Nabila Tri Amanda
Universitas Sebelas Maret

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ANALYSIS OF UNEMPLOYED YOUTH IN INDONESIA BY PANEL DATA REGRESSION WITH MODERATING VARIABLE Sri Subanti; Nabila Tri Amanda
Journal of the Indonesian Mathematical Society Vol. 30 No. 3 (2024): NOVEMBER
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.30.3.1803.338-351

Abstract

Indonesia is entering the era of demographic bonus with the productive age dominating the population. Productive age is the main focus of the government in maximizing the demographic dividend, but Indonesia has the highest percentage of Not in employment, education or training (NEET) in Asia. NEET are people on 15 – 24 years old who do other activities outside of school, work or training. This study aims to analyze NEET in Indonesia using panel data with moderate regression analysis. The analysis of multiple linear regression is focused on the relationship between the independent and dependent variables without taking into other possible outcomes. By inserting a moderating variable, this study explores the relationship between the independent and dependent variables differently and aims to strengthen or weaken it. Under certain conditions, the relationship between the independent and dependent variables can be explained by the moderating variable. The research data used were obtained from the employment book and the website of BPS Indonesia, in the form of 34 cross section and 5 years time series data that tends to be stationary. The dependent variable is NEET with 5 independent variables including Human development index, the open unemployment rate, labor force participation rate, proportion of individuals who own phone, and proportion of informal employment. The moderating variable is the proportion of youth aged with ICT skills. The best model in regression analysis panel data is FEM with 4 significant independent variables and 92.75% of R-square. Moderating variable can moderates the relationship of NEET with its independent variables and increased the R-square to 94.19%.