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	5. 	Correcting GDP for natural capital 
		a	Depletion of natural capital 
		b	Climate change 

	6. 	Changes in human capital 
		a	Formal education
		b	Early childhood development
		c	Unemployment
		d	An alternative snapshot of net changes to the 
			natural and human capital stock

	7. 	The distribution of income 		

5. Correcting GDP for natural capital 

Both GDP and NNI are measures of the flow of annual income or production. 

They measure the flow of economic value creation from year to year. 

However, in creating an index of wellbeing we care not only about the value 
we generate or receive today, but also what we can expect that level to be in 
the future. 

Such accounting is not done at all within GDP and is done only very partially – 
with regard to physical capital such as buildings and equipment within NNI. 

This leaves out other major sources of capital. 

As Table 5 shows, the net worth of Australia’s capital stocks is many times 
greater than annual levels of GDP or NNI. 

Table 5: Real/volume measures of Australia’s economy ($ billion) 

Key aggregate 	2000-01 	2009-10 	% growth 	 Value 
						(00-01 to	as a % of net 
						 09-10)		worth (2009-10) 

Real GDP 	970 		1,284 		32.4 		19 

Real GNI 	858 		1,220 		42.2 		18 

RNNDI 		728 		1,010 		38.7 		15 

Net worth 	5,562 		6,888 		23.8 		100 

Source: ABS, 2011. Australian System of National Accounts 2009-10, cat. no. 5204.0 

Even given NNI’s allowance for changes in physical capital, two major sources 
of capital are ignored – the economic value of our environment or ‘natural 
capital’ and the economic value of the knowhow embodied in our economy or 
‘human capital’. 

We now examine these subjects in turn in this and the subsequent chapter. 

a	Depletion of natural capital

Environmental degradation can affect wellbeing in these ways: 

1	The productivity of our natural resources can be impaired from 
	o 	resource depletion, 
	o 	land degradation and/or 
	o 	biodiversity loss 

	resulting from agriculture, mining or other development. 

	This can reduce the future productivity of natural resources. 

2	Pollution can impose direct health impacts such as respiratory 
	diseases or impaired development from air-or water-borne pollutants, 
	poisoning or diseases. 

3	The impacts above may also impair the amenity people enjoy 
	including from the ‘existence value’ of species or eco-systems
	that have disappeared. 

National accounting statistics poorly capture the last two sets of costs of 
environmental degradation – pollution and amenity costs – even allowing for 
a switch from GDP to NNI.

Loss of amenity will usually be invisible (12) while sickness from pollution 
could increase GDP and NNI in the short term if it leads to the health sector 
earning more income, although over time more of its full economic costs 
will be registered if they lower participation or quality in the workforce.

These impacts can be better measured through including a separate 
environmental domain as part of the overall wellbeing index. 

However, we can use information in the current system of national accounts
to adjust for the most important forms of natural resource depletion and 

GDP alone does not measure resource depletion satisfactorily. 

Resource depletion is recorded as an economic benefit as extracted 
resources are sold on the market.

But non-renewable resources are run down as they are exploited and this is 
not captured in flow measures such as GDP or predominantly flow measures 
like NNI. 


It is worth illustrating what it would take for loss of amenity to be measured by national accounting.

It would require that the amenity available in the first instance be fully captured by the market. 

For example, a national park might charge admission fees or attract travel costs that fully reflect its value to visitors. 

Then, as a result of environmental degradation, demand to visit the park falls and income would fall along with it. 

Simply to outline the kind of scenario in which national accounting might capture the value of amenity is to illustrate its 

A great deal of the amenity we enjoy about places in our lives is enjoyed as a public good, available to all in the area 
with fees being charged for the privilege that are either zero or some figure that is a small fraction of the true value
of the resource. 


The World Bank has calculated that subsoil assets account for over 50 per cent of Australia’s natural capital, although it 
does not formally include climate change liabilities in its wealth accounting estimates, given the lack of agreement over 
who `owns’ carbon emissions. 

Given the potential impacts of climate change on future wellbeing are very large, we consider this separately later in the 

The balance sheet of the national accounts includes values for the stock of 
certain natural resources – land (including rural and urban land), subsoil 
assets (minerals), native timber and electromagnetic spectrum.(14)

In 2009-10, Australia’s natural resource assets were valued at almost 
$3.3 billion in real volume terms,(15) and accounted for about 40 per cent of 
total assets included on the national balance sheet. 

Looking at changes in the national balance sheet from year to year gives us 
important information about the use of natural resources. 

In fact, the stock of Australia’s natural resources has increased over the past 

This is because new mineral discoveries have exceeded rates of mineral extraction 
and real land yields have increased, both from improved agricultural practices 
but also the rezoning of land to allow higher value uses (see Table 6). 

Table 6: Real/volume value of Australia’s natural resource assets ($ billion) 

Type of asset 			2000-01 		2009-10 

Land 				2,506 			2,749 

Subsoil assets 			  485 			  638 

Native timber 			    2			    2 

Total natural resources 	3,044 			3,397 

Total assets 			6,605 			8,791 

Source: ABS, 2011(a). Australian System National Accounts 2009-10 cat no 5204.0

Experimental estimates of natural resource depletion have been constructed by 
the ABS in 2002 (for 1993-94 to 2000-01) and 2010 (for 2002-03 to 2006-07). 

Both studies included an estimate of the annual incremental cost of land 

This was imputed by the ABS from two national studies undertaken during the 
early 2000s that reviewed the impact of accumulated land degradation on land 
values and yield rates. 


Other forms of natural capital, including renewable resources such as water, atmosphere and fish stocks, are not yet included in the 
national accounts. 


Natural resources are valued on the national balance sheet according to the net present value of identified subsoil and timber assets, 
NPV is determined based on current production rates, prices, costs and discount rates, so that known mineral reserves that are 
non-economic to exploit at current prices have an effective NPV of less than zero, and are excluded from the balance sheet. 


Nominal increases in the value of Australia’s natural resource assets have been even higher, due to rising commodity prices due to the 
mining boom. 

While real asset values abstract from price rises, increasing commodity prices may increase the economic viability of known mineral 
deposits, and so may increase the stock of economically useable mineral assets. 

