MASTER'S THESIS. Local Job Multiplier of the Mining Industry in Northern Sweden. An Econometric Study. Thomas Moritz 2015

Transcription

1 MASTER'S THESIS Local Job Multiplier of the Mining Industry in Northern Sweden An Econometric Study Thomas Moritz 2015 Master of Science in Business and Economics (Civilekonom) Business and Economics Luleå University of Technology Department of Business Administration, Technology and Social Sciences

2 Local job multiplier of the mining industry in northern Sweden An econometric study Thomas Moritz Luleå University of Technology Economic Unit SE Luleå, Sweden

3 ABSTRACT The purpose of this study is to assess the effects of the mining industry in northern Sweden by examining the job multiplier. The multiplier shows how many jobs that are created in other sectors in the region, as one new job in the mining industry is created. The job multiplier will be estimated for different parts of northern Sweden. To estimate the job multiplier an econometric model introduced by Moretti (2010) will be applied. Data on the number of employees in different sectors in northern Sweden was collected from the Central Bureau of Statistics in Sweden. The result shows that between and new jobs in the private service sector is created for each new job in the mining sector. The results vary depending on which municipalities in northern Sweden that are included. It also shows that totally between and new jobs are created for each new mining job, when all sectors are combined into one sector. Finally, the study shows that municipalities with mining employees are more affected by the mining industry than municipalities without mining workers.

4 SAMMANFATTNING Syftet med den här studien är att analysera effekterna av gruvindustrin i norra Sverige genom att undersöka hur många jobb som skapas i övriga samhället då ett nytt jobb inom gruvindustrin skapas. Studien kommer att undersöka detta för olika delar av norra Sverige. För att genomföra studien valdes en ekonometrisk modell för att estimera jobb multiplikatorn. Data över antalet anställda i olika sektorer i norra Sverige hämtades in från statistiska centralbyrån i Sverige. Resultaten visar att det skapas mellan 0,492 och 0,662 nya jobb inom privat servicesektor för varje nytt jobb i gruvsektorn, beroende på vilka kommuner i norra Sverige som inkluderas. Resultaten visar även att det totalt skapas mellan 0,852 och 0,989 nya jobb för varje nytt jobb i gruvan till alla sektorer. Slutligen så visar studien att kommuner med anställda inom gruvan påverkas mer av gruvindustrin än kommuner utan anställda i gruvindustrin.

5 ACKNOWLEDGEMENT I would like to express my gratitude to my supervisor Linda Wårell for her valuable comments and engagement throughout the whole process of this thesis. I also want to thank everyone in my environment that supported me and have given me valuable comments throughout the process.

6 TABLE OF CONTENTS CHAPTER 1 INTRODUCTION BACKGROUND PURPOSE METHODOLOGY SCOPE AND LIMITATIONS OUTLINE... 3 CHAPTER 2 MINING INDUSTRY IN SWEDEN HISTORICAL DEVELOPMENT MARKET STRUCTURE COMMODITY BOOM DEVELOPMENT IN NORTHERN SWEDEN... 7 CHAPTER 3 LITERATURE REVIEW SEARCH STRATEGY PREVIOUS LITERATURE Input-output model Econometric model CONCLUSIONS BASED ON THE LITERATURE REVIEW CHAPTER 4 THEORETICAL FRAMEWORK STAPLE THEORY JOB MULTIPLIER Multiplier for the non-tradable sector Multiplier for the tradable sector DISCUSSION CHAPTER 5 METHODOLOGY METHODS TO ESTIMATE THE LOCAL JOB MULTIPLIER ECONOMETRIC SPECIFICATION CHAPTER 6 DATA SOURCES EMPLOYMENT DATA CHAPTER 7 EMPIRICAL RESULTS AND ANALYSIS EMPIRICAL RESULTS... 29

7 7.2 ANALYSIS CHAPTER 8 CONCLUDING REMARKS REFERENCES... 38

8 LIST OF TABLES AND FIGURES Table 1: Production of metals 5 Figure 1: Iron ore monthly price in US dollars per dry metric ton 7 Table 2: Overview of Relevant Literature 13 Figure 2: The raps-model 20 Table 3: Number of jobs in Norrbotten and Västerbotten Table 4: Population Norrbotten and Västerbotten Table 5: Number of jobs in northern Sweden div in sectors Table 6: Number of jobs in Norrbotten and Västerbotten Table 7: Number of jobs in mining industry Table 8: Elasticity and job multiplier combined sector 30 Table 9: Job multiplier in all municipalities in northern Sweden 31 Table 10: Job multiplier in municipalities with mining workers 31 Table 11: Elasticity and job multiplier in Norrbotten 32 Table 12: Elasticity and job multiplier in Västerbotten 32

