Hurricane Katrina's Impact on African Americans' and European Americans' Endorsement of the Protestant Work Ethic

The Protestant work ethic (PWE), the belief that hard work leads to success, is a quintessentially American belief. The present research addresses a critical gap in psychological research on PWE: can a single, large-scale sociopolitical event (government's response to Hurricane Katrina) produce changes in PWE? We review evidence showing that the salience of Katrina led to a reduction in African Americans' (not European Americans') endorsement of PWE and that this result appears explained by African Americans' greater belief in the government's inadequate response to Katrina victims, who were predominately African American. The implications of differential endorsement of PWE for future expectations of societal treatment, motivation to pursue important goals, and willingness to endorse structural corrections of inequality are discussed.     

Framing in Legislation: The Case of Population Policy in the Philippines

This paper investigates the political framing of population in policy discourse through an analysis of legislative documents. Semantic network analysis was conducted and results were interpreted through discourse analysis of the typical arguments identified. Policy texts were classified into three sets: population management, reproductive health and family planning, and anti-abortion and anti-FP. While the “population management” frame focuses on social and economic consequences of population growth, the “reproductive health” frame defines the problem from a health perspective. Both policies propose aggressive FP programs but each frame uses distinct political rhetoric and semantic approach in its arguments. The “anti-abortion and anti-FP” frame identifies two problems: rise in incidence of abortion and existing policy that prohibit health professionals from refusing patients information on contraception. By invoking a moral argument and anchoring on rights, these policies challenge the problem and solutions identified by the first two frames.     

Feelings of Belonging: An Exploratory Analysis of the Sociopolitical Involvement of Black,Latina, and Asian/Pacific Islander Sexual Minority Women

This article compares and contrasts the sociopolitical involvement of Black, Latina, and Asian/Pacific Islander American sexual minority women within lesbian, gay, bisexual, and transgender (LGBT) communities of color. For the analysis, a sample of over 1,200 women from the Social Justice Sexuality project was analyzed. Findings indicate that, for all groups of women, feelings of connectedness to the LGBT community was the most significant predictor of sociopolitical involvement within LGBT communities of color.     

The Impact of Public Services Expenditure on the Spanish Income Distribution

This paper performs an assessment and imputation of the benefits produced to the individuals and households well-being by the public services provision. This analysis is very relevant in the current framework of economic crisis of the European economies. Although most of the papers in the literature compare countries, decentralization processes are included in this paper by accounting the effect of public expenditure on regional health care and education upon the levels of inequality and rates of poverty in each Spanish region. This case is relevant because of the high decentralization degree of the Spanish political structure and the persistent regional disparities. The results show a clear downward tendency in both phenomena following imputation, although education shows a greater impact. Furthermore, a decomposition, used in this context for the first time, of the effects shows the very relevant redistributive effect of this public expenditure.     

Testing for Serial Independence: Beyond the Portmanteau Approach

Portmanteau tests are typically used to test serial independence even if, by construction, they are generally powerful only in presence of pairwise dependence between lagged variables. In this article, we present a simple statistic defining a new serial independence test, which is able to detect more general forms of dependence. In particular, differently from the Portmanteau tests, the resulting test is powerful also under a dependent process characterized by pairwise independence. A diagram, based on p-values from the proposed test, is introduced to investigate serial dependence. Finally, the effectiveness of the proposal is evaluated in a simulation study and with an application on financial data. Both show that the new test, used in synergy with the existing ones, helps in the identification of the true data-generating process. Supplementary materials for this article are available online.     

Estimation and Inference for Linear Panel Data Models Under Misspecification When Both n and T are Large

This article considers fixed effects (FE) estimation for linear panel data models under possible model misspecification when both the number of individuals, n, and the number of time periods, T, are large. We first clarify the probability limit of the FE estimator and argue that this probability limit can be regarded as a pseudo-true parameter. We then establish the asymptotic distributional properties of the FE estimator around the pseudo-true parameter when n and T jointly go to infinity. Notably, we show that the FE estimator suffers from the incidental parameters bias of which the top order is O(T^{? 1}), and even after the incidental parameters bias is completely removed, the rate of convergence of the FE estimator depends on the degree of model misspecification and is either (nT)^{? 1/2} or n^{? 1/2}. Second, we establish asymptotically valid inference on the (pseudo-true) parameter. Specifically, we derive the asymptotic properties of the clustered covariance matrix (CCM) estimator and the cross-section bootstrap, and show that they are robust to model misspecification. This establishes a rigorous theoretical ground for the use of the CCM estimator and the cross-section bootstrap when model misspecification and the incidental parameters bias (in the coefficient estimate) are present. We conduct Monte Carlo simulations to evaluate the finite sample performance of the estimators and inference methods, together with a simple application to the unemployment dynamics in the U.S.     

On cumulative entropies

In analogy with the cumulative residual entropy recently proposed by Wang et al. [2003a. A new and robust information theoretic measure and its application to image alignment. In: Information Processing in Medical Imaging. Lecture Notes in Computer Science, vol. 2732, Springer, Heidelberg, pp. 388–400; 2003b. Cumulative residual entropy, a new measure of information and its application to image alignment. In: Proceedings on the Ninth IEEE International Conference on Computer Vision (ICCV’03), vol. 1, IEEE Computer Society Press, Silver Spring, MD, pp. 548–553], we introduce and study the cumulative entropy, which is a new measure of information alternative to the classical differential entropy. We show that the cumulative entropy of a random lifetime X can be expressed as the expectation of its mean inactivity time evaluated at X. Hence, our measure is particularly suitable to describe the information in problems related to ageing properties of reliability theory based on the past and on the inactivity times. Our results include various bounds to the cumulative entropy, its connection to the proportional reversed hazards model, and the study of its dynamic version that is shown to be increasing if the mean inactivity time is increasing. The empirical cumulative entropy is finally proposed to estimate the new information measure.     

Estimating the number of clusters

Hartigan (1975) defines the number q of clusters in a d ‐variate statistical population as the number of connected components of the set {f > c}, where f denotes the underlying density function on R^d and c is a given constant. Some usual cluster algorithms treat q as an input which must be given in advance. The authors propose a method for estimating this parameter which is based on the computation of the number of connected components of an estimate of {f > c}. This set estimator is constructed as a union of balls with centres at an appropriate subsample which is selected via a nonparametric density estimator of f. The asymptotic behaviour of the proposed method is analyzed. A simulation study and an example with real data are also included.     

A comparison of classification models to identify the Fragile X Syndrome

The main models of machine learning are briefly reviewed and considered for building a classifier to identify the Fragile X Syndrome (FXS). We have analyzed 172 patients potentially affected by FXS in Andalusia (Spain) and, by means of a DNA test, each member of the data set is known to belong to one of two classes: affected, not affected. The whole predictor set, formed by 40 variables, and a reduced set with only nine predictors significantly associated with the response are considered. Four alternative base classification models have been investigated: logistic regression, classification trees, multilayer perceptron and support vector machines. For both predictor sets, the best accuracy, considering both the mean and the standard deviation of the test error rate, is achieved by the support vector machines, confirming the increasing importance of this learning algorithm. Three ensemble methods - bagging, random forests and boosting - were also considered, amongst which the bagged versions of support vector machines stand out, especially when they are constructed with the reduced set of predictor variables. The analysis of the sensitivity, the specificity and the area under the ROC curve agrees with the main conclusions extracted from the accuracy results. All of these models can be fitted by free R programs.