metadata: authors: 0: author: Antonacci,Paulo
1: author: Chattha,Muhammad Khudadad
count: Indonesia
docna: 0: docna: Evaluation of Door-to-Door Tax Enforcement
            Strategy in Indonesia
docty: Policy Research Working Paper
theme: Public Expenditure Management,Municipal Institution Building,Infrastructure Finance,Data Development and Capacity Building,Public Finance Management,Finance for Development,Finance,Domestic Revenue Administration,Public Sector Management,Data production, accessibility and use,Public Administration
abstracts: cdata!: This paper presents an evaluation of a
            tax enforcement program conducted in Indonesia where
            officials from the tax authority visited properties to
            engage directly with owners about their property tax
            obligations. Through these visits, auditors explained
            outstanding debts and payment processes, aiming to improve
            tax compliance and revenue collection. The paper uses an
            administrative data set and a new set of machine
            learning&#x2013;based techniques to assess the
            program&#x2019;s effectiveness. The program was
            responsible for increasing tax compliance on the extensive
            margin by 4.3 percent and on the intensive margin by 5.1
            percent in the first year it was implemented. These effects
            are particularly strong as they persist in the following
            period. The findings show that the visited properties had
            better compliance history, lower value, smaller area, and
            were more likely to have some construction on them. A key
            finding from the analysis is that higher-value properties
            are less sensitive to the visits. In other words, if a
            data-driven tax-enforcement strategy is to be applied, then
            it may focus resources on enforcing taxation at the poorest
            part of the population in this case. This opens up the
            discussion of the distributional consequences of an
            algorithm-based enforcement strategy, which is increasingly
            important as machine learning techniques are used by tax authorities.
display_title: Evaluation of Door-to-Door Tax
            Enforcement Strategy in Indonesia
listing_relative_url: /research/2024/09/34387135/evaluation-door-to-door-tax-enforcement-strategy-indonesia
topicv3: Public Finance m1326315 713,Fiscal Policy m1327144 445,Tax Policy m1327616 983
publishtoextweb_dt: 2024-09-06T00:00:00Z
dois: 10.1596/1813-9450-10901
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 54
author_keys: 0: author_key: NA
1: author_key: 1662672
model_extractions: Policy Research Working Paper 10901 Evaluation of Door-to-Door Tax Enforcement Strategy in Indonesia _Paulo Antonacci Muhammad Khudadad Chattha_ Governance Global Practice September 2024
Policy Research Working Paper 10901

# **Abstract**

This paper presents an evaluation of a tax enforcement program conducted in Indonesia where officials from the tax authority visited properties to engage directly with owners about their property tax obligations. Through these visits, auditors explained outstanding debts and payment processes, aiming to improve tax compliance and revenue collection. The paper uses an administrative data set and a new set of machine learning–based techniques to assess the program’s effectiveness. The program was responsible for increasing tax compliance on the extensive margin by 4.3 percent and on the intensive margin by 5.1 percent in the first year it was implemented. These effects are particularly strong as they persist in the following period. The findings show that the visited properties had better compliance history, lower value, smaller area, and were more likely to have some construction on them. A key finding from the analysis is that higher-value properties are less sensitive to the visits. In other words, if a data-driven tax-enforcement strategy is to be applied, then it may focus resources on enforcing taxation at the poorest part of the population in this case. This opens up the discussion of the distributional consequences of an algorithm-based enforcement strategy, which is increasingly important as machine learning techniques are used by tax authorities.

This paper is a product of the Governance Global Practice. 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 paulo.antonacci@duke.edu and mchattha@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
Evaluation of Door-to-Door Tax Enforcement Strategy in

# INDONESIA<sup>*</sup>

Paulo Antonacci<sup>†</sup> Muhammad Khudadad Chattha<sup>‡</sup>

## **JEL:** H20,C54,C99,D04

*We thank Peter Arcidiacono, Christopher Alexander Hoy, Viet Anh Nguyen and Naranggi Pramudya Soko for their valued comments. In memoriam, we thank Raka Rizky Fadilla for his exceptional research assistance, work ethic and kindness. We are grateful for the cooperation and assistance of the local government representatives in Gorontalo. We thank Alma Kanani, Habib Rab, Rama Krishnan Venkateswaran, Jurgen Blum and Daniel Ortega for their guidance. The work was financed by the governments of Canada, Switzerland, and the European Union, through the World Bank’s Public Financial Management Multi-Donor Trust Fund for Indonesia. All potential errors are our own.

> †Duke University; paulo.antonacci@duke.edu

> ‡World Bank; mchattha@worldbank.org
# **1. Introduction**

Increasing tax collection in developing countries is challenging. The core issues encompass a broad spectrum, including inefficient tax systems, high levels of informality, and constrained tax bases, which lead to high levels of tax evasion and avoidance. In this paper, we present an evaluation of a tax enforcement program conducted in the City of Gorontalo, Indonesia, where the tax authorities sent officers to visit properties and engage directly with owners about their property tax obligations. By conducting visits to discuss tax liabilities and explaining the payment process, the program aimed to enhance tax compliance and augment revenue.

We had access to an administrative dataset encompassing all properties in the City of Gorontalo from 2014 until 2022. The data includes property characteristics, historical compliance, and some information on the ownership. This allowed us to use machine learning techniques to assess the program’s effectiveness and investigate heterogeneous effects. The techniques used allow us to assess the program’s effectiveness by contrasting the outcomes of visited households with comparable households that were not visited.

We are mainly interested in the program’s capacity to induce compliance in the absence of immediate penalties. More explicitly, our goals are to answer the following questions: (1) Are visited houses more likely to pay property taxes? (2) Does this effect persist over time? (3) What are the characteristics associated with higher/lower responsiveness to the visits?

We find a notable positive impact of the visits on tax compliance, underscoring the potential of direct engagement strategies in improving tax collection efforts. Specifically, these interventions increased the tax compliance on the extensive margin by 3.5% and on the intensive margin by 3.4%. Considering the baseline results for the visited properties, the program was responsible for a relative increase in the extensive margin of 4.3% and on the intensive margin of 5.1%.

Visited properties differ considerably from their counterparts, among many factors, they displayed much better historical tax compliance. The methods we use control for the unbalanced 2
selection of visited households. We found that the visited properties were much less sensitive to the program, i.e., the properties that received the visits were the ones less likely to increase compliance, compared to the properties that were not visited. Therefore, there is potential for program improvement by implementing targeting of the visitations. We estimate that if the intervention had been exclusively focused on the untreated units, the impact of the program would have been 4.7% and 3.8% for the extensive and intensive margins, respectively. Among the unvisited properties, the baseline compliance was 66.9% and 55%. Therefore, had these units been visited, we would expect that the program would be responsible for 7.0% and 6.9% of the average compliance in 2021.

The program results are stronger in the face of the persistent effects we find. The absolute effects of the program increased in 2022. We find an increase of 6.1% in the intensive/extensive margin.<sup>1</sup> Out of the baseline among the visited properties (76.3%), the program was responsible for increasing the overall compliance by 8.6% in 2022. We do not have an explanation for the increasing results. However, we strongly suspect the effect of COVID-19 policies in 2021. We speculate that the program results for 2021 would have been even greater in regular years in the absence of the pandemic.

Our methodological approach offers a significant advantage by accommodating heterogeneous effects, enabling us to pinpoint the characteristics of properties that exhibit the best and worst responses to the intervention. We discuss heterogeneous effects on several dimensions. A key finding from our analysis is that the higher the value of the tax object is, the lower is the effect of the program. That opens up a discussion on the distributional consequences of a fully data-driven enforcement strategy. Such a strategy would redirect efforts toward law enforcement in low-value properties. While the optimal strategy for enforcement is still an open question, our results make a case for the need for interpretable algorithms in using machine learning methods for policy.

This research is connected to a vast literature on tax compliance and enforcement strategies.

> 1Due to changes in payment rules, the extensive and intensive margins for 2021 are the same margin.

3
However, to the best of our knowledge, this is the first such work using tax data from developing countries. Also, unlike most previous studies,<sup>2</sup> the tax authorities have not imposed any extra penalty on the visited properties. Together, these two characteristics represent a setting where auditing programs were less likely to work and make a stronger case for the adoption of such programs in a developing country setting.

Our results, also open a discussion on the importance of considering enforcement strategies while designing tax law. The distributional impacts of a tax system are frequently addressed through tax policy design separately from tax enforcement strategies. The presence of heterogeneous effects in enforcement strategies illustrates the case for which a progressively designed tax system where the mainly poorest are induced to be compliant may yield a regressive outcome. We are investigating these issues more deeply in another working paper, "Policy Learning in Taxation".

This paper is structured to provide a comprehensive exploration of the evaluation of a tax audit program in Gorontalo, Indonesia, from theoretical models to empirical findings. Section 2 is a literature review, focusing on two pivotal areas: the economics of tax compliance, where we examine the theoretical underpinnings and empirical evidence on audit-like strategies and their effectiveness, and Machine Learning for causal inference with heterogeneous treatment effects. Section 3 offers a background description of revenue collection in Indonesia and motivates the importance of fostering tax compliance, followed by a description of the context within which the tax audit experiment was conducted in Gorontalo. Section 4 presents the empirical results of the study. Section 5 presents the conclusions.

2The exception is Hebous et al., 2023 for Norway.

4
# **2. Literature Review**

This paper intersects with two distinct bodies of scholarly work. First, it contributes to the field of public finance, more specifically to the economics of tax compliance. Second, it explores the adoption of recent machine learning techniques to examine heterogeneous effects. The following literature review is divided into two parts, each summarizing key contributions within these areas.

## **2.1. Economics of Tax Compliance**

There are three main channels through which our results can be rationalized: (i) Pecuniary Incentives; (ii) "Tax Morale"; (iii) Information Friction.

The classic economic models of tax evasion analyze the pecuniary incentives for evasion. The models are modified versions of Becker’s (1968) framework for dealing with crime and punishment. Early research rooted in economic theory posited that the deterrent effect of audits hinges on the perceived probability of being audited and the ensuing penalties if caught (Allingham and Sandmo, 1972). This perspective views the taxpayer’s decision as a gamble, weighing potential savings against the risks of evading. Slemrod and Yitzhaki (1987) extended this framework, suggesting that taxpayers adjust their compliance levels based on changing perceptions of audit risks and penalties. Moreover, Yitzhaki, 1987 explored the relationship between the penalty rate and tax evasion, suggesting that it is the combined effect of both audit probability and penalties that molds taxpayer behavior.

The visiting program we study is not technically an auditing program, as its main goal is not to identify tax evasion. However, within the framework of tax compliance through pecuniary incentives mentioned in the paragraph above, the visitation program works similarly to auditing in rationalizing the results. The visits provide a personal interaction between the 5
taxpayers and the tax authorities. The taxpayers perceive it as an increase in the authorities’ effort to recover their money; therefore, the relative return of avoidance decreases.

The problem with advocating for individual-specific interventions such as auditing or doorto-door visitations is that those are costly programs. There has been some disagreement in the literature regarding such interventions. On one hand, Slemrod and Yitzhaki, 2002 argue that due to the potentially significant costs of auditing, the optimization of audit numbers and their effective distribution emerge as a pivotal policy dilemma.

On the other hand, many studies have found high returns to investing in tax auditing. Tax authorities’ official statements seem to corroborate this understanding. The Congressional Budget Office CBO, 2020 estimated that modest investments in the IRS would generate somewhere between $60 billion and $100 billion in additional revenue over a decade. Sarin and Summers, 2020 suggest that the methodology used seriously underestimates the potential revenue. Moreover, Boning et al., 2023 find that the direct revenues collected during an audit exceed the costs of the audit by a factor of more than 2:1. The returns to audits of high-income taxpayers are substantially greater than the returns to audits of low-income taxpayers. If sticky behavioral responses are considered, the return of auditing is much higher. H. Kleven et al., 2011 conducted a seminal study in Denmark, which showed that audited taxpayers increased their reported liabilities by 55% of the audit adjustment in the subsequent year. This suggests that audits not only recover immediate lost revenue, but can also have a positive influence on future compliance. Advani et al., 2023 uses microdata from the UK to find a persistent impact of audits on increasing reported tax liabilities for five years after the audit. Moreover, effects are longerlasting for more stable sources of income, and only individuals found to have made errors respond to audits.

Similarly to our setting, Hebous et al., 2023 show that dynamic effects seem to be present despite the absence of penalties. For those found non-compliant from the audits, there was an improvement in the future up to six post-audit years. The main caveat: This study took place in Norway. It is imperative to note that the impact of audits can be contingent on the specific socioeconomic and cultural context. On the one hand, there are questions on the external validity of 6
such results. On the other hand, if there is one country in which we would expect this reaction, it is Norway. Asatryan and Peichl, 2017 underscored that in developing countries, where formal institutions might be weaker, the effect of audits could be different from that in developed economies. This is precisely the context we explore in our analysis of Indonesian data.

The second class of mechanisms behind the increase in tax compliance is often referred to as "tax morale". It represents a more recent body of literature that has investigated the role of nonpecuniary motives more broadly Kirchler, 2007, Luttmer and Singhal, 2014, Besley et al., 2023. In that sense, the visitation program would increase the intrinsic value of being compliant.

The last class of information explains compliance behavior through "information frictions" Bhargava and Manoli, 2015, H. J. Kleven and Kopczuk, 2011, Cox et al., 2020. Here, evasion could originate from taxpayers who are not aware of their obligation or who do not know how to be compliant. The visits would increase compliance to the extent that tax authorities inform the taxpayers how to fulfill their obligations Kirchler, 2007 and Slemrod et al., 2001. Recently, De Neve et al., 2021 explored this hypothesis in an experiment in Belgium using quantitative methods similar to those used in this paper.

# **2.2. Use of Machine Learning to Investigate Heterogeneous Effects**

The advent of Machine Learning (ML) techniques signifies a pivotal advancement in the toolkit available to applied researchers across various fields. It encompasses a broad spectrum of statistical learning methods such as Random Forest, Neural Networks, and Penalized Regression, which have revolutionized prediction and pattern recognition, especially in highdimensional settings (Athey and Imbens, 2019; Hastie et al., 2009;Bishop, 2006).

Machine Learning (ML) methods excel in conducting research within observational studies, particularly in scenarios where significant sample imbalances exist between treated and nontreated groups. Traditional approaches in this domain have largely relied on propensity score 7
matching. However, this method can be inefficient and may even exacerbate sample imbalances, resulting in model dependency and bias. To address these shortcomings, more efficient "pruning" strategies have been developed, including Coarsened Exact Matching (CEM) Stefano M. Iacus and Porro, 2011, k-nearest neighbors (knn) matching, and Matching After Learning to Stretch (MALTS) Parikh et al., 2022, offering more robust solutions to the challenges of observational study designs.

Estimations derived from matching strategies that employ pruning are vulnerable to extrapolation bias. In response, researchers have turned to popular interpolation methods like synthetic controls to examine causal effects using disaggregated data, though these methods can introduce interpolation bias. To address these limitations, recent methodologies propose a hybrid approach, combining elements of both matching and interpolation to achieve more efficient estimators, as suggested by Abadie and Kellog. This innovative strategy aims to mitigate the biases inherent in each method individually, offering a more balanced and accurate estimation of causal effects.

The lasso method proposed in Belloni et al., 2013 and Belloni et al., 2017 deals with variable selection within high-dimensional datasets, efficiently identifying key predictors by applying penalties to coefficients, thus enhancing treatment effect estimations. This method outperforms traditional approaches like propensity score matching, which often falls short in complex data scenarios. Chernozhukov et al., 2018 further this exploration with Double Machine Learning (DML), a method that divides the estimation into two phases: prediction and causal estimation. Initially, machine learning predicts outcomes and effects from covariates, then uses these predictions’ residuals for causal analysis. Both strategies have proven useful for causal inference in the presence of imbalanced samples.

Metalearners for causal inference represent frameworks that utilize machine learning (ML) models to estimate the Conditional Average Treatment Effect (CATE) or the Average Treatment Effect (ATE) within various subpopulations in a dataset. They streamline the causal inference process by breaking it down into simpler components that ML algorithms can more effectively 8
# tackle though its prediction capabilities.

The Single-Model Learner (S-Learner) applies a solitary ML model to assess treatment effects, treating the treatment indicator as an additional input feature Hill, 2011 and Foster et al., 2011. On the other hand, the Two-Model Learner (T-Learner) distinguishes itself by training two distinct ML models—one for individuals in the treatment group and another for those in the control group. The differential predictions from these models serve to estimate the treatment effect, as discussed by Athey and Imbens, 2016 and further elaborated by Lu et al., 2018 and Powers et al., 2018.

The X-Learner is an extension of the T-learner. It works in three steps: First, it estimates the effect of treatment on both treated and control groups separately, as in the T-learner. Second, it calculates ’imputed treatment effects’ for each group, aiming to identify the potential outcomes for untreated units had they been treated, and vice versa. Third, these imputed effects are used as outcomes in separate regression models, employing the chosen ML model and using covariates as predictors, as detailed by Künzel et al., 2019.

The R-learner, introduced by Nie and Wager, 2020 use a two-stage meta-algorithm that builds upon Robinson’s 1988 decomposition of the outcome, hence the name R-Learner. This approach distinctively incorporates the Conditional Average Treatment Effect (CATE) directly into the outcome regression model, facilitating a nuanced analysis of treatment effects across different covariate levels.

This approach is characterized by its structured process: predicting nuisance functions in the first phase and estimating CATE in the second by applying a loss function specifically tailored to it. It adeptly addresses selection bias by adjusting for propensity scores, and is particularly effective in scenarios with extreme propensity scores where treatment assignment is nearly deterministic. By employing this model, the R-learner corrects biases in treatment effect estimation, accommodating variations in treatment assignment among individuals through a unique parameter tuning mechanism. This is conducted using base learners like random forests or 9
gradient boosted trees, tailored not to the raw outcomes but to a model that compensates for covariate effects and propensity score balancing. This method significantly advances the estimation of CATE in observational studies, offering a precise and dependable approach to causal analysis.

Recent literature has developed some methods to investigate the presence of heterogenous treated effects. Chernozhukov et al., 2023 proposes a comprehensive method for machine learning inference on heterogeneous treatment effects, incorporating the Best Linear Predictor (BLP), Sorted Group Average Treatment Effects (GATES), and Classification Analysis (CLAN). This method effectively identifies and analyzes variations in treatment effects and their associated covariates. On the other hand, Semenova and Chernozhukov, 2020 focuses on model-based strategies for "partial" CATE inference, using machine learning to analyze outcomes relative to a specific, lower-dimensional set of covariates. This technique facilitates nonparametric inference on partial CATE, revealing treatment effects within selected covariate groups, though it requires arbitrary selection of baseline covariates. Together, these approaches enhance our understanding of treatment effects, addressing both broad heterogeneity and effects within specific covariate groups.

Our chosen approach, Clustered Random Causal Forest (C-RCF) approach Athey and Wager, 2019 and Wager and Athey, 2018, aligns with the R-Learner framework by employing honest trees to compute R-Learners. We chose this method for several reasons: First, it has demonstrated strong performance in recovering the Conditional Average Treatment Effect (CATE) compared to other methods Caron et al., 2022. In that sense, Causal BART as in Hahn et al., 2020 may have an even better performance in simulation studies and was a strong candidate, however, we are now allowed to proceed with inference techniques on the partial CATE.<sup>3</sup> Lastly, the design of the RCF algorithm naturally extends to the domain of policy learning in observational studies 3Causal Basian additive regression trees - CBART is not orthogonal do to the errors in nuisance components. Other inference techniques may be available within the Baysian framework to analyze the partial CATE. However, we would rather use techniques with the frequentist framework. A possible extension of this paper is to add results from C-BART and other methods as a robustness check.

