Capital Controls
Are Financial Markets an Exception to the Rules
of Free Trade?
Master Thesis
General Economics and International Economics & Business Reinout Wiersma
1156829
Abstract
1. Introduction
The International Monetary Fund (IMF) and the World Bank are known for being among the strongest advocates of international free trade. However, even in these bastions of the free market system, questions have arisen whether unrestricted trade is always the optimal policy regarding financial markets.
A strong belief and a widely accepted insight in modern economics is that free markets will allocate scarce resources in the most efficient way possible. Allowing for free trade would therefore appear to be the most desirable policy governments could employ to reach the most efficient economic outcome. But does this principle apply to financial markets the same way it does to other markets? After the East-Asian, Brazilian and Russian currency crises, it was realised that (temporary) restrictions on capital flows might be beneficial to an economy. These crises exposed the dangers of having an open capital account. Since then, a large body of economic research has been aimed at answering the question whether or not capital controls have a positive influence on economic development.
Capital controls can be defined as being restrictions on the trade of assets across international borders. Since this is a very broad definition, the first concern of this paper will be to describe what capital controls actually are and what is known of them. This description will give the background in which the rest of this paper is set. The first research question, therefore, is:
Research question 1: What constitutes a capital control and what are its theoretical influences on the state of an economy?
The aim is to investigate whether (temporary) controls on capital flows can be beneficial to economic development. Based on the theoretical framework presented in this paper, it will be assumed that capital controls influence economic growth rates through the channel of financial deepening. Financial deepening can be defined as the development and expansion of financial institutions relative to the size of a country’s economy. These institutions include, amongst others, stock markets, banks and insurance companies. This assumption implies that capital controls do not directly influence economic growth. The second research question is:
Research question 2: Do capital controls influence financial sector development and if so, what are the properties of this relationship?
Since the aim of this paper is to examine the relationship between capital controls and economic growth, the relationship between financial deepening and economic growth has to be determined. This relationship has already been investigated intensively in the economic literature, for example in King and Levine (1993) and Greenwood & Jovanovic (1990). The overall conclusion is that there is a positive relationship between financial deepening and economic development. Financial deepening can, however, be defined in many different ways. In order to make any realistic assumptions about the effects of capital controls on economic development, the assumptions regarding financial deepening used in this paper will have to be regressed with economic growth rates. Hence, the final research question is:
Research question 3: Does the empirical model presented in this paper show a significant relationship between financial deepening and economic growth?
2. Theoretical framework
Capital controls have become an ever more popular field of research over the past couple of decades. This study is concerned with empirically examining the effects of restrictions on international capital movements. Before proceeding with the actual analysis, however, it is important to create a background in which to place this research.
This section will start with a discussion on the theoretical arguments for and against the use of capital controls. Next, since the term capital controls can refer to many different ways in which international capital flows can be influenced, a short overview will be given explaining the different types of capital controls. Finally, one of the most influential and most widely discussed theoretical methods to slow down international capital flows is the Tobin tax. This theoretical restriction on international capital flows offers important theoretical and practical insights.
The case for free trade
Free trade, whether in goods, services or capital, is considered by many economists to be the most efficient economic environment in which to allocate resources. Free international (and domestic) financial markets allow economies to smooth their time profiles of investment and consumption during times of business cycle disturbances. They also facilitate the allocation of resources from low-return to high-return countries. Furthermore, free financial markets make it possible for economic agents to diversify away country specific risks. When restrictions are imposed on the free flow of capital across borders, an economy will restrict the effects these advantages can have on it.
Among the parallels Schuknecht (1999) finds that capital has a price just like any good or service. For any loan or bond for example, an interest rate has to be paid. And as with goods and services, capital is also subject to the principle of arbitrage. Traders take advantage of price differences across markets. Goods, services and capital are moved to the market which offers the highest return. This process continues until the returns in the different markets are approximately equalized. Arbitrage leads to short term- as well as long-term capital flows.
A second parallel is that the principle of comparative advantage is valid for both the international trade in goods and services as well as the international trade in capital. Suppose a firm has a comparative advantage in the form of a floating-rate dollar loan. The firm has this advantage due to the fact that is has, for example, accumulated little debt or it has access to new dollar-loans at low interest rates. Now suppose this firm wants a fixed rate loan in euros. The firm can try and find a firm that has a comparative advantage in euro loans and that wants a cheap dollar loan. Both firms can then arrange an interest rate swap.
Apart from the parallels described above, there are certain distinct differences between the trade in goods and services and the trade in capital. The first one is that the trade in capital can be much more uncertain and therefore riskier than the trade in goods and services. In the latter case, the prices of the goods and services are usually known to the trader. The risk involved with capital movements can be much higher.
Capital that flows into a country may almost immediately leave that country as an export flow. This is rarely the case for goods and services. The movement of capital across nations is much more volatile than the movement of goods and services.
