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Determinants of Age in Europe: A Pooled Multilevel Nested Hierarchical Time-Series Cross-Sectional Model

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Abstract

Age is often found to be associated with a plenitude of socioeconomic, politico-administrative, biological and thanatological variables. Much less attention has been paid by scholars, however, to explaining ‘age’. In this paper we address this unfortunate scientific lacuna by developing a model of ‘age’ as a function of several factors suggested by (post)rational choice and social constructionist theories. Using state-of-the-art multilevel statistical techniques, our analysis allows the determinants of age to vary with the institutional characteristics of European countries. Our findings convincingly show that generalized trust in strangers, support for incumbent extremist political parties in provincial elections held in the month of January, and the percentage of overqualified women in the cafeterias of national parliaments are all statistically significant explanations of ‘age’. Our findings have obvious implications for conspiracy theorists, organizational advisors, spin doctors and ordinary charlatans.

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Notes

  1. The fact that I use ‘we’ instead of ‘I’ has nothing to do with the allegations that large parts of this paper have been written by my student assistants. ‘We’ is only used to implicate that more than one person agrees with the statements of the paper, to diffuse responsibility in case of obvious mistakes, and to nourish a warm feeling of self-aggrandizement.

  2. Some scholars have hypothesized that the last relationship is mediated by gender but the empirical evidence is inconclusive.

  3. Most economic references will do, but see in particular, the work on optimal currency areas.

  4. Before we are accused of discrimination by the female part of the profession, let us point out that rational choice is obviously a manly (cold, calculating, rigid, unemotional and cynic) approach that leads to the fact that all its practitioners are male. The few counter-examples only prove the point and have not been confirmed by independent hormonal tests.

  5. This finding severely undermines the traditional fairy tale interpretation that ageing people are kept at home due to their abilities to locate grain in ant nests in times of bad harvests.

  6. This result has been confirmed by numerous psychological experiments that show that normal people cannot apply the Lagrange multiplier procedure to multidimensional constrained optimization problems.

  7. Reviewers of this paper pointed out that we could not possibly attempt to discuss social constructionism and not mention discourses. We agree, so here we go: ‘Discourses!’

  8. Readers who are interested in the intellectual predecessors of this approach to causal analysis can consult Ziliak and McCloskey (2008: 106–107), but are strongly advised to avoid the rest of the book like the plague.

  9. You coward, we gonna get you sooner or later!

  10. The time-honoured astragali method refers to tossing ankle bones of sheep or any other cloven-hoofed animals (Everitt, 1999).

  11. A few people continue to insist that we cannot conclude that the hypotheses are true from the analysis because the probability of the data, given the hypothesis is not the same as the probability of the hypothesis given the data. This claim is so confusing that it must be Bayesian. Let us remind everyone that Bayesian thinking has been officially declared by the UK Court of Appeal to venture into ‘inappropriate and unnecessary realms of theory and complexity’ (Regina v Adams, 1996).

  12. David Freedman (1997) argues that we cannot make causal claims on the basis of regression applied to observational data without making causal assumptions first. In his own words: ‘If you want to pull a rabbit out of the hat, you have to put a rabbit into the hat’ (p. 182). We can only pity Professor Freedman, who has obviously never been to a magician's performance.

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Acknowledgements

Drafts of this paper have been read aloud at the LXXIV Annual Meeting of Social Scientists with Large Research Budgets (Bali, Indonesia, Winter 2009), the First and Probably Last Conference of Young Gerontometricians, and at my brother's wedding. I acknowledge all moronic suggestions, vindictive personal attacks and malicious rumours advanced at these occasions. I am also extremely grateful to the current double-blind to nonsense peer-review system that can let such papers through. The usual disclaimer applies. (The usual disclaimer means that in the unlikely case that somebody is as bored and vicious as to attempt to replicate the analysis, I will transfer all responsibility for tampering with the data to my student assistants.)

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Bezimeni, U. Determinants of Age in Europe: A Pooled Multilevel Nested Hierarchical Time-Series Cross-Sectional Model. Eur Polit Sci 10, 86–91 (2011). https://doi.org/10.1057/eps.2010.12

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  • DOI: https://doi.org/10.1057/eps.2010.12

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