Technology recommendations and social concerns: Evidence from savings technologies in Pakistan

Last registered on January 03, 2023


Trial Information

General Information

Information-sharing and risk-sharing in social networks: Evidence from savings technologies in Pakistan
Initial registration date
December 20, 2022

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
January 03, 2023, 4:57 PM EST

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


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Primary Investigator

Stanford University

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In low-and-middle-income countries (LMICs), social networks facilitate critical risk-sharing between households facing shocks that, otherwise, would result in severe deprivation. However, LMIC social networks often fail to quickly diffuse information about beneficial new technologies, possibly slowing growth and development. In this paper, I develop a model where a household’s payoff from a new technology is jointly determined by the level of informal insurance and communication provided by the social network. When the technology is riskier than the outside option, reliance on informal insurance discourages communication about the technology. I test this model through an experiment in rural Pakistan.
External Link(s)

Registration Citation

Walsh, Mark. 2023. "Information-sharing and risk-sharing in social networks: Evidence from savings technologies in Pakistan." AEA RCT Registry. January 03.
Experimental Details


In the experiment, we will measure rural Pakistanis willingness-to-recommend a familiar savings technology, a lockbox, and an unfamiliar technology, a digital savings account. Our interventions attempt to de-risk recommendations. The first intervention makes the recommendation anonymous. In the second intervention, the recommender is made aware that their partner will receive consumer financial protection training prior to using the technology.

This experiment also includes a village-level intervention. In two thirds of the villages, we will seed socially-central individuals with information about digital savings accounts. In half of these "seeding" villages, we will also include consumer financial protection information and offer an anonymous communication channel for discussion of digital savings accounts.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Individual-level: willingness-to-recommend digital savings account to co-villager; willingness-to-recommend lockbox to co-villager
Village-level: experiences with digital savings accounts; knowledge of digital savings accounts;
Primary Outcomes (explanation)
Willingness-to-recommend is measured through a Becker-DeGroot-Marschak mechanism.
Experiences with digital savings accounts and knowledge of digital savings accounts will be indices constructed from endline survey responses following the Anderson (2008) method.

Secondary Outcomes

Secondary Outcomes (end points)
Individual-level: use of digital savings account, Uptake of digital savings account/lockbox by co-villager
Village-level: communication about digital savings accounts, amount saved, opinions about digital savings, advanced use of digital banking, resilience to shocks.
Secondary Outcomes (explanation)
Use of digital savings account will be measured via the endline survey.

Uptake of digital savings account/lockbox by co-villager is a dummy variable equaling 1 if the co-villager accepts our offer and 0 otherwise.
Communication about digital savings accounts is an index of endline responses relating to communication about digital savings accounts.
Amount saved is the total amount saved measured in PKR at endline.

Opinions about digital savings is an index of how positive the respondent views digital savings at endline.

Advanced use of digital banking is an index of how much the respondent uses digital banking products such as insurance, loans etc. at endline.

Resilience to shocks is measured by questions on how the respondent would deal with a negative shock at endline.

Experimental Design

Experimental Design
I propose to run this field experiment in 150 villages in Pakistan. Villages will be randomized into one of five experimental groups stratifying by subdistrict, digital banking knowledge, social proximity of respondents to seeds, and population size: Control, Seeding, Seeding + Anonymous-Consumer Financial Protection (CFP). In the baseline survey, we will randomize respondents into one of four groups: Control, Consumer Financial Protection (CFP) Info, Anonymous recommendation, and CFP Info + Anon. recommendation. After eight weeks, the field team will conduct phone surveys with the baseline respondents.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
Household-level for information treatments; village-level for seeding treatments
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
150 villages; 2100 households
Sample size: planned number of observations
2100 adult household members
Sample size (or number of clusters) by treatment arms
50 villages per village-level treatment arm; 525 adults per individual-level treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For the individual-level, we will have a MDE of .17 SDs between subgroups with .80 power at a .05 significance level for our main outcomes. At the village-level, we will have a MDE of .30-.35 SDs between subgroups with .80 power at a .05 significance level assuming an ICC between .03-.20 for our main outcome.

Institutional Review Boards (IRBs)

IRB Name
Stanford Institutional Review Board
IRB Approval Date
IRB Approval Number