圖 5a、QR 估計值與 OLS 估計值及 95%信賴區(1920 世代) 圖 5b、QR 估計值與 OLS 估計值及 95%信賴區(1930 世代) 圖 5c、QR 估計值與 OLS 估計值及 95%信賴區(1940 世代)
0.06 0.07 0.08 0.09 0.10
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
0.07 0.08 0.09 0.10 0.11
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
0.08 0.09 0.10 0.11 0.12
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
圖 5d、QR 估計值與 OLS 估計值及 95%信賴區(1950 世代) 圖 5e、QR 估計值與 OLS 估計值及 95%信賴區(1960 世代) 圖 5f、QR 估計值與 OLS 估計值及 95%信賴區(1970 世代)
0.07 0.08 0.09 0.10
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt. 0.090 0.095 0.100 0.105 0.110
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
0.06 0.07 0.08 0.09 0.10
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
圖 6a. 教育報酬率‐全體樣本
圖 6b. 教育報酬率‐男性樣本 0.06
0.07 0.08 0.09 0.10 0.11 0.12
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
1920s 1930s 1940s 1950s 1960s 1970s
0.04 0.06 0.08 0.10 0.12
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
1920s 1930s 1940s 1950s 1960s 1970s
圖 6c. 教育報酬率‐女性樣本
0.04 0.06 0.08 0.10 0.12 0.14
0.1 Qnt. 0.2 Qnt. 0.3 Qnt. 0.4 Qnt. 0.5 Qnt. 0.6 Qnt. 0.7 Qnt. 0.8 Qnt. 0.9 Qnt.
1920s 1930s 1940s 1950s 1960s 1970s
五、 結論
不同於過去的文獻中採用年度橫斷面資料，依傳統OLS 模型估計「平均」
的教育邊際報酬率的方式，本文選擇將時間序列橫斷面的資料重新組合為不
同年群資料並採用 QR 模型來估計在不同年群之條件薪資分配下的教育報酬
率。一方面透過不同年群的區分，避免因採用橫斷面資料時所強烈假設之相
同的個人年齡所得面相，而造成的估計偏誤。另一方面，經由 QR 模型估計
結果的分析，不但可擴大教育報酬率的觀察面向，體現個人特質存在異質性 的情況下所呈現之工資條件分配中可出現不同的報酬率差異外，更可進一步 分析教育投資與個人無法觀測之能力兩者間的可能關係。
由本文的估計結果發現，不同世代的教育報酬率存在相當程度的年群效 果，因為不同世代所面對的勞動市場均有所不同，可能來自需求面或供給面 甚至兩者同時影響而造成各世代不同的教育報酬率。本文也嘗試提出解釋：
在勞動需求上，由於經濟發展與產業結構的轉變，造成對不同技能的需求改 變；在勞動供給上，由於教育制度的改變(如九年國教、教育分流、大學擴增 等)，影響個人的教育成就，造成勞工教育結構的改變。但因所採取的調查資 料無法完整表現個人工作生命週期的行為，且所選擇之調查資料未臻詳盡等 限制，故本文並未將制度面與非制度面的因素完全納入模型考量，僅能有限 度的解釋不同世代教育報酬的年群效果。如何進一步釐清造成年群效果的原 因，似可作為未來研究的方向。
除此之外，本文進一步比較傳統OLS 模型與 QR 模型之估計結果可以發
現，以QR 模型所估計的係數在不同工資分配中有極大比例落在傳統 OLS 模
型的估計係數的 95%信賴區之外，表示不同的個人在先天上即具有能力的異
質性而表現出不同的教育報酬率，我們並發現個人能力的異質性在年輕世代
中最為明顯，顯示個別能力差異在年輕族群的重要性，因此以傳統OLS 模型
所估計（平均概念）的教育報酬率並不適用。
另一方面，由 QR 模型的估計結果亦發現在較年長的世代，教育與個人
能力呈現強化關係，而較年輕的世代，教育與個人能力則呈現彌補關係，且 愈年輕的世代，其彌補性也愈強。顯示過去較年長世代所接受之較為制式的 教育，目的乃在於加強個人的能力，深化個人專業能力進而表現在工資水準 的提高上；而年輕世代所接受為較多元和通才式的現代教育，則可補足個人 能力不足之處，亦即在年輕的世代中，教育乃做為培養多元或多樣化能力的 途徑，彌補先天能力之不足。換句話說，不同於傳統制式化的單元價值與著 重專業技能教育，現代教育一方面可彌補先天能力的弱勢，但另一方面透過 多元化教育訓練，也培養出更多樣化的能力，也豐富了異質性的內涵，體現 在愈年輕的世代其異質性也愈大。故本文的重要政策意涵為：教育除了一方 面能補充個人能力之不足外，另一方面亦可創造出具更多元化與具異質性內 涵的社會。台灣的經驗顯示，教育與能力可能是強化亦可能是彌補關係，端 視教育的內涵與產業對人才需求的特質而定。此結果更突顯出教育於二十一 世紀人力資本投資的重要性。
參考文獻
行政院主計處，《中華民國台灣地區人力運用調查報告》，台北，行政院主計處。
吳慧瑛 (2002)，「二十年來教育發展之經濟評估, 1978‐2001」，《台灣經濟預測與 政策》33，97‐130。