10
Athey and Wager, 2021, facilitating the application of various policy targeting rules.<sup>4</sup> After the estimation we proceed with the analysis proposed by Chernozhukov et al., 2023 and Semenova and Chernozhukov, 2020.

# **3. Background**

## **3.1. Revenue Mobilization in Indonesia: Low Revenue Limits Public Expenditure**

Indonesia has achieved remarkable economic stability, growth, and poverty reduction over the last two decades. With an annual growth rate of 5.3% from 2000 to 2018, surpassing the 4.9% average of lower-middle-income countries, Indonesia has seen significant economic progress. This period was characterized by a reduction in growth volatility and the creation of over 30 million service and industrial jobs, which replaced less productive agricultural roles and increased household incomes. Consequently, poverty rates dropped dramatically from 19.1% in 2000 to 9.4% in 2019, and Gross National Income (GNI) per capita increased six-fold, reaching US3,840, moving millions into the middle class.

Prudent fiscal management has been key to this success, particularly since the State Finance Law of 2003 mandated fiscal deficit and public debt limits, which have been well maintained. The public debt-to-GDP ratio declined sharply from 83 percent in 2000 to 30 percent of GDP in 2018, thereby earning Indonesia an investment-grade rating from the four major credit agencies. Fiscal policies have also contributed to poverty and inequality reduction, albeit modestly compared to peers.

Indonesia’s expenditure-to-GDP ratio (9.3% in 2018) is below the emerging economy average

> 4Notice that RCF is asymptotically equivalent to propensity score matching in recording some structural parameters such as ATE.

11
and restricts the expansion of public services. Figure 1 displays a cross-country comparison of Indonesia’s general revenue and government expenditure. In both cases Indonesia is below the average of its peers. According to the World Bank, 2020 " _The main reason for the low level of spending is the structurally low level of revenue collections. Indonesia’s revenue-to-GDP ratio is low at I4.6 percent in_ 2018 _, compared with the emerging economy average of 27.8 percent_ .

Revenue mobilization relies heavily on tax revenues, which have historically accounted for over 75% of the government’s total revenues. However, the country has experienced a decline in tax performance over the last two decades, evidenced by a significant drop in the tax-to-GDP ratio from 13.3% in 2008 to 9.1% in 2021. This decrease, even after a recovery post-pandemic, indicates a persistently low tax-to-GDP ratio compared to peers.<sup>5</sup> The World Bank identifies the compliance gap as the primary issue for the decline of revenue mobilization performance. It accounts for about 85% of the total revenue gap, or roughly 6.1% of GDP. The compliance gap arises from taxpayer non-compliance, including evasion, fraud, and avoidance, rather than policy decisions like rate reductions or exemptions. For instance, Indonesia’s audit performance is weaker than that of its peers, with fewer audits per capita between 2018 and 2020 and less tax liability generated from these audits when compared to peer countries. The system’s vulnerability to evasion and avoidance is exacerbated by an ineffective risk-based audit system, which has shown diminishing returns over time.

Improving audit performance, particularly in risk-based audits, is crucial for Indonesia to bolster its revenue mobilization. Following the deterrence model by Allingham and Sandmo, 1972, enhancing the probability of detection through more effective special and risk-based audits is key to inducing compliance and addressing the country’s revenue challenges.

5The figures presented in the last two paragraphs are from the central government revenue authority, which is different from subnational taxation, which is what we focus on in this paper. However, they reflect Indonesia’s structural problems with revenue mobilization that also occur at the local level.

12
# **3.2. Property Taxes in Kota Gorontalo**

In this paper, we will analyze compliance in tax property in Kota Gorontalo (KG),<sup>6</sup> in the state of Gorontalo Indonesia. The city covers an area of 79.59 km2, around 2.26 times larger than Duke University’s campus, with an estimated population of 201,350 in 2022. Figure 3 is a map displaying the location of where the experiment took place.

Property tax revenues in KG have had a notable rise over recent years due to increases in house prices and the number of registered tax-paying properties. The average house prices have increased 104% and the number of registered properties increased 7% between 2014 and 2022.

Despite the increase in tax revenues, there has been an increase in non-compliance. The debt associated with properties that have not cleared their dues increased 225%. This can be traced back to both an escalation in the property values that remain unpaid (88%) and a surge in the actual number of properties not meeting their tax obligations (76%). Figure 5 illustrates the evolution of the Tax Bill; Tax Revenue and Tax Debt between 2014 and 2022.

We observed that partial tax payments increased exceptionally in 2021. We speculated<sup>7</sup> that these partial payments arose from a possibility to pay taxes in installments. Later, due to the pandemic, some taxpayers opted to suspend the payments of their tax property. For this reason, it is important to consider the intensive and extensive margins in our analysis. In 2021 most properties were partially compliant, at<sup>8</sup> 48.9%, 28.3% were non-compliant, and 22.8% were fully compliant of the total number of properties. Moreover, 58% of the total property tax bill was paid, 30% by fully compliant properties, and 28% by partially compliant properties. The outstanding debt of 42% of the tax bill 46.7% comes from properties that did not exert any payment and 53.2% comes from properties that

> 6Kota Gorontalo means Gorontalo City. The city is the capital of a homonym state Gorontalo.

> 7Inquiries regarding this matter have been directed to the tax authorities in Kota Gorontalo, with response pending.

8We define a property as partially compliant in a given year if the tax property payment was positive, but inferior to the total tax bill.

13
made some sort of payment (Figures 7 and 8).

# **3.3. The Door-to-Door Intervention**

To tackle the growing evasion, the local tax authority created the door-to-door tax enforcement campaign in Kota Gorontalo. It consisted in sending tax officials to properties to directly interact with property owners regarding their tax responsibilities. The program spanned 14 months, from November 2020 to December 2021. Visits peaked in January 2021 with 8,776 monthly visits, but they significantly decreased from March 2021 due to COVID-19, resuming at a slower pace from August to December 2021 when the program was terminated (Figure 6c). If the visitation rate was kept, all properties would have been visited before the end of 2021. By the end of the program in December 2021, of 52,941 registered properties, 31,730 received visits, 59.9% of the total.

The tax officials were equipped with geolocated tablets. Once a visit is entered into the tablet, its location is recorded along with the name of the citizen who answers the door and the name of the person responsible for the property. The visit finishes after the tax official talks to the person responsible for the property or if no one was found at the house. Then, another visit should restart at the next-door neighbor. No house was visited twice.

The geolocation allows us to attest that the visits took place, as well as checking visitation patterns. Our initial analysis shows that the geolocation records are consistent with the registered addresses in our administrative dataset. In Kota Gorontalo, there are 50 neighborhoods. Of the 31,730 visitation records, no geolocation record was located more than 3 km away from Kota Gorontalo. Moreover, only 18 records were distanced more than 500 meters from the neighborhood it was supposed to be.

The high level of autonomy of tax officials in deciding where to visit first has created a major selection of what properties were visited. For example, despite covering almost 60% of the reg14
istered properties, the tax bills of these properties amounted to only 46.2% of the total tax bill, and 50.4% of the total taxes paid in 2021.

# **3.4. Selection on Treatment**

Our analysis highlights a pronounced selection. First, we look at the difference between the visited and non-visited groups across neighborhoods. Figure 6a contains a bivariate map that showcases the distribution of average compliance and the proportion of visited properties in each neighborhood. The shares of visited properties in a neighborhood range from 34% to 95%, while historical compliance ranges from 63% to 98%.

The neighborhoods more frequently visited were the ones with higher historical compliance, those are the neighborhoods in dark blue in (Figure 6a). Conversely, the neighborhoods in grey were less likely to have paid the property taxes and also less likely to be visited. Figure 6b plots illustrates this correlation. Also, notice that the number of properties in each neighborhood represented by the size of each dot is roughly the same across all the neighborhoods.

The baseline comparison in Table 2 reveals notable differences between the treated and control groups.<sup>9</sup> The mean test indicates that all variables except for the logarithm of taxable value have statistically different means. However, the composition of this variable varies wildly between treated and non-treated.

The properties of each group differ in its main characteristics: size, price and land use. On average, visited properties had lower total values, smaller land areas, and lower land prices. However, also they had much higher construction value land area, and value of the area by square meter and were more likely to have construction in it.

The properties also diverge in the characteristics of its owner. The owners of properties in the treatment group are more likely to live in the visited properties and less likely to own multiple

> 9We are using the terms treated/control as equivalent to visited/non-visited.

15
properties. On average, the value of their portfolio in KG is lower.

Treated and non-treated properties also differ vastly in historical compliance. Visited properties had better historical compliance. They were more likely to have fulfilled their tax obligations in 2020, were less likely to have made payments after the deadline, and were less likely to have received a fine beforehand.

The distinctions mentioned above highlight the complex dynamics influencing property tax compliance. Despite all the differences in the treatment and control group, given the observable characteristics in our dataset, no unity was deterministically immune or targeted by the program.<sup>10</sup> In other words, no covariable seems to play an effect strong enough to degenerate the probability distribution of treatment assignment. Therefore we can vouch for the existence of overlapping support in treatment for all observations in our dataset. We will discuss it further in our model identification assumptions.

# **4. Empirical Results**

This section is composed of two parts. First, we discuss the estimation strategies suitable for analyzing the impact of a specific intervention, focusing on the nuanced detection of heterogeneous treatment effects. We begin by delineating the array of estimation techniques at our disposal, ultimately arguing in favor of the Clustered Random Forest (CRF) method. The preference for CRF is due to the ability to do provably valid statistical inference and the robustness of the results to the possible presence of irrelevant covariates.

The second part of this section discusses the results obtained by our empirical estimation. We present empirical evidence demonstrating the positive impacts of the tax compliance pro-

> 10Figure 9 shows the distribution propensity score estimation through boosted regression trees. This is the propensity score estimation method we use as input for the causal forest. The lowest probability of receiving treatment was 14% and the highest was 91%.

