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The Casino Industry and the Corruption of U.S. Public Officials in the U.S.

The Casino Industry and the Corruption of U.S. Public Officials in the U.S. Douglas M. Walker and Peter T. Calcagno College of Charleston April 1, 2011. What is corruption?. Criminal acts perpetrated by politicians or government employees, with the goal of illegitimate personal gain

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The Casino Industry and the Corruption of U.S. Public Officials in the U.S.

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  1. The Casino Industry and the Corruption of U.S. Public Officials in the U.S. Douglas M. Walker and Peter T. Calcagno College of Charleston April 1, 2011

  2. What is corruption? • Criminal acts perpetrated by politicians or government employees, with the goal of illegitimate personal gain • Dept of Justice reports that election crimes are good indicators of corruption • Vote fraud, campaign finance crimes, political shakedowns • Bribes, favors for friends/family, racketeering, conspiracy, extortion, etc. • Glaeser and Saks (2006) argue that federal corruption convictions are better indicators state crime convictions • If the state is corrupt, it may affect the number of charges filed by the state • Federal conviction #s range from 0 to over 100 per year in a particular state

  3. Anecdotal evidence of a link • Las Vegas has a history of organized crime and corruption • Atlantic City, NJ has a long-established bad reputation • Over half of the last 9 mayors have been arrested on corruption charges • Louisiana governor Edwards convicted in 2000 of 17 corruption charges related to riverboat casino licenses • Illinois governor Blagojevich being sued by Illinois casino operators • FBI wiretaps indicate a shake-down of horse racetrack owners for support of a bill that requires casinos to share profits with the racetracks • Observers in Kentucky suggest that corruption has started there, even before casinos are being built • In AL, 11 people (including a few senators) were arrested for buying/selling votes related to casino legislation (2010)

  4. Theoretical explanations for a link • Casinos are big, cash businesses • See Table 1, Figure 1 • Gambling industry PAC and individual contributions to politicians are large • See Table 2 • Likely to be large illegal “contributions” too? • Industry requires explicit government approval to exist • Huge rents are created by this restriction and regulation of the industry • Laws and regulations that apply only to this industry • Requires interaction between law makers and industry • Politicians can be influenced • State legalization, local permits, regulatory framework, etc., are opportunities for corruption

  5. Table 1: US Casino states, 2007Source: AGA (2008)

  6. Table 2: Industry contributions to US politiciansSource: Center for Responsive Politics (http://www.opensecrets.org)

  7. A perfect recipe for corruption? • Lots of casino revenues, tax revenues • Large contributions to politicians from individuals & PACs related to casinos/gambling • Government approval needed for industry to exist • Every facet of the industry is regulated • Corruptible politicians But there’s no empirical evidence of a link (at least, in the literature)…

  8. Corruption literature • Numerous studies • Rose-Ackerman (1978), Glaeser & Saks (2006), Fisman & Gatti (2002), Alt & Lassen (2008), Lee & Chelius (1989), etc. • International studies (cross-sectional, at the national level) • Corruption is worse when firms are shielded from foreign competition • Negative relationship between corruption and • political freedom • decentralization of power • economic growth • “Law and order” and “democratic accountability” reduce corruption • US studies (at the state-level) • Higher education and income reduce corruption • Income inequality increases corruption • Racial “dissimilarity” (diversity?) increases corruption • No studies that examine a specific industry as a potential cause of corruption

  9. Our data • We are interested in determining if there is a link between commercial casinos and corruption • Corruption is measured by federal corruption convictions of state/local government employees • By year and state: 1985-2005, 11 casino states • Covers most legalization (1989-96), except PA, NV, NJ • Data source: US Dept of Justice • Casino activity is measured by casino revenues • By year and state, 1985-2005, 11 casino states • Data source: State gaming regulatory agencies • 231 observations (11 states, 21 years) • In most states there are several pre-casino observations • [For all states, 21 years, we have 1050 observations. But the model isn’t determinate using all states.]

