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Older Entrepreneurs: Do They Work Smarter Or Harder?
Zolin R1
1Queensland University of Technology
Submitting Author Contact Information
Roxanne Zolin
Queensland University of Technology
r.zolin@qut.edu.au
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Older Entrepreneurs: Do They Work Smarter Or Harder?
Abstract
Entrepreneurs starting their first businesses between the ages of 55 and 64 years
represent the fastest growing entrepreneurship segment in America and Australia. There is
sparse research on older entrepreneurs with conflicting results, particularly with respect to
generational differences. Previous literature on generational differences focuses on family
businesses, but characteristics of founders of family businesses are quite different than those
of founders of non-family businesses. Consequently, we compare characteristics of older
entrepreneurs to younger entrepreneurs as they start new ventures. Are there differences in
their work styles and venture performance? This study makes a contribution to
entrepreneurship literature by studying the growing phenomenon of older entrepreneurs. We
make a contribution to practice by helping older entrepreneurs identify their strengths, which
could lead to more successful older entrepreneurs and provide satisfying and rewarding
careers to those leaving wage and salary employment to pursue self-employment.
Introduction
Baby Boomers (born between 1946 and 1964) are enjoying longer, healthier, and
more active lifestyles. Of interest, the youngest of this influential segment has now reached
the age of 50, the age usually used as a dividing point between older and younger workers
(e.g., Government of Alberta, 2011). The size and influence of this generation make it
important to understand their career transitions, especially with respect to the innovation and
leadership involved in entrepreneurial careers. To highlight, statisticians have noted that
Australian and American entrepreneurs starting their first businesses between the ages of 55
and 64 years represent the fastest growing entrepreneurship segment (Mayhew 2014).
Although previous research has noted that seniors are less likely to engage in
entrepreneurial activity due to health issues, time allocation preferences, and other personal
reasons (Curran and Blackburn 2001; Singh and DeNoble 2003, Levesque and Minniti,
2006), mature workers may search out self-employment and business opportunities to
maintain their career, income, and self-expression (Álvarez-Herranz, Valencia-De-Lara, and
Martínez-Ruiz, 2011). Wainwright and Kibler’s (2014) qualitative British study discussed
how work-retirement balance was achieved through later stage self-employment. They
concluded that older individuals pursued self-employment (businesses from home) as a
means to stabilize and augment finances in retirement. Parker and Rougier (2007) concluded
that average retirement ages are substantially higher in self-employment than in paid
employment, implying that encouraging older retired or unemployed employees to become
self-employed could stimulate greater aggregate labour force participation.
There is sparse research on older entrepreneurs (Weber & Schaper, 2011). Previous
literature on generational differences focuses on family businesses, but characteristics of
founders of family businesses are quite different than those of founders of non-family
businesses (Morris, Allen, Kuratko, & Brannen, 2010). De Kok, Ichou, and Verheul (2010)
noted conflicting results from prior studies relating age to venturing and called for additional
research into the relationship between age and entrepreneurship (e.g. in different countries)
and into the macro-economic effects of this development. De Kok et al. (2010) proposed an
indirect relationship between age and entrepreneurship indicating that relevant mediators
could be missing from the model if a direct relationship is found. Consequently, our study
examines how older entrepreneurs compare to younger entrepreneurs in terms of performance
and whether there are differences in terms of how much effort and smarts older entrepreneurs
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put into their venture. Our culminating research question is: Can older entrepreneurs better
utilize their human and social investments to create higher returns on their asset and time
investments than younger entrepreneurs?
In this paper we review prior literature and develop a model of working harder and
smarter based upon extant entrepreneurship research. Then we propose and test hypotheses
related to older entrepreneurs, followed by quantitative analysis and discussion of findings
and implications.
This study makes a contribution to entrepreneurship literature by studying the
growing phenomenon of older entrepreneurs compared to younger entrepreneurs. We make a
contribution to practice by helping older entrepreneurs identify their strengths, which could
encourage more seniors to engage in entrepreneurship with greater success and provide
satisfying and rewarding careers to those leaving wage and salaried employment.
Literature Review
Researchers (e.g., Curran and Blackburn, 2001; Kautonen, 2008) and relevant
practitioner organizations such as the American Association of Retired Persons (AARP) and
Canada's Association for the 50 Plus (CARP) generally agree that mature workers are those
50 years of age or older. The United Nations (2007) reported that almost one-third of the
working-age population in developed countries will be aged 50 or over by 2050.
