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Competitiveness among Intermediaries in Agricultural Markets
Last registered on March 10, 2016


Trial Information
General Information
Competitiveness among Intermediaries in Agricultural Markets
Initial registration date
April 03, 2015
Last updated
March 10, 2016 11:32 PM EST
Primary Investigator
Other Primary Investigator(s)
Additional Trial Information
In development
Start date
End date
Secondary IDs
African agricultural markets are thin and weakly integrated, often resulting in lower revenues for smallholder farmers and higher food prices for consumers. However, the causes of these price disparities are poorly understood. How much is due to transaction costs -- for instance, poor roads and infrastructure, high search costs, and limited contract enforcement -- and how much is due to imperfect competition among private sector actors in these markets -- that is, to the ability of intermediaries to exert market power and drive prices away from competitive equilibria? The answer to this question has important implications for the type of market integration policies that should be promoted in order to have the greatest impact on price convergence and welfare. This study has two aims: first, to quantify how competitive rural agricultural markets are, and, second, to test whether the entry of new intermediaries into markets can enhance competition among traders and increase consumer welfare.
External Link(s)
Registration Citation
Bergquist, Lauren. 2016. "Competitiveness among Intermediaries in Agricultural Markets." AEA RCT Registry. March 10. https://doi.org/10.1257/rct.682-3.0.
Former Citation
Bergquist, Lauren. 2016. "Competitiveness among Intermediaries in Agricultural Markets." AEA RCT Registry. March 10. http://www.socialscienceregistry.org/trials/682/history/7233.
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Experimental Details
The first stage of this experiment (Stage 1) aims to quantify competition among traders in agricultural markets. It does so by offering a marginal cost subsidy and identifying what percentage of this subsidy is passed through to the price at which traders sell to customers. This pass-through is then combined with estimates of demand elasticity to yield a competitiveness parameter. The second stage of the experiment (Stage 2) measures the impact of new traders entering the market on competitiveness. It accomplishes this by offering a subsidy for new traders to enter (randomly selected) markets and measuring resulting price and quantity responses, both by the new trader and incumbents in the market.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
The primary outcome measure for both stages of the experiment are prices and quantities of maize sold in the market. These will be collecting in detailed transaction-level surveys. These outcomes will be used to estimate pass-through, demand elasticities, and underlying marginal costs.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Pass-through -- that is, how a reduction in traders' costs is passed-through to a reduction in the price at which they sell to consumers -- can reveal much about the competitiveness of a market. If markets are perfectly competitive, then any reduction in costs faced by all traders should be passed on in full to consumers, as competing traders underbid each other until the price has dropped by the exact reduction in costs. However, if markets are not perfectly competitive, then the reduction in costs will not be perfectly passed through to the price faced by consumers, as traders account for the fact that any reduction in price will affect quantity sold as well. Moreover, the amount by which the pass-through deviates from 100% gives us a quantifiable measure of the level of competition.

The first stage of this study executes exactly this experiment -- offering an exogenous shock to costs in the form of a per-bag subsidy offered to traders -- in order to measure pass-through. If possible, this experiment may simultaneously also allow us to estimate the underlying demand function; to insure against being underpowered to do this (and to measure welfare impacts at counterfactual prices), we will also run a separate experiment to estimate demand directly. From this, we will be able to back out parameters summarizing competition and strategic interaction among intermediaries in the market. To do this, we will take the following specific steps outlined in the design registry document.

The second stage of this experiment attempts to measure the impact of entry on competition. In this stage, we encourage (randomly selected) traders to enter (randomly selected) markets in which they do not currently work, by providing a subsidy that is conditional on entry. Reduced form impacts on prevailing price and quantity sold will be measured by comparison to control market-days. Structural estimation utilizing results from Stage 1 will enable us to back out the impact of entry on underlying competitiveness. Further details on these steps are available in the design registry document.
Experimental Design Details
See design registry document.
Randomization Method
Randomization done in office by a computer
Randomization Unit
The unit of randomization in the market-block, with a block consisting of four weeks in a row of market days. The experiments will be conducted with a pool of 60 markets. These markets each have weekly market-days, all of which will be observed for the duration of the 12-week study. The study is broken into three “blocks,” during which each market will cycle through S1 treatment, S2 treatment, and control in a randomized order.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
180 market-blocks
Sample size: planned number of observations
All transactions occuring in 720 market-days
Sample size (or number of clusters) by treatment arms
60 markets, each observed on 12 days (4 market-days in Stage 1 treatment, 4 market-days in Stage 2 treatment, and 4 market-days as controls)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power calculations using price variation from the pilot suggest that the sample size of 60 markets should be sufficient to detect a change in price equivalent to a pass-through rate of 20% for the 200Ksh subsidy and 10% for the 400Ksh subsidy (note that, for a larger subsidy, a given pass-through rate results in a larger change in absolute price, which is therefore easier to detect statistically).
Supporting Documents and Materials

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IRB Name
University of California, Berkeley
IRB Approval Date
IRB Approval Number
IRB Name
Maseno University
IRB Approval Date
IRB Approval Number
Post Trial Information
Study Withdrawal
Is the intervention completed?
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)