Essay: The relation between the power of the largest government party and the corruption level.

‘Power tends to corrupt, and absolute power corrupts absolutely’
John Dalberg-Acton (1887)

Introduction
Corruption is a global issue, it is a phenomenon that exists but is sometimes hidden. There are several (potential) causes for corruption (e.g. personal, cultural, political and institutional) applicable, on a greater or lesser scale, to different cultural and geographical environments. Therefore there can be no one solution to solve the problem of corruption. Corruption is an interesting topic not only because of this complexity but also because of its scale and impact: In 2014 corruption held second place as top problem in emerging and developing nations according to the ‘Global Attitudes survey’ executed by the Pew Research Center (see figure 1). This survey was held cross-country and the population contained twenty countries.

Figure 1: Overview of top problems ranked

Source: http://www.pewglobal.org/2014/11/06/crime-and-corruption-top-problems-in-emerging-and-developing-countries/country-issues-report-04/

A lot of research is done to verify the level of corruption and the influence on the economic development (Paulus and Kristoufek, 2014) of a country. Also the political system and political corruption have been popular research topics but this relationship more often focuses on a specific case or continent. However, the impact of the power concentration at the largest government party on the level of corruption is hardly researched.

This paper aims to be a (small) contribution towards the body of knowledge that provides insight into corruption.

This research paper is concerned with the following research question:
In what degree does the concentration of power at the largest government party affect the level of corruption?

Literature Review
In this section the review on the literature study is summarized. First the definition of corruption is described. Secondly, the existing literature/theories regarding the explanation, from a public administration perspective, for corruption will be outlined. This theoretical deduction results in the hypothesis for this research.
Definition of corruption
Several definitions of corruption are used in the literature. A general definition for corruption is implied in the description from Eiras (2003). She argues that corruption is a form of unethical behaviour or wrongdoing. Nye (1967) defined corruption as ‘behavior which deviates from the normal duties of a public role because of private-regarding, pecuniary or status gains; or violates rules against the exercise of certain types of private-regarding influence’. Todaro and Smith (2003) defined corruption as ”the abuse of public trust for private gain’. Also Transparency International (hereafter ‘TI’) stated that corruption is the abuse of entrusted power for private gain in the public sector.
Corruption occurs in different scales of society. Bureaucratic corruption, also called petty corruption, is the lowest level of corruption; street-level bureaucrats at the end of policy implementation are involved in the corruption, like bribes. Then there is grand corruption, which takes place in highest level of the government at policy formulation level; it involves political parties and campaigns that receive large amounts of money. Rose-Ackerman (2008) concluded that grand corruption shares a couple of features from the smallest scale pay-offs, but it can be more deeply destructive of state functioning ‘ bringing the state to the edge of outright failure and undermining the economy. Endemic corruption, where the state is involved, is the largest scale of corruption due to instability or weak organizations and processes. This is also called systemic corruption where monopolistic powers or discretionary powers rules. Political officials or actors have the power to rule on their own authority and judgement.
The several sorts of corruption that are used in the surveys of TI to conduct the Corruption Perceptions Index (‘CPI’) score also include those three levels; petty, grand and endemic corruption as mentioned above.
Neo-Marxist assumption
The causes of corruption are very divers. The neo-Marxist approach (an outcome of the Marxist theory) adopted Max Weber’s view of social science and follows the status and power ideology of Karl Marx. Marx’s concern was that the forces of production seem to require a complex and oppressive mode of organization mixed into the bureaucratic capitalism (Denhardt, 2004).
The neo-Marxists found that the phenomenon corruption was a spin-off of the capitalist democracy and thought of a corrupt international capitalist system wherein the workers, or in Marx’s words the bourgeoisie, are exploited in a recurring manner (Montinola and Jackman, 2002). The neo-Marxists did not recognize other causes of corruption. Therefore there has been hardly any debate about what the (other) causes of corruption could be (Rose-Ackerman, 1999).
Public choice theory
The public choice theory did pay attention to other causes of corruption (other than the capitalist system in general), which were ignored by the neo-Marxists. The public choice theory rests on the rational choice framework. This theory focuses on individuals and their behaviour, at micro-level perspective. Mueller (1982) argued that the design and implementation of rules, and it’s functioning, are affected by several political actors (e.g. legislators, street-level bureaucrats, voters, and political coalitions) including interest or pressure groups. According to McLean (1987) and Van Winden (1988) the underlying assumption of the public choice approach is that individual political actors are guided by self-interest when choosing a course of action that will be to their best advantage. The only political actor that counts is the person whose primary motivation is the pursuit of his self-interest (Howlett et al., 2009).
The theory of public choice underlines a diversity of arguments that are important to public policy. The public finds it practical and efficient to accept rules to control their socio-political interaction. The elected politicians do not radically change their objectives but turn into obedient, passive, and attentive servants of the voter. On the other hand public choice theorists argue that elected individuals are expected to be egoistic, rational, utility maximizers. These people seek ways to maximize their own welfare. The highest priority for the elected individuals is to get re-elected because that is in their self-interest (Mbaku, 2008).
Public choice view on corruption
Following the analysis of Montinola and Jackman (2002) the public choice school has a very functional and rational view on how incentive systems take advantage of opportunities to create and shape corrupt behaviour. They argue that competitive democracies as well as competitive markets are necessary conditions for honest government. Political competition could reduce corruption in two ways. First, the freedom of information and association characteristic of democracies helps monitoring of public officials, thereby limiting their opportunities for corrupt behaviour. Secondly, the possible turnover of power in democracies implies that politicians cannot always credibly guarantee that particular laws and regulations will continue in the future.
Public choice theorists thus argue that competitive democracies as well as economies are necessary conditions for honest government. Concluded from the analysis of Montinola and Jackman (2002) corruption is caused by a lack of competition in either or both political and economic environments. This is in line with the reasoning of Mbaku (2008), who argued that an effective way to minimize corruption and other opportunistic behaviours in society is to make the constitution self-enforcing.
Rose-Ackerman (1978) suggests that a lack of competition between politicians and also between bureaucrats stimulate corruption in government. Rasmusen and Ramseyer (1994) suggest that decision-making groups (e.g. democratic legislatures that implies a high level of competition) will create policies for the common good instead of decision-making individuals like authoritarian leaders (such as dictators, that implies a low level of competition), who will make policies in their own advantage.
Very weak institutions or instable countries cause corruption. Because of a dis-functioning system, corruption is used as a way to hold the system together for the short term (Rose-Ackerman, 2008).
Based on the literature above described the various students agree that political competition affects level of corruption. However, there are several types of political competition. It is not fully clear what type of political competition is the panacea for corruption. Therefore this paper focuses on one particular type of political competition, the concentration of political power at a government party and how political competition could influence the level of corruption.
The hypothesis for this paper is that the more power for the largest government party the higher the level of corruption in a country. The reasoning for this hypothesis is that a lack of (strong) opposition (in other words: competition) will result in less objection or political discourse. This research is not trying to establish whether corruption is caused by the largest government party it only searches for a possible association. Also the relationship between corruption and a political system such as a democracy or dictatorship is not taken into account.
Methodology
The used research design is cross-sectional to search for a relation between the following variables:
1) ‘Vote share largest government party’ is used as a proxy for power concentration of the largest government party (low level of political competition). This variable represents the percentage of votes for the largest government party and is derived of ‘The Quality of Government Standard Dataset’ from The Quality of Government Institute (‘QoG’), version January 15.
2) ‘CPI score’ is used as a proxy for level for level of corruption. This variable shows the corruption perception with grades per country, this is an indicator of the ‘CPI 2013 Global’ dataset of TI.

Vote share largest government party
The data set from the QoG contains several variables for 193 countries to measure the quality of government. The research period for the vote share largest government (‘GPVS1’) is 2007 till 2012. For 56 countries the data was not available. This could have three reasons; first there is no parliament, second there are no parties in the legislature, and finally when the government or opposition party seats are unknown. For 19 countries the percentage was zero, this means that those countries have a political system that deviate from the others. The percentages of vote share largest government party are real numbers and therefore an interval/ratio variable.

CPI
The dataset of TI with CPI consists of 177 countries. The CPI scores and ranks countries/territories based on how corrupt a country’s public sector is perceived to be. A combination of surveys and assessments of corruption, collected by a various institutions (see appendix 1) results in the composite index. The score is an ordinal scale, the score could be ranked from 0 (low) till 100 (high) (e.g. a country scores 90 points, the CPI score is high that means the level of corruption is low). This ordinal variable means that the distances between the scores are not equal across the range.

Population
To compare both datasets all the countries with missing values were excluded. In the QoG population of 193 countries 78 (22 + 56) countries were excluded due to lack of information or a mismatch between countries/territories in the population of QoG and TI (e.g. Hong Kong was in the CPI dataset but not in the dataset of QoG). The population size is 193 countries minus 78 resulted into 115 countries that are used for this research.

Data analysis univariate
In this section the statistical analysis is described. First the measures of central tendency and measures of dispersion are shown in table 1 per variable.

Table 1. Descriptive statistics for GPVS1 and CPI score
N Min. Max. Mean Median Mode Range ?? Q1 Q3 Int. Q
GPVS1 115 7.45 100.00 45.82 44.77 100.00 92.55 20.32 32.36 56.00 23.64
CPI score 115 8.00 91.00 46.61 42.00 38.00 N/A 20.56 30.00 61.00 31.00
The population (N) for this research is 115. The (arithmetic) mean shows that the average for GPVS1 is 45.82%, and 46.61 for the CPI score. The median is the exact middle, 44.77 for GPVS1 and 42 for CPI. In case of the GPVS1 the median is slightly lower than the mean. The mode, the value which occurs most frequently, for GPVS1 is 100, which means that the largest government party has all the seats in parliament, and the score of 38 is the mode for the index of the CPI.

The measures of dispersion:
1. Range is the difference between the minimum and the maximum value. This is only useful for the interval/ratio variable, in this case 92.55 for GPVS1.
2. Standard deviation (??) shows the average amount of variation around the mean. The standard deviation for GPVS1 is 20.32 and for CPI score it is 20.56.

Data analysis bivariate
GPVS1 is the independent variable and the CPI score represents the dependent variable. The hypothesis for this paper is that GPVS1 has impact on the CPI score. For this data analysis the Spearman correlation test should be used as the variables are on ordinal and interval/ratio scales (Bryman, 2012).

Table 2: Spearman’s correlation on GPVS1 and CPI score
Variable CPI Score
GPVS1 Spearman’s
rho (??) -0.27443**
Significance 0.00
N 115

** Correlation is significant corresponding to the 1% levels of significance
Table 2 shows the results of the Spearman correlation test. The outcome is -0.27443, which is statistically significant at the .01 level, the highest level of significance. Therefore, the two variables are (weakly) related to each other. The likelihood that this relation is coincidental is negligible.
Below a contingency table and a scatter plot are presented in order to provide some insight in the results.

Table 3: GPVS1 versus CPI score
Variables CPI score 2013 (1 low ‘ 100 high) Total (N)
GPVS1 Low Medium High
1 16 38 20 74
2 11 14 4 29
3 7 5 – 12
Total (N) 34 57 24 115

The cross-tabulation in table 3 shows the two variables and how many countries are categorized into a specific group. The CPI score is divided into three categories; low, medium, and high. The first contains all the scores from 1-33, second 34-66 and third 67 ‘ 99. GPVS1 is categorized into 0-50% of the votes, 51-75% and the third option 76-100%. Chosen for this categorization is because a government has the majority of votes above 50%. The threshold of 75% is chosen because in most countries the government have needs a majority of two thirds of the votes for certain decision (resulting in a higher power concentration for the government).
Based on the table above and the graph below can be seen that the countries with a GPVS1 of 75% the CPI is never above 66. The higher GPVS1 the lower the CPI scores but also fewer cases. The other way around, below a GPVS1 of 40% there are hardly any countries with a CPI score lower than 30.
Figure 2: GPVS1 versus CPI score

Limitations of the study
As most studies, this research has some inaccuracies that should be noticed. First the limitations related to the used data sources/used variables. For this research the CPI score is used as a proxy for the corruption level of a country. It is questionable whether CPI is a proper measure of the corruption level in a country. The CPI is based on the perception of a small group of respondents, so rather subjective (potentially impacting the stability of this research as well). However, it is still the key measure of the corruption level across countries (Paulus and Kristoufek, 2014). Moreover, it is also not sure whether the vote share of the largest government party is a proper proxy for measuring the power concentration (of the largest government party) and the lack of political competition. Therefore the construct validity of this research is rather limited.

Another limitation is that the moment of data collecting is different; the GPVS1 provides an overview of the situation during a period between 2007 and 2012, while the CPI is measured in 2013. However the advantage of the difference in period is that the independent variable, the largest government party, could have had impact on the dependent variable the CPI score.

