Master Thesis The Blessings of the Norwegian Resource Boom. An Input-Output approach

(1)

1

The Blessings of the Norwegian Resource Boom.

An Input-Output approach

H.W.D. van Vliet

S2244543

h.w.d.van.vliet@student.rug.nl

Master Thesis

Supervisor: T.M. Harchaoui, PhD

(2)

2

Abstract

This paper examines the effects of the resource boom in Norway over de period 2000-2011. This topic is relevant since the literature is still inconclusive about the mechanisms behind the so-called natural resource curse. Using input-output data we account for the direct and indirect input requirements of the

resource extraction sector in order to measure the size and composition of the resource economy and assess the extent to which the Norwegian economy experienced value added spillovers due to demand

for natural resources. We find that the economic contribution of resource related activities from other sectors increased from 2.1% of total nominal value added in 2000 to 3.7% in 2014.

(3)

3

Introduction

In recent decades, many countries experienced the so-called resource boom, a significant increase in the size of the natural resource sector, for instance due to the discovery of (new) gas fields and oil. The additional revenues and employment in the natural resource sector generated by this resource boom seem to be good news for those countries. However, research on the impact of such a resource boom suggests otherwise. Real exchange rate appreciation due to resource exports and the crowding out of other sectors such as manufacturing may affect the competitiveness of a country’s economy. Therefore, natural resource abundance may be a curse rather than a blessing, a phenomenon known as the Dutch Disease, named after similar developments in the Netherlands during the 1960s.

Numerous studies examined Dutch Disease (Van der Ploeg, 2011). Most of these research attempts however focused on partial equilibria, analyzing the link between the natural resource sector and only a single other sector, such as manufacturing, or they examined only one channel through which resource abundance may affect the domestic economy. Therefore, most empirical evidence is inconclusive, excluding possible important effects in play.

The strength of the approach in this thesis is the use of Input-Output tables. Input-Output tables take into account the interlinkages of all industries, which enables us to track both the direct and indirect effects of growth in sectors which are resource rich. This is a more comprehensive way to evaluate the issue compared to just focusing on the links between the resource rich sector and one or two other sectors, and it may provide new insights on the effects of a resource boom.

To perform our analysis we apply the same methodology as in Bishop and Rayner (2013), which used matrix calculations on Input-Output tables to study the ‘Resource Boom’ in Australia. The World Input-Output Database provides the necessary inputs for the analysis.

Our country of interest is Norway, a developed and resource-abundant country, which experienced a resource boom during the last decades. This country is particularly interesting since several studies suggest Norway, unlike many other countries, did not suffer from Dutch Disease.

(4)

4 resource economy of 24.9% and 25.6% respectively. The market services sector benefits most from demand for resources, with additional economic contributions to total nominal value added of 0.9% and 2.0% in 2000 and 2014 respectively, while manufacturing (resource related plus other) contributed 0.6% in 2000 and 0.8% in 2014. These findings of our input-output approach suggest that the interlinkages between sectors should be accounted for when analyzing the effects of a resource boom.

The thesis has the following structure: The first section discusses existing literature on Dutch Disease and other mechanisms in play during a resource boom. The research questions that we will answer in this thesis are also presented in section 1. Subsequently, the methodology used in this study is described in more detail. Section 3 discusses the data used to perform the analyses and provides an overview of descriptive statistics. The next section presents the results of our analyses. The last section concludes.

Literature review

In the last decades many countries experienced a ‘resource boom’, a substantial growth of the natural resource sector. These resource abundancies, however, appear to be harmful to the economies as a whole in many cases (Gylfason, 2000, 2001a, b; Leite and Weidmann, 1999; Papyrakis and Gerlagh,

2004; Rodriguez and Sachs, 1999; Sachs and Warner, 1995, 1997, 1999a). On the other hand, some countries manage to evade the ‘curse’ and profit from their richness in natural resources. In this section we first discuss several mechanisms that could be at work in those economies, starting with the Dutch Disease. Furthermore, we discuss existing literature on Norway in specific, since this is our country of interest.

The so-called Dutch Disease is the phenomenon in which natural-resource development, a ‘resource boom’, makes the rest of the economy less competitive. In the Netherlands, the discovery and development of natural gas industries in the 1960s, came with a period of real exchange rate appreciation. This resulted in a loss of competitiveness and contraction of traditional industries. Other sectors, such as manufacturing, are crowded out by the natural resource sector due to increasing factor prices. The term Dutch Disease has become a reference to similar experiences of such a phenomenon by other countries. Thus, if the resource sector does not exert positive productivity spillovers while the manufacturing sector does, the crowding out effect could reduce growth (Corden and Neary 1982, Krugman 1987, Matsuyama 1992, van Wijnbergen 1984).

(5)

5 Dutch disease is much more complex than explaining poor economic performance only by a loss of competitiveness of the traded sector due to a resource boom. The paper discusses several possible explanations for the natural resource curse. There is some support for the hypothesis that a resource boom leads to appreciation of the real exchange rate, contraction of the traded sector, and expansion of the nontraded sectors. Furthermore, cross-country evidence supports the hypothesis that there is a negative link between resources and economic growth. On the other hand, some studies suggest that resource abundance can be beneficial for countries with good institutions. However, countries with certain characteristics of the political environment, such as being nondemocratic, may experience a negative effect due to increased corruption induced by windfall gains in resource commodities. There is also some evidence suggesting that natural resources cause rent seeking and armed conflict. Ultimately, windfall gains due to natural resources could lead to unsustainable and unwise policies. In the

remainder of the section we discuss several studies on some of the issues mentioned in this paragraph. Observations suggest that over the last decades, resource-scarce countries experienced growth rates two or three times faster than resource rich countries (Auty, 2001). Compare for example the fast growing, resource scarce, East Asian countries to resource-abundant South American and African countries that experienced shrinking economies in the post-World War 2 era. On the other hand, the currency appreciation due to the resource boom in Australia was more than offset by an income- and activity boost due to high terms of trade and strong investment (Sheehan and Gergory, 2013). Therefore it is interesting to examine whether, and why, possessing abundant natural resources is a curse rather than a blessing.

Understanding this phenomenon is especially important for developing countries, since for many of them, natural resources form a significant part of their exports. However, there is no generally accepted explanation for the cause of the ‘curse of natural resources’, since the issue is hard to examine empirically.

(6)

6 Most studies typically focused on the interplay between the resource sector and only a single sector in the non-resource economy such as manufacturing or domestic output (see Hutchison (1994),

Bjornland (1998) and Dungey et al. (2014)). Their conclusion that effects of resource investment on domestic output are small may be to straightforward since the cross-sectional links between sectors in the economy are not accounted for. Beine et al. (2009) is also an example of such a partial equilibrium analysis. They pursue an econometric approach to estimate the magnitude of the Dutch Disease effect due to price/exchange rate changes, take into account the entanglement of the Canadian currency with the U.S. currency. They find that exchange rate developments related to Dutch Disease between 2002 and 2007 contributed for 42 per cent of the decline in manufacturing employment linked to exchange rate changes, while the weakness of the U.S. currency accounted for the remaining 58 percent. A weakness of the econometric approaches however is that they only identify a single source of sectoral reallocation of labor, such as windfall resource income in the study of Beine et al., ignoring the impact of productivity growth (or vice versa), instead of accounting for the quantified individual contributions of multiple determinants of sectoral resource allocations.

However, there are some general equilibrium analyses on this issue (for instance Iscan, 2014) which is more interesting in the light of the analysis in this thesis. In contrast to the econometric approach of Beine et al. (2009), Iscan (2014) pursues a model-based quantitative approach, which accounts for both sources of sectoral reallocation of labor. In his empirical study on the independent impacts of windfall income from natural resource exports and productivity growth on the changing share of employment in manufacturing, he finds that productivity growth plays a significant role in the decline of the

manufacturing employment share in Canada since 1960, but there is also a substantial contribution of windfall income from the resource sector boom during the 2000s to this decrease, following a large improvement of the terms of trade. In the small open economy model of Iscan (2014) there are two intermediating mechanisms important to both sources: the size and direction of labor reallocation across sectors are determined by differences in income elasticity of demand across the different consumption categories and non-unitary elasticity of substitution.

(7)

7 over the last decade), and both experienced a substantial growth in gross value added in the resource sector. Bjornland and Thorsrud (2014) use a simple theory model, based on the model of Torvik (2001), which takes into account direct productivity spillovers from the resource sector to the traded and non-traded sector. The model assumes there is learning by doing in the non-traded and non-non-traded sectors, and in addition, learning spillovers between these sectors. Furthermore, the model incorporates technology spillovers from the natural resource sector. In their analysis they study the commodity price changes and the windfall gains associated with resource booms separately, emphasizing the importance of making this distinction. They find that there are significant and positive productivity spillover effects on non-resource sectors from a booming resource sector. The construction, business services, and real estate sectors are most positively affected. A pitfall of this study however is that they only account for direct spillovers, while the indirect linkages of sectors may also be important. We will now focus mainly on Norway, since this country is central to the analysis in this thesis.

The development of Norway from having a relatively low GDP per capita compared to the OECD average during the 1950-70s towards having one of the highest GDP per capita in the world can be related to the growth of the natural resource sector according to Cappelen and Mjøset (2009). This contrasts many studies which conclude that natural resource abundancy is a curse rather than a blessing to economic growth. The paper argues that the implementation of the right policies and institutions may explain this success.

Holmøy and Heide (2005) arrive at a different conclusion when they examine whether the recent decline in manufacturing jobs in Norway is a symptom of Dutch Disease or an efficient response to income growth, new comparative advantages, and other actors linked to a sustainable growth path. Their main conclusion is that the wealth from Norwegian’s natural resources is not large enough to prevent the economy to suffer from Dutch Disease.

(8)

8

Research questions

Research Question 1: To what extent are the other sectors directly and indirectly linked to the resource extraction sector in terms of intermediate inputs required by the resource extraction sector?

To produce its output, the resource extraction sector also requires inputs from other sectors. We will examine the direct input requirements of the resource extraction sector. The next step is to account for the indirect input requirements, since most industries require inputs to produce the (intermediate) products they deliver to other sectors. It is worthwhile to quantify these effects in order to gain a better understanding of the impact of the resource extraction sector.

Research Question 2: What is the order of magnitude of the value added requirements of one dollar of resource output delivered to the final demand?

While it is useful to examine the direct and indirect input requirements, we are even more interested in the economic contribution of each sector to the resource economy. Therefore, for each sector we have to isolate the value added part of its gross output due to final demand for commodities.

