Time preferences, behavioral economics, and help-seeking for mental health
Daniel Eisenberg, Ph.D., University of Michigan
M3517 SPH II, 1415 Washington Heights
Dept. of Health Management & Policy, School of Public Health
Ann Arbor, MI 48109-2029, USA
[email protected], 734-615-7764
Benjamin G. Druss, M.D., M.P.H., Emory University
Rollins School of Public Health
1518 Clifton Road NE, Atlanta, GA 30322, USA
[email protected], 404-712-9602
Disclosures:
There was no funding source for this work, and the authors have no conflicts of interest.
PRELIMINARY DRAFT: PLEASE DO NOT CITE WITHOUT PERMISSION
Abstract
More than half of people with mental disorders do not receive treatment, even though longstanding barriers to mental health care such as stigma and financial factors appear to be receding.
This apparent puzzle might be more easily understood when viewed in a broader context of health
behaviors. Many people have unhealthy diets and engage in little physical activity, for example,
even when financial and attitudinal factors are not necessarily barriers to healthy behavior.
Present-orientation and procrastination have been highlighted by behavioral economists and other
social scientists as potentially important explanations for these unhealthy behaviors.
In this paper we first examine on a conceptual level why time preferences and procrastination may
be connected to mental health and help-seeking. Mental health could affect the general rate at
which people discount future utility, and it could also affect the degree to which people value the
present versus all future periods, which could in turn lead to indefinite delay (procrastination) of
help-seeking. These relationships would have important implications from both a positive
perspective (understanding why people fail to receive treatment, and what kind of interventions
might influence help-seeking) and a normative perspective (whether there is a rationale for
interventions to influence help-seeking behavior).
We present descriptive findings on these relationships, using two data survey data sets from college
student populations, a large cross-sectional data set and a small panel data set. We find that
depressive symptoms are highly correlated with both future discounting and procrastination,
controlling for other basic individual characteristics. The association between depression and
procrastination remains significantly even in the panel analysis with individual fixed effects,
implying that people procrastinate more when they are more depressed. We also find some
evidence that procrastination is significantly correlated with a lower likelihood of help-seeking for
mental health.
Although the empirical analysis is descriptive and has clear limitations, it indicates that future work
on these relationships would be useful. Specifically, we suggest four promising directions: 1)
observational, longitudinal studies with larger samples and more detailed data on mental health,
time preferences, and help-seeking; 2) experimental studies that examine immediate or short-term
responses and connections between these variables; 3) randomized trials of mental health therapies
that include outcome measures of time preferences and procrastination; and, 4) intervention studies
that develop and test strategies to influence help-seeking by addressing time preferences and
present orientation.
I. INTRODUCTION
One of the main questions in mental health services and policy research is why such a large
proportion of people with mental disorders do not receive treatment. This “treatment gap” is
typically around 50% or higher: at least half of people with mental disorders, even highly impairing
disorders such as major depression, receive no treatment at all, let alone high-quality treatment
(Wang et al., 2005). Understanding the reasons for this gap is essential for administrators and
policymakers who wish to address the positive question of what can be done to increase the use of
treatment, as well as the normative question of whether (and to what extent) something should be
done.
When addressing the treatment gap researchers and policymakers have traditionally emphasized
stigma (negative attitudes and shame), information deficits, and financial barriers. These factors
correspond to intervention strategies in relatively straightforward manners, although such
approaches have often proven difficult to implement effectively. Anti-stigma efforts have
attempted to improve attitudes through education, protest, and contact with mentally ill consumers
(Corrigan, 2004). Efforts to inform consumers have also emphasized education and contact.
Financial barriers, at least in the United States, have been addressed through efforts to increase
insurance coverage, as in the movement for parity between mental health and general medical care.
These traditional barriers undoubtedly remain important, but there are reasons to think that their
prominence is declining. Attitudes and knowledge may be improving as treatment has become
more common and familiar, particularly for less severe and more prevalent conditions such as
depression and anxiety disorders. This may be especially true for children and adolescents, who
have had large increases in treatment prevalence for mood disorders, attention deficit hyperactivity
disorder (ADHD), and other mental disorders since the 1980s (Vitiello et al., 2006; Zuvekas &
Vitiello, 2012). Financial barriers have also diminished in the United States, at least among
insured populations —mental health parity has been adopted at the federal level and in most states.
Despite these apparent reductions in barriers, treatment prevalence still remains below 50% among
people with mental disorders, as noted above. This is not entirely surprising, however, when
viewed in the broader context of health-related behaviors. Based on the public health field’s
experience with diet, exercise, and smoking, among other behaviors, we should not necessarily
expect that people will engage in healthy behavior, even when their attitudes are positive,
knowledge is reasonably high, and financial barriers are low. For these health behaviors, people
often appear to be irrational in the sense that they do not act as we would predict from their
apparent preferences, information, and financial constraints. The same is probably true for helpseeking for mental health.
Ideas from behavioral economics have been useful for gaining insights into seemingly irrational
health behaviors. Similarly, some of those ideas may yield important insights for the question of
why so many people with mental disorders go without treatment, and by extension, the questions of
what can be done to influence help-seeking and whether there is a normative justification for doing
so.
Within this broader line of thinking, the specific objective of this paper is to examine how time
preferences and related behavior (procrastination)—key aspects of behavioral economics—relate to
mental health and help-seeking behavior. The paper first discusses on a conceptual level why time
preferences may be connected to mental health and why this may have important implications for
clinical and public health interventions (Section II), then presents descriptive findings on these
relationships using two data sets with complementary strengths and limitations (Section III), and
finally suggests directions for future research (Section IV).
II. CONCEPTUAL DISCUSSION
In standard economic theory, intertemporal preferences are represented simply by a discount rate.
When making a decision that involves a stream of benefits and costs over time, people maximize a
utility function that represents a sum of weighted utility at each time point, where the weights
diminish exponentially in accordance with the discount rate, :
In recent years there has been increasing recognition that a single exponential discount rate is an
inadequate representation of how people make intertemporal tradeoffs in many contexts (Frederick
et al., 2002). Many behaviors and decisions are more accurately modeled with a “hyperbolic
discounting” function, a term which is generally used to refer to situations in which time preference
is higher initially and then declines in periods further into the future. A simple example is a utility
function that allows for extra discounting of the present relative to all future periods, via the term
in the following equation, where
(ODonoghue & Rabin, 2001):
With time-inconsistent preferences like these, a person gives special priority to the present, and the
optimal consumption/behavior path can change repeatedly as the new period becomes the present.
This present-orientation can lead to severe procrastination—the continued delay of activities or
behaviors that are regarded as optimal from the perspective of prior and even future periods—
unless a person is fully “sophisticated” (aware) about his or her time-inconsistency (ODonoghue &
Rabin, 2001).
We propose that mental health may play an important role in time preferences. Specifically, both
the degree of time-inconsistent present-orientation, , and the discount rate, , may depend on
current mental health,
:
This seems especially plausible in the case of depression. One of the core symptoms of major
depressive disorder is hopelessness about the future. This feature of depression could imply a
lower value of
(greater present-orientation), a higher value of
(higher discounting in general),
or both. On the other hand, another core feature of depression is lack of interest and pleasure from
usual activities (anhedonia). This implies a reduction in present marginal utility from any given
good or behavior. While this is different than a direct effect on time preferences, it could affect
intertemporal preferences in the sense that it could lower the valuation of present consumption and
activities, relative to those in the future. Which of these offsetting factors predominates is an
empirical question, and it seems likely that they might vary across activities and goods.
In addition, present-orientation is sometimes explained as a lack of self-control, in which the
present “self” wins out over the “future” or long-run planning self (Frederick et al., 2002)). Many
behaviors that are believed to reflect present-orientation and lack of self-control, such as substance
use and binge eating, are strongly correlated with depression. Depression may also sap the mental
energy or willpower needed to avoid procrastination and other present-oriented, impulsive
behavior. These interrelationships are again suggestive of a potential causal role for depression on
time preferences.
Help-seeking for mental health problems also seems likely to be related to time preferences. For
many people, help-seeking entails an immediate cost or disutility, including some combination of
time, money, and distress or discomfort. The expected benefits are delayed and also highly
uncertain; although antidepressant medication or psychotherapy can sometimes bring quick relief,
more commonly the main therapeutic benefits come only after several weeks.1
Present orientation and procrastination could be important reasons why so many people fail to seek
treatment, even if they have positive attitudes, high knowledge, and low financial barriers. Even
people without mental health problems are often highly present-oriented, and depression and other
mental health conditions could augment these tendencies, as argued earlier. Related to this,
procrastination of help-seeking may interact with a continually shifting view of what people
consider to be normal feelings, thoughts, and behaviors. If a person procrastinates help-seeking
when he has mild symptoms of depression, in the next period the symptoms may become slightly
more severe and yet seem normal relative to the previous period. If he again procrastinates helpseeking, which would be especially likely if depression exacerbates present-orientation, then this
cycle would continue as symptoms become progressively worse.2
Procrastination of mental health care also seems likely in light of the theory that procrastination can
be most severe for the most important and valuable tasks (ODonoghue & Rabin, 2001). Most
people probably believe, at least on a subconscious level, that achieving or maintaining good
mental health is an important goal. In the case of high-value tasks such as this, O’Donoghue and
Rabin argue that people are most willing to consider the full range of options, including tasks with
high initial costs, which are precisely the tasks that are subject to procrastination. For example, a
person might recognize that an important and difficult change needs to happen in order to relieve
her depression, and she might make a plan to implement that change (whether it is mental health
1
An interesting research direction would be to learn more about people’s perceptions of how far into the
future they anticipate the benefits of treatment.
2
For a related theory, see Biddle et al. (2007)).
care or major shifts in activities or interactions that are contributing to distress) in the next period,
which could easily begin a cycle of repeated procrastination.
