Seeding Innovation Through Reality TV

Last registered on October 17, 2022


Trial Information

General Information

Seeding Innovation Through Reality TV
Initial registration date
October 10, 2022

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
October 17, 2022, 5:13 PM EDT

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


Primary Investigator

Northwestern University (Kellogg)

Other Primary Investigator(s)

PI Affiliation
Northwestern University
PI Affiliation
Harvard University

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
The adoption of new agricultural technologies and improved farming practices are key elements of structural transformation and drivers of increased productivity in agriculture (Bustos, Caprettini and Ponticelli, 2016). Yet agricultural productivity remains far below its potential in many Sub-Saharan African countries. Our project investigates whether an innovative approach to provide technical information, consisting of agri-edutainment (educational entertainment) programs broadcasted on national television, can help to meet the informational needs of farmers. Two key distinguishing features of TV programs are that (i) they can easily convey information also to populations with relatively low literacy levels, and (ii) they are easy to distribute at scale at low marginal cost compared to traditional extension services. TV may also be a particularly useful medium to mitigate gender gaps in access and effectiveness of traditional extension services, to the extent that TV viewership is less gender-imbalanced than access to other services. Prior market research in Kenya indicates that a “makeover style” reality television show focused on farming as business helped to boost farmer incomes at a cost of only $0.50 per viewer. We leverage the planned rollout of this television program to Uganda (in 2022/23) to measure the causal impact and cost-effectiveness of the show in diffusing recommended techniques, and to better understand the process of information transmission and learning in agriculture.
External Link(s)

Registration Citation

La Ferrara, Eliana, Ritwika Sen and Christopher Udry. 2022. "Seeding Innovation Through Reality TV." AEA RCT Registry. October 17.
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Experimental Details


We evaluate the national rollout of a “makeover style” reality television program in Uganda that focuses on farming as a business. The television program helps to guide small scale farmers on a range of agricultural topics in an engaging yet informative way. Examples of core topics include soil and water management, planting, and pest and disease management. Each week, the film crew visits a different farming family and encourages them to identify their key goals and challenges in crop and livestock production. The show emphasizes why there is a problem and how best to deal with it. Local experts are brought to the farm to provide customized support to the family and clearly demonstrate simple yet affordable ways in which the household can implement the recommended adjustments on their farm.

The intervention is designed to generate exogenous variation in viewership of the farming TV program among comparable groups of farmers. We encourage a (randomly selected) group of rural farmers to watch the farming show, and another (randomly selected) group to watch a competing show that is broadcast at the same time. We will also shed light on farmers’ learning and adoption processes by studying the role of information provided to urban and rural contacts of control group farmers.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes for this study will be measured under five key domains:

0. Take Up
• The extent (0/1) and frequency of viewership of the targeted farming television program

1. Improved Knowledge of Agricultural Practices and Technologies
We will use multiple choice questions (with unambiguously correct answers) to measure the knowledge of plot managers under this domain, including:
• Theoretical knowledge of agricultural practices and technologies
• Knowledge of specific practices and technologies recommended on the TV program

2. Changes in Production Practices and Agricultural Input Use.
We will measure the following choices made by plot managers under this domain:
• The adoption (0/1) of agricultural practices and technologies that are common in the study region
• The adoption (0/1) of agricultural practices and technologies specifically recommended on the TV program
• Expenses incurred (UGX) on agricultural inputs in the reference farming season
• The choice of crops grown, and farm animals reared

3. Farm Productivity, Revenues and Sales to Market.
• Production: Self-reported crop harvests in the reference agricultural season (volume)
• Revenues: Value of crops harvested in the season, inclusive of production costs
• Productivity: Crops harvested per unit of land area (volume and value)
• Sales: Value of crops sold in unprocessed form (self-reported); average weekly income from the sales of processed crops and animal products; and crop sales as percentage of total output (commercialization).

4. The Diffusion of Information and Financial Resources.
• Flows of information on financing for agricultural purposes from (and to) social networks in rural areas and towns/cities.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
1. Female Participation in Agricultural Decision-Making.
This will be measured using the identity of decision makers on each plot cultivated by the household. We also measure changes in the decision rights of these plot managers, e.g., to build structures on the plot, rent it out to someone else, plant tree crops, or use it as a loan security.

2. Changes in attitudes related to women’s participation in business.
• We will collect both explicit (self-reported) and implicit measures of attitudes toward women’s participation in business tasks. The implicit measures are based on implicit association tests that will be administered to respondents in our study sample.

3. Changes in income and business aspirations.
• Subjective expectations about revenues from farming in the next agricultural season
• Farming goals (e.g., crop choice, livestock and area cultivated) in 5 years’ time
• Professional or business goals in 5 years’ time
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our project covers 10 districts in the Central Region of Uganda reached by the Luganda language broadcast of the farming TV show. We identify a population of farming households with access to TV and randomly assign them to one of the following experimental arms: (1) Watchers; (2) “non-Watchers” and, (3) Information Only. We use a symmetric encouragement design to minimize the possibility that ‘non-Watchers’ are directly exposed to episodes of the farming TV program. That is, households in group (1) are encouraged to watch the farming TV program, while households in group (2) are encouraged to watch a competing show that is broadcast on national television at the same time. The households in group (3) receive weekly farming tips that convey the same information as the farming show but through a different medium: mobile phones (with no entertainment or visual demonstration of steps for farmers to emulate).
Experimental Design Details
The research methodology involves a Randomized Controlled Trial (RCT) at the household level with a symmetric encouragement design. The evaluation design is as follows:
1. Draw sample of size N=3400 from population of active farmers with access to TV. All should be in the Central region of Uganda covered by the Luganda language broadcast.
2. Identify for each sample farmer:
• Up to 4 contacts who are also farmers in the same village or area who are the most important people the farmer refers to when making decisions about farming. These could be persons who the farmer is most likely to talk to about farming or who he/she is most likely to turn to for financial help.
• Up to 4 urban contacts
3. Classify households into network groups based on their baseline survey responses in Step 2 (i.e., no contacts reported, only rural contacts, only urban contacts, both contact types reported)
4. Randomize farmers into 3 groups (stratifying by gender, network group and subcounty):
▪ Control: incentivized to watch competing show using prize money (Group C)
▪ Intensive treatment: incentivized to watch the farming show using prize money (Group T1)
▪ Reminder treatment: SMS reminders to watch the farming show (Group T2)
▪ Information treatment: provided the information content of the TV show through alternate ICT medium, i.e., SMS (Group T3)
5. Randomize the Control farmers into 3 groups (stratifying by gender, network group, and subcounty):
▪ Pure Control (Group CC)
▪ Urban link treated (Group CU): One urban link of the farmer is incentivized to watch the farming show using prize money. The link is chosen randomly from among urban contacts shared by the farmer.
▪ Rural link treated (Group CR): One rural link of the farmer is incentivized to watch the farming show using prize money. The link is chosen randomly from among rural contacts shared by the farmer.
The first randomization (4) is required to identify the direct effect of watching the farming TV program. The control group is encouraged not to watch (although of course they could). The intensive treatment group is designed to ensure that enough people in the sample watch to make it possible to see if there is an effect. The comparison in outcomes for farmers in group T1 vs those in group CC is the most powerful for detecting this basic impact. However, a potential concern is that farmers in group T1 may be watching the show differently than typical, because of the incentive. So group T2 is included because they are simply responding to a form of advertising.

The second randomization (5) is designed to detect spillover effects. Are farmers who are not themselves watching the TV program adopting some of the recommended practices? And is this more true for those for whom it is their urban or rural contacts who are watching? We plan to examine the impact of rural and urban contacts on control group farmers (groups CR and CU) both jointly and separately.
Randomization Method
Randomization done in office by a computer using the software Stata.
Randomization Unit
The unit of randomization is the household.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
3400 households
Sample size (or number of clusters) by treatment arms
Targets/Estimates only:
Watchers: 1000
Non-Watchers: 2000
Information Only: 400
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Mildmay Uganda Research Ethics Committee
IRB Approval Date
IRB Approval Number
IRB Name
Uganda National Council for Science and Technology (UNCST)
IRB Approval Date
IRB Approval Number
IRB Name
Innovations for Poverty Action Institutional Review Board
IRB Approval Date
IRB Approval Number
IPA IRB Protocol # 15889


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

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Program Files

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Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials