全球价值链背景下加工贸易企业升级路径分析

金融危机以来国际贸易保护主义色彩浓郁，中国内部粗放型经济增长方式存在一定的不可持续性和人口红利开始逐渐消失，中国加工贸易嵌入发达国家主导的全球价值链低附加值和低生产率问题引起了学者的注意。随着外部贸易环境、国内新常态要素禀赋结构的变化和深化供给侧结构性改革的需要，推动加工贸易企业升级已经成为中国经济结构调整的一个重要部分。通过分析中国加工贸易的起源、发展和现状，描述全球价值链背景下加工贸易的若干特征和“低端锁定”问题，引出了融资约束对企业经营决策、出口行为以及企业全球价值链上地位的影响，并围绕以生产过程、产品升级和功能升级为主的产业内升级和以劳动密集型向技术密集型转变为特征的产业间升级两个角度，提出了中国加工贸易企业在全球价值链上攀升的路径。研究认为，着重强调人力资本积累、本土企业技术外溢的吸收能力以及自主研发能力的培养，同时结合国际产能合作和“一带一路”倡议等现实背景，实现东西双向开放，塑造以我为主的包容性的区域价值链和国内价值链等，是中国加工贸易企业升级的路径和新机遇。     

Supply Constraint from Earthquakes in Japan in Input–Output Analysis

Disasters often cause exogenous flow damage (i.e., the [hypothetical] difference in economic scale with and without a disaster in a certain period) to production (“supply constraint”). However, input-output (IO) analysis (IOA) cannot usually consider it, because the Leontief quantity model (LQM) assumes that production is endogenous; the Ghosh quantity model (GQM) is considered implausible; and the Leontief price model (LPM) and the Ghosh price model (GPM) assume that quantity is fixed. This study proposes to consider a supply constraint in the LPM, introducing the price elasticity of demand. This study uses the loss of social surplus (SS) as a damage estimation because production (sales) is less informative as a damage index than profit (margin); that is, production can be any amount if without considering profit, and it does not tell exactly how much profit is lost for each supplier (upstream sector) and buyer (downstream sector). As a model application, this study examines Japan's largest five earthquakes from 1995 to 2017 and the Great East Japan Earthquake (GEJE) in March 2011. The worst earthquake at the peak tends to increase price by 10–20% and decrease SS by 20–30%, when compared with the initial month's prices/production. The worst damage tends to last eight months at most, accumulating 0.5-month-production damage (i.e., the sum of [hypothetical] differences in SS with and without an earthquake [for eight months] is 50% of the initial month production). Meanwhile, the GEJE in the five prefectures had cumulatively, a 25-month-production damage until the temporal recovery at the 37th month.     

Finite mixture of regression models for a stratified sample

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results.     

A robust posterior preference multi-response optimization approach in multistage processes

Nowadays, many manufacturing and service systems provide products and services to their customers in several consecutive stages of operations, in each of which one or more quality characteristics of interest are monitored. In these environments, the final quality in the last stage not only depends on the quality of the task performed in that stage but also is dependent on the quality of the products and services in intermediate stages as well as the design parameters in each stage. In this paper, a novel methodology based on the posterior preference approach is proposed to robustly optimize these multistage processes. In this methodology, a multi-response surface optimization problem is solved in order to find preferred solutions among different non dominated solutions (NDSs) according to decision maker's preference. In addition, as the intermediate response variables (quality characteristics) may act as covariates in the next stages, a robust multi-response estimation method is applied to extract the relationships between the outputs and inputs of each stage. NDSs are generated by the ?-constraint method. The robust preferred solutions are selected considering some newly defined conformance criteria. The applicability of the proposed approach is illustrated by a numerical example at the end.     

一种改进型专家模糊核聚类赋权方法研究

针对传统基于判断矩阵的专家模糊核聚类赋权方法，由于归一化条件的制约，导致离群点对聚类结果产生不良影响的问题，提出一种改进型模糊核聚类算法。该方法在聚类过程中，通过放宽归一化约束条件，削弱离群点对聚类结果的影响；并且针对传统基于信息熵与一致性系数线性耦合的聚类标准的局限性，提出一种基于偏差熵的赋权方法，依据专家对自身类别的聚类贡献度，确定专家权重，克服了传统方法的不足。算例表明，该方法可行、有效。     

自然灾害冲击对农村家庭非农就业选择的影响

利用国家重点研发计划重点专项2018年调研数据,构建了自然灾害冲击对农村家庭非农就业选择影响的理论框架,采用Probit模型验证了自然灾害冲击对农户家庭成员非农就业选择的影响。研究发现:自然灾害冲击对农户家庭成员非农就业选择有显著正向影响,这一正向影响会随着自然灾害强度的增加而不断提升;人力资本和社会资本的嵌入,会弱化自然灾害对农户家庭成员非农就业选择的影响,信贷约束却会强化这一影响。这意味着现阶段面对自然灾害冲击,农户家庭成员应积极尝试非农就业,不仅能够促进资本存量重构,还可以缓解家庭生计压力,增加农户家庭收入,避免农户家庭因灾致贫。     

网络时代刑法解释论的现实抉择、挑战及其应对

法律的滞后性与社会的多变性决定了刑法客观解释论是我国应对网络犯罪理论与实践做出的双重选择。但不可否认的是,当前网络时代刑法客观解释论存在明显的扩张化趋势,并不断冲击着罪刑法定原则的底线。“主观的客观解释论”名义上是以主观性来纠偏过度的客观性,但实则为主观解释论的翻版,并不可取。从恪守罪刑法定原则、克服刑法滞后性的角度来看,在坚持刑法客观解释论的前提下,为刑法客观解释的扩张化设定刑法用语的可能含义、刑法体系解释规则及罪名规范保护目的三大约束要素,从而将其限定在罪刑法定原则的框架下是更为妥当的选择。     

Complete consistency for the estimator of nonparametric regression models based on extended negatively dependent errors

In this paper, we provide some exponential inequalities for extended negatively dependent (END) random variables. By using these exponential inequalities and the truncated method, we investigate the complete consistency for the estimator of nonparametric regression model based on END errors. As an application, the complete consistency for the nearest neighbour estimator is obtained.     

民国上海度政始末

度量衡与天文立法、音律三者是我国中华古老文明的基础,是民众从事经济活动的重要凭借,是百物制度的标准,也是国家政权统一的象征与经济社会发展的保障. 民国时期,地处东部沿海的上海市,开始了全市度量衡标准的划一与度量衡新器的推行工作,最终完成了划一工作,促进了民国时期上海经济社会的发展.     

Small Area Estimation Using Estimated Population Level Auxiliary Data

Unit level linear mixed models are often used in small area estimation (SAE), and the empirical best linear unbiased prediction (EBLUP) is widely used for the estimation of small area means under such models. However, EBLUP requires population level auxiliary data, atleast area specific aggregated values. Sometimes population level auxiliary data is either not available or not consistent with the survey data. We describe a SAE method that uses estimated population auxiliary information. Empirical results show that proposed method for SAE produces an efficient set of small area estimates.