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Abstract
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Before
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.
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After
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 measure the quality premium along the supply chain, from downstream exporters to intermediaries to upstream producers. We explore the role of two mechanisms that may diminish the quality premium upstream: one is markdowns resulting from differential market power by quality, and the second is productive substitutability, meaning the ability of downstream agents to make quality investments that substitute for those upstream. To differentiate between these two mechanisms and quantify their role in contributing to the diminishing quality gradient, we conduct experiments that offer randomized coffee production contracts throughout the supply chain (to both traders and farmers). These contracts induce quality-specific demand shocks that allow us to separately identify costs from markdowns, as well as understand how agents along the supply chain would respond to varying quality premia on investment in quality. We use the estimated model to examine the efficiency implications and distributional consequences of barriers to entry and productive substitutability, as well as how they interact to affect quality production and surplus distribution along the chain. Finally, we use the model to investigate counterfactual policies to induce quality upgrading.
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Trial End Date
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December 30, 2024
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December 31, 2024
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Last Published
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January 28, 2022 10:25 AM
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September 06, 2024 04:46 PM
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Intervention (Public)
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Before
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.
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After
The aim of this project is to identify variation in quality premiums along the supply chain, from downstream exporters to intermediaries to upstream producers, as well as understand the underlying causes and their efficiency and distributional implications. We partner with a large coffee exporter in Uganda (hereafter referred to as “the company”). We start by mapping out the supply chains emanating from this company and observing how quality premiums vary along the chain (more below). We then conduct randomized control trials with both traders and farmers along those chains. We offer both farmers and traders randomized coffee production contracts – which vary not just in whether they are offered at all, but also the quantity and quality – to induce quality-specific demand shifters. For both, we collect respondent’s “minimum willingness to accept” (WTA) the contract via a Becker-DeGroot-Marschak (BDM) mechanism. We will use this experiment to estimate trader costs and markdowns to understand how much of the declining quality premium is due to intermediary market power vs. joint quality production between farmers and traders.
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Intervention End Date
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June 30, 2023
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December 31, 2024
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Primary Outcomes (End Points)
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Before
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
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After
1. Quality-premium (how price varies by quality) and heterogeneity in this premium along the value chain
2. Quality provision (quantity of each quality type) in response to randomized contracts
These outcomes will be measured separately at each point in the value chain
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Primary Outcomes (Explanation)
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Before
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.
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After
1. Using our quality sampling survey and samples, we will measure the quality gradient (how prices vary with quality), and heterogeneity in this quality gradient along the supply chain (distance to world markets). Specifically, we will first measure supply chain length by the number of times coffee changes hand until it reaches the export-gate. We will then conduct high-frequency quality sampling visits to the field throughout the harvest season to collect physical samples of coffee that was transacted. Specifically, we ask traders and farmers to keep samples of their main coffee transactions (purchase and sale) 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 matter and total defects. These measurers will help us compute an “outturn” measure. This will allow us to have an accurate and objective measure of prices paid/received and quantities purchased/sold by quality level. Next, we will measure quality both continuously (using the outturn index used by our exporting partner) and a binary measure of high vs. low quality using the cutoff used by our exporting partner (defects under 33%, foreign matter under 4%, and moisture under 14.5%).
2. Quality provision: during the season, we will offer both farmers and traders randomized coffee production contracts – which vary not just in whether they are offered at all, but also the quantity and quality. We will combine the offer of these contracts with the high-frequency sampling visits to measure whether traders and farmers’ total production of high and low quality coffee changes during the period when they win the randomized coffee production contracts.
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Experimental Design (Public)
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Before
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.
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After
This project includes two parts: 1) detailed data collection on the supply chain network and high-frequency transaction level data and 2) randomized contract offers throughout the supply chain.
We begin by mapping the complete supply chain, from the farm gate to export gate, and collect detailed transaction-level data on prices, quantities, and laboratory quality measures along the chain. We document the layers of domestic intermediation and the price-quality gradient as we move up the supply chain.
We explore the role of two mechanisms that may diminish the quality premium upstream: one is markdowns resulting from differential market power by quality, and the second is productivity substitutability, the ability of downstream agents to make quality investments that substitute for those upstream. To differentiate between these two mechanisms and quantify their role in contributing to the diminishing quality gradient, we conduct experiments that offer randomized coffee production contracts throughout the supply chain (to both traders and farmers). These contracts induce quality-specific demand shocks that allow us to separately identify costs from markdowns, as well as understand how agents along the supply chain would respond to varying quality premia on investment in quality. We use the estimated model to examine the efficiency implications and distributional consequences of barriers to entry and productive substitutability, as well as how they interact to affect quality production and surplus distribution along the chain. Finally, we use the model to investigate counterfactual policies to induce quality upgrading.
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Randomization Method
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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.
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After
We will begin by mapping out the supply chain network for traders who sell directly to the company (the downstream traders). To do so, we 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. We will also collect their supplier information, and then call their suppliers to ask about the suppliers’ suppliers, until we reach a farmer. Additionally, we collect information on a randomly selected supplier for a quarter of all downstream suppliers, and follow the same procedure of call that randomly selected supplier and asking about the suppliers’ suppliers until we reach a farmer. We also collect data on the physical market centers where traders store their coffee among the primary parishes where the company is active. At each market center, we perform a census listing exercise where we collect information on all traders active in the market centers. From this listing, we randomly select a subset of parishes and add an additional downstream trader to the supply chain mapping exercise.
