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In Germany, Albers and Hubl (1997) used a probit technique to estimate the individual pattern of legal gambling in that country. With a sample of 1,586 adults, they estimated separate functions of participation for all forms of commercial gambling. They developed a survey in order to have a set of explanatory variables that covered the following socioeconomic characteristics: age, gender, education, income, family status, employment status, home ownership, occupation and importance of maximum prize in lotto for the gambler to explain the participation and/or non-participation in the different types of gambling – lotto, draw lotteries, TV-lotteries, soccer toto pools, horse-race betting, gaming machines and casinos. Their results point out that income, in Germany, has a positive and significant influence on the participation in most commercial games, suggesting that gambling is a widespread (superior) consumption good; the exceptions are Soccer Toto, which declines with income, and Lotto, for which income was found to have no impact.
Worthington, et al. (2003) used a regression modeling in order to predict gambling patterns in Australia. They gathered data from the Australian Bureau of Statistics Household Expenditure Survey of 6,892 households. Eight categories of gambling expenditure, from lottery tickets to casino gambles, were examined and the determining factors analyzed included income, family composition, gender, age, race, ethnicity and geographic location. They concluded that participation in lotteries in Australia is strongly influenced by age, ethnicity and household composition.
Impact on state government revenue
Evans and Zhang (2002) investigate whether the 16 states that earmark lottery revenues for K-12 education see increases in spending in this category. They conduct three empirical tests. First, they examine states that switched the allocation of lottery profits from the general fund into public education during the sample period of 1978 to 1998. Second, for the nine states that have always earmarked lottery profits for K-12 education, they examine whether period-to-period changes in lottery profits correlate with period-to-period changes in state spending on education, controlling for state and year effects and state-specific time trends. Third, they utilize a two-state least squares approach to estimating the relationship between lottery profits and K-12 spending using the introduction of lotto games and video lotteries as instruments for lottery profits. The authors find very similar results across the three approaches. A dollar increase in earmarked revenues contributes an additional 60 to 80 cents in K-12 education expenditures. In comparison, in states that deposit funds into the general fund, each dollar of lottery profit increases school spending by 40 to 50 cents; in states that earmark lottery profits for other uses, a dollar of lottery profit increases school spending by only 30 cents.
Impact on consumer behavior
Analyzing multiple sources of micro-level data, Kearney (forthcoming) finds that household lottery spending is financed entirely by a reduction innon-gambling expenditures.The main analysis examines household expenditure datafrom the 1982 to 1998 Interview Survey files of the Bureau of Labor Statistics (BLS) Consumer Expenditure Survey (CEX). During this time 21 states implemented a state lottery. The empirical analysis exploits the variation across states in the timing of state lottery introduction to compare the change in household expenditures among households in states that implement a lottery to the change among households in states that do not. The introduction of a state lottery is associated with an average decline of $46 per month, or 2.4 percent, in household non-gambling expenditures. This figure implies a monthly reduction in household expenditures of $24 per-adult, which compares to average monthly lottery sales of $18 per lottery-state adult. Among households in the lowest income third of the CEX Interview sample, nongamblingexpenditures are reduced by an average of 2.5 percent, 3.1 percent when the state lottery offers instant games. Furthermore, the data demonstrate a statistically significant reduction in expenditures on food eaten in the home (2.8 percent) and on home mortgage, rent, and other bills (5.8 percent). The data do not indicate which households purchase lottery tickets, so these average effects do not account for the fact that a substantial fraction of households do not engage in lottery gambling. For households that do purchase lottery tickets, the decline in non-gambling expenditures must therefore be considerably greater.
Lottery gambling is part investment, as consumers are making choices over risky assets, and it is part entertainment. Assuming that the entertainment and pecuniary components of the lottery gamble are separable, maximizing behavior predicts that consumer demand for lottery products should depend positively on its expected return, holding constant game characteristics. To evaluate whether this prediction holds, Kearney (forthcoming) analyzes weekly sales and characteristics data from 91 lotto games from 1992 to 1998. The analysis suggests that sales are positively driven by the expected value of a gamble, controlling for higher-order moments of the gamble and non-wealth creating characteristics. This finding is robust to alternative specifications, including controlling for unobserved product fixed effects. The data also reveal that consumers respond to nonwealthcreating, “entertaining” game features. Together, these two findings suggest that consumers are at least partly – and potentially fully – informed, rational consumers. It is consistent with these findings to claim that consumers derive an entertainment equal tothe price of the gamble (one minus expected value), and then, insofar as they are making investments, they are informed evaluators of gambles.
Kearney (forthcoming) reviews microlevelevidence on who plays the lottery from the 1998 National Survey on Gambling conducted by the National Opinion Research Council (NORC) under contract with the NGISC. The data reveal the following general trends. First, lottery gambling extends across races, sexes, and income and education groups. Second, black respondents spendnearly twice as much on lottery tickets as do white or Hispanic respondents. The average reported expenditure among blacks is $200 per year, $476 among those who played the lottery last year. Black men have the highest average expenditures. Third, average annual lottery spending in dollar amounts is roughly equal across the lowest, middle, and highest income groups. This implies that on average, low-income households spend a larger percentage of their wealth on lottery tickets than other households.
Clotfelter and Cook (1993) and Terrell (1994) – provide evidence of the“gambler’s fallacy” among lottery players. The “gambler’s fallacy” is the mistakennotion that the second draw of a signal will be negatively correlated with the first draw. For example, if a slot machine has not won in a while, some gamblers believe it is “due” to win, or vice versa. Using data from the Maryland and New Jersey numbers games respectively, they find that the amount of money bet on a particular number falls sharply after the number is drawn and that it gradually returns to its former level after several months.
Grinols and Mustard (2004) empirically investigate the relationship between casinos and crime rates using county-level crime data on the 7 FBI Index 1 offenses (robbery, aggravated assault, rape, murder, larceny, burglary, and auto theft) from 1977 to 1996. Their paper utilizes the quasi-experiment created by casino openings to identify a causal relationship. Their study includes all 3,165 counties in the U.S. and the period observed includes the introduction of casinos in all counties except those in Nevada. Their sample of casinos includes land-based, riverboat, and tribal-owned casinos. The authors find a sharp increase in most crimes after the introduction of casinos. Their results suggest that the effect on crime is low shortly after a casino opens, and grows over time. They calculate that roughly eight percent of crime in casino counties in 1996 was attributable to casinos, costing the average adult $75 per year. In addition, they confirm that border counties also experience increased crime rates, which suggests that casinos increase aggregate crime, as opposed to merely shifting crime from one county to another.