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Imputing consumption in the PSID using food demand estimates from the CEX

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  • Richard Blundell

    () (Institute for Fiscal Studies and Institute for Fiscal Studies and University College London)

  • Luigi Pistaferri

    (Institute for Fiscal Studies and Stanford University)

  • Ian Preston

    () (Institute for Fiscal Studies and University College London)

Abstract

This paper assesses the accuracy of decomposing income risk into permanent and transitory components using income and consumption data. We develop a specific approximation to the optimal consumption growth rule and use Monte Carlo evidence to show that this approximation can provide a robust method for decomposing income risk. The availability of asset data enables the use of a more accurate approximation allowing for partial sef-insurance against permanent shocks. We show that the use of data on median asset holdings corrects much of the error in the simple approximation which assumes no self-insurance against permanent shocks.

Suggested Citation

  • Richard Blundell & Luigi Pistaferri & Ian Preston, 2004. "Imputing consumption in the PSID using food demand estimates from the CEX," IFS Working Papers W04/27, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:ifsewp:04/27
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    References listed on IDEAS

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    Cited by:

    1. Sam Schulhofer-Wohl, 2011. "Heterogeneity and Tests of Risk Sharing," Journal of Political Economy, University of Chicago Press, vol. 119(5), pages 925-958.
    2. Erich Battistin & Richard Blundell & Arthur Lewbel, 2009. "Why Is Consumption More Log Normal than Income? Gibrat's Law Revisited," Journal of Political Economy, University of Chicago Press, vol. 117(6), pages 1140-1154, December.
    3. Orazio Attanasio & Corina Mommaerts & Costas Meghir, 2015. "Insurance in Extended Family Networks," Cowles Foundation Discussion Papers 1996R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2018.
    4. Martin Browning & Thomas Crossley, 2009. "Are Two Cheap, Noisy Measures Better Than One Expensive, Accurate One?," American Economic Review, American Economic Association, vol. 99(2), pages 99-103, May.
    5. Jonathan Heathcote & Kjetil Storesletten & Giovanni L. Violante, 2014. "Consumption and Labor Supply with Partial Insurance: An Analytical Framework," American Economic Review, American Economic Association, vol. 104(7), pages 2075-2126, July.
    6. Bertrand Garbinti & Pierre Lamarche, 2014. "Les hauts revenus épargnent‑ils davantage ?," Économie et Statistique, Programme National Persée, vol. 472(1), pages 49-64.
    7. Pierre Lamarche, 2015. "Can your stomach predict your total consumption?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Indicators to support monetary and financial stability analysis: data sources and statistical methodologies, volume 39, Bank for International Settlements.
    8. Tony Smith & M. Fatih Guvenen, 2007. "Inferring Labor Income Risk from Economic Choices: An Indirect Inference Approach," 2007 Meeting Papers 1024, Society for Economic Dynamics.
    9. Kuismanen, Mika & Pistaferri, Luigi, 2006. "Information, habits, and consumption behavior: evidence from micro data," Working Paper Series 572, European Central Bank.
    10. Geoffrey R. Dunbar & Chunling Fu, 2015. "Sheltered Income: Estimating Income Under-Reporting in Canada, 1998 and 2004," Staff Working Papers 15-22, Bank of Canada.
    11. George-Levi Gayle & Limor Golan, "undated". "Estimating a Dynamic Adverse Selection Model: Labor Force Experience and the Changing Gender Earnings Gap 1968-93," GSIA Working Papers 2006-E40, Carnegie Mellon University, Tepper School of Business.
    12. Gervais, Martin & Klein, Paul, 2009. "Measuring consumption smoothing in CEX data," Discussion Paper Series In Economics And Econometrics 0906, Economics Division, School of Social Sciences, University of Southampton.
    13. Daniel H. Cooper & Byron F. Lutz & Michael G. Palumbo, 2011. "Quantifying the role of federal and state taxes in mitigating income inequality," Public Policy Discussion Paper 11-7, Federal Reserve Bank of Boston.
    14. Lamarche, Pierre, 2017. "Estimating consumption in the HFCS: Experimental results on the first wave of the HFCS," Statistics Paper Series 22, European Central Bank.
    15. Campos, Rodolfo G. & Reggio, Iliana, 2014. "Measurement error in imputation procedures," Economics Letters, Elsevier, vol. 122(2), pages 197-202.
    16. José Casado, 2011. "From income to consumption: measuring households partial insurance," Empirical Economics, Springer, vol. 40(2), pages 471-495, April.
    17. Campos, Rodolfo G. & Reggio, Iliana, 2013. "Measurement error and imputation of consumption in survey data," UC3M Working papers. Economics we1219, Universidad Carlos III de Madrid. Departamento de Economía.
    18. Orazio Attanasio & Erik Hurst & Luigi Pistaferri, 2012. "The Evolution of Income, Consumption, and Leisure Inequality in The US, 1980-2010," NBER Working Papers 17982, National Bureau of Economic Research, Inc.
    19. Jürgen Maurer & André Meier, 2005. "Do the "Joneses" really matter? Peer-group versus correlated effects in intertemporal consumption choice," IFS Working Papers W05/15, Institute for Fiscal Studies.
    20. Daniel H. Cooper & Byron F. Lutz & Michael G. Palumbo, 2012. "Quantifying the role of federal and state taxes in mitigating wage inequality," Finance and Economics Discussion Series 2012-05, Board of Governors of the Federal Reserve System (U.S.).
    21. B. Garbinti & P. Lamarche, 2014. "Do the High-Income Households Save More?," Documents de Travail de l'Insee - INSEE Working Papers g2014-10, Institut National de la Statistique et des Etudes Economiques.
    22. Daniel H. Cooper, 2010. "Imputing household spending in the Panel Study of Income Dynamics: a comparison of approaches," Working Papers 10-12, Federal Reserve Bank of Boston.
    23. Etheridge, Ben, 2015. "A test of the household income process using consumption and wealth data," European Economic Review, Elsevier, vol. 78(C), pages 129-157.
    24. Jose Maria Casado, 2012. "Consumption partial insurance of Spanish households," Working Papers 1214, Banco de España;Working Papers Homepage.

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    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • D41 - Microeconomics - - Market Structure, Pricing, and Design - - - Perfect Competition
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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