relativist welfare measurement
TRANSCRIPT
RELATIVIST WELFARE MEASUREMENT
ACCOUNTING FOR COUNTRY-SPECIFIC PREFERENCES
IN INTERNATIONAL WELFARE COMPARISONS
Master’s Thesis Economics
Tilburg University
30 August 2010
D.C.W.M. (Dingeman) Wiertz, 209434
Supervisors:
Prof.dr. J.A. Smulders
Prof.dr. A.B.T.M. Van Schaik
Number of words: approximately 30 000
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ABSTRACT
Over the past few decades, there is a renewed interest for welfare measurement methods.
Recognizing the limitations of GDP per capita for accurately representing welfare, a
number of alternative measurement methods have been proposed, ranging from
seemingly ‘objective’ indicators like the Human Development Index to subjective well-
being indicators like happiness and life satisfaction. As this thesis argues, however, most
of these proposals are far from satisfactory either: whereas the ‘objective’ indicators pay
too little attention to subjective preferences and perceptions, the subjective indices seem
rather insensitive to objective living conditions. In response to these observations, this
thesis favours a synthesis of both approaches, focusing on both objective living
conditions as well as subjective preferences. Considering recent attempts at sophisticated,
comprehensive international welfare comparisons, nevertheless, hardly any attention is
paid to potential cross-country variation in preferences towards various welfare
dimensions like inequality, leisure, health and the risk of unemployment. Therefore, this
thesis aims to investigate the opportunities for and potential implications of taking into
account country-specific preferences towards various welfare dimensions in international
welfare comparisons. For this purpose, amongst others regression analyses and principal
component analyses on the basis of the World Values Survey are conducted. Despite
facing many difficulties and suffering from several shortcomings, this thesis strongly
demonstrates the potential importance of incorporating country-specific preferences in
international welfare comparisons. Consequently, it concludes that if one aims at
sophisticated, comprehensive international welfare comparisons, one should take into
consideration potential cross-country variation in preferences towards various welfare
dimensions.
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Contents
1 Introduction page 5
2 Approaches to welfare and its measurement page 10
2.1 Gross Domestic Product (GDP) and similar measures page 11
2.2 Alternative ‘objective’ indicators page 11
2.3 Welfarism and subjective well-being page 15
2.4 Hypothesis page 21
2.5 Equivalent income method page 23
3 Equivalent incomes and country-specific preferences for
inequality and leisure page 25
3.1 Equivalent incomes in Fleurbaey & Gaulier (2009) page 25
3.2 Country-specific inequality aversion preferences page 32
3.2.1 Estimating inequality aversion on the basis of the
subjective inequality convergence hypothesis page 34
3.2.2 Estimating inequality aversion via life
satisfaction regressions page 36
3.2.3 Estimating inequality aversion on the basis of
direct survey questions page 45
3.3 Country-specific preferences for leisure page 57
3.4 Final words page 67
4 Equivalent incomes and country-specific preferences regarding
health and unemployment page 68
4.1 Equivalent incomes in Fleurbaey, Decancq & Schokkaert (2009) page 68
4.2 Applying Fleurbaey et al.’s (2009) approach to country-specific
preferences towards health and unemployment page 70
4.2.1 Deriving country-specific willingness-to-pay estimates page 71
4.2.2 Obtaining equivalent incomes correcting for
health and unemployment page 84
4.3 Comparing our results with Fleurbaey et al.’s (2009) results page 88
4.4 Final words page 92
5 Conclusion page 94
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Appendices page 99
Appendix A Details on the variables used in the regressions
of Chapter 3 and 4 page 99
Appendix B Correlation matrix accompanying Table 3 page 104
Appendix C Life satisfaction regressions containing
regime-specific inequality effects page 105
Appendix D Average number of hours worked per country (2004) page 106
Appendix E Explanatory list of country abbreviations page 107
References page 108
Acknowledgements and contact information page 111
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I Introduction
“The valuable capacity of the human mind to simplify a complex situation in a
compact characterization becomes dangerous when not controlled in terms of definitely
stated criteria. With quantitative measurements especially, the definiteness of the result
suggests, often misleadingly, a precision and simplicity in the outlines of the object
measured. Measurements of national income are subject to this type of illusion and
resulting abuse, especially since they deal with matters that are the center of conflict of
opposing social groups where the effectiveness of an argument is often contingent upon
oversimplification…(…)…The welfare of a nation can scarcely be inferred from a
measurement of national income.”
(Simon Kuznets, 19341)
Despite its length, this quotation represents a telling and meaningful starting-point
for this thesis, which immediately takes us to our core subject: the measurement of
welfare. The interest for welfare has almost always existed and can be traced back to
people like Jeremy Bentham, Adam Smith and even Aristotle. Public policy has always
been considered as a means to enhance a society’s welfare and well-being. However, a
strong economic and statistical foundation of such public policies has long been absent
over the course of history. During the twentieth-century Great Depression people became
really aware of the problems of such a lack of economic foundation: politicians and
policy makers found it difficult to navigate the economy without a proper compass. In
response, Simon Kuznets was commissioned by the US government to develop a system
of national accounts.
In a 1934 report to the US Congress in which he presented his proposal for the
measurement of national income (which later became the basis of our current systems of
national accounts), Kuznets made amongst others the remarks quoted above. The
quotation clearly demonstrates that Kuznets was fully aware of the limitations of his
national income measure and that he warned for inappropriate use and interpretation of
his measure. Nevertheless, these warnings did not prevent Gross Domestic Product
(GDP) of becoming world’s most dominant indicator of economic performance, often
used as a core indicator on the effectiveness of public policy and as a measure of broader
well-being.
However, critique on the GDP measure and its applications has never vanished
completely. Van den Bergh (2005, 2009), for instance, presents an ardent plea for the
abolishment of GDP as an indicator of macroeconomic policy. To underpin his position,
Van den Bergh discusses a long list of arguments. Amongst others, he criticizes GDP for
insufficiently taking into account the distribution of income as well as for neglecting the
informal economy. As a result, GDP is an inappropriate measure of welfare in his
opinion. The fact that GDP is nonetheless an important decision criterion in politics,
financial markets and international organisations implies according to Van den Bergh a
1 National Income 1929-1932. Report to the 73rd US Congress, 2nd session, Senate document 124, pp. 5-7.
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serious information failure. A similar conclusion is drawn by the initiative of The
Declaration of Tilburg (‘De Verklaring van Tilburg’, 2008), a petition stating that GDP is
merely a speedometer reflecting how quickly we are earning money, while we actually
need altimeters containing information on the sustainability and solidarity of society.
Some recent examples further support Van den Bergh’s (2005, 2009) thesis. The recent
BP disaster in the Gulf of Mexico might, for example, very well translate into an increase
in GDP (as a consequence of all the necessary effort and expenditures to clean the mess),
whereas the environment obviously experiences a big loss, which does affect welfare, but
which is not accounted for in GDP (neither is accounted for the worsened future
prospects of the fishery sector in the region).
If we for the moment agree that the imperfections of GDP for measuring welfare
and well-being are so serious that we should abandon GDP as a welfare indicator, we
instantaneously arrive at the question of what alternative measure should be used. After
all, as the Dutch proverb says, should you throw away your old shoes before you have
new ones?
Van den Bergh (2005, 2009) seems to be in favour of a subjective well-being
approach for measuring welfare, in line with Layard (2005). It is, however, questionable
whether subjective well-being is a desirable measure for welfare and well-being. In this
context, Amartya Sen often gives the example that even though some happy slaves have
existed, slavery certainly does not mean well-being.2 Heertje (2007) is also critical
towards the use of subjective well-being measures as foundation for economic policy,
since many significant determinants of happiness are outside the domain of the
economist. Moreover, according to Heertje we should not even strive for an all-
embracing measure of well-being, as such a measure is just a utopia; we have to accept
that certain valuable things are simply not properly quantifiable.
Heertje’s work points at a tension within economic science: on the one hand,
economists should be careful to cross the borders of economic science too
enthusiastically, trying to assign a value to social issues and personal feelings that cannot
be measured that easily, while on the other hand, the dismantlement of interdisciplinary
barriers can indeed lead to valuable progress in the measurement of welfare and well-
being.
No matter what our position is in this regard, we can observe an increased interest
in broader measures of welfare and well-being over the past few years. A notable
example is the report by the Commission on the Measurement of Economic Performance
and Social Progress (Stiglitz et al., 2009), which was written by order of the French
president Nicholas Sarkozy. Furthermore, organisations like the World Bank, the OECD
and the European Union have also started projects in this field (Canoy & Lerais, 2007;
Fleurbaey, 2009). A frequent discussion at these platforms concerns the issue of
comprehensiveness versus comprehensibility. The less complex the measures are, the
easier they are to communicate and to understand, while these simple measures may give
a less accurate overview of affairs as compared with more comprehensive and more
complex measures. In a similar vein, one could also think of a dilemma of inclusiveness
versus measurability. The existence of such dilemmas implies that one always faces
tradeoffs when constructing a welfare indicator. Hence, the appropriateness of a welfare
2 See for example Sen (2001).
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measure is dependent on its intended application: e.g. whereas GDP probably is an
insufficient indicator if one is aiming at an all-embracing, broad welfare comparison, it
might still be very useful in a number of concrete policy contexts due to its high level of
measurability.
For now we want to concentrate our attention on a recent attempt of Cnossen
(2009) at a broad welfare comparison of a group of European countries. Cnossen’s
primary goal is to address whether comprehensive welfare states like the Netherlands are
effective in achieving higher levels of welfare through their extensive public policies.
What he is actually doing, is making cross-country comparison tables for a wide array of
welfare-related subjects, ranging from basic dimensions like inequality and poverty to
factors like education, discrimination, competitiveness, social trust, sustainability et
cetera. On the basis of these tables he concludes that in terms of welfare the Netherlands
but also the Nordic countries do not suffer from their high levels of tax pressure.
Unfortunately, however, Cnossen’s study suffers from a number of shortcomings.
One first criticism is that although Cnossen’s comparison tables do not allow him to
establish any causal links, he repeatedly suggests that more extensive welfare state
policies lead to higher levels of welfare. Second, with his comparison tables it is not
possible to trade-off scores on different dimensions, since a weighting scheme of the
different dimensions is lacking. Therefore, it is in fact impossible to derive a conclusive
welfare ranking of countries on the basis of his tables. Obviously, this is a severe
drawback of Cnossen’s research and, as a result, his conclusions lose a lot of their
convincing power.
A much more promising welfare ranking of countries is provided in Fleurbaey &
Gaulier (2009). These authors have created a ranking of 24 OECD countries based on
equivalent incomes. They have corrected standard GDP per capita for amongst others
leisure, health, risk of unemployment and income inequalities. Because of their
equivalent income approach, they arrive at a one-dimensional ranking and they are able
to weight several welfare dimensions against each other, mainly by way of market prices.
Nevertheless, there is still a third criticism on Cnossen (2009), for which also
Fleurbaey & Gaulier (2009) do not provide a satisfactory solution. The central idea
behind this criticism is that welfare is a relative concept, and that countries can adhere to
different definitions of welfare. Therefore, preferences regarding different welfare
dimensions may very well differ across countries, depending on culture, social context et
cetera. Alesina et al. (2004) show for example that preferences towards inequality differ
between the United States and Europe. If one does not include such preferences in one’s
welfare analyses, the resulting rankings may create misleading impressions. Apart from
shortly mentioning that welfare is a relative concept about which people and countries
may have differences of opinion, Cnossen (2009), however, does not pay any attention at
all to this issue. Instead, at several occasions in his text it seems that he has implicitly
adopted the Dutch preferences concerning welfare and a ‘civilized’ society. Yet, as we
have just made clear, it might for example not be fair to evaluate Italy’s welfare on the
basis of Dutch preferences. Fleurbaey & Gaulier (2009) score also in this respect
somewhat better than Cnossen. By applying price-based corrections to GDP per capita,
the different welfare dimensions are valued differently across countries in their analysis.
The question remains, however, to what extent these imputed valuations correspond to a
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country’s real preferences. Leisure, for instance, is valued against the average hourly
wage in a country in Fleurbaey & Gaulier (2009). While wages do indeed contain some
information on preferences for leisure (in accordance with the ‘reservation wage’
concept), variation in wages across countries is affected by variation in productivity and
other macroeconomic conditions across countries as well. In addition, Fleurbaey and
Gaulier use several preference-related parameters in their analysis (related to risk
aversion, inequality aversion and time preference) for which they assume universal
values for all countries they consider. So, from the perspective of country-specific
welfare preferences the work of Fleurbaey & Gaulier (2009) is not perfect either.
Since, as far as we know, there are no other studies available which succeed in
satisfactorily taking into account country-specific preferences, the aim of this thesis will
be to take up this issue and to explore the possibilities and implications of including
country-specific preferences in international welfare comparisons. The central research
question can thus be formulated as: What are the implications of including country-
specific preferences towards different welfare dimensions in cross-country welfare
comparisons?
Regarding this research question, it is relevant to remark that preferences may not
only vary across space, but also across time and individuals. Nevertheless, we have
chosen to demarcate our research problem to country-specific preferences, as we think
this is the most relevant dimension of variation from the viewpoint of international
welfare comparisons. Moreover, we would like to remark that we are not under the
illusion that this thesis can come up with some kind of superior welfare measure which
will resolve the debate on the measurement of welfare. As we noted earlier, the
appropriateness of a welfare measure strongly depends on its intended use. We recognize
that, by definition, every welfare indicator has some strong and some weak properties
and, besides, that a great leap forward is not easily attainable, were it only for reasons of
limited data availability concerning preferences. Instead, this thesis should be read as an
exploratory expedition concerning the relationship between the measurement of ‘the
wealth of nations’ and country-specific preferences towards various welfare dimensions.
During this expedition we hope to find answers to questions like: How can we include
country-specific preferences in welfare measures? Does this inclusion make a significant
difference for international welfare rankings? How can we proceed in improving our
measures of welfare? Important to note in this context is that, rather than contributing to
the vivid theoretical debate on proper welfare measures, this thesis mainly wants to add
to the empirical approximation of welfare.
Apart from the intrinsic value of gaining more sophisticated insights into the
wealth of nations, this thesis can hopefully help attenuating the information failure Van
den Bergh (2005, 2009) was pointing at.
This thesis proceeds as follows. Chapter two presents the theoretical background
of our study. In doing so, we first discuss our definition of welfare. Subsequently, we go
over some criticisms towards conventional welfare measures and finally we deal with
some alternative approaches, amongst others the subjective well-being literature. Chapter
three focuses on the earlier mentioned equivalent income measure of Fleurbaey &
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Gaulier (2009), which is based on a welfare / utility maximization model and market-
based income corrections. In addition to explaining their method, several attempts of
more satisfactorily taking into account country-specific preferences are presented. In
these attempts, survey data from the World Values Survey play an important role. Next,
chapter four deals with another method of calculating equivalent incomes, in line with the
work of Fleurbaey et al. (2009). In this method, there is a leading role for willingness-to-
pay estimates inferred from life satisfaction regressions. Finally, chapter five concludes.
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II Approaches to welfare and its measurement
Together with scarcity, welfare is perhaps the most central concept within
economic science. Almost everywhere around us we are confronted with scarcities of
resources, implying that we cannot have everything that we would desire. The resulting
fundamental decision problem is how to manage these scarce resources to attain welfare.
From this perspective, Mankiw & Taylor (2006) define economics as the study of how
society manages its scarce resources. In essence, most economic problems can in some
way be reduced to a welfare maximization problem under scarcity constraints.
Considering all this, it is obviously important to understand what is meant by the term
welfare.
Although the nature and definition of welfare is far from undisputed, there is a
wide consensus that it has something to do with the satisfaction of needs. However,
consensus stops already here, as a variety of opinions exists regarding questions like:
What should be considered as needs? What do we mean by the satisfaction of needs?
How should different kinds of needs be weighted? Et cetera.
In this thesis our starting-point will be a rather general notion of welfare,
following amongst others Heertje (2007). We adopt the view that welfare concerns the
satisfaction of needs and we stress that welfare should be seen as a subjective concept
without any concrete or predefined content. The subjective character of our notion
implies that welfare is dependent on one’s preferences. It is impossible to define a
universal, conclusive list of needs and to judge everyone’s welfare on this basis. Rather,
one’s needs and the degree to which they are satisfied depend on subjective preferences
and perceptions. Therefore, welfare has no concrete content: everything of which
individuals think it contributes to their satisfaction of needs is part of the welfare concept.
Thus, the answer to the question ‘what constitutes welfare?’ may differ from time to time,
from space to space and from individual to individual.
From this observation it follows that it is neither practically possible nor desirable
to aim for a single, all-inclusive measure of welfare that can act as a guideline for
policymaking. To quote the Commission on the Measurement of Economic Performance
and Social Progress (Stiglitz et al., 2009:207): “The search for an aggregate measure of
the quality of life that combines information across all its dimensions is often perceived
as the ‘holy grail’ of all efforts to go beyond conventional economic measures. This
perspective is, however, both limited and deceptive.” Nonetheless, these remarks should
mainly be interpreted as a warning against too high expectations regarding a welfare
measure as well as against ‘overinterpretation’ of such measures. Thus, these remarks do
not undermine the potential relevance of broader welfare measures in terms of richer
insights into the wealth of nations and in terms of attenuation of the information failure
related to the use of traditional welfare indicators like GDP per capita.
In the remainder of this chapter, we will discuss several (measurement)
approaches to the concept of welfare, after which we will posit the central hypothesis of
this thesis.
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2.1 Gross Domestic Product (GDP) and similar measures
As already stated in our introductory chapter, Gross Domestic Product (GDP) per
capita has been the most widely used indicator of welfare over the past century.
According to the textbook definition, GDP is the added value of all goods and services
produced within a country in a given period of time, measured at market prices (see for
example Mankiw & Taylor, 2006:468). As such, it tries to measure the flow of economic
activity during a period. Usually, attention is mainly paid to GDP per capita, to allow for
international comparisons across differently-sized countries. Starting from GDP per
capita, one could also calculate several related indicators like Gross National Product
(GNP) per capita (to include income flows across borders) and Net National Product
(NNP) per capita (to account for depreciation of fixed assets).
The most important advantage of GDP per capita is that it is an indicator which
can relatively easily be calculated as well as communicated. Moreover, while it is widely
accepted that GDP per capita is not the ideal measure of welfare, it is assumed that there
is a large and significant correlation between GDP per capita and other welfare
dimensions (e.g. health, education, poverty). Hence, GDP per capita is often considered
to be an acceptable indicator of welfare.
Nevertheless, GDP per capita can be seriously criticized from a welfare point of
view. Its main drawback is that it only measures market transactions; only products and
services which are traded on markets are included in GDP calculations, weighted at their
market price. Obviously, however, many valuable objects exist which are not traded on
markets and therefore not captured by GDP calculations (or only imperfectly). Notable
examples entail amongst others the environment, household production, leisure, health,
government services, the distribution of income and opportunities, quality of governance,
social interaction, education, freedom, rights and risks. Since no (or only imperfect)
market prices exist for these issues, ordinary GDP measures neglect them and might,
therefore, lead to efficiency losses due to underinvestment in these issues. Furthermore,
whereas GDP is by its nature a flow measure, it is far from inconceivable that stocks also
matter for welfare, for instance via the perspective of a society’s future prospects. Yet,
GDP does not provide any insights into stocks of environmental, human and social
capital et cetera. Finally, although proponents of GDP as a welfare indicator often defend
the measure on the basis of its rather neutral and objective character, this defense cannot
be accepted, since the presumption that market prices provide a proper tool for measuring
the value of production is already a value judgment in itself. Especially when markets are
imperfectly competitive and when externalities are present, this value judgment may be
problematic, because under these circumstances market prices are likely to hide society’s
true valuation. In conclusion, notwithstanding the relevance of GDP measures in certain
contexts, it is highly debatable whether GDP per capita satisfactorily captures the welfare
within a country as defined above.
2.2 Alternative ‘objective’ indicators
Recognizing the limitations of GDP per capita for ‘truly’ measuring the welfare
within a country, there have been many initiatives over the past few decades to introduce
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an indicator that could replace GDP per capita.3 Basically, these initiatives have sought to
come up with a measure that should not only take into account market transactions but
also other valuable aspects of life. In most of the cases, the goal was to create a
composite index that combines several domain indicators of economic, social and
environmental performance and progress.
Many different composite indexes have seen the light over the years and, by now,
it would not be difficult to present a list of several dozens of such indicators (see for
instance Decancq & Lugo (2010) for a selection). The reason for this abundance of
indicators is rather simple in our opinion: there can be as many indicators of welfare and
the quality of life as there are definitions of what constitutes welfare and quality of life.
Recall in this respect also our statement earlier in this chapter that we consider welfare as
a subjective concept without concrete content, which can be defined in many ways
depending on one’s preferences, normative views and social context. Keeping this idea in
mind, the abundance of composite welfare indexes comes as no surprise. Here one can
also clearly discover the truth behind Stiglitz et al.’s (2009) words that the search for an
all-comprising aggregate measure of welfare is both a limited as well as a deceptive
expedition: someone with other views and preferences can always prefer another index
and, therefore, it is practically impossible to ever reach consensus on a single composite
index. Does this then mean that composite welfare indicators have no value at all?
The answer to this question would still be a definite ‘no’. Although one should
always remember that every indicator just represents one normative view on welfare, all
indicators can provide interesting information and useful refinements as compared with
the standard GDP per capita measure. As Todaro & Smith (2009) observe, most
composite indexes have a strong tendency to rise with per capita income, as richer
countries simply have more financial resources to invest in things like health and
education. These authors point out, however, that despite this expected pattern, there are
still large deviations between income and broader measures of welfare. So, from this
perspective, looking at more variables than only income can indeed provide valuable
additional insights. Becker et al. (2005), for instance, show that the absence of income
convergence generally noticed in the growth literature is in stark contrast with the
reduction in cross-country inequality over time that can be observed after incorporating
the relatively large recent gains in life expectancy in the poorer countries. Moreover, in
addition to these advantages in terms of additional insights, most of the composite
indicators still have the main advantages of the GDP per capita welfare measure, as they
are in general easy to calculate as well as easy to communicate to the public.
As far as these alternative composite indicators have any theoretical basis, it is to
be found in Amartya Sen’s conceptual framework of capabilities and functionings.4 In
short, in this framework a person’s life is conceived as a combination of various ‘doings
and beings’: functionings. These functionings can be interpreted as a collection of the
3 As Stiglitz et al. (2009) note, this social indicator movement was particularly active in the 1960s and
1970s. In 1974, a special journal was even founded, Social Indicators Research, to publish research dealing
with the measurement of the quality of life. The journal still exists and is still a quite popular forum for the
exchange of ideas on social performance and progress, in particular among sociologists, but to a lesser
extent also among economists, anthropologists and political scientists. 4 A more comprehensive overview of this conceptual approach can be found in Schokkaert (2007) and
Alkire (2008).
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observable achievements of a person, ranging from being nourished to being able to
express oneself in public without shame. Because people’s values and experiences differ
across time and space, the list of most relevant functionings depends on contextual
circumstances. What ultimately matters according to Sen, is one’s capabilities: the
opportunities one has for choosing among the various combinations of these functionings.
Thus, at the core of the capabilities approach is the idea that welfare is determined by two
things: one’s objective living conditions and one’s freedom to choose these conditions.
Although subjective feelings can also be part of one’s functionings, the capability
approach emphasizes that people are likely to adapt to their living conditions and that,
therefore, subjective feelings are inadequate as the only measure of welfare.
Regarding the measurement of welfare, the capabilities approach faces actually
one major problem, namely the difficulty to derive concrete measures from its concepts.
In the first place, there is the issue whether we should try to measure functionings or
capabilities. Obviously, functionings are far more easily observable, but on the other
hand, capabilities are what ultimately matters according to Sen. Second, aiming for a
single welfare index, the capabilities approach also entails an indexing problem: how
should the different functionings and capabilities be weighted? We will discuss this
indexing problem at length in a few moments, after presenting a more general
classification framework for composite welfare indicators.
Decancq & Lugo (2010) provide an interesting classification and conceptual
overview of composite welfare indicators. They classify the different indicator proposals
using three criteria. First, they consider the transformation functions applied to the
underlying domain indicators, looking whether the underlying variables are rescaled,
linearly transformed, exponentially transformed, et cetera. These transformation
functions are applied to transform scores on different domain indicators, which are often
measured in different measurement units, to a common basis for aggregation.
Furthermore, transformation functions can also help correcting for outliers in the original
domain indicator distributions.
The second classification criterion considered by Decancq & Lugo (2010) is the
assumed elasticity of substitution between the different welfare domains. The central
question in this respect is how, for example, one additional year of life expectancy can be
traded off against a decrease in the education level within society. If the different
components of the indicator are assumed to be perfect substitutes, a decrease in one of the
domain indicators can perfectly and rather easily be compensated for by an increase in
one of the other domain indicators. On the other hand, if no substitutability between the
components is assumed, a decrease in one of the domain indicators necessarily implies a
decrease in the value of the composite welfare indicator. Thus, the assumed elasticity of
substitution between the elements of the composite index can obviously make a huge
difference.
Thirdly, Decancq & Lugo (2010) are interested in the weights attached to the
different components of the overall index. Like an index measure clearly requires
normative assumptions regarding the first two classification criteria, this is definitely also
the case for the choice of the component weights. According to Decancq & Lugo (2010),
the employed weighting scheme reflects by definition particular value judgments on how
‘a good life’ should look like. Even if one chooses for weights that can be objectively
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defined or derived, the choice for these weights still represents value judgments, since the
question ‘why are especially these weights the appropriate ones?’ simply cannot be
answered objectively, without any normative premises. Here we are back at the indexing
problem of the capabilities approach.
In addition to these points, it is relevant to note that weighting schemes can have a
significant impact on the scores and rankings on the obtained composite index. Becker et
al. (1987) provide a striking example in this respect. They study the quality of life in 329
metropolitan areas in the United States on the basis of standard variables like economic
performance, health and security, and find that, depending on the used weighting scheme,
there are 134 cities that could be ranked first and 150 cities that could be ranked last.
Furthermore, they find that 59 of the 329 cities could be ranked either first or last, solely
dependent on the adopted weighting scheme.
Decancq & Lugo (2010) distinguish three approaches to set the weights. On the
one hand, there is the data-driven approach, which derives the component weights
statistically, on the basis of the observed distributions of scores within society on the
different domain indicators. An example of a data-driven weighting procedure is a
frequency-based weighting method. Many multidimensional deprivation indexes impose,
for instance, an inverse relation between the frequency of deprivation in a certain
dimension and the weight of that dimension, acknowledging the idea that individuals
attach a higher importance to shortfalls in dimensions where the majority of the
population does not fall short.
At the other side of the spectrum, one can distinguish the normative approach.
Instead of basing the weights on the actually observed distribution of domain scores, this
approach merely depends on explicit value judgments regarding the tradeoffs and the
priorities of the different underlying domains. Notable examples of this weighting
procedure include amongst others equal or arbitrary weights, weights based on expert
opinions, but also price-based weights (which rely on the normative assumption that
prices reflect an appropriate basis for the dimensional weights).
The final class distinguished, is a hybrid approach, which combines a data-driven
element with subjective value judgments concerning the importance of the different
domains. Although hybrid weights are hardly ever used as compared with the other
weighting schemes, the most obvious candidates for hybrid weights are stated preference
weights and hedonic weights. Stated preferences weights are directly based on the
expressed opinions of a (representative) group of individuals within society, thus being a
combination of a data-driven element as well as individual valuations. A hedonic
weighting procedure, on the other hand, tries to derive implicit valuations for different
dimensions from data on self-reported happiness and life satisfaction.
All of these weighting approaches have their strengths and weaknesses. The main
drawback of the data-driven methods is that they do not survive ‘Hume’s guillotine’. This
criterion, named after the famous eighteenth-century philosopher David Hume, states that
it is impossible to derive normative statements (about values, about what ‘ought to be’)
from descriptive statements (about facts, about what ‘is’). Conversely, however,
normative weights can be accused of paternalism, as the normative weighting procedure
is based on value judgments which may not be supported by the individuals for which the
composite index is calculated, who may prefer other value judgments and resulting
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weights.5 Recognizing these drawbacks, Decancq & Lugo (2010:4) characterize the
process of setting weights for a multidimensional welfare index as “…a problem of
choosing between Scylla and Charybdis, between Hume’s guillotine and paternalism”.
The hybrid weighting procedures “…explore the narrow and dangerous strait of Messina
between Scylla and Charybdis”. This route can indeed be considered dangerous, as it
risks suffering from both Hume’s guillotine and paternalism. Nonetheless, this hybrid
approach also has the potential of avoiding (or at least attenuating) both problems.
One final remark regarding the weight-setting procedure is that one can also take
a less risky route by selecting wide ranges of acceptable weights instead of only one
weighting scheme. The necessary price to pay for this procedure is that the ranking of
individuals may become incomplete: it is possible that not every pair of cases can be
ranked anymore if one uses wide ranges of acceptable weights. Nonetheless, according to
Foster and Sen (1997), this does not have to be a huge problem, as one can still reach
agreed rankings in many situations.
Probably the most prominent example of a composite welfare index (and the most
serious ‘competitor’ of GDP per capita up until now) is the Human Development Index
(HDI), created in 1990 and thereafter annually published in the Human Development
Reports of the United Nations Development Programme (UNDP). Like most composite
indexes, the indicator has been inspired by the conceptual framework of the capabilities
approach. The index represents a composite measure of three functionings that are
generally thought to be important in almost everyone’s life: income, health (measured by
life expectancy) and education (measured by adult literacy rates and primary education
enrolment rates). A transformation function is applied to each dimension to rescale the
dimensional scores to a 0-1 scale. Subsequently, the HDI is calculated as the simple
arithmetic average of the three component indicators.
Despite the relatively high correlation that is generally found between GDP per
capita and HDI, the composite index evidently provides useful additional information:
countries like Sri Lanka, with relatively good health and educational performance, jump
up the welfare ladder when looking at HDI, whereas in contrast, countries like Brazil
experience a significant drop in their ranking.
However, HDI is still a measure with many deficiencies. Among other things, the
indicator assumes perfect substitutability between the included domains by simply taking
the average of the rescaled domain indicators.6 Obviously, one can easily criticize this
assumption. The same holds for the fact that the index implies an extremely large
difference in the monetary valuation of an extra year of life between rich and poor
countries, as observed by Ravallion (1997). In addition, life expectancy and primary
school enrolment rates, for example, do not provide any evidence on the ‘quality’ of
health or education. Moreover, since HDI is based on a combination of aggregate indices,
it can hide huge variation in welfare within a country, as large inequalities may exist.
5 Critics of normative weighting methods often describe these weighting methods as ‘playing God’,
deciding what is good for others, even if those people will never feel this to be so. 6 This implicit assumption of perfect substitutability is contradictory to the UNDP’s self-expressed vision:
“Progress in human development requires advances across a broad front: losses in human welfare linked to
life expectancy, for example, cannot be compensated for by gains in other areas such as income or
education.” (Human Development Report 2005, p. 22).
16
Furthermore, it is found that over the years movements in the HDI have tended to be
dominated by changes in its income component, in particular for countries whose
performance with respect to health and education is close to the top of the world. One can
question whether such a dominance of the income component is desirable (what is under
these circumstances the added value of HDI as compared with GDP per capita?). Finally,
and perhaps most importantly, the weighting method of the HDI can be severely
criticized for being arbitrary, as it simply assumes equal weights for every welfare
dimension.
2.3 Welfarism and subjective well-being
In the quest for broader welfare measures we can also identify another approach.
