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.

Modernizing American Manufacturing Bonds Act and intangibles

As readers of this blog will know, one of my pet peeves is the lack of awareness of intangibles in many of our economic polices and programs. An example I often cite is the long standing issue of the inclusion/exclusion of intangibles in the Qualified Small Issue Manufacturing Bonds program (aka Industrial Development Bonds – IBDs). As I have noted in a number of earlier postings, only traditional factories are eligible for low cost financing under this program. The 2009 stimulus bill included a minor change to allow the use of these bonds to finance facilities manufacturing intangible property. The change allowed local government to support new facilities for software development or bio-tech research facilities, for example, as well. But that provision expired at the end of 2010 and was not included in any tax extenders legislation. This simply act of putting physical and intangible investments on the same footing was forgotten and ignored.
However, new legislation was introduced in the House of Representatives just before the August recess to remedy that situation. The bill, Modernizing American Manufacturing Bonds Act (H.R. 5319), would make four changes to the Qualified Small Issue Manufacturing Bonds program, one of which deals with the intangibles issue. As a briefing paper by the Council of Development Finance Agencies (CDFA) explains:

Expanding the Definition of Manufacturing to Include both Tangible and Intangible Manufacturing Production for Qualified Small Issue Manufacturing Bonds
Issue Brief:
Qualified Small Issue Manufacturing Bonds are the bedrock financing tool for small- to mid-sized manufacturers. This financing tool has been providing affordable capital to our nation’s most important industry for over three decades. Current federal law defines a “manufacturing facility” as one that produces tangible property. However, manufacturing processes, production, and technology have changed significantly since this definition was established. Today’s manufacturers encompass more modern, high-tech, and intangible manufacturing practices such as bio-technology, energy generation, food processing, software, design and formula development, and intellectual property. In relationship to Qualified Small Issue Manufacturing Bonds (commonly known as Industrial Development Bonds or IDBs), the current definition as outlined in the tax code reflects an old philosophy and outdated approach to manufacturing. This outdated definition of manufacturing has resulted in the increasingly limited use of this job-generating economic development tool.
CDFA proposes updating the definition of manufacturing as it relates to Qualified Small Issue Manufacturing Bonds to allow for companies who produce both tangible and intangible property to access the capital markets. The measure would broaden the definition to include facilities that manufacture, create, or produce intangible property. The expanded definition would be sufficiently broad to cover software, patents, copyrights, formulas, processes, designs, patterns, know-how, format, and similar intellectual property. Under this new definition, knowledge-based businesses could access low-cost, tax-exempt IDB financing. This updated definition would align the growing high-tech manufacturing sector with the tools necessary to finance industry growth and expansion. This change will make an immediate difference throughout the country to help retain and create jobs, spur manufacturing investment, and accelerate the nation’s economy.

It is unclear what will happen to the legislation once Congress returns from recess. The proposal could once again be brushed aside as a minor point in a larger tax bill. And of course the future of any tax bill is itself very cloudy. At least CDFA is to be commended for continuing to raise the point. Maybe someday our lawmakers might just get it.
UPDATE: CDFA’s August 19 webcast on the legislation is now available online.

Improving how we use intangibles to boost productivity

Thanks to a recent posting over at the Smarter Companies blog, I am catching up on a McKinsey report on Innovation Matters: Reviving the Growth Engine from June 2013. The report introduces an index of “Innovation Capital” as a combination of “Physical Capital” (i.e. ICT infrastructure), “Knowledge Capital” and “Human Capital”. While this builds on the work of Carol Corrado, Chuck Hulten, Jonathan Haskel and others, I’m not sure it captures all the components of intangible capital. For example, the report talks about the need for collaboration and building the ecosystem, but never mentions relational capital. In fact a number of the policy prescriptions are aimed at building knowledge linkages i.e. social/relational capital. Nor are intangibles solely about innovation and productivity. In our current economy, intangibles are needed as inputs for ongoing operations as well.
But it was not the new index I found the most interesting. There was one graph that caught my attention: the relationship between “Innovation Capital” and productivity. The graph confirms that innovation capital (or intangible capital) is important for productivity growth. The striking feature, however, is that the U.S. gets less productivity growth from its investments in innovation capital than other nations. The U.K. gets the same amount of labor productivity growth as the U.S. from a smaller investment in innovation capital and Finland gets a much higher rate of labor productivity growth with about the same level as the U.K. investment.
Labor productivity v innovation capital - McKinsey 2013.png
The McKinsey report also has a graph on R&D spending that shows the U.S. basically on the trend line while a number of other nations, specifically Finland, Sweden and Germany, are significantly above the trend line. In other words, they get a much bigger productivity bank for their R&D buck.
TFP v R&D - McKinsey 2013.png
By the way, there is a variation the first graph in the work cited in the McKinsey report by Carol Corrado, Jonathan Haskel, Cecilia Jona-Lasinio and Massimiliano Iommi, “Intangible Capital and Growth in Advanced Economies: Measurement Methods and Comparative Results” which shows the same basic story with other nations, such as Finland, Ireland and even Slovenia get greater productivity growth from their investments in intangible capital. [Note the axis are reversed in this graph from the McKinsey graph.]
Intangible v MFP - Corrado 2012.png
These graphs were an eye-opener for me. For years I have been advocating policy measures to foster investment in and development of intangible assets. These include policy such as a knowledge tax credit and creating business assistance programs focused on intangible asset management (see “U.S. Policies for Fostering Intangibles“).
But the data presented here makes another important point: increasing investment in intangibles is not enough; policy must also look at the effectiveness of that investment in raising productivity. Why is it that the U.S. does so badly in the productivity return on its intangible asset investments compared to other nations (as point out in the first chart)? This will require a new line of research as to how intangibles actually work in boosting productivity in the economy.
The McKinsey report has some insight on that with its finding about the effect of investment in “Knowledge Capital” versus “Human Capital.” Their analysis shows that an investment in Human Capital generates a higher marginal return. But as I alluded to before, those two categories may be to gross for detailed investigation and may miss key elements. A more granular description is needed. In addition, the interaction between various types of intangible capital needs to be taken into account. As the McKinsey report points out, development of human capital is needed to realize any gains in other forms of capital.
Obviously, much more work needs to be done. One starting place is a more refined set of metrics about investment in specific types of intangible assets. Current efforts to collect data on these investments needs to be expanded and augmented with better official data. Likewise we need a more detailed understanding of policies in those more effective countries. A great deal of cross country studies have been done on innovation policy. But I am not aware of any that look specifically at how investments in intangible assets translate into productivity increases.
Sounds like we need to update and create a new (and rather substantial) research agenda.

Benefits of the human run factory

Sunday’s Washington Post ran an interview with Jeffrey Rothfeder about his new book, Driving Honda: Inside the World’s Most Innovative Car Company. There are a number of interesting points raised in the interview (“Honda’s global strategy? Go local”). He discussed Honda’s decentralized localization strategy and the company’s mindset of the need “to be there” i.e. to understand the product from a ground-up, hands-on approach.
One comment stood out. Rothfeder described Honda’s new plant in Lincoln, Alabama. The plant makes a number of different models on the same assembly line:

That is one of the most productive and flexible auto plants in the world. Uniquely, instead of setting up assembly line stations, where one person puts in the dashboard, the next station will put the radios in, and the next one will put the steering wheel in, at Honda they have zones of workers, so the zones put in five or six things. Those zones are also required to look at quality control.
Also, every car can be built to zone specifications. Whether it’s a Civic that’s come down the line or an Accord, if you’re putting in a dashboard, it’s going to be the same process. So workers are agnostic about what car is there.

Interesting. But what he said next was more profound:

Because of the flexibility, they are one of the least automated factories. Because they need human beings to work on these cars. If you’re going to have a robot put in a dashboard that has differences from one car to the next, you have to change the arms of the robot for every car. That can take hours.
Some would say that Honda not being automated and having more workers would hurt productivity. But it just shows what they make up for in flexibility. Again, their profit margins are better than anyone in the industry.
They do automate things once they feel like it has become a commodity. But once you automate, you can never improve anymore. A robot will never tell you, “Hey, I could do this better.” You’re limited by the technology, ironically. (emphasis added)

So maybe we are approaching this whole debate over robots replacing humans wrong. The debate is usually focused on how robots/automation can do the physical tasks while humans do the thinking tasks (such as learning). But, if Honda is right, the physical activity and the learning aspects of a job are inseparable. It is what economists call learning-by-doing. The doing/learning division of labor that seems to underpin much of the technology displacement debate comes straight from the paradigm of Frederick Winslow Taylor’s “scientific management.” While we claim to have long since abandoned Taylorism as an operating principle, we apparently have not shook off its hold on our conceptualizations.
Honda’s factories may show a way to get past the Taylorist mindset. Humans and robots working to together – not as replacements but a compliments. As the post-Taylorist organizational theories stress, learning and development of tacit knowledge can only occur through, as Honda’s philosophy say, “being there.” Luckily, much of the new “maker movement” is based on the same philosophy. The spread of this doing-and-learning philosophy may be the way to really spark a renaissance of manufacturing in the United States.
[For more on public policy and post-Tayorist idea of “high-performance work organizations”, see my somewhat dated paper “Time to Get Serious About Workplace Change.”]