Polynomial algorithms for canonical forms of orientations

We introduce canonical forms that represent certain equivalence classes of totally cyclic and acyclic orientations of graphs and present a polynomial algorithms for their constructions. The forms are used in new formulas evaluating tension and flow polynomials on graphs.     

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)