Do Casinos Cause Crime?

An ARIMA AnalysisAdam Jacobs

Department of SociologyUniversity of Wisconsin

Existing Research

• 1975 and 1998 National Commissions on Gambling note lack of conclusive research of gambling/crime link

• Extensive economic literature on multipliers, community development, addiction and choice

• Hakim studies (deposited at ICPSR)– Widely cited in lit reviews– Data problems

Background• Huge growth of gambling

and casinos nationwide• IGRA (1988) allows

Indian casinos• Most states have lotteries

and legal casinos• Growth of online casinos• Development strategy for

poor and/or remote areas– Detroit– Indian Reservations– Rust Belt

The debate on gambling

• Positive• Employment• Tax base• Non-seasonal

economy• Secondary

development (e.g. housing)

• “On the map”

• Negative• Addiction• Traffic/transport

problems• Cost of living/real

estate speculation• Quality of

life/dependence• Crime (?)

Theory – Why Would Casinos Cause Crime

• Routine Activity– Suitable Target, Motivated Offender, Lack of

Guardians– Out of town visitors, carrying cash, often drinking

• Relative Deprivation– Casinos bring conspicuous wealth to impoverished

areas• Secondary vice markets

– Vices go together– Growth/maintenance of drug markets, pimping

Ecological and PsychologicalFactors

• Increasing density• Increased stranger-to-stranger interaction• Self-contained nature of casinos • Gambling is anomic?

– National Commission found communities with casinos had higher rates of divorce and suicide

– Causal order problem: depressed places pursue gambling as development; these places may already have higher rates

Largest casino areas in US

• Las Vegas, NV– Problem: legalized gambling precedes

development of UCR• Tunica, MS

– Problem: did not report data to the UCR during the relevant time period (late 80s)

• Atlantic City, NJ– Best choice: good data from UCR and

extensive previous research on this area

Methods

• Interrupted Time-Series Analysis• ARIMA

– AutoRegressive Integrated Moving Average– AR: Effect of previous levels on current level– I: Level of differencing required to get a

stationary series– MA: unweighted average of one or more

previous terms

ARIMA model

• The causes of crime are endogenous to the model

• Rather than specifying factors causing crime (employment, gender ratio), we specify a model for the long-term trend

• H0: Casinos has no effect on crime rate• ARIMA specifies the overall level of growth

(if any) and the effect (if any) of outside shocks

What can ARIMA answer?

• What is the long-term trend in crime?• Does the introduction of casinos change

this trend?• If so, is the change:

– Permanent or temporary?– Abrupt or gradual?

Data

• UCR monthly offenses – We would suspect property crimes will be

most affected:• Car theft• Larceny• Robbery

– Because total crime is so heavily weighted to larceny, we will consider theft and non-theft offenses separately

ARIMA model

• Stationary series • Examination of correlogram• Specification of autoregression, moving

average, difference parameters– Usually these are either 1 or 0; 2nd and 3rd

order autoregression are fairly uncommon– We already know that the difference

parameters is 1• ARIMA = (?,?,?)

Total Crime and Theft0

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Source: Uniform Crime Reports

Casinos introduced in month 42 of seriesTotal Crime and Theft in Atlantic City, NJ, Jan 1975-Dec 1982

Non-theft crimes10

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Source: Uniform Crime Reports

Casinos introduced in month 42 of seriesTotal Non-Theft Crime

Differenced Crime Levels-4

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Source: Uniform Crime Reports

Casinos Introduced in Month 42 of seriesDifferenced Theft in Atlantic City

Differenced Non-Theft-1

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Source: Uniform Crime Reports

Casinos introduced in month 42 of seriesDifferenced Non Theft Crime

Seasonal Differencing – Theft-4

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Source: Uniform Crime Reports

Casinos Introduced in Month 42 of seriesSeasonally Differenced Theft in Atlantic City

Seasonal Differencing – Non-Theft-2

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Source: Uniform Crime Reports

Casinos introduced in month 42 of seriesSeasonally Differenced Nontheft Offenses

Characteristics of the Data

• Seasonality• Apparent increase in crime levels in casino

era– Is this significant?– If so, is it a one-time jump or gradual growth?

• Best described by a seasonal ARIMA model

ARIMA model specification

Stationary series • Examination of correlogram• Specification of autoregression, moving

average, difference parameters– We already know that the difference

parameters is 1• ARIMA = (?,1,?)

Autocorrelation graph (correlogram) for larceny

-0.6

0-0

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Bartlett's formula for MA(q) 95% confidence bands

Theft and Non-Theft have different trends, though both are clearly seasonal

050

010

0015

00

0 20 40 60 80 100id

larceny nontheft

ARIMA specification

Stationary series Examination of variagram• Specification of parameters:

– Theft: Seasonal moving average model• Current level of theft depends on inputs from last

month and this time last year• Theftt = at –B1at-1 – B2at-12 – B1*B2at-13

– Nontheft: seasonal autoregression

So Do Casinos Cause Crime?

