CORRELATES OF CORRUPTION

BO ROTHSTEIN SÖREN HOLMBERG

WORKING PAPER SERIES 2019:9

QOG THE QUALITY OF GOVERNMENT INSTITUTE Department of Political Science University of Gothenburg Box 711, SE 405 30 GÖTEBORG June 2019 ISSN 1653-8919 © 2019 by Bo Rothstein & Sören Holmberg. All rights reserved. Correlates of Corruption Bo Rothstein S¨oren Holmberg QoG Working Paper Series 2019:9 June 2019 ISSN 1653-8919

Bo Rothstein S¨orenHolmberg The Quality of Government Institute The Quality of Government Institute Department of Political Science Department of Political Science University of Gothenburg University of Gothenburg [email protected] [email protected]

This report has been prepared with the help of Natalia Alvarado ([email protected]) Contents

Control of Corruption 3

Economy 4 GDP per Capita ...... 4 Economic Equality (GINI Index) ...... 5 Economic Freedom Index ...... 6 GDP Growth per Capita ...... 7 Ease of Doing Business ...... 8

Welfare 9 Human Development Index ...... 9 Good Society Index ...... 10 Government Revenue ...... 11 Tax Revenue ...... 12 Average Schooling Years ...... 13 School Enrolment ...... 14

Health 15 Life Expectancy ...... 15 Healthy Life Expectancy ...... 16 Infant Mortality Rate ...... 17 Risk of Maternal Death ...... 18 Public Health Expenditure ...... 19 Private Health Expenditure ...... 20 Alcohol Consumption ...... 21

Environment 22 CO2 Emmissions per Capita ...... 22 Access to Drinking Water ...... 23 Unsafe Sanitation ...... 24

Gender 25 Gender Equality Index ...... 25 Female School Enrolment ...... 26

Violence/Crime 27 Total Police Personnel ...... 27 Homicide Rate ...... 28 Organized Crime ...... 29 Road Traffic Death Rate ...... 30

Trust 31 Trust in People ...... 31 Confidence in Parliament ...... 32 Confidence in Parliament in Democracies ...... 33

Happiness 34 Feeling of Happiness ...... 34 Life Satisfaction ...... 35

1 Change in Control of Corruption 36 Control of Corruption 2007 vs. Control of Corruption 2017 ...... 36

Quality of Government 37 Government Effectiveness ...... 37 Electoral Democracy ...... 38 Freedom on the Net ...... 39

Description of Variables by Source 40 Barro & Lee ...... 40 United Nations Office on Drugs and Crime ...... 41 Ease of Doing Business Report ...... 42 Environmental Performance Index ...... 43 Freedom House ...... 44 United Nations Development Programme ...... 45 Heritage Foundation ...... 46 International Monetary Fund ...... 47 S¨orenHolmberg ...... 48 United Nations Development Program ...... 49 Varieties of Democracy (V-Dem) Project ...... 50 The World Bank Group ...... 50 The World Bank Group ...... 52 World Economic Forum ...... 54 World Health Organization ...... 55 World Values Survey / European Values Survey ...... 56

References 57

2 Control of Corruption

The ”Control of Corruption” estimate measures perceptions of corruption, conventionally defined as the exercise of public power for private gain. The particular aspect of corruption measured by the various sources differs somewhat, ranging from the frequency of ”additional payments to get things done”, to the effects of corruption on the business environment, to measuring ”grand corruption” in the political arena or in the tendency of elite forms to engage in ”state capture”.

Clarifications: The estimate goes from -2.5 to 2.5, where lower values indicate less control of corruption, and higher values a better control.

Source: The World Bank Group http://info.worldbank.org/governance/wgi/ (Downloaded on 2018-09-24)

Dataset: The Worldwide Governance Indicators These indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 31 separate data sources constructed by 25 different organizations. These individual measures of governance are assigned to categories capturing key dimensions of governance. An unobserved component model is used to construct six aggregate governance indicators. Point estimates of the dimensions of governance, the margins of error as well as the number of sources are presented for each country. The governance estimates are normally distributed with a mean of zero and a standard deviation of one each year of measurement. This implies that virtually all scores lie between -2.5 and 2.5, with higher scores corresponding to better outcomes.

WARNING: Since the estimates are standardized (with a mean of zero and a standard devia- tion of one) each year of measurement, they are not directly suitable for over-time comparisons within countries. Kaufmann et al. (2006) however find no systematic time-trends in a selection of indicators that do allow for comparisons over time, which suggests that time-series information in the WBGI scores can be used if interpreted with caution.

3 GDP per Capita (constant 2010 US Dollar) vs. Control of Corruption

Luxembourg High ●

Norway 90000 ●

Ireland ● ● Switzerland

● Qatar Denmark● 60000 Sweden Australia ● ● U.S.A. ● ● Netherlands ● Singapore ● Austria ● ● Canada Japan Iceland● ● Andorra ● ● Belgium Germany Finland ● France ● ● U.K. United Arab Emirates ● 4 ● Israel New Zealand Kuwait● ● ● Brunei Italy ● Spain ● 30000 Bahamas ● Cyprus ● ● ● Greece● South Korea ●Slovenia ● ● ● Saudi Arabia● Portugal Estonia Bahrain ● Oman ● Barbados Turkey ● ● Poland● Chile ● ● ● Malaysia● ● ● ● Venezuela ● ● GDP per Capita (constant 2010 US Dollar) GDP per Capita (constant ● ● Equatorial Guinea Russia Brazil Bulgaria Latvia Uruguay ● Mexico ● ● ●● ● ● Mauritius ● ● China ● ● ● ●● ● ● St Lucia Libya Vietnam ●● Romania ● Botswana ● ● ● Iran ● ● ● ● ● Cuba ● Iraq Nigeria ● Peru ● ● ● ● ● ● ●● ●● Jamaica ● Grenada ●● ● ● ● South Africa Georgia Moldova● ● ● ● ● ●● ● ● ● ● ● ● Bhutan ● Congo Azerbaijan ● ● ● ● ● ● ● ● ● ● ● Guinea−Bissau● ● ● ● ●● ● ● ● ● ● Dominica ● ● ●● ●● ●●●● ●Morocco● ● ●● ● ●● ● ●● ● ● ● ● ●● ● ●● ● ●●●●●● ● ●● ● ● Tunisia ● Micronesia 0 ● Lesotho Namibia Yemen Chad Zimbabwe Laos Mali Tanzania India Jordan Rwanda Guinea

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 184 R−Squared: 0.59 Sources: The World Bank Group (2014 − 2017) & The World Bank Group (2017) GINI Index (World Bank Estimate) vs. Control of Corruption

High South Africa economic ● inequality 60 Namibia●

● Zambia

Mozambique ●

Brazil ● ● ●Panama ● 50 Honduras Colombia ● Guatemala● ● Costa Rica ● Benin ● Cameroon Paraguay Chile ● Nicaragua● Dominican Republic● Ecuador● ● Bolivia ● Mexico● ● Peru ● Argentina Togo ● Uganda ● Cote d'Ivoire● ● Kenya Turkey ● 5 ● U.S.A. El Salvador● Malaysia 40 ● ● Iran ● Sri Lanka Uruguay ● Ethiopia ● ● ● Bulgaria Lithuania Myanmar ● ● ● Yemen Russia Gabon ● Spain ● Bhutan Gambia Thailand Greece ● ● Vietnam ● ● ● ● Romania Georgia GINI Index (World Bank Estimate) (World GINI Index ● ● ● ● Niger Macedonia Burkina Faso Italy Latvia Portugal ● ● ● ● Luxembourg● Liberia● Tajikistan ● Armenia Cyprus ● Pakistan ● ● ●● ● ● ●Mongolia Estonia France U.K. ● Bangladesh Mauritania ● ● Croatia ● ● Switzerland Egypt Montenegro ● Ireland Germany ● ● 30 Hungary Austria Sweden Timor−Leste ● ● ● Malta Netherlands ● Denmark● Belgium ● Norway ● ● Kazakhstan ● ● ● ● Slovakia Iceland ● Belarus ● Czech Republic Finland Low Kyrgyzstan ● Moldova ● ● Slovenia economic Ukraine inequality −1 0 1 2 Low High Control of Corruption

Number of observations: 91 R−Squared: 0.12 Sources: The World Bank Group (2014 − 2017) & The World Bank Group (2017) Economic Freedom Index vs. Control of Corruption

● High Singapore

New ●Zealand

Australia Switzerland● Estonia ● ● Canada ● Taiwan U.A.E. Ireland U.K. ● ● ● ● ● Luxembourg Lithuania● ● ● ● Mauritius ● ●Denmark ● ● Georgia Chile Iceland ● ● 75 ● ● ● ●● Malaysia ● Qatar● U.S.A. Sweden South Korea ● Germany Norway Armenia ● Romania Botswana ● ● Uruguay Austria Kazakhstan ● ● ● ● ● ● ● ● ● ● Vanuatu Colombia ● Brunei ● ● Israel Japan● Peru ● Saudi Arabia ● ● Philippines ● ● ● ● Kuwait Hungary ● ● Rwanda Belgium ● ● ● ● ● ● ● Mexico● ●Paraguay ● ● ● ● ● ● South Africa Italy ● Dominica ● ● Indonesia ● ● ● ● Fiji ● Benin ● ● ● Spain ● France ●● ● ● ● Uganda ● Oman Namibia Portugal Tajikistan● ● Honduras ● Serbia Morocco● ● Seychelles● ● ● ● ● ● ●● Bahamas ● Russia ● ● Croatia ● ● DR Congo Nigeria ●● ● ● ● China Slovenia Bhutan ● Mali Samoa ● ● ● ● Tanzania ● ●●Senegal Mauritania● ● ● ● ● Cape Verde Barbados Guinea−Bissau ● ● ● Kenya ● Comoros ● ● ● ● ● ● ● ● Solomon Islands ● ●●●●●● ● Micronesia Yemen Burundi ●●● Lebanon ● Brazil Lesotho ● India ● ●● ● ● Chad ●Cameroon ● Iran Ecuador 50 ●● ● ● ● Kiribati Sudan ● ● ●Suriname ● Haiti ● ● Turkmenistan Guinea Ukraine ● ● ● ● Zimbabwe Timor−Leste Equatorial Guinea ● ●

● Eritrea 6 Congo

●

Economic Freedom Index Economic Freedom Index Cuba

● 25 Venezuela

North Korea ●

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 178 R−Squared: 0.51 Sources: Heritage Foundation (2015 − 2017) & The World Bank Group (2017) GDP per Capita Growth (annual %) vs. Control of Corruption

● High Libya

20

Ethiopia Maldives Romania ● China ● ● India Ireland● Guinea ● ● Benin ● ● Lithuania Poland Cambodia Iran ● ● ● ● ● ● ● ● ● ● Estonia ● ● ● Togo ●Indonesia Lesotho Hungary ● ● Slovenia ● ● ● ● ● ● ● ● Rwanda● Bhutan Singapore Guinea−Bissau Kyrgyzstan ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Fiji ● Portugal ● ● ● ● Egypt ● Belarus ● ● ● ● ● Finland Tajikistan ● Russia ● ● ● ● ● ● Senegal ●● ● ● Uruguay Japan Iceland ● ● ●● ● ●Guyana● ● ● ● ● ● Spain● ● ● ● ●Canada ● Sudan ● ● ● ● ● ● ● ● Croatia ● ● ● ● Uganda ● ● Bolivia ● ● ● ● ● Cyprus●● ● France ● ● ● ● ● Denmark ● ● ●● ● ● ●Morocco ● Germany ● ● ● DR Congo● ● ● ● ● ●● ● Colombia ● ● ● U.K. ● ● ● ● ● ● ● Costa Rica ● U.S.A. ● ● ● ● ● Peru● ● Norway 0 Afghanistan Mexico ● ● ● Italy ● Botswana ● Belgium Australia ● Haiti ● ● Brazil ● ● Sweden Angola ● Gabon Eswatini South Africa Saudi Arabia Qatar United Arab Emirates ● ●Lebanon ● ● ● Switzerland ● Nigeria Palau Belize Dominica Iraq ● Oman● ● Chad ● ● ● Kuwait ● ● Equatorial Guinea Congo Timor−Leste● 7

South Sudan ●

GDP per Capita Growth (annual %) (annual GDP per Capita Growth −20

Yemen ●

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 186 R−Squared: 0.02 Sources: The World Bank Group (2014 − 2017) & The World Bank Group (2017) Ease of Doing Business vs. Control of Corruption

New Zealand ● Easy Singapore South Korea ● ● Georgia U.S.A. ●Denmark ● ● U.K. ● ● Sweden ● Taiwan Estonia ● Norway Lithuania ● Australia Macedonia ● ● Ireland ● ● United Arab Emirates ● Iceland 80 ● ● ● ● Finland Mauritius● ● Kazakhstan Thailand ● ● Latvia Poland ● ● ● Austria Germany Canada ● Malaysia ● France Spain ● Portugal ● ● Russia Belarus Slovakia ● ● Netherlands ● ●Montenegro ● Czech Republic Slovenia Japan Switzerland Moldova Serbia ● ● ● ● Romania Mexico Bulgaria ● ● ● ● ● Armenia ● Rwanda Israel Belgium ● Italy ● ● Colombia ● Hungary ● ● Cyprus Azerbaijan ● Croatia ● Chile ● ● ● ● Peru Turkey ●Morocco Brunei ● ● Costa Rica ●Vietnam ● Oman● Luxembourg Uzbekistan ● Mongolia ● Greece ● ● Malta Kyrgyzstan● ● ● ● Panama ● China South Africa ●● ● ● ● Qatar ● Botswana Bhutan Kenya ●●El Salvador ● ● ● ● ●Samoa Uruguay Kuwait Saudi Arabia● ● Nepal Trinidad and Tobago ● St Lucia ● ● ● ● ● ● India● Seychelles ● ● 60 Paraguay Malawi ●Eswatini Lesotho ●● Fiji Dominica ● ● ● ● ● Bahamas Tajikistan Honduras● ● Sri Lanka ● Argentina Jordan ● ● Uganda ●● ● ● St Vincent and the Grenadines ● ● ● ● Iran Ecuador● ● Brazil Belize ● Cambodia ● Barbados ● Maldives ●● Senegal ● Cape Verde ● ● Djibouti Egypt Tanzania ● St Kitts and Nevis ●● ● ● Lebanon ● ● Marshall Islands ● ● Pakistan ● ●Cote d'Ivoire ● ● Grenada Nigeria ● ● Laos ●Gambia Burkina Faso ● Mauritania Kiribati Micronesia Zimbabwe ● ● ● ● ● Guinea ● ● ● ●Suriname 8 Comoros● ● ●Madagascar Ethiopia Sudan Burundi ● Sao Tome and Principe ● ● Myanmar ● Guinea−Bissau Gabon ● Iraq ●

Ease of Doing Business Equatorial Guinea● Liberia ● ● ● Angola Syria Congo ● 40 ● Bangladesh ● Chad ● ● Timor−Leste ● Haiti Afghanistan ● DR Congo South Sudan Libya ● ● ● Central African Republic Yemen Venezuela●

● Eritrea Somalia 20 ●

Not Easy −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 185 R−Squared: 0.51 Sources: Ease of Doing Business Report (DB17−19 methodology) (2017) & The World Bank Group (2017) Human Development Index vs. Control of Corruption

Norway High Switzerland ● Germany ● ● ● Sweden ● Netherlands● U.S.A. ● ● ● ● Ireland ● U.K. ● ●Denmark ● ● ● South Korea Israel Belgium ● Canada ● ● ● ● New Zealand ● Japan Luxembourg Italy Spain● ● Malta Slovenia France Greece ● ● ● Poland ● Cyprus ● Estonia ● ● Slovakia Lithuania ● ● ● ● ●● Qatar Bahrain ● ● U.A.E. ● Hungary ● Andorra ● Saudi Arabia Brunei ● Portugal Chile Argentina● ● Oman Russia● Montenegro Belarus ● ● ● Bahamas ● ● Uruguay ● ● Croatia ● ●● ● Seychelles ● 0.8 Panama ● ● ● Serbia ● Romania ● Iran ● ●● Costa Rica Georgia Barbados Mexico ●● ● ● ● Albania Turkey Mauritius ● ● ● Cuba ● Azerbaijan ● Grenada ● ● Brazil ● Thailand● ● China ● ●● ● Venezuela ● ● ● Lebanon ● ●St Lucia Ukraine● Tunisia Fiji Jamaica● ● ● ● St Vincent and the Grenadines Maldives Mongolia ● Tonga Jordan ● ● ● ● Libya Uzbekistan● ● ● ● Paraguay Philippines● Suriname ● Botswana ●● ● ● ● Samoa Turkmenistan ● ● ● Moldova ● Egypt South Africa ● Kyrgyzstan Vietnam Indonesia Iraq ● ● ●Morocco Nicaragua● El Salvador Cape Verde Tajikistan ● Namibia ● ● ● India ● Guatemala Guyana ● Micronesia Honduras● Timor−Leste ● ● Kiribati● ● Congo ● 9 ● Laos Bangladesh Vanuatu ● ● Bhutan 0.6 Equatorial Guinea Ghana● ● ● ● Zambia ● ● Cambodia● ● Kenya Nepal ● Sao Tome and Principe Angola ● Eswatini Cameroon ● ● Pakistan Solomon Islands Human Development Index Index Human Development Nigeria ● Tanzania ● ● Zimbabwe ● ● ● Uganda Mauritania Lesotho ● Syria ● ● Benin ● ● Togo ● Rwanda Sudan● ●● ● Senegal ● ●Madagascar Comoros ● Afghanistan Haiti Cote d'Ivoire ●● Gambia Ethiopia● Guinea−Bissau DR● Congo ● ● ● ● Guinea Yemen Eritrea● ● Liberia● ● Mozambique Mali ● Burundi● ● Sierra Leone Burkina Faso 0.4 ● ● Chad South Sudan ● Central African Republic ●Niger

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 187 R−Squared: 0.51 Sources: United Nations Development Program (2017) & The World Bank Group (2017) Good Society Index vs. Control of Corruption

●●Sweden High Singapore 50 Japan ●

● Qatar ● Australia ● New Zealand ● Netherlands

Slovenia ● Spain● ● ● Mexico Malaysia ● U.S.A. 40 ● ● South Korea Cyprus ● ● Germany Poland ● Chile Kuwait ● Ecuador ● Uruguay ● ● ● Thailand Estonia ● ● Argentina ● Lebanon Colombia Turkey 30 Brazil ● ● ●Bahrain

● China Kazakhstan ● Uzbekistan ● ● Libya ● Peru ● Belarus ● Kyrgyzstan ● ● 10 ● Armenia Trinidad and Tobago● ● Romania

Good Society Index Good Society Index ● Philippines Tunisia Russia Algeria Morocco● ● 20 ● Rwanda ● Jordan Azerbaijan ● ● ● ● Ghana Georgia Nigeria Ukraine ● ● Pakistan ● India South Africa Zimbabwe ● ● ●Egypt 10

● Iraq ● Yemen Low −1 0 1 2 Low High Control of Corruption

Number of observations: 57 R−Squared: 0.52 Sources: Holmberg, 2007. Made with data from: World Value Survey (Happiness) and World Development Indicators (Life Expectancy and Infant Mortality) (2014 − 2017) & The World Bank Group (2017) Government Revenue (percent of GDP) vs. Control of Corruption

Nauru● High80

● Kuwait

Micronesia ●

60

● Finland Marshall Islands ● Timor−Leste ● ● ● ● ● France ● Denmark Norway Belgium ● ● Lesotho ● ●Qatar Austria Libya Greece● Italy● ● Sweden ● Iceland Hungary Oman ● Portugal● ● Palau Germany ● Luxembourg ● ● Croatia ● ● Slovenia Netherlands Bosnia and Herzegovina Montenegro Czech Republic ● ● ● ● ● Ukraine ●Serbia ● Malta 40 ● ● Spain● ● ● ● 11 Ecuador Estonia ● ● Poland Azerbaijan ● Belarus Slovakia ● Cyprus Canada ● ● Barbados Bolivia ● ● ● Seychelles Israel ● ● Kyrgyzstan Argentina Bulgaria ● U.K. New Zealand ● ● ● Latvia Ireland ● ● Moldova ● ● ● Angola ● Romania ● Samoa Liberia Turkey ● Namibia ● Australia ● Japan Russia ● ● Brazil Lithuania ● ● ● ● ● China Botswana Maldives ● South Africa U.S.A. Djibouti ● Tajikistan Colombia ● ● ● ● ● ● ● Jamaica Jordan Fiji Georgia South Sudan Mozambique● ● ● ● ● ● ● ● ● St Lucia ● Bhutan Burundi Albania ● ● ● Uruguay Malawi ● ● Tunisia ● Bahrain Saudi Arabia Government Revenue (percent of GDP) (percent Revenue Government ● Honduras ● ● ● ● Brunei ● ● ● Senegal ● ● ● Equatorial Guinea ● Mongolia ●● ● Mexico ● Gambia● ●Niger ● Singapore ● ● ● Malaysia Antigua and Barbuda ● ● ● ● ● Cape Verde Guinea−Bissau ● ● Thailand ● ● Lebanon Gabon ● Peru ● ● Chile ● ● Armenia ● South Korea ● 20 ●● Philippines India Mauritius Turkmenistan Cameroon ● ● ● ● ●● Chad ● Nepal Bahamas ● ● ● Ghana DR Congo Kenya ●● Taiwan Mali ● Benin ● ● ● ● ● Kazakhstan● Indonesia Central African Republic ● Iran Sierra Leone ● ● ● ● ● Costa Rica Madagascar Uganda ● Sudan Guatemala Sri Lanka Nigeria●

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 164 R−Squared: 0.19 Sources: International Monetary Fund (2014 − 2015) & The World Bank Group (2017) Tax Revenue (% of GDP) vs. Control of Corruption 40 Iceland High ●

● Lesotho

● Denmark

● Seychelles 30

Namibia● Sweden Trinidad and Tobago South Africa ● ● ● ● ● ● Malta Barbados New Zealand ● ● ● Greece Solomon Islands Austria ● Jamaica ● ● ● Fiji Cyprus U.K. Luxembourg Kiribati ● ● ●St Vincent and the Grenadines Italy ● ● ● ● ● ● Uruguay ● Latvia ● Israel ● ● Belize Hungary Georgia ● Australia Togo France Belgium ● ● ● ● Portugal ● ●Nepal ● Senegal Dominica Netherlands Norway St Kitts and Nevis ● ● Mozambique Armenia Bulgaria ● ● ● Botswana Finland ● Ukraine ● ● 20 ● ● Croatia ● St Lucia ● Palau Turkey ● Ireland Moldova Marshall Islands Grenada ● ● El Salvador Albania ● ● ● ● ● Mauritius Slovenia Uzbekistan● ● ● ● ● ● ● Romania● Malawi ● Vanuatu ●Slovakia Poland Chile 12 Kyrgyzstan ● Macedonia ● ● Cote d'Ivoire ● Cambodia ● ● ● ● ● Antigua and Barbuda ● Burkina Faso Rwanda Nicaragua Zambia ● ● ● Mexico South Korea Bahamas● ● Dominican Republic● ● ● ●●Czech Republic Singapore ● ●● ● ●Peru Belarus Bhutan ● ● ● Costa Rica ● Tax Revenue (% of GDP) Revenue Tax ● Brazil ● Malaysia Laos ● Colombia Canada ● ● ● Paraguay Egypt ● ●Equatorial Guinea ●Sri Lanka Japan Germany● Guatemala Tanzania ● ● ● ● ●Indonesia U.S.A. Switzerland ● ● ● 10 Angola Russia ● ● Timor−Leste ● ● China Bangladesh Afghanistan● Sierra Leone Micronesia ● ● Myanmar Lithuania●

Iraq ● Estonia ● ● Kuwait ● 0 United Arab Emirates Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 129 R−Squared: 0.13 Sources: The World Bank Group (2014 − 2017) & The World Bank Group (2017) Average Schooling Years vs. Control of Corruption

● High Czech Republic ● ● U.S.A. Switzerland ● Slovakia Germany ● ● Israel Estonia● ● Slovenia Ireland ● Canada ● ● ● U.K. South Korea● Sweden● Norway Russia Hungary ● ● Australia● Belize ● Denmark ● Poland ● ● Kazakhstan● ● Croatia● ● ● Japan Netherlands ● Kyrgyzstan Bulgaria Cyprus ● ● Ukraine Serbia ● ● New Zealand ● ● Romania Luxembourg ● ● Lithuania Taiwan Belgium Moldova ● ● France ● Singapore Tajikistan ● Armenia ● ● ● Tonga ●Latvia ● ● ● Iceland Trinidad and Tobago ● ● Greece Spain Malta Finland 10 Albania● Malaysia ● ● ● Austria Sri Lanka ● ●Cuba ● Jamaica South Africa● ● Mongolia ● ● Chile Barbados ● Argentina Italy Fiji ● ● ● Panama Jordan Botswana● ● Peru ● ● Colombia United Arab Emirates Mexico ● ● Brunei● ● Iran Guyana ● ● ●Philippines ● Qatar ● Venezuela Mauritius Costa Rica Bolivia● Brazil ● ● Uruguay Gabon ●● China ● ● Saudi Arabia ● ●● ● Dominican Republic Ecuador● Indonesia ● ● ● ● Libya Zimbabwe Paraguay Thailand Bahrain Portugal Ghana ● Zambia ● Tunisia Iraq ● ● ● 13 Syria ● ● Kenya Egypt ● Turkey Cameroon ● ● ● ● ●Algeria Kuwait Namibia Nicaragua ● Lesotho● Uganda ● Congo ● ●India

Average Schooling Years Years Schooling Average Honduras Bangladesh ● 5 ● Tanzania ● ● Haiti Pakistan● Laos ● ●Togo Morocco ● ● Cote● d'Ivoire ● Cambodia● Guatemala ● ● ● Myanmar ● DR Congo Papua New Guinea ● Liberia Eswatini ● ● Rwanda Mauritania ● ● Benin ● Central African Republic ● Afghanistan Nepal ●Sierra Leone ● ● ●Yemen Gambia Burundi ● Senegal

Mali ●● ● Niger Mozambique Low −1 0 1 2 Low High Control of Corruption

Number of observations: 142 R−Squared: 0.40 Sources: Barro & Lee (2010) & The World Bank Group (2017) School Enrolment (Tertiary) vs. Control of Corruption

125 ● High Australia ● Greece

100 ● South Korea Turkey ● ●● Spain ● Grenada Chile Finland Belarus● ● Ukraine ● Ireland ● Argentina ●● New Zealand ● Austria ● ● Russia ● ● ● Slovenia Netherlands Denmark Norway Belgium ● 75 Estonia ● Iceland Bulgaria ● Iran ● Latvia ● Croatia ● Lithuania● Poland ● ● ● Mongolia ● France● ● Saudi Arabia ● ●Israel Japan Germany Serbia ● ● Sweden ● ● ● ● Czech Republic Italy ● Portugal Albania ● Cyprus Switzerland● ● ● Colombia Uruguay● Dominican Republic Montenegro Costa● Rica U.K.

14 ● ● Armenia ● Kazakhstan ● ● Slovakia Georgia ● China Romania ● 50 Panama Brazil ● ● ● Kyrgyzstan ● ● Malta ● ● ● Bahrain Hungary Ecuador Thailand ● ● ● Malaysia Oman ● ●

School Enrolment (Tertiary) (Tertiary) Enrolment School Syria Algeria Mauritius ● Lebanon Moldova Macedonia ● ● Jordan United Arab Emirates ● Philippines ● ● ● ● Mexico ● Tunisia ● ● Liechtenstein Tajikistan Egypt ● Cuba Brunei ● Morocco ● ● Vietnam ● ● Indonesia ● ● North Korea Azerbaijan El Salvador ● ● Jamaica ● India Botswana 25 ● Guatemala● Belize Seychelles ● ● ● Sri Lanka ● Cape Verde ● Sudan Cameroon Laos Honduras ● ● ● ● ● ● Ghana South Africa St Lucia Qatar Luxembourg ● ● ● Cambodia Maldives ● Myanmar Sao Tome and Principe ● ● ● Guinea Nepal ● ● Senegal ● Togo Benin ● Afghanistan Uzbekistan ● Pakistan ● ● ● ● Rwanda ● ● ● ● ● ● ● Lesotho Madagascar Comoros● Ethiopia ● Burundi ● ●● ● ● Mauritania Mali ● Burkina Faso Turkmenistan ● Uganda Eritrea Tanzania 0 Chad Low −1 0 1 2 Low High Control of Corruption

Number of observations: 134 R−Squared: 0.31 Sources: The World Bank Group (Ratio of total enrolment) (2014 − 2017) & The World Bank Group (2017) Life Expectancy at Birth, Total (years) vs. Control of Corruption

High ● Liechtenstein Spain Japan Italy ● Australia Canada ● ● Norway ● Israel France ● ● ● ● ● ● ● ● ● Malta Ireland ● ● Netherlands ● South Korea Portugal ● ● Sweden ● ● ● Belgium● ● ● U.K. ● Finland Greece ● ● ● Costa Rica Austria Denmark ● ● Cyprus Germany 80 ● ● Lebanon Cuba Chile U.S.A. Albania Czech Republic ● ● Croatia ● ● ● ● Estonia Maldives Panama ● Uruguay ● Bahrain ●Poland ● Mexico ● ● Oman ● ● Vietnam ● ● Slovakia ● China ● ● Brunei United Arab Emirates ●● ● Morocco ● ●Algeria ● ● Barbados Iran Thailand● ● ●● Hungary ● ● ● Antigua and Barbuda● St Lucia ● ● ● ●● ● Nicaragua Brazil ● Turkey ● Saudi Arabia ● Samoa Bahamas ● ● Peru ● ● ● Latvia● Dominican Republic ● Kuwait Malaysia ● ● ● ● Venezuela ● ● Seychelles ●Armenia● Jordan ●Lithuania ● Bulgaria ● Guatemala ● Colombia ● ● Georgia Honduras Grenada ● Libya Russia ●● Tonga Vanuatu ● ● ● St Vincent and the Grenadines ● ● ●Moldova Uzbekistan● Ukraine ● ● ● North Korea ● ● Trinidad and Tobago Solomon Islands Egypt ● ● Tajikistan Nepal ● Bhutan ● Kyrgyzstan ● Mongolia ● ● Syria ● Belize 70 Philippines Fiji Micronesia Iraq ● ●● ● ● ● ● Indonesia Cambodia Bolivia Timor−Leste ● ● India Kenya Senegal Rwanda Turkmenistan ● Pakistan Guyana ● ● ● ● ● ● ● Madagascar Laos ● ● Botswana ● ● Myanmar Tanzania Sao Tome and Principe ● ● ● Kiribati 15 Eritrea Yemen● ● Gabon ● Ethiopia ● ● Congo Comoros ●Sudan ● Namibia Afghanistan ● Mauritania ● ●Malawi Haiti ● ●South Africa Liberia ● ● Djibouti Ghana Angola ● ● Gambia ● ● Zambia Zimbabwe Togo ● Burkina Faso ● Benin ● ● ● ● Life Expectancy at Birth, Total (years) (years) at Birth, Expectancy Life Total DR Congo 60 ● Guinea Uganda Niger Mozambique Equatorial Guinea Cameroon ● ● ● Eswatini ● Guinea−Bissau ● ● ● Mali South Sudan● Burundi ● Somalia

Lesotho● Nigeria ● ● Chad● Cote d'Ivoire ● ● Central African Republic Sierra Leone Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 184 R−Squared: 0.45 Sources: The World Bank Group (2016) & The World Bank Group (2017) Healthy Life Expectancy vs. Control of Corruption

Singapore High ●

Japan● Spain ● Switzerland Cyprus France● ● ● ● Australia ● Norway ● ● ● ● ● Sweden ● Austria ● Italy ● ● South Korea ● Israel Iceland ● ● ● ● ● New● Zealand Malta ● U.K. ● ● Greece Costa Rica Denmark Finland ● Slovenia Belgium ● Germany Cuba● Chile 70 ● Panama Czech Republic ● ● Croatia ● Maldives ● Uruguay China● Qatar ● ●● ● ● Bahrain ● Colombia ● ● ● Poland ● Mexico ● ● ● Slovakia Estonia U.S.A. ● ● Macedonia● Honduras ● Brunei Nicaragua ● ● ● Bahamas ● ● ● ● ● ● Malaysia● ● Barbados ● ● Jordan Latvia ● Armenia Bulgaria● ● ● ● ● ● ● ● Lebanon● ● ● ● Samoa● United Arab Emirates Mauritius● Kuwait ● ● ● Venezuela ●Iran ● ● ● Tunisia Azerbaijan ●● ● Seychelles ● Saudi Arabia ● Algeria Belarus ● ● Uzbekistan● Morocco ● Paraguay● ● Georgia North Korea ● Brazil Grenada Cape Verde Kyrgyzstan Ukraine ● ● ● ● ● ● ● Guatemala ● Suriname● ● Tajikistan Russia ● St Vincent and the Grenadines Libya ● ● Moldova Mongolia Vanuatu ● ● Turkmenistan ● ● ● ●Philippines ● Micronesia ● Indonesia Solomon Islands ● ● Nepal Egypt ● ● Cambodia Sao Tome and Principe Rwanda Bhutan 60 India ● Kenya ● ● ● ● Myanmar Madagascar ● Guyana ● Iraq ● ● ●

16 Senegal ●Gabon● Kiribati ● ● Botswana Eritrea● ● Ethiopia ● Congo Laos ● Comoros● ●Djibouti ● ● Ghana● ● Syria Angola Mauritania ● ●● ● Malawi Tanzania ● Healthy Life Expectancy Expectancy Life Healthy ● ● Uganda Namibia Sudan ● South Africa Haiti Liberia● Gambia Yemen ● ● ● ● Zimbabwe ● Zambia Equatorial Guinea Afghanistan Togo ● ● Benin ● ● ● ● Burundi ● ● Burkina Faso DR● Congo Guinea Mozambique Guinea−Bissau ● ● Cameroon ● Eswatini South Sudan ● 50 ● Mali Somalia Nigeria ● Cote d'Ivoire ● ●Sierra Leone ●Chad Lesotho●

● Central African Republic Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 183 R−Squared: 0.45 Sources: World Health Organization (2016) & The World Bank Group (2017) Mortality Rate, Infant (per 1,000 Live Births) vs. Control of Corruption

● High Central African Republic

Sierra● Leone Somalia●

75 Chad●

DR● Congo

Mali ● ●Equatorial Guinea ● Cote d'Ivoire ● ● Lesotho Nigeria ● ● Pakistan Benin South Sudan ●

Guinea Liberia Guinea−Bissau Cameroon ● ● ● ● ● Mauritania ● ● ● ●Comoros Angola● Haiti ● Togo ● 50 Afghanistan ● Djibouti Burkina Faso ● ● Laos Niger Sudan ● ● Burundi ● Gambia Zambia ● Yemen ● ● ● Eswatini ● Papua New Guinea ●● ● Kiribati Turkmenistan Ethiopia Timor−Leste Zimbabwe ● ● ● ● Malawi Myanmar Ghana ● ● ● Uganda ●

17 ● ● Gabon Senegal● Congo ● Namibia Dominica ● ● ● Botswana Eritrea Madagascar India ● ● South Africa Tajikistan ● ● Bolivia● Nauru ● Bangladesh● ● ● Rwanda ● Venezuela ● Bhutan ●● Guyana Sao Tome and Principe ● 25 ● Guatemala● ● Micronesia Iraq Cambodia ● Philippines● ● ● ● ● Algeria ● ● Uzbekistan● ● Egypt Indonesia● Paraguay Vietnam● Tuvalu ● ● Mongolia ● ● ● Kyrgyzstan Honduras● Jordan ●St Lucia Cape● Verde North● Korea ● Panama● ● ● ● ● ● ● Mauritius ● Mortality Rate, Infant (per 1,000 Live Births) ● Iran ● Brazil ● ● ● Belize ● Samoa Seychelles Syria ● ● ● ● ● Barbados ● ● ● Turkey● ● ● ● ● Kazakhstan Mexico Macedonia ● Oman● Brunei● Georgia ● ● Malaysia ● Libya ● Uruguay ● Peru ● ● ● ● ● ● Latvia Qatar Chile ● ● ● ● China●● ● ● Saudi Arabia● ● ● U.S.A. Canada New Zealand Kuwait ● ● ● ● ● ● ● Russia ● Hungary ●Cuba Lithuania ● U.K. ● Sweden ● Bulgaria ● ● ●● Malta Bahamas● Belgium ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ●● Norway Italy Poland ● ● Portugal ● France ● Austria ● ● ●● Spain Germany Finland 0 Slovenia Estonia Japan

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 190 R−Squared: 0.36 Sources: The World Bank Group (2017) & The World Bank Group (2017) Risk of Maternal Death (%) vs. Control of Corruption

Sierra Leone 6 ● High

● Chad

Somalia ● ● Niger Nigeria ● ● Burundi Gambia ● ● 4 DR Congo ● Mali South Sudan Central African Republic ● ● Liberia Guinea ● ● ● Malawi ●Cote d'Ivoire ● Angola Cameroon ● Mauritania Guinea−Bissau ● ● Mozambique ● Eritrea Kenya ● ● Tanzania ● Uganda ● 18 ● Burkina Faso Congo ● Zimbabwe ●Benin 2 ● ● Afghanistan Togo ● ● Senegal ● ● Yemen ● ● Comoros ● Equatorial Guinea Madagascar ●

Risk of Maternal Death (%) Risk of Maternal Death (%) Lesotho ● Ghana Zambia ● ● Sudan Gabon ● Rwanda Haiti ● Eswatini ● ● Timor−Leste Namibia●

● Djibouti Guyana Sao Tome and Principe● Laos ● ● ● ● ● Nepal ● India Cambodia● South Africa Kyrgyzstan ● Algeria● ● ● ● Botswana ● ● ● Colombia ● Kiribati ● Bhutan ● ● ● ● ● ● Syria Iraq ● ● ● ● Jordan ●● ● ● ● ● Guatemala Peru ● ● ● ● ● Panama● ● Belize Morocco● Grenada Georgia ● Turkmenistan ● ● ● ● ● ● ● ● ●● ● Vanuatu Cuba ● ● ● Mexico ●● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● Chile Uruguay ● U.K. Luxembourg Norway 0 ● North Korea ● ●●● Iran ● ●● ●● ● ● ●● ● ● ● ● ● ●● ●●● ●●● ● ●● ● ● ● ●● ● ● ●●● ● ●●● ● ● ●● ●●● Uzbekistan Russia Brazil China Turkey Italy Saudi Arabia Brunei Portugal France U.S.A. Belgium Germany Sweden Finland

