Arpita Chatterjee
James Morley
Aarti Singh

estimating household consumption insurance (replication data)

Blundell, Pistaferri, and Preston (American Economic Review, 2008, 98(5), 1887-1921) report an estimate of household consumption insurance with respect to permanent income shocks of 36%. In replicating findings for their model and data, we find that this estimate is distorted by a code error and is not robust to weighting scheme for generalized method of moments (GMM) or consideration of quasi maximum likelihood estimation (QMLE), which produces a significantly higher estimate of consumption insurance at 55%. For sub-groups by age and education, the differences between estimates across methods are even more pronounced, and QMLE provides new insights into heterogeneity across households compared to the original study. Monte Carlo experiments using non-normal shocks suggest that consumption insurance estimates for the model are more accurate for QMLE than GMM, including when correcting for bias and especially given a smaller sample such as is only available when looking at sub-groups.

Data and Resources

Suggested Citation

Chatterjee, Arpita; Morley, James; Singh, Aarti (2021): Estimating household consumption insurance (replication data). Version: 1. Journal of Applied Econometrics. Dataset.