Productivity and Spatial Economic Disparities in the UK
Project reference: SH72
Application deadline: 10 April 2023
*Please note these projects are only available to Home rated applicants*
The project will study the relationship between productivity and spatial economic disparities in the UK, investigating whether the sorting of workers is becoming more concentrated, using newly micro-level census data to measure changes over time in the location of work, the residential decisions of workers, and how this spatial sorting contributes to regional differences in productivity. The project will also research the role of agglomeration externalities and place-based Net Zero transitions policies. The project offers a unique opportunity to work with an interdisciplinary supervision team and a major government department to inform policy making around key government initiatives.
The Local Growth Analysis team at DLUHC produces analysis and briefings on spatial economic policy, and will provide support for the PhD student regarding the quality and robustness of the analysis, the relevance to policy, and the use of novel data and techniques. They will also facilitate contact with Government analysts (including the Spatial Data Unit) and policy leads, providing opportunities for the PhD student to learn about government policy and analysis, also through the possibility of an internship at DLUHC.
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. (Click here for more information).
This project would be well suited to a student with a background in economics, regional studies, or economic geography. Solid data analysis skills and a strong interest in economic geography, urban economics and labour would be an advantage.