2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Sector-specific effects of the Australian Mining Boom:
Dutch Disease or Dutch Delight?
Jonathan R. Hambur* and Neville R. Norman**
Paper prepared for the CBEC Conference,
Murray Edwards College,
University of Cambridge, June 2013
*Reserve Bank of Australia, Martin Place, Sydney, NSW 2000, Australia.
** Economics Departments of the Universities of Melbourne (Victoria 3053 Australia) and Cambridge (CB3 9DD,
United Kingdom). Corresponding author: n.norman@unimelb.ed.au; nrn1v@econ.cam.ac.uk
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2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
[Abstract]
Sector-specific effects of the Australian Mining Boom:
Dutch Disease or Dutch Delight?
Studies of the economic impact of mining booms on Australia tend to be either theory without evidence or empirical work
based on broad aggregates and limited time spans. Here we investigate industry-specific impacts based on the latest data and
VAR econometric techniques. We find mixed evidence for the notion that the latest Australian mining boom is having an
adverse impact on manufacturing overall, but some specific adverse effects emerge when industry subdivisions are studied
individually. Contrarily, other subdivisions appear to have been impacted positively by the mining boom. Our findings enable
specific consequences of such a significant change-generator as the mining boom to be explored. They also offer the warning
that Dutch Disease theory is based on overly-simplified assumptions and overly-aggregated economic sectors. There are many,
not just two, speeds in a modern economy, some advanced, some retarded, and many left relatively unaffected by significant
shocks such as mining booms. This paper has many issues of concern to the business environment in a contemporary global
setting, as embraced by the Cambridge Business and Economics Conference series.
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2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
I:Introduction
Since the mid-2000s the real prices of many of Australia’s non-rural commodities have increased significantly, leading to large resource-industry
profits and investment and contributing substantially to overall economic growth. Many commentators have observed disparate growth
between resource and other sectors, creating what is widely called a “two-speed economy”. This crude characterisation has led to advocacy for
government action, ranging from resource taxes and sovereign wealth funds, to industry bailouts andeven to the devaluation of the Australian
dollar.
Such a divergence in economic performanceis directly predicted by ‘Dutch Disease’ (DD) theory. The theory contends that, through a mix of real
exchange rate appreciations and reallocation of inputs, a resource boom will causetradeable sectors of the economy, like manufacturing,
education and tourism, to shrink.
Received theory, media descriptions and previous empirical work tend to focus on broad groups such as manufacturing overall, or ‘trabeables’
and ‘non-tradeables’.Conversely, we focus onthemuch narrower sub-divisions of manufacturing in testing empirically which specific economic
activities of Australia, if any, have‘contracted’DD. Specifically we use the flexible and embracing Vector-Auto-Regression (VAR) approachto test
whether the mining boom has caused the touted ‘de-industrialisation’,while allowing for other causal-factor filters and differing responses in
each subdivision of industry. We alsoadvance from the existing literature by using a different lag-selection method that is more suited to
investigating long-term structural adjustments, such as those predicted by DD theory.
II: Literature Review
The term ‘Dutch Disease’ (DD) was coined by the Economist magazine in the 1970s to describe the apparent de-industrialisation of the Dutch
economy following the discovery of natural gas.1As noted by Sachs and Warner (1995), Mikesell (1997) and Iimi (2007), DD is part of the wider
resource-curse literature. This literature seeks to explain therelatively poor growth performance of many resource-rich countries.2 Explanations
for the resource-curse can be split into two broad categories (Devlin and Lewin, 2005): (a) those emphasising institutions and rent-seeking
behaviour; and (b) those focusing on how resources affect the economy’s structure and its overall economic growth.3DD is part of the latter
category.
1
See “The Dutch Disease”, The Economist, November 26, 1977. Pp.82-83
a review of the resource-curse literature see Van der Ploeg (2010)
3Examples of the former include Sala-i-Martin and Subramanian(2003), Stevens and Dietsche(2008) and Acemoglu and Robinson(2006), and of the latter include
Van der Ploeg and Steven(2009)
2For
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Corden and Neary (1982) (CN) is the seminal work on DD. The model it develops is the basis of most latter published work.4The CNmodel
containsa booming sector, a traded sector and a non-traded sectorwhich operate in a small open economy, meaning that the prices of booming
and traded sector output are set internationally, while those of non-tradable outputaredetermined domestically. Labour and capital are
internationally immobile,allbooming sector output is exported, real wages are perfectly flexible (implying full employment), and there are no
monetary considerations or complications. Employment, wages, prices and output commence in equilibrium. CN theory then investigates
theoretically the effect of a Hicks-neutral technological change on these variables for three input mobility structures. 5 These structures are:
i)
sector-specific capital and mobile labour;
ii)
capital mobility between the two domestic non-booming sectors and domestically mobile labour; and
iii)
full domestic capital and labour mobility.
The implications of the technological change are separated into two distinct effects, the ‘resource-movement effect’(mobile labour is drawn
from other sectors) and the ‘spending effect’ (real appreciation leads to increased domestic purchasing power and higher real imports).6
Many extensions have been made to this basic model. Several papers allow for more mobile labour and capital, both domestically (Corden and
Neary, 1982; Corden, 1984) and internationally (Bruno and Sachs,1982; Corden, 1984; Kuralbayeva and Vines, 2008).Corden (1984) also
considers a lagging tradeable sector made up of several component industries. In this case, while the whole sector may contract, some
industries may expand.7
Several papers also incorporate market imperfections. Corden (1984) considers the effects of real and nominal wage rigidities on
unemployment. Van Wijnbergen (1984), incorporates sticky real wages and real exchange rates (RER), noting that such rigidities may lead to
increased unemployment. Benjamin et al. (1989) considers the case where the domestic tradable sector’s goods are not perfectly substitutable
for goods on the world market and finds that ‘tradeable’ subdivisions may grow as a resultof the boom, if they have sufficiently low
Armingtonelasticities (elasticity of substitution between products from different countries). Similarly, Norman (1977) notes that linkages to the
booming sector - coupled implicitly with imperfect substitutability - may lead tradable industries to grow following the boom, rather than
shrinking. It further notes that the realappreciation associated with the boom may make imported inputs cheaper, lowering costs for the
tradeable sector and thus allowing it to expand output.
4Earlier works include Gregory (1976) and Snape (1977), which focus on the effects of mineral booms on Australian agriculture and manufacturing, and Forsyth
and Kay (1980, 1981) which consider the effect of increased oil exports on UK manufacturing.
5The paper contends that a Hicks-neutral technological change is analogous to the discovery of a new natural resource.
6 If no mobile inputs are used the booming sector is considered an enclave and there will be no
resource-movement effect. An enclave booming sector is assumed in most of the literature regarding oil booms, such as Algieri (2011). It is also used in the DD
literature on foreign aid (Rajan and Subramanian, 2011) and remittances (Lartey et al., 2008).
7This result is a direct implication of the Rybczynski theorem (Rybczynski, 1955), and was supported empirically by Ismail (2010)
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The empirical DD literature has had mixed results in identifying DD effects. Much of the early empirical work on DD, such as Hutchison (1990,
1994) and Bjornland (1998), focused on the effects of the discovery of North-Sea oil on the economies of Norway, the UK and the Netherlands.
More recent papers focus on other oil booms, such as in Russia(Algieri, 2011;Dobrynskaya and Turkisch, 2010), Kazakhstan (Egert and Leonard,
2008)and OPEC countries (Fardmanesh, 1991). Other natural resource booms have also been considered, such as coffee booms in Colombia
(Kamas, 1986; Raju and Melo,2003).8While some papers have usedeconometric techniques to identify DD in other industrialised countries, such
as Canada (Beine et al., 2012), few papers have used econometric techniques toconsider whether Australia has ‘contracted’ DD. 9
The empirical literature can be split between cross-country analysis and case studies. Cross-country papers, such as Harding (2010) and Ismail
(2010),tend to use panel-data techniques. Ismail (2010)uses panel techniques with subdivisional data to examine DD’seffects on
differentsubdivisions. The paper finds permanent increases in oil prices hurt the manufacturing sector, andthis damage is greatest in those
subdivisions where capital intensity is highest and those countries with more open capital markets.
