Associations between psychosocial risk factors and musculoskeletal disorders: application to the IT profession in India

The exponential growth of the information technology (IT) industry in India has been accompanied with a substantial increase in the reporting of work-related musculoskeletal disorders (WMSDs). The purpose of the current study was to identify and assess the contributions of prevalent psychosocial factors on perceived WMSD outcomes among IT professionals from India. About 77 IT professionals from India completed a survey set consisting of 26 question items from the Job Content Questionnaire and 3 separate question items pertaining to WMSD outcomes (pain/discomfort and psychological stress scores). The findings suggest prevalence of existing pain (shoulder/neck and low back) in more than one-fourth of the respondents. Additionally two-thirds of the respondents had never had any ergonomics awareness training. Co-worker support and psychological work demands were found to be the strongest contributors of psychosocial risk factors towards pain/discomfort and psychological stress outcomes. Findings from this study highlight the influence of certain psychosocial traits of the Indian IT workplace on perceived WMSD outcomes. There is a need to develop and implement intervention strategies to address these factors that may help lower the risk of work-related musculoskeletal pain.     

FROM MAIZE TO HAZE: AGRICULTURAL SHOCKS AND THE GROWTH OF THE MEXICAN DRUG SECTOR

Understanding how economic incentives affect illegal drug production is essential for crafting policies in response to the international drug trade. Policymakers typically face a choice between two strategies: targeting criminal groups via law enforcement, and offering producers incentives to engage in alternate activities. Yet, little is known about how the returns to alternate legal activities affect drug supply. We contribute to this literature by examining how shocks to legal commodity prices affect the drug trade in Mexico. Our analysis exploits exogenous movements in the Mexican maize price stemming from weather conditions in US maize‐growing regions, as well as exports of other major maize producers. Using data on over 2200 municipios spanning 1990–2010, we show that lower prices differentially increased the cultivation of both marijuana and opium poppies in municipios more climatically suited to growing maize. We also find impacts on downstream drug‐trade outcomes, including drug cartel operations and killings perpetrated by these groups. Our findings demonstrate that maize price changes contributed to the burgeoning drug trade in Mexico, and point to the violent consequences of an expanding drug sector.     

Dynamic Pricing in e‐Services under Demand Uncertainty

High volatility of the e‐services market, due to increasing competition, low life cycle of products, and easy availability of information about competing service offerings to customers, makes the demand for service offerings quite uncertain. Revenue management in such markets calls for real‐time techniques to learn the demand and its dependence on both the price and the service level associated with the service offering. We assume firms reply on exploratory approaches for demand estimation, in which firms experiment with different service offerings in order to simultaneously learn the demand while doing business. Such exploration and learning process can be costly without supervision. As reported by Rothschild (Journal of Economic Theory, 9 185‐202, 1974), traditional Bayesian dynamic control approaches may conclude with suboptimal offerings. We propose a novel demand learning approach that is guaranteed to converge to the optimal offering. The approach combines simulated annealing algorithm with Bayesian learning. We further present intelligent techniques that adaptively reduce the effort of exploration on suboptimal service offerings so as to improve the long‐run average profit.     

The impact of India’s food security policy on domestic and international rice market

Food security policy making in India is at crossroads. India has emerged as a leading rice exporter. The Government of India has introduced the National Food Security Act which requires 33.6 million tons of rice per year for its public food distribution system. In this study, we modeled India’s rice market and analyzed policy implications of the long-term impact of India’s food security act on domestic and international rice market. We developed a structural economic demand and supply model for India’s rice market and further added subsidy equations to trace the consequence of National Food Security Act on domestic rice consumption and on the international market. We specifically focus on three different scenarios: subsidy as price effect, subsidy as inelastic income effect, and subsidy as elastic income effect under the broader framework of National Food Security Act. We found that at the end of the projection period (2024–2025), as a result of rice subsidy program, the consumption of rice increases significantly in the case of price effect while the inelastic income effect has no or less impact on production, consumption, and export of rice. Hence, the policy implication of our study is that if the objective of the National Food Security Act is to increase consumption then it needs to be implemented as price effect.     

Ordinary least squares and Stein-rule predictions in regression models under inclusion of some superfluous variables

This article considers a misspecified linear regression model in which misspecification relates to the inclusion of some explanatory variables. Assuming the distribution of disturbances to be not necessarily normal, this paper investigates the efficiency properties of predictions arising from ordinary least squares and Stein-rule when the aim is to predict either the actual value or the mean value of the study variable.     

Stein-rule estimator under inclusion of superfluous variables in linear regression models

Stein-rule estimation is a well-known method to improve the unbiased OLSE in the sense of smaller Mean-Square-Error. The paper is investigating the behaviour of this efficiency relation in case of misspecification of the linear model caused by inclusion of superfluous variables     

Implementing an improved inventory control system in a small company: a case study

The paper presents the implementation of an improved inventory management control system in a small company. The project took place at Trojan Mine, a company involved in mining mineral resources. Firstly, a conceptual framework for the design of an inventory control management system is developed. Secondly, a very effective user-friendly inventory control tool for determining the category A items was developed using EXCEL spreadsheet; a tool that is an asset to the company since it can be used in future. Finally, key performance indicators were also established to give benchmark to operations. The improved inventory control management system developed is found to offer improvement to the performance of the company since capital tied up in overstocking items of high annual usage value is released.     

Estimating hybrid frequency moments of data streams

We consider the problem of estimating hybrid frequency moments of two dimensional data streams. In this model, data is viewed to be organized in a matrix form (A _i,j )_1≤i,j,≤n . The entries A _i,j are updated coordinate-wise, in arbitrary order and possibly multiple times. The updates include both increments and decrements to the current value of A _i,j . The hybrid frequency moment F _p,q (A) is defined as \(\sum_{j=1}^{n}(\sum_{i=1}^{n}{A_{i,j}}^{p})^{q}\) and is a generalization of the frequency moment of one-dimensional data streams.We present the first \(\tilde{O}(1)\) space algorithm for the problem of estimating F _p,q for p∈[0,2] and q∈[0,1] to within an approximation factor of 1±ε. The \(\tilde{O}\) notation hides poly-logarithmic factors in the size of the stream m, the matrix size n and polynomial factors of ε ^?1. We also present the first \(\tilde{O}(n^{1-1/q})\) space algorithm for estimating F _p,q for p∈[0,2] and q∈(1,2].