VOLATILITY AND GROWTH *
Viktoria Hnatkovska Norman Loayza
August 2003
Abstract
This paper studies the empirical, cross-country, relationship between macroeconomic
volatility and long-run economic growth. It addresses four central questions. The first is
whether the volatility-growth link depends on country and policy characteristics, such as
the level of development or trade openness. The second one is whether this link reflects a
statistically and economically significant causal effect from volatility to growth. The
third question concerns the stability of this relationship over time and whether it has
become stronger in recent decades. And the fourth is whether the volatility-growth
connection actually reveals the impact of crises rather than the overall effect of cyclical
fluctuations. We find that indeed macroeconomic volatility and long-run economic
growth are negatively related. This negative link is exacerbated in countries that are
poor, institutionally underdeveloped, undergoing intermediate stages of financial
development, or unable to conduct countercyclical fiscal policies. We find evidence that
this negative relationship actually reflects the harmful effect from volatility to growth.
Furthermore, we find that the negative effect of volatility on growth has become
considerably larger in the last two decades and that it is mostly due to large recessions
rather than normal cyclical fluctuations.
*We thank Megumi Kubota for able research assistance in the preparation of the database used in
the paper. We are grateful to Joshua Aizenman, Luis Servén, Brian Pinto, and specially, Ricardo
Caballero for their comments and suggestions. V. Hnatkovska is affiliated to Georgetown
University, and N. Loayza, to the World Bank. The usual disclaimer applies.
VOLATILITY AND GROWTH
In the last four decades, at least the 40 most volatile countries in the world are
developing economies. Among the most volatile, there are not just small economies,
such as the Dominican Republic or Togo, but also large countries, such as China and
Argentina. Many of them are mainly commodity exporters, like Nigeria and Ecuador, but
some are also rapidly industrializing countries, such as Chile and Indonesia. At the other
extreme of the spectrum, nine of the ten least volatile countries in the World belong to the
OECD. The connection between volatility and lack of development is undeniable, but is
volatility also related to economic growth? Judging by simple cross-country correlations,
there appears to be a negative relationship between the average and the standard
deviation of per capita GDP growth, both calculated over long periods (see Figure 1).
However, this connection is not uniform but seems to depend on structural country
characteristics. For example, while the correlation between volatility and growth is
negative for poor countries, it is basically zero for middle-income countries and even
positive for the group of rich economies (see Figure 2).
From academic and policy perspectives, there are four central questions on the
relationship between volatility and growth that we address in this paper. The first is
whether this link depends on country and policy characteristics, such as the level of
development or trade openness. The second one is whether the link reflects a causal
effect from volatility to growth and, if so, whether this effect is statistically and
economically significant. The third question concerns the stability of this relationship
over time, and in particular whether recent decades feature a stronger relationship
between volatility and growth. The fourth question is whether the volatility-growth
connection actually reveals the negative impact of crises rather than the overall effect of
cyclical fluctuations.
With these questions in mind, this study documents the relationship between
macroeconomic volatility and long-run economic growth. Its approach is mostly
empirical and relies on cross-country comparisons. However, in order to help understand
and put into context the empirical results, the first section of the paper selectively reviews
the analytical literature on the volatility-growth relationship. The second section
2
describes the data and econometric methodologies used in the empirical sections of the
paper. Of special importance is the discussion on the various measures of volatility and
economic crises.
Section III presents new empirical results, following the questions outlined above.
Thus, using interaction terms in the regression analysis, we first attempt to determine
whether there is a significant link between volatility and growth under various structural
country characteristics. These are the country's overall level of development, the degree
of openness to international trade, the extent of financial depth, the level of institutional
development, and the degree of fiscal policy procyclicality. Second, using instrumental
variables inspired from the causes-of-volatility literature, we account for the likely
endogeneity of volatility with respect to economic growth and its determinants. In this
way, we try to ascertain the causal effect from macroeconomic volatility to long-run
growth. Third, we compare the volatility-growth link for the four decades since the
1960s, paying special attention to the break that researchers have observed before and
after the 1980s. We do the decade comparison both ignoring and accounting for the
potential endogeneity of macroeconomic volatility. Finally, we analyze whether the
negative connection between volatility and growth may be due in fact to the
consequences of economic crises. We do it by contrasting the growth effects of repeated
but small cyclical fluctuations ("normal volatility") and large and lasting negative
macroeconomic fluctuations ("crises"). Section IV offers selected concluding remarks,
together with some practical quantifications of the relationship between macroeconomic
volatility and long-run economic growth.
I. ANALYTICAL BACKGROUND.
Traditionally, the literatures on long-run growth and business cycles have
remained apart. This approach, however, has been challenged by recent theories and
evidence that establish a strong connection between business-cycle behavior and long-run
performance (for reviews see Fatás 2002 and Wolf 2003; for theoretical analyses, see
Caballero and Hammour 1994, and Aghion and Saint-Paul 1998; and for empirical
evidence see Ramey and Ramey 1995, Martin and Rogers 2000, Kroft and Lloyd-Ellis
2002, and Servén 2003). One aspect of this relationship is the link between
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macroeconomic volatility and economic growth. In theory, this link could result from the
joint determination of volatility and growth as endogenous variables or could stem from a
causal effect from one variable to the other. Moreover, the relationship between
volatility and growth may be positive or negative depending on the mechanisms driving
the relationship (see Imbs 2002).
Let's consider first the case when both variables are jointly determined. Their
link could be positive if volatility and long-run growth reflect the risk and mean return
characteristics of investment projects: countries that aim at higher average growth rates
must accept correspondingly higher risks. For this argument to hold, however, it would
be necessary for countries to have sufficiently well developed financial markets and
government institutions, including judicial courts. Without risk-sharing mechanisms and
proper monitoring and enforcement of contracts, investors would not pursue risky
projects that would be otherwise optimal.
A different approach to analyze the joint determination of volatility and growth
derives from considering asymmetric effects of business-cycle fluctuations. On the one
hand, a positive link could develop as follows. If volatility is associated with the
occurrence of recessions, and if recessions lead to higher research and development
and/or the destruction of least productive firms, then higher long-run growth can occur
alongside higher volatility. This is the "creative destruction" view that dates back at least
to Schumpeter (1939). (For a modern treatment of this view, see Shleifer 1986, Hall
1991, Caballero and Hammour 1994, and Aghion and Saint-Paul 1998). Again, this
argument requires deep financial markets, active firm turnover, and the ability to conduct
counter-cyclical educational and innovation expenditures, characteristics that are usually
associated with developed economies. On the other hand, a negative link between
volatility and growth could occur if recessions are tied to a worsening of financial and
fiscal constraints, which is more likely to occur in developing countries. In this case,
recessions can lead to less human capital development --by decreasing learning-by-doing,
for instance--, lower productivity-enhancing expenditures, and, thus, smaller growth rates
(see Martin and Rogers 1997, and Talvi and Vegh 2000). Moreover, aversion to
economic recessions could prompt governments to adopt policies, such as labor-market
4
restrictions, that make firms less flexible and willing to innovate, thus deepening a
negative link between volatility and long-run growth.
The connection between volatility and growth can also result from a causal
relationship. For our purposes, we concentrate on the potential impact of volatility on
growth. This effect will be mostly negative when volatility is associated with economic
uncertainty, whether this comes from political insecurity (Alesina et al. 1996),
macroeconomic instability (Judson and Orphanides 1996), or institutional weaknesses
(Servén 2000, and Rodrik 1991). The theoretical underpinnings for a negative effect of
uncertainty on economic growth operate through conditions of risk aversion, lumpiness,
and irreversibility associated to the investment process: under these conditions,
uncertainty is likely to lead firms to under invest or invest in the "wrong" projects (see
Bertola and Caballero 1994). Some country structural characteristics are bound to
worsen the impact of volatility and uncertainty on economic growth, such as a poor level
of financial development, deficient rule of law, and procyclical fiscal policy, which
usually accompanies large public indebtedness (see Caballero 2000).
In this study, we are interested in the empirical regularities dealing with both the
overall relationship between volatility and growth and the causal effect from the former
to the latter. Considering the analytical background just summarized, we will consider
both the role of country structural characteristics in shaping this mutual relationship and
the role of factors that drive volatility in order to estimate its exogenous impact on
growth.
II. METHODOLOGY AND DATA.
We are interested in describing the empirical, cross-country connection between
macroeconomic volatility and long-run economic growth. For this purpose, we examine
a variety of empirical models where a country's economic growth is the dependent
variable and its volatility, the main explanatory variable. Our statistical units are given
by country observations with data representing averages over relatively long periods.
The majority of our empirical exercises are conducted using only cross-sectional data,
specifically, country-averages over the period 1960-2000. Since we are also interested in
testing the stability of the volatility-growth relationship over time, in some cases we work
5
with country averages by decades, spanning the same period. What follows describes our
empirical strategy and data in detail.
A. Empirical Methodology. We follow the main strand of the new growth
literature in the choice of both the dependent and explanatory variables, to which we add
two volatility measures (see Barro 1991). We proceed as follows. We start by
examining the simple regression of the growth rate of per capita GDP on each of two
measures of macroeconomic volatility (defined below). We do it for the full sample of
countries and for various country groupings determined by criteria such as the level of
overall development, financial depth, trade openness, institutional development, and
fiscal policy procyclicality. This simple growth regression is represented by,
gri = 0 + 1voli + i
where gr represents average growth rate of per capita GDP, vol is a volatility measure,
is the regression residual, and i is a country index.
Next, we assess the link between volatility and growth after controlling for other
variables that affect a country's growth process. This allows us to examine whether the
simple link between volatility and growth is channeled through regular growth
determinants. The corresponding growth regression is given by,
gri = 0 + 1voli + 2 Xi + i
where X represents a set of control variables, including the initial level of GDP per capita
(to account for transitional convergence effects), the average ratio of domestic private
credit to GDP (as proxy for financial development), and the average secondary school
enrollment ratio (to account for human capital investment). These control variables are
chosen in consideration of their robust role in the new empirical growth literature (see
Levine and Renelt 1992).1
We then conduct an extension of the regression analysis by considering whether
the size and statistical significance of the volatility-growth relationship varies according
to the structural characteristics mentioned above. We account for these multiplicative
effects through both continuous and categorical interactions between volatility and
1 We also considered an expanded set of control variables, including measures of trade openness,
government consumption, and institutional development. Although in some cases these variables presented
significant coefficients, the volatility-related results discussed in the paper were qualitatively the same.
6
country structural characteristics in the corresponding growth regressions. The
corresponding regression equations are given by,
gri = 0 + 1voli + 2voli * Structi + 3Xi + i
where Struct represents, in turn, the following structural country characteristics: overall
economic development (proxied by the level of output per capita), financial depth
(measured by the ratio of private domestic credit to GDP), international trade openness
(proxied by the ratio of real exports plus imports to GDP), the level of institutional
development (measured by a subjective index of investor perceptions the ICRG index),
and the degree of fiscal policy procyclicality (proxied by the correlation coefficient
between the growth rate of GDP and the growth rate of government consumption as share
to GDP).2
The country characteristics represented in Struct are considered in two ways. The
first one is standard and consists of Struct taking the actual values of the corresponding
measures for each country. This is the case of a "continuous" interaction with volatility
(or simple multiplicative effect). The second way of accounting for structural
characteristics is through country groups (or categories) derived from the cross-country
ranking for each characteristic; specifically, in each case, we work with three similarly-
sized groups of countries --low, medium, and high. Then, the variable Struct acts as a
"dummy" variable that indicates whether a country belongs or not to a given group. This
is the case of a "categorical" interaction with volatility, and it allows for a non-monotonic
relationship between volatility and growth.
Next, attempting to go beyond the description of mutual relationships, we take
into account the possibility that volatility may be endogenously determined together with
long-run growth. We use an instrumental-variable procedure to isolate exogenous
changes in volatility and, thus, gauge their causal impact on per capita GDP growth. The
regression model then becomes,
gri = 0 + 1voli + 2 Xi + i
voli = 1IVi + ui
2We also considered the production structure of the economy, specifically the share of agricultural value
added in GDP. However, this structural characteristic did not seem to affect the volatility-growth
relationship in a robust or significant manner.
7
E(voli *i ) 0 but E(IVi *i ) = 0
where IV represents a set of instrumental variables for volatility, whose desired properties
are that they help explain volatility but at the same time affect long-run growth only
through volatility (and the other control variables). We choose the set of instrumental
variables for their importance in the macroeconomic stabilization literature. They are the
standard deviation of the inflation rate, a measure of real exchange rate misalignment, the
standard deviation of terms of trade shocks, and the frequency of systemic banking crises.
These variables highlight the point that macroeconomic volatility can be driven by non-
policy factors (e.g., the volatility of terms of trade shocks) or a combination of policy and
non-policy elements (all the rest).
We then repeat the regression analysis --with and without interaction terms and
instrumental variables-- for the database organized as country averages by decades. For
the majority of countries, we work with 4 observations each, corresponding to the 1960s,
70s, 80s, and 90s. Our objective is to assess how the volatility-growth connection has
changed over time, and in particular, whether it has increased in the 1980s and 90s. For
this purpose, we use the pooled cross-section, time-series data to estimate jointly the
coefficients on volatility for each decade and then test whether their differences are
statistically significant. The pooled regression model is given by,
gri,t = 0 + 1 voli,t + 2 Xi,t + i,t
,t ,t
where the subscript t denotes time periods (decades). Note that we allow the volatility
coefficient to be different across decades. As mentioned above, we extend this regression
to account for the joint endogeneity of volatility and its dependence on the level of
income.
Finally, we examine whether the negative association between volatility and
growth could reflect the harmful impact of sharp negative fluctuations (crisis volatility)
rather than the effect of repeated but small cyclical movements (normal volatility). For
this purpose, we modify the regression analysis by replacing the (overall) volatility
measure by two of its components, that is, one related to "normal volatility" and the other
representing "crisis volatility." The measurement of these volatility components is
described in the next section. The growth regression equation then becomes,
8
gri = 0 + 1NormalVoli + 2CrisisVoli + 3Xi + i
where NormalVol and CrisisVol represent the normal and crisis components of volatility,
respectively. We estimate this regression both ignoring and accounting for the potential
endogeneity of the volatility components. We expand the set of instruments by
generating the "crisis" versions of our instruments and adding them to the regular set.
B. Sample and Data. We work with both a single cross-section of countries and
a pooled sample of country and time series observations. In the case of a single cross-
section, the observations correspond to country averages for the period 1960-2000. The
pooled sample consists of decade averages per country, corresponding to 1961-70, 1971-
80, 1981-90, and 1991-2000. The pooled dataset is almost fully balanced in the sense
that for close to 95% of the countries we have complete data for each of the four decades.
The resulting sample consists of 79 countries, of which 22 are OECD. Regarding
developing countries, 21 belong to Latin America and the Caribbean, 19 to Sub-Saharan
Africa, 8 to the Middle East and North Africa, 6 to East Asia and the Pacific, and 4 to
South Asia.
We measure macroeconomic volatility in two different ways. Both focus on
overall output volatility --as a summary proxy for macro volatility--, and both intend to
capture the variability of cyclical macroeconomic fluctuations. Following most of the
empirical literature on volatility, the first measure is the standard deviation of per capita
GDP growth, calculated for each country over the corresponding sample period. The
second one follows the real business cycle literature and consists of the standard
deviation of the per capita GDP gap. This involves estimating the trend of each country's
per capita GDP series, obtaining the gap between actual and trend GDP, and then
calculating the standard deviation of the gap series. We estimate each country's trend
GDP series by applying the band-pass filter developed by Baxter and King (1999) to the
country's GDP series. The first volatility measure implicitly assumes that trend GDP
grows at a constant rate, whereas the second measure allows trend GDP to follow a
richer, time- and country-dependant process. The standard deviation of GDP growth
would exaggerate macro volatility if actual GDP growth has an upward or downward
trend (which is the case for economies in transition to their long-run steady state). On the
other hand, the standard deviation of the output gap may underestimate macro volatility if
9
the trend series follows the actual one too closely. In practice, however, the two volatility
measures are highly correlated in the cross-country dimension and render quite similar
results in this paper. The coefficient of correlation between the two volatility measures is
0.98 for the full sample and above 0.89 for any of our country groups.
The measures of "normal" and "crisis" volatilities are obtained from the same
distribution as the overall volatility measure. "Crisis" volatility is the portion of the
standard deviation of GDP growth or output gap that corresponds to downward
deviations below a certain threshold (see the example in Figure 3). This threshold is set
equal to one standard deviation of the world distribution of overall volatility measures
(thus, it is common to all countries). Using a common threshold generates absolute (as
opposed to relative, country-specific) crisis measures and, thus, facilitates cross-country
comparisons. "Normal" volatility is then defined as the portion of the standard deviation
of GDP growth or output gap corresponding to deviations that fall within the threshold.
Table 2 shows the cross-country correlations between the per capita GDP growth rate and
the overall, "crisis," and "normal" volatility measures. We observe that overall volatility
is highly correlated (at least 80%) with any of its components, "crisis" or "normal." The
correlation coefficient between "crisis" and "normal" volatilities is around 55%, which is
high enough to denote a strong link but not so high as to render one of them redundant.
Including each of them in the analysis will provide independent informational content.
Finally, note that the correlation between per capita GDP growth and the volatility
measures is always negative, in the neighborhood of -35% for overall and "normal"
volatilities, and around 23% for "crisis" volatility. The lower correlation between
growth and the "crisis" component would indicate that, when competing as explanatory
variables for growth, "normal" volatility would prevail. As we see at the end of next
section, this is not the case.
Regarding the dependent variable, the rate of growth of GDP per capita is
calculated as the annualized log difference of the period's final and initial real GDP per
capita. The control variables are the period's initial level of real GDP per capita, the
average ratio of domestic private credit to GDP, and the average secondary school
enrollment ratio. The instrumental variables are calculated as follows. The volatility of
inflation and terms of trade shocks are calculated as the standard deviation of,
10
respectively, the growth rates of the consumer price index and the terms of trade over the
corresponding period. The measure of real exchange misalignment is calculated as the
absolute difference of the real exchange rate and its equilibrium level --where this is
obtained by fitting a country's consumer purchasing power on its average income,
population density, and region-specific factors. The frequency of banking crises is given
by the ratio of years a country experienced a systemic banking crises to the total number
of years in the period. See the appendix for more details on variable definitions and data
sources.
III. RESULTS.
We now present the empirical results on the relationship between macroeconomic
volatility and economic growth. For this purpose, we follow the outline explained in the
methodological section.
A. Simple Correlations. Table 1 presents the bivariate correlation coefficients
between the two measures of volatility with each other and with the growth rate of GDP
per capita for various samples of countries. For the full sample of countries the
correlation between the growth rate and the two measures of volatility is negative. This
is, however, not always the case for different sub-samples of countries. The correlation
between volatility and growth appears to decline as average income decreases. It is in
fact positive for high-income countries, close to zero for the medium-income group, and
negative for low-income countries. A somehow different pattern emerges when we
group countries according to financial development. The correlation between volatility
and growth is positive for countries of high financial development. It becomes large and
negative when we move to the medium group, and it remains negative but of smaller
magnitude for low-financial-development countries. Therefore, when breaking the
sample according to financial development, the correlations describe a nonlinear, "u"
pattern.
In the case of trade openness, the correlations between volatility and growth are
negative for all groups, but more so for medium- and highly-open economies. It would
appear that the negative association between volatility and growth increases with
openness. As we see later, this result does not survive the inclusion of additional
11
determinants for economic growth. When breaking the sample by the degree of
institutional development, the pattern of correlations resembles that by income levels
that is, it becomes less negative as development occurs. However, in this case, the
differences across groups are not as noticeable as when the sample is divided by income.
Finally, when we split the sample by the degree of fiscal policy procyclicality, the
correlation results are surprising: it would appear that highly procyclical countries have
the smallest negative association between growth and volatility. This result is
unexpected because procyclical fiscal policies tend to magnify the effect of
macroeconomic shocks. However, as we see below, this result is upturned when we
control for other growth determinants.
B. Regression Analysis: Homogeneous effect of volatility on growth. Table 3
presents the regression coefficients, associated t-statistics, and other estimation results for
simple and multiple regressions of the growth rate of GDP per capita on the volatility
measures (one by one) and the control variables.
The simple regression (Cols. 1 and 4) indicates a negative and statistically
significant association between either measure of volatility and economic growth. The
size and statistical significance of the volatility coefficient decline only marginally when
we control for initial GDP per capita. In fact, after including our full set of controls, the
volatility coefficient declines only slightly from its simple-regression value and retains its
statistical significance at usual confidence levels. It appears, then, that the direct link
between volatility and growth is not captured by the standard growth determinants.
The following sections consider, in turn, four avenues for a deeper study of the
volatility-growth connection: first, the link between volatility and growth may change
depending on the structure of the economy; second, volatility may be jointly endogenous
with economic growth; third, the volatility-growth link may have changed over time; and
fourth, large and negative fluctuations may explain the negative volatility-growth link.
C. Regression Analysis: Heterogeneous effect of volatility on growth
depending on various country characteristics. In contrast to the previous set of
regressions, here we allow the empirical link between volatility and growth to vary
according to some country structural characteristics. These are the overall level of
development (proxied by the level of per capita income), the depth of financial markets,
12
the openness of international trade, the level of institutional development, and the degree
of fiscal policy procyclicality. As explained in the methodological section, we can
account for heterogeneous volatility-growth links through "continuous" and "categorical"
interactions.
a. Continuous interaction effects. These effects are measured through the
coefficient on the multiplicative term between each volatility measure and the proxy for a
given structural characteristic. Table 4, panels A and B, reports these results. We find
strong evidence that the level of (initial) income affects the relationship between
volatility and growth, in the sense that it tends to be less negative for higher income
countries (see Col. 1 in panels A and B). As is the case for most findings in the paper, the
two measures of volatility render the same qualitative results. When we interact
volatility with institutional development (see Col. 4 in panels A and B), we also find that
the negative link between volatility and growth weakens in a statistically significant
fashion.
In the case of financial depth (see Col. 3 in panels A and B), the coefficient on
volatility remains negative but loses significance when we include the interaction term,
which itself is positive but lacks statistical significance. As we see below, this doesn't
mean that nonlinear effects are unimportant in the case of financial depth. It only means
that the volatility-growth relationship does not vary linearly with the level of financial
development, as the correlation analysis had anticipated. In the case of fiscal
procyclicality (see Col. 5 in panels A and B), the interaction term is negative, indicating
that more procyclical fiscal policies worsen the negative link between volatility and
growth. However, this result is significant --and marginally so-- only in the case of the
standard deviation of GDP growth as the measure of volatility. As in the case of
financial development this appears to indicate a more complicated pattern for the effect
that fiscal policy procyclicality has on the volatility-growth link, as we see below.
Finally, when we interact volatility with trade openness (see Col. 2 in panels A and B),
we find that although the coefficient on volatility remains negative and statistically
significant, that on the interaction is not significant. Contrary to the cases of financial
development or fiscal procyclicality, the lack of significance of the trade interaction
simply reflects the fact that openness has no impact on the volatility-growth relationship.
13
b. Categorical interaction effects. As mentioned above, the lack of significant
results on some of the continuous interactions is that they impose a monotonic
relationship between the volatility-growth link and a given structural characteristic (for
instance, the effect of volatility on growth must decline, stay constant, or increase with
financial development, but it cannot describe a non-monotonic, "u"-type of pattern). In
this section, we allow for non-monotonic effects through categorical interactions.
Categorical interaction effects are measured through the coefficient on the
multiplicative term between each volatility measure and the binary variable that indicates
whether the country belongs or not to a given group. As explained in the methodological
section, for each structural characteristic we divide the sample into three groups of
similar size (groups of low, medium, and high values for the corresponding structural
characteristic). We estimate the volatility coefficients for each of the three groups, which
allows us to test whether each of them is statistically significant. In addition, we test
whether the coefficients for the low and medium groups are different from the high
group, which, therefore, acts as the benchmark. Table 5, panels A and B, reports the
regression estimation results and related tests.
Regarding the level of income (Col. 1), we find that there is no significant
relationship between volatility and growth for countries of medium and high income. In
contrast, the volatility-growth link is significantly negative for poor countries. We find a
similar result in the case of institutional development (Col. 4), that is, the relationship
between volatility and growth is significantly negative only in the group of poorly
developed countries. A likely interpretation for these results is that as countries develop
they have the means --from stabilization policies, institutional safeguards, and insurance
markets-- to neutralize the long-run effects of volatility (see Fatás 2002). Note that the
volatility coefficient for medium countries is negative, as is for low countries, but fails to
be significant. We return to this case when we control for the potential endogeneity of
volatility.
