This paper develops a theoretical model for the formation of subjective beliefs on individual survival expectations. Data from the Health and Retirement Study (HRS) indicate that, on average, young respondents underestimate their true survival probability whereas old respondents overestimate their survival probability. Such subjective beliefs violate the rational expectations paradigm and are also not in line with the predictions of the rational Bayesian learning paradigm. We therefore introduce a model of Bayesian learning which combines rational learning with the possibility that the interpretation of new information is prone to psychological attitudes. We estimate the parameters of our theoretical model by pooling the HRS data. Despite a parsimonious parametrization we find that our model results in a remarkable fit to the average subjective beliefs expressed in the data.JEL classification: C44, D83, D91, I10
On average, "young" people underestimate whereas "old" people overestimate their chances to survive into the future. Such subjective survival beliefs violate the rational expectations paradigm and are also not in line with models of rational Bayesian learning. In order to explain these empirical patterns in a parsimonious manner, we assume that self-reported beliefs express likelihood insensitivity and can therefore be modeled as non-additive beliefs. In a next step we introduce a closed form model of Bayesian learning for non-additive beliefs which combines rational learning with psychological attitudes in the interpretation of information. Our model gives a remarkable fit to average subjective survival beliefs reported in the Health and Retirement Study. JEL classification: C44, D83, D91, I10
Ample psychological evidence suggests that people's learning behavior is often prone to a "myside bias" or "irrational belief persistence" in contrast to learning behavior exclusively based on objective data. In the context of Bayesian learning such a bias may result in diverging posterior beliefs and attitude polarization even if agents receive identical information. Such patterns cannot be explained by the standard model of rational Bayesian learning that implies convergent beliefs. Based on Choquet expected utility theory, we therefore develop formal models of Bayesian learning with psychological bias as alternatives to rational Bayesian learning. We derive conditions under which beliefs may diverge in the learning process despite the fact that all agents observe the same sample drawn from an i.i.d. process. Key to our approach is the description of ambiguous beliefs as neo-additive capacities (Chateauneuf et al., J Econ Theory 137:538-567, 2007), which allows for a flexible and parsimonious parametrization of departures from additive probability measures.Keywords Non-additive probability measures · Choquet expected utility theory · Bayesian learning · Bounded rationality JEL Classification C79 · D83 Several studies in the psychological literature demonstrate that people's learning behavior is prone to effects such as "myside bias" or "irrational belief persistence" (cf., e.g., Baron 2008, Chapter 9). For instance, in a famous experiment by Lord et al. (1979), subjects supporting and opposing capital punishment were exposed to two purported studies, one confirming and one disconfirming their existing beliefs about the deterrent efficacy of the death penalty. Despite the fact that both groups received the same information, their learning behavior resulted in an increased "attitude polarization" in the sense that their respective posterior beliefs, either in favor or against the deterrent efficacy of death penalty, further diverged. Analogous results on diverging posterior beliefs in the face of identical information have earlier been reported by Pitz et al. (1967), Pitz (1969) and Chapman (1973 in the context of Bayesian updating of subjective probabilities. In violation of Bayes' update rule the subjects in these experiments formed biased posteriors that supported their original opinions rather than taking into account the evidence. The learning behavior elicited in these experiments cannot be explained by the standard model of rational Bayesian learning according to which differences in agents' prior beliefs must decrease rather than increase whenever the agents receive identical information. In the economics literature, these phenomena have been referred to as a "confirmatory bias" by Rabin and Schrag (1999). That differential interpretation of identical information is of relevance for economic decisions is, e.g., documented in Kandel and Pearson (1995) who provide empirical evidence that news from public announcements are interpreted differently by traders in stock markets. Models of rational Bayesian learn...
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...
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