自古以來，長壽一直是人們追求的目標，如何延緩老化更是專家學者不斷研究的重要議題。老化是多種細胞損傷隨時間累積影響的結果。這將導致身體和精神能力逐漸下降，患病風險不斷上升，最終導致死亡。人們通常以外觀及自身感覺主觀地判斷老化程度，在研究上則大多使用年齡進行相對客觀的判斷，但使用年齡當作老化的標準存在著個體差異，即使是相同年齡的兩人，老化程度可能不盡相同。因此在近年來，專家學者們不斷尋找著能夠更加準確地衡量老化的生物標記(biomarker)。
    本文使用美國國家健康營養調查(National Health and Nutrition Examination Survey, NHANES)資料庫進行分析，並選取1999~2000年及2001~2002年共兩波之樣本，以端粒(Telomere)長度當作老化的生物標記，使用Robinson(1988)所提出的半母數模型(Semiparametric regression)探討工時及超時工作時數對於老化的影響，並逐步加入工時、年齡、生活型態、身體檢查、疾病變數分成五個模型進行探討。以控制函數法(Control function approach)處理內生，使用的工具變數包含家庭總人數及每日電視電腦使用時數。
    本文的研究將大量不同的變數共同納入一起進行分析，透過大量控制可能產生影響的變數將過去模稜兩可的各項研究結果得出一個確定的答案，根據最小平方法分析所估出期望值的效果顯示: 無論在哪一個模型中，無論性別，只要將年齡加入，工時及超時工作時數及其平方項都會變得不顯著。年齡項發現明顯的男女差異，男性年齡在控制了內生性後變得非常顯著地負相關，但女性年齡在控制了內生性後卻變得完全不顯著。這顯示了使用年齡作為衡量老化的不可靠性。
    根據半母數分析結果的圖形，在控制內生性前，無論男女，工時對端粒長度的影響呈現一條水平線，並在工時達到80小時後快速下降，但在控制內生性後，男女出現了不同的結果，男性端粒長度隨著工時增加而更長，女性則是相反呈現一條凹向下的走勢，女性端粒長度隨著工時增加而更短，老化進展較快。
    工時對於端粒長度的影響存在明顯的種族差異，白人之工時對於端粒長度的影響並不大；非裔美國人大多在工時達到30小時後開始下降；墨西哥裔整體而言呈現下降趨勢；其他種族在工時達到30小時下降，40小時時稍微回升，然後繼續下降。
    20-65歲之模型與20-84歲模型結果類似，並未觀察到明顯的差異。若將男女各分成21-40歲、41-60歲以及61-84歲三組分別進行半母數分析，男性21-40歲組之端粒長度會隨著工時增加而變長，其餘兩組則大致呈現水平線；女性每一組別之端粒長度都會隨著工時增加而變短，顯示不論是哪一年齡組，工時的增加都明顯會對女性的健康產生影響。

    Since ancient times, longevity has been a pursuit of humanity, and understanding how to delay aging remains a critical research topic for experts and scholars. Aging results from accumulated damage to various cells over time, leading to gradual declines in physical and cognitive abilities, increased susceptibility to diseases, and ultimately, death. While people often subjectively assess aging based on appearance and personal sensations, research typically relies on age as a relatively objective measure. However, using age as a standard for aging overlooks individual differences, as two individuals of the same age may experience aging differently. Therefore, experts and scholars have been actively seeking more accurate biological markers (biomarkers) to measure aging in recent years. This thesis deals with the potential endogeneity problem by using the control function approach. The instrumental variables includes the total number of family members and daily hours of TV and computer use.
This study utilizes data from the National Health and Nutrition Examination Survey (NHANES), specifically samples from 1999-2000 and 2001-2002 waves. Telomere length is chosen as the biomarker for aging. Robinson's (1988) semiparametric regression is employed to investigate the impact of working hours and overtime on aging. The analysis progressively includes variables related to working hours, age, lifestyle, health examinations, and diseases across five models.
By incorporating a wide range of variables into the analysis and controlling for potential confounding factors, this study aims to provide definitive answers that reconcile ambiguous findings from previous research. The results of ordinary least squares indicate that, across all models and irrespective of gender, once age is included, neither working hours nor overtime—nor their squared terms—are statistically significant. Significant gender differences in the effect of age are observed: male age shows a significant negative correlation after controlling for endogeneity, whereas female age becomes non-significant under the same conditions. This underscores the unreliability of age as a sole measure of aging.
Graphical representations of the results from the semiparametric analysis show that, before controlling for endogeneity, the impact of working hours on telomere length appears as a flat line for both genders, with a rapid decline after 80 hours. However, after controlling for endogeneity, distinct patterns emerge: telomere length for males increases with working hours, whereas for females, it decreases in a concave downward trend with increasing working hours, indicating faster-aging progression for women.
There are significant racial differences in the effect of working hours on telomere length. For white, the impact of working hours on telomere length is not substantial. For African Americans, telomere length generally starts to decrease after 30 hours of work. For Mexican Americans, the overall trend shows a decline. For other racial groups, telomere length decreases after 30 hours of work, slightly increases at 40 hours, and then continues to decrease.
The results for the models covering ages 20-65 and 20-84 indicate no significant differences. When analyzing males and females separately across three age groups—21-40 years, 41-60 years, and 61-84 years—using semiparametric methods, it was observed that in the 21-40 age group, telomere length increased with more work hours, while the telomere length in the other two age groups remained relatively stable. Conversely, in all female age groups, telomere length decreased as work hours increased, suggesting that increased work hours consistently negatively affect female health, regardless of age.

目錄
第一章 緒論						1
第一節 研究背景與動機					1
第二節 研究目的						2
第三節 研究架構						2
第二章 文獻回顧						4
第三章 資料來源與變數定義					8
第一節 資料來源						8
第二節 樣本篩選						9
第三節 變數定義						9
第四節 敘述統計						22
第四章 實證模型設定					31
第五章 實證結果分析					34
第一節 OLS廻歸結果(未控制內生)				34
第二節 OLS廻歸結果(控制內生)				37
第三節 半母數廻歸結果					41
第六章 討論						73
第一節 種族差異						73
第二節 年齡差異-65歲					75
第三節 年齡差異-20、40、60歲				76
第四節 產業差異						78
第七章 結論						79
第一節 結論						79
第二節 研究限制						82
第三節 未來發展						82
第四節 政策建議						82
第五節 延伸討論						83
參考文獻							84
附錄							90

表目錄
表 1  文獻回顧表						6
表 2  變數概述表						19
表 3  男性敘述統計表					27
表 4  女性敘述統計表					29
表 5  半母數廻歸結果-男性(工時)				45
表 6  半母數廻歸結果-女性(工時)				48
表 7  半母數廻歸結果-女性(工時)_IV1			52
表 8  半母數廻歸結果-男性(工時)_IV2			56
表 9  半母數廻歸結果-女性(工時)_IV2			59
表 10  半母數廻歸結果-男性(超時工作時數)			63
表 11  半母數廻歸結果-女性(超時工作時數)			66
表 12  半母數廻歸結果-男性(超時工作時數)_IV2		70

