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.
