Paris School of Economics - L'Agence Française de Développement

well-being regressions in which both the individual’s
own income and the comparison
income level appear: these are the empirical
counterpart to equation (1) above. This literature
has appealed to different datasets (in
terms of countries and years), different measures
of well-being (job and life satisfaction
being the most predominant), and various
measures of comparison income, yit*. The
typical finding is that own income is positively
correlated with well-being, but that the
correlation with others’ income is negative.
Clark and Oswald (1996) use the BHPS to calculate
the income of “people like me” from a
wage equation, and show that this is negatively
correlated with individual job satisfaction.
Own income attracts a positive coefficient,
and the sum of the two estimated income
coefficients is zero: pay rises for everyone
have no effect on satisfaction. More recent
work along the same lines using, respectively,
German and American data is Ferrer-i-Carbonell
(2005) and McBride (2001). Vendrik
and Woltjer (2006) extend the analysis of the
German GSOEP data in this respect, by considering
asymmetric reactions to gains and
losses (relative to the reference group).
An alternative measure of yit* is at the local
level: what do my neighbours earn? Both
Blanchflower and Oswald (2004) and Luttmer
(2005) calculate regional average income
in US data, and show that this is negatively
correlated with respondents’ well-being: an
individual earning $40,000 per year is happier
in a poorer than a richer region. However, at
the very local level of a few hundred metres,
Clark et al. (2009) find that in Danish panel
data, conditional on my own income and
local median income, my satisfaction is strongly
positively correlated with my rank in the
local income distribution. Other work here
has considered comparisons to the income
of the individual’s work colleagues (Brown et
al., 2006), partner (Clark, 1996) and parents
(McBride, 2001).
Running well-being regressions is only one
way of addressing the question of income
comparisons. One early method (the first
published contribution being Van Praag, 1971)
is that of the Welfare Function of Income.
Here individuals assign income levels (per
period) to verbal labels (such as excellent,
good, sufficient and bad): these stated values
form the basis of individual-level regressions
estimating a lognormal Welfare Function of
Income.The resulting individual estimated
means (�) reveal which individuals require
greater income in order to be satisfied.
Comparison income is introduced into the
analysis, typically as average income over age,
education and other characteristics. The
regression results (for example, Van de Stadt
et al., 1985) show that, given own income, the
higher is reference group income, the more
money individuals say they need to reach a
given verbal well-being level, which is consistent
with income comparisons.
Separate evidence on comparisons is found in
experimental economics. In Zizzo and Oswald
(2001), experimental participants paid out
of their own winnings in order to burn the
money earned by other participants. An alternative
approach is to ask individuals to choose
between hypothetical outcomes, as in Alpizar
et al. (2005), Johannsson-Stenman et al.
(2002) and Solnick and Hemenway (1998). A
typical income choice is as follows:
A: Your current yearly income is $50,000;
others earn $25,000.
B: Your current yearly income is $100,000;
others earn $200,000.
December 2011 / Measure for Measure / How Well Do We Measure Development? / © AFD [ 139]