Community and anti-poverty targeting

The standard theory of anti-poverty targeting assumes individual incomes cannot be observed, but statistical properties of income distribution in broadly defined groups are known. ‘Indicator targeting’ rules are then derived for the forms of transfers conditioned on group membership of individuals. In this literature the motivating notion of a ‘group’ is purely statistical, even when it is groups such as localities and ethnicities. We focus instead on groups which are ‘communities’, meaning thereby collections of individuals who have access to community-specific public goods, from which non-members are excluded. Such differential access constitutes a source of inequality among poor individuals belonging to different communities, which is not captured by monetary earnings. We show that this formulation of what constitutes a group changes many of the basic results of the indicator targeting literature. Optimal targeting for poverty alleviation leads to seemingly paradoxical rules, such as targeting transfers to the community that is richer. Total wealth of non-poor members of a community and its distribution both become relevant for specifying optimal indicator targeting rules. In addition, a poverty measure that is sensitive to the community identities of poor individuals, yet defined on nominal incomes, may be incompatible with some of the basic axioms in the standard literature on poverty measurement.     

Uncertainties in Biologically-Based Modeling of Formaldehyde-Induced Respiratory Cancer Risk: Identification of Key Issues

In a series of articles and a health-risk assessment report, scientists at the CIIT Hamner Institutes developed a model (CIIT model) for estimating respiratory cancer risk due to inhaled formaldehyde within a conceptual framework incorporating extensive mechanistic information and advanced computational methods at the toxicokinetic and toxicodynamic levels. Several regulatory bodies have utilized predictions from this model; on the other hand, upon detailed evaluation the California EPA has decided against doing so. In this article, we study the CIIT model to identify key biological and statistical uncertainties that need careful evaluation if such two-stage clonal expansion models are to be used for extrapolation of cancer risk from animal bioassays to human exposure. Broadly, these issues pertain to the use and interpretation of experimental labeling index and tumor data, the evaluation and biological interpretation of estimated parameters, and uncertainties in model specification, in particular that of initiated cells. We also identify key uncertainties in the scale-up of the CIIT model to humans, focusing on assumptions underlying model parameters for cell replication rates and formaldehyde-induced mutation. We discuss uncertainties in identifying parameter values in the model used to estimate and extrapolate DNA protein cross-link levels. The authors of the CIIT modeling endeavor characterized their human risk estimates as "conservative in the face of modeling uncertainties." The uncertainties discussed in this article indicate that such a claim is premature.     

Selecting IT applications in manufacturing: a KBS approach

The use of the right type of Information Technology (IT) applications or manufacturing systems is expected to usher in a competitive advantage. Selection of the right type of IT application is, however, a challenging task. When a company, with a given dominant process structure, emphasizes two or more competitive priorities, such as quality, product flexibility, etc., an unaided manager faces a complex decision problem in choosing from alternative IT applications available in the areas of product design through distribution. In this paper, we present a Knowledge Based System (KBS) that would assist managers with the identification of IT applications that are consistent with both the competitive priorities and the process structure. Validation of the system illustrates that its performance is consistent with the human experts, and it has the potential to facilitate effective and swift decision-making in the selection of appropriate IT applications that best match an organization's manufacturing strategy.     

Statistical Modelling of Brain Morphological Measures Within Family Pedigrees

Large, family-based imaging studies can provide a better understanding of the interactions of environmental and genetic influences on brain structure and function. The interpretation of imaging data from large family studies, however, has been hindered by the paucity of well-developed statistical tools for that permit the analysis of complex imaging data together with behavioral and clinical data. In this paper, we propose to use two methods for these analyses. First, a variance components model along with score statistics is used to test linear hypotheses of unknown parameters, such as the associations of brain measures (e.g., cortical and subcortical surfaces) with their potential genetic determinants. Second, we develop a test procedure based on a resampling method to assess simultaneously the statistical significance of linear hypotheses across the entire brain. The value of these methods lies in their computational simplicity and in their applicability to a wide range of imaging data. Simulation studies show that our test procedure can accurately control the family-wise error rate. We apply our methods to the detection of statistical significance of gender-by-age interactions and of the effects of genetic variation on the thickness of the cerebral cortex in a family study of major depressive disorder.     

An Integrated Approach for Modeling Sustainability Risks in Freight Transportation Systems

Integrating sustainability into freight transportation systems (FTSs) is a complex and challenging task due to the sheer number of inherent sustainability risks. Sustainability risks disrupt the economic, social and environmental objectives of freight operations and act as impediments in the development of sustainable freight transportation systems. The area of sustainability risk management is still unexplored and immature in the operational research domain. This study addresses these research gaps and contributes in a threefold manner. First, a total of 36 potential sustainability risks related to FTSs are identified and uniquely classified into seven categories using a rigourous approach. Second, the research proposes two prominent perspectives, namely, ontological and epistemological perspectives to understand risks and develops a novel framework for managing sustainability risks in FTSs. Third, a novel approach by integrating fuzzy evidential reasoning algorithm (FERA) with expected utility theory is developed to quantitatively model and profile sustainability risk for different risk preferences, namely, risk-averse, risk-neutral, and risk-taking scenarios. The proposed FERA is a flexible and robust approach, which transforms the experts’ inputs into belief structures and aggregates them using the evidence combination rule proposed in Dempster–Shafer theory to overcome the problem of imprecise results caused by average scoring in existing models. A sensitivity analysis is conducted to demonstrate the robustness of the proposed model. Unlike conventional perception, our study suggests that most of the high priority sustainability risks are behaviorally and socially induced rather than financially driven. The results provide significant managerial implications including a focus on skills development, and on social and behavioral dimensions while managing risks in FTSs.     

Extended Producer Responsibility for E‐Waste: Individual or Collective Producer Responsibility?

We investigate the implications of collective and individual producer responsibility (CPR and IPR, respectively) models of product take‐back laws for e‐waste on manufacturers’ design for product recovery (DfR) choices and profits, and on consumer surplus in the presence of product competition. We show that IPR offers superior DfR incentives as compared to CPR, and provides a level competitive ground. CPR may distort competition and allow free‐riding on DfR efforts to reduce product recovery costs. Thus, manufacturer preferences for IPR or CPR may differ because of the free‐riding implications under CPR, with even high‐end manufacturers having incentives to free‐ride under certain competitive conditions. The policy choice between IPR and CPR is not clear cut from an economic welfare perspective. This choice involves a comparison between the effects of superior recovery cost reduction through improved DfR under IPR and the operational cost‐efficiency under CPR.     

Facility Dispersion Problems Under Capacity and Cost Constraints

The MAX-MIN dispersion problem, which arises in the placement of undesirable facilities, involves selecting a specified number of sites among a set of potential sites so as to maximize the minimum distance between any pair of selected sites. We consider different versions of this dispersion problem where each potential site has an associated storage capacity and a storage cost. A typical problem in this context is to choose a subset of potential sites so that the total capacity of the chosen sites is at least a given value, the total storage cost is within the specified budget and the minimum distance between any pair of chosen sites is maximized. Since these constrained optimization problems are NP-hard in general, we consider whether there are efficient approximation algorithms for them with good performance guarantees. Our results include approximation algorithms for some versions, approximation schemes for some geometric versions and polynomial algorithms for special cases. We also present results that bring out the intrinsic difficulty of obtaining near-optimal solutions to some versions.     

Charitable conservatism, poverty radicalism and inequality aversion

In his Presidential Address to the European Economic Association, Tony Atkinson introduced the idea of a “charitable conservatism” position in public policy, which “exhibits a degree of concern for the poor, but this is the limit of the redistributional concern and there is indifference with respect to transfers above the poverty line.” This contrasts with the perspective of poverty indices, which give zero weight to those above the poverty line, which we call “poverty radicalism,” and with standard “inequality aversion” where the weights decline smoothly as we move up the income scale. The object of this paper is, first, to clarify the interrelationships between charitable conservatism, poverty radicalism and inequality aversion. We do this by showing how the patterns of welfare weights to which each of these gives rise are related to each other. Secondly, we are concerned to demonstrate the implications of these different views for optimal income taxation. In terms of levels and patterns of marginal tax rates, we show that charitable conservatism and poverty radicalism are on a continuum, and by choice of low or high inequality aversion one can approximate either outcome fairly well.