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研究生:顏玉郁
研究生(外文):YEN, YU-YU
論文名稱:金融分支機構設立的空間相關計量分析之研究
論文名稱(外文):A Study on Spatial Correlation Econometrics Analysis of the Establishment for Bank Branches
指導教授:廖鴻圖廖鴻圖引用關係
指導教授(外文):LIAW, HORNG-TWU
口試委員:高瑞鴻吳威震吳翠鳳
口試委員(外文):KAO, JUI-HUNGWU, WEI-CHENWU, TSUI-FENG
口試日期:2021-05-02
學位類別:碩士
校院名稱:世新大學
系所名稱:財務金融學研究所(含碩專班)
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:英文
論文頁數:84
中文關鍵詞:空間自相關高齡人口金融機構分行分佈
外文關鍵詞:Spatial AutocorrelationElderly PopulationBranches of Financial Institutions
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近年來在人口成長趨緩與高齡化的趨勢下,為了瞭解臺灣金融機構分佈之特性,每位銀行家必須審查自己的資源以獲得競爭優勢,以制定方向,並為分支機構的長期發展設定目標實現可持續運營。運用人口統計資料與臺灣鄉鎮市區從空間層面探討可能呈現空間聚集現象,或者是隨機分佈、分散現象之分佈情形,再將具有分佈之空間聚集特性地點進行分佈位置之分析,找出臺灣各地區之特徵。本研究為釐清上述的研究目的,回顧金融機構設立之背景,再透過瞭解各地金融機構分佈下,並由空間自相關分析之全域型 Moran's I 值與區域型 LISA 指標以臺灣各鄉鎮市區為實證範圍進行分析結果。
為探討全台灣本國銀行家數之空間群聚現象,發現臺灣各鄉鎮市區以全域型空間自相關分析人口統計屬性資料Sex ratio、The population density、Old age dependency ratio、Aging index、Total population的分析結果在空間中呈現聚集分佈,其空間關係為正相關性地區大於負相關之地區, 即表示臺灣7760個村里呈現集中分佈之現象,空間自迴歸模型最具解釋能力;進一步的以區域型自相關分析瞭解高齡人口與銀行分行設立空間單元(該空間單元及其鄰接之單元)之間的聚集強度相關連性,並將其劃分為「H-H」、「L-L」、「H-L」、「L-H」四個關聯地區,結果發現高齡人口 「H-H」關聯地區中金融機構設立分行均呈不足,而高度都市化區域是足夠(臺北市、新北市、桃園市、臺中市、臺南市、高雄市),經實證結果指出全台灣各地區本國銀行分行數確實存有空間相依性(Spatial Dependence),而其開發程度較高的區域,愈是容易吸引銀行分行的設立。

In recent years, under the trend of slower population growth and aging, in order to realize the characteristics of the distribution of domestic financial institutions in the towns and cities of Taiwan, each banker must review its own resources for competitive advantage to formulate their direction, and set goals for the long-term development of branches for achieving sustainable operations. In light of spatial level investigation, which coupled with demographic attributes data of Taiwan’s towns and cities. The possible spatial aggregation phenomenon, with the distribution of random or scattered, and then analyzes the locations of distributed spatial aggregation to find out the characteristics of each region in Taiwan. By spatial autocorrelation analysis, the scope of empirical analysis results with the global Moran's I and local indicators of spatial association (LISA) which are based on the districts of towns and cities in Taiwan.
To explore the spatial clustering phenomenon of the number of branches of financial institutions in Taiwan, it is found that the analysis results of the demographic attribute data of Sex ratio, the population density, old-age dependency ratio, aging index, and total population in Taiwan’s towns and cities based on the global spatial autocorrelation analysis illustrate clustered distribution in spatial, and the spatial relationship is positive with greater than negative correlations, which means that Taiwan’s 7760 villages are presented in concentrated distribution; and with the spatial autoregressive model has the most explanatory power. Furthermore, through the method of regional autocorrelation analysis comprehend the relationship between the elderly population and the spatial unit (the spatial unit and its adjacent units) with the bank branches establish. The aggregation strength correlation is divided into four quadrants: "HH", "LL", "HL", and "LH". It is found that the establishment of branches of financial institutions in the "HH" related areas of the elderly population is insufficient. Highly urbanized areas are sufficient (Taipei City, New Taipei City, Taoyuan City, Taichung City, Tainan City, Kaohsiung City). In conclusion, the empirical results indicate that the number of domestic bank branches in all regions of Taiwan does have spatial dependence, and regions with more development are more likely to attract the establishment of bank branches apparently.

Contents
誌謝 i
摘要 ii
Abstract iii
Contents iv
List of figures vi
List of tables viii
Chapter 1 Introduction 1
1.1 Research background and motivation 1
1.2 Research purpose 2
1.3 Research processes 3
Chapter 2 Literature review 6
2.1 Spatial theory of urban local 6
2.2 Distribution of national income inequality and aging population 7
2.3 Factors influencing the location selection of financial institutions 10
2.4 The method of Bank Branches selection measurement 11
2.5 Spatial autocorrelation analysis 12
Chapter 3 Research methodology 14
3.1 Spatial econometric analysis 14
3.2 Analysis of spatial distribution characteristics 15
3.3 Spatial autocorrelation analysis 18
3.4 Spatial hot spots and path analysis 20
3.5 Measurement of potential spatial accessibility 23
Chapter 4 Experimental results 28
4.1 Descriptive statistics 28
4.2 Discussion on population spatial correlation distribu- tion and aggregation 35
4.3 Regional distribution of the elderly population 47
4.4 Analysis of the accessibility of the elderly population to bank resources 63
Chapter 5 Conclusions 68
5.1 Conclusions 68
5.2 Future research 70
References 71

List of figures
Figure 1 1 Research Processes 5
Figure 3 1 Schematic Diagram of the Definition of the Neighbor Relationship of the Contiguity Spatial Weight Matrix: Bishop, Rook, Queen 17
Figure 3 2 Schematic diagram of positive and negative results of spatial autocorrelation 19
Figure 3 3 Schematic diagram of the geometric center position of the mean center group 27
Figure 4 1 Bank branches distribution level map of northern region 31
Figure 4 2 Bank branches distribution level map of central region 32
Figure 4 3 Bank branches distribution level map of southern region 33
Figure 4 4 Bank branches distribution level map of eastern region 34
Figure 4 5 Bank branches distribution level map of remote islands 35
Figure 4 6 Results of spatial autocorrelation analysis of population sex ratio in 2020 38
Figure 4 7 Moran's I scatterplot of population sex ratio in 2020 39
Figure 4 8 Results of spatial autocorrelation analysis of population density ratio in 2020 40
Figure 4 9 Moran's I scatterplot of population density ratio in 2020 40
Figure 4 10 Results of spatial autocorrelation analysis of old-age dependency ratio 42
Figure 4 11 Moran's I scatterplot of old-age dependency ratio in 2020 42
Figure 4 12 Results of spatial autocorrelation analysis of aging index in 2020 44
Figure 4 13 Moran's I scatterplot of aging index in 2020 44
Figure 4 14 Results of spatial autocorrelation analysis of the total population in 2020 46
Figure 4 15 Moran's I scatterplot of total population in 2020 46
Figure 4 16 Analysis of regional LISA indicators in the villages of 65-year-old in northern region of Taiwan 47
Figure 4 17 Analysis of regional LISA indicators in the villages of 65-year-old in the central region of Taiwan 51
Figure 4 18 Analysis of regional LISA indicators in the villages of 65-year-old in southern region of Taiwan 56
Figure 4 19 Analysis of regional LISA indicators in the villages of 65-year-old in eastern region of Taiwan 60
Figure 4 20 Analysis of regional LISA indicators in the villages of 65-year-old in the remote islands of Taiwan 62
Figure 4 21 Areas with shortage of financial institutions in the northern region 64
Figure 4 22 Areas with shortage of financial institutions in the central region 65
Figure 4 23 Areas with shortage of financial institutions in the southern region 66
Figure 4 24 Areas with shortage of financial institutions in the eastern region 67

List of tables
Table 3 1 Moran's I index description 19
Table 3 2 ZIi Value description 20
Table 4 1 List of financial institutions in Taiwan (end of January 2021) 29
Table 4 2 Location set up by branches of financial institutions 31
Table 4 3 Statistics of 10 or more bank branches in northern region 32
Table 4 4 Statistics of 10 or more bank branches in central region 33
Table 4 5 Statistics of 10 or more bank branches in southern region 34
Table 4 6 Comprehensive of all values of global spatial autocorrelation analysis of 7760 villages of Taiwan in 2020 36
Table 4 7 Comprehensive LISA aggregation analysis in the village of 65-year-old population in northern region of Taiwan 48
Table 4 8 Comprehensive LISA aggregation analysis in the villages of 65-year-old population in central region of Taiwan 52
Table 4 9 Comprehensive LISA aggregation analysis in the villages of 65-year-old population in southern region of Taiwan 56
Table 4 10 Comprehensive LISA aggregation analysis in the villages of 65-year-old population in eastern region of Taiwan 61
Table 4 11 Comprehensive LISA aggregation analysis in the villages of 65-year-old population in the remote islands of Taiwan 62

Table 4 12 Areas with shortage of financial institutions in the northern region 64
Table 4 13 Areas with shortage of financial institutions in the central region 65
Table 4 14 Areas with shortage of financial institutions in the southern region 66
Table 4 15 Areas with shortage of financial institutions in the eastern region 67


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