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Culture and Corruption: Using Hofstedees Cultural Dimensions to Explain Perceptions of Corruption

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Hofstede’s cultural dimensions are used to analyze the Corruption Perception Index of 47 countries. Using a linear regression model with the CPI as the dependent variable and culture as the independent variables, 65% of the perceptions of corruption is explained by four of Hofstede's cultural dimensions. Each dimension is significant at the < .05 level. The results have potential important implications for global strategic management, foreign investment, marketing, and internal and external auditing.
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Culture and Corruption:
Using Hofstede’s Cultural Dimensions
to Explain Perceptions of Corruption
Wm. Dennis Huber
© 2001
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ABSTRACT
Hofstede’s cultural dimensions are used to analyze the Corruption Perception Index of 47
countries. Using a linear regression model with the CPI as the dependent variable and culture as
the independent variables, 65% of the perceptions of corruption is explained by four of
Hofstede's cultural dimensions. Each dimension is significant at the < .05 level. The results have
potential important implications for global strategic management, foreign investment, marketing,
and internal and external auditing.
Keywords: Culture, corruption, Hofstede
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INTRODUCTION
Corruption is a problem that currently plagues, and has plagued historically, every country in the
world regardless of size, economic system or political system. It appears in various forms and to
different degrees. (World Bank Report, 1997; Tanzi, 1998; Bardhan, 1997) Its impact can hardly
be understated. Presidents, Prime ministers, entire governments and whole political classes have
either fallen or been replaced as a result of corruption, whether actual or perceived. (Tanzi, 1998;
Johnston 1997)
The growth of global trade and recent changes in the economic systems of some countries
have not only resulted in a greater awareness being placed on the presence of corruption, but also
a greater emphasis on curtailing it. (World Bank Report, 1997; Tanzi, 1998) At the same time,
there is a greater sensitivity to the diverse cultural practices and systems around the world.
The World Bank Report states that while the causes of corruption are complex, the
conditions of corruption exist in a variety of settings, which are particularly prevalent in some
developing countries. The conditions of corruption are always contextual in nature and which
originate, among other things, in a country's political social history. (World Bank Report, 1997)
Vito Tanzi (1995), Director of the International Monetary Fund’s Fiscal Affairs Division, states
that “the many factors that contribute to corruption tend to be more common in poorer countries
and in economies in transition than in rich countries.”
It has been suggested that perceptions of corruption, if not corruption itself, and culture
are related. Bardhan (1997) suggests that corruption is perceptibly different in different societies.
Dr. Frene Ginwalla, the Speaker of the South African Parliament, goes farther and argues that
perceptions of corruption are actually a function of culture. (Versi, 1996) There is some research
to support this.
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Tsalikis & Latour (1991) studied American and Greek views of bribery and extortion.
The results showed that reactions to bribery and extortion varied according to the nationality of
the person offering the bribe and the country in which the bribe was offered.
Tsalikis & Nwachukwu (1995) compared American and Nigerian views of bribery and
extortion. As with the American-Greek study, the reactions to bribery and extortion varied
according to both the nationality of the person offering the bribe and by the country in which the
bribe was offered.
Oguhebe (1991) studied American and Nigerian businessmen’s views of how the U.S.
and Nigerian business environments differed in cultural dimensions and the impact of Nigerian
culture on U.S. executives’ business decisions in Nigeria. Among other things, American
businessmen viewed Nigeria as a place where there existed a substantial amount of bribery and
corruption.
The World Bank advises that corruption "should be explicitly taken into account in
country risk analysis, lending decisions, and portfolio supervision if it affects project or country
performance and the government's commitment to deal with it is in question." (World Bank
Report, 1997) This is no less true for private business. In order to take corruption into account
when performing country risk analysis, it is important to be able to identify whether, and to what
degree, culture may impact perceptions of corruption, especially for firms just entering the
market.
However, no empirical research has been conducted to determine whether or not there is
a link between cultural systems and perceptions of corruption. The purpose of this paper is to
examine whether culture is a factor in explaining the perceived level of corruption of countries
around the world, and if so, to what degree it helps to explain such perceptions.
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LITERATURE REVIEW
Corruption
It is an overstatement to say, as Tanzi (1995) claims, that “in most cases, different
observers would agree on whether a particular behavior connotes corruption.” Nevertheless,
there are some common agreements on some aspects of corruption. While the private sector can
be and often is involved in corrupt activities, the presence of corruption in government action or
government agents is generally required for a country to be considered corrupt. In particular,
corruption is often seen as being associated with state-run monopolies and the discretionary
power of the state. (Tanzi, 1999)
The most commonly used definition of corruption is that corruption is the abuse of public
power for private benefit. The definition the World Bank uses is that it is “the abuse of public
office for private gain.” (World Bank Report, 1997)
While overlapping, distinctions are sometimes made between bureaucratic and political
corruption, with the former being considered "petty" and the latter "grand". (Tanzi, 1998)
Distinctions are also sometimes made between economic and political corruption. Political
corruption is where the gains of corruption are in terms of political power while economic
corruption is the use of public office for private gains. (Bardhan, 1997)
Third, distinctions are also sometimes made between public and private corruption.
Public corruption is where the government and its agents engage in corrupt activities. However,
when corruption is widespread in the government, private businesses are also affected in that
private businesses that conduct business with the government are participants in the corruption.
(World Bank Report, 1997)
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Finally, when one reads in a newspaper that the government of country “X” is accused of
being corrupt, it could be because it is thought to be taking bribes to overlook illegal activities,
such as drug trafficking. On the other hand, it could be because bribes are required for legal
activities, such as obtaining government contracts. Unfortunately, there is no data available with
which corruption with an illegal basis (criminal corruption) can be separated from corruption
with a legal basis (non-criminal corruption). Criminal corruption would be corruption pertaining
to illegal activities, such as paying bribes to overlook illegal activities such as drug trafficking.
Non-criminal corruption would be corruption pertaining to legal activities, such as bribes in
connection with obtaining a contract.
The costs and other negative impacts of corruption are typically thought to be borne by a
country’s economy. In particular, corruption if a major factor that distorts the efficient operation
of free market allocation of resources. (Tanzi, 1998) Furthermore, it has been shown that
corruption reduces investment and therefore economic growth. (Mauro, 1995; Wei, 1997)
Not everyone sees corruption as entirely negative, however. Among the arguments that
corruption is not entirely negative are that it can, in some cases, improve economic efficiency by
removing government obstacles. (Leff, 1964; Huntington, 1968; Lui, 1985; Beck & Maher,
1986; Lien, 1986) Nevertheless, while there may be some aspects of corruption that can be
beneficial in some circumstances, it is generally agreed that it is evil, at least in the long run.
(Perhaps one might ask, is the glass nine-tenths empty, or one-tenth full?)
The Corruption Perception Index
Transparency International is a non-governmental organization based in Germany whose
purpose is “to curb corruption by mobilising a global coalition to promote and strengthen
international and national integrity systems.” (Transparency International, 1998) It accomplishes
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this is by collecting on an annual basis information about perceptions of corruption which it calls
the Corruption Perception Index (CPI). The CPI is a “poll of polls” which draws on surveys of
both expert and general public opinions regarding their views of corruption of many countries
around the world.
The most recent CPI was published in 1998 and covered 85 countries, up from 52
countries in 1997. The surveys used included data from the Economist Intelligence Unit
(Country Risk Service and Country Forecasts), Gallup International (50th Anniversary Survey),
Institute for Management Development (World Competitiveness Yearbook), Political &
Economic Risk Consultancy (Asian Intelligence Issue), Political Risk Services (International
Country Risk Guide), World Bank (World Development Report & Private Sector Survey) and
World Economic Forum & Harvard Institute for International Development (Global
Competitiveness Survey). Twelve different surveys were used and a minimum of 3 surveys was
required in order for a country to be included in the Index. The higher the index, the lower the
perceived corruption.
****************
Put Table 1 Here
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Culture
Culture must be defined in such a way that it can be operationalized for empirical
research in order for its influence to be measured. In 1980 Geert Hofstede published Culture's
Consequences: International Differences in Work-Related Values. Culture's Consequences has
become the most widely used measurement for studying cultural influences because it can be
used in empirical studies of culture in a variety of contexts. (Sondergaard, 1997)
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Hofstede found that the solutions for social problems differed systematically among
countries in the areas of the relationship between the individual and the group, social inequality,
ways of dealing with uncertainty, and concepts of masculinity and femininity. Hofstede labeled
these dimensions collectivism versus individualism, power distance, uncertainty avoidance, and
masculinity versus femininity. Hofstede was also able to measure these dimensions numerically
with scales ranging from weak to strong, or low to high. (Hofstede, 1980) In 1988 a fifth
dimension was identified. The perception of time was also found to vary systematically among
cultures, which is referred to as long-term versus short-term time orientation. (Hofstede & Bond,
1988)
The collectivism versus individualism dimension measures the value that a society places
on individual achievement versus collective achievement. In a culture with a low individualism
score, individual initiative is socially frowned upon, there is a “we” consciousness, and policies
and practices are based on sense of loyalty and duty. In a culture with a high individualism score,
individual initiative is socially encouraged, there is an “I” consciousness, and policies and
practices are based on sense of loyalty and duty. (Hofstede, 1991)
The power distance dimension measures subordinates’ perception of the extent of
acceptability of unequal power distribution. Among the features of a culture of low power
distance, the government is based on majority vote and is frequently led by parties stressing
equality, the tax system aims at redistributing wealth, in general people feel less threatened, and
there is a stronger perceived work-ethic. In a high power distance culture, the government is
often autocratic or oligarchic and it does not stress equality of power, the tax system protects the
wealthy, power and inequality are facts of life, in general people feel more threatened, and there
is a weaker perceived work-ethic. (Hofstede, 1991)
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The uncertainty avoidance dimension measures a society’s willingness to tolerate
ambiguity when there are no rules to guide them. A low uncertainty avoidance score means a
high tolerance for ambiguity. Thus, among the features that may be observed in a culture where
there is a low uncertainty avoidance score is that it is less conservative with respect to law and
order, there is less need for written rules, a less elaborate legal system exists, and hierarchical
structures or rules may be broken for pragmatic reasons. A culture with a high uncertainty
avoidance score could include characteristics such as being more conservative with respect to
law and order, more need for written rules, a more elaborate legal system, hierarchical structures
should be and respected, and rules should not be broken. (Hofstede, 1991)
The masculinity versus femininity dimension measures the degree to which a society
values assertiveness (masculinity) versus supportiveness (femininity). Among the features that
may typically be found in cultures identified as having a low masculinity score are that trying to
be better than others is neither socially nor materially rewarded, there is weaker achievement
motivation, “Theory X” is strongly rejected, and there is less industrial conflict. On the other
hand, some of the features that might be found in cultures with a high masculinity score are that
there are rewards in the form of wealth or status for the successful achiever, there is stronger
achievement motivation, “Theory X” gets some support, and there is more industrial conflict.
(Hofstede, 1991)
The long-term versus short-term time orientation measures how societies view the
importance and meaning of time. Its characteristics are not as well developed as the core
dimensions. A culture with a high long-term orientation score is characterized by persistence,
relationships that are ordered according to status, thrift, and having a sense of shame. A culture
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with a low long-term time orientation score is characterized by personal steadiness, face-saving,
respect for tradition, and reciprocal favors and gifts. (Hofstede & Bond, 1988)
Obviously, not every culture exhibits all of the characteristics all the time. For example,
not every country with a high individualism score exhibits all of the characteristics of societies
with high individualism scores. Different societies with high individualism scores exhibit
different high individualism characteristics.
****************
Put Table 2 Here
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Hofstede’s work is not without its critics. The major criticism is that because his sample
consisted of only IBM employees, some question concerning whether the results can be
generalized to the population. However, as of 1994, there had been 61 known replications of
Hofstede’s study. The replications showed that the differences predicted by Hofstede's
dimensions were largely confirmed, with “remarkably few non-confirmations”. Several other
studies asking questions different from, and independent of, Hofstede's study have also been
meaningfully classified along the same dimensions as Hofstede’s. (Sondergaard, 1997) Thus,
while neither the dimensions, nor the scales, of Hofstede’s study are universally accepted, the
results have been confirmed in a sufficient number of subsequent studies that the reliability and
validity of Hofstede’s classification system and measurements can be applied confidently in a
variety of empirical studies. (Sondergaard, 1997)
HYPOTHESIS AND METHODOLOGY
Hypothesis
If, as the 1997 World Bank Report states, “corruption has a political dimension that
reflects the way power is exercised in a country,” then Hofstede’s measurements of cultural
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dimensions should be able to explain at least some aspects of perceptions of corruption as
measured by the CPI. In particular, the power distance and uncertainty avoidance dimensions
should have a significant relationship to the perception of corruption since these two dimensions
deal with laws and rules more than the other dimensions.
The hypothesis tested in this study is that Hofstede’s cultural dimensions can explain
perceptions of corruption. The null hypothesis that is tested is:
H0: Perceptions of corruption as measured by the CPI
cannot be explained by any of Hofstede’s dimensions of culture.
The research hypothesis is:
Ha: Perceptions of corruption as measured by the CPI
can be explained by one or more of Hofstede’s dimensions of culture.
Fifty-two countries were included in Hofstede’s (1991) core indexes (individualism,
power distance, uncertainty avoidance and masculinity), while 17 had the long-term time index.
The 1998 CPI included 85 countries. Of these, 47 appeared on both Hofstede’s (1991) core
dimensions and the CPI, and 17 were included in Hofstede’s (1991) LTO index and the CPI.
Hofstede’s indexes and the CPI are given on tables 3 and 4 respectively.
***********************
Put Tables 3 and 4 Here
***********************
Methodology
To test the hypothesis, a regression equation is used. The regression equation is:
CPI = α + (IDV) + (PD) + δ(UA) + ε(MAS) +
(LTO) +
where is a random error term.
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The equation states that the perception of corruption, as measured by the CPI, is a
function of the cultural dimensions of individualism (IDV), power distance (PD), uncertainty
avoidance (UA), masculinity (MAS) and long-term orientation (LTO) as measured by Hofstede,
plus a random error term. Given a country’s culture indexes, perceptions of corruption should be
predictable.
If Hofstede’s cultural dimensions have little or no relationship to perceived corruption,
then the results of the equation will be low, each coefficient will be insignificant and the random
error term will be large. If, however, they do have a relationship to perceived corruption, then the
results of the equation will be high, at least one of the coefficients will be significant and the
random error term will be small. The signs of the coefficients can be either positive or negative.
Two regressions were run. The first included only Hofstede’s core dimensions (with LTO
= 0). The second included the LTO dimension.
Limitations
First, no definition of corruption is suggested in this paper. Nor is a definition required to
test the hypothesis. The test is not concerned with particular types or labels of corruption, such as
bribes or kickbacks, and there is no differentiation between public and private corruption. The
test merely uses the terms, definitions and concepts contained in the Corruption Perception
Index.
Second, the question of the generalizability of Hofstede’s measurements must be kept in
mind. While the classification and scales have been validated in most studies, they have not been
validated in others. Third, the degree to which the perception of corruption reflects the reality of
corruption must also be kept in mind. To say that country “X” is perceived as corrupt is not the
same as saying that it actually is corrupt.
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Fourth, not all of the countries were defined the same by Hofstede and the CPI. For
example, in some cases Hofstede merely refers to groups of countries, such as Arab Countries,
West Africa and East Africa. That problem did not surface in this study. A related problem that
did surface is that Hofstede referred to the United Kingdom, while the CPI used Great Britain.
The distinction is not considered important for this study, and both are lumped together here as
“UK/GB” (with apologies to the Crown).
Fifth, the data were from different sources, and different time periods. However, the
difference is not expected to materially affect the results. Finally, this study does not take a
position on the costs or benefits of corruption. The only thing being tested here is whether one or
more of Hofstede's cultural dimensions explains perceptions of corruption.
RESULTS
The results of the regression (Appendix 1) are nothing less than astounding. The null
hypothesis is firmly rejected.
The results of the regression equation using Hofstede’s core dimensions explain 65% of
the perceptions of corruption, at an F Sig. of < .05. Furthermore, while only the PD and UA
dimensions were expected to be significant, each of the coefficients of Hofstede’s core
dimensions was significant at the t < .05 level. The IDV factor alone had the largest adjusted R2
of .485 at a t < .000 level of significance. Each dimension also had a correlation coefficient with
the CPI at a t < .05 level.
The result of the first regression equation is:
CPI = 8.871 + .435(IDV) - .351(PD) - .254(UA)
- .184(MAS) +
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The interpretation of the results is that higher the IDV index (i.e., greater individualism),
the higher the CPI (i.e., less corruption is perceived). On the other hand, each of the other three
had negative coefficients. Thus, the greater the power distance (i.e., a greater distance between
subordinates and superiors), uncertainty avoidance (i.e., a low tolerance for uncertainty) and
masculinity (i.e., more masculine), the lower the CPI (i.e., more corruption is perceived)
ANOVA results confirmed the regression results in that the F for each factor individually,
and all factors together, was significant at the < .000 level.
The results of the second regression equation that included Hofstede’s LTO dimension,
explain 66.8% of the perceptions of corruption, with an F Sig. of < .05. Again the coefficients
were negative. However, only the coefficients of two dimensions - PD and UA - were
significant at the t < .05 level. Neither LTO, nor the other two core factors, IDV and PD, were
significant. The difference between the first and second equations is no doubt the result of the
difference in the number of countries included. The second regression yielded the following
equation:
CPI = 13.925 -.063(PD) -.004(UA)
The interesting thing to note here is that the CPI starts out higher than the highest CPI
score, which is 10, and is decrease when power distance and uncertainty avoidance are factored
in.
ANOVA results confirmed the regression results in that F for each factor, and both
factors, was significant at the < .000 level.
SUMMARY
The implications of the results have potential importance for strategic management
decisions, foreign direct investment decisions, internal and external auditing, and marketing,
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among others. The risk of the existence of corruption, based on perception, can be better
evaluated. For example, the risk can be factored into risk/return decisions when considering
investing, facility location, or market entry. Auditors can incorporate the risk of the existence of
corruption when planning an auditing engagement.
Culture is extremely difficult to change, and generally changes very slowly. If perception
of corruption is explained by culture, then fighting corruption, or its perception, may be
tantamount to fighting culture. This may help to explain why some countries resist efforts to
change their laws to combat corruption.
Second, perceptions can change over time, but they may also remain the same. Even if a
country makes substantial progress in changing or improving the existence of corruption, the
perception may linger. Whether the perception accurately reflects the reality should be explored
more fully.
The results suggest several avenues for future research. First, better results would no
doubt be obtained if more data for more countries were available, regarding both culture and
corruption. Obtaining more date should be a priority in future research. As more data is obtained
predictions of corruption perception for countries that are not on the CPI may be made.
Second, following how the data changes over time may provide useful insights.
Sondergaard (1997) cites some studies that suggest some may have already changed.
Third, other factors may also help to explain the level of a country’s perceived level of
corruption. The World Bank states that corruption is more prevalent in borrower countries. Thus,
comparing borrowing patterns to the CPI may yield useful results.
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Future research should also distinguish corruption with a legal basis (e.g.,
giving/receiving bribes regarding legitimate business), versus corruption with an illegal basis
(e.g., giving/receiving bribes regarding drug trafficking).
