Faculty of Business and Law SCHOOL OF ACCOUNTING, ECONOMICS AND FINANCE School Working Paper - Economic Series 2006 SWP 2006/26 ESTIMATING INTERGENERATIONAL DISTRIBUTION PREFERENCES USING CHOICE MODELLING Helen Scarborough & Jeff Bennett The working papers are a series of manuscripts in their draft form. Please do not quote without obtaining the author’s consent as these works are in their draft form. The views expressed in this paper are those of the author and not necessarily endorsed by the School.
Transcript
Faculty of Business and Law
SCHOOL OF ACCOUNTING, ECONOMICS AND FINANCE
School Working Paper - Economic Series 2006
SWP 2006/26
ESTIMATING INTERGENERATIONAL DISTRIBUTION
PREFERENCES USING CHOICE MODELLING
Helen Scarborough & Jeff Bennett
The working papers are a series of manuscripts in their draft form. Please do not quote without obtaining the author’s consent as these works are in their draft form. The views expressed in this paper are those of the author and not necessarily endorsed by the School.
ESTIMATING INTERGENERATIONAL DISTRIBUTION
PREFERENCES USING CHOICE MODELLING
Helen Scarborough*
School of Accounting Economics and Finance, Faculty of Business and Law
Deakin University, Warrnambool, Vic. 3280 Australia
ESTIMATING INTERGENERATIONAL DISTRIBUTION PREFERENCES USING CHOICE MODELLING
ABSTRACT Resource management decisions influence not only the output of the economy but also the
distribution of utility between groups within the community. The theory of Cost Benefit
Analysis provides a means of incorporating distributional changes into the decision making
calculus through the application of distributional or welfare weights. This paper reports the
results of research designed to estimate distributional weights suitable for inclusion in a
Cost Benefit Analysis framework. The findings of a choice modelling experiment designed
to estimate community preferences with respect to intergenerational utility distribution are
presented.
1. INTRODUCTION Resource management policies have a range of distributional effects within the economy. It
is unlikely that those benefiting from a specific policy change will be the same group as
those who bear the cost. Assessment of the distributional impacts of policy alternatives can
be based on criteria such as the economic status, ethnicity, age, geographical or temporal
distribution of those who gain and those who lose. As awareness of the distributional
impacts of policy change has heightened, debates have increasingly involved these
distributional considerations (Serret and Johnstone 2006). Yet, the incorporation of
distribution in policy analysis is difficult “as there is no commonly accepted definition of
optimum equity: certainly nothing analogous to maximum net benefits from economic
efficiency” (Sutherland 2006).
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One method of including distributional considerations in policy analysis is through the
application of distributional weights in a cost benefit analysis (CBA) setting. This theory is
briefly outlined in section two. Johansson-Stenman (2005) illustrates a large range of cases
where the application of distributional weights is (second-best) optimal thus reinforcing the
argument that efficiency and distributional concerns must be analysed simultaneously.
However, despite the well established welfare economic theoretical underpinnings of
incorporating equity considerations in policy analysis, there has also been extensive debate
in the literature regarding the efficacy of applying distributional weights. This debate was
particularly active during the 1970s in the context of project appraisal by the World Bank
(Dasgupta, Sen et al. 1972; Little and Mirrlees 1974; Squire and van der Tak 1975; Squire
1989).
In practical terms, the approach to incorporating distributional weights in CBA varies. In
most cases, the practice of the World Bank is not to apply explicit distributional weights in
CBA (Little and Mirrlees 1994; World Bank 1996). In the UK, H.M. Treasury, has
officially endorsed distributional weights in CBA as detailed in their Green Book
(H.M.Treasury 2003). In Australia, the Commonwealth Department of Finance and
Administration recommends that, as a general rule, distributional weights not be assigned
“and that recommendations of cost-benefit analyses flag the need for distributional
judgements to be made at the political level” (Department of Finance and Administration
2006). This raises the issue of the distinction between the application of explicit or implicit
distributional weights. Adler and Possner (1999) suggest that at a national level in the US,
if distributional weights are applied, the weighting appears to be made implicitly through
the policy decision making process.
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One of the impediments to the adoption of the application of explicit distributional weights
has been the difficulty in estimating community preferences for the distribution of utility.
Revealed preferences studies such as those by Basu (1980) have estimated preferences by
policy makers based on the analysis of past decisions. Yet there is a paucity of knowledge
of the distributional preferences of the community.
In this paper, this limitation is addressed through the application of the stated choice
method of choice modelling (CM) to the estimation of distributional preferences. CM has
increasingly been recognized as a method of estimating relative values for non-marketed
environmental attributes (Bennett and Blamey 2001). An illustration of the application of
the CM methodology to the problem of estimating distributional weights is provided in
section three. Rather than taking the conventional CM focus on utility estimation, the
application reported here involves utility distributions between generations being used as
policy change attributes. The context used is that of environmental policy development.
The research is therefore particularly relevant to the sustainability debate, where
sustainable development implies some general rule about maintaining the capability of
future generations to achieve the same level of well-being as the current generation
(Tacconi 2000).
The results of the CM experiment, which are summarised in section four, suggest that the
community holds a degree of altruism towards future generations. Discussion of these
results in section five indicates that the weights estimated are within the range of recent
speculation regarding intergenerational distributional preferences. The findings indicating
community distributional preferences favouring the utility of future generations have
significant natural resource management policy implications. Furthermore, the plausibility
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of the results supports the potential of CM to estimate community distributional
preferences.
2. DISTRIBUTIONAL WEIGHTING AND BENEFIT COST ANALYSIS
Using a Bergson-Samuelson social welfare function (SWF), social welfare (Wj)
representing the social preferences of respondent, j, can be expressed as;
[1] )(...)()()( 332211 nnj xUxUxUxUW ++++=
where Ui is the utility of the i=1…n people in society, with xi the quantity of goods
consumed by individual i. The form of the SWF depends on whose preferences are being
reflected, and it is often expressed as the views of parliament or social planners [see, for
example, Mäler, 1985]. In this paper, we are interested in the SWFs of individuals within
the general community. Changes in social welfare resulting from policy changes can be
accounted for by acknowledging that for some individuals there may be positive or
negative changes in utility as a result of the introduction of a specific policy. Aggregation
across individuals in this form of the welfare function assumes that the marginal utility of
consumption is equal for all individuals: additions to consumption are valued equally by
each individual. Recognising that this may not be the case, distributional concerns can be
allowed for by assigning distributional weights to the gains and losses to various
individuals:
)(...)()()( 332211 321 nnjjjjj xUxUxUxUW
nαααα ++++= [2]
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where the distributional weights, held by person j, for each individual are given by the
’s. For further elaboration on the derivation of distributional weights see Johansson
(1987), Dreze and Stern (1987), Mäler (1985) or Layard and Glaister (1994).
jiα
The distributional weights ( ’s) that can be applied to the elements of a CBA are
variously referred to as the marginal social utilities of income (Johansson 1993), the
welfare weights (Dreze and Stern 1987), or the marginal social utilities (Boadway and
Bruce 1984). These distributional weights are the products of two components: the change
in social welfare if the utility of individual i increases marginally
jiα
)(i
j
UW
∂∂ and the
marginal utility of consumption of individual i, )(i
ix
U∂
∂ .1
i
i
i
jj
i xU
UW
∂∂⋅
∂∂
=α [3]
The first component of the weight indicates how the person, j, whose social welfare
preferences are being reflected, ranks the utility of individual i in their distributional
preferences. For example, in the view of person j, is social welfare enhanced or diminished
if the utility of a low income person is improved relative to a high income person?
Examples of characteristics that may influence these perceptions include wealth, ethnicity,
race, geography or generation.
1 Strictly speaking, this is the perception, of the person whose social welfare preferences are being reflected,
of the marginal utility of consumption of other members of the community.
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The second component of the weight reflects the assessment, by the person whose welfare
preferences are being reflected, of how the well-being of individual i changes as a result of
a change in consumption, often substituted by money as a numéraire. For example, is the
view held that a dollar of benefit increases the utility of a low income person more than it
would increase the utility of a high income person? Although this component of the weight
is often referred to in terms of income, this does not necessarily need to be the case. For
example, it could also be the marginal utility of an additional unit of an environmental
good for individual i. Medin, Nyborg et.al. (2001) illustrate the sensitivity of distributional
weights to the choice of numéraire.
The distributional weights may be different for each individual, j, in society reflecting their
distributional preferences. Assumptions regarding the first component of the distributional
weight reflect varying theories of social justice. For example, in a Benthamite or utilitarian
society i
j
UW
∂∂ = 1 for all, resulting in all changes in utility being treated equally.
Alternatively, in a Rawlsian society i
j
UW
∂∂ = 0 for all, except the worst-off, reflecting
Rawl’s view that welfare is maximised by seeking to maximise the utility of the least well-
off individual.
In practice, social justice preferences will most likely be in terms of groups within society
who share common characteristics, rather than individuals. For this reason, the proceeding
analysis is in terms of groups rather than individuals. Distributional weights are thus
dependent on the impact of money, assuming this is the chosen numéraire, on the well-
being of the group and the impact of the change in utility of a group on society’s total
welfare.
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There are few examples where distributional weights have been explicitly applied
(Markandya 1998). In part, this may be due to the prospect of an efficiency cost arising
from the incorporation of equity preferences in policy analysis. This case has been strongly
argued by Harberger (1978) and Harberger and Jenkins (2002) and countered by authors
such as Layard (1980), Dreze (1998) and Johansson-Stenman (2005). A further difficulty
has been the question of whose social welfare preferences should be considered. A lack of
knowledge of the community’s social welfare and distributional preferences and an
inability to elicit and estimate distributional preferences has also contributed to the limited
application of explicit distributional weights. There has been some work on estimating
distributional weights with respect to income distribution (Cowell and Gardiner 1999) but
even less in estimating weights that acknowledge distribution is impacted by a broader
range of marginal utilities.
To elicit community utility distribution preferences and hence a set of distributional
weights ( ), a CM experiment involving intergenerational utility redistribution arising
from changing environmental policies was conducted. Environmental policies can affect
the distribution of resources, both financial and environmental, between generations. Policy
debates have increasingly involved generational distributional considerations. The
Brundtland Commission (World Commission on Environment and Development 1987)
defined sustainable development as “development that meets the needs of the present
without compromising the ability of future generations to meet their own needs” and
Pearce and Barbier (2000) stress the “fair treatment of future generations”. Arrow,
Dasgupta, et.al. (2004) take sustainability to mean that intertemporal social welfare must
not decrease over time.
jiα
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These definitions highlight an anthropocentric policy focus that places emphasis on
achieving a quality of life that can be maintained for future generations. It is impossible to
know the exact conditions that will allow the certain existence of future generations.
Consequently, intergenerational utility distribution also depends on the extent of altruism
the current generations hold for future generations. Those advocating “environmental
justice”, both in principle and in terms of practical political action, emphasise the
distributional implications of environmental change (Agyeman, Bullard et al. 2003). They
highlight the strong link between sustainability and social justice.
Yet notions of social justice, both generally and with respect to sustainability and
intergenerational equity, vary between individuals reflecting personal judgements regarding
fairness. Broome (1995) suggests a search for a class of reasons, referred to as claims, why
one group should be given priority over another. He argues that fairness is about mediating
the claims of different people and requires that claims should be satisfied in proportion to
their strength. The important aspect of this view of equity is the need to mediate claims as
one of the paramount considerations for fairness because an aspect of justice is not just how
a group fares in relation to their claims but also involves how they fare in relation to the
rest of the claimants (Rescher 2002). In the context of sustainability, the claimants reflect
varying generations and the question becomes one of weighing the gains and losses to
different generations. Again, the application of the concepts so developed is limited
because of the lack of information regarding the strength of intergenerational utility
preferences.
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Another key feature of the literature on fairness is that it is dependent on the actor and
beliefs about what is fair are personal (Elster 1992). Elster suggests four groups of actors
that can be useful for analysing distributive justice; individuals in the organisation that is
charged with the allocative task, political actors, claimants and public opinion. While
policy makers generally have the opportunity to express their justice principles, the public
has limited forms of social choice in which to express their preferences. This is one of the
strengths of using a stated preference technique to estimate community utility distribution
preferences.
3. INTERGENERATIONAL DISTRIBUTION CM EXPERIMENT
A CM experiment has been undertaken where, rather than estimating utility as a function of
the attributes of goods consumed as in conventional applications, social welfare is the
dependent variable and the utility levels of different groups, in this case generations, are the
attributes that are varied. This application of CM addresses the question of the
distributional effects of policies and the consequent social welfare outcomes of policy
alternatives. Rather than applying the model to the estimation of individual well being and
value in a dollar measure, the emphasis is on the estimation of the relative distributional
preferences of respondents. It is the respondent’s conception of social welfare rather than
utility that is being maximised in the choices being made.
Choices between the distribution associated with the status quo and changes in policy
resulting in distributional changes were presented to respondents. The attributes of the
policy options that were varied were the levels of utility or well-being of particular groups
Page 10
within society and the measure of interest is the willingness of respondents to trade-off a
change in the utility of one group for a change in the utility of another group.
Arrow (1963) suggests that people have two distinct personalities: their self-interested
selves essentially disjoint from aspects of their ethical selves. Self-interested preferences
guide day-to-day participation in the market economy while their ethical ones apply to
participation in collective decision making. Nyborg (2000) formalises this distinction
between “Homo economicus”, the individual maximising personal well-being, and “Homo
politicus”, the individual expressing their social justice preferences. Focus on the behaviour
of “homo politicus” allows for a sense of social justice that Musgrave and Musgrave (1989)
argue is essential for the definition of a good society and the functioning of a democratic
society. Broome (1995) describes this as a notion of communal good that is separate from
the good of individuals.
Hence, this CM experiment is aimed at eliciting respondents’ distributional preferences
reflecting their social justice preferences. The ability of respondents to view policy in this
manner is supported by a study of the equity considerations of the burden of meeting the
costs of environmental policy by Atkinson, Machado et.al. (2000) who found the
proposition that respondents significantly allowed their own position to influence their
ranking of different options was not strongly supported. A degree of interpersonally
comparable cardinal utility must be assumed so that respondents are able to make
judgements about the well-being of other groups in society. In this application, respondents
use their knowledge of the well-being of groups within society under the status quo policy.
Therefore, decision-making is seen in a broader context of welfare maximization within a
social structure rather than individuals maximising their utilities. Hence, each individual
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has a personal view of social justice based on their distributional preferences. Respondents
were encouraged to adopt this approach by the following introduction to the survey
instrument:
“Many environmental policies result in a transfer of both income and resources
between generations. For example, some environmental policies are paid for by
current taxpayers with the aim of improving the environment for future generations.
We are interested in finding out what you think about the way these policies lead to
gains for some generations and costs for other generations.”
Hypothetical policies with generic labels (A, B, C etc) were used as the sources of
distributional change for the CM choice sets in an attempt to ensure that values other than
distribution preferences were not reflected in the respondent’s choices. This also
encouraged the respondent to focus on their social justice preferences. This does not mean
that respondents did not bring preconceived beliefs to the decision making process, rather
that these beliefs are part of ethical preferences regarding social welfare.
The attributes in this experiment were described in terms of the impact on the utilities of
individuals from different generations resulting from the three hypothetical and generic
policy options. Individuals with specific generational characteristics were used as proxies
for the group described. Following Mackay (1997), a time span of 25 years was taken as a
generation. The attributes and levels are described in Table 1.
