Capital Control Measures: A New Dataset

Fernández, Andrés; Klein, Michael W.; Rebucci, Alessandro; Schindler, Martin; Uribe, Martín Working Paper Capital Control Measures: A New Dataset IDB Working Paper Series, No. IDB-WP-573 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Fernández, Andrés; Klein, Michael W.; Rebucci, Alessandro; Schindler, Martin; Uribe, Martín (2015) : Capital Control Measures: A New Dataset, IDB Working Paper Series, No. IDBWP-573, Inter-American Development Bank (IDB), Washington, DC, https://hdl.handle.net/11319/6814 This Version is available at: https://hdl.handle.net/10419/115520 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Klein Alessandro Rebucci Martin Schindler Martín Uribe Department o f Research and Chie f Economist IDB-WP-573 IDB WORKING PAPER SERIES No. Inter-American Development Bank February 2015 Cap i tal Control Measures: A New Dataset Andrés Fernández* Michael W. Klein** Alessandro Rebucci*** Martin Schindler**** Martín Uribe***** * Inter-American Development Bank ** Tufts University and National Bureau of Economic Research *** Johns Hopkins University **** International Monetary Fund and Joint Vienna Institute ***** Columbia University and National Bureau of Economic Research 2015 Inter-American Development Bank Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Capital control measures: a new dataset / Andrés Fernández, Michael W. Klein, Alessandro Rebucci, Martin Schindler, Martín Uribe. p. cm. — (IDB Working Paper Series ; 573) Includes bibliographic references. 1. Capital. 2. Foreign exchange. 3. Foreign exchange administration. I. Fernández , Andrés. II. Klein, Michael W., -1958 . III. Rebucci, Alessandro . IV. Schindler, Martin, -1971 . V. Uribe, Martin . VI. InterAmerican Development Bank. Department of Research and Chief Economist. VII. Series. IDB-WP-573 http://www.iadb.org Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB’s name for any purpose other than for attribution, and the use of IDB’s logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license. Following a peer review process, and with previous written consent by the Inter-American Development 2015 Copyright © Inter-American Development Bank. 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The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. 1 Abstract* This paper presents and describes a new dataset of capital control restrictions on both inflows and outflows of 10 categories of assets for 100 countries over the period 1995 to 2013. Building on the data first presented in Schindler (2009) and other datasets based on the analysis of the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER), this dataset includes additional asset categories, more countries, and a longer time period. The paper discusses the manner in which information in the AREAER is translated into a usable dataset. The paper additionally characterizes the data with respect to the prevalence of controls across asset categories, the correlation of controls across asset categories and between controls on inflows and controls on outflows, the aggregation of the separate categories into broader indicators, and the comparison of this dataset with other indicators of capital controls. JEL classifications: C82, F21, F32, F36, F38, F40 Keywords: Capital flows, Financial integration, Capital control measures * We thank Javier Caicedo for providing excellent research assistance in the construction of this dataset described in this paper. The information and opinions presented in this work are entirely those of the authors, and express or imply no endorsement by the Inter-American Development Bank, the International Monetary Fund, the Board of Executive Directors of either institution, or the countries they represent. Fernández: Inter-American Development Bank ([email protected]) Klein: Tufts University and National Bureau of Economic Research ([email protected]) Rebucci: Johns Hopkins University ([email protected]) Schindler: International Monetary Fund ([email protected]) and Joint Vienna Institute ([email protected]) Uribe: Columbia University and National Bureau of Economic Research ([email protected]) 2 1. Introduction International capital flows are central to international macroeconomics. The interaction between the monetary and exchange rate policies of a country depends upon its stance towards capital mobility, as described by the policy trilemma. The ability of a government and its citizens to borrow and lend abroad allows domestic investment to diverge from domestic savings, which can promote economic efficiency and growth. In addition, international portfolio diversification is a potentially important means by which individuals can smooth consumption and undertake risky investments that would otherwise be unattractive. On a less salutary note, international capital flows are also blamed for being an important vector through which economic disturbances are spread across countries, or as a means by which investors prompt a sudden stop that causes an economy to crash. This range of potential outcomes from the international trade in assets has contributed to varying attitudes towards capital flows, as well as towards capital controls. Controversies over international capital flows have a long history. For example, in 1920 J.M. Keynes wrote elegiacally of a pre-war time when a person could “…adventure his wealth in the natural resources and new enterprises of any quarter of the world...” (The Economic Consequences of the Peace, Chapter II). But he took a very different tone in a 1933 speech in Dublin when he stated “… let goods be home-spun whenever it is reasonable and conveniently possible and, above all, let finance be national.”1 Keynes’ negative view of international capital flows in the midst of the Great Depression echoes through time in more contemporary calls for capital controls, especially in the wake of the recent current economic and financial crisis. While capital controls were pervasive during the Bretton Woods era, they were reduced or eliminated beginning in the late 1970s, and, increasingly, in the 1980s and 1990s. The title of Rudiger Dornbusch’s 1998 article “Capital Controls: An Idea Whose Time is Gone” reflects a broad consensus at that time. But attitudes began to shift in response to the economic crises in the late 1990s (Rodrik, 1998; Bhagwati, 1998). These changes were far from a fringe view; in 2002, Kenneth Rogoff, then serving as the Chief Economist and Director of Research of the International Monetary Fund wrote in the Fund’s publication Finance and Development “These days everyone agrees that a more eclectic approach to capital account liberalization is required.” 1 Quoted in Skidelsky (1992: 477). 3 The Great Recession has spurred a further reevaluation of the appropriate role of capital controls. Countries as diverse as Brazil and Switzerland considered (and in the case of Brazil, implemented) controls on inflows in the face of currency appreciation, while Iceland introduced controls on outflows at the time of its crisis. A number of recent IMF staff studies and policy papers accept the use of capital controls as part of a country’s “policy toolkit” under certain circumstances, a shift that The Economist magazine dubbed “The Reformation.”2 Even stronger calls for a greater role for capital controls include Jeanne, Subramanian and Williamson (2012) and Rey (2013). Some of these policy prescriptions are consistent with a new branch of theoretical research in which capital controls contribute to financial stability and macroeconomic management.3 The empirical research of others, however, emphasizes the ineffectiveness and potential costs of capital controls.4 The evolving nature of the debate on capital controls, and the policy prescriptions that follow, suggest that further careful empirical analysis is needed. One challenge facing empirical researchers in this area concerns the availability of indicators of capital controls. Although some empirical research addresses this challenge by considering the experience of a specific country,5 broader, cross-country analyses require panel data reflecting the experience of a range of countries. While a number of panel datasets exist, those with broad time and/or country coverage are typically hampered by a lack of granularity (for example, Chinn and Ito, 2006, and Quinn, 1997), often providing little information beyond a broad index of “capital account openness,” while others with finer granularity have been more limited in terms of sample coverage (such as Schindler, 2009, Miniane, 2004, and Tamirisa, 1999).6 In this paper, we introduce a new dataset based on the methodology in Schindler (2009), but including more countries, more asset categories and more years. In particular, the new dataset reports the presence or absence of capital controls, on an annual basis, for 100 countries over the period 1995 to 2013. As discussed in greater detail below, this dataset revises, extends, 2 Examples of IMF studies include Ostry et al. (2010) and Ostry et al. (2011). The article in The Economist appeared in the April 7, 2011 issue. 3 For just a few examples, see Korinek (2010), Bianchi (2011), Farhi and Werning (2012), Jeanne (2012), SchmittGrohé and Uribe (2012), and Benigno et al. (2014). 4 See, for example, Forbes (2007), Binici, Hutchison and Schindler (2010), Klein (2012), Prati, Schindler and Valenzuela (2012), and Klein and Shambaugh (2015). 5 See, for example, studies of the experiences of Chile by DeGregorio, Edwards and Valdés (2000) and Forbes (2007), and of Brazil by Forbes et al. (2012). 6 See Quinn, Schindler, and Toyoda (2011) for a comprehensive review of existing de jure measures. 4 and widens the dataset originally developed by Schindler (2009), and later expanded by Klein (2012) and Fernández, Rebucci and Uribe (2014). This dataset’s wide range of countries and its coverage of a period of changing policies make it a potentially important resource for research and policy.7 In particular, a distinguishing and important feature of these data is that the information on capital controls is disaggregated both by whether the controls are on inflows or outflows, and by 10 different categories of assets. This allows for a more detailed analysis of capital controls, including an examination of the co-movements of controls on different types of assets, and on the co-movements of controls on inflows and outflows, as well as the construction of aggregate measures of controls that are well targeted to the specific nature of the topic being studied. Variations of such aggregate measures across time serve as one indicator of the intensity of the application of restrictions on international capital movements. The next section of the paper discusses the methods used to develop this dataset from annual information published by the IMF. In Section 3 we discuss some statistics of our disaggregated dataset, including the correlation across categories of assets and directions of transactions (that is, controls on inflows or on outflows). Section 4 discusses issues related to aggregating the asset categories and also compares an aggregated index of our data with two aggregate indicators that are commonly used in panel estimation, those first introduced in Quinn (1997) and in Chinn and Ito (2006). We offer some concluding comments in Section 5. 2. Constructing the Capital Control Indicators Cross-country time series of capital controls typically draw from the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER).8 The capital control measures presented in this paper are also based on the de jure information from this source.9 There was a fundamental change in the reporting on capital controls beginning with the 1996 volume of the 7 The dataset will be publicly available on several websites, including that of the National Bureau of Economic Research (see www.nber.org/data) 8 The early works that use the AREAER to create panel datasets of capital controls include Grilli and Milesi-Ferretti (1995), Quinn (1997) and Chinn and Ito (2006). 9 That is, the measures capture legal restrictions, but not whether or to what extent they are enforced. One difficulty in trying to construct empirically-based de facto indicators of capital account restrictions is that there is not a clear benchmark of the gross capital flows consistent with free capital mobility. Furthermore, de facto indicators based on the equalization of rates of return would assume efficient markets, and require making assumptions about investors’ expectations and preferences as well as the correlations of asset returns with other measures of risk. 5 AREAER (providing information for conditions in 1995) when it began including more detailed information both across a disaggregated set of assets and by distinguishing between controls on outflows and controls on inflows; thus our data series begin in 1995 and currently include data through 2013.10 In this section we describe the dataset we have constructed and discuss the methods we have taken to translate the narrative in the annual volumes into a panel dataset. The present work revises, extends, and widens the dataset originally developed by Schindler (2009), and later expanded by Klein (2012) and Fernández, Rebucci and Uribe (2014). Schindler’s dataset covers 91 countries over the period 1995 to 2005, and considers restrictions on inflows and outflows over six asset categories, namely, equity, bonds, money market, collective investment, financial credit, and foreign direct investment. Klein (2012) extends Schindler’s dataset to include the period 2006 to 2010 but limits the coverage to 44 countries and restrictions on inflows. Fernández, Rebucci and Uribe (2014) further extend the dataset to the year 2011 for the original 91 countries in Schindler (2009). They also consider restrictions on capital inflows and outflows. The dataset discussed in this paper extends currently available data in three dimensions; asset categories, countries, and sample period. The four new asset categories are derivatives, commercial credit, financial guarantees, and real estate. Derivatives are of particular interest, given their increasing role in international transactions (Lane and Milesi-Ferretti, 2007). The nine new countries were selected through a population-based criterion, bringing the total number of countries to 100.11 The sample period has been extended to cover the period 1995 to 2013. This paper also provides the specific set of rules used for coding the narrative in the AREAER reports in order to generate the data. These rules are explained in detail below, and in even greater detail in a technical appendix that will be available online. The rules build on those used by Schindler (2009). We clarify the rules, and provide explicit criteria, in order to facilitate future updates of the dataset. These rules are also used to revise some of the observations in 10 There is very limited coverage for the years 1995 and 1996 for one category of assets, controls on bonds with maturity of greater than one year, and so the data series for this asset begins in 1997. 11 The nine added countries were those with the largest populations in 2012 (according to the World Development Indicators) that were not in the original Schindler dataset, but were included in the AREAER. These countries are Algeria, Colombia, Ethiopia, Iran, Myanmar, Nigeria, Poland, Ukraine and Vietnam. 12 Table 2. Countries In Dataset, By Income Groups, With Open/Gate/Wall Category High (42) Upper Middle (26) Lower Middle & Low (32) Australia Gate Algeria Wall Bangladesh* Gate Austria Open Angola Wall Bolivia Gate Bahrain Gate Argentina Gate Burkina Faso* Gate Belgium Open Brazil Gate Cote d'Ivoire Wall Brunei Darussalam Open Bulgaria Gate Egypt Open Canada Open China Wall El Salvador Open Chile Gate Colombia Gate Ethiopia* Gate Cyprus Gate Costa Rica Open Georgia Open Czech Republic Gate Dominican Republic Gate Ghana Gate Denmark Open Ecuador Gate Guatemala Open Finland Open Hungary Gate India Wall France Open Iran Gate Indonesia Gate Germany Gate Jamaica Gate Kenya* Gate Greece Open Kazakhstan Gate Kyrgyz Republic Gate Hong Kong Open Lebanon Gate Moldova Gate Iceland Gate Malaysia Wall Morocco Wall Ireland Open Mauritius Open Myanmar* Gate Israel Gate Mexico Gate Nicaragua Open Italy Open Panama Open Nigeria Gate Japan Open Peru Open Pakistan Wall Korea Gate Romania Gate Paraguay Open Kuwait Gate South Africa Gate Philippines Wall Latvia Open Thailand Gate Sri Lanka Wall Malta Gate Tunisia Wall Swaziland Wall Netherlands Open Turkey Gate Tanzania* Wall New Zealand Open Venezuela Gate Togo* Wall Norway Open Uganda* Gate Oman Open Ukraine Wall Poland Gate Uzbekistan Wall Portugal Gate Vietnam Gate Qatar Open Yemen Open Russia Gate Zambia Open Saudi Arabia Gate * = Low Income rather than Lower Middle Income Singapore Open Slovenia Gate Spain Open Sweden Open Switzerland Gate U.A.E. Gate United Kingdom Open United States Open Uruguay Open Open (36) / Gate (48) / Wall (16) 24 / 18 / 0 4 / 17 / 5 8 / 13/ 11 Note: Following Klein (2012), “Open” (“Walls”) countries have, on average, capital controls on less than 10 percent (more than 70 percent) of their transactions subcategories over the sample period and do not have any years in which controls are on more than 20 percent (less than 60 percent) of their transaction subcategories. “Gate” countries are neither Walls nor Open. 13 Figure 1 shows the prevalence of controls across 20 asset/direction categories. In this figure, no distinction is made between a value of ½ and 1; instead, each is treated equally as a control. The prevalence of controls ranges from 18 percent of observations (for liquidation of direct investment), to 25 percent (for inflow controls on Guarantees, Sureties and Financial Backup Facilities) to 50 percent or greater (for inflow controls on Real Estate and outflow controls on Money Market Instruments, Bonds, Equities, Collective Investments, and Derivatives). The figure also demonstrates that, with the exceptions of Real Estate and Direct Investment, there is a higher prevalence of controls on outflows than on inflows. A more detailed analysis by asset/direction category is presented in Table 3. The first set of columns shows the average control values (0, ½ or 1) for those 11 asset/direction categories that have two components for inflows or outflows, and the second set of columns shows the number of cases where controls are absent or present for the 10 asset/direction categories that 0 .05 .1 .15 .2 .25 .3 .35 .4 .45 .5 .55 Proportion with Controls mmi mmo boi boo eqi eqociicio dei deorei reofci fcocci cco gsi gsodii dioldi Asset Category and Direction (Inflow (i) or Outflow (o)) of Restriction By Asset Category and Direction of Restriction Figure 1: Proportion of Observations With Controls 14 have only one component each for inflows and outflows. The final row of the second column shows that overall, 40 percent of the observations represent cases in which there are capital controls. For the asset/direction categories that can take the value 0, ½ or 1, there are more observations of 1 than of ½ (the difference is 26 percent of observations versus 20 percent). Table 3. Prevalence of Controls, 100 Countries, 1995 – 2013, by Asset Sub-Categories 0 0.5 1 Total Pr. Cntrl 0 1 Total Pr. Cntrl mmi 1,143 346 388 1,877 0.39 fci 1,205 685 1,890 0.36 mmo 917 367 589 1,873 0.51 fco 1,119 767 1,886 0.41 boi* 980 378 327 1,685 0.42 cci 1,337 546 1,883 0.29 boo* 807 356 517 1,680 0.52 cco 1,225 644 1,869 0.34 eqi 1,024 459 399 1,882 0.46 gsi 1,384 471 1,855 0.25 eqo 914 388 584 1,886 0.52 gso 1,227 631 1,858 0.34 cii 1,152 360 335 1,847 0.38 dii 1,121 779 1,900 0.41 cio 892 398 577 1,867 0.52 dio 1,246 625 1,871 0.33 dei 1,073 219 452 1,744 0.38 ldi 1,546 334 1,880 0.18 deo 890 310 585 1,785 0.50 rei 828 1,034 1,862 0.55 reo 1,084 395 388 1,867 0.42 Total 23,469 15,134† 38,603 0.40 Pr. Cntrl. = Proportion of observations with controls (i.e. either ½ or 1) _i = control on inflows. _o = control on outflows mm – Money Market Instruments (Debt instruments with maturity 1 year or less) bo – Bonds (Debt instruments with maturity greater than 1 year) eq – Equities ci – Collective Investments de – Derivatives re – Real Estate fc – Financial Credits cc – Commercial Credits gs – Guaranties & Sureties di – Direct Investment ldi – liquidation of direct investment *Data on Bonds available 1997-2013 † This entry represents number of values equal to 0.5 or 1. The detailed nature of our dataset enables us to consider, along with differences in the prevalence of controls across asset/direction categories, the correlation of controls across these categories.17 This is of interest for a number of reasons, including how governments choose to pair controls across asset categories or between those on inflows and those on outflows, and whether such pairings strengthen the overall effect of policies. Table 4 presents correlations across the 10 asset categories that are listed in its rows and columns. The diagonal cells of the 17 The correlations are across all observations, that is, across all pairs x(t), y(t), where x and y represent asset/direction categories and t represents the time period. Correlations will be missing if the variance of an indicator is zero, but, in practice, there are relatively few instances of this, even among the Open and Walls categories. Zero variances would be more prevalent if we first calculated correlations for each country, that is the correlation of x(i,t) and y(i,t) where i represents a country, and then take the average of these correlations across countries to calculate the overall correlation. 15 table show the correlation between inflows and outflows for each asset category; for example the correlation between mmi and mmo is 0.78 and the correlation between eqi and eqo is 0.72. The upper triangular cells of the table show the correlations across asset categories for inflow controls; for example, the correlation between eqi and cii is 0.70. The lower triangular cells of the table show the correlations across asset categories for outflow controls; for example, the correlation between gso and cco is 0.74. The 100 entries in this table are color coded, with red cells representing correlations between 0.80 and 1.00, green cells representing correlations between 0.60 and 0.69, turquoise cells representing correlations between 0.40 and 0.59, yellow cells representing correlations between 0.20 and 0.39, and no color highlighting for cells with correlations less than 0.20. Table 4. Cross-Category Correlations, All 100 Countries, 1995-2013 mm bo eq ci de re fc cc gs Di Mm 0.78 0.74 0.69 0.78 0.74 0.22 0.59 0.44 0.46 0.40 Bo 0.82 0.74 0.70 0.66 0.67 0.21 0.54 0.37 0.46 0.40 Eq 0.83 0.87 0.72 0.70 0.61 0.37 0.54 0.40 0.50 0.55 Ci 0.87 0.83 0.85 0.75 0.72 0.21 0.63 0.51 0.56 0.49 De 0.84 0.80 0.80 0.80 0.86 0.16 0.60 0.41 0.47 0.32 Re 0.69 0.64 0.66 0.67 0.69 0.30 0.17 0.19 0.18 0.29 Fc 0.69 0.64 0.67 0.66 0.69 0.63 0.62 0.67 0.62 0.37 Cc 0.64 0.55 0.60 0.58 0.65 0.58 0.70 0.58 0.51 0.36 Gs 0.64 0.57 0.62 0.61 0.67 0.64 0.75 0.74 0.61 0.26 Di 0.73 0.68 0.72 0.72 0.71 0.70 0.68 0.64 0.68 0.37 Diagonal: Inflow vs. Outflow Controls Correlation Highlight Colors: Red = 0.80 – 1.00 Upper Triangular: Inflow vs. Inflow Green = 0.60 – 0.79 Turquoise = 0.40 – 0.59 Lower Triangular: Outflow vs. Outflow Yellow = 0.20 – 0.39 No Highlight = 0.00 - 0.19 mm – Money Market Instruments (Debt instruments with maturity 1 year or less) bo – Bonds (Debt instruments with maturity greater than 1 year) eq – Equities ci – Collective Investments de – Derivatives re – Real Estate fc – Financial Credits cc – Commercial Credits gs – Guaranties & Sureties di – Direct Investment The table shows that the correlation between inflow controls and outflow controls for a given asset tends to be high. The highest correlation between inflow and outflow controls is for Derivatives (86 percent) and the lowest is for Direct Investment (37 percent) and Real Estate (30 percent). This result echoes that obtained by Fernández, Rebucci and Uribe (2014), who show that the cyclical components of capital controls on inflows and outflows are positively correlated. The correlation between asset categories, for both inflow controls and outflow controls, is 16 highest among Money Market Instruments, Bonds, Equities, Collective Investments, and Derivatives. The lowest correlations are found for inflow controls between Real Estate and each of the other nine categories of assets. More broadly, the correlations are higher among the asset categories for outflow controls than for inflow controls. Countries that had almost no controls for any category over the entire sample period, as well as countries that had controls on virtually all assets in every year, will contribute to larger values of the correlations in Table 4. We call these Open countries and Wall countries, respectively, following Klein (2012). In particular, the 36 countries in the Open category (which includes 24 of the 42 High Income countries) each had capital controls on less than 15 percent of their asset/direction categories over the sample period and had no year in which capital controls were in place on more than 25 percent of the categories. The 16 countries in the Wall category (which includes 11 of the 26 Lower Middle Income and Low Income countries) each had controls on at least 70 percent of their asset/transaction categories and had no year in which capital controls were in place on less than 60 percent of the categories. The 48 countries that are neither Open nor Wall are classified as Gate countries. As mentioned above, Table 1 notes the classification of each country in terms of these three categories. Table 5A presents the correlations across asset/direction categories for the 48 Gate countries and Table 5B presents these correlations for the 52 Open and Wall countries. As expected, the correlations for the Gate countries are lower than those of the other countries, with only one greater than 80 percent (red cell) and 40 less than 40 percent (yellow cells, and cells without highlighting). In contrast, all the correlations in Table 5B among outflows are greater than 80 percent, and the majority of those among inflows (but for correlations with real estate) greater than 60 percent, with a fifth of the inflow restriction correlations greater than 80 percent. 17 Table 5A. Cross-Category Correlations, 47 Gate Countries, 1995-2013 mm bo Eq Ci De re fc cc gs di mm 0.69 0.65 0.55 0.66 0.69 0.03 0.47 0.27 0.26 0.29 bo 0.71 0.58 0.55 0.46 0.54 0.01 0.30 0.11 0.24 0.23 eq 0.67 0.81 0.55 0.51 0.43 0.22 0.30 0.10 0.27 0.44 ci 0.77 0.75 0.70 0.60 0.57 -0.01 0.46 0.33 0.35 0.41 de 0.76 0.70 0.63 0.64 0.79 -0.03 0.43 0.15 0.18 0.19 re 0.57 0.43 0.44 0.52 0.54 0.08 -0.02 -0.07 0.01 0.24 fc 0.50 0.42 0.41 0.45 0.51 0.43 0.48 0.59 0.43 0.27 cc 0.39 0.23 0.24 0.23 0.38 0.33 0.55 0.46 0.36 0.27 gs 0.41 0.29 0.31 0.31 0.46 0.41 0.65 0.60 0.44 0.17 di 0.54 0.50 0.51 0.54 0.51 0.56 0.52 0.38 0.50 0.22 Diagonal: Inflow vs. Outflow Controls Correlation Highlight Colors: Red = 0.80 – 1.00 Upper Triangular: Inflow vs. Inflow Green = 0.60 – 0.79 Turquoise = 0.40 – 0.59 Lower Triangular: Outflow vs. Outflow Yellow = 0.20 – 0.39 No Highlight = 0.00 - 0.19 mm – Money Market Instruments (Debt instruments with maturity 1 year or less) bo – Bonds (Debt instruments with maturity greater than 1 year) eq – Equities ci – Collective Investments de – Derivatives re – Real Estate fc – Financial Credits cc – Commercial Credits gs – Guaranties & Sureties di – Direct Investment Table 5B. Cross-Category Correlations, 53 Open and Wall Countries, 1995-2013 mm bo Eq Ci De re fc cc gs di mm 0.83 0.83 0.82 0.90 0.79 0.37 0.71 0.60 0.70 0.47 bo 0.89 0.86 0.83 0.85 0.80 0.37 0.78 0.63 0.73 0.53 eq 0.93 0.91 0.85 0.88 0.77 0.48 0.77 0.70 0.75 0.63 ci 0.94 0.87 0.95 0.86 0.86 0.40 0.78 0.68 0.77 0.55 de 0.88 0.87 0.91 0.87 0.93 0.31 0.76 0.67 0.73 0.41 re 0.81 0.81 0.84 0.80 0.83 0.47 0.33 0.43 0.34 0.31 fc 0.84 0.81 0.88 0.84 0.84 0.80 0.76 0.74 0.82 0.43 cc 0.86 0.82 0.90 0.87 0.89 0.81 0.84 0.70 0.69 0.43 gs 0.84 0.80 0.88 0.86 0.87 0.85 0.85 0.87 0.79 0.38 di 0.90 0.85 0.90 0.87 0.90 0.83 0.83 0.91 0.87 0.50 Diagonal: Inflow vs. Outflow Controls Correlation Highlight Colors: Red = 0.80 – 1.00 Upper Triangular: Inflow vs. Inflow Green = 0.60 – 0.79 Turquoise = 0.40 – 0.59 Lower Triangular: Outflow vs. Outflow Yellow = 0.20 – 0.39 No Highlight = 0.00 - 0.19 mm – Money Market Instruments (Debt instruments with maturity 1 year or less) bo – Bonds (Debt instruments with maturity greater than 1 year) eq – Equities ci – Collective Investments de – Derivatives re – Real Estate fc – Financial Credits cc – Commercial Credits gs – Guaranties & Sureties di – Direct Investment 18 Correlations in controls for the subset of Gate countries are a better indicator of the manner in which countries pair controls used episodically than the correlations for the full set of countries. The highest correlations for the Gate countries are those between outflow controls on Money Market Instruments, Bonds, Equities, Collective Investments and Derivatives. The lowest correlations are those for inflow controls with Commercial Credits, and Real Estate. These patterns of correlations will inform our decisions on which asset categories to use when constructing aggregate capital control indices, which is the topic of the next section. 4. Aggregate Indicators The correlations presented in Tables 4 and 5 are based on disaggregated asset/direction categories (with averages used for the categories that have two components for either inflows or outflows). In many instances it may be desirable to have a more aggregated indicator. For instance, one might be interested in studying the intensity with which capital controls are applied. By tracking variations across asset categories, directions of transactions, and time, aggregate indices capture a form of intensity of restrictions on capital movements across borders. Indeed, Fernández, Uribe and Rebucci (2014) show that an aggregate index of controls on capital inflows captures well the evolution of actual tax rates on capital inflows in the emblematic case of Brazil in the late 2000s. In this section we present a number of aggregate indicators and use them to demonstrate some characteristics of the capital control data. An aggregate of the capital control indicators is important for presenting the evolution of capital controls over time; a graph of the 32 disaggregated capital control categories would be hopelessly muddled. Therefore, we first calculate two broad indicator of the stance of each country towards capital controls, one as the average value controls on inflows for the 10 asset categories in each year, 𝐾𝐾𝑖,𝑡 𝐼𝐼𝐼𝐼𝐼𝐼 =1 10 ∑𝑋𝑋𝑖,𝑗,𝑡 𝐼𝐼𝐼𝐼𝐼𝐼10 𝑗=1 and another as the controls on outflows, 𝐾𝐾𝑖,𝑡 𝐼𝑂𝑂𝐼𝐼𝐼𝐼 =1 10 ∑𝑋𝑋𝑖,𝑗,𝑡 𝐼𝑂𝑂𝐼𝐼𝐼𝐼10 𝑗=1 19 where 𝑋𝑋𝑖,𝑗,𝑡 𝐼𝐼𝐼𝐼𝐼𝐼 represents controls on inflows of the jth asset category (e.g., Money Market Instruments, Bonds, etc.) for the ith country in year t, and 𝑋𝑋𝑖,𝑗,𝑡 𝐼𝑂𝑂𝐼𝐼𝐼𝐼 is the comparable control on outflows of the jth asset category for the ith country in year t. We cannot plot the evolution for all 100 countries, however, so we take the average value for each of the four income groups; High, Upper Middle, Lower Middle and Low. Figures 2a and 2b present the plots of these four aggregate series for controls on inflows and controls on outflows, respectively. Figures 2a and 2b show that, on average, the capital control index is inversely related to income. Specifically, the left axis in each figure is for the High Income group, and its midpoint is about 0.15 in Figure 2a and 0.17 in Figure 2b while midpoints of the right axes, which pertain to the other three groups, is about 0.53 and 0.60, respectively. This difference is not surprising, given the relatively large proportion of High Income countries that are classified as Open, and the relatively higher proportion of countries in the other three groups that are classified as Gate or Wall countries. This is also consistent with the findings of Fernandez, Uribe and Rebucci (2014), who found an inverse relation between capital controls and income levels, although their findings came from a more limited sample in terms of assets, countries and years. .4 .45 .5 .55 .6 .65 .15 .2 .25 Average Across 10 Asset Categories 1995 1998 2001 2004 2007 2010 2013 High(42) (Left Axis) Upper Middle(26) (Rt. Axis) Lower Middle(24) (Rt. Axis) Low(8) (Rt. Axis) Averages Across 10 Asset Categories Figure 2a: Average Controls on Inflows by Income Group 20 Another distinction across the income groups is the pattern of average capital controls over time. The High Income group of countries has a large decrease in its average from about 0.20 for inflows and 0.22 for outflows in the first years of the sample period to less than 0.10 in 2008 for inflows and 0.12 in 2004 for outflows before rising again in the subsequent years. The Low Income countries as a group also see a large decline in their average inflow and outflow controls in the first years of the sample period, and then an increase, especially in average controls on outflows. The range of the averages across time for both inflow controls and outflow controls for the two Middle Income groups is lower than the other groups, and the averages themselves are lower than the Low Income group but more than twice as high as those for the High Income group. The aggregate indicators used to generate Figures 2a and 2b show some differences between controls on inflows and controls on outflows. We further consider the relationship between inflow controls and outflow controls by calculating, for each country, its average controls on inflows and outflows over the full sample period, KCINFLOWi and KCOUTFLOWi, respectively. These are defined as .4 .5 .6 .7 .8 .12 .14 .16 .18 .2 .22 Average Across 10 Asset Categories 1995 1998 2001 2004 2007 2010 2013 High(42) (Left Axis) Upper Middle(26) (Rt. Axis) Lower Middle(24) (Rt. Axis) Low(8) (Rt. Axis) Averages Across 10 Asset Categories Figure 2b: Average Controls on Outflows by Income Group 21 𝐾𝐾𝑖 𝐼𝐼𝐼𝐼𝐼𝐼 =1 19 ∑ ∑ 𝑋𝑋𝑖,𝑗,𝑡 𝐼𝐼𝐼𝐼𝐼𝐼10 𝑗=1 2013 𝑡=1995 𝐾𝐾𝑖 𝐼𝑂𝑂𝐼𝐼𝐼𝐼 =1 19 ∑ ∑ 𝑋𝑋𝑖,𝑗,𝑡 𝐼𝑂𝑂𝐼𝐼𝐼𝐼10 𝑗=1 2013 𝑡=1995 . Figure 3 presents the scatterplots of these country-by-country indicators (along with a 45degree line), with the left panel representing the 42 High income countries and the right panel representing the 58 Medium and Low Income countries. The sizes of the bubbles in these figures reflect the number of countries in a small range. The two panels of this figure show a somewhat higher prevalence of outflow controls than of inflow controls, consistent with the statistics in Table 3 and Figure 1. Figure 3 illustrates that the difference in the prevalence of inflow and outflow controls is more pronounced for the Medium and Lower Income countries than for the High Income countries. The two panels of 24 Open Poland 0.25 .5 .75 1 Inflow Controls 0.25 .5 .75 1 Outflow Controls 42 High Income Countries 16 Closed 12 Open Myanmar 0.25 .5 .75 1 Inflow Controls 0.25 .5 .75 1 Outflow Controls 58 Medium & Low Income Countries Countries' Average Values for all Ten Assets, 1995 - 2012 Figure 3: Inflow Controls vs. Outflow Controls 28 IMF Strategy, Policy and Review Department. 2011. “Recent Experiences in Managing Capital Inflows: Cross-Cutting Themes and Possible Policy Framework.” Washington, DC, United States: International Monetary Fund. Available at: http://www.imf.org/external/np/pp/eng/2011/021411a.pdf Jeanne, O. 2012. “Capital Flow Management.” American Economic Review Papers and Proceedings 102(3): 203–06. Jeanne, O., A. Subramanian and J. Williamson. 2012. Who Needs to Open the Capital Account? Washington, DC, United States: Peterson Institute for International Economics. Keynes, J.M. 1920. The Economic Consequences of the Peace. New York, United States: Harcourt, Brace and Howe. Klein, M.W. 2012. “Capital Controls: Gates versus Walls.” Brookings Papers on Economic Activity 2012 (Fall): 317-355. Klein, M.W. and J. Shambaugh. 2015. “Rounding the Corners of the Policy Trilemma: Sources of Monetary Policy Autonomy.” Forthcoming in American Economic Journal: Macroeconomics. Korinek, A. 2010. “Regulating Capital Flows to Emerging Markets: An Externality View.” College Park, United States: University of Maryland. Mimeographed document. Lane, P., and G-M. Milesi-Ferretti. 2007. “The External Wealth of Nations Mark II.” Journal of International Economics 73: 223-250. Miniane, J. 2004. “A New Set of Measures on Capital Account Restrictions.” IMF Staff Papers 51: 276–308. Ostry, J. et al. 2010. “Capital Inflows: The Role of Controls.” IMF Staff Position Note SPN/10/04. Washington, DC, United States: International Monetary Fund. Ostry, J. et al. 2011. “Capital Controls: When and Why.” IMF Economic Review 59(3): 562-580. Prati, A., M. Schindler and P. Valenzuela. 2012. “Who Benefits from Capital Account Liberalization? Evidence from Firm-Level Credit Ratings Data.” Journal of International Money and Finance 31(6): 1649–1673. Quinn, D. 1997. “The Correlates of Change in International Financial Regulation.” American Political Science Review 91(3): 531–51. Quinn, D., M. Schindler and A.M. Toyoda. 2011. “Assessing Measures of Financial Openness and Integration.” IMF Economic Review 59(3): 488-522. 29 Rey, H. 2014. “Dilemma Not Trilemma: The Global Financial Cycle and Monetary Independence.” In: Global Dimensions of Unconventional Monetary Policy: A Symposium Sponsored By the Federal Reserve Bank of Kansas City, Jackson Hole, Wyoming, August 22-24, 2013. Kansas City, United States: Federal Reserve Bank. Rodrik, D. 1998. “Who Needs Capital-Account Convertibility?” In: S. Fischer et al., editors. Should the IMF Pursue Capital Account Convertibility? Essays in International Finance 207. Princeton, United States: Princeton University, Department of Economics, International Finance Section. Rogoff, K.S. 2002. “Rethinking Capital Controls: When Should We Keep an Open Mind?” Finance and Development 39(4): 55-56. Schindler, M. 2009. “Measuring Financial Integration: A New Dataset.” IMF Staff Papers 56(1): 222–38. Schmitt-Grohé, S., and M. Uribe. 2012. “Prudential Policy for Peggers.” NBER Working Paper 18031. Cambridge, United States: National Bureau of Economic Research. Skidelsky, R. 1992. John Maynard Keynes: The Economist as Saviour, 1920–1937. London, United Kingdom: Macmillan. Tamirisa, N. 1999. “Exchange and Capital Controls as Barriers to Trade.” IMF Staff Papers 46: 69–88.