Search
Close
Search
Advanced Search
Search Menu

Abstract

A new measure of cross-national corruption is constructed based on the geographic distribution of public officials involved in cross-border corruption cases. A comparison is made between the Public Administration Corruption Index (PACI) and perception-based measures, considers the extent to which differences between them are driven by systematic factors, and concludes that they are not. As more data on cases of cross-border bribery incidents become available, the PACI will provide an increasingly valid cross-national measure of corruption.

Corruption statistics, Causes of corruption, Corruption index

This research presents a new measure of corruption, the Public Administration Corruption Index (PACI), formulated using data on cross-border corruption cases. The proposal is motivated by the need to find viable alternatives to currently available cross-national measures of corruption, which, despite their shortcomings, have been and are being extensively used in academia,1 within policy circles, and in public debates at large.

There are two types of cross-national measures of corruption currently available. The most widely used type is perception-based, such as the Transparency International Corruption Perceptions Index (TI-CPI; Saisana and Saltelli 2012; Transparency International 2012) and the World Bank Control of Corruption Indicator (WB-CCI; Kaufmann, Kraay, and Mastruzzi 2009). The shortcomings of the cited type, however, are well known. For one, perceptions may have weak correlations with actual experiences of corruption (Seligson 2006; Olken 2009; Razafindrakoto and Roubaud 2010). Moreover, often the precise definition of corruption being assessed is not precisely defined. The second type of cross-national measure, meanwhile, is based on surveys assessing first-hand experiences of corruption.2 Surveys, however, are often costly endeavours, and their results are affected by respondents' reticence in answering questions related to their participation in corrupt activities.3

Proposing to use judicial statistics to develop a cross-national measure of corruption may appear ungainly. Even setting aside differences in legal definitions across jurisdictions, the fraction of observed corrupt transactions relative to their actual number is unknown and varies widely across countries. These differences could be so important that they could even imply a negative correlation between actual and observed corrupt transactions. After all, where corruption is endemic, the judiciary may also be corrupt or vulnerable to threats (Van Aaken et al. 2010).4

However, judicial statistics on cross-border corruption, which refers to corrupt transactions between firms headquartered in a particular country (hence, the “headquarters country”) and public officials elsewhere (the “foreign country”) permit the computation of a valid cross-national corruption index. A valid index in the lexicon of this article means one that increases along with the probability that a transaction is corrupt. The intuition behind such index is simple. To illustrate, the instances of corruption involving US firms caught bribing public officials abroad are not informative of the level of corruption in the United States; however, the distribution of these cases, with respect to the nationalities of the foreign public officials involved, reveals the relative level of corruption abroad. For example, if half of all the cases concerning US firms are seen to involve Chinese public officials, enough evidence would be provided that public sector corruption in China is relatively high. Obviously, such a conclusion should take into account the extent of the subject countries' bilateral transactions (the US interacts more often with China than, say, Denmark). As a suitable proxy for the number of bilateral transactions that could be vulnerable to corruption, export figures from the headquarters to the foreign country are used. In other words, the proposed measure uses information on the spatial distribution of cases enforced in a given country to evaluate levels of corruption in all other countries. Moreover, it considers cases arising not only from a single jurisdiction, but from all relevant ones.5

The necessary data are derived from reports of cross-border corruption ensuing from the criminalization of foreign bribery in several jurisdictions. With the passage of the Foreign Corrupt Practices Act (FCPA) of 1977, the United States became the first country to criminalize foreign bribery. On 15 February 1999, the Organisation for Economic Cooperation and Development (OECD) Anti-Bribery Convention came into force, requiring signatory countries to legislate measures intended to combat foreign bribery in their own jurisdictions. To date, 41 countries have signed the Convention, and hundreds of cases of foreign bribery have been investigated worldwide.6

The number of observed cases, however, is not sufficient for the computation of a reliable annual measure. In light of this, this paper consolidated data culled from corruption cases reported over a 15-year period or from 1998 to 2012. However, as more and more countries sign the OECD Anti-Bribery Convention, and as more cases of cross-border corruption are exposed, there will arise the possibility of computing the PACI for shorter periods of time. Increased data availability would also enable the computation of more granular measures, which pertain to specific sectors of the economy or particular segments of public administrations.

The PACI is described in the next section. Section II introduces the dataset and presents the computation of the index. Section III shows that the PACI is highly correlated with existing perception-based measures of corruption and considers the determinants of the observed differences. Section IV discusses available evidence regarding the assumptions that ensure the validity of the index, which are described in detail in Appendix A. Section V concludes.

I. The Public Administration Corruption Index

To illustrate the mechanics of the PACI, it is necessary at the outset to distinguish cases of cross-border corruption (involving firms from the headquarters country i bribing public officials in the foreign country j) based on where they were enforced first, that is, the country whose judiciary was the first to take action on a particular corruption case. The vast majority of cross-border corruption cases were first enforced in headquarters countries, comprised mostly of developed nations. This is expected, because the FCPA and the OECD Anti-Bribery Convention were envisioned precisely to address the lax policing of corruption in many foreign countries where multinational firms operate. It is also possible that corruption cases are first enforced in a third country, mainly because of broad interpretations of the extent of the jurisdiction of the United States, which covers companies registered in its Securities and Exchange Commission (SEC) and, generally, entities carrying out their businesses in the United States.7

The intuition behind the PACI is best described by means of a simple example, illustrated in Table 1. The available data (to be presented in detail in Section III) indicate the presence of 315 cases of cross-border alleged corruption, first enforced in the United States and involving firms headquartered in the same country. If the public officials of all countries trading with the United States were equally corrupt, the 315 cases could be expected to be distributed according to the number of bilateral transactions, which can be proxied by bilateral trade flows. Exports to China represented 9.29 percent of total US exports, implying an expected number of 29.26 cases (Table 1). However, the number of actual observed occurrences of corruption is 65, or 20.63 percent of the total. Thus, the number of cross-border corruption cases involving Chinese public officials was 2.22 times more than what could be expected if the total number of cases first enforced in the United States, and involving firms headquartered there, were distributed geographically according to the countries' shares in US exports (see the last column of Table 1). This provides evidence that the level of corruption exhibited by public officials in China is higher than the average level demonstrated by the public officials of all trade partners of the United States.

Table 1
Open in new tab

An Illustrative Example

HQFO(1) Cases, first pursued in HQ(2) Cases, as percent of total no. cases first pursued in HQ(3) Exports HQ→ FO as percent of total exports of HQRatio Col. (2)/Col. (3)
USAChina6520.63%9.29%2.22
USAAustria10.32%0.31%1.02
GermanyChina47.14%3.96%1.96
GermanyAustria35.36%5.62%0.95
HQFO(1) Cases, first pursued in HQ(2) Cases, as percent of total no. cases first pursued in HQ(3) Exports HQ→ FO as percent of total exports of HQRatio Col. (2)/Col. (3)
USAChina6520.63%9.29%2.22
USAAustria10.32%0.31%1.02
GermanyChina47.14%3.96%1.96
GermanyAustria35.36%5.62%0.95

Notes: Cases reported are those first pursued in the headquarters country (HQ), 1998–2012. FO: Foreign country. The total number of cases first pursued in the United States is 315, while the total number of cases first pursued in Germany is 56. Data sources are reported in Appendix B.

Table 1
Open in new tab

An Illustrative Example

HQFO(1) Cases, first pursued in HQ(2) Cases, as percent of total no. cases first pursued in HQ(3) Exports HQ→ FO as percent of total exports of HQRatio Col. (2)/Col. (3)
USAChina6520.63%9.29%2.22
USAAustria10.32%0.31%1.02
GermanyChina47.14%3.96%1.96
GermanyAustria35.36%5.62%0.95
HQFO(1) Cases, first pursued in HQ(2) Cases, as percent of total no. cases first pursued in HQ(3) Exports HQ→ FO as percent of total exports of HQRatio Col. (2)/Col. (3)
USAChina6520.63%9.29%2.22
USAAustria10.32%0.31%1.02
GermanyChina47.14%3.96%1.96
GermanyAustria35.36%5.62%0.95

Notes: Cases reported are those first pursued in the headquarters country (HQ), 1998–2012. FO: Foreign country. The total number of cases first pursued in the United States is 315, while the total number of cases first pursued in Germany is 56. Data sources are reported in Appendix B.

In comparison, Austria was pegged as the destination of 0.31 percent of total US exports (Table 1). It registered one reported case of bilateral corruption, corresponding to 0.32 percent of the total. Based on these figures, the level of corruption demonstrated by Austrian public officials is shown to be close to the average level, with the ratio tallied at 1.02.

Similarly, occurrences of corruption from the point of view of other countries may be considered. Germany, for instance, could be designated as the headquarters country. The available data indicate that there were four cases involving Chinese public officials, out of a total of 56 documented cases of corruption involving firms headquartered in Germany (and first enforced in the same country). The tally would also lead to the conclusion that China's public officials are more corrupt than the average trade partners of Germany. Meanwhile, Austria registered fewer cases of cross-border corruption despite having a larger share of German exports (see the last column of Table 1).

The PACI is based not only on one or two “points of observation”, but it aggregates cases involving firms headquartered in any country. This reasoning does not need any assumption with respect to the probability that cases in a given country would be enforced. Notably, that there were 315 cases of corruption involving firms headquartered in the United States and only 56 in Germany (with enforcement occurring first in the respective countries) has no implications with respect to levels of corruption in the United States or in Germany. It is the distribution of these cases (regardless of how many there are) abroad which conveys information that could help determine the level of corruption in the countries where the bribery of foreign officials takes place.

The PACI

The incidents of corruption involving firms in i that implicate public officials in j and first enforced in the headquarters' country i is indicated by |${cases\_obs\_H{Q_{i,j}}}$|⁠. The PACIz compares the total number of those cases with the expected number of corrupt transactions that would be observed if their spatial distribution reflected bilateral trade shares between the headquarters countries and z:
(1)
The numerator, |${\sum {cases\_obs\_H{Q_{i,z}}} }$|⁠, is the total number of observed corrupt exchanges between officials from country z and firms from all other headquarters countries, first enforced in those countries. The denominator is the total number of similar cases which could be observed if cases of corruption were distributed according to the ratio of exports of country i to z (⁠|${{X_{iz}}}$|⁠) with respect to the total amount of country i exports to the rest of the world:
(2)
The denominator may be interpreted as the total number of cross-border corruption cases involving country z public officials, first enforced elsewhere, that could be observed if the level of corruption of public officials were the same in all countries. If the actual and expected values are equal, then the PACI equals 100. The lowest value that the index may take is zero, which obtains when no corruption case (first enforced in all headquarters countries) is registered in country z.

The composite PACI

The |${PACI_z^{ALL} }$| or “composite PACI” follows the same logic of the simple |${PAC{I_z}}$|⁠. However, it accounts for cases that were first enforced not only in all headquarters countries but also in other countries except z. Cases that are first enforced in a third country w, meanwhile, are denominated as |${cases\_obs\_OTH_{i,j}^w }$| (where i,j ≠ w). The index is then shown as:
(3)
with i≠j, w≠i,j, D is the number of third countries that first enforced cases, and for the denominator the following holds:
$${\sum\limits_{w = {\rm 1}}^D {\sum\limits_{i = 1}^N {E(cases\_obs\_OTH_{z,i}^w )} } = \sum\limits_{w = {\rm 1}}^D {\sum\limits_{i = 1}^N {\displaystyle{{{X_{iz}}} \over {\sum\limits_{\,j = {\rm 1}}^N {{X_{ij}}} }}\sum\limits_{\,j = {\rm 1}}^N {cases\_obs\_OTH_{i,j}^w } } } }$$
The interpretation of the |${PACI_z^{ALL} }$| is conceptually the same as that of the |${PACI_z^{} }$|(Eq. 1), but it considers all available cases of observed cross-border corruption, first enforced either in the country where firms are headquartered or within the jurisdictions of third countries. This is the version of the index computed in this article.

Conditional Probability of Observing Zero Cases

It is also important to demonstrate the varying precision with which the PACI measures corruption, which is influenced by the total number of transactions between countries as represented by bilateral trade flows. To fix ideas, consider the case of a foreign country that has no reported incident of cross-border corruption. Such a situation could be interpreted as a signal that corruption in the said foreign country is relatively low, but not necessarily equal to zero. The strength of such a signal depends on the number of cross-border transactions, proxied by imports. To express this concept, the “probability of observing zero cases, conditional on the probability of corruption being equal in all foreign countries” is introduced:
$${\hbox{Pr_}zero\_cases_z^{PACI} = \hbox{Pr}(\sum\limits_{i = 1}^N {cases\_obs\_H{Q_{i,z}} = \hbox{0}\,|\,\hbox{Pr_}corr\_F{O_z} = c} )} $$
where |${\hbox{Pr_}corr\_F{O_z}}$| is the underlying probability that a public official accepts a bribe (see Appendix A) and c is a constant. The measure expresses the probability that no case involving country z public officials (first enforced in the headquarters country) is observed when the true country z probability that a cross-border transaction involving its public officials is corrupt is the same everywhere in the world. |${\hbox{Pr_}zero\_cases_z^{PACI} }$| may be computed as the product of individual probabilities:
$${\hbox{Pr_}zero\_cases_z^{PACI} = \prod\nolimits_{i = 1}^N {(\hbox{Pr_}cases\_obs\_HQ_{i,z}^{} = \hbox{0}\,|\,\hbox{Pr_}corr\_F{O_z} = c)} }$$
Each one of these probabilities, referring to a rare event, is described by a Poisson distribution and may be easily computed by making the condition |$\hbox{Pr_}corr\_F{O_z} = c$| correspond to an ideal situation where occurrences of corruption are distributed according to trade shares. High values of |${\hbox{Pr_}zero\_cases_z^{PACI} }$| indicate that the country in question has relatively few cross-border transactions – that is, information is relatively scarce.

II. Computing the PACI

Information on cases of cross-border corruption from 1977 until the end of 2012 were collected using various sources (See Appendix B). However, only cases reported in 1998 onwards were used, approximately coinciding with the effectivity of the OECD Anti-Bribery Convention. Out of a total of 979 cases detailed in this study, 796 cases were first enforced either in the headquarters country (569), in the United States acting as a third country jurisdiction (177), or in other third country jurisdictions (50). The remaining 183 cases that were first enforced in foreign countries were not considered for the purpose of computing the PACI.

Each case was coded according to the observed outcome. They were classified “positive” if the accused party was either found guilty or, while not admitting guilt, conceded to the payment of a fine (as in the case of a “consent to a cease-and-desist order” in the US);8 “not positive” if the case was eventually dropped and no action was taken; or “ongoing” if no available evidence was found to determine whether the case is “positive” or “not positive.” The term public official is used in a broad sense, encompassing both bureaucrats and politicians.

Table 2 indicates that out of 796 cases that may be used to compute the PACI, 444 are classified as positive, 272 as ongoing, while the rest have either been dropped or have resulted in an acquittal. Firms are shown to be headquartered in 40 countries, comprised mostly of industrialized nations. Among them, the United States takes the lion's share in the number of cases enforced, reflecting both its early adoption of the FCPA and the proactive stance adopted by its Department of Justice (DOJ) and SEC after its ratification of the OECD Anti-Bribery Convention. Germany, Britain, and France follow in the list.

Table 2
Open in new tab

Total Number of Cases by Headquarters' Country, 1998–2012

Country of firm's headquarterTotal CasesPositive CasesOngoing Cases
United States33122782
Germany865036
United Kingdom542524
France472718
Switzerland46406
Italy27516
Spain2208
Australia19514
Canada1948
Japan17115
Netherlands1585
Sweden15112
Korea14140
Portugal1307
Norway953
China871
Argentina612
Austria624
Brazil605
Finland541
Bermuda400
Chile302
Denmark303
Israel321
Belgium202
Hungary211
Angola100
Bangladesh110
Czech Republic100
Ghana110
Ireland101
India110
Luxembourg110
New Zealand101
Poland101
Russia100
Slovak Republic101
Turkey101
British Virgin Islands110
South Africa101
Total796444272
Country of firm's headquarterTotal CasesPositive CasesOngoing Cases
United States33122782
Germany865036
United Kingdom542524
France472718
Switzerland46406
Italy27516
Spain2208
Australia19514
Canada1948
Japan17115
Netherlands1585
Sweden15112
Korea14140
Portugal1307
Norway953
China871
Argentina612
Austria624
Brazil605
Finland541
Bermuda400
Chile302
Denmark303
Israel321
Belgium202
Hungary211
Angola100
Bangladesh110
Czech Republic100
Ghana110
Ireland101
India110
Luxembourg110
New Zealand101
Poland101
Russia100
Slovak Republic101
Turkey101
British Virgin Islands110
South Africa101
Total796444272

Notes: Cases are those first enforced in the headquarters country or in any third-country jurisdiction. The “headquarters country” is where the firm which allegedly bribed public officials abroad is headquartered. Positive Cases refer to cases that were concluded with a judgment in favor of the prosecution or a settlement. Ongoing cases are those that are still pending.

Table 2
Open in new tab

Total Number of Cases by Headquarters' Country, 1998–2012

Country of firm's headquarterTotal CasesPositive CasesOngoing Cases
United States33122782
Germany865036
United Kingdom542524
France472718
Switzerland46406
Italy27516
Spain2208
Australia19514
Canada1948
Japan17115
Netherlands1585
Sweden15112
Korea14140
Portugal1307
Norway953
China871
Argentina612
Austria624
Brazil605
Finland541
Bermuda400
Chile302
Denmark303
Israel321
Belgium202
Hungary211
Angola100
Bangladesh110
Czech Republic100
Ghana110
Ireland101
India110
Luxembourg110
New Zealand101
Poland101
Russia100
Slovak Republic101
Turkey101
British Virgin Islands110
South Africa101
Total796444272
Country of firm's headquarterTotal CasesPositive CasesOngoing Cases
United States33122782
Germany865036
United Kingdom542524
France472718
Switzerland46406
Italy27516
Spain2208
Australia19514
Canada1948
Japan17115
Netherlands1585
Sweden15112
Korea14140
Portugal1307
Norway953
China871
Argentina612
Austria624
Brazil605
Finland541
Bermuda400
Chile302
Denmark303
Israel321
Belgium202
Hungary211
Angola100
Bangladesh110
Czech Republic100
Ghana110
Ireland101
India110
Luxembourg110
New Zealand101
Poland101
Russia100
Slovak Republic101
Turkey101
British Virgin Islands110
South Africa101
Total796444272

Notes: Cases are those first enforced in the headquarters country or in any third-country jurisdiction. The “headquarters country” is where the firm which allegedly bribed public officials abroad is headquartered. Positive Cases refer to cases that were concluded with a judgment in favor of the prosecution or a settlement. Ongoing cases are those that are still pending.

The list of countries where public officials are seen to be on the receiving end of alleged bribery is more extensive (Table 3). At least one case is recorded in 128 countries. China leads the list with 88 cases, followed by Nigeria.

Table 3
Open in new tab

Total Number of Cases by Country where Alleged Corruption Takes Place, 1998–2012

Foreign countryTotal CasesPositive CasesOngoing Cases
China884934
Nigeria42326
India291214
Russia281510
Indonesia24185
Libya24616
Brazil22711
Kazakhstan2289
Angola1998
Egypt17143
Argentina1697
Philippines1595
Greece1494
Mexico14104
Thailand14113
Saudi Arabia1274
United States12120
Vietnam12111
Venezuela1173
Algeria1036
Poland1062
Turkey1061
United Arab Emirates954
Iraq936
Ghana823
Iran842
Korea835
Malaysia851
Romania833
Bangladesh752
Bulgaria741
Czech Republic724
Hungary733
Liberia741
Serbia742
Congo, Republic of642
Costa Rica633
Croatia632
Italy623
Kenya642
Panama623
Uganda632
Uzbekistan642
South Africa623
Austria522
Peru513
Pakistan541
Syria532
Bahrain440
France440
Equatorial Guinea421
Haiti421
Cambodia422
Mali430
Qatar422
Slovenia422
Tanzania440
Azerbaijan330
Côte d'Ivoire321
Ecuador330
Gabon320
Georgia311
Hong Kong330
Honduras330
Kuwait312
Morocco311
Mauritania321
Malawi311
Oman312
Rwanda321
Taiwan321
Ukraine311
Zimbabwe302
Albania211
Colombia211
Germany210
Spain211
Guinea211
Lithuania211
Latvia211
Mozambique210
Nepal220
Sudan220
Singapore211
Slovak Republic202
Senegal211
Somalia200
Turkmenistan220
Tunisia202
Yemen211
Zambia201
Afghanistan101
Bosnia110
Belgium110
Burkina Faso110
Benin110
Brunei110
Bolivia110
Bahamas, The110
Belarus100
Cameroon101
Cuba110
Djibouti110
Dominican Republic101
Eritrea101
Jamaica101
Jordan110
Japan101
North Korea101
Luxembourg110
Madagascar110
Macedonia110
Myanmar110
Mongolia110
Moldova101
Niger110
Netherlands110
Norway101
Nairu101
French Polynesia110
Portugal101
São Tomé and Príncipe100
El Salvador101
Turks and Caicos Islands101
Chad110
Trinidad and Tobago100
United Kingdom110
Uruguay100
Total796444272
Foreign countryTotal CasesPositive CasesOngoing Cases
China884934
Nigeria42326
India291214
Russia281510
Indonesia24185
Libya24616
Brazil22711
Kazakhstan2289
Angola1998
Egypt17143
Argentina1697
Philippines1595
Greece1494
Mexico14104
Thailand14113
Saudi Arabia1274
United States12120
Vietnam12111
Venezuela1173
Algeria1036
Poland1062
Turkey1061
United Arab Emirates954
Iraq936
Ghana823
Iran842
Korea835
Malaysia851
Romania833
Bangladesh752
Bulgaria741
Czech Republic724
Hungary733
Liberia741
Serbia742
Congo, Republic of642
Costa Rica633
Croatia632
Italy623
Kenya642
Panama623
Uganda632
Uzbekistan642
South Africa623
Austria522
Peru513
Pakistan541
Syria532
Bahrain440
France440
Equatorial Guinea421
Haiti421
Cambodia422
Mali430
Qatar422
Slovenia422
Tanzania440
Azerbaijan330
Côte d'Ivoire321
Ecuador330
Gabon320
Georgia311
Hong Kong330
Honduras330
Kuwait312
Morocco311
Mauritania321
Malawi311
Oman312
Rwanda321
Taiwan321
Ukraine311
Zimbabwe302
Albania211
Colombia211
Germany210
Spain211
Guinea211
Lithuania211
Latvia211
Mozambique210
Nepal220
Sudan220
Singapore211
Slovak Republic202
Senegal211
Somalia200
Turkmenistan220
Tunisia202
Yemen211
Zambia201
Afghanistan101
Bosnia110
Belgium110
Burkina Faso110
Benin110
Brunei110
Bolivia110
Bahamas, The110
Belarus100
Cameroon101
Cuba110
Djibouti110
Dominican Republic101
Eritrea101
Jamaica101
Jordan110
Japan101
North Korea101
Luxembourg110
Madagascar110
Macedonia110
Myanmar110
Mongolia110
Moldova101
Niger110
Netherlands110
Norway101
Nairu101
French Polynesia110
Portugal101
São Tomé and Príncipe100
El Salvador101
Turks and Caicos Islands101
Chad110
Trinidad and Tobago100
United Kingdom110
Uruguay100
Total796444272

Notes: Cases are those first enforced in the headquarters country or in any third-country jurisdiction. The “foreign country” is the country where the act of (alleged) corruption took place.

Table 3
Open in new tab

Total Number of Cases by Country where Alleged Corruption Takes Place, 1998–2012

Foreign countryTotal CasesPositive CasesOngoing Cases
China884934
Nigeria42326
India291214
Russia281510
Indonesia24185
Libya24616
Brazil22711
Kazakhstan2289
Angola1998
Egypt17143
Argentina1697
Philippines1595
Greece1494
Mexico14104
Thailand14113
Saudi Arabia1274
United States12120
Vietnam12111
Venezuela1173
Algeria1036
Poland1062
Turkey1061
United Arab Emirates954
Iraq936
Ghana823
Iran842
Korea835
Malaysia851
Romania833
Bangladesh752
Bulgaria741
Czech Republic724
Hungary733
Liberia741
Serbia742
Congo, Republic of642
Costa Rica633
Croatia632
Italy623
Kenya642
Panama623
Uganda632
Uzbekistan642
South Africa623
Austria522
Peru513
Pakistan541
Syria532
Bahrain440
France440
Equatorial Guinea421
Haiti421
Cambodia422
Mali430
Qatar422
Slovenia422
Tanzania440
Azerbaijan330
Côte d'Ivoire321
Ecuador330
Gabon320
Georgia311
Hong Kong330
Honduras330
Kuwait312
Morocco311
Mauritania321
Malawi311
Oman312
Rwanda321
Taiwan321
Ukraine311
Zimbabwe302
Albania211
Colombia211
Germany210
Spain211
Guinea211
Lithuania211
Latvia211
Mozambique210
Nepal220
Sudan220
Singapore211
Slovak Republic202
Senegal211
Somalia200
Turkmenistan220
Tunisia202
Yemen211
Zambia201
Afghanistan101
Bosnia110
Belgium110
Burkina Faso110
Benin110
Brunei110
Bolivia110
Bahamas, The110
Belarus100
Cameroon101
Cuba110
Djibouti110
Dominican Republic101
Eritrea101
Jamaica101
Jordan110
Japan101
North Korea101
Luxembourg110
Madagascar110
Macedonia110
Myanmar110
Mongolia110
Moldova101
Niger110
Netherlands110
Norway101
Nairu101
French Polynesia110
Portugal101
São Tomé and Príncipe100
El Salvador101
Turks and Caicos Islands101
Chad110
Trinidad and Tobago100
United Kingdom110
Uruguay100
Total796444272
Foreign countryTotal CasesPositive CasesOngoing Cases
China884934
Nigeria42326
India291214
Russia281510
Indonesia24185
Libya24616
Brazil22711
Kazakhstan2289
Angola1998
Egypt17143
Argentina1697
Philippines1595
Greece1494
Mexico14104
Thailand14113
Saudi Arabia1274
United States12120
Vietnam12111
Venezuela1173
Algeria1036
Poland1062
Turkey1061
United Arab Emirates954
Iraq936
Ghana823
Iran842
Korea835
Malaysia851
Romania833
Bangladesh752
Bulgaria741
Czech Republic724
Hungary733
Liberia741
Serbia742
Congo, Republic of642
Costa Rica633
Croatia632
Italy623
Kenya642
Panama623
Uganda632
Uzbekistan642
South Africa623
Austria522
Peru513
Pakistan541
Syria532
Bahrain440
France440
Equatorial Guinea421
Haiti421
Cambodia422
Mali430
Qatar422
Slovenia422
Tanzania440
Azerbaijan330
Côte d'Ivoire321
Ecuador330
Gabon320
Georgia311
Hong Kong330
Honduras330
Kuwait312
Morocco311
Mauritania321
Malawi311
Oman312
Rwanda321
Taiwan321
Ukraine311
Zimbabwe302
Albania211
Colombia211
Germany210
Spain211
Guinea211
Lithuania211
Latvia211
Mozambique210
Nepal220
Sudan220
Singapore211
Slovak Republic202
Senegal211
Somalia200
Turkmenistan220
Tunisia202
Yemen211
Zambia201
Afghanistan101
Bosnia110
Belgium110
Burkina Faso110
Benin110
Brunei110
Bolivia110
Bahamas, The110
Belarus100
Cameroon101
Cuba110
Djibouti110
Dominican Republic101
Eritrea101
Jamaica101
Jordan110
Japan101
North Korea101
Luxembourg110
Madagascar110
Macedonia110
Myanmar110
Mongolia110
Moldova101
Niger110
Netherlands110
Norway101
Nairu101
French Polynesia110
Portugal101
São Tomé and Príncipe100
El Salvador101
Turks and Caicos Islands101
Chad110
Trinidad and Tobago100
United Kingdom110
Uruguay100
Total796444272

Notes: Cases are those first enforced in the headquarters country or in any third-country jurisdiction. The “foreign country” is the country where the act of (alleged) corruption took place.

To compute the index of public administration corruption, |${PACI_z^{ALL} }$|⁠, all 796 cases, regardless of their outcomes, were considered (Table 4). The inclusion of all the cases is justified by the presence of a high burden of proof in order to lead to a conviction. Thus, false negatives are likely to be more numerous than false positives. However, in order to accommodate a more agnostic view on this issue, the study computes the index by excluding cases that have been dropped or have resulted in an acquittal. It also computes the PACI by excluding cases involving public agencies in charge of health and telecommunications sectors, for reasons discussed in Section IV of this paper. The combination of these alternatives results in the computation of four versions of the PACI, whose reciprocal Spearman rank correlations range between 0.885 and 0.945 (Table 5). These computations will be discussed in their entirety in the succeeding section.

Table 4
Open in new tab

Public Administration Corruption Index (PACI), 1998–2012

Country|${PACI_z^{ALL} }$||${\hbox{Pr_}zero\_cases_z^{PACI} }$|Country RankRank difference, WB CCIRank difference, TI CPI
Canada0.00001−10−11
Switzerland0.00002−2−3
Australia0.00003−4−4
Sweden0.00014−10
Ireland0.00025−9−12
Denmark0.0018644
Finland0.0128766
UK2.58773.00008−2−2
Japan3.017315.00009−10−9
Netherlands3.520985.00001021
Germany3.723345.000011−1−2
Belgium3.786931.000012−5−4
Spain8.576681.000013−5−6
France10.58484.000014−2−1
Singapore14.45795.0000151212
Portugal19.10472.005316−4−4
Norway19.3984.0058171111
USA21.54232.00001834
Italy22.29935.000019−22−11
Taiwan25.40663.000020−4−4
Mexico29.16911.000021−35−31
Korea46.02413.000022−5−7
Austria47.04836.0000231415
Dominican Republic50.16856.136224−52−42
Colombia65.02713.046225−27−17
Luxembourg80.35295.2881261315
Slovakia90.9612.110927−2−11
Bahamas99.06574.364428−97−94
El Salvador99.22466.365029−38−10
Malaysia106.3388.000530−82
South Africa107.8829.0038313−4
Jamaica119.4216.432832−33−16
Turkey119.5222.000233−12−20
Czech Republic125.0943.0037344−2
Poland126.9792.000435−5−21
China144.8671.000036−43−26
Trinidad and Tobago148.6733.510437−10−7
Morocco149.0878.133738−19−22
Ukraine154.7996.144039−43−51
Hungary168.4742.015640149
Tunisia173.0795.314841−79
Saudi Arabia190.4156.001842−8−17
Belarus192.3195.594543−54−46
Jordan198.2679.603844917
Kuwait202.4064.2271452011
Uruguay204.3132.6129462521
Brazil213.2271.000047−60
Lithuania217.4639.398648915
Cuba236.8499.655549134
India241.05.000050−14−22
Thailand255.6769.00415105
Qatar264.63.2205522929
Slovenia268.9919.2260533131
Romania276.0276.055154−1−11
Venezuela281.1082.019955−45−47
Russia310.2815.000156−35−42
Ecuador315.465.386357−29−40
Peru322.8425.21255809
Honduras339.1537.412859−28−28
Brunei351.3141.752260−63−64
Latvia367.9257.5806612720
Costa Rica378.6169.2050623022
Pakistan380.5453.268763−44−50
Greece388.5903.0272643127
Iran396.8289.133165−5−10
Oman460.9047.5215662945
Afghanistan472.1338.809167−52−27
Bosnia472.9085.80946814−6
Bolivia475.1096.810169−20−24
Benin479.1134.811670−29−1
Philippines496.6861.048871−6−21
Algeria533.9379.1536724−4
Croatia539.2692.3286733118
Yemen545.6701.693174−18−9
Cameroon576.2632.840775−33−33
Argentina586.4659.0653767−1
Macedonia672.15.8617776−5
Bulgaria696.8326.3662783435
Vietnam714.1484.186379−11−9
Sudan775.2856.772680−38−32
Indonesia846.4598.058681−15−26
Panama884.8087.5075822032
Bahrain900.8724.6414835257
Senegal924.2173.8054843823
Syria955.4548.592585227
Côte d'Ivoire1061.689.753886−26−30
Albania1144.235.839687−1−13
Burkina Faso1171.676.9181883934
Gabon1176.169.7748891521
Madagascar1219.936.9212904710
Egypt1253.273.2575911934
Iraq1268.119.491792−28−17
Azerbaijan1280.879.791193−9−17
Serbia1356.355.5968943130
Kenya1481.514.666995−6−16
Bangladesh1549.458.636596−18−24
Haiti1651.818.784997−20−21
Niger1812.204.94639814−1
Georgia1819.622.84809938−4
Mozambique1900.602.90011002721
Burma1929.848.9495101−20−18
Turkmenistan1947.478.9024102−14−15
Zambia1961.419.90301031017
Nigeria2101.201.1354104−5−10
Ghana2191.095.69411054654
Chad2207.392.9557106−9−15
Mongolia2302.75.95751073240
Tanzania2307.337.84081082835
Guinea2410.733.92031095−6
Djibouti2425.423.95961102941
Kazakhstan2475.69.4112111827
Congo3088.969.8234113812
Eq. Guinea3102.567.8790114−8−9
Mauritania3242.89.911641155552
Liberia3426.804.81521161011
Libya3550.355.50861171926
Zimbabwe4122.554.9298118533
Uzbekistan4290.974.8695119913
Cambodia4645.204.9174120916
Nepal5354.408.96331214325
Sao Tome & P.5905.44.98321222741
Uganda5911.488.90341232928
Mali6340.174.93881245854
Malawi8598.199.96571254047
Country|${PACI_z^{ALL} }$||${\hbox{Pr_}zero\_cases_z^{PACI} }$|Country RankRank difference, WB CCIRank difference, TI CPI
Canada0.00001−10−11
Switzerland0.00002−2−3
Australia0.00003−4−4
Sweden0.00014−10
Ireland0.00025−9−12
Denmark0.0018644
Finland0.0128766
UK2.58773.00008−2−2
Japan3.017315.00009−10−9
Netherlands3.520985.00001021
Germany3.723345.000011−1−2
Belgium3.786931.000012−5−4
Spain8.576681.000013−5−6
France10.58484.000014−2−1
Singapore14.45795.0000151212
Portugal19.10472.005316−4−4
Norway19.3984.0058171111
USA21.54232.00001834
Italy22.29935.000019−22−11
Taiwan25.40663.000020−4−4
Mexico29.16911.000021−35−31
Korea46.02413.000022−5−7
Austria47.04836.0000231415
Dominican Republic50.16856.136224−52−42
Colombia65.02713.046225−27−17
Luxembourg80.35295.2881261315
Slovakia90.9612.110927−2−11
Bahamas99.06574.364428−97−94
El Salvador99.22466.365029−38−10
Malaysia106.3388.000530−82
South Africa107.8829.0038313−4
Jamaica119.4216.432832−33−16
Turkey119.5222.000233−12−20
Czech Republic125.0943.0037344−2
Poland126.9792.000435−5−21
China144.8671.000036−43−26
Trinidad and Tobago148.6733.510437−10−7
Morocco149.0878.133738−19−22
Ukraine154.7996.144039−43−51
Hungary168.4742.015640149
Tunisia173.0795.314841−79
Saudi Arabia190.4156.001842−8−17
Belarus192.3195.594543−54−46
Jordan198.2679.603844917
Kuwait202.4064.2271452011
Uruguay204.3132.6129462521
Brazil213.2271.000047−60
Lithuania217.4639.398648915
Cuba236.8499.655549134
India241.05.000050−14−22
Thailand255.6769.00415105
Qatar264.63.2205522929
Slovenia268.9919.2260533131
Romania276.0276.055154−1−11
Venezuela281.1082.019955−45−47
Russia310.2815.000156−35−42
Ecuador315.465.386357−29−40
Peru322.8425.21255809
Honduras339.1537.412859−28−28
Brunei351.3141.752260−63−64
Latvia367.9257.5806612720
Costa Rica378.6169.2050623022
Pakistan380.5453.268763−44−50
Greece388.5903.0272643127
Iran396.8289.133165−5−10
Oman460.9047.5215662945
Afghanistan472.1338.809167−52−27
Bosnia472.9085.80946814−6
Bolivia475.1096.810169−20−24
Benin479.1134.811670−29−1
Philippines496.6861.048871−6−21
Algeria533.9379.1536724−4
Croatia539.2692.3286733118
Yemen545.6701.693174−18−9
Cameroon576.2632.840775−33−33
Argentina586.4659.0653767−1
Macedonia672.15.8617776−5
Bulgaria696.8326.3662783435
Vietnam714.1484.186379−11−9
Sudan775.2856.772680−38−32
Indonesia846.4598.058681−15−26
Panama884.8087.5075822032
Bahrain900.8724.6414835257
Senegal924.2173.8054843823
Syria955.4548.592585227
Côte d'Ivoire1061.689.753886−26−30
Albania1144.235.839687−1−13
Burkina Faso1171.676.9181883934
Gabon1176.169.7748891521
Madagascar1219.936.9212904710
Egypt1253.273.2575911934
Iraq1268.119.491792−28−17
Azerbaijan1280.879.791193−9−17
Serbia1356.355.5968943130
Kenya1481.514.666995−6−16
Bangladesh1549.458.636596−18−24
Haiti1651.818.784997−20−21
Niger1812.204.94639814−1
Georgia1819.622.84809938−4
Mozambique1900.602.90011002721
Burma1929.848.9495101−20−18
Turkmenistan1947.478.9024102−14−15
Zambia1961.419.90301031017
Nigeria2101.201.1354104−5−10
Ghana2191.095.69411054654
Chad2207.392.9557106−9−15
Mongolia2302.75.95751073240
Tanzania2307.337.84081082835
Guinea2410.733.92031095−6
Djibouti2425.423.95961102941
Kazakhstan2475.69.4112111827
Congo3088.969.8234113812
Eq. Guinea3102.567.8790114−8−9
Mauritania3242.89.911641155552
Liberia3426.804.81521161011
Libya3550.355.50861171926
Zimbabwe4122.554.9298118533
Uzbekistan4290.974.8695119913
Cambodia4645.204.9174120916
Nepal5354.408.96331214325
Sao Tome & P.5905.44.98321222741
Uganda5911.488.90341232928
Mali6340.174.93881245854
Malawi8598.199.96571254047

Notes: Index computed using all cases regardless of outcome and administration. Countries for which PACIz = 0 have been ranked according to the negative of |${\hbox{Pr_}zero\_cases_z^{PACI} }$|⁠. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded from the list. WB-CCI: World Bank Corruption Control Index, 2005. TI-CPI: Transparency International Corruption Perceptions Index, 2005.

