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

Publication date: 12 May 2022
Authors: Oxford Insights, Lateral Economics, Open Ownership

Main report: measuring the economic impact of BOT

Summary of findings

Existing literature already builds a strong economic case for beneficial ownership transparency. There is strong institutional logic surrounding the types of benefits we can expect from BOT reform, which when combined with the quantitative evidence available, builds a robust argument around the economic benefits of BOT.

There is a sizable body of work which points to the benefits which governments, business and civil society can expect to see from BOT reform. [45] Throughout this research, we found limited scepticism in the literature or from expert interviews around the economic and social benefits of BOT. The mechanisms by which BOT should lead to benefits are largely clear, as evidenced by the logic models underpinning this report.

We also found that there is strong evidence pointing to the size of the benefit areas identified through desk research and interviews. Combined with institutional logic, this research allows for a number of stories to be told about many of the economic impacts of reform, even without the direct quantification of benefits. Case studies also serve to bolster the existing economic case for BOT.

Research to date, however, has largely refrained from attempting to isolate the specific impacts of BOT, partly due to a lack of data, but also because of challenges surrounding benefit attribution.

Research which looks to directly quantify the impacts of BOT is scarce. Over the course of this project we only came across a limited number of papers which attempt to put a monetary value on beneficial ownership reform, one report commissioned by the European Commission, and the rest carried out by the UK government. [46] [47] [48] One interviewee mentioned that robust quantitative research can be costly and is not guaranteed to yield persuasive results, and therefore was not a focus of attention for some advocacy organisations. [49] A number of papers explicitly refrain from quantifying the impacts of BOT for two main reasons: a lack of data availability, and challenges surrounding benefit attribution. [50]

Multiple experts we spoke to cited data availability as a challenge when looking to measure the economic impact of BOT. Measuring impact often requires baseline figures off which to track change, the lack of which is a commonly cited obstacle in impact evaluations of other policy areas. [51] Baseline data for BOT benefit areas such as reducing corruption or financial crime are particularly hard to establish given the difficulties associated with measuring activities which are clandestine by design.

Attributing impact to BOT reform is another difficulty because, as two interviewees expressed, BOT is never a standalone policy, and is usually implemented in the context of wider reforms in order to have impact. [52] As such, isolating the impact of BOT can be challenging. Similarly, a number of the broader indicators which could be used to measure the benefits of an intervention such as a reduced perception of corruption or increased GDP, are affected by a wide range of factors extraneous to BOT. As such, causal models which can measure benefits in these areas are very unlikely to be feasibly conducted in the short term in a way that results in robust quantifications.

Despite these challenges, in some jurisdictions, qualitatively identifying the benefits of BOT, combined with international pressure for reform, has pushed forward policy change.

The challenges surrounding measuring the impact of BOT should not be understated. However, for the purposes of making the economic case for a policy change, what is already possible – building the economic case about the size of the target problem, pointing to the logical mechanisms by which BOT impacts the problem, and providing case studies which demonstrate impact in action – can be sufficient to push forward a BOT reform.

Indeed, according to Open Ownership’s worldwide commitments and action mapping, a few dozen have already implemented online BOT registries, despite a lack of evidence quantifying benefits. [53] In the UK, the 2014 Impact Assessment that preceded the creation of a BOT register in 2016 simply listed the kinds of economic benefits associated with the register, claiming that quantification was impossible. [54]

As such, a lack of evidence quantifying the impacts of BOT has not been a barrier to reform in some contexts. Governments have been able to push through reforms without conducting the kind of detailed cost benefit analyses which would require benefits to be monetised. Whilst this could suggest that some treasuries are convinced enough by the institutional logic behind benefits, it also raises questions about whether economic gain is really a major driver of BOT reform.

We heard from one interviewee that countries were much more likely to implement a regime under international pressure and mandates – such as the EU’s 5th Anti Money Laundering Directive, or fear of FATF greylisting – than due to an economic imperative. [55] Other interviewees, however, highlighted that compliance with international pressure in itself equates to economic benefits, given the economic harms associated with FATF blacklisting and greylisting. [56]

We did hear, however, that quantitative impact measurement is particularly important for certain stakeholders, especially in the private sector, or for governments in developing countries.

Whilst some interviewees were more sceptical about the need for hard numbers to support BOT reform, others pointed to specific stakeholder groups who are unlikely to support policies in this area unless cashable or monetised benefits can be outlined. For instance, one expert we interviewed alluded to difficulties advocating for BOT amongst industry specialists without being able to offer quantified evidence around economic gains. [57] They claimed that the broader social value arguments, which posit that BOT is crucial to public integrity, preventing corruption and reducing financial crime, were readily dismissed by stakeholders in this context.

The UK government’s first valuation of Companies House data, which includes beneficial ownership information, appears to have been driven by similar apprehensions. The valuation sits under the government’s former Industrial Strategy programme and focuses primarily on benefits to the private sector (although benefits to ‘public good’ providers such as police are also evaluated qualitatively). [58] It is perhaps unsurprising that business communities in particular have called for more economic evidence in favour of BOT, given that businesses bear a large portion of the costs of BOT through compliance and legislative familiarisation activities.

We also heard multiple suggestions in interviews that quantifying benefits might make BOT a more attractive policy for lower-income countries with scarce public resources, for whom implementation costs need more financial justification. [59] One interview in particular highlighted the need for quantification amongst governments looking to finance BOT reforms through loans from international development banks. [60] They suggested that impact quantification would support ministries of finance, as it would demonstrate that an investment in BOT will not just have positive social impacts but also bring about economic benefits that facilitate the repayment of the loan.

In the same interview, we also heard that better means of measuring the impacts of BOT could help treasuries to justify pursuing BOT over other reforms in the wider financial transparency or anti corruption toolkit, where benefits are easier to quantify. For instance, at present, it might be easier to justify using budget to increase the capacity of an anti-corruption unit (where calculating benefits could be as simple as multiplying existing detection rates by an increase in capacity), as opposed to a BOT reform. In this sense, the interviewee suggested BOT was ‘fighting an uphill battle’ in terms of the challenges associated with assessing impact in this area compared with other reforms. [61]

There are a number of survey-based, correlational and causational approaches that could be used to track the economic benefits of BOT reform. Approaches to measurement, however, always have trade-offs, often between how feasible it is to conduct an approach in the short-term, and its methodological robustness.

As outlined in the UK government’s advice on writing a policy business case, estimating the benefits of a policy is always possible in principle. [62] Indeed, there are a number of methods set forth in this report which would allow governments or advocacy organisations to measure impacts in a number of areas. However, all approaches to measurement incur trade offs, often between how readily an approach could be conducted in the short term, and the robustness of the estimates generated.

To illustrate, a number of future approaches to measurement set forth in this report are survey based, measuring value according to expert estimates, or willingness-to-pay questionnaires. Whilst these methods still require careful design, they are generally feasible in the sense that they could be conducted at present, without the need to collect a range of data points that are not readily available. However, they also generate subjective value estimations, and cannot generate cash releasing values. [63]

At the other end of the spectrum, causal studies represent an academic ‘gold standard’ which would establish a clear link between BOT and a benefit variable, addressing problems regarding attribution. Yet these approaches would require multiple data points across jurisdictions to be fed into complex regressions. As such, they would likely take years to develop, raising the important question of whether it is always proportional to commit to complex econometric approaches, especially where existing evidence is strong and the costs of measurement are high. [64]

Finally, we found that estimations of particular benefit types are likely to be more robust and persuasive than large scale complex models at this stage. As such, this report is structured in terms of measuring specific benefits, as opposed to the aggregate impact of BOT.

A number of experts we spoke to over the course of this project were sceptical about the potential for robust macroeconomic approaches which seek to measure the aggregate impact of BOT in a jurisdiction, or the global 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." [65] 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.

Furthermore, there is an argument to be made that measuring specific benefit areas has the potential to be a more useful approach, partly because the margins for error with macroeconomic estimations are especially large, but also because measuring in terms of benefit areas will appeal to specific user groups. For instance, measuring the impact of BOT upon businesses due diligence costs is likely to be persuasive to private sector stakeholders, whilst measuring BOT’s impact upon law enforcement investigation times might appeal more to certain types of government ministers.

Experts consulted during this research consistently emphasised that approaches to measurement need to be responsive to user needs. When asked about the kinds of benefits which econometric analyses of BOT should consider, a common response was that it depends on what measurement is aiming to achieve, or more specifically, who it is aiming to persuade. As such this report is structured according to the following benefit types:

impacts relating to crime and national security;
impacts relating to related to financial markets and investment environments;
impacts related to public procurement and corruption;
impacts related to tax evasion; and
impacts related to democracy and trust.

We acknowledge that these benefit categories are unlikely to cover all of the economic benefits of BOT conceivable, which are broad and multiple in nature. Given that BOT is still a relatively nascent policy area, there may even be unexpected economic benefits of reform which are yet to be felt or documented. However, these benefit categories were most commonly identified in literature and interviews, and function as a good starting point for a discussion of potential approaches to impact quantification.

Each section includes a discussion of the existing evidence to support an economic case for BOT in the benefit area, before outlining potential future approaches to measurement. For every approach identified, an outline of advantages and drawbacks is provided, as well as an illustrative feasibility score, ranked from running from 1 (impossible to conduct in the short term given data availability and resource intensity) to 3 (could be conducted in the short term). In light of the importance of measuring with purpose and user groups in mind, each benefit area section is also accompanied by a brief discussion of potential audiences for measurement.

1. Measuring impacts related to organised crime and national security

1.1 Benefits identified in logic model

Facilitating the role of law enforcement authorities

BOT should lead to a reduction in resource time spent on investigations by providing an accessible tool by which beneficial ownership and connections between companies can be discerned by law enforcement.
In line with this increase in law enforcement efficiency, BOT could lead to an increase in prosecutions or convictions in cases related to corruption and financial crime.

Reducing the incidence of illicit financial flows (deterrent effect)

BOT should increase the difficulty and risk of money laundering by forcing companies and individuals to report on ownership structures.
With more efficient law enforcement investigations and the increased difficulty of concealing illegal movement of money, BOT should have a deterrent effect on financial crime.

Increased asset seizures

Alongside an increase in law enforcement efficiency should come an increase in government asset seizures. [66]

Strengthened national security

BOT is a necessary precondition for enforcing sanctions on individuals with ties to hostile states, a sanction which is particularly high on the agenda at the time of writing, given the international community’s response to Russia’s invasion of Ukraine.
BOT should also lead to a reduction, or at least the better identification of terrorist financing, since terrorist organisations are likely to use shell companies to hide ownership of assets.

