nep-knm New Economics Papers
on Knowledge Management and Knowledge Economy
Issue of 2014‒10‒17
five papers chosen by
Laura Ştefănescu
Centrul European de Studii Manageriale în Administrarea Afacerilor

  1. Innovation, Agglomeration, and Knowledge Spillovers: An Empirical Study of Finnish and Swedish Firms By Yih-Luan CHYI; Kuei-Yen LIAO
  2. Innovation performance as a factor of socio-economic development in Kazakhstan By Aizhan Samambayeva; Manuel Fernández Grela
  3. Models of Innovation in Global ICT Firms: The Emerging Global Innovation Ecosystems By Martin Fransman
  4. Impact Analysis of Regional Knowledge Subsidy: a CGE Approach Applied to Sardinia By Patrizio LECCA; Giorgio GARAU
  5. An Assignment Model of Knowledge Diffusion and Income Inequality By Luttmer, Erzo G. J.

  1. By: Yih-Luan CHYI; Kuei-Yen LIAO
  2. 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
  3. 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
  4. By: Patrizio LECCA; Giorgio GARAU
  5. By: Luttmer, Erzo G. J. (Federal Reserve Bank of Minneapolis)
    Abstract: Randomness in individual discovery tends to spread out productivities in a population, while learning from others keeps productivities together. In combination, these two mechanisms for knowledge accumulation give rise to long-term growth and persistent income inequality. This paper considers a world in which those with more useful knowledge can teach those with less useful knowledge, with competitive markets assigning students to teachers. In equilibrium, students who are able to learn quickly are assigned to teachers with the most productive knowledge. The long-run growth rate of this economy is governed by the rate at which the fastest learners can learn. The income distribution reflects learning ability and serendipity, both in individual discovery and in the assignment of students to teachers. Because of naturally arising indeterminacies in this assignment, payoff irrelevant characteristics can be predictors of individual income growth. Ability rents can be large when fast learners are scarce, when the process of individual discovery is not too noisy, and when overhead labor costs are low.
    Keywords: Knowledge diffusion; Growth; Inequality;
    JEL: L20 O30 O40
    Date: 2014–09–16

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