MIP network member Damon Jones is an Assistant Professor at the University of Chicago's Harris School of Public Policy. He is also a Faculty Research Fellow at the National Bureau of Economic Research. Jones conducts research at the intersection of three fields within economics: public finance, household finance, and behavioral economics. His current research topics include income tax policy, social security, retirement and retirement savings, and the interaction between employer-provided benefits and labor market outcomes.

Describe your area of study and how it relates to current policy discussions surrounding inequality.

My research is at the intersection of public finance, household finance, and behavioral economics. For example, I study how the income tax and transfer system interacts with household savings and borrowing, in particular for low income households. I also study retirement savings decisions, both during working ages and near retirement. In addition, some of my work looks at how the rules of the Social Security affect older workers’ decision to work or retire. In all of these cases, the tax and benefit policies under consideration have the potential to redistribute income across households and thus have implications for inequality. Furthermore, in many cases, I examine whether behavioral biases have the potential to exacerbate inequality in these settings.

What areas in the study of inequality are most in need of new research?

Research that has the ability to link inequality at earlier ages, or even among one’s parents, to inequality later in life is a promising area. Previously, studies often relied on survey data linking households over time and across generations. A new frontier in this area involves leveraging large scale administrative data sets (such as tax data) to measure mobility during the course of one’s life and across generations within a household.

What advice do you have for emerging scholars in your field?

Think outside of the box when it comes to accessing your dream data set. You may think that linking one data set to another may be impossible, but early on in your career, you have enough time to invest in creating novel sources of data to shed new light on longstanding questions.