Quality Upgrading and Competition - Theory and Evidence from the Coffee Sector in Uganda

Last registered on January 28, 2022

Pre-Trial

Trial Information

General Information

Title
Quality Upgrading and Competition - Theory and Evidence from the Coffee Sector in Uganda
RCT ID
AEARCTR-0008703
Initial registration date
January 27, 2022

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
January 28, 2022, 10:25 AM EST

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
Harvard

Other Primary Investigator(s)

PI Affiliation
University of Michigan
PI Affiliation
Northwestern University
PI Affiliation
Harvard

Additional Trial Information

Status
On going
Start date
2021-12-01
End date
2024-12-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
How do long and potentially imperfectly competitive supply chains affect the incentives that producers face for quality provision in developing countries? We explore this question in the context of coffee in Uganda. International coffee markets often offer large returns to quality management; however, producers in developing countries consistently fall short of these quality standards. We study how the market structure of Uganda’s coffee supply chain affects the transmission of quality incentives up to producers. We identify pass-through of experimentally induced variation in quality premiums along the supply chain, from downstream exporters to intermediaries to upstream producers. To determine how variation in competition at different points in the value chain shapes the effective quality premium faced by upstream producers, the number of intermediaries eligible for the premium is experimentally varied across markets. A second experiment identifies the impact of varying quality premia on farmer investment in quality by manipulating the price schedule faced by producers directly.
External Link(s)

Registration Citation

Citation
Bai, Jie et al. 2022. "Quality Upgrading and Competition - Theory and Evidence from the Coffee Sector in Uganda ." AEA RCT Registry. January 28. https://doi.org/10.1257/rct.8703
Experimental Details

Interventions

Intervention(s)
The aim of this project is to identify pass-through of experimentally induced variation in quality premiums along the supply chain, from downstream exporters to intermediaries to upstream producers. This project consists of two randomized control trials. For both experiments, we will partner with a large coffee exporter in Uganda (hereafter referred to as “the company”). In the first experiment, the company will offer randomly selected traders a “bonus” for bringing in premium quality coffee. In order to examine the role of market structure, we generate exogenous variation in competition for high-quality products by randomizing the saturation of the quality premium offers to traders across geographic markets. In the second experiment, we will conduct direct buying from farmers. We will offer randomly selected farmers an additional “bonus” for premium quality coffee.
Intervention Start Date
2022-02-01
Intervention End Date
2023-06-30

Primary Outcomes

Primary Outcomes (end points)
1. Pass-through of experimentally induced variation in quality premiums along the supply chain, from downstream exporters to intermediaries to upstream producers
2. Quality provision at each point in the value chain
Primary Outcomes (explanation)
1. Pass-through: we will calculate pass-through rate of the experimentally-induced increase in the quality premium along the supply chain by comparing the high-quality prices among different players in the market, especially the price received by traders and the price trader paying to their upstream trader suppliers and farmer suppliers. We will collect detailed price information by quality level through high-frequency sampling surveys throughout the season to collect real physical samples of coffee. Specifically, we ask traders and farmers to keep real samples of their main coffee transactions throughout the season. For each sample, we ask traders and farmers to record the supplier/buyer of the transaction, price paid/received, and quantities purchased/sold. We will send the physical samples to a lab to measure quality along the following dimensions: moisture level, foreign matters, total defects and outturn. This will allow us to have an accurate and objective measure of prices paid/received by quality level. To complement the high-frequency quality sampling surveysm, we will conduct trader and farmer baseline, midline and endline surveys to collect additional information on how the intervention may have affected other aspects of the farmers’ coffee production and the traders’ businesses.

2. Quality provision: As described above, we will conduct high-frequency quality sampling visits to the field throughout the season to collect real physical samples of coffee. Specifically, we ask traders and farmers to keep real samples of their main coffee transactions throughout the season. We will send the physical samples to a lab to measure quality along the following dimensions: moisture level, foreign matters, total defects and outturn. This will allow us to have an accurate and objective measure of prices paid/received and quantities purchased/sold by quality level.

Secondary Outcomes

Secondary Outcomes (end points)
1. Quality investment behaviors at each point in the value chain (both intensive and extensive margins)
2. Shifting in sourcing behavior by traders (selection effects and search)
Secondary Outcomes (explanation)
1. To collect the quality investment behaviors of both traders and farmers, we will include detailed questions in the survey to ask what traders and farmers do in each stage of coffee processing, from gathering, drying, all the way to packaging. We will also collect information on assets owned by traders and farmers. During our high-frequency quality sampling visits to the field, we will further ask traders and farmers about the processing activities they’ve undertaken since our last visit. We will later compile these answers into a single index to represent the level of quality investment.
2. To measure shifts in sourcing behavior by traders, we will collect multiple samples of farmers (see “Experimental Design” below for more details). The first is a sample of farmers from whom the trader sample buys at baseline. The second is a sample of farmers from whom the trader sample newly started trading with in a given season. We will survey the “newly added suppliers” for a subsample of traders at the end of each season. For each trader, we will measure the impact of treatment on the following outcomes:
a. Number of new farmers and traders from whom bought
b. (Pre-determined) demographic characteristics of farmers from whom bought (e.g. size of land holdings, assets/machinery ownership, number of coffee trees (older than a year old), age of farmer, etc.)

Experimental Design

Experimental Design
This project is a two-pronged randomized control trial. In the first experiment, we will generate exogenous variation in quality premiums faced by intermediaries. We will then measure how much of the quality premium gets passed up the value-chain to farmers. In order to examine the role of market structure, we generate exogenous variation in competition for high-quality products by randomizing the saturation of the quality premium offers to traders across geographic markets. In the second experiment, we will directly manipulate the quality premium faced by farmers, in order to measure how farmer investment in quality upgrading responds to different price incentives. This will allow us to document how limited pass-through of quality premia by domestic supply chains affects ultimate quality provision among producers.
Experimental Design Details
Not available
Randomization Method
We will begin by mapping out the supply chain network for around 400 traders who sell directly to the company (the downstream traders). We conduct the network survey in two steps:

Round 1: Call all the downstream traders to administer a short survey to map out their main areas of operation, i.e., main parishes of purchasing and selling coffee.

At the end of the round 1 mapping, we will assign traders to parishes and divide the parishes into experiment 1 and experiment 2, stratify by the number of downstream traders in a parish. For traders who are assigned to parishes in experiment 1, we will conduct a second round phone call to trace out their supplier network.

Round 2 : call the downstream traders assigned to parishes in experiment 1 to collect their supplier information, and then call their suppliers to ask about the suppliers’ suppliers, till we reach the farmers.

For parishes in experiment 1, we will assign 1/3 to pure control and 2/3 to half-treated. Within the half-treated parishes, half of the downstream traders operating in those parishes, randomly selected, will be receiving the bonus.

For the parishes assigned to experiment 2, we will sample 150 farmers from those areas who share similar demographic characteristics as the farmer suppliers to the downstream traders in experiment 1 (using information collected from Round 2 network mapping). We will randomly assign farmers to three different levels of bonus offer, with 50 farmers in each.
Randomization Unit
Randomization for Experiment 1 will then occur in two-stages: first at the market level, and then second, at the individual trader level. This randomized saturation design is intended to produce random variation in demand for high-quality products, such that we can examine how the pass-through of quality premiums is affected by competition along the supply chain.

For the purpose of this project, we will take “the parish” as a plausible definition of “a market” when thinking about competition among traders and their interaction with farmers in the supply chain. Our pilot data reveal that 76% of traders operate in a single parish for the main purchase of coffee. We believe randomizing at the parish level strikes the right balance between minimizing spillovers while maintaining power.

Markets (parishes) will be randomized into two treatment saturation levels: pure control (0% of traders treated) and half-treated markets (50% treated). Then, within the latter, half of the traders will be randomized into treatment.

For Experiment 2, the farmer experiment, randomization will take place at the individual level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
We expect to have about 200 parishes (clusters) for the experiments.
Sample size: planned number of observations
For experiment 1, we expect to conduct full surveys with 250 traders and 150 farmers at baseline, which include 150 downstream traders (randomly selected from an estimated number of 300 downstream traders in the parishes assigned to experiment 1), 100 of their upstream trader suppliers and 150 of their farmer suppliers (identified through the network mapping exercise). This includes the baseline survey, the high-frequency quality sampling visits during each season and the endline survey at the end of the third season. Last but not least, at the end of every season, we will bring in 50 newly added farmer suppliers and 25 newly added trader suppliers to the data collection activities in the following seasons. For experiment 2, we will administer the baseline survey, high-frequency quality sampling visits and the endline survey on all 150 farmers recruited from the respective parishes assigned to experiment 2.
Sample size (or number of clusters) by treatment arms
For experiment 1, 100 (out of an estimated 300) traders will be selected to receive the 500 UGX/kg bonus for high-quality coffee.

For the intervention at the farmer side, we assign 50 farmers to zero bonus, 50 with a bonus of 250 UGX, and 50 with a bonus of 500 UGX.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Health Sciences and Behavioral Sciences Institutional Review Board (IRB­HSBS) at University of Michigan
IRB Approval Date
2019-03-23
IRB Approval Number
HUM00158205
IRB Name
Midmay Uganda Research Ethics Committee
IRB Approval Date
2019-02-22
IRB Approval Number
0302-2019