Research Question 3: What is the real size of the resource economy considering the interlinkages of the resource extraction sector with other sectors?

(9)

9

Methodology

For our analysis we apply the same methodology as in Bishop and Rayner (2013). They use the structural links embedded in input-output (I-O) tables to quantify the links from demand for natural resources to activity in the rest of the economy. This section briefly discusses I-O tables and the use of multipliers.

If we assume that that the economy is divided into n categories, an Input-Output system of equations can be written as: 𝑋₁= 𝑍₁₁+ 𝑍₁₂+ ⋯ + 𝑍_1𝑗+ ⋯ 𝑍_1𝑛+ 𝑌₁𝑓 𝑋₂= 𝑍₂₁+ 𝑍₂₂+ ⋯ + 𝑍_2𝑗+ ⋯ 𝑍_2𝑛+ 𝑌₂𝑓 ⋮ 𝑋𝑖 = 𝑍𝑖1+ 𝑍𝑖2 + ⋯ + 𝑍𝑖𝑗 + ⋯ 𝑍𝑖𝑛+ 𝑌𝑖𝑓 ⋮ 𝑋_𝑛= 𝑍_𝑛1+ 𝑍_𝑛2+ ⋯ + 𝑍_𝑛𝑗+ ⋯ 𝑍_𝑛𝑛+ 𝑌_𝑛𝑓 (1)

where 𝑋𝑖 stands for the total gross output of industry i (in dollars), 𝑌𝑖𝑓is the final demand for industry i's

product, and 𝑍𝑖𝑗 represents the intermediate input sales from industry i to industry j.

The right hand side of each row shows the sales (as intermediate inputs) from industry i to all other industries and to final demand. Final demand includes household consumption, public demand, investment, changes in inventories, and exports. The columns provide the sales of intermediate inputs to industry j. Therefore, the distribution of industry i’s output is given by row i, while the inputs of industry j and its sources are represented by column j.

Direct Input Requirements

The direct requirements matrix provides the value of intermediate inputs required to produces $1 of industry output. To calculate the direct input requirements matrix we divide the 𝑍𝑖𝑗 terms, which

represent the inter-industry flows of input and output, by the output of industry j to define the technical coefficient:

𝑎𝑖𝑗 =

(10)

10 where 𝑎𝑖𝑗 is de value of intermediate inputs required by industry j from industry i to produce $1 of gross

output in industry j. The next step is to re-write the I-O system of equations in matrix form:

𝐗 = 𝐀𝐗 + 𝐘𝐟 (2)

Where X and 𝒀𝒇are n x 1 vectors and A is the n x n matrix of technical coefficients:

𝐀 = [ 𝑎₁₁ 𝑎₁₂

⋯

𝑎21 𝑎22

⋯

⋮

𝑎_1𝑗

⋯

𝑎_1𝑛 𝑎_2𝑗

⋯

𝑎_2𝑛

⋮

𝑎_𝑖1 𝑎_𝑖2

⋯

⋮

𝑎_𝑛1 𝑎_𝑛2

⋯

𝑎_𝑖𝑗

⋯

𝑎_𝑖𝑛

⋮

𝑎_𝑛𝑗

⋯

𝑎_𝑛𝑛]

Matrix A shows the value of the intermediate inputs required by industry j from industry i to produce $1 of gross output in industry j.

Total Requirements Matrix

The total requirements matrix provides the gross output multipliers. This matrix takes into account the additional rounds of production linkages between sectors. This is useful since the industries that supply inputs to the resource extraction sector also require inputs from other industries etc. For this purpose, we change Equation (2) in:

𝐗 = (𝐈 − 𝐀)−𝟏𝐘𝐟 (3)

where I is the identity matrix and (𝑰 − 𝑨)−𝟏 is the total requirements matrix. Therefore the formula for

the so called Leontief matrix is:

𝐌 = (𝐈 − 𝐀)−𝟏

GVA Requirements Matrix

The gross value added (GVA) matrix shows the gross output from industry i to industry j, less the intermediate inputs that it uses to produce that output. This enables us to calculate the economic contribution, rather than just gross output, of the industries to the resource economy.

(11)

11 𝐆𝐕𝐀 = 𝐕𝐗 = 𝐕(𝐈 − 𝐀)−𝟏𝐘𝐟 (4)

where𝐕 = [ 𝑣₁ 0 ⋯ 0 𝑣₂ ⋯ ⋮ ⋮ ⋮ 0 ⋯ 0 0 ⋯ 0 ⋮ ⋮ ⋮ 0 0 ⋯ ⋮ ⋮ ⋮ 0 0 ⋯ 𝑣𝑖 ⋯ 0 ⋮ ⋮ ⋮ 0 ⋯ 𝑣_𝑛] and 𝑣𝑖 =𝐺𝑉𝐴_𝑋 𝑖 𝑖

Since our imports by industry data does not distinguish between intermediate and final imports, we adjust Equation (4) so that the formula for the GVA matrix becomes:

𝐆𝐕𝐀 = 𝐕(𝐈 − 𝐀 − 𝐁)−𝟏𝐘 (5) where Y = C + I + G + ΔInv + EX - M and M = 𝐌𝐢𝐧𝐭 + 𝐌𝐟 (i.e. intermediate + final imports),

