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Individual versus Aggregate Income Elasticities for Heterogeneous Populations

Author

Listed:
  • Paluch, Michal
  • Kneip, Alois
  • Hildenbrand, Werner

Abstract

This paper deals with different concepts of income elasticities of demand for a heterogenous population and the relationship between individual and aggregate elasticities is analyzed. In general, the aggregate elasticity is not equal to the mean of individual elasticities. The difference depends on the heterogeneity of the population and is quantified by a covariance term. Sign and magnitude of this term are determined by an empirical analysis based on the U.K. Family Expenditure Survey. It is shown that the relevant quantities can be identified from cross-section data and, without imposing restrictive structural assumptions, can be estimated by nonparametric techniques. It turns out that the aggregate elasticity significantly overestimates the mean of individual elasticities for many commodity groups.

Suggested Citation

  • Paluch, Michal & Kneip, Alois & Hildenbrand, Werner, 2007. "Individual versus Aggregate Income Elasticities for Heterogeneous Populations," Bonn Econ Discussion Papers 13/2007, University of Bonn, Bonn Graduate School of Economics (BGSE).
    • Handle: RePEc:zbw:bonedp:132007
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    References listed on IDEAS

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    1. Peter Hall & Jeff Racine & Qi Li, 2004. "Cross-Validation and the Estimation of Conditional Probability Densities," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1015-1026, December.
    2. Racine, Jeff & Li, Qi, 2004. "Nonparametric estimation of regression functions with both categorical and continuous data," Journal of Econometrics, Elsevier, vol. 119(1), pages 99-130, March.
    3. Blundell, Richard & Pashardes, Panos & Weber, Guglielmo, 1993. "What Do We Learn About Consumer Demand Patterns from Micro Data?," American Economic Review, American Economic Association, vol. 83(3), pages 570-597, June.
    4. Arthur Lewbel, 1992. "Aggregation with Log-Linear Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 59(3), pages 635-642.
    5. Campbell, John Y & Mankiw, N Gregory, 1990. "Permanent Income, Current Income, and Consumption," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(3), pages 265-279, July.
    6. Arns, Jürgen & Bhattacharya, Kaushik, 2005. "Modelling Aggregate Consumption Growth with Time-Varying Parameters," Bonn Econ Discussion Papers 15/2005, University of Bonn, Bonn Graduate School of Economics (BGSE).
    7. Davidson, James E H, et al, 1978. "Econometric Modelling of the Aggregate Time-Series Relationship between Consumers' Expenditure and Income in the United Kingdom," Economic Journal, Royal Economic Society, vol. 88(352), pages 661-692, December.
    8. Stefan Hoderlein & Enno Mammen, 2007. "Identification of Marginal Effects in Nonseparable Models Without Monotonicity," Econometrica, Econometric Society, vol. 75(5), pages 1513-1518, September.
    9. Manisha Chakrabarty & Anke Schmalenbach & Jeffrey Racine, 2006. "On the distributional effects of income in an aggregate consumption relation," Canadian Journal of Economics, Canadian Economics Association, vol. 39(4), pages 1221-1243, November.
    10. Deaton,Angus & Muellbauer,John, 1980. "Economics and Consumer Behavior," Cambridge Books, Cambridge University Press, number 9780521296762, January.
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    Keywords

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    JEL classification:

    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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