The effect of working time preferences and fair wageperceptions on entrepreneurial intentions among employees

Arndt Werner • Johanna Gast • Sascha Kraus

Accepted: 10 November 2013

� Springer Science+Business Media New York 2013

Abstract To date, little is known about how working

time preferences and fair wage perceptions affect

employees’ entrepreneurial intentions. Using data

from the German Socio-Economic Panel Study, we

provide first evidence that the difference between the

actual and desired amount of working hours in paid

employment is positively related to the propensity to

switch to self-employment. Furthermore, our analysis

supports the hypothesis that employees who perceive

their current wage level as very unfair are more likely

to have higher entrepreneurial intentions. However,

the closer actual wages get to the wage levels

perceived as fair, the more employees are likely to

remain in their current employment situation. We also

tested the interaction effect of working time

preferences and fair wage perceptions. In line with

our theoretical considerations, we find that employees

who perceive their wages as unfair and, simulta-

neously, prefer different work hours have the strongest

entrepreneurial intentions.

Keywords Employee entrepreneurship �Entrepreneurial intentions � Reference points �Working time preferences

JEL Classifications J3 � J24 � J28 � L26

1 Introduction

Recently, researchers have started to recognize the

importance of analyzing the very early stages of new

firm creation in more detail (Davidsson and Gordon

2012) and to explore factors that influence an individ-

ual’s intention to engage in entrepreneurship. This has

led to the rise of the relatively new research field of

‘‘nascent entrepreneurship’’ which aims to examine the

very first stages of the start-up process, namely the

factors that potentially influence the employees’ inten-

tion to switch to self-employment. Although the impact

of many of these factors is accepted, there are still many

gaps in this body of research literature.

For example, it is a standard fact in entrepreneur-

ship literature that an individual’s intention to become

self-employed is not made in a vacuum but instead

depends on different factors such as human and

A. Werner (&)

Institut fur Mittelstandsforschung Bonn (IfM Bonn),

University of Siegen, Maximilianstraße 20, 53111 Bonn,

Germany

e-mail: werner@ifm-bonn.org

J. Gast � S. Kraus

Institute for Entrepreneurship, University of

Liechtenstein, Furst-Franz-Josef-Strasse, 9490 Vaduz,

Liechtenstein

e-mail: johanna.gast@uni.li

S. Kraus

e-mail: sascha.kraus@wu.ac.at; s.kraus@uu.nl

S. Kraus

Utrecht University School of Economics, P.O. Box 80125,

3508 TC Utrecht, The Netherlands

123

Small Bus Econ

DOI 10.1007/s11187-013-9528-2

financial capital endowments, demographic and psy-

chological aspects, or regional conditions (e.g., Ve-

rheul et al. 2002; Blanchflower 2000; Parker 2004;

Grilo and Thurik 2005; Davidsson 2006; Grilo and

Irigoyen 2006). In particular, recent research literature

indicates that entrepreneurial behavior should not be

analyzed in isolation. Existing firms, for example,

have been identified as an important source of new

entrepreneurs (e.g., Hellmann 2007). In fact, the

majority of new entrepreneurs actually launch their

new venture following a period of employment in

established companies (e.g., Burton et al. 2002;

Gompers et al. 2005). Consequently, this finding has

sparked a growing interest in recent entrepreneurship

literature regarding the role of the work environment

in employees’ motivation to switch into self-employ-

ment from paid employment (e.g., Hellmann 2007;

Parker 2007, 2009). Recent literature has identified

several firm-related attributes—such as firms’ size,

labor income, or length of job tenure—which affect

the rate at which new entrepreneurs are spawned by

established firms (e.g., Evans and Leighton 1989;

Wagner 2004; Gompers et al. 2005; Campbell et al.

2012).

Although the impact of many of these factors is

accepted, there are still many gaps in this body of

research literature. Little is known, for example, about

the impact of working time preferences and fair wage

perceptions on the entrepreneurial intentions of

employees. With regard to working time preferences,

it can be argued that flexible work schedules may help

to reconcile work and private life preferences of

individuals because of more flexible work schedules in

self-employment compared to paid employment. With

regard to fair wage perceptions, it can be argued that

employees’ utility not only depends on absolute wage

levels but also on the relationship of the actual income

in paid employment to a specific reference wage (see,

e.g., Kahneman and Tversky 1979). Following this

line of thought, employees are assumed to have a

certain wage target in mind which they judge as being

appropriate for their work and which they do not want

to fall below. In this article, we argue that if the actual

income level of an employee is below but lies very

close to his or her specific reference wage, he or she

will stay in paid employment because switching to

self-employment may bear the risk of falling even

further below the target income (i.e. the wage level

perceived as fair for the work load). However, if the

difference between the absolute and the target income

is very large (i.e. the actual wage level is perceived as

very unfair), employees may consider self-employ-

ment as an occupational alternative to their paid

employment situation and, accordingly, as an occu-

pational alternative to reaching their target income in

the near future.

Finally, employees may have higher entrepreneur-

ial intentions if they perceive their wage as unfair and

at the same time prefer a different work schedule. It

can be argued, for example, that employees may

consider increasing the amount of working hours to

earn a wage level which they considered as fair.

However, due to organizational constraints, increasing

the work hours may be difficult to realize. In such

cases, switching to self-employment may become

particularly attractive.

Overall, the present study contributes a novel

approach to the entrepreneurship literature as it is—

to the best of our knowledge—the first to discuss the

role of working time preferences and fair wage

perceptions in a comparative analysis. Using data

from the German Socio-Economic Panel Study

(SOEP), we explore how these factors affect the very

early stages of the firm creation process, the intention

to switch to entrepreneurship. Our findings provide

strong evidence that employees indeed care about

working time preferences and fair wage perceptions.

On the one hand, we show that the difference between

the actual and desired amount of working hours in paid

employment positively affects the propensity to

switch to self-employment. On the other hand, the

analysis supports our hypotheses that, if the gap

between the actual wage level and the wage the

employees consider fair is high, then the entrepre-

neurial intentions will also be high. However, if this

gap is relatively low, employees are more likely to

remain in their current employment situation. Finally,

and in line with our theoretical considerations, we find

that both factors together additionally affect the

propensity of employees to become entrepreneurs.

2 Theory and hypotheses

In the following section, we develop a theoretical

framework dealing with working time preferences and

fair wage perceptions. To outline the above-mentioned

research gap, we performed a systematic literature

A. Werner et al.

123

review in a first step—closely following the guidelines

for an evidence-informed, systematic review method

proposed by Tranfield et al. (2003) and, for example,

being applied in entrepreneurship research by Cesinger

et al. (2012). After scanning six electronic databases,

we found a total of 25 peer-reviewed articles which

conducted an empirical analysis testing the influence of

different explanatory variables on the dependent var-

iable of nascent entrepreneurship or a closely related

concept (e.g., latent entrepreneurship). We found that

most of these studies base their empirical analyses on

longitudinal data for a certain country or set of countries

(e.g., Mueller 2006; Townsend et al. 2010; Fairlie and

Krashinsky 2012). Interestingly, except for five articles,

all these contributions were written after 2005, showing

that nascent entrepreneurship has recently been at the

center of research attention. The literature review

specifically reveals that the intention to leave paid

employment for self-employment can depend on a

number of different factors. On the one hand, personal

and regional factors as well as perceptional variables

seem to affect the occupational choice decision (e.g.,

Delmar and Davidsson 2000; Arenius and Minniti

2005; Bergmann and Sternberg 2007; Grilo and

Irigoyen 2006). On the other hand, pecuniary and

non-pecuniary aspects of the current employment

situation are important factors because they influence

an individual’s job satisfaction and therefore the

propensity towards entrepreneurship. Occupational

choice models have received ample attention in differ-

ent disciplines such as psychology, sociology and, more

recently, economics (e.g., Blaikie 1971; Kanbur 1979;

Jacobs 2008; Sullivan 2009).

Standard labor economic models as a rule assume

that the occupational choice between employment

alternatives is based on rational behavior and utility

maximization (Uusitalo 2001; Bergmann and Stern-

berg 2007). This basically means that individuals

choose entrepreneurship over salaried employment if

the expected utility derived from self-employment is

higher than that from salaried employment (e.g.,

Douglas and Shepherd 2000). However, while tradi-

tional research in this field has looked at the monetary

effects and/or differences in predicted earnings with

respect to the decision between paid and self-employ-

ment (e.g., Rees and Shah 1986; de Wit and van

Winden 1989; Taylor 1996; Johansson 2000; Anders-

son and Wadensjo 2013), recent work in entrepre-

neurship research has started to pay more attention to

the question of how non-monetary factors can influ-

ence the individuals’ utility and, consequently, his or

her occupational choice.

In line with this body of work, Hamilton (2000), for

example, finds that most entrepreneurs enter and

persist in self-employment despite the fact that they

have both lower initial earnings and lower earnings

growth than in paid employment. Without going into

detail, Hamilton assumes that being self-employed has

to be aligned with some kind of non-pecuniary value

(e.g., ‘being their own boss’) that individuals cannot

acquire in paid employment. The results of a study by

Benz and Frey (2004) point in a similar direction.

Focusing on household panels in England, Germany,

and Switzerland, the authors show that the self-

employed are generally more satisfied and happy with

their work situation than paid employees—even after

controlling for income and work hours (see also Taylor

1996; Blanchflower 2000; Hundley 2001; Kawaguchi

2002). Other studies by contrast find evidence that

being dissatisfied with an employment situation can be

an important reason for employees to switch to

entrepreneurship. Werner and Moog (2007) show that

including a vector of variables capturing poor working

conditions in paid employment (e.g., work climate, and

the relationship of the employees to their colleagues

and superiors) has a strong positive effect on the

intention of employees to switch to self-employment.

Also underlying the effect of job dissatisfaction,

Cromie and Hayes (1991) find that, in aggregate,

entrepreneurs were relatively less satisfied with the

jobs they held prior to founding their own business. So,

in sum, the literature shows that individuals can be

satisfied or dissatisfied with their occupation not only

because of economic outcomes but also due to the non-

pecuniary aspects of their current employment situa-

tion. Such workplace characteristics can influence an

individual’s utility and, consequently, his or her

propensity to switch to entrepreneurship.

Apart from concentrating on absolute wage levels as

well as the differences in predicted earnings, a second

line of recently evolved research provides evidence

that fairness perceptions of different wage levels may

influence the individual’s employment choice (e.g.,

Camerer et al. 1997; Fehr and Gotte 2007; Abeler et al.

2011). The key idea underlying this body of research is

the assumption that the utility an individual can derive

from a certain income not only depends on the level of

the absolute wage but also hinges on the relationship of

Effect of working time preferences

123

this absolute wage to a certain reference wage. That is,

even if the employees do not know the exact wage

levels of their co-workers, they do form reasonable

estimates of where they lie regarding pay (Bewley

1999; Mayraz et al. 2009; Clark and Senik 2010).

Accordingly, recent studies in economics have started

to emphasize the possible effect reference points can

have on an individual’s evaluation of economic

outcomes and the effectiveness of incentives (e.g.,

Mohnen and Pokorny 2005; Ockenfels et al. 2010). To

date, however, the impact of such relative wage

comparisons has received little attention in entrepre-

neurship. One notable exception is the study by

Schneck (2011). Using panel data of German males

between the age of 19 and 55, the author shows that

relative wage positions, generated as measures within

three different hierarchical positions (low, medium,

and high), have an effect on the likelihood of preferring

self-employment over wage employment. In contrast

to this approach, which is based on income compar-

isons (pay ordering) of responding employees in

different firms, we consider fairness perceptions of

employees as a direct measure of such relative wage

positions. As a result, we argue that comparisons with

co-workers will directly influence the employee’s

fairness perception of his or her own wage, and expect

that differences between absolute and the perceived

fair wage income levels will affect an individual’s

choice in favor of or against nascent entrepreneurship.

Our literature review finds an overall considerable

proof that specific non-pecuniary workplace attributes

can strongly affect the employees’ utility level and,

consequently, the decision of employees to switch to

entrepreneurship. Moreover, and to the best of our

knowledge, none of the articles have so far explicitly

dealt with the effect of working time preferences and

fair wage perceptions as potential non-pecuniary

factors affecting the decision between paid and self-

employment. We intend to fill this gap in research

literature in the following by focusing our analysis on

the relationship between working time preferences,

fair wage perceptions, and entrepreneurial intentions.

2.1 Working time preferences

and the entrepreneurial intentions

of employees

While standard labor supply models usually assume

that individuals can freely choose their amount of

working hours (Wooden et al. 2009), empirical

evidence suggests that many employees would actu-

ally prefer to work fewer hours than they have to, even

if this would mean a change in income (Wooden et al.

2009; Heineck and Moller 2012). Moreover, several

studies which empirically analyze the pattern of actual

and preferred working hours observe an increasing

tension between these two variables (e.g., Fagan et al.

2006; Heineck and Moller 2012). Heineck and Moller

(2012), for example, show that more than 60 % of full-

time working employees prefer to work fewer hours

than they actually do, taking the accompanied loss of

earnings into account. There is obviously a mismatch

between actual and preferred working hours, which

may affect employees’ utility function and, conse-

quently, their occupational decision. Concentrating on

working hours of entrepreneurs, Boden (1999) shows

that new business owners are more satisfied with their

work schedules compared to employees, even though

they work longer hours on average. Accordingly,

Golden (2001) finds that self-employed individuals are

more than twice as likely to adapt their working

schedules to their own needs compared to paid

employees. Based on this finding, the author concludes

that work schedule flexibility should be seen as a

‘‘major reason to become self-employed’’ (Golden

2001, p. 59). Thus, even though the self-employed

work longer hours, switching to self-employment

seems to help reconcile work and private life and,

consequently, increase the individuals’ utility. Simi-

larly, using panel data from Sweden, Andersson

(2008) reports a causal effect between self-employ-

ment and job as well as life satisfaction, indicating that

the self-employed are more likely to feel an increase in

job and life satisfaction compared to wage earners.

Hence, more generally, if employees have to work

more hours for their company than they are willing to,

they may consider switching to self-employment,

because this occupational alternative will reflect a

higher degree of autonomy and work schedule flexi-

bility than is found in an employment situation.

Furthermore, we expect that the larger the differ-

ence between actual and preferred working hours, the

lower the degree of individual utility and the higher

the entrepreneurial intentions will be. To be sure,

employees could also switch to a new occupation in

paid employment if they are unsatisfied with the

flexibility of their work schedule. However, we

believe that, on average, this occupational alternative

A. Werner et al.

123

will again involve a lower degree of autonomy and

work schedule flexibility than is the case with self-

employment, because the employee will again switch

to a given organizational structure with given working

positions and (more or less) predetermined (i.e. less

flexible) work schedules (e.g., Stier and Lewin-

Epstein 2003). We therefore hypothesize the

following:

H1 The larger the difference between the actual

amount of working hours and the time an employee

would prefer to work, the higher the positive effect on

the employee’s entrepreneurial intentions.

2.2 Fair wage perceptions and the entrepreneurial

intentions of employees

Recent studies in labor market research have aimed to

analyze the effect that relative wage positions and

reference points can have on an individual’s evalua-

tion of economic outcomes and the effectiveness of

incentives. Researchers have tried, especially in the

field of behavioral economics, to shed more light on

the question of how far reference-dependent prefer-

ences can influence the individual utility function

(e.g., Kahneman and Tversky 1979; Bolton and

Ockenfels 2000). The key idea underlying this field

of research is that the individual utility not only

depends on the absolute wage level but also on

comparisons of the absolute wage level to a specific

reference wage level (i.e. the wage level of co-

workers). As discussed above, the novelty of our

approach is that we define reference wages based on

the employees’ fairness perceptions and relate this

concept to entrepreneurial behavior. In particular, we

expect that differences between the absolute wage (i.e.

actual wage level in paid employment) and the target

wage (wage levels employees perceive as fair for their

work) will affect their propensity towards entrepre-

neurship. By doing this, we assume that comparisons

with co-workers will influence an employee’s fairness

perception of his or her own wages. Consequently,

when the actual income lies very close to a perceived

fair income, we expect employees to stay in paid

employment because switching to self-employment

bears the risk of falling farther below the target

income. However, if the difference between the

absolute and the target income is very large (i.e. the

actual income is perceived as very unfair), we expect

employees to consider self-employment as an

occupational alternative to wage employment because

switching to self-employment enhances the chance to

reach the desired target income. Therefore, we

hypothesize:

H2a If the difference between the actual wage and

the wage an individual considers as fair is very high,

then employees’ entrepreneurial intentions will be

high.

H2b If the difference between the actual wage and

the wage an individual considers as fair is very low,

then employees’ entrepreneurial intentions will be

low.

2.3 Interaction effect between working time

preferences and fair wages

So far, we have argued that working time preferences

and the fairness perception of wages separately have

the potential to influence the entrepreneurial intentions

of employees through their effect on job satisfaction.

Nevertheless, the literature review has demonstrated

that the choice of becoming a nascent entrepreneur

does not depend only on single factors, but rather on a

variety of several variables which should not be

analyzed in isolation. Accordingly, we believe it is

worthwhile to relate the probability of becoming a

nascent entrepreneur to the interaction between our

two explanatory variables of (1) working time pref-

erences and (2) fair wages. By analyzing this interac-

tion term instead of just the separate factors, we

examine the case in which an employee works more

hours than individually preferred and simultaneously

earns less than his/her target wage. Since the utility

and satisfaction that an individual derives from a

certain employment situation depends among other

things on non-monetary (e.g., amount of working

hours) and monetary factors (e.g., wages) (Wagner

2004), it can be expected that, in a situation as

described here, job satisfaction diminishes and the

discontent with the current employment situation

increases. According to several studies, such dissatis-

faction and poor working conditions predict higher

entrepreneurial intentions of employees (e.g., Cromie

and Hayes 1991; Werner and Moog 2007). The fact

that in this case two elements of poor working

conditions are simultaneously in place may perpetuate

job dissatisfaction even more and hence stimulate the

entrepreneurial intentions of employees. Furthermore,

to earn a wage level which is perceived as fair

Effect of working time preferences

123

compared to a certain reference group, the individual

might consider increasing his/her working hours.

However, the employer might not always allow this

due to, e.g., higher overall wage costs or organiza-

tional constraints. When this is the case, dissatisfac-

tion of the individual accordingly increases even more

and he/she will search for options to get closer to the

desired target wage outside the company. In such a

case, switching to self-employment becomes particu-

larly attractive. We thus predict the interaction term

between working time preferences and fair wages to

positively affect nascent entrepreneurship. Hypothesis

3 therefore reads:

H3 Employees have higher entrepreneurial inten-

tions if they perceive their wages as unfair while

simultaneously preferring to work different hours than

they have to in paid employment.

3 Data and variables

To test our hypotheses, our paper employs SOEP data

conducted by the German Institute for Economic

Research (DIW). The SOEP is a representative panel

study of private households in Germany that began in

1984 and is updated annually. A detailed description

of the survey can be found in Wagner et al. (2007). The

dataset provides information on socio-demographic

characteristics (e.g., gender, age, education, fields of

professional experience) and a number of firm-specific

characteristics of employees’ firms (e.g., firm size,

industry, job tenure). Our analysis makes use of the

survey wave of 2009 because it contains a special

block of questions dealing with working time prefer-

ences as well as fair wage perceptions of employees.

We restrict our sample to full-time and part-time

employees (blue-collar and white-collar workers),

between 21 and 65 years old, and working in the

private sector with a maximum job tenure of 40 years.

Our sample as a result excludes the self-employed,

public sector employees, and people not currently in

the labor force. Moreover, the data revealed some

implausibly low monthly wages. Thus, we decided to

exclude employees with wages below the marginal

employment ceiling of 400 Euros per month (see, e.g.,

Pfeiffer and Schneck 2012 for details). Put differently,

employees who earn \13.33 Euros per day are

dropped from our analysis. These restrictions result

in an estimation sample of 4,382 observations.

3.1 Dependent variable

3.1.1 Propensity to become self-employed

For the composition of our dependent variable, we rely

on the answers given to the question regarding

possible future career changes. Part of the data in the

SOEP deals with entrepreneurial activities. For exam-

ple, the interviewees were asked if they plan to

become self-employed in the near future. In particular,

respondents were asked to estimate the probability of

becoming self-employed within the next 2 years. The

exact question was as follows: ‘‘How likely is it that

the following career changes will take place in your

life within the next 2 years?—Have you become self-

employed and/or freelance, and/or a self-employed

professional?’’ The employees were asked to estimate

the probability of such an occupational change in the

near future on a scale from 0 to 100 % in increments of

10; where 0 means that such a change will definitely

not take place and 100 means that such a change will

definitely take place in the near future.

The left column of Fig. 1 shows the distribution of

this variable. While nearly three-quarters (73.5 %) of

the interviewees do not consider becoming self-

employed at all, \1 % consider a definite propensity

to become self-employed. Moreover, 20.1 % of the

respondents estimate their probability to switch to

self-employment in the next 2 years as being between

10 and 40 %. We believe that these individuals are

likely to have not yet been actively involved in starting

a new business. However, they have probably taken

entrepreneurship into consideration and/or might not

be averse to it. In other words, it is rather implausible

to assume these individuals as nascent entrepreneurs

(i.e. people who by themselves or with others are

actively involved in starting a new business); they may

rather be defined as latent entrepreneurs (e.g., Blanch-

flower et al. 2001). On the other hand, respondents

who rated their likelihood of becoming self-employed

at a minimum of 50 % are probably more likely to

already be actively involved in starting a business

(Mueller, 2006). Accordingly, the right column of

Fig. 1 groups the answers on the basis of a three-item

Likert scale ranging from ‘‘1’’ (those who do not

consider becoming self-employed at all), ‘‘2’’ (those

who rated their probability of becoming self-

employed at 10–40 %) and ‘‘3’’ (those who rated their

probability at a minimum of 50 %). This classification

A. Werner et al.

123

brings forth 20.1 % latent entrepreneurs and approx-

imately 6 % (6.4 %) nascent entrepreneurs. For easier

interpretation, the results presented in the next sections

will be based on regression models using this three-

item ordinal scale.

3.2 Independent variables

3.2.1 Working time preferences

The responding employees were asked to indicate (1)

how many hours they work per week including

possible overtime and (2) how many hours they would

prefer to work, taking a change in income into account

(based on Merz 2002; Reynolds 2003; Wooden et al.

2009; Tam 2010). We then calculated the absolute

difference between the values of these two metric

variables and, by doing so, constructed our first

explanatory variable of working time preferences,

which allows us to measure the level of time-related

under- or over-employment of employees, and to

explore the impact of the individual’s satisfaction with

regard to working time on our dependent variable of

nascent entrepreneurship. Figure 2 shows the histo-

gram of this variable. As the results show, there is a

fair amount of variation in the answers, with a mean of

5.9 h, and a standard deviation of 6.3 h.

3.2.2 Fair wage perceptions

This variable refers to the degree to which employees

perceive their wages as fair. This variable is generated

from the question of whether the current monthly

income was fair from their point of view. If the

respondents answered ‘‘no’’, they were additionally

asked to indicate how high their net income per month

would have to be in order to be perceived as fair. Of the

4,382 employees in our sample, 1,562 (35.6 %) stated

their income at their current job was not fair. In order

to generate a measure for fair wage perceptions, we

divided the amount of the monthly ‘fair wage’ for this

group by the amount of their actual monthly net wage.

We call this measure the fair wage ratio. The average

fair wage ratio between what this group perceives as a

fair wage for their work and their actual net wage is

.482 with a standard deviation of .635. Note that 2,820

(64.4 %) employees stated that their actual wage level

can be considered fair. This group will be used as the

reference group in our regressions. Further, note that

none of the respondents in our analysis said that a fair

wage should actually be lower than their current net

wage.

Figure 3 shows the distribution of the answers

reflecting the fair wage ratio. A first observation is that

most of the 1,562 employees who state that their actual

wage lies below what they perceive as fair are

characterized by a relatively high average ratio value.

In other words, roughly two-thirds report a fair wage

ratio that is higher than 25 %, around 25 % state that a

fair wage for the work they do should be at least

10–25 % higher, and around 3 % say that a fair wage

should amount to a pay raise of up to 10 %. To

summarize, most of these employees report a

substantial degree of disagreement with respect to

020

4060

80

Per

cent

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Self-reported probability to become self-employed in the next two years, in %

020

4060

80

No Intention Latent Intention Nascent Intention

Self-reported probability to become self-employed in the next two years, 3-item-Likert scale

(Percent)Propensity to become self-employed

Fig. 1 Distribution of the propensity to become self-employed. Source SOEP 27 (2011)

Effect of working time preferences

123

what they should earn, so we find a sufficient degree of

variation in our fair wage ratio measure to test how

strong different levels of wage ratios affect the

propensity of the employees to become self-employed

in the near future. To test Hypotheses 2a and 2b, we

constructed 6 % dummy variables ranging from an

average ratio of up to 10 % to a difference of more

than 50 % in increments of 10 %.

3.3 Control variables

We included several control variables in our study that

might affect employees’ working time preferences,

fair wage perceptions, and the propensity to become

self-employed. We included years of job tenure and

education because these variables capture different

aspects of human capital and have shown their

influence on the ability to evaluate reference wages

(Gachter and Thoni 2010). In particular, education

controls for differences in the initial stock of human

capital, and job tenure reflects an increase in an

employee’s productivity by on-the-job training (e.g.,

Werner and Moog 2007). Moreover, we also include

educational degree levels in our regression models

because only including number of years of education

is misleading (Jaeger and Page 1996). Thus, we

additionally control for vocational training and a

university degree level.

We also controlled for firm size because working in

small organizations makes individuals more likely to

launch their own businesses. Smaller organizations are

said to have a comparative advantage over larger

organizations in furnishing their employees with

productive experiences that are conducive to entre-

preneurship (e.g., Wagner 2004; Hyytinen and Mal-

iranta 2006). We added log net monthly wage because

this variable may also influence reference wage

assessments as well as the propensity to start a new

venture. Moreover, it is often argued that entrepre-

neurs face capital constraints when trying to acquire

010

2030

Per

cent

0 5 10 15 20 25 30 35 40

Abs(Real Work Time - Preferred Work Time), in Hours per Week

(Percent)Work Time PreferencesFig. 2 Distribution of

working time preferences.

Source SOEP 27 (2011)

02

46

810

Per

cent

0 .25 .5 .75 1 1.25

Fair Wage Ratio, in %

* Ratio between amount of fair wage and ampunt of actual wage; N=1,562

(Percent)Fair Wage Ratio*Fig. 3 Distribution of the

fair wage ratio. Source

SOEP 27 (2011)

A. Werner et al.

123

the necessary financial resources during the start-up

process (Parker and van Praag 2006; Backes-Gellner

and Werner 2007). However, if employees earn

enough money to permit them to build up sufficient

savings, it will be easier for them to overcome such

financial constraints and enter self-employment more

easily. We also included gender because in Germany,

as in many other countries, fewer women than men

start new businesses. And we controlled for age

because as individuals get older they tend to have

higher wages in paid employment while exceeding

lower levels of effort than younger individuals

(Waldman 1984; Hutchens 1989; Prendergast 1999).

Moreover, age can also be seen as a proxy of personal

wealth to overcome the capital constraints discussed

above. However, older employees may also be more

risk averse than younger ones and less capable of

working the long hours often necessary for entrepre-

neurs (Parker 2004). These arguments imply that both

younger and older employees will tend to stay with

their employer compared to those in their middle age.

Thus, we add age2 to test this concave effect of age on

the entrepreneurial intentions of employees.

To control for the rate of foreign employees and the

special conditions they face when trying to evaluate

reference wages and occupational choices, we

included two variables indicating different aspects of

the respondents’ national backgrounds. For example,

it is an established fact in ethnic minority entrepre-

neurship that entrepreneurship can offer a way out of

the discrimination perpetrated by employers in the

labor market, banks in capital markets, or consumers

in product markets (Parker 2004). Even so, minorities

may also face discrimination that hinders their ability

to practice entrepreneurship. Thus, we took a closer

look at (1) the total early stage entrepreneurial activity

(TEA) and (2) the type of economy of the countries

from which the foreign employees came. We gener-

ated these two variables based on information we

gathered from the the Global Entrepreneurship Mon-

itor (GEM) country reports (Sternberg et al. 2013). In

particular, we took into consideration that—compared

to Germany—self-employment in some foreign coun-

tries is overrepresented or underrepresented. Second,

we included a variable indicating whether the employ-

ees in our data come from innovation- or efficiency-

driven economies. A number of studies also confirm

the phenomenon that children of self-employed par-

ents are more likely to become self-employed (see,

e.g., Dunn and Holtz-Eakin 2000; Fairlie and Robb

2007). Self-employed parents can provide social

networks and human and financial resources condu-

cive to self-employment. Growing up in such a family

may also lead to a positive attitude towards entrepre-

neurship or a desire for independence or autonomy

(Mueller 2006). Additionally, children may be moti-

vated to inherit the family business. To provide an

indicator for the necessary constraints, with which an

employee may be confronted when deciding to switch

to entrepreneurship, we also controlled for the labor

force downsize factor, as well as full-time employment

to account for differences in physical presence at the

workplace, which may well influence the reference

wage and work time assessments (Fladung and Iseke

2010). Additionally, we controlled for different

industry sectors, the occupational status of the

employees, and their job satisfaction in paid employ-

ment which may also influence reference wage

assessments and the propensity to become self-

employed (Fehr and Gotte 2007).

