Research Article
Impact of Regional Fiscal Capacity and Regional Economic Growth on Improving the Welfare of the Community in Regencies/Cities in Kalimantan
@INPROCEEDINGS{10.4108/eai.18-11-2020.2311800, author={Muzdalifah Muzdalifah}, title={Impact of Regional Fiscal Capacity and Regional Economic Growth on Improving the Welfare of the Community in Regencies/Cities in Kalimantan}, proceedings={Proceedings of the 2nd Borobudur International Symposium on Humanities and Social Sciences, BIS-HSS 2020, 18 November 2020, Magelang, Central Java, Indonesia}, publisher={EAI}, proceedings_a={BIS-HSS}, year={2021}, month={9}, keywords={ikfd growth economic social welfare}, doi={10.4108/eai.18-11-2020.2311800} }- Muzdalifah Muzdalifah
Year: 2021
Impact of Regional Fiscal Capacity and Regional Economic Growth on Improving the Welfare of the Community in Regencies/Cities in Kalimantan
BIS-HSS
EAI
DOI: 10.4108/eai.18-11-2020.2311800
Abstract
The Purpose in this research to understand what the impact of fiscal capacity and regional economic growth are on improving the welfare of the community in districts / cities in Kalimantan. The methods use in research are Klassen typology and Panel Data Regression using SPSS and Eviews software. The research describe Scarter Plot IKFD and Growth, There are only 6 Regencies/ Cities that are in quadrant 1, the rest are mostly in quadrant 3 and 4. Scarter Plot IKFD and IPM, There are only 11 Regencies/Cities that are in quadrant 1, the rest are mostly in quadrant 3 and 4. The best model adalah Fixed effect model with the Chow and Hausman test, the simultaneously all independent variables (IKFD and Growth) affect the dependent variable (IPM) and Partially it is known that only the IKFD variable has a significant effect on the level of welfare in the Regency / City with a negative relationship, The coefficient of determination (R2) is 88.83%, which means that the proportion of variance in the predictable welfare variable from the IKFD variable and growth is 88.83 percent, the rest is explained by other variables outside the model.
