Entrepreneurial Police and Drug Enforcement Policy

Entrepreneurial Police and Drug Enforcement PolicyAuthor(s): Brent D. Mast, Bruce L. Benson and David W. RasmussenSource: Public Choice, Vol. 104, No. 3/4 (2000), pp. 285-308Published by: SpringerStable URL: http://www.jstor.org/stable/30026428 .

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Public Choice 104: 285-308, 2000. 285 @ 2000 Kluwer Academic Publishers. Printed in the Netherlands.

Entrepreneurial police and drug enforcement policy *

BRENT D. MAST', BRUCE L. BENSON2 & DAVID W. RASMUSSEN3 'The American Enterprise Institute, 1150 17th Street N.W, Washington, DC 20036, USA;

2Department of Economics, Florida State University, Tallahassee, FL 32306, USA; 3DeVoe Moore Center Florida State University, Tallahassee, FL, 32306 USA

Accepted 23 October 1998

Abstract. The hypothesis that drug enforcement is relatively high in local jurisdictions where state laws dictate that police retain seized assets is tested in the context of a reduced-form equation of the supply and demand for drug enforcement. The results are robust across model specifications, some of which directly control for the level of drug use: legislation permitting police to keep seized assets raises drug arrests as a portion of total arrests by about 20 percent and drug arrest rates by about 18 percent. Police bureaucrats apparently desire discretionary budget increases, and they have considerable discretion in determining resource allocation.

1. Introduction

The fact that a "drug war" was waged in the United States during the 1980s is widely recognized. Indeed, between 1984 and 1989, drug arrests per 100,000 population rose by 72 percent after a 14 year period of relatively constant drug arrests per capita, suggesting that the law enforcement effort against drugs increased dramatically.' However, the primary offensive in a war on drugs has to be waged by state and local troops - primarily city police departments - and the degree of state and local commitment to the drug war varied widely. Measures of drug enforcement in 1989 for cities in the National Institute of Justice's Drug Use Forecasting program in Table 1 demonstrate the extent of these enforcement differences. The ratio of drug arrests to total arrests varies from a high of .266 in San Diego to only .041 in Kansas City and .031 in Indianapolis. Atlanta's police made 232.8 drug arrests per 10,000 population, a figure that dwarfs the drug arrest rate in Indianapolis (23.2), San Antonio (47.2), and Phoenix (52.9).

* The authors would like to thank an anonymous referee, Tim Sass, Gary Fournier, Andy Morriss, and Garey Durden, as well as participants in seminars at Case Western Reserve Uni-

versity Law School and the University of West Virginia, and in the session Garey Durden

organized for the 1997 Western Economic Association meetings, for a number of helpful comments and suggestions.



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Table 1. Drug Arrest/Total Arrests (DA/TA) and Drug Arrest Rate (per 10,000 population), for Selected Cities, 1989

DA/TA RATE DA/TA RATE

Atlanta, GA .150 232.8 New Orleans, LA .127 109.3

Birmingham, AL .098 105.9 New York, NY .140 125.4

Chicago, IL .125 115.6 Omaha, NE .087 57.7

Cleveland, OH .168 133.3 Philadelphia, PA .150 72.6

Dallas, TX .085 67.3 Phoenix, AZ .073 52.9

Denver, CO .070 72.3 Portland, OR .147 100.1

Detroit, MI .117 105.1 St. Louis, MO .123 122.0

Houston, TX .103 55.4 San Antonio, TX .105 47.2

Indianapolis, IN .031 23.2 San Diego, CA .266 83.4

Kansas City, MO .041 92.1 San Jose, CA .228 128.7

Los Angeles, CA .189 139.0 Washington, D.C. .209 173.7

Source: FBI Uniform Crime Reporting Program.

Policing resources are scarce, and therefore some allocating mechanism is required to determine which of many alternative law enforcement duties will be pursued. Thus, for instance, clearance rates for reported crimes in the U.S. vary from less than 15 percent for burglary and auto theft to over 65 percent for murder, and such numbers can also vary considerably across cities. Similarly, as suggested above, city police departments apparently choose to devote substantially different efforts towards control of illicit drugs. The question explored here is what determines the level of effort that a local police department allocates to drug market control? Resource allocation in markets is determined by relative prices, but presumably this is not the case for public resources such as police services. The dominant hypotheses regarding the allocation of public sector resources in the economics literature can be summarized in the context of the economic (or public choice) model of bureaucratic institutions which explicitly or implicitly assumes that (1) only individuals act and make decisions; (2) these individuals recognize their alternatives, anticipate potential although uncertain outcomes, and rationally attempt to maximize their well-being in the face of incentives and constraints; and (3) information is scarce and costly to obtain so that the ability of citizens and their representatives to monitor and control bureaucracies is limited. In light of the last of these assumptions, it might be expected that bureaucrats themselves have a great deal of discretion in deciding how they should allocate resources controlled by the bureau [e.g., see Tullock (1965); Niskanen



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(1968, 1975); Breton and Wintrobe (1975, 1982); Williams (1984); Stumpf (1988); Benson, Rasmussen and Sollars (1995)]. Some observers [e.g., Fior- ina and Noll (1978); McCubbins et al. (1987, 1989); Weingast and Moran (1983)] contend that strong institutional constraints are imposed by sponsors and/or monitors, however, so that bureaucrats are not able to depart very far from the wishes of legislators and their represented constituents. Empirical evidence regarding the degree of bureaucratic discretion is mixed, although growing evidence suggests that considerable discretion exists at least in the short run.2

The following presentation can be seen in light of the evolving economic literature on bureaucratic behavior. Several observers have argued that the increase in drug enforcement after 1984 was a consequence of the federal Comprehensive Crime Act of 1984, which altered the incentives of local police agencies by allowing them to keep the proceeds from assets seized as a result of drug enforcement activities involving federal and local police cooperation in the investigation [see, for example, Benson, Rasmussen, and Sollars (1995), Levy (1996: 144-160), and Smith (1993)]. Both forfeitures and drug control efforts clearly rose after 1984.3 In other words, relative prices can drive allocation decisions in the government sector too, as entrepreneurial local police shift production efforts into drug control in order to expand their revenues.4 In this light, the underlying reason for the con- tention that the advent of the latest drug war can be traced to an exogenous change in police incentives from this federal Comprehensive Crime Act is not simply that it established a system whereby any local police agency which cooperated with federal drug enforcement authorities in a drug investigation would share in the money and/or property seized as part of that investigation. The Department of Justice actually went further, deciding that police in states whose own laws limited confiscation possibilities could circumvent these laws by having federal authorities "adopt" their seizures even when there was no federal involvement in the investigation. With adoptions, 90 percent of the proceeds from seized properties went back to the agency that initiated the action (80 percent after 1989), even if the state's law mandated that confiscated assets go to a use other than law enforcement. This statute and the federal adoptions applied only to assets confiscated in the context of drug crime investigations, thus creating incentives to devote relatively more effort towards drug control.

