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Low Priority Laws and the Allocation of Police Resources

Amanda Ross

Department of Economics

West Virginia University

Morgantown, WV 26506

Email: Amanda.ross@mail.wvu.edu

And

Anne Walker

Department of Economics

University of Colorado at Denver

Denver, CO 80202

Email: anne.w.walker@ucdenver.edu

Abstract:

There is an ongoing literature in economics examining the deterrent effect of police officers on

criminal activity. However, this literature tends to focus on the aggregate number of officers

employed versus the relative allocation of an officer’s time. In this paper, we examine how the

reallocation of police resources affects police behavior and criminal activity using the adoption

of low priority initiatives by some jurisdictions. Low priority initiatives mandated that minor

marijuana possession offenses be the lowest enforcement priority for police officers. We first

test whether adoption of the initiative decreased the arrest rate for minor marijuana possession

offenses. If police officers devote fewer resources towards minor marijuana possession crimes,

then more resources will be available to deter and solve more serious crimes. This would suggest

that if misdemeanor marijuana arrest rates decreased, there may be a reduction in crime rates or

clearance rates for more serious crimes, such as murder or robbery. Using city-level data from

California, we find that those jurisdictions that adopted low priority laws experienced a reduction

in arrests for misdemeanor marijuana offenses. However, we do not find a significant effect of

enacting a low priority initiative on the crime rate or clearance rate of more serious felony

crimes. Our findings are important for local policy makers, as we do not find evidence that the

initiatives had an impact on more serious crimes as was intended by the legislation.

Keywords: Police Resource Allocation, Police Incentives, Drug Policy, Low Priority Laws

JEL Code: H1, H4, K4, R5

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1. Introduction

Becker (1968) proposed a model of rational criminal behavior where individuals weigh the costs

and benefits of crime and, based on that calculation, choose to engage in criminal activity or not.

This model of criminal behavior predicts that an increase in police resources will increase the

probability that a criminal will be arrested. An increase in the probability of arrest will increase

the costs of engaging in criminal activity and will reduce crime rates. Since the seminar work of

Becker, numerous economists have empirically tested the effect of additional police protection

on crime rates within a jurisdiction (Benson and Rasmussen, 1991; Benson, Kim, Rasmussen, &

Zuehlke, 1992; Cornwell & Trumbull, 1994; Sollars, Benson, & Rasmussen, 1994; Levitt, 1997;

Benson, Rasmussen, & Kim, 1998; Corman & Mocan, 2000; McCrary, 2002; Levitt, 2002).

However, much of the existing empirical work examining the relationship between police

protection and deterrence has relied on the aggregate number of police officers, not on the

relative amount of time officers spend on various tasks. To determine the effect of additional

police resources on crime rates, the manner in which officers allocate their limited time is the

relevant information needed. Since direct data on the division of time and resources within

police departments is not available, policies which mandate a specific allocation of an officer’s

time provide an opportunity to test the consequences of a reallocation of police resources on

crime rates. In this paper, we draw upon such a mandate, known as a low priority initiative, to

examine how a change in the relative amount of time police officers spend on specific activities

affects criminal activity within a jurisdiction.

Low priority initiatives mandate that minor marijuana possession offenses be the lowest

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enforcement priority for local law enforcement agencies. Those cities that have adopted such an

initiative believe that by shifting attention away from low-level drug crimes, police officers will

be able to focus more time towards more serious crimes, such as murder, rape, robbery, assault,

burglary, motor vehicle theft, and larceny. Gil Kerlikowske, the police chief in Seattle in 2003,

stated that “the issue was one of limited police resources” in reference to Seattle’s policies on

drug enforcement, including a low priority law.1 While low priority initiatives have been

adopted by several localities since 1979, no research thus far has examined the impact of these

mandates on police behavior and criminal activity.

We begin by estimating the effect of the adoption of a low priority law on arrests using data

on the number of misdemeanor marijuana drug arrests in California from 2000 to 2009. Using a

city-level panel data set, we find that the adoption of a low priority law reduced the number of

arrests for misdemeanor marijuana offenses per person. This reduction in the arrest rate is

present when we look at the entire state of California, as well as when we exclude small towns

and focus on only the larger cities. We also considered the impact of the policy on arrests rates

of other crimes to address any concern that there was a change in the arrest behavior of police

officers in general versus just for misdemeanor marijuana offenses as intended by the policy.

We do not find evidence that the adoption of a low priority initiative affected the arrest rate for

any other type of crime, suggesting the policy had the intended impact and only affected arrests

of low-level marijuana offenses.

Next, we consider the effect of this reallocation of police resources away from low-level

1 http://online.wsj.com/article/SB124225891527617397.html

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marijuana crimes on more serious offenses. We look at the impact of the adoption of low

priority initiatives on both the crime rate and the clearance rate (defined as the number of crimes

for which an arrest was made divided by the number of reported crimes) for murder, rape,

robbery, assault, burglary, motor vehicle theft, and larceny. Both of these rates could be affected

by the additional police resources, as officers could either deter more individuals from

committing crimes (which would reduce the crime rate) or solve more crimes (which would

increase the clearance rate). We find that adoption of a low priority initiative did not have a

significant negative effect on the crime rate nor a significant positive effect on the clearance rate

for any of these violent and property crimes.

Overall, we find that the low priority initiative had the intended effect of reducing the

number of minor drug crime arrests, but we do not find evidence of a deterrent effect for any

other serious crime. Our findings have important implications for local policy makers regarding

the allocation of scarce police resources since we do not find that the policy had the intended

effect of enabling police officers to deter more serious crimes. Some possible explanations for

our findings are that not enough free time is freed up by the mandate to have a measurable effect

on other types of crime. Alternatively, the mandate may not cause a reallocation of resources,

but may simply cause police officers to stop engaging in one activity but do not use that time for

another productive activity. We discuss in detail the different mechanisms that may explain the

lack of an effect on other types of criminal activity in the conclusion.

