On the demand for smoking quitlines

Rajeev K. Goel

# Springer Science+Business Media New York (outside the USA) 2014

Abstract Using recent cross-state U.S. data, this paper estimates the demand for callsto smoking quitlines. Besides formal insights into the determinants of quitline demand,another key contribution is to provide unique insights on the role of related internetresources, using two novel measures. Results show that higher cigarette prices, lowerincome, and greater government resources increase the demand for quitline calls, withthe internet measures having positive but statistically insignificant effects. In terms ofmagnitude, the elasticity of quitline calls with respect to cigarette prices was about fourtimes greater than that with respect to public funds for quitlines. Policy implications arediscussed.

Keywords Quitlines . Smoking . Cigarettes . Demand . Internet .Master SettlementAgreement . United States

JEL classification I10 . I18

1 Introduction

Given the habit forming nature of smoking, smokers looking to give up smoking facedifficulties in doing so. Thus, governments often try to facilitate quitting by variousmeans, including providing information and subsidizing treatments. In this context,mainly due to their ability to aid smokers quit smoking across large geographic areas,smoking quitlines have proven to be effective tools in the fight for smoking cessation.1

As a result, public quitline services are now available in all 50 U.S. states (see www.naquitline.org for details). However, formal research on quitlines, especially on

J Econ FinanDOI 10.1007/s12197-013-9278-7

1According to the North American Quitline Consortium, “Quitlines are telephone-based tobacco cessationservices that help tobacco users quit. Services offered by quitlines include coaching and counseling, referrals,mailing materials, training to healthcare providers, Web-based services and, in some instances, free medica-tions such as nicotine replacement therapy (NRT)”, (www.naquitline.org).

Comments of a referee are appreciated.

R. K. Goel (*)Department of Economics, Illinois State University, Normal, IL 61790-4200, USAe-mail: rkgoel@ilstu.edu

economic aspects, has been lacking and the present research attempts to make acontribution by estimating the demand for quitlines. Besides purely academicinterest, policymakers facing tough resource allocation decisions would also beinterested in ascertaining factors driving the demand for quitlines. This is even morepertinent as quitlines face competition from new technologies with the spread of theinternet. How do internet resources affect the demand for quitlines?

The broader literature has in recent years considered the importance of studying thefactors driving the propensity to quit smoking (Goel (2007), Hammar and Carlsson(2005), Hsieh (1998) and Laxminarayan and Deolalikar (2004)). However, there isrelatively less attention to the study of quitlines, especially their economic aspects (seeAnderson and Zhu (2007) and Cummins et al. (2007)). With the growing popularityand effectiveness of quitlines in helping smokers quit smoking (see Anderson and Zhu(2007), American Lung Association (2010)), it is useful that a formal study of thefactors driving the demand for quitlines be conducted.

Recognizing the increasing role of the internet in information dissemination, wefocus on its effects. However, given its multifaceted nature, the role of the internet isnot easily captured. On the one hand, there are numerous general internet-basedcessation resources by various sources (health practitioners, government organizations,NGOs, general public etc.); while on the other hand, state-specific quitline internetresources by health authorities in individual U.S. states exist. These include: (i)information about the quitline in a state; (ii) information about tobacco cessation; (iii)self-directed web-based intervention; (iv) automated e-mail messages; (v) chat rooms;and (vi) interactive counseling and/or e-mail messaging with cessation counselor(www.naquitline.org). However, there is considerable variation in the prevalence ofthese web-based resources across individual states. In 2010, only two states, New Yorkand Wyoming, had all six of these internet resources available, while 17 states(Alabama, Arizona, Delaware, Idaho, Kentucky, Massachusetts Minnesota, Montana,Nevada, NewMexico, North Dakota, Pennsylvania, Rhode Island, South Dakota, Utah,Vermont and Washington) had none of the six available.