Empirical investigation of these connections between mental health, time preferences, and helpseeking is important for many reasons. From a positive perspective, we could learn something
about how to design or refine interventions that achieve outcomes such as improved mental health,
increased help-seeking, and possibly improved health on other dimensions (considering that mental
health often co-occurs with other health conditions such as cardiovascular risk factors and related
behaviors, some of which are also connected to time preferences and procrastination). For
example, if we learn that poor mental health does in fact contribute to present-orientation and
procrastination in specific ways, this could help refine clinical approaches that mitigate those
pathways. Also, if we learn that procrastination plays an important role in preventing help-seeking
for mental health, we could adapt intervention approaches that have proven successful for
mitigating procrastination in other contexts such as diet, exercise, or smoking cessation.
From a normative perspective, documenting that time-inconsistent preferences and procrastination
prevent help-seeking would offer firmer ground for paternalistic intervention to increase helpseeking and utilization of care. Many people with untreated mental health problems can probably
be considered to be making rational decisions not to seek care, based on their own preferences and
budget constraints. But people whose lack of help-seeking is largely driven by time-inconsistent
preferences are arguably irrational, much like people whose addiction to substances is driven by
time-inconsistent preferences (e.g., people who wish to quit smoking but continually put this off).
Also from a normative perspective, this line of research could increase understanding of the full
potential value of preventing and treating mental disorders, beyond alleviating symptoms per se. If
improving mental health reduces present-orientation and procrastination of a range of behaviors
such as exercise and healthy eating behaviors, then these corollary benefits of mental health
interventions should be counted in cost-benefit assessments, and they might be large. Related to
this, the link between mental health and time preferences could be relevant to investments in
human capital, both in terms of education and the development of job skills. If improvements in
mental health can diminish myopic, present-oriented tendencies, this could augment people’s
willingness to invest in human capital. In that sense, mental health may be one of the most
important “noncognitive” factors that Heckman and other labor economists have emphasized as
contributors to human capital (Heckman et al., 2006).
This discussion raises a number of specific empirical questions, including the following. How do
time preferences vary over time (within persons) as a result of fluctuating mental health—for
example, are people more present-oriented when depressed? To what extent is lack of mental
health treatment use explained by high discount rates? And to what extent is it explained by timeinconsistent preferences and procrastination? In the next section we offer preliminary evidence
related to these questions.
III. DESCRIPTIVE EMPIRICAL FINDINGS
Data sets
Our empirical analysis investigates the association between discount rates/procrastination and
mental health (depression and to a lesser extent anxiety), as well as the association between
utilization of mental health treatment and discount rates/procrastination. We use two survey data
sets of college student populations, and these data sets have complementary advantages and
limitations. One is a large cross-sectional data set, the 2011 Healthy Minds Study, which includes
12 institutions and approximately 9,000 survey respondents. The Healthy Minds survey focuses on
mental health symptoms, treatment utilization, and related issues among random samples of college
and university populations (Eisenberg et al., 2007; Eisenberg et al., 2011). The other is a small
longitudinal data set, the College Transition Study Replication (CTSR), which conducted monthly
surveys of a panel of approximately 300 first-year and new transfer students at a single large
university in fall 2010 (Brunwasser, 2012). By design these two studies have considerable overlap
in key measures related to the present analysis, but there are also important differences in measures
as explained below. The large sample in the Healthy Minds data set offers much greater precision
for estimating descriptive statistics and associations between variables, whereas the CTSR panel
data allow for fixed effects regressions that control for time-invariant individual characteristics.
Measures
Both studies use very brief measures of key constructs, because the surveys address a range of
issues (including many outside the scope of the present paper) and are designed to be completed in
20 minutes or less. In both studies the discount rate is measured with a question from a previous
study about a hypothetical choice between a monetary gain now or one year from now (Chapman,
1996).3 The full text of both the discounting and procrastination measures is shown in the
appendix to this paper. Following standard convention we calculate the discount rate as the percent
increase (or decrease for a small number of people with negative discounting) in the monetary
amount that the respondent would accept one year from now, as compared to the amount he or she
would accept now (e.g., if the respondent considers $750 one year from now equally attractive as
$500 today, the implied discount rate is 50%). Both studies measure procrastination with three
items from Lay’s Procrastination scale (Lay, 1986)). Three of the most generic descriptions of
procrastinating behavior were selected from the larger scale, so that they would be relevant to all
respondents (“I often find myself performing tasks that I had intended to do days before,” “I
generally delay before starting on work I have to do,” and “I am continually saying "I’ll do it
3
The CTSR data set also includes analogous questions about a monetary loss, a health gain, and a health
loss. For the sake of consistency across the data sets we focus in this paper only on the question about a
monetary gain, which is a more common approach to approximating discount rates.
tomorrow."”). As in the original larger scale, the items are each scored from 0 to 4 and are
summed to a total score.
