OECD on productivity

In Monday’s posting on OECD’s recent report on intellectual property there was a discussion on the role of patents in the diffusion of knowledge. Another recent OECD report from their Future of Productivity project shows just how the process of knowledge flows and technological diffusion is for the economy. The report, its accompanying policy note and the presentation make clear that diffusion is the key productivity issues of today:

Productivity growth of the globally most productive firms remained robust in the 21st century but the gap between those high productivity firms and the rest has been increasing over time. This rising gap raises questions about why seemingly accessible knowledge and technologies do not diffuse to all firms.

Productivity growth.png
According to the report, policies can help rectify the situation:

Three policy areas appear to be of key importance to sustain productivity growth: i) foster innovation at the global frontier and facilitate the diffusion of new technologies to firms at the national frontier; ii) create a market environment where the most productive firms are allowed to thrive, thereby facilitating the more widespread penetration of available technologies; and iii) reduce resource misallocation, particularly skill mismatches.
. . .
Policies to sustain productivity growth include:
• Improvements in public funding and the organisation of basic research, which provide the right incentives for researchers, are crucial for pushing out the global frontier and to compensate for inherent underinvestment in basic research.
• Rising international connectedness and the key role of multi-national enterprises in driving frontier R&D imply a greater need for global mechanisms to co-ordinate investment in basic research and related policies, such as R&D tax incentives, corporate taxation and IPR regimes.
• Productivity growth via the diffusion of innovations at the global frontier to national frontier firms is facilitated by trade openness, participation in global value chains (GVCs) and the international mobility of skilled workers. Rising GVC participation magnifies the benefits from lifting barriers to international trade and from easing services regulation.
• Well-functioning product, labour and risk capital markets as well as policies that do not trap resources in inefficient firms – including efficient judicial systems and bankruptcy laws that do not excessively penalize failure – help firms at the national frontier to achieve a sufficient scale, enter global markets and benefit from innovations at the global frontier.
• A competitive and open business environment that favours the adoption of superior managerial practices and does not give incentives for maintaining inefficient business structures (e.g. via inheritance tax exemptions that may prolong the existence of poorly managed family-owned firms) facilitates within-firm productivity improvements. Stronger competition also enables the diffusion of existing technologies to laggards, which underpins their catch-up to the national frontier.
• Innovation policies, including R&D fiscal incentives, collaboration between firms and universities and IPR protection, should be designed to ensure that they do not excessively favour applied vs basic research and incumbents vs young firms.
• Framework policies that reduce barriers to firm entry and exit and improve the efficiency of matching in labour markets can improve productivity performance by reducing skill mismatch.
• Reforms to policies that restrict worker mobility and amplify skill mismatch – e.g. high transaction costs on buying property and stringent planning regulations – and funding for lifelong learning will become increasingly necessary, to combat slowing growth and rising inequality.

This report builds on OECD’s earlier work on knowledge-based capital (KBC, aka intangible assets). As such, the report highlights the importance of investments in KBCs. But it is not just what companies spend on intangibles that is important. The report notes that “it is likely that the competitive advantage of GF [Global Frontier] firms arises not only from their investments in KBC, but how they tacitly combine different types of intangibles – e.g. computerized information; innovative property and economic competencies – in the production process.” The key role that tacit knowledge plays makes the task of knowledge diffusion that much more difficult.
A number of the policy recommendations explicitly attempt to foster the flow of tacit knowledge. Openness of the economy is one such policy. As the report notes, “Exposure to trade and FDI entails exposure to knowledge and know-how of the “best” foreign and domestic firms.” Another such facet of openness is the flow of people, especially brain circulation, “which might stimulate knowledge flows, collaboration and ultimately high impact research.”
The report ends with an outline for future research. One project will look at “the sources of the cross-country differences in aggregate productivity.” In that regard, it would be useful to look more carefully at what specific investment in which specific intangible assets make the most difference. As I’ve noted in an earlier posting, data shows that the countries differ greatly in the productivity growth they get from their investments in intangibles. Work by Carol Corrado, Jonathan Haskel, Cecilia Jona-Lasinio and Massimiliano Iommi, “Intangible Capital and Growth in Advanced Economies: Measurement Methods and Comparative Results” shows Finland, Ireland and even Slovenia get greater productivity growth from their investments in intangible capital than the U.S.
Intangible v MFP - Corrado 2012.png
I hope the new OECD research effort will take a closer look at this phenomenon.

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Thoughts on tech displacement

There’s a new paper out by Joel Mokyr, Chris Vickers, and Nicolas L. Ziebarth on “The History of Technological Anxiety and the Future of Economic Growth: Is This Time Different?” The paper looks at three forms of anxiety over technology:

First, one of the most common concerns is that technological progress will cause widespread substitution of machines for labor, which in turn could lead to technological unemployment and a further increase in inequality in the short run, even if the long-run effects are beneficial. Second, there has been anxiety over the moral implications of technological process for human welfare, broadly defined. In the case of the Industrial Revolution, the worry was about the dehumanizing effects of work, particularly the routinized nature of factory labor.
. . .
A third concern cuts in the opposite direction, suggesting that the epoch of major technological progress is behind us

While the discussion of all three is interesting, I found the comments on the first issue of tech displacement to be the most insightful. Mokyr et al. make a key point about the gains and costs of technological progress:

While the predictions of widespread technological unemployment were, by and large, wrong, we should not trivialize the costs borne by the many who were actually displaced. It is true that, in the long run, wages for laborers increased to reflect dramatically increased productivity. It is also true that, for the Industrial Revolution, by many estimates it took longer than an average working lifetime to do so, and in the long run, we are all dead.

These distributive aspects of the change are at the heart of most of the anxieties.

But the authors make a more important point as to the dynamics of the process:

More importantly, technological progress also took the form of product innovation, and thus created entirely new sectors for the economy (emphasis in original).

Essentially, the dynamic is a race between productivity and demand with job creation trying to offset job displacement. The linkage between the job displacement effects of innovation via productivity increases and the job creation aspects of innovation and productivity works in three ways. The standard way economists explain the dynamic is the classical balance between supply and demand. As David Autor notes in his new paper “Why Are There Still So Many Jobs? The History and Future of Workplace Automation”

Automation does indeed substitute for labor–as it is typically intended to do. However, automation also complements labor, raises output in ways that lead to higher demand for labor, and interacts with adjustments in labor supply.

In other words, if productivity reduces prices (as opposed to increasing either profits or leisure, i.e. less labor needed for same output), economic theory tells us that sales should increase. That demand for greater production will offset the need for fewer workers per unit of output.

The second way linkage is how process innovation both increases productivity and enables product innovation. The definition of a disruptive technology is that it allows you to do things that you couldn’t do before. Case in point is additive manufacturing aka 3D printing (see my 2014 report Additive Manufacturing as a Disruptive Technology). Additive manufacturing allow production of items that could not be made in conventional processes. This leads to product innovation in areas as disparate as medical devices and fashion.

An example from the early days of the Industrial Revolution is hog lard rendering. Steve Gordon points out (in “From Slaughterhouse to Soap-Boiler: Cincinnati’s Meat Packing Industry, Changing Technologies, and the Rise of Mass Production, 1825-1870“), “Not until the 1830s, with improvements in factory technology and the opening of new markets, did animal wastes become commercially profitable.”

As the process of rendering of hog lard became refined, the use of the product for high quality soaps and candles increased, displacing home-made products. But there were spins off products as well. According to Charles Morris (The Dawn of Innovation: The First American Industrial Revolution):

Mastery of the chemistry of lard facilitated the production of pure glycerine for a host of applications. It was important to tanners, a useful solvent, and widely used in the production of pharmaceuticals and food.

Changes in production processes open up new opportunities in product innovation and increased demand beyond the original product.

The third linkage is the non-linkage. Product innovation can occur independently from productivity increases. So far, the history of technological progress since the Industrial Revolution has been one of enough product innovation to create new jobs. It is this third dynamic of the independence of labor-creating product and labor-reducing process innovation which partially ties back to the third anxiety outlined by Mokyr et al. Will we have the new product (goods and services) development to continue to fulfill the promise of new jobs and new types of jobs? If the future of production is “just-in-time and just-for-me”, will this customization be enough to keep the dynamic going of net job creation?

For now, the dynamic seems to be working. As Peter Thiel famously said, “we wanted flying cars instead we got 140 characters.” Frankly I think we are better off with the 140 characters. And if you think there is an airspace problem with drones, image 250 million cars and truck flying around overhead. But the key question is this: which is enough to create jobs? Flying cars built in automated factories does little to increase net employment. If you take “140 characters” has short hand for “apps” in general, then 140 characters wins. Estimates are that the app economy has generated 750,000 jobs as of 2013.

As they say in the financial advice disclaimer however, “past performance is no guarantee of future results”. The balance between job displacement and job creation is not written in stone. Public policy can help maintain the balance. But only if we recognize that a positive economic outcome is not a preordained outcome.

New OECD report on intellectual property

The OECD has released a new report entitled Enquiries Into Intellectual Property’s Economic Impact. The report is part of Phase 2 of their project on New Sources of Growth: Knowledge-Based Capital. A summary of the finding of Phase 1 is contained as an annex in the new report and a synthesis report is available as a stand-alone document on-line.
[Note: Phase 1 of that project was kicked-off at a 2011 conference organized by Athena Alliance. As the result of that conference, we produced two reports. The first, Intangibles Conference Report September 2011 is our official report on the conference to OECD. The second, New Building Blocks for Jobs and Economic Growth: Intangible Assets as Sources of Increased Productivity and Enterprise Value — Conference Observations, is my observations and synthesis. For more on the conference, including background papers and reports from the sessions is available at the conference archives. In 2013 OECD published Supporting Investment in Knowledge Capital, Growth and Innovation. This book-length report was the culmination of Phase 1. An Athena Alliance working paper, Knowledge about Knowledge: The OECD Project on Knowledge-Based Capital by Dr. Brian Kahin analyzed that report.]
The new report contains an overview/synthesis chapter and a number of analytical background papers. These include: “Measuring the Technological and Economic Value of Patents”; “Approaches to the Protection of Trade Secrets”; “An Empirical Assessment of the Economic Implications of Protection for Trade Secrets”; “Copyright in the Digital Era: Country Studies”; and “Legal Aspects of Open Access to Publicly Funded Research”. I was particularly interested in the chapters on “IP-Based Financing of Innovative Firms” and on “Design and Design Frameworks: Investing in KBC and Economic Performance”. The “Summary of the Expert Workshop, ‘Society’s Gain from the Intellectual Property Exchange'” was also very insightful.
Chapter 9 on “IP-Based Financing of Innovative Firms” explores a topic that Athena Alliance has spent a great deal of time on. [See “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.]
The paper does a good job of summarizing the current situation:

In this context, intellectual property (IP) assets have two attractive features that may help firms to unlock new investment or obtain more favourable financing conditions. First, IPRs help to reveal to investors the quality of the firm’s management and of its technological capabilities. Second, as legally protected economic resources, IPRs can raise the projected profitability of a firm, and can be separated from the business and sold in case of financial distress. Notwithstanding these properties, IP-based finance appears to be under-exploited across OECD economies, especially with respect to those young SMEs who need to open new financing channels. To stimulate a more efficient use of IP-based finance, the governments of many countries are making increasing efforts to understand why IP-based finance is not well developed and are experimenting with new policy actions and initiatives.

