world corruption index and myanmar corruption index 2016 collection
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SURVEYS • 25 JANUARY 2017
CORRUPTION PERCEPTIONS INDEX 2016
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Let's get straight to the point: No country gets close to a perfect score in the Corruption Perceptions Index 2016.
Over two-thirds of the 176 countries and territories in this year's index fall below the midpoint of our scale of 0 (highlycorrupt) to 100 (very clean). The global average score is a paltry 43, indicating endemic corruption in a country's publicsector. Top-scoring countries (yellow in the map below) are far outnumbered by orange and red countries where citizensface the tangible impact of corruption on a daily basis.
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This year’s results highlight the connection between corruption and inequality, which feed off each other to create avicious circle between corruption, unequal distribution of power in society, and unequal distribution of wealth.
In too many countries, people are deprived of their most basic needs and go to bed hungry every night becauseof corruption, while the powerful and corrupt enjoy lavish lifestyles with impunity.”– José Ugaz, Chair of Transparency International
The interplay of corruption and inequality also feeds populism. When traditional politicians fail to tackle corruption, peoplegrow cynical. Increasingly, people are turning to populist leaders who promise to break the cycle of corruption andprivilege. Yet this is likely to exacerbate – rather than resolve – the tensions that fed the populist surge in the first place.(Read more about the linkages between corruption, inequality and populism.)
More countries declined than improved in this year's results, showing the urgent need for committed action to thwartcorruption.
PUTTING THE SCORES IN CONTEXT
The lower-ranked countries in our index are plagued by untrustworthy and badly functioning public institutions like thepolice and judiciary. Even where anti-corruption laws are on the books, in practice they're often skirted or ignored. Peoplefrequently face situations of bribery and extortion, rely on basic services that have been undermined by themisappropriation of funds, and confront official indifference when seeking redress from authorities that are on the take.
Grand corruption thrives in such settings. Cases like Petrobras and Odebrecht in Brazil or the saga of ex-President ViktorYanukovych in Ukraine show how collusion between businesses and politicians siphons off billions of dollars in revenuefrom national economies, benefitting the few at the expense of the many. This kind of systemic grand corruption violateshuman rights, prevents sustainable development and fuels social exclusion.
Higher-ranked countries tend to have higher degrees of press freedom, access to information about public expenditure,stronger standards of integrity for public officials, and independent judicial systems. But high-scoring countries can't affordto be complacent, either. While the most obvious forms of corruption may not scar citizens' daily lives in all these places,the higher-ranked countries are not immune to closed-door deals, conflicts of interest, illicit finance, and patchy law
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enforcement that can distort public policy and exacerbate corruption at home and abroad.
REGIONAL ANALYSIS
Corruption hurts all countries, in every region of the world. Learn more about public sector corruption in your regionbelow.
Americas: From the Panama Papers in April to the record US$3.5 billion Odebrecht settlement in Brazil inDecember, 2016 was a good year in the fight against corruption in the Americas. But there is still a long way togo. Read more
Asia Pacific: Unfortunately, the majority of Asia Pacific countries sit in the bottom half of this year’sCorruption Perceptions Index. Poor performance can be attributed to unaccountable governments, lack ofoversight, insecurity and shrinking space for civil society, pushing anti-corruption action to the margins in thosecountries. Read more
Europe and Central Asia: There are no drastic changes in Europe and Central Asia on this year’s index, withonly a few exceptions. However, this does not mean that the region is immune from corruption. The stagnationalso does not indicate that the fight against corruption has improved, but rather the opposite. Read more
Middle East and North Africa: Despite the political changes that shook the Arab region six years ago, thehope for Arab countries to fight corruption and end impunity has not seen any progress yet. This explains thesharp drop of most of Arab countries on the 2016 index – 90 percent of these have scored below 50, which is afailing grade. Read more
Sub Saharan Africa: 2016 saw elections across the African continent with the results providing a goodreflection of corruption trends in the region. In Ghana, for example, voters voiced their dissatisfaction with thegovernment's corruption record at the polls where, for the first time in Ghana's history, an incumbent presidentwas voted out. Read more
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Press release: 'Vicious circle of corruption and inequality must be tackled: Rise of populist politicians in many countries is a warning signal' | يبرع |Español | Français | Português | Русский
Analysis: 'Corruption and inequality: how populists mislead people'
Previous Corruption Perceptions Index results
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Corruption and inequality: how populists mislead peopleCorruption and social inequality are indeed closely related and provide a source for popular discontent. Yet, the track record of populist leaders in tacklingthis problem is dismal.
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Asia Pacific: Fighting corruption is side-linedThe majority of Asia Pacific countries sit in the bottom half of this year’s index.
Americas: Sometimes bad news is good newsFrom the Panama Papers to the Odebrecht settlement, 2016 was a good year in the fight against corruption in the Americas.
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Sub Saharan Africa: Corruption is a big issue in 2016 African electionsThe elections held across Africa in 2016 provide a good reflection of corruption trends in the region.
ماعل 2016 داسفلا تاكردم رشؤم ىلع اءوس دادزت ةیبرعلا لودلا بورحلاو ةیلخادلا تاعازنلاو يسایسلا رارقتسالا مادعنا ببسب ملاعلا لوح اداسف لودلا رثكأ نم ةیبرع لود ربتعت 6 ثیح ماعلا اذھل داسفلا تاكردم رشؤم ىلع ارادحنا ةیبرعلا لودلا ةیبلغأ تدھش
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يسایسلا داسفلا ةصاخو داسفلا يذغت بورحلاو تاعارصلا نا ىلع دكؤت يتلاو باھرإلا تایدحتو .
Middle East and North Africa: A very drastic declineThe majority of Arab countries have failed to fulfil the will of the people to build democratic systems allowing for greater transparency and accountability.
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Europe and Central Asia: An overall stagnationThere are no drastic changes in Europe and Central Asia in the Corruption Perceptions Index 2016 but this does not mean that the region is immune fromcorruption.
Social Media
Corruption dominating headlines is not always a bad thing... From the Panama Papers scandal to the record US$3.5 billion Odebrecht settlement in Brazil
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in December, 2016 was a good year in the fight against corruption in the Americas. #cpi2016
Americas: Sometimes bad news is good newsFrom the Panama Papers to the Odebrecht settlement, 2016 was a good year in the fight against corruption in the Americas.
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The US dropped 2 points in our corruption index. Donald Trump claims he's going to drain the swamp, but he's actually bringing in more crocodiles!http://www.transparency.org/cpi2016
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RT @ASalasTI: Venezuela, México #CPI2016 se perciben más corruptos. Preocupa futuro en EU https://t.co/Xld0sIY6KH @anticorruption https://t…
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Con 1,378 personas juzgadas por casos de #corrupción entre 2015/16, #España ocupa el puesto 41 de 176 en el… https://t.co/OeydKWWhyI
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Except where otherwise noted, this work is licensed under CC BY-ND 4.0© Transparency International 2016. Some rights reserved.
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SURVEYS • 25 JANUARY 2017
CORRUPTION PERCEPTIONS INDEX 2016:VICIOUS CIRCLE OF CORRUPTION AND
INEQUALITY MUST BE TACKLEDRise of populist politicians in many countries is a warning signal
Issued by Transparency International Secretariat
Translations: AR | RU | PT | ES | FR
2016 showed that around the world systemic corruption and social inequality reinforce each other, leading topopular disenchantment with political establishments and providing a fertile ground for the rise of populistpoliticians.
69 per cent of the 176 countries on the Corruption Perceptions Index 2016 scored below 50, on a scale from 0(perceived to be highly corrupt) to 100 (perceived to be very clean), exposing how massive and pervasivepublic sector corruption is around the world. This year more countries declined in the index than improved,showing the need for urgent action.
No equal opportunities for all
Corruption and inequality feed off each other, creating a vicious circle between corruption, unequal distributionof power in society, and unequal distribution of wealth. As the Panama Papers showed, it is still far too easy forthe rich and powerful to exploit the opaqueness of the global financial system to enrich themselves at theexpense of the public good.
“In too many countries, people are deprived of their most basic needs and go to bed hungry every nightbecause of corruption, while the powerful and corrupt enjoy lavish lifestyles with impunity,” said José Ugaz,Chair of Transparency International.
“We do not have the luxury of time. Corruption needs to be fought with urgency, so that the lives of peopleacross the world improve,” added Ugaz.
Grand corruption cases, from Petrobras and Odebrecht in Brazil to Ukrainian ex-President Viktor Yanukovych,show how collusion between businesses and politicians denies national economies of billions of dollars of
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revenues that were siphoned off to benefit the few at the expense of the many. This kind of systemic grandcorruption violates human rights, prevents sustainable development and fuels social exclusion.
Brazil’s score on the index, for example, has significantly declined compared to five years ago as onecorruption scandal after another involving top politicians and businesspeople was uncovered. Yet the countryhas shown this year that through the work of independent law enforcement bodies it is possible to hold toaccount those previously considered untouchable.
Populism is the wrong medicine
People are fed up by too many politicians’ empty assurances to tackle corruption and many are turning towardspopulist politicians who promise to change the system and break the cycle of corruption and privilege. Yet thisis likely to only exacerbate the issue.
“In countries with populist or autocratic leaders, we often see democracies in decline and a disturbing patternof attempts to crack down on civil society, limit press freedom, and weaken the independence of the judiciary.Instead of tackling crony capitalism, those leaders usually install even worse forms of corrupt systems,” saidUgaz. “Only where there is freedom of expression, transparency in all political processes and strongdemocratic institutions, can civil society and the media hold those in power to account and corruption be foughtsuccessfully.”
The index scores of Hungary and Turkey – countries that have seen the rise of autocratic leaders – havedropped in recent years. In contrast, the score of Argentina, which has ousted a populist government, isstarting to improve.
What needs to be done
Technical fixes to specific anti-corruption legislation are not enough. What is urgently needed are deep-rootedsystemic reforms that even up the growing imbalance of power and wealth by empowering citizens to stop thewidespread impunity for corruption, hold the powerful to account, and have a real say in the decisions thataffect their daily lives.
These reforms must include the disclosure through public registries of who owns companies as well assanctions for professional enablers who are complicit in moving corrupt money flows across borders.
The results
The Corruption Perceptions Index 2016 covers perceptions of public sector corruption in 176 countries. Clickhere for the full index.
Denmark and New Zealand perform best with scores of 90, closely followed by Finland (89) and Sweden (88).Although no country is free of corruption, the countries at the top share characteristics of open government,press freedom, civil liberties and independent judicial systems.
For the tenth year running, Somalia is the worst performer on the index, this year scoring only 10. South Sudanis second to bottom with a score of 11, followed by North Korea (12) and Syria (13). Countries at the bottom ofthe index are characterised by widespread impunity for corruption, poor governance and weak institutions.
Countries in troubled regions, particularly in the Middle East, have seen the most substantial drops this year.Qatar is the biggest decliner compared to the 2015 index with a drop of 10 scores. “The FIFA scandals, the
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investigations into the decision to host the World Cup in 2022 in Qatar and reports of human rights abuses formigrant workers have clearly affected the perception of the country,” said Ugaz.
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Corruption Perceptions Index 2016No country gets close to a perfect score in this year's index. A vicious cycle has developed between corruption, unequal distribution ofpower and unequal distribution of wealth.
Corruption and inequality: how populists mislead peopleCorruption and social inequality are indeed closely related and provide a source for popular discontent. Yet, the track record of populistleaders in tackling this problem is dismal.
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Asia Pacific: Fighting corruption is side-linedThe majority of Asia Pacific countries sit in the bottom half of this year’s index.
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Americas: Sometimes bad news is good newsFrom the Panama Papers to the Odebrecht settlement, 2016 was a good year in the fight against corruption in the Americas.
Sub Saharan Africa: Corruption is a big issue in 2016 African electionsThe elections held across Africa in 2016 provide a good reflection of corruption trends in the region.
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ماعل 2016 داسفلا تاكردم رشؤم ىلع اءوس دادزت ةیبرعلا لودلا بورحلاو ةیلخادلا تاعازنلاو يسایسلا رارقتسالا مادعنا ببسب ملاعلا لوح اداسف لودلا رثكأ نم ةیبرع لود ربتعت 6 ثیح ماعلا اذھل داسفلا تاكردم رشؤم ىلع ارادحنا ةیبرعلا لودلا ةیبلغأ تدھش
يسایسلا داسفلا ةصاخو داسفلا يذغت بورحلاو تاعارصلا نا ىلع دكؤت يتلاو باھرإلا تایدحتو .
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Middle East and North Africa: A very drastic declineThe majority of Arab countries have failed to fulfil the will of the people to build democratic systems allowing for greater transparency andaccountability.
Social Media
Corruption dominating headlines is not always a bad thing... From the Panama Papers scandal to the record US$3.5 billion Odebrechtsettlement in Brazil in December, 2016 was a good year in the fight against corruption in the Americas. #cpi2016
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Americas: Sometimes bad news is good newsFrom the Panama Papers to the Odebrecht settlement, 2016 was a good year in the fight against corruption in the Americas.
TRANSPARENCY.ORG
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The US dropped 2 points in our corruption index. Donald Trump claims he's going to drain the swamp, but he's actually bringing in morecrocodiles! http://www.transparency.org/cpi2016
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RT @ASalasTI: Venezuela, México #CPI2016 se perciben más corruptos. Preocupa futuro en EU https://t.co/Xld0sIY6KH @anticorruptionhttps://t…
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Con 1,378 personas juzgadas por casos de #corrupción entre 2015/16, #España ocupa el puesto 41 de 176 en el…https://t.co/OeydKWWhyI
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Except where otherwise noted, this work is licensed under CC BY-ND 4.0© Transparency International 2016. Some rights reserved.
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corruption perceptions index 2016The perceived levels of public sector corruption in 176 countries/territories around the world.
Score
0-9 10-19 20-29 30-39 40-49 50-59 60-69 70-79 80-89 90-100 No data
Very Clean
Highly Corrupt
21 Uruguay 71
22 Estonia 70
23 France 69
24 Bahamas 66
24 Chile 66
24 United Arab Emirates
66
27 Bhutan 65
28 Israel 64
29 Poland 62
29 Portugal 62
31 Barbados 61
31 Qatar 61
31 Slovenia 61
31 Taiwan 61
35 Botswana 60
35 Saint Lucia 60
35 Saint Vincent and The Grenadines
60
38 Cape Verde 59
38 Dominica 59
38 Lithuania 59
60 Italy 47
62 Sao Tome and Principe
46
62 Saudi Arabia 46
64 Montenegro 45
64 Oman 45
64 Senegal 45
64 South Africa 45
64 Suriname 45
69 Greece 44
70 Bahrain 43
70 Ghana 43
72 Burkina Faso 42
72 Serbia 42
72 Solomon Islands 42
75 Bulgaria 41
75 Kuwait 41
75 Tunisia 41
75 Turkey 41
79 Belarus 40
79 Brazil 40
1 Denmark 90
1 New Zealand 90
3 Finland 89
4 Sweden 88
5 Switzerland 86
6 Norway 85
7 Singapore 84
8 Netherlands 83
9 Canada 82
10 Germany 81
10 Luxembourg 81
10 United Kingdom 81
13 Australia 79
14 Iceland 78
15 Belgium 77
15 Hong Kong 77
17 Austria 75
18 United States 74
19 Ireland 73
20 Japan 72
rANK coUNTrY/TerrITorY Score rANK coUNTrY/TerrITorY Score
41 Brunei 58
41 Costa Rica 58
41 Spain 58
44 Georgia 57
44 Latvia 57
46 Grenada 56
47 Cyprus 55
47 Czech Republic 55
47 Malta 55
50 Mauritius 54
50 Rwanda 54
52 Korea (South) 53
53 Namibia 52
54 Slovakia 51
55 Croatia 49
55 Malaysia 49
57 Hungary 48
57 Jordan 48
57 Romania 48
60 Cuba 47
rANK coUNTrY/TerrITorY Score
79 China 40
79 India 40
83 Albania 39
83 Bosnia and Herzegovina
39
83 Jamaica 39
83 Lesotho 39
87 Mongolia 38
87 Panama 38
87 Zambia 38
90 Colombia 37
90 Indonesia 37
90 Liberia 37
90 Morocco 37
90 The FYR of Macedonia
37
95 Argentina 36
95 Benin 36
95 El Salvador 36
95 Kosovo 36
95 Maldives 36
95 Sri Lanka 36
101 Gabon 35
101 Niger 35
101 Peru 35
101 Philippines 35
101 Thailand 35
101 Timor-Leste 35
101 Trinidad and Tobago
35
108 Algeria 34
108 Côte d’Ivoire 34
108 Egypt 34
108 Ethiopia 34
108 Guyana 34
113 Armenia 33
113 Bolivia 33
113 Vietnam 33
116 Mali 32
116 Pakistan 32
116 Tanzania 32
116 Togo 32
rANK coUNTrY/TerrITorY Score
120 Dominican Republic
31
120 Ecuador 31
120 Malawi 31
123 Azerbaijan 30
123 Djibouti 30
123 Honduras 30
123 Laos 30
123 Mexico 30
123 Moldova 30
123 Paraguay 30
123 Sierra Leone 30
131 Iran 29
131 Kazakhstan 29
131 Nepal 29
131 Russia 29
131 Ukraine 29
136 Guatemala 28
136 Kyrgyzstan 28
136 Lebanon 28
136 Myanmar 28
136 Nigeria 28
136 Papua New Guinea
28
142 Guinea 27
142 Mauritania 27
142 Mozambique 27
145 Bangladesh 26
145 Cameroon 26
145 Gambia 26
145 Kenya 26
145 Madagascar 26
145 Nicaragua 26
151 Tajikistan 25
151 Uganda 25
153 Comoros 24
154 Turkmenistan 22
154 Zimbabwe 22
156 Cambodia 21
156 Democratic Republic of Congo
21
156 Uzbekistan 21
rANK coUNTrY/TerrITorY Score
159 Burundi 20
159 Central African Republic
20
159 Chad 20
159 Haiti 20
159 Republic of Congo 20
164 Angola 18
164 Eritrea 18
166 Iraq 17
166 Venezuela 17
168 Guinea-Bissau 16
169 Afghanistan 15
170 Libya 14
170 Sudan 14
170 Yemen 14
173 Syria 13
174 Korea (North) 12
175 South Sudan 11
176 Somalia 10
#cpi2016www.transparency.org/cpiThis work from Transparency International, 2017 is licensed under CC BY-ND 4.0
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Corruption Perceptions Index 2016: Frequently Asked Questions What is the Corruption Perceptions Index (CPI)? The CPI scores and ranks countries/territories based on how corrupt a country’s public sector is perceived to be. It is a composite index, a combination of surveys and assessments of corruption, collected by a variety of reputable institutions. The CPI is the most widely used indicator of corruption worldwide. Why is the CPI based on perceptions? Corruption generally comprises illegal activities, which are deliberately hidden and only come to light through scandals, investigations or prosecutions. There is no meaningful way to assess absolute levels of corruption in countries or territories on the basis of hard empirical data. Possible attempts to do so, such as by comparing bribes reported, the number of prosecutions brought or studying court cases directly linked to corruption, cannot be taken as definitive indicators of corruption levels. Instead, they show how effective prosecutors, the courts or the media are in investigating and exposing corruption. Capturing perceptions of corruption of those in a position to offer assessments of public sector corruption is the most reliable method of comparing relative corruption levels across countries. Which countries/territories are included in the CPI 2016 and why? For a country/territory to be included in the ranking, it must be included in a minimum of three of the CPI’s data sources. If a country is not featured in the ranking, then this is solely because of insufficient survey information and not an indication that corruption does not exist in the country. This year 176 countries and territories are included in the index, eight more than in 2015. Comparing to the 2015 CPI, Seychelles is no longer included in the 2016 CPI, but Bahamas, Barbados, Brunei, Dominica, Grenada, Maldives, Saint Lucia, Saint Vincent and the Grenadines, and Solomon Islands enter the 2016 CPI. What are the data sources for the CPI? The 2016 CPI draws on data sources from independent institutions specialising in governance and business climate analysis. The sources of information used for the 2016 CPI are based on data gathered in the past 24 months. The CPI includes only sources that provide a score for a set of countries/territories and that measure perceptions of corruption in the public sector. Transparency International reviews the methodology of each data source in detail to ensure that the sources used meet Transparency International’s quality standards. For a full list of the data sources, the type of respondents and the specific questions asked, please see the CPI sources description document. What is the difference between a country/territory’s rank and its score? A country/territory’s score indicates the perceived level of public sector corruption on a scale of 0-100, where 0 means that a country is perceived as highly corrupt and a 100 means that a country is perceived as very clean. A country's rank indicates its
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position relative to the other countries/territories included in the index. Ranks can change merely if the number of countries included in the index changes. Is the country/territory with the lowest score the world's most corrupt nation? No. The CPI is an indicator of perceptions of public sector corruption, i.e. administrative and political corruption. It is not a verdict on the levels of corruption of entire nations or societies, or of their policies, or the activities of their private sector. Citizens of those countries/territories that score at the lower end of the CPI often show the same concern about and condemnation of corruption as the public in countries that perform strongly. Further, the country/territory with the lowest score is the one where public sector corruption is perceived to be greatest among those included in the list. The CPI provides no information about countries/territories that are not included in the index. Can the score of a country in the 2016 Corruption Perceptions Index be compared with the previous year? Yes. As part of the update to the methodology used to calculate the CPI in 2012 we established the new scale of 0-100. Using this scale we can compare CPI scores from one year to the next. Because of the update in the methodology, however, CPI scores before 2012 are not comparable over time. In addition, due to the inclusion of a new data source in 2016, the scores of the underlying data sources are not comparable to previous years. For a more detailed description of the change in methodology in 2012, please see Corruption Perceptions Index – An updated Methodology for 2012. Which countries have improved/declined on the Corruption Perceptions Index this year? The biggest improvers this year are Suriname, Belarus, Timor-Leste, Myanmar, Guyana, Georgia, Laos, Argentina, North Korea, Burkina Faso, Cape Verde, Turkmenistan, Sao Tome and Principe and Afghanistan. The biggest decliners this year are Qatar, Kuwait, Bahrain, Saudi-Arabia, Cyprus, Lesotho, Jordan, Syria, Macedonia, Mexico, South Sudan, Chile, United Arab Emirates, Mauritania, Central African Republic, Netherlands, Mozambique, Trinidad and Tobago, Ghana, Yemen and Djibouti. Does the CPI tell the full story of corruption in a country? No. The CPI is limited in scope, capturing perceptions of the extent of corruption in the public sector, from the perspective of business people and country experts. Complementing this viewpoint and capturing different aspects of corruption, Transparency International produces a range of both qualitative and quantitative research on corruption, both at the global level from its Secretariat and at the national level through Transparency International’s network of national chapters based in over 100 countries around the world. Complementing the CPI, Transparency International’s other global research products include:
Global Corruption Barometer (GCB): Measuring people’s perceptions and experiences of corruption, the Global Corruption Barometer is a representative survey of people carried out worldwide. The most recent Europe and Central Asia edition of the Global Corruption Barometer can be found at: https://www.transparency.org/whatwedo/publication/7493. The most
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recent global edition of the Global Corruption Barometer can be found at: http://www.transparency.org/gcb2013/report
Global Corruption Report (GCR): Exploring corruption issues in detail for a specific issue or sector, the Global Corruption Report is a thematic report which draws on a variety of expert research and analysis as well as case studies. The series of Global Corruption Reports, covering issues from the judiciary to education, can be found at: http://www.transparency.org/gcr
National Integrity System assessments (NIS): a series of in-country studies providing an extensive qualitative assessment of the strengths and weaknesses of the key institutions that enable good governance and prevent corruption in a country. For more information on the National Integrity System
reports, please see: http://www.transparency.org/whatwedo/nis
Transparency In Corporate Reporting (TRAC): The study analyses the extent of transparency in the reporting on a series of anti-corruption measures by the world’s largest companies. For further information, please see http://www.transparency.org/whatwedo/publication/transparency_in_corporate_reporting_assessing_worlds_largest_companies_2014
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Corruption Perceptions Index 2016: Short Methodology Note
The Corruption Perceptions Index aggregates data from a number of different sources that provide perceptions of business people and country experts of the level of corruption in the public sector. The following steps are followed to calculate the CPI: 1. Select data sources: Each data source that is used to construct the Corruption
Perceptions Index must fulfil the following criteria to qualify as a valid source:
Quantifies perceptions of corruption in the public sector
Be based on a reliable and valid methodology, which scores and ranks multiple countries on the same scale
Performed by a credible institution and expected to be repeated regularly
Allow for sufficient variation of scores to distinguish between countries
The CPI 2016 is calculated using 13 different data sources from 12 different institutions that capture perceptions of corruption within the past two years. These sources are described in detail in the accompanying source description document.
2. Standardise data sources to a scale of 0-100 where a 0 equals the highest level
of perceived corruption and 100 equals the lowest level of perceived corruption. This is done by subtracting the mean of the data set and dividing by the standard deviation and results in z-scores, which are then adjusted to have a mean of approximately 45 and a standard deviation of approximately 20 so that the data set fits the CPI’s 0-100 scale. The mean and standard deviation are taken from the 2012 scores, so that the rescaled scores can be compared over time against the baseline year.
3. Calculate the average: For a country or territory to be included in the CPI, a minimum of three sources must assess that country. A country’s CPI score is then calculated as the average of all standardised scores available for that country. Scores are rounded to whole numbers.
4. Report a measure of uncertainty: The CPI is accompanied by a standard error and confidence interval associated with the score, which capture the variation in scores of the data sources available for that country/territory.
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Corruption Perceptions Index 2016: Technical Methodology Note
Background The Corruption Perceptions Index (CPI) was established in 1995 as a composite indicator used to measure perceptions of corruption in the public sector in different countries around the world. During the past 20 years, both the sources used to compile the index and the methodology has been adjusted and refined. The most recent review process took place in 20121, and some important changes were made to the methodology in 2012. The method that was used up until 2012 to aggregate different data sources has been simplified and now includes just one year’s data from each data source. Crucially, this method now allows us to compare scores over time, which was not methodologically possible prior to 2012. Methodology The methodology follows 4 basic steps: selection of source data, rescaling source data, aggregating the rescaled data and then reporting a measure for uncertainty. 1. Selection of data sources The CPI draws upon a number of available sources which capture perceptions of corruption. Each source is evaluated against the criteria listed below. Contact has been made with each institution providing data in order to verify the methodology used to generate scores and for permission to publish the rescaled scores from each source, alongside the composite index score.
A) Reliable data collection and methodology from a credible institution: It is necessary that we trust the validity of the data we are using. As such, each source should originate from a professional institution that clearly documents its methods for data collection. These methods should be methodologically sound, for example, where an ‘expert opinion’ is being provided, we seek assurance on the qualifications of the expert or where a business survey is being conducted, that the survey sample is representative.
B) Data addresses corruption in the public sector: The question or analysis
should relate to a perception of the level of corruption explicitly in the public sector. The question can relate to a defined ‘type’ of corruption (e.g. specifically petty corruption), and where appropriate, the effectiveness of
1 The methodology used to calculate the CPI 2016 builds on the work examining alternative approaches for constructing the CPI carried out by Prof. Andrew Gelman: Professor, Department of Statistics and Department of Political Science, Columbia University and Dr Piero Stanig: Fellow, Methodology Institute, London School of Economics and Political Science. This work was presented to Transparency International in a report that is available on request. Please email Santhosh Srinivasan at [email protected].
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corruption prevention as this can be used as a proxy for the perceived level of corruption in the country.
C) Quantitative granularity: The scales used by the data sources must allow
for sufficient differentiation in the data (i.e., at least a four-point scale) on the perceived levels of corruption across countries so that it can be rescaled to the CPI’s 0-100 scale.
D) Cross country comparability: As the CPI ranks countries against each
other, the source data must also be legitimately comparable between countries and not be country specific. The source should measure the same thing in each country scored, on the same scale.
E) Multi year data-set: We want to be able to compare a country’s score, and
indeed the index in general, from one year to the next. Sources that capture corruption perceptions for a single point in time, but that are not designed to be repeated over time, are therefore excluded.
2. Standardise data sources Each source is then standardised to be compatible with other available sources, for aggregation to the CPI scale. The standardisation converts all the data sources to a scale of 0-100 where a 0 = highest level of perceived corruption, and 100 = lowest level of perceived corruption. Any source that is scaled such that lower scores represent lower levels of corruption must first be reversed. This is done by multiplying every score in the data set by -1. Every score is then standardised (to a z score) by subtracting the mean of the data and dividing by the standard deviation. This results in a data set centred around 0 and with a standard deviation of 1. For these z scores to be comparable between data sets, we must define the mean and standard deviation parameters as global parameters. Therefore where a data set covers a limited range of countries, we impute scores for all those countries that are missing in the respective data set. We impute missing values for missing countries in each data set using the statistical software package STATA and, more specifically, the programme’s impute command. This command regresses each data set against the CPI data sources that are at least 50% complete to estimate values for each country that is missing data in each individual data set. This is with the exception of the Bertelsmann Foundation’s Transformation Index data, which is not used for the imputation of the Bertelsmann Foundation’s Sustainable Governance Indicators because there is no overlap in country coverage of these two data sources. The mean and standard deviation for the data set is calculated as an average of the complete data sets and is used as the parameter to standardise the raw data. Importantly, the complete data set with imputed values is used only to generate these parameters and the imputed values themselves are not used as source data for CPI country scores. Critically, the z scores are calculated using the mean and standard deviation parameters from the imputed 2012 scores. This is so that 2012 is effectively the baseline year for the data and the rescaled scores can be comparable year on year. When new sources enter the index, in order to appropriately reflect changes over time, the rescaling calculation allows for these to be consistent with 2012 baseline parameters. This is done by first estimating if there was a global change in the mean
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and standard deviation since 2012, and then using these new values, which may have deviated from 50 and 20 to rescale the new data set.2 The z scores are then rescaled to fit the CPI scale between 0-100. This uses a simple rescaling formula, which sets the mean value of the standardised dataset to approximately 45, and the standard deviation of approximately 20. Any score which exceeds the 0 to 100 boundaries will be capped. 3. Aggregate the rescaled data Each country’s CPI score is calculated as a simple average of all the available rescaled scores for that country (note, we do not use any of the imputed values as a score for the aggregated CPI). A country will only be given a score if there are at least three data sources available from which to calculate this average. 4. Report a measure of uncertainty The CPI score is reported alongside a standard error and 90% confidence interval which reflects the variance in the value of the source data that comprises the CPI score. The standard error term is calculated as the standard deviation of the rescaled source data, divided by the square root of the number of sources. Using this standard error, we can calculate the 90% confidence interval, assuming a normal distribution.
2 Since a new data source was added to the CPI, the above procedure was used to check if there was a change in the mean and standard deviation since 2012. We established that the mean and standard deviation had not changed and thereby maintaining year on year comparison of CPI scores.
Corruption Perceptions Index 2016: Full Source Description 13 data sources were used to construct the Corruption Perceptions Index 2016:
1. African Development Bank Governance Ratings 2015 2. Bertelsmann Foundation Sustainable Governance Indicators 2016 3. Bertelsmann Foundation Transformation Index 2016 4. Economist Intelligence Unit Country Risk Ratings 2016 5. Freedom House Nations in Transit 2016 6. Global Insight Country Risk Ratings 2015 7. IMD World Competitiveness Yearbook 2016 8. Political and Economic Risk Consultancy Asian Intelligence 2016 9. Political Risk Services International Country Risk Guide 2016 10. World Bank - Country Policy and Institutional Assessment 2015 11. World Economic Forum Executive Opinion Survey (EOS) 2016 12. World Justice Project Rule of Law Index 2016 13. Varieties of Democracy (VDEM) Project 2016
Source 1
1. African Development Bank Governance Ratings 2015 Code: AFDB Data Provider
The African Development Bank (AFDB) is a regional multilateral development bank, engaged in promoting the economic development and social progress of countries on the continent. The AfDB’s 2015 Governance Ratings are part of the Country Policy and Institutional Assessment (CPIA), which assesses the quality of a country’s institutional framework in terms of how conducive it is to fostering the effective use of development assistance. The current CPIA strives to achieve a maximum level of uniformity and consistency across all regional member countries surveyed. Also, and in order to comply with the Paris and Rome declarations on Aid Effectiveness, Harmonization and Alignment, the AfDB has modified the questionnaire and guidelines for its CPIA to be in line with those of the World Bank and the Asian Development Bank, thus increasing the comparability and synergy among systems. The CPIA is carried out by a group of country economists with vast experience in policy analysis. The knowledge of these experts is complemented with that of local contacts that provide both quantitative and qualitative insights. Peer discussions are also used to monitor the quality of the findings. Corruption Question(s)
Experts are asked to assess: Transparency, Accountability and Corruption in the Public Sector. “This criterion assesses the extent to which the executive can be held accountable for its use of funds and the results of its actions by the electorate and by the legislature and judiciary, and the extent to which public employees within the executive are required to account for the use of resources, administrative decisions, and results obtained. Both levels of accountability are enhanced by transparency in decision making, public audit institutions, access to relevant and timely information, and public and media scrutiny. A high degree of accountability and transparency discourages corruption, or the abuse of public office for private gain. National and sub-national governments should be appropriately weighted. Each of three dimensions should be rated separately: (a) the accountability of the executive to oversight institutions and of public employees for their performance; (b) access of civil society to information on public affairs; and (c) state capture by narrow vested interests.” The questionnaire for CPIA assessment can be accessed here: https://cpia.afdb.org/documents/public/cpia2015-questionnaire-en.pdf Scores
The rating scale ranges from 1 (very weak for two or more years) to 6 (very strong for three or more years) and allows for half point intermediate scores (e.g.3.5). The score is an aggregate of the three dimensions of corruption across national and sub-national government institutions in the country. Country Coverage
38 African countries are covered. Countries are scored in terms of their performance during the year of the rating vis-à-vis the criteria, which are included in the CPIA Manual for Drafters and updated every year. The CPIA is a three-phase process involving i) the rating of countries by country teams; iii) the review of all ratings by sector experts; and iii) the endorsement of final ratings at open discussions between country teams and reviewers Data availability
The data set has been published annually since 2005. The 2015 Governance Ratings were compiled during 2015 and published in March 2016. The data is publicly available online in the Bank’s web page,
https://cpia.afdb.org/?page=data
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2. Bertelsmann Foundation Sustainable Governance Indicators 2016 Code: BF (SGI) Data Provider
The Bertelsmann Stiftung was founded in 1977 as a private foundation. As a think tank they work toward improved education, a just and efficient economic system, a preventative healthcare system, a vibrant civil society and greater international understanding. The Bertelsmann Stiftung is independent and nonpartisan. It designs, launches and runs its own projects. The Sustainable Governance Indicators (SGI) examine governance and policymaking in all OECD and EU member states in order to evaluate each country's need for, and ability to carry out, reform. The indicators are calculated using quantitative data from international organisations and then supplemented by qualitative assessments from recognised country experts. Corruption Question(s)
Experts are asked to assess: “To what extent are public officeholders prevented from abusing their position for private interests?” This question addresses how the state and society prevent public servants and politicians from accepting bribes by applying mechanisms to guarantee the integrity of officeholders: auditing of state spending; regulation of party financing; citizen and media access to information; accountability of officeholders (asset declarations, conflict of interest rules, codes of conduct); transparent public procurement systems; effective prosecution of corruption. Scores are given from:
a low of 1 to 2, where 'Public officeholders can exploit their offices for private gain as they see fit without fear of legal consequences or adverse publicity'
to a high of 9 to 10, where 'Legal, political and public integrity mechanisms effectively prevent public officeholders from abusing their positions.'
Scores
Scores are given on a scale of 1 (highest level of corruption) to 10 (lowest level of corruption). Country Coverage
All 41 OECD and EU countries were scored. The quantitative data are compiled centrally by the SGI project team from official, publicly accessible statistics (primarily from OECD sources). The qualitative data are captured and examined by a worldwide network of around 100 respected researchers. The SGI Codebook, a detailed questionnaire, provides a clear explanation for each of the questions, so that all experts share a common understanding of the questions (http://www.sgi-network.org/docs/2016/basics/SGI2016_Codebook.pdf). Data availability
First published in 2009, this is now an annual publication. The Sustainable Governance Indicators 2016 data is publicly available online. It assesses a one-year period from November 2014 to November 2015.
http://www.sgi-network.org/2016/Democracy/Quality_of_Democracy/Rule_of_Law/Corruption_Prevention
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3. Bertelsmann Foundation Transformation Index 2016 Code: BF (TI) Data Provider
The Bertelsmann Stiftung was founded in 1977 as a private foundation. As a think tank they work toward improved education, a just and efficient economic system, a preventative healthcare system, a vibrant civil society and greater international understanding. The Bertelsmann Stiftung is independent and nonpartisan. It designs, launches and runs its own projects. The Transformation Index provides the framework for an exchange of good practice among agents of reform. Within this framework, the BTI publishes two rankings, the Status Index and the Management Index, both of which are based on in-depth assessments of 129 countries. The scores are based on detailed country reports which assess 52 questions divided into 17 criteria. Assessments are provided by two experts per country. Country assessments consist of two sections: the written assessment of the state of transformation and management performance in a country (country report) and the numerical assessment of the state of transformation and management performance (country ratings). Scores are given by a country expert, which are then reviewed blind by a second country expert who also provides a second independent rating of the country. These scores by experts are then verified and discussed by regional coordinators to ensure intra and inter-regional comparability in ratings. In addition, BF has also instituted an extra layer of verification to ensure the scores provided match the qualitative descriptions for each country. Corruption Question(s)
Experts are asked to assess: “To what extent are public officeholders who abuse their positions prosecuted or penalized?” Assessments range from:
a low of 1, where 'Officeholders who break the law and engage in corruption can do so without fear of legal consequences or adverse publicity.'
to a high of 10, where 'Officeholders who break the law and engage in corruption are prosecuted rigorously under established laws and always attract adverse publicity.'
“To what extent does the government successfully contain corruption?” Assessments range from:
from a low of 1, where 'The government fails to contain corruption, and there are no integrity mechanisms in place.'
to a high of 10, where 'The government is successful in containing corruption, and all integrity mechanisms are in place and effective.'
Scores
Scores are assigned on a scale of 1-10 with 10 being the lowest level of corruption and 1 being the highest. The score for each country is an average of the two questions. The BTI codebook for 2016 is accessible here: https://www.bti-project.org/fileadmin/files/BTI/Downloads/Zusaetzliche_Downloads/Codebook_BTI_2016.pdf Country Coverage
129 countries and territories are scored. Country scores pass through an intra-regional review stage followed by an inter-regional review and ratings aggregation. Data availability
The Transformation Index was first published in 2003, and has been published every two years since then. The data is taken from the BTI 2016 report, which was published in February 2016, and data is publicly available online: https://www.bti-project.org/fileadmin/files/BTI/Downloads/Zusaetzliche_Downloads/BTI_2016_Scores.xlsx. It assesses a one-year period from November 2014 to November 2015.
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4. Economist Intelligence Unit Country Risk Ratings 2016 Code: EIU Data Provider
The Economist Intelligence Unit (EIU) was established in 1946 as the research body for The Economist newspaper. Since then, it has grown into a global research and advisory firm that produces business intelligence for policy makers worldwide. 650 full-time and contributing analysts work in and on over 200 countries/territories. Country Risk Ratings are designed to provide in-depth and timely analysis of the risks of financial exposure in more than 140 countries. The EIU relies on teams of experts based primarily in London (but also in New York, Hong Kong, Beijing and Shanghai) who are supported by a global network of in-country specialists. Each country analyst covers a maximum of two or three countries/territories. The economic and political reports produced by EIU analysts are subjected to a rigorous review process before publication. Corruption Question(s)
Specific guiding questions include:
Are there clear procedures and accountability governing the allocation and use of public funds?
Are public funds misappropriated by ministers/public officials for private or party political purposes?
Are there special funds for which there is no accountability?
Are there general abuses of public resources?
Is there a professional civil service or are large numbers of officials directly appointed by the government?
Is there an independent body auditing the management of public finances?
Is there an independent judiciary with the power to try ministers/public officials for abuses?
Is there a tradition of a payment of bribes to secure contracts and gain favours? Scores
Scores are given as integers on a scale from 0 (very low incidence of corruption) to 4 (very high incidence of corruption). Country Coverage
129 countries/territories were scored in 2016. Data availability
Country risk assessments have been produced by the EIU since the early 1980s. Updated summaries are provided monthly for 100 countries and quarterly for the rest. The CPI draws on risk rating data available as of September 2016. Data is available to subscribers of EIU Country Risk Service. http://www.eiu.com
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5. Freedom House Nations in Transit 2016 Code: FH Data Provider
Founded in 1941, Freedom House is an independent watchdog organisation that supports the expansion of freedom around the world. Freedom House supports democratic change, monitors freedom, and advocates for democracy and human rights. The Nations in Transit (NIT) reports measure democratisation in 29 nations and administrative areas throughout Central Europe and the Newly Independent States (NIS). The reports focus on democratic progress and setbacks. Each report focuses on the following thematic areas: national democratic governance; electoral process; civil society; independent media; local democratic governance; judicial framework and independence; and corruption. The NIT surveys were produced by Freedom House staff and consultants. The latter were recommended by relevant authorities and are regional or country specialists. A range of sources were used in compiling the report, including: multilateral lending institutions; non-governmental organisations; and other international organisations; local newspapers and magazines; and select government data. Corruption Question(s)
The Freedom House experts are asked to explore a range of indicative questions, including:
Has the government implemented effective anti-corruption initiatives?
Is the government free from excessive bureaucratic regulations, registration requirements, and other controls that increase opportunities for corruption?
Are there adequate laws requiring financial disclosure and disallowing conflict of interest?
Does the government advertise jobs and contracts?
Does the state enforce an effective legislative or administrative process—particularly one that is free of prejudice against one’s political opponents—to prevent, investigate, and prosecute the corruption of government officials and civil servants?
Do whistleblowers, anti-corruption activists, investigators, and journalists enjoy legal protections that make them feel secure about reporting cases of bribery and corruption?
Scores
Ratings run from 1 (lowest level of corruption) to 7 (highest level of corruption) and allow for half-point and quarter-point intermediate scores (e.g. 3.25). The score is a generalised composite measure of corruption that includes an assessment of all areas covered by the indicative questions. Country Coverage
29 countries/territories were ranked in 2016. Country scores are reviewed at the regional level and then centrally by the Freedom House academic advisory board. Data availability
The report has been published annually since 2003. The 2016 Nations in Transit data coverage is from 1 January through 31 December 2015. The data is publicly available online.
https://freedomhouse.org/report/nations-transit/nations-transit-2016
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6. Global Insight Country Risk Ratings 2015 Code: GI Data Provider
Founded in 1959, IHS is a global information company employing more than 5,100 people in more than 30 countries around the world. It provides a wide range of online services covering macroeconomics, country risk and individual sector analysis. The Global Insight country risk rating system has been in operation since 1999 and provides a six-factor analysis of the risk environment in over 200 countries/territories. The six factors are political, economic, legal, tax operational and security risk. The corruption risk score used in the CPI is drawn from Global Insight Business Condition and Risk Indicators. The assessments are made by over 100 in-house country specialists, who also draw on the expert opinions of in-country freelancers, clients and other contacts. The ratings reflect IHS Global Insights expert perceptions of the comparative level of the problem in each country/territory. The ratings assess the broad range of corruption, from petty bribe-paying to higher-level political corruption and the scores assigned to each country are based on a qualitative assessment of corruption in each country/territory. Corruption Question(s)
Experts are asked to assess: The risk that individuals/companies will face bribery or other corrupt practices to carry out business, from securing major contracts to being allowed to import/export a small product or obtain everyday paperwork. This threatens a company's ability to operate in a country, or opens it up to legal or regulatory penalties and reputational damage. Scores
The ratings range from a minimum of 1.0 (maximum corruption) to 5.0 (minimum corruption) and allow for half-point intermediate scores (e.g. 3.5). Country Coverage
204 countries/territories worldwide are scored. Scores provided by country analysts are reviewed and benchmarked by IHS Global Insight's risk specialists at both the regional and global level. Data availability
The Country Risk Rating System has been available since 1999 and is continuously maintained. The data for CPI 2016 from IHS Global Insight was accessed through the World Bank World Governance Indicators portal, as IHS Global Insight stopped providing data to Transparency International since 2015. This can be accessed through: http://info.worldbank.org/governance/wgi/index.aspx#doc-sources The data
Detailed data is also available to customers of IHS’ Country Intelligence. http://www.ihs.com/products/global-insight/country-analysis/
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7. IMD World Competitiveness Yearbook 2016 Code: IMD Data Provider
IMD is a top-ranked business school with expertise in developing global leaders through high-impact executive education. 100% focused on real-world executive development, offering Swiss excellence with a global perspective, IMD has a flexible, customized and effective approach. IMD is ranked first in open programs worldwide (Financial Times 2012 & 2013) and first in executive education outside the US (Financial Times 2008 - 2013). (www.imd.org) The World Competitiveness Yearbook (WCY) measures the competitiveness of nations and, in doing so, both ranks and examines how a nation’s socio-political and economic climate affects corporate competitiveness. The study uses 333 criteria in order to obtain a multifaceted image of the competitiveness of nations, defined as following: “Competitiveness of nations is a field of economic knowledge, which analyses the facts and policies that shape the ability of a nation to create and maintain an environment that sustains more value creation for its enterprises and more prosperity for its people.” The WCY largely includes hard data but also a survey of senior business leaders who, together, reflect a cross-section of a nation’s corporate community. IMD calls upon local and foreign enterprises operating in a given economy, and surveys both nationals and expatriates, so as to add an international perspective on local environments. In 2016, 5480 business executives responded. The IMD World Competitiveness Centre works in collaboration with 54 partner institutes around the world to assure the validity and relevance of data.
https://www.imd.org/wcc/research-methodology/ Corruption Question
Survey respondents were asked: “Bribing and corruption: Exist or do not exist”. Scores
Answers are given on a 1 - 6 scale which is then converted to a 0 - 10 scale where 0 is the highest level of perceived corruption and 10 is the lowest. https://www.imd.org/uupload/imd.website/wcc/Survey_Explanation.pdf Country Coverage
61 countries/territories around the world were scored in 2016. Data availability
The IMD World Competitiveness Yearbook has been published annually since 1989. The 2016 data were published in May 2016. Data is available to customers of IMD World Competitiveness Yearbook, package or online services.
https://worldcompetitiveness.imd.org/
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8. Political and Economic Risk Consultancy 2016 Code: PERC Data Provider
The Political and Economic Risk Consultancy or PERC is a consulting firm specialising in strategic business information and analysis for companies doing business in the countries of East and Southeast Asia. As part of its services, PERC produces a range of risk reports on Asian countries, paying special attention to critical socio-political variables like corruption, intellectual property rights and risks, labour quality, and other systemic strengths and weakness of individual Asian countries/territories. PERC publishes fortnightly newsletters, which are available to subscribers, on a number of issues. The data for the CPI was gathered from the corruption newsletter, which gathers and interprets data from an executive opinion survey of local and expatriate businesspeople. All responses were either collected in face-to-face interviews or in response to e-mails directed to specific people obtained from different national business chambers, conferences, and personal name lists. All respondents provided scores and comments only for the country in which they are currently residing. Respondents for each country include local business executives who are nationals of the countries, academics and expatriate executives. Corruption Question(s)
The following three questions were asked: First, how do you grade the problem of corruption in the country in which you are working? Second, has corruption decreased, stayed the same or increased compared with one year ago? Third, what aspects or implications of corruption in your country stand out to you as being particularly important? For the CPI only the first question: how do you grade the problem of corruption in the country in which you are working was used. Scores
Answers to the question were scaled from 0 (not a problem) to 10 (a serious problem). Country Coverage
15 Asian countries/territories plus the Unites States were surveyed in 2016. The same questions and survey methodology were employed in each country surveyed. Data availability
The survey dates back 20 years and is conducted annually. The data used for the CPI 2016 was gathered in a survey carried out between January 2016 and March 2016 and published in April 2016. The data is available to subscribers. http://www.asiarisk.com/
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9. Political Risk Services International Country Risk Guide 2016 Code: PRS Data Provider
Based in the vicinity of Syracuse, New York, since its founding in 1979, Political Risk Services (PRS) has consistently focused on political risk analysis. On a monthly basis since 1980, their International Country Risk Guide (ICRG) has produced political, economic, and financial risk ratings for countries/territories important to international business. The ICRG now monitors 140 countries/territories. ICRG ratings form the basis of an early warning system for opportunities and pitfalls, country-by-country. ICRG staff collect political information and convert it to risk points on the basis of a consistent pattern of evaluation. Political risk assessments and other political information form the basis of ICRG risk ratings. It is therefore possible for the user to check through the information and data so as to assess the ratings against their own assessments, or against some other risk ratings system. Corruption Question(s)
This is an assessment of corruption within the political system. The most common form of corruption met directly by businesses is financial corruption in the form of demands for special payments and bribes connected with import and export licenses, exchange controls, tax assessments, police protection, or loans. The measure is most concerned with actual or potential corruption in the form of excessive patronage, nepotism, job reservations, exchange of favours, secret party funding, and suspiciously close ties between politics and business. Scores
The corruption scores are given on a scale of 0 (highest potential risk) to 6 (lowest potential risk). Country Coverage
The ICRG provides ratings for 140 countries on a monthly basis. To ensure consistency both between countries/territories and over time, points are assigned by ICRG editors on the basis of a series of pre-set questions for each risk component. Data availability
The ICRG model was created in 1980 and the data is made available on a monthly basis. The CPI 2016 data is an aggregate of quarterly assessments covering the period of August 2015 to August 2016. Data is available to customers of the PRS International Country Risk Guide. www.prsgroup.com
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10. World Bank Country Policy and Institutional Assessment 2015 Code: WB Data Provider
The World Bank was established in 1944, is headquartered in Washington, D.C and has more than 10,000 employees in more than 100 offices worldwide. The World Bank is made up of two development institutions: the International Bank for Reconstruction and Development (IBRD) and the International Development Association (IDA).The IBRD aims to reduce poverty in middle-income and creditworthy poorer countries, while IDA focuses on the world's poorest countries. The CPIA rates all IDA-eligible countries against a set of 16 criteria grouped in four clusters: (a) economic management; (b) structural policies; (c) policies for social inclusion and equity; and (d) public sector management and institutions. The criteria are focused on balancing the capture of those factors critical to fostering growth and poverty reduction against avoiding undue burden on the assessment process. The ratings are the product of staff judgment and do not necessarily reflect the views of the World Bank’s Board of Executive Directors or the governments they represent. The Bank has prepared guidance to help staff assess country performance, by providing a definition of each criterion and a detailed description of each rating level. Bank staff assess the countries’ actual performance on each of the criteria, and assign a rating. The ratings reflect a variety of indicators, observations, and judgments based on country knowledge, originating with the Bank or elsewhere, and on relevant publicly available indicators. Corruption Question(s)
Experts are asked to assess: Transparency, Accountability and Corruption in the Public Sector. “This criterion assesses the extent to which the executive can be held accountable for its use of funds and the results of its actions by the electorate and by the legislature and judiciary, and the extent to which public employees within the executive are required to account for the use of resources, administrative decisions, and results obtained. Both levels of accountability are enhanced by transparency in decision making, public audit institutions, access to relevant and timely information, and public and media scrutiny. A high degree of accountability and transparency discourages corruption, or the abuse of public office for private gain. National and sub-national governments should be appropriately weighted. Each of three dimensions should be rated separately: (a) accountability of the executive to oversight institutions and of public employees for their performance; (b) access of civil society to information on public affairs; and (c) state capture by narrow vested interests.” http://pubdocs.worldbank.org/pubdocs/publicdoc/2015/6/559351435159340828/cpia14-webFAQ14.pdf Scores
The rating scale ranges from 1 (low levels of transparency) to 6 (high levels of transparency) and allows for half-point intermediate scores (eg. 3.5). The score is an aggregate of the three dimensions of corruption across national and sub-national government institutions in the country/territory. Country Coverage
76 countries were scored in the CPIA 2015. The process of preparing the ratings involves two phases: (a) the benchmarking phase, in which a small, representative, sample of countries is rated in an intensive Bank-wide process; and (b) a second phase, in which the remaining countries are rated using the derived benchmark ratings as guideposts. The process is managed by the Bank’s Operations Policy and Country Services Vice-Presidency. Data availability
First released in 2005 in its current form, the CPIA is now an annual exercise. The ratings process typically starts in the fall and is concluded in the spring of the following year. The scores disclosed in June 2016 (the 2015 CPIA exercise) cover 2015 country performance. The data is publicly available online.
http://data.worldbank.org/data-catalog/CPIA
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11. World Economic Forum Executive Opinion Survey (EOS) 2016 Code: WEF Data Provider
The World Economic Forum is an independent international organisation committed to improving the state of the world by engaging business, political, academic and other leaders of society to shape global, regional and industry agendas. Incorporated as a not-for-profit foundation in 1971, and headquartered in Geneva, Switzerland, the Forum is not tied to political, partisan or national interests. The Executive Opinion Survey (EOS) is the World Economic Forum's annual survey of business executives. The survey has evolved over time to capture new data points essential to the Global Competitiveness Index (GCI) and other Forum indexes. The Forum's Global Competitiveness and Benchmarking Network works closely with a network of over 160 Partner Institutes that administer the survey in their respective countries/territories. They are selected because of their capacity to reach out to leading business executives as well as their understanding of the national business environment and their commitment to the Forum's research on competitiveness. The Partner Institutes are, for the most part, well-respected economics departments of national universities, independent research institutes or business organisations. The surveys are conducted according to detailed guidelines aiming at collecting a sample stratified by sector of activity and company size. The EOS administration process is reviewed on a yearly basis and underwent an external review in 2008 and 2012 by a renowned survey expert consultancy. See chapter 1.3 of the Global Competitiveness Report 2013-2014 for further details www.weforum.org/gcr. Corruption Question(s)
Survey respondents were asked: (On a scale of 1 - 7 where 1 means very common and 7 means never) “In your country, how common is it for firms to make undocumented extra payments or bribes connected with the following”: a) Imports and exports; b) Public Utilities; c) Annual Tax Payments; d) Awarding of public contracts and licenses; e) Obtaining favourable judicial decisions. (on a scale of 1 - 7 where 1 means very common and 7 means never) “In your country, how common is diversion of public funds to companies, individuals or groups due to corruption?” Scores
Each question is scored by respondents on a scale of 1 - 7. The results of parts a) to e) of the first question were aggregated into a single score. The results of the first and second question were then averaged across all respondents to give a score per country/territory. Country Coverage
In 2016 the survey captured the views of business executives in 134 economies. Data from the 2015 survey was used for 7 countries: Egypt, Guyana, Haiti, Hong Kong, Myanmar, Nicaragua and Swaziland. The survey is conducted in each country/territory according to the sampling guidelines and therefore in a consistent manner across the globe during the same time of year. Data availability
The World Economic Forum has conducted its annual survey for more than 30 years. The data was gathered in a survey conducted between January and June 2016. Some aggregated data is available in the appendix of the Global Competitiveness Report, the micro-level data is provided to TI by the World Economic Forum. http://www.weforum.org/
Source 12
12. World Justice Project Rule of Law Index 2016 Code: WJP Data Provider
The World Justice Project (WJP) is an independent, not-for-profit organisation working to advance the rule of law for the development of communities of opportunity and equity. The WJP’s multi-national, multi-disciplinary efforts are dedicated to developing practical programmes in support of the rule of law around the world. The work of the WJP is based on two complementary premises: the rule of law is the foundation for communities of opportunity and equity, and multi-disciplinary collaboration is the most effective way to advance the rule of law. The WJP Rule of Law Index is an assessment tool designed by The World Justice Project to offer a detailed and comprehensive picture of the extent to which countries/territories adhere to the rule of law in practice. The Index provides detailed information and original data regarding a variety of dimensions of the rule of law, which enables stakeholders to assess a nation’s adherence to the rule of law in practice, identify a nation’s strengths and weaknesses in comparison to similarly situated countries, and track changes over time. The Index’s rankings and scores are the product of a rigorous data collection and aggregation process. Data comes from a global poll of the general public and detailed questionnaires administered to local experts. To date, over 2,000 experts and 66,000 other individuals from around the world have participated in this project. Corruption Question(s)
A total of 68 questions are asked of experts and respondents from the general population (53 and 15 targeted to each group respectively) on the extent to which government officials use public office for private gain. These questions touch on a variety of sectors within government including the public health system, regulatory agencies, the police, and the courts. Individual questions are aggregated into four sub-indices:
Government officials in the executive branch do not use public office for private gain
Government officials in the judicial branch do not use public office for private gain
Government officials in the police and the military do not use public office for private gain
Government officials in the legislature do not use public office for private gain
Only the scores provided by the experts were considered for the CPI calculations.
