The Influence of the Manufacturing Industry on Indonesia’s Economic Growth for the 2013-2022 Period Viewed from an Islamic Economic Perspective
Abstract
Indonesia is a country that has the manufacturing sector as a driver of the economy. Data from Indonesia's Gross National Income (GNI) has increased from 2013-2022. GNI is related to the manufacturing industry which has a role in economic growth. This research aims to see how much influence the manufacturing industry has on Indonesia's economic growth for the 2013-2022 period from an Islamic economic perspective. The variables used are the values of the chemical and pharmaceutical industry sector and the food and beverage industry sector. This research uses several economic growth theories and Kaldor theory which discusses the manufacturing sector. The analysis method used is Ordinary Least Square (OLS). The hypothesis test used is test t-test, f test, and coefficient of determination R2. The results of this research are that the chemical and pharmaceutical sectors and the food and beverage sector do not have a significant effect on economic growth as seen from the probability >0.05.
References
Ghozali, Imam. "Quantitative and Qualitative Research Design in Accounting, Business and Other Social Sciences." Semarang: Diponegoro University Publishing Agency (2013).
Rafika Azwina and others, 'Manufacturing Industry Strategy in Increasing the Acceleration of Economic Growth in Indonesia', Profit: Journal of Management, Business and Accounting, 2.1 (2023), 44–55
Solling Hamid, Rahmad. "Practical Guide to Econometrics Basic Concepts and Applications Using Eviews 10." (2020).
Solling Hamid, Rahmad. "Practical Guide to Econometrics Basic Concepts and Applications Using Eviews 10." (2020).
Teguh, Muhammad. "Ekonomi industri." Jakarta: PT Raja Grafindo Persada (2010): 16.
Todaro, Michael P., Stephen C. Smith, and B. D. Putra. "Economic Development 11th edition volume 1." Jakarta: Erlangga (2011).
Wasilaine, Trifena L., Mozart W. Talakua, and Yopi A. Lesnussa. "Ridge Regression Model to Overcome Multiple Linear Regression Models Containing Multicollinearity." BAREKENG: Journal of Mathematical and Applied Sciences 8, no. 1 (2014): 31-37.
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