Analisis Ketimpangan Distribusi Pendapatan di Indonesia Menggunakan Geographically Weighted Panel Regression

Nindawardana, Dziban Nabil (2025) Analisis Ketimpangan Distribusi Pendapatan di Indonesia Menggunakan Geographically Weighted Panel Regression. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Ketimpangan distribusi pendapatan menunjukkan ketidakmeratanya distribusi pendapatan di suatu wilayah. Distribusi pendapatan yang tidak merata masih menjadi tantangan besar di Indonesia, meskipun pertumbuhan ekonomi dan peningkatan pendapatan telah terjadi. Sehingga dalam penelitian ini, akan dilakukan analisis rasio gini yang bertujuan untuk mengetahui faktor-faktor yang memengaruhi ketimpangan distribusi pendapatan pada provinsi di Indonesia tahun 2019 hingga 2023. Metode analisis yang digunakan adalah regresi data panel dengan mempertimbangkan aspek spasial dalam pemodelan ketimpangan distribusi pendapatan. Beberapa kasus, kondisi antar lokasi satu dengan lainnya berbeda karena dipengaruhi oleh faktor geografis yang menyebabkan heterogenitas spasial. Oleh karena itu, analisis dilakukan menggunakan pendekatan antara ekonometrika dan aspek spasial menggunakan model Geographically Weighted Panel Regression (GWPR) yang dapat menangkap heterogenitas penyebab terjadinya ketimpangan distribusi pendapatan di setiap provinsi. Analisis ini menggunakan 170 pengamatan yang terdiri dari data cross section meliputi 34 provinsi di Indonesia dan data time series dari tahun 2019 hingga tahun 2023. Hasil analisis menggunakan model GWPR, diperoleh bahwa faktor-faktor dan pengaruh yang signifikan memengaruhi ketimpangan distribusi pendapatan berbeda-beda pada tiap provinsi. Faktor tersebut meliputi Indeks Pembangunan Manusia (IPM) berpengaruh negatif, persentase penduduk miskin berpengaruh positif, upah rata-rata per jam pekerja berpengaruh positif, persentase pengeluaran transportasi dan komunikasi terhadap PDRB berpengaruh positif, serta indeks harga implisit PDRB sektor industri pengolahan berpengaruh negatif. Model GWPR menunjukkan kinerja yang lebih baik dibandingkan model regresi data panel random effect dengan nilai R2 sebesar 76,129%. Hasil ini diharapkan dapat memberikan masukan kepada pihak yang terkait untuk merancang kebijakan yang relevan dan spesifik berdasarkan karakteristik setiap provinsi dalam mengurangi ketimpangan distribusi pendapatan di Indonesia.
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Income distribution inequality indicates the unequal distribution of income in a region. Unequal income distribution is still a major challenge in Indonesia, despite economic growth and income increases. So in this study, a gini ratio analysis will be conducted to determine the factors that affect income distribution inequality in provinces in Indonesia from 2019 to 2023. The analytical method used is panel data regression by considering spatial aspects in modeling income distribution inequality. In some cases, conditions between locations are different from one another because they are influenced by geographical factors that cause spatial heterogeneity. Therefore, the analysis is conducted using an approach between econometrics and spatial aspects using the Geographically Weighted Panel Regression (GWPR) model that can capture the heterogeneity of the causes of income distribution inequality in each province. This analysis uses 170 observations consisting of cross section data covering 34 provinces in Indonesia and time series data from 2019 to 2023. The results of the analysis using the GWPR model show that the factors and influences that significantly affect income distribution inequality are different in each province. These factors include the Human Development Index (HDI) with a negative effect, the percentage of poor people with a positive effect, the average hourly wage of workers with a positive effect, the percentage of transportation and communication expenditure on GRDP with a positive effect, and the implicit price index of GRDP in the manufacturing sector with a negative effect. The GWPR model shows better performance than the random effect panel data regression model with an R2 value of 76,129%. These results are expected to provide input to relevant parties to design relevant and specific policies based on the characteristics of each province in reducing income distribution inequality in Indonesia.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Geographically Weighted Panel Regression, Ketimpangan Distribusi Pendapatan, Indonesia.
Subjects:	H Social Sciences > HA Statistics > HA30.3 Time-series analysis H Social Sciences > HA Statistics > HA30.6 Spatial analysis H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis. Q Science > QA Mathematics > QA401 Mathematical models.
Divisions:	Faculty of Vocational > 49501-Business Statistics
Depositing User:	Dziban Nabil Nindawardana
Date Deposited:	01 Feb 2025 08:29
Last Modified:	01 Feb 2025 08:29
URI:	http://repository.its.ac.id/id/eprint/117344

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