Earnings and Consumption Dynamics: A Nonlinear Panel Data Framework

• 成果类型: Article
• 署名作者: Arellano, Manuel; Blundell, Richard; Bonhomme, Stephane
• 署名单位: University of London; University College London; University of London; London School Economics & Political Science; University of Chicago
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA13795
• 发表日期: 2017
• 页码: 693-734
• 关键词: life-cycle; partial insurance; income changes; em algorithm; models; RISK; INEQUALITY; Heterogeneity; quantile; choices

摘要

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.