This annual cost was $377 million in 2006-07. 

Assuming a constant rate of land degradation, this is equivalent to $406 million
a year in 2009-10 dollars. 

The 2002 estimates calculated an annual net depletion adjustment that accounts 
for the annual level of land degradation as well as subsoil depletions and 
additions from new mineral discoveries. 

To avoid double counting, the expenditure and depreciation associated with 
mineral exploration is also removed from the underlying production or income 

The updated estimates in 2010 calculated only a gross depletion adjustment 
that does not take into account new additions to mineral resources.

The UN’s London Group on Environmental Accounting has recommended this
change because it does not consider that new mineral discoveries should be 
classified as a produced asset and hence included in current year production 
and income accounts.

Instead, under the UN standard new mineral discoveries are listed only on the 
national balance sheet as a new asset (United Nations Statistics Division 
SEEA, 2010).(17) 

Table 7: Previous ABS net resource depletion estimates 

2002 		Net depletion 		Land degradation + 
estimates (18) 	adjustment 		Subsoil depletions -
					Subsoil additions + 
		+$390m in 2000-01	cost of mineral exploration -
					consumption of fixed capital
					on mineral exploration 

2010 		Gross depletion 	Land degradation + 
estimates (19) 	adjustment 		Subsoil depletions 

		-$4 billion in 2002-03 

This distinction may make sense when the aim is to construct internally 
consistent and complete sets of national accounts, or to isolate the cost 
of using natural resources in creating current income. 


The London Group has noted that recording depletions but not additions as a charge in environmentally adjusted production
and income account is asymmetrical. 

However, it considers the objective of reflecting the cost of using natural resources in traditional economic accounts 
through creating a measure of depletion adjusted value added and operating surplus is more important. 


ABS (2003)


ABS (2010c)

However, as we are concerned about the sustainability of our natural 
resource use, the index should include a net depletion adjustment. 

This would be calculated as the ABS did in 2002, as shown in Table 8. 

Table 8: Calculating the value of net resource depletion 

$ billion (real/volume terms) 	00-01 05-06 06-07 	07-08 08-09 09-10

Land degradation 		-0.4 -0.4 -0.4 		-0.4 -0.4 -0.4 

 + Subsoil asset discoveries 	2.2 4.8 6.1 		n/a n/a n/a 

 - Subsoil asset depletions(20)	-2.5 -4.3 -4.2 		n/a n/a n/a 

 - Cost of mineral exploration   0.5 1.4 1.9 		2.4 2.2 2.2 

 + Consumption of Fixed 	-2.2 -2.3 -2.4 		-2.5 -2.7 -2.8 
   Capital (COFC) on mineral 

 = Net resource depletion 	-2.3 -0.7 1.1 		-0.3 -0.6 -1.05  

Source: Lateral Economics calculations based on ABS 5204.0 and 8412.0
N/a: Not available: not yet produced by ABS 

It is noteworthy that in all but one of the years detailed above, there was net 
depletion of natural capital.

That is, in each of those years other than 06-07 fewer resources were being 
added to our stock of land and subsoil natural capital via new discovery than 
were being subtracted from them by land degradation and mining extraction. 

In 2009-10, net resource depletion was negative, subtracting $1.05 billion or 
about 0.1 per cent of NNI to our economic welfare in that year.(21) 

b	Climate change

Though it has as hard an economic edge as resource depletion from mining or 
land degradation from farming, the resource degradation humans may be 
perpetrating on our planet from carbon emissions presents potentially much 
larger, but also much more uncertain, costs. 


Note the table is set out so that the numbers can be added together down each column to total to the net resource depletion adjustment. 

The words in italics are intended to explain the calculation but invoke a double negative.  

On this line we say that the calculation involves subtracting the sub-soil asset depletions, but the numbers in the table are already 

We are technically proposing to add the negative numbers in the table, which is the equivalent of doing what is described in row four 
– subtracting subsoil asset depletions. 


Nevertheless, we have been unable to find a way of adding in the increases in the value of rural land owing to improved agricultural

Greenhouse gas emissions have been extensively modelled, and there is a 
strong scientific consensus that human activity is warming the globe. 

Nevertheless, it is unlikely we will ever be able to make predictions over
the long periods of time required that do not have relatively high levels 
of uncertainty owing both to the complexity of weather systems themselves 
and to the extent of feedback mechanisms. 

Some of those mechanisms are negative and stabilising – for instance 
photosynthesis of carbon dioxide into oxygen increases as the atmosphere
becomes more carbon rich. 

Others more worryingly are destabilising – for instance warming releases 
methane in the arctic permafrost, which will then generate more warming. 

On top of this there is uncertainty about the impacts of climate change on 
future economic wellbeing and more widely.

Nevertheless, considerable effort has been expended to arrive at ‘best guesses’ 
about most likely scenarios, and this gives us a basis on which to make 
best guesses about the likely impact of climate change on our wellbeing 
and the contribution we are making – or not making – to protect our future 
wellbeing from global warming. 

The fourth and latest report of the Intergovernmental Panel on Climate Change 
(IPCC) has confirmed previous assessments that an increase in global mean 
average temperature of 4 degrees Celsius (22) above 1990 levels is expected to 
result in an average loss of 1 per cent to 5 per cent of global GDP by 2100. 

The Copenhagen Accord agreed to target a reduction in global carbon dioxide 
emissions to limit mean global temperature increase to 2 degrees. 

There is a tension between including our own greenhouse gas emissions over 
which we have direct control and what most directly affects our future 
wellbeing, which is actual global temperature change, which is driven by global 

Existing environmental indices have taken differing approaches. 

The Australian GPI values the cost of Australia’s emissions by dividing the 
expected cost to global GDP of global warming by the total projected amount of 
carbon emissions, generating the estimated contribution of a tonne of 
emissions to expected future damage. 

No account is made for uncertainty about the global temperature outcome, and 
tracking this variable would not tell us whether the risk of global warming was
rising or falling based on the actions of other countries. 

The Yale EPI uses a distance to target approach. 


This and all subsequent references to ‘degrees’ are degrees Celsius. 

The 2010 EPI assumes the Copenhagen Accord reflects a global consensus 
of a need to limit global average temperature increases to 2 degrees, and that 
this will ultimately require a 50 per cent reduction in global GHG emissions by 
2050, compared to 1990 levels, and calculates this to be equivalent to annual 
emissions of 2.5 Mt CO2 per capita.

The EPI compares current per capita country emissions to this global target
of 2.5 Mt per person. 

This approach would allow changes in both current Australian emission levels 
and changes in the target value to be made as climate change policy evolves. 

An alternative approach would be to focus on the likely impacts of future 
wellbeing in Australia should significant global warming occur. 

This method of calculation is set out in Box 4. 

This approach would be more directly related to wellbeing, but changes 
would be overwhelmingly due to international factors rather than Australia’s
own actions. 

Box 4:Valuing the cost of emissions

CSIRO and other scientific organisations have done significant work on the 
economic impact of climate change. 

This became an input into Treasury’s climate change modelling and that of 
the Garnaut review to predict the future economic impacts of global warming. 

The Stern Review suggested that, unmitigated climate change could reduce 
global GDP by five to ten per cent in perpetuity (Stern, 2006, p. 9). 

The Garnaut review concluded that Australia stands to be more affected than 
most other developed countries. 

It projected that the total the quantifiable economic impacts of unmitigated 
climate change on Australia, such as reduced agricultural yields, more 
frequent and severe natural disasters and greater prevalence of tropical 
diseases would rise to reduce GDP by six per cent in 2100, compared 
to the level it would reach if there was no climate change. 

On Garnaut’s preferred welfare metric of GNP,(23) the cost of climate change 
is even higher, reducing GNP by 7.5 per cent in 2100 (see Table 9). 

Table 9: Garnaut 2008 Review estimates of the reduction in future GNP due 
to climate change 

Scenario 			Reduction in GNP (%) 
				2010 	2030 	2050 	2070 	2100 

Unmitigated climate change 	0.2 	1.3 	2.3 	3.5 	7.6 
(5+ degrees warming by 2100) 

Stabilisation at 550 parts 	0.2 	2.1 	1.8 	1.9 	1.6 
per million (pmm) CO2 
(2-3 degrees) 

Stabilisation at 450 ppm 	0.2 	2.1 	1.75 	1.7 	1.3 
CO2 (under 2 degrees) 


Garnaut’s modelling focuses on the impact of climate change on GNP rather than GDP, because Garnaut considers GNP 
a better measure than GDP of the welfare impacts on Australians of climate change and its mitigation. 

This is because if Australian and global mitigation efforts include large international financial income flows from 
permit trading, the income from domestic production, becomes even less relevant a measure of national consumption 
possibilities (see Garnaut (2008) Economic Modelling Technical Paper 7: The net cost of climate mitigation for 
Australia, p. 8 for further discussion). 

Box 4(continued): Valuing the cost of emissions

The following formula can be used to calculate the impact of climate change on 
Australia’s future wellbeing: 

		Risk-weighted cost of climate change 
(NPV of the future cost to Australia from no significant mitigation scenario (a 5+ 
o warming) times the probability of no significant mitigation) 
(NPV future cost to Australia from Copenhagen Accord scenario (a 2 o warming 
times probability of significant mitigation) 
(NPV future cost to Australia from 550ppm CO2eq scenario (a 2-3 o warming 
times probability of moderate mitigation) 

To estimate the risk-weighted depletion of natural capital through climate 
change we need to calculate: 

1	The net present value of future costs to Australia of various levels of 
	global mean temperature increases. 

	The recent estimates from the 2011 Update to the Garnaut review 
	confirm his original costs as set out in Table 9 above and so remain 

	The appropriate discount rate for future costs is discussed further below. 

2	The most recent global emission projections. 

	As a starting point we compare the most recent emissions assessment
	from the International Energy Agency (IEA) against the UNEP’s 2010 
	review of climate models. 

	The UNEP determined the Copenhagen pledges may just keep global 
	emissions within levels that provide a 50 per cent likelihood of staying 
	within the 2 degree limit (UNEP, 2010). 

	However, most recent IEA estimates are that energy-related GHG 
	emissions in 2010 were the highest on record and substantially reduced
	the possibility of limiting temperature increases to 2 degrees.(24)

	Given this evidence we have assumed that the probability of meeting 
	the Copenhagen target is perhaps only 25 per cent, with a 70 per cent 
	chance of moderate mitigation and a 5 per cent chance of no significant 
	mitigation occurring. 

	The reduction in Australia’s net wealth due to climate change impacts also 
	depends critically on the discount rate assumed. 


IEA Report 

Box 5:How should we value costs we impose on future generations?

The full economic impact of climate change will not be felt until far into the 

To determine the cost of climate change in today’s dollars we need to 
apply a discount rate. 

Both Garnaut and Stern used very low discount rates to value future costs of 
climate change. 

Garnaut describes this as a normative discount rate, based on valuing the 
wellbeing of a person born in future only slightly less than our own. 

He uses a pure rate of time preference of 0.05 per cent and assumes real
per capita income growth in the future will be 1.3 per cent a year. 

He concludes that the appropriate real discount rate should sum these two 
figures to be 1.35 per cent, or 2.65 per cent if the marginal elasticity 
of utility is assumed to be 2. (that is, less needs to be spent now to 
benefit future, richer generations).(25)

In contrast, Nordhaus in earlier work used much higher rates of time preference 

(1.5 per cent or more) to generate overall discount rates that matched the 
overall cost of capital in the economy (Garnaut, 2008, p. 18–21).(26) 

Using the midpoint of Garnaut’s normative discount rates, the NPV of future 
reductions in GNP from unmitigated climate change is 86 per cent of GNP in 

If the Copenhagen scenario is met, the NPV of future GNP losses is 57 
per cent of today’s GNP.


The concept of marginal elasticity of utility is akin to the concept of the marginal utility of income discussed earlier in this report 
and relates to the way we comparethe value of utility to two persons or communities – in this case one in the present and one in the 

Garnaut illustrates this by reference to the following scenario. 

Based on Stern’s figures, global per capita annual income today is about $7,000 whereas the growth of per capita incomes of 
about 1.3 per cent until 2100 would increase that figure to per capita incomes of $100,000. 