Finally, capital markets feature some unique market problems which are not found in other markets. For example, one of the reasons that led to the Asian crisis of the 1990s was herding behaviour by investors. Investors seemed to follow each other blindly and pulled out their investments almost simultaneously.
The case for capital controls
Now that it is clear what the advantages of free trade are and what the differences between the goods and services markets and the capital market is, the discussion will continue by focusing on the reasons for imposing capital controls.
One of the founding fathers of the Bretton-Woods Agreement and one of the most influential economists of the 20th century, John Maynard Keynes, was a strong advocate of free trade in goods and services. Even Keynes did, however, plea for giving nations the right to impose restrictions on international financial transactions.
Figure 1.1: Trends in Financial Openness, medians by year, for each group of countries
Source: Quinn (1997), p.532
Following Johnston & Tamirisa (1998), the reasons for implementing capital controls can be grouped into four categories: (1) Balance of payments and macroeconomic management, (2) underdeveloped financial markets and regulatory systems, (3) prudential and (4) other reasons.
1. Balance of payments and macroeconomic management:
volatility as a result of the financial crisis. Eastern Europe and Central Africa experienced the same increase in this volatility as a result of their transition to capitalism. The other regions have seen little change in volatility since the 1980s.
Figure 1.2: Capital flow volatility by decade and by region
Source: World Bank, Global Development Finance: Country Tables
2. Underdeveloped financial markets and regulatory systems:
When the financial market of a country is not sufficiently developed, capital controls can be used in an infant-industry context. By shielding the domestic market, this market can develop through economies of scale and learning effects. Johnston & Tamirisa (1998) argue that this infant-industry argument is a second-best solution. Capital controls lead to economy wide distortions which are meant to off-set the perverse effects of the insufficient level of development of the financial market. In other words, instead of dealing with the cause of the underlying distortions, the effects of these are treated with other distortions.
3. Prudential reasons:
Capital controls can be used in a prudential manner. International capital flows expose a country’s financial institutions to different types of risk. Among these are, for example, country risks1 and transfer risk2. By limiting the exposure of these institutions,
governments can restrict the level of risk these institutions take. Furthermore, capital controls can help lengthen the maturity of a financial institution’s liabilities. Both effects of capital controls will provide stability to the financial system. However, critics point out that capital controls might in fact increase investment risk. Capital controls prevent portfolio diversification and thereby increases the risks an investor has to take. Another drawback lies in the fact that capital controls tend to slow the development process of the financial sector which leads to reduced liquidity and quality of financial assets. Investors tend to be able to circumvent the restrictive capital laws by channelling their investments in a different, legal manner. These alternative channels might well be less regulated and more risky which endangers the stability of the financial system. Johnston & Tamirisa (1998) concludes that prudential reasons for imposing capital controls are only valid in specific circumstances. An example of this is a country in which the financial system is in poor condition and the government needs time to implement reforms.
4. Other Reasons:
There are many other reasons to justify the use of capital controls. Larger countries are said to have less incentive to have an open capital account than smaller countries since they have more opportunities for investment diversification. Also, the overall openness of an economy can influence the need for capital controls. Open economies are more vulnerable to external shocks and capital controls can reduce risks associated with these shocks. In open economies, however, there are also more ways to avoid the capital flow restrictions. Capital account liberalisation is furthermore complementary to trade liberalisation. Different governments have different ideas about the desired level of government intervention in the economy. For reasons like national sovereignty or cultural reasons, a government might decide to take control of the flow of capital in its economy.
Johnston & Tamirisa (1998) is unable to find any empirical evidence to support the validity of the reasons mentioned above and is therefore very sceptical about the use of capital controls. Eichengreen (1998) offers several reasons why some developing
countries should be able to turn to the use of capital controls as a means of stabilizing their economy. While using many of the same reasons mentioned in Johnston & Tamirisa (1998), Eichengreen (1998) discusses why these reasons can be beneficial to an economy.
Central in the discussion is the vulnerability of the financial market in general, and the banking sector in particular, to sudden economic shocks. Banks are heavily intertwined with one another, which causes the unfortunate side-effect that problems in one bank can quickly spread to the entire sector. Banks operate in an imperfect information environment. Speculators therefore do not have all the relevant information they need when they make investment decisions. They tend to base their decisions partly on the decisions of other agents in the market (herding behaviour)3. When market conditions take a turn for the worst, a general panic might break out which can take down the entire financial market of an economy. Figure 1.3 demonstrates the severity of the sudden capital flow reversals for several countries which have all suffered a significant economic slowdown as a result of this. Governments can reduce the exposure of their banks by creating a financial safety net. This is done by regulating their banks’ position in foreign exchange markets and imposing limits on concentrated investments. The government can act as lender of the last resort. With an open capital account, however, banks can easily turn to foreign markets when they need to borrow money. Since a central bank cannot print foreign money, the role of the government as a lender of the last resort is eliminated. A central bank can only provide financial support to its banks to the extent of its stocks of international reserves. Even bank liabilities denominated in domestic currency put a central bank in a difficult position. Central banks often want to stabilize the exchange rate but this objective cannot be reached when the central bank needs to inject liquidity into the banking sector. In other words, since the banking sector is exposed to huge risks, the management of that risk is paramount to maintaining financial stability. Governments can play an important role in managing the risk taking behaviour of banks. In Western, industrialised countries, this safety net is well-developed and maintained. In developing countries, however, administrators may lack the capacity or the political will to put management risk regulations in place. In these countries,
management is usually underdeveloped. For these types of countries, Eichengreen (1998) argues that it would be prudent to allow for the use of capital control measures to prevent excessive risk taking.
Figure 1.3: Large reversals in net private capital flows (in percentage of gdp)
Source: World Economic Outlook (Washington, International Monetary Fund)
investors to pull their resources out of the country thereby increasing the need for the implementation of capital controls.
Eichengreen (1998) argues the need for capital controls if they allow for stabilization of an economy, but it is also recognized that capital controls have costs. They block access to investment flows from abroad, they require a large and bureaucratic administrative system and they reduce the incentive for necessary policy reforms. Capital controls are not a substitute for developing a sound financial market. Nor are they an excuse for governments to run excessive budget deficits. Capital controls can play a significant role in the transition process of a developing country to a developed one by stabilizing the financial sector of that country.
Types of Capital Controls
Capital controls include a wide variety of regulations aimed at reducing the volatility of capital flows in a country. Capital controls can be distinguished by looking at the asset transaction which is affected, or whether this transaction is taxed, limited or simply prohibited (Neely, 1999). Also, a division can be made depending on whether the restriction is aimed at long-run or short-run capital flows.
A common way to discriminate between capital controls is to differentiate them in controls on inflows and controls on outflows. Outflow restrictions have been used to deal with currency and financial crises. Edwards (1999) subdivides outflow controls in
preventive and curative controls. Preventive controls are used when a country
experiences a severe balance of payments deficit, but it has not (yet) had to face a strong devaluation of its currency. Preventive controls have the function of slowing down the drainage of international reserves. This in turn gives the authorities time to implement policies to deal with the balance of payments problem4. Overall, empirical research tends to suggest that preventive controls have been largely ineffective. This failure to control
capital outflows is due to the fact that the private sector usually manages to bypass the restrictions and move large amounts of capital out of the country quite easily. Furthermore, corruption seriously undermines the capital flow restriction. A restrictive capital flow policy may also give economic agents a false sense of security which may lead to dangerous market behaviour. This dangerous market behaviour was witnessed in South-Korea in 1997. Many believed South-Korea to be immune to the currency crisis which swept through South-East Asia since strong preventive capital controls were in place. To the surprise of many, even South-Korea too fell victim to a vicious currency crisis in late 1997.
Curative controls are used when a country is already facing a financial crisis. The idea is that curative controls give the government the means to implement reforms and restructure their financial sector. Also, the interest rate can be lowered to promote economic growth. Empirical evidence on this type of outflow control is ambiguous.
Controls on capital imports allow a country to set a higher interest rate. This type of control has been used to dampen inflationary pressures by restricting the money supply. Inflow restrictions are seen as a means to protect emerging economies from speculative attacks while they also allow these economies to undertake an independent monetary policy. Furthermore, when the banking sector is not appropriately governed and monitored, large capital inflows can give banks an incentive to expand their loans since banks tend to see capital inflows as a cheap means of borrowing. These additional loans often have a high risk attached to it which increases the vulnerability of the bank5. Empirical research on the effects of inflow controls is limited and no general conclusions can be drawn. Recently, economists pointed to the case of Chile, where the government
discouraged short-term capital inflows, while actively encouraging long-term capital
inflows (De Gregorio et al., 2000). This strategy seems to have been a huge success, since Chile witnessed remarkable economic growth and stability. Not all researchers agree, however, that Chile’s success was the result of its inflow restrictions6.
Finally, capital controls can be distinguished by whether the restriction limits capital flows through price mechanisms or through quantity controls. Price mechanisms take the form of taxes. This type of control also covers the mandatory reserve requirement which, in effect, acts like a tax and is aimed at restricting capital inflows. Foreign agents are required to deposit a certain percentage of their intended capital inflow with the domestic central bank for a specified period of time. These deposits earn the foreign agent no interest while at the same time gives the domestic central bank the means to acquire foreign money market instruments. Quantity controls take the form of quotas or outright prohibitions.
The Tobin Tax
One form of price mechanisms to avoid large capital movements is the so-called Tobin tax, after the Nobel-Prize winning economist James Tobin. Tobin suggested introducing a small (<0.5%) tax on all foreign currency exchange transactions. Tobin (1978) states that this tax rate will be small enough to deter speculators from moving large amounts of capital around the globe in order to profit from minute differentials in currency fluctuations. The tax would simply cut into the profits making these transactions less attractive. Long-term international investments would not be harmed, since the tax rate would be too low to erode away the long-term returns of the investment.
However, the Tobin tax theory is what it is: Only a theory. There are simply too many problems associated with it for the theory to ever become reality7. The Tobin tax theory has been used by some countries as a base on which to model capital inflow restrictions.
3. Review of research into capital controls and economic development
The research into capital controls has been very productive over the last couple of decades. This has yielded some important insights into the effects of capital controls. However, there is still a lot that is not known about capital controls. This section is concerned with discussing some of the key issues researchers in the field of capital controls have to deal with. Also, an overview of some of the most striking results is presented.
Table 1.1: Overview of studies on the effects of (the removal of) capital controls
Study Research Summary Main results
Quinn (1997) 58 Countries are examined using a cross section OLS to determine the relationship between GDP growth from 1960-1989 and a liberalization measure ( Quinn*).
GDP growth is positively correlated to Quinn.
Klein & Olivei
(2000) First, the relationship between Share** and GDP growth per capita is examined, followed by GDP growth being a function of the change in financial depth (FD).
Share significantly influences FD. Positive relationship between FD and GDP growth.
Bekaert, Harvey &
Lundblad (2001) The official dates of stock market liberalization are used to measure the effect on growth rates in income per capita for 95 countries.
Stock market liberalisation significantly contributes to growth in income per capita.
Kraay (1998) Samples of both Share, Volume*** and Quinn liberalisation measures are used to examine their effect on income per capita.
Only Volume has a significant effect on growth.
Desai, Foley &
Hines Jr. (2004) Relationship between capital controls and the level of Foreign Direct Investment (FDI). Firms easily circumvent capital controls but have to make costs to do this and the company cannot choose the most efficient alternative. Capital restrictions significantly reduce FDI.
Forbes (2004) Microeconomic evidence of the effects of capital
controls. Research on capital controls should focus more on microeconomic data. Capital controls create microeconomic distortions with negative long-run effects.
Johnston &
Tamirisa (1998) Examines the structure of capital controls and their determinants. A detailed structure of the structure of capital movement restrictions is presented.
Capital controls on outflows are primarily used for balance of payments objectives.
Lane &
Milesi-Ferretti (2001) Examines trends in net foreign asset positions and changes in debt equity ratios over time using a sample of 66 countries. Estimates of foreign assets and liabilities and their debt\equity components are set up.
Capital account openness is positively correlated with gross stocks of FDI and equity. A comprehensive dataset is constructed despite several shortcomings in the methodology.
Chinn & Hiro Ito
(2005) Panel data analysis of 108 countries to examine the effect of capital account liberalisation and the development of equity markets.
A certain level of institutional development needs to be attained for liberalisation to be effective.
Tamirisa (1998) Effect of exchange and capital controls on trade in a gravity-equation framework in which bilateral exports depend on country size, wealth, tariffs, capital controls and geography.
Dependent on the type of capital control and the level of development of a country, capital controls do indeed stand for a significant barrier to trade. Rodrik (1998) Cross section OLS in which the effect of Share is
computed with respect to growth in income per capita.
No significant correlation between Share and growth per capita has been found.
Edwards (1999) Discussion on the effects of capital controls with specific focus on Chile. Link between capital controls and the sequencing of economic reform.
All capital controls should eventually be lifted but only if sound economic reforms replace them. Capital controls had little positive effect in Chile.
* Quinn is Quinn’s capital intensity measure which ranks from 0 – 4.
** Share is the proportion of years that AREAR shows open capital accounts.
A recurring problem in examining capital controls is how they should be measured8. Broadly, there are two types of measures for capital controls: Rule-based measures and qualitative measures. Rule-based measures are based on the existence and level of published regulations such as financial transaction taxes, deposit requirements, etc. For example, a country with few restrictive rules for capital flows might receive a value of 1 as a capital control measure. A country with no regulation will receive a value 0 and a country with many restrictions will receive a 2. It should be noted that rule-based measures are based on the existence of regulations and not on whether these regulations are in fact enforced.
Qualitative measures of capital controls derive the level of capital control intensity from the values of economic variables. Three different sets of variables have been used in this effort: Interest rate differentials, international capital flows and the national savings rate combined with the investment rate. The main drawback of these variables is that it is extremely difficult to show a clear relationship between the variable and the effects of capital controls. For example, investment and savings might be highly correlated with each other even if a country has no restrictions on capital movement.
The International Monetary Fund’s Annual Report on Exchange Arrangements
and Exchange Restrictions (AREAR) is a widely used source for the construction of a
dummy variable to measure the extent of capital mobility restrictions. However, Edwards (1999) identifies several structural weaknesses associated with this report which limit its usefulness in empirical capital control research. First of all, the report completely ignores the fact that legal restrictions on capital flows are frequently circumvented. Second, the report does not distinguish between different types of restrictions, nor their intensities.
From the discussion so far, it is clear that the theoretical discussion is far from settled on whether restrictions on international capital flows should or should not be banished. Interestingly enough, the practical policy debate on capital controls has focused
not on whether capital controls should be eliminated, but on when and how fast this should be achieved. This debate is known as the ‘sequencing and speed of reform debate’. This discussion focuses on the sequence of reforms which should be undertaken, and the speed at which these reforms should be implemented before capital mobility restrictions can be lifted. By the end of the 1980s, a consensus was reached in the discussion. In time, capital controls should be eliminated, but this should only be done if certain preconditions have been met. Countries that wish to abolish capital controls have to implement political and economical reforms which should result in macroeconomic stability. Economists also agree that the liberalisation of the trade in goods and services should precede the liberalization of the capital account (Edwards, 1999). The Mexican and Asian crisis demonstrated that it is crucial for the reform process to have a modern banking supervisory system. Poorly supervised banks tend to handle inflows of capital in an inefficient way, which leads to an increase in the risk of a financial crisis (Calvo, 1998). An overall modern, sound banking system is vital before a country can lift its restrictions on international capital flows.
The image created in this and the previous section is one of ambiguity. Especially in the last couple of decades, the academic interest in capital controls has increased. However, the research into the effects and desirability of capital controls has often yielded mixed results. The setting in which research into capital controls is conducted, and in which this paper is situated, has now been given. This paper will be a complement to the already existing literature in several ways. First, this paper will combine data from earlier work9 and it will examine not a direct, but an indirect link between capital controls and economics growth. Furthermore, this paper will distinguish between the long-run and the short-run effects of capital controls. As mentioned in sections 1 and 2, the importance of the quality of institutions for an economy should not be underestimated. Therefore, the level of institutional quality will also be taken up in the analysis. Most research on capital controls examines both developed countries and developing countries simultaneously. The dataset used in the analysis of this paper will also consist of both groups of countries.
4. Data Description and the Empirical Models
This section of the paper is concerned with describing the data sources and discussing the empirical models which will be used to examine the effects of capital controls. The data for this paper is taken from the datasets used in Bekaert, Harvey & Lundblad (2001) (BHL), Lane & Milesi-Ferretti (2001) (LMF), and Klein & Olivei (1999).
The aim of this paper is to examine the relationship between the use of capital controls and economic development, measured in Gross Domestic Product (GDP). A growing body of research, see e.g. King & Levine (1993), Greenwood & Jovanovic (1990) and Bencivenga, Smith & Starr (1996), shows that economic growth is influenced by financial market development. Financial deepening, in turn, might be influenced by the existence of capital controls. Exploring the link between capital controls and financial deepening as a determinant of economic growth is central in this paper. Therefore, a dummy variable will be introduced into an empirical model which describes the year on year development of financial deepening of the countries under investigation. Second, a regression analysis will be conducted to test whether this financial deepening development affects GDP growth rates.
Indicator for the Presence of Capital Controls
As discussed in section 2, finding a satisfactory indicator for the presence of capital controls is difficult at best. Therefore, a measure that is widely used in the field of capital control research will be used, namely the Klein & Olivei (1999) dummy variable10. This makes the research in this paper better comparable to other research papers.
Klein & Olivei (1999) examines the relationship between capital account liberalisation and economic growth. The results indicate that by removing barriers to international capital flows, a country will indeed experience higher levels of economic growth. Strong evidence has been found that this economic gain is attained through the channel of financial market development. This economic gain is conditional upon the fact that a certain level of institutional quality has to be in place. The results do appear to be motivated largely by OECD member states, suggesting that the level of institutional quality in non-OECD countries is not ready to deal with liberalising international capital flows.
The Klein & Olivei dataset contains a 0/1 dummy variable for 149 countries, both industrialised and developing, from 1966 to 1995. When no capital controls are in place, the dummy variable takes the value of 1. The value per year for each country was determined using information from the Monetary Fund’s AREAR. The data show a regional and income-based pattern. Industrialised countries tend to have less experience with capital controls than do non-OECD countries. Of the countries that have the most experience with restricting capital flows, most are concentrated in Latin-America.
the United Kingdom had a value of 0 for the entire 1980 – 1995 period. Based on these facts, the Klein & Olivei dummy value for Switzerland between 1980 and 1991 has been set to 0.
Indicators for Financial Deepness
Financial deepening can be described by many different variables. Because of the difficulty of capturing the process of financial deepening in a single variable, three different measures of financial deepening will be used. In the analysis, financial deepening is represented by either ‘equity market turnover’, private credit/gdp, or the Net External Position (NEP) of a country. The robustness of each of these variables as a measure for financial deepening can be tested by comparing the test results from each of them.
The endogenous variable NEP is calculated based on data from the LMF dataset. The aim of the LMF paper is to build a dataset which describes the financial sector of a country with variables that have not been included in the Balance of Payments data. The financial sector is divided in three sections: Foreign Direct Investment (FDI), Portfolio Equity and Portfolio Debt. Mathematically:
NEPt = FDIAt + EQAt + DEBTAt + FDAt + FXt – FDILt – EQLt – DEBTLt – FDLt (1)
NEPt = Total Financial Assetst – Total Financial Liabilitiest (2)
The accuracy of the values of the variables is hampered by the fact that the estimates of FDI are based on book values, while the equity estimates are based on market values. Furthermore, the debt estimates are often plagued by lack of data for some countries. LMF, however, concludes that the estimates which were created provide a comprehensive dataset with great value in empirical international macroeconomic research.
Testing for stationarity
The data used in the analysis are time-series based. A description of the variables used in this paper can be found in ‘Appendix B’. The stochastic process (time series) of the variables used needs to be stationary, i.e. the mean and variance need to be constant. Non-stationary time series can lead to unreliable least square estimators, test statistics and predictors. When using non-stationary time series, the regression results may indicate a significant relationship when in fact there is none.11
The stationarity of the time series is tested using an augmented Dickey-Fuller Fisher unit root test. The null hypothesis states that the time series is a unit root-nonstationary process which is to be rejected for the alternative of a stationary process. The results of the unit root test for the variables FD (Financial Deepening), Private Credit, (Equity Market) Turnover, Quality of Institutions (QI), and GDP are presented in Table 4.1. From the results, it can be concluded that for all the variables, the null hypothesis can be rejected in favour of the alternative hypothesis which states that the variable has a stationary time series. The results for the variable ‘Turnover’ are somewhat weaker than those of the other variables. However, it should be noted that the time period for each variable is only 16 year, reducing the strength of the Dickey-Fuller test. Trends in the values of the variables can be more easily detected in longer time-series than in
short time-series. Because of the weak strength of the test, there is no reason to exclude ‘Turnover’ from the analysis.
Table 4.1 Results Augmented Dickey-Fuller Unit Root Test
GDP QI NEP Turnover Private
Credit ADF-Fisher Chi-Square 67.8033 (1.0000) 61.0176 (1.0000) 84.1716 (1.0000) 134.099 ( 0.0062) 129.357 ( 0.9953) ADF-Choi Z-Statistic 14.3404 (1.0000) 6.22510 (1.0000) 9.49457 (1.0000) -1.42241 ( 0.0775) 4.53300 (1.0000) # of Observations 1305 855 1305 720 1305 # of Cross-Sections 87 57 87 48 87
Note: Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality. Probability values are presented in parenthesis. The exogenous variables are tested for individual effects with 0 lags.
Capital Controls and Financial Deepness
The explanation of the effect of capital controls on the financial development of a country is based on the following regressions:
FDt = + 1CAPt + 2QIt + t (3)
This first regression model shows the relationship between the variables in the long-run. The relationship between capital controls and the development of financial deepness and economic growth is rather ambiguous as discussed in section1 and 2. The purpose of this paper is to examine this relationship. Hence, at this point no predictions can be made for the value of 1.
As became apparent in section 1, the role of institutions in the process of liberalising a financial sector should not be underestimated. Therefore, a quality of institutions variable has been introduced in equation (3). This variable is based on the International Country Risk Guide political risk subcomponents corruption, law & order, and bureaucratic quality. Since it is generally known that better institutions promote a healthier economy, it can be expected that the coefficient reflecting the relationship between the quality of institutions and financial development will be positive ( 2>0).
The second regression used to analyse the relationship between CAP, QI and FD focuses on the short-run:
FDt FDt – FDt-1 = + 1 CAPt + 2 QIt + 3 t-1 + t (4)
Capital Controls and Economic Growth
The effect of capital controls on economic growth is measured through the channel of financial deepening. The next step in the investigation is to test the relationship between financial deepness and economic growth. This is modelled in the following regression: logGDPt log −1 t t GDP GDP = + 1 FDt + 2FDt-1 + 3log(GDPt-1) + t (5)
where logGDPt represents the real GDP growth rate at time t, FDi is the year on year increase of financial deepness as specified in (3), FDt-1 is the initial measure of financial deepness, GDPt-1 is the initial measure of real GDP in period t-1, and i is the error term.
The effect of restrictions on international capital flows on economic development becomes apparent through the channel of financial deepening ( FDt). It is expected that financial deepening has a positive and significant effect on economic development. Therefore, it is expected that 1, 2 > 0.
equation, the effect of financial deepening on economic growth would be estimated downward.
Testing for Correlation between the Explanatory Variables and the Error Term
Before proceeding with the actual analysis, it is important to determine whether or not an explanatory variable is in any way correlated with the error term. Should this be the case, than the least squares estimator will fail. This is the errors-in-variables problem. Should correlation be an issue, the ordinary least squares estimator will be replaced by an instrumental variables estimator. With respect to (3), the relationship between CAPt and t should be examined. The regression stipulated in (5) will be checked for the relationship between FDt and t.
The presence of a correlation between an explanatory variable and the error term can be determined using the Hausman test. The null hypothesis states that both the ordinary least squares (OLS) and the instrumental variables estimator are consistent, i.e. in large samples the difference between them converges to zero. Since the least squares estimator is the more efficient of the two, this estimator will be used should the null hypothesis be accepted. The alternative hypothesis states that only the instrumental variables estimator is consistent and thus the difference between the two estimators does not converge to zero in large samples. When this is the case, the instrumental variables estimator is the better alternative.
The Hausman test is carried out by running two separate regressions.12 In the first regression, the variable under investigation (CAPt in the fist Hausman test and FDt in the second) is regressed on all the exogenous variables and instruments. The residuals derived in this first regression are then taken up as a separate exogenous variable in the second regression. In other words, the first regression is expanded with a residual variable in the second regression. For the OLS to be consistent, the residual variable
should not be statistically significant from zero in this second regression. The regression results for the Hausman tests are presented in Tables 4.2 and 4.3.
The results for the correlation analysis between CAPt and the error term show, that the null hypothesis cannot be rejected and that the OLS estimator is the appropriate tool for the analysis. For the relationship between the error term and FDt, the Hausman test results indicate that again the OLS estimator accurately captures the relationship between the variables.
Table 4.2 Hausman Test Results for the relationship between CAP and
FD is given by NEP FD is given by Turnover FD is given by Private Credit C -870.6757 (953.7695) 0.112630* (0.015667) 0.022525* (0.006580) FD-1 0.070650* (0.007431) -0.265340* (0.025772) -0.006461 (0.006977) CAP 655.4226 (1130.976) -0.047793* (0.016015) -0.018148* (0.005629) 1563.697 (3242.390) 0.059402 (1.181031) 0.012738 (0.014433) # of cross-sections included 87 49 87 # of observations 1305 735 1305
Note: * indicates significance at the 5% level. Standard errors are presented in parentheses. C represents the constant. represents the residual from the first part of the Hausman test,
Table 4.3 Hausman Test Results for the Relationship between FD and FD is given by NEP FD is given by Turnover FD is given by Private Credit C 0.037163* (0.005664) 0.040553* (0.012545) 0.025783* (0.007998) FD -1.58E-05 (1.07E-05) -0.059792 (0.146191) -0.014378 (0.617397) FD-1 1.12E-06 (7.55E-07) 0.065905 (0.043904) 0.042045* (0.014982) GDP-1 2.89E-09 (8.40E-09) -1.11E-09 (7.69E-09) -2.19E-10 (8.42E-09) 1.59E-05 (1.07E-05) 0.154711 (0.148820) 0.096232 (0.620399) # of cross-sections included 87 49 87 # of observations 1305 735 1305
5. Empirical Analyses
Now that the data has been presented and the models have been introduced, the actual analysis can take place. First, the statistical methods which are used will be discussed. Then, the regression results of the long- and short-run relationship between capital controls and financial deepening will be discussed. Next, the regression results for the relationship between financial deepening and economic development are presented. Section 6 will offer the final conclusions of the paper.
Statistical Methodology
The data is regressed using the ordinary least squares estimation (OLS) method. Section 4 has shown this to be the most efficient test for the available data. The OLS is conducted with fixed effects which involves removing cross-section or period specific means from the dependent variable and exogenous variables, and then performing the specified regression on the demean. The fixed effects model uses all the data while the intercept is allowed to vary across countries and/or time. In this way, any possibly omitted explanatory variables can be captured in the changing country intercept. Also, Cross-Section Weights are included. This means that weights are estimated in preliminary regression with equal weights and then applied in weighted least squares in second round. Using these weights accounts for possible cross-section heteroskedasticity. Finally, White diagonal standard errors and covariance estimators are used to correct for heteroskedasticity of unknown form.
Regression Results: The Effect of Capital Controls on Financial Deepening
capital controls and financial deepening. In the first regression, NEP is used a measure of financial deepness (FD). Next, in order to see if the regression results might be driven by the choice of NEP as a measure of financial deepness, FD is represented by ‘equity market turnover’ and private credit/gdp respectively.
Table 5.1 Long-run relationship between Capital Controls and Financial Deepening
FD is given by NEP FD is given by Turnover FD is given by Private Credit C 6244.484* (1141.377) 0.168432* (0.028189) -0.019229* (0.007494) CAP -12043.05* (1024.296) -0.086423* (0.016137) -0.019229* (0.007494) QI -15130.06* (1230.985) 0.227600* (0.038295) 0.108657* (0.011460) # of cross-sections included 72 49 72 # of observations 1152 784 1152 Adjusted R2 0.601169 0.618937 0.957985 Durbin-Watson Statistic 0.110742 0.988910 0.465260
Note: * indicates significance at the 5% level. Standard errors are presented in parentheses. C represents the constant.
Using the residuals from the long-run regressions, an error corrected model has been presented in (4). This model deals with the short-run relationship between capital controls and financial deepening. The error term t-1 shows the ability of the model to
return to its long-run steady state level after a shock. The short-run results are presented in Table 5.2.
