The macroeconomic determinants of cross-country efficiency in wealth maximization: A joint analysis through the SFA and GMM models

Md Harun Or Rosid, Zhao Xuefeng, Sujan Chandra Paul and Md Reza Sultanuzzaman ()
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Md Harun Or Rosid: School of Management, Huazhong University of Science and Technology (HUST), Wuhan, PRC
Zhao Xuefeng: School of Management, Huazhong University of Science and Technology (HUST), Wuhan, PRC
Sujan Chandra Paul: Department of Accounting & Information Systems, University of Barishal, Bangladesh

International Journal of Research in Business and Social Science (2147-4478), 2020, vol. 9, issue 6, 91-107

Abstract: In the arena of economic analysis, the wealth of a nation is getting more and more attention to be a better indicator to evaluate the status of an economy. This paper had studied the aggregate household wealth of different nations of the world, 106 countries, for the year 2009-2018. During these years, only two countries of the world, China and the USA have managed to increase their wealth tremendously over the last decade while others experienced a slow pace in the growth of wealth. To satisfy the query of how efficient these countries were in maximizing their wealth, a stochastic frontier approach (SFA) has been used to predict the technical efficiency dependent variable and then generalized methods of moments (GMM) and other models have been used to find out the determinants of this efficiency. The study had come up with the result that land, labor, and capital mainly contributed to the output of wealth maximization while past year level of efficiency, export, and import played the main roles in determining the wealth maximizing efficiency status of a nation. It is found that there is a negative relationship between past-year efficiency with current years and the more a country imports, the less efficient the country is while the more it exports, the more efficient the country is in maximizing wealth. Key Words: Net Wealth, Stochastic Frontier Analysis, Technical Efficiency, Generalized Methods of Moments, Principal Component Analysis

Date: 2020
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