Some of those barriers stem directly from the economic market problems of information asymmetry and various forms of moral hazard that limit financing of innovative companies. The paper also points out some of the practical difficulties of using IP for financing, including the problem of valuation and the fact that many small innovative companies tend to use informal mechanisms of protection such as trade secrets rather than formal patents. Other barriers include IP may be hard to redeploy with the accompanying assets such as other IP and employee know-how; IP exit markets are still immature; transactions costs for IP as collateral are high; and banks do not sufficiently understand IP assets
Policies to overcome these barriers include strengthening IP markets, including the creating sovereign patent funds. More direct ways of fostering the utilization of IP in financing involve government funded risk-sharing mechanisms either directly or through risk insurance. The paper also calls for building awareness and trust in IP financing. They note that improved corporate reporting of IP and other intangible assets will increase awareness of these valuable forms of capital.
Chapter 6 is on “Design and Design Frameworks: Investing in KBC and Economic Performance”. It looks at the nature of design as an intangibles asset and the mechanisms for treating design as intellectual property (“design-related IP”). The latter issue is of specific interest as nations have differing view of design-related IP. As the report points out, “Various nations and governing bodies use different terms for design intellectual property: Registered design, registered community design, design model, design patent, industrial design, etc.” Certain designs, such as websites, can also be protected under copyright. Trademarks and design are also linked. Confidentially agreements (a form of trade secrets) also play a role as an informal form of IP. The report explores issues in these areas and for numerous countries. Importantly, the report discusses the many non-IP means of protecting a competitive advantage via design. These include time-to-market, complexity, and tacit knowledge. As such it serves as an excellent primer on the subject.
The chapter recognizes the split personality of design as an intangible asset. One the one hand, it is a factor of production with an investment input (i.e. activity of design professionals) and an output (i.e. the ascetic and functional characteristics of a product or service). It is also an innovation process (aka “design thinking”). The report mostly focuses on the role of design as a factor of production, including a good discussion of measuring the value of the design inputs and the value-added of the outputs. However, while I understand the report is focused on intellectual property, I would have liked to seem more about the design-thinking process as an important intangible asset.
Chapter 8, the “Summary of the Expert Workshop, ‘Society’s Gain from the Intellectual Property Exchange'” was deliberately structured to look at topics not covered in the other sections. It could not help, however, to touch upon topics covered in other parts of the report. For example, the discussion raised the question of accounting for intangible assets in companies’ financial reports, the problem of valuing intangibles, and the issue of using intangibles as a means of company financing (a topic explored in depth in Chapter 6). Not surprisingly, this part of the discussion seems to have been lead by Tony Clayton, formerly of the UK Intellectual Property Office (IPO) and the driving force behind the UK IPO’s reports on Banking on IP? The role of intellectual property and intangible assets in facilitating business finance and Banking on IP: An Active Response.
The discussion was not just about items that often come up in such conversations. I found three points raised in the workshop that provided new insights (at least for me, I’m sure others readers will find others). The first concerned the need to view IP and intangible assets in the context of a bundle – not just a portfolio of interrelated patents but an interaction between types of IP (e.g. patents and trade secrets) and an interaction among types of intangibles (e.g. trademarks and marketing/customer relations). As the report points out in two separate areas:

Further work may need to be done on the notion of the ‘IP bundle,’ that is, where a single firm utilises a variety of IP instruments to protect its business processes and products. Although in certain situations some forms of protection–such as patents and trade secrets–may operate as substitutes, they are mostly complementary; yet these instruments may overlap and interact, so one should perhaps be wary of considering such overlapping rights in isolation.

and

The most important issue for the future is the relationship between different types of intangible assets held by a firm. Examining bundles of intangibles, Professor [Ahmed] Bounfour’s [University Paris-Sud] research identified a degree of complementarity between patents and R&D, and between trademarks and marketing. By contrast, there is a much weaker relationship between skilled labour and design rights.

I’ve noted before that a deeper understanding is needed of the relationship among the various forms of intangible assets and between the investment in specific intangibles and economic outputs (profits, GDP, productivity). I hope this is an area OECD will look at in the future.
The second new area to me was the linkage between copyright and text and data mining (TDM). I have not followed the policy discussions on “big data” closely so I don’t know how much this issue has been raised before, especially in the US. But the linkage is obvious:

Text and data mining (TDM) raises particular issues relating to copyright. Viewed as a promising means by which to both advance scientific and other research and to generate significant value for the wider economy, TDM may conflict with copyright insofar as it depends upon access to and extraction of data from large quantities of (often, proprietary) material. There is some evidence that researchers in certain jurisdictions (the EU and Brazil, for example) are inhibited from engaging in TDM due to fears of infringing copyright in the process. There are arguments both for and against crafting specific exemptions within the copyright rules to protect TDM, or relying on more general exemptions for fair use, while the scientific publishing industry argues that enhanced licensing arrangements facilitate TDM without any alteration of the existing copyright rules.

