EMMA NEUMAN
2016:11
Performance and job creation
among self-employed
immigrants and natives
in Sweden
Performance and job creation among self-employed immigrants and natives
in Sweden
Emma Neumana
Abstract
This paper uses individual panel data for all self-employed in the retail and service sectors to study how
immigrant and native owned firms perform and contribute to job creation in Sweden. In particular, we use an
individual-fixed effects model to explore how self-employment outcomes among immigrants and natives evolve
with years in self-employment. The results show that native men have higher earnings and profit level from selfemployment activities than immigrant men. Immigrant men converge towards the earnings and profit levels of
native men as self-employment experience increase, but do not reach parity. On the contrary, immigrant women
catch up with the earnings and profit levels of native women after between 3 to 5 years in business. Turnover is
highest for men, and in firms owned by non-European immigrants, independently of length of business
experience. Immigrant firms, and in particular those owned by non-European immigrants, contribute more to job
creation than firms owned by natives. As length of business experience increases immigrants’ hire additional
persons to their firms to a higher extent than natives.
JEL-classification: J15; L25; L26
Keywords: Immigrants; Job creation; Performance; Self-employment
a
Centre for Labour Market and Discrimination Studies, Linnaeus University, SE-351 95 Växjö,
[email protected]. The author thanks Lina Aldén and Mats Hammarstedt for useful comments and
suggestions. Financial support from the project “Obstacles and possibilities for self-employment among
immigrants in the retail and trade sector” funded by the Jan Wallander and Tom Hedelius Foundation is greatly
appreciated.
1 Introduction
Immigration has increased rapidly in many countries and the labour market integration of immigrants in the host
countries has received the attention of researchers and politicians. In Sweden the share of the population born
abroad nearly doubled between 1970 and 2000 (Statistics Sweden, various years).1 Over time the migration
flows have also shifted from consisting mainly of labour-force migrants from Europe, to be more likely to
comprise refugees and tied-movers from outside of Europe. It is well known that in Sweden, as in other
countries, immigrants perform worse on the labour market in comparison to natives. Self-employment has often
been suggested as a way to increase labour market participation among the foreign born. In several countries
immigration policies have been constructed to facilitate immigration of potential entrepreneurs (Schuetze and
Antecol, 2006; Fairlie and Lofstrom, 2015). In Sweden the Swedish government proposes increased selfemployment rates among the newly arrived immigrants as a way to better utilise their competencies (SOU
2015/16:100).
In previous research it has been shown that immigrants tend to be self-employed to a higher degree than natives
in many developed countries (see e.g. Borjas, 1986; Fairlie and Meyer, 1996; Fairlie and Robb, 2007; Robb and
Fairlie, 2009 for studies on the US, see e.g. Clark and Drinkwater, 2000, 2010; Hammarstedt, 2001; Constant
and Zimmermann, 2006; Andersson and Hammarstedt, 2011 for studies on European countries). Immigrants and
natives appear to enter self-employment partly due to different factors (for an overview see e.g. Simoes et al.,
2016). For instance, immigrants might face discrimination by employers in paid labour market work, and this
pushes them into self-employment (Clark and Drinkwater, 2000). Furthermore, there is reason to expect that
immigrants face more difficulties at start-up of their firms than do natives. Research has shown that immigrants
and minorities face problems, such as attaining capital and understanding laws and regulations, when they start a
new business, (e.g. Blanchflower et al., 2003; Blanchard et al., 2008; Asiedu et al., 2012; Aldén and
Hammarstedt, 2016). It is also possible that poor language skills in the host country language restrict immigrant
self-employment (Bates, 1999). We would therefore expect that immigrant firms are less successful and grow
slower than native firms in the start-up phase. However, it is still unknown whether the potential differences in
performance and job creation between immigrants and natives remain as the owners gain self-employment
experience.
Until now few studies have investigated how immigrants’ and natives’ self-employment outcomes develop with
business experience. Lofstrom (2011) studies how the earnings among self-employed immigrants and natives in
the US evolve with years in business. He finds that the earnings of self-employed immigrant men increase with
years in business and at a faster rate than for native men. Among women, self-employed immigrants have a
similar earnings growth as self-employed natives initially, but after about 5 years immigrants’ earnings decrease
with additional years in business. In this paper we contribute to this literature by studying how immigrant and
native owned firms in Sweden grow, both in terms of more employees and better performance, as they gain selfemployment experience. In contrast to Lofstrom (2011) we will focus on a broader set of performance measures,
including earnings, turnover, and profit and also study job creation. Firms started and operated by immigrants
1
A detailed description of the history of migration to Sweden is provided by Lundh and Ohlsson (1999).
1
contribute to the host economy, not the least by increasing employment and income from self-employment
(Fairlie and Lofstrom, 2015). However, there is a need for further research on the contribution of immigrant
firms to job creation (e.g. Kerr and Kerr, 2016; Fairlie and Lofstrom, 2015).
The paper makes use of Swedish longitudinal register data for the years 1998 to 2007. This data provides
information on individual and firm characteristics for all self-employed natives and immigrants in Sweden. We
will restrict the focus to self-employed in the retail and service sectors in Sweden. These sectors are of particular
interest for immigrants since many immigrants have established their businesses there (Andersson and
Hammarstedt, 2011).
2 Literature review
2.1 The self-employment performance of immigrants and natives
The self-employment performance among immigrants to different countries has been extensively studied.2
Several studies have investigated differences in performance between self-employed immigrants and natives. In
this literature it emerges that the self-employment experience of immigrants differs across host countries as well
as for different immigrant groups. In Sweden, non-European immigrants have lower earnings and profit from
their firms in comparison to their native counterparts, while the self-employment performance of immigrants
originating from Europe is more similar to native performance (Hammarstedt, 2006; Andersson and
Hammarstedt, 2011; Andersson Joona, 2011). Evidence for the US shows that immigrant businesses-owners
have lower earnings in comparison to self-employed natives (Lofstrom, 2002). However, Asian-owned
businesses, which are mainly immigrant owned, have higher sales and profits in comparison to natives (Robb
and Fairlie, 2009). In any study of immigrant earnings is it important to take into account the duration of stay in
the host country. A large literature on immigrant earnings assimilation in the wage sector has shown that
immigrants tend to have lower earnings than natives initially, but the gap reduces as time in the host country
increases (see e.g. Chiswick, 1978; Borjas, 1985). A few papers expand the immigrant assimilation literature to
the self-employment sector. Lofstrom (2002, 2011) finds that the earnings of self-employed immigrants in the
US converge or even surpass the earnings of self-employed natives as they age. In Canada and Australia it takes
longer time for self-employed immigrants to reach earnings parity with natives in comparison to the US
(Schuetze and Antecol, 2006).
Furthermore, the literature investigating important factors for job creation among the self-employed is rather
scarce (Henley, 2005; Fairlie and Miranda, 2016). In general men tend to hire more employees in their firms in
comparison to women (van Praag and Cramer, 2001; Fairlie and Miranda, 2016; Burke et al., 2002; Henley,
2005).
Moreover, there are relatively few studies on ethnic differences in job creation. Andersson and
Hammarstedt (2011) find that in Sweden firms owned by non-European immigrants tend have more employees
and higher turnover than natives’ and European immigrants’ firms. In contrast, Henley (2005) finds that ethnic
minorities in the UK have a lower likelihood to hire employees to their firms in comparison to natives.
