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Causal Relationships between Energy Consumption, Foreign Direct Investment and Economic Growth: Fresh Evidence from Dynamic Simultaneous-Equations Models

This paper examines the interrelationships between energy consumption, foreign direct investment and economic growth using dynamic panel data models in simultaneous-equations for a global panel consisting of 65 countries. The time component of our dataset is 1990-2011 inclusive. To make the panel data analysis more homogenous, we also investigate this interrelationship for a number of sub-panels which are constructed based on the income level of countries. In this way, we end up with three income panels; namely, high income, middle income, and low income panels. In the empirical part, we draw on growth theory and augment the classical growth model, which consists of capital stock, labor force and inflation, with foreign direct investment and energy. Generally, we shows mixed results about the interrelationship between energy consumption, FDI and economic growth.
- University of Sfax Tunisia
- University of Sousse Tunisia
- Ludwig-Maximilians-Universität München Germany
O4 - Economic Growth and Aggregate Productivity, Q4 - Energy, Multiple Variables, C3 - Multiple or Simultaneous Equation Models
O4 - Economic Growth and Aggregate Productivity, Q4 - Energy, Multiple Variables, C3 - Multiple or Simultaneous Equation Models
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).241 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
