Elena Stacy

uh-LAY-nuh STAY-see

she/her/hers

About

Hi there! I am a PhD student at the University of California, Berkeley in the Agricultural and Resource Economics Department. I am an environmental and development economist particularly interested in work that uses remote sensing data to investigate issues at the intersection of these two fields. See my CV here

Contact

Email: estacy@berkeley.edu 

Twitter: @ElenaEcon 

LinkedInlinkedin.com/in/elena-stacy/

Office

University of California, Berkeley

Department of Agricultural and Resource Economics

Giannini Hall 

Berkeley, CA 94720



Research

"The Gendered Impact of Floods on Labor and Time Use in India". Working Paper.

Abstract: This study investigates the time-varying impact of floods on women’s labor supply using time use and labor measures, and high frequency satellite imagery data. I exploit town-level variation in flooding using a staggered difference-in-differences design to examine this issue. Due to high rates of cloud cover during monsoon season, I use share of clear days flooded as a measure of flood intensity, and supplement the analysis with random forest flood predictions using rainfall data. My findings reveal that both women and men experience reduced mobility following a flood. However, while women reallocate their time toward more household work, men shift from outdoor to indoor leisure. Effects are not persistent in the long-run, dissipating within several months.  


"Measuring time use in rural India: Design and validation of a low-cost survey module" (with Erica Field, Rohini Pande, Natalia Rigol, Simone Schaner, and Charity Troyer Moore). Journal of Development Economics. 2023.

Abstract: Time use data facilitate understanding of labor supply, especially for women who often undertake unpaid care and home production. Although assisted diary-based time use surveys are suitable for low-literacy populations, they are costly and rarely used. We create a low-cost, scalable alternative that captures contextually-determined broad time categories; here, allocations across market work, household labor, and leisure. Using fewer categories and larger time intervals takes 33% less time than traditional modules. Field experiments show the module measures average time across the broader categories as well as the traditional approach, particularly for our target female population. The module can also capture multitasking for a specific category of interest. Its shortcomings are short duration activity capture and the need for careful category selection. The module’s brevity and low cost make it a viable method to use in household and labor force surveys, facilitating tracking of work and leisure patterns as economies develop.