Suchergebnisse

Hauptbeschreibung: In der politischen wie auch in der gesellschaftlichen Diskussion existieren kontroverse Positionen zum adäquaten Umfang staatlicher Umverteilung. Dabei beansprucht jede Partei für sich, die aus Sicht der Bürger gewünschte Umverteilungspolitik zu kennen. Bis heute fehlt jedoch eine angemessene Analyse der Umverteilungspräferenzen deutscher Bürger. Christian Pfarr beantwortet in dieser Studie auf Basis eines repräsentativ für Deutschland durchgeführten Entscheidungsexperiments (Discrete-Choice-Experiment) Fragen wie: Wie hoch sollte das Umverteilungsbudget s

This tutorial presents a hands-on introduction to a new discrete choice modeling approach based on the behavioral notion of regret-minimization. This so-called Random Regret Minimization-approach (RRM) forms a counterpart of the Random Utility Maximization-approach (RUM) to discrete choice modeling, which has for decades dominated the field of choice modeling and adjacent fields such as transportation, marketing and environmental economics. Being as parsimonious as conventional RUM-models and compatible with popular software packages, the RRM-approach provides an alternative and appealing account of choice behavior. Rather than providing highly technical discussions as usually encountered in scholarly journals, this tutorial aims to allow readers to explore the RRM-approach and its potential and limitations hands-on and based on a detailed discussion of examples. This tutorial is written for students, scholars and practitioners who have a basic background in choice modeling in general and RUM-modeling in particular. It has been taken care of that all concepts and results should be clear to readers that do not have an advanced knowledge of econometrics.

In der politischen wie auch in der gesellschaftlichen Diskussion existieren kontroverse Positionen zum adäquaten Umfang staatlicher Umverteilung. Dabei beansprucht jede Partei für sich, die aus Sicht der Bürger gewünschte Umverteilungspolitik zu kennen. Bis heute fehlt jedoch eine angemessene Analyse der Umverteilungspräferenzen deutscher Bürger. Christian Pfarr beantwortet in dieser Studie auf Basis eines repräsentativ für Deutschland durchgeführten Entscheidungsexperiments (Discrete-Choice-Experiment) Fragen wie: Wie hoch sollte das Umverteilungsbudget sein? Welchen Finanzierungsbeitrag sind die Bürger bereit dafür zu leisten? Welche Relevanz besitzt die individuelle Einkommensposition für die Umverteilungspräferenzen der Bürger? Und welchen Stellenwert nimmt die erwartete, aber auch bereits erfahrene Veränderung des Einkommens ein?

Of the commonly used discrete choice models, the probit class allows flexible covariance structures for disturbances but is computationally burdensome for problems with more than a few alternatives. The generalized extreme value (GEV) class, including the widely used logit and nested logit models, has the advantage of computational ease but suffers in general from the restriction of homoscedastic disturbances. This article generalizes the GEV class to allow heteroscedastic disturbances across decision makers as well as across choice alternatives. The resulting models include the heteroscedastic extreme value model as a special case, which is a generalized logit model with heteroscedasticity across choice alternatives. Particular attention is paid to the heteroscedastic logit and nested logit models because of their widespread use in practice. An empirical application reanalyzing data from the 1980 presidential election tests the hypothesis of information-induced heteroscedasticity across voters and finds support for a heteroscedastic logit model that reveals stronger effects of voter information on the turnout decision than suggested by the original standard logit model in Ordeshook and Zeng.

We discuss research ethics challenges experienced while running a discrete choice experiment administered to caregivers of children with treatment resistant pediatric epilepsy. We highlight ethical considerations around the study design of the discrete choice experimental paradigm that pertain to vulnerability of and caregiving burden on the population, imbalance of benefit-to-load of participation, and limitations of cultural meaningfulness and generalizability.

Multinomial logit discrete-choice models are now being used more frequently in sociological research, but often without appropriate attention to the central role played by the independence of irrelevant alternatives (IIA) property. In this article, three tests of the IIA property are discussed and then how to apply them is illustrated, using a data set previously analyzed by Hoffman and Duncan. The IIA tests decisively reject the IIA property in the single/welfare/remarriage choice model.

Purpose: The introduction of novel fuel and propulsion technologies, such as battery, (plug-in) hybrid and fuel cell electric vehicles, and the need to combat the exhaust emission of local and global pollutants from the passenger car fleet have enhanced the political interest in the vehicle purchase choices made by private households and firms, and in how these choices can be influenced through fiscal and regulatory penalties and incentives. Methods: As a tool to understand and analyse such questions, we have developed a generic nested logit model of automobile choice, based on complete disaggregate vehicle sales data for Norway for the period ranging from January 1996 until July 2011. The data set contains 1.6 million vehicle transactions. Results: Being sensitive to changes in the vehicle purchase tax and the fuel tax, the model discriminates well between various fiscal policy scenarios. In using the model for such purposes, one is greatly helped by the fact that the model distinguishes between price changes due to taxation and those originating from the manufacturing or marketing side. Conclusions: The strongly CO 2 graduated vehicle purchase tax, with exemptions granted for battery electric vehicles, is shown to have a major impact on the average type approval rate of CO 2 emissions from new passenger cars registered in Norway. The fuel tax also helps induce car customers to buy low emission vehicles.

AbstractIn the last 20 years many multi‐objective linear programming (MOLP) methods with continuous variables have been developed. However, in many real‐world applications discrete variables must be introduced. It is well known that MOLP problems with discrete variables can have special difficulties and so cannot be solved by simply combining discrete programming methods and multi‐objective programming methods.The present paper is intended to review the existing literature on multi‐objective combinatorial optimization (MOCO) problems. Various classical combinatorial problems are examined in a multi‐criteria framework. Some conclusions are drawn and directions for future research are suggested.

I propose a discrete choice method for estimating monetary policy reaction functions based on research by Hu and Phillips (2004). This method distinguishes between determining the underlying desired rate which drives policy rate changes and actually implementing interest rate changes. The method is applied to ECB rate setting between 1999 and 2010 by estimating a forward-looking Taylor rule on a monthly basis using real-time data drawn from the Survey of Professional Forecasters. All parameters are estimated significantly and with the expected sign. Including the period of financial turmoil in the sample delivers a less aggressive policy rule as the ECB was constrained by the lower bound on nominal interest rates. The ECB's non-standard measures helped to circumvent that constraint on monetary policy, however. For the pre-turmoil sample, the discrete choice model's estimated desired policy rate is more aggressive and less gradual than least squares estimates of the same rule specification. This is explained by the fact that the discrete choice model takes account of the fact that central banks change interest rates by discrete amounts. An advantage of using discrete choice models is that probabilities are attached to the different outcomes of every interest rate setting meeting. These probabilities correlate fairly well with the probabilities derived from surveys among commercial bank economists.

PurposeThe purpose of this paper is to assess the capacity of two methodological approaches – discrete choice and survival analysis models – to investigate the relationship between socio-economic characteristics and turnover in a retailing company. A comparison of the estimation results under each model and their interpretation is carried out. The study provides a guide to determine, assess and interpret the effects of different driving factors behind turnover.Design/methodology/approachThe authors use a data set containing information about 1,199 workers followed up between January 2007 and December 2009. First, not distinguishing voluntary and involuntary resignation, a binary logistic regression model and a Cox proportional hazards (PH) model for univariate survival data are set up and estimated. Second, distinguishing voluntary and involuntary resignation, a multinomial logistic regression model and a Cox PH model for competing risk data are set up and estimated.FindingsWhen no distinction is made, the results point that wage and age exert a negative effect on turnover. Risk of resignation is higher for male, single, not married and Spanish nationals. When the distinction is made, previous results hold for voluntary turnover: wage, age, gender, marital status and nationality are significant. However, when explaining involuntary turnover, all variables except wage lose explaining power. The survival analysis approach is better suited as it measures risk of resignation in a longitudinal way. Discrete choice models only study the risk at a particular cut-off point (24 months in case of this study).Originality/valueThis paper is a systematic application, evaluation and comparison of four different statistical models for analysing employee turnover in a single firm. This work is original because no systematic comparison has been done in the context of turnover.

The overall sex ratio at the national level (the number of females per 1,000 males) has improved by seven points from 933 in 2001 census to 940 in 2011 census. However, the matter of concern is the lowest child sex ratio of 914 girls per 1,000 boys at the national level in the 2011 census as against 927 in the 2001 census. It shows that aversion to daughters continues despite PCPNDT Act and state measures to protect the girl child. This article examines how family plans and uses discrete strategies to limit the family size deciding the number of children, particularly sons and daughters in the family. Why daughters have always been dispensed with and sons' number in the family is again not unlimited? A deficit in the girl child population is leading to 'male marriage squeeze', a serious implication of the shortage of marriageable age girls.