16
gram in both 2021 and 2022. The findings underscore a significant variability in outcomes, with the Average Treatment effect on the Treated (ATT) surpassing the Average Treatment effect on the Untreated (ATU). Our analysis investigates the program and highlights the characteristics that correlate with higher program effect.

# **4.1. Model**

We begin this section with a high-level overview of the C-RCF model. Our approach will be guided by the Neyman-Rubin Causal Model, as described in Rubin, 1978 and further elaborated in Imbens and Rubin, 2015.

Every property is indexed as _i_ . We consider a binary variable for treatment assignment, _Zi j_ , which can take values in the set {0, 1}. Here, _Zi_ = 1 signifies that the unit _i_ was visited, and _Zi j_ = 0 means otherwise. Potential outcomes for each unit are denoted as ( _Yi_ (0), _Yi_ (1)). The term _Yi_ (1) represents the outcome for unit _i_ when it undergoes treatment, corresponding in our study if a property was ever visited. Conversely, _Yi_ (0) represents the outcome without treatment exposure. The outcome variable is continuous, meaning ( _Yi_ (0), _Yi_ (1)) ∈ _R_<sup>2</sup> . The vector _Xi_ contains the covariates, among them the categorical variable _ui_ that indicates the neighborhood where the property is located. We proceed we a cluster robust estimation procedure.<sup>11</sup> We start by proposing a partially linear model, assuming that a unit’s baseline outcome can be described by an unknown (potentially complex) function _f_ , with a treatment assignment introducing a constant shift in the outcome by an amount _τ_ ( _Xi_ ): Our analysis will describe 4 outcomes variables: (i) Tax Participation - _Y_<sup>_Pa_</sup> - Dummy that assumes value 1 if any positive payment; (ii) Compliance - _Y_<sup>_Co_</sup> - The ratio of paid to payable; (iii) log Value Paid - _Y_<sup>_lnP_</sup> ; and (iv) Value Paid - _Y_<sup>_VP_</sup> . We are not able to find any statistically significant

> 11For a more detailed explanation, see Athey and Wager, 2019, section 1.2.

17
result for the outcome { _Y_<sup>_VP_</sup> }. We opt to leave this variable explicitly in case the reader wants to check it, but we will not comment on it in this section.

# **4.2. Estimation Results**

**Result 1** _The effect of the door-to-door intervention is positive._

**Result 2** _There was negative selection into treatment, the units with the highest potential effects were not visited._

Table 4 presents the point estimates. For all the variables the ATE and ATU are statistically different than zero. The Average Treatment Effect (ATE) and the Average Treatment Effect on the Treated (ATT) being positive indicate beneficial outcomes from the treatment. In 2021, we observed the following patterns ATT>ATE>ATU which suggests the presence of heterogeneous treatment effects and negative on the visit: The units visited on average show lower treatment effect compared to those not visited.

Tax participation increase by 4% in 2021, with the effect varying across different treatment statuses: 3.5% increase was observed in the units directly visited by tax officials, whereas notvisited are estimated to have a boost of 4.7% had they been visited. Notice here that the variance for our estimated CATE is much higher among visited properties. Considering the baseline results (Table 3), the program was responsible for between 0.2% and 8.1% increase among the visited units.<sup>12</sup> However, the results would be significantly higher if the visits took place in the houses that were not visited. We estimate that the average participation for the treatment group would be 63.9% and between 3.8% and 9.3% of the effect could be attributed to the interventions. If the program was to be fully implemented, it would be responsible for between 1.6% and 8.7% of the total tax participation.

> 12We use 95% confidence intervals.

18
Compliance increased overall by 3.5%. Among visited properties, it increased 3.4% and we estimate that it would increase 3.8% in the non-visited properties. In comparison to the baseline values (Table 3), the program had the potential to increase overall compliance by between 5.1% and 5.8%; it increased the compliance of the visited properties by between 1.2% and 8.6% and could have increased the average compliance among the control group to 58.9%, where we expect the program to be responsible for between 3.7% and 9.1% of the total compliance.

The impact on the logarithm of total payments was observed at 0.422, with visited units experiencing an effect of 0.377, and non-visited units seeing a higher impact of 0.492. These outcomes suggest an average uplift in payment values ranging from 31.9% to 76.3%. Notice that this result is mainly due to the ATU. If we consider only the group of non-visited units, there are larger potential gains, between 46.9% and 82%. Due to the presence of outliers, we do not see any statistically significant effect when analyzing just the value paid.

**Result 3** _The effect of the door-to-door visits in 2022 compared to 2021: (i) Are on average higher; (ii) Have narrower confidence intervals; (iii) Have lower difference between ATT and ATU._

First notice that as the partial payments virtually disappear from our dataset in 2022, then _Y_<sup>_Co_</sup> converges to _Y_<sup>_Pa_</sup> , therefore _τ_<sup>_Co_</sup> converges to _τ_<sup>_Pa_</sup> . Overall the intervention increased Tax Participation/Compliance by 6.3% in 2022, that is between 5.4% and 11.1% of the overall compliance. These results are higher than what we found for 2021.

Results 3 are in line with the literature on the economics of tax compliance that consistently finds time persistence in the effects of interventions. However, as time passes those results lose strength. We do not have an explanation for the increase in the effect, but we speculate that it may be due to the economic effects of the COVID-19 pandemic that lost strength in 2022. Therefore, it is also plausible to imagine that the results we find underestimate the potential gains in regular years.

The difference between ATT and ATU is lower in 2022. However, when we compare the re19
sults to the aggregated outcomes we see that the treated group had a much higher participation and compliance rate in 2021 as well as in 2022. The estimated ATT for Tax Participation/Compliance was 6.1% which is 8.0% of the observed aggregated outcome, whereas the ATU was 6.4% which represents 9.8% of the estimated Tax Participation/Compliance if the properties had been visited.

# **4.3. Heterogeneous Effects**

**Result 4** _There is statistically significant heterogeneity in the treatment effect. The heterogeneity seems to be concentrated in the control group._

Analyzing only average treatment effects can be deceiving as average may hide potentially high heterogeneity. Figure 10 illustrates the dispersion of our CATE estimator where red is the treatment and blue is the control group. We start by checking if our model is capable of addressing heterogeneity. We will do it using two different methods.

First, we compute the Average Treatment Effect (ATE) for both the upper and lower halves of the Conditional Average Treatment Effect (CATE) distribution using a doubly robust method. Subsequently, we assess the statistical significance of the mean difference between these groups. The results, presented in Table 5, indicate that for 2021, a significant difference between the high and low treatment effects groups is observed only for the outcome _τ_<sup>_lnP_</sup> . Analysis of the treated and untreated units reveals a notable difference between these groups, suggesting the presence of heterogeneous treatment effects. Furthermore, this disparity became more pronounced in 2022.

The second method to assess heterogeneity follows the suggestions of Athey and Wager, 2019 and Chernozhukov et al., 2023. This method aims to model the Conditional Average Treatment Effect (CATE) as a linear function of out-of-bag estimates from a causal forest. Two synthetic predictors are generated: _Ci_ , reflecting the deviation of treatment effects from their mean af20
ter adjusting for propensity scores, and _Di_ , capturing the interaction between these deviations and the actual treatment effects. By regressing adjusted outcomes against these predictors, the coefficient of _Di_ serves as an indicator of the precision in estimating treatment heterogeneity.

Table 6 displays the results where the estimators for _C_ and _D_ are in the lines named _Mean_ and _Differential_ . If the coefficient related to _Mean_ is close to 1, the more confident we can be that the forest prediction is correct. A coefficient of 1 for _Di_ suggests accurate calibration of heterogeneity estimates. Moreover, if the coefficient is significantly greater than 0, then we reject the null of no heterogeneity. However, if _Differential_ is far from one but significant and positive we have evidence of a useful association between the out-of-bag estimated CATE and the true CATE.

Our estimation suggests that C-RCF is capable of correctly addressing the average treatment effect and that there is heterogeneity in all the tested outcomes, except for the paid value before the log transformation - _Y_<sup>_PV_</sup> .

# **4.3.1. Heterogeneous Effects**

In this section, we explore how various property characteristics influence the program’s effectiveness. The results, presented in Tables 7 to 14, stem from fitting a doubly robust linear model _E_ [ _Y_ (1) − _Y_ (0) | _X_ = _Xi_ ] = _τ_ ( _Xi_ ) ∈ _β_ 0 + _Ai β_ , where _Ai_ represents the covariates projected onto the Conditional Average Treatment Effect (CATE). More specifically, we want _Ai_ to include the following: Land Area, Land Price per m<sup>2</sup> ; Dummy if there is construction in the Land interacted with the Construction Area and Construction price per m<sup>2</sup> ; Vector of Owner’s Characteristics; Vector of Payment History of the property; Vector of neighborhood fixed effects.

The tables detail estimates with neighborhood-level fixed effects in columns (1) to (7), while column (8) provides estimated projectors akin to those in column (7) but excludes neighborhood fixed effects.

21
**Result 5** _The visits were more effective in properties with lower tax base values. The program is more effective in inducing compliance in properties with lower area and lower value per square meter._

Tables 7 to 14 show that for most of our outcomes of interest, the effectiveness of the program is negatively correlated with the value of the tax base. When decomposing these effects by the value of the property and the value of the construction we see that the main drivers of this correlation come from the values associated with the land. The visits are more effective in inducing compliance in properties with lower area and lower value per square meter. This is reinforced by the dummy "Dummy - Lower Tax Bracket - 2021" which indicates that a property value is below 1,000,000 rupiah. Interestingly, for the year 2021, these results are not valid for the outcome that measure if any payment was done looks at any payment or the outcome of the logarithm of the paid value. However, the results are valid for all outcomes for the year 2022.

Overall, the coefficient associated with land area presents a much higher magnitude than the coefficient associated with land price. These results seem to suggest that the optimal strategy to induce compliance includes visiting property with a lower value. Then a visiting strategy that aims to induce compliance may have serious regressive consequences.

**Result 6** _The visits were more effective in properties that were fined in the past. However, the effectiveness of the visits decreases with the value of the fine._

For all the relevant outcomes, the effect of the visits was higher in properties that received fines in previous years. Moreover, the effectiveness of the visits decreases with the values of the fines. One may argue that this is mechanically driven by the fact that higher-value properties receive higher fines. However, even after controlling linearly by the value of the property, the negative correlation persists. This is valid for all previous years and all outcomes. This is aligned with the literature on the persistent effects of auditing on tax compliance.

22
**Result 7** _Characteristics of the owner, presence of the building, and its characteristics do not seem to correlate to the effects of the visits._

# **5. Conclusion**

This paper evaluates a tax enforcement program conducted in Gorontalo, Indonesia, where tax officials visited properties to engage directly with owners about their tax obligations, aiming to improve compliance and revenue collection. Initiated to address growing tax evasion, the door-to-door campaign ran from November 2020 to December 2021, peaking in January 2021 with 8,776 monthly visits before slowing due to COVID-19. By December 2021, 31,730 of 52,941 properties (59.9%) had been visited.

A strong aspect of this paper is its connection to the literature on the effects of close intervention for tax compliance and its potential to generalize findings to other contexts. Many equivalent studies in developed countries demonstrate that direct contact with tax authorities, often through audits, increases compliance. However, the literature lacks examples from developing countries, where tax compliance and state capacity are generally lower. Moreover, we find a positive effect on tax compliance even in the absence of punishments for non-compliant properties. These results suggest positive results of implementing such a policy in other settings.

While property taxes’ specificity might limit generalizability, this specificity is advantageous for statistical identification, as it allows us access to the real tax bill of both treatment and control groups, enabling a comprehensive analysis using the entire dataset, not just the treated units.

We applied observational study analysis techniques to address the imbalance between treated and untreated units and conducted cluster robust analysis to mitigate the impact of local treatment spillovers.

The baseline comparison in the study reveals notable differences between the treated (vis23
ited) and control (non-visited) groups. The mean test shows statistically significant differences in all variables except for the logarithm of taxable value, which varies greatly between the two groups. Treated properties, on average, had lower total values, smaller land areas, and lower land prices but higher construction value, land area, and value per square meter, and they were more likely to have constructions on them. Owners of treated properties were more likely to reside in them and less likely to own multiple properties, with lower overall portfolio values. Additionally, treated properties exhibited better historical compliance, being more likely to have met tax obligations in 2020, less likely to have made late payments, and less likely to have received fines. These distinctions highlight the complex dynamics influencing property tax compliance.

Our findings reveal statistically significant evidence that Gorontalo’s tax enforcement strategy through visitation notably increased compliance rates among property owners, with effects intensifying in subsequent years. The influence of the COVID-19 pandemic in 2021 might have played a significant role in these outcomes.

For 2021, if the program was implemented in all the districts, we would expect a tax participation increase of 1.6% and 8.7%; and an increase in overall compliance by between 5.1% and 5.8%. The visited properties experienced more pronounced effects than the control group, suggesting potential benefits from revising the targeting strategy of the visitations. Specifically, implementing the program for the control group could potentially increase tax participation by 7.0% and tax compliance by 6.4%. These effects are not only persistent in 2022, but also increasing. We do not have an explanation as to why the effect would increase over time, but we find it reasonable to speculate that the results in 2021 are underestimated due to the pandemic.

We find strong evidence of the presence of heterogeneity in the program’s effects, particularly more significant among the control group. Moreover, visits were more effective in properties with lower tax base values, especially those with smaller land areas and lower land values per square meter. Payment history is also highly associated with the visitation effect: properties previously fined showed higher compliance post-visit, though the visitation’s effectiveness diminished with the fine’s value, even after adjusting for property value. Also, the set of owner’s 24
characteristics we have access to does not seem to influence the program effect, this is subject to change in a setting where more variables are available.

Notably, the enhanced effectiveness in lower-value properties prompts a discussion on algorithmbased taxation strategies, which, while aiming to improve compliance, could inadvertently focus enforcement on lower-income owners, potentially leading to regressive taxation. This underscores the need for further research on algorithm-based taxation and policy learning as a natural progression from this study.

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# **A. Tables**

**Table 1: Treatment group vs Control group comparison: Outcomes**

||NotTreated/Visited<br>(N=21184)|Treated/Visited<br>(N=31725)|P-value|
|---|---|---|---|
|Tax Participation - 2021|12468 (66.9%)|24520 (83.9%)|<0.001|
|Tax Participation - 2022|10175 (59.2%)|21878 (76.3%)|<0.001|
|Compliance - 2021|0.55 ± 0.44|0.69 ± 0.38|<0.001|
|Compliance - 2022|0.59 ± 0.49|0.76 ± 0.43|<0.001|
|ln Value Paid - 2021|7.61 ± 5.46|9.10 ± 4.11|<0.001|
|ln Value Paid - 2022|6.88 ± 5.80|8.46 ± 4.80|<0.001|
|Value Paid - 2021|212335 ± 1410139|138643 ± 2237168|<0.001|
|Value Paid - 2022|246751 ± 1514197|150380 ± 2248708|<0.001|

30
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# **Figure 7: Distribution Property by Payment and Treatment Status**

<!-- Start of picture text --> Partially Paid (48.9%) Not Paid (22.8%)<br>Control<br>16.2%<br>Control Visited<br>13.1% 9.7%<br>Fully Paid (28.3%)<br>Visited<br>32.8%<br>Visited Control<br>17.6% 10.7%<br><!-- End of picture text --> 37
# **Figure 8: Distribution Payable by Payment and Treatment Status**

<!-- Start of picture text --> Partially Compliant − Paid (28.0%) Non compliant (19.7%)<br>Visited 14.0% Control 13.9% Control12.7% Visited 7.0%<br>Fully Compliant (30.0%) Partially Compliant − Debt (22.4%)<br>Visited 15.2% Control 14.8% Control12.3% Visited 10.0%<br><!-- End of picture text --> 38
# Histogram and Kernel Density Plot

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<!-- Start of picture text --> Paid.2021 Intaxbil.2021 taxbil.2021 shpaid.2021<br>12000<br>10000 9000<br>10000 10000<br>Ss38Ss333§36000 (s)§3<br>5000<br>5000 5000<br>3000<br>0 i) 0 0<br>0.00 0.05 0.10 0.15 0.20 ti) 1 oO 25000 50000 75000 0.0 04 0.2<br>CATE CATE CATE CATE<br>Paid.2022 Intaxbil.2022 taxbil.2022 shpaid.2022<br>12000<br>10000<br>9000<br>9000 9000<br>7500<br>6000<br>E 6000 = 6000 = r=<br>3 3 3 5000 3<br>° ° ° °<br>3000 3000 2500 3000<br>) i) oO 0<br>01 0.0 01 0.2 A o 1 2 3 0 25000 50000 75000 100000 0.0 0.1 0.2<br>CATE CATE CATE CATE<br><!-- End of picture text -->
# **Table 2: Baseline: Covariates**

||Not Treated/Visited<br>(N=18631)|Treated/Visited<br>(N=29226)|P-value|
|---|---|---|---|
|ln Value of Tax Object - 2021|18.28 ± 1.51|17.91 ± 1.36|<0.001|
|Dummy - Lower Tax Rate - 2021|17632 (94.6%)|28612 (97.9%)|<0.001|
|ln Land Area - 2021|5.86 ± 1.07|5.65 ± 0.91|<0.001|
|ln Taxable Value - 2021|12.39 ± 0.87|12.18 ± 0.74|0.60|
|ln Taxable Value - Land (by m2) - 2021|6.53 ± 1.48|6.54 ± 1.21|<0.001|
|Dummy - Contruction - 2021|13128 (70.5%)|25143 (86.0%)|<0.001|
|ln Value - Land (Total) - 2021|9.18 ± 5.97|11.11 ± 4.52|<0.001|
|ln Building Area - 2021|3.22 ± 2.19|3.80 ± 1.65|<0.001|
|ln Taxable Value - Building (by m2) - 2021|8.49 ± 0.72|8.54 ± 0.65|<0.001|
|Dummy - Owner Lives in The Property - 2021|1914 (10.3%)|3478 (11.9%)|<0.001|
|Dummy - Owner has multiple homes - 2021|1757 (9.4%)|1596 (5.5%)|<0.001|
|ln # properties by owner if multiple - 2021|0.75 ± 0.21|0.73 ± 0.16|<0.001|
|ln # properties by owner - 2021|0.03 ± 0.11|0.02 ± 0.09|<0.001|
|ln Value Owner’s Portfolio** - 2022|1.87 ± 5.82|1.07 ± 4.46|<0.001|
|Dummy - Property Paid >0 - 2020|13754 (73.8%)|26215 (89.7%)|<0.001|
|Dummy - Property Payable is Null - 2021|0 (0.0%)|0 (0.0%)|<0.001|
|Dummy - Tax was paid after deadline - 2020|4965 (26.6%)|6290 (21.5%)|<0.001|
|Dummy - Property fined - 2020|4870 (26.1%)|6261 (21.4%)|<0.001|
|ln Tax Fine* - 2020|2.39 ± 4.11|1.86 ± 3.64|<0.001|
|ln Tax Paid* - 2020|8.43 ± 5.13|9.79 ± 3.46|<0.001|

