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Evaluating the Impact of a Business Accelerator for Small-scale Entrepreneurs in Kenya and Uganda
Last registered on October 13, 2020

Pre-Trial

Trial Information
General Information
Title
Evaluating the Impact of a Business Accelerator for Small-scale Entrepreneurs in Kenya and Uganda
RCT ID
AEARCTR-0006577
Initial registration date
October 09, 2020
Last updated
October 13, 2020 9:20 AM EDT
Location(s)

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Primary Investigator
Affiliation
Stanford University
Other Primary Investigator(s)
PI Affiliation
PI Affiliation
PI Affiliation
Additional Trial Information
Status
On going
Start date
2017-06-01
End date
2022-06-30
Secondary IDs
Abstract
Small businesses are important players in the enterprise ecosystems of emerging markets. Given this situation, researchers and policy makers have been examining how to lift certain constraints in the hopes of enabling small businesses to enhance performance and stimulate economic growth. In particular, the focus has been on the design of managerial capital interventions (e.g., training, mentoring, consulting) aimed at building entrepreneurs’ capabilities and confidence.
To understand the mechanisms and main effects of interventions that intend to improve the performance of small businesses, we propose to measure the impact of an innovative business accelerator that combines training and mentorship on ‘lean startup’ approaches with growth-focused financing for small-scale entrepreneurs in Kenya and Uganda.

Keywords: Marketing, Entrepreneurship, Lean Startup, Product Experimentation, Customer Interaction, Firm Growth, Emerging Markets, Randomized Controlled Field Experiment
External Link(s)
Registration Citation
Citation
Anderson, Stephen J. et al. 2020. "Evaluating the Impact of a Business Accelerator for Small-scale Entrepreneurs in Kenya and Uganda." AEA RCT Registry. October 13. https://doi.org/10.1257/rct.6577-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
One key element of this business accelerator intervention is to help entrepreneurs to identify a profitable business model with less risk and more speed, thereby raising the chances of developing and scaling up a minimum viable product (MVP). For this purpose, the intervention focuses entrepreneurs on implementing ‘lean startup’ tools that emphasize product experimentation, customer interaction, field-testing and refinement to uncover customer needs/problems and obtain product-market fit as quickly as possible. The intervention also includes a financing component in which entrepreneurs leverage market/demand data – from their own experimentation and interaction activities – to ‘pitch’ to an investment panel for funding to grow the sales of their MVP.
Intervention Start Date
2017-09-15
Intervention End Date
2018-06-01
Primary Outcomes
Primary Outcomes (end points)
Main effects will be measured on firm sales, firm profits and an overall performance index.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
In addition, we will also try to identify the mechanisms or processes through which the intervention impacts firm sales and profits. In particular, we will focus on product experimentation measures (i.e., quantity, quality, and related outcomes) and also on customer interactions measures (i.e., frequency, intensity, and related outcomes).
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our sample consists of 1,024 entrepreneurs. We used the sampsi command in Stata for our a priori power calculations. The baseline data was collected and verified for this set of firms (N = 1,024), and the participants were randomly assigned into two experimental groups:

1. Group 1 (Treatment): 512 entrepreneurs will be offered the business accelerator intervention, consisting of training and mentorship on ‘lean startup’ approaches, as well as the opportunity to pitch for growth-focused financing at the end of the program.

2. Group 2 (Control): 512 entrepreneurs will form a comparison group who do not receive any intervention during the study period.
Experimental Design Details
Not available
Randomization Method
Randomization done on a computer, using statistical programming software (Stata; randtreat command).
Randomization Unit
The randomization unit is an individual firm/entrepreneur (N = 1,024 units).
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
14 batches
Sample size: planned number of observations
1,024 firms/entrepreneurs
Sample size (or number of clusters) by treatment arms
1. N = 512 in Treatment
2. N = 512 in Control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number