The remainder of this paper is organized as follows. Section two provides background

information on low priority initiatives as well as a brief overview of the literature on the

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economics of crime. Section three describes our empirical methodology while section four

discusses the data used in our analysis. Section five presents estimates of the impact of low

priority laws on the arrest rate for misdemeanor marijuana offenses, as well as other types of

crime, and section six contains estimates of the impact of low priority laws on the crime rate and

clearance rate for felony violent and property crimes. Conclusions and policy implications are

discussed in the final section.

2. Background Information

2.1. Low Priority Initiatives

Low priority initiatives mandate that minor marijuana possession offenses be the lowest

enforcement priority for local law enforcement agencies. While there are some differences in the

specific law that was implemented in each jurisdiction, a few components are common across all

the initiatives.2 First, the law only affects minor marijuana possession offenses. Felony drug

crimes, including felony-level possession and distribution offenses, were not affected by the

policy change. Second, the law only affected offenses where marijuana was intended for adult

personal use. Possession or selling of marijuana to minors are not affected by low priority

initiatives. Finally, the mandate was only intended to affect the private use of marijuana, so any

offenses committed on public property were not affected.3

The first low priority initiative was passed in Berkeley, CA in 1979. Since then, 16 other

2 For an example of a specific low priority initiative, the Oakland initiative can be found at the following link:

http://www2.oaklandnet.com/oakca1/groups/cityadministrator/documents/report/oak041645.pdf. 3 Most of the initiatives also had some language regarding who was responsible for making sure the ordinance was

enforced.

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cities have adopted a similar initiative. Table 1 contains a list of all the localities that adopted the

policy, as well as the year that the initiative was implemented and whether it was passed through

a city council vote or a voter initiative. The last column of Table 1 includes information on the

margin by which each jurisdiction passed the law. As we can see in Table 1, these initiatives

have become increasingly popular over time, as the margin of individuals who voted for the law

has steadily increased.

The goal of a low priority initiative is for police to allocate fewer resources towards the

enforcement of minor marijuana possession laws so that resources can be allocated towards more

serious offenses. For example, the initiative that was passed in Santa Monica noted that the

initiative “makes marijuana offenses, where cannabis is intended for adult personal use, the

lowest priority” and that the law “frees up police resources to focus on violent and serious

crimes, instead of arresting and jailing nonviolent cannabis users.”4 Therefore, the success of the

initiative is measured through two mechanisms. First, did the policy cause a reduction in the

arrest rate for minor marijuana offenses? Second, after observing a decrease in the number of

arrests for minor marijuana possession crimes, was there an effect on more serious crimes? This

second question is a direct test of the Becker model of rational criminal behavior and the

deterrence hypothesis.

2.2. The Deterrence Hypothesis

Becker's (1968) economic theory of crime provides the theoretical foundation for the deterrence

4 See http://stopthedrugwar.org/chronicle/2006/oct/26/feature_lowest_law_enforcement_p for more details on the

Santa Monica initiative.

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hypothesis. Becker models criminals as rational, utility-maximizing individuals. These

individuals weigh the costs and benefits of different activities and choose how to allocate their

resources between crime, a risky asset, and legitimate income, a riskless asset. Becker's model

shows that an increase in the probability of capture and punishment decreases an individual's

expected utility of committing a crime. Therefore, an increase in the probability of arrest will

reduce the likelihood that an individual commits a crime. Note that this deterrent effect of police

officers is present through two separate mechanisms. First, the additional police presence will

allow officers to be in the appropriate place to stop a crime that is about to be committed. In

addition, the additional officers may cause individuals to choose not to attempt to commit a

crime in the first place. The knowledge of the additional officers and the fear of being caught,

even if the individual may not be able to see the officer at that moment, may cause individuals to

be less likely to commit a crime. Both of these mechanisms would suggest additional police

officers reduce crime rates.

Becker's theory spawned a huge empirical literature that tests the economic model of crime

and its implications. Numerous papers have examined the impact of additional police officers on

crime rates. Initially, many researchers found additional police officers had a positive impact on

crime rates (see Cameron (1988) for a review). This positive coefficient is likely caused by a

reverse causality issue, since areas with higher crime rates will employ more police officers.

Levitt (1997) used election years to instrument for police officers and found that increases in

police resulted in a substantial reduction in violent crime. While studies questioned the validity

of his instrument (McCrary, 2002), research using other instruments has found that additional

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police resources reduce crime rates (Levitt, 2002; Corman & Mocan, 2000). Other studies have

used the reallocation of police officers to specific neighborhoods in response to terrorist threats,

which is exogenous to local crime rates, to estimate the impact of police presence on crime rates

(Di Tella & Schargrodsky, 2004; Draca et al., 2011; Klick & Tabarrok, 2005).5

2.3. Police Resources

One criticism of this literature that examines the impact of police on crime rates is that the data

on police protection only has information on the aggregate number of police officers, as opposed

to the relative allocation of police resources (Benson, et al., 1994; Benson et al., 1995). More

specifically, the incentives of police and the discretion of officers in allocating their time cannot

be measured when only the total number of officers employed is available. Once the discretion

of individual police officers is taken into account, it is no longer clear that increasing the number

of police officers will necessarily increase the probability of arrest and reduce crime rates.

However, data on how individual police officers allocate their time is not available, making it

difficult to determine exactly how police allocate their limited resources among various tasks.

Benson et al. (1998) provide empirical support for the claim that it is not simply the

aggregate level of police resources that matters, but rather the relative allocation of police

resources towards enforcement of particular types of crime. Using the “war on drugs” during the

1980's as a source of exogenous variation in how police officers allocate their time, the authors

5 In addition, numerous papers have looked at the impact of different sentencing policies on crime rates, specifically

the death penalty and more recently Three Strikes Laws and Truth-in-Sentencing Laws (Ehrlich, 1975; Ehrlich,

1977; Shepherd, 2002; Mocan & Gittings, 2003; Dezhbakhsh & Shepherd, 2006; Helland & Tabarrok, 2007;

Iyengar, 2008; Ross, 2012).

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test whether the reallocation of police resources towards drug crimes had an impact on violent

crime rates (murder, rape, robbery, and aggravated assault).6 They conclude that changing the

incentives of police officers to allocate more time and effort towards drug crimes took resources

away from other crimes and caused an increase in violent crime rates.

Sollars et al. (1994) also explored the effects of the reallocation of police resources due to

drug enforcement, but focused on the resulting changes in the property crime rate (burglary,

motor vehicle theft, and larceny). They found that the reallocation of scarce police resources

away from property crime and towards drug crime significantly decreased the risks that

individuals who commit property crimes faced, causing an increase in the property crime rate.

Other empirical studies have also shown that the probability of arrest for property crimes is

negatively related to the portion of police effort directed at drug control (Benson & Rasmussen,

1991; Benson et. al., 1992). All of these studies support the notion that the allocation of

resources within the police department matters more with regards to deterring crime than does

the aggregate amount of resources utilized by the police agencies.

We expand upon this literature by using adoption of low priority initiatives to examine the

impact of allocating resources away from a specific policing activity. The “war on drugs”

created a situation where police were incentivized to allocate additional time towards major drug

crimes and away from deterring property and violent crimes. Low priority initiatives mandate

that police officers allocate less time and effort towards arresting individuals for minor drug

6 Mast et al. (2000) shows that the policy changes associated with the “war on drugs” created an increase in drug

arrests in Florida. Their findings suggest that police have considerable discretion when deciding how to allocate

their scarce resources.

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crimes. If police heed the mandate and stop arresting individuals for minor marijuana offenses,

then additional police time has been created which can be spent deterring more serious crimes.

However, just because police officers spend less time arresting individuals for minor possession

offenses does not necessarily mean that officers use this additional time towards other productive

activities. Our paper contributes to the literature by looking at how removing incentives to

prosecute certain types of crimes affects police behavior, versus previous work which has looked

at what happens when police are incentivized to prosecute a specific type of crime.

3. Empirical Methodology

In this paper, we answer two questions. First, did low priority initiatives decrease arrest rates for

minor marijuana possession offenses? Second, if low priority initiatives caused police to devote

less time and effort towards these minor drug crimes, did this reallocation of resources have a

deterrent effect on more serious crimes? If low priority initiatives resulted in a reduction in the

arrest rates for minor marijuana possession offenses, then this would suggest that police have

allocated resources away from this activity. Resource allocation decisions by police officers

cannot be observed directly, but arrest rates reflect the consequences of that decision. If we find

that the low priority initiatives lead to a reduction in the number of arrests for misdemeanor

marijuana offenses, then we can evaluate the effect of the implicit reallocation of resources on

the deterrence of more serious violent (murder rape, robbery, and assault) and property crimes

(burglary, motor vehicle theft, and larceny).

The first question considers the effect of low priority initiatives on the number of arrests per

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capita for misdemeanor marijuana offenses. To estimate the effect of the policy on arrest rates,

we use the following specification:

log(𝑎𝑟𝑟𝑒𝑠𝑡𝑖𝑡) = 𝛽1𝐿𝑃𝑖𝑡 + 𝛽2 𝑋𝑖𝑡 + 𝛾𝑡 + 𝛼𝑖 + 𝑣𝑖𝑡 (1)

where 𝑎𝑟𝑟𝑒𝑠𝑡𝑖𝑡 is the number of arrests for minor possession of marijuana per 1,000 people in

year t in city i. 𝐿𝑃𝑖𝑡 is a dummy variable that equals one if city i had a low priority law in year t.

If the estimated coefficient on the low priority law dummy, 𝛽1, is negative and significant, then

the enactment of low priority laws had the intended effect of reducing the number of arrests for

minor drug crimes. 𝑋𝑖𝑡 is a vector of control variables including percent male, percent black,

median age, percentage households with a female head of household and children, as well as

various controls for the education level of the individuals. We also include in 𝑋𝑖𝑡 the number of

police officers per 1,000 people. 𝛾𝑡 is a year fixed effect to control for year specific shocks that

are common to all jurisdictions, 𝛼𝑖 is a city fixed effect to control for unobserved differences in

each locality, and 𝑣𝑖𝑡 is an idiosyncratic error term.

If the estimates obtained from the first regressions indicate that a reallocation of police

resources occurred, then we can consider if this reallocation affected other types of crimes. To

determine the effect of low priority laws on the deterrence of more serious crimes, we estimate

the following:

log(𝑐𝑟𝑖𝑚𝑒𝑖𝑡) = 𝛽1𝐿𝑃𝑖𝑡 + 𝛽2 𝑋𝑖𝑡 + 𝛾𝑡 + 𝛼𝑖 + 𝜀𝑖𝑡 (2)

The dependent variable in equation (2), log(𝑐𝑟𝑖𝑚𝑒𝑖𝑡), is a measure of criminal activity in city i

in year t. We use two different measures of criminal activity as the dependent variable – the

crime rate and the clearance rate (number of arrests divided by the number of reported crimes).

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Using these different measures will allow us to account for the different mechanisms through

which the reallocation of police resources may have a deterrent effect. For example, the

reallocation of police resources could deter individuals from committing a crime through

increased police presence, which would reduce the crime rate. Alternatively, police could

allocate more resources towards solving crimes and thus would increase the clearance rate. Both

effects are important to policy makers as both may deter criminals.

