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Happiness, Geography and the Environment


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In recent years, economists have been using socio-economic and socio-demographic characteristics to explain self-reported individual happiness or satisfaction with life. Using Geographical Information Systems (GIS), we employ data disaggregated at the individual and local level to show that while these variables are important, consideration of amenities such as climate, environmental and urban conditions is critical when analyzing subjective well-being. Location-specific factors are shown to have a direct impact on life satisfaction. Most importantly, however, the explanatory power of our happiness function substantially increases when the spatial variables are included, highlighting the importance of the role of the spatial dimension in determining well-being.
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Happiness, geography and the environment
Finbarr Brereton, J. Peter Clinch, Susana Ferreira
University College Dublin, Richview, Clonskeagh, Dublin 14, Ireland
Article history:
Received 2 March 2007
Received in revised form 6 June 2007
Accepted 6 July 2007
In recent years, economists have been using socio-economic and socio-demographic
characteristics to explain self-reported individual happiness or satisfaction with life. Using
Geographical Information Systems (GIS), we employ data disaggregated at the individual
and local level to show that while these variables are important, consideration of amenities
such as climate, environmental and urban conditions is critical when analyzing subjective
well-being. Location-specific factors are shown to have a direct impact on life satisfaction.
Most importantly, however, the explanatory power of our happiness function substantially
increases when the spatial variables are included, highlighting the importance of the role of
the spatial dimension in determining well-being.
© 2007 Elsevier B.V. All rights reserved.
Subjective well-being
Spatial amenities
Geographical Information Systems
1. Introduction
The economics of happiness literature developed in the early
nineteen seventies with the pioneering work of such research-
ers as Richard Easterlin. Easterlin and subsequent authors,
such as Daniel Kahneman, believe that individual utility, tra-
ditionally thought by economists to be immeasurable and
hence proxied by income, can be measured directly. One
method is to employ happiness data from surveys as empirical
approximations of individual utility. The specific question
asked varies throughout the literature in terms of subject
matter (questions on happiness and life satisfaction are fre-
quently employed) and range of scale (three-point to ten-point
scales have been employed in the literature). These questions
elicit happiness or life satisfaction from individuals and mea-
sures such as these have been found to have a high scientific
standard in terms of internal consistency, reliability and vali-
dity (Diener et al., 1999)
and have been used extensively in the
economics literature in recent decades (see, e.g., Easterlin,
1974; 1995; 2001,orFrey and Stutzer, 2000; 2002a,b; 2004).
This literature has examined the role of socio-economic
and socio-demographic variables on individual well-being.
Established findings within the field include that character-
istics of the individuals themselves, their socio-demographic
characteristics, such as their age, gender and marital status,
influence their happiness. Similarly for micro-economic
characteristics, such as income, household tenure and em-
ployment status, with unemployment having a profound
negative influence on well-being. At the macro-economic
level, contributions have focused on the impact of national
Financial support of the Environmental Protection Agency
ERTDI program is gratefully acknowledged.
Corresponding author. Tel.: +353 1 716 2751; fax: +353 1 716 2788.
E-mail addresses: (F. Brereton), ( J.P. Clinch),
(S. Ferreira).
Firstly, measures of life satisfaction show temporal reliability,
even over a period of several years; secondly, they covary with
ratings made by family and friends, with interviewer ratings and
with amount of smiling in an interview; and finally, when self-
reports of well-being are correlated with other methods of
measurement, they show adequate convergent validity (Diener
and Suh, 1999).
0921-8009/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
available at
ARTICLE IN PRESSECOLEC-02889; No of Pages 11
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
inflation (Di Tella et al., 2001) and unemployment (Clark and
Oswald, 1994) rates and also the type of governance present in
the person's area (Frey and Stutzer, 2000). Happiness is found
to be inversely related to the inflation and unemployment
rates, but to increase with the level of direct democracy.
Prior literature in the economics field has demonstrated
that the area or location where an individual lives affects
quality of life. This is especially evident in the hedonic pricing
literature where there is a long tradition of constructing qua-
lity of life indices as the weighted averages of amenities in a
particular area, usually a city or region (see Rosen, 1974;
Roback, 1982 or Blomquist et al., 1988, for seminal contribu-
tions, and Chay and Greenstone, 2005, for a recent state-of-
the-art valuation exercise).
However, it wasn't until the 1990s that researchers began to
examine this spatial aspect of well-being in the economic
psychology literature. These more recent papers found that
characteristics of people's immediate surroundings (their
locality) influenced their well-being, but also that the wider
environment had an important role to play in explaining what
makes us happy. Environmental variables such as aircraft
noise (van Praag and Baarsma, 2005), air pollution (Welsch,
2002; 2006) and the prevailing climate (Frijters and van Praag,
1998 and Rehdanz and Maddison, 2005) are found to influence
welfare, as are environmental attitudes (Ferrer-i-Carbonell and
Gowdy, 2007). Findings indicate that excess noise levels ad-
versely affect well-being, as does air pollution and the influ-
ence of climate depends on the variable in question, indicating
the potential importance of spatial factors in determining well-
In terms of examining the geography of well-being, pre-
vious studies were hindered by a lack of adequately disaggre-
grated data (Welsch, 2006; Rehdanz and Maddison, 2005). By
the authors own admission, data constraints at the local and
regional levels restricted their analysis to aggregated data at
the national level, or to focusing on a particular localised area
where richer data was available. Hence, thus far, the current
literature has stopped short of carrying out a holistic study of
the spatial element of well-being, due in no small part to these
data constrains, but also to the lack of availability of appro-
priate tools to carry out such analysis. For example, Rehdanz
and Maddison (2005) examine the influence on well-being of
climatic conditions, but including too many of their climate
variables in the model at once leads to problems of multi-
collinearity as some of their climate variables did not vary at
the national level (i.e., one record per country). They state that
their analysis was restricted to the country level and that it
would be interesting to see how climate would affect people's
happiness in different regions of a country. Ferrer-i-Carbonell
and Gowdy (2007) include a set of dummy variables indicating
the region where the individual lives to capture the (natural)
environment, proxying, for example, London and Manchester
as polluted areas. However, in the case of major cities in de-
veloped countries, pollution is, generally, a localised phenom-
enon and categorising an entire cities population under one
pollution level may severely under or overestimate their expo-
sure. Welsch (2006) uses life satisfaction scores to value air
pollution in European countries, but includes no within coun-
try variation in his estimation. Due to a lack of data, Welsch's
study was concerned with countries as the cross-sectional
units and he states that future research may address the
question how regional or local happiness profiles are affected
by the corresponding environmental conditions. It is conceiv-
able that at a more disaggregated level the linkage between
environment and happiness is even more articulate than it is
with respect to national data.van Praag and Baarsma (2005)
examine a localised problem and use postcodes to link their
respondents to objective noise burden, but due to issues of
anonymity, this application may only be available at city level
where populations are aggregated.
In this paper, we explicitly endeavour to examine the
importance of space in the determination of well-being, using
a more holistic approach. Firstly, we measure amenities at the
level of disaggregation at which individuals actually experi-
ence their surroundings, i.e. local level. This is facilitated
through the use of Geographical Information Systems (GIS), a
system for the visual display of spatial data. Using GIS, 1) the
level of disaggregation at which individuals are linked to their
surroundings is greatly improved; 2) the vector of spatial
variables included in the happiness function is expanded to
include variables with a potential influence on well-being, but
which have not been examined to date; and 3) distance mea-
sures are introduced, as one could hypothesize that the inten-
sity at which individuals experience their surroundings is a
function of proximity (as in the case of air pollution and noise).
The findings in the paper highlight the critical importance of
the role of the spatial dimension in determining well-being,
i.e., spatial variables are found to be highly significant with
large coefficients. We also find that the impact of spatial
amenities on life satisfaction is a function of distance, with the
most notable example being that of proximity to coast. This
has a large positive effect, which diminishes as one moves
further from the coast. The results may have potentially im-
portant implications for the setting of public policy, e.g. deci-
sions affecting the location of amenities negatively impacting
on well-being, such as waste facilities. Most importantly, the
explanatory power of our happiness function significantly
increases when the spatial variables are included, resulting in
three-times the variation in well-being being explained than
has been achieved in any previous cross-sectional study. This
indicates that geography and the environment have a much
larger influence on well-being than previously thought.
The paper proceeds as follows. Section 2 describes the
methodology (data, GIS requirements and the estimation stra-
tegy) used in the paper, Section 3 presents the results and
Section 4 concludes.
Roback (1982) found that the average person in her sample
would be willing to pay $69.55 per year for an additional clear day,
$78.25 per year to avoid an additional cloudy day, and $5.55 per
year to avoid an increase of 1 μg per cubic meter in particulate
matter. Blomquist et al. (1988) found that the difference in
compensation between the most and least desirable U.S. counties
in terms of the same bundle of local amenities comprising
climate, urban conditions and environmental quality was $5,146.
More recently, Berger et al. (2003) have shown that one standard
deviation changes in climate attributes (heating degree days), air
quality and crime produce annual compensation in the Russian
housing and labor markets of 7,839, 8,050 and 8,602 rubles
respectively, compared to a mean monthly salary of 1928 rubles.
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
2. Methodology
In this paper, we assume that the level of well-being attained
by an individual iin location kcan be represented by the
following indirect utility function:
ui;k¼aþb0xi;kþ0ai;kþ"i;ki¼1::::I;k¼1; ::::; Kð1Þ
where udenotes utility of individual iin location k,ais a vector
of spatial factors, some of which (e.g., commuting time, pro-
ximity toa coast) may vary at anindividual leveland
is a vector
of socio-economic anddemographic characteristics (age, gender
etc.) that are typically included in the literature (see, e.g., Clark
and Oswald, 1994; Di Tella et al., 2001 or Stutzer, 2004). In the
micro-econometric function, the individual's true utility is
unobservable, hence we use self-reported well-being as a proxy.
The well-being indicator (or proxy for individual utility) used
in this paper is based on the answers to the following question
(which was preceded by a range of questions regarding various
aspects of the respondent's life): Thinking about the good and
bad things in your life, which of these answers best describes
your life as a whole?. Respondents could choose a category on a
scale of one to seven(As bad as c an be;very bad;bad;alright;
good;very good;as good as can be).
The use of self-reported
well-being introduces measurement error as the respondents
may be unable to communicate accurately their underlying
utility level. However, as Blanchflower and Oswald (2004a) point
out, it is measurement error in the independent variables that
would be more problematic in the econometric estimation, and
there is a broad consensus among previous studies that self-
reported well-being is a satisfactory empirical proxy of individ-
ual utility (see, e.g., Stutzer, 2004; Blanchflower and Oswald,
2004b; Ferrer-i-Carbonell and Frijters, 2004). Additionally, the
use of a latent variable framework (i.e. Ordered Probit) controls
for measurement error in the dependent variable.
Data on well-being and on the socio-demographic and
socio-economic characteristics used in the analysis come
from a survey
of a representative sample of 1,500
men and
women, aged 18 and over and living in Ireland. The survey
found a high well-being, in general, in Ireland with an average
of 5.5 on the seven-point scale. What makes this data set
particularly well suited for this paper is that it can be merged
with detailed geographical information as we know the area in
which the respondent lives. This information allows us to
match the survey data spatially to a national map of Ireland
using GIS and hence it is possible to combine subjective data at
the individual level with a vector of spatial amenities (a).
These two datasets are combined at the local (electoral
level. However, to assess properly the impact on
individual well-being from changes in spatial amenities,
ideally, one would want to be able to match climate and
environmental factors to a particular individual rather than a
particular area. At present, however, the data do not allow this
and anonymity may preclude this in any case. Descriptions of
the variables and descriptive statistics are outlined in
Appendix A.
The use of data collected in Ireland is interesting in its own
right. In the last decade, the Celtic Tigereconomy grew at a
record rate for a developed country (this and other trends are
documented in, for example, Clinch et al., 2002). Meanwhile,
the Economist Intelligence Unit (2004) has ranked Ireland as
first in its quality of life league table for 2005. Nevertheless,
there has been much concern regarding the implications of the
pace of economic growth for localized environmental quality
and life satisfaction generally (EPA, 2004). This makes Ireland
an appropriate subject for the analysis of the influence of
spatial amenities on subjective well-being. Furthermore,
issues surrounding heterogeneity of preferences may not be
as problematic in a small (approximately 70,000 km
) and
relatively homogenous country like Ireland, compared to other
nations. Also, by examining one country, issues of translation
and cultural bias in the well-being question should not arise.
As elements of the vector of spatial factors, we include
those variables which previous literature has shown to be
important determinants of well-being. Roback (1982) includes
the crime rate, population density and climatic conditions to
construct Quality of Life indices for US cities using the hedonic
pricing method. Blomquist et al. (1988) include similar
variables in their analysis, also of US cities, but in addition
they include the presence of waste facilities and proximity to
coast. More recently, research in the economics of happiness
has shown proximity to transport routes to affect well-being
(van Praag and Baarsma, 2005).
The dataset contains climate (from Collins and Cummins,
1996), environmental (from EPA, 2005) and other spatial data
(Urbis Database, 2006). Several climate variables were consid-
ered but following the advice of a climatologist, mean annual
precipitation, January mean daily minimum air temperature,
July mean daily maximum air temperature, mean annual
duration of bright sunshine and mean annual wind speed
Some studies treat self-reported life satisfaction data and
happiness data interchangeably. Veenhoven (1997) states that
the word life-satisfaction denotes the same meaning and is often
used interchangeably with happiness.Di Tella et al. (2001) report
a correlation coefficient of 0.56. However, Peiro (2006) points to
happiness and satisfaction as two distinct spheres of well-being.
He concludes that the first would be relatively independent of
economic factors while the second would be strongly dependent.
Urban Institute Ireland National Survey on Quality of Life (2001).
Due to missing observations the final sample consists of ap-
proximately (depending on the model specification) 1,467 observa-
tions. The effective response rate is 66.6%. The margin of error
using the entire sample is ± 2.5% at a 95% confidence level. The
2000 Register of Electors was used as the sampling frame.
GIS works well when applied to static data, and less well when
applied to time series analysis (Goodchild and Haining, 2004) and
hence is well-suited to the cross-sectional data employed in this
There are around 3,440 electoral divisions in Ireland which
represent the smallest enumeration area used by the Irish Central
Statistics Office in the collection of Census data. These areas are
relatively small, particularly in the city regions and those
represented in our sample range in size from 18ha (in cities) to
6,189ha (open countryside) (mean = 1,767, standard deviation =
1,538), with total populations ranging from 47 individuals to 8,595
(mean = 2,040, standard deviation = 2,073).
However, the extent to which these biases are problematic is a
matter of debate (Diener and Suh, 1999).
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
were chosen (similar to those included in Frijters and van
Praag, 1998).
As in Blomquist et al. (1988), variables capturing whether the
respondent lives near the coast, theviolent crime rate and pres-
ence of waste facilities in the respondent's area were included.
There is evidence suggesting that noise, smell and other
negative externalities from waste facilities may impact nega-
tively on well-being or quality of life (DG Environment, 2000). Air
pollution and water quality were considered as indicators of
environmental quality but regional variation is minimal (EPA,
2004). Additionally, population density (total population divided
by total area in km
(CSO, 2003)), traffic congestion and average
commuting timein each area were included tocapture crowding
and congestion effects. Also, a variable capturing voter turnout
in the Irish general election in 2002 (Kavanagh et al., 2004)is
included as an indicator for social capital (as in Putnam, 2000).
Due to data constraints, traffic congestion (number of vehicles
(DELG, 2002a,b) divided by the total length of primary roads per
local authority
area (NRA, 2003)) and thehomicide rate (number
of homicides per 100,000 of population (Garda Siochana, 2002))
are measured at the local authority level.
As in van Praag and Baarsma (2005), we include proximity to
However, we also include more detailed transport
data consisting of proximity to: major roads (national primary
and national secondary) (NRA, 2003); international, national
and regional airports; railway stations, and seaports (Urbis
Database, 2006). Access to transport routes could potentially
enter the micro-econometric function in two ways, positively
through accessibility and negatively through pollution and
noise. The latter was shown to be the case by van Praag and
Baarsma (2005) in relation to airport noise in Amsterdam.
As for the socio-economic and demographic variables, the
dataset includes an employment-status variable divided into
ten separate categories which follow the International Labour
Organisation (ILO) classification: employed (self-employed,
full-time employed and part-time employed), inactive (stu-
dent, working on home duties, disabled, retired, those not
working and not seeking work, and those on a government
training scheme) or unemployed (CSO, 2006). Unemployment
is further divided into two categories of those unemployed
having lost or given up their job combined with those not
working but seeking work, and those seeking work for the first
time. Additional individual characteristics contained in the
dataset and typically employed in the literature are age,
gender, educational attainment (primary, lower secondary/
junior high school, upper secondary/senior high school and
university degree), marital status (single, married, cohabiting,
widowed and separated/divorced), log of gross household
whether the respondent is caring for a disabled
member of the family and the number of dependent children
in the household (1, 2, 3+). As an indicator of individual health
we use the number of times the respondent has visited the
doctor in the past year (never or once, two to five times and
six or more times a year). We also include household tenure
(owned outright, mortgaged, renting, or in public housing).
2.1. Geographical Information Systems methodology
GIS is a powerful computing tool that allows the visual repre-
sentation of spatially referenced data. It has advanced the tech-
nical ability to handle such data as countable numbers of points,
lines and polygons
in two-dimensional space (Goodchild and
Haining, 2004) and link various datasets using spatial identifiers
(Bond and Devine, 1991). It represents a solid base for spatial data
analysis and provides a range of techniques for analysis and
visualisation of spatial data. It provides effective decision support
through its database management capabilities, graphical user
interfaces and cartographic visualisation (Wu et al., 2001).
2.1.1. GIS in the economics literature
Research using GIS in the economics field has tended to be in
the area of environmental valuation through hedonic pricing
and a new generation of hedonic studies is using GIS to create
larger databases and define new explanatory variables in
combination with spatial econometric methods (see Bateman
et al., 2002; Lake et al., 1998). These hedonic models use a GIS
programme to develop neighbourhood characteristics that are
unique to each of their included observations (i.e. house or
property). GIS has enhanced the ability of these hedonic
models to explain variation in sale prices by considering both
proximity to, and extent of, environmental attributes (Paterson
and Boyle, 2002).
Baranzini and Ramirez (2005) use GIS to value the impact of
noise in Geneva, while Lynch and Rasmussen (2001) use GIS to
estimate the impact of crime on house prices in Jacksonville,
Florida, USA. Paterson and Boyle (2002) use GIS data to develop
variables representing the physical extent and visibility of
surrounding land use in a hedonic model of a rural/suburban
residential housing market. Bastian et al. (2002) use GIS data to
measure recreational and scenic amenities associated with
rural land, while Geoghegan et al. (1997) developed GIS data for
two landscape indices and incorporated them in a hedonic
model for Washington D.C, USA.
2.1.2. Creating variables using GIS
To capture accurately the influence of environmental and
location specific variables on individual well-being requires
variables to be measured at a high level of disaggregation i.e. at
For governance purposes, Ireland is divided into 34 different
regions called Local Authority areas. These generally equate to
one body per county and one for the three major urban areas of
Galway City, Limerick City and Cork City. Dublin is divided into
four areas and Tipperary is divided into two local authority areas.
These areas are relatively large and range in size from 2,035ha to
746,797ha (mean = 229,060, standard deviation = 226,508), with
total populations ranging from 25,799 individuals to 495,781
(mean = 177,377, standard deviation = 135,990).
All the proximity criteria are based on guidelines in Irish
Government policy documents (see, DELG, 2002b).
Income is expressed in thousands of euro. Missing values,
23.7% of those interviewed, were imputed based on the respon-
dent's socio-demographic characteristics including age, gender,
marital status, education level, area inhabited and employment
status. The original income variable was divided in 10 categories,
so mid-points were used (as in Stutzer, 2004). The survey was
carried out when Ireland was still using the Irish Pound, so we
converted to euros using the fixed rate of IR£1 = 1.26974.
A polygon is the GIS term for any multi sided figure.
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
the level at which individuals experience their environment.
Therefore they must be captured in a manner that reflects
individuals' perceptions of the amenity or disamenity in ques-
tion. Many facets of an amenity, such as intensity, frequency,
duration, variability, time of occurrence during the day etc.
(Bateman et al., 2001) will affect how an individual perceives
the amenity. GIS allows variables to be related spatially and
hence individuals can be linked to the geographic character-
istics of their surroundings. Hence, GIS could, in principle,
provide a full quantitative description of overall area quality if
all relevant data layers, for example concerning road networks
and public services, were available and were transformed in a
convenient way into spatial attributes (Din et al., 2001).
However, when specific household or property GeoCodes
(X,Ycoordinates) are unknown, as in the case of the house-
hold survey data used in this paper, neighbourhood areas
must be used as the reference point when creating environ-
mental variables. The typical method of doing this is to use
the mathematically-created centre or centroidof the area in
(as was the case in Craglia et al., 2001,whostudy
high intensity crime areas in England) and in this paper
we use the centroid of the respondents' electoral division.
This introduces a maximum measurement error equal to the
greatest distance between the centroid and the border of the
electoral division in question which will be greatest in rural
areas and smallest in the city regions.
The GIS requirements for this paper included the collection,
assimilation and pre-processing of digital, spatial datasets,
development of methods for spatio-temporal analysis and
production of summary statistics and cartographic representa-
tions. Thisprocess producedlayers of data which were mapped
into ArcView GIS. The data were entered into GIS as points (e.g.
the location of waste facilities), lines (e.g. roads), or polygons
(e.g. airports) within the categories of: meteorological; environ-
mental; transport; and administrative boundary data layers.
Different variables were entered in different ways. Some were
entered directly as the spatial coordinates for this data were
known, such as the airport co-ordinates. Others, such as the
climate layers were entered as raster maps and these were
converted to polygons for analysis purposes, as it was then
possible to link individuals to characteristics of their areas. All
data were converted to Irish National Grid co-ordinates.
Once the data layers were entered into the ArcView system,
variables were created to allow statistical analysis to take
place. For example, proximity to coast is measured as three
dummy variables; less than two kilometres from the coast,
between two and five kilometres and more than five kilo-
metres. This allows us to examine if the amenity/disamenity
values of the variables are functions of distance. We can also
disaggregate between different types of similar amenities e.g.
landfill and hazardous waste sites (EPA, 2005). Using proximity
tools within ArcMap, distance bufferswere created from the
centroid (as in Craglia et al., 2001) of each specific electoral
division to a specified distance. Buffer analysis allows the
researcher to take a point or line feature and generate a
polygon containing all the area within a certain distance of the
feature (Bond and Devine, 1991). A tool called select by
location, was then used to identify the area where a particular
environmental condition is satisfied. The variables created
were either entered as columns of 0s and 1s, i.e. where the
dummy equaled 1 for a particular electoral division if the
condition was satisfied and 0 otherwise (e.g., 1 if an electoral
division was within a 50 km radius of an airport and 0
otherwise) or as continuous variables (as in the case of the
climate variables). These variables were then exported to the
statistical software package STATA so econometric analysis
could be carried out.
2.2. Estimation strategy
The stated aim of this paper is to examine the influence of
space and place on individual well-being. As a first step
towards capturing this influence, a micro-econometric happi-
ness function is specified (Model 1) in which we distinguish
between two distinct geographical areas of Ireland, i.e.,
between those respondents living in Dublin and those living
in the rest of the country. This split was considered appropriate
in a small (approximately 70,000 km
) and relatively homog-
enous country like Ireland where the Dublin area comprises
28% of the population in only 1.3% of the land area, accounts for
39% of the national total of Gross Value Added and, with a
population of 1.122 million, is the only urban area with a
population in excess of 150,000. In Model 1, which also controls
for a broad range of socio-economic and socio-demographic
characteristics of the individuals in question (age, age-
squared, gender, employment status, educational attainment,
health, marital status, income, number of dependent children
and household tenure), a dummy for Dublin might be seen as a
rough summary measure of the amenities in that area.
However, it does not provide much information regarding
which specific amenities are most valued by the individuals.
Therefore, in order to determine which site-specific factors are
most relevant to well-being, a subsequent model is estimated
(Model 2), corresponding to the estimation of Eq. (1), where the
spatial variables equate to the amenities contained in vector a.
This model contains the spatial amenities created using GIS
and other data at the electoral division level.
Finally, because the regressions combine data at different
levels of disaggregation (individual, electoral division and
local authority levels), the standard errors in all the regres-
sions are corrected for clustering (Moulton, 1990).
3. Results assessing the importance of location
3.1. Model 1
Table 1 shows the results from the estimation of our models.
Following the recent literature (e.g., Ferrer-i-Carbonell and
Gowdy, 2007) and given the ordered nature of our dependent
Acentroid' is the mathematical term for the centre of an area,
region, or polygon, calculated from points on its perimeter. In the
case of irregularly shaped polygons, the centroid is derived
mathematically and is weighted to approximate a centre of
gravity.' These discrete XYlocations are often used to index or
reference the polygon within which they are located and some-
times attribute information is attached,' hung,' or hooked' to the
centroid location.
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
Table 1 Ordered probit regressions/dependent variable
life satisfaction
Variable name Model 1 Model 2
Age Age 0.0188 0.0100
(1.45) (0.74)
Age-squared 0.0002 0.0001
(1.46) (0.76)
Gender (female) Male 0.1665⁎⁎ 0.1719⁎⁎
(2.43) (2.20)
Employment status
(self employed)
Retired 0.0871
Engaged in
home duties
Student 0.1990 0.0251
(1.13) (0.10)
work for 1st
Unemployed 0.9182⁎⁎⁎ 0.8674⁎⁎⁎
(4.26) (3.94)
Not working,
not seeking
unable to work
Education (primary) Lower
junior high
senior high
Degree 0.0617 0.1590
(0.52) (1.15)
Health (visited the doctor
0 or 1 in the last year)
25 doctor visits 0.1555⁎⁎
6 or more
doctor visits
Marital status (single) Married 0.0138 0.0720
(0.15) (0.74)
Co-habiting 0.1239 0.2596
(0.83) (1.18)
Widowed 0.0880 0.1124
(0.57) (0.69)
Separated and
Log income Income (1000 s) 0.2103⁎⁎⁎ 0.2649⁎⁎⁎
(2.90) (2.95)
Number of children in the
household (no children)
1 Child 0.0215
2 Children 0.0829 0.1111
(0.87) (1.09)
3 or more
Household tenure
(own outright)
Own with a
Rent privately 0.0342 0.0033
(0.27) (0.02)
Public housing 0.5125⁎⁎⁎ 0.4781⁎⁎⁎
(4.69) (3.61)
Table 1 (continued)
Variable name Model 1 Model 2
Respondent is a carer 0.33140.2313
(1.70) (1.24)
Dublin dummy variable 0.7527⁎⁎⁎ 0.4430
(11.79) (1.12)
Spatial variables No Yes
Climate variables Precipitation 0.0005
Wind speed 0.3815⁎⁎
July maximum
Average commuting time 0.0057
Population density 0.0061
Congestion 0.0001
Homicide rate 0.0570
Voter turnout 0.0160
Proximity to landfill
(more than 10 km)
a landfill
Within 3 km 0.4332
Between 3
and 5 km
Between 5 and
10 km
Proximity to hazardous
waste facility (more than
10 km)
Contains a
waste facility
Within 3 km 0.1993
Between 3
and 5 km
Between 5
and 10 km
Proximity to coast (more
than 5 km)
Within 2 km 1.1299⁎⁎⁎
2 to 5 km 0.2761
Proximity to beach
(more than
10 km)
Within 5 km 0.2248
Between 5 and
10 km
Proximity to rail station
Within 2 km 0.2868
Between 2
and 5 km
Between 5
and 10 km
Proximity to airport (more than 60 km)
Regional Within 30 km 1.2726⁎⁎⁎
Between 30 and
60 km
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
variable, it contains results from ordered-probit regressions.
The reference groups for the independent variables are in
The results on the socio-economic and socio-demographic
characteristics in Model 1 are, broadly speaking, in line with
previous findings in the economic psychology literature. For
example, the coefficient on being unemployed is negative and
significant and, everything else being equal, reduces life
satisfaction substantially (see e.g., Blanchflower and Oswald,
2004a for similar results). Gender is significant and negative,
indicating that males are less satisfied with their lives than
females. Except for the study of Alesina et al. (2004) that finds
gender to be significantly related to life satisfaction in the USA,
in previous studies gender tends to emerge as insignificant in
life satisfaction regressions (Stutzer, 2004; Frey and Stutzer,
2000; Di Tella et al., 2001). We find that those with lower (junior
high school) or higher (senior high school) education are more
satisfied with life than those with a primary education level
(similar to Frey and Stutzer, 2000). As in Clark and Oswald
(1994) and Blanchflower and Oswald (2004b), being separated
or divorced is negative and significant. However, we find no
difference between married and single respondents. Having
three or more children is negative and significant at the 5%
level (similar to Clark and Oswald, 1994). Respondents visiting
their doctor two or more times a year are found to be less
satisfied with life than those not attending or attending only
once. Living in public housing is significant and negatively
related to life satisfaction at the 1% level with a large
coefficient. Perhaps surprisingly, being the carer of a disabled
family member emerges as positive and significant in the
regression. In line with the standard textbook prediction of
utility as an increasing function of income, our proxy for utility
(life satisfaction) is an increasing function of (log) income,
which emerges significant at the 1% level. Age emerges
insignificant in the regression. This is in contrast to the inter-
national literature which, generally, finds a U-shaped associ-
ation between life satisfaction and age.
Examining the influence of location on well-being, we find
the coefficient on the dummy variable for Dublin to be highly
significant and large; only the coefficients for being unem-
ployed and a discouraged worker are larger in magnitude.
Everything else being equal, those living in all areas outside
Dublin have a higher life satisfaction. This result is similar to
that in Ferrer-i-Carbonell and Gowdy (2007), who find indivi-
duals living in Inner London to be less happy, everything else
Having controlled for a large number of socio-economic and
socio-demographic characteristics, a reasonable hypothesis is
that factors related to the size of the settlement and other
location-specific factors may be responsible for lower life-
satisfaction levels in Dublin. For example, compared to any
other area in the country, unparalleled growth rates have
resulted in the capital having a much higher population
density than other areas and a significant traffic congestion
problem (DELG, 2002b). To test this hypothesis, Model 2
examines the importance of spatial amenities.
3.2. Model 2
Model 2, the results of which are reported in the last column of
Table 1, corresponds to Eq. (1). It builds on Model 1 by including
the variables with a spatial influence on well-being. These
include population density, congestion, commuting time and
the climatic and environmental variables. In this model, the
dummy for Dublin loses its significance. This result suggests
that the spatial variables explain an important part of the
difference between living in Dublin and other regions of
Ireland in terms of well-being.
The pseudo-R
of Model 2, at 0.16, exceeds all those obtained to
date in the international literature usinga cross-sectional dataset.
For example, Ferrer-i-Carbonell and Gowdy (2007) in their study of
subjective well-being and environmental attitudes, obtain a
of 0.088. Another barometer of the explanatory power
of the model is the adjusted-R
in Table 2 reporting the OLS
results. The adjusted-R
Model 2, which compares very favourably with those obtained in
other studies; Stutzer (2004) for example, in his analysis of Swiss
cantons, obtains an R
of 0.11. Since we control for similar socio-
economic and demographic characteristics of the individual as in
other studies of this nature, we believe this high adjusted-R
highlights the substantial influence of spatial amenities as
determinants of well-being.
Table 1 (continued)
Variable name Model 1 Model 2
National Within 30 km 0.1404
Between 30
and 60 km
International Within 30 km 0.4294
Between 30
and 60 km
Proximity to major road
(more than 5 km)
Contains a
major road
Within 5 km 0.5816
Proximity to sea ports
(more than 10 km)
Within 3 km 0.5826
Between 3
and 5 km
Between 5 and
10 km
Number of observations 1467 1464
Likelihood Ratio 1845.59 1692.85
Pseudo R
0.09 0.16
Note 1: significant at 10% level; ⁎⁎significant at 5% level; ⁎⁎⁎signif-
icant at 1% level.
Note 2: t-statistics in parentheses computed using White's Hetero-
skedasticity-corrected standard errors.
We also estimate OLS regressions (Table 2) and the results are
We also estimate Model 2 without the Dublin dummy variable
and the results are almost identical (results available on request
from the authors).
Additional R
obtained in the literature include Blachflower and
Oswald (2004b) at 0.10, Di Tella et al. (2001) at 0.17 and Blanchflower
and Oswald (2004a) at 0.084. However, these papers use pooled data
over a number of years and hence, may not be directly comparable.
Proximity to airport (more than 60 km)
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
Table 2 OLS regressions/dependent variable life
Variable name Model 1 Model 2
Age Age 0.0155 0.0070
(1.45) (0.70)
Age-squared 0.0002 0.0001
(1.46) (0.75)
Gender (female) Male 0.1369⁎⁎ 0.1232⁎⁎
(2.45) (2.08)
Employment status
(self employed)
Retired 0.0677
Engaged in
home duties
Student 0.1520 0.0149
(1.06) (0.08)
Seeking work
for 1st time
Unemployed 0.7810⁎⁎⁎ 0.6746⁎⁎⁎
(4.17) (3.56)
Not working,
not seeking
unable to
Education (primary) Lower
junior high
senior high
Degree 0.0582 0.0914
(0.58) (0.87)
Health (visited the doctor
25 doctor
6 or more
doctor visits
Marital status (single) Married 0.0030 0.0671
(0.04) (0.91)
Co-habiting 0.0696 0.1435
(0.55) (0.87)
Widowed 0.0773 0.0945
(0.61) (0.78)
and divorced
Log income Income (1000 s) 0.1722⁎⁎⁎
Number of children in the
household (no children)
1 Child 0.0160
2 Children 0.0828 0.1204
(1.02) (1.49)
3 or more
Household tenure
(own outright)
Own with a
Rent privately 0.0309 0.0100
(0.30) (0.08)
Public housing 0.4379⁎⁎⁎
Table 2 (continued)
Variable name Model 1 Model 2
Respondent is a carer 0.26320.2371
(1.73) (1.91)
Dublin dummy variable 0.6222⁎⁎⁎ 0.2434
(11.43) (0.78)
Spatial variables No Yes
Climate variables Precipitation 0.0003
Wind speed 0.2459⁎⁎
Average commuting time 0.0034
Population density 0.0038
Congestion 0.0001
Homicide rate 0.0501
Voter turnout 0.0124⁎⁎
Proximity to landfill
(more than 10 km)
a landfill
Within 3 km 0.2646
Between 3
and 5 km
Between 5
and 10 km
Proximity to hazardous
waste facility (more
than 10 km)
Contains a
waste facility
Within 3 km 0.1715
Between 3
and 5 km
Between 5
and 10 km
Proximity to coast (more
than 5 km)
Within 2 km 0.8351⁎⁎⁎
2 and 5 km 0.2271
Proximity to beach
(more than 10 km)
Within 5 km 0.1607
Between 5
and 10 km
Proximity to rail station
(more than 10 km)
Within 2 km 0.1705
Between 2
and 5 km
Between 5
and 10 km
Proximity to airport (more than 60 km)
Regional Within 30 km 0.8329⁎⁎⁎
Between 30 and
60 km
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
Of the climate variables, the coefficient on mean annual
precipitation is positive indicating that, for Irish people,
increased rainfall slightly increases life satisfaction. This result
may, however, be driven by a positive correlation between rain
and scenic beauty.
The most spectacular landscapes in
Ireland are found in the wettest counties in the West of
Ireland. Rehdanz and Maddison (2005) find very scarce pre-
cipitation reduces happiness, which they hypothesize might
reflect the fact that climate could have an indirect effect on
happiness through landscape effects. However, in our case the
coefficient emerges insignificant at conventional levels.
Increases in the January minimum and July maximum tem-
peratures emerge as amenities and increase life satisfaction.
Wind speed emerges negative and significant in our regres-
sion, while surprisingly, we find that total annual sunshine is
negatively related to life satisfaction. However, it may be that
this result is driven by the correlation between elements of
rainfall not captured in our variable (e.g., intensity and
frequency) and sunshine.
As in the hedonic literature (e.g., Blomquist et al., 1988), we
find the presence of waste facilities in an individual's area to
be a disamenity. However, the type of, and distance from, the
waste facility in question matters. The coefficient on the
variable capturing if a landfill site is in operation in the
respondent's electoral division emerges negative and signif-
icant compared to those who live in electoral divisions more
than ten kilometres away. There is evidence suggesting that
noise, smell and other negative externalities from waste
facilities of this kind may impact negatively on well-being or
quality of life (DG Environment, 2000). Proximity to landfill
sites has been the subject of many hedonic analyses, such as
Blomquist et al. (1988) who use the number of landfill sites per
capita as a variable to construct Quality of Life indices, also
Nelson et al. (1992) and Havlicek (1985) who examine the price
effects of landfills on house values in US cities. Proximity to a
hazardous waste facility however, does not seem to have an
individuals are less aware of the presence of these facilities
in their areas. The coefficient on population density is positive
and significant at the 10% level. This result is similar to that of
Roback (1982), who finds population density to be an amenity.
