Constructing an Index of Objective Indicators of Good Governance Steve Knack & Mark Kugler PREM Public Sector Group, World Bank October 2002

Introduction Constructing an Index of Objective Indicators of Good Governance Steve Knack & Mark Kugler PREM Public Sector Group, World Bank October 2002 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 crosscountry 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 nonrepresentative indicator for the country ranking 80 th 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) KKZ Indicators Voice & Accountability Political Stability Government Effectiveness Regulatory Quality Rule of Law Control of Corruption Second Generation Indicators Percentile Matching Index Unadjusted Standardized Index 0.55* 0.60* (0.23*) (0.27*) 0.63* 0.69* (0.19*) (0.24*) 0.70* 0.77* (0.32*) (0.41*) 0.61* 0.67* (0.33*) (0.38*) 0.69* 0.76* (0.31*) (0.40*) 0.62* 0.72* (0.19*) (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) 1.5 0 -.5-1 BLR PRY TKM NGA LKA PNG ZMB SEN CHN TZA MNG KHM PER NPL NER BDI MAR EGY BLZBWA MWI MOZ TUN MKD GTM IRN KWT NIC HRV ZAF POL SLV IDN TWN CHL ECU TUR ROM SVK BRA UZB SDN AZE ARM SYR SAU COL TGOKOR TJK ITA ARE SVN CZE JAM MLT CIV THA HUN MYS ISR CYP LTUPRT PAK PHL URY TTO VNM INDCRI BGD YEM GHA UGA ESP LVA HKG JPN LBN MUSEST FRA AUT SWEJOR CAN IRL GBR BFA ARG PAN GRC NOR HND BELKEN CMR USA DEU NLD CHE ISL NZL BOL ETH LUXNAM DNK AUSFIN BEN FJI MDA MEX MDG VEN ALB GMB SGP ZWE COG AGO BGRKGZ HTI DOM GEO UKR SLE RUS DZA MLI GAB GNB MRT GUY BHS SUR LAO GIN -1.5 KAZ -1.5-1 -.5 0.5 1 1.5 KKZ Gov't Effective, resid 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) 1 LAO.5 0 -.5-1 GNQ SAU HKG ARE SYR BLR DZA TKM SLB NGA BTN PRYRWA LKA KHM PNG SEN ZMB CHN TZAMNG TUN EGY BDIMAR PER VNM IRN CHL BWA NPL MOZ CPV BLZ MWINER PAK MDV KWT CIV MYS GTM IDNTWN UGA MKD HRV THA SLV TTO NIC GHABRB YEM TUR ECU ESP BGD UZB LBN ISR SDN COL TGOCMR JPNJOR TCD TJK USA FRAROM CAN SVN CZE CYP SVK KEN ITA KOR BRA AUT EST DEU ISL IRL GBR BFA PHL URY ZAF POL CRI IND HUN PRT MLT LVA AZE ARG FJI LUX NAM BEL HNDGRC NOR NLD CHE SWE LTU JAM MUS DNK AUS FIN BEN ARMETH PAN NZL BOL SGP MEX MDA MDG ZWE GMB ERI AGO VEN COG ALB KGZ HTI GEO BGR DOM LSO UKRSLE RUS COM MLI MRT GIN BHS SUR GUY GAB SWZ GNB -1.5 KAZ CAF -2-1.5-1 -.5 0.5 1 1.5 KKZ Voice & Account., resid N: 155 t-statistic: 2.938 R-Squared: 0.478 10.

Second Generation Index & KKZ Political Stability (Controlling for Per Capita GDP) LAO NGA.5 0 -.5 LKA MKD IDN ISR COL SDN AGO DZA SEN PRY RWA PNG ZMB CHN KHM BDI PER MAR EGY TUN BLZ NER NPL BWA GTMUGA ZAF IRN CHL TWN HRV CIV YEM PHL TTO MYS POL KWTHA TUR ECU CYP BGD LTU CZE PAK UZB LBN ROM SVN SVK GBR HUN IND GHA SLV URY NIC CRI ESPMLT BFA JAM PRT ITA TJK NAM KORBEL USA FRA HKG IRL CAN KEN BEN BRA JPN JOR DEU LVA AUT EST ARESWE TGO GRC NOR AUS BOL ARM AZE ARGLUX DNK NZL CMR NLD ISL MUS CHE PAN HND FIN MEX SYRSAU FJI ETH MDA MDG SGP ZWE VEN ALB ERI COG BLR KGZ BGR GEO HTI DOM UKR SLE RUS TZA VNM MLI GMB MNG MOZ MWI -1 GUY GIN GAB MRT BHS GNB TKM SUR -1.5-1.5-1 -.5 0.5 1 1.5 KKZ Pol. Stability, resid KAZ N: 144 t-statistic: 2.31 R-Squared: 0.506 11.

Second Generation Index & KKZ Regulatory Quality (Controlling for Per Capita GDP) 1 LAO.5 0 -.5 BLR RUS ZWE GNQ NGA PRY PNG SEN RWA LKA ZMB MNGKHM CHN TZA IRN CPV BLZ NPLEGY BDI PERNER MAR TUN BWA MOZ MWI KWT MKD ZAF IDN VNM BRB MYS HRV TWN NIC IND POL GTM CHL MLT TUR PAKCIV PHL UGA UZB ROM SVK ARE TJK TGO KOR BEL FRA SVN CZE MUS LTU ISR ECU CYP PRT YEM THA TTO GHA CRI URY HUN SLV LVA CAN JPN ISL LBN ITA BRA NOR MDA SYR SAU FJIARM ARG COL SDNHND TCD GRC USA DEU SWE BGD ESP JAM CHE AUT IRL GBR BFA HKG EST DNK KEN CMR AUS NZL FIN BEN NLD JOR AZE ETH NAMLUX PANBOL MEX MDG AGO KGZ VEN ALB GMB SGP COG BGR HTI GEO LSO DOM UKR SLE DZA MLI -1 TKM SUR GAB GNB MRT GUY BHS SWZ GIN -1.5 KAZ -2.5-2 -1.5-1 -.5 0.5 1 1.5 KKZ Reg. Quality, resid N: 149 t-statistic: 4.239 R-Squared: 0.524 12.

Second Generation Index & KKZ Rule of Law (Controlling for Per Capita GDP) 1 LAO.5 0 -.5 GNQ NGA PRY RWA LKA PNG SEN ZMB KHM MNG CHN PER NER NPLBDIEGY TUN GTM IRN ZAF SLV IDN MKD PHL NIC TWN MYS ECUTUR JAM PAK MLT BGD HRV CPV BWA MARMOZ BLZ MWI VNM YEM TTO POL CHL URY UGA CRI BRA CMR COL HND TJK ITA UZB ROM SVK CZE LBN CYP ISR LTUPRT HUN CIV ESP BFA THA KWT BRB GHA IND SVN LVA FRARE EST HKG IRL GBR MUS CAN KOR KEN FJI SDN AZE ARGPAN GRCTGO TCD BEL USABEN JPN DEU NOR NLD ISL SWE AUT CHE JOR BOL LUX DNK AUS FIN NZL SAU MEX SYR ARM MDA MDG ETH NAM BLR AGO VEN ZWE ALB GMB ERI SGP COG KGZ BGR HTI DOM GEO LSO UKR SLE RUS DZA MLI TZA -1 TKM GAB SUR GNB MRT GUY GIN BHS SWZ -1.5 KAZ -2-1.5-1 -.5 0.5 1 1.5 KKZ Rule of Law, resid N: 150 t-statistic: 3.94 R-Squared: 0.523 13.

Second Generation Index & KKZ Control of Corruption (Controlling for Per Capita GDP) 1.5 0 -.5-1 RUS LAO NGA PRY PNG LKAZMB SEN MNG CHN TZA KHM RWA BDI PER EGY NER BLZ NPLMAR BWA MOZ MWI IRN GTM IDN MKD ECU TUR MLT THA TWNPHL NIC HRV SLV MYS VNM ZAF KWTUGA POL CHL IND TTO GHA URY CRI CZE PAK CIV ARE ROM SVK LTU BGD HUN BFA LBN LVA IRL CMR KOR ITABRA UZB MUS JAM YEM JPN FRAJOR ISREST SVN CYPPRT ESP GBR ARG SDN PAN COL BOL HND TJK KEN GRC BEL USA DEU AUT CAN CHE ISL SWE TGO NOR NLD AUS LUX DNK NZL FIN SAU AZE MEX SYR ARM MDA MDG ETH FJI NAM VEN ZWE ALB ERI GMB SGP AGO KGZ BLR COG BGR DOM GEO HTI UKR SLE DZA MLI MRT TKM GUY GAB BHS SUR GIN GNB -1.5 KAZ -1.5-1 -.5 0.5 1 1.5 KKZ Cntrl. Corruption, resid 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 Volatility GDP per Capita KKZ Governmental Effectiveness Canada CAN 1.0138 0.6132 0.0000 0.9462 0.0125 0.0481 17 2 0.1210 0.0780 27840 1.7125 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 0.9727 0.6505 0.0000 0.9458 0.0000 0.0340 14 3 0.0910 27627 1.6153 Australia AUS 0.9547 0.5381 0.0000 0.1100 0.9438 0.0260 0.0364 11 2 0.1130 25693 1.5784 Ireland IRL 0.9201 0.6637 0.0000 0.0410 19 3 0.0800 29866 1.7942 Norway NOR 0.8921 0.6250 0.0000 14.0000 0.9466 0.0056 0.0359 12 4 0.0990 29918 1.3540 Iceland ISL 0.8555 0.5942 0.0000 0.9725 0.0125 0.0508 0.0600 29581 1.9312 France FRA 0.8435 0.5233 0.0000 5.9000 0.0000 10 10 0.2730 0.0610 24223 1.2395 Israel ISR 0.8195 0.5318 0.2530 12.0000 0.9696 0.0068 0.0474 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 0.7756 0.5515 0.0000 8.4000 0.0007 0.1058 21 5 0.0310 0.0680 24277 1.5086 Cyprus CYP 0.7503 0.5553 0.2774 22.8000 0.9488 0.0378 0.0433 0.0420 20824 0.9105 San Marino SMR 0.7495 0.7162 0.0000 0.0141 Switzerland CHE 0.7489 0.5718 0.0000 18.4700 0.9234 0.0113 0.0325 14 6 0.1160 28769 1.9264 Germany DEU 0.7386 0.5447 0.0000 8.7000 0.0000 0.0573 14 9 0.9850 0.0230 25103 1.6720 Belgium BEL 0.6993 0.6783 0.0216 4.0000 0.0000 16 7 0.1050 27178 1.2918 Taiwan, China TWN 0.6971 0.6843 0.0000 2.0600 15 8 22824 0.9092 Qatar QAT 0.6941 0.6146 0.0000 15.5200 0.9395 0.8157 Lao PDR LAO 0.6913 0.5704 1.0882 0.9559 1575-0.3945 Dominica DMA 0.6720 0.5548 12.0000 0.9372 Netherlands NLD 0.6680 0.5303 0.0000 0.5000 0.0000 0.0518 21 8 0.0540 25657 1.8355 Austria AUT 0.6569 0.5634 0.0000 6.2700 0.0002 0.0440 20 9 0.0490 26765 1.5131

Barbados BRB 0.6380 0.5715 0.2809 9.6200 0.9162 15494 St. Lucia LCA 0.6303 0.5139 1.1269 0.9395 5703 Andorra ADO 0.6289 0.6289 0.0000 13.6500 Oman OMN 0.6169 0.5691 0.4721 1.8400 0.8949 0.0253 0.8483 Chile CHL 0.6109 0.4935 0.0443 25.0000 0.9371 0.0612 0.0315 21 10 0.3210 0.0760 9417 1.1337 Finland FIN 0.6096 0.5552 0.0000 8.4000 0.0000 0.0815 19 7 0.0990 24996 1.6687 South Africa ZAF 0.6074 0.4559 1.1013 40.9000 0.9566 0.0305 11 9 0.1010 9401 0.2527 Luxembourg LUX 0.5681 0.6109 0.0000 7.0000 0.0000 0.1138 0.0930 50061 1.8550 Portugal PRT 0.5653 0.5344 0.2470 10.5000 0.0001 22 12 0.1130 0.0670 17290 0.9099 Spain ESP 0.5529 0.5632 0.0104 1.5000 0.0000 0.0601 20 11 0.0920 0.1500 19472 1.5652 Aruba ABW 0.5427 0.4325 1.5096 0.9316 Italy ITA 0.5414 0.5092 0.0000 16.2000 0.0001 16 13 0.3120 0.1040 23626 0.6761 Cuba CUB 0.5363 0.5363 10.0000-0.2222 Malta MLT 0.5199 0.5237 0.0834 28.4000 0.8477 0.0415 0.0710 0.0770 17273 0.7258 Kuwait KWT 0.5165 0.5453 0.0000 30.0000 0.9564 0.0278 0.1123 0.1150 15799 0.1340 Japan JPN 0.4934 0.5586 0.0000 1.7000 0.8964 16 11 26755 0.9301 New Caledonia NCL 0.4782 0.4782 0.6841 20.0663 21820 Samoa WSM 0.4759-0.5354 22.0000 0.9026 Paraguay PRY 0.4717 0.4374 0.6563 0.8699 4426-1.2008 Tunisia TUN 0.4625 0.4727 0.9462 43.0000 0.8536 0.1146 0.0492 14 9 0.0750 6363 1.2995 Bermudas BMU 0.4436 0.4436 0.0000 42.0000 Grenada GRD 0.4422 0.4682 0.0093 9.0000 0.9396 0.1824 7580 Malaysia MYS 0.4324 0.4569 0.7278 40.0000 0.9379 0.1266 0.0556 22 7 0.7860 0.1340 9068 0.5269 Belize BLZ 0.4282 0.3464 0.5927 65.6000 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 0.4131 0.4580 0.5452 37.2000 0.9499 0.1241 20 8 7184 0.8278 16.

Slovenia SVN 0.4085 0.4732 0.0754 20.5000 0.9456 0.0244 0.0348 22 9 1.9010 0.1190 17367 0.7023 Antigua & Barbuda ATG 0.4074 0.3027 59.0000 0.9499 10541 Singapore SGP 0.3972 0.3191 0.0000 0.0240 0.9344 0.0145 0.2502 20 7 0.0400 0.2100 23356 2.1636 Greece GRC 0.3870 0.4118 0.1853 10.0000 0.8560 0.0006 0.0828 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 0.3460 0.1317 6.5307 48.0000 0.8833 0.0638 11 7 0.0830 3639-0.2968 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 0.3108 0.4108 0.0000 5.6000 0.9521 0.0278 0.0572 38 10 0.2000 0.1840 9035 0.6124 Hungary HUN 0.3063 0.4412 0.1170 16.8000 0.8513 0.0289 0.1042 17 5 1.9040 0.2280 12416 0.6013 Bahrain BHR 0.3063 0.4169 0.0731 15.0000 0.9478 0.0523 0.0942 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 0.2885 0.4328 0.8086 26.0000 0.8872 0.0239 0.0546 18 11 1.6670 0.1570 9051 0.2687 Morocco MAR 0.2708 0.3697 0.1210 24.8000 0.8023 0.1591 0.0586 17 13 0.0670 3546 0.0972 Croatia HRV 0.2658 0.3866 0.8833 12.9000 0.9196 0.0613 0.0995 20 13 1.6170 0.0770 8091 0.1019 Micronesia, Fed. Sts. FSM 0.2358 0.2358 0.3155 66.1200 Mauritius MUS 0.2257 0.2909 0.9760 56.4200 0.9304 0.2764 0.0751 0.0820 10017 0.7580 Macedonia, FYR MKD 0.2158 0.2416 1.1569 21.3200 0.7916 5086-0.6268 Turkey TUR 0.2120 0.4116 0.4726 55.3700 0.9581 0.0134 0.1101 18 13 1.8710 0.1210 6974-0.1509 Peru PER 0.2112 0.3456 1.2461 17.1100 0.9191 0.0939 0.0803 35 8 1.3260 0.0910 4799-0.3482 China CHN 0.2010 0.3553 0.0453 0.8922 0.0951 20 12 0.1530 3976 0.1384 Latvia LVA 0.1956 0.2534 3.2500 28.7000 0.6929 0.0116 0.0716 19 7 0.1580 7045 0.2234 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 0.0751 0.0751 0.1386 95.0000 México MEX 0.0608 0.0523 0.1338 1.9000 0.8458 0.0431 0.1226 47 7 1.2920 0.1080 9023 0.2775 Argentina ARG 0.0581 0.2592 0.1676 17.2900 0.8758 0.0489 0.0401 32 14 1.2490 0.1910 12377 0.1751 Estonia EST 0.0569 0.2907 1.3605 19.2400 0.8296 0.0013 0.0965 1.9260 0.2390 10066 0.8616 Zambia ZMB 0.0460-0.0446 6.7307 90.8600 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 0.0444 0.1874 3.2293 0.8497 0.0156 0.1804 0.1350 1412 Djibouti DJI 0.0367 0.0470 0.0000 112.5000 0.8344 Thailand THA 0.0352 0.2677 1.6296 19.5600 0.9169 0.1116 0.1473 19 8 2.3230 0.2150 6402 0.0966 Guatemala GTM 0.0192 0.1716 1.6250 0.8323 0.1677 19 13 0.8840 3821-0.6292 Maldives MDV 0.0188 0.1330 0.0710 55.7000 0.8296 0.2921 0.1619 0.0660 4485 Mongolia MNG 0.0170 0.1500 2.6435 5.1000 0.6703 0.0748 0.0722 8 0.2350 1783 0.3935 Brazil BRA 0.0146 0.1355 0.5185 2.8100 0.9073 0.0290 0.2408 16 16 0.7960 0.2440 7625-0.2682 Senegal SEN -0.0044 0.2040 0.8208 17.0000 0.7640 30 9 1510 0.1639 Panama PAN -0.0193 0.0491 1.3530 48.0000 0.1072 0.1168 44 7 0.6620 0.1270 6000-0.1390 Rwanda RWA -0.0304-0.0232 4.0356 16.0000 0.8075 943 Brunei BRN -0.0443-0.0443 1.2522 86.2000 0.8829 Libya LBY -0.0449 0.0090 1.1707 0.7402-1.1226 Seychelles SYC -0.0462 0.1251 0.9663 43.0000 0.9213 0.4263 0.0751 0.1520 Nigeria NGA -0.0603 0.1700 1.3696 0.7355 25 9 896-0.9959 Lebanon LBN -0.0807 0.0203 0.9739 0.2807 0.1890 27 6 0.1730 4308-0.0185 Nicaragua NIC -0.0830-0.0159 9.0970 79.3000 0.9302 0.0708 0.1372 12 1.0990 0.0640 2366-0.7253 Philippines PHL -0.0921 0.1237 2.7889 5.2000 0.9030 0.1874 28 14 1.2590 0.1390 3971 0.0290 Ecuador ECU -0.1001 0.0010 0.7510 48.0000 0.9957 0.1127 33 14 1.6660 3203-0.9399 18.