Instead of constructing composite indicators based on objective living conditions, this
approach focuses on subjective well-being. It connects to the long-standing tradition
within economics of utilitarian welfarism. The origin of this utilitarian welfarism can
probably best be attributed to the eighteenth-century philosopher Jeremy Bentham, who
was a strong advocate of utilitarianism as central moral principle. Bentham argued that an
act or a policy was right if it led to ‘the greatest good for the greatest number of people’.
This proposition is also known as the ‘greatest happiness principle’. According to
Bentham’s definition, happiness or utility is concerned with the hedonic flow of pleasure
and pain, what we nowadays tend to call experienced utility, following the economic
psychologist Daniel Kahneman (e.g. see Kahneman & Krueger, 2006).
Utilitarianism has dominated the history of economic science. Nowadays, most
economic models are still based on the maximization of a social welfare function, which
can be disaggregated to individual utility functions. An important shortcoming of this
utilitarian welfarist approach has long been that it lacked an empirical foundation. Utility
was mainly a theoretical concept, which could not be measured in reality. However,
according to certain economists recent advances in social science have changed this
situation, evoking claims about a ‘revolution in economics’ (Layard, 2005; Frey, 2008).
Though this claim is certainly not undisputed, many economists argue that we are now
able to measure utility on the basis of survey questions on subjective well-being.7 8 Since
their introduction in the 1970s, survey questions on subjective well-being have received
increasingly more attention and especially during the last decade happiness research has
culminated within economic science, and the influence of happiness research is spreading
within economics. For instance, findings from happiness research have already entered
the literature on economic growth (e.g. see Strulik, 2008; Kawamoto, 2009; Valente,
2009). The ‘measurability’ of happiness has even induced some calls for the introduction
7 The widely used World Values Survey contains for example the following questions: “Taking all things
together, would you say you are very happy, quite happy, not very happy, not at all happy?”, “All things
considered, how satisfied are you with your life as a whole these days: 1-dissatisfied,…,10-satisfied?”.
Next to the World Values Survey, there are many other surveys that ask quite similar questions, for
example the rather new Gallup World Poll, the General Social Survey for the United States, the
Eurobarometer Survey Series for Europe and the Russian Longitudinal Monitoring Survey. 8 An even more recent ‘solution’ for measuring utility can be found in the field of neuroeconomics. Being a
synthesis of economics, psychology and neuroscience, this area of study tries, among other things, to obtain
information on experienced pleasure and pain by analyzing people’s brain activity using electrodes.
17
of Bentham’s ‘greatest happiness principle’ as key directive for policy evaluations.
Amongst others, Diener (1990) and Layard (2005) have proposed the replacement of
GDP per capita by a national happiness indicator. The Kingdom of Bhutan already uses
‘Gross National Happiness’ as its core development indicator and particularly England,
Australia and New Zealand are also developing a system of national well-being accounts.
Nonetheless, we should make clear that initiatives in this direction also encounter
much critique and opposition. One of the strongest opponents is, not surprisingly,
Amartya Sen. Basically, he has two main arguments against welfarism in general and
against the use of the ‘greatest happiness principle’ as a foundation for policymaking in
particular.9
First of all, Sen raises the issue of ‘physical-condition neglect’. The idea behind
this issue is that welfarism and happiness indicators mainly focus on subjective feelings
and mental states, and that insufficient attention is paid to one’s actual, objective living
conditions. In this respect, Sen (2008:21) provides amongst others the following
example: “A person who is ill-fed, undernourished, unsheltered and ill can still be high
up in the scale of happiness or desire-fulfilment if he or she has learned to have ‘realistic’
desires and to take pleasure in small mercies”. A similar example could be provided for a
rich man who experiences an improvement in his objective living conditions, but an even
higher increase in his aspirations level; his happiness score then probably deteriorates
(perhaps even under the level of the ill-fed, undernourished, unsheltered and ill person),
while it is somewhat difficult to consider him really worse-off.
The second issue put forward by Sen is the issue of ‘valuation neglect’, which is
about the nature of valuating activities. In Sen’s perception, ‘being happy’ or ‘desiring’ is
different from ‘valuating’, which is a more reflective activity and which Sen thinks to be
more related to the notion of welfare. In this regard, there may also be a discrepancy
between preferences (what one considers valuable) and subjective well-being
(satisfaction or happiness derived from what one has). Quoting Fleurbaey (2008:25), who
paraphrases the nineteenth-century philosopher John Stuart Mill: “It is better to be a rich
dissatisfied than a poor satisfied, if the rich and the poor both prefer the former’s life.” In
other words, satisfaction welfarism does not distinguish between ‘obtaining what one
wants’ (what welfare is about) and ‘being satisfied’ according to this second criticism.
At this point in the discussion, it is relevant to make a distinction between three
aspects that matter for subjective well-being, following Diener (1984): life satisfaction,
positive affect and negative affect. Life satisfaction is a cognitive concept, representing
individuals’ overall evaluation of their lives at a particular point in time. On the other
hand, positive and negative affects represent respectively positive and negative emotions,
which flow constantly through people’s minds. Though it is quite difficult to isolate these
different aspects in survey questions, questions on ‘life satisfaction’ tend to capture
mostly the cognitive part of subjective well-being, whereas questions regarding
‘happiness’ are more sensitive to affects.
Albeit all three aspects have their influence on subjective well-being, the mutual
correlation between affects and life satisfaction is rather low, as shown by Krueger &
Schkade (2008).There is in this regard some debate on what aspect of subjective well-
9 For an extensive discussion of these arguments, see for example Sen (2008).
18
being one should preferably measure. On the one hand, it may be argued that the
cognitive evaluation behind life satisfaction data provides more information on a person’s
(perceived) welfare and standard of living. On the other hand, Kahneman & Krueger
(2006) contend that information regarding experienced affects may provide a more
accurate reflection of a person’s well-being, less prone to reporting biases as compared
with life satisfaction questions. Therefore, these authors propose the ‘U-index’ for
measuring subjective well-being: a misery index based on the time spent in a negative
mood.
To get to grips with what is behind survey answers to subjective well-being
questions, we think it is illuminating to refer to Fleurbaey et al. (2009). In these authors’
conceptual framework, an individual’s expressed life satisfaction depends on four factors:
the person’s achieved functionings, her valuation ordering of all functionings in general
(her preferences regarding the various functionings), her framework of reference and a
disturbance factor.
The inclusion of the first two factors is rather obvious. The achieved functionings
factor describes how the respondent’s life looks like. The valuation ordering factor, in
turn, tells how the ‘good life’ looks like according to the respondent. It is probably wise,
however, to discuss the third factor a bit more detailed, since one of the most consistent
findings of the literature on subjective well-being is that respondents’ answers are highly
dependent on the respondent’s reference framework. As determinants of this framework,
two issues particularly stand out.
First, one uses his personal history as reference level. According to one theory,
every individual has a genetically-established personality, with a given ‘set point’ for
subjective well-being. Under this scenario, changes in the respondent’s achieved
functionings lead only to a temporal gain in subjective well-being. After a while, the
respondent gets used to his higher level of achieved functionings and his subjective well-
being level drops back to its natural ‘set point’. More likely, however, is a scenario of
partial adaptation, under which the respondent’s subjective well-being does not
necessarily fall back completely to its initial level. Anyway, it is obvious that this
‘hedonic treadmill’, as it is often called (e.g. see Kahneman, 2008), makes subjective
well-being somewhat immune to the person’s objective living conditions (his
functionings). In a similar vein, there may also be an ‘aspirational treadmill’ (again, see
Kahneman, 2008), representing the idea that once people have attained a certain level of
objective living conditions, they instantaneously and ‘automatically’ increase their
aspiration level.
The second important determinant of one’s reference framework originates from
relative interpersonal comparisons and peer effects. The underlying reasoning is that
every individual has a natural tendency to compare his situation with the situation of
people around him (living in the same street, having the same job, being member of the
same sports club, et cetera) and to strive for status. From this perspective, then,
improvements in one’s achieved functionings especially lead to an increase in the
person’s subjective well-being if his position compared to his reference group
ameliorates. In contrast, if the achieved functionings of the members of his reference
group improve to the same extent, the person’s subjective well-being does not increase or
only slightly. In spite of the fact that the debate on the extent to which relative
19
comparisons matter is still far from closed10
, there is nonetheless a certain consensus that
‘keeping up with the Joneses’ makes people somewhat immune to their objective living
conditions.
The fourth and remaining factor influencing people’s expressed subjective well-
being distinguished by Fleurbaey et al. (2009) is a disturbance factor. This factor can
possibly represent measurement or reporting biases, resulting from the fact that people
are not given enough time to reflect, from the fact that their answers are influenced by
their mood of the day or the moment11
, from framing effects related to the ordering of the
questions asked, from misinterpretation of the questions, from a feeling of duty to give a
certain answer, et cetera. Moreover, the disturbance factor can also capture some
remaining personality-12
, culture- or country-fixed effects, e.g. if every respondent in
Spain tends to give a more ‘rosy’ answer to every question as compared with respondents
in Japan. Such fixed effects stem from the fact that heterogeneous standards are used in
answering the survey questions: different individuals and cultures may use the answering
scales differently. The resulting fixed effects are expected to play a significant role in
answers to subjective well-being questions, seriously complicating cross-country and
cross-cultural comparisons of survey answers.
Over the past few decades, happiness research has been focused on investigating
the determinants of subjective well-being. By now, there is a vast empirical literature on
this topic. Although this literature in general faces difficulties concerning the distinction
between correlation and causation, some robust and intuitively appealing results stand
out. First and foremost, there is almost unanimity about the high human costs related to
unemployment (see amongst others Frey, 2008 and Stiglitz et al., 2009). Even after
controlling for income, unemployment still has a large and significant negative impact on
subjective well-being. Similarly, there tends to be a positive and robust relationship
between perceived health and reported well-being. In addition, research has also revealed
some clear-cut demographical patterns: people who are married or live together as a
couple tend to report significantly higher subjective well-being levels, the relationship
between age and subjective well-being is generally found to display a U-shaped pattern
and having kids tends to increase reported well-being.
Yet, on one of the most interesting potential determinants of well-being (at least
from an economic point of view) the jury is still out. Even though the relationship
10
Compare for example Layard (2005) with Stevenson & Wolfers (2008): whereas Layard states that it is
all about relative incomes and that absolute incomes hardly matter for subjective well-being, Stevenson and
Wolfers contend that subjective well-being is mainly affected by absolute incomes and that relative
incomes play a significantly less prominent role than argued by Layard. 11
Famous in this context is the experiment conducted by Schwarz (1987), showing the power of
experimental context. In Schwarz’s experiment, subjects were invited to the lab to fill in a questionnaire on
life satisfaction. However, when the subjects arrived at the lab, they were first asked to make a photocopy
for the experimenter. For a randomly chosen half of the subjects a dime (a coin of hardly any value) was
placed on the copy machine. When the subjects came back to the lab room and filled in the questionnaire, it
turned out that the subjects who had found a dime on the copy machine reported significantly higher life
satisfaction, a result which of course cannot be explained by the monetary value of the coin they found. 12
Regarding the importance of personality-effects, Kahneman & Krueger (2006:8) state: “In any event,
measures of temperament and personality typically account for much more of the variance of reported life
satisfaction than do life circumstances…(…)... Apparently, a person’s subjective evaluation of his or her
own well-being is to a significant extent a personality trait.”
20
between subjective well-being and income has been studied since the moment subjective
well-being data have become available about forty years ago, the debate about this
relationship is certainly not over yet.
The main catalyst of this debate has been the seminal contribution of Easterlin
(1974). In his paper, Easterlin studies three kinds of income-happiness relationships:
within countries, among countries and across time. Whereas he finds evidence for a
positive relationship within a country at one point time, he finds insufficient evidence for
such a relationship among countries or over time. In his conclusion, Easterlin (1974:119)
concisely sets the stage for the debate that has continued since his publication: “In a
sense, these results are a testimony to the adaptability of mankind. Income and
aspirations in time and space tend to go together, and people seemingly can make
something out of what appears, in some absolute sense, to be a sorry lot. At the same
time, the conclusions raise serious questions about the goals and prospective efficacy of
much social policy.”13
Over time, there have been several updates of Easterlin’s findings, based on
newly available data. The current state of the art is, for example, well-summarized in
Stevenson & Wolfers (2008) and Clark et al. (2008). Within countries, the positive
income-well-being relationship is still standing (since people’s reference groups are still
largely restricted by national borders, income-related status effects are most clearly
observable within countries) and it has now also become clear that one can observe a
positive relationship among countries, depending on the countries included in one’s
analysis: if both developing as well as developed countries are taken into consideration,
there is a significant positive relationship, whereas within more homogeneous groups of
countries there is only a weak or even no relationship observable.
Anyway, probably the most interesting relationship remains the one over time.
Especially regarding this relationship the jury is still out. Many authors take sides with
the so-called ‘Easterlin Paradox’, which points out that average happiness has remained
constant over time despite sharp rises in income per capita. Most of the time this paradox
is explained by referring to the importance of adaptation, aspirations and relative
comparisons. For example, Clark et al. (2008) show convincingly, both graphically and
technically, how the Easterlin Paradox is consistent with the positive income-happiness
relationships within countries and across countries at a point in time, if one takes into
account status effects and internal backward- and forward-looking reference points. On
the other hand, however, Stevenson & Wolfers (2008), for instance, claim to have found
evidence which rejects the Easterlin Paradox, showing a positive relationship over time.
Nevertheless, this rejection of the Easterlin Paradox is in itself also far from undisputed.
In a comment on Stevenson & Wolfers (2008), Krueger (2008) for example provides
arguments why he is not yet willing to label the Easterlin Paradox as a ‘nonparadox’.
13
One should however not think that Easterlin (1974) was proposing a radical shift in social policy like
Layard (2005) and others did later on: “The present results do not necessarily imply that a redirection of
attention is needed from economic growth to income redistribution as a vehicle for improving welfare. The
data themselves give no indication that international differences in happiness are systematically related to
inequality. And the theoretical relationship is uncertain – if relative positions were unchanged and income
differences halved, would happiness be greater?” (Easterlin, 1974:119).
21
2.4 Hypothesis
We hope this chapter has provided the reader with some insights into the massive
amount of proposals for the measurement of welfare. In order to provide an overview as
orderly as possible, we have distinguished three general approaches to the measurement
of welfare: GDP measures based on market transactions, alternative composite indexes
based on several domain indicators and, finally, subjective well-being indexes with a
utilitarian welfarist theoretical foundation. Having discussed the pros and cons of the
different approaches, we presume it is clear that neither of the measures represents the
‘holy grail’ some people are looking for. As stated earlier in this thesis, however, it is
highly questionable whether such a ‘holy grail’ measure is ever attainable, or whether we
should even aim for it. We recognize that in the end all proposals for welfare indicators
rely on certain normative judgments and assumptions. Since people can always adhere to
other normative principles, on various grounds, an unavoidable implication is that any
proposal can be validly opposed. Moreover, the appropriateness of a certain measure is
also subject to its intended application.
Nevertheless, we do not want to abstain from presenting our own position in this
respect, and an understanding of this position is also necessary with regard to the
upcoming chapters.
To begin with, we have already taken position in the first section of this chapter,
where we discussed our preferred notion of welfare, focusing on the satisfaction of needs
and acknowledging that this satisfaction of needs has a subjective character. As a result,
we do not only have trouble with GDP per capita as welfare indicator, but also with most
composite indicators. These measures hardly take into account the subjective element of
our preferred notion of welfare. Moreover, quite many composite indicators fall prey to
Hume’s guillotine or can be accused of arbitrariness in the dimensions included, but
particularly also in the adopted dimensional weights. Although an indicator like the
Human Development Index may be very convenient for communicational purposes, it is
beyond any doubt that it is a poor measure of welfare, representing only a rather minor
improvement as compared with GDP per capita.
Considering these remarks, one might perhaps suppose we feel more comfortable
with a welfare measure with subjective well-being as its foundation. Unmistakably, such
a measure pays more attention to subjective experiences, preferences and perceptions. In
addition, one can easily construct a subjective well-being index which can conveniently
be communicated to and understood by the public. Nevertheless, we do not think an
exclusive focus on subjective well-being, in line with Layard (2005) and others, is the
right way to go. First of all, if we want to construct a measure from which we can also
derive policy recommendations, subjective well-being indicators are not really
satisfactory, as they are to a large extent influenced by psychological and emotional
aspects outside the conventional policy domain. Acknowledging the importance of these
psychological and emotional factors, perhaps the best policy prescription would then be
to develop a happiness drug, as Layard (2005) proposes. We feel, however, that such a
policy prescription is neither very realistic nor very interesting from an economic point of
view. Anyway, more importantly, it has robustly been shown that subjective well-being
indexes are hugely affected by adaptation and social comparisons. The role played by
22
these processes is even that large that we generally do not observe any changes in
average happiness over time (cf. Easterlin Paradox). In contrast, however, we think it is
hard to sustain that welfare does not change over time: it is for sure that over time an
increasing amount of our needs has been satisfied. Besides, constant average happiness
over time would imply that it hardly makes any sense to invest in things like education,
health, the environment, government services, et cetera.
So, whereas GDP and most composite indicators pay insufficient attention to the
subjective aspects of welfare, subjective well-being indicators, being heavily affected by
adaptation, rising aspirations and social comparisons, too little reflect objective living
conditions. Thus, all of these indicators are in our opinion imperfect estimates of welfare.
In conclusion, we do not really want to take sides in this debate. Instead, we
would rather propose a synthesis of composite indicators and subjective preferences,
exploring the opportunities to combine the best of both worlds. We advocate a focus on
objective living conditions in relation to preferences.
Whereas subjective well-being data in itself provide a doubtful reflection of
welfare (in particular over time), they can probably, in addition to other stated preference
data, provide useful information about people’s relative valuations for various welfare
dimensions. At each point in time, one can derive a preference ordering of welfare
dimensions for every country on the basis of subjective well-being data and stated
preferences, which is relatively invariant to adaptation and social comparison processes.
The thus obtained information on country-specific preferences can then be used for the
construction of weights for the various objective living conditions included in composite
welfare indicators. Such an approach has the potential of both getting around the
adaptation and social comparison biases of subjective well-being indicators as well as
avoiding Hume’s guillotine and the critique of being paternalistic in setting the weights
for a composite welfare index. By using hybrid weights for the calculation of a composite
indicator, we can avoid a direct confrontation with ‘Scylla’ or ‘Charybdis’. Similarly, by
concentrating on subjective well-being and preferences at one point in time, we can
largely abstract from the third factor (reference framework) in Fleurbaey et al.’s (2009)
conceptual framework, allowing us to focus only on functionings and preferences.
Hence, a synthesis of objective living conditions and subjective preferences can
lead us to empirical welfare measures which are more closely related to our preferred
notion of welfare. It is our hypothesis that such an empirical synthesis can yield valuable
insights with regard to international welfare comparisons. Although we certainly do not
want to assert that such a synthesis will be free of any flaws, it definitely deserves some
exploration.
2.5 Equivalent income method
Finally, before closing this chapter, it is relevant to spend a few words on the
general welfare measurement method that will be employed in the next chapters. This
method is based on the concept of equivalent income. Equivalent incomes can help
ranking different combinations of living conditions, or in Sen’s words functionings. For
making a ranking between situations it is of course not enough to only have information
23
on every separate living condition (recall our criticism towards Cnossen, 2009). One
needs some translation mechanism through which one can compare different
combinations of living conditions. Equivalent income is one example of such a
mechanism, just as the composite indicators we discussed earlier in this chapter all have
their own translation mechanisms.
One of the central features of the equivalent income translation mechanism is that
for every functioning (except income) a certain reference value is determined. Then,
equivalent income is the amount of income that makes an individual indifferent between
her actual bundle of functionings (including her actual income) and a bundle which
contains this equivalent income and all the other functionings at their reference value; see
also Figure 1 on the next page. Thus, equivalent income captures information on how
one’s actual functionings bundle relates to the reference functionings bundle. If one’s
actual functionings bundle exactly matches the reference bundle, then equivalent income
will equal the person’s actual income. However, if one’s state of health is, for instance,
worse than the reference value for health, the equivalent income which makes the
individual indifferent between her actual functionings bundle and the reference bundle
will be lower than her actual income. In this way, actual income is corrected for
deviations of one’s actual functionings bundle from the reference bundle to attain
equivalent income, which thus practically provides information about one’s total
functionings bundle and allows for interpersonal comparisons of functionings bundles.
It is important to notice that the resulting income corrections do not only depend
on one’s actual functionings and the corresponding reference values. Corrections depend
namely on both the deviation between these two as well as the individual’s preferences
for these functionings, which determine the shape of the individual’s indifference curve.
These preferences imply a certain ‘willingness-to-pay’ for the different functionings and
the difference between actual and equivalent income can be interpreted as an individual’s
total willingness-to-pay for moving from his actual functionings bundle to the reference
bundle. In the upcoming chapters, we will try to include subjective preferences in our
willingness-to-pay estimates.
An important advantage of the equivalent income method is that it enables us to
combine information on objective living conditions and subjective preferences in a
theoretically sound manner. In addition, equivalent income measures can be easily
communicated, as a result of its intuitively easily understandable measurement units.
Moreover, the method largely releases us of the heavy responsibility of making
assumptions on the substitutability between functionings. Probably the most significant
disadvantage of the method is that we have to determine reference values for the
functionings involved. This determination unavoidably requires certain normative
assumptions. We think, however, that this is not too large of a problem, as we can apply
different methods for setting these values and because we can easily experiment with
different scenarios. Finally, a non-negligible reason for choosing the equivalent income
method is that by doing so, we can join with and elaborate on some promising recent
contributions to the literature on the measurement of welfare.
24
Figure 1 Illustration of the equivalent income concept
The figure above provides a simple illustration of the equivalent income concept, considering an individual whose utility is merely dependent on her income and her health status; U represents her indifference curve. Her actual functionings bundle equals (y0 , h0). If the reference value for health equals h*, the individual’s equivalent income is equal to y*: she derives the same utility from having the equivalent income together with health at its reference value as she derives from her actual functionings bundle. From this picture one can easily infer that the individual’s equivalent income is not only dependent on the difference between the individual’s actual functionings and the corresponding reference values, but also on the shape (slope) of the indifference curve, which reflects the person’s preferences for the different functionings.
25
III Equivalent incomes and country-specific preferences for
inequality and leisure
Having discussed the most common approaches towards measuring welfare in the
previous chapter, we now turn to investigating the opportunities and potential impact of
combining objective living conditions and subjective preferences for obtaining a better
understanding of welfare and differences in welfare among countries. More specifically,
we aim to explore the possibilities and consequences of including subjective preferences
in equivalent income measures based on objective living conditions. The basics of
equivalent income measures have already been explained in the previous chapter. The
present chapter and the next one each consider somewhat different methods for
calculating equivalent incomes, based respectively on a formal economic model and on
willingness-to-pay (WTP) measures derived from life satisfaction regressions.
3.1 Equivalent incomes in Fleurbaey & Gaulier (2009)
At the heart of this chapter is the paper ‘International Comparisons of Living
Standards by Equivalent Incomes’ by Marc Fleurbaey and Guillaume Gaulier (2009). In
their paper these authors present a formal economic model from which they derive
corrections of standard GDP for inter alia inequality, leisure, risk of unemployment,
health and household size. They calculate these income corrections for a sample of 24
OECD countries for the year 2004. Though not exclusively, the corrections of Fleurbaey
& Gaulier (2009) are mainly price-based. In this respect, the paper relates to Becker et al.
(2005), who compute a ‘full’ income measure encompassing income and life expectancy.
Decancq & Lugo (2010) qualify these two works as notable exceptions in the literature
on multidimensional well-being, in which price-based equivalent income measures are
not very common. Decancq & Lugo (2010) quote in this context Foster & Sen (1997),
who argue that even if implicit prices can be obtained, they are in general inappropriate
for well-being comparisons, a task for which they are not constructed according to these
authors. Indeed, although price-based weights can be calculated relatively easily, their
use is obviously debatable. Nonetheless, Fleurbaey (2009) concludes that the equivalent
income method might deserve more attention than it has received so far, since it takes an
interesting middle-ground position in the debate between the capabilities approach and
welfarism: while being based on objective living conditions, it leaves some room for
subjective preferences. Moreover, as Fleurbaey (2009) shows, the method respects the
Pareto principle14
, one of the core elements of welfarism.
Fleurbaey & Gaulier (2009) motivate their choice for their price-based equivalent
income approach by referring to other measures of social welfare, like the Human
Development Index (HDI) discussed in chapter 2. Fleurbaey and Gaulier note that many
14
This principle states that if every individual prefers a certain option to another, then so must the resulting
societal preference order (if everybody agrees on the ranking of all possible options, so should the group;
the collective ranking should coincide with the common individual ranking). See for instance Hindriks &
Myles (2006).
26
welfare indicators are based on the aggregation of various subindexes, and that the
weights used for this aggregation “have no rational basis and appear arbitrary” (Fleurbaey
& Gaulier, 2009:597). They claim that by using a price-based equivalent income method
most of their own calculations are, in contrast, not based on arbitrary weights reflecting
ethical assumptions; instead, their calculations are founded upon willingness-to-pay
figures that can be discussed on an empirical basis.
Albeit we tend to agree with Fleurbaey and Gaulier that their method comprises a
more sophisticated weighting scheme than, for example, the Human Development Index,
a critical remark is justified in this context: whereas Fleurbaey and Gaulier claim that
their corrections do not reflect any ethical assumptions, we argue that their choice for
using price-based weights for valuating the various dimensions of welfare is actually also
an ethical assumption. We do not think there is any truly objective reason for the use of
price-based weights. The reasoning that prices reflect people’s preferences and valuations
is already an ethical assumption in itself, as noted in chapter two.
As Fleurbaey & Gaulier (2009) note, however, prices can indeed sometimes
provide reasonable estimates of individuals’ valuations of various welfare dimensions.
When perfect-functioning markets exist on which these welfare dimensions are traded,
and where individuals can freely choose how much they want to ‘consume’ of the welfare
dimension concerned, prices can provide a reliable monetary estimate of the individuals’
marginal willingness-to-pay for this welfare dimension.
We should emphasize the word ‘marginal’ in this regard, as prices are the
outcome of bargaining processes on the market and merely reflect the individual’s
valuation for the last unit he bought (which may not be a good reflection of the
individual’s valuation for the other units he bought). Thus, when calculating equivalent
incomes, prices can only be used as a reliable basis for income corrections if the
differences between the individual’s actual functionings bundle and the reference bundle
are marginal. Otherwise, prices might not provide a proper reflection of how the
individual evaluates changes in his functionings bundle.
In addition, and at least as importantly, we should stress that prices only provide a
good estimate of subjective preferences if perfect-functioning markets exist. For welfare
dimensions for which only an imperfectly competitive market exists or for which no
market exists at all, prices do generally not reflect people’s preferences for these welfare
dimensions. If there is market power on the supply or demand side of the labour market,
for example, wages provide an imperfect estimate of people’s valuations for leisure time.
Similarly, prices have also little to tell about people’s valuations for air quality, since no
market for air quality (or air pollution) exists.
Keeping these issues, to which we will return in a few moments, in mind, we
think it is good to now first have a general look at Fleurbaey & Gaulier’s (2009)
calculations and their results.15
15
For more detailed information on the data and formulas used, please consult Fleurbaey & Gaulier (2009)
and / or Fleurbaey & Gaulier (2007), which is the working paper version of the 2009 article. As compared
with Fleurbaey & Gaulier (2009), Fleurbaey & Gaulier (2007) also add corrections for consumption of
fixed capital and environmental degradation. In general, most data can be obtained from OECD sources,
online retrievable via OECD.Stat (stats.oecd.org).
27
First, Fleurbaey & Gaulier (2009) make some obvious corrections to GDP, which
are conventionally also included in a country’s national accounts: apart from correcting
for population size (by looking at GDP per capita) and price levels (by using purchasing
power parities, PPPs, to translate all figures into US dollars), they correct for income
transfers paid to and received from other countries (by looking at gross national income
instead of gross domestic product) as well as for decreases in the country’s capital stock
(by subtracting the consumption / depreciation of capital from the gross domestic
product, arriving at the net domestic product).16
The latter correction is applied in order to
take into account the future prospects of the country. The authors follow in this respect
Weitzman (1976), who shows that in a competitive economy with a fixed interest rate the
discounted value of total consumption over the infinite future equals the discounted value
of constant consumption, which equals the current net domestic product (NDP).
Moreover, a correction is made for leisure, acknowledging the fact that people do
not only care about income, but also about their amount of leisure time. The reference
value of leisure time is set at the median amount of leisure time across the 24 OECD
countries in the sample and deviations are valued at each country’s average hourly wage.
In conformance with the observation that people do not like to be unemployed
(and do not like the risk of becoming unemployed), Fleurbaey and Gaulier also correct
for the risk of unemployment. In the calculation of this correction the authors include
amongst others the probability of falling into unemployment, the probability of exiting
unemployment and the income losses related to unemployment. Taking a zero risk of
unemployment as their starting-point, the authors calculate a ‘risk premium’ for the risk
of becoming unemployed, which is then subtracted from the country’s average income.
In order to correct for health, Fleurbaey and Gaulier use data on the health-
adjusted life expectancy per country. Health-adjusted life expectancy (HALE) equals the
expected number of ‘healthy years’ at a child’s birth. The authors use these HALE-data
instead of ordinary life expectancy data, since they assume that life only has value when
one is in good health. Taking the maximum of HALE observed in their sample of
countries as a reference, the authors then calculate income corrections for health on the
basis of the model of Becker et al. (2005).
Furthermore, recognizing that economies of scale occur in households comprising
more than one person (costs for certain goods like common rooms and heating can be
shared among the household members), Fleurbaey and Gaulier also correct for household
composition by computing the income that would yield the same utility if everyone was
member of a single-person household.
Fleurbaey and Gaulier’s one but last correction is for inequality. This correction
follows from the social welfare function employed by the authors, which incorporates a
certain preference for equality by giving priority to the worst-off, as the authors want to
avoid counting one dollar for the poor as equivalent to one dollar for the rich. The
inequality corrections are calculated based on the Atkinson Inequality Index, with an
assumed inequality aversion parameter of 1.5.
16
The correction for the consumption of capital is only made in Fleurbaey & Gaulier (2007), the working
paper version of the 2009 article.
28
Finally, the authors correct for environmental sustainability, which affects the
country’s future welfare prospects.17
Depletion of non-renewable resources like oil is
accounted for by subtracting current resource extraction valued at its average net price
from each country’s average income. In this respect, the authors have decided to attribute
to each country a share of the global welfare loss due to resource depletion that is
proportional to each country’s share in the global consumption of the resource. In
addition, air pollution and the cost of global warming are taken into account by
subtracting from each country’s average income its emissions of CO2, methane and
nitrous oxide, valued at a price of 25 dollars per ton of CO2 equivalents (which is,
according to the authors, not an unrealistic estimate of the shadow price of the emission
of greenhouse gases).
We have requested Fleurbaey & Gaulier’s (2009) calculations and we have
checked these with our own calculations, finding no discrepancies. The results of
Fleurbaey & Gaulier (2009), extended by the additional corrections of Fleurbaey &
Gaulier (2007), are summarized in Figure 2 and Table 1 on the next pages.18
Figure 2
plots the relative position of each country after every correction, where 100% refers to
the sample average. Table 1 shows for each country the absolute size of every correction
and provides percentages on how the final indicator relates to GDP per capita. Moreover,
country rankings are presented for both GDP per capita as well as the final indicator, and
we have added Atkinson Inequality Indexes for the cross-country distribution of
equivalent incomes after each correction.
From Figure 2 and Table 1 one can easily infer that the final indicator of
Fleurbaey & Gaulier (2009) is significantly correlated with GDP per capita. This is
however not a surprising observation, as GDP per capita has been the starting-point of the
calculations. Nevertheless, Fleurbaey & Gaulier (2009) note that none of the corrections
is significantly correlated to GDP per capita; this even holds for the health correction.
Although the final indicator and GDP per capita are clearly related, one can also
observe sharp distinctions, especially if we consider that corrections of only a few
percentage points can already translate into country rankings that are significantly
different from conventional rankings based on GDP per capita. From this perspective, the
15 percentage points decrease of the United States’ welfare measure, resulting among
other things from its high level of inequality and low amount of leisure time, is for
instance far from negligible. On the other hand, a country like France experiences an
increase in its welfare measure of almost the same magnitude, amongst others because of
its large positive correction for leisure. Similarly, Japan gains from its high level of
health-adjusted life expectancy.
In terms of cross-country inequality, it does not matter too much whether one
looks at the distribution of GDP per capita or the distribution of the final indicator.
Although the Atkinson Inequality Index experiences some significant changes during the
17
Just like the correction for the consumption of capital, the correction for environmental sustainability is
only applied in Fleurbaey & Gaulier (2007). 18
Being an outlier, Luxembourg is excluded from Figure 2. The sample average to which 100% refers, is
calculated for all 24 countries except Luxembourg. In Table 1 the Atkinson indices are also computed on
the basis of all 24 countries except Luxembourg. Atkinson indices are calculated for three different values
of the inequality aversion parameter: 0.5, 1.5 and 2.5.
29
Figure 2 Relative positions in terms of equivalent income after each correction, for the year 2004 (Source: Fleurbaey & Gaulier, 2007 and own calculations)
0%
20%
40%
60%
80%
100%
120%
140%
160%
Austra
liaAus
tria
Belgi
umC
anad
aD
enm
ark
Finla
ndFra
nce
Ger
man
yG
reec
eIc
elan
dIre
land
Italy
Japa
n
Korea
Net
herla
nds
New
Zea
land
Nor
way
Portu
gal
Spain
Swed
enSw
itzer
land
Uni
ted
Kingd
omU
nite
d Sta
tes
GDP per capita GNI per capita Leisure Unemployment Health Household size Inequality Capital consumption Sustainability
30
Table 1 Absolute corrections, comparison GDP per capita and final indicator and inequality of equivalent income distribution, for the year 2004 (Source: Fleurbaey & Gaulier, 2007 and own calculations)
GDP per
capita GNI per capita Leisure Unemployment Health
Household size Inequality
Capital consumption
Environmental sustainability
Final indicator
Final indicator as % of GDP
per capita
Country rank GDP per
capita Country rank final indicator
Australia 30116 -3226 -1238 -366 -671 16319 -7666 -4341 -1898 27029 90% 13 19
Austria 32176 -2286 -195 -290 -1150 18499 -6241 -4331 -1155 35026 109% 7 5
Belgium 31009 -1809 1225 -788 -1217 11827 -5871 -4199 -1551 28627 92% 11 15
Canada 31129 -2549 -2118 -388 -905 16917 -7248 -3716 -2377 28745 92% 10 14
Denmark 31974 -2334 360 -322 -1673 13655 -5885 -4516 -1215 30044 94% 9 12
Finland 29816 -2806 -1695 -455 -1113 12416 -4497 -4386 -1496 25784 86% 14 20
France 29077 -1397 2386 -602 -872 15498 -6667 -3512 -929 32983 113% 17 8
Germany 28147 -1607 476 -532 -889 12104 -4987 -3958 -1238 27516 98% 19 18
Greece 21954 -2354 84 -696 -790 12560 -5321 -1715 -933 22788 104% 22 21
Iceland 33090 -4440 1359 -179 -660 14115 -5689 -3844 -1186 32567 98% 6 9
Ireland 40058 -10058 1099 -307 -1696 25347 -8163 -3774 -1460 41047 102% 2 2
Italy 28162 -1802 1048 -641 -628 16559 -7310 -3613 -1086 30689 109% 18 11
Japan 29539 -2039 -1740 -260 0 19155 -3796 -4361 -1185 35313 120% 15 4
Korea 20371 -1991 -2275 -133 -1366 14747 -3971 -2273 -1210 21899 108% 23 22
Luxembourg 68719 -12729 -4295 -258 -2262 30775 -11512 -7628 -3248 57562 84% 1 1
Netherlands 32056 -1966 1617 -280 -1226 14635 -6944 -4565 -1681 31646 99% 8 10
New Zealand 22912 -2612 -673 -179 -866 12633 -6011 -2198 -1402 21603 94% 21 23
Norway 38288 -2048 2059 -291 -1185 16639 -6181 -5770 -1209 40301 105% 4 3
Portugal 19687 -1007 -681 -258 -1105 12428 -6107 -2850 -791 19316 98% 24 24
Spain 25341 -2291 -497 -572 -562 16458 -5491 -2968 -991 28427 112% 20 16
Sweden 29499 -2089 -200 -313 -482 11361 -4837 -3817 -805 28318 96% 16 17
Switzerland 33541 479 -2094 -263 -654 16949 -8437 -5075 -818 33629 100% 5 7
United Kingdom 30843 -2043 109 -322 -1359 15654 -8888 -3274 -1254 29466 96% 12 13
United States 39618 -3488 -2515 -432 -2306 21858 -12318 -4277 -2412 33728 85% 3 6
Average 31547 -2937 -350 -380 -1068 16213 -6668 -3957 -1397 31002 99%
Atkinson (0.5) 0.0081 0.0079 0.0097 0.0099 0.0104 0.0074 0.008 0.0079 0.0083
Atkinson (1.5) 0.0249 0.0245 0.0305 0.0311 0.0331 0.0225 0.0246 0.0241 0.0254
Atkinson (2.5) 0.0425 0.0422 0.0532 0.0542 0.0583 0.038 0.042 0.0408 0.0429
31
correction process, the Atkinson Index after all corrections is almost similar to the
Atkinson Index based on the distribution of GDP per capita.19
As Fleurbaey & Gaulier (2009) already note themselves in their text, it is not very
difficult to criticize their findings. For example, they make some ad hoc assumptions in
their calculations that can be debated: they assume that for every unemployed person,
there is one discouraged worker; they multiply the theoretical probability of falling into
unemployment by two, to account for the fact that people tend to perceive this probability
to be higher than predicted by theory; they assume that there is a social stigma on
unemployment, causing the perceived replacement rate to be 20 percentage points lower
than the actually observed replacement rate; et cetera. Anyway, it should be mentioned
that the authors’ results are quite robust to other assumptions concerning these particular
issues. A more influential assumption is the assumed inequality aversion parameter value
of 1.5. Other studies have for example estimated and applied parameter values which are
well below 1 or equal to 2 or even higher (see for instance the discussion in Lambert et
al., 2003). Such parameter values would lead to large and significant changes in
Fleurbaey & Gaulier’s (2009) inequality corrections.
Another potential criticism concerns the dimensions included in Fleurbaey &
Gaulier’s (2009) analysis. Why, for example, not taking into account other welfare
dimensions like quality of governance, education and social relationships? Adding social
relationships would especially be very interesting, as this is generally found to be one of
the most important determinants of well-being. However, Fleurbaey and Gaulier do
explicitly not dispute this criticism. Instead they argue, and we agree with them, that it is
an urgent task to seek ways to include social relationships in the computation of
equivalent incomes. As long as we do not have appropriate ways for doing this at our
disposal, it is better to leave such dimensions out of the analysis.
Above all, however, we have a more fundamental criticism regarding Fleurbaey
& Gaulier (2009), related to the subjective aspect of our preferred notion of welfare as
pointed out in the previous chapter: we doubt whether the authors sufficiently take into
account subjective preferences for the various welfare dimensions. As has been stated
earlier on, Fleurbaey and Gaulier mainly use price-based willingness-to-pay estimates to
incorporate differences in preferences among countries. Yet we have already discussed
that prices only properly reflect preferences if there are perfectly competitive markets and
if we consider marginal changes on the welfare dimensions. Observing that these two
conditions do not hold for many of the authors’ corrections, we think that the corrections
presented in Fleurbaey & Gaulier (2009) insufficiently account for differences in
subjective preferences, whilst these subjective preferences are at the core of our preferred
notion of welfare.20
19
The Atkinson Inequality Index should be interpreted as the proportion of total global income that can be
destroyed, while keeping the same level of social welfare by equally distributing the remainder of the
global income. 20
To avoid confusion, we would like to emphasize that despite the fact that Fleurbaey & Gaulier (2009) do
not merely consider marginal changes in the functionings bundle, they neither do merely consider simple
linear income corrections: e.g. by way of the choice of the value of the inequality aversion and risk
aversion parameters in respectively the inequality and unemployment corrections, the authors do include
certain non-linearities in their corrections.
32
We presume that significant differences in preferences among countries may exist
that are only partially taken into account in Fleurbaey & Gaulier’s (2009) income
corrections. Consider, for example, the correction for the risk of unemployment, which to
a large extent depends on the perceived costs of unemployment. Fleurbaey and Gaulier
rightfully observe that these perceived costs are likely to be higher than merely the
difference between one’s previous income and the replacement rate income, as a
consequence of a social stigma related to unemployment. Nonetheless, the authors
continue by assuming a uniform social stigma effect across all countries (represented by a
perceived replacement rate that is 20 percentage points lower than the observed
replacement rate). In contrast, we think that the perceived social stigma may vary widely
across the considered sample of countries (e.g. caused by cultural differences), translating
into preferences towards the risk of unemployment that are poorly captured by the
uniformly corrected replacement rate data used by Fleurbaey and Gaulier. Similarly, the
authors base their corrections for household size on the economies of scale in
consumption that occur in larger households. However, next to these price-based
advantages of larger households, people’s derived welfare from household size also
strongly depends on additional, more social preferences for household size, which may
vary significantly across countries: in Southern European countries people attach for
instance much more value to large families than in Nordic European countries. For true
welfare corrections such preferences should also be included in the analysis. Other places
in Fleurbaey & Gaulier’s (2009) model where country-specific preferences may play an
important role include the parameters of inequality aversion and risk aversion. By their
very nature, inequality and risk aversion are largely culture-specific and, therefore, very
likely to differ among countries. Nevertheless, Fleurbaey and Gaulier simply assume
uniform values across all countries for these parameters.
In conclusion, we think it is meaningful to investigate whether we can include
more direct information on subjective preferences in Fleurbaey & Gaulier’s (2009)
calculations. Preferences play a central role in the formation of people’s needs and matter
significantly for people’s perceived satisfaction of needs. Therefore, preferences deserve
utmost attention in the construction of welfare measures. We hypothesize that more
directly taking into account country-specific preferences can seriously affect the results
presented in the paper of Fleurbaey & Gaulier (2009).
Below, we investigate the opportunities for and potential impact of including
information on country-specific preferences in Fleurbaey & Gaulier’s (2009) calculations
by considering two of their corrections: for inequality and for leisure.
3.2 Country-specific inequality aversion preferences
The inequality corrections in Fleurbaey & Gaulier (2009) originate directly from
the social welfare function employed by the authors, which is formulated as:
( ) ( )ν
ν−
=
−
= ∑
1
1
1
1
1
1,...,
n
i
in yn
yyW
33
In this equation, ni ...,,1= represents all individuals in society. Moreover, iy can
be interpreted as the income of each member of society. Finally, the parameter ν is the
coefficient of inequality aversion, which plays a central role in this social welfare
function.
The type of social welfare displayed above is often referred to as a CES function,
because it exhibits a constant elasticity of substitution (CES), of which the value is
determined by the inequality aversion parameter ν. By way of this inequality aversion
parameter, this social welfare function can incorporate certain preferences for equality
within society. If ν equals zero, then the incomes of different members of society are
assumed to be perfect substitutes from the perspective of social welfare, but as ν gets
larger, the degree of substitutability decreases, resulting in a higher degree of priority for
the incomes of the worst-off in society. When ν approaches infinity, the incomes of all
members of society are assumed to be perfect complements and social welfare
maximization then requires full income equalization across all members of society.
Apart from the opportunity to incorporate preferences for equality, the CES
function above also has the benefit that social welfare is independent of population size in
this setting, hence facilitating international comparisons. Furthermore, the function is
homogeneous of the degree one and it is measured in the same measurement units as
income. This makes the value of the social welfare function more easily interpretable. In
particular, the value of social welfare derived from this formula is equal to the income
that would yield the exact same value of social welfare if everyone in society had this
specific amount of income.
For arriving at Fleurbaey & Gaulier’s (2009) inequality correction, we have to
rewrite the social welfare function:
( ) ( )
( )
( )
( )AyAyy
y
yn
yy
yn
yn
yn
yn
yn
yn
yyW
n
i
i
n
i
in
i
i
n
i
in
i
i
n
i
i
n
i
in
−=−=
−−=
+−=
=
−
=
−
−
=
−
=
=
==
−
=
−
∑
∑∑
∑∑∑
∑
1
1
1
1
1
1
11
1,...,
1
1
1
1
1
1
1
1
1
1
11
1
1
1
1
1
νν
νν
νν
In the equation above y is used to denote the average income (in the authors’
calculations GNI per capita) in society and A refers to the Atkinson Inequality Index,
which equals the proportion of income that could be given up with no loss of social
34
welfare if the remainder is distributed equally. Using information on average income
within society and income shares (all available via OECD.Stats; stats.oecd.org), this
Atkinson index can be calculated for any value of the inequality aversion parameter ν.
The final expression above represents the inequality correction applied by Fleurbaey and
Gaulier.
As can be seen from this derivation, however, the crucial element behind this
correction is the assumed value of the inequality aversion parameter ν. Fleurbaey and
Gaulier assume a uniform value of 1.5 for this parameter. As has already been mentioned,
this specific value is debatable. Of course, the authors also recognize this point and,
therefore, they conduct several robustness checks, amongst others with inequality
aversion parameters of 0.5 and 3. However, we would like to emphasize that not only the
specific value of the parameter is debatable, but also the general assumption of a uniform
parameter value across all countries in the sample. We believe that there may be
significant differences in inequality aversion preferences across countries, which should
be taken into account.21
Previous research also provides supporting evidence for this
hypothesis. Alesina et al. (2004), for instance, have shown that European countries tend
to be significantly more inequality-averse than the United States, where people
apparently worry much less about inequality. These findings evoke the following
question: if we know that such differences in preferences regarding inequality exist, is it
then correct / fair to base the inequality corrections on the assumption that all countries
have the same inequality aversion preferences? In our opinion, the answer to this question
should be a firm ‘no’. Therefore, we will try to estimate country-specific values for the
inequality aversion parameter ν.
3.2.1 Estimating inequality aversion on the basis of the subjective inequality
convergence hypothesis
One rather extreme proposal for estimating country-specific inequality aversion
can be found in Lambert et al. (2003). This is one of the very few existing papers that try
to estimate country-specific inequality aversion parameters for a sample of countries.22
Most other attempts of estimating inequality aversion parameters are based on leaky
bucket experiments or specific surveys that have been conducted only in one country
(e.g. see Amiel et al., 1999, Carlsson et al., 2005 and Pirttilä & Uusitalo, 2010) and are
21
The idea of country-specific inequality aversion parameters is certainly not new. In his seminal paper,
Atkinson (1970) for example already pointed at the possibility of country-specific inequality aversion
preferences by stating that as countries grow richer, they may become more concerned about inequality,
implying higher levels of inequality aversion in richer countries. Many years later, Atkinson (1998) has
also suggested, in a comment on the varied experiences of the G7-countries with respect to inequality, that
different social norms may be at work, leading to cross-country differences in the socially acceptable level
of inequality. 22
The only other paper estimating inequality aversion for a sample of countries we have found is Aristei &
Perugini (2010), who estimate the inequality aversion parameter for 26 European countries on the basis of
microdata on taxes and incomes. Amongst others, they find a relatively low inequality aversion parameter
in the Nordic countries (on average approximately 1.2) and a relatively high parameter value in the Anglo-
Saxon countries (on average approximately 1.7).
35
therefore not useful for our purpose of adapting Fleurbaey & Gaulier’s (2009)
corrections.
Lambert et al. (2003) posit a subjective inequality convergence hypothesis,
somewhat similar to the neoclassical income convergence hypothesis. The authors
distinguish two types of inequality: objective inequality (measured by the Gini
coefficient) and subjective inequality (measured by the Atkinson index).23 According to
the authors, there is a certain ‘natural rate of subjective inequality’, which is the same for
all countries. In the long run, objective inequality and inequality aversion in every
country are assumed to relate in such a way to each other, that this natural rate of
subjective inequality is attained. In this setting, each country eventually ends up with its
own mix of inequality aversion and objective inequality. Although in the short run
deviations from the natural rate of subjective inequality are possible, these differences
will be eliminated in the long run: e.g. if a certain change in a country’s political or social
institutions leads to an increased level of inequality aversion, subjective inequality will
increase in the short run, but on the long term subjective inequality will decrease again to
its natural rate, because society is now more inequality averse and thus more willing to
decrease objective inequality.
Following these principles, one can rather easily infer a country’s inequality
aversion parameter from the country’s Gini coefficient and Atkinson index. The specific
value of the inequality aversion parameter, however, crucially depends on the assumed
value of the Atkinson index (the assumed ‘natural rate of subjective inequality’). The
reason that we can only assume a value for the Atkinson index is that the Atkinson index
depends on the inequality aversion parameter, which Lambert et al. (2003) want to treat
as an unknown. So, in a certain sense, we are confronted with a somewhat strange
procedure (at least from the perspective of our goal to arrive at estimates of inequality
aversion preferences with a more solid empirical foundation): avoiding that one has to
make arbitrary assumptions regarding the inequality aversion parameter by instead
making arbitrary assumptions regarding the value of the Atkinson index. Obviously, this
is not an entirely satisfactory way of attaining country-specific estimates of inequality
aversion. This is only more so, because the authors assume a universal natural rate of
subjective inequality, which implies a quite restrictive relationship between the Gini
coefficient and the inequality aversion parameter. All in all, in spite of the fact that the
hypothesized negative relation between objective inequality and inequality aversion
certainly has some appeal, the authors’ convergence hypothesis seems to us somewhat
too restrictive to be realistic, even though they find some corroborating evidence for it.
Therefore, we would like to turn our attention to a less restrictive way of estimating
country-specific inequality aversion preferences.
Before doing so, however, Figure 3 presents several inequality corrections based
on Lambert et al.’s (2003) subjective inequality convergence hypothesis, assuming values
23
We do not really agree with Lambert et al. (2003) that the Gini coefficient provides an objective measure
of inequality. As Atkinson (1970) observes, any measure of inequality is based on some underlying concept
of social welfare and is, therefore, by its very nature, not objective. Nevertheless, we tend to agree with the
authors that the Atkinson index possesses a relatively more explicit subjective foundation, mainly because
of the inequality aversion parameter that can be freely chosen.
36
of the Atkinson index of 0.15, 0.25 and 0.35.24
Nevertheless, the figure clearly shows that
these corrections are not very illuminating. In the first place, the new corrections turn out
to be highly dependent on the assumed value of the Atkinson index, which is just
arbitrarily chosen in Lambert et al.’s (2003) framework. Moreover, the inequality
corrections based on Lambert et al. (2003) are for all countries the same in relative terms,
which appears not very realistic.
Figure 3 Inequality corrections based on Lambert et al. (2003) (Source: own calculations)
GNI per capita after inequality corrections
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Austra
lia
Austri
a
Belgium
Can
ada
Den
mark
Finlan
d
France
Ger
man
y
Gre
ece
Ireland
Italy
Japa
n
Korea
Net
herla
nds
New
Zea
land
Nor
way
Portu
gal
Spain
Swed
en
Switz
erla
nd UK
US
GNIcap uncorrected
Fleurbaey & Gaulier
Atkinson 0.15
Atkinson 0.25
Atkinson 0.35
3.2.2 Estimating inequality aversion via life satisfaction regressions
Recognizing that it is impossible to explicitly solve the Atkinson Inequality Index
for the inequality aversion parameter, it is useful to concentrate our attention on other
sources of information that may help us finding estimates for the inequality aversion
parameter. One source of information which we believe should certainly be considered in
this respect consists of large-scale population surveys, as these contain valuable
24
Corrections are plotted for all 24 OECD countries considered by Fleurbaey & Gaulier (2009), except for
Iceland, for which Fleurbaey and Gaulier themselves cannot calculate an inequality correction due to a lack
of data on Iceland’s income distribution, and Luxembourg, which is an outlier in terms of GNI per capita.
37
information on all kinds of preferences and thus perhaps also on inequality aversion
preferences. Very importantly, such surveys also have the benefit of providing micro-
level data, which is a desirable property if one wants to ‘measure’ preferences: because
preferences are in essence subjective and individual-specific, one can probably obtain the
most reliable data on preferences by focusing on data at the individual level.
Previous literature that has investigated preferences towards inequality using
survey data includes amongst others Alesina et al. (2004). By way of happiness
regressions, these authors have investigated whether there are differences in preferences
towards inequality between the United States and Europe. Their results suggest that
Europe may overall be more averse to inequalities. More significant, however, are the
transatlantic differences in preferences for various subgroups. While for the rich people
and political right-wingers hardly any transatlantic differences can be observed, European
poor and political left-wingers appear significantly more inequality-averse than their US
counterparts. Based on these observations, Alesina et al. (2004) conclude that the
observed transatlantic differences in inequality aversion preferences are likely not the
result of different intrinsic preferences, but more likely of different context-dependent
preferences, which are particularly influenced by the perceived degree of social mobility.
In a more recent related paper, Alesina & Giuliano (2009) have used survey data to
investigate the determinants of preferences for redistribution (which are closely related to
inequality aversion preferences). In line with Alesina et al. (2004), these authors have
amongst others found that preferences for redistribution vary substantially across
countries and that they are relatively stable over time, being dependent on religion,
histories of macroeconomic volatility and, not to forget, national culture.
Whereas these two investigations support our hypothesis that it may be
worthwhile to allow for country-specific inequality aversion preferences in Fleurbaey &
Gaulier’s (2009) framework, neither Alesina et al. (2004) nor Alesina & Giuliano (2009)
attempt to estimate country-specific values for the inequality aversion parameter.
Therefore, we will try to fill this gap, starting below with an exploration of the
possibilities to derive such estimates on the basis of similar regressions as in Alesina et
al. (2004).
For this purpose, we have run ordered logit regressions of the form:
ictiicticti
iictctictctict
WaveDumIncomeregimeDumInequalityregimeDum
regimeDumMICROMACROIncomeInequalityonsatisfactiLife
εβββ
βββββ
+++
+++++=
876
54321
**
The life satisfaction variable contains respondents’ answers to the World Values
Survey question: “All things considered, how satisfied are you with your life as a whole?
1-Dissatisfied, 2, …, 9, 10-Satisfied”. We have used an ordered logit specification,
because our dependent variable has a discrete scale. Subscripts i refer to individual ‘i’,
subscripts c to country ‘c’ and subscripts t to time ‘t’.25
25
A time dimension is incorporated in the regressions by including data from various survey waves of the
World Values Survey. By now, five waves of the World Values Survey have been conducted, respectively
covering the periods 1981-1984, 1989-1993, 1994-1999, 1999-2004 and 2005-2006. In our analysis we
38
In contrast to Alesina et al. (2004), we have chosen to take life satisfaction as
dependent variable instead of happiness. The reason for this decision is that, whereas
happiness questions are more related to people’s affects and emotions, life satisfaction
questions evoke more cognitive evaluations of people’s life. In our opinion, life
satisfaction data, therefore, relate more closely to preferences. Another difference with
Alesina et al. (2004) is that we have included dummy variables for welfare regimes and
accompanying interaction effects with inequality and income. Alesina et al. (2004), on
the other hand, just run separate regressions for the US, exploiting cross-state variation in
inequality, and for Europe, exploiting cross-country variation in inequality. However,
because we aim at insights into country-specific preferences towards inequality, we have
decided to divide the sample in groups of rather similar countries, in line with the
literature based on Esping-Andersen’s (1990) welfare regime typologies.26
Country-
specific inequality terms cannot be included, since we only have inequality data at the
national level and because within-country inequality hardly changes over time. The
inclusion of country dummies as well as interaction terms of these country dummies and
inequality would thus lead to almost perfect multicollinearity.
In our regressions we control for most of the variables that are generally found to
affect life satisfaction scores: e.g. unemployment and inflation rates at the macro level
and age and marital status at the micro level. Finally, because we have included the first
four waves of the World Values Survey in our analysis (as far as data for every wave
were available), we have also included wave dummies to control for potential general
trends in life satisfaction. More information on our variable definitions and data sources
can be found in Appendix A. Regression results are reported in Table 2 on the next page.
Considering the regression results, we should first note that, apart from our main
variables of interest (inequality and income) all regressions also include a large number
of control variables, which have generally been found important for explaining variation
in subjective well-being. All of these control variables enter each regression as being
highly significant, and their signs are robust across the different regressions and in
accordance with the existing literature on the determinants of subjective well-being.
Regarding the macro variables, both the inflation and the unemployment rate have a
significantly negative coefficient. Furthermore, the unemployment dummy (indicating
whether the respondent is unemployed) has a large and significantly negative sign. In
addition, one’s perceived health status is strongly positively correlated with life
satisfaction. For age we observe the familiar U-shaped pattern, with relatively lower life
satisfaction during the ‘rush hour of life’. Finally, men mostly report significantly lower
have used as much as possible of the information available from the first four waves. The subscript t in our
regression specification actually refers to the number of the survey wave concerned. 26
We have distinguished a group of Anglo-Saxon countries (comprising the United States, Canada, the
United Kingdom, Ireland, Australia and New Zealand), a group of Nordic countries (Sweden, Norway,
Denmark, Finland and the Netherlands), a group of Continental European countries (Germany, France,
Belgium, Austria and Switzerland), a group of Mediterranean countries (Italy, Spain and Portugal) and,
finally, a group of Asian countries (Japan and Korea). It should be noted that the Asian group is actually
more a kind of residual group with a low level of coherence and that some countries in fact fall between
different ideal types (e.g. the Netherlands could also be classified as Continental European, France as
Mediterranean, et cetera). In comparison to Fleurbaey & Gaulier (2009), Greece, Iceland and Luxembourg
are not included in our regressions due to a lack of data.
39
Table 2 Life satisfaction regressions containing regime-specific inequality effects
Dependent Variable: LIFE SATISFACTION
Ordered Logit Regression Models
All things considered, how satisfied are you with your life as a whole these days?
( 1-dissatisfied, 2, 3, 4, 5, 6, 7, 8, 9, 10-satisfied)
1 2 3
Inequality (Gini) 0.938*** -3.111*** -3.151***
(0.175) (0.476) (0.479)
Log Income 0.274*** 0.189*** 0.087***
(0.017) (0.017) (0.023)
Anglo-Saxon base base
Nordic 0.971*** 1.854***
(0.319) (0.508)
Continental -1.899*** -4.405***
(0.243) (0.411)
Mediterranean -2.611*** -3.724***
(0.500) (0.736)
Asian -2.081*** -8.172***
(0.346) (0.720)
Gini*Anglo-Saxon base base
Gini*Nordic -4.491*** -4.864***
(1.104) (1.105)
Gini*Continental 4.709*** 4.320***
(0.745) (0.752)
Gini*Mediterranean 7.061*** 7.365***
(1.454) (1.486)
Gini*Asian 2.293** 3.827***
(1.152) (1.182)
Log Income*Anglo-Saxon base base
Log Income*Nordic -0.086**
(0.042)
Log Income*Continental 0.271***
(0.038)
Log Income*Mediterranean 0.111**
(0.049)
Log Income*Asian 0.589***
(0.060)
Observations 48315 48315 48315
Pseudo R-square 0.14 0.17 0.17
-2 Log Likelihood 176520 170181 175107
Numbers in parentheses denote standard errors. Three stars indicate significance at the 1% level, two stars at the 5% level and one star at the 10% level. All regressions include the following control variables: inflation rate, unemployment rate, a dummy indicating whether the respondent is unemployed, the respondent’s perceived health status, the respondent’s sex, the age and the squared age of the respondent, dummies for the respondent’s marital status (married, living as a couple, divorced, separated, widowed) and the respondent’s number of children. Finally, wave dummies have been included.
40
satisfaction scores as compared with women, people who are married or live together as a
couple tend to report higher levels of life satisfaction than other people, and we also
observe a positive relationship between life satisfaction and a person’s number of kids.
In addition to these control variables, column 1 of the table adds inequality
(measured by the Gini coefficient, expressed on a 0-1 scale) and the natural logarithm of
income. We have used the natural logarithm of income instead of normal income,
because previous literature has consistently found that there is a logarithmic relationship
between income and subjective well-being. Pursuant to the findings of the subjective
well-being literature, the coefficient of the logarithm of income turns out to be
significantly positive in our regression. Furthermore, column 1 shows that if we do not
include country- or regime-specific effects, we find a significantly positive coefficient for
inequality as well. At first glance, the sign of this coefficient might very well be a
surprise for the reader, as one would perhaps more easily expect that inequality is a ‘bad’
thing, which therefore should lower life satisfaction scores. However, this line of
reasoning primarily approaches inequality from the perspective that people have a taste
for equality. Inequality, nevertheless, can also be interpreted in other ways. For rich
people, inequality may be a signal of their status, and thus may have a positive effect on
life satisfaction. On the other hand, if perceived social mobility is large, poor people can
interpret inequality as a signal of good opportunities for the future (the so-called ‘tunnel
effect’). Because different inequality-life satisfaction mechanisms may be at play in
different countries, the positive sign of the inequality coefficient is not really worrying.
As a matter of fact, this result for the inequality coefficient may already lend some
support to our hypothesis that preferences towards inequality may vary substantially
across countries.
The second column of Table 2 includes regime-specific inequality terms, by
including regime dummies and interaction terms of the regime dummies and the
inequality variable. The Anglo-Saxon regime is taken as the base level. Overall, all
regime-specific inequality effects seem rather significant. As can be seen from the table,
inequality has the most negative effect in the Nordic countries, followed by the Anglo-
Saxon and the Asian countries. In the Continental European and especially the
Mediterranean countries higher levels of inequality tend to correspond with higher levels
of life satisfaction. The signs, magnitudes and significance levels of the inequality
coefficients turn out to be quite robust to the inclusion of regime-specific income effects,
as has been done in the regression of column 3.27
In fact, these are quite strong results, in particular if one notes that we have also
controlled for variables like income and unemployment status. Nonetheless, for a proper
understanding of the importance of inequality for life satisfaction one should keep in
mind that the inequality variable has a 0-1 scale. This implies that, for instance, in the
Anglo-Saxon countries a decrease in the Gini coefficient from, say, 0.30 to 0.28 only
leads to a rather minor increase on the 1-10 life satisfaction scale of 0.062 (based on the
regressions in columns 2 and 3).
Besides, it is also important to remember that different forces may be at play in
different groups of countries. From the regressions presented above we cannot tell
27
Notice that income seems to have a moderately positive impact on life satisfaction in the Anglo-Saxon
countries, an even more positive impact in the Mediterranean, Continental and Asian countries, and a
roughly negligible effect in the Nordic countries.
41
whether the inequality coefficients represent intrinsic preferences towards inequality (a
‘taste’ for equality, a natural drive for status, etc.) or whether they mainly reflect context-
dependent preferences (dependent on the perceived degree of social mobility, the
perceived risks of crime and political instability, etc.). In addition, the results of Table 2
also fail to uncover potential differences in inequality preferences within groups of
countries. In response to these remarks, Table 3 on the next page presents similar
regressions as in Table 2, but now for samples restricted to several subgroups of people,
respectively people with left- and right-wing political preferences and poor and rich
people.28
Respondents are labeled left-wing it they answered 1, 2, 3 or 4 to the World
Values Survey question: “In political matters, people talk of ‘the left’ and ‘the right’.
