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Special Topic: Effects of Capital Flow Liberalization: What Is the Evidence from Recent Experience in Emerging Market Economies?

Author(s):
International Monetary Fund. Monetary and Capital Markets Department
Published Date:
September 2012
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This special topic is based on recent IMF research into the experience of emerging market economies that have liberalized capital flows over the past 15 years, in particular, the impact of liberalization on macroeconomic performance and financial stability.24 The research indicates that greater openness to capital flows is associated with higher growth, increased gross capital flows, and higher equity returns and with lower inflation and reduced bank capital adequacy ratios. The effects vary depending on thresholds.

This study focuses on the short- to medium-term effects of liberalizing capital flows on macroeconomic performance and risks to financial stability. Specifically, it analyzes the effects of liberalizing capital flows on economic growth; inflation; capital inflows, outflows, and net flows; equity returns; and bank capital adequacy ratios. The sample of countries and the econometric strategy have been selected to capture the short- to medium-term effects. The sample is therefore limited to 37 countries that liberalized capital flows during 1995–2010.25 Dynamic panel data specifications are used to capture the possibility of partial adjustment toward the steady state. The relatively short time dimension can be considered as the transition period between restricted and liberalized capital flows.

This study uses two new de jure measures of capital flow liberalization. The de jure measures are restrictiveness indices based on the AREAER, and are computed for 185 countries during 1995–2010. Higher values indicate more controls.

  • The first de jure restrictiveness index of capital flows is similar to the Schindler index (Schindler, 2009).26 The restrictiveness index is based on the AREAER and comprises 21 categories of restrictions, including restrictions on equity, bond, money market, and collective investment instruments; financial credit; and direct investment by direction. The index distinguishes between inflows (nonresidents’ investments in the country) and outflows (residents’ investments abroad). For each of the 21 categories a restrictiveness index was calculated for inflows, outflows, and overall flows (narrow index). The difference between the Schindler index and the narrow restrictiveness index is that the former includes a limited qualitative assessment of controls. For example, if a measure requires only notification of the transaction, the control covers only a few sectors of the economy, or the control is maintained for anti-money-laundering or security reasons, the Schindler index does not consider the transaction controlled. However, the differences between the two indices are minor, and for the period of the availability of the Schindler index, the correlation between the two indices is more than 0.92.
  • As a robustness check, a second de jure index was also used, which is an average of binary indicators of restrictiveness in 62 categories of capital transactions. The categories include all capital transactions, foreign exchange and domestic currency accounts of residents and nonresidents, regulatory measures related to the financial sector, and repatriation and surrender requirements. This broad restrictiveness index can have a value between zero and 1, and higher values represent more restricted cross-border capital flows. Due to its more extensive coverage, this index can measure liberalization or reversal of liberalization better than narrower indicators. The correlation between the narrow index and the broader index is 0.92 for the 185 countries and 0.90 for the sample used in the empirical part of this paper. The two indices are also highly correlated with other available de jure indices.27 The broader index was computed only for aggregate capital flow restrictiveness.

The de jure index is used to identify the sample of countries that have liberalized over the past 15 years. First, only those countries are retained that have liberalized by at least 0.1 point according to the index between 1995 and 2010. Second, for a given country, only those years are retained following the start of liberalization where the index declines by at least 0.01 point. Therefore, the sample encompasses only countries that have liberalized and only those years when controls on capital flows were relaxed. About 37 countries satisfy the above criteria (Table 11). For those countries, the mean of capital flow liberalization between 1995 and 2010 was 0.4; the maximum was 0.83; and the minimum was 0.1. This sample of countries is used in the empirical analysis. However, the actual sample for each regression varies with data availability.

Table 11.Countries That Liberalized Capital Flows during 1995–2010
AfghanistanChileIsraelSt. Kitts and Nevis
AlgeriaCyprusJordanSamoa
ArmeniaDominicaKoreaSão Tomé and Príncipe
AzerbaijanGhanaMaltaSenegal
Bosnia and HerzegovinaGuyanaMauritaniaSeychelles
BotswanaHaitiNigeriaSlovak Republic
BulgariaHondurasPapua New GuineaSlovenia
BurundiHungaryRomaniaSwaziland
CambodiaIraqRussiaUganda
Cape Verde
Source: IMF staff.
Source: IMF staff.

Methodology

The effects of capital flow liberalization were assessed using the following methodology.28 Various panel data specifications were used to estimate the impact of liberalization on the following variables: capital outflows, inflows, and net flows; real GDP growth per capita; inflation; equity returns; and capital adequacy ratios. The most general specification is:

where the subscript i denotes the country (i = 1, …,37), the subscript t denotes the year (t = 1995, …, 2010), and the subscript j denotes the specific equation for each indicator of interest Yj represents the specific equation for growth, inflation, capital flows, etc.). The approach includes country fixed effects, μ, to take account of unobserved heterogeneity among countries.29 The variable ka is the measure of capital flows liberalization, Z is a set of control variables, and v is the error term.

The dynamic specifications capture the potential inertia in the dependent variables. The presence of the lagged dependent variable in the equations means that all the estimated coefficients represent short-term effects, which are the focus of this analysis. The long-term effects can be derived by dividing each coefficient by 1 minus the coefficient of the lagged dependent variable (1 – β1).

Two econometric issues arise in estimating the above equation. First, some independent variables may be endogenous because of potential simultaneity or reverse causality. Second, with a fixed-effect estimator, the lagged dependent variable is, by construction, correlated with the error term and is therefore endogenous. As a robustness check, System Generalized Method of Moments (System GMM) estimators were also used with all right-hand variables treated as endogenous (Arellano and Bover, 1995; Blundell and Bond, 1998).

Following Kose and others (2009), the full sample is separated into two subsamples using thresholds. Countries meeting these threshold conditions are presumed to be better able to reap the growth and stability benefits of financial globalization. Kose and others (2009) identify four groups of threshold conditions: financial market development, institutional quality and governance, macroeconomic policies, and trade integration. In this analysis, a composite indicator is created by first normalizing, then averaging these four individual indicators: measures for financial development (ratio of market capitalization to GDP or private sector credit to GDP), quality of bureaucracy and corruption, ratio of fiscal balances to GDP, and ratio of trade openness (X + M) to GDP.30 Then, the median of the index is taken as a threshold to separate countries into two groups: those with an index higher than the median are “above threshold” countries, and those with an index lower than the median are “below threshold” countries.

Results

The econometric analysis, based on the sample of countries that have liberalized over the past 15 years, suggests that more liberalization is associated with the following:

  • Higher real GDP growth per capita: The coefficients of the liberalization index are significantly negative (a decline in the index means liberalization of capital flows).
  • Lower inflation rates: The coefficients of the liberalization index are significantly positive, indicating that lower inflation rates are associated with the liberalization.31
  • Higher equity returns: The coefficients of the equity liberalization index are significantly negative, reflecting the positive impact of liberalization on equity returns.
  • Lower bank capital adequacy ratios: The coefficients of the liberalization index are significantly positive, indicating that liberalization may reduce bank capital adequacy ratios. This outcome may be due to a higher credit and asset expansion associated with the liberalization of capital flows. Furthermore, an increase in riskier assets following the liberalization of capital flows may put downward pressure on capital ratios.
  • Higher capital inflows and outflows: The coefficients of liberalization are significantly negative, demonstrating the capability of liberalization to promote gross capital flows. However, the effect of liberalization on net flows is not statistically significant.

Thresholds

The main results of the relationship between the liberalization of capital flows and various dependent variables for the subsamples of countries “above threshold” and “below threshold” are as follows:

  • For countries “above threshold,” the main findings in the full sample are generally confirmed, with a few differences. For example, the coefficients of liberalization are larger than those in the full sample, indicating a larger role for capital flow liberalization in countries “above threshold.” In other words, countries that are above the thresholds reap more benefits from liberalization.
  • For countries “below threshold,” the coefficients of liberalization are not significant in most regressions, including in the growth regression, indicating a limited role for liberalization of capital flows in these countries.

Robustness Checks

The results are robust to using alternative estimation approaches or different capital flow liberalization measures:

  • Several other econometric specifications of panel data have been estimated, including System GMM. The results are broadly similar to those obtained with the fixed effects estimator.
  • Using the broad restrictiveness index of capital flows leads to similar results.
  • A further robustness test is implemented to investigate whether the effects of capital flow liberalization depend on the size of the country. Since the sample includes small countries and large countries, the effects of liberalizing capital flows may depend on the size of the country. To ensure that the conclusions are unaltered in larger countries, the same regression was estimated by using a pooled weighted least squares estimator, whereby each observation is weighted by the countries’ GDP in U.S. dollars. This approach assigns more weight to larger economies without eliminating the small countries. Compared with pooled (unweighted) least squares, the results are broadly similar.32 Therefore, this suggests that the results are also valid for larger countries.

In sum, liberalizing capital flows may encourage financial integration, promote growth, and lower inflation, although the liberalization may also be associated with potential risks to financial stability. Countries that meet some threshold conditions would be better able to reap the growth instability benefits of liberalization.

References

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    Blundell, Richard, and Stephen R.Bond,1998, “Initial Conditions and Moment Restrictions in Dynamic Panel Data Models,” Journal of Econometrics, Vol. 87, No. 1, pp. 115–43.

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    Chinn, Menzie D., and HiroIto,2008, “A New Measure of Financial Openness,” Journal of Comparative Policy Analysis, Vol. 10, No. 3, pp. 309–22.

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    Gruben, William C., and DarrylMcLeod,2002, “Capital Account Liberalization and Inflation,” Economics Letters,Elsevier, Vol. 77, No. 2, pp. 221–25.

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    International Monetary Fund (IMF), 2011, “Recent Experiences in Managing Capital Inflows—Cross-Cutting Themes and Possible Policy Framework,” IMF Policy Paper (Washington, February). www.imf.org/external/np/pp/eng/2011/021411a.pdf.

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    KoseM. Ayhan, EswarPrasad, KennethRogoff, and Shang-JinWei,2009, “Financial Globalization: A Reappraisal,” IMF Staff Papers, Vol. 56, No. 1, pp. 8–62.

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    Saadi-Sedik, Tahsin, and TaoSun, forthcoming, “Effects of Capital Flow Liberalization—What Is the Evidence from Recent Experiences of Emerging Market Economies?” IMF Working Paper (Washington: International Monetary Fund).

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    Schindler, Martin,2009, “Measuring Financial Integration: A New Data Set,” IMF Staff Papers, Vol. 56, No. 1, pp. 222–38.

24

Saadi-Sedik and Sun (forthcoming).

25

There is a structural break in AREAER data in 1995. Until 1995, the AREAER summarized a country’s openness to capital flows using a binary dummy variable, where 1 represented a restricted capital account and zero represented an unrestricted capital account. Since 1995, the AREAER has utilized a more structured approach, providing detailed information on restrictions on capital transactions in a number of subcategories.

26

The Schindler index is available only for 91 countries from 1995 to 2005.

27

The correlation with the Chinn and Ito (2008) index is 0.78 for the narrower index and 0.86 for the broader index for the period of availability of the Chinn-Ito index.

28

The main data sources are the IMF’s World Economic Outlook (WEO) database and International Financial Statistics (IFS) database; the World Bank’s World Development Indicators database; Bloomberg L.P.; Haver Analytics; and Thomson Reuters Datastream.

29

For example, the fixed effect takes account of all time-invariant country-specific factors, including geography, climate, ethno-linguistic characteristics, and unchanging political and legal systems.

30

To create a single indicator, each variable is first normalized as follows: Index = (actual value – minimum value) / (maximum value – minimum value). Then subindices are aggregated using the arithmetic mean.

31

Similar results were obtained by Gruben and McLeod (2002) and Gupta (2008). Using an illustrative model, Gupta (2008) shows that opening the capital account significantly lowers policymakers’ incentive to generate an inflationary shock. Theoretical and empirical evidence suggest a strong negative relationship between financial openness and inflation.

32

These techniques are not yet well developed for dynamic panel estimations; therefore, the results can only be compared to pooled least squares estimations.

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