indicators of well-being: the relative importance of weights
Composite indicators are
becoming mainstream tools for benchmarking and policy making. The standard
approach to build composite indicators consists in combining an underlying
set of indicators Xi through a series of treatments and algebraic manipulations.
The result of this process is a set of composite scores Yj for the various
elements (eg universities, regions, cities, etc.). Quite often, a weighted
average of the Xi s – through a set of weights wi, is used to
obtain Y. We call these weights subjective as they are customarily assigned
subjectively by the developers.
Composite indicators can also be obtained by estimating micro-econometric
models from given empirical data for each individual i, belonging to
country j, at time t: .
Here life satisfaction data are used as the dependent variable. Zjt
are well-being explanatory variables at macro level. Xijt are other
explanatory variables at individual level, eg socio-demographic factors.
is an error term and the coefficients a, ß, and ? are estimated.
The well-being composite indicator for country j at time t, is defined
as , where ? are the estimated weights. We call these weights objective
because they are obtained using a statistical estimation procedure from
We compare the standard approach with that based on objective weights
and test the robustness of the country composite scores in terms of
the various sources of uncertainty (weights plus others). The robustness
analysis can have relevant policy implications as it helps us to know
whether the two approaches provide similar policy conclusions.