Business Environment and Economic Growth: Is There a Link?
What role does the business play in economic growth? What circumstances are necessary for the stable business development? Recent literature focuses on the factors of business environment promoting or constraining firm growth. Using the country aggregate values of the firm-level World Bank Enterprise Survey (WBES) data on the subjective estimates of the business obstacles 128 countries are classified into six clusters. Due to the fact that firms report many obstacles to growth the contributions of 13 obstacles to business environment are recalculated for understanding the major business constraints. Lastly, cross tables analysis finds that there is a correlation between the prevalence of the business obstacles and national income growth, export growth, high-technology export. The results have important implications for the priority of reforms. Corruption, electricity and tax rates are the main business constraints in the world. Moreover, access to finance and competition in the informal sector of economy are also the major obstacles for business in the part of the countries.
We expect economic growth to remain strong in Poland and Latvia in 2016. Despite this robust growth, the new Polish government is likely to soften monetary and fiscal policies to further stimulate the economy, in our view. In 2015, the Latvian economy demonstrated strong resilience to external shocks.
These are the proceedings of the International Workshop on Petri Nets and Software Engineering (PNSE’13) and the International Workshop on Modeling and Business Environments (ModBE’13) in Milano, Italy, June 24–25, 2013. These are co-located events of Petri Nets 2013, the 34th international conference on Applications and Theory of Petri Nets and Concurrency.
PNSE'13 presents the use of Petri Nets (P/T-Nets, Coloured Petri Nets and extensions) in the formal process of software engineering, covering modelling, validation, and veriﬁcation, as well as their application and tools supporting the disciplines mentioned above.
ModBE’13 provides a forum for researchers from interested communities to investigate, experience, compare, contrast and discuss solutions for modeling in business environments with Petri nets and other modeling techniques.
Peculiarities of making of managerial decisions in modern business systems, predetermined by observation of the basic principles, are shown: constant monitoring of external environment for determining new possibilities and actual problems and determining the need for managerial decisions; founding on materials of marketing research, conduct of marketing communications for informing and supporting loyalty of interested parties in the process of implementation of decisions; and striving for increasing or at least preserving the uniqueness and effectiveness of business system during decision making (criterion of optimality of decisions).
The article explains and interprets the results of Russian business environment survey conducted by the new sociological tool – Business Environment Development Index.
The balance of the world economy is shifting away from the established economies of Europe, Japan, and the USA, towards the emerging economies of Asia, especially India and China. With contributions from some of the world's leading growth theorists, this book analyses the long-term process of structural change and productivity growth across the world from a unique comparative perspective. Ongoing research from the World KLEMS Initiative is used to comparatively study new sources of growth - including the role of investment in intangible assets, human capital, technology catch-up, and trade in global value chains. This book provides comparisons of industries and economies that are key to analysing the impacts of international trade and investment. This makes it an ideal read for academics and students interested in understanding current patterns of economic growth. It will also be of value to professionals with an interest in the drivers of economic growth and crisis.
Technological modernization remains to one of the main problems of the Russian economy. Appreciably it is reduced to a problem of the “subject of modernization” – presence of institutional agents in the Russian business environment, which would have motivation and abilities to invest in development of national innovation system. There is a significant amount of economic spheres in which private interests of business coincide with strategic interests of an economy only partially or don’t coincide absolutely. Technological modernization is one of such problem spheres, which requires additional tools to stimulate the investors. So, there is a key question, concerning possibilities, directions and tools of investment activity regulation in direction of strategic interests of national economy.
The fundamental idea underpinning spatial econometric models of economic growth is as follows: regional growth is determined not only by social, economic, geographic traits of a region but also by spillovers from other regions, most importantly adjacent ones. If one region starts booming, it can left neighbors unaffected (neutral mechanism), spur their growth (cooperation mechanism) or slow their growth by pulling resources over (competition mechanism). What mechanism and to which extent occurs in practice matters for designing balanced economic policy and evaluating efficiency of regional policy investment. Classic spatial econometric models make strong although simplifying assumption that the same mechanism matters for all regions in the same manner, and there is no variation in spillovers intensity across regions. This assumption seems plausible for relatively small and homogenous regions of European countries, but it looks excessively strong for large and diverse Russian regions. In this paper we attempt to relax this assumption and propose a new model, fitting better in Russian conditions and bringing only slight sophistication from the estimation point of view. We introduce sensitivity parameter governing regional exposure to externalities. We assume this parameter to be a linear function of region-level observables, like area, population density or urbanization rate. These hypotheses have been confirmed at least partially. We found that dense and urbanized regions were more sensitive to spillovers. In other words, a region surrounded by the fast-growing areas, will grow the more intense, the more its population density and the higher the level of urbanization.