Project Description: 

Adverse shocks to agricultural production can have lasting effects on affected households stemming from loss of output and income and from the coping mechanisms households take to try to deal with that loss. While many papers consider the long-term impacts of weather shocks such as drought, flooding, or irregular rainfall, few consider the impacts of agricultural pests. The desert locust is considered the world’s most dangerous and destructive migratory pest. Large parts of the Arabian Peninsula and the Horn of Africa remain under threat from a desert locust outbreak that began in 2018 and has threatened the food security of over 20 million people. 

We are interested in analyzing locust outbreaks both as an important shock in their own right, but also as a less-studied form of general economic shock. In the first sense, estimates of the costs of locust outbreaks will be useful in cost-benefit analyses to inform policy on coordinating cross-country locust monitoring, prevention, and control efforts. They may also inform policy on targeting assistance to affected areas, and perhaps on providing insurance for at-risk areas. In the second sense, understanding the economic and social impacts of a large agricultural shock is important for broader policy-making around how to protect vulnerable populations and support resilience and recovery.

This project combines data on actual desert locust monitoring observations and forecasts of desert locust risk to estimate causal impacts of desert locusts on outcomes of interest.

Work on the project involves three broad activities:

  1. Organizing data on the independent variable, locust outbreaks. This includes locust monitoring data from the FAO, but also locust forecasts and predictions, and possibly also locust control operations. The goal is to isolate quasi-random variation in the areas that are affected by desert locusts during an outbreak to allow for causal estimates of their impacts
  2. Organizing data on the dependent variables and control variables. We are taking an opportunistic approach to outcome analysis, looking for sources of household and other data that match well with the timing of desert locust outbreaks in specific contexts or across countries.
  3. Analyzing effects of locust outbreaks on outcome variables of interest at different geographic and temporal scales. We are interested in immediate impacts on agricultural production at the household, local, and national levels, but primarily in short- and long-term impacts on household well-being and broader social and economic outcomes such as migration, poverty and conflict.

At this stage, we have obtained and cleaned data on locust monitoring observations and have made progress on aggregating data on forecasts of desert locust risk. We have begun analyses of the impacts of desert locusts on risk of conflict in both the short- and long-term, and are beginning to explore impacts on agricultural production in the short-term and identifying data sources for the analysis of additional outcomes.

Undergraduate's Role: 

The primary role of the undergraduate researchers will be to assist in collecting and cleaning data - activities 1 and 2 above. In particular, the undergraduate researchers will be responsible for reviewing historical bulletins

( from the Food and Agriculture Organization of the United Nations (FAO) Desert Locust Watch and compiling coded information on desert locust conditions and forecasts. This will involve careful review of many documents and systematically and consistently entering relevant information from the documents into a coding framework to organize information into a usable dataset. The coded forecasting information will be used to predict which areas are more likely to experience a desert locust shock, which will be compared to the areas that actually experienced this shock. This information will later be shared as a public resource, with attribution, for use by other researchers. We have completed coding of forecasts for 2003-2005, the last major locust outbreak prior to the current one, and will focus efforts on coding forecasts from 2018 to the present to use in analyzing the ongoing outbreak.

Depending on skills and experience, the undergraduate researchers may contribute to developing tools to map desert locust forecasts against locust observations, and/or to working on locust forecasting/prediction modeling using machine learning methods.

In addition, the undergraduate researchers may assist in collecting and cleaning other data relevant to the project. This will include searching for, reviewing, and preparing data on outcomes of interest and control variables. A key element will be searching for countries with household survey data from before and after major locust outbreaks. After identifying data sources, data preparation will involve searching through household survey instruments, spatial data, and administrative datasets for relevant questions/information, and retrieving and cleaning the data to put in a format that can be matched with the locusts data. Depending on skills and experience, another important task will be working to estimate impacts of locust swarms on agricultural output, likely using satellite data.

Depending on the progress of the project and the undergraduate researchers’ abilities, they will also contribute to visualizing and analyzing the data and discussing approaches for the analysis. Additional tasks and responsibilities may be agreed between the undergraduates and the project manager.

Undergraduate's Qualifications: 
  • Interest in contributing to research at the intersection of environmental and development economics
  • Willingness to commit to 9-12 hours of work per week on average
  • Excellent organizational and communication skills
  • Ability to work independently given general instructions, to troubleshoot issues, and to raise concerns and propose solutions to the research team
  • Familiarity with Excel
  • Experience working in Stata, R, or Python is desirable and may expand the scope of possible student activities, but not required
  • Experience working with spatial data is desirable and may expand the scope of possible student activities, but not required
On Campus
9-12 hours