Age period cohort analysis: a review of what we should and shouldn't do
Context: Age, period and birth cohort (APC) effects have been known for decades in biological, health and social sciences. However, exact collinearity between these three (Age = Year - Birth Year) leads to difficulty estimating these effects. It is thus impossible to estimate linear components of th...
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| Format: | Book |
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Taylor & Francis Group,
2020-02-01T00:00:00Z.
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| Summary: | Context: Age, period and birth cohort (APC) effects have been known for decades in biological, health and social sciences. However, exact collinearity between these three (Age = Year - Birth Year) leads to difficulty estimating these effects. It is thus impossible to estimate linear components of these effects without strong assumptions about at least one of these. This is problematic for anyone interested in APC patterns. Attempts to 'solve' this identification problem without strong assumptions are, in fact, making hidden unintended assumptions. Objective: Provide an overview of what APC effects are and the nature of the identification problem, before reviewing and critiquing methodological literature across the health and social sciences. I also present an argument for what researchers should do. Method: Non-systematic review of methodological literature across health and social sciences. Results: Recommendations include considering non-linearities around linear APC effects and stating strong and explicit theory-based assumptions. Mechanical solutions to the identification problem do not work. Conclusion: These recommendations acknowledge there is a 'line of solutions' of possible combinations of APC effects, and not a single answer that can be estimated empirically. None of these methods solve the identification problem - rather they acknowledge that methods are limited by assumptions. |
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| Item Description: | 0301-4460 1464-5033 10.1080/03014460.2019.1707872 |