Tangible and intangible employment up in March

Good news in this morning’s employment data from BLS: employment increased by 916,000 in March (much higher than expected) and the unemployment rate dropped to 6%. As the BLS notes, the improvements reflect the continued resumption of economic activity that had been curtailed due to the pandemic – especially in industries with direct public contact. Employment in Accommodation & Food Services was up by 215,900 (1.8%) and Arts, Entertainment & Recreation employment was up 64,400 (3.7%). Importantly, in the tangible producing industries, Construction and Mining was up 130,000 (1.6%), Manufacturing was up 53,000 (0.4%), and Trade, Transportation & Utilities was up 94,000 (0.3%). For the intangible producing industries, employment in Professional & Business Services was up 60,500 (0.3%), Education & Health Services was up 102,100 (0.5%), and Government was up 136,400 (0.7%).  

As a result, the employment split between tangible-producing and intangible-producing industries remained the same.

For more on the categories, see my explanation of the methodology in an earlier posting https://intangibleeconomy.wordpress.com/2020/06/11/which-jobs-got-hit-in-the-covid-crash-tangible-versus-intangible/

Past analysis of intangible investments in federal budgets

When I converted this blog from Athena Alliance (when we shut down the think tank), many of the links to earlier posting and papers were broken. Given the recent interest in federal government budgeting and investments, below are the correct links to the two papers:

Federal investments in intangibles – President’s proposed FY 2014 budget

Federal investments in intangibles – President’s proposed FY 2016 budget

You can also find all of the links to the old papers and presentations here.

Productivity and digital capital

There is a new paper out from the Brookings Institution’s Hutchins Center on Fiscal and Monetary Policy that has important implications for how we address lagging productivity. Part of the Hutchins Center’s Productivity Measurement Initiative, the paper by Prasanna Tambe, Lorin M. Hitt, Daniel Rock and Erik Brynjolfsson is called Digital capital and superstar firms.

Building upon previous work, the paper looks at a different categorization of intangible assets labeled “digital capital” consisting of “employee training that is related to new information technologies, firm-specific human capital related to technology systems, and the development and implementation of business processes and other forms of organizational transformation required to support or use new information technologies.” (p. 5) This definition excludes brands and intellectual property, and general human capital (as opposed to the IT related firm-specific human capital).

They find that investment in digital capital accounts for increases in productivity. “Our findings suggest that the higher values the financial markets have assigned to firms with large digital investments in recent years reflect greater digital capital quantities, rather than simply higher prices for existing assets. In other words, they reflect genuine improvements to firms’ productive capacity. In fact, we find that digital capital, if included as a separate factor in firm-level production functions, predicts differences in output and productivity among firms.” (p. 19)

Importantly, most of the value of digital capital is concentrated in a small number of superstar companies.

The combination of these findings has major implications for how to raise productivity: “One interpretation of our findings is that translating organizational innovations into productive capital requires significant investment in organizational re-engineering and skill development. Therefore, even if firms have the appropriate absorptive capacity, knowledge of how to construct digital assets will not automatically generate productive digital capital any more than access to the blueprints of a competitor’s plant will directly lead to productive capacity.” (p. 19)

In other words, laggard companies face daunting obstacles in trying to catch up. Knowledge is not enough and is not easy to translate into action.

In light of that finding, the public policy question is “what can governments do to help?” Clearly, technical assistance is not enough. It may be a necessary condition, but it is not a sufficient one. Investment in organizational and human capital is also required. Government support for investment both areas is somewhat controversial. While funding for general human capital has long been acceptable, funding for firm-specific human capital is less acceptable. And government funding for organizational capital is viewed as questionable. In both cases, government support is seen as using public funds to benefit private interests.

This is not to say that there isn’t already a good deal of government support for specific industries and firms, often through the tax code. I’m just pointing out that the case needs to be made for investments in digital capital. That case rests on the fact that digital capital is a key driver of productivity, which is crucial for continued economic prosperity. Just like R&D spending, there are great public spillovers of investment in digital capital.

In addition, we need to think more about the most effective mechanisms for fostering digital capital. Funding for firm-specific human capital can be relatively straight forward. One example is funding industry-community college partnerships. Funding organizational capital may be much trickier. Current government policies focused on technical assistance. We need creative ways to go beyond this narrow focus. Otherwise, we risk digital equivalent of, as the authors of this paper put it, assuming “access to blueprints of a competitor’s plant will directly lead to productive capacity.”

SEC takes new steps on disclosure – and what should be next

In an earlier posting, I reported on how the SEC has changed its Regulation S-K to require disclosure of information on human capital as part of the MD&A (Management Discussion and Analysis) section of companies’ financial reports. I noted back then that two Democratic members of the SEC voted against the new rule because the changes didn’t go far enough in requiring disclosure on Environmental, Social, and Governance (ESG) issues. As I stated then, this may foreshadow additional action by the SEC in this area.

Now SEC is taking another step with the announcements of a task force on enforcement of disclosures on climate change and ESG and an enhanced focus on climate change risk as part of the agency’s enforcement priority. The Commission is also seeking public input on possible new disclosures on climate change.

This interest in climate change and other ESG issues comes in response to calls from investors in both the US (see “BlackRock Chief Pushes a Big New Climate Goal for the Corporate World“) and in other countries (see “Accounting needs to be stepped up for climate change costs“).

Will increased attention to ESG issues spill over to intangibles? The answer is unclear. The driving interest in the new requirements for disclosure of human capital was focused on ESG issues of diversity and inclusion – not on economic performance such as improving innovation and productivity. And the focus of these efforts seems to be on outputs (i.e. the impact, costs, and risk) rather than inputs (i.e. intangible assets).

The attention to EGS issues is, however, useful in honing in on the guiding principle that the disclosures be consistent, comparable, and reliable. The only way to achieve these goals is for the disclosures to be mandatory. Voluntary disclosures leave information gaps that undercut reliability and do not allow the enforcement of standards of uniformity required for consistency and comparability. The question of mandatory disclosure is one that has bedeviled regulators since the beginning. And is especially of importance when it comes to understanding the impact of intangibles (see my working paper Reporting Intangibles).

One way forward would be for the SEC to build on existing requirements. Specifically, the SEC could allow for an alternative reporting of companies’ sales, general, and administrative costs (SG&A). This alternative would refine the current reporting of SG&A to breakout spending on intangibles from more routine spending. As I described in an earlier posting, SG&A would be divided into four components: R&D, advertising, Maintenance Main SG&A (basically the cost of sales such as office and warehouse rents, customer delivery costs, and sales commissions, and Investment Main SG&A (which would be the residual after subtracting R&D, advertisement and Maintenance Main SG&A ). The assumption is that Investment Main SG&A reflects spending that seeks to build organizational assets. Investment in intangibles would constitute the three categories of R&D, advertising and Investment Main SG&A.

Creating this new measure of intangibles is not without difficulties (for more detail see One Job: Expectations and the Role of Intangible Investments by Michael J. Mauboussin and Dan Callahan of Morgan Stanley and “Should Intangible Investments Be Reported Separately or Commingled with Operating Expenses? New Evidence” by Luminita Enache and Anup Srivastava). However, the SEC could allow this calculation under a “safe-harbor” provision – a process that I have long advocated for that would expand reporting on intangibles.

At first blush the concerns over ESG issues and the disclosure of intangible assets seem rather distant. But they share a common underlying problem: that investors are not getting the information they need to make intelligent investment decisions. I hope the heightened discussion over ESG disclosures will raise awareness of this basic problem. And that the SEC will be able to use the current concerns over companies’ disclosures to make meaningful change on all aspects of the problem.

Tech policy may be next up in Congress – but we need a broader view

Now that the COVID-19 American Rescue Plan has been enacted, speculation is growing that the next big bill will be a technology and competitiveness act. According to the Washington Post, a Chinese-focused technology bill may be replacing an infrastructure bill as the next legislative push. Late last month, Senate Majority Leader Chuck Schumer directed Senate committees to start working on such a package, using last year’s Endless Frontier Act as their starting point.

Since that bill garnered bipartisan support, the thinking is that it would be a good follow-up to the partisan fight on the COVID-19 bill. That support is riding on wave of concern over a rising technological competition from China, the availability of medical supplies during the pandemic, and the current semiconductor shortage.

However, there are a number of approaches that future policy could take. The Endless Frontier Act calls for the reconfiguration of the National Science Foundation (NSF) into a National Science and Technology Foundation (NSTF). However, it is not clear that grafting a large technology development and commercialization organization on to the existing basic science funding agency is the best alternative (as I noted in earlier postings).

An alternative proposal to create a National Technology Foundation (NTF) was recently suggested by the National Security Commission on Artificial Intelligence (see previous posting). The NTF would be separate from but parallel to the NSF. The NTF would focus on technology development and commercialization in a number of key technologies, including AI, biotechnology, quantum computing, semiconductors and advanced hardware, robotics and automated systems, 5G telecommunications, additive manufacturing (aka 3D printing), and energy storage technology.

Another approach is the creation of a number of agency specific DARPA-like entities. The President seems to have already endorsed the creation of a Health Advanced Research Projects Agency (HARPA).

Beyond the question of NSTF, NTF or “X”ARPA, there are a number of other technology policies that the new legislation should consider. For example, both the Endless Frontier Act and the AI Commission call for a regional technology hub program in the Commerce Department. And the AI Commission report contains over 60 specific funding proposals. Then there are a number of specific technology policy bills already introduced, such as the Democracy Technology Act (S. 604) sponsored by Sen Warner and others to create an interagency International Technology Partnership Office at the State Department headed by a Special Ambassador for Technology, with a $5 billion International Technology Partnership Fund to support joint research projects. It is unclear which and how many of these policy proposals will make it into the bill.

It is also unclear whether the new bill will include elements from last year’s America LEADS Act (which Senator Schumer co-sponsored). Included in that bill was a number of manufacturing, research, and technology development proposals such as expanding the Manufacturing USA Institutes program, expanding the Manufacturing Extension Partnership (MEP) program, and expanding the National Security Innovation Capital program and other defense-related critical technology programs. But that bill went beyond science and technology policies to include provision such as sanctions on China, support for continued stationing of US troops in Japan and South Korea, and provide refugee status to resident of Hong Kong and Xinjian province.

What is clear is that the process will be messy and disjointed. I participated as Senate staff in the creation and enactment of the Omnibus Trade and Competitiveness Act of 1988. That bill, as sprawling as it was, was at least guided by the framework and rhetoric of improving US economic competitiveness. As I’ve pointed out, the competitiveness framework has been missing for a number of years. It has been replaced with a “fear-of” driven policy – in this case the fear-of-China.

I understand the importance of the fear-of approach in motivating action. The 1980s it was a fear-of-Japan industrial policy. In 1950s and 60s had a fear-of-Russia industrial policy. One could even argue that Hamilton’s Report on Manufacturing and Henry Clay’s American System were in part a fear-of-Britain industrial policy. But our policy needs to go beyond reaction. We need a way to look systematically at the foundations of our economic competitiveness. Just as monitoring one’s personal heath is better than waiting for a diagnosis and way better than just treating the symptoms, we need a mechanism to go beyond the current problems.

Unfortunately, none of the existing proposals addresses this need. Yes, they contain various study and strategy-creation provisions. But they are narrow in scope. For example, the AI Commission’s recommendation for a Technology Competitiveness Council and a National Technology Strategy is focused on the development of emerging critical technologies – not on the competitiveness of the economy. Similar, the President’s Executive Order on supply chains is focused on issues of resilience and security which will help improve competitiveness – rather than starting with the goal of competitiveness and looking at how supply chains enhance or threaten that goal.

Thus, I am renewing my call for a new push for how we address the competitiveness issue. One way is to reinstate the Competitiveness Policy Council (CPC). Created in the 1988 Trade and Competitiveness Act, the CPC was defunded in the 1990’s as part of a GOP budget cutting exercise. During its life time, the CPC published a number of good reports — but never seemed to get much political traction. [In full disclosure, I wrote the legislation for the CPC and helped get it up and operating back when I served on Senate staff — so its demise was rather painful to me].

A more aggressive approach was outline over a decade ago by the Center for American Progress. The CAP proposal calls for:

• A Quadrennial Competitiveness Assessment by an independent panel of the National Academies whose objectives are to collect input and information from many sources and perform a horizon scan that identifies long-term competitiveness challenges and opportunities
• A Biannual Presidential Competitiveness Strategy that lays out the president’s competitiveness agenda and policy priorities, and captures the attention and buy-in of cabinet principals
• An Interagency Competitiveness Task Force led by a new deputy at the National Economic Council that develops the biannual strategy, oversees White House coordination of competitiveness initiatives, and monitors their implementation by agencies
• A Presidential Competitiveness Advisory Panel of business and labor leaders, academics, and other experts who assist the administration in developing policy details.

I realize that it seems like this was tried (and failed) back in the Obama Administration with the President’s Council on Jobs and Competitiveness. But I would argue that the “Competitiveness” part of the title was basically ignored. Even the Council referred to itself as the “Jobs Council.”

Regardless of what mechanism is used, we need to refocus the discussion on broader issues of “Build Back Better” and competitiveness, not just critical technologies. Unfortunately, I doubt the coming technology package will contain this broader view. That will be a missed opportunity.

Commission report on AI includes broader tech policy recommendations

Last week the National Security Commission on Artificial Intelligence issued its Final Report. Established by Section 1051 of the John S. McCain National Defense Authorization Act for Fiscal Year 2019 to take a very broad look at Artificial Intelligence (AI). https://www.nscai.gov/

There are lots and lots and lots of AI-specific information, findings, and recommendations (including 60+ specific funding recommendations). While the vast majority of the report focuses on AI (as it should), the 756-page report includes a number of important broader technology policy recommendations that could easily be overlooked. These provisions are important for the development of AI, but affect technologies in general.

One of the biggest items is the report’s call for the creation of a National Technology Foundation (NTF) with budget starting at $1 billion for FY2022 and ramping up to $20 billion by FY 2026 (for a total five-year budget of $51 billion). In contrast, the FY2021 budget for NSF was almost $8.5 billion. Unlike other proposals, the National Technology Foundation would be separate but parallel to NSF. The NTF would focus on technology development and commercialization in a number of key technologies, including AI, biotechnology, quantum computing, semiconductors and advanced hardware, robotics and automated systems, 5G telecommunications, additive manufacturing (aka 3D printing), and energy storage technology.

In a similar vein, the report notes that there is no agreement as to which technologies are considered critical, and therefore no way to prioritize governmental strategy and actions. The report calls for “a single, authoritative list of technologies and sectors which are key to overall U.S. competitiveness, along with detailed implementation plans for each to ensure long-term U.S. leadership.”

The report recommends creation of a National Network for Regional Innovation in Emerging Technologies, with a budget of $200 million for FY2022 – FY2026. This network would coordinate and fund the creation of Technology Research Centers in each designated Regional Innovation Cluster to facilitate industry-academia-government collaboration on critical technologies.

The report also recommends modernizing export controls, reforming investment screening (through CFIUS), amending the Foreign Agents Registration Act to better protect critical technologies, and expanding STEM-oriented immigration. In addition, they recommend the creation of a University Affiliated Research Center focused on research integrity and research security.

To promote greater international cooperation, the report recommends the creation of an Under Secretary for Science, Research and Technology and the creation of an Emerging Technology Fund at the State Department to support “digital foreign assistance, digital development projects, emerging technology programs, and other related initiatives of the Department of State and the United States Agency for International Development.”

Finally, the report recommends expanding the loan authority of the U.S. International Development Finance Corporation (DFC) to include funding of domestic industrial base capabilities supporting critical technologies. Specifically, the recommendation is to delegate authority under Title III of the Defense Production Act to the DFC.

