DiRienzo Et Al. - 2007 - Corruption and the Role of Information

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Corruption and the Role of InformationAuthor(s): Cassandra E. DiRienzo, Jayoti Das, Kathryn T. Cort and John Burbridge Jr

Source: Journal of International Business Studies, Vol. 38, No. 2 (Mar., 2007), pp. 320-332Published by: Palgrave Macmillan JournalsStable URL: http://www.jstor.org/stable/4540422Accessed: 26-08-2014 15:39 UTC

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3/14

Corruption

and the

role of

information

CassandraDiRienzo

t al

321

rational behavior.

In

May

2001 the US

Department

of

State released

a

report

on

global

anti-corruption

efforts

stating:

Corruption

results from a

variety

of

economic, institutional,

political,

social,

and historical factors. It flourishes when

democratic institutions

are

weak,

laws are not

enforced,

political

will is

lacking,

and when citizens and the media

are

not allowed to be

partners

n

democracy.

Park

(2003: 30)

states

that,

in order

to

combat

corruption,

an

understanding

of

'the

major

deter-

minants of this multifaceted social

and

economic

phenomenon'

is

needed.

Empirical

studies

focusing

on the causes

of

corruption

have found that

the

determinants

of

corruption ranged

from

political

institutions,

government

regulations,

legal

systems,

GDP levels, and salaries of public employees to

gender,

religious

cultural

dimensions,

and

poverty,

as well as the role of colonialism

(Husted,

1999;

Sanyal

and

Samanta,

2001;

Ali and

Said,

2003;

Davis and

Ruhe,

2003; Park,

2003).

While

all of the above factors have been

empiri-

cally proven

to affect

corruption

levels,

a factor that

has

yet

to be

investigated

is

the

impact

of

the access

to

information on

corruption

levels.

Salbu

(2001)

states that

the

Internet

offers

people

unprecedented

access to

information,

resulting

in a

more

knowl-

edgeable

society.

Specifically,

as

citizens

acquire

more access to the Internet, they should be more

aware about the conduct of

government

and

business.

The

resulting

increase

in

transparency

should be

expected

to lower

corruption

levels,

as

such

openness

discourages

corrupt

practices.

There-

fore

it

is

logical

to

contend that

increased access

to

information

should result

in

fewer

transgressions

by

individuals,

businesses,

and

government.

Further,

nternational business

could become

more

open

and

transparent.

Can

something

as

funda-

mental

as

providing

worldwide access to

the

Internet reduce

corruption

and result in a better

climate for international business? The

policy

implications

of

such

a

finding

would be

significant.

Efforts

to increase access to information

could

result

in a

country

and the businesses that

operate

in

that environment

becoming

more

responsible

global

citizens.

Using cross-country

data from 85

countries,

the

initial thrust of this

paper

is

to

empirically explore

the

general relationship

between

the access

to

information

and

the

level of

perceived

corruption

within

a

country, using

regression

analysis.

The

second

major

thrust of this

study

will

employ

the

regression analysis

results to

explore

the

possibility

of

emerging

global patterns

of

corruption,

using

cluster

analysis.

The cluster

analysis

divides

the

individual countries into

groups

based

on

factors

that

are

found

to

significantly

affect

corruption.

The

possibilities

of

global patterns

of

corrupt

behavior are then

explored.

No

empirical

study

has

attempted

to understand the

growing impor-

tance

of

the

access

to

information on the level

of

corruption

within

a

country.

Background, hypotheses,

and

presentation

of

variables

Impact

of

corruptpractices

As

international business

expands, greater

attention

is

being

focused

on the ramifications

of

corrupt

practices. Numerous studies have explored the

definition,

costs, causes,

and effects of

corruption.1

In

regard

to the

definition, Johnston

(1996)

dis-

cusses

the various

meanings

of

corruption, ranging

from

bribery

among

public

officials

(Heidenheimer,

1989;

Van

Klaveren,

1989)

to

commercial

bribery

between

two

private

parties (Klitgaard,

1988).

The

World Bank defines

corruption

as

the

abuse

of

public

power

for

private

benefit; however,

corrupt

practices

can occur

within the

private

sector as

well.

Further,

Tanzi

(1998)

acknowledges

that,

although

corruption may

be difficult to

describe,2

corrupt

practicesare generally recognized when they occur.

In

this

study,

the

Transparency

nternational

(2005)

definition

of

corruption

as

the 'misuse of entrusted

power

for

private

gain'

is used.

Defining corruption

in

this manner embodies

both

public

and

private

corrupt

practices.

Studies have

also shown that

corruption

raises

the cost to host

countries

in

the form

of

reduced

tax

revenues,

and

also

distorts

the

impact

of industrial

policy

(Ades

and

Di

Tella,

1997;

Tanzi

and

Davoodi,

1997).

Corruption

is found to be

widespread

in

countries

with a lack of

transparency

with

respect

to

operations,

process,

and laws

(LaPalombara,

1994; Tanzi,

1998).

Further,

Mauro

(1995)

and

Bardhan

(1997)

have

highlighted

the harmful

effects

of

corruption

on income

inequality.

Alter-

natively,

Haque

and

Sahay

(1996);

Tullock

(1996)

and Van

Rijckeghern

and

Weder

(2001)

justify

the

existence

of

corrupt

practices

as traditional

gift-

giving

and a substitute for undervalued

wages,

incomes,

and

prices,

as such

practices

can

be

used

to more

equitably

allocate resources

in

heavily

distorted and

regulated

markets.

Overall,

the consensus

of these theoretical

and

empirical

studies

is

that

corruption

has

a

negative

journal

of International usiness tudies



4/14

Corruption

and the role of Information CassandraDiRienzot

al

322

impact

on a

country's ability

to become

a

signifi-

cant

player

in

the

global

economy.

Given that

corruption

has such

negative global

ramifications,

many

studies

have

explored

why corruption

exists

to the

degree

that it does. This

paper

extends these

past

studies

by considering

how the access to

information

in

conjunction

with

socio-economic,

institutional,

and cultural

variables affects

the level

of

corruption

within a

country.

Digital

access,

information

ransparency,

and

corruption

The

main

theoretical

argument

in this

study

is that

access to

digital

information should

provide

ordin-

ary

citizens with

knowledge regarding public

and

private

business transactions.

Increased

exposure

to

information creates a more open and free society.

Research has shown that a more

open

and trans-

parent economy experiences

a lower level

of

corruption.

Schroth and

Sharma

(2003)

state

that both tech-

nology

and

legal

means can be used

and,

if

deployed

synergistically,

should

significantly

reduce

corrup-

tion. Forms

of

digital

access

including

the Internet

and mobile

technology

can be

effectively deployed

to

allow

the

dissemination of

news

concerning

corruptpractices

as

well

as

providing

access. Further-

more,

Tanzi

(1998)

highlights

the

transparency

of

rules, processes, and laws as important weapons in

the war

against corruption.

Ades and

Di

Tella

(1999),

Treisman

(2000),

and Wei

(2000)

show that

more

open

and

transparent

economies

aspiring

to become

players

in

the

global economy experience

lower

levels of

corruption.

Thus this research

ndicates that

governments

and societies

desiring

to be

significant

players internationally

should strive

to

improve

access to information to create a more

open

and

transparent

society.

The

importance

of information

transparency

in

governmental

affairs is now

impacting

on

China,

one of the more information-restricted countries.

Scholars,

government

officials,

and members

of the

business

community

are

calling

for

greater

open-

ness in order to assist economic

development

and

to thwart

official

corruption.

Guangzhou

has made

history

in

China

by being

the first

jurisdiction

to

legislate

and

institutionalize

government

transpar-

ency.

Since

1

January

2003 the

Guangzhou

muni-

cipal government

has disclosed

government

information

that

complies

with the

World Trade

Organization

(WTO)

requirements,

and has

made

nondisclosure

the

exception

rather than

the rule

(Horsley,

2003).

Access

to information has been

recognized

as a

tool for

fighting corruption

in

Indonesia as well.

Schroth

and Sharma

(2003)

stated

that,

in

the mid

1990s,

Indonesian

journalists reported

the

corrupt

practices

of the Suharto

government

in

various

unofficial,

web-based

journals.

Even

though only

200,000

of Indonesia's

210 million

people

had

Internet

connections,

these web-based

publications

aided in

the

dismantling

of the Suharto

govern-

ment

in

1998. Schroth and Sharma state:

'in

short

the

Internet has

emerged

as a

powerful

tool to

fight

corruption.'

The

availability

of information

by

means of

digital

access can

create more

transparent

rules,

laws and

transactions,

resulting

in

greater

accountability.3

This

theory

is

supported by

Bhatnagar

(2000),

who

finds that increased access to the Internet and the

prevalence

of

e-government

raises

transparency

and

accountability,

and lowers unethical

practices.

Thus

openness

in

public

and

private

institutional

opera-

tions should reduce the

prevalence

of

corrupt

practices. Digital

access

provides

better

delivery

of

services

to

citizens,

and

improves

the interactive

effects

between

all

stakeholders,

while

giving

indi-

viduals

a sense of

empowerment through

access

to

information. Therefore

building

information infra-

structures and

increasing digital

access results

in

increased revenue and

lower transaction

costs,

and

can also reduce corruption levels. As stated by

Norris

and

Zinnbauer

(2002),

widespread

Internet

access and an

independent

free

press

are often

associated

with nations that

experience

greater

administrative

efficiency, improved

social and eco-

nomic

conditions,

and

lower

corruption.

Thus

it can be

argued

that access

to information

lowers

corruption

levels. The main

hypothesis

tested in

this

paper

is:

Hypothesis

1: The

higher

the access

to informa-

tion

and

technology

in

a

country,

the lower the

degree of corruption in that country.

Control variables

Before

a

statistical

analysis examining

the relation-

ship

between the

level of

corruption

and the

availability

of

information within a

country

can

be

performed,

the other factors

(socio-economic,

institutional,

and cultural

variables)

affecting

cor-

ruption

need to be controlled

in

order

to

prevent

a

model

mis-specification.

It is

necessary

to control

for these variables

in

an effort

to illuminate the true

relationship

between

corruption

and information.

The

socio-economic

and institutional factors are

Journal

f International usiness tudies



5/14

Corruption

and

the role

of

information

Cassandra

DiRienzot al

323

proxied

by

economic

freedom

and

development,

while cultural

factors

are

representedby

Hofstede's

four

main

cultural

attributes.

Each

of these

control

variables

will be

briefly

discussed.

Economic

reedom

nd

development

An

economically

free

nation

is considered

to have

a

government

that

promotes

a stable

legal

system,

provides

sound

money,

has

efficient

labor

and

product

markets,

and

does not

limit trade

and

investment,

thus

providing

an environment

for

growth.

Global businesses

thrive

in these

environ-

ments

in

which

institutions

and

policies

are

consistent,

providing

an

infrastructure

to

protect

individuals

and businesses

from

corruption

and

coercion.

Rose-Ackerman

1978),

Alam

(1995),

and

Tanzi (1998) state that unwieldy government

institutions

tend

to

increase

the chances

of officials

becoming

corrupt,

while

Ades and

Di

Tella

(1999)

and

Treisman

(2000)

point

to the

fact

that

more

open

economies

tend

to have

lower

corruption.

This research

has

found

that

countries

with a

higher

level of economic

freedom

are

generally

less

corrupt.

Furthermore,

n

regard

to economic

devel-

opment,

Mauro

(1995),

Husted

(1999),

and

Ali

(2003)

have

provided

empirical

support

that

coun-

tries

with

higher

levels

of economic

development

tend

to

have

lower levels

of

corruption.

Cultural

actors

Many

studies4

have

concluded

that

corruption

within a

country

is

very

much a

cultural

phenom-

enon.

It is

therefore

necessary

to

control

for the

cultural

make-up

of

a

country

when

considering

its

level

of

corruption.

Most

empirical

studiess

explor-

ing

the role

of cultural

values

use

Hofstede's

(1980,

2001)

four

dimensions

characterizing

cultures

across

the

world.

A discussion

of each

of

Hofstede's

work-related

values

and

their

relationship

to a

country's

corruption

levels

follows.

In

regard

to Hofstede's power distance, Takyi-

Asiedu

(1993),

Cohen

et

al.

(1996),

and

Husted

(1999)

state

that cultures

with

an

unequal

distribu-

tion

of

power

tend

to

discourage questioning

authority,

and,

as a

result,

citizens

of

such societies

tend

to

shy

away

from

whistle-blowing

activity

when

confronted

with

corrupt

behavior.

Thus

it has

been

found

that the

greater

the

power

distance,

the

higher

the

degree

of

corruption

in

the

country.

In

regard

to

individualism-collectivism,

Triandis

(1994)

and

Husted

(1999)

found

that

countries

with

high

levels

of individualism

maintain

a

belief

system

in which

individual

achievement

is

the

ideal:

people

are

responsible

for their

individual

actions,

and

they

are

not

emotionally

dependent

on

organizations

or

groups.

Conversely,

collectivist

societies

identify

with

group

membership

and

decision-making,

where

the

group protects

the individual

in

exchange

for

loyalty.

Thus

more

individualistic

countries

generally

have

lower levels

of

corruption.

Regarding

masculine-feminine

cultural

values,

a

masculine culture

emphasizes

power,

wealth,

and

status,

while

feminine

cultures

emphasize

the

quality

of

life,

sometimes

over

productivity

(Adler,

2002).

Vitell

et al.

(1993)

and Cohen

et

al.

(1996)

all

state

that

higher

levels

of

corruption

are

found

in

more

masculine

societies.

Finally,

with

regard

o the

degree

of

uncertainty

avoidance,

societies

that have

a

high

uncertainty

avoidance

are those

in which

people feel uncomfortable in unpredictable situa-

tions,

which often

results

in an

unwillingness

to

challenge

authority

and

rules.

Although

there is

not

a consensus

in the

literature,6

it is

generally

believed

that

individuals

in

high

uncertainty

avoidance

countries

tend

to be

more

prone

to

corruption.

Data and

regression

analysis

Data

description

The

Corruption

Perception

Index7

or

CPI

(Trans-

parency

International, 2005)

is selected

as

the

means for measuring corruption, as it is the most

comprehensive

quantitative

indicator

of

cross-

country

corruption

available.

The CPI

assesses

the

degree

to

which

officials

and

politicians

are

believed

to

accept

bribes

or

illicit

payments

in

public

procurement,

embezzle

public

funds,

or

commit

offenses,

making

the

measurement

of

corruption

perceptual

rather

than

absolute.

