SUMMARYThis paper compares two methods of analyzing aggregate data that is classified by period and age. Because there is a linear relationship among age, period, and cohort, it is not possible to distinguish the separate effects without employing an identifying assumption. The first method, which is applied in the economics literature, assumes that period effects are orthogonal to a linear time trend. The second method, which is applied in the statistics literature, assumes that the effect parameters change gradually. Simulation results suggest that the performances of both methods are comparable. The results of applying the second method to household saving rates suggest that period effects had a negligible influence in the United States but considerable influence in Japan.
Aggregate data of female labor participation rates in U.S. and Japan, classified by period and by age, are decomposed into age, period, and cohort effects using innovative Bayesian cohort models that were developed to overcome the identification problem in cohort analysis. The main findings are that in both countries, age effects are the largest and period effects are the smallest; in both countries, age effects are roughly consistent with life-cycle movements expected by labor economics, but the negative effects of marriage and/or childbearing on women?’s labor supply in Japan are much larger than those observed in the U.S.; and in both countries, upward movements of cohort effects during 1930s–1960s were found. However, cohort effects are larger for the U.S. than for Japan. All the cohort results are roughly consistent with the marriage squeeze hypothesis and the Easterlin hypothesis. Copyright Springer Science+Business Media, LLC 2006Age–period–cohort decomposition, Bayesian cohort model, Female labor participation rates, C10, J21,
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