9 CHAPTER 1 INTRODUCTION 1.1 Background The mining industry is an important industry for Sweden, which can be exemplified by the former Prime Minister Fredrik Reinfeldt s statement that the mining industry for Sweden is what the oil industry is for Norway (SvD, 2012). Even if this might be slightly exaggerated, as the oil industry in Norway represents a much larger share of the country s economy, this shows that the mining industry is considered as a very important industry for Sweden. In Europe, Sweden is a large mining country and especially so regarding iron ore where 90% of the production comes from Sweden (SGU, 2014). This is also reflected in Swedish exports, since the mining industry including steel, represents about 10% of Sweden s gross exports and around 60% of net exports (Mineralriket, 2014). The mining industry is important for the entire country, but especially for the most northern county s, Norrbotten and Västerbotten, where a majority of the mines are located. In 2013, 70% of the employees in the mining sector worked in Norrbotten and around 15% in Västerbotten. Thus, in total 85% of all mining jobs in Sweden are in these two counties. Most of the workers in Norrbotten work in the large iron ore mines in Kiruna and Gällivare (Statistics Sweden, 2015). Of the total exports from Norrbotten over 64% are mineral products, and the largest share of these represents iron ore products. If we look at the whole of Sweden, over 83% of the total export value from mining comes from Norrbotten (Ibid). Ejdemo (2014) has estimated the location ratio for different industries in Norrbotten. The location ratio is a measure to see if the county is self-sufficient or not regarding different products. If the ratio is equal to one it means that the county is self-sufficient, and everything over one are exported outside the county. Extraction of minerals has a location ratio of 19.27, which indicates that most of the minerals are exported. The second largest ratio is for repair and installation of machinery and equipment with a ratio of The fact that Norrbotten is over 19 times 1

10 self-sufficient in minerals is a strong indicator of how specialized Norrbotten s industry is on the mining sector. Due to the recent commodity boom, with accompanying high mineral prices, investments in the industry have been very high. A large part of the total industrial investments in Sweden are in the mining industry in northern Sweden (Swedish minerals strategy, 2013). Furthermore, there are future plans to increase the production of iron ore, by opening new mines, which will lead to even more investments in the industry (Mineralriket, 2014). Generally the mining industry is an industry that requires large capital investments, while the number of direct employment is relatively low compared to the capital investment. However, when an industry requires a lot of capital it will spill-over to the rest of the society and have positive effect on other sectors. Regarding this knowledge, it is important to find out the indirect effect of the mining industry and understand how the mining industry affects other sectors in the society. One way to investigate the indirect impacts from the mining industry on other sectors is to estimate the job multiplier (Moretti, 2010). The job multiplier describes how many jobs that will be created in other sectors, when one new job in the mining industry is created. This implies that we can find out how many non-mining jobs that are being created by the mining sector. 1.2 Purpose The purpose of this study is to assess the job multiplier of the mining industry in northern Sweden. 1.3 Methodology The method used in this study to estimate the local job multiplier of mining is based on an econometric model that Moretti introduced The simple econometric model estimates the local job multiplier of mining, by estimating the change in the number of employees in the non-mining sector, when one new job in the mining sector is created. In order to use the model, data on the number of employees in the non-mining sectors and in the mining sector are collected. The regressions are made for two five year periods, and , which means that data for 2003, 2008 and 2013 are 2

11 used. Data is collected from the Central Bureau of Statistics in Sweden and are from all the 29 municipalities in Norrbotten and Västerbotten. 1.4 Scope and limitations This study aims to investigate the effect of the mining industry in northern Sweden. The study is thus geographically limited to northern Sweden, which is represented by the two most northern county s Norrbotten and Västerbotten, mainly because this is where most of the Swedish mines are located. The study only considers the effect from the mining industry in form of new jobs and does not directly take into consideration for example environmental aspects, mainly because of the time limit of this study. This study is also limited to investigate the change in the number of jobs during the time period 2003 to Furthermore, the study uses only data from Statistics Sweden and no data is retrieved from another sources. This is because Statistics Sweden is a wellused source when it comes to statistics for research and the statistics are therefore considered as reliable. 1.5 Outline This study has 8 chapters and starts with this introduction chapter. Chapter two provides a background of the mining industry. In chapter three a literature review is presented where earlier literature on the subject are presented and discussed. Then in chapter four the theoretical framework are described, and in chapter five the method and econometric model is presented. In chapter six the data and the data sources are presented in form of tables and text. Then in chapter seven the result is presented and analyzed. And in the last chapter, chapter eight, the concluding remarks are made. 3

12 CHAPTER 2 THE MINING INDUSTRY IN SWEDEN This chapter begins by describing the historical development of the Swedish mining industry. Then the market structure is presented and discussed. The chapter then continues with describing the commodity boom from the beginning of the 2000s. Finally, the development of the mining industry in the northern Sweden is discussed. The chapter has a clear focus on iron ore, this because iron ore is the most important mineral for northern Sweden. 2.1 Historical development In order to describe the mining industry, we start with a historical perspective. The mining industry in Sweden has a long history that goes back to the early 1000 s when the first mine was opened. Until today there have been more than 3000 active mines in Sweden, and the majority of these have been located in Bergslagen in the central parts of Sweden. The large iron ore reserves in Norrbotten was discovered in 1660, and 1890 the government owned company LKAB was founded (SGU, 2015). Since then, the mining industry has been an important part of the Swedish society. The large iron ore reserves in Norrbotten are located in the northern part of Norrbotten and thus far from the coast, which meant that the problem with transportation needed to be solved. In order to export the iron ore, efficient transports was needed. A railway between Luleå - Malmberget - Kiruna solved this problem. The railway was completed in the late 1880s and made it possible to transport iron ore from the mines to Luleå, for further transport by boat (SGU, 2015). Since most of the minerals are exported the transports are important in order to efficiently compete against other competitors on the world market. In the end of 1910 there were over 500 active mines in Sweden that produced around 8 million tons of ore. Today there are just over 15 active mines that produce 80 million tons of ore (Ibid). 4