張宏基與朱敬一 (1996)，「用 Pseudo Panel Data 估計台灣男性跨期勞動替代彈性」，
《經濟論文叢刊》，24(3)，313‐337。
Ashenfelter, O. and C. Rouse (1998), “Income, Schooling and Ability: Evidence from a New Sample of Identical twins,” Quarterly Journal of Economics, 113(1), 253–84.
Blackburn, M. L. and D. Neumark (1993), “Omitted-Ability Bias and the Increase in the Return to Schooling,” Journal of Labor Economics, 11(3), 521-44.
Bound, J., D. A. Jaeger and R. Baker (1995), “Problems with Instrumental Variables Estimation When the Correlation between the Instruments and the Endogenous Explanatory Variable is Weak”, Journal of the American Statistical Association, 90, 443-450.
Brunello, G., M. Ford and G. Weber (2009), “Changes in Compulsory Schooling, Education and the Distribution of Wage in Europe,” Economic Journal, 119, 516-5393.
Buchinsky, M. (1994), “Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression,” Econometrica, 62, 405-458.
Buchinsky, M. (1998), “The Dynamics of Changes in the Female Wage Distribution in the USA: A Quantile Regression Approach,” Journal of Applied Econometrics, 13, 1-30.
Card, D. (1999), “The Causal Effect of Education on Earnings,” In Ashenfelter, O. and D.
Card, (eds) Handbook of Labor Economics, Volume 3A, Amsterdam: Elsevier.
Card, D. and T. Lemieux (2001), “Can Falling Supply Explain the Rising Return to College for Younger Men? A Chort-based Analysis”, Quarterly Journal of Economics, 116(2), 705–746.
Chu, C. Y., R. S. Tsay and R. R. Yu (2005), “Intergenerational Transmission of Sex-specific Differential Treatments: The Allocation of Education Resources among Siblings,” 2005 Conference on Panel Study of Family Dynamics, Institute of Economics, Academia Sinica.
Chu, C. Y., Y. Xie and R. R. Yu (2005), “Effects of Sibship Structure Revisited : Evidence from Intra-Family Resource Transfer in Taiwan,” 2005 Conference on Panel Study of Family Dynamics, Institute of Economics, Academia Sinica.
Chuang, Y. C. and C. Y. Chao (2001), “Educational Choice, Wage Determination, and Rates of Return to Education in Taiwan,” International Advances in Economic Research, 7, 479-504.
De Fraja, G. (2002), “The Design of Optimal Education Policies,” Review of Economic Studies, 69, 437–66.
Deaton, A. S. and C. H. Paxon (1994), “Saving, Growth and Aging in Taiwan,” in David A. Wise (ed.), Studies in the Economics of Aging, 331-361, Chicago: University of Chicago Press
Denny, K. and V. O’Sullivan (2007), “Can Education Compensate for Low Ability?
