Authors:Geraldo Da Silva E Souza, Eliseu Alves, and Ant onio Flávio Dias ávila
We define and model research production at Embrapa, the major Brazilian institution responsible for applied agricultural research.
The main theoretical framework used is Data Envelopment Analysis — DEA. The economic interpretation of these models is explored
to assess scale, congestion and cost efficiencies. Efficiency results are used to test for differences among types of research
units and for the scale of operation. A further analysis of agricultural research in Brazil is carried out with the inclusion
of three research centers in Argentina. Finally, DEA estimates are compared with the fit of a stochastic frontier.
of the population. Pelone et al. (2008) inferred that a healthcare system can be considered to be of ‘high level quality’ if it successfully achieves the expected results across six main dimensions: effectiveness, technicalefficiency, accessibility
This paper proposes a novel methodological framework for effectively measuring the production frontier performance (PFP) of
macro-scale (regional or national) R&D activities themselves associated with two improved models: a non-radial data envelopment
analysis (DEA) model and a nonradial Malmquist index. In particular, the framework can provide multidimensional information
to benchmark various R&D efficiency indexes (i.e., technical efficiency, pure technical efficiency and scale efficiency) as
well as the total factor R&D productivity change (determined by three components: “catch-up” of R&D efficiency, “frontier
shift” of R&D technology as well as “exploitation” of R&D scale economics effect) at a comparable production frontier. It
can be used to not only investigate the potential and sustainable capacity of innovation but also screen and finance R&D projects
at the regional or national level. We have applied the framework to a province-level panel dataset on R&D activities of 30
selected Chinese provinces.
Authors:Giovanni Abramo, Ciriaco D’Angelo, and Fabio Pugini
This paper presents a methodology for measuring the technical efficiency of research activities. It is based on the application
of data envelopment analysis to bibliometric data on the Italian university system. For that purpose, different input values
(research personnel by level and extra funding) and output values (quantity, quality and level of contribution to actual scientific
publications) are considered. Our study aims at overcoming some of the limitations connected to the methodologies that have
so far been proposed in the literature, in particular by surveying the scientific production of universities by authors’ name.
Based on panel data from 1995.1997, the paper focusses on the impact of ownership concentration on the performance of Russian non-financial privatised companies that constitute the group of .blue chips. of the country.s stock market. We consider three indicators of company performance . labour productivity, profitability, and Tobins.s q . and employ instrumental variables technique to correct regression results for endogeneity of ownership. We find that ownership concentration positively affects labour productivity, but has a negative impact on Tobins.s q. The relationship between ownership concentration and profitability follows a U-shaped pattern with the turning point at 56% concentration. These findings imply that ownership concentration results in higher technical efficiency of enterprises, but the benefits from productivity improvements do not accrue to all shareholders. This is consistent with the expropriation hypothesis that large owners use their power to extract private benefits of control.
This study examines the relative efficiency of the R&D process across a group of 22 developed and developing countries using
Data Envelopment Analysis (DEA). The R&D technical efficiency is examined using a model with patents granted to residents
as an output and gross domestic expenditure on R&D and the number of researchers as inputs. Under CRS (Constant Returns to
Scale), Japan, the Republic of Korea and China are found to be efficient, whereas under the VRS (Variable Returns to Scale)
framework, Japan, the Republic of Korea, China, India, Slovenia and Hungary are found to be efficient. The emergence of some
of the developing nations on the efficiency frontier indicates that these nations can also serve as benchmarks for their efficient
use of R&D resources. The inefficiency in the R&D resource usage highlighted by this study indicates the underlying potential
that can be tapped for the development and growth of nations.