Do casinos cause crime


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  • Obviously there are still strong moral/religious objections to gambling; Utah does not (and probably never will) have gambling, and Protestant groups in the South are still very active against casino development
  • Autoregression just means that previous values largely determine current values; moving average means that previous values are averaged together with the current one for a stable series
  • ARIMA is a univariate analysis, although we can also add independent variables as controls
  • Although it turns out the same model will apply to both types of crime
  • Differencing and seasonally adjusting the data gets us to the first requirement of ARIMA – a stationary series that is just white noise or a “random walk.” To figure out the shape of our model, we need to look at the autocorrelation functions.
  • So crime levels exhibit clear seasonality, with peaks in june and july, and they seem to grow over time. It’s not clear if this is a one-time jump to a new level or a gradual growth over time. Because theft is such a large portion of the overall crime count, let’s subtract it out and look at non-theft crimes.
  • With theft removed, the data still looks fairly seasonal, and there still appears to be a jump after about month 50.
  • I’ve separated crime into larceny (which has a 90+ correlation with total crime) and all non-theft offenses
  • Differencing data is just subtracting out the prior term in the time series. So it’s now centered around zero, with the values representing the deviation from the last month. There’s still some seasonality, so we’ll adjust for that too.
  • Now we have a nice stationary series. This is called a “random walk” where the months values are just scattered randomly around zero with no clear pattern. Notice that there aren’t values for the first year, because we’re now subtracting out the previous year’s value. So the value for this July subtracts out last July and this June.
  • Differencing and seasonally adjusting the data gets us to the first requirement of ARIMA – a stationary series that is just white noise or a “random walk.” To figure out the shape of our model, we need to look at the autocorrelation functions.
  • This graph shows the autocorrelations for the seasonally differenced larceny data; this suggests a moving average
  • the effects of casinos don’t appear instantly and it takes a while for the industry to get off the ground.
  • We have a large positive interruption term, a growth of more than 250 larcenies per year with the interruption.
  • Obviously there’s no effect here. I decomposed some particular crimes like auto theft with similar results – they simply followed overall trends, with no break with casino introduction.
  • This is an in-between value, suggesting a fairly rapid ascent to the new level; deltas are between 0 and 1 with 0 indicating instantaneous change.
  • Is this surprising? Clearly from the graph you can see an increase; but ARIMA offers us a way to a) test the significance of this jump b) specify the dimensions of the change (in this case rather abrupt and permanent) c) test model for different types of offenses.
  • Although the overall population of atlantic city doesn’t change much during this time, and actually drops through the 80s, surely the number of people passing through is rising gradually.
  • Do casinos cause crime

    1. 1. Do Casinos Cause Crime? An ARIMA Analysis Adam Jacobs Department of Sociology University of Wisconsin
    2. 2. Existing Research <ul><li>1975 and 1998 National Commissions on Gambling note lack of conclusive research of gambling/crime link </li></ul><ul><li>Extensive economic literature on multipliers, community development, addiction and choice </li></ul><ul><li>Hakim studies (deposited at ICPSR) </li></ul><ul><ul><li>Widely cited in lit reviews </li></ul></ul><ul><ul><li>Data problems </li></ul></ul>
    3. 3. Background <ul><li>Huge growth of gambling and casinos nationwide </li></ul><ul><li>IGRA (1988) allows Indian casinos </li></ul><ul><li>Most states have lotteries and legal casinos </li></ul><ul><li>Growth of online casinos </li></ul><ul><li>Development strategy for poor and/or remote areas </li></ul><ul><ul><li>Detroit </li></ul></ul><ul><ul><li>Indian Reservations </li></ul></ul><ul><ul><li>Rust Belt </li></ul></ul>
    4. 4. The debate on gambling <ul><li>Positive </li></ul><ul><li>Employment </li></ul><ul><li>Tax base </li></ul><ul><li>Non-seasonal economy </li></ul><ul><li>Secondary development (e.g. housing) </li></ul><ul><li>“ On the map” </li></ul><ul><li>Negative </li></ul><ul><li>Addiction </li></ul><ul><li>Traffic/transport problems </li></ul><ul><li>Cost of living/real estate speculation </li></ul><ul><li>Quality of life/dependence </li></ul><ul><li>Crime (?) </li></ul>
    5. 5. Theory – Why Would Casinos Cause Crime <ul><li>Routine Activity </li></ul><ul><ul><li>Suitable Target, Motivated Offender, Lack of Guardians </li></ul></ul><ul><ul><li>Out of town visitors, carrying cash, often drinking </li></ul></ul><ul><li>Relative Deprivation </li></ul><ul><ul><li>Casinos bring conspicuous wealth to impoverished areas </li></ul></ul><ul><li>Secondary vice markets </li></ul><ul><ul><li>Vices go together </li></ul></ul><ul><ul><li>Growth/maintenance of drug markets, pimping </li></ul></ul>
    6. 6. Ecological and PsychologicalFactors <ul><li>Increasing density </li></ul><ul><li>Increased stranger-to-stranger interaction </li></ul><ul><li>Self-contained nature of casinos </li></ul><ul><li>Gambling is anomic? </li></ul><ul><ul><li>National Commission found communities with casinos had higher rates of divorce and suicide </li></ul></ul><ul><ul><li>Causal order problem: depressed places pursue gambling as development; these places may already have higher rates </li></ul></ul>
    7. 7. Largest casino areas in US <ul><li>Las Vegas, NV </li></ul><ul><ul><li>Problem: legalized gambling precedes development of UCR </li></ul></ul><ul><li>Tunica, MS </li></ul><ul><ul><li>Problem: did not report data to the UCR during the relevant time period (late 80s) </li></ul></ul><ul><li>Atlantic City, NJ </li></ul><ul><ul><li>Best choice: good data from UCR and extensive previous research on this area </li></ul></ul>
    8. 8. Methods <ul><li>Interrupted Time-Series Analysis </li></ul><ul><li>ARIMA </li></ul><ul><ul><li>A uto R egressive I ntegrated M oving A verage </li></ul></ul><ul><ul><li>AR: Effect of previous levels on current level </li></ul></ul><ul><ul><li>I: Level of differencing required to get a stationary series </li></ul></ul><ul><ul><li>MA: unweighted average of one or more previous terms </li></ul></ul>
    9. 9. ARIMA model <ul><li>The causes of crime are endogenous to the model </li></ul><ul><li>Rather than specifying factors causing crime (employment, gender ratio), we specify a model for the long-term trend </li></ul><ul><li>H 0 : Casinos has no effect on crime rate </li></ul><ul><li>ARIMA specifies the overall level of growth (if any) and the effect (if any) of outside shocks </li></ul>
    10. 10. What can ARIMA answer? <ul><li>What is the long-term trend in crime? </li></ul><ul><li>Does the introduction of casinos change this trend? </li></ul><ul><li>If so, is the change: </li></ul><ul><ul><li>Permanent or temporary? </li></ul></ul><ul><ul><li>Abrupt or gradual? </li></ul></ul>
    11. 11. Data <ul><li>UCR monthly offenses </li></ul><ul><ul><li>We would suspect property crimes will be most affected: </li></ul></ul><ul><ul><ul><li>Car theft </li></ul></ul></ul><ul><ul><ul><li>Larceny </li></ul></ul></ul><ul><ul><ul><li>Robbery </li></ul></ul></ul><ul><ul><li>Because total crime is so heavily weighted to larceny, we will consider theft and non-theft offenses separately </li></ul></ul>
    12. 12. ARIMA model <ul><li>Stationary series </li></ul><ul><li>Examination of correlogram </li></ul><ul><li>Specification of autoregression, moving average, difference parameters </li></ul><ul><ul><li>Usually these are either 1 or 0; 2 nd and 3 rd order autoregression are fairly uncommon </li></ul></ul><ul><ul><li>We already know that the difference parameters is 1 </li></ul></ul><ul><li>ARIMA = (?,?,?) </li></ul>
    13. 13. Total Crime and Theft
    14. 14. Non-theft crimes
    15. 15. Differenced Crime Levels
    16. 16. Differenced Non-Theft
    17. 17. Seasonal Differencing – Theft
    18. 18. Seasonal Differencing – Non-Theft
    19. 19. Characteristics of the Data <ul><li>Seasonality </li></ul><ul><li>Apparent increase in crime levels in casino era </li></ul><ul><ul><li>Is this significant? </li></ul></ul><ul><ul><li>If so, is it a one-time jump or gradual growth? </li></ul></ul><ul><li>Best described by a seasonal ARIMA model </li></ul>
    20. 20. ARIMA model specification <ul><li>Stationary series </li></ul><ul><li>Examination of correlogram </li></ul><ul><li>Specification of autoregression, moving average, difference parameters </li></ul><ul><ul><li>We already know that the difference parameters is 1 </li></ul></ul><ul><li>ARIMA = (?,1,?) </li></ul>
    21. 21. Autocorrelation graph (correlogram) for larceny
    22. 22. Theft and Non-Theft have different trends, though both are clearly seasonal
    23. 23. ARIMA specification <ul><li>Stationary series </li></ul><ul><li>Examination of variagram </li></ul><ul><li>Specification of parameters: </li></ul><ul><ul><li>Theft: Seasonal moving average model </li></ul></ul><ul><ul><ul><li>Current level of theft depends on inputs from last month and this time last year </li></ul></ul></ul><ul><ul><ul><li>Theft t = a t –B 1 a t-1 – B 2 a t-12 – B 1 *B 2 a t-13 </li></ul></ul></ul><ul><ul><li>Nontheft: seasonal autoregression </li></ul></ul>
    24. 24. So Do Casinos Cause Crime? <ul><li>To answer this, we’ll introduce two interruption terms to measure short term and long term effects </li></ul><ul><li>Examining the graphs, the interruption appears 2 years after the casino’s introduction </li></ul>
    25. 25. Results from larceny ARIMA <ul><li>ARIMA regression </li></ul><ul><li>Sample: 13 to 96 Number of obs = 84 </li></ul><ul><li>Wald chi2(2) = 35.53 </li></ul><ul><li>Log likelihood = -513.4215 Prob > chi2 = 0.0000 </li></ul><ul><li>------------------------------------------------------------------------------ </li></ul><ul><li>| OPG </li></ul><ul><li>S12.larceny | Coef. Std. Err. z P>|z| [95% Conf. Interval] </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>Interruption | 259.5194 45.76395 5.67 0.000 169.8237 349.2151 </li></ul><ul><li>_cons | 64.30593 15.04811 4.27 0.000 34.81219 93.79968 </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>ARMA12 | </li></ul><ul><li>ma | </li></ul><ul><li>L1. | -.325136 .1596493 - 2.04 0.042 -.6380427 -.0122292 </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>/sigma | 108.3422 11.50894 9.41 0.000 85.7851 130.8993 </li></ul><ul><li>------------------------------------------------------------------------------ </li></ul>
    26. 26. Results from Nontheft ARIMA <ul><li>ARIMA regression </li></ul><ul><li>Sample: 2 to 96 Number of obs = 95 </li></ul><ul><li>Wald chi2(2) = 18.30 </li></ul><ul><li>Log likelihood = -501.8218 Prob > chi2 = 0.0001 </li></ul><ul><li>------------------------------------------------------------------------------ </li></ul><ul><li>| OPG </li></ul><ul><li>D.nontheft | Coef. Std. Err. z P>|z| [95% Conf. Interval] </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>interruption | -7.89021 12.86408 -0.61 0.540 -33.10334 17.32292 </li></ul><ul><li>_cons | 6.47043 9.815005 0.66 0.510 -12.76663 25.70749 </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>Seasonal AR | </li></ul><ul><li>L1. | .4379765 .1047591 4.18 0.000 .2326524 .6433006 </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>/sigma | 46.9898 3.962207 11.86 0.000 39.22402 54.75558 </li></ul><ul><li>------------------------------------------------------------------------------ </li></ul><ul><li>. </li></ul>
    27. 27. Gradual or abrupt? <ul><li>ARIMA regression </li></ul><ul><li>Sample: 14 to 96 Number of obs = 83 </li></ul><ul><li>Wald chi2(3) = 125.29 </li></ul><ul><li>Log likelihood = -497.4252 Prob > chi2 = 0.0000 </li></ul><ul><li>------------------------------------------------------------------------------ </li></ul><ul><li>| OPG </li></ul><ul><li>S12.larceny | Coef. Std. Err. z P>|z| [95% Conf. Interval] </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>Interruption | -30.35914 78.73046 -0.39 0.700 -184.668 123.9497 </li></ul><ul><li>Delta | . 4701114 .0842888 5.58 0.000 .3049084 .6353144 </li></ul><ul><li>_cons | 53.64972 8.114049 6.61 0.000 37.74648 69.55297 </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>ARMA12 | </li></ul><ul><li>ma | </li></ul><ul><li>L1. | -.876413 .2837159 - 3.09 0.002 -1.432486 -.3203401 </li></ul><ul><li>-------------+---------------------------------------------------------------- </li></ul><ul><li>/sigma | 88.0626 12.30615 7.16 0.000 63.943 112.1822 </li></ul><ul><li>------------------------------------------------------------------------------ </li></ul>
    28. 28. Conclusions <ul><li>Casinos are associated with a statistically significant increase in larceny </li></ul><ul><ul><li>Increase is gradual and permanent </li></ul></ul><ul><ul><li>(at least during this short time series) </li></ul></ul><ul><ul><li>Onset is approximately 2 years after first casino opens, consistent with gradual growth of AC </li></ul></ul><ul><li>Casinos have little effect on nontheft crimes </li></ul><ul><ul><li>Growth in nontheft crimes is entirely due to autoregression, with no perceptible effect of casino introduction </li></ul></ul><ul><ul><li>Analysis of specific crimes like car theft and robbery confirms this </li></ul></ul>
    29. 29. Effect of casinos <ul><li>Casino growth has very little effect on non-theft crime </li></ul><ul><li>This is consistent with Routine Activity Theory and the Problem-Oriented Policing Paradigm </li></ul><ul><ul><li>Greatest focus on items that are disposable, concealable, easily removable, high value </li></ul></ul><ul><ul><li>No effects on overall trends of house burglary, car theft, murder </li></ul></ul><ul><ul><li>Offenses like robbery grow but apparently independently of casino development – no significant interruption in series </li></ul></ul>
    30. 30. Revisiting the graphs
    31. 31. Further Implications <ul><li>Debate over casinos often focuses on potential crime increases </li></ul><ul><li>Casinos do not appear to increase violent crimes </li></ul><ul><li>Casinos have a strong impact on theft, probably owing to increased opportunity </li></ul><ul><li>Location may be crucial: downtown versus Indian casinos in the country </li></ul>
    32. 32. Problems <ul><li>Spuriousness </li></ul><ul><ul><li>Increased tax base leads to more police, leading to more detection/arrests </li></ul></ul><ul><li>Daytime population fluctuations </li></ul><ul><ul><li>Increase may be just proportional to visitor population </li></ul></ul><ul><li>Displacement </li></ul><ul><ul><li>As casinos raise real estate prices, crime may move further out </li></ul></ul><ul><li>Political pressure on statistics </li></ul><ul><ul><li>Officials/casino operators may want low crime stats </li></ul></ul><ul><li>Measurement </li></ul><ul><ul><li>Organized crime/corruption not recorded </li></ul></ul><ul><li>National trends </li></ul><ul><ul><li>Late 70s/early 80s a period of crime growth everywhere </li></ul></ul>
    33. 33. Further Research <ul><li>Native casino versus commercial/corporate entities </li></ul><ul><li>Rural versus urban </li></ul><ul><li>Addiction and secondary crime from gambling addiction (domestic abuse, white-collar crime) </li></ul><ul><li>Effects of market saturation </li></ul>
    34. 34. Other issues <ul><li>Comparison of different gambling mediums (lottery vs casinos vs online cardrooms) </li></ul><ul><li>Compare additional locations (Council Bluffs, IA, Cripple Creek, CO, Deadwood, SD, Hammond, IN) </li></ul><ul><ul><li>Some locations are too small to get reasonable crime data (< 1500 population) </li></ul></ul><ul><li>International research </li></ul><ul><ul><li>Casinos have a different social position in Europe </li></ul></ul><ul><ul><li>Growth in Asia (Macau) </li></ul></ul>