Capital flows, degree of openness and macroeconomic volatility.

Chakraborty, Debasish ; Boasson, Vigdis

Abstract

This paper examines the effects of capital inflow on the volatility of output and price using panel data. The major contribution of the paper lies in incorporating the effects of degree of financial openness in analyzing the effects of capital flows on macroeconomic volatility. Specifically the paper investigates whether the degree of financial openness influences the impact of capital flows on macroeconomic volatility. Our model uses the KOF globalization index as a proxy for openness. Our results show that as financial openness increases, the capital inflow has less impact on macroeconomic volatility. This could be due to an increase in the proportion of direct foreign investment compared to portfolio investment and with a highly integrated banking sector the possibilities of a currency and maturity mismatch are lower.

JEL Codes F36 F62 and F65

Keywords: Economic Globalization, Economic Integration, Capital Flows, Financial Liberalization

1. INTRODUCTION

There is a great deal of debate among economists about the effects of international capital inflow on economic growth and stability. IMF and the World Bank spearheaded the arguments that developing countries need to open up their capital account in an orderly, deliberate and sustainable fashion in order to accelerate economic growth. They also argue that international capital flows also help with macroeconomic stability. The higher growth results from efficient allocation of resources and the reduction of macroeconomic volatility came from the ability to smoothly borrow and lend internationally in response to domestic economic conditions. However, economic data suggests that international capital flows also increases volatility of output, consumption and exchange rates. So often instead of increasing macroeconomic stability (as argued by the IMF and the World Bank), capital flows can actually increase macroeconomic instability, at least in the short run. This tradeoff between growth and volatility has led to intense debates among policy planners about the efficiency of opening up of the capital account.

This paper addresses this debate by investigating the effects of capital inflow on the volatility of output and price using panel data. The major contribution of the paper lies in incorporating the effects of degree of financial openness in analyzing the effects of capital flows on macroeconomic volatility. Specifically the paper investigates whether the degree of financial openness influences the impact of capital flows on macroeconomic volatility. Our model uses the KOF globalization index as a proxy for openness. The KOF globalization index is a weighted average economic, political and social openness.

The paper is organized in the following manner. Section 2 provides a survey of literature on the effects of capital flows on economic growth and volatility, section 3 discusses the data and methodology, section 4 deals with the empirical analysis and section provides the summary and the conclusions of this paper. The paper concludes that as financial openness increases, the capital inflow has less impact on macroeconomic volatility. This could be due to an increase in the proportion of direct foreign investment compared to portfolio investment and with a highly integrated banking sector the possibilities of a currency and maturity mismatch are lower.

2. SURVEY OF LITERATURE

It has been argued that financial openness helps augment the level of domestic resources that are crucially needed for economic growth. However, it is also true that financial openness and the surge in capital inflow contributes to macroeconomic volatility, especially in developing and emerging economies. The most common effect of a surge in capital inflow is appreciation in the exchange rate. Under a flexible exchange rate this appreciation comes via the appreciation of the nominal exchange rate. Under fixed exchange rate, a surge in capital flows lead to an expansion of money supply and liquidity, which results in an increase in aggregate demand, increase in the price of non-tradable goods, and inflation. Thus the surge in financial flows has the potential of derailing the economic reform process of the type recommended by the IMF and the World Bank.

An increase in capital inflow and the consequent surplus in the capital account are often matched by a simultaneous deficit in the current account. This deficit in current account could be due to increase in private consumption (as in Latin America) or private investment (as in Asia). So a surge in capital inflow, in addition to its impact on exchange rate and inflation, thus, has some direct effect on consumption, investment and output. The impact on volatility depends largely on the composition of the capital flows. It has been argued that the volatility will be less pronounced if the proportion of foreign direct investment is higher than portfolio investment. Macroeconomic volatility resulting from surge in capital inflow also increases if the possibility of currency mismatch (bank liabilities denominated in foreign currencies and bank assets denominated in local currency) and maturity mismatch (bank liability is mainly short term in nature and bank lending is long term in nature) higher.

Since capital flows impacts both economic growth and macroeconomic stability, it is critical to weigh in the effects on each one of them before adopting a formal policy of opening up the economy. This section provides a brief summary of the effects of capital flows on economic growth. However, the survey of literature on the effects of capital flows on volatility is much more extensive, since that is the thrust of this paper.

2a. Effect of Capital Account Liberalization on Economic Growth

There is mixed evidence on the effects of capital account liberalization on economic growth. Licchetta (2006) summarizes the findings of fifteen recent studies on the effects of capital account liberalization on economic growth as shown in Table 1 as follows:

Table 1

Capital Account Liberalization and Economic Growth

 # of
Study countries Years

Alesina, Grilli and Ferretti (1994) 20 1950-89
Grilli and Ferretti(1995) 61 1966-89
Quinn (1997) 58 1975-89
Kraay (1998) 117 1985-1997
Rodrik (1998) 95 1975-89
Klein and Olivei (2000) Up to 92 1986-95
Chanda (2001) 116 1976-89
Arteta, Eichengreen, Wypolosz (2001) 51-59 1973-92
Bekaert, Harvey, Lundblad (2001) 30 1981-97
Edwards (2001) 62 1980s
O'Donnel 94 1971-94
IMF 38 1980-99
Reisen and Soto(2001) 44 1986-1997
Edison, Levine, Ricci, Slok (2002a) Up to 89 1973-1995
Edison, Levine, Ricci, Slok (2002b) 57 1980-2000

Study Effecton Growth

Alesina, Grilli and Ferretti (1994) No effect
Grilli and Ferretti(1995) No effect
Quinn (1997) Positive
Kraay (1998) No effect/mixed
Rodrik (1998) No effect
Klein and Olivei (2000) Mixed
Chanda (2001) Mixed
Arteta, Eichengreen, Wypolosz (2001) Mixed
Bekaert, Harvey, Lundblad (2001) Positive
Edwards (2001) No effects on poor
 countries
O'Donnel No effect/mixed
IMF Positive/not significant
Reisen and Soto(2001) Mixed
Edison, Levine, Ricci, Slok (2002a) Mixed
Edison, Levine, Ricci, Slok (2002b) No effect

Source: Prasad et al. (2003), IMF (2001) and Edison et al. (2002a):
reproduced from Licchetta (2006).

2b. Effects of Capital Account Liberalization on Macroeconomic Volatility

The effect of capital account liberalization on macroeconomic volatility is similarly mixed. Mendoza (1994) uses a stochastic dynamic business cycle model and finds very little connections between financial openness and volatility of output and consumption. He however finds that if the shocks are big and prolonged than output and consumption volatility increases with degree of financial openness. Baxter and Crucini (1995) find that with increased financial openness output volatility increases but consumption volatility falls (both consumption and relative consumption volatility). Possible reason for these differences can be attributed to wealth effect on consumption and the interaction of these assets on the implications of these effects on the risks associated with different assets structures.

Sutherland (1996), Senay (1998) and Buch, Dopke and Pierdzioch (2002) uses dynamic stochastic stick-price models and all these studies conclude that the impact of financial openness on macroeconomic volatility depends on some exogenous shocks. In the case of monetary shocks volatility of output increases; In the case of fiscal shocks volatility of output decreases. In the case of monetary shocks volatility of consumption decreases, while in the case of fiscal shocks the volatility of consumption increases.

The impact of financial openness on Macroeconomic volatility can be also explained by the structural conditions of the economy. If a country embarking upon financial openness has a limited diversification of exports and imports, then financial openness could contribute to macroeconomic volatility through its impact on the terms of trade and foreign demand shocks. Kose (2002) examines these effects by inspecting the effects on the terms of trade and Senhadji (1998) examines the role played by foreign demand shocks.

Countries where the level of financial openness is not that deep and countries which are highly indebted often can experience capital flow reversal which could lead to fierce macroeconomic volatility. Also in these countries, changes in the world interest rate could trigger serious macroeconomic volatility. Aghion, Banerjee, and Piketty (1999) and Caballero and Krishnamurthy (2001) shows the relationship between financial openness and macroeconomic volatility by focusing on countries with not so developed financial markets and countries who are highly indebted.

Financial openness seems to have more impact on small countries than large countries. Head (1995) and Crucini (1997) made the case that productivity shifts in large countries contribute to macroeconomic volatility in small countries. Kose and Prasad (2002) financial openness contributes to macroeconomic volatility much more in small countries (population below 1.5 million) than other developing countries.

Kaminsky and Reinhart (1999) and Glick and Hutchinson (1999) showed that countries starting financial liberalization experienced high volatility in output and consumption due to sudden loss of access to global financial markets. Mendoza (2002) and Arellano and Mendoza (2002) however, shows that sudden stop in access to financial markets did not cause any sever macroeconomic volatility.

