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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. Outline of the Presentation. 1. Motivation and objectives

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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

outline of the presentation
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

1 motivation and objectives
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)

1 motivation and objectives1
1. Motivation and 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)

2 literature review
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

3 empirical analysis
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)




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)...


3 empirical analysis1
3. Empirical analysis

Macroeconomic variables (INE, Instituto Nacional de Estadística)

Per capita GDP

Unemployment rate

Climatological variables (AEMET, Agencia Estatal de Meteorología)

T: anually averaged mean temperature (ºC)

Tmax: average mean temperature in hottest month, July (ºC)

Tmin: average mean temperature in coldest month, January (ºC)

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)

3 empirical analysis2
3. Empirical analysis

Pollution variables (MMA, Ministerio de Medio Ambiente)

CO2 emissions (tons per km2)

NO2 concentration

PM10 (number of days per year in which PM10 concentration exceeds 35 mg/m3)

3 descriptive statistics1
3. Descriptive statistics

Significant regional differences in happiness (F=4.70***)

Andalucía, Castilla-la Mancha, Cantabria

La Rioja, Canarias

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

Regional differences in subjective well-being (ANOVA test on mean differences)

Model including socio-economic individual indicators, macroeconomic, climate and pollution variables

Monetary value of non-marketed goods

4 concluding remarks
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 (PM10 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

5 future research
5. Future research

Multilevel modelling approach

Extend the analysis to the European regions