Outsourcing to a Powerful Contract Manufacturer: The Effect of Learning‐by‐Doing

The contract manufacturing industry has grown rapidly in recent years as firms have increasingly outsourced production to reduce costs. This growth has created powerful contract manufacturers (CMs) in several industries. Achieving a competitive cost position is often a primary motive for outsourcing. Outsourcing influences both the original equipment manufacturer's (OEM) and the CM's production levels, and, therefore, through learning‐by‐doing renders future costs dependent on past outsourcing decisions. As such, outsourcing should not be viewed as a static decision that, once made, is not revisited. We address these considerations by analyzing a two‐period game between an OEM and a powerful CM wherein both firms can reduce their production costs through learning‐by‐doing. We find that partial outsourcing, wherein the OEM simultaneously outsources and produces in‐house, can be an optimal strategy. Also, we find that the OEM's outsourcing strategy may be dynamic—i.e., change from period to period. In addition, we find both that the OEM may engage in production for leverage (i.e., produce internally when at a cost disadvantage) and that the CM may engage in low balling. These and other findings in this paper demonstrate the importance of considering learning, the power of the CM, and future periods when making outsourcing decisions.     

Asymptotics for Statistical Treatment Rules

This paper develops asymptotic optimality theory for statistical treatment rules in smooth parametric and semiparametric models. Manski (2000, 2002, 2004) and Dehejia (2005) have argued that the problem of choosing treatments to maximize social welfare is distinct from the point estimation and hypothesis testing problems usually considered in the treatment effects literature, and advocate formal analysis of decision procedures that map empirical data into treatment choices. We develop large‐sample approximations to statistical treatment assignment problems using the limits of experiments framework. We then consider some different loss functions and derive treatment assignment rules that are asymptotically optimal under average and minmax risk criteria.     

The flavours of love: A cross‐cultural lexical analysis

Linguists have often remarked upon the polysemous nature of love, whereby the term encompasses a wide diversity of emotional relationships. Several typologies have been constructed to account for this diversity. However, these tend to be restricted in scope, and fail to fully represent the range of experiences signified by the term ‘love’ in discourse. In the interest of generating an expanded typology of love, encompassing its varied forms, an enquiry was conducted into relevant concepts found across the world's cultures, focusing on so‐called untranslatable words. Through a quasi‐systematic search of published and internet sources, 609 relevant words were identified. These were organised through a version of grounded theory into 14 categories, representing 14 different forms or ‘flavours’ of love. The result is an expanded theoretical treatment of love, allowing us to better appreciate the nuances of this most cherished and yet polysemous of concepts.     

Postretirement work and volunteering by poverty groups informed by role theory

This study uses role theory to examine the association between postretirement work and volunteering among retirees and to determine whether this association varies across poverty status. Data came from the 2012 wave of the Health and Retirement Study (HRS). The sample was restricted to respondents of older retirees (N = 6,619). We found that postretirement work positively influenced volunteering. However, poverty can be a risk factor of volunteering among older retirees. The positive effect of post-retirement work was found to be more significant in the near-poverty group. We provide an empirical foundation to help inform volunteer programs for retirees.     

Left behind: How to encourage biological parents' involvement in their children's group homes

This paper addresses the challenges and benefits of involving biological parents in group homes in Israel and presents various means to encourage their involvement in care. Using family systems theories and the concept of co‐parenting, it analyses the fragile and complex relationship caseworkers and foster parents have with biological parents. The paper presents four components that might play a role in encouraging parental involvement to benefit their children's adjustment. The components are demonstrated through case studies and include assessing the family profile; addressing the family's needs within the child's intervention plan; training biological and foster parents; and building co‐parenting between biological and foster parents.     

Service Users with Experience of Poverty as Institutional Entrepreneurs in Public Services in Belgium: An Institutional Theory Perspective on Policy Implementation

In this article, we report on a qualitative and interpretative research project in which we gained in‐depth knowledge about dynamics in ten federal public policy units in Belgium where a service user with experience of poverty was employed. Starting from an institutional theory perspective, it was argued that these service users with experience of poverty can possibly feature as institutional entrepreneurs or agents of change who initiate a critical reflection in the mindset of fellow social administrators about taken‐for‐granted practices, routines and rationales. In order to fulfill this ambition, we equally stressed that service users with experience of poverty should be able to perform two other roles as well: to provide direct support to citizens, especially those living in poverty during their contacts with a public policy unit, and to formulate concrete propositions to change some of the policy units' internal procedures. Our research findings reveal that these two roles provide a necessary breeding ground for further interaction and (in)formal dialogue between service users with experience of poverty and fellow social administrators about the quality and accessibility of service delivery. However, installing this shared responsibility proves to be a valuable but also gradual process, and we argue that it is therefore undesirable when service users with experience of poverty act as so‐called ‘heroes’ in changing these taken‐for‐granted institutional practices, routines and rationales.     

Bayesian hierarchical joint modeling using skew-normal/independent distributions

The multiple longitudinal outcomes collected in many clinical trials are often analyzed by multilevel item response theory (MLIRT) models. The normality assumption for the continuous outcomes in the MLIRT models can be violated due to skewness and/or outliers. Moreover, patients’ follow-up may be stopped by some terminal events (e.g., death or dropout), which are dependent on the multiple longitudinal outcomes. We proposed a joint modeling framework based on the MLIRT model to account for three data features: skewness, outliers, and dependent censoring. Our method development was motivated by a clinical study for Parkinson’s disease.     

Bias and size corrections in extreme value modeling

Extreme value theory models have found applications in myriad fields. Maximum likelihood (ML) is attractive for fitting the models because it is statistically efficient and flexible. However, in small samples, ML is biased to O(N^?1) and some classical hypothesis tests suffer from size distortions. This paper derives the analytical Cox–Snell bias correction for the generalized extreme value (GEV) model, and for the model's extension to multiple order statistics (GEVr). Using simulations, the paper compares this correction to bootstrap-based bias corrections, for the generalized Pareto, GEV, and GEVr. It then compares eight approaches to inference with respect to primary parameters and extreme quantiles, some including corrections. The Cox–Snell correction is not markedly superior to bootstrap-based correction. The likelihood ratio test appears most accurately sized. The methods are applied to the distribution of geomagnetic storms.     

A bivariate distribution with Lomax and geometric margins

We develop a stochastic model describing the joint distribution of $(X,N)$, where $N$ has a geometric distribution while $X$ is the sum of $N$ dependent, heavy-tail Pareto components. Models of this form arise in many applications, ranging from hydro-climatology to finance and insurance. We present fundamental properties of this vector, including marginal and conditional distributions, moments, representations, and parameter estimation. We also include an example from finance, illustrating modeling potential of this new bivariate distribution.