Are financial development and trade openness complements or substitutes?
Kim, Dong-Hyeon ; Lin, Shu-Chin ; Suen, Yu-Bo 等
This article studies the long- and short-run relationships between
financial development and trade openness. Using the pooled mean group
estimator of Pesaran, Shin, and Smith (1999) for unbalanced panel data
for 87 countries over the 1960-2005 period, our empirical results
indicate that long-run complementarity between financial development and
trade openness coexists with short-run substitutionarity between the two
policy variables. But when splitting the data into OECD and non-OECD
country groups, this finding can be observed only in non-OECD countries.
For OECD countries, financial development has negligible effects on
trade. In addition, we find nonlinearity in the relationship in that
long-run responses of trade decrease with financial development. The
article further finds coexistence of negative trade effects of financial
fragility and positive trade impacts of financial depth.
JEL Classification: F13, G21
1. Introduction
Recent theoretical literature on financial liberalization predicts
that liberalization can generate both short-run financial instability
and long-run economic growth. On the one hand, financial intermediaries
and markets may produce information about profitable ventures, diversify
risk, and facilitate resource mobilization. Thus, a well-developed
financial system helps improve the efficiency of resource allocation and
productivity growth, thereby promoting long-run economic growth. (1) On
the other hand, financial liberalization may lead to undue lending
booms, and hence financial crises, because of limited monitoring
capacity of regulatory agencies, inability of banks to discriminate good
projects during investment booms, and/or existence of an explicit or
implicit insurance against banking failure. (2)
More recently, Loayza and Ranciere (2006) provide cross-country
evidence of coexistence of positive long-run and negative short-run
relationships between financial development and growth. They link the
negative short-run impact to financial fragility and the positive
influence to long-run effects of financial liberalization. This article
goes a step further and investigates whether such a dual role of
financial liberalization results in heterogeneous long- and short-run
responses of trade openness to financial development. If financial
intermediation indeed affects trade openness, this might offer one
mechanism through which financial development exerts its influence on
long-run economic growth and short-run economic fluctuations.
Several recent articles suggest that trade is strongly linked to
financial development. (3) If greater international trade increases
exposure to the fluctuations of the world goods market, the development
of a financial system as an insurance mechanism might reduce barriers to
trade. Feeney and Hillman (2004), for instance, demonstrate how capital
market incompleteness can affect trade policy and that the degree of
portfolio diversification determines the protectionist lobbying effort
conducted by owners of sector-specific capital. If risk can be fully
diversified, special interest groups have no incentive to lobby for
protection, and free trade will prevail. Thus, the development of
financial markets that mitigates informational asymmetries could lead to
more trade liberalization and trade flows.
Others emphasize that financial development is a source of
comparative advantage. For example, Kletzer and Bardhan (1987) augment
the Heckscher-Ohlin trade model by incorporating the financial sector
and demonstrate that countries with a relatively well-developed
financial sector have a comparative advantage in industries and sectors
that rely more on external financing. Beck (2002) goes a step further
and focuses on the role of financial intermediaries in mobilizing
savings and facilitating large-scale and high-return projects. In the
model, financial development lowers the search costs and increases the
level of external finance in the economy. Banking development may thus
shift incentives of producers toward goods with increasing returns to
scale. Accordingly, the intersectoral specialization and the structure
of trade flows are determined by the relative level of financial
intermediation. All else being equal, economies with better-developed
financial systems are net exporters of the goods with high scale of
economies.
In addition to the long-run effects, short-run considerations may
play roles in the relationship. As suggested in the financial crisis
literature, financial liberalization tends to cause financial fragility
and hence financial crises and recessions in the short run. For example,
Demirguc-Kunt and Detragiache (1998b) claim that financial
liberalization erodes banks' monopolistic power, suggesting an
increased moral hazard to banks with a low franchise value, thereby
tending to make banking crises more likely. Daniel and Jones (2007) also
reach similar conclusions. Van Order (2006) postulates that the
fragility-provoked crises may have cyclical elements in that a downturn
tends to lower asset values and/or twist the risk structure, adding more
risky loans, which can provoke a crisis. Alessandria and Qian (2005)
show that financial liberalization can lead to a lending boom and an
aggregate shift toward worse projects, which often precede financial
crises. In Stiglitz (2000) and Mishkin (2007a, b), financial
liberalization, if carried out inappropriately, may induce
destabilization in the financial system and trigger financial crises,
thereby impeding economic performance. (4) Therefore, financial
development as an insurance and comparative advantage contributor may
generate more risks that impede international trade in the short run.
Accordingly, econometric assessments of the finance-trade
relationship should ideally be capable of uncovering the relevant
long-run parameters as well as the short-run link between the two
variables. Therefore, this article revisits the issue and estimates the
long- and short-run relationship between financial development and trade
openness by using panel techniques that explicitly isolate trend effects
of financial development from short-lived considerations. This can be
accomplished by specifying an autoregressive distributed lag (ARDL)
model for each country, pooling them together in a panel, and then
testing the cross-equation restriction of a common long-run relationship
between the two variables using the pooled mean group (PMG) estimator of
Pesaran, Shin, and Smith (1999). Such a country-specific ARDL approach
allows us not only to accommodate cross-country heterogeneity (for
example, in the degree of credit market imperfections and policy
regimes), but also to capture certain interesting time-series relations
that cross-section analysis alone cannot deal with. Moreover, this
methodology can be applied to either stationary or nonstationary
variables and does not require the pretesting of unit roots. This
partially circumvents some of the problems with cointegration analysis
that focuses only on the estimation of long-run relationship among I(1)
variables, as well as low power of unit root tests against plausible
alternatives. Further, instead of averaging the data per country to
isolate trend effects, (5) both long- and short-run relationships are
estimated using a panel of data pooling time-series and cross-section
effects. (6)
Using panel data pooled from 87 developed and developing countries
for the 1960-2005 period, we find evidence of a strong link between
financial development and trade openness. While financial development is
detrimental to trade openness in the short run, it ultimately
contributes to trade openness in the long run. In other words, trade
openness and financial development are substitutes in the short run but
complements in the long run. This may partially explain why the effects
of financial development on growth differ in different time horizons.
