About

Elinor Benami is an Assistant Professor in the Department of Agricultural and Applied Economics at Virginia Tech. Her research lies at the intersection of environmental and development economics and policy, focusing on how digital data and emerging technologies can be used to improve disaster relief financing, enhance environmental compliance, and support climate-smart agricultural practices. She explores the use of satellite imagery, remote sensing, crop modeling, and novel data sources to help farmers increase resilience to weather risks and to inform public agencies for better environmental management. Her international research experience spans Brazil, Uganda, Kenya, and Indonesia, with upcoming work on irrigation adoption and drought risk financing in Morocco as a NASA-funded early career scientist. She uses both economics and land systems science to uncover how digital technologies such as mobile money, digital credit scoring, and earth observation can reshape rural markets for savings, credit, and insurance services. Her work aims to help farmers adapt to weather variability and to improve the effectiveness of public policies related to environmental and agricultural sustainability. She has held positions at Virginia Tech since August 2020, and her academic background includes a Ph.D. from Stanford University, a postdoctoral fellowship at the University of California Davis, and a B.A. from the University of North Carolina at Chapel Hill.

Research topics

• Computer Science
• Business
• Economics
• Political Science
• Artificial Intelligence
• Engineering
• Law
• Remote sensing
• Finance
• Financial system
• Psychology
• Risk analysis (engineering)
• Geography
• Environmental economics
• Public economics
• Economic growth

Selected publications

• Rain check: how data details influence payout determinations in a U.S. rainfall index insurance program

  Agricultural and Resource Economics Review · 2025-09-04 · 1 citations

  articleOpen access1st authorCorresponding

  Abstract An increasing number of disaster relief programs rely on weather data to trigger automated payouts. However, several factors can meaningfully affect payouts, including the choice of data set, its spatial resolution, and the historical reference period used to determine abnormal conditions to be indemnified. We investigate these issues for a subsidized rainfall-based insurance program in the U.S. using data averaged over 0.25° × 0.25° grids to trigger payouts. We simulate the program using 5x finer spatial resolution precipitation estimates and evaluate differences in payouts from the current design. Our analysis across the highest enrolling state (Texas) from 2012 to 2023 reveals that payout determinations would differ in 13% of cases, with payout amounts ranging from 46 to 83% of those calculated using the original data. This potentially reduces payouts by tens of millions annually, assuming unchanged premiums. We then discuss likely factors contributing to payout differences, including intra-grid variation, reference periods used, and varying precipitation distributions. Finally, to address basis risk concerns, we propose ways to use these results to identify where mismatches may lurk, in turn informing strategic sampling campaigns or alternative designs that could enhance the value of insurance and protect producers from downside risks of poor weather conditions.

  PublisherOA PDFDOI

• Seeding Change: Growing Insights From Four Programs to Support Climate‐Resilient Soil and Water Conservation in US Agriculture

  Agricultural Economics · 2025-04-23 · 2 citations

  articleOpen access1st authorCorresponding

  ABSTRACT The United States authorized unprecedented investments in agri‐environmental programs in 2022, dedicating over $19 billion to soil and water conservation practices with climate mitigation and adaptation benefits. We examine historical funding patterns, new funding allocations, and evaluation approaches for these programs. Our analysis reveals four key findings: (1) Nearly 40% of prior conservation funding has supported climate‐beneficial practices, with increasing shares reflecting growing producer demand; (2) Although enrollment of historically underserved producers (HUPs) has increased, variation across programs and higher contract non‐completion rates among this group suggests enhanced pre‐ and post‐enrollment support services could be valuable; (3) A shift toward partnership‐style programs facilitate locally‐tailored agreements and market linkages, potentially broadening producer participation while enabling more durable incentives for sustained practice adoption; (4) current evaluation approaches primarily focus on implementation metrics paired with biophysical modeling and could be strengthened through rigorous impact evaluation design. Promising techniques include conducting randomized experiments and integrating geospatial data with program records to assess the impacts on producer behavior as well as program outcomes over time and space. Such approaches can build evidence for strategic conservation finance and de‐risk future investments for other types of financial services—accelerating transformation toward sustainable agri‐food systems in the United States and beyond.

  PublisherOA PDFDOI

• Integrating Weather and Land Cover Data into Geospatial Impact Evaluations

  ArXiv.org · 2025-09-25

  preprintOpen access1st authorCorresponding

  Integrating gridded weather and earth observation data into impact evaluations holds great promise. It allows researchers to capture environmental context, external shocks, and even to measure outcomes (e.g., land cover change, agricultural production) that surveys might miss due to spatial or temporal data collection constraints. However, with great power comes great responsibility: the increasing ease of extracting time series from these datasets belies potentially complex geospatial and measurement issues that can affect the magnitude, direction, as well as interpretation of impact evaluation estimates. This chapter highlights several of the most common issues while providing resources to help guide researchers to thoughtfully use (and avoid misuse) of weather, vegetation, land cover, and extreme event data in the context of geospatial impact evaluation.

  PublisherOA PDFDOI

• Sensitivity to Data Choice for Index‐Based Flood Insurance

  Earth s Future · 2025-09-01 · 2 citations

  articleOpen access

  Abstract Despite increasing adoption of earth observations data to inform disaster response and recovery, deciding which measurements to use—and how—remains an open question. An increasing number of flood insurance programs have been using observable proxies—or indices—to activate payouts. However, convincing evaluation of important design features, including choice of index data, are lacking. This study investigates five potential flood data sets at national and regional scales in a simulated index‐based insurance program in Bangladesh: gridded precipitation, river‐height and modeled inundation from the national flood agency, and two satellite data sets of surface‐water‐extent (one state‐of‐practice, the other state‐of‐the‐art). We demonstrate that data choice determines the accuracy and timeliness of indexed payouts, as well as the uncertainty associated with their likelihood, which influences program costs. For example, while river‐height and satellite water‐extent indices activated payouts during the two worst floods in the 20‐year study period (2004 and 2007), the precipitation‐index activated for just one of them. Furthermore, our state‐of‐the‐art satellite index activated on average 1 week earlier and with 21% lower uncertainty than the satellite‐index used in practice. We propose that practitioners leverage the divergence‐of‐evidence among multiple data sets to identify regions where there is lower confidence in making accurate and timely payouts, which can help inform additional programming such as back‐up payout mechanisms. Beyond insights for practitioners leveraging insurance to protect Bangladeshi communities threatened by extreme monsoon floods, this work offers techniques to assess the sensitivity of indexed programs to different data and scales in other flood‐prone regions.

  PublisherOA PDFDOI

• Bridging Science and Practice to En(in)sure Resilience in a Changing Climate

  Journal of catastrophe risk and resilience. · 2024-02-06 · 2 citations

  article
  PublisherDOI

• How and Where Do Financial Incentives Promote Adoption of Climate Smart Agricultural Practices?

  AEA Randomized Controlled Trials · 2024-07-30

  dataset
  PublisherDOI

• Author Correction: Uniting remote sensing, crop modelling and economics for agricultural risk management

  Nature Reviews Earth & Environment · 2024-11-04 · 1 citations

  reviewOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Sub-national scale mapping of individual olive trees integrating Earth observation and deep learning

  ISPRS Journal of Photogrammetry and Remote Sensing · 2024-08-19 · 10 citations

  article
  PublisherDOI

• How and Where Do Financial Incentives Promote Adoption of Climate Smart Agricultural Practices?

  AEA Randomized Controlled Trials · 2024-07-30

  dataset
  PublisherDOI

• Drop a Line, Submit on Time? Randomized Tailored Reminders Improve Pollution Reporting Timeliness

  SSRN Electronic Journal · 2023-01-01 · 3 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Zhenong Jin

  University of Minnesota

  4 shared

• Daniel E. Ho

  3 shared

• Andrew Nourafshan

  Duke University

  3 shared

• Michaela Dolk

  3 shared

• Michael R. Carter

  3 shared

• Colby K. Fisher

  3 shared

• Andrew Hobbs

  3 shared

• Sara Lawrence

  Thompson Rivers University

  3 shared

Labs

• Department of Agricultural and Applied EconomicsPI

Education

• PhD, Interdisciplinary Program in Environment & Resources

  Stanford University

  2018

• Bachelors, Economics

  University of North Carolina System

  2010

Awards & honors

• VT Early Career Scholarly Impact Award Nominee 2025
• Association of Environmental and Resource Economists (AERE)…