Economic development and food security
Estimating the Impact of Weather on Agriculture: This work seeks to quantify the significance and magnitude of the effect of measurement error in satellite weather data on modeling agricultural production, agricultural productivity, and resilience outcomes. We combine geo-spatial weather data from a variety of satellite sources with the geo-referenced household survey data from seven sub-Saharan African countries that are part of the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture initiative. Our goal is to provide systematic evidence on which weather metrics have strong predictive power over a large set of crops and countries and which metrics are only useful in highly specific settings. More details available on OSF with a working paper on arXiv and from the World Bank. Collaborators: T. Kilic (World Bank), J.D. Michler (UArizona), and S. Murray (World Bank).
Impact Assessment of Stress-Tolerant Rice Varieties: Evaluating Impact through Remote Sensing and Econometric Methods: This study assesses the impact of key stress-tolerant rice varieties (STRVs) in South Asia. Combining administrative data on variety release with remote sensing data on rainfall, temperature, and the density of green vegetation (NDVI) and using historic data on rainfall and temperature, we determine the impact of these exogenous weather conditions on the NDVI. Then, using district level data on the release of STRVs, we measure the change in NDVI that occurs after the introduction of STRVs. Collaborators: D.A. Al Rafi (UArizona), R. Mathieu (IRRI), J.D. Michler (UArizona), V. Pede (IRRI), and the Social Pixel Lab (UArizona).
Crises and resilience
Intra-Household Dynamics, Agriculture, and COVID-19: COVID-19 has impacted agricultural supply chains across the globe and initial reports suggest that the effects of the pandemic in Sub-Saharan Africa may be particularly pronounced. This work uses the recently generated data from the LSMS COVID-19 phone surveys to investigate conditions in Africa, focusing on the outcomes of the COVID-19 pandemic and livelihood diversification and agricultural sector participation before the pandemic, as well as household decision-making, bargaining, and equity and the relationship with household coping, mitigating, and surviving COVID-19. Learn more by visiting our Github Repo (DOI: 10.5281/zenodo.4060416). Collaborators: A. Furbush (UArizona), T. Kilic (World Bank), J.D. Michler (UArizona), L. Rudin-Rush (UArizona).
Three recent publications from this research:
Measuring food security in disasters: Current food insecurity measures have been used in disaster contexts, but there are distinct challenges to food security in a post-disaster context. Providing sufficient calories while also addressing dietary quality and nutrition is difficult to do in a disaster setting, with widespread disruption to infrastructure, supply chains, and organizational and social systems. Thus, this research works to establish a new metric that captures dimensions of food insecurity affected in a disaster context is necessary to improve food security measurement, better estimate food insecurity prevalence during community-level disruptions, and enhance emergency food assistance to support families coping with widespread disruption to infrastructure, supply chains, organizational and social networks, personal property, and daily routines. Collaborators: E. Biehl (JHU), L. Clay (NYU), R. Neff (JHU), M. Niles (UVM), S. Rogus (NMSU).
Measuring outcomes of humanitarian assistance: Humanitarian assistance minimizes the human and material losses in the immediate aftermath of social and environmental shocks. Focused on emergency programming activities (e.g., local early warning systems, disaster risk reduction governance, livelihood diversification strategies), this work develops methods and indicators that reflect adaptations in behaviors and landscape uses spurred by emergency programming activities. Funded by USAID’s Bureau of Humanitarian Assistance, this work aims to develop tools for improved evaluation of programming outcomes. Collaborators: Z. Guido (UArizona), J.D. Michler (UArizona), O. Olurotimi (UArizona)