On the relation between unemployment and housing tenure...

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2013 – 2014

On the relation between unemployment and housing tenure: the European baby

boomer generation

Masterproef voorgedragen tot het bekomen van de graad van

Master of Science in de Handelswetenschappen

Miguel Vandenbussche en Maxime Verhenne

onder leiding van

Prof. Dr. Carine Smolders

GHENT UNIVERSITY

FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION

ACADEMIC YEAR 2013-2014

On the relation between unemployment and housing tenure: the European baby

boomer generation

Thesis presented in fulfillment of the requirements for the degree of

Master of Science in Commercial Sciences

Miguel Vandenbussche and Maxime Verhenne

under the supervision of

Prof. Dr. Carine Smolders

Preface

Ghent, May 2014

We made this thesis as a completion of the Master of Science in Commercial Sciences,

Finance and Risk management at the Faculty of Economics and Business Administration of

Ghent University in the academic year of 2013-2014.

The past year we have been investigating the remarkable relationship between unemployment

and home ownership that was described by a vast amount of academic literature. We wanted

to make a unique contribution to the literature and therefore decided to observe the

relationship for a specific age group that was not yet made, namely the baby boomer

generation. These days, this age group is hot topic for policy makers across Europe as the

baby boomers start to enter retirement age in large numbers.

With help of simple statistical analyses on the data of the SHARE database we were able to

make an academic contribution to the literature. Therefore we would like to thank Josette

Janssen to give us access to the dataset. We must also recognize it is one of the rare large-

scale datasets that is freely retrievable for students.

Furthermore we would also like to thank the people behind the Flemish Policy Research

Centre on Fiscal Policy to supply us with specific information concerning the recent findings

on the subject. Special thanks go to Daan Isebaert and Jan Rouwendal for their presentations

at a study day in Brussels.

Finally, with this preface we would like to underline the gratitude to our promoter, Professor

Carine Smolders, who guided us trough this academic route and gave us the opportunity to

explore this subject profoundly.

Enjoy your reading,

Miguel Vandenbussche and Maxime Verhenne

Acknowledgement

This paper uses data from SHARE wave 4 release 1.1.1, as of March 28th 2013 (DOI:

10.6103/SHARE.w4.111) and SHARE wave 1 and 2 release 2.6.0, as of November 29 2013

(DOI: 10.6103/SHARE.w1.260 and 10.6103/SHARE.w2.260). The SHARE data collection

has been primarily funded by the European Commission through the 5th Framework

Programme (project QLK6-CT-2001-00360 in the thematic programme Quality of Life),

through the 6th Framework Programme (projects SHARE-I3, RII-CT-2006-062193,

COMPARE, CIT5- CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and

through the 7th Framework Programme (SHARE-PREP, N° 211909, SHARE-LEAP, N°

227822 and SHARE M4, N° 261982). Additional funding from the U.S. National Institute on

Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169,

Y1-AG-4553-01, IAG BSR06-11 and OGHA 04-064) and the German Ministry of Education

and Research as well as from various national sources is gratefully acknowledged (see

www.share-project.org for a full list of funding institutions).

Table of Contents

1. Introduction .................................................................................................................... 1

2. Literature ........................................................................................................................ 5

2.1 Unemployment ................................................................................................... 5

2.1.1 Definition .................................................................................................. 7

2.1.2 Photograph ................................................................................................ 8

2.1.3 The rise of the unemployment rate in Europe ......................................... 10

2.2 Home ownership .............................................................................................. 12

2.2.1 Definition ................................................................................................ 12

2.2.2 Photograph .............................................................................................. 13

2.2.3 Home ownership stimuli ......................................................................... 15

2.2.4 Perverse effects of home ownership stimuli ........................................... 19

2.3 The relation between home ownership and unemployment ............................. 21

2.3.1 The Oswald hypothesis ........................................................................... 21

2.3.2 Macro academic research ........................................................................ 23

2.3.3 Micro academic research ......................................................................... 24

2.4 The role of financial assets ............................................................................... 26

3. Data ................................................................................................................................ 27

4. Methodology ................................................................................................................. 28

4.1 Oswald correlation for baby boomers across European countries (RQ1) ........ 28

4.2 The nuance in housing tenure (RQ2) ............................................................... 29

4.3 The role of financial assets (RQ3) ................................................................... 29

4.4 The unemployment equation (RQ4) ................................................................ 31

5. Results ........................................................................................................................... 32

5.1 Descriptive summary ........................................................................................ 32

5.2 Oswald correlation across European countries (RQ1) ..................................... 35

5.3 The nuance in housing tenure (RQ2) ............................................................... 37

5.4 The role of financial assets (RQ3) ................................................................... 39

5.5 The unemployment equation (RQ4) ................................................................ 41

6. Conclusion ..................................................................................................................... 43

7. Final remarks of the authors ....................................................................................... 44

8. References ..................................................................................................................... 45

Appendix A .............................................................................................................................. 50

Appendix B .............................................................................................................................. 52

Appendix C .............................................................................................................................. 54

1

On the relation between unemployment and housing tenure:

the European baby boomers generation

Abstract

At aggregated level most developed countries are found to have a strong positive correlation between

the rate of unemployment and the rate of home ownership. In academic literature, this phenomenon is

called the Oswald Hypothesis because of Andrew Oswald’s 1996’s pioneering working paper on this

issue. He argued that the rising home ownership rates in OECD countries causes higher rates of

unemployment. As a result of this proposal, a lot of academic work was written that revealed new

insights. In this paper the hypothesis is tested on the so called baby boomer generation (people born

between 1946 and 1964) because, according to academics and policy makers, this specific working age

group bears longer unemployment spells and has a higher probability on being home owner. The

statistical analysis in this paper starts with the correlation for baby boomers across a selection of

European countries that indeed confirms the hypothesis. Later on, the micro level results of this paper

are more nuanced and show that outright owners have a significantly higher chance on being

unemployed and that they are associated with smaller amounts of financial assets in comparison with

mortgagors.

1 Introduction

"There are three kinds of lies: lies, damned lies and statistics." (Mark Twain, 1898)1

This one-line joke is one that many academics and students in economic science ever get

confronted with. Nevertheless, this statement bears an important lesson on the negative

perception for both writers and readers of statistics. On the one hand it is important that

economists and statisticians explain their work at an appropriate level so it can attain

many people. On the other hand it is important that readers are precarious, but try to

understand the point where the human science of economics can meet the exact science of

statistics, so it can be clear how to interpret these statistical results and that readers are for

instance able to apply the correct interpretation of the words ‘correlation’ and ‘causality’.

Essentially, this is what the theory of Oswald is all about. During the 1990s the British

professor - and later on many more academics - found strong correlation between two

macroeconomic fundamentals, which are home ownership and unemployment. The

Oswald hypothesis (Oswald, 1996) states that the growing rate of home ownership in

most developed countries causes higher rates of unemployment. The common sense

explanation argues that living in one’s own house makes people less mobile on labor

markets. Intuitively this is logic, because owners cannot move at the same speed and cost

as renters when looking for a job. One feature that is often investigated in the literature is

the unemployment spell that should – in theory – be longer for home owners compared

with renters, because owners literally ‘stick’ to their homes. In other words, rising home

ownership seems to slowdown the inner mobility at labor markets. But is this really true?

1 Quote retrieved from the autobiography of Mark Twain (Neider, 1959)

2

At a certain moment in time the growing home ownership rate in most OECD countries2

was even mentioned as the missing puzzle part in the economic theory of the natural rate

of unemployment, that was developed in the early sixties of the previous century by

Phelps and Friedman (1968). Nonetheless, this relatively strong correlation does not mean

that - in fact - a casual relation exists between these two macroeconomic indicators.

However, since Oswald presented his rough findings in 1996, a bunch of academic work

followed to unravel the possible mechanisms behind this correlation.

In general, the results across studies are not really consistent. Most of the academic

studies confirm the theory on aggregated data such as in the United States (Green and

Hendershott, 2001) or in Belgium (Isebaert et al, 2013). Since these results suffer from

aggregation bias, more ambiguous research followed on micro data. The results on data

of individuals showed insights for the literature, however they were not really consistent

across time or across countries that could be the result of the fixed-country effects. For

example the Spanish phenomenon in housing culture where obtaining a house for the

children is a family matter (Earley, 2004). Early micro analysis focused on the

unemployment spells of individuals that showed various results. In the U.S.,

unemployment spells were shorter for home owners (Green and Henderschott, 2001) in

contrary to France were this was found to be longer for home owners (Brunet et Lesueur,

2003). Notable is that some concepts became more nuanced, such as the difference of the

effect between an outright home owner and a mortgagor3, and the difference between the

public versus the private renter. A mortgagor for instance has a strong incentive to find a

new job because the mortgage has to be paid off.

Academic literature brings, however, an important problem of the results of Oswald’s

hypothesis that is named endogeneity bias. This problem is typically situated in social and

economic science and can lead to a rejection of a hypothesis that in fact is true.

According to the lectures of Reichstein (2013) the bias can be summarized underlying

two important features. First, omitted variable bias can lead to endogeneity problem such

as described by Van Leuvensteijn and Koning (2004) that certain variables in the model

have limited characteristics that are indeed important for the mechanism. The example

that the authors phrase is job commitment that can explain the housing tenure of people.

This shows that certain variables could be overlooked – or omitted - when explaining

housing tenure or the general unemployment rate model of Oswald. Second, another form

2 The Organization for Economic Co-operation and Development (OECD) current Member countries are:

Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany,

Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New

Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey,

United Kingdom, United States 3 In this paper the mortgagor is “the borrower in a mortgage, typically a homeowner” (Oxford dictionary,

2014). The mortgagee however is defined as “the person who is the lender in a mortgage, typically a bank,

building society, or saving and loan association” (Oxford dictionary, 2014)

3

of the endogeneity problem was explained by L’Horty and Sari (2010) which is the

simultaneity bias. It is when variables are determined simultaneously and hence it is not

clear which one evokes the other one. In other words, this reverse causality can be a bias

of the theory because intuitively it is not clear whether someone is (un)employed because

he is a home owner or instead the fact that home ownership makes someone

(un)employed. This paper is however able to work with lagged variables of the tenure

status to address this latter problem. It is a good attempt to solve this problem, but not

completely (Reed, 2013).

Literature does not only link home ownership to unemployment, as there are other

variables that can be influential to both home ownership and unemployment. A possible

suggestion is the role of financial assets, of which less is known in literature. For instance,

Fratantoni (1998) linked housing tenure with the relative weight of investments in risky

assets and found mortgagors to be significantly more risk averse. Financial wealth itself

can also be an incentive to longer unemployment spells as it can be consumed during the

unemployed period (Gruber, 2001). Another relevant finding in this story is that generous

unemployment insurance benefits can undermine labor mobility. Feldstein (1973) for

instance argued that unemployment insurance was responsible for a rise in unemployment

rate. However, he also found that a lot of the benefit receivers were not especially low in

the income distribution. In this optic the Oswald theory may be specifically located with

unemployed (outright) owners who can combine the consumption of financial assets with

the benefits of unemployment insurance during the unemployment spell. That off course

is an incentive for longer unemployment spells.

In this paper the European baby boomer generation is taken into account. The United

States Census Bureau defines this generation as the people that were born in the post-war

housing and education society that started from 1946 and lasted until 1964. The argument

to work with this age group is dual. First, this age group has a significant higher

probability of being home owners in comparison with younger age groups (Andrews and

Caldera Sánchez, 2011). This is relevant, because in developed societies most of their

citizens dream to own their own house one day. In accordance with this dream, it is

plausible that in the year 2011 most of the baby boomers must have achieved their dream,

since the eldest boomers achieved the age of 65, which is the end of working age4. In the

opposite point-of-view, it is logic to perceive the baby boom renters as such that this

tenant choice is either the result of financial restrictions or either a well-thought-out

choice to be flexible in life or work situation and thus not a temporarily situation in order

to still achieve the dream of acquiring one’s own house. This paper’s scope is important

because temporarily housing situations can blur the underlying mechanism(s) of the

4 The SHARE questionnaires were hold in 2011. The oldest baby boomers (born in 1946) achieved the age

65 in the year 2011. (2011 minus 1946 equals 65)

4

theory. Second, it is proved that older workers of 55 years and older have a significant

lower mobility on labor markets because they spend far more time searching for work.

For instance the U.S. average searching period was found to be 56.1 weeks for this age

group in comparison with 35.1 weeks for unemployed at age less than 55 (Rix, 2013).

Thus, these dual finding seems to confirm the Oswald hypothesis across age groups.

Therefore this paper will investigate how strong this correlation really is across European

countries and what the underlying effects may be, such as the role of financial assets.

This paper uses the dataset of the Survey of Health, Ageing and Retirement in Europe

(SHARE) that serves data on European citizens at the age of 50 or more. In the first wave

the data extend to 30,816 observations across twelve countries while seven years later the

database of the fourth wave covers 58,489 individuals over fifteen European countries

plus Israel. In fact only twelve countries are taken into account since the regression

models required appropriate variables and lagged variables are used taken from the

second wave. Nonetheless, these data are ideal because the scope in age fits perfectly with

the age scope of the fourth wave. At the time of the interviews in 2011, the baby boomer

generations was between age 46/47 and age 64/65, which is the eldest age group of the

then labor force.

This papers’ first research question (RQ) checks if the European baby boomer generation

exhibits the correlation on a big dataset (N = 9738) across a selection of European

countries5. This correlation - that is measured with help of an ordinary least squares

(OLS) estimate – is expected to be stronger than Oswald’s original one across the OECD

countries (1997) because of the specific scope in age.

Then, the second RQ applies the nuance of the tenure status that is modeled as dependant

variable by making a sub selection between owners and renters and between outright

owners and mortgagors. In fact, this model tries to predict the probability for each of

these tenure statuses through the effect of unemployment and other control variables such

as gender, education or income. In line with other academic work, it is expected to

observe the incentive of a mortgagor to have a smaller chance on unemployment, because

- intuitively - this person needs to pay off the mortgage and is not willing to lose his or

her house and thus less unemployed.

The third RQ brings up a financial approach to the theory. First a model checks if housing

tenure of the baby boomers effects their choice of holding financial assets. In line with

Frantantoni (1998) it is expected that mortgagors give less weight to risky financial assets

5 The selected countries in the SHARE database for RQ1 are Austria, Belgium, Czech Republic, Denmark,

Estonia, France, Germany, Hungary, Italy, Netherlands, Poland, Portugal, Slovenia, Spain, Sweden and

Switzerland.

5

such as stocks, bonds and mutual funds, in comparison to outright owners.

Eventually, this paper investigates a general unemployment equation in line with Oswald

with help of a logistic model that implies the features of the previous questions, which are

the nuance of the housing tenure, financial assets, weight of risky assets in the financial

portfolio.

The second section offers a brief view on the literature that pursued Oswald’s (1996) up

to now, but the section is initiated with a photograph on the indicators ‘unemployment’

and ‘home ownership’, joined with an academic approach on these two macroeconomic

indicators. The third and fourth section incorporates the investigated data with the chosen

methodology. Eventually the results of this methods are shown in section five, just before

the conclusion in the sixth section. Finally, the paper is extended with a discussion for

future research.

2 Literature

2.1 Unemployment

“Unemployment insurance is a pre-paid vacation for freeloaders” (Ronald Reagan, 1966)

The era of Ronald Reagan is often described as ‘neoliberal’ and is in the optic of

oversimplified one-liners probably not different from other eras. The former Governor of

California depicts unemployment as a comfortable position because his statement builds a

bridge between the pleasant thought of vacation and the choice to be unemployed. In fact,

when Reagan became President of the U.S. in 1981 the unemployment rate was rising to

very high levels as a result of the lasting recession due to the oil crisis of 1973. When the

rollercoaster of the business cycle is speeding downwards, workers do not have much of a

voluntary choice when employers - en masse - go astray. Besides, academics have

pointed to the bitter fruits of joblessness. It is proved that being unemployed is a form of

distress that statistically bears significantly higher suicide rates, especially for men, since

the risk of suicide death in e.g. England and Wales (Charlton et al., 1992) was found to be

three times greater than average. Other results from the United Kingdom by Platt and

Kreitman (1985) even found a twelve time greater than average chance of attempting

suicide for long term unemployed Brits. An even more remarkable finding is Oswald’s

(1997) who observed that the rising suicide rate in Western countries since the 1970s

moves along with the rising unemployment rates. (Oswald, 1997).

However, a change of heart for this first – skeptical – paragraph may be found with the

6

time frame. Into the late 1970s, the Federal Reserve6 had a hard time making choices

between unemployment and inflation. Theory and practice used to show a strong trade-off

between these two macroeconomic indicators, that was named the Phillips curve. But by

the beginning of the decade the effect seemed to be gone for good due to the rational

expectations of workers and unions who claimed wages, that could capture the expected

inflation. As a result, the reality in the 1970s showed both high unemployment and high

inflation. This devastating finding on the Phillips curve came by Phelps and Friedman

(1968), Lucas (1976) and others. It’s because of these observations that the policy

performed under ‘the Reagan Administration’ had to make choices in order to drop the

high unemployment rates7 from the early 1980s. The chosen policy was – similar to the

introducing quote – quite harsh. It incorporated a decline in minimum wages, a full

taxation of unemployment insurance and work requirement for the unemployed.

Nonetheless, it was very successful as the unemployment rate dropped to its natural level

at the end of his Presidency8. His chief economic advisor even stated: “It was very

successful, especially in comparison with Europe, where the unemployment protection

policies led to double digit unemployment rates.” (Feldstein, 1997). Despite, critics argue

that this pride statement is questionable because the fundamental reason for change of the

economic climate and that of unemployment is not per se due to the chosen policy but

rather to the underlying business cycle that moves up and down every now and then and

is merely based on the monetary policy. (Krugman, 1982). In line with this critique,

recent work of Drehmann et al. (2012) reminds us to the financial cycle that is very

influential – more precisely procyclical - to this business cycle (see figure 1). In fact, the

financial cycle tracks the level of credit and property prices and moves along in intervals

of about sixteen years which is twice as slow as the business cycle. According to the

authors’ observations, the beginning of the 1980s showed a very strong upward trend for

this financial cycle that probably is due to the effect of financial liberalization. So it is

doubtful whether specific unemployment policies had such a great effect on lowering the

unemployment level. It seems rather the effect of bigger phenomena. Yet, less is known

about these real drivers such as the financial cycle.

6 The Federal Reserve is the central banking system of the United States that has mandate on the U.S.

monetary policy. (Meltzer, 2010) 7 According to the statistics of the United States Department of Labor, the rate of unemployment reached

9.7% in 1982. (U.S. Department of Labor, 2014) 8 The unemployment rate in 1988 was 5.5%. (U.S. Department of Labor, 2014)

7

Figure 1: The financial and business cycles in the United States (Drehman et al.,

2012)

Returning back to unemployment on individual level, financial incentives are at work as

well. A working paper of Tatsiramos (2006) argues that an unemployment insurance

reduces the incentive for leaving unemployment. When comparing the diverse

unemployment insurance systems across Europe it is clear that more generous systems

lead to longer periods of unemployment for individual unemployed persons. The writer’s

diagnose is that welfare countries in Europe have taken away the incentive to work.

Hence, it is notable that the phenomenon of unemployment is not easy to understand and

that analyzing, debating, avoiding this phenomenon should happen with an appropriate

level of distinction. For instance the distinction between structural and cyclical

unemployment that was discussed in the previous paragraph. Besides, this can be for

instance very country specific, such as the well known and unique example of Denmark.

The country is perceived as a “flexicurity” welfare state because it has a highly flexible

labor market with high mobility and a high employment rate in combination with good

protection mechanisms for workers, such as high minimum wages and long generous

unemployment insurances (Andersen, Svarer, 2007). As it is notable, these country-

specific incentives must be distinct when analyzing this macroeconomic indicator.

2.1.1 Definition

Unemployment seems easy to understand, but it in fact it’s not. Observing unemployment

rates in reports, papers or policy programs may be misleading because definitions of the

concept can vary. Briefly, two categories of definitions can be observed

First, in sensu stricto, the most known and applied definition of unemployment is the one

used for the harmonized unemployment rates in OECD countries. The applied definition

8

is based on the International Labor Organization (ILO) that defines unemployment as “a

person - at working age who - in a certain reference period is:

without work, that is, were not in paid employment or self employment during

the reference period;

currently available for work, that is, were available for paid employment or

self-employment during the reference period; and

seeking work, that is, had taken specific steps in a specified recent period to

seek paid employment or self-employment. “(ILO Conference of Labour

Statisticians, October 1982)

Second, in sensu lato, broader definitions of being unemployed are found. In line with the

application of the critique of Brandolini et al. (2004) the unemployed can be divided into

four groups:

i. persons who do not want a job

ii. persons who are not searching but might take a job if offered,

iii. persons who are looking for a job and took specific steps in the last four weeks

(this is similar as the definition of the ILO)

iv. and finally persons who are searching for a job but took their last step more

than four weeks before the observation moment.

The ILO standard only incorporates category three and defines the other categories as

inactive people. According to Brandolini et al. (2004) it is because of the negligence of

category four that approximately one fifth of the European unemployed during the 1990s

was left out of the unemployment statistics. The reason is that the arbitrary chosen four

week period does not capture this group. In fact, this was revealed during the 1990s by

the Italian Statistics Agency (Istat) that did not apply this ‘four weeks’ rule. (Brandolini et

al, 2004). Notable is that the author suggests that the best distinction for the definition is

to check whether an unemployed person is really looking for a job or not. Besides, one

can also argue whether category two or even one concerns unemployed persons, as being

long term unemployed can be demotivating for the individual.

2.1.2 Photograph

Europe’s unemployment rates are heterogeneous (see table 1). With the help from the

Organization for Economic Co-operation and Development (OECD) statistics, this

section presents a brief picture of the 2011 harmonized unemployment rates and its

evolution (see figure 2) between 1990 and 2010 for all age groups.

9

Austria 4.1 % Italy 8.4 %

Belgium 7.2 % Netherlands 4.5 %

Czech Republic 6.7 % Poland 9.7 %

Denmark 7.6 % Portugal 12.9 %

Estonia 12.4 % Slovenia 8.2 %

France 9.2 % Spain 21.6 %

Germany 6 % Sweden 7.8 %

Hungary 11 % Switzerland 4 %

source: data retrieved from OECD statistics

Table 1: Harmonized Unemployment Rates for a selection of European countries

(2010)

Figure 2: Evolution of the harmonized unemployment rates (2010)

As the second part of our paper will focus on the baby boom generation, a more closer

look on the unemployment characteristics of these age group is interesting. According to

the unemployment rates given by OECD labor force statistics, the unemployment rate of

the age category 55-64 is not especially higher than for younger workers in most

countries. Belgium for example had an unemployment rate of 5.4 per cent for the age

category older than 55 in comparison with 7.4 per cent for the age category 25-54 in the

year 2013. This false perception is described by Rones (1983) on a dataset of the 1960s

and 1970s. These unemployment figures can be explained by several things such as the

fact that older workers are less likely to get unemployed by the cyclical effects of an

economy, older workers are often more protected in the working agreements, instead of

laying off the older employers firms often offer financial provision to get an early

retirement. Also important is the described lower unemployment rate for woman.

0

2

4

6

8

10

12

14

16

18

20

22

24

1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

source: data retrieved from OECD statistics

Austria Belgium

Denmark France

Germany Italy

Spain Sweden

10

Especially in older data it is acceptable that older woman have lower unemployment rates

as they withdraw from the labor market more often because of lack of the career-

orientation (Rones, 1983). It is however proved that older workers generally have a

significant lower mobility on labor markets because they spend far more time searching

for work. For instance the U.S. average searching period was found to be 56.1 weeks for

this age group in comparison with 35.1 weeks for unemployed at age less than 55 (Rix,

2013). So this false perception of older workers is in fact true but only in case of the

unemployment spell and thus not in the unemployment rate.

2.1.3 The rise of the unemployment rate in Europe

Figure 3: The unemployment rate for EU-15 (Blanchard, 2006)

Literature is clear. Most of the European countries have seen unemployment rates

growing since the 1970s. The above figure by Blanchard (2006) - retrieved from the

OECD database – shows a significant growth of the relative share of unemployed

Europeans9 across the past decades. Moreover, the fluctuations of the unemployment rate

seems to be more volatile as it moves with the business cycle. (Blanchard, 2006) This is a

remarkably finding because during the same period the U.S. unemployment rate didn’t

show such a trend at all. In fact, the U.S. unemployment rate was quite stable over time

and before the 1970s it was consistently higher than the European rates. (Blanchard,

2006). According to Solow, the most remarkably about the European rate is that it

dominates the business cycle. (Solow, 2000) This means that the growing part of

unemployment is rather structural and less a result of recessions in the business cycle

9 The EU-15 countries are: Austria, Belgium, Denmark, Finland, France, Germany,

Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and United

Kingdom

11

rollercoaster.

So, what is this theory of the natural rate of unemployment all about? Today, most

macroeconomic course books threat this concept, that was initially formulated in the

1960s by two economists named Milton Friedman and Edmund Phelps. The formulation

was in fact very simple: “an economy will have a sort of constant rate of unemployment

that is rather structural because it is evoked by factors such as labor market imperfections,

costs of information, deceleration or non access to vacancy information and costs of

mobility” (Friedman, 1968). This statement was based on the perception of looking at

labor markets through the eyes of equilibrium models, the so called Walrasian systems.

As the professor argued, it is important to note that this rate depends on the policy of the

nation or of a central bank and, as a result of this, is not per se unchangeable over time.

Therefore, many teachers and writers need to note that the word ‘natural’ of the theory of

the natural rate of unemployment is rather confusing because it – as for example the

European rate - moves over time.

According to Blanchard and Katz (1996), the past research between the 1970s and the

1990s on the potential explanations of this rising unemployment rate are various and

rather unreliable because unrevealing this puzzle was neither consistent across time, nor

across countries. The potential determinants that were presented by academics over these

years drove from the increase of price of energy, the slowdown of productivity growth

(Bruno and Sachs, 1985), the impact of taxes on wages (Bean et al., 1986), loss of skills

due to long term unemployment (Blanchard, 1991), labor market rigidities such as the

firm’s cost and term of firing workers (OECD Jobs Study, 1994) or either the growth of

unskilled workers due to fast technological progress (Krugman, 1994). One can off course

argue about the country specific differences, but the utter diagnose, which Blanchard and

Katz mentioned in 1996 at the bottom line of their paper is the discrepancy between

macroeconomic view on the natural rate of unemployment and the micro economic

findings by labor economics. Or to use their words: “We thus end with a plea for more

joint efforts by macro and labor economists to better integrate theoretical and empirical

work on wage determination and unemployment.”

Later on, it was Flatau et al (2003) who first related the Oswald hypothesis as a possible

explanation of the rise in the natural rate of unemployment for the observed OECD

countries. The hypothesis is that the rising rate of home ownership since the second world

war is not strictly due to labor-market characteristics, but a possible consequence of the

growing home ownership rate in most developed countries.

12

2.2 Home ownership

“Deep in the hearts of most American families glows, however faintly, the spark of desire

for home ownership.” (U.S. Department of Commerce, 1942)

Owning your own house is one of the main purposes in life for most of us and is an

important part of the American Dream. The benefits of owning your own house are

enormous: it stabilizes communities and leads to more responsibility for the living

environment (diPasquale, Glaeser, 1999), houses look greater because the owner has a

strong incentive to maintain them (Coulson, 2002) and it has a positive effect to personal

satisfaction and self-esteem (Rohe, Stegman, 1994).

However, recent events - such as the U.S. housing bubble burst of 2007 - showed that the

benefits of this dream doesn’t always follow reality. This is an indication that policy

makers need to be thoughtful when implementing this part of the American Dream. Also

other perverse effects can be part of the double edged sword of home ownership.

2.2.1 Definition

Literature defines home owners as owner-occupiers: “a housing unit is owner-occupied if

the owner or co-owner lives in the unit, even if it is mortgaged or not fully paid for.” (U.

S. Census Bureau). This means for example that someone who owns a house but lives in a

rental apartment is no longer perceived as a home owner, but instead as a tenant.

However, according to Proxenos (2002) there is no strict definition of home ownership

itself. The interpretation of home ownership differs from one country to the next. Where

one country considers a mobile home as ownership, another country would not. Also the

definition of home ownership tells nothing about the quality of the owner-occupied

houses. This can lead to an over-estimation bias between the home ownership in countries

especially at a global scale. This means that in some cases it is not justified to compare

these rates as they are not based on the same funding definitions, this estimation can lead

to misleading results. Besides, it is also possible that a bias appears in surveys when the

concept of home ownership is not well defined to the respondents.

13

Since the 1990s, organizations such as the OECD and Eurostat started tracking the rates

of home owners of nations in order to benchmark. These percentages represent the sum of

dwellings that are owned outright or purchased with a mortgage in relation to the total

dwelling stock. This latter incorporates the total of home owners plus tenants. (European

Mortgage Federation, 2004). Thus, the rate is equated as follows:

2.2.2 Photograph: ownership rate

When investigating these ownership rates, two facts are consistent across academic

research. First, on a long time horizon nearly all countries experienced a growth of the

share of home owners. Second, these ownership rates strongly differ across countries

(Andrews and Caldera Sanchez, 2011). The overall average home ownership rate once

was measured across 106 countries on data from the World Bank and it was found 67.8

per cent with a median of 69.3 per cent. (Fisher and Jaffe, 2003). For a selection of

OECD countries, Andrews and Caldera Sanchez (2011) describe this evolution and build

models to decompose its determinants. However, the they strongly differ. For example in

2004 Spain had an ownership rate of 83.2 per cent while Switzerland’s did not even

reached half this percentage with 38.4 per cent.

A more nuanced way of observing the housing tenure of citizens is the distinction

between private or public renter and the distinction between a mortgagor or a household

that has no outright owner as this was confirmed recently by researchers such as Nijkamp

and Rouwendal (2007). The EU Statistics on Income and Living Conditions (EU-SILC)

survey measures a range of structural indicators of living conditions. One is the tenure

status of the individual households. (see Figure 4) A few substantial differences between

the member states arise. Northern countries as Denmark, Netherlands, Sweden and also

Switzerland have very little outright owners, but instead a very substantial share of

mortgagors. Also countries such as Germany, Austria and Switzerland have

proportionally a high share of renters compared to the other countries. For instance the

share of Swiss tenants is almost 60 per cent.

14

Figure 4: Housing tenure in Europe (2010)

When investigating the determinants of these heterogeneous rates, some predictive power

was found for the effect of housing credit. (Fisher and Jaffe, 2003) Most countries’

policies offer a contribution scheme for housing finance. However, a single equation

model for explaining the traditional economic determinants of home ownership rate

across countries was not consistent. The country specific differences on cultural, law,

economic or political fields seems to be too harsh to make a generalized econometric

model with consistent results. It seems that incentives for ownership are very diverse

across countries.

Although there is perception that home ownership is linked to the wealth and strength of

an economy, such as suggested in the American dream, the adverse is proved by the

poorer European countries where ownership rates are very high, such as Greece and Spain

(Earley, 2004). This could for instance be caused by the cultural differences between the

more northern and the southern European countries. This is explained by the fact that in

southern societies have strong traditions of the family being involved in the

accommodation choices of their children. An example of this is the proportion of children

living at home that was found the highest in these southern countries (Earley, 2004). This

means that children stay longer with their parents so that they have a better financial

support when eventually leaving the family home. It seems logic that this phenomenon

leads to lower demand of rental units in these states. And thus the rental market is

underdeveloped.

0%

20%

40%

60%

80%

100%

Owner, no outstanding mortgage or housing loan (=outright owner) Owner, with mortgage or loan (mortgagee) Tenant, rent at market price Tenant, rent at reduced price or free

source: data retrieved from Eurostat, based on EU-SILC

15

Also remarkable are the Eastern European countries that have very high ownership rates

as a result of the communist legacy. During the communistic era it was the state that

owned a large share of the housing system. These housing units were part of cooperatives

during the 1980s. After the fall of communism in 1989, most of the countries experienced

a wave of privatization of ownership. As a result homes were offered for free to its

inhabitants and almost every ex-communistic country developed these remarkable high

rates of home ownership (Struyk, 2000).

2.2.3 Home Ownership Stimuli

“When it comes to economics people have emotions, it's not like chemistry or physics”

(Shiller, 2013)

These days, the most persistent feature to promote home ownership seems to be the home

mortgage interest deduction (HMID) systems in fiscal policies. This system gained a lot

of popularity among tax payers, since they are able to reduce their fiscal expenses.

However, another pallet of less known or less rational stimuli for home ownership exists.

Irrationality and herd behavior

As houses are often seen as an important asset investment, it may be interesting to

perceive decision making stimuli with a financial approach. Let’s start with two of the

three 2013 Nobel Prize winners in Economic Science, Robert Shiller and Eugene Fama.

Something strange appears in their financial theories. These academics totally disagree.

The 1970s were - concerning financial theories - the golden decade for the “efficient

markets” theory. Fama said that capital markets were “efficient”. This is the case when

investors of capital products act completely rational because their decisions to buy or sell

are based on all available information at the moment of their transaction. Fama argued

that all available information is thus reflected in the price of this asset (Fama, 1970).

Contrary, Shiller began talking about behavior finance and argued that people do not

always behave rationally. Examples come from social science and are for instance

wishful thinking or herd behavior. These features eventually can evoke so called

“irrational exuberance” in financial markets (Shiller, 2000), but also in housing markets

(Shiller, 1989). It was in this optic that Case and Shiller warned for a housing bubble on

the U.S. housing market in 2004 (Shiller and Case, 2003). According to the researchers

housing prices could no longer solely be explained by fundamentals such as population

growth, construction costs, income growth or tax rates. Their arguments were also based

on qualitative evidence that showed homebuyers’ perception of buying a house more and

more as a result of making an investment. Also, their survey showed that a major

motivation of buying a home was the expected future appreciation of the house price.

More than 90 per cent of the respondents expected an increase in home prices in the next

16

seven years. However, in reality, the U.S. housing bubble collapsed in 2007 with negative

corrections of 30 per cent10

and more resulting in prices far beneath these expectations.

So it seems that one of the incentives to buy a house can come from herd behavior and

also can be explained by irrational expectations. Briefly, these stimulus seems to be the

result from mythical expressions such as “land is scarce, prices can only rise” that should

better be replaced by “what goes up, may come down”.

Home ownership programs

Underneath this irrational expectations may lie a certain governmental policy that evoked

this. One of the systems that led to a lot of attention because it was pointed as one of the

causes of the U.S. housing bubble in 2007 is the policy that President Clinton started in

the U.S. under the name of the National Home ownership Strategy. The president defends

his policy on stimulating ownership based on its social and economic benefits:

“Home ownership encourages savings and investment. When a family buys a

home, the ripple effect is enormous. It means new home owner consumers. They

need more durable goods, like washers and dryers, refrigerators and water

heaters. And if more families could buy new homes or older homes, more

hammers will be pounding, more saws will be buzzing. Homebuilders and home

fixers will be put to work. When we boost the number of home owners in our

country, we strengthen our economy, create jobs, build up the middle class, and

build better citizens.” (Clinton, 1995)

Before the 1960s, policies facilitating home ownership were initially established to

encourage other purposes such as to boost economy out of recessions. But by the time

such a measure was eventually operational, the actual recession was already gone.

However, as these measures were enacted they had a significant impact on housing on the

long run. (Carliner, 1998) The reason why it had grown is merely due to the overall

economic growth and lower interest rates than to any specific housing policies.

Under the Clinton Administration the home ownership rate – as measured by the U.S.

Census Bureau – rose to 68 per cent by the end of 2001. (Bratt, 2002). Next came the

American Dream Down Payment Assistance Act under the Bush administration, that had

a specific target group (Bush, 2003). A budget of 200 million dollars was provided by the

government to support low income families.11

This law and many others which aimed to

create a society of home owners eventually lead to an increase of the ownership rate to a

10

According to the Case-Shiller Home Price Index (2014)

17

record height of 69.4 percent in June 2004, according to the United States Census Bureau.

Beside these programs, other aspect of pro-home ownership policies can declare the rise

of home ownership within the U.S., namely the innovations in the mortgage financing

business and the flexibility in repayment schedules (Doms and Motika, 2006). A good

example of these innovations are Fannie Mae and Freddy Mac, which are the most known

government-sponsored mortgage enterprises that had a big impact in the mortgage

financing industry as their activities raised significantly from the 1990s tot the early

2000s (Roll, 2002). During this period they contributed to the creation of the so called

‘mortgage backed securities’ (MBS) that ignored the real credit worthiness of the

individual mortgagors, and was able to pass – or secure – the risk to the financial markets.

(Diamand and Rajan, 2009).

Home Mortgage Interest Deduction (HMID)

Most of the industrialized countries handle a fiscal policy program to offer their citizens

an incentive to buy a house. The ideology in the eyes of policymakers is that private

home ownership has many benefits for society as a whole. The applied systems across

most European nations are very similar and incorporates an income tax deduction of the

interests that are charged on the outstanding mortgage linked to the property that the

owner is acquiring. This means that governments are stimulating people to take a loan for

financing one’s own house.

As mentioned by Oswald (1999) Spain and Switzerland are two very contrary cases

within European home ownership statistics. In Switzerland, the authorities do not

promote housing on national level to increase the home ownership rate. This is reflected

in the less favorable fiscal tax systems for home owners. Although interest of a mortgage

is tax deductible, it is rather low because many taxes are added. A property tax also is

added to the taxable income, which is similar to rental income. Besides, when a house is

sold a tax performs on the capital gain. (Kirchgässner, Pommerehne, 1996) This policy

can be explained by the fact that around two-thirds of the Swiss households are renters. A

characteristic of this developed rental market are the institutional investors who own more

than one fourth of this market. (Bourassa et al., 2010) The contrary is seen in Spain where

the authorities promoted home ownership for a long time. However, since the March

2012 – as a result of the economic crises and the Spanish housing bubble – interests on

mortgages and capital payment are no longer tax deductible (PWC, 2012). It will be

interesting to see what impact this measure has on the home ownership rate in the

upcoming years.

Besides, it is also questionable whether these policy measures really are (or were)

18

effective. As told by Glaeser and Shapiro (2002) these home ownership policies mainly

aided the wealthier owners instead of following the ‘American dream’ philosophy where

it is for everyone to obtain a house. Evaluating the different mortgage deduction policies

over the years the real effect on the rate of home ownership is minimal, as it has mainly

influenced the housing consumption and as a result missed its original purpose. Glaeser

and Shapiro argue that the best evidence is the fact that the U.S. ownership rate did not

augment significantly over the past 40 years although the existence of a mortgage interest

deduction has gained much of popularity among owners.

Now returning to Europe, it certainly is interesting to compare the importance of

mortgage markets within the different countries since the growing popularity of

deductable mortgage interest systems. An indicator for this type of benchmarking is the

total mortgage debt of a country relative to its GDP. (Earley, 2004) An interesting

finding – that is presented on figure 5 - is that over EU countries a negative correlation

exists between the home ownership rate and mortgage debt to GDP. Essentially, this

seems a bit perverse because the countries with higher rates of debt have smaller rates of

owners. Or in other words, countries with a high ownership rate, such as Spain, Greece,

Poland or Belgium have smaller outstanding mortgage debt. Although every country has

its own story, it is notable that this proves again poorness of a interest deduction system.

Earley (2004) argues that the level of development of finance markets and its supply and

demand are more important factors. The Southern countries for example, where

ownership rates are higher, are also poorer and so it is more riskier for southern people to

become dependent on a mortgage, hence its level of outstanding mortgage debt is rather

small.

Figure 5: Mortgage Debt to GDP ratio vs. home ownership rates (Earley, 2004)

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2.2.4 Perverse effects of home ownership stimuli

The dream of owning your own house is widespread across Western civilization. Chasing

this dream and eventually achieving it is a self-fulfillment, a save harbor and a pleasant

thought for most people. Although ownership rates in some countries are very high,

policymakers keep pointing to the abundant benefits of owning one’s house and handle

incentives to promote this achievement. Indeed, academic literature on the benefits of

owning your own home is abundant, such as Glaeser and DiPasquale who found home

owners significantly more involved citizens which results in strong communities. On the

other hand, academics point to some perverse effects of stimulating ownership – at all

costs – for all citizens.

Credit Crisis

It’s oversimplified to find only one scapegoat for the credit crisis. In this optic the role of

home ownership is also relevant. As a result of the securitization of mortgage loans in

order to facilitate ownership to low income families, the risk that was put in the markets

was higher. Among the Americans, a perverse phenomenon rose that was named NINJA

loans that offered mortgage loans to people with No Income, No Job, or no Assets

(Coffee, 2008). At the time, prices reached higher levels relative to rent or incomes in

Ireland, Spain, the Netherlands, the United Kingdom and the United States (Diamond and

Rajan, 2009) while the general believe was that housing prices could only go up in the

long run. When the housing bubble eventually burst, the housing prices fell sharp with

corrections of 30 per cent and more (see before).

Inequality

One can also ask whether home ownership is the best way to build wealth for low-income

families. Policy makers often point to the general benefits such as the eternal rising

housing prices, that serve as a protection for the old days. Summarizing by a quote such

as “buying a house is the best investment one can make”. But is this really the full truth?

According to Galster et al. (1996) it was in 1963 that Grigsby introduced the filtering

theory of the housing market. The filtering of the housing market takes place as follows.

Prosperous citizens build new homes in new areas which are clean and modern. The

quality of these houses is outstanding, but after a few decades the quality declines

especially when these houses are not well maintained or these houses are no longer

equipped with new technologies, such as for instance energy efficient central heating.

When low-income families look for affordable houses their scope often is restricted to

these old houses that fit within their budgets. Often a miscalculation of the price is made

because future costs of maintenance are not taking into account. The fact that lower

income families tend to buy older homes that are affordable at the moment leads to higher

20

risk that the house loses its value. Especially when this old house is located in a

neighborhoods that bears a higher risk on vacancy. When demand for these houses lowers

the price will fall too. Add up a mortgage or loan that is linked to the property and this

story can even get more dangerous such as described by McCarthy, Van Zandt and Rohe

(2013). Is it fair that low income families need to make monthly mortgage repayments for

a fixed total amount that is higher as the future value of the property itself? From the

opposite point-of-view high-income families build new houses at new areas and repay a

mortgage for a total amount of which the chance that the price will rise is much higher.

Besides, the risk for low-income families is leveraged since they hold larger portions of

their wealth in housing. Therefore, Chaterjee (1996) argues that if home ownership would

not be so attractive, only households that could effort it would buy houses so there would

be smaller mortgages taken. This leads to less risk because the hold portfolios would be

more diversified.

Are HMID effective?

Mortgage deductions can even work discouraging for achieving home ownership.

Simulation models in studies have clearly shown that in some countries the value of the

deduction is capitalized into the price of the home prices themselves (Nakagami, Pareira,

1994). This certainly is a perverse effect because often young families – who often belong

to lower income groups – are financially more restrict to buy their dream. In the

simulation of Nakagami and Pareira an elimination of the interest deductibility would

mean that the price of renting becomes relatively cheaper in comparison with the price of

owning. As a result of this the rental market will bloom and as a result it will lower the

demand for houses on the housing market, which in a market economy means lowering

housing prices. What these researchers did not mention is that in real life such an

elimination may be abrupt and can lead to a shock on the housing market.

One can also question the effectiveness of offering taxation incentives for home owners.

Mann (2000) argues that countries such as Canada, New Zealand and Australia have

similar ownership rates in comparison with the U.S. despite they don’t apply HMID.

Also, when the United Kingdom reduced the deductible amounts there was no significant

drop in home ownership rates. Also for the U.S. the rate of home owners stayed rather

constant since the 1960 despite the application of HMID. (Glaeser and Shapiro, 2002)

Asset rich, income poor

Another issue an possible result of raising the home ownership rate in developed

countries is the phenomenon of “asset rich, income poor”. It describes the majority of the

elderly who have significantly higher levels of housing wealth, but are often beneath or

flirting with the poverty definition of income because pensions are less generous.

21

Research on this topic is however limited. Dolaning and Ronald (2010) point to Belgium

where this effect appears. A proposed solution for the income poorness is the reversed

mortgage in which the house is consumed in order to add an extra monthly premium to

the retirement pension income (Bradbury, 2010). In Australia Bradbury (2010)

investigated if elderly consume their housing assets during retirement. This was not really

the case. However the researcher found that the average Australian older person is indeed

asset rich but income poor.

2.3 The relation between home ownership and unemployment

This section connects the two latter sections. Next to the discussed perverse effects of

being a home owner, this section arrives at the most remarkable one among them. In

1996, British economist Andrew Oswald stated that the rising home ownership rates in

OECD countries causes higher unemployment rates (Oswald, 1996). In the pile of

academic work that followed on this statement one can distinct two levels of research that

are essential to understand the problemacy. At first level, macro academic work focuses

on the characteristics of countries or regions in order to proof the theory. Yet, a second

step of the theory has followed, that investigates the relationship on individual level. Such

a micro research is ideal to reveal the small-scale incentives of people that are underneath

the emerging correlation.

2.3.1 The Oswald Hypothesis

In 1996 Oswald published his first paper concerning the hypothesis that a rise in the

unemployment rate is explained by an increase in home ownership, or in other words a

decline of the private rental market. The original working paper shows a strong

correlation between the rate of unemployment and home ownership rate of a selection of

industrialized nations, as depicted on figure 6. Visually this positive relation is

demonstrated by a scatter plot between the two variables for a selection of industrialized

countries in the 1960s. These different countries - graphically represented by dots -

exhibit a linear relationship indicating a correlation of these two variables. Countries with

a high degree of ownership like Spain (75%) have a higher unemployment rate (18%) and

vice versa, for example, Switzerland that combines a low unemployment rate (4%) with a

low ownership rate (28%). (Oswald, 1996) However, this chart only shows a statistical

correlation. This means that unemployment and home ownership of countries tend to vary

together. However it is not known whether this relationship is causal, nor are the country

specific effects explained.

22

Figure 6: Unemployment and home ownership in the 1990s (Oswald, 1996)

The statistical method to interpret this correlation is a simple linear regression that gives

an estimation of the effect of variable X on variable Y by taking the ordinary least squares

(OLS). The equation of the simple regression line over the 1990 data (Oswald, 1996) was:

The slope of this line – that best fits the results of the individual countries - can be

retrieved from this equation and equals the coefficient of the X variable, which is 0.2208.

Hence, it is said that a general rise of 10 percentage points in home ownership is

associated with an increase of 2.2 percentage points in unemployment. Over the data of

1960 this figure was only 0.14 which indicates that the correlation seems stronger. In

2013 Blanchflower and Oswald, however, they found R² = 0.17 on data of 2010.

(Blanchflower and Oswald, 2013)

The hypothesis behind this correlation is intuitively explained by Oswald (Oswald,1999):

i. “Selling a house is expensive. Hence owner occupiers are less mobile than

renters, and therefore more vulnerable to economic downturns in their region.”

ii. ”The difficulty is not that unemployed people are themselves the home owners;

it is that unemployed men and women cannot move into the right places.”

iii. “In an economy in which people are immobile, workers do jobs for which they

are not ideally suited.”

iv. “Areas with high home ownership levels may act to deter entrepreneurs from

setting up new operations. Planning laws and restrictions on land development,

enforced by the local political power of groups of home owners, may discourage

business start-ups.”

23

v. “Home owners commute much more than renters, and over longer distances, and

this may lead to transport congestion that makes getting to work more costly and

difficult for everyone”

Although these arguments seem plausible they do not necessarily are true because

correlation on aggregated data is not able to prove the effects of individual persons, nor it

is possible to show a casual relation. Nonetheless, if this hypothesis is indeed correct it

would be a good explanation for the rise in the natural rate of unemployment in Europe

and other industrialized nations. So, despite this first indication, the question remains

whether unemployment is also one of these perverse effects of stimulating home

ownership across OECD countries.

2.3.2 Macro Academic Research

Early work that followed to Oswald’s working paper found evidence in favor of the

hypothesis. It was confirmed with Nickell and Layard (1999) for a selection of OECD

countries and with Green and Henderschott (2001) on U.S. data. According to Rouwendal

and Nijkamp (2007) the only exception is Spain where Barrios Garcia and Rodriguez

Hernandez (2004) found the opposite across Spanish provinces because the higher home

ownership rates were associated with lower unemployment rates. Also today the

hypothesis is often confirmed on macro data: for districts in Belgium (Isebaert et al.,

2013) and across OECD countries (Blanchflower and Oswald, 2013). These macro

studies suffer, however, from aggregation bias because the relationship between

unemployment and ownership is located on the individual level, that cannot only be

proven with aggregated data. Excessive generalizations from individuals based on these

macro data are dangerous since they can be totally opposite to reality.

Nonetheless, an interesting finding on the macro level is Germany (Lerbs, 2010) for

which Oswald’s hypothesis was investigated with data of 1998 and 2006 for the

individual Bundes Staten in Germany. A first cross-sectional estimate on these regional

data did not confirm the hypothesis. Neither a significant difference was found for the

dummy between East and West Germany. The researcher argued that factors such as

participation and productivity dominate the labor markets and thus the effect of housing

tenure is marginal. However, a fixed effects panel model found little evidence in favor of

the hypothesis that could be explained by the fact that this model is able to solve a major

problem in cross-sectional research that is called unobserved heterogeneity bias. This can

be the case if for instance a specific region has proportionally more high skilled workers

who have higher chances on being owner coincided with lower chances on being

unemployed.

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2.3.3 Micro Academic Research

Since it is hard to make conclusions for the underlying mechanisms of the correlation at

aggregated level, academics have applied micro analysis. A scrutiny on the individual

level is advantageous because it can track and unravel the behavior mechanism(s) behind

this correlation and hence is no longer suffering from aggregation bias. So again, the

question is raised: are owners more unemployed than renters? Or do they stay longer

unemployed than renters?

A common feature that is often tested is indeed the duration of unemployment. It were

Goss and Phillips (1997) who first searched for significance with housing tenure as a

explanatory variable. Contrary to the hypothesis, ownership was found to reduce the

duration of unemployment. However, the effect for mortgagors is stronger than for

outright owners. Intuitively the incentive to work is stronger for a mortgagor because he

wants to maintain the bought house. (Nijkamp and Rouwendal, 2007)

In contrast to their macro study of 2001, Green and Hendershott (2001), did only find

little evidence on U.S. micro data from the Panel Study of Income Dynamics (PSID) with

the help of hazard equations for the effect of home ownership on the employment spell.

More specific, the relation was positive but about eight times smaller than Oswalds’

indication which is a ten per cent raise in ownership the leads to a two per cent rise in

unemployment. Also Coulson and Fisher (2002) did a micro-investigation on these PSID

data. They found no evidence at all in favor of the Oswald hypothesis because their

models showed that home owners have a smaller chance on unemployment, experience a

smaller duration of unemployment and enjoy higher wages. These contradictory results to

Oswald could be explained by the fact that some variables were not taken into account,

for example the mobility of renters or the distinction between outright owners and

mortgagors that was applied before.

Nonetheless, even today the effect seems little in the U.S. as Taskin en Yaman (2012)

used micro data over a period between 1996 and 2011. They accounted for the

unobserved heterogeneity by the use of a special model that is called the full information

maximum likelihood (FIML) method. Such a model is capable of making stable estimates

even when sets have missing values. They found that ownership does indeed reduce the

job finding hazard but again at a much smaller level than Oswald’s suggestion.

