Presentation to ISBE 2019 by Neha Prashar.

1. Aston University
Birmingham
B4 7ET
E: mark.hart@aston.ac.uk,
n.prashar14@aston.ac.uk
Low Incidences of High Growth
Firms in Scotland: Why is
Scotland lagging behind?
Neha Prashar, Anastasia Ri, Mark Hart, Jonathan Slow,
Karen Bonner and Jun Du

2. Main Research Question
• What explains low levels of High Growth Firms in Scotland
1. Firm Characteristics
2. Regional Level Characteristics
3. Different entrepreneurial activity/ ambition
• Is there a “Scottish” effect or can differences be explained
through the points above.

3. 6
8
10
12
14
16
2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18
HGErate(%)(20%over1year)
North East North West Wales Scotland UK UK (Excl London)
• Analysis shows
that Scotland has
been below the
UK average since
2010
• This is true
whether looking
at the OECD
definition of 20%
growth over a 3
year period and
relaxing this
assumption to
looking at 10%
and 1 and 2-year
periods.
Background

4. Model
𝑮𝒓𝒐𝒘𝒕𝒉 𝑴𝒆𝒕𝒓𝒊𝒄𝒊𝒕 = α𝒊𝒕 + β𝒊𝒕 𝑿𝒊𝒕 + γ𝒊 𝒕−𝟏 𝒁𝒊 𝒕−𝟏 + δ𝒊 + θ 𝒕 + ε𝒊𝒕
• HGE – High growth episode (10% growth in employment over 1 year for firms with more than or equal to 10
employees)
• HGE_20 – High growth episode (20% growth in employment over 1 year for firms with more than or equal
to 10 employees)
• sHGE – small high growth episode (growth in employment by at least 8 employees for firms with less than
10 employees)
• allHGE - High growth episode (10% growth in employment over 1 year for firms with more than or equal to
10 employees) and sHGE (growth in employment by at least 8 employees for firms with less than 10
employees) together.
• allHGE_20 - High growth episode (20% growth in employment over 1 year for firms with more than or equal
to 10 employees) and sHGE (growth in employment by at least 8 employees for firms with less than 10
employees) together.
Data is combined from the Business Structure Database (BSD), Annual Population Survey (APS) and the Global
Entrepreneurship Monitor (GEM) for 2010-2018.
Logistic regression analysis with random effects is estimated using the panel data. The data was restricted to
those born before 2007 and still alive in 2018. This was done due to memory restrictions in the Secure Lab –
something we are working on to modify during the course of this research.

5. Model
𝑿𝒊𝒕 includes
• Age = Year – Birth of a firm
• Sector = 1 digit SIC code classifications (not including Agriculture, mining and public sectors)
• Average TEA = Total early stage entrepreneurial activity (GOR level for each year). This is taken
from the Global Entrepreneurship Monitor UK.
• UKborn = percentage of working aged population (16-64) who are non –UK born (NUTS2 level for
each year)
• Ethnic = percentage of working aged population (16-64) who are ethnic minority (NUTS2 level for
each year)
• NVQ4+ = percentage of working aged population (16-64) who have qualification of NVQ4 or over
• Netemployment = calculated from job creation and destruction estimates as the number of jobs
resulting from firm births + firm expansions – (firm deaths + firm contractions) at NUTS3 level
for each year
• GOR = GOR level dummies
• Year = Year level dummies
𝒁𝒊 𝒕−𝟏 includes
• Size = size in terms of number of employees
• Empgr = previous employment growth over one year (ie, for 2010, it would be the employment
growth between 2008 and 2009)
• Turngr = previous turnover growth (similar to empgr)
δ𝒊 represents the regional dummies at GOR level and θ 𝒕 are time dummies. Results presented are the
odds ratios.

6. Results
(1) (2) (3) (4) (5)
VARIABLES HGE HGE HGE HGE HGE
Age 0.900*** 0.906*** 0.906*** 0.906*** 0.906***
(0.000899) (0.000904) (0.000904) (0.000909) (0.000909)
Base = 10- 14 employees
15-19 employees 0.844*** 0.846*** 0.846*** 0.846*** 0.846***
(0.00769) (0.00765) (0.00765) (0.00769) (0.00769)
20-49 employees 0.937*** 0.940*** 0.940*** 0.939*** 0.939***
(0.00766) (0.00760) (0.00760) (0.00764) (0.00764)
50-249 employees 1.019* 1.017* 1.017* 1.016 1.016
(0.0104) (0.0102) (0.0102) (0.0103) (0.0103)
250+ employees 1.187*** 1.178*** 1.178*** 1.177*** 1.177***
(0.0204) (0.0200) (0.0200) (0.0201) (0.0201)
Base = Manufacturing
Electricity 1.348** 1.275** 1.275** 1.268* 1.268*
(0.167) (0.156) (0.156) (0.157) (0.157)
Water Supply 1.407*** 1.390*** 1.390*** 1.382*** 1.382***
(0.0538) (0.0522) (0.0522) (0.0523) (0.0523)
Construction 0.806*** 0.805*** 0.805*** 0.806*** 0.806***
(0.0116) (0.0114) (0.0114) (0.0115) (0.0115)
Wholesale and Retail Trade 0.993 0.992 0.992 0.991 0.991
(0.0109) (0.0108) (0.0108) (0.0108) (0.0108)
Transport and Storage 1.180*** 1.174*** 1.173*** 1.173*** 1.173***
(0.0202) (0.0198) (0.0198) (0.0199) (0.0199)
Accommodation 1.036** 1.041*** 1.041*** 1.038*** 1.038***
(0.0143) (0.0141) (0.0141) (0.0142) (0.0142)
Information and Communication 1.509*** 1.489*** 1.489*** 1.477*** 1.477***
(0.0249) (0.0241) (0.0241) (0.0241) (0.0241)
Financial and Insurance 0.981 0.976 0.976 0.967 0.968
(0.0242) (0.0237) (0.0237) (0.0236) (0.0236)
Real Estate 0.865*** 0.848*** 0.848*** 0.844*** 0.844***
(0.0201) (0.0194) (0.0194) (0.0195) (0.0195)
Professional, Scientific and Tech. Act 1.109*** 1.105*** 1.105*** 1.098*** 1.098***
(0.0145) (0.0142) (0.0142) (0.0143) (0.0143)
Admin, support services 1.188*** 1.181*** 1.181*** 1.179*** 1.179***
(0.0170) (0.0166) (0.0166) (0.0167) (0.0167)
Arts, Entertainment and Recreation 1.163*** 1.152*** 1.152*** 1.148*** 1.148***
(0.0223) (0.0217) (0.0217) (0.0218) (0.0218)
Other Service Activities 1.026 1.020 1.019 1.015 1.014
(0.0185) (0.0181) (0.0181) (0.0181) (0.0181)

7. Employment growth (t-1) 1.204*** 1.203*** 1.203*** 1.203***
(0.0105) (0.0105) (0.0106) (0.0106)
Turnover growth (t-1) 1.274*** 1.274*** 1.273*** 1.273***
(0.00845) (0.00845) (0.00849) (0.00849)
Average TEA 1.657** 1.650** 1.690**
(0.335) (0.337) (0.346)
% of working pop with NVQ4+ 1.002** 1.002**
(0.00114) (0.00114)
% of working pop that are ethnic minorities 0.999 0.999
(0.00166) (0.00166)
% of working pop that are Non-UK Born 1.005* 1.004*
(0.00258) (0.00258)
netemployment 1.004***
(0.000768)
Base = North East
North West 1.080*** 1.077*** 1.071*** 1.052** 1.050**
(0.0239) (0.0235) (0.0235) (0.0237) (0.0236)
Yorkshire and the Humber 1.042* 1.041* 1.025 1.006 1.005
(0.0239) (0.0235) (0.0239) (0.0239) (0.0238)
East Midlands 1.069*** 1.066*** 1.064*** 1.034 1.034
(0.0248) (0.0243) (0.0243) (0.0251) (0.0251)
West Midlands 1.040* 1.037 1.031 1.007 1.005
(0.0236) (0.0231) (0.0231) (0.0239) (0.0238)
East of England 1.084*** 1.078*** 1.062*** 1.020 1.019
(0.0242) (0.0236) (0.0241) (0.0255) (0.0255)
London 1.190*** 1.174*** 1.152*** 0.954 0.951
(0.0255) (0.0248) (0.0258) (0.0429) (0.0428)
South East 1.084*** 1.079*** 1.064*** 1.001 1.001
(0.0232) (0.0227) (0.0232) (0.0245) (0.0246)
South West 1.083*** 1.078*** 1.063*** 1.026 1.023
(0.0244) (0.0239) (0.0244) (0.0246) (0.0245)
Wales 0.883*** 0.882*** 0.879*** 0.871*** 0.870***
(0.0233) (0.0229) (0.0228) (0.0229) (0.0229)
Scotland 1.049** 1.049** 1.047** 1.004 1.004
(0.0243) (0.0238) (0.0238) (0.0248) (0.0248)
Northern Ireland 1.321*** 1.318*** 1.316*** 1.299*** 1.306***
(0.0353) (0.0347) (0.0346) (0.0360) (0.0362)
Constant 0.623*** 0.553*** 0.541*** 0.501*** 0.506***
(0.0157) (0.0138) (0.0144) (0.0179) (0.0181)
Observations 1,108,837 1,108,837 1,108,837 1,095,858 1,095,858
Number of ID 161,318 161,318 161,318 160,657 160,657
SE form in parentheses
*** p<0.01, ** p<0.05, * p<0.1

8. Conclusion and Future Research
• Age, size, sector and average TEA play an important role in
determining HG
• Previous growth is important in determining future high
growth.
• When adding regional controls for skills and dynamism,
most come out insignificant but change the importance of
regional differences – no ‘Scottish’ effect
• Future research will add in growth ambition (do you
anticipate growth in your business in the next 3 years) from
GEM.
• Will also re-run the model without London and also with a
continuous dependent variable looking at employment and
turnover growth over a 1 year period.

9. Thank you!
Questions/Comments?
Dr Neha Prashar (n.prashar14@aston.ac.uk)
The data used here is from the Jobs and Turnover version of the Longitudinal Business Structure Database which can be
accessed through the Secure Lab. The use of these data does not imply the endorsement of the data owner or the UK
Data Service at the UK Data Archive in relation to the interpretation or analysis of the data. This work uses research
datasets which may not exactly reproduce National Statistics aggregates