The Effect of VAT Threshold on the Behavior of Small Businesses: Evidence and Implications · 2020-05-10 · The E ect of AVT Threshold on the Behavior of Small Businesses: Evidence

The Effect of VAT Threshold on the Behavior of

Small Businesses: Evidence and Implications

Jarkko Harju, Tuomas Matikka and Timo Rauhanen

The E�ect of VAT Threshold on the Behavior of Small

Businesses: Evidence and Implications

Jarkko Harju, Tuomas Matikka and Timo Rauhanen∗

March 30, 2015

Preliminary version

Abstract

Small businesses are often regarded as important determinants of economic growth. Simultane-

ously, many tax rules and regulations are size-dependent, which might decrease e�ciency and the

economic activity of growing �rms. We study the e�ects of the value-added tax (VAT) threshold on

the behavior of small businesses. In Finland, �rms with annual sales below 8,500 euros are not liable

to pay VAT. We use detailed register data on the universe of Finnish businesses and the bunching

method to provide robust and clear evidence of behavioral e�ects of the threshold. We �nd that

the VAT threshold has notable e�ects among small business. Firms bunch actively just below the

threshold, which implies notable e�ciency implications. We �nd that changing tax incentives at the

threshold does not have a signi�cant e�ect on the extent of the response. This implies that compli-

ance costs are important in explaining observed responses. We �nd no evidence of tax avoidance or

evasion, which suggests that �rms respond by reducing output. Also, we �nd that bunching behavior

is relatively permanent, which implies that the threshold decreases the growth of small businesses.

Keywords: Small businesses, value-added tax, VAT threshold, bunching

JEL codes: H21, H25, H32

∗Government Institute for Economic Research VATT. Jarkko.Harju@vatt.�, Tuomas.Matikka@vatt.�,Timo.Rauhanen@vatt.�

1

1 Introduction

Small and especially young businesses are often regarded as important determinants of economic growth

(see e.g. Haltiwanger et al. 2013, Decker et al. 2014). Simultaneously, many tax rules and regulations

are size-dependent. These rules might reduce the e�ciency of taxation and decelerate economic activity,

in contrast to widespread objectives to enhance the growth of small businesses.

Value-added tax (VAT) is a commonly applied form of consumption taxation, and a crucial component

of tax revenue in many countries. Most VAT systems include varying thresholds below which �rms are

exempt from remitting VAT. For example, in the EU, VAT threshold varies between 0-100,000 euros.

Despite the potential detrimental e�ects of size-dependent thresholds, there is only limited evidence on

the e�ects of VAT threshold on the behavior of small businesses.

In Finland, �rms with annual sales below 8,500 euros are not liable to pay VAT and separately report

sales to the Tax Administration. Relatively low VAT thresholds are common. Half of the EU countries

apply thresholds below 25,000 euros, including for example Germany, Belgium and Denmark. In general,

small �rms comprise a large share of all businesses. In Finland, one third of all registered �rms have

turnover below 25,000 euros. Among young and potentially growing �rms, the share of businesses with

small turnover is even larger. Over 40% of �rms that are younger than three years have turnover below

25,000 euros.

In this study we present comprehensive evidence on the e�ects of the VAT threshold among small

businesses. We utilize detailed data on the universe of Finnish businesses, including also �rms below the

VAT threshold. We use the bunching method in order to provide clear and robust evidence on behavioral

e�ects.

To understand the implications of the VAT threshold, it is important to know both why and how

�rms respond to it. By utilizing changes in VAT rules at the threshold, we analyze the role of both tax

incentives and compliance costs. We study the anatomy of the response to learn whether �rms react by

changes in real economic activity or by tax avoidance and evasion. In order to illustrate the important

dynamic aspects of the VAT threshold, we analyze how the threshold a�ects growth and development of

small businesses.

First, �rms could respond to the VAT threshold both because of increased taxation and increased

compliance costs above it. We utilize variation in tax incentives and compliance costs to analyze why

�rms react to the threshold. Before 2004, VAT liability increased sharply at the threshold, as �rms

marginally above the threshold were liable to fully pay the VAT on all sales. In 2004, Finland introduced

a VAT relief scheme, in which remitted VAT increases gradually above the threshold. Thus the reform

drastically changed tax incentives at the threshold, which allow us to disentangle the e�ects of tax

incentives and compliance costs.

Second, �rms can respond to the threshold by reducing sales, or by engaging in various tax avoidance

activities or underreporting of sales. We analyze the nature of the response by studying how the pro-

duction factors of the �rms, such as equity and expenses, develop around the VAT threshold. Potential

discontinuous changes in production factors exactly at the VAT threshold indicate changes in behavior

caused by this regulation, and shed light on how �rms respond to the threshold.

Third, in terms of welfare e�ects, it is essential to know how the VAT threshold a�ects the growth of

small businesses. The threshold could signi�cantly hinder growth if �rms avoid exceeding the threshold

for a prolonged period of time. The panel structure of the data allow us to follow �rms over time, which

enable us to characterize the e�ect of the threshold on growth and the scale of business activity.

We �nd that the VAT threshold has notable e�ects among small business. Firms bunch actively

just below the threshold, which implies signi�cant e�ciency implications. We �nd that changing tax

incentives at the threshold does not signi�cantly decrease the e�ect, which implies that compliance

costs are important in explaining observed behavior. We �nd no clear evidence of tax avoidance or

evasion, which suggests that �rms respond by changes in real economic activity. Finally, we �nd that

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bunching behavior is relatively permanent, which implies that the threshold decreases the growth of

small businesses.

Despite the �scal importance of VAT and the generally applied sales thresholds, only a few previous

papers study the e�ects of these thresholds. The theoretical literature has focused on determining the

rules for optimal VAT threshold. Keen and Mintz (2004) and Kanbur and Keen (2014) show that the

optimal VAT threshold depends on administrative and compliance costs, and the extent of the e�ect of

the threshold on �rm behavior. Empirically, Onji (2009) was the �rst to detect clear e�ects of the VAT

threshold on the distribution of �rms in Japan. He shows that relatively large Japanese �rms reacted

to the introduction of a VAT threshold by splitting into smaller entities, re�ecting clear tax avoidance

behavior. Li and Lockwood (2014) show that �rms in the UK bunch actively at the relatively large VAT

threshold (approx. ¿90,000). Also, Waseem (2015) observes a strong clustering of �rms at the VAT

threshold in Pakistan.

This paper proceed as follows: Section 2 describes the VAT system and the VAT threshold in Finland.

Section 3 presents the methodology and Section 4 describes the data. Section 5 o�ers the results and

Section 6 concludes the study.

2 Institutions

2.1 Value-added taxation

Most developed countries use the value-added tax (VAT) as their primary consumption tax system. VAT

is usually a broadly based tax assessed on the value added to goods and services. The amount of value

added is calculated by subtracting the amount of externally purchased goods and services from the value

of goods and services produced.

In short, the VAT assessment process is the following: each trader in the chain of supply from

manufacturers to retailers charges VAT on the sales. Firms are entitled to deduct from this amount the

VAT paid on purchases. VAT is remitted to the tax authorities by the seller of the goods and services.

VAT is the main source of tax revenue in many developed countries. For example, among all OECD

countries almost one-�fth of all tax revenue is collected by VAT. However, the variation in VAT revenues

is large across countries.

A common feature in many VAT systems is that �rms with annual sales under a certain threshold

are not required to register and remit VAT. Figure 1 depicts these annual sale thresholds among OECD

countries in 2014. The Figure shows thresholds vary notably across countries. While some countries levy

VAT on all turnover without any threshold (e.g. Sweden and Turkey), some countries apply relatively

high thresholds around 100,000 euros (e.g. Switzerland and the UK).

2.2 VAT in Finland

Finland, as a member of the EU, applies the general EU VAT legislation. All members of the EU apply

a standard rate of at least 15%. The EU allow member countries to use a maximum of two reduced VAT

rates for speci�c products and services, such as food and pharmaceuticals.

The standard VAT rate in Finland is 24% in 2014 that applies to most of the goods and services sold.

Finland uses two reduced rates: 14% is applied to e.g. food and restaurant services, and 10% is applied

to e.g. books and pharmaceuticals.

VAT registered �rms are obliged to regularly �le periodic tax returns to the Finnish Tax Adminis-

tration. The �ling and reporting obligation covers all VAT on sales at di�erent rates, input purchases,

zero-rated sales, imports and exports. The frequency of the required reports depends on the annual

sales of a �rm: Firms with annual sales below 25,000 euros are allowed to report annually, �rms with

3

0 20,000 40,000 60,000 80,000 100000Sales (in euros)

United KingdomSwitzerland

FranceIrelandJapan

AustraliaSlovenia

Slovak RepublicNew Zealand

Czech RepublicPoland

ItalyAustria

AverageLuxembourg

CanadaKorea

HungaryGermany

IsraelEstonia

PortugalFinland

DenmarkIcelandNorwayBelgiumGreece

NetherlandsTurkey

SwedenSpain

MexicoChile

Source: OECD Statistics

VAT thresholds in OECD countries in 2014 (in euros)

Figure 1: Annual sale thresholds for VAT registration among OECD countries in 2014 (in euros)

turnover 25,000-50,000 euros must report quarterly, and �rms with sales above 50,000 euros have to

report monthly.

Some sectors and industries are exempt from VAT or have other special rules for paying VAT. These

include �nancial and insurance activities, letting and operation of dwellings, education, human health

and social work activities. A �rm that sells solely these goods or services are not liable to pay VAT.

VAT threshold before 2004. In Finland, the VAT threshold for �rms is 8,500 euros of annual sales.

Below this threshold �rms are exempt from VAT. The threshold has remained constant from 1995, even

in nominal terms. Albeit small businesses below the threshold are exempted from VAT, they need to pay

other taxes and report their income to the Tax Administration. Firms that exceeded the threshold paid

VAT for sales, including sales below the threshold. Thus the average tax rate jumps at the threshold

before 2004.

Firms that do not exceed the thresholds can voluntarily register and pay VAT. There are logical

reasons for registering even when it is not necessary. A �rm can only deduct VAT from purchases and

costs if registered. For example, voluntary registration could be important for businesses that have large

start-up costs. Also, �rms below the threshold that have a large share of business-to-business sales have

an incentive to register, as the VAT rebate is only possible from purchases of VAT registered �rms.

VAT relief scheme from 2004 onwards. The VAT rate at the threshold changed in 2004 although

the threshold itself remained at 8,500 euros. The reform introduced a VAT relief scheme for annual sales

below 20,000 euros in 2004 and 22,500 euros 2005 onwards. After the reform, �rms can apply for a VAT

relief that gradually decreases (above 8,500 euros) with the increase in sales.

Figure 2 shows the VAT remittance (above) in euros and average tax rates (below) for di�erent levels

of total annual sales of �rms for di�erent years. The data is split to 100 euro turnover bins in the Figure.

The Figure shows the introduction of the relief region in 2004 and post-2005 in comparison to pre-2003

4

period for a representative �rm that is subject to the standard VAT rate.

The Figure clearly depicts that the pre-reform system created a salient VAT notch, implying a jump

in remitted VAT and the average VAT rate from 0 to 22% at the threshold (standard VAT was 22% rate

in 2003-2009 in Finland). After the reform the notch was replaced by a VAT kink, implying a gradually

increasing remitted VAT and average VAT rates above the threshold. Within the relief scheme, gradually

increasing average VAT rate implies an increasing marginal VAT rate up to the point in which the average

VAT rate equals 22%. This leads to marginal VAT rates between 13-57% within the relief region, which

was 8,500-20,000 euros in 2004 and 8,500-22,500 euros from 2005 onwards.

An additional important detail of the VAT relief is that it is not automatically granted by the Tax

Administration. Firms needed to apply for the VAT relief using a separate tax form before 2010. From

2010 onwards, �rms can apply for the VAT relief with the same periodic tax form they use to declare

VAT. This can have important implications for the salience of the VAT relief.

030

0060

00R

emitt

ed V

AT

0 5000 10000 15000 20000 25000 30000

Remitted VAT

05

1015

2025

30A

vera

ge ta

x ra

te (

%)

0 5000 10000 15000 20000 25000 30000Annual turnover

Average tax rates

Remitted VAT and average tax rates before and after the reform

VAT pre−2003 VAT 2004VAT post−2005

Figure 2: VAT remittance and average VAT rates for di�erent levels of sales before and after the intro-duction of VAT relief region

3 Methodology

3.1 Bunching at the VAT threshold

We use the bunching methodology introduced in Saez (2010) to analyze responses to the VAT threshold.

The intuition behind the bunching approach is that if a discontinuous change in tax liability at the

threshold a�ects the behavior of �rms, we should �nd an excess mass of �rms located at the threshold.

Consider a �rm which is owned and managed by a single entrepreneur1 that maximizes the following

function

1As the VAT threshold in Finland is low, most �rms around it are managed and owned by a single owner. Therefore,it is reasonable to assume that individual owners make the relevant decisions on s. However, for conceptual simplicity,throughout the paper we denote that �rms respond to the VAT threshold, not individual owners.

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π = (s− d(s))(1− τp)− c(s)− τvatd(s)− [T (s)vat − τvatd(s) + δ(s)] · 1(s > s∗) (1)

where s denotes annual sales, and d(s) is a concave funtion of tax-deductible costs needed to generate s.

We assume that the marginal unit of sales produces positive net income for the �rm, which implies that

d′(s) ≤ 1. Net income from the �rm (s − d(s)) is taxed at a �at income tax rate τp. c(s) is a convex

function of the cost of e�ort of the owner, which is not tax-deductable. τvatd(s) is VAT paid on d(s),

where τvat denotes the �at VAT rate. T (s)vat denotes the convex VAT function, and δ(s) represents

compliance costs related to VAT reporting.

The �rm is not liable to report and pay VAT below a sales threshold s∗. Therefore, [T (s)vat − τvatd(s) + δ(s)] =

0 if s ≤ s∗, and thus exceeding s∗ creates a jump in both remitted VAT and compliance costs. However,

above the threshold the �rm can deduct the VAT on tax-deductible costs from remitted VAT. Below s∗

VAT paid on purchases is not tax-deductable.

Let us consider �rm decision making within a small sales interval (s∗−ε,s∗) below the VAT threshold.

Maximizing π with respect to s below the threshold implies that c′(s) = (1− d′(s))(1− τp)− τvatd′(s).At s∗, maximization yields c′(s) = (1 − d′(s))(1 − τp) − T ′(s)vat − δ′(s). Let us assume that d(s) is

approximately linear in sales within (s∗ − ε,s∗), which implies that d′(s) is approximately equal within

this region. Firms have incentives not to exceed the threshold because the marginal cost of additional

sales is larger at the threshold than just below it, τvatd′(s) ≤ T ′(s)vat + δ′(s). Intuitively, an additional

unit of sales is less valuable at the VAT threshold because the �rm needs to pay both VAT and compliance

costs if s∗ is exceeded.

First, we study the e�ect of the change in the VAT rate at the threshold. For now we ignore compliance

costs, which we will study in Section 3.3. In Finland, there has been two kinds of changes in the VAT

rate at the threshold: a VAT notch and a VAT kink. To start with the VAT notch, consider a VAT

schedule where sales are not taxed until the notch point s∗. If sales exceed s∗, the VAT rate will be

applied to all sales. Thus the VAT liability jumps discretely at s∗, as the �rm needs to pay VAT also for

the sales below s∗ if the threshold is exceeded. More formally, the VAT function in equation (1) in the

notch schedule is of the form Tvat = s ∗ τvat · 1(s > s∗), where τvat is the �at VAT rate.

Bunching behavior at the VAT notch is illustrated in the upper graph of Figure 3. The vertical

axis denotes the net-of-tax sales, and horizontal axis denotes sales before taxes. The straight blue lines

illustrate the tax rates, and curvy red lines the indi�erence curves of di�erent �rms (type A and type

B). 4τvat represents the VAT paid from sales below the threshold once the VAT threshold is exceeded.

A fraction of �rms originally above s∗ will locate themselves at the VAT threshold after the introduc-

tion of a discontinuous jump in VAT liability. The extent of this bunching behavior depends on the sales

elasticity with respect to VAT rate, which we will come back to in more detail below. Firms originally

at s∗ or below the threshold will not change their behavior after the introduction of the notch (type

A �rm). In the graph, s∗ + 4s denotes the hypothetical �rm with the highest sales to bunch at the

threshold (type B �rm). In other words, s∗ +4s marks the last �rm bunching at the notch, which we

call the marginal buncher. More formally, the fraction of �rms located at s∗ in response to the notch

is denoted as B(∆s) =´ s∗+∆s

s∗h0(s)ds, where h0(s) denotes the counterfactual density of sales in the

absence of the notch.

6

Net-of-tax sales

sales

Indifference curves

s* s*+∆s

Slope (1-τp- τvat)

Slope (1-τp)

Type A

Type B

sB

∆ τvat

Net-of-tax sales

saless* s*+∆s

Type A

Indifference curves

Type B

Slope (1-τp- τvat)

Slope (1-τp)

Figure 3: Bunching at a VAT notch (upper graph) and a VAT kink (lower graph)

Bunching at the VAT kink is illustrated on the lower graph side of Figure 3. In the VAT kink system,

only sales exceeding s∗ are taxed at the VAT rate, and the VAT function in equation (1) is of the form

Tvat = (s − s∗) ∗ τvat · 1(s > s∗). Type A �rm which is located at s∗ before the introduction of the

VAT kink will not respond to the kink, whereas a fraction of �rms above s∗ will locate themselves at the

VAT kink. As with notches, type B �rm in the graph represents the marginal �rm with the largest sales

(s∗ +4s) to bunch at s∗ after the introduction of the VAT kink.

Intuitively, the main di�erence between VAT notch and VAT kink is the size of the change in tax

7

incentives at the threshold. Compared to VAT notch, a VAT kink produces notably smaller incentives to

respond. Therefore, it is presumable that less �rms will bunch at the VAT kink than at the VAT notch.

Figure 4 describes bunching in the sales distribution. In the Figure, the horizontal axis denotes

the number of �rms and vertical axis denotes sales levels. The solid blue line represents observed sales

distribution, and the dotted red line the counterfactual density in the absence of the threshold. The

excess mass caused by the threshold is presented as a spike in the distribution at s∗. The excess mass at

s∗ comes from the missing above the threshold. The missing mass above the threshold is denoted as the

area between the counterfactual distribution and the obseved distribution within the region (s∗, s∗+4s).Assuming smooth and heterogenous sales elasticities across di�erent �rms, the observed density gradually

approaches the counterfactual density above s∗. Thus s∗ +4s represents the �rm with the largest sales

to bunch at the threshold. Intuitively, the larger the excess mass at the threshold is the further away

from s∗comes the last �rm to bunch at the VAT threshold. We discuss the empirical estimation in more

detail below.

Number of firms

Saless* s*+∆s

Excess mass

Missing mass

Observed distribution

Counterfactual

Figure 4: Bunching at the VAT threshold

Abstracting from compliance costs, there are also circumstances in which a �rm has no tax incentive

to bunch at the VAT threshold. The main instance is substantial VAT paid on purchases stemming

from, for example, large start-up costs. In other words, for some �rms it could be that T (s) < τvatd(s)

above the VAT threshold, and thus (marginally) exceeding the threshold does not increase tax liability.

Second, it could be that d′(s) > 1 for some small businesses in the short run, which might not induce

incentives to bunch at the threshold as τvatd′(s) > T ′(s). However, small businesses in our data are on

average pro�table and have notably larger level of sales compared to overall expenses, which indicates

that incentives to bunch at the VAT threshold exist for a large proportion of small �rms in Finland.

3.2 VAT rate elasticities based on observed bunching

We approximate the sales elasticity at the VAT threshold using a similar approach as Kleven and Waseem

(2013). We characterize the elasticity at VAT notch and VAT kink by relating the earnings response of a

marginal buncher �rm (s∗+ ∆s) to the change in tax liability caused by exceeding the threshold by ∆s.

8

This upper-bound reduced-form approximation of the sales elasticity o�ers a conveivable way to scale

the extent of the behvioral response to the threshold with the change in the VAT rate under di�erent

VAT rate schedules.

Elasticity at the VAT notch is calculated with the following quadratic formula

eN ≈ (4s/s∗)2/4tN (2)

where 4tN = (4s+ s∗)τvat/4s de�nes the relative increase in VAT payments caused by exceeding the

threshold by ∆s. Importantly, when exceeding the VAT notch, the �rm needs to pay VAT also for sales

below s∗.

Sales elasticity associated with VAT kink can be written as

eK ≈ (4s/s∗)2/4tK (3)

where 4tK = (4s)τvat/4s = τvat. Compared to the VAT notch, the �rm needs to pay VAT only for

sales above s∗ within the VAT kink system, and thus the denominator of equation (3) reduces to the �at

VAT rate.

Equations (2) and (3) imply that the change in the implicit marginal tax rate (4tN , 4tK) is largerat VAT notch compared to VAT kink. This is creates larger incentives to bunch at the VAT notch.

Therefore, assuming similar underlying (structural) elasticity regardless of the VAT system, we should

�nd larger excess bunching at the VAT notch compared to the VAT kink.

3.3 Compliance costs of VAT reporting

[To be added here later]

3.4 Empirical analysis

The excess mass of �rms at the VAT threshold is estimated by comparing the actual density function

around the threshold to an estimated smooth counterfactual density. The counterfactual density function

describes what the distribution of sales would have looked like without changes in tax liability at s∗.

We follow the methods in Chetty et al. (2011) and Kleven and Waseem (2013) to estimate the

counterfactual density. Intuitively, the counterfactual density is estimated by �tting a �exible polynomial

function to the observed distribution, excluding an area around s∗ from the observed distribution. First,

we re-center income in terms of s∗, and group �rms into small sales bins of 100¿. We estimate a

counterfactual density by regressing the following equation and excluding the region around the threshold

[sL, sH ] from the regression

cj =

p∑i=0

βi(sj)i +

sH∑i=sL

ηi · 1(sj = i) + εj (4)

In equation (4), cj is the count of �rms in bin j, and sj denotes the sales level in bin j. The order

of the polynomial is denoted by p. Thus the �tted values for the counterfactual density are given by

cj =∑p

i=0 βi(sj)i.

The excess bunching is estimated by relating the actual number of �rms close to the threshold within

(sL, s∗) to the estimated counterfactual density within the same region. We calculate excess bunching as

b(s∗) =

∑s∗

i=sL(cj − cj)∑s∗

i=sLcj/Nj

(5)

where Nj is the number of bins within [sL, s∗].

9

One important question when estimating the counterfactual density is how to determine the excluded

the region [sL, sH ]. As in earlier literature, we determine the lower limit sL based on visual observations

of the sales distribution. Intuitively, sL represents the point in the sales distribution where the bunching

behavior begins.

We follow the approach of Kleven and Waseem (2013) to de�ne the upper limit. We determine sH

such that the estimated excess mass bE(s∗) = (∑s∗

i=sLcj − cj) equals the estimated missing mass above

the threshold, bM (s∗) = (∑sH

s>s∗ cj − cj). Theoretically, this condition de�nes that �rms who bunch at

the threshold come from the region directly above it. We apply this convergence condition by starting

from a small value of sH and increasing it gradually until bE(s∗) ≈ bM (s∗). This de�nition for sH denotes

the upper bound of the excluded range, and thus the lower bound for estimated excess bunching (Kleven

and Waseem 2013).2 In addition, the convergence condition implies that we can intuitively de�ne the

sales response 4s of the marginal buncher �rm using the estimated excess mass and the upper limit sH .

This enables us to approximate sales elasticities with respect to VAT rate for both the VAT kink and

the VAT notch by relating the marginal sales response to the implied change in the tax rate.

Following Chetty et al. (2011) and Kleven and Waseem (2013), the standard errors for all the

estimates are calculated using a residual-based bootstrap procedure. We generate a large number of sales

distributions by randomly resampling the residuals from equation (4) with replacement, and generate a

large number of new estimates of the counterfactual density based on the resampled distributions. The

bootstrap procedure takes into account the iterative process to determine sH . Based on the bootstrapped

counterfactual densities, we evaluate variation in the estimates of interest. The standard errors for each

estimate are de�ned as the standard deviation in the distribution of the estimate.

4 Data and descriptive statistics

Our data are from the Finnish Tax Administration and contain all businesses that operate in Finland,

including �rms that are registered to pay VAT and �rms that are not included in the VAT register.

The data include all information needed for tax purposes, such as sales, number of employees, taxable

pro�ts, total assets and organizational form. Importantly, data include accurate information on total

sales also for �rms below the VAT threshold. This enables us to analyze the e�ects of the VAT threshold

on the distribution of sales. In addition, we can link owner-level variables to the �rm-level data, such as

personal taxable wage and capital income of the main owner of the �rm.

Figure 5 shows the distribution of sales for all businesses with annual sales between 1,500-20,000

euros in 2000-2011. The Figure shows a clear excess mass at the VAT threshold of 8,500 euros (marked

with a vertical line in the Figure). This provides strong visual evidence that �rms have responded to the

threshold. The distribution seem to be otherwise rather smooth, with the exception of �round-number

bunching�, which can be seen as spikes in the distribution at convenient round numbers such as 5,000,

10,000, and 15,000 euros. Nevertheless, bunching is much more evident at the VAT threshold compared

to any of the round numbers, implying apparent behavioral responses to the threshold.

2Kleven and Waseem (2013) apply this convergence condition to estimate the counterfactual density for an individualincome tax notch in Pakistan. For individual tax rate kink points in Denmark, Chetty et al. (2011) determine the upperlimit visually, and then iteratively adjust the counterfactual density above the kink point such that it includes the excessmass at the kink. This makes the estimated counterfactual density equal to the observed density. These procedures areintuitively similar, but the convergence method of Kleven and Waseem (2013) typically provides a smaller estimate forexcess bunching. In addition, the convergence method provides a more justi�ed approach to de�ne the upper limit of theexcluded region.

