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on Innovation |
| By: | Galina A. Kitova (National Research University Higher School of Economics) |
| Abstract: | In recent years R&D tax incentives have been characterized by increasing scale and spread on innovation activity. Approaches to integrated R&D tax incentives into "recipes" for long-term growth and competitiveness were developed and tested in many countries. For exam-ple, only 12 OECD members employed R&D tax incentives in 1995, but 27 members do so in 2013 (as well as Brazil, China, India, Russia and other countries). And their share of total government expenditure on R&D (direct and tax) by OECD member countries reached at least a third. These trends have accompanied the development and testing of approaches to estimate the costs of tax support for R&D (including tax expenditures) and its effects and to ensure that they are internationally compatible. As for Russia, there are no officially accepted estimates of the scale and effectiveness of R&D and innovation tax support yet, though efforts to calculate them have been under way since 2010. This paper includes the current state of empirical research of tax support for R&D and in-novation in the Russian Federation, as well as a survey of the demand for its tools from research institutes, universities performing R&D, and manufacturing enterprises, which was conducted in 2012-2013. The results obtained demonstrate the power of empirical analysis and optimization of R&D and innovation tax incentives in the Russian Federation, against the background of the field's best practices and current trends. |
| Keywords: | R&D, innovation, tax incentives, tax expenditures, demand for R&D and in-novation tax incentives. |
| JEL: | H21 H22 H25 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:28sti2014&r=ino |
| By: | Aizhan Samambayeva; Manuel Fernández Grela |
| Abstract: | Relationship between innovation performance and economic development is well-recognised all over the world (Mairesse, Lotti, & Mairesse, 2009, Grossman & Helpman, 1990, Hall, 2001). There are numerous of studies confirming that innovation development leads to economic growth, better productivity and increase in sustainable competitiveness. The assessment contributes to theoretical analysis on innovation and significantly broadens knowledge of innovation performance in developing countries. But the most considerable contribution is made to innovation system of Kazakhstan, which is very poor researched and published. Results of the study provide strengths and weaknesses of innovation performance in Kazakhstan and its position in the global landscape, which can be useful information for future policy making to improve social and economic development of the region. Besides The European Innovation Scoreboard, the most prominent innovation measurement indices are: 1. OECD Science, Technology, and Industry Scoreboard 2013 2. The World Bank’s Knowledge Assessment Methodology (KAM) 2012 3. The World Economic Forum’s Global Competitiveness Report 2013-2014 Taking into account data availability and level of innovation development, European Innovation Scoreboard is most appropriate tool to measure innovation performance in Kazakhstan. For example, The World Economic Forum’s Global Competitiveness Report is difficult to implement due to comprehensive nature of data required that is not publicly available. Moreover, some innovation indicators used in scoreboard are elaborated particularly for developed and sophisticated innovation system. Therefore they include variables that have interpretation value only in case of developed countries. European Innovation Scoreboard is not optimal choice to measure innovation performance in Kazakhstan. However, perfect fitting to Kazakhstan’s economy innovation measurement is unlikely will be comparable for other countries as well. Our goal was to find innovation measurement (scoreboard) that can satisfy our targets to elaborate innovation indicators that can be easily interpreted, providing exhaustive analysis of innovation situation in Kazakhstan; and to be able benchmark the country with similar economies (catching-up countries). According to Archibugi, Denni, & Filippetti (2009), European Innovation Scoreboard shoud be considered as measure of innovation performance rather than others. Because it takes into account new forms of innovation. Others mostly represent current endowment of country to develop its competitiveness and growth through technological innovations. The methodology includes 29 indicators, grouped over 7 different innovation dimensions and 3 major groups of dimensions. The group of “Enablers” captures the main drivers of innovation that are external to the firm and it is divided into two dimensions: “Human resources” and “Finance and support”, capturing in total 9 indicators. Some indicators are subject to national context. Therefore, more detailed information about issues regarding the calculation of the indicators is presented in the whole version of the paper. The results of study revealed relative competitiveness of the region in supply of human capital. However, the rapid pace of economic development requires highly skilled workforce, especially technical and engineering specialist, in order to support innovation performance in the country. Besides the importance of participation in long-life learning for on-going technical development and innovation, this number is extremely low in Kazakhstan. The main factors hampering innovation performance are insufficient R&D investments (public and private), poor infrastructure, weal linkages between main stakeholders of innovation process. This everything is a result of inefficient public policy on innovation and historical and cultural circumstances. The study has found that generally the innovation performance of the region is similar to that of the country. The indicator of the country and region are slightly different. Unsurprisingly, the indicators have shown that the region is placed at the bottom of catching-up countries. The current research was limited to evaluate factors related to qualitative characteristics of the indicator. Moreover, measuring regional innovation performance showed that more progress is needed on the availability and quality of innovation data at regional level. In general, research showed that innovation level of the country is very low even in comparison with catching-up countries. It can be explained by economic model where output is mainly driven by increased used of labour and capital. As a result a low demand for knowledge and weak linkages between key actors. “Knowledge producing and processing sectors and actors so far remain largely isolated from one another, and their activities are structurally mismatched. This may be explained by the lack of incentives in the business sector to innovate, as innovation is often not seen as necessary to maintain or develop competitive advantages. In addition, the commercial orientation of public R&D capacities (knowledge supply) remains limited. This vicious cycle seems to have locked the national innovation system into a suboptimal, low knowledge intensity equilibrium (Innovation performance review of Kazakhstan, 2012)” See above See above |
| Keywords: | Kazakhstan, Socio-economic development, Socio-economic development |
| Date: | 2014–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:ekd:006666:7897&r=ino |
| By: | Yih-Luan CHYI; Kuei-Yen LIAO |
| URL: | http://d.repec.org/n?u=RePEc:ekd:002596:259600039&r=ino |
| By: | Graziella Bonanno (Dipartimento di Economia, Statistica e Finanza, Università della Calabria) |
| Abstract: | Are Information and Communication Technology (ICT) and Research & Development (R&D) productive inputs or efficiency determinants? This is the topic of this paper which analyses a sample of 2691 Italian manufacturing firms over the period 2007-2009. The empirical setting is based on a production function estimated through the Stochastic Frontier (SF) approach. ICT and R&D are used once as inputs, once as efficiency determinants (Coelli et al., 1999). The results show that the rates of return of ICT and R&D investments are quite high (0.08 for ICT and 0.04 for R&D) when they enter into the model only as inputs. We also documented that ICT and R&D contribute positively to explain the efficiency scores. |
| Keywords: | ICT, R&D, Stochastic Frontier Approach, efficiency |
| JEL: | D22 D24 L69 O39 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:clb:wpaper:201412&r=ino |
| By: | Martin Fransman (University of Edinburgh) |
| Abstract: | This report focuses on the changing models of innovation adopted by some of the largest and most innovative global ICT companies in the world, including Apple, BT, Google, Microsoft, Skype, Telefonica and Vodafone. One of the main contributions of this report is to demonstrate that, in order to understand these innovation models, it is necessary at the same time to understand the dynamics of innovation at sector level. Beginning with an analysis of the innovation process in the ICT ecosystem, the author drills down into the company global innovation ecosystems that have been created by these global companies. In addition, he explores some of the implications that proliferating company global innovation ecosystems have for government policy. He concludes that whilst innovation is changing the world, changing global circumstances are in turn transforming the innovation model in companies, both large and small, around the world. |
| Keywords: | innovation, ICT |
| JEL: | L1 L22 L63 L86 |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc90726&r=ino |
| By: | Olof Hallonsten (University of Gothenburg) |
| Abstract: | This analytical country report is one of a series of annual ERAWATCH reports produced for EU Member States and Countries Associated to the Seventh Framework Programme for Research of the European Union (FP7). The main objective of the ERAWATCH Annual Country Reports is to characterise and assess the performance of national research systems and related policies in a structured manner that is comparable across countries. The Country Report 2012 builds on and updates the 2011 edition. The report identifies the structural challenges of the national research and innovation system and assesses the match between the national priorities and the structural challenges, highlighting the latest developments, their dynamics and impact in the overall national context. They further analyse and assess the ability of the policy mix in place to consistently and efficiently tackle these challenges. These reports were originally produced in December 2012, focusing on policy developments over the previous twelve months. The reports were produced by independent experts under direct contract with IPTS. The analytical framework and the structure of the reports have been developed by the Institute for Prospective Technological Studies of the Joint Research Centre (JRC-IPTS) and Directorate General for Research and Innovation with contributions from external experts. |
| Keywords: | European research and innovation policy, Innovation Union, ERAWATCH, European Research Area, Policy Mixes, Transnational and International Cooperation, NETWATCH, ERA Nets, Foresight, Joint programming of research, Researchers, Universities |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc90739&r=ino |
| By: | Leslie SHIELL; Nikita LYSSENKO |
| URL: | http://d.repec.org/n?u=RePEc:ekd:000215:21500085&r=ino |
| By: | Fatih Karanfil; Bilge Ozturk |
| URL: | http://d.repec.org/n?u=RePEc:ekd:000239:23900041&r=ino |
| By: | Mikel Buesa; Thomas Baumert; Joost Heijs; Monica Martinez Pellitero |
| URL: | http://d.repec.org/n?u=RePEc:ekd:002836:283600017&r=ino |
| By: | Bruno VAN POTTELSBERGHE; Nicolas VAN ZEEBROECK |
| URL: | http://d.repec.org/n?u=RePEc:ekd:000238:23800148&r=ino |
| By: | Mark Huberty; Amma Serwaah; Georg Zachmann |
| Abstract: | The inventors in PATSTAT are often duplicates: the same person or company may be split into multiple entries in PATSTAT, each associated to different patents. In this paper, we address this problem with an algorithm that efficiently de-duplicates the data. It needs minimal manual input and works well even on consumer-grade computers. Comparisons between entries are not limited to their names, and thus this algorithm is an improvement over earlier ones that required extensive manual work or overly cautious clean-up of the names. Source code on Github. Download data. |
| Date: | 2014–09 |
| URL: | http://d.repec.org/n?u=RePEc:bre:wpaper:850&r=ino |
| By: | Milan Viturka |
| URL: | http://d.repec.org/n?u=RePEc:ekd:002836:283600104&r=ino |