We then select supply chains to include in the high-frequency transaction level data collection through the season, using both main and randomly selected supply chains. Additionally, we incorporate additional downstream traders selected randomly from the market listing activities, stratified by parish.
All agents (traders and farmers) along selected main supply chains will be in the pool that is potentially eligible for the randomized contracts. The randomization of the experimental contracts will come from the BDM mechanism. Conditional on WTA, the award of the contract is randomized through the randomized price scratched off. For each contract, the quality (high vs. low) and quantity (three levels) is also randomized, with quality and quantity assignment stratified by chain length.
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Randomization Unit
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Before
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.
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Randomization will occur at the individual respondent level.
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Was the treatment clustered?
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Yes
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No
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Planned Number of Clusters
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We expect to have about 200 parishes (clusters) for the experiments.
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N/A
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Planned Number of Observations
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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.
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We expect to have about 400 respondents (drawn from 200 supply chains) in the experiments.
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Sample size (or number of clusters) by treatment arms
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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.
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After
All agents (traders and farmers) along these selected supply chains will be in the pool that is potentially eligible for the randomized contracts. The randomization of the experimental contracts will come through the BDM mechanism. Conditional on WTA, the award of the contract is randomized based on the randomized price scratched off. For each contract, the quality (high vs. low) and quantity (three levels) is also randomized, with quality and quantity assignment stratified by chain length.
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Keyword(s)
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Agriculture, Education, Firms And Productivity
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Agriculture, Firms And Productivity
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Intervention (Hidden)
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Before
In the first experiment, the company will offer randomly selected traders a “bonus” for bringing in premium quality coffee that meet the pre-determined requirements on moisture content, foreign matter and total defects. Eligible traders with qualified coffee will receive an extra bonus of up to 500 UGX/kg. This will generate exogenous variation in quality premiums offered to traders. We will then measure how much of the quality premium gets passed up the value chain to farmers and, in turn, how variation in quality premiums affect traders and farmers' incentives to invest in quality. 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 (using the same definition as in Experiment 1). Farmers in this experiment will be divided into three equal sized groups, each offered a bonus sizes ranging from 0 to 500 UGX/kg. This will therefore span the full range of possible pass-through from 0-100% and allow for non-linear estimation of supply. To ensure that the randomization is followed exactly, we will have our research staff, who are embedded at the company, be responsible for making the offer, collecting the coffee, and testing quality at farmgate. The company will reconduct these tests upon arrival at their factory gate.
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After
In the experimental component of this paper, we offer randomized coffee contracts. These experiments are run with both traders and farmers. Contracts vary randomly on quality (high vs. low) and quantity (three possible levels). Our contracts are based on the Becker-DeGroot-Marschak (BDM) mechanism. Specifically, we start by asking respondents for their willingness-to-accept (WTA) a contract to sell a randomized quantity and quality of coffee. Then, respondents receive a scratch card with prices in a randomized order and covered by stickers. Respondents choose a sticker to scratch off to reveal the underlying price; if the uncovered price is weakly higher than their willingness-to-accept (WTA), then the coffee producer or intermediary receives a contract for the randomized quantity and quality to be sold at the uncovered price. After the two weeks have elapsed, we return and purchase the coffee if it meets the quantity and quality specified in the contract. This approach has two benefits. First, conditional on respondents’ WTA, whether they win the contract is random (i.e. based on the scratched off price); this induces random variation in whether the contract is offered. Second, we can use the resulting induced exogenous shift in quantity, along with the WTA price, to identify agents’ underlying cost structure, which we allow to vary by quality type and by upstream vs. downstream agents. This will allow us to identify costs and markdowns at different points in the supply chain and by quality type, which will give insight into whether imperfect competition or productive substitutability is driving the declining quality gradient along the supply chain.
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Secondary Outcomes (End Points)
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Before
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)
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1. Minimum willingness to accept (WTA) the BDM contract
2. Sourcing behavior by traders
3. Processing activities behaviors by farmers and traders
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Secondary Outcomes (Explanation)
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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.)
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1. Minimum willingness to accept (WTA): we will collect respondents’ minimum willingness-to-accept (WTA) (i.e., the minimum price at which they will accept) the contract to sell a randomized quantity and quality of coffee. Then, respondents receive a scratch card with prices in a randomized order and covered by stickers. Respondents choose a sticker to scratch off to reveal the underlying price; if the uncovered price is weakly higher than their willingness-to-accept (WTA), then the coffee producer or intermediary receives a contract for the randomized quantity and quality to be sold at the uncovered price. This mechanism has been shown to be incentive compatible.
2. To measure sourcing behavior by traders, we will collect information about coffee purchases. We will collect physical samples of the inputs purchased used to measure whether the downstream agent provides high quality by sourcing higher quality from upstream or by upgrading the quality himself. Relatedly, we will also ask respondents about the upgrading activities they have done to the input purchased. We will also ask about identity of the supplier from whom the trader sourced to fulfill the contract, including whether it is an existing supplier or new supplier.
3. We also add questions in the survey to ask the processing activities and relevant assets owned by traders and farmers, to gain insights on the quality investment behaviors along the chain.
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