Furthermore, recent evidence from the US shows that immigrant and native business-owners tend to hire
employees to the same extent if differences in individual characteristics are taken into account (Fairlie and
2
For a review of the literature on immigrant business performance see Fairlie and Lofstrom (2015).
2
Miranda, 2016). Although, among firms having survived three years in business in the US immigrant businessowners employ additional personnel at a faster rate than natives (Kerr and Kerr, 2016).
2.2 The role of self-employment experience
It has been established in the literature that a high level of human capital is of great importance for successful
self-employment. For the self-employed, age, education, and experience can be considered as the most relevant
components of human capital.3 Older persons have longer labour market experience, more wisdom and
knowledge of institutions, better social and business networks, and more financial capital making them more
likely to enter self-employment and to be successful in it (Cowling et al, 2004; Constant et al, 2007).
Furthermore, it is well-known that individuals to a high degree start businesses in the same type of industry as
the one where they previously worked in paid employment. Shane (2003) means that one reason might be that
the industry-specific information and skills gathered when working raise the expected value of exploitation and
increase individuals’ effort at start-up. In addition to the general experience attained along with ageing and
working, specific experience from self-employment spells might prove valuable for self-employment
performance. A person with experience of being self-employed might have learnt about business opportunities
and attained skills in selling, negotiating, leading, planning, decision-making, problem-solving, organising, and
communicating (Cowling et al, 2004; Parker, 2009; Shane, 2003). Against this background it seems reasonable
to expect that self-employment performance increase with age, labour market experience, and experience from
spells of self-employment. Up to now, few studies have distinguished the impact of experience in paid
employment from that of experience in self-employment on individuals’ performance in self-employment (Van
der Sluis et al., 2008). In general, the empirical evidence for that previous experience from being self-employed
enhances performance as self-employed is mixed (Burke et al., 2002; Shane, 2003; Van der Sluis et al., 2008).
Storey (1994) argues that more experience from self-employment might capture both that individuals have learnt
skills relevant for successful self-employment, and in addition that individuals have previously been selfemployed and failed because of lack in managerial ability. The mixed empirical results on the role of selfemployment experience for self-employment performance might be due to the lack of distinction between
experience from current and previous spells of self-employment.
Previous literature has focused mainly on differences between immigrants and natives in self-employment
performance over the life-cycle, i.e. related to aging. However, differences in native and immigrant business
performance might also emerge because of variation in length of experience of self-employment. For instance,
immigrants might face additional difficulties in the business start-up process. Research has shown that
immigrants and minorities face problems, such as attaining capital and understanding laws and regulations, when
they start a new business, (e.g. Blanchflower et al., 2003; Blanchard et al., 2008; Asiedu et al., 2012; Aldén and
Hammarstedt, 2016). It is also possible that poor language skills in the host country language restrict immigrant
self-employment (Bates, 1999). We would therefore expect that immigrant firms are less successful and grow
slower than native firms initially. Few studies have investigated whether the potential differences in performance
between immigrants and natives remain as the owners gain self-employment experience. In a cross-sectional
study on Germany Constant et al. (2007) find that longer business tenure is positively correlated with
3
For an overview on human capital and self-employment see e.g. Parker (2009).
3
immigrants’ earnings, but not related to natives’ earnings. To our knowledge Lofstrom (2011) is the only study
which answers this question with help of individual panel data. Lofstrom finds that the earnings of low-skilled
self-employed immigrant men grow faster than for native men with years in business. The results for low-skilled
women show that self-employed immigrant women have a slower earnings growth than self-employed native
women.
Moreover, in an analysis of the returns to self-employment it is important to take into account differences in
survival rates, not least to avoid non-random selection out of self-employment which could also be related to
performance. It has been shown that immigrant firms do not survive for as long time as native firms (e.g. Fairlie
and Meyer, 1996; Bates, 1999; Fairlie and Robb, 2007; Andersson Joona, 2010). In this paper we use individual
panel data, which makes it possible to follow individuals and their firms over time. This enables estimations of
the returns to self-employment and taking into account unobservable time-constant individual factors.
3 Data
3.1 Sample construction
In this paper we use register data from the longitudinal data base LISA developed by Statistics Sweden. The data
contains yearly information on individual, demographic, and firm characteristics for all individuals in Sweden
older than 16 years. We restrict our sample to individuals working in the retail, trade or service sector and who
have been registered as self-employed in at least one year during the period of our study, 1998 to 2007. 4 To be
registered as self-employed earnings from self-employment need to be the individual’s main source of income.5
Only persons who entered self-employment in the period of our study are included, and thus individuals who
were self-employed during all nine years are excluded. The reason is that the data does not include information
on years of experience from self-employment prior to 1998, and this implies that information on years in selfemployment is missing for individuals who remained self-employed all nine years. The included individuals can
still have experience from earlier spells of self-employment. In the estimations the individual fixed effect model
will control for the effect of such previous experience. This implies that the return to experience will not be
biased by previous experience, but the estimated gap at start-up might be biased by omitted previous selfemployment experience.
Furthermore, in order to capture persons with high attachment to the labour market (e.g. not part-time retired
persons or students) we include only individuals aged 25 to 64 years. In total the number of individual-year
observations amounts to 431,371, with 155,081 unique entrants. About 24 per cent are immigrants and a
majority (around 61 per cent) are men. Individuals are defined as immigrants if they were born in a country other
than Sweden and as natives if they were born in Sweden.
4
About 0.6 per cent of the sample is self-employed in limited liabilities. Controlling for whether the firm is a
private firm or a limited liability has no impact on the results. The results are not displayed, but available from
the author upon request.
5
If individuals have income from both wage-employment and self-employment Statistic Sweden defines them as
self-employed if the income from self-employment is higher than the income from wage-employment. Selfemployment income is up-weighted with the factor 1.6 since income from self-employment is underestimated in
relation to the number of hours spent in this activity.
4
3.2 Descriptive statistics
Table 1 presents the performance of self-employed immigrants and natives by years in self-employment,
separately for men and women. The firms’ performance is measured by yearly income from self-employment,
yearly firm turnover (income from sales net of taxes and discounts), and yearly firm profit (income minus costs,
excluding deductions related to interest rates and taxes). In addition, the owner’s hiring decisions are described
by the average number of employees at the firm and the share of firms having at least one employee. It appears
that immigrants have more employees than natives and their firms are bigger in terms of turnover, but at the
same time their profit and earnings are lower than natives’. Moreover, men tend to hire more persons than
women, and men’s firms are more successful. All groups improve their performance as years in self-employment
increase, but not many persons stay in self-employment for the full eight years. This highlights the importance of
utilizing individual fixed effects, i.e. making comparisons within the individual, which will make non-random
selection out of self-employment caused by time-invariant factors like ability or motivation less of a concern.
