Methodology
METHODOLOGY
Our innovative approach uses methodologies created and develop by Nesta. We combine official economic statistics with other varied data sources in order to estimate the true scale and capabilities of digital tech in the UK.
Measuring and mapping the digital tech economy is challenging. In Tech Nation 2017 our innovative approach uses methodologies created and developed by Tech City UK and Nesta. We combine official economic statistics with a range of other data sources in order to estimate the true scale and capabilities of digital tech industries in the UK.
Economic Statistics
We have used official data sources to estimate economic statistics about the digital tech economy. This is based on the most rigorous selection of ‘digital’ standard industrial classification (SIC) and standard occupational classification (SOC) codes available, as developed by Nesta in previous ‘dynamic mapping’ research.
Although updated relatively infrequently, official economic statistics enable us to derive the employment, productivity and value added estimates in this report. Furthermore, they make it possible to track the progress of the digital tech industry on a consistent basis, drawing robust comparisons with our Tech Nation 2016 report.
Although updated relatively infrequently, official economic statistics enable us to derive the employment, productivity and value added estimates in this report. Furthermore, they make it possible to track the progress of the digital tech industry on a consistent basis, drawing robust comparisons with our Tech Nation 2016 report.
Our starting point was to define our digital tech clusters using Office for National Statistics (ONS) 2011 travel to work areas (TTWAs). In a few instances – Bristol & Bath, Cardiff & Swansea and Bournemouth & Poole – we have combined TTWAs to better represent a digital tech cluster.
Digital Tech Soc Codes
1136 IT and telecommunications directors
2133 IT and specialist managers
2134 IT project and programme managers
2135 IT business analysts, architects & system designers
2136 Programmers & software development professionals
2137 Web design & development professionals
2139 IT & telecommunications professionals not elsewhere classified
3131 IT operations technicians
3132 IT user support technicians
5242 Telecommunications engineers
5245 IT engineers
Digital Tech Sic Codes
26.20 Manufacture of computers and peripheral equipment
58.21 Publishing of computer games
58.29 Other software publishing
61.10 Wired telecommunications activities
61.20 Wireless telecommunications activities
61.30 Satellite telecommunications activities
61.90 Other telecommunications activities
62.01 Computer programming activities
62.02 Computer consultancy activities
62.03 Computer facilities management activities
62.09 Other IT & computer service activities
63.11 Data processing, hosting & related activities
63.12 Web portals
95.11 Repair of computers & peripheral equipment
Data Set
Our partner Frontier Economics provided measures of economic performance from the following official ONS datasets: Annual Populations Survey, The Business Structure Database and The Annual Business Survey.
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The Annual Populations Survey (APS)
The APS is a household survey with information about respondent’s occupation (SOC) and Industry of employment (SIC). Our partner Nesta it to estimate employment in the digital tech industries and the digital tech economy. Crucially, the APS has allowed us to capture digital embeddedness, that is digital experts working in non-digital industries. Furthermore, it also covers freelancers and self-employed workers, an important component of the digital workforce.
The Business Structure Database (BSD)
This is an administrative dataset which includes SIC, location, employment and turnover data for all UK businesses registered for PAYE/VAT.
The Annual Business Survey (ABS)
This survey captures detailed financial data as well as SIC and location information, allowing the estimation of approximate GVA figures.
Burning Glass online jobs data
Burning Glass provided a dataset containing detailed information about job ads posted online in the UK in 2016, including location (using TTWA 2001 codes), role, occupation, skills required and salary.
Github online software development activity
Our partner We are Flint scraped the Github open API in November 2016 to access data about recently active developers in the UK and across Europe. This dataset contains information about their location and their programming languages.
Meetup local industry networking
Our partner We are Flint scraped data about tech meetup groups and tech meetup members/attendees from meetup’s open API in November 2015.
Pitchbook investment data
We accessed data on venture capital and private equity activity in the UK and Europe from the Pitchbook database (downloaded February 2017).
JLL commercial property data
Our partner JLL provided prime office floorspace rents (quoted in £/sqft/year). Prime rent represents the top open-market rent expected for a notional unit of the highest quality and specification in the best location in the market.
Lane Registry house price data
We used Land Registry data (in October 2016) to determine average house prices in each of the 30 clusters. For ease of comparison we used the average price of a semi-detached house in each cluster.
Tech Nation 2017 survey data
The Tech Nation 2017 survey was conducted between 15 November and 5 December 2016. The survey received an enormous 2,732 completed responses. Of these, 1,841 completed interviews were with digital tech businesses and 891 completed interviews were with members of the ecosystem such as investors, accelerators, and universities.