D

WD

a

ARRA

JI

KDADSB

1

ldiArdtmm2

dtaat(

ipaf

1d

The Journal of Socio-Economics 41 (2012) 104– 109

Contents lists available at SciVerse ScienceDirect

The Journal of Socio-Economics

j o ur nal homep ag e: www.elsev ier .com/ locate /soceco

o designated drivers and workplace policies effect alcohol consumption?

esley A. Austin ∗, Rand W. Resslerepartment of Economics and Finance, University of Louisiana at Lafayette, P.O. Box 44570, Lafayette, LA 70504-4570, United States

r t i c l e i n f o

rticle history:eceived 8 October 2010eceived in revised form 4 October 2011ccepted 18 October 2011

EL classification:12, I18, I21

a b s t r a c t

Is there a link between designated driver usage and alcohol consumption? We hypothesize that theuse of a designated driver lowers the cost of drinking which, in turn, increases alcohol consumption.We examine the effect on drinking intensity (which incorporates low levels of alcohol use) and bingedrinking (which measures greater alcohol use), using a proxy for designated driver usage. If workplacerules forbid alcohol use for safety or other reasons, a large potential cost of drinking is the possible jobloss (or other penalty) incurred if an employed person tests positive for alcohol. We add variables to

eywords:rinkinglcoholesignated driverubstance use

our model related to workplace policies on alcohol use by workers to ascertain if designated drivers stillinfluence drinking. We test these hypotheses utilizing a large dataset from the 2006 and 2007 NationalSurvey on Drug Use and Health (NSDUH) which includes several measures of alcohol use, in addition to ahost of other correlates. Findings reveal that our proxy for designated driver use increases the incidenceof drinking and the results hold even after controlling for workplace alcohol testing.

© 2011 Elsevier Inc. All rights reserved.

inging

. Introduction

In many health-related and social science fields, there hasong been concern over the various harmful effects of heavyrinking. Recent evidence in economic research indicates drink-

ng, coupled with smoking, reduces income (Auld, 2005).nother related consequence of alcohol use is the potentialeduction in human capital accumulation by drinkers. Much evi-ence has established a strong negative relationship betweenhe regularity and intensity of drinking and human capital

easures such as educational attainment and academic perfor-ance (Cook and Moore, 2003; Wolaver, 2002; Williams et al.,

003).More generally, medical researchers find that heavy drinking

ecreases thinking and reasoning performance, inhibits concentra-ion and coordination, and impairs short-term memory. Prolongedlcohol consumption can have more serious consequences suchs liver and kidney failure (Rehm et al., 2001) and may leado increases in accidents, suicide, crime and alcohol poisoningHingson and Winter, 2003).

In 1988, the Harvard Alcohol Project introduced the term “des-gnated driver” to the American public and began a campaign to

romote the concept. With the help of the entertainment industry,nd the bully pulpit of the Presidency, the idea became a hallmarkor responsible drinking behavior. The resulting burgeoning popu-

∗ Corresponding author. Tel.: +1 337 296 0614/482 6662; fax: +1 337 482 6675.E-mail addresses: wes.econ@hotmail.com, waustin@louisiana.edu (W.A. Austin).

053-5357/$ – see front matter © 2011 Elsevier Inc. All rights reserved.oi:10.1016/j.socec.2011.10.013

larity of arranging for a designated driver has been credited for thedecline in some alcohol related traffic fatalities between 1988 and1994.

In 2007, 12,998 people died in alcohol related driving crashesaccording to the NHTSA’s Center for Statistics and Analysis. In1990, the number of alcohol related traffic fatalities was 22,084.Heightened awareness of the perils of drunk driving, along withencouraging the use of designated drivers, is a partial explanationof this downward trend.

This paper contributes to the economics literature on this topicby examining the relationship between designated driver utiliza-tion and drinking alcohol. It is hypothesized that use of a designateddriver encourages greater drinking, even after controlling for othercorrelates such as workplace alcohol testing, by reducing risk fac-tors involved in alcohol consumption (e.g. potential accidents, DUIarrest, etc.). Further, the analysis augments the literature by utiliz-ing a large sample of respondents.

2. Theoretical underpinnings

Consider the price ratio r = Pa/Pb where Pa < Pb. Now suppose aconstant, c, is added to both prices: r = (Pa + c)/(Pb + c). The value ofr will increase as c increases, thus rendering the more expensive

good (or behavior) increasingly attractive to consumers.

The Alchian and Allen (1968) effect refers to situations wherea fixed cost, like c above, is added to the prices of two goods, thuschanging the relative prices of the goods. As the fixed cost increases,

al of S

tr

(iooftaeplth

d2aotdtuhtawd

wtpaBttiejpltift

3

sft

tAaKaHr

a

W.A. Austin, R.W. Ressler / The Journ

he more expensive good becomes more attractive to the consumerelative to the less expensive good.1

Suppose the two goods are (1) consuming one or two drinks and2) binge drinking. These two goods each carry costs which maynclude the cost of the drinks and the subsequent consequencesf consuming the respective amounts of alcohol (hangover, riskf DUI, etc.). For example, those under 21 carry the risk of arrestor underage drinking and that additional cost accrues if either ofhe two goods is purchased. This additional cost changes the rel-tive costs facing consumers of the two goods. The Alchian–Allenffect thus encourages underage drinkers to consume more drinkser drinking episode—much like prohibition resulted in more

iquor being consumed in speakeasies as opposed to beer. It isherefore expected that underage drinkers would drink moreeavily.

Despite its illegality for those under the age of 21, excessiverinking has been associated with youth. Data from the 2006 and007 National Survey on Drug Use and Health (NSDUH) foundpproximately 18% of youths ages 15–18 and approximately 43%f young adults ages 18–25 engaged in binge drinking (defined ashe consumption of at least five alcoholic beverages in one sitting)uring the past month. In the model in Section 5, an explana-ory variable is included to identify survey respondents that arender 21 and are not eligible to legally purchase/consume alco-ol. These individuals are likely to have differing drinking habitshan those who are of legal drinking age. Though underage personsre likely to drink less often, the Alchian–Allen effect suggests thathen they do drink, they are more likely to drink heavily and bingerink.

There are also Alchian–Allen Effect implications for randomorkplace alcohol testing. Perhaps counter intuitively, it might be

hat the coefficient of a variable for random alcohol testing will beositive. Suppose two goods are put forth as above: (1) consuminglcohol moderately, and (2) consuming “heavy” amounts alcohol.ecause a test will likely reveal any amount of alcohol consumed,he product of the perceived probability of a test being adminis-ered and the consequences of a positive test is the fixed cost thats born regardless of which good is chosen. Simply stated, if anmployee decides to drink, he may correctly reason that if sub-ect to a random test, one drink will reveal a violation of employerolicy. The marginal cost of subsequent drinks, therefore, is much

ower. As a result, the employee (working at a firm that randomlyests for alcohol) who decides to drink is more likely to drink heav-ly. The Alchian–Allen effect is offered as a potential explanationor the results obtained for some of the explanatory variables inhe model outlined in Section 5.

. Literature overview

While several studies have examined the effects of alcohol con-umption on various labor market and education variables, theactors that impact drinking itself remain relatively unexplored inhe economics literature.

Previous research has indicated that alcohol consumption tendso be lower for those married than those who are single (Gius, 2005).n individual who is married is also more likely to be responsiblend less likely to engage in self-destructive behavior. As Hajema andnibbe (1998) conclude: “the acquisition of a spouse role. . .was

ssociated with a decrease in consumption or heavy drinking.”aving the additional responsibility of parenting reinforces the

eduction of alcohol consumption.

1 See Borcherding and Silberberg (1978) and Kroncke and Ressler (1993) for otherpplications of the Alchian–Allen effect.

ocio-Economics 41 (2012) 104– 109 105

Unemployed status in the family has also been linked todrinking by a number of studies (see Lundborg, 2002; Tomkinset al., 2007). In addition, unemployment of some duration canheighten the stress level of the individual which can be the impe-tus for substance abuse (O’Hare and Sherrer, 2006; Mossakowski,1998).

The literature on the determinants of drinking finds that malestend to consume more alcohol, and more often, than females(Johnson et al., 1998; York et al., 1998). Religiosity has also beenshown to curtail drinking (see Kenkel and Ribar, 1994).

Because college graduates tend to be more responsible andmature, they are less likely to put their health at risk throughheavy drinking or any other self-destructive behavior. In addition,a more educated person is probably aware of the risks associatedwith excessive drinking. Finally, those who have invested muchin the acquisition of human capital are less likely to diminishtheir rate of return via destructive behavior. Conclusions regard-ing race have been more complex: while studies find Caucasiantotal alcohol consumption exceeds that of African-Americans andHispanics, average drinks per episode for African-Americans andHispanics exceeds that of Caucasians, especially in later adulthood(see Johnson et al., 1998; Mossakowski, 1998).

There is evidence which suggests that workplace substance usepolicies–in conjunction with testing—alter employee behavior. In2007, for example, the Society for Human Resource Management(SHRM) (Gurchiek, 2007) reported that drug testing by employerswas credited for positive results in drug testing falling to their low-est levels since 1988. In other words, as the probability of beingcaught violating the employer’s drug-free policy increases, work-ers are less likely to violate such a policy. The SHRM also found ofemployers who administer drug tests, 83.5% use the test as part ofthe application process. This likely leads to a sample bias: individu-als who apply for employment at firms that drug test are less likelyto be drug users.

Pidd et al. (2006) also demonstrate the impact of workplace poli-cies regarding drug and alcohol consumption on behavior. Workersreporting an alcohol policy at their place of work consumed lessalcohol than their counterparts employed at firms without such apolicy. Beyond the mere existence of a policy, Bennett et al. (2004)reports that being proactive regarding alcohol consumption can beaffective. Specifically, when employees undergo alcohol awarenesstraining, they decrease alcohol consumption.

As the broader literature on the determinants of drinking hasdeveloped, a pair of studies in the alcohol and drug use literatureaddresses designated driver usage (Barr and Moore, 1998; Cheonget al., 2006). While alcohol use is positively associated with the useof designated drivers, these studies utilize very small datasets andsamples are restricted to college students.

4. Data

The National Survey on Drug Use and Health (NSDUH),sponsored by the Substance Abuse and Mental Health Ser-vices Administration (SAMHSA), is administered to approximately55,000 civilian, non-institutionalized individuals age 12 and over,chosen so that the application of sample weights produces a nation-ally representative sample, with approximately equal numbers ofrespondents from the 12–17, 18–25 and 26 and over age groups.Data from the NSDUH allow for both breadth and depth of cover-age on the topic. Breadth comes from the ability to study aspectsof drinking behaviors using data from an elaborate questionnaireadministered on a wide array of substance use issues. Depth is pro-

vided by variables on demographics, education and health levels,family composition, MSA size, etc.

In addition, the data include variables on work and employmentissues, which permit determination of the effects workplace alcohol

1 al of S

ps

4

uaoi

4

ectsnT

tn

mrieorari

5

irhwocotcnhS(

D

whBtae

adifiT

06 W.A. Austin, R.W. Ressler / The Journ

olicies may have on workers’ drinking behavior. Therefore twoamples are evaluated.

.1. Full sample

This sample consists of 42,069 respondents and includes thosenemployed or not in the labor force for various reasons (e.g. stay-t-home parents, full-time students, retirees, etc.). Those 16 yearsld and under are excluded completely from the analysis given theirnability to legally drive on their own.

.2. Employment sample

This sample of 20,352 respondents incorporates only thosemployed, and therefore subject to employer alcohol control poli-ies. The data heavily samples youths so those that are too youngo work and/or full-time students are excluded, as are retirees,tay-at-home parents, those self-employed who presumably faceo drug/alcohol testing, and those otherwise not in the labor force.hose 16 and under are again excluded.

Both samples exclude non-drinkers but do include respondentshat did not drink in the specified time period (past 30 days) yetonetheless still consume alcohol.

A potentially problematic attribute of the data is non-randomeasurement error emanating from the self-reported nature of

esponses. However, studies on the quality of self-reported drink-ng data suggest that such reporting bias should be minimal. Grantt al. (1998), Midanik (1998) and Reinisch et al. (1991), basedn the consistency of responses to alcohol use questions fromepeated interviews, conclude that drinking self-reports are reli-ble. Harrison and Hughes (1997) find that survey methods notequiring subjects to verbally answer questions, as in the NSDUH,ncrease the accuracy of substance use self-reports.

. Model specification

As stated, many costs are associated with excessive drinkingncluding alcohol-related health problems, the pecuniary expenseequired for alcoholic beverages, and, if drinking occurs away fromome, the risks associated with driving intoxicated. An individualho has not made arrangements for a designated driver may rec-

gnize the potential costs of driving intoxicated and consequentlyhoose to drink in moderation. While drinking establishments mayffer to contact a taxi service for those too intoxicated to drive,here are still costs to these individuals related to payment for theab and any other costs of vehicle recovery the following day. Alter-atively, if that individual has a designated driver, the full cost toim of drinking is lowered and he’s more likely to consume alcohol.everal other correlates are included to form the following model1):

rinking Intensity/Binging = ̨ + ˇ1Designated Driver + ˇ1X + ε

(1)

here Drinking Intensity is the self reported average number of alco-olic drinks an individual consumed per day in the past 30 days, andinging is the consumption of five or more drinks in one sitting inhe previous 30 days. Vector X represents a set of explanatory vari-bles that plausibly affect both drinking measures while ε is therror term. And the ˛’s and ˇ’s are parameters to be estimated.

Designated Driver is the inverse of the interaction of “drinkinglone” and “drinking outside the home.” Designated Driver is not

irectly observed in the data so a proxy is created by obtaining the

nverse of the interaction between two dichotomous variables. Therst is equal to ‘1’ if the individual has consumed alcohol alone.he second is equal to ‘1’ if the individual has consumed alcohol

ocio-Economics 41 (2012) 104– 109

away from home. The interaction of the two indicates an episodein which the individual has consumed alcohol alone and away fromhome. Such a scenario would likely indicate that the person drankand then drove without the use of a designated driver. The inverse ofthis relationship serves as our proxy for using a designated driver.This variable is an estimate of the upper bound of designated driverusage, and not the exact number of documented instances in whicha designated driver was used. Though the latter is preferable to theformer, such data are not available.

5.1. Other explanatory variables

Several other variables from the NSDUH data are consideredexplanatory in the model and included in the X vector: HPAR (HighPerceived Alcohol Risk) is a binary variable equal to ‘1’ if the indi-vidual perceives high/moderate risks of physical harm associatedwith consuming four or five drinks nearly every day, and equal to‘0’ if slight/low risk is perceived. Married with Kids is also binaryvariable equal to ‘1’ if the individual is married with children andequal to ‘0’ otherwise.

College Graduate is a binary variable equal to ‘1’ if the individualis a college graduate and equal to ‘0’ otherwise while Unemployed –past 12 months is a binary variable equal to ‘1’ if the person has beenunemployed for 12 months or longer and equal to ‘0’ otherwise.Those that are college educated would be expected to behave moreresponsibly, and therefore drink less, while extended unemploy-ment may encourage greater alcohol consumption. Arrest is equal to‘1’ if the individual has ever been arrested for any crime and equal to‘0’ otherwise. Individuals who have been arrested may be expectedto have a greater propensity to drink excessively. Arrest is expectedto identify individuals who have exhibited reckless behavior andare therefore more likely to act in a self-destructive fashion.

Religious beliefs influence decisions is a binary variable equal to ‘1’if the individual agrees or strongly agrees with the statement: myreligion influences my decisions. It is equal to ‘0’ if the respondentdisagrees or strongly disagrees with the statement. Female, Under21, African Americans, Native Americans, Pacific Islanders, Asian, non-white Hispanics and Caucasians constitute a vector of descriptivedichotomous variables, and ̨ and ε are the intercept and error term,respectively.

6. Results

Given that Drinking Intensity is a count dependent variable, aPoisson regression model is utilized while a probit regression isused for the binary Binge Drinking dependent variable. For thesedrinking measures, we apply the same estimation technique to boththe full and employment samples.

