Adoption and Impacts of Digital Payment Technologies: Evidence from Informal Transit

Last registered on October 07, 2022


Trial Information

General Information

Adoption and Impacts of Digital Payment Technologies: Evidence from Informal Transit
Initial registration date
March 28, 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
March 28, 2022, 5:05 PM EDT

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

Last updated
October 07, 2022, 11:27 AM EDT

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



Primary Investigator


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.
Digital technologies have spread rapidly in much of the world. Despite their expected impacts on productivity and growth, rates of adoption remain modest among firms in sub-Saharan Africa. This research will evaluate the impact of digital payments on business activity, and investigate the role of information asymmetry within firms in explaining businesses’ resistance to technology adoption. By mitigating moral hazard, digital technologies enable changes in the contracting space between employers and employees, in ways that may constitute a barrier to their adoption. To study these questions, I will conduct a randomized experiment by introducing digital payment technologies into widespread small businesses, taxis. In collaboration with a large mobile money company, I will test options for digitizing payments and evaluate the impact of digital payment technologies in the informal transit sector.
External Link(s)

Registration Citation

Houeix, Deivy. 2022. "Adoption and Impacts of Digital Payment Technologies: Evidence from Informal Transit." AEA RCT Registry. October 07.
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Experimental Details


Owners and drivers will be invited to use the digital payments technology. QR codes will be installed in taxis to allow passengers to pay securely. We will recruit owners and drivers in two ways: marketing meetings in collaboration with taxi associations and listing of interested drivers and owners throughout the city by hired enumerators. Owners and drivers will be invited to participate in the study to test the product before its official launch. In collaboration with a partner company, I designed a base product with three options for randomly varying the amount of information available to the owners on their drivers. The randomly selected control group will be offered nothing and on a wait list.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The analysis will include three main sets of outcomes:
(a) Technology adoption: I will measure adoption barriers on two dimensions: take-up and usage across the three options, as key variables of interest, using both survey and administrative data.
(b) Impacts on business productivity, profits, growth, and financial inclusion, including leakages, costs, quantities (passengers), prices, and business growth, savings, credit, money transfers to other sources, using both survey and administrative data.
(c) Contracts and relationships between owners and drivers, including trust, repayment, beliefs, value of the relationship, contract changes. Owners driving their own taxi alone, with no other drivers will be included and randomly offered one of the 3 options to study hiring of a new driver as an outcome.
Primary Outcomes (explanation)
Endline surveys will be conducted with studied drivers after about 3-6 months. In addition, I will conduct high-frequency bi-weekly phone surveys with drivers in all groups. Mystery passengers will also monitor technology adoption and collect data like prices. In addition, passengers will be able to reach Wave’s call center with any complaints about drivers’ compliance with digital payments.

Secondary Outcomes

Secondary Outcomes (end points)
The partner plans to offer discounts to passengers to increase take-up. The latter discounts will be randomized and studied as secondary outcomes.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Owners and drivers will be invited to use the digital payments technology that aims at reducing transaction costs. In collaboration with the partner company, I designed a base product with three options for randomly varying the amount of information available to the owners on their drivers:
A) No Visibility: The owners can only receive payments from drivers at no fee.
B) Limited Visibility / Limited Liability: The owners get notified each day via text every night of their drivers’ total collection up to the estimated daily usage of digital payments for taxis. If the total balance is above average, the owners do not get notified of the surplus.
C) Full Visibility: The owners get notified and can observe all the drivers’ transactions. They also receive an SMS about the total transactions of the day. This option is currently the only one offered to other types of businesses accepting digital payments.
D) Control group: Waitlisted for the time of the experiment and followed using both survey and administrative data.

The partner company plans to reach further taxi owners/drivers outside of this sample.

We plan to "treat" the control group with either full visibility or no-visibility at endline, with the intent to conduct another round of follow-up in the future. The visibility group will be randomly sub-divided into two groups: whether we nudge the drivers or the owners about the possible contractual changes induced by the visibility to strengthen the treatment effect and measure the trigger of the contractual change if any.
Experimental Design Details
The goal of these three treatment arms is to quantify one potential barrier for drivers to use the technology, which I call the “moral hazard” barrier to technology adoption. A short baseline survey will be conducted with participants to assess their willingness-to-pay (WTP) for their randomized option, incentivized using the BDM method, where a price is drawn, and they are invited to complete their purchase only if the price is below their WTP. Most of the sample (95%) will be “surprised” and randomized into a group irrespective of their WTP. Only 5% of respondents will actually be treated using the BDM mechanism. Thus, the proposed design will collect incentive-compatible measures of WTP (hence the 5% who are actually implemented) while preserving the randomization and thus impact estimates for the vast majority of the sample (the 95%). All participants will be asked to rank the three options. Under some assumptions about baseline beliefs (which will be measured), Option B might be a middle ground pleasing both parties, conditional on passenger take-up: it allows drivers to credibly signal “bad days” to their owners while not revealing the overall surplus on “good days”.
Surveys will be conducted with both owners and drivers to obtain their characteristics and baseline measures of the outcome variables described in the following section (e.g., productivity). We will also measure risk aversion in the field in an incentivized manner (5% of the participants will get their choices realized).
Randomization Method
Randomization done by computer.
Randomization Unit
The unit of randomization is the taxi business, i.e., the interested taxi owners. The owners and their drivers will be randomized into one of the six groups, i.e., 3 options and a control group, also each assigned to one of the three options to elicit their willingness-to-pay for it (waitlisted) (3 + 3).

I stratify the randomization by anticipated dimensions of heterogeneity, i.e.,
-- Proxy for baseline knowledge/beliefs (business type, number of taxis, and length of the relationship).
-- Proxy for baseline risk aversion, i.e., whether the taxi drivers circulate throughout the city or mostly wait for passengers in a fixed place.
-- Proxy for baseline taxi-like digital usage

The randomization will be done in several batches and listing surveys will continue to happen in addition to the baseline survey.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
The target sample is 1,200 owners along with their drivers.
Sample size (or number of clusters) by treatment arms
240 owners and their drivers in each of the three treatment arms. 480 owners and their drivers in the control group (sub-divided into three options to elicit willingness-to-pay).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
MIT Committee on the Use of Humans as Experimental Subjects (COUHES)
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents


MD5: e9acef718afb256212a45fd78b702b40

SHA1: 0af79832ca283f6124126cd003b632d8d0233367

Uploaded At: October 07, 2022


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