the impact of sociopolitical and cultural characteristics on corruption

8/6/2019 The Impact of Sociopolitical and Cultural Characteristics on Corruption

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Impact of Behavioral & Socio-Political Culture Characteristics on Prevalence of

Corruption

By

Konstantin RavvinE-mail: [email protected]

For

Research Methods in EconomicsECO 4451

Professor Richard Hofler

Spring Semester, 2010


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ABSTRACT

Studies have show that, aside from economic development, behavioral, normative and

socio-political culture has a significant impact on corruption levels in a given country.Using 79 observations, this study will attempt to evaluate the influence of

behavioral/normative cultural characteristics such as Power Distance, Individuality,Masculinity and Risk avoidance along with socio-political factors such as democratic

indices, political participation, and civil liberties on the outcome of corruption (measuredin CPI units).


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1. INTRODUCTION

Corruption has always been an obstacle impeding the progress of democracy,

rationale and self-improvement in societies across the globe. There are diverse amounts

of scholarly arguments that pin corruption as a result of two varying factors, economic

development and culture. The intuitive nature of the former argument accounts for the

presence of low-income levels, high-income inequality, low access to resources and

stagnant socioeconomic conditions as a strong contribution to corruption levels within a

country. The later factor, however, examines a cross-cultural view of a countrys

traditional values and analyzes how a nations regional distinctiveness relates to the

corruption level.

This study will focus on the later of the two factors and observe its impact on

regional corruption levels. The objectives involve obtaining quantitative data on cultural

values across a single time period of one year (2009) and finding what impact the

presence of those values has on the corruption level within a given region. The research

will attempt to determine whether traditional and culturally maintained values present a

significant underlying cause to policy formulation and efficacy within a nations borders.

The importance of this study is omnipresent in varying areas, including forecasting, risk

management, and policy formulation. In cases of political analysis, the ability to forecast

a political event based on recurring significant factors is essential to understanding how,

why and to which extent certain political regimes formulate policy & approach social,

economic, and unforeseen issues.

Although forecasting plays an important role in understanding the most likely

outcome of a sociopolitical scenario within a nation, it is overshadowed by the marginal

benefit private corporations obtain in understanding the extent to which culture plays a


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roll in establishing a lack of ethical standards (such as in corruption). A lack of ethics, in

an incentive driven world, is the antecedent to corruption. In a corporation, the number

one goal is to mitigate risk in order to minimize costs legal, moral, tangible and

intangible. The corporations ability to understand the regional attributes of culture and

their reflection on corruption could allow for a better assessment of investment risk by

reflecting the regions cultural make-up towards its corrupt tendencies, this process could

be used in both calculating the risk of a direct foreign investment or perhaps a merger

with a foreign corporation, determining the long term orientation of the investment.

As a rule of thumb, this study could be efficient for personal use for someone

traveling internationally. Understanding how cultural values differ by region is

detrimental to understanding reasons, motives and rationale behind each action taken.

For the personal safety, sanctity and security of a tourist, it would be beneficial to

understand how the cultural make-up of a nation may impact the process of traveling to

and interacting with the nations culture.

As a significant indicator of economic progress, corruption is important to stratify

and pinpoint, specifically its cause and the long-term orientation and presence. It is a

strong determinant of investment risk evaluation, political stability, as well as means and

motives of each nation, all of the preceding which have a strong influence on the outcome

of an economies stature in the global arena.

2. LITERATURE REVIEW

In an effort to further familiarize this research study with the topic, it is necessary to

obtain prior research results in an effort to make a focused, reasonable and stable

hypothesis. A key constraint of conducting and analyzing cross cultural studies is the

validity of the quantitative values put upon measuring values that are generally more

qualitatively descriptive in nature. In an effort to eliminate this error, this study will


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relate to a previous study, which contains the analysis of the impact cultural values have

on the prevalence of corruption (Das, Eargle, Esmail 2009,Journal of Social Change).

The study was a cross-national panel design focused on the presence of secular vs.

traditional values as well as a societies focus on self-expression vs. survivalist mentality

in accordance with the civil structure of the country (whether democratic or

authoritarian). Panel analyses using Seemingly Unrelated Regression (SUR) technique

are conducted, since the residuals for the panels have high likelihood of being correlated

with one another. The explanatory factors were quantified using survey analysis from

various agencies such as the World Value Survey, the United Nations, Transparency

International, Inter-Consortium of Political and Social Research and measured cultural

values based on questions including (but not limited to) the importance of God, the

justification of abortion and the acceptance of authority.

Religion was also factored into the study as an indicator variable to observe

countries that contained a 50%+ presence of a certain Abrahamic religion including

Catholicism and Islam.

Corruption level was obtained through surveyed CPI (Corruption Perception

Indices) scores from Transparency International, measured on a scale of 1-10, with 1

exhibiting a high level of corruption and 10 being the least likely to exhibit characteristics

of corruption. Corruption Level specifications will be discussed further in the Methods

and Data Section.

It should be noted that the study was done based on data in 4 distinct years including

1999, 2001, 2003 and 2005. The hypothesis of the study maintained that socioeconomic

resource attainments, greater self-expression values than survival values, greater secular

values than traditional values, and institutional arrangements for democracy reduce

corruption.


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The results of the study showed that a democratic institution indicator tended to

decrease corruption and increase the CPI score by 1%, on average in all 4 panels.

Although the self-expression factor bared little significance on determining corruption, a

secular political and civil orientation tended to increase CPI score by 7%, on average, in

all 4 panels. In regards to the religious aspect of the studies, research found that countries

that exhibiting an Islamic indicator variable (Islamic population > 50%) on average

tended to increase CPI score by 11% in the 2005 panel. Christian countries where found

to decrease CPI score by 13% on 2005 but this was a highly controversial measurement

considering much of this significance was attributed to Latin countries, exhibiting

Catholic leaning. As stated in the final conclusion, the study found that based on the

above findings, clearly, socio-economic resources, rational over traditional beliefs, self-

expression values and democratic institutional arrangements when combined together

reduce corruption more than the reform variables, thus the hypothesis was fairly

supported.

Reviewing the above findings of the Das, Eargle, Esmail study, we find evidence

that both refutes and confirms portions of the study. Aside from the various studies

conducted on the effects of economic reform and conditions on corruption level

(Coolidge, Rose-Ackerman 1997; World Bank 2000), there have also been studies that

review the influence of socio-political-cultural characteristics on corruption levels

(Paldam 2001; Welzel, Inglehart, Klingemann 2003; Inglehart, Oyserman 2004; Kaufman

2004; Kaufman, Siegelbaum 2004; Inglehart, Welzel 2005; Kaufman, Kraay, Mastruzzi

2005; Sandholtz, Taagepera 2005).

Ulrich von Alemann, a prominent German political scientist defined corruption as

a multi-faceted phenomenon, consisting of social decline, deviant behavior, logical

exchange, perceptions, and shadow politics (Politische Korruption).


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Inglehard (1997) argues that socioeconomic resources generally reinforce secular

values over traditional values in society. Secular values create a switch from survival

values to self-expression values, this is perhaps because there is more availability for

advancement, reducing the need for unethical behavior, enhancing democracy,

increasing elite integrity, and lowering the level of corruption (Inglehart, Welzel 2005),

this trend is present in the prior study showing a significance of secular values over

traditional values in having a positive effect on CPI scores. Although as separate entities,

only a couple were able to pass a significance test, the model was able to predict

corruption level significantly. Despite its absence of influence in the study, Triesman

(2005) mentions that corruption decreases when self-expression values are emphasized

over survival values. Lipset and Lenz (2000) agree with this trend, citing the lack of

democratization and self-expression values in these societies also restrict access to

resources for most people hence increasing corruption by denying the people the means

to obtain those goods.

In regards to religious tendencies, Inglehard and Welzel (2005) regarded most

Islamic societies as exhibiting a lack secular values (associated with self expression), this

characteristic is not likely to promote an effective democracy, which in turn promotes

corruption. They further concluded that the Islamic culture place an emphasis on family,

and despite the fact that Islamic society promotes the equal allocation of resources among

people by the state (David and Robinson 2006), the absence of democracy enhances the

control of resources by few in society, increase corrupt tendencies.

In regards to Christianity, Haller and Shore (2005) believed that Christian traditions do

not promote secularism and rational values, hence they fail to promote self-expression,

leading to a lack in effective democracy. Lipset and Lenz followed this notion by adding

Christian orientation promotes a communitarian lifestyle as opposed to an individualist


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and universalist lifestyle, corruption is more likely to prevail in this environment.

Paldams views on religion (2001) suggest that it has both direct and indirect effects on

corruption ranging from emphasis on conservative values to supporting group mentality,

all characteristics of a tradition based approach to culture. Although these views provide

justifications for the above findings, there is a lack of adjustment to the data, specifically

in stratifying the devotion of certain religions to their specified deities, in other words, the

studies do not account for the reforms that have taken place in various cultures to allow

for a distinction between conservative religious views and reformed views.

