Can Consumers Be Protected From Themselves?&Nbsp; the Case of Distilled Spirits

Paul N. Bloom, University of Maryland
Frank J. Franzak, Boston College
ABSTRACT - The effects of an assortment of state distilled spirits regulations were evaluated using a cross-sectional econometric modeling approach. The results suggest that these regulations have not been effective in reducing consumption of distilled spirits.
[ to cite ]:
Paul N. Bloom and Frank J. Franzak (1982) ,"Can Consumers Be Protected From Themselves?&Nbsp; the Case of Distilled Spirits", in NA - Advances in Consumer Research Volume 09, eds. Andrew Mitchell, Ann Abor, MI : Association for Consumer Research, Pages: 315-320.

Advances in Consumer Research Volume 9, 1982      Pages 315-320


Paul N. Bloom, University of Maryland

Frank J. Franzak, Boston College


The effects of an assortment of state distilled spirits regulations were evaluated using a cross-sectional econometric modeling approach. The results suggest that these regulations have not been effective in reducing consumption of distilled spirits.


Public acceptance of the concept of deregulation has grown considerably in the last few years. Government officials at all levels are currently in the process of removing or revising many of the regulations that were established over the years to protect consumers or accomplish other social objectives. Where possible, the deregulation efforts are being guided by the results of evaluation research studies on the effects of specific regulations. However, much of the evaluation work- at least in the consumer protection area - has had serious methodological weaknesses (Phillips, 1978) or has only recently commenced and has not produced final reports (Bernhardt and Mazis,1980). There remains a shortage of informative, rigorous evaluation studies of consumer protection regulations. Such studies could help to add an element of rational discussion to the emotion-charged deregulation debate.

This paper contains a report on an evaluation of the effects of state regulations that seek to protect consumers from over-consumption of distilled spirits. This study was conducted to provide input to the heated debates currently going on in states like New Jersey and Massachusetts over how much the distilled spirits industry should be deregulated (Jacobs, 1981; Kenney, 1981). The study was also designed to provide insights into the effects of regulation (or deregulation) in general. Although the distilled spirits industry certainly has unique properties that make generalizations from its experiences difficult to extract, it is an industry that has been regulated in large part out of a desire to protect consumers from themselves, just as has been the case for the drug, cigarette, gambling, and automobile industries. It does not seem unreasonable to suggest that consumer protection regulations in the distilled spirits industry could have similar effects as parallel forms of regulation in these other major industries.

The paper is organized as follows. The types of regulations that exist in the distilled spirits industry and the nature of the debate that has gone on about these regulations are reviewed first. This is followed by a description of the cross-sectional model that was tested. The results of several regressions that were done to test the model are reported next, and an extended discussion is provided on the efforts that were taken to understand several of the complex relationships that were discovered.Finally, the implications of the results for public policy makers and for future evaluation research efforts are analyzed.


Since the repeal of Prohibition -- which represented the ultimate in regulation (ant probably the ultimate in the failure of regulation) -- an enormous variety of regulatory approaches have developed in the United States to discourage excessive drinking. Individual states have tried various combinations of the following measures to encourage moderation in drinking:

1. Limiting the population of drinkers by establishing a minimum age for purchasing alcoholic beverages.

2. Limiting the hours during which people can purchase alcoholic beverages.

3. Restricting the manner in which alcoholic beverages can be promoted to the public, including prohibitions of novelty promotions, price advertising, and billboard advertising.

4. Prohibiting the use of credit cards or other credit arrangements to purchase alcoholic beverages.

5. Limiting the size of distilled spirits containers.

6. Having licensing requirements that tend to limit the availabilitY of alcoholic beverages.

7. Restricting the sale of packaged alcoholic beverages to state-owned "monopoly" stores (i.e., banning them in grocery or drug stores).

8. Saving taxation methods or policies such as resale price maintenance and price posting that tend to stimulate higher prices for alcoholic beverages.

Of course, a desire for temperance was not the sole reason for instituting many of these regulations, as some were also passed to protect the interests of certain small retailers, to assist law enforcement officials in controlling illegal traffic, or to help certain states raise more revenues from the sale of alcoholic beverages.

Most of the above regulatory approaches have been criticized for being ineffective or wasteful. For instance, it has been argued that minimum age requirements inadvertently stimulate consumption by creating a "forbidden fruit" for young people (Wilkinson, 1970). Similarly, it has been argued that people who need to drink -- who also tend to be the ones responsible for alcohol-related social problems will not buy or consume less because of inconvenient hours or locations, a lack of advertising or promotion, the absence of credit privileges, the smallness of containers,the type of retailer, or the price (Bales, 1946; Popham et al., 1976). Unfortunately, until recently there has been little empirical evidence available to help judge the various arguments.

However, two recently completed studies both provide support for the critics of regulation of the distilled spirits industry. Colon (1980) found that per capita consumption of absolute alcohol in the 50 states and the District of Columbia for the year 1970 was best explained by several socio-demographic variables, with insignificant effects appearing for regulatory variables reflecting the minimum age, the existence of state monopoly control, the extent of limitations on availability, and the size of taxes. In a more extensive study, Ornstein and Hanssens (1981) found per capita consumption in the states (for the years 1974 to 1978) to be generally explained best by socio-demographic variables, although they did find restrictions against price advertising and the existence of state monopoly control to have significant negative effects on consumption. Ornstein and Hanssens found insignificant or positive effects appearing for regulatory variables reflecting the minimum age, the extent of limitations on availability,the existence of resale price maintenance, and the existence of bans on Sunday sales, bans on sales in drug stores. and bans on outdoor advertising.

The study reported upon below was designed to replicate and extent the work tone by Ornstein and Hanssens (1981). As discussed in the next section, their work was extended by using (1) a slightly different data base, (2) different operationalizations of several variables, and (3) a larger assortment of regulatory and socio-demographic variables. The third step was felt to be particularly important given the need to use several d = y variables in the regressions. As Phillips (1978) has pointed out in his review of consumer protection evaluations, one must strive to include all possible explanatory variables to avoid obtaining highly biased coefficients when dummy variables are used in "correlational" evaluation studies.

The Model

The basic model that was tested took the following form:



Cit = per capita consumption in wine gallons of distilled spirits in state i in year t

Rj,it = regulatory variables (j=1,...,m)

Mj,it = marketing variables (j=m+1,...,n)

Sj,it = socio-demographic variables (j=n+1,...,p)

COj,it = competition variables (j=p+1, . .. ,q)

Ej,it = enforcement variables (j=q+1,...,r)

eit = error term

The model was tested in this additive form rather than in the log-linear form used by Ornstein and Hanssens (1981)because no theoretical reasons could be identified for expecting the completely multiplicative, interactive relationship implied by a log-linear model. The numerous regression runs, conducted to explore the effects of multicollinearity among the explanatory variables, were all tone using ordinary least squares. The variables used under each variable category -- and the rationale for including each of them in the model -- are discussed fully in the following subsections. Sources of data are described in the Appendix.

Per Capita Consumption

It should be noted that the data used to represent consumption actually reflected sales of distilled spirits (i.e., shipments) by wholesalers and not actual consumption behavior. These data are typically used in studies of drinking. Naturally, these data could misrepresent actual drinking if (1) people did not drink all they bought,(2) people imported or exported spirits to take advantage of price differences across states, (3) sales went unreported because of a desire to avoid taxes, or (4) people consumed spirits produced illegally by themselves. An effort to control for the last three sources of bias was made by including a border-effect variable and two enforcement variables.

Regulatory Variables

The regulatory variables that were examined included:

SUNDAY = a dummy variable set at one if the state prohibited sales of distilled spirits on Sundays (and set at zero if the state allowed it)

NOVELTY = a dummy variable set at one if the state prohibited the use of novelty promotion items

MINAGE = the minimum legal drinking age for distilled spirits

MONOP = a dummy variable set at one if the state was a "monopoly" state (and set at zero if it was a "license" state)

REMIX = a dummy variable set at one if the state prohibited retailers from selling distilled spirits along with groceries, drugs, or other items

PRCADV = a dummy variable set at one if the state prohibited price advertising

CREDIT = a dummy variable set at one if the state prohibited the use of credit to purchase distilled spirits

SIZE = the maximum allowed container size for distilled spirits

Ornstein and Hanssens (1981) tested all of these variables except CREDIT and SIZE. However, they used two dummy variables (rather than one) to represent restrictions against selling spirits in grocery stores and drug stores. They also used two dummy variables to represent restrictions against using price advertising of spirits in billboards and print. In this study, it was teemed unnecessary to use more than REMIX and PRCADV because there are very few states that ban only one type of retail mix or one type of price advertising.

If the regulations have been performing as intended, then one would expect to find negative coefficients associated with all the regulatory variables except SIZE (which would have a positive coefficient). But theory that postulates that people who are so inclined will find a way to drink regardless of the obstacles suggests that no significant coefficients should be found for the regulatory variables. This theory tends to be supported by the results of the two studies cited earlier and by reports that have described heavy consumption of spirits in highly regulated countries like Russia, while light consumption has existed in relatively regulation-free countries like Finland. Therefore, it was hypothesized that no significant coefficients would be found for the regulatory variables.

