Chapter 4: International Financial Integration And Economic Growth

INTERNATIONAL FINANCIAL INTEGRATION AND ECONOMIC GROWTH - A PANEL ANALYSIS

By Xuan Vinh Vo School of Economics and Finance University of Western Sydney, Australia Tel: 61. (0) 401 497 888 Fax: 62.2. 4620 3787 Email: [email protected] Address: Locked Bag 1797, Penrith South DC, NSW 1797 Australia.

Abstract This paper employs a new panel dataset and a wide assorted number of indicators both de jure and de facto measures to proxy for international financial integration to investigate the relationship between international financial integration and economic growth. Using 79 countries with the data covering the period from 1980 to 2003, our analysis indicates a weak relationship between international financial integration and economic growth. Our data also show that this relationship is not different even though we control for different economic conditions. JEL Classification: F3, 04, 016 Keywords: international financial integration, economic growth, panel, FDI, Portfolio Investment, Private Capital Flows. Acknowledgement The author is grateful for advices and suggestions from Kevin Daly, Tom Valentine, and Craig Ellis. Financial support from University of Western Sydney is also acknowledged. Any remaining errors are of course my own responsibilities. 1. Introduction

With the development of financial market and increased degree of international financial integration around the world, many countries especially developing countries are now trying to remove cross-border barrier and capital control, relaxing the policy on capital restrictions and deregulating domestic financial system. This paper will empirically examine the growth impacts of international financial integration.

This paper is going to contribute to the existing literature on the impacts of international financial integration on economic performance in a number of ways.

Firstly, we examine an extensive array of international financial integration indicators, both de jure and de facto of international financial integration. We examine the IMF’s official restriction dummy variable1 as well as the newly developed capital restriction measures by Miniane (2004). Furthermore, we explore various measures of capital flows and in disaggregation including total assets and liabilities, total liabilities, FDI, portfolio, and total capital flows as share of GDP (a total of 18 de facto indicators).

Moreover, we consider measures of just capital inflows as well as measures of gross capital flows (inflows plus outflows) to proxy for international financial integration because capital account openness is defined both in terms of receiving foreign capital and in terms of domestic residents having the ability to diversify their investments abroad. In addition, we examine a wide array of international financial integration proxies because each indicator has advantages and disadvantages2.

Secondly, we develop and examine a large number of new measures of international financial integration, both in flows and stock measures, especially in disaggregation.

1 This dummy variable is equal to 0 in the year when there is no restriction and 1 otherwise. Data are based on the IMF publication Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER) (various issues) 2 See Vo (2005) for a detailed discussion on this matter.

2 As proxies for international financial integration, we first examine the flow measures of capital flows. In this regard, we use FDI inflows (as a share of GDP), FDI inflows and outflows (as a share of GDP), portfolio investment (equities and debts) inflows

(as a share of GDP), gross portfolio investment inflows and outflows (as a share of

GDP), gross private capital flows3 (as a share of GDP). In addition, we use the stock measure of these indicators. Since we want to measure the average level of openness over an extended period of time, these stock measures are more useful additional indicators. Furthermore, these stock measures are less sensitive to short-run fluctuations in capital flows associated with factors that are unrelated to international financial integration, and may therefore provide more accurate indicators of international financial integration than capital flow measures. In particular, we examine both the accumulated stock of liabilities (as share of GDP) and the accumulated stock of liabilities and assets (as share of GDP). Furthermore, we break down the accumulated stocks of financial assets and liabilities into stock of FDI and stock of portfolio inflows and outflows in assessing the links between economic growth and a wide assortment of international financial integration indicators. There are some authors who previously use a subset of these indicators. For example, Lane and Milesi-Ferretti (2002) get credit as the first to compute the accumulated stock of foreign assets and liabilities for an extensive sample of countries. Edison et al. (2002) use this dataset for their study in measuring international financial integration. Thus, we advance other studies by carefully computing, developing and investigating these additional international financial integration indicators as well as a large number of countries in our dataset. As a result, we believe that our empirical investigation provides a far more complete picture of the relationship between international

3 Gross private capital flows include FDI, portfolio investments, and other private bank credits inflows and outflows.

3 financial integration and economic growth compared to other previous studies using a small subset of these indicators and smaller number of countries.

Thirdly, since theory and some past empirical evidence suggest that international financial integration will only have positive growth effects under particular institutional and policy regimes (Edison et al. 2002), we examine an extensive array of interaction terms. Specifically, we examine whether international financial integration is positively associated with growth when countries have well-developed banks, well-developed stock markets, well functioning legal systems that protect the rule of law, low levels of government corruption, sufficiently high levels of real per capita GDP, high levels of educational attainment, prudent fiscal balances, and low inflation rates. Thus, we search for economic, financial, institutional, and policy conditions under which international financial integration boosts growth.

Fourthly, we use newly developed panel techniques that control for (i) simultaneity bias, (ii) the bias induced by the standard practice of including lagged dependent variables in growth regressions, and (iii) the bias created by the omission of country- specific effects in empirical studies of the international financial integration-growth relationship. Since each of these econometric biases is a serious concern in assessing the growth-international financial integration nexus, applying panel techniques enhances the confidence we can have in the empirical results. Furthermore, the panel approach allows us to exploit the time-series dimension of the data instead of using purely cross-sectional estimators.

As mentioned above, we empirically study the relationship between broad measures of international financial integration and growth with broad controlling macroeconomic and country risk conditions. Other researchers focus instead on a much narrower issue: restrictions on foreign participation in domestic equity markets.

4 Levine and Zervos (1998) construct indicators of restrictions on equity transactions by foreigners. They show that liberalizing restrictions boosts equity market liquidity.

Henry (2000a; 2000b) extends these data and shows that liberalizing restrictions on foreign equity flows boosts domestic stock prices and domestic investment. Bekaert et al. (2004) go farther and show that easing restrictions on foreign participation in domestic stock exchanges accelerates economic growth. While it is valuable to examine the impact of liberalizing restrictions on foreign activity in domestic stock markets, it is also valuable to study whether international financial integration in general has an impact on economic growth under particular economic, financial, institutional, and specific country risk environments.

