Essays on Consumer Choice with Unobserved Choice Sets PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Essays on Consumer Choice with Unobserved Choice Sets PDF full book. Access full book title Essays on Consumer Choice with Unobserved Choice Sets by Maura Coughlin. Download full books in PDF and EPUB format.
Author: Maura Coughlin
Publisher:
ISBN:
Category :
Languages : en
Pages : 179
Get Book Here
Book Description
This dissertation consists of three essays that evaluate how consumers make decisions in settings where the researcher may not know the set of alternatives from which observed choices were selected. Many empirical analyses in economics presume the researcher knows the full set of alternatives an individual compared when selecting their most preferred. In practice, this assumption may fail to hold for a variety of reasons. In the first chapter, I introduce the economic setting of unobserved choice sets and consideration sets defining to this work. In the second chapter, my coauthors and I propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between agents' choice sets and their preferences. We first establish that the model is partially identified and characterize its sharp identification region. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. The third chapter evaluates the prescription drug insurance choices of Medicare beneficiaries. I propose an empirical model of demand for prescription drug plans where non-monetary plan attributes stochastically determine the composition of the set of plans that an individual considers, and monetary plan attributes determine the individual's expected utility over contracts in her consideration set. This model reconciles the classic view of insurance contracts as lotteries with purely monetary outcomes with the empirical finding that choice among insurance plans is driven by their non-monetary attributes and financial attributes beyond their impacts on costs. I estimate the model using data from Medicare Part D allowing for unobserved heterogeneity in risk aversion and in consideration sets. I find that the latter plays a crucial role in plan choices, and in contrast to previous literature that assumes full consideration of all plans, I uncover an important role for risk aversion in determining individual choices.
Author: Maura Coughlin
Publisher:
ISBN:
Category :
Languages : en
Pages : 179
Get Book Here
Book Description
This dissertation consists of three essays that evaluate how consumers make decisions in settings where the researcher may not know the set of alternatives from which observed choices were selected. Many empirical analyses in economics presume the researcher knows the full set of alternatives an individual compared when selecting their most preferred. In practice, this assumption may fail to hold for a variety of reasons. In the first chapter, I introduce the economic setting of unobserved choice sets and consideration sets defining to this work. In the second chapter, my coauthors and I propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between agents' choice sets and their preferences. We first establish that the model is partially identified and characterize its sharp identification region. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. The third chapter evaluates the prescription drug insurance choices of Medicare beneficiaries. I propose an empirical model of demand for prescription drug plans where non-monetary plan attributes stochastically determine the composition of the set of plans that an individual considers, and monetary plan attributes determine the individual's expected utility over contracts in her consideration set. This model reconciles the classic view of insurance contracts as lotteries with purely monetary outcomes with the empirical finding that choice among insurance plans is driven by their non-monetary attributes and financial attributes beyond their impacts on costs. I estimate the model using data from Medicare Part D allowing for unobserved heterogeneity in risk aversion and in consideration sets. I find that the latter plays a crucial role in plan choices, and in contrast to previous literature that assumes full consideration of all plans, I uncover an important role for risk aversion in determining individual choices.
