What is happening to the impact of financial deepening on economic growth?
Rousseau, Peter L. ; Wachtel, Paul
I. INTRODUCTION
Among the strongest elements of the modern economists' canon
is that financial sector development has a significant impact on
economic growth. A generation ago, economists like Goldsmith (1969) (1)
and McKinnon (1973) began to draw attention to the benefits of financial
structure development and financial liberalization. By the early 1990s,
McKinnon (1991, 12) could write with confidence that:
"Now, however, there is widespread agreement that flows of
saving and investment should be voluntary and significantly
decentralized in an open capital market at close to equilibrium interest
rates."
Since the 1990s, a burgeoning empirical literature has illustrated
the importance of financial sector development for economic growth.
Despite the growing consensus, however, we find that the link between
finance and growth in cross-country panel data has weakened considerably
over time. At the very time that financial sector liberalization spread
around the world, the influence of financial sector development on
economic growth has diminished.
The seminal empirical work that established the growth-finance link
is King and Levine (1993), which extended the cross-country framework
introduced in Barro (1991) by adding financial variables such as the
ratios of liquid liabilities or claims on the private sector to gross
domestic product (GDP) to the standard growth regression. They found a
robust, positive, and statistically significant relationship between
initial financial conditions and subsequent growth in real per capita incomes for a cross-section of about 80 countries. In the subsequent
decade numerous empirical studies expanded upon this, using both
cross-country and panel data sets for the post-1960 period. (2)
In this paper we reexamine the core cross-country panel result and
find that the impact of financial deepening on growth is not as strong
with more recent data as it appeared in the original panel studies with
data for the period from 1960 to 1989. We consider various explanations
for this clear shift. First, we suggest that financial deepening has a
positive effect on growth if not done to excess. Rapid and excessive
deepening, as manifested in a credit boom, can be problematic even in
the most developed markets because it can both weaken the banking system
and bring inflationary pressures. We test this hypothesis by looking at
the finance-growth nexus among countries that have or have not
experienced financial sector crises. We find that once crisis episodes
are removed, the finance-growth relationship remains intact. Its
weakening over time thus seems to be a result of an increased incidence
of crises in later years.
Our second and related hypothesis is that the widespread
liberalization of financial markets that occurred in the late 1980s and
early 1990s made financial deepening less effective. This is reminiscent
of Robert Lucas's (1975) critique of econometric policy evaluation
advanced three decades ago. Policies that have promoted and/or forced
increases in financial depth over the past two decades may have altered
the basic structural relationship between finance and growth. This could
occur if the observed benefits of financial deepening led many countries
to liberalize before the associated legal and regulatory institutions
were sufficiently well developed. As a consequence, the impact of
financial deepening on growth would become smaller. Our evidence does
not indicate that recent liberalizations are responsible for the
breakdown of the finance-growth link. However, there may be an indirect
link as premature financial development can lead to financial crises
that have real effects.
Third, we examine the role of global equity markets that have grown
in importance and prominence in the years over which the finance-growth
relationship disappeared. However, we do not find any evidence to
suggest that equity market growth has substituted for the role of credit
markets and banks in particular.
We also examine some sample composition effects. For example, we
distinguish between developed and developing countries. Although the
finance-growth relationship is somewhat stronger among developed
countries, the decline in the impact of finance on growth in recent
years is found in both groups.
Further, we look at several estimation techniques in order to be
confident of the robustness of our major finding, the virtual
disappearance of the finance-growth relationship in recent data. The
finance-growth literature uses various estimation approaches because of
the difficulty in adequately controlling for endogeneity. We examine
pure cross-section estimates, panels of 5-year average growth rates, and
dynamic panels in order to show that our basic result is robust to the
choice of estimation technique.
Although the finance-growth nexus has become firmly entrenched,
this is not the first study to question its importance. Economists as
disparate as Joan Robinson and Robert Lucas have expressed doubts about
the link. (3) In addition, some authors have been less than enthusiastic
about the strength of the recently established empirical consensus and
there are indications that the relationship varies and lacks robustness.
(4)
A few earlier papers, including those by Demetriades and Hussein
(1996), Rousseau and Wachtel (1998), and Arestis, Demetriades, and
Luintel (2001), have noted that the relationship between financial
deepening and growth varies considerably across countries. Rousseau and
Wachtel (2002) show that the relationship varies with the inflation
rate; financial deepening does not affect growth when annual inflation
is above a threshold of about 13%. Rioja and Valev (2004) also show that
the relationship varies with the level of economic development.
Specifically, deepening has a larger impact on growth with a moderate
level of financial sector development. However, none of the earlier
studies has provided an explanation for the weakening of the
relationship over time.
A recent paper by Loayza and Ranciere (2006) addresses the dual
role of financial deepening discussed above. They distinguish between
the short-run impact of credit expansions on growth and the long-run
positive impact of financial deepening on growth. The short-run effect
is sometimes negative, particularly during episodes of financial crisis.
Our approach to this dual role of finance is somewhat different. First,
we investigate how banking crises and liberalizations affect the impact
of financial deepening on growth. Second, we relate these phenomena to
the secular decline in the impact of financial deepening observed in the
classical cross-country panel regression framework.
The next section describes the data and the by-now-standard
approach to panel estimates of growth equations. In Section III we
present baseline estimates and show that the finance-growth nexus has
weakened over time. In Section IV we examine the hypotheses suggested
above regarding the possible causes of decline in the effect of
financial deepening on growth. Section V presents some additional
evidence on the relationship between the strength of the finance-growth
link and the levels of economic development and financial depth in a
country. Our conclusions are in Section VI.
II. DATA AND METHODOLOGY
Our study includes cross-sectional and panel data on financial and
macroeconomic indicators for 84 countries over the period from 1960 to
2004. (5) Data are from the 2007 edition of the World Bank's Worm
Development Indicators database. The selection of countries is based on
data availability from this source. To ensure comparability with King
and Levine's original study and others, we use three familiar
measures of financial development, namely, the ratios to GDP of liquid
liabilities (M3), liquid liabilities less narrow money (M3 less M1), and
credit allocated to the private sector. M3 as a percentage of GDP has
become a standard measure of financial depth and an indicator of the
overall size of financial intermediary activity in cross-country
studies. M3 less M1 removes the pure transactions asset and the credit
measure isolates intermediation to the private sector from the credit
allocated to government or state enterprises.
King and Levine's version of the Barro growth regression, and
the starting point for our analysis, has the form
(1) [Y.sub.it] = [[alpha].sub.0] + [alpha][F.sub.it] +
[beta][X.sub.it] + [u.sub.it],
where [Y.sub.it] is the growth rate of real per capita GDP,
[F.sub.it] is a measure of financial sector development, and [X.sub.it]
is a set of baseline explanatory variables that have been shown
empirically to be robust determinants of growth. The X variables include
the log of initial real per capita GDP, which should capture the
tendency for growth rates to converge across countries and over time,
and the log of the initial secondary school enrollment rate, which
should reflect the extent of investment in human capital. We include the
ratio of trade (i.e., imports plus exports) to GDP and the ratio of
government final consumption to GDP as additional explanatory variables.
Following King and Levine, we start with cross-sectional estimates
where the dependent variable is the average annual growth rate over our
entire data period (i.e., 1960-2004). To reduce any simultaneity bias
that might result from the influence of economic growth on the
development of the financial sector, we use initial values from the
start of the cross-section for all explanatory variables in the
regression. Following the subsequent literature, we also exploit the
time series variation in the data by estimating Equation (1) with a
panel of 5-year averages.
In these regressions we use instrumental variables to reduce any
simultaneity bias. Specifically, we attempt to extract the predetermined component of the financial variable by using its initial value (in each
5-year period) along with the initial values of government expenditure
and trade as percentages of GDP as instruments in each regression
equation. All panel estimates include time period fixed effects.
Finally, we will also present estimates with the system GMM dynamic
panel estimation techniques that have become common in the literature.
(6) Our fundamental result that the finance-growth relationship weakened
dramatically is robust to the choice of estimation approach.
III. THE DECLINE IN THE FINANCE-GROWTH RELATIONSHIP
Table 1 contains results from the baseline cross-section growth
equations for each of the three measures of financial depth for the full
data period, 1960-2004, and two subperiods. The first subperiod,
1960-1989, coincides with the time period analyzed by King and Levine
and others that established the consensus results that have become so
important. The results for 1960-1989 and for the full period are largely
consistent with this consensus. (7) For the 1960-1989 subperiod, the
coefficients on the financial variables are all positive and significant
at the 5% level; the same is true for the full period except for the
ratio of private credit to GDP. The contrast with the second subperiod
is dramatic; none of the finance variables is significant in the cross
section for 1990-2004. The coefficients on the log of initial real GDP
is negative and statistically significant for the 1960-1989 and
1960-2004 periods, which is consistent with the notion of beta
convergence, but they are not significant for 1990-2004. The positive
and significant coefficients on the log of the initial secondary school
enrollment rate in all specifications suggest that human capital
investment matters for growth. The other control variables--government
expenditure and trade as percentages of GDP--are not always significant
in these cross-section results but robustness tests (not shown) indicate
that their presence or absence does not have much effect on the finance
coefficients.
The dramatic difference between the initial and recent time periods
found in the cross-section estimates of Table 1 is repeated when we look
at panels with 5-year averages estimated with both standard two-stage
least squares and with dynamic system GMM. Tables 2 and 3 present these
same equations estimated with the alternative techniques. With all three
estimation approaches, the effect of financial depth on growth, which is
always significant in the first 30-year period, disappears in the next
15. Whereas all of the finance coefficients are significant at the 5%
level in the early time period, none are significant in the more recent
data and the coefficients fall to near zero.
To examine further the differences over time in the effect of
financial depth on growth, we estimated the baseline equation separately
with the cross section of data from each 5-year period. That is, from
1960 to 2004 there are nine cross sections. Instrumental variable
regressions with each of the three measures of financial depth are
summarized in Table 4, which shows only the finance coefficients from
each cross-section regression. (8)
The coefficient on M3 as a percent of GDP is positive and
statistically significant for five successive time periods running from
1965 to 1989 but insignificant in the earlier and subsequent periods.
The same is true for the coefficients on M3 less M1 with the exception
of one time period in the 1980s when the coefficient is not quite
significant. In contrast, the coefficient on the private credit ratio is
only significantly different from zero in two time periods. But the
coefficient on private sector credit is clearly positive (averaging
.025) from the late 1960s until 1989 and then falls to zero or below.
