Time Preferences and Help-seeking for Mental Health
(WORKING PAPER: PLEASE DO NOT DISTRIBUTE WITHOUT PERMISSION)
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
daneis@umich.edu, 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
bdruss@emory.edu, 404-712-9602
Disclosures: There was no funding for this work, and the authors have no conflicts of interest.
Running head: Time preferences and mental health
Key words: time preferences; present orientation; discounting; mental health; depression
Word count: 4882
Tables: 7
Figures: none
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Abstract
Fewer than half of people with mental disorders seek treatment. We examine conceptually and
empirically how this lack of help-seeking might be related to time preferences and procrastination.
Mental health might affect the general rate at which people discount future utility and also the
degree to which people value the present versus all future periods, which could in turn lead to
indefinite delay of help-seeking. Our empirical analysis, using survey data from U.S. college
student populations, generally supports these hypotheses. Additional research on interrelationships
between time preferences, mental health, and behaviors could have significant implications for
interventions and policy.
2
1. 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, Patel et al., 2010). 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 appropriate treatment, as well as the normative question of whether 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 barriers
remain important for segments of the population, but there are reasons to think their overall
prominence is declining. Attitudes and knowledge are presumably improving as treatment
becomes more common and familiar, particularly for less severe and more prevalent conditions
such as depression and anxiety disorders. This may be especially true among younger cohorts,
because children and adolescents have had large increases in treatment prevalence for mood
disorders, attention deficit hyperactivity disorder (ADHD), and other mental disorders since the
1980s (Zuvekas and Vitiello, 2012, Vitiello et al., 2006). Financial barriers have also diminished
in the United States, at least among insured populations —mental health parity (equality with
physical health coverage) has been adopted at the federal level and in most states.
Despite the apparent reductions in barriers, the treatment gap is still estimated at over 50% in all
countries of the world (Patel et al., 2010). This is not surprising, however, when viewed in the
broader context of health-related behaviors. Based on the public health field’s experience with
diet, exercise, smoking, and other behaviors, we should not assume that people will engage in
3
healthy behavior, even when attitudes are positive, knowledge is reasonably high, and financial
barriers are low. For many 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 budget
constraints. The same is probably true for help-seeking for mental health.
Ideas from behavioral economics have been useful for gaining insights into seemingly irrational
health behaviors. Similarly, some of those ideas could yield important insights about why so many
people with mental disorders go without treatment, and by extension, 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 and procrastination might be connected to mental health and why this would have
important implications for clinical and public health interventions (Section 2). The paper then
presents descriptive findings that generally support these hypothesized relationships, using two
data sets with complementary strengths and limitations (Section 3). Finally, the paper offers ideas
for future research on how ideas from behavioral economics can improve understanding of mental
health and help-seeking, and vice-versa (Section 4).
2. 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 is a weighted sum of utility at each time point, where the weights diminish
exponentially in accordance with the discount rate, :
4
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, referring to situations in which period-to-period time preference is higher
initially and then declines 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
equation, where
in the following
(O'Donoghue and 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 each 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 “sophisticated” (aware) about his or her time-inconsistency (O'Donoghue and
Rabin, 2001).
We propose that mental health may play an important role in these types of time preferences.
Specifically, both the degree of time-inconsistent present-orientation, , and the discount rate, ,
could depend on current mental health,
:
5
This seems especially plausible in the case of depression, one of the most prevalent mental health
problems. Core symptoms of major depressive disorder include feeling hopeless and like one has
“nothing to look forward to” (American Psychiatric Association, 2013). This feature of depression
could imply a lower value of
(higher present-orientation), a higher value of
(higher discounting
in general), or both. Another core symptom, feeling worthless (low self-esteem), could similarly
dampen one’s valuation of the future. On the other hand, yet 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 believed to reflect present-orientation and lack of self-control, such as substance use and
binge eating, are strongly correlated with depression. Avoiding procrastination and other presentoriented, impulsive behaviors requires expending a limited supply of mental energy or willpower in
some conceptual models (Loewenstein and O'Donoghue, 2004), and this energy or willpower could
be more limited for people experiencing depression or other mental health problems. Employee
productivity is another important behavioral outcome affected by depression (Wang et al., 2007);
to the extent that being productive on the job reflects self-control and future orientation, the link
between depression and time preferences could be an important underlying mechanism.
Help-seeking for mental health problems (and many other health conditions, for that matter) also
seems likely to be related to time preferences. For many people, help-seeking entails an immediate
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cost or disutility, including some combination of time, money, and distress or discomfort. The
expected benefits are delayed and uncertain; although psychiatric medication or psychotherapy can
sometimes bring quick relief, more commonly the main therapeutic benefits come only after
several weeks. 1 Given that depression and anxiety often impair concentration and other aspects of
cognition, this uncertainty and delay in benefits might not only make the benefits of treatment seem
less attractive but could also make it difficult to assess the benefits cognitively .
Present orientation and procrastination could be important reasons why so many people fail to seek
treatment, even as attitudes, knowledge, and financial barriers improve. Even people without
mental health problems are often highly present-oriented, and depression and other mental health
conditions could augment these tendencies. Furthermore, procrastination of help-seeking could
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 could become slightly more severe and yet seem normal relative to
the previous period. If he again procrastinates help-seeking, which would be especially likely if
depression exacerbates present-orientation, this cycle would continue as symptoms become
progressively worse. 2
Procrastination of mental health care (and other non-acute but disabling conditions) also seems
likely in light of the theory that procrastination can be most severe for the most important and
valuable tasks (O'Donoghue and 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
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).
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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 in the next period (by seeking mental health care, or modifying activities
and interactions that are contributing to distress), 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 can learn something
about how to design or refine interventions that achieve outcomes such as improved mental health,
increased help-seeking (for not just mental health but also other health conditions), and other health
behaviors such as diet and exercise (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 (such as self-management skills emphasized in psychotherapy) that
mitigate those pathways. Also, if we learn that procrastination plays an important role in
preventing help-seeking for mental health or other health conditions, we could adapt intervention
approaches that have proven successful for mitigating procrastination in contexts such as diet,
exercise, or smoking cessation.
There is also the possibility that insights from the mental health field could inform behavioral
economics, just as behavioral economics can advance understanding of mental health and helpseeking. Given that mental health problems such as depression and anxiety are highly prevalent
and are highly variable over time within individuals, these conditions could be an important part of
the explanation for why present-orientation, time-inconsistency, and procrastination are so
pervasive in human behavior. Related to this, this line of research could bring new insight into
8
poor outcomes in employment and education (i.e., low productivity in employment and human
capital accumulation), given that these outcomes are generally associated with both present
orientation and mental health.
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 are probably
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 and feel regretful
after the fact). 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 labor economists have emphasized as
contributors to human capital (Heckman et al., 2006, Currie and Stabile, 2007).
This discussion raises a number of specific empirical questions. How do time preferences vary
with respect to mental health—for example, do depressed people exhibit higher discount rates than
non-depressed people, and are people more present-oriented when their depressive symptoms are
9
higher? To what extent is lack of mental health treatment use explained by high discount rates?
And to what extent is lack of treatment use explained by time-inconsistent preferences and
procrastination? In the next section we offer evidence related to these questions, as a starting point
for empirical work in this area.
3. EMPIRICAL ANALYSIS
Data sets
Our empirical analysis investigates the association between indicators of present-orientation
(discount rates and procrastination) and mental health (depression and, to a lesser extent, anxiety),
as well as the association between present-orientation and utilization of mental health treatment.
We use two survey data sets from college student populations, with complementary advantages and
limitations. One is a large cross-sectional data set, the 2011 Healthy Minds Study, which includes
12 colleges and universities with 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., 2011, Eisenberg et al., 2007). 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
undergraduate students at a 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
important differences as explained below. The large sample in the Healthy Minds data set offers
more statistical precision and is perhaps more likely to generalize to the national population of
students, whereas the CTSR panel data allow for fixed effects regressions that control for timeinvariant individual characteristics. Both studies were approved by the University of Michigan
Institutional Review Board.
