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Equity and Efficiency of State Lotteries
Ecmromics
of Edmmfion
Vol. 13. No. 4. pp. 355-362. 1994 Copyright @ 1994 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0272-7757l94 $9.50 + 0.00 Revkw,
0272-7757(94)EOOO3-N
Review Essay Equity and Efficiency of State Lotteries The Economic Consequences of State Lotteries by MARY 0. BORG, PAUL M. MASON and STEPHEN L. SHAPIRO. New York: Praeger, 1991. xii + 140 pp. $39.95 (cloth). SAMUEL Department
of
Economics.
College
COOPER
and ELCHANAN
of Business Administration. Columbia. SC 29208. U.S.A.
COHN* University
of
South
Carolina,
Abstract-Borg et a/. argue that the Florida lottery creates an implicit tax which is both inequitable and inefficient. We analyze their models. results and interpretation of results. and conclude that their conclusidns are far less than convincing. We also find it difficiult to accept that lotteries are responsible for reductions of up to 35% in per pupil spending on elementary and secondary schools in lottery states. We find it interesting that additional work by Borg e/ al. indicates that lotteries have little, if any, negative impact on consumer spending; in fact. the opposite may be true in some cases. book The Economic bases. The penultimate chapter attempts to discern is a well written and the alternative spending displaced by lotteries, and the final chapter offers some policy recomconcise book that deals with several questions related to the economics of state lotteries. Since mendations. New Hampshire instituted its lottery in 1964. many ’ In what follows we summarize the methodologies and findings of the BMS book. In the processwe other states have followed suit. Politicians in these states have found lotteries an attractive revenue also find it imperative to reinterpret, and in some source. In The Economic Consequences of State cases challenge, the book’s conclusions. Lotteries, however, Borg et nl. (hereafter BMS) argue that the temptations of this “attractive HISTORY OF THE LOTTERY revenue” should be resisted. Their book, while only I40 pages in length, The Lottery certainly cannot be considered a new invention in the United States. As far back as addresses many issues of importance to state legiscolonial America the lottery was used to fund both latures when considering the implementation or restructuring of lotteries. In the first chapter the public and private projects. These projects covered authors give a historical overview of the lottery a considerablespectrum ranging from schoolsand instrument, and also discuss its management. From roads to the financing of the Revolutionary War. here they proceed to an analysis of both the During the colonial period there seemed to be little budgetary incidence (chapter 2) and efficiency resistance to the use of these instruments as tools of aspects (chapter 3) of the lottery. In the next chapter public finance. However, as the nineteenth century arrived, other methods of funding were being the authors turn to the relation between lottery taxes and revenue from other, more traditional, tax developed and the lottery began to lose supporters. BORG,
MASONAND SHAPIRO’S of State Lotteries
ConsequenCes
* Correspondence.
355
Economics of Educntion Review
356
In fact, considerable opposition arose, claiming that gambling was not only morally wrong, but also that it had negative social effects on lower income groups. As a result legal restrictions were imposed in the late 1800s that severely limited gambling activities in the United States. “. . . From 1894 to 1964 there were no government-sponsored or other legal lotteries in the United States” [BMS 1991, p.
dummies for city size, residence in suburb or rural area and number of household members attending a public school. In the second step, since revenues obtained by the Florida lottery are earmarked to education, a probit equation was estimated to determine the impact of household income on the number of children attending public school (call it
21.
The data used by BMS were collected from a survey mailed to randomly chosen residents of Florida. From this mailing, only 163 completed surveys were returned. To supplement this small data set, a phone survey was also conducted in the spring of 1990. This survey provided 276 additional respondents for a total of 439 observations. Given the issues addressed in this research, we think the data collection methods may lead to inaccurate findings. For example, there is obviously a selection mechanism at work in both the mail and phone surveys. With respect to the mail survey, the 163 households returning the form are likely to be fundamentally different from those not returning the form. As a result, the data may not be representative of the general population, contrary to BMS’s claim. Also, it is interesting to note that the phone survey is biased since it only reaches households with telephones and published numbers. The families not reached (i.e., those without a telephone or published numbers) are likely to represent the very poor and very rich, respectively. The very poor represent those individuals who BMS argue are most negatively affected by lotteries. Although we recognize that all data suffer some limitations, we feel that this data set may not accurately represent the poor and the very rich, and as a result it seems difficult to determine how they are influenced by the lottery. We will assume for the moment that the data are acceptable, and proceed to discuss BMS’s findings. In their first equation relating Y to L, there are only three significant explanatory variables among the 13 that were included. These are household income (at the 10% level), head self-employed (at the 1% level) and head in a blue collar occupation (also at the I% level). Concerning the household income estimate, ‘*. . . a 10% increase in income for a household with the sample’s mean income of $32,927 increases monthly lottery expenditures from $12.39 to $12.88, which is an increase of only 4%. This means that the income elasticity of lottery expenditures at the mean is only 0.4, and an income
New Hampshire was the first to implement the lottery in the twentieth century. Soon many other states followed suit, but in many cases with less than grand results. BMS provide a discussion of the problems that plagued the “re-introduction” of the lottery in their first chapter. One of the most serious of these being that revenue expectations were never fully met. The authors then proceed to a discussion of lottery advertising, lottery instruments, industry linkages, and other associated topics. These issues will only be addressed in this review as needed to supplement our discussion of the empirical analysis.
INCIDENCE
OF THE STATE
LOTTERY
“TAX”
A number of studies have attempted to measure the incidence of the “implicit tax” of state lotteries (see e.g., Clotfelter and Cook, 1987, 1989, ch. 11). Clotfelter and Cook (1989) conclude from their surveys of the studies that “the evidence is quite clear that the implicit lottery tax is decidedly regressive” (p. 227). They recognize, however, that these studies are deficient because they do not consider the budgetary incidence of state revenues derived from lotteries (1987, p. 545). An attempt to overcome this deficiency was made in Borg and Mason (1988), and BMS provide additional analysis in Chapter 2. Borg and Mason (1988) examine the budgetary incidence of the Illinois lottery, while BMS employ a similar methodology for Florida. We have serious reservations about the methodology employed in both studies, and believe that the results are biased against state lotteries. The methodology used in the BMS study includes several steps. The first step is the estimation of a simple linear regression relating household income (call it Y) to lottery expenditures (call it L). In this regression the authors include an unusually large number of controls, including age, race, marital status, years of education, number of income earners in the home, self-employment, whether the head is employed in a blue collar occupation,
A).
