Determining Spatial Distribution of the BSP Supervised Banks in the Philippines: A Multivariate Cluster Analysis

Vivian Sesgundo, Peter John Aranas
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Abstract

The goal of this study was to determine the significant variables that contribute to the number of Bangko Sentral ng Pilipinas (BSP) Supervised banks in the Philippine regions. A model that can predict the number of banks needed in the region was also presented. Spatial distribution of the banks was also analyzed. These analyses determined if the current number of banks in the region is sufficient to provide the financial services needed by the people. The ArcGIS Pro was used to perform Ordinary Least Square Regression, Global Moran’s I and Multivariate Clustering Analyses to the Regional Distribution of BSP Supervised banks in the Philippines and the categorized economic, demographic, labor market and potential market variables from the Philippine Statistics Authority (PSA) in 2020. Results of this study show that the population density, economically active population, functional literacy rate, and families’ ownership of personal computer are the significant factors, which represent every category of the independent variable, that contributed to the number of banks in the region. Using the chosen passing model, the difference between the actual and estimated number of banks was used as the indicator by which regions need more physical banks to promote financial inclusion. Regions X, CAR, and BARMM are the best locations for the future physical bank to be established. Furthermore, the results from Global Moran’s I analysis showed that there is a clustering of high number of banks in the Philippines. There were 7 clusters formed for the number of banks based on population density, economically active population, functional literacy rate, and families’ ownership of personal computer. Among these explanatory variables, population density is the greatest contributor in forming the clusters.
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确定菲律宾中央银行监管银行的空间分布:多元聚类分析
本研究的目的是确定菲律宾各地区受菲律宾中央银行(BSP)监管的银行数量的重要变量。研究还提出了一个可以预测该地区所需银行数量的模型。此外,还对银行的空间分布进行了分析。这些分析确定了该地区目前的银行数量是否足以提供人们所需的金融服务。本研究使用 ArcGIS Pro 对菲律宾中央银行监管银行的地区分布以及菲律宾统计局 (PSA)提供的 2020 年分类经济、人口、劳动力市场和潜在市场变量进行了普通最小平方 回归、全球莫兰 I 和多变量聚类分析。研究结果表明,人口密度、经济活跃人口、实用识字率和家庭个人电脑拥有率是影响该地区银行数量的重要因素,代表了自变量的每一个类别。利用所选择的传递模型,实际银行数量与估计银行数量之间的差额被用作地区需要更多实体银行来促进普惠金融发展的指标。X 地区、CAR 地区和 BARMM 地区是未来建立实体银行的最佳地点。此外,全球莫兰 I 分析结果显示,菲律宾存在银行数量较多的聚类现象。根据人口密度、经济活跃人口、实用识字率和家庭拥有个人电脑的情况,银行数量形成了 7 个聚类。在这些解释变量中,人口密度对形成聚类的贡献最大。
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