Analisis Dampak Implementasi Kebijakan Kenaikan PPN Terhadap Kinerja UMKM Menggunakan Pendekatan Difference-in-Difference (DiD) Dengan Model Regresi Data Panel

Mahanariswari, Luh Putu Ayu Eshanti (2025) Analisis Dampak Implementasi Kebijakan Kenaikan PPN Terhadap Kinerja UMKM Menggunakan Pendekatan Difference-in-Difference (DiD) Dengan Model Regresi Data Panel. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penelitian ini bertujuan untuk menganalisis dampak kenaikan tarif Pajak Pertambahan Nilai (PPN) dari 10% menjadi 11% terhadap variabel makroekonomi yakni Indeks Harga Konsumen (IHK), inflasi, dan pendapatan pajak serta implikasinya terhadap kinerja sektor Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia. Kenaikan tarif yang diberlakukan sejak April 2022 merupakan bagian dari strategi pemulihan ekonomi nasional. Penelitian ini menggunakan data panel provinsi periode 2020–2023 dan menerapkan metode Difference-in-Differences (DiD) untuk mengukur dampak kebijakan pada variabel makroekonomi, serta regresi data panel untuk mengevaluasi kinerja UMKM dengan fokus pada pendapatan, kredit, jumlah usaha, dan balas jasa pekerja. Hasil analisis DiD menunjukkan bahwa kebijakan kenaikan PPN secara signifikan meningkatkan inflasi sebesar 1,431 poin (p-value 0,005), namun secara tidak terduga menurunkan IHK sebesar 3,202 poin (p-value 0,000). Dampak kebijakan terhadap pendapatan pajak tidak signifikan (p-value 0,763), yang mengindikasikan ketidakefektifan kebijakan dalam meningkatkan penerimaan negara. Analisis regresi panel dengan pendekatan Common Effect Model dan Geographically Weighted Panel Regression (GWPR) menemukan bahwa variabel Kredit (X₁) dan Balas Jasa (X₃), yang direduksi menjadi komponen utama PC₁, serta variabel Jumlah Usaha (X₂), secara signifikan memengaruhi pendapatan UMKM. Inflasi (X₄) dan IHK (X₅) yang mencerminkan dampak kebijakan PPN juga terbukti berpengaruh terhadap penurunan pendapatan UMKM. Model GWPR menegaskan pentingnya kontribusi faktor spasial dalam menganalisis kinerja UMKM, dengan Adjusted R² terbaik mencapai 98,87% menggunakan kernel Gaussian.
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This study aims to analyze the impact of the increase in the Value-Added Tax (VAT) rate from 10% to 11% on macroeconomic variables namely the Consumer Price Index (CPI), inflation, and tax revenue and its implications for the performance of the Micro, Small, and Medium Enterprises (MSMEs) sector in Indonesia. The tax rate increase, implemented in April 2022, is part of the national economic recovery strategy. The study uses provincial panel data from 2020 to 2023 and applies the Difference-in-Differences (DiD) method to measure the policy’s impact on macroeconomic variables, as well as panel regression analysis to evaluate MSME performance, focusing on income, credit, number of enterprises, and labor compensation. The DiD analysis indicates that the VAT increase significantly raised inflation by 1,431 points (p-value 0,005), but unexpectedly reduced the CPI by 3,202 points (p-value 0,000). On the other hand, its impact on tax revenue was not significant (p-value 0,763), suggesting the policy’s ineffectiveness in boosting government revenue. Panel regression analysis using the Common Effect Model and Geographically Weighted Panel Regression (GWPR) found that the variables Credit (X₁) and Labor Compensation (X₃), which were reduced to a principal component (PC₁), as well as the Number of Enterprises (X₂), significantly influenced MSME income. Inflation (X₄) and CPI (X₅), which reflect the impact of the VAT policy, were also shown to negatively affect MSME income. The GWPR model underscores the importance of spatial factors in analyzing MSME performance, achieving the highest adjusted R² of 98,87% using the Gaussian kernel.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Difference-in-Differences, regresi data panel, PPN, UMKM, Difference-in-Differences, MSMEs, panel data regression, VAT
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 > HA31.35 Analysis of variance H Social Sciences > HA Statistics > HA31.3 Regression. Correlation. Logistic regression analysis. H Social Sciences > HA Statistics > HA31.7 Estimation
Divisions:	Faculty of Mathematics, Computation, and Data Science > Actuaria > 94203-(S1) Undergraduate Thesis
Depositing User:	Luh Putu Ayu Eshanti M.
Date Deposited:	24 Jul 2025 02:51
Last Modified:	24 Jul 2025 02:51
URI:	http://repository.its.ac.id/id/eprint/121053

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