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Individual heterogeneity and average welfare

  • Jerry Hausman


    (Institute for Fiscal Studies and MIT)

  • Whitney Newey


    (Institute for Fiscal Studies and MIT)

Individual heterogeneity is an important source of variation in demand. Allowing for general heterogeneity is needed for correct welfare comparisons. We consider general heterogenous demand where preferences and linear budget sets are statistically independent. We find that the dimension of heterogeneity and the individual demand functions are not identified. We also find that the exact consumer surplus of a price change, averaged across individuals, is not identified, motivating bounds analysis. We use bounds on income effects to derive relatively simple bounds on the average surplus, including for discrete/continuous choice. We also sketch an approach to bounding surplus that does not use income effect bounds. We apply the results with income effect bounds to gasoline demand. We find little sensitivity to the income effect bounds in this application.

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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP34/13.

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Date of creation: Jul 2013
Date of revision:
Handle: RePEc:ifs:cemmap:34/13
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  1. Beresteanu, Arie & Molinari, Francesca, 2006. "Asymptotic Properties for a Class of Partially Identified Models," Working Papers 06-07, Cornell University, Center for Analytic Economics.
  2. Richard Blundell & Joel Horowitz & Matthias Parey, 2009. "Measuring the price responsiveness of gasoline demand," CeMMAP working papers CWP11/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. Stefan Hoderlein & Anne Vanhems, 2011. "Welfare analysis using nonseparable models," CeMMAP working papers CWP01/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  4. Richard Blundell & Joel L. Horowitz & Matthias Parey, 2012. "Measuring the price responsiveness of gasoline demand: Economic shape restrictions and nonparametric demand estimation," Quantitative Economics, Econometric Society, vol. 3(1), pages 29-51, 03.
  5. Stefan Hoderlein & Jörg Stoye, 2014. "Revealed Preferences in a Heterogeneous Population," The Review of Economics and Statistics, MIT Press, vol. 96(2), pages 197-213, May.
  6. Dubin, Jeffrey A & McFadden, Daniel L, 1984. "An Econometric Analysis of Residential Electric Appliance Holdings and Consumption," Econometrica, Econometric Society, vol. 52(2), pages 345-62, March.
  7. Holger Dette & Stefan Hoderlein & Natalie Neumeyer, 2013. "Testing Multivariate Economic Restrictions Using Quantiles: The Example of Slutsky Negative Semidefiniteness," Boston College Working Papers in Economics 836, Boston College Department of Economics.
  8. Yuichi Kitamura & Jorg Stoye, 2013. "Nonparametric Analysis of Random Utility Models: Testing," Cowles Foundation Discussion Papers 1902, Cowles Foundation for Research in Economics, Yale University.
  9. Hausman, Jerry A, 1981. "Exact Consumer's Surplus and Deadweight Loss," American Economic Review, American Economic Association, vol. 71(4), pages 662-76, September.
  10. G. Burtless & J. A. Hausman, 1977. "The Effect of Taxation on Labor Supply: Evaluating the Gary Negative Income Tax Experiment," Working papers 211, Massachusetts Institute of Technology (MIT), Department of Economics.
  11. Arthur Lewbel, 2001. "Demand Systems with and without Errors," American Economic Review, American Economic Association, vol. 91(3), pages 611-618, June.
  12. Hausman, J.A. & Newey, W.K., 1992. "Nonparametric Estimation of Exact Consumers Surplus and Deadweight Loss," Working papers 93-2, Massachusetts Institute of Technology (MIT), Department of Economics.
  13. Newey, Whitney K., 1997. "Convergence rates and asymptotic normality for series estimators," Journal of Econometrics, Elsevier, vol. 79(1), pages 147-168, July.
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