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Andrés Fernández, Mr. Michael W Klein, Mr. Alessandro Rebucci, Mr. Martin Schindler, and Martin Uribe
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 2013. 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.
Ms. Eliane A. Cardoso and Mr. Ilan Goldfajn
This paper creates an index of capital controls to analyze the determinants of capital flows to Brazil, accounting for the endogeneity of capital controls by considering a government that sets controls in response to capital flows. It finds that the government reacts strongly to capital flows by increasing controls on inflows during booms and relaxing them in moments of distress. The paper estimates a vector autoregression with capital flows, controls, and interest differentials. It shows that controls have been temporarily effective in altering levels and composition of capital flows but have had no sustained effects in the long run.
Mr. Giovanni Dell'Ariccia, Mr. Paolo Mauro, Mr. Andre Faria, Mr. Jonathan David Ostry, Mr. Julian Di Giovanni, Mr. Martin Schindler, Mr. Ayhan Kose, and Mr. Marco Terrones

Economic Cooperation and Development, and trade from the IMF's Direction of Trade Statistics . The financial transparency measure is taken from issues of the World Economic Forum's Global Competitiveness Report . Capital controls data are constructed by staff based on the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions . Notes: All dependent variables in natural logarithms, and measured in end-of-year 2004 U.S. dollars. The capital control variables refer to controls on the specific type of flows, and refer to inflows for the recipients and

Mr. Stanley Fischer

, it appears that there might be a bias with regard to the countries for which capital controls data are available: among the advanced countries, the capital controls index is available for 23 out of 25 countries; for the emerging market countries, the capital controls variable is available for 32 out of 39 countries; and for the “other” category (data on which are presented in Table 3 ), capital controls data are available for only 35 of 118 countries. It is quite likely that the extent of capital controls among the countries in the “other” group, for which the

Andrés Fernández, Mr. Michael W Klein, Mr. Alessandro Rebucci, Mr. Martin Schindler, and Martin Uribe

Indicators In this section, we present some characteristics of the capital control data. We begin by considering the properties of inflow and outflow controls for the ten asset categories. We then discuss aggregating these series into broader indicators that reflect the average level of controls for the full set of assets, or for subsets consisting of two or more categories. We conclude this section with an estimation of the correlation between our broad capital control indicator and two other popular indicators of aggregate capital controls. The dataset covers 100

Mr. Peter J Montiel and Mr. Jonathan David Ostry

( ρ ) ] . ( 9 ) The combinations of b and π that satisfy equations (8) and (9) are portrayed in Figure 1 . The schedule labelled NN is the locus of combinations of b and π that clear the market for home goods (equation (8) ). The slope of the NN schedule is: Figure 1 Macroeconomic Equilibrium Under Capital Controls Data: ρ 0 is the initial terms of trade defined as the price of exports relative to imports. d π / d b | N N = − f P / L

Gurnain Kaur Pasricha

, as shown in Figure 4 above. As we see later, the mercantilist motivation is more important in explaining net NKI restricting measures, which captures all available capital control tools to stem appreciation pressure. As the capital controls index is based on qualitative information, one may ask how the interpretation of results is affected if the intensity of the changes is not perfectly captured. The dataset on capital controls captures the intensity of changes in two ways: (1) the capital controls data identifies the changes at a granular level

Gurnain Kaur Pasricha
This paper borrows the tradition of estimating policy reaction functions from monetary policy literature to ask whether capital controls respond to macroprudential or mercantilist motivations. I explore this question using a novel, weekly dataset on capital control actions in 21 emerging economies from 2001 to 2015. I introduce a new proxy for mercantilist motivations: the weighted appreciation of an emerging-market currency against its top five trade competitors. This proxy Granger causes future net initiations of non-tariff barriers in most countries. Emerging markets systematically respond to both mercantilist and macroprudential motivations. Policymakers respond to trade competitiveness concerns by using both instruments—inflow tightening and outflow easing. They use only inflow tightening in response to macroprudential concerns. Policy is acyclical to foreign debt; however, high levels of this debt reduces countercyclicality to mercantilist concerns. Higher exchange rate pass-through to export prices, and having an inflation targeting regime with non-freely floating exchange rates, increase responsiveness to mercantilist concerns.
Ms. Eliane A. Cardoso and Mr. Ilan Goldfajn

policy objectives, such as avoiding real appreciation, or that controls have enhanced welfare as suggested by theory. Data on capital controls are scarce and few empirical papers introduce them directly. Most papers use the International Monetary Fund’s Annual Report on Exchange Arrangements and Exchange Restrictions as the source of capital control data. Johnston and Ryan (1994) and Grilli and Milesi-Ferretti (1995) use panel data for industrialized and developing countries. Both papers find that the data do not support the hypothesis that control programs

Ms. Natalia T. Tamirisa and Mr. R. B. Johnston
Recourse to controls on capital flows among developing economies is generally quite pervasive. This paper examines the structure and determinants of capital controls based on a cross-sectional study of developing and transition economies. It identifies categories of capital transactions that can be aggregated for analytical purposes. Controls are found to be related to the balance of payments, macroeconomic management, market and institutional evolution, prudential and other factors. The relationship with the balance of payments, however, is not robust to simultaneous equation analysis.