The remainder of the paper is organized as follows. Section two reviews the literature and theoretical framework. Section three develops the econometric model. Section four describes the methodology. Section five presents the data. Section six reports the empirical results and section seven concludes the paper.

2. Literature Review

This section assesses the current literature about the impacts of international financial integration4 on the economic performance both theoretically and empirically.

Theoretically, evidences provided are mixed. According to some researchers who mention the pros of international financial integration, international financial integration facilitates risk-sharing, improves international diversification and thereby enhances production specialization, capital allocation, and economic growth (Obstfeld

1994; Acemoglu & Zilibotti 1997; Edison et al. 2002). Further, it is argued in the standard neoclassical growth model that international financial integration smooths the flow of capital from capital-abundant to capital-scarce countries with positive growth effects. In addition, international financial integration may boost the

4 See Vo (Vo 2005) for the definition of international financial integration.

5 functioning of domestic financial systems through the intensification of competition and the importation of financial services. This may tend to reduce profits of local firms but spillover effects through linkages to supplier industries may reduce input costs, raise profits and stimulate domestic investment and this will result in positive growth effects (Markusen & Venables 1999; Klein & Olivei 2000; Levine 2001;

Edison et al. 2002; Agenor 2003). Moreover, international financial integration can facilitate the transfer or diffusion of managerial and technological know-how, particularly in the form of new varieties of capital inputs (Grossman & Helpman

1991; Borenzstein et al. 1998; Berthelemy & Demurger 2000; Agenor 2003).

On the other hand, many other authors disbelieve the positive effects of international financial integration. Boyd and Smith (1992) state that international financial integration in countries with weak institutions and policies – for instance, weak financial and legal systems - may actually induce a capital out-flow from capital- scarce countries to capital-abundant countries with better institutions. Bhagwati

(1998), in addition, argues that international financial integration in the presence of pre-existing distortions can actually retard growth. Rodrick (1998) and Edwards

(2001) further blame that the increased degree of international financial integration was ultimately behind the succession of crises that the emerging markets experienced during the mid-1990s. According to this school of thoughts, international financial integration inflicts many costly disadvantages but offers very limited benefits to emerging nations. It has been argued that, since emerging markets lack modern financial institutions, they are particularly vulnerable to the volatility of global financial markets. This vulnerability will be exaggerated in countries with a more open capital account. Edison et al (2002) also do not support the view that international financial integration per se accelerates economic growth, even when

6 controlling for particular economic, financial, institutional, and policy characteristics.

Thus, some theories predict that international financial integration will promote growth only in countries with sound institutions and good policies.

Although theoretical disputes and the contemporaneous policy debate over the growth effects of international financial integration have produced an escalating empirical literature, resolving this issue is complicated by the difficulty in measuring international financial integration. Countries impose a complex array of price and quantity controls on a broad assortment of financial transactions. Thus, researchers face enormous hurdles in measuring cross-country differences in the nature, intensity, and effectiveness of barriers to international capital flows (Eichengreen 2001).

Empirical evidence yields conflicting conclusions about the growth effects of international financial integration. Grilli and Milesi-Ferretti (1995), Kraay (1998) and

Rodrik (1998) find no link between economic growth and the IMF capital restriction measure. Edison et al (2002) use the data of up to 57 countries over 20 years period and find out that international financial integration per se does not accelerate economic growth, even when controlling for particular economic, financial, institutional and risk characteristics.

In contrast, Edwards (2001) suggests that the positive relationship between capital account openness and productivity performance only manifests itself after the country in question has reach a certain degree of development. He thus argues that to take advantage of international financial integration, a country needs to develop to a level of sound financial institutions and advanced domestic financial market.

Edwards (2001) also finds that the IMF capital restriction measure is negatively associated with growth in rich countries but positively associated with growth in poor countries. He thus argues that good institutions are necessary to enjoy the positive

7 growth effects of international financial integration. Arteta et al. (2001) and Edison et al. (2002), however, argue that Edwards’s results are not robust to small changes in the econometric specification. While Quinn5 (1997) finds that his measure of capital account openness is positively linked with growth, Kraay (1998) and Arteta et al.

(2001) find these results are not robust.

Finally, while some studies find that foreign direct investment (FDI) inflows are positively associated with economic growth when countries are sufficiently rich

(Blomstrom et al. 1994) educated (Borenzstein et al. 1998), or financially developed

(Alfaro et al. 2004), a research by Carkovic & Levine (2002) contends that these results are not robust to controlling for simultaneity bias.

In light of the current literature, this research is significant in terms of contribution in provision of a complete picture of the growth - international financial integration relationship to the existing literature. This paper is also of interest to the policy makers and researcher to uncover the impact of international financial integration on economic performance.

3. The Model

To satisfy the conditions mentioned in the previous part, we formulate the model that is represented as follow:6

Y1 = α + βi*I +βf*IFI + βx*X + є (1)

And we interact the IFI with each of the variables in X:

Y1 = α + βi*I +βf*IFI + βx*IFI*X + βy*X + є (2)

5 This measure is based on the AREAER, however, it is not publicly available and cover only 4 years for OECD countries and 2 years for non-OECD countries. We do not use this variable in our analysis. 6 A common feature of most-country growth regression is that the explanatory variables are entered independently and linearly. This is based on the influential work of (Kormendi & Meguire 1985).

8 If we have a positive βx then it would mean that IFI will have a greater effect on economic growth with greater X and vice versa, it will have lesser impact with greater

Where:

- Y1 is per capital GDP growth

- I is a set of variables always included in the regression.

- IFI is individual international financial integration variable of interest.

- X: a subset of variables chosen from a pool of variables identified by past

studies as potentially important explanatory variables of growth. These are

controlling variables.

In this paper, I-variables include:

- the investment share of GDP (INV)

- the lagged value of real GDP per capita (LGDPCAP)

- the lagged value of secondary-school enrolment rate (EDU)

- the annual growth rate of population (POPU)

IFI variables include:

De jure indicators

- IFI01: capital control variable developed by Miniane (2004)

- IFI02: IMF dummy variable from AREAEA

It is clear that in this category, a smaller value of capital control variable or IMF dummy variable implies a greater degree of international financial integration.

De facto Measures

Flows Measures

7 This can be mathematically proved as follow: We differentiate Y with respect to IFI, equation (14) yields: δY/δIFI = βf + βx*X If βx > 0 then IFI would have stronger effect on Y in a country with a high level of X.

9 - IFI03: Gross FDI as share of GDP

- IFI04: FDI inflows as share of GDP

- IFI05: Gross Portfolio Investment as share of GDP

- IFI06: Portfolio investment inflows as share of GDP

- IFI07: Total FDI + Portfolio Investment Inflows + Outflows (as share of GDP)

= IFI03+IFI05

- IFI08: Total FDI + Portfolio Investment Inflows (as share of GDP) =

IFI04+IFI06

- IFI09: Net private capital flows as share of GDP

- IFI10: Gross Private Capital Flows as share of GDP

Stock measures

- IFI11: Total Stock of Assets and Liabilities as share of GDP

- IFI12: total stock liabilities as share of GDP

- IFI13: Gross Stock FDI as share of GDP

- IFI14: Stock FDI inwards as share of GDP

- IFI15: Gross Stock Portfolio Investment as share of GDP

- IFI16: Stock Portfolio Investment Inflows as share of GDP

- IFI17: Stock FDI + Portfolio Investment Inflows + Outflows (as share of

GDP) = IFI13 +IFI15

- IFI18: Stock FDI and Portfolio Investment Inflows (as share of GDP)

=IFI14+IFI16

The pool of X-variables in this equation includes:8

Fiscal Indicators:

- the annual rate of government consumption expenditures to GDP (GOVCON)

8 These X-variables form the basis of the conditioning information set because other academics and researchers have employed these variables (or close-related variables) to stand for fiscal, trade, monetary, uncertainty, and political-instability indicators.

10 - the government tax revenue as a share of GDP (TAX)

Trade indicators:

- the share of exports in GDP (EXPORT).

- the trade openness: this indicator is defined as total import and export as a

share of GDP (TRADE).

Monetary Indicators

- the annual inflation rate (INFLATION). The inflation rate is calculated by the

difference in natural logarithm of Consumer Price Index. In a "cash-in-

advance" theory of money, higher anticipated inflation rate is predicted to

reduce capital formation (Stockman 1981; Chowdhury 2001; Widmalm 2001).

Hence the inflation rate is also included.

Financial System Indicators, Banking System

- the ratio of domestic credit to GDP (DCREDIT)

- Financial deepening degree (M2). The degree of financial deepening in these

countries is considered using the M2/GDP ratio (Chowdhury 2001). The

assumption is that an increase in financial deepening would enhance growth.

Indicators of Stock Market Size, Activity and Efficiency

A recent and expanding literature establishes the importance of financial

development for economic growth.9 According to Beck et al. (1999), the stock

market size, activity and efficiency indicators are highly correlated with the

subsequent economic growth. It is also evidenced by many authors suggesting that

the level of stock market development exert a causal impact on economic growth.

- The size of stock market (STOCAP)

- The domestic stock market activity or liquidity (STOACT)

- The stock market efficiency (STOTO)

9 For an overview over this literature, see Levine (Levine 1997).

11 Macroeconomic Uncertainty & Political Instability Indicators

- PRS risk index. Composite International Country Risk Guide (ICRG) risk

rating is an overall index, ranging from 0 to 100 (highest risk to lowest), based

on 22 components of risk.

Classification

- A dummy variable equals to 1 in the case of developed countries (World Bank

Classification) and equals to 0 for developing countries (STATUS).

Following the suggestion from Widmalm (2001), in order to reduce multicollinearity, we do not use any pair of variables in I, X or IFI which measure the same underlying phenomenon. Therefore, in our study, we include either export or trade openness in our regressions, one of three measures of capital market development measures in each regression.

Although few empirical studies include all of these variables, most studies control for some subsets. Of the 41 growth studies surveyed by Levine & Renelt (1991), 33 include the investment share of GDP, 29 include population growth, 13 include a measure of initial income. In addition, the I-variables are consistent with a variety of

“new” growth models that rely on constant returns to reproducible inputs or endogenous technological change (Barro 1990; Romer 1990). Furthermore, with these

I-variables, we can confirm the findings of a large assortment of empirical studies; and, in recognition of the issues raised by Mc Aleer et al. (1985), it is shown that changes in the I-variables do not alter this paper’s conclusions (Levine & Renelt

1992).

Each of these I-variables has statistical and conceptual problems. In keeping with this paper’s focus on assessing the statistical sensitivity of past findings, we discuss these problems only briefly. Measurement problem with the lagged value of real GDP per

12 capita flows and the secondary-schooling enrolment rate may induce biased results. In the case of the average annual rate of population growth, census data may be very poor, and the causal links with the average annual growth rate of GDP per capita are ambiguous (Becker et al. 1990). Furthermore, in the case of the investment share of

GDP, investment in human capital represents more than formal schooling, and enrolment rates do not control for quality.

There are also problems with including the ratio of physical-capital investment to

GDP as an I-variable. The causal relationship between the investment share of GDP and the average annual growth rate of GDP per capita is ambiguous, and the justification for including many variables in growth regressions is that they may explain the investment share of GDP. If we include the investment share of GDP, the only channel through which other explanatory variables can explain growth differentials is the efficiency of resource allocation.

4. Methodology

To get an unbiased empirical result, we use an assorted number of econometric techniques to estimate the model. We first use the Ordinary Least Square (OLS) Panel

Estimator to estimate the relationship between international financial integration and economic growth. In addition, we also use other estimators including Two Stage Least

Square and Generalized Method of Moments (GMM) panel estimator developed for dynamic panel data designed by Holtz-Eakin et al. (1990), Arellano and Bond (1991),

Arellano and Bover (1995) and Blundell and Bond (1997) to extract consistent and efficient estimates of the impact of international financial integration on economic growth. The advantages of this GMM panel estimation method is to exploit the time- series variation in the data, accounts for unobserved country-specific effects, allows for the inclusion of lagged dependent variables as regressors, and controls for

13 endogeneity of all the explanatory variables. This method has been recently employed by Carkovic & Levine (2002) and Edison et al. (2002) to study economic growth.

However, as these estimators yield similar results, we do not report the results here to conserve space.

5. Data

To improve quality of the estimation, we use a long enough period data to allow us to abstract from business-cycle fluctuations, short-run political and financial shocks to focus on long-run growth. It is clear that our 24 year annual data of 79 countries ranging from 1980 to 2003 meets this requirement.

Our data are collected from a number of commercial database including 2004 World

Bank’s World Development Indicator CDRom, IMF International Financial Statistics and The PRS International Country Risk Guide.

Data Descriptive Statistics

The table 1 report the descriptive statistics of the data.

14 Table 1 Descriptive Statistics of Data

Mean Median Maximum Minimum Std. Dev. Y1 0.0159 0.0193 0.3463 -0.2518 0.0444 IFI01 0.5305 0.5000 1.0000 0.0769 0.3178 IFI02 0.5022 1.0000 1.0000 0.0000 0.5004 IFI03 0.0367 0.0202 0.4064 0.0000 0.0487 IFI04 0.0228 0.0117 0.3357 0.0000 0.0323 IFI05 0.0478 0.0206 1.9566 0.0000 0.1269 IFI06 0.0920 0.0535 2.0901 0.0001 0.1642 IFI07 0.0444 0.0283 1.0906 0.0000 0.0717 IFI08 0.0331 0.0218 0.2579 0.0000 0.0350 IFI09 0.2301 0.1045 23.1410 0.0006 0.8008 IFI10 2.1555 1.2411 35.9676 0.1948 3.7681 IFI11 1.1447 0.7574 17.4390 0.0791 1.7955 IFI12 0.3179 0.2371 2.8488 0.0137 0.3143 IFI13 0.0210 0.0085 0.8696 0.0000 0.0537 IFI14 0.1922 0.1350 1.7849 0.0030 0.2050 IFI15 0.4669 0.2585 8.2118 0.0007 0.7007 IFI16 0.2541 0.1522 3.5555 0.0000 0.3357 IFI17 0.8232 0.5079 10.0189 0.0320 0.9599 IFI18 0.4620 0.3606 5.0764 0.0182 0.4611 EDU 0.7451 0.7585 1.6070 0.0633 0.3155 EXPORT 0.3770 0.3168 1.5239 0.0506 0.2393 DCREDIT 0.5507 0.4472 2.0316 0.0288 0.3899 GOVCON 0.1675 0.1598 0.7622 0.0000 0.0643 ICRG 69.4630 71.0000 96.0000 25.5000 13.7537 INFLATION 0.3673 0.0619 117.4964 -1.0000 3.7244 INV 0.2202 0.2144 0.5973 0.0000 0.0618 LGDPCAL 8.3621 8.3681 10.9862 5.0539 1.4529 M2 0.4707 0.4004 1.7255 0.0356 0.2857 POPU 1.4141 1.4577 7.4215 -275.2720 6.9084 STOACT 0.2700 0.0882 3.2639 0.0000 0.4505 STOCAP 0.4853 0.3129 3.2996 0.0028 0.5035 STOTO 0.4889 0.3316 4.7546 0.0000 0.5699 TAX 0.2150 0.2030 0.4939 0.0000 0.0934 TRADE 0.7763 0.6708 2.9602 0.1155 0.4648 6. Empirical Results

Table 2, 3 and 4 report the correlation estimation result between variables employed in this research. A first glance at these tables we can see that the correlation between economic growth and international financial integration is characterised by the coefficients of very small value. It can be seen from these tables that GDP per capita growth is negatively associated with de jure capital restrictions variables (-0.03 and

-0.04 respectively for IFI01 and IFI02). It indicates that countries with less capital restrictions tend to grow faster. However, these correlation coefficients are not statistically significant.

On the other hand, most of correlation coefficients between GDP per capita growth and our de facto variables are positive. These capital account openness variables are positively correlated with economic growth to support the hypothesis of higher international financial integration is linked with higher growth rate. However, similarly to those de jure indicators, these correlations are not significant because of very small value of correlation coefficients.

GDP per capita growth is strongly positively correlated with domestic investment

(0.24), slightly correlated with population growth (0.06), lagged of GDP per capita in log form (0.05), education (0.10), export (0.08), trade (0.09), domestic credit (0.11), money and quasi money (0.08). These results do not support the view that countries with high educated population, sufficiently rich are growing at a slower rate. Simply, we can interpret that domestic investment very important to economic growth and other endogenous and exogenous factors (education, population growth, domestic credit and money supply) can contribute to economic development.

A strong positive correlation coefficient between ICRG risk index and GDP per capita growth (27%) indicates that in country where the lower level of risk is associated with

16 the higher rate of economic growth. This is consistent with theory which argues that a country with stable economic and political conditions enjoys a faster growth rate. In addition, our correlation estimation is according to the literature which state that the quality of government can influence economic growth as country with higher inflation rate, higher government expenditure associated with slower economic growth (-0.10 and -0.07 respectively).

Table 3 indicates that country which is sufficiently rich (high level of GDP per capita), well educated (high enrolment rate), more open in trade (export and trade openness), high level of financial development (banks and financial markets), lower inflation rate and lower level of risk tend to be more open.

17 Table 2: Correlation Matrix

Y1 IFI01 IFI02 IFI03 IFI04 IFI05 IFI06 IFI07 IFI08 IFI09 IFI10 IFI11 IFI112 IFI13 IFI14 IFI15 IFI16 Y1 1.00 -0.03 -0.04 0.09 0.11 0.12 0.10 0.14 0.14 0.10 0.02 0.00 -0.01 -0.04 -0.04 0.02 0.01 IFI01 1.00 0.76 -0.34 -0.18 -0.46 -0.39 -0.42 -0.36 0.28 -0.47 -0.55 -0.54 -0.43 -0.22 -0.54 -0.57 IFI02 1.00 -0.36 -0.31 -0.44 -0.33 -0.42 -0.40 -0.12 -0.44 -0.41 -0.42 -0.37 -0.27 -0.43 -0.49 IFI03 1.00 0.91 0.45 0.37 0.69 0.69 0.58 0.30 0.41 0.42 0.67 0.56 0.56 0.57 IFI04 1.00 0.41 0.33 0.63 0.70 0.71 0.31 0.39 0.41 0.54 0.60 0.38 0.36 IFI05 1.00 0.94 0.96 0.90 -0.05 0.60 0.54 0.55 0.49 0.48 0.82 0.73 IFI06 1.00 0.89 0.90 0.01 0.45 0.35 0.37 0.35 0.33 0.71 0.71 IFI07 1.00 0.95 0.26 0.62 0.57 0.58 0.62 0.58 0.86 0.78 IFI08 1.00 0.51 0.52 0.42 0.45 0.53 0.53 0.73 0.72 IFI09 1.00 0.32 -0.07 0.01 0.21 0.43 -0.22 -0.20 IFI10 1.00 0.74 0.74 0.35 0.36 0.53 0.33 IFI11 1.00 0.99 0.49 0.45 0.68 0.44 IFI12 1.00 0.48 0.46 0.66 0.45 IFI13 1.00 0.88 0.68 0.60 IFI14 1.00 0.48 0.37 IFI15 1.00 0.92 IFI16 1.00

18 Table 3: Correlation Matrix (cont.)

IFI17 IFI18 INV LGDPCAL EDU POPU GOVCON EXPORT TRADE INFLATION DCREDIT M2 STOCAP STOACT STOTO ICRG Y1 0.00 -0.01 0.24 0.05 0.10 0.06 -0.07 0.08 0.09 -0.10 0.11 0.08 0.12 0.12 0.19 0.27 IFI01 -0.52 -0.52 0.07 -0.75 -0.66 0.50 -0.48 -0.09 -0.09 0.17 -0.39 -0.53 -0.13 -0.26 -0.29 -0.67 IFI02 -0.42 -0.46 -0.06 -0.58 -0.52 0.23 -0.28 -0.34 -0.32 0.15 -0.53 -0.66 -0.33 -0.35 -0.15 -0.60 IFI03 0.64 0.67 0.11 0.22 0.31 -0.06 0.09 0.37 0.34 -0.07 0.20 0.19 0.49 0.43 0.09 0.32 IFI04 0.47 0.53 0.18 0.08 0.09 0.03 0.13 0.41 0.41 -0.05 0.11 0.26 0.33 0.26 -0.01 0.24 IFI05 0.77 0.75 -0.03 0.24 0.39 -0.09 0.02 0.24 0.20 -0.02 0.22 0.30 0.21 0.16 0.07 0.25 IFI06 0.64 0.67 -0.01 0.25 0.37 -0.01 0.04 0.16 0.13 -0.02 0.20 0.11 0.15 0.14 0.09 0.24 IFI07 0.84 0.83 0.02 0.23 0.39 -0.07 -0.01 0.31 0.27 -0.04 0.23 0.32 0.31 0.25 0.08 0.28 IFI08 0.72 0.77 0.09 0.22 0.35 0.01 0.03 0.31 0.28 -0.03 0.20 0.26 0.25 0.21 0.06 0.27 IFI09 -0.12 -0.01 0.35 0.09 0.07 0.04 0.20 0.35 0.36 -0.07 0.10 0.23 0.36 0.20 -0.05 0.19 IFI10 0.53 0.43 -0.01 0.16 0.19 0.01 0.09 0.32 0.30 -0.02 0.12 0.16 0.25 0.08 -0.03 0.16 IFI11 0.71 0.58 -0.19 0.11 0.14 0.19 0.13 0.49 0.45 -0.02 0.08 0.19 0.33 0.22 0.02 0.06 IFI12 0.70 0.60 -0.21 0.07 0.14 0.22 0.12 0.49 0.45 -0.01 0.04 0.18 0.32 0.19 0.01 0.01 IFI13 0.84 0.84 -0.18 0.17 0.30 0.02 0.24 0.41 0.37 -0.04 0.25 0.24 0.47 0.45 0.13 0.26 IFI14 0.65 0.72 -0.14 -0.11 -0.01 0.24 0.13 0.49 0.49 -0.04 0.04 0.18 0.21 0.17 -0.09 0.08 IFI15 0.97 0.90 -0.15 0.41 0.50 -0.27 0.10 0.20 0.13 -0.18 0.39 0.34 0.48 0.46 0.19 0.39 IFI16 0.88 0.91 -0.13 0.47 0.59 -0.34 0.20 0.12 0.05 -0.20 0.34 0.19 0.49 0.50 0.26 0.42

Table 4: Correlation Matrix (cont.)

IFI17 IFI18 INV LGDPCAL EDU POPU GOVCON EXPORT TRADE INFLATION DCREDIT M2 STOCAP STOACT STOTO ICRG IFI17 1.00 0.95 -0.18 0.34 0.45 -0.19 0.13 0.29 0.23 -0.18 0.37 0.33 0.51 0.48 0.17 0.36 IFI18 1.00 -0.17 0.28 0.48 -0.15 0.18 0.31 0.25 -0.19 0.26 0.24 0.46 0.45 0.16 0.32 INV 1.00 0.09 -0.04 0.04 -0.01 0.21 0.27 -0.05 0.16 0.22 0.16 0.12 0.12 0.22 LGDPCAL 1.00 0.84 -0.07 0.35 0.22 0.15 -0.04 0.62 0.37 0.39 0.37 0.19 0.79 EDU 1.00 0.00 0.35 0.09 0.00 -0.07 0.53 0.36 0.33 0.30 0.33 0.69 POPU 1.00 -0.01 0.02 0.03 0.01 -0.35 0.02 -0.07 -0.16 -0.23 -0.44 GOVCON 1.00 0.18 0.21 -0.06 0.11 0.32 0.06 -0.03 -0.05 0.32 EXPORT 1.00 0.98 -0.08 0.17 0.34 0.21 0.16 -0.11 0.21 TRADE 1.00 -0.08 0.14 0.38 0.17 0.15 -0.14 0.14 INFLATION 1.00 -0.06 -0.11 -0.09 -0.06 -0.03 -0.16 DCREDIT 1.00 0.66 0.58 0.51 0.27 0.63 M2 1.00 0.41 0.26 0.09 0.44 STOCAP 1.00 0.71 0.20 0.41 STOACT 1.00 0.58 0.36 STOTO 1.00 0.20 ICRG 1.00