Scores
Scores are given on a continuous scale between from a low of 0 to a high of 1. Country Coverage
113 countries were scored in the 2016 Rule of Law index. The Index is deliberately intended to be applied in countries with vastly differing social, cultural, economic, and political systems. Data availability
The first edition was published in 2010, with slight variation in methodology and country coverage. Data for computing this index was collected between May to September 2016 using 2700 experts across the various countries. Data is publicly available online. http://worldjusticeproject.org/rule-of-law-index/ http://worldjusticeproject.org/sites/default/files/media/rolindex2016_methodology.pdf
Source 13
13. Varieties of Democracy Project 2016 Code: VDEM Data Provider
Varieties of Democracy (V-Dem) is a new approach to conceptualizing and measuring democracy. V-Dem provide a multidimensional and disaggregated dataset that reflects the complexity of the concept of democracy as a system of rule that goes beyond the simple presence of elections. The V-Dem project distinguishes between seven high-level principles of democracy: electoral, liberal, participatory, deliberative, egalitarian, majoritarian and consensual, and collects data to measure these principles. 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. With four Principal Investigators (PIs), fifteen Project Managers (PMs) with special responsibility for issue areas, more than thirty Regional Managers (RMs), 170 Country Coordinators (CCs), Research Assistants, and 2,500 Country Experts (CEs), the V-Dem project is one of the largest social science data collection projects focusing on research. V-Dem is one of the largest-ever social science data collection efforts with a database containing over 16 million data points. By April 2017, the dataset will cover 177 countries from 1900 to 2016 with annual updates to follow. Corruption Question(s)
Question: How pervasive is political corruption? The directionality of the V-Dem corruption index runs from less corrupt to more corrupt (unlike the other V-Dem variables that generally run from less democratic to more democratic situation). The corruption index includes measures of six distinct types of corruption that cover both different areas and levels of the polity realm, distinguishing between executive, legislative and judicial corruption. Within the executive realm, the measures also distinguish between corruption mostly pertaining to bribery and corruption due to embezzlement. Finally, they differentiate between corruption in the highest echelons of the executive (at the level of the rulers/cabinet) on the one hand, and in the public sector at large on the other. The measures thus tap into several distinguished types of corruption: both ‘petty’ and ‘grand’; both bribery and theft; both corruption aimed and influencing law making and that affecting implementation. Aggregation: The index is arrived at by taking the average of (a) public sector corruption index (b) executive corruption index (c) the indicator for legislative corruption and (d) the indicator for judicial corruption. In other words, these four different government spheres are weighted equally in the resulting index. Scores
Scores are given on a continuous scale between a low of 0 to a high of 1. Country Coverage
76 countries were scored in the 2016 update of the index with country coverage expected to rise considerably next year. Data availability
VDEM data can be publicly accessed through: https://www.v-dem.net/en/data/data-version-6-2/ and the codebook is available at: https://www.v-dem.net/en/reference/version-6-mar-2016/
Co
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New Zealand 90 1 AP NZL 90
Denmark 90 1 WE/EU DNK 85
Finland 89 3 WE/EU FIN 91
Sweden 88 4 WE/EU SWE 86
Switzerland 86 5 WE/EU CHE 80
Norway 85 6 WE/EU NOR 80
Singapore 84 7 AP SGP 88
Netherlands 83 8 WE/EU NLD 82
Canada 82 9 AME CAN 73
Germany 81 10 WE/EU DEU 67
Luxembourg 81 10 WE/EU LUX 85
United Kingdom 81 10 WE/EU GBR 80
Australia 79 13 AP AUS 80
Iceland 78 14 WE/EU ISL 85
Hong Kong 77 15 AP HKG 82
Belgium 77 15 WE/EU BEL 73
Austria 75 17 WE/EU AUT 73
The United States of America 74 18 AME USA 65
Ireland 73 19 WE/EU IRL 83
Japan 72 20 AP JPN 78
Uruguay 71 21 AME URY 68
Estonia 70 22 WE/EU EST 76
France 69 23 WE/EU FRA 69
Bahamas 66 24 AME BHS
Chile 66 24 AME CHL 64
United Arab Emirates 66 24 MENA ARE 86
Bhutan 65 27 AP BTN 69 59
Israel 64 28 MENA ISR 69
Poland 62 29 WE/EU POL 56
Portugal 62 29 WE/EU PRT 59
Barbados 61 31 AME BRB 48
Taiwan 61 31 AP TWN 68
Qatar 61 31 MENA QAT 82
Slovenia 61 31 WE/EU SVN 58
Saint Lucia 60 35 AME LCA 69
Saint Vincent and The Grenadines 60 35 AME VCT 58
Botswana 60 35 SSA BWA 52
Dominica 59 38 AME DMA 58
Cape Verde 59 38 SSA CPV 69 49
Lithuania 59 38 WE/EU LTU 56
Costa Rica 58 41 AME CRI 46
Brunei 58 41 AP BRN 61
Spain 58 41 WE/EU ESP 51
Georgia 57 44 ECA GEO 68
Latvia 57 44 WE/EU LVA 48
Grenada 56 46 AME GRD 58
Cyprus 55 47 WE/EU CYP 49
Czech Republic 55 47 WE/EU CZE 46
Malta 55 47 WE/EU MLT 54
Mauritius 54 50 SSA MUS 53
Rwanda 54 50 SSA RWA 47 76
Korea (South) 53 52 AP KOR 49
Namibia 52 53 SSA NAM 49
Slovakia 51 54 WE/EU SVK 34
Malaysia 49 55 AP MYS 56
Croatia 49 55 WE/EU HRV 39
Jordan 48 57 MENA JOR 60
Hungary 48 57 WE/EU HUN 43
Romania 48 57 WE/EU ROM 37
Cuba 47 60 AME CUB
Italy 47 60 WE/EU ITA 47
Saudi Arabia 46 62 MENA SAU 66
Sao Tome and Principe 46 62 SSA STP 47
Suriname 45 64 AME SUR
Montenegro 45 64 ECA MON 39
Oman 45 64 MENA OMN 67
Senegal 45 64 SSA SEN 47 36
South Africa 45 64 SSA ZAF 49
Greece 44 69 WE/EU GRC 42
Bahrain 43 70 MENA BHR 66
Ghana 43 70 SSA GHA 47 30
Solomon Islands 42 72 AP SLB 35
Serbia 42 72 ECA SCG 39
Burkina Faso 42 72 SSA BFA 47
Turkey 41 75 ECA TUR 49
Kuwait 41 75 MENA KWT 43
Tunisia 41 75 MENA TUN 37
Bulgaria 41 75 WE/EU BGR 38
Brazil 40 79 AME BRA 28
China 40 79 AP CHN 53
India 40 79 AP IND 54
Belarus 40 79 ECA BLR
Jamaica 39 83 AME JAM 41
Albania 39 83 ECA ALB 41
Bosnia and Herzegovina 39 83 ECA BIH 34
Lesotho 39 83 SSA LSO 35 20
Panama 38 87 AME PAN 43
Mongolia 38 87 AP MNG 47 38
Zambia 38 87 SSA ZMB 35 31
Colombia 37 90 AME COL 32
Indonesia 37 90 AP IDN 40
The FYR of Macedonia 37 90 ECA MKD 54
Morocco 37 90 MENA MAR 42
Liberia 37 90 SSA LBR 35 45
Argentina 36 95 AME ARG 29
El Salvador 36 95 AME SLV 32
Maldives 36 95 AP MDV 35
Sri Lanka 36 95 AP LKA 35 41
Kosovo 36 95 ECA LWI 35
Benin 36 95 SSA BEN 47 20
Peru 35 101 AME PER 39
Trinidad and Tobago 35 101 AME TTO 29
Philippines 35 101 AP PHL 29
Thailand 35 101 AP THA 37
Timor-Leste 35 101 AP TLS 24
Gabon 35 101 SSA GAB 36
Niger 35 101 SSA NER 35
Guyana 34 108 AME GUY 35 25
Algeria 34 108 MENA DZA 33
Egypt 34 108 MENA EGY 42
Côte d’Ivoire 34 108 SSA CIV 35 32
Ethiopia 34 108 SSA ETH 35 37
Bolivia 33 113 AME BOL 35 18
Vietnam 33 113 AP VNM 35 34
Armenia 33 113 ECA ARM 45
Pakistan 32 116 AP PAK 35 29
Mali 32 116 SSA MLI 35 24
Tanzania 32 116 SSA TZA 35 27
Togo 32 116 SSA TGO 24
Dominican Republic 31 120 AME DOM 24
Ecuador 31 120 AME ECU 33
Malawi 31 120 SSA MWI 24 27
Honduras 30 123 AME HND 35 26
Mexico 30 123 AME MEX 29
Paraguay 30 123 AME PRY 23
Laos 30 123 AP LAO 24 45
Azerbaijan 30 123 ECA AZE 46
Moldova 30 123 ECA MDA 24 23
Djibouti 30 123 SSA DJI 24
Sierra Leone 30 123 SSA SLE 35 19
Nepal 29 131 AP NPL 35 26
Kazakhstan 29 131 ECA KAZ 45
Russia 29 131 ECA RUS 38
Ukraine 29 131 ECA UKR 27
Iran 29 131 MENA IRN 34
Guatemala 28 136 AME GTM 35
Myanmar 28 136 AP MMR 35 23
Papua New Guinea 28 136 AP PNG 35
Kyrgyzstan 28 136 ECA KGZ 35 23
Lebanon 28 136 MENA LBN 23
Nigeria 28 136 SSA NGA 35 20
Guinea 27 142 SSA GIN 24
Mauritania 27 142 SSA MRT 35 15
Mozambique 27 142 SSA MOZ 24 25
Nicaragua 26 145 AME NIC 35 28
Bangladesh 26 145 AP BGD 24 17
Cameroon 26 145 SSA CMR 24 22
Gambia 26 145 SSA GMB 13 44
Kenya 26 145 SSA KEN 35 30
Madagascar 26 145 SSA MDG 24 19
Tajikistan 25 151 ECA TJK 24 48
Uganda 25 151 SSA UGA 13 27
Comoros 24 153 SSA COM 24
Turkmenistan 22 154 ECA TKM
Zimbabwe 22 154 SSA ZWE 13 30
Cambodia 21 156 AP KHM 13 28
Uzbekistan 21 156 ECA UZB 13
The Democratic Republic of Congo 21 156 SSA COD 13 20
Haiti 20 159 AME HTI 24 20
Burundi 20 159 SSA BDI 13 24
Central African Republic 20 159 SSA CAF 24
Chad 20 159 SSA TCD 24 10
Republic of Congo 20 159 SSA COG 13
Angola 18 164 SSA AGO
Eritrea 18 164 SSA ERI 13
Venezuela 17 166 AME VEN 13
Iraq 17 166 MENA IRQ
Guinea-Bissau 16 168 SSA GNB 13
Afghanistan 15 169 AP AFG 13
Libya 14 170 MENA LBY
Yemen 14 170 MENA YEM 2 12
Sudan 14 170 SSA SDN 2
Syria 13 173 MENA SYR
Korea (North) 12 174 AP PRK
South Sudan 11 175 SSA SSD 2
Somalia 10 176 SSA SOM
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83 95 99 79 93 90
83 98 99 85 93 90
83 94 90 85 93 90
83 86 90 85 93 90
83 88 90 85 90
83 83 80 84 93 90
83 73 91 85 76 90
83 89 71 82 85 90
83 85 80 79 85 90
83 85 80 79 85 90
83 81 80 85 72
71 80 80 80 85 90
83 81 80 78 76 72
83 80 61 85 72
83 87 77 67 72
83 79 80 74 76 72
71 74 80 79 76 72
71 74 90 72 76 69 90
71 83 71 76 54
71 74 52 75 76 72
59 77 72 76 72
71 73 66 80 70 67 69 54
71 73 52 69 76 72
59 62 76
59 73 54 61 65 76 72
47 53 81 73 67 54
71 65 64
59 64 61 58 72
59 69 60 71 66 58 66 54
59 51 71 68 67 67 54
71 65
71 77 65 50 50 54
47 40 80 67 39 72
71 65 46 61 59 58 67 54
47 65
59 63
59 57 55 67 72
59 61
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59 65 53 61 58 64 54
59 65 61 50 69 54
71 41
59 38 61 65 58 72
47 53 61 65
59 57 45 71 50 67 54
47 62
47 42 67 72
59 65 47 52 62 50 54
59 52 58 54
59 49 54
59 40 53 49
47 57 47 52 69 50 54
59 49 50 54
59 61 45 52 50 54
59 49 52 41 41 54
47 61 38 52 50 50 54
34 40 53 50 50 40 54
59 53 37 33 49 50 54
59 61 37 52 49 41 52 37
47 40 41 53 54
59 39 52 57 41 37
22 36 50 54
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34 47 32 65
47 53
47 24 50 37
47 53 44 43 32 54
47 45 33 47 41 54
47 37 52 53 41 37
34 36 41 37
34 45 53 35 50 42 54
47 44
47 57 32 32 37
47 32 44 34 41 49
47 45 46 33 39 41 36 37
34 40 50 37
47 28 37 41 61 37
34 53 37 42 38 42 37
47 61 25 37 32 51 37
47 36 42 37 32 37
34 45 39 34 41 37
47 28 56 32 46 37
34 36 47 41 37
47 36 30 41 37
47 40 37 37 37
59 40 38
47 36 33 32 37
47 36 35 38 32 34 37
34 28 41 39 41 59 37
47 45 28 37 41 34 37
34 36 39 26 50 37
34 40 42 21 19
34 28 37 41 39 37
34 45 41 19 41
34 36 37 46 32 39 37
22 45 34 41 40 37
47 27
34 28 38 41 37
47 36 27
34 32 44 36
34 45 29 30 34 37
34 43 32 37
34 36 31 31 41 36 37
22 40 44 37 32 24 37
34 45
34 32 37
34 36 44 24
34 40 25 47
22 36 32 44 37
22 32 37 32 37
47 28 32 30 32 37
34 24 38 33 32 32 37
34 36 25 32 44 37
34 28 40 41 19
34 28 32 17
34 20 32 32 37
34 32 35 32
22 32 38 27 32 38 37
47 32 23 32
34 32 27 32 37
22 32 32 32 37
34 36 38 32 32 36 19
22 36 27 41 19
34 28 32 33 26 24 37
22 36 32 27 37
34 16
47 24 24 9 37
34 40 26 32 23 37
47 20
34 40 35 23 32 19
34 24 31 21
34 20 41 32 24 16 19
34 28 41 32 24 18 19
34 36 29 32 24 23 19
34 24 35 24 33 19
22 32 27 32 19
22 20 27 24 50 19
22 28 32 19
22 32 29 21
22 20 30 32 34 37
22 28 32 26 24 24 37
22 36 26 24
22 32 29
22 28 14 32 34 37
22 28 26 24 19
22 24 25 50 19
34 28 41 19 32 11 19
34 5 32
22 28 29 21 24 28 19
34 32 14 23 32
22 16 11
22 32 26 21 24 26 37
47 2
22 20 19
22 16 17 24 15 24 37
22 16 15 17 19
22 20 32 16 19
22 20 32 24 19
22 16 15
22 20 20 20
10 28 17
22 20 23
22 20 24 19
22 16 15 19
34 12 0 29
22 16 21 14 15 19
10 20 15 19 19
22 14 15
10 20 13 16
10 12 15 19
10 28 15 14 19
22 16 11 6 22 19
10 8 15 12 19
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New Zealand 90 1 7
Denmark 90 1 7
Finland 89 3 7
Sweden 88 4 7
Switzerland 86 5 6
Norway 85 6 7
89 Singapore 84 7 8
Netherlands 83 8 7
Canada 82 9 7
Germany 81 10 7
Luxembourg 81 10 6
United Kingdom 81 10 7
81 Australia 79 13 8
Iceland 78 14 6
74 Hong Kong 77 15 7
Belgium 77 15 7
Austria 75 17 7
64 The United States of America 74 18 9
Ireland 73 19 6
78 Japan 72 20 8
Uruguay 71 21 6
70 Estonia 70 22 10
France 69 23 7
Bahamas 66 24 3
Chile 66 24 8
United Arab Emirates 66 24 7
Bhutan 65 27 5
Israel 64 28 6
59 Poland 62 29 10
Portugal 62 29 8
Barbados 61 31 3
51 Taiwan 61 31 8
Qatar 61 31 7
70 Slovenia 61 31 10
Saint Lucia 60 35 3
Saint Vincent and The Grenadines 60 35 3
Botswana 60 35 6
Dominica 59 38 3
Cape Verde 59 38 3
59 Lithuania 59 38 9
Costa Rica 58 41 7
Brunei 58 41 3
Spain 58 41 7
49 Georgia 57 44 6
65 Latvia 57 44 9
Grenada 56 46 3
Cyprus 55 47 5
59 Czech Republic 55 47 9
Malta 55 47 5
Mauritius 54 50 4
Rwanda 54 50 6
50 Korea (South) 53 52 9
Namibia 52 53 5
57 Slovakia 51 54 8
44 Malaysia 49 55 8
52 Croatia 49 55 9
Jordan 48 57 8
54 Hungary 48 57 9
57 Romania 48 57 10
Cuba 47 60 5
Italy 47 60 7
Saudi Arabia 46 62 5
Sao Tome and Principe 46 62 3
Suriname 45 64 4
44 Montenegro 45 64 4
Oman 45 64 5
Senegal 45 64 8
South Africa 45 64 7
Greece 44 69 7
Bahrain 43 70 5
Ghana 43 70 9
Solomon Islands 42 72 3
52 Serbia 42 72 7
Burkina Faso 42 72 7
Turkey 41 75 9
Kuwait 41 75 5
Tunisia 41 75 7
52 Bulgaria 41 75 9
Brazil 40 79 8
39 China 40 79 8
34 India 40 79 8
30 Belarus 40 79 7
Jamaica 39 83 6
41 Albania 39 83 7
44 Bosnia and Herzegovina 39 83 7
Lesotho 39 83 5
Panama 38 87 6
Mongolia 38 87 9
Zambia 38 87 9
Colombia 37 90 8
35 Indonesia 37 90 8
49 The FYR of Macedonia 37 90 7
Morocco 37 90 7
Liberia 37 90 7
Argentina 36 95 8
El Salvador 36 95 7
Maldives 36 95 3
Sri Lanka 36 95 7
33 Kosovo 36 95 5
Benin 36 95 6
Peru 35 101 7
Trinidad and Tobago 35 101 5
43 Philippines 35 101 9
38 Thailand 35 101 9
Timor-Leste 35 101 3
Gabon 35 101 4
Niger 35 101 5
Guyana 34 108 6
Algeria 34 108 6
Egypt 34 108 6
Côte d’Ivoire 34 108 8
Ethiopia 34 108 9
Bolivia 33 113 8
35 Vietnam 33 113 8
41 Armenia 33 113 6
Pakistan 32 116 7
Mali 32 116 6
Tanzania 32 116 9
Togo 32 116 5
Dominican Republic 31 120 6
Ecuador 31 120 6
Malawi 31 120 9
Honduras 30 123 7
Mexico 30 123 8
Paraguay 30 123 6
Laos 30 123 4
25 Azerbaijan 30 123 7
33 Moldova 30 123 9
Djibouti 30 123 3
Sierra Leone 30 123 8
Nepal 29 131 6
28 Kazakhstan 29 131 9
25 Russia 29 131 9
33 Ukraine 29 131 9
Iran 29 131 7
Guatemala 28 136 6
Myanmar 28 136 8
Papua New Guinea 28 136 5
30 Kyrgyzstan 28 136 7
Lebanon 28 136 7
Nigeria 28 136 9
Guinea 27 142 5
Mauritania 27 142 5
Mozambique 27 142 8
Nicaragua 26 145 7
Bangladesh 26 145 7
Cameroon 26 145 9
Gambia 26 145 5
Kenya 26 145 9
Madagascar 26 145 7
28 Tajikistan 25 151 6
Uganda 25 151 9
Comoros 24 153 3
25 Turkmenistan 22 154 4
Zimbabwe 22 154 9
37 Cambodia 21 156 8
25 Uzbekistan 21 156 7
The Democratic Republic of Congo 21 156 7
Haiti 20 159 5
Burundi 20 159 6
Central African Republic 20 159 4
Chad 20 159 5
Republic of Congo 20 159 5
Angola 18 164 4
Eritrea 18 164 5
Venezuela 17 166 7
Iraq 17 166 5
Guinea-Bissau 16 168 4
Afghanistan 15 169 5
Libya 14 170 4
Yemen 14 170 7
Sudan 14 170 7
Syria 13 173 5
Korea (North) 12 174 3
South Sudan 11 175 5
Somalia 10 176 5
Std
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20
16
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OECD G20 BRICS EU
2.56 86 94 79 99 y
2.46 86 94 83 99 y y
1.46 87 92 83 94 y y
1.33 85 90 83 93 y y
1.57 83 89 80 90 y
1.85 82 88 80 93 y
2.35 81 88 73 91
2.32 79 87 71 90 y y
2.03 79 85 73 90 y y
2.73 77 86 67 90 y y y
1.96 78 84 72 85 y y
2.12 77 84 71 90 y y y
1.27 77 81 72 83 y y