Case study papers tend to take one of two approaches. The first is to consider counterfactuals. For example Larsen (2005, 2006) use tests for
structural breaks to compare Norway’s economy with its neighbours’ pre and post the discovery of oil. The other approach is to estimate
reduced-form equations or VAR/VECM.10 Of particular note is Hutchison (1994), which uses a VECM to analyse whether the North-Sea oil boom
created DD-type effects in the UK, Norway and the Netherlands. Itfinds that, while the increased oil production had little adverse effect on
manufacturing, increased oil prices did have an adverse impact. Bjornland (1998) carried out a similar exercise using a Structural VAR,but
focused only on Norway and the UK. Itconcludedthat the North-Sea oil discovery adversely affected UK manufacturing, but positively affected
Norwegian manufacturing.
III:The 21st Century Australian Mining Boom
The resource boom occurring in Australia since the mid-2000s has been largely driven by demand from China. This increased demand has led to
an increase in non-rural commodity prices, have more than doubled and tripled in $AUD and $USD terms respectively since the start of 2004
(Fig. 1).As a result, since the start of 2004 mining company profits before tax have risen by over 400% in nominal terms and 300% in real terms
(ABS, 2012a, 2012c). Despite this, mining production has not increased markedly, increasing by only around 30% over the decade to June 2012,
compared growth of around 40% in the preceding decade (ABS 2012g). Explanations for the inelasticity of mining production include
infrastructure bottlenecks (Bloxham, 2011), skill shortages, and the ‘Global Financial Crisis’, which may have delayed much needed investment.
8
The relevant literature also extends beyond resources into areas like development, considering the effects of other ‘Hicks-neutral shocks’ such as aid and
international remittances. See for example Rajan and Subramanian (2005, 2011), Issa and Ouattara (2008), Saab and Ayoub (2010) and Lartey et al. (2008)
9 Some non-econometric analyses includeMitchell and Bill (2006), Goodman and Worth (2008),Bloxham (2011) and Gregory and Sheehan (2012).Further, Corden
(2012) discusses possible policy responses to DD in the context of Australia.
10
Examples include Hutchison (1990) for Norway, the Netherlands and the UK, Farzanegan (2009) for Iran, Richards (1994) for Paraguay and Warr (2006) for Laos
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Figure 1
RBA Index of Commodity Prices
2008/2009=100*
Index
Index
USD
150
150
120
120
90
90
60
AUD
30
60
30
0
1982
0
1989
1996
2003
2010
* Non-rural component
Sources: RBA (2012a)
With regard to the resource-movement effect, since 2004 manufacturing workers’ ‘real-product wages’ (Nominal Wage/PPI) - which represents
wages as a cost (Corden, 2012; Lowe 2011) -have fallen by around 5% (ABS, 2012d, 2012h).This apparent fall in the cost of labour is counter to
DD’s prediction that the cost of labour in the tradeable sector should rise as a result of the rising marginal product and wages in the booming
sector. This indicates that any resource-movement effect with respect to labour has been small. Explanations for this include: the capital
intensive nature of the resource sector, spare capacity in the economy at the start of the boom, a sizeable compensating differential required
for workers to be willing to work in the mining sector, the resource and manufacturing sectors targeting different types of workers.
Despite
-
part
the
of
apparent
the
lack
income
of
resource-movement
effect
-
is
evident
effect,
in
increased
higher
income
flowing
average-weekly
and
into
the
consumer
economy
wages
(ABS 2012a, 2012d; Gregory and Sheehan, 2012). Much of this income has entered the economy through mining companies via their increased
spending on inputs and investment, higher tax and dividend payments, and through wealth effects created by their rising stock prices.
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DD predicts that this higher income will lead to a real appreciation. This has been the case in Australia, with the ‘Trade-weighted Real Exchange
Rate’ or ‘Real Effective Exchange Rate’ (REER) - often used to proxy for the RER - increasing significantly in the latter part of the decade (RBA,
2012b). Similarly, both the price ratios of tradable to non-tradable goods, and goods to services, have dropped appreciably (ABS, 2012b), while
Australia’s Terms-of-Trade has risen to record levels (ABS, 2012f). These relative price movements are further indication of a loss of
international competitiveness in the tradable sector, as predicted by DD.
As for the ‘de-industrialisation’ predicted by DD, since 2004chain-volumemanufacturing sales have fallen by around 5%, while Chain-Volume
GDP has grown by around30% (ABS, 2012e, 2012f) .Over the same period, the growth performances of manufacturing subdivisions were mixed.
While some subdivisions,such as FBT,grew moderately,some - such as textiles, transport and primary metals -shrank, and others, such as
chemicals
and
machinery,continued
to
grow
at
similar
rates
(Fig. 2 and 3).11Overall, while many would contend manufacturing in Australia has been shrinking for some time, the process does appear to
have accelerated in some subdivisions since the beginning of the mining boom.
Figure 2
Figure 3
Manufacturing Subdivision Sales
Manufacturing Subdivision Sales
Mar 1985=100*
Mar 1985=100*
Index
Index
Index
400
400
400
350
350
350
300
300
Chemicals
300
250
Manufacturing
200
150
Food, beverage
and tobacco
100
Textiles
50
0
1985
250
250
200
200
150
150
100
100
50
0
1991
* Trend series
Sources: ABS (2012h)
1997
2003
2009
50
0
1985
Index
400
Furniture
350
300
Manufacturing
250
200
Machinery
150
100
Transport equipment
Primary metals
50
0
1991
1997
2003
2009
* Trend series
Sources: ABS (2012h)
4: Empirical Methodology
While DD theory gives insight into which variables may be salient in testing for DD, it gives little insight into the exact structure and dynamics of
the adjustments. For example, as noted by Hutchison (1990, 1994), how quickly the deindustrialisation occurs will vary between economies
based on underlying structural parameters. Given this lack of guidance, a VAR approach was considered appropriate. Rather than imposing an
11The
primary metals industry’s struggles are epitomized by the closure of several of BlueScope Steel’s Australian plants in 2011, with BlueScope citing the high
Australian dollar and input prices as the main reasons for the closure (Bluescope Steel, 2011).
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a-priori structure, based on economic theory, it allows the data to reveal an appropriate structure. The VAR also provides several methods for
assessing whether the tested relationship actually exists. These include Granger-causality tests (Granger, 1969), Impulse Response Functions
(IRFs), and Variance Decompositions (VDCs).12
The VAR can be expressed as:
whereZt is a vector of k endogenous variables, πi is a matrix of k autoregressive coefficients at lag i, Xt is a vector of q exogenous variables and
is a matrix of q coefficients on the exogenous variables. The error term, et, is assumed to contain no serial correlation and have a covariance
matrix:
which is estimated using the average sums-of-squares of the LS residuals.
For the base specification, Zt consisted of mining income, the tradable subdivision’s sales and the REER. A mining income variable was chosen
over a production variable because the boom appears to bemore evident in mining profits than mining production. Regarding the sales variable,
to allow for identification of differing subdivisional responses to the resource boom theVAR was run separately using either aggregate
manufacturing’s chain-volume sales, orthe sales of a subdivision. Both sales and profits were measured in log forms. Several dummies were also
included. Seasonal dummies were used, along with two dummies to account for different stages of the GFC. Further, a boom dummy was
included to capture the exogenous mining profit boom. Finally, a dummy for Q1 2009was included to capture a large negative shock to the
REER in this quarter. A further specificationwas also considered, which included a measure of subdivisional input prices, deflated using the
subdivision’s Producer-Price-Index (PPI).13
Augmented-Dickey-Fuller and unit-root tests were used to test for stationarity. Those variables found to be non-stationary were then modelled
in differences. However, where co-integration was presentwe used aVECMalong the lines of Engle and Granger (1987).