Author: Rishin Roy
Publisher:
ISBN:
Category :
Languages : en
Pages : 344
Get Book Here
Book Description
Author: Robert Nathanael Donnelly
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
This dissertation consists of three essays related to the analysis of heterogeneity in consumer preferences based on individual level data on historical choices. In particular, they are connected by their application of modern Bayesian approaches to model consumers who differ both in their preferences for observed characteristics as well as their preferences for characteristics that are unobserved by the econometrician, but can instead be inferred from the correlations in choice behavior across different subsets of the population of consumers. The three chapters of this dissertation are also connected by their focus on scalability (both in computation and statistical efficiency) to large choice sets. Large choice sets are all around us, and the rise of E-commerce is leading to even larger sets of products that consumers can choose between. The average grocery store has tens of thousands of unique SKUs. The South Bay region around Stanford University has thousands of restaurants to choose between when you decide to go out for lunch. Large web retailers like Amazon sell hundreds of millions of distinct items. Individual level data on choices in situations like these present both opportunities and challenges. While these data sources are often large and rich in information, it is almost always the case that the number of choice occasions that we observe for any single individual is very small relative to the number of possible items they could have chosen between. Some types of products are easily described as a bundle of characteristics that consumers have preferences over, for example cars (horsepower, number of doors, leather seats) or digital cameras (resolution, zoom, flash), however for many other product categories it is more difficult to find a ''feature representation'' of products that accurately captures the heterogeneity in preferences across consumers. What are the characteristics that differ between Coke and Pepsi that lead to such strong disagreements over which is best. My work builds on recently developed approaches from machine learning for estimating models with large numbers of latent variables. This allows us to infer latent ''characteristics'' of products that are not directly observed by the econometrician, but can be inferred based on similarities in choice patterns across a large set of consumers. This allows us to model consumer preferences with heterogeneity in preferences for both observed and unobserved product characteristics. The first chapter of this dissertation is a paper written together with Susan Athey, David Blei, Francisco Ruiz, and Tobias Schmidt which analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each restaurant has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant opens or closes and compare our predictions to the actual realized outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location. The second chapter is a paper written together with Susan Athey, David Blei, and Francisco Ruiz applies a similar approach in the context of supermarket scanner data. This paper demonstrates a method for estimating consumer preferences among discrete choices, where the consumer makes choices from many different categories. The consumer's utility is additive in the different categories, and her preferences about product attributes as well as her price sensitivity vary across products. Her preferences are correlated across products. We build on techniques from the machine learning literature on probabilistic models of matrix factorization, extending the methods to account for time-varying product attributes, a more realistic functional form for price sensitivity, and products going out of stock. We incorporate the information about the product hierarchy, so that consumers are assumed to select at most one alternative within a category. We evaluate the performance of the model using held-out data from weeks with price changes. We show that our model improves over traditional modeling approaches that consider each category in isolation, when we evaluate the ability of the model to predict responsiveness to price changes (using held-out data from a large number of price changes that occurred in our sample). We show that one source of the improvement is the ability of the model to accurately estimate heterogeneity in preferences (by pooling information across categories); another source of improvement is its ability to estimate the preferences of consumers who have rarely or never made a purchase in a given category in the training data. We consider counterfactuals such as personally targeted price discounts, showing that using a richer model such as the one we propose substantially increases the benefits of personalization in discounts. The third chapter of this dissertation proposes a novel estimator for learning heterogeneous consumer preferences based on both browsing and purchase data from online retailers with large product assortments. This work was done in collaboration with Ilya Morozov. Despite increasing availability data on the product pages consumers browse prior to making a purchase, the existing marketing literature provides little guidance on how retailers can use it to make better marketing decisions. In this paper, we propose an empirical framework that allows to efficiently extract information from consumers' search histories and use it to design personalized product recommendations. Our framework is based on the standard consideration set model from the marketing literature. To extract information from the unstructured search data, we augment the model with rich consumer heterogeneity and include several unobserved product characteristics. We then propose a way to estimate this model's parameters using a latent factorization approach from the computer science literature. The proposed framework can be seen as combining a structural approach to modeling consumer consideration from marketing with nonparametric estimation methods commonly used in the computer science. We are in discussion with a large online retailer to gain access to data and to run an AB test to experimentally validate the effects of improved rankings and recommendations of products.
