nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2008‒05‒10
six papers chosen by
Philip Yu
Hong Kong University

  1. Comparing Small-Group and Individual Behavior in Lottery-Choice Experiments By Ronald J. Baker II; Susan K. Laury; Arlington W. Williams
  2. Technology adoption and herding behavior in complex social networks By Natalie Svarcova; Petr Svarc
  3. The Treatment Effect, the Cross Difference, and the Interaction Term in Nonlinear “Difference-in-Differences” Models By Puhani, Patrick A.
  4. Nonparametric Identification of Dynamic Models with Unobserved State Variables By Yingyao Hu; Matthew Shum
  5. Risk Aversion By Pavlo R. Blavatskyy
  6. Estimating Local Welfare Generated by a Professional Sports Team: An Application to the Minnesota Vikings under Threat of Relocation By John R. Crooker; Aju J. Fenn

  1. By: Ronald J. Baker II (Millersville University of Pennsylvania); Susan K. Laury (Georgia State University); Arlington W. Williams (Indiana University Bloomington)
    Abstract: Lottery-choice experiments are conducted to compare risk preferences revealed by three-person groups versus isolated individuals. A lottery-choice experiment consists of a menu of paired lottery choices structured so that the crossover point from a low-risk to a high-risk lottery can be used to infer the degree of risk aversion. A between-subjects experiment of group versus individual lottery-choice decisions reveal that there is not a significant difference in the average crossover point, but lottery choices are affected by a significant interaction between subject composition (individual or group) and lottery winning percentage. Also, a three-phased individual-group-individual sequenced experiment reveals that the count of safe lotteries chosen by groups is, on average, significantly greater than the mean of the individual members. Finally, making a phase-two group decision has a significant impact on subsequent phase-three individual decisions relative to the initial phase-one (individual) decisions.
    Keywords: lab experiments, risk preferences, group decisions
    JEL: C91 C92 D80
    Date: 2008–05
  2. By: Natalie Svarcova (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic); Petr Svarc (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic)
    Abstract: Using a simple computational model, we study consequences of herding behavior in population of agents connected in networks with different topologies: random networks, small-world networks and scale-free networks. Agents sequentially choose between two technologies using very simple rules based on the previous choice of their immediate neighbors. We show that different seeding of technologies can lead to very different results in the choice of majority of agents. We mainly focus on the situation where one technology is seeded randomly while the other is directed to targeted (highly connected) agents. We show that even if the initial seeding is positively biased toward the first technology (more agents start with the choice of the first technology) the dynamic of the model can result in the majority choosing the second technology under the targeted hub approach. Even if the change to majority choice is highly improbable targeted seeding can lead to more favorable results. The explanation is that targeting hubs enhances the diffusion of the firm’s own technology and halts or slows-down the adoption of the concurrent one. Comparison of the results for different network topologies also leads to the conclusion that the overall results are affected by the distribution of number of connections (degree) of individual agents, mainly by its variance.
    Keywords: technology adoption, simulation, networks, herding behavior
    JEL: D71 D74
    Date: 2008–05
  3. By: Puhani, Patrick A. (University of Hannover)
    Abstract: I demonstrate that Ai and Norton’s (2003) point about cross differences is not relevant for the estimation of the treatment effect in nonlinear “difference-in-differences” models such as probit, logit or tobit, because the cross difference is not equal to the treatment effect, which is the parameter of interest. In a nonlinear “difference-in-differences” model, the treatment effect is the cross difference of the conditional expectation of the observed outcome minus the cross difference of the conditional expectation of the potential outcome without treatment. Unlike in the linear model, the latter cross difference is not zero in the nonlinear model. It follows that the sign of the treatment effect in a nonlinear “difference-in-differences” model with a strictly monotonic transformation function is equal to the sign of the coefficient of the interaction term of the time and treatment group indicators. The treatment effect is simply the incremental effect of the coefficient of the interaction term.
    Keywords: identification, nonlinear models, limited dependent variable, probit, logit, tobit, difference-in-differences, interaction effect
    JEL: C21 C25 H0 I0 J0
    Date: 2008–04
  4. By: Yingyao Hu; Matthew Shum
    Abstract: We consider the identification of a Markov process {Wt,Xt*} for t = 1, 2, ... , T when only {Wt} for t = 1, 2, ... , T is observed. In structural dynamic models, Wt denotes the sequence of choice variables and observed state variables of an optimizing agent, while Xt* denotes the sequence of unobserved state variables. The Markov setting allows the distribution of the unobserved state variable Xt* to depend on Wt-1 and Xt-1*. We show that the joint distribution f Wt, Xt*, Wt-1, Xt-1* is identified from the observed distribution f Wt+1, Wt, Wt-1, Wt-2, Wt-3 under reasonable assumptions. Identification of f Wt, Xt*, Wt-1, Xt-1* is a crucial input in methodologies for estimating dynamic models based on the "conditional-choice-probability (CCP)" approach pioneered by Hotz and Miller.
    Date: 2007–12
  5. By: Pavlo R. Blavatskyy
    Abstract: Risk aversion is traditionally defined in the context of lotteries over monetary payoffs. This paper extends the notion of risk aversion to a more general setup where outcomes (consequences) may not be measurable in monetary terms and people may have fuzzy preferences over lotteries, i.e. they may choose in a probabilistic manner. The paper considers comparative risk aversion within neoclassical expected utility theory, a constant error/tremble model and a strong utility model of probabilistic choice (which includes the Fechner model and the Luce choice model as special cases). The paper also provides a new definition of relative riskiness of lotteries.
    Keywords: Risk aversion, more risk averse than, riskiness, probabilistic choice,expected utility theory, Fechner model, Luce choice model
    JEL: D00 D80 D81
    Date: 2008–04
  6. By: John R. Crooker (University of Central Missouri); Aju J. Fenn (The Colorado College)
    Abstract: The issue of public financing for a professional sports team is one that has seen vigorous debate in the state of Minnesota. This study offers the opportunity to examine the welfare contribution of the Minnesota Vikings to Minnesota households in the context of a credible threat to team relocation. We find the credibility of relocation is essential to providing unbiased estimates of welfare. This study utilizes contingent valuation methodology (CVM) and a random utility model (RUM) to analyze Minnesotans’ decision-making mechanisms for supporting a new stadium initiative. While previous studies have attempted to measure the welfare associated with a sports franchise, we develop and discuss bias that may be imparted to estimates when the researcher fails to calculate a choke price. Further, we develop an unbiased approach to identify welfare when respondents perceive a risk of losing the franchise. Our study suggests a 95% confidence interval on the welfare contribution of the Vikings to households in Minnesota is $435.4 million to $1,499.1 million.
    Keywords: Stadium Costs, Sports Economics, Contingent Valuation, Random Utility Model
    JEL: H41 L83
    Date: 2008–05

This nep-dcm issue is ©2008 by Philip Yu. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.