TAX PROGRESSIVITY AND SELF-EMPLOYMENT DYNAMICS
Wiji Arulampalam and Andrea Papini*
Abstract—Analysis of the relationship between taxes and self-employment
should account for the interplay between responses in self-employment and
wage employment. To this end, we estimate a two-state multispell duration
model which accounts for both observed and unobserved heterogeneity
using a large longitudinal administrative data set for Norway for 1993 à
2011. Our findings confirm theoretical predictions and are robust to var-
ious changes to definitions and sample selections. A policy experiment
simulating a flatter tax schedule in the year 2000 is found to encourage
self-employment, delivering a net increase of predicted inflow into self-
employment from 2.8% à 5.3%.
je.
Introduction
MODELS of choices facing wage earners typically ne-
glect the fact that taxpayers may exit or enter self-
employment because of differences in tax schedules. Since
the interplay between the occupational choices is typically
not considered in models of labor supply, these models are
silent on how tax differences across occupational choice af-
fect decisions.1,2 However, in contrast, models of choice
of the self-employed are dominated by perspectives where
decisions are based on implicit or explicit comparisons to
the wage sectors. One obvious reason for this asymmetry
is the relative sizes of the sectors. Par exemple, the self-
employment rate (as a percentage of total employment) dans
Norway is 7%, whereas the European Union average is ap-
proximately 15% (OECD, 2018).
The relationship to the wage sector is not the only fac-
tor that complicates the assessment of the effects of taxation
on self-employment. From a theoretical perspective, the tax
effects are ambiguous. D'une part, an increase in the
tax rate may diminish the self-employment rate as it reduces
expected returns. On the other hand, high taxes may encour-
age self-employment if loss offsetting is allowed because the
government provides an implicit insurance by sharing the
Received for publication June 13, 2018. Revision accepted for publication
Février 17, 2021. Editor: Brigitte C. Madrian.
∗Arulampalam: Department of Economics, University of Warwick, IZA,
Oxford CBT, and OFS Oslo; Papini: European Commission and Joint
Research Centre.
This paper is part of the research of Oslo Fiscal Studies supported by the
Research Council of Norway. We are grateful to Statistics Norway (SSB)
for providing us with access to the confidential administrative data used in
the paper. The paper has benefited from comments from many individuals.
We are very grateful to Thor O. Thoresen for his detailed comments on mul-
tiple drafts of the paper. The paper has benefited from comments received
from Frank Fossen, ˚Asa Hansson, Ben Lockwood, Jean-François Wen, le
participants at the Workshop on Self-Employment/Entrepreneurship and
Public Policy held at the University of Oslo, Septembre 2016, and the par-
ticipants at the Skatteforum held in Hadeland, Juin 2017, the referees, et
the editor of this journal. The views expressed are purely those of the au-
thors and may not in any circumstances be regarded as stating an official
position of the European Commission.
A supplemental appendix is available online at https://est ce que je.org/10.1162/
rest_a_01046.
1See Blundell and MaCurdy (1999), and Keane (2011) for reviews of the
literature on labor supply.
2“Occupational choice” here means a choice between wage employment
and self-employment.
risk associated with self-employment (Domar & Musgrave,
1944).3
A large majority of empirical studies on the effect of taxes
on the level of self-employment activity focuses on the United
États. These studies examine the extensive margin in occu-
pational choice models (see Bruce, 2000, 2002; Gentry &
Hubbard, 2000, 2004; Schuetze, 2000; Schuetze & Bruce,
2004; Cullen & Gordon, 2007; and Moore, 2004).4 Études
for other countries include Hansson (2012) for Sweden; Fos-
sen (2007, 2009) and Fossen and Steiner (2009) for Germany,
and Wen and Gordon (2014) for Canada. Results from these
studies are mixed. Results for the United States, Par exemple,
do not provide an unambiguous answer about the relationship
between tax progressivity and self-employment. Cependant, dans
other countries, tax progressivity is generally found to dis-
courage self-employment.5
The representation of the tax schedule is important in any
analysis of tax effects on self-employment. Some studies in-
clude measures of marginal and/or average taxes in a quasi-
experimental or reduced-form analysis to investigate the ef-
fect of nonlinearities in taxes on entrepreneurship.6 In other
études, authors have used measures of expected net-income
differences and/or tax progressivity to capture the tax ef-
fects. Par exemple, Gentry and Hubbard (2000, 2004) utiliser
the spread in the marginal (or average) tax rates faced by a
self-employed individual at various levels of “success,” where
success is defined as the observed distribution of the three-
year real wage growth for entrants into self-employment.
In two recent studies (Fossen, 2009; Wen & Gordon, 2014),
authors derive the tax variables within a structural framework
3The role of loss offsetting is less clear in the presence of a progressive
tax schedule. If the tax rate is an increasing function of taxable income, le
savings made because of the loss offset are usually lower in magnitude than
the taxes paid on profits (Gentry & Hubbard, 2000).
4See Hansson (2012), Gale and Brown (2013), and Clingingsmith and
Shane (2016) for surveys on taxation and self-employment.
5A positive correlation between taxes and self-employment may also
partly be attributed to the higher tax evasion or avoidance possibilities in
self-employment relative to wage employment (voir, par exemple, Schuetze
& Bruce, 2004). Our data do not allow us to address this issue. Recent
tax evasion estimates for Norway show that around 14% of the business
income is not reported (Nygård et al., 2019). This estimate is lower than
typical estimates for the United States but close to what is found among the
self-employed in Finland (Johansson, 2005) and Denmark (Kleven et al.,
2011). Slemrod (2007) estimates that around 57% of U.S. nonfarm business
income was not reported. The time and individual unobservable effects in-
cluded in our model will partially mitigate this problem if the differential
evasion possibilities are relatively constant over the time period under con-
sideration. Another issue is the possibility of a tax-induced organizational
shift. See Papini (2018) for a recent analysis of this issue. We treat a self-
employed individual who decides to incorporate and, thus, decides to earn
wages from the company, as a wage earner. We also include region fixed
effects to partly control for this issue, as this organizational shift was more
common in some regions and time periods (Papini, 2018).
6Par exemple, Bruce (2002), and Gurley-Calvez and Bruce (2008) utiliser
expected marginal tax rates, ou, alternativement, average tax rates to capture
nonlinearities in the tax schedule. These authors do not include any measure
of riskiness of income received.
The Review of Economics and Statistics, Mars 2023, 105(2): 376–391
© 2021 The President and Fellows of Harvard College and the Massachusetts Institute of Technology. Published under a Creative Commons Attribution 4.0
International (CC PAR 4.0) Licence.
https://doi.org/10.1162/rest_a_01046
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TAX PROGRESSIVITY AND SELF-EMPLOYMENT DYNAMICS
377
where the decision making is based on the difference in ex-
pected utilities. Yet the two papers differ in many aspects
and draw different conclusions. The use of different utility
functions and assumptions regarding the pretax income dis-
tribution of the individual result in different variables that
capture the effects of nonlinearities in the tax schedule. Ils
also use different statistical models (logit versus probit).
Fossen (2009) models the transitions between wage
and self-employment using data from the German Socio-
Economic Panel (GSOEP) over the period 2002 à 2006 et
a logit model in which agents are assumed to trade-off risks
and returns. He uses a constant relative risk-aversion utility
and assumes normally distributed pretax income. The two
relevant model-generated variables are (je) the difference in
net-of-tax incomes in the two occupations and (ii) la variété-
ances of the individual’s posttax income distributions in the
transition equation.
In contrast, Wen and Gordon (2014) use a pooled cross-
sectional sample from the Canadian Survey of Labour and
Income Dynamics over the years 1999 à 2005 to estimate
the probability of self-employment in a probit model.7 They
assume risk neutrality and a log-normal distribution for the
pretax income. The relevant “tax variables” are (je) the differ-
ence in log net-of-tax incomes in the occupations (netincdiff)
et (ii) a variable that they call convexity. The variable con-
vexity has an intuitive interpretation as the “increase in tax-
liability taken on by the self-employed due to the volatility
of their business income, expressed as a proportion of their
disposable income.”
Both studies use selectivity-corrected income equations
to predict individual pretax incomes and then use a tax-
transfer microsimulation model to generate the relevant ex-
pected value and variance of after-tax incomes in wage em-
ployment and self-employment. The estimated models are
subsequently used to simulate the effects of hypothetical tax
policy scenarios that reduced progressivity. Fossen finds the
“flatter-tax” reforms considered discourage individuals from
choosing self-employment;8 Wen and Gordon find a “small”
positive effect on the probability of finding someone in self-
employment.9
Here we use the two variables netincdiff and convexity used
by Wen and Gordon (2014). Although some of the tax effects
in both studies are captured via net-income differences, le
additional variable convexity in Wen and Gordon (2014) est
an individual-specific measure that intuitively captures the
interaction between the progressivity of the tax schedule and
7Ainsi, the focus is on being in self-employment at the time of interview
and not on entering self-employment.
8The interpretation given in Fossen (2009) is that a flatter tax schedule
increases expected returns in self-employment, but at the same time it also
increases the risk because the variance of the net income distribution also
increases. The second effect is found to dominate the first one, and hence,
a flatter tax schedule discourages self-employment.
9The “flatter-tax” reform considered is found to increase the probability
of finding someone in self-employment by 0.04 percentage points from the
base model prediction of 5.76%.
the volatility of self-employment income relative to wage
revenu.
Our work complements the existing empirical literature
in various ways. D'abord, our definitions of wage employment
and self-employment are based on reported incomes from tax
records and not on survey responses. We use data drawn from
various Norwegian population registers over the period 1993
à 2011. The data include rich sociodemographic informa-
tion together with highly accurate income measures from the
annual tax returns. Deuxième, we model the evolution of em-
ployment spells using a two-state multispell duration model
that controls for observed and unobserved heterogeneity cor-
related across spells and accounts for left and right censoring
in the observed spells. This contrasts with several previous
contributions, which mainly focus on self-employment en-
tries or exits using survey data with self-reported employment
status and short panels of individuals.
We generally find significant effects of both netincdiff and
convexity on the probability of exit from both types of em-
ployment spells, conforming to theoretical predictions as dis-
cussed in section VA. The increase in convexity is found to
increase the probability of exiting self-employment and to
decrease the probability of entry into self-employment; que
est, convexity has a discouraging effect on self-employment,
ceteris paribus. On the other hand, an opposite effect is found
for netincdiff: negative (positive) in the self-employment
in our base
(wage-employment) equation. En plus,
model, we find a larger effect of convexity relative to that of
netincdiff, implying that small increases in convexity will
require large increases in netincdiff to discourage the self-
employed from quitting and to encourage wage earners to
enter self-employment.
Given the way the tax variables are constructed, a change
in the progressivity of the tax schedule will have an impact
on the convexity and on the netincdiff by changing the ex-
pected net income difference in self-employment and wage
employment. From this, the total effect on the rate of self-
employment of a decrease in the progressivity of the tax
schedule is hard to predict. Ainsi, to better understand the net
effect, we simulate a tax experiment that replaces the personal
income tax structure in the year 2000 with a less progressive,
revenue-neutral tax schedule, as explained in section VB. Le
overall estimated effect of this policy change is positive on
the share of self-employment. The average exit rate from self-
employment is estimated to go down by 0.018 (s.e. 0.184)
percentage points, and the estimated exit rate from wage-
employment is estimated to increase by 0.119 (s.e. 0.010)
percentage points. This change results in a net increase of
predicted inflow into self-employment changing from about
2.8% à 5.3%.
The rest of the paper is organized as follows. Section II de-
scribes the taxation of self-employment income and wages
during our sample period. Section III sets out our economet-
ric model. In section IV we provide details of the data and
the sample selected for our analyses. We also present the pro-
cedure used for estimating the tax variables. The estimation
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378
THE REVIEW OF ECONOMICS AND STATISTICS
FIGURE 1.—MARGINAL TAX RATE FOR WAGE AND SELF-EMPLOYMENT INCOMES, YEAR 2005
(je) Solid line: Marginal tax rate for a wage earner in tax class 1 (see online appendix A.2 for the definition of tax class 1) with only wage income. Employer’s social security contributions are excluded. (ii) Dashed line:
Marginal tax rate for a self-employed individual in tax class 1 with only self-employed income and no capital invested in the firm.
results are discussed in section V along with the results from
our policy simulation and some sensitivity checks. Enfin,
section VI concludes the paper.
II. Taxation in Norway
Tax reforms undertaken in 1992 introduced a dual-income
tax system in Norway. Under this regime, all types of capital
income are taxed at a flat rate, but a progressive schedule
applies to labor and pension income. Individuals pay income
tax on two different tax bases: (je) ordinary income and (ii)
personal income.
Income from wages, self-employment, capital, transfers,
and pensions are first grouped as ordinary income. After de-
ductions, individuals pay tax at a flat rate (28% during most
of the sample period) on ordinary income.10 The other tax
base—personal income—includes wage income, transfers,
and pension income and self-employment income due to ac-
tive efforts, but not capital income. Individuals pay a sur-
tax and social security contributions levied on the personal
revenu.
As an example, consider a wage earner whose only source
of income is from wages in the year 2005. The solid line
in figure 1 represents the marginal tax rates that apply to the
wage income. No taxes and contributions are paid for income
below the tax-free threshold. This threshold was NOK 29,600
10The deductions include a standard personal allowance, a deduction for
expenses including interest payments, and a basic allowance, which is a
percentage (up to a maximum) of labor or pension income.
dans 2005.11 Above the threshold, a social security contribution
de 25% (of the personal income above NOK 29,600) is due, en haut
to the amount where the total amount is the same as one would
get using the standard rate of 7.8% on all personal income.
Thereafter the rate is 7.8%. The flat tax on ordinary income
(28% dans 2005) is paid on the part of income that exceeds
the sum of the personal allowance and the basic allowance.
The basic allowance is 31% of wage income with a lower
limit of NOK 31,800 and an upper limit of NOK 57,400. Le
personal allowance is a standard deduction from ordinary
revenu, set at NOK 34,200 dans 2005. The last two steps in
figure 1 represent the two surtaxes that raise the marginal tax
rates by 12 percentage points and 15.5 percentage points. Le
maximum marginal tax rate of 51.3% is reached after the two
surtaxes become effective.
Taxation is more complicated for the self-employed be-
cause income represents the reward to the labor of the indi-
vidual as well as the returns to the capital invested in the firm.
Given the lower tax rate on capital income, the decision about
how to declare the income was not left to the discretion of the
self-employed; rules were established to split the profits into
labor and capital income.12 The dashed line in figure 1 rep-
resents the marginal tax rates that apply to self-employment
income in the case where no capital is invested in the firm.
11The exchange rate in 2005 était 1 USD ≡ 6.45 NOK; 1 EUR ≡ 8.01
NOK.
12Capital income is calculated by multiplying the capital invested in the
firm with a rate of return annually established by the government. The labor
income is then estimated by subtracting the imputed capital income from
the reported self-employment income net of expenses.
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TAX PROGRESSIVITY AND SELF-EMPLOYMENT DYNAMICS
379
FIGURE 2.—MARGINAL TAX RATE FOR WAGE INCOME, YEARS 1995, 2005, AND 2010
(je) Marginal tax rate for a wage earner in tax class 1 with only wage income in years 1995, 2005, et 2010. Employer’s social security contributions are excluded. Thresholds are adjusted to account for income growth
during the period (base year is 2005). Marginal tax rate is reported only for income larger than 200,000 NOK. (ii) To improve readability, the case for self-employment income is not reported, because it would imply
only a proportional vertical shift of each of the three curves presented; see figure 1.
The main differences to the wage income case are the lack of
basic allowance and the higher social security contribution
(10.7% dans 2005).
Tax progressivity is achieved through the tax-free al-
lowances applied to ordinary income and the surtaxes on
personal income. Cependant, during the years under consid-
eration, the progressivity changed several times because of
changes to the tax rates, the number of surtaxes, and their
thresholds. Dans l'ensemble, tax progressivity decreased during the
period. Figures 2 et 3 show the marginal tax rates and aver-
age tax rates in different years for an individual whose only
source of income was wage income.13 Marginal tax rates in
the year 2010 were overall lower than in the year 1995, et,
for most part, they were also lower than in the year 2005. Sim-
ilarly, the average tax rates in 1995 were in general higher
than the rates in 2005 et 2010 (figure 3).
III. Econometric Model
Drawing heavily on the framework of Ham et al. (2016),
we model employment transitions using a two-state multi-
spell discrete duration model accounting for unobserved in-
dividual heterogeneity.14 The two employment states are self-
13Note that the thresholds account for wage growth.
14Following the early pioneering work by Lancaster (1979), and Nick-
ell (1979), the literature on modeling durations using survival analysis has
developed very fast. Lancaster (1990) and Van den Berg (2001) provide a
comprehensive discussion of theoretical issues as well as empirical exam-
ples that helped to develop this literature. See Carrasco and García-Pérez
employment and wage employment. The duration variable is
measured in terms of the Norwegian financial year, which is
the calendar year (January–December). Approximately 70%
of individuals in our sample have a first spell that is left cen-
sored. Without dropping these individuals from the analysis
sample, we include them and specify a different model of exit
rates for them (Ham et al., 2016). We check for sensitivity of
our estimates to excluding the left-censored spells, which is
equivalent to using an inflow sample.
With regard to the unobserved heterogeneity, we follow
the literature and assume this to be distributed independently
across individuals and of the covariates included but fixed
over the same type of spell, but correlated across the two
employment states and the type of spell (fresh versus left-
censored). A discrete distribution is assumed for the unob-
served heterogeneity.
As we closely follow the setup in Ham et al. (2016), we pro-
vide only the form of the hazard function used and refer read-
ers to their paper for further details. For notational simplicity,
we do not distinguish between duration time and calendar
temps, although the estimated model does. The duration time
random variable is denoted as ϒ. Let j = {s f , w f , sc, wc}
where the first letter denotes a self-employment (s) or a wage-
employment (w) spell, and the second letter denotes a fresh
( F ) or a left-censored spell (c). The probability that individ-
ual i would leave the spell in spell type j at the end of duration
(2015) for another recent application of a two-state multispell duration
model with discretely distributed unobserved heterogeneity.
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380
THE REVIEW OF ECONOMICS AND STATISTICS
FIGURE 3.—AVERAGE TAX RATE FOR WAGE INCOME, YEARS 1995, 2005, AND 2010
(je) Average tax rate for a wage earner in tax class 1 with only wage income in years 1995, 2005, et 2010. Employer’s social security contribution are excluded. Thresholds are adjusted to account for income growth
during the period (base year is 2005). (ii) To improve readability, the case for self-employment income is not reported, because it would imply only a proportional vertical shift of each of the three curves presented;
see figure 1.
time t, conditional on not having left in t − 1, is a discrete
time hazard λ(t ) given by
ωs f = ωe f = ωsc = ωec = 0 as a normalization, and esti-
mate the associated probability, p.
λi, j (t|xi, j, ωi, j )
= Pr(ϒi, j = t|ϒi, j > t − 1, taxationi, j (t ), xi, j (t ), ωi, j )
= F
(cid:2)
h j (t ) + xi, j (t )(cid:4)β j + un(cid:4)
j taxationi, j (t ) + ωi, j
(cid:3)
,
(1)
where h j is the duration dependence function, xi, j (t ) con-
tains time-fixed and time-varying observed individual char-
acteristics, taxation contains the tax variable(s), and ωi, j is
the unobserved heterogeneity. F is specified as the comple-
mentary log-log distribution function.15 To achieve conver-
gence with stable parameter estimates, we restrict the du-
ration dependence function to a log linear form and model
the unobserved heterogeneity to be discrete with two points
of support.16 We keep the hazard-specific intercepts, ensemble
15The distribution function is given by F (z) = 1 − exp[− exp(z)]. Some
other popular distributions used are the standard normal and the logistic
cdfs, which are symmetric distributions. The distribution we employ is not
a symmetric distribution. A discrete time hazard model derived from an
underlying continuous time proportional hazard model can be written in
this form. See Narendranathan and Stewart (1993) for an application.
16Theoretical results exist for lack of nonparametric identification in haz-
ard models when one or more of the following are present: duration depen-
dence, time-varying variables, time-varying effects, and unobserved hetero-
geneity. Par exemple, Baker and Melino (2000), using simulations, look at
the behavior of the nonparametric maximum likelihood estimator for a dis-
crete duration model with unobserved heterogeneity and unknown duration
effect, and find the estimator to be biased when both are nonparametrically
specified. Sans surprise, empirical researchers have also found the model
IV. Données, Sample, and Variable Definitions
UN. Data and Sample Selection
The present study benefits from rich longitudinal Norwe-
gian administrative data for the period 1993 à 2011. Le
main data source is the Income and Wealth Statistics for Per-
sons and Families (Statistics Norway, 2005). The data are
drawn from the annual tax returns and the education registers
(years of education and fields of studies). The data also con-
tain individual and family sociodemographic characteristics.
