X_ PS-249Sr
POLICY RESEARCH WORKING PAPER 2455
The Effects on Growth Commodity export
dependency confers ex post
of Commodity Price shocks and ex ante
Uncertainty and Shocks uncertainty upon producing
countries. What reduces
growth is not the prospect of
Jan Dehn volatile world prices, but the
actual realization of negative
shocks.
The World Bank
Development Research Group
Rural Development
September 2000
Poi.icY RESEARCH WORKING PAPER 2455
Summary findings
Dehn estimates the effects on growth of commodity price It defines the dependent variable to allow an
shocks and uncertainty within an established empirical assessment of the longer-term implications of temporary
growth model. Ex post shocks and ex ante uncertainty trade shocks.
have been treated in the empirical literature as if they * It imposes no priors on h1ow commodity price
were synonymous. But they are distinct concepts and it is movements affect growth, but compares and contrasts a
both theoretically and empirically inappropriate to treat range of competing shock and uncertainty specifications.
them as synonymous. Dehn resolves the disagreement about the long-run
He slhows that the interaction between policy and aid effect of positive shocks on growth, finding that positive
is robust to the inclusion of variables capturing shocks have no long-run impact on growth (that
commodity price movements. More important, his windfalls from trade shocks do not translate into
approach departs in three ways from earlier empirical sustainable increases in income).
studies of the subject: He shows that negative shocks have large, highly
* It deals with issues of endogeneity without incurring significant, and negative effects on growth, but that
an excessive loss of efficiency. commodity price uncertainty does not affect growth.
This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to
analyze the impact of commodity price risks on developing economies. Copies of the paper are available free from the World
Bank, 1818 H Street NW, Washington, DC 20433. Please contact Panos Varangis, room MC3-535, telephone 202-47.3-
3852, fax 202-522-1151, email address pvarangisC@worldbank.org. Policy Research Working Papers are also posted on the
Web at www.worldbank.org/research/workingpapers. The author may be contacted at jan.dehnC@ economics.oxford.ac.uk.
September 2000. (62 pages)
The Policy Research Wlorking P'aper Series dissentinates the findings of work in progress to enzconirage the exchange of ideas about
development issues. As} objective of the series is to get the findings outt quickly, even if the presentations are less than ftully polished. The
papers ca-ry the nanmes of the anthors and shouild be cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do niot necessarily represenzt the vietw, of the World Bank, its Executive Directors, or the
counirtries they represent.
Produced by the Policy Research Dissemination Center
The Effects on Growth of Commodity
Price Unicertainty and Shocks
By
Jan Dehnt
Centrefor the Study ojfAfrican Economies, University of Oxford
Preliminary Draft
Author's email address: jan.dehn@economics.ox.ac.uk
1 This paper is preliminary and is circulated for comment. The findings, interpretations, and
conclusions expressed in this paper ar. entirely those of the author. They do not necessarily represent
the view of the World Bank, its Executive Directors, or the countries they represent. Many thanks are
extended to Panayotis N. Varangis, Christopher L. Gilbert, Richard Mash, Paul Collier, and Jan
Willem Gunning for considerable help and support. Special thanks are also extended to David Dollar
and Craig Burnside for the use of thrir data, and to the Danish Trust Fund of the World Bank for
financial support.
1. Introduction
It has long been believed that commodity price variability causes problems for
primary-producing developing countries, both for the governments and for the
producers themselves. For governnents, unforeseen variations in export prices can
complicate budgetary planning and can jeopardize the attainment of debt targets. This
is a particularly serious problem .or HIPCs, all of which are highly dependent on
commodity exports. For exporters, price variability increases cash flow variability and
reduces the collateral value of invrntories: Both factors work to increase borrowing
costs. Finally, smallholder farmers, often with poor access to efficient savings
instruments, cope with revenue variability through crop diversification with the
consequence that they largely fDrego the potential benefits obtainable through
specialization. For all of these reasons, we should expect vulnerability to commodity
price variability to retard growth.
There is less agreement about which particular manifestations of commodity
price movements matter to developing countries. The literature is replete of references
to volatility, variability, and uncertainty. Other studies have paid attention to trends
and to discrete price shocks. The paper focus specifically on two manifestations of
commodity price movements, namely discrete temporary ex post commodity price
shocks and commodity price uncertainty. The latter can be thought of as the ex ante
manifestation of commodity price unpredictability. The emphasis on these particular
mnanifestations of commodity price movements is not accidental; the importance of
large discrete price changes has been recognized in the 'Dutch Disease' literature for
some time, while an older, larger amd more diverse literature has examined the effects
of commodity price uncertainty in various contexts.
This paper departs from earlier contributions in two regards. Firstly, the paper
aims to be more specific about which attributes of commodity price movements
matter to growth in developing countries, to measure their impact, and to document
their robustness. Discrete shocks cand uncertainty about future prices have been treated
in the empirical commodity price literature more or less as if they were synonymous.
Studies of shocks have invariably ignored uncertainty about future prices a potential
regressor, and similarly studies of commodity price uncertainty have not tested for the
3
effects of current period shocks. However, shocks and uncertainty are distinct
concepts and it is therefore both theoretically and empirically inappropriate to treat
them as synonymous. The paper therefore departs from Collier and Gunning (1999a),
whose analysis is restricted to positive shock episodes, by examining the effects of
both positive and negative shocks. Similarly, this paper tests for asymmetric effects of
large price changes on growth and thus departs from the analyses of Deaton and
Miller (1995) and Deaton (1999) who impose an assumption of symmetry between
small and large price changes. Finally, by modeling ex post shocks and ex ante
uncertainty jointly, it is possible to determine which of these manifestations of
commodity price movements are most relevant to growth, and, in the event both are
important, to avoid omitted variable bias.
Secondly, the paper aims to obtain better estimates of the long term effects of
exposure to shocks and uncertainty. Recently, the availability of reasonably long
panel data sets covering a substantial group of developing countries has facilitated a
more systematic evaluation of the determinants of relative growth rates in developing
countries - see Temple (1999) for a survey. It is therefore a natural step forward to
examine the importance of commodity shocks and uncertainty in the context of an
established empirical panel growth model. By using epoch growth rates rather than
annual growth rates and cyclical income changes, it is possible to obtain better
estimates of long term effects of exposure to shocks and uncertainty. This increases
the scope for resolving the debate between Deaton and Miller (1995) and Collier and
Gunning (1999a) over the medium to long run implications of positive shocks for
economic growth.
The analysis shows that per capita growth rates are significantly reduced by
large discrete negative commodity price shocks, while positive commodity price
shocks and commodity price uncertainty do not exert an influence on economic
growth. The magnitude of the effect of negative shocks on growth is very substantial,
and appears to work independently of investment, which suggests that the adjustment
is achieved through severe reductions in capacity utilization. Negative shocks also
remain highly significant after controlling for government economic policy and
institutional quality, which indicates that the result cannot be attributed exclusively to
inappropriate policy responses on the part of governments. The results are robust to
4
changes in sample composition, changing the time series dimensions of the data,
instrumenting for endogenous regressors, and across different estimation methods.
The paper is structured a; follows. Section 2 briefly summarizes the panel
growth literature and Section 3 discusses the relationships between uncertainty and
growth, and shocks and growth, respectively. The empirical literature on shocks and
uncertainty are also reviewed. Section 4 describes the structure of a new data set
compiled to evaluate commodity price effects, and Sections 5 and 6 describe the
distribution of discrete shocks and uncertainty in a sample of 113 developing
countries, respectively. In Section 7, the analytical framework for an empirical
examination of the effects of u-certainty and shocks on growth is presented. A
canonical growth model framework is augmented to include measure of commodity
price uncertainty and shocks. Secl ion 8 looks at methodological issues involved in the
estimation of panel growth models. In Section 9, the results of the regression analysis
and robustness tests are presented, and Section 10 concludes.
2. Panel growth models
In his recent review of thie growth evidence, Temple (1999) underlines the
current lack of consensus with regard to the specification of empirical growth models.
Two broad canonical models have featured in the empirical growth literature. The
models by Caselli, Esquivel and Lefort (1996), Islam (1995), Mankiw, Romer and
Weil (1992), and Hoeffler (1999), all of which are closely based on theoretical growth
models, define the first class. The other type of model, typified by Barro (1991) and
subsequently widely replicated, places far more emphasis on the role of policy
variables.
These two approaches Exe not mutually exclusive. Consider the Mankiw,
Romer and Weil (1992) augmented Solow model with convergence. The central
empirical specification is
gy, = a. + a, log(s,) +,Blog(n + t,7 - 6) - y log(y0) [1]
5
where s, denotes the total savings rate, which consists of aid, domestic savings,
foreign savings, and other foreign flows. g,, is the rate of growth of per capita GDP,
and y0 is the initial income per effective worker at some initial date. The latter is
intended to capture the extent of deviations from the steady state, while n,tp, c denote
the rates of population growth, technical progress and depreciation respectively,
which are typically assumed to grow at exogenously determined constant rates and are
thus subsumable into the intercept.
In equations such as [1], it is popular to substitute out savings in terms of its
determinants, an approach first proposed by Papanek (1972), and since widely
adopted following the influential paper by Barro (1991). Using standard national
income identities, savings may be expressed in terms of domestic investment (id,),
and foreign investment (if ) as follows:
s, - id, + if, -= i, [2]
where ti, is the total investment rate. Equation [1] can then be rewritten as
g,, = a, + a, log(ti,) - y log(y0) [3]
which makes explicit the link from investment to growth. Subsequent studies may be
grouped into three broad classes:
a) Studies that replace savings by government and private investment rates without
including policy variables of any kind (see for example Caselli, Esquivel and
Lefort (1996), Islam (1995), Mankiw, Romer and Weil (1992), and Hoeffler
(1999)). In these models, empirical specifications closely follow the underlying
theory.
b) Studies that focus on policy variables and exclude investment variables.
Prominent papers in this tradition include Bumside and Dollar (1997), Hansen and
Tarp (1999a), and Guillaumont and Chauvet (1999). The argument justifying
substitution of policy variables for investment is that policy and external
environment variables fully explain how investment influences growth. In other
words, these variables may be thought of as incentive variables.
6
c) Studies that contain a mixture af investment and incentive variables (Hadjimichael
et al. (1995), Barro (1991), and Lensink and Morrissey (1999)). The simultaneous
inclusion of both investment arid incentive variables raises issues of interpretation.
For example, when investment is included, the other variables in the model affect
growth through the 'level of efficiency', whereas when investment is omitted the
effect of other variables on growth is either via investment, via efficiency, or both.
The implication is that in certain circumstances it may be insightful to estimate
growth equations both with and without investment included as in Lensink and
Morrissey (1999).
Since our purpose is to investigate the impact of commodity price uncertainty
and shocks on developing count-y performance, we adopt an established empirical
model, which allows approxim ate comparisons of our results with those from
previous studies. In particular, w. use the data set compiled by Burnside and Dollar
(1997), and in the main we closely follow their intermediate approach - (b) in the
above classification.
A word on the measurernent of economic growth: Over a given period, a
change in income partly reflects cyclical transitory income changes and partly reflects
underlying permanent changes in income. From a theoretical point of view, economic
growth refers to the latter only. In empirical analysis, however, growth rates are
usually calculated without drawing a distinction between transitory and permanent
income changes. Since growth rates calculated thus only make use of end point
observations, they are potentially very sensitive to outliers caused by transitory
cyclical movements in income. To minimize this bias, growth rates are usually
calculated over longer periods, typically 5 to 10 years for panel estimation, and up to
20 or 30 years in cross-section studies. This paper follows other contributions to the
empirical growth literature by not drawing a distinction between transitory and
permanent changes income. The reasons are twofold: First, the number of annual
observations on GDP in most developing countries is insufficient to enable an
unambiguous decomposition of income into its permanent and transitory components.
Secondly, to the extent that the adjustment to temporary shocks and uncertainty
involves transitory changes in capacity utilization, it is useful to be able to capture
such effects. We are obviously presented with an identification problem since we
cannot determine whether the o !served income changes are transitory or permanent,
7
but if the transitory adjustment processes to shocks and uncertainty are lengthy the
distinction may be largely irrelevant, particularly if policy makers have relatively
short time horizons.
3. Commodity price uncertainty, shocks, and growth
The uncertainty variables which have received particular attention in the
empirical growth literature include measures of political instability, business cycles,
and inflation. A number of studies have found negative correlations between these
variables and growth2. One way to think about how uncertainty affects growth is via
factor accumulation, technical progress, and efficiency. Technical progress and factor
accumulation shift out the production possibility frontier, while efficiency brings the
economy from a point within the frontier to a point closer to the perimeter.
The theoretical literature shows that the link between uncertainty and factor
accumulation - investment - depends on the relationship between the expected
marginal revenue product of capital and the uncertainty variable. When the profit
function is convex, the link between investment and uncertainty is positive3. When
investments are irreversible the positive link is not broken, but a range of inaction is
created within which investment does not respond to the conventional net present
value criterion - see Dixit and Pindyck (1994) and Abel and Eberly (1994). A negative
relationship between investment and uncertainty requires either imperfect competition
or decreasing returns to scale or both (see Caballero (1991)). Additionally, aggregate
uncertainty may have effects, which are distinct from those of idiosyncratic
uncertainty. Caballero and Pindyck (1996) show that aggregate uncertainty has
asymmetric effects, because in good states there is free entry, while in bad states free
exit is not possible if investments are irreversible. Hence, positive shocks do not raise
profits, while negative shocks lower them, so the average payoff is decreased by
uncertainty.
The empirical literature shows a robust negative association between
investment and certain sources of uncertainty. Serven (1998) estimates private
2 Bleaney and Greenaway (1993) and Aizenman and Marion (1993) find that policy instability lowers growth. Similarly,
inflation has been shown to be negatively related to growth, although the correlation is not robust (Levine and Renelt (1990),
Levine and Zervos (1993)). Gyimah-Brempong and Traynor (1999) find a significant negative correlation between growth and
political instability.
I Hartman (1972) abstracted from agent attitudes to risk.Zeira (1987) shows that when investors are risk averse the investment-
uncertainty link becomes ambiguous even under the conditions specified by Hartman.
8
investment equations for a large number of developing countries and finds very robust
evidence in favor of a negative link between real exchange rate uncertainty and
investment4. Given the robust link between investment and growth (see Levine and
Renelt (1990)), it seems reasonabl- to suppose that real exchange rate uncertainty will
also have a strong negative effect on growth5. However, after controlling for real
exchange rate uncertainty Serven finds that terms of trade uncertainty per se is not a
significant determinant of investment. This suggests that to the extent that terms of
trade uncertainty affects growth it must do so via routes other than investment, for
example via efficiency and/or the rate of adoption of new technologies.
The link between uncertainty and technical progress is less well understood
and only rarely modeled empiric-ally. Ramey and Ramey (1995) cite a model by
Fischer Black which predicts a positive link between growth and uncertainty on the
grounds that agents can choose from a shelf with high risk/high return technologies
and low risk/low return technologies. Uncertainty in this model facilitates growth by
allowing agents to exploit differenit technologies as external conditions change.
The empirical evidence in favor of a growth-commodity price uncertainty link
is relatively weak. The classic study is MacBean (1966), who failed to support the
hypothesis that export instability reduces growth in developing countries. Subsequent
contributions include Erb and Sclhiavo-Campo (1969), Glezakos (1973), Knudsen and
Parnes (1975), Yotopoulos and Nugent (1976), Lutz (1994), Guillaumont,
Guillaumont Jeanneney and Brun (1999), and Guillaumont and Chauvet (1999). The
latter study finds that a broad measure of instability (which includes the variability of
terms of trade) remains significant with a negative coefficient in a growth regression
which includes investment as a regressor. This supports the notion that uncertainty
operates via efficiency or technical progress, but is it not possible on the basis of this
study to determine if the result iE due to commodity price uncertainty or to some other
component in the composite vulnerability index. There is some indication, however,
4 He examines the role of uncertainty of inflat on, the relative price of capital, real exchange rate, the terms of trade, and GDP
growth on private investment. For each of these variables he develops seven different measures of uncertainty, and finds that
each measure is negatively correlated with privw te investment.
5 Kormendi and Meguire (1985) andGrier and Tullock (1989) found output growth to be positively correlated with output
fluctuations in large cross-sections of countries. They found that this relationship was unchanged when investment was
introduced, the implication being that uncertainty may operate through technical progress, although the route may equally well
be capacity utilization. Making the distinctioi between the predictable and unpredictable components of output volatility,
Ramey and Ramey (1995) show the positive elationship between growth and volatility only holds for the variability of the
unpredictable component; the correlation between the unpredictable component and growth is negative and strong enough, in
fact, to dominate the total effect. They also argue that uncertainty exerts its negative impact on growth mainly technical progress
or efficiency, not investment.
9
that commodity prices may not be culprit. Controlling for investment, Lutz (1994)
compares the effects on growth of Net Barter Terms of Trade and Income Terms of
Trade (ITT) instability measures. His two main findings are that there is no
consistently significant and robust effect of NBTT volatility on growth, and secondly
that ITT volatility affects growth (negatively) mainly via volume rather than price
shocks. In other words, it may be that it is output rather than price volatility which
drives the negative growth effects in the index of Guillaumont and Chauvet (1999).
The theoretical literature linking growth and discrete temporary trade shocks is
very limited. The Ramsey model by Collier and Gunning (1999a), which formalises
the seminal work in Bevan, Collier and Gunning (1990), appears to be unique. The
model shows that positive boom income is initially invested, and in the post-boom
period the investment is reversed to enable a higher level of consumption. Investment
is therefore the vehicle whereby consumption is smoothed. Consumption is
permanently higher than before the boom after jumping up at the time of the shock
and then declining monotonically towards its pre-shock level after the shock. The
model shows that temporary trade shocks ought to increase the level of GDP with
accompanying short to medium term growth effects.
Rodrik (1998) proposes a linkage from temporary trade shocks to growth via a
country's institutional capacity for managing conflicts. In his model, shocks give rise
to conflicts over who should benefit from windfalls (in the event of positive shocks)
and who should bear the cost of adjustment (negative shocks). In countries with strong
institutions for conflict management, the dominant strategy is for competing interests
to cooperate. On the other hand, when conflict management institutions are weak
there are large potential returns to opportunistic behavior which makes fighting for the
spoils of (or to avoid bearing the costs of adjustment to negative) shocks the optimal
strategy irrespective of what other groups choose to do. In the presence of an
intermediate range of institutional capacity, the outcome is determined by the degree
of latent social conflicts in society.
Empirical studies of the effects of discrete ex post shocks on growuth are almost
as rare as their theoretical counterparts, possibly due to the arbitrariness involved in
defining shock episodes. In the empirical part of his paper, Rodrik (1998) specifically
considers a period in history when many developing countries experienced a decline
in their terms of trade, defining his shocks as the standard deviation of (the log)
10
difference of terms of trade over the (1971-1980). It is not clear, however, if this
variable reflects the downward tbend in prices at the time, the variability of prices,
their uncertainty, or individual episodes of powerful negative price changes. It is
therefore not possible to be entire'.y confident about what drives Rodrik's results.
Easterly et al. (1993) find a strong positive correlation between changes in the
terms of trade and economic growth in both the 1970s and 1980s, and they attribute as
much variation in economic growth t6 terms of trade shocks as to economic policies.
It is not clear, however, that th- dichotomy between terms of trade and policy is
entirely valid. Collier and Gunning (1999a) point out that policy changes are often
endogenous to shocks, such that the growth effect depends as much on the shock itself
and the policies in place at the time as on the policy changes which are subsequently
made in direct response to the shock. Collier and Gunning (1 999a) consider the effects
on annual growth rates of 19 positive shock episodes over the period 1964-1991 for a
sample of developing countries. Using a series of shock intercept dummies,
investment-shock interaction dummies, and dummies which capture the post-shock
period, they measure the effect on growth during as well as after the shock. Their
main finding is that despite init al high savings rates windfalls do not translate into
sustainable increases in income; initial positive effects are more than reversed in the
post-shock period. They attribut. the reversal to a combination of low quality public
investment projects and disincentives for private agents to lock into their savings
decisions on account of policy decisions taken prior to and during the shock itself. In
contrast, Deaton and Miller (1995) who examine the effects of commodity price
movements on growth using a VAR approach find a positive coefficient between
growth in commodity prices and growth in income. There is therefore disagreement
over the long run growth imrlications of temporary commodity price shocks. A
consensus reading of these stud'ies suggests that positive shocks tend to boost growth
in the short run, but that any long run effects may depend on the policy response, the
economy's flexibility, institutions for conflict resolution, and the importance of
commodities in the country's terms of trade. Meanwhile, the effects of negative
shocks are not well-documented. Likewise, none of the papers test whether large and
small shocks and negative and positive shocks have asymmetrical effects on growth.
11
4. Constructing a suitable commodity price index
With a few exceptions (notably Deaton and Miller (1995)), studies of the
effects of commodity price movements in developing countries have been undertaken
using either prices of individual commodities, terms of trade indices, or indices of
aggregate commodity price movements (not country specific). Neither of these
approaches are, however, satisfactory for the following reasons:
First, only a few oil producing countries are specialized to the point of
exporting only a single commodity, so for the majority of developing countries the
full ramifications of specializing in commodities cannot be determined with reference
to the movements in the price series of just a single commodity. Secondly, while
individual commodity prices typically capture the movements of too few
commodities, broad terms of trade indices arguably capture too much information,
including various non-commodity and non-export price influences; their inclusion
present a problem mainly because it is not possible with confidence to determine if the
results are due to commodity prices per se.
Until recently, it might have been seen as overkill to construct commodity
prices indices for individual countries, because the prices of even unrelated
commodities were seen to display 'excess comovement', which implied that there was
little to gain over using broad aggregates of commodity prices (Pindyck and
Rotemberg (1990)). However, recent work by Cashin, McDermott and Scott (1999)
suggests that much of the comovement in unrelated commodity prices can be
accounted for mainly by extreme outliers and structural breaks, which have powerful
influences on the correlation based measures of comovement used by Pindyck and
Rotemberg (1990). Using a concordance measure, which is insensitive to outliers,
Cashin, McDermott and Scott (1999) find that unrelated commodities do not display
comovement as hitherto thought. This has a clear implication for the choice of index
used to evaluate the effects of commodity price movement in developing countries:
Broad aggregate indices are likely to behave very differently from individual country
indices, especially if the country is specialized in a narrow range of commodities
The structure of the index used here is identical to the geometrically weighted
index used by Deaton and Miller (1995), namely
12
DM =7Pjw [4]
where W is a weighting item and P, is the dollar international commodity price for
the commodity i . Dollar prices measure cif border prices. Historicalfob prices, which
give a preferable measure of the value of a commodity to the exporting country are
not generally available. The weighting item, W, is the value of commodity i in the
total value of all commodities, n, or the constant base period j
W 5 ;. [5]
n
Since WT is country specific, each country's aggregate commodity price index is
unique. As an average of the prices of the commodities exported by each country, the
index is primarily suited to the study of macroeconomic rather than sectoral effects. A
geometrical weighting scheme is 'aseful for two reasons. After taking logs a geometric
index provides the rate of change of prices in first differences, which is a useful
property. Also, geometrically weighted indices avoid the numeraire problem which
affects deflated arithmetically weighted indices. The appendix describes the data
sources and country coverage of the indices.
5. The distribution of temporary commodity price shocks
The temporary trade shock model by Collier and Pattillo (2000) is not
restricted to discrete shocks of a particular magnitude. Nevertheless, most empirical
studies of temporary trade shock 3 have focussed specifically on events associated with
large price changes (see for exarnple the collection of case studies in Collier, Gunning
and Associates (1999)). There is therefore a slightly odd dichotomy between the
theoretical treatment of shocks, which makes no distinction between large and small
shocks, and the empirical analysis of shocks, which does make this distinction.
Larger disturbances obviously give rise to larger absolute annuity values,
larger absolute changes in consumption, and larger absolute quantities of savings.
There is therefore some intuitive appeal in focusing on large price changes to the
13
extent that larger effects are more likely to show up in the data. Additionally, there
may be theoretical reasons for paying particular attention to large price changes.
Deaton (1991) for example has argued that large negative shocks can give rise to
consumption collapses when consumers are characterized by a combination of
impatience and precautionary savings, particularly in the presence of liquidity
constraints. This is because large negative shocks are the one manifestation of the
stochastic process against which buffer stocks cannot give adequate protection.
Secondly, agents may not treat windfall and other sources of income as fully fungible
in terms of consumption (Thaler (1990)). Hatsopoulos, Krugman and Poterba (1989),
Summers and Carroll (1987), and Ishikawa and Ueda (1984) show that marginal
propensities to consume out of different types of wealth differ considerably. There is
also evidence that agents assign different consumption propensities according to the
magnitude of windfalls (Holcomb and Nelson (1989), Horowitz (1988), Benzion,
Rapoport and Yagil (1989) and Thaler (1981)). Landsberger (1966) is an early result
in the same vein based on a study of Israeli recipients of German restitution payments
after World War II. Thirdly, large and highly visible shocks may trigger discrete
government interventions, because they signal new untapped taxation possibilities.
Schuknecht (1997) has argued, for example, that many governments respond to
commodity shocks by digging deeply into the pool of rents created by increases in the
price of commodities in the 1970s. Schuknecht (1996) shows that higher revenues
from windfall taxation are associated with higher fiscal deficits, higher current
expenditure, lower shares of health and education expenditures and lower growth.
While there may therefore be good reasons to examine the specific effects of
large shocks, there are practical problems involved in finding a suitable definition of
'large'. The theoretical arguments presented above offer only limited guidance about a
suitable cut off point due to the general unobservability of the relevant conditioning
variables. The second best solution is to locate shocks using a purely statistical
definition, which is consistently applied to each country's commodity price index.
The steps are the following: First, each country's aggregate commodity price series is
made stationary by first differencing the series, which removes the any permanent
innovations6. Secondly, the remaining 'predictable' elements are removed by
6 It is assumed that the commodity price series are l(l) rather than trend stationary. In practice, determining whether a series is a
stochastic trend process or a deterministic trend process is difficult. See Leon and Soto (1 995).
14
regressing the differenced series oni its own lag, and a second lag in levels as well as a
linear time trend. This error correction specification [6] is the most efficient way to
model an integrated process, and it removes both the levels and differences
information, which may inform the data.
Ayi, = aO + ait + /Ayi,,1 + AYi±,-2 + £j,; [6]
The residuals from [6], ei,, are normalized by subtracting the mean and dividing by
the standard deviation, and finally an extreme but essentially arbitrary cut off point
can be applied to the stationary normalized residuals. The base case cut off point used
here puts 2.5% of the observations into each tail region.
A total of 179 positive and 99 negative shocks were found in this data,
constituting 4.06% and 2.25% of the total number of observations, respectively. The
disproportionate number of posilive shocks is consistent with the predictions of the
competitive storage model propcsed by Deaton and Laroque (1992). Figure 3 and 4
show the distribution of positive and negative shocks over the period 1957 to 1997 for
10 different cut off points in the xange of 1%-10%. It is evident from these figures that
shocks do not appear to be distributed randomly across time. The incidence of shocks
is low prior to the 1970s, and then suddenly increases dramatically with close to 1/3
of all countries in the sample experiencing positive shocks across several years,
notably in the 1970s. The inciclence of positive shocks then declines, but remains
higher than in the period prior to the 1970s. This pattern is consistent with the
findings of Love (1989) who cLlculates estimates of mean variability of commodity
prices in 65 developing countries over the two periods 1960-1971 and 1972-1984.
Love finds that instability inareased in the latter period using three different
deterministic trend specifications (linear, exponential, and moving average). It is also
evident that the incidence of negative shocks increased in the 1970s, although the
numbers of shocks are always smaller than those for positive shocks. Negative shocks
are particularly prevalent in the 1980s and 1990s.
It is not the objective of this paper to explain the uneven temporal distribution
of shocks. It is important, however, to establish that the high concurrence of shocks in
some years is not attributable tc some specific factors such as oil price movements, or
15
the choice of deflator. Consider first the role of oil. A total of 59 countries
experienced shocks in either 1973 or 1974 (the oil shock year), which is more than
twice the number of countries in the sample, which exports oil (23 countries). The
negative shock in 1986 could also be construed as a product of the collapse in oil
prices, but again a large number of non-oil exporting countries saw shocks in that
year. The fact that the 1979 shock is exclusive to oil producers also suggest that the
price changes for other commodities in 1974 and 1986 were not indirectly due to oil
either. Clearly, oil is not the whole story.
All indices are deflated by the same deflator; the MUV index. It is therefore
possible that the similarities in the distribution of shocks across different countries are
due to specific outliers in the common deflator. Closer inspection of the deflator,
however, reveals that its volatility is much smaller than the volatility of commodity
prices, usually by a factor between 2 and 5 depending on the time period and choice
countries. The differences are significant at the 1% level. Even in the critical year of
1986, where the MUV index has an upwards kink which could potentially account for
the high incidence of negative shocks in the commodity price indices, the price
change in the deflator is a mere 11.3% compared to 49.5% for the 40 country's whose
aggregate indices experienced negative shocks in that year. Indeed, the average
magnitude of price changes in each of the 10 commodities, which saw outliers was
51.6% in that year7. It therefore seems fairly certain that the high incidence of shocks
in particular years reflects instability in many commodities rather than oil shocks or
deflator shocks.
6. Commodity price uncertainty in developing countries
Uncertainty can be measured in many different ways, and there is no consensus
on what constitutes the 'correct' method of measurement. The lack of consensus
suggests that there is merit in considering more than one measure, and we therefore
consider three broad alternative approaches to measuring uncertainty.
The naYve approach involves treating all price movements as indicative of
uncertainty by calculating the standard deviation each country's aggregate commodity
price index. This is unsatisfactory on a number of counts. Most importantly, it does
'The standard deviations were small at 3.1% for the country shocks and 5.0% for the commodity shocks.
16
not control for the predictable cornponents aiid trends in the price evolution process,
and is therefore likely to overstate uncertainty. Both Ramey and Ramey (1995) and
Serven (1998) have shown and argued that this distinction is important.
The second approach distinguishes between predictable and unpredictable
components of the price series, bat remains time invariant. The measure is based on
the principle proposed by Ramey ,md Ramey (1995) that the 'predictable' components
of the price series can be modeled using a selection of explanatory variables. The
variance of the residuals can then be thought of as uncertainty. However, in contrast to
Ramey and Ramey (1995), we do not regress commodity prices on a series of
explarnatory variables, but adopt instead a time series approach, whereby the first
difference of real commodity prices (in logs) is regressed on its first lag, the second
lag in levels (making the regression akin to an error correction specification) plus a
quadratic trend, and quarterly durmmies:
Ay, = -a + at + a2t' + AAyi ,-, t Ayiy-2 +y1D, + -,;
t = 1..., T;
[71
The three quarterly dummies, D,, take the value of 1 for the second, third, and fourth
quarters, respectively, zero otherxise. The constant captures the base period intercept.
This approach treats as predictable the parameters on the trend, quarterly dummies,
and lagged differences and level:, of the dependent variable, which can be justified by
thinking of past values and tre ads as being accumulated as knowledge by agents,
wherefore uncertainty estimates must purge these known priors.
Cashin, Liang and McDermott (1999) argue that uncertainty worsened during
the 1970s. If this is so, it is clearly not appropriate to impose an assumption of
homoskedasticity upon the variance of the residuals. The third approach to measuring
uncertainty therefore distinguishes not only between predictable and unpredictable
components of prices, but also allows the variance of the unpredictable element to be
time varying. Time varying conditional variances can be estimated by applying a
Generalized Autoregressive Coniditional Heteroskedasticity (GARCH) model to each
country's aggregate commodity price index (Bollerslev (1986)). We use a univariate
GARCH(1,1) specification sim,ar to that adopted by Serven (1998) which we apply
uniformly across countries. We therefore estimate, for each country,
17
Ay,t = aO + -at +a2t2 +/4Ayj,, + AYi,,-2 + )7ID, + j,,;
t=l..., T; [8]
0,2 =,,io +7',gr- +),i2,__
where o;2 denotes the variance of £, conditional upon information up to period. The
fitted values of (7i, are the measure of uncertainty of y,,. Quarterly dummies, D1,
were included to remove possible detenninistic seasonal influences on the conditional
variance. Each quarterly dummy takes a value of 1 for a particular quarter, zero
otherwise, and the final quarter is catered for by the constant term.
Large shocks may dominate both the time invariant and time varying
uncertainty measures, but it is possible that agents view such large shocks as
sufficiently infrequent and atypical to effectively discount them when they form
estimates about future price uncertainty. Versions of the Ramey and Ramey and
GARCH uncertainty measures were therefore also constructed which 'dummy out'
particular events. The six uncertainty measures are summarized in Table 1.
Table 2 shows average uncertainty for different groups of countries over
different periods of time for each uncertainty measure. The columns labeled 'I' to 'VI'
correspond to the six uncertainty measures in Table 1. The first line in Table 2 shows
the average commodity price uncertainty for the full 113 countries sample. Evidently,
these highly aggregated statistics do not differ a great deal between the Ramey and
Ramey and GARCH based measures, which both record a standard deviation in the
range of 0.6-0.8. In contrast, the standard deviation measure, which does not remove
'predictable' elements from the price series, is several times larger than either of the
measures, which do remove predictable elements. This underlines the point made by
both Ramey and Ramey (1995) and Serven (1998) that the distinction between
uncertainty and variability is an important one; the large discrepancy between
uncertainty measures which do and do not control for predictable elements suggests
that much of the movement in the price series reflects 'predictable' changes such as
autoregressive paramneters and trends, and failure to account for these components
leads to considerable overstatements of actual uncertainty.
18
The second block of statistics in Table 2 shows average uncertainty by broad
regional grouping calculated over the full sample period (1957-1997). According to
the uncertainty measures, which do not control for shocks ('I', 'IV' and 'VI') the
region, which faces by far the most commodity price uncertainty is the Middle East
and North Africa. Among the reinaining regional groups, there is little difference in
commodity price uncertainty. This includes Sub-Saharan African countries, which do
not appear to experience more uncertainty on average than other developing countries.
To the extent that the commodity share of total exports is greater for African
countries, the same level of uncertainty will of course have greater effects, ceteris
paribus. When controlling for shocks, the difference in uncertainty between Middle
Eastern and North African countiies on the one hand and other regional groups on the
other diminishes considerably for the GARCH measures ('II', 'III'). The Ramey and
Ramey measure ('V') does not ciange, however, which is probably because the trend
break allowed for in this measure is a poor control for the first oil shock.
The third block of data in Table 2 splits the sample by time period in
accordance with oil price movements (1958-1972; 1973-1985; 1986-1997). On all
measures, uncertainty is higher in the 1973-1985 and 1986-1997 periods than in the
period from 1957-1972. On most measures, the increase in uncertainty is as much as
100%. There is no consistent evidence that uncertainty falls in the 1986-1997 period
relative to the 1973-1985 period. Indeed, depending on the measure used, uncertainty
is in some cases higher in the 1986-1997 period than in the 1973-1985 period. It
would therefore appear that uncertainty rose in the 1970s and has not subsequently
declined. Moreover, since this increase is also evident in the measures, which
specifically control for outliers the rise in uncertainty cannot be attributed exclusively
to a few extreme outliers.
The final eight blocks of data in Table 2 show uncertainty for each regional
group, by time period. Except for South Africa, uncertainty increased in all regions
after 1973 and increased further in East Asia and the Caribbean after 1986. In Sub-
Saharan Africa, South Asia, and the Pacific economies uncertainty fell slightly after
1986, while in the Middle East. and North Africa and in Latin America the outcome
depends on the specific uncertainty measure used.
Producers of different types of commodities may be prone to uncertainty for
different reasons, and their experience of uncertainty may therefore be different. For
19
example, agricultural commodities are widely regarded as more prone to weather
shocks, while non-food products by virtue of not being consumer goods may be more
prone to business cycles. Oil is often best treated on its own. On these grounds, it is
insightful to split the sample into agricultural food producers, agricultural non-food
producers, non-agricultural non-oil producers, and oil producers. Countries are labeled
as exporters of a particular type of commodity if their exports of that particular type of
commodity constitute 50% or more of total commodity exports. If no single
commodity type accounts for 50% of exports the country was labeled a 'mixed'
exporter. Table 3 shows average uncertainty by producer type. It is evident that oil
producers face by far the most uncertain prices on most measures. The exception is
the GARCH measure ('III'), which controls for all shocks, although the other
measures which partly control for shocks ('II', 'III', and 'V') also indicate that
uncertainty is considerably reduced by controlling for outliers.' The implication is that
the bulk of uncertainty in these countries is accounted for mainly by discrete shocks.
Meanwhile, there is very little to separate uncertainty measures for the remaining
three producer types, although it is noticeable that mixed producers appear to have
equivalent or lower uncertainty than all other non-oil producers in the 1973-1985 and
1986-1997 periods according to those measures, which do not control for shocks ('I,'
'IV' and 'VI'). Over the full sample period, the uncertainty faced by mixed producers
is equal to or lower than uncertainty in all other regions. Finally, uncertainty tends to
be higher during the 1973-1985 period than in the preceding period, and in many
cases remains at this higher level into the 1986-1997 period. Hence, regardless of
whether we disaggregate by region or by commodity producer type there appears to
have been a sustained increase in uncertainty since the early 1970s.
7. The empirical growth model
This section describes the approach which will be used evaluate if and how the
uneven distribution of discrete shocks and the increase in uncertainty since the 1970s
have impacted growth rates in developing countries. The approach involves
augmenting a canonical empirical growth equation with suitably defined variables.
Our approach departs from recent work by Guillaumont and Chauvet (1999) in two
'Since the oil producers are primarily from the Middle East and North Africa, this explains why this group of countries faced the
20
important regards. First, an established empirical growth model is used as the
canonical basis for the empirical analysis. Since the choice of explanatory variables in
the Burnside and Dollar (1997) growth model encapsulates what are regarded as the
key empirical determinants of growth in the literature, the use of this model enables
more direct comparison of our results with other papers in the growth literature.
Secondly, the uncertainty and shocks variables are different from the vulnerability
index used by Guillaumont and Chauvet (1999) which is a composite index which
picks up not only terms of trade shocks, but also the effects of ecological shocks on
agricultural output, changes in the trend in terms of trade, and the economy's
structural exposure to these types of shocks. In contrast, the measure used here is
based entirely on commodity prices. In estimating a full growth model, the present
analysis also goes considerably further than Deaton (1999), who only considers the
simple correlation between comrm[odity prices and growth.
The canonical specification has the following arguments:
g,G = f (YO, X) [9]
where the matrices {Y0, X} respectively denote initial conditions, and canonical
regressors. Two time invarying variables capture initial conditions, namely the
institutional quality index constructed by Knack and Keefer (1995), which measures
the security of property rights and efficiency of the government bureaucracy, and the
ethnolinguistic fractionalization index which has been shown to be an important
determinant of growth by Easterly and Levine (1997).
The time varying variables include the log of real GDP in the beginning of each
growth epoch, which is included to capture convergence effects, and the ratio of
money supply (M2) to GDP, which proxies for development of the financial system
(King and Levine (1993)). The latter is lagged one period to avoid endogeneity
problems. To capture political instability effects, a variable, which measures
assassinations is included, and this variable is also interacted with the ethnic
fractionalization index. Finally. Sub-Saharan Africa and East Asia dummy variables
greater uncertainty in Table 2.
21
are included to capture the sharply contrasting growth performances of these two
regions.
Instead of using a range of policy indicators, the policy incentive regime is
modeled using the policy index produced by Burnside and Dollar (1997). This index
is constructed as a product of the coefficients of the relevant policy variables in a
growth regression and the means of these variables. Their specification is:
Policy=1.28 + 6.85 Budget surplus -1.40 Inflation +2.16 Openness
where the constant is scaled to ensure that the mean of the policy index and the
dependent variable are identical. This index has been criticized on the grounds that it
does not capture what constitutes 'good policies' (Lensink and White (2000)).
However, the particular choice of variables for inclusion in a policy index is always
bound to be controversial. A strong argument for using the Burnside and Dollar
(1997) policy variable is its very impressive explanatory power in regressions.
The key objective is to explore whether and how commodity prices affect
growth. Various different manifestations of commodity price movements may
potentially affect growth, and it is important not to prejudice the analysis by excluding
any of these a priori. We therefore consider a full range of specifications. First, we
use the log of real commodity prices in levels as a potential regressor, because
commodity prices in levels may matter to growth. A levels measure may also be
important if, say, the effects of shocks and uncertainty are conditional upon the level
of commodity prices. Secondly, the first difference of (log) commodity prices can be
thought of as a base case variable, because this variable encompasses the large price
changes which form the basis for the shock variable. In particular, the first difference
of log commodity prices can be seen as a variable, which imposes an assumption of
symmetry between positive and negative price movements, and between large and
small price changes. Thirdly, interaction terms are introduced to enable distinctions to
be made between large and small commodity price changes, and between positive and
negative price changes. Large price changes - shocks - are identified in accordance
with the methodology described in Section 6.5. Finally, the full range of commodity
price uncertainty measures described in Section 6.6 are tested for their explanatory
power in the growth regression.
22
A shocks is modelled as year-specific dummy, which presents a problem in the
context of estimating a growth panel whose epoch time dimension spans more than
one year. The shock variable theiefore has to be redefined to suit the panel context.
The new shock variable takes a value of unity if a shock occurs in the epoch as
opposed to a particular year, zero otherwise. Clearly, the length of the epoch used in
the growth regression is of considerable importance. For example, if growth rates are
calculated over the full 1970-1993 sample period, the shock variables will become
near meaningless, because most countries experienced at least one positive or negative
shock during this time, wherefore the shock variable would be indistinguishable from
the constant. In the Bumside and Dollar (1997) growth panel, however, this is not a
problem, since the growth epochs are only 4 years long.
8. Estimation issues
Estimation of a panel growth equation with policy variables introduces at least
two potential estimation issues, namely country specific effects and endogeneity. This
section briefly discusses each in tam.
A number of methods exist for coping with unobserved country specific
effects in static panels. When country specific effects are present, they will give rise to
omitted variable bias (OVB) in a pooled OLS regression. One way to avoid OVB is to
include a set of n-i country specific intercept dummy variables (LSDV model).
However, given that the sample consists of a mere 275 observations in the preferred
specification, the inclusion of 55 additional parameters puts a serious drain on degrees
of freedom. An alternative way to deal with the problem is to use the Fixed Effects
(Within Groups) estimator, which sweeps out any country specific effects by
subtracting the mean from each variable, although this also means that the variables
which capture initial conditions; in the equation drop out along with the country
specific effects.
Here, we shall estimate pooled OLS and FE(WG) models and perform
Hausman tests across the specifications to check if there are gains in moving from the
former to the latter. We shall also use a Hausman test to determine if country specific
effects are best modeled as random or fixed.
23
Issues of endogeneity are potentially very important - see Bumside and Dollar
(1997), Guillaumont and Chauvet (1999), and especially Hansen and Tarp (1999b).
Both the policy variable and the investment variable (when included) are likely to be
determined by growth itself. For example, supply shocks such as droughts cause
incomes, and therefore growth, to fall. If the fall in income causes policy to worsen,
the result is that policy is positively correlated with the error term, and the coefficient
will be biased.
Deaton and Miller (1995) and Collier and Gunning (1999a) estimate the
effects of various commodity price manifestations on GDP and annual growth rates,
respectively. They both include investment as a regressor, but they are at near
opposite extremes in terms of their treatment of endogeneity issues. In the spirit of
Sims (1980), the VAR of Deaton and Miller treats all variables symmetrically by not
imposing any prior assumptions of endogeneity and exogeneity (except commodity
prices which are treated as exogenous). In contrast, Collier and Gunning (1999a) treat
growth as endogenous and investment rates as exogenous. The possible endogeneity
of investment to growth is therefore not taken into account.
Arguably, neither of these approaches are ideal. The VAR analysis produces
inefficient estimates and is not well suited for estimating long run effects, and
ignoring endogeneity can hardly be recommended either. Alternative approaches
involve simultaneous equation estimation, or instrumental variable estimation (IV).
Simultaneous equation methods typically involve the introduction of other
explanatory variables for purposes of identification, which themselves may be
endogenous, which in turn means that more equations and more variables are needed,
and so on. The methodology favored here is therefore the instrumental variable
method, which strikes a balance by correcting for the potential bias in the Collier-
Gunning paper dealing with the potential endogeneity problem, while avoiding the
inefficiency of VAR estimation.
IV techniques require that instruments be found which are correlated with the
endogenous variable, but uncorrelated with the error term. A full range of external
instruments is provided in the Burnside Dollar data. As an alternative to the
conventional instrumental variable estimation approach to dealing with endogeneity,
however, we also carry out the Systems GMM analysis proposed by Blundell and
24
Bond (1998), which uses internally generated instruments to instrument for both
policy and, as part of our robustness analysis, for investment.
The sample consists of 56 countries over the period 1970/1973 to 1990/93. The
data is an unbalanced panel with a maximum of six growth observations per country.
9. Results
In this section, we present a progression of results leading towards a preferred
model specification. Several regressions are reported in order to illustrate what does
not work. This is of some interest, because one of the objectives is to establish which
among the competing manifestations of commodity price variability actually affect
growth. We then test the robustne:,s of the preferred model specification to changes in
sample size, estimation methods, time series dimension, and equation specification.
In regression 1 of Table 4, we report the canonical growth specification, which
is identical in all respects to the canonical model reported in Burnside and Dollar
(1997). The most important determinants of growth in the canonical model are policy
and institutional quality. Ethnolinguistic fractionalization interacted with
assassinations is also significant as is the Sub-Saharan Africa dummy. In regressions
2, 3, and 4 we augment the canDnical model with the log of commodity prices in
levels and differences, and the positive and negative shock dummies, respectively.
These regressions are carried out to give a basic flavor of how commodity prices
affect growth, if at all. It is evident from regressions 2 and 3 that there is no simple
strong statistical relationship either between the log of the real commodity price in
levels or its difference (which is also the annual growth rate since the levels variable
upon which it is based is in logs). In regression 4, we enter the positive and negative
shock dummies, which, it is recalled, indicate episodes of 'large' changes in (log)
commodity prices. In contrast to the simple levels and differences specifications, the
negative shock dummy enters the growth regression with a significant negative
coefficient. The positive shock dummy is not significant. This provides a first
indication that there may be asymmetrical effects in terms of how commodity price
changes affect growth. However, since both the positive and negative shock dummy
impose an untested restriction that smaller commodity price changes do not matter to
growth, it is not clear if the significance of the negative shock dummy indicates that
25
large negative commodity price changes have asymmetric effects from smaller price
changes, or whether all negative commodity price changes would have this effect on
growth.
In order to determine if positive and negative price changes have different
effects on growth and whether the effects are sensitive to the magnitude of the price
changes, we ran a new set of regressions shown in Table 5. Regression 1 in Table 5
splits the first difference of the log of real commodity prices into positive and negative
changes, thus no longer imposing the assumption of symmetry for positive and
negative price changes. It is clear that negative price changes have a significant
negative effect on growth rates, while again positive price changes do not appear to
matter. In terms of growth, positive and negative price changes therefore have very
different effects.
The remaining question is now whether the significant coefficient on the
negative price changes variable is driven by large shocks or small commodity price
changes, or indeed by both. This question can be answered by introducing an
interaction term between the negative shocks dummy and the negative changes in
commodity prices (regression 2). The interaction term between the shock dummy and
the change in commodity prices enables large and small price changes to be
distinguished in terms of their effects on growth. It is very clear from this regression
that it is large negative price changes, which matter rather than negative price changes
per se. We also tested whether the coefficients on these variables were equal in
magnitude, but opposite in sign, which would imply that the coefficient on the shock
interaction term is zero. This was firmly rejected at the 99% confidence level (F(1,
258)=12.34)). Meanwhile, when a similar decomposition was carried out for positive
shocks, it was not possible to reject the null hypothesis that the coefficient on the
positive shock interaction term was identical but of opposite sign to the positive
changes in prices base variable (F(1,256)=0.06)). This means that large and small
positive shocks do not have different effects on growth, indeed, they do not appear to
have any effects at all. Finally, a test was carried out to verify that positive price
changes on the one hand and the disaggregated negative price changes on the other are
statistically distinct in their effect on growth. This was validated at the 5%
significance level (F(1,257)=4.08). On the basis of these tests, it is therefore possible
to conclude that statistically speaking commodity prices have highly asymmetrical
26
effects on growth in terms of both magnitude and direction. In particular, only
negative changes appear to matter to growth, and within this subset only large
negative changes appear to matter,
Shocks are 'large' price changes, and they can, by virtue of the stochastic
process, which determines their incidence, occur at any point in time. They can for
example occur at a time when the level of commodity prices is historically low, or
indeed when commodity prices are already high. It might be hypothesized that a large
negative shock is more growth reducing when it occurs at a time when commodity
prices are already low. This does not appear to be the case, however. In regression 3,
we interact the negative shock interaction term with the log of real commodity prices,
but this variable is insignificant. The implication is that negative shocks exert their
negative influence on growth regardless of whether they occur when epoch
commodity prices are on average high or low. It should be borne in mind, however,
that this test is likely to be weak because of the use of epoch averages of the levels
variable. For example, the shock may have occurred during a particular year, when the
level of commodity prices was indeed low by historical standards, which accounts for
the large effect on growth, but the epoch average of the level variable is a poor
estimator of the price level in the critical year.
In regression 4, we include both negative changes in prices, negative changes
interacted with the negative shock dummy, and the negative shock dummy itself to
capture any intercept effects. It is evident from this regression that the intercept
dummy and the negative price vhanges are not significant after controlling for the
interaction between the negative shock dummy and large price changes. This suggests
that the effect is confined to the interaction term. Thus, in regression 5, we present our
preferred model, where the insignificant positive price changes, the small negative
price changes, and the intercep: shock dummies have been dropped. The negative
shocks interaction term is signifi=ant at the 99% confidence level, and exercises a very
considerable negative effect on growth. To illustrate the magnitude of this effect,
consider the first row of numbers in Table 6. Given the estimated beta coefficient of -
62.463 from the preferred regression, the mean of the change in commodity prices
during shocks of 0.025, and the mean of the dependent variable of 1.17, the elasticity
of growth with respect to changes in price can easily be evaluated conditional upon a
large shock having occurred. At the mean, the growth elasticity is -1.345. Evaluated at
27
two standard deviations above the mean, the growth elasticity is -2.876, while
evaluated at one standard deviation below the mean the growth elasticity is -0.580.
Elasticities were also calculated for negative commodity price changes more
generally and for negative commodities price changes net of shocks (respectively the
2nd and 3rd rows in Table 6). Although these elasticities are also substantial, they are
smaller than for shocks, which is supportive of asymmetric effects from large shocks.
Moreover, it should be remembered that the coefficients upon which they are based
are statistically indistinguishable from zero.
Two conclusions can be drawn from these results. Firstly, negative shocks-are
important due to their large growth elasticities. Secondly, the fact that the elasticity is
very different depending on whether it is evaluated at, above, or below the mean
shows that, conditional upon a shock having occurred, the bigger the shock the more
severe its effect. Indeed, elasticities of this magnitude are supportive of the hypothesis
proposed by Rodrik (1998) that negative shocks can cause growth collapses, although
we are not at liberty on the basis of the information presented so far to evaluate if, as
Rodrik suggests, the mechanism whereby these collapses occur is via poor conflict
resolution. However, it is clear from the regressions that negative shocks remain
highly significant even when the canonical model includes ethnolinguistic
fractionalization and institutional quality variables. This is interesting, because in his
growth regressions, Rodrik (1998) finds that negative shocks cease to have a
significant effect on growth when these variables are introduced. Rodrik interprets the
sudden insignificance of the shock variable upon the introduction of the institutional
variables as indicative of the importance of social structures of conflict resolution in
ensuring that shocks have beneficial effects on growth. Our results suggest a different
interpretation of Rodrik's results, namely that changes in terms of trade, and their
standard deviation may be poor instruments for large negative shocks, which are
therefore not robust to the inclusion of other standard growth regressors. Thus, while
social conditions may still matter in the way that Rodrik suggests negative shocks can
clearly precipitate growth collapses even after controlling for social conditions.
A natural next step is to evaluate the robustness of these findings along several
different dimensions. First, we examine the impact of changing the sample of
countries. Table 7 reports OLS estimates of the preferred model for four different
sample specifications. Regression I excludes the five observations identified as
28
outliers by Burnside and Dollar (I 997). It is clear from the results that while these
countries may be outliers in terms of how aid have affected their growth rates, their
inclusion clearly does not alter the shock coefficient in the shock augmented growth
equation.
A more serious concern is ihe role of oil shocks, although typically one thinks
of positive shocks in this context, However, oil prices dropped dramatically in the
1980s and it is important to checkc whether the results are not simply driven by the
decline in the price of oil. Regression 2 therefore excludes oil producers defined as
countries for which oil constitutes 50% or more of total commodity exports. While
magnitude of the coefficient is reduced somewhat by their exclusion, negative shocks
are still highly significant when oil producers are omitted from the sample. This is a
strong indicator that the shock results are not driven by oil shocks alone.
Another interesting question is whether negative shocks affect the poorest
countries in the world, because the welfare implications of a fall in growth rates are
arguably more serious in the poorest countries, where people live closer to absolute
destitution. Regression 3 therefore additionally omits countries whose income per
capita in 1970 was above US$1900 in constant 1985 US Dollars. This reduces the
sample to 60% of the original sanple size, wherefore the efficiency of the estimates
declines considerably. The coefficient on negative shocks is nevertheless still
significant at the 10% level, and c f the same order of magnitude as for the full sample.
Finally, we ran the preferred model on a sample consisting of just Sub-Saharan
African countries (regression 4). This reduced the sample to just 84 observations, and
predictably the t statistic on the negative shock term is now only 1.52 (corresponding
to a p value of 13%). Again, however, the magnitude of the coefficient is close to the
previous estimates. Taking into account the small sample size, it would seem that the
effect of negative shocks on growth is quite robust to changes in sample composition,
and particularly relevant in the poorest developing countries.
A second dimension of robustness testing concerns the method of estimation,
In estimating our preferred specification using a pooled OLS estimator, we have
implicitly assumed that pooling across countries is valid so long as we include Sub-
Saharan African and East Asi,an dummies. However, it is possible that the bias
introduced by not allowing for ir,dividual country specific effects is sufficiently strong
to give grounds for concern. The other concern is endogeneity. The pooled OLS
29
model treats all right hand side variables as exogenous, although the policy variable in
particular may well be endogenous. If this is the case, the result is that coefficients in
the preferred specification are both biased and inconsistent. Table 8 reports a number
of regressions, which use different estimation methods to control for country specific
effects and endogeneity. Country specific effects may be modeled as random or fixed
effects. Regression I reports the preferred model estimated using a random effects
model. It is worth noticing that the coefficient on the negative shock variable is
entirely stable in the face of this change in estimation methodology, although the
random effects model is not the preferred estimator. This is evident from the Chi-
squared test statistic of 5.71 which fails to reject null of the Hausman test of no
systematic difference in coefficients in this model and a fixed effects within group
estimator (FE(WG)). Hence, there are efficiency gains to considering an estimator,
which allows for fixed country specific effects.
One way to do this is to is to transform the variable by subtracting their means.
This sweeps out the country specific effects, but also the time invariant variables,
which capture initial conditions. Regression 2 reports the FE(WG) estimates and
shows that the negative shock variable is robust to the transformation and remains
significant at the 5% level. The country specific effects are not jointly significant
according to the F test (F(55,208)=1.25), but this does not mean that individual
coefficients are not different from zero, and hence potentially a source of bias. What is
important is whether such biases are sufficiently important to produce systematic
differences in the coefficients between a model, which accounts for them, and one that
does not. The effect on the beta coefficients can be determined by applying a
Hausman test to a FE(WG) model against the OLS alternative. The test is unable to
reject the null that the beta coefficients for the FE(WG) are indistinguishable from the
OLS model (Chi-squared test of 10.50). We therefore take this to suggest that the OLS
model is not strongly biased by the omission of country specific effects for each
individual country.
Regression 3 reports an estimate of the preferred model using Two Stage Least
Squares (TSLS) instrumenting for policy. Burnside and Dollar (1997) argue that the
policy variable can be regarded as exogenous, which is extremely convenient given
the difficulties in finding good instruments for policy. However, we elected to take the
endogeneity issue more seriously. First, we constructed a set of instruments composed
30
variously of initial income and log of population and their squares in combination
with the Sachs-Warner openness index. The argument for using the Sachs-Warner
openness index as an instrument fox policy despite the fact that this variable is actually
part of the policy index itself is the following: Unlike the budget deficit and inflation,
which make up the other compoilents of the policy index, the openness variable
captures discrete trade policy changes, and therefore does not adjust continuously to
income shocks. Regression 3 shows that these instruments predictably perform well,
because policy remains highly signiificant. Conditional upon the Sachs-Warner index
being genuinely exogenous, the result appears to vindicate the assumption maintained
in Burnside and Dollar (1997) that the policy variable is indeed exogenous, since there
are no notable differences in the size and significance of coefficient on the negative
shock variable compared to the OLS estimate. The other coefficients are also
statistically unchanged by instrumentation as indicated by the Hausman test, which is
unable to reject the model treating all variables as exogenous in favor of the TSLS
model (Chi-squared test statistic is 0.00).
However, the close similarity between the OLS and TSLS model may simply
reflect that the correlation coefficient between the instrument, the Sachs-Warner
index, and the instrumented policy variable is high (0.78). The key question is
whether the Sachs-Warner index should be treated as exogenous. This is a valid
question since the index is partly a function of the black market premium, which is
arguably endogencus. More fundamentally, Collier and Gunning (1999a) argue that
discrete trade policy measures may to all intents and purposes be endogenous to
shocks, which therefore puts a further question mark over the validity of treating the
Sachs-Warner index as exogenouls, even if we ignore the issue of the black market
premium. In order to deal with this potential endogeneity problem, we therefore re-
estimated the preferred model using the SYS-GMM estimator of Blundell and Bond
(1998). By jointly estimating bcth levels and differenced equations, the SYS-GMM
estimator solves the problem of the Nickell bias through an Anderson-Hsiao
differencing transformation, while simultaneously finding an efficient solution to the
endogeneity problem by using internally generated lagged instruments which exploit
all available moment restrictions. In addition to the policy variable, which we treat as
endogenous, we also allow for both initial income and the financial development
31
variable to be pre-determined'. The SYS-GMM estimator requires a minimum of 5
observations per country, which reduces the sample size from 275 to 234 observations
and from 56 to 40 countries. The results are reported in regression 4 and shows that
the policy index is still significant. More importantly, the negative shock variable
remains significant (5% level). The coefficients on both the policy index and the
shock variable are smaller, which perhaps indicates that there is some bias due to
endogeneity in the OLS regressions. The Sargan test for the SYS-GMM estimates
does not reject that the instruments are optimal for this regression, although there is
some evidence of first order serial correlation.
The third and fourth dimensions of robustness, which we examine pertain to
the stability of the coefficients over time, and the sensitivity of the coefficients to the
inclusion of investment in the growth equation. Regarding stability of coefficients
over time, two issues are of importance: First, are the coefficients the same in the first
half of the sample period as in the second half? Given that the panel covers the period
from 1970-1993, a split in the middle (corresponding to 1981/1982) may be telling
because the 1970s was a period of unusually many positive shocks, while the 1980s
saw mostly negative shocks. Both periods also saw marked changes in uncertainty. In
addition, in the second period many developing countries found themselves unable to
borrow on intemational capital markets due to the debt crisis. It is therefore possible
that negative shocks are not a general problem, but one that is specifically attributable
to events, which occurred in the 1982-1993 period. The first two regressions in Table
9 report estimates of the preferred model for observations up to and including 1981
(growth epochs 1970-73, 1974-77, 1978-81) and the remaining growth epochs (1982-
85, 1986-89, and 1990-1993), respectively. These regressions show that the
coefficient on negative shocks for the latter half of the sample is indeed greater than
that the coefficient in the earlier period as one would expect, but negative shocks also
have a considerable and significant effect on growth in the 1970s, which saw
predominantly positive shocks. In other words, the growth implications of negative
shocks are clearly neither a decade specific phenomenon, nor a specific ramification
of the debt crisis.
9 Pre-determined variables are variables, whose current values are correlated with past shocks, but not with current and future
errors. Valid instruments for pre-determined variables include regressors lagged one period or more. Endogenous variables are
variables, whose current values are correlated with past and current errors, but not with future errors. Valid instruments for
endogenous variables are regressors lagged two periods or more. In the same vein, exogenous variables are variables which are
uncorrelated with any past, current or future errors, and these variables act as their own instruments.
32
Another interesting endeavor is to change the epoch length. Arguably, four
years is a very short epoch length, which means that growth rates may be more
reflective of business cycles than of actual underlying long-term growth rates. There is
therefore merit in changing the epoch length. But in the context of measuring the
effects of discrete shocks identified by dummy variables, there is clearly a limit to
how far the epoch length can be extended. As mentioned earlier, the original shock
variable is a year specific variable, but in the growth regression where the time
dimension is the epoch instead of the year, the shock variable must necessarily be
redefined to take the value of unity if a shock occurs within the epoch rather than
within a particular year. It follows that as the epoch length is expanded, the likelihood
of encountering a shock increases. Hence, in the extreme cases of an infinite number
of observations, there will be no time variation in the shock variable at all. While we
should therefore not estimate the model on growth rates calculated over the full
sample period, shocks are arguably sufficiently rare to enable an enlargement of the
epoch length from four to eight years. In this framework, the shock variable is the
redefined to take the value of unitv if a shock occurs within an eight-year epoch rather
than the default four-year perioc.. Regression 3 reports the results of running the
regression on eight year rather than four year epochs. The shock coefficient is large,
negative and highly significant. This is strong evidence that the effect of shocks on
growth is not purely a cyclical eff;>ct.
An interesting question is obviously how negative shocks manage to depress
growth rates. A possible route is via investment, which is known to be robust
determinant of growth (Levine and Renelt (1990)). So far we have assumed that
investment is fully determined ty policies, which allows us to simply estimate the
reduced form empirical growth eqjuation. The validity of this approach is supported by
regression 1 in Table 10, which is simply the canonical regression to which we have
added the ratio of private investrment to GDP as a regressor. The investment data are
from Serven (1998). Due to the obvious endogeneity of investment rates and the
possible endogeneity of policy, we have estimated the investment augmented growth
equations in Table 10 using SYS-GMM. Regression I shows that investment is
insignificant when the growth equation includes the policy variable. In regression 2,
the policy variable is dropped, and the investment equation is now significant at the
10% level. This is not a major improvement over regression 1, but it does suggest that
33
policy has some influence over investment as has been supposed so far. More
importantly, negative shocks remain highly significant regardless of whether or not
policy and/or investment are included in the regression. This suggests that the main
route whereby negative shocks affect growth is neither via a worsening of the policy
environment nor via a dramatic reduction in investment. The remaining avenue of
adjustment is via 'efficiency"'. In this view, output is adjusted downwards in the face
of shocks through a reduction in the utilization of existing capacity.
Finally, in regressions 3 and 4, we examine if the relationship between policy
and aid established by Burnside and Dollar (1997) is robust to the inclusion of the
negative shock tenn. Burnside and Dollar show that aid has a positive impact on
growth in developing countries with good fiscal, monetary, and trade policies, but has
little effect in the presence of poor policies. In regression 3, we estimate the preferred
model using the full sample of 275 observations, and we find that aid interacted with
policy and aid squared interacted with policy are both significant as found by
Burnside and Dollar. The significance of the interaction term is, however, attributed
by the authors to 5 outliers, wherefore we also ran the negative shock augmented
growth model without these five outliers. The result reported in regression 4 is
identical to what Burnside and Dollar find, namely that the aid policy variable is still
significant, while the interaction term is now no longer significant. Hence, Bumside
and Dollar's results are not reversed by the inclusion of negative shocks into their
growth model.
This paper aims to evaluate the effects on growth of commodity price
uncertainty as well as commodity price shocks, and the preferred specification
reported so far is notable for the absence of uncertainty variables among the
regressors. This is simply because uncertainty was never found to be significant in the
growth equation regression. To illustrate this, in Table 11 we report 4 growth
regressions, which include different measures of commodity price uncertainty. In
regression 1, we measure uncertainty using epoch averages of the conditional variance
of commodity prices correcting for the oil shock in the early 1970s. This measure,
which was the best performing arnong the competing specifications, is insignificant.
Similar measures, which variously did and did not 'dummy' out the effects of various
shocks produced similar effects. In regression 2, we replace the GARCH measure by
Ignoring technical progress.
34
a simple standard deviation of commodity prices variable, which can be thought of as
a measure of commodity price variability rather than uncertainty. This variable is also
insignificant. Finally, in regressions 3 and 4 we estimate uncertainty augmented
growth equations on different sub-samples of the data by splitting the sample into pre-
1982 and post-1981 samples, respectively. This is done in order to evaluate if pooling
across the highly unstable 1970s and periods of less instability is the reason for the
insignificance of the uncertainty tlerm. In both regressions, however, uncertainty is
consistently insignificant as a determinant of growth, while the negative shock
variable in all cases remains highly significant. In total, we experimented with nine
different uncertainty measures", with and without the negative shock variable, but
none of these experiments produced robust and significant coefficients in the growth
equation.
10. Conclusion
The key contributions to the empirical temporary trade shocks literature have
been made by Deaton and Miller (1995) who estimate a VAR extended to include
commodity prices in levels, and Collier and Gunning (1999a) who regress annual
growth rates of GDP on investment, positive shocks, and various lags and interaction
terms within a pooled OLS model. Deaton and Miller (1995) find that international
commodity prices strongly affect :)utput, mostly via investment. Collier and Gunning
(1999a) likewise find that output initially responds very strongly to shocks, but they
reach the conclusion that the long run overall effect of shocks on growth is negative.
They argue here and elsewhere (Collier and Gunning (1996)) that adverse policy
decisions are to blame.
Our approach has been to estimate the effects on growth of commodity price
shocks and uncertainty within arn established empirical growth model. This confers
certain advantages in that our iesults are more easily compared to other growth
models, including the influential model of Burnside and Dollar (1997). We have thus
been able to show that the interaction between policy and aid is robust to the inclusion
of variables capturing commodity price movements. More importantly, however, our
approach has made three importent methodological departures from the contributions
The six measures described in Table I plus corditional standard deviation versions of each of the GARCH measures.
35
by Deaton and Miller (1995) and Collier and Gunning (1999a). Firstly, we have
attempted to deal with issues of endogeneity without incurring an excessive loss of
efficiency. Our methodology therefore strikes a balance between these two papers by
correcting for the potential bias in the Collier-Gunning paper by employing a
methodology, which takes explicit account of endogeneity issues, while also
maximizing efficiency by not estimating fully unrestricted equations.
Secondly, we have defined our dependent variable to better enable an
assessment of the longer-term implications of temporary trade shocks. While we do
not claim to be able to discriminate between cyclical and long run growth rate effects,
the present analysis does go further towards this goal by using four and eight year
epoch growth rates as the dependent variable, since epoch averages are more likely to
erase purely cyclical effects.
Thirdly, we have not imposed any priors on how commodity price movements
affect growth. Instead, we have compared and contrasted a range of competing shock
and uncertainty specifications, which include but are not confined to the variables
used in other contributions. Thus, we both allow for the possibility of non-linearity in
the effect of commodity prices on growth, and for asymmetrical effects of positive
and negative shocks on growth. By testing for the best performing among competing
specifications, we have arguably been able to obtain more efficient and less biased
estimates of the effects of shocks.
A key contribution of this paper is to offer a resolution to the disagreement
over the long run effect of positive shocks on growth. We find that positive shocks
have no long run impact on growth. This result confirms that windfalls from trade
shocks do not translate into sustainable increases in income as suggested by Collier
and Gunning (1999a). The result is also supportive of Deaton and Miller (1995) who
find evidence of positive effect on income in the short run, but no evidence of
negative effects. The result, however, overturns the finding of Collier and Gunning
that the long run effect of positive shocks in negative.
Why might positive commodity shocks not translate systematically into higher
growth rates? Collier and Gunning (1996) attribute this to five key policy errors on the
part of governments. First, they sometimes fail to save windfalls. Secondly, even
when they save early on they then fail to lock into the savings decision, proceeding to
spend the windfall rapidly. Thirdly, windfall spending typically results in large
36
expenditures on capital projects undertaken while the boom is still in progress. Since
domestic prices are high at such timnes, the efficiency of public investment projects is
reduced. Fourthly, windfall is often channeled into low return projects for political
rather than economic reasons. Finally, governments often end up with widened fiscal
deficits after the end of the shock iSchuknecht (1996)), which must be financed by
extracting taxes from the private sector after the boom ends.
The second key contribution is to show that negative shocks have large, highly
significant and negative effects cn growth as suggested by Rodrik (1998). An
interesting difference from Rodrik's work is that Rodrik's shock variable loses
significance when indicators of latent social conflict are introduced. In contrast, our
negative shock variable remains highly significant at the introduction of such
indicators (institutional quality, etlmolinguistic fractionalization and assassinations).
The implication of this is clear: With greater attention paid to how shocks are
modeled, it can be shown that negative shocks precipitate growth collapses regardless
of whether a country is socially divided or has weak institutions. Hence, institutions
may not matter as much as RodrLk's results suggest. Indeed, the insignificance of
Rodrik's shock variable may have more to do with not distinguishing between large
and small shocks than with the inclusion of social conflict variables into his
regression.
The negative shock effect is also robust to the inclusion of investment in the
growth regression. This indicates that economies adjust to negative shocks by
lowering capacity utilization rather than by disinvesting. This interpretation is
consistent with the observation that investment decisions in developing countries are
irreversible (Collier and Gunning (1 999b)).
By modeling shocks and uncertainty simultaneously, it is possible to
determine whether growth is affected by ex post shocks, ex ante uncertainty, or indeed
by both these manifestations of commodity price movements. To the extent that both
matter, of course, this approach al so avoids omitted variable bias. The third key result,
however, is that commodity price uncertainty does not affect growth. This finding
holds for various different specifications of the uncertainty variable and across
different sample periods. Commodity price uncertainty remains insignificant
regardless of whether we include or exclude ex post shocks in the regression
37
specification. This is a surprising result, because uncertainty is often put forward as an
important determinant of investment, and therefore growth.
Our results are highly robust. In particular, we have showed that negative
shocks affect growth across different samples of countries, across different growth
epochs, and across different lengths of growth epochs. The results also hold when we
consider different specifications of the growth model, and when we include additional
regressors, such as aid and uncertainty. Our preferred model is robust to the inclusion
of country and time dummies and to estimation using TSLS and SYS-GMM methods,
which take full account of endogeneity.
38
Table 1: Uncertainty and Variatility Measures
No. Nature of Description Predictable Shocks 'dummied
uncertainty element in out'of residuals
variable process and conditional
variance
i Tine varying Garch conditional standard deviation of one LDV, T, TA2, QD
uncertainty step ahead forecast errc r
11 Time varying Garch conditional stand,ard deviation of one LDV, T, TA2, QD First oil shock only
uncertainty step ahead forecast errc r dymmying out first (1973Q3-1974Q2)
oil shock
IlIl Time varying Garch conditional stand 3rd deviation of one LDV, T, TA2, QD All 2.5% positive and
uncertainty step ahead forecast errcr dummying out all negative shocks
shocks
IV Time invariant Ramey & Ramey unconditional standard LDV, T, TA2, QD
uncertainty deviation
V Time invariant Ramey & Ramey unconditional standard LDV, T, QD Trend break and
uncertainty deviation intercept break in
1973Q3
VI Time invariant Simple unconditional standard deviation
variability
(Note: WDV', 'T TA2', and 'QD' denote laggeci dependent variable, linear time trend, trend squared, and quarterly dummies)
39
Table 2: Commodity Price Uncertainty, By Region
Reon (Group numbw) pod n I II I V v VI
A#113counhtis 1957-1997 113 0.08 (003) 0.07 (002) 0.06 (o02) 0.08 (o03) 0.08 (o03) 0.30 (013)
Sulb-ShManAfica 1957-1997 44 0.08 (oo3) 0.07 (o.o2) 0.06 (o02) 0.08 (003) 0.08 (o02) 0.27 (01f)
MkEid Cut and NorthAftta 1957-1997 16 0.12 (oo4) 0.08 (002) 0.06 (001) 0.11 (004) 0.11 (o03) 0.45 (o i)
L.in Amfka 1957-1997 17 0.07 (002) 0.07 (o02) 0.06 (foo) 0.07 (002) 0.07 (.0o2) 0.27 (009g)
SouthAsi. 1957-1997 5 0.07 (0.02) 0.07 (002) 0.07 (003) 0.07 (oo2) 0.07 (coo2) 0.35 (ois)
East Asia 1957-1997 11 0.08 (o03) 0.07 (003) 0.07 (o03) 0.08 (0a3) 0.08 (o03) 0.26 (oo0)
fic 1957-1997 5 0.07 (o0) 0.07 (002) 0.07 (o02) 0.07 (o02) 0.07 (002) 0.29 (o01)
COanbban 1957-1997 14 0.08 (004) 0.08 (o03) 0.07 (0o0) 0.09 (003) 0.08 (o03) 0.25 (014)
SouOhAbtca 1957-1997 1 0.03 0.03 0.03 0.03 0.03 0.15
ALL 1957-1972 113 0.07 (004) 0.05 (002) 0.05 (002) 0.05 (002) 0.05 (002) 0.10 (005)
4LL 1973-1995 113 0.09 (O03) 0.08 (002) 0.07 (o00) 0.10 (004) 0.10 (004) 0.24 (o01)
AL.. 1986-1997 113 0.09 (004) 0.09 (004) 0.08 (o03) 0.09 (o00) 0.09 (o04) 0.15 (007)
Sub-SahahanAfnca 1957-1972 44 0.06 (003) 0.05 (o02) 0.05 (002) 0.05 (002) 0.05 (002) 0.11 (00)
Sub-SaharanAfnca 1973-1985 44 0.09 (003) 0.08 (O02) 0.07 (o02) 0.10 (004) 0.09 (o03) 0.22 (o00)
Sub-SaharanAfria 1986-1997 44 0.08 (003) 0.08 (0,0) 0.07 (o03) 0.08 (003) 0.08 (003) 0.16 (o09)
M East and NorthAftne 1957-1972 16 0.12 (004) 0.05 (0o02 0.04 (00o) 0.04 (o01) 0.03 (o00) 0.06 (002)
94A/d East and North Aft/ca 1973-1995 16 0.13 (0o4) 0.09 (003) 0.05 (001) 0.16 (oo5) 0.15 (0o5) 0.37 (o12)
Mtdde Est and North Aftca 1986-1997 16 0.12 (004) 0.12 (o00) 0.09 (o03) 0.12 (oo4) 0.11 (o04) 0.13 (0V2)
Lfain Anwma 1957-1972 17 0.06 (003) 0.05 (002) 0.05 (o02) 0.04 (002) 0.04 (o02) 0.09 (o09)
Latin Amnanca 1973-1985 17 0.0 (00o2) 0.07 (0 02) 0.06 (001i) 0.D9 (00o3) 0.08 (00o3) 0.20 (009p)
LaIn Amnca 1986-1997 17 0.08 (003) 0.08 (o03) 0.07 (0o0 0.08 (o03) 0.08 (o03) 0.13 (005)
South Asia 1957-1972 5 0.06 (003) 0.06 (o03) 0.06 (o03) 0.06 (003) 0.06 (o03) 0.12 (oo0)
Sou/hAsie 1973-1985 5 0.08 (002) 0.08 (oa2) 0.08 (o00) 0.08 (002) 0.08 (o03) 0.27 (o01)
SouthAsa 0 1986-1997 5 0.08 (0o2) 0.07 (o03) 0.08 (oa3) 0.07 (002) 0.07 (oa2) 0.15 (007)
Esst Asia 1957-1972 11 0.06 (002) 0.06 (O02) 0.06 (002) 0.05 (002) 0.05 (002) 0.13 (007)
East Aab 1973-1985 11 0.08 (0o3) 0.07 (003) 0.07 (003) 0.09 (003) 0.08 (oo3) 0.21 (007)
East Asfa 1986-1997 11 0.09 (005) 0.09 (005) 0.08 (o00) 0.09 (000) 0.09 (o00) 0.15 (o 10)
PcfDcM 1957-1972 5 0.06 (O02) 0.06 (o02) 0.06 (o02) 0.05 (o0o) 0.05 (00t) 0.12 (0.05)
Pacifc 1973-1985 5 0.08 (002) 0.08 (002) 0.08 ()02) 0.09 (002) 0.09 (o00) 0.24 (00o)
Pacfi7c 1986-1997 5 0.07 (003) 0.07 (003) 0.07 (o03) 0.07 (003) 0.07 (003) 0.15 (0O0)
Canbbean 1957-1972 14 0.06 (o04) 0.05 (003) 0.05 (003) 0.05 (003) 0.05 (003) 0.11 (006)
Cainbben 1973-1985 14 0.09 (o0o) 0.08 (o02) 0.07 (002) 0.10 (0o4) 0.10 (oo4) 0.20 (011)
Caribbean 1986-1997 14 0.10 (005) 0.10 (oo5) 0.09 (004) 0.10 (OO5) 0.10 (004) 0.16 (007)
Sou/hA.9lca 1957-1972 1 0.03 0.02 0.02 0.02 0.02 0.03
SouLdAfFka 1973-1985 1 0.04 0.04 0.03 0.04 0.04 0.08
South Afca 1986-1997 1 0.03 0.03 0 .03 0.03 0.03 0.07
(Note: Figures in BOLD are averages, while smallerfigures in italic are standard deviations across group members)
Key:
I-Average conditional standard deviation (GARCH base case)
TI-Average conditional standard deviation (GARCH controlling for 1973/74 shock)
III-Average conditional standard deviation (GARCH controlling for all shocks)
IV-Unconditional standard deviation (Ramey and Ramey)
V-Unconditional standard deviation (Ramey and Ramey w. 1973Q3 break)
VI-Simple unconditional standard deviation
40
Table 3: Commodity Price Uncertainty, By Commodity Type
Commodity type rime period n I tl 111 IV V VI
Al 113 countries 1957-1997 113 0.08 (0.3) 0.07 (002) 0.06 (002) 0.08 (003) 0.08 (o03) 0.30 (0.13)
Agricultural food stuffs 1957-1997 52 0.07 (oo2) 0.07 (0.02) 0.07 (0o2) 0.08 (0.2) 0.08 (0.2) 0.25 (ote)
Agriculturalnon-foods 1957-1997 18 0.06 (0.)2) 0.06 (0.02) 0.06 (o02) 0.07 (0o.2) 0.07 (002) 0.24 (0oo)
Non-agro non-oil 1957-1997 17 0.07 (0.,2) 0.06 (0.02) 0.06 (o02) 0.07 (o2) 0.07 (a02) 0.23 (0o.)
Oil 1957-1997 23 0.13 (0.33) 0.09 (0.02) 0.06 (0o.) 0.12 (002) 0.12 (002) 0.60 (.o)
Mixed 1957-1997 3 0.06 (e. n) 0.05 (0.o0) 0.05 (o0o) 0.06 (o o1) 0.06 (o01) 0.24 (o 03)
Agricultural food stuffs 1957-1972 52 0.06 (0.22) 0.06 (0o2) 0.06 (o.o2) 0.05 (0a2) 0.06 (0o2) 0.11 (005)
Agricultural food stuffs 1973-19f5 52 0.08 (0o.2) 0.08 (0.02) 0.07 (0o2) 0.09 (0.2) 0.09 (0o2) 0.20 (oo0)
Agriculturat food stuffs 1986-1997 52 0.08 (0o.4) 0.08 (0o4) 0.08 (004) 0.08 (0.04) 0.08 (004) 0.17 (0.08)
Agriculturalnon-foods 1957-1972 18 0.05 (002) 0.05 (0o2) 0.06 (0o2) 0.04 (002) 0.04 (0o2) 0.09 (0.05)
Agricultural non-foods 1973-1985 18 0.07 (002) 0.07 (0.2) 0.07 (0o.2) 0.08 (0.02) 0.07 (o.2) 0.19 (0.08)
Agriculturae non-foods 1986-1997 18 0.08 (C 02) 0.08 (0.02) 0.07 (0o2) 0.08 (0o2) 0.08 (002) 0.16 (oos)
Non-agro non-oil 1957-1972 17 0.06 (1.03) 0.05 (0o3) 0.05 (0o3) 0.05 (0o3) 0.05 (o.03) 0.15 (o08)
Non-agro non-oil 1973-1985 17 0.07 (1.02) 0.07 (0.02) 0.07 (0.2 0.08 (0o3) 0.08 (0o3) 0.20 (0.o
Non-agro non-oil 1986-1997 17 0.07 (1102) 0.07 (0.02) 0.06 (002) 0.07 (0o2) 0.07 (0o2) 0.14 (009)
oil 1957-1972 23 0.12 (11.0) 0.06 (002) 0.04 (000o) 0.04 (0.00) 0.03 (o000) 0.06 (0.00)
Oil 1973-1985 23 0.14 ().30) 0.09 (002) 0.06 (0o0) 0.17 (003) 0.17 (003) 0.40 (0.09)
Oil 1986-1997 23 0.14 (.02) 0.13 (0.4) 0.10 (o02) 0.13 (o02) 0.13 (0o.) 0.12 (o02)
Mixed 1957-1972 3 0.05 (2.01) 0.04 (0.03) 0.04 (0oo) 0.04 (0o1) 0.04 (0o0) 0.09 (ao3)
Mixed 1973-1985 3 0.06 (oo0) 0.05 (0o.) 0.06 (o0)) 0.07 (0o.) 0.07 (0.03) 0.16 (0.031
Mixed 1986-1997 3 0.05 6 o 00) 0.06 (0o01) 0.04 (0oo0) 0.05 (0.o0) 0.06 (too0) 0.11 (o04)
(Note. Figures in BOLD are averages, while smalerfigures in italic are standard deviations across group members)
Key:
I-Average conditional standard deviation (GARCH base case)
lI-Average conditional standard deviation (GARCH controlling for 1973/74 shock)
III-Average conditional standard (leviation (GARCH controlling for all shocks)
IV-Unconditional standard deviation (Ramey and Ramey)
V-Unconditional standard deviati n (Ramey and Ramey w. 1973Q3 break)
VI-Simple unconditional standarcl deviation
41
Table 4
Growth regression results
Dependent variable: Growth of real per capita GDP
White heteroskedasticRty consistent standard errors in (liacs)
(", ', ~and denote significance at 1%, 5%, and 10% respectively)
No. 1 2 3 4
Pooled OLS
with 1st Pooled OLS
Pooled OLS Pooled OLS difference of with positive
Canonkal with commodity commodity and negative
Model model prices in levels prices shock dummies
initial Income (inlY) -0.65 -0.67 -0.70 -0.58
(0.53) (052) (0.52) (0.53)
Ethnollnguistic fractlonallsatlon (ethnt) -0.58 -0.60 -0.58 -0.59
(0.74) (0,73) (0 74) (0.74)
Assassinations (ASSAS) -0.44 -0.42 -0.40 -0.41
(o 27) (0.27) (0-27) (0.27)
Ethnollnguistic fractlonallsatlon x Assasinations (ethnas) 0.81 * 0.78 * 0.74 0.73
(0.45) (0.45) (0.45) (0.46)
Institutional quality (ICRGE) 0.64 * 0.64 ^ 0.65 ' 0.59 *
(0.1s) (0.18) (0.17) (0.16)
M21GDP (M21) 0.01 0.01 0.01 0.01
(001) (0.01) (0.01) (0.01)
Sub-SaharanAfrlcadummy(SSA) -1.53 -1.51 -1.58 ** -1.47 **
(0.73) (0.72) (0.71) (0 74)
EastAsian dummy (EASIA) 0.89 0.90 0.90 0.78
(0.55) (005) (0-55) (0.54)
Policy (policy) 1.00 ( 1.00 '* 0.97 1.03
(0.15) (0.14) (015) (o15)
Log(Commodity prices) (Idmav) -0.56
(0,60)
lst Difference of Log(Commodity prices) (didmav) 13.03
(10o66)
Large positive shock dummy (pos) 0.55
(o 51)
Large negative shock dummy (neg) -1.04
(0.49)
Epoch dummy (ed3) -0.01 0.13 -0.02 -0.07
(0.59) (060) (o 59) (0 60)
Epoch dummy (ed4) -1.35 -1.19 -1.24 -1.32
(085) (0.62) (0.67) (0.65)
Epoch dummy (ed6) ,3.37 -3.23 -3.20 -3.26
(059) (059) (0.63) (0.60)
Epoch dummy (e*d) -1.96 -1.96 -1.71 -1.37
(0.53) (0.53) (o 56) (056)
Epoch dummy (od?) -2.31 -2.39 .. -2.07 -2.04
(0-62) (0o63) (o 66) (0 64)
Constant 3.76 3.94 4.07 3.30
(3.80) (3.75) (3.72) (3.82)
No. countries 56 56 56 56
No. observations 275 275 275 275
F(regresslon) 18.29 17.27 17.49 16.24
R squared 0.39 0.39 0.40 0.40
42
Table 5
Growth regression results
Dependent varables Growth of real per capita GOP
Wnit heteroskedastcity consistent standard errors in (0.,.)
( , , and denote significance at 1%, 5%, and 10% respectwely)
No. 1 2 3 4 5
Pooled OLS
Pooled OLS w. with negative
Pooled OLS w. positive, smati Regression 2 price changes,
positive and negative prke with level negative shock Pooled OLS
negative price changes,and interaction dummy, and preferred
Model changes shocks terms interaction tenm specIircation
Initlal Income liniY) -0.63 -0.41 -0.38 -0.42 -0.44
(050) (052) (052) (054) (0.54
Ethnolinrgultic tractionallsatlon (ethnf) -0.51 -0.28 -0.31 -0.27 -0.30
(0.74) (0.73) (0.74) (0 74) (0.73)
Asassinatlons (ASSAS) -0.37 -0.38 -0.39 -0.38 -0.37
(0.27) (0.27) (027) (027) (0.27)
Ethnolinguistic iractlonalisatlon x Assasinations (ethnaa) 0.69 0.62 0.63 0.62 0.62
(0.44) (0.47) (0.47) (0 47) (0.47)
Institutional quality (ICRGE) 0.64 0.59 0.58 0.59 0.59
(0.17) (0.17) (017) (0.1') (0.1 )
M2IGDP (Mz2) 0.01 0.02 0.02 0.02 0.02
(0 .1) (0 01) (0 01) (001) (0.01)
Sub.aharan Afrca dummy (SSA) -1.54 -1.42 -1.42 -1.42 -1.44
(0 71) (0.70) (0. 70) (0 72) (0.72)
East Asian dummy (EASIA) 0.84 0.77 0.78 0.80 0.78
(0 50) (0.55) (o 00) (0.0) (004)
Policy(policy) 0.98 1.02 1.02 1.02 1.02
(o 15) (0.15) (0.15) (0.15) (015)
Negative commodity price changes jdldmN) -30.44 2.72 -3.58 6.39
114.34) (17.62) (24.94) (17 00)
Positive commodity price changes (didmP) -4.99 -3.51 -3.38
(22.95) (22 74) (22.60)
Neg. shockicom. price change Interacton (negdidmN) -65.90 -72.15 -76.17 -62.46
(21 40) (2950) (3070) (1705)
Neg. price changellevel Interaction (negDNldm) 19 35
(f5.96)
Shock/Neg. price changentevel Interaction (negDNtdm) 55.75
(6 57)
Negative shock dummy (nag) 0.33
(069)
Epoch dummy (ed3) 0.30 0.27 0.24 0.20 0.24
( 59) (o 58) (o 59) (0.61) (o 59)
Epoch dummy (ed4) -1.19 -1.30 -1.38 -1.32 -1 28
(0.66) (0.66) (0.67) (O 67) (0.66)
Epoch dummy (edS) -3.26 -3.43 -3.43 -3.43 -3.39
(063) (0 63) (063) (0e60) (0.09)
Epochdummy(ed6) -1.60 -1.42 -1.31 -1.51 -1.40
(0 54) (0e53) (O 54) (056) (0 5)
Epoch dummy (ed7) -2.09 -2.39 -2.30 -2.43 -2.33
(0.66) (0 66) ()072) (0.69) (0.61)
Constant 3.70 2.08 1.86 2.02 2.24
(3 64 (3.74 (3 75) (3 69) (3085)
No. countries 56 56 56 56 56
No. observatlons 275 275 275 275 275
F(regresslon) 16.64 16.26 t14.3 1552 17 82
R squared 0.40 0.41 0.42 0.41 0.41
43
Table 6
Growth elasticities of negative shocks
Coeffcient and _ _
Mean of variables standard deviation Elasticity evaluated at:
Negative
changes in
commodity Sigma Mean - Mean + Mean +
Obs Growth prices Beta (Seta) Mean I'sigma I sigma 2sigme
Shock changes 31 1.173 0.025 -62.463 0.014 -1.345 -0.580 -2.111 -2.876
Allchanges 171 1.173 0.014 -62.463 0.012 -0.763 -0.134 -1.393 -2.022
Non-shock changes 140 1.173 0.012 -62.463 0.010 -0.634 -0.119 -1.150 -1.665
44
Table 7
Growth regression results
Dependent variable: Growth of real per capita GDP
White heteroskedasticity consistent standard errors in (iics).
('-', '', and '' denote significance at 1%, 5%, and 10°k respectively)
No. 1 2 3 4
Pooled OLS
Pooled OLS preferred
preferred Pooled OLS specification
specification preferred (omitting oil Pooled OLS
(omitting 5 specification producers and preferred
Bumside Dollar (omitting oil middle income specification
Model outliers) producers) countries) (SSA only)
Initial Income (iniY) -0.46 -0.58 -0.31 0.50
(054) (048) (O87) (1.40)
Ethnolinguistic fractionalisation (ethnf) -0.30 -0.52 -0.97 2.11
(0.74) (0.76) (0.91) (1.96)
Assassinations (ASSAS) -0.37 -0.44 -0.76 9.86
(O 27) (0.29) (O 52) (7.59)
Ethnolinguistic fractionalisation x Assasinations (e,:hnas) 0.62 0.82 * 1.19 -16.34
(0.47) (0 48) (O 93) (12283)
Institutional quality (ICRGE) 0.62 0.84 0.96 0.62
(0.16) (0.17) (0.21) (0.43)
M21GDP (M21) 0.01 0.02 0.03 0.04
(0.01) (0.01) (0.02) (0.05)
Sub-Saharan Africa dummy (SSA) -1.40 -2.01 -2.04 *
(0 73) (0.40) (0.71)
East Asian dummy (EASIA) 0.74 0.03 -0.18
(0 56) (O 59) (O 71)
Policy (policy) 1.03 ** 1.06 1.12 1.06
(O 16) (O 15) (0.21) (043)
Neg. shocklcom. price change interacton (negdldniN) -65.75 -53.04 -40.72 -64.44
(1716) (17 31) (24 40) (42.37)
Epoch dummy (ed3) 0.27 0.15 0.46 -0.27
(O 59) (O 55) (O 67) (1 55)
Epoch dummy (ed4) -1.26 -0.70 -0.54 -2.36
(0.65) (0.64) (0.81) (1.69)
Epoch dummy (ed5) -3.36 -3.00 -2.26 -4.07
(0.59) (0.62) (0.79) (1.30)
Epoch dummy (ed6) -1.20 -1.05 -1.00 -1.95
(O 52) (0.52) (O 65) (1.28)
Epoch dummy (ed7) -2.28 -2.40 -2.70 -4.49
(0.63) (0.92) (0.79) (1.45)
Constant 2.34 2.03 -0.58 -7.25
(3.89) (351) (619) (9.32)
No. countrles 56 47 35 21
No. observations 275 230 166 84
F(regression) 16.76 16.41 12.43 2.78
R squared 0.41 0.47 0.48 0.26
45
Table 8
Growth regression results
Dependent variable: Growth of real per capita GDP
Wvihte heteroskedasticity consistent standard errors in (italics)
('-" '-,and "denote significance at 1%, 5%, and 10% respectively)
No. 1 2 3 4
Pooled OLS Pooled OLS w.
preferred positive, small
specification negative price TSLS SYS-GMM
(Random effects changes, and (instrumenting (instrumenting
Model model) shocks for policy) forpolicy)
Initial income (InlY) -0.45 -2.36 ** -0.44 -3.99 *
(0r37) (I 04) (054) (1.50)
Ethnolingulstic fractionalisation (ethnt) -0.30 -0.30 -2.33
(0.81) (0. 73) (1.50)
Assassinations (ASSAS) -0.38 -0.61 -0.37 -0.40
(0e30) (0e38) (0.28) (0.28)
EthnollnguistlcfractionalisationxAssasinations(ethnas) 0.63 1.10 0.63 0.80
(0.62) (0-78) (0.47) (0.44)
Institutional quality (ICRGE) 0.60 * 0.59 1.32 ^
(0 18) (018) (0.49)
M2iGDP (M21) 0.02 0.01 0.02 0.01
(0.02) (0 03) (0.01) (0.02)
Sub-Saharan Africa dummy (SSA) -1.45 "s -1.43 -3.86
(0863) (0 73) (1. 68)
EastAsian dtmmy (EASIA) 0.78 0.73 1.14
(0.89) (0.61) (1.00)
Policy (policy) 1.01 0.85 1.05 *c 0.73
(017) (021) (0.22) (0 32)
Neg. shocklcom. price change interacton (negdidmN) -62.26 -54.51 -62.59 -37.14
(2027) (21.24) (17 14) (18.92)
Epoch dummy (ed3) 0.24 0.44 0.25 0.52
(0.61) (0 62) (0.59) (0 55)
Epoch dummy (ed4) -1.28 -1.01 -1.28 -0.77
(081) (0.85) (0.85) (075)
Epoch dummy (ed5) -3.39 -3.12 -3.38 -3.08
(0.62) (0.67) (0.59) (0.70)
Epoch dummy (ed6) -1.40 ** -1.31 -1.40 -1.41
(0 86) (0 72) (0.52) (0.63)
Epoch dummy (ed7) -2.34 -1.98 -2.36 -1.68 *
(a068) (0.78) (0 65) (0a77)
Constant 2.29 19.01 ** 2.21 27.45 *
(2.69) (7 62) (3.88) (10.03)
No. countries 56 56 56 40
No. observations 275 275 275 194
F/Wald Chi2 178.39 16.26 15.53 113.03
R squared (overall) 0.41 0.11 0.41
R squared (within) 0.26 0.30
R squared (between) 0.56 0.01
Hausman(RE vs.FE) 5.71
F test for country specific effects 1.25
Hausman(FE vs. OLS) 10.50
Hausman(TSLS vs. OLS) 0.00
F test for time dummies 31.75
Test for 1st order serial correlation -2.41
Test for 2nd order serial correlation 1.16
Sargan test for Instrument optimality 39.59
First and greater
Instruments for policy SACW-iniY lags of OY
First and greater
SACW'iniY'2 lags of M21
Second and
greater lags of
SACW-LPOP policy
46
Table 9
Growth regression results
Dependent variable: Growth of real per capita GDP
White heteroskedasticity consistent standard erro s in (ta/ics)
( and denote significance at 1%, 5%, and 10% respectively)
No. 1 2 3
Pooled OLS Pooled OLS
preferred preferred Pooled OLS
specification specification with 8 year
Model (1970-1981) (1982-1993) epochs
Initial income (iniY) -0.46 -0.33 -0.10
(0.88) (0 62) (0 68)
Ethnolinguistic fractionalisation (ethnf) -0.27 -0.20 -0.02
(1 11) (1. 01) (O 88)
Assassinations (ASSAS) -0.88 0.10 -0.22
(0.59) (0.22) (0.30)
Ethnolinguisticfractlonalisation xAssasinatlons (ethnas) 1.51 -0.09 0.15
(O 93) (0 63) (0 57)
Institutional quality (ICRGE) 0.69 0.50 ** 0.47 **
(0.26) (0.24) (020)
M2/GDP (M21) 0.00 0.02 0.02
(0.03) (0.02) (0.01)
Sub-Saharan Africa dummy (SSA) -1.66 -1.23 -1.10
(1 19) (0 78) (0.82)
East Asian dummy (EASIA) 0.06 1.69 0.65
(0 84) (0.86) (0.62)
Policy (policy) 0.93 ** 1.00 1.12
(0.40) (0 16) (0.17)
Neg. shock/com. price change interacton (negdldmN) -52.25 * -82.30 -95.75
(22. 95) (25 83) (28 44)
Epoch dummy (ed3) 0.29
(0.60)
Epoch dummy (ed4) -1.19 *
(0 67)
Epoch dummy (ed5)
Epoch dummy (ed6) 2.15
(0 61)
Epoch dummy (ed7) 0.90
(0.63)
Constant 2.55 -1.96 0.08
(6.26) (4 54) (4.68)
8 year epoch dummy (v81) -2.56 *
(o 05)
8 year epoch dummy (v82) -1.93
(0.49)
No. countries 50 52 56
No. observations 136 139 149
F 7.56 13.75 16.80
R squared (overall) 0.28 0.50 0.46
47
Table 10
Growth regression results
Dependent variable: Growth of real per capita GDP
White heteroskedasticity consistent standard errors in (itasics)
and denote significance at 1%, 5%, and 10% respectively)
No. 1 2 3 4
SYS-GMM Pooled OL.S Pooled OLS
preferred with aid and with aid and
SYS-GMM specification policy policy
preferred with Investment interaction interaction
specfrication and without terms (full terms (without
Model with investment policy sample) outliers)
Initial income (IniY) -4.20 -4.32 -0.39 -0.44
(1.20) ((.23) (0.59) (0.59)
Ethnolingulstic fractionallsation (ethnf) -2.15 -2.15 -0.18 -0.19
(155) (1.71) (0 75) (0e74)
Assassinations (ASSAS) -0.33 -0.27 -0.37 -0.37
(0.25) (023) (0.27) (027)
EthnolingulstUc tractionatlsatlon x Assasinations (ethnas) 0.71 ^ 057 0.61 0.60
(0.39) (0.38) (0.48) (0.49)
Institutionalquality (ICRGE) 1.17 1.31 0.62 .. 0.64
(0.36) (0.37) (0.17) (0.19)
M2VGOP (M21) 0.00 0.00 0.02 0.01
(0.02) (0.03) (001) (001)
Sub-Saharan Africa dummy (SSA) -3.63 -3.82 -1.67 ^^ -1.67
(1.20) (124) (0.77) (0.79)
EastAsian dummy (EASIA) 0.13 1.11 1.03 ^ 1.13
(1.18) (1.16) (0.59) (0.59)
Policy (policy) 0.66 0.84 ^^ 0.77 ^
(0.29) (0 20) (0.20)
Neg. shocfitcom. price change lnteracton (negdidmN) -37.14 -36.09 -61.94 -62.10
(17.12) (18.05) (17.14) (17.10)
tnvestmentVGDP (IlY) 0.14 0.15
(009) (009)
AidlGDP (EDA) 0.03 -0.06
(0.13) (0e19)
AidlGDP x Policy (edapolA) 0.18 0.17 ^^
(0.10) (007)
(AldtGDP)^2 x Policy (eda2polA) -0.02
(096) (0 70) (0.01)
Epoch dummy (ed3) 0.25 0.13 0.23 0.24
(0.53) (0055) (0 59) (0e59)
Epoch dummy (ed4) -0.93 -1.00 -1.32 * -1.31 ^-
(0.69) (6 67) (0 66) (0 66)
Epoch dummy (ed5) -2.95 -3.24 -3.47 -3.45
(0.66) (0.70) (0,60) (0 90)
Epochdummy(ed6) -1.13 ^ -1.11 -1.46 ^^ -1.39
(0.64) (074) (0.53) (0.02)
Epoch dummy (ed7) -1.46 * -0.92 -2.37 -2.27
(0.79) (0.96) (0.65) (0.95)
Constant 28.04 28.98 1.68 2.21
(7.60) (7.79) (4.28) (4.29)
No. countries 40 40 56 56
No. observations 234 234 275 270
R squared 0.42 0.42
Wald Chi2tF 157.35 114.11 . 16.59 16.50
Wald test for time dummies 29.32 35.56
Test for 1st order serial correlation -2.90 -2.71
Test for 2nd order serial correlatlon 1.02 0.65
Sargan test for instrument optimallty 40.84 40.79
First and First and
greater lags greater lags
Instruments of iniY of iniY
First and First and
greater lags greater lags
of M21 of M21
48
Table 11
Growth regression results
Dependent variable: Growth of real per capita GDP
White heteroskedasticity consistent standard errors in (iteIcs)
(, m,and denote significance at 1%, 5%, and 10% respectively)
No. 1 2 3 4
Pooled OLS Pooled OLS
Pooled OLS Pooled OLS preferred preferred
preferred preferred speclflcation w. specification w.
specification w. specification w. GARCH GARCH
GARCH commodity uncertainty uncertainty
Model uncertainty price variability (1970-1981) (1982-1993)
Initial Income (iniY) -0.48 -0.45 -0.46 -0.46
(0r55) (0 53) (0.88) (0865)
Ethnollngulstic fractionalsation (ethnf) -0.31 -0.33 -0.27 -0.26
(0.73) (0.74) (1.11) (1.02)
Assassinations (ASSAS) -0.37 -0.36 -0.88 0.10
(0.28) (0r28) (0 59) (0.23)
Ethnolingulsct fractionalisataon x Assasinations (eti nas) 0.62 0.60 1.51 -0.06
(0.47) (0.47) (0.93) (0.63)
Institutional quality (ICRGE) 0.61 ... 0.58 ... 0.69 ... 0.55 *
(018) (018) (0.26) (0.24)
M2/GDP (M21) 0.02 0.02 0.00 0.02
(0.01) (0.01) (003) (002)
Sub-Saharan Africa dummy (SSA) -1.48 ^ -1.39 * -1.67 -1.41 *
(0.74) (0.72) (1.20) (0 78)
East Asian dummy (EASIA) 0.82 0.76 0.06 1.76 *
(0.54) (0 54) (0.64) (0.86)
Policy (policy) 1.01 - 1.02 0.93 0.99
(0.15) (0.14) (040) (018)
Neg. shocidcom. price change interacton (negdldmNtI) -65.03 -60.46 -52.30 ** -94.00
(16S5) (17.34) (2299) (24.40)
GARCH conditlonal variance (gar7O) 11 .34 0.72 28.62
(19.39) (27.04) (25.35)
Commodity price variability (std) -2.70
(3.55)
Epoch dummy (ed3) 0.20 0.43 0.28
(0.61) (0.67) (0.63)
Epoch dummy (ed4) -1.33 -1.18 * -1.19 *
(0.66) (0.84) (0.68)
Epoch dummy (edS) -3.40 -3.40
(0.59) (0.59)
Epoch dummy (ed6) -1.44 -1.33 ** 2.12 '*
(0.53) (0.53) (0.62)
Epoch dummy (ad?) -2.39 -2.33 0.80
(0.62) (0.61) (0.64)
constant 2.45 2.63 2.55 -1.19
(392) (3.89) (6.30) (4.76)
No. countriea 56 56 50 52
No. obServations 275 275 136 139
F(regression) 17.27 16.51 6,93 14.05
R squared 0.41 0.41 0.28 0.50
49
Appendix: Data Sources and Coverage
Shocks were identified using an annual index, while uncertainty was estimated
using quarterly indices. Both indices have identical composition, use similar weights,
and therefore differ only in terms of their frequency. It was necessary to use high
frequency quarterly data to obtain convergence for the GARCH models used to
estimate uncertainty, while discrete shocks are arguably better thought of as annual
events.
The indices are have constant 1990 base year weights, wherefore they do not
cope well with shifts in the structure of trade. In particular, the indices do not capture
resource discoveries and other quantity shocks after the base period. Nor do they
capture temporary volume shocks except for those, which occur in the base year itself.
However, since the purpose is to capture price rather than quantity movements, it is
desirable to hold volumes constant. This also avoids possible endogeneity problems
arising in the event of a volume response to price changes. Nevertheless, indices will
understate income effects of a given price change. The data set covers 113 countries
of which 44 are Sub-Saharan African countries, 16 are from the Middle East and
North Africa, 19 are from Latin America, 7 are from South Asia, 9 are from East Asia,
5 from the Pacific, and 12 are from the Caribbean. The final country is South Africa.
Table Al provides basic descriptive statistics on each country's structure of trade and
regional affiliation.
Each individual country's commodity price index is constructed using
international commodity price indices for 57 commodities. Table A2 lists the
commodities used. Price data are mainly from International Financial Statistics (IFS).
The single exception is the price of cocoa used for African countries, which is from
International Cocoa and Coffee Organization (ICCO), because the Ghanaian Cocoa
series in IFS is not credible, and has major gaps. A few important commodities have
not been included in the index due to lack of adequate data. These include notably
prices of natural gas and uranium ore. The indices for countries whose exports are
dominated by one or both of these commodities, such as Niger, which is a major
uranium producer, should therefore be interpreted with caution.
50
The complete quarterly data set covers the period from 1957Q1 to 1997Q4,
producing a total of 18,532 observations'2. Unfortunately, it was not possible to obtain
IFS data starting in 1957Q1 for al. commodities, but since identical sample length is
an important consideration when rmeasuring uncertainty, it was decided to generate the
missing observations. This was do ne using a combination of methods. For series with
missing values at the start of the series for which other highly correlated series were
available, the missing values were generated using a partial adjustment regression
equation:
ln(X') = A + A ln(X'-1 ) + A ln( l) +e, [Al]
where X, is the series with the missing early values and Y, is a highly correlated
series with a full set of observations. The regression was run on overlapping
observations, and then used to 'backcast' the missing observations. This method was
applied to 'fill' the initial gap oF 12 observations in the Palm Kernels and African
Cocoa series where the IFS series began only in 1960Q1. The close correlates were
IFS Palm Oil prices and Brazilian Cocoa prices, respectively. For the following series
with missing early values where no obvious correlates were available, the early gaps
were filled using annual data as far as possible: Hardwood (1958Q1-1969Q4), Lead
(1957Q1-1963Q4), Manganese (1957Q1-1959Q4), Rubber (1957Q1-1961Q4), Silver
(1957Q1-1967Q4), Sorghum (1957Q1-1966Q4) and Sugar to US ports (1957Q1-
1962Q4). Finally, for the following few commodities there were no annual
observations to indicate the movements of the quarterly series, wherefore the real
price was held constant at the value of the first available observation: Coal (1957Q1-
1966Q2), Superphosphates (195 7Q1-1962Q4), and Tobacco (1957Q1-1967Q4). The
nominal Gold price was held constant over the period of its missing observations
(1957-1962q4). A few commodities had a occasional missing observations in mid-
sample. These included Colombian coffee (1994ql-q4), Manganese (1963q2-1964q4;
1967q3-1968q4), Palm Kernels (1967q2-1967q4), Shrimps (1995q2), and Silver
(1970q3). These gaps are all veiy short and were filled by linear interpolation.
12113 countries times 164 observations per country.
51
The biases introduced by filling early gaps in the data using annual data and
holding real prices constant are unlikely to be very large for the following reasons.
First, the GARCH based uncertainty measure allows the uncertainty to vary with time,
so biases early on in the index have less of an effect in subsequent periods. Secondly,
the problem of missing data mainly affects observations in the very early part of the
indices, which is generally outside the sample range used in the core regressions.
Finally, the number of affected observations are only 332 out of a total sample of
9348 observations'3, thus affecting only 3.46% of the observations.
The annual data used to locate discrete shocks also covers the period 1957-
1997. Data availability was better than for the quarterly series. However, for a few
commodities there were missing observations in first part of the series. These included
Coal (1957-1966), Hardwood (1957), Superphosphates (1957-62), and Tobacco
(1957-1967). The missing values for these commodities were generated by holding
real prices constant at the value of the first available observation. Gold prices were
unavailable for the period 1957-1962, and its nominal price was therefore held
constant for this period. Finally, Palm kernel prices for 1957-1959 were generated as
annual averages of the quarterly observations obtained by the regression with Palm
Oil described above.
The data on export values used in constructing the weighting item are exports
(fob) in current US$ in 1990. It was not possible to obtain quarterly weights so annual
1990 weights were also used for the base year in the quarterly indices; this also avoids
biases arising from any seasonal effects affecting output. The weights data are
variously from UNCTAD's Commodity Yearbook 1994 and the UN's International
Trade Statistics Yearbook (1993 and 1994). In some cases, the weights differed
considerably across different sources for no obvious reason. In such cases, the most
reasonable figure was chosen with reference to total exports data from alternative
sources such as individual countries own national accounts statistics. In a few cases, it
was not possible to obtain weights for the year 1990. In those cases a different base
year was used for the weights. Effort was made to select a new base year as close to
1990 as possible. The cases with different base year weights are: 1994 (Aluminum, St.
Vincent and Grenadines), 1984 (Beef, Haiti), 1994 (Jute, Rice and Hardwood,
Myanmar); 1989 (Sugar, Dominica). For South Africa, weights used were those of the
1 57 commodities times 164 observations per commodity.
52
Southern African Customs Union (SACU) because data on individual member
countries were unavailable.
Given the different availability of price and weight data across commodities,
there is a trade off between including additional commodities in each country's index
and losing observations in the i:ime series dimension. For this reason, the final
specification of the index for mo ,t countries does not include a complete set of the
exported commodities. In deciding whether to drop or retain a commodity, the cost in
terms of lost observations from including an additional commodity was balanced
somewhat informally against the possible gain in terms of a more representative
index. To ensure consistency and to minimize distortion to the final index,
commodities were only dropped if they constituted less than 10% of the commodity
exports of the country question, and if the number of available observations for the
variable was lower than the nurnber of observations on all the other commodities
included in the index (i.e. the commodity constituted a data constraint). Only one
exception was made to this rule. Woodpulp was dropped from the index, because data
was only available from 1983QI onwards. But Uruguay and South Africa produce
this commodity in moderate an.ounts (5 and 10% of sampled commodity exports,
respectively). So while the omission of this commodity is unlikely to affect most
indices it may have a minor impact on the indices for these two countries.
The quarterly and annua. indices for each the countries were deflated by the
same deflator; namely the unit ralue index (1990=100) of industrial country exports
from the International FinancicI Statistics. This index ('MUV') has been used as a
deflator of commodity prices in other recent work, e.g. Cashin, Liang and McDermott
(1999).
53
Table Al: Country Characteristics
id country Region Producer 1990 Value of 1990 Value 19901ndexed 1990 Total
type Indexed of Total Commodities Exports as a
Commodities Exports as a Share of Share of GDP
(US$m) (US$m) Total Exports
1 Algeria 2 4 2,309 14,425 0.16 0.23
2 Angola 1 4 2,800 1,493 1.87 0.39
3 Argentina 3 1 3,733 14,643 0.25 0.10
4 Bahamas, The 7 4 1,525 1,664 0.92 0.61
5 Bahrain 2 4 2,939 4,888 0.60 1.22
6 Bangladesh 4 2 617 1,882 0.33 0.08
7 Barbados 7 1 32 840 0.04 0.49
8 Belize 7 1 53 257 0.20 0.64
9 Benin 1 2 99 402 0.25 0.22
10 Bhutan 4 1 1 92 0.01 0.32
11 Bolivia 3 3 450 978 0.46 0.22
12 Botswana 1 1 116 1,895 0.06 0.56
13 Brazil 3 1 8,844 34,339 0.26 0.07
14 Burkina Faso 1 2 95 352 0.27 0.13
15 Burundi 1 1 68 89 0.76 0.08
16 Cameroon 1 5 1,011 2,275 0.44 0.20
17 Cape Verde 1 1 2 56 0.03 0.18
18 CAR 1 1 54 220 0.25 0.15
19 Chad 1 2 91 234 0.39 0.19
20 Chile 3 3 4,256 10,470 0.41 0.34
21 Colombia 3 1 3,806 8,283 0.46 0.21
22 Congo 1 4 1,103 1,433 0.77 0.51
23 Costa Rica 3 1 682 1,975 0.35 0.35
24 Coted'lvoire 1 1 1,667 3,421 0.49 0.32
25 Djibouti 1 1 2 249 0.01 0.55
26 Dominica 7 1 32 70 0.45 0.46
27 Dominican Republic 7 3 571 2,301 0.25 0.34
28 Ecuador 3 4 2,345 3,499 0.67 0.33
29 Egypt 2 4 956 8,647 0.11 0.20
30 El Salvador 3 1 213 892 0.24 0.19
31 Ethiopia 1 1 212 535 0.40 0.08
32 Fiji 6 1 216 879 0.25 0.64
33 Gabon 1 4 2,462 2,740 0.90 0.46
34 Gambia 1 1 13 201 0.07 0.69
35 Ghana 1 5 1,041 993 1.05 0.17
36 Grenada 7 1 8 110 0.07 0.49
37 Guatemala 3 1 651 1,509 0.43 0.20
38 Guinea 1 1 12 870 0.01 0.31
39 Guinea-Bissau 1 2 2 26 0.09 0.11
40 Guyana 3 1 224 249 0.90 0.63
41 Haiti 7 1 21 477 0.04 0.16
42 Honduras 3 1 427 1,108 0.39 0.36
43 India 4 1 3,158 23,026 0.14 0.08
44 Indonesia 5 4 11,515 29,912 0.38 0.26
45 Iran 2 4 17,036 26,476 0.64 0.22
46 Iraq 2 4 8,881 NA NA 0.27
47 Jamaica 7 3 851 2,207 0.39 0.52
48 Jordan 2 3 215 2,489 0.09 0.62
49 Kenya 1 1 377 2,234 0.17 0.26
54
50 Korea, Republic of 5 1 781 75,544 0.01 0.30
51 Kuwait 2 4 2,607 8,281 0.31 0.45
52 Lao P.D.R 5 1 12 98 0.12 0.11
53 Lesotho 1 2 7 89 0.08 0.14
54 Liberia 1 2 288 464 0.62 0.43
55 Madagascar 1 1 111 489 0.23 0.16
56 Malawi 1 2 382 447 0.85 0.24
57 Malaysia 5 4 8,548 32,664 0.26 0.76
58 Mali 1 2 218 415 0.52 0.17
59 Mauritania 1 3 232 473 0.49 0.46
60 Mauritius 1 1 358 1,724 0.21 0.65
61 Mexico 3 4 10,460 48,866 0.21 0.19
62 Mongolia 5 3 321 436 0.74 0.21
63 Morocco 2 3 1,179 6,849 0.17 0.27
64 Mozambique 1 1 61 230 0.26 0.16
65 Myanmar 4 2 218 NA NA 0,03
66 Namibia 1 3 202 1,217 0.17 0.49
67 Nepal 4 2 6 382 0.02 0.11
68 Nicaragua 3 1 279 253 1.10 0.25
69 Niger 1 2 5 420 0.01 0.17
70 Nigeria 1 4 12,754 12,366 1.03 0.43
71 Oman 2 4 4,768 5,555 0.86 0.53
72 Pakistan 4 2 873 5,918 0.15 0.15
73 Panama 3 1 200 4,611 0.04 0.87
74 Papua New Guinea 5 3 1,164 1,309 0.89 0.41
75 Paraguay 3 1 808 1,750 0.46 0.33
76 Peru 3 3 1,549 3,937 0.39 0.12
77 Philippines 5 1 1,326 12,198 0.11 0.28
78 Qatar 2 4 2,872 NA NA 0.52
79 Reunion 1 1 142 NA NA 0.05
80 Rwanda 1 1 121 145 0.83 0.06
81 Saudi Arabia 2 4 34,168 48,366 0.71 0.46
82 Senegal 1 1 252 1,512 0.17 0.27
83 Seychelles 1 2 0 256 0.00 0.68
84 Sierra Leone 1 3 41 215 0.19 0.24
85 Singapore 5 5 2,278 73,999 0.03 1.98
86 Solomon Islands 6 2 40 99 0.40 0.47
87 Somalia 1 1 43 90 0.48 0.10
88 South Africa 8 3 3,155 27,327 0.12 0.26
89 Sri Lanka 4 1 601 2,424 0.25 0.30
90 St. Kitts and Nevis 7 1 9 75 0.12 0.59
91 St. Lucia 7 1 78 288 0.27 0.72
92 St. Vincent 7 1 48 128 0.38 0.66
93 Sudan 1 2 253 653 0.39 0.07
94 Suriname 3 3 427 420 1.02 0.43
95 Swaziland 1 1 187 690 0.27 0.83
96 Syrian Arab Republic 2 4 1,690 3,413 0.50 0.28
97 Tanzania 1 1 200 555 0.36 0.13
98 Thailand 5 1 2,828 29,130 0.10 0.34
99 Togo 1 3 225 545 0.41 0.33
100 Tonga 6 1 0 36 0.01 0.32
101 Trinidad &Tobago 7 4 858 2,214 0.39 0.44
102 Tunisia 2 4 738 5,353 0.14 0.44
103 Turkey 2 2 891 20,016 0.04 0.13
55
104 Uganda 1 1 167 312 0.53 0.07
105 United Arab Emirates 2 4 13,403 22,331 0.60 0.66
106 Uruguay 3 1 656 2,185 0.30 0.26
107 Vanuatu 6 1 11 71 0.15 0.46
108 Venezuela 3 4 10,371 19,168 0.54 0.39
109 Western Samoa 6 1 5 45 0.10 0.31
110 Yemen, Republic of 2 1 40 689 0.06 0.15
111 Zaire 1 3 949 2,758 0.34 0.30
112 Zambia 1 3 1,167 1,180 0.99 0.36
113 Zimbabwe 1 2 830 2,174 0.38 0.32
TOTAL 217,253 714,155
(Note: Regions: I-Sub-Saharan Africa; 2-Middle East and North Africa; 3-Latin America; 4-South Asia; 5-East Asia; 6-Pacific;
7-Caribbean; 8-South Africa. Type: I-Agricultural food stuffs; 2-Agricultural non-foods; 3-Non-Agricultural non-oil
commodities; 4-Oil; 5-Mixed; 'NA': not available).
56
Table A2: Commodities Used in Country Indices
ID IFS Name IFS Code 1990 Value of World 1990 Share in
Exports (US$m) World Commodity
Exports
1 ALUMINUM 15676DRDZF ... 4,514 0.021
2 BANANAS 24876U.DZF ... 1,993 0.009
3 BEEF 19376KBDZF ... 1,360 0.006
4 COAL 19374VRDZF ... 1,489 0.007
5 COCOA (Brazil) 22374R.DZF ... 992 0.005
6 COCOA (ICCO) OBCS 1,617 0.007
7 COCONUT OIL (Philippines) 56676AI.ZF ... 361 0.002
8 COCONUT OIL New York 56676AIDZF ... 163 0.001
9 COFFEE BRAZIL 22376EBDZF ... 1,283 0.006
10 COFFEE COLOMBIA 23376E.DZF ... 1,473 0.007
11 COFFEE OTHER MILDS 38676EBDZF ... 2,539 0.012
12 COFFEE UGANDA 79976ECDZF... 1,357 0.006
13 COPPER UK 11276C.DZF ... 8,889 0.041
14 COPRA PHILIPP 56676AGDZF ... 68 0.000
15 COTTON 11176F.DZFM40 3,626 0.017
16 FISHMEAL 29376Z.DZF ... 768 0.004
17 GOLD 11276KRDZF ... 617 0.003
18 GROUNDNUT OIL 69476BIDZF ... 222 0.001
19 GROUNDNUTS 69476BHDZF ... 172 0.001
20 HARDWOOD 54876RMDZF ... 1,850 0.009
21 HIDES 11176P.DZF ... 603 0.003
22 IRON ORE 22376GADZF ... 4,164 0.019
23 JUTE 51376X.DZF ... 743 0.003
24 LAMB 19676PFDZF ... 32 0.000
25 LEAD 11176V.DZF ... 272 0.001
26 LINSEED OIL 00176NIDZF ... 96 0.000
27 MAIZE 11176J.DZFM17 744 0.003
28 MANGANESE 53476W.DZF ... 717 0.003
29 NEWSPRINT 17272UL.ZF ... 143 0.001
30 NICKEL 15676PTDZF ... 939 0.004
31 OIL 00176AADZF ... 143,187 0.659
32 PALM KERNELS 54876DFDZF ... 0 0.000
33 PALM OIL 54876DGDZF ... 1,994 0.009
34 PHOSPHATE ROCK 68676AWDZF ... 902 0.004
35 RICE 57874N .ZF... 866 0.004
36 RICE THAILAND (BANGKOK) 57876N.DZFM81 923 0.004
37 RUBBER 11176L.DZF... 2,007 0.009
38 RUBBER MALAYSIA 54876L.DZF... 1,122 0.005
39 SHRIMP 11176BLDZF... 4,643 0.021
40 SILVER 11176Y.DZF... 715 0.003
41 SISAL 63976MLDZF... 54 0.000
42 SORGHUM 11176TRDZF... 24 0.000
43 SOYBEAN MEAL 11176JJDZF... 1,626 0.007
44 SOYBEAN OIL 11176JIDZF... 1,073 0.005
45 SOYBEANS 11176JFDZF... 1,932 0.009
46 SUGAR 223741.DZF... 1,861 0.009
47 SUGAR EEC IMPORT 112761.DZF... 1,406 0.006
48 SUPERPHOSPHATE 11176ASDZF... 498 0.002
49 TEA (Sri Lanka) 52474S .ZF... 493 0.002
57
50 TEA AVERAGE AUCTION 11276S.DZF ... 1,262 0.006
51 TIN (Bolivia) 21874Q.DZF ... 84 0.000
52 TIN ALL ORIGINS 11276Q.DZF ... 2,566 0.012
53 TOBACCO 11176M.DZF ... 1,050 0.005
54 UREA 17076URDZF ... 445 0.002
55 WHEAT 11176D.DZF ... 1,259 0.006
56 WOOL 11276HDDZF ... 720 0.003
57 ZINC 11276T.DZF... 733 0.003
TOTAL 217,253 1.000
(Note: 'QBCS' stands for Quaterly Bulletin of Cocoa Statistics)
58
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Devetopment: Laying the Basis for Helene Grandvoinnet 31983
Collective Action Mattia Romani
WPS2440 Lessons from Uganda on Strategies John Mackinrion September 2000 tl. Sladovr,h
to Fight Poverty Rlitva Reinikka 376&8
WPS2441 Controlling the Fiscal Costs of P3atrick Honohan September 2000 A. Yapterruc
Banking Crises Daniela Klingebiel 3852i6
Policy Research 'W\orking Paper Series
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Title Author Date for paper
WPS2442 A Firms's-Eye View of Policy and Bernard Gauthier September 2000 L. Tabada
Fiscal Reforms in Cameroon Isidro Soloaga 36896
James Tybout
WPS2443 The Politics of Economic Policy Richard H. Adams Jr. September 2000 M. Coleridge-Taylor
Reform in Developing Countries 33704
WPS2444 Seize the State, Seize the Day: Joel S. Hellman September 2000 D. Billups
State Capture, Corruption, and Geraint Jones 35818
Influence in Transition Daniel Kaufmann
WPS2445 Subsidies in Chilean Public Utilities Pablo Serra September 2000 G. Chenet-Smith
36370
WPS2446 Forecasting the Demand for Lourdes Trujillo September 2000 G. Chenet-Smith
Privatized Transport: What Economic Emile Quinet 36370
Regulators Should Know, and Why Antonio Estache
WPS2447 Attrition in Longitudinal Household Harold Alderman September 2000 P. Sader
Survey Data: Some Tests for Three Jere R. Behrman 33902
Developing-Country Samples Hans-Peter Kohler
John A. Maluccio
Susan Cotts Watkins
WPS2448 On "Good" Politicians and "Bad" Jo Ritzen September 2000 A. Kibutu
Policies. Social Cohesion. William Easterly 34047
Institutions, and Growth Michael Woolcock
WPS2449 Pricing Irrigation Water: A Literature Robert C. Johansson September 2000 M. Williams
Survey 87297
WPS2450 Which Firms Do Foreigners Buy? Caroline Freund September 2000 R. Vo
Evidence from the Republic of Simeon Djankov 33722
Korea
WPS2451 Can There Be Growth with Equity? Klaus Deininger September 2000 33766
An Initial Assessment of Land Reform Julian May
in South Africa
WPS2452 Trends in Private Sector Development Shobhana Sosale September 2000 S. Sosale
in World Bank Education Projects 36490
WPS2453 Designing Financial Safety Nets Edward J. Kane September 2000 K. Labrie
to Fit Country Circumstances 31001
WPS2454 Political Cycles in a Developing Stuti Khemani September 2000 H. Sladovich
Economy: Effect of Elections in 37698
Indian States