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系統識別號 U0026-0407201916573500
論文名稱(中文) 貨物旅行的距離是否越來越遠?全球貨物的進出口距離樣態
論文名稱(英文) Are We Shipping Farther?The Global Distance Pattern of Export and Import Freight Trip
校院名稱 成功大學
系所名稱(中) 交通管理科學系
系所名稱(英) Department of Transportation & Communication Management Science
學年度 107
學期 2
出版年 108
研究生(中文) 蕭家宜
研究生(英文) Chai-Yi Hsiao
學號 R56064099
學位類別 碩士
語文別 英文
論文頁數 128頁
口試委員 指導教授-林珮珺
口試委員-戴佐敏
口試委員-高凱聲
中文關鍵字 貿易距離  進出口  貨物旅行  空間統計  空間自相關  間計量經濟模型 
英文關鍵字 Trade distance  Export and import  Freight trip  Spatial statistics  Moran’s I  Spatial econometrics models 
學科別分類
中文摘要 本研究目的在得知全球進出口距離模式歷年的變化,並藉由分析全球每進出口$1,000所需運送的距離以了解「全球每進出口$1,000所需運送的距離是否增加?」為了建構全球進出口距離模式,我們先評估進出口$1,000所需運送的距離在全域與區域是否呈現離散或集聚的分布型態,以確保本研究可繼續進行,之後便以每進出口$1,000所需運送的距離為因變數;一個國家對貿易的脆弱程度為自變數,以建構空間計量經濟模型,並假設一個國家對貿易的脆弱程度(依賴程度)會影響其每進出口$1,000所需運送的距離。空間計量經濟模型以2001至2017年的面板數據建構以分析全球每進出口$1,000所需運送的距離的變化。其結果顯示由於每年進出口對象與進出口產品的變化,全球每進出口$1,000所需運送的距離有每年增加的趨勢,而以出口而論,越不依賴的國家就會將太遠的需求國剔除,導致他們每出口$1,000的距離低;以進口而論,越不依賴貿易的國家,通常是因其國內已發展出各種產品滿足國內需求,擁有較高的購買力,導致每進口$1,000的距離高。以與他國的關係而論,當一個國家的鄰近國家越依賴出口,本國便會出口越貴的產品到世界各國,造成每出口$1,000的距離增加;當一個國家的鄰近國家越不依賴進口,本國便會因為失去了鄰國的市場而開始與較遠的國家做生意,造成每進口$1,000的距離增加。
英文摘要 This paper is to construct trade distance pattern of freight trip through years and answer the question “Are we shipping farther?” by evaluating the trade distance per thousand US dollars globally. We construct spatial econometrics models for export and import distance per thousand US dollars. We assume the degree of vulnerability to trades affects the export and import distance per thousand US dollars of a country. The models are constructed based on a panel data form with 201 countries from 2001 to 2017. The result suggests that trade distance per thousand US dollars of each country has generally increased, and the changes in them depend on the shiftings in trades partners and values of products. In respect of export, a country excludes the customers that are too far from it when it becomes less dependent on exports, which make export distance per thousand US dollars become lesser. In respect of import, most of the major importer around the world can make profits from their own industries, hence they are less vulnerable to imports and have greater purchasing power, which also means having greater import distance per thousand US dollars. To the relationship between a country and its neighbor countries, most of the exporters prone to sell expensive products to all of the countries around the world when their neighbors become more dependent on trades; a country tend to start trading with the countries far from it when its neighbor countries become less vulnerable on trades.
論文目次 Chapter One Introduction 1
1.1 Research Background 1
1.2 Research Motivation 8
1.2.1 Changes in global distance patterns of international trades 8
1.2.2 Argument against death of distance 8
1.2.3 Scarcity of research applying spatial concepts 9
1.3 Research Objective 9
1.4 Research Area 10
1.5 Research Framework 10
Chapter Two Literature Review 12
2.1 Import and Export Freight Trip Distance 12
2.1.1 Rudimentary form relates trades to incomes and geographical distance 12
2.1.2 Death of distance 13
2.1.3 Ways to describe the effect of distance on trades 15
2.2 Distance-decay Effect 15
2.3 Spatial Autocorrelation (Moran’s I Statistics) 21
2.3.1 Global spatial autocorrelation (Global Moran’s I) 21
2.3.2 Local spatial autocorrelation (Local Moran’s I) 22
2.4 Spatial Econometric Model 28
2.4.1 Spatial autoregressive (SAR) model 28
2.4.2 Spatial Durbin model (SDM) 30
Chapter Three Methodology 32
3.1 Data Structure and Sources 33
3.1.1 Data structure 34
3.1.2 Data sources 34
3.2 Explanatory Variables 39
3.2.1 Export and import distance per thousand US dollars 39
3.2.2 Degrees of vulnerability to exports and imports 39
3.3 Spatial Weights Matrix 41
3.3.1 Weight of interaction intensity between two countries 42
3.3.2 Weight of geographical distance between two countries 44
3.3.3 Weight of Free-trade agreements between two countries 44
3.4 Spatial Data Analysis 45
3.4.1 Global spatial autocorrelation 46
3.4.2 Local spatial autocorrelation 48
3.5 Spatial Econometrics Model 49
3.5.1 Ordinary least squares (OLS) model 49
3.5.2 Spatial autoregressive (SAR) model 50
3.5.3 Spatial lag of X (SLX) model 51
3.5.4 Spatial Durbin model (SDM) 52
3.6 Direct and Indirect Effects on Trade Distance per Thousand US Dollars 53
Chapter Four Result 55
4.1 Spatial Data Analysis 55
4.1.1 Global spatial autocorrelation 55
4.1.2 Local spatial autocorrelation 60
4.2 Spatial Econometrics Model 96
4.2.1 Correlation coefficient of degree of vulnerability to trades ( ) 96
4.2.1.1 Weight of reciprocal of geographical distance 96
4.2.1.2 Weight of interaction intensity 96
4.2.2 Spatial lag coefficient of vulnerability to trades ( ) 98
4.2.2.1 Weight of reciprocal of geographical distance 98
4.2.2.2 Weight of interaction intensity 98
4.2.3 Spatial lag coefficients of trade distance per thousand US dollars ( ) 98
4.2.3.1 Weight of reciprocal of geographical distance 98
4.2.3.2 Weight of interaction intensity 99
4.2.4 Correlation coefficient of trade distance per thousand US dollars in last year ( ) 99
4.3 Direct and Indirect Effects on Trade Distance per Thousand US Dollars 104
4.3.1 Weight of reciprocal of geographical distance 104
4.3.2 Weight of interaction intensity 105
Chapter Five Conclusion 112
5.1 Summary of The Results 112
5.2 Research Contribution 117
5.2.1 Academic contribution 117
5.2.2 Practical contribution 118
5.3 Limitation and Future Research 119
Reference 121
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