Regarding financial development (Col. 3), there is no significant link between
growth and volatility in countries that are either highly or poorly financially developed.
However, there is strong evidence of a negative relationship for countries in the middle of
the financial-development spectrum. This result is consistent with the literature that
14
indicates a larger macroeconomic vulnerability in countries that have just liberalized their
financial systems (see Gaytán and Ranciere 2002). When we consider trade openness as
the structural characteristic of interest (Col. 2), we find that the volatility coefficient is
significantly negative in all country groups and that the differences across groups are not
statistically different from zero. Together with the result on the continuous interaction,
this indicates that openness has no bearing on the volatility-growth link; that is, open
countries are as likely to deal with their volatile environment and neutralize it as closed
economies are. Finally, when we classify countries by their degree of fiscal policy
procyclicality (Col. 5), we find that only in countries that conduct relatively more
counter-cyclical policies, volatility has no statistically significant link with growth (see
Imbs 2002). This is particularly noticeable when volatility is measured as the standard
deviation of GDP growth. Moreover, we find that the negative coefficient on volatility
tends to be larger (although not statistically so) for medium than for high fiscal
procyclical economies. One interpretation for this result is that medium countries are
also the most uncertain regarding how governments react to shocks, and it is this
uncertainty that worsens the volatility growth connection.
In sum, the types of countries where volatility and growth appear to be negatively
related are the relatively poor, the institutionally underdeveloped, the more-or-less
financially developed, and those that conduct mixed or highly procyclical fiscal policies.
The level of openness does not appear to worsen or improve the negative relationship
between volatility and growth.
D. Instrumental Variable Regression Analysis: Controlling for the joint
endogeneity of volatility. Here we attempt to estimate the causal effect of volatility on
growth. We do so by extracting the exogenous component of volatility through the use
of instrumental variables. We allow also for interaction effects (both continuous and
categorical) but only related to level of income, the most relevant indicator of overall
development. Table 6, panels A and B, reports the results when we don't allow for
interaction effects (Col. 1) as well as when we consider continuous (Col. 2) and
categorical (Col. 3) interaction effects between volatility and level of income.
15
The set of instrumental variables used in the analysis consists of real exchange
rate misalignment, frequency of banking crises, price volatility, proxied by the standard
deviation of inflation rate, and volatility of terms of trade shocks.
Before discussing the estimation results, the first issue to consider is whether
there are grounds to believe that the volatility measures may be subject to joint
endogeneity. For this we conduct a Hausman-type test, reported at the bottom of Table 6.
Under the null hypothesis that volatility is exogenous, the ordinary-least-squares (OLS)
estimates are both consistent and efficient, and the instrumental-variable (IV) estimates
are consistent but not efficient. In contrast, under the alternative hypothesis, only the IV
estimates are consistent. The test results lead us to strongly reject the null hypothesis of
exogenous volatility and points to the use of instrumental variables to estimate the causal
impact of volatility on growth.
Next, we need to make sure that the instrumental variable procedure is
appropriate. This depends on, first, whether the instrumental variables can explain a
large share of the variation in volatility and, second, whether they are related to economic
growth only through the explanatory variables in the regression (so that the instruments'
correlation with the regression residual is zero). In order to show the instrumental
variables' strong explanatory power on the volatility measures, we report the R-squared
coefficients of the first-stage regression. They are about 50% in the first two columns
and jump considerably when we allow for categorical interaction effects. The full first-
stage regression (not reported) indicates that all instruments exhibit the expected positive
coefficient, and all are statistically significant, except for the frequency of banking crises.
Then, to assess whether the instrumental variables are not correlated with the regression
residual we conduct a Hansen test of overidentifying restrictions and report its p-value.
Fortunately, the test clearly indicates that we should not reject the hypothesis that there is
no correlation between the instrumental variables and the error term.
The general result from the IV estimation is that the coefficient on volatility
becomes larger in magnitude and stronger in statistical significance than the
corresponding OLS estimate. Apparently, there is a positive association between
volatility and growth that comes from either simultaneous causation from third variables
or a positive feedback from growth to volatility. Once we remove this positive link, the
16
negative effect from volatility to growth is revealed to be larger in magnitude. In fact,
comparing Table 6 (Col. 1) with Table 3 (Cols. 3 and 6), the IV volatility coefficients are
more than twice as large as the OLS coefficients, whether we consider the standard
deviation of the output gap or GDP growth as the measure of volatility. We discuss the
economic significance of the estimated effect of volatility on growth in the concluding
section.
When we consider income interaction effects (Cols. 2 and 3), it is also the case
that the volatility coefficient under IV is larger than its OLS counterpart. This is
particularly noticeable when we allow for categorical interactions. Now, we find that
volatility has a negative impact on growth not only in poor countries but also in medium-
income economies (although more so in the former group). Nevertheless, it is still the
case that for rich countries volatility has no significant effect on growth.
E. Pooled regression analysis: The stability of the volatility-growth
relationship over time. We now consider whether the link between volatility and
growth has changed in recent decades. As explained in the methodological section, for
this purpose we conduct pooled regression analysis on country observations
corresponding to the four decades since the 1960s to the 90s. We carry out the analysis
first ignoring and then allowing for income interactions, and the results are reported in
Tables 7 and 8, respectively. In both cases, we obtain the regression coefficients through
OLS and IV estimators.
Let's first consider the results in Table 7, which ignore income interactions. For
both OLS and IV estimators, the largest volatility coefficients belong to the 1980s.
Focusing on the IV estimates, there is a sharp and statistically significant increase in the
size of the volatility effect on growth from the 1960s to the 70s and even further to the
80s. The 1990s coefficient is only a little smaller than that of the 1980s, and the
difference is not statistically significant. The marked change between the first two
decades and the latter two does not appear to be related to a change in the cross-country
mean or variance of either volatility measure. What seems to drive the change is the
substantial decrease in the mean growth rate, which dropped to less than one third from
the 1960s to the 80s and less than one half from the 1960s to the 90s. The world is not
more volatile now than 30 years ago, but volatility is taking a larger toll on growth.
17
Table 8 tells a similar story, implying that the volatility interaction with income
cannot explain the changes in recent decades. The coefficients on volatility and on the
income interaction term are remarkably similar between the 1960s and 70s, and also
between the 1980s and 90s; but a break occurs in the 1980s, and the difference between
the first two decades and the latter two is notable and statistically significant. The fact
that the coefficients on volatility and on the interaction term change by roughly the same
proportion indicates that the overall growth effect of a change in volatility also changes
proportionally, provided income stays constant. The gains from an increase in income --
in terms of a diminished indirect effect of volatility on growth-- are larger in the latter
decades but so is volatility' negative direct effect.
F. Regression Analysis: Volatility and Crises. It can be shown that a high
measure of volatility can result from large but infrequent swings in per capita GDP as
from small but frequent fluctuations. However, their respective real effects could be
sharply different (see Caballero 2002). The measures of volatility we have used up to
now in the paper combine normal and crisis fluctuations. In this section, we work with
the components of volatility to answer the last question we pose in this paper. This is
whether the negative relationship between volatility and growth is actually due to the
harmful impact of large negative fluctuations ("crisis" volatility) and not really to the
effect of repeated but small fluctuations around the trend ("normal" volatility).
We take the basic model (Table 3, Cols. 3 and 4) and replace overall volatility by
measures of "normal" and "crisis" volatilities. Then we estimate the model by OLS and
IV estimators. The results are reported in Table 9. We find that although both forms of
volatility present negative coefficients, only "crisis" volatility is statistically significant.
This is true whether we work with the output gap or per capita GDP growth as the proxy
for macroeconomic fluctuations; although the contrast between "crisis" and "normal"
volatility effects is sharper in the case of the output gap. As before, the IV estimates
render larger coefficients for either type of volatility, but only the "crisis" one is
statistically significant. In the case of the output gap, the effect of "crisis" volatility is
almost twice as large as that of overall volatility (compare Table 6, panel A, Col. 1 with
Table 9, Col. 2).
18
IV. CONCLUSIONS.
Analyzing cross-country data, we conclude that macroeconomic volatility and
long-run economic growth are negatively related. This negative link is exacerbated in
countries that are poor, institutionally underdeveloped, undergoing intermediate stages of
financial development, or unable to conduct countercyclical fiscal policies. On the other
hand, the volatility-growth association does not appear to depend on a country's level of
international trade openness.
Furthermore, the negative global relationship between macroeconomic volatility
and long-run growth actually reflects an even stronger, harmful effect from volatility to
growth. This is true for a worldwide sample of countries, and particularly so in low and
middle-income economies. The negative effect of volatility to growth has been present
since the 1960s, but it has become considerably larger in the last two decades. This is not
due to a change in volatility trends over time but, rather, to the reduction in growth in the
1980s and 1990s and the countries' inability to deal with volatility in that context.
Examining the components of volatility, we find that its negative impact on
growth is not the effect of small although repeated cyclical deviations but to large drops
below the output trend. Therefore, it's the volatility due to crisis, and not due to normal
times, that harms the economy's long-run growth performance.
The effects we have just described are not only statistically significant; rather,
their magnitude leads us to believe that they are also economically significant. In order
to illustrate volatility's long-run impact, Table 10 reports the growth effect of a change in
volatility under various conditions. In order to make the table figures comparable with
each other, we apply in all exercises the same benchmark change in volatility. We set it
equal to one worldwide, cross-country standard deviation of volatility, which for each
country is measured as the standard deviation of the output gap over 1960-2000. To
make this benchmark change in volatility more concrete, consider the following two
examples of sequences of countries. In each sequence, countries are presented in
ascending order of volatility, and the separation between two consecutive countries is
about one standard deviation of volatility. The first example sequence, which covers
almost the full spectrum of countries in the sample, is, France, Egypt, Uruguay, Jordan,
19
and Nigeria. The second example, which covers countries towards the middle of the
volatility distribution, is, Japan, Botswana, and Argentina.
If we ignore the endogeneity of volatility, the growth decline due to a one-
standard-deviation increase in volatility appears to be modest, at about 0.5 percentage
points of the growth rate. However, once we account for simultaneous and reverse
causation in the volatility-growth relationship, the same increase in volatility is found to
lead to a 1.3 percentage-point drop in the growth rate, which already represents a sizeable
loss. This decline in growth is magnified even further if we consider the same change in
volatility in the 1990s or under a crisis situation. In both cases, the loss would amount to
about 2.2 percentage points of the per capita GDP growth rate. For the government and
the private sector alike, macroeconomic volatility should be not only a source of short-
run concerns but also a constant preoccupation for the achievement of long-run goals.
20
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23
Appendix
Definitions and Sources of Variables Used in Correlation and Regression Analysis
Basic Variables Definition and construction Source
Real per capita GDP (in 1985 US$ PPP) Ratio of total GDP to total population. Authors' construction using Summers,
GDP is in 1985 PPP-adjusted US$. Growth Heston and Aten (2002) and The World
rates are obtained from constant 1995 Bank (2002).
US$ per capita GDP series.
Output gap Difference between the log of actual Authors's calculations.
GDP and (the log of) potential (trend)
GDP. In order to decompose the log of
GDP, the Baxter-King filter is used.
Gross secondary-school enrollment Ratio of total secondary enrollment, World Development Network (2002) and
regardless of age, to the population of The World Bank (2002).
the age group that officially corresponds
to that level of education.
Domestic Credit to the Private Sector (% Ratio to GDP of the stock of claims on the Beck, Demirguc-Kunt and Levine (2000).
of GDP) private sector by deposit money banks
and other financial institutions.
Trade Openness (% of GDP) Ratio of exports and imports (in 1995 US$) World Development Network (2002) and
to GDP (in 1995 US$). The World Bank (2002).
Structural Variables
Index of Institutional Development First principal component of four International Country Risk Guide (ICRG)
indicators: prevalence of law and order,
quality of bureaucracy, absence of
corruption, and accountability of public
officials.
Government Consumption (% of GDP) Ratio of government consumption to Summers, Heston and Aten (2002)
GDP.
Fiscal Policy Procyclicality Correlation between GDP growth rate Authors' calculations.
and growth rate of the government
consumption.
24
Instrumental Variables
Volatility of Inflation Measured by the standard deviation of The World Bank (2002).
the rate of change in the consumer price
index: annual percentage change in the
cost to the average consumer of
acquiring a fixed basket of goods and
services.
Real Exchange Rate Misalignment Absolute deviation of the real exchange Loayza and Kubota (2003)
rate overvaluation from the equilibrium
real exchange rate (set to 1).
The extent of Real Exchange Rate
disequilibrium is defined as the difference
between actual real effective exchange
rate and its equilibrium level, given by
cross-country purchasing power parity
comparisons.
Systemic Banking Crises Number of years in which a country Authors's calculations using data from
underwent a systemic banking crisis, as a Caprio and Klingebiel (1999), and
fraction of the number of years in the Kaminsky and Reinhart (1998).
corresponding period.
Volatility of Terms of Trade shocks Standard deviation of the log difference The World Bank (2000) "World
of the terms of trade. Development Indicators".
25
26
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Table 6 : Instrumental Variable Estimation
Cross-Sectional Regression Analysis, 1960 - 2000
Dependent Variable:
Growth Rate of GDP per capita
(A) Volatility: Standard Deviation of Output Gap
Homogeneous Continuous Categorical
Effect Interaction Interaction
[1] [2] [3]
Volatility -1.1950 -3.2805
(standard deviation of output gap) -4.09 -3.31
Volatility Interaction with Income 0.3201
(volatility*log average income) 2.57
Volatility, low income -1.2355
(volatility*[dummy=1 if low income country]) -2.03
Volatility, medium income -0.6484
(volatility*[dummy=1 if medium income country]) -2.9100
Volatility, high income -0.0020
(volatility*[dummy=1 if high income country]) -0.01
Control Variables:
Initial GDP Per Capita -1.2388 -1.6918 -1.3061
(in logs) -4.7 -5.15 -6.14
Education 1.6871 1.3145 0.9940
(secondary enrollment, in logs) 4.08 3.02 3.31
Financial Depth 1.0318 1.0489 0.7856
(private domestic credit/GDP, in logs) 3.33 3.31 3.21
R-squared 0.7345 0.7868 0.8359
No. Countries / No. Observations 79 / 79 79 / 79 79 / 79
R-squared 1st stage (average) 0.4782 0.4860 0.9440
SPECIFICATION TESTS (P-Values)
(a) Durbin-Wu-Hausman Test 0.0001 0.0007 0.0086
(b) Hansen J-Test for Overidentifying Restrictions : 0.9446 0.8234 0.6623
Notes: t-Statistics are presented below the corresponding coefficient
Intercept is included in all estimations but not reported
Standard errors are corrected for potential heteroscedasticity using Newey-West procedure
Source: Authors' estimation
33
Table 6 : Instrumental Variable Estimation (cont.)
Cross-Sectional Regression Analysis, 1960 - 2000
Dependent Variable:
Growth Rate of GDP per capita
(B) Volatility: Standard Deviation of GDP Per Capita Growth
Homogeneous Continuous Categorical
Effect Interaction Interaction
[1] [2] [3]
Volatility -0.6917 -2.0698
(standard deviation of GDP per capita growth) -4.25 -4.01
Volatility Interaction with Income 0.2098
(volatility*log average income) 3.16
Volatility, low income -0.7321
(volatility*[dummy=1 if low income country]) -2.53
Volatility, medium income -0.3839
(volatility*[dummy=1 if medium income country]) -2.9100
Volatility, high income 0.1790
(volatility*[dummy=1 if high income country]) 0.62
Control Variables:
Initial GDP Per Capita -1.2044 -1.6812 -1.2771
(in logs) -4.93 -5.64 -6.09
Education 1.7023 1.3011 0.9808
(secondary enrollment, in logs) 4.19 3.18 3.42
Financial Depth 1.0248 0.9800 0.7527
(private domestic credit/GDP, in logs) 3.5 3.23 3.18
R-squared 0.7445 0.8009 0.8443
No. Countries / No. Observations 79 / 79 79 / 79 79 / 79
R-squared 1st stage (average) 0.5157 0.5164 0.9473
SPECIFICATION TESTS (P-Values)
(a) Durbin-Wu-Hausman Test 0.0001 0.0006 0.0124
(b) Hansen J-Test for Overidentifying Restrictions : 0.8490 0.8082 0.7339
Notes: t-Statistics are presented below the corresponding coefficient
Intercept is included in all estimations but not reported
Standard errors are corrected for potential heteroscedasticity using Newey-West procedure
Source: Authors' estimation
34
Table 7. Homogeneous Effect of Volatility on Growth: OLS and IV Estimation
Regression Analysis of Decades, 1960 - 2000
Dependent Variable:
Growth Rate of GDP per capita
(A) Volatility: (B) Volatility:
Standard Deviation of Output Gap Standard Deviation of GDP Per Capita Growth
OLS IV OLS IV
[1] [2] [3] [4]
Volatility, 60's -0.0415 -0.6451 -0.0406 -0.4159
(Volatility*[Dummy=1 if year 61-70) -0.48 -1.66 -0.72 -1.79
Volatility, 70's -0.4022 -1.0453 -0.2359 -0.6752
(Volatility*[Dummy=1 if year 71-80) -1.77 -3.29 -1.55 -3.29
Volatility, 80's -0.7146 -2.4869 -0.5309 -1.2976
(Volatility*[Dummy=1 if year 81-90) -6.50 -2.87 -5.85 -3.73
Volatility, 90's -0.3193 -2.0165 -0.2126 -1.1917
(Volatility*[Dummy=1 if year 91-00) -1.53 -3.68 -1.57 -3.75
Control Variables:
Initial GDP Per Capita -0.6800 -0.8634 -0.7148 -0.9003
(in logs) -4.65 -4.55 -4.99 -5.07
Education 1.1780 1.0970 1.2076 1.1202
(secondary enrollment, in logs) 5.48 3.75 5.59 4.05
Financial Depth 1.1872 0.8642 1.1900 0.9455
(private domestic credit/GDP, in logs) 4.87 2.85 4.90 3.45
R-squared 0.6087 0.3273 0.6117 0.4266
No. Countries / No. Observations 79 / 310 79 / 309 310 309
R-squared 1st stage (average) 0.7906 0.7985
TESTS (P-Values)
(a) Ho : Volatility coefficient for 60's = Volatility coefficient for 70's 0.1220 0.336 0.210 0.312
(b) Ho : Volatility coefficient for 60's = Volatility coefficient for 80's 0.0000 0.030 0.000 0.015
(c) Ho : Volatility coefficient for 60's = Volatility coefficient for 90's 0.2161 0.019 0.240 0.024
(d) Ho : Volatility coefficient for 70's = Volatility coefficient for 80's 0.2132 0.082 0.094 0.075
(e) Ho : Volatility coefficient for 80's = Volatility coefficient for 90's 0.0942 0.595 0.049 0.794
SPECIFICATION TESTS (P-Values)
(a) Durbin-Wu-Hausman Test 0.0000 0.0001
(b) Hansen J-Test for Overidentifying Restrictions : 0.7525 0.6841
Notes: t-Statistics are presented below the corresponding coefficient
Intercept is included in all estimations but not reported
Standard errors are corrected for potential heteroscedasticity using Newey-West procedure
Source: Authors' estimation
35
Table 8. Heterogeneous Effect of Volatility on Growth, Continuous Interaction Effects: OLS and IV
Regression Analysis of Decades, 1960 - 2000
Dependent Variable:
Growth Rate of GDP per capita
(A) Volatility: (B) Volatility:
Standard Deviation of Output Gap Standard Deviation of GDP Per Capita Growth
OLS IV OLS IV
[1] [2] [3] [4]
Volatility, 60's -1.5392 -3.7136 -1.0594 -2.3899
(Volatility*[Dummy=1 if year 61-70) -3.01 -3.57 -3.15 -3.38
Volatility, 70's -1.5855 -4.0515 -0.9555 -2.4480
(Volatility*[Dummy=1 if year 71-80) -2.79 -4.89 -2.55 -4.55
Volatility, 80's -2.6642 -6.2465 -1.5623 -3.5250
(Volatility*[Dummy=1 if year 81-90) -4.48 -5.33 -4.49 -4.32
Volatility, 90's -3.4670 -6.3728 -1.9633 -3.7489
(Volatility*[Dummy=1 if year 91-00) -5.03 -4.81 -4.50 -4.54
Continuous Interactions between Volatility and Average Income:
Volatility Interaction, 60's 0.2249 0.3533 0.1557 0.2357
(volatility*average income*[Dummy=1 if year 61-70]) 2.84 2.46 2.97 2.47
Volatility Interaction, 70's 0.1613 0.4133 0.0973 0.2426
(volatility*average income*[Dummy=1 if year 71-80]) 2.03 3.64 1.82 3.11
Volatility Interaction, 80's 0.2573 0.6045 0.1374 0.3175
(volatility*average income*[Dummy=1 if year 81-90]) 3.23 4.28 2.89 3.20
Volatility Interaction, 90's 0.4784 0.8006 0.2670 0.4849
(volatility*average income*[Dummy=1 if year 91-00]) 5.13 3.86 4.55 3.45
Control Variables:
Initial GDP Per Capita -1.0623 -1.5513 -1.0743 -1.5600
(in logs) -6.91 -5.87 -7.19 -5.98
Education 1.0641 0.6399 1.1095 0.7517
(secondary enrollment, in logs) 4.73 1.89 4.92 2.24
Financial Depth 1.0509 0.7796 1.0648 0.8119
(private domestic credit/GDP, in logs) 5.00 3.16 5.04 3.37
R-squared 0.6445 0.4333 0.6435 0.4538
No. Countries / No. Observations 79 / 310 79 / 309 79 / 310 79 / 309
R-squared 1st stage (average) 0.8187 0.8230
TESTS (P-Values)
(a) Ho : Volatility coefficient for 60's = Volatility coefficient for 70's 0.937 0.683 0.787 0.914
(b) Ho : Volatility coefficient for 60's = Volatility coefficient for 80's 0.068 0.010 0.179 0.088
(c) Ho : Volatility coefficient for 60's = Volatility coefficient for 90's 0.007 0.016 0.043 0.046
(d) Ho : Volatility coefficient for 70's = Volatility coefficient for 80's 0.090 0.008 0.123 0.058
(e) Ho : Volatility coefficient for 80's = Volatility coefficient for 90's 0.256 0.892 0.351 0.712
(a) Ho : Interaction coefficient for 60's = Interaction coefficient for 70's 0.486 0.667 0.335 0.939
(b) Ho : Interaction coefficient for 60's = Interaction coefficient for 80's 0.722 0.103 0.750 0.423
(c) Ho : Interaction coefficient for 60's = Interaction coefficient for 90's 0.015 0.029 0.090 0.061
(d) Ho : Interaction coefficient for 70's = Interaction coefficient for 80's 0.280 0.115 0.485 0.381
(e) Ho : Interaction coefficient for 80's = Interaction coefficient for 90's 0.024 0.242 0.034 0.133
SPECIFICATION TESTS (P-Values)
(a) Durbin-Wu-Hausman Test 0.0000 0.0000
(b) Hansen J-Test for Overidentifying Restrictions : 0.6448 0.5304
Notes: t-Statistics are presented below the corresponding coefficient
36
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o
N oS
FIGURES
Figure 1. Growth and Volatility, 1960 - 2000
8
Gr = 3.1695 - 0.3355*Vol
% 6 (-3.02)
th,
4
ow
gr 2
P
D
G 0
av -2
R2 = 0.119
-4
0 2 4 6 8 10
std dev (GDP growth)
Figure 2a. Low Income Countries Figure 2b. Middle Income Countries
4 7
R2 = 0.1958
%,h 3 6 Gr = 1.7987 + 0.0463*Vol
2 %,h 5 (0.29)
1 4
growt 0 growt 3
-1 2
GDP -2 Gr = 2.9793 - 0.5088*Vol GDP 1 R2 = 0.0029
av-3 (-2.47) av 0
-4
-1
0 2 4 6 8
0 2 4 6 8 10
std dev (GDP growth)
std dev (GDP growth)
Figure 2c. High Income Countries
7
6 Gr = 1.0986 + 0.715*Vol
h
5 (2.83)
growt 4
3
GDP
2
av
1 R2 = 0.3651
0
0 1 2 3 4 5
std dev (GDP growth)
39
40
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com
De
ity
Volatility tilalo
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