Economic growth, density, and subjective well-being in Finnish regions:
The paradox of affluence in geographic context
Abstract:
Several studies have shown that individual life satisfaction grows more slowly or even declines with urban
density and economic performance. Contribution made to this thesis here is twofold; including several
measures of subjective wellbeing and ask if individuals with different educational levels will respond
differently. First, results indicate that populations with and without tertiary education experience similar
negative effect in life satisfaction if residing in Helsinki-Uusimaa region. However, tertiary educated gain in
‘thick relationships’ measuring the quality of ‘bonding ties’ while non-tertiary educated show significantly
low scores in reciprocity in social exchange, social trust and frequency of social contact if living in urban
region. Sense of competence predicts high life satisfaction for tertiary educated in urban region, while nontertiary educated draw life satisfaction from several psychological domains. Overall, results suggest that
urban life is more related to eudaimonic wellbeing consisting from active functioning rather than passive
life satisfaction. Effects of urban environment are however not equally experienced in population as a
whole.
Mikko Weckrothᵃ
Philip Morrisonᵇ
ᵃ Department of Geosciences and Geography, Division of Geography, P.O. Box 64 FI-00014 University of
Helsinki
ᵇ School of Geography, Environment and Earth Sciences , Victoria University of Wellington , PO Box 600,
Wellington, New Zealand
1
Introduction
Governances around the world have started to recognize a need to look beyond economic indicators while
defining the wellbeing of the residents (STIGLITZ, SEN and FITOUSSI, 2010). As the interest for obtaining
better measures of well-being has been expressed mostly by national governments (OFFICE FOR NATIONAL
STATISTICS, 2014), most of the research has been focused either on national accounts of wellbeing
(SAAMAH and MICHAELSON, 2009,DIENER and TOV, 2012) or on international comparisons (HUPPERT,
2013,VEENHOVEN, 2009,HELLIWELL, RICHARD LAYARD and SACS JEFFREY, 2012). However, even though
there is no reason to assume why subjective wellbeing would not have variance between subnational
territories as well researchers have paid substantially less attention on regional differences (see
MORRISON, 2014). A need to look at subnational patterns is however not purely academic as for example
the regional policy of EU explicitly aims for reducing regional disparities on the domains of job creation,
competitiveness, economic growth, and quality of life (EUROPEAN COMMISSION, 2014). Simultaneously, as
the main interests of regional studies as a discipline are on spatial dimensions of above concepts and on
policies shaping them, there exists a need for more detailed view on their variance at regional level.
Thereby, this study focuses on interaction between different subjective wellbeing measures and economic
growth and density on sub-national context.
More explicitly, this study sets out to re-examine, validate and complement so-called localization of the
paradox of affluence. This paradox derives from several studies indicating that that life satisfaction appears
at lower level in economically thriving and growing urban regions (CAMPBELL, 1981,POWDTHAVEE,
2005,BERRY and OKULICZ-KOZARYN, 2009,KNIGHT and GUNATILAKA, 2010,MORRISON, 2011,BERRY and
OKULIZC-KOZARYN, 2011,EASTERLIN, ANGELESCU and ZWEIG, 2011). This line of research can be seen as an
extension to the original Easterlin paradox suggesting that even though richer countries are happier (i.e.
more satisfied) than less rich countries there can be seen an decreasing utility from economic growth from
cross sectional analysis between rich countries (EASTERLIN, 1974).
The issue faced within countries hence is that “the geography of happiness” may not mirror the “geography
of growth”. While the country as a whole might be better off, and average levels of subjective wellbeing
might be higher on international comparison, average levels of wellbeing in growth centers themselves are
lower compared to other regions. Most of the research has addressed this difference as urban-rural
comparison (BERRY and OKULICZ-KOZARYN, 2009,BERRY and OKULIZC-KOZARYN, 2011). However, as the
most densely urban regions are usually also the most productive ones, it inevitably becomes also an
investigation on economic growth and subjective wellbeing (EASTERLIN, ANGELESCU and ZWEIG, 2011). In
this study we concentrate on negative effect of Finland dominant engine of growth, Helsinki-Uusimaa
region, to average life satisfaction with and without individual controls. We also test the effect of individual
income to life satisfaction in urban and rural contexts. We are also interested on possibility of different
negative and positive effects in different population groups.
This study aims to contribute to existing literature through three routes; 1) placing analysis into national
context with relatively homogenous population and culture and low regional differences and 2) including
more precise and extensive measures of subjective wellbeing than usual life satisfaction measure 3) taking
account possibility of different wellbeing gains of different population groups (tertiary and non-tertiary
educated).
We expect to find that, after validating the localization of paradox of affluence in Finnish context, some of
the eudaimonic measures of subjective wellbeing, treating subjective wellbeing as active positive
functioning instead of passive life satisfaction, might be at higher level in urban growth center. However,
these benefits of residing in urban agglomerations might not be experienced equally among the whole
population. Drawing from the ‘polarization of labor markets in the urban regions’ thesis (SASSEN, 1991), we
2
concentrate on population with and without tertiary education and possibility of unequal gains in different
dimensions of subjective wellbeing.
This paper argues that the reason wellbeing has been shown to fall at an increasing rate with population
density is that ‘wellbeing’ has only been measured in one particular way – as life satisfaction. However,
recently published 6th round European Social Survey data conceptualizing and measuring more extensive
aspects from personal and social wellbeing than simple life satisfaction, enables us to analyze this paradox
in with closer scrutiny.
The paper is structured as follow. First, we make a brief overview the literature or urban-rural comparisons
on life satisfaction. After this we present the exact research questions and the statistical model and the
data we applied for answering them. What follows is description of Finland as a context and descriptive
analysis based on simple life satisfaction measure. After validating localization of paradox of affluence in on
life satisfaction account we examine different conceptualization and measures prevailed in participating
disciplines in empirical happiness studies. Subsequently, we look at descriptive statistics based on social
and personal wellbeing measures and apply multivariate regression model treating life satisfaction as
dependent variable and socio-demographic variables predictors in two regional context. Eventually, the
sample is divided into four groups, respondents with tertiary and non-tertiary education living in either
Helsinki-Uusimaa or other regions, in order to reveal different gains from living on urban centers. Final
stage of empirical analysis looks at personal value orientations to clarify and confirm the suggestions made
on previous analysis of different wellbeing measures. We close with summary and discussion about
possible links for policy making.
Geography of happiness and regional wellbeing
As empirical study of happiness has spread into wide range of disciplines inside social sciences, also
geographer and regional analysis have offered to make their contribution. As the role of regional scientists
is to argue that location and context matters, this study is focuses on the effect of two regional contexts,
urban and rural, based on level of population density and difference in economic indicators.
One of earliest summaries of urban-rural happiness differences was made by VEENHOVEN and EHRHARDT
(1995) who concluded that in less-developed countries happiness (measured as life satisfaction) is greater
in urban areas but urban-rural differential tends to disappear or even reverse with economic development.
However, the first comprehensive study with worldwide focus and data was done by BERRY and OKULICZKOZARYN in (2009) who conducted a cross-sectional comparison between 80 countries drawing from World
Value Survey data from around the year 2000. Results showed that even personal characteristics and level
of development are still key driving forces for life satisfaction, in higher income countries life satisfaction
decreases with big-city residence. Later on Berry and Okulicz-Kozaryn build on this thesis and repeated the
analysis in the context United States using the data from General Social Survey from 1972 to 2008 (BERRY
and OKULIZC-KOZARYN, 2011). This analysis revealed, after controlling for personal characteristics affecting
individual happiness, an ‘urban-rural happiness gradient’ decreasing from greater life satisfaction in lower
density areas towards lower levels in large central cities.
Another comprehensive study revealing similar pattern was conducted by (EASTERLIN, ANGELESCU and
ZWEIG, 2011) who conducted both across- and within-country regression analyses of 2005-08 data from
Gallup Survey Poll. Their analysis suggested, after controlling gender, age, and marital status, economic
variables of income, education and occupation to be main factors underlying urban-rural differences in life
satisfaction. Yet another within-country analyses producing similar patterns were done by (POWDTHAVEE,
2005) in the context of South Africa and (KNIGHT and GUNATILAKA, 2010) who investigated the subjective
wellbeing of rural-urban migrants in China.
3
One obvious method for looking at contextual effects in subjective wellbeing is the use of multilevel
modelling. The results of this line of research, usually focusing on effect of income and unemployment, can
be summarized as “life satisfaction rises less with income in areas with higher economic performance”
(PITTAU, ZELLI and GELMAN, 2010,BONINI, 2008,ASLAM and CORRADO, 2012,HELLIWELL, 2008). Pittau,
Zelli et al. (2010) interpret this evidence from perspective of Post-materialism where life satisfaction in rich
societies is more related to non-materialistic issues. Accordingly, life satisfaction is more about economics
in poor regions and more about ‘‘culture’’ in rich regions (PITTAU, ZELLI and GELMAN, 2010).
As addition to test for contextual effects regional analysis needs to consider another important question for
spatial dimension of life satisfaction: the question of selective migration. The possibility of selectivity in
rural-urban migration was rised by (EASTERLIN, ANGELESCU and ZWEIG, 2011) who asked that since rural
out migrants tend to be better educated than their peers out-migration would lower the rural life
satisfaction average. Thereby, is has been suggested that people self-select their location in order to bring
their life style and residence into harmony with personal subjective preferences and traits (MORRISON,
2014). This ‘spatial equilibrium’ theory suggests that people either more or adjust in order to balance their
expectations versus realization.
Although localization of paradox of affluence has been repeatedly confirmed in the developed countries no
consensus exists for reasons behind it. Despite spatial equilibrium might at least partially explain small
variance of life satisfaction in within country, it fails to offer explanation for negative effect of urban
regions. It has hence been suggested though that people may be prepared to undergo short-term loss of
life satisfaction caused by living in urban areas where the chances for upward social mobility are believed
to be higher (MORRISON, 2014). This line of thought follows the general economic theory treating
individual as rational agent looking for maximizing personal life satisfaction in long term. Another option is
to look for answers from classic urban theories as was done in conceptual framework in (BERRY and
OKULICZ-KOZARYN, 2009,BERRY and OKULIZC-KOZARYN, 2011) papers. These authors considered the
possibility of cities negative effect in life satisfaction through writings of SIMMEL (1976) and WIRTH (1938).
Georg Simmel addressed in his seminal essays (1976, originally 1903) possibilities of psychological
correlates of urban life and suggested that steady pace of habitual behavior characterizing rural life was
replaced by external stimuli of urban life, requiring continual and conscious responses from individual.
Wirth (1938) later on built on Simmels writings saying that increased size, density and heterogeneity in
urban life was due to lead into differentiation, formalization of institutions and sense of individual anomie.
In Wirths perception of city, urban living could lead to social and economic opportunities but also on
unhappiness and dissatisfaction.
However, these classic theories on social and psychological dimensions of urban life have not made their
way directly into empirical analyses. Instead, effects of urban life has been more typically analyzed, as
addition to economic variables, against environmental factors such as commuting time (STUTZER, 2008), air
quality (MACKERRON and MOURATO, 2009), presence of green areas (AMBREY and FLEMING, 2014) or
satisfaction on availability of different services (MARANS and STIMSON, 2011).
This research hopes to shed some additional light on urban-rural differences in subjective well-being
connecting analysis back to mentioned classic theories about social and psychological characteristics of life
in urban centers. The social and personal subjective wellbeing gains caused by living in dense, economically
thriving regions are hoped to be detectable from extensive module of personal and social wellbeing
included in 6th round European Social Survey questionnaire.
The exact research questions in this analysis can thereby be formulated as follows;
4
1) Can the localization of the paradox of affluence be detected from the regional data of Finland in
individual and aggregate levels and does this hold also after controlling socio-demographic
variables?
2) If so, instead of focusing only on life satisfaction, might personal and social wellbeing be on higher
level in more dense and growing economically thriving regions?
3) Are subcomponents of personal and social wellbeing contributing to overall life satisfaction
differently in different regional contexts?
4) And finally, do different population segments with different education level and personal
characteristics reweight the domains of subjective wellbeing similarly and do they gain similar
benefits from residing in growing regions.
In order to answer these questions we apply linear multivariate regression models. In these models sociodemographic variables which have been found to be a strong predictors for life satisfaction (age, gender,
partnership, education, unemployment and income) are first entered to the model (1). After this we
included stepwise the social (Model 2) and personal wellbeing (Model 3) indicators as operationalized in
ESS wellbeing module (HUPPERT, et al., 2014).
The equations for multivariate linear regression models used are formulated as follow
𝑌ᵢ = 𝛽 0 + 𝛽ᵢ𝐴₁ + εᵢ
𝑌ᵢ = 𝛽 0 + 𝛽ᵢ𝐴₁ + 𝛽ᵢ𝐵₂
(1)
+ εᵢ
𝑌ᵢ = 𝛽 0 + 𝛽ᵢ𝐴₁ + 𝛽ᵢ𝐵₂ + 𝛽ᵢ𝐶₃ + εᵢ
(2)
(3)
Here, 𝑌ᵢ counts as predicted individual life satisfaction, 𝛽 0 as constant, 𝐴₁ for socio-demographic
variables, 𝐵₂ for social well-being variables, 𝐶₃ for personal wellbeing variables and ε for random error. This
model is repeated for relevant contexts and educational groups.
Data
As general interest for subjective wellbeing measures has been increasing, more survey data including
specific location indicators has also become available. One example of this is the latest 6th round European
Social Survey (ESS) data released in late 2013 which includes regional level location indicator from
respondents from all of the participating European countries. It also includes a comprehensive module on
social and personal wellbeing aiming to capture more nuanced picture on multidimensional concept of
subjective well-being (HUPPERT, et al., 2014). Thereby, the individual level data used in this study comes
from Finnish sample in 6th round ESS survey collected in 2012.Indicators for regional growth and economic
performance are from Statistic of Finland and Ministry of Employment and the Economy of Finland and are
from either from years of 2011 or 2012.
While the data for regional economic performance in is reported as aggregate of GDP/capita at market
prices, the survey data for individual income in ESS is measured as “households total net income” and
reported as deciles (on a scale of 1-10).
The component for social and personal wellbeing are generated as described in wellbeing module in 6th
round ESS data (HUPPERT, et al., 2014). As their subcomponents (for example competence, resilience and
thick relationships) are results of separate survey items, combining different questions with different
scales, they are presented and used in regression models as standardized Z-scores.
5
Finland as a context and descriptive analysis
The most distinctive feature in the development of Finland is its rapid transformation from rural periphery
at the beginning of 20th century into modern knowledge intensive society and a global leader in the ICT
sectors at the beginning of 21st century (OJALA, ELORANTA and JALAVA, 2006). This development
proceeded simultaneously with labor and economy structure shifting from manufacturing and processing
activities in the mid 1950´s to the dominance of service sector in the late 1970´s. The spatial dimension
related to this “first wave” of structural shift, urbanization, occurred both later and more rapidly in Finland
compared to continental Europe.
The most significant deviation to Finland´s “rags to riches” narrative took place during the early 1990´s
when the economy sank into its most severe recession of its recent history with the record unemployment
of 18 % in 1993 (CONSOLI, VONA and SAARIVIRTA, 2013). However, a less than a decade later Finland had
returned to a path of constant growth in GDP and low unemployment figures. This transition era described
also as “Finnish miracle” (CASTELLS and HIMANEN, 2002) has been said to been fuelled by mixture of
forward-looking industrial policies, public investments in higher education and research and development
which stimulated and ICT-complementary clusters in manufacturing and service sectors (HONKAPOHJA,
2001).
This revival process showing robust figures of growth in national scale from mid-1990´s onwards had a
however a backdrop which is not as widely cited in the economic literature. The growth in innovative
knowledge intensive industries during post 1990´s period has been concentrated to certain urban centers
in Southern Finland and especially Helsinki-Uusimaa region (LOIKKANEN and LÖNNQVIST, 2007) and
thereby rises up as a question of spatial equalities between skilled and unskilled labour. In other words, the
jobs created by post 1990´s revival were created to ICT sectors and were not the same which were lost in
the recession. Spatial dimension of this ‘second structural shift’ has been examined by (VAATTOVAARA and
KORTTEINEN, 2003) in the context Helsinki metropolitan and it´s urban structure.
The main interest of this paper lies however in the regional differences of wellbeing and more specific to
interaction between objective (unemployment, income etc.) and subjective (life satisfaction, social and
personal wellbeing) measures of well-being. And furthermore, the differences between Helsinki-Uusimaa
region, by far the biggest urban concentration, and rest of the country. The initial question thereby is that if
the residents of Helsinki-Uusimaa region, which has been biggest winner in net migration for last decades
and having largest shares of national R&D expenditure and investments, are also showing higher figures in
subjective wellbeing?
We begin this investigation by looking at correlations between objective indicators in aggregate regional
level and the most simplified measure of subjective wellbeing; overall life satisfaction.
Map of Finnish regions is presented in Figure 1 and an overview of regional characteristics in the following
Table 1.
Figure 1. Regions of Finland
6
Table 1. Overview of Finnish regions
7
Map code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Region
Capital city
GDP
Uusimaa
Varsinais-Suomi
Satakunta
Tavastia Proper
Pirkanmaa
Tavastia Päijänne
Kymenlaakso
South Karelia
Southern Savonia
Northen Savonia
North Karelia
Central Finland
Southern Ostrobothnia
Ostrobothnia
Central Ostrobothnia
North Ostrobothnia
Kainuu
Lappland
Åland Islands
Helsinki
Turku
Pori
Hämeenlinna
Tampere
Lahti
Kotka
Lappeenranta
Mikkeli
Kuopio
Joensuu
Jyväskylä
Seinäjoki
Vaasa
Kokkola
Oulu
Kajaani
Rovaniemi
Mariehamn
Mean
46292
31838
32732
27969
33049
28486
30162
35382
25815
29466
27126
28569
28234
35759
33076
29360
24967
29329
41547
31535
(2011)
€/resident at
market prices
Population
28,5
8,7
4,2
3,2
9,1
3,8
3,4
2,5
2,9
4,6
3,1
5,1
3,6
3,3
1,3
7,3
1,5
3,4
0,5
% of total
Density
Mean life satisfaction Unemployment rate
170,3
8,01
43,8
8,07
28,5
8,12
33,7
8,09
39,5
8,24
39,5
8,29
35,3
8,12
23,8
8,02
11,0
8,11
14,8
8,15
9,3
8,02
16,4
8,12
14,4
8,13
23,2
8,4
13,7
8,27
11,3
8,15
3,8
8,5
2,0
8,01
18,3
8,21
29,08
8,16
(2012)
(2012)
Residents/land km2
Inter-regional migration
in
out
net
29920
26797
3123
9457
9162
295
4498
4860
-362
5646
5888
-242
13753
12118
1635
5912
5823
89
4045
4498
-453
3324
3539
-215
4481
5060
-579
6536
6565
-29
4102
4394
-292
8019
8043
-24
4122
4415
-293
3595
4158
-563
1727
1971
-244
9130
9703
-573
2126
2670
-544
4888
5718
-830
353
252
101
7,0
9,5
10,4
9,0
10,6
11,7
12,1
11,8
10,7
10,8
13,4
12,2
7,7
6,0
7,8
11,0
12,5
12,9
2,9
10,0
(2011)
% of the labor force
(2012)
As expected, engine of economic growth is the nations largest urban agglomeration, Helsinki-Uusimaa,
which shows GDP/capita figure 47 % percent above the regional average. It is also the biggest net winner in
inter-regional migration, has most dense population/land area, counts more than a quarter (28,5 %) of total
population, and has one of the lowest numbers in unemployment rate.
Thereby, the localization of the paradox of affluence is confirmed from the Table 1 as Helsinki- Uusimaa
region ranks to the bottom of together with Lappland at 8.011. In order to present more detailed picture of
these regional figures, a bivariate correlation matrix is presented in Table 2.
Table 2. Bivariate correlations between regional indicators from Table 1.
GDP / Capita
GDP / Population Unemployment
Net
Mean
Capita
density
rate
migration 'sflife'
1
Population density
,700**
Unemployment rate
-,683**
-,294
1
Net migration
,673**
,890**
-,285
1
Mean satisfaction to life
-,146
-,268
-,188
-,185
1
19
19
19
19
N
19
**. Correlation is significant at the 0.01 level (2-tailed).
1
The strong correlation between regional GDP and population density (.700**) is what is expected based on
standard theory of regional growth, as is the negative correlation between GDP and unemployment rate (.683**). However, the main interest here lies in the satisfaction to life variable and it´s bivariate
correlations to the objective indicators. Thereby, also Table 2 confirms the localization of the paradox of
affluence hypothesis with negative correlation between life satisfaction and population density (-.268) and
regional GDP (-.146). The negative correlation between unemployment rate and life satisfaction (-.188) can
be understood against both theories on regional economic growth and subjective wellbeing since
unemployment has constantly been found as a strong predictor of low life satisfaction (ASLAM and
CORRADO, 2012,PITTAU, ZELLI and GELMAN, 2010,BALLAS, 2012). The negative correlation between life
8
satisfaction and ‘net migration’ is however in strong contrast to assumption of mainstream economic
theory assumptions about rationally behaving and locating individuals looking to maximize their personal
utility and life satisfaction. In other words, why would the regions associated with lower life satisfaction be
net winners in inter-regional migration as well drivers of economic growth?
Thereby, based on these results it seems fair to suggest that paradox of affluence can be found in Finland
as well based on a life satisfaction measure. The similar pattern was also confirmed from 6th round of ESS
data also from other European countries. Corresponding values of mean life satisfaction in economically
thriving regions vs. rest of the country were found as follows: Sweden: Stockholm region 7,64 < 7,94,
Norway: Oslo and Akerhus region 8,02 < 8,17, France: Ile-de-France 6,00 < 6,24.
First argument against this thesis would most likely be that the sample used in this study in HelsinkiUusimaa would have certain socio-demographic characteristics which would result as lower the mean
value. Thereby, in order to separate regional effect socio-demographic variables need to be controlled.
Thus, after controlling for individual characteristics (age, gender, partnered, education, income and
unemployment) Helsinki-Uusimaa as dummy variable to the model results negative coefficient of -,227***
in linear regression model. Helsinki-Uusimaa is the only region which results as negative coefficient as a
regional dummy.
Thereby, results confirm the localization of paradox off affluence in Finland with both with and without
controls. This thesis is however limited only to simple life satisfaction measure which is however not the
only or unquestionably most valid measure of subjective well-being. Thereby, before moving on to
descriptive analysis of other subjective wellbeing measures and proceeding regression models, next
chapter offers an overview of different accounts in measuring subjective wellbeing. It includes also
description on the “dynamic model of wellbeing” which the regression model treating life satisfaction as
dependent variable is built on.
Different accounts in measuring subjective wellbeing
As stated earlier policy makers are showing an increasing interest for the measures of subjective wellbeing.
The question on how exactly results of subjective wellbeing studies would turn into policy suggestions
remains as unsolved and highly debated topic (see for example LAYARD, 2006,WILKINSON, 2007).
Research based decision making in case of subjective wellbeing is problematic also because of competing
measures and overlapping concepts being offered from different disciplines and traditions (DIENER and
SELIGMAN, 2004,JAYAWICKREME, FORGEARD and SELIGMAN, 2012). This is at least partly due to the due
lack of interdisciplinary discussions between different traditions, perspectives and measures in happiness-,
quality of life- and subjective wellbeing studies. Encounters between different traditions have been
examined for example by RYAN and DECI (2001) and KEYES, SHMOTKIN and RYFF (2002) and calls for more
and deeper interdisciplinarity has been made by BALLAS (2013), JAYAWICKREME, FORGEARD and
SELIGMAN (2012) and WEIJER, JARDEN and POWDTHATEE (2011).
KRISTJANSSON (2010) has categorized different perspectives in empirical investigations of subjective wellbeing into three different accounts. First one (a) Hedonistic account defines happiness and well-being in
terms of pleasure attainment, pain avoidance and overall satisfaction to life. Although there are few
different self-reporting measurements of hedonistic accounts the threefold structure consisting from
negative feelings, positive feelings and overall satisfactions is common for all.
Closely related to hedonistic account is the (b) Life satisfaction account which is concerned only on the
overall life satisfaction. Thereby, the measurement is formulated into single item question such as “Taking
all together, how satisfied you currently are with your life as a whole?” scaling from 0 to 10. Because of its
9
apparent simplicity and cost efficiency as a survey item life satisfaction is the most frequently used
measure in social sciences.
The critiques of hedonistic and life-satisfaction accounts are arguing them being inevitably relative and
sensitive to both cultural context and personal traits while determining the gap between expectations and
experienced reality (RYAN and DECI, 2001,HUPPERT, et al., 2014). There has also been concerns on how
well hedonistic and life satisfaction measures capture psychological wellbeing and human potential for
optimal living (RYFF and SINGER, 2008).
