Thin Business Networks and Supply-Chain Frictions: The Impact of a Mobile Phone Networking Application on Small Businesses in Tanzania
Last registered on December 15, 2019

Pre-Trial

Trial Information
General Information
Title
Thin Business Networks and Supply-Chain Frictions: The Impact of a Mobile Phone Networking Application on Small Businesses in Tanzania
RCT ID
AEARCTR-0004430
Initial registration date
September 13, 2019
Last updated
December 15, 2019 8:49 PM EST
Location(s)
Primary Investigator
Affiliation
University of California Davis
Other Primary Investigator(s)
PI Affiliation
Cornell University
Additional Trial Information
Status
On going
Start date
2019-06-10
End date
2020-02-28
Secondary IDs
Abstract
Substantial information frictions along supply chains of small firms can generate bottlenecks which slow the dissemination of competitive terms of trade and other market information. This research investigates whether lowering search costs decreases information frictions and improves small firms' ability to source and sell goods in their local area. It leverages the scale-up of a digital phonebook application that effectively lowers the cost of accessing new business and customer networks. Participating small firms are split into a control and treatment group with two variations - 1) a business listing targeting input markets in urban areas, and 2) a business listing targeting customers in output markets in rural areas. The design allows for comparing the extent to which input or output business networks constrain business performance in rural markets and whether lowering the cost of initial contact improves firm productivity.
External Link(s)
Registration Citation
Citation
Rudder, Jessica and Brian Dillon. 2019. "Thin Business Networks and Supply-Chain Frictions: The Impact of a Mobile Phone Networking Application on Small Businesses in Tanzania." AEA RCT Registry. December 15. https://doi.org/10.1257/rct.4430-2.1.
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Experimental Details
Interventions
Intervention(s)
The program targets 3 types of participants linked through urban-to-rural supply chains: upstream urban suppliers, rural firms, and downstream rural consumers. The intervention focuses on the middle link of the supply chain: rural firms. These rural firms located in small to medium sized commercial centers are split into a control group and a treatment group with two variations - 1) a business listing in a digital phone book that targets upstream firms, and 2) a business listing in a digital phone book that targets downstream consumers. Random assignment generates variation in the likelihood that rural firms communicate with upstream or downstream contacts.
Intervention Start Date
2019-09-18
Intervention End Date
2020-01-29
Primary Outcomes
Primary Outcomes (end points)
Number of supplier contacts,
Number of customer contacts,
Firms conducts business outside their local market
Primary Outcomes (explanation)
The primary outcome variables concentrate on outcomes related to firm contact with suppliers and customers. During follow-up phone surveys, we will ask whether new suppliers and customers called or came to the storefront. Although treatments are designed to increase either upstream contacts or downstream contacts, these outcomes will be measured for all firms to allow comparison between treatment arms.

The other primary outcome variable - conducting business outside their local market - is designed to pick up changes in the geographic area over which the firm conducts its business.
Secondary Outcomes
Secondary Outcomes (end points)
Changes in input and output prices of select goods,
Sourcing costs efficiency,
Responses to stock-outs,
Marketing Activities,
Business performance index
Secondary Outcomes (explanation)
Sourcing Efficiency: Firms’ upstream inputs sourcing activity will be used to construct a variable that measures the extent to which treated firms improve productivity in their input market.

Stock-outs measures whether customers request relevant goods that the firm does not have in stock. If stock-outs decrease as a result of treatment, it provides evidence that increasing network opportunities could improve availability of goods in local market areas.

The business performance index will be a composite measure of sales, number of customers, number of workers, and hours worked.

Input and output prices: Respondents will be asked to select three primary goods and asked to provide the price they paid for that good. Those items will then be stored and asked again during follow-up surveys in order to learn whether the firm sources or sells goods at different prices.
Experimental Design
Experimental Design
First, a sample of communities was drawn by selecting 20 medium-sized villages located between three large city hubs: Dodoma City, Singida City, and Manyoni town. Communities with a population above 3,000 people (per the 2012 Tanzanian census) were eligible. Qualifying communities were selected after stratifying on population and distance to the nearest city. After selecting communities, the research team traveled to each location and invited all businesses to enroll in the digital phone directory and conducted the baseline survey. Firms that agreed to participate became the baseline sample and were randomly divided in the control and two treatments at the individual level, after stratifying on firm sector, village, gender of the firm owner, and a measure of how much the firm preferred to have their business listing shown within their local market versus the city market (see PAP for a full explanation).
Experimental Design Details
Randomization Method
Randomization done by computer after collecting baseline outcomes.
Randomization Unit
Individual (firm) level
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
20 villages
Sample size: planned number of observations
500 firms
Sample size (or number of clusters) by treatment arms
Upstream treatment: 166
Downstream treatment: 166
Control: 168
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See pre-analysis plan
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of California Davis
IRB Approval Date
2019-04-10
IRB Approval Number
1381056-1
Analysis Plan
Analysis Plan Documents
eKichabi Pre-Analysis Plan

MD5: ddd73dd8f532e6112e701622c846231a

SHA1: fc7b4b1ac876c6b7ec4a6b5bfffa6d96e20c08d9

Uploaded At: December 15, 2019

Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers