and its recent controls

Bruno Thiago Tomio Berlin School of Economics and Law Mohamed Amal Regional University of Blumenau

Abstract

With many turnarounds about the role and the effectiveness of capital controls throughout time, the doubt about its desirability remains to be conclusive answered. By researching the opinion of policymakers and academics, this paper aims to demonstrate the benefits and harms of capital controls, focusing on a specific country, Brazil. This goes in the direction of the current debate on capital controls, which gathers relevant issues, amongst others, the current increasing flows of international capital to developing countries. Not only Brazil, but also others countries have been raising their protection against speculative capital flows. After the 2007/08 financial crisis, the control on foreign capital has gained relevance because low interest rates are set in developed countries and high interest rates in developing countries. This global imbalance started generating a new wave of controlling mechanisms, also called macro prudential tools. Still inconclusive as well is if such measure is working or not and if is a sustainable policy. With an econometric model, we are going to test if Brazil and its flows are being affected by such measures. The aim is to estimate a model that can shed some light on these policies undertaken by Brazilian government. The results show that capital controls in the recent period have been ineffective.

Table of Contents

Introduction ...... 1

1. Brief History of Capital Controls ...... 1

2. Benefits, Harms, and Current Debate...... 2

3. Empirical model for Brazil ...... 6

Conclusion ...... 11

References ...... 11

Introduction

With many turnarounds about capital controls throughout time, the doubt about its desirability remains to be conclusive answered. The first part gives the definition of capital controls, and, most importantly, a brief history of it. Following, researching the opinion of policymakers and academics, the second part demonstrates the benefits and harms of capital controls. In the same way, and section, the current debate gathers relevant issues, amongst others, the current building up of controls of international capital in developing countries. Brazil current position of increasing capital controls is analyzed in depth with the help of an econometric model. Lastly, the final part concludes with the main insights.

1. Brief History of Capital Controls

In a broad definition, capital controls are the set of policies available for countries to make the regulation of capital flows (Ocampo and Stiglitz, 2008, p.357), either inflows or outflows. According to Kose et al. (2010), they may be direct, which is more related to administrative measures (e.g. limiting the volume of inflows), or indirect, also called market- based because it focus on taxing international capitals (e.g. Brazil’s recent tax on portfolio investments by non-residents). The history of capital controls is long, notably if the restrictions to gold and silver trade in the sixteenth-century are considered as such. Their “modern form” use dates from the (Cooper et al., 1999). Abdelal (2007, p.1-18) divides the recent history of capital controls into four periods, relating them to the conventional opinion about it. According to him, the world has seen capital controls as orthodoxy or heresy, depending on the rules and restrictions of the international financial system at the respective time. In between 1914-1944, capital controls were considered a heresy in the beginning, and in the end of this period, they shifted to orthodoxy. The year 1931 is the turning point because European countries, fearing the contagion of the in 1929, lifted again barriers for international capital flows. During the years 1944 to 1961, formal rules to control capital flows, especially or short-term speculative capital, were accorded by the members of three international institutions: IMF (1945), European Community (1947), and OCDE (1961). Remarkably, the change for easing the controls on capital started to occur after World War II with the Americans opposing British’s ideas, mainly from Keynes, of

1 stronger controls (Cooper et al., 1999). Consequently, the third period, from 1961 to 1986, experienced first illegal movements of capital, with the Eurodollars market, which was the lending of dollars by US banks in London; lastly, the unilateral liberalization of capital movements by the US and the , in the end of the 1970s. Germany and Japan joined the team of free capital movements in the middle of the 1980s; comprising the year 1986 and onwards as the last period, capital controls became more a heresy and less an orthodoxy. By the early and middle of the 1990s, the IMF widely supported free movement of capitals; despite the EU and OCDE countries, which were much liberalized, other economies controlled capitals as they wished to (Abdelal, 2007, p.12-13). Recently, the debate on the benefits and harms of international capital flows have increased because of financial crises: (a) the debt crisis in the 1980s, caused by foreign debt; (b) the Asian crisis in 1997-8, exposed the damage of rapidly movements of foreign capital; and, (c) today’s crisis (global financial crisis of 2007-8), explained by several reasons, but amongst them the mobility of huge amount of foreign capital flows (Evans, 2010). The history of capital controls shows that there is no unified view on the issue, and the debate about its benefits and harms still goes on.

