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Ö N K Ö P I N G

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N T E R N A T I O N A L

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U S I N E S S

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C H O O L

JÖNKÖPI NG UNIVER SITY

The Infrastructural Impact on

the Swedish Wood Industry

Analysis of profitability, productivity, localization patterns and clustering

Bachelor thesis within Economics Author: Fredrik Wareborn 810804 Tutor: Prof. Åke E Andersson

PhD. Candidate Pär Sjölander Jönköping 2005

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Kandidat uppsats inom Nationalekonomi

Titel: The Infrastructural Impact on the Swedish Wood Industry Författare: Fredrik Wareborn 810804

Handledare: Prof. Åke E Andersson PhD. Candidate Pär Sjölander

Datum: 2005

Ämnesord: Svensk trävaruindustri, infrastruktur, lokaliseringsmönster, produktivitet, kluster

Sammanfattning

I den här uppsatsen har jag valt att analysera den svenska trävaruindustrin och dess utveckling under de senaste decennierna. Syftet med analysen är att undersöka den infrastrukturella situationen inom den svenska trävaruindustrin. Har det svenska industriella lokaliseringsmönstret förändrats över tiden, och kan man dessutom se skillnader i omfattning av dessa förändringar i mer eller mindre fördelaktigt lokaliserade regioner.

Teoretiskt sett skulle all ekonomisk verksamhet ta hänsyn till var verksamheten är belägen, d.v.s. företagen kommer att bedöma sina inkomster och utgifter och därefter bedöma om man befinner sig på en ekonomiskt hållbar plats. Kommer man däremot fram till att man inte är effektiv, ska man överväga en ny industriell lokalisering för att kunna öka sin produktivitet.

Slutsatsen som jag dragit är att benägenheten för ett företag att överleva på marknaden såväl som att bibehålla en positiv sysselsättningsutveckling i relation till andra företag inom samma industri är större om företaget är lokaliserat i en mer industriellt fördelaktig region. Med detta menas en region där företagen är mer fördelaktigt lokaliserade i relation till marknaden eller de råmaterial som används i produktionen.

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Bachelor Thesis in Economics

Title: The Infrastructural Impact on the Swedish Wood Industry

Author: Fredrik Wareborn 810804

Tutor: Prof. Åke E Andersson

PhD. Candidate Pär Sjölander

Date: 2005

Subject terms: Swedish wood industry, infrastructure, location patterns, productivity, clusters

Abstract

In this thesis, I have chosen to analyse the Swedish wood industry and how it has been developing during the last decades. The purpose is to analyse the infrastructural impact on the industrial location patterns and to see to what degree these locational changes can be observed in more or less favourable regions.

Theoretically, all economic activity should take into consideration where to locate the production. This means that the firms should analyse both profits and costs and then consider if they are located in an economically efficient location. If they draw the conclusion from the analysis that their locational situation is not efficient, they should consider relocating production in order to gain higher productivity.

In conclusion, the probability of a firm’s survival in the market, as well as sustaining a positive employment development, is higher in more favourable industrial locations. A more favourable industrial location is a location with a closer proximity to the market or in some cases close proximity to the raw materials used in production.

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Table of Contents

1

Introduction... 1

2

The industry ... 2

2.1 Historical perspective ...2 2.2 1945 to present situation ...4 2.3 Infrastructural development ...5

3

Theoretical framework... 7

3.1 Fundamental approach...7 3.1.1 Transport orientation ...8 3.1.2 Labour orientation...12 3.2 Modern approach ...12

3.2.1 Agglomeration and Clustering ...14

4

Hypothesis formulation and testing ... 16

4.1 Analysis ...17 4.2 Chi-square test ...19

5

Conclusions ... 23

5.1 Further research:...24

6

References ... 25

Appendix ... 26

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1 Introduction

One of the foundational questions when considering the future development of a firm is where to locate this economic activity. Where should the production be located in order to gain maximum profit both economically and socially for the employees? Throughout the economic history these questions have been analysed by a number of economists trying to provide the optimal solutions to how a firm should locate the production within a geographical area.

The Swedish wood industry is by no means an exception for this kind of analysis. Firms within the industry have over centuries been considering these questions in order to be able to gain a more favourable economic situation. Initially production was dependent on rapid flowing water and production was naturally attracted to the rivers in the northern part of Sweden. When technological innovations were introduced e.g. the steam engine and later on the electricity, this implied that production could move to locations that before not would be possible. With the introduction of new technology and better transportation systems it became possible to locate the production closer to the final market.

The purpose of this thesis is to examine if firms within the Swedish wood industry make location decisions that correspond to the theoretical framework that tells us about how firms should distribute themselves over a geographical area. This will bring us to an analysis of the development of the location patterns within the Swedish wood industry. A starting point in the analysis of industrial locations has been the theories developed by Alfred Weber and his creation of the so called “locational figures”.

The analysis is aimed at examining the possibility of any significant connections between a favourable industrial location and a higher probability for survival in the market. Also examined, is the probability of maintaining a positive employment development in more or less favourable industrial locations. A more favourable industrial location means that there is a higher potential for future development in a region, in this case with respect to the proximity to population and wages.

The analysis made in this thesis will show that there are connections between positive development within the Swedish wood industry concerning both the total number of firms and the total number of employees within more favourable industrial locations. Despite the outcome of the tests, several questions can be asked if these results really are of the same importance for every single company when considering an expansion of the economic activity. In spite of this the result shows that there are links between a more favourable industrial location and a stronger future economic development.

The industry, with an aspect of a historical and a present discussion, is presented in chapter 2. This chapter will give a solid foundation and understanding of the historical development within the Swedish wood industry, a history that still influence the decisions made within the industry. In chapter 3 the theoretical framework is characterized. Chapter 4 is devoted to hypothesis formulation and analysis. The results from tests and empirical findings will also be analysed and discussed in this part. Finally in chapter 5 some conclusions are made. All data used can be found in the appendix in the back of this thesis.

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2 The industry

2.1 Historical perspective

The Swedish wood industry has its foundation in old and important traditions. The rich supply of timber was early exploited for different kinds of activities such as fuel support, construction and in the process of iron production. There are records of water powered sawmills as far back as in the 13th century. These sawmills had to be located close to

rapid-flowing water, often far inland. This localization had a negative impact and a limiting effect due to the dependence of rapid flowing water and the annual shifts in water level. These unfavourable conditions of the water dependant mills became even more apparent when the export of wood products started during the 16th century since the timber had to be

transported downstream to the coast for further transportation.

