Global Business Mobility, defined as GDP-weighted Google geolocation data of workplace less residential mobility for the 24 largest economies in the world, representing over two-thirds of global GDP, remains in decline despite a recent uptick:

When excluding USA from the data, the situation appears even worse, as depicted by the second chart above showing steeper decline of business mobility as well as a daily Covid19 infection rate that appears on the rise. This is due to the fact that the US business mobility is essentially flat-lined versus the other 23 economies that are mostly in mobility decline and the US has had a sharp decline in daily reported new Covid19 infections.

In fact all but two of the 10 performance metrics we track for the management of the US coronavirus outbreak are showing positive outcomes and we expect US business mobility to start its second-leg upwards shortly, especially as summer vacation comes to an end:

Additionally, the number of US states with decreasing mobility has declined to less than 5 and the number of US states with increasing daily infections has come down nicely from 45 to around 21 currently. This implies a broad-based improvement of the above 10 metrics:

Business related mobility remains an important aspect of tracking the Covid19 Recession recovery. Various mobility indices we monitor are actually showing a very high correlation to our Weekly Leading Economic Index (WLEI) which tracks mostly financial and labor market data:

You can see the full set of individual US State and country-level mobility/infection charts for the 23 largest economies in the world at the COVID-19 Menu in the US MOBILITY tab. The data has just been updated as part of the usual Thursday mobility charts update.

IMPORTANT DISCLAIMERS: Although many of our models rely heavily on back-testing, optimisation and probability methods, please note that past performance is NO GUARANTEE for future returns. No system devised by man can predict the future, let alone the future of the markets and economies. What we have are sets of mathematical models that use historical data and varying theories to pinpoint places in time when the STATISTICAL LIKELYHOOD of calling the start and end to recessions is the greatest. In the midst of all the best mathematical models, you can have geo-political events, wars, terrorist attacks, natural disasters and even nuclear accidents that tip everything upside down. This is always a risk factor you need to factor in on any recession call no matter how confident you are in a signal. The SuperIndexes are created through mathematical and statistical optimisation techniques that best fit the historical NBER data. There is no guarantee that out-of-sample performance will match that of prior in-sample performance.

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