Why the Link between Volatility and Growth is
both Positive and Negative¤
Jean Imbs
London Business School
and CEPR
April 2002
Preliminary
Abstract
This paper uses international disaggregated data to investigate the
link between volatility and growth. We …nd the relationship to depend
on the level of aggregation. At the sectoral level, volatility is associated
with high growth. We use non-parametric methods to show that within
activity volatility is typically associated with net entry and higher (total factor) productivity. This is consistent with the evidence that most
of the process of creative destruction occurs within narrowly de…ned
sectors, as surveyed in Caballero and Hammour (2000) or Davis and
Haltiwanger (1998). Once sectoral data is aggregated at the country
level however, the link becomes negative. We show how aggregation
creates this discrepancy.
Keywords: Sectors, Growth, Volatility.
JEL Classi…cation: E32, O40
¤
London Business School, Dept. of Economics, Regent’s Park, London NW1 4SA,
[email protected].
1
1
Introduction
The relationship between macroeconomic volatility and growth is an old
and important issue, both from a theoretical and an empirical standpoint.
The debate relates directly to two central questions in macroeconomics:
what are the determinants of economic growth, and if volatility is one of
them, how does this a¤ect our understanding of business cycles and their
welfare e¤ect. Theoretically, the correlation could go both ways: positive
on the one hand, as volatility could be a manifestation of the adoption of
a new (general purpose) technology, that entails costly restructuring.1 ;2 On
the other hand, the relationship could be negative as volatility can result
in wasted human capital or deter investment and the implementation of
newer technologies.3 Without a theoretical consensus, a considerable body
of literature has concerned itself with establishing empirically the sign of the
relationship. This paper contributes to this literature using a disaggregated
approach.
There are several reasons why a disaggregated approach is promising
to address the question at hand. Firstly it multiplies the cross-sectional
dimension of the data, quite usefully when estimating the determinants of
output growth, an endeavour famously sensitive to the conditioning set. In
particular, the higher dimensionality of the data relative to cross-country
studies, and the fact that the variation of interest is country-sector speci…c
opens the possibility to account for all country- and sector-speci…c deter1
The ‡uctuations resulting from the disruptive introduction of a new technology are
commonly lab eled “Schumpeterian waves”, or “creative destruction”. A large theoretical
literature, spanning macroeconomics and industrial organization, proposes to model creative destruction. See for instance Helpman and Trajtenberg (1998) or Jovanovic and Rob
(1990) for an early contribution.
2
A closely related strand of research concerns itself with the long-run e¤ects of business
cycles. In particular recessions could have positive long-run e¤ects on growth, through
“opportunity costs” arguments whereby productivity-enhancing activities are best left for
recessionary p eriods, or “cleansing” e¤ects whereby recessions eliminate less productive
units, thus increasing average productivity. This is similar to the present exercise, but the
corresponding empirical tests typically involve time-series procedures to identify shocks of
interest and track their e¤ect on productivity over time. Unless appropriate asymmetries
between recesionnary and expansionary shocks are built in these models, volatility will
however remain unrelated with growth. See Aghion and Howitt (1996) for a survey.
3
This will be the case if for instance investment is irreversible, as in Pindyck (1991), or
if growth occurs through learning-by-doing, as in Martin and Rogers (1997). Ramey and
Ramey (1991) present a model where volatility constrains the choice of technology, and
thus results in lower growth.
1
minants of growth, both in a pure cross-section and using panel techniques.
We …nd the link between (sectoral) growth and volatility to be non negative
at least, and actually signi…cantly positive in most speci…cations. Secondly,
the question at hand can be addressed both in the aggregate and at the
sectoral level using the same data, an interesting opportunity to investigate
the importance of aggregation when computing growth and volatility.
Our disaggregated data includes information on the number of establishments at the sectoral level, thus making it possible to decompose growth
into an intensive (average …rm growth) and an extensive (average …rm entry)
margin.4 This decomposition turns out to be strikingly relevant, as we …nd
volatility to be associated signi…cantly with extensive growth only. Furthermore, we use non-parametric methods to show that the link between growth
and volatility increases with the rate of (sectoral) productivity growth. Both
facts lend support to a Schumpeterian view of economic ‡uctuations where
innovative entering …rms induce both high growth and high volatility, as the
economy adjusts to the advent of a new technological paradigm. They are
also consistent with the empirical fact that a large share of (industry-level)
productivity growth originates in the reallocation of factors within narrowly
de…ned sectors.5
This surprising result stands in stark contrast with recent (aggregate)
studies by Ramey and Ramey (1995) or Martin and Rogers (2000) [RR
and MR, respectively], who document a negative relation. We explain this
contradiction with an aggregation argument. In particular, we show that
the same dataset can be used to generate both a (well-known aggregate)
negative and a (surprising disaggregate) signi…cantly positive relationship
between growth and volatility. We show that the aggregate negative link
stems from composition e¤ects, and document how the range of activities
that compose aggregate GDP appears to have a crucial impact on the nature
of the aggregate relationship.
Thus, this paper reconciles two sets of apparently contradictory theoretical propositions, whose manifestations are simply obscured by aggregation.
The negative link between aggregate growth and volatility masks a positive
4
The data come from UNIDO, and cover manufacturing sectors. An establishment is
de…ned as “a unit which engages, under a single ownership or control, in one, or predominantly one, kind of activity at a single location”. The measure may not exactly coincide
with the notion of …rm, but there is to our knowledge no other data for such a large
cross-section. We will indi¤erently use the two appelations in the paper.
5
See for instance Caballero and Hammour (2000).
2
one at the purely disaggregated level, and is to cross-country heterogeneity
in the sectoral composition of aggregate GDP. The rest of the paper is structured as follows. In section 2, we present our purely disaggregated results
and, using a variety of recent techniques, establish that sectoral growth
and volatility are positively related in most cases. In section 3, we show
the sectoral results are consistent with a Schumpeterian view of innovation
and volatility, commonly labeled “creative destruction”. Section 4 turns
to the aggregate evidence and shows how aggregation a¤ects the link between growth and volatility. There, we reconcile our (sectoral) evidence
with the literature, and cast some light on the relationship between aggregate growth and volatility. Section 5 presents some sensitivity analysis.
Section 6 concludes.
2
2.1
Sectoral Volatility and Growth
Data
We use data on sectoral value added, employment, factor content and number of establishments in manufacturing activities only, as published by the
United Nations Industrial Development Organization (UNIDO). Although
observations go as far back as 1963, the data is particularly incomplete in
the early part of the sample.In order to limit measurement error, we focus
on countries where at least 18 years of observations are available between
1970 and 1992, which selects a maximum of 36 countries.6 We also consider a sub-set of the UNIDO data, composed of OECD economies only,
which further reduces the sample to 15 countries. The purpose of a reduced
dataset is to control for the putative importance of transitional dynamics and
focus on economies at a comparable stage of development. The OECD sample excludes developing countries where industrialization -and the a¤erent
structural change- has played an important role in economic growth. There
is a maximum of 28 sectors. We de‡ated value added by Producer Price
6
We constrained the numb er of sectors in a given country to remain constant over time,
as the aggregate volatility stemming from whole sectors appearing or disappearing over
time could be an artefact of measurement error, and eliminated sectors whose de…nition
in the ISIC classi…cation system varied in our sample. We also took care of outliers, whose
inclusion only reinforced our results. The “System of National Accounts” was changed
in 1993, which is why sectoral information of a comparable nature over time and across
countries typically becomes incomplete after 1992.
3
Index series taken from the International Monetary Fund’s (IMF’s) International Financial Statistics.7 Data on aggregate (capital and output) growth
rates come from the Penn-World Tables. Table 1 presents some summary
statistics. Relative sectoral output growth and volatility are much larger on
average in the extended sample, as expected given the wider country coverage. The unconditional correlation between growth and volatility is mildly
positive in the UNIDO data, although never signi…cantly di¤erent from zero.
Figures 1 and 2 show the corresponding unconditional scatterplots.
2.2
Cross-Sectional Evidence
In this section, we focus on the pure cross-section in the variables of interest.
In particular, we follow an intuition similar to Rajan and Zingales (1998),
and hold constant all (time-invariant) country- and sector-speci…c characteristics through …xed e¤ects, and focus instead on individual country-sector
variation. Thus we alleviate parts of the issue of conditionality, quite important in standard growth regressions. A substantial literature has indeed
concerned itself with the appropriate conditioning set in growth regressions,
culminating with Levine and Renelt (1992) who propose a list of four robust conditioning variables. 8 Sectoral data make it possible to control for
all time invariant country-speci…c considerations, and thus do away with the
potential issue of sensitivity that is prevalent in this area, even in a pure
cross-section. Similarly, sector-speci…c e¤ects control for any tendency of
one sector to display systematically high growth rates, possibly because of
technological breakthrough, and focus on systematic deviations from an international average. Thus, all estimations are run controlling for countryand sector-speci…c e¤ects. Speci…cally, consider
µ
¶
yi;j;T
yi;j;0
yi;j;t
yi;j;t¡1
ln
¡ ln
= ¯ 0 + ¯ 1 VT ln
¡ ln
+ ¯ 2 Xi;j + "i;j
Yj;T
Yj;0
Yj;t
Yj;t¡1
(1)
where i and j index industry
P and country, respectively, yi;j;t is sectoral value
added at time t, Yj;t = i yi;j;t , Xi;j is a vector of control variables and
7
Or alternatively an index of industrial production when the PPI was not available.
This follows Rajan and Zingales (1998). Countries and sectors are listed in the App endix.
8
The Levine-Renelt variables are initial income p er capita, average population growth,
initial human capital as measured by schooling years and average investment rate.