In Europe, Brunet and Lesueur (2003) went through French micro data to test if the

housing tenure is able to explain the unemployment spell. Logistic results from 3965

French individuals were however not able to reject Oswald hypothesis since home

ownership was positively related to unemployment duration. Research of Ahn and

Blazques (2007) on the European Community Household Panel (ECHP) showed mixed

25

results: Spain (no), Denmark (yes), France (weak). The researchers point remarkably to

another explanation on the effect. It seems that the degree of mobility is more a result of

satisfaction with job or home. The results of Munch et al. (2006) could clearly reject the

hypothesis for the Danish people because they found that ownership lowers the

unemployment duration. The researchers instead argue that a low labor mobility may

causes unemployment and not per se the home ownership. They argue that the effect of

ownership is not relevant for countries with low labor mobility such as in most European

countries. “By contrast in the U.S. where geographical mobility is more important on the

labor market, the effect of raising ownership may lead to higher levels of

unemployment.” (Munch et al, 2006)

In the Netherlands, research was conducted to the effects of home ownership on labor

mobility. Van Leuvensteijn en Koning (2004) could not reject the hypothesis as they

concluded: "Unemployed owners seem more likely to move than unemployed tenants."

Hence, this seems logic because Netherland has a extreme high proportion of mortgagees

that – as mentioned before by Goss and Phillips – has an impact on the financial incentive

of not wanting to lose their houses. This was also confirmed in the research of Rouwendal

and Nijkamp (2007), they found that the theory is correct Oswald for owners without a

mortgage, but not for home owners with a mortgage. This incentive was also found for

Belgium by Baert et al. (2013).

The contribution to the literature of Van Leuvensteijn and De Graaff (2007) was more

broader than the hypothesis. They found that home owners have a smaller chance on

unemployment because the researchers observed lower exit rates out of the current job

spell. They argue that it is not home ownership which has a positive effect on

unemployment, but it are the transaction costs on the housing market that causes a weaker

labor mobility.

Academic literature brings up an important problem of the results of Oswald’s hypothesis

that is named endogeneity bias. This problem is typically situated in social and economic

science and can lead to a rejection of a hypothesis that in fact is true. (Reichstein, 2013)

According to the lectures of Reichstein, the bias can be summarized underlying two

important features. First, omitted variable bias can lead to endogeneity problem such as

described by Van Leuvensteijn and Koning (2004) that certain variables in the model

have limited characteristics that are indeed important for the mechanism. The example

that the authors phrase is job commitment that can explain the housing tenure of people.

This shows that certain variables could be overlooked – or omitted - when explaining

housing tenure or the general unemployment rate model of Oswald. Second, another form

of the endogeneity problem was explained by L’Horty and Sari (2010), the simultaneity

bias. That is when variables are determined simultaneously and hence it is not clear which

26

one evokes the other one. In other words, this reverse causality can be a bias of the theory

because intuitively it is not clear whether someone is (un)employed because he is a home

owner or instead the fact that home ownership makes someone (un)employed. This latter

problem can be solved by taking lagged variables that are suffering from it. This common

practice is a good attempt, but it is not completely correct (Reed, 2013).

2.4 The role of Financial Assets and Unemployment Insurance

Literature does not only link home ownership to unemployment, as there are other

variables that can be both influential to home ownership and to unemployment. A

possible variable is the role of financial assets of which less is known in literature. For

instance, Frantantoni (1998) linked housing tenure with the relative weight of investments

in risky assets. First he showed that the average home owner has a higher amount of

financial asset than the average renter. Second, he found that a mortgage commitment is

associated to reduced risky assets holdings. Also Becker and Shabani showed that

mortgagors are 10 per cent less likely to own stocks or 37 per cent less likely to own

bonds. (Becker and Shabani, 2010) This may be a possible link in the Oswald theory.

Such a financial wealth itself may be an incentive to longer unemployment spells as it can

be consumed during the unemployed period. Gruber (2002) found that the wealth of the

unemployed population is quite diverse as it undertakes a substantial heterogeneity,

especially when measured at the start of the unemployed period. His micro data of the

1980s and 1990s were very extreme as one-third of the American workers was not even

able to replace 10 per cent of their income loss. In this paper Gruber also refers to Baily

(1978) who argued that the optimal level of unemployment insurance for a worker should

be in function of the private resources that this person can consume during the

unemployment spell. This is, in our opinion, a possible incentive for outright owners to

have longer unemployment spells because they were found to be associated with higher

financial assets, especially with higher financial risky assets.

Another relevant finding in this story is that generous unemployment insurance benefits

can undermine labor mobility. Feldstein (1973) for instance argued that indeed

unemployment insurance was responsible for a rise in unemployment rate. However, he

also found that a lot of the benefit receivers were not located especially low in the income

distribution. In this optic the Oswald theory may be specifically located with unemployed

(outright) owners that can consume both financial assets as well have an unemployment

insurance during the unemployment spell. Hence, this group has a stronger incentive for

longer unemployment spells as they can afford maintaining their living standard just as

before the unemployment period.

27

3 Data12

This paper uses the dataset of the Survey of Health, Ageing and Retirement in Europe

(SHARE) that serves data on European citizen at the age of 50 or more. In the first wave

the data extends to 30,816 observations across twelve countries while seven years later

the database of the fourth wave covers 58,489 interview individuals over fifteen European

countries plus Israel. Although the questionnaires mainly focus on lifestyle facts of the

targeted age group such as physical health or children, also information concerning

themes such as employment, housing and assets is included. Our main argument to use

this database is the fact that across most European countries unemployment is largely

situated with either young people (age -25 years) or either elder people (age 55+ years).

This latter group corresponds with the target group of SHARE survey. Furthermore, this

age group is also found to have a higher probability of being a home owner (Andrews,

Caldera Sanchez, 2011). Hence, this paper will focus on the baby boomer generation (i.e.

people born between 1946 and 1964), that at the time of the fourth wave of the SHARE

interviews in 2011, were between age 46/47 and age 64/65, which is the eldest age group

of the then labor force. In addition, we must also admit that the SHARE database is one

of these rare large-scale datasets that is freely retrievable for students.

In fact, the aim of the survey is longitudinal. In 2014 four waves were available of this

specific age group. The initial idea of making a cohort was eventually extended with a

broader database that includes more interviewees and more countries. The second wave

contains 36,730 observations of which about survived the fourth wave. Because of this

number this research was able to control for endogeneity by the use of lagged variables

out of wave 2.

Code Country (language) Wave 2 Wave 4

AT Austria 2006 and 2007 2011

DE Germany 2006 and 2007 2011 and 2012

SE Sweden 2006 and 2007 2011

NL Netherlands 2007 2011

ES Spain 2006 and 2007 2011

IT Italy 2006 and 2007 2011

FR France 2006 and 2007 2011

DK Denmark 2006 and 2007 2011

GR Greece 2007 -

12

The applied data for this paper is available in a public Dropbox folder in file name extension of (SAV).

These files can be downloaded with help of the following web link:

https://www.dropbox.com/sh/xmk4m8x09faa8le/AAAskyrODAWPVnaHnIp_U7xVa

https://www.dropbox.com/sh/xmk4m8x09faa8le/AAAskyrODAWPVnaHnIp_U7xVa

28

C(g,i,f) Switzerland (German, Italian, French) 2006 and 2007 2011

B(f,n) Belgium (French, Dutch) 2006 and 2007 2011

CZ Czech Republic 2006 and 2007 2011

PL Poland 2006 and 2007 2011

HU Hungary 2011

PT Portugal 2011

SI Slovenia 2011

EE Estonia 2010 and 2011

(n) Number of observations 36,730 58,489

Table 2: SHARE fact sheet of wave 2 and 4 (Release Guide, 2013)

4 Methodology

This section describes the applied methodology to answer the four research questions as

mentioned in the introduction. In order to run the statistical equations on the data a

software package IBM SPSS was used, that is the abbreviation of Statistical Package for

the Social Sciences. The software’s output of the equations can be found in the appendix

C. The creation of the variables applied in following models can also be found in

appendix B. Definitions of these variables can be found in appendix A.

4.1 Oswald correlation for baby boomers across European countries (RQ1)

In 2013 Blanchflower and Oswald published a new working paper that essentially is very

similar to the original working paper by Oswald of 1996. The researchers reanalyze the

correlations with more recent figures across the OECD nations. Although the home

ownership rates have grown since the 1990, still the conclusion on the correlation is

identical, that raising ownership seems to be a good explanation of the rising

unemployment rates. On the 2010 data, the correlation coefficient of the simple

regression was found R² = 0.17. (Blanchflower and Oswald, 2013). RQ1 in this paper

applies the exact same equation (1.1) to analyze the correlations across a selection of

European countries with help of a simple regression model:

(1.1) with cn = country1, country2, …

This paper, however, only takes the baby boomer generation into account. Because of this

specific scope of age the correlation between home ownership rate (HO) and the

unemployment rate (U) is expected to be stronger for the selected countries. The selected

countries in the SHARE database for this question are Austria, Belgium, Czech Republic,

Denmark, Estonia, France, Germany, Hungary, Italy, Netherlands, Poland, Portugal,

Slovenia, Spain, Sweden and Switzerland.

29

4.2 The nuance in housing tenure (RQ2)

In the second RQ this paper scrutinizes the characteristics of the housing tenure of the

European baby boomer generation with respect of the individual countries since cultural

difference can play a important role. The applied model is a binary logit model that is

able to observe the odds ratios of the independent variables in the model on the housing

tenure. Because of the importance of the nuance in housing tenure the important

distinction is made as follows:

(a) between owners and renters, and

(b) between outright owners and mortgagor owners.

By comparing these models (model a vs. model b) this paper expects to track the

incentive of a mortgagor to be less unemployed than a outright owner, that was

mentioned in early literature of Goss and Phillips (1997) who proved the importance of

this distinction. This is in line with Rouwendal & Nijkamp (2010) and Baert et al. (2013)

who indeed pointed to the role of mortgage loans on European data. However, because of

the focus on the baby boomer generation in this paper results can be ambiguous. Because

the incentive can diverse for the elderly workers who are to reach retirement pension or in

the end of their mortgage payment scheme. In order to filter out these effects this paper

applies dummies13

for every five years in age.

The valuable characteristics that were retrieved out of the SHARE data are gender (GN),

age (AGE), household size (HHSIZE), unemployed (U) and education (EDU). Because

country fixed effects have an important role, the equation foresees 11 country dummies14

.

As a result, the statistical equations are:

(3.1.a)

with i = individual observations

4.3 The role of financial assets (RQ3)

This RQ brings a financial approach to the theory because literature has found a link

between the degree of investment decisions and the housing tenure as Frantantoni (1998)

found that higher degree of housing expenses for American renters and mortgagors led to

13

4 age categories are represented in 3 dummies (see appendix B) 14

12 countries are represented in 11 dummies (see appendix B)

30

significantly lower shares of holding risky financial assets15

. Frantantoni defined this

share of risky assets as:

In this paper an OLS regression model checks if the housing expenses (HOUSEXP) of the

mortgagors effects their choice of holding risky financial assets . It is expected that higher

mortgage repayments lead to a smaller share of risky assets . In line with Frantantoni

(1998) it is also expected that mortgagors give less weight to risky financial assets such as

stocks, bonds and mutual funds, in comparison to outright owners. Therefore, the second

model incorporates the effect of the variables housing tenure (HT) and ownership (OWN)

to the equation:

+ CN (3.4)

+ CN (3.5)

+ CN (3.6)

Whereas the housing expense ratio (HOUSEXP) is computed as:

Before these specific equations are investigated, the general role of financial assets is

scrutinized by taking the natural logarithm of financial assets as the dependant variable.

These models are presented as 3.1, 3.2, 3.2bis and 3.3.

In order to use valuable variables such as income, mortgage payments, financial assets as

a nominal variable in the regressions, it is necessary to apply an index of living standard

to correspond with the theory of the Power Purchase Parity (PPP) and the law of one price

(LOP) among the individual diverse countries. Because, intuitively, 100 Euro in Ireland

is not worth as much as 100 Euro in Slovakia. It is therefore important to eliminate these

biased effects that are regressed on the dependant variable. This paper obtains the Actual

Individual Consumption (AIC) per capita to withdraw the bias because this index takes

the price level differences between countries into account. The underneath table shows

that the diversion of the power purchase in our applied countries ranges from 63 per cent

in Hungary to 127 per cent in Switzerland over the EU28 average.

15

See appendix A for definition of financial assets

31

Country AIC per capita

Austria 1.19 Slovenia 0.81

Netherlands 1.12 Czech Republic 0.72

Sweden 1.15 Portugal 0.80

Denmark 1.13 Greece 0.92

Germany 1.23 Estonia 0.59

Belgium 1.13 Poland 0.70

France 1.14 Hungary 0.63

Italy 1.03 Switzerland 1.27

Spain 0.93 EU28 1.00

Table 3: Actual Individual Consumption (bron, jaar)

4.4 The unemployment equation (RQ4)

In this fourth RQ, the chance on unemployment is predicted with help of a logit

regression model. In Belgium Heylen (2012) refers to O’Connell et al. (2010) who

described models to predict the chance on long term unemployment (U(LT)),that are

often used by public employment services in developed countries. These original

equations take into account the following variables: gender (GN), age (AGE), marital

status (MAR), children (CH), education (EDU) and country (CN). This paper extends

such a model by adding two specific aspects that are questioned in RQ1 and RQ2. First,

the Oswald finding of housing tenure (HT) and also with the nuance of ownership status

(OWN). Second, we apply a financial approach by adding the amount of financial assets

(FA):

(4.1b and 4.2b)

Essentially, such an unemployment equation was the initial purpose of Oswald’s

methodology. However, it is not possible to make harsh conclusions on these odds ratios

since the models suffer from the endogeneity problem such as discussed in section 2.3.3.

Academic models usually address this problem with help of intstrumental variables (IV)

in a two stage least square (2SLS) regression. This paper, however, is only able to work

with lagged variables of housing tenure (HT(t-1)) and the nuanced ownership (OWN(t-1))

to address the simultaneity problem with unemployment (U). This is common practice is

in econometrics and a good attempt but not per se 100 per cent correct as argued by Reed.

(Reed, 2013). Essentially the models in RQ4 will be able to compare the effect of the

previous tenure status on the current (un)employment situation of the respondents.

32

5 Results

5.1 Descriptive summary

5.1.1 Age

As this paper concerns the baby boomer generation the most important criterion is age.

Out of the fourth wave exactly 28,374 observations belong to the baby boomer

generation, that is because they are born between 1946 and 1964. The age pyramid as

presented on figure 8 shows a structural lack of younger baby boomers that are at age 50

or younger. Therefore, dummy variables were created over every four age years so certain

categories could be excluded in case of non significance.

Figure 8: Age pyramid of the baby boomer generation in the SHARE database

(2011)

5.1.2 Unemployment rate

On average the unemployment rate for the European baby boomer generation in our

database is 9.8 per cent (N=7060). This rate was calculated in accordance with the

broader definition of being unemployed, because no specific questions concerning the

four week search period were applied in the questionnaire to build the ILO standard.

However, the average unemployment rate can be misleading because of individual

country effects. A more nuanced presentation is the following graph that shows the

individual rates for the selected countries.

6 4 2 0 2 4 6

46 yrs. 47 yrs. 48 yrs. 49 yrs. 50 yrs. 51 yrs. 52 yrs. 53 yrs. 54 yrs. 55 yrs. 56 yrs. 57 yrs. 58 yrs. 59 yrs. 60 yrs. 61 yrs. 62 yrs. 63 yrs. 64 yrs.

Males(%) Females(%) n = 28,374

33

Figure 9: Unemployment rates of the baby boomer generation (2011)

Even though the questionnaire comported a question that could make the distinction

between unemployed and looking for a job or not, only 693 observations were valid out

of the total dataset. In general 61.5 per cent of them was looking for a job, 38.5 per cent

was not.

A weak point of this paper is that it is not able to show the average unemployment spell

as this was not measured in the survey. Instead it is only possible to measure the share of

the long term unemployed out of the baby boomer work force. Unfortunately, making the

distinction between long term unemployed and short term unemployed reduces the

database significantly because only 424 observations16

out of the database have filled in

questions that can distinct long term unemployed or not. On these observations about 60.4

per cent were long term unemployed. It is however possible to distinct the countries in

Europe with flexible labor market, such as Denmark (11.1 per cent) and countries without

flexible labor markets such as Italy (71.4 per cent) and Belgium (80.2 per cent).

16

Germany, Poland and Sweden are not included in the long term unemployment section because no

observations were found.

0%

5%

10%

15%

20%

25%

30%

Unemployment Employment source: based on wave 4 of the SHARE database

34

Figure 10: Baby boomers unemployment: long term vs. short term (2011)

5.1.3 Housing tenure

The nuanced housing tenure of the baby boomer generation is very much in line with the

general Eurostat figures of the housing tenure in Europe (see paragraph 2.2, figure 4). The

baby boomer observations (N=7,060) show three countries that have a very high

percentage of tenants at a level of 40 per cent. These countries are Austria, Germany and

Switzerland. The highest percentage of ownership is Spain with 91.6 per cent and second

is Poland with 86.7 per cent. The average rate of owners over the selected countries is

77.3 per cent. Furthermore it is notable that some countries have huge percentages of

mortgagors out of the total of home owners. This benchmark is strong in line with the

debt-to-GDP ratio (see paragraph 2.2.4). For instance in Netherlands and in Switzerland

more than 90 per cent of baby boom owners still has a mortgage on the owner occupied

house. Also Denmark en Sweden have a similar high degrees of mortgagors. This is

strong in contrast to the former communist states where these percentages reach only 6

per cent for Poland and 10 per cent for Czech Republic.

0%

20%

40%

60%

80%

100%

unemployed (<2y)

LT unemployed (>2y)

source: based on wave 4 of the SHARE database

35

Figure 11: Housing Tenure of the baby boomer generation (2011)

5.2 Oswald correlation across European countries (RQ1)

At first, an OLS estimate shows indeed the Oswald correlation on the European baby

boomers over aggregated data for the different countries in scope. As figure 12 presents it

is clear that the effect between unemployment rate and rate of ownership is as expected

positive and quite strong. Visually every dot in this graph represents the baby boomers of

one specific country. For example the Suisse baby boomers (CH) coincide a level of 60

per cent home owners with an unemployment rate at 4 per cent. In contrary, Hungary

where the ownership rate reaches more than 95 per cent, has an unemployment rate of

about 16 per cent. Notable is the aftermath of the economic crisis that shows

unemployment levels for Spain and Portugal that yet reached over 20 per cent in 2011.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Tenant Mortgagor Outright Owner source: based on wave 4 of the SHARE database (n=7,060)

36

Figure 12: General unemployment vs. home-ownership of the baby boom generation

across 16 European countries (2011)

In addition the distinction in unemployment was made between people that are

‘unemployment’ (see figure 12) and people ‘looking for a job’ (see figure 13). This first

analyses shows a strong positive correlation with slope of 0.28 that is much higher than

Blanchflower and Oswald (2013) with slope of 0.17. However, the second analysis

applied a definition of unemployment rate that is more in line with the ILO standard. The

slope of this graph (β = 0.17) is identically to Blanchflower and Oswald’s (β = 0.17). The

explanatory power of the underneath models is also very in line with their results (R² =

0.19), as figure 12 (R² 0.21) and figure 13 (R² 0.14).

Figure 13: Unemployment and home-ownership rates of the baby boom generation

across 16 European countries (2011)

A D

S

NL

ES

I F

DK CH

BE

CZ

PL

HU

P

SLO

EST

0%

5%

10%

15%

20%

25%

55% 60% 65% 70% 75% 80% 85% 90% 95% 100%

Un

emp

loym

ent

R

ate

Homeownership Rate y = 0.280x - 0.101 R² = 0.21 n = 10,158

A D

S

NL

ES

I

F

DK CH

BE

CZ

PL

HU P

SLO

EST

0%

5%

10%

15%

20%

25%

55% 60% 65% 70% 75% 80% 85% 90% 95% 100%

Un

emp

loym

ent

R

ate

(lo

oki

ng

for

a jo

b)

Homeownership Rate y = 0.178x - 0.063 R² = 0.14 n = 9,738

37

5.3 The nuance in housing tenure (RQ2)

Table 4 presents the odds ratios of the variables in the logit regression models that are

model a and model b. These odds ratios in the table need to be transformed into

probabilities in order to interpret the models:

exp (β)

The dependant variable of the first logit regression (model a) applies the distinction17

between owner (dummy = 1) and tenant (dummy = 0). According to the Oswald

hypothesis, it is expected that the probability on being owner is higher for unemployed

persons.

The odds ratios show that an unemployed person has 71.9 per cent (1-exp(0.281)) less

chance to be a home owner instead of being a tenant. Which is a first indication that the

general Oswald theory is not confirmed on these micro data. The model was expanded

with a lagged variable (model 2.3a) for unemployment18

that shows also shows a

significant negative effect of 67.9 per cent. By adding long term unemployment (model

2.4a) the effect becomes smaller to 39.4 per cent lower chance of being home owner. The

models also show that women have a smaller chance of about 22.6 per cent on being

home owner. Higher chances are found for higher educated people and for older people.

The country specific characteristics confirm the European differences again as shown in

literature and in the summary statistics. Switzerland, Austria and Germany have

significantly lower chances on having home owners in the country. Whereas, southern

and eastern European countries have a significant higher chance for example Spain and

Poland.

In the second model (b) the distinction is made between outright owner (dummy = 1) and

mortgagor (dummy = 0). Previous research has also shown that the Oswald hypothesis

was not confirmed in general, but was more precisely situated with outright owners who

are proportionally more unemployed. The explanation argues that mortgagors are less

unemployed because of the mortgage repayment incentive. The odds ratio in model 2.2b

shows that unemployed have 33.7 per cent more chance to be outright owner instead of a

mortgagor. This partially confirms the incentive of a mortgagor to be less unemployed

because of the house that needs to be paid off. However, the results for the lagged

unemployed variable and the long term unemployed are not significant. By contrast, age

increases the chance on being an outright owner quite strong. Every additive year gives

about 14 per cent more chance of being an outright owner, which is logical because

mortgages often end before retirement.

17

See also Appendix B 18

This lagged variables were constructed by implementing SHARE data of waves 2 and 4

38

Housing Tenure (model a) Ownership (model b)

# 2.1a

2.2a

2.3a

2.4a 2.1b

2.2b

2.3b

2.4b

Constant

2,171

(0,204)

2,475

(0,047)*

8,284

(0,061)

0,001

(0,000)*

0,004

(0,000)*

0,008

(0,000)*

0,098

(0,073)

0,002

(0,022)*

Gender 0,868

(0,066)

0,774

(0,000)*

0,663

(0,000)*

1,095

(0,681)

1,138

(0,201)

1,132

(0,085)

0,983

(0,893)

0,934

(0,845)

Age

1,009

(0,420)

1,014

(0,82)

1,003

(0,888)

1,140

(0,000)*

1,099

(0,000)*

1,094

(0,000)*

1,052

(0,020)*

1,137

(0,008)*

Education 1,321

(0,003)*

0,933

(0,543)

0,962

(0,914)

Householdsize 0,954

(0,639)

0,984

(0,829)

1,009

(0,950)

1,087

(0,775)

1,083

(0,547)

1,053

(0,579)

0,998

(0,992)

0,536

(0,136)

Unemployed 0,250

(0,000)*

0,281

(0,000)*

1,315

(0,170)

1,337

(0,037)*

Unemployed

(t-1)

0,321

(0,000)*

1,519

(0,145)

LT Unempl. 0,606

(0,034)*

1,261

(0,510)

Austria 0,459

(0,000)*

0,395

(0,000)*

0,331

(0,009)*

0,734

(0,318)

2,656

(0,000)*

1,764

(0,000)*

0,658

(0,331)

Germany 0,487

(0,000)*

0,320

(0,000)*

1,594

(0,421)

0,902

(0,574)

0,743

(0,205)

Sweden 0,694

(0,754)

0,706

(0,042)*

0,400

(0,000)*

0,000

(0,999)

0,124

(0,000)*

0,111

(0,000)*

Netherlands 0,966

(0,793)

0,575

(0,032)*

1,594

(0,421)

0,064

(0,000)*

0,062

(0,000)*

Spain 4,522

(0,000)*

3,191

(0,000)*

1,346

(0,388)

5,736

(0,000)*

3,193

(0,000)*

2,213

(0,000)*

1,819

(0,026)*

Italy 1,471

(0,048)*

1,124

(0,438)

0,804

(0,484)

2,602

(0,120)

5,902

(0,000)*

3,845

(0,000)*

2,494

(0,001)*

France 1,292

(0,215)

0,925

(0,559)

0,472

(0,002)*

0,908

(0,865)

2,195

(0,000)*

1,658

(0,000)*

1,324

(0,200)

Denmark 1,334

(0,172)

1,209

(0,170)

0,756

(0,261)

11,757

(0,025)*

0,174

(0,000)*

0,120

(0,000)*

0,096

(0,000)*

Switzerland 0,395

(0,000)*

0,336

(0,000)*

0,168

(0,000)*

0,288

(0,016)*

0,058

(0,000)*

0,045

(0,000)*

0,056

(0,000)*

Czech

Republic

1,117

(0,440)

0,982

(0,874)

0,920

(0,795)

1,712

(0,120)

10,450

(0,000)*

7,246

(0,000)*

6,259

(0,000)*

Poland 1,913

(0,012)*

1,314

(0,061)

0,001

(0,000)*

11,186

(0,000)*

7,872

(0,000)*

Nagelkerke R² ,131 ,116 0.112 0.240 0.489 0.480 0.480

0.085

N 3805 7018 2240 421 2699 5209 1742 179

Table 4: Odds ratios (Exp(β)) of housing tenure in RQ 2

39

5.4 The role of financial assets (RQ3)

The underneath table presents the quartiles in financial assets across the different housing

categories. These financial assets are converted in euro and converted into the same

purchase power by implementing the AIC (see section 4.3). It is remarkable that financial

assets of mortgagors are significantly higher than outright owners. This can partially be

explained by the fact that northern countries are richer and have proportionally more

mortgage owners, while southern and eastern countries are poorer and have

proportionally more outright owners.

Mortgagor Outright owner Tenant

25 € 3751.82 € 319.56 € 349.17

50 € 25000.00 € 3465.13 € 3100.00

75 € 89804.75 € 21180.83 € 20128.28

n 3396 9592 3768

Table 5: Financial assets in quartiles per housing tenure

The results in table 6 below indeed confirm that mortgagors are related to higher amounts

of financial assets (model 3.2). However, once country specific effects are added by

dummies (model 3.2bis), the coefficients show remarkable significant differences among

the European countries. For example Suisse people have significant higher financial

assets than the reference group. Unfortunately, the nuanced ownership status does no

longer make a substantial difference in this model (3.2bis). Yet R² augments from 9.3 to

24.3 per cent. This confirms that the differences in financial assets are mainly explained

by the country specific effects. Furthermore, higher amounts of financial assets in the

financial portfolio are also associated with males, higher educated and married people.

Looking deeper into this mortgagor group it is clear that higher housing expense ratio is

negatively related with the amount of financial assets (model 3.3). In other words, high

commitment mortgagors have lower amounts of financial assets. Besides, higher housing

expense ratios are also associated with higher weights of risky assets (model 3.6). This is

the opposite to the conclusion of Frantantoni (1998) but can be caused by too few

observations (N = 132).

The last remarkable finding is that owners, in comparison with tenants, are associated

with significant smaller weights of risky financial assets. Specifically, this weight seems

to be higher in Spain and lower in countries such as France and Czech Republic.

40

Ln_PPP_Financial Assets Relative weight of Risky Assets

# 3.1 3.2 3.2bis 3.3 3.4 3.5 3.6

Constant

Housing Tenure 0.139

(0.000)*

-0.101

(0.003)*

Outright Owner

-0.198

(0.000)*

0.024

(0.246)

0.007

(0.839)

Housing Expense Ratio -0.102

(0.029)*

0.235

(0.009)*

Age 0.025

(0.072)

0.015

(0.273)

0.003

(0.873)

0.056

(0.209)

0.155

(0.000)*

0.151

(0.000)*

0.046

(0.602)

Gender -0.087

(0.000)*

-0.149

(0.000)*

-0.105

(0.000)*

-0.059

(0.208)

0.032

(0.337)

-0.011

(0.749)

-0.050

(0.570)

Education 0.148

(0.000)*

0.074

(0.000)*

0.152

(0.000)*

0.275

(0.000)*

0.012

(0.751)

-0.030

(0.376)

0.121

(0.171)

Married 0.094

(0.000)*

0.095

(0.000)*

0.062

(0.000)*

0.119

(0.008)*

0.011

(0.736)

0.009

(0.786)

0.089

(0.355)

Germany 0.020

(0.128)

0.025

(0.093)

0.020

(0.540)

Sweden 0.022

(0.089)

0.040

(0.008)*

0.012

(0.709)

Spain -0.035

(0.020)*

-0.008

(0.647)

0.100

(0.003)*

Italy 0.026

(0.075)

0.004

(0.010)*

0.196

(0.000)*

France 0.161

(0.000)*

0.213

(0.000)*

-0.130

(0.001)*

Denmark 0.106

(0.000)*

0.146

(0.000)*

-0.004

(0.908)

Switzerland 0.355

(0.000)*

0.398

(0.000)*

Czech Republic -0.015

(0.327)

-0.009

(0.613)

-0.114

(0.001)*

Poland 0.005

(0.704)

0.005

(0.739)

Adjusted R² 0.230 0.093 0.243 0.118 0.122 0.020 0.029

N 4428 5910 3342 456 874 948 132

Table 6: Risky financial assets and financial assets (RQ3)

41

5.5 The unemployment equation (RQ4)

The chance on unemployment is predicted with help of logit regression models. Table 7

presents the results of two series of models. The first models have unemployment as the

dependant variable, while the second models have long term unemployment as the

dependant variable. Long term unemployment is defined as people who are unemployed

for more than two years. The models below are free of multicollinearity and outliers that

were taken into account because of a possible correlation between education, financial

assets and the tenure states. Because the country specific results in model 4.3a are

confusing as a result of the 2011 skyrocketing unemployment rates in Spain and Portugal

the dummy variables were withhold in the other models because skewed observations

lead to indecisive results.

Similar as in RQ1 the results below confirm that home owners have smaller chance on

unemployment in comparison with renters. In this dataset this chance is about 42 per cent

smaller for home owners, which is very similar to the micro data model of Coulson and

Fisher (2002) that found approximately 35 per cent19

less chance in the United States.

Remarkable is when a five year lagged variable of housing tenure is added to the model

(4.2a), it does not show a significant stronger effect on the current unemployment

situation of the observed persons. Although, it seems that the Oswald hypotheses is

rejected again on micro level, the nuance in ownership shows a significant higher chance

for outright owners to be unemployment in comparison with mortgagors. This is very

much in line with literature such as Nijkamp and Rouwendal (2007) and points to the

incentive of mortgagors to be employed in order to make mortgage repayments for the

acquired house. This time the lagged variable makes the relation significantly stronger,

which is a remarkable confirmation of the latter finding.

The role of financial assets shows an obvious relation towards unemployment. A one

percent raise in financial assets leads to a significant smaller chance on unemployment. In

other words people with small financial assets are proportionally more unemployed.

The results of the long term unemployment equations are similar although they reveal that

women and elder people have significantly higher chances of being long term

unemployed. Which is in line with the findings of Rones (1983) that described longer

unemployment spells for older workers and for women.

19

Coulson and Fisher (2002), p 44: probability = 1 – exp(-0.43515)

42

Unemployment Long Term

Unemployent

# 4.1a

4.2a 4.3a

4.4a

4.5a 4.1b

4.2b

Constant

0,049

(0,079)

0,022

(0,020)*

0,242

(0,447)

0,003

(0,015)*

0,038

(0,112)

0,002

(0,074)

0,004

(0,038)*

Housing Tenure 0,572

(0,018)*

0,506

(0,008)*

0,514

(0,183)

Housing Tenure(t-1) 0,545

(0,000)*

Outright Owner

2,129

(0,029)*

1,159

(0,7005)

Outright Owner(t-1) 2,367

(0,000)*

PPP Financial Assets

(log)

0,713

(0,000)*

0,727

(0,000)*

0,625

(0,000)*

0,617

(0,001)*

Gender 0,746

(0,190)

0,881

(0,420)

0,771

(0,263)

0,805

(0,474)

1,046

(0,815)

1,067

(0,888)

2,069

(0,039)*

Education 0,294

(0,000)*

0,410

(0,001)*

0,272

(0,000)*

0,675

(0,393)

0,565

(0,113)

Age 1,096

(0,000)*

1,036

(0,202)

1,072

(0,023)*

1,145

(0,001)*

1,007

(0,840)

1,209

(0,003)

1,100

(0,042)*

Low Urbanization 0,797

(0,368)

0,868

(0,591)

1,264

(0,611)

Medium Urbanization 1,136

(0,693)

1,358 1,176

(0,770)

Country of birth 1,898

(0,027)*

1,624

(0,108)

1,257

(0,677)

0,430

(0,168)

0,835

(0,760)

Married 0,653

(0,066)

0,693

(0,133)

0,880

(0,705)

2,185

(0,085)

1,249

(0,571)

Austria 0,503

(0,047)*

Spain 1,220

(0,605)*

Italy 0,241

(0,019)*

France 0,339

(0,072)

Denmark 0,156

(0,019)*

Switzerland 0,307

(0,005)*

Czech Republic 0,228

(0,001)*

Nagelkerke R² 0,222 0,017 0,268 0,262 0,032 0,286 0,107

N 1297 1873 1297 992 1437 115 157

Table 7: Odds ratios in the unemployment equation (RQ4)

43

6 Conclusion

This papers investigated the Oswald hypothesis at micro level on a dataset of the

European baby boomer generation and implements two important features. First the

characteristics of the nuanced ownership status were scrutinized (see RQ2). Second, a

financial approach was brought to the theory in order to check whether the tenure choice

is associated with higher (or lower) amounts of financial assets (see RQ3). Finally, these

findings are brought together in an unemployment equation to investigate whether this

features have a significant impact on the boomers’ chance of being unemployed (see

RQ4).

The first cross sectional analysis (see RQ1) concerned the relationship between

unemployment rate and home ownership rate on aggregated country level. These results

clearly show - in line with most macro analyses - that a strong positive relationship exists

between these indicators. However, it does not explain the underlying mechanism.

Therefore, the relation is investigated at micro level with the previous mentioned RQ’s.

This was done with help of SPSS on the database of the SHARE survey.

At this micro level the results (see RQ2) show that unemployed people have a smaller

chance of being a home owner instead of a renter, which means that the Oswald

hypothesis is rejected. However, looking more deeply in this relationship, the nuance of

ownership shows that mortgagors have 30 per cent less chance on unemployment, and

hence unemployment is strongly related with outright owners. This confirms the

expectation of the incentive of mortgagors to be employed in order to pay off their

mortgage loans as they do not want to lose their acquired house.

The financial approach (RQ3) shows that owners, overall, own more financial assets than

tenants. More specifically, it are mortgagors who own significantly the most financial

assets than outright owners and tenants. The explanation for this may be country specific

because northern countries that are richer have proportionally more mortgagors, while

southern and eastern countries are poorer and have proportionally more outright owners.

