MANAGERS' COMPENSATION AND MISREPORTING: A COSTLY STATE VERIFICATION APPROACH

We look for the optimal shareholder-manager contract leading to high effort and truthful revelation of firm performance. This twofold incentive compatibility constraint calls for a convex compensation scheme (a fixed wage plus a stock option) coupled with a state contingent audit. In order to reduce expected verification costs, an optimal stock option plan assigns the manager a large number of options with high strike price. It is suggested that focusing the audit activity (and supervision) on the exercise of stock option packages is a better solution to the problem of misreporting than giving up stock options as a compensation tool. ( JEL D82, G30, M40, M52)     

Bayesian Non-Parametric Estimation of Smooth Hazard Rates for Seismic Hazard Assessment

Abstract.  Hazard rate estimation is an alternative to density estimation for positive variables that is of interest when variables are times to event. In particular, it is here shown that hazard rate estimation is useful for seismic hazard assessment. This paper suggests a simple, but flexible, Bayesian method for non-parametric hazard rate estimation, based on building the prior hazard rate as the convolution mixture of a Gaussian kernel with an exponential jump-size compound Poisson process. Conditions are given for a compound Poisson process prior to be well-defined and to select smooth hazard rates, an elicitation procedure is devised to assign a constant prior expected hazard rate while controlling prior variability, and a Markov chain Monte Carlo approximation of the posterior distribution is obtained. Finally, the suggested method is validated in a simulation study, and some Italian seismic event data are analysed.     

Objective Bayes Factors for Gaussian Directed Acyclic Graphical Models

Abstract. We propose an objective Bayesian method for the comparison of all Gaussian directed acyclic graphical models defined on a given set of variables. The method, which is based on the notion of fractional Bayes factor (BF), requires a single default (typically improper) prior on the space of unconstrained covariance matrices, together with a prior sample size hyper‐parameter, which can be set to its minimal value. We show that our approach produces genuine BFs. The implied prior on the concentration matrix of any complete graph is a data‐dependent Wishart distribution, and this in turn guarantees that Markov equivalent graphs are scored with the same marginal likelihood. We specialize our results to the smaller class of Gaussian decomposable undirected graphical models and show that in this case they coincide with those recently obtained using limiting versions of hyper‐inverse Wishart distributions as priors on the graph‐constrained covariance matrices.     

On Block Ordering of Variables in Graphical Modelling

Abstract.  In graphical modelling, the existence of substantive background knowledge on block ordering of variables is used to perform structural learning within the family of chain graphs (CGs) in which every block corresponds to an undirected graph and edges joining vertices in different blocks are directed in accordance with the ordering. We show that this practice may lead to an inappropriate restriction of the search space and introduce the concept of labelled block ordering B corresponding to a family of B - consistent CGs in which every block may be either an undirected graph or a directed acyclic graph or, more generally, a CG. In this way we provide a flexible tool for specifying subsets of chain graphs, and we observe that the most relevant subsets of CGs considered in the literature are families of B -consistent CGs for the appropriate choice of B . Structural learning within a family of B -consistent CGs requires to deal with Markov equivalence. We provide a graphical characterization of equivalence classes of B -consistent CGs, namely the B - essential graphs , as well as a procedure to construct the B -essential graph for any given equivalence class of B -consistent chain graphs. Both largest CGs and essential graphs turn out to be special cases of B -essential graphs.     

Statistical Analysis of the Hirsch Index

Abstract. The Hirsch index (commonly referred to as h‐index) is a bibliometric indicator which is widely recognized as effective for measuring the scientific production of a scholar since it summarizes size and impact of the research output. In a formal setting, the h‐index is actually an empirical functional of the distribution of the citation counts received by the scholar. Under this approach, the asymptotic theory for the empirical h‐index has been recently exploited when the citation counts follow a continuous distribution and, in particular, variance estimation has been considered for the Pareto‐type and the Weibull‐type distribution families. However, in bibliometric applications, citation counts display a distribution supported by the integers. Thus, we provide general properties for the empirical h‐index under the small‐ and large‐sample settings. In addition, we also introduce consistent non‐parametric variance estimation, which allows for the implementation of large‐sample set estimation for the theoretical h‐index.     

MOTIVATION CROWDING IN REAL CONSUMPTION DECISIONS: WHO IS MESSING WITH MY GROCERIES?

We present evidence of crowding out of intrinsic motivation in real purchasing decisions from a field experiment in a large supermarket chain. We compare three instruments, a label, a subsidy, and a neutral price change, in their ability to induce consumers to switch from dirty to clean products. Interestingly, a subsidy framed as an intervention is less effective than either a label or a neutrally framed price change. We argue that this provides a new explanation for crowding behavior: consumers are resistant to having the line of demarcation between public and private decision making moved in either direction. (JEL C93, Q18, Q54, Q58, H23, H41)     

BRAIN DRAIN IN GLOBALIZATION: A GENERAL EQUILIBRIUM ANALYSIS FROM THE SENDING COUNTRIES' PERSPECTIVE

According to the economic literature, high‐skilled emigration may either harm or benefit developing economies. Recent research highlighted several positive and negative channels through which the brain drain operates. This paper aims at evaluating the relative magnitudes of various brain drain channels and quantifying their global impact on migrants' sending countries. For this purpose, we develop a 10‐region general equilibrium model of the world economy characterized by overlapping‐generations dynamics. Our findings suggest that the short‐run impact of brain drain on resident human capital is extremely crucial, as it affects not only the number of high‐skilled workers available to domestic production, but also the sending economy's capacity to innovate/adopt modern technologies. This latter effect is particularly important in globalization, where capital investments are made in places with high production efficiencies. Hence, despite positive feedback effects, those countries facing prevalent high‐skilled emigration are the most candid victims to brain drain. (JEL F22, J24, O57)