Measuring the economic impact of beneficial ownership transparency (summary report)

  • Publication date: 16 May 2022
  • Authors: Oxford Insights, Lateral Economics, Open Ownership

What has already been done to measure the economic impacts of beneficial ownership transparency?

The benefits of any policy reform can be classed according to four categories: qualifiable, quantifiable, monetisable, and cash releasing benefits. To date, most work discussing the economic impact of BOT refers to benefits only qualitatively, without seeking to measure them.

In the UK, HM Treasury provides four definitions of benefit types which can arise from government reforms. [19]

Benefit type
Cash releasing benefit (CRB) a monetisable benefit which is cashable for a particular stakeholder group, releasing additional funding for a government, business, individual etc.
Non cash releasing benefit (Non CRB) a monetisable but not cash releasing benefit
Quantifiable benefit a quantifiable but not readily monetisable benefit
Qualitative benefit a benefit which can be qualitatively identified, but not readily quantified

With notable exceptions discussed below, most sources we consulted describe the benefits logically expected to arise from BOT, or which can be observed through case studies. But they do so only qualitatively. Even benefits that are in principle cash releasing, monetisable and quantifiable – such as reduced due diligence costs, increased law enforcement efficiency or increased criminal convictions – are often only qualitatively identified in the literature.

Similarly, whilst all the interviewees consulted over the course of this research identified a number of benefits associated with BOT, subject matter experts rarely point to potential methodological approaches for quantification, or previous work to measure benefits. This echoes findings from a 2021 European Commission report on high value datasets which found that whilst experts were able to speak to case study examples demonstrating company data value, they usually could not refer to robust figures or means of quantification. [20]

Previous work on BOT often explicitly refrains from measuring benefits arguing that it is too early to quantify economic impact, or that it is too difficult to confidently attribute a benefit to BOT.

A number of studies and interviewees have suggested the economic impact of beneficial ownership transparency is too difficult to measure, as a nascent policy field with inadequate empirical data. In 2019, an Adam Smith International report on beneficial ownership stated that it was “largely too early” to measure benefits of BOT on a systematic basis, [21] whilst a 2020 Transparency International paper outlines how the supporting evidence for BOT reform remained “anecdotal”. [22]

Even within the UK government, often regarded as a leader of BOT reform, some implementers have expressed doubt about being able to quantify impact due to a lack of policy maturity. For instance, the BEIS Post-Implementation Review of the UK PSC register from 2019 agrees that it is “too early to evaluate its wider economic effects”. [23]

Elsewhere, experts highlighted that attributing economic impact to BOT is likely to be extremely difficult. As one of our interviewees for this project pointed out, whilst beneficial ownership has been referred to as the “lynchpin” of financial transparency, [24] BOT is still “just one piece of the puzzle”, and is usually implemented as part of a broader package of transparency reforms, making it particularly difficult to measure its benefits is isolation. [25] Similarly, the UK government’s 2014 impact assessment of its Transparency and Trust programme of reforms found that, in the case of reducing law enforcement costs, there is no “reliable or systematic way” to attribute benefits “directly and exclusively” to BOT. [26]

Nonetheless, there are a very limited number of studies that have sought to quantify and even monetise the benefits of beneficial ownership transparency.

Most work we encountered to quantify the economic benefits of BOT has been undertaken by the UK government. This dates back to 2002, when a Regulatory Impact Assessment sought to place value on the benefits of hypothetical BOT implementation options for law enforcement purposes using expert estimates from police and salary data. [27] This included the creation of a “modern database” of BOT information searchable by name (an early ideation of the UK PSC register). The study found that, even using conservative estimates, the economic benefits of a BOT register for government far outweigh any additional costs.

Following the implementation of the PSC register, there has been one effort to quantify its costs and benefits for end-users. The analysis was presented in a joint Companies House and BEIS report in 2019. [28] The report used a willingness to pay (WTP) survey-based approach to measure the value of all Companies House data, including BOT data on the PSC register, for different user groups, including businesses and providers of public goods such as governments and transparency advocacy groups. The report concluded that:

  • Based on WTP survey responses, beneficial ownership data was estimated to account for 4% of the total value of all Companies House data – or approximately £40 million to £120 million of aggregate benefit per year.
  • This compares to ongoing annual costs of compliance of £78 million.
  • For “high use” users – who are characterised as “public good” users working in transparency organisations, government departments or law enforcement entities – the value of PSC data rises to 13% of the total value.
  • Introducing a subscription-based model for Companies House data would lead to a net economic welfare loss, despite revenue generated from fees. Publishing the data in a freely accessible format leads to further economic benefits. [29]

In summary, the report suggests that the value of freely accessible BOT information for end-users is likely sufficient to cover the costs of compliance. [30]

Outside of the UK context, we only found limited examples of work that explicitly quantifies the monetisable economic benefits of BOT. The first, a study conducted by PwC, focused on BOT in Italy with a similar focus on the value of the data for business. It identified a value of €60 million in 2019. However, the report is not publicly available and only referenced in a European Commission report on high value datasets, which provides no insight into its methodology, the types of benefits measured or the specific types of business information considered. [31]