Our focus is on wage earners and the self-employed who have
strong labor market attachment, and so we restrict our anal-
ysis to Norwegian citizens aged 25 à 61 and exclude those
who have reported any income from agricultural, forestry, ou
fishing activities.17
We use an income-based definition to identify periods or
spells of self-employment and wage employment. In our
main analysis, we classify an individual observation as “self-
employed” if the major source of income is self-employment
estimations to be unstable when most of the time effects are modeled in an
unrestricted manner and have thus imposed some functional form restric-
tions to identify the parameters. See Ham and Rea (1987) for a discussion
of these issues in the context of an empirical application.
17Since immigrants are a group of “selected” individuals, we exclude
eux.
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TAX PROGRESSIVITY AND SELF-EMPLOYMENT DYNAMICS
381
FIGURE 4.—ANNUAL SHARE OF SELF-EMPLOYMENT OBSERVATION
Annual self-employment observation as a share of total self-employment plus wage employment observations. Categorisation into self-employment and wage employment is described in section III.
revenu, c'est à dire., if the reported self-employment income (net of
expenses) is larger in absolute value than the wage income
and is also larger than government transfers (qui comprennent
disability insurance, unemployment benefits, and other types
of pensions).18 En plus, we restrict our sample to those
who have been classified as either being in wage employ-
ment or self-employment during the observation period 1993
à 2011.19
The majority of individuals never experience any self-
employment spells. Par exemple, the average rate of self-
employment over the sample period is around 5% (see figure
4). To reduce the computational burden of working with more
que 2 million individuals, we use a 50% random sample to
generate our tax variables. From this sample, we next ran-
domly select 2% of individuals who have never been catego-
rized as self-employed and 20% from the other group, lequel
includes individuals with periods of self-employment spells
only and individuals with a mix of types of employment. Ce
gives us a sample of 476,275 individual-year unweighted ob-
servations. All analyses presented use sample weights to ac-
count for this endogenous sample selection, following Solon,
Haider, and Wooldridge (2015).
18We also exclude individuals who do not report any wage income or
business income that is larger than the “Basic amount” during the obser-
vation period for at least three years. The “Basic amount” is the base for
calculating many of the Norwegian social insurance scheme’s payments and
était 78,024 NOK in 2011 (the approximate exchange rate in that year was:
1 USD ≡ 5.67 NOK; 1 EUR ≡ 7.79 NOK).
19Around 18% of the individuals in the sample experienced at least one
“third-state” spell (periods of time that cannot be defined either as wage
employment or as self-employment) and are omitted from the analysis.
B. Defining and Estimating the Tax Variables
Our analysis is based on the theoretical exposition of an ex-
pected utility maximization approach discussed by Wen and
Gordon (2014), who in turn base their model on the one de-
veloped by Rees and Shah (1986). Assuming risk neutrality,
a convex tax schedule, and log-normally distributed pretax
revenu, they show how the probability of self-employment
can be written as a function of the tax schedule using two
representations of the effects of taxation.20 These are (1) net-
incdiff, which is the difference in log of expected net incomes
in self-employment and wage employment, et (2) convexity,
which is a measure of how the expected tax liability changes
due to the volatility of their self-employment income relative
to the net income in wage employment (see online appendix
A.1 for further details).
The construction of the two tax variables requires net-
income distributions for each individual. We use a tax simula-
tor to generate these (see online appendix A.2). The simulator
considers the yearly rules for taxing self-employment income
net of expenses, wages, and other sources of income. Other
sources of income are taken to be exogenous; these are added
to the predicted self-employment or wage income. The sim-
ulator also accounts for the main deductions and allowances,
as well as for the system for taxation of the labor and capital
parts of net self-employment income; see section II.
20Wen and Gordon (2014) represent the convex tax function specifying
the after-tax income x j as (y j )1−τy0
τ, where the tax parameters τ and y0
are such that 0 < τ < 1, and y0 > 0 represents the income at which the tax
liability is zero. (1 − τ) is the elasticity of posttax income with respect to
pretax income (also see Musgrave & Thin [1948] and Benabou [2000]).
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THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 1.—SUMMARY STATISTICS: MEAN (STD DEVIATION)
Individual-specific variables
Females
Lower secondary school and less
Upper secondary school
University
Time-varying variables
Age at the start of the spell
Years 1993–1998
Years 1999–2002
Years 2003–2007
Years 2008–2011
Eastern Norway
Southern Norway
Western Norway
Central Norway
Northern Norway
Local unemployment rate
convexity
netincdiff
Proportion of exits
All
0.47 (0.50)
0.39 (0.49)
0.30 (0.46)
0.32 (0.47)
35.06 (9.24)
0.30 (0.49)
0.22 (0.41)
0.27 (0.44)
0.21 (0.41)
0.50 (0.50)
0.05 (0.22)
0.26 (0.44)
0.09 (0.28)
0.10 (0.30)
2.73 (0.83)
0.007 (0.008)
−0.448 (0.19)
WE Sample
0.48 (0.50)
0.35 (0.49)
0.31 (0.46)
0.34 (0.47)
34.84 (9.20)
0.30 (0.46)
0.22 (0.41)
0.27 (0.44)
0.21 (0.41)
0.49 (0.50)
0.05 (0.22)
0.26 (0.44)
0.09 (0.29)
0.10 (0.30)
2.73 (0.83)
0.007 (0.008)
−0.429 (0.17)
0.006
SE Sample
0.27 (0.44)
0.53 (0.50)
0.27 (0.45)
0.20 (0.40)
39.80 (8.80)
0.34 (0.47)
0.21 (0.41)
0.27 (0.44)
0.18 (0.39)
0.55 (0.50)
0.06 (0.24)
0.24 (0.42)
0.07 (0.26)
0.08 (0.27)
2.78 (0.83)
0.012 (0.008)
−0.825 (0.25)
0.106
(je) Years covered in the analysis are 1993–2011. (ii) Definitions of wage employment and self-employment and the sample selection criteria used are provided in section IV. (iii) All averages and proportions are
based on the weighted sample (see section IV for further details). (iv) The number of unweighted observations is 476,275, of which 362,217 are classified as wage employment and 114,058 as self-employment. (v)
The number of unweighted individuals is 34,746.
Our construction of the two tax variables closely follows
Wen and Gordon (2014). Assuming pretax income to be log-
normally distributed, y j ∼ LN (μ j, σ j ), where j = s for self-
employment, and j = e for wage employment, we have
à 0, so that convexity is associated with uncertainties in self-
employment income only.
The convexity variable for each individual in each time
period is calculated as
y j
≡ E (y j ) = exp
(cid:4)
(cid:5)
.
