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.

本研究采用2000年至2020年撒哈拉以南32个国家的平衡年度面板数据。样本选取的国家与研究时段，依据目标数据的可获得性确定。具体而言，本研究筛选出11个农业商品依赖型国家、7个能源商品依赖型国家以及14个矿产与金属矿石依赖型国家（详见附录1）。

本次年度数据集涵盖农业商品价格、全球原油价格（Global Oil Price, GOP）、矿产与金属矿石价格、目标依赖商品出口额、国家总出口额、实际国内生产总值（Real GDP, RGDP）以及贸易条件（Terms of Trade, TOT）。其中，目标依赖商品出口额、国家总出口额、实际国内生产总值与贸易条件的数据来源于世界银行数据库的世界发展指标（World Development Indicators）；农业商品价格、全球原油价格及矿产与金属矿石价格则取自世界银行商品价格数据门户。

鉴于误差项（内生性）与各国商品出口价格指数存在关联，本研究采用世界银行商品价格数据平台发布的全球商品价格数据。该定价信息覆盖农产品、全球原油、矿产及金属矿石品类。Deaton与Miller（1995）指出，采用全球商品价格的一大优势在于其通常不受一国国内经济活动的影响，此类应用案例可见于Tahar等人（2021）的相关研究。

国家i在时期t的商品依赖指数，以目标依赖商品出口额与该国总出口额的比值计算得到。商品价格波动率的测算则通过月度商品价格指数的标准差实现，以纳入月度价格波动维度（Aghion et al., 2009），该方法可解决年度数据固有的年内波动率测算难题。参照Arezki等人（2014）与Mondal & Khanam（2018）的研究范式，本研究采用标准差作为商品价格波动率的代理变量，因其具备计算简便性且不受计量单位的限制。