OJS  Vol.3 No.6 , December 2013
Rural Labor Force Transfer Training Effects Evaluation by Matching Methods: Evidence from Yunnan Province of China
ABSTRACT

Rural labor force transfer training is one of important poverty alleviation measures in China. This paper describes training participation situation and evaluates training effects by matching methods in the case of coastal autonomous minority nationality areas of Yunnan province by using 2053 rural household data. The result shows that the average training participation from 2006 to 2008 is 26.39 percent. In addition, ATE is 18.33 percent, TT is 18.99 percent, TUT is 18.09 percent. And,TUT < ATE < TT. It demonstrates that the rural labor force transfer training program is effective and well-directed in coastal autonomous minority nationality areas of Yunnan province of China.


Cite this paper
J. Xie and X. Li, "Rural Labor Force Transfer Training Effects Evaluation by Matching Methods: Evidence from Yunnan Province of China," Open Journal of Statistics, Vol. 3 No. 6, 2013, pp. 398-408. doi: 10.4236/ojs.2013.36047.
References
[1]   Household Survey Department of National Bureau of Statistics, “Poverty Monitoring Report of Rural China,” China Statistics Press, Beijing, 2010.

[2]   R. J. Lalonde, “The Promise of Public Sector-Sponsored Training Programs,” Journal of Economic Perspectives, Vol. 9, No. 2, 1995, pp. 149-168.
http://dx.doi.org/10.1257/jep.9.2.149

[3]   J. J. Heckman and N.-L. Salvador, “Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models,” The Review of Economics and Statistics, Vol. 86, No. 1, 2004, pp. 30-57.
http://dx.doi.org/10.1162/003465304323023660

[4]   J. J. Heckman and V. Edward, “Local Instrumental Variable and Latent Variable Models for Identifying and Bounding Treatment Effects,” Proceedings of the National Academy of Sciences, Vol. 96, No. 8, 1999, pp. 47304734. http://dx.doi.org/10.1073/pnas.96.8.4730

[5]   J. J. Heckman and V. Edward, “The Relationship between Treatment Parameters within a Latent Variable Framework,” Economics Letter, Vol. 66, No. 1, 2000, pp. 33-39.
http://dx.doi.org/10.1016/S0165-1765(99)00181-0

[6]   J. J. Heckman and V. Edward, “Structural Equation, Treatment Effects, and Economic Policy Evaluation,” Econometrica, Vol. 73, No. 3, 2005, pp. 669-738.
http://dx.doi.org/10.1111/j.1468-0262.2005.00594.x

[7]   J. J. Heckman, “Micro Data, Heterogeneity, and the Evaluation of Public Policy: Nobel Lecture,” The Journal of Political Economy, Vol. 109, No. 4, 2001, pp. 673-748.
http://dx.doi.org/10.1086/322086

[8]   J. J. Heckman, U. Sergio and V. Edward, “Understanding Instrumental Variables in Models with Essential Heterogeneity,” The Review of Economics and Statistics, Vol. 88, No. 3, 2006, pp. 389-432.
http://dx.doi.org/10.1162/rest.88.3.389

[9]   J. J. Heckman, U. Sergio and V. Edward, “Estimation of treatment Effects under Essential Heterogeneity,” Working Paper, University of Chicago and American Bar Foundation, Chicago, 2006.

[10]   X. Zhou and Y. Xie, “Propensity-Score-Cased Method versus MTE-Based Methods in Causal Inference,” Population Studies Center Research Report, University of Michigan, Ann Arbor, 2011.
http://www.psc.isr.umich.edu/pubs/pdf/rr11-747.pdf

[11]   P. R. Rosenbaum and D. B. Rubin, “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” Biometrika, Vol. 70, No. 1, 1983, pp. 41-55.
http://dx.doi.org/10.1093/biomet/70.1.41

[12]   H. D. Rajeev and W. Sadek, “Propensity Score Matching for Nonexperimental Causal Studies,” The Review of Economics and Statistics, Vol. 81, No. 1, 2001, pp. 151-161.