𝐁 = [ 𝑏₁₁ 𝑏₁₂

⋯

𝑏₂₁ 𝑏₂₂

⋯

⋮

𝑏_1𝑗

⋯

𝑏_1𝑛 𝑏_2𝑗

⋯

𝑏_2𝑛

⋮

𝑏𝑖1 𝑏𝑖2

⋯

⋮

𝑏_𝑛1 𝑏_𝑛2

⋯

𝑏_𝑖𝑗

⋯

𝑏_𝑖𝑛

⋮

𝑏_𝑛𝑗

⋯

𝑏_𝑛𝑛_] , 𝑏𝑖𝑗= 𝑚𝑖𝑗𝑖𝑛𝑡 𝑋𝑖

and 𝑚𝑖𝑗𝑖𝑛𝑡 is the value of imported intermediate sales from industry i to industry j. Matrix B is the direct

requirements matrix for the imports. To calculate the GVA requirements matrix we use the formula:

𝐕(𝐈 − 𝐀 − 𝐁)−𝟏

Size and Composition of the Resource Economy

(12)

12

Data and Descriptive Statistics

To perform the analysis, Output tables on the Norwegian economy are required. The World Input-Output Database (WIOD) November 2016 Release based on the work of Timmer et al. (2015) gives the necessary Input-Output data for Norway. Its databases cover 43 countries, including 28 EU countries and 15 other major countries, for the period 2000-2014. Economic activity is assigned to one of 56 industries such as Mining and quarrying, Construction, Water transport or Postal and courier activities.

Based on the definitions of Bishop and Rayner (2013), I assigned the following industry to the ‘Resource extraction’ category:

- Mining and Quarrying.

The three industries most closely related to the resource extraction sector, are allocated to the ‘Resource manufacturing’ category:

- Manufacture of coke and refined petroleum products. - Manufacture of other non-metallic mineral products. - Manufacture of basic metals.

(13)

13 Sources: wiod.org; author´s calculations.

Direct requirements

The direct requirements matrix for 2000 and 2014 is presented in table 1 and 2 respectively. Each column shows the required intermediate inputs for every $1 of output for that particular sector, while the source sectors of these intermediate inputs are in the rows. For instance, column 2 in table 1 indicates that for every $1 of output of Resource manufacturing, ca. $0.13 worth of inputs is required from the industry itself (row 2), while the Market services sector supplies ca. $0.17 worth of inputs (row 5). Column 1 shows that the resource extraction sector uses few intermediate inputs to produce its output, which is what we would expect since its products are mainly commodities. It is also clear from the tables that most sectors make substantially use of some form of market services in order to produce their industry output. Most notable is the increase of required intermediate inputs from the resource extraction sector by the resource manufacturing sector between 2000 and 2014, which rose from $0.05 (Table 1, column 2, row 1) to $0.26 (Table 2, column 2, row 1). Figure 2 presents the graph with the changes in direct input requirements for the resource extraction sector. We see a sharp increase in required input requirements following the economic crisis in 2008.

0% 5% 10% 15% 20% 25% 30% 35% 2000 2008 2014

Figure 1: Share of nominal Value Added across sectors

(14)

14 Table 1: Direct Requirements Matrix - Norway

Value of intermediate inputs required for every $1 of industry output - 2000 Resource extraction Resource manufact uring Construction Other manufact uring Market services Transport Household and public services Other industries Resource extract. 0.00 0.05 0.00 0.01 0.00 0.00 0.00 0.00 Resource manufact. 0.00 0.13 0.04 0.02 0.00 0.03 0.00 0.01 Construction 0.00 0.00 0.13 0.00 0.01 0.01 0.03 0.02 Other manufacturing 0.03 0.04 0.11 0.14 0.06 0.05 0.03 0.08 Market services 0.03 0.17 0.14 0.15 0.24 0.14 0.12 0.11 Transport 0.01 0.04 0.03 0.04 0.03 0.09 0.02 0.01

Household and pub. 0.00 0.01 0.01 0.01 0.01 0.10 0.02 0.01

Other industries 0.00 0.05 0.01 0.13 0.01 0.01 0.02 0.07

Total (exl imports) 0.08 0.48 0.47 0.51 0.37 0.44 0.23 0.31

Imported inputs 0.02 0.21 0.11 0.14 0.06 0.14 0.04 0.06

Total (incl imports) 0.10 0.70 0.59 0.65 0.44 0.57 0.27 0.37

Sources: wiod.org; author´s calculations.

Table 2: Direct Requirements Matrix - Norway

Value of intermediate inputs required for every $1 of industry output - 2014 Resource extraction Resource manufact uring Construction Other manufact uring Market services Transport Household and public services Other industries Resource extract. 0.01 0.26 0.01 0.06 0.00 0.01 0.00 0.01 Resource manufact. 0.01 0.05 0.02 0.01 0.00 0.03 0.00 0.01 Construction 0.00 0.00 0.21 0.00 0.02 0.00 0.04 0.02 Other manufacturing 0.03 0.04 0.07 0.16 0.03 0.06 0.02 0.08 Market services 0.07 0.12 0.15 0.14 0.24 0.14 0.11 0.12 Transport 0.01 0.03 0.02 0.03 0.02 0.12 0.01 0.01

Imported inputs 0.05 0.23 0.10 0.16 0.08 0.20 0.04 0.08

(15)

15 Sources: wiod.org; author´s calculations

Total requirements

The total requirements matrix for Norway in 2000 and 2014 is presented in table 3 and 4 respectively. In contrast to the direct requirements matrix, it also accounts for additional input requirements (sector A needs inputs from sector B, which in turn requires inputs from sector A, C, and D etc.). The columns show the value of intermediate inputs required from the row industry for $1 of final demand for the output of the column industry. For example, in 2000, $1.13 of gross output was generated by all industries for every $1 of final demand for the output of the resource extraction sector (table 3, column 1, row 9). Close to $1 by the resource extraction sector itself, while the remaining $0.13 is produced by the other sectors. Furthermore, the gross output multiplier for the resource extraction sector can be separated in three components:

- The initial effect: $1 of gross output from the resource extraction sector is required to provide $1

of final demand for the output in the resource extraction sector.

