The demise of Silicon Valley Bank

Tech was not the problem with SVB. The old-fashioned role of the bank as a facilitator of commerce was. But tech might indirectly benefit

Silicon Valley Bank (SVB) is often referred to as at the heart of Silicon Valley’s tech ecosystem. SVB has financed numerous ventures, both high-tech and not so high-tech (such as wineries). But SVB’s demise did not come about due to its lending. It was an old-fashioned bank run right out the plot of the movie It’s a Wonderful Life. That movie dramatically pictured what happens when lots of people want to withdraw their money at the same time. As the character George Bailey, head of Bailey, explains “You’re thinking of this place all wrong. As if I had the money back in a safe. The money’s not here. Your money’s in Joe’s house…right next to yours. And in the Kennedy house, and Mrs. Macklin’s house, and a hundred others. Why, you’re lending them the money to build, and then, they’re going to pay it back to you as best they can.” In other words, I can’t give you your money because it isn’t here. It is invested out in the economy.

To understand what did and did not happen as SVB, let us look at the role of the banking system in the economy. [Note that there are many types of entities in what I am calling the banking system, including, fintech companies, etc.] The banking system provides three interrelated services. First, it facilitates savings by providing a place (an account) where funds could be stored and a small incentive to keep (and increase) those fund through interest payments (the savings function). Second, it provides transactional services to their customers. People make payments out of those accounts. This facilitates transactions between buyers and sellers by provided instruments for the transfer of funds (the commerce function). Third, it aggregates and then allocates capital (the lending function). Banks pool the money from deposits and lend is out to business and individuals. These three are interrelated and can be combined in various ways in what is referred to banking services.

Given SVB’s position in the tech sector, one would think that the problem was with the lending function – that SVB made too many risking loans. But it wasn’t shaky loans that did in SVB. The problems were more in the savings and commerce functions. SVB put a large amount of its deposits into long term government bonds. It essentially transferred its savings function over to the bonds market. That was fine as long as interest rates were low and. But once interest rates started to go up, the value of those bonds went down. This forced SVB to sell bonds at a loss, which lead to concerns over SVB’s ability to pay off depositors wishing to withdraw funds, which lead to everyone wanting to withdraw funds at the same time, aka a bank run. As a result, SVB ceased operations and depositors were faced with the possibility of losing all their funds (except for the $250,000 covered by deposit insurance).

The problem of SVB’s collapse was made worse by its role in the commerce area. SVB was the Valley’s major provider of banking services. It was a holding spot for their customers’ funds, especially start-up companies. Start-ups would raise a large amount of fund that would be deposited in an account and then slowly withdrawn to meet payment needs (e.g., salaries, equipment purchase or rental, office rent, etc.). It was this function that potentially could have caused the greatest economic harm. If tech companies’ funds held by SVB were wiped out, these companies be unable to pay current expenses. And they would lose the funds that they had stashed away for future expenses. In other words, all the funds raised to finance these start-ups would be wiped out.

The SVB experience should be a wake-up call for banking executives, investors, and regulators. The current regulatorily system is focused on dealing with systemic risk especially the risk of contagion where there is the possibility that the failure of one financial institution will cause other financial institutions to fail. The collapse of Lehman Brothers and the financial crisis of 2007-2008 is a classic example of contagion. The SVB case illustrates the danger to the “real” economy even when there may not be a large impact on other financial institutions.

The difference is important. It is not clear to me that the current system recognizes the danger from SVB-like situations.  Under current law, a finding of a systemic risk is needed used to allow the Federal Deposit Insurance Corporation (FDIC) to provide additional deposit insurance, the so-called systemic risk exception. The SVB situation was questionable as to whether it was a systemic risk in the traditional definition. But it is clear that a write-off of the deposits over $250,000 would have a negative impact on the economy. Government officials understood that. The statement from the Federal Reserve on its actions cited the need to “minimize any impact on businesses, households, taxpayers, and the broader economy.” But the authority was based on systemic risk.

The question facing policymakers is what do we do to prevent this from happening in the future. And what is the criteria for government intervention. The systemic risk exception is meant to protect the banking system and thereby indirectly mitigate any negative impacts on the economy. The deposit insurance system is designed to protect individuals and, to some extent, small companies. But it could be argued that the $250,000 limit is too low to truly protect all but the smallest of small businesses. And it clearly does not protect start-ups or other cases where large reserves of cash are common.

I don’t have any answers to that question. At a minimum however, we should take a new look at the purpose of deposit insurance and of the meaning and relevance of systemic risk as the intervention trigger.

Now for the possible silver lining. It has been said that you should never let a crisis go to waste in terms of opening up a new opportunity. In the SVB case, the opportunity exists to learn something from their loan portfolio. As I mention above, SVB’s loan portfolio was not a factor in their demise. But the autopsy of their demise gives us an opportunity to look more closely at the process of Intellectual Property (IP)-backed debt financing.

According to one estimate, SVB’s loans given them a security interest in “tens of thousands of US patents.” I assume that most of these security interests were created as part of a routine “all-asset” or “blanket” lien included in the loan. As such, they were not specifically included in the calculation of the loan’s collateral; they were simply swept up along with everything else. On the other hand, it would be important to know the extent to which patents (and any other forms of IP) were valued as collateral.

It is unclear exactly how SVB’s loan will be dealt with. The FDIC has been looking for someone to buy SVB outright but may end up selling off the loans individually (or in packets). If the loans are put up for sale, a wise buyer would be smart to look at the value of those patents. Some may be worthless and some might be very valuable. As Joff Wild points out, “there are people who do understand IP value and have robust methodologies for working out what it might be when applied to a particular patent portfolio or family. Using this knowledge, they are able to develop profitable monetisation programmes. It is hard to believe that, in the US, they are not already taking a deep dive into what is on SVB’s books to work out whether any of the bank’s loans are worth acquiring.”

Such an exercise would be very useful to those who study patents – and for banking regulators. Assuming that the data could be made available without compromising proprietary information, it would give researchers insights into the role of IP in financing. For banking regulators, the ability to analyze a bank’s IP portfolio would add another dimension of oversight.

Banking regulations and regulators will be under intense scrutiny. Since the problems were mainly illiquidity, we can expect this to be the focus of attention. But the debate over what to do provides an opening for a wider look. Both SVB and banking regulators seem to have been caught off guard by the degree of interest rate risk. Regulators should take this opportunity to look at where there are other blind spots. And as I have argued before, the amount of IP in the loan portfolio, and its implications, is one such blind spot.

To improve oversight of banks’ loan portfolios, regulators need access to the “robust methodologies” mentioned earlier by Wild. One way of doing this is for the regulators to develop their own mechanisms for valuing IP. I’m sure that would be opposed by those with proprietary methodologies who would see the regulators as unwarranted government competition. An alternative might be similar to the system of credit ratings. The government does not have its own methodology. Instead, it relies on credit rating agencies for bonds and other securities. Officially known as nationally recognized statistical rating organizations (NRSRO), the big three, Moody’s Investors Service, Standard & Poor’s, and Fitch Ratings, control approximately 95% of the market.

As I’ve stated several times before, having an agreed upon methodology for determining IP value as loan collateral would boost the use of IP-backed financing. Having a means for regulators to assess the strength of a bank’s loan portfolio would result in banks adopting that standard for loan underwriting purposes. Having a standardized methodology would give lenders a level of comfort about including IP as part of the collateralization calculation. This, in turn, would opening up additional financing for intangible heavy companies, especially start-ups. And increase economic growth and prosperity in this age of intangible assets.

