Informal care and savings

ABSTRACT

Using panel data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, this paper examines to what extent informal care provided by couples and single individuals affects their household savings. This paper is one of the first attempts to examine the relationship between informal care and savings. There are both negative and positive effects of informal care on wealth accumulation, but we do not know which of these effects dominate. We estimate quantile regression models and find that informal care provision has limited negative impacts on changes in household wealth formation. There was no effect of caring on wealth formation for the lower quantile and the negative effects are limited to the mid to high quantile groups.

JEL CLASSIFICATION:

• D14
• G51
• I11

KEYWORDS:

• Care
• informal care
• quantile regression
• savings

I. Introduction

Populations are ageing rapidly in many developed countries, and the ageing populations have posed serious challenges to governments through increases in health and social welfare expenditures and labour shortages. While public funding is an important source of financing for care responsibilities for elderly or disabled adults, due to the government’s budget constraint, public spending related to the care of the elderly or disabled person such as long-term care is relatively low (OECD 2019). In these circumstances, most of the care provided to elderly or disabled adults is informal, and is provided by close relatives or neighbours rather than by professional caregivers from the public sector or hired in the market (OECD 2005).

Keating et al. (2014) provide a taxonomy of the costs of caring into three broad categories: reduced or forgone income/benefits; out of pocket expenses; and the benefits lost from time spent in caring. Previous studies indicate that the negative effects of informal care on the labour supply of carers are limited (Bolin, Lindgren, and Lundborg (2008), Leigh (2010), Oshio and Usui (2017) and Moussa (2019)), while informal care can affect the mental health and subjective well-being of carers (Coe and Van Houtven (2009), Colombo et al. (2011) and Van Den Berg, Fiebig, and Hall (2014)). This may affect their labour productivity and/or increase carer’s medical costs. Moreover, private and government financial transfers may mitigate the negative financial effects of informal care on the households. There are both negative and positive effects of informal care on wealth accumulation, but which effects dominate? This issue has important policy implications in that during their life-course, due to care provisions, carers may not accumulate enough wealth for their future retirement.

This paper is one of the first attempts to examine the relationship between informal care and savings. We use panel data on Australian households from the Household, Income and Labour Dynamics in Australia (HILDA) Survey to empirically examine to what extent informal care provided by couples affects their savings when the husband and/or wife are carers.¹ We also examine the effect for single individuals who are carers. Informal care covers caring for a disabled spouse or disabled adult relative and caring for elderly parents or parents-in-law. According to the Australian Bureau of Statistics (ABS) (2019), the population of carers in Australia in 2018 was around 2.65 million people, 10.8% of all Australians. Women were more likely to be carers with 12.3% (9.3%) of all females (males) being carers. Around 3.5% of all Australians are primary carers, and 71.8% of these carers were women.

Arora (2016) examines the impact of dementia onset among parents on the subsequent change in their unmarried adult children’s wealth using data from seven waves of the Health and Retirement Study. Considering that parental dementia onset could influence points other than the mean of the wealth distribution, using a quantile regression Arora finds that parental dementia reduces unmarried adult children’s wealth accumulation for those children above the median of the wealth distribution. Apart from Arora (2016), to our knowledge, there is no other study which has investigated this issue. Our research instead looks at people who provide informal care for any reason not just those providing care to parents with dementia. We examine the wealth of both couples who are caregivers and single caregivers

In the context of caring for parents with dementia, Arora (2016) focuses on caring provided by unmarried adult children. Evidence supporting the importance of couples providing informal care in the Australian context can be obtained from several sources. The Australian Bureau of Statistics (2019, Table 34.3) indicates that 36.6% of all primary carers are the spouses of the care recipient and 26.2% are the children of the care recipient. The HILDA survey shows that 69% of informal carers are members of a couple, either married couples (62%) or unmarried people who are living in a relationship (7%). When the care recipient is limited to a parent, Wave 6 of the HILDA survey indicates that around half the care givers are married children, and the other half are single children.

In order to take account of outliers and non-normality in wealth data, we estimate quantile regression models. Our findings suggest that informal care provision has limited negative impacts on changes in household asset formation. The negative effects are limited to the mid to high quantile groups. The findings are in line with previous studies which find that the effects of providing informal care have limited effects on carer’s labour market outcomes.

The rest of this paper is organized into four sections. Section II details the model to be estimated and the estimation method. The data used in this paper are discussed in sections III. Section IV presents the estimation results. Section V presents some conclusions.

II. Model

There are number of channels through which the provision of informal care can affect a carer’s savings. The first channel is an impact through an absence from work or a reduction in working hours. Carers may lose a substantial amount of earned income as a result of being absent from work for caring. It is important to note that in Australia the carers’ absence from work also directly affects their retirement savings because a reduction in their paid work will reduce their contribution and their employer’s contribution to their superannuation account. Under the current system, employers have to make superannuation contributions for their employees on top of the employees’ wages and salaries (9.5% in 2020). As a rule, access to the funds in a superannuation account is heavily restricted until the individual retires or nears retirement. The second channel is out of pocket expenses. Carers may have to incur various direct and indirect costs of informal care provision. The expenses include daily expenses, medical expenses and the costs of visiting the care recipient(s). The third channel is more indirect. The carers may experience ill health themselves due to their informal care provision (Coe and Van Houtven 2009; Van Den Berg, Fiebig, and Hall 2014). This may lead to additional medical costs and/or a loss of labour productivity.

Finally, financial transfers can have positive effects on carers’ savings through private transfers and government transfers. Private transfers include inheritances, bequests and inter vivos transfers. Such private transfers from care recipients to carers may occur in exchange for informal care provision. Parents may promise a future monetary transfer to an adult child in order to receive informal care from their children (Bernheim, Shleifer, and Summers 1985). The transferability of time and money between the partners in a couple and the potential importance of bargaining power may lead to different results for couples compared to single individuals. The empirical evidence is mixed on the importance of the bequest motive and there is some support for an exchange motive (Norton and Van Houtven 2006). In addition, care recipients may make regular/irregular financial transfers to carers to obtain informal care. In Australia, there are two major payments from the government to carers: Carer Payments and Carer Allowances. The Carer Payment provides financial assistance to people who cannot work in paid employment because they provide full-time daily care for either someone with a severe disability or medical condition or someone who is frail aged. This payment is subject to both an income and an asset test. As of May 2020, the maximum rates for couples combined are A$1,423.60 per fortnight ($A1≈$US0.70). The Carer Allowance is an income supplement for parents or carers who provide extra daily care for either someone (an adult or dependent child) with a disability or a medical condition or someone who is frail aged. It is subject to an income test. The Carer Allowance currently is A$131.90 paid fortnightly to people who provide care to someone aged 16 or older (Centrelink 2020). These private transfers and government transfers may mitigate some of the negative impacts of informal care on savings.

There exists both positive and negative impacts of informal care on savings, but we do not know which effects dominate. In the context of wealth analysis, it is highly likely that ordinary least squares (OLS) estimates suffer from outliers and non-normality (Baum, 2013). Considering the possible heterogeneity of saving propensities across households, this study estimates household saving functions based on quantile regressions, focusing on the effects of informal care.

