运用柯布-道格拉斯生产函数模型分析研发活动的成效

本文采用柯布-道格拉斯生产函数模型,基于1995-2008年研发资金的投入和产出相关数据,对我国研发活动的成效进行了分析。总体而言,我国应该加大研发经费的支出。但如果我们更注重研发成果的质量,则研发资金的分配应该倾向于加大支持的强度;如果我们更注重研发成果的数量,则研发资金的分配应该倾向于支持更多的研发人员从事研发活动。同时,从长期来看,无论是以研发活动的质量还是数量为衡量标准,都应该增加对研发活动的投入,以实现研发活动的最佳规模。     

研发投入对企业绩效影响的门槛效应:以广东省为例

一般认为,研发投入会对企业绩效产生影响,但并不是简单的促进或抑制作用,而与企业研发投入强度有关。文章基于广东省2015—2020年A股上市企业的面板数据,运用门槛模型对R&D投入与企业绩效之间的非线性关系进行实证分析。结果表明,无论是基于全样本还是民营上市企业样本,R&D投入对企业绩效均存在显著的三重门槛效应,R&D投入对企业绩效的影响在不同门槛区间内存在方向、系数值上的明显差异。同时,企业规模、企业营运能力以及资本结构等控制变量均对企业绩效具有显著影响,股权集中度在全样本回归中不具有显著性。因此,政府整体上仍应鼓励企业加强R&D投入,具体方式包括直接补贴和间接税收优惠,对R&D投入位于低水平的民营企业而言,应侧重从降低企业研发成本的角度给予支持。     

市场结构、产权结构与R&D————中国制造业的实证分析

本文运用中国四位数制造产业数据实证检验了企业规模、市场力量和产权结构对R＆D投入的影响。研究发现,市场集中与R＆D投入之间不存在相关性,但企业规模对R＆D投入有显著的促进作用,从而为熊彼特假说提供了部分支持证据。研究还发现,不同性质的产权结构在R＆D投入上具有不同的激励效果,界定清晰的产权结构有利于增加R＆D投入。     

我国研发投入要素的区域差异及时空演化特征

文章采用泰尔指数法对我国研发投入要素的区域差异进行了研究,并采用探索性空间数据分析法、时空跃迁法对我国研发投入要素的时空演化特征进行了深入分析。结果表明:我国研发经费和研发人员的区域差异具有不同的发展态势,区域间差异是导致地区总差异的主要因素,区域内中、东部地区差异最大;我国研发投入要素在空间上呈现集聚特征,主要集中在东部沿海地区,并且这种集聚趋势在不断增强;我国研发投入在空间分布上呈现"集聚"与"分异"并存,且以低-低水平"集聚"为主要时空演化特征;我国研发投入的局部空间结构稳定性较强,具有高度的路径锁定特征。     

R&D productivity rides again?

A recent analysis of R&D productivity suggests that there are grounds for ‘cautious optimism’ that the industry ‘turned the corner’ in 2008 and is ‘on the comeback trail’. We believe that this analysis is flawed and most probably wrong. We present an alternative analysis of these same data to suggest that the industry is not yet ‘out of the woods’ and suggest that many of the systemic issues affecting pharmaceutical R&D productivity are still being resolved. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessing the contribution of R&;D to total factor productivity—a Bayesian approach to account for heterogeneity and heteroskedasticity

This paper proposes a hierarchical Bayes estimator for a panel data random coefficient model with heteroskedasticity to assess the contribution of R&D capital to total factor productivity. Based on Hall (1993) data for 323 US firms over 1976–1990, we find that there appear to have substantial unobserved heterogeneity and heteroskedasticity across firms and industries that support the use of our Bayes inference procedure. We find much higher returns to R&D capital and a more pronounced downswing for the 1981–1985 period, followed by a more pronounced upswing than those yielded by the conventional feasible generalized least squares estimators or other estimates. The estimated elasticities of R&D capital are 0.062 for 1976–1980, 0.036 for 1981–1985 and 0.081 for 1986–1990, while the estimated elasticities of ordinary capital are much more stable over these periods.     

A quantile regression perspective on external preference mapping

AStA Advances in Statistical Analysis - External preference mapping is widely used in marketing and R&D divisions to understand the consumer behaviour. The most common preference map is...     

25 years of Bayesian methods in the pharmaceutical industry: a personal, statistical bummel

Twenty-five years ago the use of Bayesian methods in Pharmaceutical R&D was non-existent. Today that is no longer true. In this paper I describe my own personal journey along the road of discovery of Bayesian methods to routine use in the pharmaceutical industry.     

Visualising interaction effects

In drug discovery, statisticians often work with biologists and chemists to uncover relationships between the various chemical properties and the biological effects of compounds. The aim is to "design" drugs that have exactly the desired pharmaceutical effects. Phil Woodward and Mark Whitlock of Pfizer Global R&D show how a graph can uncover deeply hidden relationships and guide chemists in their efforts to find the next blockbuster drug.     

Professional development of statisticians in the pharmaceutical sector: evolution over the past decade and into the future

The adoption of The International Conference on Harmonization Tripartite Guideline: Statistical Principles for Clinical Trials (ICH-E9) has provided a foundation for the application of statistical principles in clinical research and raised awareness of the value of a statistical contribution to the wider pharmaceutical R&D process. In addition, over the past decade globalization of the pharmaceutical R&D process and the measures taken to address reduced productivity and spiralling costs have impacted on the roles and career opportunities for statisticians working in the pharmaceutical sector. This has enhanced the need for continuing professional development to equip statisticians with the skills to fully contribute to creating innovative solutions. In the future, key areas of focus are the establishment of professional standards for statistical work and increasing the collaboration between statisticians working in industry, regulatory agencies and academia. In addition, the diversity of roles and potential career paths for statisticians embarking on a career in the pharmaceutical sector emphasizes the importance of mentoring and coaching. For the more experienced statisticians, there are unprecedented opportunities to lead and innovate.