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Smallholder Farmers' Returns and Learning from Subsidized Inputs in Uganda
Last registered on May 20, 2019

Pre-Trial

Trial Information
General Information
Title
Smallholder Farmers' Returns and Learning from Subsidized Inputs in Uganda
RCT ID
AEARCTR-0001360
Initial registration date
July 28, 2016
Last updated
May 20, 2019 8:02 PM EDT
Location(s)

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Primary Investigator
Affiliation
University of California, Davis
Other Primary Investigator(s)
PI Affiliation
The World Bank
Additional Trial Information
Status
In development
Start date
2016-09-18
End date
2020-12-31
Secondary IDs
Abstract
Temporary farm input subsidies are one of the main policy tools for addressing low productivity in African agriculture, as they reduce the costs and risks that smallholder farmers face when experimenting with modern inputs. Without a clear exit strategy, however, subsidy programs may become fiscally unsustainable. Moreover, there is no rigorous evidence on whether program efficacy varies by subsidy level. In collaboration with the Government of Uganda, this impact evaluation aims to identify the impact of a temporary farm input subsidy on the use of, returns to, and learning about modern farm inputs by smallholder farmers. The evaluation will study these impacts both during and after the period of time when farmers receive subsidies. By studying these impacts of the pilot subsidy program, the impact evaluation will inform subsidy design for subsequent roll out of the subsidy program throughout Uganda.
External Link(s)
Registration Citation
Citation
Carter, Michael R and Maria Jones. 2019. "Smallholder Farmers' Returns and Learning from Subsidized Inputs in Uganda." AEA RCT Registry. May 20. https://doi.org/10.1257/rct.1360-3.0
Former Citation
Carter, Michael R and Maria Jones. 2019. "Smallholder Farmers' Returns and Learning from Subsidized Inputs in Uganda." AEA RCT Registry. May 20. https://www.socialscienceregistry.org/trials/1360/history/46921
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
The intervention under study is the Agriculture Cluster Development Project (ACDP), which will offer subsidized farm inputs to nearly 10% of Ugandan farm households by 2022 through an electronic voucher (e-Voucher) program. Farm inputs available through the e-voucher program will support 1 acre of production of one targeted crop in each of our four study districts; the targeted crops are beans in Ntungamo district, maize in Iganga district, rice in Amuru district, and coffee in Masaka district. The subsidy is designed such that it decreases in value each season and phases out completely after three seasons. We will study the impact and sustainability of this subsidy scheme using a randomized controlled trial design such that subsidy availability and level are randomly assigned by farmer organization.
Intervention Start Date
2019-01-01
Intervention End Date
2020-06-30
Primary Outcomes
Primary Outcomes (end points)
Productivity of targeted crop; Production of targeted crop; Food security; Sales of targeted crop; Net marketed surplus of targeted crop; Gross yield; Net yield; Farm income
Primary Outcomes (explanation)
Productivity of targeted crop: Kilograms per acre
Production of targeted crop: Kilograms
Food security: Household Hunger Scale
Sales of targeted crop: Kilograms
Net marketed surplus of targeted crop: Kilograms of sales less purchases
Gross yield: Value of production per acre in Ugandan shillings
Net yield: Gross yield minus monetary input costs per acre in Ugandan shillings
Farm income: Value of crop sales in Ugandan shillings
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
ACDP will be implemented in phases, creating a natural opportunity to randomize assignment of eligible farmers to receive subsidies in different implementation phases. Phase 1 of the e-Voucher program will be initiated in the first season of 2019 and will conclude in mid-2020. Phase 2 will start in 2020, and therefore farmers assigned to phase 2 can provide a credible control group during phase 1. The impact evaluation employs a cluster randomization design with farmers clustered by farmer organization and randomization stratified by sub-county, an administrative area of 5-10 contiguous parishes. To test the impact of the value of the initial subsidy, phase 1 farmers will additionally be assigned to a high or low initial subsidy level by farmer organization.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer to phase 1 or 2, and then public lotteries to assign Phase 1 farmer organizations to high or low initial subsidy streams.
Randomization Unit
Farmer organization
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
136 farmer organizations
Sample size: planned number of observations
2808 households
Sample size (or number of clusters) by treatment arms
720 households - directly treated with high initial subsidy
720 households - directly treated with low initial subsidy