2. Benefits, Harms, and Current Debate

There is an immense variety of studies on the benefits and harms of financial liberalization, which in a certain way are related to capital controls. Nevertheless, this discussion passes the limits of this essay, which focus is capital controls. In the recent literature, Dooley (1996) and Magud and Reinhart (2007) elaborated a survey of works on capital controls. Dooley (1996) main conclusions are: (a) regarding theoretical works, capital controls improve welfare; and, (b) on empirical works, he says that there is evidence of successful capital controls on increasing revenue for the governments and limiting domestic government debt costs, by decreasing debt-services payments. As for the main concluding insights of Magud and Reinhart (2007), control on capital inflows tend to be good for the economy (better control of monetary policies, and diminishing pressure of real ), and, the Malaysian case (1998-9) in controlling outflows is a unique case of success in these type of controls. When it comes to the theoretical ground, the arguments for and against capital controls are closely related to those of financial liberalization. This seems logical when

2 financial liberalization is defined as a process of easing controls of capital. Modenesi and Modenesi (2008) expose that the benefits from financial liberalization, i.e. against capital controls, are: (a) capital is better allocated around the world, where the profits are higher – same allocation argument is used by free trade supporters; (b) balance-of-payments are better adjusted, in favor of deficit countries; and, (c) governments would enhance their macroeconomic discipline because capital would flow more to countries with solid economies. In addition, capital controls go against the ideal of freedom, limiting the liberty of the members of societies; in another point of view, they are seen as ineffective too, capital always knows how to cheat controls illegally. Ahmed and Islam (2009, p. 5) gather the points pro financial liberalization in a group called the Goldsmith-McKinnon-Shaw school, because of these authors earlier works on financial liberalization. By contrast, arguments against financial liberalization, thus pro capital controls, argue that: (a) macroeconomic policies become more independent; (b) financially, the economy is more stable; (c) they are a necessary instrument against the overvaluation of the exchange rate by the flood of international capital; (d) avoid capital flight, which searches for higher interest rates; and, (e) can be used to foster specific changes in the structure of target sectors in the domestic economy by FDI. The main authors of this approach are Keynes, Harry D. White, Tobin, Davidson, Rodrik, and Stiglitz (Modenesi and Modenesi, 2008). A main argument is that countries tend to lose control over its exchanges rates, and, consequently, their indebtedness in foreign currencies grows, leading to balance-of-payments crises. Even rich countries are suffering some side effects of financial with the 2007-8 global financial crisis (Bresser Pereira, 2010, p. 220). In the actual scenario, sympathy for capital controls is higher worldwide. In 2007, flows and outflows of international capital for developed countries were about six or seven trillion of dollars. By 2009, they were close to zero. In the same period, the impact in emerging and developing countries was lighter (see Figure 1).

3 8

6

4

2

0 2007 2008 2009 -2

-4 DE inflows DE outflows -6 EDE inflows EDE outflows -8 Figure 1. Flows and outflows of international capital for Developed (DE) and Emerging and Developing (EDE) economies, 2007-2009 Source: Authors’ calculations with data from IMF (2010) Notes: The sum of direct investment, portfolio investment and other investment gives the amount of international capital flows and outflows. Developed economies are a compound of selected countries and areas: United States, United Kingdom and Area. Values in US dollars, trillion.