Table 4
Open in new tab

Public Administration Corruption Index (PACI), 1998–2012

Country|${PACI_z^{ALL} }$||${\hbox{Pr_}zero\_cases_z^{PACI} }$|Country RankRank difference, WB CCIRank difference, TI CPI
Canada0.00001−10−11
Switzerland0.00002−2−3
Australia0.00003−4−4
Sweden0.00014−10
Ireland0.00025−9−12
Denmark0.0018644
Finland0.0128766
UK2.58773.00008−2−2
Japan3.017315.00009−10−9
Netherlands3.520985.00001021
Germany3.723345.000011−1−2
Belgium3.786931.000012−5−4
Spain8.576681.000013−5−6
France10.58484.000014−2−1
Singapore14.45795.0000151212
Portugal19.10472.005316−4−4
Norway19.3984.0058171111
USA21.54232.00001834
Italy22.29935.000019−22−11
Taiwan25.40663.000020−4−4
Mexico29.16911.000021−35−31
Korea46.02413.000022−5−7
Austria47.04836.0000231415
Dominican Republic50.16856.136224−52−42
Colombia65.02713.046225−27−17
Luxembourg80.35295.2881261315
Slovakia90.9612.110927−2−11
Bahamas99.06574.364428−97−94
El Salvador99.22466.365029−38−10
Malaysia106.3388.000530−82
South Africa107.8829.0038313−4
Jamaica119.4216.432832−33−16
Turkey119.5222.000233−12−20
Czech Republic125.0943.0037344−2
Poland126.9792.000435−5−21
China144.8671.000036−43−26
Trinidad and Tobago148.6733.510437−10−7
Morocco149.0878.133738−19−22
Ukraine154.7996.144039−43−51
Hungary168.4742.015640149
Tunisia173.0795.314841−79
Saudi Arabia190.4156.001842−8−17
Belarus192.3195.594543−54−46
Jordan198.2679.603844917
Kuwait202.4064.2271452011
Uruguay204.3132.6129462521
Brazil213.2271.000047−60
Lithuania217.4639.398648915
Cuba236.8499.655549134
India241.05.000050−14−22
Thailand255.6769.00415105
Qatar264.63.2205522929
Slovenia268.9919.2260533131
Romania276.0276.055154−1−11
Venezuela281.1082.019955−45−47
Russia310.2815.000156−35−42
Ecuador315.465.386357−29−40
Peru322.8425.21255809
Honduras339.1537.412859−28−28
Brunei351.3141.752260−63−64
Latvia367.9257.5806612720
Costa Rica378.6169.2050623022
Pakistan380.5453.268763−44−50
Greece388.5903.0272643127
Iran396.8289.133165−5−10
Oman460.9047.5215662945
Afghanistan472.1338.809167−52−27
Bosnia472.9085.80946814−6
Bolivia475.1096.810169−20−24
Benin479.1134.811670−29−1
Philippines496.6861.048871−6−21
Algeria533.9379.1536724−4
Croatia539.2692.3286733118
Yemen545.6701.693174−18−9
Cameroon576.2632.840775−33−33
Argentina586.4659.0653767−1
Macedonia672.15.8617776−5
Bulgaria696.8326.3662783435
Vietnam714.1484.186379−11−9
Sudan775.2856.772680−38−32
Indonesia846.4598.058681−15−26
Panama884.8087.5075822032
Bahrain900.8724.6414835257
Senegal924.2173.8054843823
Syria955.4548.592585227
Côte d'Ivoire1061.689.753886−26−30
Albania1144.235.839687−1−13
Burkina Faso1171.676.9181883934
Gabon1176.169.7748891521
Madagascar1219.936.9212904710
Egypt1253.273.2575911934
Iraq1268.119.491792−28−17
Azerbaijan1280.879.791193−9−17
Serbia1356.355.5968943130
Kenya1481.514.666995−6−16
Bangladesh1549.458.636596−18−24
Haiti1651.818.784997−20−21
Niger1812.204.94639814−1
Georgia1819.622.84809938−4
Mozambique1900.602.90011002721
Burma1929.848.9495101−20−18
Turkmenistan1947.478.9024102−14−15
Zambia1961.419.90301031017
Nigeria2101.201.1354104−5−10
Ghana2191.095.69411054654
Chad2207.392.9557106−9−15
Mongolia2302.75.95751073240
Tanzania2307.337.84081082835
Guinea2410.733.92031095−6
Djibouti2425.423.95961102941
Kazakhstan2475.69.4112111827
Congo3088.969.8234113812
Eq. Guinea3102.567.8790114−8−9
Mauritania3242.89.911641155552
Liberia3426.804.81521161011
Libya3550.355.50861171926
Zimbabwe4122.554.9298118533
Uzbekistan4290.974.8695119913
Cambodia4645.204.9174120916
Nepal5354.408.96331214325
Sao Tome & P.5905.44.98321222741
Uganda5911.488.90341232928
Mali6340.174.93881245854
Malawi8598.199.96571254047
Country|${PACI_z^{ALL} }$||${\hbox{Pr_}zero\_cases_z^{PACI} }$|Country RankRank difference, WB CCIRank difference, TI CPI
Canada0.00001−10−11
Switzerland0.00002−2−3
Australia0.00003−4−4
Sweden0.00014−10
Ireland0.00025−9−12
Denmark0.0018644
Finland0.0128766
UK2.58773.00008−2−2
Japan3.017315.00009−10−9
Netherlands3.520985.00001021
Germany3.723345.000011−1−2
Belgium3.786931.000012−5−4
Spain8.576681.000013−5−6
France10.58484.000014−2−1
Singapore14.45795.0000151212
Portugal19.10472.005316−4−4
Norway19.3984.0058171111
USA21.54232.00001834
Italy22.29935.000019−22−11
Taiwan25.40663.000020−4−4
Mexico29.16911.000021−35−31
Korea46.02413.000022−5−7
Austria47.04836.0000231415
Dominican Republic50.16856.136224−52−42
Colombia65.02713.046225−27−17
Luxembourg80.35295.2881261315
Slovakia90.9612.110927−2−11
Bahamas99.06574.364428−97−94
El Salvador99.22466.365029−38−10
Malaysia106.3388.000530−82
South Africa107.8829.0038313−4
Jamaica119.4216.432832−33−16
Turkey119.5222.000233−12−20
Czech Republic125.0943.0037344−2
Poland126.9792.000435−5−21
China144.8671.000036−43−26
Trinidad and Tobago148.6733.510437−10−7
Morocco149.0878.133738−19−22
Ukraine154.7996.144039−43−51
Hungary168.4742.015640149
Tunisia173.0795.314841−79
Saudi Arabia190.4156.001842−8−17
Belarus192.3195.594543−54−46
Jordan198.2679.603844917
Kuwait202.4064.2271452011
Uruguay204.3132.6129462521
Brazil213.2271.000047−60
Lithuania217.4639.398648915
Cuba236.8499.655549134
India241.05.000050−14−22
Thailand255.6769.00415105
Qatar264.63.2205522929
Slovenia268.9919.2260533131
Romania276.0276.055154−1−11
Venezuela281.1082.019955−45−47
Russia310.2815.000156−35−42
Ecuador315.465.386357−29−40
Peru322.8425.21255809
Honduras339.1537.412859−28−28
Brunei351.3141.752260−63−64
Latvia367.9257.5806612720
Costa Rica378.6169.2050623022
Pakistan380.5453.268763−44−50
Greece388.5903.0272643127
Iran396.8289.133165−5−10
Oman460.9047.5215662945
Afghanistan472.1338.809167−52−27
Bosnia472.9085.80946814−6
Bolivia475.1096.810169−20−24
Benin479.1134.811670−29−1
Philippines496.6861.048871−6−21
Algeria533.9379.1536724−4
Croatia539.2692.3286733118
Yemen545.6701.693174−18−9
Cameroon576.2632.840775−33−33
Argentina586.4659.0653767−1
Macedonia672.15.8617776−5
Bulgaria696.8326.3662783435
Vietnam714.1484.186379−11−9
Sudan775.2856.772680−38−32
Indonesia846.4598.058681−15−26
Panama884.8087.5075822032
Bahrain900.8724.6414835257
Senegal924.2173.8054843823
Syria955.4548.592585227
Côte d'Ivoire1061.689.753886−26−30
Albania1144.235.839687−1−13
Burkina Faso1171.676.9181883934
Gabon1176.169.7748891521
Madagascar1219.936.9212904710
Egypt1253.273.2575911934
Iraq1268.119.491792−28−17
Azerbaijan1280.879.791193−9−17
Serbia1356.355.5968943130
Kenya1481.514.666995−6−16
Bangladesh1549.458.636596−18−24
Haiti1651.818.784997−20−21
Niger1812.204.94639814−1
Georgia1819.622.84809938−4
Mozambique1900.602.90011002721
Burma1929.848.9495101−20−18
Turkmenistan1947.478.9024102−14−15
Zambia1961.419.90301031017
Nigeria2101.201.1354104−5−10
Ghana2191.095.69411054654
Chad2207.392.9557106−9−15
Mongolia2302.75.95751073240
Tanzania2307.337.84081082835
Guinea2410.733.92031095−6
Djibouti2425.423.95961102941
Kazakhstan2475.69.4112111827
Congo3088.969.8234113812
Eq. Guinea3102.567.8790114−8−9
Mauritania3242.89.911641155552
Liberia3426.804.81521161011
Libya3550.355.50861171926
Zimbabwe4122.554.9298118533
Uzbekistan4290.974.8695119913
Cambodia4645.204.9174120916
Nepal5354.408.96331214325
Sao Tome & P.5905.44.98321222741
Uganda5911.488.90341232928
Mali6340.174.93881245854
Malawi8598.199.96571254047

Notes: Index computed using all cases regardless of outcome and administration. Countries for which PACIz = 0 have been ranked according to the negative of |${\hbox{Pr_}zero\_cases_z^{PACI} }$|⁠. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded from the list. WB-CCI: World Bank Corruption Control Index, 2005. TI-CPI: Transparency International Corruption Perceptions Index, 2005.

Table 5
Open in new tab

Spearman Rank Correlations between Different Indexes of Corruption

PACI1PACI2PACI3PACI4TI-CPIWB-CCITI-GCB
PACI11
PACI20.938
(123)		1
PACI30.940
(123)		0.886
(123)		1
PACI40.885
(123)		0.945
(123)		0.941
(123)		1
TI-CPI−0.779
(123)		−0.736
(123)		−0.751
(123)		−0.729
(123)		1
WB-CC−0.768
(123)		−0.711
(123)		−0.729
(123)		−0.692
(123)		0.954
(123)		1
TI-GCB0.755
(56)		0.731
(56)		0.769
(56)		0.747
(56)		−0.800
(56)		−0.767
(56)		1
PACI1PACI2PACI3PACI4TI-CPIWB-CCITI-GCB
PACI11
PACI20.938
(123)		1
PACI30.940
(123)		0.886
(123)		1
PACI40.885
(123)		0.945
(123)		0.941
(123)		1
TI-CPI−0.779
(123)		−0.736
(123)		−0.751
(123)		−0.729
(123)		1
WB-CC−0.768
(123)		−0.711
(123)		−0.729
(123)		−0.692
(123)		0.954
(123)		1
TI-GCB0.755
(56)		0.731
(56)		0.769
(56)		0.747
(56)		−0.800
(56)		−0.767
(56)		1

Notes: PACI: Public Administration Corruption Index, 1998–2012. The subscript indicates:

1: All cases, all administrations (the same values shown in Table 4; our preferred index).

2: All cases, with the exclusion of health and telecom administration.

3: Only “positive” and “ongoing” cases, all administrations.

4: Only “positive” and “ongoing” cases, with the exclusion of health and telecom administrations.

TI-CPI: Transparency International Corruption Perceptions Index, 2005. WB-CCI: World Bank Corruption Control Index, 2005. TI-GCB: Percentage of persons who answered “yes” to the question: “In the past 12 months, have you or anyone living in your household paid a bribe in any form?” Transparency International Global Corruption Barometer, 2005. Number of observations are between parentheses. All the estimated coefficients are significant at less than 1 percent. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded.

Table 5
Open in new tab

Spearman Rank Correlations between Different Indexes of Corruption

PACI1PACI2PACI3PACI4TI-CPIWB-CCITI-GCB
PACI11
PACI20.938
(123)		1
PACI30.940
(123)		0.886
(123)		1
PACI40.885
(123)		0.945
(123)		0.941
(123)		1
TI-CPI−0.779
(123)		−0.736
(123)		−0.751
(123)		−0.729
(123)		1
WB-CC−0.768
(123)		−0.711
(123)		−0.729
(123)		−0.692
(123)		0.954
(123)		1
TI-GCB0.755
(56)		0.731
(56)		0.769
(56)		0.747
(56)		−0.800
(56)		−0.767
(56)		1
PACI1PACI2PACI3PACI4TI-CPIWB-CCITI-GCB
PACI11
PACI20.938
(123)		1
PACI30.940
(123)		0.886
(123)		1
PACI40.885
(123)		0.945
(123)		0.941
(123)		1
TI-CPI−0.779
(123)		−0.736
(123)		−0.751
(123)		−0.729
(123)		1
WB-CC−0.768
(123)		−0.711
(123)		−0.729
(123)		−0.692
(123)		0.954
(123)		1
TI-GCB0.755
(56)		0.731
(56)		0.769
(56)		0.747
(56)		−0.800
(56)		−0.767
(56)		1

Notes: PACI: Public Administration Corruption Index, 1998–2012. The subscript indicates:

1: All cases, all administrations (the same values shown in Table 4; our preferred index).

2: All cases, with the exclusion of health and telecom administration.

3: Only “positive” and “ongoing” cases, all administrations.

4: Only “positive” and “ongoing” cases, with the exclusion of health and telecom administrations.

TI-CPI: Transparency International Corruption Perceptions Index, 2005. WB-CCI: World Bank Corruption Control Index, 2005. TI-GCB: Percentage of persons who answered “yes” to the question: “In the past 12 months, have you or anyone living in your household paid a bribe in any form?” Transparency International Global Corruption Barometer, 2005. Number of observations are between parentheses. All the estimated coefficients are significant at less than 1 percent. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded.

