本論文集可分為三篇研究，第一篇在探討糖尿病對就業的影響，第二篇在探討體重控制緩和糖尿病對就業衝擊效果，第三篇則在探討糖尿病對所得的影響。
   第一篇：本研究運用我國八十五年｢國民營養狀況變遷調查｣、九十年與九十四年｢國民健康訪問調查｣資料，分男女按年齡分組估計罹患糖尿病對就業負向衝擊效果。為考量糖尿病為內生，使用帶工具變數雙元Probit模型，工具變數包含糖尿病家族病史和糖尿病區域盛行率。比較三年度分析可知：八十五年與九十年因缺乏糖尿病家族病史且糖尿病區域盛行率變異不夠大，糖尿病對就業負向衝擊較不明確；惟九十四年則無此二項限制，中老年男性之糖尿病對就業衝擊效果呈顯著為-24.22%，其它各組之效果多偏小或不顯著。
   第二篇：本研究運用我國九十年與九十四年｢國民健康訪問調查｣資料，分男女按體重分組估計體重控制對糖尿病衝擊中老年就業的緩和效果。為能考量糖尿病與體重控制為內生，使用帶工具變數三元Probit模型，工具變數包含糖尿病家族病史和糖尿病與體重控制二區域盛行率。比較二年度分析可知：九十年因缺乏糖尿病家族病史且二區域盛行率變異不夠大，緩和效果較不明確；惟九十四年之分析則無此二項限制，過重以上 (BMI 24) 男性之緩和效果呈顯著為1.98%，其它各組之效果多偏小或不顯著。政策制定者估算體重控制成本效益時，宜將中老年過重以上男性之緩和效果納入考量。
   第三篇：本研究運用我國九十年與九十四年｢國民健康訪問調查｣資料，分男女按年齡分組檢視罹患糖尿病對所得(月收入)負向衝擊效果。除檢視糖尿病為外生之糖尿病對所得負向衝擊效果外，本研究依所得變量類別(月收入變量間斷與否)，考量糖尿病為內生，分別使用工具變數線性迴歸模型、帶工具變數Ordered Probit模型與半無母數Ordered Probit模型，工具變數包含糖尿病家族病史和糖尿病區域盛行率。比較兩年度實證結果可知：糖尿病為外生之糖尿病對所得衝擊，兩年度皆不顯著，然而考量糖尿病內生，九十四年中老年男性各組模型之糖尿病對所得衝擊效果呈負向顯著，但女性其它各組模型大多不顯著。

Essay 1: This paper examines the negative effect of diabetes on employment by gender and aged group. It analyzes the 1996 Nutrition and Health Survey in Taiwan (NAHSIT) and two waves of National Health Interview Survey of Taiwan (2001, 2005). Allowing for endogeneity of diabetes, it uses a bivariate probit model with instrumental variables, including family history of diabetes and area prevalence rates of diabetes. Due to lack of family history of diabetes and insufficient variations of the area prevalence rates of diabetes, the findings from the 1996 and 2001 wave are not totally according to expectation. Contrastly, the analysis of the 2005 data does not suffer similar constraints. The negative effect for middle-aged and older males (aged 40 to 64) is statistically significant, on the order of -24%. Most effects are either small or insignificant for other groups.
    Essay 2: This study examines whether weight control ameliorates the effect of diabetes on employment among middle-aged and older adults (aged 40 to 64) by gender and weight group. It analyzes two waves of National Health Interview Survey of Taiwan (2001, 2005). Allowing for endogeneity of diabetes and weight control, it uses a trivariate probit model with instrumental variables, including family history of diabetes and area prevalence rates of diabetes and weight control. Due to lack of family history of diabetes and insufficient variations of the two area prevalence rates, the findings from the 2001 wave are not totally according to expectation. Contrastly, the analysis of the 2005 data does not suffer similar constraints. The moderating effect for those overweight or obese (defined as BMI 24) males is statistically significant, on the order of 1.98%. Most effects are either small or insignificant for other groups. Taking into account the moderating effect for overweight or obese, middle-aged and older males will enhance completeness of cost-benefit analysis in evaluating weight control policy.
    Essay 3: This paper examines the negative impact of diabetes on income by gender and aged group. It analyzes two waves of National Health Interview Survey of Taiwan (2001, 2005). Besides exogeneity of diabetes, this paper also allows for endogeneity of diabetes using linear regression, ordered probit model and semi-nonparametric ordered probit model combined with the control function appaoach. The instrumental variables include family history of diabetes and area prevalence rates of diabetes. The results show that the negative impact of diabetes on income is either small or insignificant using the exogeneity assumption of diabetes. Allowing for endogeneity of diabetes, the negative effect for middle-aged and older males (aged 40 to 64), in contrast, becomes statistically significant. But for female and other aged groups the effects are either small or insignificant.

目   錄                      頁次
圖表目錄……………………………………………………….……….Ⅴ
導論………………………………………………..……………………..1
第一章  發展中國家糖尿病對就業的衝擊─以臺灣為例………...…..2
    1.1 前言…………………………………………………………….3
1.2 文獻回顧………………………………………….……..……..4
1.3 資料來源、變數設定與敘述統計……………………………...6
1.4 模型……………..…………………………………………….12
1.5 實證結果與討論……………………………………..……….14
1.6 結論……………………………………………………………21
參考文獻……………………………………………..……………22
附錄………………………………………………………………..43
第二章  糖尿病對臺灣中老年人就業的衝擊
─體重控制的緩和效果.……………………………………47
  2.1 前言…...……..……………………………………………….48
2.2 文獻回顧………………………………………….…………..49
2.3 觀念架構…….………………………………………………..52
2.4 資料來源、變數設定與敘述統計.……….……………………...53
2.5 模型……….…..………………………...…………………….64
2.6 實證結果與討論……………………………………..……….68
 2.7 結論…….…..…………………………………………………89
參考文獻……………………………………………..……………91
第三章  發展中國家糖尿病對所得的衝擊─以臺灣為例……..…….96
    3.1 前言……...……………………………………………………97
3.2 文獻回顧………………………………………….……..……98
3.3 資料來源、變數設定與敘述統計…………………….……..99
3.4 模型……...………..…………………………………………102
3.5 實證結果與討論……………………………………..…..….104
3.6 結論………………………………………………….………107
參考文獻……………………………………………..………..…107
總結論與建議…….………………………………………..………….138
參考文獻(論文集全部)……………………………………………….140

                       表  圖  目  錄
表圖號                                                 頁次
Table 1.1.1 Descriptive statistics, by gender and age 
cohorts (NAHSIT 1996)………….……………………………………...25
Table 1.1.2 Descriptive statistics, by gender and age 
cohorts (NHIS 2001)……………………………………………………26
Table 1.1.3 Descriptive statistics, by gender and age 
cohorts (NHIS 2005) ……………………………………………………27
Table 1.2.1 Employment rate – diabetic versus non-diabetic samples, by gender and age cohorts (NAHSIT 1996) ……………………………………………28
Table 1.2.2 Employment rate – diabetic versus non-diabetic samples, by gender and age cohorts (NHIS 2001) ……………………………………….………29
Table 1.2.3 Employment rate – diabetic versus non-diabetic samples, by gender and age cohorts (NHIS 2005) ……………………………….………………30
Table 1.3.1 Bivariate Probit with IVs (Male), by age cohorts….……………………31
Table 1.3.2 Bivariate Probit with IVs (Female), by age cohorts….………….………32
Table 1.3.3 Bivariate Probit with IVs (Male), by age cohorts….…………….………33
Table 1.3.4 Bivariate Probit with IVs (Female), by age cohorts….…………….……34
Table 1.3.5 Bivariate Probit with IVs (Male), by age cohorts….………….…………35
Table 1.3.6 Bivariate Probit with IVs (Female), by age cohorts….…………….……36
Table 1.4.1 Test of Instrumental Variables’ validity, by gender and
age cohorts (NAHSIT 1996) …………………………….………………37
Table 1.4.2 Test of Instrumental Variables’ validity, by gender and 
age cohorts (NHIS 2001) …………………………….………………..…38
Table 1.4.3 Test of Instrumental Variables’ validity, by gender and 
age cohorts (NHIS 2005) …………………………….………………..…39
Table 1.5.1 Marginal effect, by gender and age cohorts (NAHSIT 1996) …..………40
Table 1.5.2 Marginal effect, by gender and age cohorts (NHIS 2001) …..……….…41
Table 1.5.3 Marginal effect, by gender and age cohorts……….………………….…42
Table A.1.1 Marginal effect and main coefficient in the employment 
uniprobit model, by gender and age cohorts (NAHSIT 1996) ………....43
Table A.1.2 Marginal effect and main coefficient in the employment 
uniprobit model, by gender and age cohorts (NHIS 2001) …………….44
Table A.1.3 Marginal effect and main coefficient in the employment 
uniprobit model, by gender and age cohorts (NHIS 2005) …………….45
Table A.1.4 Comparison of NHIS 2005 empirical results and 
previous literature- impact of diabetes on employment……...…………46
表1.1 敘述統計表(九十年) …………………………….………………………..…55
表1.2 敘述統計表(九十四年) ………………………………….………………….56
表 2.1 就業率─糖尿病樣本群組相對非糖尿樣本群組(九十年) ……………….63
表 2.2 就業率─糖尿病樣本群組相對非糖尿樣本群組(九十四年) …………….63
表3.1全樣本群組之三元機率方程組模型估計結果(九十年) ….…………….….69
表3.2過重以上群組之三元機率方程組模型估計結果(九十年) .…………….….70
表3.3非過重以上群組之三元機率方程組模型估計結果(九十年) ……….….….71
表 3.4肥胖群組之三元機率方程組模型估計結果(九十年) ….……………….….72
表3.5全樣本群組之三元機率方程組模型估計結果(九十四年) .…………….….73
表 3.6過重以上群組之三元機率方程組模型估計結果(九十四年) ………….….75
表3.7非過重以上群組之三元機率方程組模型估計結果(九十四年) ……….…..76
表 4.1 檢定工具變數是否有效(九十年) ……………………….…………..….….82
表 4.2 檢定工具變數是否有效(九十四年) …………………….…………..….….83
表 5.1 三元機率方程組下之糖尿病對就業的邊際效果估計(九十年) ………….85
表 5.2 三元機率方程組下之糖尿病對就業的邊際效果估計(九十四年) ……….85
表 6.1 緩和效果之估計(九十年) ……………………….…………..………….….87
表 6.2 緩和效果之估計(九十四年) …………………….…………..………….….88
表3.1.1 敘述統計表(九十年) …………………………….………………………110
表3.1.2 敘述統計表(九十四年) ………………………….………………………111
表3.2.1 平均個人月收入─
糖尿病樣本群組相對非糖尿樣本群組 (九十年) ………………………112
表3.2.2 平均個人月收入─
糖尿病樣本群組相對非糖尿樣本群組 (九十四年) ……………………113
表 3.3.1九十年－男性－外生－模型估計結果(年齡≧18&＜65) …………..…114
表 3.3.2九十年－男性－外生－模型估計結果(年齡≧18&＜40) …………..…115
表 3.3.3九十年－男性－外生－模型估計結果(年齡≧40&＜65) …………..…116
表 3.3.4 九十年－女性－外生－模型估計結果(年齡≧18&＜65) ………….…117
表 3.3.5 九十年－女性－外生－模型估計結果(年齡≧18&＜40) ………….…118
表 3.3.6 九十年－女性－外生－模型估計結果(年齡≧40&＜65) ………….…119
表 3.3.7九十四年－男性－外生－模型估計結果(年齡≧18&＜65) ………..…120
表 3.3.8九十四年－男性－外生－模型估計結果(年齡≧18&＜40) ………..…121
表 3.3.9九十四年－男性－外生－模型估計結果(年齡≧40&＜65) ………..…122
表 3.3.10九十四年－女性－外生－模型估計結果(年齡≧18&＜65) ……….…123
表 3.3.11九十四年－女性－外生－模型估計結果(年齡≧18&＜40) ……….…124
表 3.3.12九十四年－女性－外生－模型估計結果(年齡≧40&＜65) ……….…125
表 3.3.13九十年－男性－內生－模型估計結果(年齡≧18&＜65) ………….…126
表 3.3.14九十年－男性－內生－模型估計結果(年齡≧18&＜40) ………….…127
表 3.3.15九十年－男性－內生－模型估計結果(年齡≧40&＜65) ………….…128
表 3.3.16 九十年－女性－內生－模型估計結果(年齡≧18&＜65) …………...129
表 3.3.17 九十年－女性－內生－模型估計結果(年齡≧18&＜40) …………...130
