Hrishikesh D. Vinod,

Professor of Economics and Director of the Institute of Ethics and Economic Policy, Fordham University, Bronx, NY 10458, E-Mail:




We discuss why corruption remains high and show that corruption contributes to the Banking distress and to the rapid transmission across international stock and currency markets. Undeveloped ‘derivative securities’ markets make the risk from stress-induced volatility difficult to manage. Vinod’s (1999) closed economy model is extended to include the ‘international diversification puzzle’ and the effect of corruption on the value at risk (VaR). Our theory predicts that foreign direct investment (FDI) will be low and cost of capital will be high in corrupt countries, which is supported by Asian data.  We include some policy recommendations regarding financial institutions and markets.


JEL Classification: F30, H82, O19

Key Words:  Asian crisis, Value at Risk, Derivative securities, Diversification, Hedging, Generalized Pareto.



Author’s footnote:

A version of this paper was presented at Ninth Annual Conference on Pacific Basin Finance Economics and Accounting, September 22, 2001, Rutgers University, New Brunswick, New Jersey.







Professor of Economics and Director of the Institute of Ethics and Economic Policy, Fordham University, Bronx, NY 10458, Tel. 718-817-4065

Fax: 718-817-3518,

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Corruption is defined as abuse of public office for private gain and it is often symptomatic of wider governance problems. From Bardhan’s (1997) review, Vinod (1999) and their references it is clear that corruption hurts economic development.  However, typical arguments in this literature are based on closed economy models largely ignoring open economy aspects and both domestic and international financial sectors.  This paper fills the gap. This section covers the financial sector and the next section discusses how corruption leads to some open economy market failures. These failures and inefficient financial markets explain how corruption lowers foreign direct investments (FDI) and increases cost of capital in developing countries.


Although the world of finance may seem complicated, technical, and mysterious, this paper explains how it is hurt by corruption. Transparency International (TI) report released on October 16, 2001 notes that the September 11 attacks in the US clarified the high stakes in money laundering, and that fighting corruption means fighting terrorism.


Let us first note that corruption remains high, especially in developing countries.  Even a cursory review of corruption perception index (CPI) by TI confirms this.  The question is: Why? We suggest that it is because of missing or inadequate consensus against corruption in three specific areas due to conflicts of interest.


(1) Economists define that “middle class consensus” exists in a country when the share of income for the middle class is high and the degree of ethnic polarization is low. Such a consensus is known to favor greater democracy, more health, better infrastructure, and above all, greater success in economic development. What is the role of corruption in such consensus?  In corrupt countries if the income share of the middle class is high, it may be due to bribery, money laundering, tax evasion and corruption. We need greater education to convince the middle class that long run costs of corruption are high. 


(2) In 1999, TI started publishing Bribe Payers Perceptions Index (BPI), which ranks 19 leading exporting countries in terms of the degree to which their corporations are perceived to be paying bribes abroad. Many rich countries such as Japan, which are not corrupt in terms of CPI, are shown to be big bribe payers abroad.  There is no strong consensus in rich countries that they need to open their own markets, especially to products in which the poorest countries have a comparative advantage; to prosecute those who pay bribes abroad; to forego bribery-infested export promotion, which encourages wasteful military buildups in developing countries. 


(3) The Banking privacy laws are used by corrupt entities for money laundering. Since money laundering can be profitable to the Banks, they have little incentive to report them and lose their business.  In July 2000, the G7 organization of seven leading industrialized nations announced a new campaign to deter money laundering. Their Financial Action Task Force (FATF) on money laundering named fifteen countries protecting money launderers.  DJN (2001) notes that about $1 trillion per year is laundered in an increasingly borderless world, aided by criminal development of new and more sophisticated methods for moving money, even as countries develop counter measures.  Exploiting vulnerabilities in the financial system is an area of rising concern as Internet Banking transactions are quick, easy, and anonymous.  Inadequate customer identification and account monitoring procedures allow money laundering.


September 2001 terrorist attacks have brought money laundering to the forefront and is likely to help create a new consensus against this third item, despite the efforts of Bank lobbyists. The missing consensus regarding the first two items arises from a conflict between self-interest and public good.  Hence, the invisible hand of market forces is unlikely to weed out corruption.  The consensus building requires better understanding of how corruption increases the cost of capital, slows economic growth and hurts everyone. We postpone till section 3 our evidence regarding how corruption increases the cost of capital.  The following four subsections discuss various aspects of domestic and international finance and corruption.


1.1 Corruption and Competition in Banking

This subsection briefly discusses the history and importance of competitive financial sector for economic development, the positive role of foreign Banks in reducing corruption and how corruption can hurt this sector. Competitive financial institutions and Banks are important for economic development. Although Banks do not create savings, they help economic growth by efficiently channeling the savings and paying incentives to savers in the form of interest payment.  Moreover, Banks help by record keeping and monitoring investments for all, including international investors. In the absence of corruption, Banks can efficiently allocate savings to the most productive employment and provide help in risk management.  Entry of foreign capital and foreign Banks can be important in promoting efficient financial institutions. Many of these benefits from efficient Banking are unavailable in corruption-ridden countries, especially if they disallow competition in the Banking sector from foreign Banks.


Consider some basic causes of corruption in the context of finance. Primitive societies had reciprocal barter exchanges often based on mutual trust rather than organized markets. The initial roots of corruption are found in the informal barter.  With the advent of modern markets there was no strong need for mutual trust, which was important for barter.  Laws enforced by the government replaced the trust.  Hence, good governance became very important for law enforcement when trust is lost. Some countries made no investment in social capital to build trust in good governance, and corrupt rent seeking became the norm. Ades et al’s (1999) theory suggests that high rents need not always cause corruption. However, an empirical study by these authors finds that high level of rents does indeed lead to high corruption. Also, less competition means bureaucrats extract larger rents from the monopolists, and this too is shown to lead to greater corruption.  Greater competition imposes a better control on bureaucrats than stricter supervision.  In the context of finance, foreign capital and foreign Banks can provide the necessary competitive pressures. In the sequel we use relevant Economics and Finance theory to show why it is useful to encourage foreign direct investments and expansion of sophisticated markets in futures and derivative securities to better manage all risks.


