The Impact of a Smartphone Marketing Application on Smallholder Livestock Producers in Nepal

Last registered on August 30, 2021

Pre-Trial

Trial Information

General Information

Title
The Impact of a Smartphone Marketing Application on Smallholder Livestock Producers in Nepal
RCT ID
AEARCTR-0005545
Initial registration date
March 10, 2020
Last updated
August 30, 2021, 1:33 PM EDT

Locations

Region

Primary Investigator

Affiliation
University of Florida

Other Primary Investigator(s)

PI Affiliation
PI Affiliation
PI Affiliation

Additional Trial Information

Status
Completed
Start date
2018-01-01
End date
2020-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Can smartphone applications be harnessed to raise the incomes of smallholder farmers? Although previous literature has shown that access to mobile phones and mobile phone-based interventions can positively affect smallholder farmers, little is known about the effects of introducing smartphone applications (apps) designed to solve market problems and increase economic activity. In this pre-analysis plan, we propose to evaluate the effects of a smartphone-based, information-sharing app for livestock producers known as the ``Virtual Collection Center''. Our population of interest consists of smallholder goat producers in rural Nepal, all of whom are women and are members of producer cooperatives. We will estimate average and heterogeneous treatment effects of the app in four domains: information sharing, animals sold, prices received and cooperative-level management outcomes. Our study will contribute to two areas of interest where little evidence currently exists: the potential of smartphone apps as a tool for agricultural development, and business development for agricultural cooperatives in developing countries.
External Link(s)

Registration Citation

Citation
Janzen, Sarah et al. 2021. "The Impact of a Smartphone Marketing Application on Smallholder Livestock Producers in Nepal." AEA RCT Registry. August 30. https://doi.org/10.1257/rct.5545-2.0
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Experimental Details

Interventions

Intervention(s)
We propose to evaluate the effects of a smartphone-based information-sharing app for livestock producers known as the ``Virtual Collection Center'' (VCC). Our population of interest consists of smallholder goat producers in rural Nepal, all of whom are women and members of producer cooperatives. The VCC allows rank-and-file cooperative members to regularly update leadership on available goat inventory. Cooperative leaders use this inventory information to negotiate bulk sales with traders, and then invite cooperative members to sales events through the app.
Intervention Start Date
2018-03-01
Intervention End Date
2020-12-31

Primary Outcomes

Primary Outcomes (end points)
Our outcomes of interest are organized around four research questions:
•Question 1:Does access to the VCC application improve communication within coopera-tives?
1a) Household is aware of whether or not their cooperative organizes goat sales with traders(0/1)
1b) Household was contacted at least once about cooperative goat sale in past 12 months(0/1)
1c) Administrative transparency index (continuous)
1d) Economic services transparency index (continuous)
Question 2:Does access to the VCC application increase goat sales?
2a) Number of goats sold by the household through the cooperative (count)
2b) Number of goats sold by the household (count)
Question 3:Does the VCC application increase prices received for goats?
3a) Revenue per goat sold through the cooperative (USD)
3b) Revenue per goat sold (USD)
Question 4:Does access to the VCC application improve the planning horizon and expectations of cooperative leaders?
4a) Planning time horizon (years)
4b) Number of goats expected to be sold through cooperative in next six months (count)
4c) Total revenue expected to be earned by cooperative in next six months (USD)
Primary Outcomes (explanation)
Outcome 1c:
We collect data on administrative transparency by asking identical questions to both cooperative
general managers and individual members. Our data contains seven binary variables that indicate
whether the general manager and individual member state that the cooperative makes certain
information available to its members. For each of the seven administrative transparency questions,
we calculate a binary discrepancy variable equal to 1 when the cooperative general manager and
individual member agree, and zero when they disagree.
i. Cooperative general manager and individual member agree that the cooperative makes its
mandate available to members (0/1).
ii. Cooperative general manager and individual member agree that the cooperative makes its
annual report available to members (0/1).
iii. Cooperative general manager and individual member agree that the cooperative makes its
annual budget available to members (0/1).
iv. Cooperative general manager and individual member agree that the cooperative makes its
financial report available to members (0/1).
v. Cooperative general manager and individual member agree that the cooperative makes its
meeting minutes available to members (0/1).
vi. Cooperative general manager and individual member agree that the cooperative makes its
election results available to members (0/1).
vii. Cooperative general manager and individual member agree that the cooperative makes its
sale records available to members (0/1).

Outcome 1d:
We collect data on economic services transparency by asking identical questions to both cooperative
general managers and individual members. Our data contains sixteen binary variables that
indicate whether the general manager and individual member state that the cooperative makes
certain information available to its members. For each of the seven transparency questions, we
calculate a binary discrepancy variable equal to 1 when the cooperative general manager and individual
member agree, and zero when they disagree.
i. Cooperative general manager and individual member agree that the cooperative accepts savings
deposits (0/1).
ii. Cooperative general manager and individual member agree that the cooperative offers loans
(0/1).
iii. Cooperative general manager and individual member agree that the cooperative helps members
access bank loans (0/1).
iv. Cooperative general manager and individual member agree that the cooperative sells or helps
members access livestock insurance (0/1).
v. Cooperative general manager and individual member agree that the cooperative sells animal
feed (0/1).
vi. Cooperative general manager and individual member agree that the cooperative sells seed
(0/1).
vii. Cooperative general manager and individual member agree that the cooperative sells fertilizer
(0/1).
viii. Cooperative general manager and individual member agree that the cooperative offers pesticide
or other agrochemicals for sale (0/1).
ix. Cooperative general manager and individual member agree that the cooperative sells or rents
agricultural or livestock tools (0/1).
x. Cooperative general manager and individual member agree that the cooperative sells consumer
goods, such as food (0/1).
xi. Cooperative general manager and individual member agree that the cooperative provides
access to veterinary services (0/1).
xii. Cooperative general manager and individual member agree that the cooperative provides
assistance with business planning (0/1).
xii. Cooperative general manager and individual member agree that the cooperative provides
assistance with animal husbandry (0/1).
xiv. Cooperative general manager and individual member agree that the cooperative coordinate
sales of goats to traders (0/1).
xv. Cooperative general manager and individual member agree that the cooperative gives dividend
payments to owners of cooperative shares (0/1).
xvi. Cooperative general manager and individual member agree that the cooperative provides
goat price information (0/1).
1d) weighted standardized average of variables: (i), (ii), (iii), (iv), (v), (vi), (vii),
(viii), (ix), (x), (xi), (xii), (xiii), (xiv), (xv), (xvi).
Question 2: Does access to the VCC application increase goat sales?
Outcomes:
2a) Number of goats sold by the household through the cooperative (count).
• Winsorized at the 1st and 99th percentiles.
2b) Number of goats sold by the household (count).
• Missing values replaced with zero.
• Winsorized at the 1st and 99th percentiles.
Question 3: Does the VCC application increase prices received for goats?
Outcomes:
3a) Revenue per goat sold through the cooperative (USD).
• Missing values replaced with zero.
• Converted to USD.
• Winsorized at the 1st and 99th percentiles.
3b) Revenue per goat sold (USD).
• Missing values replaced with zero.
• Converted to USD.
• Winsorized at the 1st and 99th percentiles.
Question 4: Does access to the VCC application improve the planning horizon and expectations
of cooperative leaders?
Outcomes:
4a) Planning time horizon (years).
4b) Number of goats expected to be sold through cooperative in next six months
(count).
• Missing values replaced with zero.
• Winsorized at the 1st and 99th percentiles.
4c) Total revenue expected to be earned by cooperative in next six months (USD).
• Missing values replaced with zero.
• Converted to USD.
• Winsorized at the 1st and 99th percentiles.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The impact of the VCC platform will be evaluated using a cluster RCT spanning 92 cooperatives in Nepal. Treatment was assigned at the cooperative level through a stratified randomization design as described in detail below. Treatment effects will be estimated at both the household and cooperative levels. The cooperatives in our study cover a wide geographic area. Based on conversations with HPIN, we understand cooperatives largely sell to local markets and buyers, such that broader macroeconomic price impacts (extending beyond the geographic borders within which the cooperative operates) are not anticipated. Cooperatives are geographically based, and members of a cooperative are only able to sell through their cooperative. Because cooperative membership comes through SHG membership, and SHG membership is extremely local (tole, or neighborhood), nonmembers would only be able to join their local cooperative and not a cooperative in a different treatment group.

To form strata prior to randomization, we first used the baseline data from both the household and cooperative leader survey to identify what we expect to be strong predictors of our outcomes of interest, including region (Terai and Mid-Hills), household goat revenue over the past 12 months, number of cooperative members, and cooperative revenue. Household goat revenue was top coded, replacing obvious outliers with reported number of goats sold multiplied by the median revenue per goat sold. The strata were then created using the following steps:
1. Dropping a single cooperative that was used in a pilot of the VCC app, leaving 107 cooperatives in total.
2. Sorting cooperatives within each region by average household-level goat revenue.
3. Splitting each region at its 33rd and 66th percentiles of average household goat revenue, yielding six bins.
4. Sorting cooperatives in each of the six bins by number of cooperative members.
5. Splitting each bin at the median number of cooperative members, yielding a total of 12 bins.
6. Sorting each of the 12 bins by cooperative revenue.
7. Splitting each bin at the median of cooperative-level revenue (from any source), yielding a total of 24 bins ranging in size from four to six cooperatives.
Within each cooperative, we generated a uniform random variable and assigned the two (in the case of strata with four or five members) or three (in the case of strata with six members) cooperatives with the greatest value of the random variable to treatment. For strata with five members, the odd-numbered cooperative was assigned to treatment with 50% probability using a uniform random variable.
Two complications arose around the time of treatment assignment. First, HPIN informed the research team that cooperatives in a single district could not be part of the randomization because the cooperatives work together to market goats. At that point, the randomization was complete and cooperatives had already been contacted to coordinate VCC app training. Therefore it would have been problematic to perform the randomization again. Instead, we verified that balance for our chosen indicators was maintained after dropping the cooperatives from the aforementioned district.2 The final treatment assignment includes 92 cooperatives in total, with 45 assigned to treatment and 47 assigned to control.
Second, a coding error occurred during the randomization that resulted in 39 of 92 cooperatives being assigned to strata using the wrong value of total cooperative revenue. Fortunately, the damage caused by the error was minimal. Total cooperative revenue was used at the lowest level of stratification, and no household-level variables were affected. Total cooperative revenue is almost identical on average in treatment and control cooperatives.
Experimental Design Details
Randomization Method
Randomization in office by computer (Stata).
Randomization Unit
Cooperatives
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
92
Sample size: planned number of observations
2800
Sample size (or number of clusters) by treatment arms
27
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Communication MDE % of mean # of sd's. Aware that co-op sells goats 0.1632 0.2536 0.3406 Received sale information 0.1356 0.4701 0.2992 Administrative transparency index 0.2810 9.1738 0.2872 Economic services transparency index 0.3457 27.5897 0.3519 Goat Sales MDE % of mean # of sd's. Goats Sold through Cooperative 0.1707 0.7613 0.2270 Total Goats Sold 0.2426 0.2149 0.1436 Currency measured in USD Goat Price MDE % of mean # of sd's. Revenue per Goat 7.2217 0.1714 0.1443 Revenue per Cooperative Goat 6.6429 0.7025 0.2277 Currency measured in USD Planning and Goals MDE % of mean # of sd's. Planning Time Horizon 0.5675 0.4521 0.5778 Expected Goats Sold 170.6262 0.6341 0.4744 Expected Rev. 1,208.6133 1.1945 0.5656
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Florida
IRB Approval Date
2017-01-17
IRB Approval Number
IRB201602316
Analysis Plan

Analysis Plan Documents

The Impact of a Smartphone Marketing Application on Smallholder Livestock Producers in Nepal: Pre-Analysis Plan

MD5: c5ba2b26eda8f1a24a79d5db9fa22bc1

SHA1: 7546e4b0ae3256bc01e30e2081bf1b5962514d49

Uploaded At: March 10, 2020

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
Yes
Intervention Completion Date
December 31, 2020, 12:00 +00:00
Data Collection Complete
Yes
Data Collection Completion Date
February 01, 2018, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
92
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
Final Sample Size (or Number of Clusters) by Treatment Arms
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials