Food Price Seasonality and Drought in Developing Countries
Beyond variations in the weather, another source of seasonality in food prices in thin markets is the seasonality of transportation costs. Little is known about the variability of transportation costs in each of the markets of this study, but rainfall and poor roads, increased demand for movement of goods and people during the rainy season, and the increased difficulty of distributing fuel and other necessities for transportation make it likely that transportation costs more during the growing seasons Transaction costs are the costs incurred in making an economic exchange: determining the price and the demand for a good in a market, the cost of bargaining for a fair price, and the cost of policing and enforcement in the market. All of these costs are also likely to be seasonal. These sources of non-food production variability in the seasonality of food prices can also be estimated with remote sensing data.
Societies are vulnerable to extreme weather at multiple levels. Subsistence small holders who hold livestock and consume much of the food they produce are vulnerable to food production variability. The broader society, however, is also vulnerable to extreme weather because of the secondary effects on market functioning, resource availability, and large-scale impacts on employment in trading, trucking and wage labor that are caused by weather-related shocks. Food price variability captures many of these broad impacts and can be used to diagnose weather-related vulnerability across multiple sectors.
In this study, we use a bivariate dynamic probit model for panel data to estimate the various causes of food price seasonality. 291 corn, rice, and wheat market price time series in 107 countries for Jan 2007 through Apr 2013 were downloaded for every country we could find data on. A significant portion of the data was provided by FAO GIEWS (Food and Agriculture Organization of the United Nations, Global Information and Early Warning System), with US data provided by the USDA and EU data from the UN (United Nations) Commission Report.
To estimate seasonal biophysical stressors to food markets, we use satellite remote sensing of vegetation data and drought indices derived from rainfall data. We also use local annual retail gasoline and diesel prices for each country. Economic data at the country level are also included to identify differences of economic health. Thus we use gross domestic product (GDP) per capita, import and export rates, population, inflation rates, and account balance as a percentage of GDP. Our results show that a combination of environmental and economic causes are the best predictors of market seasonality. Policy responses on the vulnerability of agricultural systems to extreme weather will also be presented.