Comparison between Unitary and Collective Models of Household Labor Supply with Taxation

34 Pages Posted: 29 Apr 2003

See all articles by Denis Beninger

Denis Beninger

ZEW – Leibniz Centre for European Economic Research

Francois Laisney

ZEW – Leibniz Centre for European Economic Research

Date Written: 2002

Abstract

Several recent papers have shown the relevance of collective models for the empirical investigation of household labor supply and consumption. Yet the estimation of collective models in the presence of non-linear budget sets and participation decisions remains a daunting task. This paper compares collective and unitary models on the basis of simulated collective data with income taxation. We distinguish the cases of individual and joint taxation. Estimating the unitary model we obtain strikingly different 'preference' parameters depending on the type of taxation. We also obtain substantial differences between predicted adjustments to labor supply following a switch between tax regimes, and hence potentially wide-ranging definitions of revenue-neutral versions of tax reforms. Finally we discuss distortions affecting the welfare analysis of reforms on the basis of unitary estimates when the model generating the data is a collective model. The results suggest that increased efforts should be devoted to the estimation of collective models with taxation.

Keywords: Pareto optimal allocation, tax reform, simulated data

JEL Classification: D11, D12

Suggested Citation

Beninger, Denis and Laisney, Francois, Comparison between Unitary and Collective Models of Household Labor Supply with Taxation (2002). ZEW Discussion Paper No. 02-65, Available at SSRN: https://ssrn.com/abstract=385424 or http://dx.doi.org/10.2139/ssrn.385424

Denis Beninger

ZEW – Leibniz Centre for European Economic Research ( email )

P.O. Box 10 34 43
L 7,1 D-68161 Mannheim
Germany

Francois Laisney (Contact Author)

ZEW – Leibniz Centre for European Economic Research ( email )

P.O. Box 10 34 43
L 7,1
D-68034 Mannheim, 68034
Germany