Household structure and housework: assessing the contributions of all household members,with a focus on children and youths

Most research into the division of household domestic labor focuses on couple households, treating other household members such as children/youths and other adults as independent variables affecting the domestic work of husbands and wives. We present an integrated analysis of variance/variance decomposition that summarizes the determinants of the housework contributions of, and the housework burden imposed by, all the individuals in four common household types, with a focus on the contributions of older children and youths. We demonstrate the importance of statistical interactions between the contributions of different household members (distinguished by partnership status, gender, and the ages and genders of children/youths), in particular for those households containing children/youths. We conclude that in order to analyze the contributions of all household members jointly, it is necessary to distinguish different household compositions for separate analysis.     

Does the retirement consumption puzzle differ across the distribution?

Previous research has repeatedly found a puzzling one-time drop in the mean and median of consumption at retirement, contrary to the predictions of the life-cycle hypothesis. However, very little is known as to whether these effects vary across the consumption distribution. This study expands upon the previous work by examining changes in the consumption distribution between the non-retired and the retired using quantile regression techniques on pseudo-cohorts from the cross-sectional data of the 1990–2007 Consumer Expenditure Survey. The results indicate that there are insignificant changes between these groups at the lower end of the consumption distribution, while there are significant decreases at the higher end of this distribution. In addition, these changes in the distribution are gradually larger in magnitude when moving from the lower end to the higher end, which is found using several different measures of consumption. Work-related expenditures are instead shown to decrease uniformly across the consumption distribution. This evidence reveals that there is a progressive distributional component to the retirement consumption puzzle.     

100 key research questions for the post‐2015 development agenda

The Sustainable Development Goals (SDGs) herald a new phase for international development. This article presents the results of a consultative exercise to collaboratively identify 100 research questions of critical importance for the post‐2015 international development agenda. The final shortlist is grouped into nine thematic areas and was selected by 21 representatives of international and non‐governmental organisations and consultancies, and 14 academics with diverse disciplinary expertise from an initial pool of 704 questions submitted by 110 organisations based in 34 countries. The shortlist includes questions addressing long‐standing problems, new challenges and broader issues related to development policies, practices and institutions. Collectively, these questions are relevant for future development‐related research priorities of governmental and non‐governmental organisations worldwide and could act as focal points for transdisciplinary research collaborations.     

Inferactive data analysis

We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise between Tukey's exploratory and confirmatory data analysis allowing also for Bayesian data analysis. We see this as a useful step in concrete providing tools (with statistical guarantees) for current data scientists. The basis of inference we use is (a conditional approach to) selective inference, in particular its randomized form. The relevant reference distributions are constructed from what we call a DAG-DAG—a Data Analysis Generative DAG, and a selective change of variables formula is crucial to any practical implementation of inferactive data analysis via sampling these distributions. We discuss a canonical example of an incomplete cross-validation test statistic to discriminate between black box models, and a real HIV dataset example to illustrate inference after making multiple queries on data.     

Uncertainty and Variability in Health-Related Damages from Coal-Fired Power Plants in the United States

The health‐related damages associated with emissions from coal‐fired power plants can vary greatly across facilities as a function of plant, site, and population characteristics, but the degree of variability and the contributing factors have not been formally evaluated. In this study, we modeled the monetized damages associated with 407 coal‐fired power plants in the United States, focusing on premature mortality from fine particulate matter (PM_2.5). We applied a reduced‐form chemistry‐transport model accounting for primary PM_2.5 emissions and the influence of sulfur dioxide (SO₂) and nitrogen oxide (NO_x) emissions on secondary particulate formation. Outputs were linked with a concentration‐response function for PM_2.5‐related mortality that incorporated nonlinearities and model uncertainty. We valued mortality with a value of statistical life approach, characterizing and propagating uncertainties in all model elements. At the median of the plant‐specific uncertainty distributions, damages across plants ranged from $30,000 to $500,000 per ton of PM_2.5, $6,000 to $50,000 per ton of SO₂, $500 to $15,000 per ton of NO_x, and $0.02 to $1.57 per kilowatt‐hour of electricity generated. Variability in damages per ton of emissions was almost entirely explained by population exposure per unit emissions (intake fraction), which itself was related to atmospheric conditions and the population size at various distances from the power plant. Variability in damages per kilowatt‐hour was highly correlated with SO₂ emissions, related to fuel and control technology characteristics, but was also correlated with atmospheric conditions and population size at various distances. Our findings emphasize that control strategies that consider variability in damages across facilities would yield more efficient outcomes.     

Key to Effective Organizational Performance Management Lies at the Intersection of Paradox Theory and Stakeholder Theory

One of the fundamental and recurring issues in performance management is the adoption of a simplistic, short‐term, narrow, metrics‐oriented approach, which often results in unintended negative outcomes, some of which could be disastrous. This paper makes the case that the key to preventing this syndrome lies at the intersection of paradox and stakeholder theories. Both theories encourage a more complex, long‐term, holistic, balanced approach to management. Stakeholder theory focuses on addressing the many (sometimes conflicting) goals of multiple stakeholders, and paradox theory provides insights into how this challenging task (i.e. of simultaneously addressing multiple conflicting priorities) can be accomplished. Thus, the former provides the ‘what’ and the latter the ‘how’ of effective organizational performance management. Accordingly, the literature at the intersection of both theories (composed of 69 scholarly outputs), was reviewed, and in so doing, identified seven domain areas and 21 constructs, all of which implicitly deal with either performance management or its communication, thereby lending support to this paper's thesis. The implications of this review for both theory and practice, including the role of paradoxical cognitive mechanisms, is discussed.     

Toward Probabilistic Prediction of Flash Flood Human Impacts

This article focuses on conceptual and methodological developments allowing the integration of physical and social dynamics leading to model forecasts of circumstance‐specific human losses during a flash flood. To reach this objective, a random forest classifier is applied to assess the likelihood of fatality occurrence for a given circumstance as a function of representative indicators. Here, vehicle‐related circumstance is chosen as the literature indicates that most fatalities from flash flooding fall in this category. A database of flash flood events, with and without human losses from 2001 to 2011 in the United States, is supplemented with other variables describing the storm event, the spatial distribution of the sensitive characteristics of the exposed population, and built environment at the county level. The catastrophic flash floods of May 2015 in the states of Texas and Oklahoma are used as a case study to map the dynamics of the estimated probabilistic human risk on a daily scale. The results indicate the importance of time‐ and space‐dependent human vulnerability and risk assessment for short‐fuse flood events. The need for more systematic human impact data collection is also highlighted to advance impact‐based predictive models for flash flood casualties using machine‐learning approaches in the future.     

The Value of Information in Decision‐Analytic Modeling for Malaria Vector Control in East Africa

Decision analysis tools and mathematical modeling are increasingly emphasized in malaria control programs worldwide to improve resource allocation and address ongoing challenges with sustainability. However, such tools require substantial scientific evidence, which is costly to acquire. The value of information (VOI) has been proposed as a metric for gauging the value of reduced model uncertainty. We apply this concept to an evidenced‐based Malaria Decision Analysis Support Tool (MDAST) designed for application in East Africa. In developing MDAST, substantial gaps in the scientific evidence base were identified regarding insecticide resistance in malaria vector control and the effectiveness of alternative mosquito control approaches, including larviciding. We identify four entomological parameters in the model (two for insecticide resistance and two for larviciding) that involve high levels of uncertainty and to which outputs in MDAST are sensitive. We estimate and compare a VOI for combinations of these parameters in evaluating three policy alternatives relative to a status quo policy. We find having perfect information on the uncertain parameters could improve program net benefits by up to 5–21%, with the highest VOI associated with jointly eliminating uncertainty about reproductive speed of malaria‐transmitting mosquitoes and initial efficacy of larviciding at reducing the emergence of new adult mosquitoes. Future research on parameter uncertainty in decision analysis of malaria control policy should investigate the VOI with respect to other aspects of malaria transmission (such as antimalarial resistance), the costs of reducing uncertainty in these parameters, and the extent to which imperfect information about these parameters can improve payoffs.     

The implementation of digital technologies for operations management: a case study for manufacturing apps

Abstract

Manufacturing applications address business to business (B2B) with highly customised applications developed for specific requirements, offering highly specialised solution-oriented and service-based software components, systems, and digital tools that aim at a fast and accurate decision-making support system. The purpose of this paper is to describe the implementation of digital technologies for operations management using manufacturing or engineering apps (eApps), for product design and manufacturing processes. In particular, starting from the specific needs of two companies from mature European industries as automotive and food, this work depicts how this kind of solutions can support companies and improve their operations. In particular, related benefits and challenges faced for the full implementation of the developed tools are highlighted. Moreover a business model to exploit the manufacturing apps is also proposed. The business model proposed for the exploitation of the eApps supports the commercialisation of all the revenue streams offered by this rapidly growing sector taking into account the specific needs of the concerned stakeholders through a diversified value proposition.