1Badan Pusat Statistik Kabupaten Hulu Sungai Selatan
2Politeknik Statistika STIS, Indonesia
BibTex Citation Data :
@article{JDEP23010, author = {Agustina Adiningtyas and Budyanra Budyanra}, title = {DETERMINANTS OF DISTRICTS/CITIES EDUCATION ATTAINMENT INEQUALITY IN EAST JAVA PROVINCE DURING 2014-2016}, journal = {JURNAL DINAMIKA EKONOMI PEMBANGUNAN}, volume = {2}, number = {3}, year = {2020}, keywords = {education inequality, panel data regression, REM}, abstract = { High school enrollment rate can reduce educational inequality. However, East Java Province which has a high school enrollment rate, education inequality is also high. Using data from the National Socioeconomic Survey (Susenas) from 2014 to 2016 and publications from several institutes, this research aims to find out the determinants of districts/cities education inequality in East Java in 2014-2016. The inferential analysis uses panel data regression with the random effect model (REM). The results show that the significant variables increasing the education inequality are the percentage of poor population and percentage of married young women, while the significant variables decreasing the education inequality are Gross Domestic Regional Product (GDRP) growth, the percentage of household head educated minimum secondary level, and teacher-school ratio on the secondary level. There are no significant effects of government spending on education inequality. }, issn = {2620-3049}, pages = {1--18} doi = {10.14710/jdep.2.3.1-18}, url = {https://ejournal.undip.ac.id/index.php/dinamika_pembangunan/article/view/23010} }
Refworks Citation Data :
High school enrollment rate can reduce educational inequality. However, East Java Province which has a high school enrollment rate, education inequality is also high. Using data from the National Socioeconomic Survey (Susenas) from 2014 to 2016 and publications from several institutes, this research aims to find out the determinants of districts/cities education inequality in East Java in 2014-2016. The inferential analysis uses panel data regression with the random effect model (REM). The results show that the significant variables increasing the education inequality are the percentage of poor population and percentage of married young women, while the significant variables decreasing the education inequality are Gross Domestic Regional Product (GDRP) growth, the percentage of household head educated minimum secondary level, and teacher-school ratio on the secondary level. There are no significant effects of government spending on education inequality.
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