Superstardom and technological turbulence: job-linked sources of earnings inequality
Peter B. Meyer1
Office of Productivity and Technology, Bureau of Labor StatisticsMarch 31, 2008
Preliminary and incompleteFeedback welcome, to [email protected]
Abstract. The paper analyzes trends in the dispersion of earnings within occupations in the Current Population Survey since 1968 and the decennial U.S. Census since 1960. New media technologies make it easier to transmit certain kinds of work, such as athletic performances, to wider audiences around the world, enhancing the relative payoffs to the most-favored performers. Earnings inequality rose within these occupations, consistent with the superstars effect described by Rosen (1981). Earnings inequality rose within occupations which call for working closely with new semiconductor and information technologies, such as electrical engineers and computer programmers. It is argued that these occupations experienced technological uncertainty, which leads to extraordinary opportunities, obsolescence, and therefore turbulence. The uncertainty and superstars effects would naturally occur to some extent in many occupations. Therefore we examine also occupations in which these effects are likely to be the weakest – those that call for personal interaction with other individuals. On average inequality within occupations at this other extreme has not risen.
1 The author thanks Christopher Taber, Joel Mokyr, Leo Sveikauskas, Sabrina Pabilonia, and many others for their comments and advice. Thanks to Anastasiya Osborne for much research assistance. Views expressed are those of the author, not the U.S. Bureau of Labor Statistics.
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1. Introduction
New information technology is one cause of the rise in income inequality in the U.S.
since 1970. Autor, Katz, and Krueger (1998) showed that wage inequality tended to rise
more in those industries which had more computers, which invested more into computers as a
fraction of overall investment, and whose employees used computers more. By their
accounting, 30 to 50 percent of the increase in income inequality since 1970 could be
attributed to the use of computer technology.
Particular capabilities of computer technology produced some of this rise in inequality.
For example, much of the computer-related rise in inequality could be explained by
reorganizations of tasks to use computers to do routine work.2 The broader category of
semiconductor technology also enabled the expansion of media markets through quicker and
cheaper communication, for example on CDs and through cable television systems. This
increased the scale of distribution of the most-preferred performers and therefore their
competitive advantage in revenue terms. This superstars effect, discussed by Rosen (1981),
will be measured here.
Influential new technologies arrive along with technological uncertainty which can cause
economic turbulence and a temporary increase in earnings inequality. It is difficult to predict
the future of an immature technology with great potential. Organizations may innovate,
reorganize, and change their products and processes to capture the benefits of reduced costs,
higher quality output, and to avoid obsolescence. The new technology can therefore produce
a wave of experimentation, new engineering standards, and entrant firms. Characteristically
many of the entrants fail and a few become big successes. This turbulence among
organizations can widen the distribution of individual earnings in affected occupations by (1)
temporarily opening up valuable opportunities (such as starting a firm, or receiving incentive
stock options); (2) making opportunities depreciate rapidly, especially for those using older
2 Autor, Levy, and Murnane (2003) demonstrates this.
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technologies and methods; and (3) expanding the range of activities and knowledge in the
affected occupation.
Subsequent sections of this paper discuss the theories of the superstars effect and
technological uncertainty, and the institutional mechanisms through which they could raise
earnings inequality. Using decennial U.S. Census data from 1960 to 2000 and March Current
Population Survey data from 1968 to 2000, occupations that experienced amplification
through the media and technological turbulence will be shown to have had rising inequality
of earnings compared to other occupations.
2. Media amplification and the superstars effectRosen (1981) modeled an effect that would occur in certain labor markets as they grow
in size. In the model, the services of sellers vary in quality, and the sellers can deliver
services to many buyers simultaneously. That is, joint consumption is possible, in the sense
that many listeners can hear a single musician and many readers can read a book, without
imposing significant costs on one another’s experience. In this environment, an expansion of
the market (quantities demanded and supplied) for the service leads to more revenue for the
top-quality sellers, but less revenue for the least-preferred sellers (who now have more
competition) and therefore there is a rise in revenue inequality among the sellers. As a
market expands, revenue inequality would increase in certain labor markets.
Several standard examples illustrate the theory. An athlete or musician before the age of
mass media performed only for those who were present. Spectators might have been almost
as likely to come to see the tenth-most famous one as the very most famous one, since it was
hard to rank them and the opportunity to see either one was rare. But once all musicians had
recordings for sale, buyers could more easily buy any of them, and the best or most famous
one may get most of sales and unknown ones no sales. Similarly, before broadcasts, if there
were only one basketball game available in town, it would not face direct competition for
basketball fans, but when there are several games on television the most appealing one may
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get all the viewers. It follows that inequality at the top of these performance occupations
would increase as more and more recorded and packaged versions of their performances
became more widely available. Availability increases with technology (e.g., the invention
and standardization of compact disks, computer networks, and cable television), and also with
expanded trade and globalization. The work of famous musicians and authors is available
around the world, and successful performers are international celebrities. These are
superstars, in Rosen’s memorable language. Labor markets with superstars have two
distinctive characteristics: (a) the outputs of different sellers are not perfect substitutes for one
another in the minds of buyers, and (b) there are economies of distribution, meaning that the
costs of production rise more slowly than the number of buyers.
Distinctive niche sellers of such services such as athletes, dancers, or reporters, can
benefit directly from expanded media outlets and indirectly from being interviewed or
discussed on cable TV, through global broadcasts of American channels, and Internet
connections to homes and workplaces. A famous athlete, musician or author can have a
larger audience now than ever before. Top earners in these professions thus benefit
disproportionately from improvements in information and communication technology, and
globalization.
The empirical definition here is made up of occupations Rosen (1981) used as examples,
adjusted for what is available in the data and also by the other occupations in which the effect
seems to be visible. The media-amplified occupation groups are defined to be: actors,
directors, or producers; artists (artistic painters, sculptors, craft-artists, and print-makers);
athletes; authors; dancers, dance teachers, and choreographers; designers; editors and
reporters; musician or composers; and photographers. Table 6 shows three definitions: this
list, Rosen’s examples, and the examples in Frank and Cook (1995). The mean earnings in
performance jobs have not risen much on average (as shown in table 3) perhaps because
performers enter these tournament-type professions and reestablish a kind of Malthusian
equilibrium.
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Rosen (1981) discussed a related effect, of more intense competition among
professionals that can occur in an environment where communication and transportation are
easier. It has become easier over time to compare surgeons or attorneys through phone
recommendations or online information, and also easier to travel to such specialists.
Therefore the value of a one percent increase in expected performance to a customer could be
increasingly valuable in these professions, and competition could have increased at the top of
the professions. Indeed there could be a tendency for superstars with international audiences
to appear. This argument is plausible, but the evidence on earnings does not show this.
The main superstars hypothesis to be taken to the data is that in professions whose output
can be reproduced or amplified by computer or television communications, earnings
inequality rose over the recent decades. This is not because the technologies are new per se,
but because they have been used increasingly to transmit work content and performances.
3. Technological uncertainty and turbulenceHere technological uncertainty means a lack of common knowledge and agreement about
what production technology will be relevant in the future. “It involves not only lack of
knowledge of the precise cost and outcomes of the different alternatives, but often also lack
of knowledge of what the alternatives are.” 3 Uncertainty in markets associated with a new
technology takes several forms such as uncertainty over prices, tools and materials, products
and customers, financing, and the work force. A core assumption in the Greenwood-
Yorukoglu (1997) theory is that people vary tremendously in how well they can apply the
“skill” of learning in response to new situations, and this leads to a rise in income inequality
during the adoption of a radically new technology. In that model there is a productivity
slowdown at the same time, as employers try to usefully adapt the new invention.
3 Dosi (1988) p.1134. The subject is also well discussed in Rosenberg (1996). No source seems to give a direct definition but this one seems to be approximately what they mean. Tushman and Anderson (1986) measured uncertainty by the magnitudes of the errors in forecasts of demand growth made by financial analysts. This was much higher in semiconductors than in other industries.
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In the theory of Greenwood and Yorukoglu (1997) and Greenwood (1997), workers have
the opportunity to improve the technology of the firm at times of radically new technology.
Workers differ in their ability to make such improvements, therefore there is an increase in
the dispersion of worker productivities and in the dispersion in the distribution of earnings
when innovations are most desired by the employers. This hypothesis, that workers differ
greatly in the ability to adapt (the adaptability hypothesis) has been made in other models of
technological change such as those of Caselli (1997), Rubinstein and Tsiddon (1999) and
Galor and Moav (2000). Offering evidence for this interpretation, Bartel and Sicherman
(1999) found wages and the wage premium to education were higher in industries which had
more technological change by several measures, including research and development as a
fraction of sales. Using a specification with fixed effects on individuals they controlled for
unmeasured abilities of workers who switched industries over time. They found that
technologically changing industries tended to employ workers who had more of this
unmeasured ability, and more years of formal education. On the employer’s side, Hunter,
Kobelsky, and Richardson (2003) and Chun, Kim, Lee, and Morck (2004) have found that
greater investment by firms in information technology was correlated with more volatile
earnings, sales growth, and stock returns subsequently. Indeed information technology
projects have for decades had high rates of finishing later than planned or with an unexpected
outcome.4
Semiconductor chips that are the physical basis of most of the new information
technology could have caused this effect before microcomputers even existed.5 The transistor
was invented in 1948, the integrated circuit in 1959, the microprocessor in about 1971, and
microcomputers soon after that. Throughout the history of the semiconductor industry there
has been plenty of evidence of uncertainty such as asset price fluctuations, retreats by firms
4 Brooks (1975) attributes this to complexity and the interrelationships of those on the project.5 Meyer (2005) showed that the adoption of mass-production steel technology in the U.S. in the 1870s was associated with a rise in earnings inequality in the iron- and steel-making industries but not in other industries.
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from product markets they had just entered, and poor understanding of production processes.
These also characterized the software and e-commerce markets as they developed.
Uncertainty about the future means today’s choices are gambles, with noisy payoffs, and this
could induce noise into the semiconductor-related occupation wages.
Below we will compare statistics measuring inequality of salaries within occupations tied
to semiconductors and information technology to inequality measures from other
occupations. This functional approach is qualitatively different from an approach of
decomposing inequality into worker attributes like demographics or education, which has
been done well elsewhere. That work leaves open the possibility that measures of education
have a signaling role or a credentialing role in a tournament game for high incomes, not a
skill role. Here we look for a more narrowly functional effect of the new technologies on a
different dimension where signaling is less likely. The hypothesis about turbulence is
different from the Greenwood-Yorukoglu hypothesis about adaptability, and also different
from the more common hypotheses about skill bias, though these are not strongly
distinguished here. Evidence for the uncertainty paradigm takes two forms here: anecdotes of
uncertainty from the players themselves, statistics about earnings inequality in some groups
changing differently from others. In principle uncertainty could also be measured by
volatility or variation in company profits, or errors in profit forecasts, as in the classic
Tushman and Anderson (1986).
Since 1968 semiconductor chips have improved dramatically and fallen in price while
the quantities produced have skyrocketed. The resulting products (such as those shown in
Table 1) and changes in work process have redefined white collar work around the world.
Semconductor performance improvements have followed an exponential pace since 1959
known as Moore’s Law. They result from the efforts of a variety of specialists including a
class of electrical engineers and other specialists, and these improvements then reverberate to
buffet the population of electrical engineers and computer specialists. Electronic design and
software design changed dramatically. Electrical engineering became less about continuous
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flows of electricity and more about digital encoding. The dramatic technological changes put
them in a state of technological turbulence that is more intense than that felt by denizens of
other occupational categories.
Table 1. Examples of new semiconductor-related technologies
New kinds of hardware
New kinds of software
Disk drives (1960s) Word processors (circa 1976) Semiconductor memory (1971) Electronic spreadsheets (circa 1978) Microprocessors (1971), pocket calculators, about 1973
Graphical user interfaces (with mice, icons, and drop-down menus)
Bit-mapped video output (beginning late 1970s) Object-oriented computer languages Internet hardware (1970s and on) Web (1990) Microcomputers (beginning in late 1970s) Client / server distinctions Mobile phones Streaming transmission of content Handheld music and game devices E-commerce Handhelds PDAs (personal digital assistants) Web search (late 1990s and on)
Making and selling a new device is involves risky predictions. Jerry Kaplan, a founder
of the company that sold the first pen-input computers, wrote, “We are building an unproven
product for an unproven market. And the key to success is to reduce risk whenever and
wherever we can.” Many of the companies that pioneered new devices were themselves
startups, adding another layer of uncertainty: “Anyone who has managed a startup knows that
predictability is an illusion.” (Kaplan, 1994). Thompson’s (1967) theory of organizations is
constructed around the idea that “the central problem for complex organizations is one of
coping with uncertainty. . . . technologies and environments are major sources of uncertainty
for organizations, . . . differences in those dimensions will result in differences in
organizations” (p. 13). Relatedly, Lindberg (1995) offers advice specifically to managers
who confront technological uncertainty.6
6 Among those recommendations: select for skills and intellectual capacity in potential employees; train existing workers; install new technology in stages, to delay some risks and to enable learning-by-doing; establish trust with potential vendors; be flexible organizationally.
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In this study, electrical engineers and software development employees are taken to be
the high technology employees who can change the employer’s products or processes in
response to technological opportunities. Therefore technological uncertainty predicts a
widening of the earnings distribution in these occupations. The next sections discuss several
dimensions of this.
3.1 Changing prices
An evolving technology causes changes in the market prices for its inputs and outputs.
Intel cofounder Gordon Moore described price trends and fluctuations for semiconductor
devices since the late 1950s this way:
A 20 to 30 percent price decrease per year is about average, although this average consists of periods of time when prices fall very rapidly and when they might even increase if supplies are tight. Not only does the price fall for a given integrated circuit, but as the complexity of the chip increases, the price per electronics function decreases from product generation to generation as more and more functions are integrated into a single structure. Today a complete circuit containing several million transistors costs less to the user than did a single transistor thirty-five years ago. 7
Microcomputer designers faced extraordinary declines and fluctuations in the prices of
components. Price change and volatility were permanent parts of the environment, as
performance of the best semiconductor devices, disk drives, and computer networks
improved so much. For example, one read-only memory chip declined in retail price from
$110 in 1983 to $10 in 1984, and to $3 in 1985. (Morris, 1990, p. 78). Kaplan (1994, p. 35)
described the engineer’s situation this way:
7 Moore, 1996, p. 56. R. J. Gordon (1990) estimated that computer hardware prices declined on average 19% per year from 1954 to 1984 period.
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Hardware people are tinkerers and gamblers. Their challenge is to assemble things out of standard manufactured parts, as cheaply and reliably as possible. The problem is that these parts are constantly changing -- in configuration, price, and availability. Hardware people must spend their leisure time poring over catalogs, price lists, and specification sheets ... A short supply of parts instantly creates a black market, with skyrocketing prices. Brilliantly designed circuits become doomed products if a single component is unavailable.
This disequilibrium affected sophisticated computer makers such as the Digital
Equipment Corporation (DEC). DEC had surprised IBM in the late 1960s and early 1970s by
delivering minicomputers that competed with IBM’s mainframes. IBM then introduced its
own minicomputers. Observing the expanding class of microcomputers in the late 1970s,
IBM released its PC in 1981 to unexpected success. DEC never effectively confronted the
low-end competition from personal computers. DEC did try a series of half-measures to
confront the challenge but relative paralysis seemed to overcome the company. By the 1990s
it was losing money, permanently. These surprising shakeups illustrate the economics of
technological uncertainty. In more predictable markets, the top firms are less likely to be
overturned.
3.2 Novelty and uncertainty over tools, materials, and products
New technologies may have “properties and characteristics whose usefulness cannot be
immediately appreciated.”8 Semiconductor work in the 1960s was characterized by failures
that were not well understood. It is now thought that the failures were results of uncontrolled
impurities in the silicon. “Sometimes the problems would disappear for no apparent reason,
only suddenly to reappear. Solutions that worked one time might not work the next.
8 Rosenberg, 1996, pp. 340-349. As an example, Rosenberg discusses early fiber optic technology: “It took a number of years for some of the attractive properties of fiber optic technology to become apparent: the lack of electromagnetic interference, the conservation of heat and electricity, and the enormous expansion in bandwidth that fiber optics can provide -- the last feature a consequence of the fact that the light spectrum is approximately 1000 times wider than the radio spectrum.” (p. 342). Analogously, it was not generally understood when integrated circuits were first made how compact and reliable they could be.
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Semiconductor manufacturing was so poorly understood that some problems were given
colorful names, such as ‘Purple Plague’ and ‘Red Death.’ Scientists routinely referred to
‘black magic’ and ‘witches' brew’ in describing their process techniques.” (Berlin, 2001, p.
83) Such language implies uncertainty even as the companies were creating and selling
products. Relatedly, the scientists were often doing engineering work. Several analysts have
observed that after the invention of the first transistors, most of the work in this area has been
fundamentally cumulative9, depending more on empirical discoveries than on scientific ones.
Rising productivity in this area depended on experimentation and imitation.
Manufacture of each generation of smaller integrated circuits requires unproven
technology which if successful gives the leader a temporary monopoly. When Intel was a
startup, its first product was immediately imitated so profits from it did not fund the company
for long, but its next product, a silicon gate metal oxide semiconductor memory chip, was not
successfully imitated for seven years. That lead-time advantage allowed the company to fund
other operations at length. The second product is an example where the technological
uncertainty worked to the advantage of the player who was first to market. Handwriting-
input computer maker GO had the opposite experience when standard manufactured chips
turned out not to behave according to their specifications outside the bounds of what regular
PCs would put them through. GO also had trouble writing on flat displays for which the
existing technology had not been used in a production environment (Kaplan, pp. 56 and 108).
GO had not planned on great difficulty with those devices -- the gamble was a surprise.
Similar problems occurred in software development, where project schedules were
dubious. Among the problems were unreliable software development tools, ambiguities in
engineering standards, and unexpected and intricate external product design problems in the
user interface. Some engineering managers believed that expanding a project development
9 The distinction between “cumulative” and “science-based” technologies has been attributed to Nelson and Winter (1982). See also Bessen and Maskin (2006).
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team could proportionately reduce the project’s duration but often this was dramatically not
the case. (Maguire, 1994, and the classic Brooks, 1975).
