Linköping University Department of Computer and Information Science 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

THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

Dedicated to Helena Svensson for introducing me to the country of Sweden.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______CONTENTS

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 2.8.3 Labour market equilibrium 2.9 Employment and Wages 2.10 Unemployment 2.11 GIS and the Labour Market

CHAPTER 3: GIS OPERATION/ IMPLEMENTATION 3.1 GIS Chain 3.1.1 Organization 3.1.2 Expertise 3.1.3 Hardware/ software 3.1.4 Structured data 3.2 GIS Implementation in the Labour Market

CHAPTER 4: STUDY AREA- ÖSTERGÖTLAND COUNTY 4.1 Geography 4.2 Population 4.3 Economy

CHAPTER 5: THE LABOUR MARKET IN ÖSTERGÖTLAND 5.1 Labour Legislation 5.2 Unemployment

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______5.3 Employment 5.4 Wages 5.5 Social Security Contributions 5.6 Pensions 5.7 Undeclared Work 5.8 Unemployment Insurance 5.9 Commuting Behaviour 5.10 Employment of Immigrants

CHAPTER 6: ANALYSIS AND DISCUSSION 6.1 Methodology and Research Development 6.1.1 Research methodology 6.2 Findings 6.2.1 Data analysis 6.2.2 Presentation and evaluation of secondary data 6.2.3 GIS analysis using Oracle 6.3 The Use of GIS within the Job Market in Östergötland 6.4 Present Position and Trends in the Labour Market in Östergötland 6.5 Conclusions and Future Work 6.6 Areas for Further Study

BIBLIOGRAPHY

APPENDIX A. List of Abbreviations B. Data Compilation: The Map Data C. Scheme D. KOMMUNNAMN XML E. Source Code (PL/SQL)

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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 Map production, and internal use of internal use of data data and shared use of data Task • storage • map • map • manipulation production production • maintenance • planning • project • presentation • facility • planning maintenance • facility • project maintenance management • 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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______(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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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 Difficult to function simulation data is in vector form 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 Process of large amount of data accommodated equally good Grid-cell systems are very compatible with Since most input data is in vector form, data raster-based output devices must undergo vector-to-raster conversion Better for showing the geographical variation of phenomena and area presentations Table 2.2: Comparison of vector and raster data (Bernhardsen 1999, Buckley 1997)

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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 DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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-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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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.

Recruitment companies of the private sector include Adecco, Manpower, Poolia, Proffice etc.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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).

Figure 2.8: Labour supply curve

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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).

Figure 2.9: Labour demand curve at a fixed price

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______The company’s demand for labour curve is based on its marginal revenue product curve. The horizontal axis computes the number of employees hired. The vertical axis computes the wage rate. While the wage falls from W1 to W2 and then to W3, the demand for labor is increasing from N1 to N2 and then to N3. This shows that the labour demand curve is exactly the same as the company’s marginal revenue product curve because the company hires the profit maximizing quantity of labour when the wage rate equals the marginal revenue product of labour. Figure 2.8 shows that the labour demand curve slopes downward and that the lower the wage, the greater the quantity of labour demanded.

Changes in the demand for labour depend on the price of the company’s output, the prices of production’s factors and new technology (Parkin et al 1999). The higher the price of the company’s output, the greater the shift in the company’s demand for labour curve, because as the company’s output increases, the marginal revenue and the marginal revenue product of labour increases. In addition, the demand for labour is influenced by a change in the prices of production’s factors such as the relative price of labour and capital, which may lead to a replacement away from the factor whose relative price has increased and towards the factor whose relative price has decreased. Furthermore, new technology that has an effect on the marginal product of labour, also influences the demand for labour when a company implements modern technology to its production process causing adjustments in the labour force used. New technology usually increases the demand for labour.

2.8.3 Labour market equilibrium

Wage rates and employment levels are determined by the equilibrium of demand and supply in labour markets (figure 2.10) (Parkin et al 1999).

Figure 2.10: Labour market equilibrium

W (wage) is the price of labour and N is the number of labour-hours employed. The demand for labour is the pMP, the price of output multiplied with the marginal productivity of labour in units of output. Moreover, the equilibrium price (wage) and the equilibrium quantity

Andreas-Nikolaos Papandreou 35

THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______demanded and supplied (employment) are at the point where the supply and demand curves interconnect.

2.9 Employment and Wages

Another characteristic of the labour market is the number of people of the economically active population (people that can work) that have employment. Each country keeps statistics of the people working according to certain demographic characteristics such as age, sex, qualifications and ethnic origin. Employment can be either full-time or part-time and it may sometimes involve overtime work.

The real wage rate is the quantity of goods and services that an hour’s work can buy (Parkin et al 1999). It is equal to the money wage rate divided by the price level. The real wage rate is an important economic variable because it measures the reward for labour.

Real wages increase over time to keep pace with the rate of inflation but the rate of this increase has become zero during the last years. In addition, although real wages increase over time, the working hours performed rises in a smaller rate.

2.10 Unemployment

People are considered to be at a state of unemployment when they do not have a job but are available for work, are willing to work and are making an effort to find work, while being registered as unemployed at the Public Employment Services office (Leat 2001). Moreover, the unemployment rate is the percentage of the economically active people that are unemployed (Parkin et al 1999). People enter unemployment when they lose their jobs, voluntarily leave their jobs, enter or re-enter the workforce.

Economic research has proved that unemployment has a different impact among various demographic groups (Leat 2001). There is more unemployment among young persons and immigrants, which gets higher during recession periods.

There are three types of unemployment (Parkin et al 1999): • Frictional unemployment, which comes from normal labour market circulation. This is the situation when new entrants continuously join the labour market and companies fire old staff and hire new • Structural unemployment, which arises when a decline occurs in the number of jobs available in a particular region or industry because of technological changes or international competition • Cyclical unemployment, which is caused from the slowdown in the progress of economic expansion

There is always some unemployment because of three reasons (Parkin et al 1999): • Job search, which is the action of people looking for acceptable available jobs. Changes in job search are affected by demographic changes, unemployment benefits and technological changes • Job rationing, which is the action of paying employed people a wage that creates an excess supply of labour and a shortage of jobs. Job rationing can come from efficiency wages (wages that maximize profits), insider interest (existing employees-insiders-

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______must offer training to new employees-outsiders) and the minimum wage (the wage below which it is illegal to employ people) • Sticky wages, which are the gradual adjustment of wage rates

2.11 GIS and the Labour Market

GIS are large-scale information systems, which among others can combine census data with other data on jobs, industries and individual enterprises (Ballas and Clarke 2000). Using GIS can integrate data on the supply and demand of a labour market from a variety of sources and build a thematic map of labour supply and demand variables.

An example of a GIS approach to a local market analysis is the system developed by the Northern Ireland Research Laboratory (NIRRL), which created, developed and applied a local labour market GIS to assist the industrial and employment policy analysis in the Belfast labour market in Northern Ireland (Hart et al. 1991). NIRRL’s objective was to provide an efficient local information system to perform decision-making and analysis to the agencies that offer services to the local labour market. In addition, the performance of GIS technology aided the analysis and examination of the local labour market by getting information of the factories in a geographic area that have certain criteria and combine this information with basic demographic information. The data could then be presented in a map form, while more information about an individual business or workplace could be obtained by pointing to it on the screen of the computer.

GIS can be used in peoples’ information/ geodemographics and be applied in locating persons with specific demographic characteristics such as commuting routes, occupations etc (Longley et al. 2001). Moreover, GIS can analyse characteristics of particular areas and correlate demographic features with geographic features.

Individuals consume time and space for going to work. GIS software can be effective in managing and analysing the large volume of data that is generated by existing commuting to work.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______CHAPTER 3: GIS OPERATION/IMPLEMENTATION

During the last twenty years the evolution of Geographical Information Systems made them play an increasingly important role in technology and information systems. More and more companies, organizations and governmental authorities apply GIS because they are useful in various business and service applications. GIS have greatly enhanced efficiency in a number of business related areas, especially marketing research. Some examples of the utilization and implementation of GIS in business comprise the following: locating potential competitors and customers, facilitating risk management decisions in insurance companies and helping real estate consultants to use property data better. Moreover, delivery services employ GIS as well in the navigation and monitoring of their fleets, routing optimization for shipping and deliveries, geo coding address matching and spatial location searches. GIS can increase companies’ productivity and improve their competitive ability. In addition, public services make decisions regarding property and tax assessment and the demography of employment in a more effective way because of GIS.

3.1 GIS Chain

According to Bernhardsen (1999), when an organization introduces a GIS system it is important that the same attention is being given to four different but linked parts, such as expertise, structured data, organization and hardware/software in order for the system to function properly. These elements constitute the so-called “GIS chain” that is illustrated in figure 3.1 (Bernhardsen 1999).

Figure 3.1: GIS chain (Bernhardsen 1999)

All the above elements are necessary for a successful GIS to work. This means that the system works only when the required expertise is available, the data are brought together, the necessary routines are organized and the software programmes are adjusted to fill the users’ needs.

3.1.1 Organization

An efficient GIS operates according to a well-designed implementation plan and business rules, which are unique to each organization. GIS is a tool that involves a lot of information technology but it can only be used effectively if the technology is properly integrated into the entire business strategy and operation of the company/organization. According to international studies only 10% of the problems arising in information technology projects are caused because of technology (Bernhardsen 1999). Approximately 90% of these problems are caused from underestimating the organizational issues and have a different impact to organizations depending on their size. It has been reported that in various authorities of the public sector insufficient training of GIS operators and users takes place with negative

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______consequences. In addition, instead of choosing external consultants for training, that are not involved in the organisation, the organisations often choose internal personnel without getting the expected results.

The reason for having organization problems comes from the lack of a clear strategy. GIS implementation can affect the whole organizational hierarchy in a private company or governmental authority. Current users from various departments will be supplied with a lot of data from many different sources that can complicate them. The current personnel will need to have training and new expertized staff will need to be hired that will increase the cost. Procedures may need to change and new routines may need to be adopted that will have an impact to the whole administrative scheme. Continuous training and education of personnel play a key role in these procedures for a smooth transition to the after GIS era. Training programs should be focused and offer adequate time for learning to the operational staff in order to ensure efficiency and increase in productivity as it is shown in the typical GIS learning and productivity curves (figure 3.2 and figure 3.3) (Buckley 1997).

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

There are many factors that influence the shape of the learning and productivity curves. Buckley lists some of them as the following (Buckley 1997):

• the choice of software and hardware platforms • the availability of data • the skills, aptitude and motivation of the staff • the commitment and priority attached to GIS technology dictated by the administration of the organization

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______• the implementation strategy • the status of data compilation

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

The above mentioned factors influence the learning and productivity curves. The typical learning curve reflects a long initial period before it starts to accelerate for sufficient understanding spatial data compilation requirements and database architecture. The threshold point of the learning curve is typically where the organization aims to reach at a specified timeframe according to its strategy.

GIS is considered a long term investment for which the organization wants to maximize its utility. That is why the organization aims to reach the threshold point of increased productivity at the soonest possible time and minimize the time in which a decrease in productivity occurs.

3.1.2 Expertise

Expertise is related to the people who are involved in the GIS process and plays a critical role in the implementation of GIS. GIS users can be technical specialists, who design and maintain the system or administrative users, who apply it in their everyday work. GIS users can come from internal permanent or temporary staff that will get GIS skills after adequate training or external consultants that are GIS experts. The basic skills are normal computer skills used to operate GIS and the advanced ones include software installation, data conversion, applications development, database creation and updates.

It is important that the management of the organization makes a plan for the transition period to an operational regime with the use of GIS. This plan should schedule training, re-training

Andreas-Nikolaos Papandreou 40

THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______and possible relocation for the current staff. Moreover, it should clearly define the new job descriptions and responsibilities and create a working environment that promotes challenging work and career advancement.

3.1.3 Hardware/ software

Hardware includes all or a part of the physical components on which a GIS operates. For example, hardware may include a computer, a monitor, a printer, a plotter, a network, digitizing tables and scanners.

Software in GIS includes the programs, procedures and rules needed to store, analyze, and display geographic information.

Hardware and software are strongly connected with the GIS personnel that will utilize the new technology in the proper organizational context in order to reach successful results.

3.1.4 Structured data

The most important and maybe the most expensive and time-consuming element of a GIS is the data (Longley & Graham, 1995). Geographic and related attribute data can be collected in- house, compiled to custom specifications and requirements, or frequently purchased from the data provider market. A GIS is able to integrate spatial data with other existing data resources, frequently stored in a corporate DBMS.

GIS can utilize data from a wide range of map and graphics file formats, images, CAD files, spreadsheets, relational databases, and many other sources. Data are free or fee-based and comes from commercial, non-profit, educational and public sources. An important concern in the data issue is the quality of data that has already been discussed in chapter 2.3.

The data users should consider a number of factors before choosing the appropriate data for their needs (Guide to Geographic Information Systems 2004):

• What should be accomplished with the data? • What are the particular geographic features needed? • What attributes of those features are needed? • What is the size of the specified area of interest? • What is the level of geography that will be examined? • What is the age of the data? • Where did the data come from? • What is the reliability of the provider? • What is the content, accuracy, completeness and timeliness of the data? • What type of computing environment will be used? • What type of GIS software will be used? • How many users will be accessing the data and in how many locations? • When is the data needed?

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______3.2 GIS Implementation in the Labour Market

According to Ballas and Clarke, GIS can be used as a tool in order to facilitate the analysis of a region’s labour market and estimate the degree of labour market segmentation and socio- economic dualism within an urban system. GIS can be used in the analysis of a local labour market since it can use census and private data about populations living and working in specific areas to create profiles of those areas, which illustrate particular characteristics of these populations and generate demographic profiles of specific locations and the people that are related to them.

GIS tools are also capable of evaluating the consequences of prospective labour market policies before these policies are actually applied (Ballas and Clarke 2000). By identifying the policy population target groups and using sophisticated model-based geographical tools it is possible to assess the spatial and socio-economic outcomes of such policies.