41
# **Table 3: Treatment group vs Control group comparison: Outcomes**

||NotTreated/Visited<br>(N=21184)|Treated/Visited <br>(N=31725)|P-value|
|---|---|---|---|
|2021<br>Tax Participation - 2021 -_Y_<sup>_Pa_</sup>|12468 (66.9%)|24520 (83.9%)|<0.001|
|2022<br>Tax Participation - 2022 -_Y_<sup>_Pa_</sup>|10175 (59.2%)|21878 (76.3%)|<0.001|
|2021<br>Compliance - 2021 -_Y_<sup>_Co_</sup>|0.55 ± 0.44|0.69 ± 0.38|<0.001|
|2022<br>Compliance - 2022 -_Y_<sup>_Co_</sup>|0.59 ± 0.49|0.76 ± 0.43|<0.001|
|2021<br>ln Value Tax Bill Paid - 2021 -_Y_<sup>_lnP_</sup>|7.61 ± 5.46|9.10 ± 4.11|<0.001|
|2022<br>ln Value Tax Bill Paid - 2022 -_Y_<sup>_lnP_</sup>|6.88 ± 5.80|8.46 ± 4.80|<0.001|
|2021<br>Value Tax Bill Paid - 2021 -_Y_<sup>_V_</sup> <sup>_P_</sup>|212335 ± 1410139|138643 ± 2237168|<0.001|
|2022<br>Value Tax Bill Paid - 2022 -_Y_<sup>_V_</sup> <sup>_P_</sup>|246751 ± 1514197|150380 ± 2248708|<0.001|

**Table 4: Treatment Effect**

||_τ_<sup>_Pa_</sup>|**2021**<br>_τ_<sup>_Co_</sup>|_τ_<sup>_lnP_</sup>|_τ_<sup>_V P_</sup>|_τ_<sup>_Pa_</sup>|**202**<br>_τ_<sup>_Co_</sup>|**2**<br>_τ_<sup>_lnP_</sup>|_τ_<sup>_V P_</sup>|
|---|---|---|---|---|---|---|---|---|
|ATE|0.04***|0.035***|0.422***|29597|0.063***|0.063***|0.678***|29645|
||(0.014)|(0.011)|(0.145)|(29156)|(0.011)|(0.011)|(0.122)|(28764)|
|ATT|0.035**|0.034***|0.377**|18638|0.061***|0.061***|0.663***|19133|
||(0.017)|(0.013)|(0.172)|(18072)|(0.013)|(0.013)|(0.136)|(17554)|
|ATU|0.047***|0.038***|0.492***|47986|0.064***|0.064***|0.693***|48431|
||(0.01)|(0.008)|(0.107)|(52113)|(0.009)|(0.009)|(0.102)|(53937)|

42
# **Table 5: High vs Low CATE**

# **2021**

|||_τ_<sup>_Pa_</sup>|||_τ_<sup>_Co_</sup>|||_τ_<sup>_lnP_</sup>|||_τ_<sup>_V P_</sup>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(H)|(L)|(Diff)|(H)|(L)|(Diff)|(H)|(L)|(Diff)|(H)|(L)|(Diff)|
|ATE|0.056**|0.023***|0.033|0.06***|0.008|0.052***|0.622***|0.238***|0.384*|-17|55178|-55195|
||(0.025)|(0.008)|(0.026)|(0.019)|(0.006)|(0.02)|(0.248)|(0.077)|(0.26)|(13377.1)|(53974.8)|(55607.8)|
|ATT|0.044*|0.025***|0.02|0.053***|0.01*|0.043**|0.513**|0.249***|0.264|767.2|32176.1|-31408.9|
||(0.031)|(0.008)|(0.031)|(0.022)|(0.006)|(0.023)|(0.3)|(0.078)|(0.31)|(10479)|(31375.7)|(33079.3)|
|ATU|0.076***|0.019***|0.057***|0.074***|0.005|0.069***|0.801***|0.204***|0.597***|-2968.1|103749.7|-106717.8|
||(0.017)|(0.007)|(0.019)|(0.014)|(0.007)|(0.016)|(0.172)|(0.082)|(0.19)|(24975.4)|(107574.8)|(110436)|

|||||||**20**|**22**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||_τ_<sup>_Pa_</sup>|||_τ_<sup>_Co_</sup>|||_τ_<sup>_lnP_</sup>|||_τ_<sup>_V P_</sup>||
||(H)|(L)|(Diff)|(H)|(L)|(Diff)|(H)|(L)|(Diff)|(H)|(L)|(Diff)|
|ATE|0.099***|0.037***|0.062***|0.098***|0.036***|0.063***|0.953***|0.507***|0.447**|-17|55178|-55195|
||(0.014)|(0.01)|(0.017)|(0.014)|(0.01)|(0.017)|(0.143)|(0.13)|(0.193)|(13377.1)|(53974.8)|(55607.8)|
|ATT|0.095***|0.04***|0.055***|0.094***|0.038***|0.056***|0.889***|0.562***|0.327*|767.2|32176.1|-31408.9|
||(0.015)|(0.011)|(0.019)|(0.015)|(0.011)|(0.018)|(0.15)|(0.141)|(0.206)|(10479)|(31375.7)|(33079.3)|
|ATU|0.108***|0.032***|0.075***|0.107***|0.032***|0.075***|1.065***|0.418***|0.647***|-2968.1|103749.7|-106717.8|
||(0.015)|(0.01)|(0.018)|(0.015)|(0.01)|(0.018)|(0.154)|(0.128)|(0.201)|(24975.4)|(107574.8)|(110436)|

43
**Table 6: Calibration Test**

||||**2021**|||**2022**||
|---|---|---|---|---|---|---|---|
|||Estimate|Sd|P-Value|Estimate|Sd|P-Value|
|_τ_<sup>_Pa_</sup>|_Mean_|1.00176|0.2718|0.00011|1.02106|0.13588|-|
||_Differential_|1.36729|0.28072|-|1.20537|0.22464|-|
|_τ_<sup>_Co_</sup>|_Mean_|1.00982|0.24418|0.00002|1.0073|0.13887|-|
||_Differential_|1.47134|0.2412|-|1.28648|0.27367|-|
|_τ_<sup>_lnP_</sup>|_Mean_|1.00429|0.26355|0.00007|1.02692|0.14324|-|
||_Differential_|1.22987|0.21412|-|0.91808|0.22855|0.00003|
|_τ_<sup>_V P_</sup>|_Mean_|3.29768|3.28017|0.15737|3.29768|3.28017|0.15737|
||_Differential_|-3.10676|3.33146|0.82447|-3.10676|3.33146|0.82447|

44
# **Table 7: Outcome: Paid - 2021**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|-0.015 **||||||-0.030|-0.058|
||(0.006)||||||(0.020)|(0.043)|
|ln Value Tax Base - LAND||-0.007||||-0.004|||
|||(0.007)||||(0.007)|||
|ln Land Area - 2021|||-0.022 **|-0.022 **|-0.063 ***||-0.029|0.014|
||||(0.011)|(0.011)|(0.016)||(0.029)|(0.067)|
|ln Taxable Value - Land (by m2) - 2021|||-0.004|-0.004|-0.024 ***||-0.007|0.006|
||||(0.007)|(0.007)|(0.009)||(0.015)|(0.039)|
|Dummy - Contruction - 2021||-0.121|-0.207|-0.210|-0.071|-0.132|-0.167|-0.159|
|||(0.119)|(0.141)|(0.143)|(0.135)|(0.118)|(0.154)|(0.142)|
|ln Building Area - 2021|||0.010|0.010|-0.008||0.006|-0.000|
||||(0.011)|(0.011)|(0.011)||(0.014)|(0.014)|
|ln Value Tax Base - BUILDING||0.010||||0.011|||
|||(0.009)||||(0.009)|||
|ln Taxable Value - Building (by m2) * - 2021|||0.018|0.018|0.010||0.015|0.018 *|
||||(0.011)|(0.011)|(0.010)||(0.011)|(0.011)|
|Dummy - Owner has lives in Property - 2021||||-0.004|-0.004|-0.000|-0.004|0.018|
|||||(0.014)|(0.014)|(0.013)|(0.014)|(0.018)|
|Dummy - Owner has multiple homes - 2021||||0.028|0.014|0.081|-0.010|0.027|
|||||(0.234)|(0.235)|(0.243)|(0.236)|(0.235)|
|ln Value Owners Portfolio** - 2021||||-0.002|-0.001|-0.004|0.000|-0.001|
|||||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|Dummy - Property Paid>0 - 2020|||||-0.244|-0.091|-0.267|-0.267|
||||||(0.188)|(0.188)|(0.187)|(0.195)|
|Dummy - Property fined - 2020|||||0.073 *|0.072 *|0.072 *|0.076 *|
||||||(0.041)|(0.041)|(0.041)|(0.045)|
|ln Value Fine - 2020|||||-0.021 ***|-0.020 ***|-0.021 ***|-0.020 ***|
||||||(0.006)|(0.006)|(0.006)|(0.007)|
|Dummy - Tax was paid after deadline - 2020|||||0.103 ***|0.100 **|0.102 ***|0.092 **|
||||||(0.039)|(0.040)|(0.039)|(0.041)|
|Dummy - Lower Tax Bracket - 2021|||||-0.029|-0.012|-0.032|-0.015|
||||||(0.021)|(0.020)|(0.021)|(0.034)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

