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Using Elicited Choice Probabilities To Estimate Random Utility Models: Preferences For Electricity Reliability

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  • Asher A. Blass
  • Saul Lach
  • Charles F. Manski

Abstract

When choice data are not available, researchers studying preferences sometimes ask respondents to state the actions they would choose in choice scenarios. Data on stated choices are then used to estimate random utility models, as if they are data on actual choices. Stated and actual choices may differ because researchers typically provide respondents less information than they would have in actuality. Elicitation of choice probabilities overcomes this problem by permitting respondents to express uncertainty about behavior. This article shows how to use elicited choice probabilities to estimate random utility models and reports estimates of preferences for electricity reliability. Copyright (2010) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Suggested Citation

  • Asher A. Blass & Saul Lach & Charles F. Manski, 2010. "Using Elicited Choice Probabilities To Estimate Random Utility Models: Preferences For Electricity Reliability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(2), pages 421-440, May.
  • Handle: RePEc:ier:iecrev:v:51:y:2010:i:2:p:421-440
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    References listed on IDEAS

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    1. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
    2. Manski, Charles F, 1999. "Analysis of Choice Expectations in Incomplete Scenarios," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 49-66, December.
    3. F. Thomas Juster, 1966. "Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design," NBER Books, National Bureau of Economic Research, Inc, number just66-2.
    4. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    5. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    6. Adeline Delavande, 2008. "Pill, Patch, Or Shot? Subjective Expectations And Birth Control Choice," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 49(3), pages 999-1042, August.
    7. Caves, Douglas W & Herriges, Joseph A & Windle, Robert J, 1990. "Customer Demand for Service Reliability in the Electric Power Industry: A Synthesis of the Outage Cost Literature," Bulletin of Economic Research, Wiley Blackwell, vol. 42(2), pages 79-119, April.
    8. Charles F. Manski, 2004. "Measuring Expectations," Econometrica, Econometric Society, vol. 72(5), pages 1329-1376, September.
    9. Charles F. Manski, 2007. "Partial Identification Of Counterfactual Choice Probabilities," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1393-1410, November.
    10. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May.
    11. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
    12. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    13. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    14. Michael J. Doane & Raymond S. Hartman & Chi-Keung Woo, 1988. "Households' Perceived Value of Service Reliability: An Analysis of Contingent Valuation Data," The Energy Journal, International Association for Energy Economics, vol. 0(Special I), pages 135-150.
    15. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
    16. Beenstock, Michael & Goldin, Ephraim & Haitovsky, Yoel, 1998. "Response bias in a conjoint analysis of power outages," Energy Economics, Elsevier, vol. 20(2), pages 135-156, April.
    17. Yongxin Cai & Iraj Deilami & Kenneth Train, 1998. "Customer Retention in a Competitive Power Market: Analysis of a 'Double-Bounded Plus Follow-Ups' Questionnaire," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 191-215.
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    More about this item

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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