The Effects of Corruption on National Education

By: Theophilos Poulopoulos

4/30/2007 Dr. Vincent Wang

PLSC 374

Abstract: Over the past couple of decades, think tanks as well as the media have come to

understand a new realm of politics, namely the realm of state corruption. This area of

political science has been monitored in recent history due to the expediential awareness

by the public of corruption amongst political elites. The increase in pubic awareness has

even brought some agencies to empirically measure levels of corruption in a state.

This research project utilizes this breakthrough in measuring corruption to attain a

better understanding of how bribery and the misallocation of public monies effects one of

the most sacred institutions of the modern world: education. Globally, education has been

appreciated as a leaping stone to industrialization and potential economic advancement.

In a world full of modern technology, it is imperative that all states, despite their religion

and culture, partake in expanding the influence of education amongst their people.

I will explore in this research paper how corruption, a once intangible concept,

can interfere with and hinder the expansion of education in a nation.

Key Words:

1. Corruption - a : impairment of integrity, virtue, or moral principle : DEPRAVITY b : DECAY, DECOMPOSITION c : inducement to wrong by improper or unlawful means (as bribery) d : a departure from the original or from what is pure or correct.

~Merriam – Webster Corruption - the misuse of entrusted power for private gain. ~Transparency International 2. Education - 1 a : the action or process of educating or of being educated; also : a stage of such a process b : the knowledge and development resulting from an educational process. ~Merriam - Webster

3. Purchasing Power Parity (PPP) - Purchasing Power Parities (PPPs) are currency conversion rates that both convert to a common currency and equalise the purchasing power of different currencies. In other words, they eliminate the differences in price levels between countries in the process of conversion. ~Organisation for Economic Co-operation and Development (OECD)

4. Literacy Rate - (% age 15 and above) The percentage of people aged 15 and above who can, with understanding, both read and write a short, simple statement related to their everyday life ~ Human Development Reports (HDR) 5. Electoral Democracy – Requirements:

A competitive multi-party political system; • Universal adult suffrage for all citizens; * • Regularly contested elections conducted in conditions of ballot secrecy,

reasonable ballot security, and in the absence of massive voter fraud that yields results that are unrepresentative of the public will;

• Significant public access of major political parties to the electorate through the media and through generally open political campaigning.

~ Freedom House

Introduction: Research Question: Does an excessive level of corruption within a state government have a negative effect on the average national level of education? The purpose of my research project is to explore the concept of corruption and

discover whether or not it has a negative impact on education. This is an important topic

because it seeks to explore rather uncharted territory in the field of political science. Is

there an association between corruption and education at the national level? The

misallocation of funds, bribery by private enterprises for tax relief, personal interests of

politicians, and other forms of corruption all suggest an obstruction to the growth of

education in a country. I would like to explore whether or not corruption actually plays a

substantive role in reducing the expansion of national education.

The topic I selected is especially interesting in that seeks to warn the public of the

dangers involved in political corruption not only for adults, but for children and their

futures as well. If I could find that these two variables are in fact correlated (strongly)

then it could be argued that the future economic potential of corrupt states is minimal.

Without an educated society in the world we live in today, a nation will sink into the

quicksand of the past. Nations need to keep their public educated and able to adapt to the

ever evolving world of technology. But first, we must investigate the factors that may be

hindering educational expansion, including the possibility of corruption.

Literature Review:

There are three main focuses of this research that required extensive substantive

outside reading. These focuses included national education, corruption, and the

distinction of government type between electoral democracy and autocratic governments.

All three of these focuses are pertinent to understanding the purpose and results of my

research. I used a bevy of both online and text sources to understand how these three

areas are related.

There has not been a great deal of research done specifically to the field of

corruption and its impact on national education levels. So I decided to research the three

independently and then come to conclusions.

The first area I researched mainly dealt with the categorization of national

governments. I purposely included government type as a variable in my research to make

conclusions as to which types of governments have a positive correlation to the national

education level of a state. My primary source in defining the governments came from the

Freedom House organization. I wanted a basic dyadic categorization and so I chose to

adopt the “electoral democracy/autocratic” scheme as a means for classifying the cases.

