Can you introduce yourself and your background?
I am part of the Modelling for Policy team within the Turing’s public policy programme, where we develop methods in computational social science to tackle some of the most challenging problems that societies face today. I have a PhD in economics from George Mason University – my dissertation focused on the use of computational methods and models to approximate social systems, specifically labour markets, housing markets and institutional design.
What are you researching at the Turing?
My research can be categorised into two main areas. Firstly, I build models of housing markets that can accurately recreate both rents and house prices in order to understand the interrelated effects of policies like rent control, rezoning and building efforts on markets. This type of model becomes especially important as housing represents one of the key challenges facing many countries. And secondly, I study productivity from a ‘worker sorting’ perspective. Sorting is about how workers with different skills come together in the economy with businesses of various types. The resulting process shapes critical outcomes in the economy like productivity, opportunities for workers, and equality.
This research is why I was chosen to be a UKRI policy fellow in the Department for Business and Trade’s Analytical Data Science team to study the productivity puzzle in the UK, where I am currently seconded for 18 months working as an academic amongst policymakers. In this role, I not only contribute academic expertise by building data-driven agent-based models but also run training courses and advise on other simulation techniques that are being used in the department.
What do you hope is the impact of your research?
It will help us to understand if workers are finding the best jobs for their skills in the economy. This is crucial because finding the right job is essential to ensuring a worker has a prosperous and satisfying career path. Society also benefits when businesses employ workers with the right skills, as the businesses can adopt new technologies that can increase productivity and provide additional opportunities to other workers. This can both increase pay for workers and reduce inequality as they can not only access better jobs where they live, but equally important, join better teams unlocking the massive, positive impact of being among good coworkers, increasing their own productivity.
Productivity growth has long been considered a major factor of economic development. However, recently this growth has slowed. This declining pace of growth in the 21st century is one of the critical challenges faced by society. The impact of matching workers’ skills with the right jobs directly shapes the pace of economic growth. By building comprehensive, emergent models, we can expand our understanding of how workers and businesses come together, as well as provide policymakers with powerful tools to shape their approaches.
What future parts of your work are you most excited about?
I am most excited by the opportunity to expand the worker matching model we have developed because the true power of this framework lies in its scalability. It can seamlessly be implemented at local, regional, national and international levels. I hope we can build and incorporate all of these models together, which would bring us closer to the possibility of a truly global labour market model. With such a tool, we could help ensure a fairer global economy where all workers have access to good opportunities and improved lives.