4. Data

Given the difficulty of finding a nation-wide data set on misdemeanor marijuana crimes, we

focus on California – a large state that had seven cities implement low priority initiatives.

Annual data on the number of arrests for misdemeanor marijuana offenses from 2000 to 2009 in

California was obtained from the California Department of Justice.7 Annual data on the Index I

violent and property crimes, as well as the number of police officers per capita, were obtained

from the Federal Bureau of Investigation’s (FBI) Uniform Crime Reports (UCR). Data for the

socio-economic control variables used in our analysis was obtained from the City and County

Data Books. Combining these various data sets, we create a panel data set on California cities

from 2000 to 2009.

One concern when considering the impact of the policy on arrest behavior as well as the

crime rates is that those areas which adopted the policy are different than those that did not.

Since we are using the non-adopting cities to model what would have happened in the absence of

7 See the following website for information on our arrest data: http://ag.ca.gov/cjsc/datatabs.php.

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the policy, it is important that the adopting and non-adopting cities are similar prior to passage of

the law. To provide evidence that this assumption is true, we present summary statistics for both

the cities that implemented a low priority law and those that did not in Table 2. The first column

gives the summary statistics for all cities in California, while columns 2 and 3 break the sample

into adopters and non-adopters. The final column presents the t-statistic of whether the means

are statistically different from one another.

Looking first at the arrest rate for misdemeanor marijuana offenses, we see that the non-

adopters appear to have a higher arrest rate than the adopters. However, when we account for

the variance of the samples and test for a difference in means we do not find that the average

arrest rate of the two groups are statistically different from one another. While the means are not

statistically different from one another, it is important to note how the potential difference would

impact our estimates. If the police in the adopting areas were already treating these offenses as

the lowest priority, then this would suggest that our point estimates are underestimating the true

effect of the policy on the arrest rate. Therefore, we argue that our estimates of the impact of

low priority initiatives on arrests of misdemeanor marijuana crimes are actually a lower bound of

the true effect of the policy.

Looking next at the difference in the means of the other crime rates, we do not find that

there is a statistically significant difference between the average crime rate of those cities that

implemented low priority laws and those that did not for murder, rape, robbery, assault, burglary,

motor vehicle theft, or larceny. We also do not find that the number of police officers per capita

is statistically different in the adopting and non-adopting jurisdictions. Overall, these results

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suggest that in terms of police protection and crime rates, there was not a significant difference

between the cities that chose to adopt the policy and those that did not.

We do see some differences in terms of the socio-economic attributes of the cities that

adopted low priority initiatives and those that did not. Specifically, we find statistically

significant differences between the adopters and non-adopters with respect to the percent of

residents within the city that are black as well as the educational attainment measures. Adopting

cities tend to have a higher percent of black residents and also have residents with higher levels

of educational attainment on average. We include these variables as controls in our regressions

to remove any possible effect these characteristics may have on our variables of interest.

5. Impact of Low Priority Laws on Arrests

5.1. Impact of Low Priority Initiatives on Misdemeanor Marijuana Arrests

Table 3 presents estimates of the impact of low priority laws on the log of the number of low-

level marijuana arrests per 1,000 people. We define low-level marijuana arrests as the number of

arrests for misdemeanor marijuana offenses. The first column presents results for the full

sample, while columns two and three restrict the sample to cities with populations above 30,000

and 50,000, respectively.8 We restrict the sample by population to account for the possibility

that the full sample results are being driven by small towns versus larger cities. Since larger

cities tended to be the adopters of the low priority initiatives, we exclude the smaller jurisdictions

8 We select these cutoffs based on the population of the adopting jurisdictions. West Hollywood has a population of

just over 30,000, so we look at both at 30,000 and at 50,000 since we are losing one of the adopting cities when

we increase the cutoff.

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so that the two samples are more comparable. Standard errors are reported in parenthesis below

the coefficients.

Looking at the first column, we see that adoption of low priority initiatives caused a

reduction in the number of arrests for minor marijuana offenses. The coefficient, which can be

interpreted as a semi-elasticity, suggests that adoption of a low priority law will reduce the arrest

rate for misdemeanor marijuana offenses by 0.28%. This effect is statistically different from

zero at the 10% level. When we restrict the sample to those cities with larger populations, we

find the effect is still negative and approximately the same magnitude, but these estimates are

more precise and are now significant at the 5% level. The increase in precision, despite the

smaller sample size, supports our exclusion of the smaller, less comparable jurisdictions.

Overall, our findings in Table 3 indicate that in the areas that adopted low priority laws, police

heeded the mandate and arrested fewer individuals for low level marijuana offenses.

We include a variety of other control variables, in addition to MSA and year fixed effects, to

control for any unobservable variables that may be present and driving our results. These

controls include the log of the number of police officers per capita, as well as a variety of other

socio-economic controls.9 We find a positive effect of the number of police officers on arrests,

which suggests that more police officers lead to more arrests. This positive effect is not

surprising given that hiring additional officers leads to more individuals to make arrests. In

general, the coefficients on the other socio-economic variables do not have a consistent pattern

9 Controls include median age, percent male, percent white, percent black, as well as the measures to control for

education. Our socio-economic data does not have information on percentage Hispanic, but this ethnic group is

included in the omitted category.

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across the different city sizes.