Marketing Variables

The marketing variables were defined to be:

PRICE = the average price charged for eight major brands -- weighted by the relative sales of each brand and deflated by the regional Consumer Price Index (CPI)

AVL = the number of on-premise ant/or off-premise outlets for distilled spirits per capita

ADV = dollar expenditures per capita for distilled spirits advertising in magazines, newspaper supplements, and outdoor -- deflated by the regional CPI

The use of the regional Consumer Price Index represents a refinement of the approach of Ornstein and Hanssens (1981) as they apparently deflated by only the national CPI. The use of AVL also represents a refinement of their approach, since they used two separate variables for the number of on-premise and number of off-premise outlets. The ADV variable goes beyond what Ornstein and Hanssens employed, as they only included a d = y variable reflecting whether out door advertising was prohibited. Unfortunately, the lack of available data on newspaper advertising expenditures (by state) forced ADV to be a less than optimal indicator of advertising intensity.

It was hypothesized that PRICE would have a negative coefficient and that AVL and ADV would have positive coefficients. Obtaining such results would tend to support marketing and economic theory, while also tenting to support arguments contenting that regulations that raise prices, lower availability,and limit advertising expenditures tend to reduce consumption. It was recognized, of course, that positive coefficients could appear for AVL and ADV because these variables sight be determined by consumption (i.e., two-was causation could exist).

Socio-Demographic Variables

The variables used to control for the differences in the economies and people of various states were:

TEMP = average temperature in degrees centigrade

INC = per capita income deflated by the regional CPI

TOUR = dollars spent in hotels, motels, and motor hotels-deflated by the regional CPI

URBAN = percent of the population living in standard metropolitan statistical areas

DENS = population per square mile

The first four variables were used by Ornstein and Hanssens (1981), although they apparently deflated INC by the national CPI and they operationalized TOUR using "the percent of the total state payroll going to workers in hotels motels, and tourist courts." They also used four other control variables representing the percent of the population in the heavier drinking age groups (18 to 44) and the percent of the population that was Southern Baptist or Mormon, Catholic, and Protestant. These variables were not used in this study because (1) the age-group variable showed no effect in Ornstein and Hanssens' work and (2) data for the religion variables could not be located for the years that were analyzed. Since Ornstein and Hanssens found the religion variables to be highly correlated with TEMPS INC, TOUR, and URBAN, perhaps these other variables picked up the effects of religion in this study.

It was hypothesized that TEMP would have a negative coefficient and that the other four socio-demographic variables would have positive coefficients. Such results would be consistent with previous studies that have shown colder climates, higher incomes, healthier tourism, and greater urbanization or population density associated with heavier spirits consumption (Simon, 1966; Cahalane, Cisin, and Crossley, 1969; Smart, 1980; Ornstein and Hanssens, 1981).

Competition Variables

The two variables used to control for the effects of competitive activity were:

BORDER = [PRICE - (Lowest PRICE in an adjacent state)]/Distance between the population center of the state and the border of the lowest-priced adjacent state [The numerator was set at zero -- implying no border effect -- if PRICE was lower than the lowest PRICE in an adjacent state.]

WINEAD = dollar expenditures per capita on wine advertising in magazines, newspaper supplements, and outdoor-deflated by the regional CPI

Ornstein and Hanssens (1981) used a somewhat different variable to control for the effect of people being attracted to border states to save money on distilled spirits. Their variable took the ratio of the lowest adjacent-state price to the state's price and divided it by the square mileage of the state. It was believed that their variable essentially measured size of the state, since a numerator showing a ratio of prices would have very little variation in comparison to the variation in a denominator reflecting the size of a state. The variable used in this stud was thought to have a similar problem but in a less severe form. On the other hand, this study's variable only attempted to control for the effect of people importing spirits from lower-priced states, and not for the effect of people exporting spirits to higher-priced states. (Ornstein and Hanssens attempted to control for both.) Accounting for the latter effect would require another complicated index. Since, as discussed later, the BORDER variable did not work out too well -- and did not, among other things, exhibit the expected negative coefficient -- a second index was not developed.

Unlike any previous studies on distilled spirits, an effort was also made to control for the effect that promotion of competing alcoholic beverages might have on distilled spirits consumption. Consequently, WINEAD was included in the model with the expectation that it would have a negative coefficient.