Finally, a word of caution is necessary. The results could lead to a feeling of cultural
superiority and/or cultural prejudice. Hopefully, this will not happen. Hopefully the results will
be used for combating the reality of corruption.
REFERENCES
Bardhan, Pranab. 1997. Corruption and development: A review of issues. Journal of Economic
Literature, Vol. XXXV, September, 1320-1346.
Beck, Paul J. & Michael W. Maher. 1986. A comparison of bribery and bidding in thin markets.
Economic Letters, 20(1), 1-5.
Hofstede, Geert. 1980. Culture's consequences: international differences in work-related values.
Newbury Park, CA: Sage.
Hofstede, Geert, & Michael H. Bond. 1988. Confucius & economic growth: New trends in
culture's consequences. Organizational Dynamics, 16(4), 4-21.
Hofstede, Geert. 1991. Cultures and organizations: Software of the mind. London: McGraw-Hill.
Johnston, Michael. 1997. Public officials, private interests, and sustainable democracy: When
politics and corruption meet. In Kimberly Ann Elliott, editor, Corruption and the global
economy. Washington, DC: Institute for International Economics).
Leff, Nathaniel. 1964. Economic development through bureaucratic corruption. American
Behavioral Scientist, 8-14.
Lien, Da Hsiang Donald. 1986. A note on competitive bribery games. Economic Letters, 22(4),
337-41.
Lui, Francis T. 1985. An equilibrium queuing model of bribery. Journal of Political Economy,
Vol. 93 (August), 760-81.
Mauro, Paolo. 1995. Corruption and growth. Quarterly Journal of Economics, Vol. 110
(August), 681-712.
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Oguhebe, Festus S. 1991. Managing in foreign cultures: An assessment of cultural problems
facing American executives in the Nigerian business environment. Unpublished D.B.A.
Dissertation, Mississippi State University, Mississippi State, Mississippi.
Sondergaard, Mikael. 1994. Hofstede's consequences: A study of reviews, citations and
replications. Organization Studies, 15(3): 447-456.
Tanzi, Vito. 1998. Corruption around the world: Causes, consequences, scope, and cures.
International Monetary Fund Staff Papers. 45(4): 559-94.
Transparency International. 1998. Corruption Perception Index.
http://www.transparency.de/documents/cpi/index.html
Tsalikis, John. & Osita Nwachukwu. 1991. A Comparison of Nigerian to American views of
bribery and extortion in international commerce. Journal of Business Ethics, 10(2), 85-98.
Tsalikis, John & Michael D. LaTour. 1995. Bribery and extortion in international business:
Ethical perceptions of Greeks compared to Americans. Journal of Business Ethics, 14(4): 249-
264.
Versi, Anver. 1996. On corruption and corrupters. African Business, November, 215-22.
Wei, Shang-Jin. 1997a. How taxing is corruption on international investors? NBER Working
Paper No. 6030, Cambridge, Massachusetts: National Bureau of Economic Research.
World Bank Group. 1997. Helping countries combat corruption: The role of the World Bank.
http://www.worldbank.org/html/extdr/
corruptn/coridx.htm
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Table 1.
Transparency International
1998 Corruption Perceptions Indexa
Country
CPIb
Country
CPI
Country
CPI
Denmark
10.0
Namibia
Ivory Coast
Finland
9.6
Taiwan
Guatemala
Sweden
9.5
South Africa
Argentina
New Zealand
9.4
Hungary
Nicaragua
Iceland
9.3
Mauritius
Romania
Canada
9.2
Tunisia
Thailand
Singapore
9.1
Greece
Yugoslavia
Netherlands
9.0
Czech
Republic
Bulgaria
Norway
9.0
Jordan
Egypt
Switzerland
8.9
Italy
India
Australia
8.7
Poland
Bolivia
Luxembourg
8.7
Peru
Ukraine
United
Kingdom
8.7
Uruguay
Latvia
Ireland
8.2
South Ko+rea
Pakistan
Germany
7.9
Zimbabwe
Uganda
Hong Kong
7.8
Malawi
Kenya
Austria
7.5
Brazil
Vietnam
United States
7.5
Belarus
Russia
Israel
7.1
Slovak
Republic
Ecuador
Chile
6.8
Jamaica
Venezuela
France
6.7
Morocco
Colombia
Portugal
6.5
El Salvador
Indonesia
Botswana
6.1
China
Nigeria
Spain
6.1
Zambia
Tanzania
Japan
5.8
Turkey
Honduras
Estonia
5.7
Ghana
Paraguay
Costa Rica
5.6
Mexico
Cameroon
Belgium
5.4
Philippines
Malaysia
5.3
Senegal
a. http://www.transparency.de/documents/cpi/index.html
b. Lowest index is perceived as most corrupt.
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Table 2.
Hofstede's Culture Indexes (Numerical Ranking)a
Countryb
IDVc
Country
PDd
Country
UAe
Guatemala
6
Austria
11
Singapore
8
Ecuador
8
Israel
13
Thailand
8
Panama
11
Denmark
18
Greece
11
Venezuela
12
New Zealand
22
Jamaica
13
Columbia
13
Ireland
28
Denmark
23
Indonesia
14
Sweden
31
Sweden
29
Pakistan
14
Norway
31
Hong Kong
29
Costa Rica
15
Finland
33
Great Britain
35
Peru
16
Switzerland
34
Ireland
35
Taiwan
17
Great Britain
35
Malaysia
36
South Korea
18
Germany
35
India
40
Salvador
19
Costa Rica
35
Philippines
44
West Africa
20
Australia
36
U.S.A.
46
Singapore
20
Netherlands
38
Canada
48
Thailand
20
Canada
39
Indonesia
48
Chile
23
U.S.A.
40
New Zealand
49
Hong Kong
25
Jamaica
45
South Africa
49
Malaysia
26
South Africa
49
Norway
50
East Africa
27
Argentina
49
Australia
51
Portugal
27
Italy
50
East Africa
52
Mexico
30
Japan
54
Netherlands
53
Philippines
32
Pakistan
55
West Africa
54
Greece
35
Spain
57
Switzerland
58
Uruguay
36
Iran
58
Finland
59
Turkey
37
Taiwan
58
Iran
59
Brazil
38
Greece
60
Germany
65
Arab Countries
38
South Korea
60
Ecuador
67
Jamaica
39
Uruguay
61
Arab Countries
68
Iran
41
Portugal
63
Taiwan
69
Argentina
46
Chile
63
Austria
70
Japan
46
East Africa
64
Pakistan
70
India
48
Peru
64
Italy
75
Spain
51
Belgium
65
Brazil
76
Israel
54
Turkey
66
Venezuela
76
Austria
55
Salvador
66
Columbia
80
Finland
63
Columbia
67
Israel
81
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Table 2. (continued)
Country
IDV
Country
PD
Country
UA
South Africa
65
France
68
Mexico
82
Germany
67
Hong Kong
68
Turkey
85
Switzerland
68
Brazil
69
South Korea
85
Norway
69
Singapore
74
France
86
Ireland
70
Thailand
74
Spain
86
France
71
India
77
Argentina
86
Sweden
71
West Africa
77
Chile
86
Denmark
74
Indonesia
78
Costa Rica
86
Belgium
75
Ecuador
78
Panama
86
Italy
76
Arab Countries
80
Peru
87
New Zealand
79
Mexico
81
Japan
92
Canada
80
Venezuela
81
Belgium
94
Netherlands
80
Philippines
94
Salvador
94
Great Britain
89
Panama
95
Uruguay
100
Australia
90
Guatemala
95
Guatemala
101
U.S.A.
91
Malaysia
104
Portugal
104
21
Table 2. (continued)
Country
MASf
Country
MAS
Sweden
5
Brazil
49
Norway
8
Malaysia
50
Netherlands
14
Pakistan
50
Denmark
16
Canada
52
Costa Rica
21
Belgium
54
Finland
26
India
56
Chile
28
Argentina
56
Portugal
31
Greece
57
Guatemala
37
Hong Kong
57
Uruguay
38
New Zealand
58
South Korea
39
Australia
61
Salvador
40
U.S.A.
62
East Africa
41
South Africa
63
Spain
42
Ecuador
63
Peru
42
Philippines
64
France
43
Columbia
64
Iran
43
Great Britain
66
Panama
44
Germany
66
Arab Countries
45
Ireland
68
Turkey
45
Jamaica
68
Taiwan
45
Mexico
69
West Africa
46
Italy
70
Indonesia
46
Switzerland
70
Israel
47
Venezuela
73
Singapore
48
Austria
79
Thailand
48
Japan
95
Country
LTOg
Country
LTO
Philippines
19
Singapore
48
Canada
23
Thailand
48
Great Britain
25
India
61
U.S.A.
29
Brazil
65
New Zealand
30
South Korea
75
Australia
31
Japan
80
Germany
31
Taiwan
87
Sweden
33
Hong Kong
96
Netherlands
44
a. Geert Hofstede, Cultures and Organizations: Software of
the Mind, Berkshire, England: McGraw-Hill, 1991.
b. Rank ordered by Index.
c. Higher index is more individualistic.
d. Higher index is greater distance.
e. Higher index is less tolerant of uncertainty.
f. Higher index is more masculine.
g. Higher index is longer time horizon.
22
Table 3.
Hofstede’s Core Indexes and CPI Index (Alphabetical)
Countrya
IDV
PD
UA
MAS
CPI
Argentina
46.0
49.0
86.0
56.0
3.0
Australia
90.0
36.0
51.0
61.0
8.7
Austria
55.0
11.0
70.0
79.0
7.5
Belgium
75.0
65.0
94.0
54.0
5.4
Brazil
38.0
69.0
76.0
49.0
4.0
Canada
80.0
39.0
48.0
52.0
9.2
Chile
23.0
63.0
86.0
28.0
6.8
Columbia
13.0
67.0
80.0
64.0
2.2
Costa Rica
15.0
35.0
86.0
21.0
5.7
Denmark
74.0
18.0
23.0
16.0
10.0
Ecuador
8.0
78.0
67.0
63.0
2.3
Finland
63.0
33.0
59.0
26.0
9.6
France
71.0
68.0
86.0
43.0
6.7
Germany
67.0
35.0
65.0
66.0
7.9
Greece
35.0
60.0
11.0
57.0
4.9
Guatemala
6.0
95.0
101.0
37.0
3.1
Hong Kong
25.0
68.0
29.0
57.0
7.8
India
48.0
77.0
40.0
56.0
2.9
Indonesia
14.0
78.0
48.0
46.0
2.0
Ireland
70.0
28.0
35.0
68.0
8.2
Israel
54.0
13.0
81.0
47.0
7.1
Italy
76.0
50.0
75.0
70.0
4.6
Jamaica
39.0
45.0
13.0
68.0
3.8
Japan
46.0
54.0
92.0
95.0
5.8
Malaysia
26.0
104.0
36.0
50.0
5.3
Mexico
30.0
81.0
82.0
69.0
3.3
Netherlands
80.0
38.0
53.0
14.0
9.0
New Zealand
79.0
22.0
49.0
58.0
9.4
Norway
69.0
31.0
50.0
8.0
9.0
Pakistan
14.0
55.0
70.0
50.0
2.7
Peru
16.0
64.0
87.0
42.0
4.5
Philippines
32.0
94.0
44.0
64.0
3.3
Portugal
27.0
63.0
104.0
31.0
6.5
Salvador
19.0
66.0
94.0
40.0
3.6
Singapore
20.0
74.0
8.0
48.0
9.1
South Africa
65.0
49.0
49.0
63.0
5.2
South Korea
18.0
60.0
85.0
39.0
4.2
Spain
51.0
57.0
86.0
42.0
6.1
Sweden
71.0
31.0
29.0
5.0
9.5
Switzerland
68.0
34.0
58.0
70.0
8.9
Taiwan
17.0
58.0
69.0
45.0
5.3
Thailand
20.0
74.0
8.0
48.0
3.0
Turkey
37.0
66.0
85.0
45.0
3.4
U.S.A.
91.0
40.0
46.0
62.0
7.5
UK/GB
89.0
35.0
35.0
66.0
8.7
Uruguay
36.0
61.0
100.0
38.0
4.3
Venezuela
12.0
81.0
76.0
73.0
2.3
23
Table 4.
Hofstede’s Culture Indexes (with LTO)
and CPI Index (Alphabetical)
Country
IDV
PD
UA
MAS
LTO
Australia
90.00
36.00
51.00
61.00
31.00
Brazil
38.00
69.00
76.00
49.00
65.00
Canada
80.00
39.00
48.00
52.00
23.00
Germany
67.00
35.00
65.00
66.00
31.00
Hong Kong
25.00
68.00
29.00
57.00
96.00
India
48.00
77.00
40.00
56.00
61.00
Japan
46.00
54.00
92.00
95.00
80.00
Netherlands
80.00
38.00
53.00
14.00
44.00
New Zealand
79.00
22.00
49.00
58.00
30.00
Philippines
32.00
94.00
44.00
64.00
19.00
Singapore
20.00
74.00
8.00
48.00
48.00
South Korea
18.00
60.00
85.00
39.00
75.00
Sweden
71.00
31.00
29.00
5.00
33.00
Taiwan
17.00
58.00
69.00
45.00
87.00
Thailand
20.00
74.00
8.00
48.00
48.00
U.S.A.
91.00
40.00
46.00
62.00
29.00
UK/GB
89.00
35.00
35.00
66.00
25.00
24
APPENDIX 1
a. Correlations (Core)
Index
CPI
IDV
PD
UA
MAS
CPI
1.000
.705
-.694
-.327
-.276
Pearson
IDV
.705
1.000
-.666
-.235
.028
Correlation
PD
-.694
-.666
1.000
.137
.113
UA
-.327
-.235
.137
1.000
-.028
MAS
-.276
.028
.113
-.028
1.000
CPI
.
.000
.000
.012
.030
Sig. (1-
IDV
.000
.
.000
.056
.425
tailed)
PD
.000
.000
.
.179
.225
UA
.012
.056
.179
.
.427
MAS
.030
.425
.225
.427
.
b. Correlations (with LTO)
Index
CPI
IDV
PD
UA
MAS
LTO
CPI
1.000
.642
-.770
-.214
-.205
-.383
Pearson
IDV
.642
1.000
-.803
.012
.000
-.681
Correlation
PD
-.770
-.803
1.000
-.161
.150
.387
UA
-.214
.012
-.161
1.000
.273
.328
MAS
-.205
.000
.150
.273
1.000
.062
LTO
-.383
-.681
.387
.328
.062
1.000
CPI
.
.003
.000
.204
.215
.064
Sig. (1-
IDV
.003
.
.000
.481
.500
.001
tailed)
PD
.000
.000
.
.268
.282
.063
UA
.204
.481
.268
.
.145
.099
MAS
.215
.500
.282
.145
.
.406
LTO
.064
.001
.063
.099
.406
.
25
c. Regression Summary (Core)
Modela
R
R2
Adjusted
R2
Std. Error of
the Estimate
1
.705b
.496
.485
1.7863
2
.766c
.587
.569
1.6352
3
.805d
.648
.624
1.5275
4
.825e
.680
.650
1.4738
Change Statistics
Model
Adjusted
R2
R2
Change
F
Change
df1
df2
Sig. F
Change
Durbin-
Watson
1
1.7863
.496
44.354
1
45
.000
2
1.6352
.091
9.702
1
44
.003
3
1.5275
.061
7.420
1
43
.009
4
1.4738
.032
4.190
1
42
.047
2.072
a. Dependent Variable: CPI Index
b. Predictors: (Constant), IDV Index
c. Predictors: (Constant), IDV Index, PD Index
d. Predictors: (Constant), IDV Index, PD Index, MAS Index
e. Predictors: (Constant), IDV Index, PD Index, MAS Index, UA Index
d. Regression Summary (with LTO)a
Model
R
R2
Adjusted
R2
Std. Error of
the Estimate
1
.770b
.592
.565
1.6457
2
.842c
.710
.668
1.4370
Change Statistics
Model
Adjusted
R2
R2
Change
F
Change
df1
df2
Sig. F
Change
Durbin-
Watson
1
.565
.592
21.777
1
15
.000
2
.668
.118
5.674
1
14
.032
2.121
a. Dependent Variable: CPI Index
b. Predictors: (Constant), PD Index
c. Predictors: (Constant), PD Index, UA Index
26
e. ANOVA (Core)
Modela
Sums of
Squares
df
Mean
Square
F
Sig.
1
Regression
141.530
1
141.530
44.354
.000b
Residual
143.590
45
3.191
Total
285.120
46
2
Regression
167.471
2
83.736
31.317
.000c
Residual
117.648
44
2.674
Total
285.120
46
3
Regression
184.786
3
61.595
26.398
.000d
Residual
100.334
43
2.333
Total
285.120
46
4
Regression
193.887
4
48.472
22.314
.000e
Residual
91.233
42
2.172
Total
285.120
46
a. Dependent Variable: CPI Index
b. Predictors: (Constant), IDV Index
c. Predictors: (Constant), IDV Index, PD Index
d. Predictors: (Constant), IDV Index, PD Index, MAS Index
e. Predictors: (Constant), IDV Index, PD Index, MAS Index, UA Index
f. ANOVA (with LTO)
Modela
Sums of
Squares
df
Mean
Square
F
Sig.
1
Regression
58.980
1
58.980
21.777
.000b
Residual
40.625
15
2.708
Total
99.605
16
2
Regression
70.697
2
35.348
17.119
.000c
Residual
28.908
14
2.065
Total
99.605
16
a. Dependent Variable: CPI Index
b. Predictors: (Constant), PD Index
c. Predictors: (Constant), PD Index, UA Index
27
g. Coefficients (Core)
Coefficients
Unstandardized
Standardized
B
Std. Error
Beta
t
Sig.
(Constant)
8.871
1.373
6.461
.000
IDV Index
4.147E-02
.011
.435
3.607
.001
PD Index
-4.000E-02
.014
-.351
-2.950
.005
MAS Index
-3.358E-02
.012
-.254
-2.858
.007
UA Index
-1.711E-02
.008
-.184
-2.047
.047
Coefficients
95% Confidence
Interval
Collinearity
B
Lower
Upper
Tolerance
VIF
(Constant)
8.871
11.642
IDV Index
4.147E-02
.065
.705
.525
1.904
PD Index
-4.000E-02
-.013
-.694
.538
1.858
MAS Index
-3.358E-02
-.010
-.276
.968
1.033
UA Index
-1.711E-02
.000
-.327
.944
1.060
a. Dependent Variable: CPI Index
h. Coefficients (with LTO)a
Coefficients
Unstandardized
Standardized
B
Std. Error
Beta
t
Sig.
(Constant)
13.925
1.344
10.364
.000
PD Index
-.101
.018
-.826
-5.659
.000
UA Index
-3.652E-02
.015
-.348
-2.382
.032
Coefficients
95% Confidence
Interval
Collinearity
B
Lower
Upper
Tolerance
VIF
(Constant)
13.925
1.344
10.364
.000
PD Index
-.101
.018
-.826
-5.659
.000
UA Index
-3.652E-02
.015
-.348
-2.382
.032
a. Dependent Variable: CPI Index
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