How would place your views on this scale, generally speaking? 1-Left, 2, …, 9, 10-
Right”. Respondents that answered 7, 8, 9 or 10 are identified as being right-wing.
Regarding the rich-poor distinction, people who are in the lowest four income deciles are
labeled poor and people in the highest two income deciles are labeled rich.29
Looking at the regime-specific inequality effects presented in Table 3, we can
observe several interesting patterns. First of all, the base level inequality coefficients
show that in the Anglo-Saxon countries aversion to inequality primarily resides in the
minds of left-wing and poor people. Whereas we observe for these groups highly
significant and relatively large negative coefficients, the rich and right-wingers in the
Anglo-Saxon countries, in contrast, do not tend to worry about inequality: these latter
groups have insignificant inequality coefficients. These results indicate that there exists
no real ‘taste for equality’ in the Anglo-Saxon countries. Otherwise, we should also have
observed significant negative coefficients for the rich and right-wing people. More
specifically, under the presence of a taste for equality we should in particular have
expected a significant negative coefficient for the rich: if we assume equality to be a
normal good, demand for equality should rise with income. Therefore, instead of
supporting a ‘taste for equality’ thesis, the results for the Anglo-Saxon countries sketch a
picture of a society with low perceived social mobility30
, where inequality represents a
signal of bad future prospects for the poor and a signal of an unthreatened status position
for the rich.
As compared with the results for the Anglo-Saxon countries, the results for the
Nordic countries exhibit a remarkably different pattern. Although the results do not
indicate significantly different coefficients for the poor and the left-wingers as compared
28
Actually, the regressions in Table 3 are of the linear form instead of the ordered logit form. The main
reason for this is that our statistical package (SPSS Statistics 17.0) does not provide the option to run
ordered logit regressions for subsamples. However, as a check we have also run linear regressions based on
the regression equations in Table 2. A comparison of these linear regressions with the ordered logit
regressions in Table 2 has shown that the differences in results between the two specifications tend to be
fairly small. 29
The correlation between the variables ‘Left’ and ‘Poor’ is small, but significantly positive (0.019); the
correlation between ‘Left’ and ‘Rich’ is small, but significantly negative (-0.028); the correlation between
‘Right’ and ‘Poor’ is small, but significantly negative as well (-0.040); and the correlation between ‘Right’
and ‘Rich’ is small, but significantly positive (0.064). See also Appendix B for a more comprehensive
correlation matrix that provides further insights into the characteristics of the various subgroups. 30
This suggestion contrasts sharply with the generally assumed high degree of perceived social mobility in
the Anglo-Saxon countries and especially the United States.
42
Table 3 Life satisfaction regressions containing regime-specific inequality effects for various subgroups within society
Dependent Variable: LIFE SATISFACTION
Linear Regression Models
All things considered, how satisfied are you with your life as a whole these days?
( 1-dissatisfied, 2, 3, 4, 5, 6, 7, 8, 9, 10-satisfied)
1 2 3 4 5 6 7 8
Subgroup Left Left Right Right Poor Poor Rich Rich
Inequality (Gini) -4.611*** -4.890*** 0.257 0.094 -3.601*** -3.208*** -1.597 -1.044
(1.211) (1.217) (0.911) (0.913) (0.867) (0.879) (1.099) (1.112)
Log Income 0.268*** 0.237*** 0.148*** 0.061 0.217*** 0.018 0.318*** 0.255***
(0.038) (0.055) (0.033) (0.044) (0.044) (0.062) (0.072) (0.084)
Anglo-Saxon base base base base base base base base
Nordic -0.437 1.079 1.098* 1.788** -0.393 -0.933 0.802 2.328
(0.680) (1.133) (0.586) (0.913) (0.603) (1.150) (0.646) (1.643)
Continental -1.863*** -3.136*** -0.442 -2.250*** -2.119*** -6.902*** -1.013* -6.527***
(0.574) (0.930) (0.486) (0.824) (0.437) (0.948) (0.542) (1.362)
Mediterranean -1.322 0.507 0.006 -1.712 -3.384*** -5.858*** -1.331 -0.195
(1.042) (1.479) (1.046) (1.640) (0.793) (1.250) (1.581) (2.903)
Asian -3.200*** -13.038*** 0.976 -6.372*** -1.438** -3.491** -2.648 -3.143
(0.829) (1.843) (0.663) (1.387) (0.618) (1.633) (1.704) (2.874)
Gini*Anglo-Saxon base base base base base base base base
Gini*Nordic 0.672 0.134 -4.054** -4.225** 0.426 -0.302 -3.511* -4.313**
(2.269) (2.268) (2.007) (2.004) (2.107) (2.125) (2.161) (2.189)
Gini*Continental 4.369*** 4.474*** 0.847 0.658 5.152*** 4.648*** 2.644* 1.832
(1.724) (1.732) (1.512) (1.524) (1.342) (1.348) (1.633) (1.650)
Gini*Mediterranean 3.767 3.043 -0.658 0.490 9.200*** 9.376*** 3.784 1.806
(3.023) (3.057) (3.024) (3.162) (2.288) (2.309) (4.575) (4.678)
Gini*Asian 6.073** 9.536*** -7.211*** -4.663** -1.523 -2.634 7.526 5.759
(2.662) (2.740) (2.177) (2.239) (2.061) (2.228) (6.610) (6.654)
Log Income*Anglo-Saxon base base base base
Log Income*Nordic -0.151 -0.072 0.089 -0.122
(0.095) (0.074) (0.102) (0.139)
Log Income*Continental 0.126 0.191*** 0.548*** 0.561***
(0.083) (0.076) (0.096) (0.128)
Log Income*Mediterranean -0.167* 0.147 0.270*** -0.055
(0.100) (0.105) (0.103) (0.220)
Log Income*Asian 0.921*** 0.689*** 0.265* 0.103
(0.156) (0.113) (0.142) (0.205)
Observations 10984 10984 11272 11272 20524 20524 6920 6920
R-square 0.16 0.17 0.15 0.16 0.17 0.17 0.13 0.14
F-value 83.858 74.633 79.094 70.380 159.401 139.566 41.702 37.053
Numbers in parentheses denote standard errors. Three stars indicate significance at the 1% level, two stars at the 5% level and one star at the 10% level. All regressions include the following control variables: inflation rate, unemployment rate, a dummy indicating whether the respondent is unemployed, the respondent’s perceived health status, the respondent’s sex, the age and the squared age of the respondent, dummies for the respondent’s marital status (married, living as a couple, divorced, separated, widowed) and the respondent’s number of children. Finally, wave dummies have been included.
43
with the Anglo-Saxon countries, they also show large and significantly negative
inequality coefficients for the rich and right-wingers in the Nordic countries. Thus, in
these countries we find more support for the existence of a widespread, culturally-
determined ‘taste for equality’. In addition, the more widespread inequality aversion in
the Nordic countries is also in line with the strong negative inequality coefficient for
these countries in Table 2.
For the Continental European countries the results point at yet another pattern.
First, these countries resemble the Anglo-Saxon countries in the sense that the rich and
the right-wingers do not care at all about inequality: for these groups we observe
insignificant inequality coefficients. Moreover, the left-wing people in the Continental
countries also seem not to care about inequality. The only subgroup for which there
appear to be significant inequality effects comprises the poor people, and here the sign of
the inequality-life satisfaction relationship tends to be positive. So, if people in these
countries care about inequality at all, it is probably not resulting from ideological beliefs
or intrinsic preferences, but more from the social context people live in: the perceived
degree of social mobility is possibly rather high, causing the poor people to interpret
inequality as a signal of potentially rosy future prospects and vast opportunities for
improvement of their situation.
Of all regimes considered, the Mediterranean and Asian countries provide the
strangest patterns, which are most difficult to explain. The Mediterranean case for the
largest part seems to display the same pattern as the Anglo-Saxon countries. However,
there is one significant difference: the inequality coefficient for the poor subgroup in the
Mediterranean countries tends to be significantly positive. This significantly positive
coefficient for the poor yields a quite surprising combination with the significantly
negative coefficient for the Mediterranean left-wingers, which is hard to explain. Is there
perhaps a tension between the ideological beliefs of the left-wingers and the context-
dependent perceptions of the poor?
Finally, it is most and for all difficult to detect any consistency in the results for
the Asian countries: for the rich we observe insignificant inequality coefficients, for the
right-wingers and the poor we find significantly negative coefficients and the coefficient
for the left-wingers seems positive. Without specific knowledge of the situation in the
Asian countries these results seem somewhat contradictory, especially as far as the left-
right distinction is concerned. Nevertheless, it is perhaps possible that the left-right
distinction in these countries is determined along different demarcation lines as compared
with the other regimes. In this case, the results for the Asian regime would already
become better understandable. Moreover, one should remember that the Asian regime is
some kind of a residual group, containing only two countries (Japan and Korea), which
besides differ in some meaningful aspects.
Concerning the discussion of the results above, it is important to note that we
want to refrain from any firm conclusions regarding the mechanisms at play in the
different groups of countries, since the results simply do not allow us to draw any
conclusions regarding the relative importance of intrinsic and context-dependent
preferences et cetera. Instead, the inferences above concern mainly suggestions derived
from the regressions and, hence, should not be interpreted as anything more than
suggestions. However, what we can conclude from Table 2 and Table 3 is that there
appear to be significant cross-regime differences in inequality aversion preferences,
44
partially also reflecting differences in the distribution of inequality aversion preferences
across various subgroups within the regimes.
One critical question that can be raised in this context is to what extent we can
derive reliable information on preferences from subjective well-being regressions. This
question certainly makes sense, not in the least because we have expressed in chapter two
that subjective well-being measures provide an unsatisfactory measure of welfare.
However, here we are dealing with a somewhat different issue. While we retain our main
criticism towards subjective well-being as a welfare indicator (that subjective well-being
over time, and sometimes also across space, is almost invariant to changes in objective
living conditions due to the dominance of adaptation, aspiration and social comparison
processes), we do not think that this criticism rejects the idea that subjective well-being
data can provide certain insights into people’s relative, ordinal valuations for different
dimensions of life at one point in time. In our regressions, the survey wave dummies for
example partly control for adaptation processes over time.
More generally, one can question whether answers to survey questions can be
cross-nationally compared at all, for instance because respondents from different
countries or cultures interpret questions differently or use different scales in answering
the questions (Heath et al., 2009). However, since our regressions include standard
regime dummies, such problems related to cross-national comparisons are substantially
diminished in our analysis.
Nevertheless, if we return to our initial goal for conducting the regression
analyses above (to explore whether we can derive inequality aversion parameter
estimates from regressions like in Alesina et al. (2004)), we are still skeptical regarding
the usefulness of the conducted regressions. Whereas it is certainly possible to think of
ways to attain inequality aversion parameter estimates on the basis of the inequality
coefficients or the relationship between the inequality and income coefficients in the
regressions above, we doubt whether this is a desirable procedure. In the first place,
whilst we were aiming for the inclusion of country-specific inequality aversion
preferences in Fleurbaey & Gaulier’s (2009) calculations, these regressions only allow us
to make a distinction between different groups of more or less similar countries.
Obviously, this is a serious drawback of the regressions. Second, we have some doubts
whether the inequality coefficients really represent what we want them to represent; in a
certain sense the regime-specific inequality coefficients are not merely a measure of
preferences towards inequality, but also a measure of the degree of similarity among the
countries within a regime. This issue has to do with the fact that we only have data on
inequality at the country level. Therefore, within each regime, we just have about three or
four possible values for the inequality variable. Thus, the degree of unity within a regime
(amongst others regarding the inequality variable) can significantly affect the slope and
significance of the inequality-life satisfaction relationship within that specific regime.31
31
We have checked the sensitivity of the results to group composition amongst others by excluding from
the regression analysis in Table 2 the countries that are difficult to assign to one of the welfare regimes:
Australia and New Zealand have been excluded from the Anglo-Saxon group, Netherlands from the Nordic
group, France from the Continental group, and the Asian group has been excluded completely. Thus, we
have repeated the regressions of Table 2 for four regimes, each consisting of four members. Results are
45
So, although the regression results are intuitively quite attractive, we do not entirely trust
them, and hence prefer to continue our exploratory expedition by investigating yet
another potential source of information on preferences towards inequality.
3.2.3 Estimating inequality aversion on the basis of direct survey questions
In fact, this section will not describe an entirely new source of information. Just as
in the previous section, we will namely rely on information from large-scale opinion
surveys. In contrast to the previous section, however, we will not try to (hedonically)
derive preferences from subjective well-being regressions, but instead rely on stated
preferences: respondents’ direct answers to questions related to inequality.
Nevertheless, we do not have any single question at our disposal that measures
inequality aversion fully satisfactorily. Due to the framing of most inequality-related
survey questions, answers to these questions are never purely an expression of one’s
preferences towards inequality. These answers always depend on certain contextual
reference levels or involve preferences towards other issues. Consider for example the
following question from the US General Social Survey: “Do you agree or disagree?
Differences in income in America are too large. (1-Strongly agree, …, 5-Strongly
disagree)” Obviously, this question does not only capture preferences towards inequality,
but is also strongly dependent on the contextual situation. Furthermore, the question may
also implicitly evoke answers that are partly based on opinions on the desired size of the
government apparatus. Therefore, answers to this question are not purely a measure of
intrinsic inequality aversion preferences.
However, the lack of availability of a single perfect question regarding
preferences towards inequality is, as a matter of fact, not such a large problem as it may
seem. By combining various questions that are somehow related to inequality aversion
preferences, it is still possible to obtain a fairly good impression of people’s attitudes
towards inequality. Indeed, such a combination method even has some benefits as
compared with looking at only one single question. By using a variety of questions, we
actually conduct a little robustness test, smoothing the influence of possible social-
desirability biases or biases due to the ordering of questions and so on, thus arriving at a
rather reliable estimator of preferences towards inequality.
The procedure that is generally followed for creating such a composite variable is
principal component analysis. The central idea of principal components analysis is to
reduce the multidimensionality of a group of interrelated variables by combining the
variables into a component variable, while still retaining as much as possible of the
reported in Appendix C. These results show that the Nordic group still tends to exhibit the highest level of
inequality aversion, followed by the Anglo-Saxon countries. However, whereas in the regressions of Table
2 the Mediterranean countries seem to be the least inequality-averse, this is no longer the case for the
regressions presented in Appendix C, where the Continental group seems to be the least inequality-averse.
Moreover, comparing the regressions in Table 2 with those presented in the appendix, the coefficients of
the inequality terms experience serious changes in their absolute value. Finally, it should be noted that the
coefficients of the control variables hardly differ between the two tables, both with respect to their
significance levels as well as with respect to their values.
46
variation in the underlying variables. The newly created variable is a linear combination
of the underlying variables, with the weights in this combination being represented by the
eigenvectors of the covariance or correlation matrix of the underlying variables.
Principal component analysis has often been used for deriving certain index
variables from a range of survey questions; see for instance Inglehart & Baker (2000),
who use survey answers from the World Values Survey to create various modernization
indexes, measuring amongst others ‘Traditional vs. Secular-Rational Values’ and
‘Survival vs. Self-Expression Values’. Here we base our principal components analysis
on six variables from the World Values Survey, videlicet:
- Income equality: Now I'd like you to tell me your views on various issues. How would
you place your views on this scale? 1 means you agree completely with the statement on
the left; 10 means you agree completely with the statement on the right; and if your views
fall somewhere in between, you can choose any number in between. Sentences: Incomes
should be made more equal vs. We need larger income differences as incentives.
- Government responsibility: Now I'd like you to tell me your views on various issues.
How would you place your views on this scale? 1 means you agree completely with the
statement on the left; 10 means you agree completely with the statement on the right; and
if your views fall somewhere in between, you can choose any number in between.
Sentences: People should take more responsibility to provide for themselves vs. The
government should take more responsibility to ensure that everyone is provided for.
- Freedom or equality: Which of these two statements comes closest to your own
opinion? A. I find that both freedom and equality are important. But if I were to choose
one or the other, I would consider personal freedom more important, that is, everyone can
live in freedom and develop without hindrance (coded 1) B. Certainly both freedom and
equality are important. But if I were to choose one or the other, I would consider equality
more important, that is, that nobody is underprivileged and that social class differences
are not so strong (coded 2).
- Self-positioning in political scale: In political matters, people talk of “the left” and “the
right.” How would you place your views on this scale, generally speaking? (1=Left, ….,
10=Right)
- Most people can be trusted: Generally speaking, would you say that most people can be
trusted (coded 1) or that you need to be very careful in dealing with people (coded 2)?
- Post-materialist index: General index indicating to what extent people adhere to either
traditional or post-materialist values, ranging from 0-Materialist to 5-Post-materialist.
The variable is a cluster of variables involving materialist values (like maintaining order,
fighting inflation, having a safe job) versus post-materialist values (freedom, tolerance,
self-expression). See also Inglehart (1997) for more detailed information about this index.
47
Clearly, the first three variables all contain directly an element of preferences for
equality or inequality aversion. The last three variables, on the other hand, have a more
general character, but are all related to preferences towards inequality as well: people on
the left side of the political spectrum tend to be more inequality-averse, generalized trust
is an indicator of social cohesion and can through this channel also be related to
preferences towards inequality, and finally, people with more post-materialist values are
likely to be more concerned with social issues like inequality.
Table 4 below shows the component loadings of the first principle component
calculated on the basis of the six variables mentioned above. The principle component
analysis includes a total number of 20,478 observations. The Kaiser-Meyer-Olkin (KMO)
Measure of Sampling Adequacy equals 0.62 and the p-value of Bartlett’s Test of
Sphericity equals 0, indicating that we have conducted an appropriate analysis. The
created first principal component accounts for almost 27 percent of the variation in the
underlying variables. Although this percentage might seem quite low, it is not really
worrying: since we have conducted our principal component analysis on the basis of
individual-level data, there is likely quite some noise caused by measurement error et
cetera. If we had conducted our principal component analysis for country-level data, the
resulting first principal component would certainly have explained a larger fraction of the
variation in the underlying variables. In the same manner, the component loadings in
Table 4 would probably also have been higher if we had used country-level data.
Nevertheless, for our purposes we prefer individual-level data, as we think it is most
correct to include as much variation as possible at the individual level when estimating
country-specific preferences.
Table 4 Component matrix
Variable Component loading
Income equality -0.57
Government responsibility 0.55
Freedom or equality 0.49
Self-positioning in political scale -0.69
Most people can be trusted -0.02
Post-materialist index 0.50
The component loadings represent the correlations between the first principal
component and the underlying variables. As can be deduced from the signs of the
component loadings, we can label the obtained first principal component as ‘Degree of
inequality aversion’ or ‘Preference for equality’. Interpreting the principal component
variable in this way, all of the correlations are in conformance with our expectations (also
recall the above mentioned coding of the underlying variables). In addition, except for the
trust variable, all component loadings are rather large.32
32
We have experimented with omitting some of the underlying variables from the principal component
analysis, particularly the trust variable and the post-materialist index. Nonetheless, this did not lead to
48
Finally, we have calculated the principal component scores for the 20,478
individuals in our sample that have been included in the principal components analysis.33
Subsequently, we have used these component scores for calculating the average
component score per country, which can be interpreted as a measure of the country’s
inequality aversion. These country-specific averages are shown in Table 5 below.34 35
Table 5 Average country scores on the principal component ‘Inequality Aversion’
Country Average score
Austria -0.15
Belgium 0.00
Canada -0.19
Denmark -0.20
Finland -0.24
France 0.26
Germany -0.14
Iceland 0.11
Ireland -0.10
Italy 0.38
Japan 0.16
Korea -0.40
Netherlands 0.13
Norway -0.16
Portugal 0.24
Spain 0.59
Sweden -0.31
United Kingdom -0.01
United States -0.44
One first interesting observation from this table is that we can distinguish several
groups of countries with quite similar average component scores. In fact, we can observe
spectacular changes in the results: the component loadings of the other variables stayed about the same, and
the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and the percentage of variance in the
underlying variables explained by the first principal component rose moderately, as one would expect when
reducing the number of variables included in a principal component analysis. 33
Due to a lack of data availability, it was not possible to include all of the individuals in our sample in the
principal components analysis. Actually, all 20,478 individuals included in the principal component
analysis were part of survey wave 2 (1989-1994). Although this is obviously not an ideal situation, it does
not have to be a large problem either: Alesina & Giuliano (2009) have for example shown that preferences
towards redistribution, which are strongly related to inequality aversion preferences, are highly persistent
over time. More generally, other literature on values and preferences has also consistently shown that
preferences and values tend to change only very slowly over time. 34
Vis-à-vis Fleurbaey & Gaulier (2009), Table 5 excludes Australia, Greece, Luxembourg, New Zealand
and Switzerland, for which countries insufficient data were available to be included in the principal
components analysis. 35
The distribution of principal component scores within each country has generally been found to be well
approximated by a normal distribution. Histograms confirming this pattern are available upon request.
49
a group division which is largely similar to the one we used in our regressions in the
previous sections: the main Nordic countries all having significantly negative average
scores, the Anglo-Saxon countries (especially the United States) having negative scores
as well, the Mediterranean countries (including France) having significant positive scores
and the remaining Continental European countries (Germany, Austria, Belgium) having
moderately negative averages. As we already noted in the previous section, the Asian
group indeed seems some kind of a residual group, with Korea and Japan having
significantly different average scores.
Recognizing the fact that we can interpret the first principal component as
measuring the degree of inequality aversion, the group differences in Table 5 have
interesting implications. According to this interpretation, the United States have the
lowest degree of inequality aversion of the whole sample, followed by the Nordic
countries and the other Anglo-Saxon countries. The Continental European countries (to
which group we should perhaps also assign the Netherlands and the United Kingdom
according to Table 5) take a middle-ground position with respect to inequality aversion,
and the Mediterranean countries are by far the most inequality-averse of all.
Strikingly, this inequality aversion ranking derived from stated preferences
appears to be completely contrary to the ranking derived from the life satisfaction
regressions in the previous section, where the Nordic countries were found to be most
inequality-averse, followed by the Anglo-Saxon countries, the Continental countries and
finally the Mediterranean countries, which were found to be the least inequality-averse.
Of course, it would be interesting to know the causes of these deviations between the two
rankings. However, determining these causes is not an easy task (and at least it is beyond
the scope of this thesis), as several possibilities can be distinguished.
To begin with, we have already pointed at several shortcomings of the derivations
in the previous section; especially that the regime-specific inequality coefficients might
be more a reflection of group unity than of preferences towards inequality. This distortion
has been eliminated in the principal components analysis above, where we are able to
estimate country-specific inequality aversion preferences. Perhaps this procedural
difference might help explaining the differences in rankings. Related to this point is the
long-standing debate on how we can best estimate preferences, by relying on stated
preferences or by hedonic derivations from regressions. This issue may also play a role in
explaining the different country rankings.
Another non-negligible point concerns the interplay of intrinsic and context-
dependent preferences. From the perspective of our goal to estimate country-specific
inequality aversion parameters, we would prefer estimates of intrinsic preferences, since
the inequality aversion parameter in the CES social welfare function acts as a kind of
pure or intrinsic societal aversion towards inequality. However, we can doubt whether we
are purely measuring these intrinsic preferences, both for the subjective well-being
regressions as well as for the principal component analysis, and the extent to which both
methods measure intrinsic preferences may possibly help explaining the differences in
findings between both methods. Whereas the hedonic regression method is by definition
based on the actual level of inequality within society, also the principal component
analysis contains context-dependent elements. Consider for example the survey questions
labeled ‘Income equality’ and ‘Government responsibility’: these questions clearly ask
the respondents to state their preferences based on the contextual situation they live in.
50
From this perspective, it is hardly surprising that Swedish or Norwegian citizens
subscribe to statements like ‘We need larger income differences as incentives’ or ‘People
should take more responsibility to provide for themselves’. If we consider the equal
societies these people live in, with lots of government intervention, it is no surprise that
people state that they would not mind somewhat more inequalities and a little bit more
personal responsibility.36 On the other hand, it would be false to conclude from such
answers that Norwegians and Swedes just have a very low degree of inequality aversion;
why would they otherwise have such large, encompassing welfare states?37
So, although
not every survey question is problematic, the preferences derived from the principal
component analysis are clearly also subject to context-dependency.38
In this regard,
finding ways to approximate intrinsic preferences more closely through surveys is both
an interesting and challenging topic for future research.
Anyway, for the moment we continue the exploratory expedition of this thesis by
considering ways to derive inequality aversion parameter estimates from the principal
component analysis above. In spite of the fact that this principal component analysis
might not provide ideal measures for this purpose, we think it provides at least better and
more convenient measures than the subjective well-being regressions in the previous
section, because it does not suffer from some of the shortcomings of the regression
method. Particularly the problematic interpretation of the regime-specific inequality
coefficients and the impossibility to derive country-specific parameter estimates are in
our opinion troublesome aspects of the regressions presented in the previous section.
Besides, concerning our choice for using the principal component scores, we should
remember that this thesis only aims to explore some possibilities for taking into account
country-specific preferences; it does not pretend to provide ideal measures. For the goal
of exploration, the principal component scores may certainly be useful.
Nevertheless, the principal component scores only estimate the relative inequality
aversion of each country as compared with the other countries in the sample. Thus, we
have to adopt a mechanism to translate these relative scores into absolute parameter
estimates that can be applied to the calculations of Fleurbaey & Gaulier (2009).
Unfortunately, however, there is no objective way of doing this. Therefore, we have
decided to use several conversion formulas, hoping to find some robust results across the
various conversion schemes. Table 6 on the next page displays the country-specific
estimates of the inequality aversion parameters, calculated for different conversion
formulas. In relation to most of the existing literature the distinctive element of this table
is that, instead of estimating the inequality aversion parameter for one specific country,
36
Compare this situation with the case of the United States. Despite the American context of large existing
inequalities and little government intervention, the United States are still the country with the lowest score
on the principal component measuring the degree of inequality aversion. Evidently, this is a remarkable
result, which can probably be attributed to the widely debated phenomenon of ‘American exceptionalism’. 37
It is very well possible to tell a similar but opposite story for the Mediterranean countries. 38
Furthermore, the preference estimates derived from the principal component analysis are also subject to
the ‘standard’ cross-country comparability problems caused by the fact that respondents in different
countries use different scales to answer the questions et cetera. This particular problem, however, is
perhaps not that serious in this case, as most underlying variables of the principal components analysis
concern questions with only very few answering options, thus leaving less room for biases due to different
interpretations et cetera.
51
we have estimated inequality aversion parameters for a range of countries, based on a
unified framework.
Table 6 Country-specific estimates of the inequality aversion parameter under various scenarios (Source: own calculations)
PC PC(0-1) Parameter estimates
(0) (1) (2) (3) (4) (5) (6) (7) (8)
Austria -0.15 0.28 1.50 1.35 1.19 1.04 1.62 0.99 0.71 1.32 1.42
Belgium 0.00 0.42 1.50 1.50 1.49 1.49 2.30 1.19 1.12 1.53 1.63
Canada -0.19 0.25 1.50 1.31 1.13 0.94 1.47 0.95 0.65 1.28 1.36
Denmark -0.20 0.24 1.50 1.30 1.11 0.91 1.43 0.94 0.63 1.27 1.35
Finland -0.24 0.20 1.50 1.26 1.02 0.78 1.23 0.88 0.56 1.22 1.27
France 0.26 0.68 1.50 1.76 2.01 2.27 3.47 1.55 2.22 1.96 1.92
Germany -0.14 0.29 1.50 1.36 1.22 1.07 1.67 1.01 0.74 1.34 1.43
Iceland 0.11 0.53 1.50 1.61 1.72 1.83 2.81 1.35 1.54 1.71 1.77
Ireland -0.10 0.33 1.50 1.40 1.29 1.19 1.84 1.06 0.83 1.39 1.49
Italy 0.38 0.79 1.50 1.88 2.26 2.63 4.02 1.71 2.91 2.21 2.04
Japan 0.16 0.58 1.50 1.66 1.82 1.97 3.03 1.41 1.75 1.79 1.82
Korea -0.40 0.04 1.50 1.10 0.70 0.30 0.50 0.66 0.41 1.04 0.85
Netherlands 0.13 0.55 1.50 1.63 1.75 1.88 2.89 1.37 1.61 1.73 1.79
Norway -0.16 0.27 1.50 1.34 1.18 1.02 1.58 0.98 0.70 1.31 1.40
Portugal 0.24 0.66 1.50 1.74 1.99 2.23 3.42 1.53 2.16 1.94 1.91
Spain 0.59 1.00 1.50 2.09 2.69 3.28 5.00 2.00 4.40 2.72 2.23
Sweden -0.31 0.13 1.50 1.19 0.87 0.56 0.89 0.78 0.46 1.13 1.11
United Kingdom -0.01 0.42 1.50 1.49 1.48 1.47 2.27 1.19 1.10 1.52 1.62
United States -0.44 0.00 1.50 1.06 0.61 0.17 0.30 0.60 0.40 1.00 0.50
Mean -0.03 0.40 1.50 1.47 1.45 1.42 2.20 1.16 1.31 1.55 1.52
Median -0.10 0.33 1.50 1.40 1.29 1.19 1.84 1.06 0.83 1.39 1.49
Minimum -0.44 0.00 1.50 1.06 0.61 0.17 0.30 0.60 0.40 1.00 0.50
Maximum 0.59 1.00 1.50 2.09 2.69 3.28 5.00 2.00 4.40 2.72 2.23
The first column shows the standard principal component scores (PC) per country, as depicted in Table 5. In the second column these component scores have been rescaled to a 0-1 scale, with 0 and 1 respectively referring to the lowest and highest principal component score within the sample. The remaining columns of the table show the inequality aversion parameter estimates calculated on the basis of different conversion formulas. Variant (0) is the Fleurbaey & Gaulier (2009) scenario, where a uniform parameter value of 1.5 is assumed. The formulas corresponding to the other scenarios are:
- Variant (1): ν = 1.5 + PC - Variant (2): ν = 1.5 + 2PC - Variant (3): ν = 1.5 + 3PC - Variant (4): ν = 0.3 + PC(0-1) * 4.7 - Variant (5): ν = 0.6 + PC(0-1) * 1.4 - Variant (6): ν = ( 2 * PC(0-1) )
2 + 0.4
- Variant (7): ν = e ^ ( PC(0-1) ) - Variant (8): ν = ( 3 * PC(0-1) )
0.5 + 0.5
52
As can be seen from the footnotes of Table 6, we have used a variety of functions
to convert the principal component scores to inequality aversion parameter estimates. The
core message of this table is that we can have a wide cross-country variation in parameter
values if we loosen Fleurbaey & Gaulier’s (2009) assumption of a uniform inequality
aversion parameter across all countries. For all scenarios the country-specific parameter
estimates lie within the range of parameter values that have earlier been proposed in the
literature; see amongst others Lambert et al. (2003), Decancq et al. (2009) and Decancq
& Lugo (2010). However, the most interesting issue is to what extent the different
scenarios lead to deviations from the equivalent income calculations of Fleurbaey &
Gaulier (2009). On the basis of this information we can then assess whether accounting
for country-specific inequality aversion preferences is likely to make a difference for
international welfare comparisons. To examine these effects, Figure 4 and 5 on the next
pages plot Fleurbaey & Gaulier’s (2009) inequality corrections for some of the scenarios
presented in Table 6.39
Recall that Fleurbaey and Gaulier calculate their inequality
corrections by multiplying GNI per capita by the Atkinson inequality index, which is
partly determined by the inequality aversion parameter, and subtracting this product from
GNI per capita. Also remember that the corrections concern the year 2004.40
The messages of Figure 4 and 5 are quite clear, the main message being that
Fleurbaey & Gaulier’s (2009) inequality corrections change significantly if we loosen the
assumption of a uniform inequality aversion parameter value of 1.5. When a uniform
parameter value of 1.5 is applied, especially the countries with relatively unequal income
distributions have to experience large negative inequality corrections, as the first panel of
Figure 4 shows. These countries comprise mainly the Anglo-Saxon and Mediterranean
countries. Most other countries experience corrections of about 15 to 20 percent of their
GNI per capita. However, if country-specific estimates of the inequality aversion
parameter are used, we can observe some dramatic changes in these patterns. Whereas
the Nordic and Anglo-Saxon countries experience significant decreases of their inequality
corrections, the Mediterranean countries are now ‘taxed’ more heavily for their relatively
high levels of inequality. The magnitudes of these changes are strongly dependent on the
conversion formulas applied to the principal component scores, as can clearly be seen
from a comparison of the second, third and fourth panel of Figure 4.
