Related Literature

Easterlin (1974) analyzes differences in wellbeing across countries and over time and points out that human wellbeing does not depend exclusively on income. Within countries his findings suggest a positive relationship between income and SWB, but when analyzing across countries this relationship diminishes. The Easterlin Paradox refers to this finding.

Easterlin (1974) concludes that individuals compare their own wealth with the wealth of their peer group. Hence, relative income matters more for wellbeing than absolute income.

Frey & Stutzer (2002) analyze the relationship between SWB and income in a cross county setting. They find that higher income on average contributes to SWB but at a diminishing rate. Therefore, one may expect large gains in SWB at lower levels of income. Frey &

Stutzer (2002) conclude that individuals’ aspirations adjust thus they always strive for more and these wants are insatiable. Di Tella et al. (2003) and Di Tella & R. MacCulloch (2006) test the effect of the macro-economic environment on SWB. They find that recessions create strong psychic loses besides the decline in GDP and the rise in unemployment. Finally, Di Tella & R. MacCulloch (2008) bring together macro and micro variables and disprove the Easterlin Paradox. After controlling for macroeconomic stability, crime rates, environmental degradation, working hours and life expectancy they find increasing rates of SWB with rising income even across countries.

Frijters & Van Praag (1998) investigate the impact of climate variables on life satisfaction.

They analyze the impact of changes in temperature, humidity and precipitation on life satisfaction with a panel of 3727 households in Russia and find that a rise in annual minimum temperatures would lead to lower heating expenses and higher life satisfaction.

Rehdanz & Maddison (2005) use country-averaged data on happiness provided by the World Database of Happiness by Veenhoven (2001) to analyze the impact of climate

variables on happiness for 67 countries over the period from 1972 to 2000. Regarding the variables for climatic conditions, they apply various indices on temperature and precipitation as well as locational parameters like absolute latitude. Results from a panel-corrected least squares approach do not prove a significant effect of changes in annual average temperature or rain on happiness. But they find a negative effect of an increase in the mean temperature of the annual hottest month and a positive effect on happiness due to an increase in the mean temperature of the coldest month. By applying predicted changes in temperature and precipitation levels for 2039 and 2069, they calculate the change in income required to keep happiness at a constant level. Their results support earlier findings that high-latitude countries will benefit from climate change, but low-latitude countries are likely to suffer most. Maddison & Rehdanz (2011) analyze potential GDP per capita loses and gains based on climate change scenarios in another country panel study. In this analysis they do not refer to the hottest and coldest month’s temperature as the variable of interest but refer to the number of “degree months” which represent the deviation from a generally appreciated temperature of 18.3°C. Again they find that countries located in northern Europe might gain, meanwhile African countries may have to face GDP loses based on the climate change scenarios. Becchetti et al. (2007) provide a similar setting as Maddison & Rehdanz (2011) but do not average the data on happiness over countries.

They use the individually reported data on happiness and find, that a rise in the number of hot months, with temperatures above 20°C, or the number of rainy days has a positive effect on happiness; meanwhile an increase in mean temperature shows a negative effect.

Brereton et al. (2008) analyze the relationship between life satisfaction and climate variables such as temperature, precipitation and wind speed in Ireland. With a geographic information system they match an individuals’ place of residence precisely with the climate data and find that an increase in the temperature of the annually coldest and hottest month leads to gains in life satisfaction meanwhile a rise in wind speed leads to a decline.⁵ There are concerns about the analysis of SWB. First of all, there are two commonly used measures of SWB, which are treated equally in the literature. One, which asks for the level of life satisfaction, and a second one, which asks for the level of happiness. Stevenson &

Wolfers (2008) point out that those measures should not be treated equally since they tend

5 For an overview on the studies concerning SWB and climate refer to Table A.1 in the Appendix to this chapter.

Related Literature 12

to measure different things. The former accounts for the individual’s perception of how his or her life has been so far. Meanwhile the later one captures the current sensation of life or a state of mood when the individual is asked: “How happy are you with your life?” This difference in the perception of the question might explain the low correlation between the two variables. Another major issue is the inconsistency of the data. Krueger & Schkade (2008) tested the correlation between test and the re-test results and conclude that there is either a strong unobserved bias when answering the questions or the people are very inconsistent in their perception of SWB. Ferrer-­‐i-­‐Carbonell & Frijters (2004) address methodological issues and point out that the assumption of cardinal or ordinal scales makes little difference, but allowing for individual fixed effects changes the results.

The results of the studies regarding life satisfaction or happiness and climate vary a lot.

This could be due to the different methods and samples applied. Rehdanz & Maddison (2005) and Maddison & Rehdanz (2011) use country averaged data on happiness and life satisfaction. They cannot control for individual characteristics such as being married or unemployed but they can control for the macroeconomic country environment such as GDP per capita growth and inflation. Frijters & Van Praag (1998) and Brereton et al.

(2008) analyze individual life satisfaction in Russia and Ireland. Hence they look more homogenous but smaller samples. They can control for individual characteristics but not for the respective macroeconomic environment.

None of the studies uses the climate data from the specific month when individuals were questioned regarding their level of SWB and none of the studies controls for generation specific effects over time. I close this gap in the literature by constructing a pseudo panel and controlling for the cohort specific effect on SWB. My findings regarding monthly mean and maximum temperature as well as precipitation remain robust over all model specifications. I find an inverse N-shaped⁶ relationship between mean monthly temperatures and SWB with a turning point at 22°C. Most of the observations have already past this turning point and a further rise in mean temperatures would on average lead to a decline in levels of SWB for this sample of 18 Latin American countries.

6 The inverse N-shaped relationship describes a curve with initially declining levels of SWB until the lower turning point, which is a minimum point. After passing through the minimum levels of SWB rise until the upper turning, which is a maximum point.