To get a handle on the role of the internet, we employ two measures of internetresources for smokers looking to quit smoking. These indicators incorporate a generaland a specific measure. A general measure, Internet, captures internet hits (number ofwebpages following an internet search) about smoking cessation resources employing awidely used internet search engine, and a specific measure, NoWeb, identifies the 17states without a specific internet program about quitlines in any of the six listedcategories. These controls are employed along with other factors to study the determi-nants of quitline demand. Which factors significantly affect the demand for quitlines?In a nutshell, the results find non-internet influences on quitline demand to be relativelymore powerful. The formal setup follows.

2 Model and data

In the absence of a specific economic study on the demand for quitlines to guide us andhelp anchor the discussion, we borrow from the broader economics literature regardingeconomic agents (in this case smokers considering quitting) weighing the relative costsand benefits of their propensity to call smoking quitlines (see Chaloupka and Warner

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(2000), Goel and Nelson (2008) and U.S. Department of Health and Human Services(2000) for authoritative reviews of the broader literature on the economics of smoking).

In general, the demand for quitlines by smokers is affected by economic, social andgovernment factors. With regard to economic factors, we consider the prices ofcigarettes (CigP) and personal income (Inc). Other things being the same, highercigarette prices would induce more smokers to consider quitting and thus induce themto call quitlines,2 while income affects the alternatives smokers considering quittingmight have. Higher income smokers might be able to afford expensive personalizedcessation programs and thus would be less likely to call quitlines. Another reinforcingincome effect might be that higher income smokers would have a higher opportunitycost of time, making them less likely to spend time calling quitlines. Further, higherincome might also proxy for greater literacy, making quitting on their own more likely(see Hsieh (1998)).3

In terms of social aspects, we examine the effect of internet awareness about quittingsmoking on the propensity to call quitlines. The internet is increasingly becoming aprimary source of information for many consumers as well as a key informationdissemination source for service providers. The internet web pages in this case caninclude numerous pertinent aspects including web pages listing government resourcesfor quitting, academic research, related advertisements, blog posts about the pros andcons of quitting, summaries of lawsuits related to smoking cessation and other relatededucation sites, etc. Also, given the borderless nature of the internet, these postings andtheir access is virtually worldwide.

To capture the effect of the internet, we construct a unique measure of internet hits inGoogle, searching with keywords, “smoking quit tobacco cigarettes cessation stopresources state “state name”” and recording the number of internet search hits. 4, 5

Google is the main search engine of choice for many internet users, and given thatthe number of search results can change almost instantaneously, the searches for allstates were conducted in a single day in February 2012. Further, to eliminate datacontamination in cases of states that could also be personal names (e.g., Georgia,Virginia) and states that share a common name (e.g., North Carolina and SouthCarolina), some refinements were undertaken so that the number of internet hits inthese cases were not “contaminated”. 6 The resulting hit count for each state wasnormalized by state population, yielding a unique measure of the internet resources

2 Laxminarayan and Deolalikar (2004) note that besides inducing quitting, higher cigarette prices might alsopromote switching to other (cheaper) tobacco products.3 With appropriate data on individual smokers, one could also incorporate smoking intensity, with heaviersmokers more likely to call quitlines, ceteris paribus.4 For example, a search in Google.com with keywords, “smoking quit tobacco cigarettes cessation stopresources state “Alabama”” yielded 180,000 hits and we recorded this number for Alabama (later normalizedby the state’s population in the analysis). As the reader might have noticed, the 180,000 hits for Alabama donot necessarily all include unique webpages about cessation resources for Alabama—there could be somemirror sites. However, further refinements are beyond the scope of this work; and in any case, all states areequally likely to have such duplication instances.5 Given that some cities and counties have their own anti-smoking initiatives, one could fine-tune the internetsearches to examine quitline resources available at the local level.6 Specifically, putting the state name in quotations in the search and adding the word “state” weeds outreferences to personal names in some internet sites. Further, in the case of states like North Carolina that sharepart of their names with other states, the search included the word “South” with a minus sign prefixed toeliminate references to South Carolina.

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available for quitting smoking (Internet). The measure Internet can alternatively beviewed as capturing internet awareness about cessation resources. While theimportance of the internet in quitline research has been recognized by others(Anderson and Zhu (2007)), we are not aware of any related state-specific measureof internet awareness about resources to help quit smoking.