One explanation for different impacts on countries’ net flows of capital may rely on capital controls. Following Chelsky (2010, p. 121-122), China and India have given more recognition to the success of capital controls; thus the IMF has started to change its view on the acceptance of these controls. Recently, the IMF supports capital controls in some specific cases (Ostry et al., 2010; IMF, 2011), which is a turnaround from the institution’s non- supportive position in the 1997-8 Asian crisis (Krugman, 2010). In fact, this change emerges in a moment when the number of countries holding capital controls increases; to cite some countries with recent application of capital controls: Brazil, Thailand, Indonesia and South Korea (, 2011). Nevertheless, these controls may shift the focus away from other needs, like solid macroeconomic policies (Moreno, 2011). Carry-trade, a situation where an investor takes money with a low in a country and invests in another country with high interest rate, is the main cause for these recent controls. Brazil, for example, has many reserves, like many other emerging economies, and is suffering with an overvalued exchange rate, due to high flows of international capital seeking high returns with its high interest rate. In such cases, i.e. to avoid balance-of-payments crash, controls are desirable. It was not a direct call of the Brazilian (BCB hereafter) to lift such controls, but it was rather up to the Ministry of Finance to take action. Moreover, the desirability of an independent Central Bank and the lack of coordination between these two institutions to elaborate common macroeconomic policies might have been crucial factors to determine the success of capital controls in Brazil. While BCB had had to pursue restrictive monetary policies (raising the interest rate) due to inflationary pressures, the Ministry of

4 Finance has been pursuing ways to reduce the amount of speculative international capital. Extremely low interest rates in developed countries have supported eager investors in the world financial market to seek easy and expressive financial gains in Brazil. The main worry is the pressure on the exchange rate that can have irreversible consequences to Brazilian exports and industries. Figure shows three moments of actions undertaken by Brazilian government to stop the flood of dollars in its domestic market. Foreigners barely invested in Brazilian bonds before the burst of the current financial crisis in July/2007. Surely due to the fact of expansive monetary policies in developed countries, non-residents’ participation in this type of investment soared from mid-2007 until late 2010, in which is stabilized at around 11 per cent. The first action to avoid a complete disaster for Brazilian exporters occurred on March/2008, when the IOF (short in Portuguese for financial operations tax) on inflows of foreign capital was raised to 1.5 per cent. Following this, the same tax was increased on October/2008 to 2 per cent, and on October/2010 to 6 per cent.

14.0% IOF 6% 12.0% IOF 1.5% 10.0%

8.0%

6.0% IOF 2%

4.0%

2.0%

0.0% jul/11 jul/10 oct/10 jan/11 oct/08 jan/10 feb/11 apr/11 jun/11 feb/10 apr/10 jun/10 sep/10 dec/07 dec/10 dec/08 dec/09 aug/11 aug/10 nov/10 mar/07 mar/08 mar/11 mar/10 may/11 may/10 Figure 2. Non-residents’ participation in the Brazilian public debt (domestic), December 2007-June 2011 Source: Authors’ highlights with data from the Brazilian Treasury (2011).

This sudden bust of capital inflows did not coincide only with an appreciation or strenghten of the Brazilian curreny, real , in respect to the US dollar. On December 2008, Brazilian currency was quite devalued against the US dollar, when it was quoted at 2.39R$ for one US$. From this point onwards there has been a strong appreciation against the dollar. The lowest exchange rate for the post-burst of the financial bubble in the US period was

5 1.56R$/US$. Nonethless, worries about future slowdowns in the world economy and also, but maybe not so effectively, Brazilian capital controls have put an upward pressure on the exchange rate, which avareged 1.86 on October 2011 (check Figure 3). 2.39

2 1.86

1.56 1

0 2007.01 2007.04 2007.07 2007.10 2008.01 2008.04 2008.07 2008.10 2009.01 2009.04 2009.07 2009.10 2010.01 2010.04 2010.07 2010.10 2011.01 2011.04 2011.07 *2011.10 Figure 3. Exchange rate Brazilian real /US dollar, January 2010-October 2011 Source: IPEA (2011). Notes: For *2011.10 (October 2011) an average of the values for the first week of the month is calculated.

The most recent manoeuver to avoid excessive flows of capital to Brazil was the higher taxation of foreign borrowing and derivatives. In the following sections, the authors elaborate more on the issue of the effectiveness of capital controls, and its policy implications. An econometric model is used to find support for the current buildup of barriers for speculative capital in Brazil.

