Does Wealth Taxation Discourage Entrepreneurship?

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Does Wealth Taxation

Discourage Entrepreneurship?

Bjørnar Drejer

Master thesis in Samfunnsøkonomisk Analyse Department of Economics

University of Oslo

June 2020

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Abstract

While redistribution and the need for government revenue typically make up the arguments for the taxation of net wealth, the distortion of savings and the adverse effect on entrepreneurship is by many held up as the gloomy backside of the coin. In this master thesis, I take a closer look at the latter statement; investigating the claim by analyzing Norwegian register data over a period of time where the wealth tax regime has been modified yearly. I employ a fixed-effects, instrumental variable approach to control for unobserved heterogeneity, both time-invariant and time-variant. I find no support of the hypothesized claim that increased net-of-tax wealth leads to a higher probability of being an entrepreneur, rather, I find unrealistically large effects of the opposite. The results most likely still suffers from unresolved endogeneity of the tax variables and is dominated by a persistent negative trend in entrepreneurship.

Salience issues, optimization frictions, and the timing of responses, all suggests that the effect will be small. The results are also conditioned on “one of the most difficult and intractable tasks faced by researchers working in this area” (Parker, 2004, p.5) – the definition and operationalization of entrepreneurship.

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Preface

With this master thesis, I conclude the five-year master program in Economics, at the Department of Economics, University of Oslo.

I thank my supervisor Ragnhild Schreiner for insightful guidance in the art of econometric modelling, and Thor Olav Thoresen for expert advice and guidance; not to mention the idea for the thesis.

My thanks also go to Statistics Norway for granting access to high quality data, free coffee, and a place to sit, pre-pandemic.

Also for quickly giving remote access once the restrictions hit in.

For the work on the thesis I received the Oslo Fiscal Studies Master Thesis Grant, which I am grateful for.

Finally, I give my greatest thanks to my wife and two sons.

Writing this thesis from the home office with the three of you has been an experience worth remembering, and I am grateful for the much-needed patience and support the last months.

And to my sons. I hope you become well-behaved, utility- maximizing agents with rational expectations, not overly risk- seeking, nor too biased to the present, always keeping to the budget line; so the world can become at bit more like economic theory.

Bjørnar Drejer,

Lindeberg, 9. juni 2020

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List of Figures

Figure 1: Nominal wealth tax rate and basic allowance from 2010 – 2017. ... 6

Figure 2: Nominal tax rate adjusted for valuation, by wealth categories. ... 7

Figure 3: Effective wealth tax rate among wealth tax payers (yearly averages) ... 12

Figure 4: Number of entrepreneurs and entrepreneurial share (percent) of total non- agricultural employment. Ages 25 – 62. ... 14

Figure 5: Decomposed entrepreneurship; by staying, entering, or leaving entrepreneurship. ... 15

Figure 6: Regional rates of entrepreneurship ... 17

Figure 7: Total tax revenue and average tax by entrepreneurial status ... 18

Figure 8: Average valuation for wealth tax payers, by entrepreneurial status ... 19

Figure 9: Gross and net wealth, by entrepreneurship status ... 20

Figure 10: Average effective tax rates and wealth tax, by entrepreneurship status .... 21

Figure 11: Entrepreneurship rates, by treatment and control groups (norm. 2014) .. 22

Figure 12: Macro-movements in GDP per cap, unemployment rate and central bank interest rate. Entrepreneurship rate included for reference. ... 29

List of Tables

Table 1: Sources and types of data ... 8

Table 2: Estimation output, pooled OLS and FE, with and without macro controls. .. 30

Table 3: Estimation output; 2SLS, reduced form and fixed effects model. ... 36

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1 Introduction

The taxation of wealth has been subject to discussion and disagreement between both economists and politicians for a long time. Today, very few countries levy a tax on net wealth (next to Norway, only Spain, France, and Switzerland), mostly argued from an efficiency perspective.

The last decade, however, the debate has sparked anew, after prominent economists like Thomas Piketty and Anthony Atkinson have argued in favor of the revival of the annual wealth tax. Both Piketty (2014) and Atkinson (2015) argue that wealth taxation will reduce excessive capital accumulation and increase redistribution;

«Among the reasons for considering an annual wealth tax more favourable in the UK today than forty years ago are the much higher income inequality» (Atkinson, 2015, p.200).

Saez and Zucman (2019) goes far in supporting the same idea: “wealth tax is likely to be the most direct and powerful tool to restore tax progressivity at the very top of the distribution” (p.51).

While most recognize the need for redistribution, many worry that the cost on efficiency is too high and argue there are better ways to reduce inequality (Boadway et al., 2010; Kopczuk, 2013). In a recent review of wealth taxation, the OECD highlights the distortion on savings and future consumption as the main argument against the wealth tax, as well as double taxation and the alleged negative effect on entrepreneurship (OECD, 2018).

In this thesis, I narrow in on the last objection and ask; does wealth taxation discourage entrepreneurship?

Although there are extensive bodies of empirical literature investigating both the relationship between wealth and entrepreneurship (Hurst & Lusardi, 2004; Corradin &

Popov (2015); see Parker (2018) for more details), and the effect of personal income taxation on entrepreneurship (see Hansson (2012) for overview), little is known about the effects of wealth taxation on entrepreneurship.

From simple reasoning one can infer that if entrepreneurship is positively correlated with wealth, then, if wealth is reduced by a wealth tax, the likelihood of entrepreneurship declines as well.

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However, there is little evidence backing the presence of real behavioral responses to wealth taxation. Seim (2017), for example, analyzes behavioral responses to wealth taxation using Swedish register data, and finds no evidence of real responses to changes in the wealth tax. Rather he finds evasion and avoidance responses, in accordance with the hierarchy of responses to taxation as presented by Slemrod (1990, 1995). In addition, salience issues, optimization frictions, attrition in occupational choice, and the timing of responses, all suggest that the effect on entrepreneurship is small.

Despite the low expectations, Hansson (2008), one of few empirical studies of wealth tax and entrepreneurship, does find some support of a negative effect on entrepreneurship of wealth taxation. Building on the work of Hansson, I hypothesize that an increase in individual net-of-tax-wealth – the share of net wealth remaining after tax – does increase the probability of being an entrepreneur.

To test this hypothesis, I estimate the relationship by using a fixed effect panel data model in combination with the instrumental variable approach, on Norwegian register data of wealth and income, spanning from 2010 to 2017.

It turns out that results of the empirical investigation are not convincing, and non-informative in considering the validity of the hypothesis. The main reason is most likely that the tax variables are endogenous and it has been hard to find good instruments within the time limit of this master thesis. In addition, the chosen operationalization of entrepreneurship, does not capture the group of entrepreneurs allegedly most affected by the changes in the wealth tax.

Thus, future work should explore the question more thoroughly, for example by finding more reliable instruments, and study alternative operationalizations of entrepreneurship.

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2 Background

2.1 Wealth taxation and entrepreneurship

Hansson (2008) is one of few researchers having studied the empirical relationship between wealth taxation and entrepreneurship. By comparing countries within the OECD, with and without taxation of wealth, she finds evidence of a negative effect of the wealth tax on the share of entrepreneurs, albeit the effect is small in magnitude and there are challenges to the identification due to cross-country comparison.

In the theoretical framework, Hansson argues that wealth taxation affect entrepreneurship through two main channels. 1) A tax on wealth reduces the available wealth in the economy and thus reduces the number of people having the means to start their own business. 2) The wealth tax reduces the expected return from entrepreneurship, since future profits will be subject to taxation. The reduction in expected returns reduces the share of the population with entrepreneurial vision., and consequently the share of entrepreneurs in the economy, both through the self-financed and loan-financed entrepreneurs.

In the Norwegian popular debate, two key mechanisms are often highlighted.

First, the wealth tax is often argued to be unpayable for entrepreneurs. Entrepreneurs initially have low personal liquidity, having invested all personal funds into the start- up. The newly started company is, however, often given a high expected value. The resulting personal wealth tax on the entrepreneur’s shares in the company, is high and difficult to pay for the constrained entrepreneur. He/she will have to borrow money from the company, or sell shares, in order to pay the personal wealth tax. This further constrains the entrepreneur and binds up capital that otherwise could have been invested into the growth and the survival of the company. Following this argument, there is reason to believe the wealth tax not only affects entrepreneurial vision, but also affects the survival of newly started businesses.

Hansson argues that the expected future profit channel is the dominating mechanism when studying effects of wealth tax changes. However, there is no

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consensus that the main motive of the entrepreneur is pecuniary. If we instead of entrepreneurial vision rather think of entrepreneurial ambition, it is not clear that slightly reduced expected future wealth will make many entrepreneurs choose to be employees. After all, employees also pay wealth tax if they accumulate wealth. I therefore question the importance of this channel.

Access to capital is generally thought to be the main hinder to successful entrepreneurship. Hansson’s first argument says that a wealth tax reduces the available wealth in the economy, leaving less entrepreneurs to self-finance. I argue that it also reduces the available capital in the economy that could be borrowed or invested into startups by others than the entrepreneur, which Hansson disregards.

The second argument from the Norwegian setting is that the differentiated valuation of personal wealth in the Norwegian tax system, e.g. primary housing at 25%

and stocks at 90% of actual value in 2017, favors investments in property over stocks.

Therefore, a disproportional share of investments go into property rather than risky investments, such as shares in start-ups. The government has made some efforts the last years to shift investments away from property by lowering the valuation of stocks and

“working capital”. The first 10% reduction was effective 2017. In Hansson’s theoretical framework, this could again be modelled as an increase in the possibility to get a loan or other funding (although Hansson disregards venture capital). An increase in the chance of funding would lower the negative impact of the wealth tax on entrepreneurship. Since the valuations were only effective the last period of the time span I study, it is unclear to me to what extent including this would make a difference.

Following the first argument from the Norwegian setting, the wealth tax is challenging entrepreneurship where the entrepreneur is cash constrained and not able to pay the tax. In my understanding, this is mostly a problem for the private limited companies (Akjseselskap), whose owners are taxed based on the value of the shares in the company. Of course, there might also be sole proprietors (Enkeltpersonsforetak) with highly valued property or machinery, who might feel the wealth tax is too high.

However, these properties must be fully financed for the tax to apply, since debt is deductible. If that is the case, the tax is at least on real values, and not expected future value. Following this argument, the wealth tax is mainly a challenge to the private

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limited companies. If this is correct, I must make sure that these are included by the chosen definition of entrepreneurship, as I will discuss below.

2.2 Defining entrepreneurship

Defining the entrepreneur is difficult, both conceptually and for applied work, and there is no clear consensus in the literature on the proper definition of entrepreneurship, nor in non-academic sources (Parker, 2009). The conceptual definitions try to capture the innovative and risk-seeking aspects, which make the entrepreneurs unique and meaningful as agents of economic renewal and increased growth.

In applied work, however, the definition needs to be precise, in order to consistently classify some individuals as entrepreneurs. This operationalization must be context-specific, and some efforts have already been made to operationalize entrepreneurship in the Norwegian setting.

Fjærli, Iancu and Raknerud (2013) provide a good discussion of the definitions applied in the literature. They separate between firm based definitions (new firms) and self-employment definitions, which has been the standards in empirical research.

Although different operationalizations have been applied (see Berglann et al. (2011) or Hvide (2009)), Fjærli et al. (2013) argues that in order to capture the real innovative essence of entrepreneurship, while keeping the resulting group relatively homogenous, the definition can best be identified purely by active ownership in a new business.

Considering the mechanism argued for in the previous section, it seems appropriate to define the entrepreneur mainly as an active owner of a new firm.

However, both the data requirement and the work requirement for such research, is beyond the scope of this thesis. Therefore, I follow the seemingly well-accepted standard of defining the entrepreneur as the self-employed.

2.3 The Norwegian wealth tax system

Norway is one of few countries that still levies a tax on net wealth, and as agents can be followed through rich individual-level register data, it offers a unique setting for analyzing the effects of wealth taxation on entrepreneurship.

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The Norwegian wealth tax schedule is a progressive tax system with two brackets, where a basic allowance defines tax exempt wealth and wealth above that is taxed by one rate. The tax base consists of all personal wealth net of debt, discounted by a valuation scheme favoring certain types of wealth, e.g. primary housing is valued at 25% of market value, secondary housing at 90%, and business property at 80% in 2017.

Over the sample period, the nominal tax rate decreased by 0.25 percentage points from 1.1% in 2010 to 0.85% in 2017. This makes a 23% decrease in relative terms. The reduction was implemented in two steps; down to 1% in 2014 and further down to 0.85% in 2015. In addition, the basic allowance has increased yearly since 2012 (figure 1) (Norwegian Tax Administration, 2020).

Over the period which I have data for in the present analysis, there has been changes in the valuations of items. Predominantly for the categories secondary housing and business property, which has increased from 40% in 2012 to 90% and 80%, respectively, in 2017. Figure 2 displays the nominal tax rate interacted with the

Figure 1: Nominal wealth tax rate and basic allowance from 2010 – 2017.

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valuation scheme, showing that for some wealth groups the increase in valuation neutralizes the reduction in the nominal rate.

The nominal tax rate reductions in 2014 and 2015 therefore have individual- specific impact depending on the composition of the individual’s wealth. Depending on the relative size of the basic allowance to net wealth, individuals whose wealth is dominated by secondary housing and business property have most likely experienced increased taxes. The categories of wealth subject to the largest tax reduction are those that are not already discounted, e.g. financial assets, bank deposits, and more.

Figure 2: Nominal tax rate adjusted for valuation, by wealth categories.

3 Data and descriptive statistics

3.1 Data

The study has been conducted using Norwegian register data on wealth and income provided by Statistics Norway, as well as macro data available from Statistics Norway, The Central Bank of Norway and Norwegian Tax Authorities. Table 1 shows what type of data is provided by the different sources.

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Table 1: Sources and types of data

Data source Type of data

Statistics Norway Register data on wealth and income Central bank of Norway Central bank interest rate,

yearly averages

Statistics Norway, online GDP per capita; Unemployment rate Norwegian Tax Authorities Tax system variables

3.1.1 Sample specification

The full data set contains forty million observations in the time period 2010 – 2017. In order to create a coherent and manageable sample, which matches earlier studies of entrepreneurship and wealth taxation, the sample has been reduced to roughly eighteen million observations by the following criteria:

• Ages 25 – 62

• Employees reporting either wage income, self-employment income, or both.

• Non-agricultural employment

Applying the selection criteria results in an unbalanced panel with roughly two and half million individuals observed from 2010 to 2017. I exclude agricultural employment, since this sector is highly subsidized and not directly comparable to the rest of the sample of self-employed. The implementation of this criteria was not straight forward, however. Next, I briefly discuss the chosen implementation.

3.1.1.1 Non-agricultural employment

Information on sector affiliation was not readily available in the data. The variables with potential for identifying agricultural affiliation changed multiple times over the period. The implementation of the exclusion criteria thus has potential to inconsistently

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exclude individuals from the sample. This is not ideal, since a non-consistent yearly exclusion of individuals will affect the yearly sample size which again affects the measure of entrepreneurship.

Keeping this in mind, I choose to implement the exclusion involving three different variables: 1) Income in farming, forestry, and fisheries, available for 2010- 2011, 2) Farmers deduction, available for 2012-2017, and 3) Income in fisheries available for 2012-2017. I identified agricultural employment if any of these variables contain positive values.

As verification, I attempted excluding individuals identified by the first variable from the whole sample, i.e. removing all individuals having income from agricultural employment in 2010 or 2011. This excludes roughly the same number of observations from the sample as the three-variable exclusion implemented (about 320.000 observations). This makes sense, if assuming the agricultural affiliation is relatively stable over time.

3.1.2 Key variables

Two key variables have been constructed for the purpose of this study; 1) the operationalization of entrepreneurship, and 2) the construction of the effective average wealth tax.

3.1.2.1 Entrepreneurship

An entrepreneur is, as previously discussed, operationalized as the self-employed and identified in the data by an indicator variable equal to one if business income contributes more than fifty percent to the total income from employment¹. In consequence, an individual can be registered as entrepreneur even when making losses, as long as wage income is not larger than the loss in absolute value. This to acknowledge that entrepreneurial endeavors often involve years of losses. The fifty percent threshold is set somewhat arbitrarily, and there are arguments for setting it lower, however the

1 Employment income = wage income + self-employment income

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fifty percent is kept as the threshold in order to filter out the many employees with a small self-employed business on the side.

Using reported self-employment income from tax reports as an identifier of entrepreneurship is not without problems. Reported income does not necessarily reflect time invested in the project. An entrepreneur can spend more working hours in self- employment than as an employee, yet have more wage income than self-employment income. To account for such an effect, the fifty percent threshold could have been set lower. At the same time, self-employment income can be substantial even with low activity, e.g. income from selling books years after writing and publishing.

Another potential challenge to the measure of entrepreneurs is tax evasion. In fact, dominant approaches of the tax evasion literature build on the assumption that the self-employed evade (and wage earners not). This potentially affects the number of identified entrepreneurs if reported self-employment income is lower than wage income, although real self-employment income might be higher than wage income. This again argues for setting a lower threshold in the operationalization of entrepreneurship.

Additionally, there might be reporting/registry issues. For example, if child leave payments and other social benefits are registered as wage income. If this is the case, an entrepreneur who takes out most of the child leave within one calendar year might be identified as an employee rather than self-employed, as most of the income is registered as wage income.

3.1.2.2 Effective average tax

Section 2.3 above describes how the different components of the wealth tax system has been subject to change every year since 2012. To capture the joined effect of the yearly changes for a given individual, I define the individual effective average wealth tax rate as the wealth tax share of net wealth.² The effective rate is constructed as:

𝐸𝑓𝑓. 𝑎𝑣𝑔. 𝑤𝑒𝑎𝑙𝑡ℎ 𝑡𝑎𝑥 𝑟𝑎𝑡𝑒_/0 = 𝑤𝑒𝑎𝑙𝑡ℎ 𝑡𝑎𝑥_/0

𝑛𝑒𝑡 𝑤𝑒𝑎𝑙𝑡ℎ_/0 (𝕀 𝑤𝑒𝑎𝑙𝑡ℎ 𝑡𝑎𝑥_/0 > 0)

2 Net wealth = gross wealth – debt

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which captures the tax payment as share of net wealth for individual, i, in year, t. The effective tax rate differs from the marginal and average rates due to the presence of a basic allowance, b, and the different valuations of different wealth categories, k, defined by the tax system. When decomposing the wealth tax, the effective rate becomes:

τ_/0^9:: = τ₀^;<=∗ (𝑣𝑎𝑙_/0∗ 𝑛𝑒𝑡 𝑤𝑒𝑎𝑙𝑡ℎ_/0− 𝑏₀)

𝑛𝑒𝑡 𝑤𝑒𝑎𝑙𝑡ℎ_/0 (𝕀𝑣𝑎𝑙_/0∗ 𝑛𝑒𝑡 𝑤𝑒𝑎𝑙𝑡ℎ_/0 > 𝑏₀)

where 𝑣𝑎𝑙_/0 = ^G_GJKABCDB0/<;_EFG ∗ ;90 H9BC0I_EFG

;90 H9BC0I_EFG GGJK

The individual effective tax rate increases in the nominal rate and the average valuation, and decreases in the basic allowance. When rewriting, it is also clear that the effective rate increases with net wealth:

τ_/0^9:: = [τ₀^;<=∗ 𝑣𝑎𝑙_/0− τ₀^;<=∗ 𝑏₀

𝑛𝑒𝑡 𝑤𝑒𝑎𝑙𝑡ℎ_/0] (𝕀 𝑣𝑎𝑙_/0∗ 𝑛𝑒𝑡 𝑤𝑒𝑎𝑙𝑡ℎ_/0 > 𝑏₀)

The effective rate equals the marginal rate and the average rate, respectively, under the following conditions:

τ_/0^9:: = τ_/0^=BNO if 𝑣𝑎𝑙_/0 = 1 and 𝑏₀ = 0 τ_/0^9:: = τ_/0^BAO if 𝑣𝑎𝑙_/0 = 1 and 𝑏₀> 0

Any observed variation in effective tax rates will reflect the sum of the variation in the four components above, i.e. nominal rate, net wealth, basic allowance and wealth composition. Changes to any one component are likely to affect segments of the wealth distribution differently. Changes in the nominal rate and the basic allowance affect wealth holders closer to the cut-off more, while changes to the valuations differently affect home owners, business owners, and asset holders.

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To illustrate the observed effective tax rates in the sample, figure 3 shows the yearly averaged effective wealth tax rate among wealth tax payers, according to the definition above. There is indeed a negative trend, and the largest drops take place in 2014 and 2015. In both these years all components to the tax system changed.

Seemingly, the reduction in the nominal rate and increase in the basic allowance, dominates the changes to the valuation scheme.

Figure 3: Effective wealth tax rate among wealth tax payers (yearly averages)

3.1.3 Correcting for shared tax liability for couples

The Norwegian wealth tax scheme implies that married couples and registered partners are taxed together. This results in observations where net wealth and the wealth tax mismatch, i.e. effective tax rates end up outside the possible range (0, τ₀^;<=).

To address this, I have summed over the wealth measures (gross wealth, net wealth, debt, and wealth tax) for married couples and registered partners every year and inserted the mean back to both. This brings out the real tax base which the tax is calculated on, and the effective rate goes back into the expected range. To correct the

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wealth for as many as possible, the procedure is performed before applying the sample selection criteria mentioned above.

The procedure resolves most nonsensical observations of effective rate outside the range zero to 1.1% (1% and 0.85% for later years), yet some outliers prevail (roughly one thousand observations on either side of the range). To my knowledge, these outliers are still due to married couples and partners being accounted for together.

I suspect it is a result of marital status changing mid-year, e.g. couples divorcing in the course of the year show up as divorced in the registers, yet they may have been tax liable for a larger or smaller net wealth parts of the year, and pay tax thereafter. In this case, the procedure fails to match the individuals and evening out the wealth.

To address these remaining outliers, I winsorize the effective average tax rate, by setting observations below zero to zero, and observations exceeding 1.1%, 1.0%, and 0.85%, to 1.1%, 1.0%, and 0.85%, for the respective years. This mitigates the potential bias from the outliers.

3.2 Descriptive statistics

3.2.1 Trends in Entrepreneurship

Yearly entrepreneurship, both in number and in share of non-agricultural employment, shows a negative trend over the sample period (figure 4). The total reduction in the number of entrepreneurs is about 13% from 2010 to 2017.

The trend adjusts slightly in 2014, becoming less steep for the rest of the sample.

Although small, the change does coincide with the first major reduction in average effective tax rate, and corresponds to the expected effect both in magnitude and direction. However, to know whether this is caused by the changes in effective tax rates, or rather by other coinciding determinants of entrepreneurship, I need to employ an econometric analysis.

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Figure 4: Number of entrepreneurs and entrepreneurial share (percent) of total non- agricultural employment. Ages 25 – 62.

3.2.2 Entrepreneurial entry and exit

The group of entrepreneurs is not static, and is affected by yearly entry and exit of new and old entrepreneurs. Figure 5 graphs the three groups: individuals switching into entrepreneurship (first-year entrepreneur), individuals switching away from entrepreneurship (first-year wage-earner), and individuals staying in entrepreneurship (second year entrepreneurs).

The latter group shows a negative trend parallel to the total share of entrepreneurs, while the two first groups (individuals entering and leaving entrepreneurship) are mostly equal and stable around 0.5 percent of yearly non- agricultural employment. Since exits are not much higher than entries, it seems that the negative trend in entrepreneurship is driven by the decrease in the stable base of entrepreneurs. Since entrepreneurs switching to wage employment is accounted for, the negative trend must be driven by entrepreneurship switching out of the sample.

That means either to unemployment or retirement (age above 62). If the decrease is

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driven by age, it reflects that new generations entering the sample, have lower rates of entrepreneurship, than the generations exiting the sample.³

Figure 5: Decomposed entrepreneurship; by staying, entering, or leaving entrepreneurship.

The slight adjustment of the trend in total entrepreneurship, observed in 2014, is not present in the group of second-year entrepreneurs. When taking a closer look at the two groups of switchers, 2014 stands out as one of few years seeing more individuals switch away from entrepreneurship than into entrepreneurship. In consequence, the share of entrepreneurs decreased more than the trend that year.

3 Decreasing shares can also be due to increasing sample size, as total non-agricultural employment increases every year, however the pattern is also present when looking at numbers rather than shares.

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The other time that more people exited entrepreneurship, than entering, was in 2012. In 2012, new regulations for incorporation of businesses were implemented, lowering the cost of incorporation. As owners of small businesses may run their activities under different organizational forms, some self-employed entrepreneurs may have chosen incorporation, and some new entrepreneurs similarly.⁴ This effect could also persist through the sample period. In most cases, incorporation means wage employment for the owner, e.g. as the CEO. A switch into incorporation should therefore be captured by the group switching away from entrepreneurship in figure 5, which we are seeing.

3.2.3 What happened in 2014?

Even though the wealth tax rate was reduced in 2014, other, maybe more, significant determinants of entrepreneurship changed as well. Firstly, the Norwegian economy experienced a negative shock to the oil price which led to economic uncertainty. It is plausible that this could have made more entrepreneurs to go bankrupt, which forced them to choose wage employment.

