•  
  •  
 

Abstract

Poor households in the agricultural sector belong to a group with high food insecurity. As one of the instruments in overcoming the problem of food insecurity, Non-Cash Food Assistance (BPNT) provides social assistance to the poor to purchase adequate and nutritious food. This study aims to analyze the impact of BPNT on the expenditure of poor households in the agricultural sector in Maluku province. Using the Propensity Score Matching analysis on March 2020 Susenas data, the results show that BPNT significantly influences increasing food expenditure but not total expenditure.

Bahasa Abstract

Rumah tangga miskin di sektor pertanian tergolong dalam kelompok dengan kerawanan pangan yang tinggi. Sebagai salah satu instrumen dalam mengatasi permasalahan kerawanan pangan, Bantuan Pangan Non-Tunai (BPNT) menyediakan bantuan sosial kepada masyarakat miskin untuk dapat membeli kebutuhan bahan pangan yang cukup dan bernutrisi. Penelitian ini bertujuan untuk menganalisis dampak BPNT terhadap pengeluaran rumah tangga miskin sektor pertanian di Provinsi Maluku. Dengan menggunakan metode analisis Propensity Score Matching pada data Susenas Maret 2020, hasil penelitian menunjukkan bahwa BPNT memberikan pengaruh signifikan terhadap peningkatan pengeluaran makanan, tetapi tidak untuk pengeluaran total. Rumah tangga miskin pertanian yang menerima BPNT secara rata-rata mengalami kenaikan pengeluaran makanan sebesar 6,52 persen.

References

[1] Abebaw, D., Fentie, Y., & Kassa, B. (2010). The impact of a food security program on household food consumption in Northwestern Ethiopia: A matching estimator approach. Food Policy, 35(4), 286-293. doi: https:// doi.org/10.1016/j.foodpol.2010.01.002.

[2] Ansah, I. G. K., Gardebroek, C., & Ihle, R. (2021). Shock interactions, coping strategy choices and household food security. Climate and Development, 13(5), 414-426. doi: https:// doi.org/10.1080/17565529.2020.1785832.

[3] Badan Pangan Nasional. (2022). Peta Ketahanan dan Kerentanan Pangan (Food Security and Vulnerability Atlas – FSVA). Diakses 2 Juni 2023 dari https://fsva.badanpangan.go.id.

[4] Banerjee, A., Hanna, R., Kyle, J., Olken, B. A., & Sumarto, S. (2018). Tangible information and citizen empowerment: Identification cards and food subsidy programs in Indonesia. Journal of Political Economy, 126(2), 451-491. doi: https:// doi.org/10.1086/696226.

[5] Banerjee, A., Hanna, R., Olken, B. A., Satriawan, E., & Sumarto, S. (2021). Food vs. food stamps: Evidence from an at-scale experiment in Indonesia. NBER Working Paper, 28641. National Bureau of Economic Research. doi: 10.3386/w28641.

[6] BPS. (2020). Buku pedoman pencacahan survei sosial ekonomi nasional Maret 2020. Badan Pusat Statistik.

[7] BPS. (2021, 15 Februari). Profil kemiskinan di Maluku September 2020. Berita Resmi Statistik, 06/02/81/Th.XXIV. Badan Pusat Statistik Provinsi Maluku. Diakses 30 Juli 2023 dari https://maluku.bps.go.id/pressrelease/2021/02/15/442/ profil-kemiskinan-di-maluku-september-2020.html#:~: text=Jumlah%20penduduk%20miskin%20di%20Maluku, peningkatan%20sebesar%200%2C55%20poin.

[8] BPS. (2022). Penghitungan dan analisis kemiskinan makro Indonesia 2022. Badan Pusat Statistik. Diakses 25 Juli 2023 dari https://www.bps.go.id/id/ publication/2022/11/30/041b11a57ce8fe671631f684/ penghitungan-dan-analisis-kemiskinan-makro-indonesiatahun- 2022.html.