12See
for example Greene(1997), Lutkepohl (2006) and Canova (1995).
This was motivated by Norman (1977).
13
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As noted by Hutchison (1994), VECMs allow for examination of the long-run level relationships between the variables lost when the variables
are modelled in differences. They may be expressed as:
where the exogenous variables, having previously been I(1), are now stationary. The deterministic structure chosen allowed for a trend in the
endogenous variables, but for only an intercept in the co-integrating relationship. This is in line with other papers, such as Hutchison (1994),
and seemed the best fit from a theoretical standpoint.
The existence of co-integrating relationships was assessed using the Trace and Maximum-Eigenvalue tests often attributed to Johansen (1991).
As exogenous variables are not accounted for in these tests’ limiting distributions, we followed Johansen(1995)in usingdummy variables
centred on zero, rather than traditional 0/1 dummies.
Unfortunately, the variables chosen were not conducive to Cholesky-decomposition(Sims, 1980). The variables were also not conducive the use
of a structural VAR approach as the literature’s standard assumption, that the mining variable cannot be affected contemporaneously, could
not be used. This is because most mining revenues are received in $USD and at least some must be converted back into $AUD. Thus, a change in
the REER may affect mining profits contemporaneously.14 Therefore, generalised impulse response functions (Pesaran and Shin, 1998), which
are invariant to ordering, were used.15
Two lag-selection methods were employed. The first was to use traditional lag-selection criteria such as the Akaike (AIC),Scwartz (SC) and
Hannan-Quinn (HQC) criteria.16 The second was to select a maximum lag and then to sequentially remove lags with the lowest joint t-statistic
until all are below some threshold, a process suggested by Bruggemann et al. (2003).17 This method was used because the structural
adjustments caused by the mining boom may be slow.Traditional lag-selection criteria may thus over-penalise longer lag choices by including all
intervening lags.
The data used are quarterly from 1985Q3 to 2012Q2.The subdivisions considered are Food, Beverage and Tobacco (FBT) Textiles, Clothing and
Leather Apparel (TCL), Machinery (Mach), Chemicals (Chem), Furniture (Furn), Primary Metals (Prim) and Transport Equipment (Trans), along
with Total Manufacturing (TM). These subdivisions were chosen for their apparent diverging performances in the boom period.
14
While many large miners may have currency hedges, both natural and via derivatives, it is difficult to quantify exactly what proportion of mining revenues are
hedged. As such we felt that the assumption that the mining variable could not be affected contemporaneously was too strong.
15This method requires the assumption that errors follow a multivariate normal distribution.
16
The removal of autocorrelation was also considered in selecting the lag-length.
17
The threshold is
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, where
is
and
for the AIC, HQ and SC, respectively.
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5: Results
5.1: Unit-root tests:
The results of the unit root tests are in Appendix A.Most of the variables present as I(1), though there is evidence that some of the input price
variables are I(0). However, treating these variables as I(0) makes little sense from a theoretical standpoint as the measures are created using
price indices.As such, all variables were treated as I(1).
5.2: Base Specification
5.2.1: Model Specification and Testing
The lag structures used under both lag selection methodologies are summarised in Table 2.18
Table 1: Lag Structure – Base Model
Subdivision
TM
FBT
TCL
Chem
Prim
Trans
Mach
Furn
Lag Selection Methodology
Lag-length criteria
Lag Exclusion
1-2
1-9
1-2
1-5, 8, 11-12
1-2
1-2, 6-12
1-2
1-5, 8-9, 12
1-2
1-2, 4, 6, 8-9, 12
1-2
1-4, 6-8
1-2
1-2, 4-5, 8 10-12
1-2
1-2, 5, 8-10
The number of co-integrating relationships used in each model is reportedin Table 3 and the Johansen test results are in Appendix A. Note,
where the number of co-integrating relationships indicated by the trace and maximum eigenvalue tests differed, the model which yielded
better information criteria was selected.19
18For
the ‘Lag Exclusion’ methodology, the maximum lag considered was twelve, or three years. Three information criteriawere used: AIC, SC and HQC. Lags were
generally removed until none of the criteria suggested further removals; however, autocorrelation was also considered in choosing the lag structure.
19The LM test, Doornik-Hansen Normality test and White Heteroskedasticity test were used to check for autocorrelation, normality and heteroskedasticity of the
errors respectively. Unless otherwise stated, all tests were passed at the 1% level.
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Table 2: Number of co-integrating relationships used – Base Model
Subdivision
TM
FBT
TCL
Chem
Prim
Trans
Mach
Furn
The
co-integrating
relationships
and
adjustment
No. of Cointegrating Relationships used
Lag-length criteria
Lag Exclusion
1
1
1
1
0
0
0
0
0
2
0
0
1
0
1
0
coefficients
from
the
co-integrated
models
are
in
Table 4.With the mining profit coefficient normalised to positive unity, a positive coefficient on the other variables indicates a negative long-run
relationship in levels between mining and that variable.A negative coefficient indicates a positive relationship. The estimated co-integrating
relationships for TM,FBT, Mach and Furn are all consistent with a positive long-run relationship between mining profits and sales, which is
inconsistent with the DD hypothesis.20 However, theresults also indicate a positive long-run relationshipbetween mining profits and REER and a
negative long-run relationship between REER and sales, both of which are supportive of DD-type effects.Note that the adjustment coefficients
on sales are numerically small and are only significant in two of the models, indicating that sales adjust very slowly - if at all - to
disequilibrium.This apparent lack of adjustment by sales is somewhat counter to the DD hypothesis.
20
Note an additional June quarter 2010 dummy was requiredto achieve normality in the in the ‘lag exclusion’ model for TM. This dummy was also required for
the Prim models in both the base and input specifications.
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Table 3: Co-integrating relationships – Base Model
Sector
Lags Selection
Co-int. Relationship
TM
FBT
Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion
1
1
1
1
Prim
Lag Exclusion
1
2
Mach
Furn
Lag-length criteria Lag-length criteria
1
1
β coefficients
Mining Profit
1
1
1
1
1
0
1
1
REER
std. dev
t-stat
-0.003
-0.009
-0.316
-0.075***
-0.018
-4.268
-0.009
-0.007
-1.283
-0.02**
-0.008
-2.431
0
1
-0.004
-0.011
-0.412
-0.0002
-0.009
-0.024
Sales
std. dev
t-stat
-2.652***
-0.400
-6.637
-3.485***
-0.594
-5.867
-2.758***
-0.329
-8.379
-3.002***
-0.389
-7.710
-3.848***
-0.844
-4.559
10.725
-11.061
0.970
-1.616***
-0.289
-5.588
-1.498***
-0.222
-6.751
-0.294***
-0.065
-4.555
-0.103
-0.063
-1.638
-0.355***
-0.079
-4.510
-0.429***
-0.115
-3.741
-0.136***
-0.049
-2.765
-0.002
-0.004
-0.406
-0.262***
-0.065
-4.016
-0.267***
-0.064
-4.146
0.602
-1.173
0.513
3.052***
-0.941
3.244
1.671
-1.426
1.172
2.114
-1.895
1.116
0.905
-0.712
1.271
-0.224***
-0.062
-3.620
0.756
-1.159
0.652
-0.114
-1.162
-0.098
0.008
-0.011
0.737
0.013
-0.014
0.898
0.054***
-0.016
3.467
0.001
-0.001
0.489
-0.004
-0.012
-0.371
0.051**
-0.021
2.400
Adj. Coef
Mining Profit
std. dev
t-stat
REER
std. dev
t-stat
Sales
0.007
0.012***
std. dev
-0.006
-0.004
t-stat
1.099
2.708
Significant using Normal Distribution at 10%*, 5%**, 1%***
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Interpreting the long-run relationships in the Prim subdivision is more difficult, given the multiple co-integrating relationships. However, the
estimates appear againto indicate a long-run negative relationship between REER and sales, and long-run positive relationships between mining
profits and sales, and mining profits and REER.