The probability of observing zero cases, under the condition that probability of corruption is equal in all foreign countries, |${\hbox{Pr_}zero\_cases_z^{PACI} }$|⁠, is used to rank those countries that have no observed cases of corruption first enforced abroad, and also to exclude very small countries for which the available information is not deemed to be sufficient. Canada and Finland, for instance, reported zero cases of corruption (first enforced abroad) involving their public officials. For Finland, such probability equals 1.3 percent—being a middle-sized economy, it is rather unlikely for Finland not to observe any cases, if the probability that public officials accept bribes were the same in Finland as in the rest of the world. For Canada, on the other hand, |${\hbox{Pr_}zero\_cases_z^{PACI} }$|=0.0000. In this case, the signal provided by the PACI, indicating that the level of corruption in Canada is low, is very strong and illustrates that Canada, which is a bigger country, generates more information than Finland.

|${\hbox{Pr_}zero\_cases_z^{PACI} }$| provides a useful indication of the precision of the PACI also for countries with at least one case of corruption on record. For smaller countries, the signal that the PACI provides is rather noisy because of the rarity of observed corruption events. For example, the probability of observing zero cases in Tunisia would be equal to 0.31 in a situation where the level of corruption is the same everywhere in the world. Notably, two cases were actually observed, close to twice as many as the expected figure if cases were distributed according to trade shares, with a resulting PACI of about 170 and a ranking measure for Tunisia similar to that indicated by the WB-CCI and the TI-CPI (see the last two columns of Table 4).

III. The PACI and Perception-Based Measures: A Comparison

The differences in the rankings yielded by the PACI and the WB-CCI and TI-CPI are also explored (see last columns of Table 4), where the perception-based measures used are for the year 2005, the middle of the time interval of reference of the PACI. A positive value indicates that a given country is considered to be relatively more corrupt based on the PACI. In most cases, differences are modest. However, results diverge considerably for a few countries.

These information are summarized by showing the Spearman rank correlations between the different versions of the PACI and the perception-based alternatives (Table 5). The rank correlations of PACI1, the version which includes all cases (presented in Table 4), with perception-based measures are rather high, typically pegged at above 0.7.9

While the ranking based on the PACI is similar to the rankings based on the two most popular perception-based indices, the scales appear to be very different (Figure 1). Panel A shows a scatter diagram of the PACI together with the WB-CCI for 2005, while Panel B shows the same indices, with the PACI log-transformed. The Pearson correlation of log(PACI) and WB-CCI is quite high (equal to −0.841).
Comparison between PACI and WB Corruption Control Index
Figure 1

Comparison between PACI and WB Corruption Control Index

Open in new tabDownload slide
Notes: In Panel B, when the PACI equals zero (countries: FI, DK, IE, SE, AU, CH, CA), it has been set equal to arbitrary small numbers for the purpose of computing the log. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded.

The study also seeks to examine whether the observed differences between the PACI and perception-based measures of corruption are systematic, and to the extent that they might be, what determines them. The residual of a linear regression between the log of the PACI and the WB-CCI (as in Figure 1.B) or the TI-CPI is considered and denoted as “measurement residuals” – mesresWB-CCI and mesresTI-CPI, respectively. Negative (positive) values indicate that a given country appears to be less (more) corrupt according to the PACI as opposed to the perception-based index used.

Whether these differences are correlated with |${\hbox{Pr_}zero\_cases_z^{PACI} }$| is determined first. If the precision of the PACI deteriorates significantly with respect to smaller countries, while the precision of the perception-based measures remains intact (or deteriorates to a lesser degree), then the absolute value of the measurement residuals could be expected to be positively correlated with |${\hbox{Pr_}zero\_cases_z^{PACI} }$|⁠. This, however, is not the case. The correlation between the absolute values of mesresWB-CCI and mesresTI-CPI and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| is seen to be insignificant, pegged at −0.0524 and −0.1001, respectively. This result indicates that any problem which may beset the precision of the PACI in measuring corruption in small countries, would also be shared by the two perception-based alternatives.

Subsequently, the study selected a set of variables which may be linked to biases in favor of or against either measure.10 Variables of an economic and demographic nature, namely: per capita GDP based on purchasing-power-parity (gdp_cap), population (pop), and the ratio of public expenditure over GDP (r_g/gdp) are considered. Variables that express the ease with which publicly relevant information is generated and debated upon, as well as the democratic attributes of a country, are also contemplated. Also included in such examination are emp_rights, an index of empowerment rights; free_press and free_speech, which respectively measure the operationalization of the guarantees to free press and free speech; voice_acc, a measure of voice and accountability; democ, an index of democratization; checks, which measures systems of checks and balances; and stability, which measures political stability. To test whether the observed residuals follow some recognizable geographic pattern, a set of geographic dummies is also considered. Pairwise correlations between mesresWB-CCI, mesresTI-CPI, and these variables are thus shown (Table 6). Results excluding the ten biggest (in absolute value) measurement residuals, roughly corresponding to ten percent of the available observations, are also presented.

Table 6
Open in new tab

Correlation Coefficients between Measurement Residuals and Selected Variables

All observations
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0604−0.1405−0.2507***0.3621***−0.0090−0.2122**−0.1182
(0.5071)(0.1211)(0.0052)(0.0000)(0.9242)(0.0185)(0.1946)
mesresTI-CPI−0.1297−0.0810−0.2854***0.4109***0.0510−0.2234**−0.1010
(0.1527)(0.3732)(0.0014)(0.0000)(0.5916)(0.0130)(0.2683)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI0.0368−0.0739−0.0604−0.0677−0.2687***−0.2180**0.1518*
(0.6876)(0.4163)(0.5067)(0.4572)(0.0033)(0.0177)(0.0938)
mesresTI-CPI0.1579*−0.1221−0.1620−0.1415−0.3310***−0.2807***0.1113
(0.0823)(0.1786)(0.0735) *(0.1184)(0.0003)(0.0021)(0.2205)
Excluding the ten biggest outliers
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0400−0.1297−0.1928**0.2860***0.0478−0.2234**−0.0933
(0.6737)(0.1708)(0.0408)(0.0021)(0.6315)(0.0174)(0.3278)
mesresTI-CPI−0.1200−0.0758−0.2216**0.3476***0.1124−0.2319**−0.0772
(0.2056)(0.4247)(0.0184)(0.0002)(0.2585)(0.0134)(0.4186)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI−0.0221−0.06730.0019−0.0391−0.1882*−0.2234**0.2073**
(0.8171)(0.4787)(0.9839)(0.6904)(0.0511)(0.0195)(0.0276)
mesresTI-CPI0.1159−0.1104−0.1161−0.1153−0.2682***−0.2818***0.1619*
(0.2238)(0.2443)(0.2208)(0.2241)(0.050)(0.0030)(0.0866)
All observations
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0604−0.1405−0.2507***0.3621***−0.0090−0.2122**−0.1182
(0.5071)(0.1211)(0.0052)(0.0000)(0.9242)(0.0185)(0.1946)
mesresTI-CPI−0.1297−0.0810−0.2854***0.4109***0.0510−0.2234**−0.1010
(0.1527)(0.3732)(0.0014)(0.0000)(0.5916)(0.0130)(0.2683)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI0.0368−0.0739−0.0604−0.0677−0.2687***−0.2180**0.1518*
(0.6876)(0.4163)(0.5067)(0.4572)(0.0033)(0.0177)(0.0938)
mesresTI-CPI0.1579*−0.1221−0.1620−0.1415−0.3310***−0.2807***0.1113
(0.0823)(0.1786)(0.0735) *(0.1184)(0.0003)(0.0021)(0.2205)
Excluding the ten biggest outliers
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0400−0.1297−0.1928**0.2860***0.0478−0.2234**−0.0933
(0.6737)(0.1708)(0.0408)(0.0021)(0.6315)(0.0174)(0.3278)
mesresTI-CPI−0.1200−0.0758−0.2216**0.3476***0.1124−0.2319**−0.0772
(0.2056)(0.4247)(0.0184)(0.0002)(0.2585)(0.0134)(0.4186)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI−0.0221−0.06730.0019−0.0391−0.1882*−0.2234**0.2073**
(0.8171)(0.4787)(0.9839)(0.6904)(0.0511)(0.0195)(0.0276)
mesresTI-CPI0.1159−0.1104−0.1161−0.1153−0.2682***−0.2818***0.1619*
(0.2238)(0.2443)(0.2208)(0.2241)(0.050)(0.0030)(0.0866)

Notes: Number of observations. Panel A: between 124 and 117; Panel B: between 104 and 113. P-values between parentheses. *p-value < .1; **p-value < .05; ***p-value < .001. For a description of the data, see Appendix B. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded.

Table 6
Open in new tab

Correlation Coefficients between Measurement Residuals and Selected Variables

All observations
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0604−0.1405−0.2507***0.3621***−0.0090−0.2122**−0.1182
(0.5071)(0.1211)(0.0052)(0.0000)(0.9242)(0.0185)(0.1946)
mesresTI-CPI−0.1297−0.0810−0.2854***0.4109***0.0510−0.2234**−0.1010
(0.1527)(0.3732)(0.0014)(0.0000)(0.5916)(0.0130)(0.2683)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI0.0368−0.0739−0.0604−0.0677−0.2687***−0.2180**0.1518*
(0.6876)(0.4163)(0.5067)(0.4572)(0.0033)(0.0177)(0.0938)
mesresTI-CPI0.1579*−0.1221−0.1620−0.1415−0.3310***−0.2807***0.1113
(0.0823)(0.1786)(0.0735) *(0.1184)(0.0003)(0.0021)(0.2205)
Excluding the ten biggest outliers
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0400−0.1297−0.1928**0.2860***0.0478−0.2234**−0.0933
(0.6737)(0.1708)(0.0408)(0.0021)(0.6315)(0.0174)(0.3278)
mesresTI-CPI−0.1200−0.0758−0.2216**0.3476***0.1124−0.2319**−0.0772
(0.2056)(0.4247)(0.0184)(0.0002)(0.2585)(0.0134)(0.4186)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI−0.0221−0.06730.0019−0.0391−0.1882*−0.2234**0.2073**
(0.8171)(0.4787)(0.9839)(0.6904)(0.0511)(0.0195)(0.0276)
mesresTI-CPI0.1159−0.1104−0.1161−0.1153−0.2682***−0.2818***0.1619*
(0.2238)(0.2443)(0.2208)(0.2241)(0.050)(0.0030)(0.0866)
All observations
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0604−0.1405−0.2507***0.3621***−0.0090−0.2122**−0.1182
(0.5071)(0.1211)(0.0052)(0.0000)(0.9242)(0.0185)(0.1946)
mesresTI-CPI−0.1297−0.0810−0.2854***0.4109***0.0510−0.2234**−0.1010
(0.1527)(0.3732)(0.0014)(0.0000)(0.5916)(0.0130)(0.2683)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI0.0368−0.0739−0.0604−0.0677−0.2687***−0.2180**0.1518*
(0.6876)(0.4163)(0.5067)(0.4572)(0.0033)(0.0177)(0.0938)
mesresTI-CPI0.1579*−0.1221−0.1620−0.1415−0.3310***−0.2807***0.1113
(0.0823)(0.1786)(0.0735) *(0.1184)(0.0003)(0.0021)(0.2205)
Excluding the ten biggest outliers
DummyEU AsiaDummy Asia PacificDummyAmericaDummyAfrica MEGdp_capPopR_g/gdp
mesresWB-CCI−0.0400−0.1297−0.1928**0.2860***0.0478−0.2234**−0.0933
(0.6737)(0.1708)(0.0408)(0.0021)(0.6315)(0.0174)(0.3278)
mesresTI-CPI−0.1200−0.0758−0.2216**0.3476***0.1124−0.2319**−0.0772
(0.2056)(0.4247)(0.0184)(0.0002)(0.2585)(0.0134)(0.4186)
Free_pressFree_speechVoice_accEmp_rightDemocChecksStability
mesresWB-CCI−0.0221−0.06730.0019−0.0391−0.1882*−0.2234**0.2073**
(0.8171)(0.4787)(0.9839)(0.6904)(0.0511)(0.0195)(0.0276)
mesresTI-CPI0.1159−0.1104−0.1161−0.1153−0.2682***−0.2818***0.1619*
(0.2238)(0.2443)(0.2208)(0.2241)(0.050)(0.0030)(0.0866)

Notes: Number of observations. Panel A: between 124 and 117; Panel B: between 104 and 113. P-values between parentheses. *p-value < .1; **p-value < .05; ***p-value < .001. For a description of the data, see Appendix B. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded.

Two of the geographic dummies are significantly correlated with both types of measurement residuals. Countries in the American continents, on average, appear to be less corrupt according to the PACI, compared to when either of the perception-based measures are used. The opposite, however, can be seen for countries in Africa and the Middle East. Populous countries also appear to fare better according to the PACI. Countries that are more democratic and have stronger checks and balances appear to be less corrupt on average when the PACI is used. The significance of some of the other variables, meanwhile, depends on which measurement residual is considered and on whether outliers are included. In interpreting the results, the squared estimated correlation coefficient should be noted to represent the fraction of the variance of the measurement residuals that is explained by a given variable (the R2 of the bivariate ordinary least-squares regression). Such fraction is always rather small, even when the estimated correlation coefficient is statistically significant.

To go beyond simple bivariate correlations, a multivariate regression is also considered (Table 7). Here, the dependent variable is either mesresWB-CCI or mesresTI-CPI and the regressors consist of all the explanatory variables described earlier.11 As in the bivariate analysis, results obtained by excluding the ten observations of the dependent variable having the greatest absolute value were also reported.

