Research Article
Analysis of Inequality in Economic Development District / City in North Sumatra
@INPROCEEDINGS{10.4108/eai.4-12-2019.2293866, author={Linda Septi Yanti Sianipar and Rahmad Sembiring and Annisa Ilmi Faried}, title={Analysis of Inequality in Economic Development District / City in North Sumatra}, proceedings={The 3rd International Conference Community Research and Service Engagements, IC2RSE 2019, 4th December 2019, North Sumatra, Indonesia}, publisher={EAI}, proceedings_a={IC2RSE}, year={2020}, month={4}, keywords={disparities gdp population development spending unemployment rate}, doi={10.4108/eai.4-12-2019.2293866} }- Linda Septi Yanti Sianipar
Rahmad Sembiring
Annisa Ilmi Faried
Year: 2020
Analysis of Inequality in Economic Development District / City in North Sumatra
IC2RSE
EAI
DOI: 10.4108/eai.4-12-2019.2293866
Abstract
Development planning in Indonesia is directed to create a society that is more prosperous, prosperous and equitable. Policy development is implemented to achieve high economic growth potential and by utilizing existing resources. But the fruits of development have not been felt evenly and sometimes there are regional disparities. This study aims to identify and analyze the influence of PDB, population, development expenditure and unemployment rate on the imbalance of economic development between districts / cities in North Sumatra. The data used is panel data from the year 2011 - 2018 on 25 districts / cities in the province of North Sumatra. Sources of data from the Central Statistics Agency of North Sumatra Province with method Fixed Effect, with testing conducted by classical assumption test and statistical tests. With the help of Eviews 6.0 program data processing, data analysis results showed that the PDB variable is negative and significant effect at α = 10%, variables of population and development expenditure has positive and significant at α = 10%, while the unemployment rate did not significantly influence the disparity of economic development in North Sumatra. Regression results on the model is R-Squared = 0.994995, which means that the independent variables affect the dependent variable was 99.49% and the remaining 0.52% is influenced other variables outside the model are analyzed.