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Field
Trial Start Date
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Before
October 01, 2024
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After
November 01, 2024
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Field
Trial End Date
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Before
November 01, 2026
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After
January 01, 2027
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Field
Last Published
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Before
October 07, 2024 07:15 PM
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After
November 19, 2024 06:21 AM
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Field
Intervention (Public)
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Before
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 allocated 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 financial 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.
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After
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.
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Field
Intervention Start Date
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Before
November 01, 2024
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After
December 15, 2024
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Field
Primary Outcomes (End Points)
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Before
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
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After
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
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Field
Experimental Design (Public)
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Before
Intervention Villages: Randomly select 75 Gram Panchayats (GP) to be reached by PANI as part of their programme. Among the list of programme participants identified by PANI in each GP, equally split 40 farmers to either randomly receive the financial management app or not. 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.
Spill over Villages: Farmers using the financial management app may discuss the information retrieved from this app and changes to their production choices with other farmers in their community. This spread of information may bias the treatment effect. We therefore propose to randomly select 20 GPs, with 20 farmers per GP, to be reached by PANI as part of their programme in which no farmer will receive the app. A comparison of the non-users of the app in the intervention villages with farmers in this spill-over villages will allow us to measure the presence of information diffusion and its impact on production choices.
Pure Control Villages: In order to obtain pure control villages, we propose to randomly sample 50 GPs, with 20 farmers per GP, identified by PANI as eligible for the programme but not included in their final sample. Firstly, a comparison of these pure control villages with the intervention villages will give us a measure of the impact of PANI’s overall programme including financial management. Secondly, a comparison of these pure control villages with the spill-over villages will give us a measure of the impact of PANI’s programme excluding financial management. Thirdly, a further comparison of the above two effects will enable us to capture the marginal impact from providing financial management over and above just information on agricultural practices.
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After
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.
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Field
Randomization Method
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Before
Randomisation will be conducted in office using a computer, based on the lists of GPs and farming households identified by PANI as beneficiaries of their programme.
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After
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.
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Field
Randomization Unit
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Before
For the purpose of the study design, the intervention and spill-over GPs will be randomly selected from the total list of GPs identified by PANI. Within the intervention villages, the farmers will be randomly selected to either receive the financial management app or not.
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After
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.
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Field
Planned Number of Clusters
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Before
4400 households across 145 Gram Panchayats
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After
4400 households across 110 Gram Panchayats
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Field
Planned Number of Observations
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Before
4400 households across 145 Gram Panchayats
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After
4400 households across 110 Gram Panchayats
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Field
Sample size (or number of clusters) by treatment arms
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Before
Number of households and GPs in each treatment arm:
1. The intervention arm will include 3000 households across 75 GPs (40 households/GP)
(a) 1500 household will receive the app and 1500 will not
2. The spill over arm will include 400 households across 20 GPs (20 households/GP).
3. The pure control arm will include 1000 households across 50 GPs (20 households/GP)
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After
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)
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Field
Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
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 3000 households will allow us to estimate a 6% change in harvest value, 3% change in expenses and 12% change in profits with 95% confidence.
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After
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.
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