–
J.M. Wooldridge, 2009,“Introductory Econometrics. A modern approach.”
A labor economist would like to examine the effects of job training on worker productivity. In this case, there is little need for formal economic theory. Basic economic understanding is sufficient for realizing that factors such as education, experience, and training affect worker productivity. Also, economists are well aware that workers are paid commensurate with their productivity. This simple reasoning leads to a model such as: wage = f ( educ , exper , training ) (1.1) where wage is hourly wage, educ is years of formal education, exper is years of workforce experience, and training is weeks spent in job training. Obviously, other factors generally affect the wage rate, but (1.1) captures the essence of the problem.
Let’s define:
Hourly Wage Equation:
The most often used in empirical analyses:
i
 
0
 
1
i
 
2
i
 
3
i

i
(1.2) where: educ (years of education), exper (years of labor market experience), and tenure (years with the current employer).
We could estimate the above regression using for example the 526 observations on workers ( i=1,…, 526 ) in wage1.gdt
.
Interpretation:
The coefficients have a percentage interpretation.
The interpretation - with ceteris paribus.
1) Holding exper and tenure fixed, another year of education is predicted to increase log( wage ) by which translates into an approximate

1
percent increase in wage .

1
,
Alternatively,
2) If we take two people with the same levels of experience and job tenure, the coefficient on educ is the proportionate difference in predicted wage when their education levels differ by one year.
Sometimes we want to change more than one independent variable at the same time to find the resulting effect on the dependent variable.
For example, in equation (1.2), we can obtain the estimated effect on wage when an individual stays at the same firm for another year: exper (general workforce experience) and tenure both increase by one year.
The total effect (holding educ fixed) is

2
 
3
.
Since exper and tenure each increase by one year, we just add the coefficients on exper and tenure and multiply by 100 to turn the effect into a percent.
We can estimate also a quadratic form of the model:
i
 
0
 
1
i
 
2
i
 
3
i
2  
3
i

i
(1.3)
However, whether it is a good estimate of the ceteris paribus return to another year of education requires us to study the statistical properties of OLS. !!!!!!!!!!!!