Lithuania LTU -0.1032 0.2147 0.9358 19.8000 0.7699 0.0129 0.0872 30 11 2.0760 0.2150 7106 0.2568 Colombia COL -0.1063-0.0151 1.9644 59.9000 0.8334 0.0732 0.0747 37 18 0.4510 0.0820 6248-0.3798 Cambodia KHM -0.1187 0.0758 2.7901 7.1722 0.7378 1.7900 1446 0.3375 Namibia NAM -0.1488-0.0096 0.6977 76.0000 0.3705 0.0710 6431 0.5986 Romania ROM -0.1649 0.1443 3.8308 35.7000 0.8683 0.0492 0.1147 28 9 2.1770 0.2320 6423-0.5441 Ghana GHA -0.1904-0.0573 1.5316 86.0000 0.6732 21 10 1964-0.0611 Nepal NPL -0.2013-0.0728 6.7005 78.8000 0.7723 0.2683 0.0877 8 0.0940 1327-1.0382 Fiji FJI -0.2050-0.1493 1.0610 132.0000 0.8761 0.2155 4668 0.3825 India IND -0.2118-0.0040 0.7545 186.0000 0.8274 0.1971 0.0807 22 10 0.1990 2358-0.1688 Belarus BLR -0.2124-0.1841 2.7410 28.3300 0.8520 0.0541 0.1367 20 1.6350 0.1250 7544-0.9902 Bangladesh BGD -0.2211-0.1073 3.2878 207.6000 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 -0.2342-0.0060 0.2543 18.1900 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 -0.2514-0.1145 3.1826 80.0000 0.8706 18 16 854-0.4948 Uzbekistan UZB -0.2655-0.1557 0.8712 92.6000 9 2.6100 2441-0.8587 Indonesia IDN -0.2793 0.0459 15.9600 0.9088 0.0254 0.1696 29 11 2.5190 0.2580 3043-0.4965 Tanzania TZA -0.2955-0.2492 1.2953 175.0000 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 -0.3331-0.2189 3.6143 80.0000 0.7613 0.1010 16 17 1208-0.3158 Malawi MWI -0.3395-0.2907 9.0953 0.7828 12 11 615-0.7726 Níger NER -0.3658-0.2427 1.0942 94.7600 0.6395 11 746-1.1613 Sao Tome & Principe STP -0.3784-0.3234 7.0796 3.9770 0.7726 Honduras HND -0.3820-0.2867 7.8166 24.0000 0.8986 32 15 0.6230 2453-0.5787 Vietnam VNM -0.3867-0.0375 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 AZE -0.4124-0.2970 1.2746 52.0000 0.5728 0.0853 0.1491 15 2.7670 0.0800 2936-0.9505 Pakistan PAK -0.4250-0.1432 1.8030 98.6000 0.7398 0.1161 30 8 2.3140 0.2540 1928-0.4766 Bosnia & Herzegovina BIH -0.4337-0.0098 2.1950 12 2.2570-0.9199 Venezuela VEN -0.4551-0.1631 2.5034 2.0000 0.8620 0.0734 41 14 1.1370 0.3390 5794-0.8108 Togo TGO -0.4716-0.4319 2.8560 61.4000 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 -0.5166-0.2858 6.2393 60.0000 0.7442 0.2826 0.0806 13 1703-0.4027 Bolivia BOL -0.5275-0.2764 0.1874 0.9011 0.0576 0.1813 44 19 1.9420 0.1350 2424-0.4659 Benin BEN -0.5382-0.4808 4.5422 76.0000 0.5871 9 990 0.1200 Burundi BDI -0.5490-0.2964 7.2587 32.4300 0.7044 0.2017 0.1319 0.2080 591-1.1351 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 -0.5716-0.4014 4.4306 