1.2 Existing evidence regarding BOT as a tool for reducing organised crime, reducing the cost of investigations, and increasing national security

Strong institutional logic and preliminary studies already indicate that effective BOT reforms should reduce the time and resources taken to investigate money laundering and other illicit financial activities, and even the incidence of these crimes.

Historically, there is a strong institutional logic behind BOT as a policy reform to pursue in order to reduce financial crime. In 2003, the Financial Action Task Force (FATF) became the first body to set forth global standards on BOT, in order to “get rid of the cloak of secrecy concerning the ultimate owner of a company, foundation, association or any other legal person, and prevent their misuse for crime and terrorism.” [67] Since FATF’s original standards were published, a number of organisations have continued to make the case for beneficial ownership transparency as a powerful tool in the anti-corruption arsenal, as outlined in this report’s literature review. However, as a rule, most advocates of BOT have not attempted to quantify the impacts of BOT relating to organised crime and national security, instead framing the case for transparency as a question of broader ‘public good’.

Whilst the quantitative evidence base for BOT as a driver of anti-corruption is admittedly small, research which has attempted to monetise the value of beneficial ownership data supports the proposition that BOT is a valuable tool when fighting financial crime. For example, in 2002, a UK government Regulatory Impact Assessment (RIA) looked to assess the value of BOT information for law enforcement. [68] Through a methodological approach which relied upon estimates from financial investigators, the report concluded that an open BOT register could lead to cost savings of £30 million annually in terms of improved police efficiency and recovery revenues – eclipsing the costs to government associated with reform. [69]

More recently, the UK government’s 2019 valuation of Companies House data used qualitative interviews to identify the value of beneficial ownership data as a tool which improves the quality and depth of investigations and can act as evidence in court. [70] However, the 2019 paper was unable to quantify this value specifically, citing the number of public sector participants as insufficient in order to run a survey-based willingness to pay (WTP) study. [71]

Whilst the benefits of BOT to law enforcement have been broadly accepted, we found no evidence of research which looks to quantify how BOT might look to actually reduce the incidence of organised crime. However the scale of the problems BOT targets in this area represents a powerful upper boundary for estimates of economic benefits.

There are obvious difficulties associated with sizing the presence and scale of money laundering and other criminal activities – which are clandestine by design. Nonetheless, a limited number of methods have been proposed as means of estimating the size of money laundering flows globally, dating back to the Walker gravity model, which first estimated the worldwide allocation of money to be laundered at 2.8 trillion USD annually in 1999. [72]

Since the publication of the Walker model, other studies have also used econometric gravity model estimations to attempt to size the amount of money laundered annually. For example, in 2020, Ferwerda et al. used suspicious transaction report (STR) data from the Netherlands to empirically test some of the Walker model’s assumptions, concluding that money laundering is likely to account for 3% of global GDP – more than 2.5 trillion USD according to 2020 World Bank estimates of global GDP. [73] [74]

Whilst significant, these kinds of macroeconomic estimates are often recycled across literature with no methodological reasoning, and start to lose persuasive power as they become what the academic Michael Levi characterises as “facts by repetition”. [75] Crucially, they also shed no light on a) the actual harms associated with money laundering and illicit financial flows and whether these outweigh the cost of AML compliance, or b) the extent to which BOT reforms can influence these harms. Fully quantifying the former may be conceptually difficult to do in any meaningful way; in their 2013 work on harms, Greenfield and Paoli suggest that measuring the harmful impact of financial crime is is impossible due to moral uncertainties surrounding what can be classed as harm, and the “infinitude” of potential harms that could be considered for measurement. [76] Others have provided rough estimates of harms as a percentage of overall revenue from financial crime, such as Unger and Walker, who in 2009 referred to the harms of financial crime, including tax evasion, fraud, drug crime and theft amongst many others, at one third of the proceeds in the Australian context. [77]

Meanwhile obstacles regarding causation and data accuracy render any causal analysis of how BOT impacts global flows of organised crime unfeasible at present. Causality is difficult to obtain due to the complex nature of money laundering and other financial crimes, which are influenced by a wide range of factors beyond BOT, whilst the broad margins for error associated with current macroeconomic estimates of the size of money laundering at present do not provide a solid basis for measurement. Nonetheless, estimates here represent a significant upper boundary for the quantity of assets that in principle could be seized, or prevented through deterrence. Based on these figures, even a small impact upon the incidence of financial crime would constitute a benefit which well outweighs the costs of BOT reform.

There is also a strong argument for BOT as a driver of national security, yet quantifying the monetary benefits of BOT in this regard is largely unfeasible. Again, even sizing the general costs of the benefit area, in comparison to the costs of BOT reform, begins to make a compelling economic case for reform here.

BOT has long been set forth as an anti-terrorism reform, as well as an AML policy. The initial 2003 FATF standards on BOT referred to preventing “crime and terrorism”, whilst the EU’s 4th AML directive bears the headline “preventing abuse of the financial system for money laundering and terrorism purposes”. [78] [79] Whilst there is little documented evidence of this occurring, terrorist organisations could use shell companies to hide beneficial ownership and launder the money used to finance their operations, and substantive BOT has the potential to help investigators uncover these connections, or reduce the risk of an organisation laundering money in a jurisdiction. [80]

At the time of writing, during the 2022 Russian invasion of Ukraine, BOT as a driver for increased national security has also risen up political agendas as a necessary precondition for sanctions on hostile states. In February 2022, the UK government released its Economic Crime Bill, [81] which calls for further transparency in terms of the beneficial ownership of property, in the context of the estimated £1.2 million with links to the Kremlin held in UK property, via shell companies. [82]

Quantifying the benefits of national security and anti-terrorism programmes is unfeasible, but also likely to be limited in terms of persuasive power, given how arguments in the national security space are rarely focussed on monetary benefit, rather geopolitical priorties and risk prevention. To make the economic case for BOT in this area using existing data, a better angle would be to examine the size of expenditures on antiterrorism specifically and national defence generally. If an intervention such as BOT can reduce funds accruing to terrorist cells, or provide a strong disincentive for states to engage in warfare, then the benefits are potentially enormous. To illustrate this, the UK government spends at least £2 billion annually on the implementation of its antiterrorism strategy, compared to the estimated set up costs of £72-£112k for the IT development of the PSC register, and £225k of annual maintenance costs. [83] [84]

1.3 Future approaches to measurement

1.3.1 Approaches to measuring potential reductions in law enforcement resource / time dedicated to investigations

Potential approach	A survey-based approach, asking law enforcement officials to estimate the impact that a BOT intervention would have on investigation resource time
	Similar to the Regulatory Impact Assessment (RIA) carried out by the UK government in 2002 to estimate the potential time savings of a hypothetical BOT register, this approach would involve working with government officials to estimate how much time a BOT intervention – such as a centralised BOT registry – would save them in terms of resource. [85] The survey would need to determine how and when officials were using (or would use) BOT data in their investigations, and collect estimations of time taken to conduct activities with and without this data available. Based on additional data such as the number of investigators, and average salary, monetary estimates of savings could then also be calculated.
Advantages	– This approach would not require an existing BOT regime to be implemented before it can be carried out. – The approach could therefore be carried out in any jurisdiction. – The method tackles the data availability problem often associated with BOT, since it requires only a limited number of data points in addition to survey estimations, such as number of investigators and average investigator salary.
Drawbacks	– Results would be dependent on subjective estimations from law enforcement. – Survey would require a significant sample (and therefore buy-in from officials) in order to produce robust estimations. – In cases where the BOT regime has not yet been implemented, there will be a need to make assumptions about data quality of a future regime.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – A feasible approach which could be carried out in any jurisdiction, does not require large datasets, and has been successfully employed in the past in a hypothetical setting (when a register had not yet been implemented).

Potential approach	A correlational study, comparing the time taken to conduct investigations before a BOT reform was implemented, with time taken after implementation.
	Simple to carry out with the right data, this correlational approach has the potential to be used to imply that BOT can have an impact upon investigation times. Data required would broadly include:– Specific uses of BOT data within an investigation context– Investigation times for these activities before implementation– Investigation times for these activities after implementationHowever, unlike a causal study, which would require much more data and effort to conduct, a correlational approach would not interrogate the reasons behind any potential change (which could be influenced by a number of factors, including BOT).
Advantages	– This approach would produce a more direct estimate of a cash releasing benefit in contrast to a survey based approach. – The approach would not require a large body of officials to be recruited as participants.
Drawbacks	– This approach would require at least one and possibly several BOT regimes to be implemented before a correlational study could be conducted. – It would also require data on pre-intervention investigation times, which may not be available in all jurisdictions.– Correlational studies help to illuminate a connection between an intervention and potential effects, but do not interrogate the causes of the changes observed, and therefore have a poor methodological reputation within academic communities.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	2 – This approach is not methodologically complex but completely dependent on the availability of internal data.

1.3.2 Approaches to measuring potential reduction in criminal activity (illicit financial flows)

Potential approach	Expert survey to estimate the % of criminal activity reliant on money laundering in a jurisdiction, and share of that money laundering activity which is affected by BOT.
	This approach would use law enforcement figures on incidence of illicit financial flows as a baseline dataset. Expert estimates would then be used to size any change in criminal activity from this baseline. Given the difficulty of quantifying the harms of financial crime, fully monetising this benefit is likely to be impossible. If available, data on the costs of investigating or policing money laundering could be used to provide some partial monetised estimates of benefit here. However, as outlined below, collecting comparable data across jurisdictions here would be challenging.
Advantages	– The approach does not require an existing BOT regime to be implemented. – The method would be relatively simple to execute. – With the right experts, the approach is likely to be persuasive.
Drawbacks	– The approach rests on subjective expert assessments. – Comparability of data between countries is likely difficult due to money laundering being defined using predicate crimes, which differ across countries. – Estimates could be difficult to make, given that money laundering is closely linked to other crimes. It may be hard to say where police work on money laundering specifically stops, and other investigation work begins. – Linked to the above, there is some risk that experts will be unwilling to estimate, or may challenge assumptions. – Benefits would be quantifiable, but potentially difficult to monetise, and not cash releasing.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – Quantification through expert surveys is definitely feasible, but risks surrounding the subjective nature of difficult estimations would mean that pre-survey exploration should be carried out before investing into this approach.

1.3.3 Approaches to measuring impact on asset seizures

Potential approach	Correlational or causational study to estimate the impact of BOT on asset seizures
	A correlational approach would compare data on asset seizures before and after a BOT intervention, either in a single jurisdiction, or across countries. The latter would produce more robust findings, but neither approach would interrogate the reasoning behind any potential change. A causative, difference-in-differences, methodology could be used here to compare impacts across jurisdictions with and without BOT reform in place. This would involve collecting asset seizure data from a range of jurisdictions (both with and without BOT interventions) and comparing changes over time.
Advantages	– Asset seizure data should be available in most jurisdictions – for example – this report* provides data on asset seizures in the UK.
Drawbacks	– A causational requires at least one or potentially multiple BOT interventions to be implemented in different jurisdictions before it can be carried out. – Assembling comparable data across jurisdictions would be challenging (requires jurisdictions to collect and publish data comparably). – From consulting with experts, we heard that revenues here were likely to be insignificant, and therefore not particularly persuasive.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	2 for correlational studies – This method should be largely feasible to implement at present, with caveats around data availability and quality. 1 for causal studies – this would require a number of comparable BOT regimes to be implemented, and comparable data on asset seizures before the method could be carried out robustly.

* Asset recovery statistical bulletin: financial years ending 2016 to 2021

Potential approach	Survey-based approach to estimate the % of asset seizures which are facilitated by BOT data
	This approach would involve working with law enforcement agencies to estimate the % of asset seizures that could have been facilitated using data from a central register (depending on whether BOT policy has been implemented or not). If available, data on the total value of asset seizures could then be used to estimate the monetary value of the % of BOT facilitated forfeitures. The method would supplement the correlational estimation outlined above, and provide some causal insight into how many asset seizures specifically are facilitated by BOT reform.
Advantages	– This approach does not necessarily require a BOT intervention to have been implemented yet.
Drawbacks	– Experts may find the effects of BOT difficult to judge without more institutional context – especially if a reform hasn’t yet been implemented. – The approach rests on subjective expert assessments – There is some risk that experts will be unwilling to estimate, or may challenge assumptions.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – This approach is feasible in the sense that it requires very limited data to carry out, although the typical caveats around survey based approaches apply. Again, pre-survey exploration might be useful before deciding to invest in this method.

1.3.4 Approaches to measuring national security benefits

Potential approach	National security survey to assess the value of BOT for fighting terrorism and hostile entity financing
	A survey-based approach which would seek to assess the value of BOT information to public servants as well as investigative journalists or banks working on anti-terrorism matters, or to impede unwanted foreign influence through sanctions. Monetising this impact in terms of the broader economic benefits to national security would be impossible, but a survey could ask national security experts to estimate values such as: – Amount of asset seizures facilitated by BOT – Number of economic sanctions more broadly facilitated by BOT – Investigation time saved by BOT
Advantages	– This approach does not require a BOT regime to be implemented yet. – The approach harnesses the authority and experience of experts – which is crucial given the lack of data availability in this sphere.
Drawbacks	– Survey data is subjective. – Monetised economic benefit of avoided harms cannot be quantified. – Experts may find the effects of BOT difficult to judge without more institutional context.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – In terms of practicality this approach is feasible (with the crucial trade off that direct monetary estimates of BOT’s influence on broader economic harms are unfeasible for this benefit area)

1.4 Potential audiences for measurement

Government stakeholders are the most likely audience for methods which seek to estimate the value of BOT in law enforcement or national security contexts. Time saving for law enforcement officials in particular is one of the most direct, monetizable benefits that a BOT register will bring about for governments – with the caveat that data needs to be accurate, easily searchable, and interoperable with other systems to save police time. [86]

Therefore, the approaches which use expert estimates or correlational techniques to quantify a saving in police time have the most potential to deepen buy-in within treasuries, particularly now that these kinds of studies could be tied to real experiences using an operational register, as opposed to theoretical projections. Findings from this type of approach could also be of interest to governments in other jurisdictions considering implementing BOT, but sceptical about economic returns.

The quantification of benefits relating to financial crime and national security potentially also has a broader audience, given how crime, law and order, and defence matters often feature amongst the top voter issues. The voting public might be more inclined to vote for a regime that can demonstrate that it implemented policies which have saved police time, led to the prosecution of more criminals, or tackled terrorist financing. Additionally, evidence around the benefits of BOT in a national security context are likely to be particularly persuasive during times of conflict, when governments might look to enforce sanctions.

However, arguably economic gain is unlikely to be the key determinant of reform in these areas, given that there is a strong moral component to these kinds of benefits, which are generally accepted as worth pursuing even without economic evidence. As such, investing in approaches which quantify the impact of BOT in a national security context should not necessarily be a priority for governments looking to pursue economic evaluations of reform.

2. Measuring impacts related to financial markets and investment environments

2.1 Benefits identified in logic model

Reduced due diligence costs, and reduced compliance costs associated with anti-money laundering procedures and human rights regulations for companies and financial institutions.

Reduced due diligence costs for companies.
Reduced AML compliance resource time and costs for regulated entities, such as banks. Notably, some interviewees did emphasise that this benefit is unlikely to be achievable at present, since currently the data available on BOT registers is not reliable enough to be compliant with regulated entities AML requirements. Nonetheless, we have included it here as an indication of a benefit which could be achievable in the future, as registers develop and introduce better data verification mechanisms. [87]

Increased reputational value for the country’s economy

Better perception of investment environment
Increased FDI from private investors

Decreased risk and consequent losses for companies and investors

Losses avoided due to bad investments, such as ‘pump and dump’ schemes. A comprehensive BOT register facilitates better investor due diligence, meaning chosen partners are less likely to be investigated and penalised.

2.2 Existing evidence in favour of BOT as a tool for increasing market efficiency and investment

Existing evidence already makes a strong case that free-to-access beneficial ownership registers benefit business environments; it is already clear that registers can help reduce the cost of due diligence procedures of businesses. More limited evidence suggests BOT will reduce the cost of Know Your Customer (KYC) compliance for regulated entities.

Evidence from the United Kingdom demonstrates this point persuasively. In 2019, the UK’s Government’s Department of Business, Energy and Industrial Strategy (BEIS) conducted an economic valuation of Companies House data, which includes data published on the UK beneficial ownership register. [88] The valuation showed that 22% of UK businesses had used the register to look up information about other businesses, which bears testament to the utility of BOT data as a due diligence tool.

The valuation also included an analysis of willingness to pay survey responses. Based on these responses, the report estimated that the beneficial ownership data accounts for 4% of the total value of all Companies House data, which translates to approximately £40 million to £120 million of aggregate benefit per year. It is important to note, however, that this economic benefit is contingent on free access to the beneficial ownership data. The report shows that if people interested in accessing the PSC register were charged an annual subscription fee, the register would suffer a net welfare loss despite the revenue which would come from the fees, due to a drop in data use. A more in depth discussion of this work, and its methodological approach can be found in this report’s literature review.

Literature also indicates that regulated entities, such as banks, can also use beneficial ownership data to support compliance with Know Your Customer (KYC) anti-money laundering regulation, which involves identifying and screening banking customers. [89] There is currently no quantified evidence about how much money a free-to-use BOT register would save banks when carrying out these procedures, however, and these benefits are unlikely to be fully felt until the quality of data on registers improves. [90] For example, a 2014 impact assessment conducted by BEIS refers to the benefits of beneficial ownership transparency for banks, but says that they cannot isolate “the costs they incur in obtaining beneficial ownership information as separate from the total costs incurred in carrying out AML due diligence”. [91]

The institutional logic that BOT reduces the incidence of corruption also implies wider macroeconomic benefits. Research demonstrates that corruption reduces the efficiency of markets and firm valuations and hampers risk management.

Multiple studies have demonstrated the negative impact of corruption on business environments. Effects depend on the type of corruption and the stage of development, but in general there is robust evidence that corruption reduces market efficiency and the problem is quantitatively highly significant. As an illustrative example, Lee and Ng’s research into international valuation from 2009 found that firms in countries with higher levels of corruption have generally lower valuations. [92] Other studies on corruption and economic growth demonstrate that corruption substantially reduces a country’s real GDP per capita, largely due to decreases in Foreign Direct Investment (FDI) and increases in inflation. [93] [94] In terms of aggregate impact, OECD broadly estimates that corruption adds up to 10% of the cost of doing business globally. [95]

Based on the logic that BOT makes it more difficult to set up anonymous companies – which are the vehicle for more than 70% of corruption cases according to the World Bank [96] – it follows that BOT should lead to a decrease in corruption in a jurisdiction, which in turn could translate into the kinds of economic benefits discussed above. However, quantifying BOT’s impact upon the incidence of corruption is unfeasible, given an inherent lack of granular data on the scale of corruption, meaning that these benefits are likely to remain speculative even in the longer term.

Whilst there is limited research which explicitly ties BOT to wider market benefits, econometric analyses of different types of financial transparency provide cause for optimism.

Mainstream economic logic supports the broad 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, [97] and George Akerlof, A. Michael Spence, and Joseph Stiglitz [98] draw a tight connection between market efficiency and other forms of transparency, by demonstrating perfect information is a key condition for perfect market efficiency. Asymmetric information, on the other hand, produces a variety of market failures.

Several studies have also identified causal connections between other forms of financial transparency, such as fiscal transparency, and positive effects on national investment environments, which might cast doubt over the argument that BOT would lead to an investment “chilling effect”. In one investigation, researchers found evidence of increased investment and wage payments after improving country-by-country reporting to European tax authorities. [99] Another study found that increasing fiscal transparency in middle and low-income countries boosts FDI, [100] while other researchers calculated that an increase of one point in a country’s transparency rankings leads to an increase of 40% in FDI. [101]

A further benefit of BOT is investor protection through a reduction in “pump-and-dump” style schemes, although evidence to demonstrate this connection, and the size of the target problem is sparse at present.

In principle, free beneficial ownership registers should protect investors from a type of fraud known as “pump and dump”. In these schemes, investors are tricked into buying artificially inflated shares, which fraudsters then sell quickly at a high price. [102] In 2019, for example, two US businessmen were caught in an FBI sting operation after making more than US $15 million over 5 years by defrauding elderly pensioners. [103] The fraudsters hid their ownership in inflated shares using offshore shell companies based in Malta.

Freely accessible beneficial ownership registers – with open access being a precondition of this benefit – give investors an accessible tool they can use to perform due diligence checks on companies. This can help investors see who has beneficial ownership of a company, if this person has involvement in other companies, and how those companies have performed in the past.