Secondly, the European Commission itself conducted a macro-economic impact assessment of the market value of company and company ownership data. To achieve this, the report looks back to a study conducted by Graham Vickery in 2011 [32] to estimate the market size of public sector information (PSI) in its broadest sense and applies forecasts from the European data market monitoring tool to predict a baseline scenario for growth up to 2025. [33] Company and company ownership data is estimated to represent 6% of total PSI market size, giving it a representative value of €3 billion euros in 2020 across all EU member states. [34] Note, however, that the report refers to “company and company ownership data” only, so that no specific estimations are attributed to BOT data. There is also no clear methodological rationale provided for the 6% market size figure, other than that this was established using “existing literature” and the study’s own research. [35]

In short, BOT-specific studies are limited and focus on only a narrow subset of the anticipated benefits. They are nonetheless encouraging.

Whilst evidence tracking the specific impacts of BOT is scarce, much more has been done to demonstrate the general economic benefits of financial transparency.

Despite limited evidence specifically concerning the economic impact of BOT, several studies have found that financial transparency produces net economic benefits. Even at the broadest level, mainstream economic logic supports the argument that BOT, as a step towards greater information transparency, will ultimately lead to better market performance. The economic theories advanced by Nobel Prize winners James Mirrlees and William Vickrey, [36] and George Akerlof, A. Michael Spence, and Joseph Stiglitz [37] draw a tight connection between market efficiency and other forms of transparency. Perfect information is a key precondition for idealised efficient markets. Asymmetric information, on the other hand, produces a variety of market failures.

Several studies have identified causal connections between other forms of financial transparency. In one investigation, researchers found evidence of increased investment and wage payments after improving country-by-country reporting to European tax authorities. [38] Another study found that increasing fiscal transparency in middle and low-income countries boosts FDI, [39] while other researchers calculated that an increase of one point in a country’s transparency rankings leads to an increase of 40% in FDI. [40]


[19] HM Treasury. (2018). Guide to Developing the Programme Business Case.

[20] European Commission. (2020). Impact Assessment study on the list of High Value Datasets to be made available by the Member States under the Open Data Directive.

[21] Davila, J., et. al. (2019). Towards a Global Norm of Beneficial Ownership Transparency. Adam Smith International., p. 34.

[22] The Transparency International paper also refers to “a dearth of empirical evidence” and the current impossibility of identifying the causal effects of implementation. See, Van der Merwe. T. (2020). U4 Helpdesk Answer, Beneficial ownership registers: progress to date. Transparency International., p. 16.

[23] Department for Business, Energy and Industrial Strategy (BEIS). (2019). Post-Implementation Review of the People with Significant Control Register., p. 40.

[24] Sharman, J.C. (2011). Testing the Global Financial Transparency Regime, International Studies Quarterly. Volume 55, no. 4.

[25] Interview with an academic subject matter expert, January 2022.

[26] Department for Business, Innovation and Skills (BIS). (2014). Final Stage Impact Assessments to Part A of the Transparency and Trust Proposals (Companies Transparency).

[27] HM Treasury / Department of Trade and Industry (DTI). (2002). Regulatory Impact Analysis: Disclosure of Beneficial Ownership of Unlisted Companies.

[28] Department for Business, Energy and Industrial Strategy (BEIS). (2019). Post-Implementation Review of the People with Significant Control Register.

[29] Department for Business, Energy and Industrial Strategy (BEIS), Companies House. (2019). Valuing the User Benefits of Companies House Data: Policy Summary.

[30] Note that some of the main benefits of BOT are expected to arise through its effects on money laundering and corruption, which are excluded from this study.

[31] European Commission. (2020). Impact Assessment study on the list of High Value Datasets to be made available by the Member States under the Open Data Directive.

[32] Vickery, G. (2011). Review of Recent Studies on PSI Re-Use and Related Market Developments. European Commission.

[33] Data landscape. Data Landscape: The European Data Market Monitoring Tool. Accessed February 2022.

[34] European Commission. (2020). Impact Assessment study on the list of High Value Datasets to be made available by the Member States under the Open Data Directive., p. 390.

[35] Ibid.

[36] Nobel Prize. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1996. Accessed Monday 7 Mar 2022.

[37] Nobel Prize. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2001. Accessed Monday 7 Mar 2022.

[38] De Simone, L and Olbert, M. (2021). Real effects of private country-by-country disclosure. The Accounting Review (forthcoming).

[39] Cicatiello, L., et. al. (2021). Assessing the impact of fiscal transparency on FDI inflows. Socio-Economic Planning Sciences. Volume 73, no. 100892.

[40] Drabek, Z. and Pane, W. (2002). The impact of transparency on foreign direct investment. Journal of Economic Integration. Volume 17, no. 4.

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