3.3 Uncertainty over potential customers and what they want
Rosenberg (1996) lists issues of technological uncertainty, among them:
the impact of an innovation depends on later complementary inventions; one does not
at the beginning see the whole technological system built around the original invention
the original invention is targeted at some particular problem to begin with, and its
useful scope may expand and evolve in a way that is hard to predict
This paper, like Rosenberg’s, conflates uncertainty about making a product with the
market uncertainty associated with selling a product under the general heading of
technological uncertainty.
There may be no common agreement about the form in which a new technology is best
delivered to customers. The technology is complex and comes in many designs, possibly
from competing vendors. In the management literature this is sometimes described as the
period before a dominant design. Thomas Edison, for example, was making awkward
phonographs that could record on and play from cylinders made of wax and cardboard for
fifteen years before it became clear that the main mass market use for the phonograph was to
play pre-recorded music. (Norman, 1998, c. p 30.) Edison lost a similar battle when his
company committed to using direct current electricity when alternating current became the
standard. In the semiconductor context, an electronics magazine wrote, “In the 1962-64
period nearly every semiconductor producer got into the [field-effect transistor] business –
and out again just as quickly when optimistic predictions failed to materialize.”10
After the microprocessor had been commercialized in 1971 it was not obvious how to
make money from it. From Freiberger and Swaine, p. 14:
10 Morris (1990, p. 44) quoting from Electronics magazine.
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Intel’s marketing department was cool to the idea of releasing the chips to the general engineering public. Intel had been formed to produce memory chips, which were easy to put to use and were sold in volume like razor blades. Microprocessors presented enormous customer support problems for the young company. [Inventor Ted] Hoff countered with ideas for applications. For instance, one could build an elevator controller around a chip. Moreover, the processor offered cost-reduction to an electronic design engineer, and the engineer would thus make the effort to design it into products. Hoff knew he would.
Even after the microprocessor was an established product, Intel did not venture into the
business of selling applications for it, although in retrospect many of these would have been
valuable businesses. In the early 1970s at Intel “talk had come up about getting into end
products, designing machines around the microprocessors, even about using a microprocessor
as the main component in a small computer. Microprocessor-controlled computers, however,
seemed to have a marginal sales potential at best. Noyce felt that microprocessors would find
their chief market in watches.”11
Starting in January, 1975, a little known store in Albuquerque offered the first extensible
microprocessor-based computer kit for sale. Customers could order it by mail and receive a
kit and build an Altair, then potentially integrate other hardware and software into it. The
company that produced the kit was operating near the edge of bankruptcy. Owner Ed Roberts
had to borrow to his limits to offer the computer kit. He was worried that hardly anyone
would buy it. In fact, electronic hobbyists placed hundreds of thousands of dollars in orders
within the first two months. The same problem occurred with the IBM PC for which IBM
under-predicted sales in its first year by a factor of six. Such mis-estimates also occurred
with the 1978 release of the first electronic spreadsheet program, Visicalc. Its publishers
believed small businesses were the natural first market but in fact it was hard to convince
them, whereas sales to corporate middle managers took off. These examples of unexpectedly
11 Freiberger and Swaine, pp. 15-16. Integrated circuit co-inventor and Intel co-founder Robert Noyce was a legend beforehand and long afterward. He was not just ill-informed.
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rapid success are legendary.12 They occurred simultaneously with others (like Intel’s Noyce)
who could have sold products into these markets but did not, and others who did try, but
failed. It is intrinsic to the uncertainty of the environment, and the brief opportunities it
presents, that there were surprising successes and numerous failures or missed opportunities.
These pioneers were the experts, but made poor predictions. Perhaps this is a kind of
failure, and they could have done better. The “designers and managers involved in each new
generation of computers consistently failed to anticipate the uses that would be found for
their machines” (Ruttan, pp. 90-91). And “The microcomputer is a product that came out of
nowhere, at least in the sense that established firms initially misunderstood its uses and
underappreciated its importance” Langlois (1992, p.5). The uncertainty perspective is that
such errors are not idiosyncratic, but are likely when the situation is novel.
In a new-technology environment, customers may specifically ask for a feature, design or
product that is inferior to another approach, or harder to deliver. “The customer is always
right” is particularly false when the customer does not know what is possible (Southwick, p.
vi and p. 161). The usual approaches to figuring out what customers want are inappropriate,
according to a psychology professor turned Apple vice president:
But focus groups can be very misleading. They tend to reveal what is relevant at the moment, not about what might happen in the future. Users have great trouble imagining how they might use new products, and when it comes to entirely new product categories – forget it. (Norman, 1998, p. 192).
Christensen (1999) and Utterback (1996) documented cases of established firms that
were ruined because they repeatedly followed the advice of customers and made only minor
updates to their product technology, when fundamental technological changes were called for
instead. Customers naturally did not know this, could not evaluate it, or had fundamentally
different interests from the vendor. Possibly this is what happened to the vacuum tube
12 On the Altair, this account is drawn from Freiberger and Swaine, p. 37; on the PC, from (Langlois 1992, p. 23); on Visicalc, from (Cringely, 1992, circa p. 64).
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producers in the 1950s. They had the financial resources to branch out into transistors, but
few did (Morris, p. 80).
Henderson (1993) offers theories of why after a radical or drastic invention, entrant
producers often overcome incumbent producers. One hypothesis is that the incumbent’s
incentives to cannibalize an existing product line are weak, whereas the entrant’s incentives
to survive and prosper are strong. Another theory is that the incumbent’s organization “falls
prey to inertia and complacency” (p. 248), partly because the incumbent is organized to
ignore certain kinds of information. For example, an incumbent firm’s research and
development department may naturally respond to product improvements by competitors by
investing in improving its own technology, even when the better strategy is to abandon its
existing technology. It can be hard for organizations to arrive at that consensus, however, so
Christensen (1999) recommends that established firms form subsidiaries to specialize in
potentially disruptive new technologies.
These issues are sources of productivity dispersion among individuals since sometimes it
is possible for individuals to avoid investing in the wrong technology, or to adapt in some
other way. Some people will wisely evaluate whether they must abandon a technology. IBM
executives did this when a manager stated that in order to compete in the new microcomputer
market it would have to abandon its risk-avoidance tactics and proprietary designs in order to
get a PC product out fast enough to be relevant. Large competitors such as Xerox, HP, and
DEC did not.
We conclude that while the technologies are new and changing and have an
undetermined future, there is not a common agreement about who might be potential
customers and what they want.
3.4 Uncertainty over industry entrants, financing, and survival
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New firms are often the pioneers of discontinuous technological innovations, and new
firms often face great difficulty obtaining financing. Much of the effort of GO executives
was devoted to finding sources of financing (Kaplan, 1994, pp. 60-80). Venture capitalists
specializing in computer technology startups have had high but variable returns. “Industry
benchmarks have it that out of ten venture capital deals, one is going to turn out to be a super
winner, two if you are really lucky; and the rest will turn out to be bankruptcies or barely
break even.” (Perez, 1986, p. 106). In this quote the break-even firms were lumped in with
the bankruptcies. For a venture capital firm, a quickly bankrupt client can actually be better
than a slowly bankrupt one or a firm that barely survives since long-lived firms that are not
acquired and do not go public take much more management time. To reduce risks across
clients, venture capital firms syndicate (share) deals.
Waves of initial public offerings of stocks represent another kind of financial uncertainty
for new firms. Payoffs to early investors are much higher if the firm can go public early
(Perez, 1986, p. 140), and this depends on a kind of cycle in venture capital financing and
public investor interest. Financing for a startup is therefore more easily available when a flow
of initial public offerings of stock is anticipated. Such waves have peaked in 1959, 1961,
1969, 1983, and 1999-2000. In 1983, for example, four times as many firms went public as
in 1982, and they raised more money than all new issues raised in the ten previous years or in
the next two years (Perez, 1986).
There was an influx of new semiconductor makers after a practical integrated circuit was
demonstrated in 1959. Tilton (1971, p 53) counted a leap in industry size from about 25
firms selling transistors in 1955-58 to about 50 in 1960-63. A number of new firms,
including Intel, spun off from the innovating firm Fairchild Semiconductor (Malone, 1985;
Berlin, 2001, pp. 84-85). There was not a great wave of entry of microprocessor producers
after its invention. But with the microprocessor, semiconductor memory, and floppy disk
drive – all commercialized about 1971 -- there was a rise of downstream industries, especially
makers of personal computers.
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Intel, Hewlett-Packard, and DEC all declined to mass-manufacture personal computers,
in the early stages when one of them could have led that industry.13 In later years when they
did try, the experienced computer makers usually failed financially in the personal computer
market. IBM was the main exception. This is a kind of productivity dispersion among the
firms illustrating the uncertainty in the market and technology.