Author: Salar Jahedi
Publisher:
ISBN:
Category :
Languages : en
Pages : 262
Get Book Here
Book Description
Author: Abdoul Karim Nchare Fogam
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
My dissertation provides a nonparametric analysis of behavioral models of consumerchoice theory. I focus especially on identification, prediction, and testing of demandand consumption under minimal assumptions.The first chapter investigates the following question: Do people, over time, buythe same insurance plans subscribe to the same utility services because they preferthose options or because they dont even pay attention to alternatives available?I propose an econometric framework that models consumer inertia with a mixturemodel of inattention: attention is a latent variable known to the consumer but unobservable by the researcher. Inattentive consumers stick with their previous choiceand attentive consumers pick among all alternatives the one that maximizes theirpreferences. This model allows disentangling inattention from other sources of inertia. For that, I rely on an exclusion restriction, some variables affect attention butnot utility. Based on this model, I derive partial identification results and suggestestimation and inference methods for parameters of interest. I then show how theidentified parameters can be used for policy and welfare analysis by providing sharpbounds on the money-metric welfare of attentive consumers.The second chapter establishes an equivalence result between the Dogit model andthe rational inattention model. The Dogit choice model introduced by Gaudry andDagenais (1979) is an extension of the multinomial logit model to allow for captive orinattentive consumers. McFadden (1981) argues that a drawback of the Dogit modelis its inconsistency with the standard random utility framework. I show that whenthe information cost is modeled using a generalized version of the Shannon entropy,the Dogit model is observationally equivalent to a rational inattention model. Thus,the Dogit model can be derived from microfoundations in term of boundedly rationalbehavior.The third chapter studies the impact of increases in SNAP benefits on food expenditure of program participants. Existing literature on the topic has focusedon the average treatment effect without considering heterogeneity in the effect ofSNAP benefit enhancements and changes in the participant population. To addressthese issues, we propose a distributional approach based on a nonparametric quantiledifference-in-differences setting that accounts for changes in the participant population. Using exogenous increases in SNAP benefits due to the American Recoveryand Reinvestment Act of 2009, we find that while participants at the 20th percentileof the distribution of the food expenditure share experienced a 0.71 percentage-pointincrease in food expenditure share, those at the 70th percentile had their food expenditure share increase by 4.89 percentage points.
Author: Fouad Sabry
Publisher: One Billion Knowledgeable
ISBN:
Category : Business & Economics
Languages : en
Pages : 271
Get Book Here
Book Description
What is Consumer Choice The theory of consumer choice is the branch of microeconomics that relates preferences to consumption expenditures and to consumer demand curves. It analyzes how consumers maximize the desirability of their consumption, by maximizing utility subject to a consumer budget constraint.Factors influencing consumers' evaluation of the utility of goods include: income level, cultural factors, product information and physio-psychological factors. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Consumer choice Chapter 2: Utility Chapter 3: Indifference curve Chapter 4: Budget constraint Chapter 5: Substitute good Chapter 6: Marginal rate of substitution Chapter 7: Income-consumption curve Chapter 8: Substitution effect Chapter 9: Law of demand Chapter 10: Utility maximization problem Chapter 11: Marshallian demand function Chapter 12: Revealed preference Chapter 13: Hicksian demand function Chapter 14: Corner solution Chapter 15: Relative price Chapter 16: Local nonsatiation Chapter 17: Quasilinear utility Chapter 18: Homothetic preferences Chapter 19: Preference (economics) Chapter 20: Robinson Crusoe economy Chapter 21: Linear utility (II) Answering the public top questions about consumer choice. (III) Real world examples for the usage of consumer choice in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Consumer Choice.
