The Demography of Employment in a Swedish County Council: Estimation and Mapping of Manpower Statistics in Östergötland

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MSc in GeoInformatics 2004-2005

LINKÖPING UNIVERSITY

Final Thesis

THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH

COUNTY COUNCIL: ESTIMATION AND MAPPING OF

MANPOWER STATISTICS IN ÖSTERGÖTLAND.

By

Andreas-Nikolaos Papandreou

2005-02-22

ISRN LIU-IDA-D20--06/005--SE

Supervisor: Vivian Vimarlund

Examiner: Vivian Vimarlund

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Andreas-Nikolaos Papandreou ₂ Dedicated to Helena Svensson for introducing me to the country of Sweden.

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Andreas-Nikolaos Papandreou ₃

ABSTRACT

The changing requirements in the modern labour market have led to a new form of economic geography of employment, where skills, wages and the uncertainty of employment play a primary role in the spatial division of labour.

The main purpose of this project is to investigate the use of Geographical Information Systems (GIS) as a tool to illustrate employment and unemployment in Östergötland County for giving information on the development of the labour market. In addition, the use of GIS for population data analysis with the help of Oracle’s map viewer is closely examined. This descriptive thesis reveals that the labour market is characterized by the geographic extension of the market and its determination by how far the supply and demand forces go and the important role that GIS plays in illustrating the distribution of workforce in Östergötland’s labour market.

GIS is an analytical tool for employer/employee demographics that can be used for visualization but also for analysis and pre-processing purposes with the use of graphic tools. With the use of thematic maps, GIS can visualise spatial data with labour data according to certain demographic criteria.

GIS technology has ways of mapping thematically the local labour market demand and supply. In addition, it is capable of constructing a comprehensive workforce development system that can integrate the job seekers and employers. GIS can facilitate the development of visual web-based mapping systems that allow users to investigate and find employees within various industries.

Keywords:

GIS, labour market, demand for labour, supply of labour, employer/employee demographics, manpower statistics, Östergötland, Oracle

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ACKNOWLEDGEMENTS

I would like to take this opportunity to thank all those who have contributed to this thesis, directly or indirectly.

I would first like to thank my supervisor Dr. Vivian Vimarlund for her great support, good ideas and patience. Furthermore, I want to thank my fellow MSc students for our fruitful discussions and good company and especially my friends Mathias Ashu Tako Tambe Ebot for his nice opponentship, Aleksander Karol Gumoś and Rahel Hamad Sedik for their critical comments. A special thanks goes to my good friend Nikos Karagiannakis for his valuable comments. In addition, I would like to thank my country Greece and its people for giving me the benefits of the Greek culture and values of life.

Last but not least I want to thank my family and friends in Greece and Sweden for encouraging and supporting me in various ways. My warmest thanks to Manolitsa Kazakou,

Olga Papandreou, Anna Papandreou, Nikos Mykoniatis and Olga Mykoniati.

Andreas-Nikolaos Papandreou

February 2005, Department of Computer and Information Science (IDA),

Linköping University, Sweden

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LIST OF TABLES

Table 1.1: Employment in Sweden (Statistics Sweden 2004) Table 2.1: Aims and uses of GIS (Bernhardsen 1999)

Table 2.2: Comparison of vector and raster data (Bernhardsen 1999, Buckley 1997)

Table 4.1: Östergötland’s population according to age and gender as of December 31st, 2003 (Facts about Östergötland 2004)

Table 4.2: Changes in Östergötland’s population during 1978-2003 (Facts about Östergötland 2004)

Table 4.3: Population in Östergötland and proportion of gainfully employed men and women as of December 31st, 2002 (Facts about Östergötland 2004)

Table 4.4: Personal income tax rates for 2004 (Facts about Östergötland 2004)

Table 4.5: Average taxable earned income, income year 2002 for Östergötland (Statistics Sweden 2004)

Table 5.1: Monthly unemployment in Östergötland during 2003 (Facts about Östergötland 2004)

Table 5.2: Number and proportion of unemployed persons aged 16-64 during 2002-2003 (Facts about Östergötland 2004)

Table 5.3: Persons that are unemployed or in labour market schemes during 2003 (Facts about Östergötland 2004)

Table 5.4: Unemployed men and women during 2002-2003 (Facts about Östergötland 2004) Table 5.5: Workplaces in 2003 by sector and region (Facts about Östergötland 2004)

Table 5.6: Economically active population in 2002 aged 20-64 by industry and level of education; unemployed or not in labour force by level of education (Facts about Östergötland 2004)

Table 5.7: 25 largest employers in Östergötland during 2003 (Facts about Östergötland 2004) Table 5.8: Average earned income among men and women of Östergötland’s municipalities during 2001 in 1000’s SEK (Alpkvist & Pettersson Molinder 2003)

Table 5.9: Social security contributions in Sweden during 2001 (Swedish Tax Agency 2004) Table 5.10: Commuting of men between municipalities within Östergötland during 2001 (Facts about Östergötland 2004)

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Table 5.11: Commuting of women between municipalities within Östergötland during 2001 (Facts about Östergötland 2004)

Table 5.12: Commuting of men and women between municipalities within Östergötland during 2001 (Facts about Östergötland 2004)

Table 5.13: Commuting from Östergötland County to other counties in Sweden during 2001 (Facts about Östergötland 2004)

Table 6.1: Statistics of Östergötland’s total population during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Table B1: Spatial reference information- Projection-Gauss –Kruger (Department of Computer Science -IDA- Linköping University 2004)

Table B2: Geographic Coordinate System RT90 Kruger (Department of Computer Science -IDA- Linköping University 2004)

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LIST OF FIGURES

Figure 1.1: The Country of Sweden (Economist 2004)

Figure 2.1: Layers of a GIS (Guide to Geographic Information Systems 2004) Figure 2.2: What is GIS (AGI 2004)

Figure 2.3: Functions and applications of GIS (Mennecke 1997) Figure 2.4: Vector and raster formats (Buckley 1997)

Figure 2.5: A digitizer (Bernhardsen 1999) Figure 2.6: A GIS model (Mennecke 1997)

Figure 2.7: Various thematic layers (Buckley 1997) Figure 2.8: Labour supply curve

Figure 2.9: Labour demand curve at a fixed price Figure 2.10: Labour market equilibrium

Figure 3.1: GIS chain (Bernhardsen 1999)

Figure 3.2: The typical GIS learning curve (Buckley 1997) Figure 3.3: The typical GIS productivity curve (Buckley 1997)

Figure 4.1: Östergötland and Sweden (Facts about Östergötland 2004) Figure 4.2: Östergötland’s population (Facts about Östergötland 2004)

Figure 4.3: Map of changes in Östergötland’s population during 1978-2003 (Facts about Östergötland 2004)

Figure 4.4: Map of regional GDP per inhabitant by county during 2002 (Facts about Östergötland 2004)

Figure 5.1: Map of unemployment in Östergötland’s municipalities during 2003 (Facts about Östergötland 2004)

Figure 5.2: Map of labour market schemes participation of Östergötland’s municipalities during 2003 (Facts about Östergötland 2004)

Figure 5.3: Map of expanded unemployment in Östergötland’s municipalities during 2003 (Facts about Östergötland 2004)

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Figure 5.4: Layoff notices and vacancies (Konjunkturinstitutet 2004)

Figure 5.5: Percentage of women and men aged 25-64 years with some form of post-upper secondary education in Östergötland’s municipalities during 2002 (Alpkvist & Pettersson Molinder 2003)

Figure 5.6: Percentage of women and men with tertiary education by sectors in Östergötland during 2002 (Alpkvist & Pettersson Molinder 2003)

Figure 5.7: Map of regional GDP per inhabitant by municipality in Östergötland during 2001 (Facts about Östergötland 2004)

Figure 5.8: Commuting to work flows in Östergötland (Facts about Östergötland 2004) Figure 6.1: Population density map of men 20-64 years old in Östergötland during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.2: Map of Employment Office (Arbetsförmedlingen-Af) in Linköping (Linköping’s Municipality 2005)

Figure 6.3: Proportion of job seekers aged 18-64 years in Norrköping as of March 31st, 2002 (Norrköping’s Municipality 2005)

Figure 6.4: Map viewer’s architecture (Oracle Corporation 2005) Figure 6.5: Map viewer’s main page

Figure 6.6: Total population per municipality in Östergötland during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.7: Total population pie per municipality in Östergötland during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.8: Detailed analysis of population distribution in Linköping Municipality during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.9: Detailed analysis of population distribution in Norrköping Municipality during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.10: Total population of men pie per municipality in Östergötland during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.11: Total population of women pie per municipality in Östergötland during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.12: Unemployment per municipality in Östergötland during 2000 (Department of Computer Science -IDA- Linköping University 2005)

Figure 6.13: Proportion of people being gainfully employed aged 20-64 years in Norrköping during 2000 (Norrköping’s Municipality 2005)

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Figure 6.14: Proportion of people’s level of education aged 20-64 years in Norrköping during 2000 (Norrköping’s Municipality 2005)

Figure 6.15: Map of Östergötland (Sverige Guiden 2005) Figure 6.16: Östergötland’s map (Stadskartan 2005)

Figure 6.17: Map of transition to tertiary education by municipality in Östergötland (Facts about Östergötland 2004)

Figure 6.18: Map of university entrants per 1000 inhabitants in Östergötland (Facts about Östergötland 2004)

Figure 6.19: Map of participants in adult educational associations per 1000 inhabitants in Östergötland during 2003 (Facts about Östergötland 2004)

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CHAPTER 1: INTRODUCTION AND BACKGROUND

1.1 Introduction

In March 2000, the European Council in Lisbon set out a ten-year strategy for the European Union (EU) “to become the most competitive and dynamic knowledge-based economy in the world, capable of sustainable economic growth with more and better jobs and greater social cohesion” (Lisbon European Council 2000). The strategy was created to enable the EU to recover the conditions for full employment and to strengthen organization in the job market by 2010. According to a survey made by the European Union within the following years most of the EU Member States will face a serious shortage of labour due to the ageing population. Because of this demographic trend that threatens future growth in the EU, extensive measures will be required to maintain full employment and sustainable development (Getaway to the European Union 2004).

The European Council also decided that the general objective of the measures should be to lift the total EU employment rate to 70% and to increase the number of women that work from an average to more than 60% by 2010. Moreover, the European Council in Stockholm in March 2001 added two intermediate and one additional aim: the employment rate should be raised to 67% overall by 2005, 57% for women by 2005 and 50% for older workers (aged 55 to 64) by 2010.

A key instrument for reaching the ambitions set from the Lisbon Council is the European Employment Strategy (EES). With its guidelines and recommendations it offers an integrated framework and the analytical support to continuously assess and monitor labour market developments in the EU.

1.2 Background

1.2.1 The country of Sweden

Sweden is the third largest country in Western Europe (after France and Spain) with a land area of 449,964 square kilometres, of which 53% is covered by forests, 17% is covered by mountains, 8% is cultivated land, 10% is swamps, 3% is residential areas and there is a 9% of lakes and rivers (Official Getaway to Sweden 2004). In the west it borders with Norway and in the northeast with Finland.

The population is approximately 9 million (8,975,670 –as of December 31st 2003) and the population growth is 0.2% (1998-2003, average). This gives a population density of about 20 people per square kilometre, which makes Sweden one of the most sparsely populated countries in Europe. About 85% live in the south part and 1.5 million live in Stockholm, which is the capital of the country. It is a multi-cultural society that has attracted many foreigners from all over the world; over 10% of the population is immigrants.

Sweden has harmonized its economic policies with those of the EU of which it is a member since January 1st_{, 1995. However, the Swedish electorate rejected the European single} currency (Euro) for the time being in the referendum held in September 14th, 2003, although the country fulfils the criteria for participating in the European Monetary Union (EMU).

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Sweden is known worldwide for its high general standard of living and life expectancy, with publicly financed systems of economic security for all people in all phases of their life.

Figure 1.1: The Country of Sweden (Economist 2004)

1.2.2 The economy of Sweden

Sweden has accomplished a desirable by many other countries standard of living, aided by adopting peace and neutrality for the whole twentieth century, under a mixed system of high-tech capitalism and extensive welfare benefits.

Although Sweden is a small country, it has a strong industrial base, characterized by internationally known companies such as Ericsson, SAAB, Volvo and ABB. The country’s economy depends to a large extent on the success of its international trade. During 2003 exports of goods and services amounted to 43.9% of the gross domestic product (GDP), while imports of goods and services were 37.3% (Statistics Sweden 2004).

The Swedish economy is in a relatively strong position among the EU member states with a growth rate of 0.7% in the GDP for the fourth quarter of 2003 (Eurostat 2004). Sweden’s GDP for the whole year 2003 slowed to 1.6%, down from 2% in 2002. The GDP per person reached $ 27,430 (2003, at purchasing power parity-PPP-weights) (Economist 2004), which is 115% of the EU average. Inflation is currently low at a rate of 2%. Sweden’s Central Bank (Riksbank 2004) has set the key-repo rate (the seven-day inter-bank lending rate) at 2.00%. Generally, the Swedish markets, such as electricity and telecommunications are quite open and competitive, while Sweden is considered a leader in market deregulation. However, the public sector continues to play a major role in the economy and is still large in terms of employment and value.

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Most important export goods include: electrical and computer equipment, motor vehicles, machinery, chemical products, pharmaceuticals, paper, iron and steel. Sweden’s exports were decreased by 0.6% during the fourth quarter of 2003.

Most important imported goods in Sweden include: Petroleum products, motor vehicles and accessories, machinery, electrical and computer equipment, food stuffs, textile products, footwear. Sweden’s imports increased by 1.5% during the fourth quarter of 2003.

However, although Sweden remains one of the wealthiest countries in the world, its tightly regulated and highly socialized economic model is now viewed far more critically than before.

1.2.3 Structure of the labour market in Sweden

Sweden has a developed labour market and has been described as the European capital of the Internet (BBC 2004). The Swedish National Labour Market Administration (AMS) runs a network of about 300 job centres and offers information and various counselling services (AMV 2004). Increasingly, private-placement firms are also offering recruitment and mediation services by helping workers find jobs, sometimes under contract with the state or local governments. Use of temporary workers is also growing in almost all sectors. Both Manpower and Adecco are active in the Swedish market and there are several large Swedish job placement firms as well.

At the end of 2003 employment had approximately 4,234,000 persons, which is about 47% of the total population (Statistics Sweden 2004). The hours of work are 40 per week. The labour cost, which is among the highest in Europe, is SEK 202.40 per hour (for wage earners) and SEK 41,370 per month for salaried employees (Statistics Sweden 2004). This is because employers pay considerable payroll taxes and also the contributions agreed by the employers’ union and the employees’ unions.

Both men and women are gainfully employed, although women often prefer part time jobs in comparison to men that prefer full time jobs (Eures 2004). In addition, women often earn less money than men because traditional female occupations have low wage levels. For example, during 2001, 71% of all employees were full-time workers but most part-timers were women. The average income of male full-time employees in 2001 was SEK 265,000 and of female full-time employees SEK 221,000. In 2001, 5% of all adults (people over 18 years old) had assessed earned incomes exceeding SEK 400,000. They received 16% of the taxable income and paid 22% of the tax (Skatteverket 2004).

About 48% of the Swedish population of 8.9 million during 2002 were either employed or self-employed, i.e. were part of the economically active population.

Sweden’s workforce is generally well educated. The retirement age for both men and women is 65 years. The public sector remains large in terms of employment and contributes 30% of all services provided in the country. Although the public sector gives a high level of employment to women, less than 60% work full-time. As in most highly developed economies, the services sector contributes most of total GDP, at over 60% (Economist 2004).

Nevertheless, during the last years this amazingly positive picture of Sweden has been darkened by budgetary troubles, high unemployment, and an ongoing loss of competitiveness

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in international markets that affected the labour market. Unemployment has reached 4.9% (average 2003) (Statistics Sweden 2004) (see table 1.1) and measures should be adopted to combat the increasing rate of it.

Employed, thousands Unemployed Unemployment rate Labour force rate

Year Total Men Women thousands Total Men Women Total Men Women 2002 4,244 2,197 2,047 176 4.0 4.4 3.6 78.0 79.8 76.1 2003 4,234 2,191 2,043 217 4.9 5.3 4.4 78.1 79.9 76.2

Table 1.1: Employment in Sweden (Statistics Sweden 2004)

1.3 Problem Formulation

The problematic that is going to be investigated is the use of Geographical Information Systems (GIS) as a tool to illustrate employment and unemployment in Östergötland County for giving information on the development of the labour market. In addition, the use of GIS for population data analysis with the help of Oracle’s map viewer will be closely examined. In order to understand the use of GIS within the labour market and the challenges faced when implementing and operating a GIS, it is important that GIS is introduced and presented, first from a general point of view and then with respect to the demography of employment. This will give answers in what precisely a GIS is, what kind of data are needed and are available, what specific applications are relevant to the above mentioned aims and which problems must be dealt with to make the system more effective.

1.4 Delimitations

During the study process some delimitations were considered:

In October 24th, 1998 the Personal Data Act (1998:204) came into force in Sweden (Hall & Beusen 2003). The Personal Data Act is based on the Directive 95/46/EC. Section 33 of the Act was amended in 1999 to implement the EU Directive on the transfer of personal data to a third country. Data protection applies to a large amount of governmental information, including the SPAR population database, certain types of statistics, certain types of real property information and geographical information (addresses, real estate unit number), vehicle registries and VAT files that restrict the access to geographical information due to the legal protection of privacy. Moreover, it was not possible to acquire primary GIS labour data of the most recent years in Östergötland, but only GIS population data for the year 2000.

1.5 Methods

The use of GIS within the labour market in general, is a new research area. As already mentioned, this thesis aims to portray the use of GIS within the job market and especially in the distribution of workforce (employees and unemployed) and take the area of Östergötland, Sweden as an example.

Research methods will include a combination of theoretical analysis (literature review) and empirical analysis (application of GIS data in making maps).

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Studying of recent publications will constitute the literature review in this field. The aim of the literature review is to show what has been done in the field and how the current study relates to earlier research. The review will give an overview of the findings of various previous studies and identify general patterns of the findings and the conclusions that can be made. As part of the study, secondary data was collected from various publications to better understand and explain the research problem. These publications include general statistics, government publications, periodicals and books, online data sources, business information, research reports and international information.

Moreover, the Department of Computer Science –IDA at Linköping University offered primary demographic data in a CD because of the difficulty and limitations faced in collecting data in GIS. This CD had detailed population data of Östergötland, which included total population (men and women) from 0 to 100 years old for the year 2000. The offered population data was helpful in showing general demographic characteristics such as total population, population of men and women according to different data sets. In addition, GIS analyses were executed with the use of Oracle to present data in the form of maps and provide additional GIS functionality. By holding location data in an Oracle database several mapping applications were used to view geospatial data and analyse population data of Östergötland. During this study, the focus on the implementation of GIS in the analysis of the labour market in the selected area and its consequences were prioritised. As a result, this thesis mainly represents a theoretical (descriptive) study of this implementation and its accompanying benefits and problems.

1.6 Thesis Outline

This study is divided into six chapters.

Chapter 1 gives a short introduction to the subject and the central theme of the thesis is highlighted. Moreover, the chosen topic is brought in and the aims of this study are being presented together with their limitations and methods. In addition, the structure of the following chapters is being provided.

Chapter 2 defines the basic terms related to GIS and the supply and demand for labour. Moreover, the use of GIS as an analytical tool for employer/employee demographics and the labour market are presented, necessary for further discussion. Furthermore, the relationship between GIS and Oracle is being introduced.

Chapter 3 explains the process of implementing GIS and the GIS chain used for identification and analysis of problems in the labour market.