Conclusion
The main question of this study is; in what degree does the concentration of power at the largest government party affects the level of corruption? The literature review shows that corruption is difficult to measure. The analysis of Montinola and Jackman (2002) confirms that (a lack of) political competition affects level of corruption, but this effect is nonlinear.
The analysis of this research results in a (weak) negative correlation between the two variables. In other words: if the vote share of the largest government party is high(er) (in this research a proxy for the concentration of power), it is likely that the CPI score (proxy for level of corruption) for the country is low. Even though the relationship is significant and a correlation present – in only a very few cases the vote share of the largest government party is high and the CPI score is low’ it is likely that this relationship is spurious. Since other variables could be involved that could affect the dependent variable. This should be researched in a follow-up study.’
References
1. Bryman, A. (2012) Social Research Methods, 4th edition. Oxford: Oxford University Press
2. Denhardt, R.B. (2004) Theories of Public Organization, 4thedition, pp. 20-28. Belmont: Thomson Wadsworth
3. Eiras, A.I. (2003) Ethics, Corruption, and Economic Freedom
4. Gelman, A., and Stanig, P. (2013) CPI 2013 Global dataset: http://www.transparency.org/cpi2013/results
5. Howlett, M., Ramesh, M. and Perl, A. (2009) Studying Public Policy. Policy Cycles and Policy Subsystems, 3rd edition pp. 31-37. Oxford: Oxford University Press
6. Mbaku, J.M. (2008) Corruption Cleanups in Africa: Lessons from Public Choice Theory. Journal of Asian and African Studies, Vol 43 (4), pp. 427’456. Weber State University, USA
7. McLean, I. (1987) Public Choice: An introduction. Oxford: Blackwell
8. Montinola, G.R., and Jackman, R.W. (2002). Sources of Corruption: A Cross-Country Study. British Journal of Political Science, 2002, Vol.32 (1), pp.147-170.
Cambridge University Press
9. Mueller, D.C. (1989) Public Choice II: A Revised Edition of Public Choice. Cambridge: Cambridge University Press.
10. Nye, J.S. (1967) ‘Corruption and Political Development: A Cost’Benefit Analysis’, American Political Science Review 61(2): 417’27
11. Paulus, M and Kristoufek, L. (2014). Worldwide clustering of the corruption perception, Physica A 428 (2015) pp 351’358. Elsevier
12. Rasmusen, E. and Ramseyer, J.M. (1994), ‘Cheap Bribes and the Corruption Ban: A Coordination Game Among Rational Legislators’, Public Choice, 78, pp. 305-27.
13. Rose-Ackerman, S. (1978) Corruption: A Study in Political Economy. New York: Academic Press
14. Rose-Ackerman, S. (1999) Corruption and Government: Causes, Consequences and Reform, Cambridge: Cambridge University Press
15. Rose’Ackerman, S. (2008) Corruption and Government. International Peacekeeping, Vol.15, No.3, June 2008, pp.328’343. Taylor and Francis
16. Teorell, J., Dahlberg S., Holmberg, S. et al. (2015). The Quality of Government Standard Dataset, version Jan15. University of Gothenburg: The Quality of Government Institute, http://www.qog.pol.gu.se.
17. Todaro, M. and S. Smith: 2003, Economic Development, 8th edition p. 711. Addison Wesley, New York, NY
18. Van Winden, F.A.A.M (1988) ‘The Economic Theory of Political Decision-Making’, in J. van den Broeck, ed., Public Choice. Dordrecht: Kluwer

Websites:
http://global.oup.com/uk/orc/sociology/brymansrm4e/01student/excel_tutorials/
http://www.transparency.org/cpi2011/in_detail#myAnchor3
http://www.u4.no/glossary/
http://www.heritage.org/research/lecture/ethics-corruption-and-economic-freedom. Lecture #813 on Trade and Economic Freedom


Appendix 1
To conduct the Corruption Perceptions Index 2013 the following 13 data sources were used:
1. African Development Bank Governance Ratings 2012
2. Bertelsmann Foundation Sustainable Governance Indicators 2014
3. Bertelsmann Foundation Transformation Index 2014
4. Economist Intelligence Unit Country Risk Ratings
5. Freedom House Nations in Transit 2013
6. Global Insight Country Risk Ratings
7. IMD World Competitiveness Yearbook 2013
8. Political and Economic Risk Consultancy Asian Intelligence 2013
9. Political Risk Services International Country Risk Guide
10. Transparency International Bribe Payers Survey 2011
11. World Bank – Country Policy and Institutional Assessment 2012
12. World Economic Forum Executive Opinion Survey (EOS) 2013
13. World Justice Project Rule of Law Index 2013

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