13 Large parts of our society are based on knowledge about how to extract and utilize minerals. From minerals all metals that are important for our society are produced, for example iron. However, ore is actually not a geological term, it is a commercial term that means a geological shared setting of one or more metal minerals that are economically profitable to mine. Iron ore is one or more minerals that can be used for the production of iron and steel (SGU, 2014). Most of the iron ore in Sweden are mined by LKAB, and they do not sell iron ore directly, instead they processes iron ore into different types of iron ore products, mostly pellets and fines. Pellets is a ball that is about 10 millimeters and it is created in a pellets plant. Fines are milled iron ore sand that is lumped together into a larger mass when it is used in steel mills (LKAB, 2015a). 2.2 Market structure The world population increases and urbanization with it, which implies that more and more people choose to live in cities and the number of megacities is predicted to increase. This places high demand on the supply of metals and minerals, not the least iron ore that is important in order to produce steel (Swedish mineral strategy, 2013). Sweden has relatively large market shares for many minerals in Europe, but small market shares of the world production. In table 1 the production in ton and market shares for the different metals are presented. Table 1: Production of metals Metal Gold Silver Copper Zinc Lead Iron Swedish production in ton million Europe's production in ton million World production in ton million 13.8 million 5.6 million 1938 million Sweden's share of European production 24% 17% 10% 23% 39% 91% Europe's share of world production 1% 8% 5% 5% 3% 1.6% Source: SGU, Statistics of the Swedish Mining Industry 2013 The market for iron ore is global and there are three large producers that account for approximately half of the global production. These three producers are Rio Tinto, BHP Billiton and Vale. The world production of iron ore in 2013 was 1928 million tons, which can be compared with Sweden s production of 27,3 million tons. China is the country that imports most of the iron ore in the world, and in 2013 they imported 820 million tons of iron ore, which can be compared with EU-28 that imported 128 million 5

14 tons (SGU, 2014). The global demand for iron ore is therefore based on China and how much iron ore they need. When it comes to other base metals the production in the world are increasing with a couple of percent per year. The largest producing countries of base metals are China, Australia and Indonesia. As with iron ore, it is China who demand most of the base metals and therefore the price for the base metals are depending on the Chinese market. Precious metals, such as gold and silver, have also increased production in recent years, slightly larger increase for gold than for silver. Gold is mostly produced in Asia and North America and silver mostly in North and South America. The prices of gold and silver are mainly depending on the US dollar (SGU, 2014). Sine the Swedish mining companies are relatively small they act as price takers and can not influence the price level by increasing or decreasing the quantity. When it comes to iron ore, most of the reserves in the world are located relatively close to the surface, which means that they can be extracted by open pit mining. Most of the iron ore in Sweden are located further down, which implies the use of underground mines to extract the minerals. The mining industry has generally large economies of scales and high entry barriers in terms of capital and infrastructure. The iron ore market has a structure where a few companies supply a large part of the market. For markets that have this type of structure, one way for smaller companies to compete is through specialization. See e.g. LKAB, they have specialized on producing high-quality pellets, which is an upgrade of the ore to make it more attractive. 2.3 Commodity boom From the early 2000s it has been a commodity boom in the world due the rapidly growing economies in Asia. The increase in demand for many minerals has led to increased profits for mining companies, and as a result new companies has entered the market (Söderholm and Svanh, 2014). The price of iron ore has increased dramatically from the early 2000 and forward, in 2003 the price was around 30 US dollars per ton and five years later, in 2008, the price was noted at 170 US dollars per ton. The financial crisis around 2008 pressed down the price for a period, but then the price started to increase again. Figure 1 illustrates the monthly price change in US dollars per dry metric ton iron ore (including costs for insurance and freight) from 2003 to

15 Figure 1: Iron ore monthly price in US dollars per dry metric ton Source: Indexmundi (2015) During the period of price increases it has become more lucrative to extract iron ore, and the existing mining companies has increased their production and new mines has opened in northern Sweden. Employment in the mining sector has also increased and new jobs have been created. However, over the past year the prices on iron ore has fallen and the consequence of that have been reduced revenues for the iron ore companies. Two companies in Sweden, Northland Resources and Dannemora mineral, have gone bankrupt and the only remaining iron ore company in Sweden is now LKAB and even those are facing major savings due to decreases in the price of iron ore (LKAB, 2015). 2.4 Development in northern Sweden In 2013 there were 12 active mines in northern Sweden, five in Norrbotten and seven in Västerbotten. In Norrbotten, Kirunavaara, Gruvberget, Malmberget and Tapuli are extracting iron ore and Aitik is extracting base metals, mainly copper. In Västerbotten, Björkdal, Kankberg and Svartliden extracts gold and Renström, Norrliden, Maurliden and Kristineberg extracting base metals (SGU, 2014). As described earlier in this 7

16 chapter the price of iron ore has decreased the last years and the mine in Pajala, Tapuli, has been closed. However, the mining industry in northern Sweden has increased considerably over the last decade. For example, the number of employees in the mining sector increased over 30% between 2003 and 2013 (Statistics Sweden, 2015). During the commodity boom investments in the mining industry in northern Sweden has been considerable, and a large part of total industry investments in Sweden are made in northern Sweden (Swedish mineral strategy, 2013). The production of ore is planned to increase by 75% to 2020, from the level produced in 2011 (ibid). Based on this the future for the mining industry in northern Sweden looks very positive. However, there is a concern for the effects of the current price decreases, implying that the future for the mining in northern Sweden is more uncertain than it was a few years ago. 8

17 CHAPTER 3 LITERATURE REVIEW The objective of this chapter is to present previous research in the area. The overview concentrates on the most relevant articles and begins with the search strategy that were used to find the articles, and then continues with a brief review of each article, followed by a table with short information of each article. Finally, conclusions based on the literature review are presented. 3.1 Search strategy In order to find relevant literature several databases have been used, the most frequently used databases are Google Scholar and Scopus. In addition to that, several studies have been found by going through references in interesting studies to find more similar articles. These studies have then been found by searching the web for the references. The words used in searches for literature are: Benefit-sharing Local multiplier Job multiplier Input-output model Mining industry 3.2 Previous literature Below is a brief summary of relevant articles. The summary is divided into two types of articles, first the articles that use an input-output model and then the articles that use an econometric model Input-output model The input-output model describes flows from industries and institutions and how an input in one industry can be an output in another. The model describes how the different 9

18 parts of the society are dependent of each other, and it combines economic and demographic variables. An example of an input-output model is the Swedish model raps that are based on five different modules. The first module is population, where a baseline for the future population is calculated. The outline from module 1 produce input to module 2, the labor market, which estimates the size of the workforce. Module 3 is the regional economy, which includes calculation of production, incomes and demand for labor. Module 4 is the housing market, which is based on results from module 1, population. Several interactions between module 2 and 3 is made in order to balance the labor market with the regional market. The last is module 5, the municipality, where the results breaks down into details, and the income and expenditures for the municipality is calculated with respect to taxes (Sweden statistics, 2015). The model then produces a job multiplier, which can for example be calculated as the effects of a new mine. Ejdemo and Söderholm (2010) investigate the impacts on the regional economy when a relatively large iron ore mine opens up in a small town in the northern Sweden (Pajala). They estimate the local job multiplier with the raps model discussed above. The model is delivered by the Swedish agency for economic and regional growth, with data from the Central Bureau of Statistics in Sweden. The study shows that the job multiplier is between 2 and 2.5 for the maximum production phase, which implies that for each new mining job created, two to two and a half new jobs are created in other sectors. Hansson et al. (2010) investigate the future need of workers in the three mining communities in Norrbotten, Gällivare, Pajala and Kiruna. In order to do this, the authors counts the numbers of jobs directly needed in the mining industry in the future and then they multiply that number with the job multiplier to get the total number of labor needed. To find out the job multiplier they use the same raps input-output model that Ejdemo and Söderholm used. The result shows a job multiplier between 0.5 and 1.7 depending on which municipality they estimate the job-multiplier for. It is estimated that the multiplier for Pajala is between 1.4 and 1.5, which is lower than the result Ejdemo and Söderholm get in their study. The difference depends mostly on different assumptions made by the author s. Hansson gives an example for the mines in Pajala, assuming that all goods and services that the mine demand can be supplied by the local society and do not need to be imported from other parts of the country. For example, 10

19 there is a local manufacture that makes the mining machines instead that these machines are imported from other parts of the country. Then the multiplier would increase to 2.5 for the mine in Pajala. Depending on how strong the assumptions are regarding how much the local society supplies the demand from the mines, the higher will the multiplier be. Leaming (2007) study the expected direct and indirect benefits from the Rosemont copper mine in Arizona, USA. The mine is located in the Helvetia and Rosemont mining district, which is in the eastern Pima County, Arizona. Direct and indirect benefits for three regions are examined, first the impacts in the local economy in Pima county, then in the whole Arizona, and finally across the whole United States. The direct impacts are calculated from the direct income for workers at the mining company, income to other businesses due to the establishment of the mine, and the direct revenue to the local government in form of taxes. In this study, like the two studies above the indirect number of jobs created from the mining are calculated using the job multiplier, the way the job multiplier is estimated is also the same and it is through an input-output model. The results indicate that Pima County will get around 500 jobs directly and in total near 1700 jobs, in the entire Arizona 2950 new jobs will be created, and in the entire United States around jobs. This translates into a job multiplier of 3.4 for Pima County, 5.9 for Arizona and 22 for the entire country. The two Swedish studies above have not calculated the multiplier for the whole country Econometric model The articles described below use an alternative way, compared to the input-output model, to estimate the local job multiplier, as they use a simple econometric model. The econometric model measures the elasticity of employment change for a specific sector with respect to change in another sector over a certain time period. This elasticity is then multiplied by a ratio in order to get the specific job multiplier. Fleming and Measham (2014) examine the local job multiplier of mining in Australia by using an econometric model. The econometric model measures the difference in employment in a specific sector with respect to the difference in employment in the mining sector. There are two different samples for the study, one with all sub-state 11

