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Casinos, hotels, and crime.
Abstract:
This paper examines the links among casinos, hotels, and crime using Indiana's counties for 1994-2004. In estimating casinos' impacts, I introduce a measure of casino activity in addition to variables related to the timing of casino opening. I test whether or not the number of hotel rooms affects crime rates. Increased casino activity reduces crime rates except for burglary, where crime rates rise after a lag. Leaving out a measure of casino activity appears to create a serious specification error. Finally, including problem crime data plagued by incomplete reporting affects the estimated impact of casinos on crime. (JEL R11, L83)

Subject:
Gambling (Political aspects)
Native Americans (Political aspects)
Native Americans (Political activity)
Hotels and motels (Political aspects)
Hotels and motels (Political activity)
Casinos (Political aspects)
Author:
Reece, William S.
Pub Date:
04/01/2010
Publication:
Name: Contemporary Economic Policy Publisher: Western Economic Association International Audience: Academic; Trade Format: Magazine/Journal Subject: Business; Economics Copyright: COPYRIGHT 2010 Western Economic Association International ISSN: 1074-3529
Issue:
Date: April, 2010 Source Volume: 28 Source Issue: 2
Topic:
Event Code: 290 Public affairs; 200 Management dynamics Computer Subject: Company business management
Product:
Product Code: 7760000 Legal Gambling; 7011000 Hotels & Motels; 7761000 Casinos NAICS Code: 7132 Gambling Industries; 72111 Hotels (except Casino Hotels) and Motels; 71321 Casinos (except Casino Hotels) SIC Code: 7011 Hotels and motels
Organization:
Government Agency: United States. Federal Bureau of Investigation; United States. Federal Bureau of Investigation
Accession Number:
225075316
Full Text:
I. INTRODUCTION

In recent decades, casinos have spread to many states in which they had previously been illegal. Nevada legalized casino gambling in 1931. In having legal casinos, it remained alone among the states until New Jersey legalized Atlantic City casinos in 1976. Recently, a total of 12 states have allowed commercial casinos (American Gaming Association, 2009). Other states have allowed gaming devices at racetracks. Following action by the U.S. Supreme Court in 1987 and the Congress in 1988, Native American casinos have opened in 28 states (American Gaming Association, 2009). This spread of casino gaming has been very controversial. The public, regulators, and researchers in health and social sciences have been interested, among other things, in the effects of casino gambling on crime.

A few researchers have empirically investigated the connection between the opening of new casinos and local area crime rates. Two of the most important of these are Evans and Topoleski (2002) (E&T) and Grinols and Mustard (2006) (G&M). (1) Using a large panel of U.S. county data on casino openings and crime rates, G&M test the hypothesis that casinos cause crime. They estimate the parameters of a fixed-effects panel model using all 3,165 U.S. counties for the period 1977 through 1996. Their dependent variables are numbers of reported offenses in each of the following seven categories: aggravated assault, rape, robbery, murder, larceny, burglary, and motor vehicle theft. The source for these crime rates is the Federal Bureau of Investigation's (FBI) Uniform Crime Reporting (UCR) program, which the Interuniversity Consortium for Political and Social Research (ICPSR) makes available to researchers. (2) G&M find that casino openings cause county crime rates in all categories except murder to rise after a lag of a few years. E&T also use UCR data to estimate a similar model to test the effects of opening Native American casinos on county crime rates. They find statistically significant positive lagged effects of new Native American casinos on crime.

In this paper, I reexamine the link between new casinos and crime rates. Here, I use the 92 counties of Indiana over the period 1994 through 2004. During this period, Indiana opened its first commercial casinos, including 10 casinos in seven counties. (Indiana has no Native American casinos.) Also, Indiana's casinos have a readily available measure of casino activity, the turnstile count of patrons, which we can use in addition to the often used dates of casino opening. Finally, I have created a database of the number of hotel rooms for each Indiana county for each of these years. I add this variable to the crime equations. The annual series of data on hotel rooms by county allows a test of whether or not the opening of other public accommodations besides casinos, specifically hotels, affects crime rates.

II. THEORY OF CASINOS AND CRIME

The economic model of crime proposed by Becker (1968), and investigated by Ehrlich (1973), Sjoquist (1973), Cornwell and Trumbull (1994), Raphael and Winter-Ebmer (2001), Gould, Weinberg, and Mustard (2002), Levitt (1998, 2004a, 2004b), Ihlanfeldt (2006), and many others asserts that potential criminals are utility maximizing agents who allocate time between legal work and crime based on the potential benefits and costs of each. (3) To the extent that this is true, increasing the opportunity cost of criminal behavior would reduce crime. Increasing the probability of arrest and conviction along with more severe penalties would increase the opportunity cost of crime. Also, increasing legitimate work opportunities for potential criminals or the wages for that work would increase the opportunity cost of crime. As explained below, this theory has testable implications for the link between new casinos and crime.

Following the economic model of crime, new casinos could either increase or decrease local crime rates. If introducing new casinos increases job opportunities in the area, new casinos could, by increasing the opportunity cost of crime, reduce crime rates. Also, increasing activity within existing casinos could have the same effect. Many studies have supported the idea that better local labor market conditions reduce local crime rates and have found this result using various levels of aggregation. Raphael and Winter-Ebmer (2001) find that higher state unemployment rates lead to higher property crime rates. Gould, Weinberg, and Mustard (2002) find that movements of wages of unskilled men explain a large fraction of the movements in county crime rates. Ihlanfeldt (2006) finds that greater neighborhood employment opportunities for young males lower neighborhood crime rates. Levitt (2004a, pp. 170-171), however, questions the importance of the direct link between wages or unemployment rates and property crime rates.

The economic model of crime suggests that closer proximity of potential criminals and potential victims would increase local crime rates. Bowes and Ihlanfeldt (2001) investigate the impact of location and transportation costs on census tract crime rates and find that lowering transportation costs between potential criminals and victims (by the presence of a nearby rapid transit rail station) increases crime rates. This locational effect suggests that new casinos could reduce local crime rates by displacing crime-ridden locations with new construction. Alternatively, new casinos could lower the opportunity cost of crime by bringing new criminal opportunities to the local area. If this effect were important, new casinos would increase crime rates.

Another consideration that may be important in investigating the link between casinos and crime is that casino openings are typically followed, often with a lag, by the opening of hotels, entertainment venues, retail shops, and other public accommodations. These new public accommodations could raise or lower the opportunity cost of crime in the same ways discussed above for new casinos. Thus, because new casinos and other new public accommodations are linked, leaving these other new public accommodations out of the model explaining crime rates could lead to a specification error. Then finding that new casinos increase local crime rates after a lag of a few years could actually be the finding that casinos cause hotels and other public accommodations with a lag and the increased presence of these public accommodations causes a contemporaneous increase in crime rates. (4)

As with new casinos, the activities associated with new hotels and other public accommodations could reduce crime rates by providing increased legal employment opportunities, by displacing crime-ridden locations, or through some other mechanism. Or, they might increase crime by bringing together potential victims and potential criminals. Tourists visiting casinos or other sites may be particularly vulnerable to crime because they often carry large amounts of cash and other valuables and because they are outside their normal home territory. (5)

Thus, the economic model of crime suggests that the opening of new casinos, the level of activity at existing casinos, and the extent of other public accommodations may help explain local crime rates. Here, I use dates of opening of new casinos, leads and lags around these opening dates, a measure of the level of casino activity, and the number of hotel rooms in the specification explaining each county's crime rates. Previous studies of casinos and crime have accounted only for the introduction of casinos--they have not examined the impact of the level of casino activity or the extent of other public accommodations. In some cases, local areas have seen substantial new activity including multiple large casinos, new hotels, new restaurants, new entertainment venues, and new shopping areas. In other areas, introduction of casinos may mean that the area has a single relatively small casino. This study is the first to empirically examine the impacts of the level of casino activity and the level of related activity, in this case the number of hotel rooms, on local crime rates.

It is possible that opening a new casino in Indiana increases crimes rates in bordering states. I do not investigate that possibility here for two reasons. First, some of the Indiana casinos are in the Chicago area, and explaining crime rates in Chicago is beyond the scope of this paper. Second, most Illinois counties did not report crime rates during the period covered here, making investigation of spillovers infeasible.