There has been a great deal of research done in this area, and many sources have

published many different data sets for government type. According to Freedom House,

the classification process focuses on how a state’s national leaders are represented. Many

political scientist and think tanks classify governments based on their functional qualities

(for example a communist state can be recognized for its condemnation of privatization),

resulting in a vast array of government type categories. For my purposes, I chose the

Freedom House classification system because it was dyadic and because the relationship

between democratic elections and corruption is important. It may be simple to say that

non-elected leaders are corrupt, and that they gain their power through coercion. It is

another matter however to find corruption amongst an elected body of representatives. If

such is the case, then citizens must take a deeper look into the election process and the

integrity of their candidates.

Corruption was the second area that I researched because it wasn’t clear to me at

first what corruption actually entails. In my previous attempt at researching corruption, I

had come up with several indicators which I wrongly assumed would describe corruption.

I took a different approach this time around by simply using the Transparency

International Corruption Index. The index consists of scores for nations based on

perceived corruption by risk agencies/national analysts in both the public and political

sectors. Although this may seem very subjective, one must also understand that

corruption is a very intangible concept. There is no straight forward empirical data which

can be used to measure corruption, mostly because there is no straight forward definition

of corruption. Nevertheless, as former IMF Director of Fiscal Affairs Vito Tanzi notes,

corruption, “like an elephant, may be difficult to describe, [it] is generally not difficult to

recognize when observed.” Likewise, the IMF goes on to define corruption as “the abuse

of public power for private gain.” 1 This is more or less the basic form that most agencies

use to define corruption. Transparency International takes an even more in-depth

approach to defining corruption by associating it strictly with bribery on both the political

and public levels of the state. I personally do not like this strict definition of corruption

because it does not take into account other factors contributing to the definition such as

1 George T. Abed & Sanjeev Gupta, Governance, Corruption, and Economic Performance. IMF; Washington D.C.: 2002, 25.

the acceptance of gifts, abusing public position for personal use (such as taking an

unwarranted vacation during public duty, abuse of taxes, and others. One must keep in

mind that Transparency International, while being one of the leading sources on global

corruption, measures corruption based on perception, and not objective statistics. There is

no true objective measure of corruption, and therefore the perception of corruption by

political analysts seems to be the best source on it to date. Therefore I utilized the index

as a means of measuring corruption in my research.

The final area which I explored dealt with my dependent variable, namely

national education. Just as with corruption, there is no single objective measurement to

describe how educated a nation’s public is as a whole. There are however, unlike

corruption, several major indicators that may be used to make generalizations on a state’s

education level. The two indicators that I used in this research and seemed most pertinent

in measuring education levels were adult literacy rate and average number of years in

school. The qualitative measurement of education can be reflected upon by the literacy

rate where as the quantitative measurement can be referenced by the average number of

years a child spends in school.

Upon researching global education, I confronted an interesting theory that seems

to be taking hold of the world. This theory proposes the decentralization of national

education for a more privatized experience. Across the globe, “Decentralization,

devolution, localization, and even marketization of public services, have become part of a

new way to think about how public schools are managed.”2 If this theory of

decentralization is true, then the implications are significant. For example, if in fact there

2 David P. Baker & Gerald LeTendre, National Differences, Global Similarities: World Culture and the Future of Schooling. Stanford University Press; Stanford, CA: 2005, 135.

is a strong negative correlation between state corruption and education, then any move

towards a decentralized educational system will minimize the effects of state corruption. I

would argue that a more privatized educational system would in fact be better for a state

with high levels of political and public corruption. There would be no misallocation of

funds for education. It will be interesting to see if this proposed reformation takes the

world by storm in the near future.

Taking a comparative approach to education will prove to be difficult. Analysts

have argues aimlessly over a proper system to quantitatively compare national education

systems. Some analysts base their reasoning of economic statistics, others use

infrastructure as an indicator. There is no clear concise manner in which to measure

education comparatively across the world.3

3 W.D. Halls, Comparative Education: Contemporary Issues and Trends. Jessica Kingsly Publishers; London, England: 1990, 35.

Hypotheses:

Although I have one target research question which I am seeking to answer (Does

an excessive level of corruption within a state government have a negative effect on the

average national level of education?), there are a couple smaller questions that I would

also like to find conclusions to. Some of these include:

1. What is the relationship between government type and educational levels?

2. What is the relationship between education levels and percent of total

workforce in agriculture?

My general hypothesis is deals with answering my initial research question. My

hypothesis is that there will be a significant negative relationship between level of

corruption and level of education. I expect that once GNI per capita (PPP) and % labor

force in agriculture are controlled for; there will still be a significant relationship between

corruption and education.

For my other more minor questions, my hypotheses respectively are:

1. Electoral democracies will on average experience higher levels of education

than autocratic nations.

2. Higher levels of the workforce engaged in agriculture will be associated with

a decrease in literacy rate and mean years spent in school. This is because the

state focuses its citizens to enter the agricultural field, requiring little or no

education.

My variables are as follows:

Dependent Variable = Level of Education as described by 1. National Adult Literacy Rate and 2. Average number of years a child spends in school.

Independent Variables = Most importantly, my independent variable is Perceived Level of Corruption. I will also use Government type as an independent variable in answering question 1.

Control for: 1. GNI per Capita (PPP) 2. % Labor Force in Agriculture

-These two controls represent national development that may impede on educational statistics. Prediction for Relationship Among Variable: Overall Hypothesis: Development Corruption Education

Hypothesis 1. Government Type: Electoral Democracy Education Autocratic Hypothesis 2. % Workforce in Agriculture Education Hypothesis 3. GNI per Capita (PPP) Education

Data and Methods / Research Design: To conduct my research, I made use of various data sets from various sources to

arrive at a multiple regression equation for education in respect to level of corruption,

while controlling for development. I used SPSS to acquire comparison of means tables,

regression coefficients, scatterplots, and univariate statistics. To attain this information, I

first devised a spread sheet which consisted of an N of 143, or the number of cases

evaluated by Transparency International. I then made a dummy variable to distinguish

these cases as either electoral democracies or autocracies. After having done this, I then

proceeded to gather the independent and dependent variable statistics which I have

written the sources for below. Once my data was collected, I ran a univariate analysis on

education in terms of average mean number of years schooled and literacy rate. Finally, I

explored each of my hypotheses by running comparison of means tests and regressions to

discover whether or not there were relationships between the given variables. The sources

are:

Dependent Variables: 1. Adult Literacy Rate – Human Development Report 2003 2. Average Number of years educated – World Development Report 2006 (The World Bank, 2006) Independent Variables:

1. Perceived Corruption – Transparency International Corruption Index 2006 2 GNI per Capita (PPP) - World Development Report 2006 (The World Bank, 2006)

3. % Workforce in Agriculture – World Resources Institute: EarthTrends 2005 4. Government Type – Freedom House: Freedom of the World 2006

*Note – In the following data table, the levels of corruption are the inverse of what they are off of the Transparency International Index. Instead of the higher values indicating less corruption, they on my data table and in my applications stand for higher levels of corruption. As value increase, so does corruption. *Note – I also redefined the corruption values into three categories. These are Least Corrupt (0-4), Corrupt (4.1-6), and Most Corrupt (6.1-9) and are labeled 1, 2, and 3 respectively.

Univaritate Statistics for Dependent Variables: Average Number of Years Receiving Education: Descriptive Statistics N Minimum Maximum Mean Mean years of Schooling

108 1.00 13.96 7.7395

Valid N (listwise) 108 Adult Literacy Rate: Descriptive Statistics N Minimum Maximum Mean Adult Literacy Rate 101 .165 .999 .78462Valid N (listwise) 101

Univariate Statistics for Independent Variables: Government type using dummy variable. 1 = Electoral democracy 0 = Autocratic Electoral Democracy

Frequency Percent Valid Percent Cumulative

Percent 0 54 37.8 37.8 37.81 89 62.2 62.2 100.0

Valid

Total 143 100.0 100.0

Corruption: Descriptive Statistics N Minimum Maximum Mean Level of state corruption 143 .40 8.20 5.9000Valid N (listwise) 143

GNI per Capita (PPP): Descriptive Statistics N Minimum Maximum Mean GNI per Capita 132 520 45470 8520.98Valid N (listwise) 132

% Workforce In Agriculture: Descriptive Statistics N Minimum Maximum Mean Std. Deviation % of National Workforce in Agriculture 72 .003 .821 .21238 .190152

Valid N (listwise) 72

Findings: Overall Hypothesis Tests – Comparison of Means between Levels of Corruption and Mean Years of Education: Report Mean years of Schooling

Level of Corruption Mean N 1.00 11.4514 212.00 8.7592 123.00 6.5371 75Total 7.7395 108

ANOVA Table

Sum of

Squares df Mean Square F Sig. Between Groups (Combined) 410.262 2 205.131 26.437 .000Within Groups 814.726 105 7.759

Mean years of Schooling * Level of Corruption

Total 1224.988 107 Measures of Association Eta Eta Squared Mean years of Schooling * Level of Corruption .579 .335

Regression between Corruption and Mean Years of Education not taking into consideration control Variables: Model Summary

Model R R Square Adjusted R

Square Std. Error of the Estimate

1 .605(a) .366 .360 2.70780a Predictors: (Constant), Level of state corruption Coefficients(a) Dependent Variable: Mean School Years

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) 13.371 .766 17.450 .0001 Level of state corruption -.932 .119 -.605 -7.815 .000

Scatterplot of Mean Years Education in terms of Corruption Index Score with Regression equation shown:

Linear Regression

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Mean years of Schooling = 13.37 + -0.93 * CORRUPTR-Square = 0.37

Comparison of Means Between Levels of Corruption and Literacy Rate: Report Adult Literacy Rate

Level of Corruption Mean N 1.00 .97377 132.00 .86771 143.00 .73568 74Total .78462 101

ANOVA Table

Measures of Association Eta Eta Squared Adult Literacy Rate * Level of Corruption .429 .184

Sum of

Squares df Mean Square F Sig. Between Groups (Combined) .739 2 .370 11.080 .000Within Groups 3.268 98 .033

Adult Literacy Rate * Level of Corruption

Total 4.007 100

Regression Between Perceived Corruption Score and Literacy Rate with no control variables: Model Summary

Model R R Square Adjusted R

Square Std. Error of the Estimate

1 .484(a) .234 .227 .176052a Predictors: (Constant), Level of state corruption Coefficients(a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) 1.102 .060 18.283 .0001 Level of state corruption -.051 .009 -.484 -5.504 .000

a Dependent Variable: Adult Literacy Rate Scatterplot of Literacy Rate in Terms of Corruption Score with Regression Equation:

Linear Regression

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Regression between Corruption Score and Mean Years of Education when controlling for Development (GNI per Capita (PPP) and % Workforce in Agriculture): Descriptive Statistics Mean Std. Deviation N Mean years of Schooling 9.1825 2.69605 56Level of state corruption 5.3054 2.36700 56% of National Workforce in Agriculture .21951 .200453 56

GNI per Capita 11080.36 9419.118 56 Correlations

Mean years of

Schooling Level of state

corruption

% of National Workforce in Agriculture

GNI per Capita

Mean years of Schooling 1.000 -.548 -.588 .590Level of state corruption -.548 1.000 .639 -.917% of National Workforce in Agriculture -.588 .639 1.000 -.725

Pearson Correlation

GNI per Capita .590 -.917 -.725 1.000Mean years of Schooling . .000 .000 .000Level of state corruption .000 . .000 .000% of National Workforce in Agriculture .000 .000 . .000

Sig. (1-tailed)

GNI per Capita .000 .000 .000 .Mean years of Schooling 56 56 56 56Level of state corruption 56 56 56 56% of National Workforce in Agriculture 56 56 56 56

N

GNI per Capita 56 56 56 56 Coefficients(a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) 10.015 2.662 3.762 .000Level of state corruption -.114 .308 -.100 -.369 .713% of National Workforce in Agriculture -4.636 2.099 -.345 -2.209 .032

1

GNI per Capita 7.11E-005 .000 .248 .824 .414a Dependent Variable: Mean years of Schooling

Model Summary

Change Statistics

Model R R Square Adjusted R

Square Std. Error of the Estimate

R Square Change F Change df1 df2 Sig. F Change

1 .636(a) .404 .370 2.14068 .404 11.747 3 52 .000a Predictors: (Constant), GNI per Capita, % of National Workforce in Agriculture, Level of state corruption Regression between Corruption Score and Literacy Rate when controlling for Development (GNI per Capita (PPP) and % Workforce in Agriculture): Descriptive Statistics Mean Std. Deviation N Adult Literacy Rate .87252 .138793 48Level of state corruption 5.7063 2.17690 48% of National Workforce in Agriculture .24006 .200465 48

GNI per Capita 8806.46 7928.208 48 Correlations

Adult Literacy

Rate Level of state

corruption

% of National Workforce in Agriculture

GNI per Capita

Adult Literacy Rate 1.000 -.419 -.485 .489Level of state corruption -.419 1.000 .546 -.900% of National Workforce in Agriculture -.485 .546 1.000 -.666

Pearson Correlation

GNI per Capita .489 -.900 -.666 1.000Adult Literacy Rate . .002 .000 .000Level of state corruption .002 . .000 .000% of National Workforce in Agriculture .000 .000 . .000

Sig. (1-tailed)

GNI per Capita .000 .000 .000 .Adult Literacy Rate 48 48 48 48Level of state corruption 48 48 48 48% of National Workforce in Agriculture 48 48 48 48

N

GNI per Capita 48 48 48 48

Coefficients(a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) .858 .165 5.203 .000Level of state corruption .002 .019 .030 .101 .920% of National Workforce in Agriculture -.196 .120 -.284 -1.636 .109

1

GNI per Capita 5.72E-006 .000 .327 .983 .331a Dependent Variable: Adult Literacy Rate Model Summary

Change Statistics

Model R R Square Adjusted R

Square Std. Error of the Estimate

R Square Change F Change df1 df2 Sig. F Change

1 .534(a) .285 .236 .121315 .285 5.839 3 44 .002a Predictors: (Constant), GNI per Capita, % of National Workforce in Agriculture, Level of state corruption Regression Equations: Uncontrolled- 1. Mean Number of Years Educated = 13.371 + (-.932)*Perceived Corruption Score Sig. = .000** R^2 = .366 2. Literacy Rate = 1.102 + (-.051)*Perceived Corruption Score Controlled – 3. Mean Number of Years Educated = 10.015 + (-.114)* Perceived Corruption Score + (-.04436)*Percent Labor Force in Agriculture + (.000075)*GNI per Capita (PPP) R^2 = .404 4. Literacy Rate = .858 + (.002)* Perceived Corruption Score + (-.00196)* Percent Labor Force in Agriculture + (.00000572)*GNI per Capita R^2 = .285

Conclusions: I will go over my conclusions step by step by hypothesis. The first conclusion I will make

will pertain to my general research question, and I will use my controlled data to make

those conclusions.

Impact of Corruption on Education:

The first indicator of education that I tested for was Mean Years of Schooling in

relation to corruption while controlling for state development as indicated by GNI per

Capita and % Labor Force in Agriculture. I predicted that there would be a significant

negative relationship between Corruption and Mean Years of Schooling. Based on the

multiple regressions I ran on SPSS, I found that when controlled for, Corruption has an

insignificant impact on Mean Years Education with a Sig. value of .713. In fact, all three

of these variables only explained about 40.4% of the dependent variable as indicated by

the R-squared value. This leads me to conclude that there are other factors contributing to

the average number of years a child is educated in a nation besides levels of perceived

corruption and developmental factors. Based on this indicator of education, my initial

hypothesis is incorrect.

The second indicator of education that I tested for was Adult Literacy Rate in

relationship to Corruption while controlling for state development as indicated by GNI

per Capita and % Labor Force in Agriculture. I predicted there would be a significant

negative relationship between increased levels of Corruption and Literacy. Based on the

multiple regressions I ran, I found that when controlled for, Corruption has an

insignificant impact on Literacy Rate with a Sig. value of .920. This means that 92.0%

can be attributed to chance. The three independent variables only explained 28.5% of the

dependent variable literacy rate. This indicator of education also forces me to reject my

hypothesis.

An interesting observation that I have made comes in the form of uncontrolled

regression for Corruption and indicators of education. In the case of both indicators,

Corruption had a significant impact on education. I made use of comparison of means by

re-labeling corruption into categories of least corrupt, corrupt, and most corrupt. Based

one the comparison of means, one can see that as the level of corruption increased, the

mean literacy rate decreased significantly and the mean years of education decrease as

well. These findings are especially interesting since they have much larger N’s than the

regressions. I could use this information as a supporter of my main hypothesis.

Question 1. What is the relationship between government type and educational levels?

Report Adult Literacy Rate

Electoral Democracy Mean N 0 .70046 411 .84213 60Total .78462 101

Measures of Association Eta Eta Squared Adult Literacy Rate * Electoral Democracy .349 .122

Sig. - .000** Report Mean years of Schooling

Electoral Democracy Mean N 0 6.3438 371 8.4669 71Total 7.7395 108

Measures of Association Eta Eta Squared Mean years of Schooling * Electoral Democracy .299 .090

Sig. - .002* Based on these four tables, one can make the deduction that based on the mean literacy

rate, electoral democracies on average have higher levels of literacy than due autocratic

states. These results are significant with a Sig. of 000** and they explain 34.9% of the

variance.

One can also make the conclusion that children living in electoral democracies on

average enjoy just over 2 years more education than do children living in autocratic

states. These results are significant with a Sig. of .002* and they only explained 9% of

the variance. Based on these findings however, Hypothesis 1 was correct.

This conclusion was especially exciting because it provided me with evidence to

support the expansion of electoral democracies across the world. I hypothesized that

electoral democracies would have higher educational levels based on the fact that they

are more likely to have leaders focused on public social issues such as education. The

theory is that if a candidate wants to win an election, or if an already elected official

wants to remain in office, they will focus on issues important to the people. Education,

throughout most developed and developing societies, is of the utmost importance to the

public, and so public officials spend a great deal of time and tax dollars in addressing this

issue. Therefore the education levels increase in electoral democracies.

Question 2. What is the relationship between education levels and percent of total workforce in agriculture? Coefficients(a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) .955 .028 34.596 .0001 % of National Workforce in Agriculture -.335 .089 -.482 -3.766 .000

a Dependent Variable: Adult Literacy Rate R^2 = .232 Coefficients(a)

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig. (Constant) 10.920 .438 24.923 .0001 % of National Workforce in Agriculture -7.914 1.480 -.588 -5.347 .000

a Dependent Variable: Mean years of Schooling R^2 =.346

Based on the bivariate regressions that I ran, I can argue that my second

hypothesis is correct since both tests proved to be significant. The first test, which dealt

with Adult Literacy Rate, proved that when uncontrolled for, for every 1% of the Labor

force in Agriculture, there is a .0035 decrease in Literacy rate. Based on the R-squared,

this explains 23.2% of the dependent variable.

The second test, which dealt with Average Years of Education, proved that when

uncontrolled for, for every 1% of the labor force in agriculture, there is a .07914 decrease

in the number of years a child receives education. 34.6% of the dependent variable is

explained by the independent variable.

