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

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

Options for future econometric analyses

Estimates of particular benefit types are likely to be more robust than large scale complex models at this stage. As such, any attempts to measure the economic benefits of BOT should focus on quantifying specific benefits, rather than the aggregate economic impact of BOT.

A number of experts we spoke to over the course of this project were sceptical about the potential for macroeconomic approaches to measure the aggregate impact of BOT in a given jurisdiction, or the total value of BOT data. In the words of one interviewee, “the inclination is to want to come up with a nice econometric model with lots of variables. I would argue for simplicity.” [41] We heard that challenges surrounding data availability and attribution were likely to be easier to control for in simpler models, which look to measure one benefit area.

Whilst our full report goes into more detail, the benefits of BOT outlined in the literature and interviews and represented in the project’s logic model can be broadly classified into the following categories:

  • Benefits relating to crime and national security
    • Improving the efficiency and success of law enforcement
    • Reducing the incidence of illicit financial flows, and the consequent profitability of organised crime
    • Increasing asset seizures
    • Strengthening national security by facilitating sanctions on individuals with ties to hostile states, and reducing terrorism funding
  • Benefits relating to markets and investment environments
    • Reducing due diligence costs for businesses
    • Reducing “Know Your Customer” compliance costs for regulated financial entities
    • Improving investor confidence and increasing Foreign Direct Investment
  • Benefits related to public procurement and corruption
    • Improving procurement outcomes by fostering competition and improving value for money
    • Reducing corruption in procurement
  • Benefits related to tax evasion
    • Reducing tax evasion and increasing tax revenue
  • Benefits related to democracy and trust
    • Reducing political corruption
    • Reducing perceived corruption and increasing citizen trust in government

There are a number of approaches to measurement, however, they often involve trade-offs between how feasible it is to conduct an approach in the short-term, and its methodological robustness.

The table below summarises the methods that could be used to estimate the economic value of BOT data. It examines their main advantages and drawbacks, and provides an illustrative feasibility score, running from 1 (unfeasible to conduct in the short term given data availability and resource intensity) to 3 (could be conducted in the short term). Our main report provides a significantly more detailed breakdown of the ways in which these broad methodologies might be applied to the measurement of particular benefit types, and how this impacts feasibility.

Method and example Feasibility Main advantages Main drawbacks
Expert estimation surveys: e.g. asking law enforcement to estimate their time savings due to BOT data 3 – Could be carried out in the short term in jurisdictions both with and without a BOT regime already in place, but requires careful design and pre-survey consultation. The method tackles the data availability problem often associated with BOT impact assessment, by replacing the need for data points which may not be accessible with expert estimates. Estimates rely upon subjective estimates. There is also some risk that experts will be unwilling to estimate, or may challenge assumptions.
Willingness-to-pay studies: e.g. asking businesses how much they would be willing to pay for access to BOT data to support activities such as due diligence checks 3 – Could be carried out in the short term in jurisdictions both with and without a BOT regime already in place, but requires careful design and pre-survey consultation. This method can be employed across different user groups, not just expert users. WTP approaches also address data availability issues, and have already been conducted in the context of the UK PSC register. WTP estimates require a sufficient sample size. For example, the 2019 Companies House valuation was unable to generate WTP estimates for ‘public good providers’ due to a small sample size.
Public polls and surveys: e.g. asking the public whether or not freely accessible BOT data would increase their trust in government and generating a % estimate 3 – Could be carried out in the short term without detailed design required for WTP or expert estimations studies. A quick way of generating descriptive statistics about the value of BOT, which would not require econometric expertise to conduct. Results generated would not be methodologically robust and the approach would generate descriptive statistics about perceptions of BOT’s economic attributes rather than monetary estimates of the value of BOT.
Correlational studies: e.g. comparing data on variables such as asset seizures before and after a BOT intervention or multiple interventions across jurisdictions. 2 – A simple approach for economists which could be conducted in the short term, but completely dependent on data availability across jurisdictions (which varies depending on the variable in question). This approach would produce a more direct estimate of benefits in contrast to survey based approaches, and is in principle simple to carry out with the right pre and post implementation data. Correlational studies illuminate changes in variables which occur after an intervention, but do not interrogate the cause of a change. They are unlikely to be robust in this context, since changes in variables are likely to be influenced by a number of factors extraneous to BOT.
Causal studies: e.g. conducting a difference-in differences [42] or regression to analyse the average changes in a given variable across a number of jurisdictions with and without BOT regimes in place. A causal analysis might use index data to track benefits such as increased FDI or better confidence in business. 1 – Could be carried out in the long term, but is time consuming and difficult to conduct. More feasible where data happens to be available, and where research questions relate to simple, easily quantified variables. Conducted well, this approach is the most academically respectable method set forth. It would be likely very challenging to assemble comparable data across jurisdictions (collected in the same way, at similar intervals).
The approach would be time consuming, potentially taking years to conduct, without the guarantee of persuasive results.

Currently, the most near-term feasible approaches for measuring the value of beneficial ownership transparency interventions are survey-based. These methods could be employed both in jurisdictions where BOT has been implemented, and jurisdictions without a BOT regime in place.

Experts surveys, willingness to pay and novel surveys all could be carried out in jurisdictions either with or without a BOT regime in place. For instance, a 2002 Regulatory Impact Assessment conducted by the UK government asked officials to indicate the police time that would be saved by a hypothetical BOT register, 14 years before the PSC Register was operationalised. They are a useful, if imperfect, method for coping with extreme data limitations on BOT. Public polls may be helpful for indicating community sentiment and political support, but cannot produce robust estimates of benefits. Willingness-to-pay and expert estimation studies come with many caveats, but are flexible and could be harnessed to provide broadly indicative estimates of economic impacts in a number of benefit areas, including, but not limited to:

  • impact upon law enforcement investigation times;
  • impact upon money laundering activity;
  • impact upon perceptions of corruption and trust;
  • percentage of asset seizures facilitated by BOT information;
  • value of data for businesses; and
  • value of data in a national security context.

Correlational and causal studies could also be possible in the longer term across countries with BOT regimes already in place. Findings generated by causal studies have the potential to be particularly robust, but these approaches would be both time-consuming and costly.

Unlike subjective survey-based estimations, correlational and causal studies have the potential to make more direct observations regarding the economic impacts of BOT for jurisdictions with BOT regimes already in place.

A typical correlational study would simply compare a variable (e.g. money laundering flows) before and after BOT policy implementation. Such analyses are generally regarded as low-quality, given variables may change due to factors other than the policy change. In the right context, however, they are useful and potentially influential. Causal studies, in contrast, attempt to control for extraneous factors and so isolate the effect of the policy change. A classic example of a causal study is a randomised control trial – but, of course, it is not possible to randomly assign nations to different BOT regimes. Instead, social scientists use various methods to control for the influence of extraneous variables, though this is more demanding in its data and analytic requirements.

Both these approaches are likely to be more difficult and costly to conduct than survey-based approaches. Unlike survey-based methods, they depend on data availability and require baseline data points from which to measure impact, which are unlikely to be readily available in a number of benefit areas. For example, data on financial crime, corruption and tax evasion is generally of a very low quality, due to the illicit nature of these activities, meaning that robust causal or correlational research into BOT’s impact on those variables is unlikely to ever be feasible.

Even in the case of other variables that are theoretically easier to measure, such as asset seizure rates or investigation times, causal or correlational studies would require jurisdictions to collect and store this data over time. Multiple experts we spoke to cited data availability as a challenge when looking to measure the economic impact of BOT. [44] Correlational and causal studies will become more important in the future, as BOT regimes are implemented, although currently insufficient baseline data is being collected to enable this.


[41] Interview with academic subject matter expert, February 2022.

[42] Difference-in-differences methodologies involve plotting out changes in a given variable over time for countries that have implemented a BOT regime and countries that have not, and then comparing the averages.

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

[44] Interview with subject matter expert, February 2022; Interview with academic subject matter expert, January 2022.

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