The coefficient on the M3 ratio falls to zero from 1990 on, as does the
coefficient on M3 less M1. Chow tests for the regressions in each table
reject the hypothesis of coefficient stability across the nine time
periods at the 1% level.
These tables provide a clear story. The effect of finance on growth
is a disappearing phenomenon. In the next section we examine several
hypotheses that might explain the result.
IV. UNDERSTANDING THE CHANGES IN THE FINANCE-GROWTH RELATIONSHIP
In this section we relate changes in the finance-growth
relationship to the hypotheses stated in the Introduction (Section I).
We start by relating the finance effect to the incidence of financial
crises. The disappearance of the finance effect on growth over time may
be related to the incidence of financial crises since such episodes are
often associated with too-rapid a financial deepening. There is a thin
line between financial deepening that comes from the expansion of
financial intermediary activity and financial deepening that is the
consequence of a credit boom. In the first instance increased
intermediation is likely to be growth enhancing, while in the second
instance credit standards deteriorate, nonperforming loans proliferate and a banking crisis ensues. The effect of financial deepening on growth
disappears in a financial crisis and the incidence of financial crises
increased in the late 1980s. Thus, the reduced effect of finance on
growth may be due to the increased incidence of financial crises.
We investigate this hypothesis by isolating episodes of financial
crisis and examining the impact of financial deepening on growth in
non-crisis episodes. We use the identification and dating prepared by
Caprio and Klingebiel (2003) for systemic banking and financial crises
around the world. Of the 84 countries in our sample, 45 have experienced
at least one major crisis. We characterize a 5-year country observation
as a crisis period if the country was in crisis at any time during the
period. Table 5 shows the number of countries in crisis at any time
during each 5-year period.
Instrumental variable estimates of the baseline growth equations
that allow the finance coefficient to vary when there is either a major
or minor financial crisis are shown in Table 6. Each equation shows the
finance variable for all observations and then the finance variable
interacted with dummies for crisis episodes. The size of the coefficient
on the finance variable indicates the impact of finance on growth in
non-crisis observations. These effects are all positive, statistically
significant, and larger than the corresponding coefficients in Table 2,
which do not account for crisis episodes. The interaction with the major
crisis dummy indicates the difference in the finance effect when a
country is in crisis. In every case, the finance effect is significantly
smaller at the 5% level when there is a financial crisis. In fact, the
impact of financial deepening in these crisis periods is often near
zero. The minor crisis episodes also have a negative impact on the
finance coefficients but the changes are small and not statistically
significant.
Since excessive credit creation can lead to instability and crisis,
and financial liberalization is usually associated with the rapid
development of financial institutions, capital flows, and increases in
liquid liabilities, the disappearance of the finance effect on growth
over time could also be related to the rapid liberalization of financial
markets in many countries in the latter period. In particular, policy
makers have busily touted the benefits of liberalization of financial
markets and the growth of financial institutions throughout the 1980s
and 1990s. However, the increases in financial depth in many countries
took place without the requisite development of lending expertise,
mechanisms for monitoring, and supervisory and regulatory skills. So the
relationships observed in the early data may have disappeared as efforts
to liberalize financial markets became widespread.
In order to explore the impact of liberalizations we use the dating
of equity market liberalizations in Bekaert, Harvey, and Lundblad
(2005). They use a variety of sources to date an important element of
financial-sector liberalization-the liberalization of access by
foreigners to the domestic equity market. This classification scheme can
be applied to virtually all of the countries in our sample, and it turns
out that a large number of countries experienced liberalization,
although most of it occurred within a rather short period of time in the
late 1980s and the early 1990s. Thus, we can associate our 5-year
average growth observations with the liberalization status of the
country. We separate our observations into four groups indicating
whether a country was always liberalized, never liberalized, the
preliberalization periods of countries that liberalized, and the
corresponding postliberalization observations.
Instrumental variable estimates of the base line growth equations
that allow the finance coefficient to vary with the country's
liberalization status are shown in Table 7. Each equation shows the
finance variable for all observations and then the finance variable
interacted with dummies for three of the liberalization groups (the
always liberalized group is omitted). Thus, the coefficients on the
interaction variables are differences in the finance effect from the
effect for always liberalized countries. The signs of the interaction
coefficients offer some indication that the finance effect is larger in
neverliberalized countries and smaller in countries prior to
liberalization, but they are never significantly different from zero. Of
course, it might be difficult to identify the effect of liberalizations
on the finance-growth relationship because the liberalization itself
often promotes growth. Indeed, Bekaert, Harvey, and Lundblad find that
equity market liberalizations increase growth rates by a full percentage
point. Further, it might be hard to distinguish a liberalization effect
from the effect of time since all the postliberalization observations
occur later in our sample and time period fixed effects are included in
all of the equations. (9)
Next, equity markets have grown in size and importance around the
world in just the years in which the effect of financial deepening on
growth seems to have disappeared. It therefore is reasonable to suggest
that equity market financing has acted as a substitute for credit market
financing so that the impact of financial deepening has been mitigated in recent years by the increasing role of equity markets. The positive
impact of equity markets on growth has been demonstrated with panel data
sets like ours by Levine and Zervos (1998) and Rousseau and Wachtel
(2000). In order to investigate this hypothesis we define a broad
financing measure which is the sum of M3 and the market capitalization of the stock market as a ratio to GDP. (10) The stock market data is not
widely available until the 1980s and even then is not available for
every country. Nevertheless, we estimated the baseline equation for the
countries that were available for each cross section after 1980 with the
broad financing measure. Table 8 shows the coefficients on the broad
financing measure from each cross section with its standard error in
parentheses and the number of countries. The finance effect is present
though not quite statistically significant in the 1980s but disappears
afterward even when the equity and credit markets are considered
together.