Liu (2016) provides some theoretical insights into factors that are potentially important when analysing the relationship between care and saving, for example, uncertainty in health status of the care recipient, the opportunity cost of caregiving, and the share of expected bequests from the parent. There is no information in the HILDA survey on the health status of the non-resident care recipient or expected bequests from parents.² For people who are working, it would be possible to use their wage rate as a measure of the opportunity cost of caregiving, but for those who are engaged in heavy caregiving (and therefore are not working) or those who are retired, we do not observe a wage rate. Instead of the wage rate, we use education levels as a proxy for this opportunity cost.

To investigate whether and to what extent the informal care affects household savings, we estimate the following quantile regression model:

(1) $\mathrm{\Delta }{W}_{it}={\alpha }_0^{\tau }+{\alpha }_1^{\tau }{\sum }_{t-3}^{t}car{e}_{it}+X_{it}{\beta }_1^{\tau }+u_{it}^{\tau },\hspace{0.17em}t=6,10,14,18$(1)

where $W_{it}$ is the level of net assets of households i in wave t; $\mathrm{\Delta }{W}_{it}$ is the change in household assets over the last 4 years³ ($\mathrm{\Delta }{W}_{it}=W_{it}-W_{i,t-4}$); the variable of interest is ${\sum }_{t-3}^{t}car{e}_{it}$, which is either the total hours or the years of informal care provision by a couple combined or single individual over the last 4 years; $X_{it}$ contains other control variables relating to both the respondent and if they are in a couple their partner such as their ages, levels of education, non-English speaking background, experience of divorce, the number of siblings and financial literacy. $\tau$ denotes a quantile, and $u_{it}^{\tau }$ is an error term for household i in wave t at the quantile $\tau$. We estimate equation (1)(1) $\mathrm{\Delta }{W}_{it}={\alpha }_0^{\tau }+{\alpha }_1^{\tau }{\sum }_{t-3}^{t}car{e}_{it}+X_{it}{\beta }_1^{\tau }+u_{it}^{\tau },\hspace{0.17em}t=6,10,14,18$(1) for the 25th, 50th and 75th quantiles.

III. Data

Our data are drawn from Waves 2–18 of a nationally representative longitudinal study, the Household, Income and Labour Dynamics in Australia (HILDA) Survey (Release 18). The HILDA survey has been conducted annually by the Melbourne Institute of Applied Economics and Social Research since 2001. The HILDA survey collects information on economic and subjective well-being, family structures, and labour market dynamics. All members of the households who participated in at least one interview in Wave 1 are pursued in each subsequent wave. The sample has been gradually extended to include any new household members to the original households. The sample was replenished in Wave 11 with an additional 2,153 households being added. This replenishment aimed to provide better coverage of migrants in the HILDA Survey. There are 7,682 households and 19,914 individuals in Wave 1, and 7,616 households and 18,234 individuals in Wave 18 (Summerfield et al. 2019).

Since wave 2, the HILDA survey has collected comprehensive information on household wealth only every 4 waves, that is, in Waves 2, 6, 10, 14 and 18. Total net assets of the household are calculated by deducting household debts from household assets. There are 5 major components of household assets: 1) financial wealth (including interest earning assets and investments in stocks, mutual funds, trust funds and life insurance): 2) business equity; 3) real estate equity (including the primary residence, holiday houses and other property); 4) vehicles (cars, trucks and recreational vehicles); and 5) pension entitlements. A derived variable is created by summing up the value of financial and non-financial assets for the household, and the missing values of any of the wealth components have been imputed. Household debts in the HILDA survey include property debt, business debt, total credit card debt, student loans (HECS debt), and other debt. From Wave 6 onwards, this also includes overdue household bills. As with assets, the missing values of debt have been imputed (see Summerfield et al. 2019). Bloxham and Bett (2009) discuss the reliability of the HILDA wealth module and compare wealth measures constructed by the Reserve Bank of Australia and the ABS to measures generated by the HILDA wealth module. Their analysis concludes that the measures are broadly similar and provides researchers with some confidence about the reliability of the HILDA wealth data. As we are interested in asset flows rather than assets stocks, we use the difference in wealth over four years ($\mathrm{\Delta }{W}_{it}=W_{it}-W_{i,t-4}$) which is available in Waves 6, 10, 14 and 18. The asset variables are adjusted for inflation using the Consumer Price Index (CPI) (2012 = 100) produced by the ABS.

It is important to discuss how to define someone who provides informal care.⁴ There is a wide spectrum for informal care. Some individuals may provide a small amount of care each week. Others may have to substantially reduce their hours of paid employment or completely give up their jobs for caregiving. We use three alternative measures to define the care provided by an informal carer. The first measure is based on time use. In each wave, the HILDA survey asks how much time in a typical week the respondent and their partner if the respondent is in a relationship spend on ‘caring for a disabled spouse or disabled adult relative, or caring for elderly parents or parents-in-law hours of informal care’. We use the responses for both the reference person and their partner to estimate the total number of informal care hours provided by couples and the responses for the reference person to estimate the total number of informal care hours provided by single individuals over the past four years. Each response is multiplied by 52 weeks to calculate the annual total hours of informal care. We then sum the annual total hours over the past four years.

The second measure is based on the self-reported information of whether the respondent is a main carer. In each wave from Wave 5, the HILDA survey asks the following question: ‘Are you the main carer of [this person/any of these people]? (That is, are you the person who provides most of their care?)’. This question is asked about care provision for both resident and non-resident person(s). In this question, a care recipient is defined as a person ‘who has a long-term health condition, who is elderly or who has a disability, and for who you care or help on an ongoing basis’. We use this information to calculate the total years of caring as a main carer by the couple or the single individual over the past 4 years. Thus, we can compute this 4 year total for Waves 10, 14 and 18 for the saving analysis.

The third measure attempts to measure the number of hours of care provided as the main carer by assuming that if a person says they are the main carer in any year, the hours of caring they report for that year are the hours of caring as the main carer.

We include other variables to control for the household information. In addition to the total number of own resident dependent children, we include the following information on both the respondent and for couples their partner: their age; their levels of education; a 0–1 dummy variable for whether they have a non-English speaking background; the number of siblings, a 0–1 dummy variable for whether they have ever divorced; and a financial literacy 0–1 dummy which takes the value 1 if they answered all 5 financial literacy questions correctly without any help, and 0 otherwise. The financial literacy dummy is included because previous studies have indicated that financial literacy has positive impacts on retirement planning and savings (van Rooij, Lusardi, and Alessie 2012; Lusardi and Mitchell 2014). We also include a global financial crisis (GFC) dummy which takes the value 1 if the data is post-GFC (2008 or later), and 0 otherwise.

The sample is restricted to respondents aged between 25 and 64 and so excludes people who are most likely to be completing their education or entering retirement. For couples, the reference person is a male, and we focus on heterosexual couples (either married couples or couples who are not married but are living in a relationship) who stayed together for at least the four years between waves (i.e. over the time period 2002–2006, 2006–2010, 2010–2014, 2014–2018). For single individuals, we focus on those (a) who have never married and are not in a relationship, (b) who are divorced or separated; or (c) who are widowed, and who have been single for at least the four years between waves (i.e. over the time period 2002–2006, 2006–2010, 2010–2014, 2014–2018).⁵ Households where the reference person reported informal care hours per week that were in the top 1%, namely people who reported caring for 128 hours or more, were dropped from the sample. Finally, information on all relevant variables must available. For couples and single individuals, the total number of observations are 5,271 and 6,401 for the analysis using the total hours of informal care, and 4,094 and 5,048 for the analyses using the total years of informal care and the hours of informal care provided as a main carer, respectively. The descriptive statistics are summarized in Table 1. It is important to note that the sample for the total hours of providing informal care in this sample is larger than for the other two care variables From wave 5 to wave 18, we find that among the respondents who have cared for someone on an ongoing basis as a main carer, around 40% report they do so for only one year, and over 70% of them report that their total years of providing informal care as a main carer are no more than 3 years.

Table 1. Descriptive statistics

	Couples			Single
	Sample Size	Mean	Std. Dev.	Sample Size	Mean	Std. Dev.
Asset Variables
Real net assets ($A) [imputation]	5,271	1,075,334	1,368,460	6,401	557,386	969,529
Real net assets – real net assets(t-4) ($A)[imputation]	5,271	248,250	899,656	6,401	61,938	739,178
Real net assets ($A) [no imputation]	2,793	1,047,937	1,248,382	3,951	505,173	818,732
Real net assets – real net assets(t-4) ($A) [no imputation]	2,793	226,345	860,559	3,951	51,513	689,971
Caring Variables
Total hours caring for disabled spouse (for couples)/relative or for elderly parents or parents-in-law for the last 4 years	5,271	410.12	1560.18	6,401	344.80	1506.74
Total years as the main carer for a disabled spouse (for couples)/relative or for elderly parents or parents-in-law for the last 4 years	4,094	0.30	0.88	5,048	0.22	0.74
Total hours caring for disabled spouse (for couples)/relative or for elderly parents or parents-in-law as a main carer for the last 4 years	4,094	214.96	1281.81	5,048	232.71	1397.46
Household Variables
Total number of own resident dependent children	5,271	1.45	1.21	6,401	0.46	0.89
Reference person
Age	5,271	47.65	9.84	6,401	45.86	11.73
Educational attainment
Bachelor degree or more	5,271	0.33	0.47	6,401	0.27	0.44
Adv diploma, diploma	5,271	0.11	0.32	6,401	0.10	0.31
Cert III or IV	5,271	0.31	0.46	6,401	0.24	0.43
Year 12	5,271	0.09	0.29	6,401	0.14	0.34
Year 11 and below	5,271	0.16	0.37	6,401	0.25	0.43
Non English speaking background	5,271	0.10	0.30	6,401	0.07	0.26
Ever divorced	5,271	0.13	0.34	6,401	0.09	0.28
Financial literacy	5,271	0.60	0.49	6,401	0.44	0.50
Number of siblings	5,271	2.75	2.03	6,401	2.73	2.05
Partner
Age	5,271	45.44	9.69
Educational attainment
Bachelor degree or more	5,271	0.36	0.48
Adv diploma, diploma	5,271	0.11	0.32
Cert III or IV	5,271	0.15	0.36
Year 12	5,271	0.14	0.34
Year 11 and below	5,271	0.23	0.42
Non English speaking backgroud	5,271	0.13	0.33
Ever divorced	5,271	0.12	0.32
Financial literacy	5,271	0.45	0.50
Number of siblings	5,271	2.77	1.95
Male				6,401	0.43	0.50
GFC dummy	5,271	0.78	0.42	6,401	0.79	0.41
Wave
6	5,271	0.22	0.42	6,401	0.21	0.41
10	5,271	0.24	0.43	6,401	0.22	0.42
14	5,271	0.23	0.42	6,401	0.24	0.43
18	5,271	0.31	0.46	6,401	0.32	0.47

IV. Results

Table 2 presents the results estimating the effects of caring on wealth changes for couples when we use a quantile regression estimator with imputed wealth data. The caring variables are is the number of hours of care (column 2.1), the years of care (column 2.2), and the number of hours of care as the primary carer (column 2.3), respectively.⁶ The main part of the Table is divided into three sections labelled q25, q50 and q75 that indicate the results for the 0.25, 0.50 and 0.75 quantiles, respectively. At the foot of the Tables are tests for the null hypothesis that the coefficients on the caring variable for the three quantiles are identical and tests for heteroskedasticity. For each equation, the test for the null hypothesis that the coefficients on the caring variable for the three quantiles are identical indicate the null is clearly rejected in each case. In addition, the results for the Breusch and Pagan (1979) /Cook and Weisberg (1983) tests for heteroskedasticity also strongly reject the null hypothesis of a constant variance. These test results support the use of quantile regression models. If we focus on column (2.1), the estimated results show that informal care when measured using the hours of care does not affect savings for households at the 0.25 quantile. The significant effects are limited to the 0.50 and 0.75 quantiles. Moreover, even among these quantiles, which show negative effects of informal care provision, the effects are moderate. Using the estimates in column (2.1), for the 0.50 and 0.75 quantiles, our estimates show that each additional hour of informal care provision reduces household assets by only $A2.45 and $A10.19, respectively. In a way, we can see this as part of the opportunity cost of informal care provision. By comparison in 2012, the minimum wage in Australia was $A15.96 per hour.

Table 2. The effects of informal care on net asset accumulation for couples (using imputed wealth data)

