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We are interested in only a subset of these data. Table 2 reports the definitions of variables that are relevant for our analysis. We can get further insight into the data set using the summarize command. Table 3 reports the summary statistics for the data set.
Variable name | Definition |
country | County of residence (categorical variable equal to 0, 1, ..., 9) |
age | Age of the woman |
education | Number of years of education of the woman |
married | Dummy variable equal to 1 if the woman is married and 0 otherwise |
children | Number of children that the woman has in their household |
wage | Hourly wage rate of the woman |
lw | Natural logarithm of hourly wage rate |
work | Dummy variable equal to 1 if the individual is in the workforce and 0 otherwise |
Variable | Obs | Mean | Std. Dev | Min | Max |
Age | 2000 | 36.208 | 8.28656 | 20 | 59 |
education | 2000 | 13.084 | 3.045912 | 10 | 20 |
married | 2000 | .6705 | .4701492 | 0 | 1 |
children | 2000 | 1.6445 | 1.398963 | 0 | 5 |
wage | 1343 | 23.69217 | 6.305374 | 5.88497 | 45.80979 |
lw | 1343 | 3.126703 | .2865111 | 1.772402 | 3.824498 |
work | 2000 | .6715 | .4697852 | 0 | 1 |
We are interested in modeling two things: (1) the decision of the woman to enter the labor force and (2) determinants of the female wage rate. It might be reasonable to assume that the decision to enter the labor force by a woman is a function of age, marital status, the number of children, and her level of education. Also, the wage rate a woman earns should be a function of her age and education.
We can use a probit regression to model the decision of a woman to enter the labor force. The results of this estimation are reported in Table 4. However, we can use the predict command to produce some results that we can use to be sure that we understand what the regression results mean. In particular, type in the following two commands:
.predict zbhat, xb
.predict phat, p
These two commands will predict (1) the linear prediction (zbhat) and (2) the predicted probability that the woman will be in the workforce (phat). Table 5 reports the values of these two variables for observations 1 through 10.
. probit work age education married children | ||||||
Iteration 0: log likelihood = -1266.2225 | ||||||
Iteration 4: log likelihood = -1027.0616 | ||||||
Probit estimates Number of obs = 2000 | ||||||
LR chi2(4) = 478.32 | ||||||
Prob>chi2 = 0.0000 | ||||||
Log likelihood = -1027.0616 Pseudo R2 = 0.1889 | ||||||
work | Coef. | Std. Err. | z | P>z | [95% Conf. Interval] | |
age | .0347211 | .0042293 | 8.21 | 0.000 | .0264318 | .0430105 |
education | .0583645 | .0109742 | 5.32 | 0.000 | .0368555 | .0798735 |
married | .4308575 | .074208 | 5.81 | 0.000 | .2854125 | .5763025 |
children | .4473249 | .0287417 | 15.56 | 0.000 | .3909922 | .5036576 |
_cons | -2.467365 | .1925635 | -12.81 | 0.000 | -2.844782 | -2.089948 |
Observation | zbhat | phat |
1 | -0.68900 | 0.24541 |
2 | -0.20290 | 0.41961 |
3 | -0.48067 | 0.31538 |
4 | -0.16818 | 0.43322 |
5 | 0.34859 | 0.63630 |
6 | 0.58758 | 0.72159 |
7 | 0.97357 | 0.83486 |
8 | 0.45978 | 0.67716 |
9 | 0.01799 | 0.50718 |
10 | 0.32628 | 0.62790 |
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