Electronic copy available at: http://ssrn.com/abstract=1346481

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Profiling the Growth Oriented Nascent Entrepreneur in the US – Evidence from

Representative Samples

Maija Renko, Florida International University

Paul Reynolds, Florida International University

Paper presented at Academy of Management Annual Meeting 2006, Atlanta, Georgia

ABSTRACT

High growth new firms are of considerable interest, in no small part due to their

disproportionate contribution to overall job growth; among the 13 million nascent entrepreneurs

present in the US in 2001, the 675,000 that aspire to provide 50 or more jobs five years after the

firm birth would account for 40% of all new firm jobs. Estimating the impact of demographic

(entrepreneur-related) and organizational variables that influence firm growth expectations at the

time of business start-up is a major step toward understanding the unique features of high growth

new firms. This assessment makes use of two publicly available, harmonized datasets, namely

GEM 2001 data from the US and the first PSED data set, assembled from 1998-2003. Gender,

start-up team size, and features of the opportunity recognition process are statistically significant

factors affecting projected job growth in the first five years of new firm operation; educational

attainment, age, and a focus on manufacturing did not have a significant impact. Comparison of

answering patterns to opportunity recognition items in GEM vs. PSED suggests that extreme

caution is needed when wording questionnaire items; individuals would rather describe

themselves as “planning entrepreneurs” instead of “necessity entrepreneurs”.

Electronic copy available at: http://ssrn.com/abstract=1346481

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INTRODUCTION

Nascent entrepreneurs with high growth ambitious should, all other things being equal, be

associated with new firms with higher growth trajectories. Thus, knowledge of the determinants

of growth expectations during the venture creation phase may be central in understanding the

growth of newly founded firms (Delmar and Davidsson, 1999) .The central research question of

the following assessment is:

What demographic (entrepreneur-related) and organizational variables at the time of

business start-up influence or are related to firm growth expectations?

As will be described in the following, previous research has established links between a

large number of explanatory variables and firm growth (or growth expectations) as a dependent

variable. In this study, the choice of micro level explanatory variables is somewhat limited by

data constraints. On the other hand, the assessment takes advantage of two large scale random

samples of the US population to develop a solid empirical base for the conclusions regarding

those factors associated with aspirations for higher levels of new firm growth.

Once it became clear that new firms were a major source of new job creation, it was

determined that most of this growth was from firms with high growth trajectories, often referred

to as gazelles (Birch, 1987). A small proportion of high growth firms is usually the source of a

major proportion of total job growth. The data from the Global Entrepreneurship Monitor 2001

assessment of the United States can be used to illustrate the importance of the contributions of

high growth aspiration firms.

The extrapolation from the sample to the full adult population is illustrated in Table 1.

Nascent entrepreneurs are classified by the expected size of their firm in terms of jobs created,

five years following the birth of an operational business. There are five future “job aspiration

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categories”: 0-1, 2-4, 5-14, 15-49, and 50 and more. As shown in column 1, the 2001 GEM

survey of the adult population involved a representative sample of 2,440 individuals 18-64 years

old, representing a total population of 199 million individuals. In the sample, 162 respondents

appeared to be active in a start-up effort where they would expect to own part of the business but

had not paid any salaries and wages for more than 3 months—the operational definition of a

nascent entrepreneur. This sample represented about 13 million in the total population, as shown

in column 4. Those expecting 15-49 jobs five years after start up represented 1.3 million in the

population, about 10% of all nascent entrepreneurs, and those expecting 50 or more jobs

accounted for almost 700 thousand in the population or 5% of all nascent entrepreneurs.

The expected job contributions are adjusted by taking account of the average team size,

as shown in column 6; 13 million nascents appear to be working on about 7.8 million new firms.

Longitudinal studies (Reynolds and Curting, 2004) suggest that about 30% of the start-up efforts

would be realized, reducing the number of active start-ups that would become operational to

about 2.4 million. The expected employment is calculated by multiplying the number of firms to

be realized by the average jobs projected in five years (column 9) by the number of active firms

(column 8). The total number of jobs created is about 13 million, but very much skewed toward

the “high growth aspiration” start-ups.

Those expecting to create 50 or more jobs are 5% of the nascents but will provide 39% of

the new jobs. Those expecting to create 15 or more jobs are 15% of the nascents but will account

for 63% of the new jobs. It is clear that the new firms with high growth aspirations will, if

realized, make a substantial contribution to economic growth.

Table 1 about here

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Understanding new firm growth is a central focus of a large number of conceptual and

empirical research studies. Although researchers have been able to show the importance of

resources for firm growth, it is not clear what other factors may have a major role to play at

various phases of new firm development. This assessment will emphasize the connection

between certain firm- and entrepreneur related variables at the time of company formation and

entrepreneurs’ expectations of their firms’ future growth.

The paper is organized as follows: First, there is an overview of the concept of firm

growth and growth orientation, i.e. the dependent variable of the empirical study. This is

followed by a discussion of the prior work and theoretical background that has led to the

selection of six independent variables as predictors of growth expectations; summarized by

presenting six explicit hypotheses. The sources of the empirical data, two representative samples

of US entrepreneurial activity, i.e. Global Entrepreneurship Monitor (GEM) 2001 and first Panel

Study of Entrepreneurial Dynamics (PSED). The research results are presented hypothesis by

hypothesis, and finally the last section of the paper presents conclusions and some suggestions

for future research.

FIRM GROWTH AND GROWTH ORIENTATION

Firm growth is often considered to be indicative of success and future performance of the

firm (Pukkinen et al., 2005, Baum and Locke, 2004). Venture growth causes valued economic

and social gains, including job creation (Aldrich, 1999), and it is a measurable and well-

understood venture goal (Kirzner, 1985). According to Covin and Slevin (1997), venture growth

is the essence of entrepreneurship.

