The Impact of Self-Employment Experience on Wages and the Risk of Unemployment

Michaela NiefertCentre for European Economic Research, Mannheim

Niefert@zew.de(competing for Young Economist Award)

Abstract: The paper analyses the impact of self-employment experience on earnings in the

wage and salary sector and on the probability of unemployment using a fixed effects model

and a random effects probit model, respectively. The results reveal that women working in

wage employment receive positive returns to self-employment experience. The returns for

men may be positive or negative depending on the industry of self-employment and the cur-

rent occupation. The probability of finding a job after leaving self-employment is generally

higher for men than for women. In any case temporary self-employment seems to be a good

alternative to unemployment for both genders.

Keywords: Self-employment experience, wage and salary work, earnings, unemployment

risk

JEL-Classification: J23, J31, J64, C23

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1 Introduction

There is extensive empirical work on the determinants of earnings in the wage and salary

sector (hereafter “wage sector”) and the risk of unemployment. A lot of research has been

dedicated to the labour market consequences of educational attainment, labour force attach-

ment, job tenure and unemployment experience. Moreover, it exists a comprehensive litera-

ture on the transition from wage employment to self-employment. There are, however, still

only relatively few studies on the transition from self-employment into other labour market

states and on the returns to self-employment experience in wage work. This is in so far sur-

prising as the experience gained from leading an own company presumably differs considera-

bly from the experience acquired as an employee and could therefore have a different impact

on earnings and employment opportunities in the wage sector. The present study wants to

contribute to fill this gap.

The question addressed here is in how far human capital specific to self-employment can be

transferred to the wage sector and whether former self-employment works as a signal to em-

ployers. The paper analyses how self-employment experience is rewarded by employers in

comparison with wage work or unemployment experience. It further dwells on the chances of

those who give up self-employment to find wage employment. The topic is currently of spe-

cial relevance since policy in Germany increasingly promotes self-employment as an alterna-

tive to unemployment by granting start-up assistance to unemployed people becoming self-

employed. For the evaluation of this policy it is important to know about the consequences of

choosing self-employment over unemployment for those who return to the wage sector later

on.

The next section describes the possible influences that self-employment experience may have

on wages and the risk of unemployment and relies on labour market theory. The relevant em-

pirical literature is surveyed in section 3. Section 4 discusses some methodological issues. The

econometric models used for the empirical analysis are explained in section 5. A description

of the underlying data set is given in section 6. Section 7 presents the results, section 8 con-

cludes.

2 Theoretical Considerations

Labour market theory and research on the gender earnings gap give important hints to how

experience in self-employment may affect earnings and employment prospects in the wage

sector. There are several kinds of potential influences. Most of them can be attributed to hu-

3

man capital effects. Firstly, during the time being self-employed a person foregoes growth in

human capital specific to the wage sector which she could have gained if she had worked as

an employee. Secondly, possible wage sector-specific human capital which a person possesses

when entering self-employment may depreciate during the absence from the wage sector.

Thirdly, a self-employed person foregoes the accumulation of employer-specific capital which

she could have acquired during working for the same employer. All these detrimental effects

are supposed to occur also in case of unemployment or labour force intermittence (Mincer and

Ofek 1982) and to reduce wages and the chances of finding employment.

But different from those who are unemployed or out of the labour force, self-employed per-

sons acquire work experience. The knowledge gained in self-employment can possibly be

transferred to the wage sector, enhance job opportunities and be rewarded by an employer.

The transferability presumably depends on the occupation, the sector of origin and the sector

of destination. It may also be affected by company sizes and by the position which the former

self-employed person – if she finds a job – takes in the new company. It could be hypothe-

sised that returns to self-employment experience are the higher, the more similar the jobs in

self-employment and paid employment are with respect to these terms. For example, one

might think that what distinguishes self-employment experience are management capabilities

which are particularly helpful in a leading position. These skills are certainly less useful in a

low-qualified job at the lower scale of the company hierarchy. However, owners of very small

firms in the service or retail sector can be supposed to have engaged not only in management

tasks but also in the “production process”. So there is less difference between the work of the

owner and of the employees in these firms, and a self-employed person can easily transfer its

knowledge to the wage sector.

Independent of the actual effects of self-employment on human capital, the fact of having

been self-employed may work as a signal to employers. These considerations are based on

statistical discrimination theory which has been used to explain the gender wage differential.

Since employers have only limited information about the potential employee, they tend to

ascribe to her the pertinent characteristics of the group she belongs to. In the case of self-

employed workers employers must probably rely even more on this strategy because they do

not have any information from references of previous employers for the periods of self-

employment (Trzcinski 1999). If employers consider the characteristics of self-employed

workers to be less advantageous than those of workers who have exclusively been employed

in the wage sector, they are probably not apt to employ them or they pay them less. Employ-

ers might stigmatise the event of self-employment, particularly if the potential employee has

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been unemployed before entering self-employment. But they might also attribute qualities like

entrepreneurial spirit and willingness to perform to the self-employed.

To summarise, the relationship between self-employment experience, earnings in the wage

sector and risk of unemployment is determined by human capital and signalling effects. The

direction of these effects is not clear ex ante. They can be assumed to depend very much on

the characteristics of the job in self-employment as well as of the job (aimed at) in paid em-

ployment.

3 Survey of Empirical Literature

The empirical evidence regarding the wage returns to self-employment experience is rather

mixed. Evans and Leighton (1989) do not observe any significant differences between the

returns to wage employment and self-employment in the wage sector analysing the National

Longitudinal Survey (NLS) of Young Men. They conclude that workers leaving self-

employment return to wage work at roughly the same wages they would have received if they

had not tried self-employment. They note, however, that it is unclear whether this is due to the

value of business experience or because those with the best wage opportunities tend to switch.

Using the NLS Youth Cohort and the NLS of Young Women, Williams (2000) finds smaller

returns to self-employment as compared to wage employment only for women. The result

does not hold for women working in sales occupations. In a further analysis of the Youth Co-

hort, Williams (2004) discovers that youth self-employment experience in contrast to wage

employment experience is not rewarded in the wage employment market at age 27. Bruce and

Schuetze (2004) observe smaller, negative returns1 to self-employment for both genders in

their analysis of the Panel Study of Income Dynamics (PSID). They explain the difference

between their results and those of Williams (2000) by the fact that they focus on short-term

self-employment, namely on those who are wage workers at the start and the end of a five-

year-period. This would imply that short spells of self-employment have a more detrimental

effect on wage earnings than longer ones. The returns to self-employment, however, are larger

than the returns to unemployment. Thus, the negative labour market consequences associated

with unemployment seem to be more severe than for self-employment.

Trzcinski´s (1999) study on basis of the PSID and the German Socioeconomic Panel

(GSOEP) is partly contradictory to the findings of Bruce and Schuetze. Controlling for time

spent in unemployment and out of the labour force she can detect a smaller, negative effect of

1 The sign of the self-employment experience coefficient depends on which other employment states are in-cluded in the regression. Bruce and Schuetze control also for the years spent in unemployment.

5

self-employment experience in comparison with experience in wage work only for American

women, not for American men. In Germany, in contrast, this result is only valid for men. For

women, the coefficient of self-employment experience is negative but insignificant. In addi-

tion to experience, Trzcinski uses the number of spells spent in self-employment as an ex-

planatory variable in order to capture the signalling effect. The corresponding coefficient

turns out to be positive for American women and German men. It seems that for these two

groups self-employment deteriorates the human capital relevant for wage work, but that at the

same time frequent self-employment spells work as positive signal to employers. Trzcinski

concludes that these individuals fare better when moving frequently into and out of self-

employment than when spending extended periods of time in self-employment. The returns to

self-employment seem to be a mixture of human capital and signalling effects. Her study also

indicates that the consequences of self-employment are less detrimental than those of unem-

ployment and time out of labour force. Williams (2003) also uses data from the GSOEP but

does not differentiate by sex. He finds that the return to self-employment experience is lower

than the return to wage employment experience. Not controlling for other labour market

states, however, the return is still positive.

Bruce and Schuetze´s (2004) study is the only one known to the author which deals also with

the effect of self-employment experience on the chances to find wage employment. Focussing

on a sample of individuals who are wage workers at the start and not self-employed at the end

of a five-year-period, Bruce and Schuetze find small positive effects of self-employment ex-

perience on the probability of unemployment which are insignificant for most periods. In any

case, self-employment experience increases the risk of unemployment by far less than unem-

ployment experience.

To sum up, there is no clear picture whether the wage returns to self-employment experience

are lower than those to wage employment experience. Some of the results support this hy-

pothesis. The effects seem to differ by sex, country and length of self-employment spell. The

finding that self-employment experience produces higher returns than time spent in unem-

ployment or out of the labour force, however, is consistent across all studies. There is only

very weak and scarce evidence that self-employment experience could increase the probabil-

ity of unemployment for those who want to return to the wage sector.

4 Methodological Issues

There are three potential sources of bias when estimating an earnings function. Firstly, wages

typically depend on factors like motivation or talent which are unobservable. If these factors

6

are correlated with the explanatory variables used in the regression, the estimated coefficients

will be biased due to unobserved heterogeneity. For example, unobserved characteristics

which are relevant for the level of earnings in the wage sector might also influence the choice

of self-employment over paid employment. In this case, individuals select non-randomly into

the different employment states. The estimated effect of self-employment experience on

wages then is biased if it is not controlled for this non-random selection. Secondly, the vari-

ables related to the employment history may be endogenous. Wages are likely to affect labour

force participation and unemployment. They might also influence the choice of self-

employment as an alternative to wage employment. As a result, the estimation will be fraught

with an endogeneity bias and it is not clear whether the coefficients reflect the effect of the

regressors on wages or the reverse. Both types of bias may also occur when estimating the

probability of unemployment. Unobserved factors affecting wages are likely to influence the

unemployment risk, too. In addition, the probability of unemployment may exert an influence

on the decision to try self-employement. Thirdly, there is the sample selection problem. The

wage equation can only be estimated for individuals in paid-employment, not for those who

are in one of the other labour market states. As a consequence, the effect of self-employment

experience, for example, can only be estimated for those who leave self-employment and en-

ter the wage sector later on. These self-employed persons might be a non-random group of the

self-employed. Individuals who remain in self-employment and therefore do not enter the

earnings estimation might have had higher or lower wages upon returning to the wage sector.

The self-employment-related coefficients then only reflect the labour market consequences of

self-employment for those who leave self-employment and not of self-employment in general.

The empirical work so far has only addressed the heterogeneity and endogeneity issues. In an

attempt to correct for non-random selection into self-employment, Williams (2000) and Bruce

and Schuetze (2004) include the wages received by self-employed workers before leaving the

wage sector into the regression, thereby accounting for a possible lower productivity of these

workers. Their results suggest that in some cases self-employment experience has a more

positive effect on wages when controlling for such productivity differentials. This would im-

ply that individuals negatively select into temporary self-employment. Evans and Leighton

(1989), Trzcinski (1999) and Williams (2003, 2004) use a standard two-step procedure to

control for selectivity. Only Trzcinski and Williams (2003) are able to detect any correlation

between selection into self-employment and earnings. Williams´ selectivity-adjusted estimate

of the return to self-employment is of about the same magnitude as the simple OLS estimate.

To correct for endogeneity bias, Williams (2003) applies an instrumental variables approach

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using a GMM estimator. But he only instruments the education variable, not self-employment

experience.

All of the studies – although using panel data – apply only cross-sectional methods to investi-

gate the effect of self-employment experience on wages. Thus they miss the opportunity to

control completely for constant unobserved individual effects which would reduce all the bi-

ases considerably (see next section). Moreover, none of them controls for sample selection

bias. Evans and Leighton (1989), Williams (2000) and Bruce and Schuetze (2004) admit that

the methodology they use does not take into account the possible selectivity emanating from

observing only those self-employed workers who left for paid employment. In an attempt to

address these methodological problems more fully and to reduce all three kinds of biases, the

present paper will apply a panel data method to account for heterogeneity and use a procedure

correcting for sample selectivity.

5 Econometric Model

5.1 Fixed-Effects Estimation of the Wage Equation

The analysis of wages is based on the typical Mincerian earnings function which has been

adapted to the panel data context by Kim and Polachek (1994):

itiitit xw εαβ ++= , Ni ,...,2,1= Tt ,...,2,1= . 1

i is an index for the individual, t is an index for the year. w is the logarithm of monthly

wages deflated by the consumer price index (1990=100), x is a vector of time-varying re-

gressors, α is a vector of unobservable individual, time-constant effects, and ε is the error

term reflecting time-varying unobservable factors.

The heterogeneity and endogeneity bias are closely interrelated. Both the omission of vari-

ables and a simultaneity between the explanatory variables and the dependent variable lead to

a correlation of the error term with the regressors, so that the exogeneity assumption

0)( =iti xE α (in the case of heterogeneity) respectively 0)( =+ ititi xE εα (in the case of

endogeneity) is violated. Heterogeneity usually implies endogeneity (Kim and Polachek 1994,

Polachek and Kim 1994). Applying panel data estimation techniques decreases the impor-

tance of both biases. Panel data allow to transform equation 1 by mean deviation so that the

unobserved constant effect iα nets out:

)()( iitiitiit xxww εεβ −+−=− , 2

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and the exogeneity condition relaxes to 0)( =−− iitii xxE εε . Estimating equation 2 by OLS

corresponds to the fixed-effects approach and eliminates the heterogeneity bias as far as it

arises from time-constant unobservables. Likewise, the endogeneity problem diminishes be-

cause invariable individual factors affecting wages are controlled for so that they cannot exert

any influence on the regressors via wages. Since employment history and wages can be sup-

posed to be largely determined by individual factors like motivation or talent, the biases are

presumably reduced considerably by applying this panel data approach.

Sample selection, the third type of bias, is caused by the fact that itw can only be observed if

individual i is working in the wage sector. Selection into the wage sector can be described by

the probit model

ittitit Xs υγ +=* ~itit Xυ Normal(0,1) 3

1=its if 0* >its , and 0 otherwise.

its takes value 1 if i works in the wage sector in period t. X should include at least one

significant explanatory variable which is not part of x in order to avoid collinearity among

the regressors. The sample selection problem arises if υ is correlated with ε in equation 1 so

that 0),( ≠ititit xE υε . Using the fixed effects approach reduces the sample selection bias to

the extent that the selection process is determined by time-constant factors. However, the de-

cision and possibility to work as a dependent employee are probably also influenced by time-

varying factors like household income, family background, regional economic situation or

existence of a promising self-employment alternative. Therefore, an extension of the two-step

selection correction procedure by Heckman (1979) to the panel data context is used. This is

done by estimating equation 3 for each t and calculating the inverse Mills ratio itλ̂ for all i

and t (Wooldrige 2002). Then equation 2 is estimated by OLS using the Mills ratios as addi-

tional regressors:

)(ˆ...ˆ1)( 1 iititTittiitiit dxxww εελρλρβ −++++−=− 4

where td1 through tdT are time dummies. Sample selection bias can be tested by a joint test

of 0:0 =tH ρ for Tt ,...,1= .

5.2 Random-Effects Probit Estimation of the Probability of Unemployment

The probability of unemployment is estimated by a random effects probit model. It can be

written

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ititit uXy += β* ~itit Xu Normal(0,1) 5

1=ity if 0* >ity , and 0 otherwise,

where ity is an indicator of unemployment. itu can be decomposed into iti εα + , where the

ia and itε are random drawings which are assumed to be independent and normally distrib-

uted, both with mean zero and respective variances 2ασ and 2

εσ . The Butler and Moffitt

(1982) formulation of the model restricts the correlation of the itu to be equal across different

time periods in order to avoid problematic computation of joint probabilities from a T-variate

normal distribution. The likelihood function then reduces to a single integral which can be

evaluated by Gaussian quadrature. The correlation of the error term over time is given by

)/(],[ 222αεα σσσρ +==isit uuCorr for st ≠ .2 Accounting for the serial correlation of obser-

vations on the same person reduces the variance as compared to the pooled probit model. It

does not eliminate a possible heterogeneity or endogeneity bias, though, since it assumes the

α and X to be uncorrelated. The random-effects probit model is still preferred to the fixed-

effects logit model because the latter would lead to a drop out of all individuals which do not

change between employment and unemployment during the observation period. This would

imply a heavy loss of information.

6 Data Description

The data base used for the empirical analysis in section 7 are the first 20 waves of the German

Socioeconomic Panel (GSOEP).3 The GSOEP is a longitudinal survey of private households

and persons which started in 1984 and included 12,245 persons in the first wave. It provides

annual information on various individual and household characteristics, e.g. in the areas of

population and demography, education, labour market and occupational dynamics, and ear-

nings and income. Its monthly employment and income calendars provide an informative da-

tabase on the duration in various labour market states. Several extensions of the GSOEP have

taken place since 1984.4 In the 20th wave (2003) the GSOEP comprises nearly 24,000 persons.

All available waves (1984-2003) and samples (A-G) are used for the empirical analysis. Peri-

2 For more details on the estimation see Greene (1997).3 See http://www.diw.de/english/sop/index.html for a detailed description of the GSOEP.4 Starting with a West German (A) and a foreign sample (B) (the latter consisting of the so-called “Gastarbeiter”)in the first wave, an East German (C) sample was added after the German reunification in 1990; an immigrantsample (D) containing persons who immigrated into Germany after 1984 started in 1994/95, a refreshment (E)and an innovation (F) sample were selected in 1998 and 2000, respectively, and a high income sample (G) wasdrawn in 2002.

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odical information, calendar information as well as life history information contained in the

GSOEP has been exploited to compile the work history of individuals. The panel estimations

comprise the waves 1990 to 2003. Variable definitions are given in the appendix.

Table 1 gives some descriptive statistics by employment history and sex using the most recent

wave of the GSOEP. It compares those who are currently employed in the wage sector and

have never been self-employed (“always paid-employed”) with those who are currently self-

employed and have never been paid-employed (“always self-employed”) and those who are

currently paid-employed but have once been self-employed (“formerly self-employed, now

paid-employed”), further differentiating between men and women. The relative quantities of

these groups reveal that, while there are only half as much females continuously self-

employed as males, there are at least as much females choosing temporarily self-employment

as males. The formerly self-employed are older than the always paid-employed but younger

than the always self-employed. The always self-employed are married more often and have

more children on average than the always paid-employed. The same holds for formerly self-

employed females, but to a lesser extent. Wage employment apparently makes it more diffi-

cult for women to have a family than self-employment. This finding is consistent with studies

by Boden (1996), Connelly (1992) and MacPherson (1988) according to which women often

choose self-employment because it provides them with more flexibility in combining market

work and household production.

Women who have worked only in the wage sector have spent a somewhat higher proportion

of their time in the labour force in unemployment than men. For women who were formerly

self-employed, however, this is not the case. Formerly self-employed employees have spent

more time in unemployment than always paid-employed workers. Their working careers seem

to be more unstable because they experience more spells of all kinds of employment states.

This result holds for both genders. Although formerly self-employed women do not seem to

be disproportionately hit by unemployment when looking at the sample of wage workers, they

have a relatively high probability of unemployment based on an analysis of the full sample of

women in the labour force excluding the self-employed. The unemployment rate among fe-

males with self-employment experience amounts to 17.9% whereas it is only 12.3% for those

without self-employment experience. There is no such difference for males; their corre-

sponding unemployment rates are 14.1% and 13.2%.

The always self-employed work more hours per week than both groups of paid-employed

workers. Formerly self-employed males work in smaller companies on average than always

paid-employed males, maybe because smaller companies are more similar in structure to the

11

company they led when they were self-employed and, consequently, the match between quali-

fication and job requirements is higher. For females this relation is less apparent. Continu-

ously self-employed people earn considerably more than paid-employed individuals. For-

merly self-employed men earn little more than men working in the wage sector, whereas

women have a lower income than their always paid-employed counterparts. Both the propor-

tion of employees with a university degree and of those without any professional qualification

is higher among the formerly self-employed than among the always paid-employed workers.

Formerly self-employed women are less qualified than formerly self-employed men. Inter-

estingly, formerly self-employed males are working in the occupation they are trained for

more often than those who were always paid-employed whereas for females holds the reverse.