There are three ways in which financial institutions can get hurt by corruption. First, attacks on physical assets. We note that March 1993 terrorist attacks on Bombay stock exchange was helped by corrupt customs officials who permitted the import of RDX explosives in India. Also, corruption in the form of money laundering did have a role in the September 2001 terrorist attack on physical assets at the World Trade Center. Second, corruption can attack the goodwill of financial institutions. Svetlana Kudryavtsev accepted $500 a month to monitor money-laundering accounts at the Bank of New York and entered guilty plea for it (NY Times, March 29, 2000). As a result of involvement in Russian money laundering the Bank lost goodwill. Third, stock manipulation and fraud through corruption hurts ordinary investors. For example, Rs 680 crore ONGC corporate funds were credited in the notorious financier Harshad Mehta's account (no 1028 with the UCO Bank, Hanuman Street, Mumbai) by a corrupt official named Deosthali (Hindustan Times, Delhi, February 17, 2001).  Mehta used these funds to defraud Indian investors to the tune of nearly a billion dollars.  Some lost their life savings and their example hurt the confidence in stock markets and Banks.


1.2 Corruption, Distressed Banks and Asian Currency Crisis of Late 1990’s:

In this subsection we discuss the role of corruption in the Asian currency crisis started with the collapse of the Thai Baht in 1997. It was preceded by the Mexican Peso crisis in 1994 and followed by unilateral Russian debt restructuring in 1998. For three decades before 1997, East Asian economies grew remarkably fast due to: (i) low inflation and high investment in human capital, (ii) high savings rate and protection for individual savers, (iii) few price distortions and imbalances between agricultural and industrial sectors and (iv) adoption of foreign technology.  The financial crisis of 1997 was due to some structural problems including: (a) tendency to finance long-term debt with short-term paper due to a lack of well-developed derivatives market, (b) inadequate attention to social safety net for the unemployed and the poor during boom times, (c) excess exploitation of natural resources as forests, fisheries, etc., and (d) poor regulation of domestic capital markets which did not check global push toward a financial bubble. The global capital flows grew too fast (30% annually) and foreign investors’ ignorance of local conditions and greed for high returns were important. The investors expected to be bailed out (moral hazard) if things go wrong.  The Asian contagion was fueled by slowing of export growth followed by competitive devaluations, and by strong trade and financial links among the East Asian countries. In addition, we argue that crony and corrupt capitalism played a critical role in worsening all four structural problems listed above.


Banking distress is defined in terms of: (i) falling growth rates of real GDP, (ii) large fluctuations of inflation rate, credit expansion rate and capital inflow rate, (iii) sharp declines in exchange rates, (iv) adverse shock in international trade, (v) sharp rise in real interest rate, Hardy and Pazarbasioglu (1999).  Vinod (1999) notes that corruption can reduce economic growth and discourage savings. Clearly, reduced supply of savings can lead to higher price in the form of higher interest rates. Corruption infested governmental waste and policies for fighting inflation can lead to large swings in inflation rate and exchange rate. If bribe demands lead to interruption or cancellation of major international trade transactions, they can create trade shocks.  Thus, corruption contributes to each of the five factors causing Banking distress. 


How does a localized Banking distress turn into a financial contagion affecting large and far-flung geographical regions? The answer may be found in routine portfolio rebalancing by investors in response to any loss or distress. We discuss some portfolio theory regarding risk and hedging in Section 2. An adverse shock affecting only one asset (one country) can lead international investors to reduce their investment levels in all risky assets (all neighboring countries) when they rebalance the portfolios. Schinasi and Smith (2000) note that such behavior is particularly strong if the investor is leveraged (uses borrowed funds) and that several traditional portfolio-rebalancing rules, including VaR rules, can induce such behavior. Of course, rebalancing remains a valid and rational response. Chang, and Velasco (1998) attribute the contagion to international Banking illiquidity and self-fulfilling pessimism, both of which are made worse by corruption.


IMF, World Bank and major multinational Banks can help fight the financial contagion before it occurs. They have begun to focus on removing the distress factors including corruption. To promote worldwide prosperity, IMF identified “promoting good governance in all its aspects, including ensuring the rule of law, improving the efficiency and accountability of the public sector, and tackling corruption” (September, 1996). World Bank also has indicated similar initiatives against corruption. More recently, IMF is trying to eliminate the opportunity for bribery, corruption, and fraudulent activity in the management of public resources. IMF directives seek to limit the scope for ad hoc decision making, for rent seeking, and for undesirable preferential treatment of individuals or organizations. However, IMF needs to be more proactive against misuse of official foreign exchange reserves, abuse of power by Bank supervisors, and similar areas where information should be available at the IMF.  However, unless the consensus is developed on two fronts mentioned above, strong action by IMF or World Bank employees against the corrupt cannot be expected. After all, these can be sensitive issues of local laws and personal safety of World Bank/ IMF staff and their families.


1.3 Corruption and Slow Growth of Derivative Markets.

This subsection first notes that that very large amounts of money (A trillion US dollars) transfer between various countries on a daily basis.  These short-term funds are called “hot money” transfers. Since these transfers create new kinds of risks, financial institutions need new tools (e.g., interest swaps) for managing them.  This subsection shows that corruption slows the growth of these tools and thereby hurts developing countries.


