Measuring Socio-economic Factors and Sensitivity of Happiness
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There is significant variation in average subjective well-being across countries. What makes people in some countries happier or more miserable than others? In this study, we decompose the difference in average subjective well-being across countries into a comprehensive set of socio-economic factors along with cross-country difference in sensitivity of happiness in order to answer the question. While an individual’s subjective well-being is affected by socio-economic status, every individual does not necessarily draw the same level of subjective well-being from a given condition of life because of different personal characteristics. Sensitivity of happiness is an umbrella term capturing such factors that are not reflected by socio-economic conditions. We introduce Data Envelopment Analysis (DEA) approach to estimate a happiness function and specify the sensitivity score for each country in a sample. We draw on a comprehensive set of well-being indicators released by the Better Life Initiative of the OECD, along with measures of income inequality. We find that the health factor and sensitivity term play the largest role in generating variation in subjective well-being. Even within countries, the average level of subjective well-being varies between different population groups. Drawing on a set of indicators that assess the life circumstances of different groups within each country, our decomposition formulation allows for a full explanation of the differences in average life satisfaction between the groups. We investigate the differences between men and women, and high income earners and low income earners.
KeywordsSubjective well-being Happiness function Data envelopment analysis Better life index
JEL ClassificationI31 D24 D63 O15
The author is grateful for two anonymous referee referees, Jiro Nemoto, Tomohiro Tasaki, Akiko Kamesaka, and Bernhard Mahlberg for their helpful comments and suggestions. This article was completed when the author visited Center for Efficiency and Productivity Analysis in the School of Economics at the University of Queensland. The good research environment offered by the institute and the department is greatly appreciated. This research was financially supported by Grant-in-Aid for Scientific Research (KAKENHI 25870922). All remaining errors are the author's responsibility.
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