A marginal elasticity of utility of one would imply that the expenditure of one per cent of our income today (worth $70 on average for 
each person on the globe) is a contribution of utility (or, if you like, economic wellbeing) that is equal to a contribution of one 
per cent of the income of people in 2010, which would be $1,000. 

With the appropriate discount rate being the sum of the pure rate of time preference (0.05 per cent) plus the growth rate in per capita 
incomes times the elasticity of utility, an elasticity of utility generates a discount rate of (1.3 + 0.05)% = 1.35%. 

It will be seen on inspection that if the elasticity of utility were 2, the appropriate discount rate would be 2.65 per cent. 

The figures 1.35 per cent and 2.65 per cent provide Garnaut’s upper and lower bound for determining the appropriate normative 
discount rate to apply for the purposes of comparing the costs of climate mitigation today with the benefits that mitigation 
generates for later, richer generations. 


Garnaut addresses criticisms of his low discount rate in his 2011 Update and concludesthat higher discount rates “would assert a 
preference for equality of income distribution far more extreme than has ever been suggested as a basis for practical policy making, 
for example on taxation or development assistance” (Garnaut, 2011 Update Paper 1: Weighing the costs and benefits of climate 
change action, p. 21). 

If the intermediate 550 ppm scenario is met, the NPV is equivalent to 60 per cent 
of current GNP.(27) 

Alternatively, if a real discount rate more reflective of our financial markets of, 
say, four per cent was used,(28) the larger climate change impacts towards the 
end of the century would be much less heavily weighted. 

If so, future GNP losses from unmitigated climate change equate to 34 per cent of 
today’s GNP, only slightly more than the two mitigation scenarios at about 30 per 
cent to 31 per cent of current GNP. 

Table 10: Risk weighted cost of climate change

Scenario 			Probability 		NPV 
						(% reduction in today’s GNP) 
						@ 2% 		@ 4% 
						discount rate 	discount rate 

No mitigation			5% 		86 		34 

Copenhagen target met 		25%		57 		30 

Moderate mitigation 		70% 		60 		31 

Risk-weighted cost 				61 		31 

As updated information becomes available on current global emissions trajectories, 
we will update the probabilities of meeting the various climate change mitigation 

Future updates would come from International Energy Agency’s annual World Energy 

If current trends continue and the likelihood of meeting the Copenhagen Accord 
target becomes less likely, then the likely damage to future economic wellbeing 
will be greater, and the HALE Index of Wellbeing will fall accordingly. 


Australia’s annual GDP for 2009-10 was $1,283 billion. 

Using our methodology, the net present value of future climate change in 2009-10 would range from $731 billion under the moderate 
warming (2-degree scenario) to $1,103 billion for unmitigated climate change. 

These figures look extremely large, compared with annual GDP or GNP, but that is because we are comparing a stock with a flow. 

The values we are looking at here are capital values or values of the extent to which climatechange might degrade our natural 
environment considered as an asset.

Such damage being done over the nearly 90 years to the end of the century would equate to much smaller shares of annual GDP. 

For example, even in the unmitigated climate change scenario, the negative effects of climate change would reduce annual GNP 
in 2025 by 1.0 per cent, increasing over time to reduce annual GNP in 2100 by 7.4 per cent compared to a no climate change 


four per cent was the discount rate used in the Garnaut-Treasury modelling of the pricing emission permits, based on a risk-free 
real interest rate of two per cent and a risk premiumin the permit market of two per cent. 

For illustrative purposes, we have assumed that from 2005 to 2010 the likelihood of 
meetingthe Copenhagen Accord target has decreased by 5 percentage points each
year (from 50 per cent in 2005 to 25 per cent in 2010) and the likelihood of moderate 
mitigation has correspondingly increased from 45 per cent chance in 2005 to 70 per 
cent chance today. 

The probability of the no mitigation target is assumed to have remained at 5 per cent 
throughout this period. 

Natural capital domain of the HALE Index

		Indicator 			Narrative		 

Resource 	National		Apart from a brief period in 2007 when 
depletion 	Accounts data 		subsoil asset discoveries exceed 
					depletion, net resource depletion is a 
					small but growing deduction to NNI 

Climate 	Change in risk-		Climate change costs have increased 
change 		weighted cost 		slowly but consistently over the period. 
		of future 		They are small in value due to the slow 
		climate change 		rate of change and long time frame for 
						impacts to be felt 

6. Changes in human capital 

One prominent alternative measure of wellbeing to GDP, the Genuine Progress 
Indicator (GPI), begins with GDP and corrects it for things that should arguably 
be included in any comprehensive measure of wellbeing but that tend to reduce 
measured wellbeing.

However, as Gruen has argued (2006), while the GPI takes most opportunities to 
deduct some of the less attractive things about recent economic growth from its 
measure of economic wellbeing – like the costs of congestion, industrial accidents 
and uninformative advertising – it pays scant attention to the positives that have 
come our way as well. 

This is well illustrated by the GPI’s deducting mineral depletion but not adding 
new mineral discoveries. 

Moreover, it makes no positive adjustment for improved life expectancy, better 
road and workplace safety. 

But the elephant in the room in this regard is accretions of human capital or the 
knowhow embodied in Australia’s people and the technologies to which they 
have access. 

While the recurrent return to human capital is captured in GDP and NNI in people’s
wages, human capital itself is not directly tracked in the national accounts. 

Wealth accounting exercises conducted by the World Bank have confirmed that 
intangible capital, which includes human capital, technological progress and other
forms of social and institutional capital, has provided the largest wealth gains 
during the 1990s and 2000s and accounts for 60 per cent to 80 per cent of total 
assets – giving it many times the value of natural, physical or financial assets
(World Bank, 2011).(29) 

The World Bank last calculated total wealth values for Australia in 2005. 

At that time Australia's total wealth was $16.3 trillion in current dollar terms, 
and had grown in real terms by $5.7 trillion, or 40 per cent, over the decade. 

Intangible capital accounted for just under 75 per cent of total wealth in 2005, 
more than three times the value of produced capital stocks. 


The World Bank's work calculates a nation's wealth as the present value of sustainable consumption over the next 25 years.

As the present value of consumption is much higher than the book value of a nation's physical and natural capital stocks 
(including net foreign assets), the World Bank imputes that the difference must be due to returns on intangible capital. 

Table 11: World Bank estimates of Australia’s total wealth 2009-10 
AUS$ trillion (% of total wealth) 

Asset type 		1995 		2000 		2005 

Produced capital 	 2.5 (22%) 	2.9 (20%) 	3.5 (22%) 
Net foreign assets	-0.4 (-3%)	-0.5 (-1%)	-0.6 (-4%) 
Natural capital (30)	 0.8 (7%) 	1.3(2%)		1.3 (8%) 
Intangible capital	 8.7 (75%) 	10.5 (80%) 	12.1 (74%) 
Total wealth 		11.6 		14.2 		16.3 

Source: World Bank (2011), translated to 2008-09 AUD using PPP 
from Penn World Tables 

Australia’s human capital stock accumulates through formal education, on-the-
job training and the attraction of skilled migrants from overseas. 

Similarly, skills are lost (due to emigration, unemployment, retirement from the
workforce and death). 

While satellite human capital accounts are not currently produced for Australia, 
ABS experimental estimates confirm that, on average, human capital stocks
have grown by well over $1 trillion during each five-year period between 

In contrast, net worth as measured in the national accounts grew by only $1.3 
trillion over the last decade. 

It is outside the scope of this project to undertake a comprehensive human 
capital stock accounting exercise. 

However, we can use the World Bank’s estimate of intangible capital stocks in 
2005 as a starting point. 

Previous experimental estimates from the ABS valued Australia’s human capital 
stocks at almost $5.6 trillion in 2001 (ABS, 2004, p. 26). 

This is equivalent to about 85 per cent of the total value of Australia’s intangible 
capital calculated by the World Bank for around the same time period.(31) 

We have assumed that human capital itself is composed roughly from 25 per 
cent early childhood learning, 25 per cent from school education, 40 per cent 
from adult education, which would include both formal post-secondary 
education and on-the-job learning. 

The final 10 per cent is from other sources of innovation.

These relative weights are based on our judgements and on evidence gleaned 
from the international literature. 


The World Bank includes in natural capital subsoil assets, land devoted to cropping, pasture and timber and protected areas. 


The World Bank calculated intangible capital in 2000 to be worth $10.5 trillion in 2008-09 constant dollars; this is equivalent 
to $6.4 trillion in 2001 dollar terms. 

The ABS’s estimate of human capital stocks in 2001 dollar terms was $5.6 trillion, or 85 per cent of total intangible capital 
stocks around the same time. 

For example, American research suggests that up to half of the inequality in the 
present value of lifetime earnings is due to differences in development through 
childhood up to the age of 18, and ABS analysis of lifetime incomes suggests
that bachelor degree qualified males earn a 68 per cent lifetime wage premium
over a person with no post-secondary qualifications (ABS, 2004, p. 21). 

Once we have calibrated an opening value for Australia’s capital stock, we are 
then able to track changes in the elements of human capital over time, and see 
how it will change overall human capital stocks. 

For example, if the quality of our school education improves so that Australian 
students perform 2 per cent better on the next OECD PISA tests in 2012, we 
would expect the value of our human capital from school education to increase 
by 2 per cent as well. 

Table 12: Human capital accumulation

Source of 		% of total 	2004-05		Variable used to track 
human capital 		stock 		$ trillion 	future change 

Early childhood 	25 		3.0		Australian Early 
development 						Development Index 
School education 	25 		3.0 		PISA test scores 
							Year 12 retention rates 

Adult education 	40 		4.9 		ABS Education and 
							Work survey 

Net innovation 		10 		1.2 		Capitalised average 
							multi-factor productivity 
							(MFP) growth 
Intangible capital 	100 		12.1 

Formal education

While expenditure on education services forms part of our national accounts, it 
cannot be assumed that every additional dollar of expenditure on education 
buys a dollar of additional human capital accumulation. 

Australia increased real school education spending per child by 258 per cent 
between 1964 and 2003, but over the same period numeracy test results 
deteriorated significantly (Jensen, 2011). 

The focus on educational inputs rather than outputs is a hangover of poor metrics
on the effectiveness of educational expenditure, though like many services, 
particularly those embodying professional expertise, output measures are often
far from straightforward. 

Box 6 below summarises some of the means adopted to more comprehensively 
track human capital growth. 

Box 6:Existing approaches to tracking human capital

The OECD Better Life Index measures the quantity and quality of education 
through two indicators. 

Overall educational attainment is measured as the proportion of 25-to 64-year-olds 
with at least a high-school qualification.

The quality of education is based on a country’s performance in the 2009 
Programme for International Student Assessment (PISA) tests. 

PISA is an OECD initiative that, every three years, tests the competency of 
15-year-olds across OECD countries in reading, mathematics and science. 

The Canadian Index of Wellbeing includes a large number of variables within 
its Education domain. 

These primarily relate to the different ways a person may develop human capital 
depending on age. 

Accordingly, human capital development of very young children is measured 
through the availability of child care places and developmental health in kindergarten; 
of school-aged children through student-educator ratios, PISA test scores, 
high school completion rates and self-reported social and emotional competence; 
and of adults through rates of post-secondary participation and attainment. 

Human capital stock-flow accounts put a monetary value on a country’s 
human capital stocks, based on the lifetime expected income generated by 
people of different skill levels.

The ABS has created experimental human capital accounts based on information
from the Census. 

Under the lifetime income approach human capital stocks will increase through 
population growth and educational attainment and will decrease when a person
ages or is unemployed for long periods. 

The ABS MAP headline indicator of educational progress is the proportion of 
25-to 64-year-olds with a vocational or higher education qualification. 

This has increased from 53.3 per cent in 2001 to 62.5 per cent in 2010. 

MAP also reports education participation rates for 15-to 19-year-olds and 
apparent school retention rates as supplementary indicators, as well as data 
on the different types of training people receive, including work-related training 
and informal training. 

All data are sourced from the ABS survey of Education and Training or its annual 
Schools publication. 

Even from this brief survey it is possible to conclude that all simple measures of 
human capital formation are flawed in important ways. 

Measures of simple inputs do not allow for the productivity with which educational 
inputs are turned into human capital outputs, while measures of educational 
achievement tend to be crude – focusing on the level of qualifications achieved 
(whether a pass or fail was obtained) rather than the quality of those qualifications. 

Our measure focuses on three different thresholds of educational attainment, giving
it at least some spread over the population. 

We measure changes in: 

1	rates of early childhood development; 

2	schooling participation and learning outcomes; and 

3	attainment of formal post-secondary school qualifications, as 
	summarised in Table 13 below. 

An important component of human capital growth – informal and on-the-job 
training – is not sufficiently well measured at present to be directly included. 

Further work on such indicators through the MAP progress may provide a 
sensible indicator that could be incorporated into later versions of the HALE 
Index of Wellbeing. 

Instead, we incorporate capitalised multi-factor productivity growth as explained

The most comprehensive measure of increases in human capital through 
school-based education comes from Australia’s performance in the OECD’s 
Programme for International Student Assessment [PISA]. 

The PISA tests are conducted every three years (latest 2009) on 15-year-olds 
in all OECD countries and have been recommended by the SSF Commission 
as one of the most relevant indicators for assessing the role of education for 
Quality of Life (p. 164). 

PISA tests the competency and accumulated learning of 15-year-olds across 
literacy, mathematics and science. 

Higher country test scores indicate that, on average, students in that country 
have learnt more in these core subjects by the time they reach testing age. 

On the PISA scale, a year’s worth of learning is equivalent to 38 points 
(Ibid, p. 7).

OECD analysis suggests that increasing student participation and performance 
on the PISA tests by one year of learning would lift long-run GDP by 1.4 per 
cent to 2 per cent. 

Australia’s PISA results in reading fell from 525 in 2003 to 513 in 2006, before
rising again to 515 in 2009. 

Based on the OECD’s analysis, this 2-point increase would be equivalent to a 
yearly 0.07 per cent increase in long-run GDP. 

To supplement PISA results, which are updated only every three years, we also 
track the apparent retention rate of secondary school students, from the ABS’s
annual Schools survey (ABS, 2011b).(32)


The years 7/8 to Year 12 Apparent Retention Rate is a measure of the number of school students in their final year of school education
expressed as a percentage of their respective cohort group in their first year of high school. 

The year of commencement varies among jurisdictions (states and territories) and over time. 

These variations are incorporated into calculation of ARRs at the Australia level. 

b	Early childhood development

Traditionally, educational interventions have been strongly influenced by 
theories of education, which privilege cognitive over non-cognitive skills such 
as motivation and self-confidence.(33)

However, as Heckman’s longitudinal analysis has shown, there is a strong 
link between early age lack of development of non-cognitive skills like motivation 
and self-confidence and subsequent dysfunction later in life as demonstrated 
by higher levels of criminal activity, teenage pregnancy and educational and 
employment underachievement (Heckman et al., 2006). 

At the same time, improved non-cognitive skills compensate for poor 
non-cognitive skills to some extent by helping to ameliorate the intergenerational
transfer of poor socioeconomic outcomes between parents and children.

In fact, Heckman’s research suggests that the return on investments in early 
childhood development, such as support services for pregnant women and their 
children, could be about 15 per cent to 17 per cent if savings from reduced crime, 
welfare and increased taxes are taken into account. 

This is far higher than the rates of return to investments in school-based or 
tertiary education. 

It would be good to incorporate into our index of wellbeing a measure of the 
human capital generated through the development of both cognitive and non-
cognitive skills in early childhood. 

To do so we need both an accurate and timely measure of levels of early 
childhood development and a sense of how to value this in terms of future 
lifetime earnings and wellbeing. 

The Australian Early Development Index (AEDI) is a national measure of early 
childhood development, assessed by asking teachers about the development 
of children in their first year of full-time schooling. 

It has been adapted from a similar instrument used in Canada for almost a decade. 

The first national AEDI survey was run in 2009 and measures development 
across five domains – 

	1	physical health and wellbeing, 
	2	social competence,
	3	emotional maturity, 
	4	language and cognitive skills
	5	communication skills and general knowledge. 

The 2009 AEDI found that 23.6 per cent of children were assessed as 
developmentally vulnerable on at least one domain (34) and 11.8 per cent were 
vulnerable across two or more (Centre for Community Child Health and 
Telethon Institute for Child Health Research, 2009).

See for example Piaget and Inhelder (1969) as quoted in Feeny, T (2006) The Case for Investing in Early Childhood: A Snapshot 
of research by James Heckman and Richard Tremblay Smith Family Research and Development Report 


Developmentally vulnerable means the child’s development was in the bottom 10 per cent of scores. 

Children scoring between the 10th and 25th percentile are classified developmentally at risk and those in the top 75 per cent 
are considered developmentally on track. 

Developmentally vulnerable children are more likely to be boys than girls, 
come from low socioeconomic background or come from a non-English-speaking
background and not be proficient in English. 

Table 13: AEDI 2009 Results 

Domain 				Australian 	% 		% 
				average score 	Develop-	Develop-
				(out of 10) 	mentally	mentally at
						vulnerable 	risk 		

Physical health & wellbeing 	9.6 		9.3 		13.0 

Social competence 		9.2 		9.5 		15.2 

Emotional maturity 		8.7 		8.9 		15.5 

Language & cognitive skills 	9.2 		8.9 		14.0 

Communication skills and 	9.4		9.2 		15.8 
general knowledge 

Total number children with	-		23.6		-
at least one developmental 			(246,421 
vulnerability 					children) 

Source: The Australian Early Development Index

c	Unemployment 

Once a person is unemployed for a long period, they become less likely to 
move out of unemployment than the newly unemployed (Jackman and Layard, 

There is consensus in the academic literature that this is partially 
explained by skills atrophy that occurs while people are unemployed.36

Skills atrophy can include both the loss of generic skills such as computer 
literacy over time, as skills get rusty or become obsolete, and the loss of
firm-specific skills that are less highly valued by other prospective 
employers of an unemployed person. 


