Higher-Order Income Risk over the Business Cycle: A Parametric Approach

Christopher Busch and Alexander Ludwig

No 1137, 2019 Meeting Papers from Society for Economic Dynamics

Abstract: We develop a novel parametric approach to estimate the relationship between idiosyncratic and aggregate labor income risk. We derive closed form expressions for the variance and skewness of persistent and transitory income shocks, and achieve identification in a Generalized Method of Moments (GMM) framework. We apply our method to German and US household data. For both Germany and the US, we find the skewness of persistent income changes to be procyclical, and the variance to be countercyclical. The existing tax and transfer schemes reduce this cyclicality, and also reduce dispersion and left-skewness of transitory shocks. We then illustrate that the estimated ``higher-order income risk'' is relevant from a macroeconomic perspective. To this end, we develop a partial equilibrium life-cycle model of consumption and savings with exogenous earnings processes, which we employ to show, first, that the welfare implications of higher-order risk are potentially large but depend on risk attitudes of households. Second, higher-order risk matters for the welfare costs of business cycles. Third, consumption insurance through self-insurance is better under higher-order income risk.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:red:sed019:1137

Access Statistics for this paper

More papers in 2019 Meeting Papers from Society for Economic Dynamics Society for Economic Dynamics Marina Azzimonti Department of Economics Stonybrook University 10 Nicolls Road Stonybrook NY 11790 USA. Contact information at EDIRC.
Bibliographic data for series maintained by Christian Zimmermann ().