4
VT (:) is the (time) variance operator, computed over period T.9 The coef…cient of interest is ¯ 1. There are numerous reasons why the residual "i;j
is liable to contain both industry- and country-speci…c e¤ects, say °i and
´j , respectively. To list only two, suppose for instance political instability
translates in both high aggregate volatility and low growth, as in Alesina
et al (1992): the residual is then negatively correlated with the regressor,
through ´ j . Suppose instead an industry speci…c technological breakthrough
is associated with both high sectoral volatility and growth: the residual is
now positively correlated with the regressor, through °i . Accounting for
these …xed-e¤ects is a serious issue in most growth regressions, especially
so when the issue is to identify the signi…cance of one particular variable,
such as volatility in our case. Here however, (1) can be estimated simply in
deviations from means as in
µ
¶
µ
¶
yi;j;T
yi;j;0
yi;j;t
± ln
¡ ln
= ¡° 0 + ¯ 1 ±VT ¢ ln
+ ¯ 2 ±Xi;j + ±"i;j
Yj;T
Yj;0
Yj;t
(2)
P
P
where ±Zi;j = Zi;j ¡ 1I i Zi;j ¡ J1 j Zi;j. The country or sector …xed e¤ects
P
P
are now controlled for, as ° 0 = ¡¯ 0 + 1I i °i + 1J j ´ j .10 Equation (2)
leaves open the question of what country-industry speci…c variables ought
to be included in the set of controls, denoted by Xi;j . We now turn to this
question.
Numerous authors have concerned themselves with the dynamics of sectoral specialization, using insights from growth and international trade theories.11 A very stylized model may be helpful here. Consider a two-country
two-sector two-factor world. Sector one is capital-intensive, sector two
labour-intensive, and country A has higher aggregate capital-labour ratio
than country B. Suppose …nally that initial sectoral specialization patterns
correspond to the balance of aggregate endowments, i.e. that sector one is
9
Basu and Fernald (1997) argue that gross output is a better measure of sectoral
activity than value added when investigating external e¤ects of aggregate activity onto
sectoral production. We checked that the results obtain as well when using gross output.
10
This is a generalization of the estimation procedure used in Rajan and Zingales (1998),
where only the dependent variable is demeaned, and the focus is on the signi…cance of
an interaction term b etween (sector-speci…c) need for and (country-speci…c) availability
of external …nance. Since they use the UNIDO data as well, Rajan and Zingales also have
to present results for manufacturing sectors only.
11
For theoretical approaches, see Ventura (1997) or Cuñat (2000). For an empirical
analysis, see Imbs and Wacziarg (2002).
5
initially larger in country A and sector two is larger in country B. With aggregate diminishing returns to capital, country B accumulates capital faster,
and factor price equalization favours growth in sector one there. Similarly,
factor price equalization and neo-classical convergence suggest sector two
will grow relatively faster in country A.12 Thus, from an empirical point
of view, the determinants of relative sectoral growth are two-fold. Firstly,
a variable capturing country-speci…c capital-labour growth interacted with
sector-speci…c capital content. This is the approach adopted in a recent
paper by Bernard and Jensen (2001), which shows sectoral factor content
is important in explaining higher than average sectoral output growth in a
cross-section of US regions. We estimate (2) with such an interaction term
between sectoral capital content and the aggregate growth of the capitallabour ratio. However, the measurement of this variable raises a number
of issues: although UNIDO provides information on the sectoral wage bill
and sectoral value added (both nominal), the resulting labour shares tend
to be quite noisy. Secondly, an assumption of constant returns to scale
must be maintained if the capital share is to be inferred from the wage bill.
Although the evidence suggests sectoral production functions by and large
display constant returns to scale, there seems to be ample cross-sectoral
variation.13 Thus, an interaction term à la Bernard and Jensen must be
taken as an approximation in the present context.
Fortunately, the previous sketch of a model suggests an obvious alternative: in both countries, the fastest growing sector is also the smallest initially.
This happens of course because of diminishing returns to capital, and because of our assumption that initial sectoral shares match initial relative
aggregate endowments. However, it suggests the inclusion of a measure of
the initial relative size of a sector in the estimation of (2), which has the
substantial advantage of being immediately available. There is at least one
additional reason to include such an “initial condition” term in (2), which
is more of an econometric nature. Transition dynamics in the usual neoclassical sense are potentially important here, as they tend to result in both
high and monotonically-decreasing growth. This may result in an upward
bias when estimating the relationship between growth and volatility.14 We
therefore include the initial sectoral share in value added in (2), and note
12
For details, see Ventura (1997). This model guided the choice of using relative growth
rates in (1) and (2).
13
See for instance Burnside, Eichenbaum and Rebelo (1996)
14
There are reasons to believe that this bias is not as imp ortant in the sectoral data as
it is in the aggregate. Imbs and Wacziarg (2002) have shown that the notion of a “steady
state economic structure”, to which growing economies would converge is not supported
6
that a negative sign could be interpreted equally as a “convergence” term
or as dictated by comparative advantage.
2.2.1
Results
Table 2 presents estimations of (2), for both UNIDO samples and various
speci…cations of Xi;j . The main result that should be taken from the table is
the signi…cantly positive sign of ¯ 1. The coe¢cient on the variance of value
added growth is positive and signi…cant in 75% of the cases, and always in
the OECD sub-sample. It is also never signi…cantly negative.15 Volatility
is also important economically. In the OECD, the smallest estimate of ¯ 1
across speci…cations is 3.832, which implies one standard deviation of the
volatility measure (measured across country-sectors) translates into around
a third of percentage point of average yearly sectoral output growth.16
Several other comments are in order. Firstly, our results are in partial agreement with Bernard and Jensen (2001), as we …nd some evidence
that capital intensive sectors grow faster in economies with high rates of
capital accumulation. This appears to be particularly prevalent in our extended sample, inclusive of developing economies. There are two important
di¤erences between this study and Bernard and Jensen’s: …rstly our sample is international whereas theirs is inter-regional in the US, secondly our
estimations are ran with both country and sector …xed e¤ects.17 When
an estimation more similar to Bernard and Jensen’s equations (7) and (8) is
performed, for instance our equation (1) with no variance term and no country …xed e¤ects, the “comparative advantage” interaction term does enter
with a very signi…cant positive sign even in the OECD sample. Thus, the
results in table 2 do not necessarily cast doubt on a role for international
trade based theories of sectoral specialization; rather, they correspond to an
estimation strategy whose purpose is elsewhere.
in the data. Countries are shown to …rst diversify, thus allocating resources across sectors
increasingly equally, but start re-specializing once they reach a relatively high level of
income p er capita.
15
From the point of view of the positive bias that could arise from transitional dynamics,
it is reassuring that estimates of ¯ 1 should be most positive in the sample where this
putative bias is a priori least prevalent, i.e. in the OECD.
16
To be precise, an average yearly growth higher by 0.36% in the OECD sample.
17
Bernard and Jensen focus on the U.S economy, and show sectoral growth is lowest in
least capital- and skill-intensive industries.
7
Secondly, table 2 provides evidence in favour of a signi…cant “convergence” term, as measured by initial sectoral value added, either in absolute
or in relative term. In both samples, initially “smaller” sectors tend to
display higher subsequent growth rates. As expected, the signi…cance of a
convergence term is strongest in the extended sample, where the volatility term ceases to be signi…cant once initial conditions are held constant.
This con…rms the importance of a bias due to “transitional dynamics” in
explaining why growth relates positively to volatility. In the reduced sample
however, volatility continues to be signi…cantly positive even controlling for
initial conditions, which calls for an alternative explanation than the aforementioned bias. There is evidence that the relationship between growth
and volatility is biased upwards by transitional dynamics, but this evidence
is quite limited between rich countries.18 The next section generalizes the
estimation procedure in making use of the panel dimension of the data.
2.3
Dynamic Panel Evidence
Recent years have seen important developments in the use of panel techniques to estimate growth equations.19 In this section, we implement some
of them, thus making use of the panel dimension of our data. A generalized
speci…cation of (1) is
µ
¶
yi;j;T
yi;j;t
yi;j;T ¡1
P
P
ln
= ¯ 1 VT ¢ ln
+ (¯ 2 + 1) ln P
y
y
i i;j;T
i i;j;t
i yi;j;T ¡1
+¯ 3 COMP:ADV:T + ®i;j + (±T ) + "i;j;T
(3)
with t 2 [T ¡1; T]. The conditioning set now includes initial relative sectoral
value added and an interaction term between initial sectoral capital content
18
We also investigate the possibility that the residuals in (2) have country- or sectorspeci…c components with implications on the structure of the covariance matrix, and thus
on the consistency of the estimates. We do this by allowing for clustered residuals at the
country and the sector level, i.e. relaxing the assumption of independence within clusters
(country or sector), but not between them. The technique is standard in the labour
literature. The results with standard errors robust to clustered residuals are available
upon request. In short, while ¯ 1 becomes less signi…cant, as all other coe¢cients, it never
falls below the 13% signi…cance level in the extended sample, and below 2% in the reduced
OECD one.
19
See Caselli, Esquivel and Montfort (1996) for a seminal contribution, and Forb es
(2000) for a recent one.
8
and aggregate capital growth over the subsequent sub-period [T ¡1; T ].20 ±T
denotes a period-speci…c indicator variable, included whenever meaningful.
The main di¤erence with equation (1) is that the …xed e¤ects ®i;j are now
generalized to be country-sector speci…c. An obvious procedure
to obtain
³
´
yi;j;T
yi;j;t
P
P
estimates for ¯ 1 involves evaluating ln
and
V
¢
ln
over
T
i yi; j;T
i yi;j;t
sub-periods, and estimating (3) in …rst di¤erences. However, a well-known
problem in doing so is that, while it controls for the presence of a timeinvariant component in the residuals, it leaves open the possibility that the
lagged dependent variable be correlated with the residuals. In other words,
estimates of ¯ 2 could be biased, with possible consequences on estimates of
¯ 1. Anderson and Hsiao (1981) suggest to instrument the …rst-di¤erenced
y
¡2
lagged dependent variable with its initial level, as indeed ln P i;j;T
is
i yi;j;T ¡2
not correlated with "i;j;T ¡ "i;j;T ¡1. Arellano and Bond (1991) generalize
this procedure by proposing to include further lags of the dependent variables as instruments, a useful possibility when the instrument set turns out
to be insu¢cient, as is often the case in the present context. Crucially,
both instrumenting techniques require no serial correlation in the residuals
for the instruments to be consistent. We next present the results of three
estimations procedures for (3), …rstly using simple within group OLS, secondly using the Anderson-Hsiao instrumental variable technique, and …nally
implementing the Arellano-Bond GMM method.