In previous studies, firm growth and related performance has been conceptualized and

measured in over thirty different ways (Brush and Vanderwerf, 1992). Because of this variety in

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conceptualizations and measures used, comparing different research results is a challenging task.

It has even been suggested that the chosen concept of growth and the related operationalizations

influence the very research results (Delmar 1997, Heshmati 2001, Roper 1999). For example,

using personnel growth as an indicator of firm growth may lead to different kinds of conclusions

than employing the measure of sales turnover growth. (Robson and Bennett, 2000). Because

growth is a dynamic process that takes place over time, reliable measurement of growth requires

multiple observations over time of whichever indicators chosen.

Just as much as the measures of growth vary, so do the independent variables that have

been suggested to influence venture growth. Personality traits, organizational factors, and

environmental factors have been studied by entrepreneurship researchers as causes of new

venture success (Baum and Locke, 2004). For example, entrepreneurial climate in a society as

well as role models have been linked to firm growth (Davidsson and Henrekson, 2002, Reynolds

et al., 2003). However, this paper focuses on selected firm- and entrepreneur related variables

and their influence on firm growth expectations. Growth is often predicated on managerial

perceptions and expectations about specific resources (Penrose, 1959). Researchers have studied

managers’ evaluations of resources and their relationships to performance over time, finding that

certain types of resources and strategies can lead to above average performance and growth

(Mosakowski, 1993).

Early firm growth has been predominantly explained by the individual characteristics of

the founder or founders of the business; age, sex, and experience (Stuart and Abetti, 1990),

cognitive constructs such as perceived competence (Chandler and Jansen, 1992) and personal

goals (Birley and Westhead, 1994). However, even though much effort has been directed e.g. to

understanding the influence of motivational make-up of the entrepreneur on growth of a new

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firm (e.g. Smith and Miner, 1984), this research direction has never been able to establish strong

influences (Autio, 2000). For example, Stuart and Abetti (1990) found only a very slight

correlation between objectives pursued by entrepreneurs and the performance of their businesses

(p = 0.21). Evidence showing that resources have an important impact on growth and

performance of young firms is more established. For example, Chandler and Hanks (1994) found

that companies with broad capabilities grew faster than those companies that did not have such

capabilities. Interestingly, however, when the relationship between entrepreneurial intentions

(like growth intentions) and resource environment is concerned, it has been shown in several

studies (Bruno and Tyebjee, 1982, Krueger and Brazeal, 1994, Krueger and Dickson, 1994) that

perceptions of resource availability are more significant than actual resource availability

(subjective vs. objective resource availability).

As mentioned above, growth is a dynamic process (e.g. Wiklund and Shepherd, 2003).

Thus, reliable measurement of firm growth needs to follow the phenomenon, i.e. firm

development, over a period of time. However, in practice, researchers’ resources seldom allow

longitudinal research designs. In the current study, instead of actual firm growth, entrepreneur’s

growth intentions (i.e. growth orientation) are in focus. Intentions are a predictor of activity in a

given environment. It has been attested in many studies (Boyd and Vozikis, 1994, Krueger and

Brazeal, 1994, Krueger et al., 2000) that entrepreneurial intentions correlate significantly with

entrepreneurial behavior. Following similar line of reasoning, many research studies show that

growth intentions of an entrepreneur and the real growth of a firm may correlate positively

(Bellu and Sherman, 1995, Kolvereid and Bullvåg, 1996, Wiklund and Shepherd, 2003).

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DEMOGRAPHIC TRAITS, ORGANIZATIONAL FACTORS, AND GROWTH

ORIENTATION

Previous research has established links between numerous entrepreneur- and firm related

variables and growth intentions as well as actual growth of the firm. In the following, gender,

human capital, source of business opportunity, as well as type of business (service vs.

manufacturing) are considered as potential explanatory variables for nascent entrepreneurs’

differing growth aspirations. Table 2 provides a simple summary of key concepts, their

definitions, as well as operationalizations in the empirical data collection. As described above,

the study subjects are nascent entrepreneurs, and the phenomenon of interest is their growth

orientation. Definitions for both concepts are provided in Table 2.

Table 2 about here

Human Capital

Human capital theory maintains that knowledge provides individuals with higher

cognitive abilities, leading to more productive and efficient activity (Becker, 1964, Mincer,

1974). Knowledge, again, may be defined as being either tacit or explicit (Polanyi, 1967). Tacit

knowledge refers to the often non-codified components of activity, whereas explicit knowledge

consists of information normally conveyed in procedures, processes, formal written documents,

and educational institutions. Making entrepreneurial decisions utilizes an interaction of both tacit

and explicit knowledge, as well as social structures and belief systems. Individuals with more or

higher quality human capital should be better at perceiving entrepreneurial opportunities, and

they should also have superior ability in successfully exploiting opportunities (Davidsson and

Honig, 2003). Thus, nascent entrepreneurs with more human capital can be expected to have

higher growth aspirations for their firms.

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The relationship between human capital and entrepreneurial activity and success may be

confounded by a number of factors. For example, education seems to be particularly important

for female entrepreneurs (Bates, 1995). Also, different types of human capital may be more

important at different stages of the entrepreneurial process. For example, the findings of

Davidsson and Honig (2003) support the role of formal education and previous start-up

experience in predicting who, among a cross-section of the general population, would attempt to

engage in nascent entrepreneurship. However, formal education did not appear to be a factor in

determining the frequency of gestation activities over time nor in predicting those who

succeeded with a first sale or a profitable venture.

