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Casino gambling and bankruptcy in new United States casino jurisdictions
Journal of Socio-Economics 29 (2000) 247–261
Casino gambling and bankruptcy in new United States casino jurisdictions夞 Mark W. Nicholsa,*, B. Grant Stittb, David Giacopassic a
Department of Economics/030, University of Nevada, Reno, NV 89557, USA Department of Criminal Justice/214, University of Nevada, Reno, NV 89557, USA c Department of Criminal Justice, University of Memphis, Memphis, TN 38152, USA b
Received 1 May 1999; accepted 1 June 1999
Abstract Using quarterly data on personal consumer bankruptcy for 1989:Q4 through 1998:Q1, this study examines the impact that the introduction of casino gambling has on per capita personal bankruptcy filings. Eight jurisdictions that have recently adopted gambling are compared with a set of matching control jurisdictions, communities without casinos that are economically and demographically similar to the eight communities. The results reveal that casino gambling is associated with an increase in personal bankruptcy in seven of the eight communities. In five of the seven the increase is statistically significant. However, an increase is not universal and in one community, Harrison County, Mississippi (Biloxi), bankruptcy per capita significantly decreased. It is speculated that this decrease is due to the features of both the community and the casino industry in Biloxi. Finally, the most significant changes in bankruptcy occur among Chapter 13, as opposed to Chapter 7, filings. This suggests that a growing portion of insolvents are creating repayment plans for their debts. Policy implications of the findings are discussed. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Bankruptcy; Casino gambling; Control jurisdictions
夞 This project was partially supported by Grant No. 98-IJ-CX-0037 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice. The support of the Graduate School at the University of Nevada, Reno is also acknowledged. * Corresponding author. Tel.: ⫹1-775-784-6850; fax: ⫹1-775-784-4728. E-mail address: [email protected] (M.W. Nichols). 1053-5357/00/$ – see front matter © 2000 Elsevier Science Inc. All rights reserved. PII: S 1 0 5 3 - 0 0 0 0 ( 0 0 ) 0 0 0 7 1 - 8
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Fig. 1. Bankruptcy per capita.
1. Introduction In 1997, personal bankruptcy filings in the United States surpassed 1.3 million, the highest in U.S. history up to that time. This equates to one in 70 families and is a 27% increase over 1996 and a 400% increase over the number of Americans who filed for personal bankruptcy in 1980 (U.S. Department of Commerce, Bureau of the Census, 1998a, 1998b; Anonymous, 1998). What is the cause behind this rapid growth in bankruptcies? Why, given a prospering economy with unemployment at near-record lows, have bankruptcies continued to rise, causing some researchers to refer to the current situation as a crisis (SMR Research, 1997)? Fig. 1 shows bankruptcies per capita in the United States for the years 1975 to 1997. With the exception 1993 through 1995, per capita bankruptcies have skyrocketed since 1985, growing 277% percent by 1997. The rapid rise in bankruptcies has drawn much attention and ultimately resulted in the formation of the National Bankruptcy Review Commission in 1994 (Pub. L. No. 103–394, 108 Stat. 4106) whose charge was to review current bankruptcy law and policy, hear from divergent views on the current bankruptcy system, evaluate various proposals, and make suggestions for change.1 There have been multiple explanations put forth for the rise in personal bankruptcies ranging from readily-available and excessive credit, the growing divorce rate, a deterioration of the stigma attached to bankruptcy, high debt to income ratios, corporate downsizing, inadequate insurance coverage, changing bankruptcy law, and the rapid spread of casino
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gambling. While the cause of rising bankruptcy rates is certainly multidimensional, encompassing many of the above factors and others as well, the purpose of the present study is to determine what role, if any, the spread of casino gambling has played. In the last 10 years (from 1988 to 1998), casino gambling has boomed. Before 1988, only two states (Nevada and New Jersey) had legalized casino gambling; today, ten states have legalized casino gambling (Gazel, 1998). When the 31 states that have Native American casinos are included in the count, the overwhelming majority of states have some form of casino gambling (Christiansen, 1998). Casino gross revenue (customer spending and gaming losses) for 1996 exceeded $19 billion in the United States. This amount is equal to about 10% of all leisure expenditures (Christiansen, 1998). This rapid spread of casino gambling has been extremely controversial. Anticasino forces focus on the problems experienced by problem gamblers and argue that casinos are bad for communities, leading to higher rates of a variety of social problems including bankruptcy, divorce, suicide and crime (e.g., Tuttle, 1996). Although personal testimonies provide a powerful account of the problems some individuals experience as a result of becoming compulsive gamblers, little empirical evidence is available to substantiate the claim that the presence of casinos leads to a higher rate of bankruptcy in a community. The countervailing argument made by casino supporters is that while a small proportion of the community may engage in problem gambling and suffer severe economic consequences, the jobs created by the casinos themselves (341,000 workers in non-Indian casinos in 1996) provide economic stability for large numbers of relatively low skilled workers ((Thompson et al., 1997; Christiansen, 1998). Although most of the casino jobs are low to moderate in wage scale, the casino workers generally are provided company health benefits that are often unavailable in comparable jobs in the lodging or entertainment industries. In addition, the economic benefits that accrue to companies that supply casinos and their patrons with the broad range of goods and services needed for the casinos to operate also contributes greatly to the economic well-being of large numbers of community residents. In addition to the plausible arguments on each side of the question (of how casinos affect the bankruptcy rate in a community), bankruptcy data that appear in the press are equally confounding. For example, an article in U.S.A. Today (March 18, 1996) reported that Tennessee, a state with no legalized gambling and an unemployment rate well below the national average, had the highest bankruptcy rate in the nation. Connecticut, a state with several types of legalized gambling available (casinos, lottery, off-track betting, Jai Alai) and an unemployment rate higher than average, had a bankruptcy rate approximately one-third that of Tennessee. Clearly, the relationship between casinos and bankruptcy is complicated and in need of studies that apply rigorous methodology. The current study analyzes bankruptcy rates per capita in a group of communities that have casinos and compares them to a control group of communities that do not have casinos. Section 2 of this article briefly reviews the literature on bankruptcy. Section 3 describes the selection of the communities, the control groups, and the basic empirical methodology. Section 4 presents results, and section 5 concludes the article.
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2. Review of the bankruptcy literature Contrary to much popular opinion, the most common causes of bankruptcy do not involve a profligate lifestyle and extravagant spending habits that go well beyond the average citizen’s means. According to the Consumer Federation of America, those who most commonly file for bankruptcy are from the lower middle class and are individuals who experience some unforeseen financial hardship, such as loss of job, divorce, or medical expense (Burke, 1998). As noted in Shephard (1984a), “extensive survey work by consumer economists has yielded a picture of the ‘typical’ bankruptcy petitioner as a head of household who is a high school graduate employed as a blue collar worker falling into the lower middle class of income workers. Petitioners are more apt to be unemployed, recently divorced, members of racial minorities and heavy users of credit” (p. 217). Consistent with this description of the typical bankruptcy petitioner, a study by the Congressional Budget Office reports that bankruptcies tend to rise not during economic downturns, but during economic expansions as people become more confident in the future and willing to increase their current levels of expenditures (Clark, 1997). When these individuals experience an economic setback, they find themselves in an unexpected economic bind, and bankruptcy becomes the tool many use to extricate themselves from what appears to be an otherwise unsolvable problem. In addition, Eckstein and Sinai (1986) note the presence of a financial cycle operating within the business cycle. Near the peak of an economic expansion, the demand for credit expands beyond the ability of households to finance their increasing commitments with current income. At some point, however, the demand for credit exceeds the supply and rising interest rates and credit rationing become widespread. At this point, note Eckstein and Sinai, “. . . households, businesses, and financial institutions find that the expectations upon which spending plans are based become falsified as balance sheets deteriorate, with loan repayments, debt burdens, and cost of financing all becoming surprisingly burdensome” (p. 53). Bankruptcy is one alternative to alleviate this burden. Other explanations attribute the increasing rate of bankruptcy in the United States to a growing “bankruptcy culture”. This perspective holds that the social stigma formerly attached to declaring bankruptcy has diminished so that individuals view bankruptcy as a normal, legal, and viable alternative to economic exigency. Most frequently, the money is owed to large and assumedly prosperous businesses (banks, credit card companies) that are seen as bureaucratic entities with little personal connection to the individual; the individual, therefore, feels little compunction if the businesses go unpaid. In addition, for someone contemplating filing for bankruptcy, the economic consequences appear to be minimal as some companies, such as credit card companies and auto dealerships, promote themselves as specialists in doing business with individuals who have “bad credit.” Lastly, there are lawyers in every large community who specialize and advertise their expertise in helping individuals declare bankruptcy, thereby stopping repossessions, garnishments, and harassing phone calls (Sullivan, 1988). The “bankruptcy culture” perspective holds that the increase in bankruptcy is due primarily to social and cultural factors apart from any significant changes in spending patterns or lifestyle changes. In addition to the above issues, several researchers have attributed increased bankruptcy
