Building Sustainable Supply Chains: A Model of Youth Input Resellers in Kenya

Last registered on April 15, 2023


Trial Information

General Information

Building Sustainable Supply Chains: A Model of Youth Input Resellers in Kenya
Initial registration date
November 20, 2021

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
November 21, 2021, 10:31 PM EST

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

Last updated
April 15, 2023, 1:54 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.


Primary Investigator

Purdue University Department of Agricultural Economics

Other Primary Investigator(s)

PI Affiliation
Purdue University
PI Affiliation
Kenya Agricultural and Livestock Research Organization

Additional Trial Information

Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
In Sub-Saharan Africa, high levels of youth unemployment and weak agricultural input supply chains have been widely documented. However, relatively few studies have tested the effectiveness of employment programs for rural youth. At the same time, adoption rates of improved agricultural technologies among smallholder farmers remain low due to supply chain complications. Our study seeks to provide insight on both fronts. We will also contribute to the understanding of psychometric measures and better business practices on the entrepreneurial success of rural youth. We accomplish this by conducting a randomized control trial where rural youth in Eastern Kenya were trained in business concepts and linked with agricultural input suppliers to become resellers of agricultural inputs to smallholder farmers.
External Link(s)

Registration Citation

Ketiem, Patrick, Wyatt Pracht and Jacob Ricker-Gilbert. 2023. "Building Sustainable Supply Chains: A Model of Youth Input Resellers in Kenya." AEA RCT Registry. April 15.
Experimental Details


We made changes to this pre-analysis plan on the AEA Registry Website in Nov. 2022 before our final survey, but the changes were not saved on the website. We are updating this document ex-poste to be be transparent in the changes made.

The study will include a treatment group and equally sized control group. The intervention will randomly select youth groups in Machakos, Kitui and Makueni counties of eastern Kenya. The randomly selected youth in each group will be paired with a local agro-dealer to sell post-harvest inputs such as hermetic bags and low-cost grain moisture meters to smallholder farmers and rural grain traders. During the post-harvest period following training, youth will sell inputs on a commission basis. The agro-dealer will provide the youth with the inputs and receive a portion of the commission from the revenue generated from the youth’s sales. The value of the inputs the youth will receive at baseline will be approximately 2500 shillings (US $25) in value. Youth will also put up 500 shillings (US $5) of their own money in collateral for the agro-dealer. The youth will also receive a one-time allotment of 1000 shillings (US $10) to cover initial travel costs to allow them to transport and sell the inputs at various locations such as markets or directly to smallholders. Since these Eastern Kenyan counties have two growing seasons in one year, treatment youth will be followed and surveyed after each post-harvest selling period. The first post-harvest period is in February-March 2021 while the second is July-August 2022. After the second post-harvest period (endline), youth in the control group will receive the same training, be paired with a local agro-dealer, value of inputs, and travel stipend. They will also be required to put up their own money in collateral.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. Income from all sources (both from the intervention and additional sources)
2. Aspirations (for both income and assets)

Updated additional outcome:
3. Expenditures
Primary Outcomes (explanation)
1. Youth will be surveyed and asked about their income sources from both the intervention and other additional activities. Treatment youth will be asked about both sources while control youth will be asked only about other income generating activities. Income was calculated by asking about total revenue earned from and total costs incurred from specific activities. By subtracting total costs from total revenue, we will have the income generated from all income sources. Total income will simply be an aggregation of income generated from each activity. We also asked about the main times of the year of these receipts from each income generating activities.
2. Aspirations will be measured as suggested by Bernard and Taffesse (2014). We will ask about what are the maximum and minimum monetary values possible for both personal income and assets. Participants will then be asked about their current level and aspired levels for the value of both dimensions. We acknowledge that directly asking about their aspirations could bias our results since they are self-reported values. However, aspirations are subjective in nature and not directly observable. Bernard and Taffesse (2014) recommend asking about aspirations as previously described for framing purposes. First, asking respondents about what is possible in their communities and then reminding them of their current level and finally asking them about a realistic goal. This approach has been used and replicated by numerous studies in the literature (for examples see Bernard et al., 2014; Janzen et al., 2017; Larue et al., 2021)


Bernard, T., Dercon, S., Orkin, K., & Tafesse, A. S. (2014). The Future in Mind: Aspirations and Forward-Looking Behaviour in Ethiopia. CSAE Working Paper no. 2014-16, Centre for the Study of African Economies, University of Oxford.

Bernard, T., & Taffesse, A. S. (2014). Aspirations: An Approach to Measurement with Validation Using Ethiopian Data. Journal of African Economies, 23(2), 1–36.

Janzen, S. A., Magnan, N., Sharma, S., & Thompson, W. M. (2017). Aspirations failure and formation in rural Nepal. Journal of Economic Behavior & Organization, 139, 1–25.

LaRue, K., Daum, T., Mausch, K., & Harris, D. (2021). Who wants to farm? Answers depend on how you ask: A case study on youth aspirations in Kenya. The European Journal of Development Research, 1-25.

3. After pre-testing our baseline survey, we decided that in addition to income, prior monthly expenditures were a good measure of economic welfare in addition to income. Our expenditure variable is a total of multiple forms of expenditures.

Secondary Outcomes

Secondary Outcomes (end points)
1. Better Business Practices (we decided this was not relevant to our study given the fact that control youth were not asked these questions given that none of them were exposed to the treatment)
2. Value of sales
3. Treatment youth aspirations (for number of customers and value of sales) (we decided that this outcome was not relevant during the baseline survey and as such will not be used in a formal analysis)
4. Subjective feeling of income
Secondary Outcomes (explanation)
1. To measure better business practices, we adapt a list of 18 appropriate questions to measure better practices from the one used in McKenzie and Woodruff (2017). A respondent’s answer to each question is assigned a one or zero with one meaning they are currently implementing the associated business practice. This variable represents the aggregate number of business practices being employed with a maximum possible score of 18 and minimum of 0.
2. Value of sales is measured as the aggregation of the value of sales for each input sold. The value of sales for each input was calculated as the number of units multiplied by the price at which the associated input was sold.
3. Aspirations were measured for value of sales and number of customers for treatment youth only. The reason for this is that treatment youth had no current levels of either dimension so they will not be included in this part of the analysis. We employ the same approach outlined in the ‘primary outcomes explanation’ section.
4. We added this measure of subjective well-being towards income to our intermediate outcomes to examine if self-beliefs were consistent with aspirations post-treatment. Specifically, we asked youth if they felt that there income was higher in the year after treatment compared to the year pre-treatment.

McKenzie, D., & Woodruff, C. (2017). Business Practices in Small Firms in Developing Countries. Management Science, 63(9), 2967–2981.

Experimental Design

Experimental Design
A sample of 40 youth groups engaged in agricultural activities will be randomly selected to participate in the intervention as either a part of the treatment or control groups (20 treatment and 20 control). Youth groups will be identified with the help of county government youth
departments in Machakos, Makeuni, and Kitui counties. 10 individually selected youth will be randomly chose from within each group can take part in one of the two groups. This will give us a total sample size of 400 youth (200 treatment and 200 control). The youth groups were also chosen to provide a list of agro-dealers in their area. Agro-dealers will be chosen on the basis of who the youth groups feel the most comfortable working with. The goal of this approach was to build necessary trust between youth groups and agro-dealers. This will give us 40 agro-dealers.
Experimental Design Details
Randomization Method
The groups will be randomly assigned to either the treatment or control group using excel. The individual 10 youth within each group will be randomly chosen via open lottery.
Randomization Unit
Youth group (treatment or control), individual youth (group members chosen to participate in the intervention)
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
40 youth groups (level of randomization and clustering), along with each agro-dealer assigned to a group. We will stratify the sample at the sub-county level to make sure that an equal number of treatment and control groups are located in each sub-county.
Sample size: planned number of observations
10 youth per youth group, 400 youth for baseline and follow-up surveys
Sample size (or number of clusters) by treatment arms
1. Treatment: 20 youth groups (200 youth)
2. Control: 20 youth groups (200 youth)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We ran a small pilot that included 10 youth resellers and one agro-dealer during the post-harvest season of July to September 2021 that follows the short growing season. Using the revenue baseline mean of 13,400 Kenyan shillings from the pilot, we assumed an increase of 5 percent as a result of the training, which would be in line with the average increase in sales from business training interventions as discussed in McKenzie (2021). We based our standard deviation assumptions based off the standard deviation of 0.05 for sales in McKezie and Puerto (2021). They ran an RCT with 3,357 firms in 157 rural markets in Kenya where firms were randomly assigned to receive business training. Knowing that we most likely wouldn’t be able to achieve nearly as large of a sample size given financial resources and time constraints, we assume a standard deviation of 0.15 for revenue. This higher value is warranted given that a smaller sample size will have higher levels of variability in the main outcome variable. We concluded that we could detect a reasonable MDE between 800 to 1300 Kenyan shillings with a sample size in the range of 250 to 350 youth participants. This was determined assuming different scenarios with intra-cluster correlation coefficients of 0.01, 0.04, 0.07, and 0.1. These scenarios gave us a cohen’s d value of 6 to 10 percent. These calculations were also done assuming 0.05 significance level, 0.8 statistical power, and a cluster size of 10 youth (per youth group). To account for expected early attrition, we determined that our ideal sample size would be to oversample and recruit 400 youth to take part in the intervention. This attrition is important to note as rural youth can be a transient group that may relocate in search of economic opportunities (Bezu & Holden, 2014). This number was also comparable to the size of the treatment and control groups in other youth employment studies conducted in Kenya (see Alvares de Azevedo et al., 2013; Hicks et al., 2015; Honorati, 2015; Brudevold-Newman et al., 2017). References: Alvares de Azevedo, T., Davis, J., & Charles, M. (2013). Testing What Works in Youth Unemployment: Evaluating Kenya’s Ninaweza Program (Summarative Report No. Volume 1). Available at pactEval_V1.pdf (accessed July 12, 2021). Bezu, S., & Holden, S. T. (2014). Are Rural Youth in Ethiopia Abandoning Agriculture? World Development, 64, 259–272. Brudevold-Newman, A., Honorati, M., Jakiela, P, & Ozier, O. (2017). A Firm of One’s Own: Experimental Evidence on Credit Constraints and Occupational Choice. World Bank Policy Research Working Paper 7977. World Bank, Policy Research Department, Washington, D.C. Hicks, J. H., Kremer, M., Mbiti, I., & Miguel, E. (2015). Vocational Education in Kenya-A Randomized Evaluation. 3ie Grantee Final Report. New Delhi: International Initiative for Impact Evaluation (3ie). Honorati, M. (2015). The Impact of Private Sector Internship and Training on Urban Youth in Kenya. World Bank Policy Research Working Paper 7404. World Bank, Policy Research Department, Washington, D.C. McKenzie, D. (2021). Small Business Training to Improve Management Practices in Developing Countries: Re-assessing the Evidence for “Training Doesn’t Work”. Oxford Review of Economic Policy, 37(2), 276-301. McKenzie, D., & Pueto, S. (2021). Growing Markets through Business Training for Female Entrepreneurs: A Market-Level Randomized Experiment in Kenya. American Economic Journal: Applied Economics, 13(2): 297–332.

Institutional Review Boards (IRBs)

IRB Name
Purdue University
IRB Approval Date
IRB Approval Number


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