An Examination of the Effects of Corruption on Financial Market Volatility

Abstract

Corruption has a profound impact on many aspects of a society, such as, productivity growth, foreign direct investment or income equality. We propose that corruption also has an impact on stability of financial markets. In this study, we demonstrate a strong correlation between corruption and financial market stability and compare corruption with other social and economic factors in terms of their correlations with financial market stability. The data used in this study include financial data of 29 countries’ index funds for the last five years (2002–07), the Corruption Perception Index 2007 and the Index of Economic Freedom 2007.

JEL Classification: D73, G15, G32

Keywords

Corruption volatility stock market Corruption Perception Index

Introduction

A recent phenomenon is that developing countries increasingly open their financial markets to the world and developed countries increasingly pour financial investments into emerging markets (Bowers et al. 2003). It was estimated that emerging markets’ investment banking revenue would soar to $90 – $115 billion by 2010 (Hutchinson 2008). Emerging markets are perceived to bear stronger influences from political factors than mature markets. Bremmer defined an emerging market as a country where politics matters at least as much as economics (Bremmer 2005: 52). Maxfield has studied how stock markets fulfil different economic and political purposes in emerging markets (Maxfield 2004). This study is going to focus on corruption as an influential factor of financial markets and compare its effect with the effects of other social and economical factors.

Corruption, as a cultural and political phenomenon, has attracted attention from industry, academia and government. Political scientists examined corruption in relation with political developments (Werner 1983). Culture studies investigated gift giving as corruption in the Western world (>Soon 2006) and as a social norm in Chinese society (Sun 2001). Some economists saw corruption as being detrimental to economic growth and associated corruption with slow productivity growth (Husted 2005; >Soon 2006) and low level of foreign direct investment (Zhao et al. 2003). Other economists considered corruption as a form of cooperation which ‘greases the wheels’ of commerce (Argandoña 2005; Leff 1964). Since corruption has such a profound impact on many aspects of a society and developing countries are especially prone to corruption (Budima 2006), a corruption study is more relevant at the time of exploring emerging markets.

In this study, we propose that corruption has a strong correlation with market stability. We provide two data analyses to support our proposal. The data used in the analyses are the financial data of 29 countries’ index funds for the past five years (2002–07), the Corruption Perception Index 2007 and the Index of Economic Freedom 2007. The rest of the article covers theoretical foundation and constructs, the research hypotheses, an explanation of variable measurements, the statistical analyses, and implications and discussions.

Theoretical Foundation and Constructs

Transaction governance structure is defined as the structure that mediates exchanges of goods or services. Transaction cost theory classifies transaction governance structure into market and firm (Coase 1937; Williamson 1979). According to classic economics, a market operates through the ‘invisible hand of the price mechanism’ (Smith 1776). Coase first suggested ‘costs of using the price mechanism’ and the concept of transaction costs (Coase 1937). According to transaction cost economics, market is not friction-free and has transaction costs due to opportunism in the market and limitations of decision-makers in solving complex problems (Williamson 1979, 1981). Market transaction costs consist of (a) costs of searching product quantity, quality and price; (b) costs of negotiating and drawing up contracts; and (c) costs of enforcing contracts including taking legal actions (Besanko et al. 1996). Firm transaction costs are based on agency theory where costs are associated with controlling, monitoring and coordinating agents’ activities within a corporate hierarchy. The selection of a transaction governance structure is based on comparisons between market and firm transaction costs (Williamson 1979, 1981). Since market has already been chosen as the transaction governance structure for stock exchanges, the current study will address market transaction cost due to opportunism.

A majority of corruption studies did not provide any theoretical foundations and simply correlated corruption with various social and economical phenomena. For instance, corruption was associated with productivity growth (Husted 2005; Soon 2006), the level of government spending (Guetat 2006) or individualism in a society (Sanyal 2005). This study proposes transaction cost economics (TCE) as the theoretical foundation. The current study maps opportunism–transaction costs’ relation onto corruption–volatility relation. Figure 1 shows the theoretical mapping.

Figure 1.

Source: Developed by the author.

To suggest that transaction cost theory is the underlying theory for the current study or the current study reconfirms transaction cost theory, we have to establish the relations projected through the magnifying glass in Figure 1. In other words, if opportunism and corruption have similar properties, and if transaction costs and volatility have similar properties, then transaction cost theory can be considered as the underlying theory. Next, we will address this issue by discussing the similarities between volatility and transaction costs, and by identifying the common construct domain shared by corruption and opportunism.

Volatility and Transaction Costs

Black and Scholes (1973) first introduced the concept of volatility in their option pricing model which uses six variables to predict option price. One of the six variables is volatility. Under normal circumstances, the option price is a monotonic increasing function of volatility. Volatility is a basis of many other stock valuation models, such as the capital asset pricing model which determines appropriate rates of return and is used to value securities (Treynor 1962).

Volatility is the most popular proxy for risk. Investopedia, the largest investing education site, explains volatility as the amount of uncertainty or risk about the size of changes in a security’s value. Volatility has been accepted by academics and practitioners as a measurement of market stability and as a representation of risk.

Similar to volatility which is factored into stock prices, transaction costs are factored into product prices. For example, transaction costs of purchasing a house include realtor commission for disseminating and gathering sales information and handling communications between a buyer and a seller, costs of house inspection and title search against opportunism of misrepresenting or withholding information, closing cost for drawing a contract and conducting legal procedures. These transaction costs are included in the total cost of purchasing the house.

According to TCE, the attributes of a transaction determine the level of transaction costs. The attributes of a transaction are asset specificity, frequency and uncertainty (Williamson 1979, 1981). Asset specificity is defined as the degree to which an asset can be redeployed to alternative uses without losing its productive value (Williamson 1981). High asset specificity, high frequency and high uncertainty induce high market transaction costs. For example, industrial machineries have relatively high transaction costs compared to consumer products because industrial machineries are made for special applications and are difficult to be redeployed for other usages. Similar to volatility which correlates with risk, level of transaction costs correlates with the degree of asset specificity and uncertainty.

In summary, transaction costs and volatility have similar properties. Both transaction costs and volatility vary with degree of risk, and both of them are factored into final prices.

Opportunism and Corruption

In order to confirm the relation between corruption and opportunism, some questions have to be answered. Are there any similarities between the two constructs? Can corruption adequately represent the domain of opportunism? To answer these questions, let us compare the definitions of the two constructs. Opportunism is defined as ‘self-interest seeking with guile’ (Williamson 1985: 47). The definition of corruption adopted by the majority of literature is ‘the misuse of public office for private gain’ (Jain 2001: 73; Treisman 2000: 399). The World Bank defines corruption as the offering, giving, receiving or soliciting of anything of value to influence the action of an official in the procurement or selection process or in contract execution (World Bank 2008).

Similar to self-interest seeking, corruption is perceived as rent-seeking activity by many researchers (Fan 2002; Guo and Hu 2004; Lambsdorff 2002; Rose-Ackerman 1999). In rent-seeking, corruption parties pursue self-interests at the expense of public interests, to gain preferential treatment, or to escape the market supply–demand mechanism by influencing policies to their advantage.

Guile is defined as insidiously cunning in attaining a goal, crafty or artful deception, duplicity (Random House 2005). Similar to guile, in opportunism, ‘the person corrupting and the person being corrupted had to have agreed to do something illegal. Only in the case of such a conspiracy was bribery assumed to have taken place. Often the corrupters and the corruptees act according to double standards. They know quite well that the public does not approve of their actions. That is why they keep them secret’ (Alemann 2004: 30).

Both corruption and opportunism are transaction related. Their effects are magnified in the context of deal making, either business deals or political deals. In fact, Alemann defined corruption as an exchange process.

Corruption is always an exchange process between two or more persons (or groups organized into two or more parties). The person who corrupts is in possession of economic goods or resources that are scarce; the person who is to be corrupted possesses power in its broadest sense—power which was transferred to him or her by a defined public body to be used for the common good and according to fixed rules. The person who corrupts wants to get a concession or a contract or wants to avoid a punishment. He or she therefore bribes the person to be corrupted, i.e. the person who has got the power to issue concessions or decide otherwise. (Alemann 2004: 29)

