TY - JOUR
AU - Thorsrud, Leif, A.
AB - Abstract Traditional studies of the Dutch disease do not account for productivity spillovers between the booming resource sector and other domestic sectors. We put forward a simple theory model that allows for such spillovers. We then identify and quantify these spillovers using a Bayesian dynamic factor model. The model allows for resource movements and spending effects through a large panel of variables, while also identifying disturbances to other shocks. Using Australia and Norway as representative cases studies, we find that a booming resource sector has substantial productivity spillovers on non‐resource sectors, effects that have not been captured in previous analysis. Over the last decade, commodity producers such as Australia and Norway have experienced growth rates exceeding those of comparable advanced economies by up to 0.5 percentage points on a yearly basis. A boom in the extraction of natural resources is important in explaining this growth performance. In particular, the value of the Norwegian oil and gas industry – including services – grew by approximately 90%, while employment in this industry grew by more than 60%. No other industry exhibited such growth rates. Even more pronounced was the development that played out in the mineral abundant country, Australia. The value of mining increased by 130%, while employment in the same industry went up by 105%. The mineral boom in Australia and the oil and gas boom in the North Sea have been the principal, but far from only, cause of the substantial growth enjoyed by both these countries. A strong rise in commodity prices has caused Australia and Norway’s terms of trade to almost double since 2003. These price rises have profound effects on economies, as they constitute both a large increase in real income, boosting aggregate demand in the wider economy, and also a large shift in relative prices, inducing resource movements between industries. For instance, while employment in the non‐tradable sectors such as construction and business service in both Australia and Norway increased by 30–40% over the last decade, employment in sectors such as manufacturing and the hotel and service industry has either fallen or at best, hardly grown. This has prompted much discussion as to whether Australia and Norway might have become two‐speed economies, see, for example, Garton (2008). There are concerns that the gains from the boom largely accrue to the profitable sectors servicing the resource industry, while the rest of the country is suffering adverse effects from increased wage costs, an appreciated exchange rate and a lack of competitiveness as a result of the boom. In the literature, such a phenomenon has commonly been referred to as the Dutch disease, based on similar experiences in the Netherlands in the 1960s.1 Figure 1 summarises these concerns for the two resource‐rich economies: a two‐speed development in the labour market coupled with an appreciating exchange rate alongside soaring commodity prices. Fig. 1. Open in new tabDownload slide Boom or Gloom? Stylised Facts Notes. The employment series are on a log scale, normalised to 100 in 1991:Q1 (Australia) and 1996:Q1 (Norway. Shorter sample due to data availability). We use the real effective exchange rate, where an increase implies appreciation. Fig. 1. Open in new tabDownload slide Boom or Gloom? Stylised Facts Notes. The employment series are on a log scale, normalised to 100 in 1991:Q1 (Australia) and 1996:Q1 (Norway. Shorter sample due to data availability). We use the real effective exchange rate, where an increase implies appreciation. Much theoretical work has been done on analysing the benefits and costs of resource discoveries, see for instance Bruno and Sachs (1982), Corden and Neary (1982), Eastwood and Venables (1982), Corden (1984), van Wijnbergen (1984) and Neary and van Wijnbergen (1984) for early contributions. There have, however, been relatively few empirical studies. In addition, the standard Dutch disease model on which these articles are based typically does not account for productivity spillovers between the resource rich sector and the rest of the economy. Experience in Australia, and Norway in particular, suggests that this could be important. For instance, as the development of offshore oil often demands complicated technical solutions, this could in itself generate positive knowledge externalities that benefit other sectors. And since these sectors trade with other industries in the economy, there may be learning‐by‐doing (LBD) spillovers to the overall economy. This could be an important explanation for the high growth rates observed in the domestic economies. To address these issues, we put forward a simple theory model that allows for direct productivity spillovers from the resource sector to both the traded and non‐traded sector. We further assume there is LBD in the traded and non‐traded sectors, as well as learning spillovers between these sectors. While the introduction of the direct productivity spillover is new to this article, the LBD mechanism is similar to that developed in Torvik (2001). Hence, we extend the model of Torvik (2001) with technology spillovers from the resource sector. To the extent that the natural resource sector crowds in productivity in the other sectors, the growth rate in the overall economy will increase. We test the predictions from our suggested theoretical model against data by estimating a Bayesian dynamic factor model (BDFM) that includes separate activity factors for the resource and non‐resource (domestic) sectors in addition to global activity and the real commodity price. Four structural shocks are identified using a simple recursive ordering: a global activity shock, a commodity price shock, a resource activity shock and a domestic activity shock. Our main focus is to examine the windfall gains associated with resource booms and commodity price changes separately, while also allowing global demand to affect commodity prices, see Hamilton (1983,2009), Barsky and Kilian (2002) and Kilian (2009) for discussions on this latter feature.2 The BDFM is particularly useful to answer the research questions we address. First, the interdependence between the different branches of an economy – traditionally measured by the input‐output tables from the national accounts – do not account for the indirect spillover effects (productivity or demand) between different sectors. Thus, co‐movement across sectors, for example, oil or non‐oil, due to common factors, is not captured by observable variables alone. Conversely, in the BDFM, latent common factors can be identified and estimated simultaneously with the rest of the model’s parameters. Thus, the size and sign of spillover effects can be derived and analysed. Second, to quantify the spillover effects across a large cross section of sectors and variables, standard multivariate time series techniques are inappropriate due to the curse of dimensionality. The BDFM is designed for data rich environments such as ours. Third, macroeconomic data are often measured with noise and errors. In the factor model framework, we can separate these idiosyncratic noise components from the underlying economic signal. The empirical analysis is applied to Norway and Australia, two small net exporters of respectively petroleum and minerals. What matters, however, is not their absolute size in the commodity market but the size of the resource sector relative to the rest of the economy. In particular, in the last decade, more than 75% of the value of their export was commodity based, while gross value added in the resource sector took up around 10% and 20% of output in Australia and Norway, respectively. Thus, the analysis could be applied to any commodity‐exporting small open economy, as long as the resource sector represents a relatively important share of the overall economy. We extend the literature in three ways. First, to the best of our knowledge, this is the first article to explicitly analyse and quantify the linkages between a booming energy sector and sectoral performance in the domestic economy using a structural model, while also allowing for explicit disturbances in real commodity prices, world activity and activity in the domestic sector. Second, given the large number of variables and industries included in the analysis, this is also the most comprehensive analysis to date of the relationship between resource booms and activity at the industry level in resource rich economies. So far there have been very few studies empirically examining the relationship between a booming resource sector and the rest of the economy. Those that have analysed the issue, have typically employed a structural vector autoregression (SVAR), including only a single sector in the non‐resource economy, such as manufacturing or domestic output, see, for example, Hutchison (1994), Bjørnland (1998) and Dungey et al. (2014), or a panel data approach studying common movements in manufacturing across numerous countries, see, for example, Ismail (2010). The overall conclusion has been that effects of, say, mining or petroleum investment on domestic output are small, c.f., Dungey et al. (2014) or Bjørnland (1998). However, neither of these approaches accounts for cross‐sectional co‐movement of variables and sectors within a country. The factor model proposed in this article does. Lastly, the use the BDFM framework to analyse the Dutch disease is novel in the literature. A related study in that regard, is recently presented in Charnavoki and Dolado (2014). They examine how changes in commodity prices due to various global shocks affect the commodity‐exporter Canada and uncover a Dutch disease effect using a structural factor model. However, as alluded to above, a windfall gain due to a change in commodity prices is only one channel through which resource wealth can affect the domestic economy. Alternatively, a resource boom could be caused by (unpredicted) technical improvements in the booming sector, represented by a favourable shift in the production function, or, say, a windfall discovery of new resources, see, for example, Corden (1984) for details. In Charnavoki and Dolado (2014) this channel is not investigated. We claim that it might be important and should be accounted for when analysing commodity exporting countries.3 Our main conclusion emphasises that a booming resource sector has significant and positive productivity spillovers on non‐resource sectors, effects that have not been captured by previous analyses. In particular, we find that the resource sector stimulates productivity and production in both Australia and Norway. What is more, value added and employment both increase in the non‐traded relative to the traded sectors, suggesting a two‐speed transmission phase. The most positively affected sectors are construction, business services and real estate. Windfall gains derived from changes in the commodity price also stimulate the economy, particularly if the rise in commodity price is associated with a boom in global demand. However, commodity price increases unrelated to global activity are less favourable, in part because of substantial real exchange rate appreciation and reduced competitiveness. Still, value added and employment rise temporarily in Norway, mostly due to increased activity in the technologically intense service sectors and a boost in government spending. For Australia, the picture is gloomier, as there is evidence of a Dutch disease effect with crowding out and an eventual decline in manufacturing. These results emphasise the importance of distinguishing between windfall gains due to volume and price changes when analysing the Dutch disease hypothesis. To the best of our knowledge, this is the first article to separate and quantify these two channels explicitly, while also allowing for explicit disturbances to global activity and the non‐resource sectors. The remainder of the article is structured as follows. In Section 1, we discuss the theoretical literature on Dutch disease and develop a simple alternative theoretical model. Section 2 details the data and the model, the identification strategy, and the estimation procedure used in the empirical investigation. Our main results are reported in Section 3, while in Section 4, we discuss robustness of results. Section 5 concludes. 1. The Model The traditional literature on the Dutch disease typically predicts an inverse long run relationship between the exploitation of natural resources and the development in the traded sector (i.e. manufacturing), see Corden (1984) for an overview of the literature. The negative effect comes about from a movement of resources out of the traded and non‐traded sector and into the booming sector that extracts the natural resource (resource movement effect). There will also be indirect (secondary) effects of increased demand by the sectors that produce goods and services for the booming sector (spending effect). This will cause a real appreciation that will hurt the traded sectors. A limitation of the traditional Dutch disease literature is that it assumes productivity to be exogenous to the model. However, in some resource‐rich countries, the exploitation of natural resources could have substantial productivity spillovers to the other sectors in the economy. For example, the development of offshore oil, as in Norway, often demands complicated technical solutions, this could in itself generate positive knowledge externalities that benefit some sectors. If these sectors trade with other industries in the economy, then there are likely to be LBD spillovers to the overall economy. To account for these features, we develop a model that allows for direct productivity spillovers from the natural resource sector to both the traded and non‐traded sector. In addition, we assume there is LBD in the traded and non‐traded sectors, as well as learning spillovers between these sectors. While the introduction of the direct productivity spillover is new to this article, the LBD mechanism is similar to that developed in Torvik (2001). In particular, Torvik (2001) assumes that both the traded and the non‐traded sector can contribute to learning and that there are spillovers between these sectors. Hence, we extend the model of Torvik (2001) with technology spillovers from the resource sector. In doing so, we show that if the natural resource sector crowds in productivity in the other sectors, the growth rate in the overall economy will increase. To focus on the new mechanisms, the following assumptions are made: there is no unemployment; the natural resource boom is exogenous (i.e. a foreign exchange gift); there is balanced trade; labour