Does Swedish policy respond to the preferences of large birth cohorts more than to equally held preferences of smaller ones, after accounting for age, period shocks, class, and institutional constraints?
Why it matters
This is the decisive empirical test for the strong claim in Boomers and democracy and for the capture condition in Intergenerational policy capture. Both notes argue the mechanism is credible; neither can currently show that policy outcomes followed cohort-weighted preferences rather than crisis pressure, party competition, or ordinary life-cycle redistribution. Until this question is answered, the fyrtiotalist capture thesis remains a hypothesis with a plausible mechanism and an unproven effect.
What is already known
Swedish Election Studies data run from 1956 to 2022 and can support cohort construction. Swedish research documents cohort effects in entry to homeownership and growing dependence on parental wealth, but no located study connects the fyrtiotalist cohort’s housing position to its longitudinal voting behaviour. The central identification problem is that age redistribution is normal: children receive education, working adults contribute taxes, and older people draw pensions and health care. Demonstrating capture requires durable asymmetry beyond life-cycle insurance, connected to political responsiveness.
What would settle it
- Birth cohorts built from the Swedish Election Studies, following party choice, turnout, and issue preferences across elections.
- Homeownership, permanent employment, union status, pension proximity, education, sex, and region modelled within those cohorts.
- Policy responsiveness to cohort-weighted preferences compared against crisis indicators and party-manifesto change.
- Negative cases in which the large cohort accepted a material loss or supported benefits concentrated on younger outsiders. A responsiveness story that survives negative cases is much stronger than one fitted to confirming episodes.
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Working out connections…
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Model contributions
Measured by git-blame lines per AI model (73 total).
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