How do the Poor Use Their Mobile Data?

Last registered on May 24, 2020
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Pre-Trial

Trial Information

General Information

Title
How do the Poor Use Their Mobile Data?
RCT ID
AEARCTR-0004594
Initial registration date
November 16, 2019

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 20, 2019, 3:00 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
May 24, 2020, 2:02 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
India
Region
Mumbai

Primary Investigator

Name
Alp Sungu
Affiliation
London Business School
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Other Primary Investigator(s)

PI Name
Kamalini Ramdas
PI Affiliation
London Business School
Contact Investigator

Additional Trial Information

Status
In development
Start date
2019-10-08
End date
2020-08-30
Keywords
Other, Welfare
Additional Keywords
engagement, accessibility, poverty
JEL code(s)
Secondary IDs
Abstract
Increased prevalence of mobile phones in slum-communities is regarded as a key opportunity for eradicating poverty. Eventual impact of a mobile-phone-based intervention is conditionally dependent on users’ interaction with their mobile phones. In this paper, we attempt to shed some light on the dynamics of mobile data usage of slum-dwellers. This research project examines how data plans with shorter replenishment cycles impact poorer users’ interactions with their mobile phones. We expect that participants who are likely to binge-use their mobile phones may prefer plans with smaller replenishment cycles and may benefit more from using such plans.
External Link(s)

Registration Citation

Citation
Ramdas, Kamalini and Alp Sungu. 2020. "How do the Poor Use Their Mobile Data?." AEA RCT Registry. May 24. https://doi.org/10.1257/rct.4594-2.0
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Experimental Details

Interventions

Intervention(s)
The intervention is different data plans with varying data replenishment cycles. We expect that participants who are likely to binge-use would prefer shorter data replenishment cycles. Further, we examine whether shorter data replenishment cycles would make users interact with better content and make them more accessible.

Intervention Start Date
2019-10-08
Intervention End Date
2020-02-28

Primary Outcomes

Primary Outcomes (end points)
Mobile phone engagement and accessibility.
Primary Outcomes (explanation)
Both engagement and accessibility will be measured as duration.

Secondary Outcomes

Secondary Outcomes (end points)
Attendance of events advertised via mobile phone. Measures for sleep quality, happiness, subjective well-being, fear of missing out, demand for mobile data restrictions, present bias and perception about mobile app services.
Secondary Outcomes (explanation)
Variable Construct: We will have 0-1 variables for attendance of events advertised via mobile phone. We use the PSQI (and Insomnia Severity Index) for sleep quality. Likert scale-based survey measures for fear of missing out, subjective well-being demand for mobile data restrictions, present bias and stated outcomes related to smartphone usage.

Experimental Design

Experimental Design
Sample.
We choose an urban slum in Mumbai, India because smartphones are widely prevalent in slum communities. Also, slum dwellers have better access to services that are advertised via mobile phones.
Treatment.
We first do a survey, where we elicit choices, next, we reduce the price of the alternative option, i.e., the one that is not chosen by the participant, to see whether they are willing to pay more to stick to their initial plan choice. We also ask choice questions for different goods such as shampoo and chips. We collect corresponding psychological measures and also capture demographic information.

Then, we randomly allocate subjects to different data plans. We provide an identical pricing scheme, lump-sum payment in advance, for all types of data plans. We pay the extra cost associated with different data plans or telecom providers.

We conduct quasi-health camps in an office in the slum community. We invite participants to our quasi-health camps over mobile phones. Every participant is invited to four such camps throughout the experiment.
Experimental Design Details
We first elicit the participant's preference for data plans and do a self-control test to link the likelihood of binge usage with user's behavioral tendencies. Then, independent of their preferences, we randomly allocate them to daily or 28-daily replenishing data plans. Further, we trace the engagement and level of accessibility of users with their smartphones.
Randomization Method
Randomization is done in office, through a computer. The hard-copy survey forms are printed and random allocation to treatment is coded in the user id (i.e., users with shorter plans have id as 44_ _ _ and user with longer plans have id as 66 _ _ _).
Randomization Unit
Individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Individual-level randomization.
Sample size: planned number of observations
1000 individuals.
Sample size (or number of clusters) by treatment arms
~500 individuals in each treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
London Business School Ethical Review Board.
IRB Approval Date
2019-08-18
IRB Approval Number
REC 574
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials