The paper uses household economic panel data from five countries—Australia, Britain, Germany, Hungary and The Netherlands—to provide a reassessment of the impact of economic well-being on happiness. The main conclusion is that happiness is considerably more affected by economic circumstances than previously believed. In all five countries wealth affects life satisfaction more than income. In the countries for which consumption data are available (Britain and Hungary), non-durable consumption expenditures also prove at least as important to happiness as income.
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This assumes that both capital gains and bequests are treated as income in the year in question.
If included in a measure of overall consumption, both these items actually lowered the correlation between consumption and subjective outcomes. These initially surprising results are probably due to the fact that housing expenditure is strongly related to how recently one bought one’s dwelling, and fuel expenditure is related to the age of a dwelling, as well as size.
Respondents gave their answers to these three questions within 12 expenditure bands (under 10 pounds, 10–19, 20–29 etc). In calculating total consumption we assumed expenditure at the mid-point of the band.
The expenditures covered were utilities, rent/mortgage, clothing, doctors, prescribed medicines, other medicines, transport, cleaning woman, nurse, baby-sitting, food, tuition fees, holidays, charity gifts, and money spent on other households.
However, in a trial run, results for the effects of wealth and income on life satisfaction were significant at the 0.001 level, although the size of the effects was small, as would be expected with a dichotomous dependent variable.
However, there were weak but significant relationships between durables expenditure and satisfaction with standard of living.
Five terms relating to the food items enter equations: the log of groceries expenditure and its square, the log of meals out and its square, and the cross-product of the logs.
However, the two items by themselves appear not to be useful in accounting for life satisfaction and standard of living satisfaction.
The variance explained was 64.5%.
1996 was preferred to 1997 because sample attrition was substantial in the final year of the panel.
Dr Zsolt Speder, a co-investigator in Tarki, confirms that research using the Hungarian panel consistently finds a close relationship between life satisfaction and standard of living satisfaction (r ∼ 0.6), and consistently finds that economic variables account for as much variance in life satisfaction as in the domain satisfaction.
Medium term correlations for Hungary, based on averaging wealth, income and consumption over 5 years, were considerably higher. The correlations between wealth and income, and between wealth and consumption were about 0.5, and the correlation of income with consumption was about 0.7.
So the effect of including subjective variables on the RHS might well have been to bias the coefficients of main interest due to covariation between the subjective variables, omitted variables like personality traits and the dependent variable of life satisfaction.
The dependent variable is really only an ordinal scale, so strictly speaking an ordinal scale technique like ordered probit analysis would be more appropriate. However, like many researchers before us, starting with Andrews and Withey (1976), we found that OLS and ordinal scale results were substantively little different, and OLS has the advantages of familiarity and ease of interpretation.
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This paper reports on research being conducted as part of the research program, “The Dynamics of Economic and Social Change: An Analysis of the Household, Income and Labour Dynamics in Australia Survey.” It is supported by an Australian Research Council Discovery Grant (DP0342970).
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Headey, B., Muffels, R. & Wooden, M. Money Does not Buy Happiness: Or Does It? A Reassessment Based on the Combined Effects of Wealth, Income and Consumption. Soc Indic Res 87, 65–82 (2008). https://doi.org/10.1007/s11205-007-9146-y
- Panel data