Nonetheless, all graphs of the various parameter scenarios (also the graphs of the
scenarios that are not presented above) tell a consistent story regarding the ‘winners and
losers’ of the inclusion of country-specific preferences in the inequality corrections: the
Anglo-Saxon and Nordic countries gain as compared with most other countries and the
Mediterranean countries, on the other hand, lose in relative terms. This pattern comes as
no surprise: according to our principal component analysis the Anglo-Saxon and Nordic
39
In addition to the countries that are not part of Table 6 (see footnote 34), also Iceland has been excluded
from these tables. Since we do not have any data for Iceland on income shares, it is impossible to calculate
the country’s Atkinson inequality index. As a consequence, it is neither possible to calculate inequality
corrections for Iceland. 40
Since preferences are generally found to change only very slowly over time, it is in our opinion no
serious problem to apply the preference estimates derived from the second wave of the World Values
Survey (1989-1994) to income corrections for the year 2004. Ideally, however, one would wish to use
preference estimates derived from survey answers of a more recent date. Nevertheless, as has been noted
already, such information is not available. See also footnote 33.
53
Figure 4 Inequality corrections for different inequality aversion parameter scenarios (as % of uncorrected GNI per capita)
Variant (0)
-40.00%
-35.00%
-30.00%
-25.00%
-20.00%
-15.00%
-10.00%
-5.00%
0.00%
1
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Ireland
Italy
Japan
Korea
Netherlands
Norw ay
Portugal
Spain
Sw eden
United Kingdom
United States
Variant (1)
-40.00%
-35.00%
-30.00%
-25.00%
-20.00%
-15.00%
-10.00%
-5.00%
0.00%
1
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Ireland
Italy
Japan
Korea
Netherlands
Norw ay
Portugal
Spain
Sw eden
United Kingdom
United States
54
Figure 4 (cont.) Inequality corrections for different inequality aversion parameter scenarios (as % of uncorrected GNI per capita)
Variant (3)
-50.00%
-45.00%
-40.00%
-35.00%
-30.00%
-25.00%
-20.00%
-15.00%
-10.00%
-5.00%
0.00%
1
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Ireland
Italy
Japan
Korea
Netherlands
Norw ay
Portugal
Spain
Sw eden
United Kingdom
United States
Variant (4)
-60.00%
-50.00%
-40.00%
-30.00%
-20.00%
-10.00%
0.00%
1
Austria
Belgium
Canada
Denmark
Finland
France
Germany
Ireland
Italy
Japan
Korea
Netherlands
Norw ay
Portugal
Spain
Sw eden
United Kingdom
United States
55
Figure 5 Gross National Income (GNI) per capita and inequality corrections according to different inequality aversion parameter scenarios
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Austri
aBel
gium
Can
adaD
enm
ark
Finla
ndFra
nce
Ger
man
yIre
land
Italy
Japa
n
Korea
Net
herla
nds
Nor
way
Portu
gal
Spain
Swed
en
Uni
ted
Kingd
omU
nite
d Sta
tes
GNI cap uncorrected
Variant (0)
Variant (1)
Variant (3)
Variant (4)
56
countries are characterized by a relatively low degree of inequality aversion, while the
Mediterranean countries have been denoted as exhibiting the highest degree of inequality
aversion within our sample. The pattern of winners and losers is also confirmed by the
country rankings in Table 7 below.
Furthermore, in absolute terms the changes are non-negligible as well, as Figure 5
shows: particularly for the United States and the Mediterranean countries enormous
changes can be observed in comparison to Fleurbaey & Gaulier’s (2009) corrections. For
the United States the results mean a recovery of its top ranking in terms of uncorrected
GNI per capita, while for the Mediterranean countries the results imply an aggravation of
their backward position. This latter phenomenon is interesting to observe, as it implies
that including country-specific preferences for various welfare dimensions does not lead
to a decrease in cross-country inequality in terms of welfare, in contrast to what one
might expect beforehand if one expects that there is a negative relationship between the
degree of inequality of a country’s income distribution and its level of inequality
aversion.
Table 7 Country rankings of GNI per capita under different scenarios (as % of sample average and absolute ranks)
uncorrected Variant (0) Variant (1) Variant (3) Variant (4)
Austria 107% 5 110% 5 112% 4 115% 4 117% 5
Belgium 105% 7 109% 6 108% 6 107% 8 104% 10
Canada 103% 9 100% 12 102% 11 107% 7 108% 7
Denmark 106% 6 111% 3 113% 3 117% 3 121% 3
Finland 97% 13 105% 9 106% 8 109% 6 116% 6
France 99% 10 98% 13 93% 13 84% 14 77% 15
Germany 95% 14 101% 11 102% 12 104% 11 106% 9
Ireland 108% 4 102% 10 103% 10 104% 10 104% 11
Italy 95% 15 89% 15 82% 15 69% 16 59% 16
Japan 99% 11 111% 4 109% 5 105% 9 108% 8
Korea 66% 18 67% 17 70% 17 76% 15 82% 14
Netherlands 108% 3 108% 7 105% 9 99% 12 90% 12
Norway 130% 1 140% 1 141% 1 143% 2 150% 2
Portugal 67% 17 59% 18 55% 18 48% 18 41% 18
Spain 83% 16 82% 16 74% 16 61% 17 55% 17
Sweden 98% 12 105% 8 108% 7 113% 5 120% 4
United Kingdom 103% 8 93% 14 92% 14 91% 13 84% 13
United States 130% 2 111% 2 123% 2 149% 1 159% 1
In conclusion of this section, we have found evidence for our hypothesis that
preferences towards inequality may very well differ across countries. This hypothesis is
supported by both the regression analyses and principal component analysis presented in
this section. Moreover, although we admit that there is still much room for discussion
regarding the context-dependency of our estimates of inequality aversion preferences and
with respect to the conversion of these estimates to inequality aversion parameter values,
57
we think that our analysis has pointed out that the inclusion of country-specific inequality
preferences may significantly alter the inequality corrections presented in Fleurbaey &
Gaulier’s (2009). As such, the results in this section point to the importance of taking into
account country-specific preferences in the construction of welfare measures.
As a further continuation of our exploratory expedition, the next paragraph of this
chapter considers Fleurbaey & Gaulier’s (2009) corrections for leisure.
3.3 Country-specific preferences for leisure
At the core of Fleurbaey & Gaulier’s (2009) leisure corrections is the idea that a
person can spend his time on either work or leisure. Work is assumed to increase utility
by enhancing one’s consumption possibilities through earning an income, while it is
assumed to decrease utility by requiring a sacrifice in terms of leisure time, which is
assumed to have a positive effect on utility. To correct standard income measures for this
leisure sacrifice, Fleurbaey & Gaulier (2009) pick a reference value for the number of
hours worked, which in this two-dimensional setting also implies a reference value for
the amount of leisure. Subsequently, they calculate the willingness-to-pay of the
representative agent of each country for having this amount of leisure time. The authors
assume that the reference value for the number of hours worked differs only marginally
from the average number of hours worked in each country, so that the willingness-to-pay
per country can be calculated on the basis of wages, which for marginal changes of
quantities are assumed to be very close to people’s marginal rates of substitution between
leisure and work.
Formally, Fleurbaey & Gaulier’s (2009) leisure correction arises from the
following utility equation, with a person’s utility derived from his actual mix of hours of
work and leisure on the left-hand side, and the utility derived from the equivalent mix on
the right-hand side, where *l represents the reference value for number of hours worked
and l
δ the person’s willingness-to-pay for having *l hours of work:
41
( )( )[ ] ( ) ( )( )[ ] ( )*
00
lll
υδβυβ −
+=−
∑∑
==
T
t
tT
t
t tyuEtyuE
Supposing that the number of hours worked is chosen under the budget constraint
0ywy += l , with w referring to the net income of an hour of work, we can calculate the
first-order condition of the agent’s utility maximization problem (the maximization of the
left-hand side of the equation above) as follows:
41
In addition, y refers to the income that the agent earns and l refers to his actual amount of hours worked.
To capture the intertemporal nature of the agent’s decision problem, time indices and a time discount factor
β have also been added.
58
( )[ ] ( )( )( )[ ] ( ) 0
0
0
0
=′−′=
−
+
∑∑
=
=l
l
ll
υβ
υβ
wtyud
ywuEdT
t
t
T
t
t
This equation implies that in the optimum the wage equals the marginal rate of
substitution between work and leisure:
( )
( )( )[ ]workleisure
work
leisure
T
t
t
MRSMU
MU
tyu
w ,
0
==
′
′=
∑=
β
υ l
So, assuming that the difference between l and *l is not too large (which makes
the marginal rate of substitution a rather good approximation for valuing changes in the
number of hours worked) and that individuals can maximize their utility, Fleurbaey &
Gaulier (2009) can estimate their leisure correction l
δ by using wage information:
( ) ( )lllll
−=−≈ *** wMRSδ
As reference value *l Fleurbaey and Gaulier use the median number of hours
worked per capita in their sample of 24 OECD countries. For each country the average
number of hours per capita is calculated under the assumption that the unemployed (twice
the official unemployment figure, to take into account hidden unemployment and
discouraged workers, who would actually have preferred to work) do not have more
leisure than the average worker in their country, amongst others because they invest time
in job search. Moreover, the authors assume that prisoners work twice as much as the
average worker in their country, reflecting the serious time constraints imposed upon
these people.42
Assessing the appropriateness of Fleurbaey & Gaulier’s (2009) leisure
corrections, we distinguish two main elements that can be criticized. First, one can debate
the way in which leisure is defined and classified by Fleurbaey and Gaulier. Of course,
one can point in this respect at the ad hoc treatment of the unemployed, the discouraged
workers and the prisoners. However, the leisure corrections are hardly sensitive to these
treatments: other ad hoc assumptions do not lead to radically different leisure corrections.
Actually, the special treatments for these groups should mainly be interpreted as a sign
that the authors have paid attention to these subpopulations. More important regarding
the definition of leisure is the fact that leisure is simply classified as all the hours that an
individual does not spend in paid employment. Evidently, this represents a highly
simplified classification. Apart from paid work, an individual for example spends time on
voluntary work, household production, travelling to work, sleeping and all kinds of
specific leisure time use such as sporting, reading, eating and so on. It is somewhat
simple to put all these different time utilizations under the single heading of leisure,
especially because not all of these activities might be perceived as leisure (e.g. travelling
42
The average number of hours worked per country, abstracting from the assumptions regarding hidden
unemployment, discouraged workers and prisoners, can be found in Appendix D.
59
to work, household production, sleeping). In this sense, Fleurbaey & Gaulier’s (2009)
leisure corrections are obviously imperfect, by defining leisure as anything except for
paid work and by valuing many different types of activities by way of one single
measure: the hourly wage. Of course, however, it would be a very complex task to
differentiate between all kinds of non-paid-work activities and to value each of them
uniquely. Thus, despite the imperfectness of their definition of leisure, we can certainly
understand Fleurbaey and Gaulier’s choice for it.43
The second debatable element of the leisure corrections in Fleurbaey & Gaulier
(2009) concerns the use of average hourly wages to value deviations from the reference
value of hours worked. The correctness of this practice hinges on the extent to which
wages reflect preferences for leisure. We do not tend to be overly optimistic in this
respect.
First, whereas the authors state that wages only provide a good approximation for
the valuation of marginal deviations from the reference level, we think that for many of
the 24 countries in the sample the deviation from the reference level, however, cannot be
considered marginal. For instance, for 15 of the 24 countries the deviation amounts to
more than 10 percent and for 5 countries it totals even more than 20 percent.44
In our
perception, it is hard to label such deviations as being marginal. Thus, wage-based leisure
corrections might not provide such a good approximation of people’s valuations after all.
Nonetheless, we do not think this is the only reason to reject purely wage-based
leisure corrections. Another condition for wages to properly reflect preferences is namely
that markets are well-defined and perfectly competitive. Again, we do not think this is
generally the case in reality. Labour markets are for instance often characterized by a lack
of transparency and imperfect mobility, to mention just a few distortions. Labour unions
and institutions like minimum wages can play a distorting role as well. If all of these
distortions influenced the wages in every country to the same extent, there would perhaps
be no problem, but the critical matter is that countries differ significantly in terms of
labour mobility and labour market institutions.
Related to this point is the fact that workers are often not free to choose their
amount of working hours: for instance, there may be only limited possibilities for part-
time work or people may be stuck in a straitjacket. Does the fact that many people in the
United States have multiple low-paid jobs, for example, necessarily mean that they attach
a low value to leisure time? We should recognize that we cannot assume that people
choose between work and leisure in the same manner as they allocate their budget among
consumption goods like shoes and clothing. In this context, we can wonder to what extent
there really exists a work-leisure choice at the micro level. Many changes in the number
of hours worked over the past decades have been reflecting national conventions instead
of decisions at the individual level: in many countries people’s number of hours worked
43
Nonetheless, progress can surely be made in this area. Consider for example Krueger et al. (2008), who
propose a system of national time accounting, collecting information on how people use their time and on
their emotional experiences during their different activities. It goes without saying that the creation of such
a system would be a very complex and demanding task and that such a system would probably also be
subject to different kinds of measurement error, but the proposal is at least a valuable suggestion for
potential ways forward. 44
The median number of hours worked in Fleurbaey & Gaulier (2009) equals 779 per year. Countries with
relatively large deviations from this reference level include amongst others Belgium (609), France (584),
Korea (1147) and Switzerland (937). See also Appendix D.
60
is determined by collective, national agreements and regulation, which often result from
the current macroeconomic conditions and historically acquired rights. In addition, recall
that wages are the outcome of the interaction between labour demand and supply, thus by
definition not merely reflecting people’s preferences for work versus leisure.
Finally, it is important to notice that jobs also yield all kinds of non-pecuniary
benefits and costs; think for instance of nice contacts with colleagues, feelings of self-
actualization and contributing to society, working stress, et cetera. Albeit such non-
pecuniary costs and benefits undoubtedly affect people’s preferences for work and
leisure, it is uncertain whether they are all fully reflected in the market wages.
Altogether, we conclude that Fleurbaey & Gaulier’s (2009) leisure corrections are
not completely satisfactory. At the same time, however, leisure is generally thought to be
“…one of the more important aspects for comparative assessment of economic well-
being”, to quote Stiglitz et al. (2009:131). Therefore, we think it is valuable to amend
Fleurbaey and Gaulier’s corrections by making a distinction between different types of
non-paid-work activities and by providing a richer reflection of people’s preferences for
leisure.
However, the former of these two tasks is hard to accomplish at this stage. At the
moment, international time use datasets allowing for cross-country comparisons are not
yet available. In the absence of such datasets, it is difficult to come up with classifications
of non-paid-work activities that are not characterized by a high degree of arbitrariness
and impreciseness. As a consequence, we feel forced to leave this element of Fleurbaey
and Gaulier’s corrections unchanged for this moment.
On the other hand, at first sight there seem more opportunities for adjusting the
second element of the authors’ leisure corrections: the wage as valuation unit.
Nonetheless, examination of the existing literature teaches us that wage-based leisure
corrections are still common practice and that hardly any other methods have been
proposed. Furthermore, whilst much subjective well-being research has been devoted to
preferences concerning work versus unemployment, the subjective well-being literature
has barely investigated preferences concerning work versus leisure. This is, nevertheless,
not a complete surprise, as most datasets on subjective well-being do not contain
variables on people’s experienced amount of leisure time or even on their amount of
hours spent on paid employment. Thus, it is almost impossible to hedonically derive
preferences for leisure from subjective well-being regressions.
Recognizing these difficulties and acknowledging our space constraints, we have
decided to confine ourselves to an investigation of a similar principal component analysis
as in the previous paragraph, to arrive at adjusted valuation units for leisure. The obtained
results can act as an indication of the potential influence of including more direct
information on leisure preferences in Fleurbaey & Gaulier’s (2009) calculations.
We have used the following questions of the World Values Survey for our
principal component analysis:45
45
We have also conducted principal component analyses in which one or more of these variables were left
out. Likewise, we have also experimented with adding some other variables such as ‘Important child
qualities: hard work’ and ‘Job satisfaction’. Nonetheless, all analyses yielded very similar results.
61
- Leisure important in life: For each of the following aspects, indicate how important it is
in your life. Leisure time. (1-Very important, …, 4-Not at all important)
- Important in a job: good hours: Here are some aspects of a job that people say are
important. Please look at them and tell me which ones you personally think are important
in a job? Good hours. (0-Not mentioned, 1-Mentioned)
- Important in a job: generous holidays: Here are some aspects of a job that people say
are important. Please look at them and tell me which ones you personally think are
important in a job? Generous holidays. (0-Not mentioned, 1-Mentioned)
- Work should come first even if it means less spare time: Do you agree or disagree with
the following statements? Work should always come first, even if it means less spare
time. (1-Strongly agree, …, 5-Strongly disagree)
- Future changes: less importance placed on work: I'm going to read out a list of various
changes in our way of life that might take place in the near future. Please tell me for each
one, if it were to happen, whether you think it would be a good thing, a bad thing, or don't
you mind? Less importance placed on work in our lives. (1-Good thing, 2-Don’t mind, 3-
Bad thing)
The principal component analysis has been based on a total number of 24,666
observations, all of them being part of the fourth wave of the World Values Survey
(1999-2004). The component matrix of the principal component analysis is shown in
Table 8 below. If we recall that the component loadings represent the correlation of each
variable with the created first principal component, we can observe from these
correlations that the principal component variable can be interpreted as measuring
people’s preferences for leisure, with higher component scores implying a stronger
preference for leisure. Using this interpretation, the signs of all correlations are as
intuitively expected: people with strong preferences for leisure find leisure relatively
important in life, they consider good hours and generous holidays to be relatively
important elements of a job, they subscribe relatively less often to the statement ‘work
should always come first, even if it means less spare time’, and they are relatively well-
disposed to societal developments that lead to a situation in which less importance is
placed on work.
Concerning the appropriateness of the conducted analysis, the Kaiser-Meyer-
Olkin Measure of Sampling Adequacy equals 0.53. Although this value is lower than for
the principal component analysis related to inequality aversion, it is still larger than 0.5,
which is often assumed to be a critical value regarding the appropriateness of the
analysis. Moreover, the p-value of Bartlett’s Test of Sphericity equals zero and the first
principal component captures almost 31 percent of the variation in the underlying
variables, which is a rather good score given the fact that we are looking at individual-
level data. Table 9 on the next page displays the average component scores per country.46
46
In comparison with Fleurbaey & Gaulier’s (2009) sample Australia, Austria, New Zealand, Norway and
Switzerland have been omitted from our analysis due to a lack of data availability.
62
Table 8 Component matrix
Variable Component loading
Leisure time important in life -0.42
Important in a job: good hours 0.76
Important in a job: generous holidays 0.77
Work should come first even if it means less spare time 0.40
Future changes: less importance placed on work -0.23
Table 9 Average country scores on the principal component ‘Preference for leisure’
Country Average score
Belgium -0.06
Canada -0.16
Denmark -0.43
Finland -0.18
France -0.18
Germany -0.51
Greece -0.04
Iceland -0.07
Ireland 0.26
Italy -0.05
Japan 0.61
Korea 0.69
Luxembourg 0.15
Netherlands -0.01
Portugal -0.22
Spain 0.03
Sweden -0.03
United Kingdom 0.31
United States 0.24
Concerning the average scores in Table 9, we should first of all remark, just like
we noted for the inequality aversion analysis, that these scores may be subject to context-
dependency. Even though only the ‘Future changes’ question explicitly refers to the
respondent’s context of living, it is very well possible that the respondents have also
answered the other questions on the basis of implicit contextual standards (possibly
unconsciously). Particularly the current number of hours worked and historically
acquired rights may play a significant role in this regard. Because of this and other
reasons, we should again be careful for ‘overinterpretation’ of cross-country differences
in the principal component scores.
As compared with the inequality aversion scores presented in the previous
paragraph, it is here somewhat more difficult to group the countries based on their
63
principal component scores. Nevertheless, the Asian countries stand out enormously:
Japan and Korea have by far the highest component scores, indicating that these countries
attach the most value to leisure.47 The Anglo-Saxon countries (except Canada) follow the
Asian countries at some distance48 and for the remainder of the sample it is rather
difficult to form groups. Except for Sweden, the Scandinavian countries tend to attach
relatively little value to leisure, just as Portugal and France, and Germany has the lowest
component score within the sample. Finally, it is interesting to note that the correlation
between the hourly wage and the average component score per country is negative (-0.15)
but highly insignificant (the p-value of the correlation equals 0.54). In this sense, the
principal component scores may provide interesting additional information regarding
country-specific preferences for leisure, vis-à-vis the wage information used by
Fleurbaey & Gaulier (2009).
The next step in our analysis is to use the country-specific principal component
scores for adjusting Fleurbaey & Gaulier’s (2009) leisure corrections. The most obvious
way of doing this is to adjust each country’s net hourly wage level on the basis of the
component scores. Since there are no theoretical suggestions for how to perform this
procedure, we have again experimented with various adjustment scenarios, which are
presented in Table 10.49 As we do not think that wages contain no information at all on
people’s preferences for leisure, we have adhered to the principle that our adjustments
preferably should not completely disrupt the ranking of countries based on hourly wages;
we are looking for a measure that provides a balanced mix of both wage information and
information derived from the principle component scores. Again, we should also note that
one can distinguish infinitely many applicable adjustments. The adjustments presented in
Table 10 are in this regard just an arbitrary selection. The consequences of (several of)
these adjustment scenarios for Fleurbaey & Gaulier’s (2009) leisure corrections have
been plotted in Table 11 and Figure 6. Recall that the leisure corrections consist of a
country’s (adjusted) net hourly wage multiplied by the difference between the country’s
number of hours worked per capita and the median number of hours worked per capita
within our sample of 19 OECD countries.50
In line with our expectations, we can observe
the most significant changes in Fleurbaey and Gaulier’s corrections for the countries with
the largest deviations between their relative wage and their relative component score.
47
Observing these outlier results for the Asian countries, we have conducted the same principal component
analysis but without these countries in order to check to what extent the results for the other countries are
affected by these two extreme observations. It turned out, however, that excluding the Asian countries from
the analysis did not make a significant difference for the scores of the other countries. 48
Observe that, while the economic literature often presumes that Europe has stronger preferences for
leisure than the United States, these component scores tend to contradict this presumed pattern. Regarding
this observation, we should however again beware of overinterpreting our results. 49
The standard net hourly wages in Table 10 (scenario 0) have been calculated on the basis of data for the
year 2004 on national income, the labour share in national income, taxes on income from labour and the
average amount of hours worked. All these data can be found in OECD.Stat (stats.oecd.org). 50
For reasons of convenience we have in our calculations abstracted from Fleurbaey & Gaulier’s (2009)
special assumptions regarding hidden unemployment, discouraged workers and prisoners: we have based
our calculations on the ‘standard’ number of hours worked per capita, as presented in Appendix D. As we
noted earlier on, Fleurbaey and Gaulier’s special treatments of the above-mentioned groups hardly affect
the correction results.
64
Table 10 Adjusted hourly wages under various wage adjustment scenarios (Source: own calculations)
PC PC(0-1) PC* Adjusted hourly wages
(0) (1) (2) (3) (4) (5) (6)
Belgium -0.06 0.38 -0.47 16.91 16.33 15.75 12.49 9.72 16.42 15.93
Canada -0.16 0.30 -0.27 14.19 12.64 11.09 11.18 10.50 13.09 11.99
Denmark -0.43 0.07 -0.82 13.98 9.69 5.39 7.46 8.31 10.98 7.98
Finland -0.18 0.28 -1.32 20.81 19.05 17.28 10.89 6.33 18.98 17.14
France -0.18 0.28 -0.70 16.71 14.94 13.17 10.88 8.81 15.23 13.76
Germany -0.51 0.00 -1.27 15.76 10.64 5.51 6.33 6.54 11.72 7.68
Greece -0.04 0.39 0.93 7.96 7.51 7.07 12.67 15.31 7.78 7.61
Iceland -0.07 0.37 -0.74 18.56 17.81 17.07 12.27 8.63 17.87 17.17
Ireland 0.26 0.64 -0.04 18.63 21.20 23.77 16.76 11.43 21.02 23.42
Italy -0.05 0.38 0.38 11.48 10.95 10.43 12.56 13.09 11.18 10.88
Japan 0.61 0.94 1.61 12.72 18.86 25.00 21.60 18.02 16.63 20.53
Korea 0.69 1.00 2.75 6.33 13.18 20.03 22.57 22.57 8.49 10.66
Luxembourg 0.15 0.55 -0.87 22.57 24.09 25.60 15.34 8.10 24.28 25.99
Netherlands -0.01 0.42 -0.32 16.65 16.58 16.51 13.18 10.31 16.59 16.53
Portugal -0.22 0.25 0.38 9.00 6.82 4.64 10.32 13.13 8.02 7.04
Spain 0.03 0.45 0.36 12.74 13.02 13.30 13.66 13.02 12.92 13.10
Sweden -0.03 0.40 0.21 12.86 12.53 12.20 12.83 12.43 12.64 12.43
United Kingdom 0.31 0.68 0.47 15.97 19.03 22.08 17.42 13.47 18.41 20.85
United States 0.24 0.63 -0.26 19.81 22.18 24.54 16.49 10.55 22.16 24.50
Mean 0.02 0.44 0.00 14.93 15.11 15.29 13.52 11.59 14.97 15.01
Median -0.04 0.39 -0.26 15.76 14.94 15.75 12.67 10.55 15.23 13.76
Minimum -0.51 0.00 -1.32 6.33 6.82 4.64 6.33 6.33 7.78 7.04
Maximum 0.69 1.00 2.75 22.57 24.09 25.60 22.57 22.57 24.28 25.99
The first column shows the standard principal component scores per country (PC), as depicted in Table 9. In the second column these component scores have been rescaled to a 0-1 scale, with 0 and 1 respectively referring to the lowest and highest principal component score within the sample. The column PC* presents the first principal component scores created on the basis of a principal component analysis including the hourly wage variable and the original PC scores from Table 9. The remaining columns of the table show the adjusted hourly wages per country calculated on the basis of various adjustment formulas. Variant (0) represents the standard hourly wage data, as have been used in Fleurbaey & Gaulier (2009). The formulas corresponding to the other scenarios are (with W referring to the standard wage):
- Variant (1): adjusted wage = W + 10 * PC - Variant (2): adjusted wage = W + 20 * PC - Variant (3): adjusted wage = Wmin + PC(0-1) * ( Wmax - Wmin ) - Variant (4): adjusted wage = Wmin + PC*(0-1) * ( Wmax - Wmin ) - Variant (5): adjusted wage = ( 1 + PC / 2 ) * W - Variant (6): adjusted wage = ( 1 + PC ) * W
65
Table 11 Leisure corrections for various wage adjustment scenarios (as % of Gross National Income (GNI) per capita)
Variant (0) Variant (1) Variant (2) Variant (3) Variant (4) Variant (5) Variant (6)
Belgium 3.76% 3.63% 3.50% 2.78% 2.16% 3.65% 3.54%
Canada -7.78% -6.93% -6.08% -6.13% -5.76% -7.18% -6.58%
Denmark 0.86% 0.59% 0.33% 0.46% 0.51% 0.67% 0.49%
Finland -6.85% -6.27% -5.68% -3.58% -2.08% -6.24% -5.64%
France 8.17% 7.30% 6.44% 5.32% 4.30% 7.44% 6.72%
Germany 1.35% 0.91% 0.47% 0.54% 0.56% 1.00% 0.66%
Greece 0.13% 0.12% 0.11% 0.20% 0.24% 0.12% 0.12%
Iceland 4.24% 4.07% 3.90% 2.80% 1.97% 4.08% 3.93%
Ireland 3.21% 3.65% 4.09% 2.88% 1.97% 3.62% 4.03%
Italy 3.65% 3.48% 3.31% 3.99% 4.16% 3.55% 3.46%
Japan -6.68% -9.90% -13.13% -11.34% -9.46% -8.73% -10.78%
Korea -12.64% -26.32% -40.01% -45.08% -45.08% -16.97% -21.30%
Luxembourg -7.97% -8.51% -9.04% -5.42% -2.86% -8.58% -9.18%
Netherlands 4.96% 4.94% 4.92% 3.93% 3.07% 4.95% 4.93%
Portugal -4.00% -3.03% -2.06% -4.59% -5.83% -3.56% -3.13%
Spain -2.58% -2.64% -2.70% -2.77% -2.64% -2.62% -2.66%
Sweden -1.08% -1.05% -1.02% -1.08% -1.04% -1.06% -1.04%
United Kingdom -0.03% -0.04% -0.04% -0.03% -0.03% -0.04% -0.04%
United States -7.36% -8.23% -9.11% -6.12% -3.92% -8.23% -9.10%
Figure 6 Gross National Income (GNI) per capita and leisure corrections according to
various wage adjustment scenarios
0
5000
10000
15000
20000
25000
30000
35000
40000
Belgium
Can
ada
Den
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Finla
nd
Franc
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Ger
man
y
Gre
ece
Icelan
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Ireland
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Japa
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Korea
Net
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Portu
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Swed
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Uni
ted
Kingd
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Uni
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GNIcap uncorrected
Variant (0)
Variant (1)
Variant (2)
Variant (3)
66
As Table 1 earlier on in this chapter already showed, Fleurbaey & Gaulier’s
(2009) corrections for leisure are relatively modest in magnitude as compared with the
authors’ inequality corrections. Table 11 and Figure 6 both confirm this pattern. They
show that the authors’ corrections are positive for quite a lot European countries
(particularly France, Italy, Belgium and the Netherlands) and negative for amongst others
the Asian countries, the United States, Canada and the Iberian countries.
Because Fleurbaey and Gaulier’s leisure corrections are quite small in relation to
GNI per capita and because we have adhered to the principle that our wage adjustments
preferably should not completely overthrow the ranking of countries based on their
hourly wages, it is no surprise that the various wage adjustment scenarios only lead to
moderate changes in the leisure corrections. Nevertheless, we should keep in mind that
changes in our income measure of about 1 percentage point are certainly not negligible.
Such a change can imply a change in the relative leisure correction of about 10 to 40
percent, which is obviously far from negligible.
Moreover, Table 11 displays a consistent pattern: for countries like Denmark and
Canada (who do not care that much about leisure, according to our principal component
analysis) the corrections under scenarios 1 to 6 tend to be less pronounced than under
scenario 0, and for countries like the United States and Luxembourg (who care relatively
much about leisure, according to our principal component analysis) the leisure
corrections under scenarios 1 to 6 tend to be more pronounced than Fleurbaey &
Gaulier’s (2009) corrections. The most significant deviations from Fleurbaey & Gaulier
(2009) are observed for the Asian countries, especially Korea. Figure 6 clearly shows that
these countries are most sensitive to the various wage adjustment scenarios. Apparently,
the relative wages and relative component scores of these countries fall widely apart,
causing the large deviations observed among the different adjustment scenarios.
Finally, nevertheless, as Table 12 on the next page indicates, the relative ranking
of countries in terms of leisure-corrected GNI per capita is not very sensitive to the
different wage adjustment formulas. However, this can again be largely explained by the
fact that we did not intend to completely overthrow the ranking of countries based on
their hourly wages.
To conclude, this paragraph can be considered as some kind of a robustness check
of the conclusion drawn after the previous paragraph on the inequality corrections. This
paragraph has argued that Fleurbaey & Gaulier’s (2009) leisure corrections insufficiently
take into account country-specific preferences for leisure. Furthermore, we have shown
that adjusting the authors’ leisure corrections by way of survey-derived information on
subjective preferences can make a modest, though significant difference. Hence, this
paragraph confirms the conclusion of the previous paragraph that it is useful to take
country-specific preferences into consideration in international welfare comparisons.
3.4 Final words
We hope that this chapter has succeeded in demonstrating that the
multidimensional method of welfare measurement of Fleurbaey & Gaulier (2009), though
being very promising, is not entirely satisfactory. Hopefully this chapter has pointed out
67
Table 12 Country rankings of GNI per capita under different wage adjustment scenarios (as % of sample average and absolute ranks)
uncorrected Variant (0) Variant (1) Variant (2) Variant (3)
Belgium 103% 6 108% 5 109% 5 110% 5 109% 5
Canada 101% 9 94% 13 96% 13 97% 12 97% 12
Denmark 104% 5 107% 7 107% 7 108% 7 108% 6
Finland 95% 13 90% 15 91% 14 93% 14 94% 14
France 98% 10 107% 6 107% 8 107% 8 106% 8
Germany 94% 14 96% 12 97% 12 97% 13 97% 13
Greece 69% 17 70% 17 71% 17 71% 17 71% 17
Iceland 101% 8 107% 8 107% 6 108% 6 107% 7
Ireland 106% 4 111% 4 112% 4 113% 4 112% 4
Italy 93% 15 98% 10 98% 10 99% 10 99% 10
Japan 97% 11 92% 14 89% 15 87% 15 88% 15
Korea 65% 19 57% 19 49% 19 40% 19 37% 19
Luxembourg 197% 1 184% 1 185% 1 185% 1 192% 1
Netherlands 106% 3 113% 3 114% 3 115% 3 113% 3
Portugal 66% 18 64% 18 65% 18 66% 18 65% 18
Spain 81% 16 80% 16 81% 16 81% 16 81% 16
Sweden 97% 12 97% 11 98% 11 99% 11 98% 11
United Kingdom 101% 7 103% 9 104% 9 105% 9 104% 9
United States 127% 2 120% 2 120% 2 119% 2 123% 2
two messages in this regard: first, that Fleurbaey & Gaulier (2009) insufficiently take
account of country-specific preferences and, secondly, that more directly taking into
account such preferences can significantly alter Fleurbaey and Gaulier’s income
corrections. Finally, it should be emphasized that our own calculations are not intended
nor considered to be superior to Fleurbaey & Gaulier’s (2009) calculations. The only
goals served by our own calculations are to show that more directly taking into account
country-specific preferences can make a difference, to explore the possibilities for
including such country-specific preferences in international welfare comparisons and to
illustrate what one can be confronted with if one wants to include such preferences.
68
IV Equivalent incomes and country-specific preferences regarding
health and unemployment
In the previous chapter we discussed one method to construct equivalent income
measures for comparing multidimensional welfare across countries. This method was
founded on a formal economic model, from which correction formulas were derived. The
corrections were mainly market-based, with some supplementary parameter assumptions.
As we noted, however, this method is not fully satisfactory in our opinion, since we doubt
whether its corrections pay sufficient attention to people’s real preferences. Therefore, we
have among other things investigated the possibilities of including (country-specific)
preferences more directly in the method’s framework.
Nevertheless, it should be emphasized that the method discussed in the previous
chapter is by no means the only way to construct equivalent incomes. Ergo, it makes
sense to also examine other methods of calculating equivalent incomes. This chapter will
go over one of these methods. This particular method is especially interesting, as it
replies to our criticism towards the method discussed in chapter 3, aiming to incorporate
subjective preferences as directly as possible in the construction of equivalent income
measures.
4.1 Equivalent incomes in Fleurbaey, Decancq & Schokkaert (2009)
The equivalent income approach that will be dealt with in this chapter is largely
borrowed from Marc Fleurbaey, Koen Decancq and Erik Schokkaert’s (2009) discussion
paper ‘What good is happiness?’, which investigates a method to incorporate information
from subjective well-being studies in the construction of equivalent incomes. We believe
that this paper deserves much more attention than it has received until now, since it
proposes an interesting new welfare measurement method and because it combines sound
and appealing theoretical and conceptual arguments with compelling empirical evidence.
Starting-point of Fleurbaey et al. (2009) is the view that in constructing a
multidimensional welfare measure one must rely on individual preferences for weighting
the various dimensions of life. As Fleurbaey et al. (2009:5) note: “It would certainly be
utterly absurd to evaluate individual situations without any connection to human needs
and goals as perceived by the individuals themselves.” Unmistakably, this statement
relates closely to our preferred notion of welfare (see chapter 2) and the underlying ideas
of this thesis. Nonetheless, despite the authors’ plea for attention for subjective
preferences and perceptions, they reject the often-drawn welfarist conclusion that we
should focus on subjective well-being measures for evaluating welfare. As the authors
namely observe, one’s level of subjective well-being and one’s preference ranking can
very well fall apart.
This idea can be illustrated by the following equations:
( )iiii ARf ,,σσ = and ( )iii dSS ,σ= .
69
The first equation represents an individual’s life satisfaction level σ, which is
determined by f (the individual’s vector of functionings describing his life), R (the
person’s valuation ordering of the various functionings, i.e. his preferences) and A (the
person’s frame of reference, including the person’s personal history and his social
reference group). Thus, one’s life satisfaction level is essentially determined by his
objective living conditions, by what he considers to be ‘the good life’ and by his frame of
reference. Intuitively, this conceptualization certainly makes sense. The second function,
in turn, specifies an individual’s expressed life satisfaction level S, which is a function of
both his true satisfaction level σ and a disturbance term d (as we cannot expect
individuals to give an answer to a life satisfaction question that is exactly equal to their
true satisfaction level, because there are biases related to personality, et cetera).
From the equations above it is evident that people’s levels of expressed life
satisfaction and their preference ranking can very well fall apart, caused by differences in
people’s frame of reference A or by differences in the disturbance term d. According to
Fleurbaey et al. (2009) this is a serious issue, which renders expressed life satisfaction
measures unsatisfactory as sole measures of welfare. To support this point, the authors
present the example of a rich life f** and a poor life f*, for individual i and j respectively,
who share the same preferences regarding what constitutes a good life (Ri = Rj). If,
however, the rich person suffers from high aspirations, while the poor person has adapted
to his miserable situation, it can very well be that σi = σj (or Si = Sj). Concluding from
these equal life satisfaction levels that both lives are equally good would, nevertheless,
neglect the persons’ own preferences. According to the authors, this is an unacceptable
practice, as their core principle is to respect people’s subjective preferences.51
As an alternative, Fleurbaey et al. (2009) propose another method of welfare
measurement, which in their opinion both avoids the physical-condition and valuation /
preference neglect of welfarism as well as the paternalism related to many composite
welfare indexes. The authors’ proposal boils down to calculating equivalent income
measures based on people’s functionings (living conditions), valued at willingness-to-pay
estimates derived from life satisfaction regressions. Thus, the resulting equivalent
incomes contain information on functionings f and preferences R, while largely
abstracting from reference frames A, which the regressions correct for by including fixed-
effects and certain control variables.
Fleurbaey et al. (2009) illustrate their approach using survey data from the Russia
Longitudinal Monitoring Survey (RLMS). This survey has been collecting data since the
early 1990s to monitor the effects of Russia’s reform policies on people’s health and
economic situation. It is an extremely rich data source and, as a consequence, very
popular among researchers. The dependent variable of Fleurbaey et al.’s (2009)
regressions consists of the respondents’ answers on a five-point scale to the question: “To
what extent are you satisfied with your life in general at the present time?” The most
51
The authors, however, do recognize that people’s preference rankings may sometimes contain ‘mistaken’
beliefs about what is important (striving for fulfillment of needs which are inherently actually not valued
that much by the individual), due to imperfect information, irrationality or conditioning by questionable
social norms. Therefore, the authors have refined their core principle to the claim that if there is no reason
to attribute flaws to people’s valuation orderings, they should be respected.
70
important functionings included in the regressions are, in line with previous findings in
the subjective well-being literature, income (expenditures), self-assessed health, housing
and (un)employment. In addition, the regressions control for age, sex, marital status,
education, social status and the average functionings vectors within the respondents’
reference groups. By including interaction terms of the functioning variables and the
control variables, the authors are able to derive amongst others age-specific and sex-
specific estimates of the willingness-to-pay for the various functionings. These estimates
are then used for calculating each individual’s equivalent income, based on the deviations
between an individual’s actual functionings bundle and the reference bundle (which
includes perfect health, not being unemployed, et cetera as reference values).
Analyzing the obtained equivalent incomes, Fleurbaey et al. (2009) compare their
preferred measure of welfare with its two most popular alternatives: income
(expenditures) and subjective well-being (as measured by expressed life satisfaction).
The authors compare the rankings of individuals that result from these different
alternatives. First, looking at the correlations between the rankings, they find that the
equivalent incomes are far from perfectly correlated with income and that there is hardly
any correlation at all with expressed life satisfaction. Second, examining intertemporal
mobility, the results indicate that expressed life satisfaction is by far the most volatile
measure, whereas equivalent income provides by far the most persistent rankings of
individuals. As the authors note, this observation comes as no surprise, since expressed
life satisfaction is likely to be more influenced by temporary moods and random events
than income measures, which are in turn likely to be more volatile than the equivalent
income measure, since the latter is by definition a weighted average of various life
dimensions (some of which change only very slowly over time). Third, and finally, the
authors have compared the characteristics of the ‘deprived’ according to the three
measurement approaches, finding amongst others that, as compared with the people with
the lowest equivalent incomes, the people with the lowest levels of expressed life
satisfaction are richer, in a better state of health, have a nicer house, are better educated
and less likely to be member of a minority group. According to the authors, these
findings support their hypothesis that subjective well-being does not properly capture
people’s preferences and their objective living conditions.
4.2 Applying Fleurbaey et al.’s (2009) approach to country-specific
preferences towards health and unemployment
Considering Fleurbaey et al.’s (2009) arguments and findings, we think it is a
relevant task to investigate whether we can apply the authors’ equivalent income
approach to our own research problem of obtaining multidimensional welfare measures
that incorporate country-specific preferences for various welfare dimensions. Recall in
this respect our hypothesis, put forward in chapter 2, that accounting for country-specific
preferences could make a significant difference for international welfare comparisons.
Therefore, whereas Fleurbaey et al. (2009) apply their approach to the case of Russia, we
will try to conduct a similar analysis for a sample of countries.
71
4.2.1 Deriving country-specific willingness-to-pay estimates
More specifically, we will investigate the same 24 OECD countries as Fleurbaey
& Gaulier (2009), except for Greece, Iceland and Luxembourg, for which countries we
are confronted with a lack of data. As data source we again use the World Values Survey
(WVS). As dependent variable we use respondents’ answers to the familiar life
satisfaction question: “All things considered, how satisfied are you with your life as a
whole these days? 1-Dissatisfied, 2, …, 9, 10-Satisfied”. Because of the discrete nature of
this variable, we run ordered logit regressions. In terms of functionings included in our
analysis, we will follow Fleurbaey et al. (2009), including variables on the logarithm of
income, self-assessed health and whether a respondent is unemployed.52
Moreover, we
add the generally used control variables related to age, sex and marital status and we
control for time trends and survey-specific effects by the inclusion of survey wave
dummies. From the perspective of our research goals the most important elements of our
regressions are the country dummies and the interaction effects of these country dummies
with the functioning variables subjective health and unemployment status.53
In short, the estimated regression equation can be specified as follows:
( ) ( ) ( ) ( )( ) ( )( ) iiwi
iiciic
iciiii
WaveControls
ntUnemploymeCountryHealthCountry
CountryntUnemploymeHealthLogIncomeonSatisfactiLife
ερπ
µλ
κγβα
++
++
++++=
)(
**
To derive willingness-to-pay estimates from this regression, we first of all have to
set reference values for the functionings considered. Such choices are rather difficult and
one cannot avoid making normative judgments in setting these values, as these choices
have an inherently normative character. We will return to this issue when determining the
exact values of these reference levels, but let us for the moment denote these reference
values by Health and ntUnemployme .
Next, in accordance with the idea of equivalent incomes, we can calculate the
amount of income that makes an individual indifferent between having his actual
functionings bundle including his actual income, and having this specific amount of
income and all other functionings at their reference value. In this context, we interpret
‘being indifferent’ as ‘having the same level of life satisfaction’.54
If we consider, for
example, an individual whose functionings bundle merely differs from the reference
52
Unfortunately, in contrast to the Russia Longitudinal Monitoring Survey (RLMS) that contains lots of
information on housing characteristics and housing prices, the World Values Survey (WVS) contains no
questions related to the value of a respondent’s housing. Therefore, we cannot include housing in our
analysis. A blessing in disguise, however, is that the WVS does provide information on self-assessed health
and unemployment status, since these two functionings are generally found to be more important than
housing for explaining subjective well-being. 53
More details concerning the definitions and sources of the data used can be found in Appendix A. 54
Of course, this interpretation is debatable. One could, for instance, argue that ‘being indifferent’ refers to
equal utilities, whereas life satisfaction data are an imperfect approximation of utility. Notwithstanding
these valid remarks, we think that our interpretation of ‘being indifferent’ is obvious, acceptable and even
almost inevitable for the sake of our analysis.
72
bundle with respect to health, we thus get the following indifference equation (with
health being denoted by H, income by Y and the equivalent income by Y*):
( ) ( ) ( ) ( ) ( ) ( )HCountryHYLogHCountryHYLog iciicii *** λβαλβα ++=++
Some rewriting of this equation yields the expression for the equivalent income Y*:
( ) ( ) ( ) ( )( ) ( ) ( )( )
( ) ( )
( )( ) ( )
( )( ) ( )
( ) ( )
−
+=⇔
−
+=⇔
−
++=⇔
−+
+=⇔
−++=⇔
−+−+=
HHCountry
YY
HHCountry
YLogY
HHCountry
YLogYLog
HHCountry
YLogYLog
HHCountryYLogYLog
HHCountryHHYLogYLog
i
ic
i
i
ic
i
i
ic
i
i
ic
i
iici
iicii
α
λβ
α
λβ
α
λβ
α
λβ
λβαα
λβαα
exp**
exp*exp*
exp*exp
*
*
*
In the final equation above the second factor on the right-hand side can be
interpreted as the willingness-to-pay factor. If the value of this factor is larger than 1, the
equivalent income Y* of individual i is larger than her actual income Yi, meaning that her
functionings bundle has a greater value than the reference bundle (e.g. her perceived
health status is better than the reference value for perceived health status). On the other
hand, a willingness-to-pay factor below 1 indicates that the person’s actual functionings
bundle has less value as compared with the reference bundle. In this case her equivalent
income is lower than her actual income, implying that her actual income represents an
overestimation of her welfare level.
So, following this procedure, we can obtain willingness-to-pay estimates from life
satisfaction regressions. The country-specific element of these estimates is represented by
the regression coefficient λc, the country-specific coefficient of the health variable.
Before presenting the regression results, it useful to shortly compare the
regression specification above with the life satisfaction regressions presented in chapter
3. Whereas the interpretation of the regression coefficients of interest in chapter 3 was
somewhat problematic due to the fact that we only had country-level data for the variable
of interest (inequality), the regressions presented here do not suffer from this problem, as
all the variables in these regressions concern individual-level data. Thus, the regressions
in this chapter seem relatively more useful and more reliable for deriving estimates on
country-specific preferences. Nonetheless, it is important to realize that general points of
criticism towards the method of deriving information on preferences from subjective
well-being regressions may still apply; the in this manner derived preference estimates
might, for instance, be subject to context-dependency (recall also our discussion on the
73
appropriateness of the regression-based preference derivation method in the previous
chapter).
The regression results accompanying the regression specification presented above
are shown in Table 13 below. First of all, these results show that the coefficients of all
control variables are strongly significant and in line with the findings of the subjective
well-being literature: the relationship between age and life satisfaction follows a U-
shaped pattern, males tend to report lower satisfaction scores than women and people
who are married or live together as a couple report on average higher levels of life
satisfaction. Furthermore, the coefficients of the wave dummies point out that it is useful
to include survey-wave fixed effects.
More interesting from the perspective of this thesis is that the table shows that the
coefficients of our three main functioning variables are all highly significant. As expected
beforehand, the income variable has a positive sign and the dummy indicating whether
one is unemployed has a negative sign. The sign of the subjective health variable might
look more doubtful at first glance, but this negative sign is also in conformance with our
expectations if we consider that this variable contains respondents’ answers to the WVS
question: “All in all, how would you describe your state of health these days? Would you
say it is… 1-Very good, 2-Good, 3-Fair, 4-Poor, 5-Very poor”. Lower scores for the
subjective health variable thus correspond to a higher level of perceived health, so a
negative coefficient for this variable is simply what we would expect. The magnitude of
the health coefficient implies that a 1 point improvement on the 1-5 subjective health
scale leads to an increase on the 1-10 life satisfaction scale of approximately 0.5 point.
Similarly, if a person is unemployed, his life satisfaction level tends to be 0.3 point lower
as compared with the scenario in which he is not unemployed. Although these effects
might seem rather minor, effects of such magnitudes are in fact far from negligible if we
recognize that almost 75 percent of all respondents report life satisfaction scores of 7 or
higher and that reported satisfaction scores tend to be highly persistent, for a significant
part being determined by personality traits.
Anyway, we should emphasize that, whereas the income coefficient captures the
sample-wide effect of income on life satisfaction, the coefficients for health and
unemployment actually only capture the effects of these variables in the United States,
which is taken as benchmark in the regression of Table 13. The country-specific health
and unemployment effects for all other countries are reflected by the country-specific
interaction terms.
The interaction terms for Australia, for instance, show that Australia does not
differ significantly from the United States in terms of the effects of health and
unemployment on life satisfaction. This also holds for Japan and the Netherlands. Other
countries, like Canada, Finland, New Zealand, Portugal and the United Kingdom differ
only in one respect from the United States. Most of the countries, however, differ
significantly from the United States on both dimensions. This is especially the case for
most European countries, no matter whether they belong to the Nordic, the Mediterranean
or the Continental group. For example, for the Continental group (comprising Austria,
Belgium, Germany, Switzerland and perhaps also France) particularly the strongly
negative effects of unemployment stand out.
74
Table 13 Life satisfaction regression containing country-specific effects of subjective health and unemployment
Dependent Variable: LIFE SATISFACTION
Ordered Logit Regression Model
All things considered, how satisfied are you with your life as a whole these days?
( 1-dissatisfied, 2, 3, 4, 5, 6, 7, 8, 9, 10-satisfied)
Log Income 0.199***
(0.017)
Subjective health -0.531***
(0.028)
Unemployment dummy -0.300***
(0.105)
Country-specific effects D_AUSL 0.131 Health*AUSL -0.013 Unem*AUSL 0.130
(0.101) (0.047) (0.238)
D_AUS 0.549*** Health*AUS -0.114** Unem*AUS -0.781**
(0.131) (0.053) (0.401)
D_BEL 0.256** Health*BEL -0.198*** Unem*BEL -0.358*
(0.112) (0.051) (0.189)
D_CAN 0.153* Health*CAN 0.003 Unem*CAN -0.290*
(0.089) (0.044) (0.155)
D_DEN 0.918*** Health*DEN -0.165*** Unem*DEN -0.587***
(0.115) (0.054) (0.209)
D_FIN 0.492*** Health*FIN -0.126** Unem*FIN -0.05
(0.133) (0.060) (0.188)
D_FRA -0.537*** Health*FRA -0.131** Unem*FRA -0.640***
(0.141) (0.058) (0.261)
D_GER 0.035 Health*GER -0.179*** Unem*GER -0.933***
(0.092) (0.039) (0.156)
D_IRE 0.602*** Health*IRE -0.231*** Unem*IRE -0.501**
(0.131) (0.064) (0.236)
D_ITA 0.334** Health*ITA -0.266*** Unem*ITA -0.620***
(0.138) (0.057) (0.247)
D_JAP -0.892*** Health*JAP 0.015 Unem*JAP -0.172
(0.109) (0.045) (0.240)
D_KOR -0.813*** Health*KOR -0.148* Unem*KOR -0.498
(0.191) (0.087) (0.328)
D_NET -0.057 Health*NET 0.009 Unem*NET -0.173
(0.143) (0.065) (0.332)
D_NEW 0.989*** Health*NEW -0.429*** Unem*NEW -0.162
(0.149) (0.072) (0.229)
D_NOR 0.224** Health*NOR -0.090** Unem*NOR -0.763***
(0.096) (0.045) (0.267)
D_POR 0.466*** Health*POR -0.242*** Unem*POR 0.435
(0.165) (0.061) (0.343)
D_SPA -0.316*** Health*SPA -0.072** Unem*SPA -0.379***
(0.088) (0.037) (0.141)
75
D_SWE 0.681*** Health*SWE -0.180*** Unem*SWE -0.421*
(0.124) (0.058) (0.255)
D_SWI 0.736*** Health*SWI -0.108* Unem*SWI -1.286***
(0.128) (0.062) (0.421)
D_UK -0.303*** Health*UK 0.032 Unem*UK -0.423***
(0.117) (0.054) (0.238)
D_US base Health*US base Unem*US base
Dummy Wave1 0.199***
(0.036)
Dummy Wave2 0.205***
(0.033)
Dummy Wave3 -0.039
(0.036)
Age -0.042***
(0.003)
Age squared 0.057***
(0.003)
Dummy Male -0.131***
(0.016)
Dummy Married 0.388***
(0.029)
Dummy As Married 0.250***
(0.040)
Dummy Divorced -0.220***
(0.047)
Dummy Separated -0.588***
(0.066)
Dummy Widowed -0.207***
(0.044)
Observations 55266
Pseudo R-square 0.18
-2 Log Likelihood 179199.8
Numbers in parentheses denote standard errors. Three stars indicate significance at the 1% level, two stars at the 5% level and one star at the 10% level. An explanatory list of the country abbreviations can be found in Appendix E.
In any case, we can conclude from Table 13 that significant differences in
preferences apparently exist among countries and that it therefore makes sense to
estimate country-specific willingness-to-pay measures. These WTP estimates, calculated
in accordance with the formulas above, are presented in Table 14.55
In this table, columns
3 and 4 present the WTP equivalence factors for respectively a positive and a negative
deviation of size 0.2 between the country’s average health score and the health reference
score. Likewise, columns 5 and 6 represent the WTP equivalence factors for respectively
a positive and a negative deviation of 2 percentage points between the country’s actual
55
For interaction terms for which the country-specific coefficient is insignificant, we have set the value of λ
or µ equal to zero in our calculations. Thus, these countries have the same willingness-to-pay estimates as
the United States.
76
unemployment rate and the reference unemployment rate.56
Recall for the interpretation
of the results in Table 14 that equivalent incomes can be obtained by multiplying
people’s actual income by these WTP equivalence factors.
Table 14 Country-specific willingness-to-pay estimates as derived from Table 13
Coefficient factors
α
λβ c+
α
µγ c+
Implied willingness-to-pay (WTP) equivalence factors
( )
−
+HHc
α
λβexp ( )
−
+UUc
α
µγexp
Health Unemployment Health Unemployment
+0.2 -0.2 +2% -2%
Australia -2.67 -1.51 0.59 1.71 0.97 1.03
Austria -3.24 -5.43 0.52 1.91 0.90 1.11
Belgium -3.66 -3.31 0.48 2.08 0.94 1.07
Canada -2.67 -2.97 0.59 1.71 0.94 1.06
Denmark -3.50 -4.46 0.50 2.01 0.91 1.09
Finland -3.30 -1.51 0.52 1.94 0.97 1.03
France -3.33 -4.72 0.51 1.95 0.91 1.10
Germany -3.57 -6.20 0.49 2.04 0.88 1.13
Ireland -3.83 -4.03 0.46 2.15 0.92 1.08
Italy -4.01 -4.62 0.45 2.23 0.91 1.10
Japan -2.67 -1.51 0.59 1.71 0.97 1.03
Korea -3.41 -1.51 0.51 1.98 0.97 1.03
Netherlands -2.67 -1.51 0.59 1.71 0.97 1.03
New Zealand -4.82 -1.51 0.38 2.62 0.97 1.03
Norway -3.12 -5.34 0.54 1.87 0.90 1.11
Portugal -3.88 -1.51 0.46 2.17 0.97 1.03
Spain -3.03 -3.41 0.55 1.83 0.93 1.07
Sweden -3.57 -3.62 0.49 2.04 0.93 1.08
Switzerland -3.21 -7.97 0.53 1.90 0.85 1.17
United Kingdom -2.67 -3.63 0.59 1.71 0.93 1.08
United States -2.67 -1.51 0.59 1.71 0.97 1.03
56
Because the unemployment variable has a dummy character, a 0.02 increase in the country’s average
value of the unemployment dummy is equivalent to a 2 percentage points increase in the country’s
unemployment rate. Note, however, that the unemployment rate definition employed here is somewhat
different than the standard definition of the unemployment rate. Whereas normally the unemployment rate
is defined as total unemployment as a percentage of the total labour force, here we define the
unemployment rate as total unemployment as a percentage of a country’s population aged 15 or older. We
have opted for this approach, because our unemployment dummy equals 1 if the respondent answered
‘Unemployed’ to the WVS question “Are you employed now or not? If yes: about how many hours a
week? If more than one job: only for the main job.”, where the answering options are: Full time, Part time,
Self Employed, Retired, Housewife, Students, Unemployed, Other. Since all survey respondents had to
answer this question, and because the survey respondents are a representative sample of a country’s adult
population (the population aged 18 or older), we thus have to use our non-standard definition of the
unemployment rate. Otherwise, the WTP factors would not be in line with the regression results, which
show the effect of unemployment as compared with the options Full time, Part time, Self Employed,
Retired, Housewife, et cetera.
77
One strong conclusion that can be derived from Table 14 is that people apparently
attach an enormous amount of value to their health status. In many countries a 0.2
decrease on the health dimension (for instance, from a perceived health status of ‘almost
good’, i.e. 2.2, to a perceived status of ‘good’, i.e. 2.0) is equivalent to an income
increase of even 100 percent or more. In this sense, the regressions and the derived
willingness-to-pay estimates provide powerful support for multidimensional welfare
measures: income seems in itself relatively unimportant as compared with a factor like
self-assessed health. Despite this overall conclusion, we can still observe wide variation
among the country-specific willingness-to-pay estimates for health: in Italy, for example,
a 0.2 decrease on the health dimension is equivalent to a 123 percent increase in income,
whereas in the United States such a health improvement is equivalent to only a 71 percent
increase in income.
Concerning the willingness-to-pay estimates related to unemployment, we
observe that unemployment tends to have a smaller importance than health (in spite of the
fact that the specific WTP estimates presented in Table 14 are obviously also dependent
on the size of the deviations we have chosen in this table: 0.2 for the health dimension
and 2 percentage points for the unemployment dimension). Nevertheless, again we can
observe quite some variation among countries; compare for example Germany or
Switzerland with the United States.
Finally, we can also trace some group-specific patterns in Table 14 for our earlier
defined groups of countries: notice, for instance, the similarities among the Anglo-Saxon
countries on the health dimension and the similarities among the Continental European
countries on the unemployment dimension.
However, at this point it is time for a piece of criticism, directed both at the results
presented above as well as to the results presented in Fleurbaey et al. (2009). Namely, if
we are accounting for country-specific preferences (or as in Fleurbaey et al. (2009), age-
specific, sex-specific, etc.) for health and unemployment, why do we then not also
account for country-specific preferences towards income? This is certainly not a strange
question, as it is intuitively quite conceivable that not everyone cares as much about
income. This is even more so if we examine a sample comprising various countries,
among which large cultural differences are likely to exist. It is very well possible that
some people have a more materialistic attitude than others, with a relatively stronger
focus on consumption and status. As a result, there may be significant differences
regarding the income-life satisfaction relationship among countries or among groups
within society. Thus, keeping in mind Fleurbaey et al.’s (2009) remark that we should
respect individuals’ preferences as much as possible, it seems a good idea to also include
country-specific income effects in our analysis. Our regression equation then becomes:
( ) ( ) ( ) ( )( ) ( )
( ) ( ) iiwiiic
iiciic
iciiii
WaveControlsntUnemploymeCountry
HealthCountryLogIncomeCountry
CountryntUnemploymeHealthLogIncomeonSatisfactiLife
ερπµ
λτ
κγβα
+++
++
++++=
)(*
**
And the expression for equivalent income Y* becomes (for a person that only
differs from the reference functionings bundle in terms of health):
78
( ) ( )
−
+
+= HH
Country
CountryYY i
ic
ic
iτα
λβexp**
Table 15 below reports the results of the accompanying ordered logit regression.
Table 15 Life satisfaction regression containing country-specific effects of income,
subjective health and unemployment
Dependent Variable: LIFE SATISFACTION
Ordered Logit Regression Model
All things considered, how satisfied are you with your life as a whole these days?