 A derivative is defined as a security whose value depends on the price of some other asset.  In modern finance, put and call options, financial futures, warrants and swaps are called derivative securities. For example, consider a manager at a large securities firm where the supply of funds is short term while the demand for funds longer term (or vice versa), which causes a mismatch.  There is also a mismatch in timing of the need for funds.  Before the derivatives were developed, the main sources of funds for solving the mismatch were Bank overnight loans and commercial paper.  The former tend to disappear in panic situations and the latter cannot be easily rolled over. 


Thanks to the derivatives, the solution is simply to float say 10-year paper with fixed rate and swap it into a floating interest rate.  Since this eliminates interest rate risk, the global market for swaps is near five trillion dollars.  Moreover, since the interest swaps have a market value reflecting the interest rate risk, both assets and liabilities move up and down correctly.  While derivatives may contribute to greater volatility of markets, they do create new avenues for people to take risk.  For sophisticated players, they provide an opportunity to keep cost of borrowing low.  Thinness of the market, lack of scale economies, lack of trust in local financial institutions and corruption are among the reasons for inadequate derivative markets in developing countries.  If Mr. Harshad Mehta could bribe Bank officials to defraud thousands of Indian investors in standard stock market, the potential for fraudulent manipulation loom even larger in thin derivative markets. Thus, corruption risk is difficult to manage since corruption itself prevents access to the modern tools of managing this risk by preventing growth of derivative markets. Accordingly, FDI is reduced in corrupt developing countries.


The derivative markets have focused on three different types of risk. (i) Credit risk refers to the ability of the borrower to generate revenue to pay back the debt. (ii) Default risk is with reference to collecting when default occurs. (iii) Transaction risk arises from possible problems with international electronic transfer of funds and enforcement of foreign exchange contracts.  Although corruption risk is currently considered as a part of one of these three, it may be helpful to have a separate category for corruption. La Porta et al (1998) find that common-law countries (US, UK, India) provide superior legal protection to investors compared to French civil law countries.  However, the CPI data used in Vinod (1999) suggests that common-law countries providing good legal protection of investors do not enjoy generally less corruption.  France and her former colonies do not, in general, have any greater corruption than UK and her former colonies.  This supports the notion that corruption imposes a somewhat unique type of risk burden with a distinct probability distribution than for the risk associated with currency fluctuations, taxes and investments.


1.4 Corruption and Private Credit Rating Agencies.

We view private rating agencies, which need to maintain their reputation by making right calls as a positive force against corruption.  Their term “country risk” refers to creditworthiness of sovereign governments who have a large leeway in structuring debts and payment schedules than private businesses. However, global money traders (hot money transfer agents) gather background information from rating agencies (S&P and Moody’s) regarding actual deficits in different countries. Since rating agencies can potentially negate domestic fiscal policies of sovereign countries, policy makers often resent the power of money traders and such rating agencies. On the other hand, this creates a balance of power and forces some fiscal discipline on various countries without involving the IMF.  For example, Thailand took on debt to build super highways and was punished for it by declining Thai Baht. In short, the positive contribution of rating agencies and hot money is that they create a countervailing power and they discourage corruption. 


We must also mention the negative role of hot money transfers when the investors rely on private rating agencies. A trillion dollars going in and out of different countries daily can obviously increase the volatility of markets.  Changes in credit ratings lead to herd behavior by investors, even if the ratings themselves are in response to valid new information.  The herd behavior creates intrinsically self-fulfilling short-run instabilities. When ratings go down, cost of borrowing for that country increases, precisely at the time when a country in trouble can least afford it.  Sometimes, investors lose money even before the country ratings are lowered. If so, they try to sell investments in other similar countries (same geographic region or similarly situated). Investors need only a limited amount of information about impending trouble, not detailed information. This can create a rush to beat the rating agencies. In any case, pessimism is self-fulfilling and can lead to further declines in balance of payments. 


On balance and in the long run, rating agencies play a mildly positive role by inviting market discipline and thereby reducing corruption. If countries do not discipline themselves, world markets do it for them through hot money transfers.  Since markets treats everyone equally, market discipline is politically more bearable than edicts from IMF bureaucrats, who can sometimes make wrong policy recommendations.  Since IMF has little expertise in tax deficits, corruption, and weaknesses of local Banks in all countries, IMF did make some wrong calls in response to the Asian contagion.


The outline of the remaining sections is as follows. Section 2 discusses two results from an open economy theoretical model for studying corruption and comments on the financial burden of corruption. It also outlines the transmission mechanism by which corruption hurts.  Subsection 2.1 focuses on the diversification puzzle and subsection 2.2 focuses on value at risk computation. Section 3 studies empirical data and policy implications.  Section 4 has concluding remarks.




It is useful to briefly mention some results of the closed economy model of corruption before discussing market failures arising in an open economy allowing international trade and finance. Capital accumulation is one of the most important features of economic development.  Vinod (1999) uses the fundamental differential equation of the neoclassical “closed economy” growth model to show that corruption reduces efficiency of capital and exponentially reduces the rate of capital accumulation. Erlich and Lui (1999) discuss corruption in an endogenous growth model. Using marginal excess burden calculation, similar to deadweight loss, in the context of applied general equilibrium model, Vinod (1999) estimated that a dollar reduction in corruption will benefit developing countries at least $1.67. He argued that compounding raises $1.67 even higher in just few years.  Top five actions recommended for reducing corruption in order of importance are: (1) reduce red tape, (2) increase efficiency of the judiciary, (3) increase per capita GNP, (4) increase economic freedom and schooling, and (5) reduce income inequality. Although some had speculated that corruption could improve efficiency by speeding up the bureaucrats, Vinod (1999) refers to recent theory proving the opposite.  In short, the burden of corruption from closed economy models is already shown to be high. The following discussion shows that open economy models, which include trade and international commerce, make the burden even heavier. We present some “results” from the extended theory in subsections 2.1 and 2.2, which should be regarded as supplemental to the earlier closed economy results.  The financial burden of corruption is felt by ordinary citizen and arises from various sources discussed in subsections 1.1 to 1.4 above.


2.1 Corruption and the Market Failure Induced by the Home Bias:

When the presence of international trade is introduced in a traditional study of the tradeoff between risk and return, we note that the tradeoff does not smoothly extend to investments abroad.  There appears to be a market failure, since investors in developed countries overwhelmingly exhibit a so-called “home bias.” This subsection explains various factors causing the home bias or “diversification puzzle,” including corruption.  More importantly, the impact of home bias is asymmetric with respect to rich and poor countries. The underlying incentives encourage flight of capital out of poor and corrupt countries.  Hence policy makers in poor countries impose capital controls to prevent flight of capital, which in turn encourage monopolies and corruption.


French and Poterba (1991) were the first to discuss home bias and estimate its size.  They assume that a representative agent in each country has one of the simplest exponential decay constant-relative-risk-aversion (CRRA) utility functions. It is defined with respect to wealth W as the argument (instead of consumption) as:


(1)           U(W)= -exp(-l W/W0),

where W0 is initial wealth.  The wealth increases by investing in portfolios consisting of possibly risky international assets. Each portfoilio is defined by a vector w of weights.  If the probability distribution of asset returns is Normal, only the mean and variance matter. Denote the mean vector by m and covariance matrix by S. Now, the expected utility is:


(2)           E(U)= -exp (-l{wm - 0.5 lw¢S w}).


Assuming expected utility theory (EUT), maximization of (2) yields a first order condition involving a simple analytic formula. If w* denotes optimal portfolio weights, we have the necessary condition:

(3)               m =l w*¢S


French and Poterba bypass the need for historical data on international equity returns m and compute estimates of S for US, Japan, UK, France, Germany and Canada. They ask what set of “optimal” expected equity returns m* would justify the observed pattern of international holdings.  They find, for example, that UK investors need about 500 basis points higher return in domestic market to justify not investing in US stocks. In general, investors in developed countries hold too high a proportion of their portfolio in domestic securities (94% for US and in excess of 85% for UK and Japan). French and Poterba (1991) argue that within the set of six rich countries, there are few institutional barriers against international investments.  Yet the “puzzle” lies in the empirically observed lack of adequate international diversification.  Baxter and Jermann (1997) argue that the puzzle is “worse than you think.” If we remember that all of our own human capital is concentrated in the home country, hedging would require us to invest even larger proportion of our wealth abroad.


The relevance of hedge component has been known at least since Merton (1973) who shows that the conditional excess market return is a linear function of its conditional variance (the risk component) and its covariance with investment opportunities. We now discuss the hedge component in Merton’s model. Let J(W; F; t) denote the indirect utility function with subscripts denoting partial derivatives, let W denote wealth as before, and let F denote a vector of state variables that describes investment opportunities.


(4) E (excess return)  = [-JWWW/JW ]Es2  + [-JWF/JW ]E(covariance),


where [-JWWW/JW ] measures the constant relative risk aversion CRRA. Clearly, the first term of (4) represents risk and the covariance term measures the hedge component.  This theory can incorporate corruption as one of the state variables in the vector F.


How do we solve the puzzle or explain the home bias? Let us first consider theoretical explanations and then institutional ones. The CRRA utility function (1) used by French and Poterba is unrealistic. In the consumption context, Carroll and Kimball (1996) and Vinod (1998), among others, argue that CRRA utility should be rejected, because it does not lead to a concave consumption function, as it should.  These authors propose using a hyperbolic or diminishing absolute risk aversion (HyDARA) utility function.  Another way of stating this issue is that CRRA is unrealistic because it gives inadequate importance to uncertainty.  We can incorporate HyDARA in (4) by making [-JWWW/JW ] term a declining function over time.


A second theoretical explanation of home bias is that EUT assumed above is unrealistic. A need for non-EUT models is explained in the survey by Starmer (2000). Vinod (2001) considers risk aversion, stochastic dominance and non-EUT in the context of portfolio theory.  These modifications lead to a revision of (3) involving a nonlinear transformation of weights for greater realism. A third theoretical explanation is that the model lumps all risk in the variance term based on the distribution of returns. Since the currency devaluation risk applies only for foreign investments, it can be large enough to cancel the hedging term.  A fourth theoretical explanation of home bias is that it is derived by assuming that return distribution is Normal. Nonnormal distributions require skewness, kurtosis and higher moments which when included can reduce the home bias.


Now we note some institutional explanations for home bias in investments. First, investing in home country avoids the risk associated with enforcement of property rights in a foreign country.  Even if no language barrier exists and the property laws are essentially similar, such as between US and UK, enforcement costs can be large. The observed difference of 500 basis points noted by French and Poterba (1991) may be reflecting such costs including attorney fees.  Second, corruption levels can be different in different countries at different times for different industrial sectors. This can mean uncertainty regarding size of bribes and financial and time costs of getting things done abroad can be larger. 


Serrat (2001) develops a two-country exchange economy dynamic equilibrium model to attribute the home bias to the role of nontraded goods. He shows that risk-adjusted expected growth rates of home and foreign endowments of tradables and nontradable goods are relevant.  Since the mean and variance of home and foreign endowments of tradables need not be constant, hedging demands causing home bias become less important in their dynamic setting with intertemporal substitution. In corrupt countries the hedging motive is likely to retain the home bias if variance of home endowments is larger.


The new point here is not only that corruption can explain home bias, but also that institutional explanations of the home bias do not apply symmetrically for investors in rich and poor countries.  With lower corruption and better legal and insurance protections for small investors in rich countries, the investors in poor countries are not as reluctant to invest abroad, i.e., less subject to home bias.


Note that it is also helpful to think of “international diversification puzzle” not as something to be solved, but a pedagogical device to learn economic behavior.  A similar use of six consumption puzzles is advocated in Vinod’s (1996) appendix. Accordingly the useful insight from this puzzle is summarized as:


Result 1:

Other things remaining the same, all investors in different countries will be generally better off in terms of diversification and hedging if they invest a large proportion of their wealth in foreign assets.  In practice, investors exhibit home bias. Various theoretical generalizations of the model and institutional barriers partially explain the home bias. However the bias against investment in poor and corrupt countries is justifiably stronger than the bias against investing in rich and less corrupt countries.


From the viewpoint of the developing country, it is desirable to let as much of the domestic savings fund domestic investments.  Accordingly, developing countries often impose controls on the outflow of capital to prevent flight of scarce capital resources to foreign countries, thereby helping domestic capital accumulation. Clearly, the government can readily identify the domestic resident investor and punish any export of capital by outlawing capital account transfers. Since there are subtle ways of exporting capital, capital flight control can manifest itself in at least five categories. 


IMF (2001) annual reports consider five control categories designed to discourage flight of capital. The data from several such reports are in the form of a “yes” or “no” answer to the following five questions: (1) Do multiple exchange rates exist? (2) Are there payment arrears and bilateral payment arrangements? (3) Are there controls on current transfers and payments for invisible transactions? (4) Are there controls on proceeds from export or invisible transactions? (5) Are there controls on capital account transactions? Appendix I of IMF(2001) has dots for some two to thirteen parts within these five categories. We assign weight 1 for each of the five categories so that our index of capital controls ranges from 0 to 5 with zero indicating no controls. If there are K subcategories within each category, we simply assign the weight (1/K) for each and add. Table 1, which reports our index values, will be discussed later in our empirical Section 3. 


Unfortunately, these controls themselves can encourage corruption and reduce “economic freedom” of entrepreneurs to create wealth around the world. Often, the first immediate effect of controls on out-going capital is to discourage in-coming investment in the form of FDI.  The policy makers in developing countries have to consider quantitative information on the extent of the following related issues: (1) Can the domestic saver illegally invest abroad anyway? (2) Do these exchange controls influence the foreign non-resident owner of capital? (3) Are the investments in the form of equity or debt? (4) Do the controls encourage corruption and encourage inefficient allocation of resources? (5) If domestic industries rely solely on domestic capital, do they strive to become competitive in global marketplace? (6) Are the foreign investors likely to be too fickle with a very short-term focus? (7) Does the international flow of funds (hot money) destabilize the exchange rate? 


The quantitative importance of answers to these questions will vary from country to country.  There is two-way causation between capital flight controls and corruption. We have shown that there is a tradeoff between capital flight controls and discouragement of FDI. Hence the optimal policy for different countries will be different. 


2.2 Corruption and the Market Failure Induced by the Value at Risk (VaR) Criterion:

This subsection discusses the role of VaR methods in discouraging FDI in corrupt countries. In recent Finance literature, value at risk has become a popular tool for practical portfolio choices.  By definition, the value at risk at time t for horizon t is the upper bound of the one-sided confidence interval:


(5)                Pr[DP(t) < -VaR] = 1-a¢,

where a¢ is the confidence level and DP(t) =DPt (t), is the relative change (return) in the

portfolio value over the time horizon t defined below in (6). Let t denote the current time, S(t) denote the portfolio value at t, and denote P(t) = logS(t). If we let the entire range of the time period be [t, T], the remaining time horizon is: t =T-t. Now we define the relative return as:


(6)                        DPt (t) = P(t+t)-P(t).

The essence of the VaR computations is estimation of low quantiles in the portfolio return distributions. Expected utility theory (EUT) leads to equal weight on all returns in computation of risks. Starmer (2000), Vinod (2001) and others show that the weights are high on extreme observations when an investor behaves according to the non-EUT. This means that worst-case scenarios are more important to a non-EUT investor leading to larger weight on perceived costs of corruption.  In other words, more realistic non-EUT worsens the impact of VaR calculations, by further lowering the lower limit of the confidence interval.


2.2.1 What is the effect of the assumed distribution on VaR?

Stable Paretian distributions are useful in modeling market and credit risks. In general, stable distributions do not have closed form expressions for the density and distribution functions. Stable random variables are commonly described by their characteristic functions and by four parameters: tail index a, skewness b, location m, and scale s. Modeling with such parameters will depict fat tails and skewness of these distributions.  Estimates of α for Japan, Singapore and Hong Kong markets are around 1.50 for almost every sum size, indicating that these markets have high probability of large returns, Omran (2001).


A linear combination of independent stable (or jointly stable) random variables with stability index a is again a stable random variable with the same a. Hence, any stable random variable can be decomposed into the “symmetry” and “skewness” parts. If s is the volatility, X*=(z1-a¢ s) is the limiting return at a given confidence level a¢, and if Y0 denotes the initial value of the portfolio, then value at risk is: VaR= -Y0X*. Thus, modeling Value at Risk (VaR) by stable Pareto distributions permits convenient decomposition of the distribution into three parts. The mean or centering part, skewness part and dependence (autocorrelation) structure, Rachev et al (2001).