Average commuting time and congestion emerge insignificant
in the regression as does the crime rate.
Proximity to coast emerges positive and significant with a
large coefficient, indicating that individuals living near the
coast enjoy higher life satisfaction, other things being equal.
Additionally there is evidence that the utility value of coast is a
function of distance with respondents living two kilometres or
less from the coast more satisfied with their lives, compared to
those living more than five kilometres from the coast. Those
living between two and five kilometres from the coast are also
more satisfied, if insignificantly so, but the coefficient is
reduced. Interestingly, proximity to beach emerges insignifi-
cant in the regression. It may be that, given Ireland's climate,
the amenity value of coastal areas is not a function of the
availability of a beach.
We find access to transport emerges as both an amenity and
disamenity, depending on the type of, and distance from, the
amenity in question. Life satisfaction is highest for those living
between thirty and sixty kilometres from an international
airport. It may be that those less than thirty kilometres away
are affected by the noise disamenity. In relation to regional
airports, the amenity value lies at less than thirty kilometres.
This result is not unexpected as these are smallairports and only
deal with smaller, less noisy aircraft and would have signifi-
cantly fewer arrivals and departures than do the larger airports.
Close proximity to a major road (less than five kilometres)
emerges as a disamenity,again with distance decay. This may be
capturing the noise affects of this transport route. Close
proximity to a seaport emerges insignificant in the regression.
4. Conclusion
In this paper we adopt a holistic approach to the examination
of the influence of geography and the environment on
happiness. Using GIS we are able to overcome many of the
difficulties that have prevented previous researchers addres-
sing this issue comprehensively. This is achieved by matching
individuals to their surroundings at a higher level of disag-
gregration and by expanding the vector of spatial variables
included in the happiness function. We also use proximity
measures to examine if the influence of spatial amenities on
life satisfaction is a function of distance.
The findings show that climate has a significant influence on
well-being, with wind speed negative and significant, but
increases in both January minimum temperature and July
maximum temperature are positive and significant. Access to
major transport routes and proximity to coast and to waste
facilities all influence well-being. However, the manner in which
Table 2 (continued)
Variable name Model 1 Model 2
National Within 30 km 0.0721
Between 30 and
60 km
International Within 30 km 0.2603
Between 30 and
60 km
Proximity to major road
(more than 5 km)
Contains a
major road
Within 5 km 0.3543
Proximity to sea ports
(more than 10 km)
Within 3 km 0.3887
Between 3 and 5
Between 5 and
10 km
Number of observations 1467 1451
Adjusted R
0.21 0.33
Note 1: significant at 10% level; ⁎⁎ significant at 5% level;
⁎⁎⁎ significant at 1% level.
Note 2: t-statistics in parentheses computed using White's Hetero-
skedasticity-corrected standard errors.
A high correlation coefficient is observed between precipita-
tion and presence of Natural Heritage Areas (0.5874), the latter
being EU-designated areas of outstanding natural beauty.
Please cite this article as: Brereton, F. et al. Happiness, geography and the environment. Ecological Economics (2007),
they enter the happiness equation differs depending on the
amenity in question. Proximity to landfill is found to have a
negative affect on well-being. Proximity to coast has a large
positive effect, but its influence is a diminishing function of
distance. Additionally, the impact of proximity to major
transport routes has different effects depending on the type of,
and distance to, the amenity in question, e.g., while reasonable
proximity to international airports increases well-being, close
proximity to major roads decreases it. It may be that, in the
former case, the positive effect of access outweighs the negative
effect of noise, while the opposite may be true in thelatter case.
These results may have potentially important implications for
the setting of public policy, such as the location of waste
facilities, the routing of major roads, location of airports etc., so
as to have as minimal negative impact as possible on well-being.
Our findings highlight the critical importance of the role of the
spatial dimension in determining well-being, i.e., spatial vari-
ables are found to be highly significant with large coefficients. In
fact, the explanatory power of our happiness function substan-
tially increases when the spatial variables are included, resulting
in three-times the variation in well-being being explained than
has been achieved in any previous cross-sectional study. This
indicates that geography and the environment have a much
larger influence on well-being than previously thought, as im-
portant as the most critical socio-economic and socio-demo-
graphic factors, such as unemployment and marital status. This
finding has potentially important implications for setting
priorities for public policy as, in essence, improving well-being
could be considered to be the ultimate goal of public policy.
We thank Andrew Oswald, Michael Hanemann, Mirko Moro,
Alan Carr and two anonymous referees for helpful comments.
We thank Daniel McInerney and Sean Morrish for technical
assistance. The usual disclaimer applies.
Appendix A. Supplementary data
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... The city shape mainly reflects a hierarchy of different subcenters or clusters across many scales, from the entire city to neighborhoods, organized around key economic functions (Batty, 2008). Brereton et al. (2008) found that location-specific factors have a direct impact on life satisfaction and also emphasized that urban spatial structure plays an important role in determining well-being. Although some existing literature paid attention to the relationship between the compact city and happiness, most of it focused on the impact of urban economic density on happiness, and only two empirical studies discussed the relationship between morphological density and happiness (Ahfeldt & Pietrostefani, 2017). ...
... These characteristics include per capita GDP, population density, the ratio of fiscal expenditure to GDP, the ratio of fiscal revenue to GDP, the total area of the road, subway mileage, the number of buses, the number of college students in school, the number of beds in hospitals, the public library collection, and the number of theaters. These factors are related to residents' lives in the city and are commonly associated with residents' subjective well-being (Brereton et al., 2008;Brown et al., 2015). To save space, descriptive statistics for the above control variables are provided in the Appendix. ...
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This paper investigates the impact of city shape variation on residents' happiness in Chinese cities. We first measure the city shape index of Chinese cities between 2000 and 2015 using matched data from the European Space Agency Global Land Cover dataset and the Chinese administrative map to delineate the changes in city shape in China's urbanization. Then we use a reduced-form estimation strategy to examine the causality between city shape variation and urban residents' happiness using our city shape dataset and the microsurvey data of CHIP (Chinese Household Income Project). We find that city shape exerts a negative influence on the happiness of urban residents. Such an effect is more pronounced in large cities. However, well-developed public transportation infrastructure can mitigate the negative impact of a non-compact city shape. Turning to the mechanism , commute time to the workplace and amenities is the main channel through which city shape works. We find that bad shape significantly increases the commute time between residential, workplace, and commercial amenities and therefore makes urban residents unhappy.
... There have been many research efforts aimed at adding a spatial dimension in the examination of happiness 1 data (Ferrer-i-Carbonell & Gowdy, 2007;Brereton et al., 2008;Bernini & Tampieri, 2019;Ala-Mantila et al., 2018; for a recent review see Ballas, 2021). However, there have been very limited research studies involving small area level analysis and in particular of spatial clustering and spatial spillover effects between small area level spatial units. ...
... The evidence shows that urban areas experience lower levels of subjective well-being (Berry & Okulicz-Kozaryn, 2011;Morrison, 2011;Okulicz-Kozaryn, 2017;Morrison & Weckroth, 2018;Helliwell et al., 2019;Burger et al., 2020;Weckroth et al., 2022). GIS related analysis and geographically environmental and climate characteristics have also been examined like pedestrian and car-oriented zones or wind speed and temperature (Ala-Mantila et al., 2018;Brereton et al., 2008). Regarding spatial clustering and spillover effects in studying happiness data, different geographical levels have been examined that identify spatial patterns and spillover effects between neighbouring countries, regions and cities. ...
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There has been a rapidly growing number of studies of the geographical aspects of happiness and well-being. Many of these studies have been highlighting the role of space and place and of individual and spatial contextual determinants of happiness. However, most of the studies to date do not explicitly consider spatial clustering and possible spatial spillover effects of happiness and well-being. The few studies that do consider spatial clustering and spillovers conduct the analysis at a relatively coarse geographical scale of country or region. This article analyses such effects at a much smaller geographical unit: community areas. These are small area level geographies at the intra-urban level. In particular, the article presents a spatial econometric approach to the analysis of life satisfaction data aggregated to 1,215 communities in Canada and examines spatial clustering and spatial spillovers. Communities are suitable given that they form a small geographical reference point for households. We find that communities’ life satisfaction is spatially clustered while regression results show that it is associated to the life satisfaction of neighbouring communities as well as to the latter's average household income and unemployment rate. We consider the role of shared cultural traits and institutions that may explain such spillovers of life satisfaction. The findings highlight the importance of neighbouring characteristics when discussing policies to improve the well-being of a (small area) place.
... Spatial factors (Brereton et al., 2008), climate, and air pollution (Cuñado & De Gracia, 2013) are also significant determinants of well-being. Evidence indicates that infrastructure can be designed in accordance to have a positive influence on well-being (Brereton et al., 2008;Sarmiento et al., 2022), and significant differences by region have been observed in this regard (Sarmiento et al., 2022). ...
... Spatial factors (Brereton et al., 2008), climate, and air pollution (Cuñado & De Gracia, 2013) are also significant determinants of well-being. Evidence indicates that infrastructure can be designed in accordance to have a positive influence on well-being (Brereton et al., 2008;Sarmiento et al., 2022), and significant differences by region have been observed in this regard (Sarmiento et al., 2022). According to several studies, high-quality landscapes and ecosystems contribute to greater well-being in terms of mental and physical health (Abraham et al., 2010;Bieling et al., 2014;Bignante, 2015;Carrus et al., 2015;Skärbäck, 2007;Summers et al., 2012). ...
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Unlabelled: Regional nature parks in Switzerland are, for the most part, protected areas that aim to promote sustainable development and residents' well-being. In recent years, research on regional nature parks and comparable protected areas has focused on questions regarding local populations' acceptance of such areas, their governance, and their economic effects. However, we know surprisingly little about the impact of protected areas on environmental resource use and life satisfaction, two essential ingredients of sustainable regional development. In this study, we survey people living in and around three regional nature parks in Switzerland on their resource use and life satisfaction (gross sample n = 3358). We propose a novel measurement of resource use based on vignettes describing different lifestyles, which we validate against the carbon footprint obtained for a subsample of our respondents. With these indicators, using multiple regression analyses, we test several hypotheses derived from the literature on the relationship between resource use and life satisfaction in and around protected areas. Contrary to our expectations, we do not find differences in resource use or life satisfaction, or the relationship between resource use and life satisfaction, across park and non-park regions. We discuss potential explanations for our findings and their implications for nature park authorities and future study designs. Supplementary information: The online version contains supplementary material available at 10.1007/s11205-023-03164-z.
... In addition, Adamowicz et al. (1997) reported that the discrete choice model of recreation based on perceptions outperforms the models based on objective measures of environmental attributes. Many scholars have used the LSA approach to value Aboriginal land use activities (Kant et al., 2014(Kant et al., , 2016 and public goods including air pollution (Luechinger, 2009), climatic conditions (Brereton et al., 2008), and natural environments and land areas (Kopmann and Rehdanz, 2013). Just (2008) demonstrated that the normative purpose of any economic policy is to improve people's well-being and the economic analysis is based on the notion that individuals' choices reflect their preferences. ...
Purpose This paper analyzes individual subjective well-being using a survey database from the Strasbourg metropolitan development council (France). The authors focus on the effects of externalities generated by public services (transport, culture and sport), environmental quality and feeling of security in the Strasbourg metropolitan area (Eurométropole de Strasbourg, EMS). Results show that EMS specificities (public facilities, environmental quality, safety and security) and individual features like opportunities to laugh or live with children significantly influence individual well-being. These findings are robust when using three subjective measures: feeling of well-being, environmental satisfaction and social life satisfaction. The authors also show that income may affect the perceived well-being of individuals belonging to a low-income group, while individuals belonging to a high-income group tend to be unsatisfied with environmental quality but satisfied with their social life. Besides, social comparison in terms of income does not matter for individual well-being in the Strasbourg metropolitan area. Design/methodology/approach Theoretical and empirical paper —Utility theory in economics—Econometric modeling using an ordered probit model. Findings Specificities of the Strasbourg metropolitan area-France (public services related to transport, culture and sport, environmental quality perceived as convenient for individual health, sense of security) significantly impact individual subjective well-being. Income does not substantially impact the individual subjective perception of happiness: income may matter for the feeling of well-being only for individuals belonging to a low-income group. Wealthy individuals tend to be unsatisfied with environmental quality but satisfied with their social life. Social comparison in terms of income does not matter for individual well-being in the Strasbourg metropolitan area. Research limitations/implications Cross-sectional data, but it is the only available database from a survey conducted by EMS in 2017 to collect information on potential elements relative to individual well-being in the Strasbourg metropolitan area. Practical implications Results shed light on the role of territorial policies in improving individual well-being and might provide some guidelines for policy-makers concerned about the population’s welfare. Policy-makers should give strong attention to public facilities (an essential element of local public action) and improve environmental quality. If they care about the population’s happiness, they have to reorient current policies in this direction. Of course, through the inquiry in 2017 giving this database, the Strasbourg agglomeration development council aimed to provide such evidence to the local administration. Nevertheless, the results were a bit upsetting for many people in the administrative and political circles, who generally prioritize economic and demographic development, while the citizens’ responses to the inquiry have revealed a strong focus on the quality of everyday life in their neighborhood. Originality/value The present study contributes to the literature on subjective well-being, with a focus on the role of local characteristics and living environment. The authors’ starting point is related to the standard utility theory, indicating that environmental quality and public services are positive externalities. The authors investigate whether the local living environment and public facilities are crucial elements explaining individual well-being. To do this, we consider three subjective measures: feeling of well-being, environmental satisfaction and social life satisfaction, which are used as proxies of individual utility. The authors consider different explicative variables representing specificities of EMS in terms of public services (transport, culture and sport), environmental quality perceived as convenient for individual health, safety and security, etc. The authors also provide a test for relative standing by including the median monthly household income at the municipality level.
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This research paper investigates the impact of air pollution and happiness among residents in Delhi, India. Delhi is one of the most polluted cities in the world, and its citizens face severe health hazards due to air pollution. While previous studies have focused on the adverse health effects of air pollution, the present study investigates its impact on subjective well-being, i.e., happiness. Using data from a survey conducted among a sample of Delhi residents, this study examines the relationship between various indicators of air pollution and self-reported levels of happiness. The results suggest a significant negative relationship between air pollution and happiness, with higher levels of pollution associated with lower levels of happiness. The study also identifies some of the key factors that moderate this relationship, such as socio-demographic variables, perception of health risks, and coping strategies.
Happiness is longed for by people from all walks of life and is construed as one of the most important attainments in life. The present chapter endeavors to put forward the literature based on strategies that can enhance happiness levels along with its most probable determinants. As the notion of happiness majorly draws from the field of positive psychology, various interventions based on the principles of positive psychology are also discussed along with their application in the Indian context. The chapter also discusses the perceived happiness-increasing strategies as identified by people. As the discussion throughout the chapter is focused on the Indian context, sociocultural factors’ role in influencing individuals’ well-being levels is also explored. Additionally, the role of positive environmental ingredients also forms a relevant part of the discussion as it highlights the imperative nature of person-environment fit in enhancing individuals’ well-being. Considering the importance of happiness, a variety of initiatives facilitated by the government, non-governmental organizations (NGOs), and communities at different societal levels are also discussed. Moreover, as per the contemporary milieu where technology has enveloped almost every field, its role in promoting happiness and well-being cannot be left uncovered.KeywordsHappiness-increasing strategiesFactors influencing happinessIndigenous practicesPositive environmentTechnology-based interventionsHappy habits
The value of linking urban environment and subjective well-being (SWB) is now well recognized. But whether the geographical context inside and outside the neighborhood has differential influence on long- and short-term SWB remains unclear. Based on the activity perspective, we used survey data from Guangzhou, China, integrating GPS data, portable environmental sensors data to analyze time-weighted and real-time geographical context inside and outside the neighborhood on long- and short-term SWB. The results show that SWB is not only influenced by the neighborhood environment, but also the geographical context outside the neighborhood. Time-weighted geographical environment inside the neighborhood has a higher impact and explanatory ability on long-term SWB, while real-time geographical environment outside the neighborhood has a higher impact and explanatory ability on short-term SWB. This study provides a new understanding for geographies of SWB through the extension of time and space, and also provides reference for more refined urban planning and governance in the future.
Using individual-level panel data representative of Chinese residents, this study examines in detail the relationship between temperature and subjective well-being (SWB). We first find that a 1 °C increase in temperature anomalies (difference between current and historical temperature) causes a 0.02 decrease in SWB (2% of 1 S.D.). Second, we present evidence of climate inequality along socioeconomic status (SES) as SWB of better educated, and higher-income Chinese residents are less affected by temperature anomalies compared to their lower SES counterparts. Closer examination reveals that adaptation mechanisms such as ownership of air-conditioners, automobiles, and indoor work help to alleviate adverse impacts of temperature anomalies. Lastly, for better comparison, we express our findings as monetized damages. We compute that a 1 °C increase in temperature anomalies causes damages equivalent to around 6.9% of income. However, these damages are mostly driven by Chinese from the lower-SES stratum as their damages are equivalent to around 9.6% of income compared to no damages for the high-SES group. Similarly, when translated into elasticity, we find that temperature-induced damages reduce by around 2% for every 1% increase in average income.
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‘Turnout’ is a key measure of participation in the democratic process. Specifically, it measures the proportion of eligible voters that turns out to vote on election day. Low (or declining) turnout rates are a cause of concern and are often taken as a measure of disaffection with the political decision‐making process. In Ireland, turnout in the 2002 general election confirmed a downward trend in voter participation and represented the lowest turnout since the foundation of the State. However, turnout rates vary markedly across the State. Until recently, it was not possible to examine turnout at a sufficiently detailed geographical scale to allow systematic analysis of the potential causes of such variations. This paper reports on a joint project, involving the Geary Institute, UCD and the National Institute for Regional and Spatial Analysis (NIRSA), NUI Maynooth, that is undertaking a comprehensive analysis of turnout in the 2002 general election at the lowest possible level of aggregation. Here, a cartographic description and introductory analysis is presented that includes the first ever electoral‐district‐level map of turnout. The resulting geographical patterns are generally coherent and explicable and provide important pointers for future research. In an unanticipated finding, this map shows that the phenomenon of low turnout in urban areas occurs beyond the main cities and their suburbanised hinterlands and shows up as a characteristic of most provincial towns.
In the final years of the twentieth century Ireland was the economic wonder of the western world. The economy is now in transition and things have changed dramatically, especially in the light of September 11th. This book explains why Ireland has made such startling progress and identifies the policies which will help in our changing circumstances and carry us through into a bright future. It examines The Irish economic policy and its performance The effect and challenges of globalisation Environmental damage and climate change Social issues, such as housing, traffic, immigration. From a background in economics, and with internationally recognised expertise, these three authors look at the current crisis and at the big quality of life issues which interest every human being.
Publisher Summary This chapter discusses the association of income and happiness. The basic data consist of statements by individuals on their subjective happiness, as reported in thirty surveys from 1946 through 1970, covering nineteen countries, including eleven in Asia, Africa, and Latin America. Within countries, there is a noticeable positive association between income and happiness—in every single survey, those in the highest status group were happier, on the average, than those in the lowest status group. However, whether any such positive association exists among countries at a given time is uncertain. Certainly, the happiness differences between rich and poor countries that one might expect on the basis of the within-country differences by economic status are not borne out by the international data. Similarly, in the one national time series studied, for the United States since 1946, higher income was not systematically accompanied by greater happiness. As for why national comparisons among countries and over time show an association between income and happiness that is so much weaker than, if not inconsistent with, that shown by within-country comparisons, a Duesenberry-type model, involving relative status considerations as an important determinant of happiness, is suggested.
The links between income, sexual behavior and reported happiness are studied using recent data on a sample of 16,000 adult Americans. The paper finds that sexual activity enters strongly positively in happiness equations. Higher income does not buy more sex or more sexual partners. Married people have more sex than those who are single, divorced, widowed or separated. The happiness-maximizing number of sexual partners in the previous year is calculated to be 1. Highly educated females tend to have fewer sexual partners. Homosexuality has no statistically significant effect on happiness. © The editors of the Scandinavian Journal of Economics 2004. Published by Blackwell Publishing.
The purpose of this paper is to examine the role of Geographic Information Systems (GIS) in survey analysis. This is done with reference to the work of the Northern Ireland Regional Research Laboratory. The paper begins by outlining the concept of a GIS and the various components of such a system. Given the importance which the United Kingdom Government and the Economic and Social Research Council attaches to the role of the Regional Research Laboratory initiative as a vehicle for developing both the practical and academic applications of GIS an overview of the initiative is given. The main role of a GIS in survey analysis, namely the linking of various datasets using spatial identifiers, is outlined. The use of some of the available GIS techniques in survey design, data collection and analysis are discussed in detail. In particular the role of GIS in stratifying samples, checking data input and in adding additional functionality to the analysis of the results are covered. The paper concludes that GIS will play an increasingly important role in survey analysis and that within the United Kingdom the ESRC through its Regional Laboratories have provided a sound foundation for this.
Institutional factors in the form of direct democracy (via initiatives and referenda) and federal structure (local autonomy) systematically and sizeably raise self-reported individual well-being in a cross-regional econometric analysis. This positive effect can be attributed to political outcomes closer to voters' preferences, as well as to the procedural utility of political participation possibilities. Moreover, the results of previous microeconometric well-being functions for other countries are generally supported. Unemployment has a strongly depressing effect on happiness. A higher income level raises happiness, however, only to a small extent.
Police forces responsible for large metropolitan areas in England and Wales have claimed that within certain parts of their urban areas there exist high-intensity crime areas (HIAs). These are areas that raise special policing problems because of the particularly violent forms of crime sometimes found within them and because of the unwillingness or inability of the resident population to co-operate fully with the police in part because of fears for their own safety. A sample of metropolitan police forces was asked to identify the location of their HIAs and this paper reports the results of a GIS-based spatial analysis to try and model the location of these areas using census data. Three police force areas were used to develop the model. This was subsequently validated against a further set of HIA data from different police forces. The model suggests that HIAs are characterised by populations that are deprived and live at high density, and by higher levels of population turnover.