How Much Consumption Insurance Beyond Self-Insurance?

Abstract

We assess the degree of consumption smoothing implicit in a calibrated life-cycle version of the standard incomplete-markets model, and we compare it to the empirical estimates of Blundell et al. (2008) (BPP hereafter). We find that households in the model have access to less consumption-smoothing against permanent earnings shocks than what is measured in the data. BPP estimate that 36% of permanent shocks are insurable (i.e., do not translate into consumption growth), whereas the model's counterpart of the BPP estimator varies between 7% and 22%, depending on the tightness of debt limits. In the model, the age profile of the insurance coefficient is sharply increasing, whereas BPP find no clear age slope in their estimate. Allowing for a plausible degree of "advance information" about future earnings does not reconcile the model-data gap. If earnings shocks display mean reversion, even with very high autocorrelation, then the average degree of consumption smoothing in the model agrees with the BPP empirical estimate, but its age profile remains steep. Finally, we show that the BPP estimator of the true insurance coefficient has, in general, a downward bias that grows as borrowing limits become tighter.

Fiscal Policy and MPC Heterogeneity

“…This result, however, is somewhat puzzling in light of their previous study that finds that most spending is on nondurables (Johnson, Parker, and Souleles 2006). 15 Kaplan and Violante (2010) investigate this issue simulating a life-cycle model in which consumers have isoelastic preferences, face income risk during their working lives, and retire at age 65. They conclude that the MPC possible explanation for this large deviation from the theoretical benchmarks is that the question does not distinguish between components of spending (durables versus nondurable consumption).…”

Consumer default with complete markets: default-based pricing and finite punishment

“…The ability of different models for understanding the evidence on risk-sharing has been the subject of an important body of quantitative literature (e.g., Krueger and Perri (2006), Kaplan and Violante (2010)). The conclusion that seems to emerge is that the debt-constrained model in the Kehoe and Levine (1993) tradition underestimates inequality.…”

Income Uncertainty and Household Savings in China

“…In order to quantify the effects of this rise in uncertainty on individual and aggregate savings, we now undertake a calibration of a precautionary savings model, building on the work of Carroll (1997) and Gourinchas and Parker (2002). 17 This enables us to provide a quantitative measure of how the increase in the variance of 17 See also Fuchs-Schündeln (2008) and Kaplan and Violante (2010). Our calibration exercise sets only a lower bound on the degree of precautionary saving attributable to earnings uncertainty.…”