Most interesting of all was the discussion of patents and knowledge flows. A standard view of patents is that of the grand bargain. Actually it is two bargains. The first is the granting of a monopoly right which impedes consumer welfare in exchange for the promise of consumer benefits from innovation. In most thinking, the second is built into the first: the granting of exclusive rights in exchange for disclosure that will spur innovation. The disclosure requirement is not part of the incentives to the inventor but is meant to be a mechanism for spurring diffusion of the technology and for fostering follow-on innovation.

At its core, IP is about both creation and application of new ideas. The temporary right to exclude is granted as a means to incentivise innovation, so that, to the extent that IP policies fail to result in the creation and use of new ideas, they cannot be justified. Moreover, the emphasis is upon competition between ideas, not within ideas. The economic incentives provided by the IP system relate to the entire value chain: from inventors and authors, to ‘second movers’ that help to diffuse innovations, to distributors and intermediate users of innovative products and processes. The incentives particularly extend to open innovation partners, who need to be aware of which rights can be used in an increasingly important economy of knowledge exchange.
. . .
Two aspects of the knowledge diffusion process are of particular importance: disclosure (the information that is revealed) and dissemination (the accessibility of that information). Disclosure should enable third parties to use patented inventions once the patent has expired. It should thus allow a ‘person skilled in the art’ to understand how the invention works, be inspired and, potentially, make improvements. (Emphasis in original).

However, it is not clear that the disclosure and dissemination side of the bargain is working well. The workshop discussed the issue at great length and came to the following conclusion:

The effectiveness of disclosure as a means of knowledge diffusion in practice, however, has been called into question. Empirical evidence presented at the workshop suggests that patent disclosures can have a positive impact on innovation, but the effects may vary between industries, and there is evidence that the quality of disclosure may be inadequate in some instances. Greater emphasis should thus be placed on the sufficiency of disclosure at the patent examination stage.

Of particular interest to me is how this is framed in the current policy debate over patent reform. It appears to me that the entire discussion is over the incentives part of the bargains and ignores the diffusion aspects. Much has been said about how particular pieces of legislation will hurt or help inventors by either weakening incentives to patent or by protecting inventors from “patent trolls.” Little seems to be said about how the legislation will affect diffusion and follow-on innovation.

– – –
In conclusion, the OECD has prepared an interesting set of background papers that could help policymakers better understand the IP ecosystem. It remains to be seem what they do with this understanding.

2015 Small Business Friendliness Survey

Earlier this week, Thumbtack.com released its 2015 survey of small business attitudes toward state and local government (see previous posting for 2013 and 2014. I find this survey especially interesting because of their sample. Thumbtack.com is a web-based service where consumers can go to find services. As they note, their data base (and therefore their sample) “tend to be very small, mobile service businesses. 90 percent have five employees or fewer, and about half are working alone.” Specifically the sample is bias toward the professional and nonprofessional services sectors.
The overall findings this year are consistent with previous years. Intangibles — effective government regulatory systems and worker skills — were more important than taxes to small business success.

• Licensing was again more important than taxes – When evaluating their cities, small businesses said the ease of compliance with licensing rules mattered far more than tax rates, and that taxes mattered far less than any measure of regulatory compliance. For example, labor rules were 88 percent more important in driving state friendliness scores when compared to tax rates.
• Effective licensing was just as friendly as no licensing – Small business owners who found licensing compliance to be “very easy” were just as favorable towards their city governments as respondents who weren’t required to be licensed at all. By contrast, licensed professionals in cities with complicated requirements or inconsistent enforcement reported the lowest approval rates.
• Training experience was the top factor in both state and city rankings – Offering training on how to build and run a business and how to navigate the local economic and policy environment was the single biggest factor that influenced perceptions of friendliness. In cities, training was 78 percent more important than the number two factor. On the state level, small businesses who had a positive training experience were 1.5 times more likely to rate their states as being very supportive.
• High quality websites matter – Investing in a high quality, easy-to-use website that provides useful information and decreases the costs of regulatory compliance improves overall perceptions of a local or state government. Business owners who said their city had a “great” website ranked their cities 13 percent higher, while there was no difference in the rankings of business owners who were either unaware of or had had a bad experience on city websites.

