Cross-Sectional Views of GDP Growth in the Light of Innovations

  title={Cross-Sectional Views of GDP Growth in the Light of Innovations},
  author={Tuhin K. Das},
  journal={Economic Growth eJournal},
This paper involves an analysis to investigate the limits to GDP growth in the present economic scenario. One objective of this study is to investigate how innovation alters these limits to economic growth. The present analysis is based on cross-section data. Results show the impact of the size of the economy on GDP growth and the relation between GDP growth and GDP across the world at particular point of time is convex. Growth increases with GDP, reaches its maximum and then begins to decline… Expand
Innovation and Investment Factors in the State Strategic Management of Social and Economic Development of the Country: Modeling and Forecasting
It was found out that the highest priority factors of a competitiveness according to the innovative component of Ukraine are the amount of scientific and technological works and the effective investments in basic capital and increase of sold innovative products volume will also cause GDP growth. Expand


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Rising educational attainment and research intensity in recent decades suggest that the U.S. economy is far from its steady state. This paper develops a model reconciling these facts with theExpand
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The term "middle-income trap" has entered common parlance in the development policy community, despite the lack of a precise definition. This paper discusses in more detail the definitional issuesExpand
China’s Economic Rise: History, Trends, Challenges, and Implications for the United States
This report provides background on China's economic rise; describes its current economic structure; identifies the challenges China faces to maintain economic growth; and discusses the challenges,Expand
Identifying and removing sources of imprecision in polynomial regression
Identification and removal of imprecision in polynomial regression, originating from random errors (noise) in the independent variable data is discussed. The truncation error-to-noise ratio (TNR) isExpand
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