However, when splitting the data into OECD and non-OECD country groups,
we find interesting results. In non-OECD countries, a positive long-run
relationship coexists with a negative short-run link. But financial
development does not exert significant effects on trade liberalization
in OECD countries. This suggests that the long-run effect of financial
development on international trade decreases with financial development.
We also find that financial development tends to have a significant and
negative short-run impact in medium-financial-development countries with
the effect insignificant for both high- and low-financial-development
countries. Finally, the article provides evidence that the short-run
negative responses of trade to finance are mainly due to financial
fragility, while the positive effects of finance on trade are largely
due to financial deepening.
The remainder of the article is organized as follows. Section 2
introduces the PMG estimator proposed by Pesaran, Shin, and Smith
(1999). Section 3 describes the data and source and analyzes various
empirical results by the PMG approach. Section 4 assesses whether
financial fragility is also relevant in the relationship, and section 5
concludes.
2. The Autoregressive Distributed Lag Approach
To examine the effect of financial development, Beck (2002, 2003)
and Svaleryd and Vlachos (2002, 2005) estimate the following
cross-sectional regression:
[trade.sub.i] = [alpha] + [beta] [finance.sub.i] + [omega]
[controls.sub.i] + [[epsilon].sub.i], (1)
where i = 1, 2, ..., N is the country indicator, trade is the
trade openness index, finance is the financial development indicator,
controls is a set of control variables, and e is the error term.
As an alternative, this article investigates the effect of finance
on trade using dynamic panel econometric techniques. In particular,
Equation 1 is extended to a panel data specification, assuming that
there exists a long-run relationship between trade and finance:
[trade.sub.it] = [[alpha].sub.i] + [beta] [finance.sub.it] +
[omega] [controls.sub.it] + [[epsilon].sub.it], (2)
where [[alpha].sub.i] is the fixed (country-specific) effect and t
= 1, 2, ..., T is the time index. In the time series framework, Pesaran
and Shin (1998) and Pesaran, Shin, and Smith (2001) propose the ARDL
models to estimate the long-run cointegrating relationship among
variables of interest. In a panel specification, we nest Equation 2 in
an ARDL specification to allow for rich dynamics in the way that trade
openness (trade) adjusts to changes in financial development (finance)
and to other explanatory variables (controls).
The ARDL(p, q, ..., q) model where the dependent and independent
variables enter the right-hand side with lags of order p, q, ..., q,
respectively, can be written as
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
where i = 1, 2, ..., N, t = 1, 2, ..., T, [y.sub.it] =
[trade.sub.it], [x.sub.it] = ([finance.sub.it], [controls.sub.it]), and
[[mu].sub.i] is the fixed effect.
By reparameterization, Equation 3 can be written as
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
By grouping the variables in levels, Equation 4 can be rewritten as
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
where [[theta].sub.i] = -([[beta].sub.i]/[[phi].sub.i]) defines the
long-run or equilibrium relationship among [y.sub.it] and [x.sub.it].
[[lambda].sup.*.sub.ij] and [[delta].sup.*.sub.ij] are the short-run
coefficients relating trade openness to its determinants. Finally, the
pooled error-correction coefficient [[phi].sub.i] measures the speed of
adjustment of [y.sub.it] toward its long-run equilibrium following a
change in [x.sub.it]; [[phi].sub.i] < 0 ensures that such a long-run
relationship exists. As a result, a significant and negative value of
[[phi].sub.i] can be treated as evidence in support of cointegration
between [y.sub.it] and [x.sub.it].
As shown in Catao and Solomou (2005) and Catao and Terrones (2005),
the ARDL specification in Equation 5, where all explanatory variables
enter the regression with lags, not only allows us to mitigate the
contemporaneous feedback and reverse causality running from trade to
finance but also accommodates the substantial persistence of finance
adjustments and captures potentially rich trade adjustment dynamics. In
addition, the model allows for heterogeneity in the relationship between
finance and trade across countries because the various parameters in
Equation 5 are not restricted to be the same across countries.
Furthermore, the ARDL approach allows us to estimate an empirical model
that encompasses the long- and short-run effects of financial
development on trade openness using a data field composed of a
relatively large sample of countries and annual observations.
There are a few existing procedures for estimating this model. In
one extreme, the simple pooled estimator assumes the fully
homogeneous-coefficient model in which all slope and intercept
parameters are restricted to be identical across countries. By contrast,
the other extreme, the fully heterogeneous-coefficient model, imposes no
cross-country coefficient constraints and can be estimated on a
country-by-country basis. This is the mean group (MG) estimator
introduced by Pesaran and Smith (1995). The approach amounts to
estimating separate ARDL regressions for each group and obtain [theta]
and [phi] as simple averages of individual group coefficients
[[theta].sub.i] and [[phi].sub.i]. In particular, Pesaran and Smith
(1995) show that the MG estimator will provide consistent estimates of
the average of parameters we are interested in.
Between these extremes, the dynamic fixed-effect (DFE) method
allows the intercepts to differ across groups but imposes homogeneity of
all slope coefficients and error variances. Alternatively, Pesaran,
Shin, and Smith (1999) propose the pooled mean group (PMG) estimator
that restricts the long-run parameters to be identical over the cross
section but allows the intercepts, short-run coefficients (including the
speed of adjustment), and error variances to differ across groups on the
cross section. If the long-run homogeneity restrictions are valid, it is
known that MG estimates are inefficient. In this case, the maximum
likelihood-based PMG approach proposed by Pesaran, Shin, and Smith
(1999) yields a more efficient estimator. (7) As suggested in Pesaran,
Shin, and Smith (1999), the validity of a cross-sectional, long-run
homogeneity restriction of the form [[theta].sub.i] = [theta], i = 1, 2,
..., N, and hence the suitability of the PMG estimator, can be tested by
a standard Hausman-type statistic.
