2Q 2014 GDP shows increase intangibles investment

Looks like good news this morning. The BEA has revised its estimate of economic growth in the 2nd quarter of 2014 upward to 4.2%. The advanced estimate released last month showed a growth rate of 4.0%. Economists had been expecting a slight downward revision to 3.9%. Growth in business investments in intellectual property products (IPP) was also revised upward to 4.4% from the previously estimated 3.5% growth rate.
IPP percent 2Q14 -2nd.png

SEC's next task: disclosure of hidden loan IP collateral

This morning the SEC unanimously adopted a long delayed rule on transparency of asset-back securities (ABS). Required under the Dodd-Frank Act, the final rule turned out to be less controversial that previously proposed versions. The new rules will require collection and disclosure of certain types of information (“data points”) about the underlying asset (i.e. the mortgage). This includes information such as the credit score and income of the borrower and information about the property (e.g. location, age, valuation). The earlier proposal had been held up in part because of concerns over privacy and the protection of this level of sensitive data. The SEC had re-opened the rulemaking process earlier this year to deal with these and other issues.
In adopting the new rules, SEC Commissioners mentioned that more to be done, including looking at other asset classes such as student loans. However, they did not mention nor include in the new rules disclosure of hidden loan collateral: IP.
Intangibles assets, specifically intellectual property, have always been part of the U.S. financial system. As I’ve noted before, the first trade secrets case in the United States involved the debt on a bond secured in part by a secret chocolate-making process in 1837. In 1884, Ara Shipman loaned Lewis Waterman $5,000 to start a pen-manufacturing business, secured by Waterman’s patent.
[Note: we have discussed that role in numerous publications such as “Commercialization of University Research – Using Intangible Asset Financing”, “Intangible Assets in Capital Markets”, “Intangible Assets: Innovative Financing for Innovation”, Intangible Asset Monetization: The Promise and the Reality and Maximizing Intellectual Property and Intangible Assets: Case Studies in Intangible Asset Finance.]
That interest appears to be growing. Gabe Fried and David Peress recently noted that “Increasingly, ABL [asset-based lending] structures incorporate intangible assets such as trademarks, patents, customer lists and other intellectual property assets in the borrowing base” (see their article “The Continued Growth of Asset-Based Lending Secured by Intangible Assets” which gives a good overview of why intangibles make good collateral). William Mann found, based on USPTO filings of a creditor’s security interest in a patent, “20% of patents held by domestic corporations during the 1990s had been used as collateral at some point in their lives” (see “Creditor Rights and Innovation: Evidence from Patent Collateral” – summarized as “Patents as Collateral”). Research by Maria Loumioti on “The Use of Intangible Assets as Loan Collateral” found that “twenty-one percent of U.S.-originated secured syndicated loans during 1996-2005 have been collateralized by intangibles, with intangible asset collateralization significantly increasing over this time period.” Importantly, she found that “loans secured by intangibles perform no worse than other secured loans.”
With this growing interest in intangible-backed lending, it is important that our financial regulatory system come to grips with this trend. As I’ve noted before, the failure to overtly include intangible assets in collateral analysis may have the following consequences:
•  Underestimation in the amount of collateral a lending institution has to call on in case of default (and therefore the undervaluation of the underlying loan).
•  Miscalculation of a lending institution’s ability to recapture collateral if the lending institution is dealing with an asset it does not understand.
•  Improperly priced loans due to a failure to assign the correct value to the intangible assets or a tendency to apply exceedingly low loan-to-value ratios that are less a reflection of risk than of the institution’s lack of knowledge about the performance of intangible assets.
•  Higher capital costs for borrowers, especially those in businesses heavily reliant on knowledge and technology.
Here we can learn from others. Our friends across “the Pond” (friends notwithstanding a nasty little incident 200 years ago) in the UK are taking steps to better utilize intangibles in the financial system. Starting with a study Banking on IP? The role of intellectual property and intangible assets in facilitating business finance, the UK Intellectual Property Office (IPO) then issued its report Banking on IP: An Active Response. As I noted in an earlier posting, one of the more important task will be to begin to standardize the process of looking at IP.

The first step will be to develop common terminology, so that lenders and businesses can talk the same language. The finance and IP worlds are both full of terms not readily understood by the lay person and which can be misused or confused. As a first step to developing a common understanding the IPO, working with businesses and the finance community, will develop a glossary of accepted definitions to be used when describing and valuing IP and intangible assets.
This common language will form a foundation on which we will develop templates and guidance which will help business accurately to document their IP assets in a way that supports the decision making of a potential lender. We recognise that most lenders already use standard templates or application forms for client businesses seeking finance. We will therefore seek to produce templates for IP related assets that can either be directly incorporated into this existing documentation or which can be used as a databank for information likely to be required by lenders.

The SEC could jumpstart a similar activity here. The databank envisioned by the UK IPO is similar in format to the data points the SEC now requires to be disclosed for ABS portfolios. Based on this experience, the next step is for the SEC to broaden the asset class covered by the new rules to include disclosure of information on intangible assets (starting with IP) used a collateral in securitized portfolio of loans. For example, an ABS using commercial loans as the underlying asset should be required to disclose information on any patents pledged as collateral on those loans. Any valuation of those patents used by the lender on those loans should also be disclosed. Such disclosures would set the template for lending institutions to use regardless of whether or not the loan is eventual part of an ABS offering. That would go a long way to helping both lenders and borrowers understand and better utilize the value of intangible assets.

OECD video on knowledge based capital

We call it “intangible capital” or “intellectual assets” — OECD calls it “knowledge-based capital” (KBC). Here is a new short video from OECD on KBC.


For more on OECD’s work on KBC, see their reports on New Sources of Growth: Knowledge-based Capital and the materials from our 2011 conference with OECD on New Building Blocks for Jobs and Economic Growth: Intangible Assets as Sources of Increased Productivity and Enterprise Value.

More on the benefits of human run factories

In an earlier posting, I made the point that human workers are better at innovation and flexible production than robots/automation. Willy Shih makes the same point in his new paper, “What It Takes to Reshore Manufacturing Successfully.” Shih looked at the experience of two companies who moved production back to the U.S. These companies faces challenges of re-establishing the work force’s factory-specific skill base and establishing a local supply base. He argues that the benefits to the companies in terms of better serving changing customer demands overcame the costs of meeting these challenges.
But his point on flexibility was more about the production process than about where the process was located (and how close it was to R&D/product development and the customer):

When choosing a location for assembly operations, it is natural to assume that higher wage rates will justify a greater use of automation. When work was offshored from the United States or Europe to Asia over the last two decades, the principal driver was labor arbitrage. With that offshoring came substantial substitution of labor for capital — the replacement of “hard” automation using expensive capital equipment with manual processes. Manual processes were less expensive, and human operators were far more flexible than machines that had to be reprogrammed with every model change. So when work comes back, most people assume that we will simply go back to using more automation.

But that is not necessarily the case. Manufacturing in China enabled rapid product changeovers, and we trained consumer markets worldwide to expect this kind of flexibility. If you want to have five million new smartphones on hand to sell on the first weekend after a new phone model launches, you will need a lot of people, not automation. While the latest automation technologies often have reduced setup or changeover times, managers should not assume that we should necessarily use more robots.
. . .
I recently visited a medical products factory in Denmark where the production engineers were continually experimenting with the balance between manual and automated processing. Having a slightly higher mix of manual operations promoted significantly more flexibility, and as the production engineers configured processing equipment for locations in eastern Europe and the Far East, they adjusted the labor mix in accordance with the labor costs. Even in high-labor-cost Denmark, the engineers were careful to avoid over-automating. Striking the right balance between capital and labor can benefit from an open mind and some experimentation. The mix may change over time with production experience and learning.

Our robot overlords may be coming in the future, but humans are still the most adaptive production system in existence today.

Robots and people

This morning at the Fed’s annual Jackson Hole symposium David Autor presented a paper on robots and people. Officially titled Polanyi’s Paradox and the Shape of Employment Growth, the paper has gotten some interesting press (see the WSJ summary – “Autor Paper at Jackson Hole: Automation Is Polarizing the Labor Market” and the more provocative Bloomberg summary – “‘Robot Overlords’ Job-Stealing Exaggerated: Jackson Hole Paper”).
Autor’s main point of the current paper is that automation is a powerful force in economic restructuring, but has its limits. He references Michael Polanyi’s insight “that our tacit knowledge of how the world works often exceeds our explicit understanding.”

