Effects of perceived product quality and Lifestyles of Health and Sustainability (LOHAS) on consumer price preferences for children's furniture in China

The effects of perceived product quality and orientation toward Lifestyles of Health and Sustainability (LOHAS) on consumer price preferences were studied with the use of survey data of the children's furniture market in two metropolitan cities of China: Shanghai and Shenzhen. Based on the results from a factor analysis and logistic regression, consumers’ perceived product quality of children's furniture was identified as a four-dimensional construct: ‘supplier attributes’, ‘extended product attributes’, ‘basic product attributes’ and ‘environmental quality attributes’. Consumer price preferences were discovered to be negatively influenced by ‘basic product attributes’, but positively influenced by consumer orientation towards LOHAS, gender and education level. In conclusion, China's children's furniture market presents a high-end niche segment with growth potential for both furniture producers and wood material suppliers.     

The puzzle of greenhouse gas footprints of oil abundance

The present inquiry lays a groundwork for the analysis of the net greenhouse gas (GHG) footprint of oil in the oil-abundant settings. To address the research question, the study puts forward a three-sector decision model, which provides a common ground for the assessment of the interaction of the structuralist and institutional factors influencing environmental pollution in the oil-reliant economies. The study shows that fossil-fuel abundance triggers forces, which induce diametrically opposed effects concerning atmospheric pollution. These are the rising carbon-intensive oil extraction and processing and fossil-fueled power generation versus shrinkage of the carbon-intensive manufacturing and growth of the low-carbon tertiarization. The theoretical analysis enables compartmentalization of the essential factors, which determine GHG emissions in the respective countries. To assess the significance of the proposed theoretical framework, the study employs multivariate panel co-integration techniques and two-stage fixed effects estimations for a dataset of 38 oil-producing countries for the time period between 1960 and 2018. In contrast to the existing literature, this study drives apart from the black box approaches that employ just one omnibus variable, per capita income.     

Creating Opportunities for Women in the Renewable Energy Sector: Findings from India

This paper identifies opportunities and constraints that low-income women face in accessing livelihoods in the renewable-energy sector in India through qualitative and quantitative research conducted in collaboration with The Energy Resources Institute (TERI) and the Self Employed Women's Association (SEWA) in 2012–13. Whereas previous research has focused on women mostly as end users of solar and biomass technologies, this research attempts also to understand women's potential as entrepreneurs, facilitators, designers, and innovators. Findings reveal that although access to technology and employment in the energy sector is limited by inadequate purchasing power and low social status, there is tremendous potential to create livelihoods for women at all levels of the energy supply chain. Broader findings indicate that women can gain optimal traction from employment in the energy sector only if there are wider socially progressive policies in place, including state intervention to create a robust social welfare infrastructure and accessible, high-quality, public services.     

Some Variants of Constrained Estimation in Finite Population Sampling

This is a review article that unifies several important examples using constrained optimisation techniques. The basic tools are three simple mathematical optimisation results subject to certain constraints. Applications include calibration, benchmarking in small area estimation and imputation. A final illustration is constrained optimisation under a general divergence loss.     

重叠滤波多音系统的频偏性能分析及优化

重叠滤波多音(O-FMT)是针对滤波多音(FMT)系统频谱利用率低的缺点而根据超奈奎斯特(FTN)概念引入子载波重叠得到的方案。鉴于O-FMT系统在频偏环境下的性能缺陷，理论分析了频偏对系统信号各部分的影响，比较了O-FMT与正交频分复用（OFDM）系统的抗频偏性能。针对频偏性能优化问题，提出了基于最佳线性无偏估计（BLUE）的改进盲估计算法，设计了基于频域滤波器系数的内嵌结构进行频偏补偿。仿真实验表明，O-FMT相比OFDM具有更好的抗频偏能力，改进的盲估计算法估计值精度更高，抗干扰、抗衰落能力更强，设计的频偏补偿结构可以使系统获得更好的性能。     

较差质量数据下的返回弹道计算及对落点影响的分析

针对某老式卫星返回时雷达跟踪数据曾发生的测量异常及质量较差等情况，为解决此类数据在返回弹道及落点计算中出现的实际困难以及给出应对措施，基于工程实际情况给出了三种不同建模类型下的弹道计算方法——当前统计解耦扩展卡尔曼滤波（EKF）、动力学建模无迹卡尔曼滤波（UKF）、多项式滑窗最小二乘估计算法，并应用该次质量较差的实际观测数据进行了算法应用效果分析与落点计算情况对比，对计算中的经验及教训进行了总结。研究结果表明，所给出的三种算法在面对较差质量数据时性能差异较大，在工程实际应用中可考虑将多种算法互为参考。研究结果及其经验教训对卫星返回弹道及落点的实时估计的工程实施有一定的参考价值。     

Incentives to move up: Effects of pay gaps between levels on employee performance

Employees of a certain rank are motivated by the pay gap between them and the levels above (upward comparison), and the pay gap between them and the levels below (downward comparison). In some cases, employees face multiple upward comparisons such as immediate and subsequent upward comparisons. We hypothesise that upward comparison matters more than downward comparison, and in the case of multiple upward comparisons, the immediate one matters more than the subsequent ones. We also hypothesise that the pay gap effect resulting from the upward/downward comparison ought to be non‐linear in that performance increases with pay gap size at a decreasing rate. The results from two empirical studies, namely, a longitudinal field study and a laboratory experiment, largely support our hypotheses.     

Applications of Statistics in the Field of General Insurance: An Overview

This is an expository paper on applications of statistics in the field of general insurance, also called non‐life insurance. Unlike life insurance where advanced statistical techniques have long been part of financial mathematics and actuarial applications, their use is only relatively recent in non‐life insurance. The business model of insurance companies, especially those active in non‐life insurance, has seen dramatic changes over the last 15 years. The aim of this paper is to convince the readers that especially today non‐life insurance is not only an exciting ground to apply existing modern statistical tools but also a fertile environment for new and challenging statistical developments. The activities of an insurance company can be viewed as an industrial process where data management and data analysis play a key role. That is why a fundamental understanding of data‐related issues (such as data quality, variability, analysis and correct interpretation) is so essential to the insurance business. These are exactly the tasks where professional statisticians excel. Also, a better understanding of the field of general insurance by statisticians will promote fruitful exchanges between actuaries and statisticians, thereby helping to bring actuarial and statistical professional societies closer to each other. Selected examples are used to cover the essential aspects of general insurance, and all of them are based on the author's experience. The paper concludes with some remarks on the role of statisticians working in general insurance.     

La gestión mecanicista de las Instituciones de Educación Superior: un estudio desde la modelación sistémica

The bureaucratic tendencies to organize work in Higher Education Institutions (HEIs) of which facilitates personnel control and the mechanical ways to measure educational content are the ones that shatter education in pieces, under the hierarchy criterion, also shattering the professor's labor. This organizational method results in an incompetent education, leaving very negative consequences for teaching development. We address this problem by using systemic modeling and Structural Equations Systems (SEM) to confirm hypotheses from scientific observation. It was proven that there was a negative impact of mechanistic management which affected teacher's development. This was proven in context in two HEIs located in Villahermosa (Tabasco, Mexico).     

Does Accrual Management Impair the Performance of Earnings‐Based Valuation Models?

This study examines how accrual manipulations affect firm valuation in the years surrounding the passage of the Sarbanes‐Oxley Act (SOX). We compare the absolute percentage pricing errors of RIM and DCF valuation models for a group of US firms suspected to have engaged in accrual manipulations to avoid a small loss or a small earnings decline vs. ‘Normal’ firms matched on industry, year and size. We find that RIM can better estimate intrinsic value than DCF for the matched Normal firms in the pre‐SOX period, but not so for accrual manipulators, and that SOX mitigates the harmful effect of accrual manipulations, completely eliminating the difference in RIM's accuracy advantage over DCF between Normal firms and accrual manipulators. As a further analysis, we redefine Suspect firms as real‐activity manipulators and find a significant across‐group difference in accuracy wedge in both sample periods, implying that SOX has prompted firms to favor real‐activity manipulations over accrual manipulations.