nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2019‒08‒12
seven papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Semi-Parametric Hierarchical Bayes Estimates of New Yorkers' Willingness to Pay for Features of Shared Automated Vehicle Services By Rico Krueger; Taha H. Rashidi; Akshay Vij
  2. An Early Warning System for banking crises: From regression-based analysis to machine learning techniques By Elizabeth Jane Casabianca; Michele Catalano; Lorenzo Forni; Elena Giarda; Simone Passeri
  3. Paying for Kidneys? A Randomized Survey and Choice Experiment By J. J. Elías; N. Lacetera; M. Macis
  4. “Re-examining the debt-growth nexus: A grouped fixed-effect approach” By Inmaculada Martínez-Zarzoso; Marta Gómez-Puig; Simón Sosvilla-Rivero
  5. The criterion validity of willingness to pay methods: a systematic review and meta-analysis of the evidence By Kanya, Lucy; Saghera, Sabina; Lewin, Alex; Fox-Rushby, Julia
  6. Effects of Self-informed knowledge of GM foods and preference of organic foods on the willingness to pay for Non-GM foods: Focusing on the case of cooking oil By Lim, Heesun; Nam, Kyungsoo; Ahn, Byeong-il
  7. Experimental Auctions versus Real Choice Experiment: An empirical application using homegrown and induced value experiments By Lagoudakis, Angelos; Caputo, Vincenzina; Shupp, Robert S.

  1. By: Rico Krueger; Taha H. Rashidi; Akshay Vij
    Abstract: In this paper, we contrast parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian multinomial logit models and leverage these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV) services. Specifically, we compare the multivariate normal (MVN), finite mixture of normals (F-MON) and Dirichlet process mixture of normals (DP-MON) mixing distributions. The latter promises to be particularly flexible in respect to the shapes it can assume and unlike other semi-parametric approaches does not require that its complexity is fixed prior to estimation. However, its properties relative to simpler mixing distributions are not well understood. In this paper, we evaluate the performance of the MVN, F-MON and DP-MON mixing distributions using simulated data and real data sourced from a stated choice study on preferences for SAV services in New York City. Our analysis shows that the DP-MON mixing distribution provides superior fit to the data and performs at least as well as the competing methods at out-of-sample prediction. The DP-MON mixing distribution also offers substantive behavioural insights into the adoption of SAVs. We find that preferences for in-vehicle travel time by SAV with ride-splitting are strongly polarised. Whereas one third of the sample is willing to pay between 10 and 80 USD/h to avoid sharing a vehicle with strangers, the remainder of the sample is either indifferent to ride-splitting or even desires it. Moreover, we estimate that new technologies such as vehicle automation and electrification are relatively unimportant to travellers. This suggests that travellers may primarily derive indirect, rather than immediate benefits from these new technologies through increases in operational efficiency and lower operating costs.
    Date: 2019–07
  2. By: Elizabeth Jane Casabianca (Prometeia Associazione per le Previsioni Econometriche, and DiSeS, Polytechnic University of Marche); Michele Catalano (Prometeia Associazione per le Previsioni Econometriche); Lorenzo Forni (Prometeia Associazione per le Previsioni Econometriche, and DSEA, University of Padua); Elena Giarda (Prometeia Associazione per le Previsioni Econometriche, and Cefin, University of Modena and Reggio Emilia); Simone Passeri (Prometeia Associazione per le Previsioni Econometriche)
    Abstract: Ten years after the outbreak of the 2007-2008 crisis, renewed attention is directed to money and credit fluctuations, financial crises and policy responses. By using an integrated dataset that includes 100 countries (advanced and emerging) spanning from 1970 to 2017, we propose an Early Warning System (EWS) to predict the build-up of systemic banking crises. The paper aims at (i) identifying the macroeconomic drivers of banking crises, (ii) going beyond the use of traditional discrete choice models by applying supervised machine learning (ML) and (iii) assessing the degree of countries’ exposure to systemic risks by means of predicted probabilities. Our results show that ML algorithms can have a better predictive performance than the logit models. All models deliver increasing predicted probabilities in the last years of the sample for the advanced countries, warning against the possible build-up of pre-crisis macroeconomic imbalances.
