3rd quarter economic growth revised upwards

BEA’s “second estimate” of the 3rd quarter GDP showed stronger economic growth than previously thought. The GDP growth was revised upwards to 2.7%, from the previous estimate of 2%. That is still weaker than many had hoped for. The Federal Reserve’s “Beige Book” economic survey also released today indicates “economic activity generally expanded modestly since the last report.”
Greater exports and private inventory investment account for the upward revision. Business investment building was significantly revised upward. However, investment in information processing equipment and software was revised downward.
As I have noted before, the data has a basic problem in that it does not give us any guidance on investment in intangibles other than software. So we do not know whether companies have increased or decreased their investments in important areas such as human and organizational capital.
As I’ve also mentioned before, BEA has plans for a major revision in the GDP calculations next year. Two big changes will help make the GDP data more accurate: capitalization of research and development (R&D)and capitalization of entertainment, literary, and artistic originals (movies, music, books, art work, etc.) Currently, both R&D and the cost of creating entertainment, literary, and artistic originals are treated as a direct expense. Under the new system, they will be treated as investments, as they should be since they have long paybacks not just immediate returns.
As part of this shift, investments in these items will be specifically captured in the nonresidential fixed investment data. There will be separate data for software (now a subcategory of equipment), R&D, and entertainment, literary, and artistic originals. This should allow us to get a better picture of the I-Cubed Economy.
Note: the third estimate (what used to be called the “final”) will be released on December 20. The GDP number is still subject to later revision.

OECD report on workforce skills – and the need to change the workplace

Earlier this year, OECD put out a report on workforce skills (Better Skills, Better Jobs, Better Lives: A Strategic Approach to Skills Policies) that contains a broad analysis of the skills gap and other training and labor force issues. It also contains a wide range of policy recommendations. But from my perspective, there is one item tucked away in the report that deserves more attention: the need to change the workplace to utilize higher level skills.
When we discuss the skills issue, much of our focus is on the individual and the need for the individual to upgrade their skills. Most of the governmental programs, such as those discussed in the OECD report, are geared this way. Yet, if the organization is not capable of utilizing those skills, the training and educational programs are for naught. As the OECD report points out, some countries are facing a skills surplus, where there are not enough high skilled jobs to employee high skilled college graduates. In other countries, such as the United States, we might be suffering from an inability to match the general skills of a worker to the very narrowly defined skill needs of the company (the (“Purple Squirrel” problem — see earlier posting).
We need a broader focus on workplace design to utilize the available skills in the most productive manner. In part, this means focusing on the creation of high performance work organizations (see earlier postings). In an article I co-authored a number of years ago, “Time to Get Serious About Workplace Change”, I argued that:

The federal government obviously cannot jump-start the transformation [to high performance work organizations] by legislative or regulatory fiat. But it can serve as a catalyst and enabler. Government policy can help foster economic, political, and social environments that favor and speed the adoption of high-performance practices and reduce the risks and costs of implementation. The government can support the development and diffusion of tools, technologies, technical assistance, and standards that make it possible for companies to move toward high-performance work systems, and it can help expand the educational and training resources required.

Starting point of such a government policy is to recognize that the workforce skills issue is not solely one of raising the skill levels of individuals. It is as much an issue of the organizational utilization of those skills. The dual focus of workforce and workplace is needed if we are to have a coherent skills policy for the I-Cubed Economy.

Twinkies versus cars

A few decades ago when America faced a previous competitive challenge, an economist asked a core question about the future of the economy: “computer chips, potato chips — what’s the difference.” The basis for this question was whether there was a place for a government policy directed at a particular sector of the economy. Well, the answer to that question turns out (and was self-evident at the time) that computer chips are much more important to economic growth and prosperity than potato chips. And government policies toward the technology sector help spark the economy.
Now, the question has reappeared in the form of “cars or Twinkies — what’s the difference between bailing out GM versus Hostess.” George Will ask that question in a semi-serious manner in yesterday’s Washington Post (“Digesting the Twinkies’ lessons”).
The answer to that question lies in the type of assets involved, specifically the intangible assets.
The assets behind Twinkies are brand and recipe. As Hostess moves through liquidation, other companies can easily pick up the brand and the recipe and resume production. In fact, Hostess claims that there has been “a flood of inquiries” on purchasing the assets. That is the way the bankruptcy system is supposed to work.
The case of GM was very different. There is a much more expansive set of intangibles assets that underpins the automotive industry.
First is the technology. One Twinkie is pretty much the same as another. And the Twinkie you bought a year ago, or 5 years ago or 20 years ago are pretty much the same as the one you would buy today. Yes, the production process may have been modernized and the original banana flavored Twinkie reintroduced. But the basic product is relatively the same. In fact, that consistency is an intangible asset.
In the automotive industry, both the product and process technology needs to be constantly upgraded. Building and trying to sell yesterday’s car is a formula for failure. The technology for Twinkies can sit on the shelve for a while and still be good (like the product itself). But if you dismantle the car companies’ innovation process, it will take time and resources to put it back together. And the tacit knowledge embedding in the innovation system could be lost.
The second type of asset is the supply chain. Twinkies pull from a vast existing supply chain to the bakery industry. But that supply chain does not rely heavily on the Twinkies and the Hostess company. Remove that product from the system and the supply chain will continue. And the supply chain can easily reintegrate Twinkies and a Hostess-replacement company into the system.
Not true about GM. Had GM gone under, a vast supply chain would likely have fallen as well. Rebuilding that supply chain would have been a daunting task — and it is not clear that it even could have been rebuilt in the United States.
Third, there is customer support network. Again Hostess fits into to a large retailing system. Shutting down Hostess means disrupting a part of that system — specifically the Hostess-specific wholesale system. But that is not major disruption to the entire baked goods wholesale/retail system. GM and the auto industry rely on a company specific dealer system. Dismantling that system and trying to recreate it would again be difficult.
Dismantling the dealer system would eliminate not just the sales but part of the service chain as well. Service is a large part of the automotive industry. Customers don’t take Twinkies back to the dealer for repairs. Nor do Twinkie buyers worry that the warrant might not be honored or that parts might not be available if the company goes under. Car dealers play a much more important role in the lifecycle process of the product than the retail stores do with respect to Twinkies.
All in all, it should be self-evident that losing a major part of the automotive industry is very different from a possible temporary lose of Twinkies. One (Twinkies) can easily transfer and use the key intangible assets to restart production. The other (cars) risks very costly disruptions and the possible permanent loss of key intangible assets.
That someone could even think of equating the two (even in a semi-serious way) demonstrates how little understanding there is about how the economic world really works.

The changing nature of manufacturing

The McKinsey Global Institute has released a new report: Manufacturing the future: The next era of global growth and innovation. (See also Neil Irwin’s summary in the Washington Post: “American manufacturing is coming back. Manufacturing jobs aren’t.”)
This is the the latest in a long series, by many authors, of important reports on manufacturing. The McKinsey report reiterates and reinforces much of the recent work on why manufacturing matters to the U.S. economy — the contribution to GDP, productivity, exports, R&D and innovation, etc. More importantly, the report makes two point that often gets overlooked in the discussion, especially the policy discussion.
The first concerns the differences within the manufacturing sector:

In order to craft effective business and policy strategies in manufacturing, it is important to start with an understanding of the fundamental differences between manufacturing industries.

They segment the manufacturing sector into five groups:
 •  Global innovation for local markets such as chemicals (including pharmaceuticals); automobiles; other transportation equipment; and machinery, equipment, and appliances.
 •  Regional processing industries such as food processing and other industries that locate close to demand and sources of raw materials.
 •  Energy and resource-intensive commodities such as basic metals.
 •  Global technology industries such as computers and electronics.
 •  Labor intensive tradables such as apparel manufacturing.

We find this segmentation a helpful way to see the global nature of different industries, anticipate where manufacturing activities are most likely to take place, and understand the role of innovation in various industries. For companies, the segmentation helps to explain the evolution of different parts of their operations, from individual business units to various stages of their supply chains. The segmentation can also clarify the differences between segments of the same industry–why suppliers of automotive electronic components respond to very different dynamics than suppliers of mechanical parts, for example. The framework also helps explain why the needs and factors of success vary even within the same industry; the carmaker that emphasizes its technological edge and precision engineering has very different requirements than the producer of low-cost models.

