Can Information and Encouragement Prevent Joint Tax Evasion? Evidence from the Construction Sector an Empirical Analysis Using a Difference-In-Differences Approach

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Can information and encouragement prevent joint tax evasion? Evidence from the Construction sector An empirical analysis using a Difference-in-Differences approach

Juni Engum

Thesis submitted for the degree of Master in Economic Theory and Econometrics 30 credits

Department of Economics Faculty of Social Sciences

UNIVERSITY OF OSLO May 2021

Can information and encouragement prevent joint tax evasion? Evidence from the Construction sector

An empirical analysis using a Difference-in-Differences approach

Juni Engum

Can information and encouragement prevent joint tax evasion? Evidence from the Construction sector. An empirical analysis using a Difference-in-Differences approach. http://www.duo.uio.no/

Printed: Reprosentralen, University of Oslo

Acknowledgments First and foremost, I would like to thank my supervisor Gaute Torsvik for great guidance, support, patience and feedback during this process. He presented me with the topic of my thesis and my data set and has been of great help.

I would also like to thank my parents for proofreading and helping me get through a challenging semester. Lastly, I would like to thank my friends for making the last five years at the University of Oslo some of the best in my life. The last few years would have been a lot less rich without you. Stata 16 is the software used for calculations.

All inaccuracies or flaws are my responsibility.

Juni Engum May 2021

Abstract

In this thesis I estimate the effect a governmental program aimed at reducing tax evasion in the construction sector. The program was introduced in Trondheim and informed consumers on how to make sure they were using approved craftsmen, and the importance of buying goods and services from the formal sector. The goal was to reduce tax evasion and the use of unreported employment.

I use a Difference-in-Differences approach to assess the effect of this program. The method uses data on paid VAT in the different municipalities in Norway to see if there is an increase in paid VAT in the treatment group (Trondheim). Using this method, I find no significant effects in VAT payment for the different sectors examined.

The data used in the analysis did not contain information on new buildings, nor did it separate VAT payments from larger companies, smaller companies and the self-employed. Smaller companies and the self-employed have a higher degree of VAT evasion then larger companies. Obtaining this information might result in a different effect from the program and leads me to believe that I was unable to capture the full effect of the program.

Contents

1. Introduction ...... 1

2. Theory ...... 3 2.1 Theoretical models for tax evasion ...... 3 2.2 Joint tax evasion ...... 5 2.3 Tax Enforcement policies ...... 8 2.3.1 Audits ...... 8 2.3.2 Informational campaigns...... 9 2.4 “Tett på” ...... 13

3. Data ...... 16 3.1 Description of data and variables ...... 16

4. Empirical Approach ...... 18 4.1. Identification ...... 18 4.2. Difference-in-Differences ...... 20

5. Results and discussion ...... 22 5.2 Regression result ...... 26 5.3 Discussion ...... 27

6. Conclusion ...... 30

Refrences ...... 31

Appendix A ...... 34

Appendix B ...... 35

iii

List of Figures

1 The effect of increased prices on demand for goods and services …………………...19 2 The effect of increased prices on demand for renovations …………………………..19 3 VAT payment for carpenters and painters before and after the reform………………24

4 VAT payment for carpenters and painters before and after the reform………………24

5 VAT payment for carpenters and painters before and after the reform………………25

6 VAT payment for carpenters and painters before and after the reform………………25

List of Tables 1 Descriptive statistic, before (2012-April 2016) and after (May 2016-2019) the reform. Trondheim municipality Trondheim (treated) and other municipalities (control)……22 2 Descriptive statistics before (2012-April 2016) and after (May 2016-2019) the reform. Trondheim municipality Trondheim (treated) and other municipalities (control)……23 3 Trondheim against all other municipalities and Trøndelag…………………………...26

1. Introduction

This thesis studies the effect of a tax compliance intervention. In May 2016 Trondheim municipality, Skatteetaten and Arbeidstilsynet initiated a project called “Tett på” where individuals who bought a home or submitted a construction application were contacted by the tax administration over the phone and reminded that if they wanted to renovate their home, they had to make sure they were using approved craftsmen that did not participate in tax evasion (Trondheim kommune, 2018b). The aim of the project was to reduce the prevalence of unreported employment and tax evasion in the construction sector.

In addition to the phone call, all individuals contacted received an informational package on email informing them about the project and providing additional information on potential penalties that would be invoked if irregularities were detected upon inspection. The email contained practical information on how to identify approved craftsmen and make sure that they follow the legal roles; the importance of receiving a valid invoice, checking that the company you are using is properly registered, paying via your bank and checking HSE-cards of the craftsmen (Trondheim kommune, 2018a)1. The aim of my thesis is to estimate if this information campaign reduced tax evasion in the construction sector.

Tax evasion is a serious problem, the building and the construction sector is one of the most tax evasion-ridden industries in Norway (KRISINO, 2011). The government uses different measures, both informational campaigns and probabilities of audits in order to increase tax compliance. In many cases tax evasion is a decision made in solitude, for example if a worker claim tax deduction that are not legitimate or a self-employed does not. In the case considered in this study the demand (customers) and supply (craftsmen) side must strike a tax evasion deal, this is called joint tax evasion. What is special and interesting in the “Tett På” case is that the tax authorities of these campaigns are directed towards the demand side of this market.

The informal sector is difficult to measure, precisely because it is hidden. I try to estimate the effect of the reform using a Difference-in-Differences (DD) approach. The idea is that if “Tett på” has an effect, that is, if the program induces customers to not buy unregistered services and to not enter into a joint tax evasion deal, then we should observe more VAT

1 HSE-cards are identification cards for workers on building and construction sites, the card identifies who you are and who you work for. 1 payment in the construction sector in Trondheim after “Tett på”. To estimate the effect, I compare VAT payments from occupations in the construction sector that are especially prone to offer services in the informal market in Trondheim to a control group that is not affected by the intervention of “Tett på”2. I use two different control groups for the regressions. The first is the paid VAT in all municipalities in Norway, excluding municipalities initiating the “Tett på” program after it started in Trondheim (Skatteetaten, 2021)3. The second control group is paid VAT for the municipalities in Trøndelag county excluding municipalities that initiated the program after 2016.

There appear to be differences in unregistered work and joint tax evasion among the professions within the constructions sector. There is reason to expect that carpenters have a higher degree of tax evasion than painters, electricians and plumbers (Kili and Aastvedt, 2008). Therefore, the expected effect of “Tett på” should be larger for carpenters, than the other professions.

I find no statistically significant effects, and some of the point estimates even go in the “wrong” direction.

The data from the analysis did not contain information on new buildings in Trondheim and did not separate VAT payments from larger companies, smaller companies and the self- employed. Smaller companies and the self-employed have a higher degree of VAT evasion than larger companies. Obtaining this information might result in a different effect from the program and leads me to believe that I was unable to capture the full effect of the program.

Since “Tett på” has been introduced in other municipalities in Norway it remains to see if the program will have an effect on reducing tax evasion in the construction sector for the other municipalities.

The thesis is organized as follows. Section 2 ‘Litterature’ gives a brief overview of the theoretical and empirical results in the literature of tax evasion as well as going into depth on the expected results from the implementation of the “Tett på” program. Section 3 ‘Data’ elaborates on the data set. Section 4 ‘Empirical approach’ will present the econometric specification of the DD model. Section 5 ‘Results and discussion’ will present results and the limitations to the model. Finally, in Section 6 the conclusion will be presented.

2 The informal sector is the part of any economy that is neither taxed nor monitored by any form of government. 3 Since 2018 “Tett på” has been introduced in 19 other municipalities in Norway, the most recent joined in early 2021. 2

2. Theory

In this section I will briefly present some of the theoretical models used to explain an individual’s incentive to participate in tax evasion from the supply and demand side, and through joint tax evasion. Then I will present the literature from an empirical stand. The section will end with a review of “Tett på” and how the program affects the consumers those who purchase goods and services from the informal sector.

2.1 Theoretical models for tax evasion Tax evasion is important for many reasons, it affects both the resource cost of raising taxes and the distribution of the tax burden. In order to reduce evasion, the government conducts audits and impose punishments on those who are caught evading. In this setting tax evasion becomes, like other criminal activity, a gamble. Evasion can either lead to higher or lower take home income than non-evasion, depending on whether evasion is detected or not. Becker (1968) was a pioneer in the field of studying criminal behavior in a rational choice economic framework, where agents weigh the costs and benefits of engaging in criminal activity. Within this framework, criminal activity depends on several factors such as penalty, detection, probability, and gain from the activity.

Allingham and Sandmo (1972) were pioneers in applying Becker’s (1968) insights to tax evasion. In their model a risk averse agent has an exogenously given income level and faces a tax rate and a detection probability. The agent trades off the expected utility from getting away with tax evasion and pays less taxes against the expected utility of being caught and having to pay the penalty rate. The objective of the model is to analyze when it is rational to evade income taxes.

The model predicts that the taxpayer evades taxes if the tax rate is larger than the probability of being caught times the penalty of evading. Meaning the taxpayer evades taxes if the expected penalty rate is less than the tax rate. If there is an increase in the probability of detection or an increase in the penalty rate, evasion will decrease. Allingham and Sandmo’s (1972) model predict a higher degree of tax evasion than the empirical evidence suggests.

Two explanations have been suggested for why the AS-model predicts more tax evasion than we observe in data (Auerbach et al., 2013). One possibility is that this is driven by a lack of moral cost in the model. It is likely that an individual is not solely driven by their own self-

3 interest, but instead act as a member of a group that is influenced by norms, customs, law, and patriotism. They will not be driven by only economic factors but also guilt and moral, that will influence their decisions, meaning that the model does not account for “moral cost”. Another explanation for why the empirical research does not match the theoretical model is that wage earners does not have the ability to evade taxes due to third-party reporting of income (Kleven et al., 2011). Third-party reporting makes the probability of detection close to 100% for wage earners. This means that the model does not work for wage earners. For the model to work for wage earners the probability of detection could be very high, making the predictions of the model more in line with the data.

The expected utility reasoning for why an individual is willing to evade their taxes from the authorities can also be used to explain why firms evade taxes. Cremer and Gahvari (1993) incorporates tax evasion into Ramsey’s optimal taxation problem. In Ramsey’s original problem the government chooses the set of tax rates that maximizes the utility of a representative consumer subject to a tax revenue constraint. If sales are unobservable by the tax administration so that tax evasion is possible, the audit strategy becomes crucial in the optimal taxation problem. The authors formulate two interesting questions from the problem. First, the question of tradeoff between optimal tax rates and audit probabilities in raising tax revenues. They provide a precise and intuitive characterization of this tradeoff and show how the government chooses the optimal combination of the two types of instruments by taking into account the cost implied by optimal tax rate and audit probabilities.

The second question concerns the structure of optimal commodity taxes in the presence of tax evasion. Modifying the traditional interpretation of Ramsey equation and showing that the requirement of proportional reduction in compensated demands is no longer valid. The traditional result on the optimality of uniform taxation when income is exogeneous is now gone, and the equivalence of wage and uniform commodity taxes does no longer hold. This is best seen by noting that the wage tax (a tax on exogeneous income) continues to be the optimal tax even in the presence of tax evasion since it does not create any distortions. But optimal commodity taxes are non-uniform and distortionary. The government considers random audits and chooses the best policy by fixing the audit probabilities for each market. The random audits can estimate the degree of tax evasion, in the different markets.

2.2 Joint tax evasion Much of the theoretical analysis of tax evasion assumes that evasion involves one party, either an individual evading their taxes or a firm. However evading taxes often require two parties to agree to deviate from the tax code. When goods and services are sold on the market suppliers and those demanding the goods, will sometimes agree to trade outside the formal framework in order to avoid regulation and taxation. This is called joint tax evasion. The supplier will benefit by reducing his tax burden by underreporting his income and the demander gains from buying a non-taxed good at a lower price. Exactly how the gain from transacting outside the formal system is shared between buyer and supplier depends on several factors.

Nygård, Slemrod and Thoresen (2016) presents a model that displays the distributional implication form joint tax evasion. They analyze data from the supply and demand side of the market, utilizing the expenditure method by Pissarides and Weber (1986) to learn about the suppliers of informal goods and services in the hidden market. The expenditure method is a method to measure tax evasion among the self-employed using food expenditure. The expenditure method relies on two assumptions, (i) workers cannot evade their income from taxes, and (ii) workers and the self-employed spends the same amount of money on food. The authors obtain information on food consumption form survey data, which is considered correctly reported. They identify households as self-employed if receive more than 25% of their total income from self-employment. This is how the authors identify households that underreport their income.

The calculations of the method consist of two parts. First, Pissarides and Weber (1986) estimate expenditure functions in terms of household characteristics and reported income. Second, they invert the expenditure function and forecast income from reported expenditure. Their two assumptions enable them to estimate a reliable food expenditure function for employees in employment and then to invert it to calculate the ‘true’ income of the self- employed. By comparing reported income with the estimated true income, they estimated the extent of tax evasion.

To evaluate the consumers side, Nygård, Slemrod and Thoresen (2016) utilizes sample survey information and incorporate the data from consumers and suppliers into a general equilibrium model. In calculations of additions to income for consumers, they use estimated evasion probabilities, but make assumptions about how prices in the hidden market deviate

5 from the formal market. The overall effect depends on how both the suppliers and consumers are positioned in the distribution of income and the price of the goods and services in the informal market depends on negotiations between supplier and consumers. This lets the authors discuss how the “hidden-economy-controlled” income distribution compares to the formal market.

The distribution of the reduction in tax burdens of suppliers and consumers due to tax evasion are related to adjustments in income. This becomes more complicated when addressing reductions in tax burdens (tax savings), instead of income. The results depend on how suppliers and consumers divide the economic gains, that is not observed. For example, there may occur a situation where the returns of the supplier are squeezed so that the consumer retains all the economic advantage, and the distribution of the economic gain is determined by the demand side alone. The authors show that the empirical evidence can be used to provide empirical illustrations of effects, given the conceptual foundations.

Nygård, Slemrod and Thoresen (2016) find that the distributional effects of tax evasion depend on the income profile of both the buyers and sellers, and how the gain is shared between the two parties. Accounting for the hidden economy increases income inequality in Norway, with informal suppliers contributing the larger share of the change in measured inequality. They also find that the compliance rate decreases with income, implying that the effective income tax progressivity is less than what is indicated by official figures.

Another interesting model on joint tax evasion and specifically VAT evasion is presented by Fedeli and Forte (1999). The model considers joint income-tax evasion and VAT-chain evasion, acknowledging that decisions are not taken by agents alone, but results from negotiation between consumers suppliers in a chain of exchanges from producers and importers to final consumers. They establish outcomes in terms of Nash-bargaining-equilibria and show that the greatest evasion is at the last stage of the chain. Then, they consider strategies that either recover lost revenues or deter tax evasion of all taxpayers in the chain.

The issue of the chain of corruption has been considered by Basu et al. (1991) in Nash bargaining games. The authors extend the Basu et al. (1991) model to consider joint income tax and VAT evasion inside a chain of interrelated choices by purchaser and sellers who are subject to VAT and income tax. They propose that these interactions provide fundamental problems for the tax authorities strategy. These are: the choice of the most effective tools, i.e.,

6 quantity of inspections and what kind and amount of punishments to recover lost revenues, and what choice who the tax agents chooses to focus the inspections on.

Consider a chain of exchanges in which the last four participants are:

→ 퐹퐼푅푀 1 → 퐹퐼푅푀 2 → 푆퐻푂푃 → 퐹퐼푁퐴퐿 퐶푂푁푆푈푀퐸푅

Assume that profits from these activities are the only form of income firm 1 and 2 and the shop’s owner receives. The authors assume further that firm 1 imports raw materials from abroad, and sells goods to firm 2, which manufactures and sells all products to the shop, which in turn, sells everything to the final consumer. 4

At each stage of the chain the reported value added is taxed and each agent is expected to pay their supplier the VAT on their purchases. At the end of the period, excluding the final consumer, each agent who has collected VAT from his customers has to give the tax authority the difference between VAT collected on his sales, and VAT paid on his purchases as reported on the invoices.

Under the assumption that VAT is levied on consumption a final consumer has the incentive to evade VAT, in order to reduce their gross expenditure in obtaining the same amount of goods. For the other traders, the answer of willingness to evade VAT is not so obvious. This is because they charge VAT on their products. In these sectors the VAT evasion must be considered with income evasion.

Fedeli and Forte (1999) find that the highest amount of taxes evaded are between the shops and consumers and that the amount evaded depends on the gains from the illegal transactions. They also find that the penalty rate on VAT evasion does not affect the size of evasion but is crucial in deciding whether the consumer is willing to evade.

Given these results they look at the implications from the tax authorities point of view. Firstly, they consider the tax authorities ability to control evasion, finding that the most efficient way to reduce tax evasion is increasing the number of inspections5. When it comes to penalties, they find that increasing the penalty rate reduces income tax evasion but does not have the same effect on VAT evasion, when the penalties are of the same size. Thus, the

4 From now on the authors refer to firm 1 and 2 and the shop as traders. 5 The authors referred to the inspections as a control of the goods and services sold by the business. Both the business and the consumer could be inspected 7 policy on increasing the probability of being inspected is the most effective at reducing tax evasion.

Lastly, the authors study what role the size of the penalties and probability of inspections has for the consumers’ willingness to evade taxes. They argue that increasing penalties and probabilities of detection for consumers can be viewed as unfair, and entail a police state, where its inhabitants are constantly being monitored. Instead they propose selective actions on VAT-registered traders. Increasing penalties and probability of inspections along the chain is the most effective way to reduce the amount of evasion. Increasing the probability of inspections at each stage of the chain for the traders can provide a disincentive for tax evasion along the chan. However, a policy like this can be very expensive, requiring skilled inspections for dealing with VAT registered traders. Instead, increasing penalties on VAT registered traders is less expensive and reduces tax evasion along the chain. The consumer will still wish to evade taxes on their goods, but the increased penalty on evasion also increases the shops cost of evading, leading to a reduction in tax evasion along the final part of the chain.

2.3 Tax Enforcement policies Broadly speaking there are two types of tax enforcement policies governments carry out to enhance tax compliance. One is audits, often targeted towards high-risk filers. The other type of intervention is information campaigns. The policy that I study, “Tett på” belong to the latter category and I will review the empirical literature on audits and information campaigns below.

2.3.1 Audits Audits are the cornerstone of governments tax enforcement policies. In operational audits the tax enforcement selects high risk filers for a thorough inspection. Slemrod, Blumenthal, and Christian’s (2001) investigate taxpayers’ response to an increased probability of audits. A group of randomly selected Minnesota taxpayers received a letter informing them that the returns they where about to file would be ‘closely examined’. This group was compared to a control group that did not receive this letter. The letter was sent out in 1995. That authors compared the change in reported income from 1993 for the treatment group and the control

8 group. They found that low- and middle-income taxpayers who received a letter promising a certain probability of an audit reported slightly more income than those who did not receive the letter. The study gives an indication that this type of intervention can reduce noncompliance in the short run. It does not give us any long run effects since the informational letter was only handed out once and the study does not handle subsequent behavior. The individuals in the treatment group where not investigated any further to see if their perceptions had been permanently changed.

Another finding by the authors was that high-income taxpayers with self-employment receiving this audit-threat letter reported lower income. The authors suggest that the high- income groups view an audit to be a negotiation, and view reported taxable income as the opening bid in a negotiation that does not necessarily result in finding and penalizing all noncompliance. This shows that high-income individuals view tax compliance in a different way than others.

In 2011 Kleven conducted an audit experiment in Denmark. 40 000 individuals were randomly selected for the experiment. The individuals either received a letter stating there was a 100% probability of an audit or a 50% probability of an audit, the control group received no letter. The letters were received shortly after they received their pre-populated returns from the government with all relevant information known to the government, and the individuals had one month to make adjustment to the return. The individuals that received the letter with a 50% probability of an audit where 1.1% more likely to adjust their income upwards than those that received no letter. The individuals that received the letter with a 100% probability of an audit where 0.9% more likely to adjust their income than the 50% threat of audit to do so.

There are several recent studies that estimate the long-term effects of audits. These studies find that audits change the behavior of those audited, see Advani et al. (2015), DeBacker, (2014) and Belnap et al. (2020).

2.3.2 Informational campaigns Informational campaigns can be an effective tool in for government and researchers to investigate change in individual’s behavior and believes. It is in many cases an effective tool to reduce tax evasion. Many of the information campaigns that are used by tax

9 administrations are conducted in cooperation with researchers. The tax authority will then typically send one type of information or encouragement to a random sample of taxpayers and compare their compliance with a control group in order to get an estimate of the intervention. The type of information treatments that has been used, can broadly be divided into letters that makes the riskiness of evading taxes salient (interventions that highlight the risks and the penalties) and letters that appeal to morality or the social obligation of paying taxes.

Bergolo et al. (2018) carried out a large-scale field experiment involving value-added-tax compliance of over 20,000 Uruguayan small- and medium-sized firms. They wanted to see if firms evade taxes by optimally trading off between the costs and benefits of evasion, that is predicted by Allingham and Sandom’s (1972) model. Firms in a control group received a letter from the tax authority with generic information about taxes. Firms in the treatment group received the same letter with an added paragraph, conveying information about past audit rates and the penalty levels for tax evasion.

In separate treatment groups they measured the effect of varying the probability of being audited by the tax authorities. The probabilities communicated to the firm was between 25 to 50 percent and added a paragraph that informs the firms that evading taxes increase the probability of being audited. They also sent out a public-goods letter listing a set of government services that could be provided if evasion in Uruguay ceased. They supplement these treatments with information on taxpayers’ subsequent perceptions about audits, measured with survey data, as well as on the actual taxes paid. Bergolo et al (2018) finds that adding the paragraph on probability of being audited and penalties of tax evasion increases tax compliance by 6.3 percent. They also find that adding the paragraph that informs firms that evading taxes increased the probability of being audited increases tax compliance by about 7.4 percent. The message about public goods did not have a statistically significant and robust effect on tax compliance.

Among the firms who were sent the audit-statistics letter, those who received higher signals of the audit probability or penalty rates did not remit significantly higher taxes, nor did the variation in actual audit probabilities induce a significant change in reporting behavior. This contrasts to the result in Kleven et al. (2011) discussed just above. The authors finding was inconsistent with Allingham and Sandmo (1972) model on making an optimal cost-benefit calculation, instead they argue that taxpayers are reacting to the information because it makes

10 the cost of evasion more salient, without any change in beliefs about the probability that evasion is penalized. They argue that the results from the survey also favor the salient channel because they suggest that on average the audit-statistics letter reduced the perceived probability of being audited. Thus, rational Allingham-Sandmo taxpayers would have reduced, rather than increased, their tax compliance, which did not happen.

Bott et al. (2019) report on a large-scale randomized field experiment conducted on a sample of more than 15 000 taxpayers in Norway. The authors aim to see how moral motivation and perceived probability of detection affects taxpayer behavior. The sample consists of taxpayers who were likely to misreport their foreign income from previous tax years but were unaware that the Norwegian tax authorities had that information. Foreign income is not pre- reported in the tax returns in Norway and taxpayers have to self-report this information. Recent changes in international collaboration among tax authorities has made it easier for the Norwegian tax authorities to confirm self-reported income.

The intervention consists of an informational letter sent by the tax authority shortly before taxpayers were due to submit their tax return for the previous year, were randomly assigned taxpayers receive different versions of the base letter and the remaining taxpayer (the control group) did not receive a letter. The base letter contained information about why and how to report foreign income There were two different versions from the base letters, one that focused on moral and one that focused on probability of detection.

The authors study two versions of moral suasion, a fairness argument, and a societal benefits argument, for correctly reporting foreign income. They assume that the main role of these letters is to make the moral argument for tax compliance salient. Studying the effect of the treatment manipulations on the self-reported foreign income both in the following tax return and on year later. They also investigate whether the effects are largely on the extensive margin or the intensive margin.6

The main result is that moral suasion has a large and significant effect on self-reported foreign income. For the following tax return, the average self-reported foreign income by taxpayers who received one of the moral letters was almost the double of the amount self- reported income by those received the base letter. They also find a large effect from the

6 Extensive margin is how many taxpayers self-report any foreign income, and the intensive margin is how much foreign income is self-reported by taxpayers who would have self-reported some foreign income in the absence of the treatment. 11 detection letter, but the moral and detection letter affected different margins of the taxpayer behavior. The moral letter only had a large effect on the intensive margin, and the detection letter had a large effect on the extensive margin. Further, they find that the base letter had some effect on self-reported income, but overall, it’s not the lack of knowledge about how to report foreign income.

Finally, they study the effect of the long-term effects of the intervention, where the main insight is that the detection letter has a large effect on taxpayers even a year after receiving the letter, whereas there are no statistically significant long-term effects of the moral letters. The long-term findings suggests that the moral letters mainly worked thorough making the moral arguments salient when receiving the letter, whereas the detection letter caused the taxpayers to permanently update their beliefs about the detection probability.

In the last study I want to present Ortega and Scartascini (2020) investigate how different ways of communication and intervention methods aimed at reducing tax evasion, affects the effectiveness of the intervention. In the paper the authors want to evaluate if the delivery method matters by conduction a field experiment in Colombia were, they send the same message to taxpayer with tax delinquencies but using different methods7.

Around 21 000 individuals with tax delinquencies were randomly assigned to one of three different treatments, a physical letter, email, and a personal visit, the control group did not receive any treatment. The authors choose these methods because they could allow the authors to see if there are differences between impersonal and personal methods of communication and if there are differences between physical and electronic delivery. The first comparison could inform the tax authority whether spending additional resources for contacting taxpayers pays off. The second comparison would allow the tax authorities to the role that new technologies could have in its enforcement strategies. Both comparisons would inform researchers about how to interpret the results coming from interventions that use only one type of communication, for example, whether finding null results in an intervention using letters could be explained by low power.

The results in the paper show that differences across communication methods are significant. Among those assigned to a letter, the probability of making a payment is 4 percentage points higher than receiving no letter. Sending an email and scheduling a personal visit have an

7 Delinquent tax is a tax that had to be paid, but the taxpayers had not deposited the payment. 12 impact that is three times larger. These results show that messages matter for reducing tax evasion and that emails seem to be particularly effective given that they have a large effect while costing basically nothing. In total the letter increased the probability of making the payments by 4 percent, the email increased the probability of making the payment by 15 percent and the personal visit increased the probability of 67 percent. Consequently, tax authorities could consider switching to electronic communication whenever possible.

To check for the robustness of their results, given that there is sizable noncompliance with assignment to the treatment the tax agency attempted to visit only about 1/3 of the taxpayers assigned to treatment, and there is some contamination across treatments, the authors perform additional exercises using nearest neighbor matching and inverse probability weighting, and the plausible exogenous approach, the results hold. The effectiveness of the communication methods is significantly different: personalized methods have a higher effect than impersonal methods, and the electronic communication surpasses the physical letter.

These studies indicate that letter interventions can substantially reduce noncompliance in the short term. They are less definitive regarding what the mechanism is, with Bergolo et al. (2018) arguing that the letter affected the salience rather than the perception of the enforcement environment and Bott et al (2019) argue that moral and perceived probability of detection affects the taxpayer’s compliance. Ortega and Scartascini (2020) argue that electronic communication and personalized methods have a higher effect of increasing tax compliance.