III. DATA

The UCR crime data used to examine links between casinos and crime have severe and well known defects, discussed below. Furthermore, researchers have lacked useful, broadly applicable measures of casino activity. (6) Here, I test the hypothesis that new casinos caused increases in crime rates in the counties of Indiana over the years 1994 to 2004 using the standard data sources previous investigators have used plus a measure of casino activity and a unique annual panel data set on hotel accommodations. The counties of Indiana are particularly well suited for a study of the links among casinos, hotels, and crime. In 1993, the Indiana legislature created the Indiana Gaming Commission (IGC) and authorized it to issue up to 11 riverboat casino licenses (IGC, 1994). This Riverboat Gaming Act specified that the city of Gary would receive the first one or two licenses. The IGC set up local referendums on the desirability of casinos in the city or county. In the 1993 elections, four counties rejected casino referendums and five counties and two cities passed casino referendums. The IGC then established a sequence of locations for consideration for riverboat casino licenses. These included four Lake Michigan cities or counties, four Ohio River counties, and Patoka Lake in the southern interior of Indiana. The Army Corps of Engineers, owner of Patoka Lake, subsequently denied permission for a riverboat casino. In 2003, the legislature approved a replacement land-based casino north of the lake in Orange County near French Lick. At the same time, the legislature created a Historic Hotel Preservation Commission to work with the IGC to develop the historic resort hotels in the area (IGC, 2003).

As a result of this and subsequent activity, Indiana, which had no casinos before 1995, opened 10 casinos in seven counties over the period 1995-2000. Four of the riverboat casinos are in Lake County in the extreme northwest of Indiana near Chicago. Six other counties have one riverboat casino each. Indiana also opened an 11th casino in an 8th county in 2006, which is outside the period for which crime rate data are currently available. (This 11th casino does, however, enter the data through the variable indicating a 2-yr lead in casino opening.)

Indiana's casinos also have a readily available measure of casino activity that we can use in addition to the dummy variables related to dates of casino opening that other researchers have used. The IGC's monthly revenue reports show "turnstile" counts of casino admissions for the entire period covered here (7) (IGC, 1996-2004). As each riverboat casino owes $3 in admissions taxes for each patron admitted to the casino, it must accurately count admissions. In the early years of Indiana's riverboat casino operations, patrons had to be readmitted for a new "excursion" every 2 h even if they had not left the casino. In 2002, the state allowed dock-side operations and amended the admissions tax scheme to eliminate the readmission process. This change did not affect the definition of the turnstile count because throughout the period each casino had reported turnstile admissions, which is a count of the number of patrons actually entering the casino. That is, before the 2002 change, patrons who were readmitted for a new excursion had not passed through the turnstile again (Klacik et al., 2003, pp. 5-6).

Two of the Lake County casinos share dock-side facilities. With the 2002 admissions tax changes, these casinos got IGC permission to operate a single turnstile granting admission to both casinos and to evenly split the admissions tax liabilities between them. (8) Thus, before this change, these casinos operated two turnstiles and all patrons went through one or the other. (In a minority of cases, patrons would go through both [Klacik, Littlepage, and Payton 2001].) After the change, all patrons for the two casinos would pass through a single turnstile, and one-half of the total admissions were assigned to each casino. Beginning with September 2002, the monthly reports of turnstile admissions and admissions taxes for these two casinos show the totals evenly split between them. With both casinos in the same county, this change had no effect on county turnstile admission totals. This 2002 change seems to have had little effect on the turnstile count, as the average turnstile count for these two casinos for the preceding years 2000 and 2001 was 1,753,209 and for the following years 2003 and 2004 was 1,738,138.

Indiana is also a good choice for studying the link between casinos and crime because there are adequate offense data for Indiana counties in the annual FBI UCR program data. (9) While researchers have often used the UCR as the source for crime rate data, these crime reports have some very important limitations: (10)

* the reported data are voluntary self reports of state or local law enforcement agencies, which may not report at all in some years or may report incompletely, with errors, or nonuniformly across jurisdictions;

* the UCR only records the most serious crime in incidents in which multiple crimes are committed;

* some agencies only report state totals; to get county offense numbers, the ICPSR allocates offenses reported by these agencies to the counties in proportion to each county's share of the state's population.

* Most interesting for our purposes, the UCR includes imputations of some incompletely reported offense data. That is, in adjusting for incomplete reports the ICPSR replaces some incompletely reported data with values not actually from the reporting agencies.

It is important to examine the imputation of incompletely reported data more closely. Some agencies report offenses for only part of the year. For agencies reporting 3-11 mo of data, the ICPSR inflates the reported data up to a 12-moequivalent. For example, if an agency reported 6 mo of data, the ICPSR doubles the number of reported offenses to get a 12-mo number. For agencies reporting 0, 1, or 2 mo of data, the ICPSR discards the reported data and replaces the number of offenses with an estimate based on reports of agencies reporting 12 mo of data within the same state and in cities or counties of the same type based on urbanization and population. If there are no cities or counties of that type having agencies reporting 12 mo of data, the ICPSR does not estimate the missing values; instead it shows the number of offenses as 0.

Thus, in some cases, the idea that the UCR provides offense data is an illusion, as the coded value is a number but it is not a reported number of offenses. Recall that the ICPSR has coded some incompletely reported values as 0. This listing of the number of crimes as "0" can be particularly important for research relating new casinos to local crime rates. For example, Iowa reported no offense data for 1991. Thus for 1991, the year in which Iowa's first casinos opened, (11) all Iowa counties show UCR crime totals of 0 for all crimes. Tunica County, Mississippi, which had its first casino open in 1992, (12) reported no crime data for 1990-1998; so that, its offense totals for those years are coded as 0. Illinois has casinos, but few Illinois counties reported offense data over the period 1993-2004; so that, the UCR shows most Illinois county offense totals as "0." Thus, 0s in the UCR are often not actual offense totals. It is important to recall that not all imputed values are 0s, however; so that, some nonzero values are also not actual reported offense totals for the county. Finally, the ICPSR's imputation method for UCR data changed beginning with 1994, causing an important break in the continuity of the crime data. The ICPSR cautions that researchers should not compare UCR county-level crime data from 1993 and earlier with UCR data from 1994 and later. (13)

Both E&T and G&M used UCR offense data in constructing their dependent variables and used data from before and after the 1994 break in continuity. E&T, however, dealt with the ICPSR's changing imputation methods to try to get consistent crime data over time. E&T (p. 38) deleted post-1993 county-year observations having inadequate data based on the coverage variable, discussed below, and pre-1994 observations based on the proportion of the county's Census Bureau population for which crimes were reported. G&M used every county in every year 1977 through 1996, including the counties and years in Mississippi, Iowa, and Illinois where many crime rates resulting from incomplete reporting are inaccurate. Furthermore, G&M treated the offense rates uniformly across the 1994 break in continuity. Problems with these crime rate data suggest that the results reported in G&M should be treated with caution.

Here, unlike previous studies, I avoid the worst of these problems. I use data from Indiana counties for 1994 through 2004. Beginning my analysis with 1994 data, I avoid the break in the data at 1993-1994. I examine the statistical relationships among crime, casinos, and hotels after deleting some observations with crime rate data having problems created by incomplete reporting, as explained below.

As part of the change in imputations, beginning with 1994 the ICPSR reports a variable called "coverage," which is the percentage of the crime rate data for each county that is not imputed. This percentage is 0, showing 100% imputation, for only 14 instances of Indiana's counties over the period 1994 through 2004. I delete those 14 observations with coverage equaling 0. Also, beginning with 1994, the ICPSR reports the county population of jurisdictions reporting crime data. For example, a county may include an incorporated city with a municipal police force and a county police force for the remainder of the county. If only the city agency reports crime data, the ICPSR data show the population of the city as the county population of jurisdictions reporting crime data. (The meaning of this population variable and the coverage variable, which depends on the population figure, is complicated by the fact that some jurisdictions, such as parks or toll roads, have 0 population. Thus, in some cases, there will be reported crimes for jurisdictions with 0 population, and these crime reports will not affect the coverage number.) To deal with this varying population coverage, I also delete those observations for which the UCR's county population of jurisdictions reporting crime data is less than 60% of the county's population as reported by the Census Bureau. For Indiana's counties for 1994 through 2004, there were 503 county-year observations having nonzero values for the coverage variable and having ratios of county population of jurisdictions reporting crime data to total county population of at least 0.6. Note that I am truncating the sample based on coverage and population reporting ratios, not the crime rate dependent variable--some of the remaining crime rates are 0 and some of the deleted observations have nonzero crime rates. I also deleted six observations in county panels having only one observation, as these observations have no effect on parameter estimates in a fixed-effects model. This leaves a data set of 497 observations. This smaller data set with adequate crime rate data has information on 69 Indiana counties, including five counties having casinos.

Data on hotel rooms by county over a long time period have not previously been available. I have constructed this data set for Indiana using a historical series of publications including AAA TourBook (AAA Publishing, 1995-2005), Mobil Travel Guide, Great Lakes, and other sources. I began by listing, for each year, all Indiana lodging establishments (with their addresses and number of rooms) included in the AAA TourBook (Illinois, Indiana, Ohio edition), assuming that the guide covers lodging available for the year preceding the Guide's copyright date. (This is clearly the case, as in a few cases the guide for one year states that a hotel was scheduled to open on a date from the previous year.) I then added any establishments that were in the appropriate Mobil Travel Guide but had not been included in the AAA guide. I then filled in any intervening years for establishments that moved in and out of the guides. I then checked Internet sources (including websites of the Association of Indiana Convention and Visitors Bureaus, Hotel-Guides.us (2007), triprewards.com, ChoiceHotels.com, and Dayslnn.com) to see if any listed establishment was still operating in early 2007. For those that were, I filled in up through 2004 from the most recent year having a guide listing. I also added any casino hotels listed in IGC (various years) riverboat casino evaluation reports but not included in the AAA or Mobil guides. Finally, in some cases, I was able to fill backwards in time using Internet travel sites, including TravelPost.com, that show the establishment's date of opening. I then used each property's town or street address to assign it to a county, using State of Indiana (2007) or, for properties near county borders, Mapquest.com. The result is a database of lodging properties by Indiana county for the years 1994 through 2004. I found 7,526 property-year observations in 78 of Indiana's 92 counties. (14) For each county and year, I added the rooms of all establishments to get the county's total rooms for that year. The 14 counties for which I found no lodging establishments have 0 rooms for all years.

IV. HOTEL ROOMS FOLLOW CASINOS

To test whether or not finding that casinos cause rising crime rates after a lag of a few years could actually be finding that casinos cause hotel rooms with a lag and new hotel rooms cause a contemporaneous increase in crime rates, I add the number of hotel rooms to the crime equation. It seems clear that new casinos and new hotels rooms are linked, as we sometimes observe that casino openings are followed, often with a lag, by new or expanded hotels. To test this more formally, I estimate the parameters of an equation explaining hotel rooms by Indiana county for the period 1994 through 2004 using a fixed-effects estimator including both county and year effects. With 92 counties and 11 yr, we have 1,012 observations. The equation to be estimated is

[rooms.sub.it] = [x.sub.it][beta] + [[alpha].sub.i] + [[delta].sub.t] + [[epsilon].sub.it].

Here [rooms.sub.it] is the number of hotel rooms in county i in year t. The row vector [x.sub.it] contains the values of the independent variables for county i at time t, and [beta] is a vector of parameters to be estimated. I use a dummy variable for the year of casino opening, 5 yr of lag dummy variables indicating year after casino opening, population, population squared, population density, and real per capita income, and various other demographic variables as independent variables to explain the number of hotel rooms in each county. (See below for data sources.) The constants [[alpha].sub.i] capture unobserved influences of county i on hotel rooms that do not vary over the time period of the sample. The constants [[delta].sub.t], capture unobserved effects of year t on hotel rooms that are the same for all counties. The term [[epsilon].sub.it] is the unobserved random error.

The direction of causality between casinos and hotels raises an econometric issue here if the State of Indiana granted casino licenses to localities because they had a large number of hotel rooms. The recent French Lick Resort Casino, seems to illustrate such an occurrence. It was constructed in French Lick, which for many years has been the site of two large resort hotels. This casino, however, opened in 2006, which is outside the range of this study. As for the remaining counties, the Riverboat Gaming Act originally envisioned all of the Indiana casinos to be on riverboats, so that all of Indiana's counties bordering Lake Michigan or the Ohio River (or Patoka Lake, discussed above) were eligible for casinos and no other counties were eligible (IGC, 1994). Waterside location had little to do with the number of hotel rooms, and indeed the counties in the Indianapolis area with the state's largest number of hotel rooms were not eligible for casinos. Other relatively large hotel markets (including South Bend, Fort Wayne, Lafayette, Richmond, and Terre Haute) were also not eligible. Thus, the state did not generally locate casinos in areas having the most hotel rooms.

Table 1 shows the results of estimating the parameters of the lodging rooms equation using all 92 counties for all 11 yr. Table Al shows the average values of the dependent and independent variables. The figures in parentheses are ratios of estimated coefficients to estimated standard errors. The results confirm the conjecture that hotel rooms follow casinos with a lag. Lags 3, 4, and 5 have statistically significant, positive coefficients. The values peak 4 yr after the casino's opening, indicating an increase of 241 rooms in the new casino's county. Other variables are also positive and highly statistically significant, including population and its square, population density, and real per capita income. The "within" [R.sup.2] goodness-of-fit measure used here, and throughout the paper, shows that variation over time in the explanatory variables within each county explains almost 75% of the variation in rooms.

We turn now to our main concern, casinos and crime.

V. RESULTS FOR CASINOS AND CRIME

To investigate the link between casinos and crime, I begin by adopting the basic model and method used by G&M. That is, I explain differing crime rates across counties and across time using a linear regression model including fixed effects for both county and year. Equation (2) shows the model:

[offenses.sub.it] = [x.sub.it][beta] + [[alpha].sub.i] + [[delta].sub.t] + [[epsilon].sub.it]

The dependent variable [offenses.sub.it] is the rate of offenses per 100,000 population in one of the six categories in county i in year t. The row vector [x.sub.it] contains the values of the independent variables for county i at time t, and [beta] is a vector of parameters to be estimated. The constant [[alpha].sub.i] captures county fixed effects for county i. The constant [[delta].sub.t] captures time fixed effects for year t. The term [[epsilon].sub.it] is the unobserved random error. As dependent variables, I use crime rates for three categories of property crime--larceny, burglary, and motor vehicle theft--and three categories of violent crime--aggravated assault, rape, and robbery. (15)

I include the same independent variables used by G&M, except that they also used a variable measuring the extent to which the county issued permits to carry concealed handguns under "shall issue" requirements. I exclude this variable here because Indiana had a statewide shall-issue requirement over the entire period examined here. (Maltz and Targonski, 2002, p. 314) The explanatory variables in the vector [x.sub.it] include dummy variables for the year in which the first casino opened in the county, dummy variables for 1-yr and 2-yr leads before casino opening and 1-yr through 5-yr lags after casino opening. It also includes control variables that vary across counties and year, including population density, real per capita income, real per capita receipts of income maintenance payments and unemployment compensation, real retirement payments per person over 64yr of age, and percentages of population in various age and race categories. My source for casino opening dates is IGC (various years). My sources for the remaining data are U.S. Census Bureau and U.S. Department of Commerce sources. (16) I augment the model with three additional variables. First, I add two variables to the G&M specification: the number of hotel rooms in the county and the turnstile measure of casino activity. Later, I will add a deterrence variable, which will also require changing to instrumental variables estimation.

A. An Aside on the Impacts of the Defective Data

We will spend little time looking at the crime equations for the full sample because as discussed above, the dependent variable is defective for about one-half of the sample. We can, however, examine the impact of the defective crime rate data on some of the results. Table 2 shows the estimated parameters for the hotel room and casino variables from Equation (2) for each category of crimes for both the full and reduced samples. For each crime category, the first column shows results using the reduced sample of 497 observations, and the second column shows the results for the same specification using the full sample of 1,012 observations. In all cases, the estimated equations include county and year fixed effects. Many of the results are qualitatively similar in the full and reduced samples. (The crime data for Indiana are not especially low in quality, as they are for Illinois, for example.) There are, however, some important differences, especially for the burglary equation. For burglary, we see that eliminating the bad data reduces the coefficients of rooms and turnstile by half and eliminates their statistical significance. It has the opposite effect on the 3-yr and 4-yr lag variables, doubling the coefficients and making them statistically significant. Thus, moving from the complete sample to the reduced sample changes some of the results. We now turn to analysis of the results and some additional specifications using the reduced sample.