  10. Granger causality analysis • Standard Granger causality for time series data (Granger, Econometrica 1969): • Xt=Xt-j+Yt-j+ • Y “causes” X if inclusion of Y improves prediction of X • Yt=Yt-j+Xt-j+ • X “causes” Y if inclusion of X improves prediction of Y • Not exactly the same meaning as the common concept of “causality” • We cannot analyze casino legalization – an event – using this empirical analysis • We maybe should focus on this instead…

  11. Granger causality for panel data • Walker & Jackson (1998) adapt Granger causality to panel data; we follow that method • We have two series: Corrupt and Revenue • Step 1 • Detrend the panel data series of state- and time-specific information • State dummies, time trend, state-trend interaction terms • Use the residuals from each equation • Test the residuals for stationarity; if hypothesis of unit root is rejected, move on

  12. Empirical analysis, continued • Step 2 • Determine the autoregressive process that generates each series of detrended residuals • We want the fewest lag periods on which the variable must be regressed so that the residuals are white noise • Iterative process, adding additional lag periods until correlograms and Box-Pierce Q-statistics indicate residuals from these regressions are white noise • If these residuals are white noise, then if adding the other variable’s residuals improves prediction of the first series, then Granger causality exists • Models indicate that Corrupt requires 2 lag periods; Revenue requires 3 lag periods • Sample size must be adjusted to account for these

  13. Empirical analysis, continued • Step 3 • Run the regressions on each detrended series: (1)Corrt = a1+ a2Corr(t-1)+ a3Corr(t-2)+ a4 Rev(t-1) + a5 Rev(t-2) + a6 Rev(t-3)+ ε (2)Revt = g1+ g2 Rev(t-1) + g3 Rev(t-2) +g4 Rev(t-3) + g5Corr(t-1) + g6Corr(t-2) + ε • Granger causality tests (F-tests): • “Revenue does not cause Corruption” • in equation (1) above, test coefficient restriction: a4 = a5 = a6 = 0 • “Corruption does not cause Revenue” • in equation (2) above, test coefficient restriction: g5 = g6 = 0

  14. Possible results • 4 possible results • Casino revenues Granger cause corruption convictions • Casino revenues are used to bribe politicians to expand the industry or loosen regulation • Corruption convictions Granger cause casino revenues • Casinos are legalized in relatively corrupt states • Independence • No causal relationship either way • “No result” is still an interesting result (fortunately) • Feedback, or simultaneous determination • Each variable is contributing to the other • e.g., quid pro quo between casinos and politicians • Timing here must be examined

  15. Actual results Table 4. Granger causality test results

  16. Discussion • The first econometric evidence of a link • Analysis does not indicate why there is a link • A reasonable story can be told whatever the result • Suggests that casinos may be more likely to be legalized in relatively corrupt states • Anecdotal evidence: IL, LA, NJ, MS; next AL? • No evidence that casinos use revenues to corrupt politicians after casinos open • Casino licenses are expensive; unlikely to be put at risk… • Consistent with a public choice perspective, that politicians have the most power to extract rents when they are formulating casino legislation • Corruption is most likely to occur at this point

  17. Discussion, cont. • Analyze legalization point instead of revenue stream? • Hazard model • Casino states only or all states? • Timing issues need to be addressed: • corruption ‘event’ and convictions • casino legalization and casino revenues • [see the following scenario] • Suggestions for robustness checks? • Granger causality provides evidence for subsequent analysis • Corruption leading to revenues/legalization

  18. Year 1 Casinos proposed Some politicians solicit bribes to support casinos Year 2 Casino bill passes Corruption charges filed and trials begin; some trials end in conviction Year 3 Casino building begins Additional trials end in conviction Year 4 Casinos open for business (revenues begin) Figure 3. Possible timing of corruption convictions and casino revenues

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