Events

Male excess mortality

Hosted by The Data Science Institutes at LSE and Imperial College London

William Penney Laboratory, Imperial College London, South Kensington Campus, London SW7 2AZ

Speaker

Astrid Krenz

Astrid Krenz

Miguel Dols Fellow, Cañada Blanch Centre

Unsolved problem

Women have a survival advantage over men, and we see an increase in the gap between male and female survival. Why did this male excess mortality emerge and grow over time?

Abstract
For a given age, adult men die at a higher rate than women. In many developed countries, increasing excess mortality of men has been demonstrated for cohorts born in the late 19th century and thereafter. It has been surmised that the decline in infectious diseases environment contributed to the increase in excess male mortality. Here, we focus on India 1990-2019, a period in which the Indian states experienced to varying degrees the epidemiological transition. We show that excess male mortality evolves positively over the observation period, is greater in later-born cohorts, and is strongly associated with the decline in infectious disease mortality. We propose a simple theory that explains these facts by a greater influence of infections on the biological aging of women compared to men. We calibrate the model with a huge data set for India and show that it can predict the feature of rising excess male mortality over time and birth year of cohorts.


About the speaker
Astrid Krenz is Professor of Economics at Ruhr University of Bochum in Germany. She is a Miguel Dols Research Fellow in the Canada Blanch Centre at the LSE, and became an Affiliate at the Data Science Institute in October 2024. In her research, she is interested in analysing various aspects of economic development. She investigates questions in demography, the link between digitalisation, automation (robots, AI), and firm dynamics (such as location choices and productivity performance), as well as developments for labour markets and economic inequality. She conducts big data analyses and uses methods from Econometrics, Machine Learning and NLP, among others.

About Unsolved Problems
This series of seminars forms part of the DSI Squared collaboration between the Data Science Institutes at LSE and Imperial College London Data Science Institute.  innovation by bridging the social sciences and computer science and STEM subjects. Researchers from both Institutes are invited to showcase their ideas in front of an audience. Attendees offer their expertise and knowledge to crowd source solutions to research challenges. At these lunchtime meetings, a light lunch is provided at the seminar. 

How can I attend? Add to calendar
This research seminar is an in-person event. If you would like to attend, please register here.

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