The conclusion to be drawn from these tests is clear. The decline
in the finance coefficients over time is not an inexplicable or
transitory time effect. The coefficients are smaller in recent years
because of the increased incidence of financial and banking crises.
Financial deepening promotes growth as long as it is not excessive. Once
excessive growth of money and credit leads to a crisis in the banking
system, the benefits of financial deepening disappear until the crisis
is cleaned up. The change in the finance effect is not due to
liberalization as measured by financial sector openness and it is not
due to the increasingly important role of equity markets.
V. ADDITIONAL EVIDENCE
In this section we examine sample composition effects that might
affect the relationship between finance and growth. To begin, we
distinguish between developed and developing countries using the World
Bank's classification and estimate the baseline growth equation for
each group. Table 9 shows the panel estimates for both country groups
for the initial sample period (1960-1989) and the subsequent period
(1990-2004) with M3 as a percentage of GDP as the finance variable. The
finance effect is significant for both in the earlier period though it
is larger for the developed countries. In the later period it is much
smaller for the developed countries and disappears for the less
developed ones.
The effect of both time and level of development on the finance
coefficient can be related to changes in the per capita income of the
countries in the sample. In order to examine this, we used a rolling
regression technique to investigate the relationship between per capita
income and the finance effect more closely. (11) In results not shown
here, we find that for very low-income countries (income below US $3,000
in the year 2000), the effect of financial deepening is positive but not
significant. The effect is imprecisely estimated because in many of
these countries increased financial depth might be due to directed
finance and poor lending standards. However, in the middle-income range
(from $3,000 to $12,000), there seems to be clear evidence of a
finance-growth relationship. The relationship disappears among very
high-income countries. These results indicate that the finance-growth
nexus appears to be stronger in certain economic environments. Countries
with moderately developed financial sectors or countries with middle
levels of per capita income have a stronger and significant impact of
financial deepening on economic growth.
We also use the roiling regression technique to investigate the
relationship between the level of financial development and the impact
of finance with IV panel regressions for the baseline equation with the
M3 ratio as the finance variable. (12) Figure 1 shows the evolution of
the finance coefficient for 20-country rolling windows; the solid line
gives the estimated coefficients and 5% confidence intervals are given
by the dotted lines. The countries are ordered by the average level of
financial depth (after adjusting for global time effects) and rolled in
as the ratio of M3 to GDP increases.
The initial regression includes the 20 countries with the lowest
levels of financial depth and rolls in additional countries and rolls
out the initial countries one by one so that each coefficient is
estimated with a 20-country window. Thus, the coefficients depicted in
Figure 1 reflect the effects of finance on growth among countries with
relatively similar levels of financial sector development. The
horizontal axis measures the average ratio of M3 to GDP among the 20
countries corresponding to each particular point estimate.
The results are striking; financial deepening matters when the M3
to GDP ratio is around the middle of the observed range (about 40%). The
20-country window that corresponds with this peak positive effect
includes M3 to GDP ratios that range from 32% to nearly 60%. Among the
financially less developed countries the coefficient is usually
negative, is quite variable, and is imprecisely measured. Among the
financially most developed countries the coefficient is about zero but
rising slowly with the level of financial depth; although finance
differs considerably among these countries it has little relationship to
growth.
[FIGURE 1 OMITTED]
VI. CONCLUSION
We examined the robustness of some now-classic findings on the
cross-country relationship between financial development and economic
growth and found that the finance-growth relationship that was estimated
with data from the 1960s to the 1980s simply disappeared over the
subsequent 15 years. One might conclude that the underlying relationship
that is so widely used is simply unstable and that with additional data
it might well reappear. Alternatively, we investigate some simple
hypotheses that might explain the time effects.
First, we test whether the incidence of domestic banking and
financial crises affects the impact of deepening. Here the evidence is
very strong. Financial deepening has a strong impact on growth
throughout the sample period as long as a country can avoid a financial
crisis. In crisis episodes, which are more often than not due to
excessive deepening, the benefits of financial deepening, not
surprisingly, disappear.
Second, we test to see whether an affect analogous to the Lucas
critique is at work. In the context of our problem, it would imply that
financial deepening causes growth as long as the relationship is not
exploited. We use international equity market opening as an indicator of
liberalization and the effort to develop financial markets. We find that
the effect of financial deepening does not weaken when liberalizations
occur.
Third, we test to see whether the disappearance of the finance
effect is due to the omission of the role of equity markets on growth.
This is of particular concern because of the increasing role of equity
markets in many countries in the recent years. We do not find any
indication that our result is due to the absence of equity markets in
the baseline model. Although market capitalization is not available for
all of the countries in our sample, when it is included the effect of
finance, broadly defined, still declines after the 1980s.