Although some work time schedules offered by

companies are more flexible than others, job and

organizational characteristics largely influence the

actual amount of working hours, as employers’ and

employees’ preferences regarding the regulation of

work hours may diverge (Reynolds 2003). These

organizational constraints may force individuals to

think about how to change their employment situation

so that their preferred working hours better match their

actual working hours, e.g., through within- or

between-employer job changes (Boheim and Taylor

2004) or by switching to self-employment (Taylor

1996). Thus, to control for currently existing work

time flexibility, we incorporated the control variable

flexible work time schedule. Last, we included a

control variable for the fact that employees might

consider to take a second job, implying that they

become self-employed while sticking with their cur-

rent employer. As we are interested in the employees’

intention to switch to entrepreneurship instead of wage

employment, not controlling for a second job might

bias our regression results. Last but not least, we

controlled for regional differences in nascent entre-

preneurship within Germany, since previous research

reported that several macro-economic aspects such as

GDP per capita, unemployment rate, population

density, and agglomeration can have an influence on

the self-employment rate and start-up behavior

Effect of working time preferences

123

Table 1 Definitions of variables and descriptive statistics

Description Mean SD Min Max

Self-employment How likely is it that you become self-employed and/or freelance, and/

or a self-employed professional within the next 2 years? 11-item

Likert scale, [0;100]

7.66 17.56 0.00 100.00

Tenure Since when have you been working for your current employer? In

years

10.78 9.27 0.00 40.00

Education (years) Generated variable. Years of education 12.52 2.58 7.00 18.00

University degree

(1 = yes)

Did you conclude your education with a university degree? 1 = yes,

0 = no

0.22 0.42 0.00 1.00

Prof. education_1 Did you have a vocational training degree? 1 = no, 0 = else 0.23 0.42 0.00 1.00

Prof. education_2 Did you have a vocational training degree? 1 = other, 0 = else 0.14 0.35 0.00 1.00

Prof. education_3 Did you have a vocational training degree? 1 = apprenticeship,

0 = else

0.54 0.50 0.00 1.00

Prof. education_4 Did you have a vocational training degree? 1 = master craftsman,

0 = else

0.08 0.27 0.00 1.00

Firm size _1 Approximately how many people does the company employ as a

whole? 1 = \ 20, 0 = else

0.25 0.44 0.00 1.00

Firm size_2 Approximately how many people does the company employ as a

whole? 1 = from 20, but \200, 0 = else

0.30 0.46 0.00 1.00

Firm size_3 Approximately how many people does the company employ as a

whole? 1 = from 200, but \2,000, 0 = else

0.21 0.41 0.00 1.00

Firm size_4 Approximately how many people does the company employ as a

whole? 1 = 2,000 or more people, 0 = else

0.23 0.42 0.00 1.00

Wage How high was your net income from employment last month? Log

net monthly wage

7.32 0.55 5.99 9.90

Gender Your sex? 1 = female, 0 = male 0.42 0.49 1.00 2.00

Age Your year of birth? (generated, years) 42.94 10.51 21.00 65.00

Entrepreneurial_Activity_1 Generated variable: total early stage entrepreneurial activity in

Country of origin: 1 = Germany, 0 = else

0.94 0.24 0.00 1.00

Entrepreneurial_Activity_2 Generated variable: total early stage entrepreneurial activity in

country of origin: 1 = lower or equal to Germany, 0 = else

0.01 0.12 0.00 1.00

Entrepreneurial_Activity_3 Generated variable: total early stage entrepreneurial activity in

country of origin: 1 = higher than Germany, 0 = else

0.05 0.22 0.00 1.00

Type of economy Generated variable (GEM): type of economy in country of Origin:

0 = efficiency-driven economy, 1 = innovation-based economy

0.97 0.18 0.00 1.00

Parent(s) self-employed Is/was your father/mother self-employed? 1 = yes, 0 = no 0.10 0.30 0.00 1.00

East Germany Place of residence? 1 = East Germany, 0 = West Germany 0.24 0.43 0.00 1.00

Full-time Are you currently engaged in paid employment? If yes, which of the

following applies best to your status? 1 = Full-time employed,

0 = part-time employed

0.81 0.39 0.00 1.00

Second job It is possible to work in addition to regular employment. Are you

engaged in a second job next to your main employment activity?

1 = yes, 0 = no

0.07 0.25 0.00 1.00

Flex. work time schedule How many hours are stipulated in your contract (excluding

overtime)? 1 = no set hours, 0 = else

0.07 0.25 0.00 1.00

Labour force downsize What developments do you anticipate in your occupational area: will

the number of employees increase, decrease, or stay the same over

the next 12 months? 1 = decrease, 0 = else

0.31 0.46 0.00 1.00

Job satisfaction How satisfied are you with your job? 11-item Likert scale, [0;10] 6.84 2.03 0.00 10.00

A. Werner et al.

123

(Wagner and Sternberg 2005; Bergmann and Stern-

berg 2007). Hence, we included a dummy variable for

East Germany. Table 1 provides the description of the

variables and detailed descriptive statistics.

3.4 Analytical approach

In the empirical models discussed below, we regress

the propensity of employees to leave paid employment

for self-employment on working time preferences, fair

wage perceptions, and the control variables discussed

above. In particular, four different specifications of the

empirical model were estimated.

In Models 1 and 2, the dependent variable is the

three-item ordinal scale variable described above

taking the value ‘1’ if the employees do not consider

becoming self-employed at all, ‘2’ if they rated their

probability of becoming self-employed at 10–40 %,

and ‘3’ if they rated their probability at a minimum of

50 %. The appropriate econometric model to use in

this case is a regression model for ordinal outcome

variables. In Model 1, we test Hypotheses 1, 2a, and 2b

by including the control variables and the variables

representing the working time preferences and the fair

wage perceptions by using the above described set of

percentage interval dummies indicating different fair

wage ratio levels. In Model 2, we test Hypothesis 3 by

replacing the interval dummies with a single dummy

variable (coded as ‘1’ if the responding employees

state that their income at their current job is not fair

and ‘0’ if the employees perceive their current income

as fair) and interacting this variable with the working

time preferences variable described above. Note, in

the cases where we illustrate our results (Figs. 4, 5),

we display the predictive probabilities and the 95 %-

confidence intervals of the likelihood of becoming an

entrepreneur at a minimum of 50 % compared to the

case where the employees do not consider becoming

self-employed at all.

In Models 3 and 4, we test for the robustness of our

results by checking what happens to the results if we

use the original 11-level ordinal variable as our

dependent variable in a Tobit model. Since our

dependent variable has an overload of employees

who have no entrepreneurial intentions at all, the

variable could be classified as left-censored at zero.

Overall, we find that the results of our central variables

remain robust and in line with our hypotheses. That is

all models show similar effects (same signs and the

statistical significance of the estimates is similar

compared to those in the ordered probit models) of

working time preferences and fair wage perceptions of

employees’ propensity to leave paid employment for

self-employment (e.g., Wooldridge 2003).

Finally, note that all the empirical models presented

here have robust standard errors with correction for

heteroskedasticity. We also computed for all the

models several regression diagnostics and checked

the variance inflation factors (VIF) to exclude multi-

collinearity. Table 3 in the appendix provides the pair-

wise correlations of key variables used in our empir-

ical analysis.

