城镇化、工业化对农业全要素生产率增长的影响研究——来自重庆37个县(区)面板数据的证据

文章基于2000—2016年重庆37个县（区）面板数据，使用序列DEA方法测算了重庆农业全要素生产率变动，在此基础上探讨了重庆农业全要素生产率增长的时间演变，并从理论和实证层面系统考察了城镇化和工业化对农业全要素生产率增长的影响。结果表明：首先，2000—2016年间重庆市农业全要素生产率呈上升趋势，2007年后重庆市农业全要素生产率增长更为明显，并且重庆市各地区呈现出明显的空间不平衡性。其次，动态面板模型的实证结果表明城镇化对农业全要素生产率增长具有显著正向影响，工业化对农业全要素生产率增长也具有显著正向影响。最后，研究还发现基础设施、教育水平以及金融发展程度对农业全要素生产率增长均具有显著正向影响，而产业结构对农业全要素生产率增长具有显著负向影响。     

Patience moderates the class cleavage in demand for redistribution

Previous studies on class voting have yielded mixed results linking income and demand for redistribution. Why do some poor people oppose redistribution, while some rich people support it? This article argues that an individual's level of patience, an important personal characteristic that influences how people calculate immediate and distinct outcomes, may moderate the effect of class on redistributive preference. In a one-shot game, redistribution between the rich and the poor is zero sum. When people extend their time horizons, however, the poor see the possibility of upward mobility, while the rich emphasize future losses, such as unemployment and economic instability. Consistent with the hypotheses, analyses of the 2014 Cooperative Congressional Election Study and a representative Taiwanese dataset from 2016 reveal a clear class cleavage in demand for redistribution among impatient poor and rich respondents, but the cleavage between their patient counterparts diminished. This pattern of convergence extends previous studies on upward mobility and risk perception theory.     

中国自主品牌形象提升的关键驱动因素及对策研究——基于矫形与塑形传播的视角

提升自主品牌形象是当前改善自主品牌消费环境、扩大自主品牌消费市场的重要现实命题,而厘清自主品牌负面刻板印象生成的根源及影响因素是探讨自主品牌形象提升的关键驱动因素与战略对策的理论基础。首先以8个论坛的受众跟帖为例,运用扎根理论进行探索性研究,发现负面企业形象、负面国货形象、负面政府形象、媒体渲染、国货意识淡漠和社会规范压力等6个主范畴是导致自主品牌负面刻板印象生成的主要影响因素;然后将范畴作为测量问项,将主范畴作为潜变量,进行问卷调查和实证分析,结果表明,6个主范畴均对自主品牌负面刻板印象存在显著的正向影响。在此基础上,根据自主品牌负面刻板印象生成的影响因素,提炼出自主品牌形象提升的5大关键驱动因素,即企业形象驱动因素、政府形象驱动因素、媒介框架驱动因素、消费者情感驱动因素和社会规范驱动因素,并对其影响效应及对策措施进行了探讨,建构了自主品牌形象提升的关键驱动因素影响效应模型。     

Values and happiness among Asian adolescents: a cross-national study

Despite policy efforts to increase adolescent happiness, their impact has been unsatisfactory. Their limited impact may be rooted from a discrepancy between values that adolescents pursued and those that the policies were based on. To provide policy implications, our study aims to identify prevailing values for South Korean, Japanese, and Chinese adolescents and to examine the relationship between the values and self-rated happiness (SRH). A cross-sectional study was conducted using survey data collected on approximately 2000 middle and high school students (7th to 12th school grade) from each country in 2008. Firstly, an explanatory factor analysis was conducted to identify salient adolescent values from each country. Subsequently, a multiple logistic regression analysis was conducted for each population group to examine the relationship between the identified values and adolescent SRH after controlling for sociodemographic characteristics. We found that benevolence and altruism were positively associated with adolescent SRH in all three population groups. Patriarchy was associated with SRH positively in Chinese yet inversely in Japanese. Success pursuit was inversely associated with SRH in Korean. Policy efforts based on values of communities or social harmony may benefit adolescents’ SRH in these three countries.     

要素禀赋结构与标准供给——基于中国制造业行业面板数据的实证研究

描述了标准供给的定义与测度，分析了要素禀赋结构对标准供给的作用机理，并对1993-2016年中国十六个制造业行业的面板数据进行了实证研究。研究结果表明：要素禀赋结构对标准供给有显着的正向影响，其中资本劳动比、研发资金投入、研发人力投入对标准供给的提升存在显著的正向影响，而且研发资金投入的影响最大，研发人力投入次之，资本劳动比的影响最小。这一结论对调整我国行业标准供给，提升我国标准市场竞争力具有重要意义。     

Mixtures of Gaussian copula factor analyzers for clustering high dimensional data

Mixtures of factor analyzers is a useful model-based clustering method which can avoid the curse of dimensionality in high-dimensional clustering. However, this approach is sensitive to both diverse non-normalities of marginal variables and outliers, which are commonly observed in multivariate experiments. We propose mixtures of Gaussian copula factor analyzers (MGCFA) for clustering high-dimensional clustering. This model has two advantages; (1) it allows different marginal distributions to facilitate fitting flexibility of the mixture model, (2) it can avoid the curse of dimensionality by embedding the factor-analytic structure in the component-correlation matrices of the mixture distribution.An EM algorithm is developed for the fitting of MGCFA. The proposed method is free of the curse of dimensionality and allows any parametric marginal distribution which fits best to the data. It is applied to both synthetic data and a microarray gene expression data for clustering and shows its better performance over several existing methods.     

Three Recommendations for Improving the Use of p-Values

ABSTRACT

Researchers commonly use p-values to answer the question: How strongly does the evidence favor the alternative hypothesis relative to the null hypothesis? p-Values themselves do not directly answer this question and are often misinterpreted in ways that lead to overstating the evidence against the null hypothesis. Even in the “post p?<?0.05 era,” however, it is quite possible that p-values will continue to be widely reported and used to assess the strength of evidence (if for no other reason than the widespread availability and use of statistical software that routinely produces p-values and thereby implicitly advocates for their use). If so, the potential for misinterpretation will persist. In this article, we recommend three practices that would help researchers more accurately interpret p-values. Each of the three recommended practices involves interpreting p-values in light of their corresponding “Bayes factor bound,” which is the largest odds in favor of the alternative hypothesis relative to the null hypothesis that is consistent with the observed data. The Bayes factor bound generally indicates that a given p-value provides weaker evidence against the null hypothesis than typically assumed. We therefore believe that our recommendations can guard against some of the most harmful p-value misinterpretations. In research communities that are deeply attached to reliance on “p?<?0.05,” our recommendations will serve as initial steps away from this attachment. We emphasize that our recommendations are intended merely as initial, temporary steps and that many further steps will need to be taken to reach the ultimate destination: a holistic interpretation of statistical evidence that fully conforms to the principles laid out in the ASA statement on statistical significance and p-values.     

地震后家庭幸福指数研究

2008年5月12日，举国震动的汶川大地震打破了平静，灾区人民的生命财产遭受巨大损失。灾后重建，八方支援，灾区人民找回失去的幸福。文章采用问卷调查地震灾区重建后家庭幸福感状况，从物质生活幸福感、环境幸福感和精神生活幸福感三因子反映地震后家庭幸福指数，构建因子模型，以期向爱心人士提供灾后重建的家庭幸福感的可靠信息。     

人工智能生成物的著作权保护路径探析——兼评人工智能生成物著作权第一案

人工智能技术的发展对传统著作权制度带来了显著的冲击。以人工智能生成物著作权第一案为例，通过审视作品构成要件判断标准发现，独创性始终贯彻以人类作者为中心的评价标准，同时，智力成果要件则是认定人工智能生成物构成著作权法意义上作品与否的重要标准。从人类是否实际参与作品的创作过程来看，多数人工智能生成物不构成作品，而保护人工智能生成物的价值追求与邻接权制度的基本理念相适应，且邻接权制度的扩张趋势也为把人工智能生成物纳入其保护范畴创造了机会。建议立法通过扩张邻接权制度的权利内容与客体类型以对人工智能生成物提供必要保护。而在当前阶段，反不正当竞争法可以为人工智能生成物相关利益主体提供一定限度的过渡保护。     

Noninformative priors for linear combinations of normal means with unequal variances

For normal populations with unequal variances, we develop matching priors and reference priors for a linear combination of the means. Here, we find three second-order matching priors: a highest posterior density (HPD) matching prior, a cumulative distribution function (CDF) matching prior, and a likelihood ratio (LR) matching prior. Furthermore, we show that the reference priors are all first-order matching priors, but that they do not satisfy the second-order matching criterion that establishes the symmetry and the unimodality of the posterior under the developed priors. The results of a simulation indicate that the second-order matching prior outperforms the reference priors in terms of matching the target coverage probabilities, in a frequentist sense. Finally, we compare the Bayesian credible intervals based on the developed priors with the confidence intervals derived from real data.