Tushman and Anderson (1986, p. 448-9 and 455-6) measured uncertainty by the errors in
forecasts of industry sales by financial analysts summarized by the publication Predicasts
Forecasts. Forecast errors were high in the minicomputer business both before and after
1971 (when semiconductor memory was adopted into minicomputers, the floppy disk drive
was first used, and the microprocessor was invented.) Uncertainty by this measure was much
greater throughout the 1967-1976 period in the minicomputer market than in their other cases
of technological discontinuity – the appearance of the DC-3 airplane in 1959 and the
widebody jets in 1969 on the airline industry, and the first feasible huge computerized kilns
to make cement on cement makers.14
Of the top ten U.S. manufacturers of vacuum tubes in 1955, only one (RCA) was a
significant producer of integrated circuits in 1978. (Morris, 1990, circa p. 80) One study
measured a rate of 2.8 new semiconductor firms per year in the 1950s (the transistor period)
13 The founders of Apple had hoped to make computers for Hewlett-Packard but HP turned them down and they instead started their own company. DEC’s founder and president did not foresee uses for home computers in 1977 (Norman, p. 234). Xerox’s Alto office computer had a user interface something like a Macintosh in 1974, and could be networked. However, the Alto was expensive and unpleasantly slow to use. Xerox executives were ambivalent about the attempt and did not rush to try again. But if Xerox had revised the product quickly and seriously they might then have led the industry. Similar technology later emerged from Apple Corporation partly because Apple cofounder Steve Jobs took a now-infamous tour of Xerox’s laboratory, and hired his tour guide to work at Apple, where they imitated parts of the Alto’s design.14 Their measure was average error in forecasts of sales, by industry analysts I believe, which was 146% in minicomputers from 1967 to 1971 and 136% from 1972 to 1976. Their other cases were the cement industry (38% from 1963-1967 and 80% from 1968 to 1972) and the airlines (16% from 1955-1959, 78% from 1960-64, 19% from 1965-1969, and 49% from 1970-74. (These figures are from their Table 4. Tushman and Anderson drew the conclusion that uncertainty by this measure was generally higher in the period after a major technological discontinuity. The uncertainty in the industry affected by semiconductors was twice as great as in the other examples.
17
and a rise to 4.7 new firms per year from 1960 to 1972 (the integrated circuit period), but then
a decline to .7 new firms per year from 1973-78. (Levin, 1982, p. 44; Wilson, Ashton, and
Egan, 1980. Berlin (2001), p. 61, says waves of spinoffs and startup firms appeared in 1961-
3 and 1968-9. These waves were centered around specific technologies – the planar
manufacturing process and the integrated circuit, first, then large-scale-integration density
techniques in the second. Often startups had specific niche markets to which they thought
they could offer a product and get established before larger companies stepped in.) In a
sample of 35 semiconductor companies started before 1975 only one-fifth remained
independent in 1980 (Morris, 1990, p. 85). Thus there were substantial flows of entrants and
exits from semiconductor manufacture.
The new developments in semiconductors made new downstream products and industries
possible, such as digital watches, microwave ovens, mobile phones, personal computers, and
personal computer software. The early years of personal computers and software were
turbulent. The first industry sales leader in microcomputers was MITS (the maker of the
Altair) in 1975, then IMSAI in 1975-78 (Langlois, 1992, p.12), then Apple (1978-81), then
IBM, then Compaq. IBM’s attempted to regain control from the clone-makers with its
proprietary standards for the PS/2 hardware design and OS/2 operating system, but these
were overturned in 1987 by revised industry standards -- the EISA hardware standard and the
Windows operating environment which evolved into a complete graphical operating system.
Downstream from the personal computer, in the personal computer software industry,
there was dramatic evidence of uncertainty and turbulence. New companies took the field
almost entirely. Few minicomputer or mainframe software developers made software for
microcomputers. Fundamental principles of software development remained the same, but
techniques and culture did not translate well (Langlois and Mowery, 1996, p. 74).
18
Consider personal computer operating systems. Before making its PC, IBM expected
DRI, the established leader in personal computer operating systems,15 to supply the new
computer’s operating system. DRI unexpectedly declined to discuss a contract with IBM.16
IBM offered the contract instead to Microsoft which seized the opportunity and agreed to
provide an operating system. Microsoft quickly acquired one, adapted it to look like the DRI
product, and came up with PC-DOS. This turned out to be an astonishingly important
product, changing the whole industry in a path-dependent way as Microsoft could define
standards, exercise leadership, and undermine alternatives. Microsoft had not been in the
operating systems business in 1979, but by 1987 had locked down a kind of dominant
position. Even so, it had to slowly develop a radically different technology, a graphical user
interface, following the Macintosh.
Applications software categories were more turbulent yet. Industry-leading word
processors repeatedly lost their position: Electric Pencil (1976), Wordstar (1978),
WordPerfect, and finally Microsoft’s Word. In the 1980s the leading database program was
Ashton-Tate’s dBase II, which ran aground on technical complexity and was replaced as the
leader by Borland’s Paradox, until Borland self-destructed. The inventors of electronic
spreadsheets made millions in the 1970s selling their Visicalc program, but it was then beaten
by the focused effort of startup Lotus to make its 1-2-3 program on the then-new IBM PC.
This program made billions in revenue but then lost out to Microsoft’s Excel.
So survival in the personal computer industry’s first ten years was hard to predict even
by experts in the technology and market. Partly because of uncertainty in the product design
and potential customer base, a shakeout often follows the industry boom of new-technology
entrants. Companies take varied approaches to meeting customer needs. Some work, but
15 DRI stood for Digital Research International, which was a toned-down version of its original name, Intergalactic Digital Research. Such hobbyist producers can exist partly for the joy of it and may not be profit maximizing. In the early pre-profitable phase of a technology, such hobbyists may have the best available technology, before conventional microeconomics has kicked in.16
? Probably this was because IBM required signing an obtrusive nondisclosure agreement A nondisclosure agreement was a routine prerequisite to seeing or hearing about secret technology or marketing plans. It stated that the signer would not tell others of what he or she would see or hear in the private discussions. According to Cringely’s account, IBM’s nondisclosure agreement required both that the IBM technology be kept secret, and also that IBM would not guarantee to keep secret the technology of the other firm. This last unusual clause may be the reason Digital Research refused to sign.
19
others are not implemented well enough, or do not become well enough known, or for some
other reason do not become established.
3.5 Uncertainty over possible colleagues
Electrical engineers design semiconductor devices. Engineers with experience with a
very new technology are by definition not widely available, and their experiences are
idiosyncratic. In labor market terms, the supply of workers is thin and heterogenous. So the
right person for a job may not exist, or may not be available. A key engineer designing a new
technology product may be irreplaceable since the market may disappear before a substitute
could be competent (Perez, 1986, p. 104-5). Hiring is chancy too since the talents and skills
needed may not be clearly identified and agreed upon. Turnover in the semiconductor and
software industries tends to be high,17 so there is uncertainty about who will be in next year’s
workforce. Almeida and Kogut (1999) show that Silicon Valley has a distinctive pattern of
job mobility compared to other areas where semiconductor patents come from, and that when
comparing regions, high mobility predicts more patent citations holding other factors
constant.
3.6 Uncertainty hypothesis for the data
If answers existed to the many questions above about the markets, methods, and timing
of the new technology, they were not common knowledge. Workers may differ dramatically
in their ability to adapt to this situation, and employers are willing to pay more for workers
within the high-tech occupations who can respond to the uncertainty in a useful way and
improve the employer’s technology. Aside from the worker’s capabilities, different
employers and different circumstances can determine whether successful adaptation is
17 This is shown in Fallick, Fleischman, and Rebitzer (2005), and is widely believed in the relevant industries.
20
possible. Very new technologies produce a kind of disequilibrium situation in which workers
and employers make various gambles on the basis of partial information. In principle these
are different forces but the general proposition can be tested indirectly. The hypothesis to be
taken to the data is that earnings dispersion rose within occupations which involved designing
semiconductor products or using novel, incomplete or malfunctioning computer systems.
The empirical definition used here includes five occupations. In the combined occupation
scheme these are titled electrical engineers, electrical engineering technicians, computer
software developers (usually called computer programmers in the original Census
definitions), systems analysts, and data processing repair persons.
4. Data sources and definitions of variablesThe data come from the annual March CPS (Current Population Survey) and from the
ten-year population Census. These are repeated cross section samples of the U.S. population.
The 1960, 1970, 1980, and 1990 Census samples have results from 1% of the U.S.
population, and the 2000 sample has results from 5% of the population. The Census data
came from the IPUMS project site (Ruggles and Sobek, 1997), and the CPS data came partly
from Unicon and partly from the IPUMS project site (Current Population Surveys, Unicon,
1999; and King, Ruggles, and Sobek, 2003).
The data set includes respondents between 16 and 75 years old who reported a positive
income and for whom an occupation was recorded. Occupations were assigned by Census
specialists, and mapped into one of several hundred codes. The category systems for these
21
occupation codes changed each decade.18 A standardized occupation category system defined
by Meyer and Osborne (2005) is used here.19
The regressions to follow exclude respondents with zero or negative earnings, by the
definition of earnings relevant to each regression. Earnings were usually measured by wages
and salaries, and in some regression also include self-employment (or “business”) income.
Data on capital gains income and stock options was not consistently available most years, and
is not used in any regression here.
Income variables are said to be top-coded if the reporting agency does not report exact
values of high incomes. The Census and CPS report only top-coded incomes to protect the
privacy of respondents with distinctive levels of income. Similarly, negative values for self-
employment income are bottom-coded. The average of top-coded incomes each year is
known, and that value was imputed for each top-coded income as if it were every such
person’s true income.20 The IPUMS data source filled in estimates for the some top-coded
incomes in the decennial Census data. Not every year has top-coding taken into account
however. Fixed effects on years may help adjust for this somewhat in the regressions to
follow.