[9] Dartanto, T., Moeis, F. R., Can, C. K., Ratih, S. P., Nurhasana, R., Satrya, A., & Thabrany, H. (2021). Good intentions, unintended outcomes: Impact of social assistance on tobacco consumption in Indonesia. Tobacco Induced Diseases, 19, 29. doi: 10.18332/tid/132966.

[10] Getler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. J. (2016). Impact evaluation in practice (2nd edition). Inter-American Development Bank and World Bank. Diakses 4 Mei 2023 dari https://www.worldbank.org/en/programs/sief-trust-fund/ publication/impact-evaluation-in-practice.

[11] Gitz, V., & Meybeck, A. (2016). Climate change and food security: risks and responses. Watch Letter, 37. Centre International de Hautes études agronomiques méditerranéennes (CIHEAM). Diakses 25 Juli 2023 dari https://www.iamm.ciheam.org/uploads/attachments/250/ 06_Meybeck_WL_37.pdf.

[12] Gupta, P., & Huang, B. (2018). In-kind transfer and child development: Evidence from subsidized rice program in Indonesia. ADBI Working Paper, 826. Asian Development Bank Institute. Diakses 23 Juni 2023 dari https://www.adb.org/publications/ kind-transfer-and-child-development-evidence-indonesia.

[13] Harris, H., & Horst, S. J. (2016). A brief guide to decisions at each step of the propensity score matching process. Practical Assessment, Research, and Evaluation, 21, 4. doi: https://doi.org/10.7275/yq7r-4820.

[14] Hirvonen, K., & Hoddinott, J. (2020). Beneficiary views on cash and in-kind payments: evidence from Ethiopia’s productive safety. Policy Research Working Paper, 9125. World Bank. Diakses 7 Juli 2023 dari https://openknowledge. worldbank.org/handle/10986/33261.

[15] Hoddinott, J., Sandström, S., & Upton, J. (2018). The impact of cash and food transfers: Evidence from a randomized intervention in Niger. American Journal of Agricultural Economics, 100(4), 1032-1049. doi: https:// doi.org/10.1093/ajae/aay019.

[16] Kemensos. (2021). Laporan kinerja Kementerian Sosial 2020. Kementerian Sosial. Diakses 14 April 2023 dari https:// kemensos.go.id/uploads/topics/16520633675317.pdf.

[17] Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on impact evaluation: quantitative methods and practices. World Bank. Diakses 22 Mei 2023 dari https://documents.worldbank.org/en/publication/ documents-reports/documentdetail/650951468335456749/ Handbook-on-impact-evaluation-quantitative-methodsand- practices.

[18] Munandar, Y. (2021). Income inequality and noncash food assistance program in Central Java Province. Eko-Regional: Jurnal Pembangunan Ekonomi Wilayah, 16(2), 84-93. doi: https:// doi.org/10.20884/1.erjpe.2021.16.2.1805.

[19] Mustofa, M., Sugiyanto, C., & Susamto, A. A. (2023). Analysis of the impact of the raskin program on food security for poor households in Indonesia. Jurnal Economia, 19(1), 127-140. doi: 10.21831/economia.v19i1.58937.

[20] Mykerezi, E., & Mills, B. (2010). The impact of food stamp program participation on household food insecurity. American Journal of Agricultural Economics, 92(5), 1379-1391. doi: https://doi.org/10.1093/ajae/aaq072.

[21] Ningtiyas, E. R. (2018). Counterproductive effects of rice for poor (raskin) program on labor supply. Jurnal Perencanaan Pembangunan: The Indonesian Journal of Development Planning, 2(2), 188-202.

[22] Otok, B.W., Musa, M., & Yasmirullah, S. D. P. (2020). Propensity score stratification using bootstrap aggregating classification trees analysis. Heliyon, 6(7), e04288. doi: https:// doi.org/10.1016/j.heliyon.2020.e04288.

[23] Putri, A. S. (2023). Karakteristik sosial demografi yang mempengaruhi kontribusi pendapatan perempuan pekerja sektor informal di Kabupaten Maluku Tengah tahun 2021 (Tesis, Program Magister Perencanaan dan Pengembangan Wilayah Universitas Hasanuddin). Diakses 2 Juni 2023 dari http://repository.unhas.ac.id/id/eprint/25539/.