[Insert Table 1 about here]
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The chosen design limited the choices to generations currently living to avoid time and
discounting complications, acknowledging the trade-offs required when considering the
cognitive demands placed on respondents. The total time period of the analysis could have
been extended by increasing the number of attributes, however, this also would have
increased the cognitive burden for respondents and there is likely to be a trade-off between
the number of attributes and valid responses (Louviere, Hensher et al. 2000).
The levels of the attributes were described in dollar terms. The dollar terms reflected the
change in utility to the individual with the specific characteristic described by the attribute.
Dollars were adopted as a metric with which respondents could associate.
The main advantage with this numéraire is that dollars are a common and well understood
metric to respondents. However, respondents were advised in the following way that the
dollar values represented the general utility of the individuals, and should not be interpreted
as financial wealth alone:
“In this survey, dollars have been used to measure the gains and losses to different
generations. The dollar amounts represent gains and losses from changes to access
to environmental resources such as air, water, forests and beaches as well as
monetary wealth.”
It is recognised that a disadvantage associated with this choice of numéraire is the difficulty
for respondents to think in terms of general well-being or welfare and not just monetary
income. The distribution of preferences may be sensitive to the choice of numéraire and it
is possible that if a different numéraire was applied, the distributional preferences may
vary.
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Theoretically, a possible solution to this difficulty would be to describe the attributes in
terms of an “index of well-being”. This has been used in making a theoretical case in an
example by Broome (1995) but not in an empirical exercise. While an index of well-being
would encourage respondents to think in terms of utility being broader than money and
therefore more in line with the notion of utility in the literature (Sen 1982; Sen 2000), the
difficulty and subjectivity in developing an index, determining the values for components
of the index and descriptors of the index make it impractical. Even if these issues were
resolved, the cognitive difficulty for respondents of making complex decisions in an
unfamiliar metric would remain a concern. For these reasons money was selected as the
numéraire.
The levels of the attributes involve the manipulation of attribute differences, not absolute
values of the attributes. The hypothetical dollar values represent a one-off loss or gain to
the individual representing the group described by the specific characteristic determining
the attribute. In this case, there are five levels for each attribute with each level varying
well-being to the value of A$500. Feedback from focus groups suggested this degree of
variation was large enough to be significant to respondents in determining a choice, and not
unrealistic in representing a once-off gain or loss.
A fractional factorial design taken from Lazari and Anderson (1994) was used to create 25
choice sets, an example of which is presented in Figure 1. The 25 sets were blocked into
groups of five so that each respondent was presented with five choice sets in a survey.
Respondents were provided with a reference key such as that in Figure 2 when asked to
complete the choice sets.
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[Insert Figures 1 and 2 about here]
A survey was conducted in July 2005 across a random sample of households in
Warrnambool, a regional city in South West Victoria, Australia. A personal drop off and
pick up form of distribution and collection was used. Acknowledging one of the strategies
for data collection suggested by Dillman (2000), respondents were provided with the
additional motivation to respond through the opportunity to participate in the draw for a
A$150 shopping voucher at a major retail chain if they completed the questionnaire. A total
of 431 questionnaires were distributed. Of the 337 that were collected or returned by mail,
295 were usable giving a response rate of 68.5%. Each of the 295 usable responses
included five completed choice sets giving a total of 1475 completed choice sets.
Each respondent also completed socio-demographic questions and two qualitative
questions; one regarding specific strategies they had employed in answering the choice set
questions and one regarding general comments they wished to make about the survey.
Comparison of the survey sample’s socio-demographics with the Australian Bureau of
Statistics (2001) census data indicates a slightly higher representation of females and
younger people completing the survey than in the general population. Table 2 provides a
comparison of the age profile of the sample with that of the 2001 census as this variable is
particularly relevant to the analysis.
[Insert Table 2 about here]
Standard choice experiment procedures were applied with the distinction that an indirect
welfare function rather than an indirect utility function has been assumed. It is assumed
; where is the deterministic component for respondent j and choice z, jz
jz
jz ewW += j
zw
Page 15
and can be decomposed in where is a vector of the attributes of
alternative z and is a vector of the characteristics of respondent j. The stochastic
component of welfare is . Hence,
][ jjz S,X=j
zw jzX
jS
jze
)( zzzjz ASCASCw ∗++= ∑∑ jj
njz SγXβ [4]
where ASCz is an alternative specific constant associated with the change options, zβ are
the coefficients associated with each attribute and is the vector of the coefficients
associated with the socio-demographic characteristics intersected with the ASC to avoid
singularities. Assuming that the random component of welfare is distributed as IID and
with an extreme value (Gumbel) distribution, then the probability of an option being
chosen can be expressed as the multinomial logit (MNL) or conditional logit (McFadden
1974).
jnγ
∑=≠∀>
z
jz
jmj
zj
m ww
mzWWPμ
μexp
)exp(),( [5]
where μ is a scale parameter which is inversely proportional to the standard deviation of the
error term and and are conditional indirect welfare functions for choice options m
and z, which are assumed to be linear in parameters.
jmw j
zw
The key outputs of the welfare based choice model are the social marginal rates of
substitution (SMRS). Given that the attributes of the choice model are the changes in utility
accruing to particular groups then the SMRS are estimated by the ratios of the marginal
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welfare changes (βs). Focussing on the ratios of the welfare parameters also overcomes the
problem of confounding presented by the scale parameter in the choice model. For
example, assuming a specific policy, m, the SMRS by respondent j, between those aged 50
and those aged 25 is:
jmAged
jmAged
jmAged
jmAgedj
AgedAgedm
SMRS25
50
25
50
2550 β
βδβδβ
== [6]
In effect, the SMRS reflects a willingness to accept distributional change, which can be
represented graphically by the slope of the SWF. This reflects the respondent’s notion of
social justice. For example, in Figure 3, a movement from R to S indicates a willingness to
trade a decrease in the utility of those in the aged 50 generation for an increase in the utility
of those in the aged 25 generation.2
[Insert Figure 3 about here]
The SRMS also yields distributional weights applicable to a CBA setting. For example, the
distributional weight, associated with Policy m, between those aged 50 and those aged 25
can be estimated by the SMRS.
25
25
25
50
50
50
2550
mAged
mAged
mAged
jm
mAged
mAged
mAged
jm
j
AgedAgedm
xU
UW
xU
UW
SMRS
∂
∂⋅
∂∂
∂
∂⋅
∂∂
= [7]
2 Figure 3 assumes a well-behaved utilitarian SWF.
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This reflects the two components of the distributional weight as indicated in Section two
and equation [3].
The hypotheses to be tested using the model are that the distributional weights for each age
group are not equal to one. For example, if there is altruism towards the younger
generations by the community then when responses are aggregated:
150
25 ≥aged
agedβ
β and 150≥
aged
newbornβ
β [8]
4. RESULTS
The MNL was estimated, using the Stata software program. The IIA (independence of
irrelevant alternatives) property was tested using the test suggested by Hausman and
McFadden (1984) and compliance was confirmed. Each of the variables used in the model
is specified in Table 3.
[Insert Table 3 about here]
Model results are summarised in Table 4. Each choice set attribute parameter is significant
at the 1% level and signed as expected a priori indicating that the utility of each age group
contributes positively to the social welfare function.
[Insert Table 4 about here]
Of the demographic characteristics, the age, income and parental status variables are
significant at the five percent level. The interpretation of the signs for the demographic
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characteristics is complicated by the changes being both positive and negative in the design
of the choice experiment.
Table 5 summarises the 95% confidence intervals for the mean social marginal rates of
substitution. These results indicate a distributional preference towards the younger
generations with the ratio of the welfare parameters being greater than 1 for both the aged
25 generation and newborns relative to the aged 50 generation. For the Aged25/Aged50,
the SMRS suggests a relative distributional weight of 1.70 and for the Newborn/Aged50 a
weighting of 2.35.