Enforcement Variables

Smith(1976) has pointed out the importance of considering how illegal activities (e.g., stills, nonreported sales, border-running) and the policing of these activities affects the distilled spirits industry. Following this lead, the following two variables were included in the motel:

MASHSZ = gallons of mash seized by law enforcement officials

ENF = per capita expenditures on enforcing liquor control laws

Neither of these variables were used by Ornstein and Hanssens (1981). It was hypothesized that both variables would have positive coefficients, since it was believed that reporting of distilled spirits sales would be greater with more enforcement activity.

The Sample

The sample included observations from 49 states and the District of Columbia for the years 1970 and 1975 (n-100). The State of Hawaii was not included in the sample because of missing data. The years 1970 and 1975 were used because of the availability of data on advertising expenditures by state for these two years. The two years were pooled to obtain more degrees of freedom - although two runs are reported below that examined each year separately. Pooling of two separated years was deemed acceptable after noting that Ornstein and Hanssens (1981) found no statistical problems with pooling five consecutive years and using ordinary least squares.


The results of the various step-wise regressions are summarized in Table l. Equations 1.1 to 1.8 in the table are displayed going from the most parsimonious to the most comprehensive, while equations 1.9 and 1.10 show the results of estimating separate comprehensive equations from the 1970 and 1975 observations. In developing this table, particular emphasis was placed on showing the effects of removing AVL and/or ADV -- the two variables producing the most problems with multicollinearity -- from the equations. The extent of multicollinearity is revealed in the correlation matrix found in Table 2.

In general, the results turned out as hypothesized. That is:

1. The regulatory variables had no significant coefficients in any of the more comprehensive equations.

2. The coefficients for the marketing variables all had the expected signs - although only PRICE had a significant coefficient in the more comprehensive equations.

3. Four of the five socio-demographic variables had coefficients with the expected signs, and three of these (TEMP, TOUR and DENS) had significant coefficients in the more comprehensive equations.

4. WINEAD and MASHSZ had coefficients with the expected signs, although neither was significant.

The only unanticipated results came in the form of unexpected signs for the nonsignificant coefficients associated with URBAN, BORDER, AVL (in the 1970 equation only), and ENF. The negative coefficients for URBAN can probably be explained by multicollinearity, as URBAN is correlated with ADV (.62), INC (.46), TOUR (.49), and DENS (.29). The positive coefficient for BORDER can probably be attributed to a variety of difficulties with this variable, including its very high correlation with DENS (.92). The result for AVL (for 1970) probably has something to do with both two-way causation and multicollinearity. Finally, the result for ENF is not very disturbing, since this variable was one of the last to enter all of the regressions. In fact, none of these unanticipated results are very disturbing, since none of the coefficients were significant.





The overall results are consistent with those of Ornstein and Hanssens (1981), differing from their findings in t four major, but relatively explainable, ways. First, they found monopoly control and price advertising restrictions (MONOP and PRCADV) to be significantly negatively related to consumption, whereas this study did not. Since these are dummy variables that are likely to pick up the effects of missing explanatory variables, their findings may be a result of not including some of the variables utilized in this study.

Second, Ornstein and Hanssens found a significant positive coefficient to be associated with number of on-premise outlets per capita, whereas this study found a positive, generally nonsignificant coefficient association with a roughly equivalent variable (AVL). The difference in results here could be a result of the different operationalizations or, most probably, the higher degrees of freedom available to Ornstein and Hanssens. In this study, the AVL coefficient was significant in several of the more parsimonious regressions having more degrees of freedom. Of course, AVL could be related to consumption because of reverse causation.

Third, Ornstein and Hanssens found their equivalents to INC and URBAN to be significantly positive related to consumption, whereas this study found marginally significant positive coefficients for INC only in a few parsimonious regressions and found negative,nonsignificant coefficients for URBAN. As with AVL, the difference in results for INC probably is a consequence of the higher degrees of freedom available to Ornstein and Hanssens -- although multicollinearity between INC and four variables that were measured differently or not used by them (ADV, AVL, TOUR, and DENS) could also explain what happened. The difference in results for URBAN is also probably a consequence of multicollinearity between this variable and several variables (cited earlier) that were measured differently or not used by Ornstein and Hanssens.

Finally, Ornstein and Hanssens obtained significant positive coefficients for their border-effect variable. However, as argued earlier, their variable essentially measured size of the state. Thus, they were probably picking up the same effect as that obtained for DENS in this study

In sum, the results suggest that the only way state regulations may be discouraging consumption of distilled spirits is in an indirect manner, through the effects they have on price. The evidence suggests that regulations or other forces that raise prices probably have a small ability to lower consumption, while regulations that try to limit consumption more directly, such as those represented by the regulatory variables, probably are ineffective. The results also suggest that regulations that try to reduce consumption through reducing availability and advertising probably have very limited effectiveness. Variation in consumption of distilled spirits across the states seems to be explained primarily by price, temperature, tourism, and population density.


