Fisheries Income Satellite Project
I am working with Giuseppe Stelluto on the Fisheries Income Satellite Project to assess how shocks to ocean chemical composition correlate with economic activity in data poor fisheries. We are assembling several databases of NASA satellite observations on sea surface temperature, chlorophyll-A, and nighttime lights. Using Google Earth Engine, Python, and other spatial data analysis software, we will examine the correlation between predicted fish abundance and economic activity as measured by nighttime lights and large household surveys of income. At this stage, we are working to generate interactive GIFs (see an example embedded below) to assess the coverage of the data, the best spatial and temporal way to analyze the data, and the raw correlation between the satellite observations. Eventually, I will use these datasets to answer a series of interesting research questions. Do these fisheries shocks have as much predictive power over income in fishing-dependent communities as rainfall shocks commonly used in agricultural regions? Is fishing best described as an income-smoothing alternative to agriculture or is the fishery a primary occupation? Is fishing income as a proportion of total income stable or is it higher when agricultural/tourism/other streams of income are lower? Does the focus on agricultural income in most development economics surveys systematically bias estimates of income in fishing-dependent communities? Can a naive prediction of income from satellite data do a better job of predicting night time lights activity than a formal survey or than rainfall? Does a shock to income in fishing-dependent communities lead to change in female empowerment, violence against women, investment in female children, marriage market outcomes, or other gendered outcomes observable through existing surveys?