Sourcing frictions in traditional retail: Evidence from Lima, Peru

Last registered on July 17, 2025

Pre-Trial

Trial Information

General Information

Title
Sourcing frictions in traditional retail: Evidence from Lima, Peru
RCT ID
AEARCTR-0016388
Initial registration date
July 14, 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
July 17, 2025, 8:02 AM EDT

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

Locations

Region

Primary Investigator

Affiliation
Harvard University

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2024-07-01
End date
2026-05-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Supply chains for everyday consumption goods are organized in strikingly similar ways across developing regions: many small retailers (corner stores) source from a few large suppliers, each specializing in a narrow product category. In this work, I ask whether corner stores in developing countries face constraints due to limited sourcing options, and whether increasing supply chain competition can benefit small businesses and downstream consumers.

I test this question through a cluster-randomized trial in Northern Lima, where marketing teams promote a digital sourcing app to corner stores in randomly selected neighborhoods. The app allows users to order a wide range of competitively priced goods with 24-hour delivery. The goal is to assess whether the tool improves inventory management and supports business growth.

I will examine whether the digital tool affects sourcing patterns (e.g., frequency and size of orders, in-person visits to markets), wholesale prices paid, and the mix of products sold. Downstream outcomes include the store’s scale and profitability, as well as retail prices. Outcomes will be measured through the firm’s administrative data and corner-store surveys in treatment and control areas, with a focus on capturing sourcing decisions up to the order and product level.
External Link(s)

Registration Citation

Citation
Santa Maria, Diego. 2025. "Sourcing frictions in traditional retail: Evidence from Lima, Peru." AEA RCT Registry. July 17. https://doi.org/10.1257/rct.16388-1.0
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Experimental Details

Interventions

Intervention(s)
A large retail firm has recently launched a service for small retailers like corner stores, where they can order competitively priced goods with 24-hour delivery through a mobile app. The available product mix is broad, comprising virtually all categories and brands that small retailers typically sell: cooking staples, snacks, drinks, dairy, and cleaning and personal care products. To promote adoption, the firm has deployed a team of sales agents that visit corner stores in person, teach their owners how to use the tool, solve issues, inform stores about products and promotions, etc. Sales agents are assigned one neighborhood per day and cover all areas weekly.

Formally, the intervention consists of weekly visits by the firm’s sales agents to encourage use of the sourcing tool. Store owners are free to decide whether they download or use the app. Users can browse the app and order goods at any time.
Intervention Start Date
2024-07-01
Intervention End Date
2025-09-30

Primary Outcomes

Primary Outcomes (end points)
- Sourcing patterns: number and frequency of wholesale purchases, mean expense per purchase, total expenses (overall and by supplier and product category)
- Product mix: categories sold, number of different categories and brands in stock
- Wholesale prices paid (overall and by supplier and product category)
- Financial outcomes: total costs, sales, and profits
- Retail prices (for key products in each category)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Mechanisms:
- Time use: hours worked, hours dedicated to sourcing, hours commuting to markets
- Other measures of business scale: number of employees, daily client count, etc.
- Inventory management: percentage of physical space used, stockouts, and waste (e.g., spoiled items)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The empirical design leverages the staggered rollout of the sourcing tool across Northern Lima, a low-income sector of the city home to about 25,000 corner stores and one-fourth of the city’s population.

We divided the area into 119 similarly sized neighborhoods with about 200 corner stores, which constitute the study strata. Each neighborhood was then divided into three smaller clusters, excluding the areas around markets, which were left out of the study (businesses in and around markets are very different and the implementing firm preferred not to include them in the randomization). Two clusters per neighborhood were randomly assigned to the treatment group to receive weekly visits by sales agents. Stores in the remaining clusters form the control group and do not receive scheduled visits until after data collection (but they can download the app and make orders independently, for example if they hear about the service from friends). Clusters were drawn to ensure that treatment areas are contiguous within each stratum regardless of the randomization result, to reduce compliance issues.

Data collection:

Outcomes will be measured through a combination of the firm's administrative data (on client transactions) and a representative survey of corner stores in the study area. The survey began in March 2025, eight months after the initial rollout, and is scheduled to end in August. Neighborhoods are randomly assigned to monthly surveying rounds.

The survey covers a random sample of 10 corner stores per stratum (3 per treatment cluster and 4 in each control cluster). Sampling was based on a 2019 corner store census provided by the partner firm. Locations were randomly drawn from among the stores in the census, and surveyors were instructed to visit each point and locate the nearest open store. Replacements—such as in cases of refusal to participate—were allowed within three blocks of the original location.

Before enrollment, stores were screened based on the number of storage shelves: surveyors had to verify that each store had at least two before beginning the survey. The reason is that some corner stores are very small and open only occasionally, and thus are not covered in the firm's marketing strategy. Still, this criterion excluded fewer than one-third of the stores in the census.

The survey consists of two visits, two weeks apart. In the first visit, the team collects information on the store owner demographics, the store's history, its operations (employees, schedule, etc.), its financial performance over the last week (sales, profits, etc.), and retail prices for the most popular product in each category. Surveyors also document recent wholesale purchases by photographing all receipts provided by suppliers during the week prior to the survey. In the second visit, store owners are asked to provide updates on their financial performance and supplier-provided receipts for the two weeks between the visits. A separate team then encodes the data from the pictures, including supplier identity, order dates and total amounts, and product-level quantities and prices. The result is a granular dataset of all purchases made by each store over a three-week window.

Stores receive a small enrollment incentive (USD 1.50) and a USD 5 gift upon completing both survey visits. Additionally, stores are paid a small incentive for each receipt they show. This incentive was randomized between USD 0.15 and USD 0.30, as a test of whether stores withhold some receipts from surveyors.
Experimental Design Details
Not available
Randomization Method
Assignment of clusters to treatment/control: computer algorithm.
Order of surveys (neighborhood level): computer algorithm.
Survey incentive for receipts: within surveyCTO.
Randomization Unit
Clusters are spatially contiguous and internally compact units drawn by the researcher, each containing a similar number of corner stores.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
357 clusters (three per neighborhood/stratum).
Sample size: planned number of observations
1190 (10 per neighborhood/stratum, 3 per treatment cluster and 4 per control cluster).
Sample size (or number of clusters) by treatment arms
119 control clusters (119 * 4 = 476 control stores surveyed), 238 treatment clusters (238 * 3 = 714 treatment stores surveyed)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Harvard University Institutional Review Board
IRB Approval Date
2024-04-03
IRB Approval Number
IRB23-1307