Digital Transformation and Competition: Evidence from Mobile Money

Last registered on September 17, 2020


Trial Information

General Information

Digital Transformation and Competition: Evidence from Mobile Money
Initial registration date
September 15, 2020
Last updated
September 17, 2020, 8:08 AM EDT



Primary Investigator

Georgia State University

Other Primary Investigator(s)

Additional Trial Information

In development
Start date
End date
Secondary IDs
As digital financial markets evolve, new actors will enter the business environment; thus, influencing market competition. What are the potential impacts of such competitive digital transformation on financial inclusion, digital transparency, and/or the overall level of consumer trust in digital markets? To answer these questions, we partner with the largest bank in Ghana (GCB Ltd) to randomize the expansion of their new Mobile Money business (called G-Money) across low-income localities.
External Link(s)

Registration Citation

Annan, Francis. 2020. "Digital Transformation and Competition: Evidence from Mobile Money." AEA RCT Registry. September 17.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Measurements (vendors): For market vendors, we plan to focus on the impacts of four non-price dimensions of the digital competitive pressure from G-Money’s entry, evaluating:
1. Quality: incidence of transactional fraud or overcharging consumers at vendor banking points,
2. Information: tariff posting or display behavior at vendor points or digital market transparency; vendor attempts to discuss transaction tariffs,
3. Services: length of carrying out transactions or consumer convenience; likelihood of declining transactions due to liquidity shortfalls or ability to manage liquidity properly,
4. Innovation: bundling M-Money with other non-M-Money services or expansions to other lines of business including total exits; changing vendor locations; hours of operations [self-reported]; vendor accessibility to previously unbanked households.

Measurements (potential customers): For consumers, we plan to examine the impacts on trust outcomes. We will field questions that will provide subjective measures of trust in (i) M-Money providers (MTN, Vodafone, AirtelTigo), (ii) market vendors, (iii) carrying out transaction on M-Money (opening accounts; cash-in, cash-out), (iv) consumers’ family and friends, (v) commercial and rural banks (e.g., ADB, GCB, etc.), and (vi) the regulator of financial services in Ghana (Bank of Ghana) if they have heard enough about them to say. This allows us to benchmark consumer trust across M-Money and the other financial alternatives. We will supplement this with objective measures of trust measured from trust games between consumers and their local vendors. In its simple form: this entails a real monetary payoff game between the consumers (i.e., trustors) and one randomly selected (anonymous) local vendor (i.e., trustee). We impose vendor anonymity to mitigate against issues of social and individual preferences. Consumers will be endowed with 50GHS and will decide how much to send to another person (i.e., M-Money vendor). We will triple it, so that the vendor receives three times the amount of money the consumer sent. The vendor will then decide how much he/she wants to send back to the consumer. The total payoffs depend on the choices made between the consumers and vendor. Our objective measure of consumer trust will reflect the amount they sent and expected the vendor to send back to them.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Through our partnership, G-Money will be implemented in eastern Ghana (for 100 select experimental low-income localities) based on our research guidance (using a two-step design):
Two-step Design: 25 localities will randomly receive “no-entry” of G-Money (control program), while the remaining 75 localities will receive entry of G-Money (treatment program). For the 75 treated localities, we will exogenously vary the density of entering vendorships (i.e., new agents or vendors who are not presently in the market) from 1 to 3: 25 localities will receive 1 additional vendor each, 25 localities will receive 2 additional vendors each, and the last 25 localities will equivalently receive the entry of 3 vendors each
Experimental Design Details
Randomization Method
Computer software and simple lotteries, while ensuring balance on observable characteristics (Bruhn and McKenzie [2009])
Randomization Unit
At the locality-level, stratified by districts
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Number of localities (markets): 100
Number of districts (i.e., a larger administrative unit containing multiple localities): 10
Sample size: planned number of observations
Number of households (customers): 100 localities x random 50 per locality = 5,000 Number of “incumbent” vendors (agents and merchants): 100 localities x roughly 3 per locality (using estimates in Annan 2020a) = 300 Number of “entrant” vendors (agents and merchants): 150 new vendors, representing about 67% increase in vendorship in the treated markets (or localities)
Sample size (or number of clusters) by treatment arms
25 localities (control program)
75 localities (treatment program) -- varying the intensity of entering vendorships across localities
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Georgia State University
IRB Approval Date
IRB Approval Number


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

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Reports & Other Materials