Level of education. Education is often treated as a proxy for human capital. Empirical

research has demonstrated a range of results regarding the relationship between education,

entrepreneurship, and success, with education frequently producing nonlinear effects on the

probability of becoming an entrepreneur, or in achieving success (Bellu et al., 1990, Gimeno et

al., 1997, Reynolds, 1997, Davidsson and Honig, 2003, Arenius and Minniti, 2005). More

specifically, formal education is one component of the explicit knowledge part of human capital,

which, again, may provide skills useful to entrepreneurs (Davidsson and Honig, 2003). Thus, it is

possible to propose:

Hypothesis 1: Higher level of education of a nascent entrepreneur has a positive effect on

his / her firm growth expectations.

Age. Human capital (general human capital, management know-how, industry specific

know-how) that entrepreneurs provide to their firms accumulates throughout their careers.

Human capital is not only the result of formal education, but includes experience and practical

learning that takes place on the job, as well as non-formal education. Thus, broad labor market

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experience, as well as specific vocationally oriented experience, has been theoretically predicted

to increase human capital (Becker, 1964). Although empirical results have been mixed

(Davidsson and Honig, 2003), there are studies showing that work life experience is significantly

related to entrepreneurial activity (Bates, 1995, Gimeno et al., 1997, Robinson and Sexton,

1994). It has been suggested that the relationship between age and the likelihood of starting a

new business picks at a relatively early age and decreases thereafter (Reynolds et al., 2003). For

growth expectations, it is proposed:

Hypothesis 2: Older age of the nascent entrepreneur has a positive effect on his / her firm

growth expectations.

Start-up team size. Human capital of a new, starting venture, is mainly available from

individuals in the startup team. The larger this startup team, the more human capital there is. The

important role of the management team for the success of a start-up firm has been confirmed in

several studies (Roure and Maidique, 1986, Delmar and Davidsson, 1999, Eisenhardt and

Schoonhoven, 1990, Doutriaux 1992). Bollinger et al. (1983) reviewed the then-existing

knowledge on factors contributing to the success of technology-based new firms. The only

empirical finding that they could cite was that the faster growing technology-based new firms

were started by greater management teams, and that more technology had been transferred from

the incubating organization to the more successful new firms. Accordingly, the following

hypothesis is developed:

Hypothesis 3: Larger startup team size has a positive effect on firm growth expectations.

Gender

Gender differences in entrepreneurial behavior have been a subject of a significant

amount of attention. Men and women entrepreneurs differ very little with respect to demographic

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and psychological variables (Brush, 1990, 1992), while more pronounced differences seem to

exist in business goals and management styles; women tend to pursue intrinsic goals (intangible,

psychological in nature) rather than financial gain (Brush, 1992, Rosa et al., 1994). Based on

Danish Global Entrepreneurship Monitor data, Bager and Schott (2004) found that expected

growth correlates positively with a number of personal characteristics, in particular being male,

having entrepreneurial competence, and having a network encompassing other entrepreneurs. To

some extent, the gender difference maybe attributable to a confound effect with differences in

human capital; women are less likely to track into technical disciplines like engineering, which

would give them skills for launching businesses in manufacturing or high technology sectors.

Businesses in largely male-dominated sectors, like telecommunications or pharmaceuticals, are

typically larger and have greater growth potential. (Carter and Brush, 2004) Finally, Anna et al.

(2000) suggest that systemic social, cultural, and work structure barriers may cause women’s’

intentions to differ from those of men. Taken this previous empirical evidence on gender

differences in entrepreneurship, it is hypothesized that:

Hypothesis 4: Male nascent entrepreneurs have higher growth expectations for their

firms than female nascent entrepreneurs.

Opportunity Recognition Process

An increasing number of entrepreneurship scholars agree that opportunity perception is

the most distinctive and fundamental characteristic of entrepreneurial behavior (Kirzner, 1973,

1979). Opportunity recognition is the beginning of the entrepreneurship process (Christensen,

Madsen, and Peterson, 1994), and Bygrave and Hofer (1991) propose a definition of the

entrepreneur as “someone who perceives an opportunity and creates an organization to pursue

it”.

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Hills and Singh (2004) cite Bhave (1994) as a seminal piece of work identifying two

types of opportunity recognition, again, based on Cyert and March: externally stimulated and

internally stimulated opportunity recognition. Externally stimulated opportunities are those,

where the decision to start a business comes before opportunity recognition. In general terms,

this compares to Kirznerian entrepreneurship (Kirzner, 1973, 1979), where entrepreneur

recognizes opportunities differently from his / her peers because of his / her sophisticated

understanding of customers, markets, and ways to serve those markets (Shane, 2000). A more

Schumpeterian view of opportunity recognition is the internally stimulated recognition, where

entrepreneur has a business idea first and only later decides to create a venture.

Davidsson (1991) finds that objective measures of ability, need, and opportunity can

explain a substantial share of the variation in actual growth of young firms, and that objective

and subjective measures of these three factors can explain a substantial share of the variation in

growth motivation. However, Hills and Singh (2004) conclude that the performance implications

of the opportunity recognition process are largely unexplored in the opportunity recognition

literature. Specifically, they state that future research should address the question “Is there some

link between performance and internally versus externally stimulated opportunities?” (Hills and

Singh, 2004, 270). Typically, Schumpeterian types of entrepreneurs that shift markets to a

disequilibrium state are viewed as “growth entrepreneurs”, whereas those that feel an urge or

need to start a business and then search for business opportunities are referred to as “necessity

entrepreneurs” and can be expected to have lower growth aspirations. Inspired by the research

gap identified by Hills and Singh (2004), it is proposed that:

Hypothesis 5: Internally stimulated opportunity recognition leads to higher growth

orientation than externally stimulated opportunity recognition.

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On a macro level, Wong et al. (2005) do not find support for the contribution of

opportunity- or necessity entrepreneurship towards economic growth of a country. Even though

these results on a macro level are somewhat discouraging, the dynamics on micro level can be

different.

Business Sector

Pukkinen et al. (2005) point out that the choice of growth measurement (e.g. personnel

growth, turnover growth, growth orientation) has a significant influence on the results of the

relationship between business sector (industry) and firm growth. Overall, their data from Finland

suggests that both service and manufacturing sectors correlate with firm growth more strongly

than other sectors when subjective and future oriented growth measures are employed. When

looking at actual growth in terms of number of employees and sales turnover, service firms have

grown fastest (Pukkinen et al., 2005).

Typically, industry sector data available for new ventures comes at a high level of

aggregation. Thus, it is difficult to study the influence of industry sector on venture growth.

Based on Swedish data, Delmar and Davidsson (1998) find that “super growers” were typically

high-technology manufacturing firms, technology-based services, education & health care, and

other knowledge-intensive service firms (advertising agencies, business consultants, and such).

Delmar and Davidsson (1998) describe these industries as “new”; they are either the result of

newly created markets (as information technology) or – typical for the Swedish context - are

earlier state monopolies being deregulated (education and health care).

Business sector and other demographic features of new start-ups may have a direct

bearing on the speed and success of completing the start-up process and future success of the

firm (Reynolds, 2004a). In this study and given the data available from GEM 2001 and PSED, it

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is hypothesized that nascent entrepreneurs starting manufacturing firms expect greater firm

growth than entrepreneurs in other business sectors. Ideally, it would be possible to distinguish

the kinds of “new” growth sectors Delmar and Davidsson (1998) were able to identify and

hypothesize on faster growth in those sectors, but given the aggregation level of the PSED as

well as GEM 2001 data this is not possible. Manufacturing businesses typically require larger

initial investments than e.g. service- or retail businesses. Logically, one would expect that the

owners of these firms would aim at faster growth in order to compensate the initial investments

and to gain rents. Also, “manufacturing” as a sector comprises high technology manufacturing

firms, that represent the largest share of super growers in Delmar and Davidsson (1998) data.

Hypothesis 6: Nascent entrepreneurs starting businesses in manufacturing sectors expect

faster growth for their businesses than entrepreneurs in other business sectors.

METHODOLOGY

Sources of Data

Over the past years, concentrated data collection efforts have brought entrepreneurship

researchers together in order to collect large scale, reliable data on entrepreneurial activities. One

of these efforts, the Global Entrepreneurship Monitor (GEM), has grown into a substantial

international project that engages researchers from dozens of countries to collect internationally

comparable data on entrepreneurial activities. Another data collection project, Panel Study of

Entrepreneurial Dynamics (PSED), was established to collect large scale, reliable data on

entrepreneurial activities within the US population (Gartner et al., 2004).

The current study makes use of both of these publicly available research databases,

namely GEM and PSED. The goals of these two data collection efforts are rather different, and

the depth of data in the GEM database is much more limited than the PSED data. Despite the

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limitations that arise from the use of secondary data, there are interesting research questions that

can be addressed using combined data from the PSED and GEM databases to expand the number

of nascent entrepreneurs in the analysis. Combining data from the GEM 2001 and PSED projects

provides a total data base of 1,129 US nascent entrepreneurs.

Both of the original datasets, GEM as well as PSED, contain cases that represent (1)

individuals not active in starting up businesses (control group in PSED) and (2) individuals

active in starting up businesses, i.e. nascent entrepreneurs. Using the definition and operational

criteria for nascent entrepreneur presented in Table 2, nascent entrepreneurs have been screened

from the original data files. This screening has resulted in a dataset, which includes 1,129

individuals (cases, nascent entrepreneurs) that have been interviewed. Out of these total cases,

845 are derived from the PSED dataset and 284 cases from the GEM 2001 database (US data

only). These are the cases used for empirical analyses in this current study.

The nature of the data used in the current study is cross-sectional. From the GEM data,

only data from the year 2001 is used. The more recent data is currently available only to

members of the national GEM teams. Year 2001 was the first year when an item tapping the

sources of opportunity recognition was included in the GEM interview schedule. This is an

important item for the current study and precludes use of GEM data from early years. GEM data

was obtained from the project website: www.gemconsortium.org, and the original data file used

in the current study is GEM 2001 Adult Population Survey Data.por. PSED data was accessed

through the University of Michigan website: http://projects.isr.umich.edu/psed and the original

data file used in the current study is called “ercw14q.sav”.

Both PSED and GEM databases are representative of the US adult population. In both

studies, random, structured data collection procedures (phone interviews) have been completed

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to collect information on individual- level variables. The data collection is described in detail

elsewhere (See e.g. Gartner et al., 2004; Reynolds et al., 2003; Reynolds, et al 2005), and an

interested reader is referred to those original sources.

Variables

Growth orientation. The growth orientation of a nascent entrepreneur is captured by

asking him / her about the number of employees expected to be working in the startup firm in

five years (continuous variable). For cases in the PSED dataset this is computed as a sum of two

questions, i.e. “By the end of the fifth year of operation, about how many full time employees,

not counting owners, do you expect to be working for pay at this new business?” and “By the

end of the fifth year of operation, about how many part-time employees do you expect to be

working for pay at this new firm?”. For cases in the GEM 2001 dataset, this value is the answer

to the following question “How many people will be working for this business, not counting

owners but including all subcontractors, when it is five years old? By subcontractors, we mean

only people or firms working exclusively for this business, and not working for others as well.”

