Data Appendix I
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The authors wish to thank Tamim Bayoumi, Michael Klein, Phillip Lane, Gian Maria Milesi-Ferreti, and David Robinson for data and helpful comments and Yutong Li and Bennett Sutton for efficient research assistance. This research was originally conducted as a background paper for Chapter 4 of the October 2001 World Economic Outlook (Washington: International Monetary Fund).
To paraphrase Eichengreen’s (,2001, p.1) insightful literature review, there are innumerable constellations of distortions for which liberalization of international capital controls will hurt resource allocation and growth. For example, in the presence of trade distortions, capital account liberalization may induce capital inflows to sectors in which the country has a comparative disadvantage.
For more detailed literature reviews of cross-country studies of the causes and effects of IFI, see Eichengreen (2001) and Edison, Klein, Ricci, and Sløk (2002). For a review of country-specific experiences with IFI, see Cooper (1999).
In 1997, however, there was structural break in the AREAER documentation of capital controls. No longer are countries categorized as having open or restricted capital accounts. Since 1997, information is provided on thirteen separate categories of capital flows, including a distinction between restrictions on inflows and outflows. Because of the structural break, we only use information on IMF-Restriction through 1996.
For each five-year period, we require that a country has three years of non-missing data for that variable or the variable is set to missing. We include the early period in the panel estimation, 1976–80, which is excluded from the pure cross-section results, because we need as many time periods as possible to have confidence in the dynamic panel estimation. For this initial period, about 25 percent of the countries have missing data.
We use a variant of the standard two-step system estimator that controls for heteroskedasticity. Typically, the system estimator treats the moment conditions as applying to a particular time period. This provides for a more flexible variance-covariance structure of the moment conditions because the variance for a given moment condition is not assumed to be the same across time. This approach has the drawback that the number of overidentifying conditions increases dramatically as the number of time periods increases. Consequently, this typical two-step estimator tends to induce over-fitting and potentially biased standard errors, which is particularly important for this paper because of data limitations. To limit the number of overidentifying conditions, we follow Calderon, Chong and Loayza (2000) and apply each moment condition to all available periods. This reduces the over-fitting bias of the two-step estimator. However, applying this modified estimator reduces the number of periods by one. While in the standard estimator time dummies and the constant are used as instruments for the second period, this modified estimator does not allow the use of the first and second period. We confirm the results using the standard system estimator.
Recall that we assume that the explanatory variables are “weakly exogenous.” This means they can be affected by current and past realizations of the growth rate but not future realizations of the error term. Weak exogeneity does not mean that agents do not take into account expected future growth in their decision to undertake IFI; it just means that unanticipated shocks to future growth do not influence current IFI. We statistically assess the validity of this assumption.
The four panel regressions in Table 3 pass the standard specifications tests. Specifically, none reject the Sargan test, i.e., they do not reject the econometric specification and the validity of the instruments. Also, the regressions do not exhibit significant serial correlation, i.e., they do not reject the null hypothesis of no serial correlation as discussed in the methodology section.