Mai Mahmoud - Research

Pricing and Allocation of New Agricultural Technologies (Job Market Paper)

World Bank Development Impact Blog post: available here

This study uses a two-stage experiment to examine whether lower prices allocate new agricultural technologies to farmers with lower returns. In stage one, I randomize a price subsidy, ranging from full to zero subsidies, for a new wheat seed variety. In stage two, I randomize free distribution across the self-selected sample of non-buyers from stage one. This design allows me to compare treatment effects across the entire population with treatment effects among the sample choosing not to buy the seed. If higher prices screen out farmers with low willingness to adopt, then the effect of stage-two free distribution on adoption by non-buyers should be trivial. Instead, I find that the stage-two free distribution increases adoption and wheat cultivation by an amount almost equal to the effect from stage one. In addition, farmers choosing not to buy in stage one do not realize lower returns to adoption -- despite there being substantial heterogeneity in returns across the sample. A potential mechanism for explaining the results is that binding credit constraints prevent some farmers from buying in stage one. Free distribution in stage two selects in farmers who are credit constrained but do not have systematically lower returns to adoption. Taken together, these findings imply that policy makers who aim to increase dissemination of agricultural technologies cannot rely on market prices as a mechanism for targeting high return farmers.

Targeting of Food Aid Programs (with Sikandra Kurdi)

Given the frequent occurrence of food crises, food aid programs remain a critical component of social safety nets in many countries. Targeting food aid programs is more complicated than simply identifying the poorest or most needy. Since in-kind transfers distort consumption choices, their impacts on nutritional outcomes vary depending on unobserved household demand functions. In this paper, we evaluate the impacts of an in-kind food aid program and use machine learning (ML) techniques to assess whether the program targeting can be improved. We use a randomized controlled trial (RCT) that randomizes different variations of the food aid program: a staple-heavy, or a nutrition-sensitive food box. We find a significant degree of heterogeneity in treatment effects and show that observable characteristics significantly predict this heterogeneity. We do not find tradeoff between targeting for impact and targeting for need within either food box type. However, we do find that characteristics predicting impact differ by outcome and by treatment modality. These findings suggest that differentiated targeting strategies can improve program effectiveness.

Are Those Who Know and Those Who Don’t Know Alike? Implication of Seed Misclassification on Adoption of Complementary Inputs and Agricultural Yield (with Kibrom Abay)

Imperfect information on agricultural inputs can impede technology adoption and farm management decisions. In this paper, we use a dataset that contrasts farmers’ perceptions of the type of seed variety with a lab-based measure using DNA-fingerprints. We show that farmers use of complementary inputs is driven by farmers’ (mis)perceptions of the seed type rather than actual features of the seed. We also provide suggestive evidence that accurate knowledge of seed variety is associated with higher yield. Ideally, the yield gap between farmers who act based on correct versus wrong perceptions should be directly observable to all farmers. However, farmers observe yield outcomes (measured by dividing total harvest by farm area) with a margin of error due to mismeasurements in both harvest quantity and size of farm plot. The combination of measurement errors in both input and output may explain the persistence of wrong perceptions of seed types over time.

Publications

Plot Size and Sustainable Input Intensification in Smallholder Irrigated Agriculture: Evidence from Egypt (with Kibrom A. Abay, Lina Abdelfattah, Hoda El-Enbaby, Clemens Breisinger), Agricultural Economics, 2022.

Increasing population pressure and population density in many African countries are inducing land scarcity and land constraints. Tightening land constraints are expected to trigger various responses, including agricultural intensification, as postulated by the Boserup hypothesis. The relevance of the Boserup hypothesis in irrigated agriculture, and in contexts where application of improved inputs is high, remains largely unexplored. Furthermore, while much of the debate on the topic in Africa has focused on how to boost agricultural intensification, there is scant evidence on whether evolving agricultural intensification practices in some parts of Africa are sustainable. In this article, we investigate the implication of land size (at the plot and farm level) on agricultural intensification. Our analysis sheds light on the relevance of the Boserup hypothesis in the context of Egypt, where irrigation dominates agriculture and input application rates are high relative to global standards. We also examine whether evolving agricultural intensification practices induced by land scarcity are agronomically appropriate. Our findings show that smaller plot and farm sizes are associated with higher application of agricultural inputs, mainly nitrogen fertilizers. Importantly, small plot size is associated with overapplication of nitrogen fertilizer relative to crop-specific agronomic recommendations. In addition, smaller plots are associated with higher rates of labor application and lower rates of mechanization.

Work-in-progress

Do Carbon Offsets in Agriculture Deliver Benefits to the Environment?: Evidence from a Randomized Evaluation (with Siddhi Doshi and Kyle Emerick)

Unpacking Social Perceptions of Technical Education and Vocational Occupations (with Adam Osman and Abu Shonchoy)