B. Results and Analysis Using the Reduced Sample

The results for the reduced sample in Table 2 show that increased casino activity (turnstile) reduces all crime rates, although the negative coefficient is not statistically significant for burglary or rape. The coefficients on the casino lead and lag variables are generally insignificant, except for aggravated assault where they are large and negative, burglary where they are positive and at the borderline of significance at Lag 3 and positive and significant at Lag 4, motor vehicle theft where they are negative at the casino opening and at the 2-yr lead, and robbery where the coefficient on casino opening is negative. The coefficient on the hotel rooms variable is also always negative, but it is statistically significant only for larceny, motor vehicle theft, and at the borderline of significance for rape. Before interpreting these results further, we examine one more important issue--allowing for deterrence in determining crime rates.

The economic model of crime suggests that increasing the opportunity cost of crime reduces the crime rate. The specification used up to this point leaves out components of the opportunity cost of crime associated with the probability of arrest and conviction, often known as deterrence. Many of the papers cited above that apply the economic model of crime try to measure the impact of deterrence variables on crime rates. Some researchers (e.g., Gould, Weinberg, and Mustard, 2002; Levitt, 1998) use arrest rates, while others use measures of enforcement effort such as the number of police (e.g., Levitt, 2002). While the results in the deterrence literature are mixed, researchers have found that deterrence variables can be important in crime equations.

Introducing deterrence variables to the crime equation leads to important potential sources of bias in estimating the crime equation's parameters. There are two main issues. The first is spurious correlation resulting from measurement error in the number of offenses (Gould, Weinberg, and Mustard, 2002; Levitt, 1998). If the dependent variable is the crime rate (offenses/ population) and we use the arrest rate (arrests/ offenses) as an explanatory variable, then the number of offenses is both the numerator of the dependent variable and the denominator of this right-hand-side variable. Any measurement error in the number of offenses will appear on both sides of the equation, creating a negative bias in the estimated coefficient of the arrest rate. Gould, Weinberg, and Mustard (2002, p. 51) call this "division bias." As we have seen, in this data set, measurement error in the number of offenses is an important problem.

The second source of bias arises from the fact that that we can expect that measures of deterrence will be jointly determined with crime rates. That is, a rise in local crime rates will almost certainly cause local officials to increase crime deterrence efforts, such as hiring more police. This creates a simultaneous equations bias in OLS estimates of the effects of deterrence variables in the crime equation.

One could potentially deal with measurement error biases and simultaneous equations bias using instrumental variables (IV). This requires having at least one instrumental variable that is correlated with the arrest rate or other deterrence variable but is uncorrelated with the error in the crime equation, [[epsilon].sub.it] in Equation (2). Levitt (1997) used local election cycles for a wide range of U.S. cities as instruments, arguing that the timing of local elections may affect the hiring of new police but the timing of local elections is not related to local crime. Using local election cycles as instruments will not work when we, unlike Levitt, are dealing with only one state. Indiana has most municipal elections on the same 4-yr cycle, so that there will be no variation in timing of elections across counties. With no cross-section variation in election timing, we cannot use election cycles as an instrumental variable in a panel equation.

Here we will use the aggregate arrest rate for the four categories of violent crime (aggravated assault, robbery, rape, and murder) as the measure of deterrence in the property crime equations. Using the violent crime arrest rate to measure deterrence in the property crime equations avoids the division bias. Furthermore, for the property crime equations, we have a variable having the properties we seek in an instrument for the violent crime arrest rate: the percentage of local government employment in total county employment. (17) First, we would expect that local government employment would be higher where local deterrence effort is higher. Second, this variable is statistically significant is explaining the violent crime arrest rate. (18) Third, this variable is not significant in explaining any of the property crime rates. (19)

Unfortunately, we cannot make the analogous argument for using the property crime arrest rate as the measure of deterrence in the violent crime equations. The problem here is that the percentage of local government employment in total county employment is significant in explaining violent crime rates. This correlation of the proposed instrument with the dependent variable rules it out as an instrument for the deterrence variable. Therefore, we proceed below with estimates including the deterrence variable for the property crimes only.

Table 3 shows the estimated parameters for the deterrence, hotel room, and casino variables from Equation (2) with various specifications of the right-hand side variables [x.sub.it]. (Table A2 shows the estimated parameter values for the other independent variables.) In all cases, the estimated equations include county and year fixed effects. For each category of offense, the first column shows results using OLS, reproduced from Table 2. The second column shows the same equation estimated using OLS with the violent crime arrest rate added. The third column for each crime category shows the equation including the deterrence variable and estimated using instrumental variables with the percentage of local government employment in total county employment as the instrument.

Looking at the center columns, for each category of property crime (OLS) the deterrence variable has a negative coefficient; but it is only at the borderline of statistical significance in a one-tailed test for burglary and motor vehicle theft. The statistical significance of deterrence disappears in the instrumental variable estimation, shown for each category of property crime in the columns on the right. Adding the deterrence variable has little effect on the estimated parameters and ratios of parameters to standard errors of the casino timing, turnstile, and rooms variables.

We can examine the patterns of coefficients on lead, casino opening, lag variables, and turnstile in Table 3 to look for impacts of new casinos on crime. The patterns are the same in all three specifications. There are no significant effects of the timing of casino openings on rates of larceny, although the coefficients on all of the lag variables are positive. (The five lag variables are not jointly significantly different from 0 in any of the three specifications.) The coefficient on the turnstile variable shows a large and significant negative effect of casino activity on larceny. The coefficient on the turnstile variable shows a significant and negative effect of casino activity on motor vehicle thefts. The casino opening and 2-yr lead variables are negative and statistically significant in explaining motor vehicle thefts, while the lag coefficients are small, insignificant, and vary in sign. (Again, the five lag variables are not jointly significantly different from 0 in any of the three specifications.) The burglary equations show a different result. For burglary, the coefficient on the turnstile variable is small and not statistically significant. The coefficients for the 3-yr lag are positive and near the borderline for significance and for the 4-yr lag are positive and significant, suggesting that increased burglary rates follow new casinos. The lags at years 3 and 4 are jointly significant at the .95 level for all three specifications. For the first OLS burglary specification, all five lag variables are also jointly significant at the .95 level (F = 2.32; critical value 2.21), while the five lag variables are at the borderline of significance in the second and third specifications.

VI. IMPLICATIONS OF THE RESULTS

These results suggest that introducing a new casino increases local burglary rates after a lag of a few years. Otherwise, contrary to the results in G&M and E&T, these results do not show that introducing new casinos increases local crime rates. The results suggest, moreover, that increasing casino activity, in this case the turnstile count of casino patrons, leads to reductions in local crime rates. (This effect of casino activity is, however, small and statistically insignificant for burglary.) These results suggest that for some crime categories, new casinos may actually reduce local crime rates. Furthermore, new casinos lead to new hotel room construction, which may lead to additional reductions in some crime rates.

We can use the estimated parameter values and the average of the turnstile variable to calculate an estimate of the quantitative effect on crime rates of the opening of a new casino in an Indiana county. Table 4 shows the averages of the turnstile variable (in thousands) by county, for counties having casinos in the reduced sample, by years since the opening of the county's first casino. In calculating the effects of new casinos, I use only the results for the first column for each crime category from Table 3. I begin with the average crime rate for the counties and years included in the reduced sample and then add the lead or lag coefficient plus the product of the turnstile coefficient and the average turnstile count for the appropriate lag from Table 4. The results show approximate average effects of new casinos on crime rates over time. (I do not attribute to casinos any of the effects of hotel rooms on crime rates.) Figures 1, 2, and 3 show these net effects of the lead, casino open, lag, and turnstile variables on the predicted crime rates for larceny, burglary, and motor vehicle theft, respectively. The figures also show the 95% confidence bounds above and below the estimated values. The net effect of a new casino on larceny rates is small, as the large and significant turnstile effect roughly cancels out the large (but insignificant) lag effects. We can see that county burglary rates rise a few years after opening a new casino because the turnstile effect is too small to cancel out the large effects increasing burglary rates 3 and 4 yr after the casino's opening. The large negative turnstile effect substantially reduces the rate of motor vehicle thefts.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

VII. CONCLUSION

This paper examines the link between casinos and crime using Indiana's counties for the years 1994 through 2004. This paper uses a specification of the equations explaining crime rates which includes the number of hotel rooms in the county and a count of casino patrons as a measure of casino activity. I have also deleted defective crime rate data that have plagued earlier studies. As a first result, I find that construction of new hotel rooms follows the introduction of casinos into the county. These new hotel rooms seem to reduce the levels of larceny and motor vehicle theft. Turning to casinos and crime, I find very limited support for the proposition that new casinos increase local crime rates. Opening new casinos appears to increase the number of burglaries in the county after a lag of a few years. Opening new casinos appears, however, to reduce the number of motor vehicle thefts and aggravated assaults. Increased casino activity, measured using turnstile count of casino patrons, seems to reduce rates of larceny, motor vehicle theft, aggravated assault, and robbery. These results do not match those of earlier studies that show large increases in a broad range of local crime rates after opening new casinos. The results presented here, however, are based on only a small segment of the nation's experience with the casino industry, and this narrow focus limits the applicability of the results. Expanding investigations like this to other states will be limited by the availability of adequate crime data, annual data by county on hotels or other establishments, and consistent measures of casino activity.

Nevertheless, the new results presented here provide guidance for future work on casinos and crime. I introduce a measure of casino activity in addition to variables related to the timing of casino opening. The measure of casino activity, turnstile count of patrons entering the casinos, is often statistically significant and its effect is negative, suggesting that increased casino activity reduces crime rates. It may also suggest that casinos aimed at attracting large numbers of patrons are more likely than small casinos to reduce local crime rates. I also test whether or not the number of hotel rooms affects crime rates. The estimated effect of the number of hotel rooms in the county on crime rates is always negative, but when restricting the sample to observations with relatively complete data, this variable is only significant in the equations for larcenies and motor vehicle theft. The statistical significance of the turnstile variable suggests that leaving out a measure of casino activity when estimating the effect of casinos on crime is a serious specification error. The significance of the hotel room variable in equations for some crime rates suggests that a measure of the level of other public accommodations in addition to casinos may be necessary to avoid specification error.

These results provide some support for the economic model of crime--the statistically significant negative effect of the turnstile measure of casino activity may suggest that increasing casino activity increases the opportunity cost of crime by providing increased legitimate employment opportunities.

Finally, researchers need to exercise care in using county-level Uniform Crime Reports data to study links between casinos and crime. In all cases, the UCR's coded number of crimes at the county level is a number, but in many cases it is not a reported number of crimes.

REFERENCES

AAA Publishing. AAA TourBook. Heathrow. FL: AAA Publishing, 1995-2005 annual editions.

Albanese, J. "The Effect of Casino Gambling on Crime." Federal Probation, 48, 1985. 39-44.

American Gaming Association. "States with Gaming." 2009, www.americangaming.org/Industry/factsheets/general_info_detail.cfv?id=15. Accessed May 20, 2009.

Association of Indiana Convention and Visitors Bureaus. "CVB Directory." 2007, www.aicvb.org/cvbdirectory.html.

Becker, G. S. "Crime and Punishment: An Economic Approach." Journal of Political Economy, 76, 1968, 169-217.

Bowes, D. R., and K. R. Ihlanfeldt. "Identifying the Impacts of Rail Transit Stations on Residential Property Values." Journal of Urban Economics, 50, 2001, 1-25.

Cornwell, C., and W. N. Trumbull. "Estimating the Economic Model of Crime with Panel Data." Review of Economics and Statistics, 76, 1994, 360-66.

Ehrlich, I. "Participation in Illegitimate Activities: A Theoretical and Empirical Investigation." Journal of Political Economy, 81, 1973, 521-65.

Ehrlich, I. "Crime, Punishment, and the Market for Offenses." Journal of Economic Perspectives, 10, 1996, 43-67.

Evans, W. N., and J. H. Topoleski. "The Social and Economic Impact of Native American Casinos." NBER Working Paper 9198, 2002.

Glaeser, E. L, B. Sacerdote, and J. A. Scheinkman. "Crime and Social Interactions." Quarterly Journal of Economics, 111, 1996, 507-48.

Gould, E. D., B. A Weinberg, and D. B Mustard. "Crime Rates and Local Labor Market Opportunities in the United States: 1979-1997." Review of Economics and Statistics, 84, 2002, 45-61.

Grinols, E. L., and D. B. Mustard. "Casinos, Crime, and Community Costs." Review of Economics and Statistics, 88, 2006, 28-45.

Grinols, E. L., and D. B. Mustard. "Correctly Critiquing Casino-Crime Causality." Econ Journal Watch, 5, 2008, 21-31.

Hotel-Guides.us. 2007. Hotel-Guides.us/indiana/in-hotels.html.

Ihlanfeldt, K. "Neighborhood Crime and Young Males' Job Opportunity." Journal of Law and Economics, 49, 2006, 249-83.

IGC. "Annual Report to the Governor, 1993" 1994. www.in.gov/igc/files/CY1993-Annual.pdf. Accessed May 20, 2009.

IGC. "Annual Report to the Governor." 2003. www.in.gov/igc/files/annual/FY2003-Annual.pdf.

IGC. "Monthly Revenue Reports." 1996-2004, www.in.gov/igc/2363.htm.

IGC. "Casino Evaluations." (Various years). WWW.in.gov/igc/2361.htm.

Iowa Racing and Gaming Commission. [No date]. "Chronology Of The Iowa Racing And Gaming Commission." www.iowa.gov/irgc/Chronology.htm. Accessed May 20, 2009.

Klacik, D., L. Littlepage, and S. Payton. "Five-Year License Renewal: Majestic Star Casino, LLC." 2001. www.in.gov/igc/files/majestic-5.pdf.

Klacik, D., L. Littlepage, S. Payton, L. DeBoer, and C. Sullivan. "Eight-Year License Renewal: Aztar Indiana Gaming Corporation." 2003. www.in.gov/igc/files/aztar-8.pdf.

Levitt, S. D. "Using Electoral Cycles in Police Hiring to Estimate the Effects of Police on Crime." American Economic Review, 87, 1997, 270-90.

--. "Why Do Increased Arrest Rates Appear to Reduce Crime: Deterrence, Incapacitation, or Measurement Error." Economic Inquiry, 36, 1998, 353-72.

--. "Using Electoral Cycles in Police Hiring to Estimate the Effects of Police on Crime: Reply." American Economic Review, 92. 2002, 1244-50.

--. "Understanding Why Crime Fell in the 1990s: Four Factors that Explain the Decline and Six that Do Not." Journal of Economic Perspectives, 18, 2004, 163-90.

Maltz, M. D. "Analysis of Missingness in UCR Crime Data." 2006, http://www.ncjrs.gov/pdffiles1/nij/grants/215343.pdf.

Maltz, M. D, and J. Targonski. "A Note on the Use of County-Level UCR Data." Journal of Quantitative Criminology, 18, 2002, 297-318.

Mapquest.com.

Mississippi Gaming Commission. "History of Licensure for Operating Casinos." 2009 www.mgc.state.ms.us/pdf/historyofmgclicensees.pdf. Accessed May 20, 2009.

Mobil Travel Guide, Great Lakes. New York: Fodor's Travel Publications (1995, 1996, 1997, 1998, 1999); Lincolnwood, IL: Publications International (2000, 2001, 2002); Park Ridge, IL: EMTG, LLC (2002, 2004, 2005).

Raphael, S., and R. Winter-Ebmer. "Identifying the Effect of Unemployment on Crime." Journal of Law and Economics, 44, 2001, 259-83.

Sjoquist, D. L. "Property Crime and Economic Behavior: Some Empirical Results." American Economic Review, 63, 1973, 439-46.

State of Indiana. "City/Town Locator." 2007. www.in.gov/mylocal/.

Stitt, B. G., M. Nichols, and D. Giacopassi, "Does the Presence of Casinos Increase Crime? An Examination of Casino and Control Communities." Crime & Delinquency, 49, 2003, 253-84.

Stokowski, P. "Crime Patterns and Gaming Development in Rural Colorado." Journal of Travel Research, 34, 1996, 63-69.

TravelPost.com.

Triprewards.com.