The second is a point I have made over and over again: The distinction between manufacturing and services has blurred.

Manufacturing has always included a range of activities in addition to production. Over time, service-like activities–such as R&D, marketing and sales, and customer support–have become a larger share of what manufacturing companies do. More than 34 percent of US manufacturing employment is in such service-like occupations today, up from about 32 percent in 2002. Depending on the segment, 30 to 55 percent of manufacturing jobs in advanced economies are service-type functions (Exhibit E5), and service inputs make up 20 to 25 percent of manufacturing output.
Manufacturing companies rely on a multitude of service providers to produce their goods. These include telecom and travel services to connect workers in global production networks, logistics providers, banks, and IT service providers. We estimate that 4.7 million US service sector jobs depend on business from manufacturers. If we count those and one million primary resources jobs related to manufacturing (e.g., iron ore mining), total manufacturing-related employment in the United States would be 17.2 million, versus 11.5 million in official data in 2010. Including outsourced services, we find that services jobs in US manufacturing related employment now exceed production jobs–8.9 million in services versus 7.3 million in production.
Just as manufacturing creates demand for services inputs, services also create demand for manufactured goods. For every dollar of output, US manufacturers use 19 cents of service inputs, creating $900 billion a year in demand for services, while services create $1.4 trillion in US manufacturing demand. In China manufacturing creates $500 billion in services demand, and services demand $600 billion a year in manufactured goods. And while manufacturing drives more than 80 percent of exports in Germany, services and manufacturing contribute nearly equal shares of value added to the country’s total exports.

The report sees manufacturing facing a new set of challenges and opportunities:

Some forces are already being felt: the shift of global demand toward developing economies, the proliferation of products to meet fragmenting customer demand, the growing importance of value-added services, and rising wages in low-cost locations. Other trends are now becoming more pronounced, such as a growing scarcity of technical talent to develop and run manufacturing tools and systems, and the use of greater intelligence in product design and manufacturing to boost resource efficiency and track activity in supply chains.

A rich pipeline of innovations promises to create additional demand and drive further productivity gains across manufacturing industries and geographies. New technologies are increasing the importance of information, resource efficiency, and scale variations in manufacturing. These innovations include new materials such as carbon fiber components and nanotechnology, advanced robotics and 3-D printing, and new information technologies that can generate new forms of intelligence, such as big data and the use of data-gathering sensors in production machinery and in logistics (the so-called Internet of Things).

The report contains a number of insights for company managers and public policy makers. From my perspective, the conclusions on policy are the most relevant:

As manufacturing evolves, policy makers must adjust their expectations and look at manufacturing not as a source of mass employment in traditional production work but as a critical driver of innovation, productivity, and competitiveness. Policies aimed at promoting the health of manufacturing industries also must incorporate the crucial contributions that service employees, services suppliers, and collaborators make. Take exports: between 2000 and 2011, services exports grew slightly faster than goods exports in most advanced economies. In addition, services such as training and maintenance are a growing complement to equipment and machinery exports.
Policy needs to be grounded in a thorough understanding of the diverse industry segments in a national or regional economy and the wider trends that are affecting manufacturing industries.

When it comes to policy prescriptions they have the following advice:

As policy makers develop new approaches to support manufacturing, they need to consider the full policy tool kit.

Unfortunately, they shy away from the difficult policy task:

We do not attempt to settle the questions about what constitutes appropriate policy–or whether policy interventions are even warranted. We do provide policy makers a framework and approach for designing and implementing effective manufacturing strategies for today’s environment, with examples of how nations have reinvented manufacturing sector strategy.

They do reiterate many of the existing policy perceptions: R&D and innovation — including technology commercialization, standard setting and overcoming the “valley of death”; education and skill development; and reducing regulatory barriers.
Probably their most important recommendation is this: Work with trends, not against them
One of those trends is the shifting nature of production and the increasing irrelevancy of the dichotomy between manufacturing and services — as the report points out. As we pointed out in our Policy Brief–Intellectual Capital and Revitalizing Manufacturing, manufacturing is an knowledge and intangible asset based activity with a emphasis on “production” beyond “manufacturing.” We need policies (as outlined in the Working Paper) that recognize that fact.
The McKinsey report is a useful addition to the policy debate. However, much still needs to be done. Let us hope that policy makers will look carefully at the report and continue the development of a 21st Century “production” strategy fitting to the I-Cubed Economy.