In terms of the relationship between financial development and
trade openness, the PMG estimator offers the best available compromise
in the search for consistency and efficiency. This estimator is
particularly useful when the long run is given by conditions expected to
be homogeneous across countries, while the short-run adjustment depends
on country characteristics such as monetary and fiscal adjustment
mechanisms, capital market imperfections, and relative price and wage
flexibility (for example, Loayza and Ranciere 2006). Therefore, we use
the PMG method to estimate a long-run relationship that is common across
countries while allowing for unrestricted country heterogeneity in the
adjustment dynamics.
3. Data and Empirical Results
Data
Our data set consists of a panel of 87 countries over the 1960-2005
period and is mainly taken from the World Development Indicator (2006),
published by World Bank. Data on financial development are obtained from
the Financial Structure Database originally compiled by Beck,
Demirguc-Kunt, and Levine (2000). Given the PMG procedure's
requirements on the time-series dimension of the data, we include only
countries that have at least 20 consecutive observations. (8) Table 1
displays the list of countries in the sample. We use three bank-based
financial development indicators: private credit (lprivo), bank assets
(ldby), and liquidity liabilities (llly). "Private credit" is
the value of credits by financial intermediaries to the private sector
divided by GDP. It is Beck, Demirguc-Kunt, and Levine's (2000)
preferred measure because it excludes credit granted to the public
sector and credit issued by the central bank and development banks.
"Bank assets" is defined as the domestic assets of deposit
money bank as a share of GDP. This thus measures the degree to which
domestic banks allocate society's savings. "Liquidity
liabilities" is equal to the sum of currency and demand and
interest-bearing liabilities of banks and nonbank financial
intermediaries, divided by GDP. This is a commonly used measure of
financial depth; although, it might involve double counting and includes
liabilities backed by credits to the public sector.
As for the trade openness index, following common practice, we use
"trade share," which is the (logarithm of) sum of imports and
exports over GDP (ltrade) as our preferred measure of the degree of
trade openness. As argued, trade share measures actual exposure to trade
interactions, accounts for the effective level of integration, and has
an advantage of being both clearly defined and well measured.
To strengthen our empirical results, we also control for
conditioning variables in the relation between financial intermediary
development and trade openness. The conditioning variables include the
initial real per capita GDP (initial) to control for a causal link from
the income level to trade openness, and the ratio of government
expenditure to GDP (lgov) and the inflation rate (lpi) computed as the
growth rate of the GDP deflator to account for macroeconomic stability.
All variables are in natural logarithm form. Table 2 provides
descriptive statistics and correlations of the variables for the
1960-2005 period.
Basic Results
Table 3 displays both PMG and MG results on specification tests and
the estimation of long- and short-run parameters linking three
bank-based financial development indicators and the trade openness
index. (9) We emphasize the outcome from using the PMG estimator,
considering its gains in consistency and efficiency over other panel
error-correction estimators. For comparison, we also present the results
obtained by the MG estimator.
For the existence of a long-run relationship (dynamic stability),
the coefficient on the error-correction term should be negative and
within the unit circle. As seen in Table 3, the pooled error-correction
coefficient estimates are significantly negative and fall within the
dynamically stable range for both PMG and MG estimators. This gives
evidence of mean reversion to a nonspurious long-run relationship and
therefore stationary residuals, meaning that financial development and
trade are cointegrated. And the Hausman test of long-run homogeneity
restriction is not rejected, indicating that the PMG estimator is more
suitable for the analysis than the MG estimator is. These results hold
for alternative financial development measures. Accordingly, the
following analysis focuses on the PMG approach.
Regarding the parameters of primary interest, we find that the
long-run coefficient of financial development is highly significant and
positive, irrespective of alternative measures of bank-based financial
development, implying that trade is positively linked to financial
development in the long run. The finding that financial development
accelerates trade openness in the long run agrees with the arguments
that financial development is an insurance mechanism and a source of
comparative advantage.
However, the short-run coefficients on finance tell a different
story. Because countries are affected by financial volatility and
banking crises to widely different degrees, the short-run coefficients
are not restricted to be the same across countries, so that we do not
have a single pooled estimate for each coefficient. Nevertheless, we can
still analyze the average short-run effect by considering the mean of
the corresponding coefficients across countries. As Table 3 shows, the
short-run average relationship between financial development and trade
appears to be significantly negative for all financial development
indicators. Therefore, financial development, on average, hinders
international trade in the short run.
Accordingly, comparing the long- and short-run estimates, the
trade-financial development relationship depends on whether their
movements are temporary or permanent. And the finding of coexistence of
positive long-run effects and negative short-run effects implies that
even though financial liberalization hinders international trade in the
short run, it eventually leads to higher integration with international
goods markets in the long run.
To further check if the results are driven by omitted variables
bias, we add three control variables into the models: income, government
size, and inflation. (10) Various studies have found these factors to be
important determinants of trade and financial development. Table 4
reports the results. The estimation outcome is qualitatively similar to
that in Table 3. The signs and statistical significance of both long-
and short-run coefficients remain unchanged, except for the short-run
coefficient when llly is used as the financial development measure.
Moreover, the pooled error-correction coefficients continue to be
significantly negative and within the unit circle. Consequently, our
findings that financial development has significantly positive effects
on international trade in the long run but significantly negative
effects in the short run do not seem to be driven by common omitted
factors.
Finally, we report the PMG estimation results for some ARDL(p,q)
specifications in Table 5 to check whether our findings are sensitive to
model specification. As can be seen, the outcome confirms our finding:
long-run cointegration and complementarity but short-run
substitutionarity between financial deepening and international trade.
The evidence holds for alternative measures of financial development and
different ARDL models.
Stock Markets and Trade Openness
This subsection investigates the long- and short-run relationship
of trade with stock market activities. To this end, we use market
capitalization and value traded to measure the degree of stock market
development. "Market capitalization" (lcap) is a size-based
measure and is the (logarithm of) value of listed shares divided by GDP.
It indicates the market size relative to that of the economy and thus
reflects the importance of financing through equity issuance in the
capital mobilization and resource allocation processes. "Value
traded" (lstv) is a transaction-based measure and is defined as the
(logarithm of) value of traded shares on domestic exchanges divided by
GDP. It measures stock trading relative to the size of the economy.