( 1-dissatisfied, 2, 3, 4, 5, 6, 7, 8, 9, 10-satisfied)
Log Income 0.199***
(0.036)
Subjective health -0.512***
(0.027)
Unemployment dummy -0.368***
(0.096)
Country-specific effects D_AUSL 2.087*** LogY*AUSL -0.196*** Health*AUSL -0.059 Unem*AUSL 0.050
(0.624) (0.063) (0.047) (0.239)
D_AUS 2.199** LogY*AUS -0.167 Health*AUS -0.147*** Unem*AUS -0.741*
(1.102) (0.111) (0.054) (0.400)
D_BEL -1.266* LogY*BEL 0.151** Health*BEL -0.163*** Unem*BEL -0.334**
(0.756) (0.077) (0.049) (0.170)
D_CAN 0.702 LogY*CAN -0.055 Health*CAN 0 Unem*CAN -0.342**
(0.608) (0.061) (0.042) (0.143)
D_DEN 1.832*** LogY*DEN -0.096 Health*DEN -0.160*** Unem*DEN -0.497***
(0.748) (0.077) (0.051) (0.179)
D_FIN 4.419*** LogY*FIN -0.403*** Health*FIN -0.175*** Unem*FIN -0.122
(1.112) (0.113) (0.060) (0.187)
D_FRA -3.066*** LogY*FRA 0.266*** Health*FRA -0.099* Unem*FRA -0.645***
(0.912) (0.094) (0.054) (0.221)
D_GER 0.085 LogY*GER -0.003 Health*GER -0.200*** Unem*GER -0.868***
(0.635) (0.064) (0.038) (0.150)
D_IRE 0.249 LogY*IRE 0.032 Health*IRE -0.194*** Unem*IRE -0.605***
(0.913) (0.096) (0.061) (0.204)
D_ITA -0.208 LogY*ITA 0.044 Health*ITA -0.238*** Unem*ITA -0.669***
(0.980) (0.103) (0.052) (0.193)
D_JAP -3.307*** LogY*JAP 0.257*** Health*JAP -0.007 Unem*JAP -0.002
(0.653) (0.066) (0.044) (0.233)
D_KOR -13.085*** LogY*KOR 1.282*** Health*KOR -0.087 Unem*KOR -0.060
(1.539) (0.159) (0.087) (0.327)
D_NET 1.297 LogY*NET -0.146* Health*NET 0.028 Unem*NET 0.021
(0.829) (0.084) (0.061) (0.278)
D_NEW 2.917*** LogY*NEW -0.191** Health*NEW -0.487*** Unem*NEW -0.182
(0.978) (0.098) (0.073) (0.231)
D_NOR 1.142 LogY*NOR -0.094 Health*NOR -0.098** Unem*NOR -0.664***
79
(0.695) (0.071) (0.044) (0.252)
D_POR 1.087 LogY*POR -0.064 Health*POR -0.275*** Unem*POR 0.506
(0.918) (0.098) (0.062) (0.340)
D_SPA -2.505*** LogY*SPA 0.230*** Health*SPA -0.033 Unem*SPA -0.289**
(0.533) (0.055) (0.036) (0.125)
D_SWE 2.520*** LogY*SWE -0.197** Health*SWE -0.178*** Unem*SWE -0.344
(0.928) (0.095) (0.055) (0.240)
D_SWI 2.310*** LogY*SWI -0.154** Health*SWI -0.146*** Unem*SWI -0.592*
(0.758) (0.074) (0.058) (0.369)
D_UK 1.750*** LogY*UK -0.212*** Health*UK 0.010 Unem*UK -0.379*
(0.682) (0.069) (0.052) (0.204)
D_US base LogY*US base Health*US base Unem*US base
Dummy Wave1 0.189***
(0.037)
Dummy Wave2 0.245***
(0.034)
Dummy Wave3 0.000
(0.037)
Age -0.038***
(0.003)
Age squared 0.052***
(0.003)
Dummy Male -0.125***
(0.015)
Dummy Married 0.471***
(0.024)
Dummy As Married 0.319***
(0.036)
Dummy Divorced -0.17***
(0.044)
Dummy Separated -0.518***
(0.063)
Dummy Widowed -0.127***
(0.040)
Observations 55266
Pseudo R-square 0.18
-2 Log Likelihood 189824
Numbers in parentheses denote standard errors. Three stars indicate significance at the 1% level, two stars at the 5% level and one star at the 10% level. An explanatory list of the country abbreviations can be found in Appendix E.
Table 15 in fact dissects some part of the general country fixed-effects captured
by the standard country dummies: a comparison of Table 13 and Table 15 shows that the
decrease in each country’s coefficient of the standard country dummy caused by the
inclusion of country-specific income effects is almost exactly equal to the country-
specific income coefficient times the country’s average value of the logarithm of income.
So, some part of the variation among countries that was previously accounted for by the
standard country dummies is now captured by the country-specific income effects.
Except for these changes, the values and significance levels of all other variables remain
80
nearly perfectly the same after the inclusion of the country-specific income effects. This
can be interpreted as a signal of the robustness of the regression results in Table 13.
As Table 15 shows, the American income coefficient is exactly equal to the
general income coefficient in Table 13. Furthermore, although it is difficult to distinguish
any particular patterns in the country-specific income effects, there indeed seems to be
some variation in income preferences across countries: compare, for instance, the
Netherlands or Australia with Spain or France. This observation supports our hypothesis
that it is relevant to allow for country-specific preferences for income when constructing
equivalent incomes. Table 16, which is similar to Table 14, next presents the willingness-
to-pay estimates based on the regressions results in Table 15.
Table 16 Country-specific willingness-to-pay estimates as derived from Table 15
Coefficient factors
c
c
τα
λβ
+
+
c
c
τα
µγ
+
+
Implied willingness-to-pay (WTP) equivalence factors
( )
−
+
+HH
c
c
τα
λβexp ( )
−
+
+UU
c
c
τα
µγexp
Health Unemployment Health Unemployment
+0.2 -0.2 +2% -2%
Australia -170.67 -122.67 1.4999E-15 6.66726E+14 0.09 11.63
Austria -3.31 -5.57 0.52 1.94 0.89 1.12
Belgium -1.93 -2.01 0.68 1.47 0.96 1.04
Canada -2.57 -3.57 0.60 1.67 0.93 1.07
Denmark -3.38 -4.35 0.51 1.96 0.92 1.09
Finland 3.37 1.80 1.96 0.51 1.04 0.96
France -1.31 -2.18 0.77 1.30 0.96 1.04
Germany -3.58 -6.21 0.49 2.05 0.88 1.13
Ireland -3.55 -4.89 0.49 2.03 0.91 1.10
Italy -3.77 -5.36 0.47 2.13 0.90 1.11
Japan -1.12 -0.81 0.80 1.25 0.98 1.02
Korea -0.35 -0.25 0.93 1.07 1.00 1.00
Netherlands -9.66 -6.94 0.14 6.90 0.87 1.15
New Zealand -124.88 -46.00 1.424E-11 70227092038 0.40 2.51
Norway -3.07 -5.19 0.54 1.85 0.90 1.11
Portugal -3.96 -1.85 0.45 2.21 0.96 1.04
Spain -1.19 -1.53 0.79 1.27 0.97 1.03
Sweden -345.00 -184.00 1.0806E-30 9.25378E+29 0.03 39.65
Switzerland -14.62 -21.33 0.05 18.62 0.65 1.53
United Kingdom 39.39 57.46 2635.95 0.00038 3.16 0.32
United States -2.57 -1.85 0.60 1.67 0.96 1.04
A quick inspection of Table 16 immediately shows that we are now confronted
with some worrisome results. The problems manifest themselves in particular for some
countries, including amongst others Australia, Finland, Sweden and the United Kingdom.
For example, Australia has extremely large WTP equivalence factors and the equivalence
factors for the United Kingdom and Finland even have counterintuitive signs: according
81
to Table 16, these two countries are willing to pay for a deterioration of their health status
or for an increase in unemployment. Obviously, such results are rather dubious.
Nonetheless, reinvestigating Table 15 the cause of these problems can easily be
discovered. All countries for which we have questionable results in Table 16 have
significant and relatively large negative country-specific income effects. As a
consequence, the overall income effect in these countries is very close to zero or even
negative (as is the case for Finland and the United Kingdom). Therefore, the denominator
( α + τcCountry ) in the formula for calculating the WTP equivalence factors approaches
zero or gets negative, leading to respectively an explosion or a sign reversal of the
equivalence factors. This is exactly what we can observe in Table 16.
Although we can thus formally explain the extreme results in Table 16, the
question remains how we should interpret them and to what extent we should be worried
about these findings. In this respect, it is at first sight quite startling that we observe a
negative income-life satisfaction relationship in some countries. This finding contrasts
sharply with the generally found positive relationship between income and life
satisfaction within a country at one point in time (e.g. see Stevenson & Wolfers, 2008).
However, we would like to argue that there is not too much reason for concern. We
suspect that there is quite some multicollinearity involved in this matter: whether one is
unemployed or not significantly affects a person’s income, and a person’s income, in
turn, is a potentially powerful determinant of a person’s health status. It is therefore very
well possible that, whereas income and life satisfaction tend to be positively correlated,
the regression coefficient becomes much less positive when controlling for health and
unemployment status. Pursuing this line of reasoning, income may be mainly an
instrumental variable with respect to life satisfaction. Under this scenario, the positive
bivariate income-life satisfaction correlation does not reflect that people care so much
about income, but instead chiefly reflects that people care about health and
unemployment, which are correlated with income. The remaining value attached to
income, apart from its correlation with health and unemployment, may then be relatively
low or even negative. Of course, some measurement error may also play a role in this
context.
Investigating this hypothesis, it is instructive to have a look at the correlations
between the logarithm of income (Y), health (H), unemployment (U) and life satisfaction
(LS). Table 17 shows per country the bivariate correlations between these variables and
Table 18 presents for each country the partial correlations between the three functionings
and life satisfaction after controlling for the other two functionings.57 Table 17 shows that
for every country each single bivariate correlation has the expected sign, with amongst
others income being positively correlated with life satisfaction and negatively correlated
with the subjective health variable and the unemployment dummy. Moreover, the table
demonstrates that health is more strongly correlated with life satisfaction than the other
two functionings. This pattern is also confirmed by the significance levels of the
functioning variables in Table 13 and Table 15, the health variable in those regressions
having by far the highest significance level. If we turn our attention to Table 18, we can
observe that, while the partial correlations between life satisfaction and health are hardly
57
All bivariate correlations in Table 17 are significant at the 1% level. Unfortunately, our statistical
package (SPSS Statistics 17.0) cannot provide us with the significance levels of the partial correlations in
Table 18.
82
Table 17 Bivariate correlations between life satisfaction and functionings
LS - H LS - U LS - Y H - Y U - Y
Australia -0.26 -0.05 0.05 -0.20 -0.24
Austria -0.33 -0.08 0.09 -0.23 -0.06
Belgium -0.31 -0.13 0.16 -0.18 -0.09
Canada -0.23 -0.16 0.12 -0.18 -0.19
Denmark -0.30 -0.12 0.13 -0.24 -0.03
Finland -0.30 -0.11 0.05 -0.15 -0.23
France -0.26 -0.15 0.20 -0.16 -0.12
Germany -0.31 -0.19 0.09 -0.19 -0.01
Ireland -0.27 -0.21 0.16 -0.25 -0.19
Italy -0.23 -0.11 0.09 -0.18 -0.09
Japan -0.24 -0.07 0.14 -0.10 -0.13
Korea -0.21 -0.08 0.24 -0.13 -0.13
Netherlands -0.26 -0.08 0.11 -0.17 -0.15
New Zealand -0.39 -0.16 0.16 -0.25 -0.27
Norway -0.28 -0.07 0.08 -0.16 -0.04
Portugal -0.32 -0.02 0.11 -0.26 -0.04
Spain -0.23 -0.13 0.15 -0.21 -0.05
Sweden -0.35 -0.09 0.02 -0.14 -0.01
Switzerland -0.25 -0.10 0.08 -0.21 -0.06
United Kingdom -0.23 -0.14 0.08 -0.26 -0.16
United States -0.23 -0.10 0.12 -0.25 -0.12
Table 18 Partial correlations between life satisfaction and functionings
LS - H LS - U LS - Y
Australia -0.26 -0.04 -0.02
Austria -0.31 -0.09 0.01
Belgium -0.29 -0.13 0.10
Canada -0.22 -0.15 0.06
Denmark -0.28 -0.14 0.06
Finland -0.31 -0.13 -0.03
France -0.26 -0.16 0.15
Germany -0.31 -0.19 0.03
Ireland -0.28 -0.16 0.06
Italy -0.24 -0.14 0.03
Japan -0.23 -0.04 0.12
Korea -0.18 -0.05 0.21
Netherlands -0.22 -0.07 0.07
New Zealand -0.37 -0.13 0.04
Norway -0.27 -0.09 0.03
Portugal -0.30 -0.01 0.03
Spain -0.22 -0.14 0.09
Sweden -0.33 -0.11 -0.03
Switzerland -0.23 -0.08 0.02
United Kingdom -0.20 -0.13 0.00
United States -0.21 -0.08 0.06
83
lower than their bivariate counterparts, the correlation factors between life satisfaction
and the logarithm of income diminish considerably. Indeed, we observe non-positive
partial LS – Y correlations for Australia, Finland, Sweden and the United Kingdom, some
of the countries for which Table 16 presented rather extreme results. Furthermore, many
other countries are also left with very small LS – Y correlations after controlling for
health and unemployment.
Having considered Table 17 and Table 18, we think that multicollinearity indeed
provides a plausible explanation for the ‘strange’ willingness-to-pay estimates in Table
16. In this regard, we after all prefer to continue our calculations without including the
country-specific preferences for income. Albeit we still think that it is more correct to
include country-specific income effects in our willingness-to-pay estimations (Table 15
has shown that for many countries income still has a positive impact on life satisfaction
after controlling for health and unemployment and that there is significant cross-country
variation in the strength of these effects), it is more convenient to abstract from these
country-specific income effects, since the influence of multicollinearity at the country-
specific level leads to too much distortions to arrive at sensible willingness-to-pay
estimates.58 Moreover, from the viewpoint of the goal of this thesis (to explore ways to
include country-specific preferences in multidimensional welfare measures) it is in our
opinion for the moment acceptable to abstract from the country-specific preferences
towards income and to proceed our calculations with the results of Tables 13 and 14.
Nevertheless, we think that Tables 15 to 18 convey an interesting conclusion for
the literature on subjective well-being. The results show that if we control for a number
of reasonable variables and include country-specific interaction terms for certain
variables, there appears for some countries to be no relationship or even a negative
relationship between income and life satisfaction at one point in time. This conclusion is
in sharp contrast with the ‘subjective well-being paradigm’, which generally assumes a
positive relationship between income and subjective well-being within a country at one
point in time (e.g. see Clark et al, 2008 and Stevenson & Wolfers, 2008). Nevertheless,
the fact that we have conducted our analysis for over 55,000 individuals from 21
countries and that most of our variables are found to be highly significant and in line with
intuition and previous research strongly contributes to the trustworthiness of our
conclusion. Besides, we consider it as quite striking that an influential paper like
Stevenson & Wolfers (2008) only controls for gender, a quartic in age, and their
interaction terms when they run regressions to underpin their conclusion that there is a
positive link between income and life satisfaction within a country at one point in time.
58
We should realize, however, that the multicollinearity between income, subjective health and
unemployment does not disappear when removing the country-specific income effects from the regression.
The general income variable, of which the regression coefficient can be interpreted as some kind of
weighted average of the country-specific income coefficients, is after all still part of our regression.
Nevertheless, we can state that the removal of the country-specific income effects does diminish the impact
of multicollinearity at the country-specific level, in particular for the countries for which the willingness-to-
pay estimates are most affected by it.
84
4.2.2 Obtaining equivalent incomes correcting for health and unemployment
Though we have now chosen to use the willingness-to-pay equivalent factors of
Table 14, we are still not ready to calculate equivalent incomes for each country. After
all, during this thesis we have several times noted that one needs two things for applying
equivalent income corrections: first, a weighting scheme for the different correction
variables (for which purpose we use our willingness-to-pay estimates) and, second,
reference values for the various functionings concerned. As usual, the choice of these
reference values has an inherently normative character and, hence, there is a wide range
of potential reference values. For example, one can opt for reference values with a
statistical / empirical foundation (e.g. the mean or median value within a certain sample)
or one can use ethical arguments (e.g. principles like ‘everybody should be in perfect
health’). Tables 19 and 20 below present equivalent income corrections, for subjective
health and unemployment separately, for a selection of reference values. Starting-point of
the corrections is the average GNI per capita over the period 2000-2007.59
Obviously, if
one has chosen which reference values one would like to use, it is relatively easy to
combine the health and unemployment corrections into one total correction. Such tables
are, however, not presented here, as we do not think that this adds many insights.
Table 19 confirms what we have already seen in Table 14: that health is a highly
valued welfare dimension. No matter what the exact reference value is, the percentage
income correction tends to be relatively large, in accordance with our hypothesis that
subjective health is more important for people’s life satisfaction than income.
Furthermore, Table 19 shows that particularly the Anglo-Saxon countries and the
Nordic countries score well on the subjective health variable, with Ireland being the top
country. As a result, as compared with most other countries, the Anglo-Saxon and Nordic
countries experience relatively positive income corrections for health. Nonetheless, we
can also clearly observe the effect of preferences, which are captured by the WTP
equivalence factors. If we compare, for instance, the United States and Sweden, we
observe almost the same average health score for both countries, but the health
corrections are significantly more pronounced for Sweden than for the United States
(irrespectively of whether these corrections are positive or negative). The obvious cause
of these differences lies in the fact that the Swedes tend to attach more value to health
than the Americans do.
The table also clearly points out that the Mediterranean, Continental and Asian
countries are the most important ‘losers’ of the health corrections. Many of these
countries combine a relatively low health score with a relatively high value attached to
health, translating into rather negative health corrections, even for quite conservative
59
Notice that, whereas we take the average GNI per capita during the period 2000-2007 as starting-point of
our corrections, the estimated WTP factors are based on a regression that includes all four waves of the
World Values Survey. As a consequence, there is a certain time inconsistency between the preference
estimates and the income corrections for which they are used. Nonetheless, we do not consider this to be a
serious problem, as previous research has systematically shown that (culturally determined) preferences
tend to be rather stable over time. The reason why we have included all four waves of the World Values
Survey in our regression is that we want to use as much as possible of the available information, to arrive at
preference estimates that are as reliable and representative as possible.
85
Table 19 Equivalent income corrections for health, based on various reference values
Correction as % of GNI per capita
Health
variable General
WTP factor GNI per capita (1) (2) (3) (4) (5)
Australia 1.90 -2.67 26,769 56% 27% 0% -23% -91%
Austria 2.31 -3.24 26,752 -55% -65% -74% -81% -99%
Belgium 1.99 -3.66 26,525 32% 0% -28% -50% -97%
Canada 1.83 -2.67 27,869 88% 53% 21% -8% -89%
Denmark 1.83 -3.50 26,914 128% 75% 28% -10% -95%
Finland 2.03 -3.30 24,920 13% -12% -35% -53% -97%
France 2.21 -3.33 25,106 -38% -52% -64% -74% -98%
Germany 2.40 -3.57 25,422 -70% -77% -83% -88% -99%
Ireland 1.74 -3.83 27,095 249% 160% 85% 26% -94%
Italy 2.31 -4.01 23,456 -62% -72% -81% -87% -99%
Japan 2.44 -2.67 23,287 -63% -70% -76% -82% -98%
Korea 2.08 -3.41 18,541 -5% -26% -46% -62% -97%
Netherlands 2.04 -2.67 29,181 7% -12% -31% -47% -94%
New Zealand 1.90 -4.82 19,177 123% 54% 0% -38% -99%
Norway 1.92 -3.12 37,295 58% 24% -6% -31% -94%
Portugal 2.62 -3.88 15,864 -88% -91% -94% -96% -100%
Spain 2.30 -3.03 21,682 -51% -61% -70% -78% -98%
Sweden 1.84 -3.57 27,492 124% 71% 24% -13% -95%
Switzerland 1.86 -3.21 30,872 94% 52% 14% -18% -94%
United Kingdom 1.99 -2.67 27,627 23% 0% -21% -40% -93%
United States 1.85 -2.67 35,733 78% 45% 14% -12% -90%
The value of the health variable in column 1 has been calculated as the country’s average answer over wave 2, 3 and 4 (the period 1989-2004) to the question “All in all, how would you describe your state of health these days? 1-Very good, …, 5-Very poor”. The GNI per capita column represents each country’s average GNI per capita over the period 2000-2007. The corrections in the subsequent columns use the following reference values for the subjective health variable:
(1) the mean value of the health variable across the 21 countries (2.0662) (2) the median value of the health variable across the 21 countries (1.99) (3) the arbitrarily chosen value of 1.9 (4) the arbitrarily chosen value of 1.8 (5) the optimal health value of 1.0
reference values. This pattern is in particular exemplified by countries like Germany,
Italy and Portugal.
Finally, whereas the first four reference values underlying the corrections in Table
19 are quite close to each other, the last column presents the corrections based on the
ethically appealing reference value of perfect health for everyone within the country. For
this scenario every country experiences an extremely large negative health correction,
reflecting both the strong universal preferences for health as well as the relatively large
distance between the countries’ actual health scores and this maximum reference value.
One of the conclusions that can be drawn from Table 20 is that the unemployment
corrections are significantly smaller than the health corrections, irrespectively of how
ambitiously one sets the reference values. Interestingly, however, the percentage
unemployment corrections are still much more pronounced than the corrections for the
risk of unemployment in Fleurbaey & Gaulier (2009), presented in the previous chapter.
86
Table 20 Equivalent income corrections for unemployment, based on various reference values
Correction as % of GNI per capita
Unemployment
variable General WTP
factor GNI per capita (1) (2) (3) (4) (5) (6)
Australia 3.6% -1.508 26,769 -0.7% -1.7% -2.4% -3.2% -3.9% -5.3%
Austria 2.6% -5.432 26,752 3.4% -0.4% -3.1% -5.7% -8.2% -13.0%
Belgium 4.1% -3.307 26,525 -3.0% -5.2% -6.7% -8.3% -9.8% -12.7%
Canada 4.6% -2.965 27,869 -4.0% -6.0% -7.3% -8.7% -10.0% -12.7%
Denmark 3.0% -4.457 26,914 0.8% -2.3% -4.4% -6.5% -8.6% -12.6%
Finland 5.3% -1.508 24,920 -3.1% -4.1% -4.8% -5.5% -6.3% -7.7%
France 5.0% -4.724 25,106 -8.2% -11.2% -13.3% -15.3% -17.3% -21.1%
Germany 5.1% -6.196 25,422 -11.3% -15.0% -17.6% -20.1% -22.5% -27.2%
Ireland 2.7% -4.025 27,095 1.9% -0.9% -2.8% -4.8% -6.7% -10.3%
Italy 4.0% -4.623 23,456 -3.6% -6.7% -8.8% -10.9% -12.9% -16.9%
Japan 2.9% -1.508 23,287 0.5% -0.5% -1.3% -2.0% -2.8% -4.2%
Korea 2.2% -1.508 18,541 1.5% 0.4% -0.3% -1.1% -1.8% -3.3%
Netherlands 2.2% -1.508 29,181 1.4% 0.4% -0.4% -1.1% -1.8% -3.3%
New Zealand 3.1% -1.508 19,177 0.1% -0.9% -1.7% -2.4% -3.1% -4.6%
Norway 2.4% -5.342 37,295 4.3% 0.5% -2.1% -4.7% -7.2% -12.0%
Portugal 3.9% -1.508 15,864 -1.0% -2.1% -2.8% -3.5% -4.3% -5.7%
Spain 5.6% -3.412 21,682 -7.9% -10.1% -11.6% -13.1% -14.5% -17.4%
Sweden 4.0% -3.623 27,492 -3.0% -5.4% -7.1% -8.8% -10.4% -13.6%
Switzerland 2.6% -7.970 30,872 5.0% -0.6% -4.5% -8.2% -11.8% -18.6%
United Kingdom 3.1% -3.633 27,627 0.4% -2.1% -3.9% -5.6% -7.3% -10.6%
United States 3.2% -1.508 35,733 0.0% -1.0% -1.8% -2.5% -3.2% -4.7%
Because the unemployment variable is a dummy variable, with a 0-1 scale, we can interpret the scores on this variable as a kind of unemployment rates (see also footnote 56). For a more convenient interpretation of the reference values, we therefore have expressed the values of the unemployment variable in this table as percentages. These numbers represent the average total unemployment as percentage of the total population aged 15 or older for the period 2000-2007. The GNI per capita column represents each country’s average GNI per capita over the period 2000-2007. The corrections in the subsequent use the following reference values for the unemployment variable:
(1) the median value of the unemployment variable across the 21 countries (3.19 %) (2) the arbitrarily chosen value of 2.5 % (3) the arbitrarily chosen value of 2 % (4) the arbitrarily chosen value of 1.5 % (5) the arbitrarily chosen value of 1 % (6) no unemployment at all, value 0 %
From this perspective, Table 20 may represent another indication that the market-based
corrections in Fleurbaey & Gaulier (2009) do not fully capture people’s preferences.
Anyway, for societies as a whole the existence of some unemployment seems to be
accepted as a fact of life. Though being unemployed is undoubtedly a serious adversity
for those who are directly confronted with it, for society as a whole the existence of
unemployment is apparently not perceived as a dramatic setback. Even for the most
ambitious reference values, the unemployment corrections exceed the 20 percent level for
hardly any country.
The most negative unemployment corrections are observed for Germany and
France, which suffer from the combination of relatively high unemployment levels and a
relatively high degree of unemployment aversion. Also some other Continental and
Mediterranean countries experience quite large negative corrections, in the case of the
87
Continental countries mainly due to their high level of unemployment aversion and for
the Mediterranean countries primarily the result of their relatively high unemployment
rates. Furthermore, whereas the Nordic countries have reasonably high levels of
unemployment aversion as well, these high levels are somewhat compensated for by the
lower levels of unemployment, thus moderating these countries’ unemployment
corrections (this holds especially for Denmark and Norway). Finally, the influence of
preferences can also clearly be seen in Table 20 by looking at the countries that
experience the least negative unemployment corrections. All of these countries share with
the United States a fairly low degree of unemployment aversion.
Tables 21 and 22 next present information on the ranking of the 21 countries
based on the various calculated corrections. In this context, the equivalent incomes can be
interpreted as each country’s average equivalent income over the period 2000-2007.
Since space constraints prevent us from elaborating extensively on these tables, we just
would like to mention the core messages conveyed by these tables. First, we can observe
significant differences between the country rankings in terms of uncorrected GNI per
capita and the rankings in terms of corrected GNI per capita. This holds in particular for
the health-corrected incomes. Moreover, the tables also show that, although the sample
range of equivalent incomes strongly depends on the chosen reference values, the
absolute country ranks with respect to equivalent income are quite robust to the choice of
different reference values.
Table 21 Country rankings in terms of health-corrected GNI per capita, based on the
health corrections in Table 19 (as % of sample mean and absolute ranks)
uncorrected (1) (2) (3) (4) (5)
Australia 103% 10 117% 9 123% 8 130% 8 137% 8 198% 3
Austria 103% 11 34% 16 34% 16 34% 16 34% 16 31% 17
Belgium 102% 12 98% 10 96% 11 92% 12 88% 11 57% 12
Canada 107% 5 147% 7 154% 7 163% 7 172% 3 248% 2
Denmark 103% 9 173% 4 170% 3 166% 5 162% 6 120% 9
Finland 96% 15 79% 13 79% 13 79% 13 78% 13 68% 11
France 96% 14 44% 15 44% 15 43% 15 43% 15 36% 15
Germany 97% 13 22% 20 21% 20 21% 20 20% 20 14% 19
Ireland 104% 8 265% 1 255% 1 242% 1 228% 1 130% 8
Italy 90% 16 25% 18 23% 19 22% 19 20% 19 10% 20
Japan 89% 17 24% 19 25% 18 27% 18 28% 18 41% 13
Korea 71% 20 50% 14 49% 14 49% 14 48% 14 38% 14
Netherlands 112% 4 88% 12 92% 12 97% 10 103% 10 148% 7
New Zealand 74% 19 120% 8 107% 9 93% 11 79% 12 20% 18
Norway 143% 1 165% 6 167% 6 170% 4 172% 4 172% 4
Portugal 61% 21 5% 21 5% 21 5% 21 4% 21 2% 21
Spain 83% 18 30% 17 31% 17 31% 17 32% 17 34% 16
Sweden 105% 7 173% 3 169% 4 165% 6 159% 7 111% 10
Switzerland 118% 3 168% 5 169% 5 170% 3 170% 5 159% 6
United Kingdom 106% 6 95% 11 100% 10 105% 9 111% 9 160% 5
United States 137% 2 179% 2 187% 2 198% 2 209% 2 301% 1
88
Table 22 Country rankings in terms of unemployment-corrected GNI per capita, based on the unemployment corrections in Table 20 (as % of sample mean and absolute ranks)
uncorrected (1) (2) (3) (4) (5) (6)
Australia 103% 10 103% 11 104% 8 106% 7 107% 6 108% 5 110% 4
Austria 103% 11 107% 6 106% 7 105% 8 104% 9 103% 11 101% 11
Belgium 102% 12 100% 12 100% 12 100% 12 100% 12 100% 12 100% 12
Canada 107% 5 104% 9 104% 10 104% 9 105% 8 105% 8 105% 7
Denmark 103% 9 105% 8 104% 9 104% 10 103% 10 103% 10 102% 10
Finland 96% 15 94% 13 95% 13 96% 13 97% 13 98% 13 100% 13
France 96% 14 89% 15 89% 15 88% 15 87% 15 87% 15 86% 15
Germany 97% 13 87% 17 86% 17 85% 17 84% 17 82% 17 80% 17
Ireland 104% 8 107% 7 107% 6 106% 6 106% 7 106% 7 105% 8
Italy 90% 16 88% 16 87% 16 86% 16 86% 16 85% 16 84% 16
Japan 89% 17 91% 14 92% 14 93% 14 94% 14 95% 14 97% 14
Korea 71% 20 73% 20 74% 20 75% 20 75% 20 76% 20 78% 19
Netherlands 112% 4 115% 4 116% 4 117% 4 119% 3 120% 3 122% 3
New Zealand 74% 19 74% 19 75% 19 76% 19 77% 19 78% 18 79% 18
Norway 143% 1 151% 1 149% 1 148% 1 146% 1 145% 1 142% 2
Portugal 61% 21 61% 21 62% 21 62% 21 63% 21 64% 21 65% 21
Spain 83% 18 77% 18 77% 18 77% 18 78% 18 78% 19 78% 20
Sweden 105% 7 103% 10 103% 11 103% 11 103% 11 103% 9 103% 9
Switzerland 118% 3 126% 3 122% 3 119% 3 116% 4 114% 4 109% 5
United Kingdom 106% 6 107% 5 107% 5 107% 5 107% 5 107% 6 107% 6
United States 137% 2 138% 2 140% 2 142% 2 143% 2 145% 2 148% 1
4.3 Comparing our results with Fleurbaey et al.’s (2009) results
Before winding up this section, there is one remaining issue we would like to
address, namely the comparison of our equivalent income corrections presented above
and Fleurbaey et al.’s (2009) equivalent income corrections for Russia. The most
remarkable difference in this comparison concerns the magnitude of the estimated
willingness-to-pay factors for health. For all countries within our sample we observe
significantly larger WTP factors for health than Fleurbaey et al. (2009) have found for
their Russian dataset. These differences are actually quite striking: whereas the maximum
general WTP factor for health in Russia is about 1.25 in absolute value, the minimum
general WTP factor in our regressions equals 2.67 in absolute value (recall that this is the
value of the WTP factor for the United States and some other countries; see Table 14).
Obviously, such a difference has tremendous implications for the tradeoff between
income and health. These implications are illustrated by Figures 7 and 8, plotting several
indifference curves that can be deduced from the regressions in Fleurbaey et al. (2009)
and the regression in Table 13 above.60
As these graphs show, the indifference curves of
60 Recall that in Fleurbaey et al. (2009) higher health scores correspond to a better state of health and that in
our analysis higher health scores correspond to a worse state of health. In addition, notice that, whereas
Figure 8 plots our own derived indifference curves in the health-income space, Figure 7 plots the
indifference curves derived by Fleurbaey et al. (2009) in the health-expenditures space. The reason for this
is that Fleurbaey et al. (2009) prefer to look at expenditures instead of income, because income is
notoriously difficult to measure within Russia due to the country’s large shadow economy. Moreover,
89
Figure 7 Indifference map in the health – expenditures space
(Source: Fleurbaey et al. (2009))
Figure 8 Indifference map in the health – income space
0
10000
20000
30000
40000
50000
60000
70000
1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7
health
inc
om
e
United States
Austria
Denmark
Italy
expenditures are in their opinion a better indicator of permanent income (which plays, according to the
authors, a more prominent role in people’s life satisfaction evaluations than actual income). For our own
analysis it has never been a real option to include expenditures instead of income, as the World Values
Survey does not contain any information on people’s expenditures.
90
the countries within our analysis are much steeper in the neighborhood of their average
values (which are around 2) than the indifference curves for Russia (where the average
health score is approximately 3.1).61 Hence, it is not surprising that the health corrections
within our sample are relatively much larger than the health corrections in Fleurbaey et
al. (2009).
