R Dataset / Package DAAG / psid2

On this R-data statistics page, you will find information about the psid2 data set which pertains to Labour Training Evaluation Data. The psid2 data set is found in the DAAG R package. You can load the psid2 data set in R by issuing the following command at the console data("psid2"). This will load the data into a variable called psid2. If R says the psid2 data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the psid2 R data set. The size of this file is about 9,733 bytes.

Labour Training Evaluation Data


A non-experimental "control" group, used in various studies of the effect of a labor training program, alternative to the experimental control group in nswdemo.




This data frame contains the following columns:


a numeric vector identifying the study in which the subjects were enrolled (0 = Control, 1 = treated).


age (in years).


years of education.


(0 = not black, 1 = black).


(0 = not hispanic, 1 = hispanic).


(0 = not married, 1 = married).


(0 = completed high school, 1 = dropout).


real earnings in 1974.


real earnings in 1975.


real earnings in 1978.


The cps1 and psid1 data sets are two non-experimental "control" groups, alternative to that in nswdemo, used in investigating whether use of such a non-experimental control group can be satisfactory. cps2 and cps3 are subsets of cps1, designed to be better matched to the experimental data than cps1. Similary psid2 and psid3 are subsets of psid1, designed to be better matched to the experimental data than psid1.




Dehejia, R.H. and Wahba, S. 1999. Causal effects in non-experimental studies: re-evaluating the evaluation of training programs. Journal of the American Statistical Association 94: 1053-1062.

Lalonde, R. 1986. Evaluating the economic evaluations of training programs. American Economic Review 76: 604-620.

Smith, J. A. and Todd, P.E. "Does Matching overcome. LaLonde?s critique of nonexperimental estimators", Journal of Econometrics 125: 305-353.

Dehejia, R.H. 2005. Practical propensity score matching: a reply to Smith and Todd. Journal of Econometrics 125: 355-364.

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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