At year of start-up
Number of employees
Table 1: Performance by years since start-up
Native men
Immigrant men
Native women
Immigrant women
0.24
0.35
0.22
0.28
9.44
19.44
8.93
14.11
Yearly real earnings(1000 SEK)
107.96
75.13
72.06
57.03
Yearly real net turnover(1000 SEK)
66.99
77.18
53.08
63.27
Yearly real operating profit/loss(1000 SEK)
20.77
13.76
13.89
11.33
Observations
69,032
25,509
48,193
12,347
After 4 years
Number of employees
0.36
0.60
0.24
0.32
Share with employees
15.06
29.45
12.06
16.25
Yearly real earnings(1000 SEK)
159.25
118.60
113.99
96.34
Yearly real net turnover(1000 SEK)
86.22
103.02
51.90
68.39
Yearly real operating profit/loss(1000 SEK)
26.06
19.30
17.22
15.80
Observations
12,087
5,029
9,021
1,908
After 8 years
Number of employees
0.41
0.68
0.26
0.23
Share with employees
18.32
29.26
14.80
15.93
Yearly real earnings(1000 SEK)
171.02
135.50
123.76
103.05
Yearly real net turnover(1000 SEK)
96.92
88.26
52.30
51.04
Yearly real operating profit/loss(1000 SEK)
27.19
21.69
18.40
15.21
Share with employees
Observations
1,212
475
1,034
182
1 svensk krona (SEK) ≈ US Dollar 0.15 in 2007.Earnings, profit, and turnover are expressed in real values, i.e. adjusted for
changes in the price level.
Table 2 displays some descriptive statistics for our sample. It emerges that natives have somewhat higher
educational attainment and are about two years older than immigrants. Almost half of the native men are active
in financial and business services, while immigrant men are over-represented among hotel and restaurant
owners. Among women, near 40 per cent of natives are active in personal and other services, while immigrants
5
are more evenly spread in a number of different sectors. On average men and native women have been running
their own firms for around 1.6 years, while immigrant women are slightly less experienced with about 1.5 years.
Immigrants are more likely to be married and have more children than natives. The average length of residence
in Sweden is about 18 years for immigrant women, and about one year shorter for immigrant men.
Primary education (%)
Table 2: Summary statistics
Native men
Immigrant men
16.88
25.08
(37.45)
(43.35)
Native women
13.03
(33.66)
Immigrant women
19.97
(39.98)
Secondary education (%)
49.12
(49.99)
43.45
(49.57)
53.77
(49.86)
44.69
(49.72)
Post-secondary or tertiary education (%)
33.77
(47.29)
28.29
(45.04)
33.07
(47.05)
32.58
(46.87)
Age
44.84
(11.25)
42.18
(9.13)
43.85
(10.58)
42.12
(9.50)
7.69
(26.64)
4.55
(20.85)
0.87
(9.29)
0.73
(8.52)
Wholesale and commission trade
6.73
(25.06)
4.41
(20.53)
3.42
(18.16)
4.35
(20.40)
Retail trade
9.83
(29.77)
18.03
(38.44)
14.69
(35.40)
19.33
(39.49)
Transportation and warehousing
10.80
(31.04)
16.52
(37.14)
1.96
(13.87)
2.35
(15.14)
Postal and telecommunication
0.38
(6.18)
0.18
(4.27)
0.09
(3.08)
0.07
(2.63)
Financial and business services
46.80
(49.90)
16.73
(37.33)
35.42
(47.83)
25.26
(43.45)
Hotels and restaurants
3.65
(18.74)
31.28
(46.37)
5.59
(22.97)
20.44
(40.33)
Personal and other services
14.12
(34.82)
8.29
(27.57)
37.96
(48.53)
27.47
(44.63)
Years in self-employment
1.61
(1.80)
1.66
(1.81)
1.67
(1.85)
1.50
(1.74)
Married (%)
46.94
(49.91)
66.40
(47.23)
50.83
(49.99)
63.79
(48.06)
Number of children
0.659
(0.990)
1.189
(1.277)
0.833
(1.043)
1.067
(1.169)
Business line (%)
Trade and repair of motor vehicles
Years since migration
16.99
(10.38)
Observations
190,107
72,975
Displayed are sample means and standard deviations within parentheses.
18.08
(11.77)
136,147
32,142
4 Empirical analysis
4.1 Self-employment performance
Our aim is to study the relative success of self-employed immigrants to self-employed natives and especially
how this is affected by self-employment experience. From the descriptive statistics it emerges that performance
differs across groups, native men have the highest earnings and profit and immigrant men have highest turnover
and most employees. However, part of the group differences could be explained by individual characteristics
6
such as education, age, family composition, or business line. To take into account the differences in observable
characteristics we estimate the following OLS regression separately for men and women.
𝑦𝑦"# = 𝑋𝑋"# 𝛽𝛽 + 𝜌𝜌) 𝑡𝑡"# +𝜌𝜌+ 𝑡𝑡"#+ + 𝜏𝜏# +𝛾𝛾# + 𝜃𝜃# + 𝜀𝜀"#
(1)
where yit is the performance of individual i in year t. As measures of performance we will use the logarithm of
individuals’ yearly earnings, and their firm’s yearly turnover and profit. This implies that we exclude
observations with values equal to or below zero.6 The matrix Xit includes individual characteristics (a quadratic
function of age, educational level, labour earnings, marital status, number of children, and a quadratic function of
years since migration for immigrants (set to zero for natives) ). tit is a trend that measures the effect of years in
self-employment, specified as a quadratic function. In addition, the regression includes year fixed effects, τt,
sector fixed effects, γt, and region fixed effects, θt.7 All independent variables are allowed to vary for immigrants
and natives, implying that they are interacted with an indicator variable for being foreign born. εit is the error
term.
Although equation (1) includes a large number of individual characteristics it is still possible that omitted
variables, such as motivation, talent, and ability, affect the self-employment performance. To reduce this bias we
will estimate regressions where we include individual fixed effects, αi, which control for all individual
unobservable factors that are constant over time. Furthermore, the individual fixed effects implicitly control for
selection out from self-employment that is related to time-constant factors, however, it is still possible that timevarying factors might affect self-employment exit of natives and immigrants differently. We estimate the
following equation by OLS:
𝑦𝑦"# = 𝛼𝛼" +𝑋𝑋"# 𝛽𝛽 + 𝜌𝜌) 𝑡𝑡"# +𝜌𝜌+ 𝑡𝑡"#+ + 𝜏𝜏# +𝛾𝛾# + 𝜃𝜃# + 𝜀𝜀"#
(2)
where all variables are defined as above. The year fixed effects are dropped in the fixed effects estimations, since
it is not possible to include both age and year of observation simultaneously as they are perfectly correlated for
each individual. We include the yearly local unemployment rate to reduce the bias from the omitted year
dummies (Barth et al, 2004). For comparative reasons, we will estimate equation (1) without year fixed effects
and controlling for the yearly local unemployment rate instead. Furthermore, a drawback with including
individual fixed effects in the model is that this makes it impossible to identify the initial performance gap at
year of start-up. In the figures presented below, the plots based on regressions including individual fixed effects
will have the starting values imposed as the sample means for each group. Our main purpose is to estimate
differences in the return to experience in self-employment between immigrants and natives. However, the
6
6,081 observations are excluded in the earnings regressions, 25,459 observations are excluded in regressions for
profit, and 8,378 observations are excluded in the turnover regressions. As a robustness check we have estimated
regressions without logarithmic values and these are presented in Table A1 in the appendix. These results are in
line with the baseline results, although the differences between immigrants’ and natives’ returns to experience
tend to be smaller.
7
A region is defined as a county and Sweden contains 21 counties. We have investigated whether the results are
robust to specifying years in self-employment and the other time variant variables (age and years since
migration) as indicator variables instead of continuous variables. This had little impact on the results and does
not change the main conclusions drawn. The results are not displayed, but available from the author upon
request.
7
performance gap at start-up is of interest especially for how the results can be interpreted in terms of immigrant
assimilation. The initial performance gap incorporates differences in observed individual characteristics, but
does not take into account potential selection into self-employment. If natives and immigrants entering selfemployment differ in terms of e.g. previous self-employment experience, talent, or ability, this can bias the
estimated difference in performance levels. In other words, the differences in levels should be interpreted with
caution.
In table 3 the results for the regressions of yearly earnings from self-employment are displayed. From column (1)
it emerges that for native women earnings increase with about 31 per cent with one additional year of selfemployment experience. The yearly return to experience is about 4 per cent lower for immigrant women. In
column (2) the year fixed effects are omitted and the local unemployment rate is included and this has little
effect on the results. Including individual fixed effects in column (3) reduces the returns to experience
substantially and there is no longer a statistically significant difference between natives and immigrants. It is
likely that a large part of the observed returns to experience is due to the fact that those with high ability,
motivation and talent stay in self-employment and the unsuccessful leave. However, when taking time-constant
factors such as entrepreneurial ability into account, experience still increases earnings, and in a similar
magnitude for natives and immigrants. The results for men show that native men have an earnings increase of
about 28 per cent for each additional year in self-employment, and immigrant men have between 1 and 3 per
cent lower returns to self-employment (columns (4) and (5)). In line with the results for women, including
individual fixed effects reduces the returns to experience and there is no longer any statistically significant
difference between immigrants and natives. General experience attained with ageing also appears beneficial for
the self-employed. Being one year older generates an increase in earnings of about 20 per cent for women and
immigrant men, and around 25 per cent for native men (columns (3) and (6)).
In figure 1 the predicted earnings from the regressions in table 3 are plotted against years in self-employment.
The graph to the left includes year fixed effects (column (1) and (4)) and the graph to the right includes
individual fixed effects (column (3) and (6)). At start-up, the predicted earnings are lower for women than for
men, and immigrants’ predicted earnings are lower than natives’. As experience increase all groups increase their
earnings and the graph without individual fixed effects shows a rather high earnings growth and no catch-up of
immigrants. Adding individual fixed effects implies that the earnings growth is reduced substantially and
immigrant women catch up with native women after about 5 years in self-employment. For men, natives still
earn more than immigrants 8 years after start-up.
8
Table 3: OLS Regressions for Yearly Self-employment Earnings
VARIABLES
Years in self-employment
Immigrant*Years in self-employment
Years in self-employment squared
Immigrant*Years in self-employment squared
Age
Age squared
Immigrant*Age
Immigrant*Age squared
Years since migration
Years since migration squared
(1)
Women
(2)
(3)
(4)
Men
(5)
(6)
0.313***
(0.005)
-0.038***
(0.012)
-0.029***
(0.001)
0.005***
(0.002)
0.095***
(0.004)
-0.001***
(0.000)
-0.045***
(0.010)
0.001***
(0.000)
0.021***
(0.003)
-0.000***
(0.000)
0.326***
(0.005)
-0.026**
(0.011)
-0.029***
(0.001)
0.003*
(0.002)
0.094***
(0.004)
-0.001***
(0.000)
-0.044***
(0.010)
0.001***
(0.000)
0.021***
(0.003)
-0.000***
(0.000)
0.106***
(0.006)
0.014
(0.014)
-0.013***
(0.001)
-0.002
(0.002)
0.206***
(0.009)
-0.002***
(0.000)
0.009
(0.031)
0.000
(0.000)
0.023
(0.018)
-0.001***
(0.000)
0.281***
(0.004)
-0.034***
(0.007)
-0.027***
(0.001)
0.003**
(0.001)
0.112***
(0.003)
-0.001***
(0.000)
-0.061***
(0.007)
0.001***
(0.000)
0.010***
(0.002)
-0.000
(0.000)
0.291***
(0.004)
-0.014**
(0.007)
-0.026***
(0.001)
0.001
(0.001)
0.110***
(0.003)
-0.001***
(0.000)
-0.063***
(0.007)
0.001***
(0.000)
0.012***
(0.002)
-0.000
(0.000)
0.094***
(0.005)
0.012
(0.009)
-0.012***
(0.001)
-0.004***
(0.001)
0.252***
(0.008)
-0.002***
(0.000)
-0.044**
(0.018)
0.001***
(0.000)
0.047***
(0.007)
-0.001***
(0.000)
Individual fixed effects
No
No
Yes
No
No
Yes
Year fixed effects
Yes
No
No
Yes
No
No
Local unemployment rate
No
Yes
Yes
No
Yes
Yes
R-squared
0.160
0.154
0.079
0.162
0.155
0.088
Prediction at sample means
6.572
6.572
6.572
6.913
6.913
6.913
Number of individuals
59,718
59,718
59,718
93,388
93,388
93,388
Number of observations
165,596
165,596
165,596
260,054
260,054
260,054
Robust standard errors, clustered on individuals, in parentheses. ***p<0.01 **p<0.05 *p<0.1 Dependent variable is logarithm of
yearly self-employment earnings. All regressions include controls for education, marital status, number of children, and income
from wage-employment. In addition, region and sector fixed effects are included.
6.5
LnEarnings
7
7.5
With individual fixed effects
6
6
6.5
LnEarnings
7
7.5
Without individual fixed effects
0
2
4
Years in self-employment
6
8
0
2
4
Years in self-employment
Immigrant women
Native women
Immigrant men
Native men
6
8
95% CI
Figure 1: Predicted yearly earnings by years in self-employment
9
Table 4 shows the results from the estimations of yearly profit from self-employment. Profit increases with
additional years in business, and the yearly return is around 20 per cent for native women (column (1) and (2))
and men (column (3) and (4)). Among women, self-employment experience has a similar impact on the profit
level of immigrant and native firms. Immigrant men have about 2 per cent lower return to self-employment
experience in comparison to native men as indicted by the results in column (4), but this result is not robust to
excluding the year dummies and adding the local unemployment rate in column (5). Controlling for individual
fixed effects reduces the returns to experience to about 5 per cent for women and 3 per cent for men.
Furthermore, the fixed effects regressions indicate that immigrant women (men) gain about 5 (1) per cent
additional profit from one more year of experience than do native women (men). Moreover, being one year older
generates an increase in profit of around 20 per cent (columns (3) and (6)).
In figure 2 the regression results from table 4 are used to plot the predicted profit against years in selfemployment. At start-up, men have higher predicted profit than women, and natives have higher predicted profit
than immigrants. When including individual fixed effects it appears that immigrant men approaches but do not
fully catch up with the profit level of native men, while immigrant women reach native women’s profit level
after about 4 years of business experience.
Table 4: OLS Regressions for Yearly Profit from Self-employment
VARIABLES
Years in self-employment
Immigrant*Years in self-employment
Years in self-employment squared
Immigrant*Years in self-employment squared
Age
Age squared
Immigrant*Age
Immigrant*Age squared
Years since migration
Years since migration squared
(1)
Women
(2)
(3)
(4)
Men
(5)
(6)
0.233***
(0.005)
0.003
(0.011)
-0.023***
(0.001)
-0.000
(0.002)
0.083***
(0.004)
-0.001***
(0.000)
-0.061***
(0.010)
0.001***
(0.000)
0.026***
(0.003)
-0.000***
(0.000)
0.233***
(0.005)
0.014
(0.011)
-0.021***
(0.001)
-0.002
(0.002)
0.082***
(0.004)
-0.001***
(0.000)
-0.061***
(0.010)
0.001***
(0.000)
0.026***
(0.003)
-0.000***
(0.000)
0.047***
(0.005)
0.045***
(0.012)
-0.006***
(0.001)
-0.004***
(0.001)
0.185***
(0.009)
-0.002***
(0.000)
-0.000
(0.027)
0.000
(0.000)
0.022
(0.014)
-0.001***
(0.000)
0.197***
(0.004)
-0.021***
(0.007)
-0.020***
(0.001)
0.001
(0.001)
0.097***
(0.003)
-0.001***
(0.000)
-0.062***
(0.007)
0.001***
(0.000)
0.018***
(0.002)
-0.000**
(0.000)
0.201***
(0.004)
-0.001
(0.007)
-0.019***
(0.001)
-0.001
(0.001)
0.095***
(0.003)
-0.001***
(0.000)
-0.064***
(0.007)
0.001***
(0.000)
0.020***
(0.002)
-0.000***
(0.000)
0.031***
(0.004)
0.013*
(0.008)
-0.004***
(0.001)
-0.004***
(0.001)
0.203***
(0.007)
-0.002***
(0.000)
-0.016
(0.017)
0.001***
(0.000)
0.038***
(0.008)
-0.001***
(0.000)
Individual fixed effects
No
No
Yes
No
No
Yes
Year fixed effects
Yes
No
No
Yes
No
No
Local unemployment rate
No
Yes
Yes
No
Yes
Yes
R-squared
0.126
0.123
0.054
0.127
0.120
0.059
Prediction at sample means
4.831
4.831
4.831
5.166
5.166
5.166
Number of individuals
57,632
57,632
57,632
90,572
90,572
90,572
Number of observations
157,524
157,524
157,524
248,748
248,748
248,748
Robust standard errors, clustered on individuals, in parentheses. ***p<0.01 **p<0.05 *p<0.1 Dependent variable is logarithm of
yearly firm profit. All regressions include controls for education, marital status, number of children, and income from wageemployment. In addition, region and sector fixed effects are included.
10
5
LnProfit
4.5
5
4.5
LnProfit
5.5
With individual fixed effects
5.5
Without individual fixed effects
0
2
4
Years in self-employment
6
8
0
2
4
Years in self-employment
Immigrant women
Native women
Immigrant men
Native men
6
8
95% CI
Figure 2: Predicted yearly profit by years since in self-employment
The regression results for yearly turnover are displayed in table 5. In line with the results for profit and earnings,
turnover increases with about 19 per cent for each additional year in self-employment. Immigrant men have
around 3 per cent lower returns to experience than native men (columns (4) and (5)). If time constant individual
factors are taken into account the increase in turnover form having longer business experience decreases for both
men and women. The fixed effects estimations for firm turnover provide no evidence for that the return to years
in self-employment differs for immigrants and natives. Furthermore, being one year older generates an increase
in turnover of close to 20 per cent (columns (3) and (6)).
Figure 3 displays the predicted turnover based on the regressions presented in table 5. In the left panel estimates
without individual fixed effects are displayed and it appears that immigrants at start-up have higher turnover than
natives, but among women natives catch up with immigrants after about 7 years of experience. However, when
individual fixed effects are included the gaps observed at start-up are of about similar magnitude after 8 years of
experience. This is in contrast to the results for earnings and profit, where a large part of the initial gaps between
immigrants and natives are closed 8 years after start-up. In general, the results for the performance measures
indicate that immigrants gain more from longer self-employment experience in comparison to natives.
Noteworthy is also the fact that failing to incorporate time-invariant individual factors severely bias the results,
and would lead to the misleading conclusion that immigrants have lower a return to experience than natives.
11
Table 5: OLS Regressions for Yearly Firm Turnover
VARIABLES
Years in self-employment
Immigrant*Years in self-employment
Years in self-employment squared
Immigrant*Years in self-employment squared
Age
Age squared
Immigrant*Age
Immigrant*Age squared
Years since migration
Years since migration squared
(1)
Women
(2)
(3)
(4)
Men
(5)
(6)
0.198***
(0.005)
-0.009
(0.011)
-0.020***
(0.001)
-0.001
(0.002)
0.083***
(0.004)
-0.001***
(0.000)
-0.057***
(0.010)
0.001***
(0.000)
0.031***
(0.003)
-0.000***
(0.000)
0.198***
(0.005)
-0.001
(0.011)
-0.018***
(0.001)
-0.003
(0.002)
0.082***
(0.004)
-0.001***
(0.000)
-0.057***
(0.010)
0.001***
(0.000)
0.032***
(0.003)
-0.000***
(0.000)
0.038***
(0.005)
0.011
(0.010)
-0.004***
(0.001)
-0.002*
(0.001)
0.169***
(0.008)
-0.002***
(0.000)
0.014
(0.021)
-0.000
(0.000)
0.015
(0.010)
-0.000
(0.000)
0.185***
(0.004)
-0.038***
(0.007)
-0.020***
(0.001)
0.001
(0.001)
0.102***
(0.003)
-0.001***
(0.000)
-0.077***
(0.007)
0.001***
(0.000)
0.034***
(0.002)
-0.000***
(0.000)
0.187***
(0.004)
-0.024***
(0.007)
-0.018***
(0.001)
0.000
(0.001)
0.101***
(0.004)
-0.001***
(0.000)
-0.078***
(0.007)
0.001***
(0.000)
0.035***
(0.002)
-0.001***
(0.000)
0.049***
(0.004)
-0.009
(0.007)
-0.004***
(0.000)
-0.002***
(0.001)
0.194***
(0.007)
-0.002***
(0.000)
-0.013
(0.014)
0.001***
(0.000)
0.024***
(0.006)
-0.001***
(0.000)
Individual fixed effects
No
No
Yes
No
No
Yes
Year fixed effects
Yes
No
No
Yes
No
No
Local unemployment rate
No
Yes
Yes
No
Yes
Yes
R-squared
0.212
0.209
0.057
0.182
0.176
0.074
Prediction at sample means
5.823
5.823
5.823
6.263
6.263
6.263
Number of individuals
60,005
60,005
60,005
93,235
93,235
93,235
Number of observations
165,075
165,075
165,075
258,278
258,278
258,278
Robust standard errors, clustered on individuals, in parentheses. ***p<0.01 **p<0.05 *p<0.1 Dependent variable is logarithm of
yearly firm turnover. All regressions include controls for education, marital status, number of children, and income from wageemployment. In addition, region and sector fixed effects are included.
6.5
LnTurnover
6
5.5
5.5
6
LnTurnover
6.5
7
With individual fixed effects
7
Without individual fixed effects
0
2
4
Years in self-employment
6
8
0
2
4
Years in self-employment
Immigrant women
Native women
Immigrant men
Native men
6
8
95% CI
Figure 3: Predicted yearly turnover by years in self-employment
12
4.2 Job creation among the self-employed
From the society’s point of view not only is it important that businesses generate income and are profitable, but
they also have the potential to generate jobs for others. Therefore, we will study hiring decisions among
immigrant and native business owners and how these change as time in self-employment increases. We will
estimate three outcomes with the same specifications as described above in equation (1) and (2). The outcomes
considered are the firm’s number of employees, the likelihood of having employees, and the probability of hiring
additional persons to the firm. All regressions will be estimated using OLS, implying that we use a linear
probability model for the two last outcomes.