	Informal care variable is hours of care			Informal care variable is years of care as the main carer			Informal care variable is hours of care as the main carer
	(2.1)			(2.2)			(2.3)
		Bootstrap Std. Err.			Bootstrap Std. Err.			Bootstrap Std. Err.
	Coef.			Coef.			Coef.
q25
Couple’s informal care	0.952	1.272		230.797	3,243.304		1.104	2.125
Total number of own resident dependent children	1,908.082	2,857.538		918.627	3,215.925		733.857	3,281.376
Reference person
Age	−2,846.156	904.809	***	−2,701.017	1,032.623	***	−2,692.493	1,002.723	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	33,017.38	14,047.29	**	32,248.45	15,011.17	**	31,751.52	15,888.88	**
Adv diploma, diploma	−3,669.158	15,668.11		2,771.92	17,066.69		1,975.368	16,641.89
Cert III or IV	4,379.995	10,395.77		7,941.525	12,154.58		7,771.794	11,358.64
Year 12	−4,913.876	14,319.91		−9,042.659	15,129.63		−9,278.421	14,363.95
Non English speaking background	21,302.29	16,490.38		33,168.05	15,560.6	**	32,392.28	15,215.78	**
Ever divorced	11,438.97	14,018.28		11,317.93	15,609.49		6,488.059	16,658.24
Financial literacy	3,785.303	8,904.525		5,716.019	9,062.697		6,616.275	9,799.225
Number of siblings	−3,875.907	1,838.401	**	−4,458.654	2,158.036	**	−4,657.185	2,180.983	**
Partner
Age	616.3289	946.576		−244.2197	970.950		−270.386	987.329
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	25,266.69	12,543.07	**	16,247.19	13,341.15		16,892.24	13,611.3
Adv diploma, diploma	−27,046.11	16,309.66	*	−38,383.79	18,250.85	**	−37,734.52	18,832.18	**
Cert III or IV	5,628.094	9,976.487		3,015.071	12,126.39		2,765.442	11,349.77
Year 12	−9,878.107	11,251.39		−11,960.83	11,828.67		−10,913.12	12,018.01
Non English speaking background	−3,517.17	15,877.48		2,206.749	14,692.78		1,965.244	15,966.77
Ever divorced	−9,579.503	14,964.46		−10,793.48	16,495.13		−9,096.824	15,687.47
Financial literacy	11,092.12	8,365.005		8,107.512	9,763.146		6,755.976	9,385.829
Number of siblings	−3,071.268	1,994.036		−2,300.719	2,146.762		−1,995.719	2,346.2
GFC dummy	−64,918.54	9199.853	***
Constant	153,460.4	22,930.48	***	119,668.9	25,493.35	***	120,616.5	26,330.85	***
q50
Couple’s informal care	−2.452	1.158	**	−10,619.710	3688.708	***	−4.390	1.756	**
Total number of own resident dependent children	6,155.212	3,334.936	*	4,618.789	3,624.543		4,646.14	3,503.882
Reference person
Age	−2,329.484	840.6837	***	−1,953.232	984.1043	**	−2,209.337	885.0037	**
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	78,368.11	12,940.24	***	80,811.04	15,149.24	***	84,322.62	14,074.03	***
Adv diploma, diploma	20,325.08	14,968.49		17,968.73	17,000.27		22,049.71	16,649.35
Cert III or IV	12,330.26	9,058.627		18,718.12	9,815.573	*	17,248.55	10,036.02	*
Year 12	11,801.76	11,947.5		5,648.039	13,720.25		8,084.517	13,795.54
Non English speaking background	16,820.69	15,239.71		35,699.17	15,561.5	**	34,677.3	17,839.9	*
Ever divorced	14,132.11	11,716.56		22,480.82	13,505.37	*	17,677.58	14,163.7
Financial literacy	8,986.69	7,914.451		13,498.47	9,124.544		17,058.69	9,678.402	*
Number of siblings	−7,659.248	1,824.767	***	−8,157.2	2,057.872	***	−8,507.523	2,172.478	***
Partner
Age	4,666.06	870.8207	***	3,636.618	999.4884	***	3,697.356	952.9388	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	84,783.89	11,690.59	***	88,353.34	13,580.19	***	86,211.02	13,985.35	***
Adv diploma, diploma	17,209.45	13,140.89		4,225.699	14,421.55		992.2952	15,169.51
Cert III or IV	4,652.575	9,945.192		8,961.564	12,185.27		7,167.869	11,968.96
Year 12	13,262.16	11,573.15		24,281.47	14,397.19	*	19,070.75	13,513.37
Non English speaking background	−12,078.29	14,689.26		−18,028.15	14,399.56		−18,265.42	14,625.94
Ever divorced	−32,582.98	11,795.28	***	−31,379.57	13,273.47	**	−29,602.36	13,218.33	**
Financial literacy	27,696.71	7681.532	***	14,025.12	9,515.432		14,844.86	8,869.72	*
Number of siblings	−4,567.085	1,830.157	**	−4,232.205	2,028.634	**	−3,914.458	1,897.299	**
GFC dummy	−79,966.52	9,756.627	***
Constant	54,491.16	25,822.57	**	4,668.358	27,367		10,180.28	27,763.28
q75
Couple’s informal care	−10.186	2.419	***	−28,019.920	6,931.614	***	−9.893	2.827	***
Total number of own resident dependent children	9,126.953	7,038.835		14,431.17	7,274.528	**	12,666.8	7,624.47	*
Reference person
Age	−651.6029	1,773.562		−573.4342	1,928.941		−404.4106	2,005.194
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	192,219.7	30,012.31	***	184,481.4	31,547	***	201,975.2	32,494.25	***
Adv diploma, diploma	70,340.13	34,555.16	**	74,964.43	42,173.06	*	87,347.3	37,326	**
Cert III or IV	10,664.51	22,224.34		18,126.93	21,958.57		32,109.85	22,991.48
Year 12	4,318.929	32,188.46		39,331.46	34,868.92		47,348.33	33,865.82
Non English speaking background	8,908.311	31,305.33		56,334.42	32,082.07	*	53,745.74	31,939.31	*
Ever divorced	59,407.68	27,439.55	**	41,886.1	30,365.01		45,029.03	29,780.93
Financial literacy	5,676.167	17,074.7		32,650.69	19,997.48		33,934.6	19,171.02	*
Number of siblings	−9,874.326	3,582.87	***	−12,002.6	3,239.928	***	−11,607.83	3,832.256	***
Partner
Age	7,311.86	1,782.272	***	7,770.965	1,870.388	***	7,517.079	1,976.195	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	160,630.6	24,117.34	***	186,642.2	24,996	***	186,908.8	24,322.02	***
Adv diploma, diploma	77,785.56	31,858.47	**	44,307.32	32,301.64		50,260.22	29,446.68	*
Cert III or IV	25298.98	21,309.72		32,388.68	22,606.89		27,113.64	23,036.59
Year 12	104,576.1	27,286.47	***	125,702.1	29,751.87	***	127,387.4	32,039.18	***
Non English speaking background	−25,527.68	30,995.28		−29,650.56	31,900.39		−29,386.75	30,941.51
Ever divorced	−23,494.63	29,983.89		−25,884.57	30,681.64		−30,674.87	31,938.73
Financial literacy	24,838.89	17,301.35		3,913.025	19,124.17		4,181.152	17,713.41
Number of siblings	−8,850.714	3,273.614	***	−10,200.18	3,569.826	***	−10,200.68	3,782.928	***
GFC dummy	−120,541.6	21,492.76	***
Constant	41,812.63	54,382.48		−121,205	55,559.01	**	−135,539	52,990.22	**
Sample size		5,271			4,094			4,094
Number of households		2,845			2,503			2,503
0.25 Pseudo R2		0.0112			0.0095			0.0096
0.50 Pseudo R2		0.0275			0.0247			0.0245
0.75 Pseudo R2		0.0411			0.05			0.0494
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity	chi2(21) = 59.39		***	chi2(20) = 56.52		***	chi2(20) = 51.03		***
Test of the null hypothesis that the coefficients of informal care variable are the same for the 0.25, 0.50 and 0.75 quantiles.	F(2, 5249) = 9.81		***	F(2, 4073) = 9.36		***	F(2, 4073) = 7.34		***

(1) Estimates of the standard errors are obtained using bootstrap methods with 400 replications. (2) ***, ** and * indicate significance at the 1%, 5% and 10% significance levels, respectively. (3) The following sample selection rules are imposed: (a) hetrosexual couples; (b) the age of the reference person and partner are >24 & age<65 (for both reference person and the partner); (c) the couple has been continuously married or living together without being married for the last 4 years; and (d) in the case of equations (2.1) and (2.3), the reported hours of care are less than 128 hours per week.

Column (2.2) in Table 2 shows the estimated results using the total years of informal care as a main carer as the variable of interest. Similar to column (2.1), we only find significant effects of informal care for the 0.50 and 0.75 quantiles. Our estimates show that an additional year of informal care provision as a main carer reduces their savings over a 4 year period by $A10,620 and $A28,020 for the 0.50 and 0.75 quantiles, respectively. When we look at the impact of the hours of care provided as a primary carer that are reported in column (2.3), we observe that an additional hour of primary care reduces savings by $A4.39 and $A9.89 for the 0.50 and 0.75 quantiles. That is, being the main carer (column (2.3)) has a slightly negative larger impact on wealth than just being one of the carers (column (2.1)) for the 0.5 quantile but not for the 0.75 quantile. The regression in column (2.1) is the only one that includes data before and after the Global Financial Crisis, but we can see that this crisis has large negative impacts on wealth for all three quantiles somewhere between just under $A65,000 for the 0.25 quantile to just over $A121,000 for the 0.75 quantile. The number of siblings has a negative impact on wealth for all three quantiles, a finding that is consistent with Cronqvist and Siegel (2015) who point out that ‘more siblings may act as insurance, reducing savings’. We should not be surprised that lower levels of education for both the reference person and their partner tend to reduce wealth in all cases as levels of income are likely to be closely related to levels of education. Interestingly, financial literacy has limited effects. There is hardly any or no effect for the high and low quantiles. Only the middle quantile shows any effects.

Table 3 reports the results for estimating the effects of caring on wealth changes for single individuals. In contrast to the results reported in Table 2, for single individuals the informal care variable is only significant when care is measured as years of care as the main carer and then only for the 0.50 and 0.75 quantiles. An additional year of care reduces savings over the 4 year period by $A3,072 and $A10,635 for the 0.50 and 0.75 quantiles, respectively, both of which are quite smaller in size than what is found in Table 2 for couples. According to the results for (3.1) the impact of the Global Finance Crisis is also smaller for single individuals. The number of siblings have a negative impact on savings for the 0.50 and 0.75 quantiles.

Table 3. The effects of informal care on net asset accumulation for single individuals (using imputed wealth data)

	Informal care variable is hours of care			Informal care variable is years of care as the main carer			Informal care variable is hours of care as the main carer
	(3.1)			(3.2)			(3.3)
		Bootstrap Std. Err.			Bootstrap Std. Err.			Bootstrap Std. Err.
	Coef.			Coef.			Coef.
q25
Informal care	0.682	2.005		118.259	3,909.955		1.872	1.878
Total number of own resident dependent children	1,739.987	2,572.592		973.630	2,802.563		1,204.743	2,842.221
Male	−1,009.552	5,818.937		1,039.001	7,942.054		2,329.02	7,114.986
Age	94.98178	292.151		−111.9439	360.209		−114.735	375.551
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	−8,933.455	8,437.378		−18,142.74	11,296.06		−18,211.38	12,156.28
Adv diploma, diploma	−23,342.62	11,563.82	**	−27,490.97	11,542.1	**	−28,710.07	11,723.85	**
Cert III or IV	4,496.356	6,068.875		2,790.261	6,984.134		1,445.723	7,306.86
Year 12	−4,564.955	10,583.18		−10,370	13,830.04		−11,211.44	14,328.71
Non English speaking background	10,832.03	7,774.067		17,491.86	9,867.905	*	17,635.99	9,066.157	*
Ever divorced	−10,698.87	9,800.782		−27,573.72	11,966.64	**	−28,656.32	12,005.85	**
Financial literacy	−1,438.004	5,869.136		−3,972.749	8,249.393		−4,363.543	7,904.633
Number of siblings	1,005.491	764.4405		1,330.279	1,094.461		1,369.646	1,083.154
GFC dummy	−24,343.68	5,079.334	***
Constant	−16,539.77	15,265.09		−26,311.48	19,424.02		−26,430.17	19,849.34
q50
Informal care	−0.288	0.909		−3,071.898	1,282.235	**	0.049	1.052
Total number of own resident dependent children	−1,271.778	1,231.139		−760.4577	1,202.623		−1,024.74	1,284.535
Male	7,129.953	3,183.489	**	5,767.795	3,396.493	*	6,643.787	3,324.9	**
Age	798.048	142.3561	***	557.5828	141.0434	***	542.781	140.5064	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	51,194.73	6110.632	***	48,664.68	6,931.476	***	48,850.48	6,814.138	***
Adv diploma, diploma	26,815.99	8,246.905	***	21,000.03	8,127.023	**	22,482.91	7,572.719	***
Cert III or IV	9,996.559	3,199.183	***	6,070.563	3,017.048	**	6,561.812	3,163.787	**
Year 12	13,180.02	3,810.566	***	9,292.023	3,499.489	***	9,403.607	3,677.879	**
Non English speaking background	3,263.862	5,511.891		4,159.152	5,311.717		3,986.729	6,076.862
Ever divorced	−8,454.769	5,822.992		−12,596.97	5,781.376	**	−12,006.82	5879.067	**
Financial literacy	15,064.15	4,023.187	***	13,123.06	3,824.501	***	13,297.06	4,201.122	***
Number of siblings	−2,294.149	571.2282	***	−1,796.734	490.8846	***	−1,839.859	519.8091	***
GFC dummy	−18,545.69	4,942.964	***
Constant	−8,667.663	8,061.269		−14,571.19	6,504.098	**	−15,131	6,994.068	**
q75
Informal care	−1.364	1.827		−10,635.080	3,318.750	***	−0.948	2.002
Total number of own resident dependent children	−9,655.362	2,978.935	***	−7,049.026	3,315.748	**	−7,832.369	3,197.047	**
Male	29,857.71	9,164.94	***	28,209.72	9,476.745	***	31,609.06	9,987.966	***
Age	2,830.435	378.1695	***	2,678.348	427.0625	***	2,529.152	408.4312	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	165,606.6	13,733.2	***	160,210	15,566.3	***	162,908	14,836.38	***
Adv diploma, diploma	87,577.12	19,457.93	***	73,888.97	17,726.82	***	75,108.7	17,009.16	***
Cert III or IV	25,233.85	8,923.329	***	25,373.52	9393.587	***	27,172.31	10,428.05	***
Year 12	40,207.86	13,658.33	***	33,428.59	14,309.53	**	35,384.62	14,694	**
Non English speaking background	13,784.21	14,774.86		4,112.288	19,204.94		10,012.06	17,877.36
Ever divorced	22,489.16	22,620.74		15,825.27	25,319.23		19,058.64	24,291.75
Financial literacy	37,060.64	10,980.93	***	39,656.84	11,152.41	***	41,029.74	11,092.81	***
Number of siblings	−8,749.401	1,432.297	***	−8,757.947	1,523.051	***	−8,482.972	1,525.047	***
GFC dummy	−66,509.55	13,495.41	***
Constant	31,888.04	22,304.88		−23,602.21	19,935.78		−23,169.72	19,591.6
Sample size		6,401			5,048			5,048
Number of households		3,730			3,255			3,255
0.25 Pseudo R2		0.0018			0.0017			0.0018
0.50 Pseudo R2		0.0088			0.0074			0.0073
0.75 Pseudo R2		0.0324			0.0317			0.0312
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity	chi2(13) = 39.68		***	chi2(12) = 39.25		***	chi2(12) = 39.46		***
Test of the null hypothesis that the coefficients of informal care variable are the same for the 0.25, 0.50 and 0.75 quantiles.	F(2, 6387) = 0.42			F(2, 5035) = 4.02		**	F(2, 5035) = 0.82

(1) Estimates of the standard errors are obtained using bootstrap methods with 400 replications. (2) ***, ** and * indicate significance at the 1%, 5% and 10% significance levels, respectively. (3) The following sample selection rules are imposed: (a) a single individual who (i) has never married and is not in a relationship, (ii) is divorced or separated; or (iii) is widowed from a hetrosexual couple; (b) the age of the individual is >24 & age<65; (c) the individual has been continuously single for the last 4 years; and (d) in the case of equations (4.1), and (4.3), the reported hours of care are less than 128 hours per week.