720 households - control (comparison for directly treated)
216 households - indirectly treated with high initial subsidy
216 households - indirectly treated with low initial subsidy
216 households - control (comparison for indirectly treated)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We conduct power calculations using parameters estimated from the 2011/12 Uganda National Panel Survey (UNPS) data. The ACDP study sample will be a more homogeneous population than the entire Ugandan population, from which the UNPS sample is drawn, as it is restricted by: (1) region and crop, including only maize farmers in eastern Uganda, bean farmers in western Uganda, rice farmers in northern Uganda, and coffee farmers in central Uganda; (2) farmer characteristics, determined by the specific eligibility rules established by ACDP. To approximate the more homogeneous ACDP target population using the UNPS data, we estimate parameters using data from a restricted sample of households farming between 3 and 5 acres, a criterion for ACDP eligibility. Even the restricted UNPS sample is more heterogeneous than what the ACDP sample will be, and we are therefore confident that our power calculations are conservative. Our power calculations take maize yields as the outcome variable. While outcome variables other than yields are of interest, yields typically exhibit higher coefficients of variation than do outcome measures of input use and other farming practices to be studied in the evaluation. We are thus confident that designing a sample with adequate power for maize yields will give us sufficient power to look at other key indicators and crops as well. We report calculations for two cases in which we assume mean yields of 345 kg/ac, the mean maize yields for farmers in eastern Uganda cultivating 3-5 acres of land in the second season of the 2011/12. The remaining parameters for the two cases are: (1) Expected parameter case - The standard deviation of yields is assumed to be 300 kg/ac, which is based on the coefficient of variation for bean yields of farmers in western Uganda, and the parish-level intra-cluster correlation is assumed to be 12%, which is just below the village-level intra-cluster correlation for bean yields of farmers in western Uganda (14%); (2) Conservative parameter case - The standard deviation of yields is assumed to be 388 kg/ac, which is based on the coefficient of variation for maize yields of farmers in eastern Uganda, and the parish-level intra-cluster correlation is assumed to be 18%, which was the village-level intra-cluster correlation for maize yields of farmers in eastern Uganda. Based on our power analysis (summarized below), we propose to select our study sample from 5 sub-counties across the four study districts. In 2 sub-counties, we will randomly select 24 farmer groups (8 with high initial subsidy, 8 with low initial subsidy, and 8 with no subsidy). In 1 sub-county, we will randomly select 12 farmer groups (4 with high initial subsidy, 4 with low initial subsidy, and 4 with no subsidy). In 1 sub-county, we will randomly select 32 farmer groups (10 with high initial subsidy, 10 with low initial subsidy, and 12 with no subsidy). In the final sub-county, we will randomly select 27 farmer groups (9 with high initial subsidy, 8 with low initial subsidy, and 10 with no subsidy), yielding 136 clusters overall for our sample. We will then randomly select 20 households per farmer organization cluster from among ACDP-eligible households, yielding a total sample size of 2160 households for our main sample of directly treated farmers. Our minimum detectable effect calculations assume a net uptake rate of 75%. At the expected voucher uptake rate of 75%, the proposed design is powered to detect effects of less than half that magnitude (19%); even under our case of conservative assumptions, the study design could detect a minimum effect of 32%. As long as net uptake of the e-voucher is no lower than 50%, the proposed study design will have adequate power to detect program impacts if ACDP achieves its 50% yield growth goal. While this yield growth goal is high, it appears achievable in Uganda based on government estimates as well as estimates from experimental trials and on-farm trials of farm inputs in Uganda. We are thus confident that we have adequate power to pick up expected yield changes, as well as changes in the other, less variable, outcome indicators of interest. Moreover, we expect the actual power of the study to be higher than what is estimated here, because we will collect multiple (seasonal) rounds of follow-up data and will ensure high levels of data quality through carefully programmed electronic survey instruments.
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Uganda National Council for Science and Technology
IRB Approval Date
2018-04-20
IRB Approval Number
SS145ES
IRB Name
UC Davis IRB
IRB Approval Date
2016-11-30
IRB Approval Number
938856-1
Analysis Plan

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