3. Empirical Results
I estimate three different specifications. The dependent variable in each specification is performance, as measured by the percentage of games won. Pay inequality and total payroll are the independent variables. Table 2 shows the results. In the first specification, I regress performance on the share earned by the top 20% of players. The coefficient on the share of top 20% is negative and statistically significant. This indicates that teams with higher pay inequality tend to win fewer games. A one percentage point increase in the share of payroll earned by the top 20% of players is associated with about half of a percentage point decline in the percentage of games won.
Table 2: Regression Results
Dependent variable: winning percentage (in %)
Top20share (in %)
Payroll (in mil. USD)
Log of Payroll
Number of observations is 60.
In the second specification I include total payroll as an independent variable. Payroll is a measure of the financial resources which can affect performance - the higher the payroll, the higher the quality of players and, generally, the better the performance. Therefore, including payroll may increase the precision of the estimated coefficient on pay inequality. More importantly, it is possible that pay inequality is correlated with total payroll. If low payroll teams tend to have more pay inequality, then the coefficient on pay inequality in specification (1) is biased. Indeed, the correlation coefficient between the share earned by the top 20% of players and total payroll is -0.5. Teams with high pay inequality may perform worse not because of pay inequality, but because they are also the teams with a lower payroll. Therefore, in order to measure the effect of pay inequality on performance, I need to control for total payroll.
Once I control for total payroll, the coefficient on the share of the top 20% remains statistically significant but the magnitude drops substantially. Holding payroll constant, a one percentage point increase in the share earned by the highest paid 20% is associated with a 0.27 percentage point decline in the percentage of games won. The impact of inequality on performance does not seem enormous. For example, a five percentage point increase in inequality for the team with median inequality would shift the team up 13 spots in the inequality ranking, but its performance ranking would drop by only 2 spots. The coefficient on total payroll is positive and statistically significant. A one million dollar increase in total payroll is associated with about 0.1 percentage point increase in the percentage of games won. This indicates that greater financial resources tend to improve performance. Adding payroll as an independent variable led to an increase in R-squared from about 0.19 to 0.29.
Finally, in specification (3) I include the logarithm of payroll instead of payroll. I want to verify that the result in specification (2) is robust to different functional forms. In addition, the effect of an additional one million dollars may be smaller for a team with a 100 million payroll than for one with a 20 million payroll. Thus, including payroll in logarithm seems appropriate. The coefficient on the share of the top 20% remains statistically significant with roughly the same magnitude. The log of payroll is statistically significant. A one percent increase in payroll is associated with about 0.07 percentage points increase in the percentage of games won.
Plan of Action
Figure 1. Schedule for completion of the literature review. The formal presentation will be on October 27, and the formal report will be completed by December 5.
ReferencesClark, Raymond L., "Background on 40 CFR Part 197 Environmental Radiation Protection Standards for Yucca Mountain," Proceedings of the 1997 Waste Management Conference (Washington, D.C.: U.S. Environmental Protection Agency, 1997).
Kerr, R., "New Way to Ask the Experts: Rating Radioactive Waste Risks," Science, vol.274, (November1996), pp. 913-914.
Murray, Raymond L., Understanding Nuclear Waste (Battelle Press, 1989).
Roush, W., "Can Nuclear Waste Keep Yucca Mountain Dry-and Safe?" Science, vol. 270, (December 1995), pp. 1761-1762.
Taubes, G., "Blowup at Yucca Mountain," Science, vol.268, (June 1995), pp. 1836-1839.
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A Proposal to Review How Geophysical Precursors
Can Help Predict Earthquakes
Justification of Proposed Review
- explain three commonly monitored geophysical precursors: ground uplift and tilt, increases in radon emissions, and changes in the electrical resistivity of rocks;
- show what happens to each of these precursors during the five stages of an earthquake; and
- discuss how each of these precursors is used for short-term earthquake predictions.
Plan of Action
Figure 1. Schedule for completion of literature review. The two triangles represent milestones for the project, the first being the formal presentation on November 11, 1996, and the second being the formal report on December 6, 1996.
Bolt, Bruce A., Earthquakes (New York: W. H. Freeman and Company, 1988).
Bolt, Bruce A., Earthquakes and Geological Discovery (New York: Scientific American Library, 1993).
Deshpande, Prof. B. G., Earthquakes, Animals and Man (Pune, India: The Maharashtra Association for the Cultivation of Science, 1987).
Hodgson, John H., Earthquakes and Earth Structure (Englewood Cliffs, NJ: Prentice-Hall, 1964).
Meyer, Larry L., California Quake (Nashville: Sherbourne Press, 1977).
Mileti, Dennis S., and Colleen Fitzpatrick, The Great Earthquake Experiment (Boulder, Colorado: Westview Press, 1993).
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