To add nuance, the oil price shock affected different regions differently. The west coast regions, with a more oil dependent industrial sector, were more affected. Figure 6 shows the entrepreneurship rates per region; surprisingly, it seems that the drop in 2014 is more pronounced in the eastern regions of the country, such as Oppland, Buskerud, and Vestfold. This can still be understood by the oil price shock, if increased unemployment in the west-coast regions caused more employees to focus on entrepreneurship, the so-called recession-pull effect, while the rest of the country experienced lower demand and economic activity, making more entrepreneurs go bankrupt, which is consistent with the recession-push effect described by Parker (2018).⁵

4 See Thoresen and Alstadsæter (2010) on choice of organizational form under the Norwegian dual income tax.

5 I come back to the recession-pull and prosperity-push effects in section 4.1.3

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Figure 6: Regional rates of entrepreneurship

Secondly, several changes to the rest of the tax system took place in 2014. Not only was the rate of wealth taxation reduced, the top effective marginal tax rate on wage income decreased as well. In addition, the inheritance tax was abolished. Lower tax rate on income makes it more attractive to become a wage earner, which is consistent with the negative shift of 2014. The abolishment of the inheritance tax increases the wealth of heirs beginning 2014. This could make them more likely to enter entrepreneurship, there is for example some evidence in Bø et al. (2019) that recipients may use bequests to start up businesses.

Both the wealth tax rate and the rate of income tax were further reduced in 2015, and, 2016 and 2017, respectively. Since only 2014 sees a notable drop in the share of entrepreneurs, I argue that the tax rates are most likely not the driving cause behind the observed drop. As to the abolishment of the inheritance tax, the effect is not clear and hard to determine, especially as it coincides with the oil price shock.

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3.2.4 Wealth tax and entrepreneurial status

Total tax revenue increased over the sample period, except the reform years 2014 and 2015 (figure 7). Average revenue followed a similar path, both for entrepreneurs and employees, although the entrepreneurs paid a higher average tax. Total revenue is determined by the nominal tax rate, the average valuations, individual net wealth, the basic allowance, and the number of tax payers, which has declined every year.

While the nominal rate has decreased, the basic allowance has increased, and the group of tax payers has decreases, what is the role of wealth composition and net wealth in causing the variation in revenue?

Figure 7: Total tax revenue and average tax by entrepreneurial status

The wealth composition changes and the changes to the valuation scheme are inseparable in the available data. However, the average valuation per individual, as defined in section 3.1.2.2, can be backed out, contingent on the observed variables. The average valuations have increased most years, except in 2011 and 2017 (figure 8).

Some of this must be attributed to increasing valuation on certain wealth categories, but the increase will also be affected by wealth composition adjustment by individuals.

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For example, the observed decrease from 2010 to 2012 could be explained by a movement of investments into property after the introduction of the new valuation scheme in 2019/2010. It is also interesting to note that entrepreneurs are having higher average valuations than employees, which suggests that they have a lower share of wealth invested in primary housing. This means they are more affected by changes in the nominal rate and could equally be more affected by the changes in the valuation scheme, if they are more invested in secondary housing and business property.

Figure 8: Average valuation for wealth tax payers, by entrepreneurial status

As for movements in net wealth, the data shows that average wealth increased for both groups (entrepreneurs and employees) over the sample period, and, as expected, net wealth is substantially higher among the entrepreneurs (figure 9).

Interestingly, average net wealth among entrepreneurs was virtually unchanged from 2013 to 2014. This is consistent with the story that 2014 was a difficult year for the entrepreneurs due to oil price shock, and suggests that the reduction in revenue and

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effective wealth tax rates was larger than if wealth would have increased according to the trend.

To conclude, the tax reductions in 2014 and 2015 were most likely driven by increasing basic allowance and decreasing nominal rate, while change in valuations pushed the revenue up in both years, while net wealth only in 2015. The large increase in revenue in 2016 and 2017, was driven by increasing net wealth both years, dampened by changes to the basic allowance, pushed up by average valuations in 2016 and dampened by average valuations in 2017.

Figure 9: Gross and net wealth, by entrepreneurship status

3.2.5 Effective tax rates and entrepreneurial status

The net effect of the changes in the tax system is represented by the effective average wealth tax, as defined in section 3.1.2.2. As expected, the entrepreneurs (right hand panel in figure 10), have a higher average effective tax rate. The rates are averaged over all employees and entrepreneurs respectively, thus also reflecting changes to group composition. The share of tax payers in each group decreases yearly, so even as the basic allowance increases, more individuals pay zero tax. This pushes down the average

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effective rates for both groups, however, if considering only the tax payers, as in figure 3, the effective rate increases in 2016 and 2017, which is consistent with section 3.2.4.

When later estimating the effect on entrepreneurship, I consider the whole sample of entrepreneurs, which to figure 10 displays relevant variation.

Figure 10: Average effective tax rates and wealth tax, by entrepreneurship status

3.2.6 Control and treatment group

A difference in differences identification strategy requires the existence of a well- defined treatment and control group. One approach to splitting the sample between treatment and control is to define a treatment group consisting of all individuals paying the wealth tax at least one of the years before the first reform year, i.e. pre-2014. This means that the control group consists only of individuals not paying wealth tax. When comparing entrepreneurship rates between the two groups, the treatment group had overall higher rates. This is expected since wealth tax payers are wealthier, and therefore more likely, as we have seen, to be entrepreneurs. When comparing the trends, the two groups have relatively equal pre-reform trends (figure 11), however, the control group surprisingly has a more positive jump in the entrepreneurship rate

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than the treatment group in 2014. This could suggest that the observed change in entrepreneurship rate does not come from the change in effective rates.

Finally, these groups are not satisfactory for estimating the effect of the change in effective tax rates. Some individuals in the control group will most likely gain wealth and pay wealth tax after the reform. Since they by construction paid no tax before, any tax after the reform will be a tax increase. The treatment group, on the other hand, is most likely subject to a tax reduction, as we have seen. The control group is thus also partly treated, however, only by an increase in the tax.

Figure 11: Entrepreneurship rates, by treatment and control groups (norm. 2014)

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4 Empirical approach

A simple regression of the probability of entrepreneurship on the effective net-of-tax rate shows that the probability of becoming an entrepreneur decreases when net-of-tax rate increases marginally – both the same year and the year to follow.⁶ This result is counter intuitive, as capital is assumed to be an entry-requirement to entrepreneurship.

Better access to capital should increase the probability of becoming an entrepreneur, not reduce it.

This counter-intuitive result of the initial regression suggests there are confounding factors affecting the estimates relationship. In the following I will explore to what extent the use of more advanced empirical techniques alters this initial result.

