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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)

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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;

Page | 1103

Figure 3. Model predicting net profit for all entrepreneurs

Figure 4. Model predicting net profit for older entrepreneurs only

Figure 5. Model predicting net profit for younger entrepreneurs only.