4 Results

Table 2 presents the regression results. As displayed

in Models 1 and 3, the effect of working time

preferences is statistically significantly different from

zero at any conventional level (Model 1: b = .006,

i \ .1; Model 3: b = .264, p \ .1). For example,

considering the effect of working time preferences

in Model 1, each hour increase or decrease in

working time preferences increases the propensity to

Table 1 continued

Description Mean SD Min Max

Working time preferences Generated variable: How many hours do your actual working-hours

consist of including possible over-time? If you could choose your

own number of working hours, taking into account that your

income would change according to the number of hours: How many

hours would you want to work? Difference in hours

5.91 6.26 0.00 39.90

Fair wage perceptions Generated variable: Is the income that you earn at your current job

fair, from your point of view? If no, how high would your net

income have to be in order to be fair? Fair wage ratio

0.17 0.44 0.00 15.48

Source SOEP 27 (2011)

Effect of working time preferences

123

entrepreneurship by .006 standard deviations, holding

all the other covariates fixed at their mean values.

Thus, Hypothesis 1 is supported by the data: employ-

ees who prefer to work other hours than they actually

do in paid employment also have a higher propensity

to become self-employed.

The predictive probabilities are displayed in the left

panel of Fig. 4 (based on Model 1). The results show

that for 5 h of increase or decrease, the probability of

becoming an entrepreneur at a minimum of 50 %

(which is true for 6.4 % of all employees in the data)

increases from by .35 % points on average compared

to those employees who actually work in their

preferred work schedule. Moreover, in Models 1 and

3, the negative and significant coefficient of the

variable Difference Fair Wage (up to 10 %) supports

Hypothesis 2a. As discussed above, this significant

influence suggests that employees who are close to

their target wage are likely to remain in their current

employment situation rather than switch to self-

employment (Model 1: b = -.381, p \ .1; Model 3:

b = -17.35, p \ .1). These employees are about to

earn their target wage, which they do not put at stake

by becoming self-employed. For Hypothesis 2b, we

also find supporting results. The coefficients of the two

categories Difference Fair Wage more than 40 and

.04

.06

.08

.1.1

2

Pr(

Nas

cent

Ent

repr

eneu

r)

0 5 10 15 20 25 30 35 40

Abs(Real Work Time - Preferred Work Time), in Hours per Week

(Predictive Margins)Work Time Preference

0.0

5.1

.15

Net = Fair Wage <10% [10; 20%[ [20; 30%[ [30; 40%[ [40; 50%[ >50%

Percentage Intervals

(Predictive Margins)Fair Wage Perceptions

Note: Confidence intervalls shown as shaded region

Fig. 4 Working time preferences, fair wage perceptions, and the probability to become a nascent entrepreneur. Source SOEP 27

(2011). All other covariates are fixed at their mean values

0.0

5.1

.15

.2.2

5

Pr(

Nas

cent

Ent

repr

eneu

r)

0 5 10 15 20 25 30 35 40

Abs(Real Work Time - Preferred Work Time), in Hours per Week

Income at Current Job is Fair Income at Current Job is not Fair

Note: Graphs include confidence intervals

(Predictive Margins)Interaction: Fair Wage Perceptions * Work Time PreferenceFig. 5 Working time

preferences, fair wage

perceptions, and the

probability to become self-

employed (moderated

effects). Source SOEP 27

(2011). All other covariates

are fixed at their mean

values

A. Werner et al.

123

Table 2 Ordered probit and tobit estimation results

DV Models 1 and 2: entrepreneurial intentions

(3-item-Likert scale)

DV Models 3 and 4: entrepreneurial intentions

(11-item-Likert scale)

Ordered probit Tobit

Model 1

Coeff.

Model 2

Coeff.

Model 3

Coeff.

Model 4

Coeff.

Controls

Tenure (in years) -0.013***

(0.003)

-0.012***

(0.003)

-0.614***

(0.126)

0.606***

(0.125)

Education (in years) 0.040***

(0.015)

0.040***

(0.015)

1.875***

(0.624)

1.895***

(0.628)

University degree (1 = yes) -0.005

(0.090)

-0.001

(0.091)

-0.928

(3.739)

-0.765

(3.753)

Professional education levels (1 = other

professional education)a0.067

(0.076)

0.066

(0.075)

3.820

(3.090)

3.757

(3.081)

Professional education levels

(1 = apprenticeship)a-0.093*

(0.059)

-0.092*

(0.059)

-3.908

(2.403)

-3.893

(2.402)

Professional education levels (1 = master

craftsman education)a0.077

(0.090)

0.076

(0.089)

4.676

(3.739)

4.647

(3.724)

Firm size (1 = \20 employees)b 0.191***

(0.068)

0.189***

(0.068)

8.785***

(2.843)

8.751***

(2.852)

Firm size (more than 20 and\200 employees)b 0.047

(0.061)

0.038

(0.061)

2.141

(2.589)

1.680

(2.586)

Firm size (more than 200 and \2,000

employees)b0.118**

(0.061)

0.115**

(0.061)

4.966*

(2.544)

4.845*

(2.546)

Log net monthly earnings (in Euros) 0.201***

(0.071)

0.172***

(0.070)

9.998***

(2.993)

8.563***

(2.929)

Gender (1 = Female) -0.224***

(0.055)

-0.230***

(0.055)

-10.23***

(2.263)

-10.53***

(2.263)

Age (in years) 0.029*

(0.017)

-0.028*

(0.017)

1.212*

(0.717)

1.166

(0.716)

Age squared (in years) -0.000***

(0.001)

-0.001***

(0.000)

-0.025***

(0.009)

-0.025***

(0.009)

Total early stage entrepreneurial activity

(1 = lower or equal to Germany)c-0.299

(0.214)

-0.297

(0.213)

-12.42

(9.522)

-12.29

(9.512)

Total early stage entrepreneurial activity

(1 = higher than Germany)c0.368***

(0.159)

0.377***

(0.156)

16.72**

(6.555)

17.09***

(6.428)

Type of economy (1 = innovation-based

economy)

0.556***

(0.198)

0.562***

(0.195)

24.39***

(8.342)

24.63***

(8.225)

Parent(s) self-employed (1 = yes) 0.149**

(0.066)

0.160***

(0.066)

6.837**

(2.703)

7.349***

(2.706)

East Germany (1 = yes) 0.088*

(0.052)

0.099**

(0.052)

4.028*

(2.163)

4.581***

(2.157)

Full-time employment (1 = yes) -0.185***

(0.075)

-0.177***

(0.075)

-9.260***

(3.143)

-8.899***

(3.155)

Second job (1 = yes) 0.374***

(0.077)

0.369***

(0.077)

18.74***

(3.192)

18.43***

(3.195)

Effect of working time preferences

123

\50 % (Model 1: b = .196, p \ .05; Model 3:

b = 9.249, p \ .05) and Difference Fair Wage more

than 50 % (Model 1: b = .130, p \ .05; Model 3:

b = 7.077, p \ .05) are significantly different from

zero at any conventional level and positive. Hence, the

intention of leaving paid employment for self-employ-

ment increases if the difference between the actual net

wage and the target wage (fair wage) increases. For

Table 2 continued

DV Models 1 and 2: entrepreneurial intentions

(3-item-Likert scale)DV Models 3 and 4:

entrepreneurial intentions (11-item-Likert scale)

Ordered probit Tobit

Model

1Coeff.

Model

2Coeff.

Model

3Coeff.

Model

4Coeff.

