A Life-Cycle Model with Ambiguous Survival Beliefs

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Non-Technical SummaryOptimal tax and transfer systems are key for the design of modern economics. One of the workhorse models used by economists to evaluate the welfare benefits of (reforms to) these systems is the life-cycle model of consumption and savings. The model can, e.g., be used to investigate the reactions of households to savings subsidies or any other kind of reform to old-age insurance, or, more general, any institutional feature of the tax-transfer system.Yet, from a quantitative perspective, it is well known that the standard model produces several "puzzles" in a sense that the standard model cannot match certain facts in the data. It is well established that, relative to an optimal saving rate according to the model, households save too little in the data. Furthermore, the decumulation speed of assets in old-age is much lower in the data than predicted by the standard model. Finally, households behave dynamically inconsistent, in a sense that they generally save less during working life for retirement than they originally planned. Such inconsistencies cannot be accommodated by the standard model.In order to generate correct quantitative predictions it is therefore important to modify the standard model in order to account for these three empirical regularities. This is the aim of the present paper.The specific model element under investigation is the life-expectancy of households which is one of the most important ingredients of the model. Obviously, survival beliefs are of high relevance for savings behavior. The standard model uses objective data on survival beliefs, traced out from population wide survival tables. However, in several datasets that explicitly ask for subjective survival beliefs, substantial biases in survival beliefs relative to such objective data can be observed. E.g., young people strongly underestimate whereas old people (after retirement) strongly overestimate their chances to survive into the future. This paper addresses the question how these biases in survival beliefs may alter model savings behavior, thereby bringing model predictions closer to the data on household savings. On the one hand, underestimation of survival beliefs may lead to lower savings than in the standard model. On the other hand, overestimation in old-age may lead to the fact that households hold on to their assets longer in life than predicted by the standard model.To test whether the observed biases in survival be...

The Impact of Biases in Survival Beliefs on Savings Behavior

“…Our results suggest that the estimates in these papers of bequest motives and of precautionary savings motives in light of health risks are upward biased because the underlying RE models do not feature overestimation of old-age objective survival risks. It is certainly a challenging endeavor-especially with respect to the identification of all the relevant model parameters and the modeling of subjective health risk-to extend our present theoretical analysis and our quantitative work in Groneck, Ludwig, and Zimper (2016) by these features. With respect to a bequest motive our results suggest that identifica-tion of preference parameters for such a motive must come from consumption rather than asset data.…”

“…The data pattern mirrors findings in numerous empirical studies on subjective survival beliefs, cf. Groneck, Ludwig, and Zimper (2016) …”

“…A detailed description of the data is contained in Ludwig and Zimper (2013), and Groneck, Ludwig, and Zimper (2016).…”

“…Each bar depicts the difference of unconditional probabilities to survive to a specific target age. Source: Groneck, Ludwig, and Zimper (2016). current age h survives until (at least) the beginning of age t > h …”

“…Eventually, it must dominate the effects of underestimation on asset holdings. How these tensions play out are quantitative questions which we address in Groneck, Ludwig, and Zimper (2016).…”