I would point out that this year’s finding as to the importance of training are even stronger than before.
– – –
Last year I looked at the Small Business Friendliness Survey versus the Kauffman Foundation’s Index of Entrepreneurial Activity. That data showed a general correlation between a state’s business friendly ranking and the amount of new entrepreneurial activity (as measured as the percentage of the adult, non-business-owner population that starts a business each month). But the relationship is not all that strong. States, as the chart shows, are all over the place.
Unfortunately, the link between state policies and entrepreneurship appears to be weakening. The second chart below is an update using this year’s data using the new Kauffman data on start-up activity. There is little correlation this time. This is not necessarily as sign that state policies don’t matter. There could be several reasons. First is that late year was a bad year for start-up activity generally. The Kauffman data shows most states in the negative part of their index.
The report makes an important point in regard to this:

Much has been written recently about a crisis of entrepreneurship in the United States, as seen the broad collapse of self-employment across industries and states and a declining rate of business starts. We believe that because of these trends, creating the right environment for small, unproven business start-ups is more important than ever.

Second, the measure is of start-ups not ongoing activity. Thus it captures only part of the dynamics of those small firms in the Thumbtack data base. Policies may be relatively easy/friendly with respect to starting a business but unfriendly when it comes to sustaining/operating the business. The discussion yesterday about the complexity of tax laws usually hits a business after it have been running for awhile. On the other hand licensing and regulatory policies can have an impact in the creation phase as well as during ongoing operations. And today’s existing policies may have a greater affect on entrepreneurship in the future than the present as policies often have a lag effect.
Friendly v activity.png
Friendly v index 2015.png
Still, the comparison of the two data sets revels some interesting points. Of particular interest are the outliers. California has high entrepreneurial activity but ranks low in small business friendly. Tennessee has a much lower level of entrepreneurial activity than its small business friendly ranking would predict. Clearly there is more going on than just the governmental environment.
The small business and entrepreneurial ecosystems are complex environments (and not necessarily the same). While there are many other elements to the ecosystem, perceptions are an important part of those environments. Perceptions are also part of the ecosystem within reach of policymakers so it would behoove them to pay attention. But policymakers need to keep in mind that perceptions are not reality and are just one element. As the weak correlation indicates, policymakers should focus on actions as well as perception – and understand the sometimes tortuous relationship between the two.

Gone the way of the horse?

As I’ve noted recently, one of the biggest questions concerning automation/robots/artificial intelligence is whether it augments human labor or is a substitute. And one of the more interesting bits of analogy is the history of the horse. Erik Brynjolfsson and Andrew McAfee asked provocatively: Will Humans Go the Way of Horses?
Remember, however, that horses still exist in the economy. According to one study by the Humane Society, there were 9.9 million horse in the United States in 2006. That is a sharp decline from the estimated 21.5 million in 1900. But it is not elimination. What happen was a transformation in the horses’ role from providing work energy to recreation. I anticipate that the same transformation will occur for human drivers. Driving a car will become recreation not transportation.
Will humans go the way of the horse? In terms of labor and work activity, probably. In terms of importance to the economy, probably not (at least as consumers). The issue confronting society is how to manage the transformation.
As Erik Brynjolfsson and Andrew McAfee note the end of their article, “It’s time to start discussing what kind of society we should construct around a labor-light economy.”
Amen to that.

The interplay of technology and know-how: the case of cotton mills

In discussions about technology and innovation is all too easy to focus on the patented technology. Other important concomitant intangible assets are thought to appear as if by magic. The reality is that these other intangible assets, including organizational knowledge and tacit know-how, are key to translating technology into practical applications. The work of Erik Brynjolfsson has explored the importance of organizational changes. I recently ran into another example in The Dawn of Innovation: The First American Industrial Revolution by Charles R. Morris. Morris notes that Richard Arkwright needed to invent more than just new cotton processing machinery such as the the water frame and the rotary carding engine.

Cotton mill management was a new discipline: it required learning how to run banks of the new machines efficiently, how to lay out the work flow, and how to manage machined cotton. The stages from machined rovings to finished yarns were subtly different from those of hand-spun cotton. Knowing what machine speeds to apply with different fibers and spotting when I yarn was about to break, knowing how to intervene and how to restart equipment after a disaster–in effect, the basic textbook of mill management–had to be invented from scratch.