As a more direct measure of the effect of state-specific quitline internet resources,NoWeb is a dummy variable that identifies the 17 states (listed above) that do not have aspecific web-based program for quitlines. Other things being the same, a web-basedprogram would make quitline calls less likely if the information on the web is asubstitute (e.g., internet chat room, web-counseling), but would make quitline callsmore likely if the internet site is complementary in that it is providing information onaccessing the telephone quitline.

Further, mainly tobacco producing states have somewhat different incentives tryingto balance the interests of tobacco growers (and smokers) against those of potentialquitters and the non-smoking public. To account for this potentially varying govern-ment involvement, we include a dummy variable (Producer) that identifies the maintobacco producing states in the United States—Georgia, Kentucky, North Carolina,South Carolina, Tennessee and Virginia. We would expect the sign on Producer to benegative, ceteris paribus.

Finally, two alternate government initiatives are considered that might affect thedemand for quitlines. While QuitSpend captures the direct resources devoted toquitlines by the government,7 the other measure—a state’s per capita share of MasterSettlement Agreement payments in that year (MSA) 8—might be complementary orsubstitute to the direct quitline funding.9 The direct quitline funding can be seen as aproxy for the quality of quitline services offered in a state. MSA funds might be used toreinforce smoking cessation initiatives, or they might be used for other purposes bylawmakers (Goel and Nelson (2007)).

The general format of the estimated demand for quitlines is the following

QuitCalli ¼ f CigPi; Inci;QuitSpendi;Produceri; Interneti;NoWebi;MSAið Þi ¼ 1;…; 49

ð1Þ

Here subscript i denotes a state and the District of Columbia, with Alaska andHawaii excluded (because they do not have bordering states as neighbors, which makescomparisons, (internet and otherwise), difficult).

The dependent variable is the prevalence of smokers calling smoking quitlines(QuitCall).10 About 3.4 % of smokers in our sample called telephone quitlines. Thus,there is substantial potential for appropriate initiatives to increase this number. Further,

7 Spending on quitlines includes expenses for maintaining quitlines and for providing related supportmaterials.8 The Master Settlement Agreement, signed in 1998 between states and tobacco companies, imposed wide-ranging limitations on the marketing and sale of tobacco products and provided cash payments to states spreadover many years (see Goel and Nelson (2007) and Viscusi (2002)).9 It is possible that some MSA funds might figure in resources specifically allocated to quitlines. If that is thecase, it is more likely that a year’s web spending would likely draw on last year’s MSA funds. Further, thispossibility is also mitigated by the modest correlation between MSA and QuitSpend (see Appendix).10 The latest figures available in this case are for the year 2006–07 (see Table 1).

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the highest number of quitline calls in our sample was in the State of Maine, while thelowest calls were in Nebraska, South Carolina and Virginia (a three-way tie). Incontrast, South Dakota had the highest quitline spending per smoker and Texas hadthe lowest.

Details about the data, including variable definitions, summary statistics and datasources, are provided in Table 1. The state-level cross-sectional observations are for theyear 2010 (or for the closest year available).11 The Appendix presents simple pairwisecorrelations between key variables. The correlation between QuitCall and QuitSpendwas 0.66.

11 As Table 1 notes, the internet search data was generated after the other data were available. There seems noeasy way to position the internet searches prior to the rest of the data. However, many of the webpagesreported in the searches had been available for a number of years.

