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: x.vo@uws.edu.au
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 countryspecific 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
4
See Vo (Vo 2005) for the definition of international financial integration.
5
growth effects. In addition, international financial integration may boost the
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 capitalscarce 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
6
international financial integration per se accelerates economic growth, even when
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
7
countries. He thus argues that good institutions are necessary to enjoy the positive
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
5
(2)
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
X.7
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-inadvance" 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.
9
-
The size of stock market (STOCAP)
-
The domestic stock market activity or liquidity (STOACT)
For an overview over this literature, see Levine (Levine 1997).
11
-
The stock market efficiency (STOTO)
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
12
problems only briefly. Measurement problem with the lagged value of real GDP per
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 timeseries variation in the data, accounts for unobserved country-specific effects, allows
13
for the inclusion of lagged dependent variables as regressors, and controls for
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
Y1
IFI01
IFI02
IFI03
IFI04
IFI05
IFI06
IFI07
IFI08
IFI09
IFI10
IFI11
IFI12
IFI13
IFI14
IFI15
IFI16
IFI17
IFI18
EDU
EXPORT
DCREDIT
GOVCON
ICRG
INFLATION
INV
LGDPCAL
M2
POPU
STOACT
STOCAP
STOTO
TAX
TRADE
Mean
0.0159
0.5305
0.5022
0.0367
0.0228
0.0478
0.0920
0.0444
0.0331
0.2301
2.1555
1.1447
0.3179
0.0210
0.1922
0.4669
0.2541
0.8232
0.4620
0.7451
0.3770
0.5507
0.1675
69.4630
0.3673
0.2202
8.3621
0.4707
1.4141
0.2700
0.4853
0.4889
0.2150
0.7763
Median
0.0193
0.5000
1.0000
0.0202
0.0117
0.0206
0.0535
0.0283
0.0218
0.1045
1.2411
0.7574
0.2371
0.0085
0.1350
0.2585
0.1522
0.5079
0.3606
0.7585
0.3168
0.4472
0.1598
71.0000
0.0619
0.2144
8.3681
0.4004
1.4577
0.0882
0.3129
0.3316
0.2030
0.6708
Maximum
0.3463
1.0000
1.0000
0.4064
0.3357
1.9566
2.0901
1.0906
0.2579
23.1410
35.9676
17.4390
2.8488
0.8696
1.7849
8.2118
3.5555
10.0189
5.0764
1.6070
1.5239
2.0316
0.7622
96.0000
117.4964
0.5973
10.9862
1.7255
7.4215
3.2639
3.2996
4.7546
0.4939
2.9602
Minimum
-0.2518
0.0769
0.0000
0.0000
0.0000
0.0000
0.0001
0.0000
0.0000
0.0006
0.1948
0.0791
0.0137
0.0000
0.0030
0.0007
0.0000
0.0320
0.0182
0.0633
0.0506
0.0288
0.0000
25.5000
-1.0000
0.0000
5.0539
0.0356
-275.2720
0.0000
0.0028
0.0000
0.0000
0.1155
Std. Dev.
0.0444
0.3178
0.5004
0.0487
0.0323
0.1269
0.1642
0.0717
0.0350
0.8008
3.7681
1.7955
0.3143
0.0537
0.2050
0.7007
0.3357
0.9599
0.4611
0.3155
0.2393
0.3899
0.0643
13.7537
3.7244
0.0618
1.4529
0.2857
6.9084
0.4505
0.5035
0.5699
0.0934
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
Y1
IFI01
IFI02
IFI03
IFI04
IFI05
IFI06
IFI07
IFI08
IFI09
IFI10
IFI11
IFI12
IFI13
IFI14
IFI15
IFI16
Y1
-0.03
IFI01
0.76
-0.04
IFI02
-0.36
-0.34
0.09
IFI03
0.91
-0.31
-0.18
0.11
IFI04
0.41
0.45
-0.44
-0.46
0.12
IFI05
0.94
0.33
0.37
-0.33
-0.39
0.10
IFI06
0.89
0.96
0.63
0.69
-0.42
-0.42
0.14
IFI07
0.95
0.90
0.90
0.70
0.69
-0.40
-0.36
0.14
IFI08
0.51
0.26
0.01
-0.05
0.71
0.58
-0.12
0.28
0.10
IFI09
0.32
0.52
0.62
0.45
0.60
0.31
0.30
-0.44
-0.47
0.02
IFI10
0.74
-0.07
0.42
0.57
0.35
0.54
0.39
0.41
-0.41
-0.55
0.00
IFI11
0.99
0.74
0.01
0.45
0.58
0.37
0.55
0.41
0.42
-0.42
-0.54
-0.01
IFI112
0.48
0.49
0.35
0.21
0.53
0.62
0.35
0.49
0.54
0.67
-0.37
-0.43
-0.04
IFI13
0.88
0.46
0.45
0.36
0.43
0.53
0.58
0.33
0.48
0.60
0.56
-0.27
-0.22
-0.04
IFI14
0.48
0.68
0.66
0.68
0.53
-0.22
0.73
0.86
0.71
0.82
0.38
0.56
-0.43
-0.54
0.02
IFI15
0.92
0.37
0.60
0.45
0.44
0.33
-0.20
0.72
0.78
0.71
0.73
0.36
0.57
-0.49
-0.57
0.01
IFI16
Table 2: Correlation Matrix
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
18
Table 3: Correlation Matrix (cont.)
IFI10
IFI07
IFI06
IFI05
IFI04
IFI03
0.72
0.84
0.64
0.77
0.47
0.64
0.43
0.77
0.83
0.67
0.75
0.53
0.67
IFI01 -0.52 -0.52
-0.21
-0.19
-0.01
0.35
0.09
0.02
-0.01
-0.03
0.18
0.11
IFI02 -0.42 -0.46 -0.06
Y1
0.53
0.58
IFI09 -0.12 -0.01
IFI08
0.71
0.60
-0.18
0.07
0.24
-0.11
0.17
0.07
0.11
0.16
0.09
0.22
0.23
0.25
0.24
0.08
0.22
-0.58
-0.75
0.05
0.50
-0.01
0.30
0.14
0.14
0.19
0.07
0.35
0.39
0.37
0.39
0.09
0.31
-0.52
-0.66
0.10
-0.34
-0.27
0.24
0.02
0.22
0.19
0.01
0.04
0.01
-0.07
-0.01
-0.09
0.03
-0.06
0.23
0.50
0.06
0.20
0.10
0.13
0.24
0.12
0.13
0.09
0.20
0.03
-0.01
0.04
0.02
0.13
0.09
-0.28
-0.48
-0.07
0.12
0.20
0.49
0.41
0.49
0.49
0.32
0.35
0.31
0.31
0.16
0.24
0.41
0.37
-0.34
-0.09
0.08
0.05
0.13
0.49
0.37
0.45
0.45
0.30
0.36
0.28
0.27
0.13
0.20
0.41
0.34
-0.32
-0.09
0.09
-0.20
-0.18
-0.04
-0.04
-0.01
-0.02
-0.02
-0.07
-0.03
-0.04
-0.02
-0.02
-0.05
-0.07
0.15
0.17
-0.10
0.34
0.39
0.04
0.25
0.04
0.08
0.12
0.10
0.20
0.23
0.20
0.22
0.11
0.20
-0.53
-0.39
0.11
0.19
0.34
0.18
0.24
0.18
0.19
0.16
0.23
0.26
0.32
0.11
0.30
0.26
0.19
-0.66
-0.53
0.08
0.49
0.48
0.21
0.47
0.32
0.33
0.25
0.36
0.25
0.31
0.15
0.21
0.33
0.49
-0.33
-0.13
0.12
0.50
0.46
0.17
0.45
0.19
0.22
0.08
0.20
0.21
0.25
0.14
0.16
0.26
0.43
-0.35
-0.26
0.12
0.26
0.19
-0.09
0.13
0.01
0.02
-0.03
-0.05
0.06
0.08
0.09
0.07
-0.01
0.09
-0.15
-0.29
0.19
0.42
0.39
0.08
0.26
0.01
0.06
0.16
0.19
0.27
0.28
0.24
0.25
0.24
0.32
-0.60
-0.67
0.27
STOCAP STOACT STOTO ICRG
IFI11
0.70
0.84
-0.14
0.41
0.59
M2
IFI12
0.84
0.72
-0.15
0.47
LGDPCAL EDU POPU GOVCON EXPORT TRADE INFLATION DCREDIT
IFI13
0.65
0.90
-0.13
IFI17 IFI18 INV
IFI14
0.97
0.91
-0.01
IFI15
0.88
0.00
IFI16
IFI17
IFI18
INV
LGDPCAL
EDU
POPU
GOVCON
EXPORT
TRADE
INFLATION
DCREDIT
M2
STOCAP
STOACT
STOTO
ICRG
Table 4: Correlation Matrix (cont.)
0.45
-0.15
-0.19
-0.01
0.18
0.13
0.22
0.21
0.31
0.29
0.00
0.15
0.27
0.25
0.23
0.01
-0.07
-0.04
-0.05
-0.19
-0.18
-0.35
0.53
0.62
0.16
0.26
0.37
0.02
0.36
0.37
0.22
0.24
0.33
-0.07
0.33
0.39
0.16
0.46
0.51
-0.16
0.30
0.37
0.12
0.45
0.48
-0.23
0.33
0.19
0.12
0.16
0.17
-0.44
0.69
0.79
0.22
0.32
0.36
STOCAP STOACT STOTO ICRG
0.34
0.48
0.04
0.35
0.09
0.03
M2
-0.18
0.28
-0.04
-0.07
0.35
0.02
LGDPCAL EDU POPU GOVCON EXPORT TRADE INFLATION DCREDIT
0.95
-0.17
0.09
0.84
0.00
-0.01
0.32
IFI17 IFI18 INV
1.00
1.00
1.00
1.00
1.00
1.00
-0.05
0.21
-0.03
-0.11
0.14
0.06
0.16
-0.14
-0.16
0.32
0.21
0.15
-0.03
0.63
0.11
0.34
0.17
-0.06
0.27
0.44
-0.06
0.17
0.38
-0.09
0.51
0.09
0.41
0.21
-0.08
0.14
-0.11
0.58
0.26
0.20
0.36
0.18
0.98
-0.08
-0.06
0.66
0.41
0.71
0.58
0.20
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
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
Miniane's measure
IMF dummy
Gross FDI
FDI Inflows
Gross Portfolio
Portfolio Inflows
Gross FDI & Portfolio
FDI & Portfolio Inflows
Private Capital Inflows
Gross Private Capital
Gross Stock Assets & Liabilities
Stock Liabilities
Gross Stock FDI
Stock FDI inflows
Gross Stock Portfolio
Stock Portfolio Inflows
Gross Stock FDI & Portfolio
Stock FDI & Portfolio Inflows
-0.02
-0.01
0.12*
0.22*
0.11*
0.07*
0.09*
0.11*
0.07
0.02*
0.01
0.01*
0.05*
0.05*
0.03*
0.04*
0.02*
0.04*
OLS111
(-0.96)
(-1.71)
(4.16)
(4.10)
(4.51)
(2.00)
(4.98)
(3.99)
(0.77)
(3.80)
(1.96)
(2.00)
(5.45)
(2.66)
(4.50)
(6.52)
(4.65)
(6.55)
OLS212
0.02
0.00
0.04
0.08
0.07*
0.08
0.06*
0.07
-0.02
0.02*
0.00
0.00
0.01
0
0.01
-0.01
0.01
0.01
(1.30)
(0.42)
(0.94)
(1.25)
(2.42)
(1.75)
(2.58)
(1.84)
(0.27)
(2.09)
(1.13)
(1.25)
(0.44)
(0.32)
(0.82)
-(0.59)
(1.54)
(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
Miniane's measure
IMF dummy
Gross FDI
FDI Inflows
Gross Portfolio
Portfolio Inflows
Gross FDI & Portfolio
FDI & Portfolio Inflows
Private Capital Inflows
Gross Private Capital
Gross Stock Assets & Liabilities
Stock Liabilities
Gross Stock FDI
Stock FDI inflows
Gross Stock Portfolio
Stock Portfolio Inflows
Gross Stock FDI & Portfolio
Stock FDI & Portfolio Inflows
Interaction Terms
Miniane's measure
IMF dummy
Gross FDI
FDI Inflows
Gross Portfolio
Portfolio Inflows
Gross FDI & Portfolio
FDI & Portfolio Inflows
Private Capital Inflows
Gross Private Capital
Gross Stock Assets & Liabilities
Stock Liabilities
Gross Stock FDI
Stock FDI inflows
Gross Stock Portfolio
Stock Portfolio Inflows
Gross Stock FDI & Portfolio
Stock FDI & Portfolio Inflows
OLS313 (with trade)
-0.0755* -(4.32)
-0.0393* -(2.64)
0.1235
(1.48)
0.2218
(1.42)
0.0749
(1.25)
-0.0454
-(0.29)
0.0927*
(2.38)
0.0000
(0.00)
0.0866
(0.38)
0.0401*
(2.58)
0.0079
(2.23)
0.0136
(2.31)
0.0046
(0.19)
-0.0181
-(0.36)
0.0263
(1.70)
0.0150
(0.45)
0.0109
(1.09)
0.0033
(0.16)
0.0812*
0.0415
-0.0062
-0.0023
0.0422
0.1768
-0.0021
0.1275
-0.0154
-0.0141
-0.0008
-0.0010
0.0468
0.0624
0.0017
0.0338
0.0105
0.0366
(6.40)
(1.53)
-(0.09)
-(0.02)
(0.66)
(0.95)
-(0.05)
(1.13)
-(0.07)
-(1.96)
-(1.48)
-(0.86)
(1.94)
(1.57)
(0.11)
(0.82)
(0.91)
(1.41)
OLS414 (with govcon)
0.0504*
(2.08)
0.0463*
(2.61)
0.1194
(0.76)
0.4007
(1.61)
0.2274*
(1.98)
0.2934*
(2.58)
0.1748
(2.00)
0.3156*
(2.24)
0.3965
(1.52)
0.0757*
(2.65)
0.0105*
(2.24)
0.0183
(2.11)
0.0205
(0.44)
-0.0027
-(0.03)
0.0219
(1.16)
0.0542
(1.87)
0.0143
(1.12)
0.0430*
(1.96)
-0.4096*
-0.2741*
0.0086
-0.9001
-0.6304
-1.3446*
-0.4258
-1.1328
-2.1004
-0.2615*
-0.0159
-0.0259
0.1278
0.2320
0.0310
-0.0495
0.0322
-0.0314
-(3.70)
-(3.52)
(0.01)
-(0.78)
-(1.04)
-(2.01)
-(0.96)
-(1.58)
-(1.24)
-(2.13)
-(1.51)
-(1.25)
(0.62)
(0.77)
(0.34)
-(0.36)
(0.52)
-(0.31)
OLS515 (with inflation)
-0.0152
-(0.96)
-0.0120
-(1.74)
0.1233*
(4.20)
0.2127*
(3.91)
0.1070*
(4.41)
0.0641
(1.72)
0.0886*
(4.95)
0.1053*
(3.93)
0.0718
(0.75)
0.0164*
(3.93)
0.0067*
(1.97)
0.0123*
(2.02)
0.0467*
(5.69)
0.0468*
(2.72)
0.0279*
(4.43)
0.0469*
(6.56)
0.0213*
(4.60)
0.0380*
(6.72)
-0.0010*
-0.0010*
-0.1110
-0.0557*
0.0155*
0.0205*
0.0133*
0.0167*
-0.0114
-0.0082
-0.0009*
-0.0012*
-0.0190*
-0.0206*
0.0008
-0.1135
-0.0533*
-0.0689*
-(6.94)
-(4.40)
-(1.01)
-(3.64)
(7.20)
(7.78)
(6.64)
(8.71)
-(0.32)
-0.0082
-(14.94)
-(14.45)
-(8.42)
-(8.84)
(0.02)
-(1.54)
-(2.55)
-(3.02)
Dependent Variable is GDP per capita growth. t-statistics is in parentheses.
* indicates statistically significant at 5%.
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
Miniane's measure
IMF dummy
Gross FDI
FDI Inflows
Gross Portfolio
Portfolio Inflows
Gross FDI & Portfolio
FDI & Portfolio Inflows
Private Capital Inflows
Gross Private Capital
Gross Stock Assets & Liabilities
Stock Liabilities
Gross Stock FDI
Stock FDI inflows
Gross Stock Portfolio
Stock Portfolio Inflows
Gross Stock FDI & Portfolio
Stock FDI & Portfolio Inflows
Interaction Terms
Miniane's measure
IMF dummy
Gross FDI
FDI Inflows
Gross Portfolio
Portfolio Inflows
Gross FDI & Portfolio
FDI & Portfolio Inflows
Private Capital Inflows
Gross Private Capital
Gross Stock Assets & Liabilities
Stock Liabilities
Gross Stock FDI
Stock FDI inflows
Gross Stock Portfolio
Stock Portfolio Inflows
Gross Stock FDI & Portfolio
Stock FDI & Portfolio Inflows
OLS616 (with domestic credit)
-0.0097
-(0.40)
-0.0157
-(1.12)
0.0713
(0.90)
0.1873
(1.41)
0.1805*
(3.09)
0.1330
(1.95)
0.1277*
(3.57)
0.1163
(1.65)
0.0435
(0.28)
0.0116
(1.49)
0.0051
(0.88)
0.0084
(0.80)
0.0372
(1.33)
-0.0053
-(0.14)
0.0515*
(7.30)
0.0550*
(7.33)
0.0364*
(7.28)
0.0431*
(5.85)
-0.0017
0.0056
0.0536
0.0306
-0.0823
-0.0837
-0.0388
-0.0144
0.0821
0.0087
0.0016
0.0039
0.0088
0.0693*
-0.0200*
-0.0115*
-0.0129*
-0.0071
-(0.07)
(0.39)
(0.81)
(0.24)
-(1.69)
-(1.09)
-(1.25)
-(0.22)
(0.39)
(1.10)
(0.52)
(0.65)
(0.41)
(2.03)
-(5.89)
-(2.00)
-(3.53)
-(0.92)
OLS717 (with risk)
-0.1333* -(3.69)
-0.1109* -(5.09)
-1.0801
-(2.46)
-1.0846
-(1.73)
-0.2062
-(0.69)
-0.6728
-(1.19)
-0.3918
-(1.74)
-1.0090* -(2.04)
-1.0386* -(3.47)
-0.0361
-(0.90)
0.0016
(1.13)
0.0006
(0.11)
-0.1189
-(1.22)
-0.1588
-(1.23)
-0.0355
-(0.53)
-0.2484* -(2.13)
-0.0420
-(1.06)
-0.0910 -0.0910
0.0016*
0.0012*
0.0149*
0.0166*
0.0040
0.0096
0.0059*
0.0141*
0.0152*
0.0007
0.0001
0.0002
0.0022
0.0031
0.0008
0.0034*
0.0008
0.0015
(3.68)
(5.05)
(2.76)
(2.10)
(1.06)
(1.38)
(2.06)
(2.25)
(3.97)
(1.22)
(1.19)
(1.19)
(1.82)
(1.90)
(0.98)
(2.61)
(1.56)
(1.93)
OLS818 (with capital market)
-0.0388
-(1.77)
-0.0250*
-(3.55)
0.0661
(0.99)
-0.0787
-(0.76)
0.0738
(2.17)
0.0087
(0.11)
0.0829*
(3.42)
0.0176
(0.33)
-0.0637
-(0.44)
-0.0142
-(0.88)
0.0204*
(3.12)
0.0280*
(2.63)
0.0394
(1.72)
0.0147
(0.39)
0.0525*
(4.10)
0.0890*
(4.02)
0.0390
0.0390
0.0634*
(4.20)
0.0441*
0.0293*
0.0559
0.2418*
0.0473*
0.1332*
0.0130
0.1032*
0.0425
0.0227*
-0.0023
-0.0013
0.0088
0.0474*
-0.0073*
-0.0132*
-0.0050*
-0.0068
Dependent Variable is GDP per capita growth. t-statistics is in parentheses.
* indicates statistically significant at 5%.
16
A number of regressions between GDP per capita on I, each of IFI, IFI interaction with Domestic
Credit and X.
17
A number of regressions between GDP per capita on I, each of IFI, IFI interaction with Country Risk
and X.
18
A number of regressions between GDP per capita on I, each of IFI, IFI interaction with Capital
Market Development (Size of Capital Market) and X.
26
(2.90)
(5.30)
(1.53)
(3.62)
(2.34)
(2.47)
(1.14)
(3.00)
(0.87)
(2.33)
-(1.84)
-(0.51)
(0.93)
(2.57)
-(2.99)
-(2.26)
-(3.52)
-(1.73)
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, welldeveloped 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
The size of the stock market capitalization to GDP
stock market
ratio which equals the value of listed
shares divided by GDP. Both numerator
and
denominator
are
deflated
appropriately, with the numerator
equalling the average of the end-of-year
value for year t and year t-1, both
deflated by the respective end-of-year
CPI, and the GDP deflated by the
annual value of the CPI.
The domestic the total value of trades of stock on
stock market domestic exchanges as a share of GDP.
activity
or Since both numerator and denominator
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
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
Symbol
Source
STOCAP Standard &
Poor's,
Emerging
Stock
Markets
Factbook
and
supplemental
S&P
data,
and World
and
STOACT Bank
OECD GDP
estimates.
STOTO
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
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