Figures and tables in Care&WorkMOD: An Australian microsimulation model projecting the economic impacts of early retirement in informal carers

Care&WorkMOD: An Australian microsimulation model projecting the economic impacts of early retirement in informal carers

Rupendra N Shrestha, Deborah Schofield, Melanie J B Zeppel, Michelle M Cunich, Robert Tanton, Simon J Kelly, Lennert Veerman, Megan E Passey

GenIMPACT: Centre for Economic Impacts of Genomic Medicine, Macquarie University, Australia; The Boden Institute of Obesity, Nutrition, Exercise and Eating Disorders, Faculty of Medicine and Health and Sydney Health Economics, Sydney Local Health District, The University of Sydney, Australia; National Centre for Social and Economic Modelling, University of Canberra, Australia; Cancer Council NSW, Woolloomooloo, Australia and School of Medicine, Griffith University, Australia; University Centre for Rural Health, The University of Sydney, Australia

Abstract

We developed a microsimulation model, Care&WorkMOD, to estimate the economic costs of early exit from the labour force, both for informal carers and the government, from 2015 to 2030. In this paper, we describe the methods used to create the model Care&WorkMOD, and the sources of data and model assumptions. Care&WorkMOD is based on the unit record data of people aged 15–64 years in the three Australian Bureau of Statistics (ABS) Surveys of Disability, Ageing and Carers (SDAC) 2003, 2009 and 2012. Population and the labour force projections from the 2015 Intergenerational Report and the outputs of an Australian microsimulation model APPSIM were used for the static aging of the base data to every five years from 2015 to 2030. The 2015 output dataset of another microsimulation model STINMOD was linked with Care&WorkMOD base data using synthetic matching. The matching process has added data on further economic variables from STINMOD into Care&WorkMOD, which are not available in SDACs. Economic data were indexed based on long-term trends on economic variables to capture the projected economic growth from 2015 to 2030. Care&WorkMOD can provide the long-term estimates of the lost labour productivity due to informal caring responsibilities and the related economic burden both at the individual and national level, and has the potential to “fill the gaps” in the current body of evidence on the costs of chronic diseases, particularly related to informal carers.

This article contains 4 figures and 2 tables.

Figures

**Figure 1.** Structure of Care&WorkMOD.

**Figure 2.** Informal carers by age and sex (SDAC, 2012).

**Figure 3.** Projected percentages of primary informal carers among the total population by age group and sex, 2015–2030. Dots represent the SDAC estimates for 2015.

**Figure 4.** Percentages of matched records by the number of matching variables.

Tables

Table 1. Synthetic matching variables and their categories in Care&WorkMOD.

Matching variables	Categories
Group A
Labour force status	Employed full-time
	Employed part-time
	Unemployed looking for work
	Not in the labour force
Income unit type	Married couple with dependents
	Married couple only
	One parent with dependents
	One person
Income quintile	1st quintile
	2nd quintile
	3rd quintile
	4th quintile
	5th quintile
Sex	Male
Sex	Female
Age group	15–19 years
	20–24 years
	25–29 years
	30–34 years
	35–39 years
	40–44 years
	45–49 years
	50–54 years
	55–59 years
	60–64 years
Group B
Hours worked per week	Not applicable
	1–15 hours
	16–24 hours
	25–34 hours
	35–40 hours
	41+ hours
Highest level of educational qualification	University
Highest level of educational qualification	Non-university
Home ownership	Yes
Home ownership	No
Receiving Carer Payment	Yes
Receiving Carer Payment	No

Table 2.

Matching variables	No of SDAC records matched with STINMOD record of different category	%
Labour force status	0	0.0%
Income unit type	0	0.0%
Income quintiles	6	0.0%
Sex	135	0.1%
Age group	3217	2.9%
Hours worked per week	1007	0.9%
Highest level of education	344	0.3%
Home ownership	308	0.3%
Receiving carer payment	395	0.4%