Accounting for Agriculture in Development: Impact of a Digital Platform for Farm Finances

Last registered on November 19, 2024

Pre-Trial

Trial Information

General Information

Title
Accounting for Agriculture in Development: Impact of a Digital Platform for Farm Finances
RCT ID
AEARCTR-0014359
Initial registration date
October 02, 2024

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 07, 2024, 7:15 PM EDT

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

Last updated
November 19, 2024, 6:23 AM EST

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

Locations

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Primary Investigator

Affiliation
Monash University

Other Primary Investigator(s)

PI Affiliation
Birkbeck, University of London
PI Affiliation
Cambridge University

Additional Trial Information

Status
In development
Start date
2024-11-01
End date
2027-01-01
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Financial record keeping is rarely practised among farmers in low/middle income countries despite it being crucial to running a successful business. Through a randomised control trial we propose to study the impact of a digital farm financial management tool developed in conjunction with the Peoples Action for National Integration (PANI). The app, which will be deployed among small and marginal farmers in the state of Uttar Pradesh, India from November 2024, will enable them to record and review their expenses and income over the agricultural season. A comparison of the farmers having received the app with those that did not will enable us to measure the direct impact of farm financial management on production choices, revenue and profits. This project will employ empirical methods for robust causal inference on the behaviour of agricultural market participants to further our understanding of barriers to economic growth within the sector and consequently highlight potential paths to reducing income/wealth disparities.
External Link(s)

Registration Citation

Citation
Boudot-Reddy, Camille, Andre Butler and Pushkar Maitra. 2024. "Accounting for Agriculture in Development: Impact of a Digital Platform for Farm Finances." AEA RCT Registry. November 19. https://doi.org/10.1257/rct.14359-1.2
Experimental Details

Interventions

Intervention(s)
We propose to study the impact of a farm financial management tool implemented by People’s Action for National Integration (PANI) -- a social development organisation working in underdeveloped regions of Uttar Pradesh. PANI developed a mobile application which enables farmers to record and review their expenses and income over the agricultural season. Downloaded on the mobile phone of farmers, the application requests farmers to record their expenses (specific to practices along the growing season including preparing land, seed and sowing, soil health, plant growth, pesticide, irrigation, and harvesting), as well as their harvest value (including both quantity, verified through a crop cutting exercise on a square meter of cultivated land, and price received if the crop is sold). This information is reviewed monthly allowing farmers to update their records on a regular basis.

PANI plans to extend their programme across Eastern Uttar Pradesh in late 2024. We propose to leverage this new role-out to investigate the impact of farm financial management for farmers using the platform. Using a randomised control trial, our experiment will randomly allocate farmers to either receive the app or not. In the first agricultural season, farmers having been selected to receive the app will be visited on a monthly basis by PANI community workers to encourage them to update their financial records on the platform. This data will be used to calculate crop-wise the total costs, value of harvest, and returns to investment -- broadly termed as financial summary. At the end of the season, PANI community workers will visit the farmers to explain their customised financial summary.

While keeping track of expenses and having a better understanding of the farm business may prove critical for making important production choices, seeing this information benchmarked to other farmers in the area may also be valuable. Preliminary evidence from PANIs trial using their digital platform suggests that there may be valuable information to share on farmer practices. We therefore propose to include a separate intervention arm wherein farmers will receive the financial management platform as explained above, with the additional feature of having this summary benchmarked to the top 25% of producers.

This project will employ empirical methods for robust causal inference on the behaviour of agricultural market participants to further our understanding of barriers to economic growth within the sector and consequently highlight potential paths to reducing income/wealth disparities.


Intervention Start Date
2024-12-01
Intervention End Date
2026-11-01

Primary Outcomes

Primary Outcomes (end points)
The key outcome variables in our analysis will be:
1. Crop choice
2. Expenses on inputs
2. Value of harvest
3. Farm profit, which will take into account imputed costs of family labour
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Some of the secondary outcome variables in our analysis will be:
1. Saving
2. Credit
3. Wellbeing
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Financial-app: Randomly select 55 Gram Panchayats reached by PANI as part of their expansion to form the "Financial app" group. In each of these Gram Panchayats, 40 of the farmers listed by PANI as programme participants will be randomly selected and allocated to either receive the financial management application (T1: Treatment) or not (C1: Control). A comparison of the farmers having received the app with those that did not will enable us to measure the direct impact of farm financial management on production choices.

Benchmarking: Randomly select 55 Gram Panchayats reached by PANI as part of their expansion to form the "Benchmarking" group. In each of these GPs, 40 of the farmers listed by PANI as programme participants will be randomly selected and allocated to either receive the financial management application + benchmarking (T2: Treatment) or not (C2: Control). The benchmarking will be implemented by using the data across the pool of all study participants and provide the summary of farm finances of each farmer benchmarked to the top 25% of producers. A comparison of the farmers having received this intervention with those that did not will enable us to measure the direct impact of benchmarked farm financial management on production choices. A post-estimation comparison to the effect capture in the "Financial app" group will tell us the additional impact of having the benchmarking.
Experimental Design Details
Not available
Randomization Method
Randomisation will be conducted in office using a computer, based on the lists of Gram Panchayats and farming households identified by PANI as beneficiaries of their programme.
Randomization Unit
For the purpose of the study design, the Gram Panchayats will be randomly selected from the total list of Gram Panchayats identified by PANI to roll-out their new programme. Within each selected Gram Panchayat, farmers identified by PANI as beneficiaries of their programme will be randomly selected and allocated to either receive the financial management app or not.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
4400 households across 110 Gram Panchayats
Sample size: planned number of observations
4400 households across 110 Gram Panchayats
Sample size (or number of clusters) by treatment arms
Number of households and Gram Panchayats in each treatment arm:
1. Financial-app arm -- 2200 households across 55 GPs (40 households/GP)
2. Benchmarking arm -- 2200 households across 55 GPs (40 households/GP)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using data from a survey in 2022 among 3451 farming households across 40 GPs in the neighbouring district of Balrampur in Uttar Pradesh (part of a project conducted by Camille Boudot-Reddy, co-PI on this project) for a study evaluating PANIs layered vegetable farming model, we conduct power calculations to identify a suitable sample size. Our results, indicate that a sample size of 2200 households (proposed sample in each intervention arm) will allow us to estimate with 95% confidence: (i) a 6% change in harvest value, (ii) 4% change in expenses, and (iii) 15% change in profit.
IRB

Institutional Review Boards (IRBs)

IRB Name
Research Committee of the Department of Land Economy at the University of Cambridge
IRB Approval Date
2024-09-17
IRB Approval Number
N/A
IRB Name
DAI Research & Advisory Services
IRB Approval Date
2024-09-22
IRB Approval Number
N/A