In 1993, the ABS found that persons unemployed less than 13 weeks had a 25 per cent chance of gaining employment in the 
next month, more than double the probability of gaining employment if unemployed for 52 weeks (12 per cent) and over 
three times the probability of someone who had been unemployed for more than three years (7 per cent) (see ABS (1994) 
‘The Dynamics of Long-term Unemployment’ in Australian Economic Indicators, June 1994, cat. no. 1350.0


This can then be compounded by employers assuming that all longer-term unemployed have had their skills reduced by 
unemployment, and so overlooking them for employment, even where some have adequate skills. 

Consistent with the phenomenon of skills atrophy, evidence from the US, 
Germany and the UK shows that when displaced workers do find work again, 
their wages are significantly and persistently lower than similarly qualified 
people who do not lose their jobs (Jacobson et al, 1993; Couch and Placzek, 

This long-term wage penalty seems to average about 10 per cent to 15 per 
cent of pre-unemployment wages. 

People with multiple periods of long-term unemployment also appear to suffer 
a compounding effect. 

For the HALE Index we use this finding to value the reduction in human capital 
from long-term unemployment, as proxied by this reduced lifetime earning 

Box 7:Calculating skills atrophy from long term unemployment

International literature suggests that the wages of long-term unemployed 
workers settle at about 90 per cent of their pre-unemployment levels when they 
are reemployed. 

As we do not have detailed data on the pre-unemployment wages of the long-
term unemployed, we assume that, at least on average, this group would have 
received a wage substantially below the average wage, and probably below the 
median wage. 

We assume 90 per cent of the median wage for our calculations. 

We also assume that, on average, long-term unemployed people would 
otherwise work for 20 years until reaching retirement age. 

A discount rate of 5 per cent is used. 

We calculate the value of human capital lost to today’s stock of long-term 
unemployed people using the following formula: 

Human capital loss = NPV (10% x 90% x median wage for 20 years) x no. of 
long-term unemployed 

However, we also want to capture the permanent human capital loss from 
people who have previously been unemployed for long periods but are now 
back in the workforce. 

To do this going forward we need to know the average outflows from long-term 
unemployment each year.

On average, about 6 per cent of people unemployed for between one and two 
years exit unemployment within a year.

So to crudely account for these people we scale up the value of human capital 
loss by 6 per cent. 

In December 2010, about 117,000 Australians had been unemployed for more 
than 12 months. 

Using the methodology outlined above, the NPV of lost human capital from 
long-term unemployment was $8.1 billion. 

This is equivalent to 0.05 per cent of our total intangible capital stocks at the

A 0.1 per cent increase in the long-term unemployment rate increases skills 
atrophy by an amount that costs about $300 million a year. 

The course of this cost over the last few years is provided in Figure 6 below. 

Figure 6: Human capital depletion from long term employment

d	An alternative snapshot of net changes to the natural and human capital stock

An alternative way of valuing the accumulation and depletion of capital that 
goes unmeasured by the national accounts would be to capitalise the value of 
current trends in multi-factor productivity (MFP) growth into an adjusted GDP 
measure, on the assumption that existing trends are indicative of future trends. 

This offers a possible way of finessing a number of problems in measuring 
changes in capital stock. 

Existing measures of human capital are very imperfect for the reasons 
documented above. 

Further, anything that improves our productivity that does not result from
increasing deployment of resources – either from nature or physical capital
accumulation – must arise from improvements in knowhow or human capital
broadly considered. 

But much continual improvement in industry is not the result of improved
levels of education, so much as the result of imported knowhow, small 
changes in operations, or on-the-job training and learning by doing, all of
which are extremely difficult to measure. 

The approach finesses another problem. 

Non-renewable resource exploitation produces two effects that pull in 
opposite directions. 

Improved knowhow increases productivity while resource depletion leads 
to progressive reductions in the productivity of resource extraction.

It is difficult to measure both of these effects on their own, but our real 
interest in them as influences on economic wellbeing is in their sum 
effects as captured in MFP.37

We can use MFP as an indicator by assuming that current trends in 
MFP will continue to play themselves out in future.

Accordingly as MFP growth trended up or down, we could capitalise
the NPV of the value of MFP growth, assuming MFP would continue to 
follow recent trends over a given time horizon. 

An additional benefit is that focusing policy making on MFP growth would be
worthwhile as, in the much quoted words of Paul Krugman, productivity isn’t
everything, but in the long run it’s nearly everything. 

Against these attractions, two problems undermine the case for capitalising 
MFP growth as a means of capturing changes in capital. 

Firstly, the methodology would yield very volatile results that would dominate
the index because of the scale of human capital in the index. 

Yet it often takes a long time to understand exactly what MFP figures are 
telling us as they are subject to significant variation through the investment 
cycle and substantial revisions between measurements. 

Secondly, the assumption that the current level of MFP growth is a predictor 
of MFP growth over the horizon for which the value of MFP growth would be 
capitalised is a strong one. 

Our fear is that giving MFP growth a strong presence in the index would tend
to focus public interest in a guessing game as to what was moving MFP in 
the short term, and the prospects of it being subsequently revised. 

On the other hand, we think that the index might play a useful role if we 
reduced its weighting substantially and incorporated it as a relatively minor 
influence on our measurement of human capital. 

Here it would play a useful role given the fact that the index does not directly 
capture output measures of the increase in productivity owing to improvements
in human capital. 

In addition, improving MFP growth should be a major preoccupation of micro-
economic policy. 

Accordingly we use a forward-looking capitalisation of the NPV of MFP as 
10 per cent of our measure of human capital. 


MFP does not measure the effects of resource depletion in the short term because increased productivity may simply reflect 
faster depletion of existing resources. 

Further, productivity varies greatly through the investment cycle. 

However, over any reasonably long period the industry must move from exhausted mines to open up new ones – or from the 
most propitious parts of existing mines to less propitious mines – and so multi-factor productivity will capture both 
effects and measure the extent to which one offsets or outweighs the other. 