When is a trade- agreement not about trade? When it is about economic integration

During my Senate staff career, I had the good fortune to be involved in the beginning and the end of the Uruguay Round (i.e., the authorizing legislation in 1988 and the implementing legislation in 1994). That experience convinced me that trade agreements were no longer about trade; they are about economic harmonization. Earlier fast track legislation and trade agreements were narrow. For example, the Trade Expansion Act of 1962 which authorized Kennedy Round of trade negotiations under the General Agreement on Tariffs and Trade, and the Trade Act of 1974, which authorized the Tokyo Round, were mostly about tariffs. As I have noted before, trade entered a new era in 1994 with the inclusion of TRIMS (Trade Related Investment Measures), TRIPS (Agreement on Trade-Related Aspects of Intellectual Property Rights) and GATS (General Agreement on Trade in Services) in the Uruguay Round. These moved trade negotiations past tariffs and at-the-border issue to internal economic and regulatory policies.

But these non-trade issues are still usually discussed as part of larger trade negotiations, such as the various free trade agreements (FTAs) with individual countries.

Now we see an example of a trade negotiation that is purely about economic harmonization. As the New York Times article “U.S. and Europe Angle for New Deal to Resolve Climate Spat” points out, “Unlike a traditional free-trade agreement, which entails reducing barriers to trade between partners, this agreement would not involve lowering tariffs on either side.” Not only are tariffs not involved, the agreement does not seek to address any non-tariff barriers (NTB). Nor, it appears, does it require the European Union to change any of its trade laws and regulations. It is strictly about the participation of European companies in a U.S. technology program.

Of course, this isn’t the only example of a negotiation/agreement on economic harmonization. For example, a major international agreement on the taxation of multinational companies was negotiated under the auspices of the OECD (an agreement has recently come under fire from the newly GOP-controlled House Ways and Means Committee).

There is an interesting twist to the story. Under the provisions of the Inflation Reduction Act (IRA), tax credits for electric vehicles are only available to those vehicles using batteries using critical mineral from the U.S. or nations that have a free trade agreement with the U.S. And since there is no US-EU free trade agreement, EU companies are not eligible for the tax break. To deal with this issue, the US and EU are negotiating a “free trade agreement” covering just this one point. Which has raised the question as to whether such an agreement is really a “free trade agreement” as meant in the Inflation Reduction Act.

Assuming that an agreement is reached (and it looks like it will), there could be consequences for future negotiation. Will negotiators latch on to this free trade agreement-like model for other issues rather than attempt to craft a full-blown agreement? Does the help move agreements forward or simply create an ad-hoc and potentially chaotic situation? There is a long-standing debate over whether bilateral free trade agreements lay the groundwork for larger multilateral agreements or remove the incentives for multilateral negotiations. That debate has just gotten more complicated with the injection of this micro-level FTA-lite option.

Personally, I think the more focused version of the process is the way the system will evolve. As I’ve discussed before, I think the large multilateral negotiation is a thing of the past. The shift from trade to economic harmonization changes the dynamics of the negotiation process. The old dynamics don’t work. It was based on the concept of reaching an agreement by linking everything in a big package. But linkage doesn’t work the way it used to. In previous negotiations with a focus on tariff reduction, the dynamic was simple. I’ll reduce my tariffs on steel if you reduce your tariffs on autos. This allowed for a win-win situation that pushed for lower and lower tariffs. Everyone agreed that the end point was lower tariffs. The question was how to get there.

In the new situation, it is unclear how the trade-offs work and in what direction the dynamics points. I’ll lower my tariffs on steel if you increase your copyright protection to 100 years? I’ll allow you to subsidize your aircraft industry if you don’t ban my genetically-modified beef? I’ll decrease my agricultural subsidies if you reduce regulations on investment banking?

It is not clear to me that trying to deal with such a complex set of trade-offs is useful. Instead, we may have to approach each of these economic integration/harmonization issues separately – possibly in separate forums, such as the OECD and the G20. Yes, this being a negotiation, there will be linkage. But the complex web of links will not become so great as to bring the entire structure down. And it will allow all parties to clearly focus on a specific issue not the trade-offs — leading, one would hope, to a better outcome.

Economy Continues Chugging Along

The U.S. labor market continued to grow in February. The Bureau of Labor Statistics reports that nonfarm payrolls grew by 311,000 jobs while the unemployment rate rose slightly to 3.6%. Employment in intangible-producing industries and tangible-producing industries continue to track one another. The growth in the tangible-producing industries was the biggest in Accommodation and Food Service (up 84,300 jobs). In intangible-producing industries, Professional & Business Services (excluding tangible services) was up 31,500, Educational & Health Services (excluding tangible services) grew by 56,100 and Government (excluding Postal Service) was up 42,600.

This continuing parallel employment growth is a structural change from the pre-2010 period when employment in intangible producing industries grew as a percentage of total employment while employment in tangible producing industries declined.  

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

“Services” Trade Surplus Down but Intangible Trade Surplus Up in January 2023

The story line from this morning news from the Bureau of Economic Analysis (BEA) is that the trade deficit is up in part because the trade surplus in services is down. That is bad news. But it is a little misleading. The good news is that the U.S.’s trade surplus in intangibles continues to grow.

The biggest gain in intangibles was made in Business Services where exports grew by more than the increases in imports in January. Revenue from Intellectual Property was also a major contribution to the overall rise where exports (payments in) declining by less than the drop in imports (payments out). The surplus in Telecommunications, Computer & Information Services grew as exports grew by more than the increases in imports. Maintenance & Repair Services are slowly recovering from their dramatic decline at the beginning of the pandemic, with exports growing slightly faster than imports. The deficit in Personal, Cultural & Recreational Services improved slightly as exports rose and imports declined. Unfortunately, the trade deficit in Insurance continued to grow as export declined and imports rose slightly. And the trade surplus in Financial Services declined as imports grew more than exports.

The real problem is with the sectors that I call “Tangible Services” which are made up of the BEA trade sectors of Transport, Travel, Construction, and Government Goods & Services. These are sectors primarily involved in physical activities. [See below.] The trade balance in tangible services turned negative in 2022 following a steady decline in the trade surplus through the past decade. Exports grew in fits and starts including drops during the financial collapse of the mid-2000’s and around the COVID-19 pandemic. However, imports have grown at a relentless pace, overtaking exports early last year. The cause of the overall balance decline is the large and increasing deficit in transport services and a smaller, but still significant, deficit in travel.

Note: Tangible activities are primarily physical activities (involving atoms); intangibles are primarily information/analytical activities (involving bits). Production of goods is almost exclusively a tangible activity. Services can be divided into tangible and intangible activities. Tangible services involve physical activities such as cutting hair, ringing up a sale at a cash register, cooking and serving a meal, and transporting someone or something. Designing a poster, negotiating a deal, writing an article, and approving a loan are examples of intangible services. For more, see my earlier postings.

UPDATE on GDP: Revised data show knowledge-related business investment declined slightly in 4Q 2022 but not as much as previously thought

BEA’s Second Estimate of GDP in the fourth quarter of 2022 (4Q 2022) shows GDP grew by an annual rate of 2.9% in 4Q 2022 and 2.1% for all of 2022. This is slight lower that the BEA’s Advanced Estimate that showed GDP grew by an annual rate of 2.9% in 4Q 2022 and 2.1% for all of 2022 see earlier posting. In a change, the revised data shows investment in R&D up by 1.7% whereas the earlier data showed a slight decline. The levels of investment in information processing equipment (down 27.5%) and software (up 14%) were not significantly changed by the new data.

Intangibles, Productivity, and Construction

In his New York Times column last week Erza Klein raised the intriguing question as to why productivity in construction is so bad. The piece was appropriately entitled “The Story Construction Tells About America’ Economy is Disturbing.” It is largely based on a recent research paper by economists Austan Goolsbee and Chad Syverson on “The Strange and Awful Path of Productivity in the U.S. Construction Sector.”

Goolsbee and Syverson conclude that the productivity slowdown is real and not, as some would claim, a case of mismeasurement or other problems with the data. As to what is causing the slowdown, they have no solid explanation. As they note, “[M]easurement problems alone likely cannot explain all of the decline and that there are some problems facing productivity in the industry that have not been documented previously.” 

Klein offers one possible explanation: increased regulation and other requirements (“paperwork, and paperwork, and more paperwork”).  Beyond paperwork, he argues that construction faces obstacles that other industries do not. “When you construct a new building or subway tunnel or highway, you have to navigate neighbors and communities and existing roads and emergency access vehicles and politicians and beloved views of the park and the possibility of earthquakes and on and on.”

Goolsbee and Syverson end with the familiar but in this case completely appropriate recommendation that “Further research is needed to test between competing explanations and sharpen the picture of what has been happening in the sector.”

I have to say that I strongly agree with the need for more analysis. But let me suggest that it might be more illuminating to turn the research question around. Rather than ask why is current productivity low compared with the 50s & 60s, let us ask why was it so high back then? In other words, what were the forces and circumstance that supported this high level of productivity? And can we create new or recreate earlier forces that will have the same effect?

A short vignette from a recent paper by Martin Baily on productivity (based on a McKinsey & Company report Baily worked on) provides some insights. In this case, he was looking at the difference between Brazil and the U.S. in residential construction:

“Productivity was very low in Brazil, only about one-fifth of the U.S. level. The conventional wisdom in Brazil was that this low productivity was the result of the low educational level of the construction workers.

. . .

Instead, the productivity difference arose from two main reasons. First, most U.S. residential construction is carried out in sites where a large area is cleared and then multiple copies of pretty much the same house is built. This allows economies of scale. Second, a U.S. construction site is carefully orchestrated by site managers. Special trade workers, such as plumbers, carpenters and electricians are brought to the site only when required. These workers move from site to site as needed. Utilization of labor is much better in residential construction in the United States.”

In other words, organizational intangible assets were the key in explaining the differences in productivity.

I would argue that these two productivity-enhancing factors of economies of scale and relatively efficient labor utilization may also explain the differences in productivity between then (50s & 60s) and now. Both appeared in the construction industry during the Golden Age. And once they were widely diffused, their ability to raise productivity diminished. So far, nothing major in the way of new organizational intangible assets has come along to boost productivity.

Neither Goolsbee and Syverson nor Klein take these intangible assets into account. Goolsbee and Syverson make an effort, but come up short. They assume the following: “[W]e can use IP capital stock as a proxy for intangible capital in the industry (which would include things like know-how, organizational strength, trade secrets, buyer-supplier relationships, sector-specific human capital, and so on).”

This assumption does not hold for construction. The measure of intangible capital they use is BEA’s data for investment in intellectual property products, which include R&D, software purchases, and artistic originals. As Goolsbee and Syverson note, construction companies have little IP capital using this definition. And, most importantly, had little to no investments in IP capital during the Golden Age of the 50s and 60s. Thus, Goolsbee and Syverson’s analysis shows little impact on productivity due to intangibles, that is ongoing investment in R&D and software. But, as the discussion above indicates, it is likely that construction companies had a high level of organizational (and other) intangible capital at one point, which dissipated over time.

If this is the case, then it is possible that the decline in productivity was due to an inability to maintain the earlier intangible capital or to create a new stock of intangibles. Unfortunately, we don’t have good data and metrics for this broader set of intangibles at the macroeconomic (System of National Accounts – GDP) level. [Which is why I use the same data as Goolsbee and Syverson in my analysis of macro-level investment in what I call knowledge-related areas].

Klein hints at another set of intangibles when he declares that “[T]he frictions are in navigating local regulations, community considerations, neighbors’ qualms and politicians’ interest.” This local knowledge and navigational skills are key intangible assets in and of itself. Based on my own experience as a government official involved in DC’s planning and zoning activities, I can attest to the fact that some companies are better at dealing with this complexity that others. I don’t doubt that the complexity facing construction projects has increased since the 50s and 60s. And I suspect that companies’ ability to navigate this complexity (which is an organizational intangible asset) has not increased accordingly.

Accepting the (unproven) hypothesis that intangibles played a (large?) role in the productivity of the construction industry, we can turn our attention to how to foster intangibles in the industry.

A number of years ago I did a report on the competitiveness of the construction industry for the then Congressional Office of Technology Assessment (part of a larger study on services). Back then, I noted that in contrast to manufacturing, construction was basically a craft-based industry. That study concluded that increase productivity in construction will require companies adopt better techniques on the job site and more extensive use of off-site prefabricated components but that there were major barriers to innovation. The industry suffers from a lack of incentives to adopt new technologies (and other intangibles). For example, as I noted back then, “Not only may superior construction methods be precluded, but the system rewards conservative choices. Once the contractor has won a job with a fixed-price bid, there is little incentive to do anything but follow the specifications the bid was based on.” In addition, companies lack of capability to utilize these intangible due in part the fragmented nature of the industry.

More recently (2017), a report from McKinsey Global Institute confirmed my decades-old findings, including the large potential gains from the use of prefabricated standardized components and the fact that “Most individual players lack both the incentives and the scale to change the system.”

The report lays out the factors behind the productivity problem:

“The industry is extensively regulated, very dependent on public-sector demand, and highly cyclical. Informality and sometimes corruption distort the market. Construction is highly fragmented. Contracts have mismatches in risk allocations and rewards, and often inexperienced owners and buyers find it hard to navigate an opaque marketplace. The result is poor project management and execution, insufficient skills, inadequate design processes, and underinvestment in skills development, R&D, and innovation.”

They describe seven areas where action is needed:

  • reshape regulation and raise transparence, including streamlining permitting and approvals processes, reducing informality and corruption, and encouraging transparency on cost and performance; “Best practice regulation would include moving toward outcome-based, more standardized building codes, and consolidating land to promote scale.”
  • rewire the contractual framework to reshape industry dynamics to encourage collaboration and problem solving and focus on best value and past performance rather than just cost;
  • rethink design and engineering processes to incorporate value engineering with a focus on constructability and repeatable design elements;
  • improve procurement and supply-chain management through digitally enabled processes;
  • improve on-site execution through adoption of existing tools and processes to improve project planning and management, ensuring that all pre-work activity is complete before starting actual constriction, greater use of key performance indicators (KPIs), and adoption of lean operating principles;
  • infuse digital technology, new materials, and advanced automation including increasing investment in IT and R&D, and appoint innovation officers to oversee technology adoption; and,
  • reskill the workforce to be able to utilize the new technologies and processes.

The report goes on to note the possibility of major changes in construction:

“Parts of the industry could move toward a manufacturing-inspired mass-production system that would boost productivity up to tenfold. Industrial and infrastructure megaprojects need to instill holistic project-operating systems on-site and in design offices. The highly non-linear and challenging nature of megaprojects underscores the difficulty of, and necessity for, moving toward an industrialized project-operating system.”

The McKinsey report’s findings fit with my hypothesis of a deterioration of the 50s and 60s intangibles and a failure to renew the stock of intangible capital. The fact that their report highlights some of the same points made in my piece of decades ago is disconcerting. But it bolsters my argument. The construction industry done little to improve its production processes. It is little wonder that its productivity has declined.

At the end of his article, Klein admits that he has no ideas on how to increase construction productivity. I think the McKinsey report points the way to a solution. The trick is to get the companies and workers to implement the recommendation actions. There is one action I would highly recommend: require government agencies use their procurement policy to encourage innovation and new approaches by prescribing means and methods of delivery or requiring use of certain technologies. Mentioned only in passing in the follow up report by McKinsey, this suggestion deserves greater attention, including as part of the Biden Administration’s policy  to use procurement to achieve environmental sustainability (Executive Order 14057).

In the end, however, it is up to the construction companies and workforce to take the actions need to increase productivity. To achieve this, the industry must accept responsibility for the problem. Rather than blame outside forces that are complicating the process, the industry (and policymakers) needs look inward. As Shakespeare told us, the fault is not in our stars but in ourselves.

Intangible Trade Surplus Down in December 2022

There was some bad economic news this morning from the Bureau of Economic Analysis (BEA) showing that the US trade deficit increased in December to $67.4 billion. Exports declined by $2.2 billion to a level of $250.2 billion and imports rose $4.2 billion to $317.6 billion.

Part of the bad news was that the surplus in intangibles shrank slightly in December as imports increasing more than exports. The trade surplus in intangibles had increased in the past 4 months.

For the year, the increase in the 2022 intangible surplus of $15.5 million is well below the $47.4 million increase recorded in 2021. But better that the actual decline in the intangible surplus of -$7.6mmillion in 2020. In aggregate, the intangibles trade surplus took a hit in 2020 (like almost everything), rebounded in 2021, and settled down to basically the historical trend line in 2022.

However, the sectoral data shows a slight shift in the composition of the surplus. The trade surplus in Financial Services held steady in 2020 and actually grew in 2021 before reverting to historical levels in 2022. Revenue from Intellectual Property fluctuated around the flat trendline of the past decade and a half. Business Services is the star of the show, growing especially strong in the past 10 years. Revenue from Maintenance and Repair Services dropped dramatically at the beginning of the pandemic and have not recovered. Interestingly, the trade deficit in Insurance sharply declined then rapidly grew again between 2018 and 2019 before flatting out in 2020.

Economy Chugging Along

Employment in intangible-producing industries and tangible-producing industries continues trend from previous months.

January was an unexpectedly strong month for the U.S. labor market. This morning the Bureau of Labor Statistics reports that nonfarm payroll grew by a whopping 517,000 jobs and the unemployment rate dropped to 3.4%. Employment in intangible-producing industries and tangible-producing industries continue to track one another. The growth in the tangible-producing industries was the biggest in Accommodation and Food Service (up 113,400 jobs). In intangible-producing industries, Professional & Business Services (excluding tangible services) was up 72,200, Educational & Health Services (excluding tangible services) grew by 80,500 and Government (excluding Postal Service) was up 72,000.

This continuing parallel employment growth is a structural change from the pre-2010 period when employment in intangible producing industries grew as a percentage of total employment while employment in tangible producing industries declined.  

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

UPDATE: Note that BLS’s press release (and many subsequent news articles) mention the large increase in employment in the industrial category of Leisure & Hospitality. For my analysis, I use the two subcategories that make up the category: Arts, Entertainment, & Recreation and Accommodation & Food Service. I classify Arts, Entertainment, & Recreation as an intangible service. Accommodation & Food Service is considered a tangible service as it mainly involves the handling of physical objects (atoms, not bits). In January, the vast majority of the 128,000 new Leisure & Hospitality jobs were in Accommodation & Food Service (113,400 new jobs), and even more specifically in the Food Service and Drinking Places part of that subcategory (up 98,600). Thus, the job growth was due to people going out to bars and restaurants, not to activities like sporting events and concerts.

In Reversal of GDP Growth Trend, Knowledge-Related Investment Slows Slightly in 4Q22

We had some good economic news yesterday as the BEA’s Advanced Estimate showed GDP grew by an annual rate of 2.9% in 4Q 2022 and 2.1% for all of 2022. That is the second straight quarter of GDP growth after declines in both 1Q 2022 and 2Q 2022.

However, the data shows a decline in investment in knowledge-related areas [information processing equipment, software, and R&D]. The decline was largely in information processing equipment, where investment dropped by over 6.4%. Investment in R&D declined by 0.2% whereas investment in software grew by 3.2%. This is only the second time since the beginning of the pandemic that investment in knowledge-related areas has declined.

Overall business (non-residential fixed) investment was up slightly, led by strong growth in investment in transportation equipment.

Obviously, those knowledge-related sectors make up only a small fraction of our $25 trillion economy. But the willingness of businesses to invest in knowledge-related areas is a strong indicator of future economic growth. In that regard, the slowdown in information processing equipment investment should be seen as at least a yellow flag warning.

[Note: I define knowledge-related investment as the combination of investment in Information Processing Equipment, R&D, and Software. The first of these three categories is reported in the GDP data as a subcategory of Non-residential Fixed Investment: Equipment. The latter two are reported as subcategories of Non-residential Fixed Investment: Intellectual Property Products.]

Covid-19, the Great Resignation, job and economic reallocation, productivity, and “destruction without creation”

The so-called “Great Resignation” of 2021-22 saw a massive job reset due to Covid-19. Maybe more accurately called the “Great Reshuffle” as more workers changed jobs rather than simply dropping out of the labor force, this somewhat unique shift in the workforce offers us a natural experiment. Theory says that productive is increased by moving resources (such as labor) from low productive uses (firms) to higher productive uses (firms). If that is true then the movement (reallocation) of all these workers should result in higher productivity. In essence, the Great Resignation should be a manifestation of creative destruction.

However, the process of reallocation is never smooth and depends on a number of factors. To what extent are workers changing industries or changing firms in the same industry? If they are switching to similar companies, are they moving to higher productivity firms? Or are they simple moving to same productivity level firms for higher wages? If the latter, then overall productivity will be going down as labor costs go up.

And it raises a more general measurement question: how to untangle reallocation effects from productivity gains dues to adoption of productivity-enhancing technologies and management practices in firm?

It also raises questions as to the importance of types of intangibles. The theory only works if one assumes that firm-specific human capital (the skills and knowledge that workers carry with them) is less important that general human capital (ability to learn, etc.) and organizational capital (ability of firm to better utilize that human capital and to integrate workers into their organization). And that firm-specific human capital (knowing “how we do things here”) is less important if it is knowledge about a less productivity way of doing things. That is not to say that organizational capital trumps human capital. Rather, organizational capital (including good management practices) is needed to make human capital relevant.

Work by Bloom et. al is answering some questions about the impact of Covid-19 using survey data of UK companies between July 2020 and April 2022. Their analysis indicates that “reallocation between firms made a positive contribution to productivity.” This is as expected.

But overall, productivity went down. Why? Because “the pandemic will have increased intermediate costs and therefore lowered productivity within firms across countries, with the adverse effects on within firm productivity likely to have been largest in industries where it is harder for jobs to be done from home.”

They go on to explain: “The negative within [firm] effects on TFP were partially offset by positive between effects – low-productivity sectors shrank more than high-productivity sectors, and the least productive firms within these sectors suffered most. The sector result arises because the lowest productivity sectors tend to involve more face-to-face activity – travel, leisure, retail etc. – and so contracted as a share of value-added. The firm result arises because the pandemic appears to have more severely affected lower-productivity firms within sectors, in part because they struggled to deal with the need for rapid pandemic re-organization.”

Ok – but here is the real kicker: “These positive between effects on productivity, however, are not the usual Schumpeterian process of creative destruction, whereby lower productivity firms are replaced by higher productivity firms. Instead, much of this was simply a lockdown of low-productivity sectors (destruction without creation). So, while this helped to push up productivity, it reduced total economic output.” (Emphasis in bold added)

I’m not sure what this portends for the future of the UK economy (and the US economy as the authors note that the findings for the UK “gives an indication of the likely direction of the impact of Covid-19 in the US and other advanced European countries given the similar nature of the pandemic impact”). But the process of “destruction without creation” worries me. Clearly there is an economic policy issue here as to how to foster the creation process in the time of destruction. With all those people changing jobs, I would hope the reshuffling is giving rise to some creation somewhere. We need to figure out how to give that a positive boost.

See Nicholas Bloom, Philip Bunn, Paul Mizen, Pawel Smietanka, and Gregory Thwaites, “The impact of COVID-19 on Productivity,” Working Paper No. 061, Programme on Innovation and Diffusion, London School of Economics and Political Science, December 2022