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How do the Poor Use Their Mobile Data?
Initial registration date
November 16, 2019
May 24, 2020 2:02 PM EDT
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London Business School
Other Primary Investigator(s)
London Business School
Additional Trial Information
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.
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
Intervention End Date
Primary Outcomes (end points)
Mobile phone engagement and accessibility.
Primary Outcomes (explanation)
Both engagement and accessibility will be measured as duration.
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.
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
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 _ _ _).
Was the treatment clustered?
Sample size: planned number of clusters
Sample size: planned number of observations
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)
INSTITUTIONAL REVIEW BOARDS (IRBs)
London Business School Ethical Review Board.
IRB Approval Date
IRB Approval Number