Supply Chain Sourcing in Remanufacturing Operations: An Empirical Investigation of Remake Versus Buy

Despite recent attention to closed‐loop supply chains and remanufacturing, there is scant information about what drives the re‐make versus buy decision for original equipment manufacturers (OEMs) engaging in remanufacturing. Based on the extant remanufacturing literature and transaction cost economics, we formulated hypotheses related to the drivers of in‐house versus contracted remanufacturing operations. The hypotheses were investigated via quantitative and qualitative data, thus offering a rich test of the formulated relationships. Consistent with the theory, the quantitative results showed that intellectual property, operational assets, and remanufacturing frequency are significant drivers of the re‐make versus buy decision. However, counter to the theory, the quantitative results did not support the significance of brand reputation, technological uncertainty, condition uncertainty, product complexity, and volume uncertainty. The qualitative results were used to enrich these findings by providing theoretical extensions and pragmatic insights of the remake versus buy decision in remanufacturing.     

The Unemployment Volatility Puzzle: Is Wage Stickiness the Answer?

I discuss the failure of the canonical search and matching model to match the cyclical volatility in the job finding rate. I show that job creation in the model is influenced by wages in new matches. I summarize microeconometric evidence and find that wages in new matches are volatile and consistent with the model's key predictions. Therefore, explanations of the unemployment volatility puzzle have to preserve the cyclical volatility of wages. I discuss a modification of the model, based on fixed matching costs, that can increase cyclical unemployment volatility and is consistent with wage flexibility in new matches.     

Variational Bayes with synthetic likelihood

Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Gaussian summary statistic for the data, informative for inference about the parameters, is available. The synthetic likelihood method derives an approximate likelihood function from a plug-in normal density estimate for the summary statistic, with plug-in mean and covariance matrix obtained by Monte Carlo simulation from the model. In this article, we develop alternatives to Markov chain Monte Carlo implementations of Bayesian synthetic likelihoods with reduced computational overheads. Our approach uses stochastic gradient variational inference methods for posterior approximation in the synthetic likelihood context, employing unbiased estimates of the log likelihood. We compare the new method with a related likelihood-free variational inference technique in the literature, while at the same time improving the implementation of that approach in a number of ways. These new algorithms are feasible to implement in situations which are challenging for conventional approximate Bayesian computation methods, in terms of the dimensionality of the parameter and summary statistic.     

Group Training in Family Therapy—The Student's Perspective*

This paper examines the experience of learning family therapy in a group training model from the trainees perspective. Five significant aspects of this learning experience are discussed. Supervision of family therapy and training within a group setting, multiple supervision, training with a peer co-therapist, using audio-visual feedback in group supervision of family therapy, and the impact on the trainee of doing family therapy for the first time. The trainees found that their learning experience was enhanced by this training approach. (Editors Note: The program referred to in this paper was described in Stier, Serena & Goldenberg, Irene, Training Issues in Family Therapy, Journal of Marriage and Family Counseling, 1975, 1, 63–68.     

Likely (and Unlikely) Voters and the Assessment of Campaign Dynamics

Only in recent years has the "likely voter" technology beenextended to polls well in advance of an election. In the caseof the 2000 U.S. presidential election, CNN/USA Today/Galluptracking polls indicated considerable fluctuations in likelyvoter preferences, greater than among the larger pool of registeredvoters surveyed. This article explores how Gallup’s likelyvoter model exaggerates the reported volatility of voter preferencesduring the campaign. Much of the reported variation in candidatepreference reported by Gallup in that election is not due toactual voter shifts in preference but rather to changes in thecomposition of Gallup’s likely voter pool. The findingshighlight dangers of relying on samples of likely voters whenpolling well before Election Day.     

The influence of gender and ethnicity on judgments of culpability in a domestic violence scenario

Using an experimental analog design, in this study we examined 503 European American, African American, and Latino undergraduate students' responses to a domestic violence scenario in which the ethnicity and gender of the perpetrator were manipulated. Results indicated that participants perceived perpetration of domestic assault significantly more criminal when committed by a man than when committed by a woman. That finding was robust across European Americans, African Americans, and Latinos and was expressed by both genders. Also, European American participants expressed significantly more criticism toward African American perpetrators of assault than they did toward European American and Latino perpetrators of the exact offense, suggestive of racial bias consistent with stereotypes about African Americans being excessively aggressive. Finally, Latino participants expressed significantly more sympathy toward women who assault their husbands than toward assaulting husbands. Implications of the findings are discussed.