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 181 R−Squared: 0.22 Sources: The World Bank Group (2015) & The World Bank Group (2017) Public Health Expenditure (% of GDP) vs. Control of Corruption 15 Tuvalu High ●

Marshall Islands ●

10

● ● Japan Sweden● Maldives ● Germany France ● Belgium Denmark ● Netherlands● ● ● ● ● U.S.A. Switzerland Norway ● ● U.K. ● Austria Canada Finland Kiribati● ● ● ● Andorra Iceland Bosnia and Herzegovina ● ● Italy ● ● 19 Spain● Slovenia ● Uruguay ● ● ● Croatia Costa Rica ● Australia ● ● Portugal Serbia ● Malta Ireland ● Namibia ● Algeria Greece Luxembourg ● ● Estonia ● ● ● Lesotho ● ● 5 Moldova Eswatini ● ● Hungary ● ● Chile Nicaragua Panama ● Bulgaria Saudi Arabia South Korea Poland ● ● ● ● ● ● ● ● Colombia ● ● ● ● ● ● ● Iran● ●South Africa Brazil ● Public Health Expenditure (% of GDP) Health Expenditure (% of Public Kyrgyzstan Paraguay ● ● ● ● Dominica ● ● Jordan ● ● ● Bahamas● Russia Belarus● Romania Micronesia Uzbekistan● Gambia Peru ● ● ● Latvia ● ● ● ● ● ● ● ● ● ● ● ● ● Oman ● ● Honduras● Turkey Seychelles ● Barbados Burundi ● ● ● China ● ● Cape● Verde Mexico ● Guyana ● ●Ukraine ● ● Mauritius Qatar ●Cyprus Madagascar ● Morocco ● ● ● ● ● ● ● ● ● ● ● Tanzania ● Singapore ● ● Djibouti ● St Lucia Brunei United Arab Emirates Bhutan ● Tajikistan ● ● ● ● Sudan ● ● ● Fiji ● Mauritania ●● ● Malaysia Rwanda Kenya ● Togo Zambia Ghana ● Venezuela ● ●● ● ● ● ● ● ● ● ● Congo ● Philippines● Burkina Faso Angola ● Laos Armenia ●● ● ● ● ● ●● Uganda● ● Nepal ● Egypt ●Indonesia Yemen ● ● ●● ● ● ● Equatorial● Guinea ● India ● ● ● ● ● Pakistan South Sudan Nigeria ●● 0 Bangladesh Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 183 R−Squared: 0.42 Sources: The World Bank Group (2014 − 2015) & The World Bank Group (2017) Private Health Expenditure (% of health exp.) vs. Control of Corruption

● High Yemen ● Armenia 80 Azerbaijan● Afghanistan● Cameroon ● ●Iraq Comoros● ● ● Bangladesh Turkmenistan ● India Equatorial Guinea ● ● ● Nigeria Nepal Myanmar ● Egypt ● ● Sudan ● ● Pakistan Philippines South Sudan ● ● Guatemala Tajikistan● St Kitts● and Nevis ● ● 60 Chad ● Ethiopia Indonesia Georgia Cambodia ● ● ● Togo Albania Senegal ● Guinea ● ● ● ● Cyprus Brazil ● Grenada ● Honduras● Morocco Eritrea ● Niger St Lucia Barbados ● ● ●● Mauritius ● ● Ukraine ● ● Bahamas Venezuela ●Ecuador Cote d'Ivoire ● U.S.A. ● ● Bulgaria ● Kenya ● Angola Kyrgyzstan● ● ●●● Moldova Vietnam ● ● ● ● Sri Lanka ● ● ● Malaysia Congo Uganda Mexico ● ● Singapore ● ● Mali ● ● Tunisia Andorra ● Mongolia ● South Korea ● ● Uzbekistan Iran Benin ● ● DR Congo ● ● ● ● China● South Africa Zimbabwe Gabon ● Latvia ● ● ● Guyana● Burkina Faso Malta ● ● Chile 40 ● Zambia● ● ● ● ● Haiti Russia ● ● Peru● Ghana Jamaica ● Nicaragua ● ● Botswana 20 Jordan● ● Guinea−Bissau Panama ● Belarus Rwanda ● Fiji ● ● ● Hungary ● ● El Salvador Bahrain ● ● Portugal Australia Algeria ● Antigua and Barbuda Lithuania ● Montenegro Poland Uruguay ● ● ● ●Colombia● Saudi Arabia Spain ● ● Madagascar Bolivia ● ● Belize ● ● ● Slovenia ● Ireland ● ● ●● ● Dominica Switzerland Tanzania Argentina Namibia United Arab Emirates ● Cape● Verde ● Palau ● Costa Rica Canada Gambia ● Italy ● ● ● Austria ● ● Estonia Finland ● Djibouti Turkey ● ● ● ● Croatia St Vincent and the Grenadines Burundi ● ● Bhutan● ● Liberia Romania ● France Netherlands 20 Thailand ● U.K. ●● Private Health Expenditure (% of health exp.) exp.) Health Expenditure (% of health Private Slovakia Czech Republic ● Maldives● ● Eswatini ● Belgium● Luxembourg Kuwait ● ● Malawi ● ● Iceland ● ●Sweden Lesotho ● Sao Tome and Principe ● Japan Germany Denmark ● ● ● ● Tonga ● Qatar Norway ● ● Timor−Leste● Marshall Islands Vanuatu Samoa ● Oman Mozambique Nauru ● ● ● Kiribati Papua New Guinea Tuvalu ● Brunei ● ● Micronesia ● ● Solomon Islands Seychelles 0 Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 183 R−Squared: 0.24 Sources: The World Bank Group (2014 − 2015) & The World Bank Group (2017) Alcohol Consumption per Capita vs. Control of Corruption

High ● 15 Estonia

Lithuania ●

Czech● Republic

Seychelles ● Bulgaria ● Ireland ● France ●● Luxembourg Hungary ● Austria ● ● Latvia Portugal ● ● ● Germany Romania● Croatia ● ● Slovenia Andorra ● ● ● Poland ● Belgium 10 ● Slovakia ●U.K. Equatorial Guinea Gabon Belarus St Lucia● ● Denmark ● ● ● ● Australia ● Nigeria ● Barbados ● South Korea Cyprus ● ● ● Uruguay● Switzerland Moldova ● New Zealand Serbia ● ● Grenada St Kitts and Nevis ● Russia Argentina ● ● ● ● Bahamas U.S.A. ● Spain Canada● Antigua and Barbuda ● ● Finland ● ● ● Trinidad and Tobago Malta Netherlands ● Namibia Chile ● Uganda ● ●● South Africa ● Eswatini ● ● Georgia Iceland ● ● Panama ● Sweden ● Greece Italy ● ● Rwanda Japan 21 ● Laos ● Brazil ● Thailand Cameroon ● ● Montenegro ● Ukraine ● ● ● ● ● Tanzania Belize Botswana Kazakhstan ● ● Norway Haiti ● ●Paraguay China Mexico ● Congo Guyana ● ●Albania Sao Tome and Principe 5 ● ● ● ● ● ● Peru ● Burkina Faso ● Angola Cote d'Ivoire ● ●

Alcohol Consumption per Capita Alcohol Consumption per ● Cuba Venezuela Kyrgyzstan Colombia Suriname ● ● ● ● Armenia Cape Verde Zimbabwe Bolivia ● ● Turkmenistan ●● ● ● Sierra Leone Jamaica ● ● ● Costa Rica Cambodia Liberia ●● North Korea ● Zambia ● Lesotho ● ● ● ● ● ● India Mauritius Israel Guinea−Bissau Gambia ● ● El Salvador ● United Arab Emirates Bahrain● Samoa ● Kenya ● Fiji ● ● Singapore● ● ● Togo Benin Micronesia ● ● ●● ● Tunisia ● ● ● ● Uzbekistan ● ● ● ● ● Maldives Turkey DR Congo Solomon● Islands Qatar ● ●Lebanon Vanuatu ● ● ● Algeria Ethiopia Tajikistan ● ●● ● ● ● ● Morocco Jordan● Kiribati Syria ● ● ● ● ● ● Brunei ● ● Nepal ●Timor−Leste ● ● Azerbaijan ● ● Oman ● 0 ● ● Iraq ●● ● Egypt ● Senegal Somalia Yemen Iran Niger Kuwait Saudi Arabia Djibouti Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 177 R−Squared: 0.28 Sources: World Health Organization (2015 − 2016) & The World Bank Group (2017) CO2 Emissions (metric tons per Capita) vs. Control of Corruption

● High Qatar

40

Trinidad● and Tobago

30

● Kuwait ● ●

Bahrain Brunei● United Arab Emirates

20 ● 22 Saudi Arabia Luxembourg U.S.A. ● ● ● Australia● Estonia● ● ● Oman Canada Kazakhstan ● Palau● Turkmenistan ● South● Korea Russia CO2 Emissions (metric tons per Capita) CO2 Emissions (metric tons Singapore● Japan ● 10 Libya Czech Republic ● ●Norway ● South● Africa ● Netherlands Iran ● ● ● ● China ● Poland ● Germany Mongolia ● ● Finland ● ● Malaysia Belgium● ● Slovakia Bahamas ● Venezuela Bulgaria Slovenia ● ● U.K. New Zealand ● ● ● ● ● ● ● Italy ● Austria ● Ukraine Serbia● ● ● Spain ● ● Malta Iraq Azerbaijan ● Turkey ● ● Iceland Denmark ● ● ● ● ● Andorra ● Mauritius ● ● ● ● ● ● ● ● ● ● Lithuania Equatorial Guinea Lebanon ● ● Vietnam ● Macedonia ● Chile ● ● ● ● ● ●St Kitts and Nevis● France Switzerland Sweden ● Brazil ●● ● Mexico ● ● ●● ● ● Montenegro St Lucia Bolivia ● Suriname ● ● ● Uruguay Syria ● ● ● ● ● ● Cuba ● ● ● ●Tajikistan ● Gabon ● ● ●● ● ● ● ● ● ● ● Morocco Tuvalu ● ●Micronesia ● ● ● ● ●●●Armenia● ●●Guyana● ● ● ● ● ● Yemen ● ● Congo ● ●● ● ● ● ● ● ● ● ● Grenada ●● ● ● ● ● ●● ●● ● ●●● ●●● ● ● Fiji Bhutan Liechtenstein 0 ● ● ●● ● ● ●● ●●● ● ● Egypt India ● ● Cape Verde Somalia Cambodia Burundi Laos Comoros Lesotho Rwanda Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 190 R−Squared: 0.13 Sources: The World Bank Group (2014) & The World Bank Group (2017) Access To Drinking Water vs. Control of Corruption 120 High

Italy Spain Armenia Greece Hungary Qatar Cyprus France Belgium Germany Sweden Finland ● ● ● ● ● ● ●●●Mauritius ● ● ●● ● ● ● ● ●● ● ● ●●● ● ●● ●●● Belarus● Israel ● ● ● ● ● ● ● ● ● ● ● Norway Egypt ● ● Croatia Latvia● Bhutan U.K. Denmark Lebanon● ● ● ● ● ● ● ● ● ● Montenegro Georgia Estonia Malaysia ● ● U.S.A. ● Kuwait Bulgaria ● Costa Rica Chile Maldives ● ● Poland ● ● ● ● Brazil Argentina ●Saudi Arabia● Ireland 90 Russia● ● Tunisia ● ● Lithuania● ● ● Botswana ● ● Thailand Jordan ● Mexico ● China Suriname ● Grenada ● Iran ● ● ● ● Vietnam ● Fiji ● Dominica ● Albania ● ● Vanuatu Cuba ● Kazakhstan Gabon ● ● ● ● ● India South Korea ● El Salvador ● Oman Venezuela Pakistan ● ● ● Guatemala● ● South Africa ● ● Namibia Cape Verde Comoros ● Colombia ● ● Kyrgyzstan ●●● Bolivia Syria ● ● Malawi Ghana ● ● ● Uzbekistan ● ● Ecuador ● ● Nicaragua ● ● ● Indonesia ● Iraq ● Algeria● Bangladesh ●Morocco ●Cote d'Ivoire ● ● Uganda ● 60 ● Lesotho Myanmar ● ● Solomon Islands Guinea−Bissau● Zimbabwe ● Guinea● Benin Senegal Congo ● ● ● Laos Rwanda● ● Burundi ●Mali ●

23 Cameroon ● Tajikistan● Liberia ● Eswatini Libya ● ● Cambodia ● Timor−Leste ● Central African Republic Kiribati● Nigeria Zambia● ● Access To Drinking Water Water Drinking Access To ● Togo● Mongolia Kenya ● Turkmenistan● ● Sierra Leone 30 Yemen ● Eritrea ● Niger ● Mauritania Sudan ● Tanzania ● Haiti ● Afghanistan ● ● Ethiopia ● DR Congo ● ● Mozambique