Table 1: Descriptive Statistics by Employment History, GSOEP 2003, weighted

mean always paid-employed always self-employed formerly self-employed,now paid-employed

male female male female male femaletotal .908 .933 .051 .022 .041 .045age 41.8 41.2 50.2 48.0 46.2 46.0married .637 .587 .748 .713 .593 .646kids .601 .507 .626 .657 .509 .552partner_full .391 .513 .509 .526 .403 .480mon_unemp 2.30 2.77 .85 1.45 4.57 3.81mon_wage 165.70 128.14 0 0 123.37 119.32mon_self 0 0 191.20 142.20 55.96 50.07spells_unemp .38 .38 .09 .06 .66 .59spells_wage 1.81 1.85 0 0 2.57 2.70spells_self 0 0 1.09 1.42 1.49 1.51hours 37.6 29.8 40.7 33.7 36.7 29.3comsiz20_199 .301 .289 .060 .052 .326 .272comsiz200_1999 .245 .212 .007 0 .097 .239comsiz2000 .282 .192 .022 .015 .196 .121income (DM) 4155 2437 6643 4543 4399 2275university .267 .228 .407 .465 .387 .272no degree .136 .147 .072 .125 .160 .224occ_trained .561 .546 .740 .644 .604 .473voctrain_req .626 .625 .650 .500 .500 .528college_req .154 .104 .241 .344 .262 .148occ_exec .070 .039 .169 .196 .189 .031occ_scien .214 .149 .371 .432 .276 .208occ_tech .174 .290 .125 .171 .167 .255occ_office .075 .202 .001 .015 .027 .236occ_serv .050 .173 .019 .090 .069 .166occ_craft .218 .024 .214 .062 .142 .073occ_mach .117 .026 .018 0 .084 .012occ_unskil .063 .093 .006 0 .030 .039

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Formerly self-employed females have less often a job for which a college degree is required

as compared to males and to always self-employed females. In contrast, the highest proportion

of males having such a job is found among the formerly self-employed. All in all, paid-

employed women who have once been self-employed earn less, are less qualified and work

less often in the occupation they are trained for than men with a comparable employment

history. Among the always paid-employed and the always self-employed this difference by

gender is less pronounced.

This is also confirmed by the distribution by occupation. The share of executives among the

always paid-employed is much higher for males than for females. But among the formerly

self-employed this difference is still by far larger. However, as compared with their female

colleagues who have never been self-employed, it cannot be said that formerly self-employed

women have on average less prestigious jobs. They are more often office workers and crafts-

men, but they also work more often as a scientist and work less often as a machinist or un-

skilled worker. Moreover, they have more often a job for which a college degree is required.

Consequently, their job quality does not compare as unfavourable with always paid-employed

women as with men who were also formerly self-employed.

The distribution of self-employment by industry sector is given in table 2. It differentiates

between the currently self-employed and the currently paid-employed who were self-

employed in their last spell (where the industry sector refers to this last spell of self-

employment). Comparing currently self-employed males and females, the most striking dif-

ferences are that women run less often a business in agriculture, construction and business

services but engage more often in personal services, education, health and social work. Thus

women tend to be self-employed in less-rewarding industries than men. Comparing currently

self-employed males with formerly self employed males it shows that men relatively seldom

give up a business in agriculture, business services, and health, education and social work

whereas they leave relatively often the manufacturing, trade and personal service sector. In

contrast, women leave disproportionally seldom manufacturing and services. They often leave

business services and – most of all – the retail sector. 43% of all formerly self-employed fe-

males come from this sector. In absolute terms, self-employed males leaving for the wage

sector mostly worked in manufacturing, business services, construction and retail trade (in

descending order). Females mostly come from the retail and business services sector.

13

Table 2: (Former) Self-Employment by Industry Sector, GSOEP 2003, weighted

share (%) self-employed paid-employed, last spellself-employed

male female male femaleagriculture, hunting, forestry; fishing 5.26 1.66 1.58 1.60manufacturing 13.73 12.57 19.99 5.33construction 15.89 2.06 13.52 2.64wholesale and intermediate trade 2.67 2.99 5.70 0retail trade 10.75 12.72 12.88 42.89transport, storage and communication 3.53 3.32 5.97 3.12financial intermediation 4.63 4.45 3.53 0.58business services 25.43 13.52 16.10 18.03personal services 7.83 17.52 11.01 9.79education, health and social work 8.17 26.19 3.99 14.22

7 Empirical Results

7.1 Impact of Self-Employment Experience on Wages

Table 3 shows the results of the fixed-effects estimation of the earnings function without and

with selection correction (equation 2 and 4, resp.) for males and females. Besides other re-

gressors typically used in earnings functions, the estimation contains the months and spells

spent in different labour market states including self-employment. Following Trzcinski

(1999), the coefficients referring to the months are interpreted as human capital effect

whereas the coefficients referring to the spells are interpreted as signalling effect. The human

capital effect can be supposed to depend on the time spent in a labour market state. The sig-

nalling effect probably depends rather on the fact whether an individual has entered a labour

market state and, if so, how often. Further, industry and occupation dummies are included and

some variables characterising possible former self-employment. These are income in last self-

employment (as an indicator of success), the time since last self-employment, size of last self-

employment, and various interaction terms between the industry sector of last self-

employment and the current occupation. In addition to these variables, the probit equations

which are estimated in the first step of the correction procedure include some wealth-related

variables, family-related variables and information on household income (results not re-

ported).

Firstly, the results of the estimation without selection correction are considered (column 2 and

4). Comparing males and females, it turns out that being married increases men´s earnings

and decreases women´s earnings, the latter being also negatively influenced by the number of

children in the household. Being a single reduces men´s wages. An explanation might be that

14

married women with children specialise in household production whereas married men spe-

cialise in market work. Empirical evidence by Coverman (1983) and Hersch and Stratton

(1997) indicates that housework and hourly earnings are negatively correlated. It corresponds

to Becker´s (1985) effort/energy hypothesis according to which devoting energy to household

production necessarily reduces the amount of effort available for market work.

Living with a partner who is full-time employed decreases the earnings of both genders. As to

be expected, earnings increase with age, hours worked, tenure and company size. Working in

the occupation trained for and having a job for which vocational training or college is required

also has a positive effect on wages. The time spent in unemployment has a detrimental influ-

ence, whereas wage sector experience increases earnings (at a decreasing rate). The coeffi-

cients of the training and wage sectors experience variables are somewhat larger for women

than for men indicating that the returns to education, training and work experience are larger

for women.

The coefficient of months spent in self-employment is negative for males and positive for

females but insignificant in both cases. According to the theoretical considerations in sec-

tion 2 one might conclude that self-employment experience does not increase human capital

specific to the wage sector but that this capital does not significantly depreciate during self-

employment, either. It is also possible that both effects cancel out each other. Consequently,

time spent in self-employment has not such a detrimental effect on wages as time spent in

unemployment. As the spells coefficients indicate, the signalling effect of self-employment is

positive for females, particularly if the last spell has been self-employment. For males the

signalling effect seems to depend on the number of spells of self-employment. Basically,

having been self-employed in the last spell has a positive effect. However, the overall signal-

ling effect becomes negative if the individual has experienced more than two self-

employment spells. This would imply that employers associate frequent self-employment

spells with negative characteristics for men but with positive characteristics for women. This

result conflicts with Trzcinski´s (1999) finding that frequent self-employment episodes have a

more beneficial effect on German men´s earnings than extended periods of self-employment.

For both genders the positive effect of having been self-employed in the last spell becomes

the smaller the longer the spell is ago. The income earned in self-employment and a company

size in last self-employment larger than nine – indicators of success and scope of former self-

employment – increase only the wages of men.5

5 The company size variable drops out of the estimation for females because there are only very few observationsfor women with more than nine employees.

15

The industry and occupation dummies contribute significant explanatory power to the estima-

tions. In addition, interaction terms between the industry of last self-employment and current

occupation give important insights into the relationship between self-employment experience

and wage level.6 Apparently the effect of self-employment depends heavily on the sector the

former self-employed operated in and the occupation she has subsequently in the wage sector.

Particularly for males the coefficients of the interaction terms are by far larger than the main

effect of self-employment and may reverse the overall effect. The returns are especially high

for them if they were self-employed in the business service sector or – to a lesser extent – in

the construction sector. It seems that returns are the larger the more qualified the occupation

in the wage sector. For example, the positive effect of a self-employment spell in business

services is largest for executives, followed by scientists and technicians. For males coming

from manufacturing the returns are mostly negative; however, they are positive if males are

occupied as scientists. The result corroborates the hypothesis put up in section 2 that man-

agement capabilities acquired in self-employment can best be transferred to the wage sector if

a leading position or a job implying a certain responsibility is achieved.

For females the interaction effects are quite different. The relationship between returns and

occupation is less clear. Former self-employment can have a positive effect on wages even in

a lower-qualified occupation (e.g. for craftsmen or unskilled workers who were self-employed

in manufacturing). For females coming from the retail sector, however, returns depend posi-

tively on the qualification level of the occupation: Returns are positive for technicians and

office workers and negative for unskilled labourers. The presumption made in section 2 that

self-employment experience in the retail or service sector might be particularly useful for re-

lated (i.e. service) occupations in the wage sector is not confirmed. However, it is supported

by the apparent positive impact of self-employment in the service sector on wages in service

occupations.

Turning to the estimations with selection correction (column 3 and 5), the joint significance of

the inverse Mills ratios indicates the presence of sample selection bias. Although the results

do not substantially alter as compared to the estimations without selection correction, there

are some changes in the magnitude of the self-employment related coefficients. The negative

coefficient of spells of self-employment for males is no longer significant now. Thus there is

no more evidence that frequent self-employment spells would work as a negative signal to

employers and decrease wages. But the positive effect of self-employment in the last spell 6 Some interactions drop out of the estimation because the corresponding transition from self-employment intothe wage sector cannot be observed. In addition, interaction terms are only included if they are significant in atleast one of the estimations.

16

Table 3: Fixed-Effects Estimation of Earnings Function

males femalesselection correction no yes no yesmarried 0.0151*** 0.0151*** -0.0296*** -0.0287***

[0.0052] [0.0052] [0.0069] [0.0069]single -0.0107* -0.0107* -0.0051 -0.0046

[0.0055] [0.0055] [0.0072] [0.0072]kids 0.003 0.0034* -0.0315*** -0.0311***

[0.0020] [0.0020] [0.0035] [0.0035]partner_full -0.0106*** -0.0109*** -0.0122** -0.0120**

[0.0030] [0.0030] [0.0052] [0.0052]age 0.0268*** 0.0260*** 0.0142*** 0.0140***

[0.0036] [0.0036] [0.0032] [0.0033]age2 -0.0004*** -0.0004*** -0.0003*** -0.0003***

[1.92e-05] [1.95e-05] [2.69e-05] [2.73e-05]hours 0.0184*** 0.0184*** 0.0357*** 0.0357***

[0.0003] [0.0003] [0.0003] [0.0003]tenure 0.0016*** 0.0014*** 0.0014*** 0.0012***

[0.0003] [0.0003] [0.0004] [0.0004]comsiz20_199 0.0493*** 0.0425*** 0.0558*** 0.0508***

[0.0044] [0.0050] [0.0058] [0.0065]comsiz200_1999 0.0644*** 0.0568*** 0.0876*** 0.0819***

[0.0052] [0.0058] [0.0067] [0.0074]comsiz2000 0.0800*** 0.0722*** 0.1040*** 0.0985***

[0.0057] [0.0062] [0.0073] [0.0079]occ_trained 0.0063* 0.0054 0.0253*** 0.0234***

[0.0035] [0.0035] [0.0057] [0.0057]college_req 0.0551*** 0.0531*** 0.0898*** 0.0875***

[0.0070] [0.0070] [0.0116] [0.0116]voctrain_req 0.0174*** 0.0166*** 0.0408*** 0.0414***

[0.0036] [0.0037] [0.0057] [0.0057]mon_unemp -0.0050*** -0.0047*** -0.0045*** -0.0044***

[0.0005] [0.0005] [0.0006] [0.0006]spells_unemp -0.005 -0.0036 -0.0033 -0.0037

[0.0040] [0.0040] [0.0061] [0.0061]mon_wage 0.0031*** 0.0032*** 0.0043*** 0.0044***

[0.0003] [0.0003] [0.0002] [0.0002]mon_wage2 -3.13e-06*** -3.13e-06*** -4.71e-06*** -4.76e-06***

[1.52e-07] [1.53e-07] [2.59e-07] [2.62e-07]spells_wage 0.0106*** 0.0088*** 0.0220*** 0.0213***

[0.0029] [0.0030] [0.0040] [0.0041]mon_selfem -0.0006 -0.0005 0.0015 0.0020*

[0.0007] [0.0007] [0.0012] [0.0012]spells_selfem -0.0261** -0.0195 0.0307** 0.0321**

[0.0126] [0.0127] [0.0141] [0.0142]lastsp_self 0.0550* 0.0472 0.2259*** 0.2175***

[0.0330] [0.0331] [0.0464] [0.0464]ylast_self -0.1165*** -0.1194*** -0.3066*** -0.3004***

[0.0352] [0.0352] [0.0531] [0.0531]income_self 0.0076*** 0.0084*** -0.0045 -0.0044

[0.0021] [0.0021] [0.0033] [0.0033]comsiz_self 0.1707* 0.1700* - -

[0.1015] [0.1014]manu_scien 0.2459* 0.2377 - -

[0.1454] [0.1452]manu_tech -0.2181** -0.2204** 0.004 -0.0082

[0.0906] [0.0905] [0.2863] [0.2860]manu_craft -0.1739** -0.1823*** 0.3890*** 0.3811***

[0.0681] [0.0681] [0.1286] [0.1285]manu_unskil 0.0034 -0.0171 0.2419* 0.2333*

[0.0881] [0.0881] [0.1374] [0.1373]

17

cons_tech 0.4147*** 0.4269*** - -[0.1361] [0.1359]

cons_serv 0.2897*** 0.3279*** 0.0063 0.0139[0.0925] [0.0926] [0.2336] [0.2334]

cons_craft 0.1841** 0.1724** - -[0.0743] [0.0745]

cons_mach -0.1777* -0.1821* - -[0.1054] [0.1053]

retail_tech 0.037 0.089 0.3978*** 0.3988***[0.0868] [0.0874] [0.1432] [0.1431]

retail_office 0.9214*** 0.9083*** 0.3237*** 0.3201***[0.2372] [0.2378] [0.1190] [0.1189]

retail_serv - - -0.1315 -0.1386[0.0865] [0.0865]

retail_unskil - - -0.2580** -0.2656**[0.1039] [0.1043]

buserv_exec 0.9569*** 0.9519*** -0.1805 -0.1865[0.2451] [0.2451] [0.2092] [0.2091]

buserv_scien 0.5204** 0.4704** - -[0.2192] [0.2191]

buserv_tech 0.3654* 0.3146 -0.0672 -0.0679[0.1918] [0.1919] [0.1312] [0.1311]

serv_serv -0.3161*** -0.3128*** 0.2450* 0.2414*[0.0740] [0.0739] [0.1327] [0.1326]

serv_craft -0.4252*** -0.4337*** -0.2654 -0.2611[0.1462] [0.1460] [0.2180] [0.2177]

industry dummies *** *** *** ***

occupation dummies *** *** *** ***

tmills90-tmills03 - *** - ***

constant 6.4229*** 6.4585*** 5.9115*** 5.9198***[0.0935] [0.0967] [0.0842] [0.0883]

number of observations 44382 44382 34735 34735number of individuals 9338 9338 8270 8270R2 within 0.2381 0.2407 0.4830 0.4845R2 between 0.3843 0.3771 0.6200 0.6221*** (**,*) indicates a significance level of 1% (5%, 10%); standard errors in brackets.

loses significance, too. Noteworthy signalling effects can yet only be observed for specific

interactions between industry of self-employment and occupation. For females, the coefficient

of months of self-employment increases slightly and becomes weakly significant, indicating

that self-employment increases human capital specific to wage employment. Altogether the

general effect of self-employment experience as revealed by the estimations with selection

correction seems to be more favourable than the one obtained from the estimations without

selection correction. The coefficient changes can be explained by the non-consideration of

two groups in the latter estimations: Firstly, unemployed workers and individuals who are not

part of the labour force are not included. They might self-select into these employment states

due to low earnings prospects in the labour market. If these inactive individuals – as to be

supposed – have less self-employment experience than average, the returns to self-

18

employment will be underestimated. Secondly, self-employed workers are not part of the

wage regressions. The estimation of the impact of self-employment on wages relies only on

those self-employed persons who leave self-employment for paid employment. If the other

self-employed workers, who could be assumed to be more successful in self-employment be-

cause they are still in this employment state, are also more successful in wage employment

the returns to self-employment will be underestimated, too. The regression results confirm at

least for men that success in self-employment (as measured by the income in self-

employment) has a positive impact on earnings in the wage sector. It should be noted, how-

ever, that there is evidently no general negative selection out of self-employment into wage

employment for all interactions between industry of former self-employment and occupation.

The contrast between the favourable general effect of self-employment on wages of females

according to the multivariate analysis and the relatively low earnings of formerly self-

employed women as revealed by the descriptive statistics can be explained as follows. The

multivariate analysis accounts for being married and having children – something which is

particularly prevalent among (formerly) self-employed women, usually implies a specialisa-

tion into housework for them, and tends to reduce their earnings. It further controls for the

fact that women who change from self-employment to paid employment are less qualified on

average, less often exert the occupation they are trained for and less often have a qualified job

than men. Empirical evidence suggests that women select negatively into self-employment

whereas selectivity is positive for men (Clain 2000). This possible selection bias is largely

corrected by the fixed-effects approach. In addition, the selection correction procedure ac-

counts for selectivity out of self-employment which seems to affect the general returns to self-

employment negatively. After considering all these issues it is possible to isolated the pure

effect of self-employment on wages.

7.2 Impact of Self-Employment Experience on the Probability of Unemployment

The results of the random effects probit estimations of the probability of unemployment

(equation 5) are given in table 4. In addition to the explanatory variables used in the fixed

effects estimation, some time-constant variables are included in the regression. They refer to

nationality, education, professional training, age at first job and kind of first job. No industry

or occupation dummies concerning the current job are included, but industry dummies refer-

ring to the last spell – if it was self-employment – are used.

Two samples are selected for each gender. The first sample only includes individuals who are

currently either wage-employed or unemployed and who have wage employment experience.

19

The corresponding estimation results are given in column 2 and 4. The intention behind the

definition of this sample is to analyse the effect of self-employment on the probability of un-

employment exclusively for those who were only temporarily self-employed and have also

experience in wage employment. The second sample additionally includes the self-employed

and the unemployed without wage employment experience, i.e. it comprises the full sample of

individuals being part of the labour force (see column 3 and 5 for estimation results). Thus, it

contains also those individuals who have spent their whole working life in self-employment.

To begin with, those coefficient estimates are discussed which do not differ very much be-

tween the two samples. Similar to the earnings estimations, family related variables have

contrasting effects on the unemployment probability of genders. Being married and having

children increases the unemployment risk of females. In contrast, married men have a com-

paratively low probability of unemployment. This corroborates the hypothesis that when

having a family, women usually specialise in household production and men in market pro-

duction. Being in charge of housework and child rearing reduces timetable flexibility and

therewith the number of acceptable jobs. Moreover, employers are often reluctant to employ

women with small children.

The negative age coefficient probably reflects a tenure effect. Having no occupational degree

increases the unemployment risk of males, and having a university degree decreases the un-

employment risk of females. Independent of gender, having completed an apprenticeship is

connected with a relatively high probability of unemployment. The occupational status in the

first job has an impact, too. Compared with the reference category “first job civil servant”, all

other professions have a higher risk of unemployment. The risk is highest for individuals who

entered the labour market as a blue collar worker. They are followed by those who started

with self-employment and those who started with a white-collar job. Time spent in unem-

ployment increases the probability of being unemployed, whereas time spent in wage em-

ployment reduces it.

There are some marked differences between the estimation results of the two samples. Being a

foreigner increases the unemployment risk of females only when accounting for those who

have exclusively work experience in self-employment. Likewise, a high school degree signifi-

cantly reduces it for males only when the always self-employed are included. For the full

sample, self-employment experience as measured in months and spells reduces the probability

of unemployment by far more than for the reduced sample. Self-employment experience cer-

tainly decreases the risk of business failure and therewith of unemployment for the currently

self-employed included in the full sample. In addition, the difference in coefficients might

20

Table 4: Random Effects Probit Estimation of the Probability of Unemployment

males femalesalways self-employed included no yes no yesmarried -0.2170*** -0.2302*** 0.3224*** 0.3014***

[0.0539] [0.0508] [0.0566] [0.0538]single 0.0174 0.0412 -0.0419 -0.0126

[0.0587] [0.0554] [0.0611] [0.0581]kids 0.0301 0.0244 0.1992*** 0.2028***

[0.0213] [0.0199] [0.0238] [0.0222]partner_full -0.1421*** -0.1531*** -0.2951*** -0.2987***

[0.0360] [0.0342] [0.0431] [0.0404]age -0.1124*** -0.1069*** -0.1279*** -0.1211***

[0.0120] [0.0109] [0.0133] [0.0122]age2 0.0018*** 0.0017*** 0.0018*** 0.0017***

[0.0001] [0.0001] [0.0002] [0.0001]foreign -0.0268 -0.009 0.1158 0.1936***

[0.0585] [0.0542] [0.0706] [0.0646]noschool 0.0976 0.1169 -0.0041 0.023

[0.0790] [0.0739] [0.1021] [0.0941]highschool -0.1096 -0.1744** -0.0136 -0.0383

[0.0848] [0.0775] [0.0888] [0.0804]nodegree 0.3045*** 0.2979*** -0.0103 0.0099

[0.0586] [0.0549] [0.0671] [0.0608]apprentice 0.2117*** 0.1931*** 0.1216** 0.1032**