表 3.3.18 九十年－女性－內生－模型估計結果(年齡≧40&＜65) …………...131
表 3.3.19九十四年－男性－內生－模型估計結果(年齡≧18&＜65) ………....132
表 3.3.20九十四年－男性－內生－模型估計結果(年齡≧18&＜40) ………....133
表 3.3.21九十四年－男性－內生－模型估計結果(年齡≧40&＜65) ………....134
表 3.3.22九十四年－女性－內生－模型估計結果(年齡≧18&＜65) ……........135
表 3.3.23九十四年－女性－內生－模型估計結果(年齡≧18&＜40) ……........136
表 3.3.24九十四年－女性－內生－模型估計結果(年齡≧40&＜65) ……........137

郭雁媚 (2008), “瘦身的經濟分析,” 碩士論文, 台北：淡江大學產業經濟學系碩士班。(Kuo, Yen-Mei (2008), “Two Economic Essays on Weight Control,” Master’s thesis, Taipei: Graduate Institute of Industrial Economics, Tamkang University.)
American Diabetes Association (2011), “Diabetes and Employment,” Diabetes Care, 34 (supplement 1), S82–S86.
Bastida, Elena and Jose A. Pagan (2002), “The Impact of Diabetes on Adult Employment and Earnings of Mexican Americans: Findings from a Community Based Study,” Health Economics, 11(5), 403–413.
Becker, Gary S. (1965), “A Theory of the Allocation of Time,” Economic Journal, 75,  493–517. 
Cappellari, Lorenzo and Stephen P. Jenkins (2003), “Multivariate Probit Regression Using Simulated Maximum Likelihood,” The Stata Journal, 3(3), 278–294.
Coppejans, Mark and A. Ronald Gallant (2002), “Cross-Validated SNP Density Estimates,” Journal of Econometrics, 110, 27–65.
De Luca, Giuseppe. (2008), “SNP and SML Estimation of Univariate and Bivariate Binary-Choice Models,” The Stata Journal, 8(2), 190–220.
De Luca, Giuseppe, and Valeria Perotti (2011), “Estimation of Ordered Response Models with Sample Selection,” The Stata Journal, 11(2), 213–239.
Fuchs, Victor R. (2004),  “Reflections on the Socio-Economic Correlates of Health,” Journal of Health Economics, 23, 653–661.
Gabler, Siegfried, Francois Laisney, and Michael Lechner (1993), “Seminonparametric Estimation of Binary-Choice with an Application to Labor-Force Participation,” Journal of Business & Economic Statistics, 11, 61–80.
Greene, William H. (1998), “Gender Economics Courses in Liberal Arts Colleges: Further Results,” Journal of Economic Education, 29(4), 291–300.
---------- (2008), Econometric Analysis, 6th ed., New Jersey: Prentice Hall.
Grossman, Michael (1972), “On the Concept of Health Capital and the Demand for Health,” Journal of Political Economy, 80(2), 223–255. 
Harris, Anthony (2009), “Diabetes, Cardiovascular Disease and Labour Force Participation in Australia: An Endogenous Multivariate Probit Analysis of Clinical Prevalence Data,” The Economic Record, 85(271), 472–484.
Kahn, Matthew E. (1998a), “Health and Labor Market Performance: the Case of Diabetes,” Journal of Labor Economics, 16(4), 878–899.
---------- (1998b), “Education’s Role in Explaining Diabetic Health Investment Differentials,” Economics of Education Review, 17(3), 257–266.
Knapp, Laura Greene and Terry G. Seaks (1998), “A Hausman Test for a Dummy Variable in Probit,” Applied Economics Letters, 5, 321–323.
Kraut, Allen, Randy Walld, Robert Tate, and Cam Mustard (2001), “Impact of Diabetes on Employment and Income in Manitoba, Canada,” Diabetes Care, 24, 24–28. 
Latif, Ehsan (2009), “The Impact of Diabetes on Employment in Canada,” Health Economics, 18, 577–589.
Lavigne, Jill E., Charles E. Phelps, Alvin Mushlin, and Wayne M. Lednar (2003), “Reductions in Individual Work Productivity Associated with Type 2 Diabetes Mellitus,” Pharmacoeconomics, 21(15), 1123–1134.
Loureiro, Maria L., Anna Sanz-de-Galdeano, and Daniela Vuri (2010), “Smoking Habits: Like Father, Like Son, Like Mother, Like Daughter?” Oxford Bulletin of Economics and Statistics 72(6), 717–743.
Maddala, Gangadharrao S. (1983), Limited-Dependent and Qualitative Variables in Econometrics, Cambridge: Cambridge University Press.
Manski, Charles F. (1993), “Identification of Endogenous Social Effects: the Reflection Problem,” Review of Economic Studies, 60, 531–542.
Minor, Travis (2011), “The Effect of Diabetes on Female Labor Force Decisions: New Evidence from the National Health Interview Survey,” Health Economics, 20, 1468–1486.
Morris, Stephen (2007), “The Impact of Obesity on Employment,” Labour Economics, 14, 413–433.
Ng, Ying Chu, Philip Jacobs, and, Jeffrey A. Johnson (2001), “Productivity Losses Associated with Diabetes in the U.S.,” Diabetes Care, 24(2), 257–261.
Ramachandran, A., C. Snehalatha, E. Latha, V. Vijay, and M. Viswanathan (1997), “Rising Prevalence of NIDDM in Urban Population in India,” Diabetologia, 40, 232–237.
Rashad, Inas and Robert Kaestner (2004), “Teenage Sex, Drugs and Alcohol Use: Problems Identifying the Cause of Risky Behaviors,” Journal of Health Economics, 23, 493–503.
Roodman, David (2011), “Fitting Fully Observed Recursive Mixed-Process Models with CMP,” The Stata Journal, 11(2), 159–206.
Shelton Brown, H., Jose A. Pagan, and Elena Bastida (2005), “The Impact of Diabetes on Employment: Genetic IVs in a Bivariate Probit,” Health Economics, 14, 537–544.
Shobhana, R., P. Rama Rao, A. Lavanya, R. Williams, V. Vijay, and A. Ramachandran (2000) “Expenditure on Health Care Incurred by Diabetic Subjects in a Developing Country--A Study from Southern India,” Diabetes Research and Clinical Practice, 48, 37–42.
Smith, James P. (1998), “Socioeconomic Status and Health,” American Economic Review, 88, 192–196.
Stern, Michael P., S. P. Gaskill, H. P. Hazuda, Lytt I. Gardner, and Steven M. Haffner (1983), “Does Obesity Explain Excess Prevalence of Diabetes among Mexican Americans? Results of the San Antonio Heart Study,” Diabetologia, 24, 272–272.
Stewart, Mark B. (2004), “Semi-Nonparametric Estimation of Extended Ordered Probit Models,” The Stata Journal, 4(1), 27–39.
Trujillo, Antonio J. (2003), “Medical Use and Selection in a Social Insurance with An Equalization Fund: Evidence from Colombia,” Health Economics, 12, 231–246.
Tunceli, Kaan, Cathy J. Bradley, David Nerenz, L. Keoki Williams, Manel Pladevall, and Jennifer Elston Lafata (2005), “The Impact of Diabetes on Employment and Work Productivity,” Diabetes Care, 28, 2662–2667.
Vijan, Sandeep, Rodney A. Hayward, and Kenneth M. Langa (2004), “The Impact of Diabetes on Workforce Participation: Results from a National Household Sample,” Health Services Research, 39, 1653–1669. 
Wooldridge, Jeffrey M. (2010), Econometrics Analysis of Cross Section and Panel Data, 2nd ed., Cambridge: MIT Press.
Zang, Xiaohui, Xueyan Zhao, and Anthony Harris (2009), “Chronic Disease and Labour Force Participation in Australia,” Journal of Health Economics, 28, 91–108.
Zellner, Arnold (1962), “An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias,” Journal of the American statistical Association, 57, 348–368.