nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2018‒03‒12
three papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Valuing Product Innovation: Genetically Engineered Varieties in U.S. Corn and Soybeans By Federico Ciliberto; GianCarlo Moschini; Edward D. Perry
  2. Do US Anglers Care about Harmful Algal Blooms? A discrete choice experiment of Lake Erie recreational anglers By Wendong Zhang; Brent Sohngen
  3. Truck driver scheduling with combined planning of rest periods, breaks and vehicle refueling By Bernhardt, A.; Melo, Teresa; Bousonville, Thomas; Kopfer, Herbert

  1. By: Federico Ciliberto; GianCarlo Moschini (Center for Agricultural and Rural Development (CARD)); Edward D. Perry
    Abstract: We develop and estimate a discrete-choice model of differentiated products for the corn and soybean seed industry in the United States to assess the welfare impact of genetically engineered (GE) crop varieties. We use a unique dataset, spanning the period 1996-2011, that contains rich information on the adoption of GE traits. Using a two-level nested logit model, we estimate that U.S. farmers are willing to pay a significant premium for GE traits, and this value has increased over time. Over the last five years of the sample, our results imply that farmers' average willingness to pay for glyphosate tolerance in soybeans was $24/acre/year. During the same period, farmers' willingness to pay for a common triple-stack in corn that includes two insect resistance traits and glyphosate tolerance was $35/acre/year. To compute overall welfare estimates, we evaluate counterfactual scenarios in which GE varieties are not available, with counterfactual non-GE seed prices predicted by a hedonic price equation. Counterfactual scenarios are adjusted to account for the fact that GE crop varieties crowded out non-GE varieties by the end of our sample. We estimate that GE innovations increased farmers' welfare by more than $14 billion over the period of study. We also find that the development and diffusion of GE traits increased U.S. corn and soybean seed industry revenues by nearly $23 billion over this period. Thus, seed firms have been able to appropriate the larger share of the ex post value of innovation created by GE technologies. Key Words: Discrete choice, Innovation, Nested logit, Product characteristics, Seed demand, Transgenic crops, Welfare JEL Codes
    Date: 2017–12
  2. By: Wendong Zhang (Center for Agricultural and Rural Development (CARD)); Brent Sohngen
    Abstract: Despite the growing awareness of harmful algal blooms (HABs) in the US and abroad, estimates of welfare losses due to their presence are missing from the literature. Using a mail survey of 767 Ohio Lake Erie recreational angler respondents and a choice experiment, this study provides the first empirical quantification of the economic impacts of HABs on US recreational anglers. Our results demonstrate a significant and substantial willingness to pay by anglers for reduction in HABs, beyond the benefits associated with conventional water quality measures such as catch rates and water clarity. For instance, we find that anglers are willing to pay $8–$10 more per trip for one less mile of boating through HABs enroute to a fishing site. This finding suggests that explicit measures of HABs need to be collected and considered when valuing water quality in nutrient-rich waterbodies. We evaluate the welfare improvements resulting from several nutrient reduction policies, and find that anglers are willing to pay on average $40-60 per trip for a policy that cuts upstream phosphorus loadings by 40%.The majority of welfare gains for anglers result from improving the non-catchable component of the fishing experience, notably water clarity and HAB reduction, as opposed to better chances of angler success. Keywords: Choice experiment, discrete choice, generalized multinomial logit model, harmful algal bloom, Lake Erie, non-market valuation, recreational angler, recreation demand, survey, water quality JEL Codes: Q51, Q53, Q57, Q15
    Date: 2018–02
  3. By: Bernhardt, A.; Melo, Teresa; Bousonville, Thomas; Kopfer, Herbert
    Abstract: Fuel is one main cost driver in the road haulage sector. An analysis of diesel price variations across different European countries showed that a significant potential for cutting fuel expenditure can be found in international long-haul freight transportation. Here, truck drivers are often on the road for several consecutive days or even weeks. During their trips, they must comply with the rules on driving hours and rest periods which in the European Union are governed by Regulation (EC) No 561/2006. In the literature, refueling problems have attracted limited attention so far. In the present study, we show why a joint consideration of drivers' rest periods and breaks and refueling is important and how the choice of time windows, the planning of driver activities, and the determination of refueling stops and quantities can be done accordingly. For a given sequence of customer locations and gas stations with different fuel prices along the route chosen to serve these customers we propose a mixed integer linear programming (MILP) model and describe the corresponding solution process. In this multicriteria optimization problem with the goals to minimize lateness, traveling time and fuel expenditures, we consider multiple soft time windows at customer locations. We extend the MILP model developed by Bernhardt et al. (2016) by integrating refueling decisions. Additionally, a preprocessing heuristic is described which reduces the number of gas stations to be considered along the route and thus the solution space and the computational effort. Numerical experiments were conducted for instances derived from real data that include vehicle routes for one week and information on gas stations along the vehicle routes. Different parameter settings for the preprocessing heuristic were analyzed.
    Keywords: road transportation,refueling,fuel cost,driver scheduling,rest periods,breaks,driving hours,Regulation (EC) No 561/2006,mixed integer linear programming models
    Date: 2017

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