In retrospect to the previous findings, the general consensus revolves around a

struggle between secular (also described as rational) values vs. and traditional values,

which tend to determine a cultures long-term orientation towards democracy. Using

these findings, it must be noted that the explanatory variables must revolve around and

encompass statistics that illustrate the secular and/or traditional leanings of a nation.

Picking up on previously included data; religion will be a significant factor and hence

must be included in the study along with socio-political tendencies such as democratic

index ratings, along with political participation as well as the existence of civil liberties.

Inquiring about ratings in regards to democracy will be most effective when survey

statistics are applied due to the difficulty in quantifying certain idiosyncrasies in cultures.

Further specifications, including the measurement and derivation methods of obtaining

the previously mentioned data will be discussed in the methods section.

Behavioral and normative characteristics of cultures that relate to secular vs.

traditional values must encompass a few key qualities: self-expression, acceptance of

authority, and uncertainty avoidance. These are important elements of cultural behavior

because they determine the willingness of a culture to accept outside influences and adapt

to change (as would a secular culture) as opposed to displaying a resistance to the


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changing times. Hofstedes 4 cultural dimensions are prominent in the field of socio-

organization and cultural studies, and are reliable for the measurement of the above

requirements. Using 4 cultural dimensions measure power distance, individuality,

masculinity and risk-averse behavior, we will be able to measure the influence of

behavioral culture on corruption within a country. Further specifications, including the

measurement and derivation methods of obtaining the cultural dimension scores will be

discussed in the methods section.

The aforementioned corruption level will consist of a CPI (Corruption Perception

Index) obtained from transparency international. The CPI score is currently the most

valid corruption measurement for the study and will provide for a reliable dependent

variable. Further specifications, including the measurement and derivation methods of

obtaining CPI will be discussed in the methods section.

Based on the analysis of previous research studies conducted for similar reasons

along with previous significant evidence supporting the impact of behavioral and socio-

political culture on corruption levels. The hypotheses of this study are as follow:

Ho: Behavioral and socio-political culture (as represented by the previously mentioned

explanatory variables) does nothave a significant impact on corruption levels (as

represented by CPI)

Ha: Behavioral and socio-political culture (as represented by the previously mentioned

explanatory variables) does have a significant impact on corruption levels (as represented

by CPI)

3. METHODS AND DATA

The variables used within this study are all gathered for the year 2009. The

independent variables are chosen for their relevance to the question at hand and will


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attempt to determine if there is a significant impact of behavioral and socio-political

culture on corruption levels (as measured by CPI scores).

The findings will allow for an in-depth analysis of the significance that the following

independent cultural variables have on corruption levels among public officials and

politicians:

o Power distanceo Individualityo Masculinityo Risk-averse behavior (uncertainty avoidance)o

Majority presence of certain religions

Christianity Islam

o Democratic Indexo Political Participation Indexo Civil Liberties Indexo Democratic Institutions vs. Authoritarian Institutions

3A. Assessment of Corruption

The dependent variable in this study is a quantitative value of corruption level

(ranging from 0 to 10 in intervals of .1) also known as the CPI as calculated by

Transparency Internationals Corruption Index. The CPI score progressively decreases

the corruption level, therefore, the higher the CPI score, the lower the corruption level.

The sources used in the aforementioned study by Das, Eargle, Esmail utilize the

same data sets and organization, differing only in the time value of the data (99,01,03,05

as opposed to this studies 09 data set).


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The sources used to provide valid scores for the CPI trace their lineage to

reputable organizations, which account for 13 different surveys that are used to surmise

the total CPI, these 10 organizations include: Africa Development Banks Country Policy

and Institutional Assessments, Asian Development Banks Country Performance

Assessment Ratings, Bertelsmann Foundations Transformation Index, Economist

Intelligence Units Country Risk Service and Country Forecast, Freedom Houses

Nations in Transit Survey, Global Insights Country Risk Rating, Institute for

Management Developments World Competitive Report, Political and Economic Risk

Consultancy Asian Intelligence Survey, World Economic Forums Global

Competitiveness Report, and the World Banks Country Policy and Institutional

Assessments Survey. For an insight on the methodology used to determine CPI scores,

please refer to the Appendix.

3B. Accounting for Hofstede Scores

The explanatory variables that pertain to behavioral and normative culture include the

Hofstede scores for the four cultural dimensions that resulted from Hofstedes cross

cultural surveys obtained from the ITIM culture and management consulting firm. Dr.