Table 1 Variable definitions, summary statistics and data sources

Variable Definition (mean; std. dev.) Source

QuitCall Prevalence of smokers calling smoking quitlines,2006–2007 (%)

(3.39; 2.59)

Centers for Disease Controland Prevention (2010)

QuitSpend Quitlines spending per smoker ($)(3.44; 4.35)

American Lung Association (2010)

CigP Retail price of cigarettes (cents/20-pack)(545.46; 99.70)

Orzechowski and Walker (2010)

Inc Per-capita disposable income ($)(36,430.37; 6,046.34)

U.S. Bureau of Census (2012)

Producer Dummy variable identifying the six major tobaccoproducing states (GA, KY, NC, SC, TN, VA)

(0.12; 0.33)

Internet Google internet search hits with keywords “smoking quittobacco cigarettes cessation stop resources state “statename””, hits per capita, February 2012

(0.13; 0.27)

www.google.com

NoWeb Dummy variable identifying states without specificinternet support for quitlinesa

(0.35; 0.48)

www.naquitline.org

MSA Master Settlement Agreement payments per capita (000 $)(0.03; 0.01)

Orzechowski and Walker (2010)

SmkPrev Adult smoking prevalence rate, 2009 (%)(18.54; 3.28)

U.S. Bureau of Census (2012)

Pop State population, (000)(6,258.67; 6,885.52)

U.S. Bureau of Census (2012)

All observations are at the state level for the year 2010, unless otherwise noted

Alaska and Hawaii are excluded, but the District of Columbia is includeda Alabama, Arizona, Delaware, Idaho, Kentucky, Massachusetts Minnesota, Montana, Nevada, New Mexico,North Dakota, Pennsylvania, Rhode Island, South Dakota, Utah, Vermont, and Washington had no webpresence in these six categories in 2010: (i) information about the quitline in a state; (ii) information abouttobacco cessation; (iii) self-directed web-based intervention; (iv) automated e-mail messages; (v) chat rooms;and (vi) interactive counseling and/or e-mail messaging with cessation counselor

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3 Results

The estimation results are reported in Table 2. Two sets of results are presented: (i)Models 2.1–2.4 report OLS estimates from base models, with evaluations of alternategovernment initiatives and of internet awareness on the demand for quitlines; and (ii)Models 2.5–2.8 allow for possible reverse causality from demand for quitlines toquitline spending (i.e., quitline spending might increase with quitline demand). Forthis purpose, a state’s population (Pop) and smoking prevalence (SmkPrev) wereemployed as additional instruments for QuitSpend. All estimations were performedusing the STATA computer software.

The overall fit of both OLS regressions and 2SLS regressions is quite decent, withR2s of at least 0.54, and statistically significant F-values. Further, a generalspecification test (RESET) shows an absence of significant specification errors in theOLS regressions, while the first-stage F-values and Sargan’s overidentification testsupport the choice of instruments in the IV regressions.

3.1 Baseline models

The effect of cigarette prices is positive and statistically significant in all cases. In fact,the order of magnitude of the price effect is also remarkably similar—a one unitincrease in the retail price of cigarettes increases the prevalence of smokers callingquitlines by one-hundredth of 1 %. This is consistent with the notion that highercigarette prices induce more smokers to quit.

Greater personal income makes quitline calls less likely. Wealthier smokers lookingto quit smoking are more likely to be able to afford personalized, more expensivecessation programs and thus are less likely to call public quitlines.12 Consistent withprior beliefs, potential quitters in the mainly tobacco producing states are less likely toaccess quitlines; however, statistical support for this effect is rather weak.

The two measures of quitline quality or public support for quitlines, QuitSpend andMSA, both positively and significantly reinforce the demand for quitlines. Whereas thefinding that direct quitline spending increases the demand for quitlines is not toosurprising (i.e., more spending on advertising and additional phone lines etc. increasingquitline demand), the positive effect of MSA funds on quitline demand is welcomenews. It seems that at least some of the MSA funds are aiding cessation. While MSApayments in each year are predetermined according to the original 1998 Agreement,QuitSpend could be a function of quitline demand and the next section considers thispossible simultaneity.

In terms of magnitude, a 10 % increase in the retail price of cigarettes would increasecalls to quitlines by about 16 %, while a similar increase in spending on quitlines persmoker would increase quitline calls by about one-fourth of that (Models 2.1–2.3, 2.5,2.7).13 The reason for the greater impact of cigarette prices is clear—smokers feel the

12 Greater income can also be seen to proxy for greater literacy and more educated smokers might feel lessinclined to obtain cessation guidance from quitlines (also see Hsieh (1998)).13 Interestingly, the elasticity of quitline calls (QuitCall) with respect to quitline spending (QuitSpend) of 0.4 isremarkably similar in magnitude to the price elasticity of cigarette demand for earlier years found in manystudies (see Chaloupka and Warner (2000), Gallet and List (2003) and U.S. Department of Health and HumanServices (2000) for informative surveys; Cebula et al. (2011) for a recent example).

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Tab

le2

Dem

andforsm

okingquitlines

(Dependent

variable:QuitCall)

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

CigP

0.01**

(2.7)

0.01**

(2.8)

0.01**

(2.5)

0.01**

(2.1)

0.01**

(2.6)

0.01**

(2.8)

0.01**

(2.3)

0.01**

(2.0)

Inc

−0.0001**(2.3)

−0.0001**(2.3)

−0.0001*

(1.9)

−0.0002**(3.6)

−0.0001**(2.0)

−0.0001**(2.2)

−0.0001*

(1.8)

−0.0002**(3.2)

Producer

−0.65(1.3)

−0.59(1.2)

−0.65(1.3)

−0.40(0.9)

−0.68(0.7)

−0.76(0.7)

−0.68(0.6)

−0.51(0.5)

QuitSpend

0.39**

(5.9)

0.38**

(6.0)

0.39**

(5.5)

0.34**

(6.2)

0.38**

(2.5)

0.33*(1.9)

0.38**

(1.9)

0.31*(1.9)

Internet

1.10

(1.2)

1.27

(1.0)

NoW

eb0.01

(0.02)

0.03

(0.05)

MSA

81.11**(2.8)

85.23**(2.5)

N49

4949

4949

4949

49

R2

0.54

0.55

0.54

0.61

0.54

0.54

0.54

0.61

F-value

20.3**

18.5**

16.4**

17.4**

4.9**

4.3**

4.0**

8.0**

RESE

T(F-value)

1.7

1.3

1.7

1.0

Estimation

OLS

OLS

OLS

OLS

2SLS

2SLS

2SLS

2SLS

First-stage

F-value

2.5**

2.1*

2.2*

2.5**

Sargan’soverid

test(p-value)

0.78

0.97

0.78

0.26

SeeTable1forvariabledefinitio

ns

Pop

andSm

kPrevwereused

asadditio

nalinstrumentsforQuitSpend

inModels2.5–2.8

Constantincluded

butnotreported

Robustt-statistics(M

odels2.1–2.4)

orz-statistics(M

odels2.5–2.8)

inabsolutevaluearereported

inparentheses,with

**and*,respectively,denotingstatisticalsignificance

atthe5%

and10

%levels

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effect of higher cigarette prices directly, while the effect of greater public spending isindirect and less immediate.

Turning to role of the internet, both measures of internet resources show positiveeffects on quitline demand, but the statistical significance is weak in each case.14 Greatergeneral and specific internet resources seem to be imparting complementary informationabout telephone quitlines; however, the impact is not statistically significant. Thisinsignificance could be partly due to the wide qualitative differences in the informationavailable on the web and the corresponding inability to effectively quantify it.15

3.2 Allowing for possible simultaneity between QuitCall and QuitSpend

Models 2.5–2.8 in Table 2 consider robustness aspects by allowing for reverse causalityfrom QuitCall to QuitSpend – greater demand for quitline calls might induce states toincrease their support for quitlines. For this purpose, a state’s population (capturingstate size and thus a constraint on resources that could be allocated for quitlines) andsmoking prevalence (capturing related interest group of smokers that have a stake inresources devoted to quitlines) are taken as additional instruments for QuitSpend.

The findings are remarkably similar to what was found in Models 2.1–2.4 – highercigarette prices, lower income, and greater government resources (both QuitSpend andMSA) increase the demand for quitline calls, with the two internet measures havingpositive but statistically insignificant effects. Thus, the estimates can be taken to bereasonably robust. The concluding section follows.