Flexible work time schedule (1 = yes) 0.085

(0.085)

0.091

(0.085)

4.922

(3.504)

5.205

(3.501)

Firm’s labour workforce (1 = downsize in next

12 month)

0.127***

(0.048)

0.127***

(0.048)

6.106***

(2.006)

6.114***

(2.004)

Job satisfaction (scale from 0 = ‘totally

unsatisfied’ to 10 = ‘totally satisfied’)

-0.077***

(0.011)

-0.077***

(0.011)

-3.689***

(0.458)

-3.713***

(0.455)

Central variables

Working time preferences (actual minus

desired working time, in hours)

0.006*

(0.003)

0.001

(0.005)

0.264*

(0.142)

-0.0237

(0.185)

Difference fair wage (up to 10 %)d -0.381*

(0.245)

-17.35*

(10.14)

Difference fair wage ([10 % B 20 %)d -0.048

(0.092)

-2.116

(3.855)

Difference fair wage ([20 % B 30 %)d 0.012

(0.078)

1.262

(3.287)

Difference fair wage ([30 % B 40 %)d -0.009

(0.089)

-0.261

(3.745)

Difference fair wage ([40 % B 50 %)d 0.196**

(0.092)

9.249**

(3.803)

Difference fair wage (more than 50 %)d 0.130**

(0.074)

7.077**

(3.109)

Difference fair wage (1 = yes) -0.049

(0.063)

-2.049

(2.602)

Difference fair wage (1 = yes)* working time

preferences

0.014**

(0.007)

0.713***

[0.272)

Log likelihood -2868.4 -2871.4 -7261.7 -7264.6

Observations 4,382 4,382 4,382 4,382

Source SOEP 27 (2011). Regressions in all columns include indicator variables for industrial sector (agriculture, energy, mining,

manufacturing, construction, trade, transport, banking and insurance), and occupational status in private employment. Models 1 and 2:

Y-standardized ordered probit regression coefficients displayed. Models 3 and 4: Tobit regression coefficients estimates displayed.

Robust standard errors in parenthesesa Reference: no professional educationb Reference: firm size with more than 2,000 employeesc Reference: country of origin is Germanyd Reference: net wage = fair wage

***, **, * indicate significance at 1, 5, and 10 % levels, respectively.

A. Werner et al.

123

these employees, reaching the target wage in the

current employment situation appears to be a distant

prospect, and the transition to entrepreneurship might

be an option to come closer to the target wage.

Moreover, note that, while the two coefficients

Difference Fair Wage more than 40 and \50 % and

Difference Fair Wage more than 50 % are statistically

different from zero compared to the reference category

Net wage = Fair Wage, they are not significantly

different from each other (p = .488). The right panel

of Fig. 4 displays the predictive probabilities.

We also predicted a positive interaction effect

between working time preferences and fair wages in

Models 2 and 4, meaning that, if the employees are

exposed to both working conditions simultaneously,

the entrepreneurial intentions are higher. Thus,

Hypothesis 3 is also born out of the data; the coefficient

of Difference Fair Wage (1 = yes) 9 Working Time

Preferences is significantly positive (Model 2:

b = .014, p \ .05; Model 4: b = .713, p \ .01). To

give a better impression of how differences in fair wage

perceptions and working time preferences impact

entrepreneurial intention, Fig. 5 also plots the predic-

tive probabilities of these variables (based on Model 2).

As the results reveal, employees have increasing

entrepreneurial intentions for every 1 h increase or

decrease in the working hours they would prefer, but

the effect is much stronger if they perceive their wage

as unfair.

With respect to the control variables, the results

show that tenure, gender, age, full-time employment,

and job satisfaction have a negative influence on the

probability of a switch to self-employment from paid

employment, whereas the estimated coefficients of

years of education, small firm size, net monthly wage,

parental self-employment, expected labor force down-

size, and having a second job next to the main or

regular position show significant positive effects. In

fact, age is nonlinear; i.e., both the very young and the

old are less likely enter self-employment compared to

those employees in middle ages.

Regarding the regional differences in Germany, we

find that the propensity to become self-employed is

significantly higher in East Germany than it is in West

Germany. We believe that this effect is due to different

labor market conditions—especially the fact that the

unemployment rate is about twice as high in the East

as it is in the West should ‘‘push’’ individuals into self-

employment (Dawson et al. 2009). Regarding

nationality differences, our findings show that

employees from countries with a higher TEA indeed

report a higher intention to quit wage-employment for

self-employment. Furthermore, under equal condi-

tions, employees who originally come from innova-

tion-driven economies are more eager to start-up their

own business than the ones coming from efficiency-

driven economies. This behavior might be traced back

to traditions which are inherent in the home countries

of the employees in our sample.

Note that we also tested for the robustness of our

results by checking what happens to the results if we

use the original 11-level ordinal variable as our

dependent variable in an ordered probit model. We

find that the results of our central variables remain

quite robust and in line with our hypotheses. More-

over, we also checked for nonlinearity graphically and

analytically (i.e. by using power terms) in the

relationship between our continuous predictors and

the outcome variable. With regard to our control

variables and working time preferences, we could not

find any statistically significant effects (besides age)

and therefore decided to not include these terms in the

regression models. With regard to our fair wage

perception variable, we found a cubic relationship.

However the predicted values were too high in the

region close to the target wage. Thus, we decided to

use the six-percentage dummy variables described

above which, in addition, also fit the model better than

when using power terms.

5 Discussion and conclusion

Our study sought to examine the environment in which

an employee decides on his/her career as a potential

nascent entrepreneur. Using data of the SOEP, we are

able to identify these potential nascent entrepreneurs

and to explore factors that affect the very early stages

of the firm creation process, the intention to switch to

entrepreneurship, and not the actual switch.

Prior research has shown that utility-maximizing

occupational choice decisions are not made randomly,

but depend on a variety of different factors and their

possible interplay with one another. To outline these

factors, we performed a systematic literature review

which revealed that, on the one hand, personal and

regional factors as well as perceptional variables,

macro-level influences, and institutional and cultural

Effect of working time preferences

123

factors matter for the occupational choice. On the

other hand, the pecuniary and non-pecuniary aspects

of the current employment situation are also impor-

tant, as they can influence an individual’s job

satisfaction and the propensity to switch to entrepre-

neurship. However, although the impact of these

factors is today generally accepted, there are still many

gaps in the research literature. Little research has been

done to date on, for example, the possible influence of

workplace and job characteristics on the entrepre-

neurial intention of employees, even though the

importance of these variables has in fact been stressed.

It is also one reason why this paper has looked at the

influence of working time preferences and fair wage

perceptions on employee entrepreneurship.

Grounded in the notion that, from the employees’

perspective, utility, and job satisfaction are derived

from income comparisons and flexible work sched-

ules, we developed hypotheses of why and how these

working conditions affect the likelihood of becoming

self-employed. In contrast to standard labor economic

models which assume that employees can freely

choose their amount of working hours, we suggest

that employees—although they do in fact want to

change their working hours—are unable to do so

because of organizational constraints in paid employ-

ment. Moreover, we argue that mismatches or tension

exist between actual and preferred working hours,

which can influence the employees’ occupational

choice due to their influence on utility and satisfaction

derived from a certain employment alternative. We

found that, compared to the situation in which

preferred hours match actual work hours, both positive

as well as negative deviations are positively related to

the probability of switching into self-employment.

This effect seems to be even stronger if the employees

want to reduce their working hours. Hence, we believe

that satisfaction with working time is one of the key

factors of overall utility that is derived by a certain

employment option. Becoming self-employed may

therefore help to reconcile work and private life while

increasing job satisfaction and individual utility.

Furthermore, we argued that an important pecuni-

ary factor which determines the occupational choice of

employees is the earned wage. Since income is part of

the utility an employee derives from a certain job,

wages can also influence an employee’s satisfaction

with the current (employment) situation. Nevertheless,

from the literature review, we infer that not many

studies have so far investigated the potential effect of

wage-related aspects on the propensity of becoming an

entrepreneur. The studies that have in fact done so

show that not only the difference between the expected

income of self-employment and wage-employment

must be considered as being relevant for the decision

to engage in entrepreneurship, as the level of co-

workers’ income in paid employment and the fairness

perception of these wage levels also seem to matter

when it comes to the decision to become an entrepre-

neur. However, the impact of relative wages in

entrepreneurship research has so far received little

attention, even though recent studies emphasize the

possible effect that reference points can have on an

individual’s evaluation of economic outcomes and the

effectiveness of incentives.