5.0 Findings about earnings dispersion
18 The 1960 Census definitions were used in the 1968-1970 CPS, the 1970 Census definitions were used in the 1971-1982 CPS, the 1980 definitions were used in the 1983-1990 CPS, the 1990 Census definitions were used for the 1991-2002 CPS, and the 2000 Census has been used in the CPS starting in 2003. Apart from this remapping, occupations are reported with error for a variety of reasons. For example, the respondent may have had several jobs during the year, and only one will be recorded. There has also been no adjustment for workers who worked only part of the year; their incomes and occupations are taken literally as describing their experience in the year.19 It was also used in Autor, Katz, and Kearney (2006) and was adopted by IPUMS.org as the definition of their occ1990 variable. 20 I am indebted to Marcela Perticala, Finis Welch, Unicon Corp., and Larry Rosenblum for their advice and methods of handling topcoding and estimation outside the reported range.
22
Average salaries for electrical engineers and software developers rose at roughly the
same pace as earnings in other occupations, as shown in Table 3. Electrical engineers and
electrical engineering technicians were a stable proportion of the workforce over the entire
period, as shown in Table 4. The software categories, however, did not exist in the first
sample period. They appeared first in the 1970 Census and the 1971 CPS for the first time.
They grew quickly. As fractions of the workforce, programmers doubled and systems
analysts quadrupled from the 1970s to 2000, by which time these software workers made up
over 1% of the U.S. workforce.
The graphs then show earnings inequality within groups representing an occupation for
each year from 1968 to 2001. One measure of within-group inequality used here is the
standard deviation of log-earnings. Another measure used here is the coefficient of variation
of incomes within an occupation or industry. The coefficient of variation of a distribution is
its standard deviation divided by its mean. Both measures of income inequality are robust to
inflation and inflation measures.21 Observations of inequality were dropped from the graphs
if they were estimated from fewer than 10 respondents.
The population within each of these occupations evolved slowly and did not change
much in terms of years of education, age distribution, or other measured attributes. Let us
assume that the populations did not change very much relative to the population. Then these
choices of groups fit a structural explanation on the labor demand side. If electrical engineers
were dramatically affected by new technology, the hypothesis that people vary greatly in their
ability to adapt to a new technology suggests that great differences will appear in the
productivity of engineer A versus engineer B. In the Greenwood and Yorukoglu (1997)
environment this would lead to increased dispersion in what employers would pay them. The
same logic holds for any group that is segmented from a broader labor market -- it should
21 Meaning: if all wages were changed by the same proportion, inequality by this measure would be unchanged. This makes it possible for inequality measures to sidestep any complicated issues about comparing prices over time. All the measures of inequality in this paper are relative to other incomes in the same year.
23
show increased earnings dispersion. There could also be substitution in and out to the broader
population, but fewer than 5% of engineers per year in this data set transition to another
occupation, even to another engineering specialty. We assume here that these transitions
between groups are caused by uncorrelated factors and do not affect the distribution of the
abilities-to-adapt within the group.
Figures 1-5 show two measures of inequality for each class of occupations listed in
Tables 5-7. The occupations shown in Figure 1 have a face-to-face component and are
providers of services which do not have economies of scale in distribution. In principle there
should not be much of a superstars effect spreading their earnings distributions over time.
These professions do not face the technological uncertainty of the semiconductor, computer,
or software industries. Earnings dispersion in these occupations trends slightly downward
since 1968.
Figure 2 shows earnings dispersion for electrical engineers, electrical engineering
technicians, computer programmers, systems analysts, and data processing repair persons.
These groups confronted technological change and uncertainty directly, from declining
semiconductor prices, quality improvements, and continuing novelty in products, processes,
and markets. They created and experienced Moore’s Law most directly. There is indeed a
trend toward rising inequality in these occupations. One might think that all technical
occupations experience this effect but Figure 3 shows that other engineering categories did
not.
Figures 4 and 5 show the closest available occupations to those mentioned in Rosen’s
article, where he forecast a superstars effect. Rosen’s examples (listed in Table 6) combine
the joint-consumption performers with specialists like surgeons and lawyers whose services
might be bid up in price by the expanded sources of demand in a market with wider or easier
communication and transportation. It seems that the joint-consumption effect is visible
(Figure 4) but the bidding-up effect is not (Figure 5). The relevance of the joint-consumption
assumption is that if consumers can simultaneously benefit from the output of the worker
24
without causing negative effects on one another, and economies of distribution improve with
better communications technologies, there will be a time trend toward greater inequality.
Figure 3 shows those occupations which seem to show this media-amplified effect. In these,
some kind of fame is possible, different sellers are not perfect substitutes for one another, and
there are extreme economies in distribution. Rosen’s superstars effect is visible, in the sense
that there is a clear rise in inequality of wages and salaries.
Some media-amplification and technological uncertainty occurs in many occupations.
For example, most academics and researchers operate in environments where some kind of
economies of distribution occur (e.g. through publication or product manufacture), where
reknown is possible, and where suppliers are imperfect substitutes for one another.
However there do exist occupations at the other extreme from the high-tech and media-
amplified groups. England, Budig, and Folbre (2002) distinguished a set of occupations in
the 1980 Census that do care work, meaning work involving face-to-face service to a
recipient which increases the recipient’s capabilities. Most of these occupations were
providers of medical services, teachers at any level, and social and religious workers. These
occupations tend to allow few economies of distribution, or and in some of them different
suppliers are near-perfect substitutes for one another. In the data we shall see these
occupations do not have widening earnings distributions over the period studied. England et
al (2002) have a broader category of interactive service work, including care work but also
attendants and sales workers of various kinds. The results to be shown are slightly weaker if
this larger interactive category is used in place of just the care workers.22
The excluded category, separate from media-amplified occupations, technologically
uncertain occupations, and care work occupations, includes most managers, sales workers,
researchers, analysts and most clerical workers. The working hypothesis about them is that
they can experience some of these effects, more than care workers do.
22 Blinder (2006) referred to a similar category of personally delivered service jobs, motivated by the principle that they would be hard to relocate in another country and keep the same customers.
25
5.1. Key regressions
Tables 5-7 define several groups of occupations. One group are the five occupations
which have the main groups of people who work with novel and uncertain semiconductor
computer technology, based on my understanding of this kind of work. Designers and testers
of new semiconductor chips, for example, are in the categories of electrical engineers and
electrical engineering technicians. The design and test of digital semiconductor chips was a
new activity which joined this field whose previous canonical activity was the design of
physical circuits. The new work looked increasingly like programming.
The category of computer programmers include any software developers who are
creating new software tools. Many of these are using recently made hardware and software
to do their work. Data processing repairers do work that is not familiar to me but involves
directly interacting with recent computer technology precisely when it is not working, that is,
under unfavorable and perhaps uncertain circumstances.
A second category of occupations includes those whose work can be jointly consumed by
many customers through amplification by some kind of communication or production
technology. Rosen (1981) discussed joint consumption, and mentioned also the increasing
effect of bidding for specialists whose work was not perfectly substitutable for one another.
Based on the evidence in the graphs it appears that the key issue is whether joint consumption
through communications and transportation media are possible. See Table 4 for the empirical
definition of these media-amplified occupations.
The dependent variables in the regressions in tables 8 and 9 are measures of dispersion of
incomes in an occupation-year in one of the two data sets. We do not have hypotheses about
the levels of dispersion but rather their changes over time. Therefore we include fixed effects
on the occupations and measure only the trends. Year fixed effects are included to help
screen out a number of possible problems, such as any effects of business cycles and any6
26
effects from adjusting for top-coding in some years but not others. Perhaps most important,
the year effects remove some effects artificially created by the sharp changes in occupational
category systems which occur in Census years. The standardized occupation system can only
imperfectly compensate for changes in the way observations were originally categorized.
The test which is most illuminating is a test of these hypotheses: that the high tech
category and the media-amplified category will exhibit rising inequality over time in the
regressions, and that the care work category will not. Results from the OLS regression on the
CPS data are in Table 8, and for the Census in Table 9. With a fixed effect on occupations
and years, we regress the measure of dispersion on a year trend for each of the (a) media-
amplified occupations, (b) occupations facing the most semiconductor-related technological
uncertainty by the earlier definition, and (c) care work occupations. Observations were
weighted by the size of the sub-samples from which the dependent variable was estimated.
Here are the central results from the first regression in the first panel of each table:
Table 2: Key predictors of trends in earnings dispersion within occupations
Predictor
Dependent variable is standard-deviation of ln(wage and salary income) within each occupation-years
in annual CPS in decennial CensusCoefficient p-value coefficient p-value
Annual trend for media-amplified occupations
0.023 0.000 0.025 0.000
Trend if high tech uncertain / turbulent occupation
0.020 0.000 0.014 0.000
Trend if care work occupation
-0.002 0.268 -0.005 0.221
Regressions include fixed effects for occupation and year. Coefficients are in bold if the p-value is smaller than .05.
27
By the definitions of the groups and the inequality measure used here, the media-
amplified (superstars) professions and the technologically turbulent (high tech) professions
experienced growing earnings inequality within them. It is detected at a high level of
statistical significance in both data sets. Furthermore the hypothesis that care work jobs do
not experience rising inequality based on the forces of technological turbulence or media-
amplification is supported. In these occupations the trend in inequality is not statistically
significantly different from zero and is more likely to be negative than positive.
It does not seem that these results are artifacts of growing or shrinking occupations, or
rising or falling wages generally in the occupations. For example, the number of systems
analysts more than quadrupled in the period, as shown in Table 4, but the number of electrical
engineers did not rise much, yet we see a rise in earnings dispersion within both groups.