4.1 Causal inference in non-experimental data

The central question in causal inference is what would have happened in absence of the treatment, i.e. the counterfactual outcome. The causal effect of the treatment would be identified as the difference between the observed outcome and the counterfactual outcome. The latter is however unobservable by definition, since the treatment status is exclusive, meaning the same individual or group cannot be both treated and not treated at the same time. Therefore, the counterfactual must be estimated, and ideally identified in a control group – a group which differs from the treatment group only by treatment status. Otherwise the estimated effect suffers from selection bias, which is a bias on the estimated effect caused by differences in groups, rather than differences in treatment. To address the selection bias, researchers can either design a randomized social experiment with a well-defined control group, or address the bias by econometric modelling.

Randomized social experiments are not feasible for most real-world policy implementation. Policy-effect estimation therefore largely depends on identification through econometric modelling. Causal inference in regression models hinges on the

6 Linear probability model including one lag of effective tax rate, with individual clustered standard errors (pooled OLS).

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conditional independence assumption, assuming, that conditional on the control variables the estimated relationship represents the causal relationship. In other words, the control variables ensure comparing comparable groups, which allows for causal interpretation.

Some popular econometric models include the difference in differences (DiD) model, instrumental variable models (IV), regression discontinuity designs, and more.

DiD models especially, are widely used to estimate reform effects, as it involves defining a treatment and control variable ex post, which is conceptually close to the theoretical potential outcome framework. To substantiate the comparability of the two groups, the diff-in-diff requires a common trend assumption, which assumes that absent of the treatment, the treatment group would move similarly to the control group. The causal effect is then the difference between the effects in the two groups.

As indicated in section 3.2.6, the DiD model is not suitable for the research question in study, as there is no clear control group to be defined, since the wealth tax (i.e. the treatment) applies to the whole population. Even though most of the population does not pay the wealth tax, and are therefore not treated per se, the taxpayers differ significantly from the non-taxpayers by the fact that they are wealthier.

In the rest of this section I present the challenges to identification given the empirical setting, and address them step by step, before I present my preferred identification technique – fixed effect panel data estimation in combination with instrumental variable approach.

4.1.1 Omitted variable bias / bad controls

Clearly there are other factors than the rate of wealth taxation affecting the choice of becoming an entrepreneur. Actors optimize based on the whole tax system, a wide list of outside options, and personal characteristics, which are all left out in the initial regression.

If a left-out variable is a significant determinant of the dependent variable, and at the same time is correlated with at least one of the independent regressors, it is an omitted variable. Omitted variables lead the OLS estimator to become biased and inconsistent, because the unaccounted for (omitted) effect ends up in the error term, which becomes correlated with the independent regressor. This is a violation of the

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strict exogeneity assumption in the Gauss-Markov theorem. For non-biased estimation of causal effects, therefore, all omitted variables should be controlled for in the model specification.

However, controlling for all omitted variables is not possible. First, not all omitted variables are observable, e.g. talent, effort, motivation. In consequence, the estimation can always be argued to be biased. Secondly, adding more controls to the specification will not unambiguously reduce the bias, it might as well increase it.

Thirdly, the variables we have available can be poor representations of the true omitted variable, or can be so called bad controls, where the bad control is in itself an outcome of the independent variable of interest.

In the register data available for this study, the variables can be sorted roughly into three categories, 1) income variables, 2) wealth variables, and 3) individual characteristics such as age, gender, marital status, education, family background and home municipality. The two first groups of variables are all endogenous, i.e. they are likely influenced by net wealth, both same period and from earlier periods, which is a key component to the individual effective tax rate. For example, net wealth likely affects an individual’s labor supply which determines income and wealth accumulation. I argue that this makes them all bad candidates as controls.

The third group, the individual-specific characteristics, are more easily considered exogenous to net wealth and the effective tax rate, and would as such be better controls. However, in light of the above considerations, theory cannot tell us which variables to include, and the resulting estimation is most likely still biased.

A better way to address the differences between individuals is to consider a panel data model with individual-specific fixed effects, controlling for unobserved heterogeneity. Since most individual-specific characteristics are close to time-invariant, the fixed effects model will control for these differences.

A different set of relevant control variables are those representing the changes in the tax system. Especially the income tax rate, which represents the alternative to entrepreneurship. Individuals optimize on the whole tax system, not on the isolated wealth tax. However, the changes to the income tax occur in 2014, 2016, and 2017. In a regression including one lag of the income tax rate, this fully absorbs the wealth tax changes in 2014 and 2015. The inheritance tax could have been entered as an indicator

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variable equal to one before the abolishment and zero after. However, adding such a dummy variable will most likely absorb most of the variation in the effective wealth tax rate that I am interested in.

4.1.2 Unobserved time-invariant heterogeneity

A pooled OLS regression disregards the panel structure of the data, and compares the change in net-of-tax wealth across the wealth distribution and the time dimension. The negative coefficients, referred to above, reflects that individuals with lower effective tax rates are less likely to be entrepreneurs. This result is driven by the variation across the wealth distribution, i.e. individuals with less wealth, and thus lower effective tax rates, are less likely to become entrepreneurs.

Panel data models, on the other hand, do take the time dimension into account and observe the same individuals over several time periods. Assuming that the error can be divided into a time-invariant individual-specific element, 𝛼_/, and an idiosyncratic time-variant individual-specific element, 𝑢_/0, allows for transforming out any unobserved time-invariant heterogeneity by the within transformation (demeaning).

The coefficient of interest, 𝛽_Y, can then be estimated with OLS on the transformed model, now having ruled out unobserved traits in individuals. The regression coefficient can be interpreted as the change in the probability of becoming an entrepreneur for a given individual, if the net-of-tax rate increases marginally.

The fixed effects model (without lag of independent variable):

𝑃(𝑒𝑛𝑡𝑟𝑒𝑝𝑟𝑒𝑛𝑒𝑢𝑟 = 1)_/0 = 𝛽_Y ln (1 − 𝑒𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝑡𝑎𝑥 𝑟𝑎𝑡𝑒₀) + 𝜀_/0 𝜀_/0 = 𝛼_/+ 𝑢_/0

Within transformation:

𝑃(𝑒𝑛𝑡 = 1)_/0− 𝑃 𝑒𝑛𝑡 = 1 _a0 = 𝛽_Y[ ln 1 − 𝜏_/0^9:: − ln 1 − 𝜏_a0^9:: ] + 𝛼_/ − 𝛼_a

c d

+ 𝑢_/0− 𝑢_a0

𝑃(𝑒𝑛𝑡 = 1)_/0 = 𝛽_Y[ ln 1 − 𝜏_a0^9:: ] + 𝑢_e0

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Table 2 presents the coefficients in the pooled OLS model and the fixed effect model, respectively. The fixed effects model reduces the magnitude of the coefficients substantially, but they are still negative and significant.

