POLICY RESEARCH WORKING PAPER 2707
Financial Intermediary Panel data for 63 countries in
1 960-97 reveal no robust
Development and Growth relationship between the
development of financial
intermediaries and the
volatility of growth.
Do Intermediaries Dampen
or Magnify Shocks?
Thorsten Beck
Mattias Lundberg
Giovanni Majnoni
The World Bank
Development Research Group
Finance
November 2001
POLICY RESEARCH WORKING PAPER 2707
Summary findings
Beck, Lundberg, and Majnoni extend the recent The authors test these predictions in a pant! data set
literature on the link between financial development and covering 63 countries over the period 1960-97, using
economic volatility by focusing on the channels through the volatility of terms of trade to proxy for rcal volatility,
which the development of financial intermediaries affects and the volatility of inflation to proxy for mcnetary
economic volatility. Their theoretical model predicts that volatility. They find no robust relationship between the
well-developed financial intermediaries dampen the development of financial intermediaries and growth
effect of real sector shocks on the volatility of growth volatility, weak evidence that financial intermediaries
while magnifying the effect of monetary shocks- dampen the effect of terms of trade volatility, and
suggesting that, overall, financial intermediaries have no evidence that financial intermediaries magnify the impact
unambiguous effect on growth volatility, of inflation volatility in low- and middle-incone
countries.
This paper-a product of Finance, Development Research Group-is part of a larger effort in the group to understand the
links between the financial system and economic growth. Copies of the paper are available free from the World Bank, 18 18
H Street NW, Washington, DC 20433. Please contactAgnesYaptenco, room MC3-446, telephone 202-473-8526, fax 202-
522-1155, email address ayaptencoCaworldbank.org. Policy Research Working Papers are also posted on the Web at
http://econ.worldbank.org. The authors may be contacted at tbeckywoeldbank.org, mlundbergaworldank.org, or
gmaj noni@oworldbank. org. November 2001. (48 pages)
The Policy Research Working Paper Series disseminates the findings of ork in progress to encourage the exchange of ideas anout
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully poltshel. The
papers cary the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed r this
paper are entirely those of the authors. They do not necessarily represent the viee of the World Bank, its Executive Directors, 8r the
Produced by the Policy Research Dissemination Center
Financial Intermediary Development and Growth
Volatility: Do Intermediaries Dampen or Magnify Shocks?
Thorsten Beck, Mattias Lundberg, and Giovanni Majnoni*
* The World Bank. We would like to thank Hosook Hwang for outstanding research
assistance and George Clarke, Robert Cull, Gregorio Impavido, Ross Levine, Rick
Mishkin, and seminar participants at the World Bank and the Conference of the Latin
American and Caribbean Economic Association for useful comments and suggestions.
1. Introduction
Do economies with higher levels of financial intermediary development
experience more or less volatility in economic growth rates? Do intermediaries dampen
the impact of external shocks on the economy or do they amplify them through the credit
channel? While the recent empirical and theoretical literature has established a positive
impact of financial sector development on economic growth, the potential links between
financial development and the volatility of economic growth have not been studied
thoroughly yet.' Still, the high growth volatility that many developing countries
experience has brought to the forefront the question whether and to what extent output
fluctuations can be related to the development of the financial sector. Explaining the
determinants of growth volatility is important for policy makers who want to secure a
high and stable growth rate for their economies.
This paper tries to shed light on the links between financial intermediary
development and growth volatility both theoretically and empirically. Previous papers
have found that financial development reduces macroeconomic volatility (Easterly,
Islam, and Stiglitz, 2000; Denizer, lygun, and Owen, 2000; Gavin and Hausmann, 1995).
These results, however, have not been proved to be robust across different measures of
financial development. Further, none of these papers has tried to identify the channels
through which financial development potentially affects growth volatility. This paper
explores the interaction of financial intermediary development and real and monetary
volatility in their effect on growth volatility. Specifically, we examine whether financial
intermediaries serve as shock absorbers mitigating the effect of real and monetary
volatility on growth volatility, or whether they magnify their impact.
I
Our work builds on three different strands of literature. First, we build on a large
empirical literature on the relation between financial development and economic growth.
Financial intermediaries and markets emerge to lower the costs of researching potential
investments and projects, exerting corporate control, managing risks, and mobilizing
savings. Economies with better-developed financial intermediaries and markets therefore
enjoy higher growth rates. This literature, however, does not explore the impact of
financial development on the volatility of economic growth rates. We use an indicator of
financial intermediary development developed by the literature on finance and growth to
explore the relation between financial intermediary development and growth volatility.
A second relevant strand of literature has emphasized the magnifying effect that
capital market imperfections have on the propagation of real sector shocks. In particular,
Bernanke and Gertler (1990) show that shocks to the net worth of borrowers amplify
economic up- and downturns, through an accelerator effect on investment. Acemoglu
and Zilibotti (1997) show that the interaction of investment indivisibility and the resulting
inability to diversify risk not only impedes economic development, but also results in
high economic volatility. Finally, Kiyotaki and Moore (1997) show that capital market
imperfections can amplify the effects of temporary productivity shocks and make them
more persistent, through their effect on the net wealth of credit-constrained borrowers.
A third related line of work is the literature on the credit channel of monetary
policy (Bernanke and Blinder, 1988 and Bernanke and Gertler, 1995).3 According to the
credit channel view, monetary policy impacts the real economy not only through its
effects on the bond market, but also through its effects on the credit market. Through
their impact on borrowers' profitability, asset value and thus collateral, interest rate
2
changes directly affect borrowers' ability to borrow (balance sheet effect) . The supply
of loanable funds is affected if banks cannot easily replace deposit liabilities and if banks'
assets are not perfectly substitutable (bank lending channel).5 While this literature
focuses on the U.S. and the impact of monetary policy on firms and banks of different
sizes, it also suggests that the banking sector can magnify monetary shocks into the real
economy.
This paper makes several contributions. Building on a model by Bacchetta and
Caminal (2000), we show that depending on their nature, shocks to the economy are
dampened or magnified by well-developed financial intermediaries.7 While real sector
shocks, i.e. shocks that affect only nonfinancial firms in the first round, are dampened in
their effect on output volatility by financial intermediaries, monetary shocks, i.e. shocks
to the banks' balance sheets, are magnified. While the results for real sector shocks
match findings by the theoretical literature on capital market imperfections and shock
propagation, the results for monetary shocks can be explained with the credit channel
view of monetary policy. Firms depend more on external resources in financially
developed economies and are therefore more exposed to monetary shocks that are
transmitted through the financial sector. Overall, our model does not predict an
unambiguous relation between financial development and growth volatility, but different
interactions of intermediaries with different sources of volatility.
Second, we test the hypotheses derived in the theoretical model in a panel data set
of 63 countries and 38 years. We explore whether financial intermediary development,
defined as outstanding credits to the private sector relative to GDP, affects the impact of
terms of trade and inflation volatility on economic growth volatility. Specifically, we
3
regress the volatility of real per capita GDP growth on our measure of financial
intermediary development, the volatility of terms of trade changes and inflation, and
interaction terms of financial development and both volatility measures, controlling for
other potential determinants of growth volatility. To test the robustness of our results, we
split the sample period in different ways and use different econometric methods.
Furthermore, we conduct a variety of specification tests.
Overall, the results give qualified support for the hypotheses derived in our
model. We do not find a robust relation between financial intermediary development and
growth volatility. We find a negative, but generally insignificant coefficient on the
interaction of financial intermediary development and terms of trade volatility,
suggesting weak evidence for a dampening effect of financial intermediary development
on the impact of terms of trade volatility. We find a positive and often significant
coefficient on the interaction of financial intermediary development and inflation
volatility. Controlling for a separate interaction in high-income countries, however, we
find a positive interaction term of financial intermediary development with inflation
volatility only for low- and middle-income countries, while we find no effect of monetary
volatility among high-income economies. We explain the differences between high-
income and low- and middle-income countries with different institutional environments
that are not captured by our model.
