Forecasting the Effects of Battery Recycling on the Global Cobalt Market
by Elena Cavallero
Abstract
This paper addresses existing concerns around a potential cobalt supply shortage driven by lithium-ion battery demand. Using econometric simultaneous equations, historical global cobalt supply and demand are estimated using data from 1981 to 2018. Based on the results of a Three-Stage Least Square estimation model of global supply and demand, this study forecasts global cobalt price and quantity in 2030. Additionally, a parametrization of battery recycling is added to study the effects of cobalt recovery on future market equilibrium. The results indicate that: 1) world GDP is a key determining driver of cobalt demand, 2) conflicts in the Democratic Republic of Congo, the world’s largest cobalt supplier, negatively impact global production, and 3) recycling lithium-ion batteries will increase global cobalt quantity supplied by 23% and decrease price by 60% in 2030 under the EU Green Deal regulations.
Professor Brian Murray, Faculty Advisor
Professor Grace Kim, Faculty Advisor
JEL Codes: C30, Q31, Q55
Identifying Supply and Demand Elasticities of Iron Ore
By Zhirui Zhu
This paper utilizes instrumental variables and joint estimation to construct efficiently identified estimates of supply and demand equations for the world iron ore market under the assumption of perfect competition. With annual data spanning 1960-2010, I found an upward sloping supply curve and a downward sloping demand curve. Both of the supply and demand curves are efficiently identified using a 3SLS model. The instruments chosen are strong and credible. Point estimation of the long-run price elasticities of supply and demand are 0.45 and -0.24 respectively, indicating inelastic supply and demand market dynamics. Back-tests and forecasts were done with Monte Carlo simulations. The results indicate that 1) the predicted prices are consistent with the historical prices, 2) world GDP growth rate is the determining factor in the forecasting of iron ore prices.
Advisor: Gale Boyd | JEL Codes: C30, Q31 | Tagged: Demand, Iron Ore, Supply, Simulation, Simultaneous Equation