Chad ● Madagascar Angola● Papua New Guinea ● Somalia● ● ● 0 Equatorial Guinea Brunei Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 180 R−Squared: 0.40 Sources: Environmental Performance Index (2015) & The World Bank Group (2017) Unsafe Sanitation vs. Control of Corruption

Safe Uzbekistan Saudi Arabia Malta Taiwan U.S.A. Japan Australia Switzerland New Zealand ● ● Italy ● ●● ● ● ● ● ● ● ● ● ● ● ● ● 100 ● ● ● Kuwait ● Germany● ● Denmark ● Croatia Spain● ● Portugal● France U.K. ●Jordan ● ● Maldives ● ●Slovenia Austria ● Greece ● ● ● Sweden ● Hungary Estonia Iceland ● ● ● ● Oman ● ● ● Libya ● ● Norway ● Serbia● Seychelles Kazakhstan Montenegro ● ● Luxembourg Ukraine ● ● Poland Syria ● ● Malaysia Tajikistan ● ● Uruguay ● ● Costa Rica Egypt ● ● Cuba ● ● Kyrgyzstan Belarus● Tunisia ● ● ● ● ● ● ● ● Venezuela ● Tonga ● Lithuania ● Thailand ● Mauritius ● Armenia ● ● Bahamas Ireland● Iran ● ● Samoa ● ● Fiji Iraq ● Belize Bulgaria ● ● ● Brazil ● ●Mexico ● ● ●● Latvia Georgia ● Colombia ●Jamaica ● ● ● Myanmar● ● ● ●Romania Dominica ● ● Peru 75 ● Russia ● ● ● Equatorial Guinea Vietnam ● ● Morocco ● China Cape● Verde Laos ● ● South Africa ●Guatemala ● ● ● Nicaragua ● Indonesia ● Turkmenistan ● Pakistan Mongolia ● Botswana ● ● Rwanda Angola Bangladesh ● ● ● ● Gambia Burundi ● Eswatini Vanuatu ● Yemen ● Bolivia ● ● Zambia ● Bhutan ● ● Senegal ●Cameroon Gabon ● Malawi ● ● ● India Kiribati● Cambodia ● ● ●Timor−Leste Sao Tome and Principe Mauritania ● Zimbabwe ● Afghanistan● Comoros Lesotho ● Kenya● Namibia ● 50 ● DR Congo● ● Solomon Islands Nigeria Ethiopia● Sudan ● ● Haiti

24 ● Mali ● Somalia Central African● Republic ● Mozambique Cote d'Ivoire ● Unsafe Sanitation Unsafe Guinea Guinea−Bissau ● Benin● ● Uganda● ● Burkina Faso Papua New Guinea ● ● Liberia Eritrea ● ● Tanzania 25 Congo ● Sierra Leone ● Ghana

●Chad

● Madagascar ● Togo

● Niger

● 0 Brunei Not Safe −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 180 R−Squared: 0.28 Sources: Environmental Performance Index (2015) & The World Bank Group (2017) Gender Equality Index vs. Control of Corruption

Norway Switzerland ● High Germany ● U.S.A. ● ● ● Netherlands Denmark Ireland● ● ● ● ● U.K. ● ● ● ● ● ● ● Israel ● Sweden South Korea ● Belgium Austria ● Finland ● ● ● ● Iceland ● Spain Slovenia France Japan ● ● ● Italy Brunei ● Estonia Greece ●● U.A.E. ● Slovakia ● ● Hungary ● Lithuania Malta ● Argentina ● ● ● ● ● Croatia ● Qatar Portugal Andorra Romania ● ● ● Kuwait Bahrain ● 0.8 ● ● ● Saudi Arabia Chile Russia ● ● ● Barbados ● Bulgaria● Oman● ● Palau ● Montenegro Bahamas● ● ● Cuba Venezuela ● Serbia ● ● Lebanon● Panama● ● Uruguay ● ● Malaysia ● ●Costa Rica Seychelles Mexico ● Albania ● ● Libya ● ● Iran Brazil ● ● ● ● ● Mauritius ● ● ● ● Grenada ● ● ● Ukraine Peru ●Macedonia ● Georgia ● ● ● Tunisia ● ● ●● Jordan ● Dominican Republic● ● ● Tonga St Vincent and the Grenadines Colombia● ● Fiji ● Turkmenistan Maldives Mongolia China Samoa ● ● ●● ● Uzbekistan● Moldova ● Egypt ● ●El Salvador ● ● ● South Africa Syria Iraq ● Gabon ● Philippines ● ● Kyrgyzstan Botswana ● ● Vietnam Nicaragua ● Guyana ● Tajikistan ● ● ● Cape Verde ● Timor−Leste ● Guatemala Morocco Namibia 0.6 ● ● Micronesia Equatorial Guinea India ● ● 25 ● Honduras ● Vanuatu Kiribati Bhutan Congo Ghana ● ● ● ●Bangladesh Zambia ● ● ● ● Cambodia ● Gender Equality Index Gender Equality Index ● Kenya Nepal ● Angola ●● Eswatini Sao Tome and Principe ● ● Myanmar Madagascar Pakistan Solomon Islands ● ● ● Yemen● Cameroon Mauritania Tanzania● ● Uganda● ● ● ●Comoros Lesotho ● Sudan Haiti Nigeria ● ● ● ● Rwanda ● Togo Zimbabwe ● ● Afghanistan Djibouti● ●● ● Senegal Guinea−Bissau● Malawi Cote d'Ivoire Eritrea● ● DR Congo ● ● Liberia ● Ethiopia South Sudan ● ● ● 0.4 ● Guinea Mozambique Mali Burundi ● ● ● Sierra Leone Chad Burkina Faso ● Central African Republic

● Niger Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 186 R−Squared: 0.49 Sources: United Nations Development Programme (2017) & The World Bank Group (2017) Female School Enrolment (Secondary) vs. Control of Corruption

● High Belgium

Finland●

Sweden 150 ●

Australia●

Netherlands ● Costa Rica ● ● U.K. ● ● ● Denmark Spain Ireland Iceland Palau ● New Zealand ● ● Portugal ● Thailand ● Kazakhstan● ● Norway Latvia France ● Argentina ● Slovenia ●● Ecuador ● Tuvalu ● ● Canada Singapore ● ● ● Bahrain Grenada Poland● Georgia Estonia Barbados Russia Cuba ● ●● ● Luxembourg ● Colombia ● ● South Africa ● ● Brazil ● ● ● ● ● ● ● ●Liechtenstein Mexico ● ● ● ● ● ● Lithuania● ● Qatar Israel ● ● ● ● ● Japan ● 100 Peru ● ● Italy U.S.A. ● ● ● Ukraine ● ●Bulgaria ● Saudi Arabia ● ● ● ● ● ● ● Chile Switzerland North Korea ● Malta Austria Germany ● Kyrgyzstan Albania ● ● Bahamas ● Armenia ● Montenegro South Korea ● ● Iran ● Tonga ● ● Seychelles ● ● ● Bhutan Uzbekistan ● ●● ● ● Slovakia ● ● ● ● ● Bolivia ● Venezuela ● ● Indonesia St Lucia Cape Verde ● Moldova Suriname Romania Turkmenistan ● Egypt ● Dominican Republic ● Jamaica 26 ● Marshall Islands India ● ● Bangladesh ● ● ● Panama El Salvador ● Nepal ● Laos Honduras ●Eswatini Jordan ● Comoros● ● Lebanon ● Lesotho ● Guatemala Myanmar ● ● ● Ghana ● Vanuatu Cameroon Benin 50 ● ● Female School Enrolment (Secondary) Enrolment Female School Sudan ● ● Burundi Senegal ● Djibouti Yemen Pakistan● ● ● Sierra Leone Rwanda Madagascar● ● ● ● Afghanistan● Mali ● Burkina Faso ● ● ● Guinea ● DR Congo ● Malawi● ● ● Ethiopia Eritrea Mozambique Liberia Niger ● Chad ● ● South Sudan Central African Republic ●

Low 0 −1 0 1 2 Low High Control of Corruption

Number of observations: 150 R−Squared: 0.43 Sources: The World Bank Group (Ratio of total enrolment) (2014 − 2017) & The World Bank Group (2017) Total Police Personnel (per 100,000 Pop.) vs. Control of Corruption

● High Grenada

Argentina●

750

● Uruguay ● Montenegro

Macedonia Malta ● ● Trinidad and Tobago Belize Croatia ● 500 ● ● ● ● ● ● ● Barbados Russia Serbia Italy Portugal Guyana Greece ● ● Latvia● Algeria ● Jamaica ● Slovakia ● Bosnia and Herzegovina ● ● Cyprus 27 El Salvador● Czech● Republic ● Peru Cape Verde ● Spain ● Belgium ● France ● ● ● Albania ● ● ● Luxembourg ● ● Thailand ● Slovenia Mexico ● Bulgaria ● Andorra Austria Netherlands ● Colombia Lithuania Chile ● ● Moldova ● ● Estonia ● ● ● ● Costa Rica Poland ● Kazakhstan Ecuador Romania ● Ireland 250 ● ● Australia ● Liechtenstein Total Police Personnel (per 100,000 Pop.) (per 100,000 Pop.) Personnel Police Total Paraguay Georgia ● ● Japan ●Sweden ● ● Switzerland ● ● ● ●Denmark Guatemala U.S.A. Iceland Canada ● ● Honduras Philippines ● ● ● Singapore Norway Burundi ● ● ● Myanmar Finland Uganda Hungary ● ● ● Kenya Tanzania

● Madagascar Low 0 −1 0 1 2 Low High Control of Corruption

Number of observations: 74 R−Squared: 0.01 Sources: United Nations Office on Drugs and Crime (2014 − 2015) & The World Bank Group (2017) Homicide Rate (per 100,000 Pop.) vs. Control of Corruption

El Salvador High ●

90

● Honduras 60 ● Venezuela 28

● Jamaica

Belize ● ● Guatemala South Africa ● ● Homicide Rate (per 100,000 Pop.) Pop.) Homicide Rate (per 100,000 30 Trinidad and Tobago Brazil ● ● Colombia

●Guyana Dominican● Republic Mexico ● DR Congo ● ● Russia Panama Costa Rica ● ● Togo ●● Suriname ● Barbados Angola Nigeria ● ● ● Guinea−Bissau ● ● ● ● ● ● ● Gabon● ●● ● Cape● Verde Uruguay ● Ethiopia● Senegal Grenada ● ● Eritrea ● ● ● ● ● ● ● Sudan Chad● ● ● ● Argentina● ● ● Laos ● Comoros ● ● Qatar U.S.A. ● ● ● ● Somalia ● North Korea● ● ● Myanmar India ● Chile ● ● Djibouti ● ●● ● ● Bhutan Australia ● ● ● ● ● ● ● ● U.K. ● ● ● ● ● Saudi Arabia Israel France ● ● Sweden Turkmenistan ● ● ● ● ● ● ● ● Georgia● ● ●● ● U.A.E. ● ● ● ●● Equatorial Guinea Uzbekistan Iran ●● ● ● ● ●● ● ● ● ● ●● Japan ●●● ● ● ● 0 Sierra Leone Portugal ● ● Libya China Bulgaria Italy South Korea Andorra Austria Germany Singapore Norway Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 133 R−Squared: 0.08 Sources: United Nations Office on Drugs and Crime (2014 − 2015) & The World Bank Group (2017) Organized Crime vs. Control of Corruption

● High El Salvador

6

Venezuela Mexico● ● ●Guatemala Honduras ● Mali Colombia Jamaica 5 ● ● ● Yemen Mozambique Macedonia ● ● ● ● ● ● Guinea Peru Chad Haiti ● South● Africa ● ● Brazil Burkina Faso ● Italy Pakistan Philippines Burundi● Uganda ● ● ● ● Bulgaria ● ● ● Kenya Sierra Leone DR Congo● Nigeria ● 4 ● Cote d'Ivoire Argentina Cameroon●Kyrgyzstan ● Albania● ● ● ● ● ● Nepal ● ● Ukraine Serbia ● ● ● Cambodia Lebanon ● Senegal Thailand India ● ● ● ● ● ● ● ● Iran ● ● Benin Angola Ecuador ●● Ghana ● ● ● Costa Rica 29 Namibia ● Russia ● Tajikistan ● Tanzania ● ● Montenegro Slovakia Cape Verde Vietnam ● China ● ● Organized Crime Organized ● Seychelles Laos ● ● Zambia ● ● Romania ● Liberia ●Malawi ● ● Croatia ● Greece ● Botswana ● 3 ● Algeria ● Hungary ● ● Germany Kazakhstan Bolivia Mongolia Poland● ● Taiwan U.S.A. ● ● ● ● Jordan Israel ● ● ● ● ● ● Denmark Mauritania Armenia Kuwait Malaysia Latvia ● Uruguay Gambia ● ● South Korea● ● ● ● ● Cyprus ● Chile U.K. Canada● Sweden ● Azerbaijan Egypt ● ● ● Rwanda France ● Zimbabwe Morocco ● Spain Ireland ●Netherlands Nicaragua Eswatini ● Slovenia ● ● ● Mauritius Georgia ● Australia ● ● Austria ● Malta ● Bahrain Lithuania ● ● Saudi Arabia ● Belgium Japan U.A.E. ● Qatar ● Switzerland Norway Lesotho● Czech Republic ● ● ● 2 ● Portugal ● ● Brunei Luxembourg New Zealand Estonia●

