(Re)counting the uncounted. Replication and Contextualisation of Dutch and Belgian Premodern Population Estimates (1350-1800)

Establishing how many people inhabited a particular area is not just an academic exercise. It is the cornerstone of historical, economic, and social scientific research. Not surprisingly, population size and growth rates are fundamental variables, catalysts moreover, in seminal studies on studies on long-term economic growth and agricultural development (Boserup 1965; North and Thomas 1973), and feature prominently in calculations of GDP (Van Zanden and Van Leeuwen 2012). Population estimates should therefore be dependable: not in the least for a highly urbanised area of the world, the Low Countries, which facilitated the ‘first modern economy’ (De Vries and Van der Woude 1997). <br/><br/>This proposal argues that the four most used and most up-to-date population estimates for the Low Countries (the area more or less covered by the Netherlands, Belgium, and Luxembourg) for the period prior to 1800 are imprecise, often inaccurate, and generally inconsistent. By using the same research protocols and primary sources, but with added information on the context of the censuses and their geographic coverage, we will replicate these four studies. This will lead to significantly improved local, provincial, and national population estimates, benefitting a wide range of scholars from economists to experts on development studies. <br/><br/>The project will revert to the same primary sources, but for the first time will use the unaggregated village-level data collected in these premodern censuses to produce more accurate and precise population figures. A newly developed GIS dataset of premodern local parish-level boundaries in the Low Countries will act as a visual control mechanism, much in the same way previous scholars have used lists of villages or their own local knowledge to account for missing areas. A new typology of premodern censuses will dramatically improve the quality and comparability of population estimates in the Low Countries.

(Re)counting the uncounted. Replication and Contextualisation of Dutch and Belgian Premodern Population Estimates (1350-1800)

Establishing how many people inhabited a particular area is not just an academic exercise. It is the cornerstone of historical, economic, and social scientific research. Not surprisingly, population size and growth rates are fundamental variables, catalysts moreover, in seminal studies on studies on long-term economic growth and agricultural development (Boserup 1965; North and Thomas 1973), and feature prominently in calculations of GDP (Van Zanden and Van Leeuwen 2012). Population estimates should therefore be dependable: not in the least for a highly urbanised area of the world, the Low Countries, which facilitated the ‘first modern economy’ (De Vries and Van der Woude 1997). <br/><br/>This proposal argues that the four most used and most up-to-date population estimates for the Low Countries (the area more or less covered by the Netherlands, Belgium, and Luxembourg) for the period prior to 1800 are imprecise, often inaccurate, and generally inconsistent. By using the same research protocols and primary sources, but with added information on the context of the censuses and their geographic coverage, we will replicate these four studies. This will lead to significantly improved local, provincial, and national population estimates, benefitting a wide range of scholars from economists to experts on development studies. <br/><br/>The project will revert to the same primary sources, but for the first time will use the unaggregated village-level data collected in these premodern censuses to produce more accurate and precise population figures. A newly developed GIS dataset of premodern local parish-level boundaries in the Low Countries will act as a visual control mechanism, much in the same way previous scholars have used lists of villages or their own local knowledge to account for missing areas. A new typology of premodern censuses will dramatically improve the quality and comparability of population estimates in the Low Countries.