THE IMPORTANCE OF LEGAL FORM OF ORGANIZATION ON SMALL CORPORATION EXTERNAL FINANCING

This article quantitatively evaluates the impact of legal form of organization (LFO) choices, C versus S corporation, on small business external financing. A treatment effect model is formulated and estimated to examine the relationship between corporate types and chances of obtaining external financing. The estimation takes into account self‐selection bias associated with LFO choices. This article finds that LFO choices mainly affect small corporations' access to external equity capital, but have no significant impact on loan financing. Specifically, when a small corporation selects the C corporate legal form, the probability of obtaining new external equity is eight times higher compared to when it selects the S corporate legal form. Furthermore, the results suggest that better access to external equity investments, loosening business capital constraints, leads to better growth prospects for small C corporations. These empirical results have important macro‐economic implications on corporate financial and fiscal policies. (JEL G32, G38)     

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.     

Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials

Early phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti‐TB drugs. The EBA of anti‐TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long‐term efficacy of anti‐TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed‐effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed‐effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent “deletion” of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.     

Evidence-based practice in Chinese social work: overcoming language and developmental barriers

This scoping review followed Cochrane guidelines to comprehensively assess the use and understanding of Evidence-Based Practice (EBP) in Chinese social science literature. We conducted a research across three electronic databases and three major social science journals for the studies published between 2004 and 2016. Chinese social work literature demonstrates a precise understanding of EBP defined as a process, but it lacks comprehensive discussions about EBP that extend beyond basic principles and process definitions. We identified language barriers and developmental issues for social work in China that contribute to these incomplete discussions. We examined the importance of broadening and enriching the discussions around EBP, especially for Chinese educators, researchers, and clinicians involved with empirically supported treatments. We also consider the implications of our findings for the development of social work in China.     

机器学习视角下中国去产能产业的识别

随着中国环境压力的增加,政府提出了供给侧改革,去产能是改革的主要内容,但是由于产业特征的实时演变,需要对政策进行完善。文章运用模糊C均值算法和支持向量机算法分析现阶段需要进行去产能的产业,结果发现在现行去产能政策中大部分行业是需要去产能的,但煤炭开采和洗选业以及铁路、船舶、航空航天和其他运输设备制造业已不适合继续去产能,同时将化学原料和化学制品制造业加入去产能行列中。     

上海长期护理供需匹配机制探索

长期护理供需不匹配,包括因评估结论失准、服务机构匹配偏差、服务质量不高导致申请人合理需求无法满足,以及因难以纠正申请人过度需求引发的供给短缺.建立和完善长期护理保险制度,必须从现实与可能的角度,建立需求识别、流程管理、服务质量管理的长期护理供需匹配机制.     

Modernity and Globalization: The Local and Global Sources of Individualistic and Materialistic Values in Shanghai

Sociological research has arrived at an intellectual crossroad where it faces the challenge of understanding how the dynamics of globalization have joined the forces of modernization in inducing social change. In this paper, using a survey conducted in Pudong, Shanghai, in 2001, which had captured conditions of the area's rapid transformations in a globalizing city, first, we have uncovered two distinctive dimensions of individualistic vs. materialistic values via factor analysis. Second, we have shown strong bivariate relationships between these two dimensions of values and several demographic and socioeconomic variables, as well as personal global connections (PGCs). Third, we have found that PGCs have uneven significant effects on the emergence of individualistic and materialistic values net of the demographic and socioeconomic variables. Finally, we discuss how modernizing and globalizing conditions are conducive to the formation of individualistic and materialistic values in Shanghai, heralding this process in other rapidly modernizing and globalizing cities in China and elsewhere.     

Cities evolution tree and applications to predicting urban growth

China’s unprecedented urbanization and economic modernization have brought about significant growth and land use changes in cities and their surroundings. Traditional analyses of the relationship between urbanization and land use change have not fully incorporated important factors like city function or developmental stage. We define and apply a new visualization and analysis tool—an evolution tree—showing how an understanding of evolutionary pathways taken by growing cities contributes to visualization and forecasting of urban development and spatial expansion. We clustered 253 Chinese cities by type and developmental stage, arranging them in a “tree” structure, with types grouped as branches and leaf position along branches reflecting developmental stage. Urban growth rates are higher for business-oriented, manufacturing-oriented, and functionally comprehensive cities than for those oriented toward tourism. Extent of spatial growth is dominated by city type and developmental stage. Cities evolve along typical pathways, punctuated by occasional leap developments across functional types.