Abstract
This article provides a novel method for estimating historical population development. We review the previous literature on historical population time-series estimates and propose a general outline to address the well-known methodological problems. We use a Bayesian hierarchical time-series model that allows us to integrate the parish-level data set and prior population information in a coherent manner. The procedure provides us with model-based posterior intervals for the final population estimates. We demonstrate its applicability by estimating the long-term development of Finland’s population from 1647 onward and simultaneously place the country among the very few to have an annual population series of such length available.
Highlights
Generalizations about historical human population development (e.g., Kremer 1993; LiviBacci 2017; Maddison 2001; Madsen et al 2019; McEvedy and Jones 1978) are complicated by a lack of available time series
Historical population trajectories have proven useful in quantifying future demographic developments and providing points of comparison how population trends shape economic growth over longer periods (e.g., Cervellati et al 2017)
By applying the proposed method, we provide a historical reconstruction of Finnish population beginning from the mid-1600s, which places Finland among the few countries that have such a long annual population series available
Summary
Generalizations about historical human population development (e.g., Kremer 1993; LiviBacci 2017; Maddison 2001; Madsen et al 2019; McEvedy and Jones 1978) are complicated by a lack of available time series. Developments in the literature on long-term economic growth (e.g., Galor and Weil 2000; Strulik and Weisdorf 2014) have sparked growing interest in pre-industrial population dynamics (e.g., Chiarini 2010; Madsen et al 2019; Møller and Sharp 2014; Nicolini 2007). This strand of research has scrutinized whether the stagnant levels of pre-industrial income per capita were due to demographic responses to variations in living standards and whether demographic fluctuations hold the key to understanding how pre-industrial poverty was eventually overcome (Lagerlöf 2003; Voigtländer and Voth 2013). Historical population trajectories have proven useful in quantifying future demographic developments and providing points of comparison how population trends shape economic growth over longer periods (e.g., Cervellati et al 2017)
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