2.4 “Tett på” “Tett på” is a program across government agencies, the construction sector and organisations protecting consumers rights (Skatteetaten, 2021b). The purpose of the program is to reduce the demand of informal labour in the construction sector. The program started in Trondheim in May 2016. A service team at Skatteetaten calls households that has applied for a building permit or bought a house in Trondheim.

The representatives at Skatteetaten start their communication by first going through the houseowners responsibilities of sending in their own construction application when renovating a house (Skatteetaten, 2021b). Then they ask the houseowners to make sure that the craftsmen they use for the renovation project are registered in the register of legal entities, the register of business enterprises and the VAT register. The households are told that they

13 can demand a tax certificate from the company, to make sure they are legally reporting the right amount of taxes. Then the representative gives the households information on how to check if the company’s employees have the right professional qualifications in the different sectors the households require work. For instance, if the households requires electrical work, they can check that the company is registered in the electrical register at the ‘Direktoratet for samfunssikkerhet og beredskap (DSB)’.

After this the representative tells the households that they can demand an HMS card, as explained in Section 1. The representative also explains the importance of having a valid contract of the construction work being done. This gives the households a written agreement of the expected result from the work, and if anything is to go wrong, the households can legally demand that if is fixed, free of charge. After the contract is written the households should demand a valid invoice, the representatives go through that the invoice should contain, invoice number, company name, organisation number, the number of estimated hours needed, a total price, delivery place and date. Lastly, the representative informs the households that payments should be completed via the bank, and not directly to the company, and tells the households to keep the documentation of the work provided. The documentation should be kept so that the households can provide it if the property is sold later, of if any problem arises with the craftsmen. As well as the phone call, all households that were called also received informational on email with the information discussed on the phone.

“Tett på” is an informational campaign. The program aims at appealing to households moral and making the choice of purchasing goods and services from the formal sector more salient. The program appeals to moral by reminding households of the importance of making sure craftsmen are properly suited for the job and reminding the households of the importance of tac compliance. The different papers in Section 2.3.2 argue that making options more salient for households, appealing to moral and electronic information increases tax compliance to a degree.

The program has been praised from both companies in the construction sector and unions working towards securing the workers in the construction sectors interests. It has been praised for its initiative to prevent tax evasion before it occurs, instead of audits and controls after the purchase of goods and services from the construction sector (Kunøe, 2016). It has been said to be great at informing the households beforehand, and that the phone call is view as a better initiative than just a letter since it is seen as more personal. It has also been a way for

14 companies using approved craftsmen as a way to be taken more seriously, and increase their profit (Hansen, 2018).

“Tett på” has been initiated in more municipalities since the program started in Trondheim. Since 2018 other municipalities started the program to reduce tax evasion and criminal activity in the building sector. The first municipality to implement the program after Trondheim were Oppdal and Orkland municipality in September 2018 (Skatteetaten, 2021b). Now there are a total of 20 municipalities implementing the program. The most recent joined in early 2021.

3. Data

In this section, I will explain what kind of information the data contains and how it will be combined to answer the research question.

3.1 Description of data and variables This thesis uses data on reported VAT and total revenue in the construction sector and population in the different municipalities in Norway obtained from Skatteetaten. I used two different datasets. The first dataset contains information on five different professions in the construction sector. They are plumbing, electrical work, carpentry and painting. The data is from 2012 to 2019 given in six terms each year. It contains the total VAT payment, revenue within and outside of the law and the number of companies in these sectors from the different municipalities in Norway8. The second dataset contains general information at the municipality level, such as population, information on age distribution and information regarding elections from 1972 to 2019 (Fiva et al. 2020).

I merged the two datasets by year and municipality, keeping municipality name and number, year, and population from the dataset on population. The dataset now contains data from 2012 to 2019, given in a total of 48 terms. Each year contains 6 terms, meaning that terms 1 to 6 is for year 2012, 7 to 12 is for year 2013 and so on. After merging the datasets, I found that there are quite a few values missing from the floor laying sector and decided to exclude the sector from my regression. The reason floor laying has a lot of missing values is because it is a small part of the construction sector compared to the other four.

In order to compare VAT payments across municipalities that differs considerably in population I normalized the VAT number by dividing on the number of inhabitants in the municipality. Hence, the empirical assessment is based on changes in VAT per inhabitant.

Given the fact that I use two different control groups for the regression, I first created a dataset where the control group is all the municipalities in Norway. The completed dataset for all municipalities in Norway excluding the municipalities that have had the treatment since 2018 (Skatteetaten, 2021a), there was a total of 477 120 observations.

8 Revenue outside of the law means services that are excluded from VAT payments but does not contain any big differences from the columns containing total revenue inside the law. 16

For the second regression with Trøndelag as the control group I removed all municipalities that were not a part of Trøndelag from the dataset. The finished dataset contains a total of 102 144 observations. I use payments for each term (six terms in each year), not the total year for both regressions.

4. Empirical Approach

In this section I will first describe the identification methods used to estimate the effect of “Tett på” to motivate the DD model used for estimating tax-noncompliance in the construction sector. I will end the section by presenting the DD equation.

4.1. Identification To estimate the effect of “Tett på” I want to do a counterfactual analysis that compares the degree of unreported employment in the construction sector in Trondheim with the “Tett på” program and without the program. To measure the change in unreported employment that comes with the “Tett på” program, we can measure change in VAT payments to the tax authorities. Assume that the construction sector reports a part a of true incomes to the tax authorities. Within a year there has been ℎ houses remodeled in a region and on average each house has income 푦 to the construction sector, so the true income is 푌 = 푦ℎ. A part (1 − 푎) of the households is willing to buy labor in the “unreported employment” leading to 푎푌 = 퐼 reported to the tax authorities. I want to see if there has been an increase in 푎.

“Tett på” was introduced in order to reduce tax evasion and the use of unreported employment in the construction sector. It is possible that the VAT response to “Tett på” will be different for different group of workers. Kili and Aastvedt’s (2008) report on tax evasion in the construction sector find that there is a larger degree of tax evasion for carpenters in Norway than for plumbers, electricians and painters. They investigate different informal markets in Norway and other countries and find that the construction sector account for 43,5 percent of the total informal sector. Carpenters account for 14,9 percent, electricians, and plumbers account for a total of 12,8 percent, meaning that they account for roughly 6,4 percent each and painter account for 5,1 percent. Assuming that Kili and Aastvedt’s (2008) estimates are correct, I would expect “Tett på” to have the largest impact on carpenters. This is because carpenters stand for the larges degree of tax evasion in the sectors I investigate.

When a house is renovated or built goods and services are needed from the different parts of the construction sector. Plumbers, electricians, painters and carpenters might all be needed to complete the renovations of the same house. Therefore, it is likely that some of the goods and services bought to complete the same house were from the formal and informal sector.

Figure 1: The effect of increased prices on demand for goods and services.

Figure 1 shows the effect on demand of goods when the price increases. When the goods and services sold from the informal sector to the formal sector it will lead to an increase in prices from 푃푂 to 푃푁. The increase in prices leads to a reduction in demand for goods and services bought. Demand decreases from 퐷푂 to 퐷푁 This leads to a fall in the number of houses renovated and built.

