Embedding as a Pitfall for Survey-Based Welfare Indicators: Evidence from an Experiment

33 Pages Posted: 8 May 2017

See all articles by Clemens Hetschko

Clemens Hetschko

Free University of Berlin (FUB)

Louisa von Reumont

Freie Universitaet Berlin (FUB)

Ronnie Schöb

Freie Universitaet Berlin; CESifo (Center for Economic Studies and Ifo Institute)

Date Written: April 04, 2017

Abstract

How can we assess the welfare of a society, its evolution over time and predict its change due to particular policy interventions? One way is to use survey-based welfare indicators such as the OECD Better Life Index. It invites people to weight a variety of quality of life indicators according to their individual preferences. 11 broad dimensions aggregate these indicators. Our experiment shows that people do not provide consistent ratings across differently labelled dimensions that embed the same indicators. They also do not adjust the rating of equally named dimensions changing sets of indicators. These results show that survey-based measures might suffer from strong embedding effects and, as a result, may fail to measure citizens’ true preferences for the indicators.

Keywords: beyond GDP, welfare measurement, survey-based welfare indicators, OECD Better Life Index, embedding effect

JEL Classification: B410, C430, C830, I310

Suggested Citation

Hetschko, Clemens and von Reumont, Louisa and Schöb, Ronnie, Embedding as a Pitfall for Survey-Based Welfare Indicators: Evidence from an Experiment (April 04, 2017). CESifo Working Paper Series No. 6419. Available at SSRN: https://ssrn.com/abstract=2964880

Clemens Hetschko

Free University of Berlin (FUB) ( email )

Van't-Hoff-Str. 8
Berlin, Berlin 14195
Germany

Louisa Von Reumont (Contact Author)

Freie Universitaet Berlin (FUB) ( email )

Boltzmannstr. 20
Berlin 14195, 14195
Germany

Ronnie Schöb

Freie Universitaet Berlin ( email )

Boltzmannstraße 20
Berlin, Berlin 14195
Germany

CESifo (Center for Economic Studies and Ifo Institute)

Poschinger Str. 5
Munich, DE-81679
Germany

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