QUALITY OF LIFE, FIRM FORMATION AND INTERNAL MIGRATION: A CASE STUDY OF POLAND'S POWIATS
By
N andaka Hugh Molagoda
Submitted to the
Faculty of the College of Arts and Sciences
of American University
in Partial Fulfillment of
the Requirements for the Degree
of Doctor of Philosophy
In
Economics
Chair: Professor Mieke Meurs
Professor Thomas Husted
Dean of the College ~~~ ~1 l\J\\ Date \
2011 American University Washington, D.C. 20016
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by
N and aka Hugh Molagoda
2011 ALL RIGHTS RESERVED DEDICATION
To my mother and father. To my wife and my children, whose love and devotion go beyond measure. To all those who have encouraged and helped me throughout this journey. ii
QUALITY OF LIFE, FIRM FORMATION AND
INTERNAL
MIGRATION: A CASE STUDY OF POLAND'S POWIATS by Nandaka Hugh Molagoda
ABSTRACT
Three essays in this dissertation look at differences in quality of life ( QoL)
in Polish counties (powiats) and, the role these differences play in the formation of firms and internal migration. Differences in quality of life or, differences in amenities
available in a powiat influence economic activity and productivity in that location. I find differences in QoL explain differences in firm formation and internal migration.
The first essay develops a method to measure QoL. Poland is an emerging market economy, where labor and land markets capitalized amenity values. Amenity values are derived from a reduced-form hedonic regression applied separately to the land and labor markets. The summations of the implicit value of all amenities, which are the coefficients of the two hedonic regression estimates, form the Quality of Life
Index ( QoLI). Differences in QoL are ranked to show the best and worst performing
Polish powiats. Rankings illustrate the scale and extent of differences in quality of life.
The second essay looks at the role differences in amenity characteristics that form QoL play in the formation of firms. I use a reduced-form model to measure the entry and exit rates of firms, and the gross rate of firm formation, in Polish lll powiats. I find variables that construct the quality of life index play a significant role in explaining firm formation in Polish powiats.
The third essay looks at the role differences of quality of life play in internal in-migration within Polish powiats. A reduced-form human capital model measures internal in-migration into Polish powiats, with quality of life as an explanatory vari able. I find quality of life plays a significant role in explaining differences in internal migration in Polish powiats. iv
ACKNOWLEDGEMENTS
I am deeply indebted to my dissertation chair, Dr. Mieke Meurs. She has been a constant source of guidance, and support, providing me with many opportunities to complete this dissertation.
I am also very grateful to my committee members Dr. Feinberg and Dr.
Thomas Husted for accepting my request for their help. Their encouragement and guidance throughout was invaluable.
Special thanks goes to my friend Mariusz Grabek for reading my work and providing insightful comments.
Lastly, to my wife and my children, for all the sacrifices they had to make:
Thank you for all you have done. v
TABLE OF CONTENTS
ABSTRACT ...... ii
ACKNOWLEDGEMENTS iv
LIST OF TABLES . viii
LIST OF FIGURES x
CHAPTER
1. INTRODUCTION . 1
1.0.l Overview . 1
1.0.2 Scope of the Study . 2
1.0.3 Software . . 2
1.0.4 Organization 3
2. ESTIMATING QUALITY OF LIFE IN POLISH POW/ATS 5
2.1 Introduction 5
2.2 Background . 8
2.2.l Residential Land and Labor Markets . 14
2.3 Valuation of Local Amenities . 17
2.4 Data and Descriptive Statistics 21
2.5 Fitting the Data ...... 29
2.5.1 Identifying Variables and Outliers 30
2.5.2 Functional Form: Bedonie Estimation 31
2.5.3 Regressions Results on Tax Revenue and Wages 32 Vl
2.5.4 Robustness ...... 37
2.5.5 Constructing QoLI and Ranking 41
2.6 Conclusion ...... 44
3. CAN AMENITY CHARACTERISTICS EXPLAIN FIRM FORMA- TION IN POLISH POW/ATS? 48
3.1 Introduction ...... 48
3.2 Literature Review and Theoretical Motivation . 50
3.3 Data and Descriptive Statistics 55
3.4 Results . . 64
3.5 Conclusion 78
4. CAN QUALITY OF LIFE EXPLAIN INTERNAL MIGRATION IN POLISH POWIATs? 81
4.1 Introduction ...... 81
4.2 Theory and Empirical Evidence . 83
4.3 Data and Estimation Model . 87
4.3.l Data ...... 87
4.3.2 Empirical Model 89
4.4 Results . . 89
4.5 Conclusion 94
5. POLICY IMPLICATIONS: QUALITY OF LIFE AND ITS INFLU- ENCE ON FIRM FORMATION AND MIGRATION 95
5.1 Overview ...... 95 5.2 Policy Implications . 96
APPENDIX
A. REAL ESTATE TAX REVENUE 97
B. PRINCIPAL COMPONENT ANALYSIS 101 vii
C. QUALITY OF LIFE INDEX RANKING 108
REFERENCES ...... 116 Vlll
LIST OF TABLES
Table Page
2.1 Regional Differences, 2003-2007 . . . . . 10
2.2 Real Estate Market in Poland, 2000-2006 15
2.3 Population, 1946-2002 ...... 16
2.4 Net Internal In-migration, 2003-2007 17
2.5 Property Tax Rates in 2003-2007 23
2.6 Summary Statistics, 2003-2007 . . 27
2. 7 Model Selection: Real Estate Tax Revenue Estimates, 2003-2007 . 34
2.8 Model Selection: Wage Estimates, 2003-2007 . . 36
2.9 Real Estate Tax Revenue Estimates, 2003-2007 38
2.10 Wage Estimates, 2003-2007 . . . 39
2.11 Sidak Pair-wise Correlation Test . 41
2.12 Summary: QoLI and Ranking of Powiats, 2003-2007 43
3.1 Summary Statistics, 2004-2007 ...... 58
3.2 Firm Entries and Exits by Ownership, 2003-2007 60
3.3 Firm Entry and Exits by Sector, 2003-2007 . 61
3.4 Gross Number of Firms, 2003-2007 62
3.5 Correlation Matrix, 2003-2007 . . . 63
3.6 Firm Formation by Sector, 2004-2007 69
3.7 Firm Formation by Sector, 2004-2007 (Cont.) 69 ix
3.8 Firm Formation by Sector Using Variables from the QoLI Alternative
2, 2004-2007 ...... 70
3.9 Firm Formation by Sector Using Variables from the QoLI Alternative
2, 2004-2007 (Cont.) ...... 71
3.10 Firm Formation by Ownership, 2004-2007 73
3.11 Firm Formation by Ownership, 2004-2007 (Cont.) 74
3.12 Firm Formation by Ownership Using Variables from the QoLI Alter-
native 2, 2004-2007 ...... 74
3.13 Firm Formation by Ownership Using Variables from the QoLI Alter-
native 2, 2004-2007 (Cont.) ...... 75
3.14 Gross Number of Firms by Size, 2004-2007 77
3.15 Gross Number of Firms by Size Using Variables from the QoLI Alter-
native 2, 2004-2007 ...... 77
4.1 Descriptive Statistics, 2003-2007 . 87
4.2 Data Correlations, 2003-2007 . . 88
4.3 In-migration Estimated Results, 2004-2007 92
4.4 In-migration Estimated Results, Using Less Correlated QoLI Vari-
ables, 2004-2007 ...... 93
A.1 Real Estate Tax Receipts, 2001 100
B.1 Description of Variables . . . . 101
B.2 Principal Components: Physical Geography 103
B.3 Principal Components: Infrastructure . . . . 104
B.4 Principal Components: Schools, Helath and Culture . 106
B.5 Principal Components: Externalities ...... 107
C.1 Quality of Life Ranking, Firm Entry and In-Migration, 2003-2007 108 x
LIST OF FIGURES
Figure Page
2.1. Annual Real Estate Tax Revenue, 2003-2007 11
2.2. Annual Wages, 2003-2007 12
2.3. Regional GDP, 2004-2007 13
2.4. Population Distribution, 2003-2007 16
2.5. Sewage Line System Usage, 2003-2007 28
2.6. School Computers with Internet Connections, 2003-2007 29
2.7. Fixed Investment in Enterprises, 2003-2007 . 30
2.8. Quality of Life Index, 2003-2007 . 42
3.1. Firm Entries, 2003-2007 57
3.2. Firm Exits, 2003-2007 63
3.3. Gross Number of Firms, 2003-2007 65
4.1. Number of In-migrants, 2003-2007 . 86
4.2. Wage Income, 2003-2007 ...... 90
B.l. Principal Components: Physical Geography, 2003-2007 103
B.2. Principal Components: Infrastructure, 2003-2007 ... 105
B.3. Principal Components: Schools, Health and Culture, 2003-2007 106 1
CHAPTER 1
INTRODUCTION
1.0.1 Overview
Economic activity and performance are unevenly distributed but not random.
Economic activity, productivity, and competitiveness are enhanced in locations that are considered attractive, because of the optimal combination of amenities. Ameni ties are location-specific goods, services, and characteristics that directly affect the utility function of residents and cost function of firms. The values of amenities such as physical geography, public infrastructure, quality of education, and externalities have to be estimated implicitly, as these are not traded in open markets. I ap ply a model developed by Rosen (1979) and Roback (1982) and later modified by
Blomquist et al. (1988) to value amenities. In this model, land prices and wages capitalize the value of amenities. The quality of life index ( QoLI) measures the total amenity value in each location.
It is plausible for people and firms to move to attractive places where valuable amenities like modern roads, good schools, and strong utility networks can improve living conditions and real income or total utility, which also increases profitability of firms. Amenities available in a particular location, therefore, can influence economic activity and performance in that location. I examine the role quality of life plays in the formation of firms and internal migration. Quality of life is the compensat- 2 ing wage, which play a significant role in influencing changes in formation of firms
and internal migration, factors which subsequently motivate local governments to invest in more amenities. This non-random relationship may explain "cumulative causation" leading to agglomeration and uneven distribution of economic activity, productivity and performance.
1.0.2 Scope of the Study
This study concentrates on three basic questions. Is it possible to measure differences in quality of life? If so, can amenity characteristics, which form the quality of life index explain differences in firm formation in Poland? Can differences in quality of life explain differences in internal migration in Poland? Finally, I discuss the policy options available to minimize differences in quality of life.
To answer the first question, I use a theoretical model developed by Rosen
(1979) and Roback (1982) and later modified by Blomquist et al. (1988) to measure regional differences in quality of life. The second and third questions are explored using reduced form models, with identified explanatory variables to measure quality of life. To test the robustness of results, I incorporate several explanatory variables that measure quality of life in Polish powiats including two different measures of quality of life, and compare results against differences in firm formation and internal migration. Finally, I use the study's empirical results to explore possible policy options that can minimize regional differences in quality of life.
1.0.3 Software
STATA version lOSE (originally developed by William Gould, 1985) for Win dows is used for estimating all models. The data is organized as panel time series and, STATA command tsset is used for the analysis. This dissertation is formatted in LaTeX (Leslie Lamport, 1980). I have also made use of the LaTeX thesis tern- 3 plate provided by Professor Allen Isaac (American University, Washington D.C.).
1 Geographic data maps have been prepared using the software ADePT version 2.0 , developed by the World Bank Group and Shapefiles for Poland provided by Map
Library.2
1.0.4 Organization
This thesis is organized in five chapters and three appendices. This introduc tory chapter puts forth the motivation and research questions the dissertation will address.
The second chapter develops a measure of quality of life. I argue that Poland is a advancing market economy, where the labor and land markets can be used to measure capitalized amenity values. Amenity values are derived from a reduced form hedonic regression applied separately to the land and labor markets. Summations of the implicit value of amenities, which are the coefficients of the two hedonic regression estimates, form the Quality of Life Index. Elements of the index are then ranked to show the differences in quality of life among Polish powzats.
The third chapter looks at the role quality of life plays in firm formation in
Polish powzats. A reduced form equation measures firm entry into Polish powzats, with variables that measure quality of life used as explanatory variables. I find quality of life plays a significant role in explaining firm entry and exit behavior in
Polish powzats.
The fourth chapter looks at the role quality of life plays in internal migration in Polish powzats. A reduced form equations measure internal in-migration in Polish powzats. I find quality of life play a significant role in explaining internal in-migration in Polish powzats.
1 http / / siteresources worldbank org /I NT POV RES/ Resources/ AdePT_Map exe
2http://www maplibrary org/mdex php 4
The fifth Chapter provides an overview of the findings and offer policy options.
There are also three appendices. The first contains a discussion of issues rel evant to the real estate tax revenue series. The second contains a discussion of the use of principal component analysis. The third contains a full ranking of the quality of life index for 379 powiats. 5
CHAPTER 2
ESTIMATING QUALITY OF LIFE IN POLISH POWIATS
2.1 Introduction
How different is quality of life among Polish powzats?1 This paper examines
that question by constructing a quality of life index (QoLI). A QoLI evaluates value
differences in a community's non-traded amenities such as good schools and solid
public infrastructure. A theoretical model developed by Rosen (1979) and Roback
(1982) explains that market-determined changes to wages and land rents implicitly reveal how individuals value non-traded amenities. In this research, I use a similar
approach to construct a quality of life index that reveals the value of amenities such
as physical geography, good schools, solid infrastructure, and externalities in Polish
powiats. Observations at the powzat level provide sufficient information on variations in quality of life, and the data set is complete. I then rank and geographically map
QoLI to provide a measure of the size and scale of differences among Polish powiats.
Economic analysis has often implied that quality of life is mainly determined to a population. The welfare of the population is discussed in terms of the total
1 An equivalent definition of a powzat is a county 6
production of the economy (GDP) or the average share of output per person (GDP
per capita). This narrow concept of welfare ignores the value of non-traded goods and
services. In the modern economy, people demand many types of non-traded goods,
such as clean air and good schools, that improve their quality of life. Ignoring non-
traded goods and services in an analysis of regional differences in human well-bemg
constitutes an omitted variable bias.
Quality of life is a measure of the social and physical (human-made and nat
ural) environment in which people pursue fulfillment of their needs. However, social
and physical environments in which people are able to live productive and comfort-
able lives are scarce and unevenly distributed. As a result, people collectively and
privately spend resources to attain them. They pay higher truces and accept lower
wages to live in amenity-rich locations. In amenity-rich locations, residents also
pay a premium for housing. Glaeser et al (2001) find urban rents rise faster than
urban wages, suggesting that amenities make up for some of the increase in rents.
Brueckner et al. (1998) find that high-income groups are attracted to high-amenity
locations. In this study, I use compensating price differences in land and labor mar- kets, estimated as the implicit value of amenities, to measure differences in quality of life.
Various scholars define quality of life differently. In this study, quality of life is described using the utilitarian approach, 2 which solves two important problems.
First, quality of life is measured in monetary terms. That is, amenities with differ- ent units of measurement, such as school classrooms with Internet connection and percentage of people using public sewage systems, can be measured in monetary terms or in one unit of measure. Using money as the unit of measure makes it easy
2 Act10ns of consumers are determmed by overall satisfact10n or utility Utility 1s where people rank all possible combmations of the least desirable to most desirable factors, consummg traded and non-traded goods and services 7 to aggregate amenity values and compare them across different locations. Second, quality of life is measured by the compensating price differences arising from land and labor markets. Employers pay high wages in low-amenity locations to attract workers. Similarly, land prices are lower in low-amenity locations, or the capitalized low-amenity value lowers land prices. Using open markets to evaluate quality of life differences improves the objective assessment of living conditions. It reduces the need to collect data on a multitude of local characteristics. It further reduces the need to make assumptions about what local amenity characteristics can be aggregated to obtain a comprehensive assessment of quality of life.
In Poland, land and labor markets are well developed as compared to many other Central and Eastern European Countries. In addition, Poland's accession to the
European Union in 2004 has improved its competitiveness in labor and land markets.
It is, then, a promising prospect to evaluate the implicit prices of amenities or the differences in quality of life that are capitalized in land markets and compensated for in labor markets in Poland.
Several previous studies by Rogerson et al. (1988), Glaeser and Shapiro (2003) and Khan (2002) have looked at quality of life differences among developed coun tries. Blomquist et al. (1988) find labor and housing markets reflect differences in the implicit prices of amenities such as climate, environmental quality, and urban conditions such as teacher-pupil ratio, lack of violent crime, and distance to a cen tral city in 253 urban counties in the United States. More recently, application of QoL analysis has been extended to transition countries such as Russia. Berger et al. (2008) find labor and housing markets reflect differences in the implicit price of amenities such as climate, environmental conditions, low levels of ethnic conflict, low crime rates, health conditions, and proximity to Western Europe in 39 cities of
32 different regions in the Russian Federation. However, there is a general lack of 8 empirical evidence on measurement of regional differences in quality of life in former transition economies. This study will add to the empirical evidence.
Similar to earlier empirical findings assessing other regions, the results in this study show that differences in amenity characteristics are capitalized in land prices and compensated for by the labor market. Amenity differences measure differences in quality of life in Polish powiats. The Quality of Life Index is interpreted in this study as an overall premium or discount as compared to other counties, which makes it possible to rank powzats in the index from the best to the worst. By ordering the index from highest to lowest, the index measure reveals large variations in quality of life in Poland. This study finds QoL is lower in central and eastern regions and higher in southwestern and northwestern regions. In particular, quality of life is higher in
Warsaw in the central region, in port cities in the north, and in border cities close to the Czech Republic and the Slovak Republic in the southwest. Across most Polish powzats, quality of life is also higher in urban locations than in rural locations. These findings are similar to empirical evidence from other, more developed economies.
The remainder of this essay is organized in the following order. First, I ex plain the theoretical foundation for the valuation of amenities in Poland. Second, I discuss the economic background and available data and estimation strategy. Third,
I explain the empirical estimate and the construct of the QoLI. Finally, I conclude with a discussion of the future direction of this research.
2.2 Background
This section will provide a general description of the physical and economic space in Poland. I explore regional differences in land rents using real estate tax revenue collections as a proxy for rents and regional differences in wages as evidenced by the labor market. In the following section, land and labor markets will be used 9 to explain a theoretical model developed to evaluate differences in quality of life.
Poland is the largest country in Central and Eastern Europe, with a landmass of 120,728 square miles and a population of over 38 million people. Poland borders
Russia to the north; Lithuania, Belarus and Ukraine to the east; Slovakia to the south; Czech Republic to the southwest, and the former East Germany to the north west. Poland's border comprises 275 miles of coastline in the north and the northwest regions. Gdansk and Gdynia are major port cities in the north. Kolobrzeg, Police,
Szczecin, and Swinoujscie are major port cities in the northwest.
Poland is divided administratively into six large regions. Table 2.1 illustrates some common regional differences in economic activity. The table shows regional differences in annual average real estate tax collection per household; average annual wage income per household, assuming at least one person works in every household; the annual unemployment rate; and regional gross domestic product (GDP). This table illustrates that mean real estate tax revenue collections are statistically similar across many regions in Poland except in the southwestern region, which has a higher mean value. The six administrative regions of Poland are further subdivided into 379 powzats. A disaggregated view of the differences in real estate tax revenue collection at the powzat level, shown in Figure 2.1, demonstrates large differences within the six regions, with revenue collection highest in urban locations highlighted in dark color. Figure 2.1 additionally shows lower regional tax revenue collections in most powzats in the eastern region.
Although living standards have steadily improved overall, wide regional dis parities can be observed in many Polish regions. These differences are illustrated by differences in real wages and GDP throughout the transition period and after EU accession in 2004. Table 2.1 and Figure 2.2 show mean wages in the southwestern region are the highest, with the lowest mean wages reported from the northern, east- 10 ern, and northwestern regions. The central region, which includes the capital city
Warsaw, lies in-between the highest and lowest mean wage regions.
The lowest mean unemployment rate is also reported from the southern region, with the highest reported from the northern region and the remaining regions sharing similar unemployment rates that place between the highest and the lowest. The average unemployment rate varies by over six percentage points between the highest and the lowest.
Table 2.1 and Figure 2.3, in addition, show similar regional variation m Gross
Domestic Product (GDP). The variation in real estate tax revenue, wages, unem ployment rates, and GDP can indicate large variations in quality of life in Polish powiats.
