Controlling for the effects of information in a public goods discrete choice model

This paper develops a reduced form method of controlling for differences in information sets of subjects in public good discrete choice models, using stated preference data. The main contribution of our method comes from accounting for the effect of information provided during a survey on the mean and the variance of individual-specific scale parameters. In this way we incorporate both scale heterogeneity as well as observed and unobserved preference heterogeneity to investigate differences across and within information treatments. Our approach will also be useful to researchers who want to combine stated preference data sets while controlling for scale differences. We illustrate our approach using the data from a discrete choice experiment study of a biodiversity conservation program and find that the mean of individual-specific scale parameters and its variance in the sample is sensitive to the information set provided to the respondents

Three Essays on the Role of Unstructured Data in Marketing Research

This thesis studies the use of firm and user-generated unstructured data (e.g., text and videos) for improving market research combining advances in text, audio and video processing with traditional economic modeling. The first chapter is joint work with K. Sudhir and Minkyung Kim. It addresses two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, we develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, we address the problem of missing attributes in text in constructing attribute sentiment scores---as reviewers write only about a subset of attributes and remain silent on others. We develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, we show superior accuracy in converting text to numerical attribute sentiment scores with our model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. The second essay, which is joint work with Aniko Oery and Joyee Deb is an information-theoretic model to study what causes selection in valence in user-generated reviews. The propensity of consumers to engage in word-of-mouth (WOM) differs after good versus bad experiences, which can result in positive or negative selection of user-generated reviews. We show how the strength of brand image (dispersion of consumer beliefs about quality) and the informativeness of good and bad experiences impacts selection of WOM in equilibrium. WOM is costly: Early adopters talk only if they can affect the receiver’s purchase. If the brand image is strong (consumer beliefs are homogeneous), only negative WOM can arise. With a weak brand image or heterogeneous beliefs, positive WOM can occur if positive experiences are sufficiently informative. Using data from Yelp.com, we show how strong brands (chain restaurants) systematically receive lower evaluations controlling for several restaurant and reviewer characteristics. The third essay which is joint work with K.Sudhir and Khai Chiong studies success factors of persuasive sales pitches from a multi-modal video dataset of buyer-seller interactions. A successful sales pitch is an outcome of both the content of the message as well as style of delivery. Moreover, unlike one-way interactions like speeches, sales pitches are a two-way process and hence interactivity as well as matching the wavelength of the buyer are also critical to the success of the pitch. We extract four groups of features: content-related, style-related, interactivity and similarity in order to build a predictive model of sales pitch effectiveness

The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Goods

This paper develops a model of demand estimation in which consumers learn about their true preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two quasi experience with a good will make consumers more certain over their preferences in both mean and variance are supported in each case.‐public goods. We find that the predictions of the theoretical exercise that additiona

Report on the First Working Group Meeting of the “AG Marketing”

This contribution reports on the first meeting of the new formed working group “Data Analysis and Classification in Marketing (AG Marketing)” of the data science society (GfKl) held at the KIT, Karlsruhe, November 14th – 15th, 2019. The abstracts of the presentations given reflect the ongoing trend to exploit a large variety of digital data sources for marketing purposes and the need for advanced and innovative analysis methods

Using discrete choice experiments for environmental valuation

This paper provides with a review of the state of the art of environmental valuation with discrete choice experiments (DCE). The growing body of literature on this field serves to emphasise the increasing role that DCE are playing in environmental decision making in the last decade. The paper attempts to cover the full process of undertaking a choice experiment, including survey and experimental design, econometric analysis of choice data and welfare analysis. The research on this field is found to be intense, although many challenges are put forward (e.g. choice task complexity and cognitive effort, experimental design, endogeneity or model uncertainty). Reviewing the state of the art of DCE serves to draw attention to the main challenges that this methodological approach will need to overcome in the coming years and to identify the frontiers in discrete choice analysis.The author acknowledges the financial support from the Department of Environment of the Basque Government through IHOBE, S.A. and from the Department of Education of the Basque Government through grant IT-334-07 (UPV/EHU Econometrics Research Group)

Thought for Food: Understanding Educational Disparities in Food Consumption

__Abstract__ Higher educated individuals are healthier and live longer than their lower educated peers. One reason is that lower educated individuals engage more in unhealthy behaviours including consumption of a poor diet, but it is not clear why they do so. In this paper we develop an economic theory of unhealthy food choice, and use a Discrete Choice Experiment to discriminate between the theoretical parameters. Differences in health knowledge appear to be responsible for the greatest part of the education disparity in diet. However, when faced with the most explicit health information regarding diet, lower educated individuals still state choices that imply a lower concern for negative health consequences. This is consistent with a theoretical prediction that part of the education differences across health behaviours is driven by the "marginal value of health" rising with education