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Measures
Both studies use 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 versus 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 unusual 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 a subset of
items from Lay’s Procrastination scale (Lay, 1986). As in the original 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 and 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 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 there is also 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
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.
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with questions about current medication and psychotherapy. We focus on simple binary (yes/no)
measures of utilization.
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 simple regressions to investigate the conditional correlations between the main variables of
interest. First, we estimate linear regressions of present-orientation (the discount rate, d, or
procrastination, p) on mental health indicators (mh) and demographic characteristics (x):
The demographic variables x are gender, age (years), and highest educational attainment of parents
(categorical dummies). In the analysis of Healthy Minds data we estimate heteroskedasticityrobust 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 probit regressions of treatment use (tx) on the discount rate and procrastination,
controlling for mental health and demographic characteristics:
12
Results from the Healthy Minds data (large cross-section)
Table I shows descriptive statistics for 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 rate (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 point is to
avoid having our estimates driven largely by extremely high values. 4 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 have negative discount rates, and there is little room for variation on the
negative end, with -1 being the theoretical minimum). 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
other national 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 II. For procrastination this association appears to
be roughly linear throughout the range of depression severity (column 6 in the table), whereas for
4
As an alternative strategy for handling outliers, we represent 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.
13
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 the relationship is smaller; a 10 point increase in depression score
is associated with a 14% increase relative to the mean and 12% increase relative to the standard
deviation.
In Table III 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 and significantly
associated with treatment. The 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. 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 low correlation between discounting and procrastination (r =
0.03).
Given the significant 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.
In the survey, respondents are asked to select all that apply from a list of reasons that might have
prevented them from using services. 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 III. Perhaps the most notable finding is that the
14
procrastination score is associated with a higher probability of reporting “I haven't had a chance but
plan to go” as a reason for not seeking services (p=0.09). 5
Lastly, in Table IV we examine how health-related and risk-related behaviors correlate with
discounting and procrastination, as another way to understand the implications of time preferences
and procrastination. Procrastination is highly correlated (p<0.001) with all six health and riskrelated 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 this
association is significant only for obesity, smoking, and exercise.
Results from the CTSR data (smaller panel)
Table V shows descriptive statistics for the CTSR panel from each of the five monthly surveys.
The mean discount rate is fairly consistent over time (during the fall semester), 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. 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 VI), 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
5
Other reported barriers associated with procrastination at p<0.10 are: “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.”
15
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 30 students (with a positive depression screen in any given
round). As shown in Table VII, 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 restrictions.
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 score in the panel data set because of the small
sample size.
16
(e.g., browsing the internet) when you were planning to work. In addition, we find that depression
score is a negative predictor of reported self-control and self-regulation in the past four weeks (as
part of the Character Strengths Scale) (p=0.11).
Summary of empirical analysis
The results are generally consistent with the idea that mental health, and particularly depression,
increase present-orientation, as measured by discounting and procrastination. Furthermore, we find
some evidence in the larger cross-sectional data that procrastination helps explain lack of helpseeking 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. These analyses have notable limitations that will require further examination in future
research: 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.
4. IDEAS FOR FUTURE RESEARCH
Collectively the conceptual discussion and the empirical 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.
We hope this paper will be the beginning of an exciting and fruitful area of research. We can
envision future studies along four complementary tracks: 1) observational, longitudinal studies
with 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)
17
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. In most cases these studies would
be using ideas from behavioral economics to understand mental health and help-seeking. But there
are also possibilities for the reverse, using ideas from mental health research to enhance
understanding of behavioral economics, especially considering that approximately one-quarter of
U.S. adults meet criteria for a mental disorder and many more have subclinical symptoms (Kessler
et al., 2005).
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 a wide range of factors, and it
plausibly affects a wide range of behaviors, symptoms, and other outcomes. Nevertheless, careful
descriptive analysis of detailed longitudinal data will be an important avenue for learning more.
Brief experimental studies can help identify causal pathways. For example, stimuli (such as music
or lighting) 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 selfcontrol (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.
