Using Financial Incentives and Screening to Increase Engagement With Mental Health Services Among College Students in Chennai, India
Phase: NA
Conditions: Depression/Anxiety
Sponsor: Massachusetts Institute of Technology

Using Financial Incentives and Screening to Increase Engagement with
Mental Health Services among College Students in Chennai, India
Pre-analysis plan
12 February 2025
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Introduction
This study will evaluate the effectiveness of interventions to increase engagement with mental
health services among a sample of college students in Chennai, India. We have partnered with
an arts and science college for women in Chennai, where the study will be conducted. The
sample will be recruited in person during college hours.
We will introduce the study to all students present in the classrooms at the time allotted
by the college administration and obtain consent from those interested after screening them
for age eligibility (between the ages of 18 and 30). Study participants will then complete a
survey via a Qualtrics link using their cell phones. The survey will include questions about
demographics, perceptions of their own mental health, mental health treatment history, and
the PHQ-ADS, a standardized composite screening tool for depression and anxiety.
The experimental design is an RCT with 4 groups, randomized at the individual level. The
investigators will measure interest in, and take-up of, psycho-therapy at the individual level.
We will also collect data on other activities that capture engagement with mental health care,
such as take-up of a cellphone app that provides tools to improve mental health, and seeking
information about mental health.
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Sample and Treatment
2.1
Sample Population
The experimental sample consists of college students at a college in Chennai who (i) are at least
18 years old, (ii) are present in the classrooms when we visit, and (iii) consent to participate in
the study. We will attempt to visit all classrooms (i.e., all majors and years) over the course
of the study. However, we rely on the college administration to make students available and
scheduling constraints on the part of the college may keep us from visiting all classrooms. In
addition, attendance rates on a given day are well below 100% (and as low as 50% during our
scoping visits). Thus, the final experimental sample will be substantially smaller than the full
college student population of approximately 3000.
2.2
Treatments
Treatments are randomized at the individual level, stratified by categories of PHQ-ADS scores.
Treatment status will be revealed via the survey administered through students’ mobile phones.
Participants will be randomized to four conditions:
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1. Control: The control group will (a) watch a video introducing them to the mental health
services available in the college, including introductory videos of the counselors, (b) have
access to therapy at no cost for the duration of the study via their college counselors
and additionally at SCARF, a mental health center in Chennai, and (c) be given links to
book a therapy session or download a mental health app.
2. Screening: In addition to the interventions in the control group, students in the screen-
ing group will receive feedback on their PHQ-ADS score and recommendations based
on that. In particular, they will be informed whether their responses to the PHQ-ADS
questions fit in the standard risk categories of no, mild, moderate, or severe distress
and corresponding recommendations for whether to make an appointment with a coun-
selor/therapist.
3. Rewards: In addition to the interventions in the control group, students in the rewards
group will be provided with a cash payment of Rs. 500 (USD 5.70 at market exchange
rates) for attending a (first) therapy session.
4. Screening and rewards: Students in this group will receive both interventions, i.e.,
both the screening information and the reward described above.
For ethical reasons, participants with PHQ-ADS scores of 30 or above (indicating severe
risk of depression and anxiety) in the non-screening treatment arms will also be informed of
their scores at the end of the survey. For outcome variables measured within the survey, we
will treat these participants as though they are part of the group to which they are randomly
assigned. One outcome variable (completing the process to make an appointment for therapy)
is measured after the conclusion of the participant survey. For this outcome variable, we will
separately run regressions both including and excluding these individuals.
2.3
Experiment timeline
We began sample recruitment on 24 January 2025. We expect to collect primary outcomes by
the end of April 2025, but this may vary as we rely on the college administration to schedule
our visits to classrooms. This pre-analysis plan was posted soon after the beginning of data
collection. In the paper, as a robustness check, we will report results for data collected solely
after the pre-analysis plan was posted.
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Outcomes
3.1
Primary Outcomes
We have two primary outcomes:
1. Expressing interest in therapy. This consists of students answering “yes” when asked
in the survey if they would like to book an appointment with a counselor, or if students
answer “yes” when asked in the survey if they would like to be contacted by a study
counselor to discuss mental health and therapy.
2. Making an appointment. If a student actually makes an appointment with a counselor
(measured using the administrative records from the counseling offices)
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3.2
Secondary Outcomes
The secondary outcomes of the study include the following:
1. Take-up of mental health app. This is measured in two ways: participants expressing
interest in accessing a mental-healthcare app in the survey, and participants actually
clicking a link to download the app.
2. Demand for information about mental health.
This is measured in two ways:
participants expressing interest in receiving more information about mental health, and
participants actually clicking a link to access the information.
3. Attendance of therapy sessions. We will collect data from the administrative records
of the counselling office on whether participants actually attended a therapy session, as
well as the number of sessions attended.
4. Other variables. We will also report exploratory analyses and mechanism tests includ-
ing: (i) whether participants report in the survey if they would consider seeking therapy
in future; (ii) what steps they report they would take to take care of their mental health in
future; (iii) reported motivating/inhibiting factors for taking up therapy and perceptions
of therapy.
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Empirical analysis
4.1
Main regression specification
Pooled treatment effects. Our main specification will be a linear regression of outcomes
on the ‘pooled’ treatment assignment, separately for the screening treatment and rewards
treatment. That is, define
Pooled screeni = Screeni + Screen and rewardsi
Pooled rewardsi = Rewardsi + Screen and rewardsi,
where Screeni is an indicator variable for i being assigned to the screening group, Rewardsi is
an indicator variable for i being assigned to the rewards group, and Screen and rewardsi is an
indicator variable for i being assigned to the combined screening and rewards group.
To identify the effect of the pooled treatments on the outcome variables, we will run the
following regression specification, separately for each pooled treatment and outcome variable:
Yi = α + βTi + X′
iλ + ϵi
where Yi represents the outcomes for individual i described above, Ti ∈{Pooled screeni, Pooled rewardsi}
represents the pooled treatment indicators described above. Xi is a vector of controls consist-
ing of the limited set of variables collected in the survey prior to treatment. This includes
categories of PHQ-ADS scores (no risk, mild risk, or moderate-to-severe risk), whether par-
ticipants report that their family would be supportive of seeking therapy, their current mood,
their perception of their current and previous mental health and well-being, whether they have
received mental health care in the past, interest in therapy at baseline, and and a measure of
their financial status.
Fully saturated model. For each outcome, we will also report a “saturated” regression which
breaks up the effects by each of the treatment cells:
Yi = α + β1Screeni + β2Rewardi + β3Screen and Rewardi + X′
iλ + ϵi
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4.2
Heterogeneity analysis
Heterogeneity by baseline mental health status.
For our primary outcome and for
selected secondary outcomes such as take-up of the app, we will analyze heterogeneity by base-
line mental health status, as measured by the PHQ-ADS. This heterogeneity is policy-relevant,
because policy-makers may especially value students at high risk of anxiety and depression
receiving therapy. It also conceptually relates to our treatments, in that screening might be
expected to cause more positive selection on symptoms of depression and anxiety while rewards
have ambiguous selection effects.
The standard cutoffs in scores in the PHQ-ADS scale in the literature are 10-19 for mild
symptoms, 20-29 for moderate symptoms, and 30 or higher for severe symptoms. We will
combine the moderate and severe categories in our analysis, as only a small share of students
are expected to show severe symptoms.
We will estimate the following regression:
Yi = α + βTi + γTi × Mildi + θTi × Moderate or Severei + λX′
i + ϵi
for each of the pooled treatment indicators and outcome variables. The vector of controls Xi
includes the indicator variables Mildi and Moderate or Severei.
We will also run a model which interacts the treatments with a continuous measure of the
PHQ-ADS score:
Yi = α + βTi + γTi × PHQ-ADSi + λX′
i + ϵi
where Xi is the vector of controls now including the continuous measure of PHQ-ADS score.
Heterogeneity by other characteristics. As exploratory analyses, we will examine hetero-
geneity by other baseline characteristics, such as participants’ financial status, their perception
of their mental health status, and their major sources of stress.
Heterogeneity by other characteristics.
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Funding and Human Subjects Review
The study has been funded by The Weiss Fund for Research in Development Economics,
Solomon Buchsbaum Research Fund, Gautam Rao’s and Emily Breza’s discretionary research
funds from Harvard University, and Frank Schilbach’s discretionary research funds from MIT
in the past and is currently being funded by Gautam Rao’s discretionary research funds from
University of California, Berkeley. The IRB at MIT is serving as the primary institution of
record (protocol 2206000666), and has entered into a reliance agreement with Harvard Uni-
versity, University of California, Berkeley, and University of Michigan. The IRB at IFMR has
also approved the study.
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