The

CPI

is a continuous

scale

from

1 to

10

(1

=

high

corruption,

10

-

no

corruption).

Despite

some

of

its

limitations,

noted

by

Husted

(1999),

this

index

has

been

used

in several

academic

studies.8

Further-

more, Lancasterand Montinola (1997) conclude in

their

study

that,

while

no index

or measure

is

perfect,

Transparency

International's

Corruption

Index

is

robust.

This

index is

not

based

upon

information

from

the

organization's

own

experts,

but

is

constructed

as a

weighted

average

of different

indexes from

10 different

organizations,

and

it

reflects

the

impressions

of business

people

and

risk

analysts

who

have been

surveyed.9

The

Digital

Access

Index or

DAI 2002

is used10

as

a

proxy

for

access to

information.

The

DAI

measures

the

overall

ability

of

individuals

across

178

countries,

to not

only

access but also use

Journal

f International

usiness

Studies



6/14

Corruption

and the role of Information

CassandraDiRienzot

al

324

information and

communication

technology.

It

consists of

eight

variables

organized

into five

categories.

The overall

country

score is based

upon:

*

infrastructure

(fixed

telephone

subscribers and

mobile cellular ubscribers,oth

per

100inhabitants);

*

affordability

of access

(Internet

access

price

as a

percentage

of GDP

per

capita);

*

knowledge

(adult

literacy,

and combined

primary,

secondary

and

tertiary

school enrolment

levels);

*

quality

of ICT services

(international

Internet

bandwidth

per capita,

Broadband

subscribers

per

100

inhabitants);

and

*

usage

(Internet

users

per

100

inhabitants).

The

DAI

index

is

represented

on a scale of 0

(low

access)

to 1

(highest

access).

The Economic Freedom of the World(EFW)ndex

is used to

measure

a

country's degree

of economic

freedom. This index is

published by Gwartney

et al.

(2002)

and

co-published by

the Fraser

nstitute.

This

index consists of five

categories:

*

size of

government

(government expenditure,

taxes,

etc.);

*

legal

structure and

security

of

property rights

(level

of

judicial independence,

protection

of

intellectual

property, military

interference

in

the

rule of

law,

etc.);

* access to sound money (growth of money supply,

freedom to own

foreign

currency,

etc.);

*

freedom to

exchange

with

foreigners

(level

of

tariff

and non-tariff

barriers,

nternational

capital

controls,

size of

the trade

sector,

etc.);

and

*

regulation

of

credit, labor,

and

business,

specifi-

cally

the ease of

entry

of a

new business.

The scale

ratings

for the Economic

Freedom

Index

range

from

0 to

10,

with 10

being

most

free,

representing

countries

with advanced socio-

economic

structures,

and 0

being

least

free,

representative

of countries with

less-developed

socio-economic

structures.

GDP

per capita

(2002)

from the World

Develop-

ment

Report

is used to measure the level of

economic

development

within a

country,

and

Hofstede's four measures of culture are used as

control

variables.11

With

the

exception

of

culture

variables,12

the

digital

access and control variables

are

lagged approximately13

two

years

behind the

CPI data.

Digital

access and the control variables are

lagged

in an effort to allow time for their values to

affect

a

country's perceived

level of

corruption.

Table

1

summarizes the data used

in

this

analysis.14

Regressionanalysis

A

set of 85 countries is used to test the

previously

stated

hypotheses.

Table

2

provides

the

descriptive

statistics

and the correlation matrix for all the

variables. As

expected,

CPI has a

positive

relation-

ship

to

DAI, IDV, EFW,

and

GDP,

and a

negative

Table

1

Variable

ummary

Variable

Proxy

name,

year

reported)

Corruption Corruption Perception Index

(CPI, 005)

Access to information

Digital

Access Index

(DAI, 2002)

Power distance Hofstede's Power Distance

(PDI)

Individualism-collectivism Hofstede's Individualism

IDV)

Masculine-feminine Hofstede's

Masculity

(MAS)

Uncertainty

avoidance Hofstede's

Uncertainty

Avoidance

(UAI)

Economicreedom EconomicFreedom f the World

Economic

development

GDP

per capita

(GDP, 2002)

Table

2

Descriptive

tatisticsand

correlationsa

or

the model

Variable Mean

s.d. CPI

DAI PDI

IDV MAS UAI EFW GDP

CPI 4.98 2.43

1

DAI 0.53 0.21

0.880**

1

PDI

61.69

20.84 0.682** 0.584** 1

IDV 40.05

22.26 0.604** 0.589** 0.597**

1

MAS

48.58 18.30 0.059

0.021 0.096 0.169

1

UAI 65.14 22.10 0.083 0.031 0.176 0.170 0.006 1

EFW

6.71

0.87 0.795** 0.649** 0.580 0.458**

0.008 0.269* 1

GDP

9,783 11,492

0.878**

0.954**

0.657** 0.614** 0.087 0.056 0.725**

1

aUsing he

Jarque-Bera

est, all

variables ere

ested

or

normality. t

95%

confidence,he

CPI, DV, ndGDPwere ound o be non-normal. sa

result,

all

paired

orrelationsor

these variables

epresent

he

Spearman

ank orrelations. he

paired

normal orrelations

epresent

he Pearson orrelation

coefficients.

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relationship

to

PDI,

MAS,

and UAI. These relation-

ships suggest

that

higher

levels of

digital

access,

individualism,

and economic freedom and devel-

opment

yield

lower levels

of

corruption.

Further,

greater power

distances,

masculinity,

and uncer-

tainty

avoidance

imply higher

levels of

corruption.

To further

examine

the

relationship

between the

degree

of

corruption

within a

country

and

these

variables,

two

ordinary

least

squares

(OLS)

regres-

sion

analyses

were

performed.

The first

regression

analysis

uses CPI as the

dependent

variable and the

six

control

variables as the

independent

variables,

and is

referred o as Model

1.

The second

regression

is the same as the first with the

exception

of the

inclusion

of

DAI,

and is

referred o as

Model

2. The

analyses

are

presented

in this

manner

in order to

highlight the additional explanatory power of DAI.

Model 1 and Model 2 are

respectively

defined:

Model

1:

CPI

=

flo

-

JIPDI

+

/2IDV

-

/3MAS

-

f4UAI

+

/f5EFW

f6GDP

+

Model 2:

CPI

=

flo

-

LlPDI

+

f2IDV

-

f3MAS

-

14UAI

+

/sEFW

+

fl6GDP

+

J7DAI

+

e

The regressionresults forboth models areprovided

in

Table 3.

The

Adjusted

R2

values and the

F

statistics indicate that the both

regression

models

provided

a

good

fit

to the data.

In an effort to

validate

these

results and test the

significance

of the

coefficients,

a series of tests for

heteroskedasticity,

normality

of the error

term,

and

multicollinearity

were carried out

for

both

models. White's

(1980)

test for

heteroskedasticity

indicated that the resi-

duals from both

regression

models were homo-

skedastic.

Further,

the residuals were tested for

normality using

the

Jarque-Bera

test,s15

nd at the

95% confidence level results indicated that

the

residuals from

both models were

normally

distrib-

uted.

Finally,

a test of

multicollinearity

for each

of

the

independent

variables

using

the variance-infla-

tion factor

(VIF)

was

performed.16

As shown

in

Table

3,

the

VIF

values

ranged

from 1.05 to 2.73

in

Model

1 and

from 1.05

to

3.63

in

Model 2. Since

VIFvalues

greater

than 5.3 have been

suggested17

as

cutoffs for

multicollinearity,

it does not

appear

that

multicollinearity

is a

problem

in this

analysis

using

the VIFs as the criterion.

However,

as shown

in

Table

2,

DAI

and GDP

are

correlated at 0.95.

Strong

correlations between

explanatory

variables

are

evidence of

multicollinearity

even if

the corre-

sponding VIFs are low. The presence of multi-

collinearity

can make the

regression

results

sensitive to

the

data used.

Further,

changes

in the

model

specification

may

result

in

significant

changes

in the coefficient estimates.

However,

the

classic

signs

of

multicollinearity

are not

present

in

the final

regression

results.18

Given

that

the

regression

results were

generally

supported

by

the tests discussed

above,

an inter-

pretation

of the coefficients and tests of

the

research

hypothesis

can be considered.

In

regres-

sion Model

1,

two of the four cultural variables

-

individualism (IDV) and masculinity (MAS)- are

significant.

The

coefficient

on IDV is

positive,

suggesting

that more individualistic societies tend

to have lower levels

(see

footnote

14)

of

corruption,

which is consistent with

existing

literature.

The

negative

coefficient on

MAS

indicates that

econo-

mies that are

more

masculine

suffer from

higher

levels19

of

corruption,

which

also adheres

to

the

existing

literature.

In

regard

to economic freedom

and

development,

the

analysis

results

show

sig-

Table

3

Regression

results

(dependent

variable

CPI

05)

Int.

PD IDV MAS UAI EFW GDP DAI

R2

F

Model 1

Estimate -1.683 -0.009 0.017* -0.018** 0.002 0.930** 0.0001**

0.847 77.64**

St. error

(1.445)

(0.007) (0.007) (0.006) (0.005)

(0.174) (0.00001)

VIF 0

2.2 2.32 1.05 1.09

2.1 2.73

Model

2

Estimate -1.258 -0.008 0.01 -0.017** -0.006 0.711** 0.00007**

4.032** 0.882 90.08**

St. error

(1.27) (0.007) (0.006) (0.005)

(0.005)

(0.159) (0.00002)

(0.819)

VIF 0 2.2

2.46 1.05 1.24

2.28 3.63 3.39

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nificant

and

positive

coefficients on both EFWand

GDP. Since

higher

values

of the EFW index

represent

countries

that are

freer and

less

regulated,

the

positive

coefficients on

EFW

and GDP

support

previous

theoretical and

empirical

research.

