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Employee Ownership Data
Advanced-HR’s employee ownership report details the equity held by executive-level employees, staff-level employees, and remaining unissued options. The report excludes Founder’s Shares and equity allocations are displayed as a percentage of fully diluted shares. Please visit www.advanced-hr.com or email firstname.lastname@example.org for more information surrounding our extensive compensation data and reports.
Founder’s Compensation Benchmarks
Advanced-HR’s compensation report details founder’s cash and equity pay as reported by participating private, venture-backed companies. Equity data is displayed as a percentage of fully diluted shares. All data is held as strictly confidential with the reports displaying aggregate data in an anonymous format. Please visit www.advanced-hr.com or email email@example.com for more information surrounding our extensive compensation data and reports.
Overall AHR/VCECS/Option Impact as a data source
Advanced-HR is the leading provider of pre-IPO compensation data. We partner with top-tier investors and portfolio companies to produce the world’s largest compensation database specific to private, venture-backed companies. Advanced-HR’s VC Executive Compensation Survey (VCECS) is for corporate use by investors, management professionals, and service providers. The VCECS results are leveraged in the Option Impact compensation database, an ongoing survey where companies maintain current information in the system in exchange for full database access at no cost.
Startup Heatmap Europe
The Startup Heatmap Europe is an annual survey among founders and the greater tech community on mobility and the attractiveness of startup hubs. The 2018 survey was collected between April and August 2018 and had 1,500 participants. After cleaning and sampling the data, 984 complete datasets remained that were weighted to adjust for regional representativeness on country level. Founders were 57.52% of respondents. For long-term trends in founder mobility, we used a combined dataset of 3 years with 1,661 distinct founder datasets.
Dealroom’s proprietary database and software aggregate data from multiple sources including processing of public news-flow, data feeds, web scraping, crowd-sourced contributions (verified by Dealroom) and manual research. Data is verified and curated manually, assisted by proprietary software.
Investment numbers refer to venture capital investment rounds such as seed, series A/B/C/etc, late stage, growth equity rounds. Excluded are debt or other non-equity funding, lending capital, grants and ICOs. Buyouts, M&A, secondary rounds, and IPOs are treated as exits, i.e. also excluded from funding data, but included in exit data.
LinkedIn defines a ‘software migrant’ as someone who is now working in a tech software industry, which is the primary employment they have and is listed as their most recently started employment, and who has come from a different country from the one they are working in now. LinkedIn analysed migration patterns using the following categories:
- International (all) source countries for arrivals in European destinations
- European destinations for international (all) migrants
- Non-European source countries for arrivals in European destinations
- European destinations for non-European migrants
- European source countries for European destinations
- European destinations for European migrants
- Non-European destinations for European migrants
- European source countries for non-European destinations
Percentages are expressed as percentage of overall flow of migrants in particular category.
LinkedIn created a pool for analysis to enable us to compare 2018’s tech professionals with those of 2017. It then took all LinkedIn members in Europe who provided enough information on job title, industry, function, and country for the position they held in September 2018 as well as in the September 2017 (this could be the same position), which is their primary employment. It then segmented those LinkedIn members in Europe who stated they worked in tech software industries in September 2018 and September 2017. LinkedIn assumed that where a member indicated they work for a company that classified itself as tech, that they were then a tech worker.
Stack Overflow has developed a revised, more reliable method for defining and identifying the largest tech hubs by number of professional developers around the world. As such, it is inappropriate to make year-over-year comparisons with the data shared for the State of European Tech 2017 on top tech hubs because of the new method for defining a metro area. Based on the old method, some cities with many professional developers, like Cologne with its large research and academic community, were missed. The methodology for defining and identifying professional developers by country remains constant, enabling year-over-year comparisons at the country level.
Job postings and job search data is provided in shares per million to take into account factors such as the size of the local labour market. To overcome varying levels of market maturity and local language inconsistencies we used searches containing "software" & "engineer" and local translations as an indicative proxy for technology searches.
We calculated the searches per million containing the words "software" and "engineer" on each of the EU Indeed country sites with over 100000 postings over the past year (2017-09-01 until 2018-09-01) over the time frames 2016-09-01 until 2017-09-01 & 2017-09-01 until 2018-09-01 and compared.
Similarly, we calculated the searches per million containing "software" & "engineer" (including local translations) coming from non-native ipaddresses (eg. someone in the UK searching on indeed.ie) over the time frames 2016-09-01 until 2017-09-01 & 2017-09-01 until 2018-09-01.
We then calculated the change (%) in both total software engineer searches per million per country, and in non-native software engineer searches per million.