• To answer this, we’ll introduce two interruption terms to measure short term and long term effects

• Examining the graphs, the interruption appears 2 years after the casino’s introduction

Results from larceny ARIMA

• ARIMA regression

• Sample: 13 to 96 Number of obs = 84• Wald chi2(2) = 35.53• Log likelihood = -513.4215 Prob > chi2 = 0.0000

• ------------------------------------------------------------------------------• | OPG• S12.larceny | Coef. Std. Err. z P>|z| [95% Conf. Interval]• -------------+----------------------------------------------------------------• Interruption | 259.5194 45.76395 5.67 0.000 169.8237 349.2151• _cons | 64.30593 15.04811 4.27 0.000 34.81219 93.79968• -------------+----------------------------------------------------------------• ARMA12 |• ma |• L1. | -.325136 .1596493 -2.04 0.042 -.6380427 -.0122292• -------------+----------------------------------------------------------------• /sigma | 108.3422 11.50894 9.41 0.000 85.7851 130.8993• ------------------------------------------------------------------------------

Results from Nontheft ARIMA

• ARIMA regression• Sample: 2 to 96 Number of obs = 95• Wald chi2(2) = 18.30• Log likelihood = -501.8218 Prob > chi2 = 0.0001

• ------------------------------------------------------------------------------• | OPG• D.nontheft | Coef. Std. Err. z P>|z| [95% Conf. Interval]• -------------+----------------------------------------------------------------• interruption | -7.89021 12.86408 -0.61 0.540 -33.10334 17.32292• _cons | 6.47043 9.815005 0.66 0.510 -12.76663 25.70749• -------------+----------------------------------------------------------------• Seasonal AR |• L1. | .4379765 .1047591 4.18 0.000 .2326524 .6433006• -------------+----------------------------------------------------------------• /sigma | 46.9898 3.962207 11.86 0.000 39.22402 54.75558• ------------------------------------------------------------------------------

• .

Gradual or abrupt?

• ARIMA regression

• Sample: 14 to 96 Number of obs = 83• Wald chi2(3) = 125.29• Log likelihood = -497.4252 Prob > chi2 = 0.0000

• ------------------------------------------------------------------------------• | OPG• S12.larceny | Coef. Std. Err. z P>|z| [95% Conf. Interval]• -------------+----------------------------------------------------------------• Interruption | -30.35914 78.73046 -0.39 0.700 -184.668 123.9497• Delta | .4701114 .0842888 5.58 0.000 .3049084 .6353144• _cons | 53.64972 8.114049 6.61 0.000 37.74648 69.55297• -------------+----------------------------------------------------------------• ARMA12 |• ma |• L1. | -.876413 .2837159 -3.09 0.002 -1.432486 -.3203401• -------------+----------------------------------------------------------------• /sigma | 88.0626 12.30615 7.16 0.000 63.943 112.1822• ------------------------------------------------------------------------------

Conclusions• Casinos are associated with a statistically

significant increase in larceny– Increase is gradual and permanent– (at least during this short time series)– Onset is approximately 2 years after first casino

opens, consistent with gradual growth of AC• Casinos have little effect on nontheft crimes

– Growth in nontheft crimes is entirely due to autoregression, with no perceptible effect of casino introduction

– Analysis of specific crimes like car theft and robbery confirms this

Effect of casinos• Casino growth has very little effect on non-theft

crime• This is consistent with Routine Activity Theory

and the Problem-Oriented Policing Paradigm– Greatest focus on items that are disposable,

concealable, easily removable, high value– No effects on overall trends of house burglary, car

theft, murder– Offenses like robbery grow but apparently

independently of casino development – no significant interruption in series

Revisiting the graphs0

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0 20 40 60 80 100index variable = 0 for Jan 1975

Theft offenses non-theft offenses

Further Implications

• Debate over casinos often focuses on potential crime increases

• Casinos do not appear to increase violent crimes

• Casinos have a strong impact on theft, probably owing to increased opportunity

• Location may be crucial: downtown versus Indian casinos in the country

Problems• Spuriousness

– Increased tax base leads to more police, leading to more detection/arrests

• Daytime population fluctuations– Increase may be just proportional to visitor population

• Displacement– As casinos raise real estate prices, crime may move further out

• Political pressure on statistics– Officials/casino operators may want low crime stats

• Measurement– Organized crime/corruption not recorded

• National trends– Late 70s/early 80s a period of crime growth everywhere

Further Research

• Native casino versus commercial/corporate entities

• Rural versus urban• Addiction and secondary crime from

gambling addiction (domestic abuse, white-collar crime)

• Effects of market saturation

Other issues

• Comparison of different gambling mediums (lottery vs casinos vs online cardrooms)

• Compare additional locations (Council Bluffs, IA, Cripple Creek, CO, Deadwood, SD, Hammond, IN)– Some locations are too small to get reasonable crime

data (< 1500 population)• International research

– Casinos have a different social position in Europe– Growth in Asia (Macau)