| 姓名 |
陳淑芬(Shu-Fen Chen)
查詢紙本館藏 |
畢業系所 |
產業經濟研究所在職專班 |
| 論文名稱 |
未成年時期債務承擔對成年債務償還能力之影響：以臺灣健保費繳納行為為例
(The Impact of Adolescent Debt on Adult Debt Repayment Ability: A Case Study of Health Insurance Premium Payment Behavior in Taiwan)
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| 摘要(中) |
本研究關注未成年時期負債對成年後債務償還能力的影響。從實際案例中發現，未成年人積欠健保費無力繳納，可能導致成年後繼續累積欠費，並且受到行政執行程序的追償。相較於無欠費的未成年人，成年即承受健保欠費壓力是否影響成年後的繳納行為，因此本研究以臺灣健保費繳納行為為例，探討未成年負債對成年債務償還能力的影響。
為了解未成年健保欠費對成年健保繳納行為的影響，本研究設定了兩個對立假說：1. 未成年健保欠費對成年健保欠費存在負向關聯，2.未成年健保欠費對成年健保欠費存在正向關聯。本研究利用2007年至2022年的健保資料，設置成年平均累積欠費為被解釋變數，未成年欠費、個人特徵和父母特徵為操作變數，建構了4個線性迴歸模型和4個羅吉斯迴歸模型，進行敘述性統計、相關分析及線性和羅吉斯迴歸分析，以驗證未成年欠費是否對成年欠費存在正向或負向關聯。
統計結果顯示未成年欠費對成年欠費具呈正向關聯。未成年時期欠費高，成年時期平均欠費金額也高。在線性迴歸模型結果未成年高欠費組的成年時期欠費金額是未成年低欠費組的1倍以上，在羅吉斯迴歸結果，勝算比均大於1，未成年高欠費組發生成年高欠費高於未成年低欠費組，未成年時期背負健保費債務確實會影響成年後健保費債務的償還能力。除了前述個模型外，本研究另以成年時期與未成年時期累積欠費之欠費差額為被解釋變數，進一步來確認保險對象是否償還欠費，實證結果為未成年高欠費組比低欠費償還較多欠費。
本研究證實未成年時期的高健保欠費會增加成年後的欠費，進而影響經濟能力並顯示出社會不公平。因此，本研究提出兩點政策建議。首先，建議在健保法中明確賦予法定代理人責任與義務，避免讓未成年人承擔健保費。具體而言，應修訂法規，規定未成年人的保費繳納責任由法定代理人或監護人負責，並在移送行政執行時，以法定代理人名義進行，以保護未成年人不受公法債務追償。此外，法定代理人或監護人應履行扶養義務，負擔未成年人的基本生活費用。
其次，本研究發現，對於未成年高欠費組，補助有助於減少累積欠費。建議健保署利用承保資料進行高風險家庭管控，如發現家庭成員入監服刑或父母死亡等情況，即進行社會關懷網絡通報，透過村里長或社工關懷父母死亡或收容人家庭成員，提供適時協助，或尋求社會資源幫助家庭維持功能，並優先補助通報對象。 |
| 摘要(英) |
This study focuses on the impact of debt incurred during adolescence on the ability to repay debt in adulthood. It has been found from actual cases that minors with unpaid health insurance premiums may continue to accumulate more debt into adulthood and face administrative enforcement procedures. Compared to minors without unpaid premiums, those who face the pressure of health insurance debt as soon as they reach adulthood may exhibit different payment behaviors. Therefore, this study explores the impact of adolescent debt on the ability to repay debt in adulthood, using Taiwan’s health insurance premium payment behavior as an example.
To understand the impact of unpaid health insurance premiums during adolescence on payment behavior in adulthood, this study sets up two opposing hypotheses: 1) Unpaid health insurance premiums during adolescence have a negative correlation with unpaid premiums in adulthood, and 2) Unpaid health insurance premiums during adolescence have a positive correlation with unpaid premiums in adulthood. This study uses health insurance data from 2007 to 2022, with average cumulative unpaid premiums in adulthood as the dependent variable, and unpaid premiums during adolescence, personal characteristics, and parental characteristics as independent variables. Four linear regression models and four logistic regression models were constructed, and descriptive statistics, correlation analysis, and linear and logistic regression analyses were conducted to verify whether unpaid premiums during adolescence have a positive or negative correlation with unpaid premiums in adulthood.
