Events and Meetings of Italian Statistical Society, Statistics and Demography: the Legacy of Corrado Gini

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Social Mobility and Mortality in southern Sweden (1813-1910)
Paolo Emilio Cardone

Last modified: 2015-09-05


Aim of this research project is to seek the influence of how intra social group mobility affected mortality patterns in Sweden, covering the transition from preindustrial to a breakthrough industrial society.  According to previous studies (see, e.g., Bengtsson: 2010; Bengtsson and Van Poppel: 2011; Bengtsson and Dribe:2011; Dribe, Helgertz, Van de Putte: 2013) Social Economical Status (SES) does not affect substantially life expectancy of Swedish population in the XIXth century, instead of this, other variables, such as public health measures or education, were key factors. However, a new question emerge for us: Could it be possible that other socio-economic factors, such as the intergenerational mobility, may affect positively life expectancy?

In order to achieve this goal, a dataset between 1813 and 1910 from the Scanian Economic-Demographic Database (SEDD) is going to be used. The database is based on local population registers for five rural Scanian coast parishes (Hög, Kävlinge, Halmstad, Sireköpinge, and Kågeröd).  Analysis is based on three periods according to historical criterion (preindustrial period: 1813-1869; early industrial period: 1870-1894 and the first part of the breakthrough of industrialization: 1895-1910).

In our study, intra social mobility is going to be defined as the chances of an individual, between ages 30 and 49, experiences a change of his SES according to SOCPO codification. SOCPO is comprised by 5-category classification scheme. Our main reason for using it is that while it focuses on social power, it is also highly correlated with education and income, as well as is that this classification can be used both for rural and industrial societies. Therefore, a Cox Proportional Hazard model is going to be applied in order to estimate the influence of social mobility, controlling for age and other possible determinant variables. We are going to estimate a model for each SOCPO category. This model includes social mobility status (a categorical variable in which 1 is when the individual experiences the upwards mobility event and 0 otherwise), age, sex, year of birth, parish of residence and position in the household. Thus, after these analyses, we expect to find a significant and positive relationship between social economic mobility and mortality.

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