Review Essay - State Lotteries
elasticity of less than one denotes a regressive pattern of expenditures” (BMS p. 19). The Fstatisticis highly significantfor this equation, but the adjusted R’ is only 0.06. The small amount of explained variance indicates either a serious misspecificationof the equation, or an extreme amount of randomnessassociatedwith lottery expenditures. If the former is true, then the income coefficient might be biased, and the results reported in BMS must be treated with great caution. The secondstep in BMS’s incidenceanalysisis, as stated above, estimationof a probit equation for the number of householdmembersattending a public school (A). This step is performed in an attempt to estimate the direct benefit received by the household from educational services funded by the lottery. The equation explains the data reasonably well with an estimatedR’ of 0.46 and with 8 of 12 independent variables being significant at the 10% level or better. The authors use this equation to draw the conclusionthat there is a positive relation between income and the number of children in the household.They do this indirectly by looking at the parameter estimatesfor educational attainment of the householdhead and from the number of income earners in the home. Both of these variables have positive and significant coefficients (although household income itself was insignificant), which BMS construe to indicate a positive correlation between “affluence” and number of children. This result sharply contrasts with the common observation that as family income rises, the number of children per householddeclineand the propensity to enroll in private schoolsincreases,resulting in an inverse relationship between income and the number of children attending public school. Since BMS’s definition of A includeschildren attending post-secondary schools, and if children of more affluent parents are more likely to attend such schools,a positive correlation betweenA and Y may be plausible.But the presencein BMS’s equation of many variables correlated with Y (e.g., race and schooling)castsdoubts about the precisionof their estimatesof the relation betweenY and A. The two equations discussedabove are used to determine the budgetary incidence of the lottery. The probit equation is usedspecifically in calculating the direct educational benefits received from funding provided through the lottery instrument. It provides estimates for the probabilities that a householdwith average characteristicswill have a
357
particular number of children attending public school. This calculation is also produced for households stratified by income. After probabilities are determined, the authorscalculate the funding available per student from the lottery. Revenuesfrom the lottery provided $700.5million to education for the 1989-1990school year. Given the 2.22 million full-time equivalent studentsin the state, this means that $315.54per student wasavailable in 1989-1990 from the lottery (p. 24). This information is usedin conjunction with the probabilitiesfrom Table 2.3 (p. 28) to derive the average benefit induced through lottery funding. Budgetary incidenceanalysis,however, mustalso include a measureof tax incidencein addition to the benefit incidencejust described.Two basicassumptions are invoked to derive the (gross)tax burden by income group. (1) An estimate of the relation between lottery expenditures and income is obtained through a linear regression. (2) Lottery expenditures are multiplied by 38% (representing the proportion of lottery revenuesremaining after administrative costs and prizes are excluded). In Table 2.4 (p. 29) BMS merge the results of their benefit and tax incidence analysis to derive an overall budgetary incidence. Their findings for “net taxes” are that all but one of the incomecategories receive a negative tax. In other words, only one group actually suffers a burden while the other groups receive net benefits through lottery financing. The group faced with the positive tax is the lowest income group (those with incomes below $10,000).The other incomegroupsreceiveprogressively larger net benefits up to the penultimate income category (which still receives a large benefit). But the distribution of lottery expenditures(L) by income (Y) is highly suspicious.First, the sample may not be representative (as discussedabove). Second, as is true of all surveys, respondentsmay not reveal their correct income; in particular, some people with high economic income might report very low “household” income, no matter how “confidential” a survey purports to be. Third, as noted earlier, the procedure employed by BMS to estimate the relation between L and Y may be in error. In addition to Table 2.4 (the end result of this chapter) Tables 2.2 and 2.3 on pages26-28 are also of interest themselves. While these tables are preliminary to Table 2.4, they still provide insight
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Ecotlomics
of Education
into certain aspects of the analysis. Table 2.2. contains the means and standard deviations for.all independent variables by income groupings. Table 2.3 provides information on the probability of a household (in a given income group) having a certain number of children in public school. Using Table 2.3, BMS conclude that the probabilities of having children in public school is very sensitive to income level. “For example, the probability that the average household in the lowest income category (below $10.000) will have no members in public school (and, thus, receive no benefit from lottery dollars) is a staggering 94.6% . . .” (p. 24). Suppose that, despite reservations voiced earlier, we accept these results. Yet it appears that there is more underlying these results than BMS point out. For example, looking at Table 2.2 we notice that the lowest income group, on average, also has the most senior members (60.54 years of age). This is not surprising since most retirees (who make up 56% of this category) have quite small income inflows. This is certainly not to say that they have not amassed considerable wealth (especially for Florida seniors). As a result, the regressivity found by BMS may simply be due to an inadequate measure of economic income (compared, say, to the Haig-Simons definition). Also, Table 2.2 shows that in this lowest income group lottery spending is less on average than any other income category. In addition, it is quite possible that households in this low income group (again largely made up of seniors) have grandchildren in public schools. To the extent that other family members’ welfare enters their own utility function, these seniors may receive considerable benefit from lottery funding. This may be either direct or indirect benefits. If the seniors find themselves contributing to their grandchildren’s educational expenses in the absence of the lottery, their direct benefit may increase in the presence of the lottery.
EFFICIENCY
OF LOTTERY
TAXATION
Having concluded that lotteries represent a regressive form of financing, BMS proceed to the question of the efficiency of lottery “taxation”. The key concept in taxation efficiency is that of excess burden. The authors provide a short explanation of excess burden and its relation to demand and supply elasticities. In this discussion BMS consider several relevant questions, including how elastic the
Review
demand for lottery tickets is and how lottery elasticities compare to spending alternatives. Since lottery tickets are usually priced at $1.00, QMS argue that the demand for tickets “is really more a function of the return received for the dollars expended” (p. 36). This return differs among the different lottery instruments. For example, instant tickets have a constant expected return based on some stated probability of winning. BMS contend that this implies that the demand curve for such tickets is horizontal. They also argue that the supply curve for lottery tickets is horizontal, so “the pre-tax supply curve is parallel to and below the after tax supply by the amount of the tax” (p. 36). Given these circumstances, the excess burden is infinite with respect to the “implicit tax”. In other words, lottery taxation on instant tickets is wholly inefficient. This argument, however, is very weak, because it is not reasonable to infer a horizontal demand curve. It is assumed that, given a price of $1 per ticket and a given probability of winning, people will purchase whatever number of tickets is placed on the market. BMS see lotto games in a different light, however. It is still assumed that the supply curve is horizontal, but the demand curve now takes on the more normal negative slope. “As a result, the purchase of a lotto ticket during a week where the pot has rolled over from the previous week appears advantageous because it inherently implies a larger probability adjusted rate of return on your dollar” (p. 36). Demand elasticity is now a function of how buyers respond to the rollover. BMS cite an example from Florida in which a rollover took place for 4 consecutive weeks. In this example the size of the pot more than doubled in each week following a rollover. The authors accept this as an indication of the demand elasticity exceeding one and thus conclude that demand is elastic. If this is true, then the tax imposed on lotto tickets is considered inefficient relative to taxes on goods with inelastic demand. In determining the relative excess burden of lottery taxation, the authors compare the demand elasticities of lottery tickets and substitutable goods. The authors consider cigarettes and alcohol to be substitutes for lottery purchases. They cite Mansur and Whalley (1984) in making the working assumption that demand elasticities for tobacco and alcohol are in the 0.2-0.8 range (in absolute value). Clearly, these estimates indicate that demand is inelastic for
Review Essay - State Lotteries these products. This is not surprising given that they are habit forming activities. The question which BMS do not address is the degree to which gambling (including playing the lottery) is also habit forming. They base their analysis on the above “approximation” of demand elasticity for tickets solely on rollover and expenditure considerations. As a result, their conclusion that the lottery is more inefficeint than sales and excise taxes on substitutes is questionable. We also question Borg and Mason’s (1988) similar argument concerning the Illinois lottery. They argue that the lottery diverts 58% of revenues (9% for administration and 49% for prizes), leaving only 42% for public expenditures. This is compared to sales and excise taxes which “divert only 5 to 10% per dollar expended to costs” (p. 81). The comparison of the 58% to the 5-10% of excise and sales taxes is clearly inappropriate, because the 49% in prizes is clearly not a tax, nor do Borg and Mason count the prizes in the gross tax calculations. The appropriate comparison is between the 9% administration cost for the lottery and the respective 5-10% for excise and sales taxes. Since the 9% for the lottery is within the 5-10% range, it cannot be argued that the implicit lottery tax is substantially less efficient than, say, sales and excise taxes. While administrative costs in Florida are slightly higher, l2%, this is still far below the 62% (12% for administrative costs and 50% prizes) not going to state coffers. In addition, gains in utility from the play of lottery games could offset wholly or in part any loss of efficiency due to administrative costs. BMS also address the notion of distributional equity. Distributional equity is closely related to incidence considerations and, given the authors’ results, their conclusion is quite predictable. We have, however, reservations about the results and their interpretation. Suppose, for the moment, that we accept BMS’s conclusion that the budgetary incidence of the Florida lottery is regressive. Does this mean that the lottery is inequitable? To the extent that people with higher income pay absolutely less in taxes, the principle of vertical equity is clearly violated. If BMS’s results of the budgetary incidence are accepted, then, indeed, the implicit lottery tax is clearly regressive. But, given our reservations about BMS’s methodology, it is quite likely that the numbers in Table 2.4 are misleading. Even if the final schedule is not progressive, we are not convinced that, overall, a
359
negative correlation between income and net tax would remain, especially if the lowest income group is disregarded. Even Clotfelter and Cook (1989, pp. 226-7) concede that regressivity is not necessarily equal to vertical inequality. To conclude that the lottery is vertically inequitable would require an agreement on the precise meaning of “ability to pay” (e.g. equal proportional sacrifice) as well as knowledge of the relevant marginal utility of income schedules (Musgrave and Musgrave, 1989, pp. 22830). It must be concluded, therefore, that BMS fail to provide sufficient support for the alleged inequity of the lottery. After briefly looking at the issue of vertical equity, the authors address what they consider a more important equity question. “Do the lottery dollars accrue to the designated beneficiary?” In other words, are the funds raised from ticket sales actually being used for educational spending? The authors cite Borg and Mason’s (1988) results which analyze the above question for the state of Illinois. Borg and Mason’s (1988) conclusion is that education was actually worse off after institution of the lottery than before. The earmarking of lottery revenues to education may not, in effect, produce a corresponding increase in educational revenues, since “money mixes”, and non-lottery revenues may actually decrease. Borg and Mason provide data for Illinois showing (1) that real state revenues for the Common School fund, net of lottery revenues, showed a secular decline after 1974, in contrast to a secular increase between 1965 and 1974; (2) that real total expenditures on education for the period 197484 also show a modest decline; but (3) that total real state revenues for the period 1974-85 have increased. The obvious conclusion seems to be that funds have been diverted from education into alternative state programs. Although Borg and Mason conceded that “these results are not necessarily all bad” (p. 83), they contend that the Illinois lottery “has not proved advantageous for the beneficiary it was designed to support”. Discussions with Illinois educators lead us to believe that Illinois did intentionally divert lottery funds from the education budget. But the data provided by Borg and Mason are not convincing. First, as shown in Table 1 (of the 1988 study), Illiniois experienced a significant decrease in enrollments in elementary and secondary schools during the period 1974-86 (enrollments decreased by 21%). Given that real state revenues for edu-
360
Economics
of Education
cation decreased during the same period by only 11% (again see Table I), real revenues per pupil actually increased during the period by 12.7%. Second, while data (not provided here) show fluctuations over the period 1974-86 in real perpupil revenues, at least some of these fluctuations may be due to shifting moods in the State Legislature concerning school finance reform (Hickrod et al., 1983), which may have nothing to do with lottery revenues. Third, even if real per-pupil revenue had declined, it is unclear as to whether such revenues might have declined even further in the absence of a lottery, as state priorities in the 1970s and 1980s began to shift from elementary and secondary education to other areas (higher education, health, welfare, social services, and criminal justice are a few examples). For the Florida lottery, BMS (1991) take a different approach to the distributional question. They now approach the question by formulating an expenditure regression model. The regression is basically that of a median voter model where the dependent variable is state expenditures per student. The data are cross sectional in nature (for three academic years: 1974-75, 1979-80, and 198485) and are measured at the state level. Included as an independent variable is a dichotomous variable indicating whether the state has an education lottery. The regression results have reasonable explanatory power (R’s ranging from 0.244 to 0.478) and highly significant F-statistics. The majority of independent variables are insignificant at standard levels, however. The educational lottery variable is significant at the 10% level or better and is negatively signed for all three academic years. In addition, the magnitude is quite large, indicating that lottery states provide considerably less funding per student than non-lottery states (other things constant). Again we find BMS’s methods less than satisfying. The regression model just described is likely inappropriate for modeling educational expenditures. As we stated above, the model is of the median voter type. While this is the traditional model used in analyzing local public spending, it relies on assumptions which are almost certainly (and grossly) violated with respect to educational expenditures. One of these assumptions is single-peakedness of preferences. This assumption is essential to the operationalization of the median voter model. There is some evidence in the literature, however,
Review
that single-peakedness is likely to be violated when school expenditures (see considering public Sonstelie (1979) and Cooper (1994) for two different presentations). Tore-Rovira (1991) provides an alternative estimation procedure that can be used when median voter assumptions are not met. Although Tor&Rovira’s method was published in 1991, the same year as the book under review, some alternative procedure would in any event have been interesting for comparison purposes. This shortcoming, we feel, introduces serious misspecifications into the model and thus limits our confidence in the results. In fact, the results by BMS for example, that, in 1984, states with lotteries spent 35% less on education per pupil than other states cannot be taken seriously as a cause-and-effect phenomenon.
LOTTERY TAXES, ALTERNATIVE REVENUE, AND CONSUMER SPENDING PATTERNS The authors take a look at two additional topics of concern for states considering either implementation or restructuring of lotteries. In chapter 4 BMS model the effect that the “lottery tax” has on other more traditional revenue sources (i.e., income, sales and excise taxes). Chapter 5 addresses the sources of lottery expenditures, and how these expenditures have changed consumption patterns. In chapter 4, a basic “macro” model is built for state economies. This model includes structural equations for state gross factor income, state government total revenue, level of savings in the state and overhead expenses of the state lottery. The authors find that the existence of a lottery does, in fact, affect revenue flows from other sources. “Specifically, those states without state income taxes, and high rates associated with sales and excise taxes, lose considerably more non-lottery revenue as a result of instituting a lottery. While these revenue losses are generally less than 15% of each dollar of revenue from the lottery, some states may be forfeiting as much as 23% of their supposed lottery proceeds indirectly through the impact of the lottery on other sources of state revenue” (p. 60). There is no question that the lottery became an important industry during the 1980s. The growth in lottery sales has been phenomenal over this period with sales raising to $19,496 million by 1990. In the absence of lotteries, this large sum would have either been saved or spent on other goods or
Review Essay -
services. From which sourceshas lottery spending been diverted? In chapter 5, B-MS search for an answerto this question.The authorsneededdata on consumerexpenditure, and in the late summerand fall of 1989 mailed 2000 surveys to residents in Georgia and Florida (1000 in each state). Of these 2000surveys, only 3.55were returned in usableform. The survey askedrespondentsto state their spending in an average month during the summerof 1989 on a representative basket of goods. Along with spending on this basket, respondentswere asked how much they spenton lottery tickets. In addition, they were askedto think back to the summerof 1987 and provide information on spendingfor the same basket (before Florida had a lottery). From these data, BMS estimated 20 separate expenditure equationson various spendingcategories.A dummy variable indicating whether or not the respondent played the lottery is included in these equations. Similar regressionswere run where the dependent variable was not category spending, but rather the changein category spendingfrom 1987to 1989. BMS found, to say the least, unexpected results for the spending equations. They had felt that lottery spendingwould decreasespendingfor such items as tobacco and groceries. What they found was that spending for these goods was actually greater among householdswho played the lottery when compared to non-players. Other spending categoriessuchasmortgagepaymentsor rent, other gambling activities. home furnishings and home serviceswere alsofound to be greater amonglottery playing homes(note that income and demographic factors were held constant). “. . . The only expenditure category in which lottery householdsspend lessthan their non-lottery counterparts is charitable giving” (p. 98). Later analysis, however. showed that lottery householdsgave lessto charity before the introduction of the lottery, so that one cannot blame the lottery on charitable behavior. With respect to the expenditure change equations(from pre- to post-lottery), the only variable showing significant difference is alcohol expenditures. This category sawa decreaseof approximately $4.50 for lottery playing households.This supportsthe claim by convenience store owners that lottery saleshad
State Lotteries
361
decreasedspending on their “bread and butter”, namely alcoholic beverages, but may actually be viewed as a positive effect of the lottery. The above resultsare for the entire data set, but when the authors segregated the data into low ($20,000 or less)and high (over $20,000) income groupings, the results prove to be what lottery proponents had feared. “It seemsthat since the lottery began lottery householdsin the low income group have significantly reducedtheir utility expenditures relative to householdsthat do not play the lottery. They have also reduced their grocery expenditures, although not significantly” (p. 98). The only category that showeda significant decline among high income lottery playing householdsis personalgroomingproducts (a decline of $3.78 per month). But, as Cain (1976) points out, truncating samplesby income levels might causeseverebiases in the results, suggestingcaution in eliciting policy implications.
CONCLUSIONS The analysis of this review has concentrated on BMS’s study of the Florida state lottery. Although we have serious reservations about the reliability and validity of the data and the empirical analysis, we nonethelessfeel that the authors have made a significant contribution by initiating a study of one of the nation’slargestlotteries. It appears,however, that additional analysesare neededto sustainBMS’s statements concerning the regressivity and inefficiency of the lottery instrument. We need to know more about the degree to which other forms of gambling may be substitutesfor state lotteries, the relation between economic income (including wealth) and lottery expenditures, and the incidence of public expenditures or tax reductions generated by the lottery. Likewise, the efficiency of the lottery tax may be quite high if revenuesfrom the lottery would have otherwise been absorbed by other gambling, so that state revenues are largely a windfall. It is quite possible, therefore, that the lottery tax is neither inequitable nor inefficient; the jury is still out on this subject.
REFERENCES BORG, National
M.O.
and MASON. Tax Jorrn~al41
P.M. (1988) The (March), 75-85.
budgetaryincidenceof a lottery to supporteducation.
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Economics CAIN.
of Ehcatio~l
Review
G.G.
(1976) The challenge of segmented labor market theories to Orthodox theory: A survey. of Economic Lirernrrrre 14 (December). 1215-1257. CLOTFELTER. C.T. and COOK. P.J. (1987) Implicit taxation in lottery finance. No/ionrr/ Tns Jowrd 40 (December), 533-546. CLOTFELTER, C.T. and COOK, P.J. (1989) Selling Hop>: S/o/e Lorreries i/r America. Cambridge. MA: Harvard University Press. COOPER. ST. (1994) Essrcps in f/re Poliricd Ecor~or~~y of Edrrctrrio~r. Unpublished doctoral dissertation. Department of Economics, University of South Carolina. Columbia. SC (USA). HICKROD, G.A.K.. CHAUDHARI. R.B. and HUBBARD. B.C. (1903) The decline and fall of school finance reform in Illinois. Jortnrnl of Edrtcnriorr Fijrnfrce 9 (Summer), 17-3X. MANSUR, A. and WHALLEY. J. (1984) Numerical specification of applied general equilihrium models: Estimation, calibration, and data. In Applied Geltern/ Equilibriwn Am~/ysis (Edited by SCARF, H.E. and SHOVEN, J.B.) New York: Cambridge University Press. MUSGRAVE, R.A. and MUSGRAVE. P.B.‘( 19X9) Phlic Firrome irk Theory md Prrccricv. 5th edn. New York: McGraw-Hill. SONSTELIE. J. (1979) Public school quality and private school enrollments. Narionnl TM Jotrrd 32.
