Competition and Entry in Digital Financial Markets

Last registered on March 16, 2022


Trial Information

General Information

Competition and Entry in Digital Financial Markets
Initial registration date
September 15, 2020

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 17, 2020, 8:08 AM EDT

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

Last updated
March 16, 2022, 1:03 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.


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Primary Investigator

Georgia State 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.
As digital financial markets evolve, new actors will enter the business environment; thus, influencing market competition. What are the potential impacts of such entry and competitive digital transformation on financial inclusion, payment services quality, innovation, transparency, consumer trust, and small businesses in the digital finance marketplace?? 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. 2022. "Competition and Entry in Digital Financial Markets." AEA RCT Registry. March 16.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We successfully piloted our experiment (key protocols, instruments, proposed design, recruitment and enrollment processes) in a pilot exercise.

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:
(i) Quality: incidence of transactional fraud or overcharging consumers at vendor retail points,
(ii) Information: tariff posting or display behavior at vendor points or digital market transparency; and whether vendor attempts to discuss transaction tariffs,
(iii) Services: likelihood of vendors’ declining transactions due to liquidity shortfalls or inability to manage liquidity properly; and length of carrying out transactions or consumer convenience,
(iv) 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. We will evaluate the impacts and various dimensions (i-iv) that businesses compete on. We will adapt Annan (2020, 2021) audit study protocols (and surveys) to measure vendor outcomes objectively (and subjectively respectively).

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 vs Vodafone vs AirtelTigo vs G-Money), (ii) market vendors, (iii) carrying out vendor-involved transactions 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.

We will combine baseline and endline market census, surveys, and audit studies to track the vendor, customer and business outcomes, supplemented with administrative data from GCB Ltd to examine market-wide impacts, including persistence and entry of new vendors.
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 design: 34 localities (~1/3rd) will randomly receive “no-entry” of G-Money (control program), while the remaining 66 localities (~2/3rd) will receive entry of G-Money (treatment program). For the 66 treated localities, we will randomly vary the density of entering vendorships (i.e., new vendors who are not presently doing M-Money business but are local microentrepreneurs) from 1 to 3: 33 localities will receive 1 additional vendor each (treatment I) and the remaining 33 localities will receive 3 additional vendors each (treatment II). We refer to existing vendors as “incumbents” (averaging at 3 vendors per locality; see Annan 2021 and our pilot). We shall focus the main experiment on localities with incumbents, which represent about 95% of localities. The design yields a total of 132 new entrant vendors, representing 69% combined increase in mean vendorship in treated markets or localities. Treatment I represents 33% increase, while treatment II represents 100% increase in vendorship. We will use this to trace out impacts of the various exogenous vendor entry levels. In a locality, we will enroll only a random subset of eligible and existing microenterprises as new G-Money entrants. Thus, overall, we create three
experimental variations: (i) only a random subset of the localities receive entry, so we can compare impacts of entered versus not; (ii) we vary the density of entry (1 vendor each=33% increase in vendorship versus 3 vendors each=100% increase in vendorship), so we can trace out the equilibrium impacts of competition; and (iiii) we enroll only a random subset of eligible microenterprises, so we can compare business impacts on enrolled enterprises versus not enrolled (all eligible).
Experimental Design Details
Not available
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 2020) = 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
34 localities (control program)
66 localities (treatment programs) -- 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