[Insert Table 5 about here]
The preference towards the utility of younger generations evident in the quantitative
analysis is consistent with comments made by respondents to the qualitative questions
regarding the strategy they had used to make choices. (One hundred and fourteen of the
295 respondents chose to explain the strategy they had used in answering the choice
questions.) Examples of these comments include:
“Help younger generation and early workforce people.”
“Picked ones that were most likely beneficial to the younger generation.”
“Thinking about effect on future generations.”
5. DISCUSSION AND CONCLUSION
The findings of this research are particularly relevant to the sustainability debate and in the
context of the trade-off between consumption today and consumption in the future. The
Page 19
magnitude of the weighting towards the utility of future generations supports the contention
of Arrow, Dasgupta et.al. (2004) that individuals “derive a positive externality (outside of
the marketplace) from the welfare of future generations”. The findings also support
estimates made by Johansson-Stenman, Carlsson et.al (2002) in an experiment involving
students’ preferences with respect to “imaginary grandchildren” and future income
distribution, where they found that respondents were willing to trade-off “non-negligible”
amounts of money for increasing their grandchild’s relative standing in society.
Arrow, Dasgupta, et.al (2004) have approached the question of intergenerational
equity by estimating the elasticity of marginal (social) utility. Theoretically, for a
consumption path in a market economy to be socially optimal, the market rate of return on
investment, i, must be equal to the social rate of interest on consumption, denoted by
r(Arrow, Dasgupta et al. 2004). If i exceeds r, markets are biased toward insufficient
saving and excessive current consumption. Based on the estimation of an intertemporal
social welfare function, the social rate of interest on consumption r, is given by the relation
gr ηδ += , where δ is the social rate of pure time preference, η is the elasticity of marginal
(social) utility and g is the rate of growth in aggregate consumption. The choice of δ and η
are value judgements which are likely to vary between individuals. The term η, which is of
relevance to the findings reported here, is interpreted by Arrow, Dasgupta et.al. as “a social
preference for equality of consumption among generations”. They speculate that the value
of η is linked to the intertemporal elasticity of consumption and based on Hall’s (1988)
time series estimates of this suggest that “plausible values for η might lie in the range of 2-
4”. Although these authors have approached the question of intergenerational distribution
from a different perspective, the distributional weights between those aged 50 and
newborns estimated in this study falls within this expected range.
Page 20
In conclusion, the results of this research demonstrate distributional weights that are not
equal to one and positively favour the younger generations. The positive distributional
preferences towards future generations may be due to a combination of factors including
altruism toward future generations and diminishing marginal utility as raised by Arrow,
Dasgupta et. al. (2004). The implication of the results is that environmental policies that
favour intergenerational transfers of utility from current to future generations will be more
favourably treated in CBA and hence more likely to be accepted as preferred options by the
community.
Furthermore, the findings of this research show that choice modelling is a useful method
for eliciting the utility distributional preferences of the community. This has implications
for the incorporation of the distributional impacts of environmental policies into CBA and
hence decision making. An advantage of this approach is that it provides the policy maker
with information regarding the community’s preferences across generations. Policy
interventions have to be sensitive to the gainers and losers, not only because that matters
from a social justice point of view, but also because the political acceptability and
effectiveness of the measures will depend on the distribution of costs and benefits.
• We appreciate the comments of Assoc. Prof. Michael Burton and participants of the
2006 conference of the Australian Agricultural and Economics Society on an
earlier draft of this paper.
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Figure 1
Example of an intergenerational utility distribution choice set
Figure 2: Reference key for choice set in Figure 1
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Figure 3
Example of social welfare function
R
SSWF
Utility of the Aged 50 generation
Utility of the Aged 25 generation
Table 1
Attributes and levels in intergenerational distribution choice experiment
Attribute Levels ($A)
Utility change Person Aged 50 -$1,000 -$500 +$500 +$1,000 +$1,500
Utility change Person Aged 25 -$1,000 -$500 +$500 +$1,000 +$1,500
