Using CAPTCHA and honesty primes to increase tax collection in Guatemala

Last registered on June 30, 2014

Pre-Trial

Trial Information

General Information

Title
Using CAPTCHA and honesty primes to increase tax collection in Guatemala
RCT ID
AEARCTR-0000424
First published
June 30, 2014, 12:58 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
Behavioural Insights Team

Other Primary Investigator(s)

PI Affiliation
Behavioural Insights Team & University of Bristol

Additional Trial Information

Status
In development
Start date
2014-07-27
End date
2015-03-31
Secondary IDs
TP4014019
Abstract
This trial involves prompting and priming honesty among Guatemalan taxpayers filing declarations for income tax (ISR) or value added tax (IVA). Messages designed to induce more honest declarations are included as part of a series of CAPTCHA security systems which participants must complete in order to complete their declaration forms for these taxes.
The primary objective of the trial is to increase honesty in tax declaration. Following previous research in this area, we argue that more honest declarations will typically be report a higher tax liability than dishonest ones. The trial is important as Guatemala has the lowest rate of tax collection in Latin America (OECD, 2014).
The trial involves all people eligible to pay at least one of four taxes who complete a tax return online between July and November 2014 (approximately 1,600,000 individuals), who will be randomly allocated to either see one of six honesty prompts, or be part of a control group. Unlike other studies of tax compliance, this study targets people who are in the process of declaring their tax, rather than late or non-payers.
The intervention is delivered at the point of accessing an online declaration form. Participants are required to complete a CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) that verifies that they are a human being completing the form rather than an automated process. Our interventions consist of a series of messages inserted into the CAPTCHA pop-up.
The trial aims to test the differential impact of the original CAPTCHA (which contains no behavioural prompts), and various prompts inspired by behavioural science. These include an honesty declaration, information about public goods paid for by taxes and punishment for noncompliance, and allowing participants to choose what they believe to be a good use for public funds, or an appropriate punishment. We hypothesise that these primes will induce more honest (higher) tax declarations through various cognitive channels.
External Link(s)

Registration Citation

Citation
Kettle, Stewart and Michael Sanders. 2014. "Using CAPTCHA and honesty primes to increase tax collection in Guatemala." AEA RCT Registry. June 30. https://doi.org/10.1257/rct.424-1.0
Former Citation
Kettle, Stewart and Michael Sanders. 2014. "Using CAPTCHA and honesty primes to increase tax collection in Guatemala." AEA RCT Registry. June 30. https://www.socialscienceregistry.org/trials/424/history/2005
Experimental Details

Interventions

Intervention(s)
The trial involves seven arms; a control arm where taxpayers receive the original CAPTCHA, and six adapted versions. The original Spanish versions of the treatment CAPTCHAs are included in appendix A. The CAPTCHA appears after the taxpayer selects the tax form to fill in on the main Declaraguate website, and before the form page.
Intervention (Hidden)
The trial involves seven arms; a control arm where taxpayers receive the original CAPTCHA, and six adapted versions. The original Spanish versions of the treatment CAPTCHAs are included in appendix A. The CAPTCHA appears after the taxpayer selects the tax form to fill in on the main Declaraguate website, and before the form page. This customer journey of taxpayers through the website is included in Annex B.
Control
This group will see the original CAPTCHA used on the Declaraguate website. This CAPTCHA will also continue to be used for all other tax forms on the Declaraguate website whilst the trial is running. Figure 1 below shows a translated version of this CAPTCHA.

