Innovations as a factor of regional manufacturing’s development in Russia
Background. The search the reasons for a manufacturing development is very important for Russia. Nowadays the extractive enterprises are the most important part of Russians economy. For example, in 2013 year the part of mineral products in common Russian export was 76,1% (data of Russians Federal State Statistics Service). But to be competitive in the global economy the country needs in production of more complex products. Methods. The method of this research is a production function which was constructed for the Russian manufacturing. The most known production functions which are used for used in the regional economics are the Cobb-Douglas function and the CES function. In spite of long life of the Cobb-Douglas production function and availability of large amount of new functions, we can say, that this instrument is still applicable today. Many scientists use this production function in their research. So in our article we used the Cobb-Douglas function and the factors were labour, capital, transport infrastructure and innovations. The coefficients for the selected function were obtained using the correlation-regression analysis approach. Results. The result is that transport infrastructure doesn’t matter for Russian manufacturing. The coefficient of innovation in logarithmic production function is 0,15 and it’s significant on the 1% level. It’s means that innovations play an important role in manufacturing production increasing. Conclusions. The first conclusion is regarding the possibility of using the Cobb-Douglas function to research the Russian industry and regarding factors, which influence the development production. The main factor except labor and capital is innovations that means that to increase manufacturing production in Russia the government should improve innovation infrastructure.
There co-exist several problems when measuring the level of competitiveness. The major one is that it represents an integral indicator of the enterprise performance. The indicator has something in common with the notion of the utility function used in economics. The latter one stamps a numerical equivalent of the utility associated by an individual from the consumption (or possession) of certain goods. Nevertheless, it stays an implicit (non-observable) function.
I report the results of observations of management practices in 20 Russian manufacturing subsidiaries of Western multinational corporations (MNCs). I argue that to counterbalance the higher country-specific risks associated with investing in Russia, MNCs impose on their Russian subsidiaries high demands for superior performance in terms of both technical and economic efficiency. My observations confirm that in most cases such demands are successfully met by the implementation of highly effective practices. Thus, I challenge several beliefs about industrial management in Russia, including the myths that Russian firms are hostile towards knowledge sharing and are wary of talent.
The chapter focuses on the alternative measures of the relative competitiveness of Russian manufacturing enterprises and on assessing the changes in the distribution of manufacturing firms by those measures between 2005 and 2009.
This study presents a snapshot of investment projects in manufacturing that were implemented by foreign investors in Russia during 2017–2018. We assemble a unique database of all new plants opened by foreign companies in Russia during 2012–2018 to clarify the distribution of investment projects implemented during 2017–2018 across industries and territories with different tax regimes. We also identify the most interesting individual investment projects, interrelated investment projects, and elements of collective actions. In general, foreign investors in manufacturing demonstrate high ingenuity in discovering and exploiting the remaining emerging growing market segments and promising niches in consumer and professional markets and express significant persistence in realizing investment projects. We also demonstrate the methods applied to decrease the uncertainty of the project costs by establishing partnerships with local foreign- and domestically owned companies and the attempts to correct the government’s decisions and regulatory measures that are uncomfortable for foreign investors.
Quality of life is a key attribute of a country. Estimating the social impact of economic development we face a problem of measuring. Because of the leading role of technologies and innovations in economic progress of modern society it is reasonable to use a corresponding global index as a measure of technological development of the country or region. Quality of life is a many-sided concept and needs particular approach for its definition in the context of the research. Two main approaches are considered. The former focuses on the population well-being and provides the objective measures of life quality; the latter is concentrated on self-assessment by the people of their quality of life. Both are significant and their using makes the analysis more comprehensive. As an indicator of innovative and technological development the Global Innovative Index (GII) can be used. There are many kinds of indicators of well-being such as Index of Economic Well-Being [Osberg, Sharp, 1998], Index of Social Progress [Estes R.J., 1998], and others. One of the most widely used indicators is the Human Development Index (HDI). A lot of interesting indicators are proposed, but many of them are focused on the particular aspect such as Health-Related Quality of Life [Andersen, 1999], Social Weather Station [Manghas, Guerro, 1998]. Other more universal indicators are often unavailable for most countries or regions of interest. So index based on self-assessment quality of life has been constructed as a first principal component of the partial indexes provided by Gallup. Various types of linear and non-linear regression models for describing the social impact of the innovative development are considered. Additional information sources have been used for explaining of the particular aspects of the problem, and the auxiliary models have been created and analyzed. As a model of the Global Innovative Index influence of the Human Development Index the logistic curve has been proposed. The explanatory power of this model is not the only reason for such a choice. The model may be considered as relevant because of the nature of the well-being indicator used. For self-assessment based quality of life index we can find another situation. Some countries with relatively high HDI show low value of the self-assessment index. It is related with the dynamic of innovative development which has an influence on the social environment of the society. In turn the social environment has a great influence on innovative and technological development of the country. Created models allow estimating social impact of innovative development indexes. The dual role of the social climate may be discussed in the context of the research. From one hand the social climate of the society is formed under influence of the technological environment. From the other hand the latter may be considered as a factor of progress in technology and innovations. Further research may be concentrated on the more comprehensive model of such interaction which evidently will be more complicated one
Already enough long time the Russian economy operates in rather stable macroeconomic conditions provided by existence of oil and gas "pillow". Even world financial crisis of 2008- 2009 didn't lead to long and essential falling of the prices for oil that allowed Russia to continue the policy on stabilizing the main macroeconomic indicators. At the same time, proclaimed by Russian Government, course on modernization of economy (actually proclaimed at the beginning of the 2000s, but not just at recent 4 years) should cause definite changes in national innovative system during this period and in these favorable financial conditions. In this article we will consider how the technological profile of the Russian innovative system has been changed and what forms of innovative behavior of key economic agents were established.
To help countries achieve their full industrialization potential and fulfil the sustainable development goals (SDGs) and thereby improve their general welfare, UNIDO is promoting the concept of comprehensive and sustainable industrial development (ISID), which was established in the Lima Declaration adopted by UNIDO Member States on 2 December 2013. The UN General Assembly recognizes the significance of ISID as an important strategic direction for fostering global development in the future. ISID is a key instrument for achieving sustainable economic growth, the creation of quality jobs, the building of equal societies, the protection of the environment, and the active shaping of comprehensive sustainable globalization. The promotion of ISID as the key driver for successful integration of economic, social and environmental factors necessary to achieve full implementation of sustainable development by creating and improving countries’ industrial potential is the main priority of UNIDO’s current activities. To successfully implement ISID, UNIDO acts as a global forum for industrial development and the establishment of relevant international standards, including standards on industrial statistics [UNIDO, 2014; 2013a]. Accordingly, UNIDO has been implementing the regional project “Improvement of industrial statistics and development of statistical indicators for the analysis of industrial development in the CIS countries” since 2013. The project’s main objective is to provide methodological assistance to the Commonwealth of Independent States’ (CIS) national statistical services in implementing international standards on industrial statistics in the statistical practice and presentation of modern, internationally comparable information for a qualitative and reliable reflection of industrial development processes. This report presents the results of the statistical analysis describing the availability, quality and measurement capabilities of official statistics in the CIS countries accumulated over the period 2005-2014.