metadata: authors: 0: author: Del Carmen,Giselle
1: author: Espinal Hernandez,Edgardo Enrique
2: author: De Gouvea Scot De Arruda,Thiago
count: Honduras
docna: 0: docna: Targeting in Tax Compliance Interventions :
            Experimental Evidence from Honduras
docty: Policy Research Working Paper
subtopic: Public Sector Economics,Public Finance Decentralization and
            Poverty Reduction,Tax Administration,Tax Law,Macro-Fiscal Policy,Economic Adjustment and Lending,Taxation & Subsidies,Financial Sector Policy,Educational Sciences
teratopic: Law and Development,Public Sector Development,Macroeconomics and Economic Growth,Education
abstracts: cdata!: Tax authorities often use low-cost
            communication with taxpayers to encourage voluntary
            compliance and avoid other costly interventions. This paper
            reports findings from an experiment with more than 30,000
            taxpayers in Honduras, designed to assess how taxpayers with
            different risk scores respond to a communication
            intervention. Across several outcomes, the average effect of
            the intervention on compliance was 0. Contrary to the
            expectation of experts surveyed, only taxpayers considered
            to be at low risk of noncompliance increase their filing and
            reported income. Using rich administrative data and a causal
            forest algorithm, the paper finds that ex-ante predicted
            risk and responsiveness to the intervention are negatively
            correlated. These findings can inform the design of targeted
            interventions by tax authorities.
geo_reg_key: 119228,517191,545133
repnme: repnme: Targeting in Tax Compliance Interventions :
            Experimental Evidence from Honduras
display_title: Targeting in Tax Compliance
            Interventions : Experimental Evidence from Honduras
listing_relative_url: /research/2022/03/33757857/targeting-tax-compliance-interventions-experimental-evidence-honduras
topicv3: Tax and Revenue Policy and Administration
            m1327008 720,Education m1327048 470,Taxation m1327615 1121,Financial Sector Development m1327681 1920,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2022-03-14T00:00:00Z
dois: 10.1596/1813-9450-9967
historic_topic: Law and Development,Public Sector Development,Macroeconomics and Economic Growth,Education
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 66
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: 1295861
model_extractions: Policy Research Working Paper 9967 Targeting in Tax Compliance Interventions Experimental Evidence from Honduras _Giselle Del Carmen Edgardo Enrique Espinal Hernandez Thiago Scot_ Development Economics Development Impact Evaluation Group March 2022
Policy Research Working Paper 9967

# **Abstract**

Tax authorities often use low-cost communication with taxpayers to encourage voluntary compliance and avoid other costly interventions. This paper reports findings from an experiment with more than 30,000 taxpayers in Honduras, designed to assess how taxpayers with different risk scores respond to a communication intervention. Across several outcomes, the average effect of the intervention on compliance was null. Contrary to the expectation of experts surveyed, only taxpayers considered to be at low risk of noncompliance increase their filing and reported income. Using rich administrative data and a causal forest algorithm, the paper finds that ex-ante predicted risk and responsiveness to the intervention are negatively correlated. These findings can inform the design of targeted interventions by tax authorities.

This paper is a product of the Development Impact Evaluation Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tscot@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Targeting in Tax Compliance Interventions: Experimental Evidence from Honduras Giselle Del Carmen Edgardo Enrique Espinal Hernandez Thiago Scot<sup>_*_</sup>

**Keywords** : Tax compliance, Tax evasion, Firms & Productivity, Targeting **JEL Codes** : H26, O17

> _*_ Development Impact Evaluation (DIME), World Bank. Corresponding author at tscot@worldbank.org. Giselle del Carmen (Enodo): g.e.delcarmen@gmail.com. Edgardo Enrique Espinal Hernandez (Servicio de Administración de Rentas (SAR) de Honduras): eespinal@sar.gob.hn. We acknowledge the collaboration of the team at the Honduran Tax Authority (SAR), in particular Oziel Aron Fernandez Herrera, David Fernando Pineda Pinto, Gabriel Ricardo Perdomo Zelaya, Wilman Alonso Ponce Nuñez, Milton Alexander Maldonado Castillo, Pedro Rafael Zuniga Figueroa, Roldan Manuel Enamorado Irias, Carlos Mauricio Zelaya Guzman, Ximena Fernanda Venis Dilworth, Mario Josué Ramos Canales, Fabián Alfredo Gómez Guillen. This project also benefited from comments from Marta Ruiz-Arranz, Martin Ardanaz, Jordi Prat Cordero, Ricardo Perez-Truglia, Reed Walker, Guo Xu, Steve Tadellis, Ernesto Dal Bo, Frederico Finan, Muhammad Yasir Khan, Petr Martynov, Arianna Legovini and participants at the UC Berkeley Development Lunch. The main email experiment was submitted to UC Berkeley Internal Review Board (IRB) under ID 2019-12-12772 and was deemed "not Human Subject Research". The forecast survey was submitted to UC Berkeley IRB under ID 2020-03-13079 and approved on March 25th 2020. This study has been enrolled in the AEA Trial Registry (RCT ID: AEARCTR-0005285)
# **1 Introduction**

Tax collection in low-income countries is very different from what is observed in higher income settings since state capacity determines the ability of governments to enforce taxation. Tax revenue is equivalent to about 16% of GDP in OECD countries but less than 12% in low-income ones. The composition of collected taxes is also very different: where high-income countries collect over 50% of their total taxes through income taxes, low- and middle-income countries collect less than 30% and rely much more on taxes on goods and services.

Collecting income taxes can be particularly hard for governments given the complexity involved in correctly assessing liabilities (Gordon & Li, 2009). However, the capacity to obtain and process the type of information needed to assess income tax liabilities is rapidly increasing in several low- and middle-income countries. International organizations are committed to improving tax capacity: approximately $200 million in Official Development Assistance (ODA) was aimed at improving tax capacity in 2014 (International Monetary Fund et al., 2016). Nonetheless, these improvements in "government intelligence" and access to technology do not translate automatically into higher compliance but require other actions by the authorities (Okunogbe & Santoro, 2021). For example, communicating to taxpayers the new information available to the tax authority might trigger higher compliance if their beliefs about the probability of punishment are updated.

In this paper we partner with the Tax Authority in Honduras (SAR, for _Servicio de Administración de Rentas_ ) to experimentally estimate the impact of providing information to taxpayers about SAR’s knowledge on their transactions. Using a recently developed risk model, we conduct an experiment with approximately 32,000 taxpayers considered to be at-risk of noncompliance. Taxpayers in the control group receive a reg– ular reminder about the income tax filing deadline a usual communication provided by the tax authority. In contrast, those in the treatment group are informed that the tax authority observes specific transactions they engaged in, implying knowledge about (part) of their realized revenue. In that sense, we see our experimental treatment as providing taxpayers with data about the information set available to the tax authority. That allows us to estimate the average effect of treatment assignment on a range of compliance outcomes, measured using administrative data on tax filing.

While there is strong evidence that the average effect of these types of interventions is positive (see Mascagni (2018) and Castro & Scartascini (2015) for recent reviews), this is not inconsistent with negative or null impacts on some subset of taxpayers (De Neve et al., 2021). We argue that measuring this heterogeneity is crucial for authorities to better target this kind of intervention. While the return on investment of these interventions is often perceived to be extremely high given the very low marginal costs of letter or 2
email communications, a less discussed potential hidden cost is the credibility of the tax authority. When capacity is limited, it is often not possible to follow-up on this type of low cost communications by performing more expensive interventions, such as in-person audits. This poses a reputational risk: revealing knowledge about potential misreporting and not acting might lead taxpayers to believe (perhaps accurately) that the expected punishment for non-compliance is low (Slemrod et al., 2001; Gangl et al., 2015)<sup>1</sup> .

We present heterogeneity exercises of the treatment effects on key dimensions built into the experimental design, but also explore the rich data on past behavior of taxpayers to predict which subjects are most responsive to the intervention. This is a classic targeting problem. Our goal is to predict, upon observing characteristics of taxpayers, what is their expected response to a communication intervention such as the one implemented in this study. In order to do so we borrow tools from the recent literature on causal machine learning and use a causal forest algorithm to estimate conditional average treatment effects (CATE) across the sample of taxpayers (Athey & Imbens, 2016; Wager & Athey, 2018). With those estimates at hand, we can assess how the treatment effects compare to the perceived risk level of each taxpayer, computed by the tax authority using non-experimental data from before the intervention. This is informative about future communication interventions since currently those with higher assessed risk are more likely to be targeted, but it is not clear whether these are the most responsive taxpayers<sup>2</sup> .

Our main finding is that, on average, the intervention had no effect on a range of compliance outcomes. Taxpayers assigned to receive the treatment were no more likely to file income taxes, declare higher revenues, or declare higher taxable income. We estimate precise zero effects: we can rule out changes in filing probability as small as one percentage point and increases in declared revenue of more than 5%. To benchmark our results, we surveyed experts before the intervention and elicited their predictions about our experimental findings. Our estimates are significantly lower than experts’ forecasts. The estimated null effects are observed both for the pooled treatment sample and for individual treatment arms that included distinct framing messages for compliance. We also show that the intervention did not increase the timeliness of tax filing or other tax compliance measures such as declared VAT sales or corrections of past filings.

The heterogeneity analysis also suggests that, across a range of dimensions, the treatment effect was mostly null. The only consistent positive treatment effect across our primary outcomes seems to be among taxpayers with low ex-ante assessed risk level. Compared to other low-risk taxpayers in the control group, those receiving the email

> 1But see Bergolo et al. (2019) for evidence that these types of interventions might function as "scarecrows", generating emotional reaction that leads to compliance rather than changing the beliefs of taxpayers about enforcement capacity.

> 2In appendix D, we present a simple model of heterogeneous treatment effects in tax compliance and illustrate the value of additional experimental information on targeting.

3
with information about their revenues available to the tax authority were more likely to file taxes (+ 2.3 p.p.) and declared higher gross revenue (+ L103,000<sup>3</sup> or 8% of the control mean). The increase in gross revenues, nonetheless, was accompanied by an increase in claimed deductions (+ L92,000) resulting in a modest increase in taxable income (L10,000 or 11% of the control mean). The offsetting increase in deductions is consistent with previous findings from Carrillo et al. (2017) in a similar setting in Ecuador.

The finding of significant impact only for taxpayers with lower perceived risk-level is contrary to what the experts we surveyed expected. As previously mentioned, it is also at odds with the current practice of the tax authority, which focuses communication interventions on medium- and high-risk taxpayers. Using the causal forest model, we predict treatment effects conditional on observed covariates for all taxpayers and show a similar finding: the correlation between (conditional) treatment effects and a continuous measure of risk-level assessed by the tax authority is negative and the average treatment effect across taxpayers is only positive for those in the low- and medium-low risk groups.

These results suggest that, while models to predict risk of non-compliance are an important tool for tax authorities, they are not sufficient to determine targeting. Governments face a menu of policy tools to assure compliance, ranging from "soft" communication encouraging truthful reporting to "hard" audits. Experimental evidence on the causal impact of different tools for distinct groups of taxpayers can help governments tailor interventions to maximize impact.

One important caveat, discussed in more detail throughout the paper, is that our intervention happened at the onset of the Covid-19 crisis in Honduras. Two weeks after we contacted taxpayers, the country entered a severe lock-down that depressed economic activity and the filing deadline for income taxes was twice postponed. These facts suggest that the intervention was likely much less salient in the mind of taxpayers. Furthermore, compliance in general was clearly affected by the crisis; 86% of taxpayers in our sample filed taxes in 2019 but only 62% did in 2020. This implies that the external validity of our findings should be taken with caution.

Our study contributes to the now large literature on communication interventions aimed at increasing taxpayers compliance, reviewed by Mascagni (2018) and Hallsworth (2014)<sup>4</sup> . Our experiment is particularly informative about exploring third-party information in communication interventions, similar to the work of Brockmeyer et al. (2019) in Costa Rica and Carrillo et al. (2017) in Ecuador. In addition to informing taxpayers about SAR’s knowledge of their transactions, we also randomly vary how we frame the importance of compliance. One third of the treatment group was reminded that non-compliance carries monetary penalties; one third was reminded that SAR can deny

> 3Approximately USD 4,000.

> 4For recent studies on similar interventions in low-income countries, see for example Collin et al. (2021), Cohen (2020), Okunogbe (2021) and Hoy et al. (2021).

4
documents necessary for their operations; and the remaining group received a message appealing to tax morale, claiming that "with your taxes we build a better country". The effects of appealing to tax morale (Luttmer & Singhal, 2014) in similar interventions have so far been mixed. De Neve et al. (2021) find that appeals to the social norm of payment or the importance of public goods have null or negative effects across the compliance spectrum in Belgium, while Castro & Scartascini (2015) find null effects in Argentina for local property taxes. Kettle et al. (2016), on the other hand, find that informing late filers in Guatemala that they were in a minority of non-compliers was as effective as sending a threatening message. Although the exact treatments are not the same, it is somewhat surprising that appeals to tax morale are more effective in Guatemala than in Argentina or Belgium, given low levels of confidence in the government: according to Latinobarometro, only 22% of Guatemalans answer having some or a lot of confidence in the government between 2015-2018, lower than the 30% reported by Argentinians. The experts we surveyed systematically predicted that the tax morale treatment would likewise be less effective than the other two. In our main estimates all three treatment arms are estimated to have null effects on compliance and we cannot reject that the effect of the tax morale arm is equal to the other interventions.

Finally, our exercise using the causal forest algorithm<sup>5</sup> contributes to a recent literature exploring heterogeneity analysis in experimental settings to inform program targeting, ranging from cash and asset transfers to the poor (Alatas et al., 2012) to the use of cellphones to monitor agricultural extension workers (Dal Bó et al., 2021). In a similar exercise but in a different setting, Hussam et al. (2020) worked with experimental grants to entrepreneurs in India, and showed that the causal forest algorithm can do no better in predicting returns to the program than assessment by other community members. We compare our treatment effect estimates with the baseline risk assessment of each taxpayer, showing that experimental results are a useful tool for the tax authority to improve targeting.

# **2 Context Taxation in Honduras**

Honduras is a lower middle-income country in Central America with GDP per capita of $5,800 PPP in 2018. Similar to other countries with comparable income levels, the country collects less than 20% of GDP in taxes and is very reliant on taxes on goods and services, making up over 50% of total tax collection (International Monetary Fund, 2018).

The fiscal year runs from January 1st to December 31st and taxpayers must file an income tax declaration by April 30th of the following year. Corporations are taxed at

> 5See Davis & Heller (2017) and Bertrand et al. (2017) for detailed discussions on the application of causal forest algorithm in experimental settings.

5
a flat rate of 25% on profits (gross revenues minus cost deductions)<sup>6</sup> . Non-incorporated taxpayers face a progressive tax schedule: in FY2019, net income below L159,000<sup>7</sup> was exempt from taxation and higher incomes are taxed with increasing marginal tax rates in three brackets of 15%, 20% and 25%<sup>8</sup> . All taxpayers with commercial activities (both corporations and non-incorporated) are also liable for monthly sales taxes of 15% over total goods and services sold. Individuals whose sole income sources are wages or capital income (dividends, interest) are withheld at source and do not have to file an income tax declaration. All taxpayers in our experiment, therefore, engage in some type of commercial activity that generate revenues that must be declared.

Between 2014-2019, the Honduran Tax Administration underwent a series of reforms and institutional changes aimed at strengthening the country’s fiscal system. These included improvements in operational management, recruitment of personnel, a new billing regime and the adoption of new technologies for data processing, among others. Since then tax revenues increased from 15% of GDP to over 18%, contributing to a decrease in the fiscal deficit of nearly 6 percentage points of GDP (7.9% in 2013 vs. 2.1% in 2018) (International Monetary Fund, 2018). Furthermore, SAR has been working to consolidate these institutional reforms and implement new tools to ensure fiscal compliance, including an internal risk model following international best practices.

# **3 Research design**

## **3.1 Intervention**

Jointly with the Tax Authority, we sent emails to approximately 32,000 taxpayers seven weeks before the original income tax filing deadline for FY2019<sup>9</sup> . Since our main goal is to estimate the effect of the content of emails, we contacted all taxpayers in the experimental sample, including those in the control group. This allows us to attribute any differential behavior among treated units to the content of messages and not to the simple fact of receiving a message from the government<sup>10</sup> .

We summarized our randomization design in Figure 1. The control group received

> 6Since 2003, corporations must also assess a 1% tax on their net assets over L3 million and pay the largest amount between asset and income taxes. In our experimental sample only 10% of corporations (3% of all taxpayers) paid the asset tax in FY2018.

> 7Approximately USD6,400 based on an average exchange rate of 25 Lempiras per US dollar for 2018.

> 8The law also allows for a L40,000 deduction of medical costs. In practice this deduction is applied to all non-incorporated taxpayers, regardless of claiming the deduction, such that the exemption threshold is higher.

> 9Due to the Covid-19 crisis, the tax filing date was postponed from April 30th to August 31st. More details on how we dealt with these changes are provided below.

> 10The tax authority sends on average 200,000 emails every month to taxpayers, notifying them about various fiscal procedures. Taxpayers in our experimental sample only received the experimental email in the weeks before and after the intervention.

6
an email<sup>11</sup> , presented in Figure 8, with a reminder about the filing deadline and the importance of truthfully reporting their tax liabilities<sup>12</sup> . It also included a link to the website of the tax administration where detailed information on how to declare taxes online was provided.

Taxpayers in the treatment group received emails containing the same informational content offered to control units, but were additionally provided with i) information available to the tax authority regarding their transactions and ii) slightly different framing messages on why they should comply with their obligations. For the majority of our experimental sample (71%) some third-party information on their transactions in FY2019 was available and that fact is included in the message. For the remaining taxpayers, no third-party information was available but their previous reporting behavior raised flags about non-compliance; that is the information included in their email. Since our treatment group includes three arms with different framings on the importance of compliance, we have in total six different types of messages, illustrated in Figure 9 through Figure 14<sup>13</sup> .

