Geographical Weighted Regression Model for Poverty Analysis in Jambi Province

Abstract

Agriculture sector has an important contribution to food security in Indonesia, but it also huge contribution to the number of poverty, especially in rural area. Studies using a global model might not be sufficient to pinpoint the factors having most impact on poverty due to spatial differences. Therefore, a Geographically Weighted Regression (GWR) was used to analyze the factors influencing the poverty among food crops famers. Jambi Province is selected because have high number of poverty in rural area and the lowest farmer exchange term in Indonesia. The GWR was better than the global model, based on high value of R2, lowers AIC and MSE and Leung test. Location in upland area and road system had more influence to the poverty in the western-southern. Rainfall was significantly influence in eastern. The effect of each factor, however, was not generic, since the parameter estimate might have a positive or negative value. Abstrak Sektor pertanian memiliki peran penting terhadap ketahanan pangan di Indonesia, tetapi juga memberi pengaruh pada angka kemiskinan khususnya di wilayah pedesaan. Penelitian dengan menggunakan model global kemungkinan tidak sesuai lagi untuk mengetahui faktor-faktor yang mempengaruhi kemiskinan berdasarkan karakteristik spasial. Oleh karena itu, penelitian ini menggunakan metode Geographically Weighted Regression (GWR) untuk menganalisis faktor yang mempengaruhi kemiskinan petani tanaman pangan. Propinsi Jambi digunakan sebagai lokasi penelitian karena wilayah ini memiliki kemiskinan di pedesaan yang tinggi dan Nilai Tukar Petani (NTP) yang paling rendah di Indonesia. GWR memberikan hasil yang lebih baik dibandingkan model global berdasarkan nilai R2 yang tinggi, nilai AIC dan MSE yang kecil, dan uji Leung. Wilayah lereng/gunung dan jaringan jalan lebih signifikan terhadap kemiskinan di wilayah barat-selatan Jambi. Curah hujan signifikan di wilayah timur. Pengaruh setiap faktor tidak sama di setiap lokasi, karena estimasi parameter dapat positif atau negatif.

The Impact of Suramadu Bridge on Rural Poverty in Kabupaten Bangkalan, East Java, Indonesia

“…As a result, the small share of farmer's incomes encourages the farmer to look for other sources of income outside the agricultural sector because the bargaining power of farmers for agricultural products produced is relatively low. Small farmers and farm workers experience a deficit in income from the agricultural sector (income from the agricultural sector alone is not enough to finance their families) so they are forced to look for labor-intensive and lesscapital jobs such as small shops, traders, handicrafts, working in services and so on (Nashwari et al, 2017).…”

“…During the Suramadu bridge operates, the mobility and accessibility in both areas becomes higher. The wide area of rice fields and irrigated ones will increase residents' businesses and reduce poverty levels (Nashwari et al, 2017). The existence of farmers will increase the number of businesses and reduce poverty (Larson et al, 2016).…”

“…(2017) who explain that the high growth of the agricultural sector creates more poverty reduction compared to the non-agricultural sector. Also, the development of the agricultural sector is the most effective poverty reduction compared to non-agricultural businesses.Rice field area has a negative effect on poverty according to the research ofMazumder & Lu (2015) andNashwari et al (2017). Increasing the area of rice fields will increase the scale of agricultural business that will encourage the growth of resident's businesses that support agricultural activities.…”

The Impact of the Suramadu Bridge on Rural Poverty in Kabupaten Bangkalan, East Java, Indonesia

“…The use of land for buildings encourages residents to open businesses in villages to decrease poverty (Akotey & Adjasi, 2015). The wide area of rice fields and irrigated grounds will increase residents' businesses and lessen poverty levels (Nashwari et al, 2017). The existence of farmers will increase the number of businesses and reduce poverty (Larson et al, 2016).…”

Analisis Spasial Kemiskinan Dengan Pendekatan Geographically Weighted Regression: Studi Kasus Kabupaten Pandeglang Dan Lebak

“…Rodriquez and Pose (1998); (Rustiadi et al (2011); Hermes et al (2017) mengungkapkan aspek wilayah sangat penting dalam pembangunan khususnya bidang sosial-ekonomi dan perubahan sosial. Untuk itu perlu ada upaya pendekatan analisis yang melibatkan unsur spasial untuk mengolah data kemiskinan (Saefuddin et al 2011;Sinaga, 2015;Vasan dan Alcantara, 2016 (Saefuddin et al 2012;Nashwari et al 2017). Pemetaan terhadap kemiskinan dilakukan pada 63 kecamatan di Pandeglang dan Lebak karena kedua daerah ini relatif tertinggal dibandingkan dengan kabupaten lain dalam Provinsi Banten.…”