The Role of R&D and Innovation in Promoting Growth across Sectors, Firms and Regions

Project reference: SH73

Application deadline: 10 April 2023

How to apply

*Please note these projects are only available to Home rated applicants*

This studentship offers an outstanding opportunity to undertake a 4-year funded integrated PhD and MSc in Data Analytics and Society based at the Department of Economics of the University of Sheffield, working in collaboration with the Sheffield Methods Institute and the UK Government Department for Business, Energy and Industrial Strategy (BEIS).

Directly addressing BEIS policy priorities, the project will explore the role of R&D in promoting innovation and productivity growth across sectors, firms and regions. Applying machine learning to big data from web-scraping of companies’ websites, the project will investigate emerging UK sectors, such as life science, renewable energy, creative and digital sectors, which are not accurately identified via traditional industrial classification. The project will also analyse the role of public R&D funding in promoting regional diversification and the importance of ring-fenced regional R&D funding and innovation hubs to promote the levelling up, applying spatial econometric techniques to data on the location of research hubs, innovators and allocated funding.

The project offers an exciting opportunity to collaborate with the BEIS Advanced Analytics team, and access granular and experimental datasets. The PhD student will be part of an active and multicultural cohort of PhD students based in the Department of Economics working on topics related to economic geography, the spatial distribution of economic activities and local labour markets. The student will also be part of the Sheffield Urban, International Trade and Environmental Economics (SUITE) research group and benefit from access to the doctoral training in economics programme, PhD reading groups, and the annual departmental PhD conference (visit for more information).

This project would be well suited to a student with a background in economics, business economics, or economic geography. Solid data analysis skills and a strong interest in economic geography and the economics of innovation would be an advantage.