nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2013‒06‒30
seven papers chosen by
Edoardo Marcucci
Universita' di Roma Tre

  1. Panel data discrete choice models of consumer demand By Michael P. Keane
  2. The Structure of Consumer Taste Heterogeneity in Revealed vs. Stated Preference Data By Michael P. Keane; Nada Wasi
  3. Learning Models: An Assessment of Progress, Challenges and New Developments By Andrew T. Ching; Tülin Erdem; Michael P. Keane
  4. Self-selection into Economics Experiments is Driven by Monetary Rewards By Johannes Abeler; Daniele Nosenzo
  5. Single-Basined Choice By Walter Bossert; Hans Peters
  6. The Impact of Residential Density on Vehicle Usage and Fuel Consumption: Evidence from National Samples By Kim, Jinwon; Brownstone, David
  7. Child Labor Variation by Type of Respondent: Evidence from a Large-Scale Study By Dammert, Ana C.; Galdo, Jose C.

  1. By: Michael P. Keane (Nuffield College and Department of Economics, University of Oxford)
    Date: 2013–06–03
  2. By: Michael P. Keane (University of Oxford, Nuffield College); Nada Wasi (University of Michigan, Survey Research Center)
    Abstract: In recent years it has become common to use stated preference (SP) discrete choice experiments (DCEs) to study and/or predict consumer demand. SP is particularly useful when revealed preference (RP) data is unobtainable or uninformative (e.g., to predict demand for a new product with an attribute not present in existing products, to value non-traded goods). The increasing use of SP data has led to a growing body of research that compares SP vs. RP demand predictions (in contexts when both are available). The present paper goes further by comparing the structure of consumer taste heterogeneity in SP vs. RP data. Our results suggest the nature of taste heterogeneity is very different: In SP data consumers are much more likely to exhibit either (i) lexicographic preferences, or (ii) “random” choice behavior. And many consumers appear to be fairly insensitive to price. This suggests that caution should be applied before using SP to answer questions about the distribution of taste heterogeneity in actual markets.
    Keywords: Discrete choice experiments, Stated preference data, Discrete choice models, Consumer demand, Consumer heterogeneity, Mixture models
    JEL: D12 C35 C33 C91 M31
    Date: 2013–02–04
  3. By: Andrew T. Ching (Rotman School of Management, University of Toronto); Tülin Erdem (Stern School of Business, New York University); Michael P. Keane (Nuffield College and Department of Economics, University of Oxford)
    Abstract: Learning models extend the traditional discrete choice framework by postulating that consumers have incomplete information about product attributes, and that they learn about these attributes over time. In this survey we describe the literature on learning models that has developed over the past 20 years, using the model of Erdem and Keane (1996) as a unifying framework. We described how subsequent work has extended their modeling framework, and applied learning models to a wide range of different products and markets. We argue that learning models have contributed greatly to our understanding of consumer behavior, in particular in enhancing our understanding of brand loyalty and long run advertising effects. We also discuss the limitations of existing learning models and discuss potential extensions. One key challenge is to disentangle learning as a source of dynamics from other key mechanisms that may generate choice dynamics (inventories, habit persistence, etc.). Another is to enhance identification of learning models by collecting and utilizing direct measures of signals, perceptions and expectations.
    Keywords: Learning Models, Choice modeling, Dynamic Programming, Structural models, Brand equity
    Date: 2013–06–13
  4. By: Johannes Abeler (School of Economics, University of Oxford); Daniele Nosenzo (School of Economics, University of Nottingham)
    Abstract: Laboratory experiments have become a wide-spread tool in economic research. Yet, there is still doubt about how well the results from lab experiments generalize to other settings. In this paper, we investigate the self-selection process of potential subjects into the subject pool. We alter the recruitment email sent to first-year students, either mentioning the monetary reward associated with participation in experiments; or appealing to the importance of helping research; or both. We find that the sign-up rate drops by two-thirds if we do not mention monetary rewards. Appealing to subjects’ willingness to help research has no effect on signup. We then invite the so-recruited subjects to the laboratory to measure a range of preferences in incentivized experiments. We do not find any differences between the three groups. Our results show that student subjects participate in experiments foremost to earn money, and that it is therefore unlikely that this selection leads to an over-estimation of social preferences in the student population.
    Keywords: Methodology; Selection bias; Laboratory experiment; Field experiment; Otherregarding behavior; Social preferences; Social Approval; Experimenter Demand.
    Date: 2013–03
  5. By: Walter Bossert; Hans Peters
    Abstract: Single-basined preferences generalize single-dipped preferences by allowing for multiple worst elements. These preferences have played an important role in areas such as voting, strategy-proofness and matching problems. We examine the notion of singlebasinedness in a choice-theoretic setting. In conjunction with independence of irrelevant alternatives, single-basined choice implies a structure that conforms to the motivation underlying our definition. We also establish the consequences of requiring single-basined choice correspondences to be upper semicontinuous, and of the revealed preference relation to be Suzumura consistent.
    Keywords: Single-basinedness, choice correspondences, independence of irrelevant alternatives, upper semicontinuity, Suzumura consistency
    JEL: D11 D71
    Date: 2013
  6. By: Kim, Jinwon; Brownstone, David
    Abstract: This paper investigates the impact of residential density on household vehicle usage and fuel consumption. We estimate a simultaneous equations system to account for the potential residential self-selection problem. While most previous studies focus on a specific region, this paper uses national samples from the 2001 National Household Travel Survey. The estimation results indicate that residential density has a statistically significant but economically modest influence on vehicle usage, which is similar to that in previous studies. However, the joint effect of the contextual density measure (density in the context of its surrounding area) and residential density on vehicle usage is quantitatively larger than the sole effect of residential density. Moving a household from a suburban to an urban area reduces household annual mileage by 18%. We also find that a lower neighborhood residential density induces consumer choices toward less fuel-efficient vehicles, which confirms the finding in Brownstone and Golob (2009).
    Keywords: Household vehicle choice; Simultaneous equations systems; Residential density
    JEL: C31 D12 R41
    Date: 2013–06–17
  7. By: Dammert, Ana C. (Carleton University); Galdo, Jose C. (Carleton University)
    Abstract: This study uses a nationally representative survey to analyze a key survey design decision in child labor measurement: self-reporting versus proxy interviewing. The child/proxy disagreement affects 20 percent of the sample, which translates into a 17.1 percentage point difference in the national rate of child labor by type of respondent. As a result, marginal effects from standard child labor supply functions show important child/proxy differences, particularly when the household experienced some adverse weather and income shocks. Moreover, we find that attitudes and social perceptions toward child labor are not related to the likelihood of disagreement, while proxy respondent's past experience as child laborers emerges as an important predictor of the disagreement. A modified bivariate choice model reports statistically significant probabilities of misclassification that ranges between 9 and 30 percent according to alternative definitions of child labor.
    Keywords: Peru, survey design, maximum likelihood, self/proxy designs, child labor
    JEL: C81 J13 J22 O15
    Date: 2013–06

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