It is noted that because the PMG procedure requires at least 20
consecutive time-series observations and because many developing
countries have no or limited data on stock markets, the sample reduces
to 44 countries, which mostly are more advanced economies. The estimated
results using both PMG and MG techniques are presented in Table 6. As
indicated, the Hausman test of the null (long-run homogeneity) is not
rejected, justifying the use of the PMG over the MG estimator. The
pooled error-correction coefficient is significantly negative, providing
evidence in support of a long run, cointegrating relationship between
stock market development and trade openness. And the long-run
coefficient is negative and highly significant. In contrast with the
previous subsection, however, the short-run coefficient is also found to
be positive and highly significant, but with smaller impacts. The
evidence thus indicates that stock market development is conducive to
greater international trade not only in the long run but also in the
short run.
OECD versus Non-OECD Countries
There are substantial studies indicating that the relationship
between banking development and economic growth is better characterized
by nonlinearity and by existence of thresholds. For example, De Gregorio
and Guidotti (1995) and Rioja and Valev (2004a) report that positive
effects between banking development and economic growth are particularly
strong in medium- and high-income countries. Deidda and Fattouh (2002)
reach similar results. By contrast, Wachtel (2003) and Calderon and Liu
(2003) provide strong evidence that the bank-growth link is not as
strong among developed countries as it is among less developed ones.
This section investigates whether the long- and short-run effects
of finance on trade differ in OECD versus non-OECD countries. The
results are presented in Table 7. For simplicity, the estimates for
control variables are not reported. For the two country groups, the
pooled error correction coefficients are significantly negative and
within the unit circle, meaning that there exists a stable long-run
relationship of trade with finance (and other control variables).
Moreover, while financial intermediation has significantly positive
long-run and negative short-run effects on external trade in non-OECD
countries, it does not exert significant effects on trade in the short
and long run in OECD countries. The intuition is that non-OECD countries
tend to have weak financial institutions, such that the costs and risks
brought about by financial liberalization may hinder international trade
in the short run. In the long run, however, positive aspects of
financial development can lead to higher trade openness. As for OECD
countries, because advanced economies tend to have more efficient
financial intermediation, further financial development may have a
negligible effect on trade. Therefore, our findings provide some
rationale for the findings of Wachtel (2003) and Calderon and Liu (2003)
that a stronger growth-enhancing effect of banking development in
developing countries than in developed ones works possibly through its
influence on trade openness.
The Effect of Financial Development
In their recent article, Rioja and Valev (2004b) propose that the
growth effects of financial development vary with the extent of
financial development. They argue that additional improvement in
financial markets has an uncertain effect on growth for countries with a
very low level of financial development but a large, positive effect on
growth for countries with more developed financial system, perhaps due
to indivisibility of investment (Acemoglu and Zilibotti 1997), improved
risk pooling and liquidity services (Saint-Paul 1992), or the
learning-by-doing effect (Lee 1996). Other studies find that such growth
effects are smaller in magnitude for countries with highly developed
financial sectors, possibly because of diminishing returns (Greenwood
and Jovanovic 1990) or increasing importance of market-based external
financing as the financial system evolves (for example, De Gregorio and
Guidotti 1995; Levine and Zervos 1998). Further, Masten, Coricelli, and
Masten (2008) claim that the positive growth effect of financial
development is higher in countries that are less developed financially
but vanishes when the financial development passes a certain threshold.
We examine whether the degree of financial development affects the
short- and long-run responses of international trade to financial
deepening. Countries are grouped into three subsamples: low-, medium-,
and high-financial-development countries, depending on the relative
ranking of the (logarithm of) initial level of private credit in the
middle of the sample period. The results are summarized in Table 8. As
indicated, the pooled error-correction coefficients for all country
subsamples and alternative financial development indicators are
significantly negative and within the unit circle, meaning there exists
a stable long-run relationship of trade with finance (and other control
variables). The long-run coefficient estimates of finance are positive
and significant for low- and medium-financial-development countries.
They are not significant for high-financial-development countries,
irrespective of the financial development measures. This suggests that
the long-run effect of finance on trade is higher in less financially
developed countries but vanishes with financial deepening. This agrees
with the finding of Masten, Coricelli, and Masten (2008). These results
provide strong support for the view that international trade is an
important channel through which finance affects growth in a nonlinear
fashion.
As for the short-run relationship, the estimates tend to be
negative and significant (except for the case when lily is used as the
financial development indicator) for medium-financial-development
countries but not significant (except for ldby in
high-financial-development countries) for both high- and
low-financial-development countries. Thus, the short-run
substitutionarity between finance and trade seems to hold only for
countries with a moderate degree of financial development.
4. Trade Openness, Financial Depth, and Financial Fragility
In this section, we go a step further and assess whether financial
fragility is also relevant for trade. We work with 5-year averages of
the data over the 1960-2005 period: that is, averages over 1960-1965,
1966-1970, ..., and 2001-2005. This gives nine observations for each
country. We use the generalized method of moments (GMM) for dynamic
models of panel data, developed by Arellano and Bond (1991) and Arellano
and Bover (1995). The GMM estimators allow us to control for unobserved
country-specific effects and potential endogeneity of the independent
variables. By extending Equation 2 to include the lagged dependent
variable and financial fragility indicators, we estimate the following
dynamic panel regression:
[trade.sub.it] = [[alpha].sub.i] + [gamma] [trade.sub.it-1] +
[beta] [finance.sub.it] + [delta] [fragility.sub.it] + [omega]
[controls.sub.it] + [[epsilon].sub.it], (6)
where finance is a measure of financial depth that is proxied by
the (logarithm of) average of private credit (lprivo); fragility
represents the indicators of financial fragility that are the frequency
of systemic banking crises (11) and the standard deviation of the growth
rate of private credit; and controls is a set of control variables
including the (logarithm of) initial real per-capita GDP (initial), the
(logarithm of) average ratio of government expenditure to GDP (lgov),
the (logarithm of) average inflation rate (lpi), foreign direct
investment (FDI), and time dummies. The regression equation is dynamic
in the sense that it includes the lagged term of trade share.
