ArticlePDF Available

Toward quantifying the abuse liability of ultraviolet tanning: A behavioral economic approach to tanning addiction


Abstract and Figures

Many adults engage in ultraviolet indoor tanning despite evidence of its association with skin cancer. The constellation of behaviors associated with ultraviolet indoor tanning is analogous to that in other behavioral addictions. Despite a growing literature on ultraviolet indoor tanning as an addiction, there remains no consensus on how to identify ultraviolet indoor tanning addictive tendencies. The purpose of the present study was to translate a behavioral economic task more commonly used in substance abuse to quantify the "abuse liability" of ultraviolet indoor tanning, establish construct validity, and determine convergent validity with the most commonly used diagnostic tools for ultraviolet indoor tanning addiction (i.e., mCAGE and mDSM-IV-TR). We conducted a between-groups study using a novel hypothetical Tanning Purchase Task to quantify intensity and elasticity of ultraviolet indoor tanning demand and permit statistical comparisons with the mCAGE and mDSM-IV-TR. Results suggest that behavioral economic demand is related to ultraviolet indoor tanning addiction status and adequately discriminates between potential addicted individuals from nonaddicted individuals. Moreover, we provide evidence that the Tanning Purchase Task renders behavioral economic indicators that are relevant to public health research. The present findings are limited to two ultraviolet indoor tanning addiction tools and a relatively small sample of high-risk ultraviolet indoor tanning users; however, these pilot data demonstrate the potential for behavioral economic assessment tools as diagnostic and research aids in ultraviolet indoor tanning addiction studies.
Content may be subject to copyright.
Many adults engage in ultraviolet indoor tanning despite evidence of its association with skin cancer.
The constellation of behaviors associated with ultraviolet indoor tanning is analogous to that in other
behavioral addictions. Despite a growing literature on ultraviolet indoor tanning as an addiction, there
remains no consensus on how to identify ultraviolet indoor tanning addictive tendencies. The purpose
of the present study was to translate a behavioral economic task more commonly used in substance
abuse to quantify the "abuse liability" of ultraviolet indoor tanning, establish construct validity, and
determine convergent validity with the most commonly used diagnostic tools for ultraviolet indoor tan-
ning addiction (i.e., mCAGE and mDSM-IV-TR). We conducted a between-groups study using a novel
hypothetical Tanning Purchase Task to quantify intensity and elasticity of ultraviolet indoor tanning
demand and permit statistical comparisons with the mCAGE and mDSM-IV-TR. Results suggest that
behavioral economic demand is related to ultraviolet indoor tanning addiction status and adequately
discriminates between potential addicted individuals from nonaddicted individuals. Moreover, we pro-
vide evidence that the Tanning Purchase Task renders behavioral economic indicators that are relevant
to public health research. The present ndings are limited to two ultraviolet indoor tanning addiction
tools and a relatively small sample of high-risk ultraviolet indoor tanning users; however, these pilot data
demonstrate the potential for behavioral economic assessment tools as diagnostic and research aids in
ultraviolet indoor tanning addiction studies.
Key words: behavioral addiction, behavioral economics, demand, hypothetical purchase task, tanning
The subdiscipline of behavioral economics
in behavior analysis initially provided a theo-
retical account of operant responding in vary-
ing forms of reinforcer economies (Hursh,
1980, 1984; Hursh & Roma, 2016; Kagel &
Winkler, 1972). Continued research has ren-
dered behavioral economics a viable approach
to understanding reinforcer value and con-
sumption under constraining conditions such
as closed economies, price increases, and
effort manipulations (Kagel, Battalio, &
Green, 1995). The analysis of reinforcer con-
sumption under constraining conditions gave
rise to a novel means of understanding drug
self-administration, which researchers quickly
translated to human models of addiction and
dependence (Bickel, DeGrandpre, & Higgins,
1993; Bickel, DeGrandpre, Higgins, & Hughes,
1990; Bickel, DeGrandpre, Hughes, & Higgins,
1991; Bickel, Hughes, DeGrandpre, Higgins, &
Rizzuto, 1992; Bickel, Madden, & Petry, 1998;
DeGrandpre, Bickel, Hughes, & Higgins, 1992;
Hursh, 1993). Contemporary behavioral eco-
nomics is now widely adoptedboth within
and outside of behavior analysisas a robust
conceptual, methodological, and analytical
framework for behavioral addictions and sub-
stance use disorders (Bickel, Jarmolowicz,
Mueller, & Gatchalian, 2011; Bickel, Johnson,
Koffarnus, MacKillop, & Murphy, 2014;
Carter & Grifths, 2009; Jarmolowicz, Reed,
DiGennaro Reed, & Bickel, 2016; MacKillop,
2016) and behavioral health, in general
(Bickel & Vuchinich, 2000).
Much of the contemporary success in behav-
ioral economics is due to its scalability in
informing public policy (Hursh & Roma,
2013) and utility in identifying potential abuse
liability of particular commodities
(Christensen, Silberberg, Hursh, Huntsberry, &
Riley, 2008; Hursh & Winger, 1995). Toward
this end, behavioral economists have begun to
rely on hypothetical purchase tasks (see Roma,
Hursh, & Hudja, 2016) to simulate consump-
tion as a means of safely and quickly quantify-
ing abuse liability (Jacobs & Bickel, 1999;
Murphy & MacKillop, 2005; Murphy & MacKil-
lop, 2006) and informing effective policies to
Address correspondence to: Derek D. Reed, Ph.D., 4048
Dole Human Development Center, 1000 Sunnyside Ave-
nue, Lawrence, KS, 660457555. Phone: 785.864.0504.
Fax: 785.864.5202. E-mail:
doi: 10.1002/jeab.216
reduce dependence (MacKillop et al., 2012).
The hypothetical purchase task is used to
quantify demand and demand elasticity for a
commodity, most typically drugs of abuse
(e.g., alcohol, tobacco, heroin), whereby indi-
viduals report how many units of a commodity
they would purchase or the probability of a
single purchase across a wide range of prices
(Roma et al., 2016). Beyond being an efcient
analog to drug administration, hypothetical
purchase tasks feature sufcient psychometrics
such as convergent/divergent validity
(MacKillop et al., 2008; Murphy, MacKillop,
Tidey, Brazil, & Colby, 2011) and temporal sta-
bility (Amlung & MacKillop, 2012; Few, Acker,
Murphy, & MacKillop, 2011; 2012). Although
these hypothetical purchase tasks demonstrate
adequate psychometric properties for tradi-
tional drugs of abuse (see also: Amlung,
Acker, Stojek, Murphy, & MacKillop, 2012;
Kiselica, Webber, & Bornovalova, 2016; MacK-
illop, 2016; MacKillop & Murphy, 2007; Reed,
Kaplan, & Becirevic, 2015), the degree to
which hypothetical purchase tasks are translat-
able to under-researched behavioral addic-
tions remains relatively unknown. The
purpose of this translational study was to
examine the degree to which a hypothetical
purchase task for ultraviolet indoor tanning
(UVIT) corresponds with known markers of
UVIT dependence used in clinical dermatology.
Over 10 million Americans are estimated to
use UVIT annually (Guy, Berkowitz, Hol-
man, & Hartman, 2015), with the highest prev-
alence among Caucasian college-aged females
(aged 1821; 31.6%) who report 27.6 UVIT
events per year, on the average (Centers for
Disease Control, 2012). These statistics suggest
relatively intense consumer demand for UVIT
in this population, which is troubling given
clear and widely publicized reports that UVIT
before the age of 25 increases ones risk of
skin cancer by up to 102% (Wehner et al.,
2012). Such substantial demand for UVIT
likely results from a host of contributing fac-
tors. Curious potential UVIT users are subject
to a multi-billion-dollar industry (Bizzozero,
2009) with advertising techniques that health
professionals have likened to those used in the
tobacco industry (Holman et al., 2013). After
trying UVIT, some users may experience pleas-
urable consequences by way of physiological
sensations, relaxation, social praise, percep-
tions of increased attractiveness, and other
reinforcement mechanisms (Cafri et al., 2006;
Feldman et al., 2004; Hillhouse & Turrisi, 2012;
Kaur, Liguori, Fleischer, & Feldman, 2006;
Schneider et al., 2013; Warthan, Uchida, &
Wagner, 2005). Indeed, Feldman and collea-
gues (2004) documented a potential reinforce-
ment effect of UVIT by demonstrating that
regular UVIT users exposed to both a typical
UV sunbed and an identical sunbed containing
aUVlter will use the sunbed emitting UV,
ceteris paribus, despite these users being blind to
which sunbed contained the lter. Thus,
UV radiation may be considered a consumable
substance, considering that exposure to UV radi-
ation in UVIT is associated with cutaneous
opioid release (Kaur, Liguori, Fleischer et al.,
2006; Kaur, Liguori, Lang et al., 2006).
The notion of UVIT dependence has been
substantiated by studies drawing parallels
between UVIT and substance-related disorders
(Ashraoun & Bonar, 2014; Mosher & Danoff-
Burg, 2010). Academic dermatologists report
many UVIT users exhibit substantial difculty
in abstaining from UVIT, as well as physiologi-
cal withdrawal symptoms upon abstinence ini-
tiation, regardless of their initial motives to
tan (Ashraoun & Bonar, 2014; Harrington
et al., 2011; Nolan, Taylor, Liguori, & Feld-
man, 2009). Reports of such dependence
have led to a proliferation of discussions on
UVIT as a commodity with abuse liability,
akin to other substance-related disorders
(Ashraoun & Bonar, 2014; Harrington
et al., 2011; Nolan et al., 2009). Moreover, the
maladaptive choices (Bickel et al., 2011,
Bickel, Jarmolowicz, MacKillop et al., 2012;
Bickel, Jarmolowicz, Mueller et al., 2012;
Bickel et al., 2014; Jarmolowicz et al., 2016)
associated with UVIT dependence appear simi-
lar to other behavioral addictions (see discus-
sion by Reed, 2014).
Despite developments in tanning-specic
addiction assessment tools (Heckman et al.,
2014; Hillhouse, Turrisi, Stapleton, & Robin-
son, 2010; Schneider et al., 2015), many UVIT
addiction studies have relied upon tools ini-
tially developed for smoking/drinking pro-
blems or behavioral addictions; notably, the
CAGE (Cut-down, Annoyed, Guilty, Eye-
opener) and the substance-related disorder
diagnostic criteria from the Diagnostic and Sta-
tistical Manual of Mental Disorders (Fourth Edi-
tion, Text Revision; DSM-IV-TR; American
Psychiatric Association, 2000; Ashraoun &
DEREK D. REED et al.2
Bonar, 2014; Cartmel et al., 2013; Harrington
et al., 2011; Heckman, Egleston, Wilson, &
Ingersoll, 2008; Mosher & Danoff-Burg, 2010;
Poorsattar & Hornung, 2007; Schneider et al.,
2013; Warthan et al., 2005). For example, Ash-
raoun and Bonar surveyed 533 university stu-
dents and found that 31% of the sample met
criteria for tanning dependence according to
a tanning-specic modication of the CAGE
(see below), as well as signicant correlations
between tanning dependence and obsessive-
compulsive and body dysmorphic disorders.
Mosher and Danoff-Burg found that 39% of
229 university students met tanning depend-
ence criteria according to the CAGE, and 31%
met dependence criteria according to tanning-
specic modied DSM-IV-TR criteria (see
below). Additionally, participants with tanning
dependence proles reported signicantly
greater prevalence of anxiety and consump-
tion of other commodities with abuse liability
(e.g., marijuana, alcohol), in line with Bickel
and colleagues(Bickel, Jarmolowicz, MacKil-
lop et al., 2012; Bickel, Jarmolowicz, Mueller
et al., 2012) view that risky health decisions
favoring immediate reinforcers (e.g., UVIT for
short-term gains despite long-term health
impacts) may be a transdisease process under-
lying addiction and spanning a range of addi-
tional clinical disorders.
Initially designed for alcohol use problems,
the CAGE is composed of four criteria
(Aertgeerts, Buntinx, & Kester, 2004): whether
individuals (1) report being told to Cut-down
use of the commodity, (2) feel Annoyed when
others comment about their consumption of
the commodity, (3) have felt Guilty for con-
suming the commodity, and (4) use the com-
modity as an Eye-opener (i.e., use the
commodity upon waking in the morning).
Afrming at least two criteria is regarded as an
indication of potential dependence. A modi-
ed CAGE (mCAGE) assessment has subse-
quently become one of the more commonly
used screeners for UVIT dependence
(Schneider et al., 2015). However, Schneider
and colleagues exposed a number of limita-
tions regarding its use in UVIT contexts:
(a) the clinical utility of the mCAGE for UVIT
has been untested, (b) potential sampling pro-
blems, and (c) lack of no-UVIT controls.
These authors demonstrated a larger and
more representative sample of UVIT users did
not replicate ndings from earlier studies
employing the mCAGE; rather, the mCAGE
over-predicted UVIT dependence.
Similar to the translation of CAGE for UVIT
purposes, researchers have adapted the lan-
guage from the substance-related disorders
diagnostic criteria in the Diagnostic and Statisti-
cal Manual of Mental Disorders (Fourth Edition,
Text Revision; DSM-IV-TR; American Psychiatric
Association, 2000). The initial translation of
these substance-related disorders criteria for
UVIT has become the standard language for
use in identifying potential UVIT dependence
(Warthan et al., 2005). As per standard DSM-
IV-TR criteria, afrming at least three of the
seven criteria is regarded as an indication of
potential dependence.
Because no gold standard for categorizing
tanning dependence has yet been established
(Schneider et al., 2015), the purpose of this
study was to compare the degree of depend-
ence on the more commonly used clinical
screeners for UVIT dependence (i.e., mCAGE
and mDSM-IV-TR) to contemporary behavioral
economic indicators of substance related dis-
orders (behavioral economic demand via
hypothetical purchase task) using recent, non-
recent, and never UVIT users. Specically, we
explored the relation between mCAGE and
mDSM-IV-TR ratings to behavioral economic
demand (Bickel et al., 1998; Hursh, 1993) for
UVIT. Behavioral economic demand is a quan-
titative marker of how consumers defend con-
sumption of a given commodity as the price of
the commodity increases. For example, a
behavioral economist assesses the degree to
which a consumer consumes a commodity
under ideal market pressures (e.g., very low
costs to the consumer). The behavioral econo-
mist also measures the elasticity of demand by
quantifying change in consumption as a func-
tion of varying costs. Relatively inelastic
demandthat is, stable consumption that is
insensitive to price increasesindicates
stronger demand for the commodity and
stronger underlying reinforcing effectiveness.
Demand elasticity also informs additional
quantitative markers of demand, such as P
(the price associated with unit elasticity where
one-unit increase in price is met with one-unit
decrease in consumption), O
at P
), and essential value (EV; a standar-
dized metric based on an elasticity rate con-
stant that succinctly summarizes an organisms
consumption of the target commodity).
Behavioral economic demand for commod-
ities of abuse serves as a major component in
the reinforcer pathologies model (Bickel,
Jarmolowicz, MacKillop et al., 2012; Bickel,
Jarmolowicz, Mueller et al., 2012; Bickel et al.,
2014), which is a theoretical approach in the
understanding and treatment of substance-
related disorders grounded in behavior sci-
ence. Specically, stronger demand is consid-
ered a marker of potential substance-related
disorder. The degree of demand inelasticity
for a commodity is thereby associated with that
commoditys abuse liability. Validating behav-
ioral economic measures of abuse liability
using common diagnostic tools for UVIT may
provide insight on the validity of our transla-
tion of the hypothetical purchase task for this
novel behavioral addiction. Demonstrating a
conceptually systematic relation between
demand and UVIT abuse will enable addi-
tional clinical research on the behavioral eco-
nomic components of UVIT use and
potentially inform novel treatment
A total of 102 undergraduate females
enrolled in an introductory psychology course
were recruited and received extra credit for
participating. The nal sample for analysis
contained 93 female participants between the
ages of 18 and 36 (mean age = 20.19 years
[SD = 2.87]) after excluding participants based
on response patterns on the behavioral eco-
nomic task (see criteria below). More than
simply a convenience sample, this demo-
graphic represents the population at the high-
est risk of UVIT-induced cancer (Karagas
et al., 2002; Wehner et al., 2012). All data were
collected two weeks prior to the universitys
scheduled spring break, which is a temporal
event associated with increased tanning rates
on college campuses (Heckman et al., 2008).
An information statement consent process
entailed a written cover letter explaining the
broad goals of the study, along with the risks
(i.e., boredom), benets (i.e., increased
understanding of consumer choices), and par-
ticipant rights (i.e., voluntary participation,
anonymity of results) associated with participa-
tion. The information statement explained
that subsequent completion of the paper-
based task (see below) was an indication of
written consent. This consent process permits
further anonymity and condentiality as no
participant names are directly linked to their
responses. All procedures, including the infor-
mation statement consent process, were
approved by the Human Subjects Committee
of the University of Kansas Lawrence Campus
Participants completed the mCAGE and
mDSM-IV-TR to determine potential depend-
ence to UVIT. For the mCAGE (Mosher &
Danoff-Burg, 2010; Warthan et al., 2005),
afrmative responses to at least two of the fol-
lowing four statements resulted in a positive
classication, whereas afrmative responses to
less than two of the following four questions
resulted in a negative classication:
1. Do you try to cut down on the time you
spend in tanning beds or booths?
2. Do you ever get annoyed when people tell
you not to use tanning beds or booths?
3. Do you ever feel guilty that you are using
tanning beds or booths too much?
4. When you wake up in the morning, do
you want to use a tanning bed or booth?
The following seven diagnostic criteria com-
prising the mDSM-IV-TR (American Psychiatric
Association, 2000; Mosher & Danoff-Burg,
2010; Warthan et al., 2005) were scored identi-
cally to past studies (Ashraoun & Bonar,
2014; Mosher & Danoff-Burg, 2010; Warthan
et al., 2005):
1. (a) Do you think you need to spend
more and more time in tanning beds
or booths to maintain your per-
fect tan?
(b) Do you think your tan will fade if
you spend the same amount of time
in a tanning bed or booth
each time?
2. Do you continue to use tanning beds or
booths so your tan will not fade?
3. When you go to tanning salons, do you
usually spend more time in the tanning
bed or booth than you had planned?
4. Do you try other non-tanning-related activ-
ities, but nd you really still like spending
time in tanning beds or booths best of all?
5. (a) How many days a week do you spend
in tanning beds or booths?
DEREK D. REED et al.4
(b) How many days a week do you spend
tanning in the sun?
(c) Do you tan year round?
(d) Have you ever missed work, a social
engagement, or school because of a
burn from tanning bed or
booth use?
6. Have you ever missed any scheduled event
(social, occupational, or recreational activ-
ities) because you decided to use tanning
beds or booths?
7. (a) Do you believe you can get skin can-
cer from the sun?
(b) Do you believe you can get skin can-
cer from tanning beds?
(c) Does this keep you from spending
time in the sun or using tanning
beds or booths?
Specically, each yesresponse is scored as
one afrmative indicator, with the following
exceptions for items 1, 5, and 7: (1) question
1 is scored as afrmative for yesresponses to
both 1a and 1b; (2) question 5 is scored as
afrmative for positive responses on at least
2 subparts (responses other than 0 are scored
positive for 5a); and (3) question 7 is scored
as afrmative for a yesresponse to 7a
and/or 7b and a noresponse to 7c. At least
three afrmative responses across the seven
items resulted in a classication of potential
dependence (i.e., positive classication); less
than three afrmative responses resulted in a
classication of no potential dependence
(i.e., negative classication).
We created a composite diagnostic based on
classications on the mCAGE and mDSM-IV-
TR. Participants who scored positive for poten-
tial dependence on at least one of the two
screening scales (mCAGE and mDSM-IV-TR)
were classied as At Risk(n= 42) and those
who scored negative across both scales were
classied as No Risk(n= 51).
In addition to completion of the screening
scales, participants reported the time they last
used a UVIT device (Table 1) and were classi-
ed according to the following criteria: Recent
users (N= 37) were classied as using UVIT
within the past month, Non-Recent users
(N= 28) were classied as using UVIT within
the past 5 years but not within the past month,
and Never users (N= 28) were classied as
never using UVIT. No participants reported
their last UVIT use more than 5 years ago.
Note, the total number of participants who
were classied as either At Riskor No Risk
or as Recent, Non-Recent, or Never users is
less than the original sample size due to exclu-
sionary criteria described in the data analysis
section below.
Finally, to measure behavioral economic
demand for UVIT, participants completed a
tanning purchase task (TPT) developed based
on previous studies using hypothetical pur-
chase tasks (Murphy & MacKillop, 2006; Reed
et al., 2015; Roma et al., 2016). The TPT was
administered via paper and pencil with the fol-
lowing instructions:
What is the likelihood that you sign up for
a month of the most basic unlimited tan-
ning? Use a value of 0 if you would
NEVER consider signing up at the given
price. Use a value between 0100 to indi-
cate the extent that you are likely to sign up
at the given price. Use a value of 100 if
you would not hesitate in signing up at the
given price.
where the given price of tanning was dened
as a base price of $30.00 (approximately 50-
75% the price of local salon packages to
ensure relative inelasticity of demand at low
Table 1
Demographic Characteristics of Study Participants
No. (%) of
93 Participants
Age, years
18-19 42 (45)
20-21 41 (44)
22-23 5 (5)
24-25 2 (2)
>25 3 (2)
Skin type
Burns, never tans 4 (4)
Burns easily, then develops light tan 17 (18
Burns moderately, then develops
light tan
18 (19)
Burns minimally, then develops
moderate tan
32 (34)
Does not burn, develops dark skin 20 (22)
Does not burn, shows no change in
noticeable appearance
1 (1)
N/A 1 (1)
UVIT Use Frequency
Never 28 (30)
Non-Recent (within past 5 years, not
within past month)
28 (30)
Recent (within past month) 37 (40)
prices in the demand curve) plus a tax ranging
from $0.00 (no tax) to $60.00. The progres-
sion of total price included the following taxes:
$0.00 (no tax), 0.30, 0.60, 1.50, 3.00, 4.50,
6.00, 7.50, 9.00, 12.00, 15.00, 18.00, 22.50,
30.00, and 60.00. Given past research suggest-
ing that most tanning facilities offer unlimited
tanning packages (e.g., Kwon et al., 2002), we
instructed participants to report the likelihood
of purchasing the month of unlimited tanning
by handwriting their numeric response (in %
likelihood) on a blank line to the right of each
total price (i.e., base price plus tax). While fre-
quency of consumption is typicallybut not
exclusivelyused in hypothetical purchase
tasks (for other examples of demand curves
using likelihood responses, see Henley, DiGen-
naro Reed, Kaplan, & Reed, 2016; Roma et al.,
2016), participants were unlikely to have expe-
rience purchasing single tanning sessions
which may have compromised the integrity of
their responses. The use of progressively
increasing tax prices modeled the effects of
potential excise tax increases on indoor tan-
ning (comparable to the 10% excise tax on
indoor tanning services levied by the Patient
Protection and Affordable Care Act 2010).
Data Analysis
Responses on the TPT were converted to a
proportion (out of 1) and subsequently ana-
lyzed according to the exponential model of
demand (Hursh & Silberberg, 2008) using a
freely available GraphPad Prism
provided by the Institutes for Behavior
Resources (
id=181; note that this GraphPad Prism
tion uses standard nonlinear regression to
minimize the sum of squares via the Mar-
quardt method; Marquardt, 1963):
logQ= logQ0+keαQ0CðÞ
where Qis the likelihood of purchase at each
tax price (i.e., C), Q
(i.e., Demand Intensity)is
the maximum likelihood of purchase associ-
ated with no tax (converted to $.01 for curve-
tting purposes), kis the range of consump-
tion in logarithmic units (in this case k=2
due to the absolute range of responses, ran-
ging from .01-1), and αis the rate of change
in elasticity across the demand curve.
Several other behavioral economic metrics
were calculated using freely available software
(Kaplan & Reed, 2014) and equations theo-
rized by Hursh (2014). P
is the price at
which likelihood of purchase disproportion-
ately decreases with increases in price
(i.e., slope = 1, unit elasticity), calculated as:
Pmax =:083k+0:65ðÞ
ðÞ ð2Þ
represents maximum expenditure, but
for the purposes of the current study repre-
sents the expected tax revenue at P
, and is
calculated by multiplying P
by Qat P
Finally, essential value (EV)reects the nor-
malized (i.e., relatively k- and Q
ent) reinforcing value of the commodity, with
larger EVs signifying relatively higher demand.
Essential value was calculated as:
EV =1
ðÞ ð3Þ
All demand indices were derived except
Intensity and k(i.e., constrained in
Equation 1). A 0value was inputted for P
, and EV for participants who indicated no
level of consumption at any price. Participants
data were excluded if their responses derived
negative demand indices (n=1) or if
responses on the TPT contained multiple
instances of consumption value increases
across increasing prices (n= 2). These exclu-
sionary criteria follow the logic of Stein and
colleagues(Stein, Koffarnus, Snider, Quisen-
berry, & Bickel, 2015) algorithm for identify-
ing nonsystematic data while also permitting
examination of zero demand datasets
(e.g., zero consumption across all prices). Six
additional participants were excluded due to
unusable P
values (range = $412,142.20 to
). All unusable P
values were arti-
facts of consumption patterns containing a
series of exclusively nondecreasing, nonzero
consumption values (extreme inelasticity)
before reporting complete suppression in con-
sumption throughout the remainder of the
TPT; such patterns return near-zero αvalues
(due to extreme inelasticity), which returns
nonsensical P
values when inputted to
Equation 2. Thus, the nal sample size after
exclusions was 93. Chi-square analyses were
DEREK D. REED et al.6
conducted using IBM
Statistics Ver-
sion (64-bit; Windows) and all other
statistical tests were conducted using Graph-
Pad Prism
7.00 for Windows.
A Pearson χ
analysis was used to examine
the relation between mCAGE and mDSM-IV-
TR and between frequency and diagnostic
status. Results indicate a signicant relation
between mCAGE and mDSM-IV-TR,
(1, N= 93) = 7.353, p= .007; d= .281, repli-
cating previous ndings (Mosher & Danoff-
Burg, 2010; Warthan et al., 2005). Among the
30 participants who scored positive on only
one of the two diagnostic tools, 24 scored
positive on the mCAGE and only six scored
positive on the mDSM-IV-TR. The relation
between usage status and diagnostic status
(Table 2) was also signicant, χ
(2, N= 93) =
34.101, p< .0001; d= .606.
The left panels in Figures 1 and 2 show
Equation 1 provided an excellent t to the
group data (R
=0.96-1.0; RMSE = .033-.096),
regardless of categorization or status. For indi-
vidual comparisons, participantsbehavioral
economic indices were graphed and descrip-
tive statistics were obtained to determine distri-
bution shape. Kruskal-Wallis and Dunns
multiple comparisons tests were performed to
compare demand indices across the three fre-
quency categorizations (right panel of Fig. 1).
Each signicance level (alpha) was adjusted to
account for multiple comparisons. Multiple
comparisons yielded signicant differences for
all of the following measures at p< .02. For
EV, results showed signicant differences
between the three groups, H(2, N= 93) =
38.67, p< .0001. Comparisons for Intensity
also showed signicant differences,
H(2, N= 93) = 34.11, p< .0001. Finally, signif-
icant differences were observed for P
N= 93] = 38.08, p< .0001) and O
N= 93] = 38.67, p< .0001). These results sug-
gest groups of participants with more recent
UVIT use exhibit signicantly greater UVIT
demand than groups with less recent or no
lifetime use.
Because data did not approximate a normal
distribution and we hypothesized At-Risk indi-
viduals would display stronger demand for
UVIT, nonparametric one-tailed Mann
Whitney Utests were performed to compare
diagnostic status across indices of demand
(right panel of Fig. 2). For EV, At-Risk indivi-
duals (M
= 60.36) displayed signicantly
higher median values than No-Risk individuals
= 36), U= 510, p< .0001. At-Risk indivi-
duals (M
= 61.30) reported signicantly
higher Intensity than No-Risk individuals
= 35.23), U= 470.5, p< .0001. For P
and O
, At-Risk individuals (M
= 60.49;
60.36) showed signicantly higher values than
No-Risk individuals (M
= 35.89; 36),
U= 504.5, p< .0001 and U= 510, p< .0001,
respectively. Taken together, results indicate
higher demand for the At-Risk group com-
pared to the No-Risk group.
Finally, we compared correlations between
behavioral economic markers of demand for
the aggregate pool of participants (i.e., regard-
less of UVIT use frequency or dependence risk
status). We used Spearman ρcorrelations given
signicant deviations from normality for all
four demand metrics (Q
,EV) based
on the DAgostino & Pearson omnibus normal-
ity test in GraphPad Prism
. Table 3 shows that
all correlations were positive and strong
(ρrange .79 to 1.00) and signicant (pvalues
range 5.55 x 10
to 0) with a .05 alpha level.
This nding is to be expected given the param-
eter dependencies and mathematical similari-
ties in calculating these behavioral economic
markers of demand.