Additionally, mature workers in developed countries are staying in the workforce longer than
in previous years (CARP, 2013). Seniors may be motivated to work longer for reasons
related to financial security, social, and personal growth (e.g., Armstrong-Stassen, 2008; Bal
and Visser, 2011; Barnes et al., 2004; Kooij, de Lange, Jansen, Kanfer, and Dikkers, 2011;
Weckerle and Shultz, 1999). These motivations for staying in the workplace may also shape
motivations for starting ventures and the performance of older entrepreneurs.
It is unclear whether older entrepreneurs perform as well, better or worse than
younger entrepreneurs. Age UK proposed that after 5 years, 70% of businesses established
by older entrepreneurs were still in operation, in contrast to 28% of younger
entrepreneurs’ ventures (Barnes, Parry, & Taylor, 2004). However, contrasting this, Dahl
and Sorenson (2012) studied 15,000 Danish small businesses and concluded that success
varies non-monotonically with age, first rising and then declining, with the highest
probability of start-up survival peaking at 42 years of age. A similar inverse u-shaped
relationship between age and decision to start a business has been confirmed by other studies
(Bönte et al.,2007; Millán, 2008). This research implies that older entrepreneurs (over 50
years) face poorer venture performance prospects.
However, individuals who choose to start businesses later in life may have more
entrepreneurial resources that can be capitalized in their businesses. Being in a later stage of
life may also shape work choices that are different from younger entrepreneurs. The
following section proposes potential choices that may distinguish the approach to work and
the performance of ventures started by older entrepreneurs from younger entrepreneurs.
Working Smarter
A significant relationship has been found between higher education and nascent
entrepreneurship (Rotefoss and Kolvereid, 2005). It is well accepted that entrepreneurs who
are able to transform resource stocks such as education, work experience and social networks
into human and social capital will enjoy greater firm success (Allen 2000; Brown, Farrel,
Sessions, 2006; Burt, 1997; Neergaard, Shaw, Carter, 2005). However, this value may not be
long-lasting, for example, a Canadian study of new ventures conducted by Doutriaux (1992)
concluded that the direct positive effect of past experiences in marketing, finance,
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government contracting, and founders’ age declined after two to four years. Additionally,
Dahl and Sorenson’s (2012) examination of 15,000 Danish small businesses found that
industry experience took a second place to how familiar the owner was with the geographic
region.
There is support for the interactional effects of prior work experience and
knowledge. For example, the combined effect of previous experiences in marketing,
finance, government contracting, and founders’ age predicted larger startup ventures
(Doutriaux 1992). Furthermore, although age is a demographic variable typically used as a
control variable, it also seems to intersect with variables related to venture experience and
industry familiarity (Álvarez-Herranz, Valencia-De-Lara, and Martínez-Ruiz, 2011; Dahl and
Sorenson 2012).
Older founders have had more time to build their networks. Accordingly, they are
expected to have better access to information in networks (indicating higher levels of social
capital) (De Kok, Ichou, and Verheul, 2010). Álvarez-Herranz, Valencia-De-Lara, and
Martínez-Ruiz (2011) examined entrepreneurial activities of nascent entrepreneurs across 22
countries with varying income levels. The results show that entrepreneurs’ characteristics
influence entrepreneurial behaviour significantly and positively, in the following order:
previous experience of the founder, age, and education. The results imply that older
individuals may be able to better capitalize on previous knowledge and experience for faster
entrepreneurial learning. Other researchers have identified the ‘carriage’ of embedded career
capital when moving from prior employment to venture start-up (Terjesen, 2005). Finally,
Eesley and Roberts (2012) concluded it was more difficult to break into mature industries
than new industries for inexperienced entrepreneurs.
H1a: The effect of industry experience on net profit is stronger for older
entrepreneurs than younger entrepreneurs.
H1b: The effect of start-up experience on net profit is stronger for older
entrepreneurs than younger entrepreneurs.
Working Harder
The perceived need for entrepreneurs to work long hours in order to found and
operate their own business has been established in the literature (ex. Cooper, Woo, and
Dunkelberg, 1988). Filion (1991) included long hours worked in his description of the
‘energy’ needed to start a business. Baum and Locke (2004) considered how putting in long
hours reflected an entrepreneur’s passion for the business.
Researchers have found that older entrepreneurs tend to proportionally employ less
staff than other age groupings (Kautonen and Down, 2012). However, De Kok, Ichou, and
Verheul found that entrepreneurs who start at older age were less likely to work fulltime in
their new venture, were less willing to take risks and had a lower perception of their
entrepreneurial skills. They may also be looking to leave the business earlier with a
sensitivity related to the opportunity cost of time (Kautonen, 2013). Levesque and Minniti
(2006) proposed an inverted U-shaped relationship between the number of total working
hours and age. Beyond a critical time point, the increase in the value of leisure time would
outweigh the increase in the wage rate, prompting individuals to work fewer hours as they
aged. Individuals for whom leisure becomes more valuable as they age would reduce the total
number of their working hours and the number of hours dedicated to starting a new firm.
