Elasticity of Consumer Demand and Welfare of Instant Retail Payments

Last registered on June 01, 2025
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Pre-Trial

Trial Information

General Information

Title
Elasticity of Consumer Demand and Welfare of Instant Retail Payments
RCT ID
AEARCTR-0015380
Initial registration date
February 13, 2025

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
February 19, 2025, 8:56 AM EST

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

Last updated
June 01, 2025, 11:59 PM EDT

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

Locations

Country
Philippines
Region

Primary Investigator

Name
Russell Toth
Affiliation
University of Sydney
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Other Primary Investigator(s)

PI Name
Philip Roessler
PI Affiliation
William and Mary
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PI Name
Hsin-Tien (Tiffany) Tsai
PI Affiliation
National University of Singapore
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PI Name
Joaquin Gonzales
PI Affiliation
Ateneo de Manila University
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Additional Trial Information

Status
In development
Start date
2025-04-28
End date
2025-12-31
Keywords
Behavior, Finance & Microfinance, Welfare
Additional Keywords
financial inclusion, instant payments, demand, price elasticity
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In this study we aim to estimate the consumer demand curve for off-net (“interoperable”) payments, by randomizing the price that consumers face for off-net payments, within an FSP's digital payments apps, with users of the Philippines' instant retail payment switch, InstaPay. An experimentally-generated estimate of demand would be informative for a number of purposes, including for FSPs’ own pricing decisions, and for pricing regulation decisions by policymakers. The demand curve can also be inverted to generate an aggregate estimate of consumer welfare from access to off-net payments, identified with experimental variation in a real-stakes, field setting. We will also study spillovers of instant payments usage on other financial inclusion behaviors, including other payments channels, and use of financial services. We aim to implement the study with a sample that can be re-weighted to be nationally-representative, so we can make statements about the welfare accrued from access to instant payments from financially included individuals in the Philippines.
External Link(s)
Innovations for Poverty Action study description

Registration Citation

Citation
Gonzales, Joaquin et al. 2025. "Elasticity of Consumer Demand and Welfare of Instant Retail Payments ." AEA RCT Registry. June 01. https://doi.org/10.1257/rct.15380-2.0
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Experimental Details

Interventions

Intervention(s)
Variation in the fee that users pay for instant payment switch, InstaPay.
Intervention (Hidden)
Users on both of RCBC's online banking apps (Diskartech, targeted at lower-income users, and Pulz, targeted at middle and upper income consumers) will be randomized into receiving lower lower transaction fees than the default (8 Philippine pesos (PHP) for Diskartech users; 25 PHP for Pulz users), each for a 3 month intervention period.

Diskartech users in the (equal-sized) 4 study treatment arms will be offered fees of 7, 4, 1, and 0 pesos.
Intervention Start Date
2025-04-30
Intervention End Date
2025-09-15

Primary Outcomes

Primary Outcomes (end points)
Use of InstaPay, measured by number and volume of daily, weekly and monthly transactions.

In line with a demand curve, we hypothesize that:
Hypothesis 1. Lower InstaPay fees will cause increased use of InstaPay in terms of number of transactions.
Hypothesis 2. Lower InstaPay fees will cause higher total volumes (i.e., the sum of transaction sizes over the time period) on InstaPay on longer time horizons (week-to-month), through overall increased use of InstaPay as a payment instrument.

We hypothesize that lower InstaPay prices will cause smaller individual transaction sizes, primarily through the mechanism of reducing bundling (bundling is the tendency for a user to bundle multiple transactions together, to avoid the cost of a higher transaction fee, an incentive which is reduced under lower fees).
Hypothesis 3. Lower InstaPay fees will cause (i) lower average transaction sizes, and (ii) lower transaction volumes, over shorter time horizons (day-to-week).
Primary Outcomes (explanation)
These outcomes will be measured directly from administrative InstaPay transaction data, on the number of transactions (over time periods including day, week month) and volume (Philippine peso value of individual transactions, and over time periods including day, week, month). The average transaction size over a time period will be measured as [sum of transaction amounts over time period]/[number of transactions over time period].

Secondary Outcomes

Secondary Outcomes (end points)
Our secondary outcomes will be taken from administrative and survey data. These include:
1. Use of other payment instruments (e.g., PesoNet, ATM, bank transfers, etc).
2. Use of other financial products and services.
3. Other subjective measures and beliefs about InstaPay and digital financial services, such as:
i. Understanding and perceptions of digital financial services.
ii. Satisfaction with digital financial services.

Hypothesis 4. The effect of lower InstaPay fees on increased InstaPay use (hypotheses 1-3) has an ambiguous effect on the use of other payments services, as InstaPay may crowd out other payment channels, but on the other hand it may induce greater use of digital payments overall (all channels).
Hypothesis 5. The effect of lower InstaPay prices on increased InstaPay use (hypotheses 1-3) will be to cause an increase in the use of other financial products and services.
Hypothesis 6. The effect of lower InstaPay prices and increased InstaPay use will cause increased subjective awareness of satisfaction with digital financial services.
Secondary Outcomes (explanation)
1. Use of other payment instruments (e.g., PesoNet, ATM, bank transfers, etc). Similarly to InstaPay, we will measure the number and volume of transactions (pesos).
These will be measured with administrative data for transactions with the partner bank, and from the endline survey for use of payments with other financial service providers, and cash.
2. Use of other financial products and services.
These will be measured with administrative data for binary measures of use of other financial products and services with the partner bank, and from the endline survey for use of products and services with other financial service providers, including the informal sector.
3. Other subjective measures and beliefs about InstaPay and digital financial services, such as:
i. Understanding and perceptions of digital financial services.
ii. Satisfaction with digital financial services.
These will be derived from survey measures as provided in our survey instruments.

Experimental Design

Experimental Design
The study will randomly vary the price that users pay for instant payment switch.
Experimental Design Details
The study will individually-randomize the InstaPay fee to an equal number of users in each of the treatment arms. The treatment arms aim to be composed about 80% of existing InstaPay users, to measure intensive-margin effects of InstaPay fees, and about 20% of bank clients who do not actively use InstaPay, to measure extensive-margin effects of InstaPay fees. The users in each treatment arm will be notified a couple days in advance of treatment, and then receive the same fee for the entire treatment period (3 months).

The study will also have two control groups: an unconsented (uncontacted) control, from which the research team will only receive anonymous data, and a consented control, which will give consent but not receive a price change. These treatment arms will allow the research team to identify demand and information effects from the recruitment phases of the study.
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Individual (bank client).
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
NA
Sample size: planned number of observations
Up to 7188 in Diskartech sample, including 5688 in intervention sample, 500 in consented control, and up to 1000 in pure control. In practice, the pure control sample was erroneously contaminated, so includes under 1000 subjects.
Sample size (or number of clusters) by treatment arms
1422 per treatment arm (4 pricing treatment arms), in Diskartech sample.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
TBD
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Innovations for Poverty Action Institutional Review Board
IRB Approval Date
2025-04-30
IRB Approval Number
16345
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials