Estimating the Risk Premium of Law Enforcement Officers

This paper is an empirical study to estimate the risk premium or compensating differential for law enforcement officers. First, we use the human capital model to form a baseline regression using data from the Current Population Survey from 1989 to 1999. The second regression includes crime rates from around the United States to determine how the risk premium of officers' change as crime rates rise and fall. A third regression includes only MSA sizes to show how the size of area affects officers' wages. Last, we estimate regressions based on splitting up the different types of officers: police officers, supervisors, and sheriffs. The results indicate that controlling for crime rates and MSA sizes do help explain the risk premium of law enforcement officers, but more day to day on the job characteristics are needed to make more accurate estimations. ____________________________________ The author would like to thank Dr. DeSimone and Dr. Schumacher for their help, advice, and guidance through all phases of this paper. INTRODUCTION Society has a number of jobs that are unpleasant or risky and would be costly to make safe and pleasant, such as coal mining and police work. There are two ways to recruit the necessary labor for such jobs. One is to compel people to do these jobs, such as the military draft. The other way is to induce people to do the jobs voluntarily. Compensating wage differentials are used to recruit labor to unpleasant jobs voluntarily. A compensating wage differential is the additional wage paid to individuals for working in undesirable working conditions. Similarly, those who choose more pleasant conditions have to "buy" them by accepting lower wages. Our prediction about the existence of compensating wage differentials grows out of the reasonable assumption that if an informed worker has a choice between a job with good working conditions and a job of equal pay with bad working conditions, he or she will choose the good job. The predicted outcome of our theory of job choice is not that employees working under bad conditions receive more than those working under good conditions do. The prediction is that, holding worker characteristics constant, employees in bad jobs receive higher wages than those working under more pleasant conditions do because employers have to pay a premium to these workers. The characteristics that must be held constant include all the other things that influence wages: skill level, age, experience, race, gender, union status, region of the country, etc. There are three assumptions that have been used to arrive at this prediction. First, workers seek to maximize their utility, not their income. This explains why compensating wage differentials exist because some people do not choose the highest paid job offered, but prefer instead a lower paying but more pleasant job. Wages do not equalize in this case. Workers net utility equalizes for the marginal worker from overall utility from pay and the psychic aspects of the type of job worked. Second, workers are aware of job characteristics of potential importance to them. A company offering a bad job with no compensating wage differential would have trouble recruiting or retaining workers, enough trouble that would eventually force it to raise its wage. Third, workers have a range of job offers from which to choose. With this range of offers, workers would not be able to select the combination of job characteristics they desired or avoid the ones to which they did not wish experience. A compensating wage differential for risk of injury would not arise if workers were able to obtain only dangerous jobs. It is the act of choosing safe jobs over dangerous ones that forces employers offering dangerous work to raise wages. This report is an empirical study using the human capital model to estimate the risk premium or compensating differential for law enforcement officers. First, we analyze a baseline regression model with log wage as the dependent variable and independent variables such as human capital, region, and year to see the risk premium for law enforcement officers. The results are consistent with economic theory in that males, being married and union workers all earn a premium over their counterparts. Also, returns to education are about 8 percent and there is a risk premium for law enforcement officers. We then see how the risk premium is affected when we control for MSA size, and more importantly, crime rates within the MSA. We attempt to examine how much influence the outside factor of the amount of crime within an MSA determines the wages of law enforcement officers. Second, this paper compares the risk premiums for law enforcement officers as they are broken up into the three occupation categories that made them up. The categories are working as a sheriff, police officer, and superintendent (detective or chief). We then control for MSA size and the crime rates within MSA to see how this affects the risk premiums.