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Citizen Demand for Corruption: Evidence from Tolls in the Democratic Republic of the Congo
Last registered on May 29, 2016


Trial Information
General Information
Citizen Demand for Corruption: Evidence from Tolls in the Democratic Republic of the Congo
Initial registration date
May 29, 2016
Last updated
May 29, 2016 2:21 PM EDT
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
Additional Trial Information
In development
Start date
End date
Secondary IDs
This proposed study examines the determinants of petty corruption in the DRC, a substantial source of lost revenue for the government. In pilot data, 42% of motorcycle taxi drivers reported paying less than the official rate at tolls. To explain this high rate of non-compliance, we randomly offer motorcycle taxi drivers one of three incentives to obtain an official receipt at the toll, or a control condition. The incentives are: a payment to the driver or one of two donation-based incentives designed to shift the perceived value of tax compliance. We cross randomize these treatments with three other treatments: two different norms treatments and incentives for toll officer to issue more receipts.
External Link(s)
Registration Citation
Reid, Otis and Jonathan Weigel. 2016. "Citizen Demand for Corruption: Evidence from Tolls in the Democratic Republic of the Congo." AEA RCT Registry. May 29.
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Experimental Details
We are providing various incentives to motorcycle taxi drivers to induce them to comply with toll payments, rather than paying bribes to toll officers. The tolls studied are located on the outskirts of the city of Kananga in the Democratic Republic of the Congo. These incentives will take the form of cash rebates and promised donations to charitable causes if the driver shows a valid receipt issued at a toll. There is also a set of treatments aimed at altering social norms. At the same time (cross-randomized), we plan to offer a set of financial incentives to toll officers to issue more receipts (proof of tax compliance) on certain days.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Our key outcomes of interest are:
- Whether or not driver has a receipt (both conditional on reporting a trip and not)
- Amount paid by driver at the toll (both conditional on not receiving a receipt and not), according to multiple measurements
- Number of trips taken
Primary Outcomes (explanation)
Our outcomes are constructed as follows:
- Whether or not driver has a receipt: dummy that is 1 if the driver brings a valid receipt (valid indicates that it is printed by the toll receipt printers, it is for a motorcycle trip, it is on the date the driver reports for his trip, it has the driver’s name, and it is from the toll the driver reported passing) and 0 otherwise
- Amount paid by driver at the toll: this will be measured 3 ways (direct self-report, writing an amount on a piece of paper that is not viewed by the enumerator, and using an “amount experiment” that allows us to estimate the driver’s reported bribe without him revealing it directly)
- Number of trips taken: integer count of trips, measured by self-report and for a sub-sample by an alternative method of verification involving direct observation
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We plan to have drivers visit our office up to 4 times: for an initial baseline survey and for 3 follow-up surveys. Drivers will be exposed to different interventions after each visit, except the final visit. Independently, toll officers will be incentivized to issue receipts on certain randomly selected days.
Experimental Design Details
Not available
Randomization Method
The randomization will done by a mixture of built-in randomization software in the SurveyODK survey program and randomization done in office using Stata with a specified seed.
Randomization Unit
The randomization unit will be the individual-by-session. Individuals will be re-randomized each visit. The norms treatment will be randomized at the individual level. We will cluster at the level of the individual to account for any common shock by individual. For the toll officer treatment, the unit of randomization is the toll-day.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
We will have approximately 1200 drivers and we will cluster at the level of the driver. For analyses using toll officer treatments, we will do a second clustering by toll-day.
Sample size: planned number of observations
Our total number of observations will be 4800 driver-visits, of which 3600 will involve randomized treatments. The initial baseline will give us 1200 data points, but will not involve a randomized treatment.
Sample size (or number of clusters) by treatment arms
Of the 3600 visits, we plan to assign:
1200 visits to control
1200 visits to cash rebates (600 to 1000FC, 300 to 2000FC, 300 to 2500 FC)
600 visits to pure charity donation
600 visits to donation via government
1200 visits to social norms “wall of pride” treatment (overlapping assignment with the final two visits by drivers to the office)

600 drivers to receive information about tax payment behavior of drivers in pilot

15% of toll-days are assigned to cash incentives for toll officers.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Our power depends on several variables besides effect size. These variables are: the proportion of total variation that is constant within individual (i.e. would be absorbed by a fixed effect), the rate at which drivers complete trips, and whether we use individual fixed effects or instead do pooled OLS. All of these calculations ignore the role of covariates. In terms of our main outcomes, the standard deviations are: 1) Receipt rate: 30 percentage points 2) Amount paid at toll: 828 FC (41% of the value of the toll) Under an assumption that 30% of the variation is constant within individual and an assumption that 65% of drivers complete trips (based on piloting), we simulated our outcomes. In the simulations, our power to detect effects is approximately the following (these are not exact due to the fact that we used simulations, not standard power calculations, because we are not aware of standard calculations that incorporate our design elements): 3) Linear effect of 0.05 standard deviations per 25% of the value of the toll cash rebate: a. Pooled OLS (1), FE (1) 4) Specific dummies for each rebate: a. 50% of toll: 0.16 standard deviations (OLS); 0.3 standard deviations (FE) b. 100% of toll: 0.19 standard deviations (OLS); 0.33 standard deviations (FE) c. 125% of toll: 0.19 standard deviations (OLS) ; 0.33 standard deviations (FE) 5) Charity: pooled OLS of 0.17 standard deviations, FE of 0.22 standard deviations 6) Government: pooled OLS of 0.17 standard deviations, FE of 0.22 standard deviations 7) Wall of pride: pooled OLS of 0.12 standard deviations, FE of 0.17 standard deviations 8) Social norms treatment: pooled OLS of 0.13 standard deviations; cannot be evaluated using FE regressions We believe that these effects are plausible, since even 1/3 of the standard deviation of receipts received is only 10 percentage points and the incentives are large. We found similar sized effects to those we are powered to detect here in pilot activities.
IRB Name
MIT Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
IRB Approval Number
Analysis Plan

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