We control for endogeneity and omitted country-specific effects by
using internal instruments (instruments based on lagged values of the
explanatory variables). We adopt the assumption of weak exogeneity of
the explanatory variables, in the sense that they are assumed to be
uncorrelated with future realizations of the error term, which is
assumed to be serially uncorrelated. Thus the lagged values of the
levels and differences of the explanatory and dependent variables can be
used as instruments.
Table 9 reports the results. Because the consistency of the GMM
estimator depends on whether lagged values of explanatory variables are
valid instruments, we perform the Sargan test of overidentifying
restrictions and the Arellano-Bond test of serial correlation. As can be
seen, the specification tests support that the orthogonality conditions
are correct and residuals are serially uncorrelated, indicating the
validity of the instrumental variable estimation results.
Regarding the estimates of particular interest, the first column of
Table 9 confirms the positive effect of financial development on trade
openness with high statistical significance. The remaining columns of
Table 9 show whether financial fragility is important for trade by
including financial volatility and the frequency of systemic banking
crises as additional explanatory variables. The data reveal that the
volatility and crisis aspects of financial intermediation are important
for trade, along with financial depth. While the financial depth measure
maintains its positive and significant coefficient in all regressions,
financial volatility and banking crises enter the regressions negatively
and significantly. It indicates that financial deepening would lead to
greater trade; whereas, financial fragility would cause trade to shrink.
Therefore, the evidence implies that the relationship between financial
development and trade openness depends upon the relative influences of
financial fragility and financial deepening accompanied by financial
liberalization. And, combined with the PMG results in section 3, it
suggests that in the short (long) run, negative effects of financial
fragility dominate (are dominated by) the positive impacts of financial
deepening.
5. Concluding Remarks
Recently, Beck (2002, 2003) and Svaleryd and Vlachos (2002, 2005)
provide empirical evidence that financial deepening facilitates
international trade, either for financial comparative advantage or
insurance considerations. However, because the process of financial
development is characterized not only by long-run financial deepening
but also by short-run financial instability (fragility), especially for
developing countries, financial development may be detrimental to
international trade at cyclical frequencies. To advance previous
empirical studies, we examine whether both cyclical and trend changes in
financial intermediation affect trade openness.
Using the PMG approach to a panel of 87 countries over the
1960-2005 period, we find that bank-based financial development exerts
positive long-run but negative short-run effects on international trade,
which is consistent with the notion that financial development is likely
to be growth-enhancing in the long run but subject to financial
fragility in the short run. As growth-enhancing policy choices, trade
openness and financial development are complements in the long run but
substitutes in the short run. The data also reveal that differential
short- and long-run responses of growth to financial development may
work through the trade mechanism.
However, when breaking the sample into OECD and non-OECD countries,
some interesting results emerge. While financial development has
positive long-run and negative short-run impacts on trade openness in
non-OECD countries, it does not exert significant long-and short-run
impacts in OECD countries. The evidence is quite consistent with recent
findings that financial development has stronger real effects for
developing countries than those for industrialized ones.
Regarding a nonlinear relationship between finance and trade, we
find that the coexistence of positive long-run complementarity and
negative short-run substitutionarity between finance and trade holds
only for the medium-financial-development countries. In
low-financial-development countries, improvement in the financial sector
has a significant positive long-run impact but insignificant short-run
effect on trade openness. In financially highly developed countries,
there is no significant correlation between the two variables. The
findings accord with the view that financial development has stronger
effects in countries with a less developed financial system.
Finally, our GMM estimation results provide evidence to support
that volatility and crisis aspects of financial liberalization and
intermediation are crucial for international trade, in additional to
financial deepening. While financial depth leads to greater trade,
financial fragility as captured by financial volatility and banking
crises is harmful to trade. Thus it implies that the relationship
between finance and trade depends upon the relative influence of
financial depth and financial fragility, which depends upon the time
horizon and the stage of economic and financial development considered
with the PMG results.
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Dong-Hyeon Kim, * Shu-Chin Lin, ([dagger]) and Yu-Bo Suen ([double
dagger])
* Department of Finance, Providence University, 200 Chung-Chi Road,
Taichung 43301, Taiwan; E-mail dhkim@pu.edu.tw.
([dagger]) Department of Economics, Tamkang University, 151
Ying-Chun Road, Tamsui 25137, Taipei County, Taiwan; Department of
Economics, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul
130-701, Korea; E-mail econscl@mail.tku.edu.tw; corresponding author.
([double dagger]) Department of Banking and Finance, Aletheia
University, 32 Chen-Li Street, Tamsui, Taipei County 25103, Taiwan;
E-mail au4390@email.au.edu.tw.
The authors are grateful to M. Hashem Pesaran for kindly making
available computer code used in this article and to two anonymous
referees for very helpful suggestions and comments. Any remaining errors
are our own responsibility.
Received April 2008; accepted January 2009.
(1) Please see Levine (1997, 2005) for an excellent survey, both
theoretical and empirical, and references therein.
(2) Please see Schneider and Tornell (2004) and Aghion, Bacchetta,
and Banerjee (2004) for theoretical discussions.
(3) While financial development may affect trade openness, others
suggest that there may exist feedback from trade to financial
development, either for political reasons (Rajan and Zingales 2003;
Braun and Raddatz 2005) or because of the demand for external finance
(Newbery and Stiglitz 1984; Do and Levchenko 2007).
(4) Empirically, Kaminsky and Reinhart (1999) find that monetary
aggregates (for example, domestic credit) may precede currency or
banking crises; in turn, banking crises usually lead to recessions, and
the expansion of domestic credit would then be associated with growth
slowdowns. Demirguc-Kunt and Detragiache (1998a) show that financial
liberalization is linked to financial fragility, especially in
developing countries where institutional development is weak.
(5) As put forth in Loayza and Ranciere (2006), while averaging
clearly induces a loss of information, it is not obvious that averaging
over fixed-length intervals effectively eliminates business-cycle
fluctuations. Averaging eliminates information that may be used to
estimate a more flexible model that allows for some parameter
heterogeneity across countries. And averaging hides the dynamic
relationship between inflation and financial development, particularly
the presence of opposite effects at different time frequencies.
(6) The PMG estimator has been recently applied to measure the
effect of exchange rate uncertainty on investment (Byrne and Davis
2005a, b), to assess the trade effect of real effective exchange rates
(Catao and Solomou 2005), to estimate the impacts of fiscal deficits on
inflation (Catao and Terrones 2005), and to investigate the relationship
between financial development and economic growth (Loayza and Ranciere
2006).
(7) The underlying ARDL specification dispenses with unit root
pretesting of the variables. Provided that there is a unique vector
defining the long-run relationship among variables involved, the MG and
PMG estimations of an ARDL regression, with the lag orders p and q
suitably chosen, yield consistent estimates of that vector, no matter
whether the variables involved are I(1) or I(0).
(8) Because the PMG methodology requires sufficiently long and
uninterrupted time series to address dynamic features in the data and
because the data on financial markets are limited in terms both of time
length and country coverage, we focus on the banking sector to draw
implications of financial development on trade openness.
(9) Loayza and Ranciere (2006) suggest that when the main interest
is on the long-run parameters, the lag order of the ARDL can be selected
using some consistent information criteria on a country-by-country
basis; however, when there is also interest in analyzing and comparing
the short-run parameters, it is recommended that a common lag structure
be imposed across countries. Thus, this article sets p = q = 1, for
simplicity. Nevertheless, we have also tried different orders for p and
q selected by Akaike information criterion (AIC), Schwarz Bayesian
criterion (SBC), and Hannan and Quinn (HQ), and find qualitatively and
quantitatively similar results.
(10) We also check whether our findings are sensitive to the
inclusion of financial openness proxied by the de facto measure, gross
foreign direct investment (FDI). Because we include only countries that
have data on FDI with at least 20 consecutive time-series observations
and only for the 1975-2005 period, the number of countries drops to 51.
However, we find qualitatively similar results as before: Financial
development has long-run positive and short-run negative effects on
trade.
(11) Data on banking crises are taken from Loayza and Ranciere
(2006) and are available for 65 countries during the 1960 2000 period.
Thus the subperiods are 1960-1965, 1966-1970, ..., and 1996-2000 for
models including the banking crisis variable.
Table 1. A List of Sample Countries
OECD countries
Australia Greece New Zealand
Austria Iceland Norway
Belgium Ireland Portugal
Canada Italy Spain
Denmark Japan Sweden
Finland Korea Switzerland
France Luxembourg United Kingdom
Germany Netherlands United States
Non-OECD countries
Belize Honduras Philippines
Bolivia Hungary Rwanda
Burkina Faso India Senegal
Burundi Indonesia Seychelles
Central African Iran Sierra Leone
Chad Israel South Africa
Chile Jamaica Sri Lanka
Costa Rica Jordan St. Kitts and Nevis
Cote d'Ivoire Kenya St. Lucia
Cyprus Madagascar St. Vincent and the Grenadines
Dominica Malawi Suriname
Dominican Malaysia Swaziland
Ecuador Mauritius Syrian
Egypt Mexico Thailand
Ethiopia Morocco Togo
Fiji Nepal Tonga
Gambia Niger Trinidad and Tobago
Ghana Nigeria Uganda
Grenada Pakistan Uruguay
Guatemala Panama Venezuela
Haiti Paraguay Zambia
Table 2. Descriptive Statistics 1960-2005
ltrade lprivo ldby lily
Panel A: summary statistics
Mean 4.0726 3.2880 3.4151 3.6152
Std. 0.5217 0.8375 0.7381 0.5824
Max. 5.2334 4.8188 4.9182 5.6608
Min. 2.7174 1.1444 1.6948 2.2576
Panel B: correlation matrix
ltrade 1.0000 0.2579 0.3409 0.3576
lprivo 1.0000 0.9299 0.8592
ldby 1.0000 0.9049
llly 1.0000
initial
lgov
lpi
initial lgov lpi
Panel A: summary statistics
Mean 7.6111 2.6617 0.0969
Std. 1.6015 0.3129 0.0771
Max. 10.2307 3.3657 0.3641
Min. 4.7971 1.9292 0.0276
Panel B: correlation matrix
ltrade 0.2452 0.4845 -0.2019
lprivo 0.8234 0.4347 -0.3210
ldby 0.8122 0.5196 -0.2909
llly 0.7545 0.4926 -0.3586
initial 1.0000 0.4985 -0.2012
lgov 1.0000 -0.1846
lpi 1.0000
Table 3. The Effects of Various Financial Development
Indicators on Openness
PMG
Panel A: lprivo
Long-run Coefficients
Finance 0.0728 (0.0192) ***
Error Correction
Phi -0.1620 (0.0165) ***
Short-run Coefficients
[DELTA]Finance -0.0586 (0.0219) ***
Constant 0.6404 (0.0623) ***
Panel B: ldby
Long-run Coefficients
Finance 0.1174 (0.0210) ***
Error Correction
Phi -0.1655 (0.0162) ***
Short-run Coefficients
[DELTA]Finance -0.1007 (0.0285) ***
Constant 0.6256 (0.0589)
Panel C: llly
Long-run Coefficients
Finance 0.1281 (0.0255) ***
Error Correction
Phi -0.1774 (0.0165) ***
Short-run Coefficients
[DELTA]Finance -0.0820 (0.0388) **
Constant 0.6654 (0.0609) ***
MG Hausman Test
Panel A: lprivo
Long-run Coefficients
Finance 0.3197 (0.2048) 1.4676 [0.2257]
Error Correction
Phi -0.2275 (0.0203) ***
Short-run Coefficients
[DELTA]Finance -0.0495 (0.0213) **
Constant 0.9260 (0.1013)
Panel B: ldby
Long-run Coefficients
Finance 0.3208 (0.1459) ** 1.9830 [0.1591]
Error Correction
Phi -0.2341 (0.0206) ***
Short-run Coefficients
[DELTA]Finance -0.1005 (0.0280) ***
Constant 0.9061 (0.1169) ***
Panel C: llly
Long-run Coefficients
Finance 0.6774 (0.4692) 1.3743 [0.2411]
Error Correction
Phi -0.2345 (0.0188) ***
Short-run Coefficients
[DELTA]Finance -0.0907 (0.0387) **
Constant 0.8066 (0.0992) ***
The models here do not include control variables. The values
in the parentheses (bracket) are the standard errors (p-value).
* p < 0.01.
** p < 0.05.
*** p < 0.1.
Table 4. Robustness Test
Financial Development Indicator
lprivo lbdy
Long-run coefficients
Finance 0.0322 (0.0185) * 0.0590 (0.0188) ***
Income 0.4233 (0.0351) *** 0.3753 (0.0325) ***
Government 0.0969 (0.0440) ** 0.1158 (0.0431) ***
Inflation -0.0623 (0.0521) 0.0303 (0.0517)
Error correction
Phi -0.1708 (0.0167) *** -0.1791 (0.0173) ***
Short-run coefficients
[DELTA]Finance -0.0503 (0.0250) ** -0.1114 (0.0313) ***
[DELTA]Income 0.0856 (0.0590) 0.1130 (0.0597) *
[DELTA]Government -0.1398 (0.0397) *** -0.1325 (0.0411) ***
[DELTA]Inflation 0.1603 (0.0627) ** 0.1296 (0.0622) **
Constant 0.1091 (0.0201) *** 0.1508 (0.0214) ***
Financial Development Indicator
llly
Long-run coefficients
Finance 0.3193 (0.0331) ***
Income -0.2519 (0.0302) ***
Government -0.0794 (0.0440) *
Inflation -0.2315 (0.0904) **
Error correction
Phi -0.1614 (0.0199) ***
Short-run coefficients
[DELTA]Finance -0.0381 (0.0395)
[DELTA]Income 0.0886 (0.0669)
[DELTA]Government -0.1247 (0.0421) ***
[DELTA]Inflation 0.1765 (0.0586) ***
Constant 0.8460 (0.1062) ***
The values in the parentheses are the standard errors.
* p < 0.01.
** p < 0.05.
*** p < 0.1.
Table 5. PMG Estimation Results for Different ARDL(p,q) Models
ARDL(2,1) ARDL(3,1)
Panel A: lprivo
Long-run coefficients
Finance 0.0872 (0.0175) *** 0.0803 (0.0175) ***
Error correction
Phi -0.1662 (0.0181) *** -0.1661 (0.0202) ***
Short-run coefficients
[DELTA]Finance -0.0660 (0.0224) *** -0.0673 (0.0245) ***
Constant 0.6529 (0.0687) *** 0.6532 (0.0781) ***
Panel B: ldby
Long-run coefficients
Finance 0.2098 (0.0125) *** 0.0845 (0.0178) ***
Error correction
Phi -0.1701 (0.0198) *** -0.1738 (0.0209) ***
Short-run coefficients
[DELTA]Finance -0.1171 (0.0279) *** -0.0973 (0.0299) ***
Constant 0.5877 (0.0643) *** 0.6815 (0.0808) ***
Panel C: llly
Long-run coefficients
Finance 0.2541 (0.0224) *** 0.1651 (0.0253) ***
Error correction
Phi -0.1848 (0.0202) *** -0.1775 (0.0204) ***
Short-run coefficients
[DELTA]Finance -0.1092 (0.0368) *** -0.0988 (0.0402) **
Constant 0.6076 (0.0625) *** 0.6427 (0.0714) ***
ARDL(4,1)
Panel A: lprivo
Long-run coefficients
Finance 0.0508 (0.0160) ***
Error correction
Phi -0.1883 (0.0247) ***
Short-run coefficients
[DELTA]Finance -0.0717 (0.0262) ***
Constant 0.7548 (0.0963) ***
Panel B: ldby
Long-run coefficients
Finance 0.2123 (0.0080) ***
Error correction
Phi -0.1863 (0.0287) ***
Short-run coefficients
[DELTA]Finance -0.1273 (0.0332) ***
Constant 0.6374 (0.0914) ***
Panel C: llly
Long-run coefficients
Finance 0.2913 (0.0203) ***
Error correction
Phi -0.1885 (0.0236) ***
Short-run coefficients
[DELTA]Finance -0.1315 (0.0424) ***
Constant 0.5898 (0.0696) ***
The models here do not include control variables. The values
in the parentheses are the standard errors.
* p < 0.01.
** p < 0.05.
*** p < 0.1.
Table 6. The Effects of Stock Market Development Indicators
PMG MG
Panel A: lcap
Long-run coefficients
Finance 0.5762 (0.1308) *** 12.4642 (12.3207)
Error correction
Phi -0.5025 (0.0573) ***
Short-run coefficients
[DELTA]Finance 0.0535 (0.0147) *** 0.0042 (0.0179)
Constant 0.2345 (0.0583) *** 1.6490 (1.1266)
Panel B: lstv
Long-run coefficients
Finance 0.0538 (0.0046) *** -0.0340 (0.0948)
Error correction
Phi -0.1786 (0.0396) *** -0.4966 (0.0558) ***
Short-run coefficients
[DELTA]Finance 0.0108 (0.0051) *** -0.0047 (0.0073)
Constant 1.5684 (0.3501) *** 0.8510 (1.0059)
Hausman Test
Panel A: lcap
Long-run coefficients
Finance 0.9311 [0.3346]
Error correction
Phi
Short-run coefficients
[DELTA]Finance
Constant
Panel B: lstv
Long-run coefficients
Finance 0.8604 [0.3536]
Error correction
Phi
Short-run coefficients
[DELTA]Finance
Constant
Control variables include income, government size, and
inflation. The values in the parentheses (bracket) are the
standard errors (p-value) of corresponding coefficient
estimates.
* p < 0.0l.