In the current study, we employed a use-
inspired, translational framework to examine
Table 2
Association Between Frequency and Diagnostic Findings
Composite Diagnostic Status
Frequency No Risk At Risk Total, No. (%)
Never 28 (30) 0 (0) 28 (30)
Non-Recent 12 (13) 16 (17) 28 (30)
Recent 11 (12) 26 (28) 37 (40)
Total, No. (%) 51 (55) 42 (45) 93 (100)
Note. Composite Diagnostic Status (Participants were
classied as No Risk if they scored negative on both
mDSM-IV-TR and mCAGE screening tools; Participants
were classied as At Risk if they scored positive on at least
one of the screening tools); Frequency (Participants were
classied as Never tanners if they had never used UVIT;
Non-Recent tanners if they had used UVIT within the past
5 years but not within the past month; Recent tanners if
they had used UVIT within the past month)
behavioral economic measures of abuse liabil-
ity of UVIT to validate these measures against
common diagnostic tools used to assess UVIT
dependence (mCAGE and mDSM-IV-TR). Our
discussion highlights the potential benets
and drawbacks of using the novel TPT in aca-
demic and clinical settings. Additionally,
through the strategic use of an increasing pro-
gression of taxes layered onto a base-price for
a month of unlimited UVIT, ndings from the
current study allow for the translation of the
aforementioned behavioral economic mea-
sures to public policy implications (discussed
In the nal sample of 93 females, 65 (70%)
reported use of UVIT in the past 5 years, with
37 of these 65 (57%) reporting use within the
last month. Chi-squared analyses suggested a
Fig. 1. Demand indices categorized by frequency of UVIT use. Participants were classied as Never tanners if they
had never used UVIT; Non-Recent tanners if they had used UVIT within the past 5 years but not within the past month;
Recent tanners if they had used UVIT within the past month. Panel A displays Equation 1 tted to the mean data. Panels
B-E show comparisons between participantsindividual behavioral economic indices with error bars indicating median
and 95% condence interval. Signicance levels between comparisons denoted by asterisks.
*p< .05. **p< .01. ****p< .0001
DEREK D. REED et al.8
relation between frequency and diagnostic sta-
tus on the mCAGE and mDSM-IV-TR. Behav-
ioral economic measures of the reinforcing
effectiveness of UVIT were signicantly greater
for the At-Risk participants, suggesting some
convergent validity in these novel metrics for
UVIT addiction. Moreover, of the 28 partici-
pants in the Never-use group, none met cri-
teria for an At- Risk status.
In examining the 42 At-Risk participants,
only 12 (29%) scored positive for potential
UVIT dependence on both mDSM-IV-TR and
mCAGE. These 12 participants may constitute
a subsample of UVIT-dependent users, but the
low proportion prohibits meaningful inferen-
tial statistical analyses. Of the remaining 30 par-
ticipants scoring positive on only one of the
diagnostic tools, 24 (80%) were due to
mCAGE scores. This disproportionate number
of participants corroborates Schneider and
colleagues(2015) critique of the mCAGEs
tendency to over-predict dependence status in
Fig. 2. Demand indices categorized by composite diagnostic status. Composite diagnostic status based on positive or
negative classications on the mDSM-IV-TR and mCAGE. Participants were classied as No Risk if they scored negative
on both screening tools. Participants were classied as At Risk if they scored positive on at least one of the screening
tools. Panel A displays Equation 1 tted to the mean data. Panels B-E show comparisons between participantsindividual
behavioral economic indices, with error bars indicating median and 95% condence interval. For all comparisons,
p< .0001.
UVIT users. Despite the majority of At-Risk
participants being identied via the mCAGEs
potentially oversensitive scoring, this group
featured signicantly greater behavioral eco-
nomic demand for UVIT than No-Risk
Demand indices have become contempo-
rary behavioral economic markers (Carter &
Grifths, 2009; MacKillop, 2016) for
(a) identifying the abuse liability of substances
(Bickel, Jarmolowicz, MacKillop et al., 2012;
Bickel, Jarmolowicz, Mueller et al., 2012;
Bickel, Jarmolowicz, Mueller, & Gatchalian,
2011; Bickel et al., 2014; Bickel et al., 1998;
Hursh, 1991; Hursh & Roma, 2016) and
(b) predicting success of treatment for
substance-related disorders (MacKillop & Mur-
phy, 2007; Madden & Kalman, 2010; McClure,
Vandrey, Johnson, & Stitzer, 2013). We posit
that another potential benet of hypothetical
purchase tasks is that they are not readily iden-
tiable diagnostic tools. That is, we speculate
that respondents cannot discern what the task
assesses and may thereby reduce reactivity in
the form of faking good.Both the mCAGE
and mDSM-IV-TR use language that indicates
the toolsgoals; the Tanning Purchase Task
(TPT) is absent of such language and may
thereby yield more honest responding. Addi-
tional empirical work is obviously necessary to
conrm our assumption. Nevertheless, the
purchase task designed for this study may be
useful to both researchers and clinicians inter-
ested in examining UVIT userspotential
abuse risk.
Because the TPT returns systematic data in
line with behavioral economic theory, this task
may be useful in identifying young adults with
substantial UVIT demand. Because demand
markers generated via similar alcohol
purchase tasks are theorized to be an impor-
tant recursive etiological markerfor later sub-
stance abuse in young adults (see review in
MacKillop, 2016, p. 677), the TPT may be simi-
larly helpful in identifying potential clinical
UVIT dependence in this young adult popula-
tion. Identifying excessive demand as an etio-
logical marker for dependence may be
particularly important for UVIT given the
long-term disease trajectory associated with
cumulative use, especially in young adults
(Zhang et al., 2012). Finally, researchers have
demonstrated that hypothetical purchase tasks
may be regularly evaluated in single-subject
design to monitor treatment success (Bujarski,
MacKillop, & Ray, 2012; Madden & Kalman,
2010; McClure et al., 2013) and provide early
indicators of treatment failures (MacKillop &
Murphy, 2007) over treatment course periods.
If the TPT exhibits these clinical advantages in
dermatological study and use, health profes-
sionals may be able to use the TPT as a unique
behavioral screener to identify potential UVIT
abuse and monitor the emergence of depend-
ence. While these advantages to hypothetical
purchase tasks are dependent on evidenced
testretest reliabilitywhich is well-
documented in cigarette and alcohol purchase
tasks (e.g., Few et al., 2011; MacKillop et al.,
2008; Murphy, MacKillop, Skidmore, & Peder-
son, 2009)we did not collect such measures;
as such, additional research on the TPT tem-
poral stability is needed.
The construct validity documented in this
study suggests that this TPT warrants further
investigation as both a research tool and diag-
nostic aid, preferably validated against more
objectively documented UVIT use. Two clini-
cal tools have recently been developed that
appear to be substantially more conservative in
assessing UVIT addiction: the Structured
Interview for Tanning Abuse and Dependence
(Heckman, Egleston, Wilson, & Ingersoll,
2008; Hillhouse et al., 2012) and the Tanning
Pathology Scale (Heckman et al., 2014; Hill-
house et al., 2010). We suggest future compar-
isons of TPT demand to scores on these two
scales, as well.
Anal broader contribution of this study
lies in its public policy implications, specically
in the P
variable to derive the tax price
associated with unit elasticity in participants
demand curves. In doing so, P
provides an
empirical basis for determining excise taxes
Table 3
Correlation Matrix (Spearmansρ) of Behavioral
Economic Demand Measures
Measure Q
.84 .99
EV .84 .99 1.00
Note. All correlations were signicant at the .05 alpha
level. See text for additional details.
DEREK D. REED et al.10
aimed at reducing problematic consumption
of unhealthy commodities, and likewise pre-
dicts potential revenues from said taxes via
computation of the O
variable (Hursh &
Roma, 2013; Roma et al., 2016). Behavioral
economists have used cigarette purchase tasks
to examine the potential policy implications of
tobacco (MacKillop et al., 2012), fuel (Reed,
Kaplan, Roma, & Hursh, 2014; Reed, Parting-
ton, Kaplan, Roma, & Hursh, 2013), or high-
caloric food (Epstein, Dearing, Roba, & Fin-
kelstein, 2010) taxation.
To date, we are unaware of any studies
examining the effects of the tanning tax levied
by the Patient Protection and Affordable Care
Act on UVIT demand. Examination of P
Panel D of Figure 2 suggests Recent tanners
would tolerate a tax of approximately $25 on a
$30 package; this constitutes an 83% tax that
far exceeds the current 10% tax levied by the
Patient Protection and Affordable Care Act.
Analysis of Non-Recent tanners suggests toler-
ance of an approximately $15 (50%) tax on
$30 tanning services. Interestingly, Never tan-
nerspoint of tolerance with taxation approxi-
mates the 10% tax currently levied. These data
collectively indicate that the 10% tanning tax
falls in the inelastic portion of the demand
curve for consumers of tanning services, sug-
gesting that present policies are (1) likely to
be ineffective in reducing demand for those
individuals who currently engage in UVIT and
(2) limiting potential revenue that could be
used toward education and other abuse-
reduction initiatives. This 10% tax may, how-
ever, effectively discourage young adults from
initiating UVIT use. These behavioral eco-
nomic markers of taxation tolerance, coupled
with data indicating a relative lack of UVIT sal-
onscompliance with the UVIT tax policy
(Jain, Rademaker, & Robinson, 2012), warrant
further investigation.
In summary, this study explored whether
behavioral economic measures of ultraviolet
indoor tanning (UVIT) demand via a novel
hypothetical tanning purchase task (TPT) are
associated with diagnostic criteria from estab-
lished measures of UVIT addiction (mCAGE
and mDSM-IV-TR) and self-reported frequency
of UVIT use.Although our ndings suggest
the TPT has promise for assessing potential
abuse liability of UVIT for current UVIT users,
more psychometric work is necessary. Speci-
cally, because this study utilized diagnostic
tools translated from addiction sciences
(i.e., behavioral economic demand), future
studies should compare the results of the TPT
with other emerging clinical tools explicitly
designed for UVIT addiction; particularly, the
Structured Interview for Tanning Abuse and
Dependence (Heckman et al., 2008; Hillhouse
et al., 2012) and the Tanning Pathology Scale
(Heckman et al., 2014; Hillhouse et al., 2010).
Further demonstration of the construct valid-
ity of the TPT may render it a benecial tool
for academic, clinical, and public health policy
applications. In doing so, behavioral economic
markers of dependence may demonstrate fur-
ther translation to other behavioral addictions
not yet researched using demand measures by
applied behavioral economists.
Aertgeerts, B., Buntinx, F., & Kester, A. (2004). The value
of the CAGE in screening for alcohol abuse and alco-
hol dependence in general clinical populations: A
diagnostic meta-analysis. Journal of Clinical
Epidemiology, 57,3039. doi:10.1016/S0895-4356(03)
American Psychiatric Association. (2000). Diagnostic and
statistical manual of mental disorders: DSM-IV-TR (4th
ed.). Washington, DC: American Psychiatric
Amlung, M. T., Acker, J., Stojek, M. K., Murphy, J. G., &
MacKillop, J. (2012). Is talk "cheap"? An initial investi-
gation of the equivalence of alcohol purchase task
performance for hypothetical and actual rewards.