Regression

Model

2

contains the

same

control

variables,

but

includes the

main

variable of

interest,

DAI. In

regard

to

the control

variables,

the

regres-

sion results are similar to

those

in

Model

1,

with

the

exception

that

IDV

becomes

insignificant

in

Model 2. Most

importantly,

the

coefficient

on DAI

is

positive

and

significant.

Given that

higher

values

of the

corruption

index indicate lower levels

of

corruption,

and

higher

values of

the

DAI index

imply

increased access to information and technol-

ogy,

the

positive

coefficient on DAI indicates that

higher levels of digital access suggest lower levels of

corruption,

thus

supporting Hypothesis

1,

which is

the main

hypothesis

of this

study.

As further

evidence,

a

partial

F

test indicated that the inclu-

sion of DAI

significantly

increases the

explanatory

power

of the

regression

at 99% confidence.20 This

result can also be

observed

by

noting

the

change

in

the

Adjusted

R2

value from Model

1

(0.847)

to

Model

2

(0.882),

suggesting

that DAI adds

approxi-

mately

3.5% additional

explanatory power

to the

regression.

In

summary,

the results of the

regression

analysis

indicate that countries with

more mascu-

line cultures with lower levels of economic freedom

and

development

and

less access to information are

more

likely

to

exhibit

high

levels

of

corruption.

The

regression

results show the

general

relation-

ship

between the level of

corruption experienced

in

a

country

and

the

cultural,

socio-economic,

institu-

tional,

and

digital

access variables.

In the

next

section,

the

analysis

is extended

to

consider how

specific

countries can be

grouped by

the

indepen-

dent

variables,21

and how

these

country

groups

differ

in

regard

to their

corruption

levels. To address

this

issue,

a

country-based

cluster

analysis

is

performed.As a consequence of the cluster analysis,

potential emerging regional

and/or

global

trends

across the

85

countries

can be illuminated.

Cluster

analysis

A cluster

analysis

is

used

to

group

the 85

countries

into distinct

clusters

using

the

cultural,

socio-

economic,

institutional,

and

digital

access

variables

found to be

statistically

significant

in

the

regression

analysis

as the criteria.

In

particular,

the

MAS, EFW,

GDP,

and

DAI

variables are used as the

country

characteristics

upon

which the clusters are formed.

A cluster

analysis groups

objects,

in this case

countries,

into

groups

such that the

objects

within

a

group

are most similar to each other with

respect

to

specified

characteristics and least similar

to

objects

in

the

other

groups.

In other

words,

the

cluster

analysis groups

the individual countries

based on how

similar or 'close'

they

are

in

regard

to

their

masculinity,

economic freedom and devel-

opment,

and

digital

access.

Segmenting

the coun-

tries

into

clusters

provides

a

more

meaningful

and

intuitive

description

of the countries

in

the

dataset,

as

one

is

able

to see how

specific

countries can be

grouped by

the

independent

variables,

and how

these

groups

differ

in their

corruption

levels.

A

non-hierarchical

cluster

analysis

is

preformed

using

the

squared

Euclidean

distance22

as

the

measure of how 'close' or similar two countries

are in regardto the four variables. Non-hierarchical

cluster

analysis

requires

that the number of

clusters

to be

created

in the

analysis

be

set

prior

to

performing

the

analysis.

There are no hard

guide-

lines

for

determining

the number of clusters to be

created

in

an

analysis,

and this is

generally

an

exploratory

process.

After

considering

many

differ-

ent cluster

analysis

results,

a

grouping

of four

countries clusters was

chosen,

and

the

results

are

presented

in

Table

4. The cluster

analysis

results

can

be

interpreted

as follows.

Country

cluster

1

repre-

sents

the

group

of countries

that

are

most

homo-

geneous in regard to their masculinity, economic

freedom

and

development,

and

digital

access,

and

most

heterogeneous

to

the

other

country

clusters

(clusters

2, 3,

and

4)

in

regard

to these

variables,

and so forth

for

the

remaining

clusters.

In

order to

gain

a more

in-depth understanding

of the

different

country

clusters,

the means

of each

of

the four variables used

to create the

clusters

are

presented

in Table 5. Table 5

shows that

the

countries

belonging

to Cluster

1

have the

highest

mean access

to

information,

level of economic

freedom and

development.

However,

compared

with the other clusters, the countries in Cluster 1

also

have a more masculine culture on

average.

It

should be noted

that this

average

is influenced

by

the inclusion

of a few countries with

exceptionally

higher

levels

of

masculinity

such

as

Japan

(95)

and

Austria

(79).