5.0000 0.6441 0.2902 1797-1.3367 Myanmar MMR -0.5854-0.5341 5.3035 172.0000 0.5213 0.0438 0.1564 0.0750-1.2464 Armenia ARM -0.5858-0.3243 20.0000 0.6224 11 2.5660 2559-1.0332 Angola AGO -0.6067-0.5936 8.5497 36.9000 0.8044 2187-1.3092 Zimbabwe ZWE -0.6075-0.4807 10.0000 223.0000 0.8586 0.2049 0.2234 13 10 0.1460 2635-1.0320 Yugoslavia, FR (Serb./Mont.) YUG -0.6097-0.4746 1.7527 16-0.9651 Algeria DZA -0.6196-0.5398 5.3687 12.0000 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 -0.6336-0.2904 3.6171 4.8000 0.7256 0.0235 0.2855 26 10 2.0030 0.4440 5710-0.2578 Georgia GEO -0.6507-0.5647 2.2301 0.0063 0.5273 0.0703 0.2328 17 12 3.1890 0.3280 2664-0.7203 Ukraine UKR -0.6602-0.5234 7.9093 34.4700 0.5692 0.0427 20 13 2.7280 3816-0.7482 Kenya KEN -0.7173-0.5135 8.1003 220.9000 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 -0.7538-0.7329 7.8328 187.0000 0.7928 8 668-1.0125 20.

Tajikistan TJK -0.7571-0.4963 124.9000 0.1364 0.2640 1152-1.3090 Moldova MDA -0.7755-0.4603 5.5100 79.0000 0.6219 0.0588 0.1374 11 2.5410 0.3730 2109-1.0993 Chad TCD -0.7872-0.5836 0.4732 48.0000 0.3623 871 Eritrea ERI -0.7968-0.7968 7.1692 57.4600 837 Albania ALB -0.8387-0.3805 4.4850 70.2000 0.6974 0.1550 0.2255 11 2.0730 0.4110 3506-0.8940 Vanuatu VUT -0.8407-0.2685 56.0000 0.9456 0.3624 0.3795 0.2110 2802 Guyana GUY -0.8934-0.8951 10.0000 87.0000 0.8425 3963 0.0245 Samoa WSM -0.9036-0.9929 10.0000 0.9026 5041 Turkmenistan TKM -0.9297-0.8983 8.4928 46.3000 0.6755 3956-1.2349 Gabon GAB -0.9598-0.9018 10.0000 67.0000 0.7738 6237-0.4498 Lesotho LSO -1.0016-0.7067 10.0000 0.9262 0.4767 0.1490 2031 Suriname SUR -1.0072-0.9584 10.0000 30.9000 0.6822 3799 0.0973 Comoros COM -1.0114-0.9764 6.3143 82.8300 0.6068 1588 Haiti HTI -1.0199-0.7895 10.0000 108.0000 0.8038 2.1700 1467-1.3221 Mauritania MRT -1.0502-1.0835 10.0000 115.0000 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 -1.1801-1.1588 7.1559 160.0000 0.9414 0.5194 4492 Madagascar MDG -1.1896-0.6961 0.0648 79.0000 0.6803 0.5186 0.5206 15 0.1230 840-0.3507 Mali MLI -1.2610-1.1499 177.6000 0.6325 14 797-1.4364 Guinea GIN -1.3702-1.1720 0.1210 62.6000 0.5505 0.7658 1982 0.4116 Liberia LBR -1.4542-1.4240 10.0000 144.0000 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 -1.5601-1.7973 4.3822 70.5000 0.2456 755-1.4769 Kazakhstan KAZ -1.5976-1.4112 10.0000 405.0000 0.7643 0.0617 0.1816 41 12 2.2100 0.4450 5871-0.6068 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 -2.7716-2.8371 1.6182 761.0000 0.9190 21.

22.