Depressive symptoms are measured in both studies using the Patient Health Questionnaire (PHQ)
screen (Kroenke & Spitzer, 2002). The Healthy Minds Study contains the PHQ-9, which is scored
0-27, and the CTSR study contains the PHQ-8, which omits the final item in the PHQ-9 about
suicidal ideation and is scored 0-24. In both studies functional impairment from depression is
measured with a standard follow-up question in the PHQ, and in the Healthy Minds Study we also
use a question about impairment to academic performance from mental or emotional difficulties.
In addition, our analysis examines anxiety symptoms in the Healthy Minds data, as measured by
the PHQ screens for panic disorder and generalized anxiety disorder (Spitzer et al., 2000). Anxiety
is not measured in the CTSR data.
Utilization of mental health treatment is measured in both studies with questions about current
medication and psychotherapy. In the present analysis we focus on simple binary measures of
utilization, although information about the amount of utilization is available for further analysis of
the Healthy Minds data.
Each data set contains additional variables that are potentially relevant to time preferences and
mental health. The Healthy Minds data set has information about health behaviors and related
variables including substance use (smoking, binge drinking, illicit drug use), physical activity,
obesity (height and weight), and gambling. The CTSR data set includes the Avoidant Behavior
Scale (ABS) (Brunwasser, 2012), which asks about a variety of behaviors related to
procrastination, and it also includes a question about self-control/self-regulation as part of the
Character Strengths Scale (CSS).
Statistical Analysis
We use OLS regressions to investigate the conditional correlations between the main variables of
interest. First, we estimate regressions of the discount rate (d) and procrastination (p) on mental
health (mh) and demographic characteristics (x):
The demographic variables are gender, age, and highest educational attainment of parents. In the
analysis of Healthy Minds data we estimate heteroskedasticity-robust standard errors with
clustering at the school level. In the analysis of the CTRS panel data we estimate the data as
pooled cross-sections without and with individual fixed effects, with standard errors adjusted for
clustering at the individual level.
We also estimate OLS regressions of treatment use (tx) on the discount rate and procrastination,
controlling for mental health and demographic characteristics:4
Results from the Healthy Minds data (large cross-section)
Table 1 shows descriptive statistics for the study variables in the Healthy Minds data. The discount
rate implied by the survey responses has a high mean (28) and standard deviation (183), largely
because of outliers with implied discount rates as high as 100 and more (i.e., some people report
that they would require more than $50,000 in a year to pass on $500 today). We adjust for outliers
by setting a ceiling of 9 as the highest discount (all values above 9 are coded as 9) and a floor of 0
(all values below zero are coded as zero). These adjustments are somewhat arbitrary, but the main
point is to avoid having our estimates driven largely by very high values that are arguably
4
In sensitivity analysis we confirm that the same basic pattern of results holds in probit regressions.
implausible.5 Setting a floor of zero can also be questioned, as previous studies suggest that some
people do in fact have negative discount rates (Frederick et al., 2002), but this adjustment does not
affect the results much in any case (only 0.8% of the sample had negative discount rates, and there
is much more room for variation in discount rates on the positive end.) The procrastination score,
on the other hand, does not require adjustments for outliers; it is bounded by definition between 0
and 4, and the mean is 2.29 (SD=1.11) in this sample, indicating that on average people are slightly
more likely to say that the procrastinating behaviors are “like me” rather than “unlike me.”
Depression and anxiety are common problems in this college student sample, with 21% screening
positive for at least one of the conditions. Only 28% of students with positive screens were
receiving psychiatric medication or psychotherapy. These numbers are roughly consistent with
previous epidemiological studies of college students (Blanco et al., 2008).
Depressive symptoms are positively associated with both discounting and procrastination at highly
significant levels (p<0.001), as shown in Table 2. For procrastination this association appears to
be roughly linear throughout the range of depression severity (column 6 in the table), whereas for
discounting the association is only positive in the lower part of the severity range (0-14) and is
roughly flat in the upper part (15-27). The magnitude of the relationship between depression and
procrastination is quite large—for example, a 10 point increase in depression score is associated
with a 0.62 increase in procrastination score, which is 27% of the mean and 56% of the standard
deviation. For the discount rate a 10 point increase in depression score is only associated with a
14% increase relative to the mean and 12% increase relative to the standard deviation.
Anxiety is significantly and positively associated with discounting, and not significantly associated
with procrastination. Women have substantially higher discount rates than men, but slightly lower
5
As an alternative strategy for handling outliers, we model the discount rate as a set of dummy variables
corresponding to intervals (0-0.5, 0.5-1.0, 1-2, 2-5, 5+) and find the same basic pattern of results.
procrastination scores. Age is not significantly associated with discounting, and is negatively
associated with procrastination. Parents’ educational attainment is significantly associated with
lower discounting and is not significantly associated with procrastination.