    Keywords: banking crises, EWS, machine learning, decision trees, AdaBoost
    JEL: C40 G01 C25 E44 G21
    Date: 2019–08
  3. By: J. J. Elías; N. Lacetera; M. Macis
    Abstract: We conducted a randomized survey with 2,666 US residents to study preferences for legalizing payments to kidney donors. We found strong polarization, with many participants supporting or opposing payments regardless of potential transplant gains. However, about 18 percent of respondents would switch to favoring payments for sufficiently large increases in transplants. Preferences for compensation have strong moral foundations; participants especially reject direct payments by patients, which they find would violate principles of fairness. We corroborate the interpretation of our findings with a choice experiment of a costly decision to donate money to a foundation that supports donor compensation.
    Keywords: repugnant transactions;morality;markets;Preferences;kidney donation
    Date: 2019
  4. By: Inmaculada Martínez-Zarzoso (Faculty of Economic Sciences, University of Göttingen, Germany and Department of Economics, University Jaume I, Castellón. Spain.); Marta Gómez-Puig (Department of Economics and Riskcenter, Universidad de Barcelona.); Simón Sosvilla-Rivero (Complutense Institute for Economic Analysis, Universidad Complutense de Madrid.)
    Abstract: This paper uses panel data for 116 countries over the period 1995-2016 to investigate the heterogeneity of the debt-growth nexus across countries and the factors underlying it. In the first step, the grouped fixed effects (GFE) estimator proposed by Bonhomme and Manresa (2015) is used to classify countries into groups, with group membership being endogenously determined. In the second step, a multinomial logit model is used to explore the drivers of the heterogeneity detected, among them the quality of institutions, the composition of debt-funded public expenditure, the relative public and private indebtedness, and the maturity of debt. Finally, the underlying factors explaining the time-varying impact of public debt on growth in the country groups identified is also investigated.
    Keywords: Public debt; economic growth; heterogeneity; grouped fixed-effects; debt-growth link; panel data; multinomial logit regression. JEL classification:C23, F33, H63, O47, O52.
    Date: 2019–07
  5. By: Kanya, Lucy; Saghera, Sabina; Lewin, Alex; Fox-Rushby, Julia
    Abstract: Background: The contingent valuation (CV) method is used to estimate the willingness to pay (WTP) for services and products to inform cost benefit analyses (CBA). A long-standing criticism that stated WTP estimates may be poor indicators of actual WTP, calls into question their validity and the use of such estimates for welfare evaluation, especially in the health sector. Available evidence on the validity of CV studies so far is inconclusive. We systematically reviewed the literature to (1) synthesize the evidence on the criterion validity of WTP/willingness to accept (WTA), (2) undertake a meta-analysis, pooling evidence on the extent of variation between stated and actual WTP values and, (3) explore the reasons for the variation. Methods: Eight electronic databases were searched, along with citations and reference reviews. 50 papers detailing 159 comparisons were identified and reviewed using a standard proforma. Two reviewers each were involved in the paper selection, review and data extraction. Meta-analysis was conducted using random effects models for ratios of means and percentage differences separately. Meta-bias was investigated using funnel plots. Results: Hypothetical WTP was on average 3.2 times greater than actual WTP, with a range of 0.7–11.8 and 5.7 (0.0–13.6) for ratios of means and percentage differences respectively. However, key methodological differences between surveys of hypothetical and actual values were found. In the meta-analysis, high levels of heterogeneity existed. The overall effect size for mean summaries was 1.79 (1.56–2.04) and 2.37 (1.93–2.80) for percent summaries. Regression analyses identified mixed results on the influence of the different experimental protocols on the variation between stated and actual WTP values. Results indicating publication bias did not account for differences in study design. Conclusions: The evidence on the criterion validity for CV studies is more mixed than authors are representing because substantial differences in study design between hypothetical and actual WTP/WTA surveys are not accounted for.
    Keywords: contingent valuation; willingness to pay; external validity; criterion validity; hypothetical values; simulated market experiments; systematic revuew; meta-analysis
    JEL: E6
    Date: 2019–07–01
  6. By: Lim, Heesun; Nam, Kyungsoo; Ahn, Byeong-il
    Keywords: Marketing
    Date: 2019–06–25
  7. By: Lagoudakis, Angelos; Caputo, Vincenzina; Shupp, Robert S.
    Keywords: Research and Development/Tech Change/Emerging Technologies
    Date: 2019–06–25

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