A Happiness Study Using Age-Period-Cohort Framework
Age effects and birth cohort effects have not been differentiated in happiness studies. In this paper, age-period-cohort decomposition is applied to happiness data in the US. Since the relationship is linear, such as age = period − cohort, it is not possible to identify the three effects. This paper considers four identification models: the polynomial age-effect model, the proxy-variable model, the orthogonal period-effect model, and the principal component model. Happiness data are obtained from the General Social Survey for 1972–2008. Except for the polynomial age-effect model, three alternative models provide similar results. In particular, there is little difference between the decomposition results obtained by the orthogonal period-effect model and by the principal component model. The age effect shows downward movements for 18–55 and for 80–89, an upward movement for 56–69, and an almost flat movement for 70–79. The period effect shows cyclical movements slightly similar to unemployment rates fluctuations. The cohort effect shows a downward movement for the birth cohorts of 1894–1936, a dip for 1945–1958 (baby boomers), an upward movement for 1959–1969, and an almost flat movement for 1970–1987. Copyright Springer Science+Business Media B.V. 2013
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Volume (Year): 14 (2013)
Issue (Month): 1 (March)
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