μ j + 1
2
σ2
j
(2)
convexity = E [T (ys)] − T ( ¯ys)
¯ys − T ( ¯ys)
.
(4)
The first tax variable, netincdiff, that enters the occupational
choice probability is given by
C.
Summary Statistics
netincdiff = [(1 − τs) ln(ys)]/[(1 − τe) ln(ye)]
(cid:6) ln [netincomes/netincomee] ,
(3)
where τ is a tax parameter from the tax function (see footnote
21). For each individual, we first estimate the selectivity-
corrected expected pretax income (y j ) for each occupation
in each period.21 We then use the tax simulator to gener-
ate the individual specific net incomes in both occupations:
netincomes and netincomee.
Suivant, we define the second individual specific tax variable
representation: convexity. This variable is defined as the dif-
ference between the expected tax liability E [T (ys)] et le
tax liability at the expected income T ( ¯ys), relative to the ex-
pected net income ¯xs = ( ¯ys − T ( ¯ys)).22 Wage employment is
generally less riskier than self-employment. Ainsi, suivre-
ing Wen and Gordon (2014), we derive our convexity variable
by setting the coefficient of variation for wage income equal
21Online appendix A.3 contains the full set of estimates from the equations
that we used to generate the income variables.
22As shown in Wen and Gordon (2014), the tax liability function T (y j )
in the theoretical model is given by y j (1 −
). This term is strictly
convex and hence the use of the term convexity; see Wen and Gordon (2014,
p. 472).
(cid:2)
y0/y j
(cid:3)τ
Summary statistics for the main estimation sample are pro-
vided in table 1. En moyenne, in the weighted sample, le
proportion of individuals exiting out of a period of work and
into a period of self-employment is less than 1%, whereas the
average share of exits out of a period of self-employment is
11%. We next turn to our tax variables.
The overall distributions of the two tax variables are pro-
vided in figures 5 et 6. netincdiff is predominantly negative,
indicating that, for the majority of observations in the sample,
the predicted net wage income is higher than the predicted
net self-employment income.23 convexity is as expected, es-
timated to be mostly positive.24 The average value of pre-
dicted netincdiff of −0.448 implies that the net income in
self-employment is about 64% of net income in wage em-
ployment. The average estimated value of convexity is 0.007
23The paradox of self-employment being characterized by higher uncer-
tainty and lower earnings than wage employment is a common finding in
previous studies (voir, Par exemple, Hamilton [2000] and Hurst & Pugsley
[2011], or Berglann et al. [2011] for the case of Norway). There are several
possible explanations for this puzzle. Among them, (je) the relevance of un-
observed nonpecuniary benefits; (ii) unobserved underreporting of income
by the self-employed; et (iii) overestimation by the self-employed of their
probability of success.
24Negative convexity values are possible if the tax function is not convex.
Estimated convexity is 0 for about 1.5% of the observations and negative
for about 5.5% of the observations.
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TAX PROGRESSIVITY AND SELF-EMPLOYMENT DYNAMICS
383
FIGURE 5.—DENSITY OF netincdiff
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netincdiff distribution across all years and observations. netincdiff is defined in equation (3).
FIGURE 6.—DENSITY OF convexity
convexity distribution across all years and observations. convexity defined in equation (4).
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384
THE REVIEW OF ECONOMICS AND STATISTICS
FIGURE 7.—BOX-AND-WHISKER PLOT FOR netincdiff
(je) netincdiff = ln[net income in SE/net income in WE]. See section IVB for further details. (ii) The box shows the median and the interquartile range (IQR). The end of the whiskers gives 1.5 times IQR.
(s.d.= 0.008), which is lower than the convexity value of
0.011 (s.d. 0.16) reported by Wen and Gordon (2014) pour
Canada.
Box-and-whisker plots in figures 7 et 8 show how these
estimated tax variables change over time. The median net-
incdiff remains stable over time without experiencing a clear
trend, and the spread decreases over time. A slightly declin-
ing trend is observed for convexity, which complies with the
reduced progressivity of the taxation during the sample pe-
riod (section II). The temporary up-tick in the median and
spread of convexity in 2000 is consistent with the fact that
two surtaxes were introduced in that year, making the overall
tax schedule more progressive.25,26
In addition to the two tax variables, the models also in-
clude time-varying and time-invariant control variables. Le
time-invariant variables are sex, age at the start of the spell, dans-
dicator variables for highest education level achieved, and re-
gional dummies to account for local labor market conditions.
Calendar time dummies control for macroeffects. The data
are an unbalanced panel; see descriptive information in table
1. Self-employed individuals are on average older and less
educated than individuals who are paid wages, and a lower
proportion of females is found among the self-employed.
Self-employment is also highly concentrated in the more
densely populated areas of eastern Norway (the Oslo region)
and western Norway (the Bergen region).
25Another possible explanation for this is the increased uncertainty due
to the recession in the early 2000s.
26We carried out an analysis of covariance to assess the contribution of
various factors to the variation of the two tax variables. We included all
the variables (sex, marital status, éducation, region, enfants, family head,
year dummies, two selection correction terms, and estimated variances)
that were used in the predictions of these two tax variables along with the
“other” tax variable (convexity or netincdiff). The model R-squared values
étaient 29% et 49% respectively in the netincdiff and convexity equations.
The top four largest contributors explained 46% of the model sum of squares
(SS) in the netincdiff equation. These were education, selection into SE, et
the regional and year dummies. With regard to the convexity variable, le
top four largest contributors were the year effects, éducation, and estimated
heteroskedastic functions, which together explained 38% of the model SS.
The convexity (netincdiff) variable in the netincdiff (convexity) equation ex-
plained less than 4% (2%) of the model variations. The largest contributions
to the model SS came from the year effects.
V. Results
UN. Main Results
Before discussing the parametric model estimation results,
we provide a plot of the empirical hazard in figure 9.27 Le
raw data self-employment (SE) hazard consistently lies above
the wage-employment (WE) hazard, implying that the condi-
tional exit rate from SE is higher relative to an exit from WE.
The WE hazard is quite low and stable over the spell dura-
tion. The probability of exiting from SE into WE is around
27This is the number of individuals exiting during the year divided by the
number of individuals in that state at the beginning of the year.
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TAX PROGRESSIVITY AND SELF-EMPLOYMENT DYNAMICS
385
FIGURE 8.—BOX-AND-WHISKER PLOT FOR convexity
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(je) See equation (4) for the definition of convexity. (ii) The box shows the median and the interquartile range (IQR). The end of the whiskers gives 1.5 times IQR.
FIGURE 9.—NONPARAMETRIC HAZARD ESTIMATES
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The figure presents the nonparametric hazard estimates for wage employment and self-employment spells. These are the OLS estimated coefficients on the duration time dummies in a linear regression of the duration
variable. The duration variable takes the value of 0 if that particular year refers to an ongoing spell and 1 when it is associated with an exit.
386
THE REVIEW OF ECONOMICS AND STATISTICS
TABLE 2.—HAZARD MODEL ESTIMATES, MAIN SAMPLE
Fresh Spells
Left-Censored Spells
netincdiff
convexity × 100
Male
Age at the start of the spell
High school
University
ln(duration)
Constant
Support points
Probability masses
p1 (constants + support points)
p2 (constants only)
N observations (unweighted)
N individuals (unweighted)
Maximized log likelihood value
SE
[1]
−0.429
(0.053)
0.049
(0.015)
−0.024
(0.027)
−0.012
(0.001)
−0.006
(0.029)
0.220
(0.028)
−0.520
(0.016)
−1.135
(0.092)
−0.531
(0.049)
0.805
(0.019)
0.195
(0.019)
476,275
34,746
−105,687.67
WE
[2]
1.685
(0.082)
−0.246
(0.021)
0.602
(0.030)
0.030
(0.002)
0.115
(0.035)
0.131
(0.037)
−0.490
(0.018)
−3.11
(0.103)
−3.042
(0.200)
SE
[3]
−0.725
(0.109)
−0.017
(0.030)
0.191
(0.058)
−0.034
(0.002)
−0.131
(0.048)
0.051
(0.051)
−0.016
(0.037)
−0.892
(0.192)
−1.337
(0.072)
WE
[4]
1.753
(0.087)
−0.163
(0.023)
0.776
(0.037)
−0.046
(0.002)
−0.008
(0.038)
0.100
(0.038)
−0.234
(0.032)
−1.930
(0.118)
−1.839
(0.094)
(je) MLE standard errors in parentheses. (ii) The models are estimated using a random sample of individuals as detailed in section IV. (iii) Omitted education category is no-education/high-school drop-out. (iv) Le
model additionally includes region and time indicators; see table 1. Complete sets of results are available in the online appendix A.4.
0.23 in the first year of the spell compared to 0.02 from WE
into SE.
Our base model estimates are presented in table 2.28 All
four hazard functions are estimated simultaneously. Except
for the left-censored SE hazard, the other three hazards show
negative duration dependence, ceteris paribus. Insignificant
duration dependence estimated for the left-censored SE spells
is consistent with the observation that the probability of ex-
iting is almost zero for high duration spells, and the sample
of left-censored spells has a higher probability of containing
large-duration spells.
We focus our discussions on the interpretation of the es-
timated effects of the tax variables. The theory predicts a
positive (negative) effect of the netincdiff variable on the
probability of exit from WE (SE). Par exemple, the higher
the proportionate increase in the net-income differential with
respect to the net income from WE, the higher the exit rate
from WE (Wen & Gordon, 2014; Taylor, 1996; Fossen, 2009).
On the other hand, the theoretical prediction of the effect of
convexity is negative on exit rate from WE since higher “con-
vexity” would be expected to discourage SE. The estimated
effects of the two tax variables conform to these theoretical
prédictions.
These estimated coefficients are also found to be higher
in absolute value for WE exit probabilities (columns 2 et
4). These results suggest that, compared to exits from SE, le
probability of an exit from WE is more sensitive to changes in
both expected net-income differences and tax progressivity.
This is consistent with the fact that the SE tend to continue
their business activities even if they experience lower earn-
ings growth (Hamilton, 2000).
These estimates also indicate that a one percentage point
increase in convexity requires an increase of approximately
nine to fourteen percentage points in netincdiff to keep these
hazards unchanged. Note that increases in convexity in this
calculation are assumed to take place via changes to the
volatility of SE income (online appendix A.1 equation [A.4])
because we assume no uncertainty in WE income in the cal-
culation of this variable. De la même manière, the increase in netincdiff
is assumed to work either via a reduction in the pretax income
in WE or via an increase in the expected pretax SE income
(not altering the variance of the SE income distribution). À
further explore these effects accounting for the relationship
between the two tax variables, we simulated a policy experi-
ment. The results are presented below.
28The bootstrapped standard errors to account for the tax variables be-
ing “generated regressors” did not change the significance of our variables
compared to the usual maximum likelihood standard errors for our base
model reported in table 2. Ainsi, we report only the usual MLE standard
errors in this table and subsequent tables.
B. Results from a Policy Experiment
So far we have looked at the effects of partial changes in the
tax variables netincdiff and convexity. Motivated by the anal-
ysis in Wen and Gordon (2014), to gain further understanding
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TAX PROGRESSIVITY AND SELF-EMPLOYMENT DYNAMICS
387
FIGURE 10.—MARGINAL TAX RATE FOR WAGE INCOME, YEAR 2000, AND HYPOTHETICAL UNIQUE SURTAX ON PERSONAL INCOME
(je) Bold line: Marginal tax rate for a wage earner in tax class 1 (see online appendix A.2) with only wage income in year 2000. Employer’s social security contributions are excluded. (ii) Dashed line: année 2000 tax
experiment. The two surtaxes are replaced by a single surtax of 11% for gross incomes exceeding 200,000 NOK. (iii) To improve readability, the case for self-employment income is not reported, because it would only
imply a proportional vertical shift of each marginal tax curve presented; see figure 1.
of how these related changes may be achieved through tax-
ation, we consider a hypothetical reform in the year 2000.
We chose this year because the Norwegian government in-
troduced two changes in the taxation of gross income from
wage and self-employment in that year. The threshold for the
1999 surtax rate of 13.5% was increased from 269,100 NOK
à 277,800 NOK. More importantly, an additional surtax was
introduced for income exceeding 762,700 NOK (dashed line
in figure 10). These changes increased the overall progres-
sivity of the Norwegian income tax system.29
Our policy experiment is to replace two of the surtaxes
applied to personal income with one surtax, to create a flat-
ter tax schedule (solid line in figure 10). The surtax value
de 11% on gross income above 200,000 NOK is chosen to
ensure revenue neutrality, given a “no behavioral reaction”
assumption. Other features of the taxation are held constant.
New values of netincdiff and convexity were generated un-
der the hypothetical scenario using our tax simulator and the
transition rates predicted from the estimated models.
The average values of the netincdiff and convexity variables
in our weighted sample are −0.374 and 0.0071 under the
new policy regime, compared to the original figures for the
année 2000 of −0.382 and 0.0087, respectivement. As expected,
the less progressive tax schedule leads to a decrease of 0.16
percentage points in convexity. The hypothetical policy also
29According to exchange rates for 2000: 1 EUR ≡ 8.11 Norwegian kroner
(NOK), et 1 USD ≡ 8.81 NOK.
leads to a small increase in the mean netincdiff, so that average
ratio of net income in SE to net income in WE changes from
68.2% à 68.8%.
The predicted transition probabilities and the correspond-
ing standard errors, under the old and the new tax regimes,
are reported in table 3.30 In the benchmark year 2000, le
model predicts that around 9.33% of self-employed individ-
uals will transit out of SE to WE (case A).31 Cependant, le
reform reduces this figure to 9.32% (case B). Under the new
regime, the predicted transitions from WE to SE are higher
à 0.68% compared to 0.56% in the base model. Since a very
large proportion of individuals are in WE compared to SE,
even with this small increase in the exit rates out of WE can
generate a substantial net inflow into SE. The change in the
exit rates induced by the policy reform is not significant for
the self-employed.