Geert Hofstede conducted perhaps the most comprehensive study of individual values in

relation to culture. From 1967 to 1973, while working at IBM as a psychologist, he

collected and analyzed data from over 100,000 individuals from forty countries. His

work continued into the next decades to encompass over 70 countries and expand on the

outreach of the surveys to be directed to students, airline pilots, office workers, and other

demographics, expanding outside of organizational research. From those results, and

later additions, Hofstede developed a model that identifies four primary dimensions to

differentiate cultures based on the survey results:


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o Power Distance: the willingness of a society to accept hierarchical superiority orview those that are at a higher status to be above lower level statuses in terms of

autocratic power and influence. (Measured from a value of 0 to 100, indicating

low power distance to high power distance respectively). A high power distance

ranking indicates that inequalities of power and wealth have been allowed to grow

within the society. These societies are more likely to follow a system that does not

allow significant upward mobility of its citizens. A low power distance ranking

indicates the society de-emphasizes the differences between citizen's power and

wealth. In these societies equality and opportunity for everyone is stressed.

(Hofstede, Geert. "Geert Hofstede's Cultural Dimensions"). Power distance is an

important factor in determining the willingness of a society to accept an

authoritarian regime, and hence procure corruption. A higher power distance is

also associated with a more traditional and conservative approach to behavioral

culture.

o Individuality: the degree to which a culture allows for an individual to create anaffinity based on their own preferences as opposed to those of the collective group

(measured from a value of 0 to 100, indicating high individuality to low

individuality respectively). A high individualism ranking indicates that

individuality and individual rights are paramount within the society. Individuals

in these societies may tend to form a larger number of looser relationships. A low

individualism ranking typifies societies of a more collectivist nature with close

ties between individuals. These cultures reinforce extended families and

collectives where everyone takes responsibility for fellow members of their

group. (Hofstede, Geert. "Geert Hofstede's Cultural Dimensions"). Individuality


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is a necessary factor in determining self-expression, which is a core value of

democratic institutions, which contribute to a decrease in corruption.

o Masculinity: the value a society places on competitiveness, assertiveness, andambition, along with the collection of material wealth, (male oriented values) as

opposed to valuing positive relationships between individuals and quality of life,

known as femininity (measured from a value of 0 to 100, indicating low

masculinity to high masculinity respectively). A High Masculinity ranking

indicates the country experiences a high degree of gender differentiation. In these

cultures, males dominate a significant portion of the society and power structure,

with females being controlled by male domination. A low masculinity ranking

indicates the country has a low level of differentiation and discrimination between

genders. In these cultures, females are treated equally to males in all aspects of the

society. (Hofstede, Geert. "Geert Hofstede's Cultural Dimensions"). Generally

speaking, masculine cultures have an affinity for competition and aggressive

behavior; this illustrates a tendency towards a traditional view of behavioral

culture, and hence put pressure on creating a democratic society, bolstering

corruption.

o Uncertainty Avoidance: the risk averse behavior of a culture, based on thewillingness of an individual from a given culture to attempt actions outside the

norm of their respective community (measured from a value of 0 to 100,

indicating low risk avoidance to high risk avoidance respectively). A high

uncertainty avoidance ranking indicates the country has a low tolerance for

uncertainty and ambiguity. This creates a rule-oriented society that institutes laws,

rules, regulations, and controls in order to reduce the amount of uncertainty. A

low uncertainty avoidance ranking indicates the country has less concern about


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ambiguity and uncertainty and has more tolerance for a variety of opinions. This

is reflected in a society that is less rule-oriented, more readily accepts change, and

takes more and greater risks. (Hofstede, Geert. "Geert Hofstede's Cultural

Dimensions"). The affinity for risk-averse behavior is present in cultures that rely

on strong norms to guide their actions, this places a restraint on self-expression as

it limits the ability of an individual to reconstruct their behavioral paradigm

towards their liking, this limits democratic expression, and hence may increase

corruption.

