nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2009‒08‒02
five papers chosen by
Philip Yu
Hong Kong University

  1. Very simple marginal effects in some discrete choice models By Kevin J. Denny
  2. Simulating WTP Values from Random-Coefficient Models By Maurus Rischatsch
  3. Social Mobility and Preferences for Income Redistribution: Evidence from a Discrete Choice Experiment By Ilja Neustadt; Peter Zweifel
  4. GPs' preferences: What price fee-for-service? By Peter Zweifel; Maurus Rischatsch; Angelika Brändle
  5. A Simple Nonparametric Estimator for the Distribution of Random Coefficients By Patrick Bajari; Jeremy T. Fox; Kyoo il Kim; Stephen P. Ryan

  1. By: Kevin J. Denny (University College Dublin)
    Abstract: I show a simple back-of-the-envelope method for calculating marginal effects in binary choice and count data models. The approach suggested here focuses attention on marginal effects at different points in the distribution of the dependent variable rather than representative points in the joint distribution of the explanatory variables. For binary models, if the mean of the dependent variable is between 0.4 and 0.6 then dividing the logit coefficient by 4 or multiplying the probit coefficient by 0.4 should be moderately accurate.
    Keywords: marginal effects, binary choice, count data
    Date: 2009–07–21
  2. By: Maurus Rischatsch (Socioeconomic Institute, University of Zurich)
    Abstract: Discrete Choice Experiments (DCEs) designed to estimate willingness-to-pay (WTP) values are very popular in health economics. With increased computation power and advanced simulation techniques, random-coefficient models have gained an increasing importance in applied work as they allow for taste heterogeneity. This paper discusses the parametrical derivation of WTP values from estimated random-coefficient models and shows how these values can be simulated in cases where they do not have a known distribution.
    Keywords: willingness-to-pay, discrete choice, simulation, random-coe±cient models
    JEL: C15 C25
    Date: 2009–07
  3. By: Ilja Neustadt (Socioeconomic Institute, University of Zurich); Peter Zweifel (Socioeconomic Institute, University of Zurich)
    Abstract: In this paper, preferences for income redistribution are elicited through a Discrete Choice Experiment (DCE) performed in 2008. In addition to the amount of redistribution as a share of GDP, attributes also included its uses (working poor, unemployed, old age, families with children, ill health) and nationality of beneficiary (Swiss, Western European, other foreigners). Willingness to pay for redistribution increases with income and education, contradicting the conventional Meltzer-Richard (1981) model. The Prospect of Upward Mobility hypothesis [Hirschman and Rothschild (1973); Benabou and Ok (2001)] receives very partial empirical support.
    Keywords: Income redistribution, preferences, willingness to pay, discrete choice experiments, stated choice, economic well-being, social mobility
    JEL: C35 C93 D63 H29
    Date: 2009–07
  4. By: Peter Zweifel (Socioeconomic Institute, University of Zurich); Maurus Rischatsch (Socioeconomic Institute, University of Zurich); Angelika Brändle
    Abstract: In mixed health care systems a crucial condition for the success of Managed Care (MC) plans is to win over a su±cient number of general practitioners (GPs) acting as gatekeepers. This contribution reports on GPs' willingness-to-accept (WTA) or compensation asked, respectively, for changing from conventional fee-for-service to MC practice. Some 175 Swiss GPs participated in discrete choice experiments which permit to put a money value on their status quo bias. Regardless of whether effects coding or dummy coding is used to measure status quo bias, Swiss GPs require at least 16 percent of their current average income to give up fee-for-service in favor of MC practice.
    Keywords: general practitioners, willingness-to-pay, preferences, market experiments, managed care, effects coding, status quo bias
    JEL: C93 D61 I11 J22
    Date: 2009–07
  5. By: Patrick Bajari; Jeremy T. Fox; Kyoo il Kim; Stephen P. Ryan
    Abstract: We propose a simple nonparametric mixtures estimator for recovering the joint distribution of parameter heterogeneity in economic models, such as the random coefficients logit. The estimator is based on linear regression subject to linear inequality constraints, and is robust, easy to program and computationally attractive compared to alternative estimators for random coefficient models. We prove consistency and provide the rate of convergence under deterministic and stochastic choices for the sieve approximating space. We present a Monte Carlo study and an empirical application to dynamic programming discrete choice with a serially-correlated unobserved state variable.
    JEL: C01 C14 C25 C31 C35 I21 I28 L0 O1 O15
    Date: 2009–07

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