In the HILDA survey, information for a sizable proportion of the wealth variables are imputed, for example, about 40% of total assets and 15% of total debts are imputed (see Hayes and Watson (2009) for details of the imputation methodology).⁷ As a robustness check in order to check whether the imputation affects our results, in Tables 4 and 5 we use non-imputed wealth data to re-estimate the equations reported in Tables 2 and 3, respectively. Columns (4.1)-(4.3) and (5.1)-(5.3) correspond to columns (2.1)-(2.3) and (3.1)-(3.3), respectively, except they use non-imputed wealth data instead of imputed wealth data. For couples, the use of non-inputted data reduces the sample sizes quite substantially from 5,271, 4,094 and 4,094 to 2,793, 2,182 and 2,182, respectively. For single individuals, the sample sizes are reduced from 6,401, 5,048, and 5,048 to 3,951, 3,123 and 3,123, respectively.

Table 4. The effects of informal care on net asset accumulation for couples (using non-imputed wealth data)

	Informal care variable is hours of care			Informal care variable is years of care as the main carer			Informal care variable is hours of care as the main carer
	(4.1)			(4.2)			(4.3)
		Bootstrap Std. Err.			Bootstrap Std. Err.			Bootstrap Std. Err.
	Coef.			Coef.			Coef.
q25
Couple’s informal care	−0.474	1.859		−2,333.588	4,953.021		0.353	3.124
Total number of own resident dependent children	354.000	3,932.798		−1,860.42	4,345.467		−2,042.06	4,210.55
Reference person
Age	−2,209.636	1,078.963	**	−2,758.564	1,389.593	**	−2,741.05	1,275.23	**
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	21,197.01	18,146.24		35,769.13	20,538.91	*	38,443.17	22,329.44	*
Adv diploma, diploma	−5,653.971	21,213.69		3,821.884	22,896.11		6,239.18	24,014.98
Cert III or IV	2,191.933	15,841.71		18,242.79	17,459.07		20,964.31	17,825.25
Year 12	6,998.39	20,838.76		10,808.1	20,278.67		12,722.26	21,312.17
Non English speaking background	30,292.34	18,954.73		52,206.03	21,278.85	**	52,713.22	22,213.69	**
Ever divorced	25,001.97	17,265.38		25,344.38	20,424.62		24,144.84	20,023.63
Financial literacy	−628.5674	10,607.06		6,272.883	11,656.38		5,210.66	11,193.27
Number of siblings	−2,956.831	2,522.384		−3,552.733	2,849.953		−3,776.99	2,933.18
Partner
Age	39.9006	1,163.099		−475.4392	1,384.566		−544.72	1,274.22
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	47,469.09	18,223.69	***	22,856.5	19,688.96		22,852.10	19,490.19
Adv diploma, diploma	−32,253.85	21,174.61		−48,327.49	22,167.55	**	−45,858.25	22,110.49	**
Cert III or IV	17,855.04	15,005.3		17,591.69	13,902.51		16,818.64	15,166.86
Year 12	−9,106.538	18,680.03		−13,190.62	18,203.57		−13,811.04	18,273.44
Non English speaking background	2938.336	19,672.22		−6,387.013	20,940.71		−5,367.35	23,371.16
Ever divorced	4,181.786	21,069.72		6,113.006	22,720.14		3,733.82	21,794.68
Financial literacy	12,708.71	12,343.3		5,589.619	12,607.44		3,772.35	13,214.50
Number of siblings	−4,604.605	2,661.306	*	−2,128.064	3,542.485		−2,025.85	3,425.04
GFC dummy	−76,544.13	11,666.03	***
Constant	153,738.6	34,912	***	119,361.6	33,883.91	***	1,20,984.30	34,223.72	***
q50
Couple’s informal care	−4.095	1.844	**	−13,333.45	4061.993	***	−5.32	2.25	**
Total number of own resident dependent children	7,531.798	4,428.139	*	5,503.173	4,054.731		2,649.29	4,563.49
Reference person
Age	−2,764.617	1,064.487	***	−2,974.251	1,095.1	***	−2,629.38	1,177.34	**
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	69,833.31	16,717.07	***	78,598.94	19,840.69	***	74,643.84	21,345.29	***
Adv diploma, diploma	9,454.064	17,546.79		−1,383.361	18,610.9		−7,173.69	18,752.61
Cert III or IV	11,220.2	11,544.35		14,962.32	12,788.23		7,097.11	12,781.89
Year 12	32,643.54	20,010.73		25,847.15	21,418.48		22,997.06	21,776.77
Non English speaking background	28,061.48	18,448.16		31,515.96	20,516.53		30,972.22	23,013.66
Ever divorced	21,134.7	14,318.52		22,355.47	16,066.05		23,905.49	17,615.59
Financial literacy	15,500.11	11,142.36		16,337.64	11,705.49		21,994.67	11,698.27	*
Number of siblings	−6,963.051	2,334.292	***	−6,365.886	2,561.9	**	−6,621.32	2,434.09	***
Partner
Age	4,751.852	1,195.712	***	4,197.994	1,219.316	***	3,458.23	1,310.46	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	103,646.2	17,109.71	***	108,681.3	18,476.12	***	109,965.10	20,215.26	***
Adv diploma, diploma	12,569.06	19,756.4		−1,633.004	19,174.62		2446.09	21,135.22
Cert III or IV	1,334.83	12,496.59		16,299.36	13,747.99		12,765.06	13,883.44
Year 12	−1,811.654	15,406.56		11,751.68	17,203.83		12,357.06	15,772.25
Non English speaking background	487.097	17,110.61		−1502.173	19,005.49		−1,661.65	18,910.49
Ever divorced	1,613.587	17,183.46		10,704.58	17,452.02		6,189.48	18,523.77
Financial literacy	21,664.81	10,959.71	**	3,475.595	12,040.38		3,834.03	11,091.31
Number of siblings	−6,321.674	2,437.956	**	−3,667.058	2,792.605		−4,176.40	2,459.25	*
GFC dummy	−88,346.61	13,704.73	***
Constant	66,444.78	38,624.13	*	6,449.295	35,483.18		26,100.74	37,073.30
q75
Couple’s informal care	−9.930	2.883	***	−33,74,6.6	7,442.388	***	−13.51	3.81	***
Total number of own resident dependent children	21,509.19	8,590.263	**	19,387.79	9,323.889	**	19,006.46	9.569.89	**
Reference person
Age	−1,465.457	2,508.609		−2,473.597	2,660.575		−2,753.76	2,709.83
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	178,201.9	39,199.12	***	195,320.9	39,940.24	***	190,121.00	43,403.10	***
Adv diploma, diploma	38,217.04	42,512.55		42,950.14	45,832.82		51,705.66	51,924.92
Cert III or IV	18288.88	28,596.16		34,704.12	27,884.99		32,086.05	30,113.31
Year 12	53,552.07	44,843.56		71,246.91	47,403.15		76,168.01	49,635.01
Non English speaking background	−8,638.93	40,542.4		62,851.98	47,348.5		67,532.28	52,444.35
Ever divorced	29,042.44	38,852.32		30,957.09	40,619.67		51,188.60	42,109.11
Financial literacy	15,179.74	22,027.75		34,782.25	24,428.69		30,721.55	24,487.36
Number of siblings	−9,545.988	4,220.782	**	−12,717.01	4,896	***	−8,763.60	4,868.03	*
Partner
Age	7,200.231	2,539.034	***	8,345.324	2,619.646	***	8,445.32	2,732.32	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	192,180.9	29,416.89	***	184,050.2	33,668.75	***	184,475.00	35,187.19	***
Adv diploma, diploma	75,642.09	39,390.68	**	52,936.2	42,134.86		50,090.64	43,431.91
Cert III or IV	17,340.17	28,430.55		29,295.31	31,224.44		14,035.05	32,747.51
Year 12	61,263.93	35,064.35	*	85,076.57	37,856.6	**	63,959.56	38,682.99	*
Non English speaking background	−16,205.8	37,321.83		−31,686.39	34,809.57		−34,468.37	39,482.92
Ever divorced	25,182.49	44,494.99		25,980.54	44,198.72		7,516.44	46,158.14
Financial literacy	10,125.37	20,938.95		−3,109.513	23,729.66		6,639.34	23,331.23
Number of siblings	−11,848.45	4,203.646	***	−10,699.79	5,318.691	**	−10,543.62	5,412.52	**
GFC dummy	−124,498.1	26,805.34	***
Constant	54,722.04	65,424.04		−80,789.66	74,121.74		−83,497.08	67,260.06
Sample size		2,793			2,182			2,182
Number of households		1,862			1,583			1,583
0.25 Pseudo R2		0.0153			0.014			0.014
0.50 Pseudo R2		0.0347			0.0302			0.0295
0.75 Pseudo R2		0.0497			0.0548			0.0536
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity	chi2(21) = 28.53			chi2(20) = 40.20		***	chi2(20) = 37.40		**
Test of the null hypothesis that the coefficients of informal care variable are the same for the 0.25, 0.50 and 0.75 quantiles.	F(2, 2771) = 5.45		***	F(2, 2161) = 8.33		***	F(2, 2161) = 4.78		***

(1) Estimates of the standard errors are obtained using bootstrap methods with 400 replications. (2) ***, ** and * indicate significance at the 1%, 5% and 10% significance levels, respectively. (3) The following sample selection rules are imposed: (a) hetrosexual couples; (b) the age of the reference person and partner are >24 & age<65 (for both reference person and the partner); (c) the couple has been continuously married or living together without being married for the last 4 years; and (d) in the case of equations (3.1) and (3.3), the reported hours of care are less than 128 hours per week.

Table 5. The effects of informal care on net asset accumulation for single individuals (using non-imputed wealth data)

	Informal care variable is hours of care			Informal care variable is years of care as the main carer			Informal care variable is hours of care as the main carer
	(5.1)			(5.2)			(5.3)
		Bootstrap Std. Err.			Bootstrap Std. Err.			Bootstrap Std. Err.
	Coef.			Coef.			Coef.
q25
Informal care	0.322	2.760		−475.540	4,489.797		0.259	3.051
Total number of own resident dependent children	3,639.959	2,891.797		3,267.579	3,591.822		3,413.87	3,262.81
Male	−8,922.718	7,790.055		−729.2413	9,056.917		−1,960.53	9,702.83
Age	345.7179	393.277		47.36624	513.3049		79.24	514.85
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	−8,942.153	10,794.78		−17,182.2	15,681.1		−17,471.90	15,674.85
Adv diploma, diploma	−24,825.71	14,657.19	*	−28,507.67	15,440.79		−28,384.48	15,757.30	*
Cert III or IV	2,502.059	6,828.356		−2,660.95	8,956.097		−3,544.54	8,894.13
Year 12	−4807.618	12,486.27		−16,024.62	19,345.41		−14,903.43	20,441.42
Non English speaking background	251.7635	14,306.32		−1,272.053	20,980.24		−1,993.48	18,979.19
Ever divorced	−18,037.17	13,676.77		−35,390.91	15,759.41		−36,949.54	16,348.00	**
Financial literacy	4,636.407	7,812.428		1,439.553	9,755.283		1,059.98	9,562.76
Number of siblings	1,569.018	809.4421	*	2,461.039	1,318.543		2,329.24	1,342.71	*
GFC dummy	−20,388.32	6,431.844	***
Constant	−30,910.43	21,244.7		−36,841.04	26,895.85		−37,194.55	28,979.31
q50
Informal care	−0.799	1.144		−3,221.825	1,351.369	**	−0.32	1.48
Total number of own resident dependent children	667.0654	1,334.155		412.8104	1,344.011		−150.61	1,279.00
Male	4,699.26	3,890.059		5,458.626	3,113.812	*	5,398.38	3,843.25
Age	901.3466	180.8123	***	608.57	162.0823	***	541.33	165.51	***
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	46,903.41	7,639.132	***	47,248.28	8,985.712	***	47,207.16	8,728.46	***
Adv diploma, diploma	20,749.21	7,609.828	***	10,710.91	7,567.228		10,603.48	7,208.06
Cert III or IV	6,324.433	3,258.006	*	3,512.881	3,022.964		3,307.72	3,374.41
Year 12	9,259.966	4,585.674	**	5,505.33	4,017.18		5,123.60	4,565.30
Non English speaking background	3,292.877	6,653.399		2,396.351	6,450.803		4,269.50	6,172.75
Ever divorced	−16,240.21	5,639.603	***	−12,526.46	5,237.184	**	−11,877.08	5,846.22	**
Financial literacy	23,599.06	4,477.826	***	17,971.47	4,377.636	***	18,518.48	4,325.83	***
Number of siblings	−1,859.485	561.8636	***	−1,753.43	564.616	***	−1,480.34	526.30	***
GFC dummy	−15,835.79	5,926.404	***
Constant	−20,238.06	8,963.709	**	−19,952.12	7,436.369	***	−18,689.51	8165.09	**
q75
Informal care	−0.971	2.261		−11,636.57	4,828.438	**	0.07	2.46
Total number of own resident dependent children	−3,050.161	3,612.683		−2,747.185	4,138.984		−2625.28	3,667.28
Male	29,109.91	10,286.72	***	30,456.32	10,816.85	***	34,716.43	10,873.27
Age	3,394.748	457.5953	***	2,953.698	464.3825	***	2,979.06	449.05
Educational attainment (base is Year 11 and below)
Bachelor degree or higher	161,560.7	16,558.37	***	162,592.9	19,206.4	***	166,170.40	18,866.65
Adv diploma, diploma	81,346.02	20,584.41	***	70,485.78	18,433.5	***	72,371.01	19,373.32
Cert III or IV	16,518.73	9,987.775	*	12,399.4	10,318.78		13,919.50	10,471.37
Year 12	21,852.63	12,676	*	15,451.76	12,874.75		19,014.07	13,138.26
Non English speaking background	−7,242.024	20,238.9		−10,969.27	19,282.84		−4,987.06	19,930.39
Ever divorced	−20,348.33	22,555.25		−23,011.24	24,479.75		−21,184.96	24,126.23
Financial literacy	46,753.46	11,313.36	***	46,968.41	12,290.56	***	48,349.43	12,563.08
Number of siblings	−9,578.651	1,944.863	***	−9,130.023	2,124.776	***	−8,982.53	2,015.86
GFC dummy	−63,643.36	17,288.94	***
Constant	−6,120.086	25,752.34		−47,063.61	22,780.7	***	−56,301.41	19,510.25
Sample size		3,951			3,123			3,123
Number of households		2,601			2,227			2,227
0.25 Pseudo R2		0.0028			0.0021			0.0021
0.50 Pseudo R2		0.0111			0.0101			0.0099
0.75 Pseudo R2		0.0431			0.0451			0.0444
Breusch-Pagan/Cook-Weisberg test for heteroskedasticity	chi2(13) = 43.92		***	chi2(12) = 42.51		***	chi2(12) = 42.18		***
Test of the null hypothesis that the coefficients of informal care variable are the same for the 0.25, 0.50 and 0.75 quantiles.	F(2, 3937) = 0.12			F(2, 3110) = 2.50		*	F(2, 3110) = 0.04