The negative coefficient in the fixed effects model means that when net-of-tax wealth – the share of taxable wealth remaining after taxation – increases marginally for a given individual from one year to another, the probability of becoming an entrepreneur decreases for that individual. For the same reasons as before, this is not intuitive. More available wealth should increase the probability of becoming an entrepreneur. There could be multiple reasons for this. 1) When introducing a time dimension, the relationship becomes sensitive to trends in entrepreneurship, resulting in the spurious estimated effect between wealth and entrepreneurship, 2) there might be unobserved time-variant heterogeneity leading to omitted variable bias, and 3) there might be simultaneous realizations of entrepreneurship and wealth, which can create reversed causality or simultaneity bias in the estimations.

Next, I address these sources of bias by controlling for macro trends and employing the instrumental variable approach.

4.1.3 Time trends

The panel data estimation is sensitive to trends in entrepreneurship, and it is likely that the simple regression with fixed effects above picks up on the overall negative trend in entrepreneurship. There are several ways of addressing time trends. In quasi- experimental studies the difference in differences model is frequently applied. However, as already discussed, the DiD model is not applicable to the research setting at hand.

Other alternatives include adding year dummies, or introducing exogenous variation on macro level. As for the time dummies, they are in this setting likely to absorb any year specific reform effects. Therefore, I address the time trend by adding macro level controls.

I introduce three macro controls into the model 1) GDP per capita, 2) the unemployment rate, and 3) the central bank interest rate (yearly averages). All three macro variables are affecting the probability of entering entrepreneurship, as well as representing the general performance of the economy and the entrepreneurial

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“climate”. When GDP is high, the economy is performing well, and the general welfare level is high. High general welfare weakens the need (and possibility) to pursue higher welfare by becoming an entrepreneur. In addition, the value of personal assets is likely to increase in times of economic growth.

As unemployment grows, more people would be in a position where becoming self-employed is a real alternative to unemployment. This suggests that higher unemployment rates increase the likelihood of entering entrepreneurship. This is, as I referred to in section 3.2.3, described by Parker (2018) as a recession-push effect, as the recession (and the unemployment) pushes individuals into entrepreneurship. On the other hand, Parker points out, increasing unemployment rates affect the general demand in the economy and reduce the market possibilities for new businesses and increase the probability of bankruptcy, a so-called prosperity-pull effect. The observed net effect of the unemployment rate on entrepreneurship thus reflects the sum of the two effects. Berglann et al. (2011) find in the Norwegian setting, positive effects of unemployment on the individual level, and negative effects from aggregate unemployment, consistent with the recession-push effect and the prosperity-pull effect, respectively.

Finally, the central bank interest rate represents the cost of financing entrepreneurship. Higher interest rates increase the cost of loans, which makes it more expensive to start new businesses. The interest rate had a negative trend over the sample period, which I expect to have had a positive effect on entrepreneurship.

The macro variables are graphed in figure 12, and the estimation output comparing the pooled OLS estimates and the Fixed effects model, with and without macro trends, are presented in table 2.

All macro variables are significant in both the pooled OLS and fixed effects model. The signs for the GDP per capita is negative as expected, and the coefficient is very small, which is reasonable given GDP is measured in NOK. The coefficient on the unemployment rate is negative in the pooled OLS model, and positive in the fixed effect model, which fits well to the findings in Berglann et al. (2011). The central bank interest rate has a positive coefficient in both specifications, which is the opposite of the expectation. The coefficients are also quite large. I do not suspect it to be a bad control

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per se, but it most likely picks up too much correlation with entrepreneurship rates, being the only other variable with a stable negative trend.

Figure 12: Macro-movements in GDP per cap, unemployment rate and central bank interest rate. Entrepreneurship rate included for reference.

All in all, the macro variables do not fundamentally change the coefficients on the effective tax rates, but reduces the magnitude of all four. In the fixed effect model, the inclusion of the macro controls reduces the significance of the lag of the effective tax rate.

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Table 2: Estimation output, pooled OLS and FE, with and without macro controls.⁷

Variable OLS FE OLS w/macro FE w/macro

𝑙𝑛(1 − 𝜏^9::) -4.953116***

(0.1200335)

-0.8586636***

(0.0892888)

-4.790685***

(0.1364948)

-0.7991944***

(0.0972339)

𝑙𝑛(1 − 𝜏^9::) (1^st lag)

-2.94088***

(0.1199275)

-0.3101063***

(0.08338)

-2.641695***

(0.1336591)

-0.2152059*

(0.0891602)

GDP per capita

(2^nd lag) -3.86e-08***

(2.03e-09)

-1.15e-08***

(1.79e-09)

Unemployment rate (2^nd lag)

-0.0727009***

(0.0121168)

0.0781532***

(0.010605)

Cent bank int.

rate (2^nd lag) 0.1641168***

(0.0166249)

0.1892707***

(0.0148871)

Constant 0.0408714***

(0.000117)

0.0628722***

(0.0016734)

Legend: * p <0.05; ** p < 0.01; *** p<0.001

4.1.4 Time-variant unobserved heterogeneity

The second source of bias I want to address is the case of time-variant unobserved characteristics of individuals affecting both the probability of becoming entrepreneur and the individual’s wealth.

The effect can be illustrated by the situation where someone marries a wealthy entrepreneur. Since married couples are taxed jointly, the spouse will now be registered with significantly more wealth, which strictly increases the effective wealth tax. At the same time, being married to an entrepreneur increases the probability of becoming an entrepreneur yourself. This change in probability of becoming an entrepreneur does not

7 Using second lag of macro variables for consistency with later estimations.

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come from being registered with more wealth, rather from being closely exposed to an entrepreneurial person. If the marriage takes place within the sample period, this unobserved heterogeneity is not captured by the fixed effects model, and thus represents an omitted variable to my specification.

This omitted variable bias will be corrected by the instrumental variable approach.

4.1.5 Reversed causality and simultaneity bias

Personal wealth is assumed to be an important determinant of entrepreneurship, since starting a business requires capital. At the same time, acquiring wealth is often considered a common motivation for becoming an entrepreneur, which suggests there is a real expectation that entrepreneurs accumulate more wealth than employees. This would mean that while entrepreneurship status depends on wealth, wealth depends on entrepreneurship status. This simultaneous causality set up makes wealth become endogenous if it was independent variable, suffering from a so-called simultaneity bias if included in the model equation. If only entrepreneurship status affect wealth realization, with no effect of wealth on the probability of being an entrepreneur, it is referred to as reversed causality.

