Evaluating Bundles of Resources and Information on Productivity and Gender- Driven Differences in the Rwanda Supply-Chain

In partnership with a leading coffee exporter, we design, implement and evaluate the impact of cash and labor-based 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. In treatment one, farmers receive only financial incentives for stumping. In treatment arm two, farmers receive financial incentives for stumping and in-kind labor support for stumping. Both incentive packages are worth the same amount.

First, we examine the take-up of different incentive packages (i.e., schemes). Second, we evaluate the effectiveness of the different schemes on the adoption of tree-stumping practices. Third, we evaluate frictions in take-up of pre-paid labor. Additionally, we cross-randomize a digital intervention and evaluate its effectiveness in improving take-up of schemes and adoption of tree rejuvenation.

We roll out this tree stumping intervention in seven coffee washing stations (CWS) in Rwanda. The intervention builds on an earlier pilot (AEARCTR-0011838) in which conducted a pilot experiment to measure the impact of only financial incentives on take-up of tree stumping by farmers in Rwanda.

We expect the final paper to analyse the RCT, the large pilot RCT conducted in 2023 (AEARCTR-0011838) and interpret their results thorough the lens of a structural model of farm upgrading.

In the study, we compare the effectiveness of promoting tree stumping among Rwandan farmers via different incentive schemes. We differentiate between two types of schemes: a) only cash-based scheme and b) in-kind labor scheme. Both schemes are worth 150 RWF per stumped tree. However, in the cash-scheme, the farmers receive the entire amount, 150 RWF per tree, in the form of cash. Whereas, in the labor arm, farmers receive 75 RWF worth in cash and 75 RWF worth in in-kind labor support per tree. Additionally, across both incentives schemes, we cross-randomize a information intervention (Tx), to measure the effectiveness of digital support in take-up of tree stumping.

Intervention design:

** Control Group (CG **
- Comprises 20% of the sample.
- No incentives are provided.
- Farmers are invited to stumping training.
- Farmers are eligible to inputs for trees stumped

** Cash Only Group (T1) **
• Comprises 40% of the sample.
• Farmers receive:
- 100 RWF cash compensation per tree for cost of stumping to be paid in fal 2024 after verification by agronomist
- 50 RWF cash bonus per tree stumped conditional on deliverying 25% of estimated production to partner CWS in harvest season 2025
• Farmers receive payment for all trees stumped in 2024.

** Labor Only Group (T2) **
• Comprises 40% of the sample.
• Farmers receive:
- 25 RWF cash compensation per tree for cost of stumping to be paid in fal 2024 after verification by agronomist
- In-kind labor support from RWACOF workers worth 75 RWF per tree
• 50 RWF cash bonus per tree stumped conditional on deliverying 25% of estimated production to partner CWS in harvest season 2025
• Farmers receive payment for all trees stumped in 2024.

We randomize intensity of labor treatment across villages:
• High intensity villages: 60% of farmers are assigned to labor arm, 20% to cash and 20% to control group.
• Low intensity villages: 40% of farmers are assigned to labor arm, 40% to cash arm and 20% to control group.

** Cross-Randomized Digital Intervention (Tx):**
• This intervention is cross-randomized across both T1 and T2 treatment arms, comprising 50% of the farmers in each arm.
• After signing, farmers receive phone calls aimed at:
• Measuring their knowledge of the contract and required next steps.
• Providing support during the stumping process.
o Reminding them of contract terms and next steps.

Next to analysing the results of the RCT and pilot RCT, we expect to also analyse results through a dynamic discrete choice model.

Farmers (T1 and T2) in the research sample receive a standardized phone call (i.e., not revealing conditions) from the call centre centre inviting them to the CWS to learn more about a new tree rejuvenation scheme. Farmers assigned to the control condition (C) receive a placebo call in which they are also invited to the CWS to attend stumping training. Upon arrival at the CWS, farmers receive information about the specific menu assigned to them. Thereafter, farmers are asked whether they want to take-up the scheme and how many trees they plan to rejuvenate. Finally, farmers are asked to sign a contract specifying the contract details and are given a receipt of this contract.

After stumping trees, the farmers are asked to call the call centre to request verification of stumping. From mid-August, agronomists will visit these farmers to verify the number of trees stumped and decide on the final payment. This process is the same across conditions and experiments.

2024-08-11

2024-10-31

1. Take-up of scheme
2. Adoption of tree stumping & number of trees stumped
3. Take-up of labor offered

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.

The second 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.

Take-up of labor offered refers to farmer take-up of pre-paid labor offered in T2 (labor arm). We will measure this variable through logging requests made by farmers for labor through the call center.

1. Coffee cherry delivery (KG)
2. Trust in CWS and Partner
3. Take-up of inputs (e.g., Lime & Fertilizer)

Coffee cherry delivery - This is a continuous variable indicating the amount of coffee
delivered by farmers to CWSs during the 2025 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.

This study partly builds on an earlier pilot (AEARCTR-0011838) in which we designed and evaluated the impact of cash-based incentives on take-up of tree stumping by farmers in Rwanda. In this study, we examine the impact both cash-based and labor-based incentives on take=up of tree stumping. Additionally, we cross-randomize digital support intervention (Tx).

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 effect of a digital support intervention (Tx).

We stratify random assignment within CWS, gender, past stumping, farm size, SMS delivery status, village, and delivery traceability.

We launch this experiment in 7 CWS in Rwanda: Kayumbu, Musasa, Mushonyi, Ngororero, Nyamiyaga, Nyamyumba and Kazo. Research sample is restricted to farmers who provided a phone number for someone who is part of the household. Farmers who gave phone numbers of neighbors etc were not considered (roughly 10% of farmers).

In this experiment, we evaluate the effectiveness of cash and labor-based incentive schemes on take-up of tree stumping. Additionally, we cross-randomize digital support intervention (Tx). We plan to use to elicit two effects: i) the (differential) effect of incentive schemes on tree rejuvenation adoption, ii) the impact of Tx calls on tree rejuvenation adoption, and iii) identify potential frictions in take-up of labor. Last, we measure preferences and psychological traits to estimate a dynamic discrete choice model.

Last, we will present both the intention-to-treat (ITT) effects (i.e., impacts of being assigned to a scheme) and the treatment effects on the treated (TOT) (i.e., impacts of taking-up and compliance with a scheme).

Randomization done in office by a computer using software STATA.

Randomization was individual Farmer-level for all treatments.

Randomization is stratified by CWS x Gender x Stumping History x Farm size x Delivery Traceability x SMS delivery status x Village.

No

1

9,207 farmers

CG: 1849 farmers
T1: 2622 farmers
T2: 4736 farmers

Cross-randomized Tx : 3679 farmers

We provide the MDE for the ITT impact on the main outcomes. The MDE for the outcome of program take-up (= 0/1) when comparing T1 to T2 is 0.03. This assumes a take-up rate of 0.25 for T1. The MDE for the outcome of Tree Stumping take-up (= 0/1) is as following (assuming a control group mean of 0.2): • Comparing CG to T1: 0.035 • Comparing CG to T2: 0.031 • Comparing T1 to T2: 0.028 The Tx comparison is estimated in two ways. First, Tx intervention when given to T1 and T2 farmers is bundled together, and a homogenous treatment effect estimated. The MDE for this estimate is as following: • For the outcome of scheme take-up (= 0/1): 0.04 • For the outcome of Tree Stumping take-up (= 0/1): 0.038 Second, we plan to estimate the impact of Tx just on the labor arm. The MDEs for this estimate are: • For the outcome of scheme take-up (= 0/1): 0.048 • For the outcome of Tree Stumping take-up (= 0/1): 0.043