(1) Estimates of the standard errors are obtained using bootstrap methods with 400 replications. (2) ***, ** and * indicate significance at the 1%, 5% and 10% significance levels, respectively. (3) The following sample selection rules are imposed: (a) a single individual who (i) has never married and is not in a relationship, (ii) is divorced or separated; or (iii) is widowed from a hetrosexual couple; (b) the age of the individual is >24 & age<65; (c) the individual has been continuously single for the last 4 years; and (d) in the case of equations (5.1), and (5.3), the reported hours of care are less than 128 hours per week.

For the results reported in Table 4 for couples, the patterns of the estimated results are similar to those for the imputed data case. The 0.25 quantile shows no significant effects of informal care variable. The significant negative effects of caring only appear in the 0.50 and 0.75 quantiles. This finding has some similarities with Arora’s (2016) results for the impact of the onset of dementia in a parent on an unmarried child’s wealth. According to the results in column (4.1), an additional hour of informal care reduces household savings by $A4.10 and $A9.93 for the 0.50 and 0.75 quantiles, respectively. From column (4.2), we observe that an additional year of care reduces household savings by $A13,333 and $A33,747 for the 0.50 and 0.75 quantiles, respectively. From column (4.3), an additional hour of primary care reduces household savings by $A5.32 and $13.51 for the 0.50 and 0.75 quantiles, respectively. Being the primary carer rather than just one of the carers reduces wealth more.

The results for single individuals when only the non-inputted data is used reported in Table 5 suggest that the care variables are only significant when care is measured using the years of care as the main carer, and even in this case only for the 0.50 and 0.75 quantiles. This result is consistent with the results reported in Table 3 when inputted data is used.

The negative effects of caring are limited to the mid to high quantile groups. No significant effects in the low quantile may be in line with previous studies which suggest that the effects of informal care have limited effects on the labour supply of carers (Bolin, Lindgren, and Lundborg (2008), Leigh (2010), Oshio and Usui (2017) and Moussa (2019)). If informal caring does not affect the labour supply for lower quantile couples, they may not suffer an income loss from informal caring. Another interpretation is that income and assets tested government transfers (Carer Payment and Carer Allowance) may mitigate the negative impacts of informal care for lower quantile couples. Due to the eligibility criteria for government transfers, we may not observe any adverse financial effects of informal care among lower quantile. Finally, there is a possibility that lower quantile couples may reduce their consumption to maintain their savings.

V. Conclusion

This paper is one of the first attempts to examine whether informal care affects household savings. Informal care may negatively affect household savings via: a loss of labour income; direct/ indirect informal care expenditures of the carer; and the carer’s medical costs. On the other hand, private transfers (inter-vivos, bequests and inheritance) and government financial transfers (Carer Payment and Carer Allowance) may mitigate the negative financial effects of informal care on the households. However, so far the existing literature did not present empirical evidence on which of these effects dominate. Our estimated results using quantile regression models show that informal care provision has limited impacts on changes in household asset formation for the lower quantile. The negative effects are limited to the mid to high quantile groups. The findings for the lower quantile may be consistent with previous studies that report the effects of informal care have limited effects on labour supply of carers. On the other hand, we observe some negative effects of informal care on savings for the 0.50 and 0.75 quantiles. This may be because government transfers may mitigate negative financial shocks of informal care for lower quantile couples or lower quantile couples may reduce their consumption to save. However, these issues need to be further examined in future research.

Acknowledgments

The authors would like to thank an anonymous referee for constructive comments that have lead to a significant improvement in the paper. This paper uses unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute: Applied Economic and Social Research, the University of Melbourne (the Melbourne Institute). The findings and views reported in this paper, however, are those of the authors and should not be attributed to DSS, the Melbourne Institute, or the Australian Institute of Family Studies. The second and third authors wish to acknowledge the financial support provided by the Japan Society for the Promotion of Science (JSPS) Grant in Aid for Scientific Research (B) No. 20H01513 for a project on “Intergenerational Interrelationships: An Analysis of Bequests, Long-term Care & Labour Supply and Consumption and Saving” (Project Leader: Colin McKenzie).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the JSPS [20H01513].

Notes

¹ Cobb-Clark, Kassenboehmer, and Sinning (2016) provide a recent analysis of saving behaviour using the HILDA data set, but the focus of their analysis is an individual’s locus of control.

² The only question in HILDA relating to bequests is whether an inheritance will be a source of funding for the individual’s retirement.

³ As is explained in section III, wealth is observed only once every 4 years (every 4^th wave).

⁴ It could be argued that different types of care may have different implications and the relationship between the care giver and care recipient may be important, The HILDA survey does not contain a lot of information on ‘types’ of care provided, but does provide information on whom the respondent cares for in terms of the carer’s relationship to the care recipient. However, the proportions for each category of care are relatively small, so that we could not take into account of the types of care. We tried to estimate equations that accounted for the different relationships between the care recipient and the care giver but they did not converge.

⁵ These sample selection rules about the state of the couple or the single individual not changing over the 4 year period are designed to eliminate the effects of partnership formation and partnership dissolution/termination on wealth changes.

⁶ It is worth noting that we also estimated these two equations including the care variables for both the reference person and the partner separately, but the coefficients of these variables were not statistically different, so we only present the estimated results for the combined hours and years of informal care provided by the couples.

⁷ According to Summerfield et al. (2019), a mixture of the Little and Su (1989) method and the nearest neighbour regression methods are used for the purpose of imputing values for those components of wealth that the respondent (or the relevant individual) does not report.