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Salience and Timely Compliance: Experimental Evidence from the Enforcement of Speeding Tickets
Last registered on November 22, 2017

Pre-Trial

Trial Information
General Information
Title
Salience and Timely Compliance: Experimental Evidence from the Enforcement of Speeding Tickets
RCT ID
AEARCTR-0002594
Initial registration date
November 22, 2017
Last updated
November 22, 2017 12:55 PM EST
Location(s)
Primary Investigator
Affiliation
Hertie School of Governance
Other Primary Investigator(s)
PI Affiliation
University of Economics, Prague
PI Affiliation
Hertie School of Governance
Additional Trial Information
Status
In development
Start date
2017-12-01
End date
2019-03-31
Secondary IDs
Abstract
In many domains, authorities rely on sending notifications to enforce compliance with outstanding payments. Typically there are laws that impose constraints on format and content of the notifications. As a result, the key information is often presented in a way that is hard to understand, making it difficult to correctly infer important attributes of the payment liabilities. This is crucial, as many of these attributes play - at least for a fully rational, attentive and cognitively unconstrained decision maker - a significant role in shaping individual responses to the notifications. This project studies the impact from increasing the salience of different attributes hidden in such complex legal texts. In particular, we focus on payment notifications for drivers who receive speeding tickets. Within a randomized controlled trial, we vary whether the payment deadline, the penalty for missing the deadline, or both features of the notification are made salient to the receiver. We then study the treatments' impact on the timing of payments for the speeding tickets.
External Link(s)
Registration Citation
Citation
Dusek, Libor, Nicolas Pardo and Christian Traxler. 2017. "Salience and Timely Compliance: Experimental Evidence from the Enforcement of Speeding Tickets." AEA RCT Registry. November 22. https://doi.org/10.1257/rct.2594-1.0.
Former Citation
Dusek, Libor, Nicolas Pardo and Christian Traxler. 2017. "Salience and Timely Compliance: Experimental Evidence from the Enforcement of Speeding Tickets." AEA RCT Registry. November 22. https://www.socialscienceregistry.org/trials/2594/history/23396.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
We use cover letters to augment a legal text. Different cover letters aim at increasing the salience of different enforcement parameters.
Intervention Start Date
2017-12-01
Intervention End Date
2018-12-01
Primary Outcomes
Primary Outcomes (end points)
We will calculate the effect of our treatments on the payment decision of the individuals by comparing payment rates across treatments for full compliance (i.e., for the payment of all fines). Our primary outcomes will be based on (1) timely compliance (pre-deadline payments) and (2) payments within 30 days. Moreover, (3) we consider the exact timing of the (first) payment.
Primary Outcomes (explanation)
Ad 1+2: To analyze the treatments' effect on compliance we will first estimate binary outcome models (using LPM). For our key outcome, we consider both payments within 15 (pre-deadline!) and 30 days. In all cases we estimate the model with and without controls.

Ad 3: Our analysis of the timing of payments mainly focuses on the period before the deadline (15 days after receiving the speeding ticket). However, to assess the treatments impact on the timing of payments we will consider duration models with an outcome (based on the timing of the first-payment) defined over a longer periods (up to 100 days). Estimates will be based on Cox proportional hazard model and/or parametric hazard models (Weibull). To assess payment hazards (conditional payment probabilities for day t), we will parametrically and non-parametrically analyze hazards over time for the different treatment groups.

Note that our analysis focuses on Intention-to-Treat (ITT) effects, as some mailings will be probably not delivered and, among those that are delivered, not all will be opened and properly read. (To the extent that our outcome data allow us to assess these problems, we will also comment on the Treatment-on-Treated (TOT) effect size.)
Secondary Outcomes
Secondary Outcomes (end points)
In addition to full payments, we will also consider partial payments ("any"-payment-made-indicators). We will again assess these for the time frames from above (payments within 15 and 30 days). To capture mid-run treatment effects on compliance, however, we will also estimate models for our outcome variables (full/partial compliance) defined over a longer time frame of 60 and 100 days.

If we are able to merge our data with information on future driving/speeding behavior, we will also examine treatment effects on subsequent driving behavior.
Secondary Outcomes (explanation)
The binary outcome models are again estimated with LPM. To assess if treatment effects differ between full and partial compliance, we will compare these estimates to results obtained for our primary outcome.