** p < 0.05.
*** p < 0.1.
Table 7. The Results for OECD versus Non-OECD Countries
Financial Development Indicator
lprivo lbdy
Panel A: OECD countries (24)
Long-run coefficients
Finance 0.0291 (0.0264) 0.0258 (0.0255)
Error correction
Phi -0.1609 (0.0228) *** -0.1659 (0.0241) ***
Short-run coefficients
[DELTA]Finance -0.0099 (0.0272) -0.0914 (0.0624)
Panel B: Non-OECD countries (63)
Long-run coefficients
Finance 0.1123 (0.0265) *** 0.1503 (0.0247) ***
Error correction
Phi -0.1664 (0.0235) *** -0.1829 (0.0244) ***
Short-run coefficients
[DELTA]Finance -0.0696 (0.0334) ** -0.1236 (0.0361) ***
Financial
Development Indicator
lily
Panel A: OECD countries (24)
Long-run coefficients
Finance -0.0258 (0.0396)
Error correction
Phi -0.1267 (0.0275) ***
Short-run coefficients
[DELTA]Finance -0.0880 (0.0563)
Panel B: Non-OECD countries (63)
Long-run coefficients
Finance 0.3438 (0.0334) ***
Error correction
Phi -0.1895 (0.0252) ***
Short-run coefficients
[DELTA]Finance -0.0062 (0.0497)
The estimates on control variables are omitted for brevity.
The values in the parenthesis are the standard errors.
* p < 0.01.
** p < 0.05.
** p < 0.1.
Table 8. The Effects of Financial Development
Financial Development Indicator
lprivo lbdy
Panel A: High-financial-development countries (29)
Long-run coefficients
Finance -0.0420 (0.0379) 0.0155 (0.0286)
Error correction
Phi -0.1120 (0.0201) *** -0.1355 (0.0211)***
Short-run coefficients
[DELTA]Finance -0.0252 (0.0454) -0.1588 (0.0660)**
Panel B: Medium-financial-development countries (29)
Long-run coefficients
Finance 0.0566 (0.0249) ** 0.1078 (0.0272) ***
Error correction
Phi -0.1884 (0.0305) *** -0.1921 (0.0314) ***
Short-run coefficients
[DELTA]Finance -0.0936 (0.0427) ** -0.1082 (0.0388) ***
Panel C: Low-financial-development countries (29)
Long-run coefficients
Finance 0.0856 (0.0408) ** 0.1027 (0.0385) ***
Error correction
Phi -0.2064 (0.0376) *** -0.2107 (0.0385) ***
Short-run coefficients
[DELTA]Finance -0.0291 (0.0479) -0.0684 (0.0524)
Financial Development
Indicator
llly
Panel A: High-financial-development countries (29)
Long-run coefficients
Finance 0.0013 (0.0519)
Error correction
Phi -0.1302 (0.0334) ***
Short-run coefficients
[DELTA]Finance -0.0272 (0.0563)
Panel B: Medium-financial-development countries (29)
Long-run coefficients
Finance 0.4857 (0.0569) ***
Error correction
Phi -0.1410 (0.0287) ***
Short-run coefficients
[DELTA]Finance 0.0325 (0.0699)
Panel C: Low-financial-development countries (29)
Long-run coefficients
Finance 0.1743 (0.0524) ***
Error correction
Phi -0.2095 (0.0373) ***
Short-run coefficients
[DELTA]Finance -0.0516 (0.0762)
Control variables include income, government expenditure,
and inflation. For simplicity, the estimates for control variables
are not reported but available upon request. The values in
the parentheses are the standard errors of corresponding
coefficient estimates.
* p < 0.01.
** p < 0.05.
*** p < 0.1.
Table 9 Openness Effects of Financial Depth, Volatility,
and Crises
[1] [2]
Trade_1 0.3543 (0.0082) *** 0.2843 (0.0098) ***
Financial depth (/privo) 0.1099 (0.0097) *** 0.0985 (0.0126) ***
Financial volatility -0.2955 (0.0295) ***
Systemic banking crises
Initial GDP per capita -0.0271 (0.0054) *** -0.0238 (0.0103) **
Government size 0.0950 (0.0163) *** 0.1675 (0.0233) ***
Inflation 0.3573 (0.0178) *** 0.3939 (0.0183) ***
Constant 2.2096 (0.0541) *** 2.3062 (0.0873) ***
Specification tests
Sargan test (P-values) 0.5500 0.9990
AR(1) test (p-values) 0.0000 0.0004
AR(2) test (p-values) 0.9958 0.4982
Obs./countries 620/87 617/87
[3] [4]
Trade_1 0.4089 (0.0247) *** 0.4994 (0.0190) ***
Financial depth (/privo) 0.1764 (0.0119) *** 0.1702 (0.0176) ***
Financial volatility -0.3039 (0.0722) ***
Systemic banking crises -0.0308 (0.0121) ** -0.0497 (0.0211) **
Initial GDP per capita -0.0857 (0.0117) *** -0.0920 (0.0153) ***
Government size 0.0369 (0.0214) * -0.0312 (0.0251)
Inflation 0.2147 (0.0201) *** 0.3892 (0.0418) ***
Constant 2.3117 (0.1575) *** 2.2412 (0.1562) ***
Specification tests
Sargan test (P-values) 0.9992 0.9447
AR(1) test (p-values) 0.0002 0.0007
AR(2) test (p-values) 0.1354 0.1449
Obs./countries 426/65 426/65
The dependent variable is the (logarithm of) the average ratio
of trade volume to GDP. The independent variables include the
lagged dependent variable, the (logarithm of) average of
private credit to GDP, the frequency of systemic banking
crises, the standard deviation of the growth rate of private
credit to GDP, the (logarithm of) initial real GDP per capita,
the (logarithm of) the average ratio of government consumption
to GDP, and the (logarithm of) average inflation rate.
Included also are time dummies that are not reported in the
table. The instruments in the GMM estimation include the
lagged values of levels and differences of dependent
variables. Because data on banking crises are not available
after year 2000, the estimation period for models 3 and 4 runs
from 1960 to 2000 for 65 countries, while models 1 and 2 run
from 1960 to 2005 for 87 countries. The values in the
parentheses are the standard errors of corresponding
coefficient estimates.
* p < 0.01.
** p < 0.05.
*** p < 0.1.
