Eigenverantwortung — Berufliche Leistung und Anforderung

Ohne Zusammenfassung     

Asymptotic Representations of the Multivariate Empirical Distribution Function and Applications

Often, many complicated statistics used as estimators or test statistics take the form of the (multivariate) empirical distribution function evaluated at a random vector (V_n). Denote such statistics by S_n. This paper describes methods for the study of various asymptotic properties of S_n. First, under minimal assumptions, a weak asymptotic representation for S_n is derived. This result may be used to show the asymptotic normality of S_n. Second, under slightly more stringent regularity conditions, an almost sure representation of S_n, with suitable order (as.) of the remainder term is studied and then a law of the iterated logarithm for S_n, is derived. In this context, strong convergence results from a sequential point of view are also studied. Finally, weak convergence to a Brownian motion process is established. As an application, we show the limiting normality of S_n, for a random number of summands.     

MONITORING IN TEAMS: USING LABORATORY EXPERIMENTS TO STUDY A THEORY OF THE FIRM

Alchian and Demsetz’s influential explanation of the classical business firm (The American Economic Review, 1972, 62, 777–795) argues that there is need for a concentrated residual claim in the hands of a central agent, to motivate the monitoring of workers. We model monitoring as a way to transform team production from a collective action dilemma with strong free riding incentives to a productivity‐enhancing opportunity with strong private marginal incentives to contribute effort. In an experiment, we have subjects experience team production without monitoring, team production with a central monitor, and team production with peer monitoring. Then subjects vote on whether to employ the central monitor, who gets to keep a fixed share of the team output, or to rely on peer monitoring, which entails a coordination or free riding problem. Our subjects usually prefer peer monitoring but they switch to the specialist when unable to successfully self‐monitor. We provide evidence for situations in which team members resist the appointing of a central monitor and succeed in overcoming coordination and free riding problems as well as for a situation in which an Alchian–Demsetz‐like firm grows in the laboratory.     

A tractable model of process fairness under risk

This paper proposes a social utility model of individual preferences for process fairness that complements the Fehr–Schmidt model for outcome fairness. The model assumes that the outcome generating process rather than the actual outcomes influences fairness perceptions, and that process fairness is evaluated through comparison of expected payoffs. The process model successfully predicts data from bargaining games involving risky payoffs that neither outcome-based nor reciprocity models can explain. In a theoretical application, Machina’s parental example for non-expected utility in a distributional problem (Machina’s Mom) is analyzed by incorporating individual level process fairness preferences under expected utility.     

A generalization of the matching distribution

The Matching Distribution converges to a Poisson Distribution with λ=1 as the parameter n converges to infinity. A generalization of the Matching Distribution is proposed. The properties of this Generalized Matching Distribution (GMD) turn out to be analogical to the case with λ=1.