Alcoholism: Clinical and Experimental Research, 36,
716724. doi:10.1111/j.1530-0277.2011.01656.x
Amlung, M., & Mackillop, J. (2012). Consistency of self-
reported alcohol consumption on randomized and
sequential alcohol purchase tasks. Frontiers in
Psychiatry, 3, 65. doi:10.3389/fpsyt.2012.00065
Ashraoun, L., & Bonar, E. E. (2014). Tanning addiction
and psychopathology: Further evaluation of anxiety
disorders and substance abuse. Journal of the American
Academy of Dermatology, 70, 473480. doi:10.1016/j.
Bickel, W. K. (2012). The emerging new science of psycho-
pathology. Addiction, 107, 17381739. doi:10.1111/
Bickel, W. K., DeGrandpre, R. J., & Higgins, S. T. (1993).
Behavioral economics: A novel experimental
approach to the study of drug dependence. Drug and
Alcohol Dependence, 33, 173192. doi:10.1016/0376-
Bickel, W. K., DeGrandpre, R. J., Higgins, S. T., &
Hughes, J. R. (1990). Behavioral economics of drug
self-administration. I. Functional equivalence of
response requirement and drug dose. Life Sciences, 47,
15011510. doi:10.1016/0024-3205(90)90178-T
Bickel, W. K., DeGrandpre, R. J., Hughes, J. R., &
Higgins, S. T. (1991). Behavioral economics of drug
self-administration. II. A unit-price analysis of
cigarette smoking. Journal of the Experimental Analysis of
Behavior, 55, 145154. doi:10.1901/jeab.1991.55-145
Bickel, W. K., Hughes, J. R., DeGrandpre, R. J.,
Higgins, S. T., & Rizzuto, P. (1992). Behavioral eco-
nomics of drug self-administration. IV. The effects of
response requirement on the consumption of and
interaction between concurrently available coffee and
cigarettes. Psychopharmacology, 107, 211216.
Bickel, W. K., Jarmolowicz, D. P., MacKillop, J.,
Epstein, L. H., Carr, K., Mueller, E. T., & Waltz, T. J.
(2012). The behavioral economics of reinforcement
pathologies: Novel approaches to addictive disorders.
APA addiction syndrome handbook, Vol. 2: Recovery, pre-
vention, and other issues. (pp. 333363): Washington,
DC: American Psychological Association.
Bickel, W. K., Jarmolowicz, D. P., Mueller, E. T., &
Gatchalian, K. M. (2011). The behavioral economics
and neuroeconomics of reinforcer pathologies: Impli-
cations for etiology and treatment of addiction. Cur-
rent Psychiatry Reports, 13, 406415. doi:10.1007/
Bickel, W. K., Jarmolowicz, D. P., Mueller, E. T.,
Koffarnus, M. N., & Gatchalian, K. M. (2012). Exces-
sive discounting of delayed reinforcers as a trans-
disease process contributing to addiction and other
disease-releated vulnerabilities: Emerging evidence.
Pharmacology & Therapeutics, 134, 287298.
Bickel, W. K., Johnson, M. W., Koffarnus, M. N.,
MacKillop, J., & Murphy, J. G. (2014). The behavioral
economics of substance use disorders: Reinforcement
pathologies and their repair. Annual Review of Clinical
Psychology, 10, 641677. doi:10.1146/annurev-clinpsy-
Bickel, W. K., Madden, G. J., & Petry, N. M. (1998). The
price of change: The behavioral economics of drug
dependence. Behavior Therapy, 29, 545565.
Bickel, W. K., & Vuchinich, R. E. (2000). Reframing health
behavior change with behavioral economics. Mahwah, NJ:
Lawrence Erlbaum Associates Publishers.
Bizzozero, J. (May, 2009). The state of the industry report
2008. Available at:
Accessed January 21, 2015.
Bujarski, S., MacKillop, J., & Ray, L. A. (2012). Under-
standing naltrexone mechanism of action and phar-
macogenetics in Asian Americans via behavioral
economics: A preliminary study. Experimental and Clini-
cal Psychopharmacology, 20, 181190. doi:10.1037/
Cafri, G., Thompson, J. K., Roehrig, M., van den Berg, P.,
Jacobsen, P. B., & Stark, S. (2006). An investigation of
appearance motives for tanning: The development
and evaluation of the Physical Appearance Reasons
For Tanning Scale (PARTS) and its relation to
sunbathing and indoor tanning intentions. Body
Image, 3, 199209. doi:10.1016/j.bodyim.2006.05.002
Carter, L. P., & Grifths, R. R. (2009). Principles of labora-
tory assessment of drug abuse liability and implica-
tions for clinical development. Drug and Alcohol
Dependence,105, S14-S25. doi:10.1016/j.
Cartmel, B., Ferrucci, L. M., Spain, P., Bale, A. E.,
Pagoto, S. L., Leffell, D. J., Mayne, S. T. (2013).
Indoor tanning and tanning dependence in young
people after a diagnosis of basal cell carcinoma.
JAMA Dermatology, 149, 11101111. doi:10.1001/
Centers for Disease Control and Prevention (CDC).
(2012). Use of indoor tanning devices by adults--
United States, 2010. Morbidity and Mortality Weekly
Report, 61(18), 323326. Available at http://www.cdc.
Christensen, C. J., Silberberg, A., Hursh, S. R.,
Huntsberry, M. E., & Riley, A. L. (2008). Essential
value of cocaine and food in rats: Tests of the expo-
nential model of demand. Psychopharmacology, 198,
221229. doi:10.1007/s00213-008-1120-0
DeGrandpre, R. J., Bickel, W. K., Hughes, J. R., &
Higgins, S. T. (1992). Behavioral economics of drug
self-administration. III. A reanalysis of the nicotine
regulation hypothesis. Psychopharmacology, 108,110.
Epstein, L. H., Dearing, K. K., Roba, L. G., &
Finkelstein, E. (2010). The inuence of taxes and sub-
sidies on energy purchased in an experimental pur-
chasing study. Psychological Science, 21, 406414.
Feldman, S. R., Liguori, A., Kucenic, M., Rapp, S. R.,
Fleischer, A. B., Jr., Lang, W., & Kaur, M. (2004).
Ultraviolet exposure is a reinforcing stimulus in fre-
quent indoor tanners. Journal of the American Academy
of Dermatology, 51,4551. doi:10.1016/j.jaad.
Few, L. R., Acker, J., Murphy, C., & MacKillop, J. (2011).
Testretest reliability of the alcohol and cigarette pur-
chase tasks. Alcoholism: Clinical and Experimental
Research, 35, 163a. doi:10.1111/j.1530-0277.2011.
Few, L. R., Acker, J., Murphy, C., & MacKillop, J. (2012).
Temporal stability of a cigarette purchase task. Nico-
tine & Tobacco Research, 14, 761765. doi:10.1093/ntr/
Guy, G. P., Berkowitz, Z., Holman, D. M., &
Hartman, A. M. (2015). Recent changes in the preva-
lence of and factors associated with frequency of
indoor tanning among US adults. JAMA Dermatology,
151, 12561259. doi: 10.1001/jamadermatol.
Harrington, C. R., Beswick, T. C., Leitenberger, J.,
Minhajuddin, A., Jacobe, H. T., & Adinoff, B. (2011).
Addictive-like behaviours to ultraviolet light among
frequent indoor tanners. Clinical and Experimental
Dermatology, 36,3338. doi:10.1111/j.1365-2230.
Heckman, C. J., Darlow, S., Kloss, J. D., Cohen-Filipic, J.,
Manne, S. L., Munshi, T., Perlis, C. (2014). Meas-
urement of tanning dependence. Journal of the
European Academy of Dermatology and Venereology, 28,
11791185. doi:10.1111/jdv.12243
Heckman, C. J., Egleston, B. L., Wilson, D. B., &
Ingersoll, K. S. (2008). A preliminary investigation of
the predictors of tanning dependence. American Jour-
nal of Health Behavior, 32, 451464. doi:10.5555/
Henley, A. J., DiGennaro Reed, F. D., Kaplan, B. A., &
Reed, D. D. (2016). Quantifying efcacy of workplace
DEREK D. REED et al.12
reinforcers: An application of behavioral economic
demand to evaluate hypothetical work performance.
Translational Issues in Psychological Science, 2, 174183.
Hillhouse, J., & Turrisi, R. (2012). Motivations for indoor
tanning: Theoretical models. In: C. J. Heckman &
S. L. Manne (Eds.), Shedding light on indoor tanning
(pp. 6986). New York, NY: Springer.
Hillhouse, J., Turrisi, R., Stapleton, J., & Robinson, J.
(2010). Effect of seasonal affective disorder and path-
ological tanning motives on efcacy of an
appearance-focused intervention to prevent skin can-
cer. Archives of Dermatology, 146, 485491. doi:10.1001/
Hillhouse, J. J., Baker, M. K., Turrisi, R., Shields, A.,
Stapleton, J., Jain, S., & Longacre, I. (2012). Evaluat-
ing a measure of tanning abuse and dependence.
Archives of Dermatology, 148, 815819. doi:10.1001/
Holman, D. M., Fox, K. A., Glenn, J. D., Guy, G. P., Jr.,
Watson, M., Baker, K., Geller, A. C. (2013). Strate-
gies to reduce indoor tanning: current research gaps
and future opportunities for prevention. American
Journal of Preventive Medicine, 44, 672681.
Hursh, S. R. (1980). Economic concepts for the analysis of
behavior. Journal of the Experimental Analysis of Behavior,
34, 219238. doi: 10.1901/jeab.1980.34-219
Hursh, S. R. (1984). Behavioral economics. Journal of the
Experimental Analysis of Behavior, 42, 435452. doi:
Hursh, S. R. (1991). Behavioral economics of drug self-
administration and drug-abuse policy. Journal of the
Experimental Analysis of Behavior, 56, 377393.
Hursh, S. R. (1993). Behavioral economics of drug self-
administrationan introduction. Drug and Alcohol
Dependence, 33, 165172. doi:10.1016/0376-8716(93)
Hursh, S. R. (2014). Behavioral economics and analysis of
consumption and choice. In F. K. McSweeney &
E. S. Murphy (Eds.), The Wiley-Blackwell handbook of
operant and classical conditioning (pp. 275305).
Oxford: John Wiley & Sons.
Hursh, S. R., & Roma, P. G. (2013). Behavioral economics
and empirical public policy. Journal of the Experimental
Analysis of Behavior, 99,98124. doi:10.1007/s00213-
Hursh, S. R., & Roma, P. G. (2016). Behavioral economics
and the analysis of consumption and choice. Manage-
rial and Decision Economics, 37, 224238. doi: 10.1002/
Hursh, S. R., & Silberberg, A. (2008). Economic demand
and essential value. Psychological Review, 115, 186198.
Hursh, S. R., & Winger, G. (1995). Normalized demand
for drugs and other reinforcers. Journal of the Experi-
mental Analysis of Behavior, 64, 373384. doi: 10.1901/
Jacobs, E. A., & Bickel, W. K. (1999). Modeling drug con-
sumption in the clinic using simulation procedures:
Demand for heroin and cigarettes in opioid-
dependent outpatients. Experimental and Clinical
Psychopharmacology, 7, 412426. doi: 10.1037//1064-
Jain, N., Rademaker, A., & Robinson, J. K. (2012). Imple-
mentation of the federal excise tax on indoor tanning
services in Illinois. Archives of Dermatology, 148,
122124. doi:10.1001/archderm.148.1.122
Jarmolowicz, D. P., Reed, D. D., DiGennaro Reed, F. D., &
Bickel, W. K. (2016). The behavioral and neuroeco-
nomics of reinforcer pathologies: Implications for
managerial and health decision making. Managerial
and Decision Economics, 37, 274293. doi: 10.1002/
Kagel, J. H., Battalio, R. C., & Green, L. (1995). Economic
choice theory: An experimental analysis of animal behavior.
New York: Cambridge University Press.
Kagel, J. H., & Winkler, R. C. (1972). Behavioral econom-
ics: Areas of cooperative research between economics
and applied behavioral analysis. Journal of Applied
Behavior Analysis, 5, 335342. doi:10.1901/
Kaplan, B. A., & Reed, D. D. (August, 2014). Essential
value, Pmax, and Omax automated Calculator. Spread-
sheet Application.
Karagas, M. R., Stannard, V. A., Mott, L. A., Slattery, M. J.,
Spencer, S. K., & Weinstock, M. A. (2002). Use of tan-
ning devices and risk of basal cell and squamous cell
skin cancer. Journal of the National Cancer Institute, 94,
224226. doi: 10.1093/jnci/94.3.224
Kaur, M., Liguori, A., Fleischer, A. B., Jr., & Feldman, S. R.
(2006a). Plasma beta-endorphin levels in frequent
and infrequent tanners before and after ultraviolet
and non-ultraviolet stimuli. Journal of the American
Academy of Dermatology, 54, 919920. doi:10.1016/j.
Kaur, M., Liguori, A., Lang, W., Rapp, S. R.,
Fleischer, A. B., & Feldman, S. R. (2006b). Induction
of withdrawal-like symptoms in a small randomized,
controlled trial of opioid blockade in frequent tan-
ners. Journal of the Amerian Academy of Dermatology, 54,
709711. doi:10.1016/j.jaad.2005.11.1059
Kiselica, A. M., Webber, T. A., & Bornovalova, M. A.
(2016). Validity of the alcohol purchase task: A meta-
analysis. Addiction, 111, 806816. doi:10.111/
Kwon, H. T., Mayer, J. A., Walker, K. K., Yu, H.,
Lewis, E. C., & Belch, G. E. (2002). Promotion of fre-
quent tanning sessions by indoor tanning facilities:
Two studies. Journal of the American Academy of
Dermatology, 46, 700705. doi:10.1067/mjd.
MacKillop, J. (2016). The behavioral economics and neu-
roeconomics of alcohol use disorders. Alcoholism: Clin-
ical and Experimental Research, 40, 672685. doi:
MacKillop, J., Few, L. R., Murphy, J. G., Wier, L. M.,
Acker, J., Murphy, C., Chaloupka, F. (2012). High-
resolution behavioral economic analysis of cigarette
demand to inform tax policy. Addiction, 107,
21912200. doi:10.1111/j.1360-0443.2012.03991.x
MacKillop, J., Miranda, R. M., Monti, P. M., Swift, R. M.,
Murphy, J. G., Rohsenow, D. J., Ray, L. A. (2008a).
Short-term testretest reliability of a behavioral eco-
nomic alcohol purchase task. Alcoholism: Clinical and
Experimental Research, 32, 53a. doi:10.111/j.1530-
MacKillop, J., & Murphy, J. G. (2007). A behavioral eco-
nomic measure of demand for alcohol predicts brief
intervention outcomes. Drug and Alcohol Dependence,
89, 227233. doi:10.1016/j.drugalcdep.2007.01.002
MacKillop, J., Murphy, J. G., Ray, L. A.,
Eisenberg, D. T. A., Lisman, S. A., Lum, J. K., &
Wilson, D. S. (2008b). Further validation of a ciga-
rette purchase task for assessing the relative reinfor-
cing efcacy of nicotine in college smokers.
Experimental and Clinical Psychopharmacology, 16,5765.
Madden, G. J., & Kalman, D. (2010). Effects of bupropion
on simulated demand for cigarettes and the subjective
effects of smoking. Nicotine & Tobacco Research, 12,
416422. doi:10.1093/Ntr/Ntq018
Marquardt, D. (1963). An algorithm for least-squares
estimation of nonlinear parameters. SIAM Journal on
Applied Mathematics, 11, 431441. doi:10.1137/0111030
McClure, E. A., Vandrey, R. G., Johnson, M. W., &
Stitzer, M. L. (2013). Effects of varenicline on absti-
nence and smoking reward following a programmed
lapse. Nicotine & Tobacco Research, 15, 139148.
Mosher, C. E., & Danoff-Burg, S. (2010). Addiction to
indoor tanning: Relation to anxiety, depression, and
substance use. Archives of Dermatology, 146, 412417.
Murphy, J., & MacKillop, J. (2005). Modeling demand for
alcohol using a simulation procedure. Alcoholism:
Clinical and Experimental Research, 29, 33a. doi:10.111/
Murphy, J. G., & MacKillop, J. (2006). Relative reinforcing
efcacy of alcohol among college student drinkers.
Experimental and Clinical Psychopharmacology, 14,
219227. doi:10.1037/1064-1397.14.2.219
Murphy J. G., MacKillop J., Skidmore J. R., &
Pederson A. A. (2009). Reliability and validity of a
demand curve measure of alcohol reinforcement.
Experimental and Clinical Psychopharmacology, 17,
396404. doi:10.1037/a0017684.
Murphy, J. G., MacKillop, J., Tidey, J. W., Brazil, L. A., &
Colby, S. M. (2011). Validity of a demand curve meas-
ure of nicotine reinforcement with adolescent smo-
kers. Drug and Alcohol Dependence, 113, 207214.
Nolan, B. V., Taylor, S. L., Liguori, A., & Feldman, S. R.
(2009). Tanning as an addictive behavior: A literature
review. Photodermatology, Photoimmunology and
Photomedicine, 25,1219. doi:10.1111/j.1600-0781.
Patient Protection and Affordable Care Act, 42 U.S.C. §
18001. 2010.
Poorsattar, S. P., & Hornung, R. L. (2007). UV light abuse
and high-risk tanning behavior among undergraduate
college students. Journal of the American Academy of
Dermatology, 56, 375379. doi:10.1016/j.
Reed, D. D. (2014). Ultra-violet indoor tanning addiction:
A reinforcer pathology interpretation. Addictive
Behaviors, 41, 247251. doi:10.1016/j.
Reed, D. D., Kaplan, B. A, & Becirevic, A. (2015). Basic
research on reinforcer value. In: F. D. DiGennaro
Reed & D. D. Reed (Eds.), Autism service delivery: Bridg-
ing the gap between science and practice (pp. 279306).
New York, NY: Springer.
Reed, D. D., Kaplan, B. A., Roma, P. G., & Hursh, S. R.
(2014). Inter-method reliability of progression sizes in
a hypothetical purchase task: Implications for empiri-
cal public policy. The Psychological Record, 64, 671679.
Reed, D. D., Partington, S. W., Kaplan, B. A.,
Roma, P. G., & Hursh, S. R. (2013). Behavioral eco-
nomic analysis of demand for fuel in North America.
Journal of Applied Behavior Analysis, 46, 651655.
Roma, P. G., Hursh, S. R., & Hudja, S. (2016). Hypotheti-
cal purchase task questionnaires for behavioral eco-
nomic assessments of value and motivation.
Managerial and Decision Economics, 37, 306323.
Schneider, S., Diehl, K., Bock, C., Schluter, M.,
Breitbart, E. W., Volkmer, B., & Greinert, R. (2013).
Sunbed use, user characteristics, and motivations for
tanning: results from the German population-based
SUN-Study 2012. JAMA Dermatology, 149,4349.
Schneider, S., Schirmbeck, F., Bock, C., Greinert, R.,
Breitbart, E. W., & Diehl, K. (2015). Casting shadows
on the prevalence of tanning dependence: An assess-
ment of mCAGE criteria. Acta Dermato-Venereologica,
95, 162168. doi:10.2340/00015555-1907
Stein, J. S., Koffarnus, M. N., Snider, S. E.,
Quisenberry, A. J., & Bickel, W. K. (2015). Identica-
tion and management of nonsystematic purchase task
data: Toward best practice. Experimental and Clinical
Psychopharmacology,23, 377386. doi: 10.1037/
Warthan, M. M., Uchida, T., & Wagner, R. F., Jr. (2005).