There are differences between corruption and opportunism. Corruption is a violation of law, while opportunism might be a fair game of the market (MacNeil 1981). The distinction between corruption and opportunism is often blurred when it comes to whether a law is violated or how a law is interpreted. A violation of law in one market might be viewed as fair game in another. For instance, in 17 out of 25 bribery settings, Singaporean participants believed that corruptions were committed at a greater degree than Chinese participants believed (Lim 2001). Although the concept of corruption is accepted worldwide, but the interpretations of the law are different. For instance, the party offering and the party receiving bribes are equally guilty of corruption according to the definition of the World Bank. However, it may not be interpreted as such in some countries. In China, punishment for corruption is normally given to the officials who receive the bribes, not to the persons who offer bribes (Levy 2002). In 2007, an official in charge of the drug approvals at China State Food and Drug Administration was sentenced to death for accepting over $300,000 in bribes from two pharmaceutical companies. ‘The government has said little about prosecuting the companies and officials who paid the bribes’ (Barboza 2007).

Opportunism applies to various kinds of participant relations (private-to-private, private-to-public or public-to-public). The terms used for defining corruption, ‘the action of an official’ and ‘the misuse of public office’, indicate that at least one participant of corruption has to be a public agent. Public corruption has been widely studied. Private corruption, by contrast, has been relatively neglected. Recently, Argandoña introduced private-to-private corruption as

the type of corruption that occurs when a manager or employee exercises a certain power or influence over the performance of a function, task or responsibility within a private organisation or corporation…thus in a way that directly or indirectly harms the company or organisation, for his own benefit or for that of another person, company or organisation. (Argandoña 2003: 255)

Private-to-private corruption, such as insider trading and accounting fraud of Enron, can cause a significant turbulence in a financial market (‘Enron Woes Pummel Stocks’ 2001).

As corruption expands into private-to-private transactions and as transaction context embraces different markets where the law is interpreted differently, the distinction between corruption and opportunism is diminishing. Let us assume that violation of law and public agent’s participation are additional features above and beyond the features of opportunism. It is still safe to argue that corruption is a special or a severe case of opportunism. Corruption shares the similar construct do-main with opportunism.

Based on transaction cost theory, our first set of hypotheses is as follows:

MODEL C: Volatility_i = β₀ + ε_i

MODEL A: Volatility_i = β₀ + β ₁CPI_i + ε_i

CPI is the Corruption Perception Index score of a country.

Null Hypothesis 1: β ₁ = 0. Corruption is not associated with financial market volatility.

Alternative Hypothesis 1: β ₁ ≠ 0. Corruption is associated with financial market volatility.

Other Social and Economic Factors

Financial market is shaped and influenced by many social and economic factors. Next, we investigate the associations of 10 social and economic factors with financial market stability. Corruption is one of these 10 factors. Among the 10 factors, we try to identify the factor that has the strongest association with financial market stability. The definitions of the 10 factors and their components are as follows (Kane et al. 2007):

Business freedom is the ability to create, operate and close an enterprise quickly and easily. Burdensome and redundant regulatory rules are the most harmful barriers to business freedom.

Trade freedom is a composite measure of the absence of tariff and non-tariff barriers that affect imports and exports of goods and services.

Fiscal freedom is a measure of the burden of government from the revenue side. It includes both the tax burden in terms of the top tax rate on income (individual and corporate separately) and the overall amount of tax revenue as a portion of gross domestic product.

Government size is defined to include all government expenditures, including consumption and transfers. Ideally, the state will provide only true public goods, with an absolute minimum of expenditure.

Monetary freedom measures the government’s ability to manage the supply of money in the economy. The average inflation rate was the main criterion for this factor.

Investment freedom is an assessment of the free flow of capital, especially foreign capital.

Financial freedom is a measure of banking security as well as independence from government control. State ownership of banks and other financial institutions, such as insurer and capital markets, are inefficient burdens.

Property right is an assessment of the ability of individuals to accumulate private property, secured by clear laws that are fully enforced by the state.

Freedom from corruption is based on quantitative data that assess the perception of corruption in the business environment, including levels of governmental legal, judicial and administrative corruption.

Labour freedom is a composite measure of the ability of workers and businesses to interact without restriction by the state.

The 10 factors are abbreviated as B, T, F, G, M, I, FIN, P, C and L in the regression model, respectively. To examine the associations of these factors with market volatility, we propose the second set of hypotheses as follows:

MODEL C: Volatility_i = β ₀ + δ_i

MODEL A: Volatility_i = β ₀ + β ₁B_i + β ₂T_i + β ₃F_i + β ₄G_i + β ₅M_i + β ₆I_i + β₇ FIN_i + β ₈P_i + β ₉C_i + β ₁₀L_i + δ_i

Null Hypothesis 2: β ₁ = β ₂ = β ₃ = β ₄ = β ₅ = β ₆ = β₇ = β ₈ = β ₉ = β ₁₀ = 0. All 10 factors have no association with financial market volatility.

Alternative Hypothesis 2: Not all β = 0. Some of the 10 factors have associations with financial market volatility.

Measurements and Data Collection

Unit of Analysis and Sample Size

The unit of analysis is ‘country’ for this study. Thus, the corruption index was measured and the volatility was calculated at country level. Our sample of 29 countries was a convenient sample. The data of 29 indexes were obtained from Yahoo financial database.

Table 1 illustrates the coverage of our study. Table 1 lists 31 stock exchanges and their domestic market capitalisation. These stock exchanges are members of the World Federation of Exchanges (WFE). The total domestic market capitalisation of these 53 exchanges is considered as world market capitalisation (Reuters 2007). The 29 countries of our study have total domestic market capitalisation of $54,371, 029 million, which is 87 per cent of the world domestic market capitalisation ($62,279,736.60 million). To be conservative in calculating the coverage (87 per cent), we did not consider NYSE Euronext and NASDAQ OMX Nordic Exchange to be included in our study. The reason is that some countries listed on these two exchanges, such as Belgium, France, Netherlands and Sweden, are covered by our study and some are not. To be clear and precise, there is a slight difference between indexes used in this study and domestic market capitalisation. These indexes include countries’ domestic stocks only. Domestic market capitalisation includes stocks of domestic companies as well as stocks of foreign companies exclusively listed on an exchange. Nevertheless, the point is that our sample provides a reasonable coverage of the world financial market. When assessing the size of the population from which our sample was drawn, one needs to consider that many countries do not have stock exchanges. There are only 53 publicly regulated stock exchanges listed under WFE.

Table 1.

Stock Exchanges and Their Domestic Market Capitalisations Covered by the Study (in US$ million)

Name of Stock Exchanges	Country	Domestic Market Capitalisation
NYSE Euronext (US)	US	15,650,832.50
Tokyo SE	Japan	4,330,921.90
NASDAQ OMX	US	4,013,650.30
London SE	United Kingdom	3,851,705.90
Shanghai SE	China	3,694,348.00
Hong Kong Exchanges	Hong Kong	2,654,416.10
TSX Group	Canada	2,186,550.20
Deutsche Börse	Germany	2,105,197.80
Bombay SE	India	1,819,100.50
BME Spanish Exchanges	Spain	1,781,132.70
National Stock Exchange India	India	1,660,096.90
BM&FBOVESPA	Brazil	1,369,711.30
Australian SE	Australia	1,298,315.00
SIX Swiss Exchange	Switzerland	1,271,047.70
Korea Exchange	South Korea	1,122,606.30
Borsa Italiana	Italy	1,072,534.70
Shenzhen SE	China	784,518.60
Taiwan SE Corp.	Taiwan	663,716.00
Singapore Exchange	Singapore	539,176.60
Mexican Exchange	Mexico	397,724.60
Oslo Børs	Norway	353,353.10
Bursa Malaysia	Malaysia	325,290.30
American SE	US	257,797.00
Tel Aviv SE	Israel	235,056.40
Osaka SE	Japan	212,147.40
Indonesia SE	Indonesia	211,693.00
Irish SE	United Kingdom	143,905.30
Egyptian Exchange	Egypt	139,273.80
Jasdaq	US	120,653.80
Buenos Aires SE	Argentina	57,070.20
New Zealand Exchange	New Zealand	47,485.60
The total domestic market capitalisation covered by the above exchanges is 87.3% of world domestic market capitalisation		54,371,029.50
The total domestic market capitalisation not included in our study is 12.7% of world domestic market capitalisation		7,908,707.10
World domestic market capitalisation		62,279,736.60

Source: Statistics section of the World Federation of Exchanges (www.world-exchanges.org).

Measurement and Calculation of Volatility

In our study, volatility is the dependent variable measuring market stability. Daily adjusted close prices of 29 countries’ index funds for the last five years (2002–07) were extracted from the Yahoo finance database. Volatility was calculated using price changes expressed in logarithmic form according to the formula (1). In the formula, u_i = ln(S_i/S_i_-1), S_i is the index price at the end of the ith day, and 252 is the number of trading days in a year (Fontanills and Gentile 2003).

V o l a t i l i t y = \sqrt{\frac{1}{n - 1} \sum_{i = 1}^{n} {(u_{i} - \bar{u})}^{2}} * \sqrt{252}

(1)

Corruption

The Corruption Perception Index (CPI) published by Transparency International (Transparency International 2007) is used as the independent variable to test the first set of hypotheses. CPI is a composite index based on 12 different surveys which are conducted by 9 independent institutions, such as the World Bank, the United Nations and the Freedom House. The countries included in CPI must have scores from at least three independent sources. CPI measures perceived levels of corruption which exist among public officials and politicians. It draws on perceptions of those who directly confront with the realities of corruption. The survey questions are related to the misuse of public power for private benefit, bribery of public officials, kickbacks in public procurement and embezzlement of public funds. The scores of CPI range from 0 to 10 with 0 indicating highest level of perceived corruption and 10 indicating lowest level of perceived corruption.

Social and Economic Factors

The Index of Economic Freedom (IEF) measures the 10 factors discussed in the previous section. IEF is used to test the second set of hypotheses. IEF has been published by the Wall Street Journal and the Heritage Foundation since 1995. Each of the 10 factors in IEF has a scale of 0 to 100. Zero indicates lowest level and 100 indicates highest level. IEF 2007 was downloaded from the Heritage Foundation’s website.¹

CPI published by Transparency International and IEF published by the Wall Street Journal and The Heritage Foundation are two independent indexes. They are designed and administrated independently. CPI is a composite index measuring perceived levels of corruption. IEF measures 10 factors and one of the 10 factors measures levels of corruption. Thus, we have two measurements of corruption, one from CPI and one from IEF. Although the two measurements of corruption are independent, they are highly correlated with a correlation of 0.99. The high correlation indicates the construct convergent validity, that is, measures of constructs that theoretically should be related to each other are, in fact, observed to be related to each other. This study uses CPI in the first analysis and IEF in the second analysis.

There are two reasons to collect 2007 data instead of longitudinal data. First, CPI index covered fewer countries in earlier years than in later years. The coverage gradually increased from 41 countries in 1995 to 179 countries in 2007. Second, fluctuations of CPI scores are rather random and there are no trends over time. Figure 2 shows CPI scores in high, medium and low ranges represented by Finland, the United States and Argentina, respectively.

Figure 2.

Source: Developed by the author.

Analysis

Volatility and Corruption

MODEL A: Volatility_i = β ₀ + β ₁CPI_i+ ε_i

Tables 2 and 3 show the statistical result of regression of Volatility on CPI. Regression R is 0.626. The coefficient of CPI (β ₁) is −0.012 with t = −4.17 and p < 0.05. The negative coefficient indicates that CPI is negatively correlated with Volatility. When CPI is low, which means high corruption, Volatility is high. This statistic rejects null hypothesis 1 and supports alternative hypothesis 1, that is, the level of corruption is associated with financial market volatility.

Table 2.

ANOVA

	Sum of Squares	df	Mean Square	F	Prob. > F
Regression	0.021	1	0.021	17.392	0.000
Residual	0.033	27	0.001
Total	0.054	28

Source: Developed by the author.

Note: Predictors (Constant), CPI; Dependent Variable: Volatility.

Table 3.

Coefficients of 1-Variable Model

Model	B	Std. Error	Beta	t	p-Value
Constant	0.269	0.019		13.84	0.000
CPI	−0.012	0.003	−0.626	−4.17	0.000

Source: Developed by the author.

Note: Dependent Variable: Volatility.

Volatility with Ten Social and Economic Factors including Corruption

MODEL A: Volatility_i = β₀ + β ₁B_i + β ₂T_i + β ₃F_i + β ₄G_i + β ₅M_i + β ₆I_i + β ₇FIN_i + β ₈P_i + β ₉C_i + β ₁₀L_i + ε_i

Backward stepwise regression is used on the data consisting volatility as the dependent variable and 10 factors from IEF as the independent variables. The backward stepwise regression starts with all 10 variables included in the model. In each step, all variables in the model are tested for their statistical significance and the least significant variable is removed from the model. The criterion for removing a variable is set at 0.10. The step iteration continues until all non-significant variables are removed from the model. Eight of 10 factors are removed in the sequence of Government Size, Property Rights, Business Freedom, Financial Freedom, Monetary Freedom, Trade Freedom, Fiscal Freedom and Investment Freedom. The two variables remained in the model are Corruption and Labour Freedom. Table 4 shows the final 2-variable model.

Based on this statistic, we can reject null hypothesis 2 and support alternative hypothesis 2, that is, some of the 10 factors (Corruption and Labour Freedom) have associations with financial market volatility. Corruption and Labour Freedom are statistically significant enough to be included in the model with p-value of 0.001 and 0.006, respectively. Between Corruption and Labour Freedom, which factor has greater predictive power on volatility? By comparing the standardised regression coefficients, −0.502 for Corruption and −0.410 for Labour Freedom, we can draw a statistical conclusion that Corruption has a stronger predictive power on Volatility because standardised coefficient of Corruption has a larger absolute value.

Table 4.

Coefficients of 2-Variable Model

	Unstandardised Coefficients		Standardised Coefficients
Model	B	Std. Error	Beta	t	p-Value
(Constant)	0.332	0.027	12.14	0.000
Corruption	−0.001	0.000	−0.502	−3.63	0.001
Labour	−0.001	0.000	−0.410	−2.97	0.006

Source: Developed by the author.

Note: Dependent Variable: Volatility.

The inclusion of Labour in the final 2-variable model does not necessarily indicate that Labour Freedom has more predictive power than the other eight variables eliminated during the stepwise regression. Table 5 shows that Corruption, Business Freedom and Labour Freedom correlate with Volatility at −0.64, −0.63 and −0.57, respectively. Although Business Freedom has a higher correlation with Volatility, Business Freedom is the third variable removed from the model. The inclusion of Corruption in the model reduces the predictive power of Business Freedom due to multicollinearity. Multicollinearity also exists between Corruption and Property Rights.

Table 5.

Pearson Correlation (1-Tailed Signification)

	V	B	T	F	G	M	I	FIN	P	C
B	−0.63
	(0.00)
T	−0.36	0.61
	(0.03)	(0.00)
F	0.10	−0.31	−0.17
	(0.30)	(0.05)	(0.20)
G	0.19	−0.41	−0.25	0.87
	(0.16)	(0.014)	(0.09)	(0.00)
M	−0.47	0.73	0.60	−0.16	−0.37
	(0.01)	(0.00)	(0.00)	(0.21)	(0.02
I	−0.23	0.63	0.46	−0.31	−0.38	0.56
	(0.12)	(0.00)	(0.01	(0.05)	(0.02)	(0.00)
FIN	−0.57	0.74	0.50	−0.18	−0.23	0.50	0.69
	(0.00)	(0.00)	(0.00)	(0.17)	(0.12)	(0.00)	(0.00)
P	−0.56	0.85	0.54	−0.33	−0.47	0.68	0.76	0.79
	(0.00)	(0.00)	(0.00)	(0.04)	(0.01)	(0.00)	(0.00)	(0.00)
C	−0.64	0.87	0.65	−0.37	−0.49	0.73	0.68	0.77	0.95
	(0.00)	(0.00)	(0.00)	(0.02)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
L	−0.57	0.38	0.35	0.52	0.37	0.26	0.09	0.45	0.27	0.32
	(0.00)	(0.02)	(0.03)	(0.00)	(0.02)	(0.09)	(0.33)	(0.01)	(0.08)	(0.04)

Source: Developed by the author.

Notes: V: Volatility, B: Business, T: Trade, F: Fiscal, G: Government Size, M: Monetary, I: Investment, FIN: Financial, P: Property Rights, C: Corruption, L: Labour.

Implications and Discussions

Emerging Market Investment

As emerging markets generate greater shares of global supply and demand, investors need better inputs to weigh political risk against financial reward. How to assess the risks of emerging markets has become a challenge for financial investors. The lack of track records and price histories in emerging markets posts a problem for stock pricing (Higgins 1996). The strong correlation between corruption and market volatility demonstrated in this study provides an alternative for financial investors to assess markets or stocks. Transparency International has been publishing the CPI to track corruption levels since 1995. The coverage of the CPI has increased from 41 countries to 179 countries. The availability of the CPI can compensate for the lack of track records and price histories.

Financial Regulation Reform of the United States

After the Great Depression, the United States government installed regulations to control market volatility. As a result, the financial sector has become the most regulated industry. Since the mid-1960s, especially since the saving and loan crisis, the pendulum of regulation has swung towards deregulation (Gerardi et al. 2006). Numerous deregulations such as the Depository Institutions Deregulation and Monetary Control Act of 1980, the Federal Deposit Insurance Improvement Act of 1991, the Riegle-Neal Act of 1994 and the Gramm-Leach-Bailey Act of 1999 fostered the development of the secondary market for mortgages and disintegrated the financial market. From October 2007 to March 2009, world market capitalisation dropped 59 per cent due to the subprime crisis which originated from the United States. The crisis generated debates on regulating Wall Street (Lind 2008).

The corruption level of the United States ranks 20th among 179 countries and is slightly above the countries such as Belgium and Chile (Table 6). As a world leader, the United States has potential to improve its rank on CPI, and consequently improve its financial market stability. Since the United States accounts for 30.64 per cent of world market capitalisation (Table 7), a lowering of corruption level in the United States will have immense impact on the world financial markets. The magnitude of the impact will be greater than any other countries can deliver. Will Obama’s regulation focus on anti-corruption or focus on government control? Is big government policy of the current administration going to increase transparency and reduce corruption? President Obama has announced that his financial reform plan would be ‘a sweeping overhaul of the financial regulatory system, a transformation on a scale not seen since the reforms that followed the Great Depression’ (The White House 2009). The US financial industry is giving its best lobbying effort against the regulation. Some financial firms were using bailout money obtained from the government to wage wars against the regulation (Kirkpatrick et al. 2009).

Table 6.

2007 CPI of the Top 30 Countries

Rank	Country	Score	Rank	Country	Score
1	Denmark	9.4	16	Germany	7.8
1	Finland	9.4	17	Ireland	7.5
1	New Zealand	9.4	17	Japan	7.5
4	Singapore	9.3	19	France	7.3
4	Sweden	9.3	20	USA	7.2
6	Iceland	9.2	21	Belgium	7.1
7	Netherlands	9.0	22	Chile	7.0
7	Switzerland	9.0	23	Barbados	6.9
9	Canada	8.7	24	Saint Lucia	6.8
9	Norway	8.7	25	Spain	6.7
11	Australia	8.6	25	Uruguay	6.7
12	Luxembourg	8.4	27	Slovenia	6.6
12	United Kingdom	8.4	28	Estonia	6.5
14	Hong Kong	8.3	28	Portugal	6.5
15	Austria	8.1	30	Israel	6.1

Source: Transparency International (www.transparency.org).

Table 7.

Market Capitalisation of the Top Four Countries

Country	% of World Market Capitalisation
Unite States	30.64
Japan	8.70
China	7.64
United Kingdom	6.38

Source: Bespoke Investment Group (http://bespokeinvest.typepad.com).

International Policy

Regarding foreign policies, one may ask, ‘What is the best way to assist developing countries?’ Given that corruption has a stronger correlation with market volatility than any other social and economical factors, fighting corruption and increasing transparency ought to be the most effective way to assist developing countries. Incentive to fight corruption can be the best financial aid that advanced countries can offer to the developing countries.

The study of Alesina and Weder (2002) indicated that Scandinavian and Australian governments gave more aid to the nations with lower levels of corruption. The United States, a country that favours democracies over dictatorships, gave more aid to the countries with higher levels of corruption. Alesina and Weder’s study also provided tentative evidence that an increase in monetary aid increases corruption. Despite billions of dollars of foreign aid that went into developing countries, there is little to show in economic development (Kane et al. 2007).

Footnotes

Note

References

Alemann

(2004), ‘The Unknown Depths of Political Theory: The Case for a Multidimensional Concept of Corruption’, Crime, Law and Social Change, 42(1): 25–34.

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