Based on all of the tests that I ran, I can conclude that when controlled for

development, there is no significant negative relationship between perceived Corruption

and education.

More Conclusions / Possible Practical Problems with Research:

Based on my initial interest on this topic, I can conclude from my findings that

there are many factors which must impact national education besides national

development and perceived Corruption. Although there were some findings that were

significant and supported my minor hypotheses, I must admit that I could make no

significant findings to support my main hypothesis negatively correlating corruption to

education. Although I am somewhat disappointed in my results, I can’t help but to

suspect that there are a number of improvements that could be made to researching these

two intangible variables. Here is a list of some suggestions for the future:

1. Have a larger N for the controlled regression. My N was 56 and 48 for Average

Years of Education and Adult Literacy Rate respectively. There were just too

many cases which lacked all four variables to be included in the regression. I

would try to find a source that contained the data for a greater majority of the

cases so that my N would increase.

2. Like I mentioned earlier, it is impossible to define corruption and education

objectively. In my testing, I only used two indicators to describe education

empirically. Though these indicators seem very valuable to measuring education,

there must by some qualitative measurements as well to describe levels of

national education. In future research, I would investigate in case studies in which

I could derive a series of qualitative educational tests.

3. One last improvement I would make on my research would be to control for more

variables besides development. Although this seems to be the most relatable

factor that could possible effect education level, I would argue that there could be

social factors such as religion which could possible sway the national education

standards.

I believe that given a larger N, and more variables to control for, one would find a more

significant relationship between corruption and education. If this area of political science

is researched intensely, one may find conclusions to suggest that state corruption is a

deterring factor in developing a global educational foundation. Though given recent

studies, it appears that the decentralization of education could possibly benefit those

nations with high levels of state corruption. There would be no misallocation of public

funds, nor would there be a corrupt curriculum for public education to follow. Perhaps it

is in the best interests of states, at least those with an abundance of private resources and

funding, to privatize their educational systems as much as possible to avoid the potential

influence of a corrupt society.

Annotated Bibliography: "Freedom In the World 2006." http://www.freedomhouse.org/template.cfm?page=15 (accessed 3/11/07).

I utilized Freedom House in defining my cases’ government type as either electoral democracies or autocratic states. Abed, George T. & Gupta, Sanjeev. Governance, Corruption, & Economic Performance. Washington D.C.: International Monetary Fund, 2002. This source helped me in defining corruption and understanding how it impacts the political and public sectors. I especially utilized the essay by Vito Tanzi on Corruption Around the World: Causes, Consequences, Scope, and Cures. Tanzi recognizes a very broad notion of corruption that differs from the strictly tailored definition offered by Transparency International which deals primarily with bribes. Andrain, Charles, and James Smith. Political Democracy, Trust, and Social Justice. London: University Press of England, 2006. The authors of this book investigate to what extent members of democratic nations trust their state governments. A series of studies including one on Interaction between Institutional Confidence and Support for Democracy help to distinguish the various practices of democracies around the globe and offers an alternative perspective to the possibility of corruption within democracies. Baker, David & LeTendre, Gerald. National Differences, Global Similarities: World Culture and the Future of Schooling. Stanford, CA: Stanford University Press, 2005. This was an especially interesting read. The authors of this book take a comparative look at global education and its future based on current trends. These authors making an intriguing argument regarding the decentralization of education at the state level. This could have a significant impact on the outcome of my research. If this trend holds true, then levels of state corruption will have little impact on the educational levels within the state. Blondel, Jean. Comparative Political Systems. New York: Praeger Publishers, 1972. Simply identifies various types of governments and distinguishes the attributes of democracies from those of autocracies. Cantori, Louis, and Andrew Ziegler, eds. Comparative Politics n the Post-Behavioral Era. Boulder, Col.: Lynne Rienner Publishers, 1988.

