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BookDOI

Financial Development, Financial Fragility, and Growth

01 Oct 2004-Journal of Money, Credit and Banking (The Ohio State University Press)-Vol. 38, Iss: 4, pp 1-35
TL;DR: In this paper, the authors study the apparent contradiction between two strands of the literature on the effects of financial intermediation on economic activity and find that a positive long-run relationship between financial intermediary and output growth coexists with a mostly negative short run relationship, and further develop an explanation for these contrasting effects by relating them to recent theoretical models.
Abstract: The authors study the apparent contradiction between two strands of the literature on the effects of financial intermediation on economic activity. On the one hand, the empirical growth literature finds a positive effect of financial depth as measured by, for instance, private domestic credit and liquid liabilities (for example, Levine, Loayza, and Beck 2000). On the other hand, the banking and currency crisis literature finds that monetary aggregates, such as domestic credit, are among the best predictors of crises and their related economic downturns (for example, Kaminski and Reinhart 1999). The authors account for these contrasting effects based on the distinction between the short- and long-run impacts of financial intermediation. Working with a panel of cross-country and time-series observations, they estimate an encompassing model of short- and long-run effects using the Pooled Mean Group estimator developed by Pesaran, Shin, and Smith (1999). Their conclusion from this analysis is that a positive long-run relationship between financial intermediation and output growth coexists with a mostly negative short-run relationship. The authors further develop an explanation for these contrasting effects by relating them to recent theoretical models, by linking the estimated short-run effects to measures of financial fragility (namely, banking crises and financial volatility), and by jointly analyzing the effects of financial depth and fragility in classic panel growth regressions.

Summary (3 min read)

FINANCIAL DEVELOPMENT, FINANCIAL FRAGILITY, AND GROWTH

  • This paper analyzes the apparent contradiction between two strands of the literature on the effects of financial intermediation on economic activity.
  • Second, the authors attempt to provide an empirical explanation to these contrasting effects.
  • In particular, on the one hand the authors relate the positive influence of financial depth on investment and growth to the long-run effect of financial liberalization; and, on the other, they describe a link between the negative impact of financial volatility and crisis and the short-run effect of liberalization.
  • For this purpose, the authors estimate an encompassing model of short- and long-run effects using a panel of crosscountry and time-series observations.
  • And third, the authors go back to the classic context of growth regressions and consider whether the volatility and crises aspects of financial liberalization are relevant growth determinants, along with the usual measures of financial depth.

II. SHORT- AND LONG-RUN GROWTH EFFECTS OF FINANCIAL INTERMEDIATION

  • In this section the authors estimate an empirical model that encompasses the short- and long-run growth effects of financial intermediation.
  • The authors use the estimation results to formulate an empirical explanation of the apparently contradictory effects of financial intermediation on economic activity.
  • This explanation is based on the distinction 1 Except for a few rather recent papers, some of the reviewed below in the paper.
  • The authors method can be summarized as a panel errorcorrection model, where short- and long-run effects are estimated jointly from a general autoregressive distributed-lag (ARDL) model and where short-run effects are allowed to vary across countries.
  • First, while averaging clearly induces a loss of information, it is not obvious that averaging over fixed-length intervals effectively eliminates business-cycle fluctuations.

A. Methodology

  • The first is the need to separate and estimate short- and long-run effects without the need to decompose directly trend and transitory components of growth, financial intermediation, and the other explanatory variables.
  • The authors treat this issue below in the context of single-country estimation.
  • The second issue is the likely possibility that the parameters in the relationship between financial intermediation and economic activity be different across countries.
  • On the other hand, the authors can expect that long-run relationships would be more homogeneous across countries.
  • 2 Similar arguments are made by Attanasio, Scorcu, and Picci (2000) in their cross-country study on the dynamic relationship between saving, investment, and growth.

Single-country estimation

  • The challenge the authors face is to estimate long- and short-run relationships without observing the long- and short-run components of the variables involved.
  • From this literature, two common misconceptions have been derived.
  • Furthermore, these methods avoid the need for pre-testing and order-ofintegration conformability given that they are valid whether the variables of interest are I(0) or I(1).
  • It is worth noting that the assumption of a unique long-run relationship underlies implicitly the various single-equation based estimators of long-run relationships commonly found in the cointegration literature.
  • Related to growth and its determinants, β are the long-run coefficients, ϕ is the speed of adjustment to the long-run relationship, ε is a time-varying disturbance, and the subscripts i and t represent country and time, respectively.

Multi-country estimation

  • The sample the authors use for estimation is a “data field,” in the sense that it is characterized by time-series (T) and cross-section (N) dimensions of roughly similar magnitude.
  • The PMG estimator also generates consistent estimates of the mean of short-run coefficients across countries by taking the simple average of individual country coefficients (provided that the cross-sectional dimension is large).
  • The choice among these estimators faces a general trade-off between consistency and efficiency.
  • The interested reader is referred to Pesaran, Shin and Smith (1999) where the PMG estimator is developed and compared with the MG estimator.
  • When the main interest is on the long-run parameters, the lag order of the ARDL can be selected using some consistent information criteria (such as the Schwartz-Bayesian Criterion) on a country-by-country basis.

B. Data and Results

  • As outlined in the previous section, the consistency and efficiency of the PMG estimates relies on several specification conditions.
  • Most importantly for their purposes, the authors find that economic growth is positively and significantly linked to the measure of financial intermediation in the long run.
  • Before the authors do that, however, they need to make sure that their results are robust to the exclusion of outlying and dynamically unstable observations.
  • In particular, the authors want to check to what extent the long-run coefficients and especially the average of short-run coefficients are sensitive to the exclusion of countries whose estimated short-run effects are considerably larger (in absolute value) than typical effects in the sample and countries that present errorcorrection coefficients that statistically fall outside the dynamic stable range.

FINANCIAL DEVELOPMENT

  • By focusing on the effects of financial intermediation at different time horizons, the analysis conducted in the previous section helps us set the basis for an explanation of the apparently contradictory effects of financial intermediation on economic activity.
  • Second, the authors consider this possibility by examining connection between country-specific measures of financial volatility and crisis and the country-specific short-run effects of financial intermediation estimated in the previous section.
  • Then, whereas the short run of financial liberalization is marked with financial crisis, volatility, and low output growth, in the long run financial liberalization is bound to improve economic growth.
  • When financial liberalization occurs and a large cohort of new firms enters the market, the absence of information capital leads to high interest rates, a risky banks’ loan portfolio, and inevitably credit misallocation.
  • Table 4 presents the tests of the difference in short-run coefficient means for the various ways of grouping countries.

Data and Methodology

  • The authors work with a pooled (cross-country, time-series) data set consisting of 82 countries and, for each of them, at most 8 non-overlapping five-year periods over 1960- 2000.
  • They are the average ratio of private credit to GDP, as measure of financial depth, and the frequency of systemic banking crises and the standard deviation of the growth rate of private credit/GDP, as measures of financial fragility.
  • Finally, the regression equation allows for both unobserved time-specific and country-specific effects.
  • Since typically the moment conditions over-identify the regression model, they also allow for specification testing through a Sargan-type test.

Results

  • Table 5 reports the regression estimation results as well as the Sargan and Serial- correlation specification tests.
  • These tests indicate that the hypothesis of correct identification cannot be rejected, thus supporting the estimation results to which the authors turn next.
  • The second and third columns include, respectively, financial volatility and the frequency of systemic banking crises as additional explanatory variables.
  • The authors can get a broad sense of the economic importance of these effects by using the point estimate of the regression coefficients to calculate the growth impact of a change in their financial measures.
  • An increase of one sample standard deviation in financial volatility leads to a decrease of 0.3 percentage points in the annual growth rate of GDP per capita, and an analogous increase in the frequency of systemic banking crises produces a 0.7 percentage point drop in annual growth.