10

2000

4000

6000

8000

Fre

quen

cy

2500 5000 7500 10000 12500 15000 17500 20000Sales

Note: Bin width=100 euro

Annual sales, all firms 2000−2011

Figure 5: Annual sales of all �rms, 2000-2011

In the following analysis, we restrict our sample by excluding those �rms for which the VAT rules

or the VAT threshold are not binding. Thus all �rms in sectors that produce �nancial and insurance

activities, letting and operation of dwellings, education, human health and social work activities are not

included in our sample. In addition, we restrict the sample to include only �rms with annual sales below

20,000 euros. Also, we exlude �rms that are taxed by assessment of the Finnish Tax Administration,

as tax record information based on assessment does not provide evidence of behavioral choices of �rms.

The most common reason for assessed taxation is that a �rm has not declared its tax forms in time.

Table 1 o�ers the descriptive statistics of the data. The upper panel of the Table shows �rm-level

statistics, and lower panel presents owner-level variables. From �rm-level statistics we can see that most

of the �rms in our sample of small businesses do not have any employees, and have low taxable pro�ts,

expenses and assets.

The lower panel of the Table shows that sole proprietor is the most common organizational form

among small �rms. The average total income of the main owner (the sum of all wage and capital

income) is relatively low, less than 11,000 euros. However, it seems that many of the owners seem to

ful�ll our de�nition of a full-time entrepreneur, as over 60% of all main owners have more annual turnover

in their �rm than they have total personal income.

11

Firm-level statistics

Sales Expenses* No. of empl. Pro�ts Assets

Mean 8,942 3,633 .195 1,705 12,600

Median 7,962 2,071 0 758 1,492

SD 5,355 14,531 1.27 9,448 75,374

N 588,505 341,754 481,407 587,677 487,047

Min 1,500 17 0 -81,852 -141,825

Max 20,000 3,716,961 90 580,561 3,111,189

Owner-level statistics

Sole proprietors Corporations Partnerships Total inc. 'Full-time'

Mean .688 .224 .088 11,821 .633

Median 1 0 0 4,390 1

SD .463 .417 .283 18,005 .482

N 588,505 588,505 588,505 586,710 588,505

Min 0 0 0 0 0

Max 1 1 1 177,759 1

Sample: Sales between 1,500-20,000 euros per year. Pooled data from 2000-2011. *Infor-

mation only from 2002 onwards.

Table 1: Descriptive statistics

5 Results

5.1 Baseline results

Figure 6 shows the sales distribution around the VAT threshold for all �rms in our estimation sample

using pooled data from 2000-2011. The �gure plots the observed sales distribution (solid line) and

counterfactual distribution (dashed line) relative to the threshold point in bins of 100¿ in the range

of +/- 7,000¿ from the threshold. The threshold is marked with a dashed vertical line. The excluded

region [sL, sH ] in the estimation of the counterfactual is marked with solid vertical lines.3 The Figure

denotes the estimate for the excess mass at the threshold with bootstrapped standard errors, and the

estimate for the upper limit of the excluded region, sH , which is determined by the iterative process

explained above. The upper limit also denotes the sales response of the marginal bunching �rm, ∆s.

Excess bunching is measured by relating the number of �rms in the observed sales distribution to the

counterfactual density within the region [sL, 0].

3The counterfactual density is estimated using a 7th-order polynomial function. Our results are not sensitive to thechoice of the order of the polynomial.

12

2000

3000

4000

5000

6000

7000

Fre

quen

cy

−70 −60 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70Distance from the threshold

Observed Counterfactual

Excess bunching: 3.195 (.179) Upper limit: 27 (2.44)

VAT threshold, all firms 2000−2011

Figure 6: Bunching at the VAT threshold, 2000-2011

Figure 6 illustrates that excess bunching is striking. A signi�cant proportion of small �rms locate

themselves just below the VAT eligibility threshold. The estimate for excess bunching is notable and

strongly signi�cant statistically. These imply that the VAT threshold clearly a�ects reported sales of

small businesses. We study how excess bunching evolves over time in Section 5.2.

In Table 2 we describe which types of �rms bunch at the VAT threshold. Column (1) of Table

2 shows the results from an OLS regression where we regress the dummy variable of locating in the

bunching region 7,600-8,500e with �rm and owner-level characteristics. We also show the results for the

regressions on belonging to sales region below 6,600-7,500e in column (2) and above 8,600-9,500e the

bunching region in column (3). These estimations provide benchmark information on the characteristics

of small businesses close to the VAT threshold. Thus by comparing estimates in column (1) with columns

(2) and (3) illustrate which chracteristics correlate with bunching behavior.

13

(1) (2) (3)

Buncher: sales 7,600-8,500e Placebo 1: 6,600-7,500e Placebo 2: 8,600-9,500e

Bunches t− 1 0.192*** 0.108*** 0.075***

Bunches t− 2 0.093*** 0.058*** 0.045***

(t-1)*(t-2) 0.272*** 0.115*** 0.061***

Ref: corporation

Partnership -0.001 0.001 -0.003**

Sole proprietor 0.006*** -0.003*** -0.006***

'Full-time' -0.000 -0.012*** -0.001**

Industry ref: Construction

Hotels and restaurants 0.006*** 0.002 0.001

Professional activities 0.008*** 0.003** -0.003***

Admin. activities 0.011*** 0.005** -0.001

Arts 0.017*** 0.000 -0.002**

N 546,277 546,277 546,277

R2 0.081 0.015 0.006Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. In column (1), buncher is 1 if sales is between 7,600 and 8,500

euros, 0 otherwise. In column (2), placebo 1 is 1 if sales is between 6,600 and 7,500 euros, 0 otherwise. In column (3), placebo 2 is 1 if

sales is between 8,600 and 9,500 euros, 0 otherwise. Estimation sample: Data from 2000-2011. Annual sales between 1,500 and 20,000

euros. Year dummies included.

Table 2: OLS regression results on locating below the VAT threshold, 2000-2011

First, we can see that past behavior signi�cantly explains bunching. Coe�cients for bunching in the

two previous periods (bunches t − 1 and bunches t − 2) and the interaction of the two (t − 1 ∗ t − 2)

are positive and highly signi�cant statisticallt. Compared to regions below or above the threshold, it is

notably more likely to bunch again below the VAT threshold if the �rm has previously located within

the bunching window. Thus it appears that bunching behavior is relatively permanent, which suggests

that the VAT threshold hinders the growth of small businesses. We study this issue in more detail in

Section 5.3.

The results indicate that sole proprietors bunch more actively than partnerships or corporations,

but the economic signi�cance of the organizational form seems to be small. Our measure for full-time

entrepreneur (turnover of the �rm greater than personal taxable income) is not signi�cant, which suggests

that all types of entrepreneurs bunch at the threshold. However, on both sides of the bunching window,

it is more likely that owners of the �rms have personal taxable income above the turnover of the �rm.

Firms who bunch at the threshold do not come from any particular industries. However, bunching is

somewhat more likely among �rms at the hotel and restaurant and arts industries.

5.2 Response to the notch vs. kink

Tax incentives at the VAT threshold changed in Finland in 2004. A VAT relief system was introduced in

2004, which implied a change from a VAT notch to a VAT kink, as explained in more detail in Section

2. This reform allows us to characterize the e�ect of the size of tax incentives at the threshold on �rm

behavior. Intuitively, if the change in VAT liability and remitted VAT at the threshold matter, we should

see notably less �rms bunching at VAT kink compared to VAT notch.

Figure 7 shows the sales distributions for all �rms around the VAT notch (upper graph) and VAT kink

(lower graph) in 2000-2003 and 2004-2011, respectively. The Figure clearly shows that excess bunching

at the threshold is highly signi�cant and very similar in size both at the VAT notch and VAT kink. This

evidence implies that the size of tax incentives is not driving the extent bunching behavior.

Second, local elasticity estimate at the threshold is almost �ve times smaller within the VAT notch

system compared to the VAT kink. This is a reasonable result because the extent of the behavioral

response remained similar after the reform, but the relative change in the implied marginal VAT rate de-

creased notably. We have no reason to assume that the underlying tax responsiveness of small businesses

14

changed abruptly at the same time as the VAT relief was introduced. Therefore, the sudden increase

in sales elasticity strongly suggests that also other issues than a pure change in VAT rate explains why

�rms actively avoid exceeding the VAT threshold.

500

1000

1500

2000

2500

Fre

quen

cy

−70 −60 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70Distance from the notch


Excess bunching: 3.449 (.148), Elasticity: .1 (.012) Upper limit: 26 (2.072)

VAT notch, all firms 2000−200310

0020

0030

0040

0050

00F

requ

ency

−70 −60 −50 −40 −30 −20 −10 0 10 20 30 40 50 60 70Distance from the kink


Excess bunching: 3.029 (.169), Elasticity: .493 (.052)Upper limit: 28 (2.328)

VAT kink, all firms 2004−2011

Figure 7: Bunching at the VAT notch and VAT kink

To further illustrate potential changes in �rm behavior over time, Figure 8 presents excess bunching

estimates (upper graph) and local elasticity estimates (lower graph) in di�erent years. The Figure shows

15

that excess bunching at the VAT threshold has remained approximately similar in 2000-2009. There is

no distinctive change in behavior in 2004 when the VAT kink was introduced. However, in 2010-2011,

excess bunching seem to have decreased slightly. This is potentially due to an increase in the salience of

the VAT relief system, which we will discuss in more detail below.

As a consequence of signi�cant and similar bunching responses under both schedules, the lower graph

in Figure 8 shows that local elasticity at the threshold jumps after the introduction of the VAT kink

system. This indicates that the VAT rate elasticity at the threshold is unlikely to be an informative

measure of actual tax rate responsiveness of small businesses, as other issues besides the discontinuous

increase in VAT rate are likely to explain the bunching behavior.

01

23

45

Exc

ess

bunc

hing

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Excess bunching

0.2

.4.6

.8E

last

icity

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011Years

Elasticity

Excess bunching and elasticity at the threshold over time

Estimate CI

Figure 8: Excess bunching and elasticity at VAT threshold over time

Negligible changes in behavior after the change in the VAT rate at the threshold point out that other

issues than the VAT rate a�ect the behavior of small businesses. One plausible explanation is various

types of compliance costs. In addition to the �tax penalty�, �rms face other costs if the VAT threshold is

exceeded. These include for example reporting and accounting costs and costs related to understanding

the details of VAT rules and regulations. In addition, more extensive reporting of sales and purchases

to the Tax Administration could make tax avoidance and evasion more di�cult, as �rms need to report

both sales and purchases to Tax Administration in more detail.

In general, it is challenging to analyze the role of compliance costs in observed behavior of �rms.

These costs are typically unobserved by the researcher, and without experimental variation it is di�cult

to identify any di�erences in compliance costs between di�erent �rms using tractable assumptions. Nev-

ertheless, the details of the Finnish VAT system allow us to characterize the e�ect of compliance costs

of VAT reporting among small businesses.

First, the Finnish system induces variation in the frequency of VAT reporting. Once a �rm exceeds

the threshold, the required frequency of VAT reports depends on the amount of annual sales. From 2010

onwards, �rms with sales between 8,500-25,000 euros report VAT annually, �rms with 25,000-50,000 of

sales report quarterly, and �rms with sales above 50,000 euros report monthly.

16

If costs related to each VAT report are important, we should �nd �rms bunching below the 25,000

and 50,000 euro thresholds. However, Figure 9 does not support this hypothesis. There is no excess

mass of �rms below these sales thresholds. The spike exactly at 25,000 euros is likely a round-number

e�ect, which is also detectable at other convenient round numbers such as 30,000 and 40,000 euros.

Nevertheless, reporting frequency thresholds only describe reporting costs at the intensive margin when

the VAT eligibility threshold is already exceeded. Costs at the extensive margin of VAT reporting might

still be notable and explain the bunching behavior at the eligibility threshold of 8,500 euros. However,

Figure 9 highlights that pure costs of frequent VAT reports are not outstanding.

200

250

300

350

400

450

Fre

quen

cy

20000 30000 40000 50000 60000Sales

Note: Bin width=100 euro

Annual sales, all firms 2010−2011

Figure 9: Annual sales of �rms and VAT reporting thresholds: 25,000e (quarterly) and 50,000e (annualreporting)

Another important issue that might a�ect excess bunching after 2004 is the transparency of the VAT

relief scheme. First, all �rms might not be aware of the existence of the VAT relief. Second, the relief

was not automatic and �rms needed to declare the eligibility to the relief by �ling a separate form.

Because of potential unawareness of the change in tax incentives at the threshold, it is possible that

excess bunching underestimates the e�ect of the change in tax incentives when comparing the VAT

notch and the VAT kink. Some fraction of �rms might bunch below the threshold after 2004 because

they do not know or fully understand the VAT relief system. We do not observe whether �rms below

the threshold are aware of the relief system or not. However, we do observe whether a �rm has �led the

separate �rm to apply for the relief. Thus we can describe the general knowledge of the relief system by

studying how many �rms above the threshold apply for the relief.

Figure 10 describes the �take-up rates� of the relief in 2004, 2007 and 2011. The vertical axis denotes

the share of �rms that applied for the VAT relief. Dashed vertical line at 20,000 euros denote the end of

the relief region in 2004 and the 22,500 line in 2007 and 2011. .

Figure 10 o�ers the following insights: First, the approximated take-up rate is only between 30-60%

just above the threshold. This implies that a notable fraction of �rms are not aware of the relief or do

not apply for it for one reason or another. Potential unawareness might increase bunching at the VAT

17

threshold if a notable fraction of �rms below the threshold are unaware of the relief. However, the share

of �rms that applied for the relief in Figure 10 probably underestimates the actual take-up rate. This is

because we cannot fully observe the actual eligibility for the relief among all �rms, but we assume that

all �rms not applying for the relief would be eligible

Second, the share of �rms that applied for the relief decreases along with sales. This is reasonable as

the monetary bene�t from the relief also decreases at larger sales levels. Naturally, the take-up rate is

(close to) zero above the relief region.

Third, there is a signi�cant increase in the take-up rate in 2011. From 2010 onwards, �rms could

apply for the relief with the same form they use to declare VAT liability. This seems to have increased

the share of �rms that applied for the relief. Importantly, the increase in take-up is re�ected in the

excess bunching estimate. Figure 8 above shows that excess bunching moderately decreased in 2010-

2011 compared to previous years. This supports the view that the non-transparency of the relief system

a�ects observed �rm behavior at the threshold, at least to some extent.

0.2

.4.6

Sha

re o

f firm

s

8500 10500 12500 14500 16500 18500 20500 22500 24500Sales

2004 20072011

Bin width = 200 euros

Share of firms that applied VAT relief: 2004, 2007 and 2011

Figure 10: Share of �rms that applied for the VAT relief in 2004, 2007 and 2011

To summarize, excess bunching is signi�cant and very similar both within the VAT notch and the VAT

kink systems. This implies that compliance costs related to the extensive margin of VAT reporting are

important in explaining why �rms actively stay below the VAT threshold. However, the negligible e�ect

of pure tax incentives is probably underestimated because of relatively low awareness and transparency

of the kink system. Nevertheless, it is unlikely that low salience fully explains the permanent bunching

e�ect over time. Observed excess bunching at the VAT kink would need to be approximately three times

smaller in order for the local VAT rate elasticities to be equal at the VAT notch and VAT kink.

5.3 Anatomy of the response and growth e�ects

Irrespective of whether �rms stay below the VAT threshold because of tax incentives or compliance costs,

it is crucial to know how �rms adjust their behavior. In terms of policy implications, it is relevant to

know whether �rms respond by decreasing real economic activity, or by engaging in active tax avoidance

18

or evasion measures. Responses along all behavioral margins a�ect tax revenue. However, changes in

real economic activity can be considered more detrimental in terms of welfare, whereas changes through

tax avoidance and evasion might not a�ect the real allocation of resources with a similar magnitude (see

for example Slemrod 1992). Furthermore, it could be easier for the government to a�ect evasion and

avoidance responses by more e�ectively monitoring small businesses. In contrast, it is more di�cult to

in�uence changes in the real economic activity of �rms.

In order to illustrate the anatomy of the response, we study how the production factors reported

to the Tax Administration, such as equity and total expenses, evolve around the VAT threshold. Our

identi�cation assumption is that in the absence of sales-based regulation, production factors should

develop smoothly as the sales of the �rm increase. Therefore any discontinuous changes in production

factors exactly at the VAT threshold would indicate changes in �rm behavior caused by this regulation

(see Almunia and Lopez-Rodriguez 2014). The existence of discontinuous jumps shed light on the nature

of the response.

Figure 11 shows the development of �rm-level factors around the VAT threshold using pooled data

in 2000-2011. In the Figure, we plot a local polynomial function with standard errors using a bandwidth

of 100 euros to illustrate any changes in production factors at the threshold. Similarly as before, sales

are centered around the VAT threshold (+/- 5,000 euros).

The upper two graphs show that �rm-level equity and wages paid increase smoothly as the sales of

the �rm increase. There are no jumps in these variables at the VAT threshold. This implies that �rms

around both sides of the threshold are equal in size, and suggests that �rms do not locate themselves

below the threshold by active tax avoidance or evasion. For example, if larger �rms would underreport

sales in order to bunch at the threshold, we would observe larger average equity levels just below the

threshold.

In contrast, the lower-left graph in Figure 11 shows that expenses jump at the threshold in a signi�cant

manner. This indicates that �rms right below the VAT threshold use less expenses to achieve similar

level of sales. Also, reporting more expenses above the threshold is more pro�table, as the �rm can

deduct the VAT from purchases within the VAT system. However, this evidence does not point to active

avoidance or evasion decisions below the threshold. If �rms would systematically underreport sales in

order to locate themselves below the threshold, we should �nd that reported expenses are larger below

the threshold, not above it.

A jump in expenses suggests that �rms below the VAT threshold have higher pro�t margins. This

notion is supported by the lower-right graph in Figure 11, which shows that �rm pro�ts decrease right

above the VAT threshold. This is a reasonable result because �rms below the threshold do not pay

VAT, and thus get more after-tax sales revenue with equal prices as �rms just above the threshold. This

suggests that VAT threshold distorts competition between small businesses, as �rms below the VAT

threshold are more pro�table.

Nevertheless, larger pro�t levels below the VAT threshold again imply that real economic decisions are

the probable source of responses. If �rms would systematically underreport income, we should observe

smaller pro�ts below the threshold. Firms that declare larger pro�ts below the threshold also pay larger

income taxes, which is not in accordance with general avoidance or evasion behavior.

In summary, the evidence in Figure 11 suggests that avoidance and evasion responses do not explain

observed behavior. However, as in other studies that utilize register-based data, we do not observe

intentional misreporting of business activity, such as operating partly in the black market. Therefore,

we cannot o�er fully conclusive evidence of potential evasion e�ects of the VAT threshold.

19

8.5

99.

5Lo

g eq

uity

−5000 −2500 0 2500 5000

Equity

7.27

.47.

67.8

88.

2Lo

g w

ages

−5000 −2500 0 2500 5000

Wages6.

87.

37.

8Lo

g ex

pens

es

−5000 −2500 0 2500 5000Distance from the threshold

Expenses

77.

58

8.5

Log

prof

its


Profits

Bandwidth: 100

Local polynomial with 100 euro bandwidthFirm−level factors around the threshold

95% CI Kernel function

Figure 11: Firm-level factors around the VAT threshold, 2000-2011

An additional avenue to avoid VAT would be to set up multiple �rms and divide sales between them

such that none of the �rms exceed the VAT threshold (see Onji 2009). The left-hand side of Figure

12 shows the average number of �rms per an individual owner (in sales bins of 100 euros around the

VAT threshold). The Figure shows that avoidance via multiple �rms appears not to explain the observed

behavior, as there is no statistically signi�cant jump in the number of �rms below the threshold. Overall,

the average number of �rms per owner is very close to one. This is driven by the fact that an individual

cannot have multiple �rms registered as sole proprietors in the Finnish business tax system. Most of small

businesses in Finland are registered as sole proprietors (69% of �rms with sales between 1,500-20,000 are

registered as sole proprietors).

The right-hand side of Figure 12 presents the number of �rms per owner when excluding sole pro-

prietors. This graph indicates an increase in the number of �rms per owner below the VAT threshold.

This implies that at least some owners utilize multiple �rms as a mean to avoid VAT. Nevertheless, this

does not fully explain the overall bunching result. Figure 14 in the Appendix shows the sales distribu-

tions around the VAT threshold separately for di�erent organizational forms. The Figure shows that

excess bunching is evident among all types of businesses, and not driven solely by partnership �rms or

corporations.

In terms of dynamic e�ciency and economic growth, it is essential to analyze whether the VAT

threshold hinders the growth of small businesses. The threshold could signi�cantly decrease or even

eliminate potential growth rates if �rms avoid exceeding the threshold for a prolonged period of time.

Figure 13 presents the growth rates of sales and �rm-level production factors around the VAT thresh-

old. In the Figure, we calculate one-year logarithmic growth rates (t − (t − 1)) of sales and various

�rm-level inputs conditional on locating in 100 euro sales bins in the base year t − 1. The upper-left

graph in the Figure shows that the growth rate of sales jumps right above the threshold. This implies

that �rms permanently bunch below the threshold, which was also shown in Table 2 above. Also, com-

pared to average growth of �rms above, the VAT threshold appears to signi�cantly decrease the growth

of sales of small businesses.

20

1.01

1.01

51.

021.

025

Num

ber

of fi

rms


All firms

1.03

1.08

1.13


Partnerships and corporations

Bandwidth: 100

Local polynomial with 100 euro bandwidthAverage number of firms per owner around the threshold


Figure 12: Average number of �rms per owner around the VAT threshold, 2000-2011

The three other graphs in the Figure show that there are also discontinuities of growth rates of �rm-

level production factors around the threshold. It seems, for example, that the growth rates of expenses

(upper-right graph) and wages (lower-right graph) are lower just below the threshold that above it.

However, these results are less apparent than that observed in the sales growth. Nevertheless, this

further suggest that the threshold creates obstacles for �rms to grow, and that the behavior is driven by

the changes in real economic activity of �rms.

21

−.0

20

.02

.04

.06

.08

−5000 −2500 0 2500 5000

sales

−.0

50

.05

.1

−5000 −2500 0 2500 5000

expenses0

.02.

04.0

6.08

.1


equity

−.1

0.1

.2.3


wages

Bandwidth: 100 euros

Local polynomial with 100 euro bandwidthGrowth of firm−level productions factors around the threshold


Figure 13: The growth rates of �rms around the VAT threshold, 2000-2011

6 Conclusions

In this paper we study the e�ects of the VAT threshold on the behavior of small businesses. In Finland,

�rms with annual sales below 8,500 euros are not liable to register and pay VAT. We use detailed tax

register data and the bunching method to provide clear and intuitive evidence on the e�ects of the

threshold.

We �nd that the VAT threshold has notable e�ects among small businesses. Bunching below the

threshold is highly signi�cant, which implies that �rms actively avoid exceeding the threshold. This

implies notable e�ciency implications.

We �nd that changing the tax system from a VAT notch to a VAT kink does not signi�cantly decrease

the e�ect. This suggests that compliance costs largely explain observed responses. We �nd no clear traces

of tax avoidance or evasion, which suggests that �rms respond by reducing output. Finally, we �nd that

bunching behavior is relatively permanent, which implies that the threshold decreases the growth of

small businesses.

22

References

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Evidence from Spanish �rms. Working paper.

[2] Chetty, R., Friedman, J., Pistaferri, L. and Olsen, T. (2011). Adjustment costs, �rm responses, and

micro vs. macro labor supply elasticities: Evidence from Danish tax records. Quarterly Journal of

Economics, 126(2): 749�804.

[3] Decker, R., Haltiwanger, J., Jarmin, R. and Miranda, J. (2014). The Role of Entrepreneurship in

US Job Creation and Economic Dynamism. Journal of Economic Perspectives, 28(3).

[4] Haltiwanger, J., Jarmin R. and Miranda, J. (2013). Who Creates Jobs? Small versus Large versus

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[5] Kanbur, R. and Keen, M. (2014). Thresholds, informality, and partitions of compliance. International

Tax and Public Finance, 21(4): 536�559.

[6] Keen, M. and Mintz, J. (2004). The optimal threshold for a value-added tax. Journal of Public

Economics, 88: 559�576.

[7] Kleven, H. and Waseem, M. (2013). Using notches to uncover optimization frictions and structural

elasticities: Theory and evidence from Pakistan. Quarterly Journal of Economics, 128: 669�723.

[8] Li, L. and Lockwood, B. (2014). VAT notches. Seminar presentation at the annual IIPF conference

in Lugano 2014.

[9] Onji, K. (2009). The response of �rms to eligibility thresholds: Evidence from the Japanese value-

added tax. Journal of Public Economics, 93: 766�775.

[10] Saez, E. (2010). Do taxpayers bunch at kink points? American Economic Journal: Economic Policy,

2(3): 180�212.

[11] Slemrod, J. (1992). Do taxes matter? Lessons from the 1980s. American Economic Review Papers

and Proceedings, 82(2): 250�256.

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working paper, work in progress.

23

Appendix

2000

4000

6000

Fre

quen

cy

−60 −40 −20 0 20 40 60


All firms

500

1000

1500

−60 −40 −20 0 20 40 60


Corporations

200

400

600

Fre

quen

cy

−60 −40 −20 0 20 40 60Distance from the threshold


Partnerships

1000

3000

5000

−60 −40 −20 0 20 40 60Distance from the threshold


Sole proprietors

Bin width = 100 euros

Excess bunching at the VAT threshold by organizational form, 2000−2011


Figure 14: Bunching at the VAT threshold for di�erent organizational forms, 2000-2011

24

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