Given the very low adjustment coefficients on sales, a likelihood-ratio test for weak exogeneity(Johansen, 1992) was used. The results are in
Table 5,along with the results from a test of Granger-causality. The weak exogeneity and Granger-causality tests seem to indicate mining profits
have little impact on sales in the FBT, Chem, Prim and Mach subdivisions. Meanwhile, mining appears to have the largest effect on sales in the
Trans subdivision and in TM, while having some effect on sales in the‘lag-length criteria’ models of the TCL and Furnsubdivisions, but not the
‘lag exclusion’ models.
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Table 4: Weak exogeneity and Granger-causality tests for Sales – Base Model
TM
FBT
TCL
Chem
Prim
Trans
Mach
Furn
Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion
Weak Exogneity LR Test (α=0)
chi^2
P-value
Granger Causality Test
Exclude Mining Profits
chi^2
P-Value
Exclude REER
chi^2
P-Value
All
chi^2
P-Value
Significant at 10%*, 5%**, 1%**
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1.032
0.310
7.609***
0.006
0.566
0.452
0.738
0.390
#N/A
N/A
#N/A
N/A
#N/A
N/A
#N/A
N/A
#N/A
N/A
14.788***
0.001
#N/A
N/A
#N/A
N/A
0.084
0.772
#N/A
N/A
5.449**
0.020
#N/A
N/A
8.041***
0.005
18.095**
0.021
0.379
0.538
7.354
0.393
7.678***
0.006
4.652
0.794
1.028
0.311
3.422
0.844
0.095
0.758
6.519
0.368
7.858***
0.005
13.842**
0.032
0.324
0.569
5.900
0.552
3.294*
0.070
7.695
0.174
5.09**
0.024
6.210
0.624
1.823
0.177
11.803
0.107
1.677
0.195
8.390
0.396
5.696**
0.017
12.784*
0.078
2.353
0.125
7.164
0.306
8.29***
0.004
14.306**
0.026
4.871**
0.027
9.655
0.209
0.044
0.834
4.112
0.533
13.726***
0.001
25.769*
0.057
2.192
0.334
20.214
0.124
8.705**
0.013
14.057
0.595
7.77**
0.021
17.099
0.251
2.557
0.278
11.992
0.446
18.194***
0.000
29.018***
0.004
5.297*
0.071
17.749
0.218
3.321
0.190
11.712
0.305
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5.2.2: IRFs
The IRFs for the responses of sales and REER to mining profits are in Appendix B. In the TM sector, the results’ support of the DD hypothesis is
mixed.The ‘lag-exclusion’ model shows the hypothesised long-run rise in REER and fall in sales following a mining profit shock. However the
‘lag-length criteria’ model shows sales’ initial negative reaction to the mining shock abating after around 15 quarters. Further, REER has a
negative long-run response to the shock. On the whole though, there does appear to be some evidence of the predicted ‘de-industrialisation’.
The results from the FBT sub-division are somewhat inconsistent with the DD theory. Both models indicate positive long-run responses by sales
and REER to the mining profits shock. This indicatesthat the FBT sector is actually helped by the shock, which accords with the subdivision’s
relatively strong growth performance over recent years.However, any such interpretation should be tempered by the fact that sales were
weakly exogenous and were not Granger-caused by mining profits.
Interestingly, the was some evidence of similar ‘industrialisation’ in the Furn subdivision, with the ‘lag-length criteria’ model indicating that the
mining shock led to a long-run positive response from sales. However, little response was evident in the ‘lag exclusion’ model.
In contrast to the FBT and, to a lesser extent, Furn subdivisions, results from the TCL, Primand Transsubdivisions support the DD hypothesis.
Both TCLmodels show a negative response by sales to the mining profits shock, though the response is only significant in the ‘lag-length criteria’
model. While both models also indicate a negative response from REER, interpretation of the REER response is best done within the TM model,
rather than the subdivisional models. Meanwhile the Prim subdivision’s ‘lag-length criteria’ shows a negative – but insignificant–responseby
sales to the mining shock, while the ‘lag exclusion’ model shows a negative long-run response, which abates somewhat after several years.
Similarly, both Trans models show sales responding negatively to the mining shock, though neither is significant.The fact that the results from
these threesubdivisions largely accord with the DD theory is not surprising given their relatively poor growthperformancesduring the boom.
Results from the Chem and Mach subdivisions seem to indicate that sales hardly respond to the mining shock. While Mach’s ‘lag-length criteria’
model indicated some positive long-run response by sales to the shock, the ‘lag exclusion’ model indicated little response. As for the Chem
subdivision, both models showed positive, but insignificant,responses from sales to the mining shock. Again, these results are not surprising
given these industries continued to grow during the boom at similar rates to beforehand.
5.3: Input Specification
5.3.1: Model Specification and Testing
The lag structures used under both lag selection methodologies are summarised in Table 6.
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Table 5: Lag Structure – Input Model
Subdivision
TM
FBT
TCL
Chem
Prim
Trans
Mach
Furn
Lag Selection Methodology
Lag-length criteria
Lag Exclusion
1-2
1-2, 5-7, 9-10, 12
1-2
1-2, 4, 7-8, 11-12
1-6
1-2, 4-12
1-2
1-3, 6, 8-9, 12
1-3
1-12
1-2
1-5, 10-12
1-2
1-2, 4-6, 8, 10-12
1-2
1-5, 7-8, 11-12
The results of the Johansen tests are in Appendix A and the numbers of co-integrating relationships used are in Table 7.
Table 6: Number of co-integrating relationships used – Input Model
Subdivision
TM
FBT
TCL
Chem
Prim
Trans
Mach
Furn
No. of Cointegrating Relationships used
Lag-length criteria
Lag Exclusion
1
1
1
0
2
1
0
1
1
2
0
1
1
1
1
2
The estimated co-integrating relationships are in Table 8.As in the base specification the TM models indicatepositive long-run relationships
between mining profits and sales, and mining profits and REER, but a negative relationship between REER andsales. 21 They also indicate a
negative relationship between mining profits and input costs, but a positiverelationship between REER and input costs. This is somewhat
puzzling as we would expect the mechanism through which the boom would lower input costs would be byraising the REER and thus lowering
the price of imported inputs. Nonetheless, the adjustment coefficient on input costs is low and insignificant, indicating theinput costs do not
really adjust to the disequilibrium.
21
Note a dummy for the March quarter in 2008 was required in both TM models to achieve normality.