4 Concluding remarks

Using cross-state U.S. data for 2010, this paper estimates the demand for calls tosmoking quitlines. Besides providing formal insights into the determinants of quitlinedemand, another key contribution is to provide unique insights on the role of relatedinternet resources, using two novel measures. Whereas the extant literature has con-sidered the decisions to quit smoking (Allwright (2008), Goel (2007), Hammar andCarlsson (2005), Harris and Harris (1996), Hsieh (1998), and Laxminarayan andDeolalikar (2004)) and provided more general oversights into quitlines (Andersonand Zhu (2007), and Cummins et al. (2007)), we are not aware of studies estimatingthe demand for quitlines. This study may also be viewed as adding to the literature onthe role of the media in general (Avery et al. (2006)) and of the internet media inparticular (Goel (2011)).

14 As an alternative to NoWeb, we considered including a dummy variable identifying the two states (NewYork and Wyoming) with comprehensive web-based quitline resources. The resulting coefficient was statis-tically insignificant. Details are available upon request.15 For instance, (i) the number of web pages might change by the second; (ii) some pages might be in differentlanguages; (iii) some driven, special interest groups in a given state might have disproportionately highnumber of web postings inflating the number of hits for those states, without a real increase in the informationcontent. To address some of these issues, we tried to also do the internet searches using Yahoo.com. However,since the underlying search algorithms for Google and Yahoo searches are qualitatively different, Yahoosearches were generating more noise or unrelated matters in the searches (which could partly be a function ofthe sequence of the search terms entered in Yahoo). Thus, the used internet hits for the variable Internet werebased only on Google search results.

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Results show that higher cigarette prices, lower income, and greater governmentresources increase the demand for quitline calls, with the two internet measures havingpositive but statistically insignificant effects. Avery et al. (2006) find that advertising ofcessation products in non-internet media to be effective, and Goel (2011) finds internetcigarette advertising to significantly affect cigarette demand. However, we are unable tosupport similar significant internet influences in the case of quitlines. This may be duepartly to about a third of the states not having a specific quitline presence on the web(Table 1) and the relatively nascent nature of the internet and its multifaceted content. Interms of magnitude, the elasticity of quitline calls with respect to cigarette prices wasabout four times greater than that with respect to funds directed towards quitlines.Quitline call demand in mainly tobacco producing states was not statistically differentfrom other states.

From a policy perspective (see Lanoie and Leclair (1998)), higher excise taxesresulting in higher cigarette prices boost quitline calls, while higher income taxesmight also be inadvertently having a similar effect. Public funds directed atquitlines are having intended effects, and MSA funds also seems to be yieldingsome dividends in the cessation context. However, since MSA funds are likely todry up in due course, lawmakers should be looking for alternate resources to makeup for the shortfall.

Further, the scale and scope of the internet-based interventions might need to bereconsidered. About a third of the states are absent on the web in all categories relatedto quitlines, and numerous others are also lacking in many respects. In fact, only twostates were found to have comprehensive internet-based cessation programs. Finally, asinternet access, related (internet) literacy and its nature (greater interactive technolo-gies) change, even states with a substantial web presence would have to maneuverdeftly. Resource-constrained policymakers looking to curb smoking would want webportals to increasingly substitute for telephone quitlines so that phone-based quitlinesbecome virtual quitlines. Thus the full potential of the internet in smoking controlseems yet to be realized. For now, the present research can be seen as making initialforays into a new and exciting area, with many insights to follow.

Appendix

Table 3 Correlation matrix of key variables

QuitCall QuitSpend CigP Inc Internet MSA SmkPrev

QuitCall 1.00

QuitSpend 0.66 1.00

CigP 0.23 −0.03 1.00

Inc −0.01 0.02 0.57 1.00

Internet 0.16 0.21 0.17 0.52 1.00

MSA 0.43 0.26 0.47 0.56 0.50 1.00

SmkPrev −0.15 −0.12 −0.48 −0.43 −0.12 −0.11 1.00

See Table 1 for variable definitions

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