2.3.1
Results
Table 3 presents the results of our three dynamic panel speci…cations, with
or without the interaction variable capturing comparative advantage. The
generality of (3) is appealing, but measurement error is likely to obscure substantially the resulting estimation. Firstly, variances are now computed on
fewer observations; secondly, the resulting measurement error is likely to be
exacerbated by …rst-di¤erencing. As data quality tends to vary with aggregate income level, measurement error is likely to be strongest in our extended
sample. In other words, within-groups estimation of (3) is likely to give estimates of ¯ 1 and ¯ 2 that are seriously biased downward. From this point of
view, the results in table 3 are remarkably robust. The top panel presents
the results of the estimation of (3) in …rst-di¤erences, where ln Pyi;j;T
i yi; j;T
³
´
yi;j;t
P
and VT ¢ ln
yi;j;t are computed over [1970,1981] and [1982,1992], rei
20
Thus initial values are measured in T ¡ 1.
9
spectively, and initial variables are measured in 1970 and 1982.21 In both
samples, the convergence e¤ect is estimated to be strong, with very negative estimates of ¯ 2. As expected, transitional dynamics as proxied by
¯ 2 are both more signi…cant and larger in magnitude (by roughly a third)
in the extended sample. Secondly, the comparative advantage variable is
non-signi…cant, but its inclusion results in the e¤ects of volatility to become
insigni…cant in the extended sample, but not in the OECD. We conclude that
the presence of country-sector speci…c …xed e¤ects is not driving the tendency for the correlation between volatility and growth to be non-negative.
However, estimates of ¯ 2 are su¤ering from a possible bias, due to the
presence of lagged dependent variables in (3). The middle panel in table
3 uses the lagged level of relative value added to instrument for its lagged
growth, following Anderson and Hsiao (1981). The results indeed point to
an important bias, as ¯ 2 now becomes non-signi…cantly di¤erent from zero
in the OECD, whereas it is still negative, but less signi…cantly so, in the
extended sample. As for volatility, its e¤ects become virtually zero in most
cases. It is however not impossible that this could largely result from the
insu¢cient …t of the chosen instruments, as the very low values for …rst-stage
R2 suggest in the penultimate row of the middle panel.
Extending the set of available instruments is exactly the purpose of the
Arellano-Bond GMM estimator, which uses all available lags of the dependent variables as instruments (both in levels and in di¤erences). We report
the results of their estimation procedure in the lower panel of Table 3. Several comments are in order: …rstly, ¯ 1 is now positive and signi…cant in all
cases. Secondly, the convergence term becomes insigni…cant in the extended
sample, and actually signi…cantly positive in the OECD. Thirdly, the coe¢cient on the comparative advantage variable is signi…cantly negative in both
samples, a somewhat surprising result. We note however that the signi…cance of the other variables, and in particular the positive estimates of ¯ 1 do
not depend on the inclusion of the comparative advantage variable. Finally,
Sargan’s tests fail to reject the hypothesis that overidentifying restrictions
are satis…ed in both samples, at any standard level of signi…cance.
As already mentioned, second-order serial correlation in the residuals of
equation (3) is liable to cast doubt on the consistency of the Arellano-Bond
21
The number of observations is lower than in table 2 b ecause of countries missing
observations in 1982.
10
estimation. Standard (unreported) t-tests suggest this is a serious problem in the extended sample, where the hypothesis of no serial correlation
is rejected in both speci…cations.22 In the reduced sample, however, it is
substantially harder to reject the hypothesis of no second-order serial correlation in the residuals. 23 We conclude the results from the OECD sample of
countries are probably to be taken most seriously, although we notice that
the coe¢cient on volatility is positive in both cases.
The maintained assumption in the previous estimations has been that
volatility is strictly exogenous to the dependent variable, output growth.
While we come back to the issue of endogeneity, and the bias in ¯ 1 that may
result from it, in section 5, the ‡exibility of the Arellano-Bond procedure
o¤ers a …rst step towards addressing the concern. The estimation
remains
³
´
yi;j;t
P
consistent even when allowing for the possibility that VT ¢ ln
be
i yi;j ;t
correlated with "i;j;S for all T > S. This makes it possible to consider current
volatility as a manifestation of past high growth. We perform once again the
Arellano-Bond estimation, eschewing the assumption of strict exogeneity,
and compare its performance to the one reported in Table 3. The resuts,
unreported for clarity, are quite clear. In all cases, allowing for volatility
to be predetermined results in rejection of the overidentifying restrictions
at standard con…dence levels. 24 Furthermore, serial correlation becomes
a serious problem in the OECD sub-samples as well. The evidence thus
favours a speci…cation that assumes perfect exogeneity of output volatility,
relative to one that allows volatility to be predetermined.
3
Why the Disaggregated Link is Positive
A common justi…cation for a putative positive link between growth and
volatility is the presence of so-called “Schumpeterian waves”, whereby the
adoption of a new technology entails high volatility as the economy adjusts
to its introduction. A direct implication is that the link between sectoral
22
The test-statistics are N(0; 1) = 3:88 and 2:62 depending whether the comparative
advantage variable is included. This rejects the null at 1% con…dence level. See Arellano
and Bond (1991) for details on the serial correlation tests.
23
The test-statistics are now N(0; 1) = 1:20 and 1:52, resp ectively. This rejects the null
at only 23% and 13% con…dence levels.
24
In the order of the sp eci…cation in the low panel of table 3, the p-values of the Sargan
tests become 0:093, 0:112, 0:002 and 0:023, respectively.
11
volatility and growth should vary systematically with the sectoral rate of
(total factor) productivity growth. In particular, the higher productivity
growth, the higher the positive correlation between growth and volatility.
In this section we investigate the empirical support for this prediction.
3.1
Sample-Splits
We do this in two ways. Firstly, we split our cross-sections according to
sectoral measures of productivity growth. For lack of reliable data on capital accumulation at the sectoral level in the UNIDO data, we use labour
productivity growth. For each sample, we present estimation results for
both (2) and (3), i.e. …rstly for a purely cross-sectional estimation with
country-sector …xed e¤ects, and secondly using the Anderson-Hsiao and the
Arellano-Bond estimation methods. Each sample is split according to the
median level of labour productivity growth across country-sectors. Table 4
presents the results for both samples in the UNIDO data. One main result transpires from all estimation methods: the coe¢cient on volatility ¯ 1
is systematically higher in the high productivity growth sub-sample. Thus,
volatility and growth are related positively mostly in country-sectors with
high labour productivity growth. Secondly, the “convergence” term is signi…cantly negative in all cases in the full sample, and weaker in the reduced
OECD sample. Thirdly, the comparative advantage term enter weakly in
most cases, and sometimes with the wrong sign. The rest of this section
discusses these results in more details.
The cross-sectional estimations yield clear results: in the full sample, ¯ 1
actually changes signs, from signi…cantly negative to signi…cantly positive
as one moves from low to high productivity growth sectors. In the reduced
sample, the coe¢cient increases in magnitude, from being virtually zero
to signi…cantly positive (as well as a point estimate almost twice larger).
The Anderson-Hsiao estimations yield similar results, if somewhat weaker.
In the extended sample, ¯ 1 goes from zero to signi…cantly positive (and a
point estimate twice larger as well), whereas in the reduced sample it is nonsigni…cantly di¤erent from zero in both sets of sectors, although a negative
(positive) point estimate obtains in low (high) productivity sectors. Once
again, the instrument set used by the Anderson-Hsiao estimator is weak,
particularly so in low productivity sectors. Nevertheless, the expanded instrument set used by the Arellano-Bond estimator leads to the same conclusion: from virtually zero in low productivity sectors, ¯ 1 becomes signi…cantly
12
positive in high-productivity sectors, or at the very least the point estimate
doubles in the OECD sample. Arellano-Bond estimations are well-speci…ed,
as the overidentifying restrictions are never rejected.25 Serial correlation
continues to be a concern for the full sample estimates, although only in
high productivity sectors, but once again we fail to reject the hypothesis of
no serial correlation in the reduced sample.26
3.2
Non-Parametric Estimations
A more general approach involves estimating the link between growth and
volatility over rolling sub-samples of country-sectors, ordered by increasing
rates of productivity growth. This avoids imposing an arbitrary split on
the data, and makes it possible to investigate the shape of the relationship
between growth and volatility with as few restrictions as possible on the
functional form. In particular, for each observation pair (y n; xn) on sectoral
growth and volatility, we run a regression akin to (2) or (3) using a small
amount of data around xn and collect the corresponding estimate of ¯ 1. We
then repeat the operation for the next pair of observations on growth and
volatility, ordered by increasing rate of labour productivity growth. Thus,
we investigate in a much more general manner than in the previous section whether estimates of the link between growth and volatility display
any systematic tendency to increase with the sectoral rate of productivity
growth. The procedure derives closely from robust locally weighted scatterplot smoothing (lowess ), and requires an arbitrary choice of two parameters:
the size, or “bandwidth”, of each sub-sample, and a step parameter governing the extent of overlap between two successive sub-samples. There are
of course many ways of choosing these, but we checked that the general
shape of the obtained curve did not depend on any particular parameter
choice. In general, kernel estimations further require the choice of a weighing scheme that assigns (often decreasing) weights onto observations farther
away from the central point of each sample. The present procedure is akin
to a “rectangular” or “‡at” kernel procedure, where equal weights are assigned to observations within the bandwidth, whereas observations outside
are disregarded altogether. 27 Another advantage of the approach relative to
25
The p-values associated with the Â2 tests reported in the table are 0.306, 0.134, 0.385
and 0.661, in the same order as in table 4.
26
In the order of Table 4, the t-statistics are N(0; 1) = 1:41, 2:66, 1:38 and 0:92.
27
For more details, see Imbs and Wacziarg (2002).
13
polynomial or semi-parametric estimations is that it is locally robust: the
shape of the relationship linking growth and volatility in, say, low productivity sectors is not a¤ected by observations in high productivity sectors.
By the same token, it highlights the possible role for outliers.
Figures 4 through 9 report the non-parametric estimates of ¯ 1 over
sub-samples ordered by increasing average labour productivity growth over
[1970;1992]. For each sample, we report results for three estimations: the
pure cross-sectional approach in equation (2), the within-OLS estimation
of (3), and the Arellano-Bond estimation of (3).28 The bold sections of
the curves correspond to estimates of ¯ 1 signi…cant at the 5% con…dence
level, and the X-axis reports average labour productivity growth between
1970 and 1992 in the median sector of the considered sub-sample. The
non-parametric evidence con…rms the results based on sample splits. In the
extended sample, estimates of ¯ 1 increase with labour productivity growth
in the three cases. As expected, measurement error is more prevalent in
dynamic estimations, that rely on measures of growth and volatility based
on shorter time periods; this is manifested by sudden jumps in the estimates
of ¯ 1 in …gures 5 and 6. In spite of noise, however, the trend is strikingly
positive in all three cases, and does not depend on the choice of a bandwidth.29 Results in the reduced sample are slightly di¤erent, and appear to
be obscured by the presence of a few outliers in low productivity growth sectors. The overall shape of the curve is ‡at in …gure 7 and 8, corresponding
to the cross-sectional and within estimators. This explains why parametric
estimates of ¯ 1 were more signi…cantly positive in the OECD sample: crosssectoral heterogeneity appears to be less of a problem there. Nevertheless,
the Arellano-Bond estimator con…rms the presence of an upward trend in ¯ 1,
partly masked by the presence of a few outliers in low productivity growth
sectors, appearing to display both high growth and high volatility.30 Insofar
as high rates of labour productivity growth re‡ect technological progress,
28
The weakness of instruments in the Anderson-Hsiao estimations resulted in essentially
‡at non-parametric estimations.
29
Figure 4 is based on a sample of 714 observations, and the bandwidth is 300. Figure
5 is based on a sample of 440 observations, with a bandwidth of 200. Figure 6 is based on
a sample of 714 observations, and a bandwidth of 300. In all three cases, the step parameter is two observations. The general upwards shape prevails for alternative (reasonable)
parameters.
30
The apparent culprit is Petroleum Re…neries (ISIC 353). When this activity is suppressed from the sample (which entails 6 lost observations), the initial section where ¯1
is signi…cantly positive disappears, while the subsequent upward trend remains prevalent.
It is reassuring that the outlier should b e a sector whose activity is closely related to the
price of a commodity, and ‡uctuations thereof.
14
the evidence in this section suggests the positive link between growth and
volatility originates in “Schumpeterian waves”, or “creative destruction”,
whereby the introduction of better technologies is accompanied by sectoral
volatility. In the next section, we investigate whether it is also accompanied
by net …rm entry.
3.3
The Mechanics of Creative Destruction
In their survey on “creative destruction”, Caballero and Hammour (2000)
argue factors shifting from low to high productivity activities account for a
large share of productivity growth; furthermore, most of this factor reallocation occurs within narrowly de…ned sectors, contrary to a ascribing this
role to structural change, or factor reallocation across sectors. Our results
are perfectly consistent with this evidence, as they underline the association
of within-sector volatility with high (productivity) growth. Several studies
have attempted to further decompose these productivity e¤ects into net entry of new …rms endowed with a better technology, and learning on the part
of incumbents. Foster, Haltiwanger and Krizan (2001), for instance, …nd
a roughly equal contribution of both in the US manufacturing data. On
the other hand, the industrial organization literature provides theoretical
arguments that newer technologies actually tend to be implemented by new
entrants.31 The question whether creative destruction is accompanied by
a wave of new entrants can be addressed in a natural way in the present
context, with two empirical shortcuts. As already mentioned, the UNIDO
data provides information on the number of establishments per sector. The
relationship between establishments and …rms is a loose one, as several establishments could very well be part of one …rm. Thus, growth in the number
of establishments captures net entry imperfectly at best. Furthermore, the
number of establishments is available from 1981 only, thus reducing the time
series over which average growth rates are computed to a little more than ten
years on average, less than half the data otherwise available. 32 With those
two caveats however, it is possible to decompose output growth into an intensive (output per …rm) and an extensive (number of …rms) margins, and
examine whether volatility is related with the former or the latter. Under
the premise that the positive link between (sectoral) volatility and growth
31
See for instance Jovanovic and Nyarko (1996) or Asplund and Nocke (2001).
This is also the reason why no dynamic estimations are possible for this growth decomposition.
32
15
re‡ects creative destruction, documented in the previous section, this may
clarify the mechanism whereby technologies are adopted.
Table 5 reports the results of this growth decomposition in both UNIDO
samples when estimating (2), i.e. controlling for country and sector- e¤ects.
They are unequivocal: the link between extensive growth and volatility is
signi…cantly positive in both cases, as if entering …rms were the innovative
units, and the source of sectoral volatility. The link between intensive growth
and volatility, on the other hand, is non-signi…cant at best. Interestingly,
the positive role for the comparative advantage variable, evident in table 2
for the full sample, seems to correspond to extensive growth as well, as if
net entry were stimulated in those sectors intensive in a factor accumulating
relatively fast in the aggregate. Taken together, the evidence in this section
suggests the link between growth and volatility is an important symptom
of technology adoption at the sectoral level, and con…rms that, at least in
manufacturing, new technologies are introduced by new entrants, or at least
by productive units newly created by incumbent …rms.
4
Why the Aggregate Link is Negative
Our result stands in stark contrast with previous similar exercises. RR and
MR used a number of aggregate cross-sections to establish what appears
to be an extremely robust feature of cross-country data: aggregate growth
(conditionally) correlates negatively with aggregate volatility. Both papers
refer extensively to the empirical growth literature to choose the conditioning
set. The choice of these controls has been the focus of an enormous literature,
culminating with Levine and Renelt (1992), who suggested the determinants
of aggregate growth are (i) initial income, (ii) average investment share of
GDP, (iii) average population growth and (iv) initial human capital. When
conditioning GDP growth on this set of variables, both papers …nd high
variance of GDP to be associated with low aggregate growth on average,
although the unconditional correlation is actually positive for a sub-sample
of OECD countries. The two papers di¤er somewhat in their methodology,
but not fundamentally in their results. RR implement a maximum likelihood
estimation and focus on the variance of innovations to GDP, using the time
series dimension of the data, while MR focus purely on a cross-section and
16
deviate somewhat from the Levine-Renelt conditioning variables.33 Both
papers show the negative impact of volatility is strongest for a sub-set of
OECD countries.34 In this section, we reconcile our results with theirs, by
examining the role of aggregation in the estimation.
4.1
Aggregation
First consider table 6. There, we report results that are meant to reproduce
the aggregate evidence. In particular, we estimate
Ã
!
X
X
X
ln
yi;j;T = ¯ 1 VT ¢ ln
yi;j;t + (¯ 2 + 1) ln
yi;j;T ¡1 + ®j + (±T ) + "j;T
i
i
i
(4)
Thus, we investigate the relationship between aggregate growth and volatility. Admittedly, the aggregates used here di¤er somewhat from standard
GDP measures, as they are sums over manufacturing activities only; the
exercise remains however interesting, at least to document the e¤ects of aggregation if not for direct comparison with RR or MR. One di¢culty in
using an aggregate of manufacturing activities is the choice of a conditioning set: the Levine-Renelt variables pertain to aggregate GDP, and thus
do not belong in (4). We choose to include initial conditions only, as well
as country …xed e¤ects and period indicator variables. That choice is of
course mainly governed by data availability: to our knowledge, there are no
equivalent to the Levine-Renelt variables applicable to the secondary sector.
Our hope is that time invariant …xed e¤ects capture other relevant conditioning variables.35 The results in table 6 stand in stark contrast with the
disaggregated evidence. Whereas ¯ 1 is not signi…cantly di¤erent from zero
in simple unconditional OLS, dynamic panel estimations (i.e. OLS-within
and Arellano-Bond) give rise to signi…cantly negative estimates of ¯ 1 . As in
33
To be precise, Martin and Rogers use cross-region as well as cross-country evidence,
and include a measure of sectoral shares to account for putative transitional e¤ects. They
also include the share of government sp ending and a measure of political instability, apparently without much e¤ect (results without a share of government are not presented in
the paper).
34
MR …nd a coe¢cient non-di¤erent from zero for a large sample of 97 countries; RR
…nd a substantially lower (in absolute value) negative coe¢cient in a sample of 92 countries
than in a sample of 24 OECD countries.
35
The next section o¤ers sensitivity analysis involving data from the OECD, covering
all economic activities. This will b etter address the concern of the conditioning set.
17
RR and MR, this is particularly true in a reduced sample of OECD. As expected, initial income enters negatively, particularly in the extended sample
inclusive of developing economies. Sargan tests suggest the Arellano-Bond
estimator is better speci…ed in the reduced OECD sample, where ¯ 1 is most
negative; it is also there that serial correlation is least present.36;37 We now
turn to an account of the discrepancy.
One immediate possibility is related with the components of aggregate
volatility. Inasmuch as it is a sum of sectoral value addeds, the variance of
GDP embeds both within sectors variances and cross-sectoral covariances.
We …rst investigate this possibility, by performing the following approximate
decomposition
X
X
X
¢ ln
yi;j;t '
!i;j;0 ¢ ln yi;j;t +
(!i;j;t ¡ ! i;j;0) ¢ ln yi;j;t
i
i
i
+
X
¢!i;j;t ln y i;j;t¡1
(5)
i
y
with ! i;j;t = P i;j;t
.38 There are two conceptually distinct components of
i yi;j;t
aggregate output growth in (5): the …rst term captures growth originating
within each sector, weighted using the initial sectoral structure. The other
two terms involve growth e¤ects of structural change, involving changes in
the sectoral structure of the economy. In table 7, we decompose volatility in estimations of (4) making use of these two elements.
P In particular,
we investigate whether the estimated coe¢cient on VT ( i ! i;j;0 ¢ ln yi;j;t)
di¤ers from P
that on the complementary
component in aggregate volatilP
ity, VT (¢ ln i yi;j;t ) ¡ VT ( i !i;j;0 ¢ ln yi;j;t ), which embeds the e¤ects
of structural change. The idea is to verify whether di¤erent components of
aggregate volatility relate di¤erently with aggregate growth. The results are
striking: estimates in the upper panel of table 7 suggest that, in dynamic
estimations, even a weighted sum of within-sector volatilities only, still correlates negatively with aggregate growth.39 The lower panel of table 7 goes
36
We fail to reject the null of no serial correlation at any standard con…dence level in
the OECD sample, but reject at 6% in the extended sample.
37
All estimates are also allowing for heteroskedasticity, for the number of sectors is
di¤erent across countries.
P
P
38
We made use of the approximation ln i yi;j;t ' i !i;j;t ln yi;j; t. The correlation
between the two measures in 0.997 in our data, and OLS of one on the other gives an R2
of 0.995.
39
¯1 is zero in OLS conditional on initial aggregate value added. The Arellano-Bond
estimator points to the presence of serial correlation in the extended sample, but, once
again, not in the reduced one.
18
one step further, and takes country averages of sector by sector estimations,
using initial relative outputs as a weighting device. Thus the measures of
aggregate growth and volatility now both contain within sector developments only. In particular, we estimate the following using our cross-section,
OLS-within and Arellano-Bond:
X
X
X
! i;j;0 ln yi;j;T = ¯ 1
(! i;j;0 )2 VT (¢ ln y i;j;t ) + (¯ 2 + 1)
!i;j;0 ln yi;j;T ¡1
i
i
i
+®0j + (±0T ) + "0j;T
(6)
The results still point to non-positive estimates of ¯ 1 , if slightly less unanimously. This suggests the key to the discrepancy does not lie in a decomposition of aggregate growth or volatility into within- and between-sector components. Country (weighted) averages of sectoral growth and volatility tend
to correlate negatively. This points to the possibility that cross-sectional
heterogeneity, and failure to account for it in aggregate estimations, is a
candidate explanation to the discrepancy.
4.2
Compositional E¤ects
In this section, we establish the importance of heterogeneity in the estimates
of ¯ 1. There are two ways in which it can matter for aggregate estimations.
Firstly, assuming economic structure is identical across countries, ¯ 1 could
be non-positive in (4) because of failure to account for the possibility that
estimates are di¤erent across the sectors composing the aggregate economy
of country j. Alternatively, assuming no heterogeneity across sectors, it
could be that ¯ 1 should be allowed to di¤er across countries, simply because
sectoral composition of aggregate output does as well. This question is easy
to address, by implementing random coe¢cient estimations of (3), as introduced for instance by Hildreth and Houck (1968). This procedure estimates
a typical relationship
Yi = Xi Bi + ei
where E(ei) = 0, E(eie0i ) = ¾2I and Bi = B +vi is allowed to vary randomly
along the i dimension, E(vi ) = 0 and E(vivi0 ) = ¡. Hildreth and Houck show
the model is equivalent to
Yi = Xi B + ´ i
19
with E(´i ) = 0 and E(´i ´ 0i) = ¾2I + Xi ¡ Xi0 which can be estimated immediately using Generalized Least Squares. In what follows, we use this
procedure in a panel context to obtain estimates of ¯ 1 in three di¤erent setups: (i) for comparison purposes, a simple estimation of growth on volatility
without any allowances for heterogeneity, (ii) an estimation allowing for ¯ 1
to depend on i and (iii) an estimation allowing for ¯ 1 to depend on j.40 The
results, reported in Table 9, are striking. Whereas estimates of ¯ 1 continue
to be signi…cantly positive in a simple random e¤ects estimation, as well
as, in most cases, when sectoral heterogeneity is allowed for, they become
non signi…cantly di¤erent from zero when country-speci…c heterogeneity is
allowed.41 We draw two inferences from these results: …rstly, the sectoral
link evidenced in the previous section, although it is heterogenous between
low and high productivity growth sectors, is a robust feature of the data.
Secondly, the nature of the aggregate link between growth and volatility
crucially depends on the sectoral composition of aggregate activity. The
composition of aggregate output, and presumably the balance of low versus
high productivity growth activities, are liable to have drastic consequences
on the aggregate evidence on this question. We leave a detailed investigation
of this possibility open for further research.
5
Extensions and robustness
5.1
An Alternative Dataset
Here, we supplement the evidence based on UNIDO data with the International Sectoral DataBase (ISDB) published by the OECD, which covers all
activities in fourteen OECD economies, but only at the two-digit level. The
reduced coverage, focused on developed economies only, is an asset from the
standpoint of robustness analysis, for it provides an alternative source of information on a homogenous group of economies. Furthermore, while ISDB
does not supply the number of establishments per sector, it does actually
contain indices of Total Factor Productivity (TFP). Time coverage is similar
40
The …rst estimation without heterogeneity is very similar to (3), although not exactly. In particular, it allows for random intercepts, and thus is not directly comparable
with previous estimations. We report the corresponding results to ascertain the role of
heterogeneity.
41
The table also reports Â2 statistics to test whether estimates of ¯1 are signi…cantly
heterogenous in the dimension under consideration. For details, see Johnston (1984).
20
to the UNIDO sample, with complete data from 1970 to 1992. Summary
statistics are reported in the lower panel of table 1, and the unconditional
correlation between growth and volatility is plotted in …gure 3.
We …rst perform our parametric estimations using the ISDB data, i.e.
cross-sectional estimations of (2) and OLS-within, Anderson-Hsiao and ArellanoBond estimations of (3). The results are reported in table 9, and are less
encouraging than those based on the UNIDO data. ¯ 1 is never signi…cantly
di¤erent from zero at usual con…dence levels, and the point estimates are
most of the time negative. However, the presence of heterogeneity across
country-sectors is liable to bias the estimations, possibly more here than in
the UNIDO dataset which is more homogenous in its coverage of sectors. In
other words, non-parametric estimations are probably even more important
here than in section 2. As a …rst pass, table 10 reports estimates where we
split our cross-sections according to sectoral averages of both labour and
total factor productivity growth. The upper panel reports the result for
a split according to the median sectoral TFP growth. Results are somewhat more encouraging, as both the cross-sectional and the Anderson-Hsiao
estimators suggest substantial heterogeneity across sub-samples, with ¯ 1 increasing with average sectoral TFP growth. Results for the Arellano-Bond
estimators, however, are only reported for completeness, as the speci…cation
is systematically rejected by the Sargan test in the low TFP growth subsample. The conclusions using a split based on the median sectoral labour
productivity growth are simillar and reported in the lower panel of table 10.
Estimates of ¯ 1 increase with sectoral productivity growth, except using the
Arellano-Bond estimator, which is once again mis-speci…ed according to the
Sargan test.
Figure 10 through 15 report the non-parametric estimates of ¯ 1 corresponding to sub-samples of sectors ordered by increasing average productivity growth, using the estimator described in section 2. Figures 10 to 12
present the results when productivity growth is measured by the TFP indices, while …gures 13 to 15 use labour productivity growth.42 As the sample
splits in table 10 suggested, the evidence that estimates of ¯ 1 increase with
productivity growth is supported by both cross-sectional and within estimations. The Arellano-Bond estimator yields opposite results when using TFP
42
The total numbers of observations for the six …gures were 181, 179, 177, 226, 224
and 211, respectively. Unsurprisingly, there are more observations on labour productivity
growth than on TFP indices. The bandwidthes were 100, 75, 75, 75, 100 and 75, and
the step parameter set at one observation in all cases. Results are robust to alternative
parameters choice.
21
indices, but, as in table 10, there is every indication that it is mis-speci…ed
as overidentifying restrictions are actually rejected, once again particularly
in sectors with low TFP growth.43 The Arellano-Bond does however lead
to similar conclusions when using labour productivity, with an upward, if
insigni…cant, trend in ¯ 1 . These estimates are however merely reported for
completeness, as overidentifying restrictions are rejected in Figure 15 as well.
Overall, results using the ISDB dataset are weaker, but not inconsistent
with those based on manufacturing sectors only. Heterogeneity seems to
be more of an issue when using data covering all sectors of the economy.
Further, aggregation in the ISDB data is coarser to start with than in the
UNIDO, leading to the possibility that disaggregated results there already
su¤er from the bias we document in the previous section. Table 11 reproduces estimations of (4) using aggregate ISDB data; we have data on all
sectoral activities in 14 countries, and thus can control for the variables
Levine-Renelt showed to be robust determinants of GDP growth. Once
again, a negative aggregation bias is apparent, as panel estimates of ¯ 1
clearly become signi…cantly negative, whether or not the standard control
variables for economic growth are included.
5.2
Sensitivity Analysis
To be completed. Clustered Standard Errors, ARCH and endogeneity, Random e¤ects.
6
Conclusion
In this paper, we reconcile results spanning two literatures. Microeconomists,
particularly students of industrial organization, often think of volatility in
economic activity as a manifestation of change, for instance in the guise of
technology adoption, and the entry and exit of …rms that accompanies it.
Macroeconomists, however, have traditionally associated economic volatility with risk, liable to deter investment, hamper the adoption of new practices, or simply re‡ecting policy instability. In this paper, we show that the
43
For clarity, the actual values of the Sargan tests are not rep orted. It is a fair approximation, however, to consider all positive estimates of ¯ 1 in …gure 12 as corresponding to
speci…cations rejected at the 5% con…dence level.
22
two approaches are not necessarily mutually exclusive. We use the same
dataset to generate both a positive and a negative link between growth and
volatility. We show that volatility and growth are particularly positively
related in activities that display fast productivity growth, as implied by the
Schumpeterian theory of creative destruction. We further establish the importance of accounting for sectoral heterogeneity when assessing the link
between growth and volatility at the country level.
23
Appendices: A. Sectoral Coverage
1. UNIDO 3-Digit Classi…cation (28 sectors)
300
311
313
314
321
322
323
324
331
332
341
342
351
352
353
354
355
356
361
362
369
371
372
381
382
383
384
385
390
Total manufacturing
Food products
Beverages
Tobacco
Textiles
Wearing apparel, except footwear
Leather products
Footwear, except rubber or plastic
Wood products, except furniture
Furniture, except metal
Paper and products
Printing and publishing
Industrial chemicals
Other chemicals
Petroleum re…neries
Miscellaneous petroleum and coal products
Rubber products
Plastic products
Pottery, china, earthenware
Glass and products
Other non-metallic mineral products
Iron and steel
Non-ferrous metals
Fabricated metal products
Machinery, except electrical
Machinery, electric
Transport equipment
Professional and scienti…c equipment
Other manufactured products
2. OECD 2-Digit Classi…cation (20 sectors)
100.
200.
310.
320.
Agriculture, hunting, forestry and …shing
Mining and quarrying
Food, beverages and tobacco
Textiles, wearing apparel and leather industries
24
330. Wood and wood products, including furniture
340. Paper and paper products, printing and publishing
350. Chemicals, chemical petroleum, coal, rubber, plastic products
360. Non-metallic mineral products excl. products of petroleum & coal
370. Basic metal industries
380. Fabricated metal products, machinery and equipment
390. Other manufacturing industries
400. Electricity, gas and water
500. Construction
610+620. Wholesale trade and retail trade
630. Restaurants and hotels
710. Transport, storage and communication
810+820 . Financial institutions and insurance
830. Real estate and business services
910. Community and Social services
920. Personal services
25
B. Geographic Coverage
Australiaa
Austria
Bangladesh
Belgium a;b
Canada a
Chile
Colombia
Costa Rica
Cyprus
Denmarka
Egypt
Finlanda
Francea;b
Germanya
Greece
Hong Kong
India
Indonesia
Ireland
Italya;b
Japana
Jordan
Kenya
Korea
Malaysia
Mexico
Netherlandsa
Norwaya;b
Pakistan
Panama
Philippines
Portugal
Singapore
South Africa
Spain
Swedena
USAa
United Kingdoma
Uruguay
Zimbabwe
a: in OECD dataset
b: not in UNIDO data
26
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29
Table 1: Summary Statistics
Extended UNIDO Sample (715 obs – 36 countries)
Mean
Std. Dev
Min
Average Yearly
-0.016
3.385
-16.070
Relative Growth
Average Yearly
3.521
4.738
0.047
Relative Variance
Correlation = 0.144
Reduced UNIDO Sample (317 obs – 15 countries)
Mean
Std. Dev
Min
Average Yearly
Relative Growth
Average Yearly
Relative Variance
Correlation = 0.118
Average Yearly
Relative Growth
Average Yearly
Relative Variance
Correlation = -0.098
Max
17.635
37.558
Max
-0.282
2.639
-16.070
9.995
1.205
2.188
0.047
21.051
ISDB Sample (232 obs – 14 countries)
Mean
Std. Dev
Min
Max
-0.367
1.916
-6.130
5.331
0.296
0.375
0.007
2.905
Table 2: Country and Sector Effects
A. Extended Sample (715 obs)
(i)
(ii)
Vt (∆lnyi,j,t )
1.974
(2.78)
1.966
(2.77)
ln(yi,j,0)
(iii)
-0.156
(0.21)
-0.201
(7.80)
ln(yi,j,0 / Σ i yi,j,0)
Comp. Adv.
Intercept
R-Square
0.072
(2.15)
0.011
0.115
(1.13)
0.071
(2.13)
0.013
B. OECD Sample (317 obs)
(i)
(ii)
Vt (∆lnyi,j,t )
4.893
(2.63)
5.007
(2.66)
ln(yi,j,0)
0.223
(2.24)
-3.864
(7.65)
0.091
(iii)
4.072
(2.16)
-0.118
(3.00)
ln(yi,j,0 / Σ i yi,j,0)
Comp. Adv.
Intercept
R-Square
0.121
(3.68)
0.022
-0.059
(0.39)
0.133
(2.94)
0.022
0.102
(0.64)
-2.425
(2.84)
0.049
(iv)
-0.661
(0.91)
-0.283
(9.46)
0.257
(2.63)
0.980
(9.69)
0.123
(iv)
3.832
(2.06)
-0.204
(4.10)
0.062
(0.41)
0.797
(4.75)
0.072
Results for estimation of (2), with country and sector fixed-effects. yi,j,t is the real
value added in sector i, country j at time t. The dependent variable is ln(yi,j,1992 /Si
yi,j,1992) - ln(yi,j,1970/Si yi,j,1970 ). Initial values are measured in 1970, or the nearest data
available. The comparative advantage variable (Comp. Adv.) is an interaction term
between average aggregate capital- labour growth between 1970 and 1985, and
measured capital share in 1970. t-statistics are reported between parentheses.
Table 3: Dynamic Panel Estimations:
OLS Within
Volatility
Initial
Relative VA
Comparative
Advantage
R-Square
# Obs.
AndersonHsiao
Volatility
Initial
Relative VA
Comparative
Advantage
First Stage
R-Square
# Obs.
ArellanoBond
Volatility
Initial
Relative VA
Comparative
Advantage
Sargan Test
# Groups
Full Sample
0.863
(2.47)
-0.967
(25.50)
0.515
641
OECD Sample
0.243
(0.62)
-0.831
(19.07)
0.057
(1.45)
0.459
440
Full Sample
1.054
(2.19)
-0.785
(2.52)
4.602
(5.28)
-0.641
(11.74)
2.838
(2.13)
-0.632
(10.80)
0.027
(0.56)
0.343
268
0.375
317
OECD Sample
9.655
(1.08)
1.417
(0.24)
0.028
-0.448
(0.79)
-1.548
(5.57)
0.113
(2.09)
0.059
0.025
77.407
(0.13)
15.743
(0.14)
-2.667
(0.14)
0.075
641
440
317
268
Full Sample
0.413
(3.20)
-0.572
(4.62)
?2 (2)=0.58
651
OECD Sample
0.478
(2.93)
-0.514
(4.29)
-0.220
(4.56)
?2 (2)=3.97
607
2.751
(2.28)
0.545
(1.87)
?2 (4)=4.38
268
2.427
(2.92)
0.321
(2.66)
-0.285
(2.61)
?2 (4)=3.55
268
The dependent variable is relative sectoral value added growth in [1970,1981] and
[1982,1992] (boundary dates are 1970, 1976, 1982, 1988 and 1992 for the ArellanoBond estimator). Initial values are measured in 1970 and 1981 (1970, 1976, 1982 and
1988 for the Arellano-Bond estimator). Variances are computed over the
corresponding two sub-periods (four for the Arellano-Bond estimator). AndersonHsiao uses ln(yi,j,70/Σ i yi,j,70) to instrument for ln(yi,j,81 /Σ i yi,j,81 )- ln(yi,j,70/Σ i yi,j,70).
Arellano-Bond uses all available lags. t-statistics are reported between parentheses.
The Sargan test of overidentifying restrictions is reported in the last row for the
Arellano-Bond estimator; the number of lagged dependent variables used as
instruments and the inclusion of time dummies were chosen to maximize the value of
the Sargan test.
Table 4: Sample Splits
Full Sample
Low Labour Productivity Growth
High Labour Productivity Growth
Vt ( ∆lnyi,j,t )
yi,j,0 / Σ i yi,j,0
Comp.Adv.
Intercept
R-Square
Sargan Test
CrossAndersonSection
Hsiao
(357 obs) (220 obs)
-2.062
1.291
(2.36)
(0.84)
-0.237
-3.160
(6.06)
(2.71)
0.202
0.302
(1.47)
(1.75)
0.661
(4.94)
0.096
0.041*
ArellanoCrossAndersonBond
Section
Hsiao
(303 obs) (358 obs) (220 obs)
0.317
2.236
2.556
(1.44)
(1.86)
(2.15)
-0.420
-0.311
-0.555
(2.71)
(7.07)
(2.17)
-0.400
0.302
-0.016
(3.76)
(2.23)
(0.24)
1.285
(8.77)
0.176
0.183*
?2 (2)=2.37
ArellanoBond
(303 obs)
1.099
(3.49)
-0.251
(4.47)
-0.166
(2.57)
?2 (2)=4.02
Reduced Sample
Low Labour Productivity Growth
High Labour Productivity Growth
Vt ( ∆lnyi,j,t )
yi,j,0 / Σ i yi,j,0
Comp.Adv.
Intercept
R-Square
Sargan Test
CrossAndersonSection
Hsiao
(158 obs) (134 obs)
2.960
-0.410
(1.30)
(0.16)
-0.136
0.679
(1.91)
(0.93)
-0.101
-0.139
(0.44)
(1.05)
0.488
(2.04)
0.042
0.076*
ArellanoCrossAndersonBond
Section
Hsiao
(134 obs) (159 obs) (133 obs)
1.469
5.391
3.286
(2.06)
(1.73)
(0.50)
0.127
-0.260
-0.934
(2.48)
(3.94)
(1.51)
-0.232
0.343
0.036
(2.68)
(1.78)
(0.21)
1.059
(4.76)
0.133
0.202*
?2 (4)=4.16
ArellanoBond
(133 obs)
4.072
(2.37)
0.035
(1.64)
-0.277
(1.12)
?2 (4)=2.41
t-statistics are reported between parentheses. The cross-section is split according to
the median sectoral labour productivity growth over [1970,1992]. The Sargan test of
overidentifying restrictions is reported in the last row for the Arellano-Bond
estimator; the number of lagged dependent variables used as instruments and the
inclusion of time dummies were chosen to maximize the value of the Sargan test.
*: first-stage R-square.
Table 5: Intensive and Extensive Growth (Country-Sector Effects)
Vt (∆lnyi,j,t )
yi,j,0 / Σ i yi,j,0
Comp. Adv.
Intercept
R-Square
Full Sample
(715 obs)
Extensive
Intensive
Growth
Growth
1.127
-0.200
(2.87)
(0.44)
-0.005
-0.088
(0.21)
(3.47)
0.182
0.041
(2.50)
(0.48)
-0.106
0.488
(1.41)
(5.57)
0.021
0.017
Reduced Sample
(317 obs)
Extensive
Intensive
Growth
Growth
5.485
-1.434
(5.11)
(1.26)
0.041
-0.036
(1.12)
(0.86)
-0.163
0.110
(1.34)
(0.85)
-0.180
0.265
(1.44)
(1.99)
0.077
0.007
Extensive growth is measured by ln(Nesti,j,1992 /Nesti,j,1981 ), and intensive growth by
ln(yi,j,1992 /Siyi,j,1992) - ln(yi,j,1981 /Siyi,j,1981) - ln(Nesti,j,1992 /Nesti,j,1981). Initial values are
computed in 1981. t-statistics are reported between parentheses.
Table 6: Aggregate Growth and Volatility
Full Sample (36 countries)
Vt (∆ lnSi yi,j,t )
OLS
Within
24.683
(1.48)
-14.991
(1.47)
-0.320
(2.79)
0.279
Initial Income
R Square / Sargan Test
0.097
ArellanoBond
-1.910
(0.72)
-0.459
(1.63)
?2 (5)=7.77
OECD Sample (15 countries)
OLS
Within
-15.996
(0.22)
-32.959
(2.28)
-0.229
(1.46)
0.371
0.005
ArellanoBond
-13.177
(2.05)
0.074
(3.30)
?2 (5)=1.15
The dependent variable is aggregate growth as measured by ∆lnSi yi,j,t between the
relevant sub-periods (1970 and 1992 in OLS, 1970, 1982 and 1992 in the within
estimation and 1970, 1976, 1982, 1988 and 1992 in the Arellano-Bond estimation).
Aggregate volatility is computed over the corresponding sub-periods. Period dummy
variables are included when meaningful. Huber-White heteroscedastic-consistent tstatistics are reported between parentheses. The reported value for the Sargan test
corresponds to the homoskedastic case, since it is specified only under that
assumption.
Table 7: Aggregate Volatility Decomposition
Full Sample (36 countries)
Vt (Si? i,j,0 ∆lnyi,j,t )
Vt (∆Silnyi,j,t )-Vt (Si? i,j,0 ∆lnyi,j,t )
OLS
Within
19.521
(2.32)
-41.169
(2.33)
-10.456
(1.35)
-57.437
(2.56)
-0.350
(3.01)
0.428
Initial Income
R Square / Sargan Test
0.284
ArellanoBond
-10.500
(2.98)
8.158
(2.29)
-1.736
(1.34)
?2 (2)=3.08
Full Sample (36 countries)
Si (? i,j,0 )2 Vt (∆lnyi,j,t )
OLS
Within
-0.815
(0.93)
-40.445
(2.57)
0.037
(11.57)
0.403
Si ? i,j,0 lnyi,j,0
R Square / Sargan Test
0.020
ArellanoBond
-1.179
(0.11)
-1.685
(0.96)
2
? (2)=2.67
OECD Sample (15 countries)
OLS
Within
-14.090
(0.19)
-58.271
(0.23)
-32.240
(1.94)
-41.563
(0.70)
-0.242
(1.32)
0.372
0.007
ArellanoBond
-12.852
(1.43)
-14.915
(0.63)
0.073
(2.40)
?2 (5)=1.14
OECD Sample (15 countries)
OLS
1.833
(0.23)
0.004
Within
-55.674
(0.36)
0.037
(14.21)
0.480
ArellanoBond
-123.86
(2.37)
-0.556
(1.55)
2
? (5)=4.92
In the upper panel, the dependent variable is aggregate growth as measured by ∆lnSi
yi,j,t between the relevant sub-periods (1970 and 1992 in OLS, 1970, 1982 and 1992 in
the within estimation and 1970, 1976, 1982, 1988 and 1992 in the Arellano-Bond
estimation). The decomposition of aggregate volatility is computed over the
corresponding sub-periods. ? i,j,0 is computed in 1981. In the lower panel, the
dependent variable is aggregate growth as measured by Si ? i,j,0 ∆lnyi,j,t , computed over
the same sub-periods. Period dummy variables are included when meaningful. HuberWhite heteroscedastic-consistent t- statistics are reported between parentheses. The
reported value for the Sargan test corresponds to the homoskedastic case, since it is
specified only under that assumption.
Table 8: Random Coefficients Estimations
Simple Random Effects
Full Sample (36 countries)
Within 2
Vt ( ∆ln yi,j,t )
Initial Income
R Square
Random Coefficients by
Sector
1.359
(3.95)
0.009
(1.45)
0.034
Initial Income
Identical Coefficients
Random Coefficients
by Country
2.185
(3.62)
0.021
(2.83)
?2 (81)=124.18
Initial Income
Identical Coefficients
Within 4
0.709
(3.22)
0.009
(2.42)
?2 (81)=134.44
Full Sample (36 countries)
Within 2
Vt ( ∆ln yi,j,t )
0.574
(4.64)
0.006
(2.09)
0.016
Full Sample (36 countries)
Within 2
Vt ( ∆ln yi,j,t )
Within 4
Within 4
0.084
0.344
(0.06)
(0.71)
-0.047
-0.024
(1.89)
(1.84)
?2 (102)=1264.14 ?2 (102)=950.89
OECD Sample (15 countries)
Within 2
2.291
(2.50)
-0.007
(0.84)
0.010
Within 4
0.562
(1.91)
-0.003
(0.73)
0.003
OECD Sample (15 countries)
Within 2
8.778
(2.42)
0.013
(0.58)
?2 (2)=284.79
Within 4
1.098
(0.78)
0.007
(0.77)
?2 (81)=327.65
OECD Sample (15 countries)
Within 2
-0.906
(0.32)
-0.003
(0.10)
?2 (39)=282.30
Within 4
0.135
(0.13)
-0.013
(0.60)
?2 (39)=197.38
The dependent variable is sectoral growth as measured by ∆lnyi,j,t between the
relevant sub-periods (1970, 1982 and 1992 in the within-2 estimation and 1970, 1976,
1982, 1988 and 1992 in the within-4 estimation). Period dummy variables are
included when meaningful.
Table 9: Estimations in ISDB
Volatility
Initial
Relative VA
Comparative
Advantage
R-Square
# Obs
CrossSection
-11.597
(1.53)
-0.117
(3.00)
0.241
(3.47)
0.074
230
OLS Within
-4.452
(1.06)
-0.354
(6.02)
0.143
232
-2.948
(0.68)
-0.350
(5.81)
-0.003
(0.10)
0.140
228
Anderson-Hsiao
-5.515
(1.22)
-0.128
(0.42)
0.045*
232
-2.105
(0.35)
0.494
(0.63)
-0.082
(1.01)
0.059*
228
Arellano-Bond
1.572
(0.90)
-0.407
(2.44)
?2 (2)=2.36
177
1.229
(0.74)
-0.492
(2.53)
-0.031
(1.15)
?2 (2)=2.89
177
Estimation of (3) with period dummies. The dependent variable is relative sectoral
value added in 1970, 1981 and 1992 (1970, 1976, 1982, 1988 and 1992 for the
Arellano-Bond estimator); variances are computed over the corresponding subperiods. Anderson-Hsiao uses ln(yi,j,70/Σ i yi,j,70 ) to instrument for ln(yi,j,81/Σ i yi,j,81 )ln(yi,j,70/Σ i yi,j,70 ). Arellano-Bond uses all available lags, and includes period dummy
variables. t-statistics are reported between parentheses.
*: first-stage R-square.
Table 10: Sample Splits in ISDB:
Low TFP Growth
Vt ( ∆lnyi,j,t )
yi,j,0 / Σ i yi,j,0
Comp.Adv.
Intercept
R-Square
Sargan Test
CrossAndersonSection
Hsiao
(90 obs)
(90 obs)
-43.286
-8.115
(3.37)
(1.19)
-0.078
0.176
(1.29)
(0.71)
0.326
-0.096
(3.35)
(0.89)
-0.034
(0.18_
0.174
0.497*
ArellanoBond
(88 obs)
4.687
(1.58)
-0.195
(6.92)
-0.041
(1.11)
yi,j,0 / Σ i yi,j,0
Comp.Adv.
Intercept
R-Square
Sargan Test
CrossAndersonSection
Hsiao
(90 obs)
(90 obs)
3.372
15.224
(0.37)
(2.02)
-0.163
-0.141
(3.07)
(0.33)
0.255
-0.072
(2.51)
(1.65)
0.621
(3.70)
0.157
0.066*
?2 (2)=6.15
Low Labour Productivity Growth
Vt ( ∆lnyi,j,t )
High TFP Growth
CrossAndersonSection
Hsiao
(112 obs) (112 obs)
-38.862
-10.826
(3.33)
(1.71)
-0.086
0.163
(1.62)
(0.74)
0.200
-0.003
(2.38)
(0.06)
0.092
(0.55)
0.118
0.244*
ArellanoBond
(88 obs)
0.099
(0.04)
0.005
(3.37)
-0.072
(1.91)
?2 (4)=1.27
High Labour Productivity Growth
ArellanoCrossAndersonBond
Section
Hsiao
(105 obs) (113 obs) (112 obs)
0.446
3.323
14.578
(0.16)
(0.34)
(1.65)
-0.203
-0.118
-0.351
(6.45)
(2.25)
(0.52)
-0.031
0.317
-0.077
(1.55)
(3.01)
(1.20)
0.434
(2.59)
0.115
0.044*
2
? (2)=5.39
ArellanoBond
(105 obs)
-0.141
(0.07)
-0.804
(0.49)
-0.007
(0.15)
?2 (2)=0.28
t-statistics are reported between parentheses. The cross-section is split according to
the median sectoral labour productivity growth over [1970,1992]. The Sargan test of
overidentifying restrictions is reported in the last row for the Arellano-Bond
estimator; the number of lagged dependent variables used as instruments and the
inclusion of time dummies were chosen to maximize the value of the Sargan test.
*: first-stage R-square.
Table 11: Aggregate Growth and Volatility in ISDB
Full Sample (14 countries)
OLS
Levine-Renelt Controls
No
-3.829
(1.19)
-0.027
(6.37)
No
R Square / Sargan Test
0.160
0.711
Vt (∆ lnSi yi,j,t )
10.061
(3.17)
Within
Initial Income
ArellanoBond
-45.777
(1.80)
-2.233
(3.64)
No
?2 (2)=3.89
OLS
Within
7.605
(0.10)
0.030
(1.44)
Yes
-52.226
(1.83)
-0.496
(2.73)
Yes
ArellanoBond
-26.405
(1.62)
-1.785
(2.15)
Yes
0.723
0.808
?2 (5)=7.64
The dependent variable is aggregate growth as measured by ∆lnSi yi,j,t between the
relevant sub-periods (1970 and 1992 in OLS, 1970, 1982 and 1992 in the within
estimation and 1970, 1976, 1982, 1988 and 1992 in the Arellano-Bond estimation).
