Knowledge Hoarding

Last registered on April 26, 2024
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Pre-Trial

Trial Information

General Information

Title
Knowledge Hoarding
RCT ID
AEARCTR-0013436
Initial registration date
April 22, 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
April 26, 2024, 12:03 PM EDT

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

Locations

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Primary Investigator

Name
Nicholas Swanson
Affiliation
University of California, Berkeley
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Other Primary Investigator(s)

PI Name
Luisa Cefala
PI Affiliation
UC Berkeley
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PI Name
Pedro Naso
PI Affiliation
Swedish University of Agricultural Sciences
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PI Name
Franck Irakoze
PI Affiliation
University of Burundi
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Additional Trial Information

Status
On going
Start date
2023-12-01
End date
2024-12-31
Keywords
Agriculture, Labor
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The diffusion of new technologies is a crucial driver for growth in low-income countries, especially in the agricultural sector. Motivated by theory, researchers and policymakers have relied on networks as a source of rapid knowledge diffusion, yet often with modest empirical results, suggesting that information transmission is not frictionless. We propose a mechanism that might prevent information diffusion: “knowledge hoarding.” In settings where early technology adopters (incumbents) derive rents from it, they might strategically halt its diffusion to preserve the rent their knowledge grants them. This project asks whether knowledge hoarding limit aggregate productivity gains because of distributional consequences generated by knowledge diffusion. We study this question in the context of modern agricultural technologies in Burundi, where agents already trained in the new technology (incumbents) have a private incentive not to share knowledge in the labor market: they face higher labor demand and a wage premium from mastering the planting technologies. We design two experiments to quantify the effect of knowledge hoarding on knowledge diffusion, and to show the distributional and aggregate consequences of knowledge hoarding on labor market equilibria, technology adoption, and productivity.
External Link(s)

Registration Citation

Citation
Cefala, Luisa et al. 2024. "Knowledge Hoarding." AEA RCT Registry. April 26. https://doi.org/10.1257/rct.13436-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
In this project, we conduct two different sets of experiments that identify different incentives that workers have to “hoard” knowledge from others. In this pre-registration we describe one set of experiments, the second set of experiments is described in a separate document, which will be published before those experiments are run.

In this experiment, we measure the effects of hoarding behavior on labor market outcomes. To do this we identify workers in each village who are skilled in row-planting/microdosage, and workers who are unskilled in these techniques. Villages are then assigned to one of three treatment arms:

1) Pure Control – Nothing occurs
2) Low Saturation – skilled workers are invited to a training event. At this training event, they are asked to spend the entire day with unskilled workers. At this event, they are told that they can use the time together to do different activies, one of which is teaching the other workers row-planting and fertilizer microdosage. Unskilled workers in this treatment are from the same village (labor market).
3) High Saturation – everything is the same as the low saturation condition except that unskilled workers in this treatment live in different villages (labor markets).
4) Placebo – same as 2) and 3), but with a placebo task (non-competitive)
Intervention Start Date
2023-12-15
Intervention End Date
2024-02-15

Primary Outcomes

Primary Outcomes (end points)
Our primary outcomes are:

1) Employment (days worked) doing row-planting and fertilizer microdosage
2) Total employment during the agricultural season (days worked) and unemployment
3) Average wage
4) Total Earnings
5) Usage of row-planting and fertilizer microdosage on own-farm fields
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes include:
- Number of fields that the worker is hired to do row planting on.
- Other amenities offered by employers in contracts
- Labor market turnover
- Attitudes and beliefs towards those who do not know how to do row-planting and microdosage
- Prestige associated with being able to do row planting.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We sample in all villages a set of laborers who have skills in row-planting/fertilizer microdosage, as well as a set of unskilled laborers. In pure control villages, no activities occur. In treatment villages, skilled workers are invited to a training event. At this training event, they are asked to spend the entire day with unskilled workers. At this event, they are told that they can use the time together to do different activities, one of which is teaching the other workers row-planting and fertilizer microdosage. In low saturation villages, unskilled workers in this treatment are from the same village (labor market). Whereas in high saturation villages, unskilled workers in this treatment are from the same village (labor market).

We then measure outcomes (labor market, technology adoption) over the course of the following agricultural season. We measure outcomes both for laborers invited to the training event, as well as spillover laborers, which comprise a random sample of laborers uninvited to the training event (both skilled and unskilled).
Experimental Design Details
Not available
Randomization Method
Office by computer
Randomization Unit
Village
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Approximately 140 villages
Sample size: planned number of observations
At most 60 individuals will be surveyed per village, meaning a total sample size of at most 8,400 individuals
Sample size (or number of clusters) by treatment arms
40-43 pure control, low saturation and high saturation villages. We sample 10 villages for the placebo arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
The Committee for Protection of Human Subjects - UC Berkeley
IRB Approval Date
2022-03-14
IRB Approval Number
2021-11-14788
IRB Name
The Committee for Protection of Human Subjects - UC Berkeley
IRB Approval Date
2024-03-25
IRB Approval Number
2024-01-17047
Analysis Plan