The evidence provided in this paper contradicts previous results that financial
intermediary development has an unambiguously negative effect on growth volatility.
The ambiguous effect can be explained by interactions of opposing signs between
financial intermediary development and different sources of volatility. While
4
intermediaries might help dampen real volatility, they help magnify monetary volatility in
low- and middle-income countries.
The remainder of the paper is organized as follows. Section 2 presents a simple
theoretical model and sets out the main testable hypotheses. Section 3 describes the data
and the testing strategy. Section 4 discusses the main findings of the econometric
analysis, while Section 5 concludes.
2. A Simple Model of Financial Development and Output Volatility
In this section, we describe a simple two-period model that builds on a model
developed by Bacchetta and Caminal (2000). Entrepreneurs differ in their level of wealth
and therefore access to the capital markets. Financial intermediaries arise due to
informational asymmetries between lenders and borrowers. Unlike in Bacchetta and
Caminal, however, we will model the financial intermediaries explicitly and will
introduce a channel for monetary policy in the form of reserve requirements. Further, we
will introduce two classes of shocks, real shocks that affect only nonfinancial firms in the
first round, and monetary shocks that affect only banks' balance sheets in the first round.
Since entrepreneurs produce at different productivity levels, depending on their level of
internal resources, real and monetary shocks will have distributional effects that will
result in a dampened or magnified effect on output depending on the nature of the shock.
2.1. The Real Sector
All individuals in our model are at the same time consumers and entrepreneurs.
Although all entrepreneurs have access to the same production technologyf(k), they are
5
endowed with different levels of wealth b. Specifically, we distinguish between two
classes of entrepreneurs, High and Low, with high and low levels of wealth. The fraction
P of agents are High entrepreneurs and the share (1-0) are Low entrepreneurs.
Entrepreneurs can use their wealth to invest in the production technology or they can
deposit their wealth with banks, earning a riskless rate rD. While High entrepreneurs can
fully finance their investment and have excess funds, which they deposit with banks, Low
entrepreneurs cannot fully finance their investment with their own funds and will borrow
funds at the lending rate rL. This might be due to investment indivisibility or required
minimum investment. Due to asymmetric information about the type of investment
entrepreneurs choose, and the resulting potential moral hazard problems, Low
entrepreneurs face agency costs (p .
Assuming decreasing returns to scale in production, we can write the profit
maximization problem for the High entrepreneurs as follows:
f'(kH) = rD, (1)
where the superscript H denotes High entrepreneurs. Since Low entrepreneurs (subscript
L) face agency costs (p, their profit maximization problem implies
f '(kL) L, rp 1 (2)
Combining eqs. (1) and (2) we obtain
f'(kL) rL
H rpD(3)
f'(k) r
The higher the agency costs or the wedge between lending and deposit rates, the higher
the ratio k"/kL and the larger the wedge between the marginal productivity of Low and
High entrepreneurs. If we take the agency costs as a negative indicator of financial
6
development, this also implies that the productivity wedge between Low and High
entrepreneurs is larger in financially less developed economies.
Given the different levels of productivity, a reallocation of funds between the two
entrepreneurial classes affects aggregate productivity and therefore output and growth in
the economy. The larger agency costs and therefore the lower the level of financial
development, the larger the effect of a reallocation.
2.2. The Financial Sector
Agents face market frictions when trying to reallocate resources between the
surplus and the deficit sector of the economy. Specifically, entrepreneurs can choose
between different investment projects that imply different degrees of efforts and thus
different probabilities of success. However, other agents cannot observe the investment
decision without costs. The asymmetric information in our economy gives rise to
financial intermediaries that can internalize the agency costs. High entrepreneurs deposit
their excess funds with financial intermediaries whereas Low entrepreneurs borrow from
intermediaries, to complement their own funds. Intermediaries operate in a perfectly
competitive environment, face no costs and can only hold loans as assets. However,
deposits are subject to reserve requirements imposed by the monetary authority, so that
loans supplied to Low entrepreneurs equal (I-) times the deposits of High entrepreneurs,
where , is the reserve requirement. There are no other liabilities and thus no other
sources of funding for banks. We assume these reserve requirements are not remunerated
and are not used for productive purposes.9 An increase in T, i.e. a monetary tightening,
implies a decrease in resources available for lending to Low entrepreneurs, whereas a
7
decrease in T, i.e. a monetary easing, implies an increase in loanable funds. Financial
intermediaries have thus two functions in our model: They arise out of market frictions
and channel flows from High to Low entrepreneurs, i.e. from the surplus to the deficit
sector, and they serve as conduit for monetary policy. Aggregate loan supply of the
financial intermediary sector can thus be written as:
(1 -r)P(b H -k H )= (kL - bL)(1-_') (4)
Since, as we show below, in equilibrium there is no uncertainty concerning
repayment by borrowers, the ratio of the lending and deposit rate depends only the
reserve requirement t.
D
r
- = (1 -'r) (5)
rL
The asymmetric information and resulting agency costs lead to sub-optimal
investment of Low entrepreneurs. While High entrepreneurs always choose the highest
level of effort and the optimal scale for their investment project, Low entrepreneurs may
choose an inefficient project, given that they share the downward risk with lenders. As
described in the appendix, assuming certain functional forms for the production function,
the nature of agency costs and for the level of equity of Low entrepreneurs, we get the
following result.
Result 1: Agency costs ip are described by the following equation. p = co(1 k L
k L
where wis a function of exogenously given technological parameters. Low entrepreneurs
are offered credit at the interest rate rL, but are credit-constrained, in the sense that their
investment level is sub-optimal.
8
The agency costs faced by Low entrepreneurs therefore increase in co and in the leverage
ratio kfr/bL. Combining eq. (2) and Result 1, we get
bL
f'(k L rLW(l L b) (6)
kL
The demand for loanable funds by Low entrepreneurs therefore decreases in rL, (o and
kL/b. The supply of loanable funds by High entrepreneurs, on the other hand, is only a
function of the interest rate rD and reserve requirement x.
Figure 1 depicts the market for loanable funds. Higher interest rates decrease the
optimal investment level for High entrepreneurs and therefore increase the excess funds
that High entrepreneurs will deposit with banks. Higher interest rates, however, will also
decrease the optimal level of investment of Low entrepreneurs, so that the demand for
loanable funds decreases. A higher level of agency costs P and thus lower level of
financial intermediary development will shift the demand for loanable funds to the left
(from D, to D2), which results in a lower level of loans k2 and a lower interest rate. If due
to monetary tightening, banks can channel less deposits to Low entrepreneurs, the supply
schedule shifts to the left (S1 to S2), resulting in a lower level of loanable funds k3 and a
higher loan interest rate. Note that due to the decreasing returns to scale of the production
technology, the sensitivity of the supply of loanable funds to interest rate changes
increases with higher levels of agency costs.
Our model thus combines the characteristics of a model with endogenous
financial intermediation with conditions for the existence of a bank lending channel of
monetary policy: (i) firms cannot substitute bank lending with alternative sources of
finance, and (ii) the monetary authority is able to affect the supply of loans.
9
S2
Interest rate rL
S,
k
ki
DI
2
D2
Loanable funds k
Figure 1: Supply and Demand for Loanable Funds
2.3. General Equilibrium
We embed the previously described partial equilibrium model of entrepreneurs
and banks into a simple two-period overlapping generations model. Agents are born with
endowments b't, i=H,L (bH>bL), and invest and produce in the first period. In the second
period they consume a fixed share of their income and leave a bequest.'0 In the next
subsection we explore how shocks affect the change in output from the first to the second
period, and how these changes differ under different levels of agency costs (p and
therefore financial development.