Chapter 4 introduces the study area, which is Östergötland County and gives a brief outline of its geography, population and economy.

Chapter 5 describes the working environment in the study area and provides information on the development of the labour market and the factors that influence it.

Chapter 6 presents the methodology and research development. The data (secondary and primary) and methods used for collecting it, its logic and limitations are highlighted.

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Moreover, analysis with GIS is being performed with the use of Oracle’s map viewer. In addition, chapter 6 discusses the conclusions regarding the use of GIS within the job market in Östergötland in connection to labour supply and demand. Finally, there is a review of the objectives of the study and some last concluding remarks concerning the results and areas of further research.

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CHAPTER 2: THEORETICAL FRAMEWORK

2.1 What is GIS?

According to the GIS dictionary of the Association for Geographic Information (AGI 2004), GIS is an abbreviation for Geographic Information Systems, a computer system for capturing, storing, checking, integrating, manipulating, analysing and displaying data related to positions on the Earth's surface. Typically, a Geographical Information System is used for handling different types of maps, which might be represented as several different layers, where each layer holds data about a particular kind of feature and that can be customers, buildings, streets, lakes, or postal codes (figure 2.1). Each feature is linked to a position on the graphical image of a map.

Figure 2.1: Layers of a GIS (Guide to Geographic Information Systems 2004)

Layers of data are organised to be studied and to perform statistical analysis. Uses are primarily government related, town planning, local authority and public utility management, environmental, resource management, engineering, business, marketing, and distribution. The figure 2.2 shows the physical components of GIS.

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Figure 2.2: What is GIS (AGI 2004)

GIS is a new but fast developing technology that already represents more than a billion US dollar industry worldwide with an annual growth rate of 25% (Bernhardsen 1999).

Implementing GIS can accomplish significant benefits, which are often related to different aims and uses of GIS, as illustrated in table 2.1:

Aim Map production Map production

and internal use of data

Map production, internal use of data and shared use of data Task • storage • manipulation • maintenance • presentation • map production • planning • facility maintenance • project management • map production • project • planning • facility maintenance • coordination • general service • facility management • economic planning • service and information Benefit/cost ratio 1:1 2:1 4:1

Table 2.1: Aims and uses of GIS (Bernhardsen 1999)

In addition, GIS functions can be used for various applications, such as spatial visualization (showing information using a coordinate system), database management (managing information), decision modelling (basis for decision making), design and planning

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(constructing spatial projects) that can be portrayed in figure 2.3. However, these functions involve explicit GIS applications such as spatial data collection and automated mapping, facility management, market analysis, transportation, logistics, strategic planning, decision making, design and engineering (Mennecke 1997).

Figure 2.3: Functions and applications of GIS (Mennecke 1997)

GIS technology puts together ordinary database operations such as query and statistical analysis with the unique visualization and geographic analysis advantages offered by maps. These capabilities differentiate GIS from other information systems such as CAD, business planning tools and economic information systems.

CAD or Computer-Aided Design is an automated system for the design, drafting and display of graphically oriented information that is mainly used in architecture and engineering applications. The difference between CAD and GIS is that the former focuses more in design rather than to analysis and sometimes cannot process the complicated information of geo-referenced data and its combination from many different sources.

Business planning tools can be books or software that helps companies to attract more customers, increase their turnover and finally their profits. However, they do not use spatial analysis in making strategic decisions like GIS does.

Economic Information Systems cover different aspects of economics and organizations with the use of information technology. Economic Information Systems involve communication and transfer of information between people, as well as the development of suitable information systems for this purpose. They also deal with the use of modern information technology and the development of structures within organizations, together with the effects

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of information technology on people and organizations. The difference with GIS is that the latter manages geo-referenced data for its analysis.

The use of GIS by organizations offers improved efficiency that can lead to an increase in revenues and a decrease in costs and time spent by the staff. Moreover, it can facilitate fast and reliable decision –making both in private and public administration. However, sometimes, projects in GIS focus more than needed on technology and underestimate organisational tasks due to a lack of a clear strategy. When successive thematic layers are added in the analysis they can create mismatches. Many different users will be supplied with a lot of data from different areas that can complicate them. During some previous GIS projects the cost was not estimated properly due to organisational problems and financial losses took place. The necessity of having a focused strategic plan will be discussed in more detail in chapter 3.

2.2 GIS Data

As mentioned earlier, a GIS stores data about the world as a compilation of themed layers that can be used together. Such data contains an explicit geographic reference, such as latitude and longitude coordinate, or an implicit reference such as an address, postal-code, or road name. A successful GIS involves explicit references, which can be generated from implicit references by cross-referencing specifically recorded x, y co-ordinates of a location and non-geographic data such as addresses or postal-codes (Guide to Geographic Information Systems 2004).

GIS data consists of three types:

• Spatial or map data that is stored in Shape files (e.g., *.SHP): this data includes areas, lines, points and give information about location, shape and relationships among, geographical features, usually stored as co-ordinates and topology (AGI 2004).

• Non-spatial or attribute data that is stored in dBase Tables (e.g., *.DBF): this data can be a trait, quality or property describing quantitative or qualitative characteristics of a geographical feature.

• Image data: this data corresponds to a graphic description of a scene that is produced by an optical or electronic device such as satellite data, aerial data, pictorial and scanned data.

Another important classification of GIS spatial data is vector and raster data, as illustrated in figure 2.4 (Bernhardsen, 1999). Vector data tries to represent the real world by presenting positional data in the form of x, y co-ordinates. In vector data, the basic units of spatial information are points, lines (arcs) and areas (polygons). Each of these units is created basically as a series of one or more co-ordinate points, for example, a line is a collection of related points, and a polygon is a collection of related lines. Points, lines and areas may also carry attributes that can be digitised and the digital information can be stored. Vector data may or may not hold topological relationships.

In raster data, representation of the real world is expressed as a matrix of grid squares (cells) or pixels, with spatial position implicit in the ordering of the pixels (AGI 2004). With the raster data, spatial data is not continuous but divided into discrete units. This makes raster data mainly appropriate for certain types of spatial operation, for example overlays or area calculations. In comparison to vector data though, there are no implicit topological

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relationships. Both raster and vector data make it possible to link information about objects to external descriptions and other databases.

Figure 2.4: Vector and raster formats (Buckley 1997)

Both vector and raster data have advantages and disadvantages that are listed in the following table and should be taken into consideration before deciding which type of spatial data to use (Bernhardsen 1999, Buckley 1997):

Vector data

Advantages Disadvantages

Good representation of reality Complicated data structures

Good graphic output Location of each vertex needs to be stored explicitly

No data conversion is required since most data is in vector form

Difficult to function simulation Efficient encoding of topology Spatial analysis and filtering within polygons

is impossible. Better for documentation, line presentations,

commercial implementations

Raster data

Advantages Disadvantages

Simple data structures Hard to access it

Easy overlay Difficulty to adequately represent linear features depending on the cell resolution. Discrete data and continuous data are

accommodated equally good

Process of large amount of data Grid-cell systems are very compatible with

raster-based output devices

Since most input data is in vector form, data must undergo vector-to-raster conversion Better for showing the geographical variation

of phenomena and area presentations

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Generally, the kind of data chosen depends on the data applications of the users.

2.2.1 Data input

For real world data to be analysed and operated by a GIS, it must be first captured and altered to digital format. It has been estimated that at least 80% of all problems can either be linked to a geographic position-geo referenced –or their area can be identified (Bernhardsen 1999). It is the possibility to geo- reference data that makes it possible to identify and analyse relationships that previously had not been recognized or had not been possible to study by connecting geological maps and maps showing labour market results, which determine correlations that can offer useful advice to people.

There are various methods, which include primary data capture that is input into the system directly e.g. by the use of remote sensing or Global Positioning System (GPS) and secondary data capture that comes from an intermediate source, e.g. hard copy maps or photos. In this case the data are input via digitisers, scanners or stereo plotters (AGI 2004). Data input raises the issues of accuracy and error, of both equipment and human operators that are discussed later on at chapter 2.3.

For a successful GIS implementation it is very important to create an appropriate digital database that encodes both types of spatial and attribute data. This is because approximately 80% of the cost made during the implementation of a GIS comes from data acquisition, data compilation and database development (Buckley 1997).

There are several methods of capturing spatial data and entering it at a GIS, which include some of the following:

Digitizing existing maps with a digitizer:

Much of GIS spatial data entry is being done by manual digitizing using a digitizer (figure 2.5) (Bernhardsen 1999). That is a device that includes a table upon which a map or drawing is placed and it used to convert map positions in digital form as x, y co-ordinates (AGI 2004). The user of the device traces the spatial characteristics with a hand-held magnetic pen, which is called a mouse or cursor. While tracing the characteristics the co-ordinates of selected points, e.g. vertices, are sent to the computer and stored (Buckley 1997). This procedure can be performed in a point mode, where single points are recorded one at a time, or in a stream mode, where a point is collected on regular intervals of time or distance, measured by an x and y movement. Digitizing can also be done blindly without an immediate viewable graphic result to the person digitizing or with a graphics terminal, which is when the digitized line work is displayed as it is being digitized on an accompanying graphics terminal.

Manual digitising has some advantages like low cost, flexibility and adaptability to various data types and sources. However, it also has some disadvantages, such as that the digitization can be a time-consuming and dull procedure.

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Figure 2.5: A digitizer (Bernhardsen 1999)

Scanning:

Another alternative for capturing spatial data in raster format is to transfer information found in photos or maps with a device called a scanner. With this method, maps or images are converted to digital raster form by systematic line by line sampling (AGI 2004).

Scanning is easy to operate and offers a fast capture of spatial characteristics. Nevertheless, some maps are hard to move and scan, reading of text and other features can be hard to distinguish and the cost can be higher in comparison to manual digitizing.

Remotely sensed imagery, such as satellite imagery:

This technique involves acquiring information about an object on earth without contacting it physically, by taking pictures through earth-orbital satellites. Satellite images can be very useful because they cover large areas and together with the help of computers they help to manipulate large areas of data.

Aerial photographs:

Another remote sensed technique includes the aerial photographs (photos, usually taken from an aeroplane, as a means of remotely recording ground level events). While satellite images are digital images, aerial photographs provide black and white, colour and infrared photographs on film, which can be taken at either vertical or oblique angles, depending on the phenomenon in question and the desired application. Aerial photography differs from satellite imagery in that the results are almost instantaneous and require only developing, as opposed to images which must undertake a great deal of processing before electromagnetic signals resemble real world features. Aerial photographs are used a lot for the production of topographic, land use, municipal maps.

Conversion of other digital data:

This technique involves the conversion to GIS data of existing public or private digital data, such as the one that comes from CAD systems. There are various data conversion programs, mostly from GIS software vendors, to alter data from CAD formats to a raster or topological GIS data format. Although this method is becoming more popular, it requires people with developed technical skills to perform the data conversion from existing digital data to GIS data.

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Other data formats that can be found in the market and can be converted to GIS data include (Buckley 1997):

• US Geological Survey’s digital data (Digital Line Graphs)

• Interactive Graphics Design Software (Intergraph / Microstation)-IGDS • Drawing Exchange Format (AutoCAD)-DXF

• Digital Elevation Models (DEM) • Other GIS data

2.2.2 Data storage and management

An important element for a GIS is the data storage and retrieval subsystem through which the spatial and attribute data are organised in a form, which allows fast retrieval for updating, querying, and analysis (Buckley 1997, Laurini & Thompson 1992). It is common that GIS software operates proprietary software for spatial editing and retrieval and a database management system (DBMS) for attribute storage. Attribute data associated with the topological definition of the spatial data are stored using a data model. Most often these internal database tables contain primary columns such as area, perimeter, length, and internal feature id number for accessing it (Bernhardsen 1999).

A GIS model that is illustrated at figure 2.6 includes four basic GIS functions such as spatial imaging, database management, decision modelling, and design and planning, where each of these functions show four areas that influence the use of GIS: human factors, GIS data management, decision making and collaboration, and planning systems (Mennecke 1997).

Figure 2.6: A GIS model (Mennecke 1997)

GIS data are organized in various themes as data layers (illustrated in figure 2.7) that simplify the storage procedure. In all projects inputting data as separate layers is needed based upon the needs and priorities of the analysis. Data layers are always defined by the requirements of the users and the availability of data so that no polygon overlay takes place in one thematic layer.

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Figure 2.7: Various thematic layers (Buckley 1997)

The proprietary organization of data layers in a horizontal fashion within a GIS is known as spatial indexing. It is a means of storing and retrieving spatial data. There are many strategies for accelerating the spatial feature retrieval process within a GIS. Most of them involve the partitioning of the geographic area into manageable subsets or tiles, which are then indexed mathematically, e.g. by quad trees, by R (rectangle) trees, to allow for quick searching and retrieval. Apart from the fact that specific indexing techniques are used to access data across map sheet (tile) boundaries, spatial indexing is analogous to the definition of map sheets. The advantage in this case is that query performance for large data sets is easily improved and data integrity across map sheet boundaries is ensured (Buckley 1997).

