Links between urban structure and life satisfaction in a cross-section of OECD metro areas

Ecological Economics 129 (2016) 112–121

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Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon

Analysis

Links between urban structure and life satisfaction in a cross-section of OECD metro areas Zachary S. Brown a,⁎, Walid Oueslati b, Jérôme Silva b a b

Department of Agricultural and Resource Economics, North Carolina State University, United States Organisation for Economic Cooperation and Development, Paris, France

a r t i c l e

i n f o

Article history: Received 12 August 2015 Received in revised form 20 January 2016 Accepted 16 May 2016 Available online xxxx JEL classification: Q51 Q56 R13 R14 I31 Keywords: Life satisfaction Urban structure Land-use Compactness Monocentric city model

a b s t r a c t Contemporary urban planning is often oriented towards encouraging compact cities and the prevention of sprawl. But relatively little empirical work has quantitatively examined how land-use fragmentation, population density and compactness determine individual wellbeing. We analyse the relationship between these aspects of urban structure and life satisfaction in 33 cities distributed across five OECD countries. We create a unique dataset merging a household survey on environmental attitudes and behaviours in these countries with geospatial data on a number of indicators related to urban structure. In support of standard urban economic theory, we find a life satisfaction trade-off in terms of households' home sizes and distances to the urban core. A novel finding from the analysis is that the degree of local land-use fragmentation around households' residence is associated strongly and negatively with life satisfaction. We also find suggestive evidence that city centralization (the relative proportion of the population living in the core) decreases life satisfaction on average for individuals residing both within and outside the core. © 2016 Elsevier B.V. All rights reserved.

1. Introduction By 2050, nearly 70% of the world's population is projected to be living in urban areas (OECD, 2015). Concerns for urban quality of life and its impact on people's welfare are highlighted as a major issue in many cities. Scales and shapes of cities necessarily induce organisational forms that affect the daily lives of residents and, therefore, their welfare. However, conventional income growth indicators are incomplete measures of welfare, because they ignore several non-market factors that may explain individual well-being, including quality of life within urban areas. The measurement of welfare has always been a central topic in economics, but in recent years, a broader perspective to the measurement of well-being is emerging (Deaton, 2008). As part of this broader perspective, recent research analyses how survey-based measures of life satisfaction vary in response to socioeconomic and environmental conditions (Fleurbaey, 2009). Whereas traditional urban economic theory has focused on the trade-off between housing costs and accessibility to markets

⁎ Corresponding author at: 2801 Founders Drive, Raleigh, NC 27607, United States. E-mail address: [email protected] (Z.S. Brown).

http://dx.doi.org/10.1016/j.ecolecon.2016.05.004 0921-8009/© 2016 Elsevier B.V. All rights reserved.

(employment and otherwise) in the city centre (Alonso, 1964), there are many other facets of urban life that are important to wellbeing, including not only environmental quality and open space (Wu and Plantinga, 2003) but also factors determining congestion and agglomeration externalities (Brereton et al., 2008). Thus, it is reasonable to suppose that city scale, urban morphology and land-use systems play a non-trivial (and likely nonlinear) role in determining individual wellbeing (OECD, 2014). In recent years, urban sprawl and land-use fragmentation have become major public policy issues in many countries, reflecting widespread complaints that city growth is paving over landscapes and degrading environmental quality, including clean air, respite from noise and light pollution, as well as access to forests and parklands. In response to these concerns, many cities and national governments have promoted ‘compact cities’ policies (OECD, 2012a), which include various restrictions on development at the urban fringe, new charges levied on builders, and public purchases of open space. However, the extent to which these measures in fact improve the subjective wellbeing of inhabitants is not well-known, and here there is scope for empirical investigation. There are clear trade-offs in different urban structures. Compactness can facilitate commerce and reduce search costs for both goods and services including employment opportunities,

Z.S. Brown et al. / Ecological Economics 129 (2016) 112–121

but it can also increase equilibrium housing prices and – depending on municipal infrastructure – congestion. Understanding the relationship between life satisfaction and urban structure is important for the economic success of cities. Of late, policymakers have become increasingly concerned with the competitiveness of cities, the policy factors that can enhance entrepreneurship and the positive ‘agglomeration’ benefits that cities provide (Fujita and Thisse, 2013). From this perspective, understanding how urban planning and local development policies interact with inhabitants' life satisfaction is key to attracting the skilled, creative labour force that fuels cities' competitiveness (Glaeser et al., 2010; Rappaport, 2009). Is there a significant relationship between different urban development patterns and life satisfaction, and what dimensions of the urban patterns are affecting well-being? The answer to this question has obvious welfare implications for land-use policies promoting, for example, compact cities. It also has political economy implications, i.e. the degree to which local populations might support different land-use policies. To address these issues, this paper explores the impact of urban structure on households' well-being. In our analysis, we merge surveyed-based measures of life satisfaction with a number of GISbased indicators of urban structure. We use regression analysis to control for a wide range of socioeconomic factors, as well as other factors related to the local and metropolitan environment. At the local level, we consider a household's distance to urban cores, as well as factors related to land-use composition in the vicinity of households, including the proportion of land which is ‘green,’ as well as local measure of general land-use fragmentation. At the metropolitan level, we examine factors directly related to both city population density and compactness. Our exploratory empirical investigation yields a number of findings relevant for subsequent research and policy analysis. First, our results provide support for the theory that households face a trade-off between distance to the city centre and home size. Additionally, we find statistically significant evidence that local land-use fragmentation in the vicinity of households is associated with lower life satisfaction. Finally, we find some suggestive evidence that one aspect of city compactness – the degree of centralization – is associated with a net decrease in average life satisfaction. The remainder of the paper is organised as follows. Section 2 reviews the related literature and sets out the hypotheses examined in this study. Section 3 describes data and discusses methodological issues related to the spatial metrics used to characterize urban structure. Section 4 presents the empirical results. Section 5 concludes with a discussion of policy implications. 2. Related Literature and Hypotheses In the standard monocentric city model, households' decisions about their location reflects a balancing of commuting costs (generally increasing with distance from the city centre), housing costs, as well as the amenities provided at any given location. In the literature examining urban sprawl and leapfrog development, the amenities typically focused on are those related to open space. Equilibrium levels of housing prices, demand and development in these models are characterised by an indifference condition – the equalisation of households' utility – regarding location choice (Wu and Plantinga, 2003; Fujita and Thisse, 2013). This utility level, which is either exogenous or endogenous, depending on the modelling framework is nevertheless treated as unobservable. Most related empirical work on the value of neighbourhood amenities and environmental quality focuses on the hedonic estimation of housing price gradients with respect to the availability of local amenities at a given location (e.g. McConnell and Walls, 2005). An emerging body of work has taken the alternative approach of estimating marginal rates of substitution between amenities and, for example, income by directly using life satisfaction data. Welsch and Ferreira (2014) provide a simple theoretical structure to show that

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both the hedonic and life satisfaction approaches are theoretically consistent, in that both approaches aim to capture the marginal rates of substitution (MRS) between locational amenities and income. The approaches differ in their distinct economic assumptions. Whereas the hedonic approach employs a spatial equilibrium condition to estimate MRS, the life satisfaction approach instead relies on the core assumption that stated life satisfaction is a valid proxy for utility. As reviewed in the OECD's Guidelines on Measuring Subjective Wellbeing (2013), life satisfaction is generally the component of overall subjective well-being considered most relevant for policy analysis. In measuring life satisfaction, the researcher's aim is to elicit the “cognitive evaluation of the respondent's life as a whole (or aspects of it)” (pp. 29). In contrast, two other components – ‘affect’ and ‘eudaimonia’ – correspond, respectively, to an individual's current emotional state and to behaviourally observed indicators of ‘flourishing’ (e.g. goalseeking, learning and altruistic behaviours). In this sense life satisfaction corresponds most closely to the typically latent ‘utility’ construct underlying much of economic theory (Marshall, 1890). However, there are important distinctions between utility, as employed in economic theory and in the psychologist's construct of life satisfaction for individual decision-making (Kahneman and Krueger, 2006). This includes the fact that the former only treats utility as an ordinal measure which need not (and generally cannot) be directly compared between individuals. In contrast life satisfaction approaches rely explicitly on the interpersonal comparison of stated life satisfaction life satisfaction. Research has explored – and tentatively supported – the construct validity of surveyed life satisfaction, in terms of behaving like more objective measures of well-being (Oswald and Wu, 2010). The key technical assumption required to use stated life satisfaction in this way is that life satisfaction is strictly monotonically related to utility both within and between individuals (Welsch and Ferreira, 2014). In other environmental valuation domains, the life satisfaction approach has emerged as a new tool for benefit-cost analysis (BCA) and other types of social welfare analysis (Welsch, 2002; Welsch and Kühling, 2009; Adler, 2012; Welsch and Ferreira, 2014). Generally, air, water and noise pollution have comprised the environmental quality factors most investigated using life satisfaction approaches (see, for example, MacKerron and Mourato, 2009; Rehdanz and Maddison, 2008; Van Praag and Baarsma, 2005). Most recently, Ferreira et al. (2013), using three waves of the European Social Survey spanning 2002 to 2007 and covering 30 European countries, provide arguably the most robust evidence to date that air pollution (measured in their study by SO2 concentrations) significantly degrades life satisfaction. Their results imply, for example, that a 0.5 μg/m3 reduction in SO2 concentrations (approximately equal to a 10% reduction in the mean concentration levels observed in the researchers' dataset) has the same average effect on life satisfaction as a 2.7% increase in household income.1 Less work has examined the effect of urban development patterns on life satisfaction, which is the motivation for the present study. Early public health research in this area focused mostly on the link between features of the urban environment, individual activity levels and health. Frank and Engelke (2001) review the literature in this area, concluding that urban design can significantly affect activity levels. A preliminary investigation specifically into the drivers of life satisfaction by Brereton et al. (2008) in Ireland finds evidence that a number of spatial

1 This type of valuation exercise, in which the regression coefficient estimate associated with the environmental quality attribute of interest is divided by the coefficient associated with income, remains a controversial approach to environmental valuation, in part because of the tendency of LS regression analysis to yield unreasonably low estimates of income coefficients, resulting in inflated monetized valuation estimates. To explain this tendency, Welsch and Ferreira (2014) note that valuation estimates elicited in this way should theoretically be interpreted in terms of “long-term social value of income” (pp. 216).

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factors play a role in determining life satisfaction: Population density appears positively associated with life satisfaction, as does warmer, less windy climates and proximity to airports and the coast. Stutzer and Frey (2008) find a clear life satisfaction cost associated with commuting time, which is likely linked to the home-size/distance trade-off described above. A number of studies find evidence that increased access to public and urban green space leads to greater life satisfaction (Ambrey and Fleming, 2013; White et al., 2013; Bertram and Rehdanz, 2015; Krekel et al., 2016). OECD (2013) proposes a conceptual framework defining well-being as the result of individual and place-based characteristics, but does not explicitly consider region-level life satisfaction data in their framework. We are not aware of previous research directly examining how landuse fragmentation might be expected to affect wellbeing. However, a number of studies have investigated how local fragmentation (and overall compactness, discussed below) may affect social capital and social interactions. Foster (2006) provides a broad conceptual overview of how urban structure, including local land-use fragmentation, can influence social capital (negatively, in the case of fragmentation). Farber and Li (2013) obtain empirical evidence that land-use fragmentation decreases the potential for social interactions, providing a plausible route for negative effects on wellbeing. From the perspective of economic theory, negative welfare effects of local land-use fragmentation must reflect some type of underlying market inefficiencies or frictions (Irwin and Bockstael, 2002), since otherwise a revealed preference argument would suggest that fragmentation arising under laissez faire conditions implies a welfare improvement. In addition to the effects of land use fragmentation and green space access, a number of factors related to the overall urban structure may influence wellbeing. In a general sense, urban structure can be classified according to population density and distribution, diversity and spatial structure (Tsai, 2005). The spatial structure of an urban area, characterised by its overall shape, may be described as compact versus sprawled. Quantitatively, compactness may be measured as the degree of concentration of the urban population in the relatively high-density areas (Gordon and Richardson, 1997). An operational indicator of compactness is the extent to which the population lives inside the urban core(s) of a city, often referred to as ‘centralization’ (Galster et al., 2001). Varying degrees of city centralization, ceteris paribus, may have both positive and negative implications for life satisfaction. Positive effects could include the aforementioned agglomeration benefits of cities, which may be more pronounced in more centralized cities. However, more centralized cities may also result in more congestion externalities for those living in the central region, and may at the same time isolate those remaining outside of the centralized area. Some of the effects of centralization may be indirect, and can be controlled for through independent observable indicators – for example, employment data can account for some degree of the agglomeration benefits of centralization, whereas residence size can partially account for congestion and the wellbeing costs of reduced living areas. Nevertheless, there are likely to be residual effects of centralization on wellbeing, which argues for the inclusion of such indicators directly in empirical analysis. Some research has suggested that centralization, working in opposition to land-use fragmentation, increases the potential for social interactions (an effect related to aforementioned agglomeration benefits; see Farber and Li, 2013). However, a number of others have suggested that there may be a significant welfare costs of urban compactness (Neuman, 2005). In a contingent valuation study of Barcelona, Garcia and Riera (2003) find that households were willing to pay a significant amount for urban planning oriented towards lower density housing. While the literature shows that the link between centralization and wellbeing is complex, we adopt the hypothesis that the residual effects of increasing centralization is negative, controlling for other key indirect effects, like overall population density and employment rates.

Motivated by this literature review, we empirically examine the following hypotheses: Hypothesis I. In light of the monocentric city model in urban economics, we hypothesise that life satisfaction is positively affected by home size and negatively affected by distance to the urban core, controlling for other factors. Hypothesis II. More green space – forests, farmland, wetlands or urban green space – is generally associated with higher life satisfaction, all other factors being equal. Hypothesis III. Based on previous research on the welfare consequences of sprawl, more fragmentation of land-uses in a household's local vicinity is associated with decreased life satisfaction. Hypothesis IV. Population density has a net positive effect on life satisfaction, due to positive agglomeration externalities. Hypothesis V. City centralization is negatively associated with life satisfaction due both to congestion externalities and to the relative isolation of those remaining outside of the core.

3. Data We merge the 2011 round of the OECD Household Survey on Environmental Policy and Individual Change (EPIC) with GIS-based indicators on land cover and urban structure. Details about the EPIC survey implementation and a full set of descriptive statistics can be found in OECD (2014). In brief, nationally representative groups of approximately 1000 households were sampled for each of the 11 countries involved.2 The target respondent for the survey was someone between 18 and 70 years of age, with some responsibility of household financial decisions, and living in a non-institutional residence. Global Market Insight (GMI), a web survey firm, conducted the survey. To obtain nationally representative samples, GMI maintains panels of respondents who receive periodic requests to participate in various webbased surveys. Respondents from GMI's panels were invited to participate in the OECD survey using a quota sampling methodology. Using national-level statistical and census data, quotas targets were set for gender, age, household income quintile, and geographic distribution within each country. The completion rate for the survey was N 75% in each of the countries. All quota targets were achieved (sample means for target variables within 20% of their population levels), except for household income quintiles: a common pattern was that the raw income distributions in a number of countries appear left-skewed relative to the quintiles reported by national statistical offices. In the sample used here, 13% of surveyed households – likely from the higher income quintiles – declined to report their income. To retain these households in subsequent analysis, we imputed their income by performing a linear ordinary least squares regression of log-income on a broad set of household socioeconomic characteristics and stated assets (such as number of cars owned), and then used predicted values from this regression for the households who did not report their income.3 Data on life satisfaction were elicited using the answers to the following question: “All things considered, please indicate how satisfied you feel with your life at the moment”. In the online questionnaire, respondents were shown a Likert scale, ranging from zero (extremely dissatisfied) to ten (extremely satisfied). This instrument is in keeping with emerging standards in the elicitation of LS (OECD, 2013). 2 Australia, Canada, Chile, France, Israel, Japan, Korea, The Netherlands, Spain, Sweden and Switzerland. 3 The R-squared for this regression was 0.54, with 10,327 observations and 25 degrees of freedom. Details available on request to the authors. Excluding these households from the analysis does not appreciably change the main results.

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Fig. 1. Locations of metropolitan areas. Source: OECD Metropolitan Database (OECD, 2012b). Note: Here are represented OECD Metropolitan Areas for Spain, France Netherlands, Sweden and Japan. Black areas are the core of the metropolitan areas, and blue areas represent the hinterlands. Dots represent approximate locations (postal codes) of surveyed households. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Table 1 Sample sizes in selected countries and in Metropolitan Areas (MA). Countries

Number of MA

National household survey sample

In MA

Survey respondents

Number of MA in our sample

Respondents in our sample

France Japan Netherlands Spain Sweden Total sample

14 33 5 8 3 63

1227 1043 1301 1101 1012 5684

43% 39% 37% 41% 30% 38%

532 406 478 455 302 2173

12 5 5 8 3 33

502 290 428 447 297 1964

The present analysis is restricted to households living inside Functional Urban Areas (FUA)4 for which the economic data is reported in the OECD Metropolitan Database (OECD, 2012b). FUAs consist of urban areas with a population above 500,000 (Fig. 1). After merging the data sources and eliminating FUAs with too few observations (b 10 surveyed households)5 the final sample consists of 1964 households distributed across 33 cities (FUAs) in 6 countries (Table 1). Finally, indicators of composition and fragmentation of land-use are derived from the Corine land cover (CLC) database maintained by the European Environmental Agency (EEA, 2000). This database provides consistent information on land cover across Europe. The satellite images are processed to form a map composed of 100 m × 100 m contiguous cells, which are each coded using the 44 land cover classes. For Japan, land-use data come from the National Land Numerical Information Service with the same resolution, i.e. 100 × 100 m. The household survey provides basic information on the individual characteristics, including the standard variables analysed in the life satisfaction literature. These variables, related to demographics, health, household structure, and financial well-being, are listed in Table 2. In particular, note that the survey collects data on households' home sizes, which while not being measured via GIS or administrative data, is certainly related to urban structure. This allows us to test Hypothesis I. In addition, it is worth noting that the location of respondents is given by their postal code. Our approach to linking individual life satisfaction (and other survey data) with urban structure is to decompose the individuals' environment into two levels (Table 2). The first level is the direct environment surrounding the place of living of the respondent. The location of each respondent is approximated by the area around the household's postal code centroid. The second (higher) level is the metropolitan area in which the respondent lives. In this paper, any references to cities formally means the FUA, a definition which provides comparable territorial and functional units in which individuals live, work and access amenities and develop social relations. Summary statistics for the merged dataset are shown in Table 3. Local factors include a household's distance to the city centre, as well as factors related to land-use. Specific land-use factors include the proportion of land within a 5 km radius6 around the household's postal code centroid which is ‘green’, i.e. devoted to agricultural, forestland or urban green spaces. This allows us to test Hypothesis II. As per Hypothesis III, we also include a measure of land-use fragmentation, again corresponding to a 5 km radius around each household in the sample. Throughout the paper we use the Shannon-Weaver entropy 4 OECD has developed a harmonised definition of urban areas that reflects the functional connections among places and applies this definition to N1000 urban areas in 28 OECD countries, defining the Functional Urban Areas ( OECD, 2012a, b). Urban cores with the FUAs are defined as contiguous 1 km2 grid cells with 1500 inhabitants per km2 (1000 in Canada), excluding small clusters of b50,000 inhabitants. In addition OECD uses the following rule for dealing with policentricity: “a municipality is defined as being part of a urban core if at least 50% of the population of the municipality lives within the urban cluster. If N15% of employed persons living in one urban core work in another urban core, these two cores are combined into a single destination (to take into account policentricity)” (OECD, 2012a, b). 5 This threshold was varied in robustness checks (available on request), and was not found to significantly change the main results. 6 The Supplementary material tests the robustness of the results to different specifications for this radius.

measure of fragmentation (Torrens, 2008).7 This defines the land-use fragmentation Vi around household i as: V i ¼ ∑ j −pij logpij

ð1Þ

where pij is the fraction of land within a 5 km radius around household i's postal code centroid which corresponds to land-use j. For M total land-uses contained in the classification, this index ranges from zero (when all land around the household is associated with a single use) to logM (corresponding to a situation where land around the household is evenly distributed across all uses considered).8 Of course, being based on the same underlying data as the ‘green space’ variable, there is a strong (but nonlinear) relationship between this fragmentation index and the green space variable (see Supplement). This motivates investigation of regression models that includes these variables separately. 4. Econometric Analysis To identify the marginal effects of the variables of interest on life satisfaction, we estimate a regression equation of the following general form: LSifc ¼ logINCifc α þ X ifc β þ Z Lifc γL þ Z Rfc γ R þ ηfc þ ϵifc

ð2Þ

where LSifc⁎ is the latent (not directly observed) life satisfaction for respondent i in FUA (city) f and country c. The survey instrument uses an 11-point Likert scale as an ordinal indicator for underlying life satisfaction. We therefore estimate an ordered probit regression model, which treats the error term ϵifc as following a standard normal distribution and uses maximum-likelihood for estimation. This means that the coefficients from different specifications of the regression cannot be directly compared with one another (i.e. across columns of the regression tables), due to potential scale differences (Greene, 2003). However, the relative magnitudes of the coefficients (i.e. their ratios) can be compared both within and across regressions. In the text we therefore discuss the regression results in terms of ‘income equivalent units,’ i.e. we discuss the coefficient estimates in terms of the percentage change in income that would have the same effect on life satisfaction as the factor of interest (Powdthavee, 2008). The other terms in the regression are as follows: logINCifc is the natural logarithm of household income, Xifc is a matrix of standard individual control variables (e.g. age, age2, gender, employment status, income, health indicators, etc.), following previous studies (Ferreira et al., 2013; Silva et al., 2012; MacKerron and Mourato, 2009; Brereton et al., 2008). The vector ZLifc is a set of locally varying spatial variables: green space as a fraction of nearby land which is green space, land-use fragmentation in the neighbourhood, and relative distance to the urban core (calculated using quartiles of each city's sample). The vector ZRfc contains indicators of urban structure at the city level: population 7 A number of other possible fragmentation indices were explored, such as the Simpson, inverted Simpson and SIEI indices (Torrens, 2008). All of these indices, being constructed from the same underlying data, are highly correlated and yield qualitatively the same results in the econometric estimation. 8 The land use types used are: Artificial Surfaces, Agricultural areas, Forests and seminatural areas, Wetlands and Water.

Z.S. Brown et al. / Ecological Economics 129 (2016) 112–121 Table 2 List of variables.

Table 3 Descriptive statistics.

Variables

Sourcea

Individual variables Income Gender

EPIC

Age Married Young child

Years of postsecondary education Unemployed

Description

Variables

Mean

Std. deviation

Min

Max

Per capita annual income, in 2007 euros Binary variable taking a value of 1 if respondent is male and 0 otherwise. Age of respondent (years). Binary variable taking a value of 1 if respondent is married, and 0 otherwise Binary variable taking a value of 1 if young children are present in the household, 0 otherwise. Number of years of post-secondary education that the respondent completed.

Income Male Age Years of post-secondary ed. Married Children Unemployed Medical conditions per person Home owner Home size (m2) Local spatial variables Land-use fragmentation Green space

€39,768 0.51 44 3.7 0.57 0.34 0.08 1.7 0.65 100

€20,688 – 13 3.2 – – – 2.0 – 50

€8602 0 18 0 0 0 0 0 0 36

€122,319 1 69 12 1 1 1 22 1 264

1.20 0.44

0.29 –

0.06 0

1.75 1

0.27 0.24 0.26 0.24

– – – –

0 0 0 0

1 1 1 1

1.12

1.02

0.07

4.10

3.82

3.43

0.78

14.46

Binary variable taking a value of 1 if respondent is unemployed and 0 otherwise. Medical conditions Number of medical conditions stated to be present in the household, divided by the number of household members Home owner Binary variable taking a value of 1 if the household owns their home. Home size Size of residence, in square meters (m2). Local (household) Computed for 5 km radius around postal code level centroids Distances to the GIS Shortest distance to the boundary of the urban core core (km) Composition of GIS/CLC Proportion of urban fabric, green space and local environment artificial surfaces Shannon-Weaver GIS/CLC Entropy-based measure of land-use index fragmentation Metro (FUA) levelb Density-city MD/GIS Population of the FUA/surface of urbanised area population density Centralization MD Population of the core/population hinterlands a EPIC: OECD Household Survey on Environmental Policy and Individual Change; MD: OECD Metropolitan Database; GIS: computed using ArcGIS; CLC: Corine land cover dataset. b FUA = Functional Urban Area.

density and centralization, as defined above. The associated vectors β ,γL and γR are vectors of life satisfaction effects to be estimated. Incomeequivalent marginal effects (for discrete variables) are calculated by dividing these coefficients by α, the regression coefficient on household income. We examine robustness of results by alternately including local-level variables ZLifc and region-level variables ZRfc over a number of regression specifications. The effect ηfc captures country- and city-level effects, which may be fixed or random. We focus on fixed effects specifications here, due to their consistency across a broader range of specifications.9 The ηfc effect may be decomposed into country- and metro-level components, with ηfc = κc + ιf. We allow at least country-level fixed effects κc throughout, and where possible articulate further to city-level fixed effects ιf. Of course, when the city-level variables ZRfc are included in the regression city-level fixed effects must be excluded, since ZRfc can be expressed as a perfect linear combination of the city-level fixed effects ιf. Table 4 presents estimation results focusing only on the role played by local factors in life satisfaction. The first column (“SES only”) estimates a regression focusing only on the factors available in the survey, i.e. the variables in Xifc and their estimated effects β. Note that this baseline specification includes the variable ‘home size,’ as this was elicited in the survey (Table 2). Most of the estimated effects in this baseline regression confirm previous findings in the literature: Age exhibits a U-shaped relationship with life satisfaction, with an average respondent's life satisfaction reaching a minimum at around 45 years of age. As is typical in this literature, unemployment is among the 9

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We also estimated random effects models, either with country-level random effects or with country-level fixed effects and metro-level random effects. While either of these specifications would be more efficient were they consistent, a Hausman test yielded pvalues too low to be comfortable to be used in the main results.

Relative distance to urban core Top quartile (closest) 2nd quartile 3rd quartile 4th quartile (farthest) Regional, metro-level variables Density (1000s of people/km2) Centralization (core pop./hinterlands pop.)

largest negative determinants of life satisfaction, as is poor health (measured as the number of health conditions per person – ranging from cancer to allergies – reported by each respondent). As per Hypothesis I, home size is positively related to life satisfaction, with an increase of 10 m2 in home size having an equivalent average impact on life satisfaction as a 2% increase in income. Interestingly, this effect becomes slightly larger and more statistically significant when distance to the urban core is included in the regression, which is consistent with the wellbeing trade-off between home-size and distance to the city centre theorized in urban economics. Potential concerns about multicollinearity were also investigated through analysis of the variance inflation factors or VIFs, shown in the Suppl (Greene, 2003). The average VIF for all included variables was 2.2, and the maximum VIF for the individually-varying factors was 1.6, well below any of the thresholds typically recommended as indicating multicollinearity. The VIFs associated with regionallyvarying factors was somewhat more concerning, but still below the threshold of 10 which is typically adopted. Regarding the overall goodness-of-fit of the regression, the generalized R-squared for this and subsequent regressions in the analysis is comparable to other studies (e.g. Brereton et al., 2008; Powdthavee, 2008), bearing in mind the differences in sample sizes (the present analysis using a sample size between the Brereton and Powdthavee studies).10 The last four columns of Table 4 present results for regressions including the local urban structure factors in the matrix ZLifc with associated effects vector γL. These columns explore the potential effects and confounding of local green space levels and land-use fragmentation, and aim to identify the appropriate specification. The land-use fragmentation index and the green space variables, being composed of the same underlying land-use data, exhibit an inverted U-shaped relationship (see Supplement) and can pose potential problems of interpreting estimated coefficients associated with each of these variables. The regressions in columns (a), (b) and (c) in Table 4 consecutively include both land-use fragmentation and green space, land-use fragmentation only, and green space only. Comparison of these

10 The Nagelkerke, Cragg, and Uhler formula for the generalized R2 is used here 2 2 (Nagelkerke, 1991): “R2 ” ¼ ½1−ðL0 Þ =½1−L0 , where L0 is the geometric mean likelihood L for an appropriately specified null model (i.e. including fixed or random effects) and L is the geometric mean likelihood for the model in question. This formula has the advantage of coinciding with the standard R2 when both are computable.

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Table 4 Effects of local factors on life satisfaction.

log(income) Male Age Age2 Years of post-secondary ed. Married Children Unemployed Medical conditions Home owner Home size (m2)

SES only

SES + local variables (a)

(b)

(c)

(d)

0.451⁎⁎⁎ (0.0602) −0.000439 (0.0481) −0.0532⁎⁎⁎

0.446⁎⁎⁎ (0.0605) 0.00262 (0.0481) −0.0532⁎⁎⁎ (0.0123) 0.000590⁎⁎⁎

0.442⁎⁎⁎ (0.0605) 0.00216 (0.0480) −0.0528⁎⁎⁎ (0.0123) 0.000584⁎⁎⁎

0.459⁎⁎⁎ (0.0612) −0.00546 (0.0485) −0.0547⁎⁎⁎

(0.0123) 0.000591⁎⁎⁎ (0.000140) −0.00128 (0.00738) 0.0470 (0.0554) −0.0432 (0.0585) −0.360⁎⁎⁎

0.444⁎⁎⁎ (0.0605) 0.00382 (0.0480) −0.0532⁎⁎⁎ (0.0123) 0.000590⁎⁎⁎ (0.000140) −0.00200 (0.00737) 0.0486 (0.0556) −0.0378 (0.0586) −0.355⁎⁎⁎

(0.000140) −0.00180 (0.00735) 0.0489 (0.0557) −0.0388 (0.0586) −0.357⁎⁎⁎

(0.000139) −0.00181 (0.00740) 0.0514 (0.0556) −0.0399 (0.0586) −0.353⁎⁎⁎

(0.0124) 0.000609⁎⁎⁎ (0.000140) −0.00190 (0.00742) 0.0649 (0.0561) −0.0590 (0.0583) −0.373⁎⁎⁎

(0.110) −0.0470⁎⁎⁎ (0.0130) 0.128⁎⁎

(0.111) −0.0480⁎⁎⁎ (0.0129) 0.135⁎⁎

(0.111) −0.0481⁎⁎⁎ (0.0129) 0.132⁎⁎

(0.111) −0.0477⁎⁎⁎ (0.0129) 0.133⁎⁎

(0.107) −0.0520⁎⁎⁎ (0.0132) 0.124⁎⁎

(0.0548) 0.000936⁎ (0.000510)

(0.0554) 0.00112⁎⁎

(0.0550) 0.00107⁎⁎

(0.0553) 0.00109⁎⁎

(0.0560) 0.00108⁎⁎

(0.000516)

(0.000514)

(0.000515)

(0.000522)

−0.183⁎⁎ (0.0895) −0.0706 (0.122)

−0.168⁎⁎ (0.0854)

−0.0847 (0.0658) 0.00554 (0.0694) −0.118 (0.0746) Fixed⁎⁎⁎

−0.0833 (0.0658) −0.00334 (0.0664) −0.138⁎⁎ (0.0658) Fixed⁎⁎⁎

−0.0909 (0.0657) −0.0134 (0.0688) −0.129⁎ (0.0741) Fixed⁎⁎⁎

None 1964 20 −3818 0.100 9.380

None 1964 19 −3818 0.100 9.057⁎

None 1964 19 −3820 0.0983 5.201

Local spatial variables Land-use fragmentation Green space

Relative distance to urban core 2nd quartile 3rd quartile 4th quartile (farthest) Country effects (Nc = 5) City effects (Nf = 33) Observations Degrees of freedom Log-likelihood Generalized R2 Log-likelihood ratio

Fixed⁎⁎⁎ None 1964 15 −3822 0.0959 1

−0.182⁎⁎ (0.0916) 0.00267 (0.116) −0.0763 (0.0665) −0.00246 (0.0664) −0.133⁎⁎ (0.0660) (Collinear) Fixed⁎⁎⁎ 1964 47 −3790 0.125 64.28⁎⁎⁎

Robust standard errors in parentheses. Statistical significance: ⁎ p b 0.1. ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.01.

regression results shows that the green space variable does not appear to have any explanatory value in the regressions, and simply adds noise. Its coefficient changes dramatically, switching signs, when landuse fragmentation is additionally included. Thus, it seems we can find no evidence in the present data that more local green space increases life satisfaction (Hypothesis II). This result of course does not mean that we accept the null hypothesis of no affect from green space, rather only that we are not able to statistically reject the null hypothesis. A number of methodological issues surrounding this result deserve attention and are addressed at the end of this paper. In contrast, land-use fragmentation appears to have much more explanatory power, and agrees with our hypothesis. Greater fragmentation in surrounding land-use is associated with a significantly negative effect on life satisfaction. Moreover, the estimated effect is quite strong and robust across multiple specifications. A household's distance to the city centre appears to have some effect on life satisfaction, though only at farther distances from the city centre (where the effect is negative). The estimated effect on LS of being in the bottom city-level quartile in terms of distance to the city centre (bottom being the farthest away) is equivalent to the estimated effect of a 25% decrease in household income. However, this effect is only statistically

significant when the green space variable is excluded from the analysis (as we argue it should be, given that the green space variable only seems to introduce noise into the regression). Thus we find some limited evidence to support Hypothesis I related to the monocentric city model, and the trade-off between distance to the city centre and home size in a life satisfaction framework. In general, however, the life satisfaction effects of distance to the city centre are likely quite heterogeneous across cities, due the large variation in amenities and employment opportunities available in the city centre and hinterlands of each city. This may also be explained by the polycentric nature of some cities in our sample.11 In general, the introduction of land-use fragmentation and city centre distance appears to increase the explanatory power of the regression by 4%, from a generalized R2 of 0.0969 in the baseline specification to a generalized R2 of 0.101 when these local urban structure factors are included. The final column (d) in Table 4 brings us to a discussion of inter-city variation in life satisfaction, and the potential impacts of city-level factors. This regression includes city-level fixed effects. 11 We replicated the regressions in this paper restricting the sample to those individuals in monocentric cities (45% of the full sample). The main results are qualitatively the same.

Z.S. Brown et al. / Ecological Economics 129 (2016) 112–121

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Table 5 Effects of regional and local factors on life satisfaction. SES only

log(income) Male Age Age2 Years of post-secondary education Married Children Unemployed Medical conditions per person Home owner Home size (m2)

SES + regional

SES + regional + local

(a)

(b)

(c)

0.451⁎⁎⁎ (0.0602) −0.000439 (0.0481) −0.0532⁎⁎⁎

0.450⁎⁎⁎ (0.0602) −0.000553 (0.0481) −0.0533⁎⁎⁎

0.450⁎⁎⁎ (0.0602) −0.000553 (0.0481) −0.0533⁎⁎⁎

(0.0123) 0.000591⁎⁎⁎ (0.000140) −0.00128 (0.00738) 0.0470 (0.0554) −0.0432 (0.0585) −0.360⁎⁎⁎

(0.0123) 0.000591⁎⁎⁎ (0.000140) −0.00126 (0.00737) 0.0472 (0.0553) −0.0432 (0.0585) −0.360⁎⁎⁎

(0.0123) 0.000591⁎⁎⁎ (0.000140) −0.00126 (0.00737) 0.0472 (0.0553) −0.0432 (0.0585) −0.360⁎⁎⁎

0.453⁎⁎⁎ (0.0602) 0.00129 (0.0482) −0.0532⁎⁎⁎ (0.0123) 0.000592⁎⁎⁎

0.450⁎⁎⁎ (0.0603) 0.00110 (0.0482) −0.0533⁎⁎⁎ (0.0123) 0.000593⁎⁎⁎

(0.000139) −0.00162 (0.00738) 0.0482 (0.0555) −0.0440 (0.0585) −0.364⁎⁎⁎

(0.000140) −0.00160 (0.00738) 0.0501 (0.0554) −0.0448 (0.0585) −0.363⁎⁎⁎

(0.110) −0.0470⁎⁎⁎ (0.0130) 0.128⁎⁎

(0.110) −0.0471⁎⁎⁎ (0.0130) 0.128⁎⁎

(0.110) −0.0471⁎⁎⁎ (0.0130) 0.128⁎⁎

(0.109) −0.0465⁎⁎⁎ (0.0130) 0.126⁎⁎

(0.109) −0.0476⁎⁎⁎ (0.0130) 0.126⁎⁎

(0.0548) 0.000936⁎ (0.000510)

(0.0548) 0.000940⁎ (0.000509)

(0.0548) 0.000940⁎ (0.000509)

(0.0549) 0.000922⁎ (0.000510)

(0.0548) 0.000955⁎ (0.000509)

Local spatial variables Land-use fragmentation (index)

−0.175⁎⁎ (0.0860)

Relative distance to urban core 2nd quartile

−0.0819 (0.0659) −0.00345 (0.0663) −0.138⁎⁎ (0.0659)

3rd quartile 4th quartile (farthest)

Regional, metro-level variables Density (1000s of people/km2)

0.00534 (0.0317)

Centralization (core pop./hinterlands) Country-level effects (Nc = 5) City-level effects (Nf = 33) Observations Number of cities Number of countries Degrees of freedom Log-likelihood Generalized R2 Log-likelihood-ratio test

Fixed⁎⁎⁎ None 1964 33 5 15 −3822 0.0959 0

Fixed⁎⁎⁎ (Collinear) 1964 33 5 16 −3822 0.0959 0.0272

−0.0234⁎ (0.0142) Fixed⁎⁎⁎

0.0441 (0.0372) −0.0334⁎⁎ (0.0166) Fixed⁎⁎⁎

0.0522 (0.0375) −0.0336⁎⁎ (0.0165) Fixed⁎⁎⁎

(Collinear) 1964 33 5 16 −3821 0.0971 2.647

(Collinear) 1964 33 5 17 −3820 0.0977 4.007

(Collinear)

21 −3816 0.102 13.24⁎⁎

Robust standard errors in parentheses. Statistical significance: ⁎ p b 0.1. ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.01.

Examining the generalized R2 of 0.125 here, we see that city-level factors could significantly increase the explanatory power of the regression. Furthermore, we see that the estimated coefficients on the other explanatory variables do not change appreciably, suggesting that our specification is robust. Table 5 presents results from regressions which account for citylevel factors of urban structure which are hypothesised to predict life satisfaction. The first regression column here simply replicates the baseline specification from Table 4, for ease of reference. Columns (a) to (c) explore the estimates from alternately including population density and centralization individually and in combination. The last column presents regression results including both regional and local-level variables. Our results indicate that centralization has a statistically significant negative effect on life satisfaction, in support of Hypothesis V. We obtain some suggestive indications that population density is associated with higher LS (Hypothesis IV), based on the positive sign for this regression

coefficient across a wide variety of alternate econometric specifications, but the estimated coefficient is statistically insignificant across all specifications analysed. Interestingly, inclusion of both the population density and centralization variables instead of only one of these variables (Table 5, column (c) compared to (a) and (b)) increases the estimated magnitudes and p-values of both coefficients (though the population density coefficient remains statistically insignificant, with a p-value = 0.24 in column (c)). Similarly to the relationship between home size and distance to the urban core, this observation is consistent with the hypothesis that these factors (i) have opposing effects on life satisfaction and (ii) are positively correlated, thus posing a potential omitted variable bias if only one of these variables is included in the regression. In general, city-level factors examined here increase explanatory power of the regression by an additional 2% increase in the R2 (comparing the last column in Tables 5 to column (b) in Table 4), far less than the 25% improvement in the R2 obtained with city-level fixed effects (comparing the last and (b) columns of Table 4). Indeed, a number of

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Z.S. Brown et al. / Ecological Economics 129 (2016) 112–121

Fig. 2. Estimated life satisfaction effects. Bars are 95% confidence intervals, calculated using the delta method. Discrete changes are estimated in WTP terms, with WTPx ¼ sgnðβx Þ½1− expð− jβαx jÞ, with βx and α being the estimated regression coefficients for the discrete factor x and log(income) respectively. For factors with βx b 0, income-equivalent is interpretable as WTP to avoid the loss.

other city-level factors were explored in these regressions (not shown here), none of which provided significant explanatory power.12 There thus seems to be an important empirical question to be explained in future analysis: What are the largest drivers of inter-city variation in life satisfaction?

5. Discussion This paper provides evidence for the conventionally theorized wellbeing trade-off between home size and distance to the city centre. In addition, we analyse the wellbeing effects of aspects relating to urban structure that have not received attention in previous research: Local land-use fragmentation and overall city compactness have a strongly negative effect on life satisfaction. We find no evidence that population density, green space or congestion have any measureable effects on life satisfaction. To better understand the implications of our results, Fig. 2 shows the magnitudes of the estimated effects of the urban structure variables on life satisfaction, in terms of an equivalent percentage change in household income. For the continuous variables (Fig. 2) – home size, level of centralization and land-use fragmentation – the figure shows the estimated wellbeing effect of a 10% increase in each variable relative to its sample mean. The average marginal effect of a 10% increase in home size is comparable to a 2% increase in household income, an effect which is almost exactly negated by a 10% increase in centralization (holding constant the household's relative distance to the urban core). A 10% increase land-use fragmentation equates to an estimated 6% loss in household income. For the discrete factors (living in the urban fringe versus the core, shown next to unemployment, for comparison), income-equivalent effects can be expressed in terms of willingness-to-pay (WTP) as a fraction of income, by the formula 1− expð− jβαx jÞ, where βx is the estimated regression coefficient for each factor x, and α is the estimated regression coefficient on log(income).13 We see that living in the urban core versus the periphery is equivalent to a 22% gain in household income, roughly a third of the negative wellbeing effect of unemployment. The estimated effects are instructive when considered in relation to the compact cities policies that are frequently advocated, including 12 These additional indicators included indices of ‘leapfrog’ development (scattering), growth in the urban area, city-level unemployment, and alternative congestion measures. 13 This formula is derived from the log-income wellbeing specification in Eq. (2). The sign of the effect shown in Fig. 2 is based on the sign of estimated coefficient: a negative income-equivalent impact is interpreted as WTP to avoid the change. An alternative would be to compute relative willingness-to-accept (WTA), which is expðjβαx jÞ−1≥ 1≥ WTP. We use WTP because it provides a more conservative estimate of income-equivalent effects for discrete factors.

development taxes, split-rate property taxes for structures and land, fuel taxes, subsidies for urban green space development and urban growth boundaries (OECD, 2012a). While the purpose of these policies is usually to correct some environmental or congestion-related externality (and thereby improve welfare), life satisfaction research of the type presented here can point to potential political economy and distributional challenges arising from these policies. Consider, for example, the case of fuel taxes or congestion charges. Such taxes can have significant pecuniary implications for household wellbeing, especially low-income households (given a well-known income inelasticity of demand for fuel and private car use). Yet fuel taxes are important policy instruments for correcting externalities associated with sprawl and traffic congestion (Timilsina and Dulal, 2011; OECD, 2012b). Given that economically meaningful fuel taxes can be anticipated to increase city centralization over time, the above analysis suggests that one side-effect of such fuel taxes would be reduced life satisfaction among current residents, not only because of the pecuniary effects of the tax due but also because of increased centralization. Compensating the ‘losers’ from a policy of higher fuel taxes or congestion charges would thus necessitate not only offsetting the wealth effect (via a lump-sum transfer), but also providing additional lump-sum compensation (to all residents in the city) for the residual wellbeing effects of increased urban centralization. Of course, the rationale for this policy argument likely depends on the precise mechanisms by which city centralization affects life satisfaction, a question or future research. Our finding of negative wellbeing effects of local land-use fragmentation also has important policy implications. It is easy to see how, under laissez faire conditions, fragmentation would arise as a negative externality of individual property development decisions (Irwin and Bockstael, 2002). While fragmentation is clearly related to sprawl and with green space access (as discussed above), it is a distinct phenomenon, and we find evidence here that it has its own effects on individual effects on life satisfaction. A core principle in environmental economics is to ensure that each market failure has its own policy instrument to address it, rather than trying to address multiple market failures with a single instrument (Baumol and Oates, 1988). Whereas there are other (arguably) effective policy instruments to address sprawl and green space access, (e.g. split rate property taxes, conservation reserves and subsidies for green space; see OECD, 2012b), zoning regulations appear to be the only widely adopted class of instruments specifically addressing fragmentation. A less coercive alternative instrument could consist of a development tax with a variable rate adjusted according to the same entropy-based measure of fragmentation used in this paper. Certainly, the above policy discussion remains exploratory, and all of these findings deserve to be probed further. The data used here, while permitting novel analysis of under-investigated research questions, have their own limitations (many of which arise frequently in the life satisfaction literature). First, as with almost all studies of this type there are potential concerns about endogeneity between regressors and the dependent wellbeing variable. Of particular concern, given the focus of our paper, is endogenous location choice. For example, households with a greater preference for living near the urban core may be intrinsically happier, a correlation which would lead to an overestimation of the wellbeing bonus of living near the core. Ideally, we would possess individual panel data with which we could control for intrinsic differences in life satisfaction. The inclusion of a relatively rich set of SES controls in the regression, along with country-level (and, where possible, city-level) fixed effects, should ameliorate this potential source of bias to some degree, but certainly our findings in this paper should be further tested with panel data in the future. An additional robustness check we have performed is to replicate the main regressions in this paper, restricting the sample to only those individuals who have resided in their homes for over 2 years (approximately 84% of the full sample). While this check certainly does not rule out endogenous location choice as a confounder, it is reassuring that our main qualitative results survive in such a regression.

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Another limitation is that we lack additional detailed local-level data on, for example, crime, school access and quality, access to groceries stores and other retail establishments, as well as walkability indicators. These place-based characteristics may be correlated with our land-use fragmentation index. The question of how these correlations relate to the wellbeing effects of fragmentation in fact draws attention to the fundamental question of the mechanisms underlying the findings presented here. An investigation into these mechanisms is warranted in future research. Lastly, we lack geocoded housing price information matched to the survey data. This limitation applies to most published studies examining the life satisfaction effects of environmental quality (with some exceptions, e.g. Rehdanz and Maddison, 2008), and means that the estimated welfare effects of local land-use fragmentation, for example, should be interpreted as the net effect on indirect utility, including both the direct effect of fragmentation on utility, as well as any indirect feedback effects on local housing prices. Disentangling the direct utility and indirect price effects of amenities on wellbeing represents a research area of broad importance to the life satisfaction literature (Welsch and Ferreira, 2014). However, even if we can only estimate the net effect of key urban structure indicators, our results remain relevant in that we find the net life satisfaction impacts of local and regional indicators to be statistically significant. This implies the existence of a direct utility or indirect price effect of these variables, which at the very least argues for the inclusion of these factors in future empirical studies on the determinants of life satisfaction. Acknowledgements The views contained in this paper are those of the authors and do not necessarily reflect those of the OECD or its member countries. Without implicating them for any remaining errors, the authors thank three anonymous reviewers of earlier versions of this manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ecolecon.2016.05.004. References Adler, M., 2012. Well-Being and Fair Distribution: Beyond Cost-Benefit Analysis. Oxford University Press. Alonso, W., 1964. Location and Land-Use: Toward a General Theory of Land Rent. Harvard University Press. Ambrey, C., Fleming, C., 2013. Public greenspace and life satisfaction in urban Australia. Urban Stud. 51 (6), 1290–1321. Baumol, W.J., Oates, W.E., 1988. The Theory of Environmental Policy. Cambridge University Press. Bertram, C., Rehdanz, K., 2015. The role of urban green space for human well-being. Ecol. Econ. 120, 139–152. Brereton, F., Clinch, J.P., Ferreira, S., 2008. Happiness, geography and the environment. Ecol. Econ. 65 (2), 386–396. Deaton, A., 2008. Income, health and wellbeing around the world: evidence from the Gallup world poll. J. Econ. Perspect. 22 (2), 53–72. EEA, 2000. CORINE land cover technical guide. European Environment Agency Technical Guides, No. 40 (available at: http://www.eea.europa.eu/publications/tech40add). Farber, S., Li, X., 2013. Urban sprawl and social interaction potential: an empirical analysis of large metropolitan regions in the United States. J. Transp. Geogr. 31, 267–277. Ferreira, S., Alpaslan, A., Brereton, F., Cuñado, J., Martinsson, P., Moro, M., Ningal, T.F., 2013. Life satisfaction and air quality in Europe. Ecol. Econ. 88, 1–10.

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