Dr Meredith Paker

I am an Associate Professor of Economic and Social History, a Fellow of All Souls College, and an Associate Member of the Department of Economics. Before joining Oxford, I was an Assistant Professor of Economics at Grinnell College in the United States (Iowa). I also worked as an economist in the U.S. federal government's Federal Housing Finance Agency (FHFA), where I contributed to policy and research on the regulation of the US housing market. I received my PhD and Master's from the University of Oxford (Nuffield College) as a Marshall Scholar, and my undergraduate degree is from the University of Georgia. My research uses novel archival data, econometrics, and machine learning methods to address fundamental questions about historical labor market dynamics, inequality, and policy responses to economic crises.

Research Interests

My research lies at the intersection of economic history and labor economics. One strand of my work focuses on mass unemployment and labor market inequality, exploring how recessions shape economic disparities, how structural and technological change interact with cyclical downturns through labor reallocation across industries, and how policy responses mediate these effects. Much of this work is centered on the Great Depression and interwar period in Britain.

Another strand focuses on labor market structures and wages, analyzing how labor market organization and worker power have changed over time, how factors beyond day wages such as job quality and career trajectories shape workers’ experiences, and how we can better measure wages to understand historical living standards.

Research interests

• Historical labor markets, wages, and unemployment
• The Great Depression and other historical recessions
• Inequality in labor market outcomes by gender, region, race, and industry
• Structural change and labor reallocation across industries
• Monopsony and labor market power
• Long-run wages, job quality, lifetime earnings, and living standards
• Policy responses to economic crises
• Machine learning and predictive modeling