Some have estimated that pump and dump schemes could cost UK investors between US$3 and US$10 billion a year. [104] Despite this rough estimate, however, there is likely not yet enough evidence of the baseline data for money lost to pump and dump schemes to support the qualitative measurement of BOT’s impact in this area.

2.3 Future approaches to measurement

2.3.1 Approaches to measuring reduced due diligence costs for businesses

Potential approach	Stated-preference / willingness to pay survey to measure the value of BOT for the private sector
	This approach would involve administering a survey to businesses to measure willingness to pay for BOT information. The methodology could reflect the approach carried out in the BEIS 2019 evaluation of Companies House data. [105]
Advantages	– This approach could be employed hypothetically and does not require a BOT regime to be implemented yet. – It would rely on credible industry-sourced estimates of benefits to industry. – The method could help to secure support from industry peak bodies.
Drawbacks	– Will not generate estimates of a direct, cash-releasing benefit. – A WTP approach might seem illogical considering BOT information should be provided for free (although introducing illustrative policy subscription fees can demonstrate a loss in willingness to pay and welfare benefits – see BEIS 2019 valuation). [106] – Requires careful design and buy-in from a significant sample of stakeholders to produce robust results.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – This approach has already been carried out and is relatively feasible for a researcher as it does not require a BOT regime to have been implemented. However, it would require investment for the careful design, administration and analysis of the survey.

2.3.2. Approaches to measuring reduced Know Your Customer costs for regulated financial entities

Potential approach	Stated preference or willingness to pay survey for regulated financial entities, such as banks
	This approach would involve administering a survey to industry officials about their willingness to pay for BOT information and how the availability of this information would affect the costs they currently pay carrying out KYC checks. The method could employ a methodology similar to that used in the BEIS 2019 evaluation of Companies House data and applying it to users in banks and other regulated entities. [107] Importantly, however, this approach is unlikely to demonstrate significant benefit, at least in the short term. During interviews, we heard scepticism about how useful BOT data is at present for KYC procedures, given concerns around data reliability on registers.
Advantages	– This approach could be employed hypothetically, and does not require an existing BOT regime to be implemented yet. – This approach would rely on credible industry-sourced estimates of benefits to industry. – The method could help to secure support from industry peak bodies.
Drawbacks	– Will not generate estimates of a direct, cash-releasing benefit. – This approach would be somewhat illogical considering BOT information should be provided for free. – Benefit unlikely to be felt at this stage because data reliability on registers is not compliant with AML requirements.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – This approach is relatively feasible for a researcher as it does not require a BOT regime to have been implemented. However, it would require investment for the careful design, administration and analysis of the survey.

2.3.3 Approaches to measuring increases in FDI

Potential approach	Correlational or causal study exploring the impact of BOT on Foreign Direct Investment
	Following a simpler, correlational approach, researchers could conduct a reasonably simple study on FDI before and after implementing BOT across jurisdictions, using FDI data from the years preceding and following an intervention. There is also scope for causal studies to be carried out to explore the relationship between BOT and FDI, using regression analysis or difference-in-differences techniques. The latter would involve plotting FDI figures over time for countries that have implemented BOT compared with those that have not, and comparing average differences.
Advantages	– FDI data should be readily available from international organisations, such as the OECD. [108]
Drawbacks	– In order to generate the most results, this approach would require several BOT regimes to be implemented first. – The impact of BOT on FDI may be too indirect a benefit to quantify. – Other factors are likely to be much more important in determining FDI flows, meaning findings from correlational studies in particular could be dismissed as spurious.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	1 For causal analysis – This approach would require that multiple BOT regimes be implemented first, and it would be a challenge to isolate the effects of BOT specifically on FDI flows. 2 For correlational analysis – This approach would be much easier to conduct, but is accompanied by the usual caveats around correlational studies, which do not interrogate in any detail the motivating factors change (which in the case of FDI could be multiple).

2.3.4 Approaches to measuring improved investor confidence

Potential approach	Measuring businesses confidence through causational or correlational studies
	For this approach, researchers would investigate business confidence using regression analysis or difference-in-differences techniques to compare outcomes in terms of business confidence for countries that have implemented BOT compared with those that have not. Researchers could also pursue the simpler approach of using a correlational study analysing business confidence before and after the implementation of BOT.
Advantages	– Business confidence data is readily available through indices such as the OECD’s business confidence index. [109] Some financial services companies also publish regular investor confidence indices, which could be used to support this approach. [110]
Drawbacks	– This approach would require at least one and possibly several BOT regimes to be implemented first. – Business confidence may be too indirect a benefit to quantify. – Other factors aside from BOT are likely to be much more important in determining business confidence, meaning findings could be perceived as spurious. – This method would not produce monetarily quantifiable estimations.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	1 For causal analysis – Researchers using this approach would face considerable challenges. At least one and possibly several BOT reforms would have to be implemented before this could be analysed. Business confidence may be too indirect a benefit to quantify and it would be difficult to isolate the effect of BOT on business confidence, which will likely be affected by multiple factors. 2 For correlational analysis – This approach would be much easier to conduct, but is accompanied by the usual caveats around correlational studies, which do not interrogate in any detail the motivating factors change (in the case of investor confidence, BOT is unlikely to be a key driver).

2.4 Potential audiences for measurement

Industry stakeholders are most likely to be receptive to the kind of estimations calculated by approaches in this area, be that businesses or regulated entities. Being able to quantitatively demonstrate the benefits is likely to be particularly useful when making the economic case for BOT, given that private sector stakeholders are set to bear a large portion of the costs of BOT through compliance, and are therefore also likely to be the most sceptical about reform. Economic quantification of impacts also lends itself to the culture of measuring Return On Investment (ROI) in the private sector, where emphasis falls on monetised benefits, as opposed to the qualitative identification of ‘public good’ benefits which might be more acceptable to some governments.

We heard from interviews that using data to build industry support for BOT would be extremely valuable for both governments and advocacy organisations when looking to advance BOT policymaking internationally. The fact that the UK government’s valuation of Companies House data focussed, in large part, on benefits to businesses, is also perhaps testament to the importance of economic impact assessment in this benefit area. Conducting similar willingness-to-pay / stated-preference approaches with business stakeholders in other jurisdictions has the potential to help to bolster arguments surrounding BOT's benefits for business.

More broadly, there is also scope for governments too to be interested in the more macroeconomic impacts of BOT, such as an increase in FDI or investor confidence. Quantitative exploration of both these benefits is largely unfeasible at present, although there is scope for relatively simple correlational studies to be undertaken, that might go some way towards quelling doubts about the ‘chilling effect’ which transparency has been said to have on FDI in a jurisdiction.

However, these approaches for measuring wider macroeconomic impacts have clear methodological limitations, in that FDI could be impacted by a number of factors aside from BOT, which correlational research cannot account for. Drawing on evidence from neighbouring financial transparency initiatives, which shows strong positive impacts on market performance, could be a good alternative to novel research here in the short-term.

3. Measuring impacts related to public procurement

3.1 Benefits identified in logic model

Reduced risk of government contracting corrupt or unfit providers

With the implementation of effective BOT policies, procurement authorities can do research on the beneficial owners of companies and their business history.
Governments should be better able to identify conflicts of interest or fraudulent providers, preventing these companies from winning a contract, and even participating in future tenders.
The use of beneficial ownership data in procurement, therefore, helps to promote fair competition. This should also lead to governments receiving better value for money and improved contract performance by the companies contracted.

3.2 Existing evidence regarding BOT as a tool for reducing corruption in the procurement sphere

A number of quantitative estimates already indicate the sizable cost of corruption in the procurement sphere. Whilst we found no work which looked to quantify the relationship between BOT and these losses, it is widely accepted that ending anonymous company’s participation in procurement processes will reduce the risk of corruption.

Procurement is a massive driver of the global economy. The World Bank estimated in 2018 that 12% of global GDP is spent following procurement regulation, approximately $11 trillion of that year’s $90 trillion global GDP. [111] However, public procurement can also be a vehicle of corruption and inefficiency, as governments risk contracting companies with conflicts of interest or firms unfit to provide the goods or services required.

Multiple studies have looked to quantify the impact of corruption on procurement, generating varying, yet sizable estimates of economic harm. In 2014, the European Commission estimated that procurement corruption costs member states around €120 billion per year, or 1% of EU GDP at the time. [112] Meanwhile, the OECD estimates that corruption adds up to 25% to the cost of public procurement globally, a significantly larger estimate than that of the European Commission. [113] The United Nations Office on Drugs and Crime has produced broader estimates, claiming that between 10 to 25% of contract value is lost to corruption worldwide. [114]

We found no evidence regarding the direct impact BOT could have on reducing the monetary losses tied to corruption. Nonetheless, it is widely accepted that by reducing the participation of anonymous companies in procurement processes, BOT is a tool which alongside open contracting data, can help to identify conflict of interest and promote fair competition. As such, many organisations have advocated for and enacted BOT reforms with the explicit purpose of reducing public procurement corruption. For instance, in 2015, more than a hundred organisations sent a letter to the World Bank urging them to require all legal entity bidders on procurements funded by the World Bank to disclose their beneficial ownership information. [115] The letter also asked the World Bank to publish this data openly. The World Bank now requires companies bidding on high value contracts to provide their beneficial ownership information. [116]

Similarly, the International Monetary Fund (IMF) has also made an explicit connection between BOT reforms and reducing corruption. They stipulated that jurisdictions receiving its emergency financing during the COVID-19 pandemic had to commit to “preventing conflicts of interest and corruption by publishing the beneficial ownership information of firms awarded procurement contracts”. [117]

Supporting this institutional logic, case studies make a compelling argument for the use of BOT in a procurement context. In a number of jurisdictions, effective BOT could have helped to prevent significant losses or risks incurred from contracting shell companies.

Numerous case studies across jurisdictions lay bare how anonymously-owned companies can be used successfully to facilitate corrupt public procurement deals, wasting procurement budgets. As just one illustrative example, in the United States, it has been estimated that the Pentagon has awarded hundreds, if not thousands of contracts to shell companies. [118]

In one high-profile case, the US Department of Defense awarded a contract to make safety gear for F-15 fighter jets to a US-based shell company which unbeknownst to government, was actually manufacturing parts in India. Upon delivery, it was discovered that these parts do not meet safety specifications, jeopardising the lives of service personnel. Used in a procurement context, effective BOT reforms could help to identify such risks, reducing budgetary wastage and the wider negative consequences of contracting under qualified suppliers, which are particularly stark in a national security context.

