FINANCIAL DEVELOPMENT, FINANCIAL FRAGILITY,
AND GROWTH*
Norman Loayza Romain Rancičre
World Bank CREI and Universitat Pompeu Fabra
October 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.
* This paper was written while Loayza and Rancičre 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.
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 with 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 "excessive" 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
2
financial liberalization and credit expansion on economic activity. Second, we attempt to
provide an empirical explanation for 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. SHORT- 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
1Except for a few rather recent papers, some of the reviewed below in the paper.
3
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.
2Similar arguments are made by Attanasio, Scorcu, and Picci (2000) in their cross-country study on the
dynamic relationship between saving, investment, and growth.
4
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:
(yi )t = [( )
p-1 q-1
i i { i i }] (1)
j (yi )t-j + j(Xi )t-j +i yi t-1 - 0 + 1 (Xi )t-1 +it
j=1 j=0
where, y is the per capita GDP growth rate, X represents a set of growth determinants
including financial depth and control variables, and 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.
5
related to growth and its determinants, are the long-run coefficients, is the speed of
adjustment to the long-run relationship, is a time-varying disturbance, and the
subscripts i and t represent country and time, respectively. The term in square brackets
contains the long-run growth regression, which acts as a forcing equilibrium condition:
(yi )t = 0 + 1(Xi )t + µi,t
i i where µi,t ~ I(0) (2)
Multi-country estimation
The sample we 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. In such conditions, there are a number of alternative methods for multi-
country estimation that vary on the extent to which they allow for parameter
heterogeneity across countries. At one extreme, the fully heterogeneous-coefficient
model imposes no cross-country parameter restrictions and can be estimated on a
country-by-country basis -- provided the time-series dimension of the data is sufficiently
large. When, in addition, the cross-country dimension is also large, the mean of long-
and short-run coefficients across countries can be estimated consistently by the
unweighted average of the individual country coefficients. This is the mean group (MG)
estimator introduced by Pesaran, Smith, and Im (1996). At the other extreme, the fully
homogeneous-coefficient model requires that all slope and intercept coefficients be equal
across countries. This is the simple pooled estimator.
In between the two extremes, there are a variety of estimators. The dynamic fixed
effects estimator restricts all slope coefficients to be equal across countries but allows for
different country intercepts. The pooled mean group (PMG) estimator, introduced by
Pesaran, Shin and Smith (1999), restricts the long-run slope coefficients to be the same
across countries but allows the short-run coefficients (including the speed of adjustment)
and the regression intercept to be country specific. 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. Estimators that impose cross-country constraints dominate the
heterogeneous estimators in terms of efficiency if the restrictions are valid. If they are
6
false, however, the restricted estimators are inconsistent. In particular, imposing invalid
parameter homogeneity in dynamic models typically leads to downward-biased estimates
of the speed of adjustment (Robertson and Symons 1992, Pesaran and Smith 1995).
For our purposes, the pooled mean group 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
vulnerability to domestic and external shocks, monetary and fiscal adjustment
mechanisms, financial-market imperfections, and relative price and wage flexibility.
Furthermore, the PMG estimator is sufficiently flexible to allow for long-run coefficient
homogeneity over only a subset of variables and/or countries.
Therefore, we use the PMG method to estimate a long-run relationship that is
common across countries (i.,e, 1 i
= 1 j for all countries i, j) while allowing for
unrestricted country heterogeneity in the adjustment dynamics and fixed effects. The
interested reader is referred to Pesaran, Shin and Smith (1999) where the PMG estimator
is developed and compared with the MG estimator. Briefly, the PMG estimator proceeds
as follows. First, the estimation of the long-run slope coefficients is done jointly across
countries through a (concentrated) maximum likelihood procedure. Second, the
estimation of short-run coefficients (including the speed of adjustment i), country-
specific intercepts 0 , and country-specific error variances is done on a country-by-
i
country basis, also through maximum likelihood and using the estimates of the long-run
slope coefficients previously obtained.4
4The comparison of the asymptotic properties of PMG and MG estimates can be put also in terms of the
general trade-off between consistency and efficiency noted in the text. If the long-run coefficients are in
fact equal across countries, then the PMG estimates will be consistent and efficient, whereas the MG
estimates will only be consistent. If, on the other hand, the long-run coefficients are not equal across
countries, then the PMG estimates will be inconsistent, whereas the MG estimator will still provide a
consistent estimate of the mean of long-run coefficients across countries. The long-run homogeneity
restrictions can be tested using Hausman or likelihood ratio tests to compare the PMG and MG estimates of
the long run coefficients. In turn, comparison of the small sample properties of these estimators relies on
their sensitivity to outliers. In small samples (low T and N), the MG estimator, being an unweighted
average, is very sensitive to outlying country estimates (for instance those obtained with small T). The
PMG estimator performs better in this regard because it produces estimates that are similar to weighted
averages of the respective country-specific estimates, where the weights are given according to their
precision (that is, the inverse of their corresponding variance-covariance matrix).
7
An important assumption for the consistency of the PMG estimates is the
independence of the regression residuals across countries. In practice, non-zero error
covariances usually arise from omitted common factors that influence the countries'
ARDL processes. We attempt to eliminate these common factors and, thus, ensure the
independence condition by allowing for time-specific effects in the estimated regression;
this is equivalent to a regression in which each variable enters as deviations with respect
to the cross-sectional mean in a particular year.
As indicated in the previous section, for each country the order of the ARDL
process must be augmented to ensure that the residual of the error-correction model be
exogenous and serially uncorrelated. At the same time, with a limited number of time-
series observations, the ARDL order should not be overextended as this imposes
excessive parameter requirements on the data. 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.
However, when there is also interest in analyzing and comparing the short-run
parameters, it is recommended to impose a common lag structure across countries, in
accordance to the characteristics of the analytical model to be studied and the limitations
of the data. We use the latter approach in this paper's implementation of the ARDL
approach.
B. Data and Results
The sample consists of 75 countries with annual data during the period 1960-
2000. See Appendix 1 for the list of countries included in the sample. Given the
procedure's requirements on the time-series dimension of the data, we include only
countries that have at least 20 consecutive observations. The dependent variable is the
growth rate of GDP per capita. The measure of financial intermediation is private
domestic credit as ratio to GDP.5 The control variables are the initial level of GDP per
capita, government consumption as ratio to GDP, the structure-adjusted volume of trade
as ratio to GDP, and the inflation rate. In order to capture proportional effects, all control
5 The other popular measure of financial intermediation is the ratio of liquid liabilities to GDP. As a
robustness check, we have repeated all empirical exercises presented in the paper substituting liquid
liabilities for private credit. Since these results are essentially the same as those using private credit/GDP,
we have omitted them in this version of the paper but can make them available upon request.
8
variables are specified in natural logs. See Appendix 2 for definitions and sources of all
variables used in the paper, and Appendix 3 for their descriptive statistics.
Table 1 presents the results on specification tests and the estimation of long- and
short-run parameters linking per capita GDP growth, financial intermediation, and other
growth determinants. We emphasize the results obtained using the pooled mean group
(PMG) estimator, which we prefer given its gains in consistency and efficiency over
other panel error-correction estimators. For comparison purposes, we also present the
results obtained with the mean group (MG) and the dynamic fixed-effects (DFE)
estimators.
As outlined in the previous section, the consistency and efficiency of the PMG
estimates relies on several specification conditions. The first are that the regression
residuals be serially uncorrelated and that the explanatory variables can be treated as
exogenous. We seek to fulfill these conditions by including in the ARDL model, 3 lags
of the growth rate, 3 lags of the measure of finance intermediation, and 1 lag of each
control variable. We could not expand the lag structure any further because we would
run into problems of lack of degrees of freedom. We chose to use a richer (longer) lag
structure for the dependent variable (growth) and the variable of interest (financial
intermediation) because our main concern was to characterize their long- and short-run
relationships.
The second specification condition is that both country-specific effects and cross-
country common factors be accounted for. We control for country-specific effects by
allowing for an intercept for each country, and we attempt to eliminate cross-country
common factors by demeaning the data using the corresponding cross-sectional means
for every period (which is algebraically the same as allowing for year-specific intercepts).
The third condition refers to the existence of a long-run relationship (dynamic
stability) and requires that the coefficient on the error-correction term be negative and not
lower than 2 (that is, within the unit circle). In the second panel of Table 1, we report
the estimates for the pooled error-correction coefficient and its corresponding standard
error. This coefficient falls within the dynamically stable range in the cases of the PMG
and dynamic fixed effects estimators. The fact that the pooled error correction coefficient
falls outside the allowed range in the case of the mean group estimator reveals that for
9
some countries the dynamic stability condition does not hold. We come back to this issue
below.
The fourth condition is that the long-run parameters be the same across countries.
As explained in the section on econometric methodology, we can test the null hypothesis
of homogeneity through a Hausman-type test, based on the comparison between the
Pooled Mean Group and the Mean Group estimators. In Table 1 we present the Hausman
test statistic and the corresponding p-values for the coefficients on each of the
explanatory variables and for all of them jointly. The homogeneity restriction is not
rejected jointly for all parameters; it is not rejected for individual parameters either
except in the case of trade openness.
Regarding the estimated parameters, our analysis focuses on those obtained with
the PMG estimator. In the long run, the growth rate of GDP per capita is negatively
related to initial income, the size of government, and the inflation rate, and positively
related to international trade openness. These are standard results from the empirical
growth literature, and it is reassuring that we are able to reproduce them with our
methodology.
Most importantly for our purposes, we find that economic growth is positively
and significantly linked to the measure of financial intermediation in the long run. This
effect is also economically significant as the point estimate implies that a 1 percent
increase in the ratio of private credit to GDP leads to a rise of 0.7 percentage points in the
growth rate of per capita GDP.
The short-run coefficients on financial intermediation tell a different story. As
explained earlier, 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. We find that the short-run average
relationship between the growth rate of GDP per capita and the measure of financial
intermediation appears to be strongly negative, with a point estimate several times larger
than that of the long-run effect of financial intermediation. Thus, comparing the long-
and short-run estimates, a first broad conclusion is that the sign of the relationship
10
between economic growth and financial intermediation depends on whether their
movements are temporary or permanent.
In the next section we will take advantage of the cross-country variation of short-
run coefficients to draw a connection between the negative effects of financial
intermediation and the occurrence of financial volatility and crisis. Before we do that,
however, we need to make sure that our results are robust to the exclusion of outlying and
dynamically unstable observations. In particular, we 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 error-
correction coefficients that statistically fall outside the dynamic stable range. We exclude
the countries whose short-run coefficients fall outside 2 standard deviations from the
mean; they are Dominican Republic, Ghana, Sierra Leone, Thailand, Turkey, and
Democratic Republic of Congo. We also exclude the countries with unstable dynamics;
they are Senegal, Uganda, and Zambia.
The results on the restricted sample are presented in Table 2. They are
qualitatively similar to those on the full sample. The signs and statistical significance of
all long-run and most short-run coefficients remain unchanged. 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.
Moreover, the long- and short-run effects of private credit on growth are not only
qualitatively but also quantitatively very similar for the restricted and full samples.
III. FINANCIAL FRAGILITY, ALONGSIDE FINANCIAL DEPTH, IN THE PATH TO
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. In
this section, we discuss and develop further this explanation. First, we review a selected
set of recent theoretical models that examine the evolution of the effects of financial
liberalization through time. All of them differentiate between short- and long-run effects
11
on economic growth. Moreover, they highlight the point that financial liberalization is
not only associated with financial depth but also to financial fragility (as captured by
banking crises and financial volatility), and it is this aspect of financial liberalization that
explain why intermediation may have a negative short-run effect on growth. Second, we
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. Finally, as a further test of the
relevance of our results, we take the usual approach of empirical growth analysis in order
to consider whether the volatility and crises aspects of financial liberalization, and not
only the customary measures of financial depth, are significant determinants of economic
growth.
A. Theoretical discussion
How can we explain the contrast between the short- and long-run effects of
financial intermediation? Recent theories on the aftermath of financial liberalization can
provide some guidance to answer this question. We do not attempt to tell them apart by
testing their particular implications; rather, we intend to draw on their similarities to build
a sensible explanation. Gaytán and Rancičre (2003) develop a model of banking
development where banks serve the purpose of providing liquidity insurance --which
avoids the need for costly asset liquidation-- but are subject to confidence crises leading
to bank runs. Banks have the option to insure against these crises, but this is costly as it
entails less money available for investment. Gaytán and Ranciere find that in emerging
countries it is optimal for banks to be only partially covered against the risk of bank runs.
This is so because in these countries the opportunity cost of full insurance, given by the
marginal rate of return to investment, is too high. As countries develop and capital
productivity decreases, it becomes optimal for banks to be fully covered against crises.
Therefore, the model predicts that in the short run after financial liberalization, there is
the chance that emerging countries will face financial crises, switch from non-crisis to
crisis equilibria, and thus experience volatility of credit and low output growth. In the
long run, however, financial development would be free from crises and would render
stable growth.
12
Dell'Arricia and Marquez (2004a, 2004b) view the aftermath of financial
liberalization as a period marked by excessive lending, as banks' screening incentives are
not at par with the rapid growth for credit demand. In their model, banks' incentives to
screen potential borrowers come from adverse selection among banks banks screen to
avoid financing firms whose projects have been tested and rejected by other banks.
When financial markets are liberalized and many new and untested projects request
funding, banks do not have strong incentives to screen its pool of applicants and rapid
credit expansion ensues. In this case the quality of banks' portfolio is likely to deteriorate
and over-lending will result in financial fragility. At the macroeconomic level, as
negative shocks occur, financial fragility will give way to financial instability and output
losses. Over time, however, as most potential borrowers are tested, banks' screening
incentives and practices are restored, and lending begins to grow at stable rates. 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.
Rajan (1994) develops a similar mechanism that focuses on bankers' incentives to
explain financial fragility. In his model, bank managers tend to hide non-performing
loans by maintaining liberal credit policies when most borrowers are solvent ("good"
times). When the economy is hit by an aggregate shock and the relative ability of
individual managers becomes less discernible, they have the incentive to tighten credit
policy. This procyclical credit practice tends to finance bad projects in good times and
squeeze credit away from worthy firms in distress times. The way to solve this
inefficient credit practice is through proper banking supervision. However, in the short
run after financial liberalization, a great need for credit expansion occurs in the face of
poor development of supervisory capacity. In this context, cycles of booms and busts in
credit and output growth are likely to arise, and they are only gradually replaced by stable
financial depth and output growth as institutional and supervisory capacities develop.
For Wynne (2002) the fundamental problem underlying financial fragility does
not reside in banks' incentives to screen and monitor potential borrowers; rather, the
problem is that it takes time and effort for firms to build financial reputation and public
knowledge about the quality of their investment projects. This is important because of
13
the intrinsic asymmetry of information between potential borrowers and creditors. Firms
create "information" capital only gradually through a higher survival rate and wealth
accumulation. 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. At the macroeconomic level,
this may result in banking crises, cycles of credit expansion and contraction, and low
output growth, particularly if the economy is hit by large shocks. Overtime, however,
good firms build information capital, lending rates decrease, and bank's loan portfolios
become safer. At the aggregate level in the long run, proper credit allocation results in
higher and more stable productivity growth.
All these models predict a positive long-run relationship between financial
intermediation and economic growth. They also predict that in the short run this
relationship may be negative and explain through what channels this might occur. What
these explanations have in common is that they all link the potential negative short-run
impact of financial intermediation with the presence of financial volatility and the
likelihood of banking crises. Given that our panel data methodology allows us to recover
country-specific short-run coefficients, we can analyze their connection with financial
volatility and crises, and to this we turn next.
B. Analysis of Short-Run Coefficients
We consider the question of whether the negative short-run relationship between
growth and financial intermediation can be linked to the occurrence of systemic banking
crisis and financial volatility. We quantify this short-run relationship by the short-run
coefficients on financial intermediation estimated previously for each country in the
sample. As a proxy for financial crises, we use the number of years in the period 1960-
2000 that the country has experienced systemic banking crises, according to Caprio and
Klingbiel (2003). We measure financial volatility as the standard deviation of the growth
rate of the private credit to GDP ratio over the period 1960-2000.
Table 3 reports the simple and rank correlation coefficients --along with the
corresponding p-values-- between the short-run coefficients and the measures of financial
crisis and financial volatility. We present statistics corresponding to both the full and
restricted samples. Not surprisingly, the correlation between crisis and volatility is
14
positive and significant, with values between 0.35 and 0.40. This is large enough to
reveal a meaningful connection but sufficiently small to indicate that each variable
contains independently relevant information. Most importantly, both financial crisis and
volatility have a negative and significant correlation (close to -0.30) with the estimated
short-run coefficients; that is, larger volatility or higher frequency of crises are related to
more negative short-run impacts of financial intermediation on output growth.
Another approach to examine this connection is by dividing the sample of
countries according to criteria given by financial crisis and volatility and then comparing
the corresponding group means of short-run coefficients. Regarding financial crises, we
divide the sample, first, according to whether countries had or not any crises during the
40 year period (about 60% had at least one year of crisis), and, second, according to
whether they are above or below the median number of crises. Regarding financial
volatility, we divide the sample using as thresholds, first, the 75th percentile of volatility
and, second, its median value.
Table 4 presents the tests of the difference in short-run coefficient means for the
various ways of grouping countries. We do so for both the full and restricted samples.
Countries that experienced financial crises in the last 40 years exhibit an average short-
run impact of financial intermediation on output growth that is significantly more
negative than the average of countries that did not have any crisis. In fact, for the non-
crisis countries, the average short-run impact of intermediation on growth is statistically
zero. A similar result is obtained when we divide the sample according to the median
frequency of crises: high-crisis countries show significantly more negative short-run
impacts than low-crisis countries, and for the latter this impact is indistinguishable from
zero. Concerning financial volatility, when we divide the sample using its 75th percentile
as threshold, we find that the group of high financial volatility has an average short-run
effect that is significantly more negative than the group of low and medium volatility.
The results are similar when we use the median volatility as threshold, but in this case the
difference in the average short-run coefficients between the two groups is not statistically
significant at conventional levels.
Figure 3 (a) presents the (smoothed) frequency distributions of short-run
coefficients for the groups of crisis and non-crisis countries. Figure 3 (b) does the same
15
for the groups of high-volatility and medium-to-low-volatility countries (using the 75th
percentile as threshold). In both cases we consider only the restricted sample of countries
(that is, without outliers and dynamically unstable observations). The distributions
appear to be basically symmetrical. We can see that the groups of crisis and high-
volatility countries not only have more negative average short-run coefficients but also
have more dispersed distributions, with fatter tails particularly towards the negative
portion of the spectrum.
C. Classical Growth Regressions: The role of financial volatility and crisis
So far our analysis has used a novel empirical estimator to distinguish between
short- and long-run effects of financial intermediation on economic growth. This
methodology uses the time-series dimension of the data at least as intensively as the
cross-country dimension. It represents a departure from the typical empirical growth
literature in which high-frequency movements in the data are averaged out prior to
estimation. Typical panel-data growth studies work with country data averaged for
periods of 5 or 10 years and, therefore, are likely to combine short- and long-run effects.
In previous sections of the paper, we have argued that the contrasting effects of financial
intermediation come from different aspects associated to the process of financial
development --financial depth and fragility. 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). In order to provide further
support to the arguments developed in the paper, we would like to use the typical growth-
regression framework to analyze whether financial fragility is also a relevant determinant
of growth.6
Data and Methodology
We 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. See Appendix 1 for the list of countries in the sample.
As is standard in the literature, our growth regression equation is dynamic in the
sense that it includes the initial level of per capita output as an explanatory variable. In
16
addition, the regression includes as control variables the average rate of secondary school
enrollment, the average structure-adjusted ratio of trade volume to GDP, the average ratio
of government consumption to GDP, and the average inflation rate. The explanatory
variables of interest were introduced above. 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.
