Does Remittance Outflow Stimulate or Retard Economic Growth?

This article evaluates the association between remittance outflow (RMO) and economic growth in the Gulf Cooperation Council (GCC) countries. The results of this evaluation indicate that RMO Granger creates gross domestic product (GDP) per capita in three countries, namely, Bahrain, Oman and Saudi Arabia. Similarly, the results for causality from GDP per capita to RMO are significant for four countries, namely, Bahrain, Kuwait, Qatar, and Saudi Arabia. The findings differ from those of the household consumption model, stating that higher RMO will decrease economic activity. GDP per capita is the main determinant of RMO, suggesting that economic growth promises and encourages continuous RMO and vice versa. The adverse impact of RMO can be minimized by encouraging the local population to be productive in the private sector, as local productivity will reduce the huge influx of foreign workers and provide valuable local investment opportunities to lessen the amount being remitted.     

The Subpoena of Ethnographic Data

This essay outlines the author's experience of having his ethnographic data subpoenaed. It outlines the challenges of subpoena's to research, and suggests four solutions: (1) Apply for and utilize the National Institutes of Health certificate of confidentiality by asking health‐related questions over the course of one's research; (2) Establish a task force that articulates clear ethical guidelines for ethnographic research, with attention to the conditions wherein ethnographers can break confidentiality (and might also comply with subpoenas). These ethical guidelines should then be made clear to research subjects as a part of informed consent processes; (3) Demand that institutions (institutional review boards) that require confidentiality as a condition of research be required to defend that confidentiality through the office of the general counsel; (4) Socialize the cost of subpoenas, wherein scholars can be part of an insurance pool that will defend them in the event of a subpoena and thereby defend the general enterprise of ethnographic research.     

Global Governance,neoliberalism and national responses: The case of Bangladesh’s ready‐made garment (RMG) sector

This article on the ready‐made garment (RMG) sector of Bangladesh shows how over‐reliance on foreign capital for development financing and deregulated investment—a hallmark of neoliberal economic arrangements—undermines the incorporation of SDGs’ and INGOs’ equity principles, contributing to biased policy responses yielding unequal outcomes. The article cautions that while countries prioritize economic growth over social and environmental nourishment and continue to adopt neoliberal economic policies to promote economic growth, inequity is unavoidable, if not inevitable. Thus, the way forward may be to shift the focus of ‘development’ from the economy to society, to building ‘good societies’ where institutions and strategies, including those that contribute to economic growth, are organized such that these complement not compromise the evolution of such societies.     

Family Economy,Rural School Choice,and Flexischooling Children with Disabilities

Most homeschooled students would be more accurately described as “flexischooled,” partly homeschooled and partly enrolled in school. The popularity of these two alternatives to public or private school enrollment has increased over the past several decades to the extent that, by 2012 over 2 million students, 4 percent of the population, were either flexischooled or homeschooled in the United States. To understand why parents have pursued these options over enrollment, we employed a family economy perspective, arguing that family, child, and locational considerations shaped parents' motivations for homeschooling or flexischooling over enrollment. For example, parents' decisions were partially shaped by the interaction between children's disability status and rural location. Using data from the National Household Education Survey 2012, this article first describes the prevalence of flexischooling and full‐time homeschooling. Then, it demonstrates how various family, child, and locational factors shape the odds of flexischooling and homeschooling, and then examines the interaction between location and children's disability status in detail. We discuss the implications of our findings for family economy theoretical perspectives, flexischooling, and rural education research.     

‘King's inheritors': understanding the ethnic discourse on the Rajbanshi as an indigenous community

The rise of ethnic struggles in various parts of the world, particularly in the post-colonial period, is an intriguing phenomenon. Having the consciousness of primordial origins, indigenous communities have pursued ethnic mobilizations along different lines in order to achieve the goals of social and economic uplift. This paper focuses on the Rajbanshi, one such community living in northeast India, as they offer an opportunity to study history and ethnic identity formation as the dynamics behind their current situation. From the standpoint of applied anthropology, processes of social change and activism intended to improve the lot of Rajbanshi communities are evaluated. Given their obscure origins, cultural diversity and divided political struggle, the Rajbanshi are far from achieving their goal of pursuing better lives. Strategies of Sanskritization and ‘sons-of-the-soil’ indigeneity have not reaped the desired results in terms of social and economic development. Furthermore, discourses rooted in immigrant-aboriginal binaries and theoretical dichotomies of primordialism–constructivism fail to make sense of this community's experience and are not helpful in guiding them toward meaningful and fruitful political and social change.     

Variable selection for survival data with a class of adaptive elastic net techniques

The accelerated failure time (AFT) models have proved useful in many contexts, though heavy censoring (as for example in cancer survival) and high dimensionality (as for example in microarray data) cause difficulties for model fitting and model selection. We propose new approaches to variable selection for censored data, based on AFT models optimized using regularized weighted least squares. The regularized technique uses a mixture of \(\ell _1\) and \(\ell _2\) norm penalties under two proposed elastic net type approaches. One is the adaptive elastic net and the other is weighted elastic net. The approaches extend the original approaches proposed by Ghosh (Adaptive elastic net: an improvement of elastic net to achieve oracle properties, Technical Reports 2007) and Hong and Zhang (Math Model Nat Phenom 5(3):115–133 2010), respectively. We also extend the two proposed approaches by adding censoring observations as constraints into their model optimization frameworks. The approaches are evaluated on microarray and by simulation. We compare the performance of these approaches with six other variable selection techniques-three are generally used for censored data and the other three are correlation-based greedy methods used for high-dimensional data.     

On UMVU estimators and Bhattacharya bounds in exponential distributions

A subfamily of exponential distributions is considered and it is shown that the variance of the UMVU estimator of an estimable function g(θ) having power series expansion is the limit of Bhattacharya bounds.     

The expanded role of nurse case managers

1. Effective nursing practice is responsive to the changing needs of clients. The community, rather than the hospital, has become the primary treatment setting in which the long-term mentally ill manage their daily activities. 2. The professional role of rehabilitative nurse case management can provide an opportunity for nurses to expand their roles and develop new career directions. 3. The primary nursing model provides an excellent foundation for acquiring the attitudes and skills necessary in the delivery of a comprehensive rehabilitative model of case management.     

Irregular Identification,Support Conditions,and Inverse Weight Estimation

In weighted moment condition models, we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models. In particular, if it is necessary for (point) identification that the weights take arbitrarily large values, then the parameter of interest, though point identified, cannot be estimated at the regular (parametric) rate and is said to be irregularly identified. This rate depends on relative tail conditions and can be as slow in some examples as n^−1/4. This nonstandard rate of convergence can lead to numerical instability and/or large standard errors. We examine two weighted model examples: (i) the binary response model under mean restriction introduced by Lewbel (1997) and further generalized to cover endogeneity and selection, where the estimator in this class of models is weighted by the density of a special regressor, and (ii) the treatment effect model under exogenous selection (Rosenbaum and Rubin (1983)), where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score. Without strong relative support conditions, these models, similar to well known “identified at infinity” models, lead to estimators that converge at slower than parametric rate, since essentially, to ensure point identification, one requires some variables to take values on sets with arbitrarily small probabilities, or thin sets. For the two models above, we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to Andrews and Schafgans (1998) for the sample selection model.