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Table 7: Co-integrating relationships – Input Model
Sector
TM
FBT
Lags Selection
Lag-length criteria Lag Exclusion Lag-length criteria
Co-int. Relationship
1
1
1
TCL
Lag-length criteria
1
2
Lag Exclusion
1
Chem
Lag Exclusion
1
Prim
Lag-length criteria
Lag Exclusion
1
1
2
Trans
Mach
Lag Exclusion Lag-length criteria Lag Exclusion
1
1
1
Furn
Lag-length criteria
Lag Exclusion
1
1
2
β coefficients
Mining Profit
1
1
1
1
0
1
1
1
1
0
1
1
1
1
1
0
REER
std. dev
t-stat
0.007
-0.009
0.725
-0.035**
-0.015
-2.352
-0.005
-0.006
-0.766
0
1
-0.056
-0.047
-1.201
-0.003
-0.008
-0.365
0.043**
-0.022
1.960
0
1
0.158***
-0.044
3.632
-0.046*
-0.025
-1.880
-0.252***
-0.066
-3.813
0.02
-0.024
0.855
0
1
Sales
std. dev
t-stat
-4.519***
-1.027
-4.398
-9.971***
-2.037
-4.894
-3.228***
-0.323
-10.006
11.152***
-1.805
6.179
87.68***
-14.700
5.965
0.446
-3.232
0.138
-0.824***
-0.240
-3.431
-5.082***
-0.930
-5.465
-4.634*** 12.581
-0.844
-11.061
-7.038
0.975
-1.243
-2.202
-0.565
0.427
-0.881
0.485
-1
-1.947
-0.513
-2.133***
-0.676
-3.155
-3.241***
-0.709
-4.571
-6.336
-12.860
-0.493
Input
std. dev
t-stat
0.547*
-0.299
1.830
1.515***
-0.523
2.900
-4.292***
-1.397
-3.072
-1.557
-1.759
-0.885
-56.877***
-14.324
-3.971
20.637***
-4.039
5.110
-14.48***
-2.300
-6.297
-14.055***
-3.970
-3.540
-7.552*** -65.1
-2.489
-48.779
-3.034
-1.335
-40.453***
-11.529
-3.509
26.404***
-7.208
3.663
33.278**
-15.590
2.135
16.708***
-3.218
5.192
19.473*** -291.905**
-7.035 -127.584
2.768
-2.288
-0.288***
-0.058
-5.002
-0.075
-0.056
-1.328
-0.411***
-0.084
-4.871
0.101**
-0.040
2.506
0.006
-0.004
1.459
-0.07***
-0.022
-3.260
-0.115
-0.086
-1.348
-0.031
-0.029
-1.049
0.002
-0.143
0.011
0.005
-0.008
0.630
-0.019
-0.021
-0.878
-0.078***
-0.025
-3.087
-0.02
-0.019
-1.081
-0.037**
-0.019
-1.999
-0.14***
-0.042
-3.311
REER
std. dev
t-stat
-0.25
-1.082
-0.231
0.098
-1.062
0.092
0.034
-1.499
0.023
0.125
-0.594
0.210
-0.213***
-0.063
-3.366
0.58
-0.363
1.598
-0.477
-1.306
-0.366
-0.948*
-0.510
-1.861
0.442
-1.865
0.237
-0.357***
-0.108
-3.298
-0.813**
-0.337
-2.411
1.183***
-0.415
2.851
1.014***
-0.262
3.870
-0.309
-0.311
-0.996
-1.647** -0.159***
-0.688
-0.051
-2.395
-3.122
Sales
std. dev
t-stat
0.008
-0.006
1.326
0.02***
-0.004
4.568
0.015
-0.012
1.291
-0.039***
-0.009
-4.126
0.002**
-0.001
2.121
0.006
-0.005
1.059
0.024
-0.017
1.400
0.015**
-0.007
2.025
0.126*** -0.004***
-0.025
-0.001
4.952
-2.851
-0.003
-0.003
-1.061
-0.004
-0.004
-0.823
0.0003
-0.003
0.117
0.007
-0.006
1.207
0.034**
-0.013
2.555
-0.0005
-0.001
-0.495
0.01
-0.007
1.426
0.006
-0.004
1.365
-0.0005
0.000
-1.079
-0.009***
-0.002
-5.309
0.025***
-0.006
4.333
0.018***
-0.004
4.911
0.054***
-0.014
3.871
0.005***
-0.001
4.331
-0.006***
-0.002
-3.208
-0.003**
-0.001
-2.435
-0.005***
-0.001
-5.937
-0.003
-0.002
-1.609
0.0002*
0.000
1.758
Adj. Coef
Mining Profit
std. dev
t-stat
Input
-0.01
0.002
std. dev
-0.019
-0.021
t-stat
-0.510
0.085
Significant using Normal Distribution at 10%*, 5%**, 1%***
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-0.001
-0.001
-1.131
15
-0.004
-0.003
-1.296
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The co-integrating relationships estimated for FBT, Chem and Trans are all somewhat at odds with DD theory, showing positive long-run
relationships between sales and mining profits. 22 All three also indicate a positive relationship between mining profits and input costs, and a
negative relationship between input costs and sales. Strangely, the Trans model also reports a significant negative relationship between mining
profits and REER, which is certainly counter to the DD hypothesis.
Meanwhile, there is mixed evidence for DD in the co-integrating relationships estimated for the Mach subdivision. While the ‘lag-length criteria’
model indicates a negative relationship between sales and mining profits, the ‘lag exclusion model indicates a positive relationship; however,
neither relationship is significant. This is in contrast to the significant positive co-integrating relationship in the base specification and may
reflect the positive relationship between profits and input costs. 23
As discussed earlier, interpretation of the point estimates of multiple co-integrating relationships is difficult. However, both TCL models appear
to indicate that there is a long-run negative relationship between sales and mining profits, while both Prim and Furn models appear to indicate
positive long-run relationships between sales and mining profits.24
Table 9contains the weak exogeneity and Granger-causality test results for the input specification. The results are reasonably similar to the base
specification, with both TM models again indicating mining profits significantly impact on sales, as do one model in each of the TCL, Trans and
Furn subdivisions.
22
Note that there is some evidence of heteroskedasticity in the Trans subdivision’s ‘lag-length criteria’ model.
Note there is some evidence of autocorrelation in the ‘lag-length criteria model’ at the tenth lag.
24 Note there is some evidence of heteroskedasticity in the ‘lag-length criteria’ model for Furn. There is also some evidence of autocorrelation at the first lag in
the ‘lag-length criteria’ model for the Prim subdivision.
23
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Table 8: Weak exogeneity and Granger-causality tests for Sales – Input Model
TM
FBT
TCL
Chem
Prim
Trans
Mach
Furn
Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion Lag-length criteria Lag Exclusion
Weak Exogneity LR Test (α=0)
chi^2
P-value
Granger Causality Test
Exclude Mining Profits
chi^2
P-Value
Exclude REER
chi^2
P-Value
Exclude Inputs
chi^2
P-Value
All
chi^2
P-Value
Significant at 10%*, 5%**, 1%**
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1.431
0.232
7.872***
0.005
1.720
0.190
#N/A
N/A
14.993***
0.001
1.893
0.169
#N/A
N/A
2.645
0.104
3.347*
0.067
45.47***
0.000
#N/A
N/A
1.284
0.257
0.455
0.500
0.020
0.889
1.315
0.251
14.023***
0.001
8.321***
0.004
21.99***
0.003
0.094
0.759
8.465
0.206
8.108
0.150
12.022
0.284
0.834
0.361
3.090
0.797
1.290
0.525
37.587***
0.000
7.562***
0.006
6.525
0.480
0.314
0.575
8.579
0.379
1.319
0.251
7.548
0.479
4.523**
0.033
16.602**
0.020
1.209
0.272
13.435**
0.037
10.591*
0.060
8.020
0.627
5.696*
0.085
5.342
0.501
5.996**
0.050
29.41***
0.002
3.926**
0.048
7.223
0.406
3.154*
0.076
9.943
0.269
0.130
0.719
12.087
0.147
0.028
0.868
11.560
0.116
0.164
0.685
3.447
0.751
8.709
0.121
16.445*
0.088
0.135
0.714
4.329
0.632
6.778**
0.034
45.692***
0.000
0.029
0.866
8.624
0.281
0.012
0.912
8.994
0.343
3.033*
0.082
30.004***
0.000
13.755***
0.003
40.408***
0.007
1.448
0.694
30.448**
0.033
34.498***
0.003
36.080
0.206
7.833**
0.050
22.489
22.489
10.467
0.106
90.255***
0.000
18.037***
0.000
32.952**
0.047
3.990
0.263
29.745
0.193
4.419
0.220
43.971***
0.008
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Meanwhile, as in the base specification, the results for the FBT, Chem and Mach subdivisions indicate that mining profits have little impact on
sales.25 In contrast, unlike in the base specification, the results of the exogneity and Granger-causality tests for the Prim subdivision indicate
that mining profits do impact on sales. This finding indicates the importance of input costs in propagating the impacts of a mining profit shock
on the Prim subdivision.
5.3.2: IRF
The IRFs are in Appendix B. For TM the IRFs for the ‘lag-length criteria’ model are similar to those from the base specification andsocontinue to
run somewhat counter to the DD hypothesis. 26 Conversely, the ‘lag exclusion’ model indicates a long-run positiveresponse by sales to a mining
profit shock, where the base specification had indicated a negative response. This difference likely reflects the negative response of input prices
to the mining profit shock. Higher mining profits lead to a higher REER, lowering the cost of imported inputs. This shifts out the supply curve
and allows for higher production.
The results for the FBT models a quite similar to those from the base specification, though sales’ positive response to the shock in the ‘lag
exclusion’ model appear less pronounced,which may reflect the positiveresponse of input costs to the shock. Input prices’ positive response to
the shock is unsurprising as the FBT subdivision may have a relatively low share of imported inputs. As such it may not benefit from lower prices
for imported inputs.
The results from the TCL, Chem and Trans subdivision models also appear quite similar to the base specification. Conversely, the Furn
subdivision’s results are quite different to those when using the base specification. While the base specification models had indicated that sales
respondednegatively to the mining profit shock, the input specification models indicate a positive response.As with the TM model, this may
reflect the negative response of input costs to the profit shock.A similar but opposite effectis evident in the Mach subdivision’s ‘lag exclusion’
model. In this case the shock appears to raise input costs, which would in turn explain the more pronounced fall in sales.
Strangely, the Prim subdivisions ‘lag exclusion’ model showed a less pronounced negative response from sales to the shock, despite the
apparent positive response of input costs.27
V: Caveats and Conclusions
A major caveat to this study is that it may be too early to gauge whether Australia has ‘contracted’ DD. While opinions differ over how far
through the boom Australia is,it is likely that there are still large amounts of capital still to be invested,and that mineral prices, mining profits
25
Note that in Chem subdivision’s the ‘lag-length criteria’ model, while mining profits do not directly Granger-cause sales, they do Granger-cause REER, which in
turn Granger-causes sales. Similarly, in both Prim subdivision models, mining profits Granger-cause inputs, which in turn Granger-causes sales.
26
The negative response by REER to the mining profit shock is again somewhat strange. It may reflect the short lag structure selected in this model, in that a
sustained rise in mining profits may be needed before the REER is affected. As such, an interesting extension may be to replace the mining profit variable with a
rolling-sum or rolling-average mining profit variable.
27 Note that heteroskedasticity tests were not available for this model due to insufficient degrees of freedom.
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and Australia’s terms of trade and dollar, will stay historically high for significantly longer. As structural changes – such as the one predicted by
DD – may take extended periods to develop and may only be truly evident once the boom is over, revisiting this study in future years may be
informative. This would further provide more observations, more accurate estimates and allow for longer lag structures.
Another timing-related limitation is the difficulty in dealing with the GFC and its aftermath, given that it coincided with a large proportion of the
boom period. While GFC dummies were included, it is still difficult to disentangle its effects from those of the resource boom. This is especially
true given the GFC’s effects on the Australian economy may have, in many senses, been similar to DD. It has lowered world demand for
manufactures, but - at least initially - didrelatively little to quash China’s demand for minerals. This kept the Australian economy strong relative
to the rest of the world and led to an appreciation in the REER, making tradeable industries less competitive. Thus, the GFC has essentially
accelerated and magnified any DD-type effects caused by the resource boom.
Some data-related limitations may also exist. Firstly, the input price variable had to be reconstructed. This may have created biases and
structural breaks. Secondly, the REER is only a proxy for the true RER, and thirdly, the Chain-volume measures used for sales are imperfect. As
such, there may be grounds to question the results and their implications based on the validity of the data used. Finally, further heterogeneity
may exist within subdivisions, and so more disaggregated data would be beneficial.
Nonetheless, the results show only mixed evidence of DD-based de-industrialisation in the TM sector, despite the apparent rise in the REER.
Further, this evidence is only apparent if we do not control for the effects on the boom on input prices.
Evidence of DD at the subdivisional level is also mixed. The results suggest ‘pro-industrialisation’ in the FBT subdivision,the DD-predicted ‘deindustrialisation’ in the TCL and Transsubdivisions, and little response from the Chem, and Mach subdivisions. Interestingly, for the Furn and - to
a lesser extent - Prim subdivisions, conclusions regarding DD are largely dependent on whether input prices are included in the model or not.
The results have implications both for the Australian economy and for the empirical DD literature. Regarding the former, the varying evidence
of DD-type effects at the subdivisional level may inform forecasting and policy formulation. The fact that these effects are not homogenous
must be considered by the Government when taking any action in response to the boom. Failing to do so may lead to non-optimal policy
decisions and unintended adverse consequences. For example, any policy, such as a tax on the mining industry, used to mitigate the boom and
protect the manufacturing industry, may hurt subdivisions which have benefited from the boom, such as FBT. 28 This underlines the importance
of these results, both with respect to responding to the mining boom, as well as with regard to more general policy making, such as trade policy
and responses to world price shocks.
28
While an in-depth discussion of the merits of different policies is beyond the scope of this paper, several countries, most notably Norway (Larsen, 2005, 2006)),
and Botswana (Iimi, 2007), have dealt with DD and the more general resource curse quite effectively and their examples may be instructive. Further, Corden
(2012) discusses the relative merits of different policy options in the Australian context.
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As for implications for the empirical DD literature, the first and most important conclusion is that, where available, disaggregated tradeable
sector data should be used to account for heterogeneity within sectors. Secondly, traditional lag-selection criteria may not be appropriate in
modelling DD-type effects as such structural changes may occur over long periods.As suchother methods of lag selection that penalise less for
intervening insignificant lags may be more appropriate. Following these prescriptions should allow for more accurate identification of DD
effects, and, more generally, identification of the effects of price shock or of changes in government policy, such as tariffs and taxes, on sectors
of the economy.
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Appendix A
Table A.1: Augmented Dickey-Fuller Test Results
Schwartz Criteria
Constant
TM Sales
FBT Sales
TCL Sales
Chem Sales
Prim sales
Trans Sales
Mach Sales
Furn Sales
Mining Profits
TM Inputs
FBT Inputs
TCL Inputs
Chem Inputs
Prim Inputs
Trans Inputs
Mach Inputs
Furn Inputs
REER
July 2-3, 2013
Cambridge, UK
Level
t-stat
p-value
-1.98
0.30
-2.08
0.25
-0.15
0.94
-1.74
0.41
-2.14
0.23
-1.48
0.54
-1.93
0.32
-1.67
0.44
-0.31
0.92
-0.89
0.79
-2.60
0.10
-2.00
0.29
-3.26
0.02
1.85
1.00
-2.75
0.07
-1.52
0.52
-5.72
0.00
-0.30
0.92
Difference
t-stat
p-value
-2.53
0.01
-3.60
0.03
-2.68
0.02
-2.00
0.20
-10.19
0.00
-4.76
0.00
-2.92
0.02
-3.78
0.01
-15.35
0.00
-7.86
0.00
-10.48
0.00
-6.99
0.00
-9.54
0.00
-8.62
0.00
-7.43
0.00
-4.38
0.00
-7.71
0.00
-8.55
0.00
Trend
Level
t-stat
p-value
0.10
1.00
-1.03
0.93
-1.83
0.68
-4.29
0.00
-1.82
0.69
-2.10
0.54
-2.38
0.39
-1.65
0.77
-2.84
0.19
-0.93
0.95
-2.81
0.20
-2.57
0.29
-3.53
0.04
0.64
1.00
-2.94
0.16
-1.90
0.65
-4.64
0.00
-1.96
0.61
Difference
t-stat
p-value
-2.84
0.06
-4.11
0.00
-3.00
0.04
-3.54
0.01
-10.14
0.00
-4.80
0.00
-3.57
0.01
-3.85
0.00
-15.51
0.00
-8.06
0.00
-10.48
0.00
-7.00
0.00
-9.49
0.00
-8.80
0.00
-7.40
0.00
-4.36
0.00
-7.89
0.00
-7.93
0.00
23
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Appendix B
Figure B.1: IRFs TM - ‘Lag-length criteria’ – Base Specification
Figure B.2: IRFs TM - ‘Lag-exclusion’ – Base Specification
Response of REER to Generalized One
S.D. MINE Innovation
Response to Generalized One S.D. Innovations
Response of REER to MINE
2.5
-.15
2.0
-.20
1.5
-.25
1.0
-.30
0.5
-.35
0.0
-0.5
-.40
5
10
15
20
25
30
35
10
40
20
30
40
50
60
70
80
90
100
Response of MANUF_S to Generalized One
S.D. MINE Innovation
Response of MANUF_S to MINE
.004
.000
.002
-.002
.000
-.004
-.002
-.004
-.006
-.006
-.008
-.008
5
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10
15
20
25
30
35
40
10
20
30
40
50
60
24
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Figure B.3: IRFs FBT - ‘Lag-length criteria’ – Base Specification
Figure B.4: IRFs FBT - ‘Lag exclusion’ – Base Specification
Response to Generalized One S.D. Innovations
Response to Generalized One S.D. Innovations
Response of REER to MINE
Response of REER to MINE
.8
.8
.6
.6
.4
.4
.2
.2
.0
.0
-.2
-.2
5
10
15
20
25
30
35
40
10
20
Response of FBT_S to MINE
30
40
50
60
70
80
90
100
80
90
100
Response of FBT_S to MINE
.012
.005
.010
.008
.004
.006
.003
.004
.002
.002
5
July 2-3, 2013
Cambridge, UK
10
15
20
25
30
35
40
10
20
30
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60
25
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Figure B.5: IRFs TCL - ‘Lag-length criteria’ – Base Specification
Figure B.6: IRFs TCL - ‘Lag exclusion’ – Base Specification
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Accumulated Response of D(REER) to D(MINE)
Accumulated Response of D(REER) to D(MINE)
3
1.0
2
0.5
1
0.0
0
-0.5
-1
-1.0
-2
-3
-1.5
2
4
6
8
10
12
14
16
18
20
5
10
15
20
25
30
35
40
45
50
Accumulated Response of D(TEXTILES_S) to D(MINE)
Accumulated Response of D(TEXTILES_S) to D(MINE)
.02
.00
.01
-.01
.00
-.01
-.02
-.02
-.03
-.03
-.04
-.05
-.04
2
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Cambridge, UK
4
6
8
10
12
14
16
18
20
5
10
15
20
25
30
26
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Figure B.7: IRFs Chem - ‘Lag-length criteria’ – Base Specification
Figure B.8: IRFs Chem - ‘Lag exclusion’ – Base Specification
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Accumulated Response of D(REER) to D(MINE)
Accumulated Response of D(REER) to D(MINE)
3
1.0
2
0.5
1
0.0
0
-1
-0.5
-2
-1.0
-3
-4
-1.5
2
4
6
8
10
12
14
16
18
20
5
10
15
20
25
30
35
40
45
50
Accumulated Response of D(CHEM_S) to D(MINE)
Accumulated Response of D(CHEM_S) to D(MINE)
.04
.02
.03
.01
.02
.01
.00
.00
-.01
-.01
-.02
2
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Cambridge, UK
4
6
8
10
12
14
16
18
20
5
10
15
20
25
30
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Figure B.9: IRFs Prim - ‘Lag-length criteria’ – Base Specification
Figure B.10: IRFs Prim - ‘Lag exclusion’ – Base Specification
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Response to Generalized One S.D. Innovations
Accumulated Response of D(REER) to D(MINE)
Response of REER to MINE
1.0
1.2
0.5
0.8
0.0
0.4
-0.5
0.0
-1.0
-0.4
-1.5
-0.8
2
4
6
8
10
12
14
16
18
20
10
Accumulated Response of D(PRIMARY_S) to D(MINE)
.03
.00
.02
-.01
.01
-.02
.00
40
50
60
70
80
90
100
90
100
-.01
2
July 2-3, 2013
Cambridge, UK
30
Response of PRIMARY_S to MINE
.01
-.03
20
4
6
8
10
12
14
16
18
20
10
20
30
40
50
60
28
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Figure B.11: IRFs Trans - ‘Lag-length criteria’ – Base Specification
Figure B.12: IRFs Trans - ‘Lag exclusion’ – Base Specification
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Accumulated Response of D(REER) to D(MINE)
Accumulated Response of D(REER) to D(MINE)
1.0
2
1
0.5
0
0.0
-1
-2
-0.5
-3
-1.0
-4
-1.5
-5
2
4
6
8
10
12
14
16
18
20
5
Accumulated Response of D(TRANS_S) to D(MINE)
.01
10
15
20
25
30
35
40
45
50
Accumulated Response of D(TRANS_S) to D(MINE)
.02
.01
.00
.00
-.01
-.01
-.02
-.03
-.02
-.04
2
July 2-3, 2013
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4
6
8
10
12
14
16
18
20
5
10
15
20
25
30
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Figure B.13: IRFs Furn - ‘Lag-length criteria’ – Base Specification
Figure B.14: IRFs Furn - ‘Lag exclusion’ – Base Specification
Response to Generalized One S.D. Innovations
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Response of REER to MINE
Accumulated Response of D(REER) to D(MINE)
2
-.40
-.42
1
-.44
0
-.46
-1
-.48
-2
-.50
-3
-.52
5
10
15
20
25
30
35
5
40
Response of FURN_S to MINE
10
15
20
25
30
35
40
45
50
Accumulated Response of D(FURN_S) to D(MINE)
.020
.04
.016
.02
.012
.00
.008
.004
-.02
.000
-.04
-.004
-.008
-.06
5
July 2-3, 2013
Cambridge, UK
10
15
20
25
30
35
40
5
10
15
20
25
30
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Figure B.15: IRFs TM - ‘Lag-length criteria’ – Input Specification
Figure B.16: IRFs TM - ‘Lag exclusion’ – Input Specification
Response to Generalized One S.D. Innovations
Response to Generalized One S.D. Innovations
Response of REER to MINE
Response of REER to MINE
-.45
0.4
-.50
0.0
-.55
-.60
-0.4
-.65
-0.8
-.70
-1.2
-.75
5
10
15
20
25
30
35
40
10
20
30
40
50
60
70
80
90
100
90
100
90
100
Response of MANUF_S to MINE
Response of MANUF_S to MINE
.008
.002
.006
.000
.004
-.002
.002
-.004
.000
-.006
-.002
-.004
-.008
5
10
15
20
25
30
35
10
40
30
40
50
60
70
80
Response of MANUF_I to MINE
Response of MANUF_I to MINE
-.008
-.008
-.012
-.012
-.016
-.016
-.020
-.020
-.024
-.024
-.028
-.028
5
July 2-3, 2013
Cambridge, UK
20
10
15
20
25
30
35
40
10
20
30
40
50
60
31
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Figure B.17: IRFs FBT - ‘Lag-length criteria’ – Input Specification
Response to Generalized One S.D. Innovations
Figure B.18: IRFs FBT - ‘Lag exclusion’ – Input Specification
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Response of REER to MINE
Accumulated Response of D(REER) to D(MINE)
-.25
4
-.30
2
-.35
0
-.40
-2
-.45
-.50
-4
5
10
15
20
25
30
35
40
5
Response of FBT_S to MINE
10
15
20
25
30
35
40
45
50
Accumulated Response of D(FBT_S) to D(MINE)
.008
.02
.007
.01
.006
.00
.005
-.01
.004
.003
-.02
5
10
15
20
25
30
35
40
5
10
15
20
25
30
35
40
45
50
Accumulated Response of D(FBT_I) to D(MINE)
Response of FBT_I to MINE
.003
.03
.002
.02
.001
.01
.000
.00
-.001
-.002
-.01
5
July 2-3, 2013
Cambridge, UK
10
15
20
25
30
35
40
5
10
15
20
25
30
35
32
40
45
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Figure B.19: IRFs TCL - ‘Lag-length criteria’ – Input Specification
Figure B.20: IRFs TCL - ‘Lag exclusion’ – Input Specification
Response to Generalized One S.D. Innovations
Response to Cholesky One S.D. Innovations
Response of REER to MINE
Response of REER to MINE
1.0
1.2
0.8
0.8
0.6
0.4
0.4
0.2
0.0
0.0
-0.2
-0.4
10
20
30
40
50
60
70
80
90
10
100
20
30
40
50
60
70
80
90
100
90
100
90
100
Response of TEXTILES_S to MINE
Response of TEXTILES_S to MINE
.008
.000
.004
-.004
.000
-.008
-.004
-.008
-.012
-.012
-.016
-.016
10
20
30
40
50
60
70
80
90
10
100
20
30
40
50
60
70
80
Response of TEXTILES_I to MINE
Response of TEXTILES_I to MINE
.005
.004
.004
.003
.000
.002
-.004
.001
.000
-.008
10
July 2-3, 2013
Cambridge, UK
20
30
40
50
60
70
80
90
100
10
20
30
40
50
60
33
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Figure B.21: IRFs Chem - ‘Lag-length criteria’ – Input Specification
Figure B.22: IRFs Chem - ‘Lag exclusion’ – Input Specification
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Response to Generalized One S.D. Innovations
Accumulated Response of D(REER) to D(MINE)
Response of REER to MINE
0.00
1.0
-0.25
0.5
-0.50
0.0
-0.75
-0.5
-1.00
-1.0
-1.25
-1.50
-1.5
2
4
6
8
10
12
14
16
18
10
20
Accumulated Response of D(CHEM_S) to D(MINE)
20
30
40
50
60
70
80
90
100
90
100
90
100
Response of CHEM_S to MINE
.02
.008
.01
.004
.00
.000
-.01
-.004
2
4
6
8
10
12
14
16
18
10
20
Accumulated Response of D(CHEM_I) to D(MINE)
30
40
50
60
70
80
Response of CHEM_I to MINE
.008
.016
.004
.012
.000
.008
-.004
.004
.000
-.008
2
July 2-3, 2013
Cambridge, UK
20
4
6
8
10
12
14
16
18
20
10
20
30
40
50
60
34
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Figure B.23: IRFs Prim - ‘Lag-length criteria’ – Input Specification
Figure B.24: IRFs Prim - ‘Lag exclusion’ – Input Specification
Response to Generalized One S.D. Innovations
Response to Generalized One S.D. Innovations
Response of REER to MINE
Response of REER to MINE
3
-.2
2
-.3
1
0
-.4
-1
-2
-.5
-3
2
4
6
8
10
12
14
16
18
10
20
Response of PRIMARY_S to MINE
20
30
40
50
60
70
80
90
100
90
100
90
100
Response of PRIMARY_S to MINE
-.002
.06
-.004
.04
-.006
.02
-.008
.00
-.010
-.02
-.04
-.012
2
4
6
8
10
12
14
16
18
10
20
Response of PRIMARY_I to MINE
20
30
40
50
60
70
80
Response of PRIMARY_I to MINE
.03
.010
.008
.02
.006
.01
.004
.002
.00
.000
2
July 2-3, 2013
Cambridge, UK
4
6
8
10
12
14
16
18
20
10
20
30
40
50
60
35
70
80
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Figure B.25: IRFs Trans - ‘Lag-length criteria’ – Input Specification
Figure B.26: IRFs Trans - ‘Lag exclusion’ – Input Specification
Accumulated Response to Generalized One S.D. Innovations ± 2 S.E.
Response to Generalized One S.D. Innovations
Accumulated Response of D(REER) to D(MINE)
Response of REER to MINE
0.4
1.0
0.5
0.0
0.0
-0.4
-0.5
-0.8
-1.0
-1.2
-1.5
2
4
6
8
10
12
14
16
18
10
20
Accumulated Response of D(TRANS_S) to D(MINE)
20
30
40
50
60
70
80
90
100
90
100
90
100
Response of TRANS_S to MINE
.01
.00
.00
-.01
-.01
-.02
-.02
-.03
-.03
2
4
6
8
10
12
14
16
18
10
20
Accumulated Response of D(TRANS_I) to D(MINE)
30
40
50
60
70
80
Response of TRANS_I to MINE
.006
.006
.004
.004
.002
.002
.000
.000
-.002
-.002
-.004
-.004
2
July 2-3, 2013
Cambridge, UK
20
4
6
8
10
12
14
16
18
20
10
20
30
40
50
60
36
70
80
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Figure B.27: IRFs Mach - ‘Lag-length criteria’ – Input Specification
Figure B.28: IRFs Mach - ‘Lag exclusion’ – Input Specification
Response to Generalized One S.D. Innovations
Response to Generalized One S.D. Innovations
Response of REER to MINE
Response of REER to MINE
1.2
2.5
2.0
0.8
1.5
1.0
0.4
0.5
0.0
0.0
-0.5
5
10
15
20
25
30
35
40
10
Response of MACHINE_S to MINE
20
30
40
50
60
70
80
90
100
90
100
90
100
Response of MACHINE_S to MINE
.005
.010
.005
.004
.000
.003
-.005
.002
-.010
.001
-.015
5
10
15
20
25
30
35
40
10
Response of MACHINE_I to MINE
20
30
40
50
60
70
80
Response of MACHINE_I to MINE
.003
.008
.002
.006
.001
.000
.004
-.001
.002
-.002
-.003
.000
5
July 2-3, 2013
Cambridge, UK
10
15
20
25
30
35
40
10
20
30
40
50
60
37
70
80
2013 Cambridge Business & Economics Conference
ISBN : 9780974211428
Figure B.29: IRFs Furn - ‘Lag-length criteria’ – Input Specification
Figure B.28: IRFs Furn - ‘Lag exclusion’ – Input Specification
Response to Generalized One S.D. Innovations
Response to Generalized One S.D. Innovations
Response of REER to MINE
Response of REER to MINE
-.38
0
-.40
-1
-.42
-2
-.44
-3
-.46
-4
-.48
-5
5
10
15
20
25
30
35
40
10
Response of FURN_S to MINE
20
30
40
50
60
70
80
90
100
90
100
90
100
Response of FURN_S to MINE
-.012
.03
-.013
.02
-.014
.01
-.015
.00
-.016
-.01
-.017
-.018
-.02
5
10
15
20
25
30
35
40
10
Response of FURN_I to MINE
20
30
40
50
60
70
80
Response of FURN_I to MINE
.000
.002
.000
-.002
-.002
-.004
-.004
-.006
-.006
-.008
-.008
5
July 2-3, 2013
Cambridge, UK
10
15
20
25
30
35
40
10
20
30
40
50
60
70
80
38