Immigration, wealth and the ‘mortality plateau’ in emergent industrial cities of nineteenth-century Massachusetts

Abstract

The mortality transition in Western Europe and the U.S. encompassed a much more complex set of conditions and experiences than earlier thought. Our research addresses the complex set of relationships among growing urban communities, family wealth, immigration and mortality in New England by examining individual-level, socio-demographic mortality correlates during the nineteenth-century mortality plateau and its early twentieth-century decline. In contrast to earlier theories that proposed a more uniform mortality transition, we offer an alternative hypothesis that focuses on the impact of family wealth and immigration on individual-level mortality during the early stages of the mortality transition in Northampton and Holyoke, Massachusetts.

The effects of wealth, occupation, and immigration on epidemic mortality from selected infectious diseases and epidemics in Holyoke township, Massachusetts, 1850−1912

“…Previous research (Leonard, Beemer, and Anderton 2012; Anderton, Beemer, and Leonard 2005) found that estimating wealth effects on general mortality in this population without an effort to control for healthy worker selection effects among more recently arrived immigrant groups can “confound selection effects from recently arriving immigrants with effects of later periods when the same immigrant group was a more stable residential population”, resulting in longitudinally inconsistent and erroneous results (Leonard, Beemer, and Anderton 2012: 447). Since census data do not record actual individual-level time of arrival over most of the study period, we follow our prior research in using a nested interaction of ethnic group and periods of peak immigration as a proxy for a ‘healthy migrant’ effect on adult mortality.…”

“…The second dataset is an urban-based geographic sample of Federal census records, linked to property tax information and death records in the year following the census, allowing us to analyze personal and family characteristics not available from the death records. Gutman (1956) demonstrates the extraordinary completeness of Massachusetts death reporting and previous research (e.g., Leonard, Beemer, and Anderton 2012) demonstrates representation of the impoverished within the tax-linked dataset. We looked at trends in mortality rates for the entire population using the first dataset, and modeled mortality rates for the sampled census population using Poisson rate regression.…”

“…Cause-specific counts of deaths are the numerators of the rates, with interpolated census population counts as the denominators. The model presented here was developed in prior research and applied to overall adult and child mortality (Leonard, Beemer, and Anderton 2012). In this note we replicate the same model, limited to mortality from the important infectious diseases of the time in this place.…”

“…The industrial structure of the township created a high level of economic and social inequality, with immigrants entering primarily to work in low-paying mill jobs. The constant influx of new workers further impacted the township’s demography by maintaining a relatively young population (Leonard, Beemer, and Anderton 2012). Immigrants of Irish and Canadian origin then experienced an accumulation in both personal and real estate wealth at a fast rate, eventually bringing their level of wealth close to that of the native-born.…”

Socioeconomic and cultural differentials in mortality in a late 19th century urban setting: A linked records study from Tartu, Estonia, 1897-1900

“…Poisson models are widely used in epidemiology to model rate data that consists of the number of events (in this study, the number of deaths) divided by the exposure time (person-years at risk) during which the events occurred (Preston 2005). Poisson regression models have been employed in a number of studies of mortality in historical populations (Leonard, Beemer, and Anderton 2012;Leonard et al 2015;Molitoris and Dribe 2016). The Poisson model can be expressed by the following formula (Dalgaard 2008: 262): log ρ = β 0 + β 1 x 1 + β 2 x 2 + …β k x k + log T in which log ρ represents the log rate of the event (death); β 0 represents the constant; x 1, x 2 … x k represent the covariates (occupational group, educational level, ethnic/language group, etc.)…”