20 OLS Regression Resuls10

Table 5 presents the results for the OLS panel regression for each of the IFI indicators and I variables (X is not included) and each of IFI indicators, I variables and X variables included. As this analysis involves a larger number of regressions, R square for each regression is not reported here to save space. It is clear that Miniane’s indicator and IFI restriction dummy do not do a good job in explaining GDP per capita growth (not statistically significant). However, the sign of these variables is negative, it means that there is a negative relationship between economic growth and capital restrictions or countries with more openness enjoy a higher growth rate. It is also consistent with the previous empirical studies that capital restrictions index measures are negatively but do not statistically associated with economic growth.

In the case of OLS1, except of private capital flows and gross stock assets and liabilities, all of IFI indicators are significant in explaining variation in GDP per capita growth. One interesting point to note here is that all of these de facto IFI variables coefficients here have a positive sign. It implies that the assumption of greater value of de facto capital control variables signify a greater degree of international financial integration hold in this case. However, when we include X variables in our regression (OLS2), there are only three IFI indicators remaining significant at 5% level (Gross Portfolio Investment as share of GDP, Gross Private

Capital as share of GDP, Gross FDI and Portfolio Investment as share of GDP).

According to the literature, these de facto do not consistently explaining variation in economic performance. In addition, the sensitivity analysis methodology (Leamer

1983; Leamer 1985; Levine & Renelt 1991) suggests that changes in the estimated coefficients here indicate a fragile the relationship between international financial

10 We let a fixed effect in our panel data analysis (all regressions) as it is argued that each country has different characteristics.

21 integration and economic growth (subject to slight alterations in the controlling variable).

Table 5 OLS Results

OLS111 OLS212 Miniane's measure -0.02 (-0.96) 0.02 (1.30) IMF dummy -0.01 (-1.71) 0.00 (0.42) Gross FDI 0.12* (4.16) 0.04 (0.94) FDI Inflows 0.22* (4.10) 0.08 (1.25) Gross Portfolio 0.11* (4.51) 0.07* (2.42) Portfolio Inflows 0.07* (2.00) 0.08 (1.75) Gross FDI & Portfolio 0.09* (4.98) 0.06* (2.58) FDI & Portfolio Inflows 0.11* (3.99) 0.07 (1.84) Private Capital Inflows 0.07 (0.77) -0.02 (0.27) Gross Private Capital 0.02* (3.80) 0.02* (2.09) Gross Stock Assets & Liabilities 0.01 (1.96) 0.00 (1.13) Stock Liabilities 0.01* (2.00) 0.00 (1.25) Gross Stock FDI 0.05* (5.45) 0.01 (0.44) Stock FDI inflows 0.05* (2.66) 0 (0.32) Gross Stock Portfolio 0.03* (4.50) 0.01 (0.82) Stock Portfolio Inflows 0.04* (6.52) -0.01 -(0.59) Gross Stock FDI & Portfolio 0.02* (4.65) 0.01 (1.54) Stock FDI & Portfolio Inflows 0.04* (6.55) 0.01 (0.84) Dependent Variable is GDP per capita growth. t-statistics is in parentheses.

* indicates statistically significant at 5%.

11 OLS1: A number of Regressions of GDP per capita growth on all variables in I and each of IFI variable only 12 OLS2: Regressions of GDP per capita growth on all variables in I, each of IFI variable and variables in X.

22 The table 6 and 7 represent the results from many regressions when we allow for the interaction between these IFI variables and other economic conditions of trade openness, country risk (including economic risk, political risk and creditor right), quality of government, economic stability and financial market development. The purpose of letting these interactions is to examine the impact of international financial integration on economic performance under different economic, financial and political environments.

As can be seen from table 6 and table 7, the results are mixed between different conditions. The de jure IFI indicators coefficients are statistically significant when we interact with trade openness, government consumption and country risk index. In addition, these interaction coefficients are also significant indicating a positive linkage between capital restriction policies and economic growth. A country with fewer restrictions on capital flows will enjoy a greater economic growth in the case of higher level of trade openness, lower government expenditure and lower lever of risk

(higher value of risk index). It also clear that there is a relationship between IFI capital restriction indicators and economic growth in countries with high degree of financial market development as those interaction coefficients are statistically significant at 5% level of significance. We could not find a statistically significant relationship between these capital restriction IFI variables and economic growth in other conditions.

The de facto IFI measures do not make any different under different economic environments as most of the interaction coefficients remain insignificant. Some of these indicator coefficients become negative in these tables representing a lesser impacts of IFI on economic growth under better conditions. For example, Gross FDI

23 as share of GDP and FDI inflows, these negative interactions indicate that international financial integration is better in explaining variation in economic growth in countries with less degree of trade openness, less government consumption expenditure as share of GDP and lower inflation rate. However, they are better in higher degree of development in domestic financial market and country risk (positive coefficients). Thus, we can conclude that there is not much different of the impact of international financial integration on economic growth under different conditions.

Our panel OLS panel regressions results show that there are not much strong empirical evidences of the relationship between international financial integration and economic growth. Some statistically significant coefficients might suggest a reversal relationship and we will further examine that in other studies.

24 Table 6: OLS results, interaction terms with Trade, Government Consumption and Inflation

OLS313 (with trade) OLS414 (with govcon) OLS515 (with inflation) Miniane's measure -0.0755* -(4.32) 0.0504* (2.08) -0.0152 -(0.96) IMF dummy -0.0393* -(2.64) 0.0463* (2.61) -0.0120 -(1.74) Gross FDI 0.1235 (1.48) 0.1194 (0.76) 0.1233* (4.20) FDI Inflows 0.2218 (1.42) 0.4007 (1.61) 0.2127* (3.91) Gross Portfolio 0.0749 (1.25) 0.2274* (1.98) 0.1070* (4.41) Portfolio Inflows -0.0454 -(0.29) 0.2934* (2.58) 0.0641 (1.72) Gross FDI & Portfolio 0.0927* (2.38) 0.1748 (2.00) 0.0886* (4.95) FDI & Portfolio Inflows 0.0000 (0.00) 0.3156* (2.24) 0.1053* (3.93) Private Capital Inflows 0.0866 (0.38) 0.3965 (1.52) 0.0718 (0.75) Gross Private Capital 0.0401* (2.58) 0.0757* (2.65) 0.0164* (3.93) Gross Stock Assets & Liabilities 0.0079 (2.23) 0.0105* (2.24) 0.0067* (1.97) Stock Liabilities 0.0136 (2.31) 0.0183 (2.11) 0.0123* (2.02) Gross Stock FDI 0.0046 (0.19) 0.0205 (0.44) 0.0467* (5.69) Stock FDI inflows -0.0181 -(0.36) -0.0027 -(0.03) 0.0468* (2.72) Gross Stock Portfolio 0.0263 (1.70) 0.0219 (1.16) 0.0279* (4.43) Stock Portfolio Inflows 0.0150 (0.45) 0.0542 (1.87) 0.0469* (6.56) Gross Stock FDI & Portfolio 0.0109 (1.09) 0.0143 (1.12) 0.0213* (4.60) Stock FDI & Portfolio Inflows 0.0033 (0.16) 0.0430* (1.96) 0.0380* (6.72) Interaction Terms Miniane's measure 0.0812* (6.40) -0.4096* -(3.70) -0.0010* -(6.94) IMF dummy 0.0415 (1.53) -0.2741* -(3.52) -0.0010* -(4.40) Gross FDI -0.0062 -(0.09) 0.0086 (0.01) -0.1110 -(1.01) FDI Inflows -0.0023 -(0.02) -0.9001 -(0.78) -0.0557* -(3.64) Gross Portfolio 0.0422 (0.66) -0.6304 -(1.04) 0.0155* (7.20) Portfolio Inflows 0.1768 (0.95) -1.3446* -(2.01) 0.0205* (7.78) Gross FDI & Portfolio -0.0021 -(0.05) -0.4258 -(0.96) 0.0133* (6.64) FDI & Portfolio Inflows 0.1275 (1.13) -1.1328 -(1.58) 0.0167* (8.71) Private Capital Inflows -0.0154 -(0.07) -2.1004 -(1.24) -0.0114 -(0.32) Gross Private Capital -0.0141 -(1.96) -0.2615* -(2.13) -0.0082 -0.0082 Gross Stock Assets & Liabilities -0.0008 -(1.48) -0.0159 -(1.51) -0.0009* -(14.94) Stock Liabilities -0.0010 -(0.86) -0.0259 -(1.25) -0.0012* -(14.45) Gross Stock FDI 0.0468 (1.94) 0.1278 (0.62) -0.0190* -(8.42) Stock FDI inflows 0.0624 (1.57) 0.2320 (0.77) -0.0206* -(8.84) Gross Stock Portfolio 0.0017 (0.11) 0.0310 (0.34) 0.0008 (0.02) Stock Portfolio Inflows 0.0338 (0.82) -0.0495 -(0.36) -0.1135 -(1.54) Gross Stock FDI & Portfolio 0.0105 (0.91) 0.0322 (0.52) -0.0533* -(2.55) Stock FDI & Portfolio Inflows 0.0366 (1.41) -0.0314 -(0.31) -0.0689* -(3.02) Dependent Variable is GDP per capita growth. t-statistics is in parentheses.

13 OLS3: A number of regressions between GDP per capita on I, each of IFI, IFI interaction with Trade and X. 14 OLS4: A number of regressions between GDP per capita on I, each of IFI, IFI interaction with Government Consumption and X.

15 OLS5: A number of regressions between GDP per capita on I, each of IFI, IFI interaction with Inflation and X.

25 Table 7 OLS results, interaction terms with Domestic Credit, Risk and Capital Market

OLS6 (with domestic credit) OLS7 (with risk) OLS8 (with capital market) Miniane's measure -0.0097 -(0.40) -0.1333* -(3.69) -0.0388 -(1.77) IMF dummy -0.0157 -(1.12) -0.1109* -(5.09) -0.0250* -(3.55) Gross FDI 0.0713 (0.90) -1.0801 -(2.46) 0.0661 (0.99) FDI Inflows 0.1873 (1.41) -1.0846 -(1.73) -0.0787 -(0.76) Gross Portfolio 0.1805* (3.09) -0.2062 -(0.69) 0.0738 (2.17) Portfolio Inflows 0.1330 (1.95) -0.6728 -(1.19) 0.0087 (0.11) Gross FDI & Portfolio 0.1277* (3.57) -0.3918 -(1.74) 0.0829* (3.42) FDI & Portfolio Inflows 0.1163 (1.65) -1.0090* -(2.04) 0.0176 (0.33) Private Capital Inflows 0.0435 (0.28) -1.0386* -(3.47) -0.0637 -(0.44) Gross Private Capital 0.0116 (1.49) -0.0361 -(0.90) -0.0142 -(0.88) Gross Stock Assets & Liabilities 0.0051 (0.88) 0.0016 (1.13) 0.0204* (3.12) Stock Liabilities 0.0084 (0.80) 0.0006 (0.11) 0.0280* (2.63) Gross Stock FDI 0.0372 (1.33) -0.1189 -(1.22) 0.0394 (1.72) Stock FDI inflows -0.0053 -(0.14) -0.1588 -(1.23) 0.0147 (0.39) Gross Stock Portfolio 0.0515* (7.30) -0.0355 -(0.53) 0.0525* (4.10) Stock Portfolio Inflows 0.0550* (7.33) -0.2484* -(2.13) 0.0890* (4.02) Gross Stock FDI & Portfolio 0.0364* (7.28) -0.0420 -(1.06) 0.0390 0.0390 Stock FDI & Portfolio Inflows 0.0431* (5.85) -0.0910 -0.0910 0.0634* (4.20) Interaction Terms Miniane's measure -0.0017 -(0.07) 0.0016* (3.68) 0.0441* (2.90) IMF dummy 0.0056 (0.39) 0.0012* (5.05) 0.0293* (5.30) Gross FDI 0.0536 (0.81) 0.0149* (2.76) 0.0559 (1.53) FDI Inflows 0.0306 (0.24) 0.0166* (2.10) 0.2418* (3.62) Gross Portfolio -0.0823 -(1.69) 0.0040 (1.06) 0.0473* (2.34) Portfolio Inflows -0.0837 -(1.09) 0.0096 (1.38) 0.1332* (2.47) Gross FDI & Portfolio -0.0388 -(1.25) 0.0059* (2.06) 0.0130 (1.14) FDI & Portfolio Inflows -0.0144 -(0.22) 0.0141* (2.25) 0.1032* (3.00) Private Capital Inflows 0.0821 (0.39) 0.0152* (3.97) 0.0425 (0.87) Gross Private Capital 0.0087 (1.10) 0.0007 (1.22) 0.0227* (2.33) Gross Stock Assets & Liabilities 0.0016 (0.52) 0.0001 (1.19) -0.0023 -(1.84) Stock Liabilities 0.0039 (0.65) 0.0002 (1.19) -0.0013 -(0.51) Gross Stock FDI 0.0088 (0.41) 0.0022 (1.82) 0.0088 (0.93) Stock FDI inflows 0.0693* (2.03) 0.0031 (1.90) 0.0474* (2.57) Gross Stock Portfolio -0.0200* -(5.89) 0.0008 (0.98) -0.0073* -(2.99) Stock Portfolio Inflows -0.0115* -(2.00) 0.0034* (2.61) -0.0132* -(2.26) Gross Stock FDI & Portfolio -0.0129* -(3.53) 0.0008 (1.56) -0.0050* -(3.52) Stock FDI & Portfolio Inflows -0.0071 -(0.92) 0.0015 (1.93) -0.0068 -(1.73)

Dependent Variable is GDP per capita growth. t-statistics is in parentheses.

26 7. Conclusion

This paper uses a new panel dataset and various new economic variables in studying the relationship between international financial integration and economic growth. This paper advances other previous studies in examining a larger number of assorted indicators to proxy for international financial integration and their relationship with economic growth.

This paper aims to enhance the literature on the international financial integration – growth nexus by providing a larger number of proxies for international financial integration in analysing the relationship under many different economic conditions. In particular, we consider both de jure and de facto measures of international financial integration. In the case of de jure measures, we consider both the popular IMF capital restrictions dummy and the newly developed Miniane’s measure. In addition, we also develop a wide array of new de facto international financial integration indicators which have never been used previously.

The main findings from our dataset are overwhelmed with evidences indicating a weak and fragile relationship between international financial integration and economic growth. This is consistent with the current literature investigating this relationship and might suggest a study on the reversal linkage between the economic growth and international financial integration. However, even though it is not statistically significant, this result should not be interpreted as that international financial integration is not associated with economic growth. Rather, we find that this relationship is not robust.

In addition, we find that this relation is not significantly different under different economic conditions and environments. By using the interaction term in our regression, we prove that the hypothesis of “international financial integration is positively associated with growth when countries have well-developed banks, well- developed stock markets, well functioning legal systems that protect the rule of law, low levels of government corruption, sufficiently high levels of real per capita GDP, high levels of educational attainment, prudent fiscal balances, and low inflation rates” does not hold in this case. Therefore, we find no different in the linkage between international financial integration and economic growth under different institutional and policy regimes.

28 Appendices

Appendix 1: Financial Market Development Indicators

Variable Description Symbol Source The size of the stock market capitalization to GDP STOCAP Standard & stock market ratio which equals the value of listed Poor's, shares divided by GDP. Both numerator Emerging and denominator are deflated Stock appropriately, with the numerator Markets equalling the average of the end-of-year Factbook value for year t and year t-1, both and deflated by the respective end-of-year supplemental CPI, and the GDP deflated by the S&P data, annual value of the CPI. and World The domestic the total value of trades of stock on STOACT Bank and stock market domestic exchanges as a share of GDP. OECD GDP activity or Since both numerator and denominator estimates. liquidity are flow variables measured over the same time period, deflating is not necessary in this case. The stock the stock market turnover ratio as STOTO market efficiency indicator of stock markets. It efficiency is defined as the ratio of the value of total shares traded and market capitalization. It measures the activity or liquidity of a stock market relative to its size. A small but active stock market will have a high turnover ratio whereas a large, while a less liquid stock market will have a low turnover ratio. Since this indicator is the ratio of a stock and a flow variable, we apply a similar deflating procedure as for the market capitalization indicator.

29 Appendix 2 – List of Countries in the sample

Developed Countries

Australia, Austria, Bahamas, Bahrain, Canada, Cyprus, Denmark, Finland, France,

Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Korea, Kuwait, Luxembourg,

Malta, Netherlands, Netherlands Antilles, New Zealand, Norway, Portugal,

Singapore, Spain, Sweden, Switzerland, United Kingdom, United States.

Developing Countries

Argentina, Barbados, Bolivia, Botswana, Brazil, Cape Verde, Chile, China, Colombia,

Congo, Costa Rica, Czech Republic, Egypt, El Salvador, Fiji, Gabon, Haiti, Hungary,

India, Indonesia, Jamaica, Jordan, Kenya, Libya, Malaysia, Mali, Mauritius, Mexico,

Morocco, Namibia, Nigeria, Pakistan, Paraguay, Peru, Philippines, Senegal,

Seychelles, South Africa, Sri Lanka, Swaziland, Thailand, Togo, Tunisia, Turkey,

Uruguay, Vanuatu, Venezuela.

30 References