Official Statistics

Background quality report: venture capital schemes statistics

Updated 17 May 2023

1. Contact

Organisation unit: Knowledge, Analysis and Intelligence (KAI) Name: M Hindley and M Solonina Function: Statistics Producers, Direct Business Taxes Mail address: HM Revenue and Customs (HMRC), 100 Parliament Street, London SW1A 2BQ Email: venturecapital.statistics@hmrc.gov.uk

2. Statistical presentation

2.1 Data description

This is a National Statistics publication produced by HM Revenue and Customs (HMRC). It provides information on the number of companies raising funds, the number of subscriptions and the amounts raised through the Enterprise Investment Scheme (EIS) and Seed Enterprise Investment Scheme (SEIS). It also includes Official Statistics on social enterprises raising funds through the Social Investment Tax Relief (SITR) scheme. Further, information on the number of advance assurance requests (AAR) received by HMRC and their outcomes can be found in the statistics.

The EIS, SEIS and SITR are 3 of 4 tax–based venture capital schemes, the other being the Venture Capital Trust (VCT) scheme. The publication also provides information on the industrial and geographical breakdown of EIS and SEIS companies, the distribution of companies by the amounts of funds raised, and the distribution of investors by the size of their investment.

The current release includes the first estimates for 2021 to 2022. The figures for 2019 to 2020 and 2020 to 2021 include small revisions and minor updates, arising from the receipt of a small number of further EIS1 and SEIS1 forms for these years.

2.2 Classification system

The publication provides breakdowns on the industrial and geographical categorisation of EIS and SEIS companies, the distribution of companies by the amounts of funds raised, and the distribution of investors who claimed Income Tax (IT) relief on their EIS or SEIS investments by investment band. A unique Company Registration Number (CRN), assigned to each registered company, is used to aggregate the data.

Industrial sector is categorised using the UK Standard Industrial Classification (SIC) 2007. It should be noted that the most recent SIC data available have been used and some companies could have changed their trade since submitting their EIS1 or SEIS1 forms, which explains some of the changes seen in the statistics year on year. SIC codes are also self–selected, and we must assume that companies have selected the right code.

The geographical distribution of EIS and SEIS investments is based on the registered address of the company raising funds. This address may place the company in a different region from the region in which the business activity is carried out, and therefore a degree of caution should be exercised in the use of these data.

2.3 Sector coverage

The EIS and SEIS companies cover all main sectors in the UK Standard Industrial Classification (SIC) 2007.

2.4 Statistical concepts and definitions

Tax year

The statistics are aggregated into tax years. A tax year stretches from 6 April until 5 April the following calendar year.

Number of companies or enterprises

The number of companies or enterprises who have submitted EIS1, SEIS1, SITR1 forms indicating they have received investment under the scheme(s) or made AARs to HMRC, in a given tax year.

2.5 Statistical unit

The unit in the statistics is companies receiving investment under EIS, SEIS and SITR or those who have submitted AARs.

2.6 Statistical population

All companies who have submitted EIS1, SEIS1, SITR1 forms in the UK. This includes UK limited companies; any foreign company with a UK branch or office; and social enterprises, for example charity or community groups.

2.7 Reference area

The geographic region covered by the data is the United Kingdom (UK).

2.8 Time coverage

The statistics cover the time periods from tax year ending:

• 1994 to 2022 for EIS
• 2013 to 2022 for SEIS
• 2015 to 2022 for SITR
• 2007 to 2023 for EIS AAR
• 2013 to 2023 for SEIS AAR
• 2015 to 2023 for SITR AAR

Note that for EIS, the advance assurance service has been provided since the start of the scheme, but these have been reported to HMRC from 2006 to 2007.

3. Statistical processing

3.1 Source data

The statistics in this release are compiled using data collected from companies’ EIS1, SEIS1 and SITR1 returns. The returns, also known as ‘compliance statements’, are statutory declarations that the company is compliant with the conditions of the EIS, SEIS or SITR scheme.

The EIS1 and SEIS1 forms include details of investors who have indicated they will be claiming EIS/SEIS relief and the amount invested. The forms also include details of the date the shares were issued; these dates are used in the production of the statistics to record the investments within particular tax years. Social enterprises have to submit similar information under the SITR scheme using the SITR1 form.

The EIS1 and SEIS1 forms also include the number of subscriptions, indication of whether the company is knowledge intensive (KI) and the registered address of the company.

IT Self Assessment returns are used to collect EIS and SEIS investor level information. Some investors will invest in both schemes in the same tax year. This information will not cover investors making IT relief claims through other systems (eg PAYE) or not making any claims.

Data on AARs are retrieved from AAR applications received by HMRC. This includes the date the applications were received and the outcome of the application (ie approved, rejected or pending).

3.2 Frequency of data collection

The data for venture capital scheme investments and AAR are collected annually. This year’s statistics are based on data extracted in March 2023.

3.3 Data collection

The data cover all EIS1, SEIS1 and SITR1 returns received by HMRC and approved by the Venture Capital Reliefs Team (VCRT) which administers the schemes. Companies are required to submit an EIS1/SEIS1 form for each share issue where EIS or SEIS relief is to be claimed. The VCRT decides if a company and a share issue qualifies for the scheme. The data from the compliance forms is then uploaded to HMRC’s electronic database.

Data on AARs are collected by the VCRT and compiled from AAR applications. The data cover all the AAR applications handled by the VCRT. The VCRT advises companies or enterprises considering using the venture capital schemes about whether HMRC will regard their planned share issues, loans and business activities as satisfying the requirements of the scheme. Similarly the data is uploaded to HMRC’s electronic database.

IT relief data is collected from Self Assessment returns received by HMRC and is available on HMRC’s Self Assessment database.

3.4 Data validation

Initial checks carried out on the data include:

• plausibility checking such as ensuring that the amount of the investment has a realistic value. Any record showing a very high amount is referred to VCRT, who will check on these cases

• checks are carried out to ensure that the companies correspond to the correct company registration number (CRN). In case of inconsistencies, the correct information is retrieved from the Companies House Database or VCRT

• checks are carried out to flag duplicate records, such as individual companies’ records with the same issue date and amount of investment are checked with VCRT

• once the data from EIS1, SEIS1 and SITR1 forms have been extracted from the analysis database, any significant changes in figures from one statistical release to the next are investigated

• in tax years 2021 to 2022 and 2022 to 2023, a number of advance assurance applications were initially returned due to either not completing a mandatory checklist, no agent authority or using an old form. The applications were subsequently resubmitted to HMRC and final outcomes reached. Therefore, we have excluded these ‘rejections’ from the number of applications rejected in these years

3.5 Data compilation

Dealing with missing data

For companies with certain fields missing (for example postcodes) the relevant fields are updated using information provided by companies to Companies House, which is accessed through Bureau van Dijk’s Financial Analysis Made Easy (FAME) product.

If the missing postcode data is not populated through FAME, the record will be marked as ‘unknown’ in the geographical classification.

Aggregating data

Data are aggregated using a unique CRN assigned to each company. This unique number does not change across tax years.

Self Assessment data are aggregated using a unique taxpayer reference (UTR) assigned to each company.

4. Quality Management

4.1 Quality assurance

All official statistics produced by KAI, must meet the standards in the Code of Practice for Statistics produced by the UK Statistics Authority and all analysts adhere to best practice as set out in the ‘Quality’ pillar.

Analytical Quality Assurance describes the arrangements and procedures put in place to ensure analytical outputs are error free and fit–for–purpose. It is an essential part of KAI’s way of working as the complexity of our work and the speed at which we are asked to provide advice means there is a high risk of error which can have serious consequences on KAI’s and HMRC’s reputation, decisions, and in turn on peoples’ lives.

Every piece of analysis is unique, and as a result there is no single quality assurance (QA) checklist that contains all the QA tasks needed for every project. Nonetheless, analysts in KAI use a checklist which summarises the key QA tasks, and is used as a starting point for teams when they are considering what QA actions to undertake.

Teams amend and adapt it as they see fit, to take account of the level of risk associated with their analysis, and the different QA tasks that are relevant to the work. At the start of a project, during the planning stage, analysts and managers make a risk–based decision on what level of QA is required.

Analysts and managers construct a plan for all the QA tasks that will need to be completed, along with documentation on how each of those tasks are to be carried out, and turn this list into a QA checklist specific to the project.

Analysts carry out the QA tasks, update the checklist, and pass onto the Senior Responsible Officer for review and eventual sign off.

4.2 Quality assessment

The QA for this project adhered to the framework described in ‘4.1 Quality assurance’ and the specific procedures undertaken were as follows:

Stage 1 – Specifying the question

Up to date documentation was agreed with stakeholders setting out outputs needed and by when; how the outputs will be used; and all the parameters required for the analysis.

Stage 2 – Developing the methodology

Methodology was agreed and developed in collaboration with stakeholders and others with relevant expertise, ensuring it was fit for purpose and would deliver the required outputs.

Stage 3 – Building and populating a model/piece of code

Stage 3 consists of the following steps:

• analysis was produced using the most appropriate software and in line with good practice guidance
• data inputs were checked to ensure they were fit–for–purpose by reviewing available documentation and, where possible, through direct contact with data suppliers
• QA of the input data was carried out
• the analysis was audited by someone other than the lead analyst – checking code and methodology

Stage 4 – Running and testing the model/code

Stage 4 consists of the following:

• results were compared with those produced in previous years and differences understood and determined to be genuine
• results were determined to be explainable and in line with expectations

Stage 5 – Drafting the final output

The final stage includes the following:

• checks were completed to ensure internal consistency (eg totals equal the sum of the components)
• the final outputs were independently proof read and checked

5. Relevance

5.1 User needs

This analysis is likely to be of interest to users under the following broad headings:

• policy makers in government
• academia and research bodies
• media
• venture capital associations
• companies raising funds under the schemes
• investors investing in venture capital schemes

5.2 User satisfaction

HMRC is committed to providing impartial quality statistics which meet our users’ needs. We encourage our users to engage with us so that we can improve our National and Official Statistics and identify gaps in the statistics that we produce.

Since the recent few years we have published statistics on the EIS, SEIS and SITR as an annual release, combining the previous April and October publications. This allows us to provide breakdowns of the EIS and SEIS data earlier in the year and makes it easier for users to find the statistics that they need.

If you would like to comment on these statistics or have any enquiries, please use the statistical contacts named at the beginning of the report.

5.3 Completeness

Tables 1 to 10, 12 to 21 and 23 include every case captured via EIS1, SEIS1 and SITR1 forms respectively.

The Self Assessment tables include every case captured via Self Assessment returns.

The AAR tables (Tables 11, 22 and 24) include all AAR cases handled by the Small Company Enterprise Centre (SCEC) at HMRC.

Since the compliance forms are mandatory for investors to claim tax relief, the data is likely to be complete.

6. Accuracy and reliability

6.1 Overall accuracy

This analysis is based on administrative data, and accuracy is addressed by eliminating non-sampling errors as much as possible through adherence to the quality assurance framework.

The potential sources of error include:

• companies entering incorrect information onto the compliance forms
• human or software error when entering the compliance form data into HMRC’s system
• the accuracy and consistency of the assignment of SIC 2007, when classifying companies by industry sector
• mistakes in the programming code used to analyse the data and produce the statistics

6.2 Sampling error

As no sampling is necessary, sampling error is not an issue.

6.3 Non-sampling error

Coverage error

The Self Assessment tables (Tables 9, 10, 20 and 21) include every case captured via Self Assessment returns. Therefore, the data only includes companies who have claimed Income Tax relief via Self Assessment forms (rather than through PAYE for example).

Measurement error

The main sources of measurement error could be categorised as respondent errors.

Companies may make errors entering their information on the EIS1, SEIS1 and SITR1 forms. The data are subsequently entered onto HMRC’s systems manually. This is another point at which data may be altered due to human error or software errors. There is a risk that errors involve very large investment amounts.

To mitigate this, checks are carried out and any incorrect large values which are detected are investigated (and potentially altered) in the analysis database before the statistics are produced.

In addition, companies are classified by industrial sector using the self-selected SIC 2007 from FAME. We must assume that companies have selected the correct industrial sector.

Nonresponse error

Nonresponse errors may arise if a company does not submit their compliance statements to HMRC. However, compliance statements are mandatory in order for companies to claim EIS, SEIS and SITR relief, thus the effect of nonresponse error should be minimal.

Processing error

It is possible that errors exist in the programming code used to analyse the data and produce the statistics. This risk is reduced through developing a good understanding of the complexities of venture capital schemes, and thoroughly reviewing and testing the programs that are used.

6.4 Data revision

Data revision – policy

The United Kingdom Statistics Authority (UKSA) Code of Practice for Official Statistics requires all producers of Official Statistics to publish transparent guidance on the policy for revisions.

Data revision – practice

The EIS, SEIS and SITR companies have a period of several years after shares are issued to submit a compliance statement. Therefore, there are a small number of returns submitted later which add to the existing data and can therefore result in minor revisions to previously published figures.

This release provides figures on the number of investors and the amount claimed through Self Assessment in value terms. The EIS and SEIS investors can claim Income Tax relief up to five years after the 31 January following the tax year in which the investment was made.

We have applied an uplift factor to the figures for 2020 to 2021 onwards, to account for returns yet to be received by HMRC. This uplift factor is based on the percentage increase in returns from the previous publication, where we have assumed the number of returns will increase at a similar proportion in next year’s release. Therefore, these figures should be treated as provisional and will be subject to revisions in future publications.

The figures for 2019 to 2020 and 2020 to 2021 have been revised as a result of more returns being received by HMRC since last year’s publication. Note that since these figures were uplifted last year, the revised figures can be slightly smaller or larger than in the previous release.

Companies can also change their registered address or selected industrial SIC 2007 which would lead to revisions to the geographical and sectoral breakdowns for EIS and SEIS.

6.5 Seasonal adjustment

Seasonal adjustment is not applicable for this analysis.

7. Timeliness and punctuality

7.1 Timeliness

All data including funds raised under the schemes, investors claiming IT relief and AAR applications are published in May 2023.

Data on amount of funds raised under the schemes covers shares issued up to the end of tax year 2021 to 2022 (5 April 2022).

Data on investors claiming IT relief covers returns received for up to the end of tax year 2021 to 2022.

Information on AAR relates to applications received up to the end of tax year 2022 to 2023.

7.2 Punctuality

In accordance with the Code of Practice for official statistics, the exact date of publication will be given not less than one calendar month before publication on both the Schedule of updates for HMRC’s statistics and the Research and statistics calendar of GOV.UK.

Any delays to the publication date will be announced on the HMRC National Statistics website.

The full publication calendar can be found on both the Schedule of updates for HMRC’s statistics and the Research and statistics calendar of GOV.UK.

8. Coherence and comparability

8.1 Geographical comparability

This analysis is presented for a single region – the United Kingdom.

8.2 Comparability over time

In 2018 to 2019, rules for Knowledge Intensive (KI) companies were introduced – these companies are entitled to receive investments up to £10 million under the EIS. Thus, there is a new category for KI investments from 2018 to 2019.

8.3 Coherence – cross domain

The most common instance of different sources being used to provide data for the same variable is for a company’s SIC 2007 code.

Companies do not report their SIC 2007 sector to HMRC as part of their compliance statements, so they have been assigned to a sector based on data from external sources; this is predominantly matching records to the ONS’s Inter–Departmental Business Register (IDBR) survey. If this is not possible, it is based on information provided by companies to Companies House and accessed through Bureau van Dijk’s Financial Analysis Made Easy (FAME) product. If SIC 2007 data is not populated through FAME, this is classified under ‘Unknown SIC2007’ in the industrial breakdowns.

Quality assurance checks are undertaken on SIC code analysis, but since the codes are standardised, coherence issues should be minimal and not impactful.

The CRNs and postcodes are also cross–checked with FAME data.

Coherence – sub-annual and annual statistics

All statistics are presented as annual outputs. No coherence issues exist.

8.4 Coherence – internal

Rounding of numbers may cause some minor internal coherence issues as the figures within a table may not sum to the total displayed. Effort has been made to ensure totals between tables remain consistent where appropriate.

9. Accessibility and clarity

9.1 News release

There haven’t been any press releases linked to this data over the past year.

9.2 Publication

The tables and associated commentary are published on the Tax-based venture capital schemes webpage of GOV.UK.

Tables are published in the OpenDocument format, and the associated commentary in HTML.

Both documents comply with the accessibility regulations set out in the Public Sector Bodies (Websites and Mobile Applications) (No. 2) Accessibility Regulations 2018.

Further information can be found in HMRC’s accessible documents policy.

9.3 Online databases

This analysis is not used in any online databases.

9.4 Micro-data access

Access to this data is not possible in micro–data form, due to HMRC’s responsibilities around maintaining confidentiality of taxpayer information.

9.5 Other

There aren’t any other dissemination formats available for this analysis.

9.6 Documentation on methodology

All up-to-date information on the methodology is found on this webpage.

9.7 Quality documentation

All official statistics produced by KAI, must meet the standards in the Code of Practice for Statistics produced by the UK Statistics Authority and all analysts adhere to best practice as set out in the ‘Quality’ pillar.

Information about quality procedures for this analysis can be found in section 4 of this document.

10. Cost and burden

Because all necessary data for the venture capital schemes National Statistics is obtained from an administrative data source, there is no additional burden on companies or HMRC tax inspectors to provide information.

It is estimated to take about 30 days FTE to produce the annual analysis and publication.

11. Confidentiality

11.1 Confidentiality – policy

HMRC has a legal duty to maintain the confidentiality of taxpayer information.

Section 18(1) of the Commissioners for Revenue and Customs Act 2005 (CRCA) sets out our duty of confidentiality.

This analysis complies with this requirement.

11.2 Confidentiality – data treatment

The statistics in these tables are presented at an aggregate level so identification of individual companies is minimised, but potentially still possible. Aggregate data categorised by SIC 2007 code and Government Office Region (GOR), has the potential to be disclosive.

Where potential risks exist, statistical disclosure control (SDC) is applied to cells within tables. SDC is the application of methods to ensure confidential data is not disclosed to parties who don’t have authority to access it.

SDC modifies data so that the risk of data subjects being identified is within acceptable limits while making the data as useful as possible.

Disclosure in this analysis is avoided by applying rules that prevent categories of data containing:

• small numbers of contributors, and
• small numbers of contributors which are very dominant

If a cell within a table is determined to be disclosive, its contents are suppressed either by removing the data or combining categories.

Further information on anonymisation and data confidentiality best practice can be found on the Government Statistical Service’s website.