WP/15/80. Capital Control Measures: A New Dataset. Andrés Fernández, Michael W. Klein, Alessandro Rebucci, Martin Schindler, and Martín Uribe - PDF

WP/15/80 Capital Control Measures: A New Dataset Andrés Fernández, Michael W. Klein, Alessandro Rebucci, Martin Schindler, and Martín Uribe International Monetary Fund WP/15/80 IMF Working Paper

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WP/15/80 Capital Control Measures: A New Dataset Andrés Fernández, Michael W. Klein, Alessandro Rebucci, Martin Schindler, and Martín Uribe International Monetary Fund WP/15/80 IMF Working Paper Institute for Capacity Development Capital Control Measures: A New Dataset 1 Prepared by Andrés Fernández, Michael W. Klein, Alessandro Rebucci, Martin Schindler, and Martín Uribe Authorized for distribution by Norbert Funke April 2015 This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. Abstract This paper presents 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 Building on the data 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 in detail the construction of the dataset and characterizes the data with respect to the prevalence and 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 Classification Numbers: F3, F36, F38 Keywords: capital control measures, capital flows; international financial integration Author s Address: 1 Author affiliations: Fernández: InterAmerican Development Bank; Klein: Fletcher School, Tufts University & NBER; Rebucci: Carey Business School, Johns Hopkins University; Schindler: International Monetary Fund & Joint Vienna Institute; Uribe: Columbia University & NBER. We thank Javier Caicedo for excellent research assistance. 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. The dataset is available for download at 3 CONTENTS PAGE Abstract...2 I. Introduction...4 II. Constructing the Capital Control Indicators...7 III. Characteristics of the Capital Control Indicators...13 IV. Aggregate Indicators...20 V. Conclusions...28 VI. References...30 TABLES Table 1: Asset and Transaction Categories for Capital Control Measures...10 Table 2: Countries In Data Set, By Income Groups, With Open/Gate/Wall Category...14 Table 3: Prevalence of Controls, 100 Countries, , by Asset Sub-Categories...16 Table 4: Cross-Category Correlations, All 100 Countries, ,...17 Table 5A: Cross-Category Correlations, 47 Gate Countries, Table 5B: Cross-Category Correlations, 53 Open and Wall Countries, Table 6: Correlation between Nine-Asset Aggregate Capital Controls and Excluded Asset Category...25 Table 7: Correlations among Aggregate Capital Controls Measures...26 FIGURES Figure 1: Proportion of Observations with Controls...15 Figure 2A: Average Controls on Inflows by Income Groups...21 Figure 2B: Average Controls on Outflows by Income Groups...22 Figure 3: Inflow Controls vs. Outflow Controls...23 Figure 4: Comparison of Aggregate Indicators...28 REFERENCES References...30 4 I. 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. 2 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 2 Quoted in Skidelsky (1992: 477). 5 Fund s publication Finance and Development These days everyone agrees that a more eclectic approach to capital account liberalization is required. 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. 3 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. 4 The empirical research of others, however, emphasizes the ineffectiveness and potential costs of capital controls. 5 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, 6 broader, cross-country analyses require panel data reflecting the experience of a range of countries. While a number of panel data sets 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 3 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. 4 For just a few examples, see Korinek (2010), Bianchi (2011), Farhi and Werning (2012), Jeanne (2012), Schmitt- Grohé and Uribe (2012), and Benigno et al. (2014). 5 See, for example, Forbes (2007), Binici, Hutchison and Schindler (2010), Klein (2012), Prati, Schindler and Valenzuela (2012), and Klein and Shambaugh (2015). 6 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 openness, while others with finer granularity have been more limited in terms of sample coverage (such as Schindler, 2009, Miniane, 2004, and Tamirisa, 1999). 7 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 As discussed in greater detail below, this dataset revises, extends, and widens the data set 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. 8 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. 7 See Quinn, Schindler, and Toyoda (2011) for a comprehensive review of existing de jure measures. 8 The dataset is publicly available for download at the National Bureau of Economic Research website (http://www.nber.org/data/international-finance/) or at request from the authors. 7 II. 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). 9 The capital control measures presented in this paper are also based on the de jure information from this source. 10 There was a fundamental change in the reporting on capital controls beginning with the 1996 volume of the 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 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 data set 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. Fernandez, 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, 9 The early works that use the AREAER to create panel data sets of capital controls include Grilli and Milesi- Ferretti (1995), Quinn (1997) and Chinn and Ito (2006). 10 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. 11 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. 