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rates to changes in bankruptcy law. For example, the Bankruptcy Reform Act of 1978 is seen largely as a “prodebtor” piece of legislation because it liberalized exemption levels.2 Shephard (1984b) and Boyes and Faith (1986) find that the 1978 Reform Act was responsible for a doubling of bankruptcy rates between 1978 and 1984. In fact, this rapid increase prompted Congress to reduce exemption levels in 1984 in an attempt to reverse the trend of rising bankruptcies. Support of this legal explanation is not unanimous, however. For example, Domowitz and Eovaldi (1993) show that once demographic and economic factors are considered, the 1978 Reform Act had no statistically significant influence on bankruptcy rates. Similarly, Sullivan et al. (1989) show that several factors, including local legal culture, economic, and social demographic characteristics, contribute to the decision to file for bankruptcy. Federal law is not the only legislation to allegedly influence an individual’s decision to file bankruptcy. States differ in their exemption levels, wage garnishing laws, and protection of assets. For example, according to Sullivan et al. (1989), “Illinois had low exemptions, Texas had high, and Pennsylvania was in the middle, so the effects predicted by the exemptionincentive model should have been directly reflected in the patterns of bankruptcy filings in those three states” (p. 240). What Sullivan et al. find, however, is no significant differences among the three states, suggesting that the decision to file for bankruptcy is driven largely by factors other than the legal environment. The above literature reveals that bankruptcy is a complicated issue influenced by many economic, social, and political factors. Nevertheless, the introduction of casino gambling into a community clearly modifies the spending patterns of many individuals (Gazel, 1998). For most of the individuals in the community, a casino simply provides another source of entertainment that, to varying degrees, may include expenditures on food, drink and live entertainment along with gaming. Studies have shown, however, that a small proportion of casino patrons become problem gamblers and that these are the individuals who are most likely to have financial problems associated with their gambling. The rate of compulsive gambling (lifetime prevalence) ranges from 1% to 3% of the gambling population, with another 3% being “potential pathological gamblers,” that is, individuals who display some of the traits associated with problem gambling and are at risk of becoming pathological gamblers (American Psychiatric Society, 1994; Lesieur and Blume, 1987). Individuals who become compulsive gamblers are likely to experience a great many social and economic problems, not the least of which is a desperate financial condition due to the compulsive behavior of “chasing” one’s losses, trying to win back money to undo past losses (American Psychiatric Society, 1994). Lesieur, in his classic study of compulsive gamblers, found that much of the gamblers’ time and energy are spent in concealing the amount of gambling losses and in the pursuit of additional money to chase the losses (Lesieur, 1977). The compulsive gambler may actually increase his or her gambling in the early stages of the problem gambling to win back the losses. Eventually, however, the individual exhausts all legitimate means of obtaining money for payment of the gambling losses and experiences a “closure state” of tightening or nonexistent resources (Lesieur, 1977). When the compulsive gambler reaches this point, bankruptcy or crime become the end game. Although it is evident that most compulsive gamblers experience severe financial problems, it is not clear how this affects the bankruptcy rate in a community. Some research
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indicates that casinos do increase the bankruptcy rate. For example, research conducted by SMR Research Corp (1997) found that the spread of legalized gambling in 298 counties was positively related to an increase in bankruptcy filings in those counties. SMR notes that the bankruptcy rate was 18% higher in counties with one gambling facility and 35% higher in counties with five or more gambling establishments. In contrast, the National Opinion Research Center (NORC) finds that there is no statistically significant casino effect with regard to bankruptcy or crime, although Type D and E gamblers are more likely than other individuals to have ever declared bankruptcy (NORC, 1999). Lifetime Type D gamblers are defined as those reporting losing $100 in a single day or year and having three or four adverse effects, while type E gamblers have five or more adverse effects. The adverse effects consist of 17 factors such as lying about gambling losses to family and friends, chasing losses, preoccupation with gambling, and conducting illegal acts to pay for gambling. Based on telephone surveys of 2406 randomly selected adults, NORC concludes that approximately 1% of the adult population is a Type D gambler while another 1% is Type E. These results likely explain the lack of any statistically significant casino effect on bankruptcy at the community level; those individuals that are most likely to declare bankruptcy because of problem gambling make up a small portion of the total population.
3. Empirical methodology This study examines personal bankruptcy in eight communities in the United States that recently adopted casino gambling. These eight communities are Sioux City, Iowa; St. Joseph, St. Louis City, and St. Louis County, Missouri; Alton, Peoria, and East Peoria, Illinois; and Biloxi, Mississippi. These communities were chosen based upon several criteria. First, the communities all had gambling for at least four years. Communities with gambling for fewer than four years were not chosen since it was felt that sufficient time should be allowed for social and economic changes to occur. In the above communities, Alton has had gambling the longest, since September 1991, whereas St. Joseph has had it the least amount of time, since June 1994. In addition, the selection of these communities is part of an ongoing study analyzing casino gaming’s impact on crime and quality of life. The above communities were able to provide police data on crime, hence their selection for study. County-level data on personal bankruptcy, including Chapter 7 and Chapter 13 filings, were obtained from SMR Research Corporation. Quarterly observations on the total number of bankruptcies in each county are available for the fourth quarter of 1989 through the first quarter of 1998. Many studies that examine bankruptcy, including those listed above, use jurisdiction-level data provided by the U.S. Office of the Courts. These jurisdictions cross several counties and often cross state lines. The data set used for this study is unique in that it is at the county level. This enables us to obtain a clearer picture of how bankruptcy rates in communities (i.e., counties) change with the introduction of casino gambling. Ideally, we would like to compare bankruptcy rates in the casino communities with the rates that would have occurred had gambling not been legalized. Since this is clearly unknown, bankruptcy rates in a matched set of control communities are used as a proxy. If casinos have no impact on bankruptcy, there should be no difference in bankruptcy rates
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between the casino and control communities over the period of study. On the other hand, significant differences between the two groups would suggest that casinos do impact bankruptcy rates. The control communities are chosen based on their similarity to the casino communities over fifteen demographic, economic, and social variables.3 The fifteen variables chosen are: percent of the population aged 15 to 34; total population; median household income; unemployment rate; percent black; percent Hispanic; percent Indian, Aleut, or Eskimo; percent below poverty for the population where poverty status is known; percent of the population not graduating high school; percent of occupied housing units that are renteroccupied; percent of total housing units in structures with three or more units; net migration; percent urban; average number of persons per square mile; and a GINI coefficient of income inequality. All data are taken from the US Census Bureau’s U.S.A. Counties 1996 CD-ROM and all variables are normalized by converting them to a Z-score relative to the US county average. The selection of control communities is based on k-means cluster analysis (Hartigan and Wong, 1979) and uses programs developed by Judson (1998). The idea is to rank control communities on their proximity to casino communities applying the following metric:
冉冘 k
d共 y, x兲 ⫽
共 yj ⫺ xj 兲
j⫽1
冊
q
1/q
(1)
where yj is the jth variable for the potential control community and xj is the same variable for the casino community. In the present study, q in Eq. (1) equals 2, the usual Euclidian distance. Summing across all k variables, the control communities can be ranked in ascending order of distance from the casino communities. Given the ranking of control communities, we select five that are located a minimum of fifty miles from a casino and have the lowest score, d(y,x), from Eq. (1). Five matching control communities were chosen for each casino community in order to ensure generality of the results. The set of casino communities and their five matching control jurisdiction are provided in Table 1.
4. Empirical results The results from comparing bankruptcy per capita (per 1000 population) for total, Chapter 7, and Chapter 13 bankruptcy petitions are provided in Tables 2 through 4. The tables report average bankruptcies per capita for the period prior to the casino opening (pre-casino) and the period since the casino opened (post-casino). The final column reports the percentage change in per capita bankruptcies and the t-statistic testing whether the change in the casino communities is statistically different than the change in the control jurisdictions.4 Table 2 reveals that in seven of the eight communities bankruptcies per capita increased more in the casino communities than in the control communities. Furthermore, in five of those seven counties the increase is statistically significant. This lends support, albeit not unanimous, to the hypothesis that the introduction of casino gambling leads to an increase in
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Table 1 Casino communities and control jurisdictions Casino community (county)
Control jurisdictions
d(y,x)a
Sioux City, Iowa (Woodbury)
Chemung, New York Black Hawk, Iowa Garfield, Oklahoma Daviess, Kentucky Ohio, West Virginia Trumbull, Ohio Stark, Ohio Clark, Ohio Richland, Ohio Winnebago, Illinois Sebastian, Arkansas Winnebago, Illinois Macon, Illinois Hamilton, Tennessee Lackawanna, Pennsylvania Sheboygan, Wisconsin Rock, Wisconsin Clark, Ohio Miami, Ohio Licking, Ohio Monroe, New York Allegheny, Pennsylvania Erie, New York Salt Lake City, Utah Montgomery, Ohio Richmond City, Virginia Norfolk City, Virginia Portsmouth City, Virginia Newport News, Virginia Roanoke, Virginia Daviess, Kentucky Sebastian, Arkansas Jasper, Missouri Lackawanna, Pennsylvania Garfield, Oklahoma Escambia, Florida Wichita, Texas New Hanover, North Carolina Mclennan, Texas Tuscaloosa, Alabama
1.060 1.060 1.080 1.120 1.330 0.876 0.882 0.905 0.999 1.010 1.220 1.230 1.240 1.340 1.420 0.793 0.806 0.883 0.916 0.967 2.120 2.420 2.500 2.630 2.820 2.870 3.560 3.820 4.400 4.540 0.846 0.850 1.080 1.120 1.250 1.050 1.460 1.520 1.530 1.590
Alton, Illinois (Madison)
Peoria, Illinois (Peoria)
East Peoria, Illinois (Tazewell)
St. Louis County, Missouri (St. Louis)
St. Louis City, Missourib (St. Louis City)
St. Joseph, Missouri (Buchanan)
Biloxi, Mississippi (Harrison)
a b
Distance Metric. See equation (1) in text for definition. St. Louis City is an independent city that is counted as a county equivalent for data collection purposes.
bankruptcy. It is interesting to note that the largest increase, 50.3%, occurred in Madison County, Illinois, where a casino has been located (in Alton) since September of 1991, longer than in any of the other jurisdictions. Moreover, Peoria and Tazewell counties, which also show significant increases, have had casinos in operation for the second longest period of time (November, 1991). They are followed by Harrison County, Mississippi (Biloxi–August
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Table 2 Bankruptcy per capita (per 1000 pop.), casino and control jurisdictions Jurisdictions
Bankruptcy per capita (pre-casino)
Bankruptcy per capita (post-casino)
Percent changea
Sioux City, IA (Woodbury) Control Alton, IL (Madison) Control Peoria, IL (Peoria) Control East Peoria, IL (Tazewell) Control St. Louis City, MO (St. Louis City) Control St. Louis County, MO (St. Louis) Control St. Joseph, MO (Buchanan) Control Biloxi, MS (Harrison) Control
2.6178
3.4718
2.7285 2.7847
3.1898 4.1844
3.6293 3.8791
3.9689 4.9463
4.5918 3.5844
4.8379 4.3333
3.0884 4.1019
3.0674 6.0950
5.2946 3.2017
7.2092 4.3322
3.3306 2.2988
3.6720 2.9388
3.1678 5.7029
3.5955 4.9627
3.4446
3.8423
32.6* (1.85) 16.9 50.3*** (2.59) 9.4 27.5** (2.24) 5.4 20.9** (2.47) ⫺0.6 48.6 (0.18) 36.2 35.3*** (2.76) 10.3 27.8 (1.05) 13.5 ⫺13.0*** (3.27) 11.5
a
Absolute value of the (two sample) t statistic in parentheses. Null hypothesis is that the change in the casino jurisdiction is equal to the change in the control jurisdiction or, equivalently, that the difference in the change in bankruptcies per capita between the casino and control jurisdictions is zero. A*, **, and *** represent significance at the 10%, 5%, and 1% level, respectively.
1992), Woodbury County, Iowa (Sioux City–January 1993), St. Louis City and County, Missouri (May 1994), and Buchanan County, Missouri (St. Joseph–June 1994). Although more evidence would be required to make any firm conclusions, this does suggest a possible link between bankruptcy and the length of time a casino has been in place. Harrison County, Mississippi (Biloxi) is the one exception to the increase in bankruptcy. There, bankruptcies have significantly declined since the introduction of casino gambling. This is noteworthy because of all of the jurisdictions examined, Biloxi is the only one that would qualify as a “destination resort.” Destination resort casinos attract a greater percentage of their clientele from tourists or visitors, effectively exporting gambling. As a result, the economic benefits–job creation, tax revenue, spinoffs to other businesses–will be greater (Eadington, 1998). In this type of environment, the creation of jobs and income may allow people to meet their financial obligations, outweighing any negative effects created by excessive gambling on the part of some individuals.5 Tables 3 and 4 provide results for Chapter 7 and Chapter 13 filings respectively. The distinction is important because Chapter 13 filings involve debt repayment plans as opposed to the nearly complete forgiveness of all admissible debt in Chapter 7 filings. The costs to
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Table 3 Chapter 7 bankruptcy per capita (per 1000 pop.), casino and control jurisdictions Jurisdictions
Bankruptcy per capita (pre-casino)
Bankruptcy per capita (post-casino)
Percent changea
Sioux City, IA (Woodbury) Control Alton, IL (Madison) Control Peoria, IL (Peoria) Control East Peoria, IL (Tazewell) Control St. Louis City, MO (St. Louis City) Control St. Louis County, MO (St. Louis) Control St. Joseph, MO (Buchanan) Control Biloxi, MS (Harrison) Control
2.5265
3.3478
2.4407 2.0946
2.9510 2.7108
2.7385 3.3557
3.0047 4.0119
2.9188 3.3170
3.1278 3.6775
2.4083 2.0021
2.3903 3.0363
4.0288 1.5297
5.0430 2.1137
2.0716 2.1562
2.2921 2.6802
2.9103 3.6669
3.2248 3.3939
1.3258
1.4872
32.5 (1.52) 20.9 29.4 (1.24) 9.7 19.6* (1.81) 7.2 10.9* (1.83) ⫺0.7 51.7 (0.08) 25.2 38.2*** (2.77) 10.6 24.3 (1.14) 10.8 ⫺7.4 (1.70) 12.2
a
Absolute value of the (two sample) t statistic in parentheses. Null hypothesis is that the change in the casino jurisdiction is equal to the change in the control jurisdiction or, equivalently, that the difference in the change in bankruptcies per capita between the casino and control jurisdictions is zero. A*, **, and *** represent significance at the 10%, 5%, and 1% level, respectively.
society and debtors who regularly pay their debts are therefore greater under Chapter 7. If individuals become insolvent due to problem gambling, incurring debt that is beyond hope of being repaid, we expect the predominant increase to be in Chapter 7 filings. Tables 3 and 4 clearly reveal that the most significant increase has occurred with Chapter 13 filings. In all jurisdictions where there is a statistically significant increase in bankruptcy, there is a corresponding significant increase in Chapter 13 filings. On the other hand, Chapter 7 filings significantly increase in only three counties. In Biloxi, a significant decrease in bankruptcy occurs among Chapter 13 but not Chapter 7 filings. While the overall increase in bankruptcies is troubling, the increase in Chapter 13 filings suggests that the proportion involving repayment plans is increasing. The greater increase in Chapter 13 runs counter to our expectations. One explanation for this result is that individuals with gambling problems seek treatment and stop gambling, thereby enabling the individual to repay debts given sufficient time to rebuild finances. Yet another possibility is that bankruptcy courts may not be willing to dismiss gambling debt if it is obtained fraudulently (i.e., the debtor has no intention of paying the creditor). Several decisions reflect the refusal of the court to discharge gambling debts based on fraud (e.g.,
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Table 4 Chapter 13 bankruptcy per capita (per 1000 pop.), casino and control jurisdictions Jurisdictions
Bankruptcy per capita (pre-casino)
Bankruptcy per capita (post-casino)
Percent changea
Sioux City, IA (Woodbury) Control Alton, IL (Madison) Control Peoria, IL (Peoria) Control East Peoria, IL (Tazewell) Control St. Louis City, MO (St. Louis City) Control St. Louis County, MO (St. Louis) Control St. Joseph, MO (Buchanan) Control Biloxi, MS (Harrison) Control
0.0913
0.1240
0.2861 0.6901
0.2388 1.4731
0.8872 0.5235
0.9629 0.9344
1.6701 0.2674
1.7084 0.6558
0.6743 2.0998
0.6763 3.0587
1.2614 1.6707
2.1626 2.2168
1.2527 0.1426
1.3769 0.2586
0.2557 2.0207
0.3695 1.5645
2.1141
2.3518
35.8*** (6.06) ⫺16.5 113.5*** (4.95) 8.5 78.5*** (2.81) 2.3 145.3*** (3.32) 0.3 45.7 (0.26) 71.4 32.7** (2.64) 9.9 81.3 (0.10) 44.5 ⫺22.6*** (6.34) 11.2
a
Absolute value of the (two sample) t statistic in parentheses. Null hypothesis is that the change in the casino jurisdiction is equal to the change in the control jurisdiction or, equivalently, that the difference in the change in bankruptcies per capita between the casino and control jurisdictions is zero. A*, **, and *** represent significance at the 10%, 5%, and 1% level, respectively.
Eashai v. Citibank South Dakota and Citibank South Dakota v. Ardet, cited in Depperschmidt and Kratzke, 1997). If an individual expects gambling and other debts not to be discharged, or is uncertain about the probability of their discharge, filing Chapter 13 may be a rational means of “buying time” to repay creditors.6 Without more detailed information on the individuals and their reason for filing, it is impossible to know for certain why the predominant increase has occurred in Chapter 13 filings. A means of comparing the overall effect that casinos have on gambling is available through the Wilcoxon Rank Sum Test (RST) under the null that the percentage change in per capita bankruptcy rates for casino and control jurisdictions come from the same probability distribution. In constructing the RST, both the casino and control communities are pooled and ranked in ascending order based on the percentage change in bankruptcies. If the two populations are identical, we would expect the rankings to be randomly distributed between the two samples. However, if casinos lower bankruptcy, the rankings of the casino sample should be relatively small, whereas if casinos increase bankruptcy the rankings should be relatively large. Table 5 contains results from the RST. The rankings of the casino and control samples are
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Table 5 Wilcoxon rank sum test for percentage change in per capita bankruptcy filings Casino jurisdictions
Sioux City, IA (Woodbury) Alton, IL (Madison) Peoria, IL (Peoria) East Peoria, IL (Tazewell) St. Louis City, MO St. Louis County, MO St. Joseph, MO (Buchanan) Biloxi, MS (Harrison)
Total
Control—Sioux City Control—Alton Control—Peoria Control—East Peoria Control—St. Louis City Control—St. Louis County Control—St. Joseph Control—Biloxi Rank Sum
Chapter 13
Rank
% change
Rank
% change
Rank
32.6 50.3 27.5 20.9 48.6 35.3 27.8 ⫺13.0
12 16 10 9 15 13 11 1
32.5 29.4 19.6 10.9 51.7 38.2 24.3 ⫺7.4
14 13 9 7 16 15 11 1
35.8 113.5 78.5 145.3 45.7 32.7 81.3 ⫺22.6
9 15 13 16 11 8 14 1
Rank Sum Casino jurisdictions
Chapter 7
% change
87** Total
86* Chapter 7
87** Chapter 13
% change
Rank
% change
Rank
% change
Rank
16.9 9.4 5.4 ⫺0.6 36.2 10.3 13.5 11.5
8 4 3 2 14 5 7 6
20.9 9.7 7.2 ⫺0.7 25.2 10.6 10.8 12.2
10 4 3 2 12 5 6 8
⫺16.5 8.5 2.3 0.3 71.4 9.9 44.5 11.2
2 5 4 3 12 6 10 7
49
50
49
H0: Percentage change in bankruptcies between casino and control communities have the same probability distribution. Ha: Probability distribution for percentage change in bankruptcies for casino communities is shifted higher or lower than probability distribution for control communities. A* and ** represent significance at the 10% and 5% level, respectively.
