Social investment policies advocate for more and better jobs by supporting families' work-life balance and investing in human capital. But do they really help to boost employment prospects for women? Earlier literature suggests a positive relationship, but not enough attention has been paid to the type of employment, or to who the actual beneficiaries of these measures are. This article combines ISSP survey data with OECD and national data in a multilevel analysis to determine whether social investment policies benefit female employment, improve job prospects, and apply to all women irrespective of their educational level. We find that training and childcare policies are associated with higher employment levels, however, the claim that social investment increases chances for better job prospects finds little empirical support. These findings suggest that active labour market and childcare policies are associated with more women's employment, but they might still be following a push to ‘just work’. 相似文献
We propose two nonparametric Bayesian methods to cluster big data and apply them to cluster genes by patterns of gene–gene interaction. Both approaches define model-based clustering with nonparametric Bayesian priors and include an implementation that remains feasible for big data. The first method is based on a predictive recursion which requires a single cycle (or few cycles) of simple deterministic calculations for each observation under study. The second scheme is an exact method that divides the data into smaller subsamples and involves local partitions that can be determined in parallel. In a second step, the method requires only the sufficient statistics of each of these local clusters to derive global clusters. Under simulated and benchmark data sets the proposed methods compare favorably with other clustering algorithms, including k-means, DP-means, DBSCAN, SUGS, streaming variational Bayes and an EM algorithm. We apply the proposed approaches to cluster a large data set of gene–gene interactions extracted from the online search tool “Zodiac.”
A truncated sample consists of realizations of two variables L and T subject to the constraint L < T. One simple solution to dependently truncated data is to take L as a covariate of T in the Cox model. We aimed at studying the probability of selection, P(L < T), in this framework. We proposed the point estimator and derived its asymptotic distribution. Both truncated-only data and censored and truncated data were generated in the simulation study. The proposed point and variance estimators showed good performance in various simulated settings. The bone marrow transplant registry data were analyzed as the illustrative example. 相似文献