附表 1  OLS廻歸結果-男性(工時)				90
附表 2  OLS廻歸結果-女性(工時)				92
附表 3  OLS廻歸結果-男性(超時工作時數)			94
附表 4  OLS廻歸結果-女性(超時工作時數)			96
附表 5  第一階段廻歸結果-男性(工時)			98
附表 6  第二階段廻歸結果-男性(工時)			101
附表 7  第一階段廻歸結果-女性(工時)			105
附表 8  第二階段廻歸結果-女性(工時)			108
附表 9  男性廻歸模型檢定結果-工時				112
附表 10  女性廻歸模型檢定結果-工時				113
附表 11  第一階段廻歸結果-男性(超時工作時數)		114
附表 12  第二階段廻歸結果-男性(超時工作時數)		117
附表 13  第一階段廻歸結果-女性(超時工作時數)		121
附表 14  第二階段廻歸結果-女性(超時工作時數)		124
附表 15  男性廻歸模型檢定結果-超時工作時數			128
附表 16  女性廻歸模型檢定結果-超時工作時數			129
附表 17  OLS廻歸結果(就業)				130
附表 18  OLS廻歸結果(就業)_IV				133
附表 19  OLS廻歸結果(超時工作)				136
附表 20  OLS廻歸結果(超時工作)_IV				139
附表 21  半母數廻歸結果(年齡)				142
附表 22  半母數廻歸結果-非西班牙裔白人(工時)		147
附表 23  半母數廻歸結果-非西班牙裔非裔美國人(工時)		152
附表 24  半母數廻歸結果-墨西哥裔美國人(工時)		157
附表 25  半母數廻歸結果-其他種族(工時)			162
附表 26  男性廻歸模型檢定結果-工時-非西班牙裔白人		167
附表 27  男性廻歸模型檢定結果-工時-非西班牙裔非裔美國人	168
附表 28  男性廻歸模型檢定結果-工時-墨西哥裔美國人		169
附表 29  男性廻歸模型(加入工具變數)檢定結果-工時-其他種族	170
附表 30  半母數廻歸結果-男性-非西班牙裔白人(工時)_IV2	171
附表 31  半母數廻歸結果-男性-非西班牙裔非裔美國人(工時)_IV2	174
附表 32  半母數廻歸結果-男性-墨西哥裔美國人(工時)_IV2	177
附表 33  半母數廻歸結果-男性-其他種族(工時)_IV2		180
附表 34  女性廻歸模型檢定結果-工時-非西班牙裔白人		183
附表 35  女性廻歸模型檢定結果-工時-非西班牙裔非裔美國人	184
附表 36  女性廻歸模型檢定結果-工時-墨西哥裔美國人		185
附表 37  女性廻歸模型檢定結果-工時-其他種族		186
附表 38  半母數廻歸結果-女性-非西班牙裔白人(工時)_IV	187
附表 39  半母數廻歸結果-女性-非西班牙裔非裔美國人(工時)_IV	192
附表 40  半母數廻歸結果-女性-墨西哥裔美國人(工時)_IV1	197
附表 41  半母數廻歸結果-女性-其他種族(工時)_IV1		200
附表 42  半母數廻歸結果-65歲以下(工時)			203
附表 43  男性廻歸模型檢定結果-工時-65歲以下		208
附表 44  女性廻歸模型檢定結果-工時-65歲以下		209
附表 45  半母數廻歸結果-男性-65歲以下(工時)_IV2		210
附表 46  半母數廻歸結果-女性-65歲以下(工時)_IV		213
附表 47  半母數廻歸結果-依照年齡分組-男性(工時)		219
附表 48  半母數廻歸結果-依照年齡分組-女性(工時)		224
附表 49  男性廻歸模型檢定結果-工時-20~40歲			229
附表 50  男性廻歸模型檢定結果-工時-41~60歲			230
附表 51  男性廻歸模型檢定結果-工時-61~84歲			231
附表 52  半母數廻歸結果-男性-20~40歲-工時(IV2)		232
附表 53  半母數廻歸結果-男性-41~60歲(工時)_IV2		235
附表 54  半母數廻歸結果-男性-61~84歲(工時)_IV2		238
附表 55  女性廻歸模型檢定結果-工時-20~40歲			241
附表 56  女性廻歸模型檢定結果-工時-41~60歲			242
附表 57  女性廻歸模型檢定結果-工時-61~84歲			243
附表 58  半母數廻歸結果-女性-20~40歲(工時)_IV1		244
附表 59  半母數廻歸結果-女性-20~40歲(工時)_IV2		247
附表 60  半母數廻歸結果-女性-41~60歲(工時)_IV1		250
附表 61  半母數廻歸結果-女性-41~60歲(工時)_IV2		253
附表 62  半母數廻歸結果-女性-61~84歲(工時)_IV2		256
附表 63  半母數迴歸結果-第二級產業(工時)			259
附表 64  半母數迴歸結果-第三級產業(工時)			264