The solution to the optimization problem of High entrepreneurs is given by
f'(kH) rD (1)
and
= y"[f (k,H) +rDt(bH - k,")] (7)
10
where y is the savings rate.1 Similarly, the optimization problem for the Low
entrepreneur yields:
bL
f ' (k ) = r (1k L) (6)
and
b,L = yL[f (kL) + rL, (bL - k,L)] (8)
We can combine eqs. (1) and (6) as above:
f'(kL) bL L b I
( ) = ((1 )(-) (9)
f'(kH) k r k 1 -
Finally, the market clearance condition for financial markets yields:
#b'H +(1fI)bL =fl,"+(1-)kL +rf(bH-k,H) (10)
Eqs. (7) - (10) describe the model. It can be shown that a unique equilibrium exists,
given certain assumptions about y" and L. We can now derive the following result:12
Result 2: The relative investment ofLow entrepreneurs kl/kFI increases in the
ratio of internal to total resources bL/kL and in their relative wealth share bL/b, and
decreases in agency costs mand reserve requirements -r The positive effect of a higher
bL/kL and bL/bH on kL/k increases in the agency costs C 13
Higher internal resources and a decreased wealth inequality decrease the
financing constraint and thereby increase the investment share of Low entrepreneurs.
They shift the loan demand curve outwards resulting in a higher level of loans and a
higher interest rates. Higher agency costs, on the other hand, increase the financing
constraint. The loan demand curve shifts inwards, resulting in a lower level of loans and
lower interest rates. Note that changes in the wealth distribution and leverage result in
11
larger movements in loanable funds at high levels of agency costs, due to the higher
sensitivity of the supply of loanable funds to changes in interest rates.14
2.4. Shocks
We can now explore the effects that different shocks have on the relative
composition of investment and output, and therefore output volatility. We will distinguish
between shocks that affect only the real sector, i.e. the internal funds available to
entrepreneurs of both classes and shocks that affect the financial sector and therefore the
external funds available to Low entrepreneurs. We are especially interested in the effect
that the agency costs, our measure of financial development, have on the scale of these
output changes.
2.4.1 Real Shocks
Consider an unanticipated shock to the production function, that hits the economy
during the first period, after all investment decisions have been made, i.e. y'=Igfk,). This
productivity shock can be caused by either improved technology or by lower input prices.
As can be seen from eqs. (7) and (8), the profits of the leveraged, i.e. the Low
entrepreneurs, increase more than proportionally. This increases the relative wealth of
Low entrepreneurs b1 /b,", and therefore relative investment by Low entrepreneurs in
the following period (Result 2). Since Low entrepreneurs produce at a higher marginal
productivity than High entrepreneurs, this shift in relative investment towards Low
entrepreneurs magnifies the effect of the productivity shock under imperfect capital
markets. The higher agency costs and thus the higher the difference in marginal
productivity are, the more magnified is the shock.15
12
Result 3: The relative output effect of a shock that leads to a change in bL/bHis
larger under asymmetric information than under perfect capital markets. The size of the
output change increases in agency costs ca
Better-developed financial intermediaries alleviate the cash-flow constraint for
Low entrepreneurs and thus dampen the impact of shocks to the production function.
Note that these shocks only affect the demand for loanable funds, but do not shift the
supply curve. Further, they affect banks' balance sheets only in the second round, through
shifts in the loan demand curve.
2.4.2. Monetary Shocks
We now consider shocks that directly affect the supply of loanable funds by
banks. A tightening of monetary policy through the increase of reserve requirement c
decreases the supply of loanable funds and increases the interest rate rL. However, it also
lowers the leverage and thus the agency cost constraint for the Low entrepreneur. This
partly offsets the negative impact of higher reserve requirements.16 This dampening
effect, however, decreases as agency costs decrease. Lower agency costs (o, i.e. more
financial development, therefore, increase the output effect of monetary shocks.
Result 4: The relative effect of a shock that changes the supply of loanable funds
to Low entrepreneurs is smaller under asymmetric information than under perfect capital
markets. The effect of the output change decreases in agency costs Ca
The financial sector thus has a magnifying effect on monetary shocks. In
financially more developed economies, Low entrepreneurs depend more on external
finance and therefore suffer more if banks' balance sheets are affected by changes in
monetary policies. Shocks that affect the financial sector in the first round therefore are
13
transmitted into the real sector, and this effect is stronger for financially more developed
economies.
This effect is comparable to the credit channel of monetary policy. However, we
concentrate on only one of the possible channels, the bank lending channel, as opposed .o
the balance sheet channel, the effect of monetary policy on borrowers' balance sheet,
their financial position and thus capacity to borrow. Unlike the theoretical literature of
the banking lending channel that focuses on the imperfect substitutability of money,
bonds and loans, we focus only on loans and on reserve requirements as monetary policy
tool. As in this literature, we focus on the distributional rather than the aggregate effects
of monetary policy. Unlike this literature, however, we do not focus on the difference
between the cost of internal and external finance, but rather on credit rationing. While
the empirical and theoretical literature on the bank-lending channel implies that monetary
shocks have larger implications for small firms with less access to finance, our model
predicts that countries with higher levels of financial intermediary development will see
their monetary shocks be amplified more. The reason for the difference is that we
assume that firms in all countries depend on bank finance and cannot substitute it for
other sources of finance. More financial intermediary development therefore translates --
unlike in the credit channel literature - into more bank-dependence. In the empirical part,
however, we will qualify this simplistic statement.
2.5. Testable Hypotheses
The theoretical model has shown that there is no unambiguous relation between
financial intermediary development and growth volatility. Financial intermediaries can
14
dampen or magnify the effect of shocks on growth volatility, depending on the nature of
the shocks. Shocks that only affect the real sector in the first round are dampened,
whereas shocks that affect the financial sector directly are magnified. While the
theoretical model only considers two periods, we could easily extend this to a multi-
period model. In our empirical analysis, we should therefore find (i) no unambiguous
relation between a measure of financial intermediary development and growth volatility,
and (ii) no independent effect of financial intermediaries on growth volatility beyond
their effect on dampening real and magnifying monetary shocks. The second hypothesis,
however, builds on the restrictions of our model to the specific channel on which we
focus in our model. In the following sections we will test these hypotheses in a panel of
63 countries and 38 years. We will use the standard deviation of terms of trade changes
to proxy for the extent to which an economy is exposed to real sector shocks and the
standard deviation of the inflation rate to proxy for the extent to which an economy is
exposed to monetary shocks. Further, we will test for a separate impact of financial
intermediary development on the effect of the volatility of terms of trade and inflation in
high-income countries. We motivate this by the observation that the institutional
environment might be sufficiently different in high-income countries to observe a
different impact of financial intermediaries, especially in the channels of monetary
policy. Further, as we will discuss below, an initial casual look at the data reveals
different relations between volatility and financial intermediary development across
different income groups.
A word of caution is due concerning the choice of terms of trade shocks to proxy
for real shocks and inflation shocks to represent monetary shocks. Terms of trade shocks
15
hit only the tradable sector of an economy directly, whereas the non-tradable sector might
be affected only indirectly. Countries with large non-tradable sectors will therefore be
relatively less affected by fluctuations in terms of trade. We partly control for this by
including the ratio of trade to GDP in our estimation below. The interaction between
financial intermediary development and inflation volatility can be interpreted as coming
from either variable, since both are subject to policy decisions, at least to a certain extent.
Further, inflation volatility might reflect not only monetary policy volatility, but other
factors as well, such as demand shocks and business cycle effects.
3. Data and Econometrics
3.1. The Data
The data come primarily from published World Bank and IMF sources.18 We
create three panel data sets by aggregating over different time periods on data from 1960
to 1997. This serves partly as a robustness check on the results, and partly to avoid the
problems caused by aggregating on unusual initial- or end-years. Our constructed data
sets are a three-period panel (aggregated over the periods 1960-72, 1973-85, and 1986-
97), a four-period panel (1960-69, 1970-78, 1979-87, 1988-97), and a six-period panel
(1960-66, 1967-72, 1973-78, 1979-84, 1985-90, 1991-97). We maintain a consistent
sample of 63 countries across all data sets, but the number of observations differs by
aggregation.'9 We will focus the discussion here and in the empirical results on the three-
period panel, since it provides the most efficient estimates of standard deviations (i.e.
based on the largest number of observations). Table I describes the data and Table 2
presents correlations.
16
The dependent variable is the standard deviation of growth in real GDP per capita
within each time period. For the three-period sample, this ranges from a minimum of less
than 1% (France and the Philippines in the first period, Sri Lanka in the middle period,
and Ghana in the most recent period) to about 11% (Lesotho in the middle period),
around a median of 2.5% (which is larger than the median growth rate for the sample of
2.1% per year).
Our measure of financial intermediary development is Private Credit, the claims on
the private sector by financial intermediaries as share of GDP. Unlike other measures of
financial intermediary development that have been used in the empirical growth
literature, such as the share of M2 in GDP, this measure is more than a simple measure of
size or financial depth. Private Credit measures the most important activity of the
financial intermediary sector, channeling funds from savers to investors, and more
specifically, to investors in the private sector. It therefore relates directly to our
theoretical model. Levine, Loayza and Beck (2000) and Beck, Levine, and Loayza (2000)
show that Private Credit has a significantly positive and economically large impact on
economic growth. Private Credit also varies significantly across countries, from less than
1% of GDP (Haiti and Congo (Zaire)) to nearly twice the level of GDP (Switzerland and
Japan).20
We use the standard deviations of terms of trade changes and inflation over the
corresponding periods to proxy for the degree to which an economy is subject to real and
monetary shocks and thus volatility. As indicated in Table 1, there is a large variation
across countries in terms of trade and monetary volatility.
17
In the multivariate analysis below, we include the log of real GDP per capita and a
measure of trade openness, specifically the log of the sum of exports and imports relative
to GDP. There is considerable evidence that wealthy countries are more stable. Easterly,
Islam and Stiglitz (2000), for example, show that the standard deviation of growth in non.-
OECD countries is more than twice that in OECD countries. Greater openness, on the
other hand, increases a country's exposure to changes in the terms of trade.
Table 2 presents correlations. We note that more developed countries, as measured
by a higher real GDP per capita, experience less variability in growth, terms of trade and
inflation. Similarly, financially more developed economies experience less volatility in
growth, terms of trade changes and inflation. Growth volatility is positively correlated
with volatility in inflation and terms of trade changes.
Table 3 summarizes the data across income classes as defined by the World Bank's
World Development Report. This table shows that high-income countries are
significantly different from other countries in almost all respects. They have more stable
growth rates, and the level of Private Credit is more than double that in low- and middle-
income countries. In general, they also experience lower standard deviations of terms of
trade changes and inflation. Also, while high-income countries are more open than on
average, the share of trade in GDP is on a par with lower-middle-income countries.21 It is
likely that these structural differences between income classes affect both the direction
and the magnitude of the impact of real and monetary volatility. In the multivariate work
below, we therefore test whether the intuitive interpretation of the data can be confirmed
by more rigorous analysis.
18
3.2. Econometric Methodology
To test our hypotheses we will run the following reduced-form regression:
SD(Growth),, = a,SD(ATOT),,, + a2SD(Inflation),,, +,/FD,,
(11)
+ yrInterl,, + y2Inter2,,, + gCV, + p, +E
where SD(Growth) is the standard deviation of real per capita GDP, SD(ATOT) and
SD(Inflation) are the standard deviations of terms of trade changes and inflation,
respectively, FD is our measure of financial intermediary development, Private Credit,
Inter] and Inter2 are the interaction terms of FD with SD(ATOT) and SD(Inflation),
respectively, CV is a vector of control variables, g is a country-specific effect, E is the
error term and i and t denote country and time period, respectively.
To explore the impact of financial intermediary development on the propagation
of real and monetary volatility, we have to consider (i) the sign and significance of the
interaction terms y and y2, and (ii) the significance of terms of trade and inflation
volatility at different levels of Private Credit. A negative sign on r; would indicate a
dampening role of financial intermediaries in the propagation of real volatility and would
thus be consistent with our theoretical model. A positive sign on 72 would indicate a
magnifying role of financial intermediaries in the propagation of monetary volatility, as
predicted by our theoretical model. However, if variance in financial intermediary
development is to explain cross-country differences in the propagation of real and
monetary volatility, the overall impact of real and monetary volatility has to vary across
different levels of financial intermediary development. We are therefore interested in
a,+ y*FD and a+ 72*FD, where FD denotes a specific level of Private Credit, at
different levels of Private Credit. Finally, our model would predict f8=0, so no significant
19
effect of financial intermediary development on growth volatility beyond its dampening
and magnifying effect on the propagation of real and monetary volatility, respectively.
We also run regressions controlling for a separate interaction term of financial
intermediary development with both terms of trade and inflation volatility for high-
income countries.
SD(Growth),,, = a,SD(ATOT),,, +a, SD(Inflation),,, + 83FD,,
+ y,1nterl,, + y2Inter2,, + yInterl,, *High, , (12)
+ y4nter2,, * High, + WV, +,a, + C,
where High is a dummy variable taking the value one for countries that are classified by
the World Development Report as high-income, and zero otherwise. The overall impact
of the interaction of financial intermediary development and real or monetary volatility in
high-income countries would then be y;+y3 and 2 +4, respectively.
The interaction terms in these regressions are by definition correlated with their
components. This gives rise to the problem of multicollinearity. While this does not
necessarily bias the estimates, it does increase the size of the estimated variance, and,
given the relatively small sample sizes, may cause instability in the parameter estimates.
Examination of variance inflation factors22 reveals that volatility in terms of trade
changes is the largest sources of collinearity. In our empirical work, this might lead to
the case where the parameter estimates on Private Credit and its interaction with the
respective volatility measure are individually insignificant, but jointly significant. We
therefore report the joint significance of the individual volatility measures and the
interaction terms.
To control for biases introduced by the estimation of panel data, we use two
different estimation strategies. The data combine cross-country and time-series, which
20
enables estimation by conventional panel-data techniques, random- or fixed-effects
regressions. These panel-data estimators are asymptotically normal as T -+ 00, but in
small samples, and especially when the number of groups exceeds the number of time
periods, these estimators yield overly optimistic standard errors, and lead to
overconfidence in the results. Our base regression is instead a pooled OLS using panel-
corrected standard errors, as suggested by Beck and Katz (1995). This allows us to
correct for errors that are both heteroskedastic (that is, they differ systematically across
countries) and correlated over time within countries. While the parameter estimates are
found by the conventional method P^ = (X' X X' Y, the estimated variance matrix is
given by (X'X)'XQX(X'X)', where 0 = xi SExi . This is similar to the
Huber-White cluster (sandwich) error correction (Q = X (i-) P, X,)), but while that
method controls for differences in errors across groups, it does not allow for correlation
within groups.
Note that the variance correction (weighting) matrix Q does not assume a specific
time-series error structure. We conduct an ad-hoc test for serial correlation, by
estimating a common serial correlation coefficient r = wr, , where r is the estimate of
the within-country serial correlation, and w; is a weight derived from the reciprocals of
the variances, which increases the efficiency of the estimates (Greene, 1993, p.457). The
ad-hoc nature of the test is that we consider the test significant if the serial correlation
coefficient is close to or above 0.3, the rule-of-thumb for correction suggested by Grubb
and Magee (1988). We find significant serial correlation only in the 6-period sample, for
21
which we present both the OLS and corrected estimates using the Prais-Winsten
transformation.
We present two additional tests. First, we present a likelihood ratio test of group-
specific heteroskedasticity, following Greene (1993, p.397). Rejection of this test
indicates that the errors differ significantly across countries, requiring the use of some
panel-correction estimation method. Second, we test for the endogeneity of Private
Credit and its interactions. Specifically, we use the Davidson-Mackinnon test of
exogeneity for Private Credit and its interactions (Davidson and Mackinnon, 1993). This
is similar to the Wu-Hausman test, with the null hypothesis that the ordinary least squares
(OLS) estimator is consistent with the instrumental variables estimator. A rejection of
the null indicates that the endogeneity of the regressors has a significant influence on the
estimates, and that the equation should be properly estimated using instrumental
variables. We use as instruments dummy variables indicating the source of legal
tradition, a dummy variable indicating commodity exporters, and the urban population
share in the total population. In no case can we reject exogeneity.
4. The Results
This section presents the regression results from a 63-country panel, with data
averaged over three, four or six sub-periods over 1960-97. We present three sets of
results. First, we discuss results from a regression without interaction terms (Table 4).
While this does not link directly to the theoretical model, it helps us relate our paper to
previous studies on the impact of financial development on growth volatility. We then
present the regression results with one interaction term (Tables 5A and B), and
22
subsequently on regressions with two interaction terms, specifically one overall
interaction term and one for high-income countries only (Tables 6A and B). We focus
on the regressions with three periods and use the regressions with four and six periods as
robustness tests.
Table 4 suggests a large and statistically significant impact of both terms of trade
and inflation volatility on growth volatility, while no robust impact of financial
intermediary development. The standard deviations of terms of trade changes and
inflation enter positively and significantly at the 1% level in all regressions, while Private
Credit enters at the 10% level in two of the regressions (3-period and 6-period OLS) and
insignificantly in the other two. These results are consistent with our theoretical model as
that there is no unambiguous relation between financial intermediary development and
growth volatility. We also note that more open economies suffer larger swings in their
growth rates, while there is no independent relation between per capita income and
growth volatility.
Tables 5A and B show only weak evidence for a dampening effect of financial
intermediary development on the propagation of terms of trade volatility, stronger
evidence for a magnifying effect on the propagation of monetary volatility, and again no
unambiguous overall relation of intermediaries with growth volatility. We first turn to the
interaction between terms of trade volatility and Private Credit. While the standard
deviation of terms of trade changes enters significantly only in the 4-period and the 6-
period AR(l) (at the 10%-level) regressions and its interaction with Private Credit only
enters significantly (at the 10%-level) in the 4-period regressions, both terms enter jointly
significant in all regressions. As discussed above, finding individual insignificance and
23
joint significance can be explained by the high multicollinearity of the individual
variables.
Table 5B presents the total effects of a change in the measures of real and
monetary volatility on growth volatility, at different levels of Private Credit. We find a
significant impact of terms of trade volatility on growth volatility at the 10th and 50th
percentile of Private Credit, while there is no significant impact at the 90th percentile. The
point estimates seem to indicate that countries with low levels of Private Credit
experience a high impact of terms of trade volatility on growth volatility, whereas we
cannot reject the hypothesis that among countries with well-developed financial
intermediaries, growth is completely insulated from the effects of terms of trade
volatility. However, although the evidence shows that countries with poorly developed
financial intermediation suffer more from the effects of real volatility, these results do not
prove that for a given country, the impact of terms of trade volatility is reduced as it
develops better financial intermediaries.
Table 5A also shows that the impact of inflation volatility on growth volatility is
larger in countries with higher levels of financial intermediary development, thus
providing evidence for a magnifying role of financial intermediaries in the transmission
of monetary shocks to the real economy. In all estimations, the interaction term of
Private Credit and the standard deviation of inflation enters positively, and it enters
significantly at the 10% level in the three- and four-period estimations. The standard
deviation of inflation and its interaction with Private Credit enter jointly significant in all
regressions and the effect of inflation volatility on growth volatility is significant at all
levels of Private Credit. Further, Table 5B shows that the overall impact of inflation
24
volatility is higher with higher levels of Private Credit. These results suggest a
magnifying role for financial intermediaries in the propagation of monetary volatility and
are consistent with our theoretical model.
The results in Table 5A also confirm that overall there is no significant relation
between Private Credit and growth volatility. Private Credit does not enter significantly
in most regressions - except for the 6-period OLS results - and together with the two
interaction terms it is insignificant at the 5% level in all regressions. Statistically, this can
be explained by the offsetting signs on the two interaction terms. Economically, this
result confirms the Table 4 results, where we do not find any robust relation between
Private Credit and growth volatility, even when not controlling for interaction terms.
The results in Table 6A and B confirm the weak evidence for a dampening effect
of financial intermediary development on the propagation of terms of trade volatility,
while providing evidence for a magnifying role of financial intermediaries in the
propagation of monetary shocks in low- and middle-income, but not in high-income
economies. Here we control for a separate interaction of financial intermediary
development with real and monetary volatility in high-income countries. As before,
terms of trade volatility and its interaction terms with Private Credit are individually
mostly insignificant, but jointly significant (Table 6A). The analysis of the marginal
impact of terms of trade volatility at different levels of Private Credit shows that both
high-income and low- and middle-income economies at the 1 0th percentile of Private
Credit experience propagation of terms of trade volatility, while economies at the 90th
percentile do not (Table 6B).
25
The results in Table 6A also indicate that Private Credit increases the impact of
inflation volatility among low- and middle-income countries, while there is no robust
evidence for high-income countries. In low- and middle-income countries, a deeper
financial system exacerbates the impact of inflation volatility on growth volatility. The
interaction term of inflation volatility and Private Credit enters significantly positive in
the 3-period and the 4-period regressions, and the standard deviation of inflation and its
interactions with Private Credit are jointly significant in all regressions. Further, the
effect of inflation volatility is significant at all levels of Private Credit in low- and
middle-income countries and increases with higher levels of financial intermediary
development. In high-income economies, financial intermediary development seems to
have a dampening impact on inflation volatility, based on the 3-period estimations. The
sum of the interaction terms of Private Credit with inflation volatility is negative and
significant. However, this result is not confirmed by the 4- and 6-period estimations.
Further, Table 6B shows that for the 3-period results, inflation volatility seems to have a
significantly negative impact on growth volatility at all levels of Private Credit, a result
not confirmed by the 4-period and 6-period regressions.
Table 6A confirms our previous findings of no robust relation between Private
Credit and growth volatility. Private Credit does not enter any of the regressions
significantly at the 5% level. Further, it is jointly insignificant with the four interaction
terms in all except the 3-period sample.
Summarizing, we find only weak evidence that financial intermediary
development might dampen the impact of terms of trade volatility on growth volatility.
Our results suggest a magnifying role of the financial sector in the propagation of
26
monetary volatility on growth volatility in low- and middle-income countries, while there
is no robust evidence for an impact of monetary volatility on growth volatility in high-
income economies. There is no robust relation between Private Credit and growth
volatility beyond the different interactions with real and monetary volatility.
The results for low- and middle-income countries are consistent with the
predictions of our theoretical model, while the results for the high-income economies do
not completely match the theoretical predictions. This might be explained both by the
limitations of our model, as well as by institutional differences between low- and middle-
income and high-income economies that are not captured by the variables in our
empirical explorations. In low- and middle-income countries, the capacity of financial
intermediaries to serve as conduit for monetary policy increases as the financial sector
develops and the real sector becomes more dependent on external financing. In most of
these economies, our assumptions that banks cannot easily substitute deposits for other
sources of funding and that firms do not have easy access to alternative source of external
financing, might be appropriate. In high-income countries, on the other hand, there are
two opposing effects. While firms depend more on external finance in financially more
developed economies, financial intermediaries also have easier access to non-deposit
sources of funding, which reduces the effectiveness of monetary policy (Ceccetti, 2001
and Kashyap and Stein, 1995). Further, firms have also easier access to alternative
sources of external financing, such as capital markets.
While we have interpreted the positive interaction term between inflation
volatility and Private Credit as evidence for a magnifying role of the financial sector in
the propagation of monetary volatility in low- and middle-income countries, one could
27
also interpret this interaction in the view of recent research that has shown the negative
impact of inflation on financial sector development (Boyd, Levine, and Smith, 2001).
This interpretation would characterize inflation volatility as decreasing the ability of
financial intermediaries to serve as shock absorber and thus its capacity to reduce growth
volatility. However, the insignificance of Private Credit in the regression without
interaction terms and the joint insignificance of Private Credit and its interaction terms in
Tables 5 and 6 are counter to this interpretation.
5. Concluding Remarks
This paper (i) assessed the impact of financial intermediary development on
growth volatility and (ii) explored potential channels through which these two variables
might be linked. In our theoretical model financial intermediaries arise to alleviate
agency costs and cash flow constraints on entrepreneurs and thus dampen the impact of
real shocks. At the same time, financial intermediaries serve as conduit for monetary
policy propagation into the real economy. Our theoretical model thus predicts a
dampening effect of financial intermediaries on the propagation of real shocks and a
magnifying effect on the propagation of monetary shocks. Depending on the shocks an
economy is exposed to and the relative size of these shocks, financial intermediaries
might therefore have an overall dampening or amplifying impact on growth volatility.
Our empirical analysis of 63 countries over the period 1960-97 confirms this prediction
and does not show any significant impact of financial intermediaries on growth volatility.
Further, we find only weak evidence for a dampening role of financial intermediaries in
the propagation of terms of trade shocks. However, we find a magnifying role of
28
intermediaries in the propagation of monetary shocks in low- and middle-income
countries, while no role is apparent in high-income economies.
Our results shed doubts on previous studies that have found a negative relation
between indicators of financial development and growth volatility. However, the
different effects that financial intermediaries have in the propagation of real and
monetary shocks can explain this insignificance. If, on average, an economy is hit by
both real and monetary shocks, the dampening and magnifying effects of financial
intermediaries cancel each other out.
Our results suggest some general conclusions. First, while well-developed
financial intermediaries foster economic growth, they do not, on average, affect its
volatility. Second, instability in macroeconomic policies, namely in the conduct of
monetary policy, may increase growth volatility, an effect that is magnified by financial
intermediaries. Finally, our results do not imply that financial sector policies are
irrelevant to the volatility that economies suffer. The ownership structure of the banking
system, for example, might be important, especially the presence of foreign banks.2'
Further, the regulatory and supervisory framework might have an impact on the extent to
which financial intermediaries serve as absorbers or as propagators of exogenous shocks
(Caprio and Honohan, 2001).
29
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33
Appendix
This appendix discusses the derivation of result 1. It follows closely the
discussion in Bachetta and Caminal (1996, 2000).
We assume that all entrepreneurs have access to the same production technology
f(k). There is a continuum of investment projects, indexed by a, which can be operated
at different scales. Specifically, if an entrepreneur selects project a and invests k units of
her resources, she obtains the following level of output y:
p(a)f (k) with probability a
0 with probability 1 - a
wheref(k) is twice continuously differentiable, with positive first and negative second
derivative. Furthermore, we assume limk-,Qf'(k)= - and limk'(k)=1. The parameter a
lies in the interval [a, I], with 0 rL.
36
See King and Levine (1993a,b) and Levine and Zervos (1998) for correlation between financial
intermediary and stock market development and economic growth. Levine, Loayza and Beck (2000), Beck,
Levine and Loayza (2000), Beck and Levine (2001), Neusser and Kugler (1998) and Rousseau and Wachtel
(2000) provide evidence for a causal impact of financial development on economic growth. Also,
Demirgiq-Kunt and Maksimovic (1998) show that firms in countries with an active stock market and large
banking sector grow faster than predicted by individual firm characteristics. Rajan and Zingales (1998)
show that industries that rely more heavily on external finance grow faster in countries with better-
developed financial systems.
2 For an overview of the theoretical literature, see Levine (1997). For the empirical literature, see the
previous footnote.
See also the literature cited in Kashyap and Stein (1995).
4 A number of papers show that liquidity constraints become binding for small firms in the U.S., which
depend more on bank loans than large firms, after the Fed tightens its monetary policy. See among others,
Gertler and Hubbard (1989), Gertler and Gilchrist (1994), Bernanke, Gertler, and Gilchrist (1996),
Kashyap, Lamont, and Stein (1994), Oliner and Rudebusch (1996), and Morgan (1998). See also the survey
in Kashyap and Stein (1994).
s Kashyap and Stein (1995) and Kishan and Opiela (2000) present evidence for the U.S. that smaller banks'
lending volume is more affected by changes in monetary policy than large banks' lending volume.
Jayaratne and Morgan (2000) show that there is a positive correlation between loan growth and insured
deposit growth in the U.S. and that this correlation increases in a bank's leverage. They interpret this as
evidence for a bank lending channel of monetary policy.
6 In a recent paper Cecchetti (2001) uses a sample of OECD countries to show that the output effect of
monetary policy is larger in countries with more concentrated and less healthy banking sectors and with
less access to non-bank finance.
We thus abstract from other channels, such as risk diversification through financial intermediaries.
' We assume thatf(k) is twice differentiable, with positive first and negative second derivative.
Furthermore, we assume Iimk,,6=k)=o and limk,f (k)=1.
9 An alternative way to introduce monetary policy in our model economy is by having bonds, i.e. assets
with a safe return, but which are not a perfect substitute to loans. Open market operations by the Central
Bank would then affect banks' bonds and due to the imperfect substitutability also loan holdings. See also
Goodfriend (1995), who points out that in the U.S. the decrease in deposits that follows monetary
tightening is due to demand shifts.
10 A constant saving rate can be obtained by assuming either log utility or a Leontief utility function.
However following Bacchetta and Caminal (2000), we assume a Leontief utility function since they imply
risk neutrality, which in turn justifies the expected profit maximization assumed for entrepreneurs in the
previous section.
" To guarantee a unique steady state equilibrium, we have to assume (1-A) <(9- )z. See proof in
Appendix 2 in Bacchetta and Caminal (1996).
12 See Bacchetta and Caminal (2000).
13 The effect of a higher bL/kL, o and r follows directly from eq. (9), as well as the result that the effect of a
higher bL/kL is increasing in to. The effect of bL/bH follows from eqs. (9) and (10).
14 Bacchetta and Caminal also show that the effects of changes in the composition of investment are
persistent over time. For the sake of shortness, we leave this out here.
" This can be seen from eq. (9) by taking the derivative with respect to bL /kL The negative derivative
increases in absolute terms in agency costs o. See also Appendix B in Bacchetta and Caminal (2000).
16 This can be seen by taking the derivative of eq. (9) with respect to t. Without agency costs, this
derivative is unambiguously positive. However, since leverage k/bL decreases with higher reserve
requirements, a negative term is added to the derivative that increases in agency costs t.
7 While our model considers output volatility, we can easily turn it into a growth model with an
exogenously given trend growth rate. Real and monetary shocks would then results in deviations from this
trend growth rate and consequently to growth volatility.
' See Appendix Table 2 for details.
37
19 See Appendix Table I for the list of countries. We restrict the set of countries to those that have at least
8, 5, and 3 observations in the 3, 4, and 6-period samples, respectively.
20 To control for potential non-linearity in the relation between growth volatility and financial intermediary
development, we include Private Credit in logs in the regressions.
21 These are medians, and do not control for the fact that many of the lower-middle-income countries are
small states (e.g. Panama, Costa Rica, Papua New Guinea, Fiji) which depend heavily on trade.
22 The variance inflation factor for a variable X, from a vector of regressors X is computed as 1/(1-R ),
where R is the multiple correlation coefficient from a regression of X, on all other elements of X. A
common rule of thumb is to be concerned with any value larger than 10.
23 For the effect of foreign banks on banking sector stability, see for example Peek and Rosengreen (2000)
and Crystal, Dages and Goldberg (2001).
38
Table 1: Descriptive Statistics
Standard
Sample Variable Median Mean deviation Minimum Maximum countries observations
3-period 63 169
Sd GDP growth (x 100) 2.539 3.351 1.964 0.634 10.968
Real GDP per capita 3,068 8,161 9,647 135 43,886
Openess 50.127 60.561 46.323 9.432 364.052
Private credit 0.274 0.418 0.357 0.010 1.961
Sd ToT changes 0.064 0.083 0.070 0.000 0.407
Sd inflation 0.036 0.093 0.217 0.006 1.619
4-period 63 218
Sd GDP growth (x 100) 2.519 3.244 1.972 0.507 11.573
Real GDP per capita 2,803 8,331 9,703 159 44,223
Openess 51.058 60.879 44.652 9.903 378.472
Private credit 0.279 0.425 0.357 0.008 2.006
Sd ToT changes 0.055 0.081 0.071 0.000 0.472
Sd inflation 0.038 0.086 0.203 0.005 1.625
6-period 63 333
Sd GDP growth (x 100) 2.392 3.125 2.146 0.432 13.529
Real GDP per capita 2,802 8,201 9,640 151 44,026
Openess 50.807 60.751 46.711 9.129 395.609
Private credit 0.284 0.418 0.358 0.003 2.043
Sd ToT changes 0.053 0.080 0.078 0.000 0.577
Sd inflation 0.029 0.073 0.173 0.004 1.602
Sd GDP growth = standard deviation of annual GDP per capita growth rates
Real GDP per capita = real GDP per capita averaged over the sample period
Openess = real exports and imports as share of real GDP
Private credit= claims on nonfinancial private sector by financial institutions as share of GDP
Sd ToT changes= standard deviation of annual terms of trade changes
Sd inflation = standard deviation of annual inflation rates
Table 2: Correlations, 1960-97
Variable Real GDP per capita Private credit Openess Sd ToT changes Sd inflatio-i
Correlations
Real GDP per capita
Private credit 0.803 *
Openess 0.079 0.175
Sd ToT changes -0.555 * -0.587 ** -0.307
Sd inflation -0.210 * -0.305 * -0.234 * 0.312 **
Sd GDP growth -0.553 -0.516 ** 0.067 0.508 * 0.288
Sd GDP growth = standard deviation of annual GDP per capita growth rates
Real GDP per capita= real GDP per capita averaged over the sample period
Openess = real exports and imports as share of real GDP
Private credit= claims on nonfinancial private sector by financial institutions as share of GDP
Sd ToT changes= standard deviation of annual terms of trade changes
Sd inflation = standard deviation of annual inflation rates
Table 3: Medians by Income Groups, 1960-97
Income class
Variable High Upper middle Lower middle Low
Sd GDP growth (x 100) 2.511 4.227 4.340 4.990
Real GDP per capita 17,074 3,050 1,268 293
Openess 55.206 40.615 54.546 38.876
Private credit 0.614 0.253 0.215 0.142
Sd ToT changes 0.040 0.119 0.092 0.135
Sd inflation 0.040 0.202 0.061 0.082
countries 24 8 18 13
Sd GDP growth= standard deviation of annual GDP per capita growth rates
Real GDP per capita = real GDP per capita averaged over the sample period
Openess = real exports and imports as share of real GDP
Private credit = claims on nonfinancial private sector by financial institutions as share of GDP
Sd ToT changes = standard deviation of annual terms of trade changes
Sd inflation = standard deviation of annual inflation rates
Table 4: Terms of Trade and Inflation Volatility, Financial Intermediaries,
and Growth Volatility
Dependent variable: Standard deviation of growth in real per capita GDP (x 100)
Sample 3-period 4-period 6-period
Method 1/ OLS OLS OLS AR(1)
[1] Ln(Real GDP per capita) -0.1496 -0.1642 -0.1405 -0.0032
(0.263) (0.142) (0.175) (0.979)
[2] Ln(Openess) 0.6930 0.6465 0.5219 0.9369
(0.001) (0.001) (0.004) (0.000)
[3] Sd dToT 7.3942 10.0246 6.7336 8.3372
(0.003) (0.000) (0.000) (0.000)
[4] Sd Inflation 1.7157 1.8842 1.7926 1.8803
(0.007) (0.007) (0.008) (0.009)
[5] Ln(Private credit) -0.4621 -0.1772 -0.3877 -0.3793
(0.072) (0.434) (0.057) (0.119)
[6) Intercept 2.6265 1.6661 2.8513 -0.0491
(0.017) (0.114) (0.002) (0.949)
LR test of homoscedasticity 425.80 1127.22 2425.12 2677.32
Chi-squared (62 df) (0.000) (0.000) (0.000) (0.000)
Exogeneity test 2/ 0.65 0.11 1.16 0.50
F(1, NT-10) (0.421) (0.738) (0.283) (0.481)
Estimated serial correlation (rho) (0.13) (0.14) (0.27)
Number of countries 63 63 63 63
Number of observations 169 218 333 333
Notes
I/ P-values in parentheses
2/ Davidson-Mackinnon test
Table 5A: Terms of Trade and Inflation Volatility, Financial Intermediaries,
and Growth Volatility; One Interaction
Dependent variable: Standard deviation of growth in real per capita GDP (x 100)
Sample 3-period 4-period 6-period
Method 1/ OLS OLS OLS AR(1)
[1] Ln(Real GDP per capita) -0.1670 -0.1913 -0.1277 -0.0062
(0.204) (0.082) (0.217) (0.960)
[2] Ln(Openess) 0.7290 0.6792 0.5392 0.9383
(0.001) (0.001) (0.003) (0.000)
[3] Sd dToT 12.9930 17.6936 6.5052 9.9797
(0.140) (0.000) (0.207) (0.057)
[4] Sd Inflation 0.0170 -0.0701 0.8694 1.0630
(0.987) (0.945) (0.370) (0.246)
[5] Sd dToT * Ln(Private credit) -2.2050 -3.0720 0.0527 -0.7072
(0.456) (0.086) (0.977) (0.710)
[6] Sd Inflation * Ln(Private credit) 0.8450 0.9403 0.4538 0.4118
(0.076) (0.034) (0.222) (0.257)
[7] Ln(Private credit) -0.3850 -0.0435 -0.4580 -0.3865
(0.216) (0.853) (0.049) (0.154)
[8] Intercept 2.3910 1.3645 2.9047 -0.0047
(0.041) (0.186) (0.002) (0.995)
Joint significance tests (Chi-squared)
[3] and [5] (2 df) 7.95 36.36 14.44 20.97
(0.019) (0.000) (0.001) (0.000)
[4] and [6] (2 df) 9.09 9.80 7.51 6.71
(0.011) (0.007) (0.023) (0.035)
[5], [6], and [7] (3 df) 6.74 6.06 5.62 3.60
(0.081) (0.109) (0.132) (0.309)
LR test of homoscedasticity 428.46 1393.62 2397.91 2570.72
Chi-squared (62 df) (0.000) (0.000) (0.000) (0.000)
Exogeneity test 2/ 0.49 0.30 1.02 0.66
F(2, NT-10) (0.690) (0.826) (0.386) (0.580)
Estimated serial correlation (rho) (0.13) (0.13) (0.27)
Number of countries 63 63 63 63
Number of observations 169 218 333 333
Notes