3C. Accounting for Indicator Variables of Religion

As in the previous study, to account for information gaps and large variations in

data, indicator variables will be used (also known as dummy variables). Measuring

religion by percentage is attainable, however, the large gaps between countries with

majority Islam, Christian and Jewish populations are more clearly defined when

measured with a dummy variable so as to understand the impact of the countrys major

religion as opposed to the average coefficient impact on the dependent variable of having

added a 1 unit increase of a certain religion to a countries population. As stated

previously in the assumption, the presence of religion emphasizes conservative values

that are characteristics of a society that stresses traditional norms for behavioral culture,

thus producing a counterproductive effect on corruption level. The indicator variable,

expressed with a (D) will be given values of 1 or 0 such that when the country has a

majority (religion>50%) religion, a value of 1 has been as opposed to a value of 0, when

the country does not have the specified religion as a majority. The dummy will be

applied as a separate regressor in the model in effort to find the interactive affect on the

intercept so that:

D= {1 or 0}


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Religion>50% (D=1)

Yi=Bo+B1X1+B2X2+Bc*(D)

Yi=Bo+B1X1+B2X2+Bc

Yi=(Bo+ Bc) +B1X1+B2X2

This indicator variable will be used to calculate the average difference in

corruption levels between countries that have a specified religion as a majority as

opposed to the countries that do not, and will account for lags in corruption levels on the

basis of religious makeup. The data on religious populations will be obtained from the

CIA World Factbook 2009 National Statistics.

3D. Accounting for Socio-Political Cultural Indicators

The Das, Eargle, Esmail study emphasized the inclusion of socio-political culture

factors along with behavioral and normative cultural values. These socio-political values

served as key determinants of the countries tolerance towards self-expression and

political/finance autonomy based on explanatory factors such as political participation,

civil liberties index, the presence of a democratic institution, ext. To account for the

presence of these socio-political factors, the Democratic Index, Political Participation

Index, and the Civil Liberties Index have been factored into the model. All such socio-

political traits are obtained from the Economist Intelligence Unit Index of Democracy

and have also been included to account for dummy variables that represent fully

democratic institutional presence (CPI score > 8) as opposed to those that do not.

D= {1 or 0}

Full Democratic (D=1)

All others (D=0)

Yi=Bo+B1X1+B2X2+Bc*(D)

Yi=Bo+B1X1+B2X2+Bc


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Yi=(Bo+ Bc) +B1X1+B2X2

*The Economist Intelligence Units index of democracy, on a 0 to 10 scale, is based on

the ratings for 60 indicators (survey questions) grouped in five categories: electoral

process and pluralism; civil liberties; the functioning of government; political

participation; and political culture. Each category has a rating on a 0 to 10 scale, and the

overall index of democracy is the simple average of the five category indexes. The

category indexes are based on the sum of the indicator scores in the category, converted

to a 0 to 10 scale. Adjustments to the category scores are made if countries do not score a

1 in the following critical areas for democracy:

1. Whether national elections are free and fair

2. The security of voters

3. The influence of foreign powers on government

4. The capability of the civil service to implement policies.

If the scores for the first three questions are 0 (or 0.5), one point (0.5 point) is

deducted from the index in the relevant category (either the electoral process and

pluralism or the functioning of government). If the score for 4 is 0, one point is deducted

from the functioning of government category index.

The index values are used to place countries within one of four types of

Regimes:

1. Full democracies scores of 8-10

2. Flawed democraciesscore of 6 to 7.9

3. Hybrid regimesscores of 4 to 5.9

4 Authoritarian regimesscores below 4

* Summarized methodology of Economist Intelligence Index of Democracy.


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The reason these factors are taken into consideration represents a multi-

dimensional view of culture that is can apprehend a behaviorally oriented variation in

culture as well as a politically motivated variation in culture and how it impacts

corruption level.

3E. Linear Regression

Although the previous study performed a Seemingly Unrelated Regression (SUR)

for their panel study, this regression will note be required as the study inquires only on

the impact of culture on corruption during a one year period of 2009. Instead a linear

regression will be performed to determine the best-fit equation with a minimal amount of

error:

Y=o+kXk

There are 14 independent variables (represented by kXk) in total along with a

constant (represented by o). The linear regression will account for all of the variation in

the dependent variable (Y, Corruption) that the 14 independent variables explain linearly.

Each coefficient will explain how on average a one unit increase in a given independent

variable affects the dependent variable.

3F. Non-Linear Double Log Regression

Although linear modeling accounts for the linear variation in corruption level,

there may be an incentive to measure the model in a double log model in order to account

for all of the variation in Y that the independent variables could explain non-linearly. To

do this we simply take the natural logs of both the dependent variable and the

independent variables to obtain the equation.

