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Trial Title
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Information, bargaining, corruption and trade: evidence from small traders in East Africa
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Information, informality and trade: evidence from small traders in East Africa
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Abstract
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Before
This research project focuses on trade barriers for small-scale cross-border traders. These traders face multiple intermediaries in the process of crossing the border, increasing their -already substantial- trade costs. Traders do not always know the official rate of each intermediary and may be taken advantage of. Indeed, an asymmetry of information between traders and intermediaries or border agents about prices and rates may play a significant role in increasing trade costs, fueling corruption and affect traders' business. Through a RCT in East-Africa, I test whether giving information to traders about market prices, border prices and taxes can affect bargaining, lower the cost at the border and affect small-scale traders’ choices of trade route. Such reductions in trade costs may increase and/or formalize trade, increase traders’ profit and have spillover effects along the value chain.
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After
This research project focuses on trade barriers for small-scale cross-border traders. These traders face multiple intermediaries in the process of trading and crossing the border, increasing their -already substantial- trade costs. Traders do not always know market prices or the official rate of each intermediary and may be taken advantage of. Indeed, an asymmetry of information between traders and intermediaries or border agents about prices and rates may play a significant role in increasing trade costs, fueling corruption and affect traders' business. Through a RCT in East-Africa, I test whether giving information to traders about market prices, border prices and taxes and exchange rates can affect bargaining, lower the cost at the border and affect small-scale traders’ choices of trade route. Such reductions in trade costs may increase and/or formalize trade, increase traders’ profit and have spillover effects along the supply chain.
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Trial End Date
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December 31, 2021
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December 31, 2022
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Last Published
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February 12, 2020 01:45 PM
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April 05, 2022 01:01 PM
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Intervention (Public)
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Traders in the treatment group will receive access to a platform (through their phone) that provides trade information (market prices, exchange rates, taxes/tariffs and trade procedures).
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Intervention Start Date
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March 23, 2020
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April 28, 2021
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Intervention End Date
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April 17, 2020
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June 30, 2022
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Primary Outcomes (End Points)
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Trade costs: transport costs, tariffs/taxes, bribes
Trade outcomes: imports/exports quantity, type of products traded, traders’ choice of formal versus informal border crossing
Sourcing/Selling outcomes and outcomes on value chain: supplier and supplier market choice, customer/selling market choice, traders' frequency of purchase from suppliers (could be farmers or traders), the number of suppliers purchased from, supplier's sales, supplier's prices
Market prices
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After
1. Business outcomes: sales, profits, costs (including transport, tariffs/taxes, ... ), probability of trading/being in business, purchasing price of goods (including inv. hyp. sine or log transformations for all monetary variables when applicable)
2. Trade outcomes: type of goods traded (take-up of new goods), number of goods traded, quantity, number of supplying and selling markets, frequency/number of trips, value of goods per trip
3. Cross border trade: traders' choice of supplier/selling market (whether it is domestic versus cross border), probability of exchanging money
4. Informality and Border costs: traders’ choice of formal versus informal border crossing, probability of paying a bribe, level of the bribe, probability of facing harassment, other non-tariff barriers (also run on cross border sample)
5. Market level outcomes: market prices, market level trade flows, market competition measures / market structure
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Primary Outcomes (Explanation)
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- For each family of outcomes outlined above, I will create an index. I will run two type of analysis, using the index created as well as run regressions separately for each outcome.
- Type 1 error will be controlled across the tested hypotheses
- I will show results for the full sample; however, I will winsorize continuous outcomes at 1 and 99 percentile.
- I consider market level outcomes (family 5) and trader level outcomes (family 1 - 4) as two distinct studies and allocate 5% type 1 error to each.
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Experimental Design (Public)
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A listing of traders will be carried out in markets located close to Busia (Uganda-Kenya crossing) and Isibania (Tanzania-Kenya crossing). A random sample of traders will be selected based on pre-selected stratas. A baseline will be administered to the sample of selected traders and their main supplier. A random sample of traders (treatment group) will receive access to a platform where they can access market prices, exchange rate information and information about tax rates and border crossing procedures. The control group will not receive access to this platform. High frequency data will be collected on trading behaviors on both treatment and control groups throughout the study. Finally, an endline study will be administered both on traders and main suppliers.
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After
A listing of traders will be carried out in markets located close to Busia (Uganda-Kenya crossing). A random sample of traders will be selected based on pre-selected stratas. A baseline will be administered to the sample of selected traders and their main supplier. A random sample of traders (treatment group) will receive access to a platform where they can access market prices, exchange rate information and information about tax rates and border crossing procedures. The control group will not receive access to this platform. High frequency data will be collected on trading behaviors on both treatment and control groups throughout the study. Finally, an endline study will be administered both on traders and main suppliers.
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Randomization Method
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Randomization done by a computer
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Randomization done by a computer. The randomization is stratified by market, trader type (domestic, formal, informal) and gender.
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Randomization Unit
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Trader level randomization, clustered at market (and possibly type of goods) level to account for spillovers
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Trader (individual) level randomization. In addition, I add variation in treatment intensity at market x industry level to account for potential spillovers.
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Planned Number of Clusters
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40 markets (located close to two borders) and 4-6 types of goods
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For the analysis that controls for spillovers, I use the following clusters: ~40 markets (located close to the border) and 2 types of industry (agriculture and shoes/clothing). For the main specification, the randomization is however at the trader level.
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Planned Number of Observations
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1000 traders, 1000-2000 suppliers (farmers or traders)
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1100 traders (agriculture and shoes/clothing), up to 1000-2000 suppliers (farmers or traders)
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Sample size (or number of clusters) by treatment arms
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500 treatment traders, 500 control traders
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~500 treatment traders, 500 control traders
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Keyword(s)
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Agriculture, Firms And Productivity, Gender, Governance
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Agriculture, Firms And Productivity, Gender, Governance
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Intervention (Hidden)
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Traders will be invited to a workshop where the platform will be introduced and given access to them. Reminder SMS (with randomized content) will be sent during the implementation to encourage traders to use the platform.
Take-up includes (1) access to the platform through a workshop and (2) use of the platform.
The study was delayed due to Covid-19. Data collection will not end before 2022 and I expect that I will start looking at/analyzing the data in May 2022.
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Secondary Outcomes (End Points)
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- Heterogeneity analysis of main outcomes by gender, trader size, trader type (cross border vs. domestic or formal vs. informal vs. domestic), industry, trader type x industry, age/experience of trader, education, prior beliefs of border costs (collected pre intervention)
- Border crossing experience outcomes, e.g., time day chosen for crossing, waiting time, negotiation experience
- Take up of platform (extensive margin) and frequency of use (intensive margin). In addition, type of information requested to platform through phone: number of requests, type of information requested
- Household wellbeing including food security
- Spillover of treatment at market level (using intensity of treatment design)
- Outcomes on value chain/suppliers: e.g., supplier's sales and profits, supplier's prices
- Effect of reminder SMS on main adoption/use of platform for traders
- For main outcomes, I will also look at ToT effect in addition to the ITT
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Secondary Outcomes (Explanation)
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- For each family of outcome listed in secondary outcomes end points, I will create an index. I will run two type of analysis, using the index created as well as run regressions separately for each outcome.
- I will show results for the full sample; however, I will winsorize continuous outcomes at 1 and 99 percentile.
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Building on Existing Work
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No
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