Explaining Gender Inequalities That Follow Couple Migration

By exploiting the unique social and economic differences between East and West Germany, the authors investigated how macro‐level opportunities interact with couple‐level decision making to explain gender differences in the determinants and economic outcomes of household migration. By incorporating regional socioeconomic conditions into household bargaining theory, 4 hypotheses for each region were derived. The hypotheses were tested using cross‐classified multilevel regressions and the German Socio‐Economic Panel (1992–2012) combined with regional economic indicators. First, gender‐specific determinants of couples' West–West (i.e., within West Germany) and East‐to‐West migration were analyzed; second, subsequent economic consequences were investigated by comparing couples with singles. The results confirm that gender differences in macro‐conditions can impose decision logics that seemingly contradict the initial power relation within couples. Despite more traditional gender arrangements in West Germany, well‐educated partnered women earn significant absolute and relative income gains from migration; their egalitarian East German counterparts suffer significant losses compared with single women and East German men.     

The Management of Redundancies in Europe: The Case of Italy

The paper presents the findings of comparative and interdisciplinary legal and economic study on managing labour redundancies in seven EU member countries. It is structured for comparability between the systems examined. The introductory section contains an account of the evolution of the Italian labour market, with special reference to redundancy trends, of the features of the programmes for managing redundancies, of the roles of firms and the ‘external environment’ in handling workforce adjustments. The second section presents a map of policies that work to prevent labour redundancies (preventive measures) such as flexibility, training etc. Next, the instruments for handling temporary labour redundancies (retentive measures) are examined (e.g. short-time working, temporary layoffs etc.). The following section is devoted to instruments and programmes involved in the management of permanent labour redundancies (expulsive measures), e.g. collective dismissals, severance pay etc. The final section provides an overall assessment of the Italian system for managing labour redundancies, and briefly discusses the national debate on the prospects for reform, in light of the principles and policies of convergence set forth by the European Union.     

Microbiological and Modeling Approach to Derive Performance Objectives for Bacillus cereus Group in Ready‐to‐Eat Salads

In this article, the performance objectives (POs) for Bacillus cereus group (BC) in celery, cheese, and spelt added as ingredients in a ready‐to‐eat mixed spelt salad, packaged under modified atmosphere, were calculated using a Bayesian approach. In order to derive the POs, BC detection and enumeration were performed in nine lots of naturally contaminated ingredients and final product. Moreover, the impact of specific production steps on the BC contamination was quantified. Finally, a sampling plan to verify the ingredient lots' compliance with each PO value at a 95% confidence level (CL) was defined. To calculate the POs, detection results as well as results above the limit of detection but below the limit of quantification (i.e., censored data) were analyzed. The most probable distribution of the censored data was determined and two‐dimensional (2D) Monte Carlo simulations were performed. The PO values were calculated to meet a food safety objective of 4 log₁₀ cfu of BC for g of spelt salad at the time of consumption. When BC grows during storage between 0.90 and 1.90 log₁₀ cfu/g, the POs for BC in celery, cheese, and spelt ranged between 1.21 log₁₀ cfu/g for celery and 2.45 log₁₀ cfu/g for spelt. This article represents the first attempt to manage the concept of PO and 2D Monte Carlo simulation in the flow chart of a complex food matrix, including raw and cooked ingredients.     

Laypeople''s and Experts'' Perception of Nanotechnology Hazards

Public perception of nanotechnology may influence the realization of technological advances. Laypeople's (N=375) and experts' (N=46) perception of 20 different nanotechnology applications and three nonnanotechnology applications were examined. The psychometric paradigm was utilized and applications were described in short scenarios. Results showed that laypeople and experts assessed asbestos as much more risky than nanotechnology applications. Analyses of aggregated data suggested that perceived dreadfulness of applications and trust in governmental agencies are important factors in determining perceived risks. Similar results were observed for experts and laypeople, but the latter perceived greater risks than the former. Analyses of individual data showed that trust, perceived benefits, and general attitudes toward technology influenced the perceived risk of laypeople. In the expert sample, confidence in governmental agencies was an important predictor of risks associated with nanotechnology applications. Results suggest that public concerns about nanotechnology would diminish if measures were taken to enhance laypeople's trust in governmental agencies.     

An Alternative FMEA Method for Simple and Accurate Ranking of Failure Modes

Failure modes and effects analysis (FMEA) is a methodology for prioritizing actions to mitigate the effects of failures in products and processes. Although originally used by product designers, FMEA is currently more widely used in industry in Six Sigma quality improvement efforts. Two prominent criticisms of the traditional application of FMEA are that the risk priority number (RPN) used to rank failure modes is an invalid measure according to measurement theory, and that the RPN does not weight the three decision criteria used in FMEA. Various methods have been proposed to mitigate these concerns, including many using fuzzy logic. We develop a new ranking method in this article using a data‐elicitation technique. Furthermore, we develop an efficient means of eliciting data to reduce the effort associated with the new method. Subsequently, we conduct an experimental study to evaluate that proposed method against the traditional method using RPN and against an approach using fuzzy logic.     

Testing a Model of Trade Union Behaviour: An Application of a Multivariate Cointegration Analysis to UK Aggregate Wage Data

ABSTRACT The first part of the paper considers a behavioural model of trade unionism. Wages and employment are characterized as the outcome of a process by which the union maximizes an objective function containing wages and employment as arguments, and is constrained by a trade-off between these two variables as represented by the firm's labour demand function. In the second part, the model's equilibrium predictions for wages are estimated in the framework of a multivariate error correction model and tested for cointegration with data from Britain's manufacturing sector from 1967 to 1986.     

Datafying anti-poverty programmes: implications for data justice

ABSTRACT

This paper seeks to illuminate the significance of datafication for anti-poverty programmes, meaning social protection schemes designed specifically for poor people. The conversion of beneficiary populations into machine-readable data enables two core functions of social protection, those of recognising entitled beneficiaries and assigning entitlements connected to each anti-poverty scheme. Drawing on the incorporation of Aadhaar, India’s biometric population database, in the national agenda for social protection, we unpack a techno-rational perspective that crafts datafication as a means to enhance the effectiveness of anti-poverty schemes. Nevertheless, narratives collected in the field show multiple forms of data injustice on recipients, underpinned by Aadhaar’s functionality for a shift of the social protection agenda from in-kind subsidies to cash transfers. Based on such narratives the paper introduces a politically embedded view of data, framing datafication as a transformative force that contributes to reforming existing anti-poverty schemes.     

Use of the scale-up methods in injury prevention research: An empirical assessment to the case of choking in children

The scale-up method estimates the size of hard to count subpopulations. This method is based on the idea that the proportion of subjects in a subpopulation E known to each member of the general population T is the same as the proportion of members of E belonging to general population T.     

Some simulations and applications of forecasting long-memory time-series models

In this paper, we show some results of forecasting based on the ARFIMA(p,d,q) and ARIMA(p,d,q) models. We show, by simulation, that the technique of forecasting of the ARIMA(p,d,q) model can also be used when d is fractional, i.e., for the ARFIMA(p,d,q) model. We also conduct a simulation study to compare the two estimators of d obtained through regression methods. They are used in the hypothesis test to decide whether or not the series has long memory property and are compared on the basis of their k-step ahead forecast errors. The properties of long-memory models are also investigated using an actual set of data.