US trade deficit in an energy self-sufficient scenario

Yesterday, the International Energy Agency published their World Energy Outlook with this shocker as part of their main forecast:
the United States becomes a net exporter of natural gas by 2020 and is almost self-sufficient in energy, in net terms, by 2035.” (emphasis added)
That outcome has lots of implications. Let me focus on one: what happens to the overall trade balance. Based on last month’s trade in intangibles data, below is what the monthly trade balance would look like if the U.S. had been self sufficient in petroleum goods over the past few years (note: while “energy” and “petroleum goods” are not quite the same thing, they are close enough for this though exercise). Even with energy self-sufficiency, the overall monthly deficit is still $21 billion. So, in other words, energy self-sufficiency doesn’t solve the trade deficit. Nor do intangibles solve the trade deficit. The only way to solve the trade deficit is through a revitalization of goods trade (aka manufacturing).
Total without petroleum-Sept12.gif

It is still booked as "goodwill"

Earlier this month, IAM blog highlighted Facebook’s recent 10-Q filing with the SEC. The 10-Q showed a huge increase in Facebook’s intangible assets on their balance sheet — growing from $162 million at the end 2011 to $1.423 billion at the end of September 2012. As those of you how follow accounting know, intangibles acquired from outside the company need to be placed on the books. So this increase was due to Facebook’s various purchases, including the purchase of Intagram and the patents that Microsoft bought from AOL and then sold to Facebook.
IAM makes a big deal that $633 million of that $1.423 billion was booked by Facebook explicitly as Intellectual Property. But I would turn that around: less than half of the intangible value is in IP and over 40% ($590 million) was booked as goodwill. In the case of Intagram, 83% of the intangibles were booked as goodwill ($435 million out of a total of $521 million).
In other words, patents might be valuable but the amorphous category of “goodwill” is still a large black hole for accounting of intangibles.

Polls, predictions and intangibles

In the last few days of the Presidential election an interesting side story developed in the form of a battle between the pollster’s numbers and the pundits expertise and gut feel. A number of high profile pundits made statements about how wrong the polling numbers were. Their focus made a social-media hero out of some of the pollsters – especially Nate Silver of the New York Times’ FiveThirtyEight blog. Turns out that polls were right – and the gut feel pundit wrong. Of course, not all the polls were as accurate as Silver (see stories in the Washington Post and the Wall Street Journal). And it must be understood that a number of these pundit predictions – such as the statements by Karl Rove – were mostly likely more about influencing the outcome rather than predicting it. But still, the number-bashers were spectacularly wrong.
The same mistake gets made repeatedly on understanding intangible assets. Part of the problem is our use of language. Too many people association the “intangible” part of intangible assets with the definition of intangibles as ethereal, indefinable or vague rather than the definition of intangibles as not physical. And indefinable means they can’t be measured.
This confusion leads some to the false conclusion that intangibles can’t be measured. They can. Metrics are an important part of understanding and managing intangibles. As we noted in our 2007 report Measuring Intangibles,

The measurement of intangibles is nothing new. Humans have been measuring intangibles for a long time. Whenever a teacher assigns a grade, they are measuring an intangible (the student’s knowledge). Whenever a boss gives or does not give an employee a raise or a bonus, they are implicitly measuring the employee’s skill level and value added to the company. Whenever a customer chooses one color, make, and model of a car over another, they are measuring a number of intangibles. Whenever an investor buys a company’s stock based on an expectation of future gain, they are investing in intangibles.

Ironically, political polls are a perfect example. Election polls measure an intangible — voters’ preferences. And political polling developed not as a public education tool for the media, but as campaign management tools. Polls tell campaigns were they are strong and where they are weak. They help determine what to do about the situation.
So if there is a lesson for intangibles coming out of the campaign, it is this: data matters. Simply going with your gut feel is a recipe for getting it wrong (as Comedy Central’s “The Daily Show” pointed out in video). If your don’t believe me, just ask Karl Rove or Joe Scarborough or Newt Gingrich or Dick Morris or George Will or Peggy Noonan.