3. Empirical model for Brazil

Based on Silva and Resende (2010), the present empirical analysis incorporates four main variables to explain oscillations in short-term capital flows to Brazil. These authors apply a VAR model to this variables, whilst in the present paper we adapted to a different method. The method used is Ordinary Least Squares (OLS). Before going to the outcomes of this study, the model is explained, corrections to make the model more robust are made, and the hypotheses are shown. Table 1 gives a full explanation of the variables employed in the model.

6

Variable Expected sign Source Short-term fixed-income investments, net (STFI) Dependent variable BCB Secretariat of the Federal Tax payments for inflows (TXOUT) - Revenue of Brazil Secretariat of the Federal Tax payments for outflows (TXIN) - Revenue of Brazil Exchange rate (ER) - IPEA Interest rate (IR) + IPEA Table 1. Variables, expected signs, and sources. The functional form of the model is given by equation 1. constant STFI t = + β1TXOUT t + β2TXIN t + β3ER t + β4IR t + ut (1) The dependent variable is short-term fixed-income investments (STFI) and the regressors are tax payments for inflows (TXOUT), tax payments for outflows (TXIN), exchange rate (ER), and interest rate (IR). Figure 4 presents the plot of each variable against time (monthly observations ranging the period between January 2008 and August 2011. STFI TXOUT 1000 350

800 300 600

400 250

200 200

STFI

0 TXOUT

-200 150

-400 100 -600

-800 50 2008 2009 2010 2011 2008 2009 2010 2011 TXIN 800 ER 2.4

700 2.3 600 2.2

500 2.1

2 400 TXIN ER 1.9 300 1.8 200 1.7

100 1.6

0 1.5 2008 2009 2010 2011 2008 2009 2010 2011 IR 1.2

1.1

1

0.9

IR 0.8

0.7

0.6

0.5 Figure 4. Plot of the variables. 2008 2009 2010 2011

7 Some variables are clearly not stationary. In practice, to comprehend “the relationship between two or more variables using regression analysis, we need to assume some sort of stability over time” (Wooldridge, 2009: 379). It is also worth mentioning that shockwaves tend to become gradually weaker and finally disappear in stationary series; when non- stationary series are used the estimations become spurious (Brooks, 2002: 319). For the purpose of classifying series as stationary or not, there are unit root tests. This paper applies the Augmented Dickey-Fuller test (ADF-test) 1. This test provides the presence of unit roots as the null hypothesis. Consequently if we accept the null hypothesis the series must be differentiated one time to become stationary. According to Brooks (2002: 326), when a series is differentiated d times to reach a stationary process this variables is classified as integrated of order d. For example, an I(0) series does not show any root and is a stationary series, whilst an I(1) series does have a unit root and is becomes stationary only after being differentiated once. Results for ADF-tests are shown in Table 2. Variable p-value Order of integration Short-term fixed-income investments, net (STFI) 0.0324 I(0) Tax payments for inflows (TXOUT) 0.0000 I(0) Tax payments for outflows (TXIN) 0.8521 I(1) Exchange rate (ER) 0.0021 I(0) Interest rate (IR) 0.5365 (1 st ), 0.7981 (2 nd ) I(2) Table 2. Unit root tests Notes: It was tested down from a maximum lag order equals to 12. According to Wooldridge (2009: 633), there is no single rule for the lag choice.

By not rejecting the null hypothesis of presence of unit root, equation 1 needs to be change accordingly. The regression obtained mutatis mutandis is equation 2, with “ ∆” representing that the variable was differentiated one time (or two times for IR) to become stationary: constant ∆ ∆ STFI t = + β1TXOUT t + β2 TXIN t + β3ER t + β4 IR t + ut (2) With this equation and the chosen method, the aim is to test three hypotheses: Hypothesis Description + = + ≠ H1: proxies of capital control (TXOUT and TXIN) H0: β1 β2 0, H 1: β1 β2 0 < H2: exchange rate (ER) H0: β3 = 0, H 1: β3 0 > H3: interest rate (IR) H0: β4 = 0, H 1: β4 0 Table 3. Hypothesis and descriptions

Hypothesis one (H1) is at the core of the research question of this paper, since the variables TXOUT and TXIN are considered as proxies of capital controls. If controls are

1 These tests and every other test, estimation, plot of data and data manipulation in this paper were carried out with the software gretl, version 1.9.5cvs.

8 really working in Brazil, this paper argues that they should be statiscally signifcant and lower than zero (a negative value). Therefore, an inverse relation between these proxies and the flows of short-term capital flows is expected. Similarly picture is drawn for the second hypothesis (H2), capital flows are inversely related to the exchange rate. This is the opposite for the last hypothesis (H3), which tries to show that upwards movements in the interest rate by the BCB would increase the flow of foreign capital flows in Brazil. The regressors are not strictly exogenous because there is a feedback between them. For example, if flows of capital grow positively, the exchange rate will naturally move. Thus, a weaker set of assumptions (Table 4) is used in this paper, justified by a precaution of such issues. Assumption Definition TS.1’ Linearity and weak dependence TS.2’ No perfect collinearity TS.3’ Zero conditional mean TS.4’ Homoskedasticity TS.5’ No serial correlation Table 4. Time series weaker assumptions

This set of assumptions is required to hold as true in time series (TS) analysis. Assumptions TS.2’, TS.4’ and TS.5’ are going to be demonstrated if they hold or not later in this paper. TS.1’ was shown to hold earlier, as well as TS.3’ (the exogeneity of the regressors). As pointed out by Wooldridge (2009: 383), the Ordinary Least Squares (OLS hereafter) estimators are considered to be consistent under TS.1’, TS.2’ and TS.3’. Further, in Theorem 11.2 in Wooldridge (2009: 385), standard errors, t statistics, F statistics and LM statistics provided by the OLS estimators are asymptotically legitimate (i.e. under the assumptions TS.1’ across TS.5’, the OLS outputs are asymptotically normally distributed. Now it is time to check if there is or not homoskedasticity. Also the presence of autocorrelation is going to be checked. Since assumptions TS.1’ to TS.3’ hold, the remaining assumptions are tested next to check for homoskedasticity (TS.4’) and for autocorrelation (TS.5’). Table 5 displays the results for the autocorrelation test, which were obtained from the previously estimated models (OLS with robust standard errors, HC0 in gretl, which produces the original "White's standard errors"). This autocorrelation test performed by gretl is also known as Breusch-Godfrey, which uses LM-statistic. The null hypothesis stands for no autocorrelation. Thus for none of these models the null can be rejected. This confirms that there is no autocorrelation in our models and that assumption TS.5’ holds. The next part of the history is to run tests to check the

9 presence of homoskedasticity (assumption TS.4’). Since this issue is clarified, a simple OLS model is estimated to check for heteroskedasticity (displayed also in Table 5). Each of the performed heteroskedasticity test here has the null hypothesis of homoskedasticity. Test p-value Result Autocorrelation 0.8490 No problem with autocorrelation White’s test 0.1324 Homoskedasticity confirmed Breusch-Pagan 0.0000 Homoskedasticity not confirmed ARCH(4) 0.8551 Homoskedasticity confirmed Table 5. Diagnostic tests

Finally, Table 6 gathers the relevant and final outcomes of the estimations. Variable Coefficient p-value Constant 616.7310 0.0317 TXOUT -0.5186 0.3458 ∆TXIN 0.0704 0.3143 ER -278.3220 0.0453 ∆IR 432.5310 0.0162 R2 0.27 Adjusted R 2 0.19 F-statistic 2.5758 (p-value 0.0534) Number of observations 42 Table 6. Results for the OLS model with Heteroskedasticity-robust standard errors, variant HC0

From the results shown in the previous table, the model supports hypotheses H2 and H3. The coefficient does not matter much because we are more interested in the sign. For ER, a negative sign is obtained with the estimated model. Then, ER and STFIT are inversely related; when the first increases, the second decreases, and vice versa. H3 is also supported because IR is statistically significant and positive. This is especially important here to confirm the consequences of restrictive in Brazil during the analyzed period on financial capital flows. Separately, TXOUT and TXIN are not statistically significant in our model, and the first presents at least the expected sign while the other not. Jointly, both variables are not equal to zero (p-value is 0.4078 for the F-statistic). It is interesting to notice that when tested for a linear restriction that TXOUT plus TXIN are equal to one, this relation is statistically significant (p-value is 0.0102 for the F-statistic). It is not a naïve conclusion but this only represents that increases in capital flows also increase their taxation, showing the willingness of Brazilian government to block the upward trend of capital flows. Nevertheless, to the extent that this approach allows, capital controls via only the increase of taxes on foreign capital is seem ineffective for Brazil in the recent years.

10 Conclusion

Finally, international capital flows play a central role in the world economy. The Asian crisis has shown how destructive they can be; millions of people jumped into poverty at that time. In today’s crisis, this number is not yet there, but it will surprise. There is no lack of support for capital controls now, and governments must act, or they will lose. However, there is a limit for such policies, since capital flows are sometimes a harm that may come for good. Brazil is already looking for partners to create measures to avoid future disasters in the world economy as it was seen in the Asian crisis. With or without support of international organizations, countries must act either unilaterally or conjointly to avoid speculative capital.

References

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11 Dooley, M. P. (1996). A Survey of Literature on Controls over International Capital Transactions. International Monetary Fund Staff Papers , 43 (4), pp. 639-649. Evans, T. (2010). Five Explanations for the International Financial Crisis. Institute for International Political Economy Berlin Working Paper , No. 08/2010. IMF (2010). Global Financial Stability Report , October. Washington, D.C.: IMF. IMF (2011). World Economic Outlook Update , January. Washington, D.C.: IMF. IPEA (2011). Several datasets. Accessed at http://www.ipeadata.gov.br/ on October 4, 2011. Kose, M. A., Prasad, E., Rogoff, K. and Wei, S. (2010). Financial Globalization and Economic Policies. In Rodrik, H. and Rosenzweig, M. (Eds.), Handbook of Development Economics , Volume 5, Chapter 65. Amsterdam: Elsevier Science Publisher. Krugman, P. (2010). Malaysian Memories. The Conscience of a Liberal . Accessed at http://krugman.blogs.nytimes.com/2010/03/04/malaysian- memories/?p...1 on February 20, 2011. Magud, N. and Reinhart, C. M. (2007). Capital Controls: An Evaluation. In Edwards, S. (Ed.), Capital Controls and Capital Flows in Emerging Economies: Policies, Practices, and Consequences , Chapter 14. Chicago: University of Chicago Press. Modenesi, A. M. and Modenesi, R. L. (2008). Capital Controls and Financial Liberalization: Removing the Ideological Bias. Journal of Post Keynesian Economics , 30 (4), pp. 563-584. Moreno, R. (2011). Policymaking from a “Macroprudential” Perspective in Economies. BIS Working Papers , No. 336. Ocampo, J. A. and Stiglitz, J. E. (Eds.) (2008). Liberalization and Development . New York: Oxford University Press. Ostry, J. D., Ghosh, A. R., Habermeier, K., Chamon, M., Qureshi, M. S. and Reinhardt, D. B. S. (2010). Capital Inflows: The Role of Controls. IMF Staff Position Note , SPN/10/04. Silva, G. J. C. and Resende, M. F. C. (2010). Eficácia dos Controles de Capitais no Brasil: uma Abordagem Teórica e Empírica Alternativa. Estudos Econômicos (USP. Impresso), 40, pp. 617-649. Wooldridge, J. (2009). Introductory Econometrics: A Modern Approach , Fourth Edition. Canada: South-Western, a part of Cengage Learning. World Bank (2011). Global Economic Prospects: Navigating Strong Currents , Volume 2, January 2011. Washington, D.C.: The World Bank.

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