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Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework

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Abstract

We develop a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption. Log-earnings are the sum of a general Markovian persistent component and a transitory innovation. The persistence of past shocks to earnings is allowed to vary according to the size and sign of the current shock. Consumption is modeled as an age-dependent nonlinear function of assets, unobservable tastes and the two earnings components. We establish the nonparametric identification of the nonlinear earnings process and of the consumption policy rule. Exploiting the enhanced consumption and asset data in recent waves of the Panel Study of Income Dynamics, we find that the earnings process features nonlinear persistence and conditional skewness. We confirm these results using population register data from Norway. We then show that the impact of earnings shocks varies substantially across earnings histories, and that this nonlinearity drives heterogeneous consumption responses. The framework provides new empirical measures of partial insurance in which the transmission of income shocks to consumption varies systematically with assets, the level of the shock and the history of past shocks.

Suggested Citation

  • Manuel Arellano & Richard Blundell & Stéphane Bonhomme, 2016. "Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework," Working Papers wp2016_1606, CEMFI.
  • Handle: RePEc:cmf:wpaper:wp2016_1606
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    1. Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework
      by maximorossi in NEP-LTV blog on 2017-03-22 00:52:08

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    Cited by:

    1. Arifur Rahman, 2017. "Heterogeneous labor earning shock process in finite horizon," LIS Working papers 721, LIS Cross-National Data Center in Luxembourg.
    2. Robert A. Moffitt & Sisi Zhang, 2018. "Income Volatility and the PSID: Past Research and New Results," NBER Working Papers 24390, National Bureau of Economic Research, Inc.
    3. Mariacristina De Nardi & Giulio Fella & Gonzalo Paz Pardo, 2016. "The Implications of Richer Earnings Dynamics for Consumption and Wealth," NBER Working Papers 21917, National Bureau of Economic Research, Inc.
    4. Peter Levell & Barra Roantree & Jonathan Shaw, 2016. "Mobility and the lifetime distributional impact of tax and transfer reforms," IFS Working Papers W16/17, Institute for Fiscal Studies.
    5. THELOUDIS Alexandros, 2017. "Consumption Inequality across Heterogeneous Families," LISER Working Paper Series 2017-18, LISER.
    6. Iourii Manovskii & Dmytro Hryshko, 2017. "How Much Consumption Insurance in the U.S.?," 2017 Meeting Papers 1584, Society for Economic Dynamics.
    7. Sanchez, Manuel & Wellschmied, Felix, 2017. "Modeling Life-Cycle Earnings Risk with Positive and Negative Shocks," IZA Discussion Papers 10925, Institute for the Study of Labor (IZA).
    8. repec:red:issued:16-340 is not listed on IDEAS
    9. Mariacristina De Nardi & Giulio Fella, 2017. "Saving and Wealth Inequality," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 26, pages 280-300, October.
    10. Robert Moffitt & Sisi Zhang, 2018. "Income Volatility and the PSID: Past Research and New Results," Working Papers 2018-016, Human Capital and Economic Opportunity Working Group.
    11. Jeremy Lise, 2016. "The Research Agenda: Jeremy Lise on Heterogeneity and dynamics in the labor market and within the household," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 17(1), April.

    More about this item

    Keywords

    Earnings dynamics; consumption; partial insurance; panel data; quantile regression; latent variables.;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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