Collecting and making use of beneficial ownership data

Header image: Photo by Markus Winkler on Unsplash

mySociety and SpendNetwork have been working on a project for the UK Government Digital Service (GDS) Global Digital Marketplace Programme and the Prosperity Fund Global Anti-Corruption programme, led by the Foreign & Commonwealth Office (FCO), around beneficial ownership in public procurement. This is one of a series of posts about that work

There are three steps to working with beneficial ownership data: collection, verification and analysis. These three areas interact – how and when data is collected affects how viable different methods of verification are, and both of these in turn affect what forms of analysis are possible.

While collection of beneficial ownership data does not have to be part of the procurement process, (for example, if there is already a national register) requirements for bidding or winning public contracts are good pressure points to require disclosure. The following diagram shows a bird’s eye view of how ownership data (green lines) might be collected by different government agencies as part of the corporate lifecycle and the government contracting process (click for more detail).

Where ownership data fits in the company lifecycle and contracting process

In an ideal world, beneficial ownership information would be available and accurate at all of these points. But realistically, choices must be made over when and where to introduce beneficial ownership data collection and how to resource verification. Such choices will have an effect on the scale and timeliness of the data collected, however, as we can explore with the following diagram:

Potential ownership collection points

For instance, if a goal is to check for bidding cartels in the process of judging the bid, this information can be collected at any point: when companies are formed; when they register as a supplier; or when they make a bid. If companies submit multiple bids, it reduces duplication if information is collected sooner. However, this also increases the potential time between submission and analysis, and so requires an update process to avoid information becoming out of date.

Collecting at different points also makes a difference to the size of the database. Each successive capture point is collecting a smaller sample of organisations. This affects analysis in two ways: scope and accuracy. The more companies covered in the dataset, the more forms of analysis become possible. If you have only collected information on bid winners, you cannot investigate bidding cartels. If your collection only includes the beneficial ownership of registered suppliers, you cannot identify the ‘sibling’ entities (which are not in the direct ownership chain of a company, but are owned by the same owners)  in a corporate structure that might contain hidden debts.

While collecting data about more companies does not inherently make data more inaccurate, there is an indirect effect in that collecting more information raises the overall cost of verification.  Collecting information about all companies creates a much larger dataset to verify than just those who win contracts. If verification resources are not increased accordingly, the dataset will have a much larger scope, but be less accurate, and so the resulting analysis may be less useful.

This inaccuracy may have a higher effect on fraud/anti-corruption analysis than its overall incidence in the dataset. This is because while some errors will be accidental, some will have been deliberately introduced to disguise ownership. Analysis that is only possible with large amounts of data may not even really be possible if the database is not supported by a strong verification system.

To provide two different models, the UK’s Persons of Significant Control (PSC) register requires declarations as part of a company’s annual statement. With a few exceptions, it includes all companies registered in the UK. This data is submitted by the company without verification, as examining suspicious statements is a resource intensive problem across the entire jurisdiction (see the recent Global Witness report for a description of the verification problems in the UK PSC register).

The Slovakian Register of public sector partners (RPVS) requires beneficial ownership information be submitted only before a high value contract is awarded and so has a much narrower scope. However, there is a much stronger verification process, with third parties (generally legal offices) submitting the information and the process they used to reach it, with an in-country individual held legally responsible for the accuracy of the data. Methods and useful concepts in the verification process are explored in more detail in an OpenOwnership briefing.

Depending on the specifics, smaller databases (with verification) may lead to more basic—but more accurate—analysis. The calculation made in Slovakia for instance, is that there is less need for using data as part of the procurement process if the post-award checks are very good, because raising the chances of being caught raises the costs of cheating. On the other hand, this is then missing out on the prospect of identification of cartels. Disclosures may be accurate while the procurement process is still distorted.

Each expansion in the number of companies included does not expand the process in the same ways. While smaller registers may be cheaper to verify, new forms of analysis may open up with small increases in size. For instance, if the overall number of companies participating in bids is not much larger than those winning bids, the additional compliance costs may be negligible and allow the possibility of cartel analysis.

See all posts in this series.