Whilst there is limited economic research exploring BOT’s impact on public procurement, there is evidence that other forms of financial transparency can have a positive impact on procurement by improving efficiency and reducing rates of corruption.

Fiscal transparency studies point to a positive relationship between transparency and procurement outcomes. Research has found that countries which rank higher on the Open Budget Index, which assesses the level of public access to information about how the central government raises and spends public money, have better public spending performance, measured using data from the World Economic Forum’s Global Competitiveness Report. [119] Elsewhere, survey-based research has demonstrated a connection between increased e-government transparency and reductions in perceptions of corruption in the EU. [120]

Whilst this work does not reveal anything about BOT’s impact upon procurement outcomes per se, it does provide methodological foundations to inform the measurement of BOT’s impact on similar indicators, as outlined in the table of future approaches to measurement below.

3.3 Future approaches to measurement

3.3.1 Approaches to measuring BOT’s impact on procurement outcomes

Potential approach	A correlational study to assess the impact of BOT upon public spending performance (mirroring approaches conducted in fiscal transparency research).
	Researchers could analyse data on better public spending performance (calculated using data from public datasets, such as those from the World Economic Forum’s Global Competitiveness Report) to assess the impact of BOT on procurement outcomes. [121] This would involve collecting data across jurisdictions which have / have not implemented BOT regimes to conduct a difference-in-differences comparison of changes to spending performance over time, with and without BOT policies in place.
Advantages	– This approach could emulate existing studies of the impact of financial transparency on procurement such as De Simone et al.’s study on the effect of fiscal transparency on the efficiency of government spending. [122]
Drawbacks	– In order to produce robust results, the approach would likely require several BOT regimes to be implemented first. – Data on how much public money is wasted on contracting corrupt firms is likely to be difficult to find. – Existing studies only obliquely capture the effects of financial transparency on procurement outcomes – this approach would not generate estimates of monetary value.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	1 For direct monetary estimate. Due to a lack of data on procurement wastage, it is not feasible to estimate the real monetary value of BOT reforms in this area. 3 For emulation of existing studies, such as De Simone et al. referenced. This approach would be feasible for an academic.

3.3.2 Approaches to measuring BOT’s impact on perceptions of corruption in a procurement setting

Potential approach	Survey of firms and governments on the effect of BOT on perceptions of government corruption in a public procurement context
	This approach would involve administering a survey to business leaders and procurement authorities to measure their perception of the effect of BOT on government corruption. It could draw on the methodology of similar financial transparency studies, such as research carried out by Bisogno and Cuadrado Ballesteros in 2021, which uses a survey based approach to demonstrate how public sector accounting reforms are tied to better governance around public spending. [123]
Advantages	– This approach could emulate existing studies on the impact of financial transparency on procurement. [124]
Drawbacks	– This approach would require at least one and possibly several BOT regimes to be implemented first – The survey would not produce a estimate value of the monetary impact of BOT on reducing corruption
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	1 For direct monetary estimate. This would be very difficult to achieve using a survey approach. 3 For emulation of existing studies. This type of study would be very feasible for an academic.

3.4 Potential audiences for measurement

Any work which demonstrates the impact of BOT on procurement outcomes is likely to be of most interest to procurement authorities and the wider open contracting community, including civil society and non-profit organisations. There is clearly already an appetite for qualitative impact evaluation in this field; in 2016 Open Contracting Partnership committed resources to designing an MEL framework in order to measure the impact of the Prozorro e-procurement system in Ukraine. [125] Measuring the impact of using open contracting and beneficial ownership data interoperably could be a further opportunity to collaborate on these issues, and raise further awareness of the importance of BOT in a procurement context.

Findings here are also likely to be of some interest to businesses participating in public tenders, who could be further persuaded of the need for BOT regulation if an approach demonstrated with confidence that it was being used to exclude shell companies from contracting processes, making contracts more accessible for legitimate entities.

However, crucially, neither of the approaches set forth in this section would lead to monetised estimations of direct benefit – in this case – the money that could be saved in a procurement context due to BOT. Such estimations, which arguably would have the most persuasive power, are unfeasible, given the lack of granular data across jurisdictions on money wasted as a result of contracting shell companies or firms owned by individuals with a conflict of interest. Therefore, whilst approaches from neighbouring policy areas could provide methodological inspiration for future work in this area, at present, case studies appear to be a more persuasive approach to demonstrating the impact of BOT in a procurement context.

4. Measuring impacts related to tax evasion

4.1 Benefits identified in logic model

Reduction in tax evasion

With open access to beneficial ownership data, governments, journalists, and civil society organisations should be better equipped to expose alleged cases of tax evasion through company networks.
BOT could also have a deterrent effect on tax evasion by making it more difficult for wealthy individuals to dodge tax obligations by placing assets in anonymous shell companies.

4.2 Existing evidence in favour of BOT as a tool for reducing tax evasion

We found no evidence of research which quantifies BOT’s impact upon tax evasion rates. However, there is strong evidence which quantifies the overall levels of tax evasion, and how they are influenced by other transparency reforms, which helps support the economic case for BOT as a means of combating tax evasion.

Quantified evidence on the global scale of tax evasion unanimously suggests that it is a sizable economic problem for governments. In 2017, a US National Bureau of Economic Research Paper estimated that approximately 10% of global GDP is held in shell companies in tax havens in order to evade taxation in other jurisdictions. [126] In terms of losses to governments, this represents a significant figure; it has been estimated that the US government loses US $100 billion in taxes annually due to multinational profit sharing. [127] Given US tax rates, this means that many hundreds of billions of profits in dollars are being shifted annually.

Despite the considerable scale of this problem, there is strong evidence that broader forms of financial transparency are effective in reducing rates of tax evasion. [128] A 2019 study which evaluated the impact of the US Foreign Account Tax Compliance Act showed that the legislation reduces annual flows through tax havens between $56.6 billion to $78 billion. [129] Improving country-by-country tax reporting has also been found to lead to reductions in tax evasion. [130] Increased financial transparency has also brought about wider economic benefits associated with a reduction in tax evasion, such as significant reductions in firm rents along with increases in market efficiency. [131] Drawing analogies between beneficial ownership transparency, and wider financial transparency reforms’ success in having impact in this area, supports the logical argument that a reduction in tax evasion should be a benefit of BOT reform.

Indeed, a number of resources have pointed to better taxation enforcement as a key benefit of BOT. The potential for ownership information to help enforce taxation is made particularly clear by case studies, particularly big exposés such as the Panama Papers scandal.

Resources on beneficial ownership transparency often have pointed to a reduction in tax evasion as a key benefit associated with BOT policies. The Tax Justice Network, in calling for beneficial ownership registration laws, claims that “identifying and registering beneficial owners is vital to making sure the wealthiest are held to the same level of transparency and accountability as everybody else”. [132] In line with this statement, most tax-orientated arguments for BOT place particular emphasis on the ways in which beneficial ownership data can support the administration and enforcement of wealth taxes in particular.

A 2020 report for the Wealth Tax Commission maps out the mechanical logic of the way in which BOT should lead to a reduction in tax evasion, particularly concerning wealth taxes. [133] The report sets out how the use of networks of private companies to manage assets is only feasible to those with substantial wealth. Without declaring their beneficial ownership, such individuals can spread wealth across these networks strategically to evade taxation, often using shell companies in different jurisdictions to avoid any association with the assets.

Leaks such as the Panama Papers, which revealed how high profile individuals had assets stored illegally in offshore accounts, demonstrate that routinely available company ownership data would empower law enforcement officials, journalists and oversight organisations to better expose potential cases of tax evasion. [134] In the UK, HMRC and HM Treasury estimated in 2019 that investigations resulting from the information leaked in the Panama Papers would yield over £190 million. [135] As this figure helps to illustrate, the order of economic benefits likely to be accessible through effective BOT reform is significant, especially considering that the Panama Papers leak covers only a minimal portion of what is estimated to be a much larger problem.

4.3 Future approaches to measurement

4.3.1 Approaches to measuring reductions in tax evasion

Potential approach	Survey of tax officials to estimate the expected impact of BOT on tax revenue
	A survey-based approach would recruit tax officials to understand their perspectives on how BOT would affect tax revenue. The survey could also ask for the officials’ perspectives on what percentage of additional tax revenue in a particular year is due to improved enforcement of tax laws resulting from BOT reforms.
Advantages	– This approach does not necessarily require an existing BOT regime to be implemented yet (although experts would arguably find it easier to make estimates if BOT reform has already occurred). – Tax officials should have a reasonable knowledge of how useful BOT is in helping them recover taxes that would otherwise be avoided or evaded. This could be a very compelling benefit item to treasuries in multiple countries. – The survey does not require the collection of multiple data points.
Drawbacks	– This approach would involve the subjective assessments of tax officials. – Tax officials may not be willing to make estimates, due to a lack of certainty. – Would require the buy-in of a reasonable sample size of experts.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – This approach could be carried out relatively easily, but would require time to develop and administer the survey and then to analyse the results. Pre survey consultation activities would need to be conducted to ensure experts would be willing to make estimates.

Potential approach	Correlative or causative studies using tax data to assess whether a BOT intervention has impacted tax evasion
	Correlational or causative studies analysing the impact of BOT on tax evasion rates would be the ideal form of evidence for demonstrating impact in this benefit area. Nonetheless, implementing these approaches is likely to be very challenging due to a lack of data availability. A correlational approach in this area would analyse the effects of BOT on reductions in tax evasion using tax data from before and after implementation in a jurisdiction. A causative study would likely use a difference in difference method to assess the changes to tax evasion rates across a range of jurisdictions after a BOT method had been implemented.
Advantages	– These approaches would produce the ideal form of evidence for measuring the effect of BOT on tax – since they would generate monetizable estimates of cash releasing benefits to governments.
Drawbacks	– This approach would require at least one and possibly several BOT regimes to be implemented first. – Tax evasion data is generally of low quality. Data on tax gaps (the difference between tax that is due, and tax that is actually paid) is likely to be more readily available, but accounts for other miscalculations in tax paid and other factors, so is a weak proxy for true rates of tax evasion. – The effect of BOT will depend on many other aspects of the tax enforcement institutions and international arbitrage opportunities, therefore, it may be difficult for these approaches to isolate the “true” impact of BOT across jurisdictions.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	2 – This approach might be more feasible as more countries implement BOT regimes, however the low quality of data on tax evasion and the difficulties of isolating the effect of BOT is likely to remain challenging in the long-term.

4.4 Potential audiences for measurement

Governments, and specifically treasuries, are likely to be most interested in any BOT benefits tied to tax, as the direct beneficiaries of higher compliance with taxation. More broadly, approaches which measure BOT’s influence on tax compliance and revenue also have potential to be of interest to electorates, given that tax is a prominent voter issue across jurisdictions.

Almost all experts identified tax as a key benefit of BOT, but we did also hear the suggestion that estimates of tax related benefits were likely to be the least persuasive tool for advocates, particularly amongst industry stakeholders. There was the suggestion that tax was a taboo topic which often “falls flat” with civil society and private sector audiences. [137]

Perhaps the most convincing story that can be told around BOT and tax evasion for civil society concerns the risks associated with not having effective BOT policies in place as opposed to observed benefits. Leaks such as the Panama, Paradise and Pandora Papers caused outrage internationally. Given the strong public reactions here, further articulating the case for beneficial ownership as a critical tool for uncovering (or even deterring) these kinds of high-profile scandals is arguably much more likely to convince civil society of the need for BOT reform than quantitative estimations.

5. Measuring impacts related to trust and economic growth

5.1 Benefits identified in logic model

Reduced perception of corruption

By increasing transparency of company ownership for citizens, improving governments’ ability to intercept alleged corruption cases, and deterring financial mismanagement, effective BOT should logically reduce the perceived level of corruption in a jurisdiction.

Increased citizen trust in government and businesses

Similarly, effective BOT should also increase trust in both public and private sector institutions, with the expectation that both will have better incentives to comply with regulatory requirements if a comprehensive, open register exists.

5.2 Existing evidence in favour of BOT as a tool in empowering democracy and trust

The final and most indirect set of benefits linked to BOT considered in this report concern democracy, trust and their wider economic effects. At present, there is a clear but only partially quantifiable link between BOT, corruption, and declining public trust in government.

As outlined earlier in this report, robust quantification of the influence of transparency reforms on corruption is challenging, due to the concealed nature of corrupt economic activity. However, strong institutional logic indicates that BOT should help to tackle corruption by making it harder for individuals to hide their ownership behind anonymous companies. It follows that BOT should also have a broader impact on levels of public trust in businesses and public institutions. With the implementation of BOT reform, individuals in both the public and private sector have strong incentives to report beneficial ownership information, and the state and civil society are equipped with better information available to more readily expose corruption.

Research shows that major events revealing political corruption lead to declines in trust and significant changes in voter behaviour. [138] [139] [140] This also applies to scandals involving BOT; the release of the Panama, Paradise, and Pandora Papers all triggered distrust in electorates where political actors were found to be harbouring assets in offshore accounts. [141] In Iceland, for example, a prime minister was forced out due to family connections with the Panama Papers. [142] It follows that a lack of beneficial ownership can equate to a lack of trust, and vice versa.

Measurements of trust are generally perceived as methodologically robust. The OECD’s Trust in Government Index [143] and Transparency International’s Corruption Perceptions Index [144] are particularly highly regarded in this field. Nannestad found in 2008 that despite concerns by social scientists about underspecification in questions about trust, the test-retest reliability for these studies is remarkably high, over 90%. [145] Measures of trust are comparable across countries, and they also correlate with actual behaviours and real effects.

Literature demonstrates that corruption and trust are not only entwined, but also together affect the performance of a given economy and the stability of its democracy. Trust, in particular, is one of the strongest determinants of economic growth in cross-country studies.

Multiple studies have analysed the relationship between trust and economic benefits. In 1997, Knack and Keefer published an early, formative study which found that “social capital” defined using indicators of trust and civic norms, was a determinant of economic growth. [146] Further research by Zak and Knack has demonstrated that low-trust environments cause economic harms to jurisdictions by reducing investment rates. [147] Other work which analyses these claims, finds that there is a strong link between social capital and economic growth [148] [149] [150] and that Zak and Knack’s conclusions are highly robust. [151] Corruption also plays an important role here; literature also demonstrates that higher levels of trust lead to reductions in corruption and subsequent increases in economic growth. [152]

There may be scope to locate financial transparency, and therefore BOT, as an important part of this story, as a determinant of corruption and trust, and in turn a determinant of the long-running performance of markets and democracies.

As this report has outlined, case studies already demonstrate that the absence of BOT can lead to lower levels of trust in government. A strong body of empirical research then demonstrates that a lack of trust in a country’s government leads to negative impacts on the functioning of democracy and the country’s economic growth.

Further empirical work could be done to strengthen the first part of the logical chain by quantifying the link between BOT and trust. Some of the approaches outlined below demonstrate how researchers could establish a stronger link between BOT reforms and indexes relating to trust. Were this to be achieved, it would technically be possible to convert the relationships between BOT and trust into broad GDP estimates using ratios established in previous research.

A study from 2011 by Ugur and Dasgupta, for example, surveyed 55 empirical studies and concluded that a 1% increase on Transparency International’s Corruption Perceptions Index is associated with a decrease in GDP per capita by 0.12 percentage points. [153] Other studies have arrived at similar estimates. [154] By demonstrating a stronger connection between BOT and indexes of trust in government, researchers could in principle estimate the impact of BOT on a country’s GDP.

5.3 Future approaches to measurement

5.3.1 Approaches to measuring increased citizen trust in government

Potential approach	Causal study seeking to estimate BOT’s relationship influence on levels of public trust in government
	This approach would involve analysing the OECD Trust in Government data across a combination of jurisdictions which have and have not implemented BOT reforms. Either a difference-in-differences, or a regression model could be developed to determine whether or not countries with BOT reform experienced higher levels of government trust according to OECD data.
Advantages	– The OECD Trust in Government index is a highly regarded data source, and would be a reliable indicator of trust.
Drawbacks	– This method would require at least one and possibly several BOT regimes to be implemented first. – Given the limitations of the OECD dataset, this method would be restricted to measuring impact across OECD countries. – Trust in government may be too indirect a benefit to quantify, especially on the short-run. – Factors other than BOT are likely to be much more important in determining trust in government. There is a strong link, however, between accounting transparency reforms and trust in government, as researchers have shown. – A major problem is that while financial transparency affects trust, it is the interaction between BOT and other aspects of the institutional structure that will be decisive in affecting trust in government so the impact of BOT cannot be genuinely quantified.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	1 – This type of investigation is more likely to be done further in the future looking more holistically at countries' transparency reforms rather than specifically at the impact of BOT.

5.3.2 Approaches to measuring reduction in perception of corruption

Potential approach	Statistical analysis of changes in Corruption Perceptions Index (CPI) linked to BOT, converted to an estimate of GDP growth.
	This approach would involve conducting a before-after statistical analysis of countries that have implemented BOT and the changes in their scores in Transparency International’s Corruption Perceptions Index (CPI). The approach would allow researchers to determine whether, and to what extent, countries implementing BOT have lower perceived corruption. If revealed, a connection between a reduction in the CPI as a result of BOT could then be converted into an estimate of GDP growth using ratios from previous research. [157]
Advantages	– Transparency International's Corruption Perceptions Index is an accessible data source, which allows comparison over time.
Drawbacks	– This method would require at least one and possibly several BOT regimes to be implemented first. – BOT is unlikely to have a detectable short-term effect on Transparency International’s index given the number of factors that affect perceived corruption. If an improvement in the index is detected this could be linked to expected growth in GDP, but it is probably impossible to robustly quantify this benefit in monetary terms.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	1 – This type of investigation is more likely to be conducted further in the future looking at how holistic transparency packages impact the CPI, rather than specifically at the impacts of BOT.

Potential approach	Novel survey or poll asking the public how much BOT would affect their perceptions of corruption and their trust in government.
	This approach would involve conducting a survey or poll of the public in different countries about their perceptions of BOT, and its impact upon trust in government. This would not be a correlational or causal study, but instead a point estimate using new statistics.
Advantages	– This approach does not require a BOT regime to be implemented yet. – A novel survey may be possibly persuasive in an advocacy context.
Drawbacks	– Like a news poll, the novel survey would have very low methodological robustness – this may not affect its persuasiveness, however. – The effect of BOT would be impossible to quantify in monetary terms using this approach. – The approach relies upon the general public having a good understanding of BOT, which might not be the case, given that this is a relatively nascent policy area which is not always well defined, even in national legislation.
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – This approach would be simple for researchers to carry out in the short term, with the caveats that it would be impossible to quantify the effect of BOT in monetary terms using this approach and that the survey would have low robustness.

5.3.3 Approaches to measuring both increased citizen trust in government and reduction in perception of corruption

Potential approach	Willingness to Pay (WTP) survey investigating the willingness of the public to pay for a BOT regime
	This approach would involve conducting a survey in one country or multiple countries investigating how much money the public would be willing to pay for a BOT regime. The method would be similar to WTP surveys conducted for other public goods, and could employ a contingent valuation approach, where individuals are asked about different hypothetical situations that could apply to an intervention. For example, participants could be asked to value BOT regimes that would lead to certain benefits, or certain BOT intervention design choices (e.g. open vs. closed register).
Advantages	– This approach does not require a BOT regime to be implemented yet. – It is a relatively simple study to carry out. – It provides an estimate of the monetary value of intangible benefits provided to the public.
Drawbacks	– Not a cashable benefit – Scenario misspecification: asking people what they would pay for BOT could be seen as somewhat poor public messaging given that advocates of BOT are campaigning for free-to-access registers
Feasibility (ranked from 1 – unfeasible at present, to 3 – could be carried out in short term)	3 – This study would be relatively simple for researchers to carry out, however, the caveats outlined above associated with WTP studies will apply.

5.4 Potential audiences for measurement

BOT’s impact in this area is unlikely to be a major direct driver of policy reform. Historically, democracies have generally addressed corruption in response to scandals and on the basis of models of institutional functioning, not because of estimates of the causal effects of a policy change on corruption and GDP.

Moreover the link between beneficial ownership and economic growth (via trust) is somewhat roundabout, to the extent that estimations connecting BOT to GDP are unlikely to be convincing as a leading argument. One interviewee in particular was sceptical about benefits arising from trust, characterising them as “nebulous”, and unlikely to persuade governments or businesses concerned about the costs of reform. [158]

Nonetheless, this class of benefits does have the potential to be a significant indirect driver of support for BOT interventions, as a motivator of effort from a wider community of civil society actors interested in the health of democracy and the market system. Case studies strongly imply a connection between BOT and trust in public institutions, and whilst quantifying the impact of trust on GDP might be a stretch too far, novel empirical research to better establish a link between BOT and trust would allow for an effective story to be told around BOT’s role in building functional democracy.