We use an estimation method that is suited to panel data, deals with a dynamic
regression specification, controls for unobserved time- and country-specific effects, and
accounts for some endogeneity in the explanatory variables. 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).
The regression equation to be estimated is the following,
yi,t - yi,t = ( -1)yi,t + ' Xi,t +FDi,t + FFi,t + µt +i + i,t
-1 -1
where, y is the logarithm of real per capita output, X is a set of control variables, FD is
the indicator of financial depth, FF represents the indicator(s) of financial fragility, µt is a
time-specific effect, i is an unobserved country-specific effect, and is the error term.
The subscripts i,t represent country and time-period, respectively.
We relax the assumption of strong exogeneity of the explanatory variables by
allowing them to be correlated with current and previous realizations of the error term .
However, we assume that future realizations of the error term do not affect current values
of the explanatory variables. Furthermore, we assume that the error term is serially
uncorrelated. We allow the unobserved country-specific effect i to be correlated with
the explanatory variables. However, following a stationarity condition, we assume that
changes in the explanatory variables are uncorrelated with the country-specific effect. 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. The
6See Hnatkovska and Loayza (2004) for a general treatment of the link between economic growth and
macroeconomic volatility, whether this comes from financial fragility, external shocks, monetary
instability, or other sources.
17
instruments corresponding to these moment conditions are appropriately lagged values of
the levels and differences of the explanatory and dependent variables. 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 we turn
next. The first column presents the results of a typical growth regression. It confirms the
negative growth impact of initial GDP per capita (conditional convergence), the size and
burden of government, and monetary instability through high inflation. It also shows the
positive growth effect of education investment, international trade openness, and, most
importantly for our purposes, financial depth. The period shifts are also significant and
indicate that world conditions deteriorated in the last twenty years making it more
difficult for countries to grow in the 1980s and 1990s than in the previous decades.
The second and third columns include, respectively, financial volatility and the
frequency of systemic banking crises as additional explanatory variables. The fourth
column includes both indicators of financial fragility at the same time. Whether by
themselves or together, financial volatility and the frequency of banking crises present a
significantly negative coefficient. Financial depth maintains its positive and significant
coefficient in all regressions. We 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 our 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. On the other hand,
an increase in one sample standard deviation of financial depth leads to economic growth
rising by 0.9 percentage points. Naturally, these are ceteris paribus calculations, and our
conjecture is that the total effect of financial liberalization and intermediation may be a
combination of these effects, with weights for financial depth and financial fragility
depending on the stage of financial development that the country is undergoing.
18
IV. CONCLUSIONS
Over the long run, financial development supports and promotes economic
growth. However, the path to development is far from smooth; and along the way,
economic growth can suffer from the financial fragility that characterizes maturing
systems. The process of financial development entails a deepening of markets and
services that channel savings to productive investment and allow risk diversification;
these positive aspects of financial development lead to higher economic growth in the
long run. As economies mature, however, the same process can present weaknesses
evidenced by systemic banking crises, cycles of booms and busts, and overall financial
volatility. 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. We 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, we can attempt to link
these effects to the aspects of financial liberalization that the literature proposes as
harmful to growth. We 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. For more stable countries, this
effect is in average nil.
Finally, attempting to relate our results to the empirical growth literature, we go
back to the classic context of growth regressions using panel data. We find that the
volatility and crisis aspects of financial intermediation are relevant growth determinants,
along with the usual measures of financial depth. However, whereas financial depth
leads to higher growth, financial fragility --as captured by financial volatility and banking
crises-- has negative growth consequences. The total impact of financial liberalization
19
and intermediation on economic growth may be a combination of these effects, where the
relative influences of financial depth and financial fragility would depend on each
country's stage of financial development.
20
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24
TABLES
Table 1. The Long- and Short-Run Effect of Financial Intermediation on Economic Growth
Estimators: Pooled mean group, mean group, and dynamic fixed efffects, all controlling for country and time effects
Dynamic specification: ARDL(3,3,1,1,1,1)
Sample: All countries, annual data 1960-2000
Pooled Mean Group Mean Group Hausman Tests Dynamic Fixed Effect
Variables Coef. St.Er. Coef. St.Er. h-test p-val Coef. St.Er.