Ideally, growth orientation should be measured with a scale comprising multiple, future

oriented items. However, in the dataset used for this study, such items are not available. In the

PSED dataset, a number of items were employed to measure expected business volume and

growth. In that dataset, the reliability statistic (Cronbach’s alpha) for four items1 inquiring about

employment growth is .644 (n=446), which is less than preferred but can be considered

satisfactory (Nunnally, 1967).

Human capital. Human capital in the current study is captured through three measures.

First, each interviewee was presented a question about his / her age (continuous variable). 1 Expected full-time jobs, 1st year; Expected part-time jobs, 1st year; Expected full-time jobs, 5th year, Expected part-time jobs, 5th year.

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Second, interviewees of PSED and GEM 2001 studies were asked to provide information on

their education. Because the response categories in the two data collection efforts were not

similar, the education variable was recoded for the current study. As described in Table 3, the

education variable employed here has five categories, four standing for highest level of

education. Age and level of education reflect human capital from the point of view of the nascent

entrepreneur interviewed for the studies.

Table 3 about here

The third human capital variable, namely size of start up team, is a firm level variable. It

is a continuous variable of the start up team size (persons only), and when the question was

asked in the GEM data collection it was specified that these people in the start up team should

“expect to share ownership”.

Gender. Gender is coded as a dummy variable (1=male; 0=female) based on interviewees

answers. From PSED data, NCGENDER variable is the one used in the current study, as

suggested by Carter and Brush (2004, 20).

Opportunity recognition process. The PSED dataset includes multiple items and scales

that have been designed to tap the opportunity recognition process (Hills and Singh 2004).

However, as we are combining data from two separate sources, the analysis is limited by the kind

of data that is common to the two sources. In the case of GEM 2001 data, opportunity

recognition is captured with one question that has a similar counterpart in the PSED dataset.

These opportunity recognition questions were recoded for the current study according to the

following scheme (Table 4):

Table 4 about here

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Response categorized as “1” represents internally stimulated opportunity recognition. The

wording of the opportunity recognition question in the two datasets is different and can

potentially lead to different kind of interpretation by the interviewees. Especially “have no better

choices of work” (in the GEM questionnaire) is a negative wording and social desirability may

bias the answers more towards the “take advantage of business opportunity” category. Hence, I

looked at the chi-square measures to test the hypothesis that the source of data (GEM 2001 vs.

PSED) and opportunity variable are independent. It turned out that the significance values were

low (.00), which indicates that there is a relationship between two variables. In the PSED data,

23 per cent of nascent entrepreneurs indicate that “business idea or opportunity came first”,

whereas in the GEM 2001 data, 55% of nascent entrepreneurs said they were starting a business

to “take advantage of business opportunity”. This discrepancy in the response pattern is

obviously a great concern for the validity of this question as a proxy for internally stimulated

opportunity recognition.

Type of business. For the current study, sector was dichotomized into two groups,

manufacturing and all other sectors. Hence, a dummy variable (1=manufacturing; 0=other) is

used as a proxy for manufacturing startups.

Obviously, these simple operationalizations are neither perfectly valid nor perfectly

reliable. However, in order to collect data from a large sample of nascent entrepreneurs, depth of

the data has to be sacrificed. As pointed out previously e.g. by Bager and Schott (2004), the

personal business related characteristics should be regarded as proxies for the variables as they

only rely on the answering of one question.

Table 5 below presents the descriptives of variables used in the analysis. The dependent

variable (growth orientation) is highly positively skewed. This is not surprising in the light of

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previous empirical analyses. Cabral and Mata (2003) present evidence that the size distribution

of a cohort of surviving firms shifts to the right and approaches a log-normal distribution over

time, after being highly skewed at birth. Also, size of start-up team is positively skewed, as well

as the dummy variable for manufacturing business. Within the dataset of 1129 nascent

entrepreneurs, there are only 34 manufacturing businesses (3%).

The distribution of the variables has been carefully analyzed, and three cases were

identified clearly as outliers for the dependent variable; three companies expected to employ

more than 1000 people in five years. These three outliers were filtered from the dataset, after

which the dependent variable was log transformed in order to normalize its distribution. Five

cases were identified clearly as outliers for the startup team size; these cases had a value of 20 or

more for this variable. These five cases were filtered from the dataset, after which the startup

team size variable was log transformed. After these procedures, the dataset comprised 593 cases

for which value of the dependent variable was available. Out of these cases, 432 come from the

PSED dataset and 161 from the GEM 2001 dataset.

Table 5 about here

Analysis and Results

Before testing the hypotheses, it is useful to consider the patterns of growth orientation

within the sample. In total, the nascent entrepreneurs in this sample is expected to create 20,650

new jobs within five years. However, this figure includes estimates from three nascent

entrepreneurs who expected to employ more than a thousand people within five years. Excluding

these three optimistic estimates reduces the total jobs expected within five years from this sample

to 12,350. Furthermore, missing data reduces the number of cases on which job projections are

available to 611 of the 1,129 nascent entrepreneurs in the sample.

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The (more cautious) total of 12,350 jobs represents 20.31 jobs per each of the 608

individuals who gave estimates. When the individuals in the dataset are weighted so that the

original weights assigned to them in the GEM and PSED data collection efforts are used as a

basis for weighting and then adjusted within the current sample, the average job count expected

within five years is 20.89 per entrepreneur.

As pointed out by Autio (2005), estimates like this should be read with some caution, as

they represent expectations, not actual job creation. Not all nascent entrepreneurial activity leads

to the actual creation of a new firm, and even when a new firm is started, the realized job

creation often falls short of expectations. Still, even with these reservations, the statistics

reported in Table 6 underline the potential of nascent entrepreneurial activity in general, and

growth oriented entrepreneurship in particular, for job creation. (See also Autio 2005 on growth

oriented entrepreneurship)

Table 6 about here

As the dataset includes cases from two different data collection efforts, the actual analysis

started by comparing the distribution of variables between the sources. T-tests were conducted

for continuous variables. The results show that in terms of age distribution as well as for startup

team size, there is no significant difference between cases from PSED dataset vs. cases from

GEM 2001 dataset. For education, the mean value for the PSED cases is significantly higher

(2.93) than the mean of GEM cases (2.62). This is most likely attributable to the different coding

schemes in the original datasets (see Table 3). Also, for the dependent variable, i.e. expected

number of employees in five years, the mean value for PSED cases is significantly higher (2.62)

than for GEM cases (1.63). This is most likely due to the level of precision when asking

interviewees about their estimates. In the GEM questionnaire, one item was used to capture a

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value for the dependent variable, whereas in the PSED questionnaire interviewees were asked

separately about full-time and part-time employees in five years (two separate questions, which

were summed to get a value for total employment estimate in five years).

For categorical variables, chi-square tests were conducted in order to detect differences in

answering patterns in the PSED vs. GEM data. No significant differences were found for gender

and type of business (manufacturing). The significant difference for opportunity recognition

variable was already discussed above.

Multiple linear regression was used to detect the effects of explanatory variables on the

dependent variable. Table 7 below presents the correlations between variables as well as mean

values and standard deviations after the necessary data transformations that were described

above.

Table 7 about here

Explanatory variables were introduced to the regression in two steps. First, the human

capital variables (age, education, and startup team size) were included in the model. At the

second step, the remaining variables (gender, opportunity recognition, and business type) were

added to the model. Table 8 below presents the results of the analysis.

Table 8 about here

Hypothesis 1 predicted that higher level of education of a nascent entrepreneur has a

positive effect on his / her firm growth expectations. In the light of the current data, however,

there seems to be no relationship between the level of education and growth orientation. As

pointed out by Arenius and Minniti (2005), the prevailing uncertainty surrounding education as a

predictor of entrepreneurial behavior and success is due in part to the fact that education levels

have primarily a contextual significance.

21

Hypothesis 2 predicted that older age of the nascent entrepreneur has a positive effect on

his / her firm growth expectations. This hypothesis is not supported by the data either. If

anything, the linear relationship between age and growth orientation seems to be a negative one.

However, a closer look at the relationship between age and growth orientation reveals that an

inverse model (b1 = 18.7412; significance .003) of age may be a better predictor of growth

expectations than a linear (b1=-.0118; significance .014), logarithmic (b1=-.4970; significance

.007), or quadratic (b1=-.0469; significance .024) model.

Hypothesis 3 (larger startup team size has a positive effect on firm growth expectations)

gets strong support from the current data; there is a strong positive relationship between startup

team size and growth orientation. Also hypothesis number four about male nascent entrepreneurs

having higher growth expectations for their firms than female nascent entrepreneurs is supported.

Results concerning the internally stimulated opportunity recognition are interesting.

Contrary to the way hypothesized, internally stimulated (i.e. Schumpeterian, opportunity driven)

entrepreneurship has a negative relationship with growth orientation. Remembering that the

explanatory variable is based on one question only and that the interpretation of that one question

may have been different in the different data collection efforts, this result should be interpreted

cautiously. However, this result seems to give grounds for further hypothesizing in subsequent

studies that externally stimulated nascent entrepreneurs actually have a higher growth

orientation. Perhaps entrepreneurs that plan for a business startup and evaluate multiple business

opportunities in the process have chosen the entrepreneurial career and aim at growing their

businesses. Also, the current data shows a weak negative correlation between startup team size

and internally stimulated opportunity recognition. It may be that opportunity driven

22

entrepreneurs are more often “solo” entrepreneurs that exploit business opportunities without

further aspirations for business growth and continuity.

The final hypothesis about the relationship between business type (manufacturing) and

growth orientation is not supported. This is not surprising taken the data constraints; there is only

a very small number of cases with a positive value for the manufacturing business variable.

The R square value of the full model, indicating that 10 per cent of the variance is

accounted for, suggests that there is plenty of space for additional explanatory variables. Even

though the explanatory power of the model is not great, the model does show that a careful

selection of few key explanatory variables and their operationalization in the form of well

thought out items makes it possible to explain one aspect of a complex phenomenon.

Discussion and Conclusions

This research has made use of two harmonized datasets, namely GEM 2001 data from the

US and PSED 1 data. Both of these original datasets are publicly available to all researchers; any

interested scholar can verify this analysis. Because both data sets are relatively new and

complicated to asses, there have been few attempts to harmonize data from the GEM and PSED

research programs for a consolidated assessment. While these two data sets are not completely

isomorphic, it is possible to harmonize certain parts of data after careful analysis of items. The

ultimate benefit of this kind of a procedure is the increased confidence in the research results

from two sources; slightly different data collection procedures improve confidence that the

phenomena is robust and the larger sample facilitates more precise estimates of model

parameters.

The key results of the study further support previous studies that have established a

relationship between startup team size and growth orientation (Delmar and Davidsson, 1999,

23

Eisenhardt and Schoonhoven, 1990, Doutriaux 1992) and gender (male) and firm growth

orientation (e.g. Brush, 1992, Anna et al. 2000). A rather speculative, but very interesting result

about the negative association between internally stimulated opportunity recognition and growth

expectations provides interesting ideas for future research. For example, the PSED dataset

comprises numerous items that tap the opportunity recognition of a nascent entrepreneur as well

as multiple items that grasp future expectations of the entrepreneur. A more detailed analysis of

those items – as well as an analysis of the actual growth pattern that is made possible by the

longitudinal nature of the PSED data collection effort – should provide further insights into the

opportunity recognition – performance relationship that has been identified as a research gap

(Hills and Singh, 2004).

Moving beyond the results that concern the actual hypotheses that were tested in the

study, we would like to point out some issues about the quality of the study as well as ideas for

future research. First of all, the fact that most constructs in this study were operationalized

through single item measures raises concerns about construct validity of the study. However, the

paper shows that even though the two major data collection efforts on entrepreneurial dynamics

have very different goals, there is still some overlap in the datasets and through a careful

combining of items from GEM and PSED, a researcher can increase the sample size and

statistical validity of the research. As data from post-2001 GEM studies will become publicly

available and as PSED 2 data collection is already under way, the opportunities for

entrepreneurial scholars to work with comprehensive, harmonized representative samples will be

expanded.

All items used in the study have been self reported by nascent entrepreneurs. Even

though there is no reason to suspect that nascent entrepreneurs would be untruthful in their

24

estimates of their firms’ future growth, evidence from the Danish GEM data, for example, shows

that the expected future size of the firm is substantially higher for nascent entrepreneurs than for

the owner-managers of existing businesses (Bager and Schott, 2004). Bager and Schott (2004)

data show that about one third of the nascent entrepreneurs expect their firm to grow to a size

larger than 10 persons in 5 years, while only about one fifth of the manager-owners expect to

reach such size. A potential explanation for this phenomenon is that entrepreneurs modify

expectations as they gain experience. Experience-based learning process may either increase or

reduce entrepreneurs’ ambitions and expectations, but – as Bager and Schott (2004) speculate -

perhaps the first option is less frequent than the latter, resulting in an overall reduction in growth

expectations. Thus, only extremely cautious predictions on the actual future size of businesses

can be made based on the kind of growth expectation data reported in this paper.

Age variable turned out to be only marginally significant in the current study, but future

research should take a closer look at the relationship between age and growth orientation without

the assumption that this relationship should be linear. Even though age in this study was used as

a proxy for human capital, an individual’s age is both a reflection of past work experiences and

current situation; older individuals may have reduced career aspirations (Reynolds, 2004b).

Thus, the fact that age does not have a linear relationship with growth orientation may not be that

surprising after all.

Finally, as the different answering patterns to the opportunity recognition item in PSED

vs. GEM data collection show, entrepreneurship researchers have to be extremely careful when it

comes to wording of questionnaire items. Even though both opportunity recognition questions,

i.e. the one in PSED and the other in GEM, give interviewees similar answer categories, the

wording of the question in GEM most likely urges people to describe themselves as opportunity

25

driven entrepreneurs. It is socially more desirable to start a business because of an opportunity,

not because of a “necessity”. The PSED question, again, presents a “planning entrepreneur” as

an alternative to the “opportunity entrepreneur”. Overall, individuals’ answers to these questions

show that they would rather describe themselves as “planning entrepreneurs” than “necessity

entrepreneurs”.

26

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32

Table 1: Estimating High Growth New

Firm Job Contributions: U

S 2001

Colum

n 1

2 3

4 5

6 7

8 9

10

110

Exp

ected size

5 years after

birth

N in

Sa

mple

N in

Po

pulatio

n (1,000

s)

Nascents in

Popu

latio

n (1,000

s)

Dist

Start-up

Team

size

Num

ber new

firm

s (1,000

s):

prop

osed

Num

ber new

firm

s (1,000

s):

realized

Avg

jobs

in 5 years

Total jo

bs

(1,000

s)

% Job

s Not nascents

2,27

8 18

6,35

5

0-1Job

s 45

5,06

2 5,06

2 38

.9%

1.41

3,58

2 1,07

5 0.33

350

2.6%

2-4 jobs

55

3,71

2 3,71

2 28

.5%

1.56

2,37

4 71

2 2.76

1,96

8 14

.8%

5-14

jobs

33

2,29

5 2,29

5 17

.6%

1.85

1,23

9 37

2 7.35

2,73

2 20

.5%

15-49 jobs

19

1,28

2 1,28

2 9.8%

2.84

451

135

22.37

3,02

7 22

.7%

50 plus jobs

10

675

675

5.2%

3.11

217

65

80.50

5,24

0 39

.3%

Total

2,44

0 19

9,38

1 13

,026

100.0%

7,86

3 2,35

9

13,318

100.0%

33

Table 2: K

ey concepts, definitions, and operationalizations.

Concept

Definition

Operationalization

Measurement

Nascent

entrepreneur

A person engaged in activities to start

a new

business

(Sternberg

and

Wennekers, 2

005)

A person that is, a

lone or with

others, currently trying

to start

a new business AND / O

R is, alone or with

others, currently

startin

g a new business or new

venture for his / her em

ploy

er

AND is currently active in th

e start-up

effort A

ND anticipates

full or part o

wnership of th

e new business.

Growth

orientation

Growth

orientation

is

nascent

entrepreneur’s vision of how

much his

/ her

firm

will grow

(firm level

outcom

e) in

the future.

(1)

Individu

al’s expectation

of the

number

of peop

le

employ

ed b

y his /her firm in

five y

ears, (2) Individu

al’s

expectation of the amou

nt of total sales, revenues, or fees of

the firm

in th

e first full y

ear of operatio

n, and

(3) Ind

ividual’s

expectation of the amou

nt of total sales, revenues, or fees of

the firm

in th

e fifth full year of o

peratio

n.

Scale

Hum

an capita

l Individu

al’s investm

ent in skills and

know

ledg

e that boo

sts earning po

wer

(Becker, 19

64)

(1) A

ge of the entrepreneur a

s a prox

y for tacit know

ledg

e; (2)

Edu

catio

n of the

entrepreneur as a

prox

y for

explicit

know

ledg

e; (3) Start-up team

size as a proxy for both tacit

and explicit know

ledg

e2 (self reported ite

ms)

(1) Sc

ale, h

igher nu

mber indicates

older

person

; (2)

Ordinal, high

er

score indicates high

er edu

catio

n; (3)

Scale,

high

er

number

indicates

larger team

Gender

Gender o

f the nascent entrepreneur

Gender o

f the nascent entrepreneur

Dum

my

Opp

ortunity

recognition

process

The cog

nitiv

e process of opp

ortunity

recognition

3 , which consists of four

major steps: (1) the

pre-recognition

stew

, (2) the

Eureka-experience, (3)

further developm

ent of the idea, and

(4) the

decision

to

proceed. (Gaglio

and Taub, 199

2)

Interviewees’ a

nswer to

the

questio

n whether h

e / she

is

invo

lved in a

start-up

to take advantage

of a

business

oppo

rtunity

or because he / she h

as n

o bette

r choices for

work.

Dum

my

Type

of

business

Econo

mic sector of the firm’s primary

business activity

. Interviewees answ

er to the qu

estio

n “W

hat will be the major

prod

uct or service o

f this n

ew b

usiness?” and

the further

refinement qu

estio

ns, when needed. (For m

ore inform

ation,

see Reyno

lds, 200

4a, 2

45-247

)

Dum

my

for

a manufacturing

bu

siness

2 Note: Start-up team

size has often been used as one proxy fo

r social capita

l of the start-up firm

. 3 O

pportunity re

cogn

ition

is th

e perceiving

of a

possibility for n

ew profit p

otentia

l throu

gh (a) th

e foun

ding

and

form

ation of a new

venture or (b) th

e sign

ificant

improv

ement o

f an existin

g venture. (H

ills and Singh, 200

4).

34

Table 3: Education categories

Education categories in the current study PSED categories GEM 2001 categories

0. Up to eighth grade Up to eighth grade None

1. Some high school Some high school Some secondary

2. Secondary degree High school degree / tech or vocational degree Secondary degree

3. College experience / college degree

Some college

Post secondary Community college / College

degree

4. Post college / graduate

Some graduate training

Graduate experience MBA, MA, MS degree

LLB, MD, PhD, EDD degree

Table 4: Opportunity recognition variable

Opportunity recognition dummy in the current study

PSED: Which came first for you, the business idea or your decision to start some kind of

business?

GEM 2001: Are you involved in this start-up to take advantage of a business opportunity or because you have no better choices for work?

1 Business idea or opportunity came first

Take advantage of business opportunity

0

Desire to start a business came first

Idea or opportunity and desire to have a business came at the same

time

No better choices for work Combination of both of the above

(i.e. opportunity and no better choice of work)

Have a job but seek better opportunities

35

Table 5: Original distribution of variables.

Variable Mean Std Dev Min Max N Growth orientation, expected number of employees in 5 years

33.79 228.38 0 5000 611

Age of nascent entrepreneur 39.82 11.32 18 74 1083 Education 2.825 .73353 0 4 1114 Size of startup team 2.0902 8.421 1 258 1087 Gender .5284 .49941 0 1 1126 Opportunity recognition, dummy for internally stimulated opportunity recognition

.31 .462 0 1 1129

Type of business, dummy for manufacturing business

.03 .171 0 1 1129

Table 6: Expected employment within five years by firms started by nascent entrepreneurs in the sample

Nascent entrepreneur’s estimate: number of employees in five years

N (611) Total jobs (20,650 / 12,3504)

N (%) Jobs (%)

0 to 1 employees 78 36 13 0 2-4 employees 156 449 26 4 5-9 employees 116 734 19 6 10-19 employees 113 1,479 18 12 20-49 employees 93 2,692 15 22 50 or more employees 55 15,260 50 or more employees excluding the 3 cases that predict more than 1000 employees

52 6,960 9 56

4 Excluding the 3 cases that predict more than 1000 employees. Used as a total for calculating Jobs (%).

36

Table 7: Correlations (Pearson) (n=593)

Variables Mean S.D. 1 2 3 4 5 6 7

Age

X1 40.44 11.32 1

Education

X2 2.85 .716 .215** 1

Size of startup team (log)

X3 .453 .490 -.098** -.015 1

Gender

X4 .556 .497 -.067 -.023 .043 1

Internally stimulated opportunity recognition

X5 .37 .484 .041 -.021 -.098* -.027 1

Manufacturing business

X6 .04 .197 .088* .020 .057 .046 .036 1

Expected number of employees in 5 years (log)

X7 2.12 1.25 -.107* .000 .226** .172** -.180** .019 1

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

Table 8: Regression analysis

Dependent variable: Expected number of employees in five years (log)

Standardized β

Age -.092* -.079 Education .029 .026 Startup team size (log) .220** .194** Gender .166** Internally stimulated opportunity recognition

-.149**

Manufacturing business .027 Adjusted R-square .055 .100 F-value 11.239** 10.670** Durbin-Watson 1.805 1.898 VIF <1.07 <1.09 * Significant at 0.05 level ** Significant at 0.01 level