學測級分對薪資之影響 : 以「台灣教育長期追蹤資料」為例

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Title:	學測級分對薪資之影響 : 以「台灣教育長期追蹤資料」為例
Other Titles:	The impact of GSAT score on wage : evidence from Taiwan education panel survey
Authors:	林柏聿;Lin, Po-Yu
Contributors:	淡江大學產業經濟學系碩士班胡登淵
Keywords:	學測級分;薪資;分量迴歸;樣本選擇;台灣教育長期追蹤資料;GSAT score;Wage;Quantile Regression;sample selection;TEPS
Date:	2016
Issue Date:	2017-08-24 23:34:41 (UTC+8)
Abstract:	勞動經濟與人力資本既存之文獻多是探討最高教育程度與薪資間的關係，本文則是探討大學入學考試學測成績與其甫進入職場平均約25歲的受訪者之月薪間的關係。本研究運用台灣教育長期追蹤資料庫2001至2010年間的調查資料，以能處理樣本選擇性偏誤的Heckman（1979）two-stage模型為基礎，延伸以處理多重樣本選擇性偏誤，將男性與女性分開估計報考學測、願意且能夠回報學測成績並有全職就業者其薪資分量迴歸模型，在控制其樣本選擇性偏誤後，將所得依低所得、中所得與高所得所區分的十分位數作分量迴歸。為修正可能因內生性問題導致的估計誤差，我們使用「國小至高一/專一無參加課外才藝班」與「高二時的BMI」做為工具變數分別對年輕男性與年輕女性進行修正。研究結果顯示：從分量迴歸的結果來看，在0.5分量時，學測級分於統計上對於薪資開始有正向影響。而在年輕女性的估計模型中，學測成績的影響比起年輕男性的估計模型為大。年輕男性的樣本在0.5至0.9分量顯著且顯著水準為0.1至0.05，而年輕女性樣本顯著於顯著水準在0.5至0.9分量至少為0.1。並於分量迴歸中加入三項Inverse Mills ratio，年輕男性的模型中在0.1與0.2分量時有樣本選擇的問題，但是在年輕女性的九個分量的分量迴歸中，Inverse Mills ratio皆在統計上不顯著，故結果表示年輕女性的大學學測總級分對薪資預測模型並無顯著的樣本選擇問題。在考慮了內生性，以工具變數控制後，男性的大學學測總級分對每月薪資對數之第二階段線性模型分析依然呈現不顯著，而女性依然在0.1的顯著水準下顯著，且影響更是明顯。 Previous studies of human capital and Labor economics discussed the relation between education and salary. This paper investigates the relation between GSAT score and salary for the young people aged 25 years old using quantile regression. The data are drawn from Taiwanese education panel survey, which surveyed from 2001 to 2010. To deal with sample selection problems, the Heckman two-stage estimation is used, and it is extended to solve the problem of multiple sample selection. To correct for the potential endogeneity problems, we use the variable regarding whether or not participated at arts class during the period from the first grade to the first year of high school (for males) and Body Mass Index (BMI) in 11th grade (for females) as instruments. The results show that GSAT score significantly affects salary only from 0.5 to 0.9 quantile. The positive correlation of GSAT score and salary is greater for females than for males. GSAT score is the significant variable in 0.5 to 0.9 quantile for young males and young females, at least, at ten-percent level. The estimates of the coefficient of GSAT score implies that salary increases for young males and young females by more than 0.9% to 1% with every additional score of GSAT. The results imply that GSAT score significantly affects salary only for high-income groups. The quantile regression models include three inverse mills ratios in the estimation, but it is only significant in the lower quantiles of young males. The effects of sample selection for young males are significant but not for young females. Although we control the regarding whether or not participated at arts class during the period from the first grade to the first year of high school for males as instruments. The results show that GSAT score still insignificant in linear model, while he effects of GSAT score for young females are greater than before.
Appears in Collections:	[Graduate Institute & Department of Industrial Economics] Thesis

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