Longin and Solnik (2001) extend stable Pareto to generalized Pareto and show with examples that cross-country equity market correlations increase in volatile times and become particularly important in bear markets. These correlations are neither constant over time nor symmetric with respect to the bull and bear markets. This means that one must reject multivariate normal as well as multivariate GARCH with time varying volatility.  The estimation of VaR is related to estimating the worst-case scenario in terms of the probability of very large losses in excess of a threshold q.  The so-called “positive q-exceedances” correspond to all observed losses that exceed q (say 10%).  Login and Solnik show that the generalized Pareto distribution is a suitable probability distribution, and estimate its parameters using 38 years of monthly data.  As q increases, the correlation across markets of large losses does not converge to zero, but increases.  This is ominous for an investor who seeks to diversify across various countries, including corrupt developing countries.  It means that losses in one country will not cancel with gains in another country. Rather, all similar countries might suffer extreme losses at the same time.


2.2.2 Do open economy international markets worsen VaR calculations?

Consider an international investor whose portfolio consists of many assets. The computation of VaR for any portfolio is computed by decomposing it into “building blocks,” which depend on some risk factors. For example, currency fluctuation and corruption are risk factors with open economy international investments. Risk professionals first use risk categories for detailed separate analysis. They need complicated algorithms to obtain total portfolio risk by aggregating risk factors and their correlations.


A well-known barrier to foreign investment is that exchange rates (currency values) fluctuate over time and can mean a loss when the return is converted into the investor’s home currency.  The individual investor would need to allow for potentially unfavorable timing of currency conversion.  However, financial markets have derivative instruments including forward and future exchange rate markets to hedge against such risks.  Hence derivative securities linked to exchange rates at a future date can mitigate if not eliminate the exchange rate risk.  Arbitrage activities by traders can be expected to price the foreign investments appropriately different from domestic investments to take account of exchange rate risk.  However, these hedging activities need free and open markets in target currencies. For developing countries like India, which have exchange control, there are black markets with a fluctuating premium over the official exchange rate. This increases the exchange rate risk and related costs even higher due to corruption.


This paper suggests corruption as an additional risk factor. Corruption can suddenly lead to a cancellation of a contract or sudden and unexpected increases in the cost of doing business. Depending on the magnitude of corrupt practices, the cost can vary considerably. When we consider almost the entire world, enforcing property rights, especially in developing countries, can be expensive and time consuming. Furthermore, the presence of corruption and lack of transparency among public institutions add to the cost of investing abroad, even if the foreign investor is not directly affected by corruption.  Since corruption increases the cost of enforcement of all property rights, it is obviously an additional burden.  Assume that the investor knows his worst-case scenario (lower limit of a 100(1-a¢)% confidence interval) return.


Assume that this VaR is r and is based on all other considerations except corruption. When the cost of corruption is considered, it is prudent to consider the worst-case scenario.  Since worst-case corruption cost can be substantial, it discourages FDI in corrupt developing countries. We may conclude that open economy international markets do worsen VaR calculations. We summarize implications from various models above as:


Result 2:

Assume that investors consider worst-case scenario by methods similar to the value at risk (VaR). Since worst-case corruption cost can be substantial, and since cross-country correlations can be unfavorable, corruption substantially increases cost associated with international investments and discourages investment in corrupt countries.


In summary, the theoretical extension of closed economy corruption models to open economy in this section predicts that investment risks are high in corrupt countries implying a need for a higher compensation (risk premium).  Hence our theory predicts that the cost of capital is high and foreign direct investment is low in corrupt countries.  Even our modest excursion into an open economy shows that the burden of corruption suggested by closed economy models in the literature becomes heavier under an open economy. 




This section first discusses our empirical study and then mentions some policy implications. To verify that corruption increases the cost of capital we cite the following direct evidence. An international accounting firm, Price-Waterhouse-Coopers, PWC (2001), surveyed chief financial officers (CFO) and PWC personnel. The survey asks information about the percentage penalty in terms of cost of capital due to lack of transparency and corruption in 34 countries.  We summarize their results by computing a weighted average (using midpoints of their intervals weighted by frequencies) and report it in Table 1 for some Asian countries under the heading “cost of capital % penalty.” For example, corruption increases the cost of capital by over 4.4 % per year in Indonesia. When one considers the large borrowings in billions of dollars the 4.4% becomes huge and avoidable.  This is our first empirical result.


Table 1 reports some empirical data on corruption (CPI), gross domestic product (GDP), foreign direct investments (FDI), ratio (FDI/GDP), and the ratio (Trade/GDP). We create an index based on IMF (2001) Appendix I to measure the extent of control on international capital transactions. In Section 2.2 subsequent to Result 1 we mention five categories of controls and our index ranging between 0 to 5 designed to prevent the flight of capital. A column entitled “capital flow control index” of Table 1 reports the index ranging from 0.077 for Hong Kong to 4.00 for Viet Nam. We include the following major Asian countries: Bangladesh, Indonesia, Pakistan, Vietnam, India, Philippines, Thailand, China South Korea, Malaysia, Taiwan, Japan, Hong Kong and Singapore. Since China threatens to not provide any data whatsoever if Taiwan and Hong Kong data are reported separately, the data sources such as the World Bank report that for Taiwan and Hong Kong data are “missing.”  However, we can compute simple correlation coefficients between countries after excluding missing rows.


Note that the correlation between corruption purity CPI and (FDI/GDP) is 0.5221. Since this is statistically significant at conventional levels (one tail, 5%), our table supports our proposition that FDI go to purer (less corrupt) countries. Similar correlation between CPI and (Trade/GDP) is even higher at 0.7341 and more significant.  This supports the notion that foreign trade exposes the country to foreign competition and discourages corruption. Conversely, lower corruption creates greater foreign trade opportunities.


The correlation coefficient between the CPI and our “capital flow control index” is -0.7522, with t-statistic of -3.786. The negative sign arises because the CPI measures purity. These estimates suggest high and statistically significant correlation, consistent with the two-way causation between corruption and controls designed to curb the flight of capital mentioned earlier at the end of Section 2.1.


