A non‐linear model of fecundability,postpartum amenorrhea,and sterility*

This paper proposes a convolution model of fecundability, controling for the effects of postpartum amenorrhea and unobserved heterogeneity in fecundability. Simulation analysis was used to assess the validity and reliability of estimates derived from the model. Analysis showed that the model captured the mean and standard deviation of age at the onset of sterility in simulated populations where sterility followed either a Gompertz, a gamma, or a lognormal distribution. The model performed well when sterility was specified by either a lognormal or a gamma distribution. The model also accurately estimated fecundability and postpartum amenorrhea. Next, the model was found to fit data from 17th and 18th century French Canadian birth histories. In this French Canadian sample the mean age at sterility was found to be 46.3 years using a gamma model. The decline in fecundability was almost linear after age 30. Thus, fecundability at age 40 had declined to about one‐third of that observed at age 30. Variability in individual fecundability was quite high. For example, women with fecundability one standard deviation above the mean had about 2.3 times as high fecundability as women one standard deviation below the mean.     

Patient-specific dose finding in phase I clinical trials

This article proposes an extension of the continual reassessment method to determine the maximum tolerated dose (MTD) in the presence of patients' heterogeneity in phase I clinical trials. To start with a simple case, we consider the covariate as a binary variable representing two groups of patients. A logistic regression model is used to establish the dose–response relationship and the design is based on the Bayesian framework. Simulation studies for six plausible dose–response scenarios show that the proposed design is likely to determine the MTD more accurately than the design that does not take covariate into consideration.     

Complexity in applying spatial analysis to describe heterogeneous air-trapping in thoracic imaging data

In this paper we consider a novel approach to analyzing medical images by applying a concept typically employed in geospatial studies. For certain diseases, such as asthma, there is a relevant distinction between the heterogeneity of constriction in airways for patients compared to healthy individuals. In order to describe such heterogeneities quantitatively, we utilize spatial correlation in the realm of lung computer tomography (CT). Specifically, we apply the approximate profile-likelihood estimator (APLE) to simulated lung air-trapping data selected based on potential interest to pulmonologists, and we explore reference values obtainable through this statistic. Results indicate that APLE values are independent of air-trapping values, and can provide useful insight into spatial patterns of these values within the lungs in situations where other common metrics, such as the coefficient of variation, reveal little. The APLE relies on a neighborhood weights matrix to define spatial relatedness of considered regions, and among a few weight structures explored, a working optimal choice seems to be one based on the inverse distance squared between regions of interest. The application yields a new method to help analyze the degree of heterogeneity in lung CT images, which can be generalized to other medical images as well.     

Investigating Heterogeneity in the Characterization of Risks Using Best Worst Scaling

This research proposes and implements a new approach to the elicitation and analysis of perceptions of risk. We use best worst scaling (BWS) to elicit the levels of control respondents believe they have over risks and the level of concern those risks prompt. The approach seeks perceptions of control and concern over a large risk set and the elicitation method is structured so as to reduce the cognitive burden typically associated with ranking over large sets. The BWS approach is designed to yield strong discrimination over items. Further, the approach permits derivation of individual‐level values, in this case of perceptions of control and worry, and analysis of how these vary over observable characteristics, through estimation of random parameter logit models. The approach is implemented for a set of 20 food and nonfood risks. The results show considerable heterogeneity in perceptions of control and worry, that the degree of heterogeneity varies across the risks, and that women systematically consider themselves to have less control over the risks than men.     

中国普惠金融对产业结构的非线性影响：基于PSTR的实证分析

作为中国经济政策改革的一项重要措施,普惠金融旨在有效、全方位地为社会所有阶层和群体提供金融服务,对包括产业结构在内的诸多经济指标的发展具有重要的影响。本文基于Sarma(2008)提出的普惠金融指标体系,测算了2005-2017年我国31个省市自治区的普惠金融指数,在此基础上,运用面板平滑转换(PSTR)模型探究普惠金融对产业结构合理化和高级化的非线性影响。研究表明,样本期内我国普惠金融发展水平呈现区域性差异,东部地区的普惠金融指数明显高于中西部地区。普惠金融的发展显著降低了产业结构偏离经济均衡状态的程度,对产业结构合理化和高级化有显著的促进作用,且这种作用表现出显著的非线性特征。当普惠金融指数大于0.2402时,普惠金融的发展对产业结构合理化的促进作用逐渐加强;当普惠金融指数大于0.5914时,普惠金融的发展对产业结构高级化的促进作用明显加强。同时,普惠金融对产业结构升级的影响存在区域异质性,普惠金融对东部、中部地区的产业结构升级有显著的促进作用,但对西部地区无显著影响。此外,本文采用系统GMM方法对模型进行稳健性检验,结论仍然成立。     

生产性服务业集聚与制造业生产率提升空间联系的机理再识别与效应新发现:从互动见融合

全球经济下行的持续与积累,为我国经济增长带来了不确定性和不稳定性,预示着进一步扩大内需和深化供给侧结构性改革,以实现更高水平的国民经济良性循环,是我国促进经济快速且高质量发展的重要之举。进一步挖掘多维联系空间内生产性服务业集聚与制造业生产率的综合互动关系,对借助两业互动逻辑来精准提升制造业生产率,进而助力国内经济循环实现具有重要意义。以长江经济带108个城市2007—2020年的面板数据为样本,通过纳入两业空间不匹配指数对“生产性服务业集聚作用制造业生产率提升”的研究框架进行完善,从复杂网络联系空间出发对两业互动关系进行重新审视。结果表明:第一,长江经济带内生产性服务业与制造业之间存在空间不匹配事实,且不匹配程度正逐渐加剧。第二,产业维度联系空间内,两业空间不匹配事实会制约两业有效互动。第三,将产业维度联系空间拓展为复杂网络联系空间时,两业互动表现出了显著正向的溢出效应,且外溢效应明显大于本地效应。第四,复杂网络联系空间内,两业互动的溢出效应具有明显的行业异质性,具体表现为仅高端生产性服务业集聚发挥了显著溢出效应。第五,对比发现,复杂网络联系空间还能通过弱化两业空间不匹配对两业互动的负向影响,间接促进两业互动有效产生。基于上述研究结论,认为以数字化赋能深化生产性服务业与制造业融合发展、以招才引智推动高端生产性服务业集聚规模扩张、以优化两业空间布局推进生产要素跨部门有序流动是助力制造业生产率提升的有效政策工具。     

Accounting for dynamic selection bias in educational transitions: The Cameron-Heckman latent class estimator and its generalizations

The sequential logit model of educational transitions has long been the dominant modeling framework for the study of inequality of educational opportunity ever since the seminal works of [Mare, 1980] and [Mare, 1981]. But conventional applications of the model are known to be biased by the ubiquitous presence of unobserved heterogeneity. Cameron and Heckman (1998) propose a logit model that allows for two or three latent classes if the selection bias is solely generated by a person-specific component of stable unobserved heterogeneity. To evaluate the latent class logit regression estimator, this study makes use of simulated data to eliminate the influences of other problems of transition modeling. The simulation is based on five independent pairs of large samples generated from standard distributional assumptions of transition modeling. The new estimator appears to be an effective way to adjust for dynamic selection bias when family background effects are transition-invariant and sample size is in the order of ten thousand or above. By contrast, the conventional sequential logit model produces results that are very different from the data generating models. This study also considers two alternative ways to improve statistical efficiency: (1) incorporate a crude indicator of stable unobserved heterogeneity; (2) pool the effect estimates across transitions, background variables, and alternative estimators to smooth out noise under the null hypothesis of transition invariance. In addition, this study examines the impact of indicator reliability and sample size on the performance of the latent class regression models and suggests practical guidelines.     

A neo-classical education transitions approach: A corrected tale for three cohorts

Analysts have regarded education transitions research as revealing late stage educational egalitarianism in the United States (e.g., Stolzenberg, 1994) and as sufficiently robust to guide policymakers (e.g., Hout, 2007). However, critics suggest parameter estimates are contaminated by selection bias (e.g., De Graaf & Ganzeboom, 1993), key parameter differences are unidentified (e.g., Cameron & Heckman, 1998), cross-transition comparisons are faulty, and the data are inappropriate. Useful modifications have been offered, (e.g., [Breen and Jonsson, 2000], [Hauser and Andrew, 2006] and [Lucas, 2001]), but analysts have yet to comprehensively address the challenges critics pose. In response, we propose a neo-classical education transitions approach that uses fuller sets of data and models that explicitly address the primary threats to proper inference. Using this approach we re-assess the educational attainment process for a baby boomer cohort, a Generation X cohort, and a Generation Y cohort. All cohorts fail to replicate the waning coefficients pattern. Methodologically, the study responds to criticisms in a way that offers methods for continued cross-national comparative research. Substantively, the study undermines confidence that standard education transitions research can provide policy guidance and the claim of late stage egalitarianism in the United States educational attainment process.     

Social Capital and Community Heterogeneity

Recent findings indicate that more pronounced community heterogeneity is associated with lower levels of social capital. These studies, however, concentrate on specific aspects in which people differ (such as income inequality or ethnic diversity). In the present paper, we introduce the number of parties in the local party system as a more encompassing measure of community heterogeneity. This builds on the argument that the number of relevant socio-economic cleavages in the population (i.e. heterogeneity) determines the level of party system fragmentation. Using data on 307 Flemish municipalities, we find that municipalities with a more heterogeneous population indeed have lower levels of social capital. Hence, our study endorses—and generalizes—previous results linking community heterogeneity to lower levels of social capital.

Does diversification promote risk reduction and profitability raise? Estimation of dynamic impacts using the pooled mean group model

This paper utilizes the pooled mean group model to explore the dynamic effects of revenue diversification on the operational risks and profitability of banks. The sample consisted of unbalanced panel data of 25 listed Taiwanese banks for the period from 1998 to 2013. The results reveal a divergence in the long- and short-run effects of revenue diversification on credit risk by the banks, and the benefits of diversification on two other operational risks and profitability are deferred. This paper provides dynamic evidence of diversification, which has been typically evaluated in previous studies, to release the aggregate effect and to explain the ambiguity in the results in the current literature.