I apply statistical and econometric methods to measure economic variables that are important for modelling and policy analysis, particularly those that have proven difficult to quantify. My projects include:
- "Pay-for-performance incentives and individual creativity: Experimental evidence" (Job Market Paper). I use nonlinear optimisation methods and graph matching algorithms to analyse the direct and indirect effects of pay-for-performance incentives on individual creativity, in multi-goal settings.
- “Price inattention: A revealed preference characterisation” (R&R, European Economic Review). I develop linear programming methods that can measure an individual’s degree of inattention to product prices using data on supermarket purchases.
- “Characterising green employment: The impacts of ‘greening’ on workforce composition” (2018, Energy Economics). I use network analysis to quantify the difficulty of job transitions from non-green to green occupations.
I have 4 years' experience teaching undergraduate courses (modules) in microeconomics and econometrics, and 2 years' experience teaching and supervising graduate students in Oxford's MPhil Economics program. I also have extensive experience with designing curricula, short courses, and teaching resources for undergraduate and postgraduate students. In 2020 I received a Teaching Excellence Award in recognition of my contributions to teaching and learning, including efforts to develop an inclusive economics curriculum at Oxford.
I am also a staff economist at CORE Econ (www.core-econ.org), an open-access platform for economics teaching used by over 160 universities worldwide. My contributions include:
- Doing Economics, an open-access eBook of empirical projects that enable users to investigate important policy issues such as climate change and inequality, using publicly available data and easily-available software (R and Excel).
- Economy, Society, and Public Policy, a textbook aimed at teaching economic concepts and data analysis skills to non-economics students, published in 2019 by Oxford University Press.
Characterising green employment: The impacts of 'greening' on workforce composition
Skills, Occupational choice, Green employment, Green economy
Characterising green employment: The impacts of ‘greening’ on workforce composition
This paper estimates the share of jobs in the US that would benefit from a transition to the green economy, and presents different measures for the ease with which workers are likely to be able to move from non-green to green jobs. Using the US O*NET database and its definition of green jobs, 19.4% of US workers could currently be part of the green economy in a broad sense, although a large proportion of green employment would be ‘indirectly’ green, comprising existing jobs that are expected to be in high demand due to greening, but do not require significant changes in tasks, skills, or knowledge. Analysis of task content also shows that green jobs vary in ‘greenness', with very few jobs only consisting of green tasks, suggesting that the term ‘green’ should be considered a continuum rather than a binary characteristic. While it is easier to transition to indirectly green rather than directly green jobs, greening is likely to involve transitions on a similar scale and scope of existing job transitions. Non-green jobs generally appear to differ from their green counterparts in only a few skill-specific aspects, suggesting that most re-training can happen on-the-job. Network analysis shows that the green economy offers a large potential for short-run growth if job transitions are strategically managed.
J24, J21, O33, J62, O51, Skills, Occupational choice, Green employment, Green economy