Relational Sourcing and Tree Rejuvenation Schemes in the Rwanda Coffee Sector

Last registered on August 10, 2023

Pre-Trial

Trial Information

General Information

Title
Relational Sourcing and Tree Rejuvenation Schemes in the Rwanda Coffee Sector
RCT ID
AEARCTR-0011838
Initial registration date
August 09, 2023

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
August 10, 2023, 1:39 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

Affiliation
Bocconi University

Other Primary Investigator(s)

PI Affiliation
London School of Economics
PI Affiliation
London School of Economics

Additional Trial Information

Status
In development
Start date
2023-07-10
End date
2024-07-31
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
The beneficial impact of tree rejuvenation on yields is well established in the agronomic literature. Practitioners seem to recognize that the real challenge – particularly in the East African context – is to increase the adoption of rejuvenation practices, as those often entail significant upfront costs and foregone incomes, for rewards that kick in only in the future. This is a problem faced in many other contexts in which poor farmers are supposed to undertake costly investment to increase adaptation and resilience to climate change. In partnership with a leading coffee exporter, we design, implement, and evaluate the impact of incentive schemes and a digital information intervention on tree stumping behaviour by farmers in the Rwanda coffee chain. We embed financial incentive schemes within pre-existing sourcing relationships between our partner and coffee farmers in Rwanda.
External Link(s)

Registration Citation

Citation
Macchiavello, Rocco , Ameek Singh and Iris Steenkamp. 2023. "Relational Sourcing and Tree Rejuvenation Schemes in the Rwanda Coffee Sector." AEA RCT Registry. August 10. https://doi.org/10.1257/rct.11838-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
In partnership with a leading coffee exporter, we design, implement and evaluate the impact of incentive schemes on tree stumping behaviour by farmers in the Rwanda coffee chain. In both schemes, farmers receive financial incentives for stumping based on the number of trees they stump. Conditions and timing of payment vary across the different schemes.

Across three related experiments, we first examine the take-up of schemes and the choice of scheme by farmers. Second, we evaluate the effectiveness of different schemes on the adoption of tree-stumping practices. Third, we evaluate the role of a relationship-building program on the choice of scheme and the effectiveness of schemes. Additionally, we cross-randomize a digital intervention and evaluate its effectiveness in improving take-up of schemes and adoption of tree rejuvenation.

In this (pilot) project, we roll out a tree stumping intervention in seven coffee washing stations (CWS) in Rwanda. The intervention builds on an earlier pilot (AEARCTR-0008725) in which we set up a farmer development program (FDP) aimed to build trusting relationships between coffee farmers and the company.
Intervention Start Date
2023-07-24
Intervention End Date
2023-09-30

Primary Outcomes

Primary Outcomes (end points)
1. Take-up of scheme
2. Choice of scheme
3. Adoption of tree stumping & number of trees stumped
Primary Outcomes (explanation)
Take-up of scheme, is a binary variable that indicates whether the farmer is willing to take-up the scheme offered and commit to stumping part of their eligible trees, or whether the farmer does not accept the scheme.

Choice of contract, refers to the type of contract that farmers will choice when offered a choice; upfront menu or the relational menu. We will measure this categorical outcome variable by recording the farmer's final choice in Survey CTO.

The third primary outcome refers to the number of trees stumped in the given period (i.e., June - September 2023). We will measure this variable through a) self-reported survey data and b) by sending an agronomist to farmer's plots to verify the actual number of trees stumped. We will create two variables, a binary indicating whether the farmers adopted stumping and a count variable indicating the number of trees stumped.

Secondary Outcomes

Secondary Outcomes (end points)
1. Coffee cherry delivery (KG)
2. Trust in CWS and Partner
3. Take-up of Lime & Fertilizer
Secondary Outcomes (explanation)
Coffee cherry delivery - This is a continuous variable indicating the amount of coffee
delivered by farmers to CWSs during the 2024 harvest season between February and
August. It is based on the administrative data on coffee delivery. To avoid results sensitivity
to extreme values or data entry errors, the coffee delivery amount will be winsorized at the
99th percentile.

