Daniel Dench

Assistant Professor

Member Of:
  • School of Economics
Related Links:
Email Address: dench@gatech.edu

Overview

Daniel Dench joined Georgia Tech's School of Economics and the Health Economics & Analytics Lab (HEAL) in the Fall of 2020. Most recently his work has focused on how access to in-person care affects birth outcomes and the effect of e-cigarettes on birth outcomes. In addition, he works on issues related to tobacco and nicotine policy. He also runs field experiments related to student motivation and cheating. Finally, he studies issues related to school choice, such as how enrollment mechanisms affect segregation in large urban schools districts. Prior to joining Georgia Tech, Daniel completed his PhD in economics at The Graduate Center of the City University of New York. In addition to Georgia Tech, Daniel is a faculty affiliate with Notre Dame's Lab for Economic Opportunity and is a research economist for the National Bureau of Economic Research.

Education:
  • Ph.D., The Graduate Center of the City University of New York
  • B.A., Temple University
Research Fields:
  • Education Policy
  • Health Economics
Issues:
  • Education Policy
Courses Taught:
  • ECON-2105: Prin of Macroeconomics
  • ECON-6140: Econometrics I
  • ECON-6510: Health Economics

Interests

Issues:
  • Education Policy

Courses

  • ECON-2105: Prin of Macroeconomics
  • ECON-6140: Econometrics I
  • ECON-6510: Health Economics

Publications

Journal Articles

  • Advances in Causal Inference at the Intersection of Air Pollution and Health Outcomes
    In: Annual Review of Resource Economics [Peer Reviewed]
    Date: October 2023

    This article provides an overview of the recent economics literature analyzing the effect of air pollution on health outcomes. We review the common approaches to measuring and modeling air pollution exposures and the epidemiological and biological literature on health outcomes that undergird federal air regulations in the United States. The article contrasts the methods used in the epidemiology literature with the causal inference framework used in economics. In particular, we review the common sources of estimation bias in epidemiological approaches that the economics literature has sought to overcome with research designs that take advantage of natural experiments. We review new promising research designs for estimating concentration-response functions and identify areas for further research.

    View All Details