nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2021‒05‒10
four papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Testing Willingness to Pay Elicitation Mechanisms in the Field: Evidence from Uganda By Burchardi, Konrad B.; de Quidt, Jonathan; Gulesci, Selim; Lerva, Benedetta; Tripodi, Stefano
  2. Identification and Estimation of average marginal effects in fixed effect logit models By Laurent Davezies; Xavier D'Haultfoeuille; Louise Laage
  3. Gains from Convenience and the Value of E-commerce By Bronnenberg, Bart; Huang, Yufeng
  4. Preferences for Sustainability and Supply Chain Essential Worker Conditions: Survey Evidence during COVID-19 By Villas-Boas, Sofia B; Copfer, Jackie; Campbell, Nica

  1. By: Burchardi, Konrad B.; de Quidt, Jonathan; Gulesci, Selim; Lerva, Benedetta; Tripodi, Stefano
    Abstract: Researchers frequently use variants of the Becker-DeGroot-Marschak (BDM) mechanism to elicit willingness to pay (WTP). These variants involve numerous incentive-irrelevant design choices, some of which carry advantages for implementation but may deteriorate participant comprehension or trust in the mechanism, which are well-known problems with the BDM. We highlight three such features and test them in the field in rural Uganda, a relevant population for many recent applications. Comprehension is very high, and 86 percent of participants bid optimally for an induced-value voucher, with little variation across treatments. This gives confidence for similar applications, and suggests the comprehension-expediency trade-off is mild.
    Keywords: Becker-DeGroot-Marschak; field experiment; Willingness to pay
    JEL: C90 C93 D44 O12
    Date: 2021–02
  2. By: Laurent Davezies; Xavier D'Haultfoeuille; Louise Laage
    Abstract: This article considers average marginal effects (AME) and similar parameters in a panel data fixed effects logit model. Relating the identified set of the AME to an extremal moment problem, we first show how to obtain sharp bounds on the AME straightforwardly, without any optimization. Then, we consider two strategies to build confidence intervals on the AME. In the first, we estimate the sharp bounds with a semiparametric two-step estimator involving a first-step nonparametric estimator. We derive the asymptotic distributions of these bounds under, mostly, a restriction on the cardinality of the support of the unobserved heterogeneity. The second, very simple strategy does not require any nonparametric estimation but may result in larger confidence intervals. Monte Carlo simulations suggest that both approaches work well in practice, the second being actually very competitive for usual sample sizes.
    Date: 2021–05
  3. By: Bronnenberg, Bart; Huang, Yufeng
    Abstract: Why do consumers value shopping online? We decompose the value of e-commerce to individual consumers and highlight the role of convenience, i.e., the avoidance of transportation costs. We complement household purchase panel data with precise locations of consumers and stores, and show that travel distance is a strong driver of consumer store choice and the substitution to the online channel. Using a structural model of retailer and channel choice, we report that during 2016-2018 the total value from e-commerce to consumers is equivalent to a 23% discount on all prices. Of this value, a quarter comes from convenience in the form of lower transportation costs, a quarter from intensified price competition, and the remaining half from new online retailers and online channels of existing offline retailers. We further demonstrate that consumer gains are heterogeneous. Consumers far from offline stores or experienced in online shopping will benefit more from e-commerce, whereas consumers who likely do not shop online still benefit indirectly from price competition. Finally, our results show that, as consumers gain more online shopping experience, substantial additional gains from e-commerce are yet to materialize in the future.
    Keywords: convenience; E-commerce; online/offline; retail; Transportation Costs
    JEL: D12 L81 M31
    Date: 2021–01
  4. By: Villas-Boas, Sofia B; Copfer, Jackie; Campbell, Nica
    Keywords: Social and Behavioral Sciences
    Date: 2021–05–03

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