The historical development of mortality in Mecklenburg-Schwerin in the 19th century
Marlen Toch, Rostock Center for the Study of Demographic Change
James E. Oeppen, Max Planck Institute for Demographic Research
Joshua R. Goldstein, Max Planck Institute for Demographic Research
Record life expectancy has risen almost linearly by a quarter of a year for 170 years. But we still do not know why the modern life expectancy revolution began. What we know is that Scandinavia has been the precursor of this development. But how did other northern parts of Europe perform? Does the Scandinavian scheme fit to all countries situated near the Baltic Sea? The northern German federal states of Mecklenburg-Western Pomerania and Schleswig-Holstein as the immediate neighbors provide ideal contrasts to Scandinavia. In particular, Mecklenburg-Western Pomerania today has the lowest average live expectancy. However, there was still a decline in mortality during the first demographic transition, but did the mortality decline set in later? Or have there been phases of stagnation? This project aims to analyze the development of the mortality in Mecklenburg-Schwerin - the biggest part of today's Mecklenburg-Western Pomerania - in the 19th and early 20th century to gather evidence to better understand the modern rise of life expectancy. Our analysis is based on the aggregate population movement data of “Großherzoglich Mecklenburg-Schwerinscher Staatskalender”, annual calendars issued by the Grand Duke from 1786 to 1910. Additionally, we will use information on age structure and migration from the statistical handbooks of the Grand Duchy and from the contributions to the statistics of Mecklenburg. First crude analyses show that migration might have caused a stagnation of population growth and thus had an impact on population development. But, to exactly understand the patterns of mortality development in Mecklenburg-Schwerin, further analyses are needed. Therefore, we will reconstruct the age specific mortality of Mecklenburg-Schwerin via Generalized Inverse Projection (GIP), which uses commonly available historical data to generate series of censuses with the best fit to the information at hand.
Presented in Session 85: Historical epidemiology