Table 7
Open in new tab

Multivariate Analysis of the Determinants of the Measurement Residuals

Dep. var: mesresWB-CCI
Dep. var. mesresTI-CPI
RegressorsAll observationsExcluding10 outliersAll observationsExcluding10 outliers
Asia & Pacific−0.4102−0.2714−0.09920.1070
(0.3213)(0.2886)(0.3243)(0.2972)
America & Caribbean−0.9691**−0.6412*−0.8116**−0.4874
(0.4018)(0.3569)(0.3778)(0.3353)
Africa & Middle East0.34500.40220.48260.4655
(0.3502)(0.3281)(0.3624)(0.3453)
Per capita GDP. PPP11.74048.819521.7674*11.8142
(10.0708)(9.5360)(11.189)(8.4577)
Pop−0.00630.0052−0.0008*−0.0010**
(0.0006)(0.0006)(0.0004)(0.0004)
R_g/gdp−0.0102***−0.0097***−0.0075*−0.0010**
(0.0035)(0.0027)(0.0041)(0.0004)
Free_press−0.0036−0.00080.01270.0068
(0.0120)(0.0116)(0.0113)(0.0109)
Free_speech−0.1566−0.0559−0.0909−0.0357
(0.2883)(0.280)(0.266)(0.24089)
Voice_acc0.49530.4670.18170.2087
(0.3785)(0.361)(0.3578)(0.3176)
Emp_right0.07340.01140.10480.0539
(0.0777)(0.0663)(0.0790)(0.0682)
Democ−0.0440**−0.0234−0.03245*−0.0247
(0.0194)(0.0176)(0.0178)(0.0164)
Checks−0.0436−0.0642−0.0332−0.0228
(0.0457)(0.0420)(0.0404)(0.0346)
Stability0.13020.15820.16050.1560
(0.1705)(0.1491)(0.1592)(0.1467)
Observations1059510596
R-squared0.3280.2950.3540.337
Dep. var: mesresWB-CCI
Dep. var. mesresTI-CPI
RegressorsAll observationsExcluding10 outliersAll observationsExcluding10 outliers
Asia & Pacific−0.4102−0.2714−0.09920.1070
(0.3213)(0.2886)(0.3243)(0.2972)
America & Caribbean−0.9691**−0.6412*−0.8116**−0.4874
(0.4018)(0.3569)(0.3778)(0.3353)
Africa & Middle East0.34500.40220.48260.4655
(0.3502)(0.3281)(0.3624)(0.3453)
Per capita GDP. PPP11.74048.819521.7674*11.8142
(10.0708)(9.5360)(11.189)(8.4577)
Pop−0.00630.0052−0.0008*−0.0010**
(0.0006)(0.0006)(0.0004)(0.0004)
R_g/gdp−0.0102***−0.0097***−0.0075*−0.0010**
(0.0035)(0.0027)(0.0041)(0.0004)
Free_press−0.0036−0.00080.01270.0068
(0.0120)(0.0116)(0.0113)(0.0109)
Free_speech−0.1566−0.0559−0.0909−0.0357
(0.2883)(0.280)(0.266)(0.24089)
Voice_acc0.49530.4670.18170.2087
(0.3785)(0.361)(0.3578)(0.3176)
Emp_right0.07340.01140.10480.0539
(0.0777)(0.0663)(0.0790)(0.0682)
Democ−0.0440**−0.0234−0.03245*−0.0247
(0.0194)(0.0176)(0.0178)(0.0164)
Checks−0.0436−0.0642−0.0332−0.0228
(0.0457)(0.0420)(0.0404)(0.0346)
Stability0.13020.15820.16050.1560
(0.1705)(0.1491)(0.1592)(0.1467)
Observations1059510596
R-squared0.3280.2950.3540.337

Notes: OLS estimates. Robust standard errors are between parentheses. **p-value < 0.1; **p-value < 0.05; ***p-value < 0.001. For a description of the variables, see the note at the bottom of Table 6 and Appendix B. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded.

Table 7
Open in new tab

Multivariate Analysis of the Determinants of the Measurement Residuals

Dep. var: mesresWB-CCI
Dep. var. mesresTI-CPI
RegressorsAll observationsExcluding10 outliersAll observationsExcluding10 outliers
Asia & Pacific−0.4102−0.2714−0.09920.1070
(0.3213)(0.2886)(0.3243)(0.2972)
America & Caribbean−0.9691**−0.6412*−0.8116**−0.4874
(0.4018)(0.3569)(0.3778)(0.3353)
Africa & Middle East0.34500.40220.48260.4655
(0.3502)(0.3281)(0.3624)(0.3453)
Per capita GDP. PPP11.74048.819521.7674*11.8142
(10.0708)(9.5360)(11.189)(8.4577)
Pop−0.00630.0052−0.0008*−0.0010**
(0.0006)(0.0006)(0.0004)(0.0004)
R_g/gdp−0.0102***−0.0097***−0.0075*−0.0010**
(0.0035)(0.0027)(0.0041)(0.0004)
Free_press−0.0036−0.00080.01270.0068
(0.0120)(0.0116)(0.0113)(0.0109)
Free_speech−0.1566−0.0559−0.0909−0.0357
(0.2883)(0.280)(0.266)(0.24089)
Voice_acc0.49530.4670.18170.2087
(0.3785)(0.361)(0.3578)(0.3176)
Emp_right0.07340.01140.10480.0539
(0.0777)(0.0663)(0.0790)(0.0682)
Democ−0.0440**−0.0234−0.03245*−0.0247
(0.0194)(0.0176)(0.0178)(0.0164)
Checks−0.0436−0.0642−0.0332−0.0228
(0.0457)(0.0420)(0.0404)(0.0346)
Stability0.13020.15820.16050.1560
(0.1705)(0.1491)(0.1592)(0.1467)
Observations1059510596
R-squared0.3280.2950.3540.337
Dep. var: mesresWB-CCI
Dep. var. mesresTI-CPI
RegressorsAll observationsExcluding10 outliersAll observationsExcluding10 outliers
Asia & Pacific−0.4102−0.2714−0.09920.1070
(0.3213)(0.2886)(0.3243)(0.2972)
America & Caribbean−0.9691**−0.6412*−0.8116**−0.4874
(0.4018)(0.3569)(0.3778)(0.3353)
Africa & Middle East0.34500.40220.48260.4655
(0.3502)(0.3281)(0.3624)(0.3453)
Per capita GDP. PPP11.74048.819521.7674*11.8142
(10.0708)(9.5360)(11.189)(8.4577)
Pop−0.00630.0052−0.0008*−0.0010**
(0.0006)(0.0006)(0.0004)(0.0004)
R_g/gdp−0.0102***−0.0097***−0.0075*−0.0010**
(0.0035)(0.0027)(0.0041)(0.0004)
Free_press−0.0036−0.00080.01270.0068
(0.0120)(0.0116)(0.0113)(0.0109)
Free_speech−0.1566−0.0559−0.0909−0.0357
(0.2883)(0.280)(0.266)(0.24089)
Voice_acc0.49530.4670.18170.2087
(0.3785)(0.361)(0.3578)(0.3176)
Emp_right0.07340.01140.10480.0539
(0.0777)(0.0663)(0.0790)(0.0682)
Democ−0.0440**−0.0234−0.03245*−0.0247
(0.0194)(0.0176)(0.0178)(0.0164)
Checks−0.0436−0.0642−0.0332−0.0228
(0.0457)(0.0420)(0.0404)(0.0346)
Stability0.13020.15820.16050.1560
(0.1705)(0.1491)(0.1592)(0.1467)
Observations1059510596
R-squared0.3280.2950.3540.337

Notes: OLS estimates. Robust standard errors are between parentheses. **p-value < 0.1; **p-value < 0.05; ***p-value < 0.001. For a description of the variables, see the note at the bottom of Table 6 and Appendix B. Countries for which PACIz = 0 and |${\hbox{Pr_}zero\_cases_z^{PACI} }$| > 0.015 and countries for which PACIz > 10000 have been excluded.

Only few of the explanatory variables considered are statistically significant and all the regressors only explain 30 to 35 percent of the total variability of the measurement residuals. A significant negative effect for the dummy variable pertaining to the American continents, in three out of four cases, can still be found. The effect of the dummy variable on Africa and the Middle East, detected in the bivariate analysis, is now insignificant. Countries where the index of democratization (democ) is higher appear to be less corrupt according to the PACI. The same applies to countries having a high share of public expenditure over GDP (r_g/gdp). Population is also seen to have a significant effect in two out of four cases. Overall, differences between the PACI and the two leading perception-based cross-national measures of corruption appear to be rather idiosyncratic, at least with respect to the set of factors considered.

IV. The Validity of the PACI

This section discusses the assumptions necessary for the PACI to be considered a valid measure of corruption. While Appendix A presents these assumptions formally, to show how they imply index validity, the focus here is on their overall meaning and, most importantly, on the extent to which they may hold in practice.

The first assumption is that the probability of observing a corrupt transaction involving firms from country i (and enforced first in the same country, or in third country jurisdictions) and public officials in country j does not depend on the identity of country j. It implies that the judiciary, when deciding which cases to pursue, does not “discriminate” based on the foreign officials' nationality. The possibility of assessing the levels of corruption in the foreign country by looking at the geographical distribution of cases first enforced elsewhere hinges on this key assumption. Whether this assumption can be relied upon is an empirical question. McLean (2012) evaluated the relevance of foreign policy considerations and opportunities for enforcement cooperation in determining the geographic distribution of FCPA cases, and found that bilateral frameworks for securities regulation and enforcement cooperation appear to be associated with higher levels of FCPA implementation. However, the magnitude of such an effect was found to be rather modest. The same study did not find other candidate explanatory variables to have any significant effect. Choi and Davis (2012) also found little evidence contradicting the first assumption.

The assumption could also be violated if the probability of detecting corruption cases depended on the conditions surrounding freedom of expression and information in the foreign country. However, the available data indicate that most cases are first enforced based on evidence gathered in the headquarters countries. Furthermore, supposing that the relevant perception-based measure is unbiased, the variables that capture a liberal environment for expression and the circulation of information in the foreign country would be expected to positively affect the measurement residuals introduced in the earlier sections (implying that the PACI in those cases would reveal a higher level of corruption compared to its perception-based counterpart). Three variables expressing the various dimensions of the ease of expression and circulation of information (free_press, free_speech and voice_acc; see Appendix B for a description) were considered and the results of both bivariate and multivariate analysis (Table 6 and 7) indicate a statistical significance only in very few cases, while pointing to the presence of marginal effects at most.12

Assumptions 2 and 3 in Appendix A are rather technical, and describe how the probability of offering a bribe, or accepting a bribe when offered, may depend on the level of corruption in the other country. Of more interest is assumption 4, which establishes that the number of cross-border transactions is proportional to bilateral trade flows. An alternative proxy for cross-border transactions would be foreign direct investments (FDI) (as in Choi and Davis 2012 and Mclean 2012). However, many transactions are not reflected in FDI flows, nor stocks, and FDI eventually enable trade flows between the countries involved. For these reasons, the choice of proxy made in this study seems to be more appropriate.

Differences in the scope of the public sector in different countries would also be a cause for concern. For instance, a pharmaceutical firm headquartered in country A successfully bribes hospital employees in country B and C in order to sell its products. If country B runs a public health system, the bribery incident would qualify as a case of cross-border corruption of a foreign public official. On the other hand, if country C's hospitals are managed by the private sector, the bribery incident would qualify as a case of private sector corruption and, as such, would not be included in this study's dataset. At first glance, neglecting this difference may be seen to lead to an underestimation of the level of corruption in country B with respect to C since, ceteris paribus, more corruption cases are registered in the former than in the latter, due to the wider scope of the public sector in country B. Apparently, in this case, trade flows would not serve as a good proxy for cross-national exchanges involving public officials in a given country, since for each dollar of imports there would be more interactions with public officials in country B than in country C. To account for this issue, this study computed the PACI without regard for cases involving procurement in the health and the telecommunication sectors (as opposed to transactions involving regulatory bodies in those sectors since they are invariably public). Arguably, these are the main sectors that are prone to stark variations in terms of government activism.13 Different choices, however, seem to deliver similar results (see Table 5).14

V. Discussion

The PACI reflects a narrow definition of corruption: the propensity of public officials to accept bribes from foreign firms.15 In denominating the index as a general “public administration” measure of corruption, the study implicitly assumes that what is observed by means of cross-country corruption statistics is also informative of the level of corruption in the public sector as a whole. Obviously, this assumption may be put to question, especially since levels of corruption may vary sensibly across public institutions within a given country. This article, however, argues that adopting narrow definitions of a phenomenon is desirable because it facilitates the testability of the assumptions on which hinges any extensive interpretation of the resulting measures.

For this reason, the PACI represents a welcome departure from most indices of governance currently available, where “sometimes it is not clear what precisely is being measured, rendering questionable the validity of at least some of the proxies” (Klitgaard and Light 1998). Such a state of affairs may reflect a situation “experienced in many ‘new areas’ of the social sciences: an explosion of measures, with little progress toward theoretical clarity or practical utility” (Klitgaard et al. 2005, 414). Quite likely, as “open data” become more widely available, more measures of governance, based on hard data, and narrowly defined like the PACI, would be ushered (see the discussion in Picci 2011, 117–119). Such a development, in turn, will facilitate greater theoretical clarity.

Data availability, as seen, poses certain constraints to the usefulness of the PACI. This limitation is the reason why this paper opted to demonstrate its application using a 15-year period. However, when comparing this attribute of the PACI with the TI-CPI, it should be noted that Transparency International has warned against making comparisons with their index across time, in light of annual changes in the methodology and country coverage. In other words, to some extent, the advantage of having a yearly measure is more apparent than real.16 As more countries sign the OECD Anti-Bribery Convention, more cases are likely to be reported, lending more precision to the PACI and allowing its computation for shorter intervals of time. Also, more data would permit the separate computation of the PACI for different sectors, and the resulting measures could eventually be aggregated into a general index.

