Investing in people or machines?

Last night, the House of Representatives House Ways and Means Committee voted to make permanent a tax break for buying machines (see The HillHouse GOP clears $287B tax break for business“). The so-called bonus depreciate that allows companies to immediately write off equipment investments as expenses (thereby lowering their pre-tax income and their overall tax bill). Many praise the move as an important step in helping business (see for example, the Heritage Foundation’s write up.) Others, such as Forbes, are more critical. Part of the debate is over how to pay for the $287 billion it would cost over ten years.
As I’ve noted before, my concern is that the this tax break is ineffective in the knowledge economy. It goes after the wrong target. The problem facing manufacturing today isn’t the ability to buy equipment. It is the ability to find trained employees. Over and over we hear that the manufacturing skill shortage is the biggest hurtle to companies’ expansion.
Back in 2011, the on-line journal The Economists’ Voice published an article on “Should the Government Invest, or Try to Spur Private Investment?”:

The U.S. economy clearly needs stimulation, but the Obama administration’s plan for accelerated depreciation is an ‘old economy’ approach to stimulating aggregate investment and unlikely to ease the Great Recession, according to Michael Cragg of Brattle Group and Joseph Stiglitz of Columbia University. The authors suggest alternative policies consisting of carefully designed carrots and sticks.

As I noted in my comment in the article (also published in The Economists’ Voice), Drs. Cragg and Stigliz are exactly correct when they point out that accelerated depreciation is a limited tool — as they put it “an ‘old economy’ approach to stimulating investment.” However, they only touch upon the reason. Accelerated depreciation applies to tangible plant and equipment. Yet, as numerous studies have shown, the composition of investment and capital formation has shifted from tangible plant and equipment to intangibles. Since investments intangibles are generally expensed rather than depreciated, any accelerated depreciation schedule completely misses the mark.
Investment tax credits are the more appropriate tool. But our policy toward tax credits for intangibles is weak at best. The Research and Experimentation Tax Credit (commonly referred to as the R&D tax credit) fights off near-death experiences on a route basis. It is also more limited in scope and scale than what is available in other developed nations. Investment incentives for other intangibles, most notably worker training, are completely absent. If we are to move beyond “old economy,” we need to focus on policy ways to provide incentives for investment in intangibles.
As I have argued too many times to recount, one policy change would be to turn the R&D tax credit into a broader knowledge tax credit. A knowledge tax credit would apply to company expenditures on worker training and education — just like the R&D tax credit applies to expenditures on research activities. In only make sense that boosting worker skill levels is a necessary compliment to any activities to raise innovation and productivity. After all, innovation doesn’t come solely from the lab any more.
Likewise, the knowledge tax credit could be paired with any job sharing programs that compensate workers for lost wages due to working fewer hours. Rather than reduce their hours, a tax credit could be given for workers spending those hours for training, either on-the-job training or in the classroom. This would have a dual effect. It would increase our human capital — a major input to productivity and economic growth. And it would immediately increase consumer demand as companies would use the funds to pay workers to take classes (thereby creating more employments slots for others to fill the working hours of those in the classes).
It seems however, that we are stuck in the past focusing on “old economy” policies. We need to think more creatively about what tax policy would work in this new situation. Key to that effort is focusing on incentives for increased investment in intangible assets.
So, for start, what if we used that $287 billion to provide a tax credit to train workers instead?

1Q 2014 GDP – second estimate shows intangibles investments up

It is now official. The U.S. economy shrank in the first quarter of 2014 at an annual rate of 1.0%, according to data released this morning by the BEA. The decline was even greater than economists had estimated. Economists surveyed by The Wall Street Journal forecast a contraction of 0.6% while Bloomberg’s survey forecast a drop of 0.5%.
However, investments in Intellectual Property Products (IPP – i.e. research and development; entertainment, literary, and artistic originals; and software) were revised significantly upwards to a 5.1% growth rate from the first estimate of 1.5%. That was the one bright spot in an otherwise gloom analysis of the investment side of the economy (which shrank by 11.7% in total).
As I’ve noted before, breaking out the IPP numbers is a new addition to the GDP calculations. We need to better understand these estimate-to-estimate revisions to understand what the really doing. That IPP investments fluctuate quarter by quarter is understandable; that are estimates of IPP investment for a specific quarter fluctuate significantly is not.
IPP percent 1Q14 -2nd.png

Inclusion in the information age redux: the new "digital divide"?

