Why Do the Elderly Save? Using Health Shocks to Uncover Bequests Motives

TL;DR

This paper introduces an advanced estimation method combining neural networks with traditional econometric techniques to better understand elderly saving motives, revealing significant bequest motives across income groups and the importance of health heterogeneity.

Contribution

It develops an adversarial structural estimation framework that improves identification of bequest motives by incorporating health and gender data, advancing previous models.

Findings

Bequest motives account for 13-19% of late-life savings.

The method precisely separates bequest motives from precautionary savings.

Health heterogeneity influences savings behavior and motive identification.

Abstract

We revisit the saving behavior of elderly singles using an adversarial structural estimation framework by Kaji, Manresa and Pouliot (2023). The method bridges the simulated method of moments (SMM) and maximum-likelihood estimation by embedding a flexible discriminator, implemented as a neural network, that adaptively selects the most informative features of the data. Applying this approach to the model of De Nardi, French, and Jones (2010) with AHEAD data, we show that including gender and health histories in the discriminator improves identification and precision of bequests motives. The resulting estimates reveal that bequest motives explain between $13\%$ and $19\%$ percent of late-life savings across all permanent-income quintiles, not only among the rich. The adversarial estimator precisely disentangles bequest motives from precautionary savings motives. These findings suggest that heterogeneity in health-related survival expectations is another important source of identifying variation to distinguishing bequest and precautionary saving motives.