Figure 1: Control CAPTCHA
The treatment arms all consist of messages and options which are entered directly above the ‘fill form’ button which the taxpayer must press to continue to the next form. Some of the treatment CAPTCHAs involve specific actions that the taxpayer has to complete before they are able to press the ‘fill form’ button, these are described further below.
(T1) Honesty Declaration
This CAPTCHA includes an honesty declaration which translates as:
‘Declaration: I will fill out this form honestly
Please sign your name to confirm this declaration’
The taxpayer must then enter their name in a box below this statement before being able to press the ‘fill form’ button .
This CAPTCHA is based around the idea that most people see themselves as honest and act according to the ethical codes of society, however need reminders that these ethical codes should apply to all their decision making. For instance, research has found that people are more honest when they are asked to recall the Ten Commandments just before making ethical decisions (Mazar et al 2008).
Specifically in the context of filing forms, Shu et al (2012), show that moving an honesty declaration to the start of a car insurance form increases truthful reporting of mileage driven each year. The intervention was simple but very successful: while traditionally people would fill out their annual insurance form and then sign their name at the bottom of the form, researchers moved both the honesty declaration and signature box to the top of the insurance form before they would fill it out. The intervention increased the amount of miles declared by 10%.
The ‘honesty declaration CAPTCHA’ applies the same considerations to tax declarations. We hypothesise that when participants sign their full name below an honest declaration, teir moral code is made more salient and hence they will be more likely to comply with it.
(T2) Public Good
This CAPTCHA involves an image of the Guatemalan flag and the following public goods message:
‘In 2013 your taxes helped pay for schools, hospitals and policemen.’
This CAPTCHA draws on the ‘non-deterrence’ approach to taxation; that citizens are fundamentally predisposed to cooperate with tax authorities. The approach contends that taxpayers’ decisions are not based on utility maximisation alone, but are influenced by morality, social norms, and public goods, amongst other concepts (Andreoni et al. 1998, Kirchler, 2007, Erard and Feinstein, 1994, Torgler, 2007). In this view, the relationship between tax authority and taxpayer is basically cooperative, with the latter asking “what should I do?” rather than “what can I get away with?” (McGraw and Scholz, 1991).
In terms of administrative policy, the non-deterrence approach recommends that taxpayers are treated fairly and respectfully. They should be provided with clear and helpful information in order to make decisions. The authority should create a helpful service that makes it easy to comply. The route to reducing non-compliance is to persuade taxpayers by emphasizing that tax compliance is an ethical activity that is practised by the great majority of people, and that it creates valued public goods (Kirchler, 2007). Following from this the CAPTCHA is designed to prime taxpayers to think about the public goods that tax money is spent on. We hypothesise that people who consider the benefits of their own contribution will weigh this more heavily, relative to their own private consumption, than they would do otherwise, and so will be more likely to make an honest declaration.
(T3) Enforcement
This CAPTCHA involves an image of a gavel and the following text:
‘5,060 taxpayers in early 2014 had legal proceedings for breach of their tax obligations.’
This CAPTCHA is based on the idea that signals of punishment enforcement can be a powerful deterrence against unlawful actions (Fehr & Gächter 2000). Knowledge that others have been caught for a crime can lead people to overestimate the likelihood that could be the next to be caught. (Tversky & Kahneman 1979)
Enforcement messages to increase taxation are based on the original economic model used to analyse tax compliance developed by Allingham and Sandmo (1972). The model sees taxpayers as rational utility maximisers and suggests that a taxpayer’s decision whether to pay or evade tax is based on the trade-off between the monetary cost of complying and the expected cost of evading. The expected cost of evading is in turn based on the probability of getting caught, the probability of punishment if caught, and the punishment that they would be liable for. Accordingly, this “deterrence” model predicts that the way to deter tax evasion is through increased sanctions for noncompliance.
The enforcement CAPTCHA draws on making enforcement more salient to the taxpayer. Similarly to the above, our hypothesis is that information on potential costs makes these costs more salient and so adjusts relative weights of private consumption and taxpaying for the participant. Enforcement messages on tax letters have been shown to be effective in a number of countries including Argentina and Venezuela (Castro and Scartascini, 2013; Ortega and Sanguinetti, 2013).
(T4) Choice Public Good
This CAPTCHA gives the taxpayer a choice of public goods that they would like to see tax money spent on. The text of the CAPTCHA reads:
‘Please choose what you want us to direct your tax money to:’
The taxpayer is then given the choice of selecting schools, hospitals, or policemen.
The design of this CAPTCHA incorporates a choice design with a public goods message. Research shows that people like to have a say in the design of a product or policy. This has been dubbed the ‘IKEA effect’ because people value their own creations (like assembling an IKEA cupboard or providing input into policy) higher than that of others (Norton et al 2011). It can thus be effective to allow people to give input towards which cause they think that their tax money should go.
This CAPTCHA intervention aims to prime the taxpayer into thinking about the public goods that tax goes towards, giving them the option to voice their preference. Our hypothesis is that even a cursory sense of control over public spending provides a sense of ownership and hence lead to taxpaying being more highly valued. The number of choices is limited to three in order to avoid potential cognitive exhaustion before filling out the form (Vohs et al 2008).
(T5) Choice Enforcement
This CAPTCHA gives the taxpayer a choice of the punishment that they think people should receive for fraudulently declaring their tax.
‘Please tell us what you think should happen to people who fill out their forms dishonestly’
The taxpayer is then given the choice of selecting; pay a fine, confiscate your assets, or go to jail.
This design is based on the same premise as above but incorporates punishment choices rather than public good choices. This CAPTCHA aims to prime the taxpayer into thinking about what should happen to people that don’t pay their tax honestly.
The message also aims to invoke cognitive dissonance in taxpayers that would have filled out the form dishonestly. We hypothesise that participants will suffer from cognitive dissonance due to the clash between their beliefs that dishonest declarers should be punished, and their dishonest actions. Under this hypothesis, participants would seek to resolve this dissonance by bringing their actions in line with their beliefs.
(T6) Self Select ‘I am honest’
This CAPTCHA allows the taxpayer to self-select into being honest. The CAPTCHA reads:
Which of the following do you identify with?
The taxpayer then has to select one of the following two options;
- I am an honest taxpayer who declares truthfully
- I'm a busy taxpayer declares quickly
Intervention Start Date
2014-07-27
Intervention End Date
2014-12-05

Primary Outcomes

Primary Outcomes (end points)
The objective of this trial can be framed in one of two ways – to increase tax revenues in Guatemala, and to encourage more honesty in the declaration of tax liabilities.
For the purposes of this trial we take the two measures to be equivalent. Following Shu et al (2012), we assume that if a participant is reporting their tax liability dishonestly (as opposed to making an error), they will systematically understate their liability relative to the truth. We further assume that misreporting through error is statistically similar to classical measurement error, and so is randomly distributed around the true value according to some unknown distribution. For the purposes of our power calculations, we assume that this error is (a) classical (b) orthogonal on treatment, and (c) does not interact with treatment – i.e. that the measurement error does not effect treatment. If (a) does not hold, we may experience a loss of power, and any treatment effect detected would be a lower bound on the true effect size; (b) holds by design of the trial, and the validity of (c) is irrelevant if (b) holds.
Hence, total tax liability declared is our primary outcome measure.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We adopt a randomised controlled trial design with parallel conditions and individual level randomisation. Participants (Guatemalans declaring their tax liability), will be randomly assigned to see one of 7 CAPTCHAs (control or one of the six treatment arms) after selecting the tax form to fill in on the main Declaraguate website, and before the form page.
This trial will be conducted over the course of 4 months. There are four types of tax included in this trial described below . The trial involves approximately 4% of Guatemalans, and all Guatemalans who declare online for these four tax regimes. For income tax and VAT general this is 100% of declarations as online declaration is mandatory for these tax regimes, for VAT small taxpayers approximately 90% of declarations are made online.
Experimental Design Details
We adopt a randomised controlled trial design with parallel conditions and individual level randomisation. Participants (Guatemalans declaring their tax liability), will be randomly assigned to see one of 7 CAPTCHAs (control or one of the six treatment arms) after selecting the tax form to fill in on the main Declaraguate website, and before the form page.
This trial will be conducted over the course of 4 months. There are four types of tax included in this trial described below . The trial involves approximately 4% of Guatemalans, and all Guatemalans who declare online for these four tax regimes. For income tax and VAT general this is 100% of declarations as online declaration is mandatory for these tax regimes, for VAT small taxpayers approximately 90% of declarations are made online.
It is reasonable to assume that our interventions may have differing effects for taxpayers declaring for the four different regimes. For this reason we produce separate results for each type of tax in our trial. This will be described in more detail later in this protocol. As well as our primary analytical objectives (determining whether more tax has been paid), we aim to gain insights into the behavioural mechanisms in action and to identify the medium-term effects of messaging (over the course of the four months of the trial). We note that choices of tax are fixed within the period of the trial, and so although these are endogenous they do not interfere with our analysis.
Income Tax
Guatemalan income tax is charged in one of two ways, selected by the taxpayer. The two taxes are paid differently (using different forms), and we are considering both in this trial.
Regime over profits from lucrative activities
Taxpayers can choose to pay income tax in this way. This is charged at a rate of 28% on non-deductible income. Deductions include income from capital. This tax must be paid every 3 months, with returns filed within ten days of the end of the quarter.
Simplified optional regime over income from lucrative activities
Participants in this scheme pay 7% of their annual income in taxation (from April 2014). Participants in this tax regime must make monthly tax returns not later than the 10th of the subsequent month, and an aggregated return to be completed annually.
There are good reasons to expect different treatment effects depending on the tax being declared. The regime over profits from lucrative activities is charged at a higher rate, and so the incentive to behave dishonestly may be higher. Individuals and businesses that complete this kind of return may however be more affluent, which in turn could reduce their incentive to be dishonest due to a lower marginal value of money
Finally, the simplified option is, as its name implies, less complex a form. This may lead to greater honesty as there are fewer dimensions across which it is possible to obfuscate tax liability.
Value Added Tax (VAT)
Two regimes exist for Value Added Tax (VAT) payments in Guatemala. VAT is levied at 12% for taxpayers under the general regime (with annual turnover of more than GTQ 150,000), and 5% for taxpayers in the small taxpayers regime (with annual turnover of less than GTQ 150,000) . VAT is charged on sales of goods within the country, sale of services in the country, any imported goods, leasing contracts, transfers of real estate, and insurance and bond sales. Exports, banking activities, payments in-kind, mergers, trade in financial instruments, and trust arrangements are exempt from VAT. We consider taxpayers under both regimes. Similar to income tax we have reason to expect differential treatment impacts for taxpayers under the two VAT regimes.
Randomization Method
Randomised by javascript randomisation code
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1,600,000
Sample size: planned number of observations
1,600,000
Sample size (or number of clusters) by treatment arms
Approximately 240,000
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Based on our calculations, a 7 arm trial will be powered to detect a rise in tax liability declared of 1% with approximately 1,687,000 observations. This is a very conservative effect size, and could be detected with roughly 4 months of data for this tax type. We will therefore run the trial for 4 months. Power may be reduced due to a lack of strict independence between observations.
IRB

Institutional Review Boards (IRBs)

IRB Name
Behavioural Insights Team Internal Review
IRB Approval Date
2014-06-30
IRB Approval Number
EC2014019

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

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