Ln(Y)=o+ln(kXk)


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There are 14 independent variables (represented by ln(kXk)) in total along with

a constant (represented by o). The double log regression will account for all of the

variation in the dependent variable (ln(Y), ln(Corruption)) that the 14 independent

variables explain non-linearly. Each coefficient will show the elasticity between the

independent and dependent variable, that is to say, what percentage effect a one percent

increase of an independent variable has on the dependent variable.

3G. Testing for Multicollinearity

Collinearity occurs when two independent variables within a study are correlated

significantly, as such the effect that this may have on the outcome of the study could

result from two seemingly different independent variables having a highly similar effect

on the dependent variable, this may skew the data significantly as the coefficient

estimates may change in an erratic fashion in response to small changes in the model or

the data. To account for multicollinearity, we simply find the correlative characteristics

of each independent variable with each of the other independent variables.

3H. Sample

The sample size will consist of 79 countries out of a total available of 180; the

size of the sample is relatively large (44%) in comparison to the requirements for

statistical significance of total sample equaling to 30 or 5% of entire population, the

larger of which is to be chosen.


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4. RESULTS

4A. Collinearity Measure

According to the above data, the democracy index is highly correlated with the

political participation index (.8509) and the civil liberties index (.9310). We are no able

to judge whether each factor should be thrown out because of this collinearity due to our

unknown significance of each of the correlated independent variables. We must first run a

linear regression.

fulldem 0.7259 -0.6515 0.6311 -0.0958 0.0498 -0.3592 0.3913 0.7114 0.5652 0.6449 1.0000 polpar 0.5923 -0.6155 0.5799 -0.1120 -0.0361 -0.5151 0.4697 0.8509 0.7072 1.0000 civillib 0.5768 -0.4375 0.4060 0.0144 0.1252 -0.6605 0.5924 0.9310 1.0000 democracy 0.6999 -0.6225 0.5567 -0.0700 0.0523 -0.6438 0.5483 1.0000 Christ 0.2656 -0.2678 0.2700 0.0177 0.1385 -0.5773 1.0000 islam -0.3851 0.3637 -0.2224 -0.0063 -0.0492 1.0000 Risk -0.2227 0.1750 -0.1814 -0.0267 1.0000 Masc -0.1136 0.1234 0.1147 1.0000 Indiv 0.6555 -0.6220 1.0000 PwrDst -0.6748 1.0000Corruption 1.0000

Corrup~n PwrDst Indiv Masc Risk islam Christ democr~y civillib polpar fulldem


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4B. Linear Regression for Multicollinearity

Because the Political Participation Index lacks a significant p-value (.110) and is

highly correlated to the Democracy Index (which has a significant p-value), it is safe to

drop the Political Participation Index variable from the regression. We can conclude the

same for the Civil Liberties Index (.645). Exempting the problem of multicollinearity,

we continue to produce the linear model without the omitted collinear variables.

_cons 4.473742 1.465874 3.05 0.003 1.547846 7.399638civillib .1074877 .2320237 0.46 0.645 -.3556335 .570609polpar -.2616047 .1615441 -1.62 0.110 -.5840482 .0608388fulldem 1.691224 .5396822 3.13 0.003 .6140134 2.768434

democracy .4389434 .3956085 1.11 0.271 -.3506944 1.228581Christ -.6315224 .4144939 -1.52 0.132 -1.458856 .1958109islam -.2747903 .5394915 -0.51 0.612 -1.35162 .8020392Risk -.021515 .0073591 -2.92 0.005 -.0362038 -.0068263Masc -.012656 .0093108 -1.36 0.179 -.0312404 .0059285Indiv .0238158 .0102009 2.33 0.023 .0034547 .0441768PwrDst -.014814 .0113666 -1.30 0.197 -.0375019 .0078739

Corruption Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 423.618441 77 5.5015382 Root MSE = 1.3197Adj R-squared = 0.6835

Residual 116.680327 67 1.74149741 R-squared = 0.7246 Model 306.938115 10 30.6938115 Prob > F = 0.0000

F( 10, 67) = 17.62Source SS df MS Number of obs = 78


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4C. Linear Regression Model

Analyzing the linear regression model, 3 of the 9 independent variables show a

significant p-value, their coefficient estimates model the following effects:

o On average, for every one unit increase in risk averse behavior exhibited by aculture, the CPI score decreases by -.0201491. This statement has a possibility of

error of 0.8% within the model. This statements states that for the data observed,

on average, risk-averse behavior had a negative impact on CPI scores and thus

increased corruption. This relates strongly to the traditionalist view of behavioral

culture, which aims to remain conservative in the face of change in an effort to

preserve itself, hence displaying a strong tendency towards avoiding risk.