Human capital domain of the HALE Index

			Indicator 		Narrative 		

Early 			Track using 	While AEDI test scores have remained 
childhood 		AEDI raw 	almost constant (except for a small dip in 
development 		scores 		2007) population growth has increased 
					overall human capital stocks 

School 			Change in 	PISA test scores and school retention 
education 		PISA test 	rates fell from 2003 to 2006/7, but have 
			scores and 	since recovered somewhat.
			change in 	School-based human capital growth in 
			secondary 	2005 was close to 0, due to falling PISA 
			school 		scores from 2005 to 2010, population growth 
			retention 	has increased overall human capital 
			rates		stocks. 
					School based human capital growth 
					in 2004-05 was unusually small, due 
					to little growth in the school population 

Adult formal 		Proportion of 	Human capital from formal adult 
education 		25-to 64-year-	education has increased consistently 
			olds with a 	over the period, due to increasing tertiary 
			post-		education attainment and population 
			secondary 	growth. 

Net innovation 		Capitalised 	Falls over the period due to falling MFP 
			trend MFP 	growth. 

Skills atrophy 		Long-term 	Skills atrophy from long-term 
from long-term 		unemployment 	unemployment (LTU) has grown overall 
unemployment 		rate x wage 	by $2 billion from 2005 to 2010. As LTU 
			penalty 	has declined since peaking in late 2009, 
					skills atrophy is also falling. 

Overall, because of the higher weighting of the top three categories, and with 
the highest (40 per cent) weighting given to adult education, the overall human 
capital domain of the index grows strongly (if in a volatile manner) through the 
period notwithstanding the fall over the period in the last two indicators. 

7. The distribution of income

Like the happiness literature more generally, the Australian Unity Wellbeing 
Index confirms common sense and the early marginal economists’ 
presumptions that the utility of additional income diminishes as income rises. 

Thus if we take seriously the idea that income is just one input into the ultimate 
objective of human wellbeing, we need to adjust additional increments of 
income earned within the Australian economy for how it is distributed.

The advantage of such SWB studies is that they give us some empirical 
evidence on which to base some calibration of this important effect. 

For the lowest income households with incomes of under $15,000 a year, 
subjectively reported wellbeing increases by one percentage point with 
just $6,000 of additional income. 

By contrast the same increment in happiness would require over $100,000 
for a household already earning more than 
$100,000 a year. 

Table 14: The marginal utility of income in Australia

Gross 		$ for 		Relative 	Assumed % 	Relative 
Household 	additional one 	value of 	of value FROM	 values, 
Income 		percentage 	additional $ 	status 		adjusted for 
($ '000) 	point (ppt) of 					assumed 
		wellbeing 					status effect 	

< 15 	 	     6,000 		4.2		35 		2.8 
15-30 		    20,000 		1.3		60 		1.0 
30-60 		    25,000		1.0 		66 		1.0 
61-100 		    33,333		0.8 		75 		1.0 
101-150 	   111,111		0.2 		80 		0.4 
151-250 	   178,571		0.1 		85 		0.3 
251+ 		 1,250,000 		0.0 		95 		0.1 

Source: Lateral Economics based on The Australian Unity Wellbeing Index

These relative values measure two things. 

We know that people value income because of the commodities and services it buys.

They also value it because of its significance for their status among other 


The positional significance of wealth is not a new phenomenon. 

As Adam Smith argued more than two centuries ago, “[T]o what purpose is all the toil and bustle of this world? . . . Is it to supply the 
necessities of nature? 

The wages of the meanest labourer can supply them. . . . To be observed, to be attended to, to be taken notice of with sympathy, 
complacency, and approbation, are all the advantages which we can propose to derive from it. It is the vanity, not the ease, or the 
pleasure, which interests us.” 

However, status is a zero-sum game – those who move up do so at the expense 
of others moving down. 

Thus there is no increase in total wellbeing across the community from the 
status effect of income. 

A survey of international literature concluded that about two-thirds of the 
marginal utility of income is due to the status effect 
(Clark et al., 2008, p. 111). 

We are unaware of strong direct evidence from SWB studies that this proportion 
varies greatly among people with relatively low or high incomes. 

However, correlations between increased income and SWB do appear to be stronger 
in poor than rich nations (Diener and Biswas-Diener, 2002), suggesting that the 
absolute value of an additional dollar is more powerful for people on lower incomes.

If this is the case, then we should correct the marginal utility of income curve 
suggested from the AUWI data in Table 14 to remove status effects. 

Figure 7 shows how the marginal utility of income may change if status effects 
are less important to low-income people than higher-income people. 

The weighting given to status impacts have been set so that the average status 
effect across all households is 66 per cent of the total value of additional 
money, consistent with the literature cited above. 

However it is only 35 per cent for the poorest Australian households, rising to 95 
per cent for the richest households. 

If the status effect is distributed evenly across all income levels, then the 
marginal utility of income would remain the steeper blue line.(39) 


The slope of the marginal utility lines can be interpreted as the elasticity of income. 

The slope of the blue line is -7.5 and the red line -1.3. 

An elasticity in the range of 1 to 2 is commonly found in literature. 

Figure 7:Accounting for status effects in income

The latest ABS data on household income distribution is from the 2007-08 
Household Income and Distribution Survey.(40)

The ABS reports data on a weekly equivalised disposable income basis, but 
when annualised these household income quintiles roughly accord with the 
first five income bands from the AUWI survey. 

Table 15 below shows the growth in average weekly income for household 
quintiles between 2005-06 and 2007-08. 

On an unweighted basis, total household incomes grew by an average of 
eight per cent a year from 2005-06 to 2007-08. 

However, as this income growth flowed mainly to high income households 
who value additional money less highly than low income households, the 
weighted value of growth was six per cent, or just three-quarters of the 
raw income increase. 


This survey is updated every two years, with 2009-10 results likely in late 2011. 

Table15:The marginal utility of income in Australia 


Household		2005-06	2007-08	Annual	Weights	Weighted
income					growth(%)	annual
distribution						growth(%)

Lowest quintile		   272 	   299 	 5.0 	2.8  	 10.5 
Second quintile 	   444 	   504 	 6.8	1.0 	  7.0 
Third quintile 		   607 	   692	 7.0 	1.0 	  7.0 
Fourth quintile 	   805 	   922 	 7.3 	1.0 	  7.2 
Highest quintile	 1,368 	 1,646 	10.2 	0.4 	  3.8 
All households		   699 	   811	 8.0 	0.3 	  6.1 

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