All of this does not detract from the basic point that at one time
countries with higher levels of financial development tended to have
higher growth rates than those with lower levels of financial
development. The question of how these countries acquired large
financial sectors and how they may have served as engines of growth,
however, remains imperfectly understood. Did finance emerge due to the
presence of deeper institutional fundamentals that had a direct impact
on growth as Acemoglu, Johnson, and Robinson (2001) suggest? Or is Joan
Robinson correct that growth is the prime mover behind financial
development? Our study, while by no means arguing that financial factors
are no longer important for economic development, serves simply as a
reminder that the link between finance and growth is more complex than
the simple relationships suggest. It would appear that deepening needs
to be accompanied by appropriate policies for financial sector reform
and regulation. Thus, the systematic study of the financial development
experiences of individual countries becomes all the more critical as the
next step in furthering our understanding of the nexus.
doi: 10.1111/j.1465-7295.2009.00197.x
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PETER L. ROUSSEAU and PAUL WACHTEL *
* This article is an extension of a paper prepared for the
UNU/WIDER conference on Financial Sector Development for Growth and
Poverty Reduction, July 1-2, 2005, Helsinki, Finland. See Economic
Growth and Financial Depth: Is the Relationship Extinct Already? WIDER
Discussion paper DP2005/10. We are also grateful for comments and
suggestions from three anonymous referees and seminar participants at
the London School of Economics, the Bank of Finland, and the European
Money and Finance Forum.
Rousseau: Professor, Department of Economics, Vanderbilt
University, Box 1819 Sta. B, Nashville, TN 37235, and Research
Associate, National Bureau of Economic Research. Phone 1-615-343-2466,
Fax 1-615-343-8495, E-mail peter.L.rousseau@vanderbilt.edu
Wachteh: Professor, Department of Economics, Stem School of
Business, New York University, 44 West 4th Street, New York, NY 10012.
Phone 1-212-998-4030, Fax 1-212-995-4218, E-mail pwachtel@stern.nyu.edu
(1.) Goldsmith, for example, found a positive relationship between
economic growth and financial development using a comparative approach
with data for 35 countries over the period from 1860 to 1963.
(2.) Levine (1997) surveys the literature through the mid-1990s,
and Levine (2005) offers a comprehensive treatment of the many
contributions that have followed. See also Temple (1999).
(3.) Lucas (1988) suggests that the role of finance is
"overstressed" and Robinson (1952, p. 80) asserts that
"where enterprise leads, finance follows."
(4.) The titles of some recent papers express the growing
skepticism, for example, "How much do we really know about growth
and finance?" Wachtel (2003) and "Finance causes growth: Can
we be so sure?" Manning (2003).
(5.) The 84 countries are Algeria, Argentina, Australia, Austria,
Bangladesh, Barbados, Belgium, Bolivia, Brazil, Cameroon, Canada,
Central African Republic, Chile, Colombia, Costa Rica, Cote
d'Ivoire, Denmark, Dominican Republic, Ecuador, Egypt, E1 Salvador,
Fiji, Finland, France, Gambia, Ghana, Greece, Guatemala, Guyana. Haiti,
Honduras, Iceland, India, Indonesia, Iran, Ireland, Israel, Italy,
Jamaica, Japan, Jordan, Kenya, Republic of Korea, Lesotho, Luxembourg,
Malawi, Malaysia, Malta, Mauritius, Mexico, Morocco, Nepal, Netherlands,
New Zealand, Nicaragua, Niger, Nigeria, Norway, Pakistan, Panama, Papua
New Guinea, Paraguay, Peru, Philippines, Portugal, Rwanda, Senegal,
Sierra Leone, South Africa, Spain, Sri Lanka, Sudan, Sweden,
Switzerland, Syrian Arab Republic, Thailand, Trinidad and Tobago, Togo,
Turkey, United Kingdom, United States, Uruguay, Venezuela, and Zimbabwe.
(6.) Our use of the "System GMM" estimator parallels that
introduced to the finance-growth literature by Levine, Loayza, and Beck
(2000). We complement this with a small sample correction of the
standard errors as described in Windmeijer (2005).
(7.) The slight differences in these results from earlier published
work with the same data definitions arise because later editions of the
World Bank's Worm Development Indicators, such as the one that we
use from 2007, provide some observations for earlier years that were
missing from previous editions.
(8.) The equations estimated include all the control variables used
in Tables 1-3.
(9.) For example, an additional explanation might be that there are
distinct characteristics of the two decades, the 1970s and 1980s, that
made the financial ratios seem to cause growth at that time but not
otherwise. Those decades are dominated by the oil shocks and periods of
high inflation in many countries. It could well be that greater
financial depth is associated with growth because these countries were
better able to withstand the large nominal shocks that characterized the
period. This would in fact be a benefit of deeper financial institutions
but would not imply that increases in financial depth cause growth.
(10.) The data on stock market capitalization are from the World
Development bulicators database and worksheets underlying Rousseau and
Wachtel (2000).
(11.) Rolling regression techniques were first applied to study of
the finance-growth nexus by Rousseau and Wachtel (2002). In that paper
we showed that the cross-sectional relationship between finance and
growth vanished in high-inflation environments.
(12.) The overall estimate for the entire sample is given in Table
1.
TABLE 1 OLS Growth Regressions with Pure Cross Section Data
Dependent Variable: % Growth of Per Capita Real GDP
1960-2004
Log of initial real -0.583 ** -0.641 ** -0.611 **
per capita GDP (0.178) (0.178) (0.186)
(2,000 US$)
Log of initial 1.311 ** 1.346 ** 1.570 **
secondary (0.389) (0.411) (0.347)
school
enrollment rate
Initial liquid 0.026 **
liabilities (M3) (0.009)
(% of GDP)
Initial M3 less 0.032 **
M1 (% of (0.016)
GDP)
Initial private 0.013
sector credit (% (0.011)
of GDP)
Initial government 0.042 0.075 0.051
expenditure (% (0.053) (0.053) (0.046)
of GDP)
Initial trade -0.008 -0.008 -0.002
(% of GDP) (0.006) (0.006) (0.004)
[R.sup.2] 0.42 0.44 0.44
(number of (77) (70) (83)
observations)
1960-1989
Log of initial real -0.552 ** -0.645 ** -0.565 **
per capita GDP (0.181) (0.199) (0.216)
(2,000 US$)
Log of initial 0.987 ** 1.067 ** 1.370 **
secondary (0.342) (0.359) (0.327)
school
enrollment rate
Initial liquid 0.042 **
liabilities (M3) (0.010)
(% of GDP)
Initial M3 less 0.058 **
M1 (% of (0.017)
GDP)
Initial private 0.027 **
sector credit (% (0.013)
of GDP)
Initial government 0.077 0.113 ** 0.075 *
expenditure (% (0.048) (0.051) (0.044)
of GDP)
Initial trade -0.020 ** -0.019 ** -0.005
(% of GDP) (0.009) (0.010) (0.006)
[R.sup.2] 0.38 0.35 0.39
(number of (75) (68) (80)
observations)
1990-2004
Log of initial real -0.041 -0.159 -0.077
per capita GDP (0.226) (0.226) (0.231)
(2,000 US$)
Log of initial 1.430 ** 1.374 ** 1.444 **
secondary (0.462) (0.464) (0.417)
school
enrollment rate
Initial liquid -0.003
liabilities (M3) (0.006)
(% of GDP)
Initial M3 less 0.007
M1 (% of (0.010)
GDP)
Initial private -0.001
sector credit (% (0.006)
of GDP)
Initial government -0.123 ** -0.120 ** -0.120 **
expenditure (% (0.051) (0.055) (0.045)
of GDP)
Initial trade 0.012 * 0.009 * 0.010 **
(% of GDP) (0.005) (0.005) (0.004)
[R.sup.2] 0.36 0.35 0.37
(number of (74) (70) (83)
observations)
Notes: Coefficient estimates are from OLS regressions with
standard errors in parentheses. Growth rates are averaged
across each data period and all explanatory variables are
measured at the start of the period. The symbols ** and *
indicate statistical significance at the 5% and 10% levels.
TABLE 2
Instrumental Variables Growth Regressions with 5-Year Panel Data
Dependent Variable: % Growth of Per Capita Real GDP
1960-2004
Log of initial real -0.059 -0.096 -0.026
per capita GDP (0.106) (0.110) (0.115)
(2,000 US$)
Log of initial 0.852 ** 0.851 ** 0.936 **
secondary (0.175) (0.179) (0.172)
school
enrollment rate
Liquid liabilities 0.014 **
(M3) (% of (0.004)
GDP)
M3 less M1 (% 0.025 **
of GDP) (0.007)
Private sector 0.005
credit (% of (0.004)
GDP)
Government -0.082 ** -0.074 ** -0.077 **
expenditure (% (0.022) (0.023) (0.022)
of GDP)
Trade (% of 0.008 ** 0.007 ** 0.010 **
GDP) (0.003) (0.003) (0.003)
[R.sup.2] 0.26 0.26 0.25
(number of (637) (632) (657)
observations)
1960-1989
Log of initial real -0.053 -0.157 -0.155
per capita GDP (.128) (0.137) (0.145)
(2,000 US$)
Log of initial 0.626 ** 0.667 ** 0.782 **
secondary (.197) (0.200) (0.194)
school
enrollment rate
Liquid liabilities
(M3) (% of
GDP)
M3 less M1 (% 0.028 ** 0.046 **
of GDP) (.006) (0.010)
Private sector 0.024 **
credit (% of (0.007)
GDP)
Government -0.075 ** -0.060 ** -0.062 **
expenditure (% (.028) (0.028) (0.028)
of GDP)
Trade (% of 0.004 0.002 0.009 **
GDP) (.005) (0.005) (0.005)
[R.sup.2] 0.30 0.30 0.29
(number of (423) (423) (427)
observations)
1990-2004
Log of initial real -0.164 -0.156 -0.069
per capita GDP (0.207) (0.206) (0.206)
(2,000 US$)
Log of initial 1.632 ** 1.577 ** 1.650 **
secondary (0.440) (0.455) (0.418)
school
enrollment rate
Liquid liabilities -0.001
(M3) (% of (0.006)
GDP)
M3 less M1 (% 0.002
of GDP) (0.009)
Private sector -0.005
credit (% of (0.005)
GDP)
Government -0.105 ** -0.110 ** -0.107 **
expenditure (% (0.039) (0.041) (0.037)
of GDP)
Trade (% of 0.011 ** 0.012 ** 0.012 **
GDP) (0.004) (0.005) (0.004)
[R.sup.2] 0.18 0.19 0.19
(number of (214) (209) (230)
observations)
Notes: Coefficient estimates are from two-stage least
squares regressions using 5-year averages of the data with
standard errors in parentheses. Instruments include initial
values of all right-hand side variables, with initial values
taken as the first observation of each 5-year period. The
regressions include a dummy variable for each time period.
