On this Picostat.com statistics page, you will find information about the nsw74psid1 data set which pertains to Labour Training Evaluation Data. The nsw74psid1 data set is found in the DAAG R package. You can load the nsw74psid1 data set in R by issuing the following command at the console data("nsw74psid1"). This will load the data into a variable called nsw74psid1. If R says the nsw74psid1 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. 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 nsw74psid1 R data set. The size of this file is about 113,463 bytes.

Labour Training Evaluation Data

Description

This data frame contains 2675 rows and 10 columns. These data are pertinent to an investigation of the way that earnings changed, between 1974-1975 and 1978, in the absence of training. Data for the experimental treatment group (NSW) were combined with control data results from the Panel Study of Income Dynamics (PSID) study.

Usage

nsw74psid1

Format

This data frame contains the following columns:

trt

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

age

age (in years).

educ

years of education.

black

(0 = not black, 1 = black).

hisp

(0 = not hispanic, 1 = hispanic).

marr

(0 = not married, 1 = married).

nodeg

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

re74

real earnings in 1974.

re75

real earnings in 1975.

re78

real earnings in 1978.

Source

http://www.columbia.edu/~rd247/nswdata.html

References

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.

Examples

print("Interpretation of Regression Coefficients - Example 6.6") nsw74psid1.lm <- lm(re78~ trt+ (age + educ + re74 + re75) +
   (black + hisp + marr + nodeg), data = nsw74psid1)
 summary(nsw74psid1.lm)$coef
options(digits=4)
sapply(nsw74psid1[, c(2,3,8,9,10)], quantile, prob=c(.25,.5,.75,.95,1))
attach(nsw74psid1)
sapply(nsw74psid1[trt==1, c(2,3,8,9,10)], quantile, 
prob=c(.25,.5,.75,.95,1))
pause()here <- age <= 40 & re74<=5000 & re75 <= 5000 & re78 < 30000 
nsw74psidA <- nsw74psid1[here, ]
detach(nsw74psid1)
table(nsw74psidA$trt)
pause()A1.lm <- lm(re78 ~ trt + (age + educ + re74 + re75) + (black +
      hisp + marr + nodeg), data = nsw74psidA)
summary(A1.lm)$coef
pause()A2.lm <- lm(re78 ~ trt + (age + educ + re74 + re75) * (black +   
      hisp + marr + nodeg), data = nsw74psidA)
anova(A1.lm, A2.lm)

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