Thereby, in contrast to previous two, eudaimonic account (c) promotes that happiness can and should be
measured objectively. For archiving this, it offers “objective lists accounts” (see also SEN 2009 and
NUSSBAUM and SEN, 1993) for evaluations on happiness drawing from classical writings of Aristotle and
especially his Nicomachean Ethics (ARISTOTELES, 2011). Accordingly, eudaimonic well-being focuses on
meaning, and self-realization and defines wellbeing in terms of living and functioning according to ones
individual potential and mental capacities (RYFF and SINGER, 2008). Also eudaimonic account has
formulated different measures for wellbeing. One of the recent ones is the instrumentalization used in 6th
round of European Social Survey combining different measures and concepts into one dynamic model of
well-being (HUPPERT, et al., 2014). It understands sustainable well-being resulting from active positive
functioning instead of relying only on passive life satisfaction or other evaluations pleasantness and
pleasure. By combining different measures (including life satisfaction) into one dynamic model it also
utilizes a procedure which has been suggested for creating room for interdisciplinary discussion; by
integrating competing measures into one model the discussion can move forward empirically instead of
arguing on optimal measures of phenomena (in this case for subjective well-being) (STROBER, 2011).
Inside the ‘geography of happiness’ literature it is also recognized that there is no commonly agreed
measure for subjective wellbeing and happiness (MARANS and STIMSON, 2011,MORRISON, 2014).For some
reason, however, the current research on geography of happiness has been limited itself mainly to (b) life
satisfaction account. For example in their seminal book ‘‘Investigating Quality of Urban Life’’ delivering a
detailed overview of empirical case studies of quality of life of cities around the world for last 50 years
Marans and Stimson (2011) formulate their conceptual frameworks as follow:
“in early studies [of happiness and quality of life] , the focus seemed to be on happiness which could be
viewed from either eudaemonistic or hedonistic philosophical perspective.[…] The eudaemonistic view of
happiness was certainly a normative view prescribing what “should be done” to be happy and lends itself to
moralistic approaches. However, the hedonistic view focusing on satisfaction is a “positive” view asking
“what is” that makes one satisfied and this lends itself to empirical approaches. Consistent with the latter
[hedonistic] view, most of the QOL literacy investigating subjective Quality of Urban Life has tended to
adopt an empirical positivist methodology.
While aiming for objectivity is surely a virtue in scientific method, the contradictory between Quality of Life
and eudaimonic objectivity requires some discussion. In order to avoid any normative or moralistic
approaches Quality of Urban Life tradition understand to have solved this issue by dividing overall
satisfaction to different life domains (satisfaction to home, location, neighborhood, or even a rather loose
concept of region) each measured with separate questions while the overall life satisfaction is then
assumed to count as a sum or average of these evaluations.
This can be seen problematic firstly due to the method being used. Surveys are based on closed
standardized questions and answers and are thereby limited set to issues to be evaluated by the
respondents. They also give respondents a little a chance to address whether they sees suggested domains
as important part of wellbeing itself on whole. Thereby, through this reductionist method, and offering
hand-picked life domains to be evaluated the domain satisfaction surveys inevitably become prescriptive
10
and normative as well. Another concern on life (and domain) satisfaction accounts is related to their
pleasantness bias making them insensitive to important life processes and not capturing processes involved
in the realization of important life projects and the engagement in long term goals and challenging activities
(HUPPERT, et al., 2014).
To sum up, quality of urban life and other regional or geographical analysis of subjective wellbeing have
treated conceptual framework as “case closed” and empirical analysis of single variable life satisfaction
have dominated the field on the expense of thorough conceptual framework. The debate of different
measures and their objectivity will surely go on in broader field of happiness studies. However, it seems fair
to suggest that as the existing literature on geographical and regional analysis of subjective wellbeing has
been inspired and steered by economic geography (MORRISON, 2014), it has overlooked other traditions in
empirical happiness studies such as eudaimonic wellbeing.
Finally, it is important to stress that regional studies and geography should not be claiming on expertize
over the concept of wellbeing itself but to create added value to analysis by looking at its spatial behavior.
And thereby it is restricted to using the most up to date instrumentalizations and measures offered by
disciplines such as psychology and sociology. Otherwise, as regional studies are eager to learn from other
disciplines, there is a risk of falling into trap of parasitic behavior by using concepts and theories from other
disciplines that we do not fully understand (SOTARAUTA, 2013).
In order to avoid this, this study follows the conceptual framework and dynamic model of well-being as is
presented in the module description of 6th round European Social Survey from which the subjective
wellbeing data is derived from. In this model this model, functioning well (eudaimonic well-being) results
from a combination of enabling material conditions and psychological resources. Enabling conditions
include opportunities and obstacles, inequalities, social norms and culture, while psychological resources
include such characteristics as resilience, optimism and self-esteem. In turn, functioning well feeds back
into enabling conditions and determines one’s experience of and cognitive judgments about life (e.g.
overall happiness or life satisfaction), and experience of life in turn feeds back into psychological resources.
A full contextual framework of the components as well as the dynamic model of wellbeing is offered in ESS
module description (HUPPERT, et al., 2014).
Returning now to empirical analysis the, below Table 3 presents the descriptive overview of sociodemographic-, social- and personal wellbeing variables in urban (Helsinki-Uusimaa) and rural (Other
regions) context.
Table 3. Descriptives of dependent (life satisfaction) and independent (socio-economic measures and
personal and social wellbeing) variables
Helsinki-Uusimaa
Variable
Dependent variable
Sftlife
Socio- demographic variables
Age
Mean
Other regions
SD
8,01
1,46
47,08 17,68
Mean
SD
8,15
1,37
50,71 19,21
Male (dummy variable)
0,46
0,50
0,50
0,50
Partnered (dummy variable)
0,62
0,49
0,61
0,49
Tertiary education (dummy variable)
0,18
0,39
0,35
0,48
Unemployed (dummy variable)
0,04
0,20
0,07
0,26
Household's total net income
6,46
2,73
5,59
2,63
11
Social well-being variables
Thick relationships
0,064
0,65
-0,019
0,63
Thin relationships
-0,026
0,56
0,016
0,55
Active involvement
-0,026
0,99
0,009
1,00
Meaning and purpose
0,034
0,75
-0,011
0,83
Autonomy and control
-0,021
0,80
0,008
0,78
Engagement
0,033
0,76
-0,003
0,78
Vitality
0,055
0,96
-0,020
1,01
-0,001
0,60
-0,004
0,80
0,057
0,67
-0,018
0,71
Personal well-being variables
Resilience
Competence
N
542
1429
First, table confirms the observations made from aggregate level data in Table1; residents of HelsinkiUusimaa are slightly younger and better educated, have higher income and are less likely to be
unemployed but are still showing lower level of life satisfaction than rest of the country.
While these results are showing robust evidence on paradox of affluence based on life satisfaction account,
we now turn into look at the subcomponents of social and personal wellbeing drawing from eudaimonic
tradition. In the broader concept of social wellbeing, consisting from thick relationships, thin relationships
and active involvement, H-U scores higher average in thick relationships. Thick relationship is described as a
core part of social element in good functioning including received support and appreciation from people
close to you. Thin relationships consists from social exchange (feeling supported and appreciated by those
in one’s wider circle and helping or supporting them) and social trust (expecting fairness from and trusting
others.
However in thin relationship and in active involvement ´Other regions´ are performing slightly better. Thin
ties measuring the equality of “bridging ties” includes reciprocity in social exchange, social trust, sense of
local belonging and frequency of social contact. Active involvement’ which is a simple measure of formal
volunteering does not show much variance between regions.
The most significant differences between the areas lies in several subconcepts of personal well-being.
Helsinki-Uusimaa performs better in all of the subconcepts of personal wellbeing except in autonomy and
control which measures perceptions of being free of others control and being able to shape ones daily life
and activities. The subconcepts of resilience (consisting from stress resistance and bouncing back from
difficult times and disturbing experiences), vitality (consisting from feeling full of energy in daily activities),
competence (consisting from sense of accomplishment and competence and a opportunities to
demonstrate it), engagement (consisting from experiences of learning and engagement during everyday
life) and meaning and purpose (consisting from orientation to future, sense of direction and significance in
overall life) are at considerably higher level in ‘Helsinki-Uusimaa’ than in ‘Other regions’
This descriptive analysis thereby suggests that residing in thriving urban region seems to be more linked to
several positive human functioning contributing to eudaimonic wellbeing rather than resulting directly as
higher life satisfaction. Interest of this study however was also so see whether these subconcepts are
effecting differently to overall life evaluations in different regional contexts. Thereby, testing the effect
socioeconomic and eudaimonic variables in in ‘ Helsinki-Uusimaa’ and ‘ other regions’ to life satisfaction is
the focus of following explanatory analysis.