Generally, this long-term orientation produces a more corrupt society based on its

emphasis on survivalist values rather than Secular values, which create a switch

_cons 3.938819 1.370658 2.87 0.005 1.204431 6.673207fulldem 1.666126 .5341911 3.12 0.003 .6004437 2.731808

democracy .3832085 .1434662 2.67 0.009 .0970013 .6694157Christ -.6322558 .4055708 -1.56 0.124 -1.441347 .1768357islam -.3343111 .5462679 -0.61 0.543 -1.424086 .7554636Risk -.0201491 .0074302 -2.71 0.008 -.034972 -.0053263Masc -.0094392 .0091826 -1.03 0.308 -.0277581 .0088796Indiv .0198156 .0101239 1.96 0.054 -.0003811 .0400123PwrDst -.0114135 .0109875 -1.04 0.303 -.0333329 .0105058

Corruption Coef. Std. Err. t P>|t| [95% Conf. Interval]





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from survival values to self-expression values. Enhancing democracy, increasing

elite integrity, and lowering the level of corruption (Inglehart and Welzel 2005)

o On average, for every one unit increase in democracy index ranking, a CPI scoreincreases by .3832085. This statement has a possibility of error of 0.9% within the

model. As previously mentioned by Lipset and Lenz (2000), a lack of

democratization restricts access to resources for most people.

o On average, a full democracy (CPI > 8) has a CPI score equivalent to 1.666126higher than a flawed democracy, a hybrid democracy or an authoritarian regime.

As compared to flawed democracies, hybrid regimes and authoritarian regimes, its

not surprising to find that CPI scores increase as democratic institutions grow.

This finding conforms with the findings of the previous study in assessing the

critical value of democracy as an impact on corruption.


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4D. Non-Linear Regression Model

A non-linear regression may account for some of the variations in the dependent

variable that wasnt explained by the independent variables in the linear model, indicator

variables are excluded from the double log non-linear regression due to their discrete

values of 1 and 0:

4E. F-Static Interpretation

The significance of the F-value is an overall significance test assessing whether

the group of independent variables, when used together reliably, predict the dependent

variable. We obtained the F-value by dividing the MSM (mean squared model) by the

MSR (mean squared residual), which is simply a ratio of explained variance over

unexplained variance , this F-value is now weighed against the critical p-value (.05). The

p-value of the F-stat helps us determine if the overall model is a good fit for predicting

CPI scores.

_cons 4.336382 2.32048 1.87 0.066 -.2894102 8.962175democracy_~g .0220904 .3612047 0.06 0.951 -.6979579 .7421387

Risk_log -.4942042 .2606253 -1.90 0.062 -1.013751 .0253429Masc_log .0478952 .2504092 0.19 0.849 -.4512864 .5470767Indiv_log .1170287 .2201664 0.53 0.597 -.321865 .5559225PwrDst_log -.3384044 .3422271 -0.99 0.326 -1.020622 .3438128

Corruption~g Coef. Std. Err. t P>|t| [95% Conf. Interval]





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Observing the linear model, there 20.91 times more explained variance than

unexplained variance in the model. This hefty statistic is also backed up by a very

significant p-value for the F-statistic of nearly 0 percent (.0000 at 4 decimal places).

Observing the non-linear model, there is stark contrast between its F-statistic as

well as its p-value for the F-stat in comparison to the linear model. There is only 1.57

times more explained variance than unexplained variance and the F-stat p-value is highly

insignificant with a 18.03% chance of being erroneous, the non-linear model is

henceforth insignificant and unfit for prediction.

4F. T-Statistic Interpretation

The t-value estimates the value of each variable and subtracts the hypothesized

value of the given variable, further dividing that value by the standard error to determine

whether there is significance in the data presented from the independent variable. The

significance is obtained by measuring the p value of the derived t-test. Generally

speaking,for a 95% confidence level, we must restrict the probability of error to 5% or

less.

4G. Coefficient of Determination

Interpreting the variation in the CPI levels in relation to the independent variables,

we look at R-squared and R-squared adjusted. R-squared is simply a measure of the

variance in CPI scores explained by the model divided by the total sum of squares (sum of

square residuals plus sum of squares explained by the model), this will give you an idea of

how much (percentage) of the variation in CPI score is explained by the model. In this

case, 70.80% of the variation in CPI scores is explained by the linear regression model as

shown above, as opposed to the 29.2% that is still unexplained by the model and left to

error.