The symbols ** and * indicate statistical significance at
the 5% and 10% levels.
TABLE 3
System GMM Growth Regressions with 5-Year Panel Data
Dependent Variable: % Growth of Per Capita Real GDP
1965-2004
Log of initial real -0.128 -0.121 -0.031
per capita GDP (.150) (.154) (.164)
(2,000 US$)
Log of secondary 0.687 ** 0.682 ** 0.720 **
school (.238) (.240) (.259)
enrollment rate
Liquid liabilities 0.008 **
(M3) (% of (.004)
GDP)
M3 less M1 (% 0.013 **
of GDP) (.007)
Private sector -0.001
credit (% of (.005)
GDP)
Government -0.055 * -0.052 -0.045
expenditure (% (.033) (.032) (.031)
of GDP)
Trade (% of 0.008 ** 0.008 ** 0.008 **
GDP) (.004) (.004) (.004)
Hansen J-Test 0.57 0.70 0.50
(p-value)
(number of (576) (571) (595)
observations)
1965-1989
Log of initial real -0.143 -0.240 -0.103
per capita GDP (.156) (.169) (.153)
(2,000 US$)
Log of secondary 0.437 0.421 0.526 *
school (.276) (.287) (.279)
enrollment rate
Liquid liabilities 0.028 **
(M3) (% of (.007)
GDP)
M3 less M1 (% 0.044 **
of GDP) (.011)
Private sector 0.017 **
credit (% of (.007)
GDP)
Government -0.091 ** -0.076** -0.080 **
expenditure (% (.037) (.035) (.033)
of GDP)
Trade (% of 0.011 * 0.011 0.014 **
GDP) (.006) (.007) (.006)
Hansen J-Test 0.91 0.87 0.95
(p-value)
(number of (363) (363) (366)
observations)
1990-2004
Log of initial real -0.243 -0.157 -0.040
per capita GDP (.265) (.246) (.290)
(2,000 US$)
Log of secondary 1.708 ** 1.523 ** 1.651 **
school (.589) (.603) (.565)
enrollment rate
Liquid liabilities 0.001
(M3) (% of (.005)
GDP)
M3 less M1 (% 0.004
of GDP) (.008)
Private sector -0.010 *
credit (% of (.006)
GDP)
Government -0.078 ** -0.098 -0.092
expenditure (% (.073) (.072) (.064)
of GDP)
Trade (% of 0.007 * 0.009 ** 0.010 **
GDP) (004) (.005) (.004)
Hansen J-Test 0.18 0.38 0.12
(p-value)
(number of (213) (208) (229)
observations)
Notes: Coefficient estimates are from "system" GMM regressions using
5-year averages of the data with standard errors in parentheses. The
regressions include a dummy variable for each time period.
The symbols ** and * indicate statistical significance at the 5% and
10% levels.
TABLE 4
Summary of Instrumental Variables Growth Regressions with Individual
5-Year Cross Sections
Dependent Variable: % Growth of Per
Capita Real GDP
1960-64 1965-69 1970-74 1975-79
Liquid liabilities -0.005 0.044 ** 0.029 * 0.040 **
(M3) (.019) (.013) (.013) (.016)
(% of GDP)
M3 less M1 -0.002 0.062 ** 0.041 * 0.043 *
(% of GDP) (.030) (.020) (.022) (.025)
Private sector credit 0.009 0.034 * 0.024 0.022
(% of GDP) (.024) (.019) (.018) (.020)
Dependent Variable: % Growth of Per
Capita Real GDP
1980-84 1985-89 1990-94 1995-99 2000-04
Liquid liabilities 0.029 * 0.020 * -0.000 -0.001 0.002
(M3) (.015) (.012) (.012) (.008) (.008)
(% of GDP)
M3 less M1 0.046 0.062 ** 0.012 -0.001 0.003
(% of GDP) (.031) (.020) (.021) (.015) (.011)
Private sector credit 0.011 0.036 ** -0.003 -0.005 0.001
(% of GDP) (.017) (.013) (.011) (.007) (.007)
Notes: Coefficient estimates are for the financial variables listed in
the left column of the table from separate two-stage least squares
regressions using 5-year averages of the data with standard errors in
parentheses. The growth regression summarized in each cell includes
initial income, secondary education, government expenditure, and trade
as controls along with the single financial variable listed.
Instruments include initial values of all right-hand side variables,
with initial values taken as the first observation of each 5-year
period. The regressions include a dummy variable for each time period.
The symbols ** and * indicate statistical significance at the 5% and
10% levels.
TABLE 5
Number of Sample Countries in Financial Crises during 5-Year Periods,
1960-2004
1960-64 1965-69 1970-74 1975-79 1980-84
Major crisis 0 0 1 4 16
Minor crisis 0 0 1 3 5
1985-89 1990-94 1995-99 2000-04
Major crisis 25 23 24 15
Minor crisis 15 22 15 8
TABLE 6
Instrumental Variables Growth Regressions with 5-Year Panel Data by
Crisis Status, 1960-2004
Dependent Variable: % Growth of Per
Capita Real GDP
Financial Variable: M3 (% GDP) M3-M1 (% GDP) Credit (% GDP)
Log of initial real per -0.073 -0.103 -0.091
capita GDP (2,000 US$) (.105) (.110) (.116)
Log of secondary school 0.817 ** 0.845 ** 0.940 **
enrollment rate (.175) (.179) (.171)
Finance 0.020 ** 0.029 ** 0.012 **
(.004) (.007) (.004)
Finance x major -0.017 ** -0.020 ** -0.015 **
financial crisis (.005) (.009) (.005)
Finance x minor -0.006 -0.007 -0.008
financial crisis (.006) (.010) (.006)
Government expenditure -0.085 ** -0.078 ** -0.079 **
(% of GDP) (.022) (.023) (.022)
Trade (%n of GDP) 0.006 * 0.006 * 0.009 **
(.003) (.004) (.003)
[R.sup.2] 0.27 0.26 0.26
(number of observations) (637) (632) (657)
Notes: Coefficient estimates are from two-stage least squares
regressions using 5-year averages of the data with standard errors in
parentheses. Instruments include initial values of all right-hand side
variables, with initial values taken as the first observation of each
5-year period. The regressions include a dummy variable for each time
period.
The symbols ** and * indicate statistical significance at the 5% and
10% levels.
TABLE 7
Instrumental Variables Growth Regressions with 5-Year Panel Data by
Liberalization Status, 1960-2004
Dependent Variable: % Growth of Per Capita
Real GDP
Financial Variable: M3 (% GDP) M3-M1 (% GDP) Credit (% GDP)
Log of initial real per -0.036 -0.076 0.007
capita GDP (2,000 US$) (.107) (.112) (.117)
Log of secondary school 0.816 ** 0.837 ** 0.935 **
enrollment rate (.178) (.180) (.174)
Finance 0.014 ** 0.023 ** 0.005
(.004) (.007) (.004)
Finance x never 0.005 0.009 -0.001
liberalized (.005) (.007) (.004)
Finance x 0.001 -0.005 -0.005
preliberalization (.006) (.009) (.007)
Finance x 0.003 0.006 0.003
postliberalization (.006) (.008) (.006)
Government expenditure -0.090 ** -0.081 ** -0.080 **
(% of GDP) (.022) (.023) (.022)
Trade (% of GDP) 0.008 ** 0.006 * 0.010 **
(.003) (.004) (.003)
Exclude liberalization 0.79 0.45 0.79
variables (p-value)
R2 0.26 0.27 0.25
(number of observations) (630) (619) (646)
Notes: Coefficient estimates are from two-stage least squares
regressions using 5-year averages of the data with standard errors in
parentheses. Instruments include initial values of all right-hand side
variables, with initial values taken as the first observation of each
5-year period. The regressions include a dummy variable for each time
period.
The symbols ** and * indicate statistical significance at the 5% and
10% levels.
TABLE 8
Coefficients on Broad Finance Measure from Baseline Equation
1980-1984 1985-1989 1990-1994
Broad finance 0.018 0.010 -0.003
(standard error) (.013) (.009) (.006)
Number of countries 39 44 51
1995-1999 2000-2004
Broad finance -0.002 0.002
(standard error) (.004) (.005)
Number of countries 61 45
TABLE 9
Instrumental Variables Growth Regressions with 5-Year Panel Data for
Developed and Less Developed Countries
Dependent Variable: % Growth of
Per Capita Real GDP
Developed
1960-2004 1960-1989 1990-2004
Log of initial real per capita -0.426 ** -0.418 * -0.650
GDP (2,000 US$) (.209) (.254) (.411)
Log of secondary school 0.569 ** 0.407 1.435 **
enrollment rate (.236) (.267) (.625)
Liquid liabilities 0.035 ** 0.045 ** 0.019
(M3) (%n of GDP) (.010) (.017) (.014)
Government expenditure (% of -0.112 ** -0.103 ** -0.127 *
GDP) (.036) (.047) (.070)
Trade (% of GDP) -0.001 -0.006 0.004
(.006) (.008) (.008)
R2 (number of observations) 0.21 0.24 0.17
(360) (238) (122)
Dependent Variable: % Growth of
Per Capita Real GDP
Less Developed
1960-2004 1960-1989 1990-2004
Log of initial real per capita -0.585 ** -0.552 ** -0.354
GDP (2,000 US$) (.210) (.256) (.392)
Log of secondary school 1.624 ** 1.447 ** 2.371 **
enrollment rate (.333) (.371) (1.075)
Liquid liabilities 0.008 ** 0.019 ** -0.006
(M3) (%n of GDP) (.004) (.005) (.005)
Government expenditure (% of -0.068 ** -0.067 * -0.122 **
GDP) (.030) (.040) (.048)
Trade (% of GDP) 0.013 ** 0.013 ** 0.016 **
(.004) (.006) (.005)
R2 (number of observations) 0.32 0.36 0.22
(277) (185) (92)
Notes: Coefficient estimates are from two-stage least squares
regressions using 5-year averages of the data with standard errors in
parentheses. Instruments include initial values of all right-hand side
variables, with initial values taken as the first observation of each
5-year period. The regressions include a dummy variable for each time
period.
The symbols ** and * indicate statistical significance at the 5%n and
10% levels.