UV light tanning as a type of substance-related disor-
der. Archives of Dermatology, 141, 963966. doi:10.1001/
Wehner, M. R., Shive, M. L., Chren, M. M., Han, J.,
Qureshi, A. A., & Linos, E. (2012). Indoor tanning and
non-melanoma skin cancer: Systematic review and
meta-analysis. BMJ, 345,e5909. doi: 10.1136/bmj.e5909
Zhang, M., Qureshi, A. A., Geller, A. C., Frazier, L.,
Hunter, D. J., & Han, J. (2012). Use of tanning beds
and incidence of skin cancer. Journal of Clinical Oncol-
ogy,30, 15881593. doi:10.1200/JCO.2011.39.3652
Received: February 6, 2016
Final Acceptance: June 22, 2016
DEREK D. REED et al.14
... In the United States, excise taxes are a common tactic to curb consumption of unhealthy commodities (Chaloupka et al., 2019). Purchase tasks have been used to simulate and evaluate the market effects of potential price increases for commodities such as ultraviolet indoor tanning (e.g., Reed et al., 2016), cigarettes (e.g., MacKillop et al., 2012), and highcalorie foods (e.g., Epstein et al., 2015). These demand curve analyses yield metrics such as O max and P max , which quantify expected peak expenditures and the price at which the demand curve transitions from inelastic to elastic demand, respectively. ...
... These data can thereby aid policy makers in estimating potential tax revenues and target excise tax amounts. For example, Reed et al. (2016) determined that demand for a month of ultraviolet indoor tanning bed use became elastic at a median P max = $60.36 for regular users of tanning services, which translated to a needed excise taxation rate of just over 100%, considering that the base price of a month of indoor tanning was $30. This amount far exceeded the planned 10% tanning tax in the Obama administration's Patient Protection and Affordable Care Act, which was presumably designed to reduce rates of indoor tanning while also increasing tax revenue. ...
Howard Rachlin and his contemporaries pioneered basic behavioral science innovations that have been usefully applied to advance understanding of human substance use disorder and related health behaviors. We briefly summarize the innovations of molar behaviorism (the matching law), behavioral economics, and teleological behaviorism. Behavioral economics and teleological behaviorism's focus on final causes are especially illuminating for these applied fields. Translational and applied research are summarized for laboratory studies of temporal discounting and economic demand, cohort studies of alcohol and other drug use in the natural environment, and experimental behavioral economic modeling of health behavior‐related public health policies. We argue that the teleological behavioral perspective on health behavior is conducive to and merges seamlessly with the contemporary socioecological model of health behavior, which broadens the contextual influences (e.g., community, economic, infrastructure, health care access and policy) of individuals’ substance use and other health risk behaviors. Basic‐to‐applied translations to date have been successful and bode well for continued applications of basic science areas pioneered by Howard Rachlin and his contemporaries.
... To date, these studies spread a wide gamut and offer scalable, policy-relevant data poised to inform environmental design. One study evaluated the use of the CPT for ultraviolet indoor showing elevated demand for a month of unlimited bed use by recent ultraviolet indoor tanning users as compared to nonusers and informing policy decisions surrounding these subscription models (Reed et al., 2016). Further, a CPT evaluating gambling behavior could distinguish between those with and without a history of disordered gambling behavior, suggesting the diagnostic utility of these procedures (Weinstock et al., 2016). ...
... The unique data produced by the CPT are readily translatable to policymakers, given the focus on economic concepts and their impacts on human behavior. Thus, policy-relevant work has historically dominated the CPT literature, providing novel behavioral economic insights for topics including but not limited to: (a) excise taxation impacts on consumer spending (e.g., Epstein et al., 2010;MacKillop et al., 2012;Reed et al., 2016), (b) drug dosing and packaging (e.g., Pacek et al., 2019;Schwartz et al., 2019), (c) vaccine framing and costs (e.g., Hursh et al., 2020;Strickland et al., 2022), and (d) drug consumption regulations and legalization (e.g., Amlung et al., 2019;. In each case, behavioral economists can rapidly obtain such insights using simulated methods, providing safe and efficient means of addressing emerging public health crises and concerns. ...
Consumers decide what to purchase, under conditions of constraint (e.g., commodity price). According to behavioral economic demand, commodity purchase task (CPT) can measure hypothetical decisions about purchases under varied simulated policy conditions (e.g., introduction of new cigarette taxes, happy hour drinking specials). These tasks permit rapid data collection without sacrificing methodological rigor or the validity of conclusions reached. The CPT allows researchers to simulate new policies, to determine their relative risks and benefits, thus offering an opportunity to optimize prior to rollout. Behavioral outcomes related to consumer purchases also make the CPT data readily translatable to policymakers, including constituent health behavior. This article provides a background on CPTs, a review of literature related to policy-aimed CPTs, and a start on best practices for other behavioral scientists interested in applying CPT to inform public policy efforts. It also serves as a primer for policymakers seeking to evaluate usage of this tool.
... Among 143 participants who answered "Yes" to both questions, 138 participants completed the survey and were given a code to enter in Amazon Mechanical Turk to receive $2.50. Given the exploratory nature of the current study, the sample size was determined based on similar previous studies with demand analyses but in different topics (e.g., Hayashi & Blessington, 2021;Reed et al., 2016). ...
... In the second step, essential value (EV) was calculated by taking the inverse of the α parameter, EV ¼ 1= α Á 100 ð Þ: Higher essential values indicate smaller change in demand elasticity (i.e., greater persistence of breastfeeding). We used essential values for the analysis of change in demand elasticity because Equation 1 could not be fitted to data from 13 participants due to the likelihood of breastfeeding being 0 for all the time, but essential values for these participants could be conceptualized as 0-the lowest possible value (Hayashi et al., 2019a;Reed et al., 2016). For 23 participants who exclusively chose 100 likelihood, Equation 1 could be fitted to their data, but the essential value of these participants was extraordinarily large (>8 trillion). ...
The present study determined whether behavioral economic demand analysis could characterize mothers' decision to exclusively breastfeed in the workplace. Females, aged between 18 and 50 who have given birth in the past three years, completed a novel demand task with hypothetical scenarios, in which they returned to work with a 2‐month‐old baby. Participants rated their likelihood of breastfeeding their baby at a workplace lactation room versus formula‐feeding their baby at their desk. The distance to the lactation room ranged from 10 s to 60 min. This assessment was conducted with and without hypothetical financial incentives for 6‐month exclusive breastfeeding. Primary dependent measures were demand intensity and change in demand elasticity, which could conceptually represent initiation and continuation of breastfeeding, respectively. Demand for breastfeeding was more intense and less elastic (i.e., more likely to initiate and continue breastfeeding) among mothers with an experience of 6‐month exclusive breastfeeding and under the condition with the financial incentives. The novel demand task can potentially provide a useful behavioral marker for quantifying mothers' decision to initiate and continue exclusive breastfeeding in the workplace, informing workplace policy regarding lactation rooms, identifying risk for early cessation, and developing and individualizing an intervention to assist mothers to exclusively breastfeed in the workplace.
... A large number of these policies have been implemented through the tax system. For example, behavior analysis has been used to investigate carbon taxes aimed to reduce consumption of CO 2 emitting products (Ionescu, 2019), cigarette taxes aimed to reduce cigarette consumption , proposed sugary-drink taxes aimed to reduce sugar consumption (Allcott et al., 2019), and indoor tanning services taxes aimed to reduce indoor UV-light tanning (Reed et al., 2016). Behavioral analysis has provided methods of predicting responses to policies like consumption taxes; for example, analysis of demand curves can be used to forecast changes in consumption of a good or use of a service across a series of prices for that good or service. ...
The success of policy involves not only good design but a good understanding of how the public will respond behaviorally to the benefits or detriments of that policy. Behavioral science has greatly contributed to how we understand the impact of monetary costs on behavior and has therefore contributed to policy design. Consumption taxes are a direct result of this; for example, cigarette taxes that aim to reduce cigarette consumption. In addition to monetary costs, time may also be conceptualized as a constraint on consumption. Time costs may therefore have policy implications, for example, long waiting times could deter people from accessing certain benefits. Recent data show that behavioral economic demand curve methods used to understand monetary cost may also be used to understand time costs. In this article we discuss how the impact of time cost can be conceptualized as a constraint on demand for public benefits utilization and public health when there are delays to receiving the benefits. Policy examples in which time costs may be relevant and demand curve methods may be useful are discussed in the areas of government benefits, public health, and transportation design.
... Demand in recent users (use within past month) is depicted in red squares, with demand in non-recent users (use within past 5 years but not past month) depicted in blue diamonds, and demand in never-users depicted in open circles. Adapted fromReed et al. (2016). ...
Behavioral economics is an approach to understanding behavior though integrating behavioral psychology and microeconomic principles. Advances in behavioral economics have resulted in quick-to-administer tasks to assess discounting (i.e., decrements in the subjective value of a commodity due to delayed or probabilistic receipt) and demand (i.e., effort exerted to defend baseline consumption of a commodity amidst increasing constraints)—these tasks are built upon decades of foundational work from the experimental analysis of behavior and exhibit adequate psychometric properties. We propose that the behavioral economic approach is particularly well suited, then, for experimentally evaluating potential public policy decisions, particularly during urgent times or crises. Using examples from our collaborations (e.g., cannabis legalization, happy hour alcohol pricing, severe weather alerts, COVID-19 vaccine marketing), we demonstrate how behavioral economic approaches have rendered novel insights to guide policy development and garnered widespread attention outside of academia. We conclude with implications on multidisciplinary work and other areas in need of behavioral economic investigations.
... In studies of condom demand, individuals had higher demand for condoms when STI risk was higher (Strickland et al., 2020) and had more demand for condoms when the absence of condoms resulted in abstinence (Harsin et al., 2021). Finally, demand for indoor tanning access was related to reported real-world use of tanning beds (Reed et al., 2016). ...
Hypothetical purchase tasks (HPTs) and operant demand analyses provide a promising methodology to determine how consumers value new products. This study used a demand analysis to determine value differences between Apple or Fitbit smartwatches and those same watches with hypothetical “added‐value” features. There were no differences between baseline smartwatches and those with additional features. However, participants who owned Apple products had significantly higher demand for Apple smartwatches, while this difference did not exist for Fitbit users. HPTs and demand analyses were sensitive enough to determine differences related to brand loyalty, but did not detect differences in added‐value of hypothetical features.
... Similarly, discounting procedures can identify the effective delay [18] or probability [19] at which behavior is altered to a given level of performance; for example, the delay associated with, say, a 50% reduction in the value of procuring a COVID-19 test. Such metrics are ripe for modeling policy effects and can provide novel and important behavioral information that is directly relatable to the design of behavior change programs [16,20,21]. ...
Full-text available
The role of human behavior to thwart transmission of infectious diseases like COVID-19 is evident. Psychological and behavioral science are key areas to understand decision-making processes underlying engagement in preventive health behaviors. Here we adapt well validated methods from behavioral economic discounting and demand frameworks to evaluate variables (e.g., delay, cost, probability) known to impact health behavior engagement. We examine the contribution of these mechanisms within a broader response class of behaviors reflecting adherence to public health recommendations made during the COVID-19 pandemic. Four crowdsourced samples (total N = 1,366) completed individual experiments probing a response class including social (physical) distancing, facemask wearing, COVID-19 testing, and COVID-19 vaccination. We also measure the extent to which choice architecture manipulations (e.g., framing, opt-in/opt-out) may promote (or discourage) behavior engagement. We find that people are more likely to socially distance when specified activities are framed as high risk, that facemask use during social interaction decreases systematically with greater social relationship, that describing delay until testing (rather than delay until results) increases testing likelihood, and that framing vaccine safety in a positive valence improves vaccine acceptance. These findings collectively emphasize the flexibility of methods from diverse areas of behavioral science for informing public health crisis management.
... Findings here indicated that the availability of a single fad or "alternative" treatment substantially decreased the baseline consumption of EBPs when compared to when EBPs were available alone. This empirical approach to public policy has been demonstrated in the use of targeted taxes to discourage unhealthy choices, such as ultraviolent tanning (Reed et al., 2016) and cigarette use (MacKillop et al., 2012;Pope et al., 2020), and to encourage sustainable practices (e.g., "green" consumerism, Kaplan et al., 2018). However, it warrants noting that further refinement of this approach will be necessary before such an approach would be helpful to inform healthcare policies. ...
Full-text available
Various treatment approaches have been determined efficacious for improving child behavior outcomes. Despite a variety of evidence-based options, consumers often disregard empirically supported treatments to pursue alternatives that lack empirical support, e.g. fad therapies. The choice to pursue therapies lacking empirical support has been considered as a 'gamble' on therapeutic outcomes and this form of risky choice has historically been explained using various cognitive heuristics and biases. This report translates quantitative analyses from the Operant Demand Framework to characterize how caregivers of children with behavioral issues consume treatment services. The operant demand framework is presented, its utility for characterizing patterns of treatment consumption is discussed, and a preliminary application of cross-price analyses of demand is performed to illustrate how various factors jointly influence treatment-related choice. Results indicated that caregivers endorsing interest in receiving behavioral parent training regularly pursued pseudoscientific alternatives as a functional substitute for an established therapy, despite explicit language stating a lack of evidence. These findings question the presumption of rationality in models of treatment choice as well as the degree to which scientific evidence influences the consumption of therapies. This report concludes with a discussion of Consumer Behavior Analysis and how quantitative analyses of behavior can be used to better understand factors that enhance or detract from the dissemination of evidence-based practices.
... These associations identify behaviors for which the task may be predictive, strengthening the utility of the purchase task and suggesting convergent validity. Indeed, demonstrations of convergent validity between established measures and demand indices have been observed in alcohol, cigarette, and indoor-tanning HPTs (MacKillop et al., 2008;Reed et al., 2016). No data has yet explored similar convergent validity with physical activity. ...
Hypothetical purchase tasks are a widely used tool to determine the reinforcing value of commodities, especially commodities which are difficult to deliver experimentally. Amazon Mechanical Turk users (N = 375) completed two novel hypothetical purchase tasks (quantity of purchase and probability of purchase) and other measures to estimate how much an individual values the opportunity to exercise in a gym. We examined correlations between demand indices generated by each task and measures related to physical activity. In addition, we compared rates of systematic and nonsystematic responding between the two tasks. Exploratory analyses of demand indices and measures of physical activity suggest initial evidence of construct validity for each task. When accounting for an order effect, the probability of purchase task generated significantly lower rates of nonsystematic responding compared to the quantity of purchase task (2.87% vs. 14.6%, respectively). We discuss how these results may inform improved construction of future hypothetical purchase tasks and implications for using likelihood and quantity purchase tasks.
The reinforcer pathology model posits that core behavioral economic mechanisms, including delay discounting and behavioral economic demand, underlie adverse health decisions and related clinical disorders. Extensions beyond substance use disorder and obesity, however, are limited. Using a reinforcer pathology framework, this study evaluates medical adherence decisions in patients with multiple sclerosis. Participants completed behavioral economic measures, including delay discounting, probability discounting, and a medication purchase task. A medical decision‐making task was also used to evaluate how sensitivity to mild side effect risk and efficacy contributed to the likelihood of taking a hypothetical disease‐modifying therapy. Less steep delay discounting and more intense (greater) medication demand were independently associated with greater adherence to the medication decision‐making procedure. More generally, the pattern of interrelations between the medication‐specific and general behavioral economic metrics was consistent with and contributes to the reinforcer pathology model. Additional research is warranted to expand these models to different populations and health behaviors, including those of a positive health orientation (i.e., medication adherence).
Full-text available
Behavioral economics is an approach to understanding decision making and behavior using principles of behavioral science and economics (Hursh, 1980). An area of investigation in behavioral economics includes evaluating demand for a commodity (such as drug and nondrug reinforcers), given changes in price, using hypothetical purchase tasks, which are a reliable and efficient assessment method. Given recent calls for organizational behavior management to examine work performance from a behavioral economic perspective (e.g., Wine, Gilroy, & Hantula, 2012), the present studies sought to extend the demand and hypothetical purchase task literature to work tasks and workplace reinforcers. Undergraduate participants completed 2 hypothetical work tasks—a variation of the hypothetical purchase task. Respondents indicated the likelihood they would complete an increasing work requirement for a fixed amount of money at 2 delays to payment (1 hr vs. 4 weeks). Exponential demand curve analyses provided excellent fits to the data and revealed (a) that individuals with and without experience completing the work behavior responded similarly, and (b) that there was higher demand for the 1-hr delay condition. These results suggest the hypothetical work task is a promising method for assessing demand for workplace reinforcers.
Background: Behavioral economics and neuroeconomics bring together perspectives and methods from psychology, economics, and cognitive neuroscience to understand decision making and choice behavior. Extending an operant behavioral theoretical framework, these perspectives have increasingly been applied to understand the alcohol use disorders (AUDs), and this review surveys the theory, methods, and findings from this approach. The focus is on 3 key behavioral economic concepts: delay discounting (i.e., preferences for smaller immediate rewards relative to larger delayed rewards), alcohol demand (i.e., alcohol's reinforcing value), and proportionate alcohol-related reinforcement (i.e., relative amount of psychosocial reinforcement associated with alcohol use). Findings: Delay discounting has been linked to AUDs in both cross-sectional and longitudinal studies and has been investigated cross-sectionally using neuroimaging. Alcohol demand and proportionate alcohol-related reinforcement have both been robustly associated with drinking and alcohol misuse cross-sectionally, but not over time. Both have also been found to predict treatment response to brief interventions. Alcohol demand has also been used to enhance the measurement of acute motivation for alcohol in laboratory studies. Interventions that focus on reducing the value of alcohol by increasing alternative reinforcement and response cost have been found to be efficacious, albeit in relatively small numbers of randomized controlled trials (RCTs). Mediators and moderators of response to these interventions have not been extensively investigated. Future directions: The application of behavioral economics and neuroeconomics to AUDs has given rise to an extensive body of empirical work, although significant gaps in knowledge remain. In particular, there is a need for more longitudinal investigations to clarify the etiological roles of these behavioral economic processes, especially alcohol demand and proportionate alcohol reinforcement. Additional RCTs are needed to extend and generalize the findings for reinforcement-based interventions and to investigate mediators and moderators of treatment success for optimization. Applying neuroeconomics to AUDs remains at an early stage and has been primarily descriptive to date, but has high potential for important translational insights into the future. The same is true for using these behavioral economic indicators to understand genetic influences on AUDs.
Background and aims: Behavioral economists assess alcohol consumption as a function of unit price. This method allows construction of demand curves and demand indices, which are thought to provide precise numerical estimates of risk for alcohol problems. One of the more commonly used behavioral economic measures is the Alcohol Purchase Task (APT). Though the APT has shown promise as a measure of risk for alcohol problems, the construct validity and incremental utility of the APT remain unclear. This manuscript presents a meta-analysis of the APT literature. Methods: Sixteen studies were included in the meta-analysis. Studies were gathered via searches of the PsycInfo, PubMed, Web of Science, and EconLit research databases. Random effects meta-analyses with inverse variance weighting were used to calculate summary effect sizes for each demand index-drinking outcome relationship. Moderation of these effects by drinking status (regular v. heavy drinkers) was examined. Additionally, tests of the incremental utility of the APT indices in predicting drinking problems above and beyond measuring alcohol consumption were performed. Results: The APT indices were correlated in the expected directions with drinking outcomes, though many effects were small in size. These effects were typically not moderated by the drinking status of the samples. Additionally, the intensity metric demonstrated incremental utility in predicting alcohol use disorder symptoms beyond measuring drinking. Conclusions: The Alcohol Purchase Task appears to have good construct validity, but limited incremental utility in estimating risk for alcohol problems.
Reinforcer value is a complex and multifaceted concept necessitating numerous levels of analyses and integration of both visual and quantitative approaches. This chapter summarizes the seminal basic operant laboratory work with both humans and nonhumans on this topic. In doing so, we provide the reader with numerous references and examples for prototypical approaches to the assessment and measurement of reinforcer demand and efficacy. Topics covered include: (a) basic preparations for measuring reinforcer value, (b) common design elements in evaluating reinforcer value, (c) quantitative analyses of reinforcer value, and (d) modulating factors.