Incentivizing quality in dairy value chains - experimental evidence from Uganda

Last registered on October 25, 2022

Pre-Trial

Trial Information

General Information

Title
Incentivizing quality in dairy value chains - experimental evidence from Uganda
RCT ID
AEARCTR-0010262
Initial registration date
October 18, 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 25, 2022, 10:41 AM 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

Affiliation
Ifpri

Other Primary Investigator(s)

PI Affiliation
CIMMYT
PI Affiliation
CIMMYT
PI Affiliation
IFPRI

Additional Trial Information

Status
In development
Start date
2022-11-11
End date
2024-04-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Quality of products transacted within value chains, and the preservation of quality throughout the chain, is central to value chain development. In Uganda, we find that there is a clear demand from dairy processors for better quality raw milk and substantial scope for quality improvement at the dairy farmer level, yet a market for quality does not develop, holding back further value chain transformation. In this study, we test two potential reasons why a market for quality does not develop through a field experiment with randomized interventions at different levels of the value chain. At the dairy farmer level, we conjecture that farmers are paying attention to the wrong quality attributes and design a video-based information campaign to point out what the quality parameters are that matter for processors. We also provide them with a small incentive to put what they learned into practice. Midstream, at milk collection centers where milk is bulked and chilled, we install technology that enables for quick and cheap testing of the milk that is brought in. We look at impact of both interventions at both farmer and milk collection center level and consider outcomes such as milk quality, prices received and quantities transacted.
External Link(s)

Registration Citation

Citation
Ariong, Richard et al. 2022. "Incentivizing quality in dairy value chains - experimental evidence from Uganda." AEA RCT Registry. October 25. https://doi.org/10.1257/rct.10262-1.0
Experimental Details

Interventions

Intervention(s)
Intervention at the milk collection center level: To make relevant quality parameters visible at the level of the milk collection centers, we focus on a technological intervention. In close collaboration with DDA, we install digital lactoscans at a random sample of milk collection centers. These can be used to test milk samples of individual farmers or traders that supply to the milk collection centers to establish quality of incoming milk, as well as to test samples from the milk tankers when milk is picked up by traders or processors.

To provide information to dairy farmers on the parameters and characteristics that processors are looking for and how farmers can produce milk that adheres to these standards, we use a short engaging video that demonstrates the inputs and practices that can be used to increase milk quality.
Intervention Start Date
2022-11-11
Intervention End Date
2023-03-31

Primary Outcomes

Primary Outcomes (end points)
The five primary outcomes at MCC level are:
1. average milk quality level of milk sold. This will be sampled from the milk tanks, and based on an index of different quality parameters (at least butter fat content and SNF).
2. average prices at which milk was bought from farmers (during last 7 days)
3. volumes collected in last 7 days
4. sold to top 5 processors (Pearl, Amos, Lakeside, GBK, Vital tomosi) (in last 7 days)
5. price at which milk was sold (in last 7 days)

Outcomes of interest at farmer level, measured in the last seven days:
1. Milk quality (butter fat content and SNF)
2. Production investment and management (based on index of five recommended practices to improve milk quality)
3. Volumes sold (liters during last week)
4. Sold to milk collection center during last week? (1=yes)
5. Price received for milk sold (inclusive of any quality premium that may have been obtained) (average during last week, UGX per liter)
Primary Outcomes (explanation)
Primary outcomes

We define five primary outcomes at each level. These five primary outcomes will be combined in a covariance weighted index to assess overall impact at that level following (Anderson, 2008). As dairy is a continuous activity, we need to define a time frame for measurement. We will use the last full week before the interview.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary outcomes at the milk collection center level include:
1. local sales - previous research found that milk collection centers are also important for local milk supply, often doubling as milk shops. Does the intervention crowd out the local market?
2. reason for selling to buyer (in particular if the buyer pays premium for quality, but also payment modalities)
3. Impact pathway: did MCC measure quality of aggregated milk before selling? In particular butter fat and SNF using a lactoscan? What equipment was used?
4. Who decided on the price? buyer made offer and MCC accepted, MCC made offer and buyer accepted, negotiation — use likert scale slider to get an idea of power balance.
5. Did the buyer pay a quality premium? What was it based on? What is the quality premium?
6. Does the MCC pay a quality premium to suppliers? What was it based on? What is the quality premium?
7. Does market for quality lead to additional investment in quality preservation - milk cans, etc
8. Does the development of a market for quality lead to more formalization (eg written contracts) between farmer and MCC? Between MCC and processor?
9. Changes in mid-stream service provision: Does the MCC provide services related to artificial insemination? Transport? Access to acaracides? Training on milk sanitation? Training on feeding practices?
10. Information on lactoscan use (for ITT-TOT analysis).

Secondary outcomes at the farmer level include:
1. Home consumption of dairy products (liters, in what form, and who consumes diary products) - test if the development of a market for quality milk crowds out animal sourced food intake within the family.
2. Reason for selling to buyer (in particular pays premium for quality, payment modalities,...)
3. Test if intervention leads to quality based market segmentation (with less rejection and more instances of lowering of price when farmer supplies substandard milk)
4. Does the buyer pay for higher quality milk.
5. Buyer checks for quality during last transaction (lactoscan, lactometer, alcohol test).
6. Number of dairy animals (improved/local) - does a market for quality lead to technology adoption for intensification? Is this stronger for the subgroup of farmers that receives the training video, where we explicitly mention that genetics also affect quality parameters?
7. Feed and pasture management - a more detailed analysis than the composite primary outcome 2 at the farmer level. This includes changes in grazing system (paddocking, free range, mixed or zero grazing) and use of different dairy feed types (hay, silage, improved forages, commercial feeds like (brewers) bran, salt and mineral blocks, multivitamin). We will differentiate between practices in the rainy season and the dry season.
8. Price of dairy animals (improved/local) - test if the development of a market for quality has an impact on the price of animals.
9. Gendered decision making outcomes - test if the development of a market for milk impacts who within the household makes the decisions to sell to a particular buyer.
10. Does the development of a market for quality lead to more formalization and less relational contracting?
11. Does the intervention also increases milk sanitation (use of milk cans)?
12. Does the intervention leads to changes in bargaining power? farmer made offer and MCC accepted, MCC made offer and farmer accepted, negotiation — use likert scale slider to get an idea of power balance.
13. Gendered labour outcomes (milking, marketing, feeding and herding or cleaning )
14. Does the intervention affect home processing? Does this have gendered effects?
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The field experiment consists of two cross-randomized interventions that are implemented at different levels. Outcomes may be measures at different levels. We randomly allocate quality testing equipment to a random subset of milk collection centers (MCCs), while another random subset of milk collection centers functions as the control group for this treatment. In the catchment area of each milk collection center, we then take a sample of dairy farmers, stratifying the sample on whether the farmer is an active supplier to the milk collection center or not. In this sample, we then randomly assign half of the farmers to the information treatment (blocking on whether the farmer is an active supplier to the milk collection center or not).
Experimental Design Details
Not available
Randomization Method
Randomization is done in office by a computer
Randomization Unit
for the MCC level treatment, we randomize at the MCC level (and so dairy farmers are exposed to this treatment in clusters); for the farmer level treatment, randomization is at the individual level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
125 Milk Collection Centers
Sample size: planned number of observations
2500 farmers (20 farmers in each catchment area of an MCC)
Sample size (or number of clusters) by treatment arms
63 MCCs control and 62 MCCs treated with lactoscan; 1250 farmers treated with information interventions and 1250 farmers as control
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We assume that the intervention at the level of the milk collection centers leads to an increase in the price of UGX30 per liter. This seems reasonable in light of the fact that processors told that they either pay a 10 percent premium for quality milk, or UGX100 per liter. However, as we assume a pretty narrow distribution of prices, even though this effect is only a 3 percent increase, this is considered a medium to large effect according to Cohen's D. At the level of the farmers, for the intervention at the MCC level, we expect an effect size of UGX40. While this represents a 4.4 percent increase, the larger variance at this level means that according to Cohen's D, this effect is considered small to medium. Finally, at the level of the farmers, the individual level randomization of the information treatment intervention allows us to estimate small effects. For our power simulation, we assumed and effect size of UGX25, which corresponds to a small effect according to Cohen's D. For the interaction, we assume a large effect (UGX50 per liter).
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Incentivizing quality in dairy value chains - experimental evidence from Uganda

MD5: 319554e870bf1ccbd7cf301d63117309

SHA1: 26ec91a23e96e4e4d27917d02e349972a46069f7

Uploaded At: October 18, 2022