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How do the Poor Use Their Mobile Data?
Last registered on May 24, 2020

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
Last updated
May 24, 2020 2:02 PM EDT
Location(s)

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Primary Investigator
Affiliation
London Business School
Other Primary Investigator(s)
PI Affiliation
London Business School
Additional Trial Information
Status
In development
Start date
2019-10-08
End date
2020-08-30
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.
Sponsors & Partners

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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
Not available
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)
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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