105
CHA PTER 2
WHERE ARE COMM ODI TY E XPORTERS H EADE D? OU TPUT GROWTH I N THE AFT ERMATH O F TH E COMMO DIT Y BOO M
International Monetary Fund | October 2015
93
In the “Dutch disease” phenomenon, a boom in
the commodity-producing sector of an economy puts
downward pressure on the output of the (noncom-
modity) tradable goods sector—essentially manufac-
turing. An extensive theoretical literature, starting with
Corden 1981 and Corden and Neary 1982, examines
the patterns and optimality of factor reallocation
between sectors following booms in commodity pro-
duction (linked to the discovery of natural resources).
周e models presented in these studies predict that an
improvement in the commodity terms of trade and
the subsequent spending of the income windfall in
the domestic economy will drive up the real exchange
rate and divert capital and labor from manufacturing
toward the commodity and nontradables sectors.1
Despite some evidence of a positive association
between the terms of trade and the real exchange
rate of commodity exporters, empirical research on
whether commodity booms hinder manufacturing
performance has been mixed, even among studies that
focus on the same countries or similar episodes:2
• No Dutch disease effects found: Studies of the 1970s
oil price boom, such as Gelb and Associates 1988
and Spatafora and Warner 1995, estimate that
higher oil prices led to real exchange rate apprecia-
tions but had no adverse effect on manufacturing
output in oil-exporting economies. Sala-i-Martin
and Subramanian (2003) find both the real
exchange rate and manufacturing activity to be
insensitive to oil price movements in Nigeria, an oil
exporter. Bjørnland (1998) argues that evidence of
Dutch disease following the United Kingdom’s oil
boom is weak and that manufacturing output in
Norway actually benefited from oil discoveries and
higher oil prices.
周e authors of this box are Aqib Aslam and Zsóka Kóczán.
1周ere are two effects at work: a “resource movement” effect,
in which the favorable price shock in the commodity sector
draws factors of production out of other activities, and a “spend-
ing effect,” which draws factors of production out of tradables
(to be substituted with imports) into the nontradables sector.
2For instance, Chen and Rogoff (2003) show that the curren-
cies of three advanced economy commodity exporters—Australia,
Canada, and New Zealand—have comoved strongly with their
terms of trade. Cashin, Céspedes, and Sahay (2004) find a long-
run relationship between the real exchange rates and commod-
ity terms-of-trade indices in about one-third of a sample of 58
commodity exporters. Arezki and Ismail(2013) argue that delays
in the response of nontradables-intensive government spending
to declines in commodity prices could weaken the empirical cor-
relation between the latter and the real exchange rate.
• Support for Dutch disease effects: Studies that have
found support for Dutch disease effects are more
recent. Ismail(2010) uses disaggregated data for
manufacturing subsectors for a sample of oil exporters
for the 1977–2004 period and shows that manufac-
turing output was negatively associated with the oil
price, especially in subsectors with a relatively higher
degree of labor intensity in production. Harding and
Venables(2013) use balance of payments data for a
broad sample of commodity exporters for 1970–2006
and find that an increase of $1 in commodity exports
tends to be accompanied by a fall of about 75 cents
in noncommodity exports and an increase of almost
25cents in noncommodity imports.
Some indirect evidence of the Dutch disease effect
can be gleaned by looking at the evolution of country
shares in global manufacturing exports, which tend
to be lower on average for commodity exporters than
for other emerging market and developing economies.
Although both groups have increased their market
shares over time (relative to advanced economies),
commodity exporters have seen a smaller increase in
their global manufacturing export shares than the
others, and the gap between the average market shares
of the two groups has widened since the early 1990s
(Figure2.1.1, panel 1).
Formal tests of whether terms-of-trade booms
hurt manufacturing export performance yield varied
results, however. 周e real exchange rate appreciates
gradually following an increase in the commodity
terms of trade (with the increase becoming statistically
significant only after the fifth year), but the impact
on manufacturing exports is not significant, which
points to a wide range of experiences across episodes
(Figure2.1.1, panels 2 and 3).
