From smiling to smirking? 3D printing,upgrading and the restructuring of global value chains

3D printing (3DP) has been heralded as a revolutionary technology that can alter the way production is organized across time and space – with important redistributive effects on geography and size of production activities. In this article, we examine the impacts that a widespread adoption of 3DP could have on restructuring, upgrading and distributing value added along manufacturing global value chains (GVC) – with brief examples from the aerospace and automotive industries. We highlight two possible scenarios for GVCs – a complementarity scenario of 3DP and traditional manufacturing overlapping, which would reproduce power relations in GVCs and the current distribution of value added in a ‘smiling curve’; and a substitution scenario of 3DP partly or fully superseding traditional manufacturing, which would have more transformational effects in terms of ‘rebundling’ activities, regionalizing or localizing GVCs, and flattening the smiling curve into a ‘smirk’.     

Does the Role of Student Factors with Adolescent Substance Use Vary by Geography?

This study assesses the association of 12th-grade student factors (e.g., grades, classroom misbehaviors) with substance use (i.e., binge drinking, cigarette smoking, marijuana) across large, medium-sized, and nonmetropolitan areas. Based on a sample of 2,189 students from the 2013 Monitoring the Future data set, logistic regression analyses showed that poor student academic and behavioral factors were associated with higher substance use after controlling for gender, race, and socioeconomic status. Interaction analyses further showed no significant differences in the influence of student factors on substance use across locales. Results suggest that prevention efforts against substance use should target student factors regardless of area of residency.     

Asymptotic properties of a generalized regression-type predictor of a finite population variance in probability sampling

A system of predictors for estimating a finite population variance is defined and shown to be asymptotically design-unbiased (ADU) and asymptotically design-consistent (ADC) under probability sampling. An asymptotic mean squared error (MSE) of a generalized regression-type predictor, generated from the system, is obtained. The suggested predictor attains the minimum expected variance of any design-unbiased estimator when the superpopulation model is correct. The generalized regression-type predictor and the predictor suggested by Mukhopadhyay (1990) are compared.     

Optimal estimation of a finite population mean under generalized random permutation models

Employing certain generalized random permutation models and a general class of linear estimators of a finite population mean, it is shown that many of the conventional estimators are “optimal” in the sense of minimum average mean square error. Simple proofs are provided by using a well-known theorem on UMV estimation. The results also cover certain simple response error situations.     

‘Public toilets for a diverse neighborhood’: Spatial purification practices in community development discourse

This paper explores the dialectic of place and community identity in Mount Pleasant, a multi–ethnic and multi–class U.S. neighborhood where definitions of place are hotly contested among its residents. In a grant proposal for public toilets, Mount Pleasant writers use linguistic strategies such as presupposition, deixis, and contrast, coupled with discursive themes of filth and geography, to construct a core of the Mount Pleasant community. The writers place themselves and people who share their values in that core, and immigrants at the margins. These strategies serve as a discursive type of spatial purification practice (cf. Sibley 1988) through which the grantwriters set up a moral and spatial order where they and other core community members are deemed to use space ‘appropriately’, and thus inhabit positive moral positions, while immigrant community members’ imputed ‘inappropriate’ use of space is used to construct negative moral positions for them.     

Real option games with R&D and learning spillovers

We demonstrate to decision makers how to optimally make costly strategic pre-investment R&D decisions in the presence of spillover effects in an option pricing framework with analytic tractability. Decisions are modeled as impulse-type controls with random outcome. Two firms face two decisions that are solved interdependently in a two-stage game. The first-stage decision is: What is the optimal level of coordination (optimal policy/technology choice)? The second-stage decision is: What is the optimal effort for a given level of the spillover effects and the cost of information acquisition? The framework is extended to a two-period closed-loop stochastic game with (path-dependency inducing) switching costs that make strategy revisions harder. When conditions of learning-by-doing exist, we find that strategy shifts are easier to observe in market environments of high growth and high volatility.