Mortality measurement

Demographic transitions to rapidly aging societies have their origins in early life mortality decline and associated fertility decline, but in societies such as the United States demographic patterns of aging are increasingly shaped by adult mortality. UC Berkeley’s core strength of formal demography has included leadership in methods for mortality measurement as a key contribution. CEDA is also committed to advancing adult mortality measurement methods for settings with weak vital statistics, as this is essential to understanding the evolution of global aging.

CenSoc Project, with Joshua Goldstein (Demography). The goal of CenSoc is the creation and dissemination of linked mortality data sets, that is, public, individual-level data sets linking the 1940 U.S. Census with the (a) Social Security Death Master File, an (b) the NARA Numident file. Two releases were issued in 2020 and researchers on campus and elsewhere are using these valuable data for mortality research on the development of new mortality rate estimation methods for linked data and for “high resolution” studies of mortality disparities and longevity determinants. Visit the CenSoc website.

Human Mortality Database, with Magali Barbieri (CEDA, INED). HMD contains original calculations of death rates and life tables for 41 national populations (countries or areas), as well as the input data used in constructing those tables. The input data consist of death counts from vital statistics, plus census counts, birth counts, and population estimates from various sources. Visit the Mortality.org website.

Social Network Methods for Demographic Issues. This area of research is headed by Dennis Feehan and Ayesha Mahmud, with other colleagues. Recent work, with Audrey Dorelien at University of Minnesota, has turned to infectious disease transmission via networks, which has immediate implications for the COVID-19 pandemic. One such project uses network methods for estimating adult death rates in the absence of vital registration, with Dennis Feehan.