As pointed out earlier, net wealth is a key component of the effective wealth tax rate; higher net wealth unambiguously increases the effective tax rate. I argue therefore, that the effective rate suffers from the same endogeneity issues as net wealth. This necessitates finding an applicable identification technique to address the simultaneous causality.

Since we are now dealing with panel data, it is appropriate to also consider the timing of the causality. The term simultaneous suggests the causality goes both ways in the same period, however we can also think of a reversed causality where the dependent variable affects only next period explanatory variable. In our setting, this would mean that while the effective tax rate affects the choice of entrepreneurship both now and next period, the entrepreneurship status affects wealth and effective tax rate only next year. If this kind of lagged reversed causality is present, I could make use of a dynamic model to address the bias, such as the Arellano-Bond (AB) estimator. The AB estimator includes the lag of the dependent variable as an independent variable and instrument

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it with the rest of the lags to address autocorrelation. However, the AB estimator will not successfully control for the unobserved time-variant heterogeneity described in the previous section. The instrumental variable approach, on the other hand, addresses all three endogeneity issues discussed above, which is the reason I choose the IV approach in my final specification.

4.2 Instrumental variable approach

In order to solve the endogeneity of the explanatory variable, discussed above, I employ an instrumental variable approach to estimate causal estimates of 𝛽_Yand 𝛽_Œ in the model specification below.

Model of interest:

𝑃 𝑒𝑛𝑡𝑟 = 1 _/0 = 𝛽_Yln 1 − 𝜏_/0^9:: + 𝛽_Œln 1 − 𝜏_/0•Y^9:: + 𝑔𝑑𝑝_0•Œ+ 𝑢𝑛_0•Œ+ 𝑖𝑛𝑡_0•Œ+ 𝜀_/0

4.2.1 The instrumental variable approach

The instrumental variable (IV) approach aims at breaking the simultaneity bias and omitted variable bias by fixing the variation in the explanatory variable by an exogenous instrument. The estimation is performed in two stages. The first stage is a regression of the explanatory variable of interest on the exogenous instrument and covariates. In the second stage the dependent variable is regressed on the predicted values from the first stage, as well as the covariates included in the first stage.

The instrument must satisfy the assumptions of relevance and excludability. The relevance assumption requires the instrument to be a significant determinant of the explanatory variable. This can typically be tested when performing the first stage, by checking whether the coefficient of the instrument is statistically significant in the first stage. A rule of thumb suggests the F-statistic (the square of the t-statistic) should be larger than ten for the instrument not to be considered a weak instrument, however, having millions of observations per year will give high t-statistics even for very small coefficients and the rule cannot be added too much weight in this setting.

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The second assumption, excludability, requires that the instrument only affects the dependent variable through the explanatory variable it is instrumenting. This is to avoid correlation between the error term and the explanatory variable in the second stage, which again is the basic regression assumption of strict exogeneity. The excludability assumption cannot be tested in data, and must rater rest on well-founded and plausible arguments.

4.2.2 Discussion of assumptions

Finding a good instrument which satisfies both assumptions is challenging. A suitable instrument in our setting, must be a significant determinant of the individual effective tax rate, while having no separate direct effect on the probability of becoming an entrepreneur.⁸ From the four components of the effective tax rate, I have chosen the nominal rate, which seemingly causes the largest variation in the effective rates. The relevance assumption is likely to hold, while the excludability assumption is more questionable. It is for example possible that there are macro trends affecting the probability of entrepreneurship, while also rendering politicians more inclined to change the nominal rate, e.g. a conservative government wanting to encourage entrepreneurship both by reducing wealth taxes and providing cheap loans or tax deductions, which has some degree of accuracy in our setting.

Additionally, you might question whether individuals optimize based on the nominal rate or the realized effective rate. This might be a question of salience, since the nominal rate is more accessible and equal to everyone, while the effective rate depends on individual average valuation for the whole year.

By employing this instrument, I assume, there are no such time trends affecting both nominal tax rate and the probability of entrepreneurship, and that individuals optimize based on the effective tax rate, rather than the nominal.

Another challenge when employing the IV approach in this setting, is the fact that individual effective tax rates are zero for the majority of the sample. When

8 Ring (2020) uses a boundary discontinuity approach, but it is beyond the scope of this study to develop more sophisticated instruments.

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instrumenting the effective rate by the nominal rate, which varies only from year to year, the estimated average effect will undoubtedly be quite small. A better instrument would be able to predict the zero rate better, for example, imagine using the marginal tax rate as the instrument. The marginal rate equals the nominal rate for the tax payers and zero for the non-taxpayers. Clearly this instrument would better predict the zero rate, but is again endogenous and breaks the excludability assumption, and is therefore not suited as an instrument.

Due to the very large sample, small coefficients can still become significant, which means that the relevance assumption might still hold in the first stage. The problem comes in the second stage. By alluding to the logic of the Wald-estimator, the second stage coefficient can be estimated as the reduced model coefficient, divided by the first stage coefficient. Since I expect a small coefficient in the first stage, I also expect to see large coefficients in the second stage. In the next section, we see that this is indeed the case.

4.2.3 IV model equations / 2SLS

I estimate the instrumental variable approach by Two Stage Least Squares (2SLS).⁹ The predicted effective net-of-tax rates from the first stages are used as explanatory variables in the second stage. I have kept the same three macro control variables as previously discussed, lagged by two periods, to avoid any endogeneity in first stage #2.

First stage #1: ln 1 − 𝜏_/0^9:: = ln 1 − 𝜏₀^;<= + 𝑔𝑑𝑝_0•Œ+ 𝑢𝑛_0•Œ+ 𝑖𝑛𝑡_0•Œ+ 𝑣_/0 First stage #2: ln 1 − 𝜏_/0•Y^9:: = ln 1 − 𝜏_0•Y^;<= + 𝑔𝑑𝑝_0•Œ+ 𝑢𝑛_0•Œ+ 𝑖𝑛𝑡_0•Œ+ 𝑣_/0•Y

Second stage:

𝑃(𝑒𝑛𝑡𝑟 = 1)_/0 = 𝛽_Yln 1 − 𝜏_a0^9:: + 𝛽_Œln 1 − 𝜏_a0•Y^9:: + 𝑔𝑑𝑝_0•Œ+ 𝑢𝑛_0•Œ+ 𝑖𝑛𝑡_0•Œ+ 𝜀_/0

9 Estimated by the Stata command –xtivreg– using the fixed effects option and standard errors clustered on individual.

Does Wealth Taxation Discourage Entrepreneurship?