Another important issue in the data storage and retrieval subsystem includes the editing and updating of data. These involve significant functions such as the interactive editing of both spatial and attribute data, the ability to add, manipulate, modify, and delete (independently or simultaneously) both spatial features and attributes and the ability to edit selected features in a batch processing mode (Buckley 1997). It is often needed to have periodic updates of data that require an increased accuracy and/ or detail of the data layer. In addition, such updates can become necessary due to changes in classification standards and procedures. It is necessary for the GIS databases to be designed properly from the beginning so that they can fulfil their aim effectively without problems that may occur during the database update.

Data models give an abstract representation of the real world by describing the behaviour of real-world entities. The database management systems (DBMS) of traditional information libraries or banks that are used to file and retrieve information take place manually and have some disadvantages based on the fact that data are dispersed in various authorities or organizations, the structure and storage methods used are not necessarily the same, verification is not certain, retrieval can be slow, data use can be restricted and only available to limited users (Bernhardsen 1999). On the other hand, DBMS of computerized data libraries and data banks include a set of software for organizing the information in a standard format and offer tools for data input, verification, storage, retrieval, query and manipulation. The latter DBMS have some advantages in comparison to traditional DBMS that include data

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storage in one place, standardized and structured data, joint data from different sources, data are open to verification and data can be accessed fast by many users. However, DBMS of computerized data libraries have also some advantages that involve expertise in the use of databases, high cost, problems faced by the users and risks of the data to be misused or lost.

2.2.3 Data manipulation and analysis

One of the main characteristics of GIS is its capability to convert and integrate spatial data in order to be able to give answers to specific queries. GIS software offers a large range of analysis capabilities that can operate on the topology or spatial aspects of the geographic data, on the non-spatial attributes of these data, or on both.

Data manipulation in GIS involves the maintenance and transformation of spatial data and the ability to input, manipulate, and transform data once it has been created. This manipulation takes place through specific functions that include coordinate thinning, geometric transformations, map projection transformations, edge matching and interactive graphic editing (Buckley 1997).

An advanced method of giving answers to complicated spatial questions is called spatial modelling. Spatial modelling uses spatial characteristics and methods in manipulating data by stringing together sets of primitive analytical functions, which include (Aronoff 1989):

1. Retrieval, reclassification and generalization 2. Topological overlay techniques

3. Neighbourhood operations 4. Connectivity functions.

Analysis is the process of identifying techniques associated with the study of geographic locations together with their spatial dimensions and associated attributes. Spatial analysis is used to evaluate the suitability, estimate, predict, interpret and understand the location and distribution of geographic features and phenomena, while aiming to a suggestion. Analysis is carried out using data from both map and attributes databases that may include logical, arithmetic, geometric and statistical operations or combinations of them (Bernhardsen 1999, Malczewski 1999):

• Logical operations use set Algebra or Boolean algebra. Set Algebra uses the operators = (equal to), > (greater than), < (less than) and combinations such as ≥, ≤, < > under SQL. Moreover, Boolean algebra uses the AND, OR, NOR and NOT operators to test whether a statement is true or false. So when we have two items like A, B the following statements may take place: A and B, A or B, A nor B, A or nor B.

• Arithmetic operations use + (addition), - (subtraction), x (multiplication), / (division), ⁿ (exponential), √ (square root), and the trigonometric functions sin, cos, tan.

• Geometric operations include computations of distances, areas, volumes and directions.

• Statistical operations include sum, maxima, minima, average, weighted average, frequency distribution, bidirectional comparison, standard deviation, multivariate and others. GIS uses statistical operations that are primarily performed on attribute data. In

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the job market, statistics can be used in DBF format from various sources. Consequently, statistical operations must be supported by the systems used to perform studies related to the supply and demand on the labour market.

Moreover, a useful analysis model is the classification process, which is an attempt to group data into classes according to some common characteristics thus reducing the number of data elements. The classification process is a method of generalisation, where attributes are grouped according to limits set by the user. Furthermore, in the reclassification process, attribute values are changed without altering geometries. In digital image processing, images are usually classified according to the spectral properties of the pixels composing the image. In spatial analysis, a map can be classified according to any attribute value, for example, soil types, population density, unemployment etc. The result of performing classification is a thematic derived map.

In addition, another important process is the superimposition, which is a method of integrating geometry and attributes by pointing to the location of a building displayed on screen and request retrieval of all information stored on the building that can be compared to a series of map overlays.

Other similar studies include:

• A polygon overlay, which is an overlay procedure, which determines the spatial coincidence of two sets of polygon features and creates a new set of polygons based upon overlay operating.

• Points in polygons, which include points superimposed on polygons. Moreover, the points are assigned the attributes of the polygons upon, which they are superimposed. • Buffer zones, which are polygons enclosing an area within a specified distance from a

point, line or polygon. Accordingly, there are point buffers, line buffers and polygon buffers that are useful for proximity analysis.

• Network operations, which include systems of connected lines represented in vector data and usually include route optimisation and allocation of resources from/to a centre.

• Raster operations, which are a process that requires discrete cell-by-cell displacements, originating from a single starting point.

• Cartographic algebra, which is based on the assumption that a set of simple operations can be found and joined sequentially to comprise relatively complex modelling.

• Digital terrain models that are digital representations of a continuous variable over a two-dimensional surface by a regular array of z values referenced to a common datum.

2.2.4 Data output and reporting

GIS can process data from a wide range of sources retrieve, analyze and present them for applications in a wide range of disciplines, examples of which are statistics, information sciences etc. For this reason, GIS software generates the data in paper or electronic form depending on the needs of GIS users. Data output can be in the form of maps, diagrams, graphs or other computer generated products. This output takes place by the use of plotters (peripheral device used in the making of hard copy maps or graphical output), computers, printers that have a high-resolution display. The quality of this output and reporting is highly connected to the data type, quality and output device.

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2.3 Data Quality and Reliability

The level of the quality of data sources in GIS processing is becoming an important issue due to the fact that in GIS, data of different origin and accuracy may be mixed. GIS is being widely used for decision-making in various business and service applications and the data quality gives indications of the degree to which data satisfies their needs, budget and available time frame. This includes information about lineage, accuracy, logical consistency and completeness of the data (Bernhardsen 1999, AGI 2004, Buckley 1997):

• Lineage is the origin of a dataset describing its source, content and the processes by which it was derived from that source. It deals with issues related to data capture specifications, collection method of the data and transformation methods applied to the data.

• Accuracy is the closeness of results of observations, computations or estimates to the true values as accepted as being true. It is related to the exactness of the result, and is distinguished from precision, which relates to the exactness of the operation by which the result was obtained. Accuracy has two types, positional and attribute accuracy: 1) Positional accuracy: the degree to which a position is measured or represented, relative to its correct value established by a more accurate process. Moreover, positional accuracy constitutes of two parts, relative and absolute accuracy. Absolute accuracy is related to the accuracy of data components in connection to a co-ordinate system, while relative accuracy deals with the positioning of map features relative to one another, 2) Attribute accuracy: illustration of estimates of the truth through attribute values that include uncertainty and resolution. (Buckley 1997)

• Logical consistency deals with determining the faithfulness of the data formation for a data set by engaging spatial data inconsistencies, such as incorrect line intersections, duplicate lines or boundaries, or gaps in lines, all of which are regarded as spatial or topological errors (Buckley 1997).

• Completeness concerns how fully a data set is and whether both map and attribute data have been entered for all properties. It consists of reflection of holes in the data, unclassified areas and any compilation procedures that may have made data to be removed (Buckley 1997).

Data quality is also influenced by the terms data timeliness and accessibility. The timeliness of data may be influenced when the geometry and attributes of existing objects have been changed. The required degree of timeliness mainly depends on the object type and data application (Bernhardsen 1999). Accessibility shows how reachable data for a specific area is, together with other conditions and limitations that apply to the acquisition and use of this data, such as cost, delivery methods etc.

To ensure data quality ISO standards are applied to data. Internationally, the standards that are developed mainly come from the International Organization for Standardization (ISO), which is the source of ISO 9000, ISO 14000 and more than 14,000 International Standards for business, government and society (International Organization for Standardization 2004). In Sweden, the Swedish project on standardisation in geographic information (STANLI) was initiated by the Swedish Development Council for Land Information and established in 1990 as a national project within the Swedish Standards Institute (SIS). The work programme includes development of a framework of standards for description and transfer of data as well as national application schema standards (Eken & Arken, 1998). SIS aims to be an effective organization for Swedish companies and authorities, where the knowledge of and the gaining

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of access to standards are concerned such as roads, addresses and other layers of geographical information. The NSDI standards in use are (Hall & Beusen 2003): 1) GGD-specification-used for mapping and elevation (height) models, 2) Swedish Standard SS 63 70 03-GGD-specification-used for postal addresses, road networks and railroads.

It should be noted, that there is a strong correlation between data quality and cost since the higher the cost the higher the quality.

Errors exist in all scientific processes and in GIS data processing. They can be introduced at any phase of the data processing and vary from minor to serious ones. There are two types of error that may reduce the quality of GIS and increase the cost, inherent error and operational error (Buckley 1997). Inherent is the error that is present in source documents and data. Operational error is the error generated during the data capture and manipulation functions of a system in GIS. Operational errors may come from inconsistencies such as mislabelling of areas on thematic maps, misplacement of positional boundaries, human errors in digitizing, GIS algorithm inaccuracies and human bias (Buckley 1997).

Depending upon the level of error inherent in the source data, and the error operationally produced through data capture and manipulation, GIS products may hold considerable quantities of error. GIS handing out should be able to spot existing error in data sources and try to diminish the amount of error added during processing. It is important to carry out control measures by expertised users to meet quality requirements that include verification routines of data and careful application of processing results.

2.4 GIS and Oracle

Relational database systems such as Oracle are particularly useful in processing geographical data. ESRI, the leader in GIS software, has fully integrated its industry-leading GIS technology with Oracle’s leading DBMS software. The use of ESRI mapping and spatial analysis software and Oracle’s DBMS allow users to manage, analyze and share data and display information more effectively.

GIS take the geographic information that is stored in a warehouse, improve it to include full latitude and longitude map reference, and present the data set in a series of maps.

Oracle has a GIS capability within the Oracle relational database management systems (RBDMS), known as the spatial option. Moreover, all the latest versions of the Oracle RDBMS have a central GIS capability, which can be fast used to improve existing databases and data warehouses with GIS, spatial and location-based features. These GIS data warehouses can then be employed as the data store for third party GIS and mapping purposes or apply Oracle map viewer, an element of Oracle application server 10g to put in mapping operation in a simple way to an existing Oracle BI and warehousing functions (Rittman 2005). Oracle’s map viewer is a Java-based visualization tool that creates maps showing different types of spatial data. It consists of parts that perform cartographic rendering and a map definition tool to manage map metadata and presentation information (Oracle Corporation [1] 2005).

Having GIS data in an Oracle database has several advantages because it integrates spatial or location-based data with demographic data, gives a common environment for spatial and

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tabular-based data and offers standard-based access through SQL. In addition, the main GIS application sellers support the Oracle database as a data source for their geographic data, using Oracle locator, a key GIS feature in the Oracle database.

Oracle locator, which is free within both standard and enterprise editions of Oracle 9i and Oracle 10g, offers core location-based functionality required for GIS applications. Adding GIS functionality to existing Oracle applications becomes easy since, with locator, location information can be directly incorporated in the various applications. This is possible because location data is fully integrated in the Oracle server itself. Geographic and location data are operated using the same meanings applied to the CHAR, DATE, or INTEGER types used in SQL. Oracle locator’s key feature is that it stores spatial data directly in the database, using native spatial data types, spatial indexes, and an open SQL interface (Oracle Corporation [2] 2005).

Standardized query routines, known as Structured Query Language (SQL) have been developed for relational databases. SQL gives users access to data in databases and the opportunity to manipulate this data. SQL’s use in relational databases is very helpful for many GIS applications.

2.5 The Swedish National Spatial Data Infrastructures

The Swedish National Spatial Data Infrastructures (NSDI) involves the capture, storage and use of geographic data at local, regional and national levels that appear to be well interlinked (Hall & Beusen 2003). NSDI are easily accessible to the users through publicly accessible Internet sites for free although with a number of exceptions. Apart from the geographic information, the basic elements of the NSDI are:

• Metadata, which is information about data and usage aspects of it • Legislative and institutional framework

• Human resources, technical systems and procedures

• Strategies and action plans, especially for interoperability and information distribution. NSDI is being co-ordinated by the Swedish National Land Survey (NLS), a governmental agency that supports the creation of an efficient and sustainable use of Sweden’s real estate, land and water under the supervision of the Ministry of Environment (Lantmäteriet 2004). Its main activities include real estate information, geographic information, image information and visualization, customised databases, atlases and tourist maps.

Another authority that is related to NSDI is the Swedish Development Council for Land Information. It is a non-profit association of more than 220 Swedish organisations working for more efficient use of geographic information (ULI 2004).

Other authorities that are involved in the use of spatial information include the Geographical Sweden Data, the National Atlas of Sweden, the Swedish Yellow Pages, the Swedish Environmental Protection Agency (SEPA), the National Road Administration, the Swedish Post and others, which are involved with and co-operate in data production and /or have responsibilities in different user sectors for geographic information.

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2.6 GIS as an Analytical Tool for Employer/Employee Demographics.

Geographic information systems and remote sensing have capabilities that are ideal for evaluating the labour market, managing human resources and make decisions on job market policy measures and other various aspects of the working environment.

Uses of GIS in labour market related issues include:

• Mapping employees according to different characteristics, such as age, gender, education, profession

• Mapping employers according to type of business, sector (public or private), number of employees, turnover, profits

• Monitoring the supply and demand for labour forces

• Analysing wages, salaries, fringe benefits, bonuses, stock option plans, vacations • Analysing income taxation and other withholding taxes

• Estimating social security contributions and other payroll levies

• Monitoring commuting behaviour patterns for different classes of workers.

• Determining geographic distribution of unemployment and unemployment insurance • Analysing spatial and temporal trends

• Targeting and planning training needs • Predicting labour shortage due to retirement

2.7 Status of Companies that Operate in the Swedish Job Market

The governmental authority that supervises the Swedish job market is the National Labour Market Administration (Arbetsmarknadsverket - AMV). The central authority of AMV is the National Labour Market Board (Arbetsmarknadsstyrelsen - AMS). In each of Sweden's 21 counties there is a County Labour Board (Länsarbetsnämnden - Lan), to which the Public Employment Services (Arbetsförmedlingar - Af) are responsible. On the isle of Gotland, the County Labour Board has been incorporated in the County Administration Board (Länsstyrelsen).

The Swedish Labour Market Administration has the task of transforming Swedish labour market policies into practice:

• To fill vacancies: ensure that vacancies are filled rapidly and that jobseekers quickly find suitable jobs.

• To prepare the individual: make it easier for people wishing to work to enter the employment sector and find the right job.

• To stimulate demand: supplement and influence labour demand, so that work will be available in the right place, at the right time and for the right person.

• To prevent redundancy and exclusion: prevent redundancy and permanent exclusion and facilitate the return of unemployed persons to work, e.g. by selling Working-Life Services to employers and to Social Insurance Offices.

• To overcome the segregation of the sexes in the labour market.

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Adecco’s 40 branches in Sweden represent the world’s largest and most successful human resources solutions company that comes from Switzerland. Adecco's services encompass staffing, career services, executive search and e-recruitment at around 5,800 offices across 70 territories.

Manpower is the second biggest private company in the employment services industry, offering customers a variety of services to meet their needs throughout the employment and business cycle. The company specializes in permanent, temporary and contract recruitment; employee assessment; training; career transition and organizational consulting services. Manpower has 40 local offices in Sweden, which constitute parts of its worldwide network of 4,300 offices in 67 countries.

2.8 The Supply and Demand for Labour

Labour includes the mental and physical actions in which people utilize their time and effort to generate goods and services (a company’s output). The labour market where these actions are allocated for these people to get income that will cover their expenses is characterized by the supply of and the demand for labour.

2.8.1 Supply of labour

The labour supply curve shows the quantity of labour that employees plan to supply at each possible real wage rate (Parkin et al 1999). Employees decide to distribute their means of production and supply their labour according to the most rewarding uses and their preferences between leisure and earning income. The labour supplied depends on the wage. The higher the wage, the larger the labour supplied, making the labour supply curve to slope upward. However, when wages per hour of labour rise, employees may choose to distribute more of their available time to leisure and other activities rather than work because their income may be considered sufficient. In that case, the quantity of labour (in hours per year) supplied decreases and the labour supply curve slopes backward at the higher wage levels (see figure 2.8).

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The labour supply shifts when there are changes in demographic characteristics and social attitudes to work (Parkin et al 1999). Demographic changes in the adult population or the number of births can decrease the supply of labour in the future. Moreover, changes in social attitudes to women, children and work have an important impact to the supply of labour. The continuous entry of women in job market sectors recently dominated by men increases the labour supply. Laws prohibiting child labour reduces the labour supply. Increasing awareness about stress at work, health and men’s caring for their children reduces the male labour supply for traditional full-time jobs.

2.8.2 Demand for labour

The economic theory says that a company that aims to maximize its profits (total revenue minus total costs) generates the output (good or service) at which marginal cost (MC) is equal to marginal revenue (MR) (Perloff 2001). According to it, the marginal cost, which is the amount by which a company’s cost changes if the company produces one more unit of output, is calculated by the increase in total cost divided by the increase in output. Moreover, the marginal revenue, which is the change in revenue a company gets from selling one more unit of output, is calculated by the change in total revenue divided by the change in quantity sold. Research from economists (Parkin et al 1999) has proved that when a company hires an additional employee, total revenue and total costs rise. However, profits increase only if the additional employee produces labour that results in higher revenue than costs. This extra revenue from hiring one more employee keeping other factors constant is called marginal revenue product (MRP), while the change in total revenue per unit change in labour is called marginal revenue product of labour (Perloff 2001).

The labour demand curve shows the quantity of labour that companies plan to hire at each possible wage rate in their attempt to maximize their profits at the highest profit maximizing output (figure 2.9) (Parkin et al 1999).

The Demography of Employment in a Swedish County Council: Estimation and Mapping of Manpower Statistics in Östergötland

MSc in GeoInformatics 2004-2005

LINKÖPING UNIVERSITY

Final Thesis

THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH

COUNTY COUNCIL: ESTIMATION AND MAPPING OF

MANPOWER STATISTICS IN ÖSTERGÖTLAND.

By

Andreas-Nikolaos Papandreou

2005-02-22

ISRN LIU-IDA-D20--06/005--SE

Supervisor: Vivian Vimarlund

Examiner: Vivian Vimarlund

ABSTRACT

Keywords:

ACKNOWLEDGEMENTS

Andreas-Nikolaos Papandreou

February 2005, Department of Computer and Information Science (IDA),

Linköping University, Sweden

CONTENTS

LIST OF TABLES

LIST OF FIGURES

CHAPTER 1: INTRODUCTION AND BACKGROUND

1.1 Introduction

1.2 Background

1.2.1 The country of Sweden

1.2.2 The economy of Sweden

1.2.3 Structure of the labour market in Sweden

1.3 Problem Formulation

1.4 Delimitations

1.5 Methods

1.6 Thesis Outline

CHAPTER 2: THEORETICAL FRAMEWORK

2.1 What is GIS?

2.2 GIS Data

2.2.1 Data input

2.2.2 Data storage and management

2.2.3 Data manipulation and analysis

2.2.4 Data output and reporting

2.3 Data Quality and Reliability

2.4 GIS and Oracle

2.5 The Swedish National Spatial Data Infrastructures

2.6 GIS as an Analytical Tool for Employer/Employee Demographics.

2.7 Status of Companies that Operate in the Swedish Job Market

2.8 The Supply and Demand for Labour

2.8.1 Supply of labour

2.8.2 Demand for labour