Link Between Volatility of Commodity Prices and Commodity Dependence on Selected Sub-Saharan Countries

The balanced annual panel data for 32 sub-Saharan countries from 2000 to 2020 was used for this study. The countries and period of study was informed by availability of data of interest. Specifically, 11 agricultural commodity dependent countries, 7 energy commodity dependent countries and 14 mineral and metal ore dependent countries were selected (Appendix 1). The annual data comprised of agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices, export value of the dependent commodity, total export value of the country, real GDP (RGDP) and terms of trade (TOT). The data for export value of the dependent commodity, total export value of the country, real GDP and terms of trade was sourced from world bank database (World Development Indicators). Data for agricultural commodity prices, global oil prices (GOP) and mineral and metal ore prices are obtained from World Bank commodity price data portal. This study used data from global commodity prices from the World Bank's commodity price data site since the error term (endogenous) is connected with each country's commodity export price index. The pricing information covered agricultural products, world oil, minerals, and metal ores. One benefit of adopting international commodity prices, according to Deaton and Miller (1995), is that they are frequently unaffected by national activities. The utilization of studies on global commodity prices is an example (Tahar et al., 2021). The commodity dependency index of country i at time i was computed as the as the ratio of export value of the dependent commodity to the total export value of the country. The commodity price volatility is estimated using standard deviation from monthly commodity price index to incorporate monthly price variation (Aghion et al., 2009). This approach addresses challenges of within the year volatility inherent in the annual data. In footstep of Arezki et al. (2014) and Mondal & Khanam (2018), standard deviation is used in this study as a proxy of commodity price volatility. The standard deviation is used because of its simplicity and it is not conditioned on the unit of measurement.