The Centre’s first World Prosperity Outlook (WPO I) tells a fact-based story, revealing a wealth of information that is hidden in the social and economic data volumes. In a nutshell, the WPO I results can be summed up by seven key messages:
GDP gives limited insight in the prosperity of a community (nation, city). Instead, disposable household income distribution – updated for purchasing power – is a rich representation of the socio-economic state of a community. Income distributions provide directly and indirectly a wealth of social information.
The Centre’s Socio-Economic Landscape reveals that the social and economic performances of nations show a strong relationship. This fundamental property tells us that a better position in the socio-economic landscape requires a connected, double investment policy. The Global Prosperity curve (‘GPR-curve’) visualizes the mean socio-economic position of nations: “The GPR-curve represents the collection of socio-economic centres in the world.”
Progress of nations occurs along well-defined pathways around the GPR-curve. This property tells us that there exists a ‘self-organizing capability’ in socio-economic development. Actually, the landscape reveals that the collective transition path of Common Development Phase nations (‘CDP cluster’) follows exactly the GPR-curve. Using the GPR-curve as an objective reference for normal socio-economic behaviour, abnormal behaviour of individual nations (‘outlying transition paths’) can be early identified and analysed
Transition paths reveal also the inequality in a community: the inequality index is given by the steepness of its transition path at the midpoint. For multi-country communities (world, clusters) inequality is only a meaningful concept if it is subdivided in inter-country and average intra-country inequality. This is also true for single nations with a clearly different rural and urban performance (think of China and India). Large economic growth of developing countries occurs at the cost of equality, caused by an increasing gap between rural and urban.
Long-term socio-economic development does not follow a continuous prosperity path. The Centre’s new insights point out that during their prosperity journey, nations have periodically to jump from one socio-economic pathway to another to avoid diminishing returns. This most important property tells us that prosperity policies must be forward looking and forecasting algorithms must recognize at an early stage when these critical step changes need to be taken. China should have already taken such a step change years ago.
Making convincing images of the future is not only a matter of applying CFGSEC’s convolutional prediction process (algorithmic part). CFGSEC also makes use of the opportunity to influence the future when the prediction process points at an alarming outlook (leadership part). As a consequence, the Centre makes alternative futures (‘fact-based scenarios’) visible, based on deviations around ‘business as usual’.
Bear in mind that the famous Moore’s law is not only telling us that Gordon Moore was an intelligent forward-looking extrapolator; it also tells us that the chip industry decided to influence the future by making this law their ambition.
CFGSEC complements each socio-economic transition path with associated ecological pathways, enabling the Centre to discover hidden socio-economic-ecological relationships. In a prospering community the social and economic performances increase and, at the same time, the involved environmental costs go down to a lower level (‘decoupling’).
The Centre wants to monitor these socio-economic-ecological relationships in real time by utilizing satellite data. Results will also point at the impact of changes in local climate on the community’s socio-economic development and ecological transition.