Table 1 presents descriptive statistics for the full sample whileTable 2 does the same for the employment sample. For the full sam-ple, the mean number of drinks consumed per day in the past 30days is about 2.3 while approximately 25% report binge drinking.For the employment sample, the mean number of drinks consumedper day in the past 30 days is about 2.6 while approximately 26%report binge drinking. Females are 51% of the full sample and 43%of the employment sample. Approximately 20% of both samplesare college graduates. Caucasians comprise approximately 63% ofboth samples; African Americans about 13%. Non-white Hispanicsaccount for about 16% of both samples and Asians about three%.In the regressions, non-white Hispanics is the omitted category.For the full sample, the mean of Unemployed – past 12 months is

quite high at approximately 0.22—this may reflect, for instance,individuals such as full-time students that have held no job, butare unaware they do not meet the standard economic definitionof unemployed. For the employment sample, approximately 83% of

W.A. Austin, R.W. Ressler / The Journal of Socio-Economics 41 (2012) 104– 109 107

Table 1Descriptive statistics (full sample) (n = 42,069).

Variable Mean Standarddeviation

Drinking intensity – # of drinks per day(past 30 days)

2.396 (4.027)

Binge drank or not – past 30 days 0.254 (0.435)Had designated driver 0.014 (0.116)Respondent is under 21 years old 0.227 (0.419)Religious beliefs influence decisions 0.680 (0.466)HPAR (respondent perceives high risk

of harm from drinking)0.906 (0.291)

Respondent graduated college 0.196 (0.397)Respondent ever been arrested 0.152 (0.359)Respondent married with kids 0.329 (0.470)Unemployed – past 12 months 0.229 (0.420)Female gender 0.518 (0.499)Race (Caucasian) 0.635 (0.481)Race (African American) 0.123 (0.328)Race (Native American) 0.014 (0.120)Race (Pacific Islander) 0.004 (0.070)Race (Asian) 0.033 (0.177)

rit

ttwd0niid

2fei

TD

Table 3Poisson regression estimates for drinking intensity (full sample) (n = 42,069).

Explanatory variables Coefficient Standarderror

Had designated driver 0.306* (0.024)Religious beliefs influence decisions −0.214* (0.006)Married with Kids −0.263* (0.007)Under 21 0.195* (0.007)HPAR −0.465* (0.008)College Graduate −0.121* (0.008)Unemployed – past 12 months 0.087* (0.007)Arrest 0.313* (0.007)Female gender −0.341* (0.006)Race (Caucasian) 0.022* (0.008)Race (African American) −0.408* (0.014)Race (Native American) 0.098* (0.026)Race (Pacific Islander) 0.499* (0.023)

Race (non-white Hispanic) 0.159 (0.365)Race (multiracial) 0.028 (0.167)

espondents report their workplace tests for alcohol during the hir-ng process and about 61% report being subject to random testinghereafter.

As shown in Table 3, Designated Driver has a positive and statis-ically significant impact on Drinking Intensity. This indicates thathe number of drinks per drinking episode is positively associatedith the upper bound of designated driver usage. Utilization ofesignated drivers increases drinking intensity by approximately.31 points—as a policy implication, this suggests that if desig-ated driver usage rises by 0.10 there is an associated expected 3.1%

ncrease in drinking intensity. In other words, this additional drink-ng may be attributable to drinkers that had designated driversuring some of their drinking episodes.

And as the Alchian–Allen Effect dictates, those drinkers under1, and who are burdened with the additional cost of possible arrest

or illegal drinking, are also more likely to drink more per drinkingpisode. Since this is a binary age variable (Under 21), if drinkingntensity increases by about 0.20, then expected drinking intensity

able 2escriptive statistics (employment sample) (n = 20,352).

Variable Mean Standarddeviation

Drinking intensity – # of drinks per day(past 30 days)

2.646 (4.273)

Binge drank or not – past 30 days 0.359 (0.479)Had designated driver 0.010 (0.093)Respondent is under 21 years old 0.144 (0.355)Religious beliefs influence decisions 0.691 (0.461)HPAR (respondent perceives high risk

of harm from drinking)0.905 (0.293)

Respondent graduated college 0.183 (0.386)Respondent ever been arrested 0.144 (0.308)Respondent married with kids 0.426 (0.494)Workplace tests for alcohol when

hiring0.831 (0.374)

Workplace has random tests foralcohol use

0.609 (0.487)

Female gender 0.436 (0.495)Race (Caucasian) 0.628 (0.483)Race (African American) 0.140 (0.344)Race (Native American) 0.015 (0.121)Race (Pacific Islander) 0.007 (0.081)Race (Asian) 0.027 (0.163)Race (non-white Hispanic) 0.157 (0.363)Race (multiracial) 0.025 (0.155)

Race (Asian) −0.287* (0.023)

* Statistically significant at 1%.

increases by about 20% for those under compared to those over21. The other explanatory variables behave as might be expectedwith Unemployed – past 12 months and Arrest possessing a posi-tive impact on Drinking Intensity while religion influences decisions,HPAR, Female, Married with Kids, and College Graduate all have neg-ative effects.

