Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework

 

By: Manuel Arellano (CEMFI, Centro de Estudios Monetarios y Financieros) ; Richard Blundell (University College London) ; Stéphane Bonhomme (University of Chicago)
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.
Keywords: Earnings dynamics, consumption, partial insurance, panel data, quantile regression, latent variables.
JEL: C23 D31 D91
URL: http://d.repec.org/n?u=RePEc:cmf:wpaper:wp2016_1606&r=ltv

 

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