Forecasting and scenarios for long-term diversification transitions in oil-based economies

We provide long-term forecasts and scenarios that could enable oil-based economies to reduce dependency - and diversify, in the future; the empirical evidence comes from the Kingdom of Saudi Arabia, and the profound dependency in Oil & Gas. This is a very timely quest, given the volatile prices of oil amidst COVID-19 pandemic and thereafter in the new VUCA1 world we live in. The task under consideration involves identifying the top-5 sectors with the highest economic contribution to the GDP in 20-years-time. The study consists of 5 sequential experiments where a quota sample of participants (n=110) who took part have varied levels of expertise; novices (n=19), semi-experts (n=73), and experts (n=18). In the 1st and 2nd experiments, participants conducted the forecasts individually using Unaided Judgment (UJ) and Structured Analogies (SA) methods respectively. However, they worked in clusters in the 3rd experiment and formed Interaction Groups (IG). During these 3 experiments, participants were not informed of the identity of the country of which they produced forecasts for. Only in the 4th experiment that the country name was revealed and participants were asked to work in clusters again and perform the same forecast task, while making the most of the new information that were given. The 5th experiment was for the outcome of the (disguised) Scottish independence referendum, aiming to assess the forecasting accuracy of the participants. The results demonstrate that the proposed forecasting framework could comprehensively identify possible areas of long-term GDP diversification, estimate the potential contribution of each GDP sector to the Saudi economy and inform policymakers of the kingdom’s future. Given the nature of the information derived at the end of the experiments, these are not considered pure forecasts, rather than scenarios/forecasts.