In broad strokes, the current major economic policy debate in the United States boils down to a simple question: is the risk in doing too much, or too little? In other words, will the drive of the Biden administration to inject more government dollars into the economy lead to overheating and runaway inflation? Or, absent those dollars, would the country continue to be mired in high unemployment and sluggish growth, with all the human misery and untapped potential that both of those entail?
The problem is that both perspectives are essentially backward-looking, framing their analysis around events from the past that are viewed as analogous to the current moment. For those on the “do more” side, the key data point is the Great Recession of 2008, with the lesson being that the Obama administration did not spend sufficiently to break the economy out of the quagmire it was trapped in.
For those on the “do less” side, they remain wary of returning to the highly inflationary economy of the 1970s and caution that the addition of more and more “stimulus” into an economy which appears to be rapidly recovering will quickly pass diminishing returns and become actively harmful. Though official inflation measures do appear to be stable for the time being, the large increases in the price of key commodities such as lumber, oil and a number of metals do signal potential trouble on the horizon, as the costs of these inputs into consumer goods inevitably get passed down the chain. This is also coming in an environment where the Federal Reserve has signaled a reluctance to raise interest rates in order to tamp down inflation, instead placing more emphasis on employment recovery.
The economic recession caused by the COVID-19 pandemic is in many ways qualitatively different from anything that modern economic and statistical techniques, not to mention data-gathering tools, has previously encountered. This is a worldwide recession caused not by fundamental flaws in the economic structure or by massive misallocations of capital but rather by a calculated decision on the part of officials to inflict (hopefully temporary) economic damage so as to prevent further spread of disease and loss of human life. The ripple effects of this sort of recession are therefore different than those in, for example, 2008, including the fact that the economy has been able to recover and adapt relatively quickly as capital as rushed into those sectors which have remained operational (construction, for example) and anticipatory consumer demand and savings have built up.
A Sophisticated Investment Response
How, then, is it best to navigate this new territory? Sophisticated investors, whether individual or institutional, are familiar with the tenants of modern portfolio management theory, including the need to diversify one’s holdings as a hedge against the various ups and downs of the market. This is a key insight that has allowed many to protect and grow their investment. At the same time, the manner in which investors diversify is not static and must change over time to accommodate new factors. Diversification into all economic areas for its own sake is not a wise strategy, as it does not respond to evolving economic conditions nor does it look to the future for likely returns. The ability to build sophisticated models of the global economy which can better guide investment allocation based on the multitude of existing data is more and more within reach; it simply needs to be harnessed effectively.
A better way of planning for the post-COVID economy, on both the individual and institutional level, would seek to understand how different discrete data sets within the global economy (such as the price of lumber or unemployment in a particular country) interact with each other. Uncovering the interlinkages, even if they do not at first appear intuitive, can help shape a new and more applicable understanding of the economic landscape. Further, the level of certainty at which forward-looking predictions can be made based on these interactions must be precisely defined in order to guide decision-making.
New technical tools, such as machine learning and real time deep economic data sets, are invaluable contributors to this understanding. For example, being able to link data on retail foot traffic in a particular area with a prior increase in flight tickets sold to a surrounding location could allow businesses to better plan good shipments as air travel reopens. One of the key challenges to the acceptance of such methods, however, is a current credibility gap compared to trusted institutional economic actors (such as the IMF or World Bank). AI performance at prediction of high level indicators, such as GDP, relative to these institutions will be a useful step in building this credibility.
Regardless of the exact tools chosen to guide decisions, relitigating past economic conflicts to decide the future coming out of COVID will get us nowhere, either in terms of individual and business success or as a society. Rather, leaders across society should be prepared to abandon their old paradigms and embrace the new tools available to guide them to make stronger, better informed decisions on our shared economic future.
The views, thoughts, and opinions expressed in this article belong solely to the author, and do not reflect the views of Conversationally Speaking Magazine