Let’s return to the wage determination example of Section 1.2. In that example, we built a model of the wage of the ith worker in a particular field as a function of the work experience, education, and gender of that worker:

WAGE_i = β₀ + β₁EXP_i + β₂EDU_i + β₃GEND_i + ε_i

where:

Y_i = WAGE_i = the wage of the ith worker

X_1i = EXP_i = the years of work experience of the ith worker

X_2i = EDU_i = the years of education beyond high school of the ith worker

X_3i = GEND_i = the gender of the ith worker (1 = male and 0 = female)

a. What is the real-world meaning of ­2?

b. What is the real-world meaning of ­3?

c. Suppose that you wanted to add a variable to this equation to measure whether there might be discrimination against people of color. How would you define such a variable? Be specific.

d. Suppose that you had the opportunity to add another variable to the equation. Which of the following possibilities would seem best? Explain your answer.

i. The age of the ith worker

ii. The number of jobs in this field

iii. The average wage in this field

iv. The number of “employee of the month” awards won by the ith worker

v. The number of children of the ith worker