[13]   A. Abadie and G. W. Imbens, “Large Sample Properties of Matching Estimators for Average Treatment Effects,” Econometrica, Vol. 74, No. 1, 2006, pp. 235-267.
http://dx.doi.org/10.1111/j.1468-0262.2006.00655.x

[14]   J. S. Sekhon and R. M. Walter Jr., “Genetic Optimization Using Derivatives: Theory and Application to Nonlinear Models,” Political Analysis, Vol. 7, No. 1, 1998, pp. 187210.
http://sekhon.berkeley.edu/genoud/genoud.pdf
http://dx.doi.org/10.1093/pan/7.1.187


[15]   J. S. Sekhon, “Alternative Balance Metrics for Bias Reduction in Matching Methods for Causal Inference,” Working Paper, 2006.
http://sekhon.berkeley.edu/papers/SekhonBalanceMetrics.pdf

[16]   J. S. Sekhon, “Opiates for the Matches: Matching Methods for Causal Inference,” Annual Review of Political Science, Vol. 12, 2009, pp. 487-508.
http://dx.doi.org/10.1146/annurev.polisci.11.060606.135444

[17]   A. Diamond and S. S. Jasjeet, “Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational in Studies,” Working Paper, 2005.
http://sekhon.berkeley.edu/papers/GenMatch.pdf

[18]   W. G. Cochran and D. B. Rubin, “Controlling Bias in Observational Studies: A Review,” Sankhya, Series A, Vol. 35, No. 4, 1973, pp. 417-446.

[19]   D. B. Rubin, “Using Multivariate Sampling and Regression Adjustment to Control Bias in Observational Studies,” Journal of the American Statistical Association, Vol. 74, No. 366, 1979, pp. 318-328.

[20]   D. B. Rubin, “Bias Reduction Using Mahalanobis-Metric Matching,” Biometrics, Vol. 36, No. 2, 1980, pp. 293-298.
http://dx.doi.org/10.2307/2529981

[21]   A. Abadie andG. W. Imbens, “Simple and Bias-Corrected Matching Estimators for Average Treatment Effects,” Technical Working Paper, 2002.
http://www.nber.org/papers/t0283.pdf

[22]   J. S. Sekhon and G. Richard, “A Nonparametric Matching Method for Covariate Adjustment with Application to Economic Evaluation,” Health Economics, 2011.
http://sekhon.berkeley.edu/papers/GeneticMatching_SekhonGrieve.pdf

[23]   M. Lechner, “Earnings and Employment Effects of Continuous Off-the-Job Training in East Germany after Unification,” Journal of Business and Economics Statistics, Vol. 17, No. 1, 1999, pp. 74-90.

[24]   G. W. Imbens, “Nonparametric Estimation of Average treatment Effects under Exogeneity: A Review,” Review of Economics and Statistics, Vol. 86, No. 1, 2004, pp. 429.
http://dx.doi.org/10.1162/003465304323023651

[25]   W. G. Cochran and D. B. Rubin, “Controlling Bias in Observational Studies: A Review,” Sankhya, Series A, Vol. 35, No. 4, 1973, pp. 417-446.

[26]   P. R. Rosenbaum and D. B. Rubin, “Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity,” American Statistician, Vol. 39, No. 1, 1985, pp. 33-38.

[27]   P. R. Rosenbaum and D. B. Rubin, “The Bias Due to Incomplete Matching,” Biometrika, Vol. 41, No. 1, 1986, pp. 103-116.

[28]   J. S. Sekhon, “Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R,” Journal of Statistical Software, Vol. 42, No. 7, 2011, pp. 1-52.

[29]   J. S. Sekhon, “Matching: Multivariate and Propensity Score Matching with Balance Optimization,” 2004.
http://sekhon.berkeley.edu/matching/

[30]   J. Mincer, “Schooling, Experience and Earnings,” National Bureau of Economic Research, New York, 1974.

 
 
Top