臺灣博碩士論文加值系統

因通勤與日常生活需求，機車為台灣最常見的交通工具。目前台灣機車強制險的保費定價僅依據四種排氣量與保險期間作為計算保費依據，而近年來因為環境保護意識抬頭，政府採取電動車的補貼措施，各家廠商也積極推出新型電動車，帶動購買電動車的人數逐年增加。因此，本論文深入比較電動車與油車的風險因子是否有差異？分析之理賠風險為理賠機率、理賠成本、與理賠幅度；即觀察理賠發生的機率、平均一張保單的理賠成本、與理賠保單的平均理賠金額。
電動車樣本的迴歸結果發現，排氣量39 hp 比1-3 hp之理賠機率較高；直接通路比保險代理人之理賠機率較高；及其他地區比北部之理賠機率較高。電動車並無任何影響風險因子會影響理賠成本。電動車理賠保單中已婚者之理賠幅度較高；18-19歲理賠幅度比30-39歲族群較低；南部理賠幅度比北部較低。
在油車樣本中，四組排氣量對理賠並無任何顯著差異。但是在廠牌方面，三葉、光陽與其他廠牌皆比三陽之理賠機率與理賠成本顯著較高。車齡方面，11年以下比至少16年之理賠機率與理賠成本顯著較高，國產機車之理賠機率相對較高。從人因素，理賠機率與理賠成本較高者為男性、年輕族群 (相對於30-39歲)、中部與南部 (相對於北部)。油車的理賠保單中發現，理賠幅度較高者為三陽(相對於三葉)、車齡8-11年 (相對於車齡至少16年)、進口車(相對於國產車)、男性、及18-19歲理賠幅度 (相對於30-39歲)、其他地區(相對於北部)。本文實證結果建議保險公司應該區分電動車與油車的不同的訂價風險因子，以作出精算公平之選擇。此外，未來可以深入了解不同廠牌的油車風險的差異。

Due to commuting and daily needs in Taiwan, motorcycles become the most common transportation on the road. The current Compulsory Motorcycle Liability Insurance premium setting is based on four types of engine horsepower and insurance policy period. Recently, various manufacturers actively promote electric motorcycles (EMs) due to the environment protection and government subsidies, resulting in the number of riding ems increase rapidly. Therefore, this thesis analyzes whether there are differences in risk factors between ems and gasoline motorcycles. The analysis of claims risks include the claim probability, average claim costs, and the claim severity; That is, the claim probability of the total policies, the average claim cost of the total policies and the average severity of the claim policies.
Our regression results show that EMs 39 hp have a higher claim probability and the average claim costs than 1-3 hp. The direct route channel has a higher claim probability than one of insurance agents, and other regions have a higher claim probability than one of the north. For the ems sample, there are no any risk factors that will affect the cost of claims. For EMs claim policies married people have a higher claim severity, insureds aged 18-19 year olds have lower claim severity than the insureds aged 30-39, and the south has a lower claim severity than one of the north.
For gasoline motorcycles, there is no significant in engine horsepower among four groups. In addition, Yamaha, Kymco and other brands have a higher claim probability and the average claim cost than one of Sym. Motorcycles aged less than 11 years significantly increase the claim probability and the average claim costs compared with one of aged over 16. Domestic motorcycles have a higher claim probability than of imported motorcycles. Male have higher claim probability and the average claim cost, younger groups have a higher claim probability and the average claim cost than 30s. The central and southern regions have higher claim probability and the average claim cost than one of the north. For claim policies, Yamaha have lower claim severities than Sym, imported motorcycles (compared to domestic motorcycles) have a higher claim severity and motorcycles aged 8-11 years (compared to motorcycles aged 16 years and over) have higher claim severities. In addition, male insureds and insureds aged 18-19 (compared to insureds aged 30-39) have higher claim severities. Other areas have higher claim severities than one of north.
Our empirical results indicate that the risk factors for ems are different from those of gasoline motorcycles. Therefore, our policy implication is that non-life insurance companies should take into account the various risk factors between EMs and gasoline motorcycles for the future premium setting. In addition, it is noted that various gasoline motorcycles brands should be further examined the risk factors.

目錄
摘 要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 vii
圖目錄 viii
壹、 緒論 1
一、 研究背景 1
二、 研究目的 4
三、 研究流程 5
貳、 文獻探討 7
一、 從車因素 7
二、 從人因素與車禍事故 10
三、 機車保險 14
參、 研究資料與方法 16
一、 資料來源 16
二、 變數定義 16
（一） 應變數 16
（二） 自變數 17
三、 研究方法 22
（一） 敘述統計 22
（二） 迴歸分析方法（Regression Analysis） 23
（三） 迴歸模型判定方法 28
肆、 實證結果分析 29
一、 敘述統計分析 29
（一） 全體樣本 29
（二） 分群樣本 32
（三） 資料分佈 33
（四） 理賠分佈 36
二、 迴歸分析 39
（一） 電動車樣本 39
（二） 油車樣本 40
（三） 油車之廠牌迴歸分析 44
伍、 結論與建議 82
一、 結論 82
二、 建議 85
參考文獻 86







表目錄
表 3-1 變數定義與敘述 19
表 4- 1全體樣本基本敘述統計 49
表 4- 2理賠分群樣本敘述統計 51
表 4- 3車型分群樣本敘述統計 53
表 4- 4各車型理賠狀況分群樣本敘述統計 55
表 4- 5 電動車PROBIT迴歸模型分析邊際效用 59
表 4- 6 電動車TOBIT迴歸模型分析 60
表 4- 7 OLS電動車理賠幅度迴歸模型分析 62
表 4- 8 油車PROBIT迴歸模型分析邊際效用 64
表 4- 9 油車TOBIT迴歸模型分析 66
表 4- 10 OLS油車理賠幅度迴歸模型分析 69
表 4- 11油車排氣量與廠牌理賠狀況敘述統計表 73
表 4- 12 油車PROBIT迴歸模型增加廠牌分析邊際效用 74
表 4- 13 油車TOBIT迴歸模型增加廠牌分析 76
表 4- 14 OLS油車理賠幅度迴歸模型增加廠牌分析 79





圖目錄
圖 1-1機車道路交通事故（2008年至2020年） 1
圖 1-2 機動車輛新增掛牌車輛數按燃料別 (2012年至2020年) 2
圖 1-3機車每星期耗油、充(租)電費依車種類別區分 3
圖 1-4 研究流程圖 6
圖 4-1 各油車排氣量分佈 33
圖 4-2 各車齡機車種類分佈 33
圖 4-3各性別機車種類分佈 34
圖 4-4各年齡機車種類分佈 34
圖 4-5 各地區機車種類分佈 35
圖 4-6油車不同排氣量之理賠發生率 36
圖 4-7油車不同車齡理賠發生率 36
圖 4-8油車不同出廠處分佈理賠發生率 37
圖 4-9電動車、油車不同性別分佈之理賠發生率 37
圖 4-10電動車、油車不同年齡理賠發生率 38
圖 4-11電動車、油車不同地區理賠發生率 38
圖 4- 12 油車排氣量124與125 CC 性別分佈差異 44
圖 4- 13 油車排氣量124與125 CC 年齡分佈差異 44
圖 4- 14 油車排氣量124與125 CC 廠牌分佈差異 45

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