Long-Run Coefficients
Financial intermediation 0.708 0.179 0.948 1.226 0.04 0.84 0.121 0.473
Initial GDP per capita -8.609 0.571 -13.803 4.52 1.34 0.25 -3.489 0.488
Government size -5.705 0.463 -9.217 4.122 0.48 0.49 -2.022 0.462
Trade openness 1.059 0.281 5.176 2.199 4.56 0.03 1.504 0.340
Inflation -5.969 0.684 4.335 5.899 0.08 0.78 -4.979 0.767
Joint Hausman Test: 8.5 0.13
Error Correction Coefficients
Phi -0.973 0.063 -2.36 0.156 -0.943 0.034
Short-Run Coefficients
Growth (-1) 0.178 0.056 1.003 0.121 0.041 0.027
Growth (-2) 0.02 0.035 0.447 0.07 -0.017 0.020
Financial intemediation -4.517 1.136 -2.814 2.008 -10.258 2.137
Financial intermediation (-1) -0.591 1.028 -0.087 1.662 4.930 2.328
Financial intermediation (-2) -0.856 1.310 0.486 1.617 -5.570 2.262
Initial GDP per capita -4.954 2.947 -9.019 5.052 -5.142 1.919
Government size -0.08 1.751 3.76 2.288 -2.894 0.738
Trade openness 3.344 2.223 -4.294 3.626 2.127 0.752
Inflation -2.258 1.842 -0.782 5.046 -0.674 0.871
Intercept -1.24 2.273 -10.121 13.104 -0.010 0.007
Sum of Coefficients on Financial Intermediation
Financial intemediation coeffs. -5.964 2.316 -2.415 4.24
No. Countries 75 75 75
No. Observations 2501 2501 2501
Avg. Rbar squared 0.454 0.67 0.484
Source: Authors' estimations
25
Table 2. The Long- and Short-Run Effects of Financial Intermediation on Economic Growth, Reduced Sample
Estimators: Pooled mean group, mean group, and dynamic fixed efffects, all controlling for country and time effects
Dynamic specification: ARDL(3,3,1,1,1,1)
Sample: Countries that are not ouliers or dynamically unstable (66 countries), annual data 1960-2000
Pooled Mean Group Mean Group Hausman Tests Dynamic Fixed Effect
Variables Coef. St.Er. Coef. St.Er. h-test p-val Coef. St.Er.
Long-Run Coefficients
Financial intermediation 0.632 0.259 0.165 1.314 0.06 0.81 0.107 0.260
Initial GDP per capita -5.181 0.513 -13.886 3.893 5.09 0.02 -3.423 0.512
Government size -2.342 0.41 -1.53 4.06 0.04 0.84 -2.022 0.462
Trade openness 2.422 0.188 2.871 3.133 0.03 0.89 1.458 0.352
Inflation -5.029 0.596 -4.568 5.849 0.01 0.94 -5.162 0.771
Joint Hausman Test: 7.72 0.17
Error Correction Coefficients
Phi -0.901 0.069 -1.867 0.118 -0.916 0.035
Short-Run Coefficients
Growth (-1) 0.144 0.049 0.739 0.091 0.056 0.028
Growth (-2) 0.001 0.031 0.314 0.053 -0.003 0.021
Financial intemediation -5.056 1.033 -4.571 1.762 -2.453 0.591
Financial intermediation (-1) -0.113 1.090 -1.027 1.675 -0.079 0.591
Financial intermediation (-2) -0.534 1.029 -0.64 1.434 -0.346 0.594
Initial GDP per capita -6.115 2.613 -7.511 4.192 -4.712 2.007
Government size -6.509 2.171 -7.14 3.525 -3.192 0.751
Trade openness 3.358 2.18 -2.819 2.719 2.660 0.816
Inflation -4.789 1.576 4.752 4.14 -1.873 0.901
Intercept -0.881 1.312 -7.696 10.063 -0.004 0.007
Sum of Coefficients on Financial Intermediation
Financial intemediation coeffs. -5.702 1.628 -6.238 4.31
No. Countries 66 66 66
No. Observations 2206 2206 2206
Avg. Rbar squared 0.449 0.65 0.47
Source: Authors' estimations
26
Table 3. Short-Run Coefficients on Financial Intermediation,
Banking Crises, and Financial Volatility
Standard correlation coefficients in upper rigth triangular matrix
Spearman rank correlation coefficients in lower left triangular matrix
(a) Sample: All Countries, average 1960-2000
Short-run coefficients Number of crisis Financial volatility
-0.303 -0.379
Short-run coefficients 1
(0.008) (0.001)
-0.2693 0.449
Number of crisis 1
(0.0195) (0.000)
-0.2980 0.3947
Financial volatility 1
(0.0094) (0.0005)
(b) Sample: 66 Countries, average 1960-2000
Short-run coefficients Number of crisis Financial volatility
-0.243 -0.345
Short-run coefficients 1
(0.049) (0.005)
-0.2740 0.366
Number of crisis 1
(0.0260) (0.002)
-0.3153 0.3565
Financial volatility 1
(0.0099) (0.0033)
Note: p-values reported in parenthesis
Source: Authors' estimations
27
Table 4. Short-Run Effects of Financial Intermediation Depending on Presence of Banking Crises and
(a) No crisis vs. Some crisis
Full sample Restricted sample
Mean short-run Std. error N° obs. Mean short-run Std. error N° obs.
coefficients coefficients
No crisis -0.199 2.58 30 -1.093 1.77 29
Some crisis -9.808 3.36 45 -9.315 2.49 37
Test of difference in means:
Ho: mean(no crisis) - mean(some crisis) = diff = 0 vs. Ha: diff > 0
diff t-value p-value diff t-value p-value
9.609 2.27 0.013 8.222 2.69 0.005
(b) Low crisis vs. High crisis
Full sample Restricted sample
Mean short-run Std. error N° obs. Mean short-run Std. error N° obs.
coefficients coefficients
Low crisis -0.343 2.33 34 -1.472 1.62 32
High crisis -10.626 3.64 41 -9.684 2.7 34
Test of difference in means:
Ho: mean(crisis below median) - mean(crisis above median) = diff = 0 vs. Ha: diff > 0
diff t-value p-value diff t-value p-value
10.283 2.38 0.01 8.211 2.61 0.006
(c) Low and medium financial volatility vs. High financial volatility
Full sample Restricted sample
Mean short-run Std. error N° obs. Mean short-run Std. error N° obs.
coefficients coefficients
Low and medium volatility -3.064 2.53 56 -4.227 1.91 51
High volatility -14.515 4.92 19 -10.717 3.16 15
Test of difference in means:
Ho: mean(fin. vol. below 75 percentile) - mean(fin. vol. above 75 percentile) = diff = 0
vs. Ha: diff > 0
diff t-value p-value diff t-value p-value
11.451 2.07 0.024 6.49 1.76 0.045
(d) Low financial volatility vs. High financial volatility
Full sample Restricted sample
Mean short-run Std. error N° obs. Mean short-run Std. error N° obs.
coefficients coefficients
Low volatility -3.933 2.9 37 -3.903 2.41 36
High volatility -7.943 3.6 38 -7.861 2.23 30
Test of difference in means:
Ho: mean(fin. vol. below median) - mean(fin. vol. above median) = diff = 0
vs. Ha: diff > 0
diff t-value p-value diff t-value p-value
4.009 0.87 0.195 3.958 1.21 0.116
Notes: - Crisis: Number of years when the country experienced a systemic banking crisis during the period
1960-2000, from Caprio and Klingbiel (2003)
- Financial volatility: Standard deviation of the growth rate of private credit/GDP
Source: Authors' calculations
28
Table 5: The growth effect of financial depth, financial volatility, and financial crises
Dependent Variable: Growth rate of GDP per capita
(Standard errors are presented below the corresponding coefficient)
Period: 1961-2000
Unit of observation: Non-overlapping 5-year averages
Estimation Technique: System GMM
[1] [2] [3] [4]
Financial Depth 0.9738 ** 0.9094 ** 0.9865 ** 1.0014 **
(private domestic credit/GDP, in logs) 0.0896 0.0943 0.0894 0.0569
Financial Volatility -5.7453 ** -3.7681 **
(Std. Dev. of growth rate of private domestic credit/GDP) 0.9959 0.9638
Systemic Banking Crises -2.6989 ** -2.6020 **
(frequency of years under crisis: 0-1) 0.1966 0.2184
Control Variables:
Initial GDP Per Capita -0.1244 * -0.1852 ** -0.2056 ** -0.3452 **
(in logs) 0.0642 0.0788 0.0721 0.0928
Education 1.6426 ** 1.6419 ** 1.8303 ** 2.0464 **
(secondary enrollment, in logs) 0.0970 0.1098 0.1288 0.1323
Trade Openness 0.4853 ** 0.3701 ** 0.7358 ** 0.1973 *
(structure-adjusted trade volume/GDP, in logs) 0.1117 0.1301 0.1200 0.1206
Government Size -1.7347 ** -1.7225 ** -1.9488 ** -1.9708 **
(government consumption/GDP, in logs) 0.1445 0.1387 0.1785 0.1898
Lack of Price Stability -3.4775 ** -2.4940 ** -1.7811 ** -1.3138 **
(inflation rate, in log [100+ inf. rate]) 0.2527 0.3582 0.2143 0.3293
Period Shifts
benchmark for Cols. 4: 71-75: -0.4348 -0.1019 -0.7126 ** -0.4403 **
76-80: -1.2907 ** -0.8449 ** -1.5133 ** -1.2029 **
81-85: -3.3432 ** -2.9626 ** -3.1966 ** -2.9614 **
86-90: -2.5525 ** -2.1066 ** -2.4078 ** -2.1162 **
91-95: -2.9852 ** -2.4348 ** -2.8070 ** -2.5337 **
96-99: -3.1688 ** -2.4931 ** -2.8753 ** -2.5298 **
Intercept 16.4400 ** 12.5041 ** 9.1010 ** 7.2593 **
1.1319 1.6125 1.0434 1.5605
No. Countries / No. Observations 82/545 82/545 82/545 82/545
SPECIFICATION TESTS (P-Values)
(a) Sargan Test: 0.202 0.245 0.263 0.206
(b) Serial Correlation :
First-Order 0.000 0.000 0.000 0.000
Second-Order 0.325 0.336 0.445 0.438
** means significant at 5% and * means significant at 10%
Source: Authors' estimations
29
FIGURES
Figure 1. Economic Growth and Financial Intermediation around Banking Crisis
Cross-country means for the years surrounding the start of a systemic banking crisis
2.5 0.1
2 0.08
1.5 0.06
1 0.04
0.5 0.02
0 0
ANTE_5 ANTE_4 ANTE_3 ANTE_2 ANTE_1 CRISIS POST_1 POST_2 POST_3 POST_4 POST_5 POST_6
-0.5 -0.02
-1 -0.04
-1.5 -0.06
-2 -0.08
-2.5 -0.1
Real Per Capita Growth Private Credit/GDP
Figure 2. Economic Growth and Financial Intermediation in the Long Run
Averages for 1960-2000
8
6
ht 4
y = 1.211x - 2.2587
ow
(5.13)
gr
a R2 = 0.318
pitac 2
r
pe
ale 0
R
0 1 2 3 4 5 6
-2
-4
Logarithm of Private credit/GDP
Note: t-statistic between parenthesis using robust OLS.
30
Figure 3. Frequency distributions of short-run coefficients
for various groupings
(a) No crisis vs. Some crisis
Sample: 66 Countries 1961-2000
0.04
0.03
0.02
0.01
0
-60 -40 -20 0 20 40
Short-run coefficients
Some crisis No crisis
(b) Low and medium financial volatility vs. High financial volatility
Sample: 66 Countries 1961-2000
0.04
0.03
0.02
0.01
0
-60 -40 -20 0 20 40
Short-run coefficients
High fin. vol. Low and Medium fin. vol
31
APPENDICES
Appendix 1: Sample of countries
Pooled Mean Group Estimation
GMM Estimation (82
Country Full Sample (75 Restricted Sample (66
countries)
countries) countries)
Algeria x x x
Argentina x x x
Australia x x x
Austria x x x
Bangladesh - - x
Belgium x x x
Bolivia x x x
Botswana - - x
Brazil x x x
Burkina Faso x x x
Canada x x x
Chile x x x
China - - x
Colombia x x x
Congo, Dem. Rep. x - x
Congo, Rep. x x x
Costa Rica - - x
Cote d'Ivoire x x x
Denmark x x x
Dominican Republic x - x
Ecuador x x x
Egypt, Arab Rep. x x x
El Salvador x x x
Finland x x x
France x x x
Gambia, The x x x
Germany - - x
Ghana x - x
Greece x x x
Guatemala x x x
Haiti x x x
Honduras x x x
Iceland x x x
India x x x
Indonesia x x x
Iran, Islamic Rep. x x x
Ireland x x x
Israel x x x
Italy x x x
Jamaica x x x
Japan x x x
Jordan - - x
Kenya x x x
Korea, Rep. x x x
Madagascar x x x
Malawi x x x
Malaysia x x x
Mexico x x x
Morocco x x x
Netherlands x x x
New Zealand x x x
Nicaragua x x x
Niger x x x
Nigeria x x x
Norway x x x
Pakistan x x x
32
Pooled Mean Group Estimation
GMM Estimation (82
Country Full Sample (75 Restricted Sample (66
countries)
countries) countries)
Panama x x x
Papua New Guinea - - x
Paraguay x x x
Peru x x x
Philippines x x x
Portugal x x x
Senegal x - x
Sierra Leone x - x
Singapore x x x
South Africa x x x
Spain x x x
Sri Lanka x x x
Sweden x x x
Switzerland x x x
Syrian Arab Republic x x x
Thailand x - x
Togo x x x
Trinidad and Tobago x x x
Tunisia x x x
Turkey x - x
Uganda x - -
United Kingdom x x x
United States x x x
Uruguay x x x
Venezuela, RB x x x
Zambia x - x
Zimbabwe - - x
33
Appendix 2: Definitions and Sources of Variables Used in Regression Analysis
Variable Definition and Construction Source
GDP per capita Ratio of total GDP to total Authors' construction using Summers and
population. GDP is in 1985 Heston (1991) and The World Bank
PPP-adjusted US$. (2002).
GDP per capita growth Log difference of real GDP Authors' construction using Summers and
per capita. Heston (1991) and The World Bank
(2002).
Initial GDP per capita Initial value of ratio of total Authors' construction using Summers and
GDP to total population. Heston (1991) and The World Bank
GDP is in 1985 PPP-adjusted (2002).
US$.
Education Ratio of total secondary World Development Network (2002) and
enrollment, regardless of age, The World Bank (2002).
to the population of the age
group that officially
corresponds to that level of
education.
Private Credit Ratio of domestic credit Author's calculations using data from IFS,
claims on private sector to the publications of the Central Bank and
GDP PWD. The method of calculations is based
on Beck, Demiguc-Kunt andLevine
(1999).
Trade Openness Residual of a regression of Author's calculations with data from
the log of the ratio of exports World Development Network (2002) and
and imports (in 1995 US$) to The World Bank (2002).
GDP (in 1995 US$), on the
logs of area and population,
and dummies for oil
exporting and for landlocked
countries.
Government Size Ratio of government The World Bank (2002).
consumption to GDP.
CPI Consumer price index (1995 Author's calculations with data from IFS.
= 100) at the end of the year
Inflation rate Annual % change in CPI Author's calculations with data from IFS.
Systemic Banking Crises Number of years in which a Author's calculations using data from
country underwent a Caprio and Klingebiel (1999), and
systemic banking crisis, as a Kaminsky and Reinhart (1998).
fraction of the number of
years in the corresponding
period.
Period-specific Shifts Time dummy variables. Authors' construction.
34
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