8 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 The sample period has been extended to cover the period 1995 to 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 available from the authors. 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 Schindler s original dataset in order to ensure a harmonization of those data with the new observations included in this expanded dataset. 13 The AREAER reports the presence of rules and regulations for international transactions by asset categories. The 10 asset categories in our dataset allow us to capture a large proportion of global cross-national asset holdings. The categories, with their two-letter abbreviations, are the following: Money market instruments, which includes securities with an original maturity of one year or less, in addition to short-term instruments like certificates of deposit and bills of exchange, among others. (mm) 2. Bonds or other debt securities with an original maturity of more than one year. (bo) 3. Equity, shares or other securities of a participating nature, excluding those investments for the purpose of acquiring a lasting economic interest which are addressed as foreign direct investment. (eq) 12 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 data set, but were included in the AREAER. These countries are Algeria, Colombia, Ethiopia, Iran, Myanmar, Nigeria, Poland, Ukraine and Vietnam. 13 Specifically, whenever a discrepancy arose in a particular asset/country category between Schindler s original data set and ours in 2005 (the last year of Schindler s dataset), the data was revised for that category in that year and backwards until no discrepancy was detected. If there was no discrepancy in 2005 then there was no revision backwards for that country/asset subcategory. In total, only 145 observations (less than one percent of the original dataset) was modified. These observations are listed in the master data file. 14 Where applicable, the notation follows that in Schindler (2009). 9 4. Collective investment securities such as mutual funds and investment trusts. (ci) 5. Financial credit and credits other than commercial credits granted by all residents, including banks, to nonresidents, or vice versa. (fc) 6. Derivatives, which includes operations in rights, warrants, financial options and futures, secondary market operations in other financial claims, swaps of bonds and other debt securities, and foreign exchange without any other underlying transaction. (de) 7. Commercial Credits for operations directly linked with international trade transactions or with the rendering of international services. (cc) 8. Guarantees, Sureties and Financial Back-Up Facilities provided by residents to nonresidents, and vice versa, which includes securities pledged for payment or performance of a contract such as warrants, performance bonds, and standby letters of credit and financial backup facilities that are credit facilities used as a guarantee for independent financial operations. (gs) 9. Real Estate transactions representing the acquisition of real estate not associated with direct investment, including, for example, investments of a purely financial nature in real estate or the acquisition of real estate for personal use. (re) 10. Direct investment accounts for transactions made for the purpose of establishing lasting economic relations both abroad by residents and domestically by nonresidents. (di) The AREAER distinguishes across types of transactions according to the residency of the buyer or the seller, and whether the transaction represents a purchase or a sale or issuance. For five asset categories, Money Market, Bonds, Equities, Collective Investments and Derivatives, there are four categories of transactions controls: two categories of controls on inflows, including Purchase Locally by Non-Residents (plbn) and Sale or Issue Abroad by Residents (siar); and two categories of controls on outflows, which are Purchase Abroad by Residents (pabr) and Sale or Issue Locally by Non-Residents (siar). The Real Estate category includes the inflow transaction category plbn and the outflow control transaction categories pabr and Sale Locally by Non- Residents (slbn). There is only a broader classification of inflow controls or outflow controls for 10 the three categories of Financial Credits (fci and fco), Commercial Credits (cci and cco), and Guarantees, Sureties and Financial Backup Facilities (gsi and gso). Direct Investment includes the categories of controls on inflows (dii), controls on outflows (dio), and controls on the Liquidation of Direct Investment (ldi) which captures controls on capital inflows or outflows from the liquidation of direct investment abroad or domestically. Thus, in its most disaggregated format, our dataset provides information on 32 transaction categories. Table 1 summarizes those categories. Table 1. Asset and Transaction Categories for Capital Control Measures Assets that Each Include Four Transaction Categories mm Money Market (Bonds with Maturity of 1 year or less) bo Bonds (Bonds with Maturity of greater than 1 year) eq Equities ci Collective Investments de Derivatives Categories Inflow Controls: _plbn Purchase Locally By Non-Residents _siar Sale or Issue Abroad By Residents Outflow Controls: _pabr Purchase Abroad By Residents _siln Sale or Issue Locally By Non-Residents Assets that Include Only Inflow (i) or Outflow (o) Categories gsi & gso Guarantees, Sureties & Financial Backup Facilities fci & fco Financial Credits cci & cco Comm
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