For this mortgagor group it is also found that high mortgage repayment-to-income ratios

are associated with low amounts of financial assets. Or in other words, mortgagors who

own high financial assets do not take as much risk as people with low financial assets in

order to pay off their house.

Contrary to Frantantoni (1998) we find that in Europe the risk in the financial portfolio is

positively associated with risk in mortgage repayments. This seems not logic, but this

ambiguity may be the result of the country-specific expects within Europe that could not

be included in this last analysis. However, this points again to the major importance of

44

these country specific results for European research.

Taking everything together in the unemployment equation (RQ4) it is found that people

with small financial assets are proportionally more unemployed. Also it was found that

home ownership and more specific mortgagees have a smaller chance of being

unemployed, confirming the results of RQ2 and the many micro literature such as

suggested by Henley (1998), and confirmed by Nijkamp and Rouwendal (2007) and also

found by Baert et al. (2013).

7 Final remarks of the authors

Our statistics show that most European baby boomers succeeded very well in owning

their own homes before the age of 65. However, this is not per se a good thing for our

economy as we show that outright owners are associated with higher chances on

unemployment and lower amounts of financial assets. This seems to be an indication that

the phenomenon of “asset rich and income poor” may be one of the most important

challenges for policy makers in the upcoming decades. Especially because the European

baby boomer generation is abundant and is getting in retirement since 2011. Despite,

European policies in the last three decades have been focusing on pension reforms of the

legal state pensions (Holzmann, 2003) economist are not yet convinced if our economies

can keep contributing to the status quo of old age pensions. Some of them even compare

the social security pension systems with a Ponzi scheme. (Tanner, 2011)

Also, we saw a rising consensus in the academic world that fiscal ownership stimuli have

perverse effects for lower income groups that are not socially acceptable. Therefore the

academic consensus argues that policy makers should be tenure neutral instead of

stimulating ownership.

Concerning future research on the Oswald theory we advise to incorporate the financial

wealth as this may play an important role in the incentive to work or not. Especially in

Europe, where unemployment insurance is more generous. We were however not able to

scrutinize the incentive of unemployed outright owners to stay longer unemployed when

they own significant amounts of financial assets. This remains a question.

Moreover, further research on the specific baby boomer generation can be conducted in

the near future because a fifth wave of the SHARE survey will be released in 2015.

Hence this research will be able to make longitudinal analyses at micro level.

45

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50

Appendix A

Home owner

Our definition of home ownership is in line with academic literature. The owner or co-

owner lives in an housing unit that he or she occupies with his or her household. A

mortgage linked to this housing unit does not change the status of the owner-occupier.

Tenant

Oxford Dictionary states: “a tenant is a person who occupies land or property rented from

a landlord.”. This paper defines the status of ‘under tenant’ also as a tenant. However,

free residents and members of cooperations were excluded from our definition of tenants.

Mortgagor

This is a owner-occupier who has an outstanding loan or mortgage linked to the property

of which he is the owner-occupier.

Outright Owner

This is a owner-occupier who has no outstanding loan or mortgage linked to the property

that he owns and occupies.

Home ownership Rate

This rate equals the total number of home owners divided by the sum of home owners and

tenants.

Unemployment

Only Wave 4 makes the discrepancy between the real situation of being unemployed and

looking for a job. Which is more or less in line with the definition of the International

Labour Organisation (ILO).

Long Term Unemployment

The respondent is perceived as long term unemployed in case this person reported to be

unemployed for more than two years.

Unemployment Rate

Both Eurostat and the OECD defines the unemployment rate as the fraction of total

unemployed persons in relation to the total labor force. This latter is the sum of the

employed people plus the unemployed people. (This definition is based on the

International Labour Organisation – ILO.). Wave 1 and Wave 2 do not distinct the

fraction of unemployed people that have the actual intention to look for a job.

51

Age

The purpose of the SHARE questionnaires was to investigate the health status of

European citizen at the age of 50 or more. The sub selection of people that are either

employed or unemployed counts 28,374 observations of whom the age curve is

distributed as in figure 8.

Income

The SHARE database offers the respondents’ earnings per year before taxes which is - in

case of a non Euro country - automatically conversed in Euro with correspondence of the

then exchange rates of the specific country. A first remark is that these earnings do not

represent the net income of the observations. However, computing the net income of the

individuals is a too time consuming calculation because taxation rates vary between

countries and income is taxed progressively according to the household composition.

Second, these earnings – even if conversed to Euro - do neither represent the same

purchase power in these individual countries. Luckily, this problem can be fixed with the

economic theory of the purchase power parity (PPP). The methodology in this paper

divides the earnings before taxes by the Actual Individual Consumption (AIC) per capita

index of 2011 in order to filter out the different levels of living standard and express the

different European incomes into one general income. This AIC is an alternative for GDP

per capita and is a better indicator to describe the difference of the material welfare

situations of European households.

Housing Expense Ratio

The amount of mortgage payments on the outstanding property of the last twelve months

is divided by the annual income of this person. This ratio should not be more than thirty

per cent because of a financial ethic law. However, since individual interviewees are

applied, this mortgage can be paid off by two persons in the family. Since the dataset only

disposes the person’s individual income, the income is arbitrarily doubled if the

individuals are living together.

Financial Assets

Financial assets are bank deposits, bond, stocks, mutual funds, retirement accounts,

contractual savings and life policies.

Risky Assets

In line with Fratantoni (1998) this paper perceives stocks, bonds and mutual funds as

risky assets. The characteristic of a risky assets is that the repayment of this finical

product is not guaranteed.

52

Appendix B

Dummy variables

Name Dummy Dummy

Label

Question

Code

Dummy = 1,

if

Dummy = 0, if Reference

category

Excluded

categories

HousingTenure HT ho002 Owner(1) Tenant(4) or

subtenant(3)

Tenants (3

and 4)

Member of

coörperation(2)

And Free rent(5)

Outrightowner OWN ho013 Outright

owner (5)

Mortgagee (1) Mortgagees

(1)

HousingTenure_t

_1

HT_t_1 ho002_t_1 Owner(1) Tenant(4) or

subtenant(3)

Tenants (3

and 4)

Member of

coörperation(2)

And Free rent(5)

Outrightowner_t_

1

OWN_t_1 ho013_t_1 Outright

owner (5)

Mortgagee (1) Mortgagees

(1)

Gender GN dn042 Female(2) Male(1) Males (1)

Age5155 AGE5155 dn003 1956-1960 1947-1955 &

1961-1965 &

Age46-50

Age5660 AGE5660 dn003 1951-1955 1947-1950 &

1956-1965

Age46-50

Age6164 AGE6164 dn003 1947-1950 1951-1965 Age46-50

Children CH ch001 ≥1 =0 No children

(=0)

Householdsize HHSIZE dn046 >2 =1 or =2 1 or 2 persons

Education EDU dn012 Further

education:

college,

university

(≥1)

None (=0) No college or

no university

(96)

Unemployed U ep005 Unemployed(

3)

Employed(2) Unemployed

people(3)

Pension(1), Long-

term sickness or

invalid (4),

housewife/houseman

(5)

Long Term

Unemployed

ULT ep050 Unemployed

and last time

worked in

≤2009

Unemployed

and last time

worked in 2010

and 2011

Short term

unemployed

(=2010 or

=2011)

Austria CNAT Country 11 All other

Belgium (23-

24)

Germany CNDE Country 12 All other

Sweden CNSE Country 13 All other

Netherlands CNNL Country 14 All other

Spain CNES Country 15 All other

Italy CNIT Country 16 All other

France CNFR Country 17 All other

Denmark CNDK Country 18 All other

Switzerland CNCH Country 20-22 All other

Czech Republic CNCZ Country 28 All other

Poland CNPL Country 29 All other

Medium MEDURB iv009 A large town A big city (1), Reference

53

Urbanization (3) The suburbs of

a big city (2), A

small town (4)

& A rural area

or village (5)

category is

High

Urbanization,

(1) A big city

& (2) the

suburbs of a

big city

Low

Urbanization

LOWUR

B

iv009 A small town

(4) & A rural

area or village

(5)

A big city (1) ,

The suburbs of

a big city (2) &

A large town

(3)

Car CAR as049 1-10 cars 0 cars 0 cars

Country Of Birth COB dn004 No (5) Yes (1) Yes (1)

Marriage MAR dn014 Married and

living together

with spouse

(1) Registered

partnership

(2)

Married, living

separated from

spouse (3),

Divorced (5) &

Widowed (6)

Computed variables

Name Variable Label Question Code Compute Remark

Income INC ep205 =Income

Income in Purchase

Power Parity

PPPINC ep205 =INC* /100

(with i = country)

Log Income LogPPPINC ep205 =ln of PPPincome

Financial Assets FA as003e: Bank

as007e: Bonds

as011e:Stocks

as017e:Mutual Funds

as021e:Retirement Acc

as027e: Contract Saving

as030e: Life policy

=as003e+ as007e+

as011e+ as017e+

as021e+ as027e+

as030e

Financial Assets in

Purchase Power

Parity

PPPFA =FA* /100

(with i = country)

Log Financial Assets LogPPPFA =ln of PPPFA

Risky Assets RA as007e: Bonds

as011e:Stocks

as017: mutual funds

=as007e+as011e+as01

7e

Risky Assets in

Purchase Power

Parity

PPPRA =RA* /100

(with i = country)

Weight of Stocks wStocks as011e:Stocks =as011e/FA Relative weight

Weight of Bonds wBonds as007e: Bonds =as007e/FA Relative weight

% Risky assets w = RA/FA Relative weight

House expenditure HOUSEXP ho020: annual mortgage

payment

ep205: income

= HO020e/EP205

Relative weight

54

Appendix C

SPSS Output RQ2

2.1a

Case Processing Summary

Unweighted Casesa N Percent

Selected Cases Included in Analysis 3805 53,9

Missing Cases 3255 46,1

Total 7060 100,0

Unselected Cases 0 ,0

Total 7060 100,0

a. If weight is in effect, see classification table for the total number of cases.

Dependent Variable Encoding

Original Value Internal Value

,00 0

1,00 1

Block 0: Beginning Block

Classification Tablea,b

Observed

Predicted

HT

Percentage Correct

,00 1,00

Step 0 HT ,00 0 1062 ,0

1,00 0 2743 100,0

Overall Percentage 72,1

a. Constant is included in the model.

b. The cut value is ,500

Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 0 Constant ,949 ,036 689,342 1 ,000 2,583

55

Variables not in the Equation

Score df Sig.

Step 0 Variables Gender 4,661 1 ,031

Age ,521 1 ,470

Education 1,300 1 ,254

Householdsize ,056 1 ,812

Unemployed 106,032 1 ,000

Austria 65,707 1 ,000

Sweden ,017 1 ,897

Spain 59,186 1 ,000

Italy 12,800 1 ,000

France 5,543 1 ,019

Denmark 17,188 1 ,000

Switzerland 59,191 1 ,000

CzechRepublic 15,544 1 ,000

Overall Statistics 349,828 13 ,000

Block 1: Method = Enter

Omnibus Tests of Model Coefficients

Chi-square df Sig.

Step 1 Step 362,889 13 ,000

Block 362,889 13 ,000

Model 362,889 13 ,000

Model Summary

Step -2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1 4143,049a ,091 ,131

a. Estimation terminated at iteration number 5 because parameter estimates

changed by less than ,001.

Classification Tablea

Observed

Predicted

HT

Percentage Correct

,00 1,00

56

Step 1 HT ,00 147 915 13,8

1,00 81 2662 97,0


a. The cut value is ,500



Step 1a Gender -,142 ,077 3,377 1 ,066 ,868

Age ,009 ,011 ,650 1 ,420 1,009

Education ,278 ,092 9,076 1 ,003 1,321

Householdsize -,047 ,100 ,220 1 ,639 ,954

Unemployed -1,388 ,123 127,465 1 ,000 ,250

Austria -,779 ,134 34,088 1 ,000 ,459

Sweden -,365 1,164 ,098 1 ,754 ,694

Spain 1,509 ,222 46,180 1 ,000 4,522

Italy ,386 ,195 3,915 1 ,048 1,471

France ,256 ,207 1,537 1 ,215 1,292

Denmark ,288 ,211 1,862 1 ,172 1,334

Switzerland -,929 ,138 45,230 1 ,000 ,395

CzechRepublic ,111 ,143 ,595 1 ,440 1,117

Constant ,775 ,611 1,612 1 ,204 2,171

a. Variable(s) entered on step 1: Gender, Age, Education, Householdsize, Unemployed, Austria, Sweden, Spain, Italy, France,

Denmark, Switzerland, CzechRepublic.

57

2.2a

Logistic Regression




Missing Cases 42 ,6

Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

HT

Percentage Correct

,00 1,00

Step 0 HT ,00 0 1720 ,0

1,00 0 5298 100,0






Step 0 Constant 1,125 ,028 1643,373 1 ,000 3,080

58


Score df Sig.


Age 2,871 1 ,090



Austria 105,480 1 ,000

Germany 10,476 1 ,001

Sweden ,227 1 ,634

Netherlands 8,042 1 ,005

Spain 67,258 1 ,000

Italy 12,093 1 ,001

France 3,037 1 ,081

Denmark 25,816 1 ,000



Poland 10,346 1 ,001




Chi-square df Sig.

Step 1 Step 567,465 15 ,000

Block 567,465 15 ,000

Model 567,465 15 ,000

Model Summary


Cox & Snell R

Square Nagelkerke R Square

1 7248,758a ,078 ,116



59


Observed

Predicted

HT

Percentage Correct

,00 1,00

Step 1 HT ,00 112 1608 6,5

1,00 52 5246 99,0





Gender -,256 ,059 18,917 1 ,000 ,774

Age ,014 ,008 3,032 1 ,082 1,014

Householdsize -,016 ,076 ,047 1 ,829 ,984

Unemployed -1,269 ,090 198,216 1 ,000 ,281

Austria -,930 ,104 79,804 1 ,000 ,395

Germany -,719 ,163 19,509 1 ,000 ,487

Sweden -,347 ,171 4,146 1 ,042 ,706

Netherlands -,035 ,133 ,069 1 ,793 ,966

Spain 1,160 ,176 43,531 1 ,000 3,191

Italy ,117 ,151 ,602 1 ,438 1,124

France -,078 ,133 ,342 1 ,559 ,925

Denmark ,187 ,136 1,882 1 ,170 1,206

Switzerland -1,091 ,100 118,776 1 ,000 ,336

CzechRepublic -,018 ,116 ,025 1 ,874 ,982

Poland ,649 ,257 6,360 1 ,012 1,913

Constant ,906 ,456 3,950 1 ,047 2,475

a. Variable(s) entered on step 1: Gender, Age, Householdsize, Unemployed, Austria, Germany, Sweden, Netherlands,

Spain, Italy, France, Denmark, Switzerland, CzechRepublic, Poland.

60

2.3a

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

HT

Percentage Correct

,00 1,00

Step 0 HT ,00 0 471 ,0

1,00 0 1769 100,0






Step 0 Constant 1,323 ,052 651,366 1 ,000 3,756

61


Score df Sig.


Age ,383 1 ,536


Unemployment_t_1 25,147 1 ,000

Austria 1,219 1 ,270

Germany 16,761 1 ,000

Sweden 2,428 1 ,119

Netherlands ,839 1 ,360

Spain 10,935 1 ,001

Italy 4,560 1 ,033

France ,973 1 ,324

Denmark 8,261 1 ,004



Poland 7,494 1 ,006



Chi-square df Sig.

Step 1 Step 166,898 15 ,000

Block 166,898 15 ,000

Model 166,898 15 ,000

Model Summary


Cox & Snell R

Square Nagelkerke R Square

1 2137,216a ,072 ,112




Observed

Predicted

HT

Percentage Correct

,00 1,00

Step 1 HT ,00 20 451 4,2

1,00 16 1753 99,1


62



Gender -,411 ,111 13,709 1 ,000 ,663

Age ,003 ,019 ,020 1 ,888 1,003

Householdsize ,009 ,141 ,004 1 ,950 1,009

Unemployment_t_1 -1,137 ,192 35,214 1 ,000 ,321

Austria -1,107 ,427 6,727 1 ,009 ,331

Germany -1,139 ,248 21,089 1 ,000 ,320

Sweden -,916 ,255 12,893 1 ,000 ,400

Netherlands -,554 ,258 4,618 1 ,032 ,575

Spain ,297 ,344 ,746 1 ,388 1,346

Italy -,218 ,311 ,489 1 ,484 ,804

France -,751 ,245 9,360 1 ,002 ,472

Denmark -,280 ,249 1,261 1 ,261 ,756

Switzerland -1,781 ,239 55,323 1 ,000 ,168

CzechRepublic -,084 ,322 ,068 1 ,795 ,920

Poland ,273 ,347 ,618 1 ,432 1,314

Constant 2,114 1,130 3,500 1 ,061 8,284

a. Variable(s) entered on step 1: Gender, Age, Householdsize, Unemployment_t_1, Austria, Germany, Sweden,

Netherlands, Spain, Italy, France, Denmark, Switzerland, CzechRepublic, Poland.

63

2.4a

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

HT

Percentage Correct

,00 1,00

Step 0 HT ,00 0 206 ,0

1,00 0 215 100,0






Step 0 Constant ,043 ,097 ,192 1 ,661 1,044

64


Score df Sig.

Step 0 Variables Gender ,280 1 ,597

Age 13,546 1 ,000


LongtermUnemployment 2,364 1 ,124

Austria 9,502 1 ,002

Netherlands ,032 1 ,858

Spain 36,381 1 ,000

Italy 1,012 1 ,314

France 1,225 1 ,268

Denmark 5,264 1 ,022


CzechRepublic ,914 1 ,339




Chi-square df Sig.

Step 1 Step 83,597 12 ,000

Block 83,597 12 ,000

Model 83,597 12 ,000

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 499,841a ,180 ,240



65


Observed

Predicted

HT

Percentage Correct

,00 1,00

Step 1 HT ,00 143 63 69,4

1,00 75 140 65,1





Gender ,091 ,221 ,169 1 ,681 1,095

Age ,131 ,033 15,885 1 ,000 1,140

Householdsize ,084 ,293 ,082 1 ,775 1,087

LongtermUnemployment -,500 ,235 4,517 1 ,034 ,606

Austria -,309 ,310 ,999 1 ,318 ,734

Netherlands ,467 ,579 ,649 1 ,421 1,594

Spain 1,747 ,354 24,284 1 ,000 5,736

Italy ,956 ,615 2,413 1 ,120 2,602

France -,096 ,566 ,029 1 ,865 ,908

Denmark 2,464 1,103 4,990 1 ,025 11,757

Switzerland -1,243 ,515 5,822 1 ,016 ,288

CzechRepublic ,538 ,346 2,418 1 ,120 1,712

Constant -7,278 1,837 15,689 1 ,000 ,001

a. Variable(s) entered on step 1: Gender, Age, Householdsize, LongtermUnemployment, Austria, Netherlands, Spain,

Italy, France, Denmark, Switzerland, CzechRepublic.

66

2.1b

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 0 OWN ,00 0 1235 ,0

1,00 0 1464 100,0






Step 0 Constant ,170 ,039 19,383 1 ,000 1,185


Score df Sig.


Age 22,530 1 ,000

Education 112,686 1 ,000


67


Austria 34,614 1 ,000

Sweden 3,560 1 ,059

Spain 45,087 1 ,000

Italy 71,810 1 ,000

France 1,513 1 ,219

Denmark 172,962 1 ,000






Chi-square df Sig.

Step 1 Step 1230,931 13 ,000

Block 1230,931 13 ,000

Model 1230,931 13 ,000

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 2491,225a ,366 ,489

a. Estimation terminated at iteration number 20 because maximum

iterations has been reached. Final solution cannot be found.


Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 1 OWN ,00 802 433 64,9

1,00 151 1313 89,7



68



Step 1a Gender ,129 ,101 1,636 1 ,201 1,138

Age ,095 ,015 40,490 1 ,000 1,099

Education -,069 ,113 ,370 1 ,543 ,933

Householdsize ,080 ,132 ,363 1 ,547 1,083

Unemployed ,274 ,199 1,885 1 ,170 1,315

Austria ,977 ,150 42,136 1 ,000 2,656

Sweden -21,197 22795,050 ,000 1 ,999 ,000

Spain 1,161 ,176 43,329 1 ,000 3,193

Italy 1,775 ,213 69,754 1 ,000 5,902

France ,786 ,196 16,038 1 ,000 2,195

Denmark -1,746 ,266 43,134 1 ,000 ,174

Switzerland -2,842 ,247 131,881 1 ,000 ,058

CzechRepublic 2,347 ,178 174,127 1 ,000 10,450

Constant -5,492 ,828 43,979 1 ,000 ,004

a. Variable(s) entered on step 1: Gender, Age, Education, Householdsize, Unemployed, Austria, Sweden, Spain, Italy, France,

Denmark, Switzerland, CzechRepublic.

69

2.2b

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 0 OWN ,00 0 2570 ,0

1,00 0 2639 100,0






Step 0 Constant ,026 ,028 ,914 1 ,339 1,027


Score df Sig.


Age 2,572 1 ,109


70


Austria 54,754 1 ,000

Germany 4,900 1 ,027

Sweden 84,980 1 ,000


Spain 105,823 1 ,000

Italy 142,403 1 ,000

France 41,970 1 ,000

Denmark 297,249 1 ,000



Poland 99,471 1 ,000




Chi-square df Sig.

Step 1 Step 2326,285 15 ,000

Block 2326,285 15 ,000

Model 2326,285 15 ,000

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 4894,008a ,360 ,480

a. Estimation terminated at iteration number 5 because parameter

estimates changed by less than ,001.

71


Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 1 OWN ,00 1745 825 67,9

1,00 329 2310 87,5





Step 1a Gender ,124 ,072 2,962 1 ,085 1,132

Age ,090 ,010 78,400 1 ,000 1,094

Householdsize ,051 ,092 ,308 1 ,579 1,053

Unemployed ,291 ,139 4,357 1 ,037 1,337

Austria ,568 ,117 23,395 1 ,000 1,765

Germany -,103 ,183 ,315 1 ,574 ,902

Sweden -2,085 ,204 104,616 1 ,000 ,124

Netherlands -2,756 ,192 207,042 1 ,000 ,064

Spain ,795 ,132 36,058 1 ,000 2,213

Italy 1,347 ,159 71,514 1 ,000 3,845

France ,506 ,128 15,699 1 ,000 1,658

Denmark -2,117 ,150 200,542 1 ,000 ,120

Switzerland -3,093 ,189 266,886 1 ,000 ,045

CzechRepublic 1,980 ,146 183,866 1 ,000 7,246

Poland 2,415 ,372 42,065 1 ,000 11,186

Constant -4,882 ,569 73,606 1 ,000 ,008

a. Variable(s) entered on step 1: Gender, Age, Householdsize, Unemployed, Austria, Germany, Sweden, Netherlands, Spain,

Italy, France, Denmark, Switzerland, CzechRepublic, Poland.

72

2.3b

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 0 OWN ,00 882 0 100,0

1,00 860 0 ,0






Step 0 Constant -,025 ,048 ,278 1 ,598 ,975

73


Score df Sig.


Age 9,760 1 ,002


Austria ,446 1 ,504

Germany 5,938 1 ,015

Sweden 61,124 1 ,000


Spain 42,332 1 ,000

Italy 54,594 1 ,000

France 39,774 1 ,000

Denmark 146,600 1 ,000



Poland 92,051 1 ,000

Unemployment_t_1 20,208 1 ,000




Chi-square df Sig.

Step 1 Step 776,527 15 ,000

Block 776,527 15 ,000

Model 776,527 15 ,000

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 1638,120a ,360 ,480



74


Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 1 OWN ,00 631 251 71,5

1,00 105 755 87,8





Step 1a Gender -,017 ,126 ,018 1 ,893 ,983

Age ,051 ,022 5,373 1 ,020 1,052

Householdsize -,002 ,161 ,000 1 ,992 ,998

Austria -,419 ,431 ,945 1 ,331 ,658

Germany -,298 ,235 1,609 1 ,205 ,743

Sweden -2,195 ,260 71,362 1 ,000 ,111

Netherlands -2,777 ,281 97,861 1 ,000 ,062

Spain ,598 ,268 4,984 1 ,026 1,819

Italy ,914 ,285 10,274 1 ,001 2,494

France ,281 ,219 1,641 1 ,200 1,324

Denmark -2,339 ,223 109,653 1 ,000 ,096

Switzerland -2,879 ,341 71,375 1 ,000 ,056

CzechRepublic 1,834 ,380 23,268 1 ,000 6,259

Poland 2,063 ,420 24,153 1 ,000 7,872

Unemployment_t_1 ,418 ,287 2,123 1 ,145 1,519

Constant -2,321 1,294 3,220 1 ,073 ,098

a. Variable(s) entered on step 1: Gender, Age, Householdsize, Austria, Germany, Sweden, Netherlands, Spain, Italy, France,

Denmark, Switzerland, CzechRepublic, Poland, Unemployment_t_1.

75

2.4b

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 0 OWN ,00 0 51 ,0

1,00 0 128 100,0






Step 0 Constant ,920 ,166 30,881 1 ,000 2,510

76


Score df Sig.


Age 7,888 1 ,005

Householdsize 1,956 1 ,162

LongtermUnemployment 1,591 1 ,207

Education ,320 1 ,571




Chi-square df Sig.

Step 1 Step 10,913 5 ,053

Block 10,913 5 ,053

Model 10,913 5 ,053

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 203,005a ,059 ,085




Observed

Predicted

OWN

Percentage Correct

,00 1,00

Step 1 OWN ,00 6 45 11,8

1,00 4 124 96,9



77



Gender -,068 ,348 ,038 1 ,845 ,934

Age ,128 ,049 6,974 1 ,008 1,137

Householdsize -,623 ,418 2,221 1 ,136 ,536

LongtermUnemployment ,232 ,352 ,433 1 ,510 1,261

Education -,039 ,361 ,012 1 ,914 ,962

Constant -6,143 2,682 5,245 1 ,022 ,002

a. Variable(s) entered on step 1: Gender, Age, Householdsize, LongtermUnemployment, Education.

SPSS Output RQ3

3.1

Regression

Descriptive Statistics

Mean Std. Deviation N

Log_Financial_Assets_PPP 8,7866 2,47440 4482

HT ,7320 ,44295 4482

AGE 63,9630 10,15174 4482

GN ,5455 ,49798 4482

EDU ,5315 ,49907 4482

MAR ,6466 ,47808 4482

CNAT ,3021 ,45922 4482

CNDE ,0013 ,03657 4482

CNSE ,0009 ,02986 4482

CNNL ,0000 ,00000 4482

CNES ,0754 ,26409 4482

CNIT ,0542 ,22647 4482

CNFR ,1959 ,39693 4482

CNDK ,0366 ,18778 4482

CNCH ,1843 ,38777 4482

CNCZ ,1481 ,35529 4482

CNPL ,0011 ,03339 4482

78

Variables Entered/Removeda

Model Variables Entered Variables Removed Method

1 CNPL, CNSE, CNDE,

CNDK, CNIT, GN,

CNES, HT, AGE,

CNCZ, CNCH, MAR,

EDU, CNFRb

. Enter

a. Dependent Variable: Log_Financial_Assets_PPP

b. Tolerance = ,000 limit reached.

Model Summary

Model R R Square Adjusted R Square

Std. Error of the

Estimate

1 ,483a ,233 ,230 2,17066

a. Predictors: (Constant), CNPL, CNSE, CNDE, CNDK, CNIT, GN, CNES, HT, AGE, CNCZ,

CNCH, MAR, EDU, CNFR

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1 Regression 6388,272 14 456,305 96,844 ,000b

Residual 21047,379 4467 4,712

Total 27435,652 4481


b. Predictors: (Constant), CNPL, CNSE, CNDE, CNDK, CNIT, GN, CNES, HT, AGE, CNCZ, CNCH, MAR, EDU, CNFR

Coefficientsa

Model

Unstandardized Coefficients Standardized Coefficients

t Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

(Constant) 6,717 ,256 26,208 ,000

HT ,775 ,078 ,139 9,986 ,000 ,889 1,125

AGE ,006 ,003 ,025 1,802 ,072 ,897 1,115

GN -,431 ,069 -,087 -6,289 ,000 ,904 1,107

EDU ,733 ,079 ,148 9,295 ,000 ,679 1,473

MAR ,485 ,074 ,094 6,560 ,000 ,840 1,191

CNDE 1,352 ,889 ,020 1,521 ,128 ,994 1,006

79

CNSE 1,848 1,088 ,022 1,699 ,089 ,997 1,003

CNES -,328 ,141 -,035 -2,320 ,020 ,756 1,323

CNIT ,284 ,160 ,026 1,779 ,075 ,804 1,244

CNFR 1,003 ,102 ,161 9,862 ,000 ,645 1,550

CNDK 1,397 ,185 ,106 7,560 ,000 ,874 1,145

CNCH 2,267 ,098 ,355 23,221 ,000 ,734 1,363

CNCZ -,102 ,104 -,015 -,980 ,327 ,771 1,298

CNPL ,370 ,974 ,005 ,380 ,704 ,995 1,005


Excluded Variablesa

Model Beta In t Sig. Partial Correlation


Tolerance VIF Minimum Tolerance

1 CNAT .b . . . -6,240E-14

-

16024754644380,553 -6,240E-14


b. Predictors in the Model: (Constant), CNPL, CNSE, CNDE, CNDK, CNIT, GN, CNES, HT, AGE, CNCZ, CNCH, MAR, EDU, CNFR

80

3.2

Regression




OWN ,7924 ,40563 5910

AGE 64,9277 9,93562 5910

GN ,5726 ,49474 5910

EDU ,5541 ,49710 5910

MAR ,6726 ,46931 5910



1 MAR, EDU, OWN,

GN, AGEb

. Enter


b. All requested variables entered.

Model Summary


Std. Error of the

Estimate

1 ,307a ,094 ,093 2,42791

a. Predictors: (Constant), MAR, EDU, OWN, GN, AGE

ANOVAa


1 Regression 3611,392 5 722,278 122,530 ,000b

Residual 34802,462 5904 5,895

Total 38413,854 5909


b. Predictors: (Constant), MAR, EDU, OWN, GN, AGE

Coefficientsa

81

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.



1 (Constant) 8,996 ,243 36,984 ,000

OWN -1,246 ,082 -,198 -15,206 ,000 ,903 1,107

AGE ,004 ,003 ,015 1,095 ,273 ,874 1,144

GN -,767 ,066 -,149 -11,596 ,000 ,931 1,075

EDU ,378 ,064 ,074 5,896 ,000 ,981 1,020

MAR ,516 ,072 ,095 7,199 ,000 ,882 1,134


82

3.2bis

Regression




OWN ,7142 ,45184 3342

AGE 63,9928 10,05429 3342

GN ,5272 ,49933 3342

EDU ,5239 ,49950 3342

MAR ,7050 ,45613 3342

CNAT ,2501 ,43316 3342

CNDE ,0018 ,04234 3342

CNSE ,0018 ,04234 3342

CNNL ,0000 ,00000 3342

CNES ,0910 ,28760 3342

CNIT ,0640 ,24485 3342

CNFR ,2059 ,40439 3342

CNDK ,0413 ,19900 3342

CNCH ,1562 ,36309 3342

CNCZ ,1867 ,38974 3342

CNPL ,0012 ,03458 3342



1 CNPL, CNSE, CNDE,

CNDK, CNIT, GN,

CNES, AGE, CNCH,

MAR, CNCZ, EDU,

CNFR, OWNb

. Enter



Model Summary

83


Std. Error of the

Estimate

1 ,496a ,246 ,243 2,11671

a. Predictors: (Constant), CNPL, CNSE, CNDE, CNDK, CNIT, GN, CNES, AGE, CNCH, MAR,

CNCZ, EDU, CNFR, OWN

ANOVAa


1 Regression 4858,650 14 347,046 77,457 ,000b

Residual 14906,554 3327 4,480

Total 19765,204 3341


b. Predictors: (Constant), CNPL, CNSE, CNDE, CNDK, CNIT, GN, CNES, AGE, CNCH, MAR, CNCZ, EDU, CNFR, OWN

Coefficientsa

Model


Standardized

Coefficients

t Sig. B Std. Error Beta

1 (Constant) 7,721 ,304 25,375 ,000

OWN ,127 ,109 ,024 1,160 ,246

AGE ,001 ,004 ,003 ,160 ,873

GN -,511 ,077 -,105 -6,630 ,000

EDU ,742 ,089 ,152 8,290 ,000

MAR ,330 ,086 ,062 3,858 ,000

CNDE 1,458 ,868 ,025 1,679 ,093

CNSE 2,323 ,870 ,040 2,670 ,008

CNES -,069 ,150 -,008 -,458 ,647

CNIT ,438 ,171 ,044 2,569 ,010

CNFR 1,284 ,116 ,213 11,096 ,000

CNDK 1,781 ,210 ,146 8,463 ,000

CNCH 2,664 ,141 ,398 18,834 ,000

CNCZ -,057 ,113 -,009 -,506 ,613

CNPL ,354 1,062 ,005 ,333 ,739


84

Excluded Variablesa



Tolerance

1 CNAT .b . . . ,000


b. Predictors in the Model: (Constant), CNPL, CNSE, CNDE, CNDK, CNIT, GN, CNES, AGE, CNCH, MAR, CNCZ, EDU,

CNFR, OWN

3.3

Regression




HOUSEXP ,2590 ,19806 456

AGE 54,9715 4,45604 456

GN ,4474 ,49777 456

EDU ,6864 ,46446 456

MAR ,7675 ,42286 456



1 MAR, AGE, EDU,

GN, HOUSEXPb

. Enter


b. All requested variables entered.

