The issues regarding executive compensation have received extensive media attention. The government is even considering a cap on high-lying salaries for executives (New York Times, February 9, 2009). Consider a regression model that links executive compensation with the total assets of the firm and the firm’s industry.

Dummy variables are used to represent four industries:

Manufacturing Technology d1, Manufacturing Other d2, Financial Services d3 and Nonfinancial Services d4. A portion of the data for the 455 highest-paid CEOs in 2006 is given in the accompanying table; the entire data set, labeled Industry_Compensatfon, can be found on the text website.

a. Estimate the model: y = β₀ + β₁x + β₂d₁ + β₃d₂ + β₄d₃ + ε, where y and x denote compensation and total assets, respectively. Here the reference category is nonfinancial services industry.

b. Interpret the estimated coefficients.

c. Use a 5% level of significance to determine which industries, relative to the nonfinancial services industry, have a different executive compensation.

d. Reformulate the model to determine, at the 5% significance level, if compensation is higher in Manufacturing Other than in Manufacturing Technology. Your model must account for assets and all industry types.

Compensation Assets (in $ million) (in $ millions) d d d 5 d 16.58 20,917.5 32659.51 2 26.92 2.30 44,875.0