Public education provision,private schooling and income redistribution

Availability of free public education induces a transfer in kind among households with school age children. We provide evidence of the redistributive character of public education provision. We estimate structural quantile treatment effects of household income on the distribution of expected educational transfers in kind. Under the assumption that education quality is a normal good, better services (ancillary to the core education mission) supplied by private schools increase quality therein and reduce the incentives for wealthy households to enroll in public education. Because of these incentives, rich families benefit less from educational transfers in kind and the public education system is redistributive. Using household survey data from Italy, we find that an increase in net income reduces the value of the expected educational in kind transfers for compulsory education.     

Happily ever after? Intrahousehold bargaining and the distribution of utility within marriage

Using a rich longitudinal data set of married couples from the Korean Labor and Income Panel Study, this article seeks to uncover the intramarital allocation of experienced utility and its drivers. We find both substantial gains from marriage and effects of relative predicted earnings outside of marriage on relative gains from marriage. These findings are consistent with cooperative bargaining models and with models viewing individuals as having a demand for household production services by a spouse, with market forces influencing the price of such services. In addition, we find that men benefit more from marriage than women and that this gender gap is more pronounced for older couples. This is likely to be due to social norms and prescribed gender roles which are more prevalent within the older generation.     

No free lunch,buddy: past housing transfers and informal care later in life

Previous empirical literature on the relation between intergenerational transfer of assets and services has mostly focused on contemporary exchanges. By contrast, we provide novel evidence showing that parents who helped their adult children in the past are rewarded by higher chances of receiving informal care later in life. To this end we use Italian data containing precise retrospective information about the help with housing that couples received from their parents when they got married, such as a real estate donation or down payment. Our estimates show that this type of past help is positively associated with the current provision of informal care to the parents. This result is robust to controlling for a large set of individual and family characteristics and is only partially due to increased geographical proximity. We suggest that this finding can be explained by mixed self-interest motives, related to theories based on either bilateral exchange or the presence of a third generation (grandchildren), such as the demonstration effect model or the family constitution model.     

Sexual Beliefs and Sexual Functioning: The Mediating Role of Cognitive Distraction

Dysfunctional sexual beliefs are vulnerability factors for sexual dysfunction. This cross-sectional study aimed to test the mediating role of cognitive distraction on the relationship between dysfunctional sexual beliefs about sexual functioning shared by men and women and sexual function. The authors used a sample of 421 cisgender heterosexual participants involved in a monogamous relationship. The hypothesized mediation model was tested using a bootstrapped cross product of coefficients approach. Results showed a significant negative, indirect effect between dysfunctional sexual beliefs and women's sexual function through cognitive distraction. The discussion of this study highlights the importance of cognitive factors in sexual functioning.     

Hypothesis Tests for Neyman's Bias in Case–Control Studies

Survival bias is a long recognized problem in case–control studies, and many varieties of bias can come under this umbrella term. We focus on one of them, termed Neyman's bias or ‘prevalence–incidence bias’. It occurs in case–control studies when exposure affects both disease and disease-induced mortality, and we give a formula for the observed, biased odds ratio under such conditions. We compare our result with previous investigations into this phenomenon and consider models under which this bias may or may not be important. Finally, we propose three hypothesis tests to identify when Neyman's bias may be present in case–control studies. We apply these tests to three data sets, one of stroke mortality, another of brain tumors, and the last of atrial fibrillation, and find some evidence of Neyman's bias in the former two cases, but not the last case.     

COWORDS: a probabilistic model for multiple word clouds

Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm.     

Some new constructions of circular balanced repeated measurements designs

Repeated measurements designs are widely used in medicine, pharmacology, animal sciences, and psychology. In this article, some infinite series are developed to generate the balanced repeated measurements designs for p (periods) even. For p odd, construction procedures are also described. Catalogues of the proposed designs are also presented for p = 5, 7, 9, when v ≤ 100.     

A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach

The generalized linear model (GLM) is a class of regression models where the means of the response variables and the linear predictors are joined through a link function. Standard GLM assumes the link function is fixed, and one can form more flexible GLM by either estimating the flexible link function from a parametric family of link functions or estimating it nonparametically. In this paper, we propose a new algorithm that uses P-spline for nonparametrically estimating the link function which is guaranteed to be monotone. It is equivalent to fit the generalized single index model with monotonicity constraint. We also conduct extensive simulation studies to compare our nonparametric approach for estimating link function with various parametric approaches, including traditional logit, probit and robit link functions, and two recently developed link functions, the generalized extreme value link and the symmetric power logit link. The simulation study shows that the link function estimated nonparametrically by our proposed algorithm performs well under a wide range of different true link functions and outperforms parametric approaches when they are misspecified. A real data example is used to illustrate the results.     

Reweighted estimators for additive hazard model with censoring indicators missing at random

Survival data with missing censoring indicators are frequently encountered in biomedical studies. In this paper, we consider statistical inference for this type of data under the additive hazard model. Reweighting methods based on simple and augmented inverse probability are proposed. The asymptotic properties of the proposed estimators are established. Furthermore, we provide a numerical technique for checking adequacy of the fitted model with missing censoring indicators. Our simulation results show that the proposed estimators outperform the simple and augmented inverse probability weighted estimators without reweighting. The proposed methods are illustrated by analyzing a dataset from a breast cancer study.