Hi all,

I would like to share a dataset recently published in Electoral Studies. The ‘Regulation of Political Finance Indicator’ (RoPFI) is a large-N dataset which compares and classifies systems of political finance regulation. Using the International IDEA Political Finance Database as a foundation, the RoPFI is developed through a combined application of Multiple Correspondence Analysis and Model Based Clustering. It accounts for information on party and candidate regulations in a 180-nation sample and, thus, paints a truly global picture of political finance. To summarize, the dataset incorporates the following:

  • A continuous variable which ranks nations from most regulated to least regulated
  • A three level categorical variable to classify political finance systems as Unregulated, Partially Regulated, or Strongly Regulated
  • A statistical measurement of the uncertainty of the nation's RoPFI classification (0 = high certainty, 1 = low certainty)

To access the data or view more information on the RoPFI, you can visit my project website at www.ropfi.com. The open access Electoral Studies article is linked below:


Horncastle, W.C.R. (2022) ‘Model based clustering of political finance regimes: Developing the Regulation of Political Finance Indicator’, Electoral Studies, Vol.72. https://doi.org/10.1016/j.electstud.2022.102524

​​​​​​​I’m always looking to collaborate with academics from around the world and wondered if anyone would have any interest in working with this data. I’m open to all ideas and would love to hear any thoughts at William.horncastle1[at]beds.ac.uk

Best wishes,

Will Horncastle