Assessing policy to address the medium-run impact of Covid-19 on income and health inequality with models informed by the history of disease outbreaks

This research aims to inform policy by assessing interventions to mitigate the medium-run implications of Covid-19 on income and health inequality. The uniqueness of our work is that we will develop models to predict inequalities that are consistent with medium-run historical postoutbreak dynamics in a large city, Glasgow, demonstrating similar inequalities to those seen across the UK today. The medium run is important because impacts of Covid-19 for inequalities are expected to persist for many years. Assessing mitigation policies requires models that can correctly predict the evolution of income and health distributions many years after an outbreak. To achieve this, we need to combine models typically applied to modern datasets with quantitative data from historical periods that, unlike contemporary data, cover sufficiently extended post-outbreak periods. Records have been especially high quality in Glasgow since the last quarter of the 19th century, covering a period of intense and volatile economic activity, and multiple disease outbreaks.

Building on team expertise, we will: (1) construct mathematical models that predict income and health inequality after disease outbreaks; (2) compile quantitative historical data alongside contemporary datasets that capture the pre-Covid-19 situation for calibration and validation; and (3) assess the effects of policies relating to e.g. tax and benefits, upskilling subsidies, and diseasespecific or general health service provision. Our framework will allow us to assess policy interventions conditional on socioeconomic and health characteristics such as health status, professional class and income position, thus providing fine-grained results for economic and public health policy advisers nationally and locally.

Lead investigator:	Konstantinos Angelopoulos
Affiliation:	University of Glasgow
Primary topic:	Inequality & poverty
Secondary topic:	Lessons from history
Region of data collection:	Europe
Country of data collection	UK
Status of data collection	In Progress
Type of data being collected:	From private company
Unit of real-time data collection	County/Municipality
Frequency	Periodic (other)