科技金融结合试点政策对地区经济增长影响研究——基于科技创新与产业结构合理化的视角

科技金融是"第一生产力"与"第一助推力"的有机结合,承担着带动地区经济高质量发展的重要使命.基于当前我国科技金融结合不断推进的背景,文章以2007—2016年我国共285个地级市的面板数据为例,运用双重差分法(DID)对科技部、中国人民银行等五部门推行的科技金融结合试点政策对试点地区经济增长的带动效应及机制进行检验.结果表明:第一,科技金融结合试点政策能够有效带动试点地区经济增长,这一结论具有稳健性;第二,科技金融结合试点政策对试点地区经济增长的带动作用存在显著的地域性差异,东部最强、中部次之、西部最弱;第三,进一步的机制检验表明科技金融结合试点政策能够通过提升地区科技创新水平和推动产业结构合理化的双重路径带动地区经济增长.为了进一步推动科技与金融相结合,提升地区经济发展水平,各地政府应高度重视科技金融结合试点政策对地区经济发展的正外部性,积极申报试点城市建设.同时,应结合各地市情社情制定差异化试点政策,大力提升科技创新与产业结构合理化水平,充分发挥其对地区经济增长的中介作用.     

治理的发轫与嬗变：中国历史视野下的考察

国内学术界一般以西方学术发展史作为背景，把治理作为一种社会互动来进行研究。中国历史视野下的治理，发轫于中国社会生活，其内涵最初主要是为了获取生存资料，即有收成才能够形成秩序也就是治理。从“乂”这个象形文字来看，乂就是治理，其原意为“收割”。不过，围绕生存资料的生产和分配逐渐产生了更加丰富的社会权力关系，而巩固和拓展这种权力关系则使得构筑更加严格的秩序成为必然。神秘感是构筑秩序的重要依据，而赋予仪式以神秘感的则是觋、巫等知识群体，他们将天、帝、王、人通过仪式联系在一起，建构起一套以礼为基础的秩序。但是，西周末年，觋、巫的神秘感被世俗的君权摧毁以后，“君臣秩序”以及由此构筑起来的社会秩序都被颠覆，从而导致“君臣之礼既坏”和“礼坏乐崩”的情形。“以智力相雄长”不仅表现在王室内部、诸侯之间、社会层面上，而且也表现在知识分子身上。从此以后，士绝大多数以追逐爵禄而骄天下为荣，以至于两千多年来一直存在“士贱而君肆”的情形。“君士互动”的治理模式，正是随着神秘主义的礼的崩溃而崩溃的。     

技术创新加剧了企业技能短缺吗?

随着劳动力价格上涨,制造业企业人口红利逐渐消失,以互联网、人工智能提升改造传统产业的同时,推动“技术红利”替代“人口红利”,成为制造业企业优化升级的必然选择。技术创新在促进经济发展的同时是否加剧了技能短缺?考察技术创新对技能短缺的影响作用,结果表明:技术创新对企业技能短缺产生了显著的正效应,研发支出增加了对技能劳动力的需求,并且伴随企业规模扩大、技能劳动力比例上升,技能短缺程度加剧。同时,技术创新对技能短缺在区域和产业上存在显著异质性。在超大城市中,技术创新对技能短缺的拉动作用更为显著。在中小城市,技术进步效应并不显著;而技术创新对制造业技能缺口的影响效应也显著强于服务业,进一步验证了技术创新水平的快速提升使现有技能水平无法与之相匹配,加剧了企业技能结构性短缺。     

局部平稳部分线性面板数据模型的统计推断

部分线性模型是一类非常重要的半参数回归模型,由于它既含有参数部分又含有非参数部分,与常规的线性模型相比具有更强的适应性和解释能力。文章研究带有局部平稳协变量的固定效应部分线性面板数据模型的统计推断。首先提出一个两阶段估计方法得到模型中未知参数和非参数函数的估计,并证明估计量的渐近性质,然后运用不变原理构造出非参数函数的一致置信带,最后通过数值模拟研究和实例分析验证了该方法的有效性。     

Transformer in navigation: Diverse government roles for open innovation in China's high-speed rail

Based on a longitudinal case study of China's high-speed rail embedded in the emerging economy context, we focus on what role the government plays and how that matters to open innovation (OI) for competitive advantage. By linking the OI literature with the national political institutions literature to motivate our research question, we propose a statist-based OI view to differentiate diverse government roles, investigating how government adopts roles in a combined way to push OI in stages. Our findings suggest that government is an important strategic decision-maker for OI. Specifically, the government plays various roles as commander, protector, cultivator, and intermediator, reflecting state activism derived from national political institutions, to construct institutional-level OI for domestic OI activities, and inbound and outbound OI across national borders. We find government can deliberately and strategically use its diverse roles in a combined way to push OI for competitive advantage through the industrial evolutionary process over time. Our study contributes to the OI literature and integrates the strategic management literature with the study of OI to provide new insights to explain the origins of competitive advantage from the state perspective.     

工业劳动力流动、资本创造与经济地理空间均衡

资本创造模型（CC模型）忽视了要素流动对产业空间分布的影响。而发展的新的资本创造模型则认为资本集聚的过程必然伴随着工业劳动力的流动过程。另外，是资本的实际收益而不是名义收益决定资本是否创造。研究结果表明，随着贸易自由度、工业品支出份额及资本贴现率的变大，替代弹性及资本折旧率的变小，将降低对称结构的稳定性，而提高中心-外围结构的稳定性；经济地理空间的产业均衡是集聚力和分散力相互作用的结果。当企业生产工业品的规模报酬递增程度足够显著，或者工业品支出份额很高时，市场拥挤效应将彻底消失，并转化成为促进产业集聚的动力；突破点与持续点的大小比较可以形成不同的关系，这意味着随着贸易自由度的变化，本文发展的资本创造模型可以体现出多样化的产业空间动态演化行为。     

Midwives’ views of caseload midwifery – comparing the caseload and non-caseload midwives’ opinions. A cross-sectional survey of Australian midwives.

BackgroundMidwife-led continuity of care has substantial benefits for women and infants and positive outcomes for midwives, yet access to these models remains limited. Caseload midwifery is associated with professional satisfaction and lower burnout, but also impacts on work-life boundaries. Few studies have explored caseload midwifery from the perspective of midwives working in caseload models compared to those in standard care models, understanding this is critical to sustainability and upscaling.AimTo compare views of caseload midwifery – those working in caseload models and those in standard care models in hospitals with and without caseload.MethodsA national cross-sectional survey of midwives working in Australian public hospitals providing birthing services.FindingsResponses were received from 542/3850 (14%) midwives from 111 hospitals – 20% worked in caseload, 39% worked in hospitals with caseload but did not work in the model, and 41% worked in hospitals without caseload. Regardless of exposure, midwives expressed support for caseload models, and for increased access to all women regardless of risk. Fifty percent of midwives not working in caseload expressed willingness to work in the model in the future. Flexibility, autonomy and building relationships were positive influencing factors, with on-call work the most common reason midwives did not want to work in caseload.ConclusionsThere was widespread support for and willingness to work in caseload. The findings suggest that the workforce could support increasing access to caseload models at existing and new caseload sites. Exposure to the model provides insight into understanding how the model works, which can positively or negatively influence midwives’ views.     

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.