We have constructed a suitably weighted average of the percentage penalties in terms of cost of capital from opinion surveys of knowledgeable persons in various countries by Price-Waterhouse-Coopers accounting firm. It is reported in a column entitled “cost of capital % penalty” in Table 1.  It is clear that the penalty is high for corrupt countries, namely 4.4 % for Indonesia. Note that the penalty steadily decreases as corruption decreases. It is only 0.021 % for Singapore.  The correlation coefficient between the CPI and this “cost of capital % penalty” is -0.827, which is also statistically significant, as seen from the t-statistic of -3.8919 reported in Table 1.


The open economy theory of Section 2 predicts that corruption discourages foreign direct investments. An obvious implication from these empirical results summarized in Table 1 is that the theoretical prediction is supported by the data.  The closed economy models have already noted that corruption imposes serious burden on the economies of developing countries.  Open economy models suggest an even heavier burden of corruption and imply that any policy actions designed to reduce corruption and improve governance will yield rich long-term dividends.  The general public and policy makers at all levels need a greater appreciation of this result.


What is the policy implication of home bias result? The home bias is stronger for investors in rich countries than for corruption-ridden developing countries. This means that capital flight from developing countries is larger than incoming FDI.  To the extent that investors must overcome institutional and other impediments before achieving optimal diversification and hedging on their own, this is an example of market failure.  To correct the imbalance, investors in rich countries may need incentives to invest in developing countries. The main point of Result 1 is that both sets of investors can be better off with safer and easier international investments.  A long-term solution is to develop a financially well-integrated global economy with similar property laws for resolution of disputes and common accounting standards. Despite the tendency to support herd behavior leading to self-fulfilling pessimism, private independent credit and investment rating agencies need to be expanded and encouraged, since they promote efficient allocation of global resources. Thus, greater financial integration and transparency will benefit everyone (Pareto superior).


During the years of socialist dogma in India, the central planners had rightly been afraid that the Indian rich will invest most of their capital assets in US and UK, if permitted to do so. Despite liberalization of 1990s India still retains controls on outflow of capital. A massive flight of capital from India can create balance of payment problems and hurt domestic capital formation.  The evidence from Asian contagion suggests that Indian Rupee was largely immune to the volatility induced by the contagion due to her capital controls.  Mexico does not have controls similar to India and has always faced the problem of flight of Mexican capital to US.  Mexican Peso fluctuates more than Indian Rupee. However, there is far greater direct investment by Americans in Mexico than in India and faster poverty reduction in Mexico. 


Empirical evidence suggests that as capital flow controls increase foreign direct investments decrease. We favor less controls for following reasons. Controls on capital flows introduce bureaucratic delays, room for corruption and discourage all foreign trade. Reduced foreign trade means less competition for domestic suppliers leading to monopolistic practices, rent seeking and corruption. This slows innovation and growth of world-class competitive products, needed for long-term prosperity in a global marketplace.


Another policy implication of our analysis is to recommend a greater role for international agencies similar to the IMF, World Bank, and others. Blanket guarantees of invested principal amounts granted to the international lenders have been shown to create a moral hazard and encourage reckless investments. It is necessary, therefore, to develop some prudent forms of encouragement for international investing and lending. Legal infrastructure, including property laws against abuse, confiscation, corruption and fraud need to be strengthened. International court needs to be established to enforce property laws and contracts. A better international allocation of savings and investments can be possible if the cost of enforcement of property laws in diverse jurisdictions can somehow be shared equitably, some of which should be borne by international agencies.  Partial loan guarantees and small subsidies for foreign direct investments (FDI) in equities are also worthy of consideration.  We suggest subsidized (not free) insurance for FDI and active help in enforcing laws against corrupt abuses of international investors. 


Foreign aid to developing countries can be effective if adopted in the form of encouragement for FDI.  The advantages of FDI compared to short-term loans (which comprise “hot money transfers” of international funds) are obvious from the viewpoint of developing countries.  The FDI funds are not withdrawn too readily on a whim, and help long-term investments.  Foreign aid could be more beneficial if it is given in the form of matching grants to encourage honest entrepreneurs from disadvantaged countries create wealth.  An effective aid program should have accountability and explicit punishments for corruption. The foreign aid dollars and technical assistance with modern surveillance equipment for local law enforcement against corruption are also worth a serious consideration. Some aid dollars should be allocated to educate the general public and policy makers in poor countries about the serious harm from corruption.




Reviews of corruption literature show that typical arguments use closed economy models largely ignoring both domestic and international financial sectors and open economy aspects.  This paper fills these gaps. Subsections 1.1 to 1.4 discuss the financial sector. Section 2 discusses how corruption leads to some open economy market failures stated as two “results.” Our first result essentially states that investors are better off with large FDI in terms of diversification and hedging, although institutional barriers, corruption and non-EUT risk perceptions discourage them. Our second result is that value at risk (VaR) worst-case scenario method tends to discourage foreign direct investments in corrupt countries.  Generalized Pareto distribution is perhaps the most realistic distribution for investment returns, and it suggests that simultaneous large losses are possible in various countries.  Hence the worst-case scenario is that corruption can cause large simultaneous losses to international investors.


We list many such mechanisms by which corruption can decrease FDI.  Whether it significantly decreases FDI in Asian countries is formulated as an empirical question. We use Transparency International’s empirical data on corruption (CPI). We construct ratios of “total international trade” and FDI to gross domestic product (GDP) and compute correlation coefficients with CPI.  Our analysis suggests that correlations are statistically significant, suggesting that FDI is indeed discouraged by corruption.


We show with a new summary measure using Price-Waterhouse-Coopers’ data that corruption increases the cost of capital. We provide specific examples to show that corruption can damage the physical assets of financial institutions as bribery and money laundering facilitate terrorist attacks. Other examples show how corruption hurts bank goodwill and creates significant losses to ordinary investors. We then discuss five factors causing Banking distress and show how corruption can worsen each of them. We show that in response to the distress in one country, (e.g., Thailand in 1997) routine portfolio rebalancing by investors can lead to financial contagion affecting several countries.  While capital flow controls can immunize a country from such contagions, they are shown to encourage corruption and monopolies. The correlation coefficient between a new index based on IMF data and CPI is used for this.


We discuss the potential role of derivative markets (swaps) in helping better management of risk associated with international investments, including corruption risk.  Encouraging competition to domestic Banks by admitting foreign Banks and encouraging the development of sophisticated futures and derivative markets are shown to be potentially useful. We note the positive contribution of independent rating agencies (e.g., S&P and Moody’s) in instilling discipline on national economies and in encouraging FDI. We also note their negative contribution in creating self-fulfilling pessimism.


Worldwide corruption persists unabated, because the middle classes in poor countries often benefit from corruption. The policy makers in rich countries are unwilling to import goods produced in poor countries in large quantities.  Also, they do not actively eliminate bribes designed to win contracts involving exports to poor countries, since both employment and living standards in rich countries are linked to such exports. We cite Bribe Payers Perceptions Index (BPI) to show that many rich countries indulge in harmful bribery in poor countries for export promotion. It would take strong leadership and education to build effective consensus in both rich and poor countries against corruption.


This paper represents an initial step in filling the gap in the corruption literature regarding open economy and financial institutions. The recent terrorist attacks have focused on money laundering.  An important component of any long-term solution to the terrorism problem must involve several anticorruption efforts supporting good governance. Our novel theoretical and empirical results and our policy recommendations deserve further study and are subject to the usual caveats. 




Ades, Alberto and Rafael Di Tella (1999) Rents competition and corruption, American Economic Review, 89(4), 982–993.


Bardhan, Pranab (1997) Corruption and Development: A Review of Issues. Journal of

Economic Literature 35: 1320-1346.


Baxter Marianne and Urban J. Jermann (1997) The international diversification puzzle is worse than you think, American Economic Review, 87(1), 170–180.


Chang, Roberto and Andres Velasco (1998) Financial crises in emerging markets: A canonical model, 98–21, C.V. STARR CENTER, New York University, Department of Economics, New York, NY


Carroll, C. D. and M. S. Kimball (1996) On the concavity of the consumption function, Econometrica 64 (4), 981–992.


DJN (2001) Dow Jones Newswires (AP), May 22, 2001


Erlich, I. and F. T. Lui (1999) Bureaucratic corruption and endogenous economic growth, Journal of Political Economy 107 (6), s270–s293.


French, Kenneth R. and James M Poterba (1991) Investor diversification and international equity markets, American Economic Review, 81(2), 222–226.


Hardy, D. C. and C. Pazarbasioglu (1999) Determinants and leading indicators of Banking crises: Further evidence, IMF Staff Papers 46 (3), 247–258.


ICRG(2001)  International country risk guide. An annual index of corruption since 1982 by political risk services, a private agency.


IMF (2001) International monetary fund, annual report on exchange restrictions.


La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer and Robert W. Vishny  (1998) Law and finance, The Journal of Political Economy, 106 (6), 1113–1155.


Longin, Francois and Bruno Solnik (2001) Extreme correlation of international equity markets, Journal of Finance 56 (2), 649–676.


Merton, R. (1973) An intertemporal capital asset pricing model, Econometrica, 41, 867–887.


Omran, M. F. (2001) International evidence on extreme values and value at risk of stock returns, 2001 proceedings of the Business and Economic Statistics section of the American Statistical Association. ASA: Alexandria, Virginia.


PWC (2001) Price-Waterhouse-Coopers, Transparency Project, Tables 21 and 22


Rachev, Svetlozar, Eduardo Schwartz and Irina Khindanova (2001) Stable modeling of market and credit value at risk,” 2001 proceedings of the Business and Economic Statistics section of the American Statistical Association. ASA: Alexandria, Virginia.


Schinasi, G. J. and R. T. Smith (2000) Portfolio diversification, leverage and financial contagion, IMF Staff Papers 47 (2), 159–176.


Serrat, Angel (2001) A dynamic equilibrium model of international portfolio holdings, Econometrica 69 (6), 1467-1490.


Starmer, C. (2000) Developments in non-expected utility theory: the hunt for a descriptive theory of choice under risk. Journal of Economic Literature 38:  332–382.


Vinod, H. D. (1996) Consumer behavior and a target seeking supply side model for income determination. Ch. 18 in M Ahsanullah,  D Bhoj (eds.).  Applied Statistical Science, I: Carbondale , IL: Nova Science Publishers. 219–233


Vinod H. D. (1998) Concave consumption, euler equation and inference using estimating functions, 1997 proceedings of the Business and Economic Statistics section of the American Statistical Association. ASA: Alexandria, Virginia, 118–123.


Vinod, H. D.(1999) Statistical analysis of corruption data and using the internet to reduce corruption, Journal of Asian Economics 10, 591–603.


Vinod, H. D. (2001) Non-expected utility, stochastic dominance and new summary measures for risk, Journal of Quantitative Economics (to appear).










times 100


times 100


Cost of Capital % penalty

































































South Korea
































Hong Kong
















Correlation coefficient with CPI
















 5% critical t and d.f. n-2




1.7823 (n=12)

1.7709 (n=13)







-1.8331 (n=9)


Notes:  CPI means corruption perception index from Transparency International. FDI is foreign direct investment. TRADE is sum of exports and imports. NA denotes missing data. The correlation coefficient (r) after deleting the rows with NA missing values and the t-statistic = r / [ (1-r2)/(n-2)]0.5, where n number of available valid rows are reported along bottom rows.  In each case the observed value is less extreme than the critical.