3.81 71 84 61 85 y
2.62 73 82 67 87
1.55 74 79 72 83 y y
1.36 73 77 71 80 y y
3.15 69 80 64 90 y y
4.31 66 80 54 83 y y
3.02 67 77 52 78 y y
2.68 66 75 59 77
2.16 66 73 54 80 y y
2.97 64 74 52 76 y y y
5.2 57 74 59 76
2.65 61 70 54 76 y
5.7 56 75 47 86
2.12 62 69 59 71
2.27 60 68 58 72 y
1.77 59 65 54 71 y y
2.58 58 66 51 71 y y
6.91 50 73 48 71
3.79 55 67 50 77
7.02 49 72 39 82
2.44 57 65 46 71 y y
6.8 49 71 47 69
1.66 57 63 58 63
3.1 55 66 52 72
0.85 58 60 58 61
5.72 50 68 49 69
1.36 57 61 53 65 y
3.17 53 63 46 69
8.85 43 72 41 71
4.09 51 65 38 72 y y
3.61 51 63 47 68
2.96 52 62 45 71 y y
4.63 48 63 47 62
5.94 46 65 42 72 y
2.24 51 59 46 65 y y
1.39 53 58 52 59 y
2.14 50 57 49 59
5.07 46 62 40 76
2.33 49 57 47 69 y y
2.03 49 55 49 59
3.09 46 57 34 61 y y
2.46 45 53 41 59
2.39 45 53 38 61 y
3.03 43 53 34 60
2.89 43 53 33 59 y y
3 43 53 37 61 y
2.9 42 52 40 54
3.34 42 53 37 59 y y y
7.54 33 58 22 66 y
0.93 44 47 44 47
7.53 32 57 32 65
2.89 41 50 39 53
7.07 33 56 24 67
2.63 40 49 32 54
2.55 41 49 33 54 y y
2.5 40 48 37 53 y y
5.96 33 53 34 66
2.89 39 48 30 54
3.34 36 47 35 47
3.69 36 48 32 57
2.47 38 46 32 49
1.8 38 44 33 49 y y
2.67 37 45 34 50
3.9 35 47 28 61
2.2 38 45 34 53 y
4.34 33 47 25 61 y y
2.39 37 44 32 53 y y
2.47 36 44 34 54 y y
3.93 33 46 28 56
1.84 36 42 34 47
1.99 36 42 30 47
1.7 37 42 34 47
6.15 29 49 20 59
2.29 34 42 32 47
1.7 35 41 32 47
2.91 34 43 28 59
2.27 34 41 28 47
2.39 33 41 26 50 y
4.97 29 45 19 54
1.74 34 40 28 42
3.43 31 43 19 45
1.76 33 39 29 46 y
2.76 31 40 22 45
5.66 27 46 27 47
1.64 34 39 28 41
3.17 31 41 27 47
3.8 29 42 20 47
2.04 32 39 29 45
2.48 31 39 29 43
1.58 33 38 29 43
2.44 31 39 22 44
5.97 25 44 24 45
0.97 33 36 32 37
3.25 29 40 24 44
3.57 29 40 25 47
2.94 29 39 22 44
2.72 29 38 22 42
2.03 31 38 28 47
1.37 31 36 24 38
2.85 28 37 18 44
2.46 29 38 19 41
4.01 26 40 17 45
2.12 28 35 20 37
1.75 29 35 24 35
1.84 29 35 22 38
4.21 25 39 23 47
1.89 28 34 24 37
1.96 28 35 22 37
2.11 28 35 19 38
3.05 25 35 19 41
1.56 28 33 24 37 y y
2.68 25 34 22 37
6.19 20 40 16 45
5.13 22 39 9 47
2.18 27 34 23 40
8.23 17 44 20 47
2.94 25 35 19 40
2.33 25 33 21 35
3.35 23 34 16 45
2.73 24 33 18 41 y y
1.97 25 32 19 36
2.47 25 33 19 35
2.58 24 32 19 35
3.69 22 34 19 50
3.01 23 32 19 35
2.08 24 31 21 35
2.5 24 32 20 37
1.98 24 31 20 37
2.54 22 31 22 36
3.62 21 33 15 35
2.57 23 31 14 37
1.98 23 29 19 35
4.13 19 33 17 50
3.04 21 31 11 41
7.2 14 38 5 44
1.72 24 29 19 35
2.88 21 30 14 34
5.26 16 34 11 48
2.24 22 29 13 37
12.81 3 45 2 47
1.32 20 24 19 25
2.59 18 26 13 37
2.82 16 26 13 37
2.35 17 25 13 32
2.13 18 25 13 32
1.81 17 23 15 24
1.53 18 23 13 24
4.04 13 27 10 28
2.67 16 24 10 24
1.78 17 23 13 24
1.68 15 21 15 22
6.24 8 28 0 34
1.41 15 20 13 22
1.87 14 20 10 20
2.09 13 20 13 22
1.74 12 17 10 20
2 11 17 10 19
3.05 9 19 2 28
2.99 9 19 2 22
1.97 10 16 8 19
1.39 10 15 10 15
3.21 5 16 2 19
2.98 5 15 0 17
Arab states
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
y
Co
un
try
CP
I2016
sco
re
CP
I2015
sco
re
Sco
re
dif
fere
nce
CP
I2016
ran
k
CP
I2015
ran
k
Ran
k
dif
fere
nce
Qatar 61 71 -10 31 22 9
Kuwait 41 49 -8 75 55 20
Bahrain 43 51 -8 70 50 20
Saudi Arabia 46 52 -6 62 48 14
Cyprus 55 61 -6 47 32 15
Lesotho 39 44 -5 83 61 22
Jordan 48 53 -5 57 45 12
Syria 13 18 -5 173 154 19
The FYR of Macedonia 37 42 -5 90 66 24
Mexico 30 35 -5 123 95 28
South Sudan 11 15 -4 175 163 12
Chile 66 70 -4 24 23 1
United Arab Emirates 66 70 -4 24 23 1
Mauritania 27 31 -4 142 112 30
Central African Republic 20 24 -4 159 145 14
Netherlands 83 87 -4 8 5 3
Mozambique 27 31 -4 142 112 30
Trinidad and Tobago 35 39 -4 101 72 29
Ghana 43 47 -4 70 56 14
Yemen 14 18 -4 170 154 16
Djibouti 30 34 -4 123 99 24
Thailand 35 38 -3 101 76 25
Uruguay 71 74 -3 21 21 0
Republic of Congo 20 23 -3 159 146 13
Korea (South) 53 56 -3 52 37 15
El Salvador 36 39 -3 95 72 23
Hungary 48 51 -3 57 50 7
Japan 72 75 -3 20 18 2
Mali 32 35 -3 116 95 21
Moldova 30 33 -3 123 103 20
Botswana 60 63 -3 35 28 7
Madagascar 26 28 -2 145 123 22
Egypt 34 36 -2 108 88 20
Norway 85 87 -2 6 5 1
Gambia 26 28 -2 145 123 22
Chad 20 22 -2 159 147 12
Ireland 73 75 -2 19 18 1
Armenia 33 35 -2 113 95 18
Lithuania 59 61 -2 38 32 6
Greece 44 46 -2 69 58 11
Libya 14 16 -2 170 161 9
Croatia 49 51 -2 55 50 5
Dominican Republic 31 33 -2 120 103 17
Algeria 34 36 -2 108 88 20
Jamaica 39 41 -2 83 69 14
The United States of America 74 76 -2 18 16 2
Comoros 24 26 -2 153 136 17
Sweden 88 89 -1 4 3 1
Benin 36 37 -1 95 83 12
Iceland 78 79 -1 14 13 1
Honduras 30 31 -1 123 112 11
Bolivia 33 34 -1 113 99 14
Cameroon 26 27 -1 145 130 15
Taiwan 61 62 -1 31 30 1
France 69 70 -1 23 23 0
Portugal 62 63 -1 29 28 1
Czech Republic 55 56 -1 47 37 10
Panama 38 39 -1 87 72 15
Burundi 20 21 -1 159 150 9
Namibia 52 53 -1 53 45 8
Tajikistan 25 26 -1 151 136 15
Austria 75 76 -1 17 16 1
Canada 82 83 -1 9 9 0
Nicaragua 26 27 -1 145 130 15
Guinea-Bissau 16 17 -1 168 158 10
Mongolia 38 39 -1 87 72 15
Peru 35 36 -1 101 88 13
Ecuador 31 32 -1 120 107 13
Turkey 41 42 -1 75 66 9
Finland 89 90 -1 3 2 1
Denmark 90 91 -1 1 1 0
Singapore 84 85 -1 7 8 -1
Sri Lanka 36 37 -1 95 83 12
The Democratic Republic of Congo 21 22 -1 156 147 9
Malaysia 49 50 -1 55 54 1
Malta 55 56 -1 47 37 10
Estonia 70 70 0 22 23 -1
Kyrgyzstan 28 28 0 136 123 13
United Kingdom 81 81 0 10 10 0
Togo 32 32 0 116 107 9
Eritrea 18 18 0 164 154 10
Poland 62 62 0 29 30 -1
Belgium 77 77 0 15 15 0
Spain 58 58 0 41 36 5
Oman 45 45 0 64 60 4
Russia 29 29 0 131 119 12
Australia 79 79 0 13 13 0
Switzerland 86 86 0 5 7 -2
Rwanda 54 54 0 50 44 6
Luxembourg 81 81 0 10 10 0
Cambodia 21 21 0 156 150 6
Cuba 47 47 0 60 56 4
Liberia 37 37 0 90 83 7
Guatemala 28 28 0 136 123 13
Germany 81 81 0 10 10 0
Malawi 31 31 0 120 112 8
Venezuela 17 17 0 166 158 8
Lebanon 28 28 0 136 123 13
Philippines 35 35 0 101 95 6
Zambia 38 38 0 87 76 11
Bhutan 65 65 0 27 27 0
Uganda 25 25 0 151 139 12
Bulgaria 41 41 0 75 69 6
Slovakia 51 51 0 54 50 4
Colombia 37 37 0 90 83 7
Senegal 45 44 1 64 61 3
Iraq 17 16 1 166 161 5
Mauritius 54 53 1 50 45 5
Ethiopia 34 33 1 108 103 5
Niger 35 34 1 101 99 2
Gabon 35 34 1 101 99 2
Sierra Leone 30 29 1 123 119 4
Morocco 37 36 1 90 88 2
Bangladesh 26 25 1 145 139 6
Kazakhstan 29 28 1 131 123 8
Slovenia 61 60 1 31 35 -4
Zimbabwe 22 21 1 154 150 4
South Africa 45 44 1 64 61 3
Indonesia 37 36 1 90 88 2
Azerbaijan 30 29 1 123 119 4
Kenya 26 25 1 145 139 6
Bosnia and Herzegovina 39 38 1 83 76 7
Montenegro 45 44 1 64 61 3
Pakistan 32 30 2 116 117 -1
Guinea 27 25 2 142 139 3
Ukraine 29 27 2 131 130 1
Nigeria 28 26 2 136 136 0
Nepal 29 27 2 131 130 1
India 40 38 2 79 76 3
Brazil 40 38 2 79 76 3
New Zealand 90 88 2 1 4 -3
Somalia 10 8 2 176 167 9
Serbia 42 40 2 72 71 1
Iran 29 27 2 131 130 1
Romania 48 46 2 57 58 -1
Tanzania 32 30 2 116 117 -1
Sudan 14 12 2 170 165 5
Cote d'Ivoire 34 32 2 108 107 1
Uzbekistan 21 19 2 156 153 3
Latvia 57 55 2 44 40 4
Hong Kong 77 75 2 15 18 -3
Vietnam 33 31 2 113 112 1
Papua New Guinea 28 25 3 136 139 -3
Haiti 20 17 3 159 158 1
Paraguay 30 27 3 123 130 -7
Kosovo 36 33 3 95 103 -8
Costa Rica 58 55 3 41 40 1
Albania 39 36 3 83 88 -5
Tunisia 41 38 3 75 76 -1
Israel 64 61 3 28 32 -4
Angola 18 15 3 164 163 1
China 40 37 3 79 83 -4
Italy 47 44 3 60 61 -1
Afghanistan 15 11 4 169 166 3
Sao Tome and Principe 46 42 4 62 66 -4
Turkmenistan 22 18 4 154 154 0
Cape Verde 59 55 4 38 40 -2
Burkina Faso 42 38 4 72 76 -4
Korea (North) 12 8 4 174 167 7
Argentina 36 32 4 95 107 -12
Laos 30 25 5 123 139 -16
Georgia 57 52 5 44 48 -4
Guyana 34 29 5 108 119 -11
Myanmar 28 22 6 136 147 -11
Timor-Leste 35 28 7 101 123 -22
Belarus 40 32 8 79 107 -28
Suriname 45 36 9 64 88 -24
Bahamas 66 #N/A #N/A 24 #N/A #N/A
Barbados 61 #N/A #N/A 31 #N/A #N/A
Brunei 58 #N/A #N/A 41 #N/A #N/A
Dominica 59 #N/A #N/A 38 #N/A #N/A
Grenada 56 #N/A #N/A 46 #N/A #N/A
Maldives 36 #N/A #N/A 95 #N/A #N/A
Saint Lucia 60 #N/A #N/A 35 #N/A #N/A
Saint Vincent and The Grenadines 60 #N/A #N/A 35 #N/A #N/A
Solomon Islands 42 #N/A #N/A 72 #N/A #N/A
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Canada 82 9 AME CAN 73
The United States of America 74 18 AME USA 65
Uruguay 71 21 AME URY 68
Bahamas 66 24 AME BHS
Chile 66 24 AME CHL 64
Barbados 61 31 AME BRB 48
Saint Lucia 60 35 AME LCA 69
Saint Vincent and The Grenadines 60 35 AME VCT 58
Dominica 59 38 AME DMA 58
Costa Rica 58 41 AME CRI 46
Grenada 56 46 AME GRD 58
Cuba 47 60 AME CUB
Suriname 45 64 AME SUR
Brazil 40 79 AME BRA 28
Jamaica 39 83 AME JAM 41
Panama 38 87 AME PAN 43
Colombia 37 90 AME COL 32
Argentina 36 95 AME ARG 29
El Salvador 36 95 AME SLV 32
Peru 35 101 AME PER 39
Trinidad and Tobago 35 101 AME TTO 29
Guyana 34 108 AME GUY 35 25
Bolivia 33 113 AME BOL 35 18
Dominican Republic 31 120 AME DOM 24
Ecuador 31 120 AME ECU 33
Honduras 30 123 AME HND 35 26
Mexico 30 123 AME MEX 29
Paraguay 30 123 AME PRY 23
Guatemala 28 136 AME GTM 35
Nicaragua 26 145 AME NIC 35 28
Haiti 20 159 AME HTI 24 20
Venezuela 17 166 AME VEN 13
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
83 85 80 79 85 90
71 74 90 72 76 69 90
59 77 72 76 72
59 62 76
59 73 54 61 65 76 72
71 65
47 65
59 63
59 61
59 65 61 50 69 54
47 62
47 40 41 53 54
34 47 32 65
47 61 25 37 32 51 37
34 36 47 41 37
47 36 33 32 37
47 45 28 37 41 34 37
34 36 37 46 32 39 37
22 45 34 41 40 37
34 45 29 30 34 37
34 43 32 37
34 40 25 47
34 36 25 32 44 37
34 32 27 32 37
22 32 32 32 37
22 36 27 41 19
34 28 32 33 26 24 37
22 36 32 27 37
22 32 27 32 19
22 28 26 24 19
22 16 15
22 16 21 14 15 19
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
PE
RC
Asia
Ris
k G
uid
e
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Canada 82 9 7 2.03 79 85
64 The United States of America74 18 9 3.15 69 80
Uruguay 71 21 6 2.68 66 75
Bahamas 66 24 3 5.2 57 74
Chile 66 24 8 2.65 61 70
Barbados 61 31 3 6.91 50 73
Saint Lucia 60 35 3 6.8 49 71
Saint Vincent and The Grenadines60 35 3 1.66 57 63
Dominica 59 38 3 0.85 58 60
Costa Rica 58 41 7 3.17 53 63
Grenada 56 46 3 4.63 48 63
Cuba 47 60 5 2.9 42 52
Suriname 45 64 4 7.53 32 57
Brazil 40 79 8 4.34 33 47
Jamaica 39 83 6 1.84 36 42
Panama 38 87 6 2.29 34 42
Colombia 37 90 8 2.27 34 41
Argentina 36 95 8 1.76 33 39
El Salvador 36 95 7 2.76 31 40
Peru 35 101 7 2.04 32 39
Trinidad and Tobago35 101 5 2.48 31 39
Guyana 34 108 6 3.57 29 40
Bolivia 33 113 8 2.85 28 37
Dominican Republic31 120 6 1.89 28 34
Ecuador 31 120 6 1.96 28 35
Honduras 30 123 7 3.05 25 35
Mexico 30 123 8 1.56 28 33
Paraguay 30 123 6 2.68 25 34
Guatemala 28 136 6 2.58 24 32
Nicaragua 26 145 7 1.98 23 29
Haiti 20 159 5 1.81 17 23
Venezuela 17 166 7 1.41 15 20
Min
Max
73 90
64 90
59 77
59 76
54 76
48 71
47 69
58 63
58 61
46 69
47 62
40 54
32 65
25 61
34 47
32 47
28 47
29 46
22 45
29 45
29 43
25 47
18 44
24 37
22 37
19 41
24 37
22 37
19 35
19 35
15 24
13 22
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
New Zealand 90 1 AP NZL 90 83
Singapore 84 7 AP SGP 88 83 73
Australia 79 13 AP AUS 80 83
Hong Kong 77 15 AP HKG 82 83
Japan 72 20 AP JPN 78 71
Bhutan 65 27 AP BTN 69 59 71 65
Taiwan 61 31 AP TWN 68 71 77
Brunei 58 41 AP BRN 61 71
Korea (South) 53 52 AP KOR 49 47 57
Malaysia 49 55 AP MYS 56 59 49
Solomon Islands 42 72 AP SLB 35 47
China 40 79 AP CHN 53 47 36
India 40 79 AP IND 54 34 45
Mongolia 38 87 AP MNG 47 38 47 36
Indonesia 37 90 AP IDN 40 34 36
Maldives 36 95 AP MDV 35 47
Sri Lanka 36 95 AP LKA 35 41 34 28
Philippines 35 101 AP PHL 29 34 36
Thailand 35 101 AP THA 37 22 40
Timor-Leste 35 101 AP TLS 24 34
Vietnam 33 113 AP VNM 35 34 34 28
Pakistan 32 116 AP PAK 35 29 34 20
Laos 30 123 AP LAO 24 45 34 16
Nepal 29 131 AP NPL 35 26 34 24
Myanmar 28 136 AP MMR 35 23 22 20
Papua New Guinea 28 136 AP PNG 35 22 28
Bangladesh 26 145 AP BGD 24 17 22 24
Cambodia 21 156 AP KHM 13 28 22 16
Afghanistan 15 169 AP AFG 13 10 20
Korea (North) 12 174 AP PRK 10 12
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
PE
RC
Asia
Ris
k G
uid
e
95 99 79 93 90
91 85 76 90 89
81 80 78 76 72 81
87 77 67 72 74
74 52 75 76 72 78
64
65 50 50 54 51
41
47 52 69 50 54 50
52 41 41 54 44
44
42 37 32 37 39
39 34 41 37 34
35 38 32 34 37
39 26 50 37 35
27
38 41 37
31 31 41 36 37 43
44 37 32 24 37 38
45
40 41 19 35
32 32 37
31 21
27 24 50 19
32 19
25 50 19
15 17 19 37
13 16
15
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
Max
New Zealand 90 1 7 2.56 86 94 79 99
Singapore 84 7 8 2.35 81 88 73 91
Australia 79 13 8 1.27 77 81 72 83
Hong Kong 77 15 7 2.62 73 82 67 87
Japan 72 20 8 3.02 67 77 52 78
Bhutan 65 27 5 2.12 62 69 59 71
Taiwan 61 31 8 3.79 55 67 50 77
Brunei 58 41 3 8.85 43 72 41 71
Korea (South) 53 52 9 2.33 49 57 47 69
Malaysia 49 55 8 2.46 45 53 41 59
Solomon Islands 42 72 3 3.34 36 47 35 47
China 40 79 8 2.39 37 44 32 53
India 40 79 8 2.47 36 44 34 54
Mongolia 38 87 9 1.7 35 41 32 47
Indonesia 37 90 8 2.39 33 41 26 50
Maldives 36 95 3 5.66 27 46 27 47
Sri Lanka 36 95 7 1.64 34 39 28 41
Philippines 35 101 9 1.58 33 38 29 43
Thailand 35 101 9 2.44 31 39 22 44
Timor-Leste 35 101 3 5.97 25 44 24 45
Vietnam 33 113 8 2.46 29 38 19 41
Pakistan 32 116 7 2.12 28 35 20 37
Laos 30 123 4 6.19 20 40 16 45
Nepal 29 131 6 2.33 25 33 21 35
Myanmar 28 136 8 3.69 22 34 19 50
Papua New Guinea 28 136 5 3.01 23 32 19 35
Bangladesh 26 145 7 4.13 19 33 17 50
Cambodia 21 156 8 2.82 16 26 13 37
Afghanistan 15 169 5 1.74 12 17 10 20
Korea (North) 12 174 3 1.39 10 15 10 15
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
United Arab Emirates 66 24 MENA ARE 86 47
Israel 64 28 MENA ISR 69 59
Qatar 61 31 MENA QAT 82 47
Jordan 48 57 MENA JOR 60 34
Saudi Arabia 46 62 MENA SAU 66 22
Oman 45 64 MENA OMN 67 47
Bahrain 43 70 MENA BHR 66 34
Kuwait 41 75 MENA KWT 43 34
Tunisia 41 75 MENA TUN 37 47
Morocco 37 90 MENA MAR 42 34
Algeria 34 108 MENA DZA 33 22
Egypt 34 108 MENA EGY 42 22
Iran 29 131 MENA IRN 34 34
Lebanon 28 136 MENA LBN 23 22
Iraq 17 166 MENA IRQ 10
Libya 14 170 MENA LBY 10
Yemen 14 170 MENA YEM 2 12 10
Syria 13 173 MENA SYR 10
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
53 81 73 67 54
64 61 58 72
40 80 67 39 72
40 53 50 50 40 54
36 50 54
24 50 37
36 41 37
40 50 37
28 37 41 61 37
28 37 41 39 37
36 32 44 37
32 37 32 37
24 35 24 33 19
20 30 32 34 37
20 15 19 19
12 15 19
28 15 14 19
8 15 12 19
PE
RC
Asia
Ris
k G
uid
e
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
United Arab Emirates66 24 7 5.7 56 75 47
Israel 64 28 6 2.27 60 68 58
Qatar 61 31 7 7.02 49 72 39
Jordan 48 57 8 3.03 43 53 34
Saudi Arabia 46 62 5 7.54 33 58 22
Oman 45 64 5 7.07 33 56 24
Bahrain 43 70 5 5.96 33 53 34
Kuwait 41 75 5 2.67 37 45 34
Tunisia 41 75 7 3.9 35 47 28
Morocco 37 90 7 1.74 34 40 28
Algeria 34 108 6 2.94 29 39 22
Egypt 34 108 6 2.72 29 38 22
Iran 29 131 7 2.47 25 33 19
Lebanon 28 136 7 2.5 24 32 20
Iraq 17 166 5 1.87 14 20 10
Libya 14 170 4 2 11 17 10
Yemen 14 170 7 3.05 9 19 2
Syria 13 173 5 1.97 10 16 8
Max
86
72
82
60
66
67
66
50
61
42
44
42
35
37
20
19
28
19
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Botswana 60 35 SSA BWA 52
Cape Verde 59 38 SSA CPV 69 49
Mauritius 54 50 SSA MUS 53
Rwanda 54 50 SSA RWA 47 76
Namibia 52 53 SSA NAM 49
Sao Tome and Principe 46 62 SSA STP 47
Senegal 45 64 SSA SEN 47 36
South Africa 45 64 SSA ZAF 49
Ghana 43 70 SSA GHA 47 30
Burkina Faso 42 72 SSA BFA 47
Lesotho 39 83 SSA LSO 35 20
Zambia 38 87 SSA ZMB 35 31
Liberia 37 90 SSA LBR 35 45
Benin 36 95 SSA BEN 47 20
Gabon 35 101 SSA GAB 36
Niger 35 101 SSA NER 35
Côte d’Ivoire 34 108 SSA CIV 35 32
Ethiopia 34 108 SSA ETH 35 37
Mali 32 116 SSA MLI 35 24
Tanzania 32 116 SSA TZA 35 27
Togo 32 116 SSA TGO 24
Malawi 31 120 SSA MWI 24 27
Djibouti 30 123 SSA DJI 24
Sierra Leone 30 123 SSA SLE 35 19
Nigeria 28 136 SSA NGA 35 20
Guinea 27 142 SSA GIN 24
Mauritania 27 142 SSA MRT 35 15
Mozambique 27 142 SSA MOZ 24 25
Cameroon 26 145 SSA CMR 24 22
Gambia 26 145 SSA GMB 13 44
Kenya 26 145 SSA KEN 35 30
Madagascar 26 145 SSA MDG 24 19
Uganda 25 151 SSA UGA 13 27
Comoros 24 153 SSA COM 24
Zimbabwe 22 154 SSA ZWE 13 30
The Democratic Republic of Congo 21 156 SSA COD 13 20
Burundi 20 159 SSA BDI 13 24
Central African Republic 20 159 SSA CAF 24
Chad 20 159 SSA TCD 24 10
Republic of Congo 20 159 SSA COG 13
Angola 18 164 SSA AGO
Eritrea 18 164 SSA ERI 13
Guinea-Bissau 16 168 SSA GNB 13
Sudan 14 170 SSA SDN 2
South Sudan 11 175 SSA SSD 2
Somalia 10 176 SSA SOM
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
59 57 55 67 72
59
59 49 54
59 40 53 49
59 49 50 54
47 44
47 53 44 43 32 54
47 45 33 47 41 54
34 45 53 35 50 42 54
47 32 44 34 41 49
59 40 38
34 28 41 39 41 59 37
34 45 41 19 41
34 32 44 36
34 32 37
34 36 44 24
47 28 32 30 32 37
34 24 38 33 32 32 37
34 32 35 32
22 32 38 27 32 38 37
47 32 23 32
34 36 38 32 32 36 19
47 20
34 40 35 23 32 19
22 28 32 26 24 24 37
22 36 26 24
22 32 29
22 28 14 32 34 37
34 28 41 19 32 11 19
34 5 32
22 28 29 21 24 28 19
34 32 14 23 32
22 32 26 21 24 26 37
47 2
22 16 17 24 15 24 37
22 20 32 24 19
22 20 20 20
10 28 17
22 20 23
22 20 24 19
22 16 15 19
34 12 0 29
22 14 15
22 16 11 6 22 19
10 16 5 19
10 8 0 15 17
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
PE
RC
Asia
Ris
k G
uid
e
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Botswana 60 35 6 3.1 55 66
Cape Verde 59 38 3 5.72 50 68
Mauritius 54 50 4 2.14 50 57
Rwanda 54 50 6 5.07 46 62
Namibia 52 53 5 2.03 49 55
Sao Tome and Principe 46 62 3 0.93 44 47
Senegal 45 64 8 2.63 40 49
South Africa 45 64 7 2.55 41 49
Ghana 43 70 9 2.89 39 48
Burkina Faso 42 72 7 2.47 38 46
Lesotho 39 83 5 6.15 29 49
Zambia 38 87 9 2.91 34 43
Liberia 37 90 7 3.43 31 43
Benin 36 95 6 3.8 29 42
Gabon 35 101 4 0.97 33 36
Niger 35 101 5 3.25 29 40
Côte d’Ivoire 34 108 8 2.03 31 38
Ethiopia 34 108 9 1.37 31 36
Mali 32 116 6 1.75 29 35
Tanzania 32 116 9 1.84 29 35
Togo 32 116 5 4.21 25 39
Malawi 31 120 9 2.11 28 35
Djibouti 30 123 3 8.23 17 44
Sierra Leone 30 123 8 2.94 25 35
Nigeria 28 136 9 1.98 24 31
Guinea 27 142 5 2.54 22 31
Mauritania 27 142 5 3.62 21 33
Mozambique 27 142 8 2.57 23 31
Cameroon 26 145 9 3.04 21 31
Gambia 26 145 5 7.2 14 38
Kenya 26 145 9 1.72 24 29
Madagascar 26 145 7 2.88 21 30
Uganda 25 151 9 2.24 22 29
Comoros 24 153 3 12.81 3 45
Zimbabwe 22 154 9 2.59 18 26
The Democratic Republic of Congo21 156 7 2.13 18 25
Burundi 20 159 6 1.53 18 23
Central African Republic 20 159 4 4.04 13 27
Chad 20 159 5 2.67 16 24
Republic of Congo 20 159 5 1.78 17 23
Angola 18 164 4 1.68 15 21
Eritrea 18 164 5 6.24 8 28
Guinea-Bissau 16 168 4 2.09 13 20
Sudan 14 170 7 2.99 9 19
South Sudan 11 175 5 3.21 5 16
Somalia 10 176 5 2.98 5 15
Min
Max
52 72
49 69
49 59
40 76
49 59
44 47
32 54
33 54
30 54
32 49
20 59
28 59
19 45
20 47
32 37
24 44
28 47
24 38
24 35
22 38
23 47
19 38
20 47
19 40
20 37
22 36
15 35
14 37
11 41
5 44
19 35
14 34
13 37
2 47
13 37
13 32
13 24
10 28
10 24
13 24
15 22
0 34
13 22
2 22
2 19
0 17
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
Denmark 90 1 WE/EU DNK 85 83
Finland 89 3 WE/EU FIN 91 83
Sweden 88 4 WE/EU SWE 86 83
Switzerland 86 5 WE/EU CHE 80 83
Norway 85 6 WE/EU NOR 80 83
Netherlands 83 8 WE/EU NLD 82 83
Germany 81 10 WE/EU DEU 67 83
Luxembourg 81 10 WE/EU LUX 85 83
United Kingdom 81 10 WE/EU GBR 80 71
Iceland 78 14 WE/EU ISL 85 83
Belgium 77 15 WE/EU BEL 73 83
Austria 75 17 WE/EU AUT 73 71
Ireland 73 19 WE/EU IRL 83 71
Estonia 70 22 WE/EU EST 76 71 73
France 69 23 WE/EU FRA 69 71
Poland 62 29 WE/EU POL 56 59 69
Portugal 62 29 WE/EU PRT 59 59
Slovenia 61 31 WE/EU SVN 58 71 65
Lithuania 59 38 WE/EU LTU 56 59 65
Spain 58 41 WE/EU ESP 51 59
Latvia 57 44 WE/EU LVA 48 59 57
Cyprus 55 47 WE/EU CYP 49 47
Czech Republic 55 47 WE/EU CZE 46 59 65
Malta 55 47 WE/EU MLT 54 59
Slovakia 51 54 WE/EU SVK 34 59 61
Croatia 49 55 WE/EU HRV 39 47 61
Hungary 48 57 WE/EU HUN 43 59 53
Romania 48 57 WE/EU ROM 37 59 61
Italy 47 60 WE/EU ITA 47 59
Greece 44 69 WE/EU GRC 42 47
Bulgaria 41 75 WE/EU BGR 38 34 53
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
PE
RC
Asia
Ris
k G
uid
e
98 99 85 93 90
94 90 85 93 90
86 90 85 93 90
88 90 85 90
83 80 84 93 90
89 71 82 85 90
85 80 79 85 90
81 80 85 72
80 80 80 85 90
80 61 85 72
79 80 74 76 72
74 80 79 76 72
83 71 76 54
66 80 70 67 69 54 70
73 52 69 76 72
60 71 66 58 66 54 59
51 71 68 67 67 54
46 61 59 58 67 54 70
53 61 58 64 54 59
38 61 65 58 72
45 71 50 67 54 65
42 67 72
47 52 62 50 54 59
52 58 54
45 52 50 54 57
38 52 50 50 54 52
37 33 49 50 54 54
37 52 49 41 52 37 57
39 52 57 41 37
37 52 53 41 37
37 42 38 42 37 52
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
Max
Denmark 90 1 7 2.46 86 94 83 99
Finland 89 3 7 1.46 87 92 83 94
Sweden 88 4 7 1.33 85 90 83 93
Switzerland 86 5 6 1.57 83 89 80 90
Norway 85 6 7 1.85 82 88 80 93
Netherlands 83 8 7 2.32 79 87 71 90
Germany 81 10 7 2.73 77 86 67 90
Luxembourg 81 10 6 1.96 78 84 72 85
United Kingdom 81 10 7 2.12 77 84 71 90
Iceland 78 14 6 3.81 71 84 61 85
Belgium 77 15 7 1.55 74 79 72 83
Austria 75 17 7 1.36 73 77 71 80
Ireland 73 19 6 4.31 66 80 54 83
Estonia 70 22 10 2.16 66 73 54 80
France 69 23 7 2.97 64 74 52 76
Poland 62 29 10 1.77 59 65 54 71
Portugal 62 29 8 2.58 58 66 51 71
Slovenia 61 31 10 2.44 57 65 46 71
Lithuania 59 38 9 1.36 57 61 53 65
Spain 58 41 7 4.09 51 65 38 72
Latvia 57 44 9 2.96 52 62 45 71
Cyprus 55 47 5 5.94 46 65 42 72
Czech Republic 55 47 9 2.24 51 59 46 65
Malta 55 47 5 1.39 53 58 52 59
Slovakia 51 54 8 3.09 46 57 34 61
Croatia 49 55 9 2.39 45 53 38 61
Hungary 48 57 9 2.89 43 53 33 59
Romania 48 57 10 3 43 53 37 61
Italy 47 60 7 3.34 42 53 37 59
Greece 44 69 7 2.5 40 48 37 53
Bulgaria 41 75 9 2.2 38 45 34 53
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Georgia 57 44 ECA GEO 68 47
Montenegro 45 64 ECA MON 39 47
Serbia 42 72 ECA SCG 39 47
Turkey 41 75 ECA TUR 49 47
Belarus 40 79 ECA BLR 47
Albania 39 83 ECA ALB 41 47
Bosnia and Herzegovina 39 83 ECA BIH 34 47
The FYR of Macedonia 37 90 ECA MKD 54 34
Kosovo 36 95 ECA LWI 35 47
Armenia 33 113 ECA ARM 45 34
Azerbaijan 30 123 ECA AZE 46 47
Moldova 30 123 ECA MDA 24 23 34
Kazakhstan 29 131 ECA KAZ 45 34
Russia 29 131 ECA RUS 38 34
Ukraine 29 131 ECA UKR 27 34
Kyrgyzstan 28 136 ECA KGZ 35 23 22
Tajikistan 25 151 ECA TJK 24 48 22
Turkmenistan 22 154 ECA TKM 22
Uzbekistan 21 156 ECA UZB 13 22
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
53 61 65 49
53 44
57 32 32 37 52
45 46 33 39 41 36 37
28 56 32 46 37 30
36 30 41 37 41
40 37 37 37 44
40 42 21 19 49
36 27 33
28 32 17 41
24 24 9 37 25
40 26 32 23 37 33
20 41 32 24 16 19 28
28 41 32 24 18 19 25
36 29 32 24 23 19 33
32 29 21 30
16 11 28
20 19 25
20 32 16 19 25
PE
RC
Asia
Ris
k G
uid
e
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
Georgia 57 44 6 3.61 51 63 47
Montenegro 45 64 4 2.89 41 50 39
Serbia 42 72 7 3.69 36 48 32
Turkey 41 75 9 1.8 38 44 33
Belarus 40 79 7 3.93 33 46 28
Albania 39 83 7 1.99 36 42 30
Bosnia and Herzegovina39 83 7 1.7 37 42 34
The FYR of Macedonia37 90 7 4.97 29 45 19
Kosovo 36 95 5 3.17 31 41 27
Armenia 33 113 6 4.01 26 40 17
Azerbaijan 30 123 7 5.13 22 39 9
Moldova 30 123 9 2.18 27 34 23
Kazakhstan 29 131 9 3.35 23 34 16
Russia 29 131 9 2.73 24 33 18
Ukraine 29 131 9 1.97 25 32 19
Kyrgyzstan 28 136 7 2.08 24 31 21
Tajikistan 25 151 6 5.26 16 34 11
Turkmenistan 22 154 4 1.32 20 24 19
Uzbekistan 21 156 7 2.35 17 25 13
Max
68
53
57
49
56
47
47
54
47
45
47
40
45
41
36
35
48
25
32
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Denmark 90 1 WE/EU DNK 85 83
New Zealand 90 1 AP NZL 90 83
Finland 89 3 WE/EU FIN 91 83
Sweden 88 4 WE/EU SWE 86 83
Switzerland 86 5 WE/EU CHE 80 83
Norway 85 6 WE/EU NOR 80 83
Netherlands 83 8 WE/EU NLD 82 83
Canada 82 9 AME CAN 73 83
Germany 81 10 WE/EU DEU 67 83
Luxembourg 81 10 WE/EU LUX 85 83
United Kingdom 81 10 WE/EU GBR 80 71
Australia 79 13 AP AUS 80 83
Iceland 78 14 WE/EU ISL 85 83
Belgium 77 15 WE/EU BEL 73 83
Austria 75 17 WE/EU AUT 73 71
The United States of America 74 18 AME USA 65 71
Ireland 73 19 WE/EU IRL 83 71
Japan 72 20 AP JPN 78 71
Estonia 70 22 WE/EU EST 76 71
France 69 23 WE/EU FRA 69 71
Chile 66 24 AME CHL 64 59
Israel 64 28 MENA ISR 69 59
Poland 62 29 WE/EU POL 56 59
Portugal 62 29 WE/EU PRT 59 59
Slovenia 61 31 WE/EU SVN 58 71
Spain 58 41 WE/EU ESP 51 59
Latvia 57 44 WE/EU LVA 48 59
Czech Republic 55 47 WE/EU CZE 46 59
Korea (South) 53 52 AP KOR 49 47
Slovakia 51 54 WE/EU SVK 34 59
Hungary 48 57 WE/EU HUN 43 59
Italy 47 60 WE/EU ITA 47 59
Greece 44 69 WE/EU GRC 42 47
Turkey 41 75 ECA TUR 49 47
Mexico 30 123 AME MEX 29 34
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
98 99 85 93 90
95 99 79 93 90
94 90 85 93 90
86 90 85 93 90
88 90 85 90
83 80 84 93 90
89 71 82 85 90
85 80 79 85 90
85 80 79 85 90
81 80 85 72
80 80 80 85 90
81 80 78 76 72
80 61 85 72
79 80 74 76 72
74 80 79 76 72
74 90 72 76 69 90
83 71 76 54
74 52 75 76 72
73 66 80 70 67 69 54 70
73 52 69 76 72
73 54 61 65 76 72
64 61 58 72
69 60 71 66 58 66 54 59
51 71 68 67 67 54
65 46 61 59 58 67 54 70
38 61 65 58 72
57 45 71 50 67 54 65
65 47 52 62 50 54 59
57 47 52 69 50 54
61 45 52 50 54 57
53 37 33 49 50 54 54
39 52 57 41 37
37 52 53 41 37
45 46 33 39 41 36 37
28 32 33 26 24 37
PE
RC
Asia
Ris
k G
uid
e
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
Denmark 90 1 7 2.46 86 94 83
New Zealand 90 1 7 2.56 86 94 79
Finland 89 3 7 1.46 87 92 83
Sweden 88 4 7 1.33 85 90 83
Switzerland 86 5 6 1.57 83 89 80
Norway 85 6 7 1.85 82 88 80
Netherlands 83 8 7 2.32 79 87 71
Canada 82 9 7 2.03 79 85 73
Germany 81 10 7 2.73 77 86 67
Luxembourg 81 10 6 1.96 78 84 72
United Kingdom 81 10 7 2.12 77 84 71
81 Australia 79 13 8 1.27 77 81 72
Iceland 78 14 6 3.81 71 84 61
Belgium 77 15 7 1.55 74 79 72
Austria 75 17 7 1.36 73 77 71
64 The United States of America74 18 9 3.15 69 80 64
Ireland 73 19 6 4.31 66 80 54
78 Japan 72 20 8 3.02 67 77 52
Estonia 70 22 10 2.16 66 73 54
France 69 23 7 2.97 64 74 52
Chile 66 24 8 2.65 61 70 54
Israel 64 28 6 2.27 60 68 58
Poland 62 29 10 1.77 59 65 54
Portugal 62 29 8 2.58 58 66 51
Slovenia 61 31 10 2.44 57 65 46
Spain 58 41 7 4.09 51 65 38
Latvia 57 44 9 2.96 52 62 45
Czech Republic 55 47 9 2.24 51 59 46
50 Korea (South) 53 52 9 2.33 49 57 47
Slovakia 51 54 8 3.09 46 57 34
Hungary 48 57 9 2.89 43 53 33
Italy 47 60 7 3.34 42 53 37
Greece 44 69 7 2.5 40 48 37
Turkey 41 75 9 1.8 38 44 33
Mexico 30 123 8 1.56 28 33 24
Max
99
99
94
93
90
93
90
90
90
85
90
83
85
83
80
90
83
78
80
76
76
72
71
71
71
72
71
65
69
61
59
59
53
49
37
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Canada 82 9 AME CAN 73 83
Germany 81 10 WE/EU DEU 67 83
United Kingdom 81 10 WE/EU GBR 80 71
Australia 79 13 AP AUS 80 83
The United States of America 74 18 AME USA 65 71
Japan 72 20 AP JPN 78 71
France 69 23 WE/EU FRA 69 71
Korea (South) 53 52 AP KOR 49 47
Italy 47 60 WE/EU ITA 47 59
Saudi Arabia 46 62 MENA SAU 66 22
South Africa 45 64 SSA ZAF 49 47
Turkey 41 75 ECA TUR 49 47
Brazil 40 79 AME BRA 28 47
China 40 79 AP CHN 53 47
India 40 79 AP IND 54 34
Indonesia 37 90 AP IDN 40 34
Argentina 36 95 AME ARG 29 34
Mexico 30 123 AME MEX 29 34
Russia 29 131 ECA RUS 38 34
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
85 80 79 85 90
85 80 79 85 90
80 80 80 85 90
81 80 78 76 72
74 90 72 76 69 90
74 52 75 76 72
73 52 69 76 72
57 47 52 69 50 54
39 52 57 41 37
36 50 54
45 33 47 41 54
45 46 33 39 41 36 37
61 25 37 32 51 37
36 42 37 32 37
45 39 34 41 37
36 39 26 50 37
36 37 46 32 39 37
28 32 33 26 24 37
28 41 32 24 18 19 25
PE
RC
Asia
Ris
k G
uid
e
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
Canada 82 9 7 2.03 79 85 73
Germany 81 10 7 2.73 77 86 67
United Kingdom 81 10 7 2.12 77 84 71
81 Australia 79 13 8 1.27 77 81 72
64 The United States of America74 18 9 3.15 69 80 64
78 Japan 72 20 8 3.02 67 77 52
France 69 23 7 2.97 64 74 52
50 Korea (South) 53 52 9 2.33 49 57 47
Italy 47 60 7 3.34 42 53 37
Saudi Arabia 46 62 5 7.54 33 58 22
South Africa 45 64 7 2.55 41 49 33
Turkey 41 75 9 1.8 38 44 33
Brazil 40 79 8 4.34 33 47 25
39 China 40 79 8 2.39 37 44 32
34 India 40 79 8 2.47 36 44 34
35 Indonesia 37 90 8 2.39 33 41 26
Argentina 36 95 8 1.76 33 39 29
Mexico 30 123 8 1.56 28 33 24
Russia 29 131 9 2.73 24 33 18
Max
90
90
90
83
90
78
76
69
59
66
54
49
61
53
54
50
46
37
41
Co
un
try
CP
I2016
Ran
k
Reg
ion
WB
Co
de
Wo
rld
Ban
k C
PIA
Wo
rld
Eco
no
mic
Fo
rum
EO
S
Glo
bal In
sig
ht
Co
un
try
Ris
k R
ati
ng
s
Bert
els
man
n
Fo
un
dati
on
Tra
nsfo
rmati
on
In
dex
South Africa 45 64 SSA ZAF 49 47 45
Brazil 40 79 AME BRA 28 47 61
China 40 79 AP CHN 53 47 36
India 40 79 AP IND 54 34 45
Russia 29 131 ECA RUS 38 34 28
Afr
ican
Develo
pm
en
t
Ban
k C
PIA
IMD
Wo
rld
Co
mp
eti
tiven
ess
Yearb
oo
k
Bert
els
man
n
Fo
un
dati
on
Su
sta
inab
le
Go
vern
an
ce In
dex
Wo
rld
Ju
sti
ce P
roje
ct
Ru
le o
f L
aw
In
dex
PR
S In
tern
ati
on
al
Co
un
try R
isk G
uid
e
Vari
ties o
f D
em
ocra
cy
Pro
ject
Eco
no
mis
t In
tellig
en
ce
Un
it C
ou
ntr
y R
ati
ng
s
Fre
ed
om
Ho
use
Nati
on
s in
Tra
nsit
Rati
ng
s
PE
RC
Asia
Ris
k G
uid
e
33 47 41 54
25 37 32 51 37
42 37 32 37 39
39 34 41 37 34
41 32 24 18 19 25
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
Max
South Africa 45 64 7 2.55 41 49 33 54
Brazil 40 79 8 4.34 33 47 25 61
China 40 79 8 2.39 37 44 32 53
India 40 79 8 2.47 36 44 34 54
Russia 29 131 9 2.73 24 33 18 41
Co
un
try
CP
I2016
Ran
k
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WB
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Bert
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Fo
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dati
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Tra
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In
dex
Denmark 90 1 WE/EU DNK 85 83
Finland 89 3 WE/EU FIN 91 83
Sweden 88 4 WE/EU SWE 86 83
Netherlands 83 8 WE/EU NLD 82 83
Germany 81 10 WE/EU DEU 67 83
Luxembourg 81 10 WE/EU LUX 85 83
United Kingdom 81 10 WE/EU GBR 80 71
Belgium 77 15 WE/EU BEL 73 83
Austria 75 17 WE/EU AUT 73 71
Ireland 73 19 WE/EU IRL 83 71
Estonia 70 22 WE/EU EST 76 71 73
France 69 23 WE/EU FRA 69 71
Poland 62 29 WE/EU POL 56 59 69
Portugal 62 29 WE/EU PRT 59 59
Slovenia 61 31 WE/EU SVN 58 71 65
Lithuania 59 38 WE/EU LTU 56 59 65
Spain 58 41 WE/EU ESP 51 59
Latvia 57 44 WE/EU LVA 48 59 57
Cyprus 55 47 WE/EU CYP 49 47
Czech Republic 55 47 WE/EU CZE 46 59 65
Malta 55 47 WE/EU MLT 54 59
Slovakia 51 54 WE/EU SVK 34 59 61
Croatia 49 55 WE/EU HRV 39 47 61
Hungary 48 57 WE/EU HUN 43 59 53
Romania 48 57 WE/EU ROM 37 59 61
Italy 47 60 WE/EU ITA 47 59
Greece 44 69 WE/EU GRC 42 47
Bulgaria 41 75 WE/EU BGR 38 34 53
Afr
ican
Develo
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Vari
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Pro
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tellig
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Un
