This project is a collaboration between the University of Edinburgh, Global Open Finance Centre of Excellence, and Fair By Design looking at developing a poverty premium measure.

Key Information
Deadline The deadline for this scholarship has now passed
Overseas or UK tuition fees plus £20,000 stipend awarded annually for up to 4 years
Contact Email Email Scholarship Support Team

Project Background

This collaborative project, between the University of Edinburgh, Global Open Finance Centre of Excellence, and Fair By Design, offers a unique and exciting opportunity to work with a range of stakeholders and data suppliers to develop an approach to measure poverty premium and address the inequalities that affect society’s most financially vulnerable individuals.

Poverty premium is the term used to describe the extra cost that households on low incomes incur when purchasing the same goods and services as households on higher incomes (‘the poor pay more’).

The data-driven approach will seek to identify and utilise appropriate data to develop and validate a measure and impact of the poverty premium on UK households and individuals. An objective data-driven approach to understanding the scale and impact of poverty premium will help inform appropriate action with implications for policy and practice.

Project Objectives:

  • To define and develop an appropriate data-driven approach (drawing on public and private sector data) to inform action required to redress the poverty premium.
  • To build a Scottish national (data) evidence base of those affected by the poverty premium to inform the creation of a UK wide rolling poverty premium measure and index.
  • To support data driven poverty premium research, examining the changing dynamics and social outcomes of poverty representativeness across Scotland and wider UK, and what this means for poverty measurement and promising alleviating interventions.
  • Create appropriate data evidence to establish whether the poverty premium affects certain domains of everyday life, demographics and geographies more than others and to what degree, and how poverty impacts on the welfare of households over time and what this means for current understandings of low-income dynamics.
  • To produce internal and external stakeholder research briefings on the methodological and policy implications of the poverty premium measure, index, and research findings.


Academic Requirement

Meet the PhD in Financial Technology programme academic requirements. This normally requires a minimum qualification (or expected qualification if you are a current Master's student) of above-average academic achievement, quantified as 70% or above overall at the Masters level, with a distinction level dissertation (or UK equivalent) in the subject of: finance, economics, informatics, physics, mathematics, engineering, or another relevant programme with significant quantitative elements.

Students with significant finance and tech industry experience or with relevant professional qualifications and that also have a minimum of a Bachelor's degree in the programmes stated above will be given due consideration on a case-by-case basis.

English Requirements

The most commonly approved certificate is an IELTS, for which the minimum accepted score is 7.0 overall with at least 6.0 in each section.

Project Requirements

Essential Criteria:

  • Experience working with data and data engineering
  • Strong mathematical or statistical background, with the ability to conduct modelling
  • Programming skills, with a very good knowledge of R and Python
  • Experience or ability to work with large, varied data sets
  • Experience working on a Linux based virtual desktop environment, Linux/Unix, command lines, shells, core Linux utilities (grep, make, awk, and so on) and git are relevant
  • Strong team player, communicator, and problem solver
  • Strong ethical and moral sensibilities
  • Willingness to work in a secure data environment with strong information governance and strict data handling, confidentiality requirements, and regulations

Desirable Criteria:

  • Experience with SQL databases would be an advantage
  • Experinece in any of these focus areas is an advantage:
    • Creating data driven evidence for policy making
    • Informing support provision and fair access to financial services
    • Algorithmic bias and reduction of social harms
  • Knowledge of creating data evidence for societal impact and socially-driven positive action
  • Knowledge of the creation of economic indices, particularly to progress financial inclusion and social progress
  • Understanding and experience of third-party data sharing and use of data for public interest
  • Experience working with multi-source industry derived data sets and understanding of related data governance
  • Experience and/or research interests in addressing poverty and inequality, social exclusion, and justice with expertise in quantitative data analysis and social statistics for related policy areas

How to Apply

Project applicants must apply online using our application form. When submitting, you should include a cover letter detailing why you are a good candidate for the particular project you would like to be considered for, and any other required documents.

If your application is successful, you will then be asked to submit additional details in order to be registered as a student.

The deadline to apply is 23:59 (GMT) on 24 September 2021.

Selection Process

Eligible applicants will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview by 8 October 2021.

Interviews will be scheduled during the week commencing 25 October 2021. Candidates will be notified of the outcome of their application in the week commencing 1 November 2021.

We anticipate the awarded candidate will join the programme in November 2021.


Professor Tina Harrison

Postgraduate Research Admissions Team

University of Edinburgh Business School
29 Buccleuch Place