In Table 4, we employ the same explanatory variables as inTable 3, but use the binary dependent variable: Binge Drink-ing. Use of a binary variable reveals whether designated driverusage directly impacts binge drinking and not merely intensity ofdrinking–which incorporates very low levels of alcohol consump-tion.

As the results in Table 4 indicate, the explanatory variableshave a remarkably similar effect on Binge Drinking as on Drink-ing Intensity. Perhaps the most interesting result centers on theprimary explanatory variable—Designated Driver. As the previ-ously stated hypothesis would suggest, the option of choosinga designated driver increases the likelihood of heavier alcoholconsumption—specifically the probit estimate is 0.033. Those indi-viduals that have designated drivers are roughly 13% more likely tobinge drink. Furthermore, as Alchian–Allen implications suggest,those under age are more likely to binge drink. Those Under 21 areabout 18% more likely to binge drink compared to those over 21.

6.1. Workplace alcohol policies

When employees drink heavily, employers often bear costs inthe form of probable lower productivity and higher rates of absen-teeism. Employee, customer and public safety may also be at risk.

Table 4Probit estimates for binge drinking (full sample) (n = 42,069).

Explanatory variables Coefficient Standarderror

Had designated driver 0.033* (0.016)Religious beliefs influence decisions −0.037* (0.003)Married with Kids −0.051* (0.003)Under 21 0.046* (0.004)HPAR −0.109* (0.006)College Graduate −0.055* (0.003)Unemployed – past 12 months 0.021* (0.003)Arrest 0.077* (0.004)Female gender −0.077* (0.003)Race (Caucasian) −0.001 (0.003)Race (African American) −0.071* (0.004)Race (Native American) 0.008 (0.013)Race (Pacific Islander) 0.114* (0.027)Race (Asian) −0.044* (0.007)

* Statistically significant at 1%.

108 W.A. Austin, R.W. Ressler / The Journal of Socio-Economics 41 (2012) 104– 109

Table 5Poisson regression estimates for drinking intensity (employment sample)(n = 20,352).

Explanatory variables Coefficient Standarderror

Had designated driver 0.391* (0.038)Religious beliefs influence decisions −0.215* (0.009)Married with Kids −0.281* (0.011)Under 21 0.135 (0.011)HPAR −0.454* (0.012)College Graduate −0.135* (0.013)Alcohol testing at hire −0.041* (0.012)Random alcohol testing 0.029* (0.009)Arrest 0.261* (0.010)Female gender −0.384* (0.010)Race (Caucasian) 0.010 (0.012)Race (African American) −0.417* (0.019)Race (Native American) 0.093* (0.035)Race (Pacific Islander) 0.670* (0.040)Race (Asian) −0.237* (0.035)

*

Mdeweohiith

dvoweqkh

spruinedds

stawpdidAAwa

Table 6Probit estimates for binge drinking (employment sample) (n = 20,352).

Explanatory variables Coefficient Standarderror

Had designated driver 0.051** (0.029)Religious beliefs influence decisions −0.041* (0.005)Married with Kids −0.054* (0.004)Under 21 0.032* (0.006)HPAR −0.113* (0.010)College Graduate −0.056* (0.005)Alcohol testing at hire −0.002 (0.006)Random alcohol testing 0.010** (0.004)Arrest 0.069* (0.006)Female gender −0.077* (0.004)Race (Caucasian) 0.002 (0.006)Race (African American) −0.081* (0.005)Race (Native American) 0.019 (0.019)Race (Pacific Islander) 0.185* (0.043)Race (Asian) −0.033** (0.013)

19, 103–113.Borcherding, T.E., Silberberg, E., 1978. Shipping the good apples out: the Alchian and

Allen theorem reconsidered. Journal of Political Economy 86, 131–138.

Statistically significant at 1%.

any employers have no alcohol policy forbidding their workers torink alcohol. However, some employers test for alcohol in employ-es’ bloodstream as part of the hiring process; and some follow upith subsequent random alcohol tests. These policies may impact

mployees’ drinking habits. Utilizing the employment sample, twother explanatory variables are included in (1): alcohol testing atire and random alcohol testing. Alcohol testing at hire is equal to ‘1’f the worker is subject to an alcohol test as part of the interview-ng/hiring process and ‘0’ otherwise. Random alcohol testing is equalo ‘1’ if the worker is subject to random alcohol testing after beingired, and “0” if not.

The model in (1) is the same except a variable for alcohol testinguring the hiring process is incorporated into the X vector as is aariable for random alcohol testing. It is anticipated the coefficientf alcohol testing at hire will be negative indicating that such a policyill be correlated with lower levels of alcohol intake. This observed

ffect is likely due to the presence of negative work-related conse-uences associated with drinking (such as possible dismissal). Ineeping with Alchian–Allen implications, random testing for alco-ol may well engender greater alcohol consumption.

Results from a Poisson regression for Drinking Intensity are pre-ented in Table 5. Again Designated Driver is significant and itositively affects the intensity of alcohol consumption—intensityises by about 0.39 points. This suggests that if designated driversage rises by 0.10 there is an associated expected 3.9% increase

n drinking intensity. In addition, workplace testing at hiringegatively impacts drinking while random testing has positiveffects—those respondents subject to random testing increaserinking intensity by about 0.03 points. Again Alchian–Allen effectso indeed hold: random testing encourages respondents to con-ume more drinks per drinking episode.

Table 6 shows results from using the binary Binge Drinking mea-ure. The presence of a designated driver increases the likelihoodhat a respondent binge drinks–those with designated drivers arepproximately 15% more likely to binge even after controlling fororkplace policy variables regarding alcohol. In addition, work-lace testing during the hiring process negatively impacts bingerinking, although it is not statistically significant. Random testing

s significant and has positive effects. Those that are subject to ran-om testing have about a 3% increased probability of binge drinking.gain, the other explanatory variables have expected effects withrrest and Under 21 possessing positive effects on binge drinking,

hile religion influences decisions, HPAR, Female, Married with Kids,

nd College Graduate all lower the probability of binging.

* Statistically significant at 1%.** Statistically significant at 5%.

7. Concluding remarks

This article contributes to the literature on the factors that deter-mine alcohol use by assessing the effects a proxy variable for thepresence of a designated driver has on drinking intensity (i.e. num-ber of drinks consumed per episode), and the probability of bingedrinking. The results indicate both drinking intensity and bingedrinking increase when the respondent has what can be construedas a designated driver. In addition, the prevalence of drug/alcoholtesting in many of today’s workplaces may actually engender moredrinking, depending on how testing is conducted (e.g. randomly).Even after controlling for workplace alcohol testing and a host ofother correlates, use of designated drivers increases alcohol con-sumption.

The results do not contravene the goals of organizations thatattempt to lower the prevalence of drunk drivers. In fact, if thepresence of designated drivers encourages alcohol consumptionto the point of drunkenness (as the above results indicate), then,provided that designated drivers provide transportation home, thepromotion of designated drivers does fulfill its purpose.

While there is no direct analysis of the effectiveness of lawsand other programs designed to curtail drinking, the conclusionsin this study simply support the premise that encouraging the useof designated drivers has the unintended consequence of increas-ing alcohol consumption per drinking episode. While promotion ofdesignated drivers certainly mitigates some of the harmful effectsof drinking, the results of this analysis suggest that campaigns thatraise awareness of the risks inherent in consuming alcohol, andother programs aimed at reducing drinking, could be more fruitfulprospective policy tools.

References

Alchian, A., Allen, R., 1968. University Economics, second ed. Wadsworth PublishingCompany, Belmont.

Auld, M., 2005. Smoking, drinking, and income. Journal of Human Resources 15 (2),505–518.

Barr, A., Moore, D., 1998. Designated driving among college students. Journal ofStudies on Alcohol and Drugs 59, 549–554.

Bennett, J., Patterson, C., Reynolds, S., Shawn, W., Wyndy, W., Lehman, W., 2004.Team awareness, problem drinking, and drinking climate: workplace socialhealth promotion in a policy context. American Journal of Health Promotion

Cheong, J., Hall, N., MacKinnon, D., 2006. Use of designated boat operators and des-ignated drivers among college students. Journal of Studies on Alcohol and Drugs67, 616–619.

al of S

C

G

G

G

H

H

H

J

K

K

L

W.A. Austin, R.W. Ressler / The Journ

ook, P., Moore, J., 2003. The economics of alcohol abuse and alcohol-control policies.Health Affairs 21, 120–133.

ius, M., 2005. An estimate effects of age, taxes, and other socioeconomic variableson the alcoholic beverage demand of young adults. Social Science Journal 42,13–24.

rant, B., Harford, T., Grigson, B., 1998. Stability of alcohol consumption amongyouth: a national longitudinal survey. Journal of Studies on Alcohol 49, 253–260.

urchiek, K., 2007. Employer Testing Credited for Drop in Worker Drug Use. HRNews. Society for Human Resource Management.

ajema, K., Knibbe, R., 1998. Changes in social roles as predictors of changes indrinking behaivour. British Journal of Addiction 93, 1717–1727.

arrison, L., Hughes, A., 1997. The validity of self-reported drug use. NIDA ResearchMonograph 167, 1–16.

ingson, R., Winter, M., 2003. Epidemiology and consequences of drinking and driv-ing. Alcohol Research and Health 27, 63–78.

ohnson, F., Gruenewald, P., Treno, A., Taff, G., 1998. Drinking over the life coursewithin gender and ethnic groups: a hyperparametric approach. Journal of Stud-ies on Alcohol 59, 568–581.

enkel, D., Ribar, D., 1994. Alcohol consumption and young adults’ socioeconomicstatus. In: Baily, M.N., Reiss, P.C., Winston, C. (Eds.), Brookings Papers on Eco-nomic Activity: Microeconomics. Brookings Institution, Washington, DC, pp.

119–175.

roncke, C.O., Ressler, R.W., 1993. The Alchian-Allen Effect in higher education: pub-lic versus private enrollment. Economics of Education Review 12 (4), 345–349.

undborg, P., 2002. Young people and alcohol: an econometric analysis. British Jour-nal of Addiction 97, 1573–1582.

ocio-Economics 41 (2012) 104– 109 109

Midanik, L., 1998. Validity of self-reported alcohol use: a literature review andassessment. British Journal of Addiction 83, 1019–1029.

Mossakowski, K., 1998. Is the duration of poverty and unemployment a risk factorfor heavy drinking? Social Science and Medicine 67, 947–955.

O’Hare, T., Sherrer, M., 2006. Stress, recent changes in alcohol consumption leveland problem drinking in freshman first offenders. Journal of Human Behavior inthe Social Environment 13, 33–50.

Pidd, K., Boeckmann, R., Morris, M., 2006. Adolescents in transition: the role of work-place alcohol and other drug policies as a prevention strategy. Drugs: Education,Prevention & Policy 13, 353–365.

Rehm, J., Gutjahr, E., Gmel, G., 2001. Alcohol and all-cause mortality: a pooled anal-ysis. Contemporary Drug Problems 28, 337–362.

Reinisch, E., Bell, R., Ellickson, P., 1991. How Accurate are Adolescent Reports of DrugUse? Rand Corporation, Santa Monica, CA.

Tomkins, S., Saburova, N., Kiryanov, N., Andreev, E., McKee, M., Shkolnikov, V., Leon,D., 2007. Prevalence and socio-economic distribution of hazardous patterns ofalcohol drinking: study of alcohol consumption in men aged 25–54 years inIzhevsk, Russia. British Journal of Addiction 107, 544–553.

Williams, J., Powell, L., Wechsler, H., 2003. Does alcohol consumption reduce humancapital accumulation? Evidence from the college alcohol study. Applied Eco-nomics 35, 1227–1239.

Wolaver, A., 2002. Effects of heavy drinking in college on study effort,grade point average, and major choice. Contemporary Economic Policy 20,415–428.

York, J., Welte, J., Hirsch, J., 1998. Gender comparison of alcohol exposure on drinkingoccasions. Journal of Studies on Alcohol 64, 790–801.