1/ P-values in parentheses
2/ Davidson-Mackinnon test
Table 5B: Partial Derivatives: Marginal Impact of Terms of Trade and Inflation
Volatility and Financial Intermediaries on Growth Volatility (from Table 5A)
Sample 3-period 4-period 6-period
Method I/ OLS OLS OLS AR(1)
Impact of terms of trade volatility on
growth volatility
10th %ile financial development 8.07 10.76 6.62 8.39
(0.009) (0.000) (0.001) (0.000)
50th %ile financial development 5.70 7.47 6.68 7.61
(0.042) (0.002) (0.002) (0.001)
90th %ile financial development 2.98 3.78 6.74 6.81
(0.592) (0.367) (0.076) (0.088)
Impact of inflation volatility on growth
volatility
10th %ile financial development 1.90 2.05 1.89 1.99
(0.004) (0.004) (0.008) (0.010)
50th %ile financial development 2.81 3.06 2.39 2.44
(0.003) (0.002) (0.008) (0.013)
90th %ile financial development 3.86 4.19 2.90 2.91
(0.008) (0.003) (0.016) (0025)
Notes
1/ P-values in parentheses
Table 6A: Terms of Trade and Inflation Volatility, Financial Intermediaries,
and Growth Volatility; Two Interactions
Dependent variable: Standard deviation of growth in real per capita GDP (x 100)
Sample 3-period 4-period 6-period
Method 1/ OLS OLS OLS AR(1)
[I] Ln(Real GDP per capita) -0.1878 -0.1784 -0.0949 0.0699
(0.208) (0.175) (0.427) (0.606)
[2] Ln(Openess) 0.8225 0.7070 0.5554 0.8858
(0.000) (0.001) (0.003) (0.000)
[3] SddToT 14.9865 18.2214 6.5833 8.4299
(0.097) (0.000) (0.221) (0.131)
[4] Sd Inflation -0.3414 -0.1989 0.8294 1.1793
(0.750) (0.850) (0.393) (0.192)
[5] Sd dToT * Ln(Private credit) -3.1826 -3.3423 0.0480 0.0751
(0.317) (0.107) (0.981) (0-972)
[6] Sd dToT * Ln(Private credit) 1.5596 0.3399 0.5155 -1.5385
(high-income countries) (0.192) (0.775) (0.212) (0.164)
[7] Sd Inflation * Ln(Private credit) 1.1222 1.0396 -0.2751 0.3778
(0.045) (0.036) (0.790) (0.326)
[8] Sd Inflation * Ln(Private credit) -1.7784 -0.8645 -0.8081 -0.7484
(high-income countries) (0.001) (0.164) (0.184) (0.271)
[9] Ln(Private credit) -0.3547 -0.0387 -0.4563 -0.4145
(0.253) (0.870) (0.051) (0.128)
[10] Intercept 2.1275 1.1742 2.6148 -0.1911
(0.080) (0.289) (0.008) (0.809)
Joint significance tests (Chi-squared)
[3], [5], and [6] (3 df) 12.78 38.35 14.85 21.20
(0.005) (0.000) (0.002) (0.000)
[4], [7], and [8] (3 df) 13.15 9.21 7.17 6.67
(0.004) (0.027) (0.067) (0.083)
[5], [6], [7], [8], and [91 (5 df) 16.85 7.07 7.66 8.45
(0.005) (0.216) (0.176) (0.133)
LR test of homoscedasticity 460.83 1079.31 2411.38 2868.18
Chi-squared (62 df) (0.000) (0.000) (0.000) (0.000)
Exogeneity test 2/ 0.46 0.18 0.58 0.90
F(3, NT-11) (0.804) (0.970) (0.717) (0.481)
Estimated serial correlation (rho) (0.13) (0.13) (0.26)
Number of countries 63 63 63 63
Number of observations 169 218 333 333
Notes
1/ P-values in parentheses
2/ Davidson-Mackinnon test
Table 6B: Partial Derivatives: Marginal Impact of Terms of Trade and Inflation
Volatility and Financial Intermediaries on Growth Volatility (from Table 6A)
Sample 3-period 4-period 6-period
Method 1/ OLS OLS OLS AR(1)
Impact of terms of trade volatility on growth
volatility, low- and middle-income countries
10th %ile financial development 7.88 10.68 6.69 8.60
(0.009) (0.000) (0.001) (0.000)
50th %ile financial development 4.46 7.10 6.74 8.68
(0.161) (0.016) (0.008) (0.001)
90th %ile financial development 0.53 3.08 6.80 8.77
(0.933) (0.548) (0.128) (0.066)
Impact of inflation volatility on growth volatility,
low- and middle-income countries
10th %ile financial development 2.16 2.15 1.99 2.03
(0.004) (0.004) (0.008) (0.010)
50th %ile financial development 3.37 3.26 2.55 2.44
(0.004) (0.003) (0.011) (0.018)
90th %ile financial development 4.75 4.51 3.14 2.87
(0.008) (0.005) (0.023) (0.038)
Impact of terms of trade volatility on growth
volatiliiy, high-income countries
10th %ile financial development 9.51 8.22 5.82 3.53
(0.002) (0.006) (0.032) (0.221)
50th %ile financial development 8.33 5.94 5.65 2.41
(0.039) (0.114) (0.101) (0.504)
90th %ile financial development 7.24 3.95 5.50 1.46
(0.179) (0.392) (0.201) (0.744)
Impact of inflation volatility on growth volatility,
high-income countries
10th %ile financial development -2.56 0.38 -0.15 -0.06
(0.041) (0.830) (0.933) (0.976)
50th %ile financial development -3.03 0.52 -0.37 -0.34
(0.044) (0.812) (0.864) (0.892)
90th %ile financial development -3.47 0.63 -0.56 -0.59
(0.049) (0. 802) (0.823) (0841)
Notes
1/ P-ialues in parentheses
Appendix Table 1: Countries Included in Sample
High income (24)'
Australia, Austria, Belgium, Canada, Denmark, Spain, Finland, France, Great Britain,
Greece, Ireland, Iceland, Israel, Italy, Japan, Korea, Netherlands, Norway, New Zealand,
Portugal, Singapore, Sweden, Switzerland, United States
Upper-middle income (8)
Argentina, Brazil, Chile, Mexico, Mauritius, Malaysia, South Africa, Uruguay
Lower-middle income (18)
Colombia, Costa Rica, Dominican Republic, Ecuador, Egypt, Fiji, Jordan, Sri Lanka,
Morocco, Panama, Peru, Philippines, Papua New Guinea, Paraguay, Swaziland, Syria,
Thailand, St. Vincent
Low income (13)
Burundi, Bangladesh, Cameroon, Ghana, Haiti, India, Kenya, Lesotho, Nepal, Pakistan,
Sierra Leone, Congo (Zaire), Zimbabwe
Income groups according to the World Development Indicators database.
Appendix Table 2: Definitions and Sources of Data
Variable Definition Source
Within-period standard deviation of World Bank, World Development
Sd GDP growth annual change in In(Real GDP per Indicators database (WDI)
capita)
Real GDP per capita 1995 dollars WDI
Openess Sum of real exports and imports as WDI
share of real GDP
Claims on the private sector by Beck Demirguc-Kunt and Levine
Private credit financial intermediaries as share of
GDP (2000)
GDP
Within-period standard deviation of
Sd ToT changes the annual change in the ratio of WDI
import and export price indices
Within-period standard deviation of
Sd inflation the December-to-December change in WDI
the consumer price index
Dummies indicating source of legal La Porta, Lopez-de-Silanes,
Legal origin tradition (British, French, German, Shleifer and Vishny (1999)
Scandinavian, Socialist)
Dummy indicating primary exports
Commodity exporter comprise more than half of total WDI
exports
Urban Urban share of population WDI
s
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