● Oman Iceland● ● Singapore

● Finland Low 1 −1 0 1 2 Low High Control of Corruption

Number of observations: 139 R−Squared: 0.43 Sources: World Economic Forum (2017) & The World Bank Group (2017) Road Traffic Death Rate (per 100,000 Pop.) vs. Control of Corruption

Venezuela High ●

40

●

Malawi● Thailand

Liberia● DR● Congo ● ● Iran ● Tanzania ● ● Sao Tome and Principe ● ●Togo ● Rwanda Burundi Mozambique Burkina Faso Gambia ● 30 Kenya ● Madagascar● ● South Sudan ● ● Comoros Lesotho● ● Zimbabwe ● Benin ● ● Uganda ● Saudi Arabia ● ● ● ● Senegal ● ● Cameroon Niger Guinea−Bissau ● Guinea ● Ghana Jordan Cape Verde Mali ● ● ● Somalia● Congo ● South Africa ● Oman Djibouti ● ● Sudan ● ● ● ● Zambia Belize ● Eritrea ● ● ● ● ● ● ● ● Namibia● ● Algeria● Tunisia ● Chad Kazakhstan ● ● Eswatini Malaysia ● Libya ● Botswana ● Bolivia Brazil Equatorial Guinea Yemen Lebanon● Gabon ● El Salvador● ● Morocco ●North Korea Kyrgyzstan ● ● ● ● 30 ● ● ● Mongolia 20 Iraq Nigeria Ecuador Suriname Syria ● ● ● ● ● Kuwait ● ● Russia Armenia ● China Solomon Islands St● Lucia Tajikistan ● ● ● ● ● ● Guatemala ● India Cambodia ● ● ● ● ● ● Turkmenistan Samoa Nepal Timor−Leste Albania Vanuatu ● Uruguay ● Haiti ● ● ● Bhutan Afghanistan ● Nicaragua ● Dominica ● ● ● Pakistan● Peru Qatar ● ● Belarus ● Bahamas ● ● Costa Rica ● ●Egypt Bangladesh ● Argentina Mauritius Chile ● Moldova ● South Korea Georgia ● ● ● ● Mexico ● Road Traffic Death Rate (per 100,000 Pop.) Death Rate (per 100,000 Pop.) Road Traffic ● United Arab Emirates Ukraine Montenegro Lithuania ● ● Philippines Jamaica ● ● Uzbekistan ● ● 10 ● ● Latvia ● U.S.A. ● Greece Croatia● Poland Azerbaijan Macedonia ● ● ● Seychelles● ● ● ● Andorra Turkey ● Romania Cuba ● Portugal● Luxembourg ● ● ● ● ● Belgium Brunei ● Barbados Serbia Hungary ● Slovakia ● ● ● Slovenia● ● ● Australia Canada● ● ● ● ● Estonia Fiji Czech Republic Austria● ● Palau Italy ● ● ● Iceland Finland ● Marshall Islands Grenada Japan ● ● ● Malta Cyprus France ● ● Singapore Norway ● ● ● ● ● Ireland Germany ● ● ● Maldives ● Spain Israel ● ● Denmark Kiribati ● U.K. Sweden Micronesia Low 0 −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 187 R−Squared: 0.40 Sources: World Health Organization (2013) & The World Bank Group (2017) Most People Can Be Trusted vs. Control of Corruption

● High Netherlands

● China ● Sweden 0.6

● New Zealand

● Australia

Germany ●

Yemen Estonia 0.4 ● ● Kazakhstan ● ● ● Japan ● Kyrgyzstan Belarus Singapore ● U.S.A.● Bahrain● ● Iraq ● ● India Thailand ● 31 ● ● Taiwan Russia Kuwait South Korea ● Ukraine ● South Africa ● Poland Most People Can Be Trusted Can Be Trusted Most People ● ● Pakistan Egypt● Qatar ● Spain ● 0.2 ● ● Argentina Slovenia Algeria● Rwanda● Nigeria ● Uruguay ● ● Tunisia ● ● Azerbaijan Morocco ● Chile Uzbekistan ● ● ● Armenia ● Jordan ● Mexico● ● Turkey Lebanon Libya Peru Malaysia ● ● ● ● Ecuador ● Georgia ● Zimbabwe ● ● Brazil Romania Cyprus ● Ghana Colombia● ● ● Philippines Trinidad and Tobago Low 0.0 −1 0 1 2 Low High Control of Corruption

Number of observations: 58 R−Squared: 0.22 Sources: World Values Survey (2010 − 2014) & The World Bank Group (2017) Confidence in Parliament vs. Control of Corruption

Uzbekistan High ● 3.5 Qatar ●

● China

3.0 Bahrain● ● Singapore

Azerbaijan● ● ● ● Kazakhstan India Malaysia Rwanda Philippines ● ● Ghana Sweden● ● ● Kyrgyzstan Turkey ●

2.5 Zimbabwe● Kuwait Belarus ● ● Germany ● ● South● Africa Thailand New Zealand 32 ● ● ● ● Estonia Morocco Cyprus ● Netherlands Nigeria Spain ● ● ● Uruguay ● ● Confidence in Parliament Confidence in Parliament Russia Trinidad and Tobago ● Australia Ecuador ● ● ● Japan ● ● Georgia Taiwan ● Lebanon Pakistan Argentina South Korea ● ● Algeria ● 2.0 ● ● U.S.A. ● Chile Mexico Armenia ● ● ● ● Jordan Egypt ● Poland Iraq Ukraine ● Colombia● Romania Brazil ● Libya● ● ● ● Yemen Peru ● Slovenia

1.5 Tunisia ●

Low −1 0 1 2 Low High Control of Corruption

Number of observations: 58 R−Squared: 0.01 Sources: World Values Survey (2010 − 2014) & The World Bank Group (2017) Confidence in Parliament vs. Control of Corruption in Democracies

● High ● Ghana Sweden

Germany ● 2.4 South● Africa

New Zealand● Cyprus●

Estonia● ● Spain ● ● Uruguay Netherlands

● Australia● Trinidad and Tobago Georgia Taiwan 2.1 South Korea ● ● ● Japan ● ●

Ecuador ● ● U.S.A. Argentina ●Chile

33 ● Mexico Poland ● Confidence in Parliament Colombia 1.8 ● Romania ● ● Brazil ●

● Slovenia Peru

1.5

Tunisia●

Low −1 0 1 2 Low High Control of Corruption

Number of observations: 28 Adjusted R−Squared: 0.21 Sources: World Values Survey (2010 − 2014) & The World Bank Group (2017). Democracies are understood as countries with more than 0.6 in V−Dem's electoral democracy. Feeling of Happiness vs. Control of Corruption

Uzbekistan Mexico High ● ● Qatar Malaysia ● Ecuador ● ● 3.5 Colombia●

Trinidad● and Tobago Nigeria ● ● Sweden Philippines Kuwait ● Kyrgyzstan ● ● ● ● ● Thailand ● Australia● Singapore ● ● ● Rwanda U.S.A. Ghana ● ● ● Pakistan Brazil New Zealand Libya Argentina ● Netherlands ● ● Uruguay ● Poland ● Japan Zimbabwe ● Taiwan ● Kazakhstan ● Turkey ● Peru ● India ● ● ● Germany Azerbaijan South Africa South Korea Cyprus ● ● Armenia ● ● Chile ● ● China● ● 3.0 Jordan Spain Slovenia Lebanon ● ● Morocco● Russia ● ● Algeria Yemen ● Tunisia ● ● ● ● Bahrain Estonia Ukraine Georgia Romania● Belarus ●Iraq ● 34

Feeling of Happiness 2.5

2.0

● Egypt Low −1 0 1 2 Low High Control of Corruption

Number of observations: 58 R−Squared: 0.01 Sources: World Values Survey (2010 − 2014) & The World Bank Group (2017) Satisfaction with Life vs. Control of Corruption

● High Mexico ● Colombia

Qatar 8 ● Uzbekistan Ecuador● ● ● Brazil

● Uruguay● Thailand● New Zealand ● Netherlands ● Argentina ● Pakistan ● ● Sweden Trinidad and Tobago Germany Slovenia● ● ● ● Turkey ● U.S.A. ● ● Philippines ● ● Cyprus Chile ● Libya Malaysia ● Kazakhstan ● Kuwait ● Australia Peru ● Poland 7 ● ● Kyrgyzstan China ● ● Singapore ● Taiwan Japan ● ● Bahrain Spain Azerbaijan ● ● ●Romania Jordan● South Africa ● South Korea● Lebanon ●

35 Rwanda Nigeria ● Estonia ● Algeria ● ● Ghana Satisfaction with Life Satisfaction with Life Russia ●

6 Iraq ● Yemen ● Ukraine ● ● Morocco Zimbabwe ● Belarus●

● Tunisia ● Georgia Armenia ●

● 5 India

Egypt●

Low −1 0 1 2 Low High Control of Corruption

Number of observations: 58 R−Squared: 0.05 Sources: World Values Survey (2010 − 2014) & The World Bank Group (2017) Control of Corruption (10 year change) 45 degree line New Zealand Norway● ● High Sweden ● ● Denmark ● Luxembourg Switzerland 2 Liechtenstein ● ● U.K. ● ●● Canada ● Germany ● Iceland Countries that improved Australia Bhutan ● Japan Ireland ● Austria● ● Belgium● ● Barbados Uruguay ● ● U.S.A. ● ● ● Estonia Andorra ● France Bahamas Taiwan ● 1 ● Chile Cape Verde Portugal● ● ● Georgia ●● ● ● Brunei Qatar Seychelles● ● Micronesia ● ● Rwanda ● ● ●Lithuania St Kitts and ●Nevis Kiribati ● ● ● Saudi Arabia ● Spain ● ● Namibia Fiji ● Antigua and Barbuda Croatia ● ● Oman ● ● ● ● ● Italy Mauritius● Tuvalu● Marshall Islands● ● ● Hungary 0 ● ● ● ● South Africa

36 Morocco ● Lesotho ● ● India ●● Suriname Kuwait● China ● ●

● ●● ● Benin ● ● ● ● Control of Corruption in 2017 Control ● ● Brazil Paraguay ● ● ● Mali ● ● ● ●Iran ● Russia Mexico● ● Countries that deteriorated ● ● −1 ● ● Uganda Nigeria Eritrea ● ● Haiti● ● ● Burundi ● ● Zimbabwe● ● Iraq ●● Venezuela ●● Syria North Korea ● ● Yemen Guinea−Bissau ● ● Somalia Libya ● Equatorial Guinea ● Low −2 Low −1 0 1 2 High Control of Corruption in 2007

Number of observations: 116 Adjusted R−Squared: 0.9 Sources: The World Bank Group Government Effectiveness vs. Control of Corruption

Singapore High ● Switzerland ● Norway 2 Andorra ● ● Netherlands Sweden ● ● ● ● Finland ● ● ● Germany● Japan ● Denmark U.S.A. ● Australia ● ● United Arab Emirates ● ●Iceland Israel● ● ● ● ● Austria Portugal ● U.K. Brunei ● France Ireland ●Slovenia ● South Korea ● ● ● Czech Republic Belgium ● ● ● Estonia 1 Mauritius ● Malta ● ● ● Chile Barbados ● ● ● ● Latvia Lithuania Cyprus Malaysia ● Slovakia Qatar ● ● Bahamas Bhutan Jamaica Hungary ● ● Samoa ● ● ● ● ● ● Botswana Thailand ● Italy ● ● Uruguay● ● South Africa Seychelles ● China ● Serbia ● ● Saudi Arabia● ● ● ● ● ● ● ● ● ● ● Costa Rica●Cape Verde Panama ● Oman ● Namibia ● India ● ● ● ● ● Mexico ● ● ● ● Vietnam● Colombia Turkey Fiji Micronesia 0 ● ● ● ● ● Russia ● ● ● ● ● ● ●Tunisia Cuba Grenada● ● Ghana● ● Iran Brazil● ● ● ●● ● Tonga ● Armenia ● El Salvador ● Kenya ● ● ● Kiribati Dominica ● ● ● ● Belarus Ukraine ● Ecuador● 37 ● ● ● ● Burkina Faso ●Guatemala● Algeria● Zambia ● ● ● ● ●● ● ● ● ● Uganda ● ●●● ● ● ● Suriname Tuvalu Cambodia ● Niger ● Tanzania ● ● ●Mauritania ● Malawi Lesotho ● ● ● Mali ● Cameroon ● ● ● Government Effectiveness Effectiveness Government ● Solomon● Islands −1 Angola ● Nigeria ● Nepal ● ● Timor−Leste Congo ● ●Madagascar ● Djibouti ● ● ● ● Afghanistan ● Zimbabwe Togo Sierra Leone ● ● Iraq ● ● ● Burundi ● ● Liberia Equatorial Guinea Chad Marshall Islands Sudan ● ● ● DR Congo● Comoros Eritrea● ●● ● Libya Central African Republic ● Syria

−2 Yemen ● Haiti Somalia●

South Sudan ●

Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 192 R−Squared: 0.81 Sources: The World Bank Group (2017) & The World Bank Group (2017) Electoral Democracy Index vs. Control of Corruption

Estonia Sweden High ● Norway Switzerland● ● Costa Rica ●France Belgium ● ● ● Portugal ● ● ● ●U.K.● Denmark ●● Czech Republic ● Uruguay ● ● ● ● Chile ● Ireland ● ● Finland Slovakia ● Jamaica ● ● ● Slovenia ●● Germany Canada ● Italy Latvia Austria ● ● Cyprus ● Suriname ● Mauritius Taiwan U.S.A. ● ● ● Greece ● Lithuania ● South Korea ● Barbados Panama Argentina● Cape Verde ● ● ● Spain Brazil● Burkina Faso● Namibia Georgia 0.75 ● ● ● ● ● ● Peru ● ● South Africa Tunisia● ● Poland ● Timor−Leste ● Botswana ●Guyana Sao Tome and Principe ● ● Bulgaria ● ● Israel ● Bolivia ● Paraguay ●Mongolia Romania ● ● ● Ghana Croatia Sri Lanka ● Mexico Liberia ● ● ● ● ● Hungary ● Bhutan ● Ecuador ● ● Nepal ● Indonesia Solomon Islands ● ● Malawi Macedonia Nigeria ● ● ● India ● ● Guinea−Bissau Mali ● ● ● Moldova ●● Albania Lesotho Seychelles Haiti ● Niger ● Lebanon ● Dominican Republic● 0.50 Kyrgyzstan ●Philippines ● Togo Mozambique ● ● ● ● ● Comoros Fiji ● ● ● Montenegro ● ● Kenya ● ● ● Pakistan Serbia Singapore Guinea ● Gabon ●Mauritania 38 Iraq ● ● ● ● Ukraine Myanmar Bangladesh● ● Afghanistan ● Algeria Zambia ● ● ● Turkey Zimbabwe Uganda Maldives ● ● ● ● ● Bosnia and Herzegovina Malaysia ● ● Electoral Democracy Index Index Electoral Democracy Cameroon ●Gambia DR Congo Kuwait ● ●● Rwanda Sudan● ● Congo Nicaragua Morocco ● ● ● ● ● ●Djibouti Libya ●Venezuela Russia Belarus ● 0.25 ● ● Angola ● Jordan Kazakhstan● Ethiopia Cambodia ● Equatorial Guinea ● ● Iran ● Egypt Oman Tajikistan Uzbekistan ●● ● ● Somalia Azerbaijan Cuba ● Turkmenistan● Eswatini● United Arab Emirates ● ● ● South Sudan Burundi ● Bahrain Syria Thailand ● Yemen ● Laos China Eritrea ● ● ● ● Qatar● North Korea

● 0.00 Low −2 −1 0 1 2 Low High Control of Corruption

Number of observations: 171 R−Squared: 0.44 Sources: Varieties of Democracy (V−Dem) Project (2017) & The World Bank Group (2017) Freedom on the Net Score vs. Control of Corruption

Estonia ● ● High Iceland

Canada ●

Germany● ● U.S.A.● Australia ● South Africa ● ● ● ● ● U.K. 75 Armenia ● Italy Georgia France Japan Argentina● ● Brazil● ● Colombia● Hungary ● Kenya Philippines Nigeria ● ● Tunisia South Korea ● Malawi ● Angola ● ● Kyrgyzstan● ● ● Ecuador ● Uganda Mexico ● India Singapore Zambia ● Ukraine● Morocco● ● Lebanon ● Malaysia ● ● Indonesia Sri Lanka ● Libya 50 ● ● Jordan Zimbabwe ● Bangladesh 39 Gambia ● ● Rwanda● Cambodia

Azerbaijan●

Freedom on the Net Score Freedom on the Net Score Kazakhstan● Myanmar Belarus Sudan ● ● ● ● ● Thailand ● Venezuela ● Turkey Russia ● Egypt ● United Arab Emirates ● Pakistan● Bahrain Saudi● Arabia 25 ● Vietnam Uzbekistan ● Cuba ● Ethiopia ● ● Syria ● Iran ● China Low −1 0 1 2 Low High Control of Corruption

Number of observations: 65 R−Squared: 0.27 Sources: Freedom House (2017) & The World Bank Group (2017) Description of Variables by Source Barro & Lee http://www.barrolee.com/ (Downloaded on 2018-07-13)

Dataset: Educational Attainment Dataset The Barro-Lee Data set provide data disaggre- gated by sex and by 5-year age intervals. It provides educational attainment data for 146 countries in 5-year intervals from 1950 to 2010. It also provides information about the distribution of ed- ucational attainment of the adult population over age 15 and over age 25 by sex at seven levels of schooling - no formal education, incomplete primary, complete primary, lower secondary, upper secondary, incomplete tertiary, and complete tertiary. Average years of schooling at all levels - primary, secondary, and tertiary - are also measured for each country and for regions in the world. Aside from updating and expanding our previous estimates (1993, 1996, and 2001), we improve the accuracy of estimation in the current version by using more information and better method- ology. To reduce measurement error, the new estimates are constructed using recently available census/survey observations from consistent census data, disaggregated by age group, and new es- timates of mortality rate and completion rate by age and by education.

Average Schooling Years, Female and Male (25+) Average Schooling Years, Female and Male (25+).

40 United Nations Office on Drugs and Crime https://data.unodc.org/ (Downloaded on 2018-12-04)

Dataset: Crime and Criminal Justice The UN-CTS deals with information, primarily ad- ministrative statistics, on the main components of the criminal justice system (police, prosecution, courts and prisons). In addition, the UN-CTS collects available data from crime victimization surveys.

Intentional homicide rate (per 100,000 pop.) Intentional homicide, counts and rates per 100,000 population. Unlawful death purposefully inflicted on a person by another person. Data on intentional homicide should also include serious assault leading to death and death as a result of a terrorist attack. It should exclude attempted homicide, manslaughter, death due to legal intervention, justifiable homicide in self-defence and death due to armed conflict.

For Belgium and Romania, the refer to offences, not victims, of intentional homicide. For New Zealand, the data for 2000-2006 refer to offences, data for 2007 onwards refer to victims of inten- tional homicide. The data for Kazakhstan, the Philippines and Sri Lanka reports a change in the definition and(or) counting rules and it entails a break in the time series.

Total police personnel at national level (per 100,000 pop.) Total police personnel at na- tional level per 100,000 population. personnel in public agencies as at 31 December whose principal functions are the prevention, detection and investigation of crime and the apprehension of alleged offenders. Data concerning support staff (secretaries, clerks, etc.) should be excluded. Data sup- plied by countries may not exactly reflect the definition provided.

Argentina, Burundi, Colombia, Estonia, Latvia, Mexico, Netherlands, Paraguay, Russia, Serbia and Thailand reported changes in definitions and/or counting rules are reported by the Member State to indicate a break in the time series.

41 Ease of Doing Business Report http://www.doingbusiness.org/en/doingbusiness (Downloaded on 2018-11-01)

Dataset: Ease of Doing Business - Historical Data The Doing Business project provides objective measures of business regulations and their enforcement across 190 economies. This EOB 2019 report covers 11 indicator sets and 190 economies. Most indicator sets refer to a case scenario in the largest business city of each economy, except for 11 economies that have a population of more than 100 million as of 2013 (Bangladesh, Brazil, China, India, Indonesia, Japan, Mexico, Nigeria, Pakistan, the Russian Federation and the United States) where Doing Business, also collected data for the second largest business city.

The ease of doing business score captures the gap between an economy’s performance and a measure of best practice across the entire sample of 41 indicators for 10 Doing Business topics (the labor market regulation indicators are excluded). For starting a business, for example, New Zealand and Georgia have the lowest number of procedures required (1). New Zealand also holds the shortest time to start a business (0.5 days), while Slovenia has the lowest cost (0.0).

Calculating the ease of doing business score for each economy involves two main steps. In the first step individual component indicators are normalized to a common unit where each of the 41 component indicators y (except for the total tax and contribution rate) is rescaled using the linear transformation (worst - y)/(worst - best). In this formulation, the highest score represents the best regulatory performance on the indicator across all economies since 2005 or the third year in which data for the indicator were collected.

Both the best regulatory performance and the worst regulatory performance are established ev- ery five years based on the Doing Business data for the year in which they are established and remain at that level for the five years regardless of any changes in data in interim years. Thus, an economy may establish the best regulatory performance for an indicator even though it may not have the highest score in a subsequent year. Conversely, an economy may score higher than the best regulatory performance if the economy reforms after the best regulatory performance is set. For example, the best regulatory performance for the time to get electricity is set at 18 days. In the Republic of Korea it now takes 13 days to get electricity while in the United Arab Emirates it takes just 10 days. Although the two economies have different times, both economies score 100 on the time to get electricity because they have exceeded the threshold of 18 days.

For scores such as those on the strength of legal rights index or the quality of land adminis- tration index, the best regulatory performance is set at the highest possible value (although no economy has yet reached that value in the case of the latter).

Due to the changes in methodologies, some variables are presented separately, given that they are not comparable given these said changes.

Ease of doing business score global (DB17-19 methodology) Ease of doing business score global (DB17-19 methodology)

42 Environmental Performance Index http://epi.yale.edu/downloads (Downloaded on 2018-11-20)

Dataset: Environmental Performance Index Data The Environmental Performance Index provides a ranking that shines light on how each country manages environmental issues. The Environmental Performance Index (EPI) ranks how well countries perform on high-priority envi- ronmental issues in two broad policy reas: protection of human health from environmental harm and protection of ecosystems. Within these two policy objectives the EPI scores country perfor- mance in nine issue areas comprised of 20 indicators. Indicators in the EPI measure how close countries are to meeting internationally established targets or, in the absence of agreed-upon tar- gets, how they compare to the range of observed countries.

Note: In many cases the EPI variables lack actual observations and rely on imputation. Please refer to the original documentation on more information about this. Also, some values (usually the value 0) are very unlikely, please use your judgement whether to treat these as the value 0 or as ”Data missing”.

Unsafe Sanitation Exposure to unsafe sanitation and population lacking access to sanitation.

Access to Drinking Water Population lacking access to drinking water

43 Freedom House https://freedomhouse.org/report/freedom-net/freedom-net-2017 (Downloaded on 2018-11-13)

Dataset: Freedom on the Net Freedom on the Net is a Freedom House project consisting of cutting-edge analysis, fact-based advocacy, and on-the-ground capacity building. It features a ranked, country-by-country assessment of online freedom, a global overview of the latest de- velopments, as well as in depth country reports. Freedom on the Net measures the subtle and not-so-subtle ways that governments and non-state actors around the world restrict our intrinsic rights online. Each country assessment includes a detailed narrative report and numerical score, based on methodology developed in consultation with international experts. This methodology includes three categories:

1. Obstacles to Access details infrastructural and economic barriers to access, legal and own- ership control over internet service providers , and independence of regulatory bodies;

2. Limits on Content analyzes legal regulations on content, technical filtering and blocking of websites, self-censorship, the vibrancy/diversity of online news media, and the use of digital tools for civic mobilization;

3. Violations of User Rights tackles surveillance, privacy, and repercussions for online speech and activities, such as imprisonment, extralegal harassment, or cyberattacks.

Freedom on the Net is a collaborative effort between a small team of Freedom House staff and an extensive network of local researchers and advisors in 65 countries.

Freedom on the Net: Score Freedom on the Net, Score: Measures the subtle and not-so-subtle ways that governments and non-state actors around the world restrict our intrinsic rights online by looking at Obstacles to Access, Limits on Content and Violations of User Rights. The scores are usually based on a scale of 0 to 100 with 0 representing the best level of freedom on the net progress and 100 the worst. For this publication, 0 represent the lowest freedom and 100 the highest.

44 United Nations Development Programme http://hdr.undp.org/en/data (Downloaded on 2018-12-04)

Dataset: Human Development Report The Gender Inequality Index (GII) reflects gender- based disadvantage in three dimensions—reproductive health, empowerment and the labour mar- ket—for as many countries as data of reasonable quality allow. It shows the loss in potential human development due to inequality between female and male achievements in these dimensions. It ranges from 0, where women and men fare equally, to 1, where one gender fares as poorly as possible in all measured dimensions.