Since comparisons with co-workers seem to influ-

ence an employee’s fairness perception of their own

wages, and because the occupational choice is based

on the utility derived from a certain employment

situation, we argued that the difference between the

absolute and the target income can affect an individ-

ual’s choice for or against nascent entrepreneurship.

Moreover, in the case where the absolute wage is close

to the target wage, we argued that employees most

probably perceive the target income to be fair and

within reach because an employee will not risk lost

income by leaving his/her job to start their own

business. When the difference is larger, however, and

the target income is perceived to be unfair and out of

reach, we argue that an individual might consider self-

employment as a real alternative to wage employment.

We also argue, that, to earn a wage level which is

perceived as fair, employees can increase their work-

ing hours. However, if employees are unable to choose

the number of work hours they prefer (e.g., due to

organizational constraints), this will additionally

stimulate their entrepreneurial intentions. Put differ-

ently, individual dissatisfaction with the employment

situation will increase even more, and employees will

have an incentive to search for options outside the

company to get closer to the target wage. Our results

provide evidence that fair wage perceptions influence

the propensity to switch to self-employment. Those

employees who perceive their current wage level as

very unfair are more likely to have entrepreneurial

intentions. However, those whose wages are very

close to the wage level they perceive as fair are more

likely to remain in their current employment situation.

A. Werner et al.

123

Moreover, those employees who perceive their wages

as unfair, while simultaneously preferring more flex-

ible work hours, show especially high entrepreneurial

intentions.

Regarding our control variables, most of our results

are in line with the expectations based on previous

research. However, there are some interesting corre-

lations which deserve further mention, especially

control variables related to working conditions, such

as firm size, second jobs, and flexible work time

schedule. The results for the firm size measures

supports the notion that small firms are more likely

to produce more entrepreneurs than larger ones. This

might, at least partly, be explained by the fact that

employees in small firms are confronted with various

task packages which increase their learning and

experience on-the-job faster or because small firms

offer limited promotion possibilities due to their firm

size. Additionally, the fact that ‘‘having a second job

while entering nascent entrepreneurship’’ is, com-

pared to some other control variables, quite strong,

which implies that employees are likely to hold on to

their current job, at least for some time, while starting-

up their own venture. This might help the young

entrepreneur to acquire the necessary finances and

might represent a fallback option in case their own

start-up does not render the (financial) results

expected. Next, existing flexible work time schedules

were found to be insignificant in our model, although

we expected that existing organizational constraints

would enhance the individual’s likelihood of thinking

about job changes, either within or between employ-

ers, or by switching to self-employment.

In view of our results concerning not only our

independent variables working time preferences and

fair wage perceptions but also some workplace-related

control variables, we can conclude that it will be

scientifically fruitful to further address the question of

whether and to what extent direct exposure to specific

working conditions is beneficial for entrepreneurship.

Some limitations should be kept in mind when

considering our results. First, our data do not (yet)

offer any information regarding whether the respond-

ing employees did in fact start their own business.

Thus, we cannot directly test whether and how our

central variables impact the real occupational deci-

sion. We do acknowledge the limitation of using

entrepreneurial intentions as our dependent variable

instead of actual switches to entrepreneurship. How-

ever, the new SOEP data of 2010 and 2011 showed a

strong correlation between higher entrepreneurial

intentions in 2009 and entrepreneurial entry in 2010

and 2011. Nevertheless, employees with strong entre-

preneurial intentions in 2009 may plan to start a new

business in the next 3–5 years but not within the next

2 years, which limits the possibility to test the

influence of our variables on the real switch to

entrepreneurship. But as we aim to shed more light

on the relatively new research field of nascent

entrepreneurship, we have focused in this paper on

the factors that affect the very early staged of the firm

creation process, hence the intention to switch to

entrepreneurship and not the actual switch. It can also

be argued that employees who, for example, state that

the income they earn is not fair are the ones that have a

higher propensity to switch to new occupations

altogether. Again, we cannot rule out this explanation

because we do not currently have the data to observe

the real occupational choice of the respondents.

Moreover, one could reverse the interpretation of the

results and say that an increasing intention of switch-

ing from paid employment to entrepreneurship is not a

consequence of different working time preferences

and fair wage perceptions, but its cause. Thus, we note

that the reverse causality problem as an additional

limitation of our study. But even with these limitations

in mind, we are quite confident that our results

represent a contribution to entrepreneurship research.

For the entrepreneurship discussion, we deliver the

first insights into why specific working conditions—

i.e. fair wage perceptions and flexible work time

schedules—affect the propensity to become an entre-

preneur in the near future.

Appendix

See Table 3.

Effect of working time preferences

123

Ta

ble

3P

air-

wis

eco

rrel

atio

ns

amo

ng

key

var

iab

les

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10

)(1

1)

(12

)(1

3)

(1)

Sel

f-em

plo

ym

ent

[0;1

00

]

1

(2)

Ten

ure

(yea

rs)

-.1

31

(3)

Ed

uca

tio

n

(yea

rs)

.14

-.0

91

(4)

Un

iver

sity

Deg

ree

(1=

yes

)

.11

-.0

5.8

11

(5)

Pro

f.ed

uca

tio

n

(1=

no

)

.06

-.0

8.3

1.4

71

(6)

Pro

f.ed

uca

tio

n

(1=

oth

er)

.03

-.0

1.0

3-

.07

-.2

31

(7)

Pro

f.ed

uca

tio

n

(1=

app

ren

tice

ship

)

-.0

9.0

4-

.29

-.3

2-

.60

-.4

51

(8)

Pro

f.ed

uca

tio

n

(1=

mas

ter

craf

tsm

an)

.04

.05

.00

-.0

5-

.16

-.1

2-

.32

1

(9)

Fir

msi

ze

\2

0

.07

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size

[20

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81

(11

)F

irm

size

[20

0;2

,00

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9-

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1-

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41

(12

)F

irm

size

[2

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1.1

6.1

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1

(13

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age

(lo

gn

etm

on

thly

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e)

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1

(14

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end

er

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ntr

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vit

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(1=

Ger

man

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.01

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6

A. Werner et al.

123

Ta

ble

3co

nti

nu

ed

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10

)(1

1)

(12

)(1

3)

(17

)E

ntr

epre

neu

rial

acti

vit

y

(1B

Ger

man

)

-.0

2-

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1.0

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1

(18

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ntr

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vit

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(1=

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man

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.01

-.0

1-

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-.0

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0-

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5-

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1.0

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6

(19

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yp

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ased

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(20

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aren

t(s)

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ast

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eco

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(26

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bsa

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(14

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Deg

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Effect of working time preferences

123

Ta

ble

3co

nti

nu

ed

(14

)(1

5)

(16

)(1

7)

(18

)(1

9)

(20

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1)

(22

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(24

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(1=

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(8)

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(1=

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Fir

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irm

size

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size

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A. Werner et al.

123

Ta

ble

3co

nti

nu

ed

(14

)(1

5)

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(23

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eco

nd

job

(1=

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abo

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0-

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41

(26

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bsa

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81

(27

)W

ork

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So

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eS

OE

P2

7(2

01

1)

Effect of working time preferences

123

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