For the population of people in the

For the population of people in the work force in 1976, let y
wage, where wage is measured in dollars per hour. Thus, for a particular person, if wage
6.75, the hourly wage is
$6.75. Let x
educ denote years of schooling; for example, educ
12 corresponds to a
complete high school education. Since the average wage in the sample is $5.90, the consumer price index indicates that this amount is equivalent to $16.64 in 1997 dollars.
Using the data in WAGE1.RAW where n
526 individuals, we obtain the following OLS
regression line (or sample regression function):
waˆge
0.90 0.54 educ. (2.27)
We must interpret this equation with caution. The intercept of 0.90 literally means that a
person with no education has a predicted hourly wage of 90 cents an hour. This, of
course, is silly. It turns out that no one in the sample has less than eight years of education,
which helps to explain the crazy prediction for a zero education value. For a person with
eight years of education, the predicted wage
is waˆge
0.90 0.54(8)
3.42, or
$3.42 per hour (in 1976 dollars).
The slope estimate in (2.27) implies that
one more year of education increases hourly
wage by 54 cents an hour. Therefore, four
more years of education increase the predicted wage by 4(0.54)
2.16 or $2.16 per hour. These are fairly large effects. Because of
the linear nature of (2.27), another year of education increases the wage by the same
amount, regardless of the initial level of education. In Section 2.4, we discuss some methods that allow for nonconstant marginal effects of our explanatory variables.