Subjective measures of risk aversion, fixed costs, and portfolio choice

The paper investigates risk preferences among different types of individuals. We use several different measures of risk preferences, including questions on choices between uncertain income streams suggested by Barsky, Juster, Kimball, and Shapiro (1997) and a number of ad hoc measures. As in [Barsky et al., 1997] and [Arrondel and Calvo-Pardo, 2002], we first analyze individual variation in the risk aversion measures and explain them by background characteristics (both “objective” characteristics and other subjective measures of risk preference). Next we incorporate the measured risk preferences into a household portfolio allocation model, which explains portfolio shares, while accounting for incomplete portfolios and fixed costs. Our results show that a measure based on factor analysis of answers to a number of simple risk preference questions has the most explanatory power. The Barsky et al. (1997) measure has less explanatory power than this “a-theoretical” measure, suggesting that sophisticated measures based on economic theory may exceed the financial capability of respondents. Fixed costs turn out to provide an economically and statistically highly significant explanation for incomplete portfolios.     

Evaluating Effects of Training within a Company: Methods,Problems and One Application

ABSTRACT: In the Netherlands, interest in the measurement of the effects of training has increased. In most cases this measurement has been done by drawing conclusions on analyses with samples of firms. However, individual companies will be more interested in the effects of training within their own organisation. In this paper we discuss possible methods of measuring the effects of training within an organisation. A crucial element of such an analysis is the use of performance indicators of individual workers. A first application of such a method in the Civil Service shows positive effects of training. However, the size of the effects should not be overrated.     

The matching extension problem in general graphs is co-NP-complete

A simple connected graph G with 2n vertices is said to be k-extendable for an integer k with \(0<k<n\) if G contains a perfect matching and every matching of cardinality k in G is a subset of some perfect matching. Lakhal and Litzler (Inf Process Lett 65(1):11–16, 1998) discovered a polynomial algorithm that decides whether a bipartite graph is k-extendable. For general graphs, however, it has been an open problem whether there exists a polynomial algorithm. The new result presented in this paper is that the extendability problem is co-NP-complete.     

Political leaders in the SYMLOG space: Perceptions of right and left wing leaders by right and left wing constituencies

Systematic Multiple Level Observation of Groups (SYMLOG) was applied to studying images of political leaders. Three left wing and three right wing Israeli leaders were evaluated by left and right wing voters along the SYMLOG's three dimensions—friendliness, task orientation, and dominance. It was found that right wing voters rated right wing leaders higher on friendliness and task orientation than left wing leaders. In a similar fashion, left wing voters viewed left wing leaders as more friendly and task-oriented than right wing leaders. Leaders of the right were viewed as more dominant than leaders of the left by left and right wing voters alike. Right and left wing voters assigned different SYMLOG types to right and left wing leaders. The dimension that distinguished between the various images was the dimension of friendliness. Whereas for right wing voters, all images of right wing leaders included a component of friendliness, none of these three images included an accented component of friendliness when rated by left wing voters. In a similar fashion, left wing voters saw two out of the three left wing leaders as including a component of friendliness, whereas none of the three left wing images included this component for right wing voters. Respondents from the right and the left rated their “ideal leader” as reflecting values of “dominance”, “friendliness,” and “task-orientation”. However, respondents saw leaders affiliated with their own political camp as closer to their image of the ideal leader on friendliness and task-orientation than leaders of the opposite political camp. Both right and left wing voters saw all political leaders as equally similar to their image of “ideal leader” on the “dominance” dimension regardless of that leader's political affiliation.     

Evaluating the administrative efficiency of courts

In addition to being held accountable for judicial decision, courts, like other public agencies, can and should be evaluated in terms of their administrative efficiency. This paper illustrates how courts can be evaluated in terms of their relative administrative efficiency, using a new approach—data envelopment analysis (DEA)—first proposed by Charnes et al. [1]. The DEA is based upon the economic notion of Pareto optimality which states that a given decision making unit (DMU) is inefficient if some other DMU, or some combination of other DMUs, can produce at least the same amounts of all outputs with less of some resource input and not more of any other resource. Conversely a DMU is said to be efficient if the above is not possible. Charnes et al. [1] generalized the usual input/output ratio measure of efficiency for a given unit in terms of a fractional linear program with fractional constraints. In the case of courts, the efficiency of any particular court is calculated by forming the ratio of a weighted sum of outputs to a weighted sum of inputs, where the weights for both outputs and inputs are to be selected in a manner that calculates the Pareto-Koopmans efficiency of the court. This paper reviews the DEA method and illustrates its application to a data base for 100 criminal superior courts in North Carolina.     

警惕绩效测量可能发生的功能失调

绩效测量能够加强责任、改善绩效,但是,为达到其中的一个目的所做的安排却可能并非实现另一目的的理想途径。绩效测量涉及以高尚的借口从“生产”中汲取资源用作一般管理费用,但是却不能促使金钱产生更大的价值。     

Probabilistic Inversion in Priority Setting of Emerging Zoonoses

This article presents methodology of applying probabilistic inversion in combination with expert judgment in priority setting problem. Experts rank scenarios according to severity. A linear multi‐criteria analysis model underlying the expert preferences is posited. Using probabilistic inversion, a distribution over attribute weights is found that optimally reproduces the expert rankings. This model is validated in three ways. First, consistency of expert rankings is checked, second, a complete model fitted using all expert data is found to adequately reproduce observed expert rankings, and third, the model is fitted to subsets of the expert data and used to predict rankings in out‐of‐sample expert data.     

The individual welfare function: A review

The Individual Welfare Function (IWF), introduced by Van Praag (1968), is a cardinal utility function. It can be measured by means of survey questions. Since its introduction, the IWF has been used extensively in both theoretical and empirical research. This research is reviewed, with an emphasis on policy applications.     

Asymptotic Properties for a Class of Partially Identified Models

We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV). An SVRV is a mapping that associates a set (rather than a real number) with each element of the sample space. Examples of population features in this class include interval‐identified scalar parameters, best linear predictors with interval outcome data, and parameters of semiparametric binary models with interval regressor data. We extend the analogy principle to SVRVs and show that the sample analog estimator of the population identification region is given by a transformation of a Minkowski average of SVRVs. Using the results of the mathematics literature on SVRVs, we show that this estimator converges in probability to the population identification region with respect to the Hausdorff distance. We then show that the Hausdorff distance and the directed Hausdorff distance between the population identification region and the estimator, when properly normalized by , converge in distribution to functions of a Gaussian process whose covariance kernel depends on parameters of the population identification region. We provide consistent bootstrap procedures to approximate these limiting distributions. Using similar arguments as those applied for vector valued random variables, we develop a methodology to test assumptions about the true identification region and its subsets. We show that these results can be used to construct a confidence collection and a directed confidence collection. Those are (respectively) collection of sets that, when specified as a null hypothesis for the true value (a subset of values) of the population identification region, cannot be rejected by our tests.