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.

Conclusion

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”]

A bad November for the labor market

It could have been worse. At least employment grew by 245,000 in November – according to this morning’s employment data from the BLS. Economists had expected employment to increase by 440,000. Employment rose by 610,000 in October. Hiring slowed the most in tangible producing goods and services industries. The biggest change was in Accommodation & Food Services where employment in November actually decline by 12,000, compared to a rise in October of 227,000.

Employment in the intangible-producing sectors grew slightly in November by 68,000 compared to an increase of 57,000 in October. But this maybe an anomaly as the decline in employment in Government slowed (due in part to the cut back in Census workers in September and October). And this is in sharp contrast to the increase in intangible employment by 929,000 in August.

The slow-down was most pronounced in Professional & Business Services, which grew by only 44,000 in November compared to 208,000 in October. This slow-down almost completely offset the improvement in Government employment. Employment in Arts, Entertainment and Recreation increased by the essentially the same amount in November as in October.

Clearly the economy continues to struggle with the impact of COVID-19. While the biggest problems are in sectors with direct contact with the public, the impact is being felt by all.

SEC Requires New Human Capital Disclosures

I have long advocated for greater disclosure of information on intangible assets in company financial reports. Specifically, the MD&A (Management Discussion and Analysis) section of SEC-required financial statements should require more qualitative disclosure of intangibles. This would allow for more information on intangibles while sidestepping the difficult problem of assigning a financial value to the asset.

Earlier this year, the SEC took a major step forward in that direction by finalizing a rule amending Regulation S-K to require disclosure of information on a company’s human capital. [It should be noted that this new rule, which took effect November 9, makes a number of changes beyond disclosure of human capital.]

The rule takes a principles-based approach to disclosure rather than a prescriptive approach. This means that the requirement is for general disclosure of material information rather than requiring specific types of information. The rule requires a “description of the registrant’s human capital resources, including the number of persons employed by the registrant, and any human capital measures or objectives that the registrant focuses on in managing the business (such as, depending on the nature of the registrant’s business and workforce, measures or objectives that address the development, attraction and retention of personnel).”

The key point is that whatever human capital metrics or other information the company uses to manage must be disclosed.

While this may sound vague, the new rules won’t operate in a vacuum. For example, as one commentator points out, there are already International Standards Organization (ISO) recommendations for human capital metrics, such a development and training costs, and turnover rates. Not surprisingly, accounting/consulting firms (such as PWC) also have approaches to help companies decide what and how to disclose.

Some argue that the SEC should have gone farther to require more disclosure on Environmental, Social, and Governance (ESG) issues. It should be noted that the two Democratic members of the SEC voted against the new rule because the changes didn’t go far enough – in both the scope of the ESG items covered and the lack of any prescriptive requirements. This may foreshadow additional action by the SEC in this area, especially given the incoming Biden Administration. I suspect, however, the SEC will want to see how the new requirements actually work before making any changes. I also suspect, however, that this is just the beginning of additional disclosures in company’s MD&A filings.

Early innovation at Amazon

But not what you think.

This insight is from a review (by James Ledbetter) of a new book collected writings by Jeff Bezos:

Bezos and his wife initially packed up Amazon orders while kneeling on a concrete floor. His idea for improvement was kneepads; when an employee suggested packing tables, Bezos declared him a genius. “The next day I went and bought packing tables and doubled our productivity,” he writes.

Score one for the importance of business process innovation!

October was a “so-so” month for intangible employment

This morning’s employment data from BLS for October is better than expected but still rather disappointing (even though the unemployment rate dropped significantly). Employment rose by 638,000 compared to the 600,000 economists expected. Almost all of that growth was in tangible producing goods and services industries. Similar to the previous months, increases occurred in industries where there is physical presence with customers, specifically Accommodation & Food Services and Trade, Transportation & Utilities.

On the intangible-producing side of the economy, employment in Professional & Business Services expanded at a healthy rate. Almost every other industry grew only very modestly, if at all. But once again, there was a large drop in government employment offsetting most of the gains.

I’ll repeat myself from the last two months. Once again, under normal circumstances this would be a positive increase. However, in the age of COVID-19, this is only a modest rebound in employment. And keep in mind the worrisome trend of furloughed workers being permanently let go.

Much more needs to be done.

Trade in Intangibles – Sept 2020

Earlier this year I posted an analysis of how the then-new pandemic economic shock was affecting our intangibles trade surplus. Back the, IP trade was affected but other sectors only suffers slightly. With all the ups and downs of the past 6 months, it is time for an update based on BEA’s latest trade data for September.

Overall, the intangibles trade surplus has rebounded somewhat since hitting bottom in April. While not yet back to January’s level, it is at least closer to the pre-pandemic trendline. This is being driven by Business Services and Financial Services which dropped in the beginning of the pandemic and are beginning to rebound slightly. Maintenance & Repair Services and net revenues from Intellectual Property Products also took a hit at the beginning of the pandemic but have flatten rather than rebounding. Telecommunications, Computer & Information Services remained flat for the past year or so. Insurance Services and Personal, Cultural & Recreational Services continued their steady decline.

A closer look at specific industries reveals a more nuanced and worrisome picture.

First, there is a new sector added to this analysis: Personal, Cultural, and Recreational Services (see more detailed discussion below on BEA’s revisions to the data). Two points to make here. One, the balance of trade in this category has seen a 5-year steady and dramatic decline. Two, trend was not substantially interrupted by the pandemic. The pandemic caused a slightly turnaround as exports saw a blip in the summer rebound. But exports have flattened recently and imports have continued a steady rise.

The trade surplus in Maintenance and Repair services took a nose dive at the beginning of the pandemic and has not yet begun to recover as exports remain stuck at a lower level.

Our surplus in Intellectual Property also seems stuck at a lower level as payments out (imports) grew at about the same as revenues received (exports). As I noted back in December, the trade surplus in IP products has been declining for almost a decade as revenues (export) have remain essentially flat while payments (imports) have grown.

The pandemic seems to have had little impact on our trade deficit in Insurance Services. But that is not good news as the trendline continues to go straight down with imports climbing and exports declining slightly over the past few years.

The picture for Financial Services is somewhat better. Exports are rising while imports are flat.

The case is similar for Business Services with the surplus rebounding as exports grew faster than imports.

For Telecommunications, Computer, and Information Services, the pandemic had almost no net impact on the trade surplus as exports and imports first dropped and recovered at the same amount. This flat level, however, interrupted a 5-year trend in growth in the trade surplus in this sector.

NOTE: As part of its annual revision, BEA has updated the categories it uses to collect services trade data. As mentioned above, this includes creating a new category called Personal, Cultural, and Recreational Services. This category consists of the following subcategories (some of which were previously included in the Intellectual Property and Business Services categories:

  • Audiovisual services, which covers production of audiovisual content, end-user rights to use audiovisual content, and outright sales and purchases of audiovisual originals
  • Artistic-related services, which includes the services provided by performing artists, authors, composers, and other visual artists; set, costume, and lighting design; presentation and promotion of performing arts and other live entertainment events; and fees to artists and athletes for performances, sporting events, and similar events
  • Other personal, cultural, and recreational services, which includes services such as education services delivered online, remotely provided telemedicine services, and services associated with museum and other cultural, sporting gambling, and recreational activities, except those acquired by customers traveling outside their country of residence

BEA also created a new category called Construction Services separating the data out from the existing Business Services category. Since this category seems to cover physical construction activities, I have decided not to include it as an intangible creating activity, similar to how we treat the Travel and Transportation categories.

For more information, see the BEA article “Preview of the 2020 Annual Update of the International Economic Accounts.”

Learning from an innovation failure

Over at Digital Tonto, Greg Satell has an interesting analysis of why the streaming service Quibi failed. He points out four major flaws: too much money; no hair-on-fire use; no addressing the key bottlenecks; and, not having an adaptable strategy.

I won’t address the “too much money” issue – one that says you need to keep the company lean. I will accept his argument that “limiting the amount of money you have around forces people to face up to problems and solve them,” although my experience has been seeing undercapitalization as the problem.

The other three I think fall into the cardinal principles of innovation: experiment, expand, adapt. The three work together. What Satell calls the “hair-on-fire use case” is having a must use. Rather than identify the largest addressable market, you look for a problem with an immediate need. You use this market to refine and further develop the innovation and follow a flexible strategy to take advance of what you learn (including new opportunities).

This is a variation of what we used to call the “thin opening wedge.”

The process of learning and adapting based on real-time market information is key to success. It is almost axiomatic in innovation research that the first iterations of a new technology are inferior to the existing technology – except in one crucial characteristic. In the case of semiconductors, their advantage over vacuum tubes was in weight and power requirements. The need for low weight and energy in space and defense uses overcame the higher cost. These early markets provided not only a source of funding for further development of the technology (both product and process). They also provided a beta test function that generated important information.

Expanding and adapting is the other key. For example, look at Apple. The iPod was a cute device for music lovers (especially teenagers). It replaced the Walkman with much easier to use technology (digital rather than audio tape) both for play back (no need to carry and change tapes) and for song acquisition (via download). That was the thin opening wedge to a much more powerful platform: the iPhone. Once the iPod was married to a cell phone, the possibilities exploded. Not only was it a voice communications tool (the phone), it was a digital communications device and a digital interconnection device (email, web browsing, GPS, and all those apps).

Remember that Airbnb started out as a means to people to identify places to crash for the night. Uber was an on-demand sedan service. Amazon was a book seller based on the arbitrage between publishers’ prices and the retail bookstore prices. Each of these expanded by using the infrastructure (physical and organizational) created to service that first market.

Tied into this process of experimenting, expanding and adapting is making sure you are focusing on the right questions. Satell notes that successful innovations address the hard problems first. These are the bottlenecks that will cause the innovation to be an also-ran in a crowded field. The example he uses is Tesla and battery technology. Electric vehicles have been around since the dawn of the automobile age. In a more recent (relatively speaking) case, in the late 70’s / early 80’s I worked on a technology assessment of electric vehicles for Detroit Edison (and was licensed to drive their test vehicles – modified VW Rabbits with a ton of batteries in the back). Our conclusion was not surprising: limitations of battery technology would keep EVs in niche markets such local delivery vehicles with limited range, limited speed and the ability to recharge overnight. But even in that market, there was no great advantage for EVs over gasoline powered vehicles. Satell notes that Tesla’s breakthrough was to combine an improved, good-enough batter technology with a niche market of affluent consumers who would pay for the cache of an eco-friendly car.

In conclusion, let me just note that each of these examples of successes illustrate the principles of experiment, expand, adapt. Satell’s analysis of the failure of Quibi provided a useful counterpart to the success stories. Since the mantra of innovation includes learning from failure, I hope would-be entrepreneurs will take the lessons Satell provides to heart.