A fab IMF paper on the topic.
It is an important paper as it mixes the two views on oil prices. Econs (and technologists) believe that if oil prices increase and it becomes scarce, people will figure something else..some innovation will come in and people will move on. Infact, this is what has happened with many projections of food/oil etc running out not coming true. Geologists however believe oil is scarce and will run out soon. Hence, we need to preserve oil etc. Similar views of Geologists are held by Physicists as well.
We discuss and reconcile two diametrically opposed views concerning the future of world oil production and prices. The geological view expects that physical constraints will dominate the future evolution of oil output and prices. It is supported by the fact that world oil production has plateaued since 2005 despite historically high prices, and that spare capacity has been near historic lows. The technological view of oil expects that higher oil prices must eventually have a decisive effect on oil output, by encouraging technological solutions. It is supported by the fact that high prices have, since 2003, led to upward revisions in production forecasts based on a purely geological view.
The authors use insights from both the econ and geologist model and make a hybrid. Geog model gives better handle on supply and gives better results:
In this paper we find that our ability to forecast future developments in the oil market, and by implication in aggregate activity, can be dramatically improved by combining the geological and economic/technological views of oil supply, and by estimating their respective contributions. We develop a simple macroeconomic model that combines a conventional linear demand specification with a nonlinear supply equation, the latter combining a mathematical formaliztion of the geological view with a conventional price sensitive oil production. We find that this model can predict oil prices far better out of sample than a random walk, and that it can predict oil production far better than the historical track record of official energy agencies on the one hand, and of advocates of pure versions of the geological view on the other hand.
We also use the model to identify which driving force has been most responsible for the recent run-up in oil prices. We find that the geological, price-insensitive component of supply is the key reason for the recent accuracy of the model’s predictions because it captures the underlying trend in prices. But we also find that shocks to excess demand for goods and to demand for oil, the latter probably due to phenomenal recent growth in China and India, have been key to explaining persistent and sizeable deviations from that trend. These deviations work through the price channel.
Looking into the future, both of these factors continue to be important, and point to a near doubling of real oil prices over the coming decade. But there is substantial uncertainty about these future trends that are rooted in our fundamental lack of knowledge, based on current data, about ultimately recoverable oil reserves, and about long-run price elasticities of oil demand and supply.
Hmmm.. This will be quite a challenge for world economy:
This is uncharted territory for the world economy, which has never experienced such prices for more than a few months. Our current model of the effect of such prices on GDP is based on historical data, and indicates perceptible but small and transitory output effects. But we suspect that there must be a pain barrier, a level of oil prices above which the effects on GDP becomes nonlinear, convex. We also suspect that the assumption that technology is independent of the availability of fossil fuels may be inappropriate, so that a lack of availability of oil may have aspects of a negative technology shock. In that case the macroeconomic effects of binding resource constraints could be much larger, more persistent, and they would extend well beyond the oil sector. Studying these issues further will be a priority of our future research.
Much of the paper talks about the model and is fairly math oriented. But we do see attempts being made to merge knowledge from the two fields. That surely should lead to better understanding..