Even those for whom leisure becomes less valuable as they age would not reduce the total
number of working hours but they would still reduce the portion of these hours dedicated to
starting a new firm since the present value of the return to entrepreneurship declines.
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Parker and Rougier (2007) found that aging self-employed workers were likely to
work longer past retirement age than wage and salary workers, regardless of their health.
Their results support other research (e.g., Quinn & Kozy, 1996; Quinn 1999) that indicates
that self-employment may be used to bridge wage and salary employment with retirement,
specifically by allowing aging individuals to reduce the amount of hours they work. In
particular, Quinn and Kozy (1996) attributed the net inflow of workers into self-employment
late in life, to a desire for flexible hours and gradual retirement.
Such different findings may be a result of different clusters of older entrepreneurs,
related to habitual, novice and hobby. In addition older entrepreneurs may just operate
differently. Canadian research indicates that older entrepreneurs may involve their family
and children more in their businesses (Uppal, 2001), and this could reflect a desire on the
older entrepreneur’s part to put less of his own hours into the business. A large empirical
study by Bates (1990) noted a lessening of owner effort after the age of 55, moreover he
noted that those entrepreneurs over the age of 55 were least likely to stay in business,
whereas entrepreneurs aged 45-54 were more likely to endure.
H2a: The effect of hours worked on net profit is stronger for older entrepreneurs than
younger entrepreneurs.
Investing in the Business
Seniors over 50 are more likely to have built up financial assets than younger
entrepreneurs. A Canadian study (Uppal, 2011) identified that higher-‐income seniors are
more likely to be self-‐employed; a clear reflection on funding requirements for senior
start-‐ups. Although some seniors may pursue hobby micro-enterprises aimed at continuing and promoting an active lifestyle (Hantman and Gimmon 2014; Halabinsky, Potter,
Kautonen, 2012), we contend that older entrepreneurs have the resources to invest
significantly in their business venture.
H2b: The effect of financial assets on net profit is stronger for older entrepreneurs
than younger entrepreneurs.
Methodology
We use the Comprehensive Australian Study of Entrepreneurial Emergence (CAUSEE) data
collected from a representative sample of 559 respondents who owned (or partly owned) a
young firm (less than four years old). CAUSEE is a panel study that follows nascent and
young firms over time. The firms were identified via random digit dialing phone interviews
of over 30,000 Australian households, which provides a random sample of the population
increasing the confidence of generalization. Young firms are defined as businesses that were:
four years or younger at the time of the screening interview; had already experienced a 12-
month period with revenues exceeding costs for at least half of the time; and were sole or co-
owned. Applying this procedure, 559 owners completed the first round interview in 2007
(Wave 1). We dropped firms operated by entrepreneurial teams to establish accurately the age
of the entrepreneur.
Two path models (i.e., Harder and Smarter models) are tested using AMOS 21.
Goodness of fit of the path models under test is evaluated with the χ2 statistic divided by
degree of freedom (χ2/d.f.), Comparative Fit Index (CFI), Root Mean Square Error of
Approximation (RMSEA), and the statistical significance (PClose) for RMSEA, as reported
in AMOS 21. The absolute goodness-of-fit index χ2 goodness-of-fit statistic is sensitive to
sample size, so that the probability of rejecting a hypothesised model increases as sample size
increases. Instead, good fit is assumed for χ2/df less than 3, CFI greater than 0.95 and
RMSEA lower than 0.05 with an insignificant PClose of at least 0.05 (as suggested by Kline,
1998).
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Entrepreneurs were judged to be older if they were over 50 when they started their
current business. Working harder was measured by hours worked and assets invested in the
business (loans and equity). Working smarter was measured by industry experience and start-
up experience. Firm performance was measured by Net Profit.
To test our hypotheses we constructed a model of hours worked, assets invested in the
business, industry experience, and start-up experience having a positive effect on net profit.
This model was run for All Entrepreneurs (See Figure 3), older entrepreneurs (See Figure 4)
and younger entrepreneurs (See Figure 5). This group model also had a good fit (See table 7:
χ2/d.f. = 2.456, CFI=.902, RMSEA=.051 and PClose=.447).
Insert Figures 3 to 5 about here
Results
Table 1 gives the descriptive statistics for the whole sample. Table 1 shows that of the
sample of 286 entrepreneurs 34% are older. Table 2 provides descriptive statistics for the
older entrepreneurs and shows that older entrepreneurs are on average 56.93 year of age.
Prior research has identified differences between novice and habitual older entrepreneurs
(e.g., Weckerle and Shultz, 1999). Consequently we conducted a t-test to determine whether
there was a significant difference between novice and habitual older entrepreneurs in our
sample. The t-test showed no significant difference, so we proceeded to treat the older
entrepreneurs in our sample as a homogenous group. Table 3 provides the descriptive
statistics for young entrepreneurs and shows that they are on average 38 years of age. Table 4
shows the correlation of variables for the whole sample.
Insert Tables 1 to 4 about here
Table 4 shows a highly significant positive correlation between older entrepreneur
and industry experience (μ = .293, p<.001). Table 4 also shows a positive correlation
between older entrepreneur and start-up experience (μ = .137, p<.05). But Table 4 shows an
insignificant but negative correlation between older entrepreneurs and hours worked (μ =
.143, n/s). And Table 4 shows an insignificant positive correlation between older
entrepreneur and financial assets (μ = .102, n/s). Finally, Table 4 shows no correlation
between older entrepreneur and net profit (μ = .002, n/s). Now we move on to test our
hypotheses.
Hypothesis 1a stated that the effect of industry experience on net profit is stronger for
older entrepreneurs than younger entrepreneurs. In our model there is no significant
difference (Table 8: z-score = .624, n/s) in industry experience between older entrepreneurs
(b = -.049) and younger entrepreneurs (b= .020). Hence Hypothesis 1a, that the effect of
industry experience on net profit is stronger for older entrepreneurs than younger
entrepreneurs is not supported.
Hypothesis 1b proposed that the effect of start-up experience on net profit is stronger
for older entrepreneurs than younger entrepreneurs. In our model there is no significant
difference (Table 12: z-score = -0.002, n/s) in start-up experience between older
entrepreneurs (b =.042) and younger entrepreneurs (b= .087). Thus we find that the effect of
start-up experience on net profit is the same for older entrepreneurs and younger
entrepreneurs.
Hypothesis 2a stated that the effect of hours worked on net profit is stronger for older
entrepreneurs than younger entrepreneurs. In our model there is a significant difference
(Table 12: z-score = -2.01, p<.05) in hours worked between older entrepreneurs (b = .196)
Page | 1097
and Younger Entrepreneurs (b= .082). Thus we find support for Hypothesis 2a, that the effect
of hours worked on net profit is stronger for older entrepreneurs than younger entrepreneurs.
Hypothesis 2b hypothesized that the effect of financial assets on net profit is stronger
for older entrepreneurs than younger entrepreneurs. In out model there is a significant
difference (Table 12: z-score = .2.436, p<.05) in financial assets between older entrepreneurs
(b = .826) and younger entrepreneurs (b= .566). Thus Hypothesis 2b, that the effect of
financial assets on net profit is stronger for older entrepreneurs than younger entrepreneurs, is
supported.
In summary, out model shows that being older significantly strengthens the positive
effect of Hours Worked on Net Profit and being older significantly strengthens the positive
effect of Financial Assets on Net Profit. There are no significant differences between older
and younger entrepreneurs with regard to the effects of Experience in Industry or Experience
in Start-ups on Net Profit. Thus this shows that older entrepreneurs work smarter, not harder.
Insert Tables 5 to 7 about here
Discussion and Implications
This research compares older and younger entrepreneurs’ impact on net profit by working
harder, through longer hours or higher investment, or by working smarter, through greater
industry experience or start-up experience.
We found that although older entrepreneurs have more industry and start-up
experience, they do not work more hours or invest more money. In fact, older entrepreneurs
work fewer hours. Hence, the older entrepreneurs in our sample did not work significantly
harder than younger entrepreneurs. However, we found that the effect of hours worked by
older entrepreneurs had a significantly bigger effect on net profit than the time spent by
younger entrepreneurs. Similarly, we found that the financial assets invested by older
entrepreneurs also had a significantly bigger effect on net profit than the assets invested by
younger entrepreneurs. Consequently we concluded that older entrepreneurs do work smarter,
not harder.
This study contributes to entrepreneurship theory by indicating that age may not have
a direct impact on firm performance but may be mediated by other variables, such as industry
experience, start-up experience, hours worked, and assets invested. We also indicate that age
could moderate the effects of these variables on firm profit.
This study contributes to entrepreneurship practice by showing that older
entrepreneurs can be just as profitable as younger entrepreneurs, despite working fewer
hours. This should encourage more seniors to enter entrepreneurship. This should also be an
indicator to government policy makers that entrepreneurship can be a viable option to extend
the working life and contributions to society of mature workers. Training and funding
programs should be developed to assist mature workers to make the transition from the
workforce into entrepreneurship.
Our study is limited by its focus on only one country, Australia. We are also limited
by taking our data from only one point in time to maximise the size of the sample used.
Future research is needed in other countries and to test the relationships of age to other
variables to broaden our understanding of the role of older entrepreneurs.
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Conclusion
This research finds that older entrepreneurs work smarter, not harder. This dispels the
age-related myths of the entrepreneurial underperformance of older entrepreneurs. These
myths, if left unchallenged, could result in inappropriate policy decisions and, more
importantly, could discourage mature workers from establishing new ventures.
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Table 1. Descriptive statistics (whole sample)
N Min. Max. M SD
Older Entrepreneur 286 0 1 .34 .48
Owner Age 286 22.00 74.00 44.74 10.81
Hours Worked 146 .00 85.00 35.55 21.53
Assets 121 700.00 10500000.00 211010.12 978067.48
Exp. Industry 286 .00 62.00 10.10 10.31
Exp. Start 286 .00 1.00 .38 .49
Net Profit 286 -600000.00 1560000.00 58040.05 186578.72
Table 2. Descriptive statistics for older entrepreneurs
N Min. Max. M SD
Owner Age 98 50.00 74.00 56.93 5.49
Hours Worked 59 .00 70.00 31.81 18.50
Assets 50 700.00 10500000.00 329604.80 1487508.88
Exp. Industry 98 .00 62.00 14.29 13.25
Exp. Start 98 .00 1.00 .47 .50
Net Profit 98 -600000.00 1560000.00 58581.75 264010.73
Table 3. Descriptive statistics younger entrepreneurs
N Min. Max. M SD
Owner Age 188 22.00 49.00 38.39 6.64
Hours Worked 87 .00 85.00 38.08 23.12
Assets 71 1000.00 1500000.00 127492.73 271871.79
Exp. Industry 188 .00 34.00 7.93 7.55
Exp. Start 188 .00 1.00 0.33 .47
Net Profit 188 -80000.00 775894.00 57696.98 115105.03
Table 4: Pearson correlations of major variables for All Entrepreneurs
1 2 3 4 5
1. Older Entrepreneur -
2. Hours Worked -.143 -
3. Asset .102 .156 -
4. Experience in Industry .293** .141 .060 -
5. Experience in Start-ups .137* .087 .137 .120* -
6. Net Profit .002 .259** .784** .002 .156
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Table 4. Regression weights whole sample
DV IV B b S.E. C.R. P
Net Profit <--- Hours Worked 1454.73 .173 480.35 3.03 .002
Net Profit <--- Asset .14 .751 .011 13.44 <.001
Net Profit <--- Exp. Industry -561.59 -.032 988.73 -.568 .570
Net Profit <--- Exp. Start 24445.31 .066 20998.86 1.164 .244
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Table 5. Regression weights older entreprenurs only
DV IV B b S.E. C.R. P
Net Profit <--- HoursWorked 2671.19 .196 1036.51 2.58 .010
Net Profit <--- Asset .141 .826 .013 11.07 <.001
Net Profit <--- Exp. Industry -922.89 -.049 1426.95 -.65 .518
Net Profit <--- Exp. Start 20791.75 .042 37702.91 .55 .581
Table 6. Regression weights younger entrepreneurs only
DV IV B b S.E. C.R. P
Net Profit <--- Hours Worked 399.85 .082 449.76 .889 .374
Net Profit <--- Assets .24 .566 .040 6.096 <.001
Net Profit <--- Exp. Industry 296.32 .020 1335.35 .222 .824
Net Profit <--- Exp. Start-up 20724.45 .087 21400.99 .968 .333
Table 7. Squared Multiple Correlations (R2) of Net Profit:
Whole sample Older Entrepreneurs Younger Entrepreneurs
.599 .724 .335
Table 8. Differences in effects between two groups
IV Older Entrepreneur Younger Entrepreneur Difference
b P b P z-score
Hours Worked .196 .010* .082 .374 -2.01*
Assets .826 ** .566 ** 2.436*
Exp. Industry -.049 .518 .020 .824 0.624
Exp. Start-up .042 .581 .087 .333 -0.002
Notes: ** p-value < 0.01; * p-value < 0.05;
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