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.
2.
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.
3.
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.
5.
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.
6.
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.
7.
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.
9.
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.