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Measurement of Preferences and Mistakes

Area Description

The work program of the research group is divided into three areas. "Measurement of Preferences and Mistakes" is one of them. Each area is divided into three projects. This area's projects are:

Code Project Leader Project Title Institution
P1 Ebert, Schildberg-Hörisch Self-Control and Time Preference: Causes, Implications and Measurement Heidelberg, HHU
P2 Heiß Measuring Consumer Behavior Using Big Data HHU
P3 Romahn, Zulehner The Informational Content of Consumer Choice in Differentiated Product Markets HHU, Wien

P1: Self-Control and Time Preference: Causes, Implications and Measurement

This project contributes expertise in foundational questions of behavioral economics and decision-making to the unit. We develop novel methodological tools for the measurement of time preferences, the sophistication about them, and their stability over time. One important aspect of time preferences is time-(in)consistency. Actual behavior may deviate from planned and rational behavior even though the decision environment has not changed. We aim at advancing our understanding of time-inconsistency, theoretically with regard to its causes, empirically through its measurement, and in an applied way, by investigating its stability and implications for real-world behavior as well as by providing tractable modelling approaches for other economists. Advancing the latter will involve axiomatic, applied theoretical, and econometric approaches. Moreover, since time-inconsistency is one important way to model self-control failure, we will address an open question concerning the nature of self-control, namely whether it can be understood as a unitary construct or, alternatively, as an umbrella term for a number of potentially related but ultimately different constructs. We will address this issue empirically by providing a comprehensive synopsis of empirical measurement tools of self-control from various disciplines in order to examine whether different self-control measures converge in what they suggest about a given individual (i.e., study their “convergent validity”).

P2: Measuring Consumer Behavior Using Big Data

In classical discrete choice models, consumers are assumed to make a comprehensive and careful choice between the available alternatives in terms of their utility consequences. It is well known that this is not an accurate description of the real-world consumer. Brand loyalty and inertia are often found to have a substantial on consumer choice. If inertia and other deviations from strict rationality are strong determinants of daily shopping decisions, this has important consequences for (i) understanding consumers themselves, (ii) the validity of standard choice analyses, and (iii) the strategic behavior of producers and retailers. Our goal in this project is to shed light on all of these aspects. While previous literature mostly focusses on single product categories, the fact that during a shopping trip consumers make dozens of choices among different categories suggests great opportunities to identify a more wholistic picture of behavior from a wealth of repeated and interrelated decisions and large amounts of data. By building on techniques from the machine learning literature, demand can be modelled simultaneously for many product categories. The approach is based on splitting behavioral parameters across individuals and categories. This allows the individual parameters to be identified from choice behavior in all categories at the same time: If we observe many yoghurt purchases of a household, this helps to identify the individual behavior for toothpaste. We will study various aspects of joint decision making using a large consumer panel data set by Nielsen. How often do people automatically pick the same kind of cereal or tooth paste without a deliberate choice? How loyal to a brand are they? How much do these kinds of effects differ across individuals? Is this an individual trait which is correlated over product categories? How much are future choices affected by current shocks such as advertising or product availability? How does a consumer react to bundling and similar store strategies? Finally, we will use the developed methods and behavioral insights to study the strategic behavior of producers and retailers in highly concentrated markets and discuss whether there is any role for legislation or regulation.

P3: The Informational Content of Consumer Choice in Differentiated Product Markets

Consumers often make decisions without being fully informed. We aim to structurally estimate the extent to which different consumers take into account the available choices in differentiated products markets. To obtain credible estimates of limited information of consumers, our modelling approach is going to allow consumers to differ in the extent to which available options are considered and, in their willingness, to pay for product characteristics. Joint identification of preference and consideration set heterogeneity will be based on observed asymmetries in the substitution between different products. Structural demand models with a full information assumption imply symmetric diversion of demand between different products. Deviations from this symmetry indicate that not all consumers are fully informed. Such observed asymmetries therefore allow us to identify parameters that determine the probability that specific products are considered. The approach avoids imposing an exclusion restriction as for example assuming that marketing expenditure only shifts consumer attention but not consumer utility. This also allows price and other product characteristics to determine consumer attention and thereby yields a substantially more flexible demand model. Our aim is to apply this modelling approach to a large number of categories from the U.S. consumer packaged goods industry, as covered in the Kilts-Nielsen data at Chicago Booth. The data offers both scanner, consumer panel and marketing exposure data. Obtaining estimates of consumers’ consideration technology will allow us to quantify if firms have been able to raise their profitability over the last decade by steering consumers to their products through shifting consumer attention. This can shed some light on the potential drivers for systematic markup changes in the U.S. economy. It can also provide important insights on the effect of horizontal mergers in differentiated product markets, where typically full information is assumed. If and how merged firms adjust their efforts of gaining consumer attention post-merger is to the best of our knowledge an under investigated question with potentially important implications for competition policy.