Author: Nahyeon Bak
Publisher:
ISBN:
Category :
Languages : en
Pages : 85
Get Book Here
Book Description
This dissertation is a collection of essays on consumer's state dependent choice. In many consumers packaged goods markets, consumer's brand choice is highly persistent because of state dependence where past choice directly influence present choice. Chapter I investigates why consumer choices show state dependence by testing two competing theories: learning and switching costs. To test them, I used a Nielsen consumer panel data set including a long history of repeated purchases by 28,724 households from 2006-2015. Reduced form estimates suggest that the results align with learning, but not switching costs. I also find the only the first and second brand experiences affect present choice. In Chapter II, consistent with reduced-form analysis, I hypothesize that under learning behavior, if consumers try a new brand, consumers are likely to choose a smaller size than before because of uncertainty on product information, if not, consumers are likely to choose a bigger size than before because of lower price per unit with a bigger size. However, under switching cost behavior, consumers size choice will not be affected by brand switching decision. To test this causal relationship between brand switching decision and size choice, I adopt double machine learning method. Compared to previous reduced-form analysis, double machine learning model specifies a set of control variables without human judgement and it provides a causal parameter. Also, compared to naive or prediction based machine learning models, it overcomes the regularization bias by using Neyman orthogonality and over-fitting problems by using sample splitting method. As a result, I find that consumer's new trial on a brand leads to choose a smaller size choice than before where it supports learning behavior, not switching costs behavior. These reduced form studies of Chapter I and II motivate structural approaches to empirical modeling. Chapter III tests the two competing theories with a structural demand model that incorporated variety-seeking behavior. Previous studies failed to explain how states affect two decisions: not only persistent brand choice, but also brand switching that usually variety-seeker have shown. To incorporate these decisions, I develop a dynamic panel demand model with multiple discreteness choices for estimating preferences where some consumers switch brand frequently even most consumers show persistent brand choice. I first find that consumers learn fast, which disputes previous slowdown learning models such as Bayesian learning. Second, state dependence of consumer choice diminishes with time elapsed from each purchase. These findings are robust to controlling variety seeking behavior or not. Combining Chapter I, II, and III, I conclude that with the assumption on myopic consumers, because of learning behavior, consumers show persistent brand choice in the initial shopping period, but as they exposure to the same brands again and again, they become satiated the brand. In other words, consumers show diminishing marginal utility over quantity consumed. Therefore, consumers switch a brand.
Author: Man Xie
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Consumers make decisions under incomplete information. In addition to the generic information availability and accessibility, how firms selectively provide information and how consumers collect information all influence consumer decisions, including product choice, purchase, and reselling. In Essay One, we study the impacts of firms providing information on product list prices on online consumer purchases. Our examination of Amazon data finds that (1) displaying list price in information-rich online markets has no impact on sales when used as a standalone marketing strategy, but can positively or negatively influence sales if implemented concurrently when the price decreases; (2) the direction of that influence depends on user-generated information; and (3) list price interacts with price promotion via both price sensitivity and demand shift. Specifically, when a product with favorable consumer reviews lowers its price, simultaneously displaying list price boosts the effectiveness of price promotion by (i) shifting demand upwards and (ii) increasing price sensitivity. However, when a product with unfavorable reviews lowers its price, simultaneously displaying list price shifts demand downwards, which decreases sales or even destroys the sales gain that price promotion would have generated without list price. In Essay Two, we study the impacts of imperfect information from the initial choice set and post-purchase consumption on consumers' reselling price decisions in C2C (consumer to consumer) markets. We model C2C markets and show they significantly differ from traditional B2C (business to consumer) markets. For example, consumers (as buyers) tend to buy products with overlooked weaknesses rather than overlooked strengths, resulting in over-optimistic choices and post-choice remorse (i.e., "buyer's remorse"). Surprisingly, both increase with a more exhaustive choice search. Moreover, even without buyer's remorse, imperfect information alone causes consumers' valuations (as owners) and asking prices (as resellers) to decrease with the duration of ownership as residual uncertainty decreases. Hence, unlike traditional B2C selling, we find that C2C reselling asking prices depend on reseller expected utility from prolonged consumption, the original consideration set size, the duration of ownership, and residual uncertainty. Our empirical analyses provide evidence from both experimental data and aggregate real estate data.
Author: Uzma Khan
Publisher:
ISBN:
Category :
Languages : en
Pages : 202
Get Book Here
Book Description
Author: Pavitra Jindahra
Publisher:
ISBN:
Category :
Languages : en
Pages : 78
Get Book Here
Book Description
