Another 'futile quest'? A simulation study of Yang and Land's Hierarchical Age-Period-Cohort model

Background: Whilst some argue that a solution to the age-period-cohort (APC) 'identification problem' is impossible, numerous methodological solutions have been proposed, including Yang and Land's Hierarchical-APC (HAPC) model: a multilevel model considering periods and cohorts as cross-classified contexts in which individuals exist. Objective: To assess the assumptions made by the HAPC model, and the situations in which it does and does not work. Methods: Simulation study. Simulation scenarios assess the effect of (a) cohort trends in the Data Generating Process (DGP) (compared to only random variation), and (b) grouping cohorts (in both DGP and fitted model). Results: The model only works if either (a) we can assume that there are no linear (or non-linear) trends in periods or cohorts, (b) we control any cohort trend in the model's fixed part and assume there is no period trend, or (c) we group cohorts in such a way that they exactly match the groupings in the (unknown) DGP. Otherwise, the model can arbitrarily reapportion APC effects, radically impacting interpretation. Conclusions: Since the purpose of APC analysis is often to ascertain the presence of period and/or cohort trends, and since we rarely have solid (if any) theory regarding cohort groupings, there are few circumstances in which this model achieves what Yang and Land claim it can. The results bring into question findings of several published studies using the HAPC model. However, the structure of the model remains a conceptual advance that is useful when we can assume the DGP has no period trends

Current practice in the modelling of Age, Period and Cohort effects with panel data: a commentary

This comment assesses how age, period and cohort (APC) effects are modelled with panel data in the social sciences. It considers variations on a 2-level multilevel model which has been used to show apparent evidence for simultaneous APC effects. We show that such an interpretation is often misleading, and that the formulation and interpretation of these models requires a better understanding of APC effects and the exact collinearity present between them. This interpretation must draw on theory to justify the claims that are made. By comparing two papers which over-interpret such a model, and another that in our view interprets it appropriately, we outline best practice for researchers aiming to use panel datasets to find APC effects, with an understanding that it is impossible for any statistical model to find and separate all three effects

Investigating the macro determinants of self-rated health and well-being using the european social survey : methodological innovations across countries and time

At present, there is a debate over the relative importance and contribution of household income to well-being, and the link between economic growth, welfare, and well-being is not fully understood. We sought to examine how changes in contextual and individual income (spanning the Great Recession) are associated with changes in self-reported well-being in the European Social Survey (ESS) 2002–2011. A multivariate multilevel analysis was performed on 237,253 individuals nested within 128 country cohorts covering 30 countries. In this article, we focus specifically on the analysis and some of the methodological challenges and issues faced when making international comparisons across nations and time

Predicting the Brexit vote: getting the geography right (more or less)

In April 2016 in two contributions to this blog Ron Johnston, Kelvyn Jones and David Manley predicted the likely geography of support for Brexit in the EU referendum. In this concluding piece they compare their predictions to the result. The general pattern of their predictions turned out to be very accurate, but regional differences were more pronounced than anticipated, with variations in both late electoral registrations and turnout introducing unexpected impacts on the geography of the outcome

The geography of the Brexit vote – what difference will turnout make?

In their recent analysis, Ron Johnston, Kelvyn Jones and David Manley used a large body of YouGov polling data to explore which social groups are most likely to vote Leave in the upcoming EU Referendum and where they live, producing a clear geography of support for Brexit. Here, they explore how differential turnout rates across those groups could alter the pattern of support for the Leave campaign, which suggests where the Remain campaign might most effectively target its campaigning resources

Can we really not predict who will vote for Brexit, and where?

In a recent Guardian article, Simon Jenkins suggested that voter decisions regarding the EU referendum will be made on the basis of gut instinct alone, and that personal characteristics and previous party support provide no guide. Using a new modelling strategy applied to a large body of YouGov opinion poll data, Ron Johnston, Kelvyn Jones and David Manley address Jenkins’ claim, and find it wanting. The young and the well-qualified appear much more inclined to Remain than the elderly, and voting intentions to Leave seem to match, not surprisingly, support for UKIP