Geospatial Analysis of Average Rice Consumption in South Kalimantan and Central Kalimantan
DOI:
https://doi.org/10.24203/ajafs.v12i1.9341Keywords:
geospatial analysis, geographically weighted regression, rice consumption, rice price, per capita incomeAbstract
This study aims to analyze the per capita rice consumption pattern and the factors that affect it at the regency/city level in South Kalimantan and Central Kalimantan. This study uses per capita rice consumption data, rice prices, and per capita income data by regency/city in South Kalimantan and Central Kalimantan in 2023. The data used is sourced from the Central Statistics Agency for National Socio-Economic Survey activities in March 2023 as well as data on food price developments by the National Food Agency. Regression analysis was used to identify the relationship between rice consumption per capita and rice prices and per capita income. Furthermore, a geospatial approach is applied to understand the variation of rice consumption and the factors that affect it in each regency/city. The results showed that there was a significant relationship between per capita rice consumption and rice price and per capita income with a determinant coefficient, R2 = 0.49 (higher than the determination coefficient in the usual regression analysis (R2 = 0.35)) The rice price elasticity of -0.40 showed that the increase in rice prices could reduce per capita rice consumption but did not have a major impact. Meanwhile, from the estimated results, the amount of income elasticity of -0.33 indicates that the increase in income will reduce rice consumption. But in fact, rice must be a normal commodity with a positive income elasticity. But it does not apply to all income levels. At a relatively high incomes level and food needs through rice have been met in good quantity, the increase in income no longer has a positive influence which is reflected in the increasing quantity of demand for rice. Even at a higher income level, it is possible that the quantity of demand for rice will decrease. The need for food is no longer met by rice but, by using the potential income, people have other alternatives through noodles, bread, and ready-to-eat foods such as fried rice, ketupat, lontong, satay, and others.
References
Anselin, L., & Rey, S. J. (2010). Perspectives on spatial data analysis. Dalam Advances in Spatial Science (Vol. 61). https://doi.org/10.1007/978-3-642-01976-0_1
Badan Pusat Statistik. (2022). Distribusi Perdagangan Komoditas Beras Indonesia 2022. Jakarta.
Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4). https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
Dewan Ketahanan Pangan, Kementerian Pertanian, & World Food Programme (WFP). (2015). Peta Ketahanan dan Kerentanan Pangan Indonesia 2015. Jakarta.
Engel, J. F., Kollat, D. T., & Blackwell, R. D. (1973). Consumer behavior, 2nd ed. Dalam Consumer behavior, 2nd ed.
Kementerian Pertanian. (2020). Panduan Teknis Penyusunan Prognosa Ketersediaan dan Kebutuhan Pangan Strategis Tahun 2020. Jakarta.
Mankiw, N. G. (2018). Pengantar Ekonomi Makro (Edisi 6). Dalam Salemba Empat: Vol. 3 Nomor 3.
Nicholson, W., & Snyder, C. (2009). Intermediate Microeconomics and Its Application. Dalam South-Western.
Pindyck, R., & Rubinfeld, D. (2018). Microeconomics Ninth Edition. Dalam Pearson Education Limited.
Schumpeter, J. A., & Keynes, J. M. (1936). The General Theory of Employment, Interest and Money. Journal of the American Statistical Association, 31(196). https://doi.org/10.2307/2278703
Turvey, R., & Duesenberry, J. S. (1950). Income, Saving and the Theory of Consumer Behavior. Economica, 17(68). https://doi.org/10.2307/2549507
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Copyright (c) 2024 Arrahman Adnani, Sadik Ikhsan, Yudi Ferianta

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