ANALYSIS OF THE EXISTENCE OF THE AGRICULTURAL SECTOR IN MODELING POVERTY IN BENGKULU PROVINCE USING GAUSSIAN COPULA MARGINAL REGRESSION

  • Sigit Nugroho Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Bengkulu, Indonesia https://orcid.org/0000-0003-4535-2045
  • Dyah Setyo Rini Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Bengkulu, Indonesia https://orcid.org/0000-0003-3758-4292
  • Pepi Novianti Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Bengkulu, Indonesia https://orcid.org/0000-0003-1970-0827
  • Riki Crisdianto Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Bengkulu, Indonesia https://orcid.org/0009-0005-2080-748X
  • Elisabeth Evelin Karuna Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Bengkulu, Indonesia https://orcid.org/0009-0001-3871-1882
  • Athaya Fairuzindah Statistics Study Program, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Bengkulu, Indonesia https://orcid.org/0009-0009-6857-0046
Keywords: Copula, Gaussian Copula Marginal Regression (GCMR), Poverty

Abstract

Bengkulu Province ranks second in the category of the highest percentage of poor people in the Sumatra region, at 14.62% in March 2022, and sixth in Indonesia, which is undoubtedly one of the fundamental problems that requires mutual attention. The phenomenon of high poverty in Bengkulu Province is inseparable from the lives of people whose main livelihood is in the agricultural sector, especially tenant farmers. Therefore, in this study, the Copula and Gaussian Copula Marginal Regression (GCMR) methods are applied to determine how the agricultural sector affects poverty in Bengkulu Province using secondary data obtained from the Bengkulu Provincial Statistics Agency (Susenas 2022). The results show that the Copula model can identify various types of dependency between the number of poor households in each district/city in Bengkulu Province in 2022  and each of the  variables, namely the Number of Agricultural Business Households , the Growth Rate of the Agricultural Sector , the Human Development Index   , and the Open Unemployment Rate ( ) by considering the different characteristics of dependency such as top-tail, bottom-tail, or negative dependency. Meanwhile, the GCMR model can provide the direction of influence of the independent variables on the dependent variable Y, where it can be seen that the variables , , and  have a negative influence on the variable , whie the variable  has a positive impact on the variable . Therefore, in general, it can be concluded that either positive or negative dependencies identified by the Copula model can influence the resulting GCMR model by providing more profound complexity regarding the relationship between the variables analyzed.

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Published
2025-04-01
How to Cite
[1]
S. Nugroho, D. S. Rini, P. Novianti, R. Crisdianto, E. E. Karuna, and A. Fairuzindah, “ANALYSIS OF THE EXISTENCE OF THE AGRICULTURAL SECTOR IN MODELING POVERTY IN BENGKULU PROVINCE USING GAUSSIAN COPULA MARGINAL REGRESSION”, BAREKENG: J. Math. & App., vol. 19, no. 2, pp. 1251-1262, Apr. 2025.