Effects of Remanufacturable Product Design on Market Segmentation and the Environment

Despite documented benefits of remanufacturing, many manufacturers have yet to embrace the idea of tapping into remanufactured‐goods markets. In this article, we explore this dichotomy and analyze the effect of remanufacturable product design on market segmentation and product and trade‐in prices by studying a two‐stage profit‐maximization problem in which a price‐setting manufacturer can choose whether or not to open a remanufactured‐goods market for its product. Our results suggest that it is optimal for a manufacturer to design a remanufacturable product when the value‐added from remanufacturing is relatively high but product durability is relatively low and innovation is nominal. In addition, we find that entering a remanufactured‐goods market in and of itself does not necessarily translate into environmental friendliness. On the one hand, the optimal trade‐in program could result in low return and/or remanufacturing rates. On the other hand, a low price for remanufactured products could attract higher demand and thereby potentially result in more damage to the environment. Meanwhile, external restrictions imposed on total greenhouse gas emissions draw criticism in their own right because they risk stifling growth or reducing overall consumer welfare. Given these trade‐offs, we therefore develop and compare several measures of environmental efficiency and conclude that emissions per revenue can serve as the best proxy for emissions as a metric for measuring overall environmental stewardship.     

ON BOOTSTRAP HYPOTHESIS TESTING

We describe methods for constructing bootstrap hypothesis tests, illustrating our approach using analysis of variance. The importance of pivotalness is discussed. Pivotal statistics usually result in improved accuracy of level. We note that hypothesis tests and confidence intervals call for different methods of resampling, so as to ensure that accurate critical point estimates are obtained in the former case even when data fail to comply with the null hypothesis. Our main points are illustrated by a simulation study and application to three real data sets.     

The Higher Education White Paper: The Good,the Bad,the Unspeakable – and the Next White Paper1

This article argues that reforms of higher education finance for undergraduates in England introduced by the Blair government in 2006 provided a progressive strategy for achieving the central objectives of higher education of quality (better), access (wider) and size (larger). Reforms in 2012 are a not a strategy but a collection of ad hoc arrangements. They include the good (a higher fees cap, a higher interest rate on student loans, better information and improved support for part‐time study), the bad (abolishing most taxpayer support for teaching in the arts and humanities and the social sciences, and raising excessively the threshold at which loan repayments start) and the unspeakable (abolishing Education Maintenance Allowances and AimHigher). The reforms are fiscally costly and hence perpetuate the central problem of capped student numbers, and will not stand the test of time. The concluding section outlines the next White Paper.     

Design and analysis considerations for comparing dynamic treatment regimens with binary outcomes from sequential multiple assignment randomized trials

In behavioral, educational and medical practice, interventions are often personalized over time using strategies that are based on individual behaviors and characteristics and changes in symptoms, severity, or adherence that are a result of one's treatment. Such strategies that more closely mimic real practice, are known as dynamic treatment regimens (DTRs). A sequential multiple assignment randomized trial (SMART) is a multi-stage trial design that can be used to construct effective DTRs. This article reviews a simple to use ‘weighted and replicated’ estimation technique for comparing DTRs embedded in a SMART design using logistic regression for a binary, end-of-study outcome variable. Based on a Wald test that compares two embedded DTRs of interest from the ‘weighted and replicated’ regression model, a sample size calculation is presented with a corresponding user-friendly applet to aid in the process of designing a SMART. The analytic models and sample size calculations are presented for three of the more commonly used two-stage SMART designs. Simulations for the sample size calculation show the empirical power reaches expected levels. A data analysis example with corresponding code is presented in the appendix using data from a SMART developing an effective DTR in autism.     

Extrapolative simulation of neighborhood networks based on population spatial distribution: Do they predict crime?

Objectives

Previous criminological scholarship has posited that network ties among neighborhood residents may impact crime rates, but has done little to consider the specific ways in which network structure may enhance or inhibit criminal activity. A lack of data on social ties has arguably led to this state of affairs. We propose to avoid this limitation by demonstrating a novel approach of extrapolatively simulating network ties and constructing structural network measures to assess their effect on neighborhood crime rates.

Methods

We first spatially locate the households of a city into their constituent blocks. Then, we employ spatial interaction functions based on prior empirical work and simulate a network of social ties among these residents. From this simulated network, we compute network statistics that more appropriately capture the notions of cohesion and information diffusion that underlie theories of networks and crime.

Results

We show that these network statistics are robust predictors of the levels of crime in five separate cities (above standard controls) at the very micro geographic level of blocks and block groups.

Conclusions

We conclude by considering extensions of the approach that account for homophily in the formation of network ties.     

Real-life closeness of social media contacts and depressive symptoms among university students

ABSTRACT

Objective: To examine the association between degree of real-life closeness of social media (SM) contacts and depressive symptoms. Participants: Students ages 18–30 (N = 1124) were recruited in August 2016. Methods: Participants completed an online survey assessing SM use and depression. We used multivariable logistic regression to assess associations between real-life closeness of SM contacts and depressive symptoms. Results: After controlling for covariates, each 10% increase in the proportion of SM friends with whom participants had no face-to-face relationship was associated with a 9% increase in odds of depressive symptoms (AOR = 1.09; 95% CI = 1.05–1.13). However, each 10% increase in the proportion of SM friends with whom participants had a close face-to-face relationship was associated with a 7% decrease in depressive symptoms (AOR = 0.93; 95% CI = 0.89–0.97). Conclusions: Having no in-person relationship with SM contacts is associated with increased depressive symptoms; however, having close in-person relationships with SM contacts is associated with decreased depressive symptoms.     

A Further Opinion on Reimagining Nursing Homes

A commentary of the article discussing the possible reimagining of long-term care (LTC) is provided. The reimagining article examines how to diminish the role of nursing homes in the LTC system. The text is provocative. It is also thought-provoking. In this commentary I provide a further opinion that we could do better within the current system using the current resources.