20 regions in Australia and one that is limited to the mining regions, in order to see if there are any differences between the mining regions and other regions. To estimate the multiplier the author use statistics on the number of employment in the mining sector and in other sectors over two time periods. The data is then put into the econometric model and the result show that the elasticity for the sectors that are statistically significant is between 3 and 13% for the full sample and for the limited sample between 12 and 23%. The job multiplier is around 7 for the full sample and 1.4 for the limited. For the full sample the 1.4 new jobs from the limited sample are included plus all other new jobs created in the country because of the mining industry. Moretti (2010) examines how the employment changes over time in the tradable and non-tradable sectors due to an increase in the number of jobs in the tradable sector from an exogenous parameter. In the tradable sector the produced goods are traded at the international market, and the price of the goods cannot be determined locally. In a nontradable sector the price is set locally, and it can for example be education or a realestate broker. The objective of this study is to estimate the local job multiplier in the tradable and non-tradable sector when an increase in the number of jobs in the tradable sector occurs. The results show that the multiplier is different depending on the type of job that is created. For a new job in manufacturing there will be 1.6 new jobs in the nontradable sector, and for a new skilled job there will be 2.5 new jobs in the non-tradable sector. The results of the study indicate that the local job multiplier is higher for skilled jobs than for non-skilled jobs. Moretti and Thulin (2012) examine what happens when a local economy attracts a new business that creates jobs to the local society. When a new job is created in the tradable sector it will lead to new jobs in the non-tradable sector. The author s estimates the local job multiplier in order to see how many other jobs that will be created. The multiplier is found by comparing the change in the number of jobs in the tradable and the non-tradable sector. The study has been made both in Sweden and the US, and the results from Sweden and US are compared to se if there are any differences in the two countries. The results from the study indicate that the multiplier is larger in the US than it is in Sweden, since an increase of one job in the tradable sector in Sweden gives 0.48 new jobs in the non-tradable sector and in the US the same increase lead to around 1.6 new jobs in the non-tradable sector. Both in Sweden and the US the job multiplier of 12

21 adding one high skilled worker is higher than for a normal worker, around 2.5 new jobs in the non-tradable sector in the US and around 3 new jobs in Sweden. Van Dijk (2014) criticizes Moretti and the studies he made 2010, and the one in 2012 that he made together with Thulin. Van Dijk repeats Morettis estimates and get approximately the same results. However, he also creates a new estimate with the same data where he changes five things to improve the estimate. The changes are: Remove industries from the analysis that are not observed in every period Do not treat mining and agriculture as non-tradable industries Use a more shift-share instrument Weigh both time intervals in the dataset equally Provide a more accurate estimate for the relative size of the non-tradable sector to the tradable sector. When Van Dijk makes these changes, he still concludes that there is a positive job multiplier, but it is significantly smaller than the one Moretti calculated. A new job in the tradable sector will result in 1.02 new jobs in the non-tradable sector, compared to 1.6 that Moretti estimated. Table 2 below summarizes the literature described in this chapter. Table 2: Overview of Relevant Literature Author Thomas Ejdemo and Patrik Söderholm G. Hansson, A-K. Lundström, P. Malmsten, C. Anderstig, J. Börjesson and C. Annetorp Hörnsten Publishing Year Objective Method Results 2011 Evaluate the Input-Output Average job regional economic model raps multiplier of 2- impacts of a iron ore 2.5 during mine in the northern maximum Sweden. production Examines future labor needs in three mine sites in the northern Sweden. Input-Output model raps Local job multiplier between 0.5 and

22 George F. Leaming 2007 Examines how large the direct and indirect economic impact is from a copper mine in Arizona, US. Direct and indirect impacts, Input-output model 500 direct jobs, totally 1700 jobs in Pima County, 2950 in Arizona and around new jobs in the whole US. David A. Freming and Thomas G. Measham 2014 Estimating the local job multipliers of the mining in Australia. Econometric model of local job multipliers Job multiplier 7 for the full sample and 1.4 for the limited sample. Enrico Moretti 2010 Enrico Moretti and Per Thulin Estimating the local job multiplier in the tradable and nontradable sector in US Estimating the local job multiplier in the tradable and nontradable sector in Sweden and compare to US. J.J. van Dijk 2014 Improve Morettis estimates of the local job multiplier in the tradable and nontradable sector in US. Econometric model of local job multipliers Econometric model of local job multipliers Improvement of Morettis Econometric model of local job multipliers One new job in manufacturing gives 1.6 jobs in non-tradable sector, One new skilled job gives 2.5 jobs in nontradable sector. Sweden average job multiplier 0.48, US 1.6. High skilled jobs 3 in Sweden and 2.5 in US. One new job in manufacturing gives 1.02 jobs in non-tradable sector, One new skilled job gives 1.87 jobs in nontradable sector. 3.3 Conclusions based on the literature review In all the articles above the job multiplier is discussed and estimated. In some of the studies the mining sector is in focus and in others the tradable and non-tradable sectors. The above studies differ as they use different methods to implement their estimates; three of the studies use an input-output model (Ejdemo and Söderholm, 2010; Hansson et al., 2010; Leaming, 2007) and four uses an econometric model (Fleming and Measham, 2014; Moretti, 2010; Moretti and Thulin, 2012; van Dijk, 2014). The job 14

23 multiplier has been estimated by the econometric model in Sweden, but only for the tradable and non-tradable sectors (Moretti and Thulin, 2012). No previous attempts to estimate the job multiplier for the mining industry in Sweden with the econometric approach have to the best of our knowledge been performed. This approach has though been applied to the mining industry in Australia. The job multiplier for the Swedish mining industry has previously been estimated with the traditional input-output model. One general conclusion from these studies is that the results vary considerably depending on the assumptions made. The econometric method Moretti (2010) presented gives an alternative way to estimate the job multiplier. This method avoids some of the potential error specifications that can occur with the input-output model. However, Van Dijk argues that there are some drawbacks with the method, mainly since it is possible that the method overestimates the job multiplier when it examines the impact of a change in the employment in the tradable sector on the employment in the non-tradable sector. The criticism is especially true when using the econometric approach to estimate the tradable and non-tradable, which not is the objective of this study. Since the econometric approach has not been used to estimate the job multiplier of the mining industry in Sweden there is a gap in the previous literature that this study aims to fill. 15

24 CHAPTER 4 THEORETICAL FRAMEWORK The objective of this chapter is to present the theoretical framework of this study, which is mainly the theories behind the linkage between the mining sector and other sectors, the job multiplier and Moretti s theoretical framework behind the multiplier. 4.1 Staple theory Staple theory describes the economic linkages between the mining industry and the local economy. The theory is used to analyze the local effects of extracted natural resources, which are exported outside the region (Gunton, 2003). The mining industry in northern Sweden provides a good example of an industry where the resource is exported, but where the extraction creates spillovers to the rest of the society. In staple theory, the linkages of the effects from the industry are divided into four different sectors: Forward, backward, demand and fiscal linkages. Forward linkages means that the mineral from the mining industry can be used as a raw material in another local industry. An example of this is that the mining industry in Norrbotten delivers iron ore to the steel industry in Norrbotten. Backward linkages are the production of inputs that are used in the mining industry. For example, suppliers needed for investments and services for example machinery and infrastructure. According to Ejdemo (2011) these types of impacts can be reduced because of the fly-in-fly-out practices of the mining sector. Demand linkages refer to the production of goods that are being produced because of the increased demand from employed in the mining industry, for example consumer goods. Fiscal linkages, which is the enlargement of the public revenues when taxes from the mining industry goes directly to the local government. This means that the local government has more money to spend and invest in the local community. 16

25 The size of the spillover from the mining industry will depend on how well the local community can take advantage of the opportunities offered by the mining industry. An example of this is that the job multiplier will be greater for a larger area, if there is one new job created in the local area because of the mines and totally two new jobs in the whole country, the local community have only been able to take advantage of some of the spillover (Leaming, 2007). 4.2 Job multiplier A multiplier is the marginal effect that occurs when one economic variable changes because of a change in another economic variable. In this case the change is in the employment in the mining sector, and the other economic variable is the change in employment in other sectors. This implies that the job multiplier in this case describes how many jobs that will be created in other sectors of the economy, when one new job in the mining sector is created. The framework for the theory that Moretti (2010) use to estimate the local job multiplier is based on an econometric model, which determines the impact on the employment in the non-tradable sector when an exogenous change in the number of jobs in the tradable sector occurs. The tradable sector represents the industries that trade goods at the international market and the prices of the produced products are set internationally. Non-tradable sector is the sector that is available locally and where prices are set locally, such as housing brokers, health care and education. It is assumed that the local community are an own competitive economy that produce traded goods x 1, x 2, x 3,.x n that are traded on the international market and the price are set there, and non-traded goods z 1, z 2, z 3,..z n where the price are set locally. It is further assumed that labor can move between sectors in the local community and that labor is perfectly mobile, which is an assumption that is likely to hold in the long run. The model thus assumes that workers are free to move between cities, and that the local labor supply has an upward slope that depends on the valuations of leisure, how the residents are distributed and how mobile people are between cities. The higher the mobility is geographically, the higher is also the elasticity of labor supply. Suppose now that there is an exogenous shock to the market and the demand for labor increases permanent in x 1. The direct effect of this is that the number of employees in x 1 increases. The indirect effect is what 17

26 happens for the rest of the traded good markets x 2, x 3,.x n and the non-traded good markets z 1, z 2, z 3,..z n Multiplier for the non-tradable sector When the exogenous shock occurs and employment in x 1 increases, the budget in the local community increases, both because of higher local taxes and because of more workers and higher wages. When the budget constraint increases, the demand for nontraded goods z 1, z 2, z 3,..z increases and the number of employees in restaurants, health care, education and other similar jobs increase. These new jobs are divided between people that are moving to the area and people who already live there. How the distribution looks depends on the degree of mobility. The size of the multiplier effect depends on three factors: The technology, since a labor-intensive technology provides a higher multiplier. It will also depend on the preferences for the non-tradable goods locally. The type of jobs that are created in the tradable sector, since high skilled jobs with higher wages will generate a larger multiplier and create more jobs in the non-tradable sector. This because the higher wage give the workers more money to spend on local services. The opposing effects on wages and prices since the exogenous shock increases the cost of labor, which can lead to a decline in the supply of local services. This decrease can partially equalize the increase that occurred in local services. If the supply of labor is elastic the crowding out effect will be smaller and the multiplier will be larger Multiplier for the tradable sector The external shock to x 1 may also effect the other tradable sectors x 2, x 3,.x n. It is though unclear if the effect will be positive or negative. The effect will however depend on three different forces: When the cost for labor increases locally because of the new jobs in x 1 that will have a negative effect on the other actors at the market x 2, x 3,.x n due to higher costs. Unlike the non-tradable sectors, the prices are set internationally and it is not possible to change them locally. In the long term this can cause business in 18

27 the tradable sector to move their production to places where the labor are less expensive. The increased output of x 1 may lead to an increasing demand for intermediate goods and services. This can affect the other companies in the tradable sector, depending on how dependent they are to the international market. If they are not completely dependent on the international market this can give a positive effect. Companies that are similar or have related activities tend to place their business close to each other, which can have a positive effect in the tradable sector. 4.3 Discussion As this study is intended to examine the job multiplier of the mining industry in northern Sweden it is important to understand the linkages from the mining industry to the rest of the society. The staple theory illustrates these linkages and shows which types of connections there are between mining and other actors in the society. The theoretical framework that explains how an exogenous shock creates new jobs can easily be adapted to the mining industry. The mining industry is dependent on the price of the resource that they are extracting, which can explain the increases in sales the last ten years and also an increase in the number of employees. The theoretical framework is related to the method applied in the study, since the study will use the basic idea of how an exogenous shock in the mining industry affects the rest of the local society. The econometrics will be performed with data from a time period when the number of mining employment increased mainly because of increased prices. 19

28 CHAPTER 5 METHODOLOGY The objective with this chapter is to present the methodology and the econometric model used to estimate the job multiplier from the mining industry to other sectors in the local economy in northern Sweden. 5.1 Methods to estimate the local job multiplier When estimating the local job multiplier either an input-output model or an econometric model can be used. The most frequently used method is an input-output model. The input-output model is a model that produces local multipliers in regional economies due to different exogenous effects. Input-output models are based on large amounts of data that are combined in order to describe the linkages between different industries and local markets. The combinations will together represent the local economy (Ejdemo and Söderholm, 2011). In Sweden there is an input-output model called raps provided by Statistics Sweden. Figure 2 shows a schematic overview of the raps-model in order to provide an example of what an input-output model can look like. Figure 2: The raps-model Source: Statistic Sweden (2015) 20

29 However, there are some drawbacks with the input-output model and there are studies that show that the method often cannot generate reliable estimates (Fleming and Measham, 2014). One drawback is that input-output models are based on strong assumptions, e.g., that regional suppliers, including labor, are perfectly elastic (Weber, 2012). Another limitation of the input-output model is that it is based on equations that define present conditions, which means that it cannot predict changes over time (Kilkenny and Partridge, 2009). The models also ignore that there may be a reduced number of jobs in other sectors, because the labor has become more expensive. And one last drawback is that they also ignore that businesses that are similar often position themselves close to each other. However, the econometric model also has drawbacks. One drawback that van Dijk (2014) discusses is that the model can overestimate the job multiplier when applied on the tradable and non-tradable sectors. One reason for this is that industries can be a part of different sectors during the time periods. To avoid this, this study use the SNI 2007 as a base for the data during the whole time period. Another issue is that the mining industry and agriculture are treated as non-tradable sectors, which not is an issue in this study because it does not treat any industry as non-tradable sector. As the drawbacks arise when the model is used in the tradable and non-tradable sectors, and not on the mining industry, it is not possible to do all the changes that Van Dijk suggests. Because of the shortcomings with the input-output model and also because the inputoutput model earlier have been applied in northern Sweden, this study will use the econometric method that Moretti (2010) presented. The original method uses two types of sectors, the tradable and non-tradable sectors. This study will use the mining sector and other sectors, similar to the Fleming and Measham (2014) study. To find out how many jobs the mining industry in Norrbotten generates to other sectors, data for 2003, 2008 and 2013 are collected. The data are then used to create two time periods and Two time periods are used to see if there is any difference between them. The time period 2003 to 2013 has been chosen because of the increase in the number of employees in the mining industry during this time period. If no increase in the number of jobs in the mining industry had occurred during the time period, it is not meaningful to estimate the job multiplier. All data were collected from the Central 21

30 Bureau of Statistics in Sweden. The data represents employment in the mining sector in municipalities in northern Sweden and in other sectors in the same municipalities. 5.2 Econometric specification The econometric model used to estimate the relationship between the changes over time in mining employment in a region and change over time in non-mining employment in the same region is given by the model that Flemming and Measham (2014) use to estimate the multiplier of the mining industry in Australia This is the same econometric model that Moretti (2010) started to use, but designed for the mining industry. The model estimates how many jobs in the non-mining sectors that are created when one new job in the mining sector is created. This is also called the local job multiplier. We begin by showing the basic model Moretti used, which is not specialized for the mining sector: ln nts!,! ln nts!!!,! = β! + β! ln ts!,! ln ts!!!,! + β! year + e! (1) In equation (1) ln nts!,! ln nts!!!,! is the change over time in the log number of nontradable jobs. Nts t,i represents employment in the non-tradable sector where t is time and i region. ln ts!,! ln ts!!!,! is the change over time in the log number of jobs in the tradable sector. Ts t,i shows employment in the tradable sector in region i at time t. Year is a dummy variable and will be equal to one in the first period and equal to zero in the second period, β! is the intercept and e i an error term. This is the basic model that Moretti started to use in order to study the effects from the tradable sector on other sectors in the local economy. Since this study examines the mining industry the tradable and non-tradable sector are changed to the mining and non-mining sectors instead, which give us a model specified for the mining sector. The equation below shows the specified model with the parameters mining and non-mining employment and the only different from the first model is that the tradable and non-tradable sector is replaced in order to fit the mining industry: ln nme!,! ln nme!!!,! = β! + β! ln me!,! ln me!!!,! + β! year + e! (2) 22

31 In the model above ln nme!,! ln nme!!!,! is the change over time in the log number of jobs in the non-mining sector. Nme t,i shows employment in another sector in time t and region i. ln me!,! ln me!!!,! is the change over time in the log number of jobs in the mining sector. Me t,i represents employment in the mining industry in region i at time t. Year is a dummy variable and will be equal to one in the first period and equal to zero in the second period, β! is the intercept and e i is an error term. This econometric specification will be estimated for a number of sectors in northern Sweden. The same econometric specification will be used to estimate the relationship between different sectors, and then all other sectors except the mining sector are put together in order to find the total effect. This estimation will give the elasticity for each non-mining sector to a change in the mining employment. From the estimated elasticities it is then possible to calculate the local multiplier for every sector, which is done by multiplying their value by the ratio, the ratio is obtained by the following equation: Sectoral employment Mining employment = Ratio (3) Equation 3 calculate the number of employees in the sector, divided by the number of mining employees in the chosen area, this gives the ratio used to get the job multiplier. Because this study uses two time periods the average ratio in the beginning of each time periods will be used, i.e., 2003 and From this we get the job multiplier from the mining sector to the other non-mining sectors. The job multiplier thus shows how many non-mining jobs that will be created when one new job in the mining sector is created. 23

32 CHAPTER 6 DATA SOURCES The purpose of this chapter is to discuss the data sources that have been used to collect data for this study. The chapter will also present the collected data in forms of tables and text. 6.1 Employment data In order to estimate the local job multiplier with the econometric model presented in the previous chapter, data over the number of employees in different sectors has been collected. Data on the number of employees in the mining sector has then been compared against the data in other sectors, as suggested by Moretti (2010). To get the number of employees in different sectors in different towns in Sweden, data from the Central Bureau of Statistics are used. This is the most common way to collect this type of data and previous studies have used similar data see e.g., Ejdemo and Söderholm (2011) who used data from the Central Bureau of Statistics in Sweden and Fleming and Measham (2014) who used data from The Central Bureau o Statistics in Australian. The collected data from the Central Bureau of Statistics are divided into 17 different sectors. One of the sectors represents the mining sector and the other 16 sectors are divided after SNI2007. SNI stands for standard for Swedish classification of economic activities, which is a standard classification of sectors in Sweden. SNI2007 is the newest standard from Statistics Sweden, which replaced the previous standard SNI2002 a few years ago. SNI2007 is divided into the following 16 sectors; agriculture, forestry and fishing, manufacturing and mining, energy and environment, contraction, retail trade, transport, hotels and restaurants, information and communication, credit institutions and insurance companies, real estate, business services civil authorities and military, education, health and social care, personal and cultural services and finally unknown. The manufacturing sector also includes mining, therefore mining has been 24