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PROJECT PROPOSAL
EU HORIZON 2020 / CO-CREATION-07-2017- COORDINATION AND SUPPORT ACTION
Willingness to Pay as a Means to Determine Weights for Composite Well-Being Indices
In recent years, there has been a lively debate on the question, whether well-being and social
progress can be measured by GDP alone, or one needs to look “Beyond GDP”. In order to
comprehensively evaluate how countries or regions compare to one another with respect to the
well-being of their citizens and how they are developing over time, various alternative well-being
measures have been suggested. Generally they combine economic indicators (such as GDP) with
other social, political or environmental indicators (e.g. unemployment, housing, health, education,
civil engagement, pollution, etc.). Well-known examples of such alternative well-being indices include
the OECD (2014)1 Better Life Index and the Social Progress Index (Stern et al. 2016)2.
To ease comparisons across countries and over time, and to attract sufficient public attention, one
might consider it desirable to aggregate the different well-being dimensions into a “single number”.
The main problem here is how to weight the different well-being dimensions. Common approaches
are to simply weight all dimensions equally or to use statistical tools, e.g. factor analysis, to obtain
such weights. While these methods are easy to apply, they obviously have severe
shortcomings.3Equal weights ignore that people might not value all considered aspects equally and
that the weights should depend on the specific choice of the indicators. There is also no reason to
believe that a statistical property, such as the correlation between indicators obtained by factor
analysis, captures conceptually meaningful trade-offs between these indicators with respect to wellbeing.
In economic analysis, people’s valuation of some good is generally measured by their “willingness to
pay” for it.4 Economics has developed tools to reveal people’s willingness to pay even for goods that
are not traded in markets, e.g. public infrastructure or environmental quality. One method is
contingent valuation, where survey respondents are asked directly about the trade-offs between the
different well-being dimensions.5 Typically, one would ask directly about how much income
respondents would be willing to give up for enjoying an improvement in some particular well-being
dimension. Contingent valuation methods have been applied extensively in the valuation of
environmental goods. Another method is conjoint analysis. Here, respondents are asked to rank
alternative scenarios, i.e. different combinations of outcomes in the various well-being dimensions.
Then, statistical tools are used to reveal the underlying preferences that best explain the reported
rankings. Conjoint analysis has been extensively applied in marketing and consumer research. Both
methods have been proposed in the scientific literature as means to tackle the weighting problem
1
OECD (2014). How’s Life in Your Region? Measuring Regional and Local Well-Being for Policy Making (Paris: OECD
Publishing). http://www.oecd.org/regional/how-s-life-in-your-region-9789264217416-en.htm.
2
Stern, S., A. Wares, and T.Hellman (2106). Social Progress Index 2016 – Methodological Report.
http://13i8vn49fibl3go3i12f59gh.wpengine.netdna-cdn.com/wp-content/uploads/2016/07/SPI-2016-MethodologicalReport.pdf
3 Döpke et al. (2016). Multidimensional Well-being and Regional Disparities in Europe. IWH-Discussion Papers 13/2016.
https://ideas.repec.org/p/iwh/dispap/13-16.html.
4 See, e.g. Adamowicz (2004): What's it worth? An examination of historical trends and future directions in environmental
valuation, The Australian Journal of Agricultural and Resource Economics 48:3, 419-443 and F. Reed Johnson, Erin E. Fries,
H. Spencer Banzhaf, Valuing morbidity: An Integration of the willingness-to-pay and health-status index literatures, Journal
of Health Economics, Volume 16, Issue 6, 1997, Pages 641-665 and the literature cited therein.
5 See, e.g. Carson, Richard T. 2011. Contingent Valuation: A Comprehensive Bibliography and History, Northampton, MA:
Edward Elgar.
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when aggregating well-being indices. While a few small-scale pilot studies6 have shown that this is
practically feasible, no representative large-scale study has been undertaken to obtain results that
could directly be applied to commonly used well-being indices.
In this research project, we want to
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develop a survey instrument that uses contingent valuation and conjoint analysis to obtain
weights for composite well-being indices that are relevant in the EU (e.g. European Social
Progress Index, OECD Better Life Index),
conduct representative surveys in a number of European countries,
determine weights, assess their quality (validity, reliability), and apply them to obtain wellbeing rankings of European regions based on sound weighting methods, and
compare the results with commonly used methods, e.g. factor analysis or budget allocation
process (as in the individually chosen weights in the OECD Better Life Index).
In case of interest, please contact: Dr. Cornelia Lang, IWH ([email protected])
For further information regarding the project team: Please visit our homepages:
Dr. Cornelia Lang, Halle Institute for Economic Research (IWH), Germany
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http://www.iwh-halle.de/e/fdz/start.asp?lang=e&Abteilung=mud
Dipl.-Volksw. Philip Maschke, Halle Institute for Economic Research (IWH), Germany
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http://www.iwh-halle.de/e/fdz/start.asp?lang=e&Abteilung=mud
Prof. Dr. Andreas Knabe, Otto-von-Guericke University Magdeburg, Germany
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http://www.vwl1.ovgu.de/vwl1/en/Team/Prof_+Dr_+Andreas+Knabe-p-573.html
Prof. Dr. Jörg Döpke, University of Applied Sciences Merseburg, Germany
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https://www.hs-merseburg.de/ww/organisation/mitarbeiterdetailansicht/?tx_bcstaff%5Bbackpid%5D=880&tx_bcstaff%5Bdetail%5D=277
6
See, e.g, Ülengin B., Ülengin F., Güvenç Ü., (2001), A multidimensional approach to urban quality of life: the case of
Istanbul. European Journal of Operational Research 130, 361-374.
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