If local police did in fact respond to the incentives created by the asset seizure section of the 1984 federal Comprehensive Crime Act, then the hypothesis that police bureaucrats have considerable discretion is supported. After all, a significant response to incentives created by this statute implies that state and local sponsors do not have the strong control over their agencies



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that Fiorina and Noll (1978), McCubbins et al. (1987, 1989), and Weingast and Moran (1983) contend exists. Of course, the federal statute could reflect the wishes of state and local government decision-makers who want their police departments to expand drug control efforts, but doubt is cast on this possibility by an examination of the congressional hearings leading up to pas- sage of this seizure-sharing law and in subsequent hearings that considered its repeal (Benson, Rasmussen, and Sollars, 1995). No representative of any state or local legislature or chief executive testified, and representatives of large numbers of local, state, and federal law enforcement agencies were its primary advocates. Supporting the hypothesis that causation is from the change in the federal law to increased local drug enforcement is difficult, however, because this was a one-time change in incentives and its effect cannot be separated statistically from other factors that might have changed at the same time. While Benson, Rasmussen, and Sollars (1995) used 1989 data from Florida to show that police agencies were able to increase their discretionary budgets through the asset forfeiture process, for instance, this fact does not demonstrate that they intentionally focus on drug control to do so. If federal asset forfeiture legislation explains the rise in drug enforcement during the 1984-1989 period, however, then state variations in asset seizure laws also should be a determinant of local differences in drug enforcement.

There is significant variation in drug enforcement activity across states and cities, as well as through time. Therefore, if the argument made above provides an accurate explanation for trends in drug-enforcement policy, it should help explain cross sectional variation in enforcement policy as well as time-series variation. Variations in state asset forfeiture legislation may be important in part because the federal authorities do not have unlimited capacity to process adopted seizures and they do not have an incentive to do so. In fact, the Department of Justice will not adopt a vehicle seizure of less that $5,000 in value or a real property seizure of less than $20,000 (Levy, 1996:149). Furthermore, small seizures can be quite attractive to local police. Indeed, most seizures are apparently quite small. In California, for instance, prosecutors conducted over 6,000 seizures in 1992, and over 94 percent of them involved $5,000 or less (Levy, 1996:127). Therefore, if a state's laws allow a local agency to keep seizures without dealing with the federal authorities, then local operations which do not involve federal inputs can avoid the accompanying transactions costs, including the 20 percent federal service charge (10 percent before 1989), and capture benefits from smaller seizures than the federal authorities will process.

State statutes governing the distribution of the proceeds from asset forfeiture activities vary considerably; some states allow police to retain the proceeds while others still mandate that they go into the general fund or



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be used for specific purposes, such as education. Many states allow police agencies to keep only a portion of the proceeds, and several states did not permit police to keep any of the proceeds from assets seized during at least some of the period of escalating drug enforcement (many state laws have been changing, as indicated below.) Thus, in some states police could only gain discretionary budgets by processing their seizures through the federal government, with the size limitations noted above. Drug arrests per 100,000 population in states with significant limits on police retention averaged 363 during 1989, while the drug arrest rate in states where police kept seizure proceeds averaged 606 drug arrests per 100,000. Of course, other factors, such as the level of drug use and/or property crime could explain these differences. Therefore, support for the hypothesis that police focus more effort on drugs when they can retain asset seizures requires an empirical analysis that controls for other factors affecting the level of drug enforcement.

The remainder of the paper is organized as follows. A model of drug enforcement is developed in Section 2, while the empirical evidence regarding the hypothesis is discussed in Section 3. These empirical results provide strong support for the notion that police agencies respond to the financial incentives offered by asset forfeiture laws. Concluding comments follow in Section 4.

2. Determinants of local drug enforcement policy

The factors that affect the level of police resources allocated to drug offenses relative to other crimes are of interest, so drug enforcement policy (7r) is measured by drug arrests as a proportion of total arrests and by the drug arrest rate. Our empirical test of the hypothesis that asset seizure laws influence the allocation of police resources is a reduced-form model derived from a two equation theoretical model. Drug enforcement (7r) in a jurisdiction is a function of the extent of drug use (y), a vector (I) of incentives and constraints facing the police agency, and the community preferences for drug enforcement (Z),

7r = 7r(y, I, Z). (1)

Included in (I) are the opportunity cost of police resources and state confiscation laws. The second equation is the demand for drug enforcement (Z):

Z = d(y, X), (2)



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where X is vector reflecting socio-economic variables affecting the demand for drug enforcement. Substituting (2) into (1) yields a model that can be tested with available data:

7 = r (y, I, X). (3)

Two different samples of cities are employed to test the implications of this model. The use of two samples is motivated by the fact that fully specifying the model is not possible for a large sample because there are no reliable estimates of the prevalence of drug use within most political jurisdictions. Surveys of drug use among households and high school seniors by the Na- tional Institute of Drug Abuse (NIDA) are inadequate, for example, in part because they ignore some of the most problematic drug users: those who are not in households or who have dropped out of school. A more severe con- straint is that cross-section analysis is precluded since these NIDA measures are not available for local jurisdictions.

Jurisdiction level data on drug use for a limited sample of cities is provided by the National Institute of Justice's Drug Use Forecasting (DUF) program. This program provides a measure of drug use among arrestees in 24 cities. Use of this sample carries a high price in terms of degrees of freedom, but the ability to control for drug use makes it very attractive, particularly when supplemented by an analysis of a larger sample of cities that does not have such a direct measure of drug use. Under the DUF program, voluntary and anonymous urine specimens are obtained from a sample of booked arrestees on fourteen consecutive evenings each quarter. Approximately 70 percent of the arrestees agree to be tested (National Institute of Justice, 1992). Drug arrestees are intentionally under sampled, resulting in an underestimate of drug use among the entire arrestee population. These data have been gathered in 10 to 24 major cities in the United States since 1987.

Our measure of drug use in a city is the percentage of male DUF subjects who were not arrested for a drug offense that tested positive for any drug.5 These data clearly do not provide a perfect estimate of the prevalence of drug use in a city since they do not take into account persons not arrested - a population that would include the majority of infrequent drug users.6 This may not be a severe shortcoming, however. If drug use among those who are arrested for non-drug crimes is a primary source of information for police regarding their estimates of "problem" drug use, or even if it is simply correlated with the police department's perception of the extent to which drugs are used by property offenders and violent criminals, the DUF data may be a reasonable proxy measure for the patterns of drug use that lead police to increase their relative effort against drug offenders. Thus, drug use's impact on supply-of-enforcement decisions may be measured quite well with the DUF data.



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Perceptions about the level of drug use also influence demand for drug enforcement and in this case the usefulness of the DUF measure in equation (2) may be less obvious. As Rhodes (1977:13) argues, however, "as far as crime policy and legislation are concerned, public opinion and attitudes are generally irrelevant. The same is not true, however, of specifically interested criminal justice publics". Clearly, police bureaucracies are "specifically interested criminal justice publics", as demonstrated in Berk et al. (1977), Benson (1990), Rasmussen and Benson (1994), and elsewhere. Police have incentives to lobby directly and to influence the opinion of other relevant "publics", as they must compete for resources. Furthermore, as Breton and Wintrobe (1982: 37-38) explain, competitive strategies employed include "variations in the quality or quantity of information leaked to the media, to other bureaus in the organization, to special-interest groups, and/or to opposition parties and rival suppliers". These strategies and selective behavior in general are possible because of the way bureaucratic organizations and hierarchies work, including the fact that monitoring by sponsors is costly and the measurement of bureaucratic performance is generally difficult. Indeed, the use of such strategies can increase monitoring costs and make measurement of performance even more difficult. Considerable evidence suggests that public opinion about criminal justice policy is shaped by "expert opinion" offered by criminal justice interest groups. In the specific case of drug enforcement policy, for instance, a good deal of information about the alleged impacts of drug use has been generated by police agencies (Kaplan, 1983: 53; Michaels 1987: 312- 324). Thus, to the degree that the DUF data influences police opinion about drug use, it also may actually be a reasonable proxy for public perception of problem drug use as well.

To take advantage of these data, the first regressions presented below are limited to the DUF cities for which other required data are available in the years 1987 through 1993 (the regressions using this sample include year dummies since the annual cross-section samples are being pooled to control for the possibility of unobserved economic and or social factors that may be changing over time).7 Possession offenses account for over 70 percent of all drug arrests in the U.S. (Maguire and Pastore, 1995) and presumably are affected by the prevalence of drug use in a city, but some variations in trafficking offenses may reflect city characteristics that can be measured. For instance, cities that serve as an entry port for regional or national drug trafficking might have relatively more arrests for drug offenses, given their level of use, because of greater police efforts to combat the flow of drugs from these cities to other markets. Most drug shipments to the U.S. from abroad arrive in California, Florida, New York, and Texas (National Institute of Justice, 1992). Cities in these four states are identified by a dummy variable in the



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DUF sample of cities because of the role they are likely to play in wholesale activities of the drug trade.

Other regressions use a larger sample of cities with a population exceeding 100,000 in 1990 for which all the required data except direct measures of drug use are available for the years 1984-1993. Indirect controls for drug use are employed for this sample of cities. For example, drug use is presumably related to demographic characteristics of the population. Thus, demographic controls discussed below may provide some control for drug use in this larger sample. In addition, we include the percentage of the population ages 15- 24 to control for those most at-risk for drug use (as demonstrated by the NIDA surveys, for instance). Furthermore and importantly, the characteristics of drug use vary greatly among jurisdictions (Haaga and Reuter, 1990), suggesting that there are many unmeasured location-specific characteristics that affect drug use which could be controlled for by using a fixed effects specification. Therefore, a fixed effects model with vectors of city and year dummy variables is employed with the larger sample. Fixed effects may also capture unmeasured differences in the demand for drug enforcement.8

The proportion of limited police resources allocated to drug enforcement will depend on the incentives and constraints affecting the agency. One con- straint is simply the relative size of the police department, so sworn officers per 10,000 population is included in each regression. Existing data permit us to identify important opportunity costs of drug arrests as well as the asset- forfeiture statutes which are hypothesized to provide an incentive to increase drug control. In particular, recent studies by Benson et al. (1992) and Benson, Kim and Rasmussen (1998) provide empirical support for the assumption that drug arrests come at the cost of reduced enforcement in other areas. Therefore, an opportunity cost of increased drug arrests is assumed to be reduced police activity against other offenses, suggesting that jurisdictions with high reported crime rates have a higher opportunity cost for incremental drug enforcement. Thus, a relatively high property and/or violent crime rate in a jurisdiction might reduce the allocation of police resources to drug enforcement activity. We include both crime rates (property crime includes reported burglary and larceny while violent crime includes reported murder, manslaughter, robbery, and aggravated assaults) in the regressions.

Confounding the notion that high crime rates will constrain drug arrests is the possibility that police agencies view drug users as the property and violent offenders that need to be apprehended. Drug enforcement policy from the drugs-cause-crime perspective might be expected to result in a "positive sum game" because arrests for drug offenses simultaneously deter drug use and incapacitate a property and/or violent offender, if the arrested drug offender is incarcerated. A substantial research literature suggests that there is no reliable



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association between drug use and major non-drug crimes, however (Chaiken and Chaiken, 1990; Nurco et al., 1991; Rasmussen and Benson, 1994: 39-66). For instance, a study of the crack epidemic in New York (Johnson, Golub and Fagan, 1995) concludes that the advent of crack did not substantially increase non-drug criminality, other than for women engaged in prostitution. Nevertheless, police agencies have argued that drug enforcement can allevi- ate other crime. Attempts to influence public opinion in this way obviously reduce the opportunity cost constraints on police that are imposed by property and violent crime rates, so the signs of the crime rate coefficients cannot be predicted a priori.

If drug enforcement is a normal good, the demand for drug arrests would be expected to increase with income. Average real pay in a city's metropolitan area, controlling for geographic cost of living differences and changes in the CPI, is used as one proxy for income.9 The metropolitan area's unemployment rate is also included to capture the proportion of the labor force receiving wages. A related control variable is the proportion of the population that is black. Since blacks tend to earn less than whites with similar labor market attributes and in identical locations, the percentage black variable complements the pay and unemployment variables by providing evidence of the proportion of the population that is likely to have relatively low income. The demand for enforcement is expected to be positively correlated to pay, while higher unemployment and the percentage black are expected to be associated with relatively lower demand for drug enforcement. Population size and density are also included as control variables.

Economic opportunity may also affect the supply of drug enforcement. Recreational drug use is not adequately measured in the DUF program because there is substantial evidence that most drug users do not commit other crime (Rasmussen and Benson, 1994: 39-66). Police may nevertheless arrest and seize assets from recreational drug users (the DEA has seized fraternity houses at the University of Virginia, for instance). Depending on whether illegal drugs can, on average, be characterized as normal or inferior goods, the number of people consuming these substances will be higher or lower in jurisdictions with relatively better economic opportunity.10 If drugs are normal (inferior) goods then the predicted signs are the same as (opposite of) those

expected from the demand for enforcement equation, but without information on the relative magnitudes of the effects, the reduced-form model cannot dis- tinguish between these alternative interpretations.11 Meier (1992) contends that drug enforcement is disproportionately directed against blacks, implying that drug arrests as a proportion of total arrests will also be positively correlated with the relative size of the black population.



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Finally and most importantly, our hypothesis is that state confiscations laws permitting police to keep assets seized provide an institutional incentive for police to commit relatively more resources to drug offenses.12 State confiscation laws are specified in alternative ways to capture various dimensions of the statutes. First, these laws are represented by a variable that takes a value from zero to one, depending on the fraction of the year police were allowed to keep the proceeds from seized assets. Most observations are either zero or one, but in some cases a new seizure law went into effect at some point during the year. No change involved allocating seizures to non-policing functions in states that previously allowed police to keep seized assets, so all observations between zero and one involve shifting the proceeds from confiscations to police (e.g., a movement from zero to one). Second, since the percentage of seizure proceeds the police can keep varies among states, the forfeiture variable is alternatively specified to reflect the proportion they get to keep. The fraction of the proceeds police can keep is not known for all states, however, so two variables are used for this specification: 1) the fraction of the year police can keep proceeds in states where the fraction is not known, and 2) the fraction of the year police can keep proceeds in states where the fraction kept is known interacted with the fraction they can keep.13

A third specification investigates whether the impact of forfeiture laws on drug arrests in 1989 through 1993 differed from their effects during the 1984-1988 period. There are at least two reasons to consider this possibility. First, in 1988 the National Criminal Justice Association (NCJA), in cooperation with the National Institute of Justice, prepared a manual to aid states in developing and maintaining asset seizure capability. The manual includes a model curriculum to instruct prosecutorial and law enforcement staff on the forfeiture process and a case study to "provide a means for examining strengths and weaknesses of current laws and procedures" (NCJA, 1988b: 4). The forfeiture variables in this specification test the hypothesis that agencies learned to better use seizure laws after 1988. Second, in 1989 the portion of federal adoptions turned back to local agencies fell from 90 percent to 80 percent. Therefore, at the margin, state laws might be expected to provide stronger incentives beginning in 1989 than they did before.

3. Empirical results

The variables used in the empirical analysis are listed in Table 2, which also gives their mean value and data sources. Several specifications and alternative dependent variables are presented to indicate the robustness of the results.

Empirical results with the DUF sample. Table 3 presents weighted least squares (WLS) estimates of the reduced form model using the data available



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Table 2. Definitions of variables and sources

Mean

DUF cities All cities

Drug arrests/total arrests1 .116 .090

Drug arrest ratel (per 10 population) .065

Property crime2 (per 1,000 population) 65.70 74.76

Violent crime2 (per 1,000 population) 16.17 12.28

Police officers per capita3 (per 10,000 population) 29.45 22.74

Real average yearly pay4 (in thousands) 27.08 25.01

Unemployment rate (x10)5 5.95 6.05

Percent black6 32.21 23.91

% population 15-247 n.a. 15.68

City population8 (in thousands) 1,374.0 440.93

Population density8 6.20 4.38

Any drug use9 57.83 n.a.

Import city10 .39 n.a. Forfeiture11 .76 .68

% forfeiture retained .55 .45

% forfeiture unknown .12 .14

Forfeiture 87-88 .13 n.a.

Forfeiture 84-88 n.a. .29

Forfeiture 89-93 .63 .39

Sources: 1. FBI Uniform Crime Reporting Program. 2. Sourcebook of Criminal Justice Statistics, annual. 3. Uniform Crime Reports. 4. Bureau of Labor Statistics. 5. Employment and Earnings, Bureau of Labor Statistics, May, annual. These data are

for the metro area and for some observations the data were interpolated. 6. City data were interpolated and extrapolated from the 1980 and 1990 Census of the

Population. 7. Data for 1985 are taken from the State and Metropolitan Area Data Book, U.S.

Bureau of the Census, 1991. Data for 1990 are from the decennial census and 1984, 1886-89, and 1991-93 are interpolated or extrapolated.

8. Statistical Abstract of the United States and the decennial census. Data for odd years were interpolated or extrapolated.

9. National Institute of Justice, Drug Use Forecasting Program. 10. Cities in California, Florida, New York, and Texas. 11. "Use of Forfeiture Sanctions in Drug Cases", National Institute of Justice (July

1985); National Criminal Justice Association (1988a, 1991); and inspection of state statutes with telephone inquiries to state attorney's general, city district attorneys, and other state and local authorities.



