A Quantitative Analysis of the Effects of Postsecondary Institution Conversions from Not-For-Profit to For-Profit

This study highlights the changes that have occurred at postsecondary institutions after conversion from not-for-profit to for-profit control. Using Delta Cost Project Data and a pre-post study design with a control group of not-for-profit institutions that did not convert, comparisons are drawn. The findings suggest that institutions that convert experience greater enrollment growth, a decline in fulltime employment levels per full time equivalent (FTE) student, no change in average expenses per FTE student, a decrease in total revenue, a decrease in Pell Grants received, a decrease in tuition and fees revenue, and a decline in average subsidy per student post conversion. These findings are critical to the creation of informed policy decisions regarding institutional conversions.     

A tighter formulation of the <Emphasis Type="Italic">p</Emphasis>-median problem

Given a set of clients and a set of potential sites for facilities, the p-median problem consists of opening a set of p sites and assigning each client to the closest open facility to it. It can be viewed as a variation of the uncapacitated facility location problem. We propose a new formulation of this problem by a mixed integer linear problem. We show that this formulation, while it has the same value by LP-relaxation, can be much more efficient than two previous formulations. The computational experiment performed on two sets of benchmark instances has showed that the efficiency of the standard branch-and-cut algorithm has been significantly improved. Finally, we explore the structure of the new formulation in order to derive reduction rules and to accelerate the LP-relaxation resolution.     

Interval estimation for the breakpoint in segmented regression: a smoothed score‐based approach

This paper is concerned with interval estimation for the breakpoint parameter in segmented regression. We present score‐type confidence intervals derived from the score statistic itself and from the recently proposed gradient statistic. Due to lack of regularity conditions of the score, non‐smoothness and non‐monotonicity, naive application of the score‐based statistics is unfeasible and we propose to exploit the smoothed score obtained via induced smoothing. We compare our proposals with the traditional methods based on the Wald and the likelihood ratio statistics via simulations and an analysis of a real dataset: results show that the smoothed score‐like statistics perform in practice somewhat better than competitors, even when the model is not correctly specified.     

Nonparametric Shrinkage Estimation for Aalen's Additive Hazards Model

Aalen's nonparametric additive model in which the regression coefficients are assumed to be unspecified functions of time is a flexible alternative to Cox's proportional hazards model when the proportionality assumption is in doubt. In this paper, we incorporate a general linear hypothesis into the estimation of the time‐varying regression coefficients. We combine unrestricted least squares estimators and estimators that are restricted by the linear hypothesis and produce James‐Stein‐type shrinkage estimators of the regression coefficients. We develop the asymptotic joint distribution of such restricted and unrestricted estimators and use this to study the relative performance of the proposed estimators via their integrated asymptotic distributional risks. We conduct Monte Carlo simulations to examine the relative performance of the estimators in terms of their integrated mean square errors. We also compare the performance of the proposed estimators with a recently devised LASSO estimator as well as with ridge‐type estimators both via simulations and data on the survival of primary billiary cirhosis patients.     

Digit ratios (2D:4D) as predictors of risky decision making for both sexes

Many important decisions involve financial risk, and substantial evidence suggests that women tend to be more risk averse than men. We explore a potential biological basis of risk-taking variation within and between the sexes by studying how the ratio between the length of the second and fourth fingers (2D:4D) predicts risk-taking. A smaller 2D:4D ratio has been linked to higher exposure to prenatal testosterone relative to estradiol, with men having lower ratios than women. In financially motivated decision-making tasks, we find that men and women with smaller 2D:4D ratios chose significantly riskier options. We further find that the ratio partially explains the variation in risk-taking between the sexes. Moreover, for men and women at the extremes of the digit-ratio distribution the difference in risk-taking disappears. Thus, the 2D:4D ratio partially explains variation in financial risk-taking behavior within and between sexes and offers evidence of a biological basis for risk-taking behavior.     

The Distribution of Returns to Education for People with Disabilities

This article takes a first look at the distribution of returns to education for people with disabilities, a particularly disadvantaged group whose labor market performances have not been well studied or documented. Using a nonparametric approach, we uncover significant heterogeneity in the returns to education for these workers, which is drastically masked by conventional parametric methods. Based on these estimates, we construct the Sharpe ratio of human capital investment (taking into account its substantial risk), and our results corroborate the claimed importance of human capital in improving these workers’ wages. Our stochastic dominance tests show that the returns to education for workers with disabilities, as a group, may have been affected more adversely in the most recent recession, relative to their non-disabled counterparts.     

“Service” Municipal Administration as Part of the Development of Youth Entrepreneurship in Russia

The study offers a development of the social technologies of “service” municipal administration. The consumers’ opinions should be taken into consideration when determining the nomenclature and standards of their provision, which include the quality parameters that the consumers consider important. In addition, “service” relations imply the assessment of consumer satisfaction with the services and the correction of services based on the results of the assessment. Since this interaction implies communication between the municipal administration body and service users, it is necessary to focus on communicative technologies that are capable of providing a full cycle of service development and improvement for young entrepreneurs. Communications should help determine the expectations associated with a service, raise awareness thereof, and involve young people in the decisions related to the provision of services.     

Are sponsors blamed for edging off? Consumer reactions to sponsorship terminations

Firms regularly terminate sponsorships, even without publicly known misconduct by the sponsee such as athlete doping. Consumer reactions to these sponsorship terminations by firms have not been studied despite being a regular occurrence. Using a set of experimental studies, this paper analyzes consumer reactions to these sponsorship terminations (i.e., early and non-renewal) that were not caused by a sponsee’s misconduct, the underlying process that causes the reactions, and the role of several moderating factors (trust, power balance, and locus of control). Our findings reveal that sponsorship terminations have a negative effect on sponsors’ brand images—particularly early terminations that occur before the end of a contract—because consumers perceive these sponsorship terminations as unfair. The results also suggest that a termination is particularly harmful for the sponsor’s perceived fairness if the sponsor is powerful and if the termination decision is under the sponsor’s control. Further, the termination effect is particularly strong for firms that consumers trust.     

Penalized empirical likelihood inference for sparse additive hazards regression with a diverging number of covariates

High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by applications in high-throughput genomic data analysis. In this paper, we propose a class of regularization methods, integrating both the penalized empirical likelihood and pseudoscore approaches, for variable selection and estimation in sparse and high-dimensional additive hazards regression models. When the number of covariates grows with the sample size, we establish asymptotic properties of the resulting estimator and the oracle property of the proposed method. It is shown that the proposed estimator is more efficient than that obtained from the non-concave penalized likelihood approach in the literature. Based on a penalized empirical likelihood ratio statistic, we further develop a nonparametric likelihood approach for testing the linear hypothesis of regression coefficients and constructing confidence regions consequently. Simulation studies are carried out to evaluate the performance of the proposed methodology and also two real data sets are analyzed.