Local governments and economic development officials, especially those in urban areas, know that high crime rates adversely affect residential and business immigration to their communities. They know that crime rates—along with educational quality, infrastructure and employment opportunity—are part of what determines whether a city or region is attractive and whether it is an economic success. Research on the effects of crime on the general economic growth of local areas generally finds that areas with higher crime rates experience lower rates of economic growth and development.
Economists explain an individual’s propensity to commit a crime by examining the expected costs and benefits of criminal activity. Empirical research on crime has modeled the direct cost to an individual as the probability of arrest and incarceration (i.e., deterrence) and the direct benefit as the value of the illegally acquired goods.
Criminal behavior also depends upon other cost comparisons, such as forgone wages and employment opportunities. The reasoning is that higher wages and employment opportunities decrease the attractiveness of acquiring assets through criminal activity. Much work has been done to estimate the effect of deterrence and economic conditions on crime, but the mixed results from these studies do not allow a definitive conclusion.
We recently completed a report that explores the effects of deterrence and economic conditions on crime in U.S. cities. Part of the report presents descriptive statistics on crimes and arrests for seven major crimes (murder, rape, robbery, assault, burglary, larceny and motor vehicle theft) in the Eighth District cities of St. Louis, Little Rock, Memphis, and Louisville. These data are shown in the Table. Crime rates and arrest rates are per 100,000 in population and have been normalized by each city’s 1990 population, thus providing average crime and average arrest rates for each city over the respective sample period. (See notes below table for the sample periods for each city.)
Some differences across the cities are worth noting. Of the four cities, St. Louis has the highest average murder rate (3.5 per 100,000), robbery rate (81.2 per 100,000), assault rate (312.1 per 100,000), burglary rate (224.1 per 100,000) and motor vehicle theft rate (170.9 per 100,000). The rate of rapes in Memphis (9.7 per 100,000) is higher than the rate of the other three cities. Little Rock has the highest rate of larceny at 582.8 per 100,000.
Note: The rates shown above were found by normalizing the mean values from the sample of each city by the 1990 population (per 100,000) for each city. The sample period for each city is: St. Louis—December 1983 to December 2004; Louisville—January 1993 to December 2002; Little Rock—December 1983 to December 2004; Memphis—January 1985 to December 2004. The 1990 population for each city was: St. Louis—396,685; Louisville—269,838; Little Rock—177,086; Memphis—618,894.
Deterrence and Business Cycles
It appears that, at least when comparing averages across cities, there is a positive relationship between arrest rates and crime rates. Of course, this positive relationship does not reveal any causal relationship. It may certainly be the case that a long-run negative relationship between arrests and crime exists and that the direction of causality is not from arrests to crime, but rather from crime to arrests as police allocate more resources to combat an increase in crime.
We also conducted statistical analyses to explore whether changes in each of the seven criminal offenses can be explained by changes in the city’s business cycle (as measured by changes in unemployment and real wages), as well as changes in deterrence (as measured by arrests).
Unlike previous time series studies that looked at long-run relationships (i.e., 20-year trends) between economic conditions and crime, the current study explores whether short-run (i.e., month-to-month) changes in city economic conditions and deterrence influence changes in city crime growth rates. We used monthly data for 23 large cities in the United States, as well as the Eighth Federal Reserve District cities of St. Louis, Louisville and Little Rock (Memphis is included in the top 23). In addition, we empirically tested the hypothesis that arrests follow an increase in crime.
Our study found weak evidence across U.S. cities that changes in economic conditions significantly influence short-run changes in crime. However, we did find that short-run changes in economic conditions influence property crimes in a greater number of cities. This likely reflects the fact that nonviolent property crimes are more likely to result in financial gain than more violent crimes.
In addition, we found little evidence to support the deterrence hypothesis in the short run, as changes in arrests had no influence on crime in many U.S. cities. It may be that arrests are not the best measure of deterrence, and thus the lack of a large number of statistically significant relationships between arrests and criminal activity reflects this fact. This supports the suggestion by previous authors that criminals are myopic with regard to changing probabilities of arrest and do not consider the likelihood of the negative consequences of committing a crime. Similarly, the results may reflect the reasonable possibility that criminals do not have perfect information regarding changes in deterrence and thus are not able to adjust their criminal activity accordingly.
The hypothesis that arrests respond to increases in crime was also empirically tested. We found much stronger evidence that, in many U.S. cities, an increase in the growth rate of crime results in an increase in the growth of arrests for that crime. In other words, arrests follow crimes. This supports the notion that law enforcement reallocates its resources in response to increases in crime. One interesting finding was that the causal relationship from robbery to robbery arrests was statistically significant for 17 of the 23 cities, and the relationship from vehicle thefts to vehicle theft arrests was statistically significant for 12 of the 23 cities in the sample.
It is reasonable to expect that, over time, an increase in all types of crimes would garner an increased response from law enforcement, especially the more violent crimes of murder and rape. Several factors explain why increases in less violent crimes garner a law enforcement response in the short run while increases in the most violent crimes do not.
First, violent crimes are committed with less forethought than property crimes and are often part of an overall increase in criminal activity, such as drugs and gangs. These activities may require years of law enforcement planning and strategy via task forces and interagency cooperation to reduce. A classic example is New York City in the 1980s. Second, preventing less violent crimes may also reduce the number of more violent crimes, as suggested by the “broken windows” hypothesis of law enforcement. Thus, combating a rise in less violent crimes is relatively less costly in terms of law enforcement resources and may, in fact, reduce the number of violent crimes.
Finally, it seems reasonable that crimes that are more visible to businesses and tourists—such as robbery, vehicle theft and assault—are likely to result in greater attention from law enforcement in the short run, possibly through a relatively inexpensive increase in police presence.
The degree to which the effect of crime on arrests persists over time is quite different across cities. For example, robbery arrests are a result of the change in robberies from only the prior month in some cities to the last 10 months in other cities. This may reflect differences in the effectiveness of law enforcement across cities to respond to crime.
Two points should be considered, however, when attempting to infer the effectiveness of law enforcement.
First, the initial level of crime and arrests is an important factor in evaluating the effectiveness of changes in law enforcement. For a city that is already allocating a large percentage of its law enforcement resources to combat robberies, for example, the opportunity cost of allocating further resources to robberies is much higher than it would be in cities that have a lower level of initial law enforcement resources allocated to combat robberies. Thus, cities already having a relatively large percentage of their resources allocated to combat robberies may be unwilling (or unable) in the short run to allocate further resources to combat a further increase in robberies.
Second, our analysis does not consider the optimal allocation of law enforcement resources to combat other crimes. Clearly, zero crime in a city is not an optimal level of crime, given the nearly infinite resources it would require to achieve this objective, if it could be achieved at all. The optimal level of each crime and the desired level of resources to combat each crime certainly differ across cities and are based on the preferences of the citizenry, public officials and law enforcement, as well as different law enforcement strategies.
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