA

BOL

SLV

MLI

MWI

SGP

ZMB

N: 149 t-statistic: 4.239 R-Squared: 0.524

12.

Second Generation Index & KKZ Rule of Law(Controlling for Per Capita GDP)

KKZ Rule of Law, resid-2 -1.5 -1 -.5 0 .5 1 1.5

-1.5

-1

-.5

0

.5

1

GNQ RUS

BLR

DZA

AGO

COL

VEN

TKM

GTM

MEX

PRY

KAZ

HTI

HND

GAB

SLVIDN

ZWE

BRA

PERIRN

FJI

ALB

ECU

ZAF

UKR

AZE

SUR

SDN

TUR

ARG

PHLMKD

SAUCMR

KGZ

TJK

NIC

SYR

GNB

ITAUZBKORPAN

TWN

KENROMJAMSVK

GRC

PAK

DOM

MLT

RWALKA

BGR

CHN

MYS

NGA

GEO

BGDHRV

TGO

CZEYEM

GIN

LAO

VNMTTO

COG

ISR

NER

LBNCYP

BOL

LTU

BHS

ARM

SVN

MRT

MDA

LVAPRT

CPVPOLCIVHUN

NPL

FRA

SWZ

PNG

ESPURYBFA

BELTCD

UGACRI

GUY

THA

BDI

AREESTHKGUSA

KWT

LUX

LSO

IRL

KHMEGYBRBBWA

MDG

BEN

MLI

JPN

GHA

DEUMUSNOR

CANGBR

DNKNLD

SEN

ISLAUS

TUNBLZMAR

SWE

GMB

IND

ERI

AUT

CHL

CHE

ZMB

JORFIN

MOZ

NZL

SGP

MWI

ETHNAM

MNG

SLE

TZA

N: 150 t-statistic: 3.94 R-Squared: 0.523

13.

Second Generation Index & KKZ Control of Corruption(Controlling for Per Capita GDP)

KKZ Cntrl. Corruption, resid-1.5 -1 -.5 0 .5 1 1.5

-1.5

-1

-.5

0

.5

1

RUS

TKM

ARG

KAZ

PRY

SAU

IRN

UKR

GAB

AZE

PNG

ECUIDN

VEN

TURARE

ZWE

ROM

DZA

MEX

MLT

AGO

THA

SYRSDNPAN

LBNCOL

GTMMKD

KGZ

CMR

TWN

KOR

ALBARM

PHL

ITACZE

DOM

NIC

BLR

GUY

GEO

SVKBRA

HRV

MDA

MRT

SLV

BOL

LVAMYS

BGR

UZBPAK

HND

VNM

CHN

TJK

KWT

KEN

HTI

BDIZAF

BHS

UGA

GRC

LTU

BEL

PER

CIVIRL

NGA

EGY

MUS

POLBGDHUNINDBFA

NERTTO

JPNHKGJAMFRA

ERIMDG

LKA

USAJORISRESTSVN

SUR

ZMB

TGO

GHA

LUX

URYCYPDEU

SEN

YEM

BLZ

LAO

PRT

MNG

AUT

TZA

CRINPL

ESP

NOR

COG

MARBWATUN

GIN

AUS

MLI

CHE

GMBETH

GBRCAN

CHL

DNK

SLE

ISL

FJINLDNAM

KHM

SGP

NZLSWE

MOZ

FIN

GNB

RWA

MWI

N: 143 t-statistic: 2.25 R-Squared: 0.502

14.

Appendix II: The Second Generation Indicator Index and Its Components

Country

World Bank Code

Simple Index

Percentile Matching Index

Telephone Wait Time

Phone Faults

Contract Intensive

Money

Trade Tax

Revenue Budgetary Volatility

Contract Enforcement

Regulation of Entry

Bribes Mean

(BEEPS)

Revenue Source

VolatilityGDP per Capita

KKZ Governmental Effectiveness

Canada

CAN 0.61321.0138 0.0000 0.9462 0.0125 0.0481 17 2 0.07800.1210 1.712527840

New Zealand NZL 1.0118 0.4629 0.0000 30.7000 0.9786 0.0186 12 2 0.0590 20070 1.2651

United Kingdom GBR 0.9787 0.6055 0.0000 4.1000 0.0000 0.0627 12 5 0.1440 0.0520 23509 1.7730

Denmark DNK 0.65050.9727 0.0000 0.9458 0.0000 0.0340 14 3 0.0910 1.615327627

Australia AUS 0.53810.9547 0.11000.0000 0.9438 0.0260 0.0364 11 2 0.1130 1.578425693

Ireland IRL 0.66370.9201 0.0000 0.0410 19 3 0.0800 1.794229866

Norway NOR 0.62500.8921 14.00000.0000 0.9466 0.03590.0056 12 4 0.0990 29918 1.3540

Iceland ISL 0.59420.8555 0.0000 0.9725 0.0125 0.0508 0.0600 1.931229581

France FRA 0.52330.8435 5.90000.0000 0.0000 10 10 0.06100.2730 1.239524223

Israel ISR 0.53180.8195 12.00000.2530 0.9696 0.04740.0068 19 5 0.0490 20131 0.8729

United States USA 0.8157 0.5916 0.0000 13.4300 0.9139 0.0102 0.0246 12 5 1.1520 0.0730 34142 1.5823

Sweden SWE 0.55150.7756 8.40000.0000 0.0007 0.1058 21 5 0.06800.0310 1.508624277

Cyprus CYP 0.55530.7503 22.80000.2774 0.9488 0.04330.0378 0.0420 20824 0.9105

San Marino SMR 0.7495 0.7162 0.0000 0.0141

Switzerland CHE 0.57180.7489 18.47000.0000 0.9234 0.03250.0113 14 6 0.1160 28769 1.9264

Germany DEU 0.54470.7386 8.70000.0000 0.0000 0.0573 14 9 0.02300.9850 1.672025103

Belgium BEL 0.67830.6993 4.00000.0216 0.0000 16 7 0.1050 1.291827178

Taiwan, China TWN 0.6971 0.6843 0.0000 2.0600 15 8 22824 0.9092

Qatar QAT 0.61460.6941 15.52000.0000 0.9395 0.8157

Lao PDR LAO 0.6913 0.5704 1.0882 0.9559 1575 -0.3945

Dominica DMA 0.55480.6720 12.0000 0.9372

Netherlands NLD 0.53030.6680 0.50000.0000 0.0000 0.0518 21 8 0.0540 25657 1.8355

Austria AUT 0.56340.6569 6.27000.0000 0.0002 0.0440 20 9 0.0490 1.513126765

Barbados

BRB 0.57150.6380 9.62000.2809 0.9162 15494

St. Lucia LCA 0.6303 0.5139 1.1269 0.9395 5703

Andorra ADO 0.62890.6289 13.65000.0000

Oman OMN 0.56910.6169 1.84000.4721 0.8949 0.0253 0.8483

Chile CHL 0.49350.6109 25.00000.0443 0.9371 0.03150.0612 21 10 0.07600.3210 1.13379417

Finland FIN 0.55520.6096 8.40000.0000 0.0000 0.0815 19 7 0.0990 1.668724996

South Africa ZAF 0.6074 0.4559 1.1013 40.9000 0.9566 0.0305 11 9 0.1010 9401 0.2527

Luxembourg LUX 0.61090.5681 7.00000.0000 0.0000 0.1138 0.0930 1.855050061

Portugal PRT 0.53440.5653 10.50000.2470 0.0001 22 12 0.06700.1130 0.909917290

Spain ESP 0.56320.5529 1.50000.0104 0.0000 0.0601 20 11 0.15000.0920 1.565219472

Aruba ABW 0.43250.5427 1.5096 0.9316

Italy ITA 0.50920.5414 16.20000.0000 0.0001 16 13 0.10400.3120 0.676123626

Cuba CUB 0.53630.5363 10.0000 -0.2222

Malta MLT 0.52370.5199 28.40000.0834 0.8477 0.07100.0415 0.0770 17273 0.7258

Kuwait KWT 0.54530.5165 30.00000.0000 0.9564 0.11230.0278 0.1150 15799 0.1340

Japan JPN 0.55860.4934 1.70000.0000 0.8964 16 11 26755 0.9301

New Caledonia NCL 0.4782 0.4782 0.6841 20.0663 21820

Samoa WSM -0.53540.4759 22.0000 0.9026

Paraguay PRY 0.43740.4717 0.6563 0.8699 4426 -1.2008

Tunisia TUN 0.47270.4625 43.00000.9462 0.8536 0.04920.1146 14 9 0.0750 1.29956363

Bermudas BMU 0.44360.4436 42.00000.0000

Grenada GRD 0.46820.4422 9.00000.0093 0.9396 0.1824 7580

Malaysia MYS 0.45690.4324 40.00000.7278 0.9379 0.05560.1266 22 7 0.7860 0.1340 0.52699068

Belize BLZ 0.34640.4282 65.60000.5927 0.8960 0.7530 5606 0.5542

United Arab Emirates ARE 0.4184 0.5209 0.0054 0.2000 0.9326 0.0000 27 10 17935 0.5997

Trinidad & Tobago TTO 0.4137 0.3918 0.5388 75.0000 0.9455 0.0574 0.1251 0.3940 0.1350 8964 0.6165

Botswana BWA 0.45800.4131 37.20000.5452 0.9499 0.1241 20 8 7184 0.8278

16.

Slovenia

SVN 0.47320.4085 20.50000.0754 0.9456 0.03480.0244 22 9 1.9010 0.1190 0.702317367

Antigua & Barbuda ATG 0.4074 0.3027 59.0000 0.9499 10541

Singapore SGP 0.31910.3972 0.02400.0000 0.9344 0.0145 0.2502 20 7 0.0400 0.2100 2.163623356

Greece GRC 0.41180.3870 10.00000.1853 0.8560 0.08280.0006 15 16 0.0550 16501 0.6475

Solomon Islands SLB 0.3753 0.4174 0.1367 5.0000 0.7828 1648

Korea, Rep. KOR 0.3657 0.4742 0.0000 1.0500 0.9600 0.0639 0.0974 23 13 0.0820 17380 0.4422

Jamaica JAM 0.13170.3460 48.00006.5307 0.8833 0.0638 11 7 0.0830 -0.29683639

Costa Rica CRI 0.3353 0.4167 0.3268 65.1000 0.9216 0.0487 0.0769 21 11 0.7710 0.1310 8650 0.7353

Sri Lanka LKA 0.3255 0.4060 1.8985 11.0000 0.8807 0.1135 0.1112 17 8 0.1350 3530 -0.4426

Uruguay URY 0.41080.3108 5.60000.0000 0.9521 0.0278 0.0572 38 10 0.18400.2000 0.61249035

Hungary HUN 0.44120.3063 16.80000.1170 0.8513 0.10420.0289 17 5 0.22801.9040 0.601312416

Bahrain BHR 0.41690.3063 15.00000.0731 0.9478 0.09420.0523 0.2470 0.6204

Cape Verde CPV 0.2933 0.2898 0.6084 47.0000 0.8503 4863

Czech Republic CZE 0.2925 0.4416 0.1622 16.9900 0.8878 0.0210 0.0672 16 10 2.2110 0.1610 13991 0.5811

Poland POL 0.43280.2885 26.00000.8086 0.8872 0.05460.0239 18 11 0.15701.6670 0.26879051

Morocco MAR 0.36970.2708 24.80000.1210 0.8023 0.05860.1591 17 13 0.0670 0.09723546

Croatia HRV 0.38660.2658 12.90000.8833 0.9196 0.09950.0613 20 13 0.07701.6170 0.10198091

Micronesia, Fed. Sts. FSM 0.2358 0.2358 0.3155 66.1200

Mauritius MUS 0.29090.2257 56.42000.9760 0.9304 0.07510.2764 0.0820 0.758010017

Macedonia, FYR MKD 0.2158 0.2416 1.1569 21.3200 0.7916 5086 -0.6268

Turkey TUR 0.41160.2120 55.37000.4726 0.9581 0.11010.0134 18 13 0.12101.8710 -0.15096974

Peru PER 0.34560.2112 17.11001.2461 0.9191 0.08030.0939 35 8 0.09101.3260 -0.34824799

China CHN 0.35530.2010 0.0453 0.8922 0.0951 20 12 0.1530 3976 0.1384

Latvia LVA 0.25340.1956 28.70003.2500 0.6929 0.07160.0116 19 7 0.1580 0.22347045

St. Vincent & the Grenadines VCT 0.1783 0.2881 1.1107 8.5700 0.9314 0.3633 0.0810 5555

Iran, Islamic Rep. IRN 0.1716 0.3192 1.1566 2.5600 0.9189 0.0742 0.0938 9 0.2920 5884 -0.2073

Slovak Republic SVK 0.1458 0.3493 0.6792 27.0400 0.8797 0.0432 0.1117 11 2.0870 0.1360 11243 0.2287

17.

Papua New Guinea PNG 0.1401 0.2640 0.1082 10.1000 0.9029 0.3194 2280 -0.6684

Egypt, Arab Rep. EGY 0.1334 0.3033 1.9203 6.8700 0.8628 0.1256 0.1546 17 13 0.1010 3635 0.2686

El Salvador SLV 0.0808 0.1676 3.9537 14.5000 0.9261 0.0672 0.1235 0.3510 0.3140 4497 -0.2471

Kiribati

KIR 0.07510.0751 95.00000.1386

México MEX 0.05230.0608 1.90000.1338 0.8458 0.0431 0.1226 47 7 0.10801.2920 0.27759023

Argentina ARG 0.25920.0581 17.29000.1676 0.8758 0.04010.0489 32 14 1.2490 0.1910 0.175112377

Estonia EST 0.29070.0569 19.24001.3605 0.8296 0.09650.0013 1.9260 0.2390 0.861610066

Zambia ZMB -0.04460.0460 90.86006.7307 0.8646 16 6 780 -0.7452

Saudi Arabia SAU 0.0453 0.1948 2.5580 2.7900 0.8513 13 11367 -0.0002

Bhutan BTN 0.18740.0444 3.2293 0.8497 0.0156 0.1804 0.1350 1412

Djibouti DJI 0.04700.0367 112.50000.0000 0.8344

Thailand THA 0.26770.0352 19.56001.6296 0.9169 0.14730.1116 19 8 2.3230 0.2150 0.09666402

Guatemala GTM 0.17160.0192 1.6250 0.8323 0.1677 19 13 0.8840 3821 -0.6292

Maldives MDV 0.13300.0188 55.70000.0710 0.8296 0.16190.2921 0.0660 4485

Mongolia MNG 0.15000.0170 5.10002.6435 0.6703 0.0748 0.0722 8 0.2350 0.39351783

Brazil BRA 0.13550.0146 2.81000.5185 0.9073 0.0290 0.2408 16 16 0.24400.7960 -0.26827625

Senegal SEN 0.2040-0.0044 17.00000.8208 0.7640 30 9 1510 0.1639

Panama PAN 0.0491-0.0193 48.00001.3530 0.1072 0.1168 44 7 0.12700.6620 -0.13906000

Rwanda RWA -0.0232-0.0304 16.00004.0356 0.8075 943

Brunei BRN -0.0443-0.0443 86.20001.2522 0.8829

Libya LBY 0.0090-0.0449 1.1707 0.7402 -1.1226

Seychelles SYC 0.1251-0.0462 43.00000.9663 0.9213 0.07510.4263 0.1520

Nigeria NGA 0.1700-0.0603 1.3696 0.7355 25 9 896 -0.9959

Lebanon LBN 0.0203-0.0807 0.9739 0.2807 0.1890 27 6 0.1730 -0.01854308

Nicaragua NIC -0.0159-0.0830 79.30009.0970 0.9302 0.13720.0708 12 1.0990 0.0640 -0.72532366

Philippines PHL 0.1237-0.0921 5.20002.7889 0.9030 0.1874 28 14 1.2590 0.1390 3971 0.0290

Ecuador ECU 0.0010-0.1001 48.00000.7510 0.9957 0.1127 33 14 1.6660 3203 -0.9399

18.

Lithuania

LTU 0.2147-0.1032 19.80000.9358 0.7699 0.08720.0129 30 11 2.0760 0.2150 0.25687106

Colombia COL -0.0151-0.1063 59.90001.9644 0.8334 0.07470.0732 37 18 0.08200.4510 -0.37986248

Cambodia KHM 0.0758-0.1187 7.17222.7901 0.7378 1.7900 1446 0.3375

Namibia NAM -0.0096-0.1488 76.00000.6977 0.3705 0.0710 0.59866431

Romania ROM 0.1443-0.1649 35.70003.8308 0.8683 0.11470.0492 28 9 0.23202.1770 -0.54416423

Ghana GHA -0.0573-0.1904 86.00001.5316 0.6732 21 10 1964 -0.0611

Nepal NPL -0.0728-0.2013 78.80006.7005 0.7723 0.08770.2683 8 0.0940 1327 -1.0382

Fiji FJI -0.1493-0.2050 132.00001.0610 0.8761 0.2155 4668 0.3825

India IND -0.0040-0.2118 186.00000.7545 0.8274 0.08070.1971 22 10 0.1990 2358 -0.1688

Belarus BLR -0.1841-0.2124 28.33002.7410 0.8520 0.13670.0541 20 0.12501.6350 -0.99027544

Bangladesh BGD -0.1073-0.2211 207.60003.2878 0.8645 0.2256 15 7 1.8710 1602 -0.5373

St. Kitts & Nevis KNA -0.2316 0.0458 0.9563 0.3704 0.1500 12510

Jordan JOR -0.0060-0.2342 18.19000.2543 0.8353 0.1993 32 14 3966 0.4238

Equatorial Guinea GNQ -0.2443 -0.2208 2.4783 62.0000 0.7276 15073

Mozambique MOZ -0.1145-0.2514 80.00003.1826 0.8706 18 16 854 -0.4948

Uzbekistan UZB -0.1557-0.2655 92.60000.8712 9 2.6100 2441 -0.8587

Indonesia IDN 0.0459-0.2793 15.9600 0.9088 0.16960.0254 29 11 2.5190 0.2580 -0.49653043

Tanzania TZA -0.2492-0.2955 175.00001.2953 0.7485 14 13 523 -0.4325

Bahamas, The BHS -0.3318 -0.1797 0.9577 0.5455 0.0755 0.1450 17012 1.0401

Uganda UGA -0.2189-0.3331 80.00003.6143 0.7613 0.1010 16 17 1208 -0.3158

Malawi MWI -0.2907-0.3395 9.0953 0.7828 12 11 615 -0.7726

Níger NER -0.2427-0.3658 94.76001.0942 0.6395 11 -1.1613746

Sao Tome & Principe STP -0.3784 -0.3234 7.0796 3.9770 0.7726

Honduras HND -0.2867-0.3820 24.00007.8166 0.8986 32 15 0.6230 2453 -0.5787

Vietnam VNM -0.0375-0.3867 0.7356 0.1773 28 10 1996 -0.3029

Cote d'Ivoire CIV -0.3949 -0.1840 0.7840 100.0000 0.5790 0.4654 18 10 0.0840 1630 -0.8133

Dominican Republic DOM -0.3985 -0.1837 0.8761 0.3954 0.1054 19 20 1.1290 0.1230 6033 -0.2406

19.

Syrian Arab Republic SYR -0.4079 -0.2739 10.0000 50.0000 0.6706 0.1178 0.1068 10 0.1300 3556 -0.8079

Azerbaijan

7.2587

AZE -0.2970-0.4124 52.00001.2746 0.5728 0.14910.0853 15 2.7670 0.0800 -0.95052936

Pakistan PAK -0.1432-0.4250 98.60001.8030 0.7398 0.1161 30 8 2.3140 0.2540 -0.47661928

Bosnia & Herzegovina BIH -0.4337 -0.0098 2.1950 12 2.2570 -0.9199

Venezuela VEN -0.1631-0.4551 2.00002.5034 0.8620 0.0734 41 14 1.1370 0.3390 -0.81085794

Togo TGO -0.4319-0.4716 61.40002.8560 0.6427 1442 -1.3168

Yemen, Rep. YEM -0.4889 -0.3195 3.7843 0.5763 0.1032 13 0.1400 893 -0.7659

Burkina Faso BFA -0.5015 -0.3457 2.1673 59.3000 0.6931 15 976 -0.0221

Cameroon CMR -0.2858-0.5166 60.00006.2393 0.7442 0.08060.2826 13 1703 -0.4027

Bolivia BOL -0.2764-0.5275 0.1874 0.9011 0.0576 0.1813 44 19 0.13501.9420 -0.46592424

Benin BEN -0.4808-0.5382 76.00004.5422 0.5871 9 990 0.1200

Burundi BDI -0.2964-0.5490 32.4300 0.7044 0.13190.2017 0.2080 -1.1351591

Russian Federation RUS -0.5695 -0.4004 5.1312 35.2100 0.7247 0.1285 0.2027 16 19 2.1440 8377 -0.5749

Sudan SDN -0.4014-0.5716 5.00004.4306 0.6441 0.2902 1797 -1.3367

Myanmar MMR -0.5341-0.5854 172.00005.3035 0.5213 0.15640.0438 0.0750 -1.2464

Armenia ARM -0.3243-0.5858 20.0000 0.6224 11 2.5660 2559 -1.0332

Angola AGO -0.5936-0.6067 36.90008.5497 0.8044 2187 -1.3092

Zimbabwe ZWE -0.4807-0.6075 223.000010.0000 0.8586 0.22340.2049 13 10 0.1460 -1.03202635

Yugoslavia, FR (Serb./Mont.) YUG -0.6097 -0.4746 1.7527 16 -0.9651

Algeria DZA -0.5398-0.6196 12.00005.3687 0.7216 0.1405 18 5308 -0.8105

Gambia, The GMB -0.6324 -0.5906 5.9819 76.0000 0.7474 1649 0.4116

Bulgaria BGR -0.2904-0.6336 4.80003.6171 0.7256 0.0235 0.2855 26 10 0.44402.0030 -0.25785710

Georgia GEO -0.5647-0.6507 0.00632.2301 0.5273 0.0703 0.2328 17 12 0.32803.1890 -0.72032664

Ukraine UKR -0.5234-0.6602 34.47007.9093 0.5692 0.0427 20 13 2.7280 3816 -0.7482

Kenya KEN -0.5135-0.7173 220.90008.1003 0.8710 0.1379 25 11 1022 -0.7608

Kyrgyz Republic KGZ -0.7261 -0.5217 6.9162 37.0000 0.3908 0.0297 9 2.5200 2711 -0.6073

Etiopía ETH -0.7329-0.7538 187.00007.8328 0.7928 8 668 -1.0125

20.

Tajikistan

TJK -0.4963-0.7571 124.9000 0.1364 0.2640 -1.30901152

Moldova MDA -0.4603-0.7755 79.00005.5100 0.6219 0.13740.0588 11 0.37302.5410 -1.09932109

Chad TCD -0.5836-0.7872 48.00000.4732 0.3623 871

Eritrea ERI -0.7968-0.7968 57.46007.1692 837

Albania ALB -0.3805-0.8387 70.20004.4850 0.6974 0.22550.1550 11 0.41102.0730 -0.89403506

Vanuatu VUT -0.2685-0.8407 56.0000 0.9456 0.37950.3624 0.2110 2802

Guyana GUY -0.8951-0.8934 87.000010.0000 0.8425 3963 0.0245

Samoa WSM -0.9929-0.9036 10.0000 0.9026 5041

Turkmenistan TKM -0.8983-0.9297 46.30008.4928 0.6755 3956 -1.2349

Gabon GAB -0.9018-0.9598 67.000010.0000 0.7738 6237 -0.4498

Lesotho LSO -0.7067-1.0016 10.0000 0.9262 0.4767 0.1490 2031

Suriname SUR -0.9584-1.0072 30.900010.0000 0.6822 3799 0.0973

Comoros COM -0.9764-1.0114 82.83006.3143 0.6068 1588

Haiti HTI -0.7895-1.0199 108.000010.0000 0.8038 2.1700 -1.32211467

Mauritania MRT -1.0835-1.0502 115.000010.0000 0.8270 1677 -0.6558

Congo, Rep. COG -1.1308 -0.8542 0.7970 0.4461 0.0516 0.4370 825 -1.5787

Swaziland SWZ -1.1588-1.1801 160.00007.1559 0.9414 0.5194 4492

Madagascar MDG -0.6961-1.1896 79.00000.0648 0.6803 0.52060.5186 15 0.1230 -0.3507840

Mali MLI -1.1499-1.2610 177.6000 0.6325 14 797 -1.4364

Guinea GIN -1.1720-1.3702 62.60000.1210 0.5505 0.7658 1982 0.4116

Liberia LBR -1.4240-1.4542 144.000010.0000 0.7027 -0.9403

Sierra Leone SLE -1.4733 -1.2041 10.0000 23.0000 0.6007 0.4860 490 -1.6041

Guinea-Bissau GNB -1.7973-1.5601 70.50004.3822 0.2456 755 -1.4769

Kazakhstan KAZ -1.4112-1.5976 405.000010.0000 0.7643 0.18160.0617 41 12 2.2100 0.4450 -0.60685871

Congo, Dem. Rep. ZAR -2.1052 -1.3759 7.0000 0.3698 0.3322 0.5798 0.3200 -1.3785

Central African Republic CAF -2.1142 -2.0233 10.0000 61.9100 0.2473 1172

Tonga TON -2.8371-2.7716 761.00001.6182 0.9190

21.

22.