Mobile-izing Savings: Evaluating a Phone-Based Defined-Contribution Account in Afghanistan
Last registered on February 24, 2014


Trial Information
General Information
Mobile-izing Savings: Evaluating a Phone-Based Defined-Contribution Account in Afghanistan
Initial registration date
Not yet registered
Last updated
February 24, 2014 10:17 PM EST
Primary Investigator
U.C. Berkeley
Other Primary Investigator(s)
PI Affiliation
University of California, Los Angeles
PI Affiliation
University of California, Berkeley
Additional Trial Information
In development
Start date
End date
Secondary IDs
Through our project, we will make a basic phone-based defined contribution account available to approximately 1,000 employees at a large Afghan firm. Building on insights from recent focus groups conducted in Kabul and an ongoing field experiment studying the impact of mobile salary payments, we will use a randomized control trial to determine how such a product can best be designed to encourage contributions, paying particular attention to the role of the “default option” in subsequent decisions. Through face-to-face and phone-based interviews, as well as administrative data collected from the company, we will measure the extent to which salary-linked defined contribution accounts can have a lasting impact on the financial decisions of participants. In Afghanistan, where only 3% of the population are banked but over 60% own a mobile phone, such products are badly needed. If successful in Afghanistan, this product could provide a scalable mechanism for banks and mobile phone operators to improve the financial capabilities of millions of potential clients in some of the world’s poorest and most fragile countries. Likewise, firms in these countries may benefit from reduced turnover and increased employee motivation, contributing to overall productivity improvements.
External Link(s)
Registration Citation
Blumenstock, Joshua, Michael Callen and Tarek Ghani. 2014. "Mobile-izing Savings: Evaluating a Phone-Based Defined-Contribution Account in Afghanistan." AEA RCT Registry. February 24.
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Experimental Details

Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
1. Savings in salary-linked defined contribution accounts (M-Pasandaaz)

2. Changes in savings in other formal and informal savings accounts.

3. Employee Welfare

Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our primary intervention, in conjunction with Roshan, will be to make the mobile savings account (M-Pasandaaz) available to Roshan’s salaried employees. The savings account intervention relies heavily on existing infrastructure within Roshan, most notably the mobile salary payment platform (M-Paisa). Each employee currently owns an M-Paisa (Mobile Money) account, and Roshan disburses all salaries directly to the M-Paisa account. Roshan employees will be given a separate M-Pasandaaz bank-linked savings account, which is associated with their primary M-Paisa account. Each month, Roshan will deposit the employee’s savings contribution into the employee’s M-Pasandaaz savings account and the remainder of the employee’s salary will be paid into the employee’s normal M-Paisa account. We will randomly assign each employee to a variant of the basic M-Pasandaaz account. We will also randomly vary the information provided to employees about M-Pasandaaz through a second SMS treatment.
Experimental Design Details
Randomization Method
Randomization done in office by computer.
Randomization Unit
Individual employee
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Not clustered
Sample size: planned number of observations
Approximately 1,000 employees
Sample size (or number of clusters) by treatment arms
Of the total population of roughly 1,000 employees, we will cross randomize two treatments: Default Enrollment Status (two groups), Incentives to Save (two groups), and SMS reminder content (3 groups).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Savings status is likely to be a highly persistent outcome, so we assume a moderate scenario with an autocorrelation parameter of 0.7: Under this assumption and with 400 treatments and 400 controls, we can achieve power of 0.8 with a treatment effect of 0.132 standard deviations. In a more restrictive setting in which we have only a baseline and endline observation for every worker, we would need a treatment effect of approximately 0.2 standard deviations.
IRB Name
University of California, San Diego Human Research Protections Program
IRB Approval Date
IRB Approval Number
IRB Name
University of California, San Diego Human Research Protections Program
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)