Dynamic Panel Data Analysis of Poverty in Indonesia
Heffi Christya Rahayu, Julianus Johnny Sarungu, Lukman Hakim, Albertus Maqnus Soesilo, Etty Puji Lestari, Diah Astuti, Tri Kuniawati Retnaningsih
Heffi Christya Rahayu
Available Online 25 May 2020.
- https://doi.org/10.2991/aebmr.k.200522.028How to use a DOI?
- education, geography, health, poverty
- Poverty is one of the main problems in most developing countries including Indonesia. Indonesia has five large islands consisting of Sumatra, Java and Bali, Kalimantan, Sulawesi, and Papua - Maluku - Nusa Tenggara. This study aims to analyze the determinants of poverty in villages by focusing on geography, education and health aspects. The analysis model used is modeling using the dynamic panel data regression method. The results of this study that urban villages have lower number of poor people than rural villages. In addition, villages with lowland topography have lower number of poor people. For distance variable, the farther away the villages from the regency/city capitals, the higher the number of poor people. In the education aspect, in villages with better education facilities, the number of poor people is generally lower. This also occurs for the health facilities and health workers variables. This shows that the characteristic of poverty determinant in several islands in Indonesia are heterogeneous. In conclusion, the determinant of poverty are geographical locations and the availability of education and health facilities.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Heffi Christya Rahayu AU - Julianus Johnny Sarungu AU - Lukman Hakim AU - Albertus Maqnus Soesilo AU - Etty Puji Lestari AU - Diah Astuti AU - Tri Kuniawati Retnaningsih PY - 2020 DA - 2020/05/25 TI - Dynamic Panel Data Analysis of Poverty in Indonesia BT - Proceedings of the 2nd International Seminar on Business, Economics, Social Science and Technology (ISBEST 2019) PB - Atlantis Press SP - 137 EP - 141 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.200522.028 DO - https://doi.org/10.2991/aebmr.k.200522.028 ID - Rahayu2020 ER -