Relational Frictions Along the Supply Chain: Evidence from Social Commerce Among Senegalese Traders

Last registered on December 01, 2023


Trial Information

General Information

Relational Frictions Along the Supply Chain: Evidence from Social Commerce Among Senegalese Traders
Initial registration date
November 18, 2023

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
December 01, 2023, 4:52 AM EST

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



Primary Investigator


Other Primary Investigator(s)

PI Affiliation

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
With the rise of smartphone ownership, social media, and mobile money, there has been a surge in 'social commerce’. This growing phenomenon is especially pertinent for small and medium enterprises (SMEs) seeking foreign market access, historically hindered by search and trust frictions. We study on how these recent digital advancements might reduce frictions in searching and contracting along international supply chains.
External Link(s)

Registration Citation

Houeix, Deivy and Edward Wiles. 2023. "Relational Frictions Along the Supply Chain: Evidence from Social Commerce Among Senegalese Traders." AEA RCT Registry. December 01.
Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
Experimental Details


We will connect Senegalese importers to intermediaries in Turkey who interact with manufacturers, and we will use social media to vary the information environment to capture key frictions. Our experiment is grounded in theory, allowing us to precisely map the treatments to the different frictions identified.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)

For buyers, we will measure three sets of outcomes described below:
1. Supplier-related outcomes: We will administer a detailed survey where we ask for the price, quantity, quality, and contract structure of all input purchases for each of their suppliers. We are interested in how the treatments affect both propensity to transact with new suppliers and their relationship with existing suppliers. We will also measure beliefs about reliability and quality of suppliers, and ultimately the value of the relationships.
2. Buyer-related outcomes: Revenues and profits, and downstream contract with customers. We will refer to the literature on measurement (e.g., de Mel, McKenzie, Woodruff, 2007) and our previous surveys in this setting.
3. Quality and price outcomes: We will use mystery shoppers to measure these. We are interested in quality because we want to see if the experiment leads to quality upgrading. To measure quality, we will hire a few retail experts to assess a random sample.

We will survey suppliers separately and collect similar survey data on (1) and (2). The mystery shopping activity will also be used to accelerate initial orders, induce trade, beyond directly measuring some of the outcomes described above.
Primary Outcomes (explanation)
Endline surveys will be conducted with studied merchants and suppliers after about 2-6 months. In addition, we may conduct high-frequency phone surveys with merchants. Mystery shoppers will also allow us to collect data like prices, product delivery and fit, price, product quantity and quality, and contract between suppliers and merchants, but also between merchants and end customers.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our sample will comprise ~1500 Senegalese wholesalers and medium-sized retailers in Dakar within markets (“buyers”), and ~30 intermediaries that live in Turkey or visit regularly (“suppliers”). We choose Turkey as our origin country because it represents a large minority of apparel imports to Senegal and is generally viewed as higher quality than China, though we are open to switching origin country if we find a good reason. We will focus on apparel because (1) it is large, (2) concerns about quality mean contracting frictions are important, (3) variation in fashions and preferences mean search is important.
Design and Treatments: There will be four treatment arms:

1. Search: At the buyer level, we will randomise access to three supplier WhatsApp groups. These are currently existing private groups run by suppliers where they advertise products and prices using high quality pictures and videos. This reduces two types of search friction: (1) cost of finding a new foreign supplier, (2) cost of seeing varieties and quality offered.

2. Reputation building: The theory predicts that if buyers share information about suppliers’ types, they will trade more and learn faster. Thus, within merchants in supplier groups, we will randomise half of them into small buyer groups without suppliers to share information about suppliers and learn about types through "social feedback". We will design this so that buyers in groups are all matched with the same suppliers.

3. Moral Hazard: The theory predicts that buyers can use joint punishment strategies to incentivize the supplier more strongly than they could on their own (as in Greif, 1993). We will thus inform all suppliers about the existence of buyer groups. We will make clear to the suppliers that we will help buyers coordinate and share feedback to ensure that the seller realizes that a joint punishment equilibrium is realistic. We will inform a random part of the buyers that the suppliers have that motivation to measure the moral hazard impact on buyers.

These three treatments shut down/activate some existing features of WhatsApp, ubiquitously used by merchants for their business, to disentangle mechanisms of the impact of social commerce.

4. E-Commerce training: We will inform and train a random subset of merchants on a traditional e-commerce B2B platforms available in Senegal to test differences between traditional e-commerce and social commerce.
Experimental Design Details
Not available
Randomization Method
Randomization done by computer.
Randomization Unit
The unit of randomization is the merchant/firm.

We stratify the randomization by anticipated dimensions of heterogeneity, i.e.,
-- Experience in international import
-- Physical store vs. purely online
-- Clothing industry codes

The randomization will be done on a computer and established pre-randomized IDs dispatched to surveyors.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
The target sample is about 1,500 merchants in Senegal and 30 suppliers abroad.
Sample size (or number of clusters) by treatment arms
- Search / Supplier social groups (80%): 1200 merchants
- Reputation Building / Merchant social groups (40+10 %): 750 merchants, including 150 not in supplier groups
- Moral Hazard (40%): 600 merchants
- E-Commerce: (10%): 150 merchants
- Pure Control (5%): 75 merchants
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