Aggregate volatility is computed over the corresponding sub-periods. Period dummy
variables are included when meaningful. Huber-White heteroscedastic-consistent tstatistics are reported between parentheses. The reported value for the Sargan test
corresponds to the homoskedastic case, since it is specified only under that
assumption.
Figure 1: Unconditional Relative Growth and Volatility in UNIDO
0.4
Variance of relative growth rate
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
-4
-3
-2
-1
0
1
2
3
4
Relative growth rate
Figure 2: Unconditional relative Growth and Volatility in reduced UNIDO
0.25
Variance of relative growth rate
0.2
0.15
0.1
0.05
0
-4
-3
-2
-1
0
1
2
3
Relative growth rate
Figure 3: Unconditional Relative Growth and Volatility in ISDB
0.03
Volatility of relative growth rate
0.025
0.02
0.015
0.01
0.005
0
-1.5
-1
-0.5
0
Relative growth rate
0.5
1
1.5
Median Productivity Growth
1.03
1.01
0.99
0.96
0.94
0.91
0.89
0.86
0.84
0.83
0.81
0.79
0.78
0.76
0.74
0.73
0.70
0.68
0.66
0.64
0.63
0.61
0.59
0.57
0.56
0.54
0.53
0.51
0.49
0.47
0.44
0.42
0.39
0.37
0.35
0.33
0.32
0.29
0.27
0.25
0.23
0.22
0.9
7
0.9
4
0.9
2
0.9
1
0.8
9
0.8
7
0.8
5
0.8
4
0.8
3
0.8
2
0.8
0
0.7
8
0.7
7
0.7
6
0.7
3
0.7
3
0.7
1
0.6
9
0.6
6
0.6
4
0.6
3
0.6
1
0.5
8
0.5
7
0.5
6
0.5
4
0.
53
0.
51
0.4
9
0.4
8
0.4
6
0.4
2
0.4
0
0.3
9
0.3
6
0.3
4
0.3
2
0.2
9
0.2
8
0.
25
-0.5
0.20
Coefficient on Volatility
Coefficient on Volatility
0.9
9
1.0
1
0.8
6
0.8
9
0.9
1
0.9
4
0.9
6
0.8
3
0.8
4
0.7
4
0.7
6
0.7
8
0.7
9
0.8
1
0.7
0
0.7
3
0.5
7
0.5
9
0.6
1
0.6
3
0.6
4
0.6
6
0.6
8
0.5
4
0.5
6
0.3
9
0.4
2
0.4
4
0.4
7
0.4
9
0.5
1
0.5
3
0.3
5
0.3
7
0.3
2
0.3
3
0.2
5
0.2
7
0.2
9
0.2
0
0.2
2
0.2
3
Coefficient on Volatility
Figure 4: Coefficient on Volatility in Cross-Sectional Estimations, Increasing Labour
Productivity Growth (UNIDO)
3
2
1
0
-1
-2
-3
Median Productivity Growth
Figure 5: Coefficient on Volatility in Within Estimations, Increasing Labour Productivity
Growth (UNIDO)
2.5
3
1.5
2
1
0.5
0
-1
Median Productivity Growth
Figure 6: Coefficient on Volatility in Arellano-Bond Estimations, Increasing Productivity
Growth (UNIDO)
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
-1
-2
-3
Median Productivity Growth
1.06
1.04
1.03
1.00
0.98
0.96
0.93
0.92
0.91
0.88
0.86
0.85
0.84
0.83
0.83
0.81
0.78
0.77
0.76
0.75
0.73
0.73
0.71
0.70
0.68
0.66
0.64
0.63
0.63
0.61
0.58
0.56
0.54
0.54
0.52
0.51
0.49
0.47
0.43
0.41
0.39
0.36
0.34
0.32
Coefficient on Volatility
0.8
5
0.8
6
0.8
7
0.8
8
0.8
9
0.9
1
0.9
1
0.9
2
0.
93
0.9
4
0.8
2
0.8
3
0.
83
0.
84
0.8
5
0.7
6
0.7
7
0.7
7
0.
78
0.
80
0.8
1
0.7
1
0.7
3
0.7
3
0.
73
0.7
5
0.6
6
0.6
7
0.6
9
0.
70
0.7
1
0.5
3
0.5
5
0.5
6
0.5
7
0.5
9
0.6
2
0.6
3
0.
63
0.6
4
Coefficient on Volatility
0.9
6
0.9
2
0.9
3
0.8
8
0.
89
0.9
1
0.8
5
0.8
6
0.
84
0.8
5
0.8
1
0.
83
0.8
3
0.7
8
0.8
0
0.7
5
0.
76
0.7
7
0.7
3
0.7
4
0.
71
0.7
2
0.6
7
0.
69
0.7
0
0.6
4
0.6
6
0.6
2
0.
63
0.6
3
0.
58
0.6
1
0.5
4
0.
55
0.5
6
0.5
2
0.5
3
0.4
9
0.5
1
0.4
3
0.
46
0.4
8
Coefficient on Volatility
Figure 7: Coefficient on Volatility in Cross-Sectional Estimations, Increasing Labour
Productivity Growth (Reduced UNIDO)
8
7
6
5
4
3
2
1
0
Median Productivity Growth
Figure 8: Coefficient on Volatility in Within Estimations, Increasing Labour Productivity
Growth (Reduced UNIDO)
7
6
5
4
3
2
1
0
Median Productivity Growth
Figure 9: Coefficient on Volatility in Arellano-Bond Estimations, Increasing Labour
Productivity Growth (Reduced UNIDO)
6
5
4
3
2
1
0
-0.5
-1.5
Median TFP Growth
0.5
0
0.4
7
0.4
6
0.4
4
0.4
3
0.4
2
0.4
0
0.3
9
0.3
8
0.3
4
0.3
3
0.3
2
0.2
9
0.2
8
0.2
7
0.2
6
0.2
5
0.2
2
0.2
2
0.2
0
0.1
7
0.1
4
0.1
2
0.1
2
0.1
1
0.0
8
0.0
7
0.0
5
0.0
4
0.0
2
0.0
0
-0.0
2
-0.0
3
-0.
05
Coefficient on Volatility
0.5
1
0.5
0
0.4
6
0.4
6
0.4
4
0.4
3
0.4
1
0.4
0
0.3
9
0.3
6
0.3
3
0.3
4
0.3
1
0.2
9
0.2
8
0.2
7
0.2
6
0.2
4
0.2
2
0.2
2
0.1
8
0.1
7
0.1
4
0.1
2
0.1
1
0.0
9
0.0
6
0.0
8
0.0
5
0.0
3
0.0
1
-0.
01
-0.
02
-0.
05
-0.
04
Coefficient on Volatility
0.4
3
0.4
4
0.4
6
0.3
9
0.3
9
0.4
0
0.
41
0.4
2
0.3
3
0.3
4
0.3
4
0.
35
0.3
8
0.2
8
0.2
8
0.2
8
0.
29
0.3
2
0.2
4
0.2
6
0.
26
0.2
6
0.2
2
0.2
2
0.1
8
0.
19
0.2
2
0.1
4
0.1
7
0.0
9
0.1
1
0.1
1
0.
12
0.1
3
0.0
4
0.0
5
0.0
6
0.
08
0.0
8
0.
01
0.0
3
Coefficient on Volatility
Figure 10: Coefficient on Volatility in Cross-Sectional Estimations, Increasing TFP
Growth (ISDB)
20
10
0
-10
-20
-30
-40
-50
Median TFP Growth
Figure 11: Coefficient on Volatility in Within Estimations, Increasing TFP Growth (ISDB)
30
20
10
0
-10
-20
-30
Median TFP Growth
Figure 12: Coefficient on Volatility in Arellano-Bond Estimations, Increasing TFP Growth
(ISDB)
7.5
6.5
5.5
4.5
3.5
2.5
1.5
0.5
Coefficient on Volatility
-1
-2
-3
-4
-5
-6
Median Productivity Growth
0.83
0.82
0.78
0.77
0.76
0.75
0.74
0.73
0.70
0.70
0.67
0.67
0.65
0.64
0.61
0.60
0.58
0.57
0.56
0.54
0.52
0.52
0.51
0.49
0.47
0.45
0.44
0.43
0.42
0.38
0.37
0.35
0.34
0.34
0.31
0.31
0.28
0.27
0.25
0.23
0.22
0.21
0.18
0.7
5
0.7
6
0.7
7
0.
73
0.7
5
0.
70
0.7
1
0.
67
0.6
8
0.
65
0.6
6
0.6
1
0.6
2
0.5
6
0.5
7
0.5
8
0.
53
0.5
4
0.
51
0.5
2
0.4
8
0.5
0
0.4
3
0.4
4
0.4
6
0.
40
0.4
2
0.
36
0.3
8
0.
34
0.3
5
0.3
1
0.3
2
0.2
6
0.2
7
0.3
0
0.2
2
0.2
5
0.
20
0.2
1
-5
0.16
0.14
0.
15
0.1
7
Coefficient on Volatility
0.8
3
0.8
5
0.6
7
0.6
9
0.7
0
0.7
3
0.7
5
0.7
6
0.7
7
0.7
9
0.5
8
0.6
1
0.6
4
0.6
6
0.5
2
0.5
3
0.5
5
0.5
7
0.3
4
0.3
5
0.3
6
0.3
8
0.4
2
0.4
4
0.4
5
0.4
8
0.5
0
0.0
9
0.1
1
0.1
3
0.1
5
0.1
7
0.2
1
0.2
2
0.2
5
0.2
7
0.3
0
0.3
1
Coefficient on Volatility
Figure 13: Coefficient on Volatility in Cross-Sectional Estimations, Increasing Labour
Productivity Growth (ISDB)
20
10
0
-10
-20
-30
-40
-50
-60
Median Productivity Growth
Figure 14: Coefficient on Volatility in Within Estimations, Increasing Labour Productivity
Growth (ISDB)
20
15
10
5
0
-10
-15
-20
-25
Median Productivity Growth
Figure 15: Coefficient on Volatility in Arellano-Bond Estimations, Increasing Labour
Productivity Growth (ISDB)
4
3
2
1
0