12
Table 4. Regression coefficients of life satisfaction in two regional contexts
Helsinki- Uusimaa
Model 1
Other regions
Variable
B
Model 2
B
Model 3
B
Socio-demographic variables
Age
-,004
-,001
-,003
Male (dummy variable)
-,210 *
-,034
-,122
Model 1
B
,001
-,287 ***
Model 2
B
,002
-,142 **
Model 3
B
,001
-,207 ***
Partnered (dummy variable)
,138
,135
Tertiary education (dummy
variable)
-,138
-,187
-,173
-,127
-,164 **
-,185 **
Unemployed (dummy variable)
-,405
-,594 **
-,483 *
-,567 ***
-,443 ***
-,514 ***
,097 ***
,066 ***
,044 ***
,689 ***
,433 ***
,312 ***
,039
,022
Household's total net income, all
sources
,136 ***
,266 **
,082 ***
,039
,674 ***
,535 ***
,254 ***
,091
,010
,151 **
Social well-being variables
Thick relationships
Thin relationships
Active involvement
-,074
,274 ***
-,061
,223 ***
Personal well-being variables
Meaning and purpose
,373 ***
,262 ***
Autonomy and control
,228 ***
,263 ***
Engagement
,076
,126 ***
Vitality
,198 ***
,079 ***
Resilience
,267 ***
Competence
,092
Constant
Adjusted R square
N
,239 ***
-,041
7,373
7,503
7,840
8,520
7,503
7,919
0,071
0,223
,367
0,121
0,232
,333
581
581
581
1616
1616
1616
* Statistically significant at 0.10 level; **Statistically significant at 0.05 level; ***Statistically significant at 0.01 level
Note: Model 1; Only socio-demographic variables included to the model, Model 2; Socio-demographic and social
well-being variables included, Model 3; Socio-demographic, social- and personal well-being variables included .
First, age appears to have only marginal effect to life satisfaction in both contexts. This is consistent with
previous research (REALO and DOBEWALL, 2011). Being a male is however strong predictor for low life
satisfaction in ´other regions´ and this result hold even after including social and wellbeing variables in
models 2 and 3. Furthermore, being partnered (living with a spouse) has substantially higher effect to life
satiasfaction in Helsinki-Uusimaa than in ’Other regions’ after including social and personal wellbeing
variables. Being unemployed has stronger negative and statistically more significant effect in the context of
‘Other regions’.
However, the main interest of this analysis is in two variables associated closely to economic growth,
education and income and their performance in different regional contexts. Based on standard economic
theory level of education (often defined as human capital) is seen as proxy measure predicting higher
income and accordingly higher subjective well-being.
First of all, significant feature is that ‘tertiary education’ has negative effect to life satisfaction in both
contexts. However, it is statistically significant only in ‘other regions’. Other indicator with economic
13
relevance, households total net income’ behaves more as expected as it has small but positive effect to life
satisfaction. However, after adding social and personal well-being variables decreases it´s effect in both
contexts and it is thus not significant predictor in model 2 in ‘Helsinki-Uusimaa’ context.
These results, together with overall framework of this study, suggests us to take closer look at both of these
variables. The relationship between education, income and life satisfaction has naturally been controversial
and highly debated issue in the literature of happiness economics (STEVENSON and WOLFERS, 2008,FRANK,
2009). However, as noted earlier these discussions and empirical analyses have been operating mostly on
national and international level. The negative effect of tertiary education found in this data is interesting
feature geographically especially against recent study arguing that human capital (i.e. education level) is
significant predictor in “happiness of cities” but not in other regional contexts (FLORIDA, MELLANDER and
RENTFROW, 2013). This thesis is highly relevant to framework of this study and the results presented this
far are in sharp contrast to “happiness of the cities” thesis.
Thereby, the bivariate relationship of levels of human capital and households income against the life
satisfaction is presented in Figures 2 and 3.
Figure 2. Measured and predicted values life satisfaction ‘stflife’ in ‘other regions’ against
‘households total net income’.
Figure 3.Measured and predicted values of ‘stflife’ in Helsinki-Uusimaa against households total
net income.
14
Note. Predicted values from socio-demographic variables of ‘age’, ‘male’, ‘partnered’, ‘unemployed’ and ‘highest level of
education’.
Figure 3 shows that relationship between income and life-satisfaction appears more linear in other regions.
Also the predicted and reported values are closer in in the context of ‘Other regions’ indicating better fit of
the model. Overall, the model seems to predict ‘sflife’ better in the context of ‘Other regions’. Interesting
remarks is that there is a significant drop at the levels of 2nd and between 8th and 9th deciles in ‘HelsinkiUusimaa’. Threby, these figures are supporting the results of previous studies indicating that income is
better predictor of ‘stflife’ in areas with lower economic performance (PITTAU, ZELLI and GELMAN,
2010,ASLAM and CORRADO, 2012).
Next, figure 4 takes a closer look at relationship between different levels of education and ‘stflife’ in two
different contexts ‘Helsinki-Uusimaa’ and ‘Other regions’ after controlling other socio-demographic
variables.
Figure 4. Measured and predicted values of ‘stflife’ in ‘Other regions’ against highest level of education
15
Figure 5. Measured and predicted values of ‘stflife’ in ‘Helsinki-Uusimaa’ against highest level of education
16
Note. Predicted values from socio-demographic variables ‘age’, ‘male’, ‘partnered’, ‘unemployed’ and households total
net income’.
First of all, figure 4 does not suggest strong positive relationship between education and life satisfaction in
either contexts. Instead, in the context of ‘Helsinki-Uusimaa’ human capital seems to be far weaker
predictor for life satisfaction whereas in ‘Other region’ measured values seem to follow closely to predicted
values. Furthermore, in ‘Other regions’ measured values are slightly higher than predicted, whereas in
‘Helsinki-Uusimaa’ relationship is reversed which is a similar pattern to income levels in Figure 3. These
results underline the observation that ‘Helsinki-Uusimaa’ seems to hold some latent characteristic
associated with low life satisfaction.
Even though these results strongly suggest rejecting the ‘happiness of the cities’ argument (FLORIDA,
MELLANDER and RENTFROW, 2013) 2it leaves us with some relevant and important questions concerning
spatial interaction of education and subjective wellbeing. Even though education performs as poor
predictor of life satisfaction in this data it is reason to suspect that social and personal wellbeing have
variance in different educational groups. Negative effect of dummy variable ‘tertiary education’ to life
satisfaction observed from Table 4 as well as significantly lower levels of life satisfaction reported by
respondents with ‘doctoral’ or ‘licentiate degree’ give support to studies suggesting that increasing
aspirations are likely to mediate the effect between education and life satisfaction (CLARK and OSWALD,
1994,FREY and STUTZER, 2002). However, even if education does not have direct positive effect at
increasing life satisfaction is likely to have indirect association to subjective wellbeing though social
relations and capital as suggested by HELLIWELL (2003).
Thereby, is likely that the variable of education is significant part of the analysis when it comes to other
measures of social and personal wellbeing and their regional variance between different educational
groups. Thereby, we might ask if the residents of ‘Helsinki-Uusimaa’ with tertiary education have gains
17
from their location through some other measures of subjective wellbeing which would explain residing in
region associated with lower life satisfaction? And subsequently, drawing from global city thesis originally
posed by (SASSEN, 1991), suggesting the polarization of labor markets in economically driving urban
regions, are people without tertiary education experiencing the same benefits in their social and personal
well-being? In other words, what is the role of spatial inequalities in the geography of subjective wellbeing
based on eudaimonic account?
In order to answer these questions, the sample is further divided into tertiary and non-tertiary educated
segments living in ´Helsinki-Uusimaa´ and ´Other regions´. The regression model used in Table 4 is then
repeated for the resulting four subgroups. Descriptive statistics from this analysis are first presented in
Table 5.
Table 5. Descriptives of dependent (life satisfaction) and independent (socio-economic measures and
personal and social wellbeing) variables in two regional contexts for population with tertiary and nontertiary education.