is the only production factor; and labour mobility between sectors is perfect. The resource boom is denoted Rt, and is measured in terms of traded sector productivity units.4 In line with Corden (1984), we assume that an increase in Rt can be thought of as happening in one of three ways: (i) an (unpredicted) technical improvement in the booming sector, represented by a favourable shift in the production function; (ii) a windfall discovery of new resources; or, (iii) an exogenous rise in the world real price of the resource that is exported. In line with the literature, we consider case (i) or (ii) in the analysis below. In the empirical analysis, however, we also allow for a windfall gain due to an increase in the real prices of the natural resources (i.e. case (iii)). We denote production at time t in the non‐traded and traded sectors as XNt and XTt, respectively. The total labour force is normalised to one, and ηt denotes the labour force employed in the non‐traded sector. The traded and the non‐traded sectors have production functions XNt = HNtf(ηt) and XTt = HTtg(1 − ηt), respectively, where HNt and HTt are the respective sectoral productivities for the non‐traded and the traded sectors. We assume diminishing returns to labour in each sector and the productivity parameters enter with constant returns to scale, as is standard in the endogenous growth literature with one factor of production, see Torvik (2001). Total income in the economy (measured in traded goods), Yt, will now be the value of production in the non‐traded and traded sectors, plus the value of the foreign exchange gift: Yt = PtXNt + XTt + HTtRt, where Pt is the price of the non‐traded goods in terms of traded goods, that is, the real exchange rate. Finally, we assume productivity in the traded and non‐traded sectors to have the following growth rates: H˙Nt/HNt=uη(λt,Rt)+vδT[1−η(λt,Rt)]+δRRt,0≤δT≤1,(1) H˙Tt/HTt=uδNη(λt,Rt)+v[1−η(λt,Rt)]+δRRt,0≤δN≤1,(2) where a ‘dot’ above a variable indicates the derivative of the variable with respect to time and λt = HTt/HNt is the relative productivity level between the two sectors. One unit of labour use in the non‐traded (traded) sector contributes to a productivity growth rate of u (v) in the non‐traded (traded) sector. Further, a fraction δT (δN) of the learning from employment in the traded (non‐traded) sector spills over to the non‐traded (traded) sector. Finally, we allow for a direct LBD spillover from the resource to domestic sector. Resource extraction implies learning and we assume the spillover to domestic sectors is governed by the learning parameter δR ≥ 0. It is reasonable to assume that the more technologically advanced the resource sector, the higher the δR. Importantly, we see from (1) and (2) that the growth in productivity is endogenous, as it depends on the labour share and the resource boom. Consumers allocate spending on non‐traded and traded goods according to a utility function with constant elasticity of substitution, σ. Total income is given by the value of production in the non‐traded and traded sector, plus the value of the foreign exchange gift. In equilibrium, demand must equal supply of non‐traded goods. The real exchange rate can then be characterised as a function of the employment share in the non‐traded sector, the relative productivity level and the resource boom (see Torvik, 2001 for derivations): Pt=λt1/σ[g(1-ηt)+Rt]/f(ηt)1/σ.(3) Furthermore, equilibrium in the labour market requires the value of the marginal productivity of labour in the two sectors to be equal: PtHNtf′(ηt) = HTtg ′(1 − ηt), thus: Pt=λt[g′(1−ηt)/f′(ηt)].(4) Figure 2 displays the relationship between the real exchange rate and the employment share in the non‐traded sector for given values of the foreign exchange gift and sectoral productivities. Equation (3) is drawn as the downward sloping NN curve. It reflects the non‐traded market equilibrium when expenditure is always equal to income.5 Equation (4) is drawn as the upward sloping LL curve. It reflects the labour market equilibrium.6 The (static) equilibrium between the real exchange rate and the labour share is given by the intersection of the two curves, at point E1. Assuming this is a steady state, the growth rates in the productivities must be equal, so that (equating (1) and (2)): η*=v(1−δT)/[u(1−δN)+v(1−δT)].(5) Fig. 2. Open in new tabDownload slide Resource Boom Shock and Learning‐by‐doing Dynamics Fig. 2. Open in new tabDownload slide Resource Boom Shock and Learning‐by‐doing Dynamics We can now study the effect of a resource gift. An exogenous shock to Rt causes the NN curve to shift up. At the new intersection, E2, the exchange rate has appreciated, and the amount of labour used in the non‐traded sector has increased. This is what is commonly referred to as the Dutch disease effects. However, since the growth rates of the productivities are endogenous in this model, the relative productivity level λt between the two sectors also changes: λ˙t/λt=H˙Tt/HTt−H˙Nt/HNt=−u(1−δN)η(λt,Rt)+v(1−δT)[1−η(λt,Rt)].(6) The derivative of (6) with respect to Rt is equal to: d(λ˙t/λt)/dRt=−[u(1−δN)+v(1−δT)][dη(λt,Rt)/dRt]<0.(7) Thus, an increase in Rt not only shifts the NN curve up, it also causes the productivity gap between the traded and non‐traded sector to diminish over time.7 This causes the LL and NN curves to shift down over time. This is depicted by the curves N′N′ and L′L′ in Figure 2. The new (dynamic) equilibrium is reached at point E3, where the real exchange rate has actually depreciated. The intuition is as follows: after the initial resource boom more people are employed in the non‐tradable sector, which therefore experiences higher productivity growth. This in turns narrows the productivity gap between the two sectors and shifts the NN and LL curves down over time. Labour is pushed out of the sector with the fastest productivity growth. This process will continue until the labour share is back at its original value. In the new steady‐state, relative production of the two sectors will have shifted in favour of the non‐traded sector as is conventional in models of the Dutch disease. However, this is not due to new factor allocations but because of a shift in the steady‐state relative productivity between the two sectors.8 As in Torvik (2001), the steady‐state labour share between the two sectors does not change after an exogenous shock to Rt. However, equilibrium output (productivity) growth will now be directly affected. To see this, insert the steady‐state labour share in (5) into one of the two equations for sectoral productivity growth. The steady state growth rate, denoted g*, is then given by: g*=δRR+[uv(1−δNδT)]/[u(1−δN)+v(1−δT)].(8) At this point, the rate of growth in the economy will be a direct function of the spillover from the resource gift. The effect depends on the size of the spillover. If δR > 0, the resource gift crowds in productivity in the traded and non‐traded sectors. Hence, output (and productivity) growth in both sectors increases, which is contrary to standard Dutch disease models. This is a new feature of our model. In addition, there is a secondary effect due to the spillovers between the traded and the non‐traded sectors. This mechanism is similar to the one described in Torvik (2001). The direction of this shift depends on the parameters u, v, δT and δN, which describe the direct and indirect spillovers on the productivity growth in the two sectors. In particular, if the indirect effect dominates in the traded sector while the direct effect dominates in the non‐traded sector, (i.e. u/v > 1/δN) output (productivity) growth in both sectors will increase.9 To sum up, our model has two implications for the dynamic adjustment after a resource boom. First, when the resource boom crowds in productivity spillovers in the non‐resource sectors, productivity (and production) in the overall economy will increase. Second, LBD spillovers between the traded and non‐traded sectors may enforce this mechanism, by allowing productivity in the non‐traded sector to increase relative to the traded sector. Hence, we could expect to see a two‐speed adjustment in the process, with the non‐traded sectors growing at a faster pace than the traded sector. 2. Theory Meets Empirical Model To investigate the empirical relevance of the theory model and to answer our main research questions, we specify a dynamic factor model (DFM). Here the co‐movement of a large cross section of variables can be represented more parsimoniously than with standard time series techniques, and the direct and indirect spillovers between the different sectors of the economy can be estimated simultaneously.10 In line with the theory model, the DFM includes four factors with associated shocks that have the potential to affect all sectors of the economy. Two shocks are related directly to the Dutch disease literature: a resource boom/activity shock and a commodity price shock (we use the terms resource booms and resource activity shocks interchangeably). Here, the former is similar to the exogenous shocks to Rt from the theory model in the previous Section, while the latter is what is commonly used in the empirical (time series) literature on Dutch disease. We postulate that it is important to distinguish between these two shocks, as only the Rt shock can plausibly lead to strong LBD spillovers (as described above) between the sectors. In addition, we allow for a global activity shock and domestic (non‐resource) activity shock. The global activity shock controls for higher economic activity driven by international developments. Importantly, the global shock also allows for higher commodity prices due to increased global demand for commodities. As such, the commodity price shocks themselves should be interpreted as shocks unrelated to global activity, that can change the commodity price on impact. Lastly, the domestic activity shock controls for the remaining domestic impulses (tradable and non‐tradable) contemporaneously unrelated to the resource sector.11 The factors and shocks will be linearly related to a large panel of domestic variables, including both tradable and non‐tradable sectors of the economy. The simple theory model proposed in the previous Section makes a clear distinction between these sectors. In the data, this distinction is less clear. However, within the DFM framework the sectors of the economy that are more exposed to foreign business cycle developments, that is, the tradable sectors, will be endogenously determined through their exposure to the global factor(s) and shocks. Moreover, the direct and indirect spillovers between sectors related to resource extraction and those that are not will be caught up by the dynamic relationship between the resource activity factor and the domestic activity factor, and through the different sectors’ exposure to these factors, respectively. These are additional advantages with our empirical strategy. We do not need to make ad hoc classifications of the sectors but are still able to model the direct and indirect spillovers between sectors of the economy in a consistent manner.12 Generally, within the DFM framework, the factors are latent. In our application two of the factors are treated as observables, namely global activity and the real commodity price. The two domestic factors are treated as unobservable and have to be estimated based on the available data. For the same reason as above, this allows us to capture the direct and indirect spillovers between the resource and non‐resource driven parts of the economy endogenously in a consistent manner. On a final note, while the theory model focuses on a windfall discovery due to, say, a permanent increase in the production possibilities in the resource sector (an increase in Rt), the windfall discovery in the empirical model will be temporary but can potentially be very persistent. This is in line with the empirical model framework adopted, where the focus is on developments at the business cycle frequencies. It is also in line with experiences in the two resource rich countries analysed here, where there have been several periods of booms and busts in the resource sectors, as new fields and production possibilities have developed and declined. 2.1. The Dynamic Factor Model We specify one DFM for each of the countries we study: Australia and Norway. In state space form, the DFM is given by (9) and (10): yt=λ0ft+⋯+λsft−s+ϵt,(9) ft=ϕ1ft−1+⋯+ϕhft−h+ut,(10) where the N × 1 vector yt represents the observables at time t. λj is a N × q matrix with dynamic factor loadings for j = 0, 1, … , s, and s denotes the number of lags used for the dynamic factors ft. The q × 1 vector ft contains both the latent and observable factors. ϵt is an N × 1 vector of idiosyncratic errors. Lastly, the dynamic factors follow a VAR(h) process, given by (10), where, ut is a q × 1 vector of VAR(h) errors. The idiosyncratic and VAR(h) errors are assumed to be independent: ϵtut∼i.i.d.N00,R00Q.(11) Further, in our application R is assumed to be diagonal. The model described above can easily be extended to the case with serially correlated idiosyncratic errors. In particular, we consider the case where ϵt,i, for i = 1, …, N, follows independent AR(l) processes: ϵt,i=ρ1,iϵt−1,i+⋯+ρl,iϵt−l,i+ωt,i,(12) where l denotes the number of lags, and ωt,i is the AR(l) errors with ωt,i∼i.i.d.N(0,σi2) ⁠. that is: R=σ120⋯00σ22⋱0⋮⋱⋱⋮0⋯⋯σN2.(13) 2.2. Identification As is common for all factor models, (9) and (10) are not identified without restrictions. To separately identify the factors and the loadings, and to be able to provide an economic interpretation of the factors, we enforce the following identification restrictions on (9): λ0=λ0,1λ0,2,(14) where λ0,1 is a q × q identity matrix, and λ0,2 is left unrestricted. As shown in Bai and Ng (2013) and Bai and Wang (2012), these restrictions uniquely identify the dynamic factors and the loadings, but leave the VAR(h) dynamics for the factors completely unrestricted. Accordingly, the innovations to the factors, ut, can be linked to structural shocks that are implied by economic theory. In our application, q = 4, and we identify four factors: global activity; the real commodity price; resource‐specific activity; and non‐resource activity. The number of factors and names are motivated by the model as discussed above.13 Of these four factors, the first two are observable and naturally load with one on the corresponding element in the yt vector. The two latter must be inferred from the data. For Australia and Norway, we require that the resource‐specific activity factor loads with one on value added in the mining industry and value added in the petroleum sector, respectively. Likewise, the non‐resource activity factor loads with one on total value added excluding mining and petroleum in Australia and Norway, respectively.14 Note that while these restrictions identify the factors, that does not mean that the factors and the observables are identical as we use the full information set (the vector yt) to extract the factors. Based on a minimal set of restrictions, we identify four structural shocks: a global activity shock, a commodity price shock, a resource activity shock (resource booms) and a non‐resource (domestic) activity shock. The shocks are identified by imposing a recursive ordering of the latent factors in the model, that is, ft=[ftgact,ftcomp,ftract,ftdact]′ ⁠, where gact, comp, ract, and dact denote global activity, commodity price, resource activity and domestic activity, respectively. Specifically, the mapping between the reduced form residuals ut and structural disturbances et, ut = A0et, is given by: utgactutcomputractutdact=a11000a21a2200a31a32a330a41a42a43a44etgactetcompetractetdact,(15) where eti are the structural disturbances, for i = [gact, comp, ract, dact], with Σe = I, such that Q=A0A0′ ⁠. We follow the usual assumption from both theoretical and empirical models of the commodity market and restrict global activity to respond to commodity price disturbances with a lag. This restriction is consistent with the sluggish behaviour of global economic activity after each of the major oil price increases in recent decades, see for example, Hamilton (2009). Furthermore, we do not treat commodity prices as exogenous to the rest of the macro economy. Any unexpected news regarding global activity is assumed to affect real commodity prices contemporaneously. This is consistent with recent work in the oil market literature, see, for example, Kilian (2009), Lippi and Nobili (2012) and Aastveit et al. (2015a). In contrast to these articles, and to keep our empirical model as parsimonious as possible, we do not explicitly identify a global commodity supply shock.15 Still, in the online Appendix, we show that our results are robust to the inclusion of such a shock. In the very short run, disturbances originating in either the Australian or the Norwegian economy cannot affect global activity and real commodity prices. These are plausible assumptions, as Australia and Norway are small, open economies. However, both the resource and the domestic activity factors respond to unexpected disturbances in global activity and the real commodity price on impact. In small open economies such as Australia and Norway, news regarding global activity will affect variables such as the exchange rate, the interest rate, asset prices and consumer sentiments contemporaneously, thereby affecting overall demand in the economy. Australia and Norway are also, respectively, net mineral and oil exporters. Thus, any disturbances to the real commodity price will, most likely, rapidly affect both the demand and supply sides of the economy. The restrictions imposed on the two last shocks in the model, a resource and domestic activity shock, follow from the theory model described in Section 1. That is, on impact domestic activity shocks can have no effect on the resource activity factor (it is predetermined) but resource activity shocks can have an effect on the domestic activity factor contemporaneously (for instance via productivity spillovers). However, at longer horizons it is plausible to assume that due to, for example, capacity constraints, the domestic economy eventually also affects the resource sector. Thus, after one period we allow the resource sector to respond to the dynamics in the domestic activity factor. This restriction slightly relaxes the assumptions implied by the theory model. We emphasise that all observable variables in the model, apart from the ones used to identify the factors, may respond to all shocks on impact inasmuch as they are contemporaneously related to the factors through the unrestricted part of the loading matrix (i.e. the λ0,2 matrix in (14)). Thus, the recursive structure is only applied to identify the shocks. Together, (14) and (15) make the structural BDFM uniquely identified.16 2.3. Data To accommodate resource movement and spending effects, the observable yt vector includes a broad range of sectoral employment and production series. The full list, for each country, is reported in Appendix A. Although we can construct labour productivity estimates directly from our model estimates (since we include both production and employment at the sectoral level), we also include productivity as an observable variable within the yt vector. Naturally, we also include the real exchange rate, which is a core variable in the Dutch disease literature. To account for wealth effects and to facilitate the interpretation and identification of the structural shocks, we also include wage and investment series (in real terms), the terms of trade, stock prices, consumer and producer prices, and the short term interest rate. In Norway, the real commodity price is the real price of oil, which is constructed on the basis of Brent Crude oil prices (US dollars). In Australia, we use the Reserve Bank of Australia’s (RBA) Index of Commodity Prices (US dollars), which is a weighted average of recent changes in commodity prices, where the weight given to each commodity reflects its importance in total commodity export values in a base period.17 Both commodity prices are deflated using the US consumer price index (CPI). However, we test robustness to using a nominal commodity price, and to deflating the commodity price with domestic CPI. Our main consideration when constructing global (or world) activity has been to include countries whose economic activity may largely affect the global oil market. In addition, to capture possible direct trade linkages, we include the most important trading partners. Hence, for Norway, we construct global activity as the simple mean of four‐quarter logarithmic changes in real GDP in Denmark, Germany, the Netherlands, Sweden, the UK, Japan, China and the US. For Australia, global activity included the countries: New Zealand, Singapore, the UK, Korea, India, Japan, China and the US. Results are, however, robust to other measures of global activity. In sum, this gives a panel of roughly 50 international and domestic data series (for each country), covering a sample period from 1991:Q1 to 2012:Q4 (Australia), and 1996:Q1 to 2012:Q4 (Norway).18 To capture the economic fluctuations of interest, we transform all variables to four‐quarter logarithmic changes; log(yi,t) − log(yi,t−4). Lastly, all variables are demeaned before estimation and all production series are measured in real terms, deflated by their respective industry deflators. 2.4. Estimation Let y~T=[y1,…,yT]′ and f~T=[f1,…,fT]′ ⁠, and define H = [λ0, … , λs], β = [ϕ1, … , ϕh], Q, R, and pi = [ρ1,i, … , ρl,i] for i = 1, … , N, as the model’s hyper‐parameters. Inference in our model can be performed using both classical and Bayesian techniques. In the classical setting, two approaches are available, two‐step estimation and maximum likelihood estimation (ML). In the former, f~T ⁠, H and R are first typically estimated using the method of principal components analysis (PCA). Following this, the dynamic components of the system, A and Q, are estimated conditional on f~T ⁠, H and R. Thus, the state variables are treated as observable variables. If estimation is performed using ML, the observation and state equations are estimated jointly. However, ML still involves some type of conditioning. That is, we first obtain ML estimates of the model’s unknown hyper‐parameters. Then, to estimate the state, we treat the ML estimates as if they were the true values for the model’s non‐random hyper‐parameters. In a Bayesian setting, both the model’s hyper‐parameters and the state variables are treated as random variables. We have estimated the DFM using both the two‐step procedure and Bayesian estimation. The results reported in Section 3 are not qualitatively affected by the choice of estimation method. However, we prefer the Bayesian approach primarily for the following reasons: (i) in contrast to the classical approach, inferences regarding the state are based on the joint distribution of the state and the hyper‐parameters, not a conditional distribution; (ii) ML estimation would be computationally intractable given the number of states and hyper‐parameters; and (iii) our data are based on logarithmic year‐on‐year differences. This spurs autocorrelation in the idiosyncratic errors. In a Bayesian setting, the model can readily be extended to accommodate these features of the error terms. In a classical two‐step estimation framework, this is not the case. Furthermore, in the two‐step estimation procedure, it is not straightforward to include lags of the dynamic factors in observation equation. Thus, our preferred model is a BDFM. We set, s = 2, h = 8, and l = 1. That is, we include 2 lags for the dynamic factors in the observation equation (see (9)), 8 lags in the transition equation (see (10)) and let the idiosyncratic errors follow AR(1) processes (see (12)).19 Bayesian estimation of the state space system is based on Gibbs simulation. We simulate the model using a total of 50,000 iterations. A burn‐in period of 40,000 draws is employed, and only every fifth iteration is stored and used for inference.20 In the online Appendix, we explain the choice of this particular specification, analyse its robustness, and explain in greater detail the estimation procedure and prior specifications. 3. Results Below we first present the identified factors and the impulse responses associated with the international part of the model before describing the resource sectors in the two countries in more detail. We then investigate the main propagation mechanisms following an unexpected resource gift in terms of either a resource boom or commodity price shock. Finally, we examine sectoral reallocation following these shocks. 3.1. Global and Domestic Factors and Impulse Responses The global activity factor and commodity prices are observable variables in our model and are graphed in the first row in Figure 3. We note how the real oil price is more volatile than the relevant real commodity price index for Australia.21 The two indexes of global activity are very similar, except the Asian crisis is more visible in the global activity index used in the model for Australia. Fig. 3. Open in new tabDownload slide Estimated Factors Notes. The Figures display the observed and estimated latent factors. The black solid lines are median estimates. The grey shaded areas are 68% probability bands. Fig. 3. Open in new tabDownload slide Estimated Factors Notes. The Figures display the observed and estimated latent factors. The black solid lines are median estimates. The grey shaded areas are 68% probability bands. The second row in Figure 3 reports the estimated resource and domestic activity factors for Norway (top) and Australia (bottom). The resource activity factor for Australia captures developments specifically linked to the mining industry, while in Norway, the factor is associated with extraction of oil and gas. As expected, the volatilities of the resource activity factors are rather large and, for Norway, larger than the volatility of the domestic activity factor. There are also some difference in persistence between the two countries, with a long lasting boom in the oil and gas sector in Norway in the early and mid 1990s and also in the period between 2000 and 2004 but followed by a long period of downsizing in the mid‐2000s. For Australia, the many boom and bust periods in the mining industry in the 1990s are clearly visible but then there is a long period of stable and high growth from 2002/3. Note also the recession in the domestic economy in Australia at the beginning of the 1990s, which was the worst recession for decades. The impulse responses associated with the international part of the model are reported in Figure 4. As seen there, the global shocks in the model are well identified. That is, the global activity shock increases both the global activity level and real commodity prices, while unexpected commodity price shocks generate a temporary inverse relationship between the commodity prices and global activity.22 While the temporary inverse relationship between commodity prices and the global economy is in line with Hamilton (2009), the results are also consistent with recent studies emphasising the role of global demand as a driver of oil prices, see, for example, Kilian (2009), Lippi and Nobili (2012), Peersman and Van Robays 2012 and Aastveit et al. (2015a) among many others. Fig. 4. Open in new tabDownload slide Global Impulse Responses Notes. The Figures display impulse responses in levels. The Global activity shock is normalised to increase global activity by 1%, while the commodity price shock is normalised to increase the commodity price index and the real price of oil with 5% and 10%, respectively. The black solid lines are median estimates. The grey shaded areas are 68% probability bands. Fig. 4. Open in new tabDownload slide Global Impulse Responses Notes. The Figures display impulse responses in levels. The Global activity shock is normalised to increase global activity by 1%, while the commodity price shock is normalised to increase the commodity price index and the real price of oil with 5% and 10%, respectively. The black solid lines are median estimates. The grey shaded areas are 68% probability bands. In the interest of brevity, we report the impulse responses associated with the domestic activity shock in Figure B1 in Appendix B. As seen there, a domestic activity shock raises GDP, employment, wages, and prices in the domestic economy. The effect on investment is also positive but the variation explained by the domestic shock is modest, at least in Norway (see Tables 2 and 3). The effect on the real exchange rate or the terms of trade is negligible in both countries. Hence, this shock may capture the effect of domestic demand.23 Fig. B1. Open in new tabDownload slide Norway and Australia. Domestic Activity Shock and Domestic Responses Notes. The domestic activity shock is normalised to increase the domestic activity factor in each country by 1%. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. See also the note to Figure 5. Fig. B1. Open in new tabDownload slide Norway and Australia. Domestic Activity Shock and Domestic Responses Notes. The domestic activity shock is normalised to increase the domestic activity factor in each country by 1%. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. See also the note to Figure 5. 