The Bayesian Corruption Index

Worldwide corruption perception estimates from 1984 to 2014



Map of the BCI values in 2014.
Notes: Countries and regions marked by high corruption are indicated in red, those with little or no corruption are colored dark green.
Source: Standaert, 2015.




About

The Bayesian Corruption Index is a composite index of the perceived overall level of corruption:
  • Corruption: With corruption, we refer to the “abuse of public power for private gain”
  • Perceived corruption: Given the hidden nature of corruption, direct measures are hard to come by, or inherently flawed (e.g. the number of corruption convictions). Instead, we amalgamate the opinion on the level of corruption from inhabitants of the country, companies operating there, NGOs, and officials working both in governmental and supra-governmental organizations.
  • Composite: It combines the information of 20 different surveys and more than 80 different survey questions that cover the perceived level of corruption.
It is an alternative to the other well-known indicators of corruption perception: the Corruption Perception Index (CPI) published by Transparency International and the Worldwide Governance Indicators (WGI) published by the World Bank. Methodologically, it is most closely related to the latter as the methodology used in the construction of the BCI can be seen as an augmented version of the Worldwide Governance Indicators’ methodology.
The augmentation allows an increase of the coverage of the BCI: a 60% to 100% increase relative to the WGI and CPI, respectively. In addition, in contrast to the WGI or CPI, the underlying source data are entered without any ex-ante imputations, averaging or other manipulations. This results in an index that truly represents the underlying data, unbiased by any modeling choices of the composer.

The construction of the index and its advantages are described in greater detail in the paper listed below.




How do I use and interpret the BCI values?


The BCI index values lie between 0 and 100, with an increase in the index corresponding to a rise in the level of corruption. This is a first difference with CPI and WGI where an increase means that the level of corruption has decreased.

There exists no objective scale on which to measure the perception of corruption and the exact scaling you use is to a large extent arbitrary. However, we were able to give the index an absolute scale: zero corresponds to a situation where all surveys say that there is absolutely no corruption. On the other hand, when the index is one, all surveys say that corruption is as bad as it gets according to their scale. This is another difference with CPI and WGI, where the scaling is relative. They are rescaled such that WGI has mean 0 and a standard deviation of 1 in each year, while CPI always lies between 0 and 100.

In contrast, the actual range of values of the BCI will change in each year, depending how close countries come to the situation where everyone agrees there is no corruption at all (0), or that corruption is as bad as it can get (100). By way of illustration, the figure below shows the histogram of the BCI in 2014. The country with the lowest level of corruption is New Zealand (15.4), while corruption is most problematic in Somalia (70.9).

The absolute scale of the BCI index was obtained by rescaling all the individual survey data such that zero corresponds to the lowest possible level of corruption and 1 to the highest one. We subsequently rescaled the BCI index such that when all underlying indicators are zero (one), the expected value of the BCI index is zero (one).

Histogram of the 2014 BCI values


Significance of changes over time and differences between countries


Unlike the WGI and BCI indicators, the values of the BCI index can be used to compute the significance of changes in the level of corruption over time or differences between countries.

The reason is that estimating the BCI index results not just in a point estimate (BCIi,t) of the level of corruption for each country i and year t. Instead it returns thousand of draws from the entire distribution of this estimate. The significance can easily be computed using these draws. If less than 5% of all draws, the level of corruption of country A is lower than that of B, we can state that B’s level of corruption is significantly lower at the 5% significance level.

For example: in 2014, the level of corruption of Liechtenstein (20.42) is more than 5% points higher than that of New Zealand (15.04) but this difference is not significant (at the 5% level). In contrast, while Denmark’s score (18.89) is lower than that of Liechtenstein, it is significantly higher than that of New Zealand. The reason is that the confidence interval of Liechtenstein’s estimate is almost twice as wide as that of Denmark.

Every year, the Corruption Perception Index will publish a ranking of countries based on their level of corruption. However, these rankings have been criticized for being very sensitive to the smallest of differences in the actual scores of countries. To address this problem, the ranking of the countries in the BCI dataset only uses these significant differences. A country will have a higher rank if, and only if, it is significantly more corrupt than at least one country with a lower ranking. This allows for a more meaningful comparison of the level of corruption between countries than a simple ranking would allow. Only if a decrease in corruption is large enough, will a country actually achieve a higher ranking.

A dataset containing a thousand random draws of the BCI values is posted on the website, allowing users to perform their own analyses. The article “Divining the level of corruption” (Standaert, 2014) discusses various ways in which these draws can be used to make robust inferences. Desbordes and Koop (2015) discuss an alternative (frequentist) way in “The known unknowns of governance.”




Cite


Please cite as: Samuel Standaert (2015) "Divining the Level of Corruption: a Bayesian State Space Approach", Journal of Comparative Economics, 43 (3) 782-803. DOI: 10.1016/j.jce.2014.05.007




Download


Click here to download the worldwide BCI indicator in csv format or in Stata 12's dta format from 1984 to 2012. It contains the following variables:
  • iso: the 3 digit ISO codes
  • name: the country names
  • year
  • BCI: the Bayesian Corruption Indicator
  • BCI_std: the standard deviation of each estimate
  • ranking: ranking of countries per year, based on significant differences
  • sources: number of individual indicators available in that year
In order to correctly take the uncertainty of estimates into account (for example when studying changes over time), the extended BCI dataset contains all of the information above, as well as a sample of 1000 randomly drawn values from the posterior distribution. Each of these draws reflects the time-dependency of the corruption estimates and can be used such that any subsequent computations or (Bayesian) estimations completely take the inherent uncertainty of the corruption estimates into account. To gain access to this dataset, simply send an email to
Samuel.Standaert@UGent.be




Contact


For questions or comments, contact me at Samuel.Standaert@UGent.be
Funding provided by the Flemish Fund for Scientific Research (FWO) and the Belgian National Bank.






Significant rankings in 2014


The following table ranks all countries according to significant differences in their BCI score. A country has rank x if it is significantly less corrupt than at least one country with rank x+1. For example, while Denmark has a higher BCI score than Sweden, the difference not big enough to be significant and both have rank 1.
Ranking Country name BCI stand. dev.
0 Finland 17.11291 1.511722
0 New Zealand 15.30374 1.490112
0 Liechtenstein 20.42452 2.895225
0 Singapore 17.00397 1.493587
1 Netherlands; The 22.11505 1.533328
1 Bermuda 25.70388 3.429705
1 Switzerland 20.7061 1.515558
1 Qatar 20.65844 1.452646
1 Norway 20.01638 1.515661
1 Luxembourg 20.72681 1.486286
1 Anguilla 26.14614 3.640825
1 Denmark 18.89078 1.513569
1 United Arab Emirates 21.89025 1.462716
1 Sweden 21.92458 1.517101
2 Andorra 25.79503 3.384658
2 Netherlands Antilles 31.25975 3.55631
2 Cayman Islands 28.17068 2.90285
2 Saint Lucia 26.14327 3.160184
2 Hong Kong SAR; China 24.29333 1.504359
2 Japan 25.2046 1.50879
2 Ireland 25.43923 1.575514
2 Germany 27.45856 1.570554
2 Iceland 25.84319 1.522012
2 Bahamas; The 28.18556 2.71265
2 Saint Kitts and Nevis 29.12919 3.631434
2 Antigua and Barbuda 28.76499 3.480403
2 Channel Islands 28.25625 4.198492
2 Aruba 30.16115 3.025376
2 United Kingdom 26.20193 1.513143
2 Greenland 27.954 3.734113
2 Canada 26.48467 1.528952
2 Australia 26.69915 1.465276
2 Saint Vincent and the Grenadines 28.02118 3.278393
3 Guam 32.82679 3.615013
3 French Guiana 31.14218 2.891837
3 Virgin Islands (US) 33.77722 3.605662
3 Rwanda 28.88322 1.537757
3 Reunion 33.64158 3.616768
3 Dominica 32.43019 3.115751
3 Belgium 29.45454 1.486096
3 Brunei Darussalam 30.58625 1.969181
3 Uruguay 31.05557 1.614913
3 Oman 31.52852 1.533604
3 Chile 30.69321 1.592247
3 Saudi Arabia 32.08049 1.534977
4 Austria 33.21942 1.538522
4 Nauru 46.47737 8.239945
4 Bhutan 36.84966 2.00462
4 American Samoa 38.51171 3.603691
4 Bahrain 33.98215 1.52582
4 Macao SAR; China 37.26621 2.371162
4 Palau 50.9367 8.565971
4 Grenada 36.70076 3.07451
4 Estonia 32.69712 1.565235
4 Barbados 35.78271 1.540072
4 France 33.39292 1.549358
4 Martinique 36.13492 3.391442
5 Samoa 41.15735 3.063844
5 Georgia 36.94461 1.613683
5 Malaysia 38.40291 1.547137
5 Vanuatu 41.22598 3.191209
5 Cyprus 38.91918 1.572298
5 United States 36.55696 1.537145
5 Botswana 38.33523 1.603971
5 Israel 37.55096 1.578329
5 Taiwan; province of China 36.47764 1.538419
5 Cuba 40.70272 2.685516
6 Marshall Islands 48.19703 5.575447
6 Poland 43.35084 1.630114
6 Cape Verde 41.15395 1.58911
6 Malta 43.22354 1.568608
6 Portugal 40.17162 1.611759
6 Jordan 42.3728 1.545217
6 Kiribati 43.88436 3.06235
6 Fiji 46.18017 3.330195
6 Micronesia; Federated States of 45.61199 3.349717
7 Costa Rica 45.14185 1.691019
7 Tunisia 46.41325 1.688597
7 Puerto Rico 43.85739 1.563987
7 Montenegro 46.15821 1.572731
7 Mauritius 43.75022 1.585082
7 Tuvalu 48.20965 3.577218
7 Korea; Republic of 47.01177 1.632405
7 Spain 45.81554 1.69789
7 Macedonia; the former Yugoslav Republic of 44.3711 1.643719
7 Seychelles 45.19896 1.665812
7 Palestinian authority; West Bank and Gaza 49.26038 2.931551
7 Gambia; The 45.39096 1.565311
7 Latvia 46.70143 1.63618
7 Slovenia 45.71141 1.606776
8 Morocco 47.5635 1.635706
8 Liberia 50.47889 2.84901
8 Bosnia-Herzegovina 51.30381 2.038961
8 Sao Tome and Principe 51.30457 2.580914
8 South Africa 50.2835 1.679332
8 Lithuania 47.37046 1.644391
8 Kuwait 47.95704 1.613972
8 Djibouti 51.61469 2.555734
8 China 47.64567 1.583527
8 Belarus 50.75587 2.606853
8 Lesotho 49.04944 1.620531
8 Kosovo 50.69155 2.714863
8 Solomon Islands 50.26783 3.03282
8 Maldives 53.07365 2.958623
8 Eritrea 52.56839 2.705286
8 Lao; People's Democratic Republic of 52.10073 2.333209
8 Tonga 52.19205 3.142369
8 Turkey 47.37593 1.620931
8 Namibia 48.26109 1.643748
9 Egypt; Arap Republic of 54.05168 1.670319
9 Romania 54.17169 1.698116
9 Indonesia 53.11787 1.638235
9 Thailand 54.7754 1.651623
9 Serbia 54.92931 1.716405
9 Kazakhstan 52.4282 1.647104
9 Armenia 54.0205 1.688567
9 Azerbaijan 53.96277 1.685322
9 Viet Nam 54.87807 1.682444
9 Gabon 52.23746 1.979679
9 Senegal 54.46265 1.721111
9 Croatia 52.45889 1.660783
9 Panama 53.96136 1.750821
9 Hungary 51.43204 1.647114
9 South Sudan 60.31121 5.476564
9 Tajikistan 52.69576 1.630999
9 Italy 54.4729 1.673661
9 Czech Republic 53.89086 1.689983
9 Zambia 53.02699 1.662755
9 Niger 55.72139 2.518939
9 Bulgaria 54.22601 1.672913
9 Sri Lanka 53.04787 1.650193
9 Jamaica 54.00863 1.65038
9 Iran; Islamic Republic of 52.98623 1.650316
9 Ethiopia 54.52135 1.688569
9 India 54.44148 1.636619
9 Ghana 53.25646 1.708901
9 Swaziland 51.98512 1.616768
10 Kenya 58.65153 1.75128
10 Trinidad and Tobago 57.62801 1.658342
10 Mongolia 57.87292 1.703343
10 Cote d'Ivoire 55.97467 1.660882
10 Tanzania; United Republic of 58.50023 1.710934
10 Ecuador 57.31291 2.142943
10 Syrian Arab Republic 58.8425 2.509536
10 Peru 55.58389 1.712549
10 El Salvador 55.33266 1.719273
10 Guyana 57.97472 1.648626
10 Suriname 56.20963 1.661167
10 Colombia 58.56053 1.743363
10 Brazil 56.33785 1.73444
10 Honduras 58.13248 1.708817
10 Comoros 60.03138 2.831972
10 Philippines 55.95221 1.648318
10 Malawi 55.82753 1.679867
10 Guatemala 57.128 1.774799
10 Nicaragua 57.75349 1.761495
10 Albania 57.14623 1.742697
10 Bolivia; Plurinational State of 58.35564 1.781123
10 Greece 55.20106 1.71597
10 Timor-leste 56.71439 1.632445
10 Slovakia 57.16147 1.664317
10 Belize 58.2616 3.08478
10 Mexico 56.78981 1.72057
11 Burkina Faso 60.3859 1.739451
11 Congo; Republic of 63.92278 2.774713
11 Central African Republic 63.1887 2.640536
11 Madagascar 61.12887 1.735128
11 Zimbabwe 59.02651 1.725708
11 Mozambique 59.25505 1.741078
11 Russian Federation 59.13538 1.719492
11 Iraq 61.97423 2.682116
11 Benin 61.21629 2.13727
11 Moldova; Republic of 60.13398 1.741508
11 Afghanistan 64.00063 2.947875
11 Dominican Republic 61.8275 1.821356
11 Togo 61.54382 2.80486
11 Pakistan 59.76432 1.743949
11 Algeria 60.69022 1.741764
11 Cambodia 58.7837 1.717342
11 Libya 60.31041 1.718593
11 Sierra Leone 60.56484 1.837303
11 Nepal 60.30784 1.706709
11 Cameroon 62.11279 1.750659
11 Uzbekistan 62.07022 2.642261
12 Kyrgyzstan 64.18823 1.750223
12 Myanmar 64.57575 2.699222
12 Equatorial Guinea 67.49809 2.910891
12 Argentina 63.82468 1.787667
12 Mali 62.33665 1.738997
12 Burundi 64.51138 1.728376
12 Paraguay 65.81917 1.835648
12 Turkmenistan 65.77632 2.669452
12 Mauritania 63.3464 1.724637
12 Bangladesh 64.60314 1.755249
12 Nigeria 65.58304 1.798533
12 Papua New Guinea 64.22837 2.578149
12 Congo; Democratic Republic of 67.11106 2.570817
12 Haiti 63.26932 1.736104
12 Uganda 63.50602 1.768148
12 Ukraine 62.62499 1.753889
12 Sudan 66.18832 2.538595
13 Lebanon 66.78853 1.787608
13 Venezuela; Bolivarian Republic of 68.30649 1.827198
13 Korea; People's Democratic Republic of 68.34975 2.83479
13 Guinea 66.58957 1.810952
13 Guinea-Bissau 69.00919 2.808171
13 Chad 67.75989 1.760472
13 Angola 67.14995 1.826767
13 Yemen (North Yemen/ Yemen Arab Republic) 68.56915 1.85548
13 Somalia 70.92777 2.824635