Causal Relationship between TFP and Economic Growth across Different Income Group Countries: Evidence from Combine Co-integration and VECM Granger Causality Approach
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The study tries to address and understand the causal relationship between growth of total factor productivity (TFPG) and growth of an economy in different income level group in the world. Quarterly data for the variables over the years 1990-2016 are used in Granger causality test by using Bayer and Hanck combine co-integration and VECM Granger Causality test approach. To verify whether the data is stationary or not Augmented Dicky-Fuller test was conducted. Standard statistical methods (P-value and t-statistics) can be accustomed to test the significance of variables and to testify the hypothesis. For the growth of an economy and TFP the researcher uses growth rate of the variables. The study was conducted by Eviews and Excel data analysis software. The Bayer and Hanck combine co-integration test indicated that there is an association between the variables for HIC, UMIC, and LMIC and there is no association between the variables for LIC. The VECM Granger causality result showed that the existence of bidirectional causal relationship for UMIC, unidirectional causal relationship running from GDP to TFP for HIC and TFP to GDP for LMIC. The causality relationship between two variables depends on a country’s income level. Therefore, it is important to understand the TFP growth of an economy directional relationship while developing and implementing economic development policies and this research put some recommendations for each income level countries on in which direction should the government focused in policy making process.
Abstract
The study tries to address and understand the causal relationship between growth of total factor productivity (TFPG) and growth of an economy in different income level group in the world. Quarterly data for the variables over the years 1990-2016 are used in Granger causality test by using Bayer and Hanck combine co-integration and VECM Granger Causality test approach. To verify whether the data is stationary or not Augmented Dicky-Fuller test was conducted. Standard statistical methods (P-value and t-statistics) can be accustomed to test the significance of variables and to testify the hypothesis. For the growth of an economy and TFP the researcher uses growth rate of the variables. The study was conducted by Eviews and Excel data analysis software. The Bayer and Hanck combine co-integration test indicated that there is an association between the variables for HIC, UMIC, and LMIC and there is no association between the variables for LIC. The VECM Granger causality result showed that the existence of bidirectional causal relationship for UMIC, unidirectional causal relationship running from GDP to TFP for HIC and TFP to GDP for LMIC. The causality relationship between two variables depends on a country’s income level. Therefore, it is important to understand the TFP growth of an economy directional relationship while developing and implementing economic development policies and this research put some recommendations for each income level countries on in which direction should the government focused in policy making process.
참고문헌 (21)
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