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from the 24 cities in the Drug Use Forecasting (DUF) program from 1987 to 1993.14 The three regressions presented in the table are identical except for the forfeiture variable, and are estimated using the minimum chi-square method with the dependent variable specified as a logistic transformation of the drug arrest/total arrest ratio. Coefficients for the intercepts and year dummy variables are not reported.

Higher property crime rates are associated with relatively fewer drug arrests, a result consistent with hypothesis that property crime control represents an opportunity cost of drug enforcement and constrains police effort against drugs. Violent crime, on the other hand, has a significant positive coefficient in these regressions which could mean that the violence associated with competitive drug markets leads the police to devote more resources to drug control. An alternative interpretation is that more enforcement actually disrupts the spatial equilibrium in local drug markets, leading to violent spatial competition (turf wars) to re-establish market niches (Rasmussen, Benson and Sollars, 1993). More police personnel appear to increase drug arrests as a proportion of total arrests, although this coefficient is only significant in regression 2. Drug use among males arrested for non-drug crimes is not significant in these regressions, but drug arrests are significantly higher in import cities, those in California, Florida, New York, and Texas."1 We have no a priori expectations as to the sign of other control variables; real pay, population and population density are significant while percent black and unemployment are not.

The forfeiture variables test the hypothesis that police increase drug arrests when they are permitted to keep some of the proceeds from assets seized during drug investigations. This coefficient is positive and significant in regression 1, where the confiscation variable measures the fraction of the year that police agencies are allowed to keep some portion of the assets seized. Asset forfeiture laws vary among states in the percentage of the proceeds the agency is able to keep, however, and this information is incorporated in the percent forfeiture retained variable used in regression 2.16 The percent retained coefficient has the expected positive sign and is significant at the .10 level, while the percent unknown coefficient is not significant. The third specification in Table 3 investigates whether the impact of forfeiture laws on drug arrests in 1989 through 1993 differed from their effects during 1987 and 1988. The results are consistent with the hypothesis that forfeiture laws should have a larger impact after 1988; the forfeiture coefficient is positive in each period, but the coefficient is larger and significant at the .05 level after 1988.17

The estimated percentage increase in the DA/TA ratio due to a law permitting police to keep at least some seized assets (regression 1), evaluated



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Table 3. Drug Arrest/Total Arrest Model for DUF Cities, 1987-1993

Variable 1 2 3

Property crime -.0149* -.0158* -.0150*

(7.79) (7.90) (7.73) Violent crime .0353* .0348* .0354*

(6.06) (5.70) (6.03) Officers per capita .0079 .0100* .0080

(1.57) (1.98) (1.57)

Drug use .0040 .0055 .0040

(.97) (1.32) (.97)

Import city .8466* .8375* .8483*

(8.38) (8.08) (8.34) Real pay -.0539* -.0575* -.0547*

(3.06) (3.17) (3.06)

Unemployment -.0389*** -.0384 -.0377

(1.66) (1.58) (1.58) Percent black -.0030 -.0040 -.0030

(1.05) (1.38) (1.03)

Population -.0003* -.0003* -.0003*

(6.25) (6.00) (6.22)

Population density .0623* .0620* .0620*

(3.99) (3.72) (3.93) Forfeiture .1924**

(2.16) % forfeiture retained .2189***

(1.83) % forfeiture unknown 0771.

(.58) Forfeiture 87-88 .1677

(1.32) Forfeiture 89-93 .2049**

(2.04)

Adj R2 .547 .534 .543

F 9.792 8.884 9.245

Intercepts and year dummy coefficients are not reported Absolute value of t value in parentheses * significant at the .01 level ** significant at the .05 level *** significant at the .10 level (two tailed test) n= 126



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at the weighted means of the other explanatory variables, is 18.54. For the 1987-88 period the relative number of drug arrests increase 15.75 percent compared to 19.44 percent for the 1989-93 period. The elasticity of DA/TA with respect to the fraction of proceeds from seized assets that the agency can keep, evaluated at the means of the other variables, is .093.18

The WLS estimates presented above might be biased if some of the explanatory variables are endogenous. Therefore, the Hausman (1978) method is used to test four variables for endogeneity: forfeiture laws, property and violent crime rates, and drug use. The instrument for the two-stage weighted least squares (2SLS) model with drug use endogenous is a measure of the population most likely to use drugs, the percentage of the population ages 18 to 24. Endogeneity of forfeiture laws was tested by creating a dummy variable for states that allowed agencies to keep some seizure proceeds at least 50 percent of the year.19 Instruments for this test included variables reflecting the political strength of police agencies (the per capita number of police agencies in each state with more than 100 officers and the total number of sworn officers per capita in each state) along with the percent of each state's population residing in metropolitan areas and state population density. The political strength of police agencies is included in light of Benson, Rasmussen, and Sollars (1995) finding that police interests were the primary sources of demand for the 1984 federal statute and the hypothesis is that the same is likely to be true at the state level. Since deterrence theory suggests that crime rates are a function of arrest rates, arrest rates are used as instruments to test for the endogeneity of crime rates. Endogeneity of forfeiture laws and crime rates is rejected by the Hausman test and the resulting estimates are very similar to those reported above. The results presented in Table 3 are not robust when drug use is treated as endogenous, since that specification yields an insignificant F test for the regressions.20

Empirical results for cities with population greater than 100,000. The

sample of cities used in the Drug Use Forecasting program allow a measure of drug use, but it is also limited to only 24 large cities. An alternative sample includes 133 cities in 40 states plus the District of Columbia that had a population exceeding 100,000 in 1990 for which all other required data are available. Data were gathered for the 1984-1993 period. The regression results are reported in Table 4 for fixed effects models for which the intercepts, year and city dummy coefficients are not reported. The specification of these models mirrors those in Table 3, except for inclusion of a variable measuring the population most at risk for drug use (% age 15-24) and the absence of the DUF measure of drug use and the import city dummy. In these regressions we control for drug use by including the age 15-24 variable and other economic and demographic variables in the context of a fixed effects model.



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Table 4. Drug Arrest/Total Arrest Model for All Cities, 1984-1993

Variable 1 2 3

Property crime .0022*** .0027** .0024**

(1.92) (2.33) (2.06) Violent crime -.0036 -.0062 -.0039

(.67) (1.14) (.71) Officers per capita -.0153* -.0147* -.0188*

(2.73) (2.67) (3.33) % age 15-24 .0826* .0916* .0929*

(3.38) (3.74) (3.78) Real pay .0525* .0573* .0555*

(2.73) (2.98) (2.89)

Unemployment -.0622* -.0708* -.0634*

(5.60) (6.31) (5.72) Percent black .0105 .0110 .0103

(1.25) (1.31) (1.25)

Population .0003*** .0004** .0003**'

(1.70) (2.32) (1.80)

Population density .0270 -.0212 -.0093

(.55) (.43) (.12) Forfeiture .1259*

(2.66) % forfeiture retained .2413*

(3.63) % forfeiture unknown -.0624

(.89) Forfeiture 84-88 .0737

(1.44) Forfeiture 89-93 .2222*

(3.71)

Adj R2 .586 .590 .588

F 11.231 11.341 11.270

Intercepts, year, and city dummy coefficients are not reported. Absolute value of t value in parentheses * significant at the .01 level ** significant at the .05 level *** significant at the .10 level (two tailed test) n = 1109



300

Many coefficients reported in Table 4 have a different sign than those reported in Table 3. There are at least two possible reasons for this. One may be that this sample of cities is very different from the DUF cities. For instance, the average city size in the DUF sample is 1,374,000 compared to 441,000 for this sample. Perhaps public decision makers and voters in big cities face very different problems and trade-offs than those arising in smaller cities. Haaga and Reuter (1990) explain that the nature of the drug market and associated problems differs significantly across cities, for instance. In this context, if relatively high levels of income in the large city sample (the DUF sample) are associated with less "serious" drug use (i.e., a relatively more extensive use of marijuana with less use of heroin or cocaine), police agencies in those cities may allocate their resources to combat other crimes. Given that the opportunity cost of police resources used for drug enforcement are relatively high in these jurisdictions, relatively high income could be associated with relatively lower levels of drug enforcement, as shown in Table 3. On the other hand, in smaller cities drug enforcement appears to be a normal good since in the all city sample, relatively high levels of income appear to be associated with greater levels of drug enforcement. This is highly speculative, of course (and another perhaps more valid explanation is offered below), but it also could explain why the property crime coefficient is negative in the DUF sample but positive in this sample. The positive sign is consistent with the trade-off hypothesis (Benson et al., 1992; Benson, Kim and Rasmussen 1998) that the allocation of police resources to drug offenses lowers property crime enforcement and leads to more crimes of this type, but perhaps in large cities where more drug users are consumers of relatively expensive heroin or cocaine, drug users are relatively more likely to be involved in property crime, so the trade-off does not arise. Endogeneity tests do not support such simultaneity, however, as indicated below, so another explanation may be more likely: controlling for important fixed-effects in Table 4 probably gen- erate better estimates than those with the small DUF sample where adding fixed effects was not possible. Thus, where the signs differ, the coefficients in Table 3 may be biased by the omission of unobservables fixed over time within the cities, while such biases have been eliminated from the results in Table 4.

No matter what the explanation is for the changes in signs on some of the coefficients between the two samples, note the percent of the population aged 15-24 has the expected positive sign in Table 4, and it is significant at the .01 level. Furthermore, and most important, estimates regarding the impact of seizure laws are robust across samples and specifications. The forfeiture coefficients are consistent with the hypothesis that police agencies increase drug arrests when they have a financial incentive to do so: in each regression



301

the forfeiture coefficient is positive and significant. The fact that the 1989- 93 forfeiture coefficient is much larger that the 1984-88 period also suggests that state asset seizure laws became more important over time. The estimated percentage increase in the DA/TA ratio due to a law permitting police to keep at least some seized assets, evaluated at the weighted means of the other explanatory variables, is 11.87. For the 1987-88 period the relative number of drug arrests increase 6.75 percent compared to 21.8 percent for the 1989-93 period. The elasticity of DA/TA with respect to the fraction of proceeds from seized assets that the agency can keep, evaluated at the means of the other variables, is .095.21

An alternative dependent variable is the logistic transformation of the drug arrest rate per 10 population.22 The results using this variable are reported in Table 5 and corroborate the previous findings: allowing police agencies to keep the proceeds from asset forfeiture programs increases the drug arrest rate. Agencies allowed to keep assets under state law increase the drug arrest rate by an average of 17.49 percent over those in states without such legislation. As in the previous cases, the elasticity of the drug arrest rate with respect to the fraction of proceeds that agencies can keep is low, .104.

The possible endogeneity of crime rates is examined using the Hausman (1978) method.23 As in the case of the DUF model, arrest rates are used as instruments to test for the endogeneity of crime rates. Endogeneity of crime rates is rejected by the Hausman test for both the DA/TA and drug arrest rate models and the resulting estimates are very similar to those reported above.

4. Conclusions

Asset seizure laws vary across states. Most states' laws now require that at least a portion of the assets go to the law enforcement agency making the seizure but some states require that those assets go into general funds or to specific functions such as education or drug treatment. Tests of the prediction that drug enforcement efforts will be relatively high in local jurisdictions where state laws dictate that the police can retain the assets that they seize are provided above in the context of a reduced form equation of the supply and demand for drug enforcement. The model is estimated with cross-section time-series pooling of: (1) data from 24 Drug-Use-Forecasting program cities which provides a measure of drug use, and (2) data from all cities with a population over 100,000. The results for the impact of asset seizure laws are robust across model specification and the alternative samples of cities: police focus relatively more effort on drug control when they can enhance their budgets by retaining seized assets. Legislation permitting police to keep



302

Table 5. Drug Arrest Rate Model for All Cities, 1984-1993

Variable 1 2 3

Property crime .0050* .0052* .0049*

(3.78) (3.96) (3.75) Violent crime -.0127** -.0146** -.0129**

(2.06) (2.35) (2.09) Officers per capita .0022 .0052 .0029

(.35) (.81) (.45) % age 15-24 .1164* .1222* .1151*

(4.07) (4.26) (4.00) Real pay .0224 .0232 .0218

(1.03) (1.06) (1.00)

Unemployment -.1108* -.1143* -.1105*

(8.80) (8.97) (8.76) Percent black .0174*** .0235** .0168***

(1.72) (2.29) (1.66)

Population .0004*** .0004** .0004***

(1.90) (2.11) (1.80)

Population density .1258** .1075*** .1322**

(2.21) (1.86) (2.30) Forfeiture .1762*

(3.27) % forfeiture retained .2561*

(3.40) % forfeiture unknown .0609

(.75) Forfeiture 84-88 .1887*

(3.22) Forfeiture 89-93 .1502**

(2.23)

Adj R2 .555 .557 .554 F 10.044 10.029 9.945

Intercepts, year, and city dummy coefficients are not reported. Absolute value of t value in parentheses. * significant at the .01 level ** significant at the .05 level *** significant at the .10 level (two tailed test) n= 1109



303

a portion of seized assets raises drug arrests as a portion of total arrests by about 20 percent and drug arrest rates by about 18 percent.

These results support the economic theory of bureaucracy's conclusion that bureaucrats desire increases in discretionary budgets, and that they also have a good deal of discretion in deciding how to allocate resources in the short run. In other words, like market entrepreneurs, entrepreneurial bureaucrats will respond to relative prices. When the price they expect to be "paid" to do one thing rises relative to what they expect to be paid to do another, they will reallocate resources.

In addition to providing a test of hypotheses about bureaucratic behavior, these results have important implications in the context of the developing economic literature on drug control policy. Some economists have questioned the wisdom of a criminal enforcement based drug policy, for instance, a recent

prominent example being Miron and Zweibel's (1995) argument that legalization is preferred to the current policy regime. Others are critical of calls for such a radical policy change, however, claiming that even more adverse unintended consequences might accompany legalization (MacCoun, Reuter, and Schelling, 1996). Whatever the merits of drug enforcement may be, it is clear from the results presented here that the current laws encourage police agencies to increase drug enforcement activity beyond the level they would choose in the absence of the asset forfeiture incentive. If asset forfeiture laws

provide a strong deterrent effect they might be appropriately used for that

purpose, of course, but according to a recent study, another dollar spent on

drug treatment is seven time more effective at reducing cocaine use than another dollar spent of criminal justice drug control activities (Rydell and

Everingham, 1994). This research suggests that reducing criminal justice ex- penditures on drug control by 25 percent and doubling treatment offered to users would reduce cocaine use and save $2 billion. Thus, requiring that the proceeds from seized assets go to police agencies appears to be creating incentives to inefficiently expand drug enforcement activities. Indeed, criminal justice drug control activities are generally ineffective (Miron and Zweibel, 1995) and they impose substantial unintended social costs by diverting scarce police resources away from other public safety concerns (Rasmussen and Benson, 1994). Benson et al. (1992) and Benson, Kim and Rasmussen (1998) find that increases in drug enforcement in Florida between 1984 and 1989 was accomplished in part by reallocating police resources, for instance, and this led to increases in property crime as a result of the reduction in the probability of arrest for such crimes. Rasmussen, Benson, and Sollars (1993) similarly find that changes in relative drug enforcement efforts across jurisdictions increases violent crime because it disturbed the spatial equilibrium in these local markets.24 While requiring that asset seizures go to general revenue



304

instead of police agencies is not the panacea for drug policy envisioned by advocates of legalization, our estimates suggest that this marginal policy change could reduce the opportunity costs of criminal justice system drug control efforts by a substantial margin.

Notes

1. Drug arrests per capita in the United States rose twenty fold from 1960 to 1989 but most of this increase was confined to two periods which, combined, account for a decade. From 1965 to 1970 drug arrests per 100,000 population rose from 34 to 228, an average annual increase of 114 percent. The second "drug war" started in 1984 and ended in 1989, a

period which saw drug arrests per 100,000 population rise from 312 to 538. This recent escalation of drug enforcement accounted for 46 percent of the increase in drug arrests

per capita from 1960 to 1989. 2. See Benson (1995) for a review of the theoretical and empirical public choice literature

on bureaucratic behavior and discretion. 3. As Deputy Attorney General Mark Richard testified in 1988, before 1984 "we counted

federal forfeitures in the tens of millions of dollars. Today ... we count federal forfeitures in the hundreds of millions of dollars" (Subcommittee on Federal Spending, Budget and Accounting, 1988: 9). Forfeiture receipts have roughly doubled every year from 1985 onward (Levy, 1996: 153). See Endnote 1 regarding the related rise in drug control efforts.

4. Since Miqu6 and Belanger (1974) and Niskanen (1975), the predominant assumptions in the literature are that bureaucrats wish to maximize utility, but that important determinants of satisfaction arise through the bureau's discretionary budget (e.g., ability to buy various perquisites, ability to reward supporters and trade with others within political networks, ability to expand output that is viewed to be important). It does not follow that utility- maximizing bureaucrats are "bad people", of course; indeed, they may be very "good people" who chose their jobs in part because they see it as a way to impact an "important" issue that they feel must be addressed. But as Breton and Wintrobe (1982: 152) explained, "One need not assume Machiavellian behavior, deceit, or dishonesty on the part of bureaucrats, because in all likelihood the pursuit of their own interest will be, as it is for everyone else, veiled in a self-perception of dedication and altruism".

5. Data on female arrestees were not available for all cities in each year. 6. Relatively casual drug use is the norm according to the NIDA survey. In 1990 11.3 percent

of the sample population had used cocaine sometime in their life, 3.1 percent used it in the last year, and only .8 percent used it in the last week.

7. The sample consists of the following cities: Atlanta, GA; Birmingham, AL; Chicago, IL; Cleveland, OH; Dallas, TX; Denver, CO; Detroit, MI; Ft. Lauderdale, FL; Houston, TX; Indianapolis, IN; Kansas City, MO; Los Angeles, CA; Miami, FL; New Orleans, LA; New York, NY; Omaha, NE; Philadelphia, PA; Phoenix, AZ; Portland, OR; St. Louis, MO; San Antonio, TX; San Diego, CA; San Jose, CA and Washington, DC.

8. Use of a fixed effects specification is also suggested by recent studies in the economics of crime literature (Cornwall and Trumbull, 1994; Levitt, 1998; Benson, Kim, and Rasmussen, 1998).

9. Geographic cost of living variations are provided by DuMond, Hirsch, and Macpherson (forthcoming). They construct cost of living indices using the Chamber of Commerce's Cost of Living Index for 182 CMSA/MSAs, which include all the cities in our sample.



305

Metropolitan income was adjusted by 40 percent of the cost of living differences in

light of DuMond et al.'s evidence that the indexes overstate differences in wages across

metropolitan areas by about 60 percent. 10. Many people have argued for drug enforcement on the grounds that drug users are ir-

rational and will do anything to support their drug habit, implicitly assuming that the demand for drugs is price inelastic. The literature does not support this view, however, and provides no evidence to support the argument that on average drugs are inferior goods. Illegal drugs include some whose demand may be quite inelastic, such as heroin, implying they may be inferior, but they also include some whose demand is quite elastic, like marijuana. Indeed, nationwide, marijuana still accounts for about 30 percent of all drug arrests. Furthermore, the degree of competition in a local drug market influences the price and therefore the price elasticity (White and Luksetich, 1983). See also, Benson et al. (1992) for a review of these arguments.

11. This argument would suggest that the relatively low income black population will have a lower rate of drug use. The evidence on this point is mixed: the NIDA Survey of House- holds suggests whites have higher use rates than blacks for PCP, hallucinogens, inhalants, and cocaine. Blacks in the survey report higher use rates of marijuana and heroin.

12. Unlike the 1984 federal seizure sharing law, state seizure laws can apply to both drug and non-drug crime, but since drug crime involves more liquid assets compared to most other crimes, this is not expected to matter with regard to the sign of the coefficient. It could affect the magnitude, of course, by making control of some non-drug crimes relatively more or less attractive.

13. Besides variation in the distribution of the proceeds, the other potentially important dif- ference in state drug forfeiture laws lies in the treatment of real property. In 1984, 4 states allowed confiscation of real property for drug crimes. By 1993, 47 states and Washington, D.C. allowed the forfeiture of real property in such cases. Although the forfeiture of real property may be an important deterrent to drug crime, it is not likely to account for a significant portion of drug arrests where the primary assets seized appear to be cash and vehicles. Nevertheless, we estimate models controlling for state differences in the treatment of real property. We considered three variables: 1) the fraction of the year police could keep proceeds from some property but not real property; 2) the fraction of the year police could keep proceeds from both real and other types of property; and 3) the fraction of the year that police could confiscate real property but could not keep any proceeds from real property. The results indicate that for the measures of drug enforcement considered in this study, the key aspect of state forfeiture laws is how proceeds are distributed.

14. In 1987 there were only 10 cities in the DUF program and the maximum number during this time period is 24. The total number of observations is 126. The weights are TAx(DA/TA)x(l-(DA/TA))2 where TA is total arrests and DA represents drug arrests. Since the sample includes multiple observations for most cities, it is possible that the error terms for those cities could be correlated over time. If this is true, panel estimation methods might be preferred to ordinary least squares. When city fixed effects are included in the model for the DUF sample, none of the remaining regressors have significant coefficients. We could not estimate a random-effects model with each city as a group, due to a negative estimated variance component.

15. Some criminal justice officials suggest that crack cocaine creates the greatest drug-related problems. Thus, it is possible that cocaine use is what drives drug arrests in most cities. When the percent of male non-drug arrestees testing positive for cocaine is used as an alternative to the "any drug use" specification, however, the estimated coefficients and significance of levels remained quite stable. We also included another variable with "any



306

drug use": the ratio of those testing positive for marijuana to those testing positive for cocaine. The marijuana/cocaine ratio was not significant in any specifications (results are available upon request).

16. The laws of some states specify that the police agency's share of seized assets varies by the amount seized. The percent forfeiture retained variable is specified as the percentage corresponding to the smallest amount of seized assets. For example, if a state allowed police to keep 70 percent of proceeds up to $50,000 and 50 percent above $50,000, the forfeitures retained variable is specified to be 70 percent multiplied by the fraction of the

year police were allowed to keep the proceeds. 17. Regressions were run using the drug arrest rate as the dependent variable but these results

are not reported here. The forfeiture coefficients did not have a significant impact on drug arrests per capita, however. The results are available upon request.

18. Because the average fraction police agency can keep is quite high (.854) and the proportion of the year they can keep the assets is .986, this elasticity is quite low. For this sample, there were 82 cases where police could keep proceeds and the fraction they kept was known and 15 instances where police could keep proceeds and the fraction was unknown.

19. The method for obtaining 2SLS estimates with an endogenous dummy variable was taken from Barnow, Cain, and Goldberger (1980).

20. The Durbin (1954) rank method was also used to treat the endogeneity of drug use, where the dummy variable for cities with the percentage of the population ages 15-24 is equal to or greater than the median was employed as an instrument. The F statistic for the equation was insignificant.

21. Because the average fraction police agency can keep is quite high (.844) and the proportion of the year they can keep the assets is .971, this elasticity is quite low. For this sample, there were 609 cases where police could keep proceeds and the fraction they kept was known and 161 instances where police could keep proceeds and fraction was unknown.

22. The drug arrest rate per 10 population was the largest multiple of 10 that the rate could be multiplied by and still be within the unit interval. Weighted least squares is employed with the weights being [population*drug rate*(1-drug rate)]-5.

23. The forfeiture variable in the all city fixed effects model cannot be treated as endogenous because our data violates a condition required for dummy variables in logit models (Greene, 1990): specifically, for some states there is no variation in state law regarding forfeitures.

24. There are other opportunity costs associated with the seizure laws themselves. For instance, many legal scholars point to the growing threat to civil liberties and property rights that has accompanied more aggressive seizure activities.

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Entrepreneurial Police and Drug Enforcement Policy