Author
Listed:
- Mohammed, Animotu
- Shahtahmassebi, Golnaz
- Ribeiro, Haroldo V.
- Scriven, Peter
- Sutton, Jack
- Hanley, Quentin S.
Abstract
A variety of transparency initiatives have been introduced by governments to reduce corruption and allow citizens to independently evaluate the effectiveness and efficiency of spending. In 2010, the UK government mandated transparency for many expenditures exceeding £25,000. The resulting data is dispersed across a range of governmental organizations and presents an opportunity to understand expenditure at scale, interrogate organizational structures and develop transparency measures. Here, we focus on data from the top two layers of the National Health Service (NHS) within England, including NHS England (NHSE) and Integrated Care Boards (ICBs). As one of the largest government run healthcare organizations in the world and potentially the sixth largest employer globally, the NHS provides a distinctive case for studying healthcare delivery, contractor dynamics, and organizational self-organization. We find that limiting transparency to larger transactions conceals a substantial share of spending from scrutiny, including most transactions. The rank-frequency distributions of suppliers, expense types, and spending categories exhibit multiple scaling regimes similar to patterns observed in word frequency and urban scaling studies indicating these methodologies can be deployed to analyze financial transparency data at scale.
Suggested Citation
Mohammed, Animotu & Shahtahmassebi, Golnaz & Ribeiro, Haroldo V. & Scriven, Peter & Sutton, Jack & Hanley, Quentin S., 2026.
"Nonlinear rank scaling and hidden structure in NHS expenditure transparency data,"
Applied Mathematics and Computation, Elsevier, vol. 521(C).
Handle:
RePEc:eee:apmaco:v:521:y:2026:i:c:s0096300326000238
DOI: 10.1016/j.amc.2026.129971
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:521:y:2026:i:c:s0096300326000238. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.