Thus far, we have argued that those cities which adopted low priority initiatives are not

different from those that did not. While there are differences across cities with regards to the

decision to enact a low priority initiative or not, there are also differences across the adopting

cities in terms of how the policy was implemented. We draw upon differences in whether cities

adopted through voter initiative or city council vote to see if there are heterogeneous effects of

the policy based on how the initiative was passed. Of the seven cities in California that adopted

the law, only two, San Francisco and West Hollywood, passed the law through a city council

vote. The rest of the cities adopted the policy through voter initiatives. In Table 4, we include

an interaction between an indicator variable equal to one if the city adopted a low priority law

and another indicator variable equal to one if the law was passed by a city council vote. We find

that adoption by the city council does not have a statistically different effect than if the law was

passed through a vote by the general public. Therefore, it appears that the method through which

cities passed a low priority law did not have a differing effect with regards to the police’s

response to the mandate.

5.2. Impact of Low Priority Initiatives on Arrests for Felony Crimes

Low priority initiatives were intended only to affect how police approached low-level,

misdemeanor marijuana possession offenses. Adoption of the policy should not have caused

police to adjust arrest behavior with regards to more serious felony marijuana crimes and other

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felonies, such as murder or motor vehicle theft.10 To examine if police changed their arrest

behavior in general or only for the crime indicated by the initiative, we examine in Table 5 the

impact of low priority laws on arrests of felony crimes. In the first column, we again include the

full sample and then stratify by if the city has a population over 30,000 and over 50,000.

The coefficients in the first row provides estimates of the impact of implementation of low

priority initiatives on arrests for all felony drug crimes. This would include felony marijuana

arrests, as well as arrests for any other type of drug. These types of crimes were not intended to

be affected by the low priority laws, as the only offense that the law mandated that police make

the lowest priority were misdemeanor marijuana crimes. As we can see in the first row of Table

5, adoption of the law did not have any effect on arrests for felony drug crimes. In addition, we

consider the impact of adopting low priority initiatives on the arrest rate for other types of

felonies – murder, rape, robbery, assault, burglary, motor vehicle theft, and larceny. We do not

find that implementation of the policy had an effect on the arrest rate for any other type of

crime.11 These findings indicate that police did not change their arrest behavior in general, only

that they reduced arrests for misdemeanor marijuana offenses as instructed by the initiative.

10 The adoption of the policy could have an effect on arrest behavior for other crimes. Since there are additional

police resources that can be devoted to catching other types of criminals, this would increase the number of

arrests. However, if the additional police officers have a deterrent effect, this would cause fewer crimes to be

committed and would thus decrease the number of arrests made. Given these two mechanisms, the possible

effect of the policy on arrests is theoretically ambiguous. For that reason, we focus on arrests in this section to

determine if there was a change in police behavior in general versus using arrests as a measure of deterrence. 11 We have also looked at misdemeanor offenses that could have been impacted, such as disorderly conduct,

trespassing, and vandalism. We continue to find no statistically significant increase in arrests for these low-level

crimes. Since the next section focuses on felony arrests, we only show the results for these more serious crimes

in the interest of space.

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6. Impact of Low Priority Laws on Crimes

Cities enacted low priority initiatives with the stated purpose of freeing up police resources from

dealing with minor drug offenses so that police could concentrate efforts on more serious crimes.

In the previous section, we found that the enactment of a low priority law led to a reduction in

the number of arrests per capita for misdemeanor marijuana offenses. Next, we examine if the

reallocation of police resources had a deterrent effect on more serious Index I violent and

property crimes – murder, rape, robbery, assault, burglary, motor vehicle theft, and larceny.

There are two ways that police can affect crime – they can stop crimes from happening or they

can solve more crimes that do happen. The first relationship can be examined through the crime

rate and the second effect can be seen through the clearance rate (number of reported crimes

divided by the number of arrests).

We first consider the impact of low priority laws on the crime rate for the different violent

and property crimes in Table 6a. If police are devoting fewer resources towards low-level

marijuana offenses, this frees up resources to devote towards patrolling and preventing more

serious crimes. Therefore, if the deterrence hypothesis is true, we expect that adoption of a low

priority initiative will cause a reduction in the rate at which more serious crimes are committed.

As we can see in Table 6a, we do not find that adoption of low priority initiatives caused a

statistically significant reduction in any type of violent or property crime. In fact, the only

statistically significant effect we obtain is that adoption of the low priority initiative increases the

aggravated assault rate in large cities.

In Table 6b, we look at the impact of low priority initiatives on the clearance rate for the

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different types of violent and property crimes. In general, we do not find a statistically

significant effect of the law on the clearance rate. The exception to this, however, is that we find

adoption of low priority laws reduce the clearance rate for larceny. In other words, police are

finding the guilty party for fewer larcenies after adoption of the policy.

The goal of the low priority initiative was to reallocate police resources away from low-level

marijuana possession offenses towards more serious crimes. While we did find there was an

impact of the laws on the arrest behavior for minor marijuana crimes, we did not find an impact

on the crime rate for more serious crimes, nor did we find there was a positive impact on the

clearance rate for other types of crimes. Overall, our findings indicate that even though police

devoted fewer resources towards arresting individuals for misdemeanor marijuana offenses, there

was no measurable effect on deterring more serious violent and property crimes through either

lower crime rates or higher clearance rates.

7. Conclusions and Policy Implications

Low priority laws were adopted to allow police agencies to focus less attention on minor drug

crimes and more attention towards violent and property crimes. Using data from California

between 2000 and 2009, a large state where seven cities adopted a low priority initiative, we

examine first if adoption of low priority laws had an effect on the number of arrests. If we find

evidence that police reallocated resources as a result of the policy, we can then examine if this

reallocation deterred individuals from committing other crimes.

We do find that low priority initiatives caused a reduction in the number of arrests for

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misdemeanor marijuana offenses. In addition, we do not find any effect on arrests for other

types of crimes, which suggests that the effect was specific to only these minor marijuana

offenses. However, we do not find any significant deterrent effect through either a reduction in

the crime rate or an increase in the clearance rate of other crimes, including murder, rape,

robbery, aggravated assault, motor vehicle theft, burglary, and larceny. These results hold for

both the entire sample, as well as when we focus on only the larger cities.

We found that the policy successfully caused police to focus less attention on low-level drug

crimes. However, we did not find evidence that after spending less time arresting individuals for

these low-level crimes that police spent more time and effort on more serious offenses. There

are several reasons why this could be the case. First, while we know that there was a reduction

in the number of arrests for minor marijuana offenses, we do not know exactly how much time

was created as a result of the policy. Minor marijuana offenses may be an extremely low cost

crime for police with regards to amount of time spent on this activity, so one explanation is that

not enough additional time was created to have a measurable impact on other crimes.

Second, it is possible that police are using the free time to engage in non-productive

behavior. Previous studies that have examined changes in the relative allocation of police

resources have focused on changes that incentivized officers to spend more time on a specific

activity. For example, Baicker and Jacobson (2007), Mast et al. (2000), and Benson et al. (1995)

provide empirical evidence that asset forfeiture laws have a positive effect on drug enforcement

and that these positive effects are primarily due to the financial incentives created by such laws.

Asset forfeiture laws allow police agencies to keep a substantial share of assets they seize during

21

drug arrests, thereby increasing their discretionary budget. The low priority initiative mandates

low-level marijuana offenses be the lowest priority, but does nothing to incentivize police to

allocate those additional resources towards more serious crimes. Therefore, another reason that

we may not find an effect on more serious crimes is because police are not reallocating their time

towards another activity because they lack any incentive to do so, and instead use that time they

were spending arresting misdemeanor marijuana offenses doing nothing.

Finally, even if reducing the time spent enforcing minor drug crimes did create a significant

amount of additional police time to have a measurable impact on other types of crimes, this does

not mean the different activities are close substitutes. The assumption of the policy is that time

spent patrolling and arresting individuals for one type of crime versus another are perfect

substitutes. There may be different skills and knowledge required to police the different types of

activities, which may explain why we do not find an effect of the policy on other types of crime.

Future work should consider how substitutable police time is between different types of crime

prevention. If police resources and effort towards different crimes are not substitutes, than

policies such as low priority initiatives are not viable options to reduce the crime rate of more

serious offenses.

22

References

Baicker, K, and M. Jacobson. 2007. Finders keepers: Forfeiture laws, policing incentives, and

local budgets. Journal of Public Economics 91 (11-12) (12): 2113-36.

Becker, G.S. 1968. Crime and punishment: An economic approach. The Journal of Political

Economy, 76(2), 169-217.

Benson, B.L., I. Kim, and D.W. Rasmussen. 1994. Estimating deterrence effects: A public

choice perspective on the economics of crime literature. Southern Economic Journal, 61(1),

161-168.

Benson, B.L., I. Kim, D.W. Rasmussen, and T.W. Zuehlke. 1992. Is property crime caused by

drug use or by drug enforcement policy? Applied Economics, 24 (7), 679.

Benson, B.L., and D.W. Rasmussen. 1991. Relationship between illicit drug enforcement policy

and property crimes. Contemporary Policy Issues, 9(4), 106-15.

Benson, B.L., D.W. Rasmussen, and I. Kim. 1998. Deterrence and public policy: Trade-offs in

the allocation of police resources. International Review of Law and Economics, 18(1), 77-

100.

Benson, B.L., D.W. Rasmussen, and D.L. Sollars, 1995. Police bureaucracies, their incentives,

and the war on drugs. Public Choice, 83(1-2), 21-45.

Cameron, S., 1988. The economics of crime deterrence: A survey of theory and evidence.

Kyklos, 41 (2): 301-23.

Cornwell, C., and W.N. Trumbull, 1994. Estimating the economic model of crime with panel

data. The Review of Economics and Statistics, 76(2), 360-366.

Corman, H. and H.N. Mocan, 2000. A Time-Series Analysis of Crime and Drug Use in New

York City. The American Economic Review, 90, 584-604.

Dezhbakhsh, H. and J. Shepherd, 2006. The Deterrent Effect of Capital Punishment: Evidence

from a “Judicial Experiment.” Economic Inquiry, 44, 512-535.

Di Tella, R., and E. Schargrodsky, 2004. Do Police Reduce Crime? Estimates using the

Allocation of Police Forces after a Terrorist Attack. The American Economic Review, 94,

115-133.

23

Draca, M., S.J. Machin, and R. Witt, 2011. Panic in the Streets of London: Police, Crime, and

the July 2005 Terrorist Attacks. The American Economic Review, 101, 2157-2181.

Ehrlich, I., 1975. The Deterrent Effect of Capital Punishment: A Question of Life and Death.

The American Economic Review, 65, 397-417.

Ehrlich, I., 1977. Capital Punishment and Deterrence: Some Further Thoughts and Evidence.

Journal of Political Economy, 85, 741-788.

Helland, E. and A. Tabarrok, 2007. Does Three Strikes Deter? A Non-Parametric Estimation.

The Journal of Human Resources, 22, 309-330.

Iyengar, R., 2008. I’d Rather be Hanged for a Sheep than a Lamb: The Unintended

Consequences of California’s Three Strikes Laws. National Bureau of Economic Research

Working Paper Number W13784.

Klick, J. and A. Tabarrok, 2005. Using Terror Alert Levels to Estimate the Effect of Police on

Crime. Journal of Law and Economics, 48, 267-279.

Levitt, S.D. 1997. Using electoral cycles in police hiring to estimate the effect of police on

crime. The American Economic Review, 87(3), 270-290.

Levitt, S.D, 2002. Using Electoral Cycles in Police Hiring to Estimate the Effects of Police on

Crime: Reply. The American Economic Review, 92(4), 1244-1250.