Table 5.2 Short-run relationship between Capital Controls and Financial Deepening
FD is given by NEP FD is given by Turnover FD is given by Private Credit C -1055.160* (17.03016) 0.009711* (0.001418) 0.007020* (0.000875) CAP 85.24313 (160.9060) -0.004540 (0.011556) -0.002192 (0.005871) QI -286.0457 (452.2632) 0.107811* (0.042074) 0.045579* (0.016721) t-1 -0.002461 (0.004456) -0.391004* (0.038864) -0.132631* (0.015234) # of cross-sections included 72 49 72 # of observations 1080 735 1080 Adjusted R2 0.146581 0.133057 0.103071 Durbin-Watson Statistic 1.520847 2.085162 1.686069
Note: * indicates significance at the 5% level. Standard errors are presented in parentheses. C represents the constant.
measures leads to a model which can absorb shocks which bring it off its long-run steady state.
Regression Results: The Effect of Financial Deepening on Economic Growth Rates
The second part of the empirical analysis is concerned with the relationship between financial deepening and economic growth. Since the assumption of this paper is that capital controls influence financial sector development, capital controls indirectly affect economic growth rates through the channel of financial deepening. The relationship between financial deepening and economic growth is modelled in (5). Table 5.3 shows the basic regression results.
Table 5.3 Regression output for Financial Deepening and Economic Growth
FD is given by NEP FD is given by Turnover FD is given by Private Credit C 0.012694 (0.012265) 0.111881* (0.020252) 0.040562* (0.014498) FDt 3.21E-08 (2.53E-08) 0.089301* (0.016981) 0.130948* (0.056909) ( 2- 1)FDt-1 2: 7.03E-08* (2.03E-08) 2: 0.103656* (0.013831) 2: 0.029180* (0.011884) Log(GDPt-1) 0.004047* (0.001064) -0.006743* (0.001898) -0.000122 (0.001645) # of cross-sections included 87 49 87 # of observations 1305 735 1305 Adjusted R2 0.116466 0.183477 0.120838 Durbin-Watson Statistic 1.629966 1.481963 1.631451
As in the earlier regressions which are concerned with the relationship between capital controls and financial deepness, using NEP as a measure for financial deepness yields surprising results. There is no significant relationship between FDt = NEP and logGDPt. Furthermore, the sign of the Log(GDPt-1) coefficient is positive which does not correspond with the idea of conditional convergence.
When financial deepening is described by equity market turnover, the results of the analysis provide strong evidence for the initial assumptions about the properties of the coefficients. There is strong empirical evidence that financial deepening has a positive effect on GDP growth rates. Also, the sign of the Log(GDPt-1) coefficient is negative implying conditional convergence.
6. Conclusion
The field of research into the effects of capital controls is surrounded with ambiguity. This paper is an effort to relinquish (some of) that ambiguity.
Section 2 of this paper discussed the pros and cons of using capital controls as a means of stabilising a country’s economy. Also, the different types of capital controls used around the world were highlighted. Some research issues were debated which play an important part in any research regarding capital controls in section 2. This section also gave an overview of some of the more important achievements in the field of capital control research. These two sections answered the first research question about what constitutes a capital control.
With the theoretical framework of section 2 in mind, an empirical model was set up in section 4. The datasets that were used were discussed and methodological concerns were addressed. The statistical results of the models have been presented in section 5.
economies should strive to refrain from using restrictions on international capital movements. The short-run effects of capital controls remain illusive.
With respect to the methodological approach to capital control research, this paper offers some lessons. The choice of how to model financial deepening is one of the key research considerations. As it turns out, using the NEP as an endogenous variable for financial deepening in the models of this paper results in statistically questionable outcomes. These results are plagued with high standard errors and the sign of the QI coefficient in (3), the CAP coefficient in (4) and the Log(GDPt-1) coefficient in (5) differ from the results when financial deepening is represented by either equity market turnover or private credit/gdp. An important difference between the variables used to describe financial deepening, is that the NEP variable is a much broader one than the other two. As shown in (1), the NEP is calculated using a broad set of data which gives a very general description of the financial market of an economy. Capital controls, on the other hand, are often aimed at a specific part of the financial system with a specific goal in mind. Since these capital controls then affect only a specific part of the financial system, its effects tend to get lost when a larger part of the financial market is under investigation. In contrast, using more detailed measures of financial deepening such as equity market turnover or private credit/gdp, yields much stronger empirical results. However, determining exactly which segment of the financial system is affected by which specific capital control policy is a daunting task. Most measures which describe capital controls do not distinguish (at all or to a large enough extent) between type or purpose. This is true even for some of the most widely used measures such as the Klein & Olivei (1999) or the Quinn (1997) dummy variables. A way forward for the field of research into capital controls would be to examine the types and purposes of capital controls more accurately and identify more precisely which part of the financial system is affected.
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