Taxpayers for which third-party information was available (Figure 9 through Figure 11) received a message informing that "In the sources of information available to the Tax Administration, your following commercial transactions for FY2019 period have been identified", followed by up to four types of transactions: sales to other taxpayers; sales through debit/credit cards; sales or services to the State; and exports<sup>14</sup> . These messages are personalized, so that each taxpayer is only informed of categories for which the TA observes their transactions (i.e. there is no deceit or bluffing involved).

Taxpayers flagged for risk of non-compliance but for whom third-party information was not available were notified (Figure 12 through Figure 14) that "In the sources of information available to the Tax Administration, the following behavior has been identified in your tax returns", followed by up to three "anomalies" in their past filings: declaring three or more years with losses in the previous five fiscal periods; financial transactions incompatible with declared revenue; and declared tax liability "atypical" for tax units in similar industries and revenue size.

As seen in the messages, we have three different treatment arms in which we change a

> 11All emails, for treatment and control taxpayers, included the same subject "Important: Notice of Tax Obligation" and were sent from an institutional email address used by the tax authority.

> 12The main part of the email reads, in English: "The Revenue Administration Service (SAR) reminds you that the obligation to file and pay the Sworn Declaration of Income Tax period 2019 expires on April 30, 2020. You are reminded that the Declaration must contain exact and truthful information, reporting all income obtained and that deductions will have to be supported by valid tax documents.".

> 13To further strengthen the intervention, the content of messages were informed by previous experiments using insights from behavioral economics (Dalton et al., 2019), such as making the text simpler, personalized to each taxpayer and including actionable information (link to SAR’s website). The messages were also analyzed in focus group discussions with SAR officials (including communication experts) and policymakers in Honduras.

> 14Unlike Brockmeyer et al. (2019) or Carrillo et al. (2017), the emails did not include monetary values on specific transactions or information on trading partners due to legal restrictions.

7
small part of the email, emphasizing different reasons why taxpayers should comply with their tax obligations.

**Sanctions treatment** : these messages emphasize the sanctions associated with nonor late-filing, by stating that "In case of not fulfilling your obligation, you will be subject to the sanctions established by the Tax Code in Articles 160 and 163."<sup>15</sup> These are similar to other "threat" messages used in the literature, explaining or making more salient to subjects the monetary costs of non-compliance.

**Procedure denial treatment** : this treatment arm also includes a threatening message, but instead of mentioning possible fines it invokes the right of the TA to withhold important documents necessary for business’ operations in case they are non-compliant. The additional message reads "In case of not fulfilling your obligation, you will be affected in obtaining proofs of "pagos a cuenta", solvency and fiscal documents"<sup>16</sup> .

**Tax morale treatment** : in this treatment arm the email contains two pieces of additional content: a motto upfront stating "For you, for your kids, for Honduras, pay your taxes!" and a paragraph stating "The Honduras we all want for our children with education, health, infrastructure and security is the fruit of the efforts of all its good citizens, thanks to their taxes we build a better country". This message appeals to the fact that taxes are used to finance public goods and it is the duty of "good citizens" to pay their taxes.

All emails were sent between March 11th-12th 2020, approximately seven weeks before the original FY2019 tax filing deadline. Due to the Covid-19 crisis and the strict lockdown implemented in the country, however, the filing date was initially postponed to June 30th and subsequently to August 31st. The email service used by the tax authority to send mass communications allows us to observe the "outcome" of every email sent, including whether they reached the taxpayers’ mailbox and if they ever opened it. We consider taxpayers to be compliant, i.e. effectively receiving the treatment, if they ever open the email.

> 15The two articles mentioned determine fines for non-presentation or late presentation, as well as nonor late payments of tax obligations (see https://www.sar.gob.hn/leyes/).

> 16"Pagos a cuenta" refers to a special regime in which clients do not have to withhold income taxes on services offered by independent professionals - this is a benefit to these professionals since it preserves cash flow until the tax payment date. "Solvency" is a statement by the TA that the taxpayer is up to date with their obligations and is required to perform transactions with state entities. "Fiscal documents" is understood to be fiscal receipts, which need to be approved and provided by the tax authority so that firms can legally issue them.

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# **3.2 Experimental sample and randomization**

The experimental sample consists of 31,396 taxpayers considered to be at-risk of noncompliance in FY2018. The risk was assessed using SAR’s internal risk model ( _Modelo de Gestión de Riesgo de Honduras_ , MGR-H)<sup>17</sup> that considers both discrepancies between declared income by taxpayers and information reported by third-parties, as well as anomalies, defined as outcomes that seem inconsistent with other similar tax units. Each taxpayer is assigned a score between zero and one that determines the predicted level of non-compliance risk<sup>18</sup> . Based on the risk management model, SAR determines which treatment actions should be implemented to mitigate risks and prioritizes the allocation of enforcement resources to the high-risk taxpayers.

The tax authority uses five main sources of information on the income of taxpayers provided by third-parties. First, the Monthly Declaration of Purchases ( _Declaración Mensual de Compras_ , DMC) is an informative declaration filed monthly by large taxpayers. They can use declared purchases from other registered taxpayers as credits against their liabilities on sales taxes (effectively a VAT system). The Monthly Declaration of Withholding ( _Declaración Mensual de Retenciones_ , DMR) is also filed monthly by taxpayers designated as "withholding agents", such as firms that retain and pay income taxes of their employees. Third, the Declaration of Credit Card Administrators ( _Declaración de Retenciones de las Administradoras de Tarjetas de Débito y Crédito_ , ATC) is filed by credit and debit card companies about point-of-sales purchases using their system. Credit and Debit Card administrators are also withholding agents, paying a share of sales taxes due in each transaction. Finally, two sources of third-party information are provided by other government agencies: the Integrated System of Financial Administration ( _Sistema de Administración Financiera Integrada_ , SIAFI) provides information on all revenue made by sales to government entities, and all export sales are also informed to the tax authority.

The risk model also performs a series of risk analyses in the absence of third party information. These include, but are not limited to, flagging taxpayers reporting repeated losses and performing cluster analyses that group "similar" units and flag those with reported outcomes (such as tax liabilities) that are inconsistent with their peers. The tax authority also has access to information about financial transactions such as loans, and uses that to flag taxpayers that declare revenues inconsistent with their financial activities.

> 17The model follows the ISO 31000 risk management standard and international best practices regarding fiscal procedures as established by the International Monetary Fund (IMF), Organization for Economic Cooperation and Development (OECD) and the Center Inter-American Tax Administrations (CIAT).

> 18The model uses risks of non-compliance identified throughout the life-cycle of taxpayers (registration, presentation, payment and truthfulness) in order to maximize tax compliance. It combines probability variables (frequency with which the risk occurs) and consequence (materiality or economic damage caused by the risk).

9
Starting from a broader set of at-risk taxpayers, we performed two restrictions to obtain the final experimental sample. First, in order to avoid spillovers between treatment and control units, only taxpayers with a unique primary email address were included in the experimental sample<sup>19</sup> . Second, power calculations exercises suggested we could significantly increase minimum detectable effects (MDE) by dropping extremely large taxpayers. Figure A5 reports MDE for our four main outcomes of interest when we trim our sample at different percentiles. Considering all primary outcomes, we decided to trim the sample at the 97th percentile of declared revenue distribution, arriving at our final sample of 31,396 taxpayers<sup>20</sup> .

We implement a stratified randomization, at the taxpayer level, using 60 strata defined by whether third-party information was available or not; whether the taxpayer was a corporation; municipality of operations defined as Distrito Central (capital Tegucigalpa), San Pedro Sula (second largest city, often referred as the industrial capital) or other; and five risk levels as defined by the tax authority (2*2*3*5 = 60 strata). In each strata 49 percent of taxpayers were allocated to the control group and the remaining 51 percent in three equally sized treatment arms. Following Bruhn & McKenzie (2009) we deal with "misfits" (remaining taxpayers in each strata) by randomly assigning them to one of the four groups (control + 3 treatments) using the above weights as assignment probabilities<sup>21</sup> .

# **3.3 Benchmarking results: Expert forecast survey**

In order to benchmark the magnitude of our results, we follow DellaVigna & Pope (2018) and DellaVigna et al. (2020) and surveyed academics and policymakers on their predictions about the impacts of this experiment. We focused on collecting forecasts from three groups of subjects: academic economists (faculty, PhD students and researchers at academic institutions); public sector workers in Honduras, in particular those working at the tax authority; and policymakers or researchers in international development organizations<sup>22</sup> .

The main results of the survey are presented in Table 7. In terms of magnitudes of

> 19 Approximately 4,400 taxpayers were also deemed at-risk of non-compliance but shared a primary contact email with other units, either due to joint ownership or an accounting firm as primary contact. These taxpayers were excluded from the main sample and were assessed in a separate, smaller experiment to estimate spillover effects.

> 20Details about power calculations used in Figure A5 are discussed in Appendix C. Taxpayers with declared gross revenue above L19.4 million (approximately USD 780,000) in either FY2017 or FY2018 were excluded from the sample

> 21The randomization was implemented in Stata using the _randtreat_ command to deal with misfits.

> 22We sent the invitation to take the survey to approximately 120 academics, 100 practitioners and 50 public sector workers. We received 111 partial survey answers and dropped 35 incomplete surveys and 11 with mostly inconsistent answers. Our final sample consisted of 65 complete surveys. Details about the exclusion process, further trimming of outliers and complete survey questionnaires are provided in Appendix E.

10
the treatment effects, experts predicted the treatment would induce an increase in filing probability of 4 p.p. among compliers, an increase of 7% in declared gross revenues and of 6% in declared taxable income. Three additional patterns in experts’ forecasts are worth emphasizing. First, experts consistently predict the Sanctions and Procedures treatment arms to be more effective than the tax morale treatment. This is true across the different outcomes and, as seen in Figure 7, across different categories of respondents. The average predicted effect on filing probability, for example, is 6.7 p.p. for the Sanctions arm and only 1.6 p.p. for the tax morale arm. The survey questionnaire was explicit in stating that different treatment arms included only an additional message to encourage compliance, but the main treatment was always the provision of information about taxpayers’ transactions. This implies that respondents either believed the main treatment would come from the encouragement messages or that the tax morale message would have a negative effect, diluting the impact of the information provision.

Second, we also collected forecasts about heterogeneous treatment effects across risklevels defined by the tax authority, directly eliciting perceptions of the relationship between ex-ante assessed risk-level and response to the intervention. Experts predicted how the impact on reported gross revenue would vary across the distribution of risk. Though results are noisy, on average the predictions pointed to a larger effect among high-risk taxpayers: experts predicted an increase of 9% in reported gross revenue among the top risk group vs. 6% for the lowest risk group. This expectation lines up with the current strategy of the tax authority of focusing communications to high-risk taxpayers, whether this holds or not is an empirical question. As discussed in the text and formally exemplified in the stylized model, high-risk taxpayers might be less reactive to such "soft-touch" interventions and therefore present a smaller treatment effect.

Finally, we note that academic economists are often less sanguine than both practitioners and, particularly, government employees when it comes to the magnitude of effects. With the exception of the tax morale treatment arm, which is deemed to have small effects by all experts, academic economists often predict smaller effects on average.

# **4 Data**

## **4.1 Baseline descriptives and balance**

We present baseline descriptive statistics of our experimental sample in Table 1. As previously mentioned, all taxpayers in the study have some type of commercial activity. One-third of taxpayers are corporations, 40% are individual business (non-incorporated firms) and 6% are self-employed service providers (often professionals like lawyers or 11
doctors)<sup>23</sup> . Half of all taxpayers are located in the two largest municipalities in the country, Distrito Central and San Pedro Sula.

On Panel B we present descriptive statistics of variables related to past tax filing. The majority of taxpayers were flagged to be at-risk for under declaring tax liabilities and not for non-declaration: 86% of the sample filed an income tax declaration for FY2018. Conditional on declaring, average gross revenue was L1.5 million (USD 60,000) while median gross revenue was L381,000 (USD 15,200) - even after excluding outliers the gross revenue distribution presents a long right-tail. Almost 50% of taxpayers were not liable to pay income taxes in FY2018, either because they declared taxable income below the minimum threshold to pay taxes (for non-incorporated entities) or because they declared losses. Conditional on declaring, the average taxable income was L115,000 (median = L41,000) and the average tax liability L15,000.

We also provide information on the indicators used by the tax authority to assess risk on Panel C. Third-party information on revenues is available for 71% of the experimental sample and, among those, 88% are informed on sales to other parties, 22% by credit/debit cards (point-of-sales, or POS) operators; 3% by the government and 1% by customs authority<sup>24</sup> . Considering anomalies, almost half of our experimental sample is flagged for having "atypical declared revenues" when compared to peers. A much smaller share is flagged for declaring three or more years with losses in the last five fiscal periods (8%) or having financial transactions inconsistent with declared revenues (7%). These indicators, among others, are then aggregated by the TA in a global "risk factor", which is used to classify every taxpayers in five risk levels. Our sample is fairly evenly divided among the four lowest levels, with only 10% deemed to be "high-risk" by the tax authority’s risk model.

In Table 2 we present balance tests between our control and treatment samples. Columns (1) and (2) present the mean and standard deviation of each variable in the control group, respectively. In Column (3) we present the difference in means between control and the pooled treated sample, and test whether we can reject the null hypothesis of equal means. Overall our sample is balanced and the few statistically significant differences are very small in magnitude. Columns (4) through (6) present differences between each of the treatment arms and the control group, and again indicate balance in observables<sup>25</sup> .

> 23The remaining 21% are not incorporated and also not registered as individual businesses or service providers. The nature of their transactions suggest these are mostly small businesses that never officially registered as such.

> 24These are the categories used to fill in the message sent to taxpayers and they are not exclusive: some taxpayers are informed in these four categories.

> 25We do not include in the balance tables variables used to stratify the sample, since they are balanced by construction.

12
# **5 Results**

## **5.1 Primary outcomes**

To obtain Intention-to-Treat (ITT) estimates on the effect of our experimental intervention on compliance, we estimate regressions of the following form: where _Yi_ is one of the four primary outcomes of interest in FY2019 (indicator for tax filing, amount of gross income, amount of deductions and amount of taxable income); _Ti_ is a dummy that takes value 1 if the unit was assigned to treatment and 0 otherwise; _Xi_ are baseline controls and _γs_ are strata fixed-effects. Baseline controls include a dummy for presenting tax declaration in FY2018; amount of declared gross revenue, third-party informed gross revenue and declared taxable income in FY2018; and amount of declared sales tax revenues in FY2019. Our main coefficient of interest is _β_ 1, measuring the difference in mean outcomes between units in treatment and control groups. Reported outcomes for non-filing taxpayers are considered to be zero. While our continuous outcome variables are highly skewed, we control for previous FY outcomes to reduce residual variance and diminish the influence of outliers. We do not treat outliers in our main specification but perform robustness tests using winsorized outcome variables at the 99th percentile.

To account for partial compliance, we also present instrumental variable regressions (Local Average Treatment Effect or LATE estimates) where we instrument opening the email (i.e. receiving the actual treatment) with treatment assignment. We estimate regressions of the form where _Openi_ is an indicator of whether the taxpayer opened the email sent to them and the remaining variables are the same as above. The parameter of interest is _δ_ 1, which measures the LATE on taxpayers that complied with treatment assignment.

Our main results are presented in Table 3, where Panel A shows the ITT estimates and Panel B LATE estimates. Across our four primary outcomes, we estimate null treatment effects. The point estimate for change in filing probability (Column 1) is -0.1 p.p. and we can rule out effects as small as 1 p.p. change in the treatment group. Similarly, the estimates for treatment effects on declared gross revenue, deductions and taxable income are all not statistically different from zero and small in economic magnitude (less than 1% of the control mean in each case). In Figure 2 we present the cumulative distribution 13
functions of reported gross revenue for treatment and control groups, as an illustration. We observe no meaningful difference between the two distributions, consistent with the null treatment effect estimated. Since, as previously discussed, just over one third of taxpayers assigned to the treatment group actually opened the email, the LATE estimates are approximately three times as large as the ITT but similarly not statistically different from zero. We therefore cannot reject the hypothesis that the intervention had an overall null effect on the compliance behavior of taxpayers.

While the previous results pool all taxpayers assigned to the three different treatment arms together, we also estimate separately the effect of each of the treatment arms, augmenting Equation 1 to estimate ITT as: where _Tj_ are the dummy indicators for each of the treatment arms and we are interested in the coefficients _β_ 1 _, β_ 2 _, β_ 3. Similarly, we estimate LATE using the different treatment arms and present both ITT and LATE in Table 4. We again cannot reject the null hypothesis of zero treatment effect. ITT are very small across the treatment arms and never statistically significant<sup>26</sup> . While LATE estimates are somewhat larger in magnitude they are similarly imprecisely estimated.

# **5.2 Secondary outcomes**

While the four outcomes listed above are of primary interest, we also pre-specified evaluating the impact of the email intervention in other dimensions of compliance with tax obligations. As discussed by Brockmeyer et al. (2019), it is an empirical question whether interventions focused on increasing tax compliance on a specific dimension (truthful income tax declarations) will affect other behavior. On the one hand it is possible that the intervention shifts taxpayers’ beliefs about the tax authority’s capacity, increasing their perception of risk and inducing more compliance across all their obligations. On the other hand, if the intervention is seen as signaling an increased oversight on a narrow dimension, taxpayers might increase compliance on that specific dimension while decreasing compliance in others. Since we observed a zero treatment effect on the primary targeted outcomes, it is no surprise we also estimate null effects across the board for secondary outcomes.

> 26In Table A1 we present similar estimates trimming outcomes at the 99th percentile. We emphasize that this specification, unlike other estimates in the paper, was not pre-specified. Overall we find similar results: point estimates are small in magnitude, often negative and not statistically different from zero. The only exception is the estimate for the impact of the Moral Duty treatment on taxable income, which is positive (L3,460 or approximately 5% of the control mean) and significant at the 5% level. While estimates for the impact of that treatment arm on declared revenue is negative, deductions seem to fall by even more, increasing taxable income. We do not infer much from these estimates since they were not pre-specified and only a single coefficient is significant.

14
We first assess whether subjects in the treatment group were more likely to file their taxes by the established deadlines - this is an important measure of compliance since it is costly for the tax authority to follow late filers. Filing dates were twice postponed, initially from April 30th to June 30th and subsequently to August 31st, so we consider both initial deadlines. We present graphical evidence of zero treatment effect in Figure 3, where we plot the cumulative share of taxpayers that filed in each week since the intervention, in control and pooled treatment (panel A) or each treatment arm (panel B). The figure indicates that approximately 25% of the experimental sample filed by April 30th, 60% by June 30th and 65% by the final August 31st deadline. There is no differential timing in filing among control and treatment taxpayers, however. In columns (1) and (2) of Table 5 we present regression estimates showing no differential filing probability by each deadline.

We also assessed the impact on the amount of taxes actually paid, since taxpayers might declare tax liabilities but not pay, also generating costs for the TA to recover those due taxes. The intervention might also impact compliance with sales taxes, a higher frequency outcome since it’s filed and paid monthly. We thus estimated the impact on total reported sales in the April-August period. Finally, Brockmeyer et al. (2019) also document that their treatment changed compliance with income tax declaration and payment in previous years: treated firms were more likely to file late income tax declarations and pay overdue taxes. We estimated whether the intervention caused a difference in rectifications of previous years’ income or sales taxes. These results are presented in columns (3) through (5) in Table 5. Both ITT and LATE estimates suggest no statistically significant impact of the intervention on any of the secondary outcomes examined.

# **5.3 Heterogeneity analysis**

Our ITT estimates document, across a range of outcomes, null effects on average from our intervention. While the average effect of the email across the experimental sample is of much interest, understanding whether some taxpayers are responsive (and which taxpayers) is of crucial importance from a policy standpoint. Under limited capacity to audit and follow-up on low-cost interventions like emails, the tax authority wants to target those units that will adjust their behavior in response to the intervention and avoid losing credibility by targeting non-responsive units.

In our main set of heterogeneity results, we interacted treatment assignment with the four variables used to stratify the experimental sample<sup>27</sup> : indicator for corporations; indicators for location (Distrito Central, San Pedro Sula and others); indicator of whether third-party information is available for that taxpayer; and the five risk-levels. These

> 27These specific heterogeneity dimensions were pre-specified before the experiment.

15
results are presented in Table 6.

We first investigate whether taxpayers for which third-party information is available respond differently from those not informed. Pomeranz et al. (2014) document in Chile that all the response from a similar communication intervention is driven by transactions not subject to paper trail, suggesting third-party information is sufficient to assure compliance. In an intervention similar to ours in Costa Rica, Brockmeyer et al. (2019), on the other hand, document that taxpayers for which third-party information is available respond to emails at least as strongly as those without information. As discussed above, third-party information seems to cover only a fraction of total revenues declared by taxpayers, which suggests there might be scope to improve compliance<sup>28</sup> . In panel A we estimate that the treatment had a _negative_ impact on the probability of filing by taxpayers with no third party information available (-2.4 p.p.) and a zero effect on those with information available. Impacts on reported gross revenue and deductions are estimated to be zero for both groups, while we estimate a positive but only marginally significant increase in reported taxable income for taxpayers with no third-party information. Overall, we interpret these results as evidence of zero effects for taxpayers with available third-party information and mixed evidence for those with no information available.

A second key heterogeneity dimension is whether taxpayers at different risk levels, as defined by the tax authority, respond differently to the intervention. The current practice is to send communications similar to emails in this experiment to those taxpayers considered to be at higher risk, both because that is seen as leading to maximize revenue collection and in order not to "rattle the boat" with taxpayers that are seen as compliant. In this experiment we include subjects in the entire range of positive perceived risk, from very low levels to very high, allowing us to empirically assess whether the intervention’s effects are heterogeneous across the distribution of risk.

We present results in panel B. We estimate positive and statistically significant effects across all four outcomes for taxpayers with low risk-level: those in the treated group are 2.3 p.p. more likely to file a declaration and increase reported gross revenue by approximately L100,000. Consistent with previous findings in the literature, however, treated taxpayers also increase their claimed deductions by about L90,000, such that the increase in taxable income is just L10,000. For all taxpayers with higher assessed risk, estimates are not statistically different from zero and always smaller in absolute value. This finding is the opposite of what experts predicted in our survey: they expected larger impacts for high-risk taxpayers.

We interpret these results, with all the caveats related to the external validity of an experiment conducted during the Covid-19 crisis, as evidence that the risk model is not

> 28While Brockmeyer et al. (2019) include a smaller treatment arm with similar messages to both groups, we cannot disentangle whether differential responses by these two groups are driven by different email content of the communication or by heterogeneity between the groups.

16
ideally suited for targeting email communications. One possible reason is that the risk model is not fully assessing true risk: it is possible that "low-risk" taxpayers are actually higher risk and that was not entirely captured by the model. Another possibility, in line with the discussion in the model, is that "compliance risk" is just one dimension that matters for targeting. Even if risk is correctly assessed, it is possible that a "soft" email intervention is not ideal for high-risk taxpayers but works for those with small noncompliance risk. We go back to the issue of how ex-ante assessed risk relates to ex-post reaction to the intervention when discussing the use of a causal forest model below.

Finally, we explore heterogeneity in two highly salient dimensions of taxpayers characteristics: corporate form and location. Corporations face a distinct income tax regime (flat rate of 25% on profits instead of a progressive schedule), are larger and often seen as much more sophisticated entities than non-incorporated taxpayers, so a "soft" touch intervention such as communication emails might be less effective to induce compliance. In panel C we estimate that the intervention did not affect declared revenue, deductions or taxable income for either group, but we do find significant impacts on the probability to file taxes. We estimate the intervention increased the probability of filing by 1.3 p.p. for non-incorporated taxpayers, but _decreased_ the probability of filing by 3 p.p. for corporations.

Geographical differences in responses to compliance are also of relevance for the tax authorities: San Pedro Sula is the industrial center of Honduras and Distrito Central, where the capital Tegucigalpa is located, is the most populated municipality in the country. In panel D we estimate no differential impact of the treatment across regions: for all primary outcomes we estimate null treatment effects for taxpayers in all three regions.

# **5.4 Targeting interventions: Causal forest model**

While the heterogeneity dimensions analyzed in the previous section were built into our experimental design, the tax authority has access to much richer data that can be explored to answer the question: which taxpayers are more responsive to email interventions? As discussed above, improving targeting for these types of interventions is crucial even if the monetary marginal cost of one extra email is close to zero, since there are reputation costs associated with revealing information to taxpayers and not following up with oversight.

In order to explore a larger set of possible predictors of differential treatment effects and allow for much more flexible heterogeneity<sup>29</sup> , we use a causal forest algorithm (Athey & Imbens, 2016; Wager & Athey, 2018) to predict conditional average treatment effects

> 29We use as covariate indicators for whether taxpayer was individual business, salaried worker or selfemployed; indicator for corporate form; dummies for geographical region; third-party reported revenue in 2019; third-party reported and self-reported revenue in 2019; taxable income in 2018; declared sales in VAT forms in 2019; continuous measure of assessed risk in 2018; and number of filed VAT declarations in 2018.

17
(CATE) on subgroups in our experimental sample (De Neve et al., 2021)<sup>30</sup> . Causal forests predict CATE for each taxpayer, taking the average effects estimated from several _causal trees_ : similarly to decision trees, which partition data in order to find best predictions of some observed label, causal trees partition data in order to maximize heterogeneous effects across partitions (leaves). Since treatment effects are not observed but estimated from the data, Athey & Imbens (2016) recommend using "honest trees", which use separate sub samples to perform partition and estimate treatment effects.

In Figure 4 we present a histogram of predicted treatment effect on the filing probability for our entire sample<sup>31</sup> . The distribution of CATE is centered around zero and 90% of predicted effects fall in the range [-3.7,+3.2] percentage points. Overall, the null average treatment effect does not seem to be driven by a large positive effect for some group of taxpayers and negative effects for others, but is more consistent with null effects for the majority of subjects in the experiment.

One specific dimension of interest for targeting these interventions is whether taxpayers deemed to be high-risk are more responsive. In Figure 5, panel A, we document that is not the case. The scatter plot of predicted treatment effect using the causal forest model and the continuous risk measure used by the tax authority shows a slightly negative correlation: predicted treatment effects are positive, albeit small, for taxpayers with very low risk, and become negative for taxpayers with higher assessed risk. We summarize that relationship in panel B, where we present the average predicted treatment effect (weighted by baseline declared revenue) across the five risk-categories created from the continuous risk measure. The average is positive and small for taxpayers in the low and medium-low risk-levels, and negative for taxpayers with higher assessed risk<sup>32</sup> .

# **5.5 Costs of the experiment**

While the marginal cost of sending emails is zero, any campaign involving thousands of taxpayers requires several activities from dozens of workers in the tax authority<sup>33</sup> . These include meetings to define the content of messages, time to prepare databases, train frontline workers who might receive calls or visits from taxpayers, among others. Using back-of-the-envelope calculations of time invested and hourly wage of involved workers,

> 30For a detailed explanation of the causal forest algorithm and application in economics, see Davis & Heller (2017).

> 31We show in Figure A1 that results are similar if we consider the amount of gross revenue declared as the outcome of interest.

> 32Magnitudes are different from our estimated heterogeneous effects obtained in Table 6, where we present results linear regressions that interact the dummy for treatment with baseline assessed risk. In here we estimate a much more flexible model, using causal trees and a richer set of covariates, and then aggregate the predicted effects based on the baseline risk.

> 33While the intervention discussed in this paper required further time from researchers outside the tax authority (e.g. developing experts’ forecast survey, preparation of the pre-analysis plan and preregistration), this cost-benefit analysis focus on what an "usual" intervention, developed entirely by the tax authority, would cost.

18
SAR estimates that the intervention cost approximately L155,000 or USD 6,200. Since there were 31,396 taxpayers receiving emails, the per email costs was less than USD 0.20.

While we estimate overall null effects, we do observe an increase in the probability of filing, in the amount of revenue declared and in the amount of taxable income declared for taxpayers with ex-ante low assessed risk. We can use our back-of-the-envelope calculations for costs of the intervention to estimate a return for that specific group - if we consider that the TA, going forward, decides to target those individuals. The cost of each email is approximately USD 0.20. We estimate that the intervention increased declared taxable income for the low-risk taxpayers by L10,700 or USD 430, so each USD 1 invested in the intervention led to an increase in USD 2,150 in declared taxable income by taxpayers. The actual impact on net tax payment, however, is much smaller: the point estimate for the impact among low-risk taxpayers is only L300 or USD 12, and not statistically different from zero (not reported). Taking that number at face-value, the return of USD 1 would still be a very large USD 60, but we caution about the imprecision of our estimates for those measures.

# **6 Conclusion and policy implications**

We experimentally informed thousands of taxpayers in Honduras that the tax authority had information about their previous transactions in order to assess whether that increased tax compliance. Overall, we found null results. Across several measures of compliance, taxpayers in the treatment groups behaved no differently from those in the control group. We emphasize that the impact of the Covid-19 crisis might have significantly decreased the salience of our treatment and makes the external validity of our findings of null effects harder to assess.

In the key dimension of ex-ante predicted risk, we find that the treatment had a positive effect for taxpayers deemed to be at low-risk of non-compliance. Treated taxpayers in that group were more likely to file taxes and report higher revenue and taxable income. We complemented the heterogeneity analysis using a random forest model that allows for much more complex heterogeneity in treatment effects. Our finding of a negative correlation between predicted treatment effect and ex-ante assessed risk suggests that targeting of policies aimed at increasing compliance should not only consider the level of perceived evasion, but also the likelihood that taxpayers will respond to specific interventions. In contemporary work, Hoy et al. (2021) find that the only taxpayers who respond to a similar intervention in Papua New Guinea are those with low compliance costs; namely, taxpayers who had previously filed taxes and those who filed but claimed to be exempt. This is consistent with our findings and the idea that "soft" interventions such as threatening emails might only change the behavior of "marginal" non-compliers.

When deciding which taxpayers to audit, tax administrations already invest heavily in 19
identifying those with highest risk of non-compliance. Our findings suggest they should also think carefully about how to target their remaining tools of enforcement. Sending – emails or letters to severe non-compliers individuals or firms with large predicted tax liabilities and who do not file or severely misreport their income – might be innocuous at best or counterproductive at worst, if threats are not followed by enforcement actions. In order to improve targeting, new methods that combine experimental variation, the rich administrative data often available to tax authorities, and machine learning tools are a promising avenue for exploration.

20
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Table 1: Descriptive Statistics - Final Sample

||Mean|SD|p50|N|
|---|---|---|---|---|
|_Panel A: Taxpayers’ characteristics_|||||
|Corporations|0.33|0.47||31,396|
|Individual Business|0.41|0.49||31,396|
|Self-employed service providers|0.06|0.23||31,396|
|Corporations, IB or self-employed|0.79|0.41||31,396|
|Distrito Central|0.27|0.45||31,396|
|San Pedro Sula|0.22|0.41||31,396|
|_Panel B: Past fling behavior_|||||
|Reported revenue (Sales) (2019) (L1,000s)|617.79|2,876.15|39.60|31,396|
|Declared revenue (Sales) (2019) (L1,000s)|1,432.67|9,745.31|190.36|31,396|
|_Income Tax 2018_|||||
|Declared income tax in 2018|0.86|0.34|1.00|31,396|
|Reported revenue (Income) (2018) (L1,000s)|544.36|2,477.44|46.78|31,396|
|Declared revenue (Income) (2018) (L1,000s)|1,305.15|2,670.35|274.22|31,396|
|Declared revenue 2018 | declaring (L1,000s)|1,509.82|2,817.80|381.22|27,140|
|Not liable for taxes|0.47|0.50|0.00|27,140|
|Liable for income taxes|0.50|0.50|1.00|27,140|
|Liable for asset taxes|0.03|0.16|0.00|27,140|
|Taxable base 2018 | declaring (L1,000s)|115.47|265.81|41.47|27,140|
|Tax liability 2018 | declaring (L1,000s)|15.34|75.74|0.14|27,140|
|Efective tax rate|0.12|0.11|0.06|14,374|
|_Income Tax 2017_|||||
|Declared income tax in 2017|0.81|0.40|1.00|31,396|
|Reported revenue (Income) (2017) (L1,000s)<br>|483.26|2,538.67|5.40|31,396|
|Declared revenue (Income) (2017) (L1,000s)|1,233.77|2,499.54|256.28|31,396|
|Declared revenue 2017 | declaring (L1,000s)|1,532.62|2,702.42|421.76|25,274|
|Taxable base 2017 | declaring (L1,000s)|121.47|260.80|62.22|25,274|
|Tax liability 2017 | declaring (L1,000s)|16.57|71.99|0.29|25,274|
|_Panel C: Third-party information_|||||
|Third-party information available (2019)|0.71|0.45||31,396|
|Revenue reported by other taxpayers|0.88|0.33||22,423|
|Revenue reported by POS operators|0.22|0.42||22,423|
|Revenue reported by government|0.03|0.17||22,423|
|Revenue reported by customs|0.01|0.09||22,423|
|_Anomalies_|||||
|Declared losses for fve years|0.08|0.27||31,396|
|Atypical fnancial transactions|0.07|0.26||31,396|
|Atypical declared revenue|0.49|0.50||31,396|
|_Risk assessment_|||||
|Low risk|0.21|0.40||31,396|
|Medium-low risk|0.28|0.45||31,396|
|Medium risk|0.23|0.42||31,396|
|Medium-high risk|0.19|0.39||31,396|
|High risk|0.10|0.30||31,396|

_Note_ : This table presents descriptive statistics for the entire experimental sample. The first panel presents taxpayers characteristics such as type (corporation, individual business or self-employed service providers) and location. The second panel presents descriptive statistics on past tax paying behavior, for both income and sales taxes, in FY2017 and FY2018. Finally, the last panel describes the sources of third-party information and behavioral anomalies used in the tax authority’s risk model, and also the distribution of taxpayers across the five broad risk-levels used by the TA.

24
Table 2: Balance Table - Baseline Characteristics

||(1)<br>Control<br>mean|(2)<br>Control<br>s.d.|(3)<br>Treatment v.<br>Control<br>dif.|Diference in<br>(4)<br>Sanctions v.<br>Control<br>dif.|Means (t-test)<br>(5)<br>Procedures v.<br>Control<br>dif.|(6)<br>Moral duty v.<br>Control<br>dif.|
|---|---|---|---|---|---|---|
|Individual Business|0.40|(0.49)|-0.01**|-0.02**|-0.01|-0.01|
|Self-employed service providers|0.06|(0.24)|0.01**|0.01|0.01**|0.01|
|Corporations, IB or self-employed|0.79|(0.41)|-0.01|-0.01*|0.00|-0.01|
|Reported revenue (Sales) (2019) (L1,000s)|615.33|(3142.60)|-4.83|32.59|-46.69|-0.22|
|Declared revenue (Sales) (2019) (L1,000s)|1391.46|(4749.50)|-80.87|-264.16|13.36|6.86|
|Declared income tax in 2018|0.86|(0.35)|-0.01|-0.01*|-0.01|0.00|
|Reported revenue (Income) (2018) (L1,000s)|533.62|(1830.09)|-21.08|26.93|-68.67|-21.25|
|Declared revenue (Income) (2018) (L1,000s)|1303.26|(2672.35)|-3.70|11.18|-9.87|-12.30|
|Declared revenue 2018 | declaring (L1,000s)|1512.46|(2823.38)|5.18|30.78|0.89|-16.18|
|Not liable for taxes|0.47|(0.50)|0.01|0.01|0.00|0.01|
|Liable for income taxes|0.50|(0.50)|-0.01|-0.01|-0.00|-0.01|
|Liable for asset taxes|0.03|(0.16)|0.00|-0.00|0.00|0.00|
|Taxable base 2018 | declaring (L1,000s)|116.40|(270.68)|1.82|0.32|3.56|1.58|
|Tax liability 2018 | declaring (L1,000s)|15.52|(73.46)|0.36|0.14|0.25|0.70|
|Efective tax rate|0.12|(0.11)|0.00|0.00|0.00|0.00|
|Declared income tax in 2017|0.80|(0.40)|-0.00|-0.01|-0.00|0.00|
|Reported revenue (Income) (2017) (L1,000s)|466.91|(1709.31)|-32.10|3.91|-64.74|-35.28|
|Declared revenue (Income) (2017) (L1,000s)|1230.89|(2513.12)|-5.64|-30.70|9.53|4.07|
|Declared revenue 2017 | declaring (L1,000s)|1531.06|(2719.64)|-3.05|-22.44|14.46|-1.11|
|Taxable base 2017 | declaring (L1,000s)|122.99|(272.04)|2.98|2.32|2.49|4.14|
|Tax liability 2017 | declaring (L1,000s)|17.11|(76.76)|1.06|1.46|1.07|0.65|
|Revenue reported by other taxpayers|0.88|(0.33)|0.01**|0.01*|0.01*|0.00|
|Revenue reported by POS operators|0.22|(0.42)|-0.01|-0.00|-0.00|-0.01*|
|Revenue reported by government|0.03|(0.17)|-0.00|-0.00|-0.00|-0.00|
|Revenue reported by customs|0.01|(0.09)|0.00|0.00|0.00|0.00|
|Declared losses for fve years|0.08|(0.27)|-0.00|0.00|-0.00|0.00|
|Atypical fnancial transactions|0.07|(0.26)|0.00|0.00|-0.00|0.00|
|Atypical declared revenue|0.48|(0.50)|-0.01**|-0.02**|-0.01|-0.01|
|Observations|15399|15399|31396|20705|20731|20758|

_Note_ : This table compares average characteristics between control and treatment arms. Column (1) presents average taxpayer characteristics and previous filing behavior in FY2018 and FY2017, while Column (2) standard errors. Column (3) presents the difference in averages between the pooled treatment group and control, while columns (4) through (5) presents the differences between taxpayers in control group and each of treatment arms (Sanctions, Procedures and Tax morale arms, respectively) and indicate whether we reject the null of equal averages. (* p<0.1, ** p<0.05, *** p <0.01) 25
Table 3: Primary Outcomes - Estimating Program Effects

||(1)<br>Filed declaration|(2)<br>Gross Revenue|(3)<br>Deductions|(4)<br>Taxable Income|
|---|---|---|---|---|
|_ITT estimates_|||||
|Treatment|-0.001|7.284|2.750|0.383|
||[-0.010,0.009]|[-48.146,62.714]|[-51.333,56.833]|[-9.868,10.635]|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.186|0.493|0.492|0.052|
|Control mean|0.65|1,267.88|1,207.31|90.61|
|_LATE estimates_|||||
|Opened email|-0.002<br>[-0.030,0.026]|21.277<br>[-140.918,183.472]|7.958<br>[-150.301,166.217]|1.172<br>[-28.829,31.173]|
|Observations|31,390|31,390|31,390|31,390|
|R-Squared|0.186|0.493|0.492|0.052|

_Note_ : This table presents the estimated impact of the intervention on primary outcomes. The first panel presents Intention-to-Treat (ITT) estimates using the specification in Equation 1. The second panel presents Local Average Treatment Effect (LATE) estimates using the specification in Equation 2. The dependent variables are an indicator equal to 1 if the taxpayer filed a declaration (Column (1)), the amount of gross revenue declared (Column (2)), the amount of deductions declared (Column (3)) and the amount of taxable income declared (Column (4)). 95% confidence-intervals using robust standard errors are presented in brackets. (* p<0.1, ** p<0.05, *** p <0.01) 26
Table 4: Primary Outcomes - Different Treatment Arms

||(1)<br>Filed declaration|(2)<br>Gross Revenue|(3)<br>Deductions|(4)<br>Taxable Income|
|---|---|---|---|---|
|**ITT estimates**|||||
|Sanctions treatment|0.004<br>[-0.009,0.018]|24.804<br>[-56.611,106.218]|8.857<br>[-70.397,88.112]|4.078<br>[-10.504,18.659]|
|Tax procedures treatment|-0.005<br>[-0.019,0.008]|23.813<br>[-54.347,101.974]|27.387<br>[-50.125,104.899]|-1.905<br>[-15.422,11.611]|
|Moral duty treatment|-0.002<br>[-0.015,0.012]|-26.499<br>[-96.645,43.646]|-27.805<br>[-97.125,41.515]|-0.996<br>[-11.780,9.788]|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.186|0.493|0.492|0.052|
|Control mean|0.65|1,267.88|1,207.31|90.61|
|_β_1 =_β_2|0.247|0.984|0.704|0.454|
|_β_1 =_β_3|0.475|0.266|0.421|0.454|
|_β_2 =_β_3|0.652|0.264|0.221|0.890|
|**LATE estimates**|||||
|Opened (Sanctions)|0.013<br>[-0.026,0.052]|71.777<br>[-163.608,307.162]|25.612<br>[-203.561,254.786]|11.803<br>[-30.362,53.968]|
|Opened (Procedures)|-0.015<br>[-0.054,0.023]|67.637<br>[-154.072,289.347]|77.789<br>[-142.066,297.643]|-5.413<br>[-43.774,32.948]|
|Opened (Tax morale)|-0.005<br>[-0.046,0.036]|-81.077<br>[-295.647,133.492]|-85.083<br>[-297.145,126.979]|-3.046<br>[-36.022,29.930]|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.185|0.493|0.492|0.052|

_Note_ : This table presents the estimated impact of each of the treatment arms on primary outcomes. The first panel presents Intention-to-Treat (ITT) estimates using the specification in Equation 4. The second panel presents Local Average Treatment Effect (LATE) estimates. The dependent variables are an indicator equal to 1 if the taxpayer filed a declaration (Column (1)), the amount of gross revenue declared (Column (2)), the amount of deductions declared (Column (3)) and the amount of taxable income declared (Column (4)). 95% confidence-intervals using robust standard errors are presented in brackets. (* p<0.1, ** p<0.05, *** p <0.01) 27
Table 5: Secondary Outcomes

||(1)<br>Filed 1st deadline|(2)<br>Filed 2nd deadline|(3)<br>Total taxes paid|(4)<br>Total sales taxes|(5)<br>Revised previous fling|
|---|---|---|---|---|---|
|_ITT estimates_||||||
|Treatment|0.003<br>[-0.006,0.011]|-0.002<br>[-0.012,0.008]|-0.408<br>[-2477.610,2476.794]|-1024.434<br>[-2841.898,793.031]|0.000<br>[-0.002,0.002]|
|Observations|31,396|31,396|31,396|31,396|31,396|
|R-Squared|0.032|0.101|0.014|0.116|0.027|
|Control mean|0.18|0.47|4,189.98|13,940.39|0.01|
|_LATE estimates_||||||
|Opened email|0.008<br>[-0.016,0.032]|-0.006<br>[-0.036,0.025]|-1.526<br>[-7251.354,7248.301]|-2994.794<br>[-8313.479,2323.892]|0.001<br>[-0.005,0.007]|
|Observations|31,390|31,390|31,390|31,390|31,390|
|R-Squared|0.033|0.101|0.014|0.115|0.027|

