Household Wealth and the Aged:
An Income Distribution Survey Analysis.
Bruce R Bacon
Retirement Income Modelling Unit
Treasury

Abstract
The RIM Task Force has been estimating the distribution of household incomes and wealth for a number of years. This paper documents the approach we have adopted and documents a number of studies we have conducted to investigate the incomes and wealth of the aged using the ABS Income Distribution Surveys (IDS).  In doing so, the paper demonstrates the potential range and scope of analysis that can be undertaken using unit record data sources like the IDS.

Introduction

The RIM Task Force has used a number of data sources to research wealth distribution in Australia.  One approach was to combine administrative data from the Department of Social Security and from the Australian Tax Office to estimate asset values of older Australians (Brown 1996). This study suffers from two main problems as discussed in that paper:

1	the processes of merging these two different data sources raises a number of conceptual and data definitional problems; and
2	the data sources capture social security recipients on the one hand and tax payers on the other.  This leave a significant section of the population unrepresented.

Another approach was to use data on asset incomes from ABS income distribution surveys to impute the value of the asset producing that income stream.  The RIM Task Force initially undertook this work in 1994 using a unit record file from the 1989/90 Income Distribution Survey (IDS). (see Tulip and Stott 1994 and Bacon 1995).  In August 1995, the RIM Task Force provided the Senate, estimates of “the total amount of capital invested by retired Australians.  More recently the Wallis Committee drew on the work of the RIM Unit to provide analysis of household wealth and its distribution (Wallis 1997).

Wealth distribution studies in Australia using unit record data were initiated by Dilnot (Dinot 1990). As well as RIM research, recent studies have been undertaken by NATSEM (Baekgaard 1995, Baekgaard and King 1996, Baekgaard  1997a and Baekgaard 1997b).  

The problems of using incomes data from surveys to impute asset values are well documented.  For example Dilnot notes that;

	“The choice of relevant multipliers is always difficult in such work, and at the level of disaggregation of investment income available it is no doubt foolish to believe that a single multiplier for each form of investment income will produce an entirely reliable result…….The problem of underreporting is unavoidable in a voluntary survey, and is undoubtedly present in the IDS.” (Dilnot 1990).

Both Dilnot and NATSEM use the IDS to estimate not only the distribution of wealth but the aggregate amount of wealth.  RIM has taken a slightly different approach.  We believe that the aggregate level of household wealth can be better estimated from other sources and that many of the shortcomings of using survey data can be avoided by using the IDS to provide the proportional distribution information only.

Aggregate Wealth Statistics

The Treasury publishes annual estimates of Australian Net Private Wealth, the latest found in the “Economic Round Up” Summer 1998.  The Treasury estimates of Australian net private sector wealth are constructed using the inventory approach, largely following the methodology of Callen (1991). This approach involves aggregating across different asset types and adjusting for the public and/or foreign ownership of assets.  The estimates are largely based on Australian Bureau of Statistics estimates of the dwelling stock, capital stock, stock of consumer durables and Australia’s international investment position. Reserve Bank of Australia (RBA) data are used for holdings of public securities and RBA liabilities. Some private sector data and estimates from previous studies also enter these estimates.

The calculation of aggregate private wealth is simplified greatly by the consolidation of the private household and business sectors. This consolidation does, however, result in loss of detail on the liabilities of these two sectors. As a result, the data on asset types contained in the attached tables and charts should not be used to infer relative ownership by either the household or business sectors or the level of personal wealth.

In particular, consolidating the private household and business sectors implies that the bulk of financial instruments held by households (such as bank deposits, debt instruments and superannuation) are netted out in the analysis.

It is therefore impossible to use these data to fully separate the household and business wealth components.  However, estimates of physical assets in the form of dwellings, motor vehicles and household durables can be directly allocated to the household.  Financial assets of the household are estimated in the Australian National Accounts: Financial Accounts (Cat No. 5232.0), which are in turn tabulated in Table 3.25, RBA - Australian Economic Statistics: 1949-50 to 1994-95 (Foster 1996).  Estimates of household debt are apportioned and netted off gross asset values to reveal the wealth story.

