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usiness characteristics (3.1). It continued looking at quality with regards to wage, hours
worked and reliability of jobs created (3.2).
Business characteristics
The recent study by Ross et al. (2015) partly applied Bosma’s et al. (2008) methodology and
explained variation in small growing firms across Scottish regions by supply, demand,
culture, policy and agglomeration benefits. Those factors influencing entrepreneurs were
reasonable and consistent with previous theories (Krugman, 1990; Rittenberg & Tregarthen,
2009) and empirical findings. To name a few, the distribution of entrepreneurial activity
across geographic space was investigated in connection with variations in spatial
characteristics, such as knowledge (Obschonka et al., 2015). Those variations were partly
explained by differences in regions. For example, Stuetzer et al. (2015) revealed that British
regions with large industries, such as textile mile in Manchester, in the 19th century, had
lower entrepreneurship rates and weaker entrepreneurship culture today. Those findings
suggested that by concentrating on one area and investigating policies (see Deas, 2013), it
might be possible to highlight the influence of business characteristics on small firms’ ability
to create new jobs.
One of the most popular methodology employed with microdata was Gibrat’s Law of
Proportionate Effect (Gibrat, 1931) claiming that firm size and growth should be
independent, which was partly supported even by recent studies (Lotti et al., 2009).
However, many European (Hart & Oulton, 1996; Konings, 1995) studies concluded that
Gilbrat’s Law was not necessarily valid. The more sophisticated method was introduced by
Davis and Haltiwanger (1992). This method relied on descriptive analysis and included
growth and death rates to show that new firms created more dynamic jobs than older ones.
It was followed by such studies as Baldwin and Picot (1995). Although more advanced
techniques were employed, further studies (J de Kok et al., 2011; Neumark, Wall, & Junfu,
2011) had very similar conclusions. This may be explained by Jovanovic’s (1982) passive
learning model, which claimed that entrepreneurs learned most when they entered the
market. Contrary, Anyadike-Danes et al. (2015) maintained that neither the initial size
distribution nor survival rates explained job growth. The only explanation they found was a
small number of rapidly growing micro firms.
Anyadike-Danes et al. (2015) empirical findings were based on Birch’s (1979, 8) pioneering
idea that ‘small firms generated 66% of all new jobs generated in the U.S.’ Although Birch’s
theories (1987) were criticized by studies such as Dennis and Phillips (1994), small firms
were recognised as the main creators of jobs (A. Coad et al., 2014; Cowling et al., 2015; Du
& Temouri, 2015). Recently, Anyadike-Danes et al. (2009, 4) estimated the contribution to
job creation in the UK economy. This revealed that ‘11,530 high growth firms were
responsible for 1.3 million out of the increase in 2.4 million new jobs in established
businesses employing ten or more people between 2005 and 2008’. Later, those numbers
were slightly lowered by taking longer time span (Anyadike-Danes et al., 2015).
Furthermore, Butcher and Bursnall (2013) showed that job creation was evenly spread
across different size bands, which slightly contradicted with Anyadike-Danes et al. (2015)
results and should be further tested. Those studies provided useful understanding about
national HGF populations. However, as Anyadike-Danes et al. (2015:22) acknowledged,
those firms ‘require further analysis … to understand the process of small business growth.’
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