INDIVIDUAL WELFARE WHEN CONSUMERS CAN SHOP FOR HEALTH INSURANCE

The statewide system of health insurance exchanges established by the Affordable Care Act (ACA) of 2010 will allow millions of U.S. citizens to change their health care policies more easily than they can switch automobile or homeowner insurance coverages, because deniability based on prior claim history is illegal. Focusing on this consumer endogeneity of health insurance policy choice, we examine the individual moral hazard welfare implications of a reduction in the price of medical care, which is a potential consequence of the ACA. We show that endogenous policy choice plays a key role in determining the welfare outcome. While moral hazard welfare improvement is not precluded, a distinctly possible outcome is that the consumer revises his/her choice of insurance policy so as to retain some portion of the reduction in expenditure risk caused by the medical care price decrease. In this event, moral hazard welfare loss is higher than it was before the price decrease, although the increased loss is tempered by the endogenous contract choice effect. This result resuscitates an old conventional wisdom. (JEL I11, I13, I30)     

Robotic-Cell Scheduling: Special Polynomially Solvable Cases of the Traveling Salesman Problem on Permuted Monge Matrices

In this paper, we introduce the 1 − K robotic-cell scheduling problem, whose solution can be reduced to solving a TSP on specially structured permuted Monge matrices, we call b-decomposable matrices. We also review a number of other scheduling problems which all reduce to solving TSP-s on permuted Monge matrices. We present the important insight that the TSP on b-decomposable matrices can be solved in polynomial time by a special adaptation of the well-known subtour-patching technique. We discuss efficient implementations of this algorithm on newly defined subclasses of permuted Monge matrices.     

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.     

Loss Averse Behavior

A behavioral condition of loss aversion is proposed and tested. Forty-nine students participated in experiments on binary choices among lotteries involving small scale real gains and losses. At the aggregate level, a significant proportion of the choices are in the direction predicted by loss aversion. Individuals can be classified as loss averse (28 participants), gain seeking (12), and unclassified (9). A comparison with risk behavior for binary choices on lotteries involving only gains shows that risk attitudes vary across these domains of lotteries. A gender effect is also observed: proportionally more women are loss averse. In contrast to the predictions of comonotonic independence, the size of common outcomes has systematic influence on choice behavior. JEL Classification: D81, C91     

Comparing Thistles and Roses: The Application of Governmental–Voluntary Sector Relations Theory to Scotland and England

Typologies of government–voluntary sector relations mostly categorize nation-level situations, and omit consideration of intra-national differences. They are also rarely subject to empirical testing and subsequent theoretical development. This paper reports on research to apply Kuhnle and Selle's (2002) relational typology, which characterizes the experience of “Britain,” to a study of Scottish and English voluntary organizations' relations with government. Contrary to Kuhnle and Selle, the paper demonstrates — through survey data — a divergence between Scotland and England, within the “British” context, and advances the case for further testing of such conceptual typologies. The methodological challenges in moving from a theoretical framework to a research design capable of field application, using survey methods, are illustrated and discussed. Further directions for theoretically led work are suggested.     

A novel feature selection scheme for high-dimensional data sets: four-Staged Feature Selection

Classification of high-dimensional data set is a big challenge for statistical learning and data mining algorithms. To effectively apply classification methods to high-dimensional data sets, feature selection is an indispensable pre-processing step of learning process. In this study, we consider the problem of constructing an effective feature selection and classification scheme for data set which has a small number of sample size with a large number of features. A novel feature selection approach, named four-Staged Feature Selection, has been proposed to overcome high-dimensional data classification problem by selecting informative features. The proposed method first selects candidate features with number of filtering methods which are based on different metrics, and then it applies semi-wrapper, union and voting stages, respectively, to obtain final feature subsets. Several statistical learning and data mining methods have been carried out to verify the efficiency of the selected features. In order to test the adequacy of the proposed method, 10 different microarray data sets are employed due to their high number of features and small sample size.     

Poverty Among The Elderly: An Application of a Collective Consumption Model in Brazil

Social Indicators Research - The growth of the elderly population and changes in household composition raise important questions regarding the level of well-being of the elderly in developing...     

Trajectories of Breadth of Participation in Organized Activity During Childhood

This study aimed to identify the trajectories of breadth of participation in organized activities during childhood and to examine the predictors of membership in these trajectories (child's individual and family characteristics measured in Kindergarten). A sample of 1038 children, recruited in Kindergarten, was assessed yearly between Kindergarten and grade 4. Semiparametric group‐based modeling brought out four trajectories: the no participation group (13.5 percent), the increasing group (26.4 percent), the decreasing group (14.1 percent), and the high group (46.1 percent). Prosociality predicted membership in the no participation group, as compared with the increasing group. Social withdrawal predicted membership in the no participation group, as compared with the high group. High family income and higher maternal education predicted membership in the increasing, decreasing, and high trajectory groups, as compared with the no participation group. Higher paternal education predicted membership in the high group, as compared with the increasing group. Overall, family variables had a greater impact than individual variables on the probability that the child would participate in a broader range of organized activities.     

Zero-inflated compound Poisson distributions in integer-valued GARCH models

In this paper we introduce a wide class of integer-valued stochastic processes that allows to take into consideration, simultaneously, relevant characteristics observed in count data namely zero inflation, overdispersion and conditional heteroscedasticity. This class includes, in particular, the compound Poisson, the zero-inflated Poisson and the zero-inflated negative binomial INGARCH models, recently proposed in literature. The main probabilistic analysis of this class of processes is here developed. Precisely, first- and second-order stationarity conditions are derived, the autocorrelation function is deduced and the strict stationarity is established in a large subclass. We also analyse in a particular model the existence of higher-order moments and deduce the explicit form for the first four cumulants, as well as its skewness and kurtosis.