Tables 8 and 9 show other inequality measures. One of them is the coefficient of
variation, which is the standard deviation of the population of wages divided by the mean
wage. This measure (like the standard deviation measure above) is fairly robust to inflation
and measures of it -- that is, by both measures, an inflation that affects all wages would not
change the inequality measure. Another measure used in these tables is the interquartile
range, meaning the difference between the 75th percentile wage and the 25th percentile wage.
A third measure includes not only the wage and salary income, and also self-employment
income, and takes the standard deviation of the log of this measure of income. Using any of
these inequality measures, the media-amplified professions are increasingly unequal to a
statistically significant degree, and in most of the regressions the high tech ones are too.
The remaining regressions test variants of these hypotheses. Consider first this
alternative. One might think that the high tech professions are not so narrowly determined.
Perhaps the turbulence hypothesis would apply to larger categories of engineers, technicians,
or mathematically sophisticated workers. To test this, regression 2 in panel A of table 8
shows the same regression as above with groups of engineering occupations and technician
occupations added to the regression. The coefficients are opposite in sign to those for the
28
turbulent semiconductor-influenced occupations. In fact, the earnings distributions of
engineering and technician professions seems to have become more compressed over time.
Any force of technological turbulence has been overwhelmed by something else -- perhaps,
greater standardization in training, certification, and tools available to these other categories
of work.
A second alternative comes from Rosen (1981), which defined two kinds of superstars
occupations -- not only those which have electronically transmissible work content, but also
those which might draw greater and greater competition from buyers. Rosen gave the
example of doctors and lawyers (and others, listed in table 6). In regressions 3, 4, and 5 of
Table 8, the distributions of earnings seem to be compressing in the medical and legal
professions. More information technology may induce greater standardization in these
professions which overwhelms any superstars properties. For example, access to Westlaw
and Lexis-Nexis may put lawyers at different employers on a more even footing than they
were decades ago. In the medical area, standardized data about pharmaceutical drugs and
other treatments may be better verified and certified, or more widely available, than it was in
1970. Or, insurance companies may face more perfect competition than they did in 1970.
There is not firm confirmation of earnings compression from the regressions in table 9, whose
coefficients for doctors and lawyers jump around and are not statistically significant in most
cases.
Frank and Cook, in their 1995 book The Winner-Take-All Society, have a similar, yet
more expansive category of professions in which competition could become more intense and
incomes more unequal. Interpretations are required to test their propositions in this data,
since there is not a perfect mapping between the occupations they used as examples and the
ones identified in the data, but Frank and Cook do indicate that researchers, faculty,
managers, and sales professions should experience some winner-take-all phenomena, which
is roughly analogous to the superstars effect. In the regressions in tables 8 and 9, these
professions do not seem to have rising inequality. Some of these jobs are also defined as care
29
work occupations, and seem to look more like care professions according to the inequality
trends than like superstars professions.
A kind of direct test of uncertainty, analogous to the one in Meyer (2005) is possible.
Predictors of wages ought to lose some traction, that is, predictive power, in a period of
uncertainty. The regression in Table 10 covers CPS wages from 1992 to 2002 (the time
restriction has to do with the data available temporarily) using occupation, weeks working in
the year, age, and education as predictors. Subtracting the predicted wages from the
observed wages generates a series of wage residuals. Regressing the square of these residuals
on various predictors tells us how the magnitudes of the errors in the predictions are a
function of time, occupation, or other regressors. (The procedure is potentially analogous to
an ARCH specification of volatility in finance, but not precise enough here for the analogy to
be strong.) In Table 11, we see that high tech occupations had slightly improving
predictability over this period. Perhaps this is because the education variable includes more
relevant computer experience in the population at the end of the period than it did at the
beginning. More usefully, perhaps, we see that for the media-amplied professions, the wages
regressions has lost a lot of traction – for performers, formal education may matter less and
less. If this finding is reliable in various studies, it could be used to define the superstars
professions in a statistically reliable way, rather than depending on the ad hoc beliefs or
examples of particular researchers.
Generally speaking, the results for the high tech effect were weaker in the regressions
from the Census than from the CPS. One possible reason for this comes from the Census
1960 measures of inequality for electrical engineers and electrical engineering technicians,
which are higher than in 1970. In 1960, there were almost no semiconductor engineers.
Perhaps inequality among electrical engineers (such as experts in telephone systems and
power systems) was declining at that time, and the population of semiconductor engineers
was too few to make a difference. If so, inequality among electrical engineers would have hit
a low some time between 1960 and 1980, and only then would the effect to be estimated have
30
dominated. A re-specification which would allow this could strengthen the results,
statistically. A second reason that there would be noise in the 1960 data is that the Census
had so many fewer classifications of occupations then. Careful work (outlined in Meyer and
Osborne, 2005) could make it possible to impute 1970 occupations to 1960 data on the basis
of dual-coded data sets with other variables taken into account besides the 1960 occupation
classification. These are avenues for further improvement.
6. Unmeasured income or wealthSome effects of technological uncertainty could be under-measured using the measure of
wage and salary income that has been used above. First, wage and salary measures do not
include all income coming from high technology employers. Employees of startups in the
new microcomputer-related industries often received stock options, whose returns were
dramatically unequal. In one survey of high technology employees, 10% of executives, 85%
of managers, and 42% of other employees participated in stock option plans in 1997
(Southwick, 1999, p. 165). 24.1% of electrical engineers in one survey were offered
employee stock option plans (IEEE, 1995, p. 5-2). The CPS has a measure of capital gains
income but it is not incorporated into the dependent variables in this paper. The Census does
not have a measure of capital gains.
Second, technologically turbulent occupations could well have more self-employment or
consulting income than other occupations do. However, a comparison of regression 10 in
table 8 to regression 1 suggests the opposite; the high tech effect is weaker once self-
employment income is taken into account. This is a surprise and calls for further
investigation.
Third, some expansion in payoffs in an industry or occupation could take the form of
expanded opportunities for promotion or demotion and unemployment. For example, if some
electrical engineers became managers, or founders of startups, then there was prospective
diversity in the population that the displayed measures of current-year income did not
31
include. The founders of Apple, Microsoft, Cisco, Google, and other technical companies
would disappear from the technologically-uncertain category at the time they became
managers, and yet their extraordinary wealth afterward is partly attributable to the
opportunities that came about because of their special knowledge of the relevant technologies
and the great, fast-depreciating opportunities. And on the other side, large fractions of high
tech startups go under. So the present value of a high tech employee’s career path may be
volatile compared to others even when taking present-day income into account. These
differences have not been measured here. CPS respondents report their occupation and
industry in both the previous year and the current one. By this measure (not shown),
transitions into and out of these occupations do not seem to have changed over time for these
occupations. It could be that the later state – manager, or founder, for example – has a more
volatile income or wealth prospect in the later period than the earlier period. Particularly
undercounted are the volatile effects of engineers leaving stable firms to start their own or
those who join one and receive stock in the new venture. If in the future it were possible to
use income tax data for this study the effects might be stronger.
Relatedly, high income values for wage-and-salary, have been censored (“top-coded”) to
protect the privacy of respondents. When possible, estimates of the true values were used in
this study (as explained earlier), based on averages over high-income groups. Using these,
measures of the mean incomes of a group are unbiased, but most measures of inequality like
standard deviation are reduced by the substitution.23 This bias probably hides some of the
uncertainty and superstars effects, since huge fortunes were made through these processes.24 23 The standard deviation will usually decline, and cannot increase, if one replaces observations by their average. To see this, consider a set {1, 2, 3} and its standard deviation, and what the standard deviation would be if we replaced any two of those observations by their average. This would not change the mean of the population, but it reduces the influence by observations far from the mean on the sum of squared differences from the mean. 24 Consider the top incomes of star athletes, or of high tech company founders who are now the richest Americans. (The great fortunes of Bill Gates and other Microsoft executives, for example, arose through a highly path-dependent, technologically uncertain process.) Replacing these incomes by averages introduces a bias across occupations. Further research may be possible in data sets with high-income Americans such as the Survey of Consumer Finances, or Census and CPS data before the
32
7. ConclusionThere is evidence here of a rise in earnings dispersion within the media-amplified
occupations since 1968, supporting Rosen’s superstars hypothesis about joint consumption
and trends in economies of distribution. The data here did not support the hypothesis of
Rosen (1981) and Frank and Cook (1995) that expanded markets would produce substantially
more unequal incomes in the larger class of professions where the output of different workers
is not substitutable.
There is evidence also of a long term rise in earnings dispersion within the high tech
occupations. Turbulence or uncertainty in information technology seems to have been high
throughout the period, presumably because it is closely related to the rapid improvements in
the capabilities of semiconductor chips. This would produce in the population several
effects: obsolescence of previous skills; big opportunities; and qualitative differentiation in
the tasks of people in these jobs.
Hopefully methods like these can enable observers to detect long-term technological
change and turbulence in a statistical way. The U.S. “productivity slowdown” after 1973
may be related to the chaotic economic processes of inventing new devices, competing in
disequilibrium markets, and setting new standards. It also could help discussion of inequality
move beyond a generic, one-dimensional “skill bias”, and toward a substantive understanding
of the effects of particular technological changes on particular jobs. Another long term
benefit could be a clarification of the kinds of labor market regulation that restrict adaptations
to technological change. To gain the benefits of what is technologically possible, it may be
necessary for a subset of workers to operate in disequilibrium environments for long periods,
and some economic and regulatory environments may support this better than others.
top-coding in public use samples.
33
Std dev of ln(weekly earnings) for care work occupationsyear
Std dev of log-oc incs in Censu Std dev of log-oc incs in CPSPhysicians
0
.5
1
1.5
Dentists Optometrists Podiatrists Other health and therapy jobs Registered nurses Respiratory therapists
Occupational therapists
0
.5
1
1.5
Physical therapists Speech therapists Therapists, n.e.c. Physicians' assistants Earth, environmental, and marine Biology instructors
Chemistry instructors
0
.5
1
1.5
Physics instructors Psychology instructors History instructors Sociology instructors Math instructors Education instructors
Law instructors
0
.5
1
1.5
Theology instructors Home economics instructors Humanities instructors Other academic subject instructo Kindergarten and earlier school Primary school teachers
Secondary school teachers
0
.5
1
1.5
Special education teachers Other teachers, pre-college Vocational and educational couns Librarians Social workers Recreation workers
Clergy and religious workers
1960 20030
.5
1
1.5
Dental hygenists
1960 2003
Licensed practical nurses
1960 2003
387
1960 2003
Dental assistants
1960 2003
Health aides, except nursing
1960 2003
Child care workers
1960 2003
Figure 1a. Inequality within care work occupations for years 1960-2000 (Census) and 1968-2003 (CPS).
Within these occupations, earnings dispersion generally drifted down over this period.
A core activity of these jobs is face-to-face contact with the recipient of the service. The care work category was defined by England, Budig, and Folbre (2002). The hypothesis in the text is that these occupations are not affected by changes in technology and globalization that produce economies of scale in distribution for other occupations.
The coefficient of variation (defined to be the standard deviation divided by sample mean) is a measure of earnings dispersion. It is unaffected by a general inflation changing all wages by the same percentage.
34
Each observation displayed represents a sample size of at least ten salaries.
35
CV of ln(weekly earnings) for care work occupationsCV of ln(weekly earnings) for care work occupationsyearyear
in decennial Census in decennial Census in CPS in CPSPhysiciansPhysicians
00.5.511
1.51.5
DentistsDentists OptometristsOptometrists PodiatristsPodiatrists Other health and therapy jobsOther health and therapy jobs Registered nursesRegistered nurses Respiratory therapistsRespiratory therapists
Occupational therapistsOccupational therapists
00.5.511
1.51.5
Physical therapistsPhysical therapists Speech therapistsSpeech therapists Therapists, n.e.c.Therapists, n.e.c. Physicians' assistantsPhysicians' assistants Earth, environmental, and marineEarth, environmental, and marine Biology instructorsBiology instructors
Chemistry instructorsChemistry instructors
00.5.511
1.51.5
Physics instructorsPhysics instructors Psychology instructorsPsychology instructors History instructorsHistory instructors Sociology instructorsSociology instructors Math instructorsMath instructors Education instructorsEducation instructors
Law instructorsLaw instructors
00.5.511
1.51.5
Theology instructorsTheology instructors Home economics instructorsHome economics instructors Humanities instructorsHumanities instructors Other academic subject instructoOther academic subject instructo Kindergarten and earlier school Kindergarten and earlier school Primary school teachersPrimary school teachers
Secondary school teachersSecondary school teachers
00.5.511
1.51.5
Special education teachersSpecial education teachers Other teachers, pre-collegeOther teachers, pre-college Vocational and educational counsVocational and educational couns LibrariansLibrarians Social workersSocial workers Recreation workersRecreation workers
Clergy and religious workersClergy and religious workers
19601960 2003200300.5.511
1.51.5
Dental hygenistsDental hygenists
19601960 20032003
Licensed practical nursesLicensed practical nurses
19601960 20032003
387387
19601960 20032003
Dental assistantsDental assistants
19601960 20032003
Health aides, except nursingHealth aides, except nursing
19601960 20032003
Child care workersChild care workers
19601960 20032003
Figure 1b. Coefficients of variation (standard deviation divided by sample mean) for years 1960-2000 (Census) and 1968-2003 (CPS).
Within these occupations, earnings dispersion generally drifted down over this period.
A core activity of these jobs is face-to-face contact with the recipient of the service. The care work category was defined by England, Budig, and Folbre (2002). The hypothesis in the text is that these occupations are not affected by changes in technology and globalization that produce economies of scale in distribution for other occupations.
36
The coefficient of variation (defined to be the standard deviation divided by sample mean) is a measure of earnings dispersion. It is unaffected by a general inflation changing all wages by the same percentage.
Each observation displayed represents a sample size of at least ten salaries.
37
Std dev of ln(weekly earnings) for high-tech/uncertain occupationsyear
Std dev of log-oc incs in Censu Std dev of log-oc incs in CPS
Electrical engineers
0
.5
1
1.5
Computer systems analysts, admin Electrical engineering technicia
1960 2003Computer software developers
1960 20030
.5
1
1.5
Data processing equipment repair
1960 2003
Figure 2a. Integrated circuit chips doubled in capacity each 18 months over this period, and the work content of many holders of these occupations changed dramatically in response. These induced great uncertainty about future technologies which the text argues generated an increase in earnings dispersion. We see above a rise in the dispersion of salaries by this inequality measure.
The standard deviation of log-incomes is a measure of earnings inequality that is unaffected by a general inflation changing all wages by the same percentage. Each observation displayed represents a sample size of at least ten salaries.
38
CV of ln(weekly earnings) for high-tech/uncertain occupationsyear
in decennial Census in CPS
Electrical engineers
0.51
1.5
Computer systems analysts, admin Electrical engineering technicia
1960 2003Computer software developers
1960 20030
.51
1.5
Data processing equipment repair
1960 2003
Figure 2b. The coefficient of variation of a sample of wages is the standard deviation divided by the mean. This is a measure of earnings inequality that is unaffected by a general inflation changing all wages by the same percentage. Each observation displayed represents a sample size of at least ten salaries.
39
Std dev of ln(weekly earnings) for other engineers and techniciansyear
Std dev of log-oc incs in Censu Std dev of log-oc incs in CPS
Aerospace engineers
0
.5
1
1.5
Materials and metallurgial engin Petroleum, mining, geo engineers Chemical engineers
Civil engineers
0
.5
1
1.5
Industrial engineers Mechanical engineers
1960 2003
Engineers not elsewhere classifi
1960 2003Engineering technicians, n.e.c.
1960 20030
.5
1
1.5
Mechanical engineering technicia
1960 2003
Figure 3a. Other engineering and technical jobs, apart from those closely associated with semiconductor improvements, do not show a rising trend in the standard deviation measure.
40
CV of ln(weekly earnings) for other engineers and techniciansyear
in decennial Census in CPS
Aerospace engineers
0.51
1.5
Materials and metallurgial engin Petroleum, mining, geo engineers Chemical engineers
Civil engineers
0.51
1.5
Industrial engineers Mechanical engineers
1960 2003
Engineers not elsewhere classifi
1960 2003Engineering technicians, n.e.c.
1960 20030
.51
1.5
Mechanical engineering technicia
1960 2003
Figure 3b. By the coefficient of variation measure.
41
Std dev of ln(weekly earnings) for media-amplified occupationsyear
Std dev of log-oc incs in Censu Std dev of log-oc incs in CPS
Writers and authors
0
.5
1
1.5
Designers Musicians and composers Actors, directors, and producers
Art and craft makers
0
.5
1
1.5
Photographers Dancers
1960 2003
Art and entertainment performers
1960 2003Editors and reporters
1960 20030
.5
1
1.5
Athletes, sports instructors, an
1960 2003
Figure 4a. In these jobs different producers are imperfect substitutes for one another, and they have great economies of distribution as large scale computer and other information networks grew. Following Rosen (1981), a superstars effect is possible at the top. Indeed there is a rise in the dispersion of earnings above.
Within most of these occupations, earnings inequality rose substantially. The text argues that this was partly because of the growth in markets and technological aspects of distribution which made it easier for the demand for their services to be satisfied by a few “superstars”.
42
CV of ln(weekly earnings) for media-amplified occupationsCV of ln(weekly earnings) for media-amplified occupationsyearyear
in decennial Census in decennial Census in CPS in CPS
Writers and authorsWriters and authors
00.5.511
1.51.5
DesignersDesigners Musicians and composersMusicians and composers Actors, directors, and producersActors, directors, and producers
Art and craft makersArt and craft makers
00.5.511
1.51.5
PhotographersPhotographers DancersDancers
19601960 20032003
Art and entertainment performersArt and entertainment performers
19601960 20032003Editors and reportersEditors and reporters
19601960 2003200300.5.511
1.51.5
Athletes, sports instructors, anAthletes, sports instructors, an
19601960 20032003
Figure 4b. In these professions, different producers are imperfect substitutes for one another, and they have great economies of distribution as large scale computer and other information networks grew. Following Rosen (1981) we may expect a rise in earnings dispersion within these professions, where a superstars effect is possible at the top. We see above a rise in the dispersion of salaries by this inequality measure.
Within most of these occupations, earnings inequality rose substantially. The text argues that this was partly because of the growth in markets and technological aspects of distribution which made it easier for the demand for their services to be satisfied by a few “superstars”.