This paper shows that, under a set of assumptions, the PACI is valid in the sense that it registers a higher value in countries where the probability that a transaction is corrupt is higher. The validity of the index is sufficient in delivering a correct ranking of countries. In a previous version of this article, it was shown that under a further assumption, the PACI represents the probability that a transaction is corrupt, relative to a world average. To illustrate, if the PACI equals 200 for a given country, it would imply that the probability of corruption in that country is twice a world average. However, it is left for future studies to explore the plausibility of such an interpretation, which may be appealing due to its practical applications but may be unrealistic. A better understanding of this issue would be welcome, considering that current cross-national measures of corruption are wanting in this respect. For example, in the 2014 TI-CPI, Germany and Turkey registered scores of 79 and 45 and rank 12th and 64th in the list of countries, respectively. The differences imply that the (perceived) level of corruption in Turkey is considerably higher than in Germany, but the index used does not allow any interpretation regarding how much more corruption there is in the latter compared to the former.

Under this light it should also be interpreted the finding that the PACI is highly correlated with the main perception-based measures of corruption, but it has a different scale. It was shown earlier that the scales become comparable once the log of the PACI is taken. If the PACI indeed approximates the probability that a transaction is corrupt with respect to a world average, then the exponential of the WB-CCI and the TI-CPI would be approximately proportional to levels of corruption. Thus, further work in this direction might help clarify the scales of existing perception-based indicators.

Other venues for future research could be foreseen. First, the availability of the PACI spurs a reassessment of the literature on the causes and consequences of corruption. As mentioned earlier, more data would allow the computation of the PACI for separate sectors of the economy. It would also be interesting to study the differences among several versions of the same indices, computed by focusing on different jurisdictions (along the lines of the presentation in Table 1). Since they all measure the same concept of corruption, they are expected to provide similar results. Thus, any variance among them could be explained by sampling error but also, possibly, by the violation of one or more of the maintained assumptions—the bigger the violation, the bigger the divergence. Tests for the validity of the maintained assumptions could be developed by leveraging on the magnitude of such observed differences.

Lastly, the same intuition behind the use of judicial statistics in this article, could be applied to other domains. The essential ingredients needed for this are data culled from different jurisdictions that convey information on crimes committed in a given country by actors residing in another country. Some types of financial crimes may possibly lend themselves to a treatment based on the methodology introduced in this paper.

Appendix A. Assumptions and Validity of the PACI.

For a corrupt transaction to occur, both parties must be willing to engage in it. Firms headquartered in country i may decide to offer bribes to public officials in the foreign country j, with a probability that depends on the attributes of the foreign country. In particular, the probability that firms would offer bribes may be higher if the perceived level of corruption in the foreign country is high. This is because high levels of corruption would imply a lower risk of being caught and a higher social tolerance for bribery. The probability that a public official in the foreign country accepts a bribe when offered one may also depend on the attributes of the headquarters country. For example, if the latter is known to be proactive in curbing cross-border corruption, public officials may be deterred because the discovery of a corrupt act in the headquarters country may be followed by prosecution in the public officials' respective countries.

The probability that a public official in country j accepts a bribe, when he perceives that there is no risk of getting caught following a case first enforced in the headquarters country, is defined by |${\,pr_corr\_F{O_j}}$|⁠. The advantage of this concept is that, logically, it does not depend on the attributes of the headquarters country, as it purely reflects the propensity of country j's public officials to accept bribes. This probability is identified as the level of corruption demonstrated by public officials in the foreign country. Meanwhile, a measure of corruption is deemed valid if it is monotonically increasing in the level of corruption.

Definition. The PACI is valid if: |$\partial PAC{I_z}/\partial {\,pr_corr\_F{O_z}} \gt 0$|⁠.

The expected number of corruption cases observed and enforced first in the headquarters country i, involving public officials in the foreign country j, is determined as follows:
(A1)
where |${corr\_exc{h_{i,j}}}$| is the number of occurrences of corruption involving firms headquartered in country i and public officials in country j (which will later be equate to its expected value) and |${\,pr_obs\_H{Q_{i,j}}}$| is the probability that the corrupt exchange is observed, and enforced first in the headquarters country.
The expected number of corrupt exchanges is:
(A2)
where |${\,pr_bribe\_H{Q_{i,j}}}$| is the probability that a firm headquartered in country i proposes a bribe to public officials in foreign country j, while|${\,pr_bribe\_F{O_{i,j}}}$| is the probability that the public official in j accepts the bribe offered by the firm headquartered in i. Meanwhile, transactionsi,j is the total number of business transactions involving firms in country i and public officials in foreign country j.

This formulation simply states that in order for a corrupt transaction to occur, both parties have to agree upon its execution. The possibility of extortion is ruled out, so that the probability that a transaction is corrupt is equal to a product of probabilities. Statistical independence between these events is not assumed, and the possibility that the probabilities of offering and accepting bribes are interdependent will be explicitly discussed subsequently.

Assumption 1

The probability that a corrupt transaction involving firms from country i and public officials in country j is observed and first enforced in country i does not depend on the identity of the foreign country j:
$${\,pr_obs\_H{Q_{i,j}}} = {\,pr_obs\_H{Q_i}}$$
This assumption is of key importance because it is the basis for using the geographic distribution of the cases involving country i firms to infer levels of corruption in all other countries.

Assumption 2

The probability that for a given cross-border transaction, a firm headquartered in country i offers a bribe to public officials in country j is as follows:
$${\,pr_bribe\_H{Q_{i,j}} = pr_corr\_H{Q_i} \cdot \theta (pr_corr\_F{O_j})},$$
where |$\theta (.)$|is a continuous and differentiable monotonically increasing function, and|$\theta (0) = 1$|⁠.

This assumption describes how the probability that a firm will propose a bribe increases with the level of corruption in the foreign country. The probability that a firm will offer a bribe abroad if it perceives that there is no risk of being caught following a case initiated in the foreign country is expressed by |${\,pr_corr\_H{Q_i}}$|. If such a risk is present, it would serve as a disincentive for firms in country i to propose a bribe. The second multiplicative factor, |$\theta (.)$|⁠, is meant to capture such a deterrence effect. It equals one when there is no perceived risk that a corrupt transaction will be caught following a case first enforced in the foreign country, and it increases with the level of corruption of the foreign country.17

Assumption 3

The probability that a public official in country j accepts a bribe when offered one by a firm headquartered in country i is as follows:
$${\,pr_bribe\_F{O_{i,j}} = pr_corr\_F{O_j} \cdot \delta (\,pr_corr\_H{Q_i})}$$
where|$\delta (.)$|is a continuous and differentiable function, and|$\delta (0) = 1$|⁠.

Symmetrically, with respect to Assumption 2, this assumption expresses the probability that a public official accepts a bribe, and its dependence on the level of corruption in the headquarters' country. The probability that a public official accepts a bribe if he perceives that there is no risk of getting caught following a case first enforced in the foreign country and then “spilling over” into the domestic jurisdiction, is expressed by |${\,pr_corr\_F{O_j}}$|⁠. This may be seen as the “underlying” probability of corruption of public officials in the foreign country j. Assumption 3 admits the possibility that, all else being equal, public officials in a given country may be more wary of accepting a bribe from a firm headquartered in a country that is very proactive in curbing cross-border corruption. It affirms that such a deterrence effect is functionally the same for all foreign countries – the function |$\delta (.)$|does not depend on j.

Assumption 4

Bilateral transactions are proportional to the value of exports from country i to country j,|${{x_{ij}}}$|, according to a constant factor k:|${transaction{s_{ij}} = k \cdot {x_{ij}}}$|

This assumption makes the number of cross-border transactions dependent on an observable variable – bilateral trade.

Given Assumptions 1, 2, 3 and 4, equation (Eq. A1) becomes:
$$\eqalignno{{cases\_obs\_H{Q_{i,j}} = pr_obs\_H{Q_i} \cdot pr_corr\_H{Q_i} \cdot \theta (pr_corr\_F{O_j})}\cr{ pr_corr\_F{O_j} \cdot \delta (pr_corr\_H{Q_i}) \cdot k \cdot {x_{ij}}}. }}$$

By substituting this expression into the definition of the PACIz (Eq. 1), proving the following becomes straightforward:

Proposition 1. The PACIz is valid:|${\partial PAC{I_z}/\partial pr_corr\_F{O_z} \ge 0}$|⁠.

Assumptions 1–4 guarantee that the PACI is valid in the sense that higher levels of probability of corruption of foreign public officials results in a higher value for the index.

Appendix B. Data Sources

Corruption cases. The main sources of data are Trace International Compendium's, a database on international anti-bribery enforcement (http://www.traceinternational.org/compendium), US DOJ and SEC documents, and OECD reports (various years). Other databases and publications, such as Shearman and Sterling 2013, Transparency International 2009 and 2013, and Cheung et al. 2012 were consulted. Various news sources, such as the Wall Street Journal Risk and Compliance Journal (http://www.wsj.com/news/risk-compliance-journal), and anti-corruption blogs like the FCPA Blog (http://www.fcpablog.com) were considered. Cases reported in multiple sources were laboriously consolidated to avoid double counting. The reference period for each case is the year when the bribe was allegedly paid. However, in some instances, this date had to be presumed from the available data. A small number of cases could not be considered for the computation of the PACI because of lack of information on the identity of one of the two countries involved. All cases linked to the United Nations Oil-for-Food Programme, which allowed Iraq to sell oil in exchange for food, medicine and other humanitarian goods, were excluded because of their peculiar origin. All the coding reflects information available on April 1, 2015. Raw data are available at http://www2.dse.unibo.it/picci/measure_corruption.html

Exports: Barbieri and Keshk 2012.

Gdp_cap: Gross domestic product based, per capita. Expressed in PPP dollar per person. Source: IMF-World Economic Outlook, October 2014 (variable name: PPPPC) (http://www.imf.org/external/Pubs/ft/weo/2015/01/).

The secondary source of the following variables is the “Standard data” of the Quality of Government Institute dataset (Teorell et al. 2013) which assembles various sources. The May 2014 release of the dataset was used. A brief description of each variable is provided, taken verbatim from that dataset's codebook. The original source, which the codebook identifies, is indicated. “Variable name” indicates how the variable is tagged in the codebook.

Pop: population. Source: Heston, Summers and Haten 2012. Variable name: pwt_pop).

R_g/gdp: the share of public expenditure over GDP (Source: Heston, Summers and Haten 2012. Variable name: pwt_gsg)

Emp_right: empowerment rights index (Source: Cingranelli and Richard 2010. Variable name: ciri_empinx_new). It is an “additive index constructed from the Foreign Movement, Domestic Movement, Freedom of Speech, Freedom of Assembly & Association, Workers' Rights, Electoral Self-Determination, and Freedom of Religion indicators. It ranges from 0 (no government respect for these seven rights) to 14 (full government respect for these seven rights).”

Free_press: freedom of the press index (Source: Freedom House. Variable name: fh_fotpc3). “The press freedom index is computed by adding four component ratings: Laws and regulations, Political pressures and controls, Economic Influences and Repressive actions. The scale ranges from 0 (most free) to 100 (least free).”

Free_speech: freedom of speech (Source: Cingranelli and Richard 2010. See Teorell et al. for details. Variable name: ciri_speech). “This variable indicates the extent to which freedoms of speech and press are affected by government censorship, including ownership of media outlets”.

Voice_acc: Voice and Accountability (The World Bank. Variable name: wbgi_vae). “‘Voice and Accountability’ includes a number of indicators measuring various aspects of the political process, civil liberties and political rights. These indicators measure the extent to which citizens of a country are able to participate in the selection of governments. This category also includes indicators measuring the independence of the media […].”

Checks: a measure of “checks and balances” (Source: Database of Political Intitutions Variable name: dpi_checks). “Equals 1 if the Legislative Index of Political Competitiveness (dpi_lipc) or the Executive Index of Political Competitiveness (dpi_eipc) is less than six. In countries where dpi_lipc and dpi_eipc are greater than or equal to six, dpi_checks is incremented by one if there is a chief executive, by a further one if the chief executive is competitively elected (dpi_eipc greater than six), and by a further one if the opposition controls the legislature […].”

Democ: index of democratization (Source: Vanhanen 2011 Variable name: van_index). “This index combines two basic dimensions of democracy – competition and participation – measured as the percentage of votes not cast for the largest party (Competition) times the percentage of the population who actually voted in the election (Participation). This product is divided by 100 to form an index that in principle could vary from 0 (no democracy) to 100 (full democracy). (Empirically, however, the largest value is 49).”

Stability: political stability (Source: The World Bank. Variable name: wbgi_pse). “‘Political Stability’ combines several indicators which measure perceptions of the likelihood that the government in power will be destabilized or overthrown by possibly unconstitutional and/or violent means, including domestic violence and terrorism.”

References

Alt
J. E.
,
Lassen
D. D.
.
2012
. “
Prosecutement and Public Corruption: Evidence from the American States.
”
Journal of Law, Economics, and Organization
.

Google Scholar

OpenURL Placeholder Text

 

Barbieri
K.
,
Keshk
O.
.
2012
.
Correlates of War Project Trade Data Set Codebook, Version 3.0
.
Online:http://correlatesofwar.org.

Carr
I. M.
,
Outhwaite
O.
.
2008
. “
The OECD Anti-Bribery Convention Ten Years Later
.”
Manchester Journal of International Law
5
(
1)
:
3
–
55
.

Google Scholar

OpenURL Placeholder Text

 

Chang
E.
,
Golden
M.
,
Hill
S.
.
2010
. “
Legislative Malfeasance and Political Accountability
.”
World Politics
62
(
2)
:
177
–
220
.