Non tertiary education
Variable
Dependent variable
Sftlife
Helsinki - Uusimaa
Tertiary education
Other
Regions
Helsinki-Uusimaa
Other
regions
7,96
8,14
8,09
8,18
Age
48,01
51,96
45,16
45,19
Male (dummy variable)
0,48
0,51
0,43
0,47
Partnered (dummy variable)
0,55
0,59
0,75
0,72
Unemployed (dummy variable)
Household's total net income
0,04
0,08
0,03
0,07
5,91
5,28
7,49
6,87
Thick relationships
,024
-,035
,139
,054
Thin relationships
-,088
,003
,090
,074
Active involvement
-,146
-,057
,207
,300
Meaning and purpose
-,023
-,031
,146
,075
Autonomy and control
-,001
,012
-,055
-,009
Engagement
-,022
-,043
,128
,167
Vitality
,090
-,039
-,005
,064
Resilience
-,040
-,027
,079
,136
Competence
-,063
-,074
,283
,228
379
1318
200
298
Socio- demographic variables
Social wellbeing variables
Personal wellbeing variables
N
First, the table confirms once more the pattern where residents in ‘Helsinki-Uusimaa’ report lower level of
life satisfaction than residents in other regions. In the variables of ‘age’ and ‘male’ there is no significant
difference between regional groups, but the respondents with tertiary education are much more likely to
be partnered and even more so if residing in ‘Helsinki-Uusimaa’. In ‘unemployment’ variable both groups
benefit from residing in ‘Helsinki-Uusimaa’ region. And finally, both group benefit economically from
residing in ‘Helsinki-Uusimaa’ region through higher average in ‘Household's total net income’.
18
However, once again the most significant differences in this comparison can be found from social and
personal well-being measures. The group with ‘tertiary education’ gains significantly in ‘Thick relationships’
if residing in ‘Helsinki-Uusimaa’ region whereas the group with ‘non-tertiary education’ has also positive
but much smaller effect. This difference is most likely related to higher rate of ‘partnered’ respondents in
the group of ‘tertiary education’. On ‘Thin relationships’ the group with ‘tertiary education’ gains from
residing in ‘Helsinki-Uusimaa’ while on ‘non-tertiary educated’ the situation is reversed rather dramatically
. In other words, ‘non-tertiary educated’ who reside in Helsinki-Uusimaa are reporting much lower
reciprocity in social exchange (feeling supported and appreciated by those in one’s wider circle and helping
or supporting them) and social trust (expecting fairness from and trusting others) than the corresponding
group residing in ‘other regions’. And finally, both groups show lower levels of ‘active involvement’ if
residing in ‘Helsinki-Uusimaa’.
When it comes to the subcomponents of personal well-being, the same pattern of ‘tertiary educated’
gaining more from residing in ‘Helsinki-Uusimaa’ is observed in ‘Meaning and purpose’ but the relationship
is reversed in ‘Autonomy and control’. The variable of ‘Engagement’ does not hold much difference but the
group with ‘non-tertiary education’ shows significantly higher levels of ‘Vitality’ if residing in ‘HelsinkiUusimaa’ whereas in ‘tertiary education’ group the situation is reversed. If residing in Helsinki-Uusimaa’
both groups show higher levels of average ‘Resilience’ measuring stress resistance and ability to bounce
back from difficult times and disturbing experiences .Finally, as the group with ‘tertiary education’ have in
both regions significantly higher average on variable ‘Competence’, consisting from opportunities to
demonstrate and sense competence, they also benefit significantly more from residing in ‘HelsinkiUusimaa’ region.The results of corresponding regression models are presented in the Table 6 below.
Table 6. Regression coefficients predicting life satisfaction in two regional contexts for respondents with
tertiary education and non-tertiary education
Non tertiary
Helsinki-Uusimaa
Other regions
Model 1
Model 2
Socio-demographic variables
Age
-,003
-,001
-,005
Male (dummy)
-,158
,036
-,100
Partnered (dummy)
Unemployed (dummy)
Household's total net income
Social wellbeing
Thick relationships
,165
-,194
,151 ***
,119
-,386
,082 **
,657 ***
,637 ***
-,100
Thin relationships
Active involvement
Personal wellbeing
Meaning and purpose
Autonomy and control
Engagement
Vitality
Resilience
Competence
Constant
N
Adjusted R square
Model 3
,002
-,318 ***
,212
,014
-,260
,055 *
-,495 ***
,103 ***
,185
,339 **
-,123 *
,431
,238
,175
,233
,264
,012
7,213
Model 1
Model 2
Model 3
,002
,001
-,180 **
-,013
-,239 ***
,134 *
-,357 ***
,071 ***
-,422 ***
,048 ***
,602 ***
,230 ***
,512 ***
,030
,317 ***
,009
***
***
*
***
***
,225
,263
,123
,076
,249
-,012
7,816
7,711
379
7,493
379
7,912
1318
7,795
1318
379
0,07
0,24
0,38
0,05
0,22
0,32
***
***
**
**
***
1318
19
Tertiary
Helsinki-Uusimaa
Other regions
Model 1
Model 2
Socio-demographic variables
Age
-,007
-,001
,001
-,001
-,001
,000
Male (dummy)
-,263
-,131
-,125
-,139
-,012
-,108
,023
,149
,326
Partnered (dummy)
Unemployed (dummy)
Household's total net income
-,919
,115 **
-,935 *
Model 3
-,911 *
Model 1
,324 *
-,888 ***
,062 *
Model 2
,159
-,885 ***
,081 *
,014
,035
Thick relationships
,733 ***
,386 **
Thin relationships
,326 *
,159 **
1,085 ***
,104
,001 **
,097 *
Model 3
,323 **
-1,023 ***
,003
Social wellbeing
Active involvement
-,063
,645 ***
-,148
,087 *
Personal wellbeing
Meaning and purpose
,253 *
,353 ***
Autonomy and control
,162
,266 ***
Engagement
-,192
,177
Vitality
,152 *
,102
Resilience
,267 **
,216 **
Competence
,342 **
Constant
N
Adjusted R square
-,113
7,682
7,389
7,567
7,705
7,826
200
200
200
298
298
7,959
298
0,07
0,22
0,33
0,08
0,34
0,47
* Statistically significant at 0.10 level; **Statistically significant at 0.05 level; ***Statistically significant at 0.01
level
Note: Model 1; Only socio-demographic variables included to the model, Model 2; Socio-demographic and
social well-being variables included, Model 3; Socio-demographic, social- and personal well-being variables
included .
Again, the variable ‘age’ does not seem to hold much significance as predictor of life satisfaction. The ‘male’
variable however reveals interesting difference between regional contexts; being a non-tertiary educated
male living in ‘other region’ seems to be strongly associated to low life satisfaction. The positive effect of
being ‘partnered’ seems to strengthen after including personal and social wellbeing variables and being
especially important in the group of ‘tertiary educated’. The comparison on the effects of ‘unemployment’
reveals significant differences regarding both education groups and contexts. Overall, the negative effect of
unemployment is much stronger on the group of ‘tertiary educated’ as expected based on previous studies
(CLARK and OSWALD, 1994). However, for ‘tertiary educated’ it appears to be roughly similar in both
regional context, whereas for the group of ‘non-tertiary-educated’ effect is much less significant in HelsinkiUusimaa region. In other words, the negative effect of unemployment in much more place bind in the
group of ‘non-tertiary educated’. This can be understood against research on the role of local labour
markets (MORRISON, 2005,CONSOLI, VONA and SAARIVIRTA, 2013). Thereby, the negative effect of
unemployment of ‘non-tertiary educated’ is smaller in a region which more low-wage employment
20
opportunities at reasonable commuting cost available (Helsinki-Uusimaa) compared to context of ‘other
region’. Whereas, for ‘tertiary educated’ labour market is less restricted to local regional context.
And finally, as expected the positive effect ‘Household’s total net income’ fades when adding social and
personal wellbeing variables to the model. This effect is especially clear for the group of ‘tertiary educated’
and even more so if residing in ‘Helsinki-Uusimaa’. Overall, these results offer support for suggestions that
education (and income) turn into subjective wellbeing through indirect linkages such as social capital
(HELLIWELL, 2003), or in this case social and personal well-being. However, this effect is not identical for
different educational groups and regional contexts.
When looking at the effect of social wellbeing, the first notion is that different components of social wellbeing appear to be weighted differently between education groups in different region. Briefly, the ‘life
satisfaction of the group of ‘tertiary educated’ is strongly effect by ‘thick relationships’ whereas the life
satisfaction of ‘non-tertiary’ group is more affected by ‘thin relationships’.
The life satisfaction of ‘non-tertiary’ educated is derived rather equally from all of the components of
personal wellbeing except ‘competence’ in both regional contexts. The life satisfaction of ‘tertiary
edycated’ is mostly affected by ‘resilience’ and ‘competence’ and to some extent by ‘meaning and
purpose’. The most distinctive feature for this group in ‘other region’ is the fact that ‘competence’ results a
negative, although not significant coefficient.
Final part of this empiric analysis is asking if personal characteristic and value orientations play role in
difference between urban and rural regions. And furthermore could they be linked to interpretations made
from analysis of personal and social wellbeing measures.
Values orientations
Final part of the analysis offers a descriptives on the measures of universal value orientation included in the
ESS survey based on well-established Shwartz´s Human Value Scale. Results of this analysis are presented in
below in Table 7
Table 7. Value orientations in two regional contexts for respondents with tertiary education and nontertiary education
Non tertiary education
Variable
Power
Helsinki - Uusimaa
Tertiary education
Other
Regions
Helsinki-Uusimaa
Other
regions
2,83
2,76
3,05
2,99
Achievement
3,35
3,37
3,66
3,47
Hedonism
4,17
3,99
4,15
4,03
Stimulation
3,79
3,59
3,80
3,73
Self_direction
4,60
4,56
4,65
4,72
Universalism
4,98
4,92
5,08
4,99
Benevolence
5,05
5,05
5,03
5,01
Tradition
4,00
4,18
3,78
3,84
Conformity
3,92
4,16
3,86
3,97
Security
4,52
4,65
4,23
4,40
N
379
1318
200
298
21
This analysis confirms the difference of value orientations associated with regional contexts; ‘other regions’
score higher of values domain of ‘Conservation’ (tradition, security and conformity) whereas value
orientation of ‘Helsinki-Uusimaa is more related to domains of self-enhancement and ‘Openness to
change’. One particular detail, related to framework of this study, is that average of ‘achievement’ is higher
for ‘tertiary-educated’ is residing in Helsinki-Uusimaa but for ‘non-tertiary’educated’ this is not the case.
And finally, similar reversed effect of ‘self-direction’ is most likely to be connected to behavior of the
‘autonomy and control’ in variable in Table 5.
Summary and discussion
The contribution of this study to the existing literature is threefold; first, it places the paradox of affluence
originally suggested at country level into a regional context, secondly by adding more precise measures of
subjective wellbeing into the analysis it offers new routes for understanding the paradox, and finally results
raises up a question of spatial inequalities of eudaimonic wellbeing since the benefits of living in urban
agglomerations are nor experienced identically between educational groups.
However, when it comes to objective measures of wellbeing, both educational groups experience gains in
higher income and lower unemployment if residing in Helsinki-Uusimaa region. They also both show similar
negative effect from life satisfaction residing in dense ‘economically thriving region.They however respond
differently to dimensions of social and personal well-being. Population with tertiary education gains
significantly in ‘thick relationships’ if residing in urban region. These relationships are also strong predictor
for overall life satisfaction. Population without tertiary education experiences also slightly in form of ‘thick
relationships’ if residing in urban region but in ‘thin relationships’ situation is reversed. This results hence
suggests that it is mostly the non-tertiary educated which are experiencing the effects of social isolation
and sense of anomie of urban life (Simmel 1974; Wirth 1938). Whereas the population with tertiary
education have better psychological resources to counter nervous stimulation, psychological overload and
alienation associated with fast paced and more complex urban life.
On dimensions of personal well-being, tertiary educated again are, again, the biggest beneficiaries of urban
environment. They gain significantly from residing in nations biggest urban agglomeration through higher
sense of meaning and purpose in life and also on possibilities to show and experience competence. Non
tertiary educated fail to experience similar benefits in any of the areas of personal wellbeing except vitality
which measures subjective experience of energy in daily activities. Both educational groups however show
a drop in sense of autonomy and control if residing in urban growth center. This effect is significant among
tertiary educated. This can be interpreted as a trade-off between for a chance to show and experience
competence and hence reflecting the adaptation to the requirements of bureaucracy, efficiency and
institutional rationality of urban life as suggested by Weber (1922). This interpretation receives support
also from fact that ‘self-direction’ was the only value of the otherwise urban set of values which scored
lower mean in Helsinki-Uusimaa among tertiary educated. Finally, the sense of competence is the most
significant predictor for life satisfaction in urban areas for tertiary educated, while non-tertiary educated
draw life satisfaction from several components of personal well-being.
Overall, results of this study underline that focusing only on life satisfaction, while trying to understand the
spatial patterns and dynamics of subjective wellbeing, might leave some serious flaws and blind spots to
the analysis. Regional analysis of happiness, currently inspired and steered by economic though, could
thereby sharpen its vision through recognizing the progress and results made by neighboring disciplines on
conceptualizing and measuring subjective wellbeing.
22
From this remark merges also the possible contributions of this study to policies in regional development.
The literacy of ‘economic of happiness’ has been struggling with some awkward realities and the mismatch
between life-satisfaction and economic growth being the most central one. And especially, policy
implications based on this pattern. It is not likely to find a form of government of governance who would
not have their citizens being satisfied with their lives. However, during the present austerity era calls for
better economic performance and resilience are as timely as ever.
The framework and results of this study suggests than economic activity, either in regional or national level,
results from people functioning actively, not passive states of feeling satisfied. It is thereby plausible to
suggest that urban life is more related to eudaimonic wellbeing consisting from active functioning and
doing, rather than passive state of being and feeling satisfied. As stated in ESS module description used in
this study, life satisfaction measures are both predictive of and responsive to more passive activities and to
episodes characterized by routine and familiarity (HUPPERT, et al., 2014). These are mainly characteristics
of rural rather than urban life. As also seen from on value orientations presented in Table 7. Thereby, we
could claim that more rural environment has some contextual feature causing individual report higher life
satisfaction, all other thing being equal, than individuals in urban environment. As Campbell suggests
“metropolitan people are most inclined to believe that they have not had their full share of happiness if life’
(Campbell 1981, 150). In other words, unsatisfied people. Thereby, the measures of eudaimonic wellbeing
might be better instrument for policy making than life satisfaction which can be increased simply through
lowering the expectations of individuals.
To conclude, this study is naturally subject to several limitations and deficiencies. First, due to the nature of
cross sectional survey data available it was not possible to do any longitudinal analysis. Thereby, also any
interpretations made about selective migration cannot be empirically confirmed as there exists no available
data on subjective well-being of migrants at both origin and destination. Another object for criticism might
be in dichotomatization of the sample into urban and rural regions and threating rural as homogenous
entity which is naturally not the case. The main interest of the study however lied on the specific
characteristics of urban life and hence the comparison of biggest urban agglomeration against rest of the
county seems justified. And finally, as multilevel modelling has almost become a standard in regional
analysis using hierarchical data one could argue it should have used here as well. However, as the research
framework was controversial as such (using other measures of subjective wellbeing rather than
conservative life satisfaction) we wanted to first gain deeper understanding about ‘dynamic model of
wellbeing’ in different contexts before moving into more sophisticated spatial modelling.
All of these issues are something which need to be addressed in future research. Thereby, the results of this
study is hoped to work as inspiration for future research as they subject the negative relationship between
life satisfaction and regional growth to closer scrutiny.
The difference in mean life satisfaction between Helsinki-Uusimaa and other regions is statistically significant in
independent sample T-test.
Although, we see several details of deficiency in the paper (FLORIDA, MELLANDER and RENTFROW, 2013) the most
severe one is placing objective (for example body mass index BMI, housing value, and days absent from work) and
subjective (life evaluations and emotional health) indicators of wellbeing to both sides of equation and referring to
them loosely as measures of happiness, subjective wellbeing, quality of life. It also fails to offer any insight on
relationships between these sub-indices used in the data.
23
References:
AMBREY C. and FLEMING C. (2014) Public Greenspace and Life Satisfaction in Urban Australia, Urban
Studies 51, 1290-321.
ARISTOTELES,. (2011) Aristotle's Nicomachean Ethics, Chicago University Press, Chicago.
ASLAM A. and CORRADO L. (2012) The geography of wellbeing, Journal of Economic Geography 12, 627-49.
BALLAS D. (2012) Happy People or Happy Places? A Multilevel Modeling Approach to the Analysis of
Happiness and Well-Being, International Regional Science Review 35, 70-102.
BALLAS D. (2013) What makes a ‘happy city’?, Cities 32, Supplement 1, S39-50.
BERRY B. and OKULIZC-KOZARYN A. (2011) An urban-rural happiness gradient, Urban Geography 32, 87183.
BERRY B. J. L. and OKULICZ-KOZARYN A. (2009) Dissatisfaction with city life: A new look at some old
questions, Cities 26, 117-24.
BONINI A. (2008) Cross-national variation in individual life satisfaction: effect of national wealth, human
development, and environmental conditions, Social Indicators Research 87, 223-36.
CAMPBELL A. (1981) The sense of wellbeing in America, McGraw Hill, New York.
CASTELLS M. and HIMANEN P. (2002) The information society and the welfare state : the Finnish model,
Oxford University Press, Oxford.
CLARK A. E. and OSWALD A. J. (1994) Unhappiness and Unemployment, The Economic Journal 104, 648-59.
CONSOLI D., VONA F. and SAARIVIRTA T. (2013) Analysis of the Graduate Labour Market in Finland: Spatial
Agglomeration and Skill–Job Match, Regional Studies 47, 1634-52.
24
DIENER E. and SELIGMAN M. (2004) Beyond money: toward and economy of well-being, Psychological
Science in the Public Interest 5, 1-31.
DIENER E. and TOV W. (2012) National accounts of Wellbeing, in K. LAND, A. MICHALOS and J. SIRGY (Eds)
Handbook of Social Indicators and Quality of Life Research, pp. 137-157.
EASTERLIN E. (1974) Does economic growth improve the human lot: Some empiricial evidence., in P. DAVID
and M. REDER (Eds) Nations and Households in Economic Growth; Essays in honor of Moses Abramowitz,
pp. 89-125. Academic Press, New York.
EASTERLIN R. A., ANGELESCU L. and ZWEIG J. S. (2011) The Impact of Modern Economic Growth on Urban–
Rural Differences in Subjective Well-Being, World Development 39, 2187-98.
EUROPEAN COMMISSION. (2014) Overview of regional policy 2014.
FLORIDA R., MELLANDER C. and RENTFROW P. J. (2013) The Happiness of Cities, Regional Studies 47, 61327.
FRANK R. H. (2009) The Easterlin Paradox revisited, HAPPINESS, ECONOMICS AND POLITICS: TOWARDS A
MULTI-DISCIPLINARY APPROACH, 151-7.
FREY B. and STUTZER A. (2002) Happiness and economics : how the economy and institutions affect wellbeing, Princeton University Press, Princeton.
HELLIWELL J. (2008) Life satisfaction and quality of development.
HELLIWELL J., RICHARD LAYARD and SACS JEFFREY. (2012) World Happiness Raport.
HELLIWELL J. F. (2003) How's life? Combining individual and national variables to explain subjective wellbeing, Economic Modelling 20, 331-60.
25
HONKAPOHJA S. (2001) The economic crisis of the 1990s in Finland, 52-101.
HUPPERT F. A. (2013) Flourishing Across Europe: Application of a New Conceptual Framework for Defining
Well-Being, Soc Indic Res 110, 837.
HUPPERT F. A., MARKS N., SIEGRIST J., VAZQUEZ C. and VITTERSO J. (2014) ESS6th round rotating module;
Personal and social well-being 2014.
JAYAWICKREME E., FORGEARD M. and SELIGMAN M. (2012) The Engine of Well-Being, Review of General
Psychology 16, 327-42.
KEYES C., SHMOTKIN D. and RYFF C. (2002) Optimizing Well-Being: The Empirical Encounter of Two
Traditions, Journal of Personality and Social Psychology 82, 1007-22.
KNIGHT J. and GUNATILAKA R. (2010) Great Expectations? The Subjective Well-being of Rural–Urban
Migrants in China, World Development 38, 113-24.
KRISTJANSSON K. (2010) Positive Psychology, Happiness, and Virtue: The Troublesome Conceptual Issues,
Review of General Psychology 14, 296-310.
LAYARD R. (2006) Happiness: lessons from a new science, Penguin Books, London.
LOIKKANEN H. A. and LÖNNQVIST H. (2007) Metropolitan Housing Markets: a case of Helsinki, in Å
ANDERSSON, L. PETTERSON and U. STRÖMQVIST (Eds) European Metropolitan Housing Markets, pp. 63-81.
Springer, Berlin.
MACKERRON G. and MOURATO S. (2009) Life satisfaction and air quality in London, Ecological Economics
68, 1441-53.
MARANS R. and STIMSON R. (2011) Investigating quality of urban life: Theory, methods and empirical
research Springer, Netherlands.
26
MORRISON P. S. (2005) Unemployment and Urban Labour Markets, Urban Studies 42, 2261-88.
MORRISON P. S. (2011) Local Expressions of Subjective Well-being: The New Zealand Experience, Regional
Studies 45, 1039.
MORRISON P. S. (2014) The Measurement of Regional Growth and Well-Being, in M. FISCHER and P.
NIJKAMP (Eds) Handbook of Regional Science Springer.
NUSSBAUM M. C. and SEN A. (1993) The quality of life, Clarendon Press, Oxford.
OFFICE FOR NATIONAL STATISTICS. (2014) Measuring National Well-being programme.
OJALA J., ELORANTA J. and JALAVA J. (. (2006) The road to prosperity : an economic history of Finland
Gummerus, Jyväskylä.
PITTAU G., ZELLI R. and GELMAN A. (2010) Economic Disparities and Life Satisfaction in European Regions,
Social Indicators Research 96, 339-261.
POWDTHAVEE N. (2005) Unhappiness and Crime: Evidence from South Africa, Economica 72, 531-47.
REALO A. and DOBEWALL H. (2011) Does life satisfaction change with age? A comparison of Estonia,
Finland, Latvia, and Sweden, Journal of Research in Personality 45, 297-308.
RYAN R. M. and DECI E. (2001) On happiness and human potentials: a review of research on hedonic and
eudaimonic well-being, Annu Rev Psychol 52, 141.
RYFF C. and SINGER B. (2008) Know Thyself and Become What You Are: A Eudaimonic Approach to
Psychological Well-Being, Journal of Happiness Studies 9, 13-39.
SAAMAH A. and MICHAELSON J. (2009) National Accounts of Well-being.
SASSEN S. (1991) The global city : New York, London, Tokyo, Princeton University Press, Princeton, N.J.
27
SEN A. (2009) The idea of justice, Belknap Press of Harvard University Press, Cambridge (Mass.).
SIMMEL G. (1976) 'The metropolis and mental life', Free Press, New York.
SOTARAUTA M. (2013) Constant Flux Makes Regional Studies ‘Amoeba’ Strong: Evolution of Regional
Studies in Tampere, Finland, Regions Magazine 291, 3-4.
STEVENSON B. and WOLFERS J. (2008) Economic Growth and Subjective Well-Being: Reassessing the
Easterlin Paradox.
STIGLITZ J. E., SEN A. and FITOUSSI J. (cop. 2010) Mismeasuring our lives : why GDP doesn't add up, New
Press, New York.
STROBER M. H. (2011) Interdisciplinary conversations: challenging habits of thought, Stanford University
Press.
STUTZER A. (2008) Stress that Doesn't Pay: The Commuting Paradox, Scandinavian Journal of Economics
110, 339-66.
VAATTOVAARA M. and KORTTEINEN M. (2003) Beyond Polarisation versus Professionalisation? A Case
Study of the Development of the Helsinki Region, Finland, Urban Studies (Sage Publications, Ltd.) 40, 212745.
VEENHOVEN R. (2009) Measures of Gross National Happiness., Psychosocial Intervention / Intervencion
Psicosocial 18, 279-99.
VEENHOVEN R. and EHRHARDT J. (1995) The cross-national pattern of happiness; Test of predictions
implied in three teories of happiness., Social Indicators Research 34, 33-68.
WEIJER D., JARDEN A. and POWDTHATEE N. (2011) Promoting research on wellbeing, International Journal
of Wellbeing 1, 1-3.
28
WILKINSON W. (2007) In Pursuit of Happiness Research: Is It Reliable? What Does It Imply for Policy? 2014.
WIRTH L. (1938) Urbanism as a way of life, The American journal of sociology 44, 1-24.
1
The difference in mean life satisfaction between Helsinki-Uusimaa and other regions is statistically significant in
independent sample T-test.
2
Although, we see several areas of deficiency in the paper (FLORIDA, MELLANDER and RENTFROW, 2013) the most
severe one is placing objective (for example body mass index BMI, housing value, and days absent from work) and
subjective (life evaluations and emotional health) indicators of wellbeing to both sides of equation and referring to
them as measures of happiness, subjective wellbeing, quality of life or overall wellbeing. It also fails to offer any
insight on relationships between these sub-indices used in the data.
29