[24] Rachman, B., & Agustian, A. (2018). Efektivitas dan perspektif pelaksanaan program beras sejahtera (Rastra) dan bantuan pangan non-tunai (BPNT). Analisis Kebijakan Pertanian, 16(1), 1-18.

[25] Rahut, D. B., Aryal, J. P., Manchanda, N.,&Sonobe, T. (2022). Expectations for household food security in the coming decades: a global scenario. In: Bhat, R. (ed.), Future foods: global trends, opportunities, and sustainability challenges (pp. 107-131), Elsevier Science. doi: https://doi.org/10.1016/B978- 0-323-91001-9.00002-5.

[26] Satriawan, E., & Shrestha, R. (2018). Mistargeting and regressive take up of the Indonesian Rice Subsidy Program. Asian Economic Journal, 32(4), 387-415. doi: https:// doi.org/10.1111/asej.12164.

[27] Savy, M., Fortin, S., Kameli, Y., Renault, S., Couderc, C., Gamli, A., ... & Martin-Prével, Y. (2020). Impact of a food voucher program in alleviating household food insecurity in two cities in Senegal during a food price crisis. Food Security, 12, 465-478. doi: https://doi.org/10.1007/s12571-019- 00996-x.

[28] Schmidt, L., Shore-Sheppard, L., & Watson, T. (2016). The effect of safety-net programs on food insecurity. Journal of Human Resources, 51(3), 589-614. doi: https://doi.org/10.3368/jhr.51.3.1013-5987R1.

[29] Sosilawati, Nababan, M. L., Wahyudi, A. R., Mahendrea, Z. A., Massudi,W., & Utami, S. (2017). Sinkronisasi program dan pembiayaan pembangunan jangka pendek 2018-2020: keterpaduan pengembangan kawasan dengan infrastrukturPUPRKepulauan Maluku dan Pulau Papua. Pusat Pemrograman dan Evaluasi Keterpaduan Infrastruktur PUPR, Badan Pengembangan Infrastruktur Wilayah, Kementerian Pekerjaan Umum dan Perumahan Rakyat. Diakses 25 Juli 2023 dari https://bpiw. pu.go.id/publication/book/pdf/Buku_1MalukuPapua.pdf.

[30] Sulistyaningrum, E. (2016). Impact evaluation of the school operational assistance program (BOS) using the matching method. Journal of Indonesian Economy and Business, 31(1), 33-62. doi: https://doi,org/10.22146/jieb.10319.

[31] TNP2K. (2015). Tantangan meningkatkan efektivitas Program Raskin. Tim Nasional Percepatan Penanggulangan Kemiskinan. Diakses 22 Mei 2023 dari https://www.tnp2k.go.id/images/uploads/downloads/ Laporan%20TNP2K%20Tantangan%20Meningkatkan% 20Efektifitas%20Program%20Raskin%20Final.pdf.

[32] Wordofa, M. G., Hassen, J. Y., Endris, G. S., Aweke, C. S., Moges, D. K., & Rorisa, D. T. (2021). Adoption of improved agricultural technology and its impact on household income: a propensity score matching estimation in eastern Ethiopia. Agriculture & Food Security, 10, 5. doi: https://doi.org/10.1186/s40066-020-00278-2.

[33] Wu, Z., Zheng, W., & Yang, Z. (2023). Influence of farmland confirmation on farmland abandonment in China. Plos one, 18(5), e0285174. doi: https:// doi.org/10.1371/journal.pone.0285174.

[34] Zhang, Z., Kim, H. J., Lonjon, G., & Zhu, Y. (2019). Balance diagnostics after propensity score matching. Annals of Translational Medicine, 7(1), 16. doi: 10.21037/atm.2018.12.10.

[35] Zhao, Q. Y., Luo, J. C., Su, Y., Zhang, Y. J., Tu, G. W., & Luo, Z. (2021). Propensity score matching with R: conventional methods and new features. Annals of Translational Medicine, 9(9), 812. doi: 10.21037/atm-20-3998.

Included in

Economics Commons

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.