Jolrmn/
343-353. TODO-ROVIRA. median voter
A. (I99 model.
I) Empirical
Public
analysis
Firuum
46(3).
of the
100-5
provision I I.
of local
public
goods:
An
alternative
to the
of Edmmfion
Vol. 13. No. 4. pp. 355-362. 1994 Copyright @ 1994 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0272-7757l94 $9.50 + 0.00 Revkw,
0272-7757(94)EOOO3-N
Review Essay Equity and Efficiency of State Lotteries The Economic Consequences of State Lotteries by MARY 0. BORG, PAUL M. MASON and STEPHEN L. SHAPIRO. New York: Praeger, 1991. xii + 140 pp. $39.95 (cloth). SAMUEL Department
of
Economics.
College
COOPER
and ELCHANAN
of Business Administration. Columbia. SC 29208. U.S.A.
COHN* University
of
South
Carolina,
Abstract-Borg et a/. argue that the Florida lottery creates an implicit tax which is both inequitable and inefficient. We analyze their models. results and interpretation of results. and conclude that their conclusidns are far less than convincing. We also find it difficiult to accept that lotteries are responsible for reductions of up to 35% in per pupil spending on elementary and secondary schools in lottery states. We find it interesting that additional work by Borg e/ al. indicates that lotteries have little, if any, negative impact on consumer spending; in fact. the opposite may be true in some cases. book The Economic bases. The penultimate chapter attempts to discern is a well written and the alternative spending displaced by lotteries, and the final chapter offers some policy recomconcise book that deals with several questions related to the economics of state lotteries. Since mendations. New Hampshire instituted its lottery in 1964. many ’ In what follows we summarize the methodologies and findings of the BMS book. In the processwe other states have followed suit. Politicians in these states have found lotteries an attractive revenue also find it imperative to reinterpret, and in some source. In The Economic Consequences of State cases challenge, the book’s conclusions. Lotteries, however, Borg et nl. (hereafter BMS) argue that the temptations of this “attractive HISTORY OF THE LOTTERY revenue” should be resisted. Their book, while only I40 pages in length, The Lottery certainly cannot be considered a new invention in the United States. As far back as addresses many issues of importance to state legiscolonial America the lottery was used to fund both latures when considering the implementation or restructuring of lotteries. In the first chapter the public and private projects. These projects covered authors give a historical overview of the lottery a considerablespectrum ranging from schoolsand instrument, and also discuss its management. From roads to the financing of the Revolutionary War. here they proceed to an analysis of both the During the colonial period there seemed to be little budgetary incidence (chapter 2) and efficiency resistance to the use of these instruments as tools of aspects (chapter 3) of the lottery. In the next chapter public finance. However, as the nineteenth century arrived, other methods of funding were being the authors turn to the relation between lottery taxes and revenue from other, more traditional, tax developed and the lottery began to lose supporters. BORG,
MASONAND SHAPIRO’S of State Lotteries
ConsequenCes
* Correspondence.
355
Economics of Educntion Review
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In fact, considerable opposition arose, claiming that gambling was not only morally wrong, but also that it had negative social effects on lower income groups. As a result legal restrictions were imposed in the late 1800s that severely limited gambling activities in the United States. “. . . From 1894 to 1964 there were no government-sponsored or other legal lotteries in the United States” [BMS 1991, p.
dummies for city size, residence in suburb or rural area and number of household members attending a public school. In the second step, since revenues obtained by the Florida lottery are earmarked to education, a probit equation was estimated to determine the impact of household income on the number of children attending public school (call it
21.
The data used by BMS were collected from a survey mailed to randomly chosen residents of Florida. From this mailing, only 163 completed surveys were returned. To supplement this small data set, a phone survey was also conducted in the spring of 1990. This survey provided 276 additional respondents for a total of 439 observations. Given the issues addressed in this research, we think the data collection methods may lead to inaccurate findings. For example, there is obviously a selection mechanism at work in both the mail and phone surveys. With respect to the mail survey, the 163 households returning the form are likely to be fundamentally different from those not returning the form. As a result, the data may not be representative of the general population, contrary to BMS’s claim. Also, it is interesting to note that the phone survey is biased since it only reaches households with telephones and published numbers. The families not reached (i.e., those without a telephone or published numbers) are likely to represent the very poor and very rich, respectively. The very poor represent those individuals who BMS argue are most negatively affected by lotteries. Although we recognize that all data suffer some limitations, we feel that this data set may not accurately represent the poor and the very rich, and as a result it seems difficult to determine how they are influenced by the lottery. We will assume for the moment that the data are acceptable, and proceed to discuss BMS’s findings. In their first equation relating Y to L, there are only three significant explanatory variables among the 13 that were included. These are household income (at the 10% level), head self-employed (at the 1% level) and head in a blue collar occupation (also at the I% level). Concerning the household income estimate, ‘*. . . a 10% increase in income for a household with the sample’s mean income of $32,927 increases monthly lottery expenditures from $12.39 to $12.88, which is an increase of only 4%. This means that the income elasticity of lottery expenditures at the mean is only 0.4, and an income
New Hampshire was the first to implement the lottery in the twentieth century. Soon many other states followed suit, but in many cases with less than grand results. BMS provide a discussion of the problems that plagued the “re-introduction” of the lottery in their first chapter. One of the most serious of these being that revenue expectations were never fully met. The authors then proceed to a discussion of lottery advertising, lottery instruments, industry linkages, and other associated topics. These issues will only be addressed in this review as needed to supplement our discussion of the empirical analysis.
INCIDENCE
OF THE STATE
LOTTERY
“TAX”
A number of studies have attempted to measure the incidence of the “implicit tax” of state lotteries (see e.g., Clotfelter and Cook, 1987, 1989, ch. 11). Clotfelter and Cook (1989) conclude from their surveys of the studies that “the evidence is quite clear that the implicit lottery tax is decidedly regressive” (p. 227). They recognize, however, that these studies are deficient because they do not consider the budgetary incidence of state revenues derived from lotteries (1987, p. 545). An attempt to overcome this deficiency was made in Borg and Mason (1988), and BMS provide additional analysis in Chapter 2. Borg and Mason (1988) examine the budgetary incidence of the Illinois lottery, while BMS employ a similar methodology for Florida. We have serious reservations about the methodology employed in both studies, and believe that the results are biased against state lotteries. The methodology used in the BMS study includes several steps. The first step is the estimation of a simple linear regression relating household income (call it Y) to lottery expenditures (call it L). In this regression the authors include an unusually large number of controls, including age, race, marital status, years of education, number of income earners in the home, self-employment, whether the head is employed in a blue collar occupation,
A).