_Note_ : This table presents the estimated impact of each of the treatment arms on secondary outcomes. The first panel presents Intention-to-Treat (ITT) estimates using the specification in Equation 1. The second panel presents Local Average Treatment Effect (LATE) estimates using the specification in Equation 2. The dependent variables are an indicator equal to 1 if the taxpayer filed a declaration by the original deadline of April 30th (Column (1)); if they filed by the second deadline of June 30th (Column (2)); the amount of taxes paid (Column (3)), the amount of sales taxes declared between March and August (Column (4)); an indicator equal to 1 if the taxpayer rectified income tax declarations for any year in the period 2014-2018 (Column (5)). 95% confidence-intervals using robust standard errors are presented in brackets. (* p<0.1, ** p<0.05, *** p <0.01) 28
Table 6: Heterogeneity Analysis

||(1)<br>|(2)<br>|(3)<br>|(4)<br>|
|---|---|---|---|---|
||Filed declaration|Gross Revenue|Deductions|Taxable Income|
|Treatment|-0.024**<br>(0.009)|35.906<br>(41.373)|21.270<br>(44.146)|11.154*<br>(6.515)|
|Treatment * Third party info available|0.032***<br>(0.011)|-40.069<br>(55.216)|-25.926<br>(56.281)|-15.078<br>(9.495)|
|Third party info available|0.093**<br>|500.667<br>|620.852**<br>|-43.908*<br>|
||(0.041)|(312.136)|(298.499)|(26.405)|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.186|0.493|0.492|0.052|
|Third-party efect (p-value)|0.150|0.908|0.893|0.567|
|Treatment|0.023**|103.221**|92.741*|10.740*|
||(0.011)|(47.219)|(47.450)|(5.882)|
|Treatment * Medium-low risk|-0.026*|-112.899*|-120.320*|-11.226|
||(0.014)|(65.735)|(66.185)|(9.272)|
|Treatment * Medium risk|-0.034**|-149.615**|-126.888*|-15.872*|
||(0.015)|(71.452)|(72.057)|(8.407)|
|Treatment * Medium-high risk|-0.041***|-138.890|-107.568|-19.686|
||(0.016)|(94.713)|(89.265)|(23.093)|
|Treatment * High risk|-0.017|-42.755|-73.463|0.771|
||(0.020)|(135.415)|(133.553)|(9.735)|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.186|0.493|0.492|0.052|
|Medium-low risk efect (p-value)|0.803|0.833|0.550|0.946|
|Medium risk efect (p-value)|0.307|0.384|0.526|0.394|
|Medium-high risk efect (p-value)|0.106|0.662|0.843|0.691|
|High risk efect (p-value)|0.673|0.633|0.877|0.138|
|Treatment|0.013**|10.678|5.220|2.355|
||(0.006)|(23.005)|(22.598)|(3.129)|
|Treatment * Corporation|-0.043***<br>(0.010)|-10.381<br>(75.998)|-7.557<br>(74.023)|-6.033<br>(14.889)|
|Corporation|0.131***|485.694|611.590**|-48.464*|
||(0.041)|(312.441)|(298.872)|(26.933)|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.186|0.493|0.492|0.052|
|Corporation efect (p-value)|0.000|0.997|0.974|0.801|
|Treatment|0.005|23.814|24.246|0.921|
||(0.007)|(34.390)|(34.683)|(2.803)|
|Treatment * Distrito Central|-0.005|-42.821|-44.075|-6.895|
||(0.012)|(69.938)|(66.987)|(16.621)|
|Treatment * San Pedro Sula|-0.020|-22.412|-43.710|6.174|
||(0.012)|(74.773)|(74.066)|(8.735)|
|Distrito Central|-0.106***|-458.701|-585.382*|55.018**|
||(0.041)|(313.115)|(299.202)|(27.654)|
|San Pedro Sula|0.007|-48.828|-68.546|-10.617|
||(0.031)|(315.594)|(307.384)|(23.484)|
|Observations|31,396|31,396|31,396|31,396|
|R-Squared|0.186|0.493|0.492|0.052|
|Distrito Central efect (p-value)<br>|0.968<br>|0.757<br>|0.731<br>|0.721<br>|
|San Pedro Sula efect (p-value)|0.140|0.983|0.765|0.390|

_Note_ : This table presents the estimated impact of the intervention for different sub-populations of the experimental sample. Columns (1) through (4) use each of the primary outcomes as dependent variable and all estimates are ITT, using the same specification in Equation 1 augmented by interacting the treatment dummy with the characteristics of interest. The first panel presents estimates of differential treatment by availability of third party information; the second panel presents estimates by risk-levels; the third panel presents estimates for corporations and non-incorporated entities; and the last panel presents estimates by three different geographical regions. Robust standard errors presented in parentheses. (* p<0.1, ** p<0.05, *** p <0.01) 29
Table 7: Descriptive Statistics - Forecast survey

||Mean|p50|SD|Min|p25|p75|Max|N|
|---|---|---|---|---|---|---|---|---|
|_Filing probability (p.p.)_|||||||||
|Pooled|3.9|4.0|3.4|-6.0|2.0|5.0|20.0|64|
|Sanctions|6.7|5.5|4.6|1.0|4.0|8.0|22.0|62|
|Procedures|5.3|4.0|5.5|0.0|2.0|7.0|23.0|63|
|Tax morale|1.6|1.0|3.8|-5.0|0.0|3.0|20.0|61|
|_Gross revenue (%)_|||||||||
|Pooled|6.7|5.0|6.2|-3.0|2.5|10.0|25.0|64|
|Sanctions|11.5|9.5|10.8|-4.0|5.0|13.0|50.0|64|
|Procedures|9.3|5.0|12.6|-2.0|1.0|11.0|58.0|63|
|Tax morale|2.3|0.5|6.1|-18.0|0.0|3.0|36.0|62|
|Low risk|6.3|1.0|9.4|-5.0|0.0|8.0|38.0|61|
|Medium-low risk|6.1|4.0|8.0|-7.0|0.0|8.0|28.0|61|
|Medium risk|7.0|4.0|8.8|-3.0|0.0|11.0|50.0|61|
|Medium-high|8.8|7.0|10.6|-3.0|1.0|12.0|50.0|61|
|High risk|9.1|6.0|11.5|-5.0|0.0|15.0|51.0|61|
|_Taxable income (%)_|||||||||
|Pooled|5.7|4.0|5.7|-2.0|2.0|10.0|30.0|64|
|Sanctions|8.6|6.0|9.2|-4.0|3.0|12.0|50.0|63|
|Procedures|6.5|4.0|10.0|-2.0|1.0|8.0|57.0|63|
|Tax morale|1.4|0.0|4.7|-12.0|0.0|2.0|21.0|63|

_Note_ : This table presents descriptive statistics for the expert forecast survey. The first panel refers to forecasts of treatment effects on filing probability, the second panel on declared gross revenue and the third panel on declared taxable income. See Appendix E for details on sample restrictions.

30
<!-- Start of picture text --> Experimental Sample<br>31,396<br>Treatment Control<br>15,997 15,399<br>Sanctions<br>5,306<br>Tax Procedures<br>5,332<br>Moral Duty<br>5,359<br><!-- End of picture text -->
Figure 3: Cumulative share of taxpayers filing per week <!-- Start of picture text --> % filed declaration<br>Original deadline First postponement Final deadline<br>.6<br>.4<br>.2<br>0<br>-6 0 10 20 30<br>Weeks since treatment<br>Control Treatment<br><!-- End of picture text --> <!-- Start of picture text --> (a) Pooled treatment arms<br><!-- End of picture text --> <!-- Start of picture text --> % filed declaration<br>Original deadline First postponement Final deadline<br>.6<br>.4<br>.2<br>0<br>-6 0 10 20 30<br>Weeks since treatment<br>Control Sanctions<br>Procedures Moral Duty<br><!-- End of picture text --> <!-- Start of picture text --> (b) Separate treatment arms<br><!-- End of picture text --> _Note:_ This figure presents the cumulative share of taxpayers filing income tax by each week since the intervention. Panel A presents results for the pooled treatment arms vs. control group whereas Panel B presents results separately for each treatment arm. E-mails were sent on March 11-12th 2020 and the original deadline for tax filing was April 30th. That was initially postponed to June 30th ("first postponement") and subsequently to August 31th ("final deadline").

32
<!-- Start of picture text --> 900 :<br>€ 600 :<br>= : :<br>{e) : :<br>O : :<br>300 : ,<br>0 : : —<br>-0.10 -0.05 0.00 0.05<br>Predicted treatment effect<br><!-- End of picture text -->
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Figure 6: Survey estimates <!-- Start of picture text --> Filing probablity (p.p.) Gross revenue (%) Gross revenue by risk (%) Taxable income (%)<br>PooledSanctionsProcedureTax morale PooledSanctionsProcedureTax morale LowMedium-lowMediumMedium-high High PooledSanctionsProcedureTax morale<br>15<br>10<br>5<br>0<br><!-- End of picture text --> _Note:_ This figure presents means and 95% confidence-intervals for predictions of treatment effect-size on each of the primary outcomes. See Appendix E for details on sample restrictions.

35
Figure 7: Forecasts by groups of respondents <!-- Start of picture text --> Pooled sample Sanctions arm Procedure denial arm Tax morale arm Pooled sample Sanctions arm Procedure d enial arm Tax morale arm<br>(a) Filing probability (b) Declared gross income<br>Pooled sample Sanctions arm Procedure denial arm Tax morale arm<br>(c) Declared taxable income<br>AcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workers AcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workers<br>AcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workersAcademicsGovt employeesPolicy workers<br>15 30<br>10 20<br>5 10<br>0 0<br>p.p. change in filing probability<br>% change in declared gross income<br>-5 -10<br>20<br>15<br>10<br>5<br>% change in declared taxable income 0<br><!-- End of picture text --> _Note:_ This figure presents means and 95% confidence-intervals for predictions of treatment effect-size on each of the primary outcomes. See Appendix E for details on sample restrictions.

36
Figure 8: Letter 1 - Control group

## **<mark>Señor Obligado Tributario</mark>**

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