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Statistical modeling to analyze factors affecting the middle-income trap in Indonesia using panel data regression

The Middle-Income Trap (MIT) refers to the situation in which a country's per capita income can reach middle-class levels but remains there for years, making it difficult to move up to a higher income level. Indonesia was declared trapped in MIT in 2014, and in 2019, it was added to the group o...

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Detalles Bibliográficos
Autores principales: Ratnasari, Vita, Audha, Salsabila Hidayatul, Dani, Andrea Tri Rian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522890/
https://www.ncbi.nlm.nih.gov/pubmed/37771499
http://dx.doi.org/10.1016/j.mex.2023.102379
Descripción
Sumario:The Middle-Income Trap (MIT) refers to the situation in which a country's per capita income can reach middle-class levels but remains there for years, making it difficult to move up to a higher income level. Indonesia was declared trapped in MIT in 2014, and in 2019, it was added to the group of upper-middle-income (UM) countries. However, due to COVID-19, it returned to the lower-middle income (UM) group in 2020. Previous studies inspired to see the existence of provinces in MIT Indonesia, so an MIT analysis was carried out for each province. The analysis aims to determine the characteristics of MIT in Indonesia and the factors that influence it and obtain a panel data regression model formed from MIT modeling. Analysis using panel data regression method. The panel data regression model was obtained based on panel data, namely data consisting of a combination of cross-section and time series data. The data used is GRDP per capita for each province in 2010 – 2020 published by the Central Bureau of Statistics (BPS). GRDP per capita data is used as an approach to calculating the MIT index. The results of the study, obtained three variables that have a significant effect on MIT, namely Life Expectancy, Gross Participation Rate, and Gross Fixed Capital Increase with an accuracy model of 97.65 %.