Agency Conflicts: Some Economic Solutions

A presentation byAkobundu Chinesom Ejiasa

Who? What? Where? When? and why?

What is Agency Theory?What is Agency Cost?

The Approach

Hypothesis: Firms can reduce their susceptibility to agency costs through the use of dependency variables. Firms can control agents by tying compensation structures to operational dependencies.

a. Company 1: high leverage and low programmability

b. Company 2: low leverage and high programmability

Firms Dependencies

Debt Equity Sales R &DIndustry:Airline x xAerospace x xAutomotive x x xPharmaceutical x x xToy x xComputers x x xBiotechnology xEnergy x xFood x xUtilities x xTelecommunication x x x xApparel x x

Firm Leverage

1. A firms leverage implies much about its dependencies.

a. Consider a firm with high leverage; it is more likely to rely on debt than a firm that requires little debt financing. Therefore this type of debt laden firm is highly dependent on creditors

b. Consider a firm with low leverage; it is more likely to rely on equity financing than a firm that is highly levered. Therefore this type of firm is highly dependent on equity financiers.

Diagram of Leverage

DEBT EQUITYRATIO(HIGH)

DEBT EQUITYRATIO(LOW)

AVG. COSTOf

DEBT(+)

AVG. COSTOf

DEBT(+)

FREE CASHFLOW(+)

FREE CASHFLOW(-)

*The debt to equity ratio is used to determine the leverage of each firm. In this case leverage is based on the industry of each firm. If a firm posts higher leverage than its industry, it will be considered highly levered, and the opposite for low levered firms. The average cost of debt is as a performance measure. The assumption is high-levered firms will have an average cost of debt greater than the industry’s average and that free cash flows have a negative relationship with the debt equity ratio.

Firm Programmability

1. Another aid in reducing a firms susceptibility to agency costs

is how well the internal monitoring structure functions.

a. The size of a board and the number of times it meets is reflective of how well the company is functioning.

2. Boards of a membership twelve and greater are considered inefficient while memberships of nine and smaller are considered efficient.

3. Boards that held meetings of twelve and higher were considered efficient, while annual meetings that summed below six were considered inefficient.

Diagram of Monitoring

BOARDSIZE

FREQ ofMEETINGS

ROA(-)

ROA(+) ROE(+)

ROE(-)

MTBVALUE(+)

MTBVALUE(-)

*The board size variable is separated into large board sizes and small board sizes. A board of twelve and higher represents high programmability whereas a board size of six and below represents low programmability. The mean of the return on equity and market to book value will be taken for both the high programmable firms and the low programmable firms.

Combining leverage and monitoring

a. Board size has a negative relationship to the performance variables return on equity and/ or market to book value, and frequency of board meetings has a positive relationship to the performance variables return on equity and/or market to book value.

b. As the debt equity ratio increases, the quantity of free cash flow should decrease, which implies a negative relationship between free cash flows and debt equity and indicates that managerial actions are better aligned with shareholders’ interest.

Results for Programmability(board size)

Programmability HIGH LOWMean 0.04614 0.132105Variance 0.710124 0.154374Observations 57 38Hypothesized Mean Difference 0df 85t Stat -0.66881P(T<=t) one-tail 0.252713t Critical one-tail 1.662979P(T<=t) two-tail 0.505426t Critical two-tail 1.988269

t-Test of significance for programmability (board size).

Results for Programmability(frequency)

Programmability LOW HIGHMean 0.264186 0.152182Variance 3.985996 0.112006Observations 43 55Hypothesized Mean Difference 0df 44t Stat -1.35277P(T<=t) one-tail 0.09152t Critical one-tail 1.68023P(T<=t) two-tail 0.18304t Critical two-tail 2.015367

t-Test of significance for programmability (meeting frequency).

Results for Leverage

Leverage HIGH LOWMean 4373.476 3422.485Variance 29204454 16387605Observations 21 103Hypothesized Mean Difference 0df 25t Stat 0.763906P(T<=t) one-tail 0.226037t Critical one-tail 1.70814P(T<=t) two-tail 0.452073t Critical two-tail 2.059537

t-Test of Significance for Leverage and Free Cash Flows.

WHAT HAPPENED?

1. THE SIZE OF THE DATA SET

2. THE VARIATION IN THE DATA SET

3. AIRLINE INDUSTRY

CONCLUSION

ACKNOWLEDGEMENTS

1. Thanks to Judy for being a pain in my side for 2 months.

2. Thanks to Aju Fen for assisting me in during my times of dire need.

3. Thanks to Kate and Shannon for keeping things humorous during our many hours of lab work.

4. Thanks to everyone else who I might have forgotten.