Essays on Individual and Household Decision-Making: Experimental and Survey Evidence

This dissertation presents a collection of four papers which are inspired by my interest in the phenomenon that economic agents are heterogeneous in their behavior and which are concerned with the investigation of the relationship between individual-specific characteristics and economic decisions. All four papers in this dissertation are self-contained studies which can be read independently. While this dissertation touches different disciplines and discusses their methodological differences, the collection of papers is united by the objective to obtain inference about determinants of economic behavior in empirical studies. I investigate behavior in distinct contexts, and I use data which have been collected in different ways: in a controlled laboratory setting and in a nationwide household survey. The first study, co-authored with Cornelia Betsch, investigates individual decision behavior in a static decision context and it combines elements of behavioral modeling in economics and in psychology. We use data from a laboratory experiment and find that there is a systematic relationship between psychometric measures of individual decision modes (intuitive vs. deliberative decision mode) and the curvature of individual utility functions. That is, we identify a stable individual disposition measure that is related to behavior in risky choice situations. The second paper explores individual decision behavior in a dynamic decision context. I develop utility-based models of sequential decision behavior and design a laboratory experiment in order to investigate the relationship between individual preferences and behavior in sequential decision tasks. The data show that individual loss aversion is systematically related to the observed behavior in a way that is consistent with the predictions of a newly developed reference point model; that is, loss aversion helps to predict heterogeneity in dynamic decision behavior, whereas risk attitude is not linked to observed behavior. The third paper is concerned with the saving decisions of German households. I use data from a representative socio-economic survey – the SAVE survey on household saving behavior – and analyze to what extent four co-existing saving motives are related to the saving decisions of private German households. The results show that heterogeneity with respect to the saving rate and the saver type (classified based on questions about the extent to which households engage in regular and planned saving) is systematically associated with the importance that households attach to different saving motives. This suggests that policy reforms that substantially change the importance of certain saving motives in the eyes of private households, e.g. based on appropriate motivation and information campaigns, might alter household saving behavior in various ways. While surveys are an important source of data for the study of household behavior, incomplete information, such as item and unit nonresponse, is a data problem that is particularly prevalent in surveys dealing with sensitive financial issues. Therefore, the last paper of this dissertation is concerned with designing an iterative statistical algorithm, a so-called Markov-Chain Monte-Carlo algorithm, for dealing with the problem of incomplete information that results from item nonresponse to questions in the German SAVE survey. The procedure leads to a multiply imputed and complete data set that allows the uncertainty due to imputation to be reflected in subsequent analyses of the SAVE data. Additionally, I find that there is heterogeneity in nonresponse behavior across different questions and that – in line with existing findings in cognitive psychology – item nonresponse is not occurring randomly but is related to the individual-specific characteristics of the survey participants. In a sense, this finding in the last paper – obtained using an involved statistical methodology – leads back to the first paper of this dissertation, since it underlines the importance of interdisciplinary collaboration: Cognitive psychologists investigate the phenomenon of item nonresponse and develop models for the processes that lead to nonresponse behavior. In a collaborative effort, these findings can help to understand and model the mechanisms of survey nonresponse better, and – a complementary approach to correcting data problems ex post based on involved statistical methods – they can help to address the problem of item nonresponse by improving the data collection methodology.