Trump Entertainment Resorts, Inc. 2005, "Form 10-K." period ending December 31, 2004.

U.S. Department of Justice, Federal Bureau of Investigation. Uniform Crime Reporting Program Data [United States]: County-Level Detailed Arrest And Offense Data, 1994-2004 [Computer files]. 2nd ICPSR edition. Ann Arbor. MI: Interuniversity Consortium for Political and Social Research [producer and distributor], various years.

U.S. Department of Justice, Federal Bureau of Investigation. "Codebook," in Uniform Crime Reporting Program Data [United States]: County-Level Detailed Arrest And Offense Data, 2004 [Computer files]. Ann Arbor. MI: Interuniversity Consortium for Political and Social Research [producer and distributor], 2006, ICPSR04466-v1.

Walker, D. M. "Do Casinos Really Cause Crime?" Econ Journal Watch, 5, 2008, 4-20.

Wilson, J. M. "Riverboat Gambling and Crime in Indiana: An Empirical Investigation." Crime & Delinquency, 47, 2001, 610-40.

(1.) Albanese (1985), Stitt, Nichols, and Giacopassi (2003), and Stokowski (1996) also study the link between casinos and crime. These studies use small data sets. One feature of these studies, emphasized by Walker (2008), is that they assert that the population at risk of crime in areas having casinos includes casino visitors from outside the local area in addition to the local population. This suggests that using the local population as the denominator in calculating the crime rate overstates crime rates in casino areas. Following G&M, I use local population in calculating crime rates. Wilson (2001) studies crime in two Indiana casino cities using weekly and monthly data extending 1 yr beyond the casino openings.

(2.) U.S. Department of Justice, Federal Bureau of Investigation (various years). Researchers may freely download these UCR crime rate data subject to the restriction that the researchers may not distribute the data to others. On request, the author will provide all of the data used here except for the crime data. For the crime data, the author will provide the link to the ICPSR source of the data and the calculations that convert the raw data to the variables used here.

(3.) See Ehrlich (1996) for a more complete explanation of the economic model of crime. See also Glaeser, Sacerdote, and Scheinkman (1996) for a generalization of the economic model of crime to include social interactions (rather than independent decisions) in explaining criminal behavior.

(4.) As Walker (2008) and others have pointed out, the new casinos and other new public accommodations would increase the local area's population at risk of being involved in crime and thereby increase the number of crimes. This would increase the calculated crime rate if the local population rather than the entire population at risk were included in the denominator of the crime rate.

(5.) See, for example. Walker (2008).

(6.) Walker (2008) discusses problems with G&M's data, including the defects in the UCR crime data and the lack of a measure of casino activity. Grinols and Mustard (2008) responds to these comments.

(7.) The monthly reports arc available beginning 3 mo after Indiana's first casino opened. Here I assume each of the first turnstile counts for that casino was 100,000. The first recorded month's turnstile count was 108.205 for March 1996. I also incorporated two later corrections for monthly turnstile figures for March 2000 and May 2004. Also, using the IGC's monthly revenue reports allows us to construct consistent calendar year totals despite the shift of IGC's annual reporting from calendar year to fiscal year during 2002.

(8.) Trump Entertainment Resorts, Inc. (2005).

(9.) This is notably in contrast to Illinois and Mississippi. which lack adequate crime data to investigate the link between casinos and crime.

(10.) Maltz (2006), Maltz and Targonski (2002). U.S. Department of Justice. Federal Bureau of Investigation (2006).

(11.) Iowa Racing and Gaming Commission (undated).

(12.) Mississippi Gaming Commission (2009).

(13.) U.S. Department of Justice. Federal Bureau of Investigation (2006), p. i.

(14.) Indiana has many small counties. In 1995. it had 15 counties with populations less than 17,000.

(15.) I do not report results for the murder category. The model explained little of the variation in county murder rates, and the casino variables were not statistically significant in the murder equation.

(16.) Population: 1990-1999 Intercensal State and County Characteristics (www.census.gov/popest/archives/est90intercensal/stch-intercensal/stch-intercensal_layout.txt). County Population Estimates by Age, Sex, Race and Hispanic Origin: April 1. 2000 to July 1. 2005 (http://www.census.gov/popest/counties/asrh/files/cc-est-alldata_layout.txt); income, unemployment compensation, income maintenance payments, and retirement payments: U.S. Department of Commerce. Bureau of Economic Analysis, REIS (http://bea.gov/bea/regional/reis); County land area: http://www.fedstats.gov/qf/download/DataSet.txt

(17.) The local government employment data (U.S. Department of Commerce, Bureau of Economic Analysis, REIS) had 14 missing values for counties and years used here. I replaced the missing values with the most recent prior available value.

(18.) Estimating the equation from Table 3 (and Table Al) with the violent crime arrest rate as the dependent variable and the local government employment instrumental variable added as an explanatory variable yields a t-value for the local government employment variable of 2.36.

(19.) Estimating the equation from Table 3 (and Table Al) with each offense rate as dependent variable and the local government employment instrumental variable added as an explanatory variable yields t-values for the local government employment variable ranging between 0.23 and 0.50.

ABBREVIATIONS

E&T: Evans and Topoleski

FBI: Federal Bureau of Investigation

G&M: Grinols and Mustard

ICPSR: Interuniversity Consortium for Political and Social Research

IGC: Indiana Gaming Commission

UCR: Uniform Crime Reporting

WILLIAM S. REECE *

* Without implicating them in any of the results, the author thanks Craig Depken, Earl Grinols, Rich Harrill, Brad Humphreys, Peter Leeson, David Mustard, Santiago Pinto, Russell Sobel, William Trumbull, seminar participants at the Alfred P. Sloan Foundation Travel and Tourism Industry Center at the University of South Carolina, and three anonymous referees for helpful and generous comments on earlier drafts.

Reece: Professor, Department of Economics, College of Business and Economics, West Virginia University, PO Box 6025, Morgantown, WV 26506-6025. Phone (304) 293-4039, Fax (304) 293-5652, E-mail william. reece@mail.wvu.edu

doi: 10.1111/j.1465-7287.2009.00172.x
TABLE 1
Hotel Rooms Regression Results--92 Indiana Counties, 1994-2004

                  Rooms

Casino open  -148.0 * (3.76)
LAG1         -110.3 * (2.80)
LAG2             49.0 (1.24)
LAG3          179.6 * (4.56)
LAG4          241.4 * (6.14)
LAG5          187.0 * (4.43)
Population    -37.3 * (7.23)
Pop squared  0.0612 * (22.01)
Density        9.78 * (5.00)
Real income    42.1 * (5.42)
Male          -71.4 * (3.40)
White          51.0 * (2.01)
Black         189.1 * (7.52)
Pop 10-19     -53.9 * (2.97)
Pop 20-29     -45.0 * (2.88)
Pop 30-39     -92.3 * (3.85)
Pop 40-49       -28.0 (1.60)
Pop 50-64     -89.5 * (5.19)
Pop over 64   -58.4 * (3.12)
Constant       3176.3 (0.95)
[R.sup.2]         .7356
Num obs            1012

Note: t-values are given in parentheses.

* Statistically significant in a two-tailed test at the .95 level.


TABLE 2
Crime Regression Results for Full and Reduced Samples--Property and
Violent Crimes

                              Larceny

Sample             Reduced            Full

Rooms         -0.245 * (2.02)   -0.283 * (2.03)
Turnstile    -0.0845 * (2.61)  -0.0888 * (2.15)
LEAD2           -303.9 (1.48)     -421.5 (1.91)
LEAD1           -141.7 (0.69)     -277.8 (1.23)
Casino open     -248.1 (1.27)     -337.9 (1.56)
LAG1             136.7 (0.76)       76.7 (0.38)
LAG2             102.4 (0.59)       98.7 (0.49)
LAG3             216.9 (1.26)      157.4 (0.78)
LAG4             141.1 (0.82)       85.1 (0.42)
LAG5              58.2 (0.34)       52.3 (0.25)
[R.sup.2]          .164              .094
Num obs            497               1012

                         Burglary

Sample           Reduced            Full

Rooms         -0.046 (0.97)   -0.098 * (2.12)
Turnstile    -0.0146 (1.14)  -0.0287 * (2.12)
LEAD2          30.21 (0.37)      -66.4 (0.91)
LEAD1          -10.2 (0.13)      -92.9 (1.25)
Casino open    -42.1 (0.55)     -112.6 (1.58)
LAG1           -9.21 (0.13)      -44.2 (0.66)
LAG2           -70.5 (1.02)      -60.9 (0.92)
LAG3           131.8 (1.94)       71.6 (1.08)
LAG4         146.3 * (2.16)       74.1 (1.12)
LAG5            84.0 (1.26)       61.7 (0.88)
[R.sup.2]        .118              .071
Num obs          497               1012

                     Motor Vehicle Theft

Sample            Reduced             Full

Rooms         -0.049 * (2.33)   -0.053 * (2.91)
Turnstile    -0.0444 * (7.87)  -0.0432 * (7.99)
LEAD2          -78.4 * (2.19)    -70.7 * (2.44)
LEAD1            -49.4 (1.39)      -56.2 (1.89)
Casino open    -71.4 * (2.11)    -63.0 * (2.22)
LAG1              45.2 (1.45)       41.9 (1.58)
LAG2              -6.0 (0.20)        7.2 (0.27)
LAG3               4.3 (0.14)       16.2 (0.61)
LAG4             -11.6 (0.39)      -3.81 (0.14)
LAG5              -6.8 (0.23)       2.22 (0.08)
[R.sup.2]          .286              .143
Num obs            497               1012

                           Assault

Sample             Reduced             Full

Rooms           -0.063 (1.58)     -0.024 (0.83)
Turnstile    -0.0436 * (4.10)  -0.0460 * (5.41)
LEAD2         -156.0 * (2.31)   -106.5 * (2.34)
LEAD1         -194.8 * (2.90)   -135.4 * (2.90)
Casino open   -128.9 * (2.01)      -59.0 (1.32)
LAG1          -162.0 * (2.74)      -65.9 (1.58)
LAG2          -165.0 * (2.87)      -68.6 (1.65)
LAG3          -152.6 * (2.69)    -89.3 * (2.15)
LAG4          -147.5 * (2.61)   -102.0 * (2.46)
LAG5             -79.9 (1.44)      -57.5 (1.31)
[R.sup.2]          .319              .182
Num obs            497               1012

                          Robbery

Sample             Reduced            Full

Rooms          -0.0063 (0.71)    -0.0087 (1.32)
Turnstile    -0.0110 * (4.63)  -0.0113 * (5.83)
LEAD2            -8.65 (0.57)      -8.21 (0.79)
LEAD1            -2.45 (0.16)      -5.16 (0.48)
Casino open    -28.3 * (1.99)      -19.8 (1.94)
LAG1              9.98 (0.76)       13.3 (1.39)
LAG2             -2.34 (0.18)       0.76 (0.08)
LAG3             -2.03 (0.16)       2.86 (0.30)
LAG4             -6.05 (0.48)      -3.10 (0.33)
LAG5             -6.78 (0.55)      -2.57 (0.26)
[R.sup.2]          .182              .104
Num obs            497               1012

                            Rape

Sample             Reduced            Full

Rooms        -0.0067 * (1.96)  -0.0062 * (2.07)
Turnstile      -0.0017 (1.91)  -0.0019 * (2.12)
LEAD2            -4.48 (0.78)      -7.04 (1.48)
LEAD1            0.421 (0.07)      -2.44 (0.50)
Casino open       1.23 (0.23)      -2.84 (0.61)
LAG1              2.87 (0.57)      -1.99 (0.46)
LAG2              1.55 (0.31)       0.63 (0.14)
LAG3             -1.70 (0.35)       1.30 (0.30)
LAG4              5.31 (1.10)       1.61 (0.37)
LAG5              6.50 (1.37)       7.76 (1.69)
[R.sup.2]          .139              .064
Num obs            497               1012

Note: t-values are given in parentheses.
* Statistically significant in a two-tailed test at the .95 level


TABLE 3
Crime Regression Results for Reduced Sample--Property Crimes

                                        Larceny

OLS                     OLS                IV               OLS

Violent arrest                       -14.53 (0.59)     -48.52 (0.23)
rate

Rooms            -0.245 * (2.02)   -0.244 * (2.01)   -0.242 * (1.98)

Turnstile       -0.0845 * (2.61)  -0.0846 * (2.61)  -0.0849 * (2.61)

LEAD2              -303.9 (1.48)     -303.0 (1.47)     -301.0 (1.45)

LEAD1              -141.7 (0.69)     -140.3 (0.68)     -137.0 (0.66)

Casino open        -248.1 (1.27)     -247.4 (1.27)     -245.6 (1.25)

LAG1                136.7 (0.76)      145.8 (0.81)      167.0 (0.75)

LAG2                102.4 (0.59)      105.7 (0.60)      113.3 (0.62)

LAG3                216.9 (1.26)      215.3 (1.25)      211.7 (1.21)

LAG4                141.1 (0.82)      140.6 (0.82)      139.5 (0.81)

LAG5                 58.2 (0.34)       56.5 (0.33)       52.6 (0.31)

[R.sup.2]             .164              .164              .160

Num obs               497               497               497

                                          Burglary

OLS                          OLS             IV             OLS

Violent arrest rate                    -14.76 (1.51)    38.87 (0.45)

Rooms                  -0.046 (0.97)   -0.045 (0.95)   -0.048 (0.97)

Turnstile             -0.0146 (1.14)  -0.0147 (1.15)  -0.0144 (1.08)

LEAD2                    30.2 (0.37)     31.1 (0.38)     27.9 (0.33)

LEAD1                   -10.2 (0.13)    -8.81 (0.11)    -14.0 (0.17)

Casino open             -42.1 (0.55)    -41.4 (0.54)    -44.1 (0.55)

LAG1                    -9.21 (0.13)   -0.011 (0.00)    -33.4 (0.37)

LAG2                    -70.5 (1.02)    -67.2 (0.97)    -79.2 (1.07)

LAG3                    131.8 (1.94)    130.2 (1.92)    136.0 (1.91)

LAG4                  146.3 * (2.16)  145.8 * (2.15)  147.6 * (2.10)

LAG5                     84.0 (1.26)     82.3 (1.24)     88.5 (1.27)

[R.sup.2]                 .118            .123            .056

Num obs                   497             497             497

                                 Motor Vehicle Theft

OLS                     OLS                IV

Violent arrest                        -6.98 (1.62)     -13.04 (0.36)
rate

Rooms            -0.049 * (2.33)   -0.049 * (2.31)   -0.048 * (2.28)

Turnstile       -0.0444 * (7.87)  -0.0444 * (7.90)  -0.0445 * (7.88)

LEAD2             -78.4 * (2.19)    -78.0 * (2.18)    -77.7 * (2.16)

LEAD1               -49.4 (1.39)      -48.7 (1.37)      -48.1 (1.34)

Casino open       -71.4 * (2.11)    -71.1 * (2.10)    -70.8 * (2.08)

LAG1                 45.2 (1.45)       49.6 (1.58)       53.4 (1.38)

LAG2                -6.02 (0.20)      -4.46 (0.15)      -3.10 (0.10)

LAG3                 4.27 (0.14)       3.52 (0.12)       2.86 (0.09)

LAG4                -11.6 (0.39)      -11.8 (0.40)      -12.0 (0.40)

LAG5                -6.80 (0.23)      -7.62 (0.26)      -8.33 (0.28)

[R.sup.2]             .286              .291              .287

Num obs               497               497               497

Note: t-values are given in parentheses.
* Statistically significant in a two-tailed test at the .95 level


TABLE 4 Average Turnstile Count (in Thousands) for Counties Having
Casinos in the Reduced Sample

