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

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1 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.

2 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 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.

3 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.

4 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 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.

5 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.

6 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.

7 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.

8 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) 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 KKZ Gov't Effective, resid N: 142 t-statistic: 4.06 R-Squared: 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.

9 References de Soto, Hernando 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 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 Kaufmann, Daniel, Aart Kraay, and Pablo Zoido-Lobaton Governance Matters II: Updated Indicators for 2000/01. World Bank Policy Research Working Paper

10 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 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 KKZ Voice & Account., resid N: 155 t-statistic: R-Squared:

11 Second Generation Index & KKZ Political Stability (Controlling for Per Capita GDP) LAO NGA 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 KKZ Pol. Stability, resid KAZ N: 144 t-statistic: 2.31 R-Squared:

12 Second Generation Index & KKZ Regulatory Quality (Controlling for Per Capita GDP) 1 LAO 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 KKZ Reg. Quality, resid N: 149 t-statistic: R-Squared:

13 Second Generation Index & KKZ Rule of Law (Controlling for Per Capita GDP) 1 LAO 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 KKZ Rule of Law, resid N: 150 t-statistic: 3.94 R-Squared:

14 Second Generation Index & KKZ Control of Corruption (Controlling for Per Capita GDP) 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 KKZ Cntrl. Corruption, resid N: 143 t-statistic: 2.25 R-Squared:

15 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 New Zealand NZL United Kingdom GBR Denmark DNK Australia AUS Ireland IRL Norway NOR Iceland ISL France FRA Israel ISR United States USA Sweden SWE Cyprus CYP San Marino SMR Switzerland CHE Germany DEU Belgium BEL Taiwan, China TWN Qatar QAT Lao PDR LAO Dominica DMA Netherlands NLD Austria AUT

16 Barbados BRB St. Lucia LCA Andorra ADO Oman OMN Chile CHL Finland FIN South Africa ZAF Luxembourg LUX Portugal PRT Spain ESP Aruba ABW Italy ITA Cuba CUB Malta MLT Kuwait KWT Japan JPN New Caledonia NCL Samoa WSM Paraguay PRY Tunisia TUN Bermudas BMU Grenada GRD Malaysia MYS Belize BLZ United Arab Emirates ARE Trinidad & Tobago TTO Botswana BWA

17 Slovenia SVN Antigua & Barbuda ATG Singapore SGP Greece GRC Solomon Islands SLB Korea, Rep. KOR Jamaica JAM Costa Rica CRI Sri Lanka LKA Uruguay URY Hungary HUN Bahrain BHR Cape Verde CPV Czech Republic CZE Poland POL Morocco MAR Croatia HRV Micronesia, Fed. Sts. FSM Mauritius MUS Macedonia, FYR MKD Turkey TUR Peru PER China CHN Latvia LVA St. Vincent & the Grenadines VCT Iran, Islamic Rep. IRN Slovak Republic SVK

18 Papua New Guinea PNG Egypt, Arab Rep. EGY El Salvador SLV Kiribati KIR México MEX Argentina ARG Estonia EST Zambia ZMB Saudi Arabia SAU Bhutan BTN Djibouti DJI Thailand THA Guatemala GTM Maldives MDV Mongolia MNG Brazil BRA Senegal SEN Panama PAN Rwanda RWA Brunei BRN Libya LBY Seychelles SYC Nigeria NGA Lebanon LBN Nicaragua NIC Philippines PHL Ecuador ECU

19 Lithuania LTU Colombia COL Cambodia KHM Namibia NAM Romania ROM Ghana GHA Nepal NPL Fiji FJI India IND Belarus BLR Bangladesh BGD St. Kitts & Nevis KNA Jordan JOR Equatorial Guinea GNQ Mozambique MOZ Uzbekistan UZB Indonesia IDN Tanzania TZA Bahamas, The BHS Uganda UGA Malawi MWI Níger NER Sao Tome & Principe STP Honduras HND Vietnam VNM Cote d'ivoire CIV Dominican Republic DOM

20 Syrian Arab Republic SYR Azerbaijan AZE Pakistan PAK Bosnia & Herzegovina BIH Venezuela VEN Togo TGO Yemen, Rep. YEM Burkina Faso BFA Cameroon CMR Bolivia BOL Benin BEN Burundi BDI Russian Federation RUS Sudan SDN Myanmar MMR Armenia ARM Angola AGO Zimbabwe ZWE Yugoslavia, FR (Serb./Mont.) YUG Algeria DZA Gambia, The GMB Bulgaria BGR Georgia GEO Ukraine UKR Kenya KEN Kyrgyz Republic KGZ Etiopía ETH

21 Tajikistan TJK Moldova MDA Chad TCD Eritrea ERI Albania ALB Vanuatu VUT Guyana GUY Samoa WSM Turkmenistan TKM Gabon GAB Lesotho LSO Suriname SUR Comoros COM Haiti HTI Mauritania MRT Congo, Rep. COG Swaziland SWZ Madagascar MDG Mali MLI Guinea GIN Liberia LBR Sierra Leone SLE Guinea-Bissau GNB Kazakhstan KAZ Congo, Dem. Rep. ZAR Central African Republic CAF Tonga TON

22 22.