More recently, the ABS has commenced publishing a National Balance Sheet for Australia (Cat No. 5241.0).  These data not only extend the scope of aggregate measures of wealth, but include a balance sheet of assets and liabilities for the household and unincorporated sector.  The current analysis combines these sources to produce time series of household wealth.

Tables 1 and 2 show the time series of estimated net wealth in the form of:
•	Cash and interest bearing deposits (includes all non-housing loans as liabilities);
•	Equities;
•	Technical reserves of life offices and pension funds;
•	Other Financial Claims;
•	Dwellings and land;
•	Motor vehicles;
•	Household durables; and
•	Other non-financial assets.

Dwellings are the most significant component of wealth for most Australians at around 50-56%.
Financial Assets have grown from around 22% in 1981 to some 35% in 1996, with households increasingly holding wealth as superannuation and as equities (Chart 1).

Table 1.  Estimates of Net Wealth
	
Estimates of Household Net Wealth  $b	$b				
	interest bearing assets	equities	super and life office	other financial assets	dwellings	motor vehicles	consumer durables	Other non financial assets	Total	
1960	4.0	1.3	3.1	2.3	19.0	1.0	5.3	5.7	41.7	
1961	4.1	1.4	3.4	2.3	20.4	1.2	5.2	5.8	43.7	
1962	4.2	1.5	3.8	2.4	22.4	1.4	5.0	5.7	46.4	
1963	4.5	1.7	4.2	2.4	23.3	1.7	5.0	6.0	48.7	
1964	4.9	2.1	4.6	2.6	26.8	1.9	5.0	6.2	54.1	
1965	5.4	1.9	5.1	2.7	29.1	2.2	5.1	6.5	58.1	
1966	5.7	2.0	5.7	2.9	31.0	2.3	5.3	6.8	61.7	
1967	6.1	2.9	6.2	3.1	32.0	2.5	5.4	7.1	65.3	
1968	6.6	4.0	6.9	3.4	34.9	2.7	5.6	7.5	71.7	
1969	7.1	4.5	7.7	3.6	39.2	3.0	6.0	8.2	79.4	
1970	7.7	5.1	8.7	3.9	45.5	3.4	6.4	8.8	89.5	
1971	8.6	4.7	9.6	4.2	52.4	3.8	7.0	9.7	100.1	
1972	9.9	5.9	10.7	4.7	60.0	4.2	7.7	10.7	113.8	
1973	11.9	5.9	12.1	5.4	73.0	4.6	8.6	11.8	133.3	
1974	13.1	4.4	13.2	5.7	96.7	5.3	10.5	14.0	162.9	
1975	16.2	4.3	14.5	6.2	109.1	6.5	13.2	17.4	187.5	
1976	18.7	5.4	16.3	8.2	124.5	8.0	15.6	20.7	217.4	
1977	15.0	5.4	18.3	9.2	138.0	9.0	18.1	23.6	236.6	
1978	17.0	5.9	20.7	10.5	146.6	9.8	19.8	25.6	255.9	
1979	19.7	7.4	23.1	12.0	164.2	10.3	21.1	27.1	284.8	
1980	21.9	12.9	25.9	13.7	191.2	11.2	23.6	29.8	330.3	
1981	25.1	16.3	29.9	15.0	230.7	12.5	26.0	32.7	388.0	
1982	26.5	11.1	34.1	14.7	251.1	14.1	29.2	36.5	417.3	
1983	31.5	15.0	43.4	19.8	265.9	15.5	32.3	40.1	463.6	
1984	34.5	17.2	50.4	24.1	299.6	16.3	34.3	42.2	518.5	
1985	36.5	24.0	58.7	26.1	333.9	18.5	36.4	45.5	579.7	
1986	37.2	35.8	72.8	27.7	351.7	21.5	41.4	51.7	639.9	
1987	43.5	63.1	97.2	34.0	368.4	24.2	45.9	57.2	733.6	
1988	45.0	58.3	118.3	37.3	483.7	25.4	49.2	60.5	877.7	
1989	38.7	57.4	145.5	43.4	614.5	27.5	53.6	65.3	1045.9	
1990	36.3	55.2	164.4	43.3	644.8	29.3	55.7	62.7	1091.7	
1991	38.1	50.6	178.3	51.3	675.6	29.8	57.4	56.4	1137.5	
1992	41.6	63.8	203.1	33.1	668.9	30.3	58.8	54.4	1154.0	
1993	46.2	86.7	218.2	26.1	699.8	32.0	59.6	57.6	1226.2	
1994	58.6	109.8	238.4	33.8	733.1	32.5	62.0	59.9	1328.1	
1995	53.1	105.9	253.4	41.9	761.0	34.8	63.7	59.2	1373.0	
1996	47.1	107.0	282.5	41.9	773.8	36.3	66.2	61.7	1416.5	
1997	42.9	125.8	332.5	55.9	804.8	35.4	68.7	62.5	1528.4	

Table 2.  Net Wealth in Percentage Terms

Household Net Wealth Percentage					
	interest bearing assets	equities	super and life office	other financial assets	dwellings	motor vehicles	consumer durables	Other non financial assets	Total	
1960	9.6%	3.1%	7.4%	5.5%	45.7%	2.4%	12.8%	13.6%	100.0%	
1961	9.3%	3.2%	7.7%	5.2%	46.7%	2.8%	11.8%	13.2%	100.0%	
1962	9.1%	3.2%	8.1%	5.1%	48.3%	3.0%	10.8%	12.4%	100.0%	
1963	9.2%	3.4%	8.5%	5.0%	47.8%	3.4%	10.2%	12.3%	100.0%	
1964	9.1%	3.9%	8.6%	4.9%	49.5%	3.5%	9.2%	11.4%	100.0%	
1965	9.3%	3.3%	8.8%	4.7%	50.1%	3.8%	8.7%	11.3%	100.0%	
1966	9.3%	3.3%	9.2%	4.6%	50.2%	3.8%	8.5%	11.1%	100.0%	
1967	9.4%	4.4%	9.5%	4.8%	49.0%	3.8%	8.2%	10.8%	100.0%	
1968	9.3%	5.6%	9.6%	4.8%	48.6%	3.8%	7.8%	10.5%	100.0%	
1969	9.0%	5.7%	9.7%	4.6%	49.3%	3.8%	7.6%	10.3%	100.0%	
1970	8.6%	5.7%	9.7%	4.4%	50.8%	3.8%	7.2%	9.9%	100.0%	
1971	8.6%	4.7%	9.6%	4.2%	52.4%	3.8%	7.0%	9.7%	100.0%	
1972	8.7%	5.2%	9.4%	4.2%	52.7%	3.7%	6.8%	9.4%	100.0%	
1973	9.0%	4.4%	9.1%	4.0%	54.8%	3.4%	6.5%	8.8%	100.0%	
1974	8.0%	2.7%	8.1%	3.5%	59.4%	3.2%	6.4%	8.6%	100.0%	
1975	8.7%	2.3%	7.7%	3.3%	58.2%	3.5%	7.0%	9.3%	100.0%	
1976	8.6%	2.5%	7.5%	3.8%	57.2%	3.7%	7.2%	9.5%	100.0%	
1977	6.3%	2.3%	7.7%	3.9%	58.3%	3.8%	7.6%	10.0%	100.0%	
1978	6.7%	2.3%	8.1%	4.1%	57.3%	3.8%	7.7%	10.0%	100.0%	
1979	6.9%	2.6%	8.1%	4.2%	57.7%	3.6%	7.4%	9.5%	100.0%	
1980	6.6%	3.9%	7.9%	4.1%	57.9%	3.4%	7.2%	9.0%	100.0%	
1981	6.5%	4.2%	7.7%	3.9%	59.5%	3.2%	6.7%	8.4%	100.0%	
1982	6.3%	2.7%	8.2%	3.5%	60.2%	3.4%	7.0%	8.8%	100.0%	
1983	6.8%	3.2%	9.4%	4.3%	57.4%	3.4%	7.0%	8.7%	100.0%	
1984	6.7%	3.3%	9.7%	4.6%	57.8%	3.2%	6.6%	8.1%	100.0%	
1985	6.3%	4.1%	10.1%	4.5%	57.6%	3.2%	6.3%	7.8%	100.0%	
1986	5.8%	5.6%	11.4%	4.3%	55.0%	3.4%	6.5%	8.1%	100.0%	
1987	5.9%	8.6%	13.3%	4.6%	50.2%	3.3%	6.3%	7.8%	100.0%	
1988	5.1%	6.6%	13.5%	4.2%	55.1%	2.9%	5.6%	6.9%	100.0%	
1989	3.7%	5.5%	13.9%	4.1%	58.8%	2.6%	5.1%	6.2%	100.0%	
1990	3.3%	5.1%	15.1%	4.0%	59.1%	2.7%	5.1%	5.7%	100.0%	
1991	3.3%	4.4%	15.7%	4.5%	59.4%	2.6%	5.0%	5.0%	100.0%	
1992	3.6%	5.5%	17.6%	2.9%	58.0%	2.6%	5.1%	4.7%	100.0%	
1993	3.8%	7.1%	17.8%	2.1%	57.1%	2.6%	4.9%	4.7%	100.0%	
1994	4.4%	8.3%	18.0%	2.5%	55.2%	2.4%	4.7%	4.5%	100.0%	
1995	3.9%	7.7%	18.5%	3.1%	55.4%	2.5%	4.6%	4.3%	100.0%	
1996	3.3%	7.6%	19.9%	3.0%	54.6%	2.6%	4.7%	4.4%	100.0%	
1997	2.8%	8.2%	21.8%	3.7%	52.7%	2.3%	4.5%	4.1%	100.0%	