Earlier this month, the Obama Administration published a report on Big Data: Seizing Opportunities, Preserving Values (see also the summary on the White House blog). Much of the focus of attention surrounding the report has been on the positive potential of big data and the concerns over privacy. However, the report also addressed another issue: the potential for discrimination. As The Economist (“Regulating big data: Rules for the new tools“) noted about the report:

But the most novel examination is the way it looks beyond privacy to consider how technology can discriminate in subtle ways. For example, some online retailers use “predictive pricing” algorithms that charge different prices to customers based on a myriad factors, such as where they live, or even whether they use a Mac or a PC. Though there may be innocuous reasons for the price discrimination, there are few safeguards to ensure that the technology does not perpetuate unfair approaches.
It is important to “examine how algorithmically-driven decisions might exacerbate existing socio-economic disparities beyond the pricing of goods and services, including in education and workforce settings,” it states. “The increasing use of algorithms to make eligibility decisions must be carefully monitored for potential discriminatory outcomes for disadvantaged groups, even absent discriminatory intent.”
This discrimination manifests in different ways. For instance, an app called Street Bump, released by the city of Boston, uses a smartphone’s sensors to identify whenever a car is jolted, relaying that information to the city’s road-maintenance team. But the system has an inherent bias: richer and younger people are more inclined to have a smartphone and download the app, so bad roads in less tech-savvy places might not get fixed so readily because there’s less reporting. The bias was accidental but its effects are real. In this case, however, the designers realised the potential shortcoming at the outset and corrected for it by giving more weight to bumps from less posh areas.

As the report states:

Just as neighborhoods can serve as a proxy for racial or ethnic identity, there are new worries that big data technologies could be used to “digitally redline” unwanted groups, either as customers, employees, tenants, or recipients of credit. A significant finding of this report is that big data could enable new forms of discrimination and predatory practices.

Interestingly, the report recommends using the technology to monitor the technology:

The same big data technologies that enable discrimination can also help groups enforce their rights. Applying correlative and data mining capabilities can identify and empirically confirm instances of discrimination and characterize the harms they caused. The federal government’s civil rights offices, together with the civil rights community, should employ the new and powerful tools of big data to ensure that our most vulnerable communities are treated fairly.
To build public awareness, the federal government’s consumer protection and technology agencies should convene public workshops and issue reports over the next year on the potential for discriminatory practices in light of these new technologies; differential pricing practices; and the use of proxy scoring to replicate regulated scoring practices in credit, employment, education, housing, and health care.

– – –

The dangers that the White House reports raise are not new – but deserve the renewed attention they are receiving. This attention is part of a new look at the issue of the “digital divide.” For example, last November, the Washington Post held a forum on the topic (see earlier posting).
Almost a decade and half ago, Athena Alliance held a conference on Inclusion in the Information Age: Reframing the Debate. That was followed by our report Extending the Information Revolution: A White Paper on Policies for Prosperity and Security. As we argued back then, the major issues of the digital age are not just access to technology (aka computers and broadband). The issues of the “digital divide” go deeper to access to and use of information. As we pointed out in the key points that came out of our 2000 conference:

Point one: Focus on the transformation, not the technology.
The issue of concern is the transformation to the Information Age. It is not simply a question of technological deployment. The end purpose is not to narrow some gap, but to ensure that everyone has access to the expanded opportunities. Our framework should be one of inclusion for all in the broader activities that make up society and the economy.
Point two: Review and coordinate efforts.
The problem has aspects of telecommunications policy, such as infrastructure and standards and elements of technology policy, such as research and development and technology deployment. But it also has aspects of policies on training and workforce development, education, economic development, housing and community development, human services and trade. Reaching our goal requires a coordinated approach — in the private, public and non-governmental sectors – that combines the various elements of providing opportunity and inclusion in the information age. To coordinate policy, the focus of governmental digital opportunity efforts should be the White House, not in any one department or agency.
It is also time to take a new look at some policy areas. For example, a comprehensive approach is needed toward all parts of managing the information commons: privacy, intellectual property rights, “right-to-know” policies and other related areas.
Point three: Work to ensure that everyone has access to the technological infrastructure.
Barriers to access to the infrastructure are many. Ways of overcoming those barriers are also varied, including public access facilities that can combine access with training and other activities, as well as home access. With respect to access in the home, we must return to the question of universal access. We also need to address the development of broadband capabilities – both at home and at work. Both home use and public access points are important. Multiple access public points are needed, such as existing public facilities, training centers, libraries, and after-school centers. For these facilities, sustainability is the key. But, it is not enough to simply provide access. We must work to weave information technology into the operations of community groups in a way that will both help individuals use the technology and will make those groups more efficient and effective in their core mission.
Some of the barriers to digital inclusion are physical: the usability of the technology. This is not, as commonly thought of, an issue only for those with disabilities. The problems of usability and human-machine interfaces affect all of us and research on ways to increase access for those with disabilities will pay off in increased usability for all.
Point four: Encourage and facilitate participation and involvement by all in the digital economy and information society.
To foster participation and involvement, the technology must meet people’s needs – not define those needs. Information technology can help people in their day-to-day lives if it is designed and structured in such a way that it helps answer their questions and solve their problems. Otherwise it becomes a barrier and a source of frustration. This is the danger of what some refer to as the “over-wired” world.
It is important to understand that individuals have different needs. A one-size-fits-all may help some – and increase their participation and involvement – but will block others. By focusing on “demand-pull,” rather than “technology-push,” we can better tailor the technology to meet individual needs.
Development of meaningful content, including more locally-based content, is one of the ways to increase the level of participation. E-government is one important form of meaningful content. But, we must also insure that those who are not on-line are not left behind. No services or information should be removed or dramatically cut back from traditional means of dissemination in favor of electronic dissemination until and unless all members of the community have access to that electronic means as easily as they have to the traditional means.
Point five: Focus economic development on the Information Economy, not the Internet Economy.
The information age will require a new approach to economic development. Key to the process is using and developing assets: financial, social, skill-based, and information assets. We must focus on building the local economy’s vitality and ability to compete in the age of globalization and help people make the switch to the new economy.
Our priorities should include:
• development of processes for identifying and assessing local assets,
• revitalizing programs for training the existing workforce,
• helping small and medium size enterprises make use of IT, and
• fostering entrepreneurship at all levels and in all sectors.
We must also develop and utilize new mechanisms for financing the transformation, including Individual Development Accounts and new ways of financing intangible assets.
Collaborative learning and sharing of information is also important in the larger process of economic development. There are a number of examples of information assets being applied within businesses and with local economies. We need to utilize new knowledge management techniques and old-fashioned communications techniques to collect, disseminate and better utilize that information.
Point six: We need a better understanding of what is going on.
We need to re-look at the data needed for economic development in the information economy. The problem of data extends beyond the scope of local economic data. We need both better data and expanded analysis of the socioeconomic aspects of the information technology. That research must be translated into policy relevant terms. For this reason, Congress should seriously consider re-establishing the Office of Technology Assessment (OTA).
Point seven: The decision making process must be open.
True inclusion and opportunity can only occur if the process of decision making is open and transparent. Information technology has a tremendous potential for opening and maintaining channels for general input and advocacy. However, decisions made about the technology can have the effect of closing off the process rather than opening it up. We must insure that all parties are at the table when decisions, including issues such as standard setting, are made.
Point eight: Innovate and experiment.
We are in a time of transformation and change. The speed of that change and the pace of economic activity will vary. Yet the change is real and will continue. In such a time, we must often invent new ways of coping with our problems and new policies for guiding our economy and society. Such experimentation will require great policy discipline, however. It requires a strong, unbiased means of evaluating programs and policies – and the political discipline to follow the guidance of that evaluation. We must also find means to ensure that the evaluations are timely for the fast moving policy arena. The goal in evaluation is not simply proving the effectiveness of an action – it is to facilitate learning. Learning is the hallmark of the Information Age. Our public policy process must embrace that concept as tightly as the rest of our economy and society already have.

– – –

The policies outlined in the White House report on Big Data are good steps forward. As we move ahead to confront the challenges of the era of “Big Data” let us keep in mind that last point on the need for policy experimentation and innovation. And use big data to monitor how we are doing.

The link between growth and inequality from an intangible capital framework

Ideas can be powerful but are often hidden. Case in point: the conceptual model of intangible capital as the driver of economic growth is becoming fixed in the public discourse. Whether people know it or not, they are implicitly using the concepts without explicitly referencing the model. An example is a recent speech by Chairman Jason Furman, Chairman of the President’s Council of Economic Advisers (“Global Lessons for Inclusive Growth”) that touches on the link between growth and inequality.
The standard argument on growth and inequality is a follows: inequality is an unavoidable and necessary ingredient for growth. Some people contribute more to economic growth. They need to be rewarded for the risks they take, otherwise they would not take those risks. The result is this group of risk takers are rewarded more — i.e. unequally. The natural result is some level of inequality.
However, in recent years, an alternative description of the link between growth and inequality has emerged. This argues that inequality reduces growth in a number of ways by undercutting the factors that lead to economic growth. As Furman puts it:

The traditional theoretical macroeconomic literature also emphasized a tradeoff between greater equality and growth: One point often emphasized is that to the degree that high-income households save more, greater inequality would translate into more savings and investment, and in turn, a higher level of output. Also, linking to microeconomic foundations, the traditional macroeconomic literature assumed that greater inequality provides a greater incentive for education, investment and entrepreneurship to capture those income gains.

A newer theoretical literature has also identified a number of mechanisms by which greater equality could increase the level of output or growth. This literature starts from the observation that the traditional emphasis on the quantity of capital, even if true, is dwarfed by the importance of the quality of capital, technology, and entrepreneurship. Moreover, pervasive market failures and incomplete markets mean that the efficiency of outcomes may depend on the distribution of income. In particular, this approach emphasizes a number of channels by which inequality could harm growth: (1) by reducing access to the education necessary for the full population to reach its full potential; (2) by reducing entrepreneurship and risk taking; (3) by undermining the trust necessary for a decentralized market economy and increasing monitoring costs; and (4) by leading to increased political instability, growth-reducing policies and uncertainty.

Without realizing it, Furman’s four points invoked the elements of intangible capital models. Let us do a quick review of intangible capital. One version of the model contains four types of capital:

 • Human Capital – This includes all the talent, competencies and experience of your employees and managers. This is the intangible capital that “goes home at night.”
 • Relationship Capital – This includes all key external relationships that drive your business, with customers, suppliers, partners, outsourcing and financing partners, to name a few. This kind of capital also includes organizational brand and reputation. Due to the growing importance of networks in organizational structures, this is also sometimes called Network Capital.
 • Structural Capital – This includes all knowledge that stays behind when your employees go home at the end of the day. There is significant structural capital in today’s organizations including recorded knowledge, processes, software and intellectual property.
 • Strategic Capital – This is a category that is not always included in academic definitions of IC. However, in our experience, this category of knowledge is the glue that holds the whole system together. It gives logic and purpose that attracts the right people, partners and knowledge to your organization–and puts it to work inside a business model that connects with a market need to generate the revenues and profits to sustain the organization. It includes culture, business model and external factors.

Another version embeds three forms of intangible capital in a larger model that also includes financial capital and tangible capital (both “manufactured” capital and natural resources). The three are:

 • Intellectual capital – Organizational, knowledge-based intangibles, including intellectual property and “organizational capital” such as tacit knowledge, systems, procedures and protocols;
 • Human capital – People’s competencies, capabilities and experience, and their motivations to innovate.
 • Social and relationship capital – The institutions and the relationships within and
between communities, groups of stakeholders and other networks, and the ability to share information to enhance individual and collective well-being.

While the terminology may be slightly different, the two models are talking about the same thing.
As Furman describes it, inequality undermines the development of various forms of intangible capital that are needed to sustain economic growth. Inequality undermines human capital development (education)–point one in Furman’s argument is all about human capital. Inequality undercuts strategic capital (entrepreneurship)–point two. It destroys relationship capital (trust)–point three. Finally, inequality undercuts social capital (through political instability and uncertainly)–point four. In short, the entire new view of the linkage between inequality and economic growth is based implicitly on the idea that intangible capital is the driver of economic growth.
Thus has the concept of intangible capital seeped into the policy debate at the highest level. Our next task is to help policymakers and other use the concepts/models as an explicit and coherent tool of analysis.

Why go to college?

There is a new study out on the benefit (or not) of attending college. The 2014 Gallup-Purdue Index Report (download full report) looks at the experiences of graduates while in school and how that relates to their current life situation. The study looks specifically at college graduates’ engagement at work and their sense of personal well-being.
Workplace engagement is an important aspect of this new I-Cubed (Information-Innovation-Intangibles) Economy. As the report points out:

People who are engaged at work are involved in and enthusiastic about their work. They are loyal and productive. Those who are not engaged may be productive and satisfied with their workplaces, but they are not intellectually and emotionally connected to them. Workers who are actively disengaged are physically present but intellectually and emotionally disconnected from their work and workplace. They are unhappy with their work, share their unhappiness with their colleagues, and are likely to jeopardize the performance of their teams.

Well-being is also a key metric of the health of the economy and society. Well-being

is about the interaction and interdependency between many aspects of life such as finding fulfillment in daily work and interactions, having strong social relationships and access to the resources people need, feeling financially secure, being physically healthy, and taking part in a true community.

And those who are engaged at work score higher on measures of well-being.
The study found that “the type of schools these college graduates attended — public or private, small or large, very selective or less selective — hardly matters at all to their workplace engagement and current well-being.”

Instead, the study found that support and experiences in college had more of a relationship to long-term outcomes for these college graduates. For example, if graduates recalled having a professor who cared about them as a person, made them excited about learning, and encouraged them to pursue their dreams, their odds of being engaged at work more than doubled, as did their odds of thriving in all aspects of their well-being. And if graduates had an internship or job in college where they were able to apply what they were learning in the classroom, were actively involved in extracurricular activities and organizations, and worked on projects that took a semester or more to complete, their odds of being engaged at work doubled as well.

In other words, as the report states:

When it comes to finding the secret to success,it’s not “where you go,” it’s “how you do it” that makes all the difference in higher education.

On that score, however, the finding raise some serious questions. While 63% of graduates said they had at least one professor who made them excited about learning, only 22% said they had a mentor who helped them. And I would question how good that 63% score is. That means over a third of college graduates never encountered a professor who helped made them excited about learning.
If you believe that the purpose of higher education is to acquire specific (often technical) skills, then the previous statements probably doesn’t worry you. If, however, you believe that the goal of higher education is to learn to learn, then you might be concerned that the system is failing one-third of its customers.
By the way, the baseline data on the work engagement and personal well-being of college graduates is not great either. Only 39% are engaged at work; 49% are not engages; and 12% are actively disengaged. If only a little more than a third of our “best and brightest” are engaged at work, then we are clean not utilizing our human capital to its fullest potential.
This is the first year for this study. It will be interesting to see how (and if) the finding change over time.

March trade in intangibles

Some good news from BEA this morning on trade: the trade deficit declined by $1.5 billion to $40.4 billion. Exports were up by $3.9 billion while imports grew by only $2.5 billion. The news wasn’t quite a good as hoped for as economists had predicted that the deficit would fall to $40.0 billion. The increase in exports was particularly good news, however. And could result in an upward revision to the 1Q GDP numbers. The not so good news hidden in the data is that the overall improvement in the deficit was due to a decline in the deficit in petroleum goods. The trade deficit in non-petroleum goods actually increased slightly (see chart below). UPDATE: Since the trade deficit was larger that what BEA projected in its GDP calculation, economists now believe that 1Q GDP will be revised downward to show a slight economic contraction.
Our trade surplus in pure intangibles also improved. The March surplus increased by $915 million to $16.4 billion. Reversing last months surge, royalty payments (imports) declined. Royalty receipts (exports) continued to increase. As we mentioned last month, we suspected that the February rise in royalty payments was due to payments for the television rights to the Winter Olympics. The trade surplus in business services grew as exports rose faster than imports.
The deficit in Advanced Technology reversed its trend – to grow slightly in March after declining dramatically in recent months. A surge in information and communications technology imports was offset by an increase in aerospace technology exports.
Advanced Technology goods also represent trade in intangibles. These goods are competitive because their value is based on knowledge and other intangibles. While not a perfect measure, Advanced Technology goods serve as an approximation of our trade in embedded intangibles. Adding the pure and embedded intangibles shows an overall surplus of approximately $12.6 billion, up from $12.3 billion in February.
Intangibles trade-Mar14.png
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Note: we define trade in intangibles as the sum of “royalties and license fees” and “other private services”. The BEA/Census Bureau definitions of those categories are as follows:

Royalties and License Fees – Transactions with foreign residents involving intangible assets and proprietary rights, such as the use of patents, techniques, processes, formulas, designs, know-how, trademarks, copyrights, franchises, and manufacturing rights. The term “royalties” generally refers to payments for the utilization of copyrights or trademarks, and the term “license fees” generally refers to payments for the use of patents or industrial processes.

Other Private Services – Transactions with affiliated foreigners, for which no identification by type is available, and of transactions with unaffiliated foreigners. (The term “affiliated” refers to a direct investment relationship, which exists when a U.S. person has ownership or control, directly or indirectly, of 10 percent or more of a foreign business enterprise’s voting securities or the equivalent, or when a foreign person has a similar interest in a U.S. enterprise.) Transactions with unaffiliated foreigners consist of education services; financial services (includes commissions and other transactions fees associated with the purchase and sale of securities and noninterest income of banks, and excludes investment income); insurance services; telecommunications services (includes transmission services and value-added services); and business, professional, and technical services. Included in the last group are advertising services; computer and data processing services; database and other information services; research, development, and testing services; management, consulting, and public relations services; legal services; construction, engineering, architectural, and mining services; industrial engineering services; installation, maintenance, and repair of equipment; and other services, including medical services and film and tape rentals.

Action items from UK IP & Banking report

Last November I posted an item on the UK Intellectual Property Office’s report on Banking on IP? The role of intellectual property and intangible assets in facilitating business finance. A follow up report is now available Banking on IP: An Active Response. This new report outlines a number of specific steps to be taken to increase the use of intangibles in financing decisions.

In 2014/15 the IPO will therefore focus attention on improving the ability of IP rich businesses to secure access to growth finance – by building understanding of IP in the business and the financial services communities, by enabling a more productive dialogue between businesses and lenders, and by building greater confidence in the value of IP assets as collateral. In particular it will:

• Actively promote improved understanding of IP and its value to business to SMEs and lenders through its IP for Business tools;

• Develop a series of real life case studies which show how a business has, through management of their IP, been able to secure finance, attract new business or increase their profitability;

• Work in partnership with the BBA and its Business Finance Roundtable to promote understanding of IP across the financial services sector, including frontline business managers;

• Lead development of an IP Finance toolkit, in collaboration with business and the financial services community, including templates and guidance on identifying and managing IP assets;

• Encourage dialogue between insurers and lenders with a view to developing appropriate insurance policies;
• Undertake an assessment of existing IP trading platforms;

• Reach out to operators of existing risk sharing schemes to see how IP can be taken into account in credit scoring in ways which are likely to reduce risk overall.

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.

While the UK IPO is looking at a number of other activities, this standardization of information may be the best beginning. It is also something that U.S. officials need to keep an eye on. If a set of templates can be developed for identifying and incorporating IP assets into existing lending documents, then I see no reason why those templates can’t be used on both sides of the “the Pond.” That would be a huge step forward.

April employment

This morning’s employment report for April shows a relatively healthy economy. The unemployment rate fell by 0.4% to 6.3% and payrolls grew by 288,000. Economists had expected an unemployment rate of 6.6% and 218,000 new jobs.
However, the number of involuntary underemployed (part time for economic reasons) grew slightly in April. The good news is that the number of those who could only find part time work dropped. But number of workers part time because of slack work rose. The rise in slack work could signal a future slowdown. Involuntary underemployment remains well above pre-Great Recession levels.
Involuntary underemployed April 2014.png


As noted in past postings, the GDP data now treats R&D spending as an investment rather than an expense. The most recent Survey of Current Business includes a “BEA Briefing: Treatment of Research and Development in Economic Accounts and in Business Accounts.” As the title indicates, the briefing compares how R&D is treated in the System of National Accounts (which produces the GDP measure) and the generally accepted accounting principles (GAAP) for companies. Both are based on basic accounting principles, such as double entry bookkeeping. However, there is a big conceptual difference when it come to R&D. As the briefing points out:

Under U.S. GAAP, R&D is not recognized as capital formation because of the uncertainty of future economic benefits associated with R&D–that is, U.S. rule makers are traditionally conservative in the treatment of expenditures. Immediate expensing implies that R&D expenditures contribute to sales and the related profits in the current period with no contribution to sales and profits in future periods.

After careful consideration over a number of years, the economists in charge of the System of National Accounts can to a different conclusion. In the new GDP measurement (adopted in 2008 and implemented this year),

[R&D] Costs should be capitalized regardless of the actual commercial or technological success of an endeavor because all costs form part of a future successful endeavor. While some R&D may require many failures to reap one success, businesses are not presumed to incur costs related to R&D with an expectation of ultimate failure.

To me, it only makes sense that R&D is treated as an investment and not an expense. The easiest way to show that is through a simple thought experiment. Expenses are costs that are needed to keep an enterprise operating (which is why they are often referred to as “operating expenses”). Rent, salaries, electricity, and raw materials/supplies are all operating expenses. If they are not paid, the ongoing activities of the enterprise are in jeopardy (and may actually constitute technical bankruptcy). What happens if R&D stops? Nothing — at least immediately. Eliminate the R&D budget and the enterprise will generally continue to operate. Yes, long term, eliminating R&D will probably have an impact on the future functioning of the enterprise. But not in the short term (which is why R&D budgets are sometimes cut to make short term financials look better). Clearly, R&D is an investment (costs associated with long term benefits) not an operating expense (costs associated with immediate activity).
Accountant worry that the path from R&D costs to future benefits is not always clear. But is that any different from any other investment? Building a new building (which clearly is treated under GAAP as an investment) is fraught with uncertainty. Will the company be able to pay for the building? Are the goods and services made in the building right for the market? Will they sell? Is there enough demand? Are they priced right to produce a profit? Will the building last long enough to re-coop the investment? Treating R&D as an investment will simply put it on par with all other outlays that are rightfully considered investments.
There is another reason, as I’ve noted before, why R&D should be treated as an investment. Right now, there is a differential treatment of R&D expenses/technology between R&D undertaken in-house (an expense) and technology purchased from outside (an investment). Thus, if a company spends $1 million to develop a new technology, that is counted as an immediate expense. If a company spends $1 million to buy another company to acquire that technology, it is counted as an investment and must be capitalized. From an accounting point of view, this process may make sense because an internal expense is different from an acquisition. However, from an operating perspective, this difference between R&D expensing and R&D capitalization can be profound.
There are two possible problems: a decrease in R&D spending (a “lock up” effect) and a distortion of R&D spending toward acquisition versus internal R&D (the “balance” issue). The lock-up effect occurs when a company is hesitant to spend funds on R&D for fear of lowering reported profit — and thereby having a negative impact on shareholder value. The case of the stock market’s reaction to the divergent paths of Pfizer and Merck is a illustration of how increased R&D can be viewed by investors as a negative. This effect may be more pronounced for the small to medium sized (mid-capitalized) companies where stock analysts may not have the time to dig far beyond the profit and loss statements.
The balance issue affects where R&D takes place and who is making the research decisions. If acquired R&D is treated more favorable from a shareholder’s perceptive, there may be incentives created for existing companies to acquire technology from outside the company rather than development the technology in-house. Because of that incentive, the decision (consciously or unconsciously) regarding to what research is undertaken may not be made on the nature of the research but rather by who does it. For example, a company may decide not to pursue a certain line of research because it would have to be done in-house. Instead the company may wait to see what is developed externally and then seek to acquire that technology.
The balance issue may also impact the financial situation of start-up companies. Start-ups are not well capitalized and are not in the position to acquire R&D from others. While start-ups may have access to R&D from universities and other public-private platforms funded by R&D activities flowing from the government or corporations, it is typical that a new, small business is creating their own R&D internally. As a result of the accounting rules, the smaller company which is reliant on internal R&D may be disadvantaged in the capital markets because of an appearance of lower profitability.
All of this has serious implications for the interaction between start-ups and existing companies — and the dynamics of the innovation process. Ideally, research decisions should not be biased one way or the other; the rules should be balanced or neutral between internal and external R&D. If a bias is to exist, there should be a solid public policy reason for that externally imposed bias. There may be good policy reasons to encourage the start-up/acquisition model of R&D over the in-house R&D model. But the possible existing bias toward acquisition due to the account rules exists because of historical circumstances, not a deliberate policy choice.
It is time to look more carefully at this issue to see whether the way in which account rules treat R&D (and intangibles in general) have become an impediment to innovation. And we need to ask a fundamental question: if we can treat research as an investment in our national accounts, why can’t we do the same in our company accounts?