To further explore how the model predicts responses to
separate changes in the two tax variables, we look at these
effects separately. In case C we hold the convexity variable
fixed at a value that is the same as in the base case scenario
and let the netincdiff variable change. Inversement, in case D
we see a change in the convexity variable only. Tableau 3 shows
that the partial effect of a change in netincdiff is an increase
in transitions out of both SE and WE. This result is consistent
30All predictions including the differences in predicted exit rate, et le
associated standard errors, use all four hazards. These are calculated using
STATA’s margins command.
31The observed exit rates in 2000 étaient 9.813% et 0.595%.
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Case
UN
B
C
D
TABLE 3.—AVERAGE PREDICTED EXIT PROBABILITIES (%) UNDER THE TAX REFORM SCENARIO
Tax Scenario
from SE, %
from WE, %
Probability of Exit
Base model: année 2000, two surtaxes
(s.e.)
Reform scenario: année 2000, one surtax
(s.e.)
Change A − B
(s.e.)
Sample size in year 2000
convexity: unchanged from baseline netincdiff: reform
(s.e.)
netincdiff: unchanged from baseline convexity: reform
(s.e.)
9.334
(0.227)
9.316
(0.289)
0.018
(0.184)
6,043
9.622
(0.234)
9.034
(0.276)
0.562
(0.011)
0.682
(0.016)
−0.119
(0.010)
130,019
0.571
(0.011)
0.673
(0.015)
(je) Actual exit rates in 2000 étaient 9.813% et 0.595%. (ii) Predicted exits are based on the estimated model from table 2. (iii) The percentage exits are calculated with respect to the stocks in each of the occupational
catégories. (iv) Case A refers to the actual situation as it was in year 2000 with two surtaxes; Calculated convexity and netincdiff in this scenario were used in the estimation of the main model. (v) Case B refers to a
hypothetical reform scenario that replaces two surtaxes with just one surtax. New values of convexity and netincdiff are recalculated given the new tax rules. (vi) Case C considers values of convexity from the baseline
scenario and values of netincdiff from the reform scenario. (vii) Case D considers values of netincdiff from the baseline scenario and values of convexity from the reform scenario. (viii) The above predictions and
the associated standard errors were calculated using the delta method in STATA’s command margins. Average exit rates as well as the differenced average exit rates were all calculated using all four hazards. (ix) All
calculations are based on the weighted sample.
with the fact that mean netincdiff experiences a decrease in the
reform scenario for the self-employed, whereas it increases
for wage earners. A possible explanation for this effect is that
the reduced progressivity of the tax system would encourage
a larger share of wage earners who expect to be successful
in self-employment to transit into SE. On the other hand,
because a majority of self-employed individuals have been
predicted to have a higher posttax income in regular employ-
ment, a flatter tax scenario would increase the proportion of
them leaving SE for WE. In contrast, the decrease in convex-
ville, common to both WE and SE observations, reduces the
transitions from SE and increases the exit from WE. In sum-
mary, the hypothetical tax scenario is found to encourage the
net inflow into SE. Translating these estimates to numbers,
we find that such a policy would have resulted in an increase
depuis 2.76% à 5.34% in the net inflow into SE.32
Enfin, we briefly compare our results to the findings of
Wen and Gordon (2014), given that the same variables are
used to capture the effects of taxes and uncertainty. Wen and
Gordon (2014) also simulated the effect of a flatter tax sched-
ule in the year 2000 using Canadian data. Their policy reform
implied decreases in the average values of (je) netincdiff and
convexity from −22.5% to −23.3% (a decrease of 4%) et
(ii) depuis 1.2% à 0.8% (a reduction of 33%). The policy re-
form we considered increased the average values of netincdiff
by around 2%, and reduced the average values of convexity
par 18%. From the simulated policy reform, Wen and Gordon
(2014) estimate an increase in the number of self-employed
individuals of 0.78% (5.76% à 5.80%), which is substan-
tially below our estimate of 2.6% (our experiment implies an
increase of the self-employment share in 2001 depuis 4.56%
à 4.68%). One should however note that Wen and Gordon
(2014) do not model transitions.
32The predicted probability of exit from SE in the reform scenario is not
statistically significantly different from the base model, and so we use the
base model predicted probability. With the reform scenario prediction, le
predicted net inflow would rise to 5.36%.
C.
Sensitivity Checks
In this subsection we present results of some of our inves-
tigations into key assumptions of our empirical approach. Nous
consider the following: (je) redefinition of a self-employment
spell; (ii) estimation based only on the inflow sample;
(iii) trimming the netincdiff with respect to extreme values;
(iv) controlling for local unemployment rates; (v) y compris
a dummy variable for individuals receiving some unemploy-
ment insurance during the year; et (vi) allowing for the
share of capital in SE income to be nonzero. Tableau 4 reports
the results of these investigations. The estimated effects of
the tax variables are qualitatively unchanged. The full set of
results is available in the online appendix A.3.
Our first investigation examines the influence of the defini-
tion of an SE spell. In our base model we included individuals
in the sample if they had at least three years of labor market
attachment, c'est, if the net SE income or WE is larger in ab-
solute value than the basic amount for at least three years over
the years the individual is observed in data. We now redefine
the sample requiring only one year of labor market attach-
ment. The results using this new definition are presented in
panel B of table 4. Individuals with less attachment to the la-
bor market would be expected to be more sensitive to changes
in the tax variables, and this is what we find when we include
these individuals in the estimation sample. The results are
qualitatively similar to the results from our base case (panel
UN). Cependant, the coefficient for convexity in the SE fresh
spells hazard decreased substantially. Individuals with less
attachment to the labor market with low predicted SE income
might be expected to be less sensitive to the progressivity of
the tax system.
The base model was estimated using both the left-censored
and fresh spells. We reestimate our model using only the in-
flow sample. This reduces the total number of unweighted
observations to 229,036. The definition of an SE spell is the
same as the one used in our base model. The results are pre-
sented in panel C of table 4. The results are broadly similar
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389
TABLE 4.—SENSITIVITY CHECKS: HAZARD MODEL ESTIMATES
Fresh Spells
Left-Censored Spells
Variables
UN. Base case
netincdiff
convexity × 100
B. Changes to sample definition
netincdiff
convexity × 100
C. Excluding left-censored spells
netincdiff
convexity × 100
D. Using trimmed netincdiff
netincdiff
convexity × 100
E. Including regional unemployment rate 1996–2011
netincdiff
convexity × 100
F. Including regional dummies 1996–2011
netincdiff
convexity × 100
G. Including unemployment benefits dummy
netincdiff
convexity × 100
H. Using 3.7% capital income invested in SE
netincdiff
convexity × 100
SE
[1]
−0.429
(0.053)
0.049
(0.015)
−0.493
(0.016)
0.011
(0.005)
−0.405
(0.053)
0.055
(0.015)
−0.333
(0.068)
0.065
(0.017)
−0.531
(0.057)
0.045
(0.018)
−0.519
(0.057)
0.036
(0.018)
−0.415
(0.053)
0.049
(0.015)
−0.434
(0.052)
0.058
(0.016)
WE
[2]
1.685
(0.082)
−0.246
(0.021)
1.734
(0.026)
−0.277
(0.007)
1.920
(0.083)
−0.292
(0.022)
2.281
(0.108)
−0.222
(0.025)
1.709
(0.096)
−0.292
(0.026)
1.762
(0.095)
−0.314
(0.026)
1.698
(0.082)
−0.252
(0.022)
1.712
(0.083)
−0.264
(0.024)
SE
[3]
−0.725
(0.109)
−0.017
(0.030)
−0.615
(0.034)
0.016
(0.009)
−0.871
(0.138)
−0.061
(0.032)
−0.718
(0.110)
0.047
(0.029)
−0.754
(0.110)
0.038
(0.030)
−0.694
(0.109)
−0.014
(0.030)
−0.719
(0.108)
−0.008
(0.031)
WE
[4]
1.753
(0.087)
−0.163
(0.023)
1.768
(0.028)
−0.187
(0.007)
2.998
(0.134)
−0.237
(0.026)
1.568
(0.083)
−0.115
(0.025)
1.607
(0.081)
−0.140
(0.024)
1.763
(0.087)
−0.165
(0.023)
1.761
(0.086)
−0.166
(0.025)
(je) Standard errors in parentheses. (ii) See section VC for further details. (iii) Panels E and F report results with no unobserved heterogeneity (see footnote 35). (iv) Also see notes to table 2. (v) A full set of results
are available in the online appendix A.3.
to our base model results. As expected, dropping those spells
for which we have no information about the length of time
they had spent in a particular state prior to the sample start
slightly increases the estimates.
The third investigation involves omitting observations with
extreme predicted values for the variable netincdiff. Comme indiqué
in figure 5, the distribution of netincdiff exhibits some lumpi-
ness in the tails. To assess the effect of extreme values of
netincdiff, we drop those individuals who have at least one
occupation-specific netincdiff above the top 1% or below the
1% cut-off values.33 Since individuals with very high or low
netincdiff would be expected to be less sensitive than the oth-
33To preserve a continuous series of observations, all observations be-
longing to an individual are dropped if we find at least one neticdiff that
is either less than the first percentile or above the 99th percentile value for
that individual resulting in a loss of more than 2% of the sample. We lose
à propos 9% of the observations, resulting in 432,409 observations in our un-
ers, we would expect the estimated effects of netincdiff to be
higher in absolute values. This is what we see with the results
reported in panel D. In the base model (panel A), we found
the WE exits to be more sensitive than the SE exits, and now
we see that the effect of netincdiff goes up for the WE exits
without much change for in SE exits.
The next investigation examines the influence of local labor
market conditions. In the main specification we use regional
dummies to partially control for labor market conditions. Per-
haps a better control for local labor market conditions would
be the use of local unemployment rates. Unfortunately such
information is available only from 1996, so we report two
sets of results. In panel E we substitute the regional dummies
with regional unemployment rates. In panel F we reestimate
weighted sample. The definition of a SE spell is the same as the one used
in our base model.
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THE REVIEW OF ECONOMICS AND STATISTICS
our base model using the restricted sample of 1996 à 2011.
The results are very similar to each other and qualitatively
similar to the baseline results.34
As described in section IV, in our base model we drop
individuals who received more in social security benefits than
their self-employment income or wages in any year. Cependant,
it can be the case that individuals are unemployed for a short
period and the unemployment insurance is small enough so
that the individual is still defined as a self-employed or a wage
earner. Individuals with an interruption to their work might
behave differently from individuals transiting directly from
WE to SE. We therefore include a dummy variable for those
individuals who received unemployment insurance during the
année. As panel G shows, the results are similar to those from
the base model.
In Norway self-employed individuals have the option of
having a share of the self-employed income declared as capi-
tal income, which is taxed at a lower rate than labor income, comme
explained in section 2. Tax variables used in our main model
are generated under the assumption that the share of capital
income in total income is zero (see online appendix A.2).
We believe our assumption is reasonable for the following
raisons. D'abord, it is not clear what is an appropriate assump-
tion regarding the proportion of capital income used in the
generation of counterfactual SE income distributions for the
wage earners, which are also exogenous. Deuxième, during our
sample period, the share declared as capital income is either
0 or very small (median value is 0.037). Cependant, we check
for sensitivity by regenerating our tax variables allowing for
3.7% of the predicted SE income to be reported as capital
income instead of 0. The results are given in panel H. Le
effect of convexity is slightly stronger on the SE exit rates,
and the rest of the estimated effects remain similar to the base
model estimates.
VI. Conclusion
We look at the effect of taxation on self-employment and
wage employment durations. Our work complements the ex-
isting literature on many dimensions. D'abord, in contrast to
many existing studies, our definitions of self-employment
and wage employment are based on income reported in Nor-
wegian tax returns. The rest of the variables used come from
various other registry data. Norwegian registry data are con-
sidered to be exceptional in terms of coverage and reliability
(Blundell, Graber & Mogstad, 2015). Deuxième, we look at the
evolution of self-employment and wage employment spells
over a very long period, depuis 1993 à 2011. We model these
transitions using a two-state multispell duration model allow-
ing for correlated unobserved heterogeneity and controlling
for a rich set of sociodemographic characteristics.
34We made multiple attempts but were unable to find significant unob-
served heterogeneity in these models with the reduced number of years.
We therefore report results from the model where we set the unobserved
heterogeneity component to 0.
We focus on the effects of two tax variables: netincdiff
and convexity, obtained from Wen and Gordon (2014).
netincdiff is defined as the difference in log net-of-tax income
in the two occupations, and convexity is an individual-specific
measure that captures the interaction between the progressiv-
ity of the tax schedule and the volatility of self-employment
income relative to wage income. We use the model to pre-
dict the transitions under a simulated tax regime that reduced
the progressivity of the tax schedule in the year 2000. Nous
also provide some sensitivity checks with respect to the def-
inition of self-employment, the selection of the estimation
sample, and other factors. The estimated effects of our two
tax variables of interest are qualitatively unchanged, et le
quantitative differences are as expected.
The main finding is that, as predicted by theory, higher
expected net earnings in self-employment relative to wage
employment reduces the probability of exiting out of a
self-employment spell. The entry into self-employment—
or equivalently the exit out of wage employment—is found
to be more sensitive to changes in the two variables than
exit from self-employment. In our base model, the estimated
effect of changes to netincdiff that are required when con-
vexity changes by a percentage point, to encourage self-
employment, is about nine to fourteen times larger in per-
centage point terms. To shed further light on this, we carried
out a policy experiment by implementing a flatter tax sched-
ule in the year 2000 that resulted in reduced tax progressiv-
ville. The hypothetical scenario was found to encourage entry
into self-employment but not significantly the exit from self-
employment, with the estimated inflow into self-employment
increasing to 5.34% from the base model prediction of 2.76%.
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