18
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. Trials with multiple time points for follow-up assessment will be especially
interesting, as one can observe the time relative time dynamics of time preferences, procrastination,
and symptoms of depression and other mental disorders.
Finally, interventions may be able to influence help-seeking behavior for mental health by targeting
time-inconsistent preferences and procrastination. For example, people could be offered
“commitment devices” to address their mental health, such as pre-scheduling a brief assessment
(with potential for referral) at regular intervals. In addition, other approaches could strategically
present information about treatment options in ways that makes the delayed benefits more
immediately salient. These are just a couple examples from 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.
19
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22
Table I: 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%
23
Table II: 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
0.041
0.007
<.001
0.040
0.008
<.001
Depression score (intervals)
0-4
5-9
10-14
15-19
20-27
Anxiety (0/1)
Female
Age
Parents: < bachelor's degree
Parents: bachelor degree
Parents: graduate degree
0.437
0.140
0.011
0.323
0.145
0.050
0.467
0.097
0.001
0.009
0.014
0.536
(ref)
-0.685
0.110
<.001
-0.935
0.153
<.001
discount
(ref)
0.330
0.089
0.004
0.583
0.077
<.001
0.589
0.216
0.021
0.400
0.312
0.229
0.372
0.153
0.035
0.459
0.100
0.001
0.010
0.014
0.523
-0.683
0.111
<.001
-0.931
0.154
<.001
procrast.
procrast.
0.063
0.002
<.001
0.062
0.002
<.001
-0.099
0.064
0.156
-0.082
0.066
0.239
-0.070
0.018
0.003
-0.011
0.004
0.015
(ref)
0.004
0.023
0.853
-0.056
0.035
0.144
procrast.
(ref)
0.425
0.026
<.001
0.643
0.034
<.001
0.814
0.040
<.001
0.928
0.046
<.001
0.019
0.073
0.805
-0.076
0.018
0.001
-0.011
0.004
0.013
(ref)
0.001
0.023
0.967
-0.056
0.037
0.160
24
Table III: 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 probit regression
with marginal effects reported. In each cell the top row shows the marginal effect, 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
-0.003
0.004
0.401
-0.004
0.004
0.341
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
-0.020
0.003
<.001
tx use
tx use
tx use
-0.003
0.004
0.383
-0.005
0.010
0.604
-0.005
0.004
0.293
-0.020
0.010
0.041
-0.002
0.002
0.210
0.123
0.021
<.001
-0.003
0.002
0.117
0.123
0.020
<.001
-0.021
0.009
0.028
-0.002
0.001
0.198
0.122
0.021
<.001
0.049
0.037
0.190
0.122
0.048
0.011
0.177
0.054
0.001
0.043
0.033
0.200
0.115
0.047
0.014
0.171
0.055
0.002
0.049
0.037
0.182
0.120
0.049
0.015
0.178
0.055
0.001
0.035
0.025
0.153
0.024
0.033
0.467
0.094
0.034
0.006
0.103
0.018
<.001
0.009
0.003
0.010
(ref)
0.034
0.019
0.085
0.104
0.023
<.001
0.039
0.024
0.103
0.038
0.034
0.266
0.108
0.036
0.003
0.100
0.016
<.001
0.008
0.003
0.014
(ref)
0.035
0.023
0.128
0.107
0.023
<.001
0.038
0.025
0.124
0.032
0.033
0.345
0.105
0.036
0.003
0.103
0.017
<.001
0.009
0.004
0.012
(ref)
0.031
0.021
0.129
0.103
0.024
<.001
25
Table IV: Association between health/risk-related behaviors and discount rate/procrastination (Healthy Minds data)
Notes: Each column shows results from a separate probit regression. In each cell the top row shows the marginal effect, 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
0.006
0.001
<.001
0.022
0.003
<.001
-0.003
0.002
0.196
0.032
0.005
<.001
discount rate (d)
procrastination (p)
exercise 3+
illicit drug use smoke cigarettes
times/wk (past
(past 30 days)
(past 30 days)
30 days)
gamble (past
year)
0.148
0.127
0.565
0.155
0.000
0.002
0.978
0.026
0.004
<.001
0.003
0.001
0.001
0.024
0.006
<.001
-0.005
0.001
0.001
-0.048
0.005
<.001
-0.010
0.002
0.540
0.024
0.004
<.001
26
Table V: Descriptive statistics: Key variables in College Transition Study Replication panel (N=281, Aug.-Dec. 2010)
Survey 1 (Aug.)
Survey 2 (Sep.)
Survey 3 (Oct.)
Survey 4 (Nov.)
Survey 5 (Dec.)
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Female
Age
Parent education: less than HS degree
Parent education: high school degree
Parent education: bachelor's degree
Parent education: graduate degree
60%
19.2
1%
17%
33%
48%
Discount rate (d)
Discount rate (d) adjusted for outliers
d<=0
0<d<1
1<=d<3
d>=3
12.2
2.11
6%
28%
40%
26%
121
2.75
Procrastination score (0-4)
2.2
1.07
PHQ-9 score (0-27)
Major depression (PHQ-9)
Other depression (PHQ-9)
3.8
2%
6%
3.9
Impairment from depression
Not difficult at all
Somewhat difficult
Very difficult
Extremely difficult
68%
29%
3%
0%
Psychiatric medication (current)
Psychotherapy (current)
Any treatment (medication or therapy)
Any treatment if mental health problem
5%
3%
6%
12%
2.4
20.9
1.93
8%
31%
37%
25%
4.16
1%
6%
60%
37%
1%
1%
3.95
1.87
1.15
4.94
5%
6%
4.42
47%
47%
5%
1%
4.75
4%
4%
46%
47%
6%
2%
180
2.72
4.21
2
1.2
5.45
7%
5%
5.05
50%
41%
7%
3%
6%
7%
10%
23%
27
Table VI: 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 heteroscedasticity-robust
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
0.064
0.034
0.062
0.046
0.034
0.182
0.057
0.281
0.840
0.114
0.073
0.123
(ref)
-1.160
0.470
0.014
-1.25
0.472
0.009
0.029
0.041
0.484
0.053
0.012
<.001
0.049
0.012
<.001
-0.137
0.116
0.237
0.019
0.016
0.239
(ref)
-0.253
0.172
0.144
-0.241
0.162
0.137
0.030
0.011
0.008
28
Table VII: Association between treatment use and discount rate/procrastination in CTSR data
Each column shows results from a separate probit regression with marginal effects reported. In each cell the top row shows the
marginal effect, the second row shows heteroscedasticity-robust standard errors (clustered by individual), and the third row shows the pvalue.
Dep. Var.:
Individual fixed effects?
tx use
no
tx use
no
discount rate (d)
-0.020
0.009
0.028
-0.015
0.006
0.016
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
0.011
0.012
0.384
tx use
no
tx use
yes
0.005
0.004
0.146
0.003
0.012
0.783
0.004
0.003
0.261
-0.015
0.019
0.429
0.003
0.006
0.618
0.061
0.032
0.055
0.138
0.059
0.018
n/a
n/a
n/a
0.042
0.027
0.129
-0.009
0.015
0.549
(ref)
0.027
0.059
0.648
0.098
0.058
0.094
0.056
0.025
0.025
0.163
0.056
0.004
0.042
0.092
0.645
0.013
0.025
0.604
-0.003
0.046
0.562
(ref)
-0.032
0.041
0.439
0.063
0.036
0.079
0.050
0.036
0.170
0.223
0.134
0.097
0.033
0.197
0.866
tx use
no
tx use
no
-0.021
0.010
0.031
0.016
0.014
0.243
-0.015
0.006
0.018
0.012
0.011
0.271
0.005
0.004
0.148
0.057
0.032
0.077
0.129
0.058
0.026
n/a
n/a
n/a
0.042
0.027
0.124
-0.007
0.014
0.611
(ref)
0.026
0.058
0.659
0.094
0.057
0.099
29
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)
30