Faktor-Faktor yang Menyebabkan Kemiskinan di Provinsi Papua: Analisis Spatial Heterogeneity

  • Ribut Nurul Tri Wahyuni Sekolah Tinggi Ilmu Statistik, Badan Pusat Statistik
  • Arie Damayanti Program Pascasarjana Ilmu Ekonomi Fakultas Ekonomi dan Bisnis Universitas Indonesia
Keywords: Geographically Weighted Regression, Kemiskinan, Multivariate K-means Clustering, Variasi Wilayah Spatial Heterogeneity

Abstract


AbstractPro-poor growth program has not been effective reducing poverty in Papua because the government does not have complete information about the spatial variation of poverty-causing factors (spatial heterogeneity). Therefore, this study will analyze poverty-causing factors using Geographically Weighted Regression (GWR) model. This study finds that the influence of the cultivated land area, use of technical irrigation, source of drinking water, and the electrical infrastructure vary spatially. In additions, multivariate K-means clusteringshows that subdistricts are spatially clustered by geographical conditions. These results imply that poverty alleviation interventions should be dierent for different areas.Keywords: Geographically Weighted Regression, Poverty, Multivariate K-means Clustering, Spatial Heterogeneity AbstrakProgram pro-poor growth (program pembangunan ekonomi yang berpihak kepada penduduk miskin) belum efektif mengurangi kemiskinan di Papua karena pemerintah tidak memiliki informasi lengkap mengenai faktor-faktor yang menyebabkan kemiskinan menurut variasi wilayah (spatial heterogeneity). Oleh karena itu, studi ini akan menganalisis faktor-faktor tersebut dengan menggunakan model Geographically Weighted Regression (GWR). Studi ini menemukan pengaruh luas lahan yang diusahakan, penggunaan irigasi teknis, sumber air minum, dan listrik terhadap kemiskinan bervariasi secara spasial. Sementara itu, multivariate K-means clustering menunjukkan kecamatan mengelompok menurut kondisi geografis. Ini menyiratkan bahwa intervensi pengentasan kemiskinan seharusnya berbeda untuk wilayah berbeda.Kata kunci: Geographically Weighted Regression, Kemiskinan, Multivariate K-means Clustering, Variasi Wilayah Spatial Heterogeneity

Downloads

Download data is not yet available.

References

Benson, T., Chamberlin, J., & Rhinehart, I. (2005). An Investigation of the Spatial Determinants of the Local Prevalence of Poverty in Rural Malawi. Food Policy, 30 (5-6), 532-550.

BPS. (2008). Analisis dan Penghitungan Tingkat Kemiskinan 2008. Jakarta: Badan Pusat Statistik.

Brenneman, A., & Kerf, M. (2002). Infrastructure & Poverty Linkage: A Literature Review. Mimeo. The World Bank. Switzerland: International Labour Organization. http://www.oit.org/wcmsp5/groups/public/@ed_emp/@emp_policy/@invest/documents/publication/wcms_asist_8281.pdf (Accessed October 12, 2012).

Buddelmeyer, H., & Cai, L. (2009). Interrelated Dynamics of Health and Poverty in Australia. IZA Discussion Paper, 4602. Bonn, Germany: The Institute for the Study of Labor. http://ftp.iza.org/dp4602.pdf (Accessed October 12, 2012).

Farrow, A., Larrea, C., Hyman, G., & Lema, G. (2005). Exploring the Spatial Variation of Food Poverty in Ecuador. Food Policy, 30 (5-6), 510-531.

Gachassin, M., Najman, B., & Raballand, G. (2010). The Impact of Roads on Poverty Reduction: A Case Study of Cameroon. Policy Research Working Paper, 5209. Transport Unit, Africa Region, The World Bank. https://openknowledge.worldbank.org/bitstream/handle/10986/19924/WPS5209.pdf?sequence=1 (Accessed October 12, 2012).

Hussain, I., & Hanjra, M. (2004). Irrigation and Poverty Alleviation: Review of the Empirical Evidence. Irrigation and Drainage, 53, (1), 1-15.

IPTRID. (1999). Poverty Reduction and Irrigated Agriculture. Issues Paper, 1. Rome, Italy: International Programme for Technology and Research in Irrigation and Drainage (IPTRID), Food and Agriculture Organization of the United Nations (FAO-UN). ftp://ftp.fao.org/docrep/fao/005/x1000e/x1000e00.pdf (Accessed October 30, 2012).

Kam, S-P., Hossain, M., Bose, M. L., & Villano, L. S. (2005). Spatial Patterns of Rural Poverty and Their Relationship With Welfare-Infuencing Factors in Bangladesh. Food Policy, 30, (5-6), 551-567.

Peraturan Presiden Republik Indonesia Nomor 65 Tahun 2011 tentang Rencana Aksi Percepatan Pembangunan Provinsi Papua dan Provinsi Papua Barat Tahun 2011-2014.

Ray, D. (1998). Development Economics. New Jersey: Princeton University Press.

Scoones, I. (1998). Sustainable Rural Livelihoods: A Framework for Analysis. IDS Working Paper, 72. Brighton, England: Institute of Development Studies (IDS), University of Sussex.

Shrestha, P. M. (2006). Comparison of Ordinary Least Square Regression, Spatial Autoregression, and Geographically Weighted Regression for Modeling Forest Structural Attributes Using a Geographical Information System (GIS)/Remote Sensing (RS) Approach. Thesis. Canada: University of Calgary. http://people.ucalgary.ca/~mcdermid/Docs/Theses/Shrestha_2006.pdf (Accessed October 30, 2012).

TNP2K. (2010). Penanggulangan Kemiskinan: Situasi Terkini, Target pemerintah, dan Program Percepatan. Jakarta: Tim Nasional Percepatan Penanggulangan Kemiskinan (TNP2K). http://data.tnp2k.go.id/file_data/Publikasi/Publikasi%20Buku/be2_situasi_terkini_target_pemerintah_dan_program_percepatan_pk_ed2_webs.pdf (Accessed October 30, 2012).

UNDP. (2005). Kajian Kebutuhan Papua: Ringkasan Temuan dan Pengaruh terhadap Perumusan Program Bantuan Pembangunan. Jakarta: United Nations Development Programme (UNDP). http://www.undp.or.id/Papua/docs/pna_indo.pdf (Accessed October 30, 2012).

Wijayanti, D., & Wahono, H. (2005). Analisis Konsentrasi Kemiskinan di Indonesia Periode Tahun 1999-2003. Jurnal Ekonomi Pembangunan, 10, (3), 215-225.

Published
2014-01-01
Views
  • Abstract 7261


  • PDF 6375
Abstract Metrics
Abstract view : 7261 times

PDF - 6375 times
How to Cite
Tri Wahyuni, R. N., & Damayanti, A. (2014). Faktor-Faktor yang Menyebabkan Kemiskinan di Provinsi Papua: Analisis Spatial Heterogeneity. Jurnal Ekonomi Dan Pembangunan Indonesia, 14(2), 128-144. https://doi.org/10.21002/jepi.v14i2.441
Section
Articles