The Economic Effect of Refugee Crises on Neighbouring Host Countries: Empirical Evidence from Pakistan

There is a considerable debate in terms of opportunities and challenges about the presence of refugees in the neighbouring host countries. Most of the existing discourse has focused only on their humanitarian and security implications. This article, on the other hand, seeks to uncover this issue through a purely economic lens, focusing on the economic impact of more than three million Afghan refugees in Pakistan. Utilizing data for the period 1979–2014 and the ARDL bounds testing approach, we arrive at few important conclusions. The empirical results indicate that Afghan refugees have a strong negative impact on economic growth in Pakistan. The effect holds in both the short run and the long run, suggesting that the influx of refugees lowers real economic activity in the country. Ultimately, the study implies that hosting refugees can never be a boon to Pakistan's economy.     

When to Mass Customize: The Impact of Environmental Uncertainty*

Previous research on mass customization (MC) has focused on what it is and how it is implemented. In this study we examine when MC is an appropriate strategy for firms to follow by scrutinizing the effects of three environmental uncertainty variables (demand uncertainty, competitive intensity, and supply chain complexity) on the MC–performance relationship. Specifically, we distinguish the direct effect of environmental uncertainty on MC ability and the moderation effect of environmental uncertainty on MC ability's impact on customer satisfaction. We examine six competing hypotheses using data collected from 266 manufacturing plants. Our results show that competitive intensity has a direct positive impact on MC ability. However, demand uncertainty moderates the relationship between MC ability and customer satisfaction, and the direct and positive relationship between MC ability and customer satisfaction holds only when customer demand is highly uncertain. Supply chain complexity neither has a direct relationship with MC, nor moderates the MC–performance relationship. Implications of these research findings are discussed and future research directions are identified.     

英语词块强化注意教学模式的实证研究

注意力是促使语言输入转化为内在知识的必要和充分条件。本研究通过为期一学期的教学实验,对英语词块强化注意教学模式和传统教学模式的教学效果进行了比较,探究了词块强化注意教学模式的有效性。结果发现:该模式教学能显著提高词块应用水平、英语综合水平和作文水平,而对听力和阅读理解水平的提高则无明显效果。研究证实:通过教学手段提高和强化输入词块的频率与突显程度、以及设计以词块输出为中心的教学任务可以促进词块由输入转变成内在知识、提高语言加工深度,从而提高语言产出水平。     

Explaining Anomalous High Performance in a Health Care Supply Chain*

An implicit assumption in distributing and coordinating work among independent organizations in a supply chain is that a focal organization can use financial or contractual mechanisms to enforce compliance among the other organizations in meeting desired performance objectives. Absent contractual agreement or financial gain, there is little incentive for independent organizations to coordinate their process improvement activities. In this study, we examine a health care supply chain in which the work is distributed among independent organizations. We use a detailed case study and an abductive reasoning approach to understand how and why the independent organizations choose to coordinate and collaborate in their work. Our study makes two contributions to the literature. First, we use well‐established lean principles to explain how independent organizations achieve superior performance despite highly uncertain and variable customer demand—a context considerably different from the origins of lean principles. Second, we forward relational coordination theory to explain why the organizations in this decentralized supply chain coordinate their work. Relational coordination includes the use of shared goals, shared knowledge, and mutual respect for one another's work as primary mechanisms to explain process improvement in the absence of any contractual incentives. Our study constitutes a first step in generating theory for work design and its improvement in decentralized supply chains.     

Sequential analysis applied to testing the mean of an inverse gaussian distribution with known coefficient of variation

Given an inverse Gaussian distribution I(.μ,a²μ) with known coefficient of variation a, the hypothesis H_O: .μ <ce:glyph name="dbnd6"/> μ_o is tested against H₁: μ <ce:glyph name="dbnd6"/> μ₁ using the sequential probability ratio test. The maximum of the expected sample number is shown to occur when μ is approximately equal to the geometric mean of μ_oand μ₁ and it is shown that this maximum value depends on .μ_o and μ₁ only through their ratio. It is observed that the test can be used to discriminate between two one-sided hypotheses.     

Bayes Estimation of Shift Point in Geometric Sequence

A sequence of independent lifetimes X ₁, X ₂,…, X _m , X _m+1,… X _n were observed from geometric population with parameter q ₁ but later it was found that there was a change in the system at some point of time m and it is reflected in the sequence after X _m by change in parameter q ₂. The Bayes estimates of m, q ₁, q ₂, reliability R ₁ (t) and R ₂ (t) at time t are derived for symmetric and asymmetric loss functions under informative and non informative priors. A simulation study is carried out.     

Likelihood Ratio Tests for Interval Hypotheses with Applications

In this article, we derive the likelihood ratio tests (LRTs) for simultaneously testing interval hypotheses for normal means with known and unknown variances, and also with unknown but equal variance. Special cases when the interval hypotheses boil down to a point hypothesis are also discussed. Remarks regarding comparison of the LRT with tests based on combination of p-values are made, and several applications based on real data are mentioned.     

Without-replacement sampling for particle methods on finite state spaces

Combinatorial estimation is a new area of application for sequential Monte Carlo methods. We use ideas from sampling theory to introduce new without-replacement sampling methods in such discrete settings. These without-replacement sampling methods allow the addition of merging steps, which can significantly improve the resulting estimators. We give examples showing the use of the proposed methods in combinatorial rare-event probability estimation and in discrete state-space models.