An important point to keep in mind.

Automation: Labor augmenting or substituting? Yes.

One of the biggest questions concerning automation/robots/artificial intelligence is whether it augments human labor or is a substitute. A recent “Open Letter on the Digital Economy” outlined a research agenda that called for “(i)dentifying business models in which technology is a complement to – not a substitute for – labor and creating a taxonomy of their common characteristics.”
But the issue is not an either/or. The classic case example is where the same technology augments parts of a job while substituting for others. In the past this has been the case for machines and physical labor. A backhoe allows one worker to dig a ditch faster than a person with a shovel, even if a worker with a shovel may be needed to finish up.
More recently, technology has been substituting and augmenting knowledge work. For example, Institutional Investor recently pointed out that “RIAs Shouldn’t Fear the Robots” [RIAs are Registered Investment Advisers.] So-called “robo” advisers can handle routine transactions like portfolio rebalancing, freeing humans for more high touch activities such as discussing changes to the client’s situation.
More interesting is when a technology in one phase of development might augment a job but substitute for it later on. This appears to be the case with autonomous vehicles. A New York Times story earlier this year on Volvo’s pilot assist technology explains how the technology currently works:

After a driver pressed a button on the steering wheel, sensors scanned the road and locked on to the vehicle a few car lengths ahead. A white icon lit up on the dashboard, and the wheel began moving on its own.
As the road curved, the Volvo steered itself through it, automatically adjusting the throttle and steering. The vehicle seamlessly kept on going, though after about five seconds, a subtle dashboard light asked the driver to keep a gentle touch on the wheel.
Not that it was needed — the Volvo could keep going hands-free for miles at speeds up to 30 miles per hour on a properly marked road. But for now Volvo has programmed the XC90 to start slowing down if a driver does not heed the warning light, making the vehicle a bridge between “lane keeping” and the truly hands-free technology set to hit the market soon.
“This is about making the tedious parts of people’s drives less stressful,” said Jim Nichols, a spokesman for Volvo. “We’re not talking about a driver simply checking out and not paying attention.”

Wired ran a similar story on “The World’s First Self-driving Semi-truck Hits the Road”:

The Freightliner Inspiration offers a rather limited version of autonomy: It will take control only on the highway, maintaining a safe distance from other vehicles and staying in its lane. It won’t pass slower vehicles on its own. If the truck encounters a situation it can’t confidently handle, like heavy snow that covers lane lines, it will alert the human that it’s time for him to take over, via beeps and icons in the dashboard. If the driver doesn’t respond within about five seconds, the truck will slow down gradually, then stop.

But, as today’s Wall Street Journal (“Truckers Gain an Automated Assist”) points out,

Manufacturers consider these systems a crucial primer for developing demand for automated vehicles . . .
Eventually, they could lead to trucks that drive themselves entirely.

That might not be the end of truck drivers, however. A Washington Post story on “How self-driving tractor-trailers may reinvent what it means to be a truck driver” points out an alternative:

. . . trucking companies could be expected to find ways to turn their cabs into mobile offices for drivers.

This is an example of a third possibility, what I call “transformative”. Some technologies will go beyond either eliminating the need for humans to undertake certain activities or helping them do those activities better. They will transform the activity completely. These are the truly disruptive technologies.
To illustrate what I mean, let’s take a non-human example: the horse. In a recent article, Erik Brynjolfsson and Andrew McAfee ask a provocative question: Will Humans Go the Way of Horses? Remember, however, that horses still exist in the economy. According to one study, there were 9.2 million horse in the United States in 2003. That is a sharp decline from the estimated 21.5 million in 1900. But it is not elimination. What happen was a transformation in the horses’ role from providing work energy to recreation. It anticipate that the same transformation will occur for human drivers. Driving a car will become recreation not transportation.
What does this mean for other types of human activity? I don’t know exactly. I do know that there will be technological substitution and displacement, technological augmentation of human activities, and technologies that will disrupt and transform human activities. We need to prepare our society from all three.