The Post-Industrial State

Arnold Kling

Summer 2018

Over the past several decades, our economy has come to be driven less by tangible inputs and outputs and more by intangible factors. As workers and consumers, we have become much more specialized than was the case 150 years ago, when the conceptual framework of modern economics was developed. Yet most mainstream economists have not properly acknowledged this transformation, remaining committed to models and concepts that served to explain an economy that no longer exists. Understanding the world we now inhabit will require letting go of many established methods, and acknowledging the complexity of an economy that responds to new, and still evolving, strategies and incentives.

To properly study the economy of the post-industrial era, economists must change the way they treat the individual, the firm, and the composition of overall economic activity. Consumer well-being can no longer be measured by the cost of a particular basket of goods. The strategy of a firm is no longer described as capital accumulation and resource deployment. The economy is no longer straightforwardly quantifiable with inputs and outputs; it is driven by services, skills, coordination, and information — intangible factors — that must be monetized in creative ways.

While most economists seem unable to see past their old models, some modern thinkers are creating new theories and frameworks to better analyze our post-industrial economy, and to take account of our dynamic new economic ecosystem. These theories will prove crucial as consumer values, technologies, and the larger economy continue to adapt to new circumstances, and policymakers reckon with post-industrial realities.


In certain respects, the work of John Kenneth Galbraith can help us understand the problems we face. Decades ago, Galbraith challenged conventional wisdom (a phrase that he coined) by providing a much richer view of the modern corporation's economic role than was commonly found in mainstream economics. Galbraith's magnum opus, The New Industrial State, offered a bold, original conception of corporate power, political economy, and sociology. Published in 1967, definitively revised in 1971, and later revised again, the book was widely praised for a time as a significant work. But Galbraith is no longer being studied. History had revealed some major flaws in his thinking, not least of which was his argument that the differences between Soviet communism and American capitalism were exaggerated. In general, Galbraith overestimated planning as a stabilizing force. The dynamism of market competition proved capable of overthrowing not just the Soviet system but also many of the industrial giants that Galbraith regarded as permanent fixtures in the United States.

But Galbraith was ahead of his time in many ways, too, particularly in his approach to corporations. He emphasized that, in a modern economy, production takes many steps and incorporates an almost unimaginable number of specialized sub-tasks. While mainstream economics treats the individual firm as a sort of black box for transforming resources into outputs, Galbraith recognized that the large industrial corporation requires a skilled bureaucracy to plan and coordinate its multilayered operations. To take a modern example, a single smartphone can contain tens if not hundreds of thousands of separate patented technologies. The complexity of modern production is still almost completely ignored by mainstream economics, with its simple, two-dimensional models of inputs leading directly to outputs.

As he wrote in the 1978 edition of The New Industrial State, "far from being the controlling power in the economy, markets were more and more accommodated to the needs and convenience of the great business organizations." To Galbraith, such "great business organizations" were not small, anonymous price-takers vibrating to the rhythm of the market along with other small, anonymous price-takers; on the contrary, they largely controlled their markets. In order to operate effectively and send the right signals to their suppliers, they needed to be able to forecast, and if possible to govern, the demand for their products.

From the inside, then, a corporation must look like a state run by a Weberian bureaucracy. A corporation's managerial class, which Galbraith dubbed the "technostructure," possesses decision-making power of the sort granted to cabinet departments and government agencies. Shareholders and their representatives on boards of directors are akin to voters and nominal leaders in a representative government. Retained earnings are equivalent to tax revenue, and advertising and marketing are comparable to regulations, which the technostructure can use to bend consumers to its will. A major corporation, like a government, has significant power over its environment. In other words, it does not simply reallocate inputs in response to circumstances beyond its control.

Galbraith believed that large firms are controlled by their technostructures rather than by their shareholders, assuming that the latter would lack the knowledge and expertise necessary to question decisions made by the "managers." He also argued that the prestige and power of the managerial class is more closely correlated with corporate growth than with profits. This insight certainly applies today, as leading technology companies have managed to convince Wall Street to buy into growth as the primary indicator of their corporate strength. Investors are impressed with Amazon, Facebook, Google, and Apple because of the ways in which they dominate existing markets and open up new ones.

In another testament to Galbraith, the determination of the technostructure to manipulate business environments is even more evident in 2018 than it was in the 1970s. Facebook and other social-media platforms have turned friendship into a sort of competitive game, encouraging users to pursue "likes," "shares," online followers, and so on. Developers for Apple and other smartphone companies have created countless highly addictive apps. Techniques that combine state-of-the-art psychological manipulation with artificial intelligence have allowed Google, Amazon, Facebook, and other companies to influence behavior in ways that make the advertising and marketing of 50 years ago seem hopelessly naïve and ineffectual. Galbraith was prescient in calling attention to these phenomena.

And yet in several respects, Galbraith's challenge to the prevailing opinions of his day did not go far enough. By focusing so closely on heavy industry, he was just as guilty as his mainstream opponents of missing the shift toward services, the increased importance of diverse individual preferences, and the ever-rising significance of intangible sources of wealth and competitive advantage.


Economics today is beset with a number of problems, not least of which is a fundamentally flawed approach toward measuring one of the most important features of the economy: the standard of living. Economists generally track this key measure using benchmarks such as real income per household, real GDP per capita, or the real wage (with the prefix "real" indicating that figures are expressed in terms of purchasing power). But calculating living standards in this way assumes that everyone purchases roughly the same mix of goods and services. That was true when a large share of income went toward traditional necessities like food, as well as mass-market durable goods like refrigerators. This is no longer the case.

Economic historian Robert Fogel studied changes in overall consumer budgets, including changes in the income that is "spent" on leisure — that is, time that could otherwise have been taken up with paid work — and found that, between 1875 and 1995, leisure increased from 18% of the consumer budget to 68%. By contrast, consumer spending on food, clothing, and housing went from 74% to just 12%.

Because tangible products make up such a small portion of consumer well-being, using the prices of those items as the standard for purchasing power is clearly misguided. Moreover, trends in relative pricing (changes to individual prices in response to the supply and demand for various goods) vary widely.

Our consumption patterns are dominated by how we choose to enjoy leisure. Because our tastes vary widely, the long-accepted concept of a standard market basket — a group of "typical" products that many people buy and that can be used to measure a variety of economic indicators — becomes suspect.

For example, the cost of information and telecommunication services has been plummeting; a smartphone now possesses capabilities that would have cost over $3 million in 1991. And since so many information services are now provided to users at no charge, much of the "consumer's surplus" — the difference between what consumers are willing and able to pay for a good or service, and what they actually do pay — that is derived from the internet is not included in the measured GDP.

On the flip side, the costs of education and health care have soared in recent decades. Between 1980 and 2014, college tuition increased by almost 260%, compared to an average increase of nearly 120% for prices in the economy as a whole. Between 1980 and 2013, health-care spending in the United States rose from about 9% of GDP to over 17%. Much of the increase in health spending is attributable to higher-quality inputs (such as specialists and high-tech equipment), but there has not been a commensurate improvement in overall health outcomes.

The result of all this is that individuals diverge significantly in the value they place on, and receive from, information services, higher education, and health care. Consequently, it is no longer possible to speak accurately about a single, widely applicable cost of living.

Individuals also diverge in their preferences regarding employment. Compensation is one important factor in job satisfaction, but it is not the only one. To his credit, Galbraith recognized that pecuniary incentives are not sufficient to motivate the "knowledge members" of the technostructure — those employees with specialized knowledge or skills. To be effective, knowledge members must perform well in teams, which means they must identify with the goals of the firm and believe they have some ability to shape those goals.

Decades after Galbraith's insight, most major corporations have adopted "mission statements" providing lofty visions for their businesses. Even so, many young people today are attracted to the non-profit sector because they find it easier to identify with the goals of such organizations.

In short, the variation in preferences across individuals means that living standards cannot be measured objectively. To even begin tracking such a standard, it will be necessary to introduce subjective measures.


Some social scientists have recognized this necessity, and have proposed using subjective happiness as an indicator of broad economic performance. While this is a worthy goal, the usual method of measuring happiness through surveys tends to fall short. Measuring happiness in this way generally involves asking people to rate their happiness on a scale of, say, one to seven. While this approach yields numbers, it is unclear what those numbers actually mean.

Subjective happiness is, of course, not an absolute. In responding to a survey on happiness, one would necessarily assess one's happiness relative to some baseline. But what baseline? One's happiness could be relative to how one was feeling an hour, a day, or a week ago, or it might be relative to one's perception of the happiness of other people. Further, a typical happiness survey may not make important parameters clear, such as whether we are supposed to describe how happy we are at the exact moment we are taking the survey, how happy we have been over the course of our entire lives, or how happy we have been in the past month.

Even if happiness were measured well, the determinants of happiness include non-economic factors. If we hope to accurately evaluate economic policies and outcomes, overall happiness is simply too broad a measure.

Instead of using either GDP or general subjective happiness, therefore, it may make sense to use occupational satisfaction as a broad indicator of economic performance. Occupational satisfaction is the core economic component of happiness. Unlike GDP, which is rooted in a materialistic understanding of value, occupational satisfaction reflects the understanding that value is largely subjective.

There are many drivers of job satisfaction. Monetary compensation, status, and work relationships can all play a role, as can a sense of accomplishment, meaning, structure, and control, among other things. And, as with happiness, occupational satisfaction is comparative. Often, we become annoyed at the thought of others attaining higher status or earning more money based on relatively few skills or little effort, but we tend to neglect to consider the hardworking people with advanced skills who have relatively low-status and low-income employment.

One way to take these factors into consideration might involve listing all the possible drivers of job satisfaction, including monetary compensation, the quality of one's work relationships, travel requirements, and so forth. The survey respondent would be asked to weigh the importance of each characteristic and indicate his level of satisfaction with that characteristic in his current job.

The weights on importance in such a survey would be made to add up to 100%, and the weighted average of the satisfaction rankings for each component would constitute the respondent's "job-satisfaction index." Breaking the question down into characteristics, rather than simply asking respondents to "rate your overall job satisfaction on a scale of one to seven," would likely encourage an answer that incorporates more thought.

Of course, developing such a tool would entail a great deal of trial and effort. Once the survey instrument has been established, though, it could be widely administered to a large sample on an annual basis, yielding a national occupational-satisfaction index. I suspect that such an index would be revealing, and specifically that it would show two positive trends and one negative trend among the American workforce: Jobs are getting safer, and the chances of obtaining a meaningful job are higher, but (as the decline in labor-force participation suggests) some are not finding satisfying paid work.

Figures from the Department of Labor have shown dramatic improvement in occupational health and safety over the past few decades. The rate of reported workplace injuries and illnesses fell from 8.4% in 1994 to 2.9% in 2016, which is likely due at least in part to a shift away from jobs that require dangerous physical labor. And, as noted earlier, young people are increasingly choosing jobs based on organizational missions. There has been a proliferation in recent years of new types of jobs, from specialty yoga instructors to web designers, which allow a wider variety of people to make meaningful use of their skills.

The slump in labor-force participation, on the other hand, shows that not every trend concerning job satisfaction is positive; presumably, if more people thought that the currently available jobs were sufficiently rewarding, fewer people would have stopped looking for work.


There are still other ways in which the intangible economy has confounded economic statistics. The framework for calculating GDP, known as the National Income Accounts, was originally developed in the 1930s and 1940s. New ways of breaking down the economy into meaningful sub-components can come from sources that were not available when that system was devised. Data from product scanners, web searches, and Yelp reviews have provided researchers with interesting insights, but such research falls outside of the conventional accounting framework.

The National Income Accounts decomposes economic output into consumption and investment, and income into labor income and capital income. This system of analysis has facilitated Marxist and Keynesian interpretations of economic aggregate statistics. In an intangible economy, though, these old decomposition methods are problematic.

In conceiving of output solely as consumption and investment, the accounting system was designed to treat only the acquisition of tangible assets as investment. For the most part, this does not include investment in education, training, brand recognition, management information and accounting systems, strategic relationships, learning by doing, and other activities that produce intangible assets.

The division between labor and capital income is also misleading. Treating labor as a homogenous quantity overlooks the importance of skill differences between workers. Capital or business income no longer represents a return on tangible assets, because intangible assets are now the main determinant of profits.

Economists will need to develop new ways of decomposing national economic activity to reflect these and other emerging realities. For example, regional variation in economic activity is widening; globally, economic activity is gravitating toward major urban centers. In the United States, we are seeing great concentrations of wealth in cities like San Francisco, New York, Boston, Los Angeles, and Washington, D.C., while rural areas and small towns are in decline. Researchers have also found sharp differences within cities. This heterogeneity should be taken into account when developing methods of decomposition.

As explained above, measures of the national standard of living implicitly assume that people have virtually identical tastes. But there are important cultural differences in consumer preferences, some of them regional, some due to social class. Using cluster analysis, market researchers have divided American consumers into over 50 different segments, with important implications for business. Economists may wish to pay attention to this, too, and develop culture-specific data on living standards.

The old distinction between consumer goods and investment goods is of little use as an industrial-classification system for today's economy. Instead, we need to recognize divergence along other lines. For example, in education and health care, demand has been growing faster than productivity for decades, while in manufacturing, the reverse has been true. Today, only in the state of Washington is a manufacturing firm (Boeing) the largest employer. In over 20 states, the largest employer is Walmart. In more than 20 other states, the largest employer is either a health-care provider or a university consortium. Economists should try to discern where the main fault lines between industries appear — whether they fall between those that provide in-person services and those that deliver information services, or between both of those industries and those that produce tangible goods.

Finally, skill differentials are increasingly important. Research that classifies jobs by the level of digital skills needed has found that annual salaries for jobs requiring high digital skills are more than double those of low-digital-skill jobs. Moreover, between 2002 and 2016, the share of jobs requiring high digital skills soared from 5% to 23%, while the share of jobs requiring low digital skills plummeted from 56% to 30%.

Trying to understand the economy by looking at consumption vs. investment, or labor income vs. capital income, is decreasingly useful. We must instead think in terms of a complex matrix of skills, locations, intangible assets, and consumer markets.


Mainstream economists treat the firm as if it were an inorganic particle that does nothing but react to forces around it. But the increased importance of intangible factors has turned the world of business into a complex ecosystem, one that is capable of changing faster than biological systems, because of the faster pace of human cultural evolution. We must look away from accepted models and examine the world itself. This will entail developing taxonomies or classification systems, rather than traditional economic frameworks.

Amar Bhidé's work provides an important example of this way of thinking. In his 2000 book, The Origin and Evolution of New Businesses, he employed a two-factor matrix to classify new business ventures. One factor involves the level of investment required to undertake an initiative, and the other involves the level of ambiguity, or irreducible uncertainty, in forecasting the outcome of an initiative.

For example, when a semiconductor manufacturer undertakes to design a new microprocessor and gears up for production, the investment required is high. But the firm can be fairly confident about its ability to forecast demand for the product and the cost of its development. These sorts of high-investment, low-ambiguity projects are readily undertaken by large, incumbent firms such as Intel.

Sometimes, both the investment and the ambiguity of an initiative are low, such as when an entrepreneur launches a small firm offering accounting, law, programming, or other professional services. The same holds true for the sorts of businesses found in strip malls — restaurants, hair salons, martial-arts studios, and the like. Bhidé refers to such initiatives as "marginal startups"; the combination of low investment and low ambiguity is attractive to individuals and families, even though the profits from such ventures are not likely to be spectacular. In some cases, business owners opt to pursue this type of work for non-financial reasons, as suggested by the phrase "lifestyle business."

When small teams of entrepreneurs gamble on their ideas, such as a group that designs a digital game in the hope of selling it as an app, ambiguity is high and investment is low. Bhidé refers to such small, speculative ventures as "promising startups."

In projects involving venture capital, both ambiguity and investment are high. The ride-sharing company Uber, for example, has spent a great deal of capital to obtain consumer acceptance, but has yet to prove its profitability.

Bhidé's classification system helps to explain variations in organizational structure and financing methods. Instead of treating every firm as solving an identical sort of problem, he has offered a picture of the business world as an ecosystem, with separate niches for large incumbents, routine marginal start-ups, speculative and promising start-ups, and venture-backed start-ups.


Hal Varian and Carl Shapiro have also provided promising strategies for the digital economy and useful ways of thinking about digital business models. In their 1998 book, Information Rules, they anticipated the opportunities and challenges posed by goods and services that arrive in the form of bits rather than atoms. In order for producers to charge for the goods they create, they must be able to prevent people from accessing their goods for free. In the digital world, however, this is no simple task. In a sense, it may even be unnatural.

It is relatively easy to prevent people from obtaining physical goods, such as a loaf of bread. If you don't pay the baker, you don't get the bread. This is a default characteristic of the physical-goods market. In the world of digital goods, however, nearly the opposite is true. The default characteristic of the digital marketplace is non-excludability. To prevent someone from accessing content on the internet, we must establish artificial barriers, such as paywalls. But paywalls pose a chicken-and-egg problem: Before paying for content, consumers want to know what it is they are buying. The producers of the content, however, want people to pay before accessing it.

Another difference between physical and digital goods concerns the cost to producers. When I consume a loaf of bread, it costs the baker something to make another loaf available to someone else. Yet it costs the writer of an essay nothing extra in terms of time or resources if more than one person reads the essay online. The same is true of cases where more than one person searches a database, uses a software algorithm, or downloads a song. In short, information wants to be free, but creators need to get paid.

Varian and Shapiro offer a list of approaches for addressing this dilemma, including price discrimination, providing free samples, "versioning," "bundling," advertising, and creating network and "lock-in" effects.

Because producing information goods requires the same amount of effort whether the producer serves many customers or just one, the fixed cost is high and the marginal cost is close to zero. When the marginal cost is so low, and if someone is willing to pay very little for the product, one can still benefit by charging a very low price. But if someone else would be willing to pay a high price, then naturally one would want to charge more in that case. Charging different prices to different people in this way is an example of price discrimination.

When asked by high-school economics students about real-world business, I often stressed that price discrimination clarifies everything. Price discrimination explains why retailers offer discount coupons and why airlines vary their ticket prices — these techniques allow businesses to maintain high prices for those who are less price-sensitive or whose needs are more urgent, while still making sales to consumers who are more price-sensitive or who have less need of the product or service.

Sellers of digital goods currently make extensive use of price discrimination, and could probably do so even more creatively. Amazon, for example, charges different prices at different times for the same e-book on its Kindle e-reading platform, as well as different prices to consumers who opt for the "unlimited" option. In theory, Amazon could also anticipate which consumers would be willing to pay more for a particular book, and offer different prices to different readers.

Varian and Shapiro's suggestion of free samples also provides a solution to the chicken-and-egg problem regarding paywalls. On the New York Times website, for example, a limited number of articles are free, but a subscription is required for unlimited reading. And websites like Spotify employ Varian and Shapiro's idea of "versioning," or offering information in different forms, by making available a free version of its streaming-music service as well as a subscriber's version. The free version includes advertising that interrupts the music, and lacks some of the paid version's features.

"Bundling," or combining different goods into one consumer package, is another potentially useful tool for firms selling digital products. Some companies (like this journal) bundle a digital good with a physical product, such as a subscription to a print version of a publication that includes unlimited access to the online version. As another example, an Amazon Prime subscription includes both physical delivery services and access to digital content, such as streaming movies.

Bundling can also make digital subscription models more appealing. Most people would not be willing to subscribe to a single label's online music offerings, but might pay a monthly fee to Spotify or Pandora, which offer unlimited music. In the 1990s, Microsoft was famous for bundling application software (word processing, spreadsheets, and, most notoriously, a web browser) with its operating system.

Advertising can allow information providers, such as Google and Facebook, to earn revenue without charging users. Though some Facebook users might find its advertising overly intrusive and prefer an ad-free subscription option, Facebook is unlikely to implement such a model because its value depends on retaining as many users as possible. This is an example of network effects, another of Varian and Shapiro's proposals. Whereas each additional consumer of a physical good imposes a cost on the producer of those goods, each additional user provides a benefit to businesses like Google or Facebook, directly increasing the value of their services.

A business that is dependent on network effects strives to attract as many users as possible in the short run in order to profit from consumers in the long run. Uber is another example; it has burned through investment capital in order to offer low prices, which are necessary to build a robust network of drivers and riders. Uber will be able to raise prices only once drivers develop confidence that they will be able to find riders, and riders develop confidence that they will be able to find drivers. Uber's challenge, therefore, is to find "lock-in" effects similar to those used by Facebook, which has multiple ways of keeping users engaged with its service. It would, of course, not do Uber any good to subsidize users in the short run if they desert the company once it begins charging enough to earn a profit. Like Facebook, Uber relies on network effects, but it has yet to be determined if it can keep users committed to its service while remaining financially solvent.


Another mode of classifying the business sector is found in Richard Bookstaber's The End of Theory, published in 2017. In it, Bookstaber depicted the financial sphere using agent-based economics, which means looking at all meaningful differences in economic actors, rather than trying to abstract them away in a mathematical model. Seeking to explain the dynamics of the financial crisis of 2008, he developed a scheme for categorizing the roles played by financial intermediaries. He particularly focused on the effects of maturity transformation, credit transformation, and risk transformation.

Maturity transformation occurs when financial institutions issue short-term liabilities while holding long-term assets. A typical bank deposit, for example, presents a short-term liability to a bank; the deposit will accrue interest at a low rate and can be withdrawn at any time. By contrast, a commercial loan that will not come due for many months and accrues interest at a higher rate presents a long-term asset to a bank.

Credit transformation is a process by which a highly reputable firm effectively "rents" its good standing to less-reputable firms. For example, the AIG insurance company wrote credit-default swaps on mortgage-backed securities. Thanks to AIG's backing, other institutions did not have to rely solely on their own credit strength to participate in mortgage-security trading. Unfortunately for all concerned, these arrangements were not durable when the mortgage-securities market crashed in 2008.

Finally, risk transformation refers to changing the relationships between outcomes from uncertain investments and the returns to investors. For example, a mutual fund that holds a diversified portfolio of stocks can allow investors to obtain better risk-reward trade-offs than they could by simply holding one stock at a time.

By examining the real-world financial sector, Bookstaber found that each type of financial institution, or agent — including large banks, hedge funds, cash providers (including money-market funds), securities lenders, and institutional investors — has its own set of objectives and operates using its own set of heuristics. As he wrote in The End of Theory,

Each agent observes its environment and takes action accordingly....Each has a different business model, a different level of risk taking, and a different culture. Some of this will be spelled out in the governance structure and policies and procedures, some will be communicated to their investors. And during times of crisis, some of the heuristics are hard wired, without any ability for the agents to alter their course....

To the economic layman, this way of looking at financial institutions and their behavior may seem only sensible. Most economists, however, have no inclination whatsoever to study the actual workings of the financial system. The typical economist tends to view the financial sector through a few key interest rates; the task of trying to understand the wide variety of actual financial instruments, trading strategies, and operating principles that have emerged in the real world, however, they find too daunting.


Finally, recent work by Jonathan Haskel and Stian Westlake highlights the gap between the market value of modern corporations and the value of their tangible assets. Their 2017 book, Capitalism without Capital, accounts for the importance of the digital economy as depicted by Varian and Shapiro. It also shares the emphasis on intangible value found in the 2009 book From Poverty to Prosperity, which Nick Schulz and I co-wrote and reissued in 2011 as Invisible Wealth.

There are many types of intangible assets. Patents and copyrights constitute formal intellectual property, while other forms of knowledge are protected informally by being embedded in corporate culture. Brand recognition is an intangible asset, as are network and lock-in effects of the kind employed by Facebook. The talents, skills, and experience of a firm's employees and managers, and even the concentration of talent in a particular location — such as Hollywood or Silicon Valley — are intangible assets as well. So are common social norms, a common language, and legal and political systems that promote social cooperation and limit corruption.