Of course, it is interesting to wonder what the reasons behind these substantial
differences might be. In this context, one could think of many potential explanations.
Most of these explanations probably relate to Russia’s highly atypical character as
compared with the countries within our sample. For instance, the observed discrepancy
may have something to do with the degree of heterogeneity with respect to the health
variable. Looking at Russia’s communist history, it is not inconceivable that the countries
investigated in our analysis contain more variation in the health variable than is the case
for the Russian republic. It is namely very well possible that there is hardly any variation
in reported health within Russia, for example because there are only minor differences in
physical living conditions and access to health care due to the country’s communist
history. However, although this seems in itself a plausible hypothesis, it cannot explain
the differences observed between our results and Fleurbaey et al. (2009)’s analysis. After
all, if there is hardly any variation in self-assessed health in Russia, this would ceteris
paribus result in a rather large regression coefficient for the subjective health variable
and, thus, in a relatively high willingness-to-pay for health. What we observe, on the
other hand, is that the Russian regression coefficient for health is smaller in absolute
value than the health coefficient for any country in our analysis. Therefore, the
discrepancies between the willingness-to-pay estimates cannot be explained by the fact
that Russia potentially has a lower degree of heterogeneity with respect to the subjective
health variable.
Anyway, apart from different degrees of heterogeneity with regard to the health
variable, the differences in the willingness-to-pay estimates can also simply be the result
of different preferences. Russia is obviously a country with a unique history, which is
very different from the countries considered in our analysis. Consequently, it is very well
possible that Russia differs from the countries within our sample in terms of the value
attached to health, especially if we recognize that preferences are generally strongly path-
dependent. However, not only history plays a role in this context, but also the fact that,
whereas we have focused on OECD countries, with a relatively high level of economic
development, Russia is still in another stage of the development process. Often it is
assumed that people’s preferences may evolve as a country develops, in conformance
with ideas like Lipset’s (1959) modernization hypothesis and Maslow’s (1943) hierarchy
of needs. Hence, the fact that Russian people attach less value to health than people in
OECD countries may also be a reflection of Russia’s position on the ladder of economic
development. In a similar vein, Russian people may have relatively stronger preferences
for income. At least, we can observe that the Russian regression coefficients for income
(expenditures) are significantly higher than the income coefficients in most countries
61
At first glance the steepness of the curves in both graphs might seem quite similar, but notice the
difference in the scales of the horizontal axes of the graphs. The indifference curves for the countries within
our sample are actually so steep that we could only get convenient graphs by restricting the horizontal axis
to the 1.5 – 2.7 range.
91
within our sample. As willingness-to-pay measures indicate the preference tradeoff
between income and health, these stronger preferences for income may also contribute to
Russia’s lower willingness-to-pay for health.
A final potential reason we would like to discuss for the deviation between the
willingness-to-pay estimates in this thesis and in Fleurbaey et al. (2009) relates to the
distribution of life satisfaction scores. Whereas satisfaction scores within our sample are
relatively high and stable, with almost 75 percent of the respondents reporting life
satisfaction scores of 7 or higher, people in Russia generally report much lower scores
and these scores are also more volatile over time, amongst others due to the economic
turmoil and political instability that has been characterizing Russia after the collapse of
the Soviet Union. These differences are illustrated by Figures 9 and 10. Obviously, such
differences in the distribution of life satisfaction scores may also play a part in the
explanation of the differences in the willingness-to-pay estimations between this thesis
and Fleurbaey et al. (2009).
Figure 9 The distribution of life satisfaction scores within Russia (Source: Fleurbaey et al. (2009))
92
Figure 10 The distribution of life satisfaction scores within our OECD sample (1 – dissatisfied, …, 10 – satisfied)
4.4 Final words
Just like the previous chapter, this chapter has explored the possibilities for the
construction of welfare measures that respect country-specific preferences. In particular,
we have investigated the construction of an equivalent income measure based on
willingness-to-pay estimates as derived from life satisfaction regressions. Apart from the
general and unavoidable drawback of the equivalent income method related to the
impossibility to arrive at a unique ranking of cases without being paternalistic in the
determination of the reference functionings bundle, the correctness of the method
proposed in this chapter is also subject to the degree to which preferences for welfare
dimensions can be derived from life satisfaction regressions. This topic has already been
discussed in the previous chapter and we repeat our point of view that we indeed consider
it possible to obtain useful information on preferences from such regressions. However,
we have to acknowledge that life satisfaction regressions, as most other preference
estimation methods, do not perfectly reflect preferences. One particular problem in this
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context is that there is often a lot of multicollinearity, endogeneity and heterogeneity
involved in life satisfaction regressions. This severely complicates the derivation of
preference estimates from these regressions. We have seen this, for instance, when we
included country-specific income effects in our regressions: if income is for some
countries hardly important for life satisfaction, it is difficult to obtain useful willingness-
to-pay estimates for equivalent income calculations. So, in conclusion, we think that a
large-scale application of the welfare measurement method presented in this chapter is for
the moment still constrained by methodological and practical difficulties. Nevertheless,
this conclusion does not alter the fact that also this chapter has pointed at the existence of
cross-country differences in preferences towards various welfare dimensions. Thus, this
chapter provides additional supporting evidence for our central hypothesis that it is
relevant to account for country-specific preferences in the construction of welfare
measures.
94
V Conclusion
The purpose of this final chapter is to round off the exploratory expedition we
have undertaken in this thesis in search of more sophisticated welfare measures.
However, it is worth mentioning that the fact that we here conclude our exploratory
expedition does not mean that we have reached anything like a final destination in this
regard. Quite the contrary, although we may have learnt many interesting things along
our way, there still remains much to be explored in the field of welfare measurement and
this thesis just underlines this assertion.
Notwithstanding these remarks, it is from the perspective of rounding off useful to
recapitulate where we started our exploratory expedition. Analyzing various attempts at
comprehensive international welfare comparisons, we were triggered by the study of
Cnossen (2009). Despite being impressed by the wide variety of data included in this
study, we were equally surprised that the study actually disregards one of the, in our
opinion, important elements of the concept of welfare, namely its subjective nature. In
line with many authors, we essentially define welfare as concerning the satisfaction of
needs. Observing that needs may vary across time, space and individuals, welfare has in
our perception a clearly subjective character: it is not merely determined by one’s
objective living conditions, but also dependent on one’s preferences. Therefore, we think
that studies aimed at comprehensive international welfare comparisons should allow for
possible cross-country differences in preferences towards welfare dimensions like health,
leisure and unemployment. We hypothesized that allowing for such variation in
preferences could make a significant difference.
Having determined that we would like to investigate the opportunities for and
potential implications of including country-specific preferences in international welfare
comparisons, we formulated our research question as: What are the implications of
including country-specific preferences towards different welfare dimensions in cross-
country welfare comparisons? Regarding the above-mentioned observations, it would of
course be interesting as well to focus on time-specific and individual-specific
preferences. However, recognizing that preferences towards welfare dimensions tend to
be rather stable over time, and anticipating the complexities related to the inclusion of
individual-specific preferences, we preferred to demarcate our research topic to country-
specific preferences, which are probably most relevant from the perspective of
international welfare comparisons.
We next commenced our expedition by examining several welfare measurement
alternatives, ranging from measures related to the Gross Domestic Product (GDP) per
capita, via composite indexes based on objective living conditions, to subjective well-
being indicators like happiness and life satisfaction. Nevertheless, none of these
alternatives could really meet our objectives and satisfy our needs. Instead, we proposed
some kind of a synthesis of the various approaches, combining information on objective
living conditions with information on subjective preferences. More specifically, we
decided to use the method of equivalent incomes for investigating our central research
question, because of its potential of linking together both sources of information.
95
In this context, we first looked at the equivalent income measure proposed by
Fleurbaey & Gaulier (2009). We concluded that their measure paid insufficient attention
to country-specific preferences towards different welfare dimensions. Hence, we
investigated the opportunities for more directly taking into account country-specific
preferences in Fleurbaey & Gaulier’s (2009) calculations. In particular, we focused on
their corrections for leisure and inequality, and we mainly used information from the
World Values Survey for deriving preference estimates. Albeit we encountered numerous
difficulties in including preference estimates in the equivalent income corrections, our
results did show some consistent patterns and thus pointed at the potential relevance of
accounting for country-specific preferences: amongst others, we found that the inequality
corrections for the Anglo-Saxon countries were relatively much less pessimistic if we
took into consideration that Anglo-Saxon countries do not tend to care too much about
inequality.
Subsequently, we analysed another equivalent income proposal. Whereas the
measure of Fleurbaey & Gaulier (2009) is primarily based on a formal economic model,
the corrections of this second proposal, attributable to Fleurbaey, Decancq & Schokkaert
(2009), are founded on willingness-to-pay estimates derived from life satisfaction
regressions. Since Fleurbaey, Decancq & Schokkaert (2009) only apply their method to a
Russian dataset, we decided to investigate the implications of applying their method to a
sample of countries, with special attention for people’s self-assessed health status and
whether they are unemployed or not. Our findings were essentially the same as for the
other equivalent income method: even though we again encountered significant
difficulties, our results strongly conveyed the message that it makes sense to allow for
country-specific preferences towards different welfare dimensions in international
welfare comparisons.
Returning to our central research question, we think we should be careful in
drawing any substantive conclusions from the results presented in this thesis. During our
discourse we have already repeatedly warned for ‘overinterpretation’ of our results.
Because of the difficulties and imperfections in our calculations, we believe it is of
utmost importance to be cautious in the interpretation of our results. Nonetheless, one
firm conclusion can certainly be drawn, namely that accounting for country-specific
preferences in international welfare comparisons can make a significant difference. We
have provided quite some evidence that relative welfare rankings can be substantially
altered by the inclusion of information on country-specific preferences.
Since until now no serious attempts have been made at including country-specific
preferences in international welfare comparisons, we thus think that this thesis provides a
modest though valuable contribution to the economic literature. Our main contribution is
that we have empirically investigated the opportunities for arriving at welfare measures
that respect the subjective character of the concept of welfare, and that we have
empirically shown that it is relevant to pay attention to this subjective character in
international welfare comparisons. At least, we think this thesis has shown that the topic
of accounting for preferences in welfare comparisons deserves further attention in future
research.
96
Future research on this topic is indeed welcome, as it should be emphasized that,
although this thesis may be considered an interesting introductory exploration of the
topic, it definitely suffers from a number of shortcomings, several of which we will
shortly mention here. In the first place, there is the general drawback of the equivalent
income method related to the choice of reference values for the various functionings
concerned. These choices necessarily involve a high degree of subjectivity. Nevertheless,
we do not consider this to be the most alarming weakness of our analysis, since one can
experiment with different reference values and test the robustness of the obtained results
with respect to the chosen reference values. Moreover, as we noted earlier in this thesis,
every welfare measure inherently rests on certain subjective, normative assumptions.
Another weakness concerns the way in which we in Chapter 3 converted our country-
specific preference estimates into information that could be used for adjusting Fleurbaey
& Gaulier’s (2009) equivalent income corrections, since the employed transformation
formulas are characterized by a high level of arbitrariness. Although we have accepted
this practice for the sake of our exploratory expedition, it goes without saying that ideally
one would like to avoid such arbitrary elements. In Chapter 4, in turn, we were
confronted with some problems related to the estimation of preferences on the basis of
life satisfaction regressions. Apart from the theoretical issue whether it is appropriate to
estimate preferences on the basis of subjective well-being regressions, we also faced
practical difficulties as a consequence of multicollinearity and endogeneity among our
independent variables. Finally, perhaps the most fundamental weakness of this thesis
arises from the context-dependency of our preference estimates. This context-dependency
stems from the fact that people use, either consciously or unconsciously, certain
contextual standards when answering survey questions from the World Values Survey.
Therefore, the country-specific preference estimates derived from these survey questions
are context-dependent as well. Due to this context-dependency, it is somewhat
problematic to compare our preference estimates across countries and to apply these
estimates to international welfare comparisons.
We think that addressing this issue of context-dependency is a prominent task for
future research in this area, just as it may be worthwhile to seek ways to diminish the
other above-mentioned shortcomings. Furthermore, whereas we have focused on the
equivalent income method, future research may also search for different ways of
including country-specific preferences in welfare measures. Lastly, additional challenges
for future work include to also account for different preferences towards other welfare
dimensions like social relationships and the environment and to allow for time-specific or
even individual-specific preferences as well. Anyway, we should realize that the
opportunities for progress in many of these areas are significantly constrained by a lack
of (adequate) data related to preferences.
All in all, we believe that, in spite of the shortcomings of the research presented in
this thesis, its main conclusion still holds its ground. Even though we simply have to
admit that one should be careful when interpreting the specific results we have presented,
we think this thesis has demonstrated that significant cross-country differences in
preferences towards various welfare domains are likely to exist. Accordingly, this thesis
actually makes a plea for the acknowledgement of the relativist nature of welfare.
Welfare is about the satisfaction of needs, needs are based on preferences, and
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preferences vary across time, space and individuals. Hence, similar achievements in
terms of objective living conditions do not necessarily translate into similar welfare
levels. Studies aiming at comprehensive international welfare comparisons should
therefore pay attention to the relativity of the notion of welfare and respect country-
specific preferences towards different welfare dimensions. From this observation it
follows that GDP per capita and other indicators that solely include objective living
conditions are in fact unsuitable for the purpose of a sophisticated cross-country welfare
comparison.
However, it is certainly not our intention to advocate the full-blown abandonment
of GDP per capita measures. In this respect, we would like to emphasize that, just like
welfare itself has a relativist nature, all welfare measures are also subject to some
relativism. Acknowledging that, as we noted earlier in this thesis, it is an illusion to
search for the ‘holy grail’ of welfare measures, the usefulness and appropriateness of any
welfare measure mainly depends on its intended use and the ambitions of the user with
regard to the required level of precision of the welfare measure.
Interesting to consider in this context is Robert Kennedy’s famous speech on
Gross National Product (GNP) measures (1968)62
: “…Yet, the Gross National Product
does not allow for the health of our children, the quality of their education, or the joy of
their play; it does not include the beauty of our poetry or the strength of our marriages,
the intelligence of our public debate or the integrity of our public officials. It measures
neither our wit nor our courage, neither our wisdom nor our learning, neither our
compassion nor our devotion to our country. It measures everything in short, except that
which makes life worthwhile. And it can tell us everything about America except why we
are proud that we are Americans.” Clearly, GNP measures do not suffice for Kennedy’s
intended use of a welfare measure; indeed, GNP measures do not include matters like the
courage of a nation or the beauty of poetry. Nevertheless, this does not imply that GDP or
GNP measures are not useful at all. GDP per capita, for instance, does quite a good job as
indicator of a country’s average level of economic activity. Depending on one’s
ambitions regarding preciseness, it may even provide a satisfactory approximation of a
country’s welfare level. However, if one has more ambitious intentions, measures like
GDP per capita do not suffice anymore. Thus, and this is the crucial message of this
thesis, when aiming for sophisticated, comprehensive international welfare comparisons,
one should pay attention to the relativist nature of the concept of welfare and be aware of
the fact that country-specific preferences for various welfare dimensions may exist.
Otherwise, one runs the risk of actually comparing apples and oranges and ending up
with misleading insights.
62
Speech of Robert F. Kennedy at the University of Kansas, 18 March 1968. A transcript of Kennedy’s
speech is available at http://www.jfklibrary.org/Historical+Resources/Archives/Reference+Desk/Speeches/
RFK/RFKSpeech68Mar18UKansas.htm. An audio fragment of the specific passage is available at
http://www.youtube.com/watch?v=77IdKFqXbUY.
98
Appendix A Details on the variables used in the regressions of Chapter 3 and 4
A.1 Regressions Chapter 3
The dependent variable of all regressions in Chapter 3 is the familiar World Values
Survey question on life satisfaction: “All things considered, how satisfied are you with
your life as a whole these days? 1-Dissatisfied, 2, …, 9, 10-Satisfied”.
The right-hand side of each regression equation in Chapter 3 contains both macro-level
and micro-level variables. The macro-level data (measured at the national level) have
been derived from the OECD.Stat database and the micro-level data (measured at the
individual level) have been derived from the World Values Survey. Below we will
discuss the definitions of these independent variables.
Macro variables:
- Inequality (Gini):
Gini coefficients measuring the level of after-tax income inequality, using a 0-1
scale, where larger coefficients correspond to higher levels of inequality.
Observing that for all countries within our sample the level of income inequality
hardly changes over time (for most countries the fluctuations over a period of 20
years are within a range of 0.02 points), we have for reasons of convenience
chosen to assign just one value for the inequality variable to each country. This
value has been calculated as the average level of after-tax income inequality over
the period 1985-2004, which is approximately the same period as covered by the
World Values Survey variables within our regressions.
- Unemployment rate:
Total unemployment as a percentage of the total labour force. For each country
we have calculated wave-specific unemployment rates by calculating the average
unemployment rate during the periods covered by the four waves of the World
Values Survey: 1981-1984, 1989-1993, 1994-1999 and 1999-2004.
- Inflation rate:
The average percentage increase in the general price level during a year. On the
basis of price indexes we have calculated wave-specific inflation rates for each
country. These rates represent the average inflation rate during each survey period
of the World Values Survey: 1981-1984, 1989-1993, 1994-1999 and 1999-2004.
Micro variables:
- Log Income:
The natural logarithm of a person’s estimated annual income in 2009 US dollars.
Since the World Values Survey does not contain precise information on
respondents’ income levels, but merely on the income deciles the respondents
99
belong to, we could only estimate the respondents’ income levels. We have done
so by using data from the OECD.Stat database on Gross National Income (GNI),
population size and income shares. Using this information, we have been able to
calculate for every country-wave combination the average annual income within
each income decile. Subsequently we have matched these numbers with the
respondents in our dataset.
- Unemployment dummy:
Dummy variable that equals 1 if the respondent answered ‘Unemployed’ to the
World Values Survey question: “Is the chief wage earner employed now or not?”,
where the answering options were ‘Full time’, ‘Part time’, ‘Self employed’,
‘Retired’, ‘Housewife’, ‘Students’, ‘Unemployed’ and ‘Other’. If the respondent
did not choose the option ‘Unemployed’, the dummy variable equals 0.
- Subjective health (Perceived health status):
Respondents’ answers to the World Values Survey question: “All in all, how
would you describe your state of health these days? Would you say it is… 1-Very
good, 2-Good, 3-Fair, 4-Poor, 5-Very poor?”
- Age:
The respondents’ age in years.
- Age squared:
The square of the respondents’ age in years.
- Dummy Male:
Dummy variable that equals 1 if the respondent is a man and 0 otherwise.
- Dummy Married:
Dummy variable that equals 1 if the respondent answered ‘Married’ to the World
Values Survey question: “Are you currently… Married, Living together as
married, Divorced, Separated, Widowed, Single / Never Married, Divorced /
Separated / Widow, Living apart but steady relation (married / cohabitation)?”
Otherwise, the dummy variable is equal to 0.
- Dummy As Married:
Dummy variable that equals 1 if the respondent answered ‘Living together as
married’ to the World Values Survey question: “Are you currently… Married,
Living together as married, Divorced, Separated, Widowed, Single / Never
Married, Divorced / Separated / Widow, Living apart but steady relation (married
/ cohabitation)?” Otherwise, the dummy variable is equal to 0.
- Dummy Divorced:
Dummy variable that equals 1 if the respondent answered ‘Divorced’ to the World
Values Survey question: “Are you currently… Married, Living together as
married, Divorced, Separated, Widowed, Single / Never Married, Divorced /
100
Separated / Widow, Living apart but steady relation (married / cohabitation)?”
Otherwise, the dummy variable is equal to 0.
- Dummy Separated:
Dummy variable that equals 1 if the respondent answered ‘Separated’ to the
World Values Survey question: “Are you currently… Married, Living together as
married, Divorced, Separated, Widowed, Single / Never Married, Divorced /
Separated / Widow, Living apart but steady relation (married / cohabitation)?”
Otherwise, the dummy variable is equal to 0.
- Dummy Widowed:
Dummy variable that equals 1 if the respondent answered ‘Widowed’ to the
World Values Survey question: “Are you currently… Married, Living together as
married, Divorced, Separated, Widowed, Single / Never Married, Divorced /
Separated / Widow, Living apart but steady relation (married / cohabitation)?”
Otherwise, the dummy variable is equal to 0.
- Number of children:
The respondents’ number of children.
- Wave dummies:
Four dummy variables that have the value 1 if a respondent took part in
respectively survey wave 1 (1981-1984), 2 (1989-1993), 3 (1994-1999) or 4
(1999-2004) of the World Values Survey and 0 otherwise. All regressions in this
thesis take survey wave 4 (1999-2004) as base level.
- Dummy Anglo-Saxon:
Dummy variable that equals 1 for all individuals from the United States, Canada,
the United Kingdom, Ireland, Australia and New Zealand. For all other
individuals the variable equals 0. All regressions in Chapter 3 take this Anglo-
Saxon group of countries as base level.
- Dummy Nordic:
Dummy variable that equals 1 for all individuals from Sweden, Norway,
Denmark, Finland and the Netherlands. For all other individuals the variable
equals 0.
- Dummy Continental:
Dummy variable that equals 1 for all individuals from Germany, France, Belgium,
Austria and Switzerland. For all other individuals the variable equals 0.
- Dummy Mediterranean:
Dummy variable that equals 1 for all individuals from Italy, Spain and Portugal.
For all other individuals the variable equals 0.
- Dummy Asian:
101
Dummy variable that equals 1 for all individuals from Japan and Korea (South
Korea). For all other individuals the variable equals 0.
- Interaction terms inequality – regime (Gini*Regime):
Variables that have been included in the regressions of Chapter 3 to capture the
regime-specific effects of inequality on life satisfaction. These variables have
been calculated by multiplying the ‘Inequality (Gini)’ variable by each separate
regime dummy (i.e. Dummy Anglo-Saxon, Dummy Nordic, Dummy Continental,
Dummy Mediterranean and Dummy Asian). All regressions including these
interaction terms take ‘Gini*Anglo-Saxon’ as base level.
- Interaction terms log income – regime (Log Income*Regime):
Variables that have been included in the regressions of Chapter 3 to capture the
regime-specific effects of log income on life satisfaction. These variables have
been calculated by multiplying the ‘Log Income’ variable by each separate regime
dummy (i.e. Dummy Anglo-Saxon, Dummy Nordic, Dummy Continental,
Dummy Mediterranean and Dummy Asian). All regressions including these
interaction terms take ‘Log Income*Anglo-Saxon’ as base level.
A.2 Regressions Chapter 4
The regressions presented in Chapter 4 mostly contain the same variables as the
regressions of Chapter 3. The dependent variable is again the familiar World Values
Survey question on life satisfaction: “All things considered, how satisfied are you with
your life as a whole these days? 1-Dissatisfied, 2, …, 9, 10-Satisfied”. In addition, most
independent variables are also the same as the ones included in the regressions of Chapter
3. Only the macro-level variables ‘Inequality (Gini)’, ‘Unemployment rate’ and ‘Inflation
rate’ have been omitted from the regressions in Chapter 4. Moreover, instead of including
regime-specific effects, the regressions presented in Chapter 4 include country-specific
effects. Thus, the additional variables as compared with the regressions of Chapter 3 are:
- Country dummies (D_Country):
Dummy variables that equal1 for all individuals coming from the particular
country concerned. For all other individuals the dummy variables equal 0. All
regressions take the United States dummy (D_US) as base level.
- Interaction terms subjective health – country (Health*Country):
Variables that have been included in the regressions of Chapter 4 in order to
capture the country-specific effects of perceived health status on life satisfaction.
These variables have been calculated by multiplying the ‘Subjective health’
variable by each separate country dummy. All regressions including these
interaction terms take ‘Health*US’ as base level.
- Interaction terms unemployment – country (Unem*Country):
102
Variables that have been included in the regressions of Chapter 4 to capture the
country-specific effects of unemployment on life satisfaction. These variables
have been calculated by multiplying the ‘Unemployment dummy’ variable by
each separate country dummy. All regressions including these interaction terms
take ‘Unem*US’ as base level.
- Interaction terms log income – country (LogY*Country):
Variables that have been included in the regressions of Chapter 4 in order to
capture the country-specific effects of log income on life satisfaction. These
variables have been calculated by multiplying the ‘Log Income’ variable by each
separate country dummy. All regressions including these interaction terms take
‘LogY*US’ as base level.
103
Appendix B Correlation matrix accompanying Table 3
LEFT RIGHT RICH POOR LIFE SAT UNEM HEALTH AGE MALE MARRIED AS MARRIED DIVORCED SEPARATED WIDOWED NKIDS
LEFT
RIGHT -0.379**
RICH -0.028** 0.064**
POOR 0.019** -0.040** -0.346**
LIFE SAT -0.085** 0.078** 0.087** -0.130**
UNEM 0.041** -0.036** -0.062** 0.099** -0.123**
HEALTH 0.018** -0.027** -0.119** 0.193** -0.310** 0.014**
AGE -0.095** 0.097** -0.087** 0.208** 0.018** -0.099** 0.288**
MALE 0.028** 0.029** 0.041** -0.059** 0.000 0.021** -0.053** -0.017**
MARRIED -0.069** 0.046** 0.079** -0.178** 0.114** -0.102** 0.024** 0.250** 0.027**
AS MARRIED 0.033** -0.022** 0.010** -0.030** 0.022** 0.028** -0.052** -0.132** 0.007* -0.267**
DIVORCED 0.021** -0.017** -0.057** 0.091** -0.048** 0.044** 0.009* 0.046** -0.046** -0.251** -0.048**
SEPARATED 0.007 -0.012** -0.020** 0.047** -0.062** 0.033** 0.007 -0.005 -0.017** -0.150** -0.029** -0.027**
WIDOWED -0.035** 0..031** -0.090** 0.208** -0.050** -0.037** 0.160** 0.397** -0.144** -0.325** -0.062** -0.058** -0.035**
NKIDS -0.079** 0.061** -0.025** 0.018** 0.035** -0.052** 0.112** 0.444** -0.063** 0.362** -0.099** 0.019** 0.021** 0.148**
Two stars denote significance at the 1% level; one star denotes significance at the 5% level. LEFT: dummy indicating whether the respondent is classified as having left-wing political preferences. RIGHT: dummy indicating whether the respondent is classified as having right-wing political preferences. RICH: dummy indicating whether the respondent is classified as being rich (i.e. belonging to the top two income deciles). POOR: dummy indicating whether the respondent is classified as being poor (i.e. belonging to the bottom four income deciles). LIFE SAT: the respondent’s answer to the WVS question: “All things considered, how satisfied are you with your life as a whole these days? (1-dissatisfied, …, 10-satisfied)”. UNEM: dummy indicating whether the respondent is unemployed. HEALTH: the respondent’s answer to the WVS question: “All in all, how would you describe your state of health these days? Would you say it is: 1-Very good, …, 5-Very poor?” AGE: the respondent’s age. MALE: dummy indicating whether the respondent is male. MARRIED: dummy indicating whether the respondent is currently married. AS MARRIED: dummy indicating whether the respondent is currently living together as married. DIVORCED: dummy indicating whether the respondent is currently divorced. SEPARATED: dummy indicating whether the respondent is currently separated. WIDOWED: dummy indicating whether the respondent is currently widowed. NKIDS: the respondent’s number of children.
104
Appendix C Life satisfaction regressions containing regime-specific inequality effects
The table below investigates the sensitivity of the regression results presented in Table 2.
In the regressions in the table below, four groups of countries have been included: an
Anglo-Saxon group (United States, Canada, United Kingdom, Ireland), a Nordic group
(Sweden, Norway, Finland, Denmark), a Continental group (Germany, Austria, Belgium,
Switzerland) and a Mediterranean group (Italy, Spain, Portugal, Greece).
Dependent Variable: LIFE SATISFACTION
Ordered Logit Regression Model
All things considered, how satisfied are you with your life as a whole these days?
( 1-dissatisfied, 2, 3, 4, 5, 6, 7, 8, 9, 10-satisfied)
1 2 3
Inequality (Gini) -0.145 -1.096** -1.080**
(0.189) (0.547) (0.549)
Log income 0.127*** 0.142*** 0.127***
(0.020) (0.020) (0.028)
Anglo-Saxon base base
Nordic 2.663*** 3.691***
(0.799) (0.894)
Continental -3.459*** -4.222***
(0.270) (0.443)
Mediterranean -3.728*** -4.403***
(0.523) (0.752)
Gini*Anglo-Saxon base base
Gini*Nordic -9.874*** -9.554***
(3.005) (3.011)
Gini*Continental 11.491*** 10.933***
(0.853) (0.875)
Gini*Mediterranean 10.066*** 10.434***
(1.526) (1.552)
Log Income*Anglo-Saxon base base
Log Income*Nordic -0.117***
(0.048)
Log Income*Continental 0.096**
(0.043)
Log Income*Mediterranean 0.059
(0.051)
Observations 37220 37220 37220
Pseudo R-square 0.14 0.15 0.15
-2 Log Likelihood 134527 134138 134121
Numbers in parentheses denote standard errors. Three stars indicate significance at the 1% level, two stars at the 5% level and one star at the 10% level. All regressions include the following control variables: inflation rate, unemployment rate, a dummy indicating whether the respondent is unemployed, the respondent’s perceived health status, the respondent’s sex, the age and the squared age of the respondent, dummies for the respondent’s marital status (married, living as a couple, divorced, separated, widowed) and the respondent’s number of children. Finally, wave dummies have been included.
105
Appendix D Average number of hours worked per country (2004)
Country Number of hours worked per capita
Belgium 609
Canada 875
Denmark 752
Finland 788
France 584
Germany 677
Greece 688
Iceland 752
Ireland 744
Italy 656
Japan 917
Korea 1147
Luxembourg 1005
Netherlands 700
Portugal 822
Spain 712
Sweden 773
United Kingdom 785
United States 879
Median 752
Source: OECD.Stat (stats.oecd.org)
106
Appendix E Explanatory list of country abbreviations
Table 13 and 15 of chapter four use abbreviations for the names of the countries included
in the regression analyses. The abbreviations refer the following countries:
AUSL - Australia
AUS - Austria
BEL - Belgium
CAN - Canada
DEN - Denmark
FIN - Finland
FRA - France
GER - Germany
IRE - Ireland
ITA - Italy
JAP - Japan
KOR - Korea
NET - Netherlands
NEW - New Zealand
NOR - Norway
POR - Portugal
SPA - Spain
SWE - Sweden
SWI - Switzerland
UK - United Kingdom
US - United States
107
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Acknowledgements
During the research project from which this thesis stems I have been able to profit from
the helpfulness and benevolence of several people. Amongst others, I am greatly indebted
to Marc Fleurbaey and Guillaume Gaulier for providing me with the calculations behind
their Fleurbaey & Gaulier (2007) and Fleurbaey & Gaulier (2009) articles. My special
thanks in this respect go to Guillaume Gaulier, who offered some additional explanation
of these calculations. Furthermore, I would like to express my thanks to Sjak Smulders
and Ton van Schaik for their excellent supervision of my thesis project. This supervision
consisted of a well-balanced mix of both granting me a significant amount of freedom
and responsibility as well as offering me subtle guidance on the course to follow. Their
critical questions, useful suggestions and their comments on earlier drafts of several
chapters have been highly appreciated and have improved the quality of this thesis.
Contact information
Dingeman Wiertz
Postal address: Van Riebeecklaan 19
4818 EA Breda
The Netherlands
Permanent email address: [email protected]
Additional information on all calculations and statistical analyses in this thesis is
available upon request; please send an email to the above address.