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Adjusted R-squared is simply adjusting for the amount of explanatory

independent) terms in the model:

Where p is the total number of explanatory variables and n is the total number of

observations. When adjusting for a degree of freedom, generally R-squared adjusted is

used to determine the impact on the ability of the model to explain variation with every

additional independent variable that is added.

5. CONCLUSIONS

Conclusively, the study produced significant results that can be applied to reject

the null hypothesis that Behavioral and socio-political culture (as represented by the

previously mentioned explanatory variables) does nothave a significant impact on

corruption levels (as represented by CPI). The evidence produced by this study shows

that there exists a significant relationship between behavioral and socio-political culture

and corruption level.

5A. Comparative Analysis

In comparison to the Das, Eargle, Esmail study, many similarities can be found.

Similarities between the significance of secular values (significant in the 2005 panel of

the Das study) and their impact on the reduction of corruption level have also been found

within this study. The significance of risk averse behavior in reducing CPI scores and

increasing corruption reflects the views of the Das study that secular cultural values, such

as low risk avoidance have the potential to decrease corruption.


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In relation to democratic institutions, this socio-political cultural trait exhibits a strong

significance in both the Das and the current study, given credence to the previously

presented findings that secular beliefs along with democratic institutions have a reductive

effect on corruption.

Although religion played a fairly regressive roll in the Das study, it seemed to

have no significant impact on predicting CPI scores in the current study, this can be

attribute to the lack in the paneling process in the current study.

In regards to the Das study, significance of the same explanatory variables varied

across panels, this is perhaps a flaw that this study has incurred as opposed to the Das

study. The research has only accounted for the year 2009 and may have diverse

significance if it were compared amongst panels, as it did in the Das study.

Another drawback of this study as well as that of the aforementioned Das study is

the validity of survey results and subjective quantification of such results. Cross-cultural

studies within the fields of sociology, anthropology, economics and psychology aim at

identifying the interaction of given cultural traits with behaviors. Upon the adoption of

such studies, the respective specialization of cross-cultural sociology, anthropology, and

economics has come under scrutiny since its inception. The criticism is deeply rooted in

the qualitative nature of culture and behavior, as a result, cross-cultural studies have

relied heavily on quantified surveys to obtain results. Therefore it is very difficult to

account for the validity and reliability of surveys. When attempting to replicate the

results of this study, it would be highly advisable to find more concrete determinants of

cultural values that would yield more reliable results, not necessarily based on subjective

interpretation. It would be beneficial to dwell deep into understanding what quantitative

factors are strongly influenced by culture, such as income inequality, and perhaps

extrapolate on those factors to get more reliable results.


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In reference to validity of surveys, there is always the existence of omitted

variable bias. The studys linear regression was not able to account for 29.2% of the

variation in corruption, this is undoubtedly as a result of a missing and significant

variable. This does not necessarily mean that adding independent variables at random

will solve the problem. Although R-squared will usually always increase when an

independent variable is added, regardless of the significance, it is nonetheless important

to obtain intuitively convincing explanatory variables that have empirical precedence.

The time and resource constraint of this study could not be ignored. While the

Das study was conducted over a period of time which was sufficient enough to include

data from 4 separate years and account for a generally more professional regressive

analysis, the current study was undertaken within the time of one semester, limited to the

knowledge of undergraduate economics students. As such, when replicating results it

would be beneficial to budget time wisely in an effort to use resources more effectively

and efficiently.


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Transparency International Corruption Perception Index. 2009.< http://www.transparency.org/>

CIA World Factbook. 2009. < https://www.cia.gov/library/publications/the-world-factbook/>

Das, Shyamal, Ashraf Esmail, and Lisa Eargle. 2007. Are the Economic Reforms Real Devils?:

A Cross-National Comparison of the Roles that Economic Reforms, SocioeconomicResources, Values, and Culture Play in Shaping Corruption levels in Developing

Countries.


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Appendix


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Summary Statistics of Applied and Omitted Variables

polpar 78 5.514103 2.027717 1.11 10civillib 78 7.522564 2.479667 1.18 10

democracy 78 6.67 2.015092 1.9 9.88 Masc 78 50.08974 17.53082 5 110 Indiv 78 41.35897 22.67041 6 91 PwrDst 78 61.44872 21.24663 11 104Corruption 78 4.923077 2.345536 1.5 9.4

Variable Obs Mean Std. Dev. Min Max


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Frequency Histograms of Applied Variables

Power Distance

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Best Fit Line of Each Independent Variable in Regards to Residuals

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