Google Scholar

Crossref
Search ADS

 

Cheung
Y. L.
,
Rau
P. R.
,
Stouraitis
A.
.
2012
. “
How Much Do Firms Pay as Bribes and What Benefits Do They Get? Evidence From Corruption Cases Worldwide.
”
National Bureau of Economic Research Working Paper No
.
17981
:
1
–
38
.

Google Scholar

OpenURL Placeholder Text

 

Choi
S.
,
Davis
K. E.
.
2012
. “
Foreign Affairs Prosecutement of the Foreign Corrupt Pratices Act
.”
Unpublished paper
.

Clausen
B.
,
Kraay
A.
,
Murrell
P.
.
2011
. “
Does Respondent Reticence Affect the Results of Corruption Surveys? Evidence from the World Bank Enterprise Survey for Nigeria.
” In
Rose-Ackerman
S.
, ed.,
International Handbook on the Economics of Corruption
.
Cheltenam UK
:
Edward Elgar Publishing House
.
Vol. 2
.
428
–
50
.

Google Scholar

Crossref
Search ADS

 

Fisman
R.
,
Gatti
R.
.
2002
. “
Decentralization and Corruption: Evidence from US Rederal Transfer Programs
.”
Public Choice
113
(
1)
:
25
–
35
.

Google Scholar

Crossref
Search ADS

 

Glaeser
E. L.
,
Saks
R. E.
.
2006
. “
Corruption in America.
”
Journal of Public Economics
90
(
6)
:
1053
–
1072
.

Google Scholar

Crossref
Search ADS

 

Goel
R.
,
Nelson
M.
.
2011
. “
Measures of Corruption and Determinants of US Corruption
.”
Economics of Governance
12
:
155
–
76
.

Google Scholar

Crossref
Search ADS

 

Golden
M.
,
Picci
L.
.
2005
. “
Proposal for a New Measure of Corruption, Illustrated with Italian Data
.”
Economics & Politics
17
(
1)
:
37
–
75
.

Google Scholar

Crossref
Search ADS

 

Golden
M.
,
Picci
L.
.
2008
. “
Pork Barrel Politics in Postwar Italy, 1953–1994
.”
American Journal of Political Science
52
(
2)
:
268
–
89
.

Google Scholar

Crossref
Search ADS

 

Kaufmann
D.
,
Kraay
A.
,
Mastruzzi
M.
.
2009
. “
Governance Matters VIII: Aggregate and Individual Governance Indicators, 1996–2008.
”
World Bank Policy Research Working Paper
4978
.
World Bank, Policy Research Department
,
Washington, DC
.

Google Scholar

Crossref
Search ADS

 

Klitgaard
R.
,
Light
P. C.
.
1998
. “
Economic Growth and Social Indicators: An Exploratory Analysis
.”
Economic Development and Cultural Change
,
46
(
3)
:
455
–
89

Google Scholar

OpenURL Placeholder Text

 

Klitgaard
R.
,
Fedderke
J.
,
Abramamov
K.
.
2005
. “
Choosing and Using Performance Criteria
.” In
Klitgaard
R.
,
Light
P. C.
, eds.,
High Performance Government. Structure, Leadership, Incentives
.
Santa Monica, CA
:
Rand Corporation

Google Scholar

OpenURL Placeholder Text

 

Kraay
A.
,
Murrell
P.
.
2013
. “
Misunderestimating Corruption.
”
World Bank Policy Research Working Paper 6488
.
World Bank, Policy Research Department
,
Washington, DC
.

Google Scholar

Lambsdorff
J. G
.
2006
. “
Consequences and Causes of Corruption: What Do We Know from a Cross-Section of Countries?
” In
Rose-Ackerman
S.
, ed.,
International Handbook on the Economics of Corruption
.
Cheltenam UK
:
Edward Elgar Publishing House
.
3
–
52
.

Google Scholar

OpenURL Placeholder Text

 

Lambsdorff
J. G
.
2007
.
The Institutional Economics of Corruption and Reform: Theory and Policy
.
Cambridge, UK
:
Cambridge University Press
.

Google Scholar

Crossref
Search ADS

 

McLean
N. M
.
2012
. “
Cross-National Patterns in FCPA Prosecutement.
”
The Yale Law Journal
121
(
7)
:
1970
–
2012
.

Google Scholar

OpenURL Placeholder Text

 

OECD, Various years
. “
Country Reports on the Implementation of the OECD Anti-Bribery Convention.
”
http://www.oecd.org/daf/antibribery/countryreportsontheimplementationoftheoecdanti-briberyconvention.htm.

Olken
B. A
.
2009
. “
Corruption Perceptions vs. Corruption Reality
.”
Journal of Public Economics
93
(
7)
:
950
–
64
.

Google Scholar

Crossref
Search ADS

 

Picci
L
.
2011
.
Reputation-based Governance
.
Stanford, CA
:
Stanford University Press
.

Google Scholar

Crossref
Search ADS

 

Putnam
T. L
.
2009
. “
Courts without Borders: The Domestic Sources of US Extraterritorial Regulation
.”
International Organization
63
(
3)
:
459
–
90
.

Google Scholar

Crossref
Search ADS

 

Razafindrakoto
M.
,
Roubaud
F.
.
2010
. “
Are International Databases on Corruption Reliable? A Comparison of Expert Opinion Surveys and Household Surveys in Sub-Saharan Africa
.”
World development
38
(
8)
:
1057
–
1069
.

Google Scholar

Crossref
Search ADS

 

Reinikka
R.
,
Svensson
J.
.
2006
. “
Using Micro-surveys to Measure and Explain Corruption
.”
World Development
34
(
2)
:
359
–
70
.

Google Scholar

Crossref
Search ADS

 

Rose-Ackerman
S
.
1999
.
Corruption and Government: Causes, Consequences and Reform
.
Cambridge, UK
:
Cambridge University Press
.

Google Scholar

Crossref
Search ADS

 

Saisana
M.
,
Saltelli
A.
.
2012
. “
Corruption Perceptions Index 2012 Statistical Assessment
.”
JRC Scientific and Policy Reports, European Commission
.

Seligson
M
.
2006
. “
The Measurement and Impact of Corruption Victimization: Survey Evidence from Latin America
.”
World Development
34
(
2)
:
381
–
404
.

Google Scholar

Crossref
Search ADS

 

Sherif
M.
,
Cantril
H.
.
1945
. “
The Psychology of ‘Attitudes’: Part I
.”
Psychological Review
,
52
(
6)
:
295
–
319
.

Google Scholar

Crossref
Search ADS

 

Shearman & Sterling Llp
.
2013
. “
FCPA Digest of Cases and Review Releases Relating to Bribes to Foreign Officials under the Foreign Corrupt Practices Act of 1977.
”
Shearman and Sterling LLP
,
New York
.
http://www.shearman.com/files/Publication/287c1af0-f9cb-4c11-805d-91c409975b41/Presentation/PublicationAttachment/83d9dc0b-b80c-4ca4-877b-9efbba0952e7/FCPA-Digest-Jan2013_010213.pdf.

Google Scholar

Teorell
J.
,
Charron
N.
,
Dahlberg
S.
,
Holmberg
S.
,
Rothstein
B.
,
Sundin
P.
,
Svensson
R.
.
2013
.
The Quality of Government Dataset, version 20. Dec13. University of Gothenburg: The Quality of Government Institute
,
http://www.qog.pol.gu.se
.

Transparency International
.
2009
. “
OECD Anti-Bribery Convention Progress Report 2009: Prosecutement of the OECD Convention on Combating Bribery of Foreign Public Officials in International Business Transactions
.”
http://www.transparency-usa.org/news/documents/FinalOECDProgressReport2009.pdf.

Transparency International
.
2012
. “
Corruption Perceptions Index 2012: An updated methodology.
”
http://cpi.transparency.org/cpi2012/in_detail/.

OpenURL Placeholder Text

Transparency International
.
2013
. “
Exporting Corruption: Progress Report 2013: Assessing prosecutement of the OECD Convention on Combating Bribery
.
http://www.transparency.org/whatwedo/pub/exporting_corruption_progress_report_2013_assessing_prosecutement_of_the_oecd.

Treisman
D
.
2007
. “
What Have We Learned About the Causes of Corruption From Ten Years of Cross-National Empirical Research?
”
Annual Review of Political Science
10
:
211
–
44
.

Google Scholar

Crossref
Search ADS

 

Van Aaken
A.
,
Feld
L.
,
Voigt
S.
.
2010
. “
Do Independent Prosecutors Deter Political Corruption? An Empirical Evaluation across Seventy-Eight Countries
.”
American Law and Economics Review
12
(
1)
:
204
–
44
.

Google Scholar

Crossref
Search ADS

 
1

On the use of perception-based measures to inquire into the nature, causes and consequences of corruption, see Lambsdorff (2006 and 2007), Treisman (2007), and Rose-Ackerman (1999).

2

A well-known example is Transparency International's “Global Corruption Barometer” (TI-GCB). See http://www.transparency.org/research/gcb/overview.

3

See for instance Treisman (2007), Clausen et al. (2011), and Kraay and Murell (2013). Other types of measures of corruption have been proposed, but so far not at the cross-national level. See for example Golden and Picci (2005).

4

Judicial statistics have been used to measure corruption in studies at the sub-national level, where the assumption of spatially homogenous enforcement is plausible. See for instance, Glaeser and Saks (2006), Goel and Nelson (2011), Fisman and Gatti (2002), Alt and Lassen (2012), for the US; Chang et al. (2010), and Golden and Picci (2008).

5

This paper is arguably the first to propose an index of corruption based on cross-border incidents of corruption. McLean (2012) contemplates the possibility of computing such an index; see in particular his Figure 1.

6

See Carr and Outhwaite (2008) and http://www.oecd.org/daf/anti-bribery/oecdantibriberyconvention.htm.

7

After a case is first enforced in the headquarters country or in a third-country jurisdiction, it may also fall under the radar of the foreign country's judiciary.

8

It should be emphasized that coding the outcome of a case as positive does not imply conviction or guilt on the part of the accused.

9

Note that more corruption corresponds to lower values of the WB-CCI and the TI-CPI indices, but to higher values for the PACI and the TI-GCB.

10

A detailed description of the variables used is found in Appendix B.

11

The dummy for Europe and Central Asia was excluded to avoid the dummy variable trap. The estimated coefficients of the other dummies should then be interpreted as the estimated effect relative to that reference group of countries.

12

The probability of detection and enforcement may also be sector-specific. For example, corruption in the arms trade is likely to be more difficult to detect as national security concerns may limit the actions available to the judiciary (see also Rose-Ackerman 1999). To address such issues, the PACI could be computed separately for different sectors of the economy.

13

Alternatively, the number of cases for the relative size of the public sector in different countries could be corrected. However, the concept of public sector is blurry, particularly in some countries. Generally, the decision on how to account for the variable scope of public sectors worldwide depends on the position which the researcher takes on conceptual issues that are the subjects of debate.

14

If the interest lies in measuring the magnitude of public sector corruption, instead of its frequency, then cross-country variations in the scope of activities of the public sector should not be a cause for concern. The scope of government is also arguably endogenous over the long-run since corrupt elites have an interest in maintaining and possibly expanding their reach.

15

A Bribe Payer's Corruption Index (BPCI) may also be computed along the same lines as the PACI, but using cases first enforced in the foreign country. However, the scarcity of data proves to be an obstacle towards its computation.

16

Recent changes in the way the TI-CPI is computed should assure that “[it] will better capture changes in perception of corruption in the public sector of [a given] country over time. However, due to the update in the methodology, 2011 CPI scores are not comparable with CPI 2012 scores” (Transparency International 2012).

17

In fact, the argument of the function |$\theta (.)$| should more appropriately be taken as expectations of levels of corruption in the foreign country. This, in particular, would lead to the possibility of expectations being partly self-fulfilling: in a country with a (initially, possibly undeserved) reputation for corruption, foreign firms would be prone to offer bribes more often, leading to more corrupt transactions. Considering |${\,pr_corr\_H{Q_i}}$| instead of expectations is justifiable if the latter is assumed to depend monotonically from that underlying probability.

Author notes

Laarni Escresa is an assistant professor at the School of Economics, University of the Philippines; her email is [email protected]. Lucio Picci (corresponding author) is a professor at the Department of Economics, University of Bologna, Strada Maggiore 45, I-40125 Bologna, Italy. They would like to thank, for their comments on a previous version of this paper, Rajeev Goel, Miriam Golden, Johann Graf Lambsdorff, Jerg Gutmann, Robert Klitgaard, Nicholas McLean, Angela Reitmaier, Susan Rose-Ackerman, participants to a workshop at the Inter-American Development Bank in Washington, DC, to the European Political Science Association 4th Annual Conference in Edinburgh, and to the 10th Annual Conference of the Italian Society of Law and Economics in Rome and three anonymous referees. Laarni Escresa acknowledges research funding from the Deutsche Forschungsgemeinschaft (German Research Foundation). Lucio Picci acknowledges the financial support of the Fondazione Cassa dei Risparmi di Forlì. All Website contents were accessed on June 8, 2015.

© The Author 2015. Published by Oxford University Press on behalf of the International Bank for Reconstruction and Development / THE WORLD BANK. All rights reserved. For permissions, please email: [email protected]
JEL
C18 - Methodological Issues: General C43 - Index Numbers and Aggregation D73 - Bureaucracy; Administrative Processes in Public Organizations; Corruption F53 - International Agreements and Observance; International Organizations F55 - International Institutional Arrangements H11 - Structure, Scope, and Performance of Government H50 - General K42 - Illegal Behavior and the Enforcement of Law
Download all slides