Evidence from British Data,” Applied Economics Letters, 14, 657-660.
Duflo, E. (1999), “Schooling and Labor Market Consequences of Schooling in Indonesia:
Evidence from an Unusual Policy Experiment,” Mimeo, Department of Economics, MIT
Eide, E. and M. H. Showalter (1998), “The Effect of School Quality on Student Performance: A Quantile Regression Approach,” Economics Letters, 58, 345-350.
Garcia, J., P. Hernandez and A. L’opez-Nicolas (2001), “How Wide Is the Gap? An Investigation of Gender Wage Difference Using Quantile Regression,” Empirical Economics, 26, 149-167.
Gindling, T. H., M. Goldfarb and C.C. Chang (1995), “Changing Return to Education in Taiwan,” World Development, 23(2), 343-356.
Griliches, Z. (1977), “Estimating the Return to Schooling: Some Econometric Problems,”
Econometrica, 45(1), 1-22.
Harmon, C. and I. Walker (1995). “Estimates of the Economic Return to Schooling for the UK,” American Economic Review, 85, 1279-1286
Hartgo, J., P. T. Pereira, and J. A. Vieira (2001), “Changing Return to Education in Portugal during the 1980s and Early 1990s: OLS and Quantile Regression Estimators,”
Applied Economics, 33(8), 1021-1037.
Heckman, J. J. and E. Vytlacil (1999), “Local Instrumental Variable and Latent Variable Models for Identifying and Bounding Treatment Effects,” Proceedings of the National Academy of Sciences, 96, 4730-4734.
Heckman, J. J. (1979), “Sample Selection Bias as a Specification Error,” Econometrica, 47(1), 153-161.
Jappelli, T. (1999), “The Age-wealth Profile and the Life-cycle Hypothesis: A Cohort Analysis with a Time Series of Cross-sections of Italian Households,” Review of Income and Wealth, 45(1), 57–75.
Juhn, C., M. M. Kevin and P. Brooks (1993), “Wage Inequality and the Rise in Returns to Skill”, Journal of Political Economy, 101(3), 410–442.
Koenker, R. (2005), Quantile Regression, Cambridge; New York: Cambridge University Press
Koenker, R. and G. J. Bassett (1978), “Regression Quantiles,” Econometrica, 46, 33-50.
Koenker, R. and K. F. Hallock (2001), “Quantile Regression,” Journal of Economic Perspectives, 15, 143-156.
Machado, J. and J. Mata (2005), “Counterfactual Decomposition of Changes in Wage Distributions Using Quantile Regression,” Journal of Applied Economics, 20, 445-465.
Mincer, J. (1974), Schooling, Experience an Earning, New York: Columbia University Press.
Mincer, J. and S. Polachek (1974), “Family Investments in Human Capital: Earnings of Women,” Journal of Political Economy, 82, 76-108
Mwabu, T. and T. P. Schultz (1996), “Education Return Across Quantiles of the Wage Function: Alternative Explanation for Education by South Africa,” American Economic Review, 86(2), 335-339.
Pereira Pedro and Pedro Silva-Martins (2002), “Is there a return-risk relationship in education?” Economics Letters, 75(2), 31-37.
Polachek, S. (1981), "Occupational Self-Selection: A Human Capital Approach to Sex Differences in Occupational Structure," Review of Economics and Statistics, 63(1), 60-69.
Psacharopouls, G. (1985), “Return to Education: A Further International Update and Implications,” Journal of Human Resources, 20(4), 583-604.
Steiger D. and J. H. Stock (1997), “Instrumental Variables Regression with Weak Instruments,” Econometrica, 65(3), 557-586.
Trostel P., I. Walker and P. Woolley (2002), “Estimates of the Economic Return to Schooling for 28 Countries,” Labour Economics, 9(1), 1-16.
Willis, R. J. (1986), “Wage Determinants: A Survey and Reinterpretation of the Human Capital Earning Function,” in O. Ashenfelter and R. Layard (eds), Handbook of Labor Economics, 525-602, Amsterdam, North-Halland: Elsevier.