Around 1850 there were three prominent sawmilling regions in Sweden. These were Piteå, Härnösand and Sundsvall all favourably situated on the coast of the Gulf of Bothnia in the north of Sweden. After the 1850s the development of the industry continued at a higher pace in the more southern regions of Sweden because of improved transportation systems e.g. railways and shorter distances for further transports. Other reasons for the favourable southern locations were that the rivers were better maintained and cleared out which made the rafting of timber much easier. Also, the supply of forest resources and annual growth of the industry were larger in the southern regions. This led to a situation where the dominant position of the north slowly started to shift south (Svenskt arbete och liv, 1980). The Swedish wood industry, that in the beginning was limited almost only to the sawmill industry, had its breakthrough in the 1850s. The development of the industry at this time had a strong impact on Swedish society when other important industries like the ore industry could not employ a lot of new labour. A particular topic of discussion is why the Swedish wood industry did not get its breakthrough at an earlier stage when more than half of the country’s area was covered with forests. The answers are rather easy to find.

To start with, Norway had a much better location towards the market in Western Europe. There was no reason for sailing into the Baltic Sea to collect wood when you could get it in the North Sea. The Norwegian geographical and technological advantage also played an important role in pushing the Swedish wood industry even further behind. Another reason for the early slow development was the preferential treatment from the Swedish government of the Swedish ore industry. The ore industry, at the time, was the most important partner for the wood industry. Also, at this time the production of charcoal was the single most important wood product. The last important reason was a restraining British tariff policy which was introduced during the Napoleon war that favoured Canadian wood exports and made imports from other countries very difficult (Svenskt arbete och liv, 1980).

The breakthrough of the Swedish wood industry was a combination of several factors. The Norwegian forest reserves particularly close to the coastline were diminishing which led to an inability to supply wood. At the same time, the demand for wood products was strongly increasing as a result of the enormous economic growth that the industrialisation brought during the second part of the 19th century. During this time the British government also

started a revolutionary change in their trade policies toward free trade which gave even better opportunities for further increasing exports.

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The biggest breakthrough within the industry was the introduction of the steam engine. The worlds first steam powered sawmill was built in 1849 close to Sundsvall. The steam engine gave sawmills the opportunity to relocate their production to more favourable locations in the river deltas closer to the coast. They were no longer dependant on the use of rapid-flowing water. The steam based production also gave sawmills the opportunity for a more continuous production throughout the year. In this way the sawmills could make use of the rivers for transportation of timber and at the same time they could gain easier access to export markets through closer proximity of water ways (Svenskt arbete och liv, 1980).

During this time, due to extensive structural changes, there was a strong increase in production with a high concentration in the area around Sundsvall which by the 1890s had approximately 40 established sawmills. This led to an enormous increase in export of Swedish timber. The industry gained even more independence from the water ways with the introduction of the railroad system. This made it possible to locate production to other places inland not before economically conceivable. In the beginning of the 20th century

Sweden had a world leading position of export of sawn timber.

The next important technological innovation was the introduction of electric power during the 1920s. The extensive availability of electric power led to a diversification within the industry. Already well established sawmills could increase their productivity and size and also a large number of small sawmills were established mainly to supply local markets all over Sweden. Electricity made it possible to run small scale sawmills. This would not have been possible with the out-dated steam engine technology.

The industrial revolution that was introduced much earlier in the United Kingdom started to spread throughout the European continent which led to an overall increase in living standards. The economic growth and the worldwide increase in trade created many new markets and the development of the transportation system gave the industry opportunities for new raw material supplies. The rapid increase in population and labour was followed by an increase in the demand for goods and housing. The rapid increase in the production of goods also resulted in a large increase in demand for raw material. These factors all contributed in making the 1850s one of the most expansive eras in European history (Svenskt arbete och liv, 1980).

During this period England, the Netherlands, Belgium and northern France all had a deficit in their wood production, and when the economic expansion began the imports of timber became more and more important. The type of the transportation system became the most significant factor when choosing the location of the import. The best choice of wood import to Belgium and the Netherlands was regarded to be the forests along the river Rhine due to the cheap transports downstream. Also the southern parts of the coastal areas of the Baltic Sea and Norway had an outstanding position for exports to Western Europe. The exceptional expansion of the Swedish wood industry came to an end in the end of the 19th century. The Swedish primeval forests were by this time almost gone and the supply of

timber started to decrease. Despite the decrease of raw material this did not lead to a major decline of the industry, however it definitely led to an economic stagnation.

During the late 20th century another innovation made its arrival on the economic scene, the

use of the wood fibre. Instead of using wood as construction material, wood was used as raw material in the pulp industry. The pulp industry experienced an even stronger industrial development than the sawmill industry. The pulp industry grew tenfold in half the time it

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took the sawmill industry to grow to the same level, which contributed to the Swedish pulp industry gaining a world leading position as pulp exporter by 1913. The exceptional position as a large exporter of paper just before 1914 and the breakout of the World War I resulted in an increase in the demand of paper and therefore an increase in the price. This helped the industry into a very favourable industrial situation during the war, and also contributed to a rapid recovery after the war.

An important fact to consider is that although the pulp industry experienced rapid development it had a much smaller social impact than the earlier development of the sawmill industry. The reason for this is that the pulp industry was employing a very small number of people in relation to the size of the value of production. The pulp industry, in this respect, could in no way compare to the development within the sawmill industry. The main concern from a social standpoint was how to make the pulp industry more productive in order to make up for the loss in manpower experienced by the sawmill industry (Svenskt arbete och liv, 1980).

2.2 1945 to present situation

Sawmills and pulp industries are natural collecting points for taking care of, transporting and drying the wood. The sawmills are today spread all over Sweden due to the vast geographical areas, the large supply of wood and a widespread knowledge of the production processes. In 1987, 63% of the sawmill production capacity was private owned, 20% was controlled by large corporations, 8% was controlled by the government and the remaining 9% was controlled by forest owner’s associations.

