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Including cost income ratio into utility function as a way of dealing with ‘exploding’ implicit prices in mixed logit models

Author

Listed:
  • Giergiczny, Marek
  • Valasiuk, Sviataslau
  • Czajkowski, Mikolaj
  • De Salvo, Maria
  • Signorello, Giovanni

Abstract

The estimates of mean WTP are typically of main interest in non-market valuation studies. In the case of mixed logit models the distribution of WTP for an attribute is derived from the distribution of the ratio of individual coefficients. Since the cost coefficient enters the denominator, its distribution plays a major role in the distribution of WTP. A standard practice in analysing the data from choice experiments is to assume the cost coefficient is fixed, which implies that there is no heterogeneity in price sensitivity. The three most commonly given reasons for this are: (i) the distribution of the marginal willingness-to-pay for an attribute is then simply the distribution of that attribute's coefficient; (ii) in this way analysts wish to restrict the price variable to be non-positive for all individuals; and (iii) analysts avoid assuming log-normal cost because it is often found to produce behaviourally implausible estimates. Constraining a price coefficient to be fixed can however have serious consequences, i.e. a constant price coefficient implies that the standard deviations of unobserved utility is the same for all observations which can lead to biased results. Respondents are also likely to vary in price sensitivities, thus ignoring this variation can lead to erroneous interpretation and conclusions. In this paper we demonstrate a choice experiment exercise in which specifying a log-normal cost results in implausibly large WTP, however, adding into the utility function the cost income ratio prevents implicit prices from ‘exploding’.

Suggested Citation

  • Giergiczny, Marek & Valasiuk, Sviataslau & Czajkowski, Mikolaj & De Salvo, Maria & Signorello, Giovanni, 2012. "Including cost income ratio into utility function as a way of dealing with ‘exploding’ implicit prices in mixed logit models," Journal of Forest Economics, Elsevier, vol. 18(4), pages 370-380.
  • Handle: RePEc:eee:foreco:v:18:y:2012:i:4:p:370-380
    DOI: 10.1016/j.jfe.2012.07.002
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    1. Scarpa Riccardo & Thiene Mara & Marangon Francesco, 2007. "The Value of Collective Reputation for Environmentally-Friendly Production Methods: The Case of Val di Gresta," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 5(1), pages 1-28, September.
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    3. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    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. Andrew Daly & Stephane Hess & Kenneth Train, 2012. "Assuring finite moments for willingness to pay in random coefficient models," Transportation, Springer, vol. 39(1), pages 19-31, January.
    6. Carlsson, Fredrik & Frykblom, Peter & Liljenstolpe, Carolina, 2003. "Valuing wetland attributes: an application of choice experiments," Ecological Economics, Elsevier, vol. 47(1), pages 95-103, November.
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    Cited by:

    1. Piotr Ćwiakowski & Marek Giergiczny & Michał Krawczyk, 2013. "Pirates in the lab. Using incentivized choice experiments to explore preference for (un)authorized content," Working Papers 2013-25, Faculty of Economic Sciences, University of Warsaw.
    2. Wiktor Budziński, 2015. "The effects of non-constant marginal utility of cost for public goods valuation," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 43.
    3. Tsegaye T. Gatiso & Suzie Greenhalgh & Isoa Korovulavula & Teddy Fong & Ratu Pio Radikedike, 2025. "Climate change-induced relocation dilemma between preserving cultural heritage and embracing modern infrastructure: insights from Fijian coastal villages," Climatic Change, Springer, vol. 178(7), pages 1-24, July.
    4. Curtis Rollins, 2023. "Investigating cost non‐attendance as a driver of inflated welfare estimates in mixed‐logit models," Journal of Agricultural Economics, Wiley Blackwell, vol. 74(3), pages 921-934, September.
    5. Morrissey, Karyn & Plater, Andrew & Dean, Mary, 2018. "The cost of electric power outages in the residential sector: A willingness to pay approach," Applied Energy, Elsevier, vol. 212(C), pages 141-150.
    6. Danley, Brian & Sandorf, Erlend Dancke & Campbell, Danny, 2021. "Putting your best fish forward: Investigating distance decay and relative preferences for fish conservation," Journal of Environmental Economics and Management, Elsevier, vol. 108(C).
    7. Giergiczny, Marek & Czajkowski, Mikołaj & Żylicz, Tomasz & Angelstam, Per, 2015. "Choice experiment assessment of public preferences for forest structural attributes," Ecological Economics, Elsevier, vol. 119(C), pages 8-23.

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    JEL classification:

    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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