summed and compared to a lower or upper critical value to determine significance (McClave and Dietrich, 1985, p. 792). At the 5% level of significance, the lower value, TL, equals 49 while the upper value, TU, equals 87. At the 10% level of significance TL ⫽ 52 and TU ⫽ 84. With equal sample sizes, the rank sum for either the casino or control communities can be used. So, if the rank sum of the casino counties is greater than or equal to 87 (or less than or equal to 49), we can say with 95% confidence that the probability distributions for casino and control communities are not identical. As seen in Table 5, the rank sum for casino counties for both total and Chapter 13 bankruptcy filings is 87, whereas the rank sum for Chapter 7 is 86. The probability distributions between casino and control communities are clearly not identical. Furthermore, given the cluster of large rankings, it appears that casino communities are associated with growing rates of bankruptcy. Finally, the analysis in Tables 2 through 5 was repeated when the post-casino date is one year later than the actual opening date. This was done to allow for the possibility that some insolvents may have begun the bankruptcy process prior to the opening of the casino, but not completed it until after the casino opened. If a significant number of bankruptcies were filed
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during this period, our results may be sensitive to the time period chosen to divide pre- and post-casino observations. Of course, lagging the post-casino period also introduces the possibility of counting post-casino bankruptcies as pre-casino bankruptcies. Nevertheless, it provides a conservative estimate of the impact that casino gambling has on bankruptcies and ensures the robustness of our results. In general, lagging the post-casino period does not change our conclusions. Total bankruptcies still increase in seven of the eight jurisdictions. The increase is statistically significant in four of the seven cases (the t statistic for Sioux City falls to 1.34, and is no longer statistically significant). The results for Chapter 7 and Chapter 13 are qualitatively identical. Moreover, repeating the RST for total, Chapter 7 and Chapter 13 bankruptcies resulted in rank sums of 84, 80, and 86. With the exception of Chapter 7, these are all significant at the 10% level. Overall, the results reported in Tables 2 through 5 appear to be robust to the date dividing pre- and post-casino time periods.
5. Conclusion This study compares per capita bankruptcy rates in eight communities that recently adopted casino gambling with a set of economically and demographically similar control jurisdictions. The results reveal that bankruptcy rates increased in seven of the eight communities and decreased in one. Standard two sample t statistics revealed that the increase was statistically significant in five of the seven communities; the decrease in Harrison County, Mississippi was also significant. Finally, the Wilcoxon rank sum test, a nonparametric test for significance, revealed that the casino communities are associated with rising rates of bankruptcy. Interestingly, both tests confirm the more pronounced increase in Chapter 13 bankruptcies as opposed to Chapter 7. While an increase in Chapter 13 relative to Chapter 7 bankruptcy filings is good news to the extent that a greater portion of insolvents are repaying their debt, the shadow of an ever growing number of total bankruptcies still looms. Perhaps the more interesting case for policy makers is Biloxi, Mississippi, where bankruptcy rates significantly declined. Biloxi shows that casino gambling need not inevitably lead to higher bankruptcy rates. More specifically, it supports the theory postulated by Eadington (1998) that the economic benefits associated with casino gambling are likely to be greatest when casinos are built in a destination resort environment as opposed to an urban setting. Of all of the communities included in this study, Biloxi is the only community with multiple casinos and the only one that would be characterized as a destination that tourists would travel to in order to gamble. In this type of environment, the economic benefits from casino gambling are greater and more likely to exceed the social costs. The creation of jobs and income is likely to be much greater when tourist dollars are infused into an economy as opposed to a recirculation of dollars in an urban environment where the majority of casino customers are from the immediate area. The findings above do suggest a positive correlation between casino gambling and consumer bankruptcy. Nevertheless, research into this issue is still in its infancy and more is needed. For example, other communities with casino gambling should be examined to see
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if the results found here generalize. A further investigation between urban and destination resort casinos would also be useful. Lastly, if communities are going to adopt casino gambling, policies to minimize gambling’s impact on bankruptcy should be explored. One policy being considered by states such as Iowa and by the National Gambling Impact Study Commission is the removal of automatic teller machines and credit card cash advance machines from casinos. Opponents of the placement of these machines in casinos argue that they make cash too readily available, causing gamblers, and problem gamblers in particular, to spend more than they originally intended. Proponents argue that the placement of cash machines is merely good business practice and a convenience factor for the large majority of customers without a gambling problem. More analysis of this debate focusing on who withdraws money, how much is withdrawn, what if any problems it creates, and whether it was a significant factor in rising bankruptcies appears warranted. Notes 1. The final report was due to Congress on October 20, 1997. That report was received with criticism by both consumer and creditor interests, and no proposals became law. 2. The 1978 Reform Act was the first major change to the Bankruptcy Code since the turn of the century. Up until 1978, the Bankruptcy Code of 1898 had been in effect and remained largely unchanged with the exception of the 1938 amendments that introduced Chapter 13. The changes made under the 1978 Reform Act are too numerous to go into here. In essence, both the number and level of exemptions were increased. Complicating matters further was the fact that states were allowed to opt out of the federal exemption levels. See Domowitz and Eovaldi (1993) and the references cited therein for more detail. 3. The selection of control communities was performed by D. Judson using the program developed by Judson (1998). 4. Formally, the two sample t statistic tests the null hypothesis that the difference in the change in bankruptcies per capita over time between the casino and control communities is zero. The two sample t statistic is calculated as t⫽{(x1⫺x2)⫺(u1⫺u2)}/w, where x1 and x2 are post- and pre-casino bankruptcies per capita for the casino community and u1 and u2 are per capita bankruptcies for the control jurisdictions over the same period; w equals {s2(1/n1⫹1/n2)}1/2, where s2 is the pooled estimate of variance and n1 and n2 are the number of post- and pre-casino observations in the casino community. 5. While anecdotal, two of the authors spoke with a credit counselor in Biloxi who indicated that the presence of casinos had made her job easier by providing people with income, allowing them to pay their bills. 6. Uncertainty is likely to be high as the decision to dismiss is up to a bankruptcy judge. As mentioned above, some have decided not to discharge gambling debts whereas others have agreed with the argument that the gambler had every intention to repay the debt once they won their previous losses back. Chasing one’s losses is behavior frequently seen in problem gamblers and several courts have accepted the argument that the person did intend to repay the debt even though their ability to do so is
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obviously questionable given the statistical advantage of the casino (see AT&T Universal Card vs. Alvi, cited in Depperschmidt and Kratzke, 1997).
References American Psychiatric Society, 1994. Diagnostic and Statistical Manual of Mental Disorders, fourth ed. American Psychiatric Society, Washington, DC. Anonymous, 1998. Busting out all over. The Economist, 25. Boyes, W.J., Faith, R.L., 1986. Some effects of the Bankruptcy Reform Act of 1978. J. Law Econ. 29 (April), 139 –149. Burke, A., 1998. Why Americans go broke. The American Prospect 41, 34 –38. Clark, K., 1997. Why so many Americans are going bankrupt. Fortune 24 –26. Christiansen, E., 1998. Gambling and the American economy. Ann. Am. Acad. Political Soc. Sci. 556, 36 –51. Eadington, W.R., 1998. Contributions of Casino-style gambling to local economies. Ann. Am. Acad. Political Soc. Sci. 556, 53– 65. Eckstein, O., Sinai, A., 1986. The Mechanisms of the Business Cycle in the Postwar Era. In: Gordon, R.J. (Ed.), The American Business Cycle. University of Chicago Press, Chicago, pp. 39 –122. Depperschmidt, T., Kratzke, N., 1997. Bankruptcy for gamblers: the questions of fraudulent intent, dischargeability, and remedial policy in credit card cash advance cases. Bankruptcy Devel. J. 13 (2), 389 – 419. Domowitz, I., Eovaldi, T., 1993. The impact of the Bankruptcy Reform Act of 1978 on consumer bankruptcy. J. Law Econ. 36 (October), 803– 835. Gazel, R., 1998. The economic impacts of casino gambling at the state and local levels. Ann. Am. Acad. Political Soc. Sci. 556, 66 – 84. Hartigan, J.A., Wong, M.A., 1979. A k-means clustering Algorithm. Appl. Statistics 28, 100 –108. Judson, D.H., 1998. CLUSTER: Stata module to perform nonhierarchical k-means (or k medoids) cluster analysis. http://ideas.uquam.ca/ideas/data/Softwares/bocbocodeS358403.html Lesieur, H., 1977. The Chase. Anchor Books, New York. Lesieur, H., Blume, S., 1987. The South Oaks Gambling Screen (SOGS): a new instrument for the identification of pathological gamblers. Am. J. Psychiatry 144 (9), 1184 –1188. McClave, J.T., Dietrich, F.H., 1985. Statistics, third ed. Dellen Publishing, San Francisco, CA. National Opinion Research Center, 1999. Overview of National Survey and Community Database Research on Gambling Behavior. Report to the National Gambling Impact Study Commission. Shephard, L., 1984a. Accounting for the rise in consumer bankruptcy rates in the united states: a preliminary analysis of aggregate data (1945–1981). J. Consumer Affairs 18 (2), 213–230. Shephard, L., 1984b. Personal Failures and the Bankruptcy Reform Act of 1978. J. Law Econ. 37 (October), 419 – 437. Sullivan, T., 1988. Laws, models, and real people: choice of chapter in personal bankruptcy. Law. Soc. Inquiry 13 (4), 661–706. Sullivan, T., Warren, E., Westbrook, J.L., 1989. As We Forgive Our Debtors: Bankruptcy and Consumer Credit in America. Oxford University Press, New York. SMR Research, 1997. The Personal Bankruptcy Crisis, 1997. SMR Research, Hackettstown, NJ. Thompson, W., Gazel, R., Rickman, D., 1997. Social and legal costs of compulsive gambling. Gaming Law Review 1 (1), 81– 89. Tuttle, R., 1996. Gambling: A Study for Congregations. Evangelical Lutheran Church in America, St. Louis. U.S. Department of Commerce, Bureau of the Census, 1998a. Statistical Abstract of the United States, 1998. US Government Printing Office, Washington, DC. U.S. Department of Commerce, Bureau of the Census, 1998b. USA Counties, 1996. US Government Printing Office, Washington, DC.
Casino gambling and bankruptcy in new United States casino jurisdictions夞 Mark W. Nicholsa,*, B. Grant Stittb, David Giacopassic a
Department of Economics/030, University of Nevada, Reno, NV 89557, USA Department of Criminal Justice/214, University of Nevada, Reno, NV 89557, USA c Department of Criminal Justice, University of Memphis, Memphis, TN 38152, USA b
Received 1 May 1999; accepted 1 June 1999
Abstract Using quarterly data on personal consumer bankruptcy for 1989:Q4 through 1998:Q1, this study examines the impact that the introduction of casino gambling has on per capita personal bankruptcy filings. Eight jurisdictions that have recently adopted gambling are compared with a set of matching control jurisdictions, communities without casinos that are economically and demographically similar to the eight communities. The results reveal that casino gambling is associated with an increase in personal bankruptcy in seven of the eight communities. In five of the seven the increase is statistically significant. However, an increase is not universal and in one community, Harrison County, Mississippi (Biloxi), bankruptcy per capita significantly decreased. It is speculated that this decrease is due to the features of both the community and the casino industry in Biloxi. Finally, the most significant changes in bankruptcy occur among Chapter 13, as opposed to Chapter 7, filings. This suggests that a growing portion of insolvents are creating repayment plans for their debts. Policy implications of the findings are discussed. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Bankruptcy; Casino gambling; Control jurisdictions
夞 This project was partially supported by Grant No. 98-IJ-CX-0037 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. Points of view in this document are those of the authors and do not necessarily represent the official position or policies of the U.S. Department of Justice. The support of the Graduate School at the University of Nevada, Reno is also acknowledged. * Corresponding author. Tel.: ⫹1-775-784-6850; fax: ⫹1-775-784-4728. E-mail address: [email protected] (M.W. Nichols). 1053-5357/00/$ – see front matter © 2000 Elsevier Science Inc. All rights reserved. PII: S 1 0 5 3 - 0 0 0 0 ( 0 0 ) 0 0 0 7 1 - 8
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Fig. 1. Bankruptcy per capita.
1. Introduction In 1997, personal bankruptcy filings in the United States surpassed 1.3 million, the highest in U.S. history up to that time. This equates to one in 70 families and is a 27% increase over 1996 and a 400% increase over the number of Americans who filed for personal bankruptcy in 1980 (U.S. Department of Commerce, Bureau of the Census, 1998a, 1998b; Anonymous, 1998). What is the cause behind this rapid growth in bankruptcies? Why, given a prospering economy with unemployment at near-record lows, have bankruptcies continued to rise, causing some researchers to refer to the current situation as a crisis (SMR Research, 1997)? Fig. 1 shows bankruptcies per capita in the United States for the years 1975 to 1997. With the exception 1993 through 1995, per capita bankruptcies have skyrocketed since 1985, growing 277% percent by 1997. The rapid rise in bankruptcies has drawn much attention and ultimately resulted in the formation of the National Bankruptcy Review Commission in 1994 (Pub. L. No. 103–394, 108 Stat. 4106) whose charge was to review current bankruptcy law and policy, hear from divergent views on the current bankruptcy system, evaluate various proposals, and make suggestions for change.1 There have been multiple explanations put forth for the rise in personal bankruptcies ranging from readily-available and excessive credit, the growing divorce rate, a deterioration of the stigma attached to bankruptcy, high debt to income ratios, corporate downsizing, inadequate insurance coverage, changing bankruptcy law, and the rapid spread of casino
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gambling. While the cause of rising bankruptcy rates is certainly multidimensional, encompassing many of the above factors and others as well, the purpose of the present study is to determine what role, if any, the spread of casino gambling has played. In the last 10 years (from 1988 to 1998), casino gambling has boomed. Before 1988, only two states (Nevada and New Jersey) had legalized casino gambling; today, ten states have legalized casino gambling (Gazel, 1998). When the 31 states that have Native American casinos are included in the count, the overwhelming majority of states have some form of casino gambling (Christiansen, 1998). Casino gross revenue (customer spending and gaming losses) for 1996 exceeded $19 billion in the United States. This amount is equal to about 10% of all leisure expenditures (Christiansen, 1998). This rapid spread of casino gambling has been extremely controversial. Anticasino forces focus on the problems experienced by problem gamblers and argue that casinos are bad for communities, leading to higher rates of a variety of social problems including bankruptcy, divorce, suicide and crime (e.g., Tuttle, 1996). Although personal testimonies provide a powerful account of the problems some individuals experience as a result of becoming compulsive gamblers, little empirical evidence is available to substantiate the claim that the presence of casinos leads to a higher rate of bankruptcy in a community. The countervailing argument made by casino supporters is that while a small proportion of the community may engage in problem gambling and suffer severe economic consequences, the jobs created by the casinos themselves (341,000 workers in non-Indian casinos in 1996) provide economic stability for large numbers of relatively low skilled workers ((Thompson et al., 1997; Christiansen, 1998). Although most of the casino jobs are low to moderate in wage scale, the casino workers generally are provided company health benefits that are often unavailable in comparable jobs in the lodging or entertainment industries. In addition, the economic benefits that accrue to companies that supply casinos and their patrons with the broad range of goods and services needed for the casinos to operate also contributes greatly to the economic well-being of large numbers of community residents. In addition to the plausible arguments on each side of the question (of how casinos affect the bankruptcy rate in a community), bankruptcy data that appear in the press are equally confounding. For example, an article in U.S.A. Today (March 18, 1996) reported that Tennessee, a state with no legalized gambling and an unemployment rate well below the national average, had the highest bankruptcy rate in the nation. Connecticut, a state with several types of legalized gambling available (casinos, lottery, off-track betting, Jai Alai) and an unemployment rate higher than average, had a bankruptcy rate approximately one-third that of Tennessee. Clearly, the relationship between casinos and bankruptcy is complicated and in need of studies that apply rigorous methodology. The current study analyzes bankruptcy rates per capita in a group of communities that have casinos and compares them to a control group of communities that do not have casinos. Section 2 of this article briefly reviews the literature on bankruptcy. Section 3 describes the selection of the communities, the control groups, and the basic empirical methodology. Section 4 presents results, and section 5 concludes the article.
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2. Review of the bankruptcy literature Contrary to much popular opinion, the most common causes of bankruptcy do not involve a profligate lifestyle and extravagant spending habits that go well beyond the average citizen’s means. According to the Consumer Federation of America, those who most commonly file for bankruptcy are from the lower middle class and are individuals who experience some unforeseen financial hardship, such as loss of job, divorce, or medical expense (Burke, 1998). As noted in Shephard (1984a), “extensive survey work by consumer economists has yielded a picture of the ‘typical’ bankruptcy petitioner as a head of household who is a high school graduate employed as a blue collar worker falling into the lower middle class of income workers. Petitioners are more apt to be unemployed, recently divorced, members of racial minorities and heavy users of credit” (p. 217). Consistent with this description of the typical bankruptcy petitioner, a study by the Congressional Budget Office reports that bankruptcies tend to rise not during economic downturns, but during economic expansions as people become more confident in the future and willing to increase their current levels of expenditures (Clark, 1997). When these individuals experience an economic setback, they find themselves in an unexpected economic bind, and bankruptcy becomes the tool many use to extricate themselves from what appears to be an otherwise unsolvable problem. In addition, Eckstein and Sinai (1986) note the presence of a financial cycle operating within the business cycle. Near the peak of an economic expansion, the demand for credit expands beyond the ability of households to finance their increasing commitments with current income. At some point, however, the demand for credit exceeds the supply and rising interest rates and credit rationing become widespread. At this point, note Eckstein and Sinai, “. . . households, businesses, and financial institutions find that the expectations upon which spending plans are based become falsified as balance sheets deteriorate, with loan repayments, debt burdens, and cost of financing all becoming surprisingly burdensome” (p. 53). Bankruptcy is one alternative to alleviate this burden. Other explanations attribute the increasing rate of bankruptcy in the United States to a growing “bankruptcy culture”. This perspective holds that the social stigma formerly attached to declaring bankruptcy has diminished so that individuals view bankruptcy as a normal, legal, and viable alternative to economic exigency. Most frequently, the money is owed to large and assumedly prosperous businesses (banks, credit card companies) that are seen as bureaucratic entities with little personal connection to the individual; the individual, therefore, feels little compunction if the businesses go unpaid. In addition, for someone contemplating filing for bankruptcy, the economic consequences appear to be minimal as some companies, such as credit card companies and auto dealerships, promote themselves as specialists in doing business with individuals who have “bad credit.” Lastly, there are lawyers in every large community who specialize and advertise their expertise in helping individuals declare bankruptcy, thereby stopping repossessions, garnishments, and harassing phone calls (Sullivan, 1988). The “bankruptcy culture” perspective holds that the increase in bankruptcy is due primarily to social and cultural factors apart from any significant changes in spending patterns or lifestyle changes. In addition to the above issues, several researchers have attributed increased bankruptcy
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rates to changes in bankruptcy law. For example, the Bankruptcy Reform Act of 1978 is seen largely as a “prodebtor” piece of legislation because it liberalized exemption levels.2 Shephard (1984b) and Boyes and Faith (1986) find that the 1978 Reform Act was responsible for a doubling of bankruptcy rates between 1978 and 1984. In fact, this rapid increase prompted Congress to reduce exemption levels in 1984 in an attempt to reverse the trend of rising bankruptcies. Support of this legal explanation is not unanimous, however. For example, Domowitz and Eovaldi (1993) show that once demographic and economic factors are considered, the 1978 Reform Act had no statistically significant influence on bankruptcy rates. Similarly, Sullivan et al. (1989) show that several factors, including local legal culture, economic, and social demographic characteristics, contribute to the decision to file for bankruptcy. Federal law is not the only legislation to allegedly influence an individual’s decision to file bankruptcy. States differ in their exemption levels, wage garnishing laws, and protection of assets. For example, according to Sullivan et al. (1989), “Illinois had low exemptions, Texas had high, and Pennsylvania was in the middle, so the effects predicted by the exemptionincentive model should have been directly reflected in the patterns of bankruptcy filings in those three states” (p. 240). What Sullivan et al. find, however, is no significant differences among the three states, suggesting that the decision to file for bankruptcy is driven largely by factors other than the legal environment. The above literature reveals that bankruptcy is a complicated issue influenced by many economic, social, and political factors. Nevertheless, the introduction of casino gambling into a community clearly modifies the spending patterns of many individuals (Gazel, 1998). For most of the individuals in the community, a casino simply provides another source of entertainment that, to varying degrees, may include expenditures on food, drink and live entertainment along with gaming. Studies have shown, however, that a small proportion of casino patrons become problem gamblers and that these are the individuals who are most likely to have financial problems associated with their gambling. The rate of compulsive gambling (lifetime prevalence) ranges from 1% to 3% of the gambling population, with another 3% being “potential pathological gamblers,” that is, individuals who display some of the traits associated with problem gambling and are at risk of becoming pathological gamblers (American Psychiatric Society, 1994; Lesieur and Blume, 1987). Individuals who become compulsive gamblers are likely to experience a great many social and economic problems, not the least of which is a desperate financial condition due to the compulsive behavior of “chasing” one’s losses, trying to win back money to undo past losses (American Psychiatric Society, 1994). Lesieur, in his classic study of compulsive gamblers, found that much of the gamblers’ time and energy are spent in concealing the amount of gambling losses and in the pursuit of additional money to chase the losses (Lesieur, 1977). The compulsive gambler may actually increase his or her gambling in the early stages of the problem gambling to win back the losses. Eventually, however, the individual exhausts all legitimate means of obtaining money for payment of the gambling losses and experiences a “closure state” of tightening or nonexistent resources (Lesieur, 1977). When the compulsive gambler reaches this point, bankruptcy or crime become the end game. Although it is evident that most compulsive gamblers experience severe financial problems, it is not clear how this affects the bankruptcy rate in a community. Some research
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indicates that casinos do increase the bankruptcy rate. For example, research conducted by SMR Research Corp (1997) found that the spread of legalized gambling in 298 counties was positively related to an increase in bankruptcy filings in those counties. SMR notes that the bankruptcy rate was 18% higher in counties with one gambling facility and 35% higher in counties with five or more gambling establishments. In contrast, the National Opinion Research Center (NORC) finds that there is no statistically significant casino effect with regard to bankruptcy or crime, although Type D and E gamblers are more likely than other individuals to have ever declared bankruptcy (NORC, 1999). Lifetime Type D gamblers are defined as those reporting losing $100 in a single day or year and having three or four adverse effects, while type E gamblers have five or more adverse effects. The adverse effects consist of 17 factors such as lying about gambling losses to family and friends, chasing losses, preoccupation with gambling, and conducting illegal acts to pay for gambling. Based on telephone surveys of 2406 randomly selected adults, NORC concludes that approximately 1% of the adult population is a Type D gambler while another 1% is Type E. These results likely explain the lack of any statistically significant casino effect on bankruptcy at the community level; those individuals that are most likely to declare bankruptcy because of problem gambling make up a small portion of the total population.
3. Empirical methodology This study examines personal bankruptcy in eight communities in the United States that recently adopted casino gambling. These eight communities are Sioux City, Iowa; St. Joseph, St. Louis City, and St. Louis County, Missouri; Alton, Peoria, and East Peoria, Illinois; and Biloxi, Mississippi. These communities were chosen based upon several criteria. First, the communities all had gambling for at least four years. Communities with gambling for fewer than four years were not chosen since it was felt that sufficient time should be allowed for social and economic changes to occur. In the above communities, Alton has had gambling the longest, since September 1991, whereas St. Joseph has had it the least amount of time, since June 1994. In addition, the selection of these communities is part of an ongoing study analyzing casino gaming’s impact on crime and quality of life. The above communities were able to provide police data on crime, hence their selection for study. County-level data on personal bankruptcy, including Chapter 7 and Chapter 13 filings, were obtained from SMR Research Corporation. Quarterly observations on the total number of bankruptcies in each county are available for the fourth quarter of 1989 through the first quarter of 1998. Many studies that examine bankruptcy, including those listed above, use jurisdiction-level data provided by the U.S. Office of the Courts. These jurisdictions cross several counties and often cross state lines. The data set used for this study is unique in that it is at the county level. This enables us to obtain a clearer picture of how bankruptcy rates in communities (i.e., counties) change with the introduction of casino gambling. Ideally, we would like to compare bankruptcy rates in the casino communities with the rates that would have occurred had gambling not been legalized. Since this is clearly unknown, bankruptcy rates in a matched set of control communities are used as a proxy. If casinos have no impact on bankruptcy, there should be no difference in bankruptcy rates
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between the casino and control communities over the period of study. On the other hand, significant differences between the two groups would suggest that casinos do impact bankruptcy rates. The control communities are chosen based on their similarity to the casino communities over fifteen demographic, economic, and social variables.3 The fifteen variables chosen are: percent of the population aged 15 to 34; total population; median household income; unemployment rate; percent black; percent Hispanic; percent Indian, Aleut, or Eskimo; percent below poverty for the population where poverty status is known; percent of the population not graduating high school; percent of occupied housing units that are renteroccupied; percent of total housing units in structures with three or more units; net migration; percent urban; average number of persons per square mile; and a GINI coefficient of income inequality. All data are taken from the US Census Bureau’s U.S.A. Counties 1996 CD-ROM and all variables are normalized by converting them to a Z-score relative to the US county average. The selection of control communities is based on k-means cluster analysis (Hartigan and Wong, 1979) and uses programs developed by Judson (1998). The idea is to rank control communities on their proximity to casino communities applying the following metric:
冉冘 k
d共 y, x兲 ⫽
共 yj ⫺ xj 兲
j⫽1
冊
q
1/q
(1)
where yj is the jth variable for the potential control community and xj is the same variable for the casino community. In the present study, q in Eq. (1) equals 2, the usual Euclidian distance. Summing across all k variables, the control communities can be ranked in ascending order of distance from the casino communities. Given the ranking of control communities, we select five that are located a minimum of fifty miles from a casino and have the lowest score, d(y,x), from Eq. (1). Five matching control communities were chosen for each casino community in order to ensure generality of the results. The set of casino communities and their five matching control jurisdiction are provided in Table 1.
4. Empirical results The results from comparing bankruptcy per capita (per 1000 population) for total, Chapter 7, and Chapter 13 bankruptcy petitions are provided in Tables 2 through 4. The tables report average bankruptcies per capita for the period prior to the casino opening (pre-casino) and the period since the casino opened (post-casino). The final column reports the percentage change in per capita bankruptcies and the t-statistic testing whether the change in the casino communities is statistically different than the change in the control jurisdictions.4 Table 2 reveals that in seven of the eight communities bankruptcies per capita increased more in the casino communities than in the control communities. Furthermore, in five of those seven counties the increase is statistically significant. This lends support, albeit not unanimous, to the hypothesis that the introduction of casino gambling leads to an increase in
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Table 1 Casino communities and control jurisdictions Casino community (county)
Control jurisdictions
d(y,x)a
Sioux City, Iowa (Woodbury)
Chemung, New York Black Hawk, Iowa Garfield, Oklahoma Daviess, Kentucky Ohio, West Virginia Trumbull, Ohio Stark, Ohio Clark, Ohio Richland, Ohio Winnebago, Illinois Sebastian, Arkansas Winnebago, Illinois Macon, Illinois Hamilton, Tennessee Lackawanna, Pennsylvania Sheboygan, Wisconsin Rock, Wisconsin Clark, Ohio Miami, Ohio Licking, Ohio Monroe, New York Allegheny, Pennsylvania Erie, New York Salt Lake City, Utah Montgomery, Ohio Richmond City, Virginia Norfolk City, Virginia Portsmouth City, Virginia Newport News, Virginia Roanoke, Virginia Daviess, Kentucky Sebastian, Arkansas Jasper, Missouri Lackawanna, Pennsylvania Garfield, Oklahoma Escambia, Florida Wichita, Texas New Hanover, North Carolina Mclennan, Texas Tuscaloosa, Alabama
1.060 1.060 1.080 1.120 1.330 0.876 0.882 0.905 0.999 1.010 1.220 1.230 1.240 1.340 1.420 0.793 0.806 0.883 0.916 0.967 2.120 2.420 2.500 2.630 2.820 2.870 3.560 3.820 4.400 4.540 0.846 0.850 1.080 1.120 1.250 1.050 1.460 1.520 1.530 1.590
Alton, Illinois (Madison)
Peoria, Illinois (Peoria)
East Peoria, Illinois (Tazewell)
St. Louis County, Missouri (St. Louis)
St. Louis City, Missourib (St. Louis City)
St. Joseph, Missouri (Buchanan)
Biloxi, Mississippi (Harrison)
a b
Distance Metric. See equation (1) in text for definition. St. Louis City is an independent city that is counted as a county equivalent for data collection purposes.
bankruptcy. It is interesting to note that the largest increase, 50.3%, occurred in Madison County, Illinois, where a casino has been located (in Alton) since September of 1991, longer than in any of the other jurisdictions. Moreover, Peoria and Tazewell counties, which also show significant increases, have had casinos in operation for the second longest period of time (November, 1991). They are followed by Harrison County, Mississippi (Biloxi–August
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Table 2 Bankruptcy per capita (per 1000 pop.), casino and control jurisdictions Jurisdictions
Bankruptcy per capita (pre-casino)
Bankruptcy per capita (post-casino)
Percent changea
Sioux City, IA (Woodbury) Control Alton, IL (Madison) Control Peoria, IL (Peoria) Control East Peoria, IL (Tazewell) Control St. Louis City, MO (St. Louis City) Control St. Louis County, MO (St. Louis) Control St. Joseph, MO (Buchanan) Control Biloxi, MS (Harrison) Control
2.6178
3.4718
2.7285 2.7847
3.1898 4.1844
3.6293 3.8791
3.9689 4.9463
4.5918 3.5844
4.8379 4.3333
3.0884 4.1019
3.0674 6.0950
5.2946 3.2017
7.2092 4.3322
3.3306 2.2988
3.6720 2.9388
3.1678 5.7029
3.5955 4.9627
3.4446
3.8423
32.6* (1.85) 16.9 50.3*** (2.59) 9.4 27.5** (2.24) 5.4 20.9** (2.47) ⫺0.6 48.6 (0.18) 36.2 35.3*** (2.76) 10.3 27.8 (1.05) 13.5 ⫺13.0*** (3.27) 11.5
a
Absolute value of the (two sample) t statistic in parentheses. Null hypothesis is that the change in the casino jurisdiction is equal to the change in the control jurisdiction or, equivalently, that the difference in the change in bankruptcies per capita between the casino and control jurisdictions is zero. A*, **, and *** represent significance at the 10%, 5%, and 1% level, respectively.
1992), Woodbury County, Iowa (Sioux City–January 1993), St. Louis City and County, Missouri (May 1994), and Buchanan County, Missouri (St. Joseph–June 1994). Although more evidence would be required to make any firm conclusions, this does suggest a possible link between bankruptcy and the length of time a casino has been in place. Harrison County, Mississippi (Biloxi) is the one exception to the increase in bankruptcy. There, bankruptcies have significantly declined since the introduction of casino gambling. This is noteworthy because of all of the jurisdictions examined, Biloxi is the only one that would qualify as a “destination resort.” Destination resort casinos attract a greater percentage of their clientele from tourists or visitors, effectively exporting gambling. As a result, the economic benefits–job creation, tax revenue, spinoffs to other businesses–will be greater (Eadington, 1998). In this type of environment, the creation of jobs and income may allow people to meet their financial obligations, outweighing any negative effects created by excessive gambling on the part of some individuals.5 Tables 3 and 4 provide results for Chapter 7 and Chapter 13 filings respectively. The distinction is important because Chapter 13 filings involve debt repayment plans as opposed to the nearly complete forgiveness of all admissible debt in Chapter 7 filings. The costs to
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Table 3 Chapter 7 bankruptcy per capita (per 1000 pop.), casino and control jurisdictions Jurisdictions
Bankruptcy per capita (pre-casino)
Bankruptcy per capita (post-casino)
Percent changea
Sioux City, IA (Woodbury) Control Alton, IL (Madison) Control Peoria, IL (Peoria) Control East Peoria, IL (Tazewell) Control St. Louis City, MO (St. Louis City) Control St. Louis County, MO (St. Louis) Control St. Joseph, MO (Buchanan) Control Biloxi, MS (Harrison) Control
2.5265
3.3478
2.4407 2.0946
2.9510 2.7108
2.7385 3.3557
3.0047 4.0119
2.9188 3.3170
3.1278 3.6775
2.4083 2.0021
2.3903 3.0363
4.0288 1.5297
5.0430 2.1137
2.0716 2.1562
2.2921 2.6802
2.9103 3.6669
3.2248 3.3939
1.3258
1.4872
32.5 (1.52) 20.9 29.4 (1.24) 9.7 19.6* (1.81) 7.2 10.9* (1.83) ⫺0.7 51.7 (0.08) 25.2 38.2*** (2.77) 10.6 24.3 (1.14) 10.8 ⫺7.4 (1.70) 12.2
a
Absolute value of the (two sample) t statistic in parentheses. Null hypothesis is that the change in the casino jurisdiction is equal to the change in the control jurisdiction or, equivalently, that the difference in the change in bankruptcies per capita between the casino and control jurisdictions is zero. A*, **, and *** represent significance at the 10%, 5%, and 1% level, respectively.
society and debtors who regularly pay their debts are therefore greater under Chapter 7. If individuals become insolvent due to problem gambling, incurring debt that is beyond hope of being repaid, we expect the predominant increase to be in Chapter 7 filings. Tables 3 and 4 clearly reveal that the most significant increase has occurred with Chapter 13 filings. In all jurisdictions where there is a statistically significant increase in bankruptcy, there is a corresponding significant increase in Chapter 13 filings. On the other hand, Chapter 7 filings significantly increase in only three counties. In Biloxi, a significant decrease in bankruptcy occurs among Chapter 13 but not Chapter 7 filings. While the overall increase in bankruptcies is troubling, the increase in Chapter 13 filings suggests that the proportion involving repayment plans is increasing. The greater increase in Chapter 13 runs counter to our expectations. One explanation for this result is that individuals with gambling problems seek treatment and stop gambling, thereby enabling the individual to repay debts given sufficient time to rebuild finances. Yet another possibility is that bankruptcy courts may not be willing to dismiss gambling debt if it is obtained fraudulently (i.e., the debtor has no intention of paying the creditor). Several decisions reflect the refusal of the court to discharge gambling debts based on fraud (e.g.,
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Table 4 Chapter 13 bankruptcy per capita (per 1000 pop.), casino and control jurisdictions Jurisdictions
Bankruptcy per capita (pre-casino)
Bankruptcy per capita (post-casino)
Percent changea
Sioux City, IA (Woodbury) Control Alton, IL (Madison) Control Peoria, IL (Peoria) Control East Peoria, IL (Tazewell) Control St. Louis City, MO (St. Louis City) Control St. Louis County, MO (St. Louis) Control St. Joseph, MO (Buchanan) Control Biloxi, MS (Harrison) Control
0.0913
0.1240
0.2861 0.6901
0.2388 1.4731
0.8872 0.5235
0.9629 0.9344
1.6701 0.2674
1.7084 0.6558
0.6743 2.0998
0.6763 3.0587
1.2614 1.6707
2.1626 2.2168
1.2527 0.1426
1.3769 0.2586
0.2557 2.0207
0.3695 1.5645
2.1141
2.3518
35.8*** (6.06) ⫺16.5 113.5*** (4.95) 8.5 78.5*** (2.81) 2.3 145.3*** (3.32) 0.3 45.7 (0.26) 71.4 32.7** (2.64) 9.9 81.3 (0.10) 44.5 ⫺22.6*** (6.34) 11.2
a
Absolute value of the (two sample) t statistic in parentheses. Null hypothesis is that the change in the casino jurisdiction is equal to the change in the control jurisdiction or, equivalently, that the difference in the change in bankruptcies per capita between the casino and control jurisdictions is zero. A*, **, and *** represent significance at the 10%, 5%, and 1% level, respectively.
Eashai v. Citibank South Dakota and Citibank South Dakota v. Ardet, cited in Depperschmidt and Kratzke, 1997). If an individual expects gambling and other debts not to be discharged, or is uncertain about the probability of their discharge, filing Chapter 13 may be a rational means of “buying time” to repay creditors.6 Without more detailed information on the individuals and their reason for filing, it is impossible to know for certain why the predominant increase has occurred in Chapter 13 filings. A means of comparing the overall effect that casinos have on gambling is available through the Wilcoxon Rank Sum Test (RST) under the null that the percentage change in per capita bankruptcy rates for casino and control jurisdictions come from the same probability distribution. In constructing the RST, both the casino and control communities are pooled and ranked in ascending order based on the percentage change in bankruptcies. If the two populations are identical, we would expect the rankings to be randomly distributed between the two samples. However, if casinos lower bankruptcy, the rankings of the casino sample should be relatively small, whereas if casinos increase bankruptcy the rankings should be relatively large. Table 5 contains results from the RST. The rankings of the casino and control samples are
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Table 5 Wilcoxon rank sum test for percentage change in per capita bankruptcy filings Casino jurisdictions
Sioux City, IA (Woodbury) Alton, IL (Madison) Peoria, IL (Peoria) East Peoria, IL (Tazewell) St. Louis City, MO St. Louis County, MO St. Joseph, MO (Buchanan) Biloxi, MS (Harrison)
Total
Control—Sioux City Control—Alton Control—Peoria Control—East Peoria Control—St. Louis City Control—St. Louis County Control—St. Joseph Control—Biloxi Rank Sum
Chapter 13
Rank
% change
Rank
% change
Rank
32.6 50.3 27.5 20.9 48.6 35.3 27.8 ⫺13.0
12 16 10 9 15 13 11 1
32.5 29.4 19.6 10.9 51.7 38.2 24.3 ⫺7.4
14 13 9 7 16 15 11 1
35.8 113.5 78.5 145.3 45.7 32.7 81.3 ⫺22.6
9 15 13 16 11 8 14 1
Rank Sum Casino jurisdictions
Chapter 7
% change
87** Total
86* Chapter 7
87** Chapter 13
% change
Rank
% change
Rank
% change
Rank
16.9 9.4 5.4 ⫺0.6 36.2 10.3 13.5 11.5
8 4 3 2 14 5 7 6
20.9 9.7 7.2 ⫺0.7 25.2 10.6 10.8 12.2
10 4 3 2 12 5 6 8
⫺16.5 8.5 2.3 0.3 71.4 9.9 44.5 11.2
2 5 4 3 12 6 10 7
49
50
49
H0: Percentage change in bankruptcies between casino and control communities have the same probability distribution. Ha: Probability distribution for percentage change in bankruptcies for casino communities is shifted higher or lower than probability distribution for control communities. A* and ** represent significance at the 10% and 5% level, respectively.
summed and compared to a lower or upper critical value to determine significance (McClave and Dietrich, 1985, p. 792). At the 5% level of significance, the lower value, TL, equals 49 while the upper value, TU, equals 87. At the 10% level of significance TL ⫽ 52 and TU ⫽ 84. With equal sample sizes, the rank sum for either the casino or control communities can be used. So, if the rank sum of the casino counties is greater than or equal to 87 (or less than or equal to 49), we can say with 95% confidence that the probability distributions for casino and control communities are not identical. As seen in Table 5, the rank sum for casino counties for both total and Chapter 13 bankruptcy filings is 87, whereas the rank sum for Chapter 7 is 86. The probability distributions between casino and control communities are clearly not identical. Furthermore, given the cluster of large rankings, it appears that casino communities are associated with growing rates of bankruptcy. Finally, the analysis in Tables 2 through 5 was repeated when the post-casino date is one year later than the actual opening date. This was done to allow for the possibility that some insolvents may have begun the bankruptcy process prior to the opening of the casino, but not completed it until after the casino opened. If a significant number of bankruptcies were filed
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during this period, our results may be sensitive to the time period chosen to divide pre- and post-casino observations. Of course, lagging the post-casino period also introduces the possibility of counting post-casino bankruptcies as pre-casino bankruptcies. Nevertheless, it provides a conservative estimate of the impact that casino gambling has on bankruptcies and ensures the robustness of our results. In general, lagging the post-casino period does not change our conclusions. Total bankruptcies still increase in seven of the eight jurisdictions. The increase is statistically significant in four of the seven cases (the t statistic for Sioux City falls to 1.34, and is no longer statistically significant). The results for Chapter 7 and Chapter 13 are qualitatively identical. Moreover, repeating the RST for total, Chapter 7 and Chapter 13 bankruptcies resulted in rank sums of 84, 80, and 86. With the exception of Chapter 7, these are all significant at the 10% level. Overall, the results reported in Tables 2 through 5 appear to be robust to the date dividing pre- and post-casino time periods.
5. Conclusion This study compares per capita bankruptcy rates in eight communities that recently adopted casino gambling with a set of economically and demographically similar control jurisdictions. The results reveal that bankruptcy rates increased in seven of the eight communities and decreased in one. Standard two sample t statistics revealed that the increase was statistically significant in five of the seven communities; the decrease in Harrison County, Mississippi was also significant. Finally, the Wilcoxon rank sum test, a nonparametric test for significance, revealed that the casino communities are associated with rising rates of bankruptcy. Interestingly, both tests confirm the more pronounced increase in Chapter 13 bankruptcies as opposed to Chapter 7. While an increase in Chapter 13 relative to Chapter 7 bankruptcy filings is good news to the extent that a greater portion of insolvents are repaying their debt, the shadow of an ever growing number of total bankruptcies still looms. Perhaps the more interesting case for policy makers is Biloxi, Mississippi, where bankruptcy rates significantly declined. Biloxi shows that casino gambling need not inevitably lead to higher bankruptcy rates. More specifically, it supports the theory postulated by Eadington (1998) that the economic benefits associated with casino gambling are likely to be greatest when casinos are built in a destination resort environment as opposed to an urban setting. Of all of the communities included in this study, Biloxi is the only community with multiple casinos and the only one that would be characterized as a destination that tourists would travel to in order to gamble. In this type of environment, the economic benefits from casino gambling are greater and more likely to exceed the social costs. The creation of jobs and income is likely to be much greater when tourist dollars are infused into an economy as opposed to a recirculation of dollars in an urban environment where the majority of casino customers are from the immediate area. The findings above do suggest a positive correlation between casino gambling and consumer bankruptcy. Nevertheless, research into this issue is still in its infancy and more is needed. For example, other communities with casino gambling should be examined to see
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if the results found here generalize. A further investigation between urban and destination resort casinos would also be useful. Lastly, if communities are going to adopt casino gambling, policies to minimize gambling’s impact on bankruptcy should be explored. One policy being considered by states such as Iowa and by the National Gambling Impact Study Commission is the removal of automatic teller machines and credit card cash advance machines from casinos. Opponents of the placement of these machines in casinos argue that they make cash too readily available, causing gamblers, and problem gamblers in particular, to spend more than they originally intended. Proponents argue that the placement of cash machines is merely good business practice and a convenience factor for the large majority of customers without a gambling problem. More analysis of this debate focusing on who withdraws money, how much is withdrawn, what if any problems it creates, and whether it was a significant factor in rising bankruptcies appears warranted. Notes 1. The final report was due to Congress on October 20, 1997. That report was received with criticism by both consumer and creditor interests, and no proposals became law. 2. The 1978 Reform Act was the first major change to the Bankruptcy Code since the turn of the century. Up until 1978, the Bankruptcy Code of 1898 had been in effect and remained largely unchanged with the exception of the 1938 amendments that introduced Chapter 13. The changes made under the 1978 Reform Act are too numerous to go into here. In essence, both the number and level of exemptions were increased. Complicating matters further was the fact that states were allowed to opt out of the federal exemption levels. See Domowitz and Eovaldi (1993) and the references cited therein for more detail. 3. The selection of control communities was performed by D. Judson using the program developed by Judson (1998). 4. Formally, the two sample t statistic tests the null hypothesis that the difference in the change in bankruptcies per capita over time between the casino and control communities is zero. The two sample t statistic is calculated as t⫽{(x1⫺x2)⫺(u1⫺u2)}/w, where x1 and x2 are post- and pre-casino bankruptcies per capita for the casino community and u1 and u2 are per capita bankruptcies for the control jurisdictions over the same period; w equals {s2(1/n1⫹1/n2)}1/2, where s2 is the pooled estimate of variance and n1 and n2 are the number of post- and pre-casino observations in the casino community. 5. While anecdotal, two of the authors spoke with a credit counselor in Biloxi who indicated that the presence of casinos had made her job easier by providing people with income, allowing them to pay their bills. 6. Uncertainty is likely to be high as the decision to dismiss is up to a bankruptcy judge. As mentioned above, some have decided not to discharge gambling debts whereas others have agreed with the argument that the gambler had every intention to repay the debt once they won their previous losses back. Chasing one’s losses is behavior frequently seen in problem gamblers and several courts have accepted the argument that the person did intend to repay the debt even though their ability to do so is
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obviously questionable given the statistical advantage of the casino (see AT&T Universal Card vs. Alvi, cited in Depperschmidt and Kratzke, 1997).
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