- The first round effect: the value of intermediate inputs required from all industries in order to

produce the initial $1 of gross output (in table 1, column 1, row 9: $0.08).

0.00 0.05 0.10 0.15 0.20 0.25 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 2: Direct Intermediate Input Requirements

Norway

Resource extraction sector

(16)

16 - The industrial support effect: the extra output resulting from all industries having to produce the

first round of intermediate inputs ($1.13 (table 3, column 1, row 9) - $1.00 (initial effect) - $0.08 (table 1, column 1, row 9) = $0.05).

The tables indicate that market services and manufacturing are the most imported suppliers of inputs for the resource extraction sector. The market sector supplies $0.05 worth of total inputs in 2000 and $0.11 in 2014, while these values are $0.04 and $0.05 for non-resource manufacturing.

However, the gross output multipliers exceed 1 since intermediate inputs are double counted. In our calculations of the gross value added multipliers we control for this.

Table 3: Total Requirements Matrix - Norway

Value of intermediate inputs required for every $1 of final demand for industry output - 2000 Resource extraction Resource manufact uring Construction Other manufact uring Market services Transport Household and public services Other industries Resource extract. 1.00 0.06 0.01 0.02 0.00 0.01 0.00 0.01 Resource manufact. 0.01 1.16 0.06 0.04 0.01 0.04 0.01 0.02 Construction 0.00 0.01 1.15 0.01 0.02 0.02 0.03 0.02 Other manufacturing 0.04 0.08 0.18 1.21 0.10 0.10 0.06 0.12 Market services 0.05 0.31 0.29 0.30 1.37 0.26 0.19 0.20 Transport 0.01 0.07 0.06 0.07 0.06 1.12 0.03 0.03

Imported inputs 1.02 1.26 1.13 1.16 1.07 1.16 1.04 1.07

Table 4: Total Requirements Matrix - Norway

Value of intermediate inputs required for every $1 of final demand for industry output - 2014 Resource extraction Resource manufact uring Construction Other manufact uring Market services Transport Household and public services Other industries Resource extract. 1.01 0.28 0.03 0.08 0.01 0.02 0.01 0.02 Resource manufact. 0.01 1.05 0.04 0.02 0.01 0.03 0.01 0.01 Construction 0.00 0.01 1.27 0.02 0.03 0.01 0.05 0.03 Other manufacturing 0.05 0.08 0.13 1.22 0.06 0.10 0.04 0.12 Market services 0.11 0.24 0.30 0.27 1.36 0.24 0.18 0.22 Transport 0.02 0.06 0.04 0.05 0.04 1.15 0.02 0.03

Imported inputs 1.06 1.27 1.12 1.19 1.09 1.24 1.05 1.10

(17)

17

Gross value added

The following tables (5 and 6) show the Gross Value Added (GVA) requirements matrix for Norway in 2000 and 2014. Each column shows the amount of GVA generated by the row industries for every $1 of final demand for the output of the column industry. In both years the GVA generated by the resource extraction sector due to its own demand is, except for the market services sector in 2014, the highest among all industries: $0.91 (table 5, column 1, row 1) and $0.82 (table 6, column 1, row 1) per $1 of final demand for industry output in 2000 and 2014 respectively. The graph in figure 3 shows the value of GVA generated outside the resource extraction sector for every $1 of final demand for the output of the resource extraction industry over the period 2000-2014. The GVA spillovers to other sectors increased over the period 2000-2014 from $0.09 to $0.17 per $1 of final demand for commodities, which indicates that the rest of the economy increasingly benefits from the extraction of natural resources in relative terms. The market services sector benefits the most with an economic contribution of $0.04 in 2000 and $0.09 in 2014. This indicates an increasing reliance of the resource extraction sector on all kinds of services in order to operate.

Table 5: GVA Requirements Matrix - Norway

Value of GVA generated for every $1 of final demand for industry output - 2000 Resource extraction Resource manufact uring Construction Other manufact uring Market services Transport Household and public services Other industries Resource extract. 0.91 0.10 0.02 0.04 0.01 0.02 0.01 0.01 Resource manufact. 0.00 0.40 0.03 0.03 0.01 0.03 0.01 0.01 Construction 0.00 0.01 0.47 0.01 0.01 0.01 0.01 0.01 Other manufacturing 0.02 0.07 0.12 0.47 0.07 0.07 0.04 0.07 Market services 0.04 0.25 0.23 0.23 0.81 0.20 0.13 0.15 Transport 0.01 0.04 0.03 0.04 0.03 0.48 0.02 0.02

Total 1.00 0.97 0.99 0.98 0.99 0.99 1.00 0.99

(18)

18 Table 6: GVA Requirements Matrix - Norway

Value of GVA generated for every $1 of final demand for industry output - 2014 Resource extraction Resource manufact uring Construction Other manufact uring Market services Transport Household and public services Other industries Resource extract. 0.82 0.31 0.05 0.09 0.02 0.05 0.01 0.03 Resource manufact. 0.01 0.24 0.01 0.01 0.00 0.02 0.00 0.01 Construction 0.00 0.01 0.49 0.01 0.01 0.01 0.02 0.01 Other manufacturing 0.03 0.07 0.10 0.47 0.05 0.08 0.03 0.08 Market services 0.09 0.23 0.25 0.24 0.84 0.23 0.14 0.18 Transport 0.01 0.04 0.03 0.04 0.03 0.55 0.02 0.02

Total 0.99 0.98 0.99 0.98 0.99 0.99 1.00 0.99

Notes: Value of GVA generated outside the resource extraction sector for every $1 of final demand for the output of the resource extraction sub-industries. Sources: wiod.org; author´s calculations.

Figure 4 presents Final Demand for the resource extraction sector as a share of nominal GVA over de period 2000-2014. Its final demand was at the lowest in 2003 being 18.7% of nominal GVA, while its highest relative share was 26.2% in 2008. The export share of resource commodities contracted in 2009

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

(19)

19 to 18.5%, coming from 24.8% in 2008. This is probably due to the financial crisis. Figure 5 shows an increase in natural resource investment from ca 0.5% of nominal GVA in 2000 to over 2% in 2014. Figure 6 provides the same data for resource related manufacturing, in which the investment share moves between 3.5 and 6 percent.

Notes: All data are measured in basic prices; final demand is domestic final demand plus exports less imports. Sources: wiod.org; authors’ calculations

-5.00% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 4: Final Demand – Resource Extraction

Share of nominal GVA, financial year, Norway

(20)

20 Notes: All data are measured in basic prices; imports have negative values since they are subtracted from

total investment; source: wiod.org; authors’ calculations

Notes: All data are measured in basic prices; imports have negative values since they are subtracted from total investment; source: wiod.org; authors’ calculations

-0.50% 0.00% 0.50% 1.00% 1.50% 2.00% 2.50% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 5: Final Demand - Resource Invesment

Share of nominal GVA, financial year, Norway

Resource Extraction

Domestic content of investment Capital imports Total investment

-2.00% -1.00% 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 6: Final Demand - Resource Investment

Share of nominal GVA, financial year, Norway

Resource Manufacturing

(21)

21

Results

First we examine the size of the resource economy as a share of nominal GVA. Figure 7 shows that the share of the resource economy varied roughly between 20% and 30% over de period 2000-2014. Following the economic crisis in 2008, the resource economy experienced a sharp decline of ca. 6% (from 30% in 2008 to 24% in 2009) in its relative size. Most of the volatility in the total relative size is due to changes in the gross value added (GVA) as share of nominal GVA generated by the resource extraction sector. The share of GVA of the resource economy generated by the other sectors due to final demand of the resource extraction sector steadily increased from 1.9% in 2000 to 3.3% in 2014. This resource related GVA is a substantial addition to the relative size of the resource economy.

Notes: All data are measured in basic prices; source: wiod.org; authors’ calculations

Tables 7 and 8 provide more insight in the composition of the resource economy at the beginning and at the end of our period. The contribution of resource related GVA, which is GVA generated by the other sectors due to final demand for the output of the resource extraction sector, is decomposed for those sectors. In both years the market services sector appears to benefit the most from final demand for commodities. This sector accounted for ca. 43% (0.9 percentage point of 2.1 percent) of the resource related GVA in 2000. In 2014 this share was even higher, being ca. 54% (2.0 percentage point of 3.7

0% 5% 10% 15% 20% 25% 30% 35% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 7: GVA Resource Economy - % nominal GVA

Norway

(22)

22 percent). This outcome is what we would expect since the resource extraction ‘extracts’ rather than ‘manufactures’ (using intermediate inputs) its commodities, while it does require administrative, communication, legal and accounting services, and all kinds of other services in order to carry out its activities. Figure 8 shows the development of resource-related GVA as a share of nominal GVA for the different sectors over the whole period. The graph indicates that the share of GVA due to final demand for commodities increased most of the period, falling back a little after reaching its highest point in 2010. The graph in Figure 9, which focuses only on the investment part of final demand, shows a similar trend, although the peak of relative GVA is now in 2009. Appendix B presents the results for the composition of the resource economy using a different definition.