Ever since the development of the sawmill industry during the 19th century the sawn

products had been most important for the wood industry. However, strictly measuring by volume, the pulpwood has since the 1950s been larger. Forest owners in general receive a larger profit from timber usable in sawmill production than in pulp production which consequently lead to a focus on producing high quality timber. Thick and straight logs with good quality are sent to sawmills, while smaller and curved logs of poor quality are considered as pulpwood. This development can be reflected in the forestry law which among other thing prescribes that the forests should be maintained in order to ensure lasting and durable growth.

The market of timber has always been of a great importance since sawmills and the forests are often owned by different interests. The timber price is renegotiated at regular intervals by the market actors. Apart from the period during the Korean War that lasted from 1950 to 1953 the prices during the 1950s and 1960s were very stabile. From 1972 to 1977 the prices of timber rose very rapidly. This rapid increase was followed by five years of restrained demand and stabile prices due to the oil-crisis. At 1983, once again, sawmills were exposed to very strong competition from other sawmills and the pulp industry that by 1987 had doubled the timber prices. However the price change that occurred during this time was corresponding to the general inflation rate of the present day.

The demand for sawn timber has a strong relation to the construction industry, and the price fluctuations also have large similarities to the changes of the sawn timber price fluctuations. The market was very stabile during the 1950s and 1960s with the exception of the Korean War. Since then the prices has increased considerably with a number of peaks followed by price falls during the 1970s and 1980s.

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The total annual Swedish production of sawn wood fluctuated between 5 and 8 million m3

in the beginning of the 20th century until the 1950s. From the 1960s, step by step, the

production increased and it reached a peak at 14 million m3 in 1974. Since then, during the

1980s and 1990s, there has been a slight decrease of production of sawn timber. Today the annual production is close on 17 million m3 sawn timber, 154.000 tons of fibreboards,

466.000 m3 of particle boards and 75.000 m3 of plywood (SVO, 2005). The export shares that in the beginning also were very high (80%) decreased during the war in the 1940s and has since then regained an export scare of approximately 70% (SNA, 1990).

Today the wood industry is one of the most important Swedish industries. It represents about 4% of the Swedish GDP and has a relatively large impact on the Swedish economy in comparison to other European Union countries, with the exception of the Finnish economy. Within the total industry the wood industry represents approximately 10% of the employment, roughly 14% of the total processing value and about 12% of the Swedish export value. Large investments are being made annually in the amount of several billion Swedish Crowns (Skogsindustrierna, 2004).

The vast accessibility of forests is still the backbone of the Swedish Wood industry and makes Sweden among one of the world leaders in the global industry. Today Sweden is the fourth largest exporter of pulp, third largest exporter of paper and the second largest exporter of wood in the world (1995). Within Europe Sweden is the third largest producer of pulp after Germany and Finland, and covers roughly 10% of the European Union paper demand (Skogsindustrin, 2005).

The Nordic wood industries have been playing a big role in the global restructuring process that the industry has been experiencing during recent years. In the sawmill industry extensive restructuring has been made, and today the ten largest companies represent approximately 60% of the production. The Nordic wood industries also control about half the production capacity in Western Europe of pulp and paper. There is an obvious tendency towards specialisation within each area of the industry with an increasing share of foreign owned production capacity (approximately 50%).

The industry consists of many complex processes and products that demand a high degree of knowledge and competence in production especially in the pulp and paper industry. The wood industries are using more complex methods in production than ever before which creates demand for higher levels of knowledge among employees (Skogsindustrierna, 2004).

2.3 Infrastructural development

During the last century, extensive changes and development have been made in the Swedish infrastructure. In 1841 the total length of the public road net in Sweden amounted to 42.900 km. In 1900 the public roads had increased to 54.800 km, in 1920 to 63.700 km, in 1940 to 88.900 km and by the 1990 the public roads consisted of 98.600 km roads. Since then efforts have been made in improving the quality of the current roads. The total road net today consists of approximately 422.900 km roads. Out of these roads about 23% are public roads (owned by the state) and about 50% of the total road length is open for public use (Statistics Sweden, 2005; SNA, 1992).

In 1845 the first construction plans of railroads in Sweden were presented. The breakthrough came during 1853 and 1954 when it was decided that the state should build and run a net of main lines. The lines were decided to be built not along the coasts but in

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the inland. The reasons behind this decision were that the railroad net naturally was of strategic importance for the military. But it was also an opportunity to bring new life to the less developed countryside in the inland, even though the underlying idea had been to connect the more developed industrial districts within Sweden.

In the beginning of the 20th century the Swedish railroad net measured approximately

11.300 km, of which about one third was owned by the state. The development of the railroad continued until the breakout of the World War I when the material prices went high. The depression after the war also held the construction back of further lines. The strong development of the motorization in the 1920s also put a hold on many new investments in railways. In 1938 the Swedish railroad reached its peak when it consisted of approximately 16.900 km of tracks, which since then has decreased until today’s situation. Since then today only a fraction of the railroad is in use. In practice only 9.700 km is in use which means about 57% of the railroad net that existed at the peak year of 1938 (SNA, 1992).

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3 Theoretical framework

In this chapter the reader will receive an underlying theoretical background that will help in understanding how the Swedish wood industry has developed throughout time. The theories will explain the reasons and the incentives why industries choose to locate the production in specific areas and describe the agglomeration of economic activity.

A natural starting point is to ask yourself where to locate a new economic establishment when the locations of all other economic activities are known. This classic problem of location of the economic activities has been analysed over a long period of time. Two economists that have been dealing with these questions are Alfred Weber and Martin J Beckmann. They are the creators of two classical studies dealing with the location of an industry in a region. Alfred Weber who was actively researching at the beginning of the 20th

century created a baseline of the theoretical approach in location theory. Martin J Beckmann has further complemented the traditional Weberian theoretical approach and managed to extend the theory to match the existing complex economic situation.

3.1 Fundamental approach

Alfred Weber is analyzing the theoretical aspect of the location of industries. In every kind of industry and in each phase of the economic and technical development, the location of the development has to be considered. This has to be considered in production, distribution and consumption. Weber is discussing the term “locational factors” which imply the advantage that the industry is gaining when the economic activity takes place in a particular place rather than anywhere else.

The favourable location gives the industry an advantage in both the productive and distributive process of a certain product. An advantage implies a saving of cost. Weber then classifies the locational factors into general and specific factors. The general factor is something that every industry has to consider more or less in one way or another e.g. cost of transportation, labour and rent. The specific factor on the other hand concerns and affects only particular industries. Example of a specific factor could be the need for certain raw materials in production (Weber, 1929).

All locational factors must be classified into regionally distributed and agglomerative or deglomerative factors according to what kind of influence they have on the industry. To be distributed regionally means that the industry is directed toward places in a prearranged geographical framework in order to create a fundamental industrial location. This will be the situation if the industry is influenced by the cost of transportation or by the geographical differences in the cost of labour. The industry will be drawn to a distinct geographical point despite the fact that the industry will change when the industry develops.

To agglomerate or deglomerate means to develop the industries inside the geographical framework independent of the regional distribution. Factors that bring industries closer in certain points are for example more economical use of machinery, advantages of being closer to market- and trade places or that the agglomeration in itself reduces price. On the other hand industries can be driven from one of these favourable places due to for example high rent. The industries within the geographical network is agglomerated and spread more or less independent of geography. In other words the factors that influence the geographical industrial patterns are agglomerative or deglomerative factors (Weber, 1929).

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3.1.1 Transport orientation

If we assume that the only factor that influence the distribution within the industry is the transportation costs, to what places will the industry be most tempted to establish the production? It is natural that the industry is drawn to a location where the transportation costs are lower then in other locations, taking into account both location of consumption and the place of storage and distribution. The fundamental factors which determine the transportation costs are the distance that has to be covered and the weight that is to be transported. Since the two factors with ease can be shown in mathematically exact numbers it gives possibilities to create a foundation for the theory. Besides the distance and weight other things that have impact on the transportation cost are what type of transportation system that exists in the region, the infrastructure and the nature of the good itself (for example the quality or size that affects the degree of difficulty of transportation).

If there are only two factors that determines the transportation cost, the distance and the weight, the industries will be drawn towards the point where the transportation costs are the lowest. But how will these locations of production be distributed throughout a region? We assume that for every product the size and the location of consumption are given and that each location of raw material also is given. To make it simple we also assume that the production is taking place in only one stage where raw material is turned into a finished product (Weber, 1929).

Figure 3.1.1. “Locational figures” Source: Weber, 1929

Let us assume that there are only one place of consumption and two most favourable locations for raw material deposits. The location of production will somehow arrange itself in the “locational figure” see Figure 3.1.1. In this case where there is only one location of consumption and two locations of raw material deposits the locational figure will take the shape of a triangle. One corner (C) represents the demand for the final product and the other two (M) the raw material deposits. These “locational figures” represent the foundation in formulating the theory.

When the production is influenced only by the costs of transportation and implemented with the “locational figures”, this means that the production will find the point with absolute lowest costs. This point will be called the transportational location. In order to find the transportational location we have to consider some practical circumstances. The

M2 M1 M2 M1 M2 M1 C C C

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entire weight that comes from the raw material deposits have to be transported to this location and the entire weight of the production must be transported from the transportational location to the place of consumption (Weber, 1929).

Figure 3.1.2. “Locational figures” Source: Beckmann, 1969

The transportational location will be connected with the corners along lines where the weights belonging to each corner will be transported. Material is transported from the lines originating from the raw material deposits and produced goods are transported along the line originating from the place of consumption. The numbers that are printed in figure 3.1.2 nr.1 represent the weight that is transported on each line. The weight represents the proportional force that is pulling the transportational location closer to the corner in the triangle (figure 3.1.2. nr.3 and nr.4). Somewhere inside the triangle the lines will meet which will correspond to the optimal location of production. If instead of being able to locate production anywhere inside the locational figure the production are restricted to the three given locations in the corners of the figure, then the production would take place in the place of consumption. Ma L Wa Wb Wc M2 M1 M2 C M1 C 1W 1,5W 2W C=1 M1=1/2 M2=3/4 Mb C

1

3

2

4

C= Place of consumption M= Place of raw material deposits W= Weight of transport

(a, b, c)= Proportional distance from place of consumption/raw material to production location. Nr. 3 and 4 show the relative force towards place of consumption and the raw material deposits.

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Now let us assume that the input of resources Ma and Mb per unit of output are the same as Wa and Wb (Table 3.1.2 nr.2). Also, production takes place at a distance from each of the three locations in the locational figure at distance Ra, Rb and Rc from the place of material deposit a and b and the place of consumption respectively. The optimal production location would be at point L that still is unknown. The connection between the optimal location and point Ma, Mb and C represent the force Wa, Wb and Wc. In order to minimize the transportation cost per unit we find that:

(1) (Wa*Ra) + (Wb*Rb) + (1*Rc) Source: Beckmann, 1968

To find the optimal location for L we need to find the point where the three forces are in equilibrium:

(2) (Wa*Ra) = (Wb*Rb) = (1*Rc) Source: Beckmann, 1968

This implies that the closer the location of production is to a certain location the greater the weight is that are being transported to or from there. In fact if the weight is large enough the best possible place of production will be right at the location of the raw material or at the market place.

Until now the discussion has been dealing with a single locational figure. What will be the difference in an entire industry? The foundation of the analysis of an entire industry is basically the same as the analysis of the isolated locational figure but with the difference that a number of locational figures have to be considered and coordinated.

Figure 3.1.3. Optimal industrial location with only one variety of transportation i.e. transportation on railroad Source: Beckmann, 1999 M2 M1 C P’ P L

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The theory so far has assumed the transportation system to be uniform in the entire geographic area. This does not have to be true since there in one territory the transportation system can be divided into several separated parts which in its turn can be divided into highways, railroad or waterways. Figure 3.1.3 shows a locational figure and the transportational location taking the railway system into account. The production will theoretically take place where the lines from each of the two material deposits and the consumption meet (L). In reality the connections between the material deposits, the production location and the place of consumption will not be formed as straight lines due to various geographical conditions. The actual location will still be placed close or near the ideal location.

The location of the actual firm will be chosen with respect to the vicinity to the ideal location. When deciding on the location of an industry like the one in figure 3.1.3 more than one point near the ideal location might be considered. In this case, P’ will be chosen in spite of the fact that it is situated further away from the ideal point than P. P’ corresponds better with the demand and requirements in the relation between the material deposits and the place of consumption. The infrastructure and development of the infrastructure in a geographical area will therefore be an important factor for an industry i.e. in order to give an industry the possibility to maximize profit from a location decisions (Beckmann, 1968).

Figure 3.1.4. Optimal industrial location with more than one variety of transportation and different transportation costs i.e. railroads and waterways

Source: Beckmann, 1999 P C M’2 M1 M2 P’

This figure illustrates two locational figures that each result in a different outcome for the optimal industrial location, P and P’, as a result from a variety of transportation with different costs.

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In figure 3.1.4 the influence of a different transportation system is presented, in this case a waterway is crossing through the geographic area (transportation on waterway is assumed to be cheaper than on railway). If the waterway would not exist a localization triangle with production at point P and consumption at point C would be present. However, with the waterway, production will take place at point P’. The distance between M2 and C is shorter

than the distance between M’2 and C but the latter will still be the economically better alternative. An effect of a waterway in this case will lead to a situation where M1 and M’2

are locations of the material deposits and the production is placed at point P’ (Beckmann, 1999).

3.1.2 Labour orientation

In this part regarding labour orientation Weber will embrace the cost of labour in his analysis. The labour cost will only become a factor in the location if the cost of labour differs from one place to another. This saving of labour cost has to be associated with a particular geographical location in order to make use of this specific factor when finding the most favourable place of production (Weber, 1929).

3.2 Modern approach

Martin J. Beckmann bases his analysis on Alfred Weber’s theory about the localization of industries. However, the theoretical framework in this analysis is being developed to meet the modern economic situation. According to Martin J. Beckmann the location of production is the heart of location theory. It is not necessarily the location of a firm but the location of each plant within the firm that is the essential part in the analysis. When analysing these locations of economic activity the entrepreneur tend to list a number of important factors (Beckmann, 1968);

 Space for expansion

 Available labour, with needed skills and at low cost  Low taxes or subsidies

 Infrastructure

 Accessibility through transportation  Amenities

These factors are all important components in order to reach higher profitability. The profitability in itself is an important factor for the firm’s long term survival on the market. Economic theory suggests that by maximizing profits the firm will create better conditions for future survival. Therefore the objective of maximizing profits will affect the locational choices of the industrial development.

The result of the analysis is that if the finished product is heavier or in other ways more difficult to handle than the raw material, the material will be transported to the production facilities which will be located closer to the actual place of consumption. Beckmann calls this choice of location “market orientation”. Examples of market oriented economic activities are restaurants, services of different kinds and distribution of various goods such as food, drugs and cosmetics. The market orientated industry has increased its importance when new technologies are being introduced. Another alternative of industrial location is what he calls “resource orientation”. This means that when the raw material is more costly

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or more difficult to transport then the finished product, the location of the industrial development will lie closer to the raw material deposits. Examples of industries that can be considered as resource orientated are smelting and refining ores, hydro-power, saw mills and paper mills (Beckmann, 1968).

Figure 3.2.1. Market orientation vs. resource orientation. Source: Beckmann, 1969

A fundamental question is where an economic activity will choose to locate when the locations of other activities already are given. In some cases the production activity will be drawn to either the location of the raw material deposit or the place of consumption. This can be shown by a number of examples; crops must be harvested where it is grown, minerals have to be extracted where it is found and buildings or bridges has to be constructed at the point of consumption. Other activities not necessarily tied to either the raw material deposit or the place of consumption can be called footloose. As will be shown the transportation cost will always be a determining factor in the choice of location whether it will be the cost of going shopping or the cost of communication (Beckmann, 1968).

When the price of a commodity is absolutely arbitrary, not much can be said concerning the location of the production site. For each production location a profit function can be created consisting of the price of inputs and outputs in production. The profit made in production must then be compared to the cost of rent for the particular site offered by competing activities. The location where the profit after rent is maximized is the most favourable place of production.

A new force is now introduced. This is the cost of labour. There are in fact local differences in the cost of labour which in its turn influences the locational choices that firms have to make. In figure 3.2.2 (below), a region with a number of different wage levels are shown. Locations with identical wage level are connected by isocurves. The force represents the amount of which the wage rate increases. The force will pull in the direction where the wage rate is experiencing the greatest decrease. When taking the wage differences into account the optimum production location will be reached only when the wage rate patterns are in balance with the force created by the transportation cost. A cheap labour force is therefore a competing factor alongside the raw material deposits when plant locations are analysed and examined (Beckmann, 1999).

Production Material

deposit

Place of Consumption

If the finished products are heavier or in other ways harder to handle than the actual raw material the production of the specific good will move towards the place of consumption (market orientation) and vice versa.

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Figure 3.2.2. Labour cost

Source: Beckmann, 1968

3.2.1 Agglomeration and Clustering

Agglomeration implies local or regional activity between several different industries. In the concept of agglomeration there are two extremes; clustering which is a form of agglomeration and repulsion which lead to a scattered location situation.

The term agglomeration was first created by Alfred Weber in the beginning of the 20th century. The meaning of agglomeration represents the attraction between industries which result in the joint location decisions. The most obvious result of the agglomeration mechanism is the attraction between the supplier and the producer of a good. This can be explained like a market orientation where the market is exchanged by another industry. An example of this kind of activities is the automobile industry where there are strong and large numbers of linkages between the producers and the suppliers. These so called “just in time” systems are dependant on the proximity and close relations between the industries to reduce transportation costs in order to be more economically efficient.

If there are no relations between supplier and producer the industry can still gain from an agglomeration of economic activity. The firm can make use of the service, support network and the qualified labour force that is offered in a specific location. Examples of services that can be offered are packaging, repairs, training, marketing, finance etc. As a result of

Direction of the force that pulls the industrial location towards a more optimal location

Isocost-curves: i.e. same wage-level along the lines

Location of Industry

The figure show Labour cost distribution in an uneven geographical area. The location of firms will be pulled in the direction where the decrease of labour cost is the greatest i.e. where the isocost-curves are closer together.

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this a new industry might choose to locate close to an already established industry to profit from the existing facilities such as a social infrastructure and the labour market (Beckmann, 1968).

Figure 3.2.3. Porter’s Diamond Source: Dicken, 2003

The first writer within the Business management field that gave the geographical clusters a central role as a competitive influence was Michael E. Porter. Porter meant that it was not only a competition between countries but that the competitive advantages are created through internal competition in a country. In other words, the combinations of conditions that are present within a nation can have a tremendous effect on the competitiveness of a region. Porter illustrates his ideas as a “diamond” (Figure 3.2.3), where there are four parts that are all inter-connected. The four sections all depend on each other e.g. the suppliers and industries need to have access to advanced factor conditions in production, home demand in order to signal for appropriate product development and active rivalry between firms for creating competition with the intention of pressuring the firms to innovate both products and processes.

“Competitors in many internationally successful industries, and often entire clusters of industries, are often located in a single town region within a nation… Geographic concentration of firms in internationally successful industries often occurs because of the influence of the individual determinants in the ‘diamond’ and their mutual reinforcement are heightened by close geographical proximity within a nation…”

(Dicken, 2003, s. 144)

Firm strategy, structure and rivalry

Management, attitudes of workers toward authority and vice versa, education system, rivalry creates competition and innovations

Related and supporting industries Proximity to world-class suppliers or other supporting activities Factor conditions

Skills and knowledge of the population and physical infrastructure

Demand conditions Proximity to the ”right” type of buyers and connections to the international market

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4 Hypothesis formulation and testing

In this analysis I intend to examine if there are any distinct connections between the theoretical approach and the actual empirical findings that will be presented further into this paper. In order to see if there have been any changes within the Swedish wood industry and the location patterns I have chosen to study the number of establishments and the total number of employees within these establishments. The number of establishments is chosen because it can illustrate a picture on how the number of firms actually has changed over the years across regions. The number of employees will give us a further insight of the structure within the wood industry e.g. a high concentration of firms does not necessarily mean the same concentration of employees within the wood industry in the same region.

The hypothesis that are being examined and discussed in this thesis is the question if the proximity to either the market or the supply of material might have a considerable effect on the location patterns of a particular industry. Is there empirical evidence that firms within the Swedish wood industry have moved to more favourable industrial locations during the last decades? A more favourable location means a location that gives the firm a higher potential for future development with respect to the proximity to population and wages. Below I have listed 2 hypotheses that will be examined and discussed from a statistical viewpoint. By examining the data I aim to see if the H0 statement will hold or be rejected.

If the H0 statement is rejected I can assume that the H1 statement is true.

1) H0: Firms within the Swedish wood industry do not move to more favourable

industrial locations between 1990 and 2002.

H1: Firms within the Swedish wood industry move towards more favourable

industrial locations between 1990 and 2002.

2) H0: The number of employees within more favourable locations in the Swedish

wood industry has been constant in relation to less favourable locations between 1990 and 2002.

∆µLess favourable - ∆µMore favourable = 0

H1: The number of employees within more favourable locations in the Swedish

wood industry has increased more in relation to less favourable locations between 1990 and 2002.

∆µMore favourable - ∆µLess favourable > 0

The analysis of the hypotheses is going to be compared to statistics and the empirical findings. Hypothesis nr.1 is the core in this analysis. It aims to conclude if there has happened anything within the Swedish wood industrial location pattern during 1990 and 2002. Hypothesis nr.2 is aimed to give an understanding on how the situation within the workforce has developed during these years i.e. number of employees within each region and the shares of each region compared to the total industry. To have in mind, the total

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number of employees within the Swedish wood industry should be a stronger measurement on the structural change than solely notice the changes in the number of establishments.

Data

Empirical data on municipality level has been collected between 1990 and 2002. The data tells us about the number of establishments and the number of employees within these establishments. In order to make the data easier to process and analyse the data is transformed into county level instead of the municipality level i.e. the analysis are going to deal with 21 or 24 counties depending on the year instead of 290 municipalities.

The problem in choosing fewer and much larger regions in this analysis is of course that the precision in explaining the movement of firms in a geographical area will decrease. There are also possibly large differences within a specific region when dealing with these locational movements that will not be captured in an analysis on the county level.

Problem

Sweden is today dividend into 21 counties which individually have the overall responsibility for the administration and the follow-up of the national governments decisions. The problem that I’m facing in this thesis is that on 1st of January 1997 it was decided to reform

the current system with 24 counties to the present situation with only 21 counties. The counties of Kristianstad and Malmöhus now creates the county of Skåne and the three counties of Göteborgs and Bohus, Älvsborg and Skaraborg now together make the county of Västra Götaland. This is very important to have in mind in further analysis since data have been collected from both before and after this transitional period.

4.1 Analysis

To be able to make a correct analysis of the infrastructural impact on the Swedish wood industry I have collected data from SCB (Statistics Sweden) concerning the number of firms and the number employed within the industry at the year 1990 and 2002. The reason for choosing to study these variables is to see if there have been any significant changes occurring over the last decade that will either strengthen or contradict the theoretical aspect of the location structure within the Swedish wood industry.

The analysis will be divided into several parts dealing with each area of the statistics that ultimately will lead to a compiled final analysis of the separate results. The different analyses will consequently deal with the number of working sites over the years of 1990 to 2002 and the number of employed within the wood industry from 1990 to 2002.

In the perspective of the number of establishments from 1990 to 2002 we can see that there has been a slight decrease of the number of working sites within the Swedish wood industry (Appendix: Table A1 and Table A2). The total number of establishments has decreased from 4576 in 1990 to 3913 in 2002. These numbers only give us information about the absolute number of firms and do not tell us anything about the actual size of any single firm or the entire industry. To be able to better examine the location process among the firms they are all divided into groups according to their size. The determining factor in what group each firm will be placed is the number of employees (1-9, 10-49, 50-99, 100-199, 200-499 and 500-).

During 1990 there are four regions in Sweden that I want to point out in this analysis. These are the counties of Stockholm, Jönköping, Älvsborg and Dalarna which during this

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year can show a prominent situation compared to other Swedish regions (Appendix: Table A1). Stockholm during this time has the largest concentration of firms within the wood industry i.e. 514, but they are mainly represented by small and to some extent medium sized firms. The industrial situation in the counties of Jönköping, Älvsborg and Dalarna are quite different where the industrial situation among the firms is much more evenly distributed between small-, medium- and large scale establishments.

In 2002 (Appendix: Table A2) the overall industrial locational situation has to a large extent changed. The number of firms situated around Stockholm has decreased by about 20% and this mainly being represented by a decrease among small firms. Another location where there is a negative industrial development is Dalarna County where we also can observe an extensive decrease within the wood industry however still belonging to the more successful regions. The county of Jönköping has during the period been able to maintain its prominent role within the Swedish wood industry with a wide representation of all sizes of establishments. The overall effect in the number of establishments within the Swedish wood industry has during the years from 1990 to 2002 decreased nearly 14.5 % and the primary reason for this is a general decrease within most Swedish regions.

There are only three regions where we can observe rising numbers of firms within the wood industry. These are the counties of Södermanland, Jönköping and Västmanland which all increase the number of establishments within the region by about 10%. The remaining 18 regions consequently experienced a decrease of the number of firms of around 5-35% between these years. The restructuring of the county classification when the counties of Göteborg and Bohus, Älvsborg and Skaraborg now makes Västra Götaland county and the counties of Kristianstad and Malmöhus now making the county of Skåne has had a significant impact on the locational map of the wood industry. Västra Götaland is now creating a region of great importance with its outstanding number of establishments and the Skåne region is today also one of the larger within the industry.

In contrast to the more successful regions there are also a few regions that are experiencing a difficult period during this period on top of the general decrease. Three regions worth pointing out when analysing the size and share of the decrease are the county of Kronoberg which during these years experienced a decrease of about 23% and the counties of Västerbotten and Norrbotten that respectively decreased 28% and 34%. These decreases are mainly a consequence of a falling number in small and medium sized establishments. When analysing the patterns concerning the number of employed within the Swedish wood industry we can see that there has been a fairly large decrease of these numbers between 1990 and 2002. The total number of employed by the industry decreased from 49103 in 1990 to 34762 in 2002 which can be translated into a decrease in the neighbourhood of 29% (Appendix: Table A3 and Table A4). In general we can observe a drop in the number of people employed in every region but does also make the observation that there are three exceptions. These are the counties of Kronoberg, Blekinge and Västmanland. When examining the number of employed within the wood industry we observe that during 1990 to 2002 the number of workers has increased by 1% in the county of Kronoberg, by 41% in the county of Blekinge and by 5% in the county of Västmanland compared to the general decrease of 29%.

By 1990 there were four regions that were employing more than 3000 people. These were the counties of Jönköping, Kalmar, Västerbotten and Dalarna. They respectively represented 5788, 4282, 3565 and 3524 employed within the wood industry this year. However by considering the future reconstruction of the Swedish counties we can add that

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the area of the future Västra Götaland would employ 6035 workers and the area of Skåne County would employ 4690 workers by 1990 within the industry. By 2002 the situation observed are quite different.

As a result of the general decrease of employed within the wood industry we now see the two new up comers among the four largest employing regions. The largest regions with respect to the number of employed are by 2002 the counties of Västra Götaland, Jönköping, Skåne and Kalmar which respectively employ 4042, 3831, 3181 and 3180. In order to visualize the process within the Swedish wood industry even further we can study diagrams A1 and A2 in the appendix that show the share of establishments within each county in relation to the total wood industry. We can by looking at the diagrams see that the share of the industry between the counties has not changed over time.

In the same way as we just looked at the number of firms we can also show this trend with diagram A3 and A4 in the appendix, dealing with the share of employees within the wood industry in each separate region. When comparing diagram A3 and diagram A4 we can draw the conclusion that there has not been any large shift of the internal regional shares relative to each other. However we can clearly see the large impact from the structural changes within the Swedish administrative regions.

The reason why Jönköping is experiencing an increase in the share of firms but at the same time a diminishing share of employed during this period is simply the fact that even if the number of firms increased during these years the average number employed within each firm has heavily decreased. The average number employed per establishment was 17.4 in 1990 in comparison to 10.4 employed per establishment in 2002. These numbers we can compare to the corresponding number for the entire industry which was 10.7 in 1990 and 8.9 in 2002.

4.2 Chi-square test

In order to be able to analyse whether the ideas of “more favourable industrial locations” influence firms within the Swedish wood industry and the location patterns within the industry it is crucial to divide the counties into more or less favourable regions. More or less favourable locations are created by classifying the Swedish counties by their accessibilities to the market (Appendix: Table A5).

Figure 4.2.1. Flow diagram of the process of development Infrastructure

e.g. roadnet, railroads or waterways Development of the industry Regional development of industries Accessibility Measurement of the potential within a region

This flow diagram shows the process within a more favourable location. A good infrastructure generates a high potential for economic expansion in the region. This will boost the industrial development.

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The analysis will focus on whether there are connections between the number of firms in more or less favourable locations and the number of employees and more or less favourable locations. In order to compare and analyse the empirical findings and in order to find a baseline that we later can use when drawing our conclusions, we need to calculate an expected value of the number of firm or the number of employees within a region. This value will depend on whether the region is characterized by a favourable location or not, the number of firms that have disappeared from the market and the total number of firms in the region.

The results of the analysis aim to answer whether the probability of survival is higher of a firm in a more favourable location and vice versa. When making the analysis the empirical data concerning the firms are plotted into a table that consists of four sections. These are the number of survivors vs. not survivors and favourable vs. not favourable locations. The table looks like the one in table 4.1.1. In order to be able to say that there are connections between a higher probability to survive in a more favourable location we want to see higher values than expected in A and D and lower values than expected in B and C.

Table 4.1.1. Expected and observed number of firms/employees

Favourable Location Not Favourable Location

Survive (A) A (B) B

Not survive (C) C (D) D

Observed values (with the expected values in parentheses).

Table 4.1.2. Chi-square test results.

Nr. of Employees 2

χ

All firms 2845.24 Small 54.70 Large 60.54 Nr. of Establishments 2

χ

All firms 47.36 Small 51.13 Large 1.14 Chi-square test: E E) -(0 2 cells all 2

=

χ

3.84 critical 2 observed 2 = >

χ

χ

Scourse: Aczel & Sounderpandian, 2006

χ2 = Chi-square value Expected value: n C R E =

E= Expected value of firms/employees R= Total number of firms/employees in

favourable vs. not favourable locations C= Total number of firms/employees that

survive vs. not survive in the market n= Total number of firms/employees

0= Number of firms/employees observed in each part of Table 4.1.1

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The empirical findings from the Chi-square test show if there are any significant connections between the data collected concerning the number of firms and the number of employees. In order to say that there is a connection between the empirical data and the hypothesis that there is higher probability for a firm to survive in a more favourable location we need to observe aχ2-value greater than 3.84. If we observe a value greater than

3.84 it means that the H0 statement described earlier in this chapter can be rejected and the

alternative H1 statement can be accepted. The analysis of the outcome from the Chi-square

test is divided into six parts in order to give an as broad analysis as possible. Initially the sample data is divided into the analysis concerning firms or employment. These two are then in each turn divided into the analysis concerning all firms that deal with firms of all sizes, small firms (1-99 employees) and large firms (100 employees and larger).

Table 4.1.3. Number of Establishments (all firms)

All firms Favourable Location Not Favourable Location Total

Survive (2378) 2458 (1535) 1455 3913

Not survive (403) 323 (260) 340 663

2781 1795 4576

Observed values (with the expected values in parentheses).

Table 4.1.4. Number of Establishments (small firms)

Small Favourable Location Not Favourable Location Total

Survive (2341) 2423 (1505) 1423 3846

Not survive (393) 311 (252) 334 645

2734 1757 4491

Observed values (with the expected values in parentheses).

Table 4.1.5. Number of Establishments (large firms)

Large Favourable Location Not Favourable Location Total

Survive (37) 35 (30) 32 67

Not survive (10) 12 (8) 6 18

47 38 85

Observed values (with the expected values in parentheses).

The result from the Chi-square tests suggests that there are connections between the number of surviving firms or a sustainable employment situation within an area that is more favourable with respect to other regions in a location viewpoint. In table 4.1.3 we can observe the total development of the number of establishments between 1990 and 2002. By calculating theχ2-value to 43.36 we assume that there are connections between survivability and overall firm location. In the case of small firms that are shown in table 4.1.4 we can also observe aχ2-value larger than 3.84 (χ2=51.13) that suggest that that

there are links between the survivability and firm location. In the third case that only includes larger firms (see table 4.1.5) there is an observedχ2-value that is less than 3.84

namelyχ2=1.14 which mean that there are no statistical connection between survivability

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Table 4.1.6. Number of Employees (all firms)

All firms Favourable Location Not Favourable Location Total

Survive (20224) 17574 (14538) 17188 34762

Not survive (8343) 10993 (5998) 3345 14341

28567 20536 49103

Observed values (with the expected values in parentheses).

Table 4.1.7. Number of Employees (small firms)

Small Favourable Location Not Favourable Location Total

Survive (12216) 12497 (12187) 11906 24403

Not survive (3786) 3505 (3777) 4058 7563

16002 15964 31966

Observed values (with the expected values in parentheses).

Table 4.1.8. Number of Employees (large firms)

Large Favourable Location Not Favourable Location Total

Survive (5823) 5573 (4536) 4786 10359

Not survive (3961) 4211 (3087) 2837 7048

9784 7623 17407

Observed values (with the expected values in parentheses).

In the case of examining and analysing the number of employees in more or less favourable locations within the Swedish wood industry in relation to the survivability we can observe a similar pattern that occurred from the analysis of the development of the number of firms. In table 4.1.6 the total employment situation within the wood industry is considered and by calculating theχ2-value to 2845.24 we assume that there are connections between a higher

likelihood of long employment and a favourable industrial location.

When analysing the development of employment within small firms and the links between the survivability in more or less favourable locations (see table 4.1.7) we see that there are connections. Theχ2-value that corresponds to small firm survivability in a more

favourable location is 54.70. The last part in this analysis is dealing with how the employment has been changing and if we can observe any links between the numbers of employed in larger firms and the infrastructural situation in the region (table 4.1.8). Unlike in the case of large firm establishments we can observe that there according to the Chi-square test (χ2=60.54), there are links connecting positive employment development and a

more favourable industrial location.

The total number of employees within the industry should be considered to be a stronger measurement than the total number of firms in the final analysis on the development within the Swedish wood industry. The total number of firms or establishments within the industry can naturally give an idea about the trend over the years but the total number of employed within an industry in a specific region should be able to give a better and stronger picture on the actual changes and the development of the Swedish wood industry. By strictly considering the Chi-square tests, we can observe that there in fact are connections between these more favourable located regions and a more positive development within this particular industry.

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5 Conclusions

The aim for this thesis is to examine if the firms make location decisions that correspond to the theoretical framework that tells us about how firms should distribute themselves over an area. The theory in this case tells us that there should be a strong relation between the locations of the industrial development and where the market is situated or where the raw material deposits are situated.

My conclusion from the results in chapter 4 is that there are definitely links between being situated in a more favourable location and the development of the Swedish wood industry with respect to the number of established firms and the number of employees. In both cases concerning the number of firms and the number of employees we can observe a positive relationship in relation to more favourable locations. The only exception where we don’t detect sufficient correlation between these factors is the analysis of the number of larger establishments. In relation to the outcome of this assessment we have to consider the outcome from the analysis of the employment within the large establishments that in fact show that there are connections between a favourable location and a positive development of employment. Also to keep in mind in this case is that the total number of employees within a specific region might be a better and stronger measurement when analysing the changes over time.

The Chi-square tests suggest that there are significant connections in order to make the conclusion that there are links between a more favourable industrial location to both the survivability of firms and a positive employment development.

A problem that can be observed is of course that all the firms within the Swedish wood industry don’t have the same possibility to relocate parts or perhaps the entire production to a more favourable location simply since the costs tied to new investment e.g. new machinery and buildings would be too high. These problems affect firms in different ways where the smaller firms which do not hold large capital stocks naturally have fewer possibilities to relocate than larger actors. Another factor that might have an impact on how the locational patterns look like within the wood industry could be that firms historically are tied to certain locations or regions which make the decision makers within the firms less influenced from pure economical factors. An example of this kind of firm could be a family company or other kinds of very locally based firms.

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5.1 Further research:

 It could be interesting to analyse locational decision effects deriving from being located close to e.g. universities or other research institutes. What kind of industries is more or less interested in these kinds of proximities?

 More detailed analysis of how favourable locations affect locational choices on a more local scale.

 Research in order to determine the factors which have the greatest impact on a firm’s industrial location development.

 To what extent does the historical development of a firm affect its future development plans? Are there higher probabilities in some industries for relocation than in others and if so which are these industries and what are their characteristics.

References

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