nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2016‒03‒10
five papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre

  1. Estimation and implementation of joint econometric models of freight transport chain and shipment size choice By Abate , Megersa; Vierth , Inge; Karlsson , Rune; de Jong , Gerard; Baak , Jaap
  2. Willingness to Pay for Ethanol in Motor Fuel: Evidence from Revealed and Stated Preference for E85 By Kenneth Liao; Sebastien Pouliot
  3. Modeling the Effects of Grade Retention in High School By Stijn BAERT; Bart COCKX; Matteo PICCHIO
  4. Identifying the Discount Factor in Dynamic Discrete Choice Models By Abbring, Jaap H; Daljord, Øystein
  5. The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences By Armin Falk; Anke Becker; Thomas Dohmen; David Huffman; Uwe Sunde

  1. By: Abate , Megersa (VTI); Vierth , Inge (VTI); Karlsson , Rune (VTI); de Jong , Gerard (Significance); Baak , Jaap (Significance)
    Abstract: As part of the further development of the Swedish national freight model system (SAMGODS), we developed a stochastic logistics model in the form of a disaggregate random utility-based model of transport chain and shipment size choice, estimated on the Swedish Commodity Flow Survey (CFS) 2004-2005. Moving from the current deterministic logistics model within the SAMGODS model to a stochastic one, is important because it bases the model on a stronger empirical foundation. The deterministic model was not estimated on observed choice outcomes, but just postulates that the least cost solution will be chosen. We estimated logit models which explain the joint choice of shipment size (in discrete categories) and transport chain separately for sixteen different commodity types. A transport chain (e.g. truck-vessel-truck) is a sequence of modes used to transport a shipment between the locations of production and consumption. Transport cost, travel time and value density are some of the main determinants included in the models. It is important to note that by their very nature these probabilistic models account for the influence of omitted factors. A deterministic model effectively assumes that the stochastic component can be ignored – in other words, that the researcher has full knowledge of all the drivers of behaviour and that there is no randomness in actual behaviour. As a result of adding the stochastic component in the random utility model, the response functions (now expressed in the form of probabilities) become smooth instead of lumped at 0 and 1 as in a deterministic model. This in turn will address the problem of “overshooting” that is prevalent in a deterministic model when testing different scenarios or policies. For two of the commodity types (metal products and chemical products) for which we estimated a transport chain and shipment size choice model, we also implemented the model in the SAMGODS framework. The implementation takes place at the level of the annual firm-to-firm flows by commodity type between producing and consuming firms that are generated by the first steps of the SAMGODS model (PC flows between zones that have been allocated to individual firms at both ends). For every firm-to-firm flow, shipment size and transport chain choice probabilities are calculated and added over the firm-to-firm flows of the PC relation (sample enumeration, as used in several disaggregate transport models). From this, the aggregate OD matrices by mode can be derived straightforwardly, as well as results in terms of tonne-kilometres by mode. It was not possible to empirically model transshipment location choices, because they are not stated in the CFS. Therefore, the determination of the optimal transshipment points for each available chain type from the set of available locations is still done deterministically. The implemented models were applied to produce elasticities of demand expressed in tonne-kilometres for various changes in cost and time for road, rail and sea transport. These elasticities are compared to those for the same commodity types in the deterministic model and to the available literature. The elasticities clearly differ between the two models, they are usually smaller (in absolute values) in the stochastic model, as expected. In the paper, we report the basic differences between a stochastic and a deterministic logistics model, the estimation results for the sixteen commodities, the way the stochastic model was implemented within the SAMGODS model, the elasticities that we obtained for the implemented stochastic model and the comparison with elasticities from the deterministic model and the literature.
    Keywords: Freight; Choice model; SAMGODS
    JEL: R40
    Date: 2016–02–22
  2. By: Kenneth Liao; Sebastien Pouliot (Center for Agricultural and Rural Development (CARD))
    Abstract: This paper estimates the relative preferences of motorists for E10 and E85 in different regions of the United States. We conducted an intercept survey of motorists with flex-fuel vehicles at E85 fuel stations in Iowa, Colorado, Oklahoma, Arkansas and California. The information collected includes prices observed at fuel stations, fuel choices by flex motorists, and responses to a series of opinion questions about ethanol and gasoline. We also proposed a hypothetical scenario to each motorist where either the price of the fuel selected was increased or the price of the fuel not selected was decreased. We estimate fuel preferences first using the revealed preference data from the observed choices and second using the stated preference data from the hypothetical price scenario. The empirical models correct for endogenous stratification within the sample and for endogeneity from unobservable demand shifters that carry over to the stated preference empirical model. We find that motorists significantly discount E85 compared to E10 even when adjusting for the different energy content of the two fuels and that the distribution of willingness to pay for E85 does not vary significantly between regions, except that flex motorists in California are willing to pay more for E85.
    Keywords: Ethanol, Gasoline, Renewable Fuel Standard, Willingness to pay. JEL codes: Q18, Q41, Q42.
    Date: 2016–03
  3. By: Stijn BAERT (Sherppa, Ghent University, University of Antwerp, Universit‚ catholique de Louvain; IZA); Bart COCKX (Sherppa, Ghent University, IRES, Universit‚catholique de Louvain, IZA; CESifo); Matteo PICCHIO (Universit… Politecnica delle Marche, Dipartimento di Scienze Economiche e Sociali)
    Abstract: A dynamic discrete choice model is set up to estimate the effects of grade retention in high school, both in the short- (end-of-year evaluation) and long-run (drop-out and delay). In contrast to regression discontinuity designs, this approach captures treatment heterogeneity and controls for grade-varying unobservable determinants. We deal with initial conditions and with partial observability of the track choices at the start of high school. Forced track downgrading is considered as an alternative remedial measure. In the longrun, grade retention and its alternative have adverse effects on schooling outcomes and, more so, for less able pupils.
    Keywords: Education, dynamic discrete choice models, grade retention, heterogeneous treatment effects, track mobility
    JEL: C33 C35 I21
    Date: 2016–02
  4. By: Abbring, Jaap H; Daljord, Øystein
    Abstract: The identification of the discount factor in dynamic discrete models is important for counterfactual analysis, but hard. Existing approaches either take the discount factor to be known or rely on high level exclusion restrictions that are difficult to interpret and hard to satisfy in applications, in particular in industrial organization. We provide identification results under an exclusion restriction on primitive utility that is more directly useful to applied researchers. We also show that our and existing exclusion restrictions limit the choice and state transition probability data in different ways; that is, they give the model nontrivial and distinct empirical content.
    Keywords: discount factor; dynamic discrete choice; empirical content; identification
    JEL: C14 C25 D91 D92
    Date: 2016–02
  5. By: Armin Falk (Universität Bonn); Anke Becker (Bonn Graduate School of Economics); Thomas Dohmen (Universität Bonn); David Huffman (University of Pittsburgh); Uwe Sunde (University of Munich)
    Abstract: This paper presents an experimentally validated survey module to measure six key economic preferences { risk aversion, discounting, trust, altruism, positive and negative reciprocity in a reliable, parsimonious and cost-effective way. The survey instruments included in the module were the best predictors of preferences revealed in incentivized choice experiments. We also offer a streamlined version of the module that has been optimized and piloted for applications where time efficiency and simplicity are paramount, such as international telephone surveys.
    Keywords: survey validation, experiment, preference measurement
    JEL: C81 C83 C90
    Date: 2016–02

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