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The econometric modelling of social preferences

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  • Anna Conte
  • Peter Moffatt

Abstract

Experimental data on social preferences present a number of features that need to be incorporated in econometric modelling. We explore a variety of econometric modelling approaches to the analysis of such data. The approaches under consideration are: the Random Utility approach (in which it is assumed that each possible action yields a utility with a deterministic and a stochastic component, and that the individual selects the action yielding the highest utility); the Random Behavioural approach (which assumes that the individual computes the maximum of a deterministic utility function, and that computational error causes their observed behaviour to depart stochastically from this optimum); and the Random Preference approach (in which all variation in behaviour is attributed to stochastic variation in the parameters of the deterministic component of utility). These approaches are applied in various ways to an experiment on fairness conducted by Cappelen et al. (Am Econ Rev 97(3):818–827, 2007 ). Various models that we estimate succeed in capturing the key features of the dataset. Conclusions concerning fairness-related behaviour depend crucially on the choice of econometric model. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Anna Conte & Peter Moffatt, 2014. "The econometric modelling of social preferences," Theory and Decision, Springer, vol. 76(1), pages 119-145, January.
  • Handle: RePEc:kap:theord:v:76:y:2014:i:1:p:119-145
    DOI: 10.1007/s11238-012-9309-4
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    13. Anna Conte & John D. Hey & Peter G. Moffatt, 2018. "Mixture models of choice under risk," World Scientific Book Chapters, in: Experiments in Economics Decision Making and Markets, chapter 1, pages 3-12, World Scientific Publishing Co. Pte. Ltd..
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    2. Adrian Bruhin & Ernst Fehr & Daniel Schunk, 2019. "The many Faces of Human Sociality: Uncovering the Distribution and Stability of Social Preferences," Journal of the European Economic Association, European Economic Association, vol. 17(4), pages 1025-1069.
    3. Philomena M. Bacon & Anna Conte & Peter G. Moffatt, 2024. "Gender and gambling preference," Applied Economics, Taylor & Francis Journals, vol. 56(4), pages 426-439, January.
    4. Jordi Brandts & Enrique Fatas & Ernan Haruvy & Francisco Lagos, 2015. "The impact of relative position and returns on sacrifice and reciprocity: an experimental study using individual decisions," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 45(3), pages 489-511, October.
    5. Luis Miller & Heiko Rauhut & Fabian Winter, 2011. "The emergence of norms from conflicts over just distributions," Jena Economics Research Papers 2011-018, Friedrich-Schiller-University Jena.
    6. Rodriguez-Lara, Ismael, 2016. "Equity and bargaining power in ultimatum games," Journal of Economic Behavior & Organization, Elsevier, vol. 130(C), pages 144-165.
    7. Breitmoser, Yves, 2013. "Estimation of social preferences in generalized dictator games," Economics Letters, Elsevier, vol. 121(2), pages 192-197.
    8. Robson, Matthew, 2021. "Inequality aversion, self-interest and social connectedness," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 744-772.
    9. Anna Conte & Peter G Moffatt & Mary Riddel, 2019. "The Multivariate Random Preference Estimatorfor Switching Multiple Price List Data," University of East Anglia School of Economics Working Paper Series 2019-04, School of Economics, University of East Anglia, Norwich, UK..
    10. Anna Conte & Daniela T. Di Cagno & Emanuela Sciubba, 2015. "Behavioral Patterns In Social Networks," Economic Inquiry, Western Economic Association International, vol. 53(2), pages 1331-1349, April.
    11. Alice Becker, 2013. "Accountability and the fairness bias: the effects of effort vs. luck," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 41(3), pages 685-699, September.
    12. Carpenter, Jeffrey P. & Robbett, Andrea, 2022. "Measuring Socially Appropriate Social Preferences," IZA Discussion Papers 15590, Institute of Labor Economics (IZA).
    13. Winter, Fabian & Rauhut, Heiko & Miller, Luis, 2018. "Dynamic Bargaining and Normative Conflict," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 74(C), pages 112-126.
    14. Bruhin, Adrian & Janizzi, Kelly & Thöni, Christian, 2020. "Uncovering the heterogeneity behind cross-cultural variation in antisocial punishment," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 291-308.

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    More about this item

    Keywords

    Econometric modelling and estimation; Model evaluation; Individual behaviour; Fairness; C51; C52; C91; D63;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement

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