Gender Inequality Index The GII is an inequality index. It measures gender inequalities in three important aspects of human development—reproductive health, measured by maternal mor- tality ratio and adolescent birth rates; empowerment, measured by proportion of parliamentary seats occupied by females and proportion of adult females and males aged 25 years and older with at least some secondary education; and economic status, expressed as labour market participation and measured by labour force participation rate of female and male populations aged 15 years and older. The GII is built on the same framework as the IHDI—to better expose differences in the distribution of achievements between women and men. It measures the human development costs of gender inequality. Thus the higher the GII value the more disparities between females and males and the more loss to human development.

45 Heritage Foundation http://www.heritage.org/index/explore (Downloaded on 2018-07-17)

Dataset: Index of Economic Freedom The Index of Economic Freedom covers 10 freedoms - from property rights to entrepreneurship - in 186 countries.

Note: For the 2015, most data covers the second half of 2013 through the first half of 2014. To the extent possible, the information considered for each factor was current as of June 30, 2014. It is important to understand that some factors are based on historical information. For example, the monetary policy factor is a 3-year weighted average rate of inflation from January 1, 2011, to December 31, 2013.

Economic Freedom Index The Economic Freedom index uses 10 specific freedoms, some as composites of even further detailed and quantifiable components:

- Business freedom - Trade freedom - Fiscal freedom - Freedom from government - Monetary freedom - Investment freedom - Financial freedom - Property rights - Freedom from corruption - Labor freedom.

Each of these freedoms is weighted equally and turned into an index ranging from 0 to 100, where 100 represents the maximum economic freedom. Although changes in methodology have been undertaken throughout the measurement period, continuous backtracking has been used to maximize comparability over time.

46 International Monetary Fund https://www.imf.org/external/pubs/ft/weo/2017/01/weodata/index.aspx (Downloaded on 2018-12-04)

Dataset: World Economic Outlook Database The World Economic Outlook (WEO) database contains selected macroeconomic data series from the statistical appendix of the World Economic Outlook report, which presents the IMF staff’s analysis and projections of economic developments at the global level, in major country groups and in many individual countries. The WEO is re- leased in April and September/October each year. Use this database to find data on national accounts, inflation, unemployment rates, balance of payments, fiscal indicators, trade for countries and country groups (aggregates), and commodity prices whose data are reported by the IMF. Data are available from 1980 to the present, and projections are given for the next two years. Addition- ally, medium-term projections are available for selected indicators. For some countries, data are incomplete or unavailable for certain years.

Government revenue (Percent of GDP) Government revenue (% of GDP). Revenue consists of taxes, social contributions, grants receivable, and other revenue. Revenue increases government’s net worth, which is the difference between its assets and liabilities (GFSM 2001, paragraph 4.20).

Note: Transactions that merely change the composition of the balance sheet do not change the net worth position, for example, proceeds from sales of nonfinancial and financial assets or incurrence of liabilities.

47 S¨orenHolmberg https://qog.pol.gu.se/digitalAssets/1551/1551579 2014 13 holmberg rothstein.pdf

Dataset: Good Society Index The Good Society Index builds on three basic premises. First, the index consists of birth and deaths of human beings as well as the quality of life of people. The second premise is that the Good Society Index should adhere to lex parsimoniae, that is to the principle of Ockham’s razor, meaning that a model should use a minimum number of explanatory variables. Third, the index measures subjective as well as objective characteristics. Subjective and objective indicators need to be combined, neither is sufficient as of its own. Given these three premises the Good Society Index is operationally constructed using:

- Infant mortality data from the World Bank (World Development Indicators) (2017) - Life expectancy data from the World Bank (World Development Indicators) (2016) - Feeling of Happiness (World Values Survey) (2010-2014)

The three indicators all carry the same weight. Furthermore, the index is based on ranks, not on rates, which means that the countries’ rank orders are utilized to build the composite index. The rank orders of each country have been summed and divided by three to yield an index value that in theory can vary between 1 (top nation on the Good Society Index) and 149 (bottom country). A top index value of 1 and a bottom value of 149 thus tell us that these specific countries are closest and furthest away respectively from the good society among the investigated nations. But the figures do not tell how close or how far away from the maximum good society the countries are. The index is not continuous; it is a rank order scale. (Holmberg, 2007)

48 United Nations Development Program http://hdr.undp.org/en/data (Downloaded on 2018-11-14)

Dataset: Human Development Report The Human Development Report (HDR) is an an- nual report published by the Human Development Report Office of the United Nations Develop- ment Programme (UNDP).

The entire series of Human Development Index (HDI) values and rankings are recalculated every year using the same the most recent (revised) data and functional forms. The HDI rankings and values in the 2014 Human Development Report cannot therefore be compared directly to indices published in previous Reports. Please see hdr.undp.org for more information.

The HDI was created to emphasize that people and their capabilities should be the ultimate cri- teria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes.

Human Development Index The HDI was created to emphasize that people and their capabili- ties should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key di- mensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by gross national income per capita. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. Refer to Technical notes for more details. The HDI simplifies and captures only part of what human development entails. It does not reflect on inequalities, poverty, human security, empowerment, etc. The HDRO offers the other compos- ite indices as broader proxy on some of the key issues of human development, inequality, gender disparity and human poverty.

49 Varieties of Democracy (V-Dem) Project https://v-dem.net/en/data/ (Downloaded on 2018-07-09)

Dataset: Varieties of Democracy Dataset version 8 Varieties of Democracy (V-Dem) is a new approach to conceptualizing and measuring democracy. It is a collaboration among more than 50 scholars worldwide which is co-hosted by the Department of Political Science at the University of Gothenburg, Sweden; and the Kellogg Institute at the University of Notre Dame, USA.

Electoral Democracy Index This index is based on the question: To what extent is the ideal of electoral democracy in its fullest sense achieved?

Clarifications: The electoral principle of democracy seeks to embody the core value of making rulers responsive to citizens, achieved through electoral competition for the electorate’s approval under circumstances when suffrage is extensive; political and civil society organizations can operate freely; elections are clean and not marred by fraud or systematic irregularities; and elections affect the composition of the chief executive of the country. In between elections, there is freedom of expression and an independent media capable of presenting alternative views on matters of politi- cal relevance. In the VDem conceptual scheme, electoral democracy is understood as an essential element of any other conception of (representative) democracy - liberal, participatory, deliberative, egalitarian, or some other. Aggregation: The index is formed by taking the average of, on the one hand, the sum of the indices measuring freedom of association (thick), suffrage, clean elections, elected executive (de jure) and freedom of expression; and, on the other, the five-way interaction between those indices. This is half way between a straight average and strict multiplication, mean- ing the average of the two. It is thus a compromise between the two most well known aggregation formulas in the literature, both allowing ”compensation” in one sub-component for lack of pol- yarchy in the others, but also punishing countries not strong in one sub-component according to the ”weakest link” argument. The aggregation is done at the level of Dahls sub-components (with the one exception of the non-electoral component).

The World Bank Group http://info.worldbank.org/governance/wgi/ (Downloaded on 2018-09-24)

Dataset: The Worldwide Governance Indicators These indicators are based on several hundred individual variables measuring perceptions of governance, drawn from 31 separate data sources constructed by 25 different organizations. These individual measures of governance are assigned to categories capturing key dimensions of governance. An unobserved component model is used to construct six aggregate governance indicators. Point estimates of the dimensions of governance, the margins of error as well as the number of sources are presented for each country. The governance estimates are normally distributed with a mean of zero and a standard deviation of one each year of measurement. This implies that virtually all scores lie between -2.5 and 2.5, with higher scores corresponding to better outcomes.

WARNING: Since the estimates are standardized (with a mean of zero and a standard devia- tion of one) each year of measurement, they are not directly suitable for over-time comparisons within countries. Kaufmann et al. (2006) however find no systematic time-trends in a selection of

50 indicators that do allow for comparisons over time, which suggests that time-series information in the WBGI scores can be used if interpreted with caution.

Government Effectiveness, Estimate Government Effectiveness - Estimate: ”Government Ef- fectiveness” combines into a single grouping responses on the quality of public service provision, the quality of the bureaucracy, the competence of civil servants, the independence of the civil service from political pressures, and the credibility of the government’s commitment to policies. The main focus of this index is on ”inputs” required for the government to be able to produce and implement good policies and deliver public goods.

51 The World Bank Group http://data.worldbank.org/data-catalog/world-development-indicators (Downloaded on 2018-10-05)

Dataset: World Development Indicators The primary World Bank collection of develop- ment indicators, compiled from officially-recognized international sources. // This is an adaptation of an original work by The World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by The World Bank.

CO2 emissions (metric tons per capita) Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring.

Domestic general government health expenditure (% of GDP) Domestic general govern- ment health expenditure (% of GDP). Public expenditure on health from domestic sources as a share of the economy as measured by GDP.

Domestic private health expenditure (% of current health expenditure) Domestic pri- vate health expenditure (% of current health expenditure). Share of current health expenditures funded from domestic private sources. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.

GDP per capita (constant 2010 US dollar) GDP per capita is gross domestic product di- vided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2010 U.S. dollars.

GDP per capita growth (annual %) Annual percentage growth rate of GDP per capita based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. GDP per capita is gross domestic product divided by midyear population. GDP at purchaser’s prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources.

School enrollment, secondary, female (% gross) Total female enrollment in secondary edu- cation, regardless of age, expressed as a percentage of the female population of official secondary education age. GER can exceed 100% due to the inclusion of over-aged and under-aged students because of early or late school entrance and grade repetition.

School enrollment, tertiary (% gross) Total enrollment in tertiary education (ISCED 5 to 8), regardless of age, expressed as a percentage of the total population of the five-year age group following on from secondary school leaving.

GINI index (World Bank estimate) Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve

52 and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Life expectancy at birth, total (years) Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

Lifetime risk of maternal death (%) Life time risk of maternal death is the probability that a 15-year-old female will die eventually from a maternal cause assuming that current levels of fertil- ity and mortality (including maternal mortality) do not change in the future, taking into account competing causes of death.

Mortality rate, infant (per 1,000 live births) Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.

Tax revenue (% of GDP) Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social secu- rity contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue.

Note: The value for San Marino for 1995 was extremely high (44326) and has been recoded to missing.

53 World Economic Forum http://reports.weforum.org/global-competitiveness-index-2017-2018/downloads/ (Downloaded on 2018-11-27)

Dataset: Global Competitiveness Report 2017-2018 The Global Competitiveness Index 4.0 assesses the competitiveness landscape of 140 economies, measuring national competitive- ness—defined as the set of institutions, policies and factors that determine the level of productivity. The Report presents information and data that were compiled and/or collected by the World Eco- nomic Forum organized into 12 pillars: Institutions, Infrastructure, ICT adoption, Macroeconomic Stability, Health, Skills, Product Market, Labor Market, Financial System, Market Size, Business Dynamism, and Innovation Capabilities.

The new methodology is presented in the report of 2018, while also back casting the scores for 2017.

Organized crime. 1-7 (best) Organized crime. 1-7 (best). In your country, to what extent does organized crime (mafia-oriented racketeering, extortion) impose costs on businesses? [1 = to a great extent—imposes huge costs; 7 = not at all—imposes no costs] Original sources: World Economic Forum, Executive Opinion Survey

54 World Health Organization http://www.who.int/gho/database/en/ (Downloaded on 2018-11-28)

Dataset: Global Health Observatory data repository The GHO data repository is WHO’s gateway to health-related statistics for its 194 Member States. It provides access to over 1000 indicators on priority health topics including mortality and burden of diseases, the Millennium De- velopment Goals (child nutrition, child health, maternal and reproductive health, immunization, HIV/AIDS, tuberculosis, malaria, neglected diseases, water and sanitation), non communicable diseases and risk factors, epidemic-prone diseases, health systems, environmental health, violence and injuries, equity among others.

Alcohol consumption per capita (2010-) Alcohol consumption per capita (2010-)

Healthy Life Expectancy, Total Healthy Life Expectancy, Total

Infant mortality rate (probability of dying between birth and age 1 per 1000 liv Infant mortality rate (probability of dying between birth and age 1 per 1000 liv

Estimated road traffic death rate (per 100 000 population) Estimated road traffic death rate (per 100 000 population)

55 World Values Survey / European Values Survey http://www.worldvaluessurvey.org/ (Downloaded on 2018-09-12)

Dataset: World Values Survey dataset and European Values Studies dataset The World Values Survey is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS associ- ation and secretariat headquartered in Stockholm, Sweden.

The variables are country averages calculated using the population weight provided by WVS/EVS.

Confidence: Parliament I am going to name a number of organizations. For each one, could you tell me how much confidence you have in them: Parliament

1. None at all 2. Not very much 3. Quite a lot 4. A great deal

Feeling of happiness Taking all things together, would you say you are:

1. Not at all happy 2. Not very happy 3. Rather happy 4. Very happy

Satisfaction with your life All things considered, how satisfied are you with your life as a whole these days?

1. Completely dissatisfied 2. 3. 4. 5. 6. 7. 8. 9. 10. Completely satisfied

Most people can be trusted Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?

0. Need to be very careful 1. Most people can be trusted

56 References

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