南充市城区已婚育龄妇女二孩生育意愿研究

2018年4—5月在四川省南充市城区采用便利抽样,以已婚且未育或已育一个小孩的育龄妇女为研究对象,采用自制匿名问卷,以面对面的形式收集资料,了解实施全面二孩政策后南充市育龄妇女的生育意愿,为南充市的人口研究提供参考依据。在213名调查对象中,56人(26.3%)有二孩生育意愿,140人(65.7%)无二孩生育意愿,17人(8.0%)不确定是否生育二孩。南充市被调查者的理想子女个数均值为1.46,意愿子女个数均值为1.27。不同文化程度(χ2=7.512)、配偶月收入(χ2=12.641)、理想子女数(χ2=66.401)、一孩性别(χ2=5.441)的育龄妇女间二孩生育意愿差异有统计学意义(P<0.05)。Logistic回归分析显示,第一个孩子性别为女孩(OR=2.641,P=0.021)、丈夫月收入越高(OR=1.761,P=0.028)、理想子女数多于1个(OR=29.040,P<0.001)的育龄妇女更有意愿生育二孩。南充市城区二孩生育意愿处于较低水平,经济收入、理想子女数、一孩性别会影响育龄妇女的二孩生育意愿。全面二孩政策对于促进育龄妇女生育二孩的效应有限,应当建立相关的配套政策,以促进全面二孩政策的实施。     

Internalized Other Interviewing in Relational Therapy: Three Discursive Approaches to Understanding its Use and Outcomes

For over 20 years, family therapist Karl Tomm has been engaging families and couples with a therapeutic intervention he calls Internalized Other Interviewing (IOI). The IOI (cf. Emmerson‐Whyte, 2010; Hurley, 2006) entails interviewing clients, from the personal experiences of partners and family members as an internalized other. The IOI is based on the idea that through dialogues over time, one can internalize a sense of one's conversational partner responsiveness in reliably anticipated ways. Anyone who has thought in a conversation with a family member or partner, “Oh there s/he goes again,” or anticipates next words before they leave the other's mouth, has a sense of what we are calling an internalized other. For Tomm, the internalized anticipations partners and family members may have offers entry points into new dialogues with therapeutic potential—particularly, when their actual dialogues get stuck in dispreferred patterns.     

On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.     

A Note on Selection and Gender Unemployment Gaps

Previous research has documented the impact of positive selection into employment on the ranking of countries by gender wage gaps. This note focuses on the impact of selection into labor force on cross-country differences in gender unemployment gaps. We construct the Manski bounds for the selection-free gender unemployment gaps in 26 EU countries and show that - without additional assumptions - the observed gender unemployment gaps carry little information about the selection-free gender differences in unemployment. Contrary to the common assumption of positive selection into labor force (similar to positive selection into employment documented in the gender wage gap research), we also point at an example of negative-selection bias. We show that labor force withdrawal of mothers on job-protected family leaves may lead to an overestimation of the selection-free gender unemployment gaps by as much as 1 p.p.     

Multilevel maximum likelihood estimation with application to covariance matrices

The asymptotic variance of the maximum likelihood estimate is proved to decrease when the maximization is restricted to a subspace that contains the true parameter value. Maximum likelihood estimation allows a systematic fitting of covariance models to the sample, which is important in data assimilation. The hierarchical maximum likelihood approach is applied to the spectral diagonal covariance model with different parameterizations of eigenvalue decay, and to the sparse inverse covariance model with specified parameter values on different sets of nonzero entries. It is shown computationally that using smaller sets of parameters can decrease the sampling noise in high dimension substantially.     

Housing and children: simultaneous decisions?—a cohort study of young adults’ housing and family formation decision

This study contributes to earlier research on homeownership and childbearing by taking into account the potential simultaneity between these two life events. A dataset comprising three different Swedish birth cohorts suggests that these are events that are indeed simultaneous. Different tests indicate that taking this simultaneity into account gives an overall statistically significant improvement of the model fit. However, this result is most obvious for those young adults who faced increasing problems on the housing market. The childbearing decision of these cohorts also seems to be more sensitive to changes in the user cost.