Chart 1.  Household Financial Assets

Incomes of the Retired

Studies of the aged look at different populations depending on the purpose of the study.  One may be interested in all persons over 65 years of age, persons on age pension or all persons who have retired from the work force.
To highlight the change in circumstances often experienced by the aged, researchers and policy advisers often compare the financial characteristics of the group of interest to that of the population as a whole or to another reference group.  This section of the paper indicates how the IDS can be used to highlight the difference between retired aged households (income units) and households in the prime of their working life.
The 1994 IDS reveals that in 1993/94 the average income of a retired household was $12,254 compared to an average income for prime-age worker households of $45,088.
The average proportions of retired incomes are tabulated in Table 3, along with those for prime-age worker households.  Note the significance of the shift towards reliance of incomes generated by financial assets and government support.
Table 3.
     1993/94	  wage & salary   	   own business	  rental income 	  invest income 	   age  pension  	other aged pension	other govn income         	super income  	  other income  	
Retired	0.0%	0.0%	4.5%	15.6%	39.9%	11.8%	15.7%	12.9%	0.0%	
Working	85.5%	9.0%	1.4%	1.6%	0.0%	0.0%	3.1%	0.3%	0.1%	

The IDS can then be used to provide the underlying distributional information.  In this example we look at how these incomes vary with income.  Table 4 shows how the average incomes and the proportions vary with income for retired households.
Table 4.
Incomes of Retired Households 1993/94	  wage & salary   	   own business	  rental income 	  invest income 	   age  pension  	other aged pension	other govn income         	  super income  	  other income  	
	Average									
$0 to $10000        	$0	$0	$61	$381	$4,900	$459	$1,220	$89	$10	
$10000 to $20000  	$0	$0	$385	$1,417	$6,109	$2,656	$2,679	$1,124	$0	
$20000 to $45000  	$0	$0	$2,692	$7,378	$2,946	$2,255	$3,021	$8,523	$0	
$45000 to $90000  	$0	$0	$7,867	$26,577	$0	$515	$2,547	$16,023	$0	
over $90000       	$0	$0	$0	$81,464	$0	$0	$4,385	$14,309	$0	
	Proportions								
$0 to $10000        	0.0%	0.0%	0.9%	5.4%	68.9%	6.5%	17.2%	1.3%	0.1%	
$10000 to $20000  	0.0%	0.0%	2.7%	9.9%	42.8%	18.6%	18.8%	7.9%	0.0%	
$20000 to $45000  	0.0%	0.0%	10.1%	27.8%	11.1%	8.5%	11.4%	32.1%	0.0%	
$45000 to $90000  	0.0%	0.0%	13.9%	46.9%	0.0%	0.9%	4.5%	28.3%	0.0%	
over $90000       	0.0%	0.0%	0.0%	81.3%	0.0%	0.0%	4.4%	14.3%	0.0%	
Data Source:  ABS 1994/95 Income Distribution Survey.
Prime-age working means income units receiving wage or business income and with reference person’s age between 25 and 54.
Retired means income units not receiving wage or business income.  For an income unit without a spouse (ie not married) the reference person is 55+ and ‘Not in the Labour Force’ (NILF).  For a coupled income unit (ie married) both partners must be NILF and at least one must be 55+.
“Other aged pensions” are age pension substitutes.  Service pensions and/or war widow pensions.

The Estimation of Assets and Wealth

The section looks at four asset types:
•	Interest bearing assets;
•	Equities;
•	Owner Occupied Dwellings; and
•	Rental Properties.

Imputation of asset values from reported income flows in income distribution surveys is problematic.  Standard practice is to use a single interest rate or yield.  If one is trying to estimate the asset value, the choice of this interest rate becomes crucial.  However, if one is simply after a proportional allocation then an interest rate or yield is not required as all the distributional information is contained within the income flows.
That is, assuming that all households face a single effective return on each class of asset, then distributing the aggregate wealth estimates proportionally to an individual’s income received from that asset produces an appropriate value for the asset held. This approach was used for equities and rental incomes.
However for assets in the form of interest bearing deposits, we have left this simple paradigm. It is well known that there is a range of interest rates on offer from the various forms of interest bearing assets.  Banks, in particular offer a range of interest rates on deposits depending on the size of the deposit and sometimes on the length of time a deposit is invested with the bank.  We do not have useful information on returns associated with length of time of the deposit, but we do have from the RBA interest rates for different amounts on some types of accounts. 
Further, the 1994 IDS carries information on whether the respondent received interest income from a range of sources. Namely:
•	Banks;
•	Other financial institutions;
•	Debentures;
•	Bonds;  and
•	Trusts.
Each of these sources offer different interest rates.  In view of the lack of information to the contrary, we assume that the interest income received comes primarily from the source with the highest yield. 
We have used this information to construct a simple model which captures the minimum and a representative upper limit of interest rates which are on offer depending on the size of the investment and the source.

Table 5.  1993-94 Interest Rates
Asset Type		Amount	Range			Rate used
Banks deposit	<$2000	1.25 – 2.0			1.625
			<$10000	1.25 - 2.75			2.0
			>$10000	2.5 - 3.75			3.125
Banks Fixed	5-100K	4.35 – 4.8			4.575
Other finance	Call		1.95 – 2.1			2.025
			Fixed		4.5 – 6.0			5.25
Trusts				3.82 – 4.05			3.925
Debentures				5.25 – 5.8			5.5125
Bonds				5.1 – 6.5			5.8

Dividends				3.0 – 3.4			3.2
Source RBA Bulletin Dec 1995

The rental yield simple reflects the simple “Sydney rule” that a $300,000 home will get $300 per week if rented, implying a return of 5.2% per annum.
	
Supperannaution assets can not be estimated from the IDS, as the income stream does not reflect in any meaningful way the value of the underlying assets.  To impute superannuation assets across households, as reflected by the aggregate value of technical reserves of life offices and pension funds, we use distributional information from RIMGROUP.

Assets of the Aged

In their publication “Ageing Society”, EPAC estimated the private wealth of pensioners over 65 to be $140 billion.  This is a top down, back of the envelope estimate. It was derived as follows:

Private Sector Wealth (Treasury estimate June 1991)		$1434bn
	(includes household sector, private financial sector and private trading sector)
Pensioners make up over 11% of the population and
Pensioners own 10% of the private wealth			10%
Private wealth of pensioners				$140bn.