This book includes a chapter comparing the advantages and disadvantages of democratic versus other systems of government. It does so by analyzing a series of case studies from both the democratic and non-democratic sectors. In addition, there is also a section comparing the public policy of both government types. Halls, W. D.. Comparative Education: Contemporary Issues and Trends. London, England: Jessica Kingsley Publishers, 1990. This source helped me define education as well as offer a glimpse at how intangible a concept it is. In the first two chapters, the various contributors seek to define education and measure it comparatively at a global level. The conclusion that they arrive simply is that education cannot be measure objectively at the quantitative or qualitative level. This leads me to believe that no matter how many indicators I include in defining my dependent variable, there can be no full explanation for the national level of education. It also leaves me to believe that comparing education at national levels will prove to be difficult given the number of factors which can contribute to its status. Kane, Timothy. "Index of Economic Freedom 2007." http://www.heritage.org/research/features/index/countries.cfm (accessed 3/11/07). This freedom index specifically measures a nation’s economic freedom by analyzing several indicators. Some of these indicators include business freedom, trade freedom, fiscal freedom, freedom from government, labor freedom, property rights, and freedom from corruption. Lambsdorff , Graf. "Corruption Perceptions Index 2006 ." http://www.transparency.org/policy_research/surveys_indices/cpi/2006 (accessed 3/11/07). This web page will also serve as a device for measuring a state’s positive or negative status based on its level of corruption. Perceptions of corruption through the eyes of business people and country analysts serve as the source for the aggregate data. The scores each country receives vary from 0 (highly clean of corruption) to 10 (highly corrupt). This is my sole source for empirically measuring corruption in my research. Lederman, Daniel. "Accountability and Corruption: Political Institutions MatterThe World Bank (2001), http://www-wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2001/12/17/000094946_01120404004589/Rendered/PDF/multi0page.pdf. (accessed March 11, 2007). This paper focuses on the impact of political institutions and how each type contributes to levels of corruptibility. The main hypothesis is that political institutions affect corruption in two ways: through political accountability and the structure of the provision of public goods. The results suggest that democracies and parliamentary systems are positively correlated with lower corruption.

Mauro, Paulo. "Corruption and GrowthQuarterly Journal of Economics 110. (1995), 681-712, http://www-wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2001/12/17/000094946_01120404004589/Rendered/PDF/multi0page.pdf. (accessed March 11, 2007). This journal article analyzes subjective indices of corruption, the amount of red tape, efficiency of the judicial system, and various categories of political stability for a large sample of both democracies and non-democracies. Included in the study is a bureaucratic efficiency index for the sample states. This article may be utilized in identifying a connection between bureaucratic efficiency, government type, and growth of state corruption. Rose-Ackerman, Susan. Corruption and Government: Cause, Consequences, and Reform. Cambridge, England: Cambridge University Press, 1999. Offers the definition, corruption describes a relationship between the state and the private sector. Chapter 7 gives an in-depth look at corruption and politics in general, and Chapter 8 argues that democratic forms of government reduce the chances for corruption to exist. These two chapters will be utilized in supporting the hypothesis that democracies in general do in fact lower the possibility for state corruption. Scott, James C. Comparative Political Corruption. Englewood Cliffs, N.J.: Prentice-Hall Inc, 1972 This book takes a comparative look at corruption in states under various government types in different time periods. The opening chapter of this book offers an in depth definition for corruption that will play a role in defining the independent variable of this research. Warren, Mark. "What does Corruption Mean in a Democracy?." American Journal of Political Science 48, no. 2 (2004): 328-43. http://www.jstor.org/view/00925853/ap050002/05a00110/0?currentResult=00925853%2bap050002%2b05a00110%2b0%2cFFFF01&searchUrl=http%3A%2F%2Fwww.jstor.org%2Fsearch%2FBasicResults%3Fhp%3D25%26si%3D1%26Query (11 March 2007). This author argues that political corruption is the most prominent pathology in democracies. Includes that corruption reduces the influence of the public sphere in decision making, thus reducing the effectiveness of the democratic principle. This article also offers a modern conception of political corruption in democracies and how they should be interpreted compared to other forms of corruption in non-democracies.