Spatial modelling techniques help GIS being implemented in the labour market and calculate the spatial interaction of different variables, such as the residences of the employees and their workplaces.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______CHAPTER 4: STUDY AREA- ÖSTERGÖTLAND COUNTY

4.1 Geography

The study area in this study is going to be Östergötland (see fig. 4.1). It is located in the south-east of Sweden and south west of the capital Stockholm. Although the area is 11,630 square kilometres, only 10,562 square kilometres is land area (2.6% of Sweden) (Länsstyrelsen Östergötland 2004). In the northwest it borders with Örebro county, in the northeast with Södermanlands county, in the southwest with Jönköpings county and in the southeast with Kalmar county. Östergötland consists of 13 municipalities of which Linköping and Norrköping are the largest. Together the two neighbouring cities represent the fourth largest urban area in Sweden (Invest in Sweden Agency 2004).

Figure 4.1: Östergötland and Sweden (Facts about Östergötland 2004)

4.2 Population

The county is the forth largest in Sweden with a population of 414,897 people (4.6% of Sweden) and a population density of 39.2 inhabitants per square kilometre (9th highest in Sweden) (Facts about Östergötland 2004) (see fig. 4.2, table 4.1). The population in Östergötland County has approximately the same age structure as Sweden. However, the age group 20 to 24 years is larger because of the many university students in the county.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

Age Östergötland County Sweden Per cent Per cent Men Women Men Women Men Women 0-4 10,670 10,221 2.6 2.5 2.7 2.6 5-9 11,895 11,364 2.9 2.7 2.9 2.7 10-14 15,128 14,153 3.6 3.4 3.6 3.4 15-19 13,670 12,414 3.3 3.0 3.2 3.0 20-24 14,436 12,765 3.5 3.1 3.0 2.8 25-29 13,540 12,337 3.3 3.0 3.2 3.0 30-34 13,982 12,862 3.4 3.1 3.5 3.3 35-39 15,242 14,366 3.7 3.5 3.8 3.6 40-44 13,645 12,760 3.3 3.1 3.4 3.3 45-49 13,304 12,920 3.2 3.1 3.3 3.2 50-54 13,754 13,629 3.3 3.3 3.3 3.3 55-59 14,903 14,682 3.6 3.5 3.6 3.6 60-64 11,739 11,747 2.8 2.8 2.8 2.8 65-69 8,960 9,412 2.2 2.3 2.1 2.3 70-74 7,469 8,881 1.8 2.1 1.8 2.1 75-79 6,557 8,701 1.6 2.1 1.5 2.0 80-84 5,038 7,739 1.2 1.9 1.2 1.8 85-89 2,361 4,454 0.6 1.1 0.5 1.0 90-94 728 1,891 0.2 0.5 0.2 0.5 95-99 125 393 0.0 0.1 0.0 0.1 100- 12 48 0.0 0.0 0.0 0.0 Total 207,158 207,739 49.9 50.1 49.5 50.5

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

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______According to the information taken from the personal register (Facts about Östergötland 2004), the population of Östergötland has increased from 392,057 people in 1978 to 414,897 in 2003, which is an increase of 5.8% during the last 25 years (see table 4.2, figure 4.3).

Municipality/ Number, 31 December Change, Change, per Region number cent 1978 2003 1978-2003 1978-2003 Boxholm 5,824 5,308 -516 -8.9 Finspång 25,044 21,052 -3,992 -15.9 Kinda 10,608 9,981 -627 -5.9 Linköping 111,424 136,231 24,807 22.3 Mjölby 25,837 25,191 -646 -2.5 Motala 42,144 42,015 -129 -0.3 Norrköping 120,251 123,971 3,720 3.1 Söderköping 10,916 14,082 3,166 29.0 Vadstena 7,598 7,582 -16 -0.2 Valdemarsvik 9,110 8,195 -915 -10.0 Ydre 4,282 3,943 -339 -7.9 Åtvidaberg 12,881 11,818 -1,063 -8.3 Ödeshög 6,138 5,528 -610 -9.9 Östergötland 392,057 414,897 22,840 5.8 County Sweden 8,284,437 8,975,670 691,233 8.3

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

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

In this project focus will be in the economically active population in Östregötland (see table 4.3) (Facts about Östergötland 2004).

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

Ag Number Proportion (%) e Population Employed or Unemployed or Employed or Unemployed self-employed not in labour self-employed or not in force labour force Men Women Total Men Wome Men Women Men Women Men Women n 0-4 10,536 9,956 20,492 10,536 9,956 0.0 0.0 2.5 2.4 5-9 12,578 12,086 24,664 12,578 12,086 0.0 0.0 3.0 2.9 10- 15,075 14,011 29,086 15,075 14,011 0.0 0.0 3.6 3.4 14 15- 13,002 11,961 24,963 1,730 2,063 11,272 9,898 0.4 0.5 2.7 2.4 19 20- 14,328 12,728 27,056 7,345 6,439 6,983 6,289 1.8 1.6 1.7 1.5 24 25- 13,878 12,525 26,403 10,407 8,630 3,471 3,895 2.5 2.1 0.8 0.9 29 30- 13,933 12,852 26,785 11,887 9,912 2,046 2,940 2.9 2.4 0.5 0.7 34 35- 15,390 14,219 29,609 13,315 11,264 2,075 2,955 3.2 2.7 0.5 0.7 39 40- 13,456 12,722 26,178 11,496 10,359 1,960 2,363 2.8 2.5 0.5 0.6 44 45- 13,296 13,061 26,357 11,421 10,650 1,875 2,411 2.8 2.6 0.5 0.6 49 50- 14,012 13,873 27,885 11,892 11,147 2,120 2,726 2.9 2.7 0.5 0.7 54 55- 14,822 14,406 29,228 11,803 10,485 3,019 3,921 2.9 2.5 0.7 0.9 59 60- 10,995 11,200 22,195 6,496 5,579 4,499 5,621 1.6 1.3 1.1 1.4 64 65- 8,686 9,215 17,901 1,334 746 7,352 8,469 0.3 0.2 1.8 2.0 69 70- 7,498 9,008 16,506 306 107 7,192 8,901 0.1 0.0 1.7 2.2 74 75- 6,795 8,846 15,641 138 42 6,657 8,804 0.0 0.0 1.6 2.1 79 80- 4,974 7,634 12,608 4,974 7,634 0.0 0.0 1.2 1.8 84 85- 2,299 4,467 6,766 2,299 4,467 0.0 0.0 0.6 1.1 89 90- 719 1,849 2,568 719 1,849 0.0 0.0 0.2 0.4 94 95- 129 374 503 129 374 0.0 0.0 0.0 0.1 99 100 6 38 44 6 38 0.0 0.0 0.0 0.0 - Tot 206,407 207,031 413,438 99,570 87,423 106,837 119,608 24.1 21.1 25.8 28.9 al

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

4.3 Economy

The regional GDP in Östergötland for 2002 per inhabitant was SEK 236,000, which is 90% of the average value of Sweden (SEK 263,000) (Facts about Östergötland 2004).

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

As all tax offices in the country, the tax offices in Östergötland have a record of everyone living within their area of responsibility, which they use for taxation purposes. Details such as name, address, date of birth, family circumstances and place of residence are registered for each individual. Everyone registered is given a civic registration number (personnummer) consisting of the date of birth (yy/mm/dd) followed by a four figure number for each individual.

Individuals resident in Sweden are subject to municipal and national income taxes on their worldwide income. Income tax is first levied at the local (municipality) level for earnings up to SEK 301,000 (Skatteverket 2004). Tax rates for individuals in Östergötland are being set by each municipality and vary from 30.25% to 31.50% (see table 4.4). Incomes exceeding the threshold of SEK 301,000 and up to SEK 447,200 -for income year 2003- are subject to another tax rate of 20% (state income tax). The portion exceeding SEK 447,200 is subject to an additional 5% state income tax. It should be noted that only expenses that are directly connected with individuals’ work, such as travelling expenses to and from work, can be deducted from the individuals’ income.

Andreas-Nikolaos Papandreou 47

THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Municipality/Region Municipal tax rate, Of which to % Municipality County Council Boxholm 31.00 21.15 9.85 Finspång 31.15 21.30 9.85 Kinda 31.00 21.15 9.85 Linköping 30.25 20.40 9.85 Mjölby 31.00 21.15 9.85 Motala 31.25 21.40 9.85 Norrköping 31.30 21.45 9.85 Söderköping 30.85 21.00 9.85 Vadstena 31.10 21.25 9.85 Valdemarsvik 31.00 21.15 9.85 Ydre 30.50 20.65 9.85 Åtvidaberg 31.50 21.65 9.85 Ödeshög 31.35 21.50 9.85 Östergötland County 30.87 21.02 9.85 Sweden 31.49 20.79 10.71

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

The average taxable earned income, which is calculated as the earned income minus the deduction for general national pensions and personal allowance for Östergötland during 2002 is presented at table 4.5.

Average, taxable earned income Ranking Per cent order among change Total Women Men all from municipalities, previous total year Sweden 168,100 133,500 203,900 2.7 Östergötland 159,600 124,200 195,800 3.1 Ödeshög 133,200 103,800 162,900 268 4.1 Ydre 136,300 107,100 164,000 255 4.5 Kinda 140,300 108,700 171,700 235 3.2 Boxholm 143,900 107,900 178,000 206 3.0 Åtvidaberg 152,700 117,400 187,900 141 2.6 Finspång 159,000 117,200 200,000 92 3.0 Valdemarsvik 139,500 109,300 169,100 238 4.8 Linköping 171,400 132,100 211,100 46 2.7 Norrköping 156,800 123,700 191,500 107 3.5 Söderköping 156,800 126,500 187,600 109 3.5 Motala 154,300 121,900 187,600 126 2.4 Vadstena 158,600 127,800 192,500 97 4.4 Mjölby 151,500 115,900 187,800 153 3.2

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

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Individual income taxes on earned income from employment or business (self-employment) are categorised as direct taxes on labour income, while social security contributions paid by employers are seen as indirect taxes on labour.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______CHAPTER 5: THE LABOUR MARKET IN ÖSTERGÖTLAND

5.1 Labour Legislation

Employment in Östergötland and in Sweden in general, can be on a permanent basis or until further notice or for a given period of time. Temporary employment is allowed for temporary (stand-in) positions or impermanent projects. A trial period of employment for a maximum of six months is permitted in most collective agreements.

The main labour laws that designate the labour market in Sweden are the following (Regeringskansliet 2004): • The Security of Employment Act (SFS 1982:80) states that employees shall not be dismissed without any reason. • The Co-determination at Work Act (SFS 1976:580) provides for consultation with the workforce on major issues affecting the company. • The Board Representation for Employees Act (SFS 1987:382) provides for employee representation on company boards in some cases. • The Equality of Men and Women at Work Act (SFS 1991:433) prohibits discrimination based on sexual grounds and encourages measures to promote equality. • The Work Environment Act (SFS 1977:1160) governs health, safety and environmental standards in the workplace and provides for joint employer- employee safety committees, whose functions are developed in collective agreements. • The General Hours of Work Act (SFS 1982:673) sets a normal workweek of 40 hours and an overtime limit of 200 hours per year. • The Holidays Act (SFS 1977:480) provides a statutory right to five weeks (25 working days) of annual holiday from the first year of employment, of which four consecutive weeks may be taken during the June–August period.

There is no legislation directly affecting remuneration, since this is completely governed by collective agreements between employer organizations, such as the Confederation of Swedish Enterprise and trade unions, such as LO (the Swedish Confederation of Trade Unions), TCO (the Central Organisation of Salaried Employees) and SACO (the Confederation of Professional Associations) that also regulate employment conditions and other terms between employers and employees.

Most of the people working in Sweden are trade union members. LO represents more than 2,300,000 blue-collar workers and 24 unions, with its strongest representation among municipal and local public employees, in the engineering/metal-working trade and in the building and retail trades. The TCO represents 1,000,000 white-collar employees organised into 20 unions, and SACO represents 292,000 professionals organised into 26 associations. The Federation of Salaried Employees in Industry and Sciences (PTK), the co-ordinating organ for the major TCO unions and some small SACO unions, represents 564,000 white- collar workers in the private sector and negotiates with the Confederation of Swedish Enterprise. LO, TCO and SACO together represent 90% of Swedes working full-time.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______5.2 Unemployment

Unemployment in Östergötland has been rising since 2002 (see table 5.2). This increase was especially rapid during the autumn of 2003 (see table 5.1).

Municipali Year 2003 ty Jan Feb Mar April May June July Aug Sept Oct Nov Dec /Region Boxholm 101 87 86 86 97 108 108 109 114 105 101 117 Finspång 270 246 244 217 232 315 338 320 300 322 307 391 Kinda 162 147 139 142 167 199 225 209 175 161 166 219 4,01 Linköping 2,732 2,691 2,803 2,837 2,985 4,268 4,683 3,994 3,512 3,459 3,445 0 Mjölby 518 520 508 521 517 701 735 646 643 608 591 707 1,14 Motala 787 753 773 764 785 1,037 1,189 1,016 900 901 942 7 5,01 Norrköping 4,014 3,938 4,003 3,992 4,248 5,224 5,459 5,015 4,592 4,548 4,574 1 Söderköping 217 230 198 190 221 267 285 235 223 216 248 294 Vadstena 100 96 88 83 105 118 126 131 111 110 125 154 Valdemarsvik 99 98 90 79 95 124 147 126 112 94 116 168 Ydre 40 36 39 38 48 65 72 57 52 45 41 59 Åtvidaberg 257 235 225 221 258 325 354 258 256 261 265 304 Ödeshög 97 86 89 80 83 97 99 96 95 96 97 133 Östergötland 11,01 12,7 9,394 9,163 9,285 9,250 9,841 12,848 13,820 12,212 11,085 10,926 County 8 14 204,98 195,32 192,71 266,26 252,92 226,87 222,61 225,6 258, Sweden 188,043 195,254 246,95 8 8 2 4 3 9 4 92 631 County/Swede 4.6 4.7 4.8 4.9 5.0 5.2 5.2 4.8 4.9 4.9 4.9 4.9 n Table 5.1: Monthly unemployment in Östergötland during 2003 (Facts about Östergötland 2004)

From all the municipalities of Östergötland the highest unemployment during 2003 was recorded in Norrköping and reached 5.8%, while Linköping and Mjölby followed with 3.9% respectively (see figure 5.1, tables 5.2, 5.3).

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

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Municipality/ 2002 2003 Region Number of Proportion of Number of Proportion of unemployed unemployed unemployed unemployed persons persons persons persons Boxholm 82 2.6 102 3.2 Finspång 275 2.1 292 2.3 Kinda 150 2.5 176 3.0 Linköping 2,472 2.8 3,451 3.9 Mjölby 432 2.8 601 3.9 Motala 675 2.6 916 3.5 Norrköping 3,609 4.6 4,551 5.8 Söderköping 193 2.2 235 2.7 Vadstena 82 1.8 112 2.5 Valdemarsvik 90 1.8 112 2.2 Ydre 39 1.7 49 2.1 Åtvidaberg 213 3.0 268 3.8 Ödeshög 78 2.4 95 2.9 Östergötland 8,390 3.2 10,961 4.2 County Sweden 185,801 3.3 222,982 3.9 Table 5.2: Number and proportion of unemployed persons aged 16-64 during 2002-2003 (Facts about Östergötland 2004)

However, the above data represents the relative open unemployment and it does not include the persons that participate in governmentally funded labour market programs.

The percentages of the people that participate in labour market schemes during 2003 are being shown in figure 5.2.

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

As a result the unemployment rate is actually higher than it is stated above (see figure 5.3, table 5.3).

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

Municipality Unemployed In labour market Unemployed+ in /Region schemes labour market schemes Number Per cent Number Per cent Number Per cent Boxholm 102 3.2 49 1.5 151 4.7 Finspång 292 2.3 273 2.1 565 4.4 Kinda 176 3.0 89 1.5 265 4.5 Linköping 3,451 3.9 1,393 1.6 4,844 5.5 Mjölby 601 3.9 243 1.6 844 5.4 Motala 916 3.5 436 1.7 1,352 5.2 Norrköping 4,551 5.8 1,697 2.2 6,247 7.9 Söderköping 235 2.7 137 1.6 373 4.2 Vadstena 112 2.5 68 1.5 180 4.0 Valdemarsvik 112 2.2 81 1.6 193 3.9 Ydre 49 2.1 45 1.9 95 4.1 Åtvidaberg 268 3.8 177 2.5 445 6.3 Ödeshög 95 2.9 46 1.4 141 4.3 Östergötland 10,961 4.2 4,734 1.8 15,695 6.0 County Sweden 222,982 3.9 91,960 1.6 314,942 5.5 Table 5.3: Persons that are unemployed or in labour market schemes during 2003 (Facts about Östergötland 2004)

Moreover, the annual average proportion of unemployed aged 18-64 of population aged 18-64 according to gender, regarding during 2002 and 2003, shows that unemployment was higher among men than in women in all municipalities of Östergötland (see table 5.4).

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Municipality/ 2002 2003 Region Men Women Men Women Boxholm 3.1 2.2 3.5 3.1 Finspång 2.1 2.3 2.6 2.1 Kinda 3.1 2.2 3.4 2.8 Linköping 3.3 2.5 4.7 3.4 Mjölby 3.2 2.5 4.4 3.7 Motala 3.0 2.4 4.2 3.1 Norrköping 5.3 4.3 6.7 5.3 Söderköping 2.4 2.2 2.8 2.8 Vadstena 2.0 1.8 3.1 2.1 Valdemarsvik 1.8 2.0 2.5 2.2 Ydre 1.9 1.6 2.3 2.2 Åtvidaberg 3.7 2.5 4.5 3.3 Ödeshög 2.8 2.2 3.3 2.9 Östergötland 3.7 3.0 4.9 3.8 County Sweden 3.8 3.0 4.6 3.5 Table 5.4: Unemployed men and women during 2002-2003 (Facts about Östergötland 2004)

It should be noted that from 2001, the amount of newly reported vacancies at AMS offices in Sweden has been decreasing. The number of newly reported vacancies has fallen in most industries but especially in general-government activities (Konjunkturinstitutet 2004). During 2004, the number of layoff notices was down to some extent in manufacturing as well as computer and related activities, which accounted for the bulk of layoff notices during 2003 but had increased substantially in general-government activities and in construction. There have also been more layoff notices in the private services sector, for example in transport, communications and wholesale and retail trade.

Figure 5.4: Layoff notices and vacancies (Konjunkturinstitutet 2004)

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______5.3 Employment

Since the spring 2001, the labour market has been characterized by soft demand (Konjunkturinstitutet 2004). The number of persons at work has decreased because of the slumping economy.

The labour market can be divided into two principal sectors, the public sector and the private sector. Just over half of all women in the labour market work in the public sector. A majority of men, about 80%, work in the private sector. In the whole labour market the number of women and men is approximately equal.

During the 1st quarter of 2003, the number of employees in Östergötland was 170,500 of whom 144,700 were permanent employees (Statistical yearbook of Sweden 2004). Moreover, 62,500 persons were working in the public sector of which 47,000 were permanent employees. In addition, 108,000 persons were working in the private sector, of which 97,700 were permanent employees. At the same period of time and compared to the whole country, Sweden had 3,864,800 employees of who 3,244,800 were permanent employees. Moreover, 1,367,100 persons were working in the public sector of which 1,007,800 were permanent employees. In addition, 2,497,700 persons were working in the private sector, of which 2,237,000 were permanent employees.

The average number of hours worked by employed persons decreased last year. In the past four years, primarily rising absenteeism, but also the number of ordinary hours worked and overtime, have contributed to the decline in average hours worked.

The greatest change for both men and women in Östergötland has been the great reduction in employment in the land-based sector (farming, forestry, hunting, fishing and reindeer herding). In manufacturing, employment has increased together with the service sector.

Municipality Agriculture, Manufacturing Service Total /Region forestry, fishing Number Per Number Per Number Per Number Per cent cent cent cent Boxholm 203 38.1 84 15.8 246 46.2 533 100.0 Finspång 386 27.6 223 15.9 791 56.5 1,400 100.0 Kinda 673 45.6 212 14.4 591 40.0 1,476 100.0 Linköping 1,813 17.1 1,244 11.7 7,547 71.2 10,604 100.0 Mjölby 783 32.7 352 14.7 1,262 52.6 2 397 100.0 Motala 768 24.8 527 17.0 1,802 58.2 3,097 100.0 Norrköping 1,117 12.5 1,398 15.6 6,424 71.9 8,939 100.0 Söderköping 430 31.1 233 16.8 721 52.1 1,384 100.0 Vadstena 228 27.3 122 14.6 486 58.1 836 100.0 Valdemarsvik 332 33.0 193 19.2 480 47.8 1,005 100.0 Ydre 324 56.9 81 14.2 164 28.8 569 100.0 Åtvidaberg 386 35.1 168 15.3 546 49.6 1,100 100.0 Ödeshög 296 47.2 85 13.6 246 39.2 627 100.0 Östergötland 7,739 22.8 4,922 14.5 21,306 62.7 33,967 100.0 County Sweden 174,582 20.1 122,028 14.1 571,312 65.8 867,922 100.0

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Table 5.5: Workplaces in 2003 by sector and region (Facts about Östergötland 2004)

The education level of the population in the county is continuously rising. The percentage of the population in the age group 25-64 years with some form of post-upper secondary education is increasing among both women and men in the county. An upper secondary education is no longer the privilege of a minority, but rather the normal case, particularly among the younger generations.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Industry Total Level Elementary Upper Tertiary unkno /comprehensive school secondary education wn school Elementary Comprehensive ≤2 3 <3 ≥3 years years years years % % % % % % % Economically 180,527 0.1 6.2 9.7 31.9 20.8 13.9 17.4 active, total Agriculture, forestry, 4,193 0.2 13.0 15.3 37.9 20.8 9.0 3.8 fishing Manufacturin 39,488 0.2 8.5 13.6 33.9 21.9 11.0 11.0 g and mining Energy and water supply, 1,313 0.1 8.3 9.8 30.8 20.3 18.4 12.3 sewage Construction 10,183 0.1 10.3 11.4 48.1 21.9 6.4 1.8 Trade, transport, storage, 30,021 0.1 8.5 12.9 35.9 27.4 10.0 5.3 communicatio ns Credit institutes, property 20,626 0.1 4.5 9.0 24.9 23.9 16.5 21.0 management, business services Research and development, 21,572 0.1 2.0 3.3 17.5 9.7 21.6 45.8 education Health and medical care, social 29,396 0.1 2.7 5.2 38.7 16.5 15,5 21.2 services veterinarians Personal and cultural 10,555 0.4 7.1 11.8 28.4 29.3 11.9 11.0 services Civil authorities, 10,852 0.1 3.3 6.7 23.8 16.9 20.2 28.9 defence, etc. Not specified 2,328 0.5 9.9 13.2 30.8 20.2 13.5 11.9 industry Unemployed or not in 61,169 2.7 13.9 14.3 24.8 17.7 17.0 9.5 labour force, total

participants 18,271 0.9 1.9 7.6 11.7 22.1 39.7 16.1 of education not participants 42,898 3.5 19.1 17.1 30.5 15.9 7.3 6.8 of education Total 241,696 0.8 8.1 10.9 30.1 20.0 14.6 15.4

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______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)

In addition, tertiary education has successively become more common. Women have passed men as regards level of education. In the age group 20-64 years 33% of women and 28% of men respectively have some kind of post-upper secondary education. However, men maintain a big advantage regarding research education. Among both women and men there is a predominance of post-upper secondary education of less than three years.

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)

According to figure 5.5, more women than men have tertiary education, both in the county and in Sweden. Among municipalities in the Östergötland, Linköping is having a fairly higher percentage of men than women with higher education. In the county 31.5% of women have a post-upper secondary education and 28.5% of men. The highest proportion of persons with a post-upper secondary education is found in the municipality of Linköping.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

Tertiary education among personnel is most frequent in sectors such as education and research, public administration, care and social care.

A lower proportion of gainfully employed women in Östergötland County work full-time, than women in the whole of Sweden. The proportion of women in full-time work has increased as much in the county has in Sweden as a whole. Both in the county and in Sweden, 90% of the men work full time.

It is very common for young people in the age group 16-19 to work part-time. Of all age groups, this age group has the highest proportion of part-time employees, which applies to both sexes. This can be explained partly because about half of the young people working are studying at the same time. Another explanation is that the youngest ones on the labour market often have temporary jobs that are not full-time jobs. Part-time work is also very common in the age group of 60-65 years. A large group of people who are not working full-time are people who have some kind of part-time pension.

In addition, the number of persons receiving sickness and activity allowances is continuing to go up and has become an issue of major concern for the government and social partners. In 2002, 5.7% of the women and 2.8% of the men in the County of Östergötland had been on an

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______ongoing extended sick leave for at least 1 year (Alpkvist & Pettersson Molinder 2003). Corresponding figures for the whole country are 4.9% of the women and 2.7% of the men (age group 16-64). This is a social problem, which requires improving the conditions at work and establishing a regulatory framework that promotes re-integration in the labour market in order for the long-term sick-leaves to be reduced.

In case of illness of a staff member, the employer pays a sick pay (80% of salary) to the employee from day 2 to day 21. From the 22nd day of sickness the National Social Insurance Office takes over and pays the sickness benefit, calculated as 80% of 97% of the employee’s previous salary (up to a standard ceiling of 7.5 price base amounts-that is SEK 39,300 for 2004). No benefit is payable for the first day of illness, which is known as the “waiting” day (Försäkringskassan 2004).

From all the companies in Östergötland the ones that have the most employees are listed in table 5.7.

Rank Employer Number of Proportion (%) of employees all employees in the county 1 Östergötland County Council 12,775 7.7 2 Norrköping Municipality 9,875 5.9 3 Linköping Municipality 7,125 4.3 4 Motala Municipality 4,975 3.0 5 Saab Aktiebolag 4,225 2.6 6 Linköping University 4,125 2.5 7 Mjölby Municipality 2,275 1.4 Demag Delaval Industrial 8 2,125 1.3 Turbomachinery AB 9 Finspång Municipality 2,075 1.3 10 ISS Sverige AB 1,525 0.9 11 Posten Sverige AB 1,475 0.9 12 Söderköping Municipality 1,225 0.7 13 Samhall Aktiebolag 1,125 0.7 14 Ericsson AB 1,075 0.6 15 BT Products Aktiebolag 1,075 0.6 16 Åtvidaberg Municipality 1,025 0.6 17 Rikspolisstyrelsen 975 0.6 18 Kinda Municipality 875 0.5 19 Dometic Aktiebolag 875 0.5 20 Swedish Meats Ek. För. 875 0.5 21 Holmen Paper Aktiebolag 825 0.5 22 Valdemarsvik Municipality 775 0.5 23 Connex Sverige AB 725 0.4 24 Whirlpool Sweden Aktiebolag 725 0.4 25 Billerud Skärblacka Aktiebolag 725 0.4

Number of employees by the 25 largest employers in the county 65,509 Proportion (%) of all employees in the county 39.5

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Table 5.7: 25 largest employers in Östergötland during 2003 (Facts about Östergötland 2004)

5.4 Wages

Sweden has no legislation on minimum levels of pay. In addition, many labour agreements now provide for most wage formation to take place locally, with no centrally specified wage increases (Konjunkturinstitutet 2004).

During 2001 the aggregate wages in Östergötland reached SEK 38.6 bn, while the preliminary tax was SEK 13.5 bn and the numbers of statements of income were 306,245 (Statistical yearbook of Sweden 2004).

As women and men often choose different study courses, their choice of occupation is also often different. After upper secondary studies, women and men often work in traditionally female and male occupations respectively. University level studies often lead to a specific occupation, but here too the choice of occupation varies for women and men. A higher level of education is most worthwhile for men. The length of the education is assumed to have a positive influence on the individual's salary. This applies to both to women and men even though the salary level generally is higher for men. For those in Östergötland with a tertiary school education of two years or more (excl. research studies), women have a monthly salary of SEK 21,700 while men's average salary is SEK 27,900. Comparative monthly salaries for the nation are SEK 22,500 for women and SEK 29,700 for men during 2001. (Alpkvist & Pettersson Molinder 2003). Moreover, in Östergötland women's full-time salaries correspond to 84% of men's. In the nation as a whole, women's full-time salary average 82% of men's.

In 2001, the average monthly salaries of women and men in Östergötland were SEK 18,600 and SEK 22,200 respectively (Alpkvist & Pettersson Molinder 2003). The national average was SEK 19,200 for women and SEK 23,300 for men.

Women have a lower full-time salary than men. This is because salaries are lower in the public sector than in the private sector. However, even in the private sector women have lower salaries since it is usually men the ones that pursue a career although not always with better education. Women in general have a longer education than men, but of a different type. Salary differences are smaller in the low-paid sectors and bigger in the well-paid sectors. Women are concentrated to certain low-paid jobs, such as care-oriented work and pre-school teaching in the public sector and office, sales and cleaning work in the private sector. In these groups of work the salary differences between the genders are small. Men work to a higher degree than women in well-paid professions both in the public and the private sector. It is anticipated that more women in managerial positions, in both the public and private sector, could reduce the salary differences between women and men.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

Table 5.8: Average earned income among men and women of Östergötland’s municipalities during 2001 in 1000’s SEK (Alpkvist & Pettersson Molinder 2003)

It should be noted that the earned income includes gross salary, unemployment benefit, sick pay, pension, income from business activities, etc.

In calculating the index of the following figure 5.7, the value assigned to Sweden is given an index value of a 100 and then the regional values have been calculated as a percentage of this. For example, Linköping and Norrköping have index values of 95 to 98, which mean that their regions’ value is 2 to 5 per cent lower than the value of Sweden (Facts about Östergötland 2004). Ydre on the other hand has an index value of 48 to 58, which means that the region’s value is 42 to 52 per cent lower than the value of Sweden.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

5.5 Social Security Contributions

Social security contributions are considered to be genuine taxes only to the extent that there is no direct link between the amount paid and the level of pensions and benefits one is entitled to (Taxes in Sweden 2003). About 60%t of these contributions could be regarded as taxes and the remaining 40% as compulsory social security premiums. Nevertheless, in the Swedish tax system, all compulsory social security contributions are regarded as taxes.

There are three categories of social security contributions: The main part is paid either by employers as a payroll tax at the rate of 32.82% (during 2001) or by self-employed people themselves at the rate of 31.01%. In addition to this, all taxpayers pay a general pension contribution. In 2001, the rate was 7%. Because some of the social security contributions are in fact taxes, there is also a special wage tax on those items of remuneration that do not provide entitlement to State pensions or benefits. In 2001, SEK 398 billion was paid as social security contributions.

SEK billion Tax rate 1. Basic social security contributions paid by: a. employers or 299 32.82% b. self-employed 7 31.01% 2. General pension 66 7% contribution paid by all active persons 3. Special wage tax 25 24.26% Total 398

Table 5.9: Social security contributions in Sweden during 2001 (Swedish Tax Agency 2004)

It should be noted that countries that have higher levels of payroll taxes have tended, in recent years, to face higher unemployment.

5.6 Pensions

The normal age of retirement is 65 years (Försäkringskassan 2004). However, pensions may be drawn at any age from 60 to 70, but they are reduced before and increased after the official retirement age. A package of part-time pay and a partial pension may be arranged for persons aged 60–65 who work 17–35 hours a week. However, lower salaries and part-time gainful employment later in life lead to a smaller pension.

The ratio of elderly people in the population is gradually increasing. Sweden has already had a strong increase in the age group of 65 years and older. A new increase is expected to last until the beginning of the 2030's (Alpkvist & Pettersson Molinder 2003). It is the baby boom from the 1940's and the late 1960's that will reach the retirement age. It is expected that the old people will represent a considerably larger proportion of the population than today. The

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______structure of the population in Östergötland has changed considerably, with a large increase in the number of old people and a large decrease in the number of young people.

5.7 Undeclared Work

Undeclared work in Östergötland is growing but it is rather complicated and difficult to estimate since there are no official statistics relating to this kind of work. However, according to the Swedish National Audit Office the whole country is annually losing 20-40 billion SEK or 3-4.5% of the GDP due to undeclared work (Riksrevisionen 2004). Moreover, the main group of employees engaged in the informal economy is students. In addition, the cleaning, restaurant and construction sectors employ many undeclared employees.

5.8 Unemployment Insurance

Unemployment insurance pays compensation for the part of the salary that is lost because of unemployment. It is handled by 38 formally independent member funds, which are connected to trade unions and it is supervised by IAF, the Swedish Unemployment Insurance Board, which substituted AMS at the end of 2003 (Swedish Institute 2004). In Sweden, 9 out of 10 people of the labour force (whether employed or unemployed) are members of an unemployment insurance fund (Swedish Unemployment Insurance Board 2004).

Unemployment insurance has two parts, the basic insurance and the voluntary income-related insurance (Eures 2004). Unemployment benefit is granted to people who meet the basic conditions and a work condition or else the “student condition”. Persons over twenty years old can get unemployment benefit under the basic insurance. The basic insurance is a basic sum of SEK 320 per day, 5 days per week irrespective of previous income.

The voluntary income-related insurance is paid to persons that were members of an unemployment insurance fund for at least one year and have fulfilled the work condition during that time. They receive 80% of their previous income subject to a maximum of SEK 730 for the first 100 days of benefit, and SEK 680 thereafter.

Unemployment benefits are almost entirely financed by taxes. Moreover, they are taxable and also provide the basis for pension.

5.9 Commuting Behaviour

A commuter is a person whose place of work is located at another municipality than the municipality that he/she is registered for living (Facts about Östergötland 2004). Commuting between municipalities has become intensive since 1980 and during the 90’s more than one fourth of the employed persons was commuters in that respect (Carlsson et al 1993). In tables 5.10, 5.11, 5.12 we can see the level of commuting between municipalities within Östergötland County.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

Men From To Box- Fin- Kinda Lin- Mjölby Motala Norr- Söder- Vad- Valde- Ydre Åtvida- Ödes- holm spång köping köping köping stena mars- berg hög vik Boxholm 760 2 74 282 16 8 8 8 8 Finspång 4,074 2 111 7 22 688 14 Kinda 3 1,451 556 11 6 25 1 1 3 15 37 3 Linköping 29 134 170 27,521 558 396 1,211 31 38 9 3 186 14 Mjölby 120 7 5 1,291 3, 971 309 80 4 68 3 8 37 Motala 17 77 6 1,167 326 7, 333 81 4 238 1 5 8 Norrköping 23, 5 682 12 1,629 40 62 430 10 92 18 2 271 Söderköping 1 23 3 190 3 10 1,533 1,321 1 76 5 Vadstena 1, 6 2 2 114 167 340 20 1 24 024 Valdemarsvik 1 12 1 28 1 298 89 1, 340 1 12 Ydre 8 23 16 2 8 3 1 477 1 Åtvidaberg 4 21 898 9 10 77 10 1 40 1 1, 666 1 Ödeshög 13 93 187 39 7 34 4 1 695

Table 5.10: Commuting of men between municipalities within Östergötland during 2001 (Facts about Östergötland 2004)

Women From To Box- Fin- Kinda Lin- Mjölby Motala Norr- Söder- Vad- Valde- Ydre Åtvida- Ödes- holm spång köping köping köping stena mars- berg hög vik Boxholm 563 2 90 240 15 5 1 3 3 7 Finspång 3,485 54 1 7 546 8 1 Kinda 1,511 385 5 14 3 14 28 Linköping 14 39 119 26,226 324 245 822 31 18 1 1 119 12 Mjölby 52 1 2 1,165 3,456 272 30 2 74 3 1 37 Motala 2 19 816 165 7,096 21 1 181 1 2 Norrköping 1 301 2 955 25 9 22,669 344 2 37 4 Söderköping 7 102 1,302 1,420 72 3 Vadstena 81 72 358 3 1,021 26 Valdemarsvik 13 1 223 95 1,165 11 2 Ydre 1 16 11 2 1 1 470 1 Åtvidaberg 1 8 646 3 1 30 4 26 1,634 1 Ödeshög 8 48 82 32 11 20 1 748

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

From tables 5.10, 5.11 we see that there are more men than women that commute within Östergötland.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Men and women From To Box- Fin- Kinda Lin- Mjölby Motala Norr- Söder- Vad- Valde- Ydre Åtvida- Ödes- holm spång köping köping köping stena mars- berg hög vik Boxholm 1,323 4 164 522 31 13 1 11 11 15 Finspång 7,559 2 165 8 29 1,234 22 1 Kinda 3 2,962 941 16 6 39 4 1 3 29 65 Linköping 43 173 289 53,747 882 641 2,033 62 56 10 4 305 26 Mjölby 172 8 7 2,456 7 427 581 110 6 142 6 9 74 Motala 19 96 6 1,983 491 14,429 102 5 419 1 6 10 Norrköping 6 983 14 2,584 65 71 45,940 774 12 129 22 2 Söderköping 1 30 3 292 3 10 2,835 2,741 1 148 8 Vadstena 6 2 2 195 239 698 23 1 2,045 50 Valdemarsvik 1 12 1 41 2 521 184 2,505 1 23 2 Ydre 9 39 27 4 9 3 1 1 947 2 Åtvidaberg 5 29 1,544 12 11 107 14 1 66 1 3,300 2 Ödeshög 21 141 269 71 18 54 5 1 1,443

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

From the above table we notice that most of the commuting within Östergötland takes place to Linköping and Nörköping since as they are the biggest municipalities in the county, they also offer the highest number of jobs.

Figure 5.8: Commuting to work flows in Östergötland (Facts about Östergötland 2004)

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

Regarding commuting out of Östergötland (see table 5.13), we see that most of it takes place to Stockholm County as it is the capital of Sweden and there is also a short distance from Östergötland in comparison to commuting to other municipalities. Stockholm is still enjoying a high employment growth that explains this commuting, while less metropolitan counties follow.

To Out-commuting Men Women Total Stockholm County 2,274 1,124 3,398 Jönköping County 1,019 698 1,717 Södermanland County 854 488 1,342 Västra Götaland County 625 315 940 Kalmar County 393 217 610 Skåne County 422 141 563 Örebro County 362 129 491 Uppsala County 237 70 307 Västmanland County 142 58 200 Kronoberg County 99 48 147 Blekinge County 102 31 133 Dalarna County 81 52 133 Halland County 68 31 99 Värmland County 67 31 98 Gävleborg County 50 27 77 Västernorrland County 49 23 72 Jämtland County 40 25 65 Västerbotten County 30 24 54 Norrbotten County 33 15 48 Gotland County 14 19 33 Unknown County 219 56 275 Total 7,180 3,622 10,802

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

The commuting distance is different among different classes of employees. It has been found that men cover on average longer commuting distances/time than women and the commuted distance has a tendency to increase dependent on the commuters’ level of education (Olsson 2002). Therefore, men follow a bigger geographical area than women and people that have higher education have a bigger geographical area to commute than the ones with lower education. This is because as it is already mentioned; men are more likely than women to have a full-time job and work in the private sector, while women are more willing to take more home responsibilities. In addition, men on average have a higher salary than women who earn less per hour (Statistics Sweden 2004).

5.10 Employment of Immigrants

There are an increasing number of foreigners that come to work in Sweden and in Östergötland. Although they have similar rights in the labour market to the Swedish residents

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______they face a higher unemployment than the people that are born in Sweden (Swedish Institute 2004).

Generally, people with foreign backgrounds encounter special difficulties while trying to be integrated into the labour market (Εslund & Runeson 2002). However, immigrants from other Nordic countries manage to find jobs and earn higher incomes -followed by people born in EU/EES countries- than the others and especially persons born outside the Western World (non-OECD).

People that come from EU member states may enter and work in the country without residence or work permits, or may enter Sweden and find employment within 3 months (Migrationsverket 2004). Before the EU expansion on May 1st, 2004, there were worries that there would be a fast increase in immigration to Sweden from the ten new member states (Statistics Sweden 2004). In addition, nationals of non- EU member states must have residence or work permits or visas (depending on the country they come from) issued before entering Sweden.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______CHAPTER 6: ANALYSIS AND DISCUSSION

It is the study’s intention to identify the impact of GIS within the job market in Östergötland County. Thus, relevant literature was studied to identify concepts and facts related to the use of GIS in the distribution of workforce. In addition, GIS analysis in detailed population data was executed with the use of Oracle (see chapter 6.2.3) to display population data in a series of maps.

6.1 Methodology and Research Development

6.1.1 Research methodology

In order to establish a strong support for this thesis topic the author included in his methodology a literature review and GIS data analysis that can help him achieve a critical analysis of this thesis.

The literature review was the first research analysis method to be used in chapters two, three, four and five and it provided an insight into the approaches and methodologies adopted by different researchers. The author was able through systematic search to enlist the support of relevant contributions in the fields of GIS, economics, demography of employment and manpower statistics. Through the analysis of recent writings and publications from various sources of information relevant to the subject matter (see references for details), he was able to gain the needed support for the topic studied. The need for the literature review is to serve as a foundation for rational reasoning on which the thesis topic can be built upon. This gave an insight of the current status of GIS and especially its applications in labour market issues.

As far as these methodologies are concerned, the author was aiming to combine both theoretical analyses (literature review) with GIS data analysis to arrive at a desired conclusion.

6.2 Findings

6.2.1 Data analysis

The data collected from the literature review was used to emphasize prior conceptualisations in the use of GIS in connection to the distribution of workforce in the researched area of Östergötland. By describing and analysing the data, the author was hoping to explain how important is the use of GIS in illustrating employment and unemployment.

6.2.2 Presentation and evaluation of secondary data

As earlier mentioned, the author desired to give a broader view of the GIS use and its implications within the job market that led him to study various journal articles, reviews, databases, statistical reports and conference publications. The use of secondary data was necessary due to the problems in collecting primary data. In addition, secondary data, which provided adequate information to answer the research questions, was easier to access and costed less in time and money spent.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______However, the descriptive research reveals that the labour market is also characterized by the geographic extension of the market and its determination by how far the supply and demand forces go. Employees use their skills to offer their services to employers according to the companies’ needs for a given salary. In today’s volatile work environment, people often decide to travel to neighbouring to their hometown cities for work, especially if the return from commuting is higher than its costs. Such choices depend on factors such as the difficulty in getting a job, the acceptance of long commuting time, the road network, traffic problems, the family situation, age, gender, education etc. In all cases, people choose a residence location and follow different patterns of commuting to a specific workplace location under monetary and time related costs.

GIS is another recognized 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 (see figure 6.1). Popular software packages include ArcGIS and ArcView of ESRI and MapInfo. The use of software can identify the highest population areas regarding density and actual population numbers, the main age and gender group for the residential areas studied.

Figure 6.1: Population density map of men 20-64 years old in Östergötland during 2000 (Department of Computer Science -IDA- Linköping University 2005)

GIS is a mainstream trend, which requires a strategic response.Some organizations/companies consider it as a vector for future growth and development and have adopted aggressive technology. The effectiveness of the GIS implementation depends on the choice of a proper strategy, related to the company’s organizational environment and on the management’s devotion to overcome institutional and individual difficulties (Budic and Godschalk 1994). By visualizing relationships, connections and patterns in data of land and people, they can make proper decisions based on knowledge and increase efficiency throughout their organization/company, while making new customers.