Table 7: Industry Composition of the Resource Economy - Norway

Estimated share of nominal GVA, per cent - 2000

Resource Economy: 24.9% of which: Resource Extraction 22.8% Resource Related 2.1% of which: Resource manufacturing 0.1% Construction 0.0% Other manufacturing 0.5% Market services 0.9% Transport 0.2%

Household and public services 0.2%

Other industries 0.2%

Table 8: Industry Composition of the Resource Economy - Norway

(23)

23 Notes: All data are measured in basic prices; source: wiod.org; authors’ calculations

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 8: GVA-Related to Resource Extraction - Norway

Share of nominal GVA, financial year

(24)

24 Notes: All data are measured in basic prices; source: wiod.org; authors’ calculations

Using a narrower definition of the resource economy, excluding intermediateand final sales of resources to the domestic economy, enables us to only capture the effects of changes in external demand (resource exports and resource investment). We included all resource investment in the narrower measure since we assumed, just as in Bishop and Rayner (2014), that all resource investment is linked to future resource exports. Figure 11 provides the results. Since external demand accounts for most of the final demand, the difference between the broad and narrow definition is not very spectacular. The largest difference (3.2 percentage point) occurs in 2013, with a share of 24.4% and 27.6% for the narrow and broad resource economy respectively. Inclusion of resource manufacturing in the resource extraction sector may provide a more interesting pattern, since the share of external demand in final demand is smaller for resource manufacturing. 0.00% 0.05% 0.10% 0.15% 0.20% 0.25% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 9: GVA-Related to Resource Investment - Norway

Share of nominal GVA, financial year

(25)

25 Notes: All data are measured in basic prices; World Bank; authors’ calculations

Conclusion

The goal and contribution of this thesis was to quantify the direct and indirect input requirements for the resource extraction sector, to find the order of magnitude of the economic contribution for each sector in the production of output for the resource extraction sector due to final demand, and to measure the real size and composition of the resource economy in terms of gross value added. In order to do so, we used an input-output approach, which has the advantage of taking into account the interlinkages between sectors.

We find a substantial economic contribution of resource related activities: taking into account the interlinkages between sectors allocates almost 4% of additional nominal value is added to the resource economy in the most recent years of our results. Its share increased from 2.1% in 2000 to 3.7% in 2014, resulting in a total size of the resource economy of 24.9% and 25.6% respectively. The market services sector benefits most from demand for natural resources in terms of nominal value added, followed by manufacturing.

Our findings indicate that it is important to account for the effects of the interlinkages of sectors when examining Dutch disease. The additional value added generated by other sectors due to demand

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure 11: GVA - Resource Economy - Norway

Share of nominal GVA, financial year

(26)

26 for natural resource is, depending on the order of magnitude, offsetting the negative effects more or less, and should therefore be included in studies on the impact of resource abundance on a country’s economy.

One of the limitations of this paper is the fact that the Output tables from the World Input-Output Database only include one broad resource extraction sector: mining & querying. Therefore it was not possible to examine whether there are differences between specific natural resources (i.e. gas or petroleum) in terms of impact on the rest of the economy. I would suggest to use more detailed input-output data to measure the separate effects. It may also be interesting to break down the composition of the economic contribution of the market services sector to the resource economy in order to find which particular services are responsible for the increase of this economic contribution.

Currently, data on the real price changes and employment figures is not yet available for the 2016 release of the World Input Output Database, which is another limitation. Data on real price changes would enable us to measure the real growth of the resource economy and compare it to the real growth of the rest of the economy. Insights in the employment related to the resource economy, in terms of size and sectoral distribution, and its developments over time would be a valuable addition.

Furthermore, I used National Input-Output Tables (NIOT) for Norway, which enables us to gain valuable insight on the interplay between the resource extraction sector and the rest of the domestic economy. In terms of final demand however, these tables only provide general export data per sector. It could be interesting to use the World Input-Output Tables to study for example the bilateral effects of changes in the size of the resource extraction sector in one of the countries on all the industries in both countries.

(27)

27

Literature list

Allcot, H., Keniston, D. (2014), DUTCH DISEASE OR AGGLOMERATION? THE LOCAL ECONOMIC EFFECTS OF NATURAL RESOURCE BOOMS IN MODERN AMERICA. NBER Working Paper No. 20508 September 2014

Beine, M., Bos, C., Coulombe, S. (2009), "Does the Canadian economy suffer from Dutch Disease"

Cappelen, A., Mjøset, L. (2009), Can Norway Be a Role Model for Natural Resource Abundant Countries? Research Paper No. 2009/23

Corden, W. M. (1984), Booming sector and Dutch disease economics: Survey and consolidation. Oxford Economic Papers 36 (3), 359-380.

Gylfason, T. (2000), Resources, agriculture, and economic growth in economies in transition. Kyklos 53, 545–580.

Gylfason, T. (2001a), Natural resources, education, and economic development. European Economic Review 45,847–859.

Gylfason, T. (2001b). Nature, power and growth. Scottish Journal of Political Economy 48, 558–588.

Holmøy, E., Heide, K.M. (2005), Is Norway immune to Dutch Disease? CGE Estimates of Sustainable Wage Growth and Deindustrialisation. Discussion Papers No. 413, March 2005 Statistics Norway, Research Department

Leite, C., Weidmann, J. (1999), Does mother nature corrupt? Natural resources, corruption and economic growth. IMF Working Paper No. 99/85, International Monetary Fund, Washington, DC.

(28)

28 Van der Ploeg, F. (2011), Natural Resources: Curse or Blessing? Journal of Economic Literature 2011, 49:2, 366–420.

Rayner, V., Bishop, J. (2013), Industry Dimensions ofthe Resource Boom:An Input-Output Analysis. RDP 2013-02.

Rodriguez, F., Sachs, J.D. (1999), Why do resource-abundant economies grow more slowly? Journal of Economic Growth 4, 277–303.

Sachs, J.D., Warner, A.M. (1995), Natural resource abundance and economic growth, NBER Working Paper No. 5398. National Bureau of Economic Research, Cambridge, MA.

Sachs, J.D., Warner, A.M. (1997), Fundamental sources of long-run growth. American Economic Review 87,184–188.

Sachs, J.D., Warner, A.M., (1999a), The big push, natural resource booms and growth. Journal of Development Economics 59, 43–76.

Sheehan, P., Gregory, R.G. (2013), The Resources Boom and Economic Policy in the Long Run, The Australian Economic Review, vol. 46, no. 2, pp. 121–39.

Talan, I. (2014.. "Windfall Resource Income, Productivity Growth, and Manufacturing Employment"

Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R. and de Vries, G. J. (2015), "An Illustrated User Guide to the World Input–Output Database: the Case of Global Automotive Production", Review of International

Economics., 23: 575–605

Torvik, R. (2001), Learning by doing and the Dutch disease. European Economic Review 45 (2), 285-306.

(29)

29

Appendix A

Table 1: Industry allocation using WIOD 2016 release

Category Industry codes*

Resource extraction B Resource manufacturing C19, C20, C23, C24 Construction F Other manufacturing C10-C12, C13-C15, C16, C17, C18, C21, C22, C25, C26, C27, C28, C29, C30, C31_C32, C33 Market services G45, G46, G47, H53, I, J58, J59_J60, J61, J62_J63, K64, K65, K66, K68, M69_M70, M71, M72, M73, M74_M75, N Transport H49, H50, H51, H52

Household and public services O84, P85, Q, R_S, T, U

Other industries A01-A03, D35, E36, E37-E39

Source: wiod.org; *industry specifications are given in table 2.

Table 2: Industries WIOD 2016 release

Industry Code

Crop and animal production, hunting and related service activities A01

Forestry and logging A02

Fishing and aquaculture A03

Mining and quarrying B

Manufacture of food products, beverages and tobacco products C10-C12 Manufacture of textiles, wearing apparel and leather products C13-C15 Manufacture of wood and of products of wood and cork, except furniture; manufacture

of articles of straw and plaiting materials

C16

Manufacture of paper and paper products C17

Printing and reproduction of recorded media C18

Manufacture of coke and refined petroleum products C19

Manufacture of chemicals and chemical products C20

Manufacture of basic pharmaceutical products and pharmaceutical preparations C21

Manufacture of rubber and plastic products C22

Manufacture of other non-metallic mineral products C23

Manufacture of basic metals C24

Manufacture of fabricated metal products, except machinery and equipment C25

Manufacture of computer, electronic and optical products C26

(30)

30

Manufacture of machinery and equipment n.e.c. C28

Manufacture of motor vehicles, trailers and semi-trailers C29

Manufacture of other transport equipment C30

Manufacture of furniture; other manufacturing C31_C32

Repair and installation of machinery and equipment C33

Electricity, gas, steam and air conditioning supply D35

Water collection, treatment and supply E36

Sewerage; waste collection, treatment and disposal activities; materials recovery; remediation activities and other waste management services

E37-E39

Construction F

Wholesale and retail trade and repair of motor vehicles and motorcycles G45

Wholesale trade, except of motor vehicles and motorcycles G46

Retail trade, except of motor vehicles and motorcycles G47

Land transport and transport via pipelines H49

Water transport H50

Air transport H51

Warehousing and support activities for transportation H52

Postal and courier activities H53

Accommodation and food service activities I

Publishing activities J58

Motion picture, video and television programme production, sound recording and music publishing activities; programming and broadcasting activities

J59_J60

Telecommunications J61

Computer programming, consultancy and related activities; information service activities J62_J63 Financial service activities, except insurance and pension funding K64 Insurance, reinsurance and pension funding, except compulsory social security K65 Activities auxiliary to financial services and insurance activities K66

Real estate activities L68

Legal and accounting activities; activities of head offices; management consultancy activities

M69_M70 Architectural and engineering activities; technical testing and analysis M71

Scientific research and development M72

Advertising and market research M73

Other professional, scientific and technical activities; veterinary activities M74_M75

Administrative and support service activities N

Public administration and defence; compulsory social security O84

Education P85

Human health and social work activities Q

Other service activities R_S

Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use

T

Activities of extraterritorial organizations and bodies U

(31)

31

Appendix B

Alternative definition of resource extraction and resource related GVA

In Figure A and B GVA from the resource extraction sector is defined as the GVA produced in the resource extraction sector itself, due to final demand for the output of all sectors. Resource related GVA is defined as GVA generated by the other sectors due to final demand for the output of the resource extraction sector. The total size of the resource economy is the same under the different definitions. As Figure B shows, the market services sector benefits most from demand for the resource extraction sector. Figure C presents the findings for GVA generated by investment. Table A and B show the composition of the resource economy in 2000 and 2014.

0% 5% 10% 15% 20% 25% 30% 35% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure A: GVA Resource Economy - % nominal GVA

Norway

Alternative calculations

(32)

32 0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure B: GVA-Related to Resource Extraction - Norway

Share of nominal GVA, financial year

Resource manufacturing Construction Other manufacturing Market services Transport Household and public services Other industries 0.0% 0.1% 0.1% 0.2% 0.2% 0.3% 0.3% 0.4% 0.4% 0.5% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Figure C: GVA-Related to Resource Investment - Norway

Share of nominal GVA, financial year

(33)

33

Table A: Industry Composition of the Resource Economy - Norway

Table B: Industry Composition of the Resource Economy - Norway