PhD opportunities

Find a PhD opportunity that aligns with your interests and career ambitions.

We want to ensure that your time spent with us is as rewarding as possible. To allow you to explore your various options, here is a list of some of our currently available PhD opportunities.

We do, however, offer many more options, so please contact us for further information. You can propose your own project that aligns with our research. Find out more about how to apply for a PhD, and identify and contact a supervisor.

About the project: A vast literature has studied the determinants of gender pay gaps (see for reviews Altonji and Blank, 1999; Weichselbaumer and Winter-Ebmer, 2005; Blau and Kahn, 2017). Explanations for the labour market differences between men and women are typically grouped into three broad categories: productivity, preferences and discrimination, which are all interrelated (Altonji and Blank, 1999).

However, with the diminishing of gender gaps in the majority of developed countries, the importance and focus on explanations from the first category, especially human capital-based ones, has lessened. Nonetheless, pay gaps persist and are pervasive. More recent work has looked to gender differences in preferences and psychological attributes, and how these impact on productivity, choices and beliefs (see for reviews Croson and Gneezy, 2009; Bertrand, 2011; Azmat and Petrongolo, 2014).

The role of firms, in particular where men and women work, cuts across across these sets of explanations. Early work found that women in the US were more likely to work for lower wage firms than men, and vice versa regarding higher wage firms (Blau, 1977; Groshen, 1991; Bayard et al., 2003). More recent studies have found that low wage growth within an establishment for women plays a bigger role in the US gender pay gap than how women are (not) sorted into higher wage firms (Goldin et al., 2017; Barth et al., 2017).

More widely and in several countries, studies have begun to document the importance of which firms men and women work for in accounting for the level and trends of the gender pay gap (e.g. US: Sorkin, 2017; Portugal: Card et al., 2016, Cardoso et al., 2016; Germany: Bruns, 2018; France: Coudin et al., 2018; Denmark: Gallen et al., 2017; UK: Jewell et al., 2018).

Even as it becomes clearer that firms have an important role to playing in closing the gender pay gap throughout the earnings distribution, there is a significant evidence gap in understanding what particular firm-based policies and management practices work for both the individual firm and gender equality. Filling this evidence gap will allow advice to be given on effective future policy making in this area.

This project will involve working with secure access sources of UK data. Due to the nature of the datasets and access requirements, distance learning is not possible. The PhD student would benefit from working as a team n this project with at least 2 established academics in the Department of Economics at the University of Reading, one of whom would be the PhD supervisor. They can expect to co-author research outputs (publications) with these academics, which would contribute to some part to their PhD thesis. In addition, they would develop their own independent research agenda, advised by their supervision team and building on what they learn from collaboration with on the project. This would contribute to the remainder of their PhD thesis.

Pre-requisites: Ideally the PhD student would have experience of handling large datasets, statistical software such as SAS, SPSS, Stata or R, and have studied MSc level topics in microeconometrics and labour economics.

Funding note: Applicants should be able to self-fund. Once an offer is made, some bespoke support may be given with regards applying for external sources of funding to cover some costs.

About the project: In Functional data analysis (FDA), the variable of interest can be naturally viewed as a smooth curve or function, rather than scalars in univariate analysis or vectors in multivariate analysis. The field has witnessed rapid development over the last two decades. While the central ideas and methods of FDA has achieved a certain mature level, its applications in various subjects is still in an accelerating speed.

Among them, finance is one of fields that are largely benefited from the widely use of tools from FDA. In finance, some of the prominent examples that can be naturally viewed as curves include: intraday price curves (Kokoszka et al, 2015), term structure of interest rates (Barsley, 2017), forward curves of commodity futures (Horváth et al, 2019), and price signatures (Oomen, 2019).

The nature of this PhD project is employing newly developed tools in FDA to produce new insights for financial study, which cannot be revealed from the conventional methods. Thus, your first and second chapters will mainly be applied work. It is likely that you may find some limitations in the current toolkit of FDA for some specific finance problems. Then, the third chapter can develop a new method in FDA, devoting to expand the applicability of FDA in finance.

References: Bardsley, P., Horváth, L., Kokoszka, P., & Young, G. (2017). Change point tests in functional factor models with application to yield curves. The Econometrics Journal, 20(1), 86-117. Horváth, L., & Kokoszka, P. (2012). Inference for functional data with applications (Vol. 200). Springer Science & Business Media. Horváth, L., Liu, Z., Rice, G., & Wang, S. (2019). A functional time series analysis of forward curves derived from commodity futures. International Journal of Forecasting. Kokoszka, P., Miao, H., & Zhang, X. (2015). Functional dynamic factor model for Intraday price curves. Journal of Financial Econometrics, 13(2), 456-477. Kokoszka, P., & Reimherr, M. (2017). Introduction to functional data analysis. CRC Press. Oomen, R. (2019). Price signatures. Quantitative Finance, 19(5), 733-761. Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis. Springer.

Pre-requisites: a solid background from mathematics or statistics with knowledge in finance; proficiency in using Matlab, R, and Python; a good understanding of major textbooks in FDA, including Ramsay and Silverman (2005), Horváth and Kokoszka (2012), Kokoszka and Reimherr (2017).

Funding note: Applicants should be able to self-fund. Once an offer is made, some bespoke support may be given with regards applying for external sources of funding to cover some costs.