Irving Wladawsky-Berger

Impact of Covid-19 on Job Automation

“Why are there still so many jobs?,” asked MIT economist David Autor in a 2015 article on the history of workplace automation. Given that technologies have been automating human work for the past couple of centuries,“should we not be somewhat surprised that technological change hasn’t already wiped out employment for the vast majority of workers?”

As Autor explained in the article, the answer is based on an economic reality that’s frequently overlooked. “Automation does indeed substitute for labor - as it is typically intended to do. However, automation also complements labor, raises output in ways that lead to higher demand for labor, and interacts with adjustments in labor supply. … journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor.”

Professor Autor was co-chair of MIT’s Work of the Future Task Force, which was launched in the spring of 2018 to better understand the impact AI and automation on jobs. The Task Force released an interim report in September of 2019. Its overriding conclusion was that the likelihood that AI and automation would wipe out major workforce sectors in the near future was exaggerated.

However, it raised important reasons for concern, especially the rising polarization of employment and wage distribution over the past few decades, which has disproportionately benefited high-skilled professionals while reducing opportunities for mid- and low-skilled workers. The report emphasized that “at a time of historic income inequality, a critical challenge is not necessarily a lack of jobs, but the low quality of many jobs and the resulting lack of viable careers for many people, particularly workers without college degrees.”

The Covid-19 pandemic came a few months later, pretty much turning our world upside down.

The Complex Interplay Between Cybersecurity and Regulatory Compliance

Cybersecurity threats have significantly increased since March of 2020 when much of the economy was forced online to help us cope with the Covid crisis, including a number of high profile attacks by international criminal groups and adversarial governments. This past June, FBI Director Christopher Wray compared the danger of ransomware attacks on US firms by Russian criminal groups to the 9/11 terrorist attacks. When Biden and Putin met in Geneva a few weeks later, cyberweapons control was at the top of the agenda, a spot previously occupied by the control of nuclear weapons.

It’s been clear for a while that in a world increasingly governed by digital data and transactions, our existing cybersecurity methods have been far from adequate. To learn more about this very important area, earlier this year I joined  CAMS, MIT’s interdisciplinary cybersecurity initiative, and started attending its research seminars.

At a recent seminar, I heard a very interesting presentation on Compliance and Cybersecurity by CAMS research affiliate Angelica Marotta. Her seminar was based on Convergence and divergence of regulatory compliance and cybersecurity, a recent paper she co-authored with MIT professor Stuart Madnick.

Marotta’s opening slide included succinct definitions of cybersecurity and compliance that highlight their different objectives:

• Cybersecurity is responsible for the “internal procedures established by a company to improve the security of its operations.”
• Compliance is responsible for the “external regulations established by governments or industries groups that must be met.”

Today, in order to be successful, every organization needs to be both compliant and cyber secure. But, the extent to which compliance helps or hinder security for an organization continues to be a very important research question.

The Supply Chain Economy - A New Categorization of the US Economy

A few weeks ago I attended The Supply Chain Economy: Understanding Innovation in Services, a virtual seminar sponsored by the Council on Foreign Relations with economists Mercedes Delgado and Karen Mills discussing their recent paper A New Categorization of the U.S. Economy.

The debate about the drivers of innovation and job creation has long been centered on manufacturing versus services. The predominant view has been that manufacturing drives good wages, economic growth, and innovation as measured by its large share of patents, while services provide lower-wage jobs, less innovation and significantly fewer patents.

But Delgado and Mills argue that categorizing the economy in terms of manufacturing versus services is no longer meaningful. Instead, they’ve proposed an alternative framework for understanding the drivers of innovation and economic performance that’s focused on the suppliers of both goods and services: the supply chain economy.

“A long academic and policy debate has focused on the role of the manufacturing capacity of a country in its economic and innovative performance,” wrote the authors. “This question has become even more relevant as the U.S. economy has shown a large decline in manufacturing employment in recent decades, in part due to increased import competition. In this debate, the predominant view is that a country's manufacturing capacity drives innovation because of externalities associated with the production of intermediate goods (e.g., machine tools, automation equipment, and semiconductors) that improve the efficiency of the innovation process. Most prior work on innovation focused on a narrow view of suppliers as producers of intermediate goods. However, in today's economy suppliers increasingly produce services (e.g., enterprise software).”

A Pandemic Is Fundamentally an Information Problem

A few weeks ago I attended a very interesting online seminar, Economics in the Age of Covid-19, by University of Toronto professor Joshua Gans. Over the past 18 months, professor Gans has been conducting research and writing extensively on the impact of Covid-19, including a number of articles, a newsletter, and two books. His key thesis is that a pandemic is fundamentally an information problem. If you know that someone you interact with is potentially infectious, you can take actions to limit the interactions. However, if you have to guess whether a person is infectious, you’re taking a risk. Not only can you become infected, but you might also pass that infection on to others.

“The difference between perfect knowledge and no knowledge is what causes an infectious disease to have an impact on social and economic interactions,” wrote Gans in The Pandemics Information Gap, first published in April of 2020, followed by an expanded second edition in November of 2020. “With perfect knowledge, some people get sick, they are isolated, and life is (for most of us) essentially unchanged. … perfect knowledge allows you to avoid all infected people. No knowledge makes it near certain that you will encounter at least one infected person.” Furthermore, when we don’t know who is infected, we have to act as if everyone is infected, which then leads to major economic and social disruptions, including near-empty offices and city centers, decreased travel and leisure activities, and learning from home instead of in school.

In his seminar, Gans cited the 2002 SARS outbreak in China, Hong Kong, and Taiwan and the 2015 MERS outbreak in South Korea as examples of pandemics that were quickly contained. With SARS and MERS, people only became infectious when they developed a fever, cough, and other easily identifiable flu-like symptoms. Anyone suspected of being infected could thus be quickly isolated before infecting many others. This made it possible to contain the SARS and MERS viruses within a few short months, and to suppress them completely a few months later.

The 2020 COVID-19 outbreak was different. About a third of the people infected with the virus were asymptomatic carriers who didn’t develop noticeable symptoms but were still capable of infecting others. Of the people who showed symptoms, around 80% were only mild to moderate. And, there was generally a delay of several days between the time a person first became infected and the appearance of the first symptoms. In other words, the information problem was relatively straightforward with SARS and MERS, which made them easier to manage and contain, whereas managing the information problem with COVID-19 has been much, much harder.

Rethinking Digital Strategy for the Post-Pandemic Era

“One year into the COVID-19 crisis, our newest McKinsey Global Survey on digital strategy indicates that the pandemic has increased the pace of business and that technology capabilities will be critical to companies’ COVID-19 exit strategies as well as to what comes next,” said a recent McKinsey report, The new digital edge: Rethinking strategy for the postpandemic era. The survey was conducted online in January of 2021, and received responses from over 1,100 C-level executives, business unit heads and senior managers from different industries, functions, company sizes and regions.

The survey’s overriding finding is that the pandemic has sped up the adoption of digital technologies by several years. “The imperative for a strategic approach to technology is universal, yet some companies are already leading the pack; their responses show that better overall technology capabilities, talent, leadership, and resources (what we call a company’s technology endowment) are linked to better economic outcomes. At the same time, the results confirm that many organizations could be missing opportunities to invest in the areas of their business models that are most at risk of digital disruption.”

The technology endowments of the top-decile economic performers include cloud adoption and infrastructure, both public and private; a common source of data across the organization; a modern architecture for technology elements; sufficient cybersecurity to mitigate current risks and threats; prioritization of tech resources to the most strategically important efforts; increased investment in talent; filling key technology roles with high-quality individuals; increased R&D spending; and creation of new partnership.