Review Essay - State Lotteries
elasticity of less than one denotes a regressive pattern of expenditures” (BMS p. 19). The Fstatisticis highly significantfor this equation, but the adjusted R’ is only 0.06. The small amount of explained variance indicates either a serious misspecificationof the equation, or an extreme amount of randomnessassociatedwith lottery expenditures. If the former is true, then the income coefficient might be biased, and the results reported in BMS must be treated with great caution. The secondstep in BMS’s incidenceanalysisis, as stated above, estimationof a probit equation for the number of householdmembersattending a public school (A). This step is performed in an attempt to estimate the direct benefit received by the household from educational services funded by the lottery. The equation explains the data reasonably well with an estimatedR’ of 0.46 and with 8 of 12 independent variables being significant at the 10% level or better. The authors use this equation to draw the conclusionthat there is a positive relation between income and the number of children in the household.They do this indirectly by looking at the parameter estimatesfor educational attainment of the householdhead and from the number of income earners in the home. Both of these variables have positive and significant coefficients (although household income itself was insignificant), which BMS construe to indicate a positive correlation between “affluence” and number of children. This result sharply contrasts with the common observation that as family income rises, the number of children per householddeclineand the propensity to enroll in private schoolsincreases,resulting in an inverse relationship between income and the number of children attending public school. Since BMS’s definition of A includeschildren attending post-secondary schools, and if children of more affluent parents are more likely to attend such schools,a positive correlation betweenA and Y may be plausible.But the presencein BMS’s equation of many variables correlated with Y (e.g., race and schooling)castsdoubts about the precisionof their estimatesof the relation betweenY and A. The two equations discussedabove are used to determine the budgetary incidence of the lottery. The probit equation is usedspecifically in calculating the direct educational benefits received from funding provided through the lottery instrument. It provides estimates for the probabilities that a householdwith average characteristicswill have a
357
particular number of children attending public school. This calculation is also produced for households stratified by income. After probabilities are determined, the authorscalculate the funding available per student from the lottery. Revenuesfrom the lottery provided $700.5million to education for the 1989-1990school year. Given the 2.22 million full-time equivalent studentsin the state, this means that $315.54per student wasavailable in 1989-1990 from the lottery (p. 24). This information is usedin conjunction with the probabilitiesfrom Table 2.3 (p. 28) to derive the average benefit induced through lottery funding. Budgetary incidenceanalysis,however, mustalso include a measureof tax incidencein addition to the benefit incidencejust described.Two basicassumptions are invoked to derive the (gross)tax burden by income group. (1) An estimate of the relation between lottery expenditures and income is obtained through a linear regression. (2) Lottery expenditures are multiplied by 38% (representing the proportion of lottery revenuesremaining after administrative costs and prizes are excluded). In Table 2.4 (p. 29) BMS merge the results of their benefit and tax incidence analysis to derive an overall budgetary incidence. Their findings for “net taxes” are that all but one of the incomecategories receive a negative tax. In other words, only one group actually suffers a burden while the other groups receive net benefits through lottery financing. The group faced with the positive tax is the lowest income group (those with incomes below $10,000).The other incomegroupsreceiveprogressively larger net benefits up to the penultimate income category (which still receives a large benefit). But the distribution of lottery expenditures(L) by income (Y) is highly suspicious.First, the sample may not be representative (as discussedabove). Second, as is true of all surveys, respondentsmay not reveal their correct income; in particular, some people with high economic income might report very low “household” income, no matter how “confidential” a survey purports to be. Third, as noted earlier, the procedure employed by BMS to estimate the relation between L and Y may be in error. In addition to Table 2.4 (the end result of this chapter) Tables 2.2 and 2.3 on pages26-28 are also of interest themselves. While these tables are preliminary to Table 2.4, they still provide insight
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Ecotlomics
of Education
into certain aspects of the analysis. Table 2.2. contains the means and standard deviations for.all independent variables by income groupings. Table 2.3 provides information on the probability of a household (in a given income group) having a certain number of children in public school. Using Table 2.3, BMS conclude that the probabilities of having children in public school is very sensitive to income level. “For example, the probability that the average household in the lowest income category (below $10.000) will have no members in public school (and, thus, receive no benefit from lottery dollars) is a staggering 94.6% . . .” (p. 24). Suppose that, despite reservations voiced earlier, we accept these results. Yet it appears that there is more underlying these results than BMS point out. For example, looking at Table 2.2 we notice that the lowest income group, on average, also has the most senior members (60.54 years of age). This is not surprising since most retirees (who make up 56% of this category) have quite small income inflows. This is certainly not to say that they have not amassed considerable wealth (especially for Florida seniors). As a result, the regressivity found by BMS may simply be due to an inadequate measure of economic income (compared, say, to the Haig-Simons definition). Also, Table 2.2 shows that in this lowest income group lottery spending is less on average than any other income category. In addition, it is quite possible that households in this low income group (again largely made up of seniors) have grandchildren in public schools. To the extent that other family members’ welfare enters their own utility function, these seniors may receive considerable benefit from lottery funding. This may be either direct or indirect benefits. If the seniors find themselves contributing to their grandchildren’s educational expenses in the absence of the lottery, their direct benefit may increase in the presence of the lottery.
EFFICIENCY
OF LOTTERY
TAXATION
Having concluded that lotteries represent a regressive form of financing, BMS proceed to the question of the efficiency of lottery “taxation”. The key concept in taxation efficiency is that of excess burden. The authors provide a short explanation of excess burden and its relation to demand and supply elasticities. In this discussion BMS consider several relevant questions, including how elastic the
Review
demand for lottery tickets is and how lottery elasticities compare to spending alternatives. Since lottery tickets are usually priced at $1.00, QMS argue that the demand for tickets “is really more a function of the return received for the dollars expended” (p. 36). This return differs among the different lottery instruments. For example, instant tickets have a constant expected return based on some stated probability of winning. BMS contend that this implies that the demand curve for such tickets is horizontal. They also argue that the supply curve for lottery tickets is horizontal, so “the pre-tax supply curve is parallel to and below the after tax supply by the amount of the tax” (p. 36). Given these circumstances, the excess burden is infinite with respect to the “implicit tax”. In other words, lottery taxation on instant tickets is wholly inefficient. This argument, however, is very weak, because it is not reasonable to infer a horizontal demand curve. It is assumed that, given a price of $1 per ticket and a given probability of winning, people will purchase whatever number of tickets is placed on the market. BMS see lotto games in a different light, however. It is still assumed that the supply curve is horizontal, but the demand curve now takes on the more normal negative slope. “As a result, the purchase of a lotto ticket during a week where the pot has rolled over from the previous week appears advantageous because it inherently implies a larger probability adjusted rate of return on your dollar” (p. 36). Demand elasticity is now a function of how buyers respond to the rollover. BMS cite an example from Florida in which a rollover took place for 4 consecutive weeks. In this example the size of the pot more than doubled in each week following a rollover. The authors accept this as an indication of the demand elasticity exceeding one and thus conclude that demand is elastic. If this is true, then the tax imposed on lotto tickets is considered inefficient relative to taxes on goods with inelastic demand. In determining the relative excess burden of lottery taxation, the authors compare the demand elasticities of lottery tickets and substitutable goods. The authors consider cigarettes and alcohol to be substitutes for lottery purchases. They cite Mansur and Whalley (1984) in making the working assumption that demand elasticities for tobacco and alcohol are in the 0.2-0.8 range (in absolute value). Clearly, these estimates indicate that demand is inelastic for
Review Essay - State Lotteries these products. This is not surprising given that they are habit forming activities. The question which BMS do not address is the degree to which gambling (including playing the lottery) is also habit forming. They base their analysis on the above “approximation” of demand elasticity for tickets solely on rollover and expenditure considerations. As a result, their conclusion that the lottery is more inefficeint than sales and excise taxes on substitutes is questionable. We also question Borg and Mason’s (1988) similar argument concerning the Illinois lottery. They argue that the lottery diverts 58% of revenues (9% for administration and 49% for prizes), leaving only 42% for public expenditures. This is compared to sales and excise taxes which “divert only 5 to 10% per dollar expended to costs” (p. 81). The comparison of the 58% to the 5-10% of excise and sales taxes is clearly inappropriate, because the 49% in prizes is clearly not a tax, nor do Borg and Mason count the prizes in the gross tax calculations. The appropriate comparison is between the 9% administration cost for the lottery and the respective 5-10% for excise and sales taxes. Since the 9% for the lottery is within the 5-10% range, it cannot be argued that the implicit lottery tax is substantially less efficient than, say, sales and excise taxes. While administrative costs in Florida are slightly higher, l2%, this is still far below the 62% (12% for administrative costs and 50% prizes) not going to state coffers. In addition, gains in utility from the play of lottery games could offset wholly or in part any loss of efficiency due to administrative costs. BMS also address the notion of distributional equity. Distributional equity is closely related to incidence considerations and, given the authors’ results, their conclusion is quite predictable. We have, however, reservations about the results and their interpretation. Suppose, for the moment, that we accept BMS’s conclusion that the budgetary incidence of the Florida lottery is regressive. Does this mean that the lottery is inequitable? To the extent that people with higher income pay absolutely less in taxes, the principle of vertical equity is clearly violated. If BMS’s results of the budgetary incidence are accepted, then, indeed, the implicit lottery tax is clearly regressive. But, given our reservations about BMS’s methodology, it is quite likely that the numbers in Table 2.4 are misleading. Even if the final schedule is not progressive, we are not convinced that, overall, a
359
negative correlation between income and net tax would remain, especially if the lowest income group is disregarded. Even Clotfelter and Cook (1989, pp. 226-7) concede that regressivity is not necessarily equal to vertical inequality. To conclude that the lottery is vertically inequitable would require an agreement on the precise meaning of “ability to pay” (e.g. equal proportional sacrifice) as well as knowledge of the relevant marginal utility of income schedules (Musgrave and Musgrave, 1989, pp. 22830). It must be concluded, therefore, that BMS fail to provide sufficient support for the alleged inequity of the lottery. After briefly looking at the issue of vertical equity, the authors address what they consider a more important equity question. “Do the lottery dollars accrue to the designated beneficiary?” In other words, are the funds raised from ticket sales actually being used for educational spending? The authors cite Borg and Mason’s (1988) results which analyze the above question for the state of Illinois. Borg and Mason’s (1988) conclusion is that education was actually worse off after institution of the lottery than before. The earmarking of lottery revenues to education may not, in effect, produce a corresponding increase in educational revenues, since “money mixes”, and non-lottery revenues may actually decrease. Borg and Mason provide data for Illinois showing (1) that real state revenues for the Common School fund, net of lottery revenues, showed a secular decline after 1974, in contrast to a secular increase between 1965 and 1974; (2) that real total expenditures on education for the period 197484 also show a modest decline; but (3) that total real state revenues for the period 1974-85 have increased. The obvious conclusion seems to be that funds have been diverted from education into alternative state programs. Although Borg and Mason conceded that “these results are not necessarily all bad” (p. 83), they contend that the Illinois lottery “has not proved advantageous for the beneficiary it was designed to support”. Discussions with Illinois educators lead us to believe that Illinois did intentionally divert lottery funds from the education budget. But the data provided by Borg and Mason are not convincing. First, as shown in Table 1 (of the 1988 study), Illiniois experienced a significant decrease in enrollments in elementary and secondary schools during the period 1974-86 (enrollments decreased by 21%). Given that real state revenues for edu-
360
Economics
of Education
cation decreased during the same period by only 11% (again see Table I), real revenues per pupil actually increased during the period by 12.7%. Second, while data (not provided here) show fluctuations over the period 1974-86 in real perpupil revenues, at least some of these fluctuations may be due to shifting moods in the State Legislature concerning school finance reform (Hickrod et al., 1983), which may have nothing to do with lottery revenues. Third, even if real per-pupil revenue had declined, it is unclear as to whether such revenues might have declined even further in the absence of a lottery, as state priorities in the 1970s and 1980s began to shift from elementary and secondary education to other areas (higher education, health, welfare, social services, and criminal justice are a few examples). For the Florida lottery, BMS (1991) take a different approach to the distributional question. They now approach the question by formulating an expenditure regression model. The regression is basically that of a median voter model where the dependent variable is state expenditures per student. The data are cross sectional in nature (for three academic years: 1974-75, 1979-80, and 198485) and are measured at the state level. Included as an independent variable is a dichotomous variable indicating whether the state has an education lottery. The regression results have reasonable explanatory power (R’s ranging from 0.244 to 0.478) and highly significant F-statistics. The majority of independent variables are insignificant at standard levels, however. The educational lottery variable is significant at the 10% level or better and is negatively signed for all three academic years. In addition, the magnitude is quite large, indicating that lottery states provide considerably less funding per student than non-lottery states (other things constant). Again we find BMS’s methods less than satisfying. The regression model just described is likely inappropriate for modeling educational expenditures. As we stated above, the model is of the median voter type. While this is the traditional model used in analyzing local public spending, it relies on assumptions which are almost certainly (and grossly) violated with respect to educational expenditures. One of these assumptions is single-peakedness of preferences. This assumption is essential to the operationalization of the median voter model. There is some evidence in the literature, however,
Review
that single-peakedness is likely to be violated when school expenditures (see considering public Sonstelie (1979) and Cooper (1994) for two different presentations). Tore-Rovira (1991) provides an alternative estimation procedure that can be used when median voter assumptions are not met. Although Tor&Rovira’s method was published in 1991, the same year as the book under review, some alternative procedure would in any event have been interesting for comparison purposes. This shortcoming, we feel, introduces serious misspecifications into the model and thus limits our confidence in the results. In fact, the results by BMS for example, that, in 1984, states with lotteries spent 35% less on education per pupil than other states cannot be taken seriously as a cause-and-effect phenomenon.
LOTTERY TAXES, ALTERNATIVE REVENUE, AND CONSUMER SPENDING PATTERNS The authors take a look at two additional topics of concern for states considering either implementation or restructuring of lotteries. In chapter 4 BMS model the effect that the “lottery tax” has on other more traditional revenue sources (i.e., income, sales and excise taxes). Chapter 5 addresses the sources of lottery expenditures, and how these expenditures have changed consumption patterns. In chapter 4, a basic “macro” model is built for state economies. This model includes structural equations for state gross factor income, state government total revenue, level of savings in the state and overhead expenses of the state lottery. The authors find that the existence of a lottery does, in fact, affect revenue flows from other sources. “Specifically, those states without state income taxes, and high rates associated with sales and excise taxes, lose considerably more non-lottery revenue as a result of instituting a lottery. While these revenue losses are generally less than 15% of each dollar of revenue from the lottery, some states may be forfeiting as much as 23% of their supposed lottery proceeds indirectly through the impact of the lottery on other sources of state revenue” (p. 60). There is no question that the lottery became an important industry during the 1980s. The growth in lottery sales has been phenomenal over this period with sales raising to $19,496 million by 1990. In the absence of lotteries, this large sum would have either been saved or spent on other goods or
Review Essay -
services. From which sourceshas lottery spending been diverted? In chapter 5, B-MS search for an answerto this question.The authorsneededdata on consumerexpenditure, and in the late summerand fall of 1989 mailed 2000 surveys to residents in Georgia and Florida (1000 in each state). Of these 2000surveys, only 3.55were returned in usableform. The survey askedrespondentsto state their spending in an average month during the summerof 1989 on a representative basket of goods. Along with spending on this basket, respondentswere asked how much they spenton lottery tickets. In addition, they were askedto think back to the summerof 1987 and provide information on spendingfor the same basket (before Florida had a lottery). From these data, BMS estimated 20 separate expenditure equationson various spendingcategories.A dummy variable indicating whether or not the respondent played the lottery is included in these equations. Similar regressionswere run where the dependent variable was not category spending, but rather the changein category spendingfrom 1987to 1989. BMS found, to say the least, unexpected results for the spending equations. They had felt that lottery spendingwould decreasespendingfor such items as tobacco and groceries. What they found was that spending for these goods was actually greater among householdswho played the lottery when compared to non-players. Other spending categoriessuchasmortgagepaymentsor rent, other gambling activities. home furnishings and home serviceswere alsofound to be greater amonglottery playing homes(note that income and demographic factors were held constant). “. . . The only expenditure category in which lottery householdsspend lessthan their non-lottery counterparts is charitable giving” (p. 98). Later analysis, however. showed that lottery householdsgave lessto charity before the introduction of the lottery, so that one cannot blame the lottery on charitable behavior. With respect to the expenditure change equations(from pre- to post-lottery), the only variable showing significant difference is alcohol expenditures. This category sawa decreaseof approximately $4.50 for lottery playing households.This supportsthe claim by convenience store owners that lottery saleshad
State Lotteries
361
decreasedspending on their “bread and butter”, namely alcoholic beverages, but may actually be viewed as a positive effect of the lottery. The above resultsare for the entire data set, but when the authors segregated the data into low ($20,000 or less)and high (over $20,000) income groupings, the results prove to be what lottery proponents had feared. “It seemsthat since the lottery began lottery householdsin the low income group have significantly reducedtheir utility expenditures relative to householdsthat do not play the lottery. They have also reduced their grocery expenditures, although not significantly” (p. 98). The only category that showeda significant decline among high income lottery playing householdsis personalgroomingproducts (a decline of $3.78 per month). But, as Cain (1976) points out, truncating samplesby income levels might causeseverebiases in the results, suggestingcaution in eliciting policy implications.
CONCLUSIONS The analysis of this review has concentrated on BMS’s study of the Florida state lottery. Although we have serious reservations about the reliability and validity of the data and the empirical analysis, we nonethelessfeel that the authors have made a significant contribution by initiating a study of one of the nation’slargestlotteries. It appears,however, that additional analysesare neededto sustainBMS’s statements concerning the regressivity and inefficiency of the lottery instrument. We need to know more about the degree to which other forms of gambling may be substitutesfor state lotteries, the relation between economic income (including wealth) and lottery expenditures, and the incidence of public expenditures or tax reductions generated by the lottery. Likewise, the efficiency of the lottery tax may be quite high if revenuesfrom the lottery would have otherwise been absorbed by other gambling, so that state revenues are largely a windfall. It is quite possible, therefore, that the lottery tax is neither inequitable nor inefficient; the jury is still out on this subject.
REFERENCES BORG, National
M.O.
and MASON. Tax Jorrn~al41
P.M. (1988) The (March), 75-85.
budgetaryincidenceof a lottery to supporteducation.
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Economics CAIN.
of Ehcatio~l
Review
G.G.
(1976) The challenge of segmented labor market theories to Orthodox theory: A survey. of Economic Lirernrrrre 14 (December). 1215-1257. CLOTFELTER. C.T. and COOK. P.J. (1987) Implicit taxation in lottery finance. No/ionrr/ Tns Jowrd 40 (December), 533-546. CLOTFELTER, C.T. and COOK, P.J. (1989) Selling Hop>: S/o/e Lorreries i/r America. Cambridge. MA: Harvard University Press. COOPER. ST. (1994) Essrcps in f/re Poliricd Ecor~or~~y of Edrrctrrio~r. Unpublished doctoral dissertation. Department of Economics, University of South Carolina. Columbia. SC (USA). HICKROD, G.A.K.. CHAUDHARI. R.B. and HUBBARD. B.C. (1903) The decline and fall of school finance reform in Illinois. Jortnrnl of Edrtcnriorr Fijrnfrce 9 (Summer), 17-3X. MANSUR, A. and WHALLEY. J. (1984) Numerical specification of applied general equilihrium models: Estimation, calibration, and data. In Applied Geltern/ Equilibriwn Am~/ysis (Edited by SCARF, H.E. and SHOVEN, J.B.) New York: Cambridge University Press. MUSGRAVE, R.A. and MUSGRAVE. P.B.‘( 19X9) Phlic Firrome irk Theory md Prrccricv. 5th edn. New York: McGraw-Hill. SONSTELIE. J. (1979) Public school quality and private school enrollments. Narionnl TM Jotrrd 32.
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