Numerous explanations have been offered for the
absence of major Dutch disease symptoms follow-
ing commodity terms-of-trade booms. 周ese include
policy-induced production restraints in the oil sector
(especially in the 1970s), the “enclave nature” of
the commodity sector (that is, its limited participa-
tion in domestic factor markets), limited spending of
the windfall on nontradables (with a ramping up of
imports instead), and government protection of the
manufacturing sector.3
A further explanation could be linked to the pickup
in global economic activity that, in some episodes,
3See Ismail2010, Sala-i-Martin and Subramanian 2003, and
Spatafora and Warner 1995.
Box 2.1. The Not-So-Sick Patient: Commodity Booms and the Dutch Disease Phenomenon
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139
WORLD ECONOMIC OUTLOOK: ADJUSTING TO LOWER COMMODITY PRICES
94
International Monetary Fund | October 2015
could be contributing to the booms in world com-
modity prices. Stronger global activity could lead to
stronger foreign demand for manufactured goods in all
countries, commodity exporters included, and provide
some offset to the loss of competitiveness associated
with an appreciating real exchange rate. 周is explana-
tion seems consistent with the varying findings in the
empirical literature. Dutch disease symptoms appear
to be stronger in studies that examine the performance
of the manufacturing sector over longer time periods,
which would include episodes of resource discoveries
and consequent increases in commodity production
volumes. Such country-specific episodes would not
necessarily be expected to coincide with episodes of
strong growth in global demand.
A question that has received much attention
among policymakers is whether commodity boom
effects on the manufacturing sector weigh on longer-
term growth. In principle, commodity booms could
compromise the longer-term outlook for the economy
if they weaken features of the manufacturing sector
that support longer-term growth—such as increas-
ing returns to scale, learning by doing, and positive
technological externalities.4
However, the evidence
is inconclusive.5 One explanation for the lack of an
apparent correlation between Dutch disease symptoms
and longer-term growth could be that learning-by-
doing externalities are not necessarily exclusive to man-
ufacturing; the commodity sectors could also benefit
from that effect (Frankel 2012). Another explanation
proposes that a manufacturing sector that contracts
and shifts toward greater capital intensity as a result of
a commodity boom—and that, in turn, uses higher-
skilled labor—may generate more positive externalities
for the economy than a larger manufacturing sector
using low-skilled labor (Ismail 2010).
4周eoretical models that incorporate learning-by-doing
externalities in the manufacturing sector include Matsuyama
1992, van Wijnbergen 1984a, Krugman 1987, and Benigno
and Fornaro 2014. Rodrik (2015) also argues that premature
deindustrialization can reduce the economic growth potential of
developing economies by stifling the formal manufacturing sec-
tor, which tends to be the most technologically dynamic sector.
5A comprehensive survey of the literature on this topic is in
Magud and Sosa2013. Rodrik (2008) analyzes the effect of the
real exchange rate on economic growth and the channels through
which this link operates; he concludes that episodes of undervalua-
tion are associated with more rapid economic growth. Eichengreen
(2008), however, notes that the evidence of a positive growth effect
from a competitive real exchange rate is not overwhelming.
Box 2.1 (continued)
0.0
0.1
0.2
0.3
0.5
0.6
0.7
0.8
0
2
4
6
8
10
1975
80
85
90
95
2000
05
08
1. Average Market Share in Global
Manufacturing Exports, 1975–2008
(Percent, five-year moving average)
–1
0
1
2
4
5
6
7
8
–1
0
1
2
3
4
5
6
7
2. Response of Real Effective Exchange Rate
to a Commodity Terms-of-Trade Shock
(Percentage points)
3. Response of Manufacturing Exports to a
Commodity Terms-of-Trade Shock
(Percentage points)
Commodity-exporting EMDEs
Other EMDEs
Advanced economies (right scale)
Figure 2.1.1. Manufacturing Export
Performance
Sources: UN Comtrade; United Nations Industrial
Development Organization; and IMF staff estimates.
Note: Impulse responses are estimated using the local
projection method; t = 0 is year of the shock; solid lines
denote response of variables to a 10 percentage point
increase in the shock variable; dashed lines denote 90
percent confidence bands. For panel 2, sample of 27
commodity-exporting emerging market and developing
economies (EMDEs) from 1970 through 2007. For panel 3,
sample of 45 commodity-exporting EMDEs from 1970
through 2007. See Annexes 2.1 and 2.4 for data definitions
and estimation methodology.
–3
–2
–1
0
1
2
3
4
5
–1
0
1
2
3
4
5
6
7
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138
CHA PTER 2
WHERE ARE COMM ODI TY E XPORTERS H EADE D? OU TPUT GROWTH I N THE AFT ERMATH O F TH E COMMO DIT Y BOO M
International Monetary Fund | October 2015
95
A commodity resource windfall can support
economic development in low-income developing
countries where potential returns to public investment
are high and access to international and domestic
credit markets is limited. When managed well, invest-
ments in productivity-enhancing public capital, such
as infrastructure, can help raise output and living
standards over the long term (Collier and others2010;
IMF2012, 2015).1
A model calibrated to a low-income developing
country is presented here to illustrate how a com-
modity windfall can raise public investment and
boost income levels over the long term if capital is
scarce and credit is constrained.2 周e model captures
the key trade-offs in public investment decisions.3 In
particular, public investment in low-income devel-
oping countries has the potential for high returns
but exhibits low levels of efficiency.4 周e long-term
effects of the boom on the growth of output depend
on the rate of return of public capital (relative to the
cost of funding), the efficiency of public investment,
and the response of private investment to the increase
in public capital.
周e analysis examines the behavior of nonresource
GDP in two scenarios—“no scaling up” (the base-
周e authors of this box are Rudolfs Bems and Bin Grace Li.
1For example, public investment can help close infrastructure
gaps, which are an important impediment to trade integration
and total factor productivity catch-up (see Chapter 3 of the April
2015 Regional Economic Outlook: Sub-Saharan Africa).
2Berg and others (forthcoming) find that low levels of effi-
ciency may be correlated with high rates of return because the
low efficiency implies very scarce public capital. In this situation,
the rate of return to investment spending may not depend on
the level of efficiency. Increasing efficiency would nonetheless
increase the return to public investment spending.
3周e model extends the work of Berg and others (2013) and
Melina, Yang, and Zanna (2014). A detailed presentation of the
model calibration is provided by Gupta, Li, and Yu (2015). 周e
modeled economy features the same structure as the commodity
exporter in the IMF’s Global Economy Model (GEM) used in
the chapter, including three sectors: tradables, nontradables, and
commodities. However, it excludes some of the real and nominal
frictions featured in the GEM, which makes it more suitable
for studying long-term effects rather than fluctuations over the
commodity cycle. 周e calibration of the model pays particular
attention to the lower levels of public investment efficiency and
limited absorptive capacity in low-income countries.
4Albino-War and others 2014 and IMF 2015 discuss the defi-
nition and measurement of public investment efficiency. 周ese
papers also highlight possible reforms that could help make
public investments more efficient, such as steps to strengthen
project appraisal, selection, and budget planning.
line) and “invest as you go”—both of which feature
a20percent increase in commodity prices followed by
a 15percent drop after year 10 (consistent with the
scenario discussed in the chapter) (Figure2.2.1):
• No scaling up: In the baseline case, the public invest-
ment ratio stays constant at 6percent of GDP.
• Invest as you go: In the alternative scenario, all
royalties from the commodity boom are spent on
public investment, whose share of GDP increases
1 percentage point, to 7percent, during the boom
(the initial 10 years) and subsequently falls in
tandem with the commodity price. Nevertheless,
it stays elevated in the long term in line with the
permanent gain in the commodity price.
Box 2.2. Commodity Booms and Public Investment
0
5
10
15
20
25
10
20
30
40
50
5.8
6.2
6.6
7.0
7.4
7.8
10
20
30
40
50
2. Public
Investment
(Percent of GDP)
1. Commodity
Price Index
No scaling up
Invest as you go
58
60
62
64
66
68
70
10
20
30
40
50
0.0
0.4
0.8
1.2
1.6
2.0
2.4
10
20
30
40
50
4. Nonresource
GDP
3. Public
Investment
Efficiency
(Percent)
Figure 2.2.1. Long-Term Effects of Heightened
Public Investment during Commodity Booms
(Percent deviation, unless noted otherwise; years on
x-axis)
Source: IMF staff calculations.
Note: “Public investment efficiency” refers to the share of
investment that ends up embedded in the capital stock.
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51
WORLD ECONOMIC OUTLOOK: ADJUSTING TO LOWER COMMODITY PRICES
96
International Monetary Fund | October 2015
As in the model simulation shown in the chapter’s
second section, nonresource GDP increases by 0.5per-
cent over the long term if the government maintains
an unchanged investment ratio. Under invest as you
go, the additional public investment increases long-
term nonresource output by about 2percent because
of the direct impact of a higher stock of public capital
and the crowding-in of private investment.5 周e
magnitude of this positive impact on output is broadly
consistent with the empirical findings for developing
economies in Chapter 3 of the October2014 World
Economic Outlook.
周e gains from higher public investment in low-
income developing countries depend crucially on
efficiency levels, which vary across the two scenarios
5While the increase in the long-term output under this
alternative scenario might appear small, it should be considered
against the relatively small size of the increase in public invest-
ment (1 percent of GDP at the peak). In comparison, Chapter
3 of the October 2014 World Economic Outlook finds that in a
typical public investment boom, the increase is about 7 percent-
age points of GDP. However, a large scaling up of public invest-
ment may also result in the implementation of inframarginal
projects, lowering its impact (see Warner 2014).
(Figure 2.2.1). In the baseline case, 35percent of
public investment is lost. In the alternative scenario,
the ramping up of public investment reduces the
efficiency level by about 6percentage points—about
41 percent is lost. 周e decline in efficiency in the
scenario highlights the trade-off between the need for
public investment and investment efficiency, with the
latter calibrated to match levels reported in empirical
studies.6
In sum, a ramping up of public investment in
response to a commodity boom can bring long-term
benefits to commodity exporters. But considering
the limited absorptive capacity of many developing
economies, a more gradual investment profile can yield
higher efficiency levels and lead to more favorable
long-term outcomes. 周e more gradual pace can also
curb the demand pressures during the boom phase of
the commodity cycle.
6周ese levels are consistent with the cost overruns in low-
income developing countries in Africa, as reported by develop-
ment agencies (see Foster and Briceño-Garmendia 2010). Gupta
and others (2014) document the decrease in public investment
efficiency during the 2000–08 boom.
Box 2.2 (continued)
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126
CHA PTER 2
WHERE ARE COMM ODI TY E XPORTERS H EADE D? OU TPUT GROWTH I N THE AFT ERMATH O F TH E COMMO DIT Y BOO M
International Monetary Fund | October 2015
97
Improvements in education and health help a coun-
try increase its economic potential over time by build-
ing larger and more-skilled pools of human capital.
Increasing their investments in human development
is therefore one way in which commodity-exporting
emerging market and developing economies can use
commodity windfall gains to boost their longer-term
living standards. 周e following discussion considers
whether commodity exporters have had an advantage
in boosting human development.1
Does Being a Commodity Exporter Matter for
Human Development?
To set the stage, it is useful to investigate whether
being a commodity exporter matters for the level and
pace of improvement in human development. Examina-
tion of the average levels of key human development
indicators over the past five decades reveals no clear
pattern across exporters and others (Figure2.3.1).2 For
instance, in terms of educational attainment at the sec-
ondary school level, commodity-exporting low-income
developing countries have on average had better out-
comes than noncommodity exporters, while commodity-
exporting emerging market economies on average have
had poorer outcomes than their noncommodity-export-
ing peers. For life expectancy and infant mortality, levels
of indicators have been similar across the two different
types of economies, but the relative pace of improvement
has varied between the groups over time.
Controlling for basic country characteristics—
including initial conditions, population size, GDP,
and political variables—does not reveal statistically
significant differences between commodity exporters
and other similar emerging market and developing
economies in terms of educational attainment, life
expectancy, or infant mortality (Figure2.3.2).3
周e authors of this box are Aqib Aslam and Zsóka Kóczán.
1McMahon and Moreira (2014) find that in the 2000s,
human development improved more rapidly in extractive com-
modity exporters than in countries that are not dependent on
extractive industries. Gylfason (2001) suggests that educa-
tion levels were inversely related to resource abundance in the
1980–97 period.
2周ese particular indicators of human development have
been shown to have an impact on the quality of human capital
(for example, Kalemli-Özcan, Ryder, and Weil 2000 and Oster,
Shoulson, and Dorsey 2013).
3周ese results are obtained using propensity score match-
ing (Rosenbaum and Rubin 1983). 周is estimation technique
tests for statistically significant differences between commodity
exporters and noncommodity exporters while ensuring that they
Box 2.3. Getting By with a Little Help from a Boom: Do Commodity Windfalls Speed Up Human Development?
0
10
20
30
40
50
1960
65
70
75
80
85
90
95
2000
05
10
1. Educational Attainment at Secondary Level
(Percentage of population that has
completed secondary schooling)
Figure 2.3.1. Human Development Indicators
Commodity-exporting EMs
Other EMs
Commodity-exporting LIDCs
Other LIDCs
40
45
50
55
60
65
70
75
1960
65
70
75
80
85
90
95
2000
05
10
2. Life Expectancy
(Years)
0
50
100
150
200
1960
65
70
75
80
85
90
95
2000
05
10
3. Infant Mortality
(Deaths per thousand births)
Sources: Barro and Lee 2010, April 2013 update; United
Nations Department of Economic and Social Affairs, UNdata;
United Nations Development Programme; World Bank, World
Development Indicators; and IMF staff calculations.
Note: Simple averages are taken over balanced samples for
each group. EM = emerging market; LIDC = low-income
developing country.
94
WORLD ECONOMIC OUTLOOK: ADJUSTING TO LOWER COMMODITY PRICES
98
International Monetary Fund | October 2015
Do Changes in the Commodity Terms of
Trade Predict Changes in the Pace of Human
Development?
Like the macroeconomic variables examined in the
chapter, key human development indicators tend to
are otherwise comparable in terms of key characteristics such
as population, level of GDP, political factors (regime change,
conflict), and lagged measures of human development. Figure
2.3.2 illustrates how commodity exporters compare with
noncommodity exporters in both an unmatched and a matched
sample. 周e former provides a simple comparison across groups
without controlling for any differences between them, whereas in
the latter, commodity exporters are compared with (hypothetical)
noncommodity exporters similar to them in regard to a number
of key characteristics.
move in tandem with the commodity terms of trade.
Educational attainment and life expectancy rise faster
during commodity terms-of-trade upswings than dur-
ing downswings (Figure2.3.3). 周is comovement is
not surprising, since education and health outcomes
are likely to benefit from higher social spending
by the public sector and a faster-growing economy
during a commodity boom. However, the differences
between average changes in educational attainment
and life expectancy during upswings and downswings
are not statistically significant, which is probably
attributable to other contextual factors affecting these
variables during these episodes.
Using the local projection method allows some
contextual factors such as the output growth of
trading partners, domestic conflict, and political
Box 2.3 (continued)
0
20
40
60
80
Unmatched
Matched
Unmatched
Matched
Unmatched
Matched
Pre-2000 CEEMDEs
Pre-2000 OEMDEs
Post-2000 CEEMDEs
Post-2000 OEMDEs
Life expectancy
Infant mortality
Figure 2.3.2. Comparing the Performance of
Commodity and Noncommodity Exporters
(Percent)
Sources: Barro and Lee 2010, April 2013 update; United
Nations Department of Economic and Social Affairs, UNdata;
United Nations Development Programme; World Bank, World
Development Indicators; and IMF staff calculations.
Note: CEEMDEs = commodity-exporting emerging market
and developing economies; OEMDEs = other emerging
market and developing economies. None of the differences
between matched samples are statistically significant at the
10 percent level.
Educational
attainment at
secondary level
0
1
2
3
4
5
6
Pre-2000 upswings
Pre-2000 downswings
Pre-2000 upswings (median)
Pre-2000 downswings (median)
Educational attainment
at secondary level
Life expectancy
Figure 2.3.3. Event Studies: Average Changes
in Human Development Indicators during
Upswings and Downswings
(Percent)
Sources: Barro and Lee 2010, April 2013 update; United
Nations Department of Economic and Social Affairs, UNdata;
United Nations Development Programme; World Bank, World
Development Indicators; and IMF staff calculations.
Note: Sample includes only cycles with peaks before 2000.
See Annex 2.2 for the cycle dating methodology. Infant
mortality is omitted from the event studies because data are
available only in five-year intervals and interpolation would
confound the effects.
23
CHA PTER 2
WHERE ARE COMM ODI TY E XPORTERS H EADE D? OU TPUT GROWTH I N THE AFT ERMATH O F TH E COMMO DIT Y BOO M
International Monetary Fund | October 2015
99
regime change to be controlled for. Estimates from
that method show that the responses of educational
attainment are barely statistically significant following
changes in the net commodity terms of trade; those of
life expectancy are not statistically significant.
Infant mortality has a statistically significant nega-
tive response, but this result appears sensitive to the
inclusion of data from the1970s and early1980s,
when commodity windfalls allowed commodity
exporters to catch up with their noncommodity-
exporting peers—infant mortality among commodity
exporters fell by 30 to 50percent over that period.
周e result weakened during later decades, when the
pace of improvement slowed for both commodity
exporters and noncommodity exporters. During those
years upswings no longer brought statistically signifi-
cant reductions, as marginal improvements appear to
have become progressively more difficult to achieve.
Box 2.3 (continued)
90
WORLD ECONOMIC OUTLOOK: ADJUSTING TO LOWER COMMODITY PRICES
100
International Monetary Fund | October 2015
周e model simulations presented in this chapter
predict that commodity booms will tend to be accom-
panied by overheating: if prices and wages adjust only
slowly to higher demand, the volume of output will
overreact and rise above its potential level (defined as
the level of output consistent with stable inflation). 周e
event studies presented in the chapter provide indirect
evidence of overheating during booms, documenting
that actual output tends to grow faster than trend out-
put during prolonged upswings in the commodity terms
of trade (Figure 2.8, panel 4). Such a growth differential
would be likely to push actual output above potential
output over the duration of the boom.
周e discussion here presents direct evidence of
overheating in six net commodity exporters during
the global commodity boom of the 2000s. Multivari-
ate filtering is used to estimate potential output and
the output gap, both of which are unobserved. 周e
technique combines information on the relationship
between unemployment and inflation (Phillips curve)
on the one hand, and between unemployment and the
output gap (Okun’s law) on the other.1
It is based on
the notion that a positive (negative) output gap will
be correlated with excess demand (slack) in the labor
market and lead to increases (decreases) in inflation.
周e six net exporters of commodities are Australia,
Canada, Chile, Norway, Peru, and Russia.2 周e infla-
tion process in these countries largely conforms to that
predicted by economic theory, with a broadly stable
relationship between inflation and unemployment.
周e authors of this box are Oya Celasun, Douglas Laxton,
Hou Wang, and Fan Zhang.
1Chapter 3 of the April 2015 World Economic Outlook uses the
multivariate-filter methodology to estimate potential output for 16
countries. A detailed description of the methodology can be found
in Annex 3.2 of that report and in Blagrave and others 2015.
2周e countries and time period chosen for the analysis reflect
the data requirements. Reliable unemployment series are not
available for a large number of commodity exporters, nor do
many countries show a broadly stable relationship between infla-
tion and unemployment. To ensure a focus on the link between
the terms of trade and the output gap, estimates are shown for
the uninterrupted phase of the commodity boom prior to the
2008–09 global financial crisis.
周e discussion focuses on the period 2002–07: the
uninterrupted phase of the boom in world commod-
ity prices ahead of the volatility associated with the
2008–09 global financial crisis.
周e analysis finds that the six economies moved into
excess demand as the commodity boom progressed
(Figure 2.4.1). 周e results are striking in that all six
economies show positive output gaps toward the end of
the prolonged commodity price boom. Moreover, the
changes in the output gap exhibit a positive correla-
tion with the commodity terms of trade, even if the
estimation does not incorporate information on the
latter variable (Figure 2.4.2). 周at result underscores the
important role of the commodity terms of trade in driv-
ing cyclical fluctuations in net commodity exporters.
However, estimates of output gaps based on
multivariate filtering benefit from hindsight, in the
Box 2.4. Do Commodity Exporters’ Economies Overheat during Commodity Booms?
–2.0
–1.0
0.0
1.0
2.0
3.0
2002
03
04
05
06
07
Australia
Canada
Chile
Norway
Russia
Peru
Figure 2.4.1. Output Gaps in Six Commodity
Exporters
(Percent)
Source: IMF staff calculations.
Note: Output gaps are estimated using the multivariate-
filter technique.
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