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
, 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
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
( 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
, 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
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