Year Casino Opens  Lag 1   Lag 2   Lag 3   Lag 4   Lag 5

899.6              2887.1  3447.9  3519.4  3591.2  3542.9


APPENDIX

TABLE A1

Descriptive Statistics

                            Crime Rate Equations

                               Reduced Sample

Variable          Observations  Average     SD      Minimum  Maximum

Turnstile             497        295.00   1324.5     0.00    11429
Rooms                 497        942.63   2232.6     0.00    16202
Larceny               497       1817.07   1073.8    24.23     4685.7
Burglary              497        527.02    298.6     3.01     1417.3
MV theft              497        191.93    185.9     0.00     1225.5
Assault               497        176.84    158.4     5.83     1021.4
Robbery               497         46.63     70.8     0.00      403.7
Rape                  497         18.90     15.2     0.00      118.3
Density               497        254.89    385.7    23.31     2178.6
Male                  497         49.28      0.96   47.24       52.4
White                 497         95.57      5.99   72.35       99.9
Black                 497          3.66      5.73    0.02       26.7
Pop 10-19             497         14.82      0.89   12.71       18.4
Pop 20-29             497         13.24      3.06    9.00       29.6
Pop 30-39             497         14.50      1.41   11.10       19.7
Pop 40-49             497         14.99      1.16   11.17       17.6
Pop 50-64             497         15.54      1.83   10.45       22.7
Pop over 64           497         13.13      1.96    7.38       16.9
Real income           497         14.61      2.18   10.84       25.7
Retirement            497          7.67      0.63    5.32        9.4
Maintenance           497        149.38     54.77   41.08      324.3
Unemployment          497         46.19     26.91    9.41      129.1
Viol arrest rate      497          0.75      0.97    0.00        9.50
Local government      497          0.085     0.026   0.043       0.149

                             Crime Rate Equations

                                  Full Sample

Variable          Observations  Average     SD      Minimum   Maximum

Turnstile             1012       164.42    952.9     0.00    11429
Rooms                 1012       569.99   1616.9     0.00    16202
Larceny               1012      1211.68   1102.8     0.00     4685.7
Burglary              1012       341.53    311.8     0.00     1639.9
MV theft              1012       122.53    156.0     0.00     1225.5
Assault               1012       125.54    136.9     0.00     1021.4
Robbery               1012        27.68     55.2     0.00      403.7
Rape                  1012        13.36     14.5     0.00      177.5
Density               1012       173.19    287.7    22.34     2178.6
Male                  1012        49.40      0.92   47.24       54.0
White                 1012        97.05      4.64   72.35       99.9
Black                 1012         2.33      4.39    0.00       26.7
Pop 10-19             1012        14.84      0.86   12.71       18.4
Pop 20-29             1012        12.80      2.56    9.00       29.6
Pop 30-39             1012        14.63      1.33   11.10       19.9
Pop 40-49             1012        15.03      1.08   11.17       17.9
Pop 50-64             1012        15.58      1.69   10.41       22.7
Pop over 64           1012        13.35      1.80    7.38       16.9
Real income           1012        14.18      2.17    9.73       25.7
Retirement            1012         7.55      0.63    5.30        9.4
Maintenance           1012       142.16     50.96   41.08      324.3
Unemployment          1012        45.27     26.98    9.41      159.4
Viol arrest rate      1012         1.80      3.69    0.00       58.0
Local government      1012         0.091     0.026   0.043       0.187

                             Rooms Equation

Variable     Observations  Average      SD     Minimum   Maximum

Rooms            1012        569.99   1616.9     0.00    16202
Population       1012         65.56    108.7     5.49      863.29
Popsq            1012      16098     79949      30.2    745268
Density          1012        173.19    287.7    22.34     2178.65
Male             1012         49.40      0.92   47.24       53.95
White            1012         97.05      4.64   72.35       99.91
Black            1012          2.33      4.39    0.00       26.71
Pop 10-19        1012         14.84      0.86   12.71       18.44
Pop 20-29        1012         12.80      2.56    9.00       29.59
Pop 30-39        1012         14.63      1.33   11.10       19.95
Pop 40-49        1012         15.03      1.08   11.17       17.91
Pop 50-64        1012         15.58      1.69   10.41       22.69
Pop over 64      1012         13.35      1.80    7.38       16.87
Real income      1012         14.18      2.17    9.73       25.70

TABLE A2

Crime Regression Results for the Reduced Sample--Property Crimes

                                 Larceny

                      OLS             OLS              IV

Density         -0.155 (0.06)    -0.144 (0.06)    -0.119 (0.05)
Male          -465.2 * (2.89)  -468.5 * (2.91)  -476.2 * (2.83)
White         -370.3 * (2.62)  -372.5 * (2.63)  -377.5 * (2.60)
Black         -285.2 * (2.04)  -285.8 * (2.04)  -287.2 * (2.04)
Real income      -1.18 (0.03)    -0.805 (0.02)     0.080 (0.00)
Retirement       -9.51 (0.08)     -8.72 (0.07)     -6.87 (0.06)
Maintenance       1.40 (0.54)      1.45 (0.56)      1.55 (0.58)
Unemployment     1.761 (0.80)     1.862 (0.85)     2.099 (0.79)
Pop 10-19     -354.7 * (3.27)  -354.6 * (3.27)  -354.5 * (3.26)
Pop 20-19     -356.9 * (3.63)  -355.5 * (3.61)  -352.3 * (3.50)
Pop 30-19       -250.0 (1.59)    -247.5 (1.57)    -241.6 (1.49)
Pop 40-19       -115.5 (1.08)    -112.6 (1.05)    -105.6 (0.92)
Pop 50-64       -179.8 (1.64)    -179.5 (1.64)    -179.0 (1.63)
Pop over 64     -209.0 (1.81)    -209.2 (1.81)    -209.8 (1.81)
Constant         82231 (3.98)     82484 (3.99)     83078 (3.95)
Num obs             497              497              497

                                 Burglary

                    OLS             OLS             IV

Density         0.840 (0.84)    0.851 (0.86)    0.811 (0.78)
Male            107.4 (1.69)    104.0 (1.64)    116.2 (1.69)
White            6.77 (0.12)     4.60 (0.08)     12.5 (0.21)
Black            13.4 (0.24)     12.8 (0.23)     15.0 (0.26)
Real income    -0.693 (0.04)   -0.309 (0.02)   -1.704 (0.10)
Retirement       68.9 (1.41)     69.7 (1.43)     66.8 (1.31)
Maintenance    -0.602 (0.59)   -0.559 (0.55)   -0.715 (0.66)
Unemployment    0.485 (0.56)    0.588 (0.68)    0.214 (0.20)
Pop 10-19     -88.0 * (2.06)  -87.9 * (2.06)  -88.1 * (1.99)
Pop 20-19       -24.9 (0.64)    -23.5 (0.61)    -28.6 (0.70)
Pop 30-19       -52.8 (0.85)    -50.2 (0.81)    -59.5 (0.90)
Pop 40-19        12.5 (0.30)     15.6 (0.37)     4.59 (0.10)
Pop 50-64       -4.53 (0.10)    -4.29 (0.10)    -5.15 (0.11)
Pop over 64      29.1 (0.64)    28.79 (0.63)     29.8 (0.63)
Constant        -3997 (0.49)    -3740 (0.46)    -4676 (0.54)
Num obs            497             497             497

                            Motor Vehicle Theft

                    OLS             OLS               IV

Density         0.065 (0.15)    0.070 (0.16)     0.075 (0.17)
Male             26.2 (0.94)     24.6 (0.88)      23.3 (0.80)
White            31.6 (1.28)     30.6 (1.24)      29.7 (1.18)
Black            37.4 (1.53)     37.1 (1.52)      36.8 (1.51)
Real income   19.16 * (2.71)  19.34 * (2.74)   19.50 * (2.73)
Retirement       21.4 (0.99)     21.8 (1.01)      22.1 (1.02)
Maintenance    -0.591 (1.31)   -0.571 (1.27)    -0.553 (1.19)
Unemployment    0.119 (0.31)    0.168 (0.44)     0.210 (0.46)
Pop 10-19      -10.88 (0.58)   -10.85 (0.58)    -10.83 (0.57)
Pop 20-19        8.62 (0.50)     9.28 (0.54)      9.86 (0.56)
Pop 30-19       18.40 (0.67)    19.61 (0.72)     20.66 (0.73)
Pop 40-19     37.44 * (2.02)  38.86 * (2.10)  -40.10 * (2.00)
Pop 50-64       10.93 (0.57)    11.05 (0.58)     11.14 (0.58)
Pop over 64     24.79 (1.23)    24.67 (1.23)     24.56 (1.22)
Constant        -5823 (1.62)    -5701 (1.59)     -5595 (1.53)
Num obs            497             497              497

Note: t-values are given in parentheses.
* Statistically significant in a two-tailed test at the .95 level
Gale Copyright:
Copyright 2010 Gale, Cengage Learning. All rights reserved.