Given the rapid spread of the COVID-19 virus, the State has had to respond rapidly and quite severely to flatten the curve and slow the spread of the virus. This has had significant implications on many aspects of life, health, economic and social and has been highly asymmetric with different groups affected in different ways.
The aim of this project is to develop a mechanism to delivery real-time analysis of the economic, social and health implications across the income distribution to facilitate policy making. This helps us identify who is most likely to suffer from loss of income, leading to more effective/efficient targeting of income support measures and improved cost estimates of these measures.
In order to understand how policy, economic and social changes affect people in different ways, household survey data is used. However there is a time lag of two years between collection and release of household income survey data for research and analysis. In normal times quite a lot happens in a two year period, but in a crisis the changes are so significant that should a lag can mean the data is relatively meaningless.
We propose to overcome this data gap by develop a “nowcasting” methodology to use more recent data on employment and prices etc to calibrate a simulation model of household incomes, taxes and benefits to produce a real time picture of the population and who is affected differentially, known as microsimulation.
We will use this model simulate counter factual real-time income distributions as a function of more timely data to predict the economic, social and health distributional impact of the COVID and to assist policy makers to assess the differential impact of the mitigation measures proposed.