Statistical results indicate a positive correlation between unpaid premiums during adolescence and those in adulthood. Higher unpaid premiums during adolescence are associated with higher average unpaid premiums in adulthood. Linear regression model results show that the unpaid premiums in adulthood for those with high unpaid premiums during adolescence are more than double those of the low unpaid premiums group. In logistic regression results, the odds ratios are greater than 1, indicating that those with high unpaid premiums during adolescence are more likely to have high unpaid premiums in adulthood compared to those with low unpaid premiums during adolescence. This demonstrates that carrying health insurance debt during adolescence indeed affects the ability to repay health insurance debt in adulthood. In addition to the aforementioned models, this study also uses the difference in cumulative unpaid premiums between adulthood and adolescence as the dependent variable to further confirm whether individuals repay their debt. The empirical results show that those with high unpaid premiums during adolescence repay more debt than those with low unpaid premiums.
This study confirms that high unpaid health insurance premiums during adolescence increase unpaid premiums in adulthood, thereby affecting economic capacity and highlighting social inequality. Therefore, this study presents two policy recommendations. First, it is recommended to clarify the responsibilities and obligations of legal guardians in the National Health Insurance Act to prevent minors from bearing health insurance premiums. Specifically, regulations should be amended to stipulate that the responsibility for paying premiums for minors falls on the legal guardian, and administrative enforcement should be conducted in the name of the legal guardian to protect minors from public debt recovery. Legal guardians should also fulfill their obligation to provide for the essential living expenses of minors.
Secondly, the study finds that subsidizing health insurance for those in the high unpaid premiums group during adolescence can help reduce cumulative unpaid premiums. It is recommended that the National Health Insurance Administration utilize insurance data to monitor high-risk families, such as those with incarcerated family members or deceased parents, and initiate social care network notifications. Through the care of village heads or social workers, families of deceased or incarcerated individuals can receive timely assistance or seek social resources to maintain family functionality. Priority should be given to subsidizing those identified through these notifications. |
| 關鍵字(中) |
★ 全民健保
★ 未成年欠費
★ 弱勢
★ 保費補助
★ 線性迴歸模型
★ 羅吉斯迴歸模型 |
關鍵字(英) |
★ National Health Insurance
★ adolescent unpaid premiums
★ disadvantaged groups
★ premium subsidies
★ linear regression model
★ logistic regression model0 |
| 論文目次 |
中文摘要 i
英文摘要 iii
誌謝 v
目錄 vi
圖目錄 viii
表目錄 ix
一、 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究流程 4
二、 文獻探討 6
2.1 債務態度 6
2.2 健保相關研究 7
2.3 兒少與弱勢 11
2.4 未成年人健保在保與欠費資料 13
三、 資料來源與研究方法 19
3.1 資料來源與樣本配對 19
3.2 各變數操作型定義 20
3.2.1 被解釋變數 20
3.2.2 解釋變數 20
3.3 研究假說與模型 27
3.3.1 線性複迴歸模型(Linear Multiple Regression Model) 28
3.3.2 羅吉斯迴歸模型(Logistic Regression Model) 29
四、 實證結果與討論 32
4.1 敘述性統計 32
4.2 線性複迴歸模型分析 41
4.2.1 未成年欠費與個人特徵變數對成年欠費線性迴歸分析 41
4.2.2 個人特徵加父母特徵變數線性迴歸分析 45
4.3 羅吉斯迴歸模型 49
4.3.1 個人特徵變數羅吉斯迴歸分析 50
4.3.2 個人特徵加父母特徵羅吉斯迴歸分析 53
4.4 進一步分析 57
4.5 小結 63
五、 結論及政策建議 67
5.1 結論 67
5.2 研究限制 70
5.3 政策建議 70
參考文獻 73
附錄 76
附錄1 以未成年欠費分組統計各解釋變數成年時期累積欠費、未成年時期累積欠費、欠費差額之平均值 76 |
| 參考文獻 |
一、中文部分
1.王濟川，郭志剛（2003）。Logistic迴歸模型 : 方法及應用 = Logistic regression models : methods and application，臺北市：五南。
2.古允文、詹宜璋(1998)。臺灣地區老人經濟安全與年金政策：社會排除觀點初探。人文及社會科學集刊。10(2)，191-225。
3.全民健康保險統計（2023）。取自https://www.nhi.gov.tw/ch/lp-3416-1.html。
4.江宛霖、江東亮（2016）。台灣全民健康保險的減貧效果。台灣公共衛生雜誌，35(2)，164-171。https://doi-org.ezproxy.lib.ncu.edu.tw/10.6288/TJPH201635104082
5.李雅萍（2015）。經濟弱勢家戶子女成年後再度落入貧窮循環歷程之質性分析。﹝碩士論文。東吳大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/g7aez8。
6.呂建德（2003）。與弱勢者的團結―尋找全民健保中的正義基礎。台灣社會研究季刊，(51)，51-94。
https://doi-org.ezproxy.lib.ncu.edu.tw/10.29816/TARQSS.200309.0002
7.呂朝賢（2010）。臺灣兒童貧窮：輪廓與成因。臺灣社會福利學刊，9(1)，97-137。
https://doi-org.ezproxy.lib.ncu.edu.tw/10.6265/TJSW.2010.9(1)。
8.林季平（2008）。影響加入台灣全民健保的社會經濟不均等要素。社會政策與社會工作學刊，12(2)，91-122。
https://doi-org.ezproxy.lib.ncu.edu.tw/10.6785/SPSW.200812.0091
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10.陳杏容、陳易甫（2019）。早期家庭經濟劣勢與成年初顯期發展成果之潛在剖析分析與其影響因子。中華心理衛生學刊，32(1)，89-117。
https://doi-org.ezproxy.lib.ncu.edu.tw/10.30074/FJMH.201903_32(1).0005
11.曹孟儀（2012）。影響健保鎖卡率的區位因素分析。﹝碩士論文。國立臺北大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/89gh78。
12.張紹勳（2018）。羅吉斯迴歸及離散選擇模型 : 應用STaTa統計 = Logistic regression and discrete choice models in STaTa。臺北市：五南。
13.連賢明（2011）。如何使用健保資料推估社經變數。人文及社會科學集刊，23(3)，371-398。
https://doi-org.ezproxy.lib.ncu.edu.tw/10.6350/JSSP.201109.0371
14.蔡貞慧、林美雀（2010）。健保欠費、中斷與2003年健保特赦條款。臺大社會工作學刊，(21)，115-152。
https://doi-org.ezproxy.lib.ncu.edu.tw/10.6171/ntuswr2010.21.03
15.薛承泰 (2008)。臺灣地區兒少貧窮：1991-2005 年的趨勢研究。臺灣社會學刊，(40)，89-130。
16.簡有璋、許綉華、蔡淑娟、陳春桃、蔣文敏、劉玉香（2018）。健保弱勢協助向前行–欠費催收及協助之策略探討 （以中區業務組為例），衛生福利部107年度自行研究計畫研究報告。
二、英文部分
1.Almenberg, J., Lusardi, A., Säve‐Söderbergh, J., & Vestman, R. (2021).” Attitudes towards debt and debt behavior”. The Scandinavian Journal of Economics, 123(3), 780-809.
2.De Gayardon, A., Callender, C., & DesJardins, S. L. (2022). “Does student loan debt structure young people’s housing tenure? Evidence from England”. Journal of Social Policy, 51(2), 221-241.
3.Harris, CR, & Jenkins, M. (2006) “Gender Differences in Risk Assessment: Why do Women Take Fewer Risksthan Men?” Judgment and Decision Making. 2006;1(1):48-63.
4.Knol, M. J., van der Tweel, I., Grobbee, D. E., Numans, M. E., & Geerlings, M. I. (2007). “Estimating interaction on an additive scale between continuous determinants in a logistic regression model”. International journal of epidemiology, 36(5), 1111-1118.
5.VanderWeele, T. J. (2009). “Sufficient cause interactions and statistical interactions”. Epidemiology, 20(1), 6-13.
6.Velez, E., Cominole, M., & Bentz, A. (2019). “Debt burden after college: the effect of student loan debt on graduates’ employment, additional schooling, family formation, and home ownership”. Education Economics, 27(2), 186-206. |
| 指導教授 |
蔡明宏
蔡栢昇(Min-Hung Tsay
Pak-Sing Choi)
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審核日期 |
2024-7-24 |
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