Model Summary


Std. Error of the

Estimate

1 ,358a ,128 ,118 2,02239

a. Predictors: (Constant), MAR, AGE, EDU, GN, HOUSEXP

ANOVAa


85

1 Regression 270,223 5 54,045 13,214 ,000b

Residual 1840,530 450 4,090

Total 2110,753 455


b. Predictors: (Constant), MAR, AGE, EDU, GN, HOUSEXP

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) 7,241 1,265 5,725 ,000

HOUSEXP -1,109 ,508 -,102 -2,185 ,029

AGE ,027 ,022 ,056 1,258 ,209

GN -,254 ,201 -,059 -1,261 ,208

EDU 1,274 ,210 ,275 6,083 ,000

MAR ,604 ,228 ,119 2,646 ,008


3.4

Regression



w ,4451 ,29778 874

HT ,8146 ,38881 874

AGE 63,3844 9,83348 874

GN ,3959 ,48932 874

EDU ,7277 ,44540 874

MAR ,7586 ,42819 874

CNAT ,2002 ,40040 874

CNDE ,0023 ,04781 874

CNSE ,0034 ,05852 874

CNNL ,0000 ,00000 874

CNES ,0297 ,16999 874

CNIT ,0584 ,23454 874

CNFR ,1739 ,37925 874

CNDK ,0950 ,29334 874

CNCH ,3684 ,48265 874

86

CNCZ ,0686 ,25300 874

CNPL ,0000 ,00000 874



1 CNCZ, CNDE, CNSE,

EDU, GN, HT, AGE,

CNES, CNAT, MAR,

CNIT, CNDK, CNFRb

. Enter

a. Dependent Variable: w


Model Summary


Std. Error of the

Estimate

1 ,367a ,135 ,122 ,27909

a. Predictors: (Constant), CNCZ, CNDE, CNSE, EDU, GN, HT, AGE, CNES, CNAT, MAR,

CNIT, CNDK, CNFR

ANOVAa


1 Regression 10,424 13 ,802 10,294 ,000b

Residual 66,986 860 ,078

Total 77,410 873


b. Predictors: (Constant), CNCZ, CNDE, CNSE, EDU, GN, HT, AGE, CNES, CNAT, MAR, CNIT, CNDK, CNFR

87

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) ,188 ,082 2,293 ,022

HT -,077 ,026 -,101 -3,005 ,003

AGE ,005 ,001 ,155 4,530 ,000

GN ,019 ,020 ,032 ,960 ,337

EDU ,008 ,025 ,012 ,317 ,751

MAR ,008 ,023 ,011 ,338 ,736

CNAT ,052 ,027 ,070 1,938 ,053

CNDE ,122 ,198 ,020 ,614 ,540

CNSE ,061 ,162 ,012 ,373 ,709

CNES ,176 ,059 ,100 2,973 ,003

CNIT ,249 ,047 ,196 5,340 ,000

CNFR -,102 ,031 -,130 -3,328 ,001

CNDK -,004 ,037 -,004 -,116 ,908

CNCZ -,135 ,040 -,114 -3,379 ,001


Excluded Variablesa



Tolerance

1 CNCH .b . . . ,000


b. Predictors in the Model: (Constant), CNCZ, CNDE, CNSE, EDU, GN, HT, AGE, CNES, CNAT, MAR, CNIT, CNDK,

CNFR

88

3.5

Regression



w ,4214 ,29390 948

OWN ,6076 ,48854 948

AGE 63,2901 9,39148 948

GN ,4241 ,49446 948

EDU ,6783 ,46739 948

MAR ,7711 ,42035 948

Correlations

w OWN AGE GN EDU MAR

Pearson Correlation w 1,000 ,054 ,155 -,020 -,042 -,013

OWN ,054 1,000 ,267 ,086 -,285 -,088

AGE ,155 ,267 1,000 -,063 -,072 -,156

GN -,020 ,086 -,063 1,000 -,053 -,234

EDU -,042 -,285 -,072 -,053 1,000 ,012

MAR -,013 -,088 -,156 -,234 ,012 1,000

Sig. (1-tailed) w . ,048 ,000 ,268 ,098 ,345

OWN ,048 . ,000 ,004 ,000 ,003

AGE ,000 ,000 . ,027 ,014 ,000

GN ,268 ,004 ,027 . ,050 ,000

EDU ,098 ,000 ,014 ,050 . ,359

MAR ,345 ,003 ,000 ,000 ,359 .

N w 948 948 948 948 948 948

OWN 948 948 948 948 948 948

AGE 948 948 948 948 948 948

GN 948 948 948 948 948 948

EDU 948 948 948 948 948 948

MAR 948 948 948 948 948 948



1 MAR, EDU, AGE,

GN, OWNb

. Enter

89

Model Summary


Std. Error of the

Estimate

1 ,159a ,025 ,020 ,29095

a. Predictors: (Constant), MAR, EDU, AGE, GN, OWN

ANOVAa


1 Regression 2,057 5 ,411 4,859 ,000b

Residual 79,743 942 ,085

Total 81,800 947


b. Predictors: (Constant), MAR, EDU, AGE, GN, OWN

Coefficientsa

Model


Standardized

Coefficients

t Sig.



1 (Constant) ,129 ,075 1,718 ,086

OWN ,004 ,021 ,007 ,204 ,839 ,849 1,177

AGE ,005 ,001 ,151 4,456 ,000 ,897 1,115

GN -,006 ,020 -,011 -,320 ,749 ,925 1,081

EDU -,019 ,021 -,030 -,886 ,376 ,917 1,090

MAR ,006 ,024 ,009 ,272 ,786 ,915 1,093


90

3.6

Regression



w ,3232 ,26831 111

HOUSEXP ,2134 ,15572 111

AGE 54,8739 4,49468 111

GN ,3243 ,47024 111

EDU ,8829 ,32302 111

MAR ,8739 ,33350 111

CNAT ,0991 ,30015 111

CNDE ,0000 ,00000 111

CNSE ,0090 ,09492 111

CNNL ,0000 ,00000 111

CNES ,0090 ,09492 111

CNIT ,0090 ,09492 111

CNFR ,1171 ,32302 111

CNDK ,3694 ,48482 111

CNCH ,3874 ,48936 111

CNCZ ,0000 ,00000 111

CNPL ,0000 ,00000 111



1 CNCH, GN, CNIT,

CNSE, MAR, CNES,

CNAT, HOUSEXP,

CNFR, EDU, AGEb

. Enter



Model Summary


Std. Error of the

Estimate

1 ,343a ,118 ,020 ,26565

a. Predictors: (Constant), CNCH, GN, CNIT, CNSE, MAR, CNES, CNAT, HOUSEXP, CNFR,

EDU, AGE

91

ANOVAa


1 Regression ,932 11 ,085 1,201 ,297b

Residual 6,987 99 ,071

Total 7,919 110


b. Predictors: (Constant), CNCH, GN, CNIT, CNSE, MAR, CNES, CNAT, HOUSEXP, CNFR, EDU, AGE

Coefficientsa

Model


Standardized

Coefficients


1 (Constant) ,117 ,384 ,306 ,760

HOUSEXP ,463 ,178 ,269 2,609 ,010

AGE ,002 ,007 ,032 ,275 ,784

GN -,029 ,057 -,051 -,510 ,611

EDU ,067 ,090 ,081 ,749 ,455

MAR -,060 ,081 -,075 -,741 ,460

CNAT ,102 ,095 ,114 1,078 ,284

CNSE -,036 ,272 -,013 -,133 ,895

CNES -,195 ,284 -,069 -,686 ,494

CNIT ,300 ,284 ,106 1,058 ,292

CNFR -,115 ,086 -,139 -1,335 ,185

CNCH ,016 ,071 ,029 ,225 ,822


Excluded Variablesa



Tolerance

1 CNDK .b . . . ,000


b. Predictors in the Model: (Constant), CNCH, GN, CNIT, CNSE, MAR, CNES, CNAT, HOUSEXP, CNFR, EDU, AGE

SPSS Output RQ4

92

4.1a

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

U

Percentage Correct

,00 1,00

Step 0 U ,00 1187 0 100,0

1,00 110 0 ,0






Step 0 Constant -2,379 ,100 569,619 1 ,000 ,093


Score df Sig.

Step 0 Variables HousingTenure 22,685 1 ,000

93

Log_Financial_Assets_PPP 70,066 1 ,000

Gender ,229 1 ,632

Education 57,371 1 ,000

Age 11,371 1 ,001

Low_Urbanization 6,210 1 ,013

Medium_Urbanization 1,359 1 ,244

Country_of_birth 11,210 1 ,001

Marriage 14,956 1 ,000




Chi-square df Sig.

Step 1 Step 133,205 9 ,000

Block 133,205 9 ,000

Model 133,205 9 ,000

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 620,003a ,098 ,222




Observed

Predicted

U

Percentage Correct

,00 1,00

Step 1 U ,00 1180 7 99,4

1,00 102 8 7,3



94



HousingTenure -,559 ,237 5,553 1 ,018 ,572

Log_Financial_Assets_PPP -,338 ,061 30,723 1 ,000 ,713

Gender -,293 ,223 1,718 1 ,190 ,746

Education -1,225 ,227 29,160 1 ,000 ,294

Age ,092 ,029 9,685 1 ,002 1,096

Low_Urbanization -,227 ,252 ,810 1 ,368 ,797

Medium_Urbanization ,127 ,322 ,156 1 ,693 1,136

Country_of_birth ,641 ,290 4,868 1 ,027 1,898

Marriage -,426 ,232 3,367 1 ,066 ,653

Constant -3,021 1,721 3,080 1 ,079 ,049

a. Variable(s) entered on step 1: HousingTenure, Log_Financial_Assets_PPP, Gender, Education, Age,

Low_Urbanization, Medium_Urbanization, Country_of_birth, Marriage.

95

4.2a

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

U

Percentage Correct

,00 1,00

Step 0 U ,00 1690 0 100,0

1,00 183 0 ,0






Step 0 Constant -2,223 ,078 815,977 1 ,000 ,108


96

Score df Sig.

Step 0 Variables HousingTenure_t_1 13,296 1 ,000

Gender ,305 1 ,581

Age 1,807 1 ,179




Chi-square df Sig.

Step 1 Step 14,773 3 ,002

Block 14,773 3 ,002

Model 14,773 3 ,002

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 1183,981a ,008 ,017




Observed

Predicted

U

Percentage Correct

,00 1,00

Step 1 U ,00 1690 0 100,0

1,00 183 0 ,0



97



Step 1a HousingTenure_t_1 -,608 ,166 13,418 1 ,000 ,545

Gender -,127 ,158 ,649 1 ,420 ,881

Age ,035 ,028 1,629 1 ,202 1,036

Constant -3,805 1,642 5,370 1 ,020 ,022

a. Variable(s) entered on step 1: HousingTenure_t_1, Gender, Age.

Correlation Matrix

Constant HousingTenure_t_1 Gender Age

Step 1 Constant 1,000 -,078 -,113 -,995

HousingTenure_t_1 -,078 1,000 ,105 ,007

Gender -,113 ,105 1,000 ,060

Age -,995 ,007 ,060 1,000

4.3a

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1

98


Observed

Predicted

U

Percentage Correct

,00 1,00

Step 0 U ,00 1187 0 100,0

1,00 110 0 ,0






Step 0 Constant -2,379 ,100 569,619 1 ,000 ,093


Chi-square df Sig.

Step 1 Step 163,251 16 ,000

Block 163,251 16 ,000

Model 163,251 16 ,000

Model Summary

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square

1 589,957a ,118 ,268




Observed

Predicted

U

Percentage Correct

,00 1,00

Step 1 U ,00 1174 13 98,9

1,00 98 12 10,9



99



HousingTenure -,680 ,257 6,982 1 ,008 ,506


Gender -,260 ,232 1,251 1 ,263 ,771

Education -,891 ,258 11,929 1 ,001 ,410

Age ,070 ,031 5,140 1 ,023 1,072

Low_Urbanization -,141 ,263 ,289 1 ,591 ,868


Country_of_birth ,496 ,308 2,589 1 ,108 1,642

Marriage -,367 ,244 2,260 1 ,133 ,693

Austria -,687 ,346 3,958 1 ,047 ,503

Spain ,199 ,384 ,268 1 ,605 1,220

Italy -1,423 ,604 5,541 1 ,019 ,241

France -1,082 ,601 3,244 1 ,072 ,339

Denmark -1,855 ,789 5,531 1 ,019 ,156

Switzerland -1,181 ,423 7,813 1 ,005 ,307

CzechRepublic -1,479 ,451 10,784 1 ,001 ,228

Constant -1,419 1,867 ,578 1 ,447 ,242

a. Variable(s) entered on step 1: HousingTenure, Log_Financial_Assets_PPP, Gender, Education, Age,

Low_Urbanization, Medium_Urbanization, Country_of_birth, Marriage, Austria, Spain, Italy, France, Denmark,

Switzerland, CzechRepublic.

100

4.4a

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

U

Percentage Correct

,00 1,00

Step 0 U ,00 933 0 100,0

1,00 59 0 ,0






Step 0 Constant -2,761 ,134 422,974 1 ,000 ,063

101


Score df Sig.

Step 0 Variables Outrightowner 24,024 1 ,000


Gender ,134 1 ,715

Education 45,628 1 ,000

Age 11,821 1 ,001

Country_of_birth ,004 1 ,952

Marriage 2,197 1 ,138



Chi-square df Sig.

Step 1 Step 99,162 7 ,000

Block 99,162 7 ,000

Model 99,162 7 ,000

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 348,275a ,095 ,262




Observed

Predicted

U

Percentage Correct

,00 1,00

Step 1 U ,00 928 5 99,5

1,00 54 5 8,5



102



Outrightowner ,756 ,346 4,769 1 ,029 2,129


Gender -,217 ,303 ,513 1 ,474 ,805

Education -1,304 ,312 17,412 1 ,000 ,272

Age ,135 ,042 10,527 1 ,001 1,145

Country_of_birth ,228 ,549 ,173 1 ,677 1,257

Marriage -,128 ,339 ,143 1 ,705 ,880

Constant -5,767 2,382 5,861 1 ,015 ,003

a. Variable(s) entered on step 1: Outrightowner, Log_Financial_Assets_PPP, Gender, Education, Age, Country_of_birth,

Marriage.

4.5a

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1

103


Observed

Predicted

U

Percentage Correct

,00 1,00

Step 0 U ,00 1317 0 100,0

1,00 120 0 ,0






Step 0 Constant -2,396 ,095 631,170 1 ,000 ,091


Score df Sig.

Step 0 Variables OutrightOwner_t_1 20,369 1 ,000

Gender ,097 1 ,756

Age ,000 1 ,986




Chi-square df Sig.

Step 1 Step 20,101 3 ,000

Block 20,101 3 ,000

Model 20,101 3 ,000

104

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 805,464a ,014 ,032




Observed

Predicted

U

Percentage Correct

,00 1,00

Step 1 U ,00 1317 0 100,0

1,00 120 0 ,0





Step 1a OutrightOwner_t_1 ,862 ,196 19,423 1 ,000 2,367

Gender ,045 ,193 ,054 1 ,815 1,046

Age ,007 ,035 ,041 1 ,840 1,007

Constant -3,261 2,050 2,529 1 ,112 ,038

a. Variable(s) entered on step 1: OutrightOwner_t_1, Gender, Age.

Correlation Matrix

Constant OutrightOwner_t_1 Gender Age

Step 1 Constant 1,000 -,100 -,131 -,996

OutrightOwner_t_1 -,100 1,000 -,016 ,044

Gender -,131 -,016 1,000 ,087

Age -,996 ,044 ,087 1,000

105

4.1b

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1



Observed

Predicted

ULT

Percentage Correct

,00 1,00

Step 0 ULT ,00 0 52 ,0

1,00 0 63 100,0






Step 0 Constant ,192 ,187 1,049 1 ,306 1,212

106


Score df Sig.

Step 0 Variables HousingTenure ,048 1 ,827


Gender ,084 1 ,772


Age 9,895 1 ,002


Marriage 3,368 1 ,066




Chi-square df Sig.

Step 1 Step 27,686 7 ,000

Block 27,686 7 ,000

Model 27,686 7 ,000

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 130,684a ,214 ,286




Observed

Predicted

ULT

Percentage Correct

,00 1,00

Step 1 ULT ,00 31 21 59,6

1,00 16 47 74,6



107



HousingTenure -,665 ,499 1,772 1 ,183 ,514


Gender ,065 ,462 ,020 1 ,888 1,067

Education -,393 ,460 ,731 1 ,393 ,675

Age ,190 ,064 8,931 1 ,003 1,209

Country_of_birth -,843 ,612 1,898 1 ,168 ,430

Marriage ,782 ,454 2,964 1 ,085 2,185

Constant -6,335 3,546 3,191 1 ,074 ,002

4.2b

Logistic Regression





Total 7060 100,0


Total 7060 100,0




,00 0

1,00 1


108


Observed

Predicted

ULT

Percentage Correct

,00 1,00

Step 0 ULT ,00 0 64 ,0

1,00 0 93 100,0






Step 0 Constant ,374 ,162 5,295 1 ,021 1,453


Score df Sig.

Step 0 Variables Outrightowner 1,015 1 ,314

Gender 3,545 1 ,060


Age 4,433 1 ,035

Medium_Urbanization ,003 1 ,956

Low_Urbanization ,191 1 ,662


Marriage ,172 1 ,679




Chi-square df Sig.

Step 1 Step 12,960 8 ,113

Block 12,960 8 ,113

Model 12,960 8 ,113

109

Model Summary


Cox & Snell R

Square

Nagelkerke R

Square

1 199,300a ,079 ,107




Observed

Predicted

ULT

Percentage Correct

,00 1,00

Step 1 ULT ,00 31 33 48,4

1,00 17 76 81,7





Outrightowner ,148 ,390 ,143 1 ,705 1,159

Gender ,727 ,353 4,245 1 ,039 2,069

Education -,570 ,360 2,505 1 ,113 ,565

Age ,095 ,047 4,136 1 ,042 1,100


Low_Urbanization ,234 ,461 ,259 1 ,611 1,264

Country_of_birth -,180 ,591 ,093 1 ,760 ,835

Marriage ,222 ,392 ,321 1 ,571 1,249

Constant -5,551 2,675 4,306 1 ,038 ,004

a. Variable(s) entered on step 1: Outrightowner, Gender, Education, Age, Medium_Urbanization, Low_Urbanization,

Country_of_birth, Marriage.

On the relation between unemployment and housing tenure...

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