In August 1995, the RIM Task Force provided the Senate, estimates of “the total amount of capital invested by retired Australians”.  These estimates were between $330 billion and $360 billion of assets held by retirees as of the March Quarter 1995, compared with some $1200 billion for all Australians. That is, retirees held some 28% of the wealth of the nation.

These estimate combined wealth share information of retirees from the 1990 IDS with Financial Asset information for households from the Australian National Accounts - Financial Accounts.

At the time retirees where defined as:
•	single persons, over 55 years of age and not in the labour force; and
•	married couples, one person over 55 years of age and both not in the labour force.

The estimates made the following assumptions:
•	assets holding ratios of the retired persons to the population has not changed significantly;
•	retired persons hold between 5% and 15% of reserves of life offices and pension funds;
•	housing equity moves with the dwelling implicit price deflator; and
•	estimates of rental property assets were not included.

The approach was to estimate the shares of financial assets held by retirees from the 1990 IDS and to apply these shares to corresponding estimates of financial assets held by the household sector from ANA Financial Accounts.  Housing equity was inflated by the growth dwelling IPD of 7.9% (see Table B1).

This current research extends the methodology previously used by RIMTF to estimate total wealth of pensioners.  The extensions include:
•	all males over 65 years of age;
•	all females over 60 years of age;
•	the inclusion of rental property;
•	a simple distribution model of interest rates;
•	modelling of full rate and part rate pensioners; and
•	splitting of assets between couples.

DATA SOURCE

The 1990 Income Distribution Survey is a sample of 30444 persons aged 15 years and over.

The scope of the survey excludes people in institutions.  This means that a number of pensioners in nursing homes are excluded.

The number of age/wives/carers pensioners in the IDS is compared to DSS statistics in Table 6.

Table 6.  Population Estimates

	IDS	DSS	DSS	
Males	Nov-90	Jun-90	Jun-91	
60-64				
65-69	116421	116531	129578	
70-74	100822	101542	100235	
75+	173303	186413	188552	
Total	390546	404486	418365	
				
Females				
60-64	164875	167252	175269	
65-69	200930	197859	199645	
70-74	186220	185058	188300	
75+	327757	385813	394252	
Total	879782	935982	957466	

Asset Modelling

The method estimates assets from the reported incomes which flow from the asset using an appropriate asset yield.

Incomes are calculated for:
•	interest bearing assets;
•	equities; and
•	rental properties.

For each of these income types, appropriate interest rates (similar to Table 4) are used to model the size of the underlying asset, which in turn determines the proportional allocations to be applied to the aggregate estimates of wealth.

Analysis shows that many of married couples have all the assets reported for either the husband or the wife.  Further, many of these cases report the partner with the asset as having low or zero income.  In many cases this may reflect asset shifting within the income unit to reduce income tax burden on the income unit.  This study assumes that assets of married couples are jointly shared, and are evenly split between them (analysis without this assumption is also available).

Housing equity is estimated as the difference between the reported value of the home less the reported amount owing on the home.  For married couples in this study, home equity is evenly split between husband and wife.

Pension Status Modelling

For this study the pension is defined as either:
•	age pension;
•	wives pension;
•	carers pension; or
•	service pension.

Pensioners are identified by income from one of these pension streams.  These are split into full rate pensions and part rate pensions by using the appropriate income and asset thresholds (changes to this threshold due to children under 13 years of age are captured by the variable reporting the number of children 14 years and under).

DETAILED TABLES

Attachment A contains detailed tables of estimates of:
•	Share of wealth;
•	Average wealth;
•	Total wealth;
for each of the four asset types
•	annual income
•	interest bearing deposits
•	equities
•	rental property
•	equity in home
•	wealth less home
•	total wealth.
Split by:
•	Married and single;
•	Home owners and nonhome owners (including total wealth and total wealth less housing);
•	Full rate pensioners, part rate pension and nonpensioners;
•	Pension wealth class;
•	Below $50,000;
•	Below full pension threshold;
•	Below part pension threshold;
•	Below double part pension threshold;
•	Above double part pension threshold.

These pension asset tests vary as follows:

Table 7.  Pension Thresholds

	Home Owner		Nonhome Owner		
	Married	Single	Married	Single	
Full Pension Threshold	$68500	$96000	$103300	$164500	
Part Pension Threshold	$124000	$163500	$158500	$231500	
Double Part Pension Threshold	$248000	$327000	$317000	$463000	

This analysis shows that as at November 1990 (excluding superannuation assets and durables):
Males over 65 and females over 60 years of age hold some 27.7% of the wealth.
Under the assumption of asset splitting for married couples, males hold 21% and females hold 33%.
Pensioners hold some 13.7% of the wealth, with male pensioners at 10.7% and female pensioners at 16.5%.

Savings Functions

The last example demonstrates the use of the IDS to model savings functions, which can be used in asset accumulation models.
In this case we are interested in how asset values are related to income.  To get a handle on saving elasticities we estimated smoothed age-specific savings functions from the 1989/90 IDS using a log-linear model specification.

The results show how the value of asset held and the elasticity vary with income and age, and how they vary with the type of asset. 

Dwelling elasticities, for example, are relatively flat and constant across age (Chart 2).  The value of the dwelling asset increases dramatically with age as the housing mortgage is paid off.

Chart 2.  Dwelling Assets

Savings elasticities for interest bearing assets show greater growth with income (Chart 3).  The functions are tightly bunched at younger ages and only show significant growth after 55 years of age, possibly reflecting greater liquidity as homes are paid off and or mortgage repayments decrease.

Chart 3. Interest Bearing Assets

Lastly we see that the savings elasticities for equities grow exponentially at around an annual income of 60 thousand dollars (chart 4).  Again the effect is more pronounced at higher ages but the income effect is more sensitive than effect with age. 

Chart 4. Equities

Conclusion

The ABS income distribution survey provides a rich source of data for the analysis of saving, asset accumulation and wealth.  This paper demonstrates that, although many data issues arise when using this source of data, appropriate assumptions and careful modelling of the underlying processes can give extremely useful results.

References

Bacon, B. R. (1995) ‘Labour Force Status, Earnings, Asset Accumulation, Retirement Behaviour and Long-run Projections’ 24th Conference of Economists, September 1995, Adelaide (RIM conference paper 96/1).

Baekgaard, H. and King, A. (1996) ‘Modelling the Accumulation and Distribution of Austrlaian Household Assets’, International Association for Research in Income and Wealth, August 1996, Lilehammer.

Baekgaard, H. (1997a) ‘The  Distribution of Household Wealth in Australia: New Estimates for 1986 and 1993’, 1997 National Social Policy Conference, UNSW, July 1997, Sydney.

Baekgaard, H. (1997b) ‘The Distribution of Household Wealth in Australia from 1989-90’, 24th Conference of Economists, October 1995, Adelaide.

Baekgaard, H. (1995) ‘The  Changing Patterns of the Distribution of Household Wealth in Australia from 1986 and 1993’, 26th Conference of Economists, October 1997, Hobart.

Brown, C. (1996) ‘Sources of Income and the Assets of Older Australians’ 4th Colloquium of Superannuation Researchers, July 1996, University of Melbourne. (RIM conference paper 96/2)

Callen, T. (1991) ‘Estimates of Private Sector Wealth’, Research Discussion Paper 9109, Reserve Bank of Australia, Sydney.

Foster, R.A. (1996) ‘Australian Economic Statistics: 19494-50 to 1994-95’, Occasional Paper No. 8, Reserve Bank of Australia, June 1996, Sydney.

Rothman, G. (1998) ‘Projections of Key Aggregates for Australia’s Aged’ 6th Colloquium of Superannuation Researchers, July 1998, University of Melbourne.

Tulip, P. and Stott, D. (1994) ‘The effect of Compulsory Superannuation on Private Saving’ Treasury Seminar, July 1994, Canberra.

Wallis, S. (1997) ‘Financial System Inquiry Final Report’, Australian Government Publishing Service, March 1997, Canberra.