[0.0471] [0.0444] [0.0561] [0.0508]university 0.0527 0.0381 -0.1996** -0.2300***

[0.0724] [0.0665] [0.0802] [0.0721]agefjob -0.0213** -0.0185** -0.0047 -0.0098

[0.0086] [0.0079] [0.0075] [0.0069]fjselfe 0.5085** 0.5312*** 1.0776*** 0.7979***

[0.2337] [0.2037] [0.3892] [0.2987]fjblue 0.7022*** 0.6157*** 1.5291*** 1.1427***

[0.1587] [0.1423] [0.2682] [0.2178]fjwhite 0.3066* 0.2451* 1.0207*** 0.6727***

[0.1586] [0.1430] [0.2629] [0.2133]mon_unemp 0.0183*** 0.0205*** 0.0096*** 0.0117***

[0.0016] [0.0013] [0.0014] [0.0016]spells_unemp 1.1267*** 1.1499*** 1.2746*** 1.2854***

[0.0264] [0.0239] [0.0298] [0.0288]mon_wage -0.0052*** -0.0063*** -0.0050*** -0.0077***

[0.0005] [0.0005] [0.0007] [0.0006]mon_wage2 7.82e-06*** 9.76e-06*** 1.03e-05*** 1.58e-05***

[1.23e-06] [1.17e-06] [1.88e-06] [1.77e-06]spells_wage -0.9995*** -1.0835*** -1.1375*** -1.2241***

[0.0295] [0.0269] [0.0329] [0.0302]mon_selfem -0.0022* -0.0091*** -0.0024 -0.0148***

[0.0013] [0.0010] [0.0026] [0.0020]spells_selfem -0.0227 -0.8289*** 0.2252*** -0.2934***

[0.0785] [0.0772] [0.0741] [0.0665]lastsp_self 0.4275 0.4528* -0.2637 0.1582

[0.2915] [0.2670] [0.3374] [0.3646]income_self 0.0107 0.0103 -0.0099 0.004

[0.0165] [0.0126] [0.0210] [0.0162]ylast_self -0.0751 0.2115*** 0.1137 0.2886**

[0.0785] [0.0683] [0.1061] [0.1333]comsiz_self 0.9679 0.4046 - -

[1.1169] [1.1998]nace_agri0 1.0223 1.9920** -8.9342 -5.3046

[0.9680] [0.8874] [3.1633e+07] [1.4185e+06]nace_manu0 -0.0223 0.4609 -1.4230 -1.2523

[0.4479] [0.4325] [1.1358] [0.7624]

21

nace_cons0 -0.0485 0.6858 3.4453*** 3.2199**[0.5223] [0.4511] [1.2517] [1.3780]

nace_wtrade0 0.7444 2.2497*** -9.3793 -5.0268[0.4745] [0.7870] [4.6020e+06] [2.0451e+05]

nace_retail0 2.1868*** 1.4584*** 1.0759** 1.2934***[0.7492] [0.4584] [0.4754] [0.4714]

nace_buserv0 0.2402 1.5522** -11.8663 -9.7224[0.7272] [0.6055] [1.9734e+07] [5.6449e+06]

nace_serv0 0.2273 0.4045 0.571 0.6576[0.4148] [0.3973] [0.6726] [0.6144]

nace_transcom0 -0.833 -0.0434 1.7949 1.7202[0.6043] [0.5262] [1.5504] [1.5702]

nace_finance0 2.4052*** 3.1053*** - -6.8893[0.7748] [0.7176] [1.1579e+06]

nace_edusoc0 2.2398* 2.7467** -2.4984*** -2.2861**[1.2957] [1.1691] [0.8960] [0.9702]

constant 0.0798 0.3259 -0.2815 0.4412[0.3400] [0.3107] [0.3897] [0.3395]

number of observations 52830 58328 42552 45967number of individuals 8959 9930 7954 8692*** (**,*) indicates a significance level of 1% (5%, 10%); standard errors in brackets.

reflect that the temporarily self-employed are a negative selection out of the group of indi-

viduals with self-employment experience. When considering the whole group, self-

employment experience clearly reduces the probability of unemployment. This holds irre-

spective of the fact that having been self-employed in the last spell slightly increases the

probability of unemployment. This indicator takes value one for those which have abandoned

a business either to start a new one, to become wage employed or to become unemployed. It

partly absorbs the apparent negative selectivity out of self-employment. The probability of

unemployment increases with the time elapsed since self-employment for the full sample.

Like in the wage regressions, the effect of self-employment experience depends heavily on

gender. There is some evidence that, as measured in months, it decreases the unemployment

risk of men even when looking only at the reduced sample. It seems that men during self-

employment accumulate human capital specific to the wage sector which enhances their

chances of finding wage work, even though the effect is smaller than for wage employment

experience. For females, in contrast, the time spent in self-employment does not affect the

probability of unemployment. Moreover, the number of self-employment spells even signifi-

cantly increases it. Thus, while frequent self-employment spells generally reduce the prob-

ability of unemployment, this is not the case for females who only temporarily try self-

employment.

In addition, the effect depends very much on the industry of former self-employment. While

the overall signal of having been self-employed is not significant using the reduced sample, a

detrimental effect on employment chances can be observed for self-employed males coming

22

from the retail, transport and communication, or the education, health and social work sector.

For women the effect is particularly strong if they were self-employed in the construction or

retail sector. It may be cancelled out, however, if a woman ran her business in the education,

health and social work sector. The detrimental effect becomes larger for some industries when

considering the full sample, reflecting additional negative selection effects. Especially men

who have always been self-employed and give up a business in agriculture, wholesale and

intermediate trade or business-related services seem to have a high probability of unemploy-

ment.

8 Conclusion

The paper analyses the impact of self-employment experience on earnings in the wage sector

and on the probability of unemployment. The results reveal that the signalling effect of former

self-employment is more important than the human capital effect with respect to wages. Pos-

sibly existing human capital specific to wage employment depreciates during self-

employment while some of the human capital acquired in self-employment can be transferred

to the wage sector. The two effects might cancel out each other. Insofar self-employment is to

be preferred to unemployment which decreases human capital, but compares negatively with

wage employment experience. These findings are largely consistent with the existing empiri-

cal literature.

Provided that an employer engages a formerly self-employed woman, he generally values her

self-employment as a positive signal and pays her higher wages. But many employers are re-

luctant to employ her in the first place. A somewhat different picture arises for men. They do

not generally receive positive returns to self-employment experience in wage employment.

The signalling effect depends heavily on the industry they operated in when self-employed

and on their current occupation. However, men who give up self-employment and try to re-

turn to wage employment usually find a job more easily than women. There are some excep-

tions depending again on the industry of former self-employment. The human capital acquired

by men during self-employment generally reduces their probability of unemployment.

There is no obvious explanation for these gender differences. They might be due to different

motivations for choosing self-employment. Women seem to enter often self-employment be-

cause it gives them more flexibility in combining housework and market work. In exchange

for this flexibility they possibly accept to do work which does not correspond to their qualifi-

cation. Self-employment is rather a compromise than another step on the career ladder. This

makes it difficult for them to find an adequate position when they try to return to the wage

23

sector. If they find a job, however, it is probably one where self-employment experience is

particularly useful and appreciated by the employer. Given the lower educational level and

therewith the lower earning prospects of formerly self-employed women as compared to their

male counterparts, self-employment experience might contribute relatively more to women´s

wages than to men´s.

The paper accounts for selection out of self-employment into wage employment. It uses a

selection correction procedure along with the fixed-effects estimation of the wage function.

As to the probit estimations of unemployment, it compares results for the full sample of indi-

viduals in the labour force with those for a reduced sample excluding individuals which have

always been self-employed. There is some evidence for a negative selection out of self-

employment into wage employment. Individuals which temporarily engage in self-

employment and try to return to the wage sector later seem to have some unobserved charac-

teristics which vitiate their success in the wage sector. If continuously self-employed indi-

viduals tried to enter the wage sector, the labour market consequences of self-employment

experience would be more favourable for them than they are for temporarily self-employed

workers. It is important to note, however, that also for the selective group of those who leave

self-employment, temporary self-employment seems to be a good alternative to unemploy-

ment. The chances to find wage employment and the potential earnings are generally higher

after self-employment than after unemployment.

9 Appendix

Table 5: Variable Definitions

Variable name Definitionincome log of monthly gross earningsmarried =1 if marriedsingle =1 if not living with a partner in the same householdkids number of kids in the householdpartner_full =1 if partner full-time employedage ageage2 age squaredforeign =1 if not Germanhours working hours per weektenure length of time with firm (years)comsiz20_199 =1 if size of company between 20 and 199 employeescomsiz200_1999 =1 if size of company between 200 and 1,999 employeescomsiz2000 =1 if size of company 2,000 employees or morenoschool =1 if having no school degreehighschool =1 if having a high school degree

24

nodegree =1 if not having any professional qualificationapprentice =1 if having completed an apprenticeshipuniversity =1 if having a university degreeocc_trained =1 if working in occupation trained forvoctrain_req =1 if vocational training is required for jobcollege_req =1 if college is required for jobEmployment history variablesagefjob age at first jobfjselfe =1 if first job was self-employmentfjblue =1 if first job was blue collarfjwhite =1 if first job was white collarREFERENCE CATEGORY first job was civil servantmon_unemp months spent in unemploymentmon_wage months spent in paid employmentmon_wage2 months spent in paid employment squaredmon_self months spent in self-employmentspells_unemp spells spent in unemploymentspells_wage spells spent in paid employmentspells_self spells spent in self-employmentlastsp_self =1 if last spell was self-employmentylast_self years since last self-employment (=0 if never self-employed)income_self income in last self-employment (=0 if never self-employed)comsiz_self =1 if size of company in last self-employment >9 employees

(=0 if never self-employed)Occupation dummiesocc_exec =1 if occupation is executive, managerocc_tech =1 if occupation is technician or coequal non-technical occupationocc_scien =1 if occupation is scientistocc_office =1 if occupation is office worker, commercial clerkocc_serv =1 if occupation is service occupation, sales assistantocc_craft =1 if occupation is craftsmanocc_mach =1 if occupation is machine operator, assemblerocc_unskil =1 if occupation is unskilled labourerREFERENCE CATEGORY occupation is farmer, fishermanIndustry dummiesnace_agri =1 if sector is agriculture, hunting, forestry, fishingnace_min =1 if sector is miningnace_manu =1 if sector is manufacturingnace_cons =1 if sector is constructionnace_wtrade =1 if sector is wholesale and intermediate tradenace_retail =1 if sector is retail tradenace_buserv =1 if sector is business servicesnace_serv =1 if sector is personal services, hotels and restaurantsnace_transcom =1 if sector is transport, storage and communicationnace_finance =1 if sector is financial intermediationnace_edusoc =1 if sector is education, health and social workREFERENCE CATEGORY sector is public administration and defence, compulsory social security,

private households; other community, social and personal service activi-ties; extra-territorial organisations

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Interaction termsnace_agri0, nace_min0, ... =1 if last spell was self-employment in agriculture, mining, ... resp.manu_tech, manu_scien ...,cons_tech, cons_serv, ...

interaction between sector of last self-employment and current occupation,e.g. manu_tech= nace_manu0*occ_tech

tmills90-tmills03 inverse Mills ratios from probit regression (equation 3) for each year from1990 to 2003, interacted with year dummies

References

Becker, G. (1985): Human Capital, Effort, and the Sexual Division of Labor, Journal of La-bor Economics 3, 1, part 2, 33-58

Boden, R. J. (1996): Gender and Self-Employment Selection: An Empirical Assessment,Journal of Socio-Economics 25, 6, 671-682

Bruce, D. and Schuetze, H. J. (2004): The Labor Market Consequences of Experience in Self-Employment, Labour Economics 11, 5, 575-598

Butler, J. and Moffitt, R. (1982): A Computationally Efficient Quadrature Procedure for theOne Factor Multinomial Probit Model, Econometrica 50, 761-764

Clain, S. H. (2000): Gender Differences in Full-Time Self-Employment, Journal of Econom-ics and Business 52, 499-513

Conelly, R. (1992): Self-Employment and Providing Child Care, Demography 29, 17-29

Coverman, S. (1983): Gender, Domestic Labor Time, and Wage Inequality, American Socio-logical Review 48, 623-637

Evans, D. S. and Leighton, L. S. (1989): Some Empirical Aspects of Entrepreneurship,American Economic Review 79, 3, 519-535

Greene, W. (1997): Econometric Analysis, Prentice Hall, New Jersey, 3rd edition

Heckman, J. J. (1979): Sample Selection Bias as a Specification Error, Econometrica 47,153-161

Hersch, J. and Stratton, L. S. (1997): Housework, Fixed Effects, and Wages of MarriedWorkers, Journal of Human Resources 32, 285-307

Hundley, G. (2001): Why Women Earn Less than Men in Self-Employment, Journal of LaborResearch 22, 4, 817-829

Kim, M.-K. and Polachek, S. W. (1994): Panel Estimates of Male-Female Earnings Functions,Journal of Human Resources 29, 2, 406-428

MacPherson, D. (1988): Self-Employment and Married Women, Economic Letters 28, 3, 281-284

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Mincer, J. and Ofek, H. (1982): Interrupted Work Careers: Depreciation and Restoration ofHuman Capital, Journal of Human Resources 17, 3-24

Polaschek, S. and Kim, M. (1994): Panel Estimates of the Gender Earnings Gap: IndividualSpecific Intercept and Individual Specific Slope Models, Journal of Econometrics 61, 23-42

Trzcinski, E. (1999): Returns to Self-Employment Experience in the Wage and Salary Sector -A Comparative Analysis of Germany and the United States, DIW-Vierteljahresbericht, 177-183

Williams, D. R. (2000): Consequences of Self-Employment for Women and Men in theUnited States, Labour Economics 7, 5, 665-687

Williams, D. R. (2003): Returns to Education and Experience in Self-Employment: Evidencefrom Germany, Schmollers Jahrbuch 123, 1, 139-150

Williams, D. R. (2004): Youth Self Employment: Its Nature and Consequences, Small Busi-ness Economics 23(4), 323-336

Wooldridge, J. M. (2002): Econometric Analysis of Cross Section and Panel Data, MITPress, Cambridge, Massachusetts