To Haskel and Westlake, intangible investments can be characterized by the "four Ss": sunk costs, spillovers, scalability, and synergies. Sunk costs look very different when discussing investment in intangible goods as opposed to physical products. For example, when a pharmaceutical company invests in physical infrastructure such as factories or laboratory equipment, it can hope to recoup at least some of the cost of that investment by selling the infrastructure when it is no longer needed. By contrast, if a company spends hundreds of millions of dollars on research to develop a new drug, and then the drug does not make it to the market, the entire research effort must be written off. All the costs will be sunk.

The second "S," spillovers, refers to the ways in which ideas can be copied for free. For the economy as a whole, spillovers provide a benefit. But for an individual firm trying to profit from its ideas, spillovers are a problem. For example, drug companies can obtain patents for their drugs, which protect their investments. If this were not the case, a pharmaceutical company could potentially spend hundreds of millions on identifying a new drug and proving its efficacy only to have a different company produce the same drug and bring it to market at a lower profit margin. This could keep the first company from ever recovering its research costs. Spillovers illustrate the principle mentioned earlier: Information wants to be free, but creators need to get paid.

Scalability refers to the fact that intangible assets are often not subject to diminishing returns. If a car manufacturer wanted to manufacture more cars, it would be necessary to build more manufacturing plants. But someone who developed an app for smartphones could make it available to an unlimited number of customers without expending additional resources.

Finally, synergies reflect the reality that ideas in combination may be much more valuable than ideas considered individually. The value of a smartphone, for instance, is much greater than the value of each of its individual components.

Taken together, the "four Ss" indicate that there is only a weak correlation between the value of intangible assets and the costs of acquiring them. The entire expense of building an intangible asset can be wasted, and the widely dispersed social benefits of ideas can be far greater than the benefit that accrues to any one individual or firm. On the other hand, a firm's ability to earn revenue can far surpass the book value of its investment.

All of this implies that economists' "neoclassical" approach to explaining the distribution of rewards is in peril. For employees, we can no longer treat productivity and earnings potential as individual characteristics, because the context in which one works matters a great deal. For firms, we can no longer expect to find a close connection between the revenues from investment and the amount of capital invested, because outcomes depend so much on strategic interaction.


Business management today does not consist primarily of deploying tangible resources. Companies must instead focus on the best strategies for capturing the value of ideas. Mainstream economics textbooks still treat incomes as returns to "factors of production," notably labor and capital; in the real world, however, the highest incomes increasingly result from management strategy, mobilizing internal talent, and exploiting opportunities to use synergy, spillovers, and scalability in the external environment. Based on the methods used by scholars — often found in business schools rather than in economics departments — who closely observe the real-world environment, we can see the changes economists must make to improve their study of the economy as a whole, as well as at the level of the individual and the firm.

Primarily because of the dramatic changes that our economy has undergone in the past several decades, we can no longer depend on models; we must look at the world itself. The business strategies that have evolved in today's economy can tell us a great deal about how the market actually functions; abstracting away from those strategies can doom the economist to a limited understanding of what is happening. The financial crisis in 2008 powerfully illustrated this.

Accordingly, we must focus on developing classification systems, not testing hypotheses. The increased importance of intangible factors has served to make the economy more opaque from a quantitative perspective. Better qualitative understanding will become increasingly helpful as quantitative methods yield fewer insights.

All of this will entail a psychological shift. The intangible economy has brought with it enormous changes; the meaning of the standard of living, as well as our understanding of the real GDP, corporations, and regional economies, among other factors, have all been rearranged. Time-honored economic models, conceptual tools, and aggregate statistics no longer adequately explain the economic environment. Fortunately, around the edges of the economics profession there are those who have examined the real-world business environment and have developed promising approaches for understanding it. Eventually, the field of economics will adopt conceptual frameworks that fit the post-industrial state.

Arnold Kling is an adjunct scholar with the Cato Institute and a member of the Financial Markets Working Group at the Mercatus Center at George Mason University.


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