Figure 2: The effect of increased prices on demand for renovations.

If we assume the extreme case that “Tett på” has influenced all consumers to purchase goods from the formal sector, there will be a decrease in the number of houses renovated and built, because it is relatively more expensive to renovate now. This is shown in figure 2, at point C.

We might also see that the program had no effect on consumers choices and continue to use the formal sector in the same way as before. The total number of renovations will remain the same, as well as the amount of tax evasion. This is shown in point A in figure 2.

From prior empirical evidence reviewed in Section 2, it would be unreasonable to assume that the program has led all tax evaders to compliance. It is more likely that some individuals will change their behavior and purchase goods from the formal sector, and others will continue to purchase goods from the informal sector. This will lead to a larger amount of paid VAT in sectors with more tax evasion. We will then be at point B.

In order to see if “Tett på” has affected the degree of unreported employment and tax evasion I will run a Difference-in-Difference regression comparing Trondheim before and after “Tett på” and then compare it to a region without “Tett på”.

4.2. Difference-in-Differences Difference-in-Differences (DD) is a quasi-experimental method that separates the observations you analyze into a treatment group and a control group (Stock and Watson, 2015, p. 542). The treatment group receives some form of treatment, while the control group does not. By removing all observable and unobservable differences between the treatment- and control group you will get the effect of the treatment, expressed by 훿, of the differences are continual over time. Theoretically it is noted in the following way:

훿 = (푌̅푡푒푟푎푡푚푒푛푡, 푎푓푡푒푟 − 푌̅푡푒푟푎푡푚푒푛푡, 푏푒푓표푟푒) − (푌̅푐표푛푡푟표푙, 푎푓푡푒푟 − 푌̅푐표푛푡푟표푙, 푏푒푓표푟푒)

= ∆푌̅푡푒푟푎푡푚푒푛푡 − ∆푌̅푐표푛푡푟표푙

Where ∆푌̅푡푒푟푎푡푚푒푛푡 is the average change in observations in the treatment group, ∆푌̅푐표푛푡푟표푙 is the average change in observations in the control group. If treatment is random 훿 would be an expected and consistent estimator of the causal effect (Stock and Watson, 2015, p. 542).

The assumption of common trend is the key assumption for the Difference-in-Differences approach. The assumption states that in the absence of treatment, the two groups should follow the same trend over time. Stated differently, the differences in the expected potential

20 nontreatment outcomes over time, conditional on some variable X, are unrelated to belonging to the control or treatment group post-treatment period (Lechner, 2011, p. 179). If this assumption holds and we observe a counterfactual trend break of the treatment group in the post treatment period, we have evidence for an effect of the treatment.

To perform the DD-estimation you need as mentioned, a control group that is not affected by the treatment, where the treatment in this case is the effect of “Tett på”. I choose to use two different control groups for my regression. The first control groups is the average VAT payments per person in all of Norway’s municipalities and the second control group was an average VAT payment per person in Trøndelag. The reason I used two different control groups is to see if the effect of the program differs when we look at Norway as a whole, from when we look at different parts of Norway. The following model estimates the effect of “Tett på”:

푦푖,푡 = 훼푖 + 푡푒푟푚𝑖푛푡 + 훿퐷퐷푖푡 + 휀푖푡 (1)

Where 푦푖,푡 is the outcome variable for municipality 𝑖 in year 푡, 훼푖 are municipality fixed effect and 푡푒푟푚𝑖푛푡 are year fixed effect. The variable 퐷퐷푖푡 is an indicator variable equal to 1 for the treated municipality Trondheim in the post-reform period, and 0 otherwise. Note that the indicator variables for the post-reform period and for the treated municipality are absorbed by the fixed effects. 휀푖푡 is the error term.

As mentioned, the identifying assumptions for the DD framework are that the average VAT payments among all municipalities in Norway (except Trondheim) and in the treated municipality (Trondheim) would have followed common trends in the absence of the reform. To see if this is plausible, I will provide graphical evidence of common trends in the next section.

Finally, I also estimate the effects of the reform with the different control groups using the DD approach.

5. Results and discussion

In this section I present descriptive statistics, graphical evidence and regression results based on the methodology outlined above. Further, I provide explanations to the results and discuss implications of the findings.

5.1 Validation of the DD assumptions

I start by providing descriptive statistics. Table 1 and 2 tabulates the before and after mean values of some key variables since “Tett på” was introduced. Comparing levels before the treatment (2012-April 2016) and after the reform (May 2016-2019). Looking at column 3 in Table 1 we can see that there is an increase in VAT payments for the treatment group in all sectors. From column 6 we can see that the control group has had an even larger increase in VAT payments for all sectors after the program started. This could indicate that “Tett på” has not had an effect on tax evasion in the construction sector.

Table 1. Descriprive statistics, before (2012-April 2016) and after (May 2016-2019) the reform. Trondheim municipality Trondheim (treated) and other municipalities (control)

Trondheim All of Norway

Before After % Change Before After % Change

Population 188 058 197 941 5.3 4 901 241 5 067 016 3.4

VAT Carpenters 33,8 41.9 24.0 28.9 39.3 36.0

VAT Painters 58.3 72,4 24.2 20.0 25.1 25.5

VAT Plumber 132.4 147.5 11.5 75.2 94.6 25.8

VAT Electrician 129.1 153.5 18,9 96.6 134.3 39.0

Notes: Table 1 compares the municipality sector in Trondheim to all other municipality sectors in Norway before and after the reform. The numbers are averaged over the respective time intervals (2012-April 2016 and May 2016-2019)

In Table 2 we see similar results as in Table 1. The control group has had a larger percentage change after the treatment then the treatment group. The largest difference between the two tables is that VAT payments for carpenters in the control group has had a larger increase when Trøndelag is the control group. The smallest difference in percentage change of VAT payments is between painters. However, since the control group has had a larger percentage

22 increase in VAT payments than the treatment group, it is likely that the DD regression will have no significant results.

Table 2. Descriptive statistics, before (2012-April 2016) and after (May 2016-2019) the reform. Trondheim municipality Trondheim (treated) and Trøndelag (control)

Trondheim Trøndelag

% Before After % Change Before After Change

Population 188 058 197 941 5.3 483 595 499 042 3.2

VAT Carpenters 33,8 41,0 24.0 22.0 33.8 53.6

VAT Painters 58.3 72,4 24.2 13.7 17.2 25.5

VAT Plumber 132.4 147.5 11.5 58,6 75.3 28.5

VAT Electrician 129.1 153.5 18,9 78.7 106.2 38.9

Notes: Table 1 compares the municipality sector in Trondheim to all other municipality sectors in Trøndelag before and after the reform. The numbers are averaged over the respective time intervals (2012-April 2016 and May 2016-2019)

In order to provide evidence on the validity of the common trend assumption I present graphical evidence. Figure 3 plots paid VAT from 2012 to 2019 for Trondheim and the control municipalities in all of Norway for carpenters and painters. The x-axis indicates VAT payments each term, the y-axis shows the VAT payments per individual and the vertical line indicates the time of the start of the program.

If we look at the pre-reform development in paid VAT, there are clear similarities in payments among the treatment and control groups and a common trend for the treatment and control group. There is a much larger difference in paid VAT for painters in the control group and the treatment group, than in other sectors. This likely has to do with the fact that Trondheim is large municipality, and control group is an average of all municipalities in Norway.

Figure 3: VAT payment for carpenters and painters before and after the reform

Notes: Figure 3 compares the municipality sector in Trondheim to all municipality sectors in Norway, for each year in the time interval of my analysis [2012, 2019] by terms. There are 6 terms each year, resulting in a total of 48 terms. The left panel shows carpenters and the right panel shows painters.

From figure 4 we can see that the pre-trend appears to be the same in Trondheim and the comparisonFigure 4: VAT municipalities, payment for and carpenters we can assumeand painters that thebefore common and after trend the assumption reform holds.

Notes: Figure 2 compares the municipality sector in Trondheim to all municipality sectors in Norway, for each year in the time interval of my analysis [2012, 2019] by terms. There are 6 terms each year, resulting in a total of 48 terms. The left panel shows carpenters and the right panel shows painters.

Figure 4: VAT payment for plumbers and electricians before and after the reform

Notes: Figure 4 compares the municipality sector in Trondheim to all municipality sectors in Norway, for each year in the time interval of my analysis [2012, 2019] by terms. There are 6 terms each year, resulting in a total of 48 terms. The left panel shows carpenters, and the right panel shows painters.

For the second DD regression with the control group containing all municipalities in Trøndelag excluding the municipalities that informed “Tett på” in 2018 we can see similar results from when the municipalities in all of Norway was used. From figure 5 we it appears that we have a common trend before “Tett på” was introduced.

Figure 5: VAT payment for carpenters and painters before and after the reform Notes: Figure 5 compares the municipality sector in Trondheim to all municipality sectors in Trøndelag, for each year in the time interval of my analysis [2012, 2019] by terms. There are 6 terms each year, resulting in a total of 48 terms. The left panel shows carpenters, and the right panel shows painters.

From figure 6 we can see that there is no visible change in payments for carpenters and plumbers, but we can see that the pre-trend appears to be the same in Trondheim and the comparison municipalities implying that we could assume that the common trend assumption holds.

Figure 6: VAT payment for plumbers and electricians before and after the reform

Notes: Figure 5 compares the municipality sector in Trondheim to all municipality sectors in Trøndelag, for each year in the time interval of my analysis [2012, 2019] by terms. There are 6 terms each year, resulting in a total of 48 terms. The left panel shows carpenters, and the right panel shows painters.

5.2 Regression result

To quantify the effects shown in Figures 2-5, I estimate equation 푦푖,푡 = 훼푖 + 푡푒푟푚𝑖푛푡 +

훿퐷퐷푖푡 + 휀푖푡, that was presented in Section 4. I consider 2012-April 2016 as pre-treatment periods, and May 2016-2019 as post- treatment periods.

Table 3. Trondheim against all other municipalities and Trøndelag (T). GROUP Carpenter Painter Plumber Electrician

Coefficients -7.48 6.56 -4.11 -6.60

P-value (Pinto-Ferman) 0.69 0.43 0.89 0.91

푹ퟐ 0.034 0.027 0.067 0.071

Observations 17040 17040 17040 17040

Coefficients (T) -6.58 0.94 -9.07 -11.47

P-value (Pinto-Ferman) (T) 0.71 0.91 0.77 0.79

푹ퟐ (T) 0.043 0.025 0.061 0.061

Observations (T) 3 648 3 648 3 648 3 648

Notes: This table reports regression results from equation 푦푖,푡 = 훼푖 + 푡푒푟푚𝑖푛푡 + 훿퐷퐷푖푡 + 휀푖푡 for the different groups. The dependent variable is VAT paid per municipality inhabitant for different groups in the construction sector. This Table shows the results when all other municipalities with complete records (for all 48 terms) are included in the analysis.

The cluster robust standard errors at the municipality level are not reliable when we have only one treated cluster (e.g., Conley and Taber (2011)). The inference assessment in Difference-in-Differences with few treated groups proposed by Ferman (2019) indicates that we can expect over-rejections at the order of 91% for a 5%-level test we rely on cluster robust standard errors at the municipality level. Therefore, in order to evaluate whether such effect is statistically different from zero, I estimate coefficients and calculate p-values using the method proposed by Ferman and Pinto (2019). This method is an extension of the inference method proposed by Conley and Taber (2011) and is suited for settings with only one treated municipality where there is heteroskedasticity generated from variation in the municipality sizes. The method allows for unrestricted serial correlation in the errors (a well- documented problem by Bertrand et al. (2004) for DD designs), and, since we have only one

26 treated municipality, it also even allows for some kinds of spatial correlation (Ferman, 2020). I present in Appendix B more details on the implementation of this inference method.

The first four rows in Table 3 show us the results from the DD regression when all of Norway is the control group, and the last four rows show us the results from the DD when Trøndelag is the control group.

From Table 3 column 3 and 4 we see that there are no significant values for plumbers and electricians for either of the control groups. The p-values are 0.89 and 0.91 for the first control group, and 0.77 and 0.79 for the second control group.

When we consider the estimated effect on painters and carpenter, the p-value is equal 0.69 for carpenter and 0.43 for painters. This means that we cannot reject H0, meaning that “Tett på” did not have an effect on the two sectors. This is seen in column 1 and 2 in Table 3. The same holds for painters and carpenters when Trøndelag is the control group, p-values of 0.71 and 0.91. This means that there is no significant effect, and I cannot reject the null hypothesis of there being no effect from the program.

The standard DD found significant values for painters at a 10% level when all municipalities in Norway was the control group, and at a 5% level when the control group was Trøndelag. The results are found in Appendix A.

5.3 Discussion The main finding here is that measured as changes in VAT in the construction sector there is no indication that “Tett På” has had an effect on tax evasion in the construction sector. Why is it that the program has not affected the construction sector? One explanation could be that the data I have used here are not well suited for a complete assessment of the intervention. The data we got from Skatteetaten only contains construction work related to renovation of homes. The data did not contain VAT paid for construction of new buildings. It could be that a more complete data set would have given a different result, although we should expect also to see traces of effects in our limited data. Since 2012 there has been built around 13 000 new buildings in Trondheim (Espeland et al., 2021), not having data on these buildings has likely affected the outcome of the regression.

Another issue regarding the data is that I did not separate between large companies, small private companies and the self-employed. NOU (2009) reports that the small private businesses and the self-employed account for a large degree of VAT evasion. Larger companies are generally organised in joint stock companies with external accountants. Tax evasion in these companies is usually done through paying fake invoices in cash. The money from the fake invoices goes to the workers and owners of the companies (NOU, 2009). In order to separate between these businesses, we would need internal accounts, this is not public information. Obtaining the internal accounts would allow us to obtain information from the smaller companies and the self-employed, and see if the program has had an effect on these sectors.

I did not separate “Tett på” from other programs that work toward reducing the amount unreported employment and tax evasion. “SMSØ” is an informational program that informs the youth and consumers on the importance of buying goods and services from the formal sector. The program has worked in all of Norway since 2017 and could have affected the degree of tax evasion in the control municipalities (Departementene 2017). “Spleiselaget” is another program that holds two-hour courses for high schools on tax and the informal sector (Skatteetaten, 2021a). These and other programs like “Handlehvitt” (Skatteetaten, 2021a) could have increased tax compliance for the control municipalities and affected the results from “Tett på”.

If it is possible to retain information about regions that have had a larger amount programs to reduce tax evasion, we could exclude these municipalities from the regression and receive a more accurate result.

Based on the caveats above, it could be that our result is a false negative; that there is an effect there, but it is not recovered in the data we have used. On the other hand, it could also be that there is no effect on “Tett på”. “Tett på” is an informational pamphlet and a phone call from the municipality workers in Trondheim on the importance of buying goods and services from the formal market. This intervention may not give households sufficient incentives to comply with the tax code. From Section 2 about the empirical evidence of tax evasion, we see that informational campaigns have had a lower effect on tax evasion compared to audits. The theory on joint tax evasion focuses on the implications of either penalties or probabilities of audits. Fedelini and Forte (1999) find that penalties and probabilities of audits both strongly effects a consumer’s decision to evade taxes.

They also find that the amount evaded depends on the gains from the financial transaction. This could help explain the consumers’ willingness to continue evading even after “Tett på” was introduced. The consumer might not view the moral and ethical obligation of not evading taxes as high as the potential financial gain for purchasing goods and services from the informal sector.

The empirical evidence reviewed in Section 2.3.2 all have different arguments for why reduction in tax evasion occurs. Bergolo et al. (2018) argues that the letter affected the salience rather than the perception of the enforcement environment, Bott et al (2019) argue that moral and perceived probability of detection affects the taxpayer’s compliance. Ortega and Scartascini (2020) argue that electronic communication and personalized methods have a higher effect of increasing tax compliance. This could mean that different treatment groups respond differently letter information. For the partitioners of “Tett på” this could mean that moral and increased information does not affect the individuals to reduce their degree of tax evasion. Instead expanding the program to include probabilities of detection (through random controls) and penalties (included in the email), might produce better compliance effect.

Since “Tett på” has been introduced in other municipalities in Norway since 2018 (Skatteetaten, 2021), we might see other results when the number of treatment groups increases and if other municipalities has different effects from the program, that will lead to a reduction in tax evasion.

6. Conclusion

The aim of this thesis was to contribute to the literature with an analysis on informational policies to reduce tax evasion. Using VAT payments as a measure of estimating tax evasion has had certain complications but is effective. The VAT payments in the construction sector would capture potential increases in tax compliance coming from the consumer. Meaning that if the treatment group would have a higher degree of reported VAT payments than the control group, we would see an effect of the program.

The empirical analysis suggest that the program did not have an effect of reducing tax evasion in the building sector. This conclusion is based on the Difference-in-Differences analysis using the inference method. The data does not include complete information on new buildings in Trondheim and does not separate between smaller businesses and self/employed contractors, where there is a larger degree of VAT evasion. This leads me to believe that I have not captured the full effect of the program, and that I cannot conclude with whether or not the program has any effect on decreasing VAT evasion.

Since the program started in Trondheim in 2016 it has been initiated in other municipalities in Norway. It remains to see if the program effects tax evasion in the new municipalities.

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Appendix A: Tables on DD regression

Tabel A1. Trondheim against municipalities in Norway. Original standard errors and p- values of regressions

GROUP Carpenter Painter Plumber Electrician

Coefficients -2.26 9.34 -3.45 -12.26

Standard Error 12.34 5.58 18.90 32.21

P-value 0.86 0.090 0.86 0.70

푹ퟐ 0.034 0.027 0.067 0.071

90% confidence interval [-22,56, 18.04] [-0.16, 18.52] [-34.54, [-65.25, 40.73] 27.64] Observations 17040 17040 17040 17040

Tabel A2. Trondheim against municipalities in Trøndelag. Original standard errors and p-values of regressions .Trondheim against municipalities in Trøndelag. GROUP Carpenter Painter Plumber Electrician

Coefficients -2.91 11.75 2.20 1.68 Standard Error 10.42 5.73 17.65 24.73 P-value 0.78 0.041 0.90 0.946

푹ퟐ 0.043 0.025 0.061 0.061

95% confidence [-23.33, 17.51] [0.50, 22.99] [-32.41, 36.80] [-46.80, 50.16] interval

Observations 3 648 3 648 3 648 3 648

Notes: This table reports regression results from equation 푦푖,푡 = 훼푖 + 푡푒푟푚𝑖푛푡 + 훿퐷퐷푖푡 + 휀푖푡 for the different groups. The dependent variable is VAT paid per municipality inhabitant for different groups in the construction sector. This Table shows the results when all other municipalities in Trøndelag with complete records (for all 48 terms) are included in the analysis.

Appendix B: Inference method

I present in more details the inference method considered in Section 5.2 for the DD estimator, which is based on Ferman and Pinto (2019).

One of the most common inference methods used in DD applications is the cluster-robust variance estimator. These inference methods does not work well when there is only one treated group. The inference method proposed by Conley and Taber (2011) works well when there is only one treated group but does not allow for heteroskedasticity in the municipality × time aggregated model. The assumption that homoskedasticity can be a restrictive assumption. For example, if there is variation in the number of observations in each municipality × time cell, then the municipality × time DD aggregate model should be inherently heteroskedastic. These methods would tend to (under-) overreject the null hypothesis when the number of observations in the treated groups is (large) small relative to the number of observations in the control groups.

Ferman and Pinto’s (2019) model is similar to the inference method proposed Conley and Taber (2011), but allowing for heteroskedasticity. More specifically, allowing for heteroskedasticity that arises from the fact that different municipalities have different population sizes, and therefore municipality × time aggregates will have lower variance when a municipality has a larger population.

Assumptions would be satisfied when heteroskedasticity arises from variation in the number of observations per group. Under this assumption, the authors can rescale the pre-post difference in average residuals of the control groups using the estimated heteroskedasticity structure, so that they become informative about the distribution of the pre-post difference in average errors of the treated groups. By focusing on this linear combination of the errors, they circumvent the need to impose strong assumptions and specify a structure for the intragroup × time and serial correlations.

They show that a cluster residual bootstrap with this heteroskedasticity correction provides asymptotically valid hypothesis testing when the number of control groups goes to infinity, even when there is only one treated group. Their Monte Carlo (MC) simulations and simulations with real data sets suggest that their method provides reliable hypothesis testing when there are around 25 groups in total (1 treated and 24 controls). No heteroskedasticity- robust inference method in DD performs well with one treated group. Therefore, although 35 their method is not robust to any form of unknown heteroskedasticity, it provides an important improvement relative to existing methods.