Studi Kasus: Aksesbilitas Petani Kopi terhadap Kredit dari Lembaga Keuangan Bank

Abstract

The development of coffee plants in Indonesia shows a rapid growth to fulfill domestic and foreign needs. However, there are still farmers who have not been able to maximize their market potential by using post-harvest technology to increase value added caused by the low accessibility of farmers to farming capital.This research aims to identify accessibility of coffee farmers to financial institution and analyze the factors of credit accessibility towards bank as financial institution of coffee farmers at Kubangsari 2 Farmers Group. The research design used quantitative design with survey research technique. The research sampling used simple random sampling with 31 sample of farmers picked. The data analysis used logit regression method with SPSS 21as a statistical tools. The results show that the coffee farmers take credit accessibility to bank (41,94%), non-bank (3,23%), and non-formal financial institution (51,61%). Besides that, there is 3,23% respondents did not access credit. The result of logit regression shows the factors that effect to credit accessibility of coffee farmers is age, formal education, farming experience, number of coffee trees, and coffee farming income.

Akses Pembiayaan Informal Petani Kentang Di Desa Margamulya Kecamatan Pangalengan Kabupaten Bandung

“…Selain itu, perbedaan pembiayaan formal dan informal terletak pada pengambilan keputusan. Karyani, 2018). Beberapa faktor yang mempengaruhi akses petani terhadap sumber pembiayaan, yaitu faktor internal dan faktor ekonomi (Yoko & Prayoga, 2019).…”

Faktor Penentu Pemanfaatan Sertifikat Redistribusi Tanah untuk Mengakses Modal: Studi Kasus Kabupaten Pasaman Barat

“…Ini berarti penerima sertifikat redistribusi tanah yang memiliki pengeluaran tinggi berpeluang lebih besar untuk mengagunkan. Hal ini sejalan dengan hasil penelitian Sartika & Karyani (2018) yang menyatakan petani dengan jumlah pendapatan yang lebih tinggi memiliki kesempatan mendapatkan akses kredit dari lembaga keuangan bank. Pendapatan yang lebih tinggi diasumsikan berbanding lurus dengan peningkatan pengeluaran.…”

What Makes Small Industries Apply for Loan?

“…Several previous studies provide an overview and input to the authors based on the studies discussed from each journal. Many previous studies used samples in the agricultural sector, such as in the study of Moahid & Maharjan (2020), Chandio et al (2017), andSartika &Karyani (2018) who use farmer household respondents in analyzing credit demand. Most studies also tend to analyze credit demand in the business sector in general, as conducted by Parida & Pradhan (2020) and Umiyati et al (2019) analyzing micro, small and medium enterprises in taking credit, Xu et al (2020) who analyzed informal businesses in accessing formal credit, Magboul & Hassan (2016) and Messah & Wangai (2011) who analyzed micro and small businesses in accessing formal microcredit and business credit.…”

“…Most studies also tend to analyze credit demand in the business sector in general, as conducted by Parida & Pradhan (2020) and Umiyati et al (2019) analyzing micro, small and medium enterprises in taking credit, Xu et al (2020) who analyzed informal businesses in accessing formal credit, Magboul & Hassan (2016) and Messah & Wangai (2011) who analyzed micro and small businesses in accessing formal microcredit and business credit. The tendency of the analytical method used in several countries to see the decision to take credit is the logit regression method, as done by Umiyati et al (2019) and Sartika & Karyani (2018) in Indonesia and study conducted by Messah & Wangai (2011) and Ssonko & Nakayaga (2014) in Kenya and Uganda. Meanwhile, another method that is also used in analyzing the decision to take credit is the probit regression analysis method.…”

“…The socio-demographic variable that is often used is the age of the entrepreneur, as in study Ajagbe (2012), Chandio et al (2017), Magboul & Hassan (2016), Messah & Wangai (2011), Mkandawire & Duan (2016), Parida & Pradhan (2020), Siswanto et al (2019), Umiyati et al (2019), andXu et al (2020). The education level variable is also often used, as in research Ajagbe (2012), Chandio et al (2017), Magboul & Hassan (2016), Moahid & Maharjan (2020), Sartika & Karyani (2018), Umiyati et al (2019), andXu et al (2020). Various other variables have also been used as determinants of credit demand, including financial records variables by Magboul &Hassan (2016), andMulandi (2013), information access variables by Mulandi (2013) and Zabri et al (2021), social group membership variables and associations by Magboul & Hassan (2016), Moahid &Maharjan (2020), andSsonko &Nakayaga (2014), and business partnership fabrication variables by Siswanto et al (2019).…”