A key observation of the paper is that journalists and expert commentators overstate the extent of machine substitution for human labor and ignore the strong complementarities. The challenges to substituting machines for workers in tasks requiring adaptability, common sense, and creativity remain immense. Contemporary computer science seeks to overcome Polanyi’s paradox by building machines that learn from human examples, thus inferring the rules that we tacitly apply but do not explicitly understand.

Those complementarities, however, are not to be found at the low end, creating a polarization in the labor market. In earlier work he constructs a Routine Task-Intensity (RTI) measure (see David Autor and David Dorn “The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market”). As he points out in this earlier paper, there is a displacement effect:

Critically, automation of routine tasks neither directly substitutes for nor complements the core jobs tasks of low education occupations–service occupations in particular–that rely heavily on “manual” tasks such as physical dexterity and flexible interpersonal communication. Consequently, as computerization erodes the wage paid to routine tasks in the model, low-skill workers reallocate their labor supply to [low-skilled] service occupations.

But Autor believes that

employment polarization will not continue indefinitely. While many middle skill tasks are susceptible to automation, many middle skill jobs demand a mixture of tasks from across the skill spectrum. To take one prominent example, medical support occupations–radiology technicians, phlebotomists, nurse technicians, etc.–are a numerically significant and rapidly growing category of relatively well-­‐‑remunerated, middle skill employment. While not all of these occupations require a college degree, they do at least demand two years of post-­‐‑secondary vocational training. Significantly, mastery of “middle skill” mathematics, life sciences, and analytical reasoning is indispensable for success in this training.

Interestingly, he puts bus and taxi drivers in the low RTI category. One would think that Google cars (and others) have shown how easily that task can be automated. However, he argues that

the Google car, unlike a human vehicle operator, cannot pilot on an “unfamiliar” road; it lacks the capability to process, interpret and respond to an environment that has not been pre-­processed by its human engineers. Instead, the Google car navigates through the road network primarily by comparing its real-­time audio-­visual sensor data (collected using LIDAR) against painstakingly hand-­curated maps that specify the exact locations of all roads, signals, signage, obstacles, etc. The Google car adapts in real time to obstacles (cars, pedestrians, road hazards) by braking, turning and stopping. But if the car’s software determines that the environment in which it is operating differs from the key static features of its pre-­specified map (e.g., an unexpected detour, a police officer directing traffic where a traffic signal is supposed to be), then the car signals for its human operator to take command. Thus, while the Google car appears outwardly to be as adaptive and flexible as a human driver, it is in reality more akin to a train running on invisible tracks.

He also points out the limitations of automated assembly lines and warehouses.
He uses these examples to highlight the adaptability and flexibility of humans. One thing he doesn’t point out is the inability of automated systems to innovate, as I pointed out in an earlier posting (“Benefits of human run factory”).
I do have to quibble a bit with him putting pharmacists in the category of high RTI scores. The pharmacist profession, at least, likes to tout the importance of their tacit knowledge, such as the knowledge of their patients drug interactions.
But Autor’s points about the role of tacit knowledge are worth remembering. Almost a decade ago, I noted in a posting that the changing nature of the economy made tacit knowledge more important:

The ability to innovate and to “design a compelling experience” are the important intangible assets. Routine activities — no matter how technically sophisticated or important — will gravitate to the cheapest workforce or be automated. Key to non-routine activities is a person’s tacit knowledge as well as problem solving abilities.

That being said, we should not underestimate the impact of these changes. As Autor notes in conclusion, adaptation “is frequently slow, costly, and disruptive.”

Intangibles and knowledge management

Yesterday, I had the enjoyable privilege of participated in a session at the Digital Government Institute’s Government Knowledge Management Conference. The session was a lively discussion among the panelists and audience kicked off with brief opening remarks by the panelists. The full background set of slides upon which my remarks were based are now available on line at “Measuring Intangibles as part of KM”.
Bottom line: government needs to pay more attention to its intangible assets – and knowledge management activities of the federal government can help.