45
# **Table 8: Outcome: Sh Paid - 2021**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|-0.020 ***||||||0.025|-0.018|
||(0.007)||||||(0.022)|(0.029)|
|ln Value Tax Base - LAND||-0.025 **||||-0.024 **|||
|||(0.010)||||(0.010)|||
|ln Land Area - 2021|||-0.048 ***|-0.049 ***|-0.089 ***||-0.117 ***|-0.051|
||||(0.014)|(0.014)|(0.021)||(0.035)|(0.049)|
|ln Taxable Value - Land (by m2) - 2021|||-0.025 **|-0.025 **|-0.044 ***||-0.058 ***|-0.026|
||||(0.010)|(0.010)|(0.013)||(0.019)|(0.029)|
|Dummy - Contruction - 2021||-0.111|-0.211|-0.214|-0.098|-0.135|-0.018|-0.089|
|||(0.121)|(0.144)|(0.147)|(0.138)|(0.121)|(0.147)|(0.133)|
|ln Building Area - 2021|||0.019 *|0.019 *|0.003||-0.009|-0.003|
||||(0.011)|(0.011)|(0.012)||(0.015)|(0.013)|
|ln Value Tax Base - BUILDING||0.011||||0.013|||
|||(0.009)||||(0.009)|||
|ln Taxable Value - Building (by m2) * - 2021|||0.016|0.016|0.010||0.006|0.012|
||||(0.012)|(0.012)|(0.011)||(0.011)|(0.011)|
|Dummy - Owner has lives in Property - 2021||||-0.007|-0.006|-0.002|-0.007|0.006|
|||||(0.014)|(0.014)|(0.014)|(0.014)|(0.017)|
|Dummy - Owner has multiple homes - 2021||||-0.178|-0.190|-0.106|-0.171|-0.158|
|||||(0.226)|(0.228)|(0.237)|(0.230)|(0.226)|
|ln Value Owners Portfolio** - 2021||||0.009|0.009|0.005|0.008|0.008|
|||||(0.011)|(0.011)|(0.012)|(0.012)|(0.011)|
|Dummy - Property Paid>0 - 2020|||||0.017|0.160|0.037|0.027|
||||||(0.187)|(0.192)|(0.186)|(0.192)|
|Dummy - Property fined - 2020|||||0.104 **|0.105 **|0.105 **|0.119 **|
||||||(0.047)|(0.046)|(0.047)|(0.049)|
|ln Value Fine - 2020|||||-0.022 ***|-0.021 ***|-0.022 ***|-0.023 ***|
||||||(0.006)|(0.006)|(0.006)|(0.007)|
|Dummy - Tax was paid after deadline - 2020|||||0.072|0.070|0.072|0.071|
||||||(0.047)|(0.048)|(0.047)|(0.048)|
|Dummy - Lower Tax Bracket - 2021|||||-0.084 ***|-0.067 **|-0.080 ***|-0.059|
||||||(0.031)|(0.031)|(0.031)|(0.037)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

46
# **Table 9: Outcome: Ln Tax Bill - 2021**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|-0.133 **||||||-0.264|-0.555|
||(0.066)||||||(0.222)|(0.432)|
|ln Value Tax Base - LAND||-0.059||||-0.032|||
|||(0.079)||||(0.076)|||
|ln Land Area - 2021|||-0.209 *|-0.205 *|-0.574 ***||-0.277|0.150|
||||(0.115)|(0.114)|(0.178)||(0.333)|(0.678)|
|ln Taxable Value - Land (by m2) - 2021|||-0.038|-0.036|-0.206 **||-0.059|0.058|
||||(0.076)|(0.075)|(0.095)||(0.169)|(0.398)|
|Dummy - Contruction - 2021||-1.479|-2.518 *|-2.554 *|-1.311|-1.577|-2.159|-2.088|
|||(1.274)|(1.521)|(1.548)|(1.479)|(1.271)|(1.697)|(1.535)|
|ln Building Area - 2021|||0.152|0.155|-0.005||0.120|0.056|
||||(0.120)|(0.122)|(0.125)||(0.162)|(0.152)|
|ln Value Tax Base - BUILDING||0.121||||0.133|||
|||(0.095)||||(0.095)|||
|ln Taxable Value - Building (by m2) * - 2021|||0.204 *|0.206 *|0.135||0.180|0.216 *|
||||(0.118)|(0.120)|(0.112)||(0.123)|(0.115)|
|Dummy - Owner has lives in Property - 2021||||-0.036|-0.037|0.002|-0.034|0.186|
|||||(0.142)|(0.143)|(0.139)|(0.142)|(0.191)|
|Dummy - Owner has multiple homes - 2021||||0.554|0.300|0.943|0.095|0.510|
|||||(2.455)|(2.460)|(2.546)|(2.474)|(2.450)|
|ln Value Owners Portfolio** - 2021||||-0.030|-0.017|-0.049|-0.006|-0.026|
|||||(0.124)|(0.124)|(0.128)|(0.124)|(0.122)|
|Dummy - Property Paid>0 - 2020|||||-1.498|-0.162|-1.707|-1.649|
||||||(2.082)|(2.103)|(2.058)|(2.136)|
|Dummy - Property fined - 2020|||||0.687|0.686|0.679|0.729|
||||||(0.444)|(0.441)|(0.444)|(0.486)|
|ln Value Fine - 2020|||||-0.221 ***|-0.215 ***|-0.220 ***|-0.215 ***|
||||||(0.070)|(0.069)|(0.070)|(0.072)|
|Dummy - Tax was paid after deadline - 2020|||||1.179 **|1.160 **|1.179 **|1.065 **|
||||||(0.459)|(0.461)|(0.458)|(0.473)|
|Dummy - Lower Tax Bracket - 2021|||||-0.211|-0.066|-0.245|-0.065|
||||||(0.276)|(0.267)|(0.278)|(0.391)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

47
# **Table 10: Outcome: Tax Bill - 2021**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|222556.640||||||145230.336|106586.402|
||(238387.509)||||||(201134.874)|(165276.316)|
|ln Value Tax Base - LAND||141312.019||||-10856.498|||
|||(124510.525)||||(61185.407)|||
|ln Land Area - 2021|||72004.568|96342.613|-297889.522||-461245.351|-422964.872|
||||(85054.791)|(98539.560)|(339333.927)||(551840.292)|(492083.566)|
|ln Taxable Value - Land (by m2) - 2021|||72557.910|85813.642|-85330.750||-166327.192|-200313.282|
||||(47950.435)|(56428.892)|(138920.357)||(244258.347)|(251479.182)|
|Dummy - Contruction - 2021||-1773094.134|-2711378.936|-2846357.515|-1557781.564|-1129181.664|-1091813.908|-990397.573|
|||(1736977.289)|(2574868.952)|(2670915.225)|(1240293.393)|(956958.886)|(783220.229)|(794946.760)|
|ln Building Area - 2021|||459088.192|472430.159|284093.842||215631.170|206154.638|
||||(450239.627)|(459576.851)|(250928.046)||(179683.003)|(180829.272)|
|ln Value Tax Base - BUILDING||135769.624||||92088.337|||
|||(133091.175)||||(79755.672)|||
|ln Taxable Value - Building (by m2) * - 2021|||80765.418|88473.464|18876.799 *||-5974.395|-6335.561|
||||(74747.500)|(80241.888)|(11111.874)||(35908.242)|(32132.195)|
|Dummy - Owner has lives in Property - 2021||||-72756.846|-80161.736|-48808.148|-81853.644|-43829.009|
|||||(67744.420)|(78066.459)|(49222.839)|(79962.813)|(46748.139)|
|Dummy - Owner has multiple homes - 2021||||3571921.222|3483366.365|3964645.613|3596353.590|3537330.581|
|||||(2347234.344)|(2335380.049)|(2681344.929)|(2440665.161)|(2404088.996)|
|ln Value Owners Portfolio** - 2021||||-190296.461|-186934.613|-210400.184|-192791.815|-189404.135|
|||||(125270.934)|(125816.473)|(142768.543)|(131337.370)|(129360.885)|
|Dummy - Property Paid>0 - 2020|||||151592.047|158639.105|266208.731|459714.822|
||||||(1022722.255)|(1002016.777)|(903613.994)|(738871.049)|
|Dummy - Property fined - 2020|||||-2331957.402|-2311408.205|-2327587.929|-2409083.337|
||||||(2126048.710)|(2104967.352)|(2119490.815)|(2181472.530)|
|ln Value Fine - 2020|||||-218064.353|-219624.406|-218475.117|-213020.195|
||||||(214449.852)|(216701.669)|(215206.130)|(208956.165)|
|Dummy - Tax was paid after deadline - 2020|||||4333982.569|4329002.477|4334433.707|4320873.143|
||||||(4053395.258)|(4054572.019)|(4054654.588)|(4018702.211)|
|Dummy - Lower Tax Bracket - 2021|||||-83810.999|-64311.203|-64652.907|-148636.771|
||||||(194584.078)|(184923.738)|(190673.323)|(230035.851)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

48
# **Table 11: Outcome: Paid - 2022**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|-0.027 ***||||||0.041|0.015|
||(0.009)||||||(0.026)|(0.031)|
|ln Value Tax Base - LAND||-0.005||||0.008|||
|||(0.011)||||(0.011)|||
|ln Land Area - 2021|||-0.042 **|-0.041 **|-0.093 ***||-0.142 ***|-0.103 *|
||||(0.017)|(0.017)|(0.029)||(0.040)|(0.053)|
|ln Taxable Value - Land (by m2) - 2021|||-0.000|0.000|-0.025 *||-0.048 **|-0.041|
||||(0.012)|(0.012)|(0.014)||(0.020)|(0.029)|
|Dummy - Contruction - 2021||0.041|-0.016|-0.021|0.138|-0.032|0.273 *|0.199|
|||(0.094)|(0.121)|(0.121)|(0.141)|(0.094)|(0.164)|(0.173)|
|ln Building Area - 2021|||-0.000|0.000|-0.022||-0.042 **|-0.039 *|
||||(0.012)|(0.012)|(0.016)||(0.019)|(0.020)|
|ln Value Tax Base - BUILDING||-0.001||||0.005|||
|||(0.007)||||(0.007)|||
|ln Taxable Value - Building (by m2) * - 2021|||0.001|0.001|-0.007||-0.015|-0.006|
||||(0.010)|(0.010)|(0.011)||(0.012)|(0.012)|
|Dummy - Owner has lives in Property - 2021||||0.001|0.001|0.008|0.000|0.012|
|||||(0.015)|(0.015)|(0.014)|(0.015)|(0.015)|
|Dummy - Owner has multiple homes - 2021||||0.099|0.102|0.217|0.140|0.219|
|||||(0.342)|(0.337)|(0.334)|(0.340)|(0.332)|
|ln Value Owners Portfolio** - 2021||||-0.006|-0.006|-0.012|-0.008|-0.012|
|||||(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|
|Dummy - Property Paid>0 - 2020|||||-0.044|0.226|-0.010|0.029|
||||||(0.168)|(0.175)|(0.171)|(0.186)|
|Dummy - Property fined - 2020|||||0.141 *|0.143 **|0.143 **|0.141 *|
||||||(0.072)|(0.072)|(0.072)|(0.072)|
|ln Value Fine - 2020|||||-0.023 ***|-0.022 ***|-0.023 ***|-0.022 ***|
||||||(0.007)|(0.007)|(0.007)|(0.007)|
|Dummy - Tax was paid after deadline - 2020|||||0.060|0.055|0.060|0.047|
||||||(0.056)|(0.057)|(0.056)|(0.056)|
|Dummy - Lower Tax Bracket - 2021|||||-0.052 *|-0.041|-0.049 *|-0.030|
||||||(0.027)|(0.025)|(0.027)|(0.032)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

49
# **Table 12: Outcome: Sh Paid - 2022**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|-0.028 ***||||||0.041|0.016|
||(0.009)||||||(0.026)|(0.031)|
|ln Value Tax Base - LAND||-0.006||||0.008|||
|||(0.011)||||(0.011)|||
|ln Land Area - 2021|||-0.044 **|-0.043 **|-0.093 ***||-0.143 ***|-0.105 **|
||||(0.017)|(0.017)|(0.029)||(0.040)|(0.053)|
|ln Taxable Value - Land (by m2) - 2021|||-0.001|-0.001|-0.025 *||-0.049 **|-0.042|
||||(0.012)|(0.012)|(0.014)||(0.020)|(0.029)|
|Dummy - Contruction - 2021||0.038|-0.021|-0.025|0.129|-0.037|0.266|0.195|
|||(0.095)|(0.122)|(0.122)|(0.142)|(0.095)|(0.166)|(0.176)|
|ln Building Area - 2021|||0.000|0.000|-0.021||-0.041 **|-0.038 *|
||||(0.012)|(0.012)|(0.016)||(0.019)|(0.021)|
|ln Value Tax Base - BUILDING||-0.001||||0.005|||
|||(0.007)||||(0.007)|||
|ln Taxable Value - Building (by m2) * - 2021|||0.001|0.002|-0.006||-0.015|-0.006|
||||(0.010)|(0.010)|(0.011)||(0.012)|(0.012)|
|Dummy - Owner has lives in Property - 2021||||0.001|0.001|0.008|0.000|0.013|
|||||(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|Dummy - Owner has multiple homes - 2021||||0.096|0.096|0.210|0.134|0.213|
|||||(0.348)|(0.343)|(0.341)|(0.345)|(0.338)|
|ln Value Owners Portfolio** - 2021||||-0.006|-0.006|-0.011|-0.008|-0.012|
|||||(0.018)|(0.017)|(0.017)|(0.017)|(0.017)|
|Dummy - Property Paid>0 - 2020|||||-0.040|0.229|-0.005|0.040|
||||||(0.166)|(0.176)|(0.170)|(0.185)|
|Dummy - Property fined - 2020|||||0.138 *|0.139 *|0.140 **|0.138 *|
||||||(0.072)|(0.072)|(0.071)|(0.071)|
|ln Value Fine - 2020|||||-0.023 ***|-0.022 ***|-0.024 ***|-0.023 ***|
||||||(0.007)|(0.007)|(0.007)|(0.007)|
|Dummy - Tax was paid after deadline - 2020|||||0.069|0.064|0.069|0.055|
||||||(0.057)|(0.057)|(0.056)|(0.057)|
|Dummy - Lower Tax Bracket - 2021|||||-0.046 *|-0.035|-0.043|-0.025|
||||||(0.028)|(0.026)|(0.028)|(0.033)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

50
# **Table 13: Outcome: Ln Tax Bill - 2022**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|-0.245 **||||||0.437|0.183|
||(0.100)||||||(0.273)|(0.321)|
|ln Value Tax Base - LAND||-0.009||||0.125|||
|||(0.141)||||(0.135)|||
|ln Land Area - 2021|||-0.377 *|-0.357 *|-0.869 ***||-1.392 ***|-1.032 *|
||||(0.203)|(0.203)|(0.313)||(0.434)|(0.558)|
|ln Taxable Value - Land (by m2) - 2021|||0.038|0.048|-0.195||-0.447 **|-0.411|
||||(0.146)|(0.144)|(0.166)||(0.228)|(0.302)|
|Dummy - Contruction - 2021||0.350|-0.288|-0.369|1.199|-0.345|2.645|1.943|
|||(1.061)|(1.375)|(1.382)|(1.569)|(1.051)|(1.793)|(1.905)|
|ln Building Area - 2021|||0.026|0.033|-0.189||-0.394 *|-0.375 *|
||||(0.137)|(0.136)|(0.175)||(0.205)|(0.224)|
|ln Value Tax Base - BUILDING||-0.004||||0.052|||
|||(0.079)||||(0.078)|||
|ln Taxable Value - Building (by m2) * - 2021|||0.011|0.015|-0.065||-0.153|-0.061|
||||(0.109)|(0.110)|(0.118)||(0.128)|(0.134)|
|Dummy - Owner has lives in Property - 2021||||0.006|0.002|0.074|-0.005|0.129|
|||||(0.163)|(0.166)|(0.159)|(0.166)|(0.168)|
|Dummy - Owner has multiple homes - 2021||||2.375|2.338|3.473|2.738|3.671|
|||||(3.866)|(3.797)|(3.758)|(3.826)|(3.734)|
|ln Value Owners Portfolio** - 2021||||-0.131|-0.130|-0.186|-0.151|-0.196|
|||||(0.198)|(0.194)|(0.192)|(0.195)|(0.190)|
|Dummy - Property Paid>0 - 2020|||||0.350|2.937|0.715|1.232|
||||||(1.810)|(1.868)|(1.827)|(2.035)|
|Dummy - Property fined - 2020|||||1.323|1.339|1.346|1.317|
||||||(0.852)|(0.853)|(0.849)|(0.848)|
|ln Value Fine - 2020|||||-0.251 ***|-0.240 ***|-0.253 ***|-0.243 ***|
||||||(0.085)|(0.084)|(0.085)|(0.085)|
|Dummy - Tax was paid after deadline - 2020|||||0.938|0.890|0.936|0.788|
||||||(0.674)|(0.677)|(0.672)|(0.670)|
|Dummy - Lower Tax Bracket - 2021|||||-0.468|-0.365|-0.437|-0.239|
||||||(0.386)|(0.370)|(0.387)|(0.430)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

51
# **Table 14: Outcome: Tax Bill - 2022**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|ln Value Tax Base|191860.798||||||202568.216|169489.533|
||(192775.827)||||||(203550.247)|(175560.689)|
|ln Value Tax Base - LAND||136749.389||||-24377.663|||
|||(120279.781)||||(68229.876)|||
|ln Land Area - 2021|||80894.376|97515.643|-286346.122||-529133.247|-502186.600|
||||(50414.784)|(61683.601)|(375396.769)||(598098.463)|(553014.873)|
|ln Taxable Value - Land (by m2) - 2021|||72248.362 **|81222.329 *|-91849.304||-208611.677|-246587.359|
||||(35833.826)|(43098.857)|(158008.785)||(265315.244)|(279355.853)|
|Dummy - Contruction - 2021||-1777883.746|-2483952.412|-2572881.388|-1318861.544|-1066597.281|-648312.950|-579828.070|
|||(1787569.246)|(2410305.617)|(2493156.799)|(1043026.195)|(960797.511)|(628525.904)|(657482.555)|
|ln Building Area - 2021|||411714.230|419787.780|232699.958||137801.804|134621.209|
||||(430228.651)|(437731.582)|(222052.519)||(157865.419)|(159945.240)|
|ln Value Tax Base - BUILDING||136166.811||||86767.259|||
|||(137106.085)||||(78814.779)|||
|ln Taxable Value - Building (by m2) * - 2021|||80918.511|86364.893|23508.452||-17345.846|-17901.532|
||||(65369.652)|(70302.607)|(15815.938)||(46178.666)|(41811.290)|
|Dummy - Owner has lives in Property - 2021||||-53032.328|-63491.737|-34891.594|-66494.618|-32618.266|
|||||(60608.882)|(74880.861)|(44483.350)|(77194.959)|(47027.638)|
|Dummy - Owner has multiple homes - 2021||||2303081.400|2343631.583|2835274.643|2529147.274|2428627.282|
|||||(1964964.565)|(2090330.960)|(2537803.185)|(2213191.889)|(2130124.220)|
|ln Value Owners Portfolio** - 2021||||-124017.307|-127307.062|-151569.926|-136943.942|-131686.607|
|||||(105912.077)|(113653.800)|(135885.423)|(120103.760)|(115807.715)|
|Dummy - Property Paid>0 - 2020|||||134380.306|335277.291|303938.866|574382.588|
||||||(1053525.329)|(824420.603)|(932896.724)|(753696.650)|
|Dummy - Property fined - 2020|||||-2312804.727|-2291088.091|-2302214.253|-2382672.164|
||||||(2120201.685)|(2095679.564)|(2109916.180)|(2169174.609)|
|ln Value Fine - 2020|||||-233711.557|-234338.303|-234440.845|-229381.495|
||||||(202172.162)|(203378.248)|(203139.217)|(197133.854)|
|Dummy - Tax was paid after deadline - 2020|||||4432343.180|4423667.898|4431304.303|4422784.682|
||||||(3913812.603)|(3909139.023)|(3914042.956)|(3880265.760)|
|Dummy - Lower Tax Bracket - 2021|||||-73330.674|-81400.968|-58962.942|-146508.051|
||||||(159224.152)|(160205.064)|(156616.772)|(199614.531)|

*** p < 0.01; ** p < 0.05; * p < 0.1.

52