Technology Adoption and Firm Employment: Evidence from Burundi

Last registered on March 15, 2024


Trial Information

General Information

Technology Adoption and Firm Employment: Evidence from Burundi
Initial registration date
March 04, 2024

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
March 15, 2024, 3:04 PM EDT

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


Primary Investigator

Swedish University of Agricultural Sciences

Other Primary Investigator(s)

PI Affiliation
University of Burundi and Université Clermont Auvergne

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Introducing more advanced technologies into firms holds the promise of elevating job quality and productivity, fostering higher wages and more efficient societal outputs. However, in developing countries, data on how technology adoption influences factors such as employment rates and wages remain sparse. To bridge this gap, our study focuses on the textile sector in Burundi, where most small firms rely solely on manual
sewing machines. Through a randomized controlled trial (RCT), we will equip selected firms with automatic sewing machines and offer operation training. Our analysis will track short-term (3 months) and long-term (9 months) effects on three key variables: employment, productivity, and real wages.
External Link(s)

Registration Citation

Armel Ndayikeza, Michel and Pedro Naso. 2024. "Technology Adoption and Firm Employment: Evidence from Burundi." AEA RCT Registry. March 15.
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Experimental Details


We will equip selected dress-making firms in Burundi with automatic sewing machines and offer operation training.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Firm employment, productivity and wages.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Characteristics of who is hired and who is laid-off. Changes in firm structure and investment.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We assume that technology affects labour via three channels: (i) replacement, (ii) reinstatement, and (iii) real income effect (Hötte and Theodorakopoulos (2022)). Replacement refers to technology-induced job losses. Reinstatement refers to technology-induced job creation. Real income effect refers to new technology raising real wages via an increased marginal product of labour. The historical account demonstrates that, at the aggregate level, channels (ii) and (iii) overcome channel (i). However, replacement can become a strong force at the micro level in the short run. We will run an RCT with textile firms in Burundi to measure the relative importance of each of these channels. We will have a sample of small dressmaking firms that use manual sewing machines to produce dresses and other relatively expensive clothes. These firms are usually composed of around 3 employees and are prevalent both in the main cities,
such as Bujumbura and Gitega, and in small towns in the countryside. We will randomly lend to a subset of these firms automatic sewing machines and power banks at a low cost.

Automatic sewing machines, usually preferred by professional tailors, are faster and more efficient than manual sewing machines. We will also provide training to tailors in treated firms on how to operate these machines.

We will run three main surveys with our sample of firms. The first survey will happen before the intervention and will collect baseline data on firms’ characteristics, such as number of employees, wages, sales and revenue, and information on firm history. The second survey
will happen 3 months after the intervention and will measure the short run effects of technological adoption. The final survey will happen after 9 months of the intervention and will collect the same variables of the second survey.
Experimental Design Details
Not available
Randomization Method
Stratified randomization (urban vs rural, large vs small firm) done in office by a computer.
Randomization Unit
Firm (atelier de couture).
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
540 dress-making firms.
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
270 firms treatment and 270 firms control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
150 treated firms

Institutional Review Boards (IRBs)

IRB Name
Ministry of Interior, Burundi
IRB Approval Date
IRB Approval Number