Does R&D spur productivity growth in Australia’s broadacre agriculture? A semi-parametric smooth coefficient approach

© 2018 Informa UK Limited, trading as Taylor & Francis Group This article analyses the role of research and development (R & D) in Australia’s broadacre farming by using the semi-parametric smooth coefficient model. While the conventional production function approach only captures the direct effects of R & D, this methodology captures both the direct impact of a change in R & D on output and the indirect impact through changes in efficiency of use of factor inputs in the production process. Moreover, technical inefficiency is introduced in the model allowing it as a function of R & D. Using a unique state-level dataset covering the period 1995–2007, this empirical study finds that once both the direct and indirect effects are taken into consideration, R & D investments significantly increase outputs. The results also show that there are substantial variations in the effects of R & D on output across the state-level average farm through technology parameters as well as through technical inefficiency. Such variations need to be taken into account when designing policies for investing public R & D in agriculture

Dynamic input demand functions and resource adjustment for US agriculture: state evidence

The paper presents an econometric model of dynamic agricultural input demand functions that include research based technical change and autoregressive disturbances and fits the model to annual data for a set of state aggregates pooled over 1950–1982. The methodological approach is one of developing a theoretical foundation for a dynamic input demand system and accepting state aggreage behavior as approximated by nonlinear adjustment costs and long-term profit maximization. Although other studies have largely ignored autocorrelation in dynamic input demand systems, the results show shorter adjustment lags with autocorrelation than without. Dynamic input demand own-price elasticities for the six input groups are inelastic, and the demand functions possess significant cross-price and research effects

Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses

The Cooperative Patent Classifications (CPC) jointly developed by the European and US Patent Offices provide a new basis for mapping and portfolio analysis. This update provides an occasion for rethinking the parameter choices. The new maps are significantly different from previous ones, although this may not always be obvious on visual inspection. Since these maps are statistical constructs based on index terms, their quality--as different from utility--can only be controlled discursively. We provide nested maps online and a routine for portfolio overlays and further statistical analysis. We add a new tool for "difference maps" which is illustrated by comparing the portfolios of patents granted to Novartis and MSD in 2016.Comment: Scientometrics 112(3) (2017) 1573-1591; http://link.springer.com/article/10.1007/s11192-017-2449-

Marginalization of end-use technologies in energy innovation for climate protection

Mitigating climate change requires directed innovation efforts to develop and deploy energy technologies. Innovation activities are directed towards the outcome of climate protection by public institutions, policies and resources that in turn shape market behaviour. We analyse diverse indicators of activity throughout the innovation system to assess these efforts. We find efficient end-use technologies contribute large potential emission reductions and provide higher social returns on investment than energy-supply technologies. Yet public institutions, policies and financial resources pervasively privilege energy-supply technologies. Directed innovation efforts are strikingly misaligned with the needs of an emissions-constrained world. Significantly greater effort is needed to develop the full potential of efficient end-use technologies

Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling:empirical study in EU regions

The main aim of this paper is to investigate the impact of patent applications, development level, employment level and degree of technological diversity on innovation efficiency. Innovation efficiency is derived by relating innovation inputs and innovation outputs. Expenditures in Research and Development and Human Capital stand for innovation inputs. Technological knowledge diffusion that comes from spatial and technological neighborhood stands for innovation output. We derive innovation efficiency using Data Envelopment Analysis for 192 European regions for a 12-year period (1995–2006). We also examine the impact of patents production, development and employment level and the level of technological diversity on innovation efficiency using Structural Equation Modeling. This paper contributes a method of innovation efficiency estimation in terms of regional knowledge spillovers and causal relationship of efficiency measurement criteria. The study reveals that the regions presenting high innovation activities through patents production have higher innovation efficiency. Additionally, our findings show that the regions characterized by high levels of employment achieve innovation sources exploitation efficiently. Moreover, we find that the level of regional development has both a direct and indirect effect on innovation efficiency. More accurately, transition and less developed regions in terms of per capita GDP present high levels of efficiency if they innovate in specific and limited technological fields. On the other hand, the more developed regions can achieve high innovation efficiency if they follow a more decentralized innovation policy