Mast, B.D., B.L. Benson, and D.W. Rasmussen. Entrepreneurial Police and Drug Enforcement

Policy. Public Choice, 104(3-4), 285-308.

Mocan, H., and R. Gittings, 2003. Getting off Death Row: Commuted Sentences and the

Deterrent Effect of Capital Punishment. Journal of Law and Economics, 46, 453-478.

McCrary, J., 2002. Using Electoral Cycles in Police Hiring to Estimate the Effects of Police on

Crime: Comment. The American Economic Review, 92(4), 1236-1243.

Ross, A., 2012. Crime, Police, and Truth-in-Sentencing: The Impact of State Sentencing Policy

on Local Communities. Regional Science and Urban Economics, 42, 144-152.

Shepherd, J., 2002. Police, Prosecutors, Criminals, and Determinate Sentencing: The Truth about

Truth-In-Sentencing Laws. Journal of Law and Economics, 45, 509-534.

Sollars, D.L., B.L. Benson, and D.W. Rasmussen. 1994. Drug enforcement and the deterrence of

property crime among local jurisdictions. Public Finance Quarterly, 22(1), 22-45.

24

Table 1: Cities with Low Priority Initiatives

Jurisdiction Year Passed Enacted By Vote Percentage

1 Berkeley, CA 1979 Voter Initiative Not available.

2 Seattle, WA 2003 Voter Initiative Passed with 58% of the vote.

3 Oakland, CA 2004 Voter Initiative Passed with 65% of the vote.

4 Santa Barbara, CA 2006 Voter Initiative Passed with 66% of the vote.

5 Santa Cruz, CA 2006 Voter Initiative Passed with 64% of the vote.

6 San Francisco, CA 2006 City Council

San Francisco Board of Supervisors

passed the ordinance in an 8-3 vote.

7 Santa Monica, CA 2006 Voter Initiative Passed with 65% of the vote.

8 West Hollywood, CA 2006 City Council

West Hollywood City Council

passed the resolution in a 4-0 vote.

9 Eureka Springs, AR 2006 Voter Initiative Passed with 62% of the vote.

10 Missoula County, MT 2006 Voter Initiative Passed with 54% of the vote.

11 Denver, CO 2007 Voter Initiative Passed with 55% of the vote.

12 Fayetteville, AR 2008 Voter Initiative Passed with 66% of the vote.

13 Hawaii County, HI 2008 Voter Initiative Passed with 53% of the vote.

14 Hailey, ID 2010 Voter Initiative Passed with 54% of the vote.a

15 Kalamazoo, MI 2011 Voter Initiative Passed with 66% of the vote.

16 Tacoma, WA 2011 Voter Initiative Passed with 65% of the vote.

17 Ypsilanti, MI 2012 Voter Initiative Passed with 74% of the vote.

Notes: Information was obtained from Marijuana Policy Project: http://www.mpp.org/reports/lowest-law-

enforcement.html and The Berkeley Daily Planet: http://www.berkeleydailyplanet.com/issue/2006-03-

31/article/23788?headline=Legal-Limbo-for-Pot-Users-By-SUZANNE-LA-BARRE aThe initiative passed with 51% of the vote in 2007, and again in 2008 with 54% of the vote. Due to a

redaction by a district court judge, the measure did not officially go into effect until 2010.

25

Table 2: Summary Statistics

Variable Full Sample Adopters Non-Adopters t-statistic

Misdemeanor marijuana arrests per

capita 2.35 1.74 2.36 0.12

Murder rate 0.06 0.08 0.06 0.68

Rape rate 0.29 0.44 0.29 1.11

Assault rate 11.72 13.73 11.69 0.18

Robbery rate 2.27 3.55 2.24 0.08

Burglary rate 9.09 8.85 9.10 0.09

Motor vehicle theft (MVT) rate 10.57 7.61 10.62 0.09

Larceny rate 29.49 32.62 29.43 0.01

Median age 34.57 35.33 34.55 0.32

Percent male 49.54 50.09 49.53 0.40

Percent black 4.20 9.64 4.11 2.39

Percent white 65.56 65.37 65.56 0.19

Percent with no HS 11.49 7.03 11.56 1.06

Percent with some HS 11.77 7.29 11.84 1.98

Percent with HS degree 20.98 14.06 21.10 2.85

Percent some college 23.37 19.13 23.44 2.05

Percent BA 16.02 27.16 15.83 3.07

Percent college or higher 9.32 19.37 9.15 3.26

Police officers per capita 14.59 17.24 14.55 0.09

Notes: Misdemeanor marijuana arrests per capita, crime rates (murder, rape, assault, robbery,

burglary, MVT, and larceny), and police per capita are all reported per 1,000 residents. The last

column reports results for a test of if the mean between the adopters and non-adopters are

statistically different. The t-statistic in the last column represents the statistic generated when testing

if the two means are different. The values have the standard cutoff values as when determining

statistical significance in a regression format.

26

Table 3: Impact of Low Priority Laws on the Log of the Number of Misdemeanor Marijuana Arrests per

Capita (standard errors are reported in parenthesis)

All Cities

Cities with Population

above 30,000

Cities with Population

above 50,000

Low Priority Law in Effect -0.282* -0.303** -0.306**

[0.159] [0.135] [0.137]

Log Number of Police Officers 0.295*** 0.561*** 0.791***

[0.088] [0.162] [0.223]

% Male -0.021*** 0.086** 0.020

[0.005] [0.039] [0.024]

Median Age -0.127*** 0.078*** 0.091***

[0.014] [0.013] [0.013]

% Black -0.024** 0.017 0.027***

[0.010] [0.018] [0.010]

% White -0.024*** 0.008 0.016**

[0.008] [0.013] [0.007]

% Less than HS -0.066*** 0.089** 0.104***

[0.010] [0.037] [0.017]

% Some HS 0.051*** 0.026 -0.056

[0.015] [0.027] [0.047]

%HS Grad -0.073*** -0.078 0.0392

[0.019] [0.049] [0.040]

% Some college 0.1046*** 0.137*** -0.019

[0.015] [0.035] [0.052]

% Associates Degree -0.165*** 0.063 0.411***

[0.035] [0.060] [0.089]

% Bachelor’s Degree 0.007 0.049 0.047

[0.014] [0.048] [0.035]

Observations 3,312 1,698 1,205

R-squared 0.77 0.81 0.82

Notes: The dependent variable is the arrest rate per 1,000 residents. Arrest data was obtained from the

California Justice Department and the police office data was obtained from the UCR.

27

Table 4: Impact of Low Priority Laws on the Log of the Number of Misdemeanor Marijuana Arrests per Capita

(standard errors are reported in parenthesis)

All Cities

Cities with Population

above 30,000

Cities with Population

above 50,000

Low Priority Law in Effect -0.318* -0.334** -0.333**

[0.177] [0.150] [0.153]

Low Priority * City Council Interaction 0.181 0.153 0.135

[0.395] [0.332] [0.337]

Log Number of Police Officers 0.294*** 0.559*** 0.788***

[0.088] [0.162] [0.223]

% Male -0.021*** 0.087** 0.020

[0.005] [0.039] [0.024]

Median Age -0.127*** 0.078*** 0.091***

[0.014] [0.013] [0.014]

% Black -0.024** 0.017 0.027***

[0.010] [0.018] [0.010]

% White -0.024*** 0.008 0.016**

[0.008] [0.013] [0.007]

% Less than HS -0.066*** 0.089** 0.104***

[0.010] [0.037] [0.017]

% Some HS 0.051*** 0.026 -0.056

[0.015] [0.027] [0.047]

%HS Grad -0.074*** -0.078 0.039

[0.019] [0.049] [0.040]

% Some college 0.105*** 0.137*** -0.019

[0.015] [0.035] [0.052]

% Associates Degree -0.165*** 0.063 0.411***

[0.035] [0.060] [0.089]

% Bachelor’s Degree 0.007 0.049 0.047

[0.014] [0.048] [0.035]

Observations 3,312 1,698 1,205

R-squared 0.77 0.81 0.82

Notes: The dependent variable is the arrest rate per 1,000 residents. The indicator variable is an interaction

between whether the city has a low priority law and if it was adopted by the city council.

28

Table 5: Impact of Low Priority Laws on the Log of the Number of Arrests per Capita for

(standard errors are reported in parenthesis)

Dependent Variable All Cities

Cities with

Population above

30,000

Cities with

Population above

50,000

Felony Drug Arrests 0.060 0.024 0.001

(0.109) (0.070) (0.064)

Murder 0.044 0.049 0.053

(0.185) (0.192) (0.196)

Rape -0.049 -0.026 -0.019

(0.149) (0.149) (0.143)

Robbery 0.013 -0.029 -0.053

(0.143) (0.111) (0.098)

Assault -0.033 -0.030 -0.039

(0.079) (0.048) (0.043)

Burglary -0.084 -0.073 -0.094

(0.114) (0.072) (0.069)

Motor Vehicle Theft -0.116 -0.117 -0.114

(0.144) (0.107) (0.099)

Larceny -0.073 -0.081 -0.088

(0.110) (0.063) (0.057)

Notes: Each coefficient and standard error reported corresponds to a different regression.

Each row represents a regression with a different dependent variable. All dependent variables

are rates, and the regressions are run separately for the different population cutoffs.

29

Table 6a: Impact of Low Priority Laws on the Log of the Reported Crime Rate for Index I

Crimes(standard errors are reported in parenthesis)

Dependent Variable All Cities

Cities with

Population above

30,000

Cities with

Population above

50,000

Murder 0.002 0.006 0.014

(0.146) (0.148) (0.145)

Rape -0.093 -0.060 -0.053

(0.136) (0.108) (0.095)

Robbery 0.047 0.038 0.023

(0.103) (0.060) (0.057)

Assault 0.104 0.132** 0.119**

(0.077) (0.051) (0.050)

Burglary 0.011 0.010 -0.012

(0.077) (0.054) (0.049)

Motor Vehicle Theft 0.023 0.057 0.051

(0.092) (0.059) (0.056)

Larceny 0.001 -0.019 -0.019

(0.066) (0.046) (0.043)

Notes: Each coefficient and standard error reported corresponds to a different regression.

Each row represents a regression with a different dependent variable. All dependent variables

are rates, and the regressions are run separately for the different population cutoffs.

30

Table 6b: Impact of Low Priority Laws on the Log of the Clearance Rate for Index I

Crimes(standard errors are reported in parenthesis)

Dependent Variable All Cities

Cities with

Population above

30,000

Cities with

Population above

50,000

Murder -0.086 -0.087 -0.077

(0.156) (0.162) (0.165)

Rape -0.105 -0.083 -0.077

(0.159) (0.157) (0.153)

Robbery -0.002 -0.024 -0.017

(0.132) (0.107) (0.094)

Assault 0.030 0.079 0.095

(0.105) (0.081) (0.073)

Burglary -0.160 -0.119 -0.1311

(0.139) (0.109) (0.102)

Motor Vehicle Theft 0.113 0.115 0.129

(0.169) (0.148) (0.132)

Larceny -0.228* -0.260*** -0.238***

(0.131) (0.085) (0.077)

Notes: Each coefficient and standard error reported corresponds to a different regression.

Each row represents a regression with a different dependent variable. All dependent variables

are rates, and the regressions are run separately for the different population cutoffs.