# jtrain2: jtrain2 In JustinMShea/wooldRidge: 111 Data Sets from "Introductory Econometrics: A Modern Approach, 6e" by Jeffrey M. Wooldridge

## Description

Wooldridge Source: R.J. Lalonde (1986), “Evaluating the Econometric Evaluations of Training Programs with Experimental Data,” American Economic Review 76, 604-620. Professor Jeff Biddle, at MSU, kindly passed the data set along to me. He obtained it from Professor Lalonde. Data loads lazily.

## Usage

 `1` ```data('jtrain2') ```

## Format

A data.frame with 445 observations on 19 variables:

• train: =1 if assigned to job training

• age: age in 1977

• educ: years of education

• black: =1 if black

• hisp: =1 if Hispanic

• married: =1 if married

• nodegree: =1 if no high school degree

• mosinex: # mnths prior to 1/78 in expmnt

• re74: real earns., 1974, \$1000s

• re75: real earns., 1975, \$1000s

• re78: real earns., 1978, \$1000s

• unem74: =1 if unem. all of 1974

• unem75: =1 if unem. all of 1975

• unem78: =1 if unem. all of 1978

• lre74: log(re74); zero if re74 == 0

• lre75: log(re75); zero if re75 == 0

• lre78: log(re78); zero if re78 == 0

• agesq: age^2

• mostrn: months in training

## Notes

Professor Lalonde obtained the data from the National Supported Work Demonstration job-training program conducted by the Manpower Demonstration Research Corporation in the mid 1970s. Training status was randomly assigned, so this is essentially experimental data. Computer Exercise C17.8 looks only at the effects of training on subsequent unemployment probabilities. For illustrating the more advanced methods in Chapter 17, a good exercise would be to have the students estimate a Tobit of re78 on train, and obtain estimates of the expected values for those with and without training. These can be compared with the sample averages.

Used in Text: pages 18, 340-341, 626

## Examples

 `1` ``` str(jtrain2) ```

JustinMShea/wooldRidge documentation built on May 13, 2018, 1:26 p.m.