For example, the municipality of Linköping offers an interactive map through its site (Linköping’s Municipality 2005) that is fully accessible from the public for information purposes. The map (see figure 6.2), which has high resolution, can be used as a tool for

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______searching addresses and real estate properties, view neighbourhoods and population centres, retrieve information for drivers and show detailed plans of the municipality. In addition, the map shows parking places, bus stops, bicycle paths and nature conservation programmes. Moreover, the public has the opportunity to check various types of information about services and activities offered in Linköping area, such as public data (pharmacies, museums, libraries, post-offices, police stations etc) and commercial data (banks, coffee-shops, hotels, restaurants etc). The available scales vary from 1:1000 to 100000 and the viewers of the maps can zoom in, zoom out, have full view, pan, measure, erase and print. Different types of information are presented by varying the size of symbols, varying the density of patterns and colour and varying shape and orientation. Retrieval is fast and is available to all users.

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

The municipality of Norrköping offers a similar service with informative maps but it also has maps that present statistics and detailed information in tables linked to the respective maps (Norrköping’s Municipality 2005). Figure 6.3 shows the proportion of job seekers (unemployed) in the area aged 18-64 years on March 31, 2002. The user can determine the geographic distribution of unemployment and monitor specific areas or search using addresses.

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

6.2.3 GIS analysis using Oracle

Primary data costs a lot in time and money to collect and depends on the willingness and ability of the people asked. However, the Department of Computer Science –IDA at Linköping University kindly provided a CD with 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.

Location data can be complex, and many of the important location relationships between demographic data need to be presented as a map to be understood and appreciated.

A web server is a computer that delivers (serves up) web pages (Webopedia 2005). Every Web server has an IP address and maybe a domain name. For example, if somebody enters the URL http://www.liu.se/index.html in his browser, this sends a request to the server whose domain name is liu.se. The server then fetches the page named index.html and sends it to his browser. Any computer can be turned into a web server by installing server software and connecting the machine to the Internet. There are many Web server software applications,

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______including public domain software from NCSA and Apache and commercial packages from Microsoft, Netscape Oracle Application Server and others.

The main use of a map viewer is for processing various map requests. Oracle’s map viewer supports the requirement to hold location information in the most effective way possible, by visualizing this information in mapped form (Oracle Corporation [3] 2005). A map viewer is a component (plug in) of Oracle’s application server that matches with it and aims to provide html pages with maps (svg, jpeg, bmp). In this project, the map viewer was downloaded from the link http://www.oracle.com/technology/software/products/mapviewer/index.html-->download the kits. This is a stand-alone map viewer, a small application server with full capabilities of the normal map viewer used for educational purposes. Its download is a zip of about 38.9 MB. After the software is downloaded, the computer automatically becomes a web server that listens at port 8888 (see figure 6.5). This means that every computer which is at the network can ask from its browser (e.g. Internet explorer) a page at URL http://machine_run_mapviewer:8888/omserver which is the engine of the map viewer and get back a map as a reply. However, the request should have a specific format. In this study, the request should be at xml form (e.g. kommunnamn.xml-see Appendix D) so that when it is sent to the web server it will receive it, check whether it is a valid xml and then construct the map to show the requested information, after it gets connected with the database to get the necessary spatial data.

The structure (scheme) of the database is shown at Appendix C.

The use of map viewer requires the following Java packages and Oracle products (Oracle Corporation 2005): • Oracle Application Server 10g release 2 (10.1.2), or a standalone version of Oracle Application Server Containers for J2EE (OC4J) release 9.0.4 or later, which is available from the Oracle Technology Network at http://www.oracle.com/technology/ • Oracle Spatial or Oracle Locator (release 8.1.6 or later) • J2SE SDK (Java 2 Platform Standard Edition, Software Development Kit from Sun Microsystems) 1.4 or later, with SDK 1.4.2_04.

A map is generated when a map request arrives at the map viewer server; the server picks a free renderer associated with the master data source in the request. The process that the map viewer server follows to generate a map. is described in the following steps: 1. Parse and process the incoming XML map request. 2. Prepare the data for each theme (executed in parallel). 3. Render and label each theme. 4. Generate final images or files.

After all themes have been rendered and (when needed) labelled, map viewer plots any additional map features (such as a legend) on the internal map image. Map viewer then converts that image into the desired format (such as PNG or GIF) specified in the original map request; however, for SVG maps, instead of using an internal image, map viewer initially creates an empty SVG map object, then creates an SVG document as a result of the rendering process, and inserts it into the map object.

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

As illustrated in the above figure 6.4 (Oracle Corporation [4] 2005):

• Map viewer is part of the Oracle Application Server middle tier. • Map viewer includes a rendering engine. • Map viewer can communicate with a client Web browser or application using the HTTP protocol. • Map viewer performs spatial data access (reading and writing Oracle spatial and Oracle locator data) through JDBC calls to the database. • The database includes Oracle spatial or Oracle locator, as well as mapping metadata.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

Figure 6.5: Map viewer’s main page

Oracle’s application server map viewer is a programmable tool for making maps using spatial data managed by Oracle spatial or Oracle locator. Map viewer provides instruments that cover the difficulty of spatial data queries and cartographic rendering, while providing customizable options for more advanced users. Such tools can be organized in a platform-independent manner and are designed to get combined with map-rendering applications (Oracle Corporation [5] 2005).

The map viewer includes the following key parts (Oracle Corporation [5] 2005): 1. A rendering engine (Java class library) that makes available cartographic rendering competencies (map renderer) 2. An Extensible Mark-up Language (XML) API that gives a programmable interface to the map viewer.

The rendering engine connects to the Oracle database through Java Database Connectivity (JDBC). It also loads the map metadata (e.g. map definitions, styling rules, and symbology) from the database and implements it to the retrieved spatial data. The XML API offers application developers with a suitable interface for submitting a map request to the map viewer and handling the map response database (Oracle Corporation [5] 2005). Apart from these components, there is also the map definition tool, which simplifies the process of creating and managing map, theme and symbology metadata in a spatial database. This tool is a standalone application that allows creating and managing mapping metadata that is stored in the database. This mapping metadata can be used by applications that use map viewer to generate customized maps.

When an application uses the map viewer, it applies specific styles (e.g. colours and patterns) to specific themes (various collections of spatial features, such as cities, rivers, and highways) to render a map (such as a GIF image for illustration on a Web page) (Oracle Corporation [6]

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______2005). Map viewer allows defining styles, themes and base maps, including the rules for applying one or more styles to each theme. Such styles, themes, base maps and associated rules are kept in the database in map definition tables under the MDSYS schema and they are visible through metadata views. All styles in a database instance are shared by all users. The mapping metadata (i.e. the set of styles, themes, and base maps) that can be accessed is determined by the map viewer metadata views (for example, USER_SDO_STYLES, USER_SDO_THEMES, and USER_SDO_MAPS) (Oracle Corporation [6] 2005). Styles, themes, and base maps can be managed with the map definition tool.

Thematic mapping is the drawing of spatial characteristics based on their attribute values (Oracle Corporation [7] 2005). Map viewer uses thematic mapping to generate maps in which colours or symbols are applied to features to indicate their attributes. For example, a municipalities theme can be drawn using colours with different hues that map directly to the population density of each municipality. Thematic mapping is accomplished by first creating a highly developed style that is suitable for the type of thematic map and then creating a theme for the features specifying the highly developed style as the rendering style. In the styling rules for the theme, attribute columns in the table must also be specified or view whose values will be used to determine exactly how a feature will be rendered thematically by the highly developed style.

Some basic statistics involved, are included in the following table:

Count 20453 Minimum 1,000000 Maximum 1305,000000 Sum 411345,000000 Mean 20,111720 Standard 58,241757 Deviation

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

Oracle’s tools help to prepare maps that give satisfactory answers to questions of obtaining information from population data. Moreover, they also identify spatial relationships between datasets. The following questions helped in making clear the nature of the data, analytical tools and methods: • Where in Östergötland County is the highest population? • Which is the dominant population age group for the given residential areas? • Which areas offer the best demographic characteristics for site-suitability decision- making?

In proceeding with this analysis the following assumptions regarding the population were taken into account: • The demographic characteristics considered were population distribution, land use and age groups. There were four main population age classes (age 0-19, age 20-34, age 35- 64 and age 65-100)

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______• The age groups 20 to 34, 35 to 64 represent the economically active population (labour force). It is therefore expected that these classes of people show the highest interest towards employment/unemployment issues. • There are socio-demographic differences among the four population groups in terms of their employment preferences. • Population data are of great interest to authorities that observe education needs. • Population data are of great use to authorities that monitor labour shortage due to retirement.

The population data was divided in four data sets according to the age characteristic (age 0- 19, age 20-34, age 35-64 and age 65-100). The total of the age group 0-19 was 99,457 persons. The total of the age group 20-34 was 81,891 persons. The total of the age group 35- 64 was 157,598 persons. The total of the age group 65-100 was 72,399 persons.

Map viewer supports the output of maps in SVG formats. When creating an SVG map, attributes can be specified for a theme that will be returned with the resulting SVG map, which can then be displayed in a pop-up window that follows the user’s cursor as it moves around in the SVG map. Customization and control of the layers in a generated SVG map can take place through such tools as JavaScript (Oracle Corporation [8] 2005).

The map viewer JavaScript application-programming interface (API) for SVG maps contains predefined functions that can be called from outside the SVG map, typically from the HTML document in which the SVG map is embedded. In addition, JavaScript functions can be created to be called when certain mouse-click actions occur. The use of any of these JavaScript functions requires that the end users must use Microsoft Internet Explorer to view the SVG maps, and Adobe SVG Viewer 3.0 or a later release must be installed on their systems.

Map viewer provides control functions to enable and disable the display of informational tips, the map legend, hidden themes, and the animated loading bar. The display control functions include the following: • switchInfoStatus() enables or disables the display of informational tips. (Each call to the function reverses the previous setting.) • the initial display of informational tips can be controlled by using the element in theme styling rule definition and the infoon attribute in a map request. The switchInfoStatus() function toggles (reverses) the current setting for the display of informational tips. • switchLegendStatus() enables or disables the display of the map legend. (Each call to the function reverses the previous setting.) The legend is initially hidden when the map is displayed. • showTheme(theme) sets the specified theme to be visible on the map, and hideTheme(theme) sets the specified theme to be invisible on the map. • showLoadingBar() displays the animated loading bar. The animated loading bar provides a visible indication that the loading of a new map is in progress. The bar is removed from the display when the loading is complete.

Map viewer provides several predefined mouse-click event control functions to enable and disable theme feature, rectangle, and polygon selection in SVG maps. It also provides functions to get information about selections and to toggle the selection status on and off.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______The calculations produced the following results (see figures 6.6 to 6.12).

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

The above illustrates the map viewer display resulting from a map request of showing the total population per municipality in Östergötland. The legend shown has one column. With the zoom in function a municipality can be observed in more detail. Here we can see that Linköping has the highest population followed by Norrköping.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

The above is a thematic map with a pie chart theme. The result shows a map in which each municipality in Östergötland County contains a pie chart in which the size of each slice reflects the proportion of the specified total population age groups. It illustrates a map display that uses the pie chart theme for each municipality that is labelled with a small pie chart. In each pie chart, the red slice shows the proportion of all the people aged 0-19 years old, the yellow slice shows the proportion of all the people aged 20-34 years old, the green slice shows the proportion of all the people aged 35-64 years old and the light blue shows the proportion of all the people aged 65-100 years old.

Map viewer also provides JPEG maps (figures 6.8, 6.9).

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

The above illustrates the map viewer display resulting from a map request of showing the population of Linköping Municipality.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

The above illustrates the map viewer display resulting from a map request of showing the population of Norrköping Municipality.

In both the above maps of Linköping and Norrköping Municipalities, when directing the mouse on the red points of the map (in the SVG format), the numbers of people from the specified age groups (age 0-19, age 20-34, age 35-64 and age 65-100) are being identified.

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

The above result shows a map in which each municipality in Östergötland County contains a pie chart in which the size of each slice reflects the proportion of the specified men population age groups. It illustrates a map display that uses the pie chart theme for each municipality that is labelled with a small pie chart. In each pie chart, the red slice shows the proportion of the men aged 0-19 years old, the yellow slice shows the proportion of the men aged 20-34 years old, the green slice shows the proportion of the men aged 35-64 years old and the light blue shows the proportion of the men aged 65-100 years old.

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

The above result shows a map in which each municipality in Östergötland County contains a pie chart in which the size of each slice reflects the proportion of the specified women population age groups. It illustrates a map display that uses the pie chart theme for each municipality that is labelled with a small pie chart. In each pie chart, the red slice shows the proportion of the women aged 0-19 years old, the yellow slice shows the proportion of the women aged 20-34 years old, the green slice shows the proportion of the women aged 35-64 years old and the light blue shows the proportion of the women aged 65-100 years old.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

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

The above illustrates a population map as a background in which the text style used for the municipality name label for each of several Östergötland municipalities reflects the unemployment of each municipality as well.

6.3 The Use of GIS within the Job Market in Östergötland

There has been significant progress in the development of computer-based tools such as GIS for socioeconomic purposes. New developments in hardware and software systems have made it possible for major advances to be made in the storage, retrieval, processing and presentation of spatially referenced data.

GIS plays an important role as it can integrate data sets from different sources, such as land registries and spatially referenced share ownership records with other private databases to create an urban labour market GIS. For example, figure 6.13 shows the employment rates of the economically active population in Norrköping.

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

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, employers and education and training organizations. GIS can facilitate the development of visual web-based mapping systems that allow users to investigate and find employees, employers and training programs within various industries.

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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)

In spatial analysis, GIS uses overlays, which are spatial operations in which a first thematic layer containing polygons is superimposed on another to form a new thematic layer. It is a very common technique in GIS, especially when maps and information from different sources are involved. In labour market issues, overlays can be used to combine layers obtained from different attribute data.

GIS can use spatial interaction models to model commuting to work flows and perform basic spatial labour market analysis under different scenarios and variables. It is a new promising mechanism for managing information in a local labour market.

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Figure 6.15: Map of Östergötland (Sverige Guiden 2005)

6.4 Present Position and Trends in the Labour Market in Östergötland

Sweden’s structural transformation from an industrialized to a more knowledge-intensive society has increased the employers’ need for specific knowledge, while the number of unskilled jobs has decreased substantially (Regeringskansliet 2004). 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. Moreover, changes in demand and supply of labour affect the equilibrium price and quantity and the income received (as discussed in chapter 2.8).

The increasing education and subsequent entry of women in the job market has increased the supply of labour (discussed in chapters 2.8 and 5.3). Moreover, their attitude towards family and children raises the supply of labour for part-time jobs. In addition, the increasing number of immigrants (skilled or non-skilled) in the area adds more in the supply of labour (discussed in chapters 2.8 and 5.10). On the other hand, men’s attitude to contribute more time in families’ tasks decreases the supply of labour.

Real wages have increased over time to keep pace with the rate of inflation but the rate of this increase has decreased during the last years. Especially for some professions (such as in the land-based sector) the introduction of new technology has affected the real wage rates. New technology has increased the demand for labour and the real wages in full-time, high-skilled jobs and in manufacturing and the services sector (consistency with the economic theory of chapter 2.8). This shifts the labour demand curve to the right as we have discussed. In addition, new technology has created good jobs but also destroyed many low skilled jobs in some industries.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Employees with different education, training and work experience receive different wages. Education and training has a cost to get it, but usually leads to a higher wage after graduation because it leads to higher productivity in white-collar jobs. In addition, men have different and better-paid jobs than women (discussed in chapters 5.3 and 5.4).

Unemployment has mainly hit young people, foreigners and the area of Norrköping (discussed in chapters 2.10, 5.2 and 5.10).

There is an increasing importance of the mobility and flexibility factors, especially in a county that is in Sweden, which is largely sparsely populated.

Figure 6.16: Östergötland’s map (Stadskartan 2005)

Education plays an important role in the job market, which can be mapped by GIS. The percentages of the people that had completed secondary school in 2000 and began tertiary education within 3 years are being shown in figure 6.17.

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

The percentages of the university entrants per 1000 inhabitants in Östergötland’s municipalities are being shown in figure 6.18.

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

The number of participants in adult educational associations per 1000 inhabitants in the municipalities of Östergötland is being shown in figure 6.19.

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

Regional and local collaborations can use GIS as a tool for achieving effective regional and labour market policies in Östergötland in order to reach a sustainable regional development.

6.5 Conclusions and Future Work

This descriptive study presented the important role of GIS and how it can be used in the labour market in Östergötland to show the quantity of labour that people plan to supply and the quantity of labour employers plan to hire at particular real wage rates. Employment and wages can be understood with the use of the demand and supply model and its application in the labour market. Labour supply and labour demand interact to verify the level of employment and the equilibrium real wage rate.

There was a review of the recent restructuring of the labour market and the existing geographical approaches to labour market research, while the potential for GIS to be used in the factors’ analysis of local labour markets was investigated through the use of recent publications and GIS population data analysis.

With the use of thematic maps, good GIS systems can visualise spatial data with labour data according to certain demographic criteria. In a similar way, at chapter 6.2.3 an example was given by successfully visualising spatial data with population data managed by Oracle to display specific populations living in specific areas of Östergötland. Oracle’s map viewer, a programmable tool for delivering simple maps using spatial data, contributed in GIS data analysis by reducing development time via tight integration with the JDeveloper environment. Map viewer increases the value of location data in the Oracle database by providing a tightly coupled tool to visualize these data. Map viewer improves applications summarizing complex demographic and geographic data and relationships in maps.

GIS technology can help explore the current labour market, manage the supply and demand and also make forecasts of the likely impacts of prospective changes in the job market.

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______6.6 Areas for Further Study

This thesis has made an effort to shed some light on the value that the use of Geographical Information Systems brings to the labour market in Östergötland as a tool for examining geographic distributions and for describing what will happen to these distributions in relation to employee demographics. Obviously, there exist other possible studies related to the current project that can be researched in more depth. These include the performance of specific industrial sectors, multiregional demographics, occupational patterns of foreign-born population, highly skilled workers mapping, global labour mobility and others. In addition, this thesis was focused in the area of Östergötland County. New studies may include other counties or municipalities in Sweden.

There are several technological and other scientific fields that after further research can be used to aid future developments of GIS solutions in the demography of employment. These consist of technological developments in the IT and telecommunication sector, including computer hardware and software and the Internet. Further research in database systems, data modelling and programming languages together with a higher availability of data can lead to a better data analysis and display of geographical information. A continuous advancement in software and hardware functionality together with an easier to use data of lower cost that will provide economies of scale may encourage human resources and recruiting companies to use GIS more for the analyses of trends and forecasts related to the labour market. Finally, a broad international standardization and cooperation between the systems suppliers is required for data that comes from various dissimilar sources.

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Appendix A

List of Abbreviations

AMS Swedish National Labour Market Board

AMV Swedish National Labour Market Administration

API Application Programming Interface

CAD Computer-Aided Design

DBF dBase Format

DBMS Database Management System

DEM Digital Elevation Models

DXF Drawing Exchange Format

EES European Employment Strategy

ESRI Environmental Systems Research Institute

EMU European Monetary Union

EU European Union

GIS Geographical Information Systems

GDP Gross Domestic Product

GPS Global Positioning System

IGDS Interactive Graphics Design Software

ISO International Organization for Standardization

IT Information Technology

MC Marginal Cost

MR Marginal Revenue

MRP Marginal Revenue Product

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NLS Swedish National Land Survey

NSDI Swedish National Spatial Data Infrastructures

OECD Organisation for Economic Co-operation and Development

PPP Purchasing Power Parity

RBDMS Relational Database Management Systems

SEK Swedish Krona

SEPA Swedish Environmental Protection Agency

SHP Shape Format

SIS Swedish Standards Institute

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Data compilation: The map data

The spatial reference information of the available data is shown in the following tables B1 and B2:

Projection-Gauss -Kruger False Easting 1500000.0000000 False Northing: 0.0000000 Central Meridian 15.8082778 Scale Factor 1.0000000 Latitude of Origin 0.000000

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

Geographic Coordinate System- RT90 Angular Unit Degree (0.017453292519943295) Prime Meridian Greenwich (0.000000000000000000) Spheroid Bessel 1841 Semi-major Axis 6377397.15500000030000 Semi-minor Axis 6356078.96281818860000 Inverse Flattening 299.15281279999999

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

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Scheme

CREATE TABLE BEF_00_MI ( LAN VARCHAR2(60), KOMMUN VARCHAR2(60), RUTA VARCHAR2(100), TOTAL VARCHAR2(60), M0 VARCHAR2(60), K0 VARCHAR2(60), M1 VARCHAR2(60), K1 VARCHAR2(60), M2 VARCHAR2(60), K2 VARCHAR2(60), M3 VARCHAR2(60), K3 VARCHAR2(60), M4 VARCHAR2(60), K4 VARCHAR2(60), M5 VARCHAR2(60), K5 VARCHAR2(60), M6 VARCHAR2(60), K6 VARCHAR2(60), M7 VARCHAR2(60), K7 VARCHAR2(60), M8 VARCHAR2(60), K8 VARCHAR2(60), M9 VARCHAR2(60), K9 VARCHAR2(60), M10 VARCHAR2(60), K10 VARCHAR2(60), M11 VARCHAR2(60), K11 VARCHAR2(60), M12 VARCHAR2(60), K12 VARCHAR2(60), M13 VARCHAR2(60), K13 VARCHAR2(60), M14 VARCHAR2(60), K14 VARCHAR2(60), M15 VARCHAR2(60), K15 VARCHAR2(60), M16 VARCHAR2(60), K16 VARCHAR2(60), M17 VARCHAR2(60), K17 VARCHAR2(60), M18 VARCHAR2(60), K18 VARCHAR2(60), M19 VARCHAR2(60),

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______K44 VARCHAR2(60), M45 VARCHAR2(60), K45 VARCHAR2(60), M46 VARCHAR2(60), K46 VARCHAR2(60), M47 VARCHAR2(60), K47 VARCHAR2(60), M48 VARCHAR2(60), K48 VARCHAR2(60), M49 VARCHAR2(60), K49 VARCHAR2(60), M50 VARCHAR2(60), K50 VARCHAR2(60), M51 VARCHAR2(60), K51 VARCHAR2(60), M52 VARCHAR2(60), K52 VARCHAR2(60), M53 VARCHAR2(60), K53 VARCHAR2(60), M54 VARCHAR2(60), K54 VARCHAR2(60), M55 VARCHAR2(60), K55 VARCHAR2(60), M56 VARCHAR2(60), K56 VARCHAR2(60), M57 VARCHAR2(60), K57 VARCHAR2(60), M58 VARCHAR2(60), K58 VARCHAR2(60), M59 VARCHAR2(60), K59 VARCHAR2(60), M60 VARCHAR2(60), K60 VARCHAR2(60), M61 VARCHAR2(60), K61 VARCHAR2(60), M62 VARCHAR2(60), K62 VARCHAR2(60), M63 VARCHAR2(60), K63 VARCHAR2(60), M64 VARCHAR2(60), K64 VARCHAR2(60), M65 VARCHAR2(60), K65 VARCHAR2(60), M66 VARCHAR2(60), K66 VARCHAR2(60), M67 VARCHAR2(60), K67 VARCHAR2(60), M68 VARCHAR2(60), K68 VARCHAR2(60), M69 VARCHAR2(60),

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______K69 VARCHAR2(60), M70 VARCHAR2(60), K70 VARCHAR2(60), M71 VARCHAR2(60), K71 VARCHAR2(60), M72 VARCHAR2(60), K72 VARCHAR2(60), M73 VARCHAR2(60), K73 VARCHAR2(60), M74 VARCHAR2(60), K74 VARCHAR2(60), M75 VARCHAR2(60), K75 VARCHAR2(60), M76 VARCHAR2(60), K76 VARCHAR2(60), M77 VARCHAR2(60), K77 VARCHAR2(60), M78 VARCHAR2(60), K78 VARCHAR2(60), M79 VARCHAR2(60), K79 VARCHAR2(60), M80 VARCHAR2(60), K80 VARCHAR2(60), M81 VARCHAR2(60), K81 VARCHAR2(60), M82 VARCHAR2(60), K82 VARCHAR2(60), M83 VARCHAR2(60), K83 VARCHAR2(60), M84 VARCHAR2(60), K84 VARCHAR2(60), M85 VARCHAR2(60), K85 VARCHAR2(60), M86 VARCHAR2(60), K86 VARCHAR2(60), M87 VARCHAR2(60), K87 VARCHAR2(60), M88 VARCHAR2(60), K88 VARCHAR2(60), M89 VARCHAR2(60), K89 VARCHAR2(60), M90 VARCHAR2(60), K90 VARCHAR2(60), M91 VARCHAR2(60), K91 VARCHAR2(60), M92 VARCHAR2(60), K92 VARCHAR2(60), M93 VARCHAR2(60), K93 VARCHAR2(60), M94 VARCHAR2(60),

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______K94 VARCHAR2(60), M95 VARCHAR2(60), K95 VARCHAR2(60), M96 VARCHAR2(60), K96 VARCHAR2(60), M97 VARCHAR2(60), K97 VARCHAR2(60), M98 VARCHAR2(60), K98 VARCHAR2(60), M99 VARCHAR2(60), K99 VARCHAR2(60), M100 VARCHAR2(60), K100 VARCHAR2(60), X VARCHAR2(60), Y VARCHAR2(60), ID NUMBER);

CREATE INDEX BEFF_KOMM_IDX ON BEF_00_MI (KOMMUN);

CREATE INDEX BEFF_LAN_IDX ON BEF_00_MI (LAN);

------CREATE TABLE BEF_00_MI_POINT ( GID NUMBER, GEOMETRY SDO_GEOMETRY); ------CREATE TABLE RE_AD97 ( FORSAMLING NUMBER, FORSAMLI0 VARCHAR2(200), KOMMUNKOD NUMBER, LANSKOD NUMBER, FOR_KOD VARCHAR2(200), KOM_KOD VARCHAR2(200), LAN_KOD VARCHAR2(200), ID NUMBER);

CREATE INDEX AD97_IDX ON RE_AD97 (ID);

CREATE INDEX AD97_KOMM_IDX ON RE_AD97 (KOMMUNKOD); ------CREATE TABLE RE_AD97_REGION ( GID NUMBER, GEOMETRY SDO_GEOMETRY);

CREATE INDEX AD97_REGION_IDX ON RE_AD97_REGION (GID); ------CREATE TABLE RE_KN97 ( KOMMUNKOD NUMBER(10), KOMMUNNAMN VARCHAR2(100),

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______LANSKOD NUMBER, KOM_KOD VARCHAR2(100), LAN_KOD VARCHAR2(100), ID NUMBER, UNEMPLOYED_NUM NUMBER, UNEMPLOYED_PROP NUMBER); ------CREATE TABLE RE_KN97_REGION ( GID NUMBER, GEOMETRY SDO_GEOMETRY); ------CREATE OR REPLACE VIEW V_BEF_00 (LAN, KOMMUN, TOTAL) AS SELECT lan,kommun,sum(total) total from BEF_00_MI where lan=5 group by lan,kommun; ------CREATE OR REPLACE VIEW V_BEF_00_AGE (LAN, KOMMUN, M19, M34, M64, M100, K19, K34, K64, K100, TOTAL19, TOTAL34, TOTAL64, TOTAL100) AS select inline."LAN",inline."KOMMUN",inline."M19",inline."M34",inline."M64",inline."M100",inli ne."K19",inline."K34",inline."K64",inline."K100",M19+K19 AS TOTAL19,M34+K34 AS TOTAL34,M64+K64 AS TOTAL64,M100+K100 AS TOTAL100 FROM ( SELECT lan ,kommun

,sum(M0+M1+M2+M3+M4+M5+M6+M7+M8+M9+M10+M11+M12+M13+M14+M15+M 16+M17+M18+M19) M19

,sum(M20+M21+M22+M23+M24+M25+M26+M27+M28+M29+M30+M31+M32+M33+M 34) M34

,sum(M35+M36+M37+M38+M39+M40+M41+M42+M43+M44+M45+M46+M47+M48+M 49+M50 +M51+M52+M53+M54+M55+M56+M57+M58+M59+M60+M61+M62+M63+M64) M64

,sum(M65+M66+M67+M68+M69+M70+M71+M72+M73+M74+M75+M76+M77+M78+M 79+M80

+M81+M82+M83+M84+M85+M86+M87+M88+M89+M90+M91+M92+M93+M94+M95+ M96+M97+M98+M99+M100) M100

,sum(K0+K1+K2+K3+K4+K5+K6+K7+K8+K9+K10+K11+K12+K13+K14+K15+K16+K1 7+K18+K19) K19

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

,sum(K20+K21+K22+K23+K24+K25+K26+K27+K28+K29+K30+K31+K32+K33+K34) K34

,sum(K35+K36+K37+K38+K39+K40+K41+K42+K43+K44+K45+K46+K47+K48+K49+K5 0 +K51+K52+K53+K54+K55+K56+K57+K58+K59+K60+K61+K62+K63+K64) K64

,sum(K65+K66+K67+K68+K69+K70+K71+K72+K73+K74+K75+K76+K77+K78+K79+K8 0

+K81+K82+K83+K84+K85+K86+K87+K88+K89+K90+K91+K92+K93+K94+K95+K96+ K97+K98+K99+K100) K100 from BEF_00_MI where lan=5 group by lan,kommun ) inline; ------CREATE OR REPLACE VIEW V_BEF_00_AGE_SPECIAL (GEOMETRY, LAN, KOMMUN, M19, M34, M64, M100, K19, K34, K64, K100, TOTAL19, TOTAL34, TOTAL64, TOTAL100, TOTAL_DESC) AS select inline2."GEOMETRY",inline2."LAN",inline2."KOMMUN",inline2."M19",inline2."M34",inl ine2."M64",inline2."M100",inline2."K19",inline2."K34",inline2."K64",inline2."K100",inline 2."TOTAL19",inline2."TOTAL34",inline2."TOTAL64",inline2."TOTAL100", 'T19:'||total19||'| T34:'||total34||'| T64:'||total64||'| T100:'||total100 as total_desc from ( select geometry,inline."LAN",inline."KOMMUN",inline."M19",inline."M34",inline."M64",inline." M100" ,inline."K19",inline."K34",inline."K64",inline."K100",M19+K19 AS TOTAL19 ,M34+K34 AS TOTAL34,M64+K64 AS TOTAL64 ,M100+K100 AS TOTAL100 FROM ( SELECT geometry ,lan ,kommun

,(M0+M1+M2+M3+M4+M5+M6+M7+M8+M9+M10+M11+M12+M13+M14+M15+M16+ M17+M18+M19) M19

,(M20+M21+M22+M23+M24+M25+M26+M27+M28+M29+M30+M31+M32+M33+M34) M34

,(M35+M36+M37+M38+M39+M40+M41+M42+M43+M44+M45+M46+M47+M48+M49+ M50

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______+M51+M52+M53+M54+M55+M56+M57+M58+M59+M60+M61+M62+M63+M64) M64

,(M65+M66+M67+M68+M69+M70+M71+M72+M73+M74+M75+M76+M77+M78+M79+ M80

+M81+M82+M83+M84+M85+M86+M87+M88+M89+M90+M91+M92+M93+M94+M95+ M96+M97+M98+M99+M100) M100

,(K0+K1+K2+K3+K4+K5+K6+K7+K8+K9+K10+K11+K12+K13+K14+K15+K16+K17+K 18+K19) K19 ,(K20+K21+K22+K23+K24+K25+K26+K27+K28+K29+K30+K31+K32+K33+K34) K34

,(K35+K36+K37+K38+K39+K40+K41+K42+K43+K44+K45+K46+K47+K48+K49+K50 +K51+K52+K53+K54+K55+K56+K57+K58+K59+K60+K61+K62+K63+K64) K64

,(K65+K66+K67+K68+K69+K70+K71+K72+K73+K74+K75+K76+K77+K78+K79+K80

+K81+K82+K83+K84+K85+K86+K87+K88+K89+K90+K91+K92+K93+K94+K95+K96+ K97+K98+K99+K100) K100 from BEF_00_MI ,BEF_00_MI_POINT where id=gid and lan=5 and kommun in (580) ) inline )inline2; ------CREATE OR REPLACE VIEW V_KN97 (GEOMETRY, KOMMUNNAMN, TOTAL, UNEMPLOYED_NUM, UNEMPLOYED_PROP, DESCR) AS select geometry ,kommunnamn ,total ,unemployed_num ,unemployed_prop ,kommunnamn||':'||unemployed_prop descr from RE_KN97_REGION a ,RE_KN97 b ,V_BEF_00 c where a.gid=b.id and b.kommunkod=c.kommun; ------CREATE OR REPLACE VIEW V_KN97_AGE (GEOMETRY, KOMMUNNAMN, M19, M34, M64, M100, K19, K34, K64, K100, TOTAL19, TOTAL34, TOTAL64, TOTAL100) AS

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______select geometry ,kommunnamn ,M19 ,M34 ,M64 ,M100 ,K19 ,K34 ,K64 ,K100 ,TOTAL19 ,TOTAL34 ,TOTAL64 ,TOTAL100 from RE_KN97_REGION a ,RE_KN97 b ,V_BEF_00_AGE c where a.gid=b.id and b.kommunkod=c.kommun; ------CREATE OR REPLACE VIEW V_KN97_AGE_SPECIAL (GEOMETRY, KOMMUNNAMN, M19, M34, M64, M100, K19, K34, K64, K100, TOTAL19, TOTAL34, TOTAL64, TOTAL100) AS select a.geometry ,kommunnamn ,M19 ,M34 ,M64 ,M100 ,K19 ,K34 ,K64 ,K100 ,TOTAL19 ,TOTAL34 ,TOTAL64 ,TOTAL100 from RE_KN97_REGION a ,RE_KN97 b ,V_BEF_00_AGE_SPECIAL c where a.gid=b.id and b.kommunkod=c.kommun; ------

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______CREATE OR REPLACE VIEW V_KN97_AGE_SPECIAL_DETAIL (GEOMETRY, KOMMUNNAMN, M19, M34, M64, M100, K19, K34, K64, K100, TOTAL19, TOTAL34, TOTAL64, TOTAL100) AS select a.geometry ,kommunnamn ,M19 ,M34 ,M64 ,M100 ,K19 ,K34 ,K64 ,K100 ,TOTAL19 ,TOTAL34 ,TOTAL64 ,TOTAL100 from RE_KN97_REGION a ,RE_KN97 b ,V_BEF_00_AGE_SPECIAL c where a.gid=b.id and b.kommunkod=c.kommun; ------CREATE OR REPLACE VIEW V_KN97_SPECIAL (GEOMETRY, KOMMUNNAMN, TOTAL) AS select geometry ,kommunnamn ,total from RE_KN97_REGION a ,RE_KN97 b ,V_BEF_00 c where a.gid=b.id and b.kommunkod=c.kommun and kommunnamn in ('Linköping'); ------

======CREATE TRIGGER DBASE_TRG_BF after insert on BEF_00_MI for each row begin pkg_trg.add; insert into temp values(pkg_trg.value,:new.rowid); end; ------CREATE TRIGGER RE_AD97_TRG

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______after insert on RE_AD97 for each row begin pkg_trg.add; insert into temp values(pkg_trg.value,:new.rowid); end; ------CREATE TRIGGER RE_KN97_TRG after insert on RE_KN97 for each row begin pkg_trg.add; insert into temp values(pkg_trg.value,:new.rowid); end; ------CREATE package dbase_pkg as

-- procedure to a load a table with records -- from a DBASE file. -- -- Uses a BFILE to read binary data and dbms_sql -- to dynamically insert into any table you -- have insert on. -- -- p_dir is the name of an ORACLE Directory Object -- that was created via the CREATE DIRECTORY -- command -- -- p_file is the name of a file in that directory -- will be the name of the DBASE file -- -- p_tname is the name of the table to load from -- -- p_cnames is an optional list of comma separated -- column names. If not supplied, this pkg -- assumes the column names in the DBASE file -- are the same as the column names in the -- table -- -- p_show boolean that if TRUE will cause us to just -- PRINT (and not insert) what we find in the -- DBASE files (not the data, just the info -- from the dbase headers....)

procedure load_Table( p_dir in varchar2, p_file in varchar2, p_tname in varchar2, p_cnames in varchar2 default NULL,

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______p_show in BOOLEAN default FALSE);

/* begin pkg_trg.init;

execute immediate 'truncate table RE_KN97';

dbase_pkg.load_Table( p_dir =>'SPATIAL_DIE', p_file =>'RE_KN97.dbf, p_tname =>'RE_KN97' );

for rec in (select * from temp) loop update RE_KN97 set id=rec.id where rowid=rec.r; end loop;

commit;

end; */ end; ------CREATE package body dbase_pkg as

-- Might have to change on your platform!!! -- Controls the byte order of binary integers read in -- from the dbase file BIG_ENDIAN constant boolean default TRUE; type dbf_header is RECORD ( version varchar2(25), -- dBASE version number year int, -- 1 byte int year, add to 1900 month int, -- 1 byte month day int, -- 1 byte day no_records int, -- number of records in file, -- 4 byte int hdr_len int, -- length of header, 2 byte int rec_len int, -- number of bytes in record, -- 2 byte int no_fields int -- number of fields );

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______type field_descriptor is RECORD ( name varchar2(11), type char(1), length int, -- 1 byte length decimals int -- 1 byte scale ); type field_descriptor_array is table of field_descriptor index by binary_integer;

type rowArray is table of varchar2(4000) index by binary_integer;

g_cursor binary_integer default dbms_sql.open_cursor;

-- Function to convert a binary unsigned integer -- into a PLSQL number function to_int( p_data in varchar2 ) return number is l_number number default 0; l_bytes number default length(p_data); begin if (big_endian) then for i in 1 .. l_bytes loop l_number := l_number + ascii(substr(p_data,i,1)) * power(2,8*(i-1)); end loop; else for i in 1 .. l_bytes loop l_number := l_number + ascii(substr(p_data,l_bytes-i+1,1)) * power(2,8*(i-1)); end loop; end if;

return l_number; end;

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

-- Routine to parse the DBASE header record, can get -- all of the details of the contents of a dbase file from -- this header procedure get_header (p_bfile in bfile, p_bfile_offset in out NUMBER, p_hdr in out dbf_header, p_flds in out field_descriptor_array ) is l_data varchar2(100); l_hdr_size number default 32; l_field_desc_size number default 32; l_flds field_descriptor_array; begin p_flds := l_flds;

l_data := utl_raw.cast_to_varchar2( dbms_lob.substr( p_bfile, l_hdr_size, p_bfile_offset ) ); p_bfile_offset := p_bfile_offset + l_hdr_size;

p_hdr.version := ascii( substr( l_data, 1, 1 ) ); p_hdr.year := 1900 + ascii( substr( l_data, 2, 1 ) ); p_hdr.month := ascii( substr( l_data, 3, 1 ) ); p_hdr.day := ascii( substr( l_data, 4, 1 ) ); p_hdr.no_records := to_int( substr( l_data, 5, 4 ) ); p_hdr.hdr_len := to_int( substr( l_data, 9, 2 ) ); p_hdr.rec_len := to_int( substr( l_data, 11, 2 ) ); p_hdr.no_fields := trunc( (p_hdr.hdr_len - l_hdr_size)/ l_field_desc_size );

for i in 1 .. p_hdr.no_fields loop l_data := utl_raw.cast_to_varchar2( dbms_lob.substr( p_bfile, l_field_desc_size, p_bfile_offset )); p_bfile_offset := p_bfile_offset + l_field_desc_size;

p_flds(i).name := rtrim(substr(l_data,1,11),chr(0)); p_flds(i).type := substr( l_data, 12, 1 ); p_flds(i).length := ascii( substr( l_data, 17, 1 ) ); p_flds(i).decimals := ascii(substr(l_data,18,1) ); end loop;

p_bfile_offset := p_bfile_offset +

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______mod( p_hdr.hdr_len - l_hdr_size, l_field_desc_size ); end;

function build_insert ( p_tname in varchar2, p_cnames in varchar2, p_flds in field_descriptor_array ) return varchar2 is l_insert_statement long; begin l_insert_statement := 'insert into ' || p_tname || '('; if ( p_cnames is NOT NULL ) then l_insert_statement := l_insert_statement || p_cnames || ') values ('; else for i in 1 .. p_flds.count loop if ( i <> 1 ) then l_insert_statement := l_insert_statement||','; end if; l_insert_statement := l_insert_statement || '"'|| p_flds(i).name || '"'; end loop; l_insert_statement := l_insert_statement || ') values ('; end if; for i in 1 .. p_flds.count loop if ( i <> 1 ) then l_insert_statement := l_insert_statement || ','; end if; if ( p_flds(i).type = 'D' ) then

l_insert_statement := l_insert_statement || 'to_date(:bv' || i || ',''yyyymmdd'' )'; else l_insert_statement := l_insert_statement || ':bv' || i; end if; end loop; l_insert_statement := l_insert_statement || ')';

return l_insert_statement; end;

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

function get_row ( p_bfile in bfile, p_bfile_offset in out number, p_hdr in dbf_header, p_flds in field_descriptor_array ) return rowArray is l_data varchar2(4000); l_row rowArray; l_n number default 2; begin l_data := utl_raw.cast_to_varchar2( dbms_lob.substr( p_bfile, p_hdr.rec_len, p_bfile_offset ) ); p_bfile_offset := p_bfile_offset + p_hdr.rec_len;

l_row(0) := substr( l_data, 1, 1 );

for i in 1 .. p_hdr.no_fields loop l_row(i) := rtrim(ltrim(substr( l_data, l_n, p_flds(i).length ) )); if ( p_flds(i).type = 'F' and l_row(i) = '.' ) then l_row(i) := NULL; end if; l_n := l_n + p_flds(i).length; end loop; return l_row; end get_row;

procedure show( p_hdr in dbf_header, p_flds in field_descriptor_array, p_tname in varchar2, p_cnames in varchar2, p_bfile in bfile ) is l_sep varchar2(1) default ',';

procedure p(p_str in varchar2) is l_str long default p_str; begin while( l_str is not null ) loop dbms_output.put_line( substr(l_str,1,250) ); l_str := substr( l_str, 251 );

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______end loop; end; begin p( 'Sizeof DBASE File: ' || dbms_lob.getlength(p_bfile) );

p( 'DBASE Header Information: ' ); p( chr(9)||'Version = ' || p_hdr.version ); p( chr(9)||'Year = ' || p_hdr.year ); p( chr(9)||'Month = ' || p_hdr.month ); p( chr(9)||'Day = ' || p_hdr.day ); p( chr(9)||'#Recs = ' || p_hdr.no_records); p( chr(9)||'Hdr Len = ' || p_hdr.hdr_len ); p( chr(9)||'Rec Len = ' || p_hdr.rec_len ); p( chr(9)||'#Fields = ' || p_hdr.no_fields );

p( chr(10)||'Data Fields:' ); for i in 1 .. p_hdr.no_fields loop p( 'Field(' || i || ') ' || 'Name = "' || p_flds(i).name || '", ' || 'Type = ' || p_flds(i).Type || ', ' || 'Len = ' || p_flds(i).length || ', ' || 'Scale= ' || p_flds(i).decimals ); end loop;

p( chr(10) || 'Insert We would use:' ); p( build_insert( p_tname, p_cnames, p_flds ) );

p( chr(10) || 'Table that could be created to hold data:'); p( 'create table ' || p_tname ); p( '(' );

for i in 1 .. p_hdr.no_fields loop if ( i = p_hdr.no_fields ) then l_sep := ')'; end if; dbms_output.put ( chr(9) || '"' || p_flds(i).name || '" ');

if ( p_flds(i).type = 'D' ) then p( 'date' || l_sep ); elsif ( p_flds(i).type = 'F' ) then p( 'float' || l_sep ); elsif ( p_flds(i).type = 'N' ) then if ( p_flds(i).decimals > 0 ) then p( 'number('||p_flds(i).length||','|| p_flds(i).decimals || ')' || l_sep ); else p( 'number('||p_flds(i).length||')'||l_sep );

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______end if; else p( 'varchar2(' || p_flds(i).length || ')'||l_sep); end if; end loop; p( '/' ); end;

procedure load_Table( p_dir in varchar2, p_file in varchar2, p_tname in varchar2, p_cnames in varchar2 default NULL, p_show in boolean default FALSE ) is l_bfile bfile; l_offset number default 1; l_hdr dbf_header; l_flds field_descriptor_array; l_row rowArray; begin l_bfile := bfilename( p_dir, p_file ); dbms_lob.fileopen( l_bfile );

get_header( l_bfile, l_offset, l_hdr, l_flds );

if ( p_show ) then show( l_hdr, l_flds, p_tname, p_cnames, l_bfile ); else dbms_sql.parse( g_cursor, build_insert(p_tname,p_cnames,l_flds), dbms_sql.native );

for i in 1 .. l_hdr.no_records loop l_row := get_row( l_bfile, l_offset, l_hdr, l_flds );

if ( l_row(0) <> '*' ) -- deleted record then for i in 1..l_hdr.no_fields loop dbms_sql.bind_variable( g_cursor, ':bv'||i, l_row(i), 4000 ); end loop; if ( dbms_sql.execute( g_cursor ) <> 1 ) then

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______raise_application_error( -20001, 'Insert failed ' || sqlerrm ); end if; end if; end loop; end if;

dbms_lob.fileclose( l_bfile ); exception when others then if ( dbms_lob.isopen( l_bfile ) > 0 ) then dbms_lob.fileclose( l_bfile ); end if; RAISE; end; end; ------CREATE package pkg_trg is value number; procedure init; procedure add; end; ------CREATE package body pkg_trg is procedure init is begin execute immediate 'truncate table temp'; value:=-1; end;

procedure add is begin value:=value+1; end;

end; ------

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______Appendix D

KOMMUNNAMN XML

- - - select geometry,KOMMUNNAMN,TOTAL from V_KN97 - select geometry,forsamli0 from RE_AD97,RE_AD97_region where id=gid - - - -

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-

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Source Code (PL/SQL) for creating the maps:

Figure 6.6: Total Population per KOMMUNNAMN declare l_http_req utl_http.req; l_http_resp utl_http.resp; l_url varchar2(4000):= 'http://localhost:8888/mapviewer/omserver'; l_value varchar2(4000); img_url varchar2(4000); response sys.xmltype; output varchar2(255); map_req varchar2(4000); begin utl_http.set_persistent_conn_support(TRUE); map_req := '

select geometry,KOMMUNNAMN,TOTAL from V_KN97

select geometry,forsamli0 from RE_AD97,RE_AD97_region where id=gid

select geometry from BEF_00_MI, BEF_00_MI_POINT where id=gid and lan=5 and kommun=512

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

'; l_http_req := utl_http.begin_request(l_url, 'POST', 'HTTP/1.0'); -- -- Sets up proper HTTP headers. -- utl_http.set_header(l_http_req, 'Content-Type','application/x-www-form- urlencoded'); utl_http.set_header(l_http_req, 'Content-Length', length('xml_request=' || map_req)); utl_http.set_header(l_http_req, 'Host', 'localhost'); utl_http.set_header(l_http_req, 'Port', '8888'); utl_http.write_text(l_http_req, 'xml_request=' || map_req); -- l_http_resp := utl_http.get_response(l_http_req); utl_http.read_text(l_http_resp, l_value); response := sys.xmltype.createxml (l_value); utl_http.end_response(l_http_resp); --img_url := response.extract('/map_response/map_image/map_content/@url').getstringval() ; --dbms_output.put_line(img_url); exception when others then dbms_output.put_line(sqlerrm); end; /

Figure 6.7: Total Pie per KOMMUNNAMN declare l_http_req utl_http.req; l_http_resp utl_http.resp; l_url varchar2(4000):= 'http://localhost:8888/mapviewer/omserver'; l_value varchar2(4000); img_url varchar2(4000); response sys.xmltype; output varchar2(255); map_req varchar2(4000); begin utl_http.set_persistent_conn_support(TRUE); map_req := '

select geometry,KOMMUNNAMN,TOTAL from V_KN97

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______ select geometry ,kommunnamn ,TOTAL19,TOTAL34 ,TOTAL64 ,TOTAL100 from V_KN97_AGE

'; l_http_req := utl_http.begin_request(l_url, 'POST', 'HTTP/1.0'); -- -- Sets up proper HTTP headers. --

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______utl_http.set_header(l_http_req, 'Content-Type','application/x-www-form- urlencoded'); utl_http.set_header(l_http_req, 'Content-Length', length('xml_request=' || map_req)); utl_http.set_header(l_http_req, 'Host', 'localhost'); utl_http.set_header(l_http_req, 'Port', '8888'); utl_http.write_text(l_http_req, 'xml_request=' || map_req); -- l_http_resp := utl_http.get_response(l_http_req); utl_http.read_text(l_http_resp, l_value); response := sys.xmltype.createxml (l_value); utl_http.end_response(l_http_resp); --img_url := response.extract('/map_response/map_image/map_content/@url').getstringval() ; --dbms_output.put_line(img_url); exception when others then dbms_output.put_line(sqlerrm); end; /

Figure 6.8: Detailed Analysis of Population (Linköping) declare l_http_req utl_http.req; l_http_resp utl_http.resp; l_url varchar2(4000):= 'http://localhost:8888/mapviewer/omserver'; l_value varchar2(4000); img_url varchar2(4000); response sys.xmltype; output varchar2(255); map_req varchar2(4000); begin utl_http.set_persistent_conn_support(TRUE); map_req := '

select GEOMETRY,KOMMUNNAMN,TOTAL from V_KN97_SPECIAL

select geometry,forsamli0 from RE_AD97,RE_AD97_region where id=gid and kommunkod=580

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select geometry,total_desc from V_BEF_00_AGE_SPECIAL

'; l_http_req := utl_http.begin_request(l_url, 'POST', 'HTTP/1.0'); -- -- Sets up proper HTTP headers. -- utl_http.set_header(l_http_req, 'Content-Type','application/x-www-form- urlencoded'); utl_http.set_header(l_http_req, 'Content-Length', length('xml_request=' || map_req)); utl_http.set_header(l_http_req, 'Host', 'localhost'); utl_http.set_header(l_http_req, 'Port', '8888'); utl_http.write_text(l_http_req, 'xml_request=' || map_req); -- l_http_resp := utl_http.get_response(l_http_req); utl_http.read_text(l_http_resp, l_value); response := sys.xmltype.createxml (l_value); utl_http.end_response(l_http_resp); --img_url := response.extract('/map_response/map_image/map_content/@url').getstringval() ; --dbms_output.put_line(img_url); exception when others then dbms_output.put_line(sqlerrm); end; /

Figure 6.9: Detailed Analysis of Population (Norrköping) declare l_http_req utl_http.req; l_http_resp utl_http.resp; l_url varchar2(4000):= 'http://localhost:8888/mapviewer/omserver'; l_value varchar2(4000); img_url varchar2(4000); response sys.xmltype;

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______output varchar2(255); map_req varchar2(4000); begin utl_http.set_persistent_conn_support(TRUE); map_req := '

select GEOMETRY,KOMMUNNAMN,TOTAL from V_KN97_SPECIAL

select geometry,forsamli0 from RE_AD97,RE_AD97_region where id=gid and kommunkod=581

select geometry,total_desc from V_BEF_00_AGE_SPECIAL

'; l_http_req := utl_http.begin_request(l_url, 'POST', 'HTTP/1.0'); -- -- Sets up proper HTTP headers. -- utl_http.set_header(l_http_req, 'Content-Type','application/x-www-form- urlencoded'); utl_http.set_header(l_http_req, 'Content-Length', length('xml_request=' || map_req)); utl_http.set_header(l_http_req, 'Host', 'localhost');

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______utl_http.set_header(l_http_req, 'Port', '8888'); utl_http.write_text(l_http_req, 'xml_request=' || map_req); -- l_http_resp := utl_http.get_response(l_http_req); utl_http.read_text(l_http_resp, l_value); response := sys.xmltype.createxml (l_value); utl_http.end_response(l_http_resp); --img_url := response.extract('/map_response/map_image/map_content/@url').getstringval() ; --dbms_output.put_line(img_url); exception when others then dbms_output.put_line(sqlerrm); end; /

Figure 6.10: Men Pie per KOMMUNNAMN declare l_http_req utl_http.req; l_http_resp utl_http.resp; l_url varchar2(4000):= 'http://localhost:8888/mapviewer/omserver'; l_value varchar2(4000); img_url varchar2(4000); response sys.xmltype; output varchar2(255); map_req varchar2(4000); begin utl_http.set_persistent_conn_support(TRUE); map_req := '

select geometry,KOMMUNNAMN,TOTAL from V_KN97

select geometry ,kommunnamn ,M19,M34 ,M64 ,M100,K19,K34,K64,K100 from V_KN97_AGE

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______

'; l_http_req := utl_http.begin_request(l_url, 'POST', 'HTTP/1.0'); -- -- Sets up proper HTTP headers. -- utl_http.set_header(l_http_req, 'Content-Type','application/x-www-form- urlencoded'); utl_http.set_header(l_http_req, 'Content-Length', length('xml_request=' || map_req)); utl_http.set_header(l_http_req, 'Host', 'localhost'); utl_http.set_header(l_http_req, 'Port', '8888'); utl_http.write_text(l_http_req, 'xml_request=' || map_req); -- l_http_resp := utl_http.get_response(l_http_req); utl_http.read_text(l_http_resp, l_value); response := sys.xmltype.createxml (l_value); utl_http.end_response(l_http_resp);

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______--img_url := response.extract('/map_response/map_image/map_content/@url').getstringval() ; --dbms_output.put_line(img_url); exception when others then dbms_output.put_line(sqlerrm); end; /

Figure 6.11: Women Pie per KOMMUNNAMN declare l_http_req utl_http.req; l_http_resp utl_http.resp; l_url varchar2(4000):= 'http://localhost:8888/mapviewer/omserver'; l_value varchar2(4000); img_url varchar2(4000); response sys.xmltype; output varchar2(255); map_req varchar2(4000); begin utl_http.set_persistent_conn_support(TRUE); map_req := '

select geometry,KOMMUNNAMN,TOTAL from V_KN97

select geometry ,kommunnamn ,K19,K34 ,K64 ,K100 from V_KN97_AGE

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'; l_http_req := utl_http.begin_request(l_url, 'POST', 'HTTP/1.0'); -- -- Sets up proper HTTP headers. -- utl_http.set_header(l_http_req, 'Content-Type','application/x-www-form- urlencoded'); utl_http.set_header(l_http_req, 'Content-Length', length('xml_request=' || map_req)); utl_http.set_header(l_http_req, 'Host', 'localhost'); utl_http.set_header(l_http_req, 'Port', '8888'); utl_http.write_text(l_http_req, 'xml_request=' || map_req); -- l_http_resp := utl_http.get_response(l_http_req); utl_http.read_text(l_http_resp, l_value); response := sys.xmltype.createxml (l_value); utl_http.end_response(l_http_resp); --img_url := response.extract('/map_response/map_image/map_content/@url').getstringval() ; --dbms_output.put_line(img_url); exception when others then dbms_output.put_line(sqlerrm);

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THE DEMOGRAPHY OF EMPLOYMENT IN A SWEDISH COUNTY COUNCIL: ESTIMATION AND MAPPING OF MANPOWER STATISTICS IN ÖSTERGÖTLAND. ______end; /

Figure 6.12: Unemployment Population per KOMMUNNAMN declare l_http_req utl_http.req; l_http_resp utl_http.resp; l_url varchar2(4000):= 'http://localhost:8888/mapviewer/omserver'; l_value varchar2(4000); img_url varchar2(4000); response sys.xmltype; output varchar2(255); map_req varchar2(4000); begin utl_http.set_persistent_conn_support(TRUE); map_req := '

select * from V_KN97

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'; l_http_req := utl_http.begin_request(l_url, 'POST', 'HTTP/1.0'); -- -- Sets up proper HTTP headers. -- utl_http.set_header(l_http_req, 'Content-Type','application/x-www-form- urlencoded'); utl_http.set_header(l_http_req, 'Content-Length', length('xml_request=' || map_req)); utl_http.set_header(l_http_req, 'Host', 'localhost'); utl_http.set_header(l_http_req, 'Port', '8888'); utl_http.write_text(l_http_req, 'xml_request=' || map_req); -- l_http_resp := utl_http.get_response(l_http_req); utl_http.read_text(l_http_resp, l_value); response := sys.xmltype.createxml (l_value); utl_http.end_response(l_http_resp); --img_url := response.extract('/map_response/map_image/map_content/@url').getstringval() ; --dbms_output.put_line(img_url); exception when others then dbms_output.put_line(sqlerrm); end; /

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