As I said, there is a lot in this report. Even if the report had limited itself to just AI-specific recommendations, this would be a pathbreaking set of recommendations. Importantly the commission recognized that our AI strategy must exist in the context of a larger technology strategy. Adding these broader technology policy recommendations strengthens the overall impact on AI development. In doing so it goes above and beyond in setting the direction for technology policy for years to come.

The report lays out an ambitious agenda for policymakers. I hope that they will embrace the broader recommendations and not just cherry-pick some of the AI specific ones. Embedded in this report is an important set of ideas that offer the opportunity to dramatically move technology policy in the right direction. Policymakers need to seize this opportunity.

Pandemic hits front-line tangible and intangible employment

December is following a bad November for the labor market. According to this morning’s employment data from the BLS, employment dropped by 140,000 in December. The biggest hit was in Accommodation & Food Services – down by almost 400,000. The second largest drop was in Arts, Entertainment & Recreation where employment decline by 102,600. Both of these industries are directly hit by the pandemic. The bright spots were increases of 191,000 in Trade, Transportation & Utilities and 157,200 in Professional & Business Services.

The most important part of the story, however, may be the across-the-board slowdown in hiring. While the four sectors discussed above had the biggest changes, the rest of the economy was essentially flat. Employment in some tangible-producing industries, such as Construction & Mining, Manufacturing, and Tangible business services, increased slightly; employment in others, such as Repair & Maintenance, Personal & Laundry Services, Telecommunications, and Tangible educational & health services declined. Employment in the intangible-producing sectors showed the same mixed pattern. Employment in Membership Associations, Education & Health Services, and Government declined somewhat. Employment in Information and in Financial Activities increased.

For more on the categories, see my explanation of the methodology in an earlier posting.

New data on adoption advanced information technologies

There is an exciting new survey out on firms’ adoption of advanced information technology. The survey is part of the Census Bureau’s 2018 Annual Business Survey and is described in a new NBER paper  Advanced Technologies Adoption and Use by U.S. Firms: Evidence from The Annual Business Survey.

The survey finds “that digitization is quite widespread, as is some use of cloud computing. In contrast, advanced technology adoption is rare and generally skewed towards larger and older firms. Adoption patterns are consistent with a hierarchy of increasing technological sophistication, in which most firms that adopt AI or other advanced business technologies also use the other, more widely diffused technologies.”

The implication is that these firms using these technologies will continue to be the driver of productivity. As the report states, “while few firms are at the technology frontier, they tend to be large so technology exposure of the average worker is significantly higher.”

Use of business technologies by industry

For me, the most interesting finding are about the use of advanced business technologies. One of the more interesting finding concerns how different industries use different business technologies. The findings are both expected (in hindsight) and illuminating. (see table 16.)

As would be expected, warehousing and wholesalers are the top users of RFID technologies, along with motor vehicle parts manufacturers. Manufacturing industries are the biggest user of robotics, machine learning and machine vision. Surprisingly, software publishers are a top adopter of machine vision as well. Software publishers are also top users of augmented reality, and natural language processing. Motion picture and video industries, and specialized design services are the other heavy users of augmented reality.

The more enlightening finding of the survey concerned uses in industries not generally thought of as heavy into advanced technologies. Augmented guided vehicles are most used in the subindustry groups of Support Activities for Crop Production, Farm Product Raw Material Merchant Wholesalers, and Highway, Street and Bridge Construction. The usage rates in these industries are well about the mean usage rate for all industries. This indicates the existence of niche markets for autonomous/semi-autonomous vehicles.

Voice recognition technologies are most heavily used in the health-related industries of Medical and Diagnostic Laboratories, Offices of Physicians, and Outpatient Care Centers. Again, not where one might expect to find the greatest use of this technology.

Most intriguing is the use of touchscreens. The top user is Nursing Care Facilities (Skilled Nursing Facilities) followed by Beverage Manufacturing and Other Amusement and Recreation Industries. Given the heterogenous mix of industry users, it would be interesting to see how the technology is being used in the different industries. I can see touchscreens begun used in nursing. And other recreation industries might reflect the seemingly ubiquitous use of touchscreens in professional sports. But beverage manufacturing seems like an anomaly.

Looking at the data in a different way, the survey found that touchscreens technology is the most used business technology in every sector of the economy except manufacturing where it is ranked third. Machine learning and voice recognition are the technologies that show up as the second most used technology across all sectors. (see table 15)

I should note that none of these advanced business technologies should be considered widespread. As I mentioned earlier, while digitization is wide spread, the utilization of advanced business technology is still very limited (see fig 9). The most adopted technology is touchscreens with only a 7% overall use rate (in current use or being tested for future use). All the rest have overall use rates between 3.6% and 1% (see table 13).

The data provides interesting indications of where the technology is being used and which niche markets may provide a foundation for future adoption. More detailed research is needed, based on this data, as to what might be the opportunities and challenges to the spread of the technology across industry sectors.

Clustering of business technologies and technological hierarchies

One of the helpful insights in the paper is the discussion of technological hierarchies and complementarities. The data shows a clear hierarchy of technological sophistication. Logically, digitization is the first step. The Sankey diagram in fig. 9a shows that adoption of advanced business technologies is predicated on digitization and the usage of cloud computing. And, as the example machine learning shows, the adoption of the machine learning is helped by higher levels of digitization and usage of cloud computing (see the Sankey diagram in fig. 9b).

The data also shows that adoption of business technologies are tied together. For example, adoption of machine learning is correlated with machine vision (table 14). Interestingly, automated vehicles is correlated with augmented reality and with RFID. As the report notes, “These correlations suggest that certain technologies may need to be adopted in tandem to fully reap the benefits of the technology.”

Some remaining questions

As mentioned earlier, the paper reports at the data on size and age indicates greater adoption in general by larger, established firms. However, this overall finding leaves me with questions. First, it is unclear as to the differences in utilization among larger and older firms. Why does one incumbent adopt while another does not?

I would also like to see more analysis of the different industries use different advanced business technologies. Specifically, is the difference in utilization by size & age consistent across industries? Are there industries and business technologies where younger, smaller firms lead in adoption (rather than the overall finding for large, established companies)? For example, while I would expect the use of robotics in manufacturing to tend toward larger, established firms, is the same true for use of touchscreens in manufacturing? Or touchscreens in nursing care facilities?

The paper hints at this when it states “there are important differences in the diffusion and intensive use rates across sectors. The analysis of the connection between the prevalence of different technologies and firm life-cycle indicators (firm size and age) reveals that technology adoption and use is not always monotonically related to these indicators.”

Business technologies, productivity and public policy

The survey opens up a number of questions about the impact of business technologies. From a technology and economy policy perspective, the goal is to facilitate the adoption of technologies that increase productivity. Adoption patterns aside, what does the data tell us about use of business technologies and productivity? Are there business technologies that increase productivity across the board? If so, are the barriers to adoption of those technologies the same across industries? If so, the broad-based policies might be the most appropriate (such as an expansion of the MEP centers). If barriers are either technology or industry specific, then more targeted policies might be called for.

The discussion on technological hierarchies mentioned earlier also raises questions about public policy. The example of machine learning (fig. 9b) shows that the leap from digitization and use of the cloud to business technologies is still daunting. Thus, from a public policy perspective, this points to the need for both facilitating more intensive digitization and use of cloud computing and for strategies to help move beyond digitization and cloud computing.


The discussion above is but a small discussion of the findings of the report. And the paper itself only begins to scratch the surface of the richness of the data. As the report notes, “It is our hope that this paper serves as an impetus for further research using this new data set to help answer these important questions.”

I second that call. There is a lot more here waiting to be discovered.

A new narrative for rural America

Earlier this month, the Kauffman Foundation published a piece on “After generations of disinvestment, rural America might be the most innovative place in the U.S.” arguing that the current narrative of rural decline is wrong. Rather, the author, Chris Harris, notes that rural America has the underlying foundation for prosperity:

“We must recognize that innovation, diversity of ideas and people, and new concepts don’t need to be imported to rural communities – they’re already there. Rural entrepreneurs and community leaders have always, by necessity, been innovative.”

The problem is not the lack of potential; the problem is the lack of investment to unleash that potential.

He goes on to argue that because of the misdiagnose of the problem, policy makers follow the wrong solutions. Using tax breaks to recruit businesses to rural locations have the perverse result of lowering communities’ tax base and thereby hampering their ability to make the long-term community and infrastructure investments needed to sustain the local economy. In other words, tax incentives to attract companies in the short term end up making the communities less attractive in the long run. Furthermore, he argues that the often-utilized strategy of targeting corporate retailers ends up damaging local entrepreneurs.

Instead, communities should follow a “build, not buy” strategy of growing local businesses. He cites the examples of Emporia, KS and Ord, NE as success in investing in technical support and capital to local entrepreneurs.

Of course, the idea of investing locally is not new. In the past, this was often referred to as “economic gardening.” But Harris’ argument of underlying strengths in rural America is worth repeating. A decade and a half ago, I make a similar argument in the piece called “Building on Local Information Assets.”

In that piece, I pointed out that “All communities have the opportunity to benefit from capturing and using their local knowledge. In this new age of information and knowledge, rural areas can continue to thrive by being the special places they are.” I argued that communities must first map their intangible assets to better focus their strategies. Especially important are the hidden assets such as the tacit skills of the local workforce. It needed to be noted that tacit knowledge is only partially based in the individual; it also resides in the special circumstances and situation of the community.

At the end of my article, I closed with the thought that “it can be economically ‘cool’ to be rural.” For that to happen, however, we need to embrace Harris’ new narrative about rural America and build upon the positive strengths. Let’s hope that policymakers are listening.

The convoluted path of innovation: the COVID-19 vaccine

The general perception of drug development is as close to the linear model of innovation as any process can be. Starting with basic the research on a new compound, then turning that compound into a drug to combat a specific disease, then testing and certification by the FDA, and finally scaling up mass production.

A look at the example of using synthetic messenger RNA (mRNA) to fight COVID-19 reveals how convoluted drug development really can be.

The following is from Damian Garde and Jonathan Saltzman, “The story of mRNA: How a once-dismissed idea became a leading technology in the Covid vaccine race.”

The story starts with an idea by Dr. Katalin Karikơ, working with her collaborator Dr. Drew Weissman, on how that mRNA could be used to trigger cells to manufacture certain proteins — including antibodies to fight infection. That work, itself, built upon years of work by other researchers on DNA, going back of course to Watson and Crick (and Franklin) and the discovery of the DNA Double Helix, which built on work even further in the past.

For Karikơ and Weissman, it was years of grant rejections and frustration until they came up with a way to prevent the injected mRNA from triggering a massive immune response and making matters worse.

The idea was then picked up by Dr. Derrick Rossi as a focus of his work on a replacement for embryonic stem cells. That work came to the attention of Dr. Robert Langer – who recognized its potential for creating vaccines, among other uses. Langer contacted the venture capital firm Flagship Ventures which lead to the creation of Moderna.

As Moderna was pursuing its research, two scientists in Germany, Dr. Ugur Sahin and Dr. Özlem Türeci, were also working with mRNA technology. The established a company in the US, BioNTech, and even hired Dr. Karikơ. Meanwhile, Moderna ran into technical problems with the general use of mRNA for multiple conditions and focused instead on vaccines. As Garde and Saltzman tell it, the two companies took different directions.

Then the pandemic hit – and the rest is history. Moderna tailored a mRNA vaccine to the coronavirus. BioNTech partnered with Pfizer on its own vaccine.

Which brings the technology almost back to the starting point. With all the subsequent high-level attention, mRNA technology is now being touted as newest breakthrough for multiple uses – as envisioned by Karikơ, Weissman, Rossi and others.

So, when the newest mRNA-based therapy for some disease is announced in the future, remember that the road from at-the-time-ignored research in the 1990s to medical breakthroughs today has been a long and convoluted path.

[And note, the Garde and Saltzman article says little about the role of the government in the process – especially in the latter stages. For a description of that, see this article in today’s Washington Post on Operation Warp Speed – “How the ‘deep state’ scientists vilified by Trump helped him deliver an unprecedented achievement”]