3.2. What Describes a Resource Boom? Norway and Australia are both net resource exporters. The resource industry in the two countries is, however, very different. In Norway, resource wealth is extracted almost exclusively from oil and gas extraction offshore, hundreds of metres below the sea surface. In recent decades, the exploration of natural resources has also moved further north and to deeper depths, requiring even more sophisticated technology to accommodate the harsher conditions and subsea exploration. Australia extracts and exports a large range of minerals (including some oil and gas), though the iron ore industry was the principal factor fuelling the recent boom. The main technical difficulty with extracting iron ore is not necessarily finding it, the grade or size of the deposits, but rather the position of the iron ore relative to the market and the energy and transportation costs required to get it to the market. Another important difference between the two countries is the degree of openness. In terms of the openness indicator computed by Penn World Table, Norway is almost twice as open as Australia.24 Common for both the oil and gas industry in Norway and the mining industry in Australia is the fact that both industries are highly capital intensive. Our results are consistent with these facts. The resource activity shock, together with the commodity price shock, explain as much as 70–90% of the variation in production, employment wages, and investment in the resource sectors in Norway and Australia, see Table 1. In both countries, the resource boom is particularly associated with increased value added and employment dynamics. This is consistent with the interpretation of the shock as an (unpredicted) technical improvement in the booming sector, represented by a favourable shift in the production function, or a windfall discovery of new resources. Table 1 Variance Decompositions: Resource Sector . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP – oil and gas 0.86, 0.65 0.07, 0.09 0.02, 0.15 0.05, 0.10 Employment – oil and gas 0.59, 0.58 0.33, 0.34 0.07, 0.05 0.01, 0.03 Wages – oil and gas 0.47, 0.34 0.33, 0.25 0.15, 0.23 0.05, 0.18 Investment – oil and gas 0.02, 0.06 0.72, 0.43 0.17, 0.29 0.09, 0.21 Australia GDP – mining 0.91, 0.86 0.05, 0.11 0.03, 0.02 0.01, 0.01 Employment – mining 0.79, 0.58 0.06, 0.15 0.09, 0.24 0.06, 0.03 Wages – mining 0.26, 0.24 0.13, 0.15 0.06, 0.05 0.55, 0.55 Investment – mining 0.27, 0.21 0.66, 0.59 0.06, 0.20 0.02, 0.01 . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP – oil and gas 0.86, 0.65 0.07, 0.09 0.02, 0.15 0.05, 0.10 Employment – oil and gas 0.59, 0.58 0.33, 0.34 0.07, 0.05 0.01, 0.03 Wages – oil and gas 0.47, 0.34 0.33, 0.25 0.15, 0.23 0.05, 0.18 Investment – oil and gas 0.02, 0.06 0.72, 0.43 0.17, 0.29 0.09, 0.21 Australia GDP – mining 0.91, 0.86 0.05, 0.11 0.03, 0.02 0.01, 0.01 Employment – mining 0.79, 0.58 0.06, 0.15 0.09, 0.24 0.06, 0.03 Wages – mining 0.26, 0.24 0.13, 0.15 0.06, 0.05 0.55, 0.55 Investment – mining 0.27, 0.21 0.66, 0.59 0.06, 0.20 0.02, 0.01 Note. Each row–column intersection reports median variance decompositions for horizons 4 (left) and 8 (right). Open in new tab Table 1 Variance Decompositions: Resource Sector . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP – oil and gas 0.86, 0.65 0.07, 0.09 0.02, 0.15 0.05, 0.10 Employment – oil and gas 0.59, 0.58 0.33, 0.34 0.07, 0.05 0.01, 0.03 Wages – oil and gas 0.47, 0.34 0.33, 0.25 0.15, 0.23 0.05, 0.18 Investment – oil and gas 0.02, 0.06 0.72, 0.43 0.17, 0.29 0.09, 0.21 Australia GDP – mining 0.91, 0.86 0.05, 0.11 0.03, 0.02 0.01, 0.01 Employment – mining 0.79, 0.58 0.06, 0.15 0.09, 0.24 0.06, 0.03 Wages – mining 0.26, 0.24 0.13, 0.15 0.06, 0.05 0.55, 0.55 Investment – mining 0.27, 0.21 0.66, 0.59 0.06, 0.20 0.02, 0.01 . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP – oil and gas 0.86, 0.65 0.07, 0.09 0.02, 0.15 0.05, 0.10 Employment – oil and gas 0.59, 0.58 0.33, 0.34 0.07, 0.05 0.01, 0.03 Wages – oil and gas 0.47, 0.34 0.33, 0.25 0.15, 0.23 0.05, 0.18 Investment – oil and gas 0.02, 0.06 0.72, 0.43 0.17, 0.29 0.09, 0.21 Australia GDP – mining 0.91, 0.86 0.05, 0.11 0.03, 0.02 0.01, 0.01 Employment – mining 0.79, 0.58 0.06, 0.15 0.09, 0.24 0.06, 0.03 Wages – mining 0.26, 0.24 0.13, 0.15 0.06, 0.05 0.55, 0.55 Investment – mining 0.27, 0.21 0.66, 0.59 0.06, 0.20 0.02, 0.01 Note. Each row–column intersection reports median variance decompositions for horizons 4 (left) and 8 (right). Open in new tab Interestingly, the bulk of the variation in petroleum and mining investment is explained by the commodity price shocks (that drive up commodity prices), see Table 1. In Australia, mining investments increase for one to two years after this shock, while for petroleum investments in Norway, the increase is delayed for a year but picks up and peaks after three years (these responses are not shown, but can be obtained on request). The fact that petroleum investments increase with a lag relative to mining investments in Australia, is consistent with a shorter lead time from discovery to exploration in the mining industry than in the offshore petroleum industry. Lastly, global demand shocks (that drive up commodity prices) also affect activity and employment in the mining and petroleum sectors and, in particular, investment. Between 20% and 30% of the variation in petroleum and mining investment refers back to global demand and its effect via higher commodity prices. As the most open of the two countries, Norway is also the most affected by global demand. 3.3. Resource Booms and Domestic Impulse Responses Now we focus on our core question: How do the domestic variables respond to the resource activity shock described above? Figure 5 reports the responses for the key variables in the domestic economy: (non‐resource) GDP, productivity, (non‐resource) employment and the real exchange rate, after a resource boom. Fig. 5. Open in new tabDownload slide Norway and Australia. Resource Gifts and Domestic Responses Notes. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. The responses are displayed in levels of the variables. The resource boom shock is normalised to increase the resource activity factor by 1%. The shaded areas (dark and light grey) represent 68% probability bands, while the lines (solid and dotted) are median estimates. Fig. 5. Open in new tabDownload slide Norway and Australia. Resource Gifts and Domestic Responses Notes. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. The responses are displayed in levels of the variables. The resource boom shock is normalised to increase the resource activity factor by 1%. The shaded areas (dark and light grey) represent 68% probability bands, while the lines (solid and dotted) are median estimates. In line with the predictions from the theory model, we confirm that there are large and positive spillovers from the exploration of natural resources to the non‐resource industries in both Australia and Norway. In particular, in the wake of the resource boom, production and productivity increases for a prolonged period of time in both countries. This suggests that productivity spillovers are important for the resource boom shock. As productivity measures the efficiency of production, this also explains why GDP in the domestic economy increases substantially following this shock. This is interesting, as it highlights the empirical relevance of alternative theoretical Dutch disease models, like the one put forward above. Variance decompositions in Table 2 confirm that the expansion in Norway is substantial; after one to two years, 25–30% of the variation in non‐resource GDP is explained by the resource boom, while the comparable numbers are 43–50% for productivity. In Australia the expansion is more modest; 10–15% of value added in non‐mining is explained by the resource boom, while 5–6% of productivity is explained by the same shock. The effect on employment, however, is initially weak but increases slightly in both countries and is highly significant after about one to two years in Norway. After two years, the resource boom shock explains about 10% of the variation in employment in Norway and about 5% in Australia, see Table 2. Table 2 Norway and Australia. Resource Gifts and Domestic Variance Decompositions . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP 0.23, 0.30 0.05, 0.02 0.56, 0.54 0.16, 0.14 Productivity 0.43, 0.50 0.18, 0.14 0.25, 0.23 0.13, 0.13 Employment 0.06, 0.09 0.09, 0.07 0.31, 0.44 0.53, 0.39 Real exchange rate 0.08, 0.16 0.69, 0.63 0.24, 0.21 0.00, 0.00 Australia GDP 0.11, 0.12 0.02, 0.11 0.27, 0.22 0.59, 0.56 Productivity 0.05, 0.06 0.46, 0.56 0.06, 0.05 0.43, 0.33 Employment 0.04, 0.03 0.16, 0.42 0.15, 0.07 0.66, 0.48 Real exchange rate 0.40, 0.42 0.06, 0.11 0.49, 0.44 0.06, 0.03 . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP 0.23, 0.30 0.05, 0.02 0.56, 0.54 0.16, 0.14 Productivity 0.43, 0.50 0.18, 0.14 0.25, 0.23 0.13, 0.13 Employment 0.06, 0.09 0.09, 0.07 0.31, 0.44 0.53, 0.39 Real exchange rate 0.08, 0.16 0.69, 0.63 0.24, 0.21 0.00, 0.00 Australia GDP 0.11, 0.12 0.02, 0.11 0.27, 0.22 0.59, 0.56 Productivity 0.05, 0.06 0.46, 0.56 0.06, 0.05 0.43, 0.33 Employment 0.04, 0.03 0.16, 0.42 0.15, 0.07 0.66, 0.48 Real exchange rate 0.40, 0.42 0.06, 0.11 0.49, 0.44 0.06, 0.03 Note. See Table 1. Open in new tab Table 2 Norway and Australia. Resource Gifts and Domestic Variance Decompositions . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP 0.23, 0.30 0.05, 0.02 0.56, 0.54 0.16, 0.14 Productivity 0.43, 0.50 0.18, 0.14 0.25, 0.23 0.13, 0.13 Employment 0.06, 0.09 0.09, 0.07 0.31, 0.44 0.53, 0.39 Real exchange rate 0.08, 0.16 0.69, 0.63 0.24, 0.21 0.00, 0.00 Australia GDP 0.11, 0.12 0.02, 0.11 0.27, 0.22 0.59, 0.56 Productivity 0.05, 0.06 0.46, 0.56 0.06, 0.05 0.43, 0.33 Employment 0.04, 0.03 0.16, 0.42 0.15, 0.07 0.66, 0.48 Real exchange rate 0.40, 0.42 0.06, 0.11 0.49, 0.44 0.06, 0.03 . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP 0.23, 0.30 0.05, 0.02 0.56, 0.54 0.16, 0.14 Productivity 0.43, 0.50 0.18, 0.14 0.25, 0.23 0.13, 0.13 Employment 0.06, 0.09 0.09, 0.07 0.31, 0.44 0.53, 0.39 Real exchange rate 0.08, 0.16 0.69, 0.63 0.24, 0.21 0.00, 0.00 Australia GDP 0.11, 0.12 0.02, 0.11 0.27, 0.22 0.59, 0.56 Productivity 0.05, 0.06 0.46, 0.56 0.06, 0.05 0.43, 0.33 Employment 0.04, 0.03 0.16, 0.42 0.15, 0.07 0.66, 0.48 Real exchange rate 0.40, 0.42 0.06, 0.11 0.49, 0.44 0.06, 0.03 Note. See Table 1. Open in new tab The difference in the importance of the spillovers in Australia and Norway could reflect the fact that the resource sector represents a larger share of the economy in Norway than in Australia (20% versus 10%). Yet, it cannot explain the very substantial productivity spillovers from the resource sector to domestic variables in Norway. The continuous development of new drilling and production technology discussed in subsection 3.2 could be an important factor in explaining the success and the boost in productivity in Norway. We return to this issue below when we discuss sectoral responses. Lastly, the responses in the real exchange rate differ across the two countries. In Norway, the response is small and mostly insignificant, if anything, showing evidence of real depreciation. This also helps explain why energy booms can have such stimulative effects on the mainland economy. For Australia, there is first an appreciation but then the exchange rate depreciates slightly. This is in line with the predictions given by the theory model above, when there are productivity spillovers. 3.4. Commodity Prices and Domestic Responses There are two structural shocks that increase commodity prices, a global activity shock and a commodity (specific) price shock. When the increase in commodity prices is due to a global activity shock, the effect on the domestic economy is primarily positive, as GDP and employment rise for a prolonged period of time in both countries, see Figure B2, Appendix B. This is in line with what others have also found, see for example, Aastveit et al. (2015,a). What is interesting to note here, is the large positive effect on the Norwegian economy relative to Australia. As documented in Table 2, global activity shocks explain roughly 55% and 25% of the variation in overall activity in Norway and Australia, respectively. This is perfectly in match with the fact that Norway is close to twice as open as Australia by conventional estimates. It is also in line with the fact that since global demand boosts commodity prices, this may have benefited the more resource rich economy, Norway, to a larger extent. Norway also experienced a temporary appreciation of the real exchange rate following this shock. The finding that foreign factors are important, but to varying degrees, for small open economies is also well documented in, for example, Furlanetto et al. (2013) and Aastveit et al. (2015b). Fig. B2. Open in new tabDownload slide Norway and Australia. Global Activity Shock and Domestic Responses Notes. The global activity shock is normalised to increase the global activity factor by 1%. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. See also the note to Figure 5. Fig. B2. Open in new tabDownload slide Norway and Australia. Global Activity Shock and Domestic Responses Notes. The global activity shock is normalised to increase the global activity factor by 1%. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. See also the note to Figure 5. We now turn to the commodity (specific) price shock, which is the shock typically analysed in the empirical Dutch disease literature. An increase in commodity prices due to this shock, will be contemporaneously unrelated to global activity. Figure 6 reports the key responses. While productivity and production in both Australia and Norway increase for a prolonged period of time following a resource boom shock, the effect of a commodity price shock is less favourable. In particular, a commodity price shock has either no effect on productivity (Norway), or affects productivity negatively (Australia). Fig. 6. Open in new tabDownload slide Norway and Australia. Commodity Price Shocks and Domestic Responses Notes. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. The commodity price shock is normalised to increase the real price of oil (commodity price index) with 10 (5)%. See also the note to Figure 5. Fig. 6. Open in new tabDownload slide Norway and Australia. Commodity Price Shocks and Domestic Responses Notes. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. The commodity price shock is normalised to increase the real price of oil (commodity price index) with 10 (5)%. See also the note to Figure 5. Further, in Norway, the commodity price increase is strongly associated with a real exchange rate appreciation. Over 60% of the variation in the real exchange rate is explained by this shock. The strong appreciation increases cost and reduces competitiveness and will potentially hurt the sectors exposed to foreign competition. As a consequence, output and employment only increase marginally following this shock. In Australia, the commodity price explains much less of the variation in the exchange rate. Still, the negative productivity effects and modest appreciation of the exchange rate are also coupled with a large drop in employment and production. Our result of an appreciating exchange rate is well in line with other empirical studies of commodity currencies, see, for example, Chen and Rogoff (2003). The results are also in line with what Charnavoki and Dolado (2014) find for Canada. As in our model, two structural oil market shocks, world activity and the commodity price shocks, are found to be important, and important to distinguish: they uncover a Dutch disease effect in Canada following a commodity price shock but not following a world activity shock – much in line with what our results suggest for Australia, although less so for Norway. However, in contrast to us, Charnavoki and Dolado (2014) do not identify a resource activity shock. As shown above, and predicted by the theory model developed in Section 1, this is an important mechanism that need to be accounted for in commodity exporting countries. Hence, our results highlight that it is important to distinguish between windfall gains due to volume and price changes when analysing the Dutch disease hypothesis. Still, although the production and productivity responses following a resource boom shock show large similarities across the two countries, in line with the theory model, the variance decompositions for both shocks and the results regarding the commodity price shock, in particular, suggest that the transmission channels might differ. We examine and discuss these and to what extent the two economies show Dutch disease symptoms and two‐speed patterns, in greater detail below. 3.5. Additional Transmission Channels Central to our theory model are productivity spillovers, discussed above. Central to more classical Dutch disease theories are resource movement and spending effects, including substantial income and wealth effects. In Figure 7 and Table 3 we examine how domestic (non‐resource) investment, wages, producer prices (PPI), consumer prices (CPI), stock prices and the terms of trade are affected by the resource activity shocks and commodity price shocks, respectively. Fig. 7. Open in new tabDownload slide Norway and Australia: Wealth, Cost and Investment Responses Notes. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. See also the note to Figures 5 and 6. Fig. 7. Open in new tabDownload slide Norway and Australia: Wealth, Cost and Investment Responses Notes. In each plot, Norway (Australia) is the solid (dotted) line with the associated dark (light) grey probability bands. See also the note to Figures 5 and 6. Table 3 Norway and Australia: Wealth, Cost and Investment Variance Decompositions . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway PPI 0.01, 0.02 0.67, 0.59 0.31, 0.38 0.01, 0.01 CPI 0.04, 0.05 0.70, 0.61 0.14, 0.16 0.12, 0.17 Stock price 0.01, 0.04 0.69, 0.63 0.29, 0.33 0.01, 0.01 Terms of trade 0.04, 0.07 0.53, 0.41 0.42, 0.50 0.02, 0.02 Investment non‐resource 0.16, 0.28 0.17, 0.07 0.60, 0.60 0.06, 0.05 Wages non‐resource 0.11, 0.11 0.05, 0.03 0.40, 0.54 0.44, 0.32 Australia PPI 0.06, 0.04 0.60, 0.43 0.33, 0.52 0.01, 0.02 CPI 0.25, 0.22 0.38, 0.27 0.27, 0.42 0.09, 0.09 Stock price 0.02, 0.02 0.87, 0.71 0.10, 0.26 0.01, 0.01 Terms of trade 0.02, 0.01 0.72, 0.54 0.26, 0.44 0.00, 0.01 Investment non‐resource 0.19, 0.25 0.26, 0.15 0.36, 0.42 0.19, 0.18 Wages non‐resource 0.07, 0.08 0.05, 0.26 0.10, 0.08 0.78, 0.58 . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway PPI 0.01, 0.02 0.67, 0.59 0.31, 0.38 0.01, 0.01 CPI 0.04, 0.05 0.70, 0.61 0.14, 0.16 0.12, 0.17 Stock price 0.01, 0.04 0.69, 0.63 0.29, 0.33 0.01, 0.01 Terms of trade 0.04, 0.07 0.53, 0.41 0.42, 0.50 0.02, 0.02 Investment non‐resource 0.16, 0.28 0.17, 0.07 0.60, 0.60 0.06, 0.05 Wages non‐resource 0.11, 0.11 0.05, 0.03 0.40, 0.54 0.44, 0.32 Australia PPI 0.06, 0.04 0.60, 0.43 0.33, 0.52 0.01, 0.02 CPI 0.25, 0.22 0.38, 0.27 0.27, 0.42 0.09, 0.09 Stock price 0.02, 0.02 0.87, 0.71 0.10, 0.26 0.01, 0.01 Terms of trade 0.02, 0.01 0.72, 0.54 0.26, 0.44 0.00, 0.01 Investment non‐resource 0.19, 0.25 0.26, 0.15 0.36, 0.42 0.19, 0.18 Wages non‐resource 0.07, 0.08 0.05, 0.26 0.10, 0.08 0.78, 0.58 Note. See Table 1. Open in new tab Table 3 Norway and Australia: Wealth, Cost and Investment Variance Decompositions . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway PPI 0.01, 0.02 0.67, 0.59 0.31, 0.38 0.01, 0.01 CPI 0.04, 0.05 0.70, 0.61 0.14, 0.16 0.12, 0.17 Stock price 0.01, 0.04 0.69, 0.63 0.29, 0.33 0.01, 0.01 Terms of trade 0.04, 0.07 0.53, 0.41 0.42, 0.50 0.02, 0.02 Investment non‐resource 0.16, 0.28 0.17, 0.07 0.60, 0.60 0.06, 0.05 Wages non‐resource 0.11, 0.11 0.05, 0.03 0.40, 0.54 0.44, 0.32 Australia PPI 0.06, 0.04 0.60, 0.43 0.33, 0.52 0.01, 0.02 CPI 0.25, 0.22 0.38, 0.27 0.27, 0.42 0.09, 0.09 Stock price 0.02, 0.02 0.87, 0.71 0.10, 0.26 0.01, 0.01 Terms of trade 0.02, 0.01 0.72, 0.54 0.26, 0.44 0.00, 0.01 Investment non‐resource 0.19, 0.25 0.26, 0.15 0.36, 0.42 0.19, 0.18 Wages non‐resource 0.07, 0.08 0.05, 0.26 0.10, 0.08 0.78, 0.58 . Shock . Variable and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway PPI 0.01, 0.02 0.67, 0.59 0.31, 0.38 0.01, 0.01 CPI 0.04, 0.05 0.70, 0.61 0.14, 0.16 0.12, 0.17 Stock price 0.01, 0.04 0.69, 0.63 0.29, 0.33 0.01, 0.01 Terms of trade 0.04, 0.07 0.53, 0.41 0.42, 0.50 0.02, 0.02 Investment non‐resource 0.16, 0.28 0.17, 0.07 0.60, 0.60 0.06, 0.05 Wages non‐resource 0.11, 0.11 0.05, 0.03 0.40, 0.54 0.44, 0.32 Australia PPI 0.06, 0.04 0.60, 0.43 0.33, 0.52 0.01, 0.02 CPI 0.25, 0.22 0.38, 0.27 0.27, 0.42 0.09, 0.09 Stock price 0.02, 0.02 0.87, 0.71 0.10, 0.26 0.01, 0.01 Terms of trade 0.02, 0.01 0.72, 0.54 0.26, 0.44 0.00, 0.01 Investment non‐resource 0.19, 0.25 0.26, 0.15 0.36, 0.42 0.19, 0.18 Wages non‐resource 0.07, 0.08 0.05, 0.26 0.10, 0.08 0.78, 0.58 Note. See Table 1. Open in new tab First, in Norway, a resource activity boom is accompanied by a substantial investment boom in the domestic economy as well. With a lag, wages also increases, while the responses in PPI, CPI, stock prices and the terms of trade are minor and hardly significant. From Table 3 we see that the resource boom does not explain a large share of the variation in these variables but does explain a substantial part of the variation in domestic investment. Thus, the resource boom shock in Norway does not affect costs but does change the distribution of wealth due to productivity spillovers (learning‐by‐doing) and subsequent movement of resources, higher income and increased spending in the overall economy. In Australia, the resource boom shock is of less importance for the domestic economy (as shown in Table 2) and investment in the domestic economy actually falls for a prolonged period after this shock. This suggests a crowding out effect from mining in Australia. As in Norway, the resource boom shock does not explain much of the variation in PPI, stock prices, the terms of trade and wages, albeit somewhat more of the variation in CPI. Overall, these results confirm the interpretation and identification of the resource boom shock as linked to either an (unpredicted) technical improvement in the booming sector, represented by a favourable shift in the production function, or as a windfall discovery of new resources. Second, a commodity price shock has some positive effects on the domestic economy. Investment, in particular, increases temporarily in both countries, most likely as petroleum and mining investment also rise. As discussed in Spatafora and Warner (1999), a commodity price shock can lead to a reduction in the relative price of investment goods, which are predominantly tradable, implying a positive correlation between commodity prices and investment. Wages also gradually increase, suggesting there are spending effects owing to the windfall gains associated with increased commodity prices. The responses in investments, wages, PPI and CPI are remarkably similar across the two countries. The rise in consumer and producer costs erodes the real effect of spending and may explain why the commodity price shock has less stimulating (or negative) effects on the economy. As expected, the terms of trade also improves in both countries but the response in Australia is substantially more pronounced. The stock market responses are in line with the fact that the shock has a positive effect on overall activity level in the Norwegian economy but a negative effect on the Australian economy (see Figure 5). While the fall in the stock price index for Australia might seem surprising, it is also found in, for example, Ratti and Hasan (2013). Moreover, asset prices are the present discounted values of the future net earnings of the firms in the economy. Unexpected commodity price shocks that increase (decrease) the production possibilities for the whole economy should be positively (negatively) related to stock returns. To sum up, both Norway and Australia have benefited from having highly profitable commodity sectors. In Norway, windfall gains due to energy booms have had positive spillover effects on the mainland economy but the shock does not affect costs. In Australia, the resource boom in the mining industry has had similar, although more modest, spillovers. In terms of the theory model developed in Section 1, this suggests positive direct spillovers (δR) for both countries, although stronger effects in Norway than in Australia. On the other hand, the large share of the variance explained by the resource boom and the shocks driving commodity prices in Norway also suggests that Norway, as an economy, is more dependent on petroleum resources than Australia on mining. The commodity price shock affects costs and wages across the two countries in the same manner, but has a clear negative effect on production and employment in Australia. This is evidence of more classical Dutch disease‐like symptoms. We examine this issue in greater detail below, focusing in particular on sectoral responses in the private sector and the role of the public sector as shock absorber in the resource rich economies. 3.6. Dutch Disease or Two‐speed Boom? The standard theory of Dutch disease predicts that some sectors of the economy (tradables) will contract and others expand (non‐tradables) as a result of an unexpected resource gift. The theory model outlined in Section 1 allows, but does not constrain, all sectors to move in the same direction. The results presented in Figures 8 and 9 cast light on the empirical relevance of these two competing theories. In the Figures we display the responses in value added and employment across a large panel of sectors to an energy boom (top panels) and a commodity price shock (bottom panels) in Norway and Australia, respectively. The Figures display the quarterly average of each sector’s response (in levels) to the different shocks, while white bars indicate that the shock explains less than 10% of the variation in that sector. Table B1 in Appendix B reports the exact numbers. Fig. 8. Open in new tabDownload slide Norway: Sectoral Responses Notes. Each plot displays the quarterly average of each sector i’s response (in levels) to the different shocks. The averages are computed over horizons 1–12. The resource activity shock is normalises to increase the resource activity factor by 1%, while the commodity price shock is normalised to increase the real price of oil by 10%. White bars indicate that the shock explains <10% of the variation in the sector. Fig. 8. Open in new tabDownload slide Norway: Sectoral Responses Notes. Each plot displays the quarterly average of each sector i’s response (in levels) to the different shocks. The averages are computed over horizons 1–12. The resource activity shock is normalises to increase the resource activity factor by 1%, while the commodity price shock is normalised to increase the real price of oil by 10%. White bars indicate that the shock explains <10% of the variation in the sector. Fig. 9. Open in new tabDownload slide Australia: Sectoral Responses Note. The commodity price shock is normalised to increase the commodity price index by 5%. See also the note to Figure 8. Fig. 9. Open in new tabDownload slide Australia: Sectoral Responses Note. The commodity price shock is normalised to increase the commodity price index by 5%. See also the note to Figure 8. Overall, the results presented in the Figures show that the resource boom shock and the commodity price shock have contributed to turn Australia and Norway into two‐speed economies, with some industries growing at a fast speed and others growing more slowly, or in fact declining.25 However, while most sectors are positively affected by the resource boom shock in both Norway and Australia, the commodity price shock works almost in opposite directions across the two countries. Starting with Norway, Figure 8 emphasises how energy booms stimulate value added in all industries in the private sector, although to varying degrees. The construction and business sectors are among the most positively affected. Between 30% and 40% of the variance in these sectors is explained by energy booms, see Table B1 in Appendix B. These are industries with moderate direct input into the oil sector but the indirect effects are large. Value added in manufacturing is also positively affected but less so than in the non‐tradable sectors. Nonetheless, there is no evidence of Dutch disease wherein the sector eventually contracts.26 Turning to the labour market, we can confirm that the resource boom shock has contributed to making Norway a two‐speed economy, with employment in non‐traded sectors such as construction, the business service sector and real estate growing at a much faster pace than traded sectors such as manufacturing. However, and as above, there is no evidence of Dutch disease: manufacturing does not contract. Interestingly, the effect on the public sector (for both value added and employment) is much smaller than for most of the other sectors, suggesting only a minor government spending effect following this shock. As seen in Table B1, and indicated by the white bars in Figure 8, the commodity price shock generally explains a substantially smaller share of the variance of the value added at the industry level in Norway than the resource activity shock does. Sectors such as scientific services and manufacturing are among the most positively affected. This is interesting, as these sectors are also technology intensive and enjoy spillovers from the significant boost in petroleum investments following the commodity price shock. As offshore oil often demands complicated technical solutions, the shock seems to generate positive knowledge externalities that benefit employment in these sectors in particular. Thus, the theory of Dutch disease is turned on its head following this shock. Furthermore, compared to the responses reported for the resource activity shock, the public sector is now also positively affected, suggesting the presence of a substantial spending effect in Norway. In light of the higher commodity prices of the past decade, this can have worked to boost demand in the Norwegian economy relative to other oil‐importing countries. However, as emphasised in the previous Section, the increased spending could also suggest why cost competitiveness has declined and may be a concern in the long run.27 The results for Australia are displayed in Figure 9. For the resource boom shock, they show a similar pattern to Norway. Service sectors such as construction, transportation, and retail are particularly stimulated by the boom. However, in Australia, there are no low‐speed industries. Instead, several industries show evidence of actual decline in both value added and employment. In particular, industries such as hotel and food, business and to a certain extent manufacturing, contract following the resource boom. After a commodity price shock, most industries in Australia decline. Construction, real estate and public employment are the ones that are positively affected. These results are very much in line with the classical Dutch disease effects, where resources are moved out of tradable sectors and into non‐tradables and the tradable sector thus contracts. Finally, a natural question arises after reading these results: why does a commodity price shock affect the two commodity exporting countries so differently? One suggestion, discussed briefly above, is that offshore oil may demand complicated technical solutions, a process which generates positive knowledge externalities. This may have benefited petroleum producer Norway to a larger extent than mining abundant Australia. Another possible answer to this question is the role of the governmental sector. As seen from Figures 8 and 9, employment in the public sector in both Norway and Australia responds positively to a commodity price shock. In Norway, value added in the public sector also increases but it falls slightly in Australia. Measuring the size of the public sector as the number of persons employed relative to the total population, we find that the governmental sector in Australia is only one‐fifth the size of Norway’s.28 Thus, the governmental sector might work as a shock absorber in Norway, simply by virtue of its size. In addition, Norway has a generous welfare system that distributes wealth across the country, as well as a sovereign wealth fund, explicitly funded by petroleum revenues, which allows for extra spending of petroleum income when business cycles turn bad (as it does internationally after a commodity price shock). Hence, procyclical fiscal policy may have worked to exacerbates the fluctuations in economic activity in Norway following a commodity price shock. 4. Additional Results and Robustness We have estimated the model using a number of alternative data compositions and model specifications. As described in greater detail in the online Appendix, the main conclusions of the article are robust to all of these robustness checks. Below, we provide a brief summary. First, the model specification is uncertain. The number of factors and lags employed in the model should be tested. We do this primarily by running a quasi‐real‐time forecasting experiment. The results show that our benchmark model, outlined in subsection 2.1, performs significantly better than simple univariate autoregressive processes. The benchmark specification is also among the best performing specifications and the best model specification over shorter forecasting horizons. Second, we have estimated the models using a truncated estimation sample, ending in 2007:Q4. This alternative experiment excludes the financial crisis and the period thereafter from the sample. The importance of productivity spillovers and of separating between resources activity and commodity price shocks is prevalent even when the latter part of the estimation sample is excluded. In fact, for most of the main variables we look at, the benchmark responses and the responses obtained using the truncated sample are not significantly different from each other. If anything, for Australia, the results based on the truncated estimation sample are stronger. Third, global activity is not observed. As discussed in the online Appendix, the results reported in Section 3 are not affected by changing how we measure this variable. Fourth, we show that the results are robust to using the nominal rather that the real commodity price for both countries. In fact, the results are virtually unchanged. Fifth, the online Appendix shows that the results are robust to an alternative identification scheme, allowing for contemporaneous spillovers between the resource activity and domestic activity shocks. Finally, we show that the results (for Norway) are robust, also after controlling for shocks to global oil supply. We have also conducted a series of other robustness checks, for which details can be provided on request. In particular, the inclusion/exclusion of additional variables (to capture, for example, wealth effects) will potentially also affect the factor estimates. Still, the main results are robust to estimating the models using only a subset of the variables in the observable yt vector, that is, excluding time series for wages, investments, the terms of trade, stock prices, consumer and producer prices, and the short term interest rate. Lastly, as mentioned in subsection 2.4, our results are robust to estimating the model using classical two‐step estimation techniques, and as described in the online Appendix, our results seem robust to different prior specifications. 5. Conclusion This study examines the empirical validity of the classical Dutch disease theory versus a theory model that allows for direct productivity spillovers from the resource sector to both the traded and non‐traded sector. Using Australia and Norway as representative case studies, we take the theory to the data by developing and estimating a BDFM, that includes separate activity factors for resource and non‐resource sectors in addition to global activity and the real commodity price. In doing so, we explicitly identify and quantify windfall gains from a booming resource sector or higher commodity prices and the associated sectoral performance in the rest of the economy. We have two main results: first, a booming resource sector has significant and positive productivity spillovers on non‐resource sectors, effects that have not been captured in previous analyses. In particular, we find that the resource sector stimulates productivity and production in both Australia and Norway. Value added and employment increases in the non‐traded relative to the traded sectors, suggesting a two‐speed transmission phase. The most positively affected sectors are construction, business services and real estate. Second, windfall gains due to changes in the commodity price also stimulate the economy, particularly if the commodity price increase is associated with a boom in global demand. However, commodity price increases unrelated to global activity are less favourable, in part because of a substantial real exchange rate appreciation and reduced competitiveness. Still, value added and employment increase temporarily in Norway, mostly due to increased activity in the technologically intense service sectors and the boost in government spending. For Australia, the picture is more gloomy, as there is evidence of a Dutch disease effect with crowding out and an eventual decline in manufacturing. These results emphasise the importance of distinguishing between windfall gains due to volume and price changes when analysing the Dutch disease hypothesis. To the best of our knowledge, this is the first article to separate and quantify these two channels explicitly, while also allowing for explicit disturbances to world activity and the non‐resource sectors. Appendix A. Data and Sources Table A1 Descriptive Statistics Sector . Norway . Australia . Variable in national accounts . . Mean . SD . Mean . SD . . GDP Res. extraction −0.63 6.28 3.73 4.10 Oil and natural gas extraction/mining Res. service 6.89 25.65 4.25 12.84 Service activities in oil and gas/mining Manufacturing 1.56 3.53 1.03 3.44 Manufacturing Construction 3.04 5.02 4.37 7.44 Construction Retail 4.39 3.46 3.64 2.18 Wholesale and retail trade Transp. ocean −5.32 16.22 Ocean transport Transportation 1.03 5.26 3.66 3.25 Transport activities excl. ocean transport Hotel and food 1.76 4.84 2.70 3.80 Accommodation and food service activities Financial 3.90 7.88 4.70 3.77 Financial and insurance activities Real estate 9.20 10.81 2.66 4.46 Real estate activities Scientific 4.12 4.75 4.96 4.43 Professional, scientific and technical activities Business 6.79 6.91 3.49 5.29 Administrative and support service activities Non‐resource 2.79 1.95 3.12 1.73 Total excl. oil and gas extraction/mining Public 1.64 1.46 2.65 2.89 General government Employment Res. extraction 2.43 5.32 4.69 9.98 See above Res. service 11.14 14.30 Manufacturing −0.59 3.35 −0.57 3.14 Construction 3.95 4.05 2.81 4.77 Retail 1.28 1.99 1.72 2.75 Transp. ocean 0.91 2.79 Transportation 0.70 2.22 1.50 4.36 Hotel and food 1.10 2.70 2.52 3.79 Financial −0.13 2.92 0.85 4.47 Real estate 5.98 6.61 2.53 7.25 Scientific 3.61 3.75 4.00 5.12 Business 5.66 6.32 3.65 6.36 Non‐resource 1.33 1.54 1.66 1.70 Public 1.33 0.92 2.29 4.25 Other Wages resource 9.90 6.87 5.13 3.00 Wages petroleum sector/mining Wages public 6.04 1.77 4.16 1.40 Wages public Wages non‐res. 6.06 2.38 4.20 1.55 Total excl. wages to petroleum sector/mining Invest. res. 4.52 22.62 12.01 25.47 Investment petroleum sector/mining Invest. non‐res. 4.06 8.60 5.60 12.43 Total excl. invest. in petroleum sector/mining Exchange rate 0.57 4.79 0.11 2.05 BIS effective exchange rate index, broad basket Int. World activity 2.78 1.90 4.54 2.10 See text, subsection 2.3 Com. Price 9.01 33.11 2.89 15.64 Commodity price. See text, subsection 2.3 Sector . Norway . Australia . Variable in national accounts . . Mean . SD . Mean . SD . . GDP Res. extraction −0.63 6.28 3.73 4.10 Oil and natural gas extraction/mining Res. service 6.89 25.65 4.25 12.84 Service activities in oil and gas/mining Manufacturing 1.56 3.53 1.03 3.44 Manufacturing Construction 3.04 5.02 4.37 7.44 Construction Retail 4.39 3.46 3.64 2.18 Wholesale and retail trade Transp. ocean −5.32 16.22 Ocean transport Transportation 1.03 5.26 3.66 3.25 Transport activities excl. ocean transport Hotel and food 1.76 4.84 2.70 3.80 Accommodation and food service activities Financial 3.90 7.88 4.70 3.77 Financial and insurance activities Real estate 9.20 10.81 2.66 4.46 Real estate activities Scientific 4.12 4.75 4.96 4.43 Professional, scientific and technical activities Business 6.79 6.91 3.49 5.29 Administrative and support service activities Non‐resource 2.79 1.95 3.12 1.73 Total excl. oil and gas extraction/mining Public 1.64 1.46 2.65 2.89 General government Employment Res. extraction 2.43 5.32 4.69 9.98 See above Res. service 11.14 14.30 Manufacturing −0.59 3.35 −0.57 3.14 Construction 3.95 4.05 2.81 4.77 Retail 1.28 1.99 1.72 2.75 Transp. ocean 0.91 2.79 Transportation 0.70 2.22 1.50 4.36 Hotel and food 1.10 2.70 2.52 3.79 Financial −0.13 2.92 0.85 4.47 Real estate 5.98 6.61 2.53 7.25 Scientific 3.61 3.75 4.00 5.12 Business 5.66 6.32 3.65 6.36 Non‐resource 1.33 1.54 1.66 1.70 Public 1.33 0.92 2.29 4.25 Other Wages resource 9.90 6.87 5.13 3.00 Wages petroleum sector/mining Wages public 6.04 1.77 4.16 1.40 Wages public Wages non‐res. 6.06 2.38 4.20 1.55 Total excl. wages to petroleum sector/mining Invest. res. 4.52 22.62 12.01 25.47 Investment petroleum sector/mining Invest. non‐res. 4.06 8.60 5.60 12.43 Total excl. invest. in petroleum sector/mining Exchange rate 0.57 4.79 0.11 2.05 BIS effective exchange rate index, broad basket Int. World activity 2.78 1.90 4.54 2.10 See text, subsection 2.3 Com. Price 9.01 33.11 2.89 15.64 Commodity price. See text, subsection 2.3 Notes. The Table lists the core variables used in the benchmark model. All activity, investment, wages and employment series are collected from the Quarterly National Accounts database of Statistics Norway and Statistics Australia, respectively. The international series are from Datastream. The real exchange rates are from the Bank of International Settlements (BIS). The moments are computed based on the transformed variables, that is, log(xi,t) − log(xi,t−4)) × 100. See subsection 2.3 for the details. The data and a replication file are available online. Open in new tab Table A1 Descriptive Statistics Sector . Norway . Australia . Variable in national accounts . . Mean . SD . Mean . SD . . GDP Res. extraction −0.63 6.28 3.73 4.10 Oil and natural gas extraction/mining Res. service 6.89 25.65 4.25 12.84 Service activities in oil and gas/mining Manufacturing 1.56 3.53 1.03 3.44 Manufacturing Construction 3.04 5.02 4.37 7.44 Construction Retail 4.39 3.46 3.64 2.18 Wholesale and retail trade Transp. ocean −5.32 16.22 Ocean transport Transportation 1.03 5.26 3.66 3.25 Transport activities excl. ocean transport Hotel and food 1.76 4.84 2.70 3.80 Accommodation and food service activities Financial 3.90 7.88 4.70 3.77 Financial and insurance activities Real estate 9.20 10.81 2.66 4.46 Real estate activities Scientific 4.12 4.75 4.96 4.43 Professional, scientific and technical activities Business 6.79 6.91 3.49 5.29 Administrative and support service activities Non‐resource 2.79 1.95 3.12 1.73 Total excl. oil and gas extraction/mining Public 1.64 1.46 2.65 2.89 General government Employment Res. extraction 2.43 5.32 4.69 9.98 See above Res. service 11.14 14.30 Manufacturing −0.59 3.35 −0.57 3.14 Construction 3.95 4.05 2.81 4.77 Retail 1.28 1.99 1.72 2.75 Transp. ocean 0.91 2.79 Transportation 0.70 2.22 1.50 4.36 Hotel and food 1.10 2.70 2.52 3.79 Financial −0.13 2.92 0.85 4.47 Real estate 5.98 6.61 2.53 7.25 Scientific 3.61 3.75 4.00 5.12 Business 5.66 6.32 3.65 6.36 Non‐resource 1.33 1.54 1.66 1.70 Public 1.33 0.92 2.29 4.25 Other Wages resource 9.90 6.87 5.13 3.00 Wages petroleum sector/mining Wages public 6.04 1.77 4.16 1.40 Wages public Wages non‐res. 6.06 2.38 4.20 1.55 Total excl. wages to petroleum sector/mining Invest. res. 4.52 22.62 12.01 25.47 Investment petroleum sector/mining Invest. non‐res. 4.06 8.60 5.60 12.43 Total excl. invest. in petroleum sector/mining Exchange rate 0.57 4.79 0.11 2.05 BIS effective exchange rate index, broad basket Int. World activity 2.78 1.90 4.54 2.10 See text, subsection 2.3 Com. Price 9.01 33.11 2.89 15.64 Commodity price. See text, subsection 2.3 Sector . Norway . Australia . Variable in national accounts . . Mean . SD . Mean . SD . . GDP Res. extraction −0.63 6.28 3.73 4.10 Oil and natural gas extraction/mining Res. service 6.89 25.65 4.25 12.84 Service activities in oil and gas/mining Manufacturing 1.56 3.53 1.03 3.44 Manufacturing Construction 3.04 5.02 4.37 7.44 Construction Retail 4.39 3.46 3.64 2.18 Wholesale and retail trade Transp. ocean −5.32 16.22 Ocean transport Transportation 1.03 5.26 3.66 3.25 Transport activities excl. ocean transport Hotel and food 1.76 4.84 2.70 3.80 Accommodation and food service activities Financial 3.90 7.88 4.70 3.77 Financial and insurance activities Real estate 9.20 10.81 2.66 4.46 Real estate activities Scientific 4.12 4.75 4.96 4.43 Professional, scientific and technical activities Business 6.79 6.91 3.49 5.29 Administrative and support service activities Non‐resource 2.79 1.95 3.12 1.73 Total excl. oil and gas extraction/mining Public 1.64 1.46 2.65 2.89 General government Employment Res. extraction 2.43 5.32 4.69 9.98 See above Res. service 11.14 14.30 Manufacturing −0.59 3.35 −0.57 3.14 Construction 3.95 4.05 2.81 4.77 Retail 1.28 1.99 1.72 2.75 Transp. ocean 0.91 2.79 Transportation 0.70 2.22 1.50 4.36 Hotel and food 1.10 2.70 2.52 3.79 Financial −0.13 2.92 0.85 4.47 Real estate 5.98 6.61 2.53 7.25 Scientific 3.61 3.75 4.00 5.12 Business 5.66 6.32 3.65 6.36 Non‐resource 1.33 1.54 1.66 1.70 Public 1.33 0.92 2.29 4.25 Other Wages resource 9.90 6.87 5.13 3.00 Wages petroleum sector/mining Wages public 6.04 1.77 4.16 1.40 Wages public Wages non‐res. 6.06 2.38 4.20 1.55 Total excl. wages to petroleum sector/mining Invest. res. 4.52 22.62 12.01 25.47 Investment petroleum sector/mining Invest. non‐res. 4.06 8.60 5.60 12.43 Total excl. invest. in petroleum sector/mining Exchange rate 0.57 4.79 0.11 2.05 BIS effective exchange rate index, broad basket Int. World activity 2.78 1.90 4.54 2.10 See text, subsection 2.3 Com. Price 9.01 33.11 2.89 15.64 Commodity price. See text, subsection 2.3 Notes. The Table lists the core variables used in the benchmark model. All activity, investment, wages and employment series are collected from the Quarterly National Accounts database of Statistics Norway and Statistics Australia, respectively. The international series are from Datastream. The real exchange rates are from the Bank of International Settlements (BIS). The moments are computed based on the transformed variables, that is, log(xi,t) − log(xi,t−4)) × 100. See subsection 2.3 for the details. The data and a replication file are available online. Open in new tab Table A2 Correlations with Commodity Price Country . Variable . Y . YR . M . ME . Norway 0.28 0.08 0.38 −0.09 (0.02) (0.53) (0.00) (0.47) Australia 0.14 0.05 0.23 −0.09 (0.21) (0.63) (0.03) (0.41) Country . Variable . Y . YR . M . ME . Norway 0.28 0.08 0.38 −0.09 (0.02) (0.53) (0.00) (0.47) Australia 0.14 0.05 0.23 −0.09 (0.21) (0.63) (0.03) (0.41) Notes. The Table reports the contemporaneous correlations coefficients (p‐value in parenthesis) between commodity prices and non‐resource GDP (Y), resource GDP (YR), value added in Manufacturing (M), and employment in Manufacturing (ME). The raw data are measured as four‐quarter logarithmic changes. The sample is 1991:Q1–2012:Q4 and 1996:Q1–2012:Q4, for Australia and Norway, respectively. Open in new tab Table A2 Correlations with Commodity Price Country . Variable . Y . YR . M . ME . Norway 0.28 0.08 0.38 −0.09 (0.02) (0.53) (0.00) (0.47) Australia 0.14 0.05 0.23 −0.09 (0.21) (0.63) (0.03) (0.41) Country . Variable . Y . YR . M . ME . Norway 0.28 0.08 0.38 −0.09 (0.02) (0.53) (0.00) (0.47) Australia 0.14 0.05 0.23 −0.09 (0.21) (0.63) (0.03) (0.41) Notes. The Table reports the contemporaneous correlations coefficients (p‐value in parenthesis) between commodity prices and non‐resource GDP (Y), resource GDP (YR), value added in Manufacturing (M), and employment in Manufacturing (ME). The raw data are measured as four‐quarter logarithmic changes. The sample is 1991:Q1–2012:Q4 and 1996:Q1–2012:Q4, for Australia and Norway, respectively. Open in new tab Appendix B. Additional Figures and Tables Table B1 Variance Decompositions: Non‐resource Sectors . . Shock . Variable . Sector and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP Construction 0.24, 0.34 0.02, 0.01 0.57, 0.54 0.16, 0.11 Business 0.24, 0.35 0.13, 0.05 0.51, 0.50 0.12, 0.10 Hotel and food 0.06, 0.18 0.09, 0.03 0.67, 0.64 0.17, 0.14 Retail 0.66, 0.72 0.02, 0.03 0.27, 0.22 0.05, 0.03 Transportation 0.14, 0.24 0.13, 0.06 0.67, 0.65 0.06, 0.06 Financial 0.33, 0.46 0.45, 0.33 0.20, 0.17 0.02, 0.04 Scientific 0.06, 0.04 0.42, 0.27 0.44, 0.58 0.08, 0.11 Real estate 0.08, 0.11 0.78, 0.74 0.14, 0.15 0.00, 0.00 Manufacturing 0.06, 0.11 0.22, 0.12 0.69, 0.74 0.03, 0.03 Public 0.03, 0.02 0.49, 0.40 0.07, 0.12 0.41, 0.46 Employment Construction 0.21, 0.33 0.15, 0.08 0.50, 0.47 0.14, 0.11 Business 0.07, 0.13 0.11, 0.04 0.65, 0.68 0.17, 0.14 Hotel and food 0.26, 0.12 0.13, 0.12 0.34, 0.48 0.27, 0.29 Retail 0.37, 0.39 0.05, 0.02 0.37, 0.44 0.22, 0.15 Transportation 0.34, 0.16 0.13, 0.09 0.05, 0.27 0.49, 0.48 Financial 0.31, 0.33 0.15, 0.11 0.53, 0.51 0.02, 0.05 Scientific 0.23, 0.10 0.02, 0.06 0.23, 0.45 0.52, 0.39 Real estate 0.17, 0.19 0.17, 0.05 0.51, 0.62 0.15, 0.13 Manufacturing 0.09, 0.07 0.15, 0.14 0.26, 0.40 0.50, 0.39 Public 0.28, 0.16 0.19, 0.18 0.08, 0.15 0.45, 0.51 Australia GDP Construction 0.31, 0.41 0.22, 0.13 0.13, 0.11 0.34, 0.36 Business 0.04, 0.08 0.20, 0.25 0.57, 0.48 0.19, 0.19 Hotel and food 0.21, 0.20 0.02, 0.17 0.23, 0.18 0.54, 0.46 Retail 0.13, 0.13 0.19, 0.29 0.08, 0.04 0.60, 0.54 Transportation 0.19, 0.13 0.03, 0.07 0.56, 0.56 0.22, 0.24 Financial 0.06, 0.06 0.44, 0.57 0.30, 0.20 0.20, 0.17 Scientific 0.85, 0.78 0.01, 0.09 0.01, 0.05 0.13, 0.08 Real estate 0.10, 0.12 0.23, 0.19 0.08, 0.07 0.60, 0.62 Manufacturing 0.19, 0.10 0.31, 0.43 0.47, 0.39 0.03, 0.08 Public 0.29, 0.30 0.31, 0.24 0.00, 0.06 0.39, 0.40 Employment Construction 0.15, 0.13 0.23, 0.38 0.14, 0.06 0.48, 0.42 Business 0.75, 0.61 0.07, 0.26 0.01, 0.04 0.17, 0.09 Hotel and food 0.59, 0.48 0.14, 0.36 0.13, 0.07 0.14, 0.09 Retail 0.23, 0.16 0.14, 0.28 0.16, 0.12 0.47, 0.44 Transportation 0.16, 0.08 0.23, 0.21 0.52, 0.59 0.09, 0.12 Financial 0.07, 0.05 0.20, 0.09 0.59, 0.77 0.14, 0.10 Scientific 0.41, 0.40 0.21, 0.27 0.20, 0.20 0.18, 0.14 Real estate 0.06, 0.19 0.56, 0.52 0.34, 0.24 0.04, 0.04 Manufacturing 0.03, 0.02 0.33, 0.47 0.38, 0.30 0.26, 0.21 Public 0.14, 0.11 0.57, 0.45 0.28, 0.43 0.00, 0.01 . . Shock . Variable . Sector and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP Construction 0.24, 0.34 0.02, 0.01 0.57, 0.54 0.16, 0.11 Business 0.24, 0.35 0.13, 0.05 0.51, 0.50 0.12, 0.10 Hotel and food 0.06, 0.18 0.09, 0.03 0.67, 0.64 0.17, 0.14 Retail 0.66, 0.72 0.02, 0.03 0.27, 0.22 0.05, 0.03 Transportation 0.14, 0.24 0.13, 0.06 0.67, 0.65 0.06, 0.06 Financial 0.33, 0.46 0.45, 0.33 0.20, 0.17 0.02, 0.04 Scientific 0.06, 0.04 0.42, 0.27 0.44, 0.58 0.08, 0.11 Real estate 0.08, 0.11 0.78, 0.74 0.14, 0.15 0.00, 0.00 Manufacturing 0.06, 0.11 0.22, 0.12 0.69, 0.74 0.03, 0.03 Public 0.03, 0.02 0.49, 0.40 0.07, 0.12 0.41, 0.46 Employment Construction 0.21, 0.33 0.15, 0.08 0.50, 0.47 0.14, 0.11 Business 0.07, 0.13 0.11, 0.04 0.65, 0.68 0.17, 0.14 Hotel and food 0.26, 0.12 0.13, 0.12 0.34, 0.48 0.27, 0.29 Retail 0.37, 0.39 0.05, 0.02 0.37, 0.44 0.22, 0.15 Transportation 0.34, 0.16 0.13, 0.09 0.05, 0.27 0.49, 0.48 Financial 0.31, 0.33 0.15, 0.11 0.53, 0.51 0.02, 0.05 Scientific 0.23, 0.10 0.02, 0.06 0.23, 0.45 0.52, 0.39 Real estate 0.17, 0.19 0.17, 0.05 0.51, 0.62 0.15, 0.13 Manufacturing 0.09, 0.07 0.15, 0.14 0.26, 0.40 0.50, 0.39 Public 0.28, 0.16 0.19, 0.18 0.08, 0.15 0.45, 0.51 Australia GDP Construction 0.31, 0.41 0.22, 0.13 0.13, 0.11 0.34, 0.36 Business 0.04, 0.08 0.20, 0.25 0.57, 0.48 0.19, 0.19 Hotel and food 0.21, 0.20 0.02, 0.17 0.23, 0.18 0.54, 0.46 Retail 0.13, 0.13 0.19, 0.29 0.08, 0.04 0.60, 0.54 Transportation 0.19, 0.13 0.03, 0.07 0.56, 0.56 0.22, 0.24 Financial 0.06, 0.06 0.44, 0.57 0.30, 0.20 0.20, 0.17 Scientific 0.85, 0.78 0.01, 0.09 0.01, 0.05 0.13, 0.08 Real estate 0.10, 0.12 0.23, 0.19 0.08, 0.07 0.60, 0.62 Manufacturing 0.19, 0.10 0.31, 0.43 0.47, 0.39 0.03, 0.08 Public 0.29, 0.30 0.31, 0.24 0.00, 0.06 0.39, 0.40 Employment Construction 0.15, 0.13 0.23, 0.38 0.14, 0.06 0.48, 0.42 Business 0.75, 0.61 0.07, 0.26 0.01, 0.04 0.17, 0.09 Hotel and food 0.59, 0.48 0.14, 0.36 0.13, 0.07 0.14, 0.09 Retail 0.23, 0.16 0.14, 0.28 0.16, 0.12 0.47, 0.44 Transportation 0.16, 0.08 0.23, 0.21 0.52, 0.59 0.09, 0.12 Financial 0.07, 0.05 0.20, 0.09 0.59, 0.77 0.14, 0.10 Scientific 0.41, 0.40 0.21, 0.27 0.20, 0.20 0.18, 0.14 Real estate 0.06, 0.19 0.56, 0.52 0.34, 0.24 0.04, 0.04 Manufacturing 0.03, 0.02 0.33, 0.47 0.38, 0.30 0.26, 0.21 Public 0.14, 0.11 0.57, 0.45 0.28, 0.43 0.00, 0.01 Note. Each row‐column intersection reports median variance decompositions for horizons 4 (left) and 8 (right). Open in new tab Table B1 Variance Decompositions: Non‐resource Sectors . . Shock . Variable . Sector and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP Construction 0.24, 0.34 0.02, 0.01 0.57, 0.54 0.16, 0.11 Business 0.24, 0.35 0.13, 0.05 0.51, 0.50 0.12, 0.10 Hotel and food 0.06, 0.18 0.09, 0.03 0.67, 0.64 0.17, 0.14 Retail 0.66, 0.72 0.02, 0.03 0.27, 0.22 0.05, 0.03 Transportation 0.14, 0.24 0.13, 0.06 0.67, 0.65 0.06, 0.06 Financial 0.33, 0.46 0.45, 0.33 0.20, 0.17 0.02, 0.04 Scientific 0.06, 0.04 0.42, 0.27 0.44, 0.58 0.08, 0.11 Real estate 0.08, 0.11 0.78, 0.74 0.14, 0.15 0.00, 0.00 Manufacturing 0.06, 0.11 0.22, 0.12 0.69, 0.74 0.03, 0.03 Public 0.03, 0.02 0.49, 0.40 0.07, 0.12 0.41, 0.46 Employment Construction 0.21, 0.33 0.15, 0.08 0.50, 0.47 0.14, 0.11 Business 0.07, 0.13 0.11, 0.04 0.65, 0.68 0.17, 0.14 Hotel and food 0.26, 0.12 0.13, 0.12 0.34, 0.48 0.27, 0.29 Retail 0.37, 0.39 0.05, 0.02 0.37, 0.44 0.22, 0.15 Transportation 0.34, 0.16 0.13, 0.09 0.05, 0.27 0.49, 0.48 Financial 0.31, 0.33 0.15, 0.11 0.53, 0.51 0.02, 0.05 Scientific 0.23, 0.10 0.02, 0.06 0.23, 0.45 0.52, 0.39 Real estate 0.17, 0.19 0.17, 0.05 0.51, 0.62 0.15, 0.13 Manufacturing 0.09, 0.07 0.15, 0.14 0.26, 0.40 0.50, 0.39 Public 0.28, 0.16 0.19, 0.18 0.08, 0.15 0.45, 0.51 Australia GDP Construction 0.31, 0.41 0.22, 0.13 0.13, 0.11 0.34, 0.36 Business 0.04, 0.08 0.20, 0.25 0.57, 0.48 0.19, 0.19 Hotel and food 0.21, 0.20 0.02, 0.17 0.23, 0.18 0.54, 0.46 Retail 0.13, 0.13 0.19, 0.29 0.08, 0.04 0.60, 0.54 Transportation 0.19, 0.13 0.03, 0.07 0.56, 0.56 0.22, 0.24 Financial 0.06, 0.06 0.44, 0.57 0.30, 0.20 0.20, 0.17 Scientific 0.85, 0.78 0.01, 0.09 0.01, 0.05 0.13, 0.08 Real estate 0.10, 0.12 0.23, 0.19 0.08, 0.07 0.60, 0.62 Manufacturing 0.19, 0.10 0.31, 0.43 0.47, 0.39 0.03, 0.08 Public 0.29, 0.30 0.31, 0.24 0.00, 0.06 0.39, 0.40 Employment Construction 0.15, 0.13 0.23, 0.38 0.14, 0.06 0.48, 0.42 Business 0.75, 0.61 0.07, 0.26 0.01, 0.04 0.17, 0.09 Hotel and food 0.59, 0.48 0.14, 0.36 0.13, 0.07 0.14, 0.09 Retail 0.23, 0.16 0.14, 0.28 0.16, 0.12 0.47, 0.44 Transportation 0.16, 0.08 0.23, 0.21 0.52, 0.59 0.09, 0.12 Financial 0.07, 0.05 0.20, 0.09 0.59, 0.77 0.14, 0.10 Scientific 0.41, 0.40 0.21, 0.27 0.20, 0.20 0.18, 0.14 Real estate 0.06, 0.19 0.56, 0.52 0.34, 0.24 0.04, 0.04 Manufacturing 0.03, 0.02 0.33, 0.47 0.38, 0.30 0.26, 0.21 Public 0.14, 0.11 0.57, 0.45 0.28, 0.43 0.00, 0.01 . . Shock . Variable . Sector and horizon . Resource activity . Commodity price . Global activity . Domestic activity . . . 4, 8 . 4, 8 . 4, 8 . 4, 8 . Norway GDP Construction 0.24, 0.34 0.02, 0.01 0.57, 0.54 0.16, 0.11 Business 0.24, 0.35 0.13, 0.05 0.51, 0.50 0.12, 0.10 Hotel and food 0.06, 0.18 0.09, 0.03 0.67, 0.64 0.17, 0.14 Retail 0.66, 0.72 0.02, 0.03 0.27, 0.22 0.05, 0.03 Transportation 0.14, 0.24 0.13, 0.06 0.67, 0.65 0.06, 0.06 Financial 0.33, 0.46 0.45, 0.33 0.20, 0.17 0.02, 0.04 Scientific 0.06, 0.04 0.42, 0.27 0.44, 0.58 0.08, 0.11 Real estate 0.08, 0.11 0.78, 0.74 0.14, 0.15 0.00, 0.00 Manufacturing 0.06, 0.11 0.22, 0.12 0.69, 0.74 0.03, 0.03 Public 0.03, 0.02 0.49, 0.40 0.07, 0.12 0.41, 0.46 Employment Construction 0.21, 0.33 0.15, 0.08 0.50, 0.47 0.14, 0.11 Business 0.07, 0.13 0.11, 0.04 0.65, 0.68 0.17, 0.14 Hotel and food 0.26, 0.12 0.13, 0.12 0.34, 0.48 0.27, 0.29 Retail 0.37, 0.39 0.05, 0.02 0.37, 0.44 0.22, 0.15 Transportation 0.34, 0.16 0.13, 0.09 0.05, 0.27 0.49, 0.48 Financial 0.31, 0.33 0.15, 0.11 0.53, 0.51 0.02, 0.05 Scientific 0.23, 0.10 0.02, 0.06 0.23, 0.45 0.52, 0.39 Real estate 0.17, 0.19 0.17, 0.05 0.51, 0.62 0.15, 0.13 Manufacturing 0.09, 0.07 0.15, 0.14 0.26, 0.40 0.50, 0.39 Public 0.28, 0.16 0.19, 0.18 0.08, 0.15 0.45, 0.51 Australia GDP Construction 0.31, 0.41 0.22, 0.13 0.13, 0.11 0.34, 0.36 Business 0.04, 0.08 0.20, 0.25 0.57, 0.48 0.19, 0.19 Hotel and food 0.21, 0.20 0.02, 0.17 0.23, 0.18 0.54, 0.46 Retail 0.13, 0.13 0.19, 0.29 0.08, 0.04 0.60, 0.54 Transportation 0.19, 0.13 0.03, 0.07 0.56, 0.56 0.22, 0.24 Financial 0.06, 0.06 0.44, 0.57 0.30, 0.20 0.20, 0.17 Scientific 0.85, 0.78 0.01, 0.09 0.01, 0.05 0.13, 0.08 Real estate 0.10, 0.12 0.23, 0.19 0.08, 0.07 0.60, 0.62 Manufacturing 0.19, 0.10 0.31, 0.43 0.47, 0.39 0.03, 0.08 Public 0.29, 0.30 0.31, 0.24 0.00, 0.06 0.39, 0.40 Employment Construction 0.15, 0.13 0.23, 0.38 0.14, 0.06 0.48, 0.42 Business 0.75, 0.61 0.07, 0.26 0.01, 0.04 0.17, 0.09 Hotel and food 0.59, 0.48 0.14, 0.36 0.13, 0.07 0.14, 0.09 Retail 0.23, 0.16 0.14, 0.28 0.16, 0.12 0.47, 0.44 Transportation 0.16, 0.08 0.23, 0.21 0.52, 0.59 0.09, 0.12 Financial 0.07, 0.05 0.20, 0.09 0.59, 0.77 0.14, 0.10 Scientific 0.41, 0.40 0.21, 0.27 0.20, 0.20 0.18, 0.14 Real estate 0.06, 0.19 0.56, 0.52 0.34, 0.24 0.04, 0.04 Manufacturing 0.03, 0.02 0.33, 0.47 0.38, 0.30 0.26, 0.21 Public 0.14, 0.11 0.57, 0.45 0.28, 0.43 0.00, 0.01 Note. Each row‐column intersection reports median variance decompositions for horizons 4 (left) and 8 (right). Open in new tab Footnotes 1 " Following the discovery and development of natural gas industries in the 1960s, the Netherlands experienced a period of real exchange rate appreciation and a corresponding loss of competitiveness and eventual contraction of traditional industries. 2 " This is important. Table A2 in Appendix A shows that GDP growth and growth in the manufacturing industry are positively correlated with the commodity price. However, this positive correlation could easily just be the result of higher global demand, not evidence against any Dutch disease pattern in itself. 3 " We differ from Charnavoki and Dolado (2014) also in other aspects. In contrast to their factor model approach our model yields unique identification of the factors, as well as taking into account uncertainty related to the unobserved factors. Moreover, they identify the structural shocks using sign restrictions, while we employ a Cholesky decomposition (but show that our results are also robust to alternative identification schemes). Finally, Charnavoki and Dolado (2014) also identify a global supply shock (in addition to global demand and commodity price shocks) by using global inflation to proxy the supply of commodities. However, consistent with many other recent studies, they do not find this shock to be empirically important. In the online Appendix we show that the same holds in our study. 4 " To ensure that the (flow of the) foreign exchange gift does not die out as a share of income, we assume that it grows over time. 5 " If ηt increases, there will be an excess supply of non‐traded goods and Pt has to fall (a real exchange rate depreciation) to restore balance by shifting demand from traded to non‐traded goods. 6 " An increase in Pt causes the marginal productivity of labour in the non‐traded sector to be higher than in the traded sector. To re‐establish the equality between the value of the marginal productivity of labour in the two sectors at the new exchange rate, labour use in the non‐traded sector has to increase. 7 " Note that relative productivity is not affected by the direct productivity spillovers, δR. Hence, expressions (6) and (7) are similar to (13) and (14) in Torvik (2001). He shows that when the elasticity of substitution σ is less than one, the model has a stable interior solution. 8 " As a results of the same shift, and because a change in Rt does not affect η*, the real exchange rate also has to depreciate, see Torvik (2001) for a formal proof. 9 " Earlier studies of the implication of LBD for Dutch disease, that is, van Wijnbergen (1984) and Krugman (1987), find unambiguously that productivity will decline. The agreement rests upon the assumption that LBD is only generated in the traded sector. Since the foreign exchange gift decreases the size of the traded sector, productivity is reduced. In our set up, this is equivalent to assuming u = δT = δN = δR = 0, so that (1) and (2) reduce to HNt˙/HNt=0 and HTt˙/HTt=v(1−ηt) ⁠, respectively. 10 " Geweke (1977) is an early example of the use of the DFM in the economic literature. Kose et al. (2003) and Mumtaz et al. (2011) are more recent examples, while Stock and Watson 2005 provide a brief overview of the use of this type of models in economics. 11 " Note that our aim is to control for aggregate domestic impulses, not to identify monetary or say, fiscal policy shocks explicitly. 12 " For example, if the resource activity shock explains a lot of the variation in the oil production and service sector in Norway, any sector that supplies intermediates to this sector is likely to also be affected by the resource activity shock. In particular, to produce output, the oil sector demands supply of goods and services from the other sectors in the economy. As such, any disturbances in the oil producing sector will automatically affect the suppliers. 13 " Moreover, as shown in the online Appendix, four factors also explain a large fraction of the variance in the data. 14 " Australia has a rich resource sector that produces many different commodities. However, the iron ore sector is by far the largest, and is therefore used to identify the resource boom factor and shocks. 15 " However, as shown in Kilian (2009), and a range of subsequent articles, such supply shocks explain a trivial fraction of the total variance in the price of oil, and do not account for a large fraction of the variation in real activity either. 16 " One could envision cases where both the resource activity and the domestic activity shocks cause contemporaneous spillover effects. In the online Appendix we explore this possibility by identifying the structural shocks using a combination of zero and sign restrictions. The main results are robust to this alternative identification scheme. 17 " Australia produces a variety of commodities, of which iron ore is by far the most important, with a weight of 33% in the index (last observed weight from 2011/2012), followed by coal (24%), gold (8%), crude oil (6%) and LNG (6%). 18 " The sample periods reflect the longest possible time period for which a full panel of observables is available for the two countries, respectively. 19 " Note that we let s = 0 and l = 0 when estimating the DFM using the two‐step estimation procedure. 20 " Standard MCMC convergence tests confirm that the Gibbs sampler converges to the posterior distribution. Convergence statistics are available on request. 21 " Recall that the real commodity price in Australia is an index based on the weight in commodity exports, see Section 3 for details. 22 " Following the standard convention in the oil market literature we have normalised the commodity price shock to an initial 10% increase in the real price of oil (Norway). Since the standard deviation of the commodity price index is half the size of the real price of oil, we have normalised the shock to the commodity price index to an initial 5% increase. 23 " Interestingly, employment and wages are explained mainly by the domestic shock whereas for investment, the global activity shock plays a larger role. We believe the dichotomy relates to the usual transmission mechanisms, whereas wages and employment respond quickly to domestic impulses (public and private demand), investment requires more foreign capital inflow, and is linked more closely with global dynamics. 24 " According to Penn World Table, openness in Norway and Australia is, respectively, 73% and 40% on average the last decade (current or constant prices). 25 " The two‐speed pattern observed in the data, see, for example, Figure 1, is a function of all potential shocks, including idiosyncratic disturbances. Our focus is on the two resource gift shocks, which are at the core of (theoretical) classical Dutch disease models, and in the model presented in Section 1. 26 " Notice also from Table B1, Appendix B, that the global activity shock explains much more of the variation in manufacturing than in the public sector. Since the manufacturing and the public sector are typically considered as tradable and non‐tradable sectors, respectively, this provides evidence that our model indeed captures the tradable versus non‐tradable distinction, see Section 2. 27 " Note that much of Norway’s petroleum income is directly managed by the Norwegian Petroleum Fund, a specially created body with the express purpose of shielding the domestic economy from potential spending effects caused by the resource endowment. A fiscal rule, however, permits the government to spend approximately 4% of the fund (expected return) every year. 28 " The same holds if we compare the number of persons employed in this sector to the total number of persons employed. References Aastveit , K.A. , Bjørnland , H.C. and Thorsrud , L.A. ( 2015a). ‘ What drives oil prices? Emerging versus developed economies ’, Journal of Applied Econometrics (forthcoming). OpenURL Placeholder Text WorldCat Aastveit , K.A. , Bjørnland , H.C. and Thorsrud , L.A. ( 2015b). ‘ The world is not enough! Small open economies and regional dependence ’, Scandinavian Journal of Economics (forthcoming). OpenURL Placeholder Text WorldCat Bai , J. and Ng , S. ( 2013 ). ‘ Principal components estimation and identification of static factors ’, Journal of Econometrics , vol. 176 ( 1 ), pp. 18 – 29 . Google Scholar Crossref Search ADS WorldCat Bai , J. and Wang , P. ( 2012 ). ‘Identification and estimation of dynamic factor models’ , Working Paper No. 38434, University Library of Munich. Barsky , R.B. and Kilian , L. ( 2002 ). ‘Do we really know that oil caused the great stagnation? A monetary alternative’, in ( B.S. Bernanke and K. Rogoff, eds.), NBER Macroeconomics Annual 2001 , pp. 137 – 83 , Cambridge, MA : MIT Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Bjørnland , H.C. ( 1998 ). ‘ The economic effects of North Sea oil on the manufacturing sector ’, Scottish Journal of Political Economy , vol. 45 ( 5 ), pp. 553 – 85 . Google Scholar Crossref Search ADS WorldCat Bruno , M. and Sachs , J. ( 1982 ). ‘ Energy and resource allocation: a dynamic model of the “Dutch Disease” ’, Review of Economic Studies , vol. 49 ( 5 ), pp. 845 – 59 . Google Scholar Crossref Search ADS WorldCat Charnavoki , V. and Dolado , J. ( 2014 ). ‘ The effects of global shocks on small commodity‐exporting economies: lessons from Canada ’, American Economic Journal: Macroeconomics , vol. 6 ( 2 ), pp. 207 – 37 . Google Scholar Crossref Search ADS WorldCat Chen , Y. and Rogoff , K. ( 2003 ). ‘ Commodity currencies ’, Journal of International Economics , vol. 60 ( 1 ), pp. 133 – 60 . Google Scholar Crossref Search ADS WorldCat Corden , W.M. ( 1984 ). ‘ Booming sector and Dutch disease economics: survey and consolidation ’, Oxford Economic Papers , vol. 36 ( 3 ), pp. 359 – 80 . Google Scholar Crossref Search ADS WorldCat Corden , W.M. and Neary , J.P. ( 1982 ). ‘ Booming sector and de‐industrialisation in a small open economy ’, Economic Journal , vol. 92 ( 368 ), pp. 825 – 48 . Google Scholar Crossref Search ADS WorldCat Dungey , M. , Fry‐McKibbin , R. and Linehan , V. ( 2014 ). ‘ Chinese resource demand and the natural resource supplier ’, Applied Economics , vol. 46 ( 2 ), pp. 167 – 78 . Google Scholar Crossref Search ADS WorldCat Eastwood , R. and Venables , A.J. ( 1982 ). ‘ The macroeconomic implications of a resource discovery in an open economy ’, Economic Journal , vol. 92 ( 366 ), pp. 285 – 99 . Google Scholar Crossref Search ADS WorldCat Furlanetto , F. , Sarferaz , S. and Ravazzolo , F. ( 2013 ). ‘Business cycles in commodity‐exporting countries’ , mimeo, Norges Bank. Garton , P. ( 2008 ). ‘ The resources boom and the two‐speed economy ’, Economic Round‐up , ( 3 ), pp. 17 . OpenURL Placeholder Text WorldCat Geweke , J. ( 1977 ). ‘The dynamic factor analysis of economic time series’, in ( D.J. Aigner and A.S. Goldberger, eds.), Latent Variables in Socio‐economic Models , pp. 365 – 83 , Amsterdam : North‐Holland . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Hamilton , J.D. ( 1983 ). ‘ Oil and the macroeconomy since World War II ’, Journal of Political Economy , vol. 91 ( 2 ), pp. 228 – 48 . Google Scholar Crossref Search ADS WorldCat Hamilton , J.D. ( 2009 ). ‘ Causes and consequences of the oil shock of 2007–08 ’, Brookings Papers on Economic Activity , vol. 40 ( 1 ), pp. 215 – 83 . Google Scholar Crossref Search ADS WorldCat Hutchison , M.M. ( 1994 ). ‘ Manufacturing sector resiliency to energy booms: empirical evidence from Norway, the Netherlands, and the United Kingdom ’, Oxford Economic Papers , vol. 46 , pp. 311 – 329 . Google Scholar Crossref Search ADS WorldCat Ismail , K. ( 2010 ). ‘ The structural manifestation of the “Dutch Disease”: the case of oil exporting countries ’, IMF Working Papers 10/103 . OpenURL Placeholder Text WorldCat Kilian , L. ( 2009 ). ‘ Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market ’, American Economic Review , vol. 99 ( 3 ), pp. 1053 – 69 . Google Scholar Crossref Search ADS WorldCat Kose , M.A. , Otrok , C. and Whiteman , C.H. ( 2003 ). ‘ International business cycles: world, region, and country‐specific factors ’, American Economic Review , vol. 93 ( 4 ), pp. 1216 – 39 . Google Scholar Crossref Search ADS WorldCat Krugman , P. ( 1987 ). ‘ The narrow moving band, the Dutch disease, and the competitive consequences of Mrs. Thatcher: notes on trade in the presence of dynamic scale economies ’, Journal of Development Economics , vol. 27 ( 1 ), pp. 41 – 55 . Google Scholar Crossref Search ADS WorldCat Lippi , F. and Nobili , A. ( 2012 ). ‘ Oil and the macroeconomy: a quantitative structural analysis ’, Journal of the European Economic Association , vol. 10 ( 5 ), pp. 1059 – 83 . Google Scholar Crossref Search ADS WorldCat Mumtaz , H. , Simonelli , S. and Surico , P. ( 2011 ). ‘ International comovements, business cycle and inflation: a historical perspective ’, Review of Economic Dynamics , vol. 14 ( 1 ), pp. 176 – 98. Google Scholar Crossref Search ADS WorldCat Neary , J.P. and van Wijnbergen , S. ( 1984 ). ‘ Can an oil discovery lead to a recession? A comment on Eastwood and Venables ’, Economic Journal , vol. 94 ( 374 ), pp. 390 – 5 . Google Scholar Crossref Search ADS WorldCat Peersman , G. and Van Robays , I. ( 2012 ). ‘ Cross‐country differences in the effects of oil shocks ’, Energy Economics , vol. 34 ( 5 ), pp. 1532 – 47 . Google Scholar Crossref Search ADS WorldCat Ratti , R.A. and Hasan , M.Z. ( 2013 ). ‘ Oil price shocks and volatility in Australian stock returns ’, Economic Record , vol. 89 , pp. 67 – 83 . Google Scholar Crossref Search ADS WorldCat Spatafora , N. and Warner , A.M. ( 1999 ). ‘Macroeconomic and sectoral effects of terms‐of‐trade shocks – the experience of the oil‐exporting developing countries’ , Working Paper No. 99/134, International Monetary Fund. Stock , J.H. and Watson , M.W. ( 2005 ). ‘Implications of dynamic factor models for VAR analysis’ , Working Paper No. 11467, National Bureau of Economic Research. Torvik , R. ( 2001 ). ‘ Learning by doing and the Dutch disease ’, European Economic Review , vol. 45 ( 2 ), pp. 285 – 306 . Google Scholar Crossref Search ADS WorldCat van Wijnbergen , S. ( 1984 ). ‘ The “Dutch Disease”: a disease after all? ’, Economic Journal , vol. 94 ( 373 ), pp. 41 – 55 . Google Scholar Crossref Search ADS WorldCat Author notes " The authors thank the Editor, Morten Ravn, two anonymous referees, Francesco Ravazzolo, Ragnar Torvik, and Benjamin Wong, as well as seminar and conference participants at the Reserve Bank of New Zealand, the IAAE 2014 annual conference in London, the CAMP‐CAMA Workshop on Commodities and the Macroeconomy at Australia National University and the CAMP Workshop on Commodity Price Dynamics and Financialisation in Oslo for valuable comments. This article is part of the research activities at the Centre for Applied Macro and Petroleum economics (CAMP) at the BI Norwegian Business School. The usual disclaimers apply. © 2015 Royal Economic Society
TI - Boom or Gloom? Examining the Dutch Disease in Two‐speed Economies
JF - The Economic Journal
DO - 10.1111/ecoj.12302
DA - 2016-12-01
UR - https://www.deepdyve.com/lp/oxford-university-press/boom-or-gloom-examining-the-dutch-disease-in-two-speed-economies-cSg0GRoQg0
SP - 2219
VL - 126
IS - 598
DP - DeepDyve
ER -