圖目錄
圖 1  半母數廻歸結果-男性(工時)				47
圖 2  半母數廻歸結果-女性(工時)				50
圖 3  半母數廻歸結果-女性(工時)_IV1			54
圖 4  半母數廻歸結果-男性(工時)_IV2			58
圖 5  半母數廻歸結果-女性(工時)_IV2			61
圖 6  半母數廻歸結果-男性(超時工作時數)			65
圖 7  半母數廻歸結果-女性(超時工作時數)			68
圖 8  半母數廻歸結果-男性(超時工作時數)_IV2		72

附圖 1  半母數廻歸結果-男性(年齡)				145
附圖 2  半母數廻歸結果-女性(年齡)				146
附圖 3  半母數廻歸結果-非西班牙裔白人-男性(工時)		150
附圖 4  半母數廻歸結果-非西班牙裔白人-女性(工時)		151
附圖 5  半母數廻歸結果-非西班牙裔非裔美國人-男性(工時)	155
附圖 6  半母數廻歸結果-非西班牙裔非裔美國人-女性(工時)	156
附圖 7  半母數廻歸結果-墨西哥裔美國人-男性(工時)		160
附圖 8  半母數廻歸結果-墨西哥裔美國人-女性(工時)		161
附圖 9  半母數廻歸結果-其他種族-男性(工時)			165
附圖 10  半母數廻歸結果-其他種族-女性(工時)		166
附圖 11  半母數廻歸結果-非西班牙裔白人-男性(工時)_IV2	173
附圖 12  半母數廻歸結果-非西班牙裔非裔美國人-男性(工時)_IV2	176
附圖 13  半母數廻歸結果-墨西哥裔美國人-男性(工時)_IV2	179
附圖 14  半母數廻歸結果-其他種族-男性(工時)_IV2		182
附圖 15  半母數廻歸結果-非西班牙裔白人-女性(工時)_IV1	190
附圖 16  半母數廻歸結果-非西班牙裔白人-女性(工時)_IV2	191
附圖 17  半母數廻歸結果-非西班牙裔非裔美國人-女性(工時)_IV1	195
附圖 18  半母數廻歸結果-非西班牙裔非裔美國人-女性(工時)_IV2	196
附圖 19  半母數廻歸結果-墨西哥裔美國人-女性(工時)_IV1	199
附圖 20  半母數廻歸結果-其他種族-女性(工時)_IV1	        202
附圖 21  半母數廻歸結果-65歲以下-男性(工時)	        206
附圖 22  半母數廻歸結果-65歲以下-女性(工時)	        207
附圖 23  半母數廻歸結果-65歲以下-男性(工時)_IV2            212
附圖 24  半母數廻歸結果-65歲以下-女性(工時)_IV1        	217
附圖 25  半母數廻歸結果-65歲以下-女性(工時)_IV2	        218
附圖 26  半母數廻歸結果-20~40歲-男性(工時)		        221
附圖 27  半母數廻歸結果-41~60歲-男性(工時)		        222
附圖 28  半母數廻歸結果-61~84歲-男性(工時)		        223
附圖 29  半母數廻歸結果-20~40歲-女性(工時)		        226
附圖 30  半母數廻歸結果-41~60歲-女性(工時)		        227
附圖 31  半母數廻歸結果-61~84歲-女性(工時)		        228
附圖 32  半母數廻歸結果-20~40歲-男性(工時)_IV2		234
附圖 33  半母數廻歸結果-41~60歲-男性(工時)_IV2		237
附圖 34  半母數廻歸結果-61~84歲-男性(工時)_IV2		240
附圖 35  半母數廻歸結果-20~40歲-女性(工時)_IV1		246
附圖 36  半母數廻歸結果-20~40歲-女性(工時)_IV2		249
附圖 37  半母數廻歸結果-41~60歲-女性(工時)_IV1		252
附圖 38  半母數廻歸結果-41~60歲-女性(工時)_IV2		255
附圖 39  半母數廻歸結果-61~84歲-女性(工時)_IV2		258
附圖 40  半母數迴歸結果-第二級產業-男性(工時)		262
附圖 41  半母數迴歸結果-第二級產業-女性(工時)		263
附圖 42  半母數迴歸結果-第三級產業-男性(工時)		267
附圖 43  半母數迴歸結果-第三級產業-女性(工時)		268

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