Footnotes

[45] See literature review for an overview of work which alludes to (but rarely measures) the economic benefits of BOT.

[46] European Commission. (2014). Report from the commission to the council and the European Parliament: EU anti-corruption report. https://eur-lex.europa.eu/resource.html?uri=cellar:058aecf0-d9b7-11e3-8cd4-01aa75ed71a1.0012.01/DOC_1&format=PDF

[47] Department for Business, Energy and Industrial Strategy (BEIS), Companies House. (2019). Valuing the User Benefits of Companies House Data, Policy Summary. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/833764/valuing-benefits-companies-house-data-policy-summary.pdf

[48] See literature review for a more in depth discussion of existing work which quantifies the value of BOT.

[49] Interview with BOT advocacy organisation, February 2022.

[50] See for example: Department for Business, Energy and Industrial Strategy (BEIS). (2014). Final Stage Impact Assessments to Part A of the Transparency and Trust Proposals (Companies Transparency). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/324712/bis-14-908a-final-impact-assessments-part-a-companies-transparency-and-trust.pdf

[51] See, for example, this paper which looks to measure the impact of an open contracting intervention, and cites a lack of pre-intervention data as a key obstacle: Kovalchuk, A., Kenny, C. and Snyder, M. (2019). Examining the Impact of E-Procurement in Ukraine. Center for Global Development. https://www.cgdev.org/publication/examining-impact-e-procurement-ukraine

[52] Interview with academic subject matter expert, January 2022; Interview with subject matter expert, March 2022.

[53] Open Ownership. Worldwide commitments and action map. Accessed January 2022. https://www.openownership.org/map/#map

[54] Department for Business, Innovation and Skills (BIS). (2014). Final Stage Impact Assessments to Part A of the Transparency and Trust Proposals (Companies Transparency). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/324712/bis-14-908a-final-impact-assessments-part-a-companies-transparency-and-trust.pdf

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

[56] Interview with subject matter experts, March 2022.

[57] Interview with BOT advocacy organisation, January 2022.

[58] Department for Business, Energy and Industrial Strategy (BEIS), Companies House. (2019). Valuing the User Benefits of Companies House Data, Policy Summary. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/833764/valuing-benefits-companies-house-data-policy-summary.pdf

[59] Interview with academic subject matter expert, January 2022; Interview with BOT advocacy organisation, January 2022.

[60] Interview with subject matter experts, March 2022.

[61] Ibid.

[62] HM Treasury. (2018). Guide to Developing the Programme Business Case. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/749085/Programme_Business_Case_2018.pdf

[63] For instance, the UK Government’s 2019 Valuation of Companies House data uses a willingness to pay method, given a lack of data availability, but concedes that this would not be a first choice approach from a methodological perspective, given that Companies House data is provided without a cost.

[64] HM Treasury business case guidance raises the question of whether it is always “practical and proportionate” to pursue an impact measurement approach for these same reasons; HM Treasury. (2018). Guide to Developing the Programme Business Case. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/749085/Programme_Business_Case_2018.pdf

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

[66] Nonetheless, in a discussion with BOT subject matter experts and economists in January 2022 we heard that any revenue generated here is likely to be minimal in comparison to other benefits.

[67] Financial Action Task Force (FATF). (2019). Best Practices on Beneficial Ownership for Legal Persons. https://www.fatf-gafi.org/publications/methodsandtrends/documents/best-practices-beneficial-ownership-legal-persons.html

[68] HM Treasury / Department of Trade and Industry (DTI). (2002). Regulatory Impact Analysis: Disclosure of Beneficial Ownership of Unlisted Companies. https://webarchive.nationalarchives.gov.uk/ukgwa/+/http:/www.hm-treasury.gov.uk/media/9/9/ownership_long.pdf

[69] Ibid.

[70] Department for Business, Energy and Industrial Strategy (BEIS), Companies House. (2019). Valuing the User Benefits of Companies House Data: Policy Summary. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/833764/valuing-benefits-companies-house-data-policy-summary.pdf

[71] Ibid.

[72] Walker, J. (1999). How big is global money laundering? Journal of Money Laundering Control. Volume 3, no.1. https://ag-pssg-sharedservices-ex.objectstore.gov.bc.ca/ag-pssg-cc-exh-prod-bkt-ex/327%20-%20How%20Big%20is%20Global%20Money%20Laundering%20-%20Journal%20of%20Money%20Laundering%20Control-Walker-1999.pdf

[73] Ferwerda, J., et. al. (2020). Estimating money laundering flows with a gravity model-based simulation. Scientific reports. Volume 10, no. 18552. https://www.nature.com/articles/s41598-020-75653-x

[74] World Bank. GDP (current US $). Accessed March 2022. https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?end=2020&name_desc=false&start=1960&view=chart

[75] Levi, M. (2020). Evaluating the Control of Money Laundering and Its Underlying Offences: the Search for Meaningful Data. Asian Journal of Criminology. Volume 15. https://doi.org/10.1007/s11417-020-09319-y

[76] Greenfield, V. and Paoli, L. (2013). A framework to assess the harms of crimes. The British Journal of Criminology. Volume 53, Issue 5. https://academic.oup.com/bjc/article-abstract/53/5/864/336249?login=false

[77] Unger, B. and Walker, J. (2009). Measuring Global Money Laundering: "The Walker Gravity Model". Review of Law and Economics. Volume 5, no. 2. https://www.degruyter.com/document/doi/10.2202/1555-5879.1418/html

[78] Financial Action Task Force (FATF). (2019). Best Practices on Beneficial Ownership for Legal Persons. https://www.fatf-gafi.org/publications/methodsandtrends/documents/best-practices-beneficial-ownership-legal-persons.html

[79] Official Journal of the European Union. (2015). Directive (EU) 2015/849 of the European Parliament and of the Council. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32015L0849&from=EN

[80] Interview with subject matter expert, March 2022.

[81] Home Office, BEIS and HM Treasury. (2022). Draft Economic Crime Bill. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1057822/DRAFT_Economic_Crime_Transparency_and_Enforcement_Bill.pdf

[82] Transparency International. (2022). Press release: Transparency measures must be fast-tracked to kick-start dirty money crackdown. https://www.transparency.org.uk/russia-sanctions-UK-latest-news-property-dirty-money-suspect-wealth

[83] HM Government. (2018). CONTEST: The United Kingdom’s Strategy for Countering Terrorism. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/716907/140618_CCS207_CCS0218929798-1_CONTEST_3.0_WEB.pdf

[84] BEIS. (2014). Transparency & Trust – Enhanced Transparency of Company Beneficial Ownership. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/434546/bis-15-320-enhanced-transparency-of-company-beneficial-ownership-enactment-impact-assessment.pdf

[85] See literature review for a more detailed discussion of the RIA’s methodological approach.

[86] Caveat emphasised in interview with subject matter expert, March 2022.

[87] Interview with subject matter experts. March 2022.

[88] Department for Business, Energy and Industrial Strategy (BEIS). (2019). Review of the Implementation of the PSC Register. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/822823/review-implementation-psc-register.pdf

[89] The Secretariat of the Global Forum on Transparency and Exchange of Information for Tax Purposes, Inter-American Development Bank. (2019). A Beneficial Ownership Implementation Toolkit. https://www.oecd.org/tax/transparency/beneficial-ownership-toolkit.pdf

[90] Interview with subject matter experts, March 2022.

[91] BEIS. (2014). Transparency & Trust – Enhanced Transparency of Company Beneficial Ownership. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/434546/bis-15-320-enhanced-transparency-of-company-beneficial-ownership-enactment-impact-assessment.pdf

[92] Lee, C. and Ng, D. (2009). Corruption and international valuation: does virtue pay? The Journal of Investing. Volume 18, no. 4. https://joi.pm-research.com/content/18/4/23.short

[93] Grundler, K. and Potrafke, N. (2019). Corruption and economic growth: new empirical evidence. European Journal of Political Economy. Volume 60, no. 101810. https://www.sciencedirect.com/science/article/abs/pii/S0176268019301156

[94] Wei, S. (2000). Local corruption and global capital flows. Brookings Papers on Economic Activity. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.631.7870&rep=rep1&type=pdf

[95] Gurria, A. (2012). OECD, an active partner of the G20 and B20 in the global fight against corruption. Organisation for Economic Co-operation and Development (OECD). https://www.oecd.org/corruption/oecdanactivepartneroftheg20andb20intheglobalfightagainstcorruption.htm

[96] Van der Does de Willebois, E., et al. (2011). The Puppet Masters How the Corrupt Use Legal Structures to Hide Stolen Assets and What to Do About It. The World Bank and United Nations Office on Drugs and Crime (UNODC). https://openknowledge.worldbank.org/bitstream/handle/10986/2363/9780821388945.pdf?sequence=6&isAllowed=y

[97] Nobel Prize. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1996. Accessed Monday 7 Mar 2022. https://www.nobelprize.org/prizes/economic-sciences/1996/summary/

[98] Nobel Prize. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2001. Accessed Monday 7 Mar 2022. https://www.nobelprize.org/prizes/economic-sciences/2001/summary/

[99] De Simone, L and Olbert, M. (2021). Real effects of private country-by-country disclosure. The Accounting Review (forthcoming). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398116

[100] Cicatiello, L., et. al. (2021). Assessing the impact of fiscal transparency on FDI inflows. Socio-Economic Planning Sciences. Volume 73, no. 100892. https://www.sciencedirect.com/science/article/abs/pii/S0038012119305142

[101] Drabek, Z. and Pane, W. (2002). The impact of transparency on foreign direct investment. Journal of Economic Integration. Volume 17, no. 4. https://www.jstor.org/stable/23000835

[102] Corporate Finance Institute. What is pump and dump? Accessed March 7 2022. https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/pump-and-dump/

[103] de Haldevang, M. and Rohrlich, J. (2019). How an undercover FBI sting busted an alleged multimillion-dollar stock fraud. Quartz. https://qz.com/1675811/how-an-undercover-fbi-sting-busted-a-multimillion-dollar-stock-fraud/

[104] Ibid.

[105] Department for Business, Energy and Industrial Strategy (BEIS), Companies House. (2019). Valuing the User Benefits of Companies House Data: Policy Summary. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/833764/valuing-benefits-companies-house-data-policy-summary.pdf

[106] Ibid.

[107] Ibid.

[108] Organisation for Economic Co-operation and Development (OECD). (2021). Foreign Direct Investment Statistics: Data, Analysis and Forecasts. https://www.oecd.org/investment/statistics.htm

[109] Organisation for Economic Co-operation and Development (OECD). Business Confidence Index (BCI). Last accessed March 2022. https://data.oecd.org/leadind/business-confidence-index-bci.htm

[110] State Street. Investor Confidence Index. Last accessed March 2022. https://www.statestreet.com/ideas/investor-confidence-index.html

[111] Bosio, E. and Djankov, S. (2020). How large is public procurement? The World Bank. https://blogs.worldbank.org/developmenttalk/how-large-public-procurement

[112] European Commission. (2014). Report from the commission to the council and the European Parliament: EU anti-corruption report. https://eur-lex.europa.eu/resource.html?uri=cellar:058aecf0-d9b7-11e3-8cd4-01aa75ed71a1.0012.01/DOC_1&format=PDF

[113] Gurria, A. (2012). OECD, an active partner of the G20 and B20 in the global fight against corruption. Organisation for Economic Co-operation and Development (OECD). https://www.oecd.org/corruption/oecdanactivepartneroftheg20andb20intheglobalfightagainstcorruption.htm

[114] United Nations Office on Drugs and Crime. (2013). Guidebook on anti-corruption in public procurement and the management of public finances. https://www.unodc.org/documents/corruption/Publications/2013/Guidebook_on_anti-corruption_in_public_procurement_and_the_management_of_public_finances.pdf

[115] Transparency International. (2015). World Bank should end corporate anonymity in bank-funded projects. https://www.transparency.org/en/press/world-bank-should-end-corporate-anonymity-in-bank-funded-procurements

[116] World Bank. (2018). Update on World Bank commitments following the UK anti-corruption summit in May 2016. https://www.worldbank.org/en/topic/governance/brief/update-on-world-bank-group-commitments-following-the-uk-anti-corruption-summit-may-2016

[117] International Monetary Fund (IMF). (2021). How the IMF is promoting transparent and accountable use of COVID-19 financial assistance. https://www.imf.org/en/About/Factsheets/Sheets/2020/04/30/how-imf-covid19-financial-help-is-used

[118] Voreacos, D. and Weinberg, N. (2020). How the Pentagon gets duped by contractors using shell companies. Los Angeles Times. https://www.latimes.com/business/story/2020-01-08/pentagon-shell-companies

[119] De Simone, E., et. al. (2019). The effect of fiscal transparency on government spending efficiency. Journal of Economic Studies. Volume 46, no. 7. https://www.emerald.com/insight/content/doi/10.1108/JES-03-2019-0123/full/html

[120] Faura-Martinez, U. and Cifuentes Faura, J. (2020). Does e-government promote transparency and the fight against corruption in the European Union? International Journal of Electronic Government Research. Volume 16, no. 4. https://www.igi-global.com/article/does-e-government-promote-transparency-and-the-fight-against-corruption-in-the-european-union/269392

[121] World Economic Forum. (2020). Global Competitiveness Report Special Edition 2020: How Countries are Performing on the Road to Recovery. https://www.weforum.org/reports/the-global-competitiveness-report-2020

[122] E.g. De Simone, E., et. al. (2019). The effect of fiscal transparency on government spending efficiency. Journal of Economic Studies. Volume 46, no. 7. https://www.emerald.com/insight/content/doi/10.1108/JES-03-2019-0123/full/html

[123] Cuadrado-Ballesteros, B. and Bisogno, M. (2020). Public sector accounting reforms and the quality of governance. Public Money & Management. https://www.tandfonline.com/doi/full/10.1080/09540962.2020.1724665?scroll=top&needAccess=true

[124] Ibid.

[125] Open Contracting Partnership. (2017). Learning Insights: Measuring results from open contracting in Ukraine. https://archive.open-contracting.org/2017/04/19/learning-insights-measuring-results-ukraine/

[126] Alstadsaeter, A., et. al. (2017). Who owns the wealth in tax havens? Macro evidence and implications for global inequality. National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w23805/w23805.pdf

[127] Clausing, K. A. (2020). Profit shifting before and after the tax cuts and jobs act. National Tax Journal. Volume 73, no. 4. https://www.journals.uchicago.edu/doi/abs/10.17310/ntj.2020.4.14

[128] Capasso, S., et. al. (2021). Fiscal transparency and tax ethics: does better information lead to greater compliance? Journal of Policy Modeling. Volume 43, no. 5. https://www.sciencedirect.com/science/article/abs/pii/S0161893820300910

[129] De Simone, L., et. al. (2020). Transparency and tax evasion: evidence from the Foreign Account Tax Compliance Act (FATCA). Journal of Accounting Research. Volume 58, no. 1. https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-679X.12293

[130] De Simone, L. and Olbert, M. (2019). Real effects of private country-by-country disclosure. The Accounting Review (forthcoming). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398116

[131] Johannesen, N. and Larsen, D. (2016). The power of financial transparency: an event study of country-by-country reporting standards. Economics Letters. Volume 145. https://www.sciencedirect.com/science/article/abs/pii/S0165176516301859

[132] Tax Justice Network. (2020). Beneficial ownership. https://taxjustice.net/topics/beneficial-ownership/

[133] Russell-Prywata, L. (2020). Beneficial ownership and wealth taxation. Wealth Tax Commission. https://www.wealthandpolicy.com/wp/BP124_BeneficialOwnership.pdf

[134] Ibid.

[135] Ibid.

[136] HM Revenue and Customs (HMRC). (2022). Measuring Tax Gaps 2021 Edition. https://www.gov.uk/government/statistics/measuring-tax-gaps/measuring-tax-gaps-2021-edition-tax-gap-estimates-for-2019-to-2020

[137] Interview with advocacy organisation. January 2022.

[138] Zimmer, T. (1979). The impact of Watergate on the public’s trust in people and confidence in mass media. Social Science Quarterly. Volume 59, no. 4. https://www.jstor.org/stable/42860478

[139] Welch, S. and Hibbing, J. (1997). The effects of charges of corruption on voting behavior in congressional elections, 1982-1990. The Journal of Politics. Volume 59, no. 1. https://www.journals.uchicago.edu/doi/abs/10.2307/2998224

[140] Peters, J. and Welch, S. (1980). The effects of charges of corruption on voting behavior in congressional elections. American Political Science Review. Volume 74, no. 3. https://www.cambridge.org/core/journals/american-political-science-review/article/abs/effects-of-charges-of-corruption-on-voting-behavior-in-congressional-elections/479882C6E1A49228DF01D7CF6FF7F1FA

[141] Martyn-Hemphil, R. (2017). Iceland goes to polls amid scandals, distrust and disgust. New York Times. https://www.nytimes.com/2017/10/28/world/europe/iceland-elections-gunnlaugsson.html

[142] Ibid.

[143] Organisation for Economic Co-operation and Development (OECD). (2021). Trust in Government. https://www.oecd.org/gov/trust-in-government.htm

[144] Transparency International. (2021). Corruption Perceptions Index. https://www.transparency.org/en/cpi/2021

[145] Nannestad, P. (2008). What have we learned about generalized trust, if anything? Annual Review of Political Science. Volume 11, no. 1. https://www.annualreviews.org/doi/abs/10.1146/annurev.polisci.11.060606.135412

[146] Knack, S. and Keefer, P. (1997). Does social capital have an economic payoff? A cross-country investigation. The Quarterly Journal of Economics. Volume 112, no. 4. https://academic.oup.com/qje/article-abstract/112/4/1251/1911732

[147] Zak, P. and Knack, S. (2001). Trust and growth. The Economic Journal. Volume 111, no. 470. https://onlinelibrary.wiley.com/doi/abs/10.1111/1468-0297.00609

[148] Whiteley, P. (2000). Economic growth and social capital. Political Studies. Volume 48, no. 3. https://journals.sagepub.com/doi/abs/10.1111/1467-9248.00269

[149] Horvath, R. (2012). Does trust promote growth? Journal of Comparative Economics. Volume 41, no. 3. https://www.sciencedirect.com/science/article/pii/S014759671200087X

[150] Dincer, O. and Ulsaner, E. (2009). Trust and growth. Public Choice. Volume 142, no. 59. https://link.springer.com/article/10.1007/s11127-009-9473-4

[151] Beugelsdijk, S., et. al. (2004). Trust and economic growth: a robustness analysis. Oxford Economic Papers. Volume 56, no. 1. https://academic.oup.com/oep/article-abstract/56/1/118/2472415

[152] Ulsaner, E. (2004). Trust and corruption. In The New Institutional Economics of Corruption. https://books.google.com.au/books?hl=en&lr=&id=JeB-AgAAQBAJ&oi=fnd&pg=PA76&dq=Uslaner+

[153] Ugur, M. and Dasgupta, N. (2011). Corruption and economic growth: a meta-analysis of the evidence on low-income countries and beyond. Munich Personal RePEc Archive. https://www.researchgate.net/publication/254443921_Corruption_and_economic_growth_A_meta-analysis_of_the_evidence_on_low-income_countries_and_beyond

[154] Swaleheen, M. (2011). Economic growth with endogenous corruption: an empirical study. Public Choice. Volume 146. https://www.researchgate.net/publication/225325767_Economic_Growth_with_Endogenous_Corruption_An_Empirical_Study; Dreher, A. and Herzfeld, T. (2005). The economic costs of corruption: a survey and new evidence. In De Luca F.N. (ed.) Economic Corruption: Detection, Costs and Prevention.https://econpapers.repec.org/paper/wpawuwppe/0506001.htm

[155] Organisation for Economic Co-operation and Development (OECD). (2021). Trust in Government. https://www.oecd.org/gov/trust-in-government.htm

[156] Cuadrado-Ballesteros, B. and Bisogno, M. (2021). Public sector accounting reforms and the quality of governance. Public Money & Management. https://www.tandfonline.com/doi/full/10.1080/09540962.2020.1724665?scroll=top&needAccess=true

[157] Ugur, M. and Dasgupta, N. (2011). Corruption and economic growth: a meta-analysis of the evidence on low-income countries and beyond. Munich Personal RePEc Archive. https://www.researchgate.net/publication/254443921_Corruption_and_economic_growth_A_meta-analysis_of_the_evidence_on_low-income_countries_and_beyond

[158] Interview with academic expert, March 2022.

Next page: Concluding recommendations

Contents