Paul Marmora


+1(603) 641-7592



2015 – Ph.D., Temple University, Economics
2011 – M.A., Temple University, Economics
2009 – B.S., The College of New Jersey, Economics

I joined the Department of Economics and Business at Saint Anselm College in the fall of 2017. I teach courses in macroeconomics, finance, and econometrics. After graduating from The College of New Jersey with an undergraduate economics degree in 2009, I decided to pursue my graduate studies at Temple University, where I obtained a masters degree in 2011 and Ph.D. in 2015.

  • Teaching

    The guiding principle I use when teaching is to always emphasize the underlying intuition behind a concept, no matter how simple or abstract the subject may be. This not only provides students with a deeper level of insight and helps keep them engaged, but also makes technical aspects of the material more accessible without having to sacrifice mathematical rigor in the process.

    In addition to building basic intuition, I also find that encouraging students to voice their opinions and ask questions plays a crucial role in their learning experience. Therefore, I try to promote a classroom atmosphere that is conducive to discussion through a combination of i) expressing enthusiasm for the subject matter, ii) taking a conversational approach to lecturing, iii) not being overly critical when a question is answered incorrectly, and iv) being receptive to alternative viewpoints. These practices make students feel comfortable to discuss ideas and pose questions, which in turn enhances their ability to digest the material.

  • Research

    My research is primarily concerned with the production and diffusion of information in financial markets. A common assumption in economics and finance models is that agents are endowed with all relevant information, something that is not only at odds with actual experience, but is also inconsistent with the large amount of time and resources that individuals devote to knowledge-based services like financial consulting and economic forecasting. My research aims to fill that gap by exploring models in which rational agents decide what to learn about and how much to learn, subject to a cost. By understanding the circumstances under which individuals are willing to incur these learning costs, one can account for a broader range of observed phenomena while still maintaining a tractable framework.