Optimizing Subsidy Allocation in Two-sided Markets: Evidence from Digital Payment

Last registered on September 12, 2024
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Pre-Trial

Trial Information

General Information

Title
Optimizing Subsidy Allocation in Two-sided Markets: Evidence from Digital Payment
RCT ID
AEARCTR-0014304
Initial registration date
September 09, 2024

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
September 12, 2024, 6:43 PM EDT

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

Locations

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Primary Investigator

Name
Zhe Liu
Affiliation
UC Berkeley
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Other Primary Investigator(s)

PI Name
Francis Annan
PI Affiliation
UC Berkeley
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PI Name
Apoorv Gupta
PI Affiliation
Dartmouth College
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Additional Trial Information

Status
In development
Start date
2024-09-30
End date
2025-03-31
Keywords
Finance & Microfinance, Firms & Productivity
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In collaboration with MTN Ghana, we are conducting a Randomized Controlled Trial (RCT) in Eastern Ghana to evaluate the effectiveness of different subsidy allocations on digital payment usage among small retailers. We will divide 120 communities into one control and three treatment groups, each receiving a budget-neutral subsidy: to merchants, to consumers, or evenly split between the two. Participants earn digital lottery tickets with each digital payment transaction, and a single winner receives a reward of 400 Ghana Cedis each month for three months. This study aims to determine how different subsidy allocations to either side of the market can most effectively enhance digital payment adoption and usage within a fixed budget.
External Link(s)

Registration Citation

Citation
Annan, Francis , Apoorv Gupta and Zhe Liu. 2024. "Optimizing Subsidy Allocation in Two-sided Markets: Evidence from Digital Payment." AEA RCT Registry. September 12. https://doi.org/10.1257/rct.14304-1.0
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Experimental Details

Interventions

Intervention(s)
Our study will feature one control group and three distinct treatment groups: T1 (subsidies to merchants), T2 (subsidies to consumers), and T3 (evenly split subsidies). The novelty of our approach lies in employing a budget-neutral subsidy structure: we will have a fixed amount of subsidy available for each treatment group. The subsidy will be implemented as lottery prizes, and lotteries will be conducted monthly for three months.
Intervention Start Date
2024-10-01
Intervention End Date
2024-12-31

Primary Outcomes

Primary Outcomes (end points)
number and value of digital payments, total and new merchants using digital payments, total and new customers using digital payments
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
number of customers, number of new customers, revenues, income, business theft, activity types, store days, store hours, household expenditure, liquidity, inventories, prices at merchant stores, knowledge of digital payment, failed digital transactions, whether persuaded by consumers to use digital payments in the last 90 days, whether persuaded by stores to use digital payments in the last 90 days
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
A total of 120 communities will be assigned into the following groups:
1. Control: no subsidy or lottery prizes is offered in the community.
2. Treatment 1 (subsidies to merchants): lottery tickets are only offered to merchants that accept MoMoPay. Based on digital transactions, merchants will receive digital lottery tickets. At the end of each month, a cash reward will be drawn from all the tickets.
3. Treatment 2 (subsidies to consumers): lottery tickets are only offered to consumers based on their digital transactions made during the experiment period.
4. Treatment 3 (evenly split subsidies): both consumers and merchants will get lottery tickets based on digital transactions.
Experimental Design Details
Not available
Randomization Method
randomization done in office by a computer
Randomization Unit
Village
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
120 villages
Sample size: planned number of observations
600 stores and 6000 customers
Sample size (or number of clusters) by treatment arms
40 villages control, 40 villages for each treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The proportion of stores adopted DF in a village increases by 5 percentage, or 21% compared to a baseline of 23%.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan