Abstract: The Cincinnati Post published its last edition on New Year's Eve 2007, leaving the Cincinnati Enquirer as the only daily newspaper in the market. The next year, fewer candidates ran for municipal office in the suburbs most reliant on the Post, incumbents became more likely to win re-election, and voter turnout and campaign spending fell. We exploit a difference-in-differences strategy and the fact that the Post's closing date was fixed 30 years in advance to rule out some non-causal explanations for these results. We show that local politics changed even though the Enquirer increased its coverage of the Post's former strongholds. Although our findings are statistically imprecise, they demonstrate that newspapers -- even underdogs such as the Post, which had a circulation of just 27,000 when it closed -- can have a substantial and measurable impact on public life.

Abstract: This paper proposes a new way of modeling age, period and cohort effects that improves substantively and methodologically on the conventional linear model. The linear model suffers from a well-known identification problem: If we assume an outcome of interest depends on the sum of an age effect, a period effect and a cohort effect, then it is impossible to distinguish these three separate effects because, for any individual, birth year = current year − age. Less well appreciated is that the model also suffers from a conceptual problem: It assumes that the influence of age is the same in all time periods, the influence of present conditions is the same for people of all ages, and cohorts do not change over time. We argue that in many applications, these assumptions fail. We propose a more general model in which age profiles can change over time and period effects can have different influences on people of different ages. Our model defines cohort effects as an accumulation of age-by-period interactions. Although a longstanding literature on theories of social change conceptualizes cohort effects in exactly this way, we are the first to show how to statistically model this more complex form of cohort effects. We show that the additive model is a special case of our model and that, except in special cases, the parameters of the more general model are identified. We apply our model to analyze changes in age-specific mortality rates in Sweden over the past 150 years.