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Rising income and the subjective well-being of nations

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TLDR
It is found that changes in household income were associated with concomitant changes in life evaluations, positive feelings, and negative feelings, which suggest that income standards are now largely global, with little effect of national social comparison.
Abstract
We explored whether rising income in nations is associated with increasing subjective well-being (SWB), with several advances over earlier work. Our methods are improved in that across time, the same well-being questions were asked in the same order, and we employed broad and equivalent representative samples over time from a large number of nations. We also assessed psychosocial factors that might mediate the relation of income and SWB. We found that changes in household income were associated with concomitant changes in life evaluations, positive feelings, and negative feelings. The effects of gross domestic product (GDP) change were weaker and significant only for life evaluations, perhaps because GDP was a less certain index of the standard of living of the average household. The association of income and SWB is more likely to occur when the average person's material welfare accompanies rising income, when people become more satisfied with their finances, and when people become more optimistic about their futures. People did not adapt to the income rises during the period of years we studied, in that income rises produced SWB increases that did not return to earlier levels. It appears that previous researchers failed to come to agreement because of the small sample sizes of the nations, the inconsistent methods across years and surveys, and the lack of measures of potential mediating variables. Analyses of income relative to people in one's nation and between-nation slopes together suggest that income standards are now largely global, with little effect of national social comparison. (PsycINFO Database Record (c) 2013 APA, all rights reserved)

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References
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Book

Hierarchical Linear Models: Applications and Data Analysis Methods

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