The results of this study provide support for the advocate of deregulation of the distilled spirits industry. As with previous studies, the results suggest that regulation has not been accomplishing very much in terms of reducing consumption. People who want to drink seem to find a way to buy spirits regardless of the obstacles.

The mounting evidence against regulation in this industry deserves careful consideration from public policy-makers who are interested in eliminating wasteful, ineffective, government initiatives. However, there remains a need for further research in this area to help shore up some of the weaknesses of this study. Models are needed that will test for curvilinear and/or interactive effects of certain variables. The effects of regulations on price and availability also require study. Further, something should be done to cope with multicollinearity and, most importantly, the possible existence of two-way causation between advertising and consumption and between availability and consumption. The use of structural equation models (Bagozzi,1980) to explore the interrelationships between regulation, price, advertising, availability, and consumption may have some promise for dealing with these issues. The authors are currently pursuing a refinement and extension of this study using a structural equation approach.

Other issues that deserve attention in future research involve the effects of population density and border-running Questions that could be addressed include: Is density an adequate variable for picking up the border-running effect If so, why is this the case? Do densely populated states have more low-price retailers near their borders in order to attract out-of-staters? What else about population density tends to produce higher consumption?

Future research activity could also be devoted toward longitudinal studies of states going through a deregulation experience. It might be more appropriate to draw cause-and-effect inferences about regulatory impacts from such a study than from the correlational-type study reported here.


Although further research is needed, this study contains reasonably convincing evidence, in conjunction with other recent studies, that consumers cannot be protected from themselves with respect to distilled spirits. The implications of such a conclusion for other industries are highly unclear, but worth some speculation. It may be that government attempts to control where, when, and how people gamble, smoke, eat, or engage in other self-indulgences are doomed to failure. Perhaps human drives or addictions are too strong to make many health and safety regulations worthwhile.


The sources of the data for each variable are listed below:

SUNDAY, Novelty, MINAGE, MONOP, REMIX, PRCADY, CREDIT, and SIZE: Distilled Spirits Council of the United States, Summary of State Laws and Regulations Relating to Distilled Spirits, 1972 and 1977, Washington, D.C.

PRICE: "Retail Prices of Leading Brands -- Fifth Sizes," Liquor Handbook, 1971 and 1976, New York: Gavin-Jobson,Inc

AVL: Distilled Spirits Council of the United States, Annual Statistical Review. 1970 and 1975, Washington, D.C.

ADV: 1970 and 1975 "Beer, Wine, and Liquor Advertising Expenditures by State," prepared for the National Institute on Alcohol Abuse and Alcoholism by Leading National Advertisers, Inc.

TEMP: United States Department of Commerce, National Climatic Center, National Oceanic and Atmospheric Administration, Climatological Data National Summary, vol. 21, no.13. 1971, ,and vol. 26, no 13, 1976.

TOUR: United States Department of Commerce, Department of the Census, Census of Service Industries. 1972 and 1977, Washington, D.C., issued 1976 and 1980.

URBAN: United States Department of Commerce, Department of the Census, Current Population Reports. Population Estimates and Projections Estimates of the Population of Counties and Metropolitan Areas: July 1 1975 and 1976, Series P-25, No. 739, Washington, D.C., 1978.

INC: United States Department of Commerce, Bureau of Economic Analysis, Survey of Current Business, 57:17, May 1977.

Other data used to compute the variables include:

Consumer Price Index for all urban consumers, selected areas, United States Department of Labor, Bureau of Labor Statistics, Handbook of Labor Statistics, Bulletin 2070, Washington, D.C., December 1980.

Consumption of distilled spirits by liquor category, "Apparent Consumption of Distilled Spirits," Liquor Handbook, 1971 and 1976, New York: Gavin-Jobson, Inc.

Population by state in 1970, United States Department of Commerce, 1970 Census of the PoPulation, vol. 1, Characteristics of the Population, Washington, D.C., June 1973.

Population by state in 1975, United States Department of Commerce, Current Population Reports, Population Estimates and Projections, Series P-25, Washington, D.C. January 1980.

State size in square miles of land mass, United States Department of Commerce, Bureau of the Census, Statistical Abstract of the United States.

State center of population, United States Department of Commerce. Centers of Population for States and Counties, Washington, D.C.. 1974.


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