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s
Fre
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use
Nati
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s in
Tra
nsit
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ng
s
PE
RC
Asia
Ris
k G
uid
e
98 99 85 93 90
94 90 85 93 90
86 90 85 93 90
89 71 82 85 90
85 80 79 85 90
81 80 85 72
80 80 80 85 90
79 80 74 76 72
74 80 79 76 72
83 71 76 54
66 80 70 67 69 54 70
73 52 69 76 72
60 71 66 58 66 54 59
51 71 68 67 67 54
46 61 59 58 67 54 70
53 61 58 64 54 59
38 61 65 58 72
45 71 50 67 54 65
42 67 72
47 52 62 50 54 59
52 58 54
45 52 50 54 57
38 52 50 50 54 52
37 33 49 50 54 54
37 52 49 41 52 37 57
39 52 57 41 37
37 52 53 41 37
37 42 38 42 37 52
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
mb
er
of
So
urc
es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
Max
Denmark 90 1 7 2.46 86 94 83 99
Finland 89 3 7 1.46 87 92 83 94
Sweden 88 4 7 1.33 85 90 83 93
Netherlands 83 8 7 2.32 79 87 71 90
Germany 81 10 7 2.73 77 86 67 90
Luxembourg 81 10 6 1.96 78 84 72 85
United Kingdom 81 10 7 2.12 77 84 71 90
Belgium 77 15 7 1.55 74 79 72 83
Austria 75 17 7 1.36 73 77 71 80
Ireland 73 19 6 4.31 66 80 54 83
Estonia 70 22 10 2.16 66 73 54 80
France 69 23 7 2.97 64 74 52 76
Poland 62 29 10 1.77 59 65 54 71
Portugal 62 29 8 2.58 58 66 51 71
Slovenia 61 31 10 2.44 57 65 46 71
Lithuania 59 38 9 1.36 57 61 53 65
Spain 58 41 7 4.09 51 65 38 72
Latvia 57 44 9 2.96 52 62 45 71
Cyprus 55 47 5 5.94 46 65 42 72
Czech Republic 55 47 9 2.24 51 59 46 65
Malta 55 47 5 1.39 53 58 52 59
Slovakia 51 54 8 3.09 46 57 34 61
Croatia 49 55 9 2.39 45 53 38 61
Hungary 48 57 9 2.89 43 53 33 59
Romania 48 57 10 3 43 53 37 61
Italy 47 60 7 3.34 42 53 37 59
Greece 44 69 7 2.5 40 48 37 53
Bulgaria 41 75 9 2.2 38 45 34 53
Co
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try
CP
I2016
Ran
k
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S
Glo
bal In
sig
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Co
un
try
Ris
k R
ati
ng
s
United Arab Emirates 66 24 MENA ARE 86 47
Qatar 61 31 MENA QAT 82 47
Jordan 48 57 MENA JOR 60 34
Saudi Arabia 46 62 MENA SAU 66 22
Oman 45 64 MENA OMN 67 47
Bahrain 43 70 MENA BHR 66 34
Kuwait 41 75 MENA KWT 43 34
Tunisia 41 75 MENA TUN 37 47
Morocco 37 90 MENA MAR 42 34
Algeria 34 108 MENA DZA 33 22
Egypt 34 108 MENA EGY 42 22
Djibouti 30 123 SSA DJI 24 47
Lebanon 28 136 MENA LBN 23 22
Mauritania 27 142 SSA MRT 35 15 22
Comoros 24 153 SSA COM 24 47
Iraq 17 166 MENA IRQ 10
Libya 14 170 MENA LBY 10
Sudan 14 170 SSA SDN 2 22
Yemen 14 170 MENA YEM 2 12 10
Syria 13 173 MENA SYR 10
Somalia 10 176 SSA SOM 10
Bert
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Vari
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s
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Nati
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Tra
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s
53 81 73 67 54
40 80 67 39 72
40 53 50 50 40 54
36 50 54
24 50 37
36 41 37
40 50 37
28 37 41 61 37
28 37 41 39 37
36 32 44 37
32 37 32 37
20
20 30 32 34 37
32 29
2
20 15 19 19
12 15 19
16 11 6 22 19
28 15 14 19
8 15 12 19
8 0 15 17
PE
RC
Asia
Ris
k G
uid
e
Co
un
try (
2)
CP
I2016 (
2)
Ran
k (
2)
Nu
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of
So
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es
Std
Err
or
2016
Lo
wer
CI
Up
per
CI
Min
United Arab Emirates66 24 7 5.7 56 75 47
Qatar 61 31 7 7.02 49 72 39
Jordan 48 57 8 3.03 43 53 34
Saudi Arabia 46 62 5 7.54 33 58 22
Oman 45 64 5 7.07 33 56 24
Bahrain 43 70 5 5.96 33 53 34
Kuwait 41 75 5 2.67 37 45 34
Tunisia 41 75 7 3.9 35 47 28
Morocco 37 90 7 1.74 34 40 28
Algeria 34 108 6 2.94 29 39 22
Egypt 34 108 6 2.72 29 38 22
Djibouti 30 123 3 8.23 17 44 20
Lebanon 28 136 7 2.5 24 32 20
Mauritania 27 142 5 3.62 21 33 15
Comoros 24 153 3 12.81 3 45 2
Iraq 17 166 5 1.87 14 20 10
Libya 14 170 4 2 11 17 10
Sudan 14 170 7 2.99 9 19 2
Yemen 14 170 7 3.05 9 19 2
Syria 13 173 5 1.97 10 16 8
Somalia 10 176 5 2.98 5 15 0
Max
86
82
60
66
67
66
50
61
42
44
42
47
37
35
47
20
19
22
28
19
17
U4 Expert Answer
Author(s): Maíra Martini, Transparency International, [email protected]
Reviewed by: Marie Chêne, Transparency International, [email protected]
Date: 23 February 2016 Number: 2016:3
U4 is a resource centre for development practitioners who wish to effectively address corruption challenges in
their work. Expert Answers are produced by the U4 Helpdesk – operated by Transparency International – as
quick responses to operational and policy questions from U4 Partner Agency staff.
Query
Can you provide an overview of corruption indicators in the following countries: Nepal, Afghanistan, Pakistan, Bangladesh, India, Kyrgyzstan, Tajikistan, Myanmar/ Burma?
Content
1. Corruption levels in selected Asian countries
2. Other governance and corruption-related
indicators
3. References
Summary This answer provides an overview of governance and corruption-related indicators in Afghanistan, Bangladesh, India, Kyrgyzstan, Myanmar, Nepal, Pakistan and Tajikistan.
Corruption and governance indicators in selected Asian countries
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 2
1. Corruption levels in selected
Asian countries
Available indicators show that corruption in
Afghanistan, Bangladesh, India, Kyrgyzstan,
Myanmar, Nepal, Pakistan and Tajikistan is a
significant problem, affecting a wide range of
sector and institutions.
There has been limited progress in fighting
corruption in these countries, with serious
consequences for the population. In particular, the
majority of these countries have a poor track
record of promoting transparency and
accountability. Opaqueness and secrecy have
been for many years the norm in the public sector
and more needs to be done to ensure that
government decision-making happens in a
transparent and accountable manner so that
corruption can be prevented and, when it
happens, detected and punished.
Most companies in the majority of these countries
consider corruption as a major impediment for
doing business.
Analysis of available governance indicators also
points to money laundering as an issue of concern
in the region. Most of the countries of interest
have an inadequate legal framework to combat
money laundering.
Another problem identified in these countries
relates to the lack of transparency in the
management of public finances. This is
particularly relevant since many of the countries of
interest rely extensively on funds from
development assistance. Improvements are
required to ensure that money entering the
domestic budget is spent adequately and that
there are enough safeguards to prevent abuses.
Corruption Perceptions Index (CPI)
CPI measures the level of perceived corruption in
the public sector in countries. It is a composite
index, based on global surveys and expert
assessments of corruption. Since 2012, CPI
scores can be compared from one year to the
next, but changes in scores do not necessarily
mean that a country has improved or declined. A
more thorough analysis is necessary to ensure
that the change is statistically significant
(Transparency International 2016).
The 2015 CPI assessed 168 countries and
territories, ranking them using a scale of 0 (highly
corrupt) to 100 (very clean). As shown in the table
below, all countries of interest score below 40 out
of 100. India is the best performer of the group
with a score of 38 points and occupying place 76
in the ranking. Afghanistan is the worst performer
of the group and also one of the worst overall,
ranking 166, behind only North Korea and
Somalia (Transparency International 2016).
Analysis of the scores over time shows that the
perception of corruption has remained rather
stable in the region. None of the countries of
interest has improved or declined since 2012 –
the small variations seen below, such as for
Myanmar or Afghanistan, are not statistically
significant as they fall within the confidence
interval, meaning that the change is within the
confidence interval and does not necessarily
reflect a real improvement.
Corruption Perceptions Index’s scores
Country 2015 2014 2013 2012
Afghanistan 11 12 8 8
Bangladesh 25 25 27 26
India 38 38 36 36
Kyrgyzstan 28 27 24 24
Myanmar 22 21 21 15
Nepal 27 29 31 27
Pakistan 30 29 28 27
Tajikistan 26 23 22 22
Source: Transparency International
Global Corruption Barometer (GCB)
GCB is a worldwide public opinion survey on
perceptions and experiences of corruption. As a
poll of the general public, it provides an indicator
of how corruption is viewed and experienced at
national level and how efforts to curb corruption
around the world are judged on the ground.
The last available data for the majority of the
countries of interest is from 2013. Myanmar and
Tajikistan were not part of the assessment. In the
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 3
current round (2015-2016), the survey is being
carried out at regional level.
Respondents to the 2013 GCB in the countries of
interest perceive corruption in the public sector as
a significant problem (see table below), with more
than 70% of the surveyed population perceiving
corruption as a problem or as a serious problem.
In Kyrgyzstan and in Pakistan, only 9% of the
population think corruption in the public sector is
not a problem. Similarly, a significant percentage
of individuals surveyed in these countries perceive
corruption to have increased in the two years
preceding the survey (see below). For instance, in
Nepal and in Pakistan corruption seems to have
increased for 72% of respondents. In Afghanistan,
on the other hand, the majority of people believe
that corruption levels either stayed the same
(32%) or decreased a little (22%).
The approval of governments’ action to fight
corruption varies across countries. In Afghanistan,
back in 2013, 49% of respondents thought the
government was effective in fighting corruption. In
India, only 9% of the respondents considered the
government’s actions effective, and in Nepal only
13% (see below).
Global Corruption Barometer 2013
% of citizens who
think corruption is a
problem1
% of citizens who
believe corruption has
increased2
% of citizens that believe
the gov’t is effective in
fighting corruption
Afghanistan 71 40 49
Bangladesh 76 60 25
India 80 71 9
Kyrgyzstan 91 41 17
Nepal 85 72 13
Pakistan 91 72 16
Source: Transparency International 2013
1 Answers include both citizens who consider corruption to be a problem and citizens who consider corruption to be a
serious problem. 2 Surveyed individuals answered to the following question: “Over the past two years how has the level of corruption in
this country/territory changed?” The percentage data include citizens who believe corruption increased a little or
increased a lot.
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 4
The police, political parties and public officials are
perceived by citizens in the countries of interest to
be the most corrupt among 12 institutions
analysed.
When asked about their experience with
corruption, respondents in the countries of interest
confirm that corruption is a reality in several
institutions and sectors. In Afghanistan, for
instance, 65% of citizens who had been in contact
with the judiciary in the year preceding the survey
reported paying bribes. This corroborates with the
fact that 60% of individuals surveyed perceive the
judiciary as the most corrupt institution in the
country. Of the Afghan respondents, 58% also
reported paying bribes to access registry and
licences, and 51% to the police (Transparency
International 2013).
In Bangladesh, 72% of citizens who had been in
contact with the police in the year preceding the
survey reported paying bribes. The police are also
perceived as the most corrupt institution in
Bangladesh by 60% of citizens. Of the
Bangladeshi respondents, 63% also reported
paying bribes to judiciary services and 44% to
land services.
In India, experience with corruption also seems to
be high among citizens who have had contact with
the police, with 62% of people reporting having
paid bribes. The percentage of individuals
reporting paying bribes to access registry and
permit services (61%) and land services (58%)
are also high.
In Nepal, 40% of those who had contact with the
land services reported paying bribes. In the same
country, 37% reported paying bribes to the
judiciary services and 30% to the police. Political
parties and public officials are perceived as the
most corrupt for 90% and 85% of individuals
surveyed, respectively.
In Pakistan, 75% of those who had contact with
the land services and 65% of those in contact with
the police reported paying bribes. Bribery
incidence in the utilities services and tax revenue
are also high, with 57% and 55% of those
surveyed reporting paying bribes. The police is
perceived by 82% of the population as corrupt/
extremely corrupt, followed by public officials
(81%) and political parties (76%).
Enterprise surveys
Enterprise Surveys, conducted by the World Bank
Group, measure firms’ perceptions of country
business environments and experience with
government processes, including informal
payments and corruption. They measure, among
other things, the percentage of firms that expect to
engage in bribery to access public services or
secure government contracts, and provides an
estimate of the number of businesses that
consider corruption to be a major constraint for
doing business in the country (World Bank Group
2016).
An analysis of the countries of interest shows that
in all of them, firms’ perception and experience
with corruption is high (see table below). In
Afghanistan, for instance, 34.6% of companies
surveyed reported having had to give gifts or
make informal payments to access services.
Almost 50% of the companies surveyed, reported
being expected to give gifts to secure a
government contract. The value of the gift/
informal payments is also higher than in other
countries in the region, reaching approximately
4.5% of contract value. Overall, 62.6% of
companies consider corruption a major constraint
for doing business in the country.
In Bangladesh, bribery incidence is also much
higher than the average of countries in South
Asia, with more than 47% of respondents to the
survey reporting having had to pay a bribe (see
table below), while 43.9% reported that a gift or
informal payment was requested when dealing
with utilities access, permits, licences and taxes.
The percentage of enterprises that reported being
expected to give gifts to access procurement
contracts is also high (48.9%). Corruption seems
to be particularly rampant for an import licence,
with 77.2% of firms reporting being expected to
give gifts against a regional average of 27.4%. As
a consequence, it is not surprising that 49.6% of
enterprises surveyed consider corruption as a
major impediment for doing business in the
country.
In India, enterprises’ perceptions and experiences
with corruption are slightly below the regional
average but still higher than the average of all
countries assessed (see table below). For
instance, 19.6% of firms declared that an informal
payment or gift was requested to access services.
