{"title":"Analisis Regresi Data Panel Untuk Pemodelan Kemiskinan Pulau Sumatera Dengan Variabel Pendidikan Tahun 2016 – 2021","authors":"Ananda Rizal, Nadya Yantieka","doi":"10.54543/etnik.v1i7.91","DOIUrl":null,"url":null,"abstract":"The poverty rate on the island of Sumatra is still categorized as high.This is due to the lack of professional human resources who canproduce and develop their own natural resources. One of theGovernment's current priority programs is human resourcedevelopment. Human resource development can be implemented bytaking into account the level of education of the community. In otherwords, taking into account the level of education will be able to reducepoverty. Therefore, in this study, poverty modeling in 10 provinces onthe island of Sumatra will be carried out and analyze what factorsinfluence poverty. The research method used is panel data regression,where the data involves cross section and time series. In this study, thedata obtained are secondary data sourced from the Central StatisticsAgency (BPS). There are three dependent variables used, namely thepercentage of poor people, the poverty depth index and the povertyseverity index. While the independent variables used are educationvariables which include Literacy Rate, Average Years of Schooling,Gross Enrollment Rate (APS), Pure Participation Rate (APM) andSchool Participation Rate (APS). The results showed that the bestestimation method for the three dependent variables was the RandomEffect Model estimation method. The independent variables that areequally significant for the three models include literacy rates and pureparticipation rates. Meanwhile, the independent variables which areboth insignificant for the three models include gross enrollment rateand school participation rate.","PeriodicalId":380922,"journal":{"name":"ETNIK: Jurnal Ekonomi dan Teknik","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ETNIK: Jurnal Ekonomi dan Teknik","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54543/etnik.v1i7.91","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Analisis Regresi Data Panel Untuk Pemodelan Kemiskinan Pulau Sumatera Dengan Variabel Pendidikan Tahun 2016 – 2021
The poverty rate on the island of Sumatra is still categorized as high.This is due to the lack of professional human resources who canproduce and develop their own natural resources. One of theGovernment's current priority programs is human resourcedevelopment. Human resource development can be implemented bytaking into account the level of education of the community. In otherwords, taking into account the level of education will be able to reducepoverty. Therefore, in this study, poverty modeling in 10 provinces onthe island of Sumatra will be carried out and analyze what factorsinfluence poverty. The research method used is panel data regression,where the data involves cross section and time series. In this study, thedata obtained are secondary data sourced from the Central StatisticsAgency (BPS). There are three dependent variables used, namely thepercentage of poor people, the poverty depth index and the povertyseverity index. While the independent variables used are educationvariables which include Literacy Rate, Average Years of Schooling,Gross Enrollment Rate (APS), Pure Participation Rate (APM) andSchool Participation Rate (APS). The results showed that the bestestimation method for the three dependent variables was the RandomEffect Model estimation method. The independent variables that areequally significant for the three models include literacy rates and pureparticipation rates. Meanwhile, the independent variables which areboth insignificant for the three models include gross enrollment rateand school participation rate.