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  1. NTU Theses and Dissertations Repository
  2. 公共衛生學院
  3. 健康政策與管理研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49966
標題: 肥胖對薪資的影響:多層次交叉分類模型分析
The Effect of Obesity and Wage:Cross-Classified Multilevel Analysis
作者: Pei-Hsun Lee
李佩勳
指導教授: 陳端容(Duan-Rung Chen)
關鍵字: 肥胖,薪資,多階層,交叉分類模型,
Obesity,Wage,Multilevel Analysis,Cross-Classified,
出版年 : 2016
學位: 碩士
摘要: 研究背景與目的:肥胖目前已是一個全球性的健康問題,也造成龐大的醫療及經濟負擔。肥胖不只會影響一個人的健康狀況,也可能會影響個人投入勞動市場的結果。在個人的生涯中,進入勞動市場是一個很重要的階段,對於初入職場的新鮮人來說,沒有什麼工作經驗,雇主很難依據過去經驗來做判斷,因此雇主會利用其他訊息來決定給予多少薪資,例如:第一印象、教育程度等等。除了個人能力之外,是否利用社會資本當作求職管道也可能造成薪資差異。因此,本研究探討肥胖、個人教育特性以及社會資本對薪資是否有影響,其次,台灣不同縣市鄉鎮以及各行業的薪資結構不同,肥胖造成薪資的差異是否因地區、行業不同而有所不同,值得進一步討論。
研究方法:本研究選擇「台灣教育長期追蹤資料庫」(Taiwan Education Panel Survey,簡稱TEPS)以及其後續調查「台灣教育長期追蹤資料庫後續調查:教育與勞力市場的連結(TEPS-B)」,選取Panel-1 SH 2003年高中/高職/五專三年級學生以及2010年25-26歲的資料,分析肥胖(BMI>=27)、個人教育特性以及社會資本與薪資之關係,控制變項包含性別、第一份薪資、父親教育程度、所屬公司大小、就業型態、工作時數以及憂鬱情緒。另外,本研究將利用多階層分析中交叉模型方法,進一步探討肥胖與薪資之關係是否因就業地區以及行業不同而有差異。
研究結果:根據多階層分析結果,2003年至2010年體重變化中,若為非肥胖變肥胖或是一直都肥胖相較於一直都非肥胖的人,其在25-26歲時工作薪資較低。除此之外,教育程度為大學以上相較於教育程度為高中或高職、畢業學校為公立學校相較畢業於私立學校的人薪資較高。跨層次分析中,薪資在不同地區以及行業間有顯著差異。
結論:個人、地區以及行業層次納入考量後,25-26歲青壯年之薪資會受到肥胖的影響,除此之外,教育程度以及畢業學校特性也會對個人投入勞動市場之薪資有影響。多階層交叉模型分析結果中,薪資之差異可被個人層次變項所解釋,推論其可能原因為本研究之年齡層為25-26青壯年,此族群之初入職場薪資受個人因素影響較大。教育單位以及企業單位應擬定相關健康促進方案。
Background: Obesity has grown into a global health issue and caused the huge burden of health and economic. It can have serious impact on a person’s health and results in the labor market. Entering the labor market is an important phase in one’s career. The employers is difficult to judge based on past experience if someone who did not have any working experience. Therefore, the employers will use other messages to determine how many wages to give. For instance, the first impression, education and so on. In addition to the personal ability, the use of social capital as the way to search job can also cause the wage inequality. Thus, this study aimed to examine the effect of obesity, characteristics of personal education and social capital on wages. Moreover, the structures of wages is different from the regions and industries in Taiwan so that it’s worthy of further discussion whether the wage inequality caused by obesity is different from regions and industries.
Methods: The data is mainly based on the Taiwan Education Panel Survey and Taiwan Education Panel Survey and Beyond. We choose the wave 2 of Panel-1 SH which population is from senior/vocational high school and junior college students and the face to face interview follow-up survey of Panel-1 SH to examine the relationship of obesity, characteristics of personal education and social capital on wages. We control gender, wage of the first job, education of father, types of works, weekly working hours, emotion of depression and type of employment in the labor market. We use Cross-Classified Multilevel Analysis to analysis whether the wage inequality caused by obesity is different from regions and industries.
Result: According to the results of the first level, a person’s obesity status from 2003 to 2010 was associated with their wage in the age of 25 or 26. People who became or maintained obesity from 2003 to 2010 had lower wages than those who were non-obese in the same period. People who graduated from university had higher wages than those who graduated from senior or vocational high school. Those who graduated from public school also had higher wages than private school. Cross-Classified Multilevel Analysis shows that wages is different from regions and industries.
Conclusion: Considering the factors of individual, regional and industries, wages in the age of 25 or 26 are affected by obesity. In addition, the educational level and school type also have influence on wages The difference of wages was determinate by the variables of individual level. The major reason might be the population were predominated by novices works. In this population, the wages of entering the workplace affected by personal factors greatly. We suggested that health and enterprise organizations must develop relevant health promotion programs to reduce obesity rates. Multilevel analysis has a key role to clarify the complex issue and explore the relationship between different levels.
URI: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49966
DOI: 10.6342/NTU201602181
全文授權: 有償授權
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