Table 2.1: Regional Differences, 2003-2007
Obs Mean Std Dev Mm Max Annual Real Estate Tax Revenue m zloty per Household
Central reg10n 330 787 433 290 3,069 Southern reg10n 290 880 342 283 2,301 Eastern 1eg10n 400 691 232 295 1,743 Northwestern 1eg10n 350 995 332 562 3,637 Southwestern region 205 1,043 482 557 4,595 Northern reg10n 320 837 232 470 1,716 Annual Wages m zloty
Central reg10n 330 25,722 4,953 19,016 47,997 Southern reg10n 290 26,118 4,554 18,645 50,558 Eastern reg10n 400 24,508 3,227 18,935 42,799 Northwestern region 350 24,537 3,412 16,662 36,957 Southwestern region 205 26,103 5,434 20,523 61,600 Northern reg10n 320 24,218 3,539 18,877 40,660
Unemployment Rate
Central reg10n 330 12 2 48 24 28 3 Southern region 290 89 3 1 29 18 6 Eastern reg10n 400 12 2 36 46 24 3 Northwestern reg10n 350 12 5 52 2 1 25 2 Southwestern reg10n 205 11 9 45 29 21 9 Northern reg10n 320 15 3 55 1 6 28 3
Contmued on next page lll
Table 2 1 - Contmued Obs Mean Std Dev Mm Max Reg10nal GDP m Million zloty
Central reg10n 264 14,736 14,447 6,154 142,731 Southern reg10n 232 14,632 6,157 6,172 33,698 Eastern reg10n 320 9,190 3,440 4,274 17,595 Northwestern region 280 11,418 3,596 5,581 31,773 Southwestern region 164 12,575 3,663 5,953 25,702 Northern rcg10n 256 11,756 5,301 4,034 30,365
Source Central Stat1st1cal Office, Poland
Lecend D10Q7fOo 7 0 ~9' 78'1 2 ~Sol 2283 0
Figure 2.1 Annual Real Estate Tax Revenue, 2003-2007
Among the most stnkmg features of regional differences m Poland are the geo- graphic disparities in real estate tax revenue, wages, unemployment rates, and GDP
These measurements record clear distmct1ons m regional d1fferences between east- ern regions that border the former Russian republics and the western and southern regions closer to the European Umon.3
3It should be noted that there is a h1stoncal context to the differences between the western 12
Leg ll=-cll 90km Figure 2.2. Annual Wages, 2003-2007 The Polish government has recognized the importance of minimizing large regional disparities to lift its citizens' sense of well-being. It decentralized govern- ment to three lower levels, namely the v01vodeship(regional), powiat(county), and gmma(commumty) over a period of time, facilitatmg decision making at the local community level. With the efforts to decentralize government, local bodies were fur- ther formalized and given more responsibilities and power. A time-line of a series of administrative and legal changes is given below. The new law on Local Self- Government, enacted in 1990, established local autonomy and clear responsibilities and eastern parts of Poland This context 1s related to the loss of Pohsh mdependence between the 3rd partit10nmg m 1795 (by Prussia, Austria and Russia) and 1918, when Poland briefly regamed its mdependence only to lose 1t agam after World War II It is hkely that the Prussian and Austrian systems of government, commerce, and bankmg left their footpnnts upon their respective local populat10ns, as well as the1r percept10ns and tradit10ns of entrepreneurship, trade and eqmty footprmts that were not left m areas under Russian traditions of governance This legacy probably predisposed the western populat10ns towards entrepreneurship and self-governance, even 1f exercised on smaller or more restricted scales 13 Legenc 0 4~7~ 0551 •9 'I 11J21 I 1>121125~70 Figure 2.3. Regional GDP, 2004-2007 for public functions at the community or gmina level. Several new laws were enacted to authorize more expenditure and revenue responsibilities, such as the Act of Local Revenue and Rules of Finance (1990), the Budget Law Act (1991), and the Act on Tax Law and Fees (1991). Three hundred and seventy-nine powiats, or counties, were re-created in 1999 after the introduction of the Polish Local Government Re- forms in 1998. Sixteen voivodeships, or larger administrative regions that cover the 379 powiats, were also created. Some of the main responsibilities that devolved to local levels of government were health, education, energy, land development, hous- ing, sanitation, water supply, and transportation. Locally collected tax revenue and transfers from the central government finance these new responsibilities. Government decentralization has also provided much needed legal structure and responsibilities to elected local governments to deliver differentiated public goods and services. The 14 overall objective of these initiatives is to improve living standards and quality of life in local communities in Poland. 2.2.1 Residential Land and Labor Markets The functioning of the residential land and, labor markets, with a focus on labor migration to equalize differences in real income, is central to estimating the QoLI. In this section, I provide background information on the development of the residential land and labor markets in Poland. The residential land market is largely shaped by the quality of housing and local amenities. Until 1989, Poland was left only with low-quality housing which had been owned and heavily subsidized by the state during the communist regime. Since shortly after 1989, private ownership of residential housing and the development of a commercial real estate market in Poland has positively influenced the overall development of the land market. To provide a general view of the land market in Poland, I present a limited set of data made available by the European Council of Real Estate Professionals and the International Real Estate Digest, shown in Table 2.2. The table highlights that the price of a square meter of housing in Warsaw increased from euro 734 in 2000 to euro 930 in 2004, and then dropped to euro 875 in 2006. The monthly rent on commercial and real estate space also fell between 2004 and 2006. This variation in prices reflects market conditions as suggested by the International Real Estate Digest Mitropolitski ( 2008). People in Poland are traditionally not very mobile, with an economy that has been largely dependent on agriculture. However, with the development of new industries and the growth of cities, labor is more mobile than before. People moving from rural to urban locations characterize internal migration in many parts of Poland. Table 2.3 shows that 62 percent of the population chooses to live in urban locations. Looking at this table, one can argue that most of the population had already moved 15 to urban centers before the transition period. However, during the transition period, old industrial urban centers failed, forcing people to move to urban locations closer to western Europe and other major trade centers, such as Warsaw and port cities. These centers were able to grow jobs and retain people. Table 2 4 shows that this trend is continuing. These changes are likely to influence living standards and quality of life. Table 2 2 Real Estate Market m Poland, 2000-2006 2000 2004 2006 Populat10n (mn) 38 1 38 1 Number of dwellmgs (mn) 12 6 14 Owner occupied dwellmgs (%) 56 90 Real estate profess10nals (number) 4,800 6,514 Property managers (number) 14,000 14,610 Interest rate on housmg loans (%) 18 0 8 7 59 Licenses to bmld (number) 114,874 Sales Transactions (number) 250,000 Sale pnce of house-Waisaw (m2) 1 733 9 930 875 Sale pnce of house-2nd city (m2) i 820 830 Sale pnce of Apt -Warsaw (m2) i 1,000 1,000 Sale pnce of Apt -2nd city (m2) i 800 500 Sale of retail space- Warsaw (m2) i 1,450 Sale price of office space-Warsaw (m2) 1 1,400 Monthly rent of house-Warsaw (m2) i 12 5 11 3 Monthly rent of house-2nd city (m2) i 90 40 Monthly rent of 2 BR Apt -Warsaw (m2) i 80 89 Monthly rent of 2 BR Apt -m 2nd city (m2) i 50 25 Monthly rent of retail space-Warsaw (m2) 1 29 8 42 5 Monthly rent of office space-Warsaw (m2) i 17 5 15 0 Pncc of water-Warsaw (m3) i 06 08 Monthly charges of 2 BR Apt -Wa1saw 1 65 0 Pnce of electricity-Warsaw (KwH) i 0 1 0 1 Fiscal allowance for first time home buyers yes yes Low mcome assistance to pay rent yes yes Fmancial assistance for refurbishmg yes no Source European Council of Real Estate Professionals and lnternat10nal Real Estate Digest (2000, 2004 and 2008) [l]m euro Figure 2.4 portrays a geographic map of the location of people in Poland Pop- ulat10n concentrates m urban centers such as Warsaw in the central region, Karakow in the southern region, and port cities such as Gdansk in the northern reg10n. 16 Table 2 3 Populat10n, 1946-2002 1946 1950 1960 1970 1978 1988 2002 Total ('000) 23,930 25,008 29,776 32,642 35,061 37,879 38,230 Urban areas ('000) 7,517 9,605 14,206 17,064 20,150 23,175 23,610 Urban areas (%of total populat10n) 31 4 38 4 47 7 52 3 57 5 61 2 61 8 Source Central Stat1st1cal Office, Poland Lege no D111816DS4< 06'~449-, ')' D 947L a 1tQJ7~.; Figure 2 4 Population D1stnbu.t10n, 2003~2007 There can be many reasons why people move from rural to urban locat10ns One important motivatmg factor m the expectatwn of higher wages and a better quahty of li.fe Between 2003 and 2005 over 600,000 people, on average, moved mternally every year m Poland From 2006 to 2007, over 700,000 people moved mternally, on average, every year m Poland Most of this population moved to 17 central and northwestern regions and away from northern and eastern regions, as shown in Table 2.4. Warsaw is situated in the central region, and the northwestern region borders the European Union; both regions are prosperous. Northern and eastern regions border Russia and the former Russian Republics of Belarus, Lithuania and Ukraine. These regions are relatively less affluent. The data also illustrate that internal migration is large (1.8 percent of total population or 50 percent of internat10nal migration),4 and illustrate the extent and direction of labor mobility within Poland. Table 2 4 Net Internal In-m1grat10n, 2003-2007 2003 2004 2005 2006 2007 Central reg10n 12,413 11,847 12,956 14,497 13,293 Southern reg10n 389 -328 78 -359 -255 Eastern reg10n -10,824 -10,447 -10,978 -11,856 -12,123 Northwestern reg10n 418 993 1,048 561 1,102 Southwestern reg10n -1,759 -1,311 -1,563 -433 -398 Northern reg10n -637 -754 -1,541 -2,410 -1,619 Source: Central Statistical Office, Poland. 2.3 Valuation of Local Amenities Rosen (1979) first developed a theoretical model that considered both the labor and real estate markets to measure quality of life in urban locations. Roback (1982) and Blomquist et al. (1988) applied this theoretical framework by using observat10ns on wages, land rents, and amenity characteristics to estimate hedonic wage and land rent equations. Roback (1982) used the empirical estimates to rank 20 large urban cities in the United States. Blomquist et al. (1988) importantly extended Roback's study to rank 253 urban counties in the United States, with 16 different amenity characteristics. 4 Close to 2 mtlhon people on average work outside Poland 18 In this section, I describe Roback's (1982) and Blomquist et al. 's (1988) spec ification of the Rosen model. This model specification is applied to Poland, with observations of wages and land rents made at the powzat or county level for five years. In this framework, households gain utility by consuming a composite good 1 produced by firms, land, and local amenities. Amenities of the k h powzat are avail able to households by purchasing land qk, where k E 1, 2. In powzat k, each household is endowed with one unit of labor and sells this unit of labor to local firms. House hold labor earnings are fully exhausted with the purchase of the composite good produced by firms and land. In this model, wages are the only source of income and household savings are zero. All labor is assumed the same or homogeneous and labor transportation costs are positive. The utility attained by each household in powzat k is: (2.3.1) where vk (.) is the indirect utility function, rk is the land rent, wk is the wage earnings and ak is an index of local amenities, and the price of the composite commodity is held fixed. The composite commodity is a set of traded goods of which either the relative price does not change or we can ignore the price effects of the composite commodity. The only relative price changes that affect utility are land rents, wages, and amenities. The use of a fixed priced composite commodity in the model helps 1 to isolate the price effects of land rents, wages, and amenities. Utility in the k h powzat increases with wages Wk and decreases with land rents rk. The availability of amenities in a powzat increases utility if it is beneficial v:k > 0 and decreases if it is not beneficial v~, < 0. Households in powzat k demand land qk = v; / v!. Given that the land available in each powiat is fixed and is equal to Qk, the population of powzat k is Nk = Qk/ qk. This implies from the indirect utility function that population is a function of each powzat's wage, land rent, and amenities. 19 Firms produce the composite commodity C, using local labor, capital, and technology, which exhibit constant returns to scale in labor and capital. The objec tive of all firms is to minimize costs. Firms take the price of the composite commodity and the price of capital as given and fixed, since they are nationally traded. Wages and land rents are normalized or are relative to the price of the composite good. The cost of production of the composite good for a firm located in powiat k is· (2.3.2) where ck ( ) is the unit cost of production. The price of capital is implicit and is not shown explicitly in the cost function. Population size of the powiat is N and is a measure of agglomeration or congestion. Depending on the city size, the cost of firms will mcrease c > 0, remain the same c = 0, or fall c < 0. Unit costs decrease with beneficial local amenities c~k and increase with non-beneficial amenities c~k. By Shepard's Lemma,5 c!i > 0. Land and labor markets clear when people and firms locate in different places. A spatial equilibrium develops, as firms cannot reduce costs by relocating and indi- viduals cannot improve utility by moving. Intra-region equilibrium develops where the common good is produced at the same unit cost between the powiats in a region and all people in the powiats within the region achieve the same utility. An inter- region equilibrium follows where the unit price of the composite good is equal to the unit production costs in all regions and each region attains the same level of utility. The set of wages, residential land rents, and city size that defines an intra- 5 Consumers buy a umque bundle of goods by cons1dermg the mm1mal pnce of that bundle or the cost, which also max1m1ze utility 20 regional and inter-regional equilibrium satisfies the following system of equations: 1 = ck( wk; aki N) (2.3.3) uo vk(wk, rk; ak) (2.3.4) 2 N = LNk (2.3.5) k=l where k E 1, 2. The first equation is the firms' cost minimizing solution; the second equation is the individuals' utility maximizing choice; and the third equation (2.3.5) in the system links the equilibrium wages and land rents within powzats through city size and firms' productivity. Solving the system of equations will give the implicit price f, of amenities ak and the comparative static effects of a change in ak on equilibrium wages, rent, and amenities. The total differential of the indirect utility function is zero at the maximum utility, given by totally differentiating equation (2.3.4), which gives: (2.3.6) for each powzat k. Rearranging the terms in equation (2.3.6), we find, Va _ Vr dr dw ( ) .f _ 2 3 7 ' - Vw -- Vw da - da · · The full implicit price of any amenity is f, = .l'.l!., where - .3!L is the quantity of Vw Vw residential land purchased, ~: is the equilibrium land rent differential, and ~: is the equilibrium wage differential. From Roy's identity I can rewrite _.3!r. as qk where equation (2.3.6) becomes: Vw (2.3.8) The above equation shows that the full implicit price of any one amenity is a com- bination of the effects of residential land and labor markets (Roback, 1982). 21 To measure differences in quality of life, I use results from the empirical valu- ation of amenity characteristics. The quality of life index ( QoLI) is the sum of the value of amenities in each powiat. It is calculated by multiplying the full implicit price of each amenity given in equation (2.3. 7) by the endowment of the respective amenities for each powiat. QoLI for each powiat is given by the equation (2.3.9): 4 QoLln = L fiAin, n = 1 ... 379 (2.3.9) i=l where f, is the amenity's price, n is the number of counties and z is the type of amenity. 2.4 Data and Descriptive Statistics I construct the QoLI using annual data for all 379 powzats in Poland for the years 2003 to 2007. Real estate tax revenue per household is used as a proxy for residential land rents, since residential land rents are not systematically collected or readily available. Real estate tax receipts6 come from different tax rates applied to buildings or their parts; other architectural structures; land which is not part of agriculture or forests; lakes and water reservoirs; and agricultural land or forests that are used for commercial activity. The tax base for most types of properties is measured in square meters, ex cept for other architectural structures, 7 which are measured according to the value 6 Total tax revenue is obtained by multiplying the applicable tax rate for different types of real estate properties by the area. The tax rates are set by the central government, allowmg counties some flexibility in setting their own tax rates. Though the area-based real estate tax revenue is purely a fiscal objective to expand the tax base beyond income, it also captures wealth diverted mto real property assets 7 A distinction is made between buildings (budynki), such as residential houses, offices, fac tories, and other architectural structures (budowla). Polish law specifies that "budowla" (other architectural structures) are all objects which are not buildings. Examples of other architectural structures are airports, roads, bndges, antenna masts, sewage treatment plants, and waste disposal plants. 22 used to depreciate the asset base. Local authorities collect information on the tax base using the Land Title Registry (for ownership) and the Land and Building Evi dence cadastral information (land maps, building description, land area, and parcel information). Table 2.5 shows the upper limit of property taxes set by the central government. The property taxes are adjusted annually based on the retail price increases in goods and services. There are some particular issues that need to be carefully addressed when con sidering real estate tax revenue as a proxy variable to capture residential land rents. First, the property tax rates in different locations can influence total tax revenue collection. A property in an urban location is likely to be taxed at a higher rate than a property in a rural location. Secondly, different types of real estate properties, such as a larger property, can potentially influence total tax revenue collection. Therefore, however, the property tax revenue series can potentially capture not only changes in the value of real estate, but changes in tax rates by type and location of properties. In this study, I consider the way different tax rates show what residents are willing to pay for differentiated amenities. For example, a household that moves to an af fluent location is willing to pay a premium for local amenities in a combination of higher tax rates and more expensive real estate. A more serious concern is that real estate tax revenue is derived from both residential and commercial real estate. To capture differences in wages and land rents as the implicit value for amenities to be evaluated by households, residential real estate needs to be isolated from commercial real estate. I do not have a real estate tax revenue series separated by residential and commercial properties. Instead, I introduce control variables to single out the impact on residential real estate of commercial real estate and housing characteristics. A discussion of this issue is provided in Appendix A. 23 Table 2 5 Property Tax Rates m 2003-2007 2003 2004 2005 2006 2007 Commercial land (zloty per sq m) 0 62 0 63 0 66 0 68 0 69 Res1dent1al bmldmgs (zloty per sq m) 0 51 0 52 0 54 0 56 0 57 Commercial bmldmgs (zloty per sq m) Mam rate 17 31 17 42 17 98 18 43 18 60 health care 3 46 3 49 3 61 3 71 3 75 seed material marketing 8 06 8 11 8 37 8 58 8 66 Other Bmldmgs (zloty per sq m) 5 78 5 82 6 01 6 17 5 78 Other structures 2% of value Source Sw1amcw1cz (2003) and Pohsh Mm1stry of Fmance In this essay I make the argument that amenities potentially influence land values. It is difficult to find data on real estate values, as properties do not turn over at the same rate in all locations each year. Since there is only scattered information on the value of land in Polish powiats, I use actual real estate tax revenue as a proxy for the value of land. The value of land may vary with building characteristics and types of commercial and residential real estate properties. To isolate the effect of amenities 8 on residential land values, I collect data to control for different types of commercial and residential buildings and building characteristics. Consumption of electric power, consumption of water, dwelling stocks with central heating, and dwelling space per person control for buildmg characteristics; new residential building space, new non-residential building space, waste water treatment plants, and private forest lands control for different types of real estate properties. My empirical studies use distance from a major city center as a proxy for transportation costs as a control variable for regional differences in land values (Cavaihes, Peeters, Sekeris, and Thisse, 2004). I include the measure of distance to capital city Warsaw as a proxy for transportation costs. Amenities also can potentially compensate for lower average wages. Average wages in a particular location can be sensitive to employment and worker charac- 8 An amenity is defined as a tangible or intangible asset in a specific location. 24 teristics. To isolate the effect of amenities on average wage rates, I collect data to control for worker and employment characteristics. Hazardous jobs are likely to re ceive a premium over average wages that may not be associated with lower levels of amenities. Similarly, a large number of job offers in a particular location indicates a demand for labor that is likely to result in a premium over average wages. Including workers with hazard related work and the number of job offers in a powzat controls for employment characteristics. Average wages can also be influenced by different pay scales for male and female workers. Many empirical studies find wages for women are relatively lower than for men (Blau and Kahn, 1994). A higher ratio of working men in a location can increase average wages relative to a similar location with more workers who are women. The number of workingmen as a ratio to total number of workers controls for worker characteristics. There can be many other reasons why average wage differences may not fully reflect differences in amenities. Given the available data, I have identified the above variables as some of the major differences that can influence average wages. To construct the QoLI, I collect data on 22 amenity variables for each Polish powzat over the period 2003-2007. Including all of the 22 amenity variables can bias the estimated results, as these variables can be highly correlated with each other. To resolve the problem of correlated independent variables, I first use principal component analysis to segregate the 22 variables into smaller groups. Thereafter, I select the most representative variable in each group to estimate the reduced form equation described in the next section of this essay. Large differences in economic outcomes across Polish powzats can be observed in geographic variation in population, wages, real estate tax revenues, and sub regional GDP (see Figures 2.1, 2.2 and 2.4). These differences seem to suggest that physical geography is important when accounting for differences in quality of life. I 25 use the northwestern, southwestern and southern regions bordering the EU; coastal locations; northern and eastern borders; areas with large areas of private forests, and road distance away from Warsaw as characteristics of physical geography. Natural advantages in locations closer to the coast and the European Union (EU) which develop and extend access to international markets may influence quality of life. Adam Smith (1776) in The Wealth of Nations was perhaps the earliest economist to observe that economies close to coastal regions with easy access to sea trade outperform economies in inland regions. Most recent empirical research finds similar evidence (Black and Henderson, 2003; Cieslik, 2004) that can potentially explain differences in quality of life. Recent literature shows that public investment in infrastructure can explain differences in economic outcomes (Bardhan, 2002; Haughwout, 2002) and may pos itively influence quality of life. One of the major responsibilities of decentralized local government in Poland is to ramp up investments in utilities, transportation networks, and housing at the local level. Such investments are needed to upgrade infrastructures that, in time, positively influence living standards and quality of life. I use public utilities sewage, gas, electric, water, the transportation network of paved roads, the number of bus routes over paved roads, waste water treatment plants, and new residential and new commercial building spaces as infrastructure characteristics. Current literature also shows that the quality of education, healthcare, and cultural institutions (Blomquist, Berger, and Hoehn, 1988; Berger, Blomquist, and Sabirianova, 2008) can explain differences in economic outcomes that may also ex plain differences in quality of life. I use the number of out patient facilities, physicians and dentists, and the infant mortality rate as characteristics of healthcare quality; nursery schools and computers with Internet connections as characteristics of good schools; number of libraries and cinema seats as quality of culture characteristics; and 26 the number of professional local council members as a characteristic of the quality of government. In Poland, more than 61 percent of the population lives in urban locations. Not surprisingly, business investment concentrates around urban profit centers. As more people migrate to urban centers, rising pollution levels may increase their levels of discomfort, making them move to less crowded locations in search of a healthier environment and better quality of life. I use air pollution, industrial waste, investment in fixed assets in business enterprises, investment outlays in business enterprises and net number of firms as characteristics of externalities. The current literature shows that externalities explain differences in economic outcomes such as firm formation and migration (Krugman, 1991; Dale-Johnson, Redfearn, and Brzeski, 2005), which may also explain the influence of externalities on quality of life. Table 2.6 presents summary statistics for real estate tax revenue per household, household income, amenity characteristics, and control variables used in the hedonic estimations. A full list of all the variables, their sources, and descriptions are provided in B.l in Appendix B. The data for this study is provided by the regional data bank of Poland's Central Statistical Office (GUS),9 10 except data on distance, which is provided by Indigo Sp., Aqurat Sp., and Geosystems Polska. 9 http //www stat gov pl/bdren_n/app/strona mdeks 10 Accordmg to GUS, the terntonal umts m Polish Statistics (NTS) were prepared based on European Nomenclature of Terntonal Umts of Stat1st1cs (NUTS) NUTS nomenclature is used to analyze social and economic developmental levels in terms of regional differences. 27 Table 2 6 Summary Statistics, 2003-2007 Van able Obs Mean Std Dev Mm Max Dependent Variables Real estate tax expenditure 1895 855 361 283 4,595 Real wages 1895 25,094 4,201 16,662 61 600 Local Amenity Characteristics A. Physical Geography Northeast 1895 0 069 0 253 0 1 Coast 1895 0 053 0 224 0 1 Southwest 1895 0 098 0 297 0 1 B. Infrastructure Sewage network 1895 51 3 21 8 44 98 7 Gas network 1895 39 7 29 5 0 115 5 Water network 1895 83 9 13 6 24 99 7 C. Schools and Health Computers with Internet 1895 11 9 3 1 50 23 5 Outpatient fac1ht1es 1895 33 9 48 1 2 571 D. Externalities Fixed mvestment 1895 0 672 0 588 0 056 7 300 Air pollution 1895 298 5 619 8 5,8470 Investment outlay 1895 0 6076 0 587 0 0047 0 7208 Control Variables A. Housing Hedonic Distance to Warsaw 1895 275 9 120 5 0 649 7 Dwelling space 1895 23 2 2 1 18 8 33 6 Waste water treatment plants 1895 11 1 82 0 113 Pnvate fmests 1895 4,199 5,783 0 36,978 New residential bmldmgs 1895 172,880 343,859 2118 6 891,267 New non-residential bmldmgs 1895 326,437 556,170 10,333 10,400,000 Houses with central heatmg 1895 81 9 10 1 0 98 1 B. Wage Hedonic Workmg age men 1895 13,771 23,031 2,014 392,235 Hazards at work 1895 1,167 2,083 0 43,432 Job offers 1895 61 4 217.2 0 4,887 Figure 2.5 shows significant usage of the sewage network in the northern and northwestern regions. As expected, the sewage network extensively covers urban cen- ters. Considering the distribution of population in Figure 2.4, a disproportionately higher level of usage is reported from the northern and northwestern region, while a disproportionately lower usage level is reported from the central, southern, and southwestern regions of Poland. Sewage network services have poor access rates in these areas that can be a result of over crowding, lower investment, or both In Ta- ble 2.4, it is clear that migration is away from the northern and towards the central region, which may show that over crowdmg in urban centers in the central, southern, 28 and southwestern regions may be havirug a signi.ficarut irufluence on usage of sewage network facilities. Legena Oo38 DlE'S ~ -.898 Figure 2 5. Sewage Line System Usage, 2003-2007 Access to good schools, m contrast to .infrastrncture measures, is widely avad~ able across Poland. As shown m Figure 2.6, standard deviations are smaller for access to good schools than for infrastructure access. Figure 2.6 illustrates that urban centers have the highest concentration of good schools. Business fixed investment is generally distributed in the northwestern, south- western, southern, and central regions of Poland. Figure 2.4 illustrates that the highest concentration in business investment rn around Warsaw, port cities, and bor~ ders dose to tlhe Czech Republic and Slovak Republic Physical geography, good pubhc infrastructure, good schools, and externalities show a significant effect on economic outcomes in Poland. These non-traded goods 29 Figure 2.6 School Computers with Internet Connections, 2003-2007 and services that are not transmitted directly through the price mechamsm can affect economic outcomes and the overall quality of life. 2.5 Fitting the Data For the analysis m this paper, three separate sets of results are reported The prmcipal component analysis identifies groups of common variables with similar un derlymg dimensions. Prmcipal component analysis is also useful m identifymg data outliers These results are used to identify variables that construct the reduced form model. Detailed results of the principal component analysis are provided m Ap pendix B Second, four different reduced form models and their estimates will be compared and the empirical model that best fits the data will be selected The four different estimates use different constructs of the dependent variable I will also test 30 l0 gend 01&0%2177 a111772'21' •221551411 Figure 2. 7 Fixed Investment in Enterprises, 2003-2007 the robustness of the hedonic estimates by adding different amenity characteristics Finally, I will construct the quality of life mdex using the best empmcal estimates of amemty characteristics to measure differences in quality of life among Polish powzats 2.5.1 Identifying Variables and Outliers Using the results of principal component analysis, I group common variables and also identify outliers. The principal component analysis is helpful to formulate the reduced form model with variables that have similar underlying dynamics. The reduced form model 11 can address the problem of multicollinearity among amenity variables that can bias results For example, mfrastructure investments in water and sewage networks can have similar underlymg dynamics that allow me to drop one of 11 A reduced form model allows est1mat1on of parameters with fewer explanatory variables 31 these variables in the analysis. In addition to identifying outliers and common groups of variables used for the principal component analysis, I also filter powiats that are outliers with a large agricultural and mining presence in the local economy. Households that own agricul tural properties do not pay real estate taxes. In other words, agricultural properties will bias estimated coefficients downward. Similarly, mining land will have a future resource value capitalized in land values that are taxed at a higher rate, but it does not necessarily reflect better amenities. Mining land may have an upward bias on the coefficients that are associated with higher real estate tax expenditure. Powiats that have a large agricultural and mining presence in the local economy are identified using firm entry data. I do not have a direct method to systematically filter across all counties that are predominantly mining and agricultural lands. Out liers are identified by net firm entry into the mining and agricultural sectors as an indicator of business concentration for above-average entry of at least three years over the sample period 2003 to 2007. Referencing available official websites that pro vide information on some of the identified outlier counties, the identification method I use successfully captures powiats with a large agricultural and mining presence in the local economy. 2.5.2 Functional Form: Hedonic Estimation For estimating the implicit price of amenities, I use the hedonic regression tech nique. I use the semi-log functional form, which takes into account non-linearities in amenity characteristics that best fit the data. Amenity characteristics therefore enter the value of land and labor equations multiplicatively and not additively (linearly). The advantage of using a hedonic regression is that land rents and wages can be expressed in monetary values of different amenity characteristics. For example, in the literature, many studies find land rents can be decomposed into housing char- 32 acteristics (Brown and Rosen, 1982; Bartik, 1987; Smith and Ju-Chin, 1995). A disadvantage of using a hedonic regression method is that it can be data- intensive. To form an unbiased value estimate of a series of amenity characteristics, many control variables are needed. In search of a reduced form model, I use a relatively wide and comprehensive data set provided by the National Statistical Office (GUS) of Poland. I make use of market conditions in the housing and labor markets to estimate a reduced form model and construct a Quality of Life Index (QoLI) for the sample period 2003-2007. 2.5.3 Regressions Results on Tax Revenue and Wages Reduced Form Model Two semi-log equations (2.5.1) and (2.5.2) are estimated for the land and labor markets using pooled time series cross-section data. In the land price equation, 'LLP' is the natural log of real property tax expenditure per household (proxy for land rents), 'C' is the constant, li is the implicit price, Ain is the amenity, 'i' is the type of amenity, 'n' is the number of counties, and 't' is the time period. Similarly, in the wage equation, 'LW' is the natural log of wages per household, 'C' is the constant, li is the implicit price, Ain is the amenity, 'i' is the type of amenity, 'n' is the number of counties, and 't' is the time period. The full implicit price of each amenity is illustrated in the equation (2.3.7). 4 LLP~ = C + L liA;n +controls~+ t:~, n = 1 ... 379 (2.5.1) i=l 4 LW~ = C + L wiA;n +controls~+ t:~, n = 1 ... 379 (2.5.2) i=l (2.5.3) 33 Table 2. 7 presents coefficient estimates for the four amenity variables in the housing hedonic regression using equation (2.5. l ). In this table, estimates for four possible models are shown. The dependent variable is the only variable that is different in each of the four models. The dependent variable in Model A is the real estate tax expenditure per household adjusted for inflation. Model B presents the real estate tax expenditure adjusted upwards 20 percent for tax concessions in powiats with a population of 5,000 to 20,000; 16 percent in tax expenditure in powiats with a population of 20,000 to 50,000; and 8 percent in powiats with a population of 50,000 or more (see Appendix A). Model C reports the real estate tax expenditure series adjusted for outliers identified by the principal component analysis (see Appendix B) and firm formation data to exclude powiats with a large agriculture and mining presence in the local economy. Model D reports the real estate tax expenditure per household taking into account the loss in tax expenditure and the outliers identified in Model C. Table 2. 7 also shows coefficient estimates, the p-value, and the Z-value for the four amenity variables. In all the models, amenities very clearly influence housing expenditures. Parameter estimates of public infrastructure, quality of schools, and externalities have a positive and significant influence on housing expenditures, as shown by the p-value12 of less than 0.001, with the Z-value (figures in brackets, below each coefficient) indicating the estimate is significant and reliable. Physical geography, on the other hand, has a higher p-value of less than 0.01, with a Z-value indicating that the estimate is significant and reliable as well. The proxy variable to capture transportation costs (distance to Warsaw) and housing with central heating both have an unexpected sign. Transportation costs measure distance traveled by 12P-value indicates the reliability of the estimated coefficient if the experiment was repeated. The null hypothesis suggests that the higher the p-value, the lower the reliability of the estimate 34 surface road from capital city Warsaw. It is likely the distance measure is picking higher costs of housing expenditure in other major city centers close to the European Union. Housing with central heating, on the other hand, is likely correlated to new residential buildings, where it may be capturing supply side effects where greater supply leads to lower housing expenditures, or a negative relationship. Comparing Models A through D, amenities in Models B and D explain 60 percent of the variation in housing expenditure per household, while amenities in Models A and C explain 44 percent of the variation in housing expenditures per household. These regression results are consistent with the housing hedonic regres- sions in Berger et al. (2008), where the authors find 60 percent of the variation in housing expenditure in Russia is explained by the housing hedonic estimation. From the results presented in Table 2.7, it is clear that Models Band D fit the data better than Models A and C. Model B uses the full data set with 379 powiats for observa- tions over the five years, while Model D uses a subset of this data. Using the full dataset improves the point estimates of the coefficients by observing the Z-values between Models B and D. I use the amenity coefficient estimates from the housing hedonic in Model B to construct the quality of life index. Table 2 7 Model Select10n Real Estate Tax Revenue Estnnates, 2003-2007 Model A Model B Model C Model D Amenities: Northeast -0 156** -0 171 ** -0 150** -0 170* (-2 98) (-2 64) (-2 76) (-2 56) Sewage network 0 00609*** 0 0127*** 0 00626*** 0 0131 *** (8 87) (16 17) (8 7) (15 89) Computers with Internet 00124*** 0 00979*** 0 0123*** 0 00964*** (11 43) (8 45) (10 79) (7 96) Fixed mvestment 0 174*** 0 167*** 0 173*** 0 168*** (11 64) (10 38) (11 31) (10 18) Housing Characteristics: Distance to Warsaw 0 000399*** 0 000577*** 0 000398** 0 000575*** (3 43) (4 08) (3 28) (3 93) Contmued on next page 35 Table 2 7 - Contmued Model A Model B Model C Model D Dwellmg space 0 0494*** 0 0524*** 0 0489*** 0 0520*** (9 06) (8 56) (8 47) (8 07) Waste wate1 0 00226* -0 00029 0 00226* -0 0004 -treatment plants (2 44) (-0 30) (2 37) (-0 40) Pnvate forests -4 8E-06 -0 00000790* -4E-06 -0 00000686* (-1 80) (-2 46) (-1 37) (-1 96) New res1dent1al -2 76E-08 -9 06E-09 -3 84E-08 -1 74E-08 -bmldmg (-1 39) (-0 45) (-1 61) (-0 72) New non-res1dent1al 2 20E-09 2 72E-09 2 73E-09 3 42E-09 -bmldmg (0 23) (0 29) (0 28) (0 34) Housmg with -0 00019 -0 00292 -0 00024 -0 00305 -central hcatmg (-0 14) (-1 78) (-0 17) (-1 83) Constant 4 874*** 5 010*** 4 881 *** 5 014*** (28 63) (25 35) (27 53) (24 51) Observations 1895 1895 1805 1805 R2 0 4396 0 5963 0 4334 06 Source: Central Statistical Office of Poland t stat1st1cs m parentheses *P < 0 05, * * p < 0 01, * * *P < 0 001 Table 2.8 illustrates coefficient estimates of the amenity characteristics con- trolled for worker characteristics. Infrastructure, good schools, and externalities have a positive and significant influence on wage earnings. Worker characteristics that control for wage differences are also significant and have the expected signs The model also shows that wages are not significantly influenced by geography, with the expected negative sign. Table 2.8 presents hedonic regression estimates for the wage equation in (2.5.2). Model E uses the full dataset, while Model F uses a subset of the full dataset by excluding outlier powiats as earlier. Using the full dataset is consistent with the earlier estimation of the housmg hedonic, while improving the point estimates of the amenity characteristics. Therefore, coefficient estimates in model E is used to construct the quality of life index. 36 Table 2 8 Model Selection Wage Estima