Suppose that you have many observations of the employee level longevity and the wage earned by that employee that year. You run the regression Longevityi = ?0 + ?1 Wagei +Ui, and get an estimate of ?1. Now, suppose that a member of the analytics team suggests that education is an omitted variable in your regression and is likely biasing your estimate of ?1. Suppose you knew that, conditional on Wage, more educated employees tended to have shorter stints with the company (lower longevity) and that the error, ?i, in the equation Longevityi = ?0 + ?1Wagei + ?2 Educationi + Ui, was uncorrelated with both Wage and Education. How would you sign the bias on your estimate of ?1?
A. Argue that education is positively correlated with wage and that your estimate of ?1 is a lower bound.
B. Argue that education is negatively correlated with wage and that your estimate of ?1 is a lower bound.
C. Argue that education is positively correlated with wage and that your estimate of ?1 is an upper bound.
D. Argue that education is positively correlated with longevity and that your estimate of ?1 is an upper bound.
Answer: A
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