1. Environment and Happiness: New Evidence for Spain Juncal Cuñado Fernando Pérez de Gracia (University of Navarra) * Financial support from the Ministerio de Ciencia y Tecnología (Spain) and European Science Foundation is acknowledged

2. Outline of the Presentation 1. Motivation and objectives 2. Literature review 3. Empirical analysis (Spanish regions) - Significant regional differences in happiness (after controlling for socio-economic variables) - Impact of regional climate and pollution variables on happiness - Monetary value of non-market goods 4. Concluding remarks 5. Future research

3. 1. Motivation and objectives -Economics of happiness: monetary socio-economic indicators (per capita GDP) are insufficient measures of well-being of citizens (United Nations, 1954; Erikson, 1993) -Evaluate welfare effects of different factors, such as -Health (Berger and Leigh, 1989, Blanchflower and Oswald, 2008) -Education (Di Tella et al, 2001) -Macroeconomic variables (Di Tella et al, 2001) -Terrorism (Frey et al, 2009) -Noise (Van Praag et al, 2005) -Air pollution (Welsch 2002, 2006, 2007; Di Tella and MacCulloch, 2006; Ferrer-i-Carbonell, 2007; Luechinger, 2009, 2010; MacKerron and Mourato, 2009) -Climate (Frijters and van Praag, 1998; Rehdanz and Maddison, 2005 2008; Brereton et al., 2008),... -This paper: implications of environmental policies on individual well-being (Spanish regions)

4. 1. Motivation and objectives Objectives: - Impact of climate and air pollution conditions on happiness in Spanish regions using individual-level data from the European Social Survey and regional data on macroeconomic, climate and pollution from INE, AEMET and MMA - Do climate and pollution variables at regional level affect individual happiness? - Are these variables more significant than macroeconomic variables such as per capita GDP or unemployment in explaining individual happiness? - Do these variables explain regional differences in subjective well-being (individual happiness)? - Monetary value of non-market goods (climate, pollution)

5. 2. Literature review Climate and pollution on happiness: - Rehdanz and Maddison (2005): temperature plays a significant role in explaining happiness (data for 67 countries) - Becchetti (2007): non-linear effects of climate variables on happiness - Brereton et al. (2008): empirical analysis for Ireland - Welsch (2006): negative and significant effect of air pollution, using data for ten European countries - Luechinger (2010): air pollution affects negatively on SWB - Ferrer-i-Carbonell and Gowdy (2007): concern about ozone pollution and concern about species extinction - Zidanseck (2007): happier people tend to care more about the environment and people who live in a better environment tend to be happier

6. 3. Empirical analysis -Happiness (ESS): individual´s responses to the question “ How happy are you ”. The respondent answers on a scale from 1 (not happy at all) to 10 (completely happy). -Socio-economic individual variables (ESS) -Gender -Age -Income -Subjective general health: discrete variable with takes the following values: 1 (very good), 2 (good), 3 (fair), 4 (bad), 5 (very bad) -Marital status: 1 (married), 2 (in a civil paternship), 3 (separated), 4 (divorced), 5 (widowed), 6 (never married, never civil paternship) -Children: 1 (yes), 0 (no) -Main activity: 1 (paid work); 2 (education); 3 (unemployed looking for job)... -...

7. 3. Empirical analysis -Macroeconomic variables (INE, Instituto Nacional de Estad í stica) - Per capita GDP -Unemployment rate -Climatological variables (AEMET, Agencia Estatal de Meteorolog í a) -T: anually averaged mean temperature ( º C) -Tmax: average mean temperature in hottest month, July ( º C) -Tmin: average mean temperature in coldest month, January ( º C) -R: regional averaged mean precipitation, July and January (mm) -H: regional relative humidity -DR: rain (number of days) -DN: snow (number of days) -DT: storms (number of days) -DF: fog (number of days) -DH: freeze (number of days) -DD: sun (number of days) -I: sun (number of hours)

8. 3. Empirical analysis -Pollution variables (MMA, Ministerio de Medio Ambiente) -CO 2 emissions (tons per km 2 ) -NO 2 concentration -PM 10 (number of days per year in which PM 10 concentration exceeds 35  g/m 3 )

9. 3. Descriptive statistics

10. Significant regional differences in happiness (F=4.70***) Andalucía, Castilla-la Mancha, Cantabria La Rioja, Canarias

11. 3. Descriptive statistics

12. -Higher temperatures in Southern regions (Extremadura, Andalucía, Murcia) -Higher precipitation values in Northern regions (Galicia, Asturias) -More polluted regions: Aragón, Castilla-León (thermic centrals)

13. 3. Methodology 1.Regional differences in subjective well-being (ANOVA test on mean differences) 2.Model including socio-economic individual indicators, macroeconomic, climate and pollution variables 3.Monetary value of non-marketed goods

14. 3. First results

15. 4. Concluding remarks -Increasing number of papers relating subjective well-being with environmental variables - Climate and pollution variables help explaining regional differences in subjective well-being - Negative significant impact of pollution variables (PM 10 concentration) - Other geographical variables ( “ coast ” dummy variable) - Multicolinearity among climate variables - Negative impact of higher July minimum temperature - Usual results of individual socio-economic variables on happiness: health, income, being unemployed, age... - Non significant effects of regional macroeconomic variables (per capita GDP, unemployment rate) on individual happiness - Monetary value of climate and pollution variables

16. 5. Future research -Multilevel modelling approach -Extend the analysis to the European regions

17. THANK YOU