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Consumer welfare and unobserved heterogeneity in discrete choice models: The value of alpine road tunnels

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  • Cerquera Dussán, Daniel
  • Ullrich, Hannes

Abstract

We investigate the sensitivity of consumer surplus estimates to parametric assumptions on individual preference heterogeneity in a discrete choice framework. We compare results from a parametric random coefficients logit model and a recently proposed nonparametric sieve estimator. In particular, we provide an assessment of the direct economic value of crossing the Alps for the European road freight sector. Using revealed preference data from a detailed survey on transalpine road freight traffic, we estimate the yearly cost of closing the Mont-Blanc Tunnel, which was closed for 3 years following a large accident in early 1999. Ultimately, our results permit the economic evaluation of security and transport policy measures affecting transalpine traffic. Our findings suggest that the way we model unobserved heterogeneity significantly affects our welfare results.

Suggested Citation

  • Cerquera Dussán, Daniel & Ullrich, Hannes, 2010. "Consumer welfare and unobserved heterogeneity in discrete choice models: The value of alpine road tunnels," ZEW Discussion Papers 10-095, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  • Handle: RePEc:zbw:zewdip:10095
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    References listed on IDEAS

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    1. Fox, Jeremy T. & Kim, Kyoo il & Ryan, Stephen P. & Bajari, Patrick, 2012. "The random coefficients logit model is identified," Journal of Econometrics, Elsevier, vol. 166(2), pages 204-212.
    2. von Haefen, Roger H., 2003. "Incorporating observed choice into the construction of welfare measures from random utility models," Journal of Environmental Economics and Management, Elsevier, vol. 45(2), pages 145-165, March.
    3. Fosgerau, Mogens & Hess, Stephane, 2008. "Competing methods for representing random taste heterogeneity in discrete choice models," MPRA Paper 10038, University Library of Munich, Germany.
    4. Austan Goolsbee & Amil Petrin, 2004. "The Consumer Gains from Direct Broadcast Satellites and the Competition with Cable TV," Econometrica, Econometric Society, vol. 72(2), pages 351-381, March.
    5. Patrick Bajari & Jeremy T. Fox & Kyoo il Kim & Stephen P. Ryan, 2009. "A Simple Nonparametric Estimator for the Distribution of Random Coefficients," NBER Working Papers 15210, National Bureau of Economic Research, Inc.
    6. Stephen Hynes & Nick Hanley & Riccardo Scarpa, 2008. "Effects on Welfare Measures of Alternative Means of Accounting for Preference Heterogeneity in Recreational Demand Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 1011-1027.
    7. Steven T. Berry & Philip A. Haile, 2009. "Nonparametric Identification of Multinomial Choice Demand Models with Heterogeneous Consumers," Cowles Foundation Discussion Papers 1718, Cowles Foundation for Research in Economics, Yale University, revised Mar 2010.
    8. Arne Risa Hole, 2007. "Fitting mixed logit models by using maximum simulated likelihood," Stata Journal, StataCorp LP, vol. 7(3), pages 388-401, September.
    9. Patrick Bajari & Jeremy T. Fox & Stephen P. Ryan, 2007. "Linear Regression Estimation of Discrete Choice Models with Nonparametric Distributions of Random Coefficients," American Economic Review, American Economic Association, vol. 97(2), pages 459-463, May.
    10. Prady, Delphine & Ullrich, Hannes, 2010. "Entry and competition in freight transport: the case of a prospective transalpine rail link between France and Italy," ZEW Discussion Papers 10-010, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
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    Cited by:

    1. Fifer, Simon & Rose, John & Greaves, Stephen, 2014. "Hypothetical bias in Stated Choice Experiments: Is it a problem? And if so, how do we deal with it?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 61(C), pages 164-177.

    More about this item

    Keywords

    Discrete Choice; Consumer Surplus; Nonparametric Estimation; Transalpine Freight;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • L91 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Transportation: General
    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures

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