The results from the regressions of the number of employees at the firm are displayed in table 6. It emerges that
both men and women hire more persons as they gain more business experience. With and additional year of selfemployment experience the number of employees increase with about 0.02 persons from the mean of 0.24 for
women and around 0.06 persons from the mean of 0.34 for men. It appears to be no statistically significant
differences between natives and immigrants as regards how many more they hire when self-employment
experience increases. It emerges that older persons have more employees at their firms, being one year older is
associated with about 0.06 more employees for women and native men, and about 0.22 additional employees for
immigrant men (columns (3) and (6)).
In figure 4 the firm’s predicted number of employees is plotted against years in self-employment. It appears that
immigrant men tend to have almost twice as many employees as the other groups. Immigrant women have more
employees than native women initially, but after eight years native women instead tend to have more persons
employed than immigrant women. However, the average number of employees is never more than one person, so
many of the firms might never have any employees. Therefore, we also consider the likelihood of having
employees and how this changes with experience in table 7. The predicted probability of having employees
(evaluated at the sample means) is about 12 per cent for women and 15 per cent for men. With an additional year
of self-employment this probability increases with between 1 to 2 percentage points. The regression results do
not indicate that the rate at which the probability of having employees increases with experience is different for
immigrants and natives. In figure 5 it appears that immigrants have a higher probability of having employees
independently of how long experience in self-employment they had, and the gaps in the predicted probability
between immigrants and natives remain quite the same over all eight years.
13
Table 6: OLS Regressions for Number of Employees at Firm
VARIABLES
Years in self-employment
Immigrant*Years in self-employment
Years in self-employment squared
Immigrant*Years in self-employment squared
Age
Age squared
Immigrant*Age
Immigrant*Age squared
Years since migration
Years since migration squared
(1)
Women
(2)
(3)
(4)
Men
(5)
(6)
0.023**
(0.009)
0.012
(0.014)
-0.001
(0.001)
-0.004**
(0.002)
0.017***
(0.005)
-0.000***
(0.000)
-0.015
(0.010)
0.000
(0.000)
0.012***
(0.003)
-0.000**
(0.000)
0.024***
(0.007)
0.011
(0.013)
-0.001
(0.001)
-0.003*
(0.002)
0.017***
(0.005)
-0.000***
(0.000)
-0.015
(0.010)
0.000
(0.000)
0.012***
(0.003)
-0.000**
(0.000)
0.010**
(0.005)
0.008
(0.011)
0.000
(0.001)
-0.002
(0.002)
0.061***
(0.008)
-0.001***
(0.000)
0.047
(0.035)
-0.001
(0.000)
-0.000
(0.009)
0.000
(0.000)
0.065***
(0.019)
-0.010
(0.021)
-0.007***
(0.003)
0.002
(0.003)
0.025***
(0.007)
-0.000***
(0.000)
-0.016
(0.010)
0.000
(0.000)
0.024***
(0.002)
-0.000***
(0.000)
0.069***
(0.018)
-0.009
(0.020)
-0.007***
(0.003)
0.004
(0.003)
0.024***
(0.007)
-0.000***
(0.000)
-0.017
(0.010)
0.000
(0.000)
0.024***
(0.002)
-0.000***
(0.000)
0.049***
(0.016)
-0.036
(0.022)
-0.003
(0.002)
0.001
(0.003)
0.065***
(0.009)
-0.001***
(0.000)
0.153***
(0.042)
-0.001***
(0.000)
-0.017
(0.012)
0.001
(0.000)
Individual fixed effects
No
No
Yes
No
No
Yes
Year fixed effects
Yes
No
No
Yes
No
No
Local unemployment rate
No
Yes
Yes
No
Yes
Yes
R-squared
0.009
0.009
0.001
0.010
0.010
0.003
Prediction at sample means
0.24
0.24
0.24
0.34
0.34
0.34
Number of individuals
60,972
60,972
60,972
94,676
94,676
94,676
Number of observations
168,289
168,289
168,289
263,082
263,082
263,082
Robust standard errors, clustered on individuals, in parentheses. ***p<0.01 **p<0.05 *p<0.1 All regressions include controls for
education, marital status, number of children, and income from wage-employment. In addition, region and sector fixed effects
are included.
.6
.2
.4
Number of employees
.6
.4
.2
Number of employees
.8
With individual fixed effects
.8
Without individual fixed effects
0
2
4
Years in self-employment
6
8
0
2
4
Years in self-employment
Immigrant women
Native women
Immigrant men
Native men
6
8
95% CI
Figure 4: Predicted number of employees at firm by years in self-employment
14
Table 7: OLS Regressions for Probability of Having Employees at Firm
VARIABLES
Years in self-employment
Immigrant*Years in self-employment
Years in self-employment squared
Immigrant*Years in self-employment
squared
Age
Age squared
Immigrant*Age
Immigrant*Age squared
Years since migration
Years since migration squared
(1)
Women
(2)
(3)
(4)
Men
(5)
(6)
1.625***
(0.132)
0.097
(0.354)
-0.127***
(0.024)
-0.122*
1.666***
(0.129)
0.282
(0.347)
-0.116***
(0.023)
-0.098
0.933***
(0.135)
-0.022
(0.361)
-0.061***
(0.019)
-0.033
1.671***
(0.121)
1.242***
(0.274)
-0.162***
(0.023)
-0.267***
1.787***
(0.119)
1.503***
(0.270)
-0.139***
(0.022)
-0.213***
1.295***
(0.128)
0.396
(0.300)
-0.096***
(0.019)
-0.180***
(0.065)
0.403***
(0.108)
-0.006***
(0.001)
-0.410
(0.281)
0.004
(0.003)
0.423***
(0.090)
-0.006***
(0.002)
(0.063)
0.393***
(0.108)
-0.006***
(0.001)
-0.417
(0.282)
0.004
(0.003)
0.418***
(0.090)
-0.006***
(0.002)
(0.051)
2.492***
(0.249)
-0.024***
(0.003)
0.745
(0.701)
-0.000
(0.008)
0.769***
(0.209)
-0.016***
(0.005)
(0.049)
0.651***
(0.093)
-0.008***
(0.001)
-0.819***
(0.230)
0.006**
(0.003)
0.947***
(0.067)
-0.014***
(0.001)
(0.048)
0.615***
(0.093)
-0.008***
(0.001)
-0.890***
(0.231)
0.007**
(0.003)
0.968***
(0.067)
-0.014***
(0.001)
(0.042)
3.497***
(0.220)
-0.033***
(0.002)
3.815***
(0.664)
-0.018***
(0.006)
0.428
(0.346)
-0.018***
(0.005)
Individual fixed effects
No
No
Yes
No
No
Yes
Year fixed effects
Yes
No
No
Yes
No
No
Local unemployment rate
No
Yes
Yes
No
Yes
Yes
R-squared
0.114
0.112
0.021
0.122
0.116
0.034
Predicted probability at sample means
11.59
11.59
11.59
15.46
15.46
15.46
Number of individuals
60,972
60,972
60,972
94,676
94,676
94,676
Number of observations
168,289
168,289
168,289
263,082
263,082
263,082
Robust standard errors, clustered on individuals, in parentheses. ***p<0.01 **p<0.05 *p<0.1 All regressions include controls for
education, marital status, number of children, and income from wage-employment. In addition, region and sector fixed effects
are included. The coefficients are reported in percentage points.
30
25
10
15
20
Probability having employees
25
20
10
15
obab ty a
g e p oyees
30
35
With individual fixed effects
35
Without individual fixed effects
0
2
4
Years in self-employment
6
8
0
2
4
Years in self-employment
Immigrant women
Native women
Immigrant men
Native men
6
8
95% CI
Figure 5: Predicted probability of having employees at firm by years since start-up
15
Finally we consider the probability of hiring more persons (independently of the firm’s number of employees in
the previous period). The results from the regressions with the dependent variable being the probability of hiring
are displayed in table 8. The predicted probability of hiring is about 3 per cent for women and 5 per cent for men
at the sample means. Increasing self-employment experience by one more year implies an increase by around 2
percentage points of hiring for natives and an about twice as large increase for immigrants. From figure 6 we can
see that the probability of hiring follows an inverted u-shaped pattern for all groups. Initially, the likelihood of
hiring increases but after about four years it starts to decrease. When individual fixed effects are included the
probability of hiring is always higher for immigrants than for natives.
Table 8: OLS Regressions for Probability of Hiring more Employees to Firm
VARIABLES
Years in self-employment
Immigrant*Years in self-employment
Years in self-employment squared
Immigrant*Years in self-employment squared
Age
Age squared
Immigrant*Age
Immigrant*Age squared
Years since migration
Years since migration squared
(1)
Women
(2)
(3)
(4)
Men
(5)
(6)
2.242***
(0.073)
1.585***
(0.197)
-0.263***
(0.013)
-0.247***
(0.036)
0.095**
(0.044)
-0.002***
(0.000)
0.055
(0.117)
-0.001
(0.001)
0.053
(0.034)
-0.001
(0.001)
2.259***
(0.072)
1.582***
(0.195)
-0.257***
(0.013)
-0.227***
(0.035)
0.088**
(0.044)
-0.002***
(0.000)
0.056
(0.117)
-0.001
(0.001)
0.049
(0.034)
-0.001
(0.001)
2.203***
(0.089)
1.390***
(0.243)
-0.259***
(0.013)
-0.166***
(0.035)
0.969***
(0.145)
-0.010***
(0.002)
0.948**
(0.429)
-0.012**
(0.005)
0.426***
(0.126)
-0.004
(0.003)
2.509***
(0.070)
3.331***
(0.170)
-0.298***
(0.013)
-0.462***
(0.031)
0.123***
(0.041)
-0.002***
(0.000)
-0.230**
(0.108)
0.001
(0.001)
0.231***
(0.029)
-0.004***
(0.001)
2.559***
(0.070)
3.318***
(0.168)
-0.291***
(0.013)
-0.419***
(0.031)
0.109***
(0.041)
-0.002***
(0.000)
-0.258**
(0.108)
0.002
(0.001)
0.233***
(0.029)
-0.004***
(0.001)
2.493***
(0.085)
2.893***
(0.207)
-0.275***
(0.013)
-0.400***
(0.032)
1.450***
(0.131)
-0.016***
(0.001)
2.135***
(0.437)
-0.015***
(0.004)
-0.275
(0.235)
0.000
(0.003)
Individual fixed effects
No
No
Yes
No
No
Yes
Year fixed effects
Yes
No
No
Yes
No
No
Local unemployment rate
No
Yes
Yes
No
Yes
Yes
R-squared
0.040
0.038
0.017
0.058
0.055
0.026
Prediction at sample means
3.47
3.47
3.47
5.25
5.25
5.25
Number of individuals
60,972
60,972
60,972
94,676
94,676
94,676
Number of observations
168,289
168,289
168,289
263,082
263,082
263,082
Robust standard errors, clustered on individuals, in parentheses. ***p<0.01 **p<0.05 *p<0.1 All regressions include controls for
education, marital status, number of children, and income from wage-employment. In addition, region and sector fixed effects
are included. The coefficients are reported in percentage points.
16
15
0
5
10
Probability hiring employees
10
0
5
obab ty
g e p oyees
15
20
With individual fixed effects
20
Without individual fixed effects
0
2
4
Years in self-employment
6
8
0
2
4
Years in self-employment
Immigrant women
Native women
Immigrant men
Native men
6
8
95% CI
Figure 6: Predicted probability of hiring additional employees to firm by years since in self-employment
4.3 Heterogeneity
Previous research has shown that self-employment performance among immigrants in Sweden differ depending
on whether they are from European or non-European countries (e.g. Andersson Joona, 2011; Hammarstedt,
2006; Andersson and Hammarstedt, 2011). In order to test for this we have estimated the regressions as specified
in equation [2], but distinguished between immigrants from European and non-European countries. The
predicted earnings, profit, turnover and probability of having employees are plotted in figure 7 for European
immigrants, non-European immigrants, and natives separately. The solid lines are the predictions for women,
while the dashed lines display men’s predicted outcomes. As regards earnings and profit, women from nonEuropean countries earn the least to start with but experience a steeper growth than natives and European
immigrants as they get more experienced. However, after eight years there are no statically significant
differences between the groups among women. Similarly, earnings and profit are lowest for men from nonEuropean countries at start-up of the firms. Male business owners from non-European countries increase their
earnings and profit more than the other groups as they gain experience and they eventually reach the level of
native men. On the contrary, European immigrant men have a lower return to experience and do not reach the
profit and earnings level of native men.
Furthermore, the turnover and probability of having employees are much higher for non-European immigrants
than for European immigrant and natives, irrespectively of gender. The firms of non-European immigrants
continue to be bigger and employ more persons as business experience increases.
17
2
6
8
4
6
8
Probemployees
6.5
6
LnTurnover
5.5
Years in self-employment
4.4 4.6 4.8 5 5.2 5.4
LnProfit
0
4
Years in self-employment
0
2
0
2
4
6
8
4
6
8
Years in self-employment
10 20 30 40 50
7.5
7
6.5
LnEarnings
6
2
7
0
Years in self-employment
Immigrant women, Non-European
Immigrant men, Non-European
Immigrant women, European
Immigrant men, European
Native women
Native men
95% CI
Figure 7: Predicted outcomes by immigrant group
In addition, we have tested for heterogeneous effects across individuals depending on their age and years since
migration.8 We split the sample by the median age (43 years), and by the median years since migration for
immigrants (16 years). In general, persons younger than 43 years tend to have higher returns to self-employment
experience than persons above 43 years. This could be because older persons have already attained the relevant
experience in other ways. Moreover, we find that having experience from self-employment has a larger impact
on the outcomes of immigrants who resided in Sweden for more than 16 years. For those arriving more recently
it is possible that other experience is more relevant, i.e. host country specific knowledge such as learning the
language.
5 Conclusions
The large increase of migration to many countries has implied that the labour market integration of immigrants is
now high on the agenda. Increasing the self-employment rates has been suggested among the solutions to poor
labour market outcomes among the foreign born. Whether this will be successful or not depends on how well
immigrants perform in self-employment. In this paper we study self-employment performance among
immigrants and natives in Sweden. In particular we focus on how performance is related to self-employment
experience. Previous research has indicated that immigrants face difficulties at start-up of their businesses, but is
still unknown whether these are dampened as they gain more experience.
The results show that earnings and firm profit are higher for self-employed natives than for self-employed
immigrants and that men gain more from their business than women. These results are in line with the findings
in previous studies for Sweden (e.g. Andersson Joona, 2011; Hammarstedt, 2006; Andersson and Hammarstedt,
8
The results are available from the author upon request.
18
2011). As self-employment experience increases immigrant men approaches the earnings and profit levels of
native men, but they do not reach parity. On the contrary, immigrant women catch up with the earnings and
profit levels of native women after between 3 to 5 years in business. This is in contrast to the findings of
Lofstrom (2011) who finds that immigrant men in the US catch up with natives, while immigrant women do not.
Furthermore, the results highlights the importance of taking into account that individuals who leave selfemployment might be different from those who stay, in particular the most successful might be more likely to
remain in self-employment. Neglecting to incorporate non-random selection out from self-employment, i.e. not
controlling for individual fixed effects, implies an overestimation of the returns to experience and an
underestimation of immigrants’ catch-up in both the earnings and profit regressions.
Further on, immigrants’ firms are bigger than natives’ firms in terms of turnover. The gap in firm size remains as
business experience increases and is unaffected by controlling for individual fixed effects. Moreover, men tend
to have higher turnover in their firms than women, and the turnover is higher in firms owned by non-European
immigrants in comparison to European immigrants’ and natives’ firms.
As regards employment outcomes it seems that immigrants’ firms, and in particular those owned by nonEuropean immigrants, contribute more to job creation than natives’ firms. Immigrant business owners have more
employees at their firms on average and are more likely to have employees and make additional hires to their
firms. As self-employment experience increase immigrants’ businesses grow faster in terms of additional hires in
comparison to firms owned by natives. This is in line with the results for the US presented in Kerr and Kerr
(2016).
As immigrants gain more self-employment experience their earnings and profit from self-employment
approaches the level for natives. It is possible that immigrants face obstacles initially, related to financing and
knowledge of laws and regulations, which hinder their firm’s performance. However, with a few years of
experience such obstacles might be reduced and they reach the earnings and profit levels of natives.
How firms contribute to job creation is one of the most central aspects of self-employment. In this paper we add
to the scarce literature on job creation and focus in particular on differences between immigrants and natives.
The results show that immigrant firms have higher turnover and more employees, and remain larger also when
they have been in business for several years. This study is among the first that highlights the particular
importance of immigrant firms in the job creation process. Future research should contribute more to our
understanding of the mechanism behind the fact that immigrants’ firms have higher turnover and contribute more
to job creation than natives’ firms. In particular, more research on who are the persons that the immigrant firms
hire is needed.
19
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21
Appendix
Table A1: OLS Performance Regressions with Non-logarithmic Outcomes
VARIABLES
Years in self-employment
Immigrant* Years in self-employment
Years in self-employment squared
Immigrant* Years in self-employment
squared
Age
Age squared
Immigrant*Age
Immigrant*Age squared
Years since migration
Years since migration squared
(1)
Women
246.896***
(5.042)
-39.701***
(15.176)
-20.504***
(0.878)
3.408
(2.239)
87.172***
(3.916)
-1.070***
(0.045)
-57.933***
(9.500)
0.664***
(0.114)
11.259***
(3.846)
-0.063
(0.077)
Earnings
Men
(2)
(3)
102.409***
(5.231)
-7.039
(10.149)
-8.601***
(0.694)
-0.378
(1.443)
203.470***
(8.511)
-1.599***
(0.088)
-41.138*
(22.054)
0.334
(0.222)
11.214
(10.498)
0.078
(0.170)
315.334***
(19.315)
-104.418***
(20.497)
-28.506***
(2.479)
9.907***
(2.769)
121.961***
(8.411)
-1.386***
(0.110)
-68.880***
(10.202)
0.695***
(0.130)
8.225***
(2.218)
0.045
(0.052)
Women
(4)
(5)
128.836***
(6.084)
-30.078***
(9.356)
-10.511***
(0.834)
1.081
(1.246)
292.486***
(9.938)
-2.219***
(0.104)
-64.616***
(17.721)
0.657***
(0.197)
10.950
(6.889)
0.064
(0.145)
36.491***
(2.847)
-4.233
(3.799)
-4.000***
(0.426)
0.292
(0.588)
13.812***
(1.748)
-0.172***
(0.019)
-9.209***
(2.707)
0.102***
(0.031)
2.960***
(0.676)
-0.030**
(0.013)
Profit
Men
(6)
(7)
11.414***
(2.930)
-1.221
(3.644)
-1.011***
(0.356)
-0.344
(0.466)
33.506***
(4.398)
-0.320***
(0.042)
-6.150
(6.243)
0.082
(0.067)
2.633
(1.630)
0.027
(0.047)
56.276***
(7.698)
-27.096***
(7.886)
-7.102***
(1.017)
3.695***
(1.074)
23.088***
(2.900)
-0.251***
(0.034)
-15.496***
(3.244)
0.143***
(0.038)
2.566***
(0.585)
-0.002
(0.013)
Women
(8)
(9)
10.919*
(5.639)
-4.511
(5.814)
-0.588
(0.664)
-0.140
(0.694)
45.376***
(3.933)
-0.393***
(0.038)
-5.619
(4.927)
0.083*
(0.051)
2.691**
(1.284)
0.002
(0.028)
64.113***
(20.804)
37.191
(31.491)
-6.409**
(2.760)
-8.371*
(4.348)
41.066***
(9.156)
-0.510***
(0.102)
-66.050**
(29.701)
0.716**
(0.331)
21.005***
(4.639)
-0.341***
(0.093)
Turnover
Men
(10)
(11)
15.290*
(8.626)
15.609
(16.021)
0.019
(2.369)
-5.391*
(2.962)
93.585***
(11.725)
-0.736***
(0.149)
44.571
(37.986)
-0.597
(0.451)
8.678
(12.369)
0.190
(0.404)
139.047***
(28.623)
-5.757
(36.601)
-16.496***
(3.903)
-2.109
(5.028)
62.690***
(13.900)
-0.711***
(0.169)
-57.885***
(21.408)
0.504*
(0.259)
40.405***
(4.968)
-0.648***
(0.107)
(12)
56.031**
(22.065)
3.047
(25.692)
-2.198
(3.238)
-6.018*
(3.643)
147.261***
(11.494)
-1.228***
(0.195)
81.933**
(37.962)
-0.628
(0.461)
12.909
(8.602)
-0.056
(0.242)
Individual fixed effects
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Year fixed effects
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
R-squared
0.157
0.105
0.024
0.090
0.015
0.005
0.016
0.009
0.008
0.001
0.008
0.001
Number of individuals
60,972
60,972
94,676
94,676
60,972
60,972
94,676
94,676
60,972
60,972
94,676
94,676
Observations
168,289
168,289
263,082
263,082
168,289
168,289
263,082
263,082
168,289
168,289
263,082
263,082
Robust standard errors, clustered on individuals, in parentheses. ***p<0.01 **p<0.05 *p<0.1. All regressions include controls for education, marital status, number of children, and income from wageemployment. In addition, region and sector fixed effects are included.
22