For evidence that mainstream economists are taking the challenge of covid-19 seriously, look no further than the comments of Gabriela Ramos, chief of staff at the OECD, at aconference in April: For many institutions, including the OECD, which has traditionally emphasized the need for efficiency, it is not easy to accept that we should build slack, buffers, and spare capacity into our systemsbut as we now see this is literally a question of life or death.
This is the first plank of the professions response to the pandemic: questioning whether national economies, individual companies, and markets should be optimized to maximize return on capital, or to ensure resilience in the face of a crisis.
The second clear trend concerns methodology and a willingness for economists to move away from strict mathematical models. The pandemic has, in many cases, decreased our reliance on traditional economic metrics such as GDP, says Chen Long, director of the Luohan Academy, an open research institute initiated by the Alibaba Group. This, he says, means thinking outside the box and looking for non-traditional indicators, such as digital apps and internet services. It also signifies a significant shift as economists dig into high-frequency information to illustrate what is happening to our economy.
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The pandemic has seen a flowering of interdisciplinary research between economists and academics in fields that would not typically have been considered adjacentepidemiologists and anthropologists for example, rather than mathematicians and statisticians.
Behavioral economics, which begins from a standpoint that social norms can have as much influence over human behavior as the rational self-interest of individual actors, has featured heavily in advice to policymakers.
One example comes from India. At Mumbais Monk Prayogshala Research Institute, behavioral economist Anirudh Tagat worked with psychologistHansika Kapoor tomake policy recommendationsthat nudge Indians into conformity with social distancing. These include drawing a line of chalk beyond the door to a home to encourage families to stay home, an idea borrowed from the Hindu Lakshmana Rekha myth.
Behavioral economics has also been used to highlight risks that may require attention. For example, a much-discussed paper drew attention to a correlation betweencultural attitudes to hand washingin different countries and the size of covid-19 outbreaks.
The pandemic has also gone some way to breaking the silo between development economics, and its mainstream counterpart. The study of extreme market failuresshutdowns due to war, for examplehas generally been the preserve of the former discipline, but the pandemic has forced the wider economics profession to switch focus.
The rush of stimulus spending by developed world governments has emboldened development economists to call for areconsideration of public sector financing. Rgis Marodon of the Agence Franaise de Dveloppement is compiling a database of global development banks. So far, he counts 400 institutions with more than $11 trillion in assets that are responsible for 10% of world gross fixed capital formation each year. He expects to make the database publicly available in November.
Absent multilateral funding, developing economies have been unable to match the stimulus efforts of their rich world counterparts. A McKinsey study shows the stimulus programmes of India, South Africa, and Brazilhave been much smaller, as a percentage of GDP, than developed countries such as Germany and France.
Back in 2009, Andrew Haldane, chief economist at the Bank of England, famously described the collapse of Lehman Brothers and the 2002 SARS pandemic in China, as two examples of the same phenomenon: the behavior under stress of a complex, adaptive network.
This description is equally appropriate for the current pandemic, and economists are once again starting to conceptualize the economy not as a robust and self-correcting market, but a delicate and complex organism, in which a general resilience needs to be fostered, rather than individual problems remedied.
After the financial crisis, building resilience involved higher capital requirements for banks, and regular stress testing. By definition this meant lower returnsbecause banks had to leave some capital idle rather than deploy it.
Business economists are arguing over what the equivalent measures now would be to ensure that governments and companies are able to meet the needs for basic medical supplies in a future crisis. One area of focus is supply chains, where in the past three decades shareholder optimization has led to an emphasis on endless subcontracting.
Yossi Sheffi, director ofMITs Center for Transportation and Logistics, does not see subcontracting and geographically distant supply chains as necessarily a bad thing, but has called for more transparency. For instance, it is crucial to know if every ventilator producer, for example, relies on the same supplier at the fifth or sixth level of their supply chain.
Oxford Universitys Professor Doyne Farmer, an expert in the economics of complexity, has called for governments to incentivize companies to reveal supply chain information, or simply require them to do so. We need to be able to make better economic models that we build from the bottom up if we ever want to really understand macro properly, he told the OECD conference in April. Having data about global supply networks is a fundamental aspect of that.
This could pave the way for collaboration between economists and technologists, with the use of blockchain, for example, to track every component that goes into a product, increasing the transparency of dependencies within systems of production.
Again, there is potentially much to learn from development economics. Farmer points to the example of Chiles VAT system, which requires both counterparties in any trade to report the transaction details and priceelectronically in real time. Implemented on a global level, this could allow supply chains to be retrospectively reconstructed by economists from public records.
A more prosaic part of the response to covid-19 has been for economists to reconsider the data they provide to policymakers and the wider public. To satisfy the need for timely data, government statistical releases are generally based on surveys, but response rates to those surveys have fallen during the pandemic, bringing into question the accuracy of numbers derived from them.
Some economists have responded by gathering hard data in close to real time to measure the impact of the pandemic and government responses to it. In apaper released in September, economists including Raj Chetty at Harvard University pulled together anonymized credit and debit card spending data to provide azip code level viewof both consumer spending and business receipts in the US during the pandemic.
The conclusion: the wealthiest American households are not fully spending the stimulus checks issued to all families by the federal government because avenues for consumption, such as restaurants, are closed. Rather than try to save companies by stimulating spending, the government might be better served providing social insurance to those that will inevitably lose jobs. This is real-time feedback as the government embarks on a gigantic program of public spending.
Mohamed El-Erian, chief economic advisor to insurance firm Allianz, has called formore humilityamong forward-looking forecasters. When forecasts have to be made, he advocates the use of fan charts, where a range of possible outcomes are shown, rather than one central case, which suggests an unrealistic amount of certainty about the future path of growth, for example, on which companies, individuals, and governments may then act.
Fan charts are a staple of forecasting in the UK. In an amusing moment in aRoyal Economic Society webinar on forecasting, Garry Young of the National Institute for Economic and Social Research showed the forecasts for UK GDP that his organization issued in February 2020 and May 2020, side by side. In the February chart, the GDP growth rate was forecast to remain within a range of 0 to 5% for the next 5 years. By May, with the UK in a pandemic-induced lockdown, forecasts ranged from -20 to +20% growth: tougher to make fixed plans, but this is the point in a time of uncertainty.
In the future, economics may become more like an interdisciplinary data science discipline. With the digitization of the economy and the explosion of data, both the objects and the ways of research are going through fundamental changes, says Chen. It is becoming more and more reliant on data science and code and transforming into a field of study that encompasses many different subjects, from psychology to computer science.
If the economics profession wants to respond in a more diverse manner, as many in the field have earnestly professed, one statistic to have come out of the pandemic gives cause for concern.
A study of the number economic working papers issued so far this yearshows a sharp increasecompared to 2019 or 2018. That makes sense; economists have rushed to analyze disruptions to economic activity and government responses. However, the study also revealed a pronounced drop in publications authored by women, with the writers suggesting the burden of caregiving was limiting publications by female economists.
IMF economists meanwhile havepointed outthe tiny number of articles in top economics publications that deal with race. The IMF authors suggested fertile grounds for future interdisciplinary study, for example sociological studies of everyday interpersonal discrimination, as well as a redoubling of attempts to increase diversity among economists.
Covid-19 has triggered economists to rethink their profession all the way from the philosophical down to the practical. This is no mere academic exercisethe pandemic has shown us that citizen welfare, economic recovery, and future resilience are at stake.
ThePandemic Economy Tracker (PET) projectfrom the Luohan Academy offers real time estimates of economic activity and mobility based on anonymized data from providers including Apple and Google.
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Econ 3.0? What economists can contribute to (and learn from) the pandemic - MIT Technology Review