We will make use of the information about ticket receivers in our dataset to analyze the difference in our outcomes for private and corporate car owners. Furthermore, based on the information on zip codes, we split the sample into local vs non-local car owners. We also analyze differential effects for different speed levels keeping the amount of the fine constant, and, conditional on data availability we will consider heterogeneous effects between first-time speeders and individuals with past speeding infractions.

As part of this heterogeneity analysis, we will make use of the observable characteristics to explore if type heterogeneity drives the pattern in hazard rates.

Using the fact that the fine is a stepwise function of the speed at which individuals were driving, and that the application of fines higher than the lowest one is discretionary, we implement a regression discontinuity design around the first speed threshold that triggers an increase in the fine (using speed as the running variable), to analyze the effect of the size of the fine on timely payment. For this we estimate non-parametrically an equation with dummies capturing total or partial payments within different periods of time on the left hand side and the measured speed, a dummy equal to one if the vehicle's speed is above the cutoff, and a function that captures how the outcome variable varies with the speed (this function is allowed to differ on either side of the cutoff), on the left hand side.

We will estimate these equations for the full sample first. Depending on the number of observations per treatment, we will perform the analysis for the control group sample only. Furthermore, and also contingent on the number of observations, we will investigate if there are treatment differences by including interactions with the treatment dummies to assess if the impact of the higher fines differs across the different treatments.
Experimental Design
Experimental Design
We conduct a randomized control trial with the universe of recipients of speeding violation tickets during the period of the study. Each speeding case is randomly allocated into a control group or one of three treatments. Due to technical constraints at our cooperation partner, the randomization will not use any stratification.

Our experimental approach is based on a 2 x 2 factorial design. All speeders in all groups receive the legal notification message, i.e., the usual summons used by the enforcement authority. Three treatment groups [T1-T3] will receive an additional cover letter. The control group [T0] does not get any cover letter. The cover letter includes a simple text that aims at communicating in a clear way either the deadline, the costs associated with not paying the fine on time, or both. (Note that a subset of speeders, in particular, cars registered for companies that use an electronic way of communicating with authorities, will receive the cover letter (as well as the legal notification) as a PDF.)
Experimental Design Details
An English versions of the different cover letters' texts are provided below: [T1] Deadline Salience: Dear Sir/Madam, We summon you to pay the prescribed amount for a speeding violation. We encourage you to carefully read the information contained in the attached pages and take appropriate action. Please pay the amount in full and make sure it is credited to the city's account within 15 days after receiving this summons. The city office of Ricany. [T2] Penalty Salience: Dear Sir/Madam, We summon you to pay the prescribed amount for a speeding violation. We encourage you to carefully read the information contained in the attached pages and take appropriate action. If you do not pay the whole amount the office will continue investigating the offense. The amount that you will potentially have to pay may be as high as CZK 2,500. (For medium-severity speeding violations i.e., for a speed of more than 20km/h above the limit, this part reads 'as high as CZK 5,000'). In addition, the driver may be added points within the demerit point system. The city office of Ricany. [T3] Deadline + Penalty Salience: Dear Sir/Madam, We summon you to pay the prescribed amount for a speeding violation. We encourage you to carefully read the information contained in the attached pages and take appropriate action. Please pay the amount in full and make sure it is credited to the city's account within 15 days after receiving this summons. If you do not pay the whole amount the office will continue investigating the offense. The amount that you will potentially have to pay may be as high as CZK 2,500. (This part is again adjusted for medium-severity speeding violations, see [T2]). In addition, the driver may be added points within the demerit point system. The city office of Ricany.
Randomization Method
The randomization will be made by a computer (using software installed at the municipality office). The software draws a random number and, based on this number, assigns each case to one of the four conditions T0, T1, T2 or T3 (with a rate of 1/4 each).
Randomization Unit
Speeding ticket
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
At this stage, there is unfortunately considerable ambiguity regarding the length of the trial (at least 9 month) and regarding the number of observations collected during this time period (speeders who will get a ticket). Based on what we know, this could be anywhere between 5,000 to 20,000 speeding tickets.
Sample size: planned number of observations
We would aim at 20,000 speeding tickets, but -- as pointed out above -- there is considerable ambiguity whether we could reach this number.
Sample size (or number of clusters) by treatment arms
Sample Size / 4. I.e., if we would reach the planned 20,000 experimental speeding tickets, this would be 5,000 per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Given the ambiguity about the number of observations and the size of the (deadline) treatment effects, we did not conduct a fully fledged power analysis.
Supporting Documents and Materials

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IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers