Complementary person-supervisor fit: An investigation of supplies-values (S-V) fit,leader-member exchange (LMX) and work outcomes

By applying the supplies-values (S-V) fit approach from the complementary person-environment (P-E) fit literature to the leader-employee perspective, and drawing upon social exchange theory, we examine how fulfillment of different work values is related to Leader-Member Exchange (LMX) and work outcomes. First, polynomial regression analyses combined with response surface analysis of data collected at two time points (N = 316) showed that LMX (Time 2) was higher the more the leader fulfills the employee's work values (Time 1). Second, LMX (Time 2) was higher when leader supplies (Time 1) and employee work values (Time 1) were both high than when both were low. Third, analyses of data from a sub-sample of matched leader-employee dyads (N = 140), showed that LMX (Time 2) played a mediating role on the relation between S-V fit (Time 1) and work outcomes (Time 2). Specifically, we found eight out of 10 relationships between S-V fit (Time 1) and leader-rated task performance and OCB (Time 2) to be fully mediated by LMX (Time 2). LMX (Time 2) partially mediated the relation between S-V fit (Time 1) and job satisfaction (Time 2) as only two out of five relationships were fully mediated.     

Treatment Evaluation in the Presence of Sample Selection

Sample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem into (i) a selective subpopulation (e.g., working—selection on unobservables) and (ii) a binary treatment (e.g., training—selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

THE COMPETITIVE RETURNS TO THE GLOBAL SEARCH FOR TALENT: PROFESSIONAL SPORTS MARKETS AND FOREIGNERS

We examine the geographical and racial diversity of a market's labor supply. While the likely impact of expanding supply is to increase the level of productivity, it is not obvious what impact the expansion would have on the overall level of competition. If the added labor increases productivity differentials or are disproportionately populated on high achieving firms, then the likely result would be greater imbalance. We find that while increases in the foreign‐born component are associated with an increase in the overall level of competition, the impact has diminished across time. (JEL C22, D23, D30)     

PROVIDING GLOBAL PUBLIC GOODS: ELECTORAL DELEGATION AND COOPERATION

This study experimentally examines the effect of electoral delegation on providing global public goods shared by several groups. Each group elects one delegate who can freely decide on each group member's contribution to the global public goods. Our results show that people mostly vote for delegates who assign equal contributions for every group member. However, in contrast to standard theoretical predictions for our delegation mechanism, unequal contributions across groups drive cooperation down over time, and it decreases efficiency by almost 50% compared to the selfish benchmark. This pattern is not driven by delegates trying to exploit their fellow group members, as indicated by theory. It is driven by conditional cooperation of delegates across groups. Since one of the potential sources of the resulting inefficiency is the polycentric nature of global public goods provision together with other‐regarding preferences, we use the term P‐inefficiency to describe our finding. (JEL C92, D72, H41)     

Load-Oriented Order Release (LOOR) revisited: bringing it back to the state of the art

In the workload control literature, the Load-Oriented Order Release (LOOR) approach has been neglected since its robustness was questioned at the end of the 1990s. This paper revisits LOOR and evaluates whether its performance can be improved in two ways. First, an intermediate pull release mechanism is added to avoid starvation between periodic release events. This mechanism was recently shown to be effective at improving the performance of a state-of-the-art release method known as LUMS COR. Second, an integer linear programming model is used to manage the trade-off between the timing and load balancing functions of order release. The two refinements are assessed using simulations of different shop configurations, which allow us to evaluate robustness. Results demonstrate that the refinements contribute to improving the performance of LOOR such that it can even outperform LUMS COR. Perhaps counter-intuitively, putting more emphasis on load balancing than on the urgency of individual orders is shown to lead to a lower percentage of tardy orders. Overall, the improvements mean that concerns about LOOR’s robustness are no longer valid – it now appears suitable for a wide range of shops found in practice.     

Learning discrete decomposable graphical models via constraint optimization

Statistical model learning problems are traditionally solved using either heuristic greedy optimization or stochastic simulation, such as Markov chain Monte Carlo or simulated annealing. Recently, there has been an increasing interest in the use of combinatorial search methods, including those based on computational logic. Some of these methods are particularly attractive since they can also be successful in proving the global optimality of solutions, in contrast to stochastic algorithms that only guarantee optimality at the limit. Here we improve and generalize a recently introduced constraint-based method for learning undirected graphical models. The new method combines perfect elimination orderings with various strategies for solution pruning and offers a dramatic improvement both in terms of time and memory complexity. We also show that the method is capable of efficiently handling a more general class of models, called stratified/labeled graphical models, which have an astronomically larger model space.