IV. CONCLUSIONS

  • Over the long run, financial development supports and promotes economic growth.
  • Whether intrinsic to the process of development or induced by policy mistakes, these elements of financial fragility can hurt economic growth and will do so until maturity is reached.
  • Recognizing the possibility of a dual effect of financial intermediation on economic growth, this paper estimates an encompassing empirical model of long- and short-run effects using a sample of cross-country and time-series observations.
  • For more stable countries, this effect is in average nil.
  • Finally, attempting to relate their results to the empirical growth literature, the authors go back to the classic context of growth regressions using panel data.

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FINANCIAL DEVELOPMENT, FINANCIAL FRAGILITY,
AND GROWTH
*
*
This paper was written while Loayza and Rancière worked in the research department of the Central
Bank of Chile. Research assistance from Megumi Kubota and Guillermo Vuletin is gratefully
acknowledged. We benefited from comments and advice by J. Benhabib, S. Bond, J. Cummings, F.
Gallego, A. Gaytán, P.O. Gourinchas, O. Jeanne, B. Jovanovic, P. Martin, D. Ray, L. Reichlin, K. Schmidt-
Hebbel, M. Spiegel, A. Rose, A. Tornell, R. Soto, Paul Evans –the journal editor--, two anonymous
referees, and seminar participants at New York University, Stanford University, University of California at
Irvine, the Bank of England, the Central Bank of Chile, the Royal Bank of Sweden, the 2001 CESifo
Summer Institute in Venice, and the 2001 Meetings of the Latin American and Caribbean Economic
Association in Montevideo. The usual disclaimer applies. Correspondence: nloayza@worldbank.org.
Norman Loayza
World Bank
Romain Rancière
CREI and Universitat Pompeu Fabra
September 2004
Abstract
This paper studies the apparent contradiction between two strands of the literature on the effects
of financial intermediation on economic activity. On the one hand, the empirical growth
literature finds a positive effect of financial depth as measured by, for instance, private domestic
credit and liquid liabilities (e.g., Levine, Loayza, and Beck 2000). On the other hand, the banking
and currency crisis literature finds that monetary aggregates, such as domestic credit, are among
the best predictors of crises and their related economic downturns (e.g., Kaminski and Reinhart
1999). The paper accounts for these contrasting effects based on the distinction between the
short- and long-run impacts of financial intermediation. Working with a panel of cross-country
and time-series observations, the paper estimates an encompassing model of short- and long-run
effects using the Pooled Mean Group estimator developed by Pesaran, Shin, and Smith (1999).
The conclusion from this analysis is that a positive long-run relationship between financial
intermediation and output growth co-exists with a, mostly, negative short-run relationship. The
paper further develops an explanation for these contrasting effects by relating them to recent
theoretical models, by linking the estimated short-run effects to measures of financial fragility
(namely, banking crises and financial volatility), and by jointly analyzing the effects of financial
depth and fragility in classic panel growth regressions.

2
FINANCIAL DEVELOPMENT, FINANCIAL FRAGILITY, AND GROWTH
I. INTRODUCTION
This paper analyzes the apparent contradiction between two strands of the
literature on the effects of financial intermediation on economic activity. On the one
hand, the empirical growth literature finds a positive effect of measures of private
domestic credit and liquid liabilities on per capita GDP growth (as illustration, see Figure
1). This is interpreted as the growth enhancing effect of financial development (e.g.,
King and Levine, 1993; Levine, Loayza, and Beck, 2000). On the other hand, the
banking and currency crisis literature finds that monetary aggregates, such as domestic
credit, are among the best predictors for crises (e.g., Demirguc-Kunt and Degatriache,
1998 and 2000; Gourinchas, Landerretche, and Valdés, 1999; Kaminsky and Reinhart,
1999). Since banking crises usually lead to recessions, an expansion of domestic credit
would then be associated to growth slowdowns (see Figure 2).
A similar divide exists at the theoretical level.
1
According to the endogenous
growth literature, financial deepening leads to a more efficient allocation of savings to
productive investment projects (see Greenwood and Jovanovic, 1990; Bencivenga and
Smith, 1991). Conversely, the financial crisis literature points to the destabilizing effect
of financial liberalization as it leads to an unduly large expansion of credit. Overlending
would occur through a combination of channels, including a limited monitoring capacity
of regulatory agencies, the inability of banks to discriminate good projects during
investment booms, and the existence of an explicit or implicit insurance against banking
failures (Schneider and Tornell, 2004; Aghion, Bacchetta and Banerjee, 2003). Not
surprisingly, each strand of the literature has produced its own set of policy implications.
Thus, researchers that emphasize the findings of the endogenous growth literature
advocate financial liberalization and deepening (e.g., Roubini and Sala-i-Martin, 1992),
while those that concentrate on crises caution against “excesive” financial liberalization
(e.g., Balino and Sundarajan, 1991; Gavin and Hausman, 1995).
This paper contributes to the debate on the effects of financial deepening from an
empirical perspective. First, we want to highlight and illustrate the contrasting effects of

3
financial liberalization and credit expansion on economic activity. Second, we attempt to
provide an empirical explanation to these contrasting effects. In particular, on the one
hand we relate the positive influence of financial depth on investment and growth to the
long-run effect of financial liberalization; and, on the other, we describe a link between
the negative impact of financial volatility and crisis and the short-run effect of
liberalization. Although it is not our purpose to test competing theories, our empirical
results provide support to some recent theoretical models predicting that financial
liberalization can both generate short-run instability and higher long-run growth.
The paper is organized as follows. In section II we examine the output growth
effects of cyclical and trend changes of financial intermediation. For this purpose, we
estimate an encompassing model of short- and long-run effects using a panel of cross-
country and time-series observations. Our basic econometric technique is the Pooled
Mean Group estimator developed by Pesaran, Shin, and Smith (1999). By focusing on
effects at different time horizons, we set the basis for an explanation of the apparently
contradictory effects of financial intermediation on economic activity. In section III, we
discuss and develop further this explanation. First, we link our short- and long-run
results to recent theoretical models on the effects of financial liberalization. Second,
since our econometric methodology allows us to estimate country-specific short-run
effects of financial intermediation on output growth, we analyze their relationship with
country-specific measures of financial fragility, namely, banking crises and volatility.
And third, we go back to the classic context of growth regressions and consider whether
the volatility and crises aspects of financial liberalization are relevant growth
determinants, along with the usual measures of financial depth. Section IV concludes.
II. S
HORT- AND LONG-RUN GROWTH EFFECTS OF FINANCIAL INTERMEDIATION
In this section we estimate an empirical model that encompasses the short- and
long-run growth effects of financial intermediation. We use the estimation results to
formulate an empirical explanation of the apparently contradictory effects of financial
intermediation on economic activity. This explanation is based on the distinction
1
Except for a few rather recent papers, some of the reviewed below in the paper.

4
between the cycle and trend changes of financial intermediation and their corresponding
effects on output growth.
Instead of averaging the data per country to isolate trend effects, we estimate both
short- and long-run effects using a data-field composed of a relatively large sample of
countries and annual observations. Our method can be summarized as a panel error-
correction model, where short- and long-run effects are estimated jointly from a general
autoregressive distributed-lag (ARDL) model and where short-run effects are allowed to
vary across countries.
We propose this panel error-correction method as an alternative to the traditional
method of time averaging for the following reasons. First, while averaging clearly
induces a loss of information, it is not obvious that averaging over fixed-length intervals
effectively eliminates business-cycle fluctuations. Second, averaging eliminates
information that may be used to estimate a more flexible model that allows for some
parameter heterogeneity across countries. Third, and most importantly for our purposes,
averaging hides the dynamic relationship between financial intermediation and economic
activity, particularly the presence of opposite effects at different time frequencies.
2
A. Methodology
Empirical estimation poses two issues. The first is the need to separate and
estimate short- and long-run effects without the need to decompose directly trend and
transitory components of growth, financial intermediation, and the other explanatory
variables. We treat this issue below in the context of single-country estimation. The
second issue is the likely possibility that the parameters in the relationship between
financial intermediation and economic activity be different across countries. It can be
argued that country heterogeneity is particularly relevant in short-run relationships, given
that countries are affected by overlending and financial crises to widely different degrees.
On the other hand, we can expect that long-run relationships would be more
homogeneous across countries. We discuss below the issue of heterogeneity in the
context of multi-country estimation.
2
Similar arguments are made by Attanasio, Scorcu, and Picci (2000) in their cross-country study on the
dynamic relationship between saving, investment, and growth.

5
Single-country estimation
The challenge we face is to estimate long- and short-run relationships without
observing the long- and short-run components of the variables involved. Over the last
decade or so, a booming cointegration literature has focused on the estimation of long-run
relationships among I(1) variables (Johansen 1995, Phillips and Hansen 1990). From this
literature, two common misconceptions have been derived. The first one is that long-run
relationships exist only in the context of cointegration among integrated variables. The
second one is that standard methods of estimation and inference are incorrect.
A recent literature, represented in Pesaran and Smith (1995), Pesaran (1997) and
Pesaran and Shin (1999), has argued against both misconceptions. These authors show
that simple modifications to standard methods can render consistent and efficient
estimates of the parameters in a long-run relationship between both integrated and
stationary variables and that inference on these parameters can be conducted using
standard tests. Furthermore, these methods avoid the need for pre-testing and order-of-
integration conformability given that they are valid whether the variables of interest are
I(0) or I(1). The main requirements for the validity of this methodology are that, first,
there exist a long-run relationship among the variables of interest and, second, the
dynamic specification of the model be sufficiently augmented so that the regressors are
strictly exogenous and the resulting residual is serially uncorrelated.
3
Pesaran and co-
authors label this the “autoregressive distributed lag (ARDL) approach” to long-run
modeling.
In order to comply with the requirements for standard estimation and inference,
we embed a long-run growth regression equation into an ARDL (p, q) model. In error-
correction form, this can be written as follows:
() () ( ) () ( )
{}
[]
it
t
i
ii
t
i
i
q
j
jt
i
i
j
jt
i
p
j
i
j
t
i
XyXyy
εββϕδγ
++++=
=
=
1
10
1
1
0
1
1
(1)
where,
y is the per capita GDP growth rate, X represents a set of growth determinants
including financial depth and control variables,
γ
and
δ
are the short-run coefficients
3
It is worth noting that the assumption of a unique long-run relationship underlies implicitly the various
single-equation based estimators of long-run relationships commonly found in the cointegration literature.
Without such assumption, these estimators would at best identify some linear combination of all the long-
run relationships present in the data.

Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors examined whether there is a threshold above which financial development no longer has a positive effect on economic growth, and they used dierent empirical approaches to show that there can indeed be too much finance.
Abstract: This paper examines whether there is a threshold above which …nancial development no longer has a positive eect on economic growth. We use dierent empirical approaches to show that there can indeed be "too much" …nance. In particular, our results suggest that …nance starts having a negative eect on output growth when credit to the private sector reaches 100% of GDP. We show that our results are consistent with the "vanishing eect" of …nancial development and that they are not driven by output volatility, banking crises, low institutional quality, or by dierences in bank regulation and supervision.

1,073 citations

Journal Article
TL;DR: Šonje et al. as mentioned in this paper used a sample of 35 countries for the period between 1860 and 1963 to show the relationship between income and financial depth measured by the ratio between bank's assets and GDP.
Abstract: relationship. All subsequent studies confirmed it (see for example King and Levine, 1993, and the review in: Pagano, 1993). Goldsmith used a sample of 35 countries for the period between 1860 and 1963 to show the relationship between income and financial depth measured by the ratio between bank's assets and GDP. He also showed that in periods of rapid growth, financial depth grows faster than income. More details about measuring financial depth can be found in this paper. FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH Velimir Šonje

891 citations

Journal ArticleDOI
TL;DR: This article found that remittances boost growth in countries with less developed financial systems by providing an alternative way to finance investment and helping overcome liquidity constraints, and also provided evidence that there could be an investment channel trough which remittance can promote growth especially when the financial sector does not meet the credit needs of the population.

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TL;DR: In this article, the authors show that real exchange rate volatility can have a significant impact on productivity growth, but the effect depends critically on a country's level of financial development, and they also offer a simple monetary growth model in which real exchange-rate uncertainty exacerbates the negative investment effects of domestic credit market constraints.

699 citations

BookDOI
TL;DR: The Global Financial Development Database (GFDB) as discussed by the authors is a dataset of financial system characteristics for 205 economies from 1960 to 2010, which includes measures of the size of financial institutions and markets (financial depth), degree to which individuals can and do use financial services (access), efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and stability of financial institution and markets.
Abstract: This paper introduces the Global Financial Development Database, an extensive dataset of financial system characteristics for 205 economies from 1960 to 2010. The database includes measures of (a) size of financial institutions and markets (financial depth), (b) degree to which individuals can and do use financial services (access), (c) efficiency of financial intermediaries and markets in intermediating resources and facilitating financial transactions (efficiency), and (d) stability of financial institutions and markets (stability). The authors document cross-country differences and time series trends.

592 citations

References
More filters
Journal ArticleDOI
TL;DR: In this article, the generalized method of moments (GMM) estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables.
Abstract: This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables. We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying restrictions and Hausman specification tests.

26,580 citations


"Financial Development, Financial Fr..." refers background or methods in this paper

  • ...This is the generalized method of moments (GMM) for dynamic models of panel data developed by Arellano and Bond (1991) and Arellano and Bover (1995)....

    [...]

  • ...As Arellano and Bond (1991) and Arellano and Bover (1995) show, this set of assumptions generates moment conditions that allow estimation of the parameters of interest....

    [...]

Report SeriesDOI
TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.

19,132 citations

Journal ArticleDOI
TL;DR: In this paper, a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables is presented. But the authors do not consider models with predetermined variables that have constant correlation with the effects.

16,245 citations


"Financial Development, Financial Fr..." refers background or methods in this paper

  • ...This is the generalized method of moments (GMM) for dynamic models of panel data developed by Arellano and Bond (1991) and Arellano and Bover (1995)....

    [...]

  • ...As Arellano and Bond (1991) and Arellano and Bover (1995) show, this set of assumptions generates moment conditions that allow estimation of the parameters of interest....

    [...]

ReportDOI
TL;DR: For 98 countries in the period 1960-1985, the growth rate of real per capita GDP is positively related to initial human capital (proxied by 1960 school-enrollment rates) and negatively related to the initial (1960) level as mentioned in this paper.
Abstract: For 98 countries in the period 1960–1985, the growth rate of real per capita GDP is positively related to initial human capital (proxied by 1960 school-enrollment rates) and negatively related to the initial (1960) level of real per capita GDP. Countries with higher human capital also have lower fertility rates and higher ratios of physical investment to GDP. Growth is inversely related to the share of government consumption in GDP, but insignificantly related to the share of public investment. Growth rates are positively related to measures of political stability and inversely related to a proxy for market distortions.

9,420 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined a cross-section of about 80 countries for the period 1960-89 and found that various measures of financial development are strongly associated with both current and later rates of economic growth.
Abstract: Joseph Schumpeter argued in 1911 that the services provided by financial intermediaries - mobilizing savings, evaluating projects, managing risk, monitoring managers, and facilitating transactions -stimulate technological innovation and economic development. The authors present evidence that supports this view. Examining a cross-section of about 80 countries for the period 1960-89, they find that various measures of financial development are strongly associated with both current and later rates of economic growth. Each measure has shortcomings but all tell the same story: finance matters. They present three main findings, which are robust to many specification tests: The average level of financial development for 1960-89 is very strongly associated with growth for the period. Financial development precedes growth. For example, financial depth in 1960 (the ratio of broad money to GDP) is positively and significantly related to real per capita GDP growth over the next 30 years even after controlling for a variety of country-specific characteristics and policy indicators. Financial development is positively associated with both investment rate and the efficiency with which economies use capital. Much work remains to be done, but the data are consistent with Schumpeter's view that the services provided by financial intermediaries stimulate long-run growth.

8,204 citations


"Financial Development, Financial Fr..." refers background in this paper

  • ...This is interpreted as the growth enhancing effect of financial development (e.g., King and Levine, 1993; Levine, Loayza, and Beck, 2000)....

    [...]

  • ...The growth literature has emphasized the role of financial depth and found a positive and significant effect on growth (see King and Levine 1993 a, b; and Levine, Loayza, and Beck 2000)....

    [...]

Frequently Asked Questions (11)
Q1. What are the contributions in "Financial development, financial fragility," ?

This paper studies the apparent contradiction between two strands of the literature on the effects of financial intermediation on economic activity. The paper accounts for these contrasting effects based on the distinction between the shortand long-run impacts of financial intermediation. Working with a panel of cross-country and time-series observations, the paper estimates an encompassing model of shortand long-run effects using the Pooled Mean Group estimator developed by Pesaran, Shin, and Smith ( 1999 ). The paper further develops an explanation for these contrasting effects by relating them to recent theoretical models, by linking the estimated short-run effects to measures of financial fragility ( namely, banking crises and financial volatility ), and by jointly analyzing the effects of financial depth and fragility in classic panel growth regressions. 

Recognizing the possibility of a dual effect of financial intermediation on economic growth, this paper estimates an encompassing empirical model of long- and short-run effects using a sample of cross-country and time-series observations. The authors find that a positive long-run relationship between financial intermediation and output growth can coexist with a negative short-run relationship, which indeed is the case for the average country in the sample. Since the methodology allows us to obtain the short-run effects of financial intermediation on growth country by country, the authors can attempt to link these effects to the aspects of financial liberalization that the literature proposes as harmful to growth. The authors find that financially fragile countries, namely those that experience banking crises or suffer high financial volatility, tend to present significantly negative short-run effects of intermediation on growth. 

The main requirements for the validity of this methodology are that, first, there exist a long-run relationship among the variables of interest and, second, the dynamic specification of the model be sufficiently augmented so that the regressors are strictly exogenous and the resulting residual is serially uncorrelated. 

whereas financial depth leads to higher growth, financial fragility --as captured by financial volatility and banking crises-- has negative growth consequences. 

The first are that the regression residuals be serially uncorrelated and that the explanatory variables can be treated as exogenous. 

When the main interest is on the long-run parameters, the lag order of the ARDL can be selected using some consistent information criteria (such as the Schwartz-Bayesian Criterion) on a country-by-country basis. 

The Hausman test does not reject the joint homogeneity of all long-run parameters; and it does not reject the homogeneity of individual long-run coefficients except for that on initial income. 

The authors can get a broad sense of the economic importance of these effects by using the point estimate of the regression coefficients to calculate the growth impact of a change in their financial measures. 

the estimation of the long-run slope coefficients is done jointly across countries through a (concentrated) maximum likelihood procedure. 

On the other hand, an increase in one sample standard deviation of financial depth leads to economic growth rising by 0.9 percentage points. 

Countries that experienced financial crises in the last 40 years exhibit an average shortrun impact of financial intermediation on output growth that is significantly more negative than the average of countries that did not have any crisis.