The source of this data is the administrative. The data is recorded automatically on the CWS data system. The data includes the amount of coffee delivered to CWS by individual farmers, the price paid to farmers, the number of transactions, whether a farmer received a transport premium. We do not expect to find a short-term impact of the tree rejuvenation schemes on the delivery volumes coffee. Coffee delivery volume is treated as a long-term outcome, given that implementation of stumping techniques will only result in increase produce volume over an average of 2-3 years.

Trust in CWS - These are indices that take a value between 0 and 10, where 0 reflects no
trust at all and 10 indicates full trust. These indices involve trust in CWS RM, CWS promises,
CWS management, and CWS farmers.

Take-up of Lime and Fertilizer – a separate binary variable for lime and fertilizer indicating whether the farmer came to the CWS to pick-up freely distributed inputs in September 2023 essential to tree stumping.

Experimental Design

Experimental Design
This study partly builds on an earlier pilot in which we designed and evaluated a farmer development program (FDP) aimed at building relationships between farmers and the company.

Across three related experiments, our empirical strategy focuses on the following elements: i) take-up of tree rejuvenation schemes, ii) the (differential) impact of tree rejuvenation incentive schemes on tree stumping, iii) the impact of FDP on the effectiveness of choice and take-up of scheme, and iv) the effect of a digital support intervention.

We stratify random assignment within each individual CWS and over phone ownership, gender, FDP status, traceability indicators, and distance to CWS and relative distance to partner and other competitors.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer using software STATA.
Randomization Unit
Randomization was individual Farmer-level for all treatments.

Experiment A - Farmers were stratified by the following:

“CWS X Intensity of FDP in Zone X Gender X FDP Tier X Certification Survey Done X Clarity-Credibility Calls Receipt X Relative Distance to Partner’s Coffee Collection Site X Relative Distance to Competitor’s CWS”

Experiment B - Farmers were stratified by the following:

“CWS X Intensity of FDP in Zone X Gender X Certification Survey Done X Clarity-Credibility Calls Receipt X Phone Ownership X Relative Distance to Partner’s Coffee Collection Site X Relative Distance to Competitor’s CWS”

Experiment C - Farmers were stratified by the following:

“CWS X Intensity of FDP in Village X Gender X FDP Eligibility (=0,1,2) X FDP Tier X Certification Survey Done X Clarity-Credibility Calls Receipt X Phone Ownership X Relative Distance to Partner’s Coffee Collection Site X Relative Distance to Competitor’s CWS”

Additionally, in experiment C, FDP Status was randomized at individual level in January 2023.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1
Sample size: planned number of observations
Experiment A: 1477 farmers Experiment B: 1482 farmers Experiment C: 3045 farmers
Sample size (or number of clusters) by treatment arms
Experiment A:

T1: 377
T2: 374
T3: 367
T4: 359

Experiment B:
C: 767
T1: 344
T3: 371

Experiment C: 3045 farmers
Choice only: 1522
Choice + Tx: 1523
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power is estimated for a two-way comparison for each experiment separately. We describe below the MDE for ITT impact on following main outcomes. Experiment A: 1. For Extensive margin take-up of tree stumping: MDE is 4.9%age points (baseline rate of 13.2%) 2. For average percent trees stumped: MDE is 2.9%age points (on a baseline rate of 6.5%) 3. For average number of trees stumped: MDE is 33.1 trees, (on a baseline rate of 53 trees) Experiment B: 1. For Extensive margin take-up of tree stumping: MDE is 3.9%age points (baseline rate of 4%) 2. For average percent trees stumped: MDE is 2.25%age points (on a baseline rate of 3.8%) 3. For average number of trees stumped: MDE is 30.2 trees, (on a baseline rate of 23.1 trees) Experiment C: 1. For Extensive margin take-up of tree stumping: MDE is 2.4%age points (baseline rate of 6.2%) 2. For average percent trees stumped: MDE is 1.1%age points (on a baseline rate of 2.1%) 3. For average number of trees stumped: MDE is 15.8 trees, (on a baseline rate of 16 trees)
IRB

Institutional Review Boards (IRBs)

IRB Name
London School of Economics IRB
IRB Approval Date
2023-03-29
IRB Approval Number
188838