I am a development and environmental economist studying how people cope with droughts, floods, and other environmental shocks. I am particularly interested in how household
power structures lead the costs of those shocks to be unevenly distributed. Much of my research relies on linking satellite data with ground-based survey datasets to study how droughts affect people, their crops, and natural landscapes.
Before starting my PhD, I lived and worked for several years in Mozambique studying climate smart agriculture. Prior to that, I worked on renewable energy and energy efficiency policy in the U.S., Germany, and China.
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with Julian Arteaga and Michael Carter
Abstract: The novel insurance contracts for crop losses and livestock mortality that have been developed in low income countries typically protect against shocks in the male sphere of economic activity. Often overlooked are women, the particularities of their indirect exposure to this risk, and their socially constructed responsibility to manage family well-being. To fill this lacuna, this paper studies the effect of a low-cost intervention that reformulates a livestock insurance contract so that it directly addresses women’s risk and is sold in units that are commensurate with women’s expenditure responsibilities. We measure the effect of this contractual reformulation using a randomized trial amongst pastoralist communities in Kenya. Twenty-four percent of previously subsidized households that received the novel contractual formulation purchased insurance (without subsidy), compared to only 13% of previously subsidized households offered insurance under the standard male-risk formulation. Households that had not received prior insurance subsidies purchased no insurance, irrespective of the framing. Protecting women, their assets and those who depend on them will require a combination of smart subsidies and gender-intentional insurance contract design.
with Aboli Khairnar
Abstract: Smallholder farmers in developing countries have always been ad- versely affected by year-to-year variation in weather patterns. Low rain- fall, high temperatures, floods, and other disasters can wreak havoc on their livelihoods. Crop insurance has the potential to partially solve this problem, but traditional indemnity-based insurance is generally too costly to administer for smallholder agriculture. Index insurance, which provides payouts based on regional satellite, weather, or crop cut data offers a potential low-cost solution. However, developing accurate indices requires ground-truth data, which itself is costly to collect. This paper explores a new solution to this problem by combining existing household survey data from the World Bank’s Living Standards Measurement Survey (LSMS) with satellite data to develop a hypothetical index for maize production. We show that by combining remotely sensed data and machine learning techniques, we can construct an accurate crop production index. We com- pare regularized regression, neural networks, and random forests, and are able to obtain reasonably good yield predictions with neural networks and random forests. This method is a promising new approach for developing accurate index insurance products at low cost with large potential benefits for smallholder farmers and governments seeking to address climate risk.
Abstract: In agricultural settings where men control the most valuable crops or livestock, shocks to men’s incomes and assets are often the primary sources of risk to the household. Negative economic shocks lead men to reduce their contributions to household public goods such as school fees and food, shifting the burden to women. Insurance has the potential to reduce this burden. However, insurance is generally linked conceptually to crops or livestock, obscuring its potential role in preventing costly cuts to household public goods. This paper uses data from an lab-in-the-field experiment in Samburu County, Kenya to show that framing index-based livestock insurance as a financial product that can help households buy food and keep children in school during droughts increases demand among women relative to a livestock-focused framing. The effect is stronger in households that score lower on empowerment indices, suggesting it is due to traditional gender roles.