Razin and Rose (1994) uses cross section data to estimate volatility and found no significant link between financial openness and macroeconomic volatility. They also argue that financial openness seems to amplify monetary shocks and dampens fiscal shock.

Easterly, Islam and Stiglitz (2004) uses 2 periods panel OLS and IV method and concludes that neither the level nor the volatility of private capital flows have any significant impact on output growth volatility. Koseand Plummer (2003) use a sample of 76 countries over a period of 1960-1999. He uses two indicators of financial openness: (a) dummy variable for capital account restrictions and (b) private capital flows. Financial openness is not significant in explaining output and consumption volatilities. Financial openness has a significant and non-linear impact on relative consumption volatilities (ratio of consumption to output volatilities).

Eozenou Patrick (2008) uses GMM -IV panel estimation method as proposed by Arellano and Bover (1995) and Blundell and Bond (1998) to estimate the following two equations:

[[sigma].sub.i,j,t] = [sigma][[alpha].sub.i,j,t-1] + [[beta].sub.1][Q'.sub.i,t] + [[beta].sub.2][FD.sub.i,t] + [[eta].sub.i] + [[epsilon].sub.it,t] (2)

[[sigma].sub.i,j,t] = [sigma][[alpha].sub.i,j,t-1] + [[beta].sub.1][Q'.sub.i,t] + [[beta].sub.2][FD.sub.i,t] + [[beta].sub.3][FI.sub.i,t] + [[beta].sub.4] ([FD.sub.i,t] *[FI.sub.i,t]) + [[eta].sub.i] + [[epsilon].sub.it,t] (3)

Where j = Y, C and C+G;

[Q'.sub.i,t] are a set of control variable;

[FD.sub.i,t] is a measure of financial development;

[FI.sub.i,t] is a measure of financial openness.

They concluded that lagged dependent variable has significant and positive effects on volatility in terms of trade volatility and share of agricultural sector on GDP (all control variables). It also has a positive impact on output growth volatility. However, these terms have no impact on consumption growth volatility. They found evidence to show that financial openness has a positive impact on output growth volatility up to a certain level of financial development, but the coefficient is not statistically significant. The marginal impact of financial development on consumption is negative for both private and total consumption growth volatility. The marginal impact of financial openness on consumption is positive for both private and total consumption growth volatility. Both these coefficients are not significant. So taken independently, each of these two variables has no impact on consumption growth volatility. However inclusion of the interaction term matters. [B.sub.3] is positive and significant. [B.sub.4] is a significant negative coefficient. This suggests that as financial development increases, the positive impact of financial openness on consumption volatility becomes weaker. Financial integration has a positive impact on consumption volatility when financial development is low, but when financial system is strong enough, then financial integration lowers consumption volatility. This paper concludes that the impact of financial development on consumption depends upon the level of financial development.

3. METHODOLOGY AND DATA

Severalempirical concerns help determine the choice of our econometric model: (i) the potential endogeneity of the domestic savings; (ii) the dynamic relationship between domestic savings and investment as both are impacted by the prior values of each other; and (iii) unobserved country- specific effects. (1) This leads us to specify the dynamic panel GMM estimator proposed by Arellano and Bond (1991) to overcome these potential issues. According to this technique, the model is transformed in two-step GMM estimator to eliminate the fixed effects to derive unbiased and consistent estimates. Under this transformation, the lagged values of the endogenous variables are used as suitable instruments to overcome the potential endogeneity problem.

For the measure of volatility, we use the measure employed by Rodrik (1998) and Iverson (2001). Thus volatility is measured by standard deviation of the concerned economic aggregate. To measure output volatility we use the standard deviation of real GDP and to measure price volatility we use the GDP deflator. In order to maintain sufficient number of data for empirical analysis we use the five year period standard deviation.

We estimate the following two equations in the paper:

Output Volatility

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

Where [[sigma].sub.GDP] is the five-year standard deviation of real GDP;

KF is the capital flow;

G is the globalization index.

Price Volatility

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)

Where [[sigma].sub.PR] is the five-year standard deviation of GDP deflator;

KF is the capital flow;

G is the globalization index.

Panel Data

We collected macroeconomic indicator data on real GDP, GDP deflators, and CPI across 208 countries for the time-series of 1966-2009 from the World Bank's database called "World Databank". We computed five-year moving standard deviations on real GDP growth rates, GDP deflators, and CPI for the period of 1970 -2009. We then matched our volatility variables with the 2012 KOF Index of Globalization across 208 countries for the time-series of 1970-2009. This yielded a panel dataset with 8320 observations. KOF Index of Globalization was introduced in 2002 (Dreher, 2006) and is updated and described in detail in Dreher, Gaston and Martens (2008).

Table 2 presents the summary statistics on the variables of our panel study. The variable on capital flows includes data on trade, FDI and portfolio investment. The sources of the data are from the World Bank, UNUTAD, and IMF. Specifically, trade data are the sum of a country's exports and imports; FDI is a country's net flows of inflows and outflows of FDI; portfolio investment is the sum of a country's stock of assets and liabilities. All these variables are normalized by GDP. The entire cross-sectional and time-series sample has a mean capital flows of 51.6 per cent of GDP with a standard deviation of 22% and coefficient of variations (CV) 0.43.

The KOF globalization index measures the three dimensions of globalization on economic, social and political globalization. The higher value of the globalization index indicates a higher level of a country's openness. The index calculated on a yearly basis. The globalization index variable has a mean of 45.3, a standard deviation of 17.6 and coefficient of variation of 0.39.

We measure macroeconomic volatilities by computing the five-year moving standard deviations of a country's real GDP annual growth rates, GDP deflator, and consumer price index (CPI). The average standard deviation for the GDP growth rates, and GDP deflator and CPI are approximately 4%, 66%, and 6% respectively.

4. EMPIRICAL RESULTS

For empirical econometric analysis, we estimate several panel models for variability in output (as measured by standard deviation of GDP growth) and variability in price (as measured by standard deviation in GDP deflator). Table 3, 4 and 5 summarize the results of the effects of capital inflow on variability in output and Table 6 summarizes the effects of capital inflow on price variability. Table 3 reports the results of the panel model with fixed effects and Arellano robust standard errors.

The lagged dependent variable is positively correlated with the volatility on GDP growth and the result is statistically significant at 1 per cent level. Even though capital flows is negatively correlated with the GDP volatility, the result is not statistically significant. However, the globalization index which is the proxy for a country's openness is negatively correlated with the GDP volatility and the result is statistically significant at 1 per cent level. This result indicates that the higher the level of openness, the lower level of the volatility on GDP growth. In other words, if a country increases its level of globalization or openness, it can help reduce its volatility on GDP growth and stabilize its economy. More interestingly, the interaction between capital flows and globalization is positively correlated with the volatility on GDP growth and this result is statistically significant at 1 per cent level. This suggests that as the level of capital flow increases, it increases the volatility of GDP growth when the country's level of openness is low. This indicates that the level of openness is low, the beneficial effects of globalization is not large enough to offset the adverse effects of capital flows on volatility of GDP growth. Thus, the impact of capital flows on GDP growth volatility depends on the level of globalization or financial openness.

The lagged dependent variable is positively correlated with the volatility on GDP growth and the result is statistically significant at 1 per cent level. Capital flows is positively correlated with the GDP volatility; the result is statistically significant at the 5% level. However, the globalization index which is the proxy for a country's openness is negatively correlated with the GDP volatility and the result is statistically significant at 5 per cent level. This result also indicates that the higher the level of openness, the lower level of the volatility on GDP growth. In other words, if a country increases its level of globalization or openness, it can help reduce its volatility on GDP growth and stabilize its economy. This estimation model also suggests that the impact of capital flows on GDP growth volatility depends on the level of globalization or financial openness.

With a pooled OLS estimation (Table 5), both the capital flow coefficient and the interaction term between the capital flow and globalization index are statistically insignificant. The globalization index coefficient has the right sign and is also statistically significant at 1% level.

The effect of capital flows on the price volatility is quite the opposite. It is clear from Table 6 that capital flow coefficient has the right sign but is statistically not significant. The globalization term is significant at the 10% level but has a positive sign. This indicates that higher the level of openness, greater the price volatility. However, the interaction term is statistically significant and has a negative sign, indicating that increased capital flows would increase price volatility if the level of openness is high.

5. SUMMARY

The effects of capital flows on output volatility are nearly identical irrespective of the estimation techniques used for estimation. Increase in capital flows generally increases output volatility; whereas output volatility is reduced more open the economy. For the same level of capital flows generally the volatility is higher more open the economy. However volatility in price is increased with globalization and for the same level of capital inflows, price volatility is reduced as the level of openness increases.

References

Aghion, P., A. Banerjee, and T. Piketty, (1999), Dualism and Macroeconomic Volatility, Quarterly Journal of Economics, Vol. 114, pp. 1359-97.

Alesina, A., Grilli, V. and Ferretti, G., (1994), The Political Economy of Capital Controls. In Capital Mobility: The Impact on Consumption, Investment and Growth, ed. L. Leiderman and Razin, Pp. 289-321, Cambridge University Press.

Arellano, C., and E. G. Mendoza, (2002), Credit Frictions and Sudden Stops in Small Open Economies: An Equilibrium Business Cycle Framework for Emerging Markets Crises, NBER Working Paper No. 8880 (Cambridge, Massachusetts: National Bureau of Economic Research).

Arellano, M., and S. Bond, (1991), Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations. Review of Economic Studies 58 (2): 277-297.

Arnello, M and O. Bover., (1995), Another Look at the Instrumental Variable Estimation of Error-Components Models, Journal of Econometrics 68 (1): 29-51.

Arteta, C., Eichengreen, B., Wyplosz, C., (2001), When Does Capital Account Liberalization Help More Than It Hurts? NBER Working Papers, no. 8414.

Barba Navaretti, G., G. Calzolari, A. Pozzole and M. Levi, (2010), Multinational banking in Europe: Financial Stability and Regulatory Implications-lessons from the Financial Crisis, mimeo, University of Milan.

Baxter, M., and M. Crucini, (1995), Business Cycles and the Asset Structure of Foreign Trade, International Economic Review, Vol. 36, pp. 821-54.

Bekaert, G., Campbell, R., and Christian, L., (2005), Does Financial Liberalization Spur Growth? Journal of Financial Economics, 77(1): 3-55.

Blanchard, O., M. Das and H. Faruqee, (2010), The Initial Impact of the Crisis on Emerging Market Countires, Brookings Papers on Economic Activity, Spring, 263-307.

Broda, C., P. Ghezzi and E. Levy-Yeyati, (2009), The New Global Balance, Braclay Capital, 23 March.

Buch, C. M., J. Dopke, and C. Pierdzioch, (2002), Financial Openness and Business Cycle Volatility, Working Paper, Kiel: Kiel Institute for World Economics.

Caballero, R. J., and A. Krishnamurthy, (2001), International and Domestic Collateral Constraints in a Model of Emerging Market Crises, Journal of Monetary Economics, Vol. 48, pp. 513-48.

Chmn, M. and H. Ito, (2008), A New Measure of Financial Openness, Journal of Comparative Policy Analysis, 10(3), 307-20.

Crucini, M., (1997), Country Size and Economic Fluctuations, Review of International Economics, Vol. 5, No. 2, pp. 204-20.

Dreher, Axel, (2006), Does Globalization Affect Growth? Evidence from a New Index of Globalization, Applied Economics 38, 10: 1091-1110.

Dreher, Axel; Noel Gaston and Pim Martens, (2008), Measuring Globalization--Gauging its Consequence, New York: Springer. Europa World Yearbook, Various Years, Routledge.

Easterly, W., R. Islam, and J. E. Stiglitz, (2001), Shaken and Stirred: Explaining Growth Volatility, Annual World Bank Conference on Development Economics, ed. by B. Pleskovic and N. Stem.

Edison, H. J., M. Klein, L. Ricci, and T. Slok, (2002), Capital Account Liberalization and Economic Performance: Survey and Synthesis, IMF Working Paper No. 02/120 (Washington: International Monetary Fund).

Edison, Hali J., Ross Levine, Luca Antonio Ricci, and TorstenSlok, (2002), International Financial Integration and Economic Growth, Journal of International Money and Finance, 21(6): 749-76.

Edwards, Sebastian. (2001), "Capital Mobility and Economic Performance: Are Emerging Economies Different?" NBER Working Papers, no. 8076.

Eozenou, Patrick, (2008), Financial Integration and Macroeconomic Volatility: Does Financial Development Matter? MPRA Paper 12738, University Library of Munich, Germany.

Gavin, M., and R. Hausmann, (1996), Sources of Macroeconomic Volatility in Developing Economies, IADB Working Paper (Washington: Inter-American Development Bank).

Glick, R., and M. Hutchison, (1999), Banking and Currency Crises: How Common are Twin Crises? in Financial Crises in Emerging Markets, ed. by R. Glick, R. Moreno, and M. Spiegel, (Cambridge: Cambridge University Press).

Grilli, V. and Ferretti, G., (1995), Economic Effects and Structural Determinants of Capital Control, IMF Staff Paper, International Monetary Volume, Vol. 42 No 3 Pp. 517-551.

Head, A. C., (1995), Country Size, Aggregate Fluctuations, and International Risk Sharing, Canadian Journal of Economics, Vol. 28, pp. 1096-19.

Hoggarth, G., L. Mahadevaandj. Martin, (2010), Understanding International Bank Capital Flows during the Recent Financial Crisis, Bank of England Financial Stability Paper 8.

International Monetary Fund, (2012), International Financial Statistics Indicators.

Kaminsky G. L., Reinhart C. M., (1999), The Twin Crises: the Cause of Banking and Balance-of-payments Problems, The American Economics Review, 89(3), 473-500.

Karras, G., and E. Song, (1996), Sources of Business-Cycle Volatility: An Exploratory Study on a Sample of OECD Countries, Journal of Macroeconomics, Vol. 18, No. 4, pp. 621-37.

Kim, S. H., M. A. Kose, and M. Plummer, (2003), Dynamics of Business Cycles in Asia, Review of Development Economics, Vol. 7, No. 3.

Klein, M., and Olivei, G., (1999), Capital Account Liberalization, Financial Depth and Economic Growth. NBER Working Paper. 7384, Cambridge, Massachusetts.

Kose, M. A., (2002), Explaining Business Cycles in Small Open Economies, Journal of International Economics, Vol. 56, pp. 299-327.

Kose, M. A., and K. Yi, (2003), The Trade-Comovement Problem in International Macroeconomics, IMF Working Paper, Washington: International Monetary Fund.

Kraay, Aart. (1998), In Search of the Macroeconomic Effects of Capital Account Liberalization, Unpublished, World Bank Group.

Licchetta M., (2006), Macroeconomic Effects of Capital Account Liberalization in Emerging Markets, Ph.D. in Economics Working Papers, University of Rome [Online], Available at http://dep.eco.uniroma1.it/phd/wp/01200604_licchetta_macroeconomicseffects.pdf (January 12, 2007).

Mendoza, E. G., (2002), Credit, Prices, and Crashes: Business Cycles with a Sudden Stop, in Preventing Currency Crises in Emerging Markets, ed. by Frankel, J., and S. Edwards, (Chicago: University of Chicago Press).

Mendoza, E. G., (1994), The Robustness of Macroeconomic Indicators of Capital Mobility, in Capital Mobility: The Impact on Consumption, Investment, and Growth, ed. by Leonardo Leiderman and Assaf Razin (Cambridge: Cambridge University Press), pp. 83-111.

Obstfeld, M., and K. Rogoff, (1995), Exchange Rate Dynamics Redux, Journal of Political Economy, Vol. 103, pp. 624-60.

Obstfeld, M., J. Shambaugh and A. Taylor, (2009), Financial Instability, Reserves, and Central Bank, Swap Lines in the Panic of 2008, NBER Working Paper 14826, March.

Prasad, E., K. Rogoff, S. Wei, and M. A. Kose (2003), The Effects of Financial Globalization on Developing Countries: Some Empirical Evidence, IMF Working Paper (Washington: International Monetary Fund).

Quinn, D., (1997), The Correlates of Change in International Financial Regulation, American Political Science Reviews, Vol. 91, No, Pp. 535-571.

Razin, Assaf and Andrew K. Rose, (1994), Business-Cycle Volatility and Openness: an Exploratory Cross-Sectional Analysis, in Capital Mobility: The Impact on Consumption, Investment, and Growth, ed. by Leonardo Leiderman and Assaf Razin (Cambridge: Cambridge University Press), pp. 48-76.

Reinhart, Carmen, and Kenneth Rogoff, (2002), The Modern History of Exchange Rate Arrangements: A Reinterpretation, NBER Working Paper (Cambridge, Massachusetts: National Bureau of Economic Research).

Reisen, H. and Soto, M., (2001), Which Type of Capital Inflows Foster Developing Country Growth? International Finance, Vol. 4, Issue 1, Pp. 1-14.

Rodrik, Dani. (1998), Who Needs Capital-Account Convertibility? In Should the IMF Pursue Capital-Account Convertibility. Essays in International Finance, no. 207, Princeton: International Finance Section, Department of Economics, Princeton University, 55-65.

Schindler, M. (2009), Measuring Financial Integration: A New Data Set, IMF Staff Papers, 56(1), 222-38.

Senay, O., (1998), The Effects of Goods and Financial Market Integration on Macroeconomic Volatility, The Manchester School Supplement, Vol. 66, pp. 39-61.

Senhadji, A., (1998), Dynamics of the Trade Balance and the Terms-of-Trade in LDCs: The S-Curve, Journal of International Economics, Vol. 46, pp. 105-31.

Sutherland, A., (1996), Financial Market Integration and Macroeconomic Volatility, Scandinavian Journal of Economics, Vol. 98, pp. 521-39.

UNCTAD 2012, UNCTAD Stat, http://unctadstat.unctad.org.

World Bank 2012, World Development Indicators, Washington, DC.

DEBASISH CHAKRABORTY * AND VIGDIS BOASSON, College of Business Administration, Central Michigan University, Mt Pleasant, MI 48859

Table 2

Summary Statistics, using the Observations for 208 Cross-sectional
Countries over 40 periods (1970-2009)

Variable Mean Median Min Max

capital flows 51.604 50.531 4.632 100.000
globalization index 45.340 42.352 12.216 92.836
St. Dev of GDP growth 3.97 2.85 0.00 55.49
St. Dev of GDP deflator 66.26 4.73 0.00 27767.50
St. Dev of CPI 5.81 3.87 0.00 775.47

Variable Std. Dev. C. V.

capital flows 22.136 0.429
globalization index 17.591 0.388
St. Dev of GDP growth 3.944 0.993
St. Dev of GDP deflator 1003.220 15.141
St. Dev of CPI 18.198 3.134

Table 3

Panel Model with Fixed-effects for 208 Countries over the Period
of 1970-2009 (robust (HAC) standard errors)

Dependent variable: SD_GDPg

 Coeff S.E. t-ratio p-value

constant 2.224 0.304 7.306 <0.00001 ***
capital flows -0.003 0.008 -0.381 0.704
globalization index -0.041 0.006 -6.852 <0.00001 ***
cap flows * glob 0.000 0.000 2.915 0.004 ***
SD_GDPg_1 0.789 0.008 99.932 <0.00001 ***

Table 4

Dynamic Panel Model with Two-Step GMM Estimator for 208 Countries
over the Period of 1970-2009

Dependent variable: SD_GDPg

 Coeff S.E. z

constant 0.87118 0.1706 5.1036
SD_GDPg (-1) 0.84319 0.01878 44.9049
capital flows 0.00686 0.00329 2.0835
globalization index -0.02125 0.01080 -1.9669
Cap flow*glob 0.00046 0.00028 1.6626

 p-value

constant <0.00001 **
SD_GDPg (-1) <0.00001 ***
capital flows 0.03721 *
globalization index 0.04919 **
Cap flow*glob 0.09640

Table 5

Pooled OLS, using 5417 Observations

Included 172 cross-sectional units

Dependent variable: SD_GDPg

 Coefficient Std. Error t-ratio

constant 1.02061 0.170303 5.9929
Capflow*glob 4.64452e-05 6.09103e-05 0.7625
Capital flows 0.00403009 0.00299558 1.3453
Global index -0.016056 0.00405876 -3.9559
SD_GDPg_1 0.837506 0.00642824 130.2855

 p-value

constant <0.00001 ***
Capflow*glob 0.44578
Capital flows 0.17857
Global index 0.00008 ***
SD_GDPg_1 <0.00001 ***

Table 6

2-step Dynamic Panel GMM, using 5456 Observations

Included 208 cross-sectional countries
Time-series length: 1979-2009
Dependent variable: Price Volatility=SD_GDPdeflator

 Coeff S.E. z

constant -20.0155 9.74967 -2.0529
SD_GDPdefl(-1) 1.03969 0.00971279 107.0438
Capflow* glob -0.0091765 0.00463033 -1.9818
capitalflows 0.371151 0.263914 1.4063
Globalization index 0.612927 0.37188 1.6482

 p-value

constant 0.04008 **
SD_GDPdefl(-1) <0.00001 ***
Capflow* glob 0.04750 **
capitalflows 0.15962
Globalization index 0.09931 *