Colonial legacies,post-colonial (in)securities,and gender(ed) representations in South Asia's nuclear policies

Through a comparative study of India and Pakistan's national security discourses, this article explores the linkages between post-colonial India and Pakistan's nationalist/communalist identities, configurations of masculinities, and gendered representations underpinning their nuclear (in)securities. This paper contends that the colonial politics of place-making in the sub-continent has not only inscribed a process of ‘othering’ between these states but has also facilitated the rise of divergent visions of post-colonial nationalisms, which, at each of their phases and with particular configurations of masculinities, have used women's bodies to re-map India-Pakistan's borders and national (in)securities. This article particularly draws attention to a new form of gendered manipulation in South Asian politics in the late 1990s, whereby both states, embedded in colonial notions of religious/cultural masculinities, have relied on discourses of Hindu/Indian and Muslim/Pakistani women's violence and protection from the ‘other’ to pursue aggressive policies of nuclearization. It is at this conjectural moment of a Hinduicized and Islamicized nationalism (flamed by the contestations of a Hindu versus an Islamic masculinity) that one needs to provide a feminist re-interpretation of India-Pakistan's nationalist identities, gendered imaginaries, and their re-articulation of national (in)securities – that represents a religious/gendered ‘otherness’ in South Asia's nuclear policies.     

Prevalence of job strain among Indian foundry shop floor workers

Global competition in manufacturing sector demand higher productivity levels. In this context, workers in this sector are set with high output targets, leading to job strain. In addition to the strain, hazardous conditions also prevail in some of the manufacturing processes like foundry activities. This paper attempts to appraise the prevalence of job strain among foundry shop floor workers in India with the help of Demands-Control model [8]. In this study, data was collected through a survey using 49-item Job Content Questionnaire (JCQ) [9], a widely used and well-validated test for job strain. Then the data was subjected to statistical analysis after ascertaining the reliability. This survey has revealed that 25% of workers in foundry were experiencing high job strain. Hazardous working conditions, limited decision making authority, etc. appear to be the main contributing factors for the higher levels of strain.     

Spatial Sampling Design for a Demographic and Health Survey

The recent advances in global position systems (GPS), geographic information systems (GIS), and remote sensing (RS) can be exploited for spatial sampling design for demographic and health surveys. These technologies are particularly useful when a sampling frame is unavailable and/or location (of household) is important for data collection (e.g., location of residence might greatly impact exposure to ambient air pollution among members of a population). Building on these technologies, this article presents a methodology of spatial sampling adopted for the respiratory health and demographic survey conducted in Delhi and its environs from January through April 2004. The overall goal of the survey was to select households that adequately represented exposure to ambient air pollution. The proposed methodology involved constructing a sampling frame of residential areas and the simulation of weighted random points within residential areas. The simulated locations were navigated with the aid of GPS to identify households at these locations and to acquire residents’ consent to participate in the survey; a total of 1,576 households at the 2,000 simulated locations were found suitable and participated in the survey. The average ambient air pollution at the sample sites was not significantly different from the average air pollution observed in the study area, which demonstrates the robustness of the proposed sampling method.     

Methods of decomposing the difference between two rates with applications to race-sex inequality in earnings

"Four general methods of decomposing the difference between two rates into several components are provided. For data cross-classified by several factors, all four methods can be used treating the factors either hierarchically or symmetrically, and within each treatment, the methods are expected to give very similar results. Since the results may differ significantly depending on whether the factors are hierarchical or symmetrical and since there is no objective way of arranging the factors hierarchically, symmetrical treatment of the factors is recommended. The latter approach is used to study race-sex inequalities in mean annual earnings for year-round full-time workers in the United States based on the 1980 census data cross-classified by education, age, and occupation." (SUMMARY IN FRE)     

A general method of decomposing a difference between two rates into several components

In her work on the components of a difference between two rates, Kitagawa (1955) was successful in dividing the difference into the rate effect and the effect of the factor, for data classified by one factor. Her formulation for data classified by two factors, however, involves an interaction term which is difficult to interpret. Retherford and Cho (1973) devised a method that does not include any interaction terms. However, their method has other limitations, such as the dependence of the results on the order in which the effects of the factors are computed. In this paper, we provide a general method capable of handling any number of factors, which is developed along the lines suggested by Kitagawa and by Retherford and Cho but without the limitations of their methods.     

Estimation of smooth regression functions in monotone response models

We consider the estimation of smooth regression functions in a class of conditionally parametric co-variate-response models. Independent and identically distributed observations are available from the distribution of (Z,X)$(Z,X)$, where Z is a real-valued co-variate with some unknown distribution, and the response X conditional on Z   is distributed according to the density p(·,ψ(Z))$p(·,\psi (Z))$, where p(·,θ)$p(·,\theta )$ is a one-parameter exponential family. The function ψ$\psi$ is a smooth monotone function. Under this formulation, the regression function E(X|Z)$E(X|Z)$ is monotone in the co-variate Z   (and can be expressed as a one–one function of ψ$\psi$); hence the term “monotone response model”. Using a penalized least squares approach that incorporates both monotonicity and smoothness, we develop a scheme for producing smooth monotone estimates of the regression function and also the function ψ$\psi$ across this entire class of models. Point-wise asymptotic normality of this estimator is established, with the rate of convergence depending on the smoothing parameter. This enables construction of Wald-type (point-wise) as well as pivotal confidence sets for ψ$\psi$ and also the regression function. The methodology is extended to the general heteroscedastic model, and its asymptotic properties are discussed.     

CONFLICT RESOLUTION IN INVESTMENT DECISIONS: IMPLEMENTATION OF GOAL PROGRAMMING METHODOLOGY FOR DUAL-PURPOSE FUNDS*

Goal programming (GP) is designed to resolve allocation problems with conflicting goals. Both goals and constraints are incorporated in the allocational decision, and the objective function is stated in a way that, upon solution, yields a result “as close as possible” to the priority-weighted goals. The present paper applies GP methodology to the investment decision of dual-purpose funds (DPFs), that are required by law to pursue allocational decisions with potentially conflicting objectives. It provides an empirical demonstration that DPF managers could have improved their investment selection and subsequent performance by the use of GP methodology. Finally the paper stresses the importance of sensitivity analysis to improve both the goal-ranking and target-selection aspects of the methodology and provides a limited but illuminating empirical demonstration of post-optimality analysis.