Following the success of the LEEPin2019 conference, and given the current situation, we have decided to move our seminar series online. The LEEPout series is designed as a platform for hosting a line-up of internationally recognised researchers to showcase their work at the cutting edge of environmental and resource economics. The LEEP Institute therefore invites you to the latest webinar in this series:
Jennifer Alix-Garcia – Remotely Incorrect? Accounting for Nonclassical Measurement Error in Satellite Data on Deforestation
Event details
Date: Thursday 9 June 2022
Time: 16:00-17:00 BST (17:00-18:00 Central Europe,11:00-12:00 Eastern; 08:00-09:00 Pacific)
Location: This webinar series is hosted online, a link with joining instructions will be sent within the confirmation of registration email
Biography
Jennifer Alix-Garcia is a microeconomist with interests in economic development and the environment. Her work includes analysis of land use change and policies to address it, the impacts of forced migration, and the use of satellite data in economic analysis. She has published peer-reviewed papers in key field journals of environmental economics, economic history, public economics, development economics, and economic geography, as well as in general interest economics journals and major science outlets. She received a Ph.D. from UC Berkeley in 2005, and has since worked at the University of Montana, the University of San Francisco, and the University of Wisconsin-Madison. She is currently a full professor and department head of the Department of Applied Economics at Oregon State University.
Abstract
Research relying on remotely sensed data on land use and deforestation has exploded in recent years. While satellite-based measures have clear advantages in terms of coverage, the presence of measurement error within these products is sometimes overlooked. Here, we detail the econometric implications of these errors when analyzing the determinants of binary measures of deforestation. We then discuss estimators that exploit knowledge of the remote sensing process to obtain consistent estimates. Finally, we assess our estimators via simulation and an impact evaluation of a conservation program in Mexico. We find that both geography and characteristics of the raw data can lead to systematic under-reporting of deforestation. However, accounting for these sources of error, which are common across many satellite-based metrics, can limit the bias from misclassification.
