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Environment and Happiness: New Evidence for Spain

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Abstract

This paper explores the relationship between air pollution, climate and reported subjective well-being (or happiness) in Spanish regions. The results show that, after controlling for most of the socio-economic variables affecting happiness, there are still significant regional differences in subjective well-being. Evidence also suggests that climate and air pollution variables play a significant role in explaining these regional differences in happiness. The analysis also allows us to calculate the monetary value of air quality and climate, deriving the average marginal rate of substitution between income and air quality and climate for the Spanish regions.

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Notes

  1. Throughout the paper we use the terms happiness and life satisfaction interchangeably. Many recent studies assume happiness and life satisfaction as synonymous (see, for example, Rehdanz and Maddison 2005 and Caporale et al. 2009). In this paper, we use both happiness and life satisfaction scores as measures of subjective well-being (i.e., dependent variable in our empirical analysis). The results are very similar with both proxy variables (happiness and life-satisfaction). The life satisfaction empirical evidence is available upon request.

  2. An excellent survey on the implications of happiness research for environmental economics has appeared recently in Ecological Economics (Welsch 2009).

  3. In a seminal paper, Van Praag (1988) analyzed the influence of climate on a household’s cost level.

  4. Other related papers also focused on the effect of extreme event such as floods (Luechinger and Raschky 2009) and droughts (Carrol et al. 2009) on happiness.

  5. We have compared these data with that obtained by Goerlich (2010), who constructs historical climate variables for Spanish regions using GIS techniques, and the correlation coefficient are in all the cases close to 1.

  6. It is worth mentioning here that the most polluting thermic centrals in Spain are As Pontes (in Galicia), Andorra (in Aragon) and Compostilla (in Castilla y Leon).

  7. The model is in the spirit of Van Praag et al. (2003).

  8. The dependent variable in Eq. (1), happiness, is an index which takes values 1–10, so that the econometric analysis could be performed using an Ordered Logit model, similar to Cunado and Perez de Gracia (2012). Ferrer-i-Carbonell et al. (2004) show that assuming cardinality or ordinality of the answers to subjective well-being questions is relatively unimportant to results. In this paper, we have estimated Eq. (1) using both methodologies, OLS and Ordered Logit models, and the results are very similar. However, we report OLS estimates in order to facilitate the interpretation of the results when calculating monetary values of climate and pollution variables. The Ordered Logit model estimation is available upon request. For a detailed description of Ordinal Logit models see Maddala (1983).

  9. Based on the definition of the income variable (see Table 2), the estimated income coefficient will measure the increase in happiness due to a change in income equivalent to a change in a decile.

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Acknowledgments

We thank the editor and an anonymous referee for helpful comments and valuable suggestions on this paper. The authors gratefully acknowledge financial support provided by the Ministerio de Ciencia y Tecnologia (ECO2008-02458-E) within the EUROCORES HumVIB programme of the European Science Foundation. An earlier version of this paper is published as Working Paper in FUNCAS.

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Authors and Affiliations

  1. Facultad de Ciencias Económicas, Universidad de Navarra, Campus Universitario, Edificio Biblioteca, Entrada Este, 31080, Pamplona, Spain

    Juncal Cuñado & Fernando Pérez de Gracia

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  1. Juncal Cuñado
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  2. Fernando Pérez de Gracia
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Correspondence to Fernando Pérez de Gracia.

Appendix: Happiness and Environment for 1st, 2nd, 3rd and 4th Waves

Appendix: Happiness and Environment for 1st, 2nd, 3rd and 4th Waves

Although the main results of the paper are referred to year 2008, in this Annex, we present the data for the main variables referred to years 2002, 2004, 2006 and 2008. Table 5 reports the regional differences in happiness across the Spanish regions, using data from the 4 waves of the European Social Survey. When we estimate Eq. (1) including as explanatory variables the socio-economic variables outlined in the paper, the main results are the same for each of the four waves:

Table 5 Regional differences in happiness
Full size table

  • The effect of socio-economic variables (age, gender, marital status, health, family size, income, education) is similar for each of the four waves.

  • Once included all those socio-economic variables, there are still significant differences in happiness across Spanish regions. We believe that, as in the paper, pollution, climate and other geographical variables (such as coast, population density) might also explain these regional differences in previous years.

However, and for pollution variables, we do not have comparable regional data for NO2 and PM10 before 2005. Table 6 shows the CO2 regional emissions for 2002, 2004, 2006 and 2008. When including this variable in happiness regression, the results suggest that this variable has a negative and significant effect on individual happiness.

Table 6 CO2 regional emissions (Tons per km2)
Full size table

In order to compare the results in different periods, in Table 7 we present the estimation of the Monetary Value of Pollution for 2006 and 2008, periods in which we have available and comparable data for pollution. As shown in this table, the monetary value of pollution ranges from 325 to 560 euros (see models 2 and 3).

Table 7 Monetary value of pollution (PM10)
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Cuñado, J., de Gracia, F.P. Environment and Happiness: New Evidence for Spain. Soc Indic Res 112, 549–567 (2013). https://doi.org/10.1007/s11205-012-0038-4

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