In Table 3 we examine how discounting and procrastination correlate with utilization of mental
health treatment, among students with a positive screen for depression or anxiety. Discounting is
not significantly associated with treatment, whereas procrastination is negatively associated with
treatment at a marginally significant level (p=0.09) once we control for mental health and
demographic characteristics. This negative coefficient implies that each one point increase in
procrastination score corresponds to a 0.02 decrease in the probability of treatment, which is about
7% relative to the mean probability of 0.28. It is also important to note that including the discount
rate and procrastination in the same regression (column 6) has little impact on their respective
associations with treatment use, as compared to separate regressions with only the discount rate and
only procrastination (columns 2 and 4). This is consistent with the very low correlation between
discounting and procrastination (r=0.03), and confirms empirically that they are very different
concepts.
Given the association between procrastination and lack of treatment use, it is also useful to
examine how procrastination correlates with self-reported reasons for not receiving treatment (or
receiving less treatment than one would have otherwise, among those who do receive treatment).
To examine this we estimate separate regressions (not shown in tables but available on request)
with each self-reported reason as a binary dependent variable and with the same righthandside
variables as in Table 3. The probability of reporting the following self-reported reasons is
positively and significantly associated with procrastination (at p<0.10): I haven't had a chance but
plan to go; I prefer to deal with issues on my own; I worry someone will notify my parent; I
question the quality of my options; I question how serious my needs are; I think stress is normal in
college/grad school.
Lastly, in Table 4 we examine how health-related and risk-related behaviors correlate with
discounting and procrastination, as another way to understand the possible implications of time
preferences and procrastination. Procrastination is highly correlated (p<0.001) with all six health
and risk-related behaviors in the expected direction (positive for obesity, binge drinking, illicit drug
use, smoking, and gambling; negative for exercise), controlling for mental health and demographic
characteristics. Discounting, by contrast, has a smaller association with the behaviors, and it is
only significant for obesity, smoking, and exercise.
Results from the CTSR data (smaller panel)
Table 5 shows the descriptive statistics in each of the five monthly CTSR surveys for the main
study variables. The mean discount rate is fairly consistent over time, after adjusting for outliers,
and is somewhat lower than in the Healthy Minds data. The mean procrastination score is also
fairly consistent over time, with a slight decline, and is also slightly lower than in the Healthy
Minds data. The depression score and impairment from depression are lower than in the Healthy
Minds data, but they increase modestly over time during the fall semester. Similarly, utilization of
treatment is lower than in the Healthy Minds data but increases between the baseline survey and
the final follow-up survey.
Depression is significantly associated with procrastination (Table 6), as in the Healthy Minds data,
and this significant association holds (albeit at a reduced magnitude) after controlling for individual
fixed effects. This indicates that people are more likely to procrastinate when they are more
depressed, whereas the cross-sectional analysis could only tell us that depressed persons are
generally more likely to procrastinate than other people.6 Discounting is also positively associated
with depression, but these estimates are somewhat imprecise and are not significant after
controlling for the demographic covariates.
When investigating how treatment use is associated with discounting and procrastination, we do
not limit the sample to students with mental health problems as we do with the Healthy Minds data,
because this would reduce the sample to about 30 students (with a positive depression screen in any
given round). As shown in Table 7, discounting is significantly and negatively associated with
treatment use, whereas procrastination is not significantly associated with treatment use. This is
the essentially the reverse of our findings with the Healthy Minds data, although the comparison is
complicated by the different sample definitions.
Lastly, we find more evidence on how depression relates to procrastination, by estimating a
separate regression of each item in the Avoidant Behavior Scale on the depression score (not
shown in tables but available on request). We find that depression score is a highly significant
predictor (p<.001) of each the 20 avoidant behaviors in the scale, in regressions both with and
without fixed effects regressions (note that avoidant behaviors and depression are both measured in
survey rounds 2-5). These avoidant behaviors include several examples that could be considered
procrastination: kept worrying about a task rather than getting it done; left an assignment till the
last minute because it was unpleasant; kept thinking about something bad that happened rather than
doing something to make the situation better; regretted having put something off; passed on an
exciting opportunity (e.g., a class or extracurricular activity) because it seemed too challenging;
started getting work done much later in the day than planned; let chores pile up rather than getting
them done; couldn't get motivated to start on your work; spent too much time on aimless activities
6
We did not examine a nonlinear specification of depression in the panel data set because of the small
sample size.
(e.g., browsing the internet) when you were planning to work. In addition, we find that depression
score is a marginally significant (p=0.11) negative predictor of reported self-control and selfregulation in the past four weeks (as part of the Character Strengths Scale).
Summary of empirical analysis
These analyses have obvious limitations: the measures are simple and brief; the panel data set is
small; and the estimates may be biased from true causal effects because of omitted variables and
reverse causality. Nevertheless, the results are generally consistent with the idea that mental
health, and particularly depression, may increase the discount rate and increase present-orientation
and procrastination. Furthermore, we find some evidence in the larger cross-sectional data that
procrastination helps explain lack of help-seeking for mental health problems. In addition,
procrastination, and to a lesser extent discounting, are correlated with health behaviors and risks
including obesity, substance use, exercise, and gambling. Collectively these findings highlight the
importance of learning more about how mental health and help-seeking relate to time preferences
and procrastination. This understanding may inform the development of interventions on clinical
and community levels, and it pertains to normative rationales for interventions and policies to
increase help-seeking behavior.
IV. IDEAS FOR FUTURE RESEARCH
We hope this paper will be the beginning of a fruitful area of research, and we can envision future
studies along four complementary tracks: 1) observational, longitudinal studies with larger samples
and more detailed data on mental health, time preferences, and help-seeking; 2) experimental
studies that examine immediate or short-term responses and connections between these variables;
3) randomized trials of mental health therapies that include outcome measures of time preferences
and procrastination; and, 4) intervention studies that develop and test strategies to influence helpseeking by addressing time preferences and present orientation.
Observational studies offer perhaps the broadest range of possibilities, as one can attempt to
measure just about any construct in surveys, and mobile technologies such as smartphones offer
exciting new ways to collect real-time data both actively and passively. The most obvious
limitation is the difficulty in identifying definitive causal relationships. Mental health is a highly
endogenous variable in almost any context; it is plausibly affected by almost everything, and it
plausibly affects almost everything. Nevertheless, careful descriptive analysis of detailed
longitudinal data will be an important avenue for learning more.
Brief experimental studies can help identify causal pathways at least in terms of immediate, shortterm responses. For example, brief stimuli that elevate or depress mood (or otherwise affect
symptoms of depression and other mental disorders) could be paired with tasks or decisions that
reflect present orientation or self-control (e.g., abstaining from something tempting or completing
an onerous task in order to win a reward later). Some of these studies might also benefit from
measures of neurocognitive processes, to elucidate how moods, cognition, and behaviors are
influenced by or influence different parts of the brain.
Standard randomized trials of mental health therapies could also add measures of time preferences
and procrastination as secondary outcomes. This type of study would help identify additional
pathways by which mental health treatment can improve health, aside from direct impacts on
symptom levels. For certain types of therapies, it is also possible that effects on time preferences
and procrastination might be observed even in the absence of direct therapeutic benefits on
symptom levels.
Finally, as already mentioned, interventions may be able to influence help-seeking behavior for
mental health by drawing on lessons about how time-inconsistent preferences and procrastination
have been overcome in other contexts. As an example of a potential approach, people could be
offered software that periodically (e.g., once every two weeks) “locks” their computers and mobile
devices until they complete a brief quiz about their mental health and help-seeking behaviors.
These brief screens could be followed by specific information about options for improving mental
health, depending on the nature and severity of symptoms. Some people may be interested in using
such software as a pre-commitment to address their mental health regularly rather than risk
indefinite procrastination. This is just one of many possibilities for adapting ideas from
psychology and behavioral economics to interventions for mental health and help-seeking. This
paper has only begun to explore the possibilities for importing insights from behavioral economics
into the mental health field, and vice-versa.
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Table 1: Descriptive statistics: cross-sectional 2011 Healthy Minds survey data
N=8,806 (from 12 colleges and universities)
Mean
SD
Min
Max
5.0
18
43
-1
0
1999
9
Female
Age
Parent education: less than HS degree
Parent education: high school degree
Parent education: bachelor's degree
Parent education: graduate degree
67%
21.8
2%
27%
31%
40%
Discount rate (d)
Discount rate (d) adjusted for outliers
d<=0
0<d<1
1<=d<3
d>=3
28
2.81
4%
20%
42%
35%
183
3.23
Procrastination score (0-4)
2.29
1.11
PHQ-9 score (0-27)
Major depression (PHQ-9)
Other depression (PHQ-9)
6.32
9%
8%
5.1
Panic disorder (PHQ screen)
Generalized anxiety (PHQ screen)
5%
6%
Any MH problem (depression or anxiety)
21%
Impairment from depression
Not difficult at all
Somewhat difficult
Very difficult
Extremely difficult
49%
31%
13%
8%
Psychiatric medication (current)
Psychotherapy (current)
Any treatment (medication or therapy)
Any treatment if mental health problem
11%
7%
15%
28%
Table 2: Association between mental health and discount rate/procrastination (Healthy Minds data)
Each column shows results from a separate OLS regression. In each cell the top row shows the coefficient, the second shows
heteroscedasticity-robust standard errors (clustered by school), and the third shows the p-value.
Dep. Var.:
Depression score (linear)
discount
discount
0.0407
discount
procrast.
procrast.
0.0396
0.0628
0.062
0.00719
0.0075
0.00228
0.00228
<.001
<.001
<.001
<.001
procrast.
Depression score (intervals)
0-4
(ref)
(ref)
5-9
0.33
0.425
0.089
0.026
0.004
<.001
0.583
0.643
0.0768
0.0337
<.001
<.001
0.589
0.814
0.216
0.0399
0.021
<.001
0.4
0.928
10-14
15-19
20-27
Anxiety (0/1)
Female
Age
Parents: < bachelor's degree
Parents: bachelor degree
Parents: graduate degree
0.437
0.323
0.14
0.145
0.011
0.05
0.467
0.312
0.046
0.229
<.001
0.372
-0.0987
-0.0823
0.0185
0.153
0.064
0.0657
0.0728
0.035
0.156
0.239
0.805
0.459
-0.0696
-0.0761
0.0968
0.1
0.0177
0.0175
0.001
0.001
0.003
0.001
0.00918
0.00951
-0.0106
-0.0109
0.0143
0.0144
0.00362
0.00363
0.536
0.523
0.015
0.013
(ref)
(ref)
-0.685
-0.683
0.00435
0.00101
0.11
0.111
0.0229
0.0234
<.001
<.001
0.853
0.967
-0.935
-0.931
-0.0557
-0.0557
0.153
0.154
0.0352
0.0367
<.001
<.001
0.144
0.16
(ref)
Table 3: Association between treatment use and discount rate/procrastination (Healthy Minds data)
Sample limited to students with mental health problems (N=1,882). Each column shows results from a separate OLS
regression. In each cell the top row shows the coefficient, the second row shows heteroscedasticity-robust standard errors
(clustered by school), and the third row shows the p-values.
Dep. Var.:
discount rate (d)
tx use
tx use
-0.00322
0.0038
0.417
-0.00434
0.0045
0.357
procrastination (p)
Depression score
Anxiety (0/1)
Impairment from depression
Somewhat difficult
Very difficult
Extremely difficult
Impairment to academic performance
1 or 2 days
3 to 5 days
6+ days
Female
Age
Parents: < bachelor's degree
Parents: bachelor degree
Parents: graduate degree
tx use
0.0197
0.00294
<.001
tx use
tx use
tx use
-0.0328
0.00374
0.401
-0.00495
0.00979
0.624
-0.00458
0.00437
0.319
-0.0199
0.0106
0.091
-0.00164
0.00164
0.342
0.126
0.0219
<.001
-0.0022
0.0162
0.357
0.125
0.021
<.001
-0.0209
0.0103
0.071
-0.00148
0.0015
0.351
0.125
0.0219
<.001
0.0303
0.0293
0.326
0.107
0.0458
0.042
0.173
0.0544
0.01
0.0249
0.0263
0.366
0.101
0.0459
0.053
0.169
0.0572
0.015
0.0298
0.0287
0.325
0.104
0.0466
0.049
0.174
0.0554
0.01
0.0303
0.0229
0.214
0.0179
0.0317
0.584
0.0945
0.0348
0.022
0.0997
0.0164
<.001
0.00978
0.0395
0.033
(ref)
0.0332
0.0173
0.084
0.1068
0.0239
0.001
0.0337
0.0224
0.164
0.0308
0.0324
0.364
0.1066
0.0364
0.015
0.0961
0.0147
<.001
0.00917
0.00387
0.039
(ref)
0.0355
0.0208
0.118
0.11
0.0235
0.001
0.033
0.0232
0.185
0.0254
0.0322
0.448
0.105
0.0364
0.017
0.1
0.0158
<.001
0.00967
0.004
0.036
(ref)
0.0311
0.0182
0.118
0.1054
0.0244
0.001
Table 4: Association between health/risk-related behaviors and discount rate/procrastination (Healthy Minds data)
Notes: Each column shows results from a separate OLS regression. In each cell the top row shows the coefficient, the second row shows heteroscedasticityrobust standard errors (clustered by school), and the third row shows the p-value. All regressions control for depression score, anxiety screen (yes/no),
impairment from depression, impairment to academic performance, gender, age, parents' education.
Dep. Var.:
obese
binge drink 3+
times (past 2
weeks)
Mean of Dep. Var.:
0.102
0.180
0.148
0.127
0.565
0.155
0.00614
0.00148
0.002
0.0209
0.0032
<.001
-0.00276
0.00206
0.209
0.0301
0.00555
<.001
-0.000154
0.0016
0.925
0.0238
0.00423
<.001
0.00288
0.001
0.014
0.0225
0.00506
0.001
-0.00476
0.00141
0.007
-0.0476
0.00466
<.001
-0.00127
0.00151
0.418
0.0228
0.00385
<.001
discount rate (d)
procrastination (p)
smoke
exercise 3+
illicit drug use
cigarettes (past times/wk (past
(past 30 days)
30 days)
30 days)
gamble (past
year)
Table 5: Descriptive statistics: Key variables in College Transition Study Replication panel (N=281, Aug.-Dec. 2010)
Survey 1 (Aug.)
Mean
SD
Survey 2 (Sep.)
Mean
SD
Survey 3 (Oct.)
Mean
SD
Survey 4 (Nov.)
Mean
SD
Female
60%
Age
19.2
Parent education: less than HS degree
1%
Parent education: high school degree
17%
Parent education: bachelor's degree
33%
Parent education: graduate degree
48%
Discount rate (d)
12.2
121
20.9
180
Discount rate (d) adjusted for outliers
2.11
2.75
1.93
2.72
d<=0
6%
8%
0<d<1
28%
31%
1<=d<3
40%
37%
d>=3
26%
25%
Survey 5 (Dec.)
Mean
SD
2.4
Procrastination score (0-4)
2.2
1.07
PHQ-9 score (0-27)
3.8
3.9
Major depression (PHQ-9)
2%
1%
5%
4%
7%
Other depression (PHQ-9)
6%
6%
6%
4%
5%
Not difficult at all
68%
60%
47%
46%
50%
Somewhat difficult
29%
37%
47%
47%
41%
Very difficult
3%
1%
5%
6%
7%
Extremely difficult
0%
1%
1%
2%
3%
Psychiatric medication (current)
5%
6%
Psychotherapy (current)
3%
7%
Any treatment (medication or therapy)
6%
10%
Any treatment if mental health problem
12%
23%
4.16
3.95
1.87
1.15
4.94
4.42
4.75
4.21
2
1.2
5.45
5.05
Impairment from depression
Table 6: Association between depression and discount rate/procrastination in CTSR data
Notes: Each column shows results from a separate OLS regression. In each cell the top row shows the coefficient, the second row shows heteroscedasticityrobust standard errors (clustered by individual), and the third row shows the p-value.
Dep. Var.:
Individual fixed effects?
Depression score
Female
Age
Parents: < bachelor's degree
Parents: bachelor degree
Parents: graduate degree
discount
discount
discount
procrast.
procrast.
procrast.
no
no
yes
no
no
yes
0.0639
0.0341
0.062
0.046
0.0344
0.182
0.0567
0.281
0.84
0.114
0.0734
0.123
(ref)
-1.16
0.47
0.014
-1.25
0.472
0.009
0.0289
0.041
0.484
0.053
0.0118
<.001
0.0489
0.0121
<.001
-0.137
0.116
0.237
0.0186
0.0158
0.239
(ref)
-0.253
0.172
0.144
-0.241
0.162
0.137
0.0298
0.0111
0.008
Table 7: Association between treatment use and discount rate/procrastination in CTSR data
Each column shows results from a separate OLS regression. In each cell the top row shows the coefficient, the second row
shows heteroscedasticity-robust standard errors (clustered by individual), and the third row shows the p-value.
Dep. Var.:
Individual fixed effects?
tx use
no
tx use
no
discount rate (d)
-0.0104
0.00291
<.001
-0.00848
0.00313
0.007
procrastination (p)
Depression score
Impairment from depression
Somewhat difficult
Very difficult
Extremely difficult
Female
Age
Parents: < bachelor's degree
Parents: bachelor degree
Parents: graduate degree
tx use
no
0.0109
0.0124
0.381
tx use
no
tx use
yes
0.00526
0.00663
0.429
0.00474
0.0125
0.705
0.00381
0.00433
0.38
-0.0149
0.0188
0.429
0.00313
0.00627
0.618
0.0697
0.0415
0.094
0.308
0.19
0.106
-0.0491
0.036
0.174
0.0442
0.0257
0.086
-0.00187
0.00261
0.474
(ref)
0.0217
0.03
0.469
0.0858
0.0342
0.013
0.053
0.0265
0.046
0.29
0.119
0.015
0.0341
0.137
0.803
0.0162
0.0266
0.544
-0.00194
0.0029
0.511
(ref)
-0.0119
0.0337
0.725
0.0691
0.0382
0.071
0.0497
0.0361
0.17
0.223
0.134
0.097
0.0332
0.197
0.866
tx use
no
tx use
no
-0.0102
0.00532
0.055
0.0165
0.0137
0.231
-0.00828
0.00313
0.009
0.01411
0.0144
0.327
0.00518
0.00679
0.446
0.0668
0.0409
0.103
0.302
0.189
0.112
-0.061
0.0375
0.105
0.0478
0.027
0.078
-0.00221
0.00262
0.402
(ref)
0.025
0.031
0.42
0.0876
0.0352
0.013
APPENDIX: Text of discounting and procrastination measures in survey questionnaires
Discounting/Future Orientation (Chapman, G.B. 1996. J Exp Psych 22(3): 771-791)
Future Orientation Scale Directions: The next four questions will provide you with hypothetical
scenarios. Please read the scenarios carefully and then provide your answer in the text box provided.
There are no right or wrong answers to these questions.
Imagine you have won some money at a casino and have a choice between two prizes:
Prize A: Gain $500 right now
or
Prize B: Gain $_____ one year from now
What amount of money (in dollars) would have to appear in the blank for Prize B to make it just as
attractive as Prize A?
Procrastination (three items from Lay’s Procrastination Scale: Lay, C. 1986. J Res Personality, 20, 474495.)
How well does each statement describe you?
I often find myself performing tasks that I had intended to do days before.
I generally delay before starting on work I have to do.
I am continually saying "I’ll do it tomorrow."
Extremely unlike me (0)
Moderately unlike me (1)
Neutral (2)
Moderately like me (3)
Extremely like me (4)