Bribery incidence is particularly high to get an
electrical connection (according to 51.5%) and
water connection (52.5). A significant percentage
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 5
(39.8%) of firms also reported being expected to
give gifts amounting to 0.1% of contract value to
secure government contracts. Overall, 35.8% of
firms identified corruption as a major constraint for
doing business in the country.
Private sector’s perception and experience with
corruption in Kyrgyzstan is particularly high with
59.8% of firms surveyed reported having to pay
bribes to access services, compared to 17.4% of
firms surveyed in the Europe and Central Asia
region. A majority (55.1%) of respondents
reported being expected to pay an average of
2.4% of contract value in bribes/gifts to secure
government contract. Within this framework, more
than 60% of firms surveyed consider corruption a
major constraint for doing business in the country.
Bribery incidence among the private sector in
Myanmar is slightly higher than in other countries
in East Asia, with 42.9% of firms surveyed
reporting having experienced corruption to access
public services in Myanmar compared to 38.9% in
the East Asia & Pacific region. Of the firms
surveyed, 32.5% also reported being expected to
give gifts to secure government contracts and
more than 53% to get an import licence. However,
probably given political instability and other issues
afflicting the country, only 9.3% of enterprises
surveyed perceived corruption a major constraint
for doing business in Myanmar.
Nepal has the lowest rates of corruption as
experienced by the private sector in comparison
to the other countries analysed. Of those
Nepalese firms surveyed, 14.4% reported having
experienced corruption to access public services.
The average in the South Asia region is 24.8%.
Nevertheless, the percentage of surveyed firms
that report having to give gifts to secure
government contracts is high: 64.5%. The amount
expected to be paid is similar to Afghanistan and
only lower than in Pakistan: 4.4% of contract
value. Overall, corruption is perceived a major
constraint for doing business in the country by
44.7% of firms.
The private sector’s experience with corruption in
Pakistan paints a dark picture of the business
environment in the country. Of the firms surveyed
in 2013, 30.8% reported paying bribes to access
public services and 88.2% reported being
expected to give gifts to secure public contract.
Moreover, the expected value of the gift/informal
payment required to access such contracts is
extremely high: 8.2% of the contract value
(against 2.9% in the South Asia region). Overall,
68.3% of enterprises identify corruption as a major
constraint for doing business in the country.
Bribery incidence in Tajikistan is high in
comparison with other countries in the Europe and
Central Asia region but lower than in neighbouring
Kyrgyzstan, according to enterprises surveyed
(see table). Of the firms surveyed, 33.6% declared
being expected to give gifts to access public
contracts, paying an average of 2% of the contract
value. Corruption is perceived as a major
constraint for doing business in the country by
23.7%, slightly above the regional average of
22.4%.
Enterprise Surveys
% of
firms
reporting
bribery
incidence
% of
firms
expected
to give
gifts to
secure
public
contracts
% of firms
identifying
corruption
as a major
constraint
Afghanistan
(2014)
46.8 46.9 62.6
Bangladesh
(2013)
47.7 48.9 46.9
India (2014) 22.7 39.8 35.8
Kyrgyzstan
(2013)
59.8 55.1 60.2
Myanmar
(2014)
42.9 32.5 9.3
Nepal
(2013)
14.4 64.5 44.7
Pakistan
(2013)
30.8 88.2 68.3
Tajikistan
(2013)
36.8% 33.6 23.7
Source: World Bank Group, Enterprise Surveys
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 6
United Nations Convention against Corruption (UNCAC)
All the countries of interest have signed and
ratified the UNCAC. When countries ratify or
accede to the UNCAC, they must align national
laws, institutions, policies, procedures, and
programmes with the convention, and report
periodically on their anti-corruption initiatives and
impact.
Within this framework, the UNCAC Review
Mechanism analyses the level of implementation
of the convention in a given country. However, the
majority of countries of interest have not published
the assessment or an executive summary of the
findings, making it difficult for citizens, civil society
and other relevant stakeholders to assess
whether or not the country has made any
progress.
Information about the review process is published
on UNODC website country profile pages. As of
February 2016, only Bangladesh has published
the executive summary of the implementation
review, but no final report has been made
available. There is no information on whether
Afghanistan, India, Myanmar, Pakistan and
Tajikistan have already finalised their review.
Source: UNCAC website
World Bank Worldwide Governance Indicators (WGI)
WGI provide an assessment of the quality of six
broad dimensions of governance: voice and
accountability; political stability and absence of
violence; government effectiveness; regulatory
quality; rule of law; and control of corruption
(World Bank 2015).
WGI report aggregate and individual governance
indicators for 215 economies over the period
1996–2014, and can be used to observe trends
over longer periods of time. However, as is the
case with CPI, the control of corruption dimension
is also based on perceptions-based data.
The results of the last assessment, which was
conducted in 2014, show that corruption is
perceived as a significant problem across the
region. All countries of interest scored below the
40 percentile rank (100 being highest control of
corruption; see table below).
Afghanistan performs particularly poorly (six
percentile rank) and it has shown no real
improvement since 2003 when data was first
available for the country.
Kyrgyzstan follows as the second worst performer
(12 percentile rank), and the country has also not
shown any real improvement in the last years.
UNCAC status
Status
Afghanistan Signed 20/02/04, Ratified 25/8/08
Bangladesh Accession 27/02/07
India Signed 9/12/05, Ratified 9/05/11
Kyrgyzstan Signed 10/12/03, Ratified
16/09/05
Myanmar Signed 2/12/05, Ratified 20/12/12
Nepal Signed 10/12/03, Ratified
31/03/11
Pakistan Signed 9/12/03, Ratified 31/08/07
Tajikistan Accession 25/09/06
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 7
India and Nepal are the best performers among
the group. India has maintained a score oscillating
between the 40 and 39 percentile ranks from 1996
onwards. Nepal’s 2014 assessment puts the
country in the 36 percentile rank. An analysis of
the country’s scores in previous years shows quite
a lot of variation (60 percentile rank in 1996 to 28
in 2009, for example), but this variation could be
explained by the number of sources used rather a
real change in perception.
Source: World Bank Worldwide Governance
Indicators Control of Corruption
2. Other governance and
corruption-related indicators
Anti-money laundering index (AML Index)
The Basel AML Index scores provide an overall
picture of a country’s anti-money laundering
framework and risk level. The index takes into
consideration a country’s money
laundering/terrorist financing risk, corruption risk,
financial transparency and standards, public
transparency and accountability as well as the
political and legal risk (Basel Institute on
Governance 2015).
Four of the countries of interest are considered as
having extremely high risks.
For instance, Afghanistan ranks second out of 152
countries assessed. It is considered to have a
high risk of money laundering with an overall
score of 8.48, where 0 means low risk and 10 high
risk. Tajikistan ranks third with a score of 8.26.
Myanmar ranks 10 with a score of 7.78, and
Nepal ranks 12 and scores 7.62.
Other countries of interest also perform relatively
poorly: Pakistan is in position 44 with a score of
6.52; Bangladesh ranks 52 and scores 6.43;
Kyrgyzstan ranks 56 and scores 6.27, and India is
the best performer in place 79 of the ranking with
a score of 5.77.
Financial Action Task Force (FATF)
FATF is an inter-governmental body that has as
its main objective to set standards and promote
effective implementation of legal, regulatory and
operational measures for combating money
laundering, terrorist financing and other related
threats to the integrity of the international financial
system.
As part of its review mechanism, the FATF
identifies jurisdictions which have strategic anti-
money laundering or terrorist financing (AML/CFT)
deficiencies for which the body develops an action
plan recommending improvements. In February
2016, FATF published a note containing the latest
review update and the list of jurisdictions
considered as having strategic deficiencies. Of the
countries of interest, Afghanistan and Myanmar
are part of the list and the FATF will continue to
monitor their progress in the future.
In 2012, Afghanistan made a high-level political
commitment to work with the FATF and the
relevant regional group to improve its strategic
AML/CFT deficiencies. In spite of recent
measures undertaken by the government, the
FATF considered that more needs to be done to
ensure a sound AML framework. In particular, the
FATF has recommended: (i) implementation of
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 8
the legal framework for identifying, tracing and
freezing terrorist assets; (ii) establishment of an
adequate AML/CFT supervisory and oversight
programme for all financial sectors; and (iii)
implementation of effective controls for cross-
border cash transactions (FATF 2016).
Myanmar’s commitment to the FATF was made in
2010 and since then has taken a series of
important steps to address key deficiencies. It
has, among others, improved its legal framework
to criminalise money laundering and terrorist
financing and implemented the legal framework
for identifying, tracing and freezing terrorist
assets. The FATF will conduct an on-site visit to
confirm that the process of implementing the
required reforms and actions is underway to
address deficiencies previously identified by the
FATF.
Freedom in the World
Freedom in the World is Freedom House’s
flagship annual report, assessing the condition of
political rights and civil liberties around the world.
Countries are classified into free, partially free and
not free (Freedom House 2016).
Considering the countries of interest, Bangladesh
and Tajikistan are among the countries that
experienced a decline in freedom in comparison
with previous assessments. Bangladesh is
assessed as partially free and Tajikistan as not
free. India is the only country of interest assessed
as free; all the others are considered partially free
(Kyrgyzstan, Nepal, Pakistan) or not free
(Afghanistan and Myanmar) (Freedom House
2016).
Global Right to Information Rating (RTI), Access Info & Centre for Law and Democracy
RTI Rating comparatively assesses the strength
of legal frameworks for the right to information
from around the world.
As of 2016, 103 countries have been assessed
and ranked according to the strength of their legal
framework. The rating does not analyse how well
the legal framework has been implemented.
Within this framework, India is the best performer
among the countries of interest, ranking third out
of the 103 countries assessed. Bangladesh ranks
20; Nepal 23, Kyrgyzstan 28, Afghanistan 64, and
Pakistan and Tajikistan are the worst performers
ranking 85 and 101, respectively (RTI 2016).
Myanmar still lacks an access to information law.
Government Defence Anti-Corruption Index (GI), Transparency International
GI assesses the existence, effectiveness and
enforcement of institutional and informal controls
to manage the risk of corruption in defence and
security institutions.
Many of the countries of interest have
experienced a massive expansion in military
expenditure in the past years, making
transparency and accountability in the sector even
more relevant: India’s military spending has
increased 147% in the last decade, Pakistan by
107%, and Bangladesh by 202% (TI Defence &
Security 2015).
In spite of such expansion, the risks of corruption
in the defence establishments are found to be
significant. According to the index, in Bangladesh
and India, corruption risks are assessed as high,
in Afghanistan and Pakistan as very high, and in
Myanmar as critical (TI Defence & Security
2015).3
The report highlights several problematic issues:
In Pakistan, for example, there is no transparency
or effective oversight of the military’s business
empire, estimated in 2007 to be worth $10 billion.
In India, in 2013, the army was found to be
illegally running golf courses on government-
owned land; air force officials have used defence
land for unauthorised use such as the building of
shopping malls and cinema halls. India’s defence
institutions have also been found to be involved in
the exploitation of the country’s natural resources.
In Bangladesh, the report provides evidence of
military officials involved in the country’s natural
resource exploitation through timber businesses
and the “grabbing” of land and forest resources.
At the institutional level, the military operates a
range of businesses directly and indirectly through
Sena Kalyan Shangstha, a retired officials’ welfare
association (TI Defence & Security 2015).
3 Kyrgyzstan, Nepal and Tajikistan were not part of the
assessment.
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 9
Open Budget Index (OBI)
OBI, produced by the International Budget
Partnership, assesses government budget
transparency, focusing specifically on whether the
government provides the public with timely access
to comprehensive information contained in eight
key budget documents in accordance with
international good practice standards
(International Budget Partnership 2015).
All the countries of interest perform poorly in the
assessment and do not publish sufficient
information on their budgets. The majority of
them, including Bangladesh, Kyrgyzstan, India,
Pakistan, and Afghanistan published only limited
information. Nepal and Tajikistan published
minimal information and Myanmar scant or none
(International Budget Partnership 2015). More
information on the performance of each of the
countries is available here.
Open Government Index
The World Justice Project Open Government
Index measures government openness using four
dimensions: publicised laws and government
data, right to information, civic participation, and
complaint mechanisms. Scores range from 0 to 1
(greatest openness).
Among the countries of interest4, India is the best
performer followed by Nepal. India ranks 37 out of
102 countries assessed and first among countries
in the South Asia region, with an overall score of
0.57. Nepal ranks 40 and second in the South
Asia region, with a score of 0.56.
All the other countries have a relatively poor
performance. Kyrgyzstan ranks 64 out of 102
countries and 8 among the 13 countries assessed
in the Eastern Europe and Central Asia region,
with a score of 0.50. Bangladesh is in position 73
of the overall ranking and ranks four among South
Asian countries, with a score of 0.47. Pakistan
ranks 83 in the overall rank and fifth among South
Asian countries, followed by Afghanistan in
position 89 of the rank and the worst performer in
South Asia. Finally, Myanmar performs very
poorly, ranking 100 out of 102 countries assessed
with a score of 0.32. Myanmar is also the worst
performer in the East Asia & Pacific region.
4 Tajikistan is not part of the assessment.
The World Justice Project Rule of Law Index
The Rule of Law Index produced by the World
Justice Project provides original data on how the
rule of law is experienced by the general public in
102 countries around the globe.5 The index is
based on household and experts surveys covering
eight categories: Constraints on Government
Powers, Absence of Corruption, Open
Government, Fundamental Rights, Order and
Security, Regulatory Enforcement, Civil Justice,
and Criminal Justice (World Justice Project 2015).
The absence of corruption category analyses
three forms of corruption: bribery, improper
influence by public or private interests, and
misappropriation of public funds or other
resources. These three forms of corruption are
examined with respect to government officers in
the executive branch, the judiciary, the military
and police, and the legislature, encompassing a
wide range of possible situations in which petty
and grand corruption can occur (World Justice
Project 2015).
Afghanistan is the worst performer in the 2015
assessment. The country ranked 102 out of 102
countries assessed in the Rule of Law Index, with
an overall score of 0.35 (scores range from 0 –
lowest - to 1 – highest). In the category “absence
of corruption”, Afghanistan received a score of
0.23 (1 being highest), with the judiciary and the
legislature perceived as most corrupt among the
areas assessed (executive, legislature, military
and police and judiciary).
Pakistan occupied position 98 in the ranking, with
an overall score of 0.38. The perception of
absence of corruption is also very low (0.35). The
majority of individuals surveyed perceive
corruption within the military / police to be
relatively higher than in the other assessed areas.
Bangladesh ranked 93 and received an overall
score of 0.42. Its score on absence of corruption
is even lower (0.27), with the military / police
perceived as being the most corrupt among the
areas assessed.
Myanmar ranked 92 out of 102 countries
assessed, with an overall score of 0.42. The
country also performs poorly in the absence of
5 Tajikistan is not part of the assessment.
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 10
corruption category, receiving a score of 0.42. The
judiciary is the area perceived as being more
prone to corruption among the areas assessed.
Kyrgyzstan ranked 74, with an overall score of
0.47. The country performs poorly on the absence
of corruption indicator (0.3), with the legislature
among the areas perceived as most prone to
corruption.
India ranked 59, with a score of 0.51. The country
also performs below average in the category
absence of corruption (0.4). The legislature is
perceived as the most prone to corruption among
the areas assessed.
Nepal ranked 48 out of the 102 countries
assessed with an overall score of 0.53. The
country is the best overall performer across the
countries of interest. The category absence of
corruption received a score of 0.39, and
corruption within the legislature appears as the
most problematic area.
World Press Freedom Index
The Reporters without Borders World Press
Freedom Index ranks the performance of 180
countries according to a range of criteria that
include media pluralism and independence,
respect for the safety and freedom of journalists,
and the legislative, institutional and infrastructural
environment in which the media operate. The
index is based upon the organisation's
assessment of the countries’ press freedom records in the previous year (Reporters without
Borders 2015).
Overall, the 2015 assessment shows that media
freedom is in retreat on all five continents.
Considering the countries of interest, a slight
improvement in media freedom can be seen in
some of them compared to the 2014 assessment,
including Nepal, which was up 15 places thanks to
a decline in violence by the security forces against
journalists, and Kyrgyzstan, up nine places
(Reporters without Borders 2015).
Nevertheless, all the countries of interest in the
Asia Pacific region are assessed as having either
“noticeable problems”, scoring between 25.01 to
35 points (100 being the worst possible), such as
Nepal; or being in a “difficult situation” with scores
between 35.01to 55 points, including Afghanistan,
Bangladesh, Myanmar and Pakistan.
In Central Asia, Tajikistan is also assessed as
having noticeable problems (score of 36.19).
Kyrgyzstan performs a bit better with a score of
30.69 (Reporters without Borders 2015).
3. References
Basel Institute on Governance. 2015. 2015 Basel AML Index.
International Centre for Asset Recovery.
https://index.baselgovernance.org/ranking
FATF. 2016. Improving global AML/CFT compliance.
http://www.fatf-gafi.org/countries/a-
c/afghanistan/documents/fatf-compliance-february-2016.html
Freedom House. 2016. Freedom in the World.
https://freedomhouse.org/report/freedom-world/freedom-
world-2016
International Budget Partnership. 2015. Open Budget Index.
http://www.internationalbudget.org/wp-
content/uploads/OBS2015-OBI-Rankings-English.pdf
Transparency International Defence & Security. 2015.
Government Defence Anti-Corruption Index: Afghanistan.
http://government.defenceindex.org/countries/afghanistan/
Transparency International. 2016. Corruption Perceptions
Index.
www.transparency.org/cpi
Transparency International. 2013. Global Corruption
Barometer.
www.transparency.org/gcb
Transparency International Defence & Security. 2015.
Government Defence Anti-Corruption Index. Regional
Results Asia Pacific.
http://government.defenceindex.org/downloads/docs/GI-Asia-
Pacific-Regional-Results-web.pdf
Reporters without Borders. 2015. World Press Freedom
Index.
https://index.rsf.org/#!/index-details
RTI. 2016. Global Right to Information Rating.
http://www.rti-rating.org/
World Bank. 2014. Worldwide Governance Indicators.
http://info.worldbank.org/governance/wgi/index.aspx#home
World Bank Group. 2015. Enterprise Surveys.
http://www.enterprisesurveys.org/
World Justice Project. 2015. WJP Open Government Index.
http://data.worldjusticeproject.org/opengov/#/groups/IND
Corruption and governance indicators in selected Asian countries
www.U4.no U4 EXPERT ANSWER 11
World Justice Project, 2015. Rule of Law Index.
http://data.worldjusticeproject.org/
|
By MizzimaOn Thursday, 28 January 2016
Home » News » Domestic » Myanmar ranked 147 on Corruption Perceptions Index
Myanmar ranked 147 on CorruptionPerceptions Index
Photo: Hong Sar/Mizzima
Myanmar has been ranked 147 out of 168 countries in the 2015 edition ofTransparency International’s Corruption Perceptions Index. The country sharesjoint position with the Democratic Republic of the Congo and Chad.
Overall, two-thirds of the 168 countries on the 2015 index scored below 50, ona scale from 0 (perceived to be highly corrupt) to 100 (perceived to be veryclean). Myanmar had a score of 22 a slight increase from 21 the previous year.
“The 2015 Corruption Perceptions Index clearly shows that corruption remains ablight around the world. But 2015 was also a year when people again took tothe streets to protest corruption. People across the globe sent a strong signalto those in power: it is time to tackle grand corruption,” said José Ugaz, Chair
of Transparency International.
Grand corruption is the abuse of high-level power that benefits the few at the expense of the many and causes serious andwidespread harm to individuals and society. It often goes unpunished.
Brazil was the biggest decliner in the index, falling 5 points and dropping 7 positions to a rank of 76. The unfolding Petrobras
MYANMAR TV
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U.S. announces new increase in support to World Food Programme (WFP)
scandal brought people into the streets in 2015 and the start of the judicial process may help Brazil stop corruption.
Denmark took the top spot for the 2nd year running, with North Korea and Somalia the worst performers, scoring just 8 pointseach.
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