Alternatively,

Cluster

4

has the lowest

mean access

to

information,

level

of

economic

freedom

and

development.

Unexpectedly,

the

countries

in Cluster

4

tend to have a more

moderate

degree

of

masculinity

within their

cul-

tures.

Again,

this result can be

explained

by

a few

outliers

within the

cluster,

such as Sri

Lanka,23

with

exceptionally

low levels of

masculinity

within their

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Corruption

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Table 4 Cluster

analysis

resultsa

Cluster Cluster

Cluster Cluster

Australia Brazil

Argentina Angola

Austria

Bulgaria

China

Bangladesh

Belgium

Chile Colombia

Egypt

Canada Costa Rica Dominican

Republic Ethiopia

Denmark

Croatia

Ecuador Ghana

Finland Czech

Republic Hungary

Guatemala

France El

Salvador

Jamaica

Honduras

Germany

Estonia

Mexico India

Hong Kong

Greece

Philippines

Indonesia

Iceland Israel Poland Iran

Ireland

Jordan

Slovakia

Kenya

Italy

Korea

South Africa

Malawi

Japan

Kuwait Venezuela

Morocco

Luxembourg

Malaysia

Namibia

Netherlands

Malta

Nigeria

New Zealand Panama Pakistan

Norway

Peru Romania

Singapore Portugal

RussianFederation

Sweden

Slovenia

Senegal

Switzerland

Spain

Serbia

United

Kingdom

Thailand

Sri Lanka

United States

Trinidad

and

Tobago Syria

Uruguay

Tanzania

Turkey

Vietnam

Zambia

aCluster

'Least

Corruption',

luster

'Moderately

ow

Corruption',

luster

'Moderately ighCorruption',

nd Cluster

'HighCorruption'.

Table

5

Independent

variablemeans

by

cluster

Table

6

CPI

means

by

cluster

Cluster DAI

MAS

EFW

GDP

Cluster Mean

CPI) Sample

ize

Cluster

1

0.76 49.00 7.70

27,053.91

Cluster

1

Least

corruption

8.42

22

Cluster

2

0.58

41.00 6.85

6,874.15

Cluster

2

Moderately

ow

corruption

5.01 23

Cluster 3 0.49

70.23 6.21

3,380.15

Cluster 3

Moderately

high

corruption

3.43

13

Cluster

4

0.29

44.23 5.97

944.22 Cluster

4

High corruption

2.77 26

culture.

Compared

with

the

other

clusters,

clusters

2 and 3 fall into the middle

ranges

in

regard

to their

mean

access to information and

levels of economic

freedom and

development.

The

primary objective

of

the

cluster

analysis

is

to

explore

how

these countries differ in their

levels

of

corruption

levels.

Having

described the

country

clusters,

a

series of tests of

means are

now

performed

to determine

whether the

country

clusters have

statistically

different

mean

levels

of

corruption.

The

regression

results

indicate that these four

variables

influence the level of

corruption

in a

particular

country and, by clustering

the countries

by

these

variables,

differences

in their

corruption

levels

should

be detectable.

Table 6 shows the

sample

mean

corruption

index for each of the four clusters

and

the

corresponding sample

size.

In

order to

statistically

determine whether

the

mean

level

of

corruption

differs

pair-wise

across the

country

clusters,

a test of means is

required.

Given

the

sample

sizes

(each

country

cluster has a

sample

size

less than

30),

it is

necessary

to assume that the

corruption

index is

normally

distributed

in

order to

use

a

t-test

to

test

for

differences

in

the means. The

Jarque-Bera

test was used to test for

normality

of

the

corruption

index,

and the

results

indicated that

the

corruption

index is not

normally

distributed.

Given the small

sample

sizes

and

the non-normal-

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Corruption

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330

the United Nations' World

Telecommunication

Development Report

(2003).

There are other

indices,

such

as the

Networked

Readiness Index

published by

CID

(Harvard),

nd World Bank

ndica-

tors

measuring

individual

phone

line and

cellular

use,

but

DAI

s

the

largest,

most

comprehensive

ndex

thus far.

11Hofstede's

measureof national

culture

s

based

on

the

surveys

hat were

conducted

during

he

late 1960s

and

early

1970s

from more than

116,000

employees

of

72

IBM international

subsidiaries.

Initially,

Hof-

stede's

surveys

assessed

personal

values and

aspira-

tions

in

the

workplace

across53 countries.

Using

actor

analysis,

Hofstede

developed

indices

ranging

rom 0

to

100 to measure

power

distance,

individualism-collec-

tivism,

masculinity-femininity,

nd

uncertainty

avoid-

ance. The higherthe score (from0 to 100), the more

the

country

is

individualistic nd

masculine,

and has a

stronger

level of

uncertainty

avoidance

with a

greater

power

distance.

12Hofstede's

cultural

variables are not

reported

annually.

The

values for the cultural

variables

were

collected from

Hofstede's2001

text.

13Given hat the

indicesare

based

partially

n

survey

data,

the

'reported'year

for an

index is not

necessarily

the

year

in

which the data

were

collected.

For

example,

the

2005

CPI

is

based

partially

on

survey

data

collected

in

2003-2004.

14Higher

valuesfor IDVrepresentmore individualis-

tic

societies.

15sThe

arque-Bera

est

evaluatesthe

null

hypothesis

that the

residuals

are

normality

distributed

with

unspecified

mean and

variance

against

the

alternative

that the

residualsare not

normally

distributed.

16The

VIF

determines the

effects of the

correlations

among

the

independent

variablesand

their

influence

on

the variancesof the

regression

coefficients

(Mad-

dala, 1988;

Kennedy,

1992).

17

Kennedy

1992),

Studenmund

(1992),

and

Burns

and Bush

(2003)

suggest

a cutoff of

10,

and Hairet

al.

(1992)

suggest

a stricter

cutoff of

5.3.

18Some

classic

signs

of

multicollinearity

n

regression

resultsare

insignificant

oefficients n

conjunction

with

a

high

R2

and/or

signs

of

coefficients that are

inconsistent with

theory.

In

Model

2,

coefficients on

GDP and

DAIare both

significant

and have

signs

that

are

consistentwith

theory.

19Higher

values for

MAS

represent higher

levels of

masculinity.

20The

F

statistic or the

partial

F

test is 24.24

and the

critical value is

3.96,

yielding

a

P-value less

than

0.0001. This

ndicates hat the null

hypothesis

hat the

inclusion of DAIdoes not significantlyincrease the

explanatorypower

of

the

regression

model

should be

rejected.

21Jain

(2001)

suggests

using

a

multiple

variable

approach

to cluster

analysis.

22The

squared

Euclidean

istance between countries

--

EN-1

(Ci"n- jn)2,

weeNi

and

j

is

defined as

dij=

_

c,

-

cn)2,

where

N is

equal

to

6,

the number

of

country

characteristics

considered,

ci,n

represents

haracteristic for

country

,

andc/

represents

characteristic for

country j.

23Sri

Lanka

has an

MASvalue of

10.

241n

his case the

Mann-Whitney

U

test

evaluates he

null hypothesisthat the populationrelative requency

distributions or

country

clusters

i

and

j

are

identical

(the

population

mean

corruption

levels are

equal

in

clusters

i

and

j) against

the

alternative

that the

population

relative

frequency

distributionof

country

cluster

i

is shifted to the

right

(or left)

of

the

relative

frequency

distribution of

country j

(cluster

i

has a

higher

(or

lower)

corruption

evel than

cluster

j).

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A. and Di

Tella,

R.

1997)

'The

auses

and

consequences

f

corruption:

a

review of

recent

empirical

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Institute f

Development

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Ades,

A. and Di

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R.

(1999)

'Rents,

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Adler,

N.

(2002)

International

imensions f

Organizational

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outh

Western:New York.

Alam,

M.S.

(1995)

'A

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419-435.

Ali,

A.M.

(2003)

'Institutional

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tlantic

conomic

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348-362.

Ali,

A.M.

and

Said,

I.H.

(2003)

'Determinants f economic

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Cato

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449-466.

Bardhan,

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About the

authors

Cassandra

E

DiRienzo

is

an Assistant Professor of

Economics at

Elon

University.

She

holds an ME

and

PhD from North Carolina State University. Her

research nterests

are

econometrics,

and the econom-

ic and business

applications

of

nonparametric

and

spatial

statistics.

She has

published

in

Challenge

nd

the

Journal

f

Global

Competitiveness,mong

others.

Jayoti

Das

is an

Associate Professorof Economics at

Elon

University.

She holds a Masters and

PhD

from

the

University

of

Cincinnati. Her

research interests

are international

trade,

global

business,

and devel-

opment.

She has

published

in

the

Journal

of

InternationalTrade and Economic

Development

and

the

Journal

of

Global

Information Management,

among

others.

Kathryn

T

Cort is an Assistant Professor of

Marketing

at North CarolinaA &T State

University.

She holds a Masters from Ohio State

University

and a

PhD

from Kent

State

University.

Her

research interests include

global marketing

and

entrepreneurship.

She

has

published

in

Market-

ing Management

and Business

Horizon,

among

others.

John J

Burbridge

Jr

is Dean of the

Love School

of

Business

at

Elon

University.

He

received

a

BS, MS,

and PhD in Industrial

Engineering

from

Lehigh

University.

He

has

published

many

articles

in

journals

such as the

Journal

f

Global

Competitiveness

and

Journal

of

Global

Information

Management,

among

others.

Accepted y

Jose'

Manuel

Campa,Departmental

ditor,

0

June

006. This

paper

has beenwiththe authors

or

three evisions.

Journal

f International usiness

Studies

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DiRienzo Et Al. - 2007 - Corruption and the Role of Information