43
44
Std dev of ln(weekly earnings) for doctors and lawyersyear
Std dev of log-oc incs in Censu Std dev of log-oc incs in CPS
Physicians
0
.5
1
1.5
Dentists Veterinarians
Optometrists
1960 20030
.5
1
1.5
Podiatrists
1960 2003
Lawyers
1960 2003
Figure 5a. Inequality within groups of doctors and lawyers
45
CV of ln(weekly earnings) for doctors and lawyersyear
in decennial Census in CPS
Physicians
0
.5
1
1.5
Dentists Veterinarians
Optometrists
1960 20030
.5
1
1.5
Podiatrists
1960 2003
Lawyers
1960 2003
Figure 5b. In these professions, different producers are imperfect substitutes for one another, but they do not have strongly growing economies of distribution as a result of new technologies. Because they are competing in larger markets in which comparison and travel are easier, Rosen (1981) predicted there would be a rise in earnings dispersion within these professions, where a superstars effect is possible at the top. However this is not supported in the evidence, above. It seems to be necessary for there to be joint consumption of the output for the changes in communication and transportation to have this effect as seen in Figure 4. The predominant effect seen here seems to be that these are face-to-face occupations in which technology uncertainty and superstars effects actually have the least effect on the earnings distribution. What we see looks more like increasingly perfect competition as forecast by Stigler (1960).
46
Table 3. Illustrative rises in nominal wage-and-salary change by occupation
Job titleChange in average
nominal wage-and-salary income, 1970 Census-
2000 CensusPhotographers 200%Judges 266%Computer systems analysts 290%Economists 307%Industrial engineers 326%Metallurgical and materials engineers 333%Mechanical engineers 338%Aerospace engineers 341%Editors and reporters 348%Chemical engineers 362%Civil engineers 369%Electrical and electronic engineers 378%Architects 387%Accountants and auditors 390%Secretaries 414%Librarians 439%Computer software developers 534%Nurses 667%Lawyers 899%Podiatrists 1059%
These are ratios of the average salaries in selected occupations based on unweighted observations from the 1970 Census and 2000 Census.
They illustrate (in a rough way) that the high tech occupations are not receiving disproportionately more pay per capita than thirty years ago.
47
Table 4. Frequency of selected occupations
Data come from the U.S. Census data downloaded from IPUMS. NA stands for “not available.” The occupation classification is that of Meyer and Osborne (2005).
Percentages of US population aged 16-75, weighted by Census weights
Occupation 1960 1970 1980 1990 2000Accountants and auditors 0.46% 0.61% 0.68% 0.98% 0.98%Architects 0.03% 0.04% 0.07% 0.09% 0.11%Aerospace engineer 0.05% 0.05% 0.06% 0.09% 0.06%Metallurgical and materials engineers 0.02% 0.01% 0.02% 0.01% 0.02%
Chemical engineers 0.04% 0.04% 0.04% 0.04% 0.04%Civil engineers 0.15% 0.14% 0.14% 0.16% 0.15%Electrical engineer 0.16% 0.23% 0.21% 0.29% 0.20%Industrial engineers 0.09% 0.14% 0.13% 0.11% 0.11%Mechanical engineers 0.14% 0.15% 0.13% 0.12% 0.16%Engineers not elsewhere classified 0.09% 0.16% 0.20% 0.24% 0.01%
Computer systems analysts and computer scientists NA 0.07% 0.13% 0.28% 0.38%
Registered nurses 0.78% 0.88% 0.95% 1.19% 1.31%Librarians 0.10% 0.12% 0.14% 0.13% 0.11%Economists 0.02% 0.05% 0.07% 0.09% 0.01%Lawyers and judges 0.18% 0.22% 0.34% 0.46% 0.49%Photographers 0.05% 0.06% 0.07% 0.10% 0.08%Editors and reporters 0.11% 0.14% 0.15% 0.17% 0.15%Licensed practical nurses 0.29% 0.25% 0.33% 0.28% 0.36%Electrical and electronic engineering technicians 0.09% 0.13% 0.18% 0.25% NA
Computer software developers NA 0.13% 0.21% 0.40% 0.76%
Secretaries 2.13% 3.08% 3.08% 2.72% 2.32%
The software occupations have quadrupled as a fraction of the work force since 1970. Programmers and systems analysts were not separately counted in the earliest classification. Electrical engineers did not grow much as a fraction of the population. We have not matched particular occupations to the electrical engineering technician category from the 2000 Census.
48
Lawyer and judges, and accountants and auditors were also growing occupations over this time period.
Table 5. Conjectured Moore’s Law occupations
Jobs in these categories were especially sensitive to change and turbulence in information and communications technologies.
Electrical engineersElectrical engineering technologistsComputer programmersSystems analystsData processing equipment repairers
49
Table 6. Media-amplified jobs, experiencing superstars effect
Panel A. Rosen (1981) illustrated the superstars discussion with these examples. The phrasings do not conform perfectly to Census occupation classifications. These were examples of occupations providing services which were imperfectly substitutable with one another:
artistsauthors (3 times)authors of textbookscomediansdoctors (3 times)economic theorists and methodologistslawyers (2 times)musical soloistsnetwork news broadcastersnews reportersperformer on televisionperformers (theater, TV, and movies)performers (2 times)
pro athletes (3 times)scholars (as writers)singerssurgeonswriters
Then, quoting Marshall (1947), who described this phenomenon too:business menbarristersjockeyspaintersmusicians
Doctors, lawyers and other experts could experience more intense bidding for their services in larger markets. This could generate a superstars effect, though it seems to be overwhelmed by other factors in the data; see Figure 5. The other occupations on the list also have the key property of joint consumption on the part of the client or consumer, which is to say they are the ones subject to media amplification, and this does seem to support a superstars effect in the data.
Panel B. The definition used in this paper, and tested in the data. Services by these occupations can be amplified through larger markets, which are generated by more advanced communications and transportation technologies. These support joint consumption of output as hypothesized by Rosen (1981). In the context of imperfect competition, these economies of scale in distribution leads to superstars effect – growing inequality of earnings. The occupations examined here for this effect in Figure 4 are:
Actors, directors, or producersArtists (artistic painters, sculptors, craft-artists, and print-makers)AthletesAuthorsDancers, dance teachers, and choreographersDesignersEditors and reporters
50
Musician or composersPhotographers
Panel C. Frank and Cook (1995) extend this kind of discussion substantially. Their book refers to an even wider category of professions within which increasingly intense competition would be possible. Their book refers to these occupations at various points as experiencing winner-take-all phenomena:
fashion modelsscreenwritersactors directorscomposersbusiness consultantsfinanciersjournalistsaccountantschiropractorsdentistssalespeople
painterswritersmusiciansathletesbusiness managersauthorslawyersacademic facultyscientistsresearchers news reportersperformers on television
For this paper, media-amplified jobs are the ones shown in listed in the first part of table 4. Engineers have occupations between code 44 and 60, inclusive. Technicians have jobs from 203 to 225, inclusive. Scientists are those with jobs between 68 and 83, inclusive. Academic faculty are those with jobs between 113 and 154, inclusive. Managers are those with jobs between 4 and 22, inclusive. Doctors have jobs 84 through 88, thus including dentists and veterinarians. Lawyers have occupation 178.
51
Table 7. Care work jobs
These are the care work occupations, as defined by England, Budig, and Folbre, 2002, as mapped into the occupation categories used here. These kinds of work involve face-to-face interactions with clients or customers and involve increasing the recipient’s capabilities.
PhysiciansDentistsOther health and therapyOptometristsPodiatristsNursesPhysical therapistsSpeech therapistsPhysicians' assistantsBiological science instructorsChemistry instructorsPhysics instructors History postsecondary teachersPostsecondary teachers of sociologyMath teachers, postsecondaryPostsecondary teachers of educationTeachers of law, generally postsecondaryPostsecondary theology teachersHome economics postsecondary teachersAcademic subject instructors, n.e.c.Teachers (secondary, primary, and earlier)LibrariansClergy and religious workersDental hygienistsLicensed practical nursesDental assistants Child care
52
Table 8. Predictors of earnings dispersion in occupation-years in CPS
Panel A. Dependent variable is standard deviation of log-wage-and-salary within occupation-year
PredictorsRegression 1 Regression 2 Regression 3 Regression 4 Regression 5 Regression 6
coeff p-value coeff p-
value coeff p-value coeff p-
value coeff p-value coeff p-
valueTrend if media-amplified job 0.0023 0.016 0.0022 0.019 0.0022 0.018 0.023 0.000 0.018 0.000 0.018 0.000
Trend if high tech job 0.0069 0.000 0.0068 0.000 0.023 0.000 0.020 0.000 0.016 0.000 0.020 0.000
Trend if care work -.0003 0.549 -.0004 0.386 -0.002 0.254 -0.002 0.382 -0.005 0.018 -0.005 0.017
Trend if engineer -0.007 0.034 -0.010 0.001
Trend if technician -0.006 0.016 -0.009 0.000
Trend if doctor -0.017 0.011 -0.018 0.007 -0.019 0.006
Trend if lawyer -0.007 0.062 -0.011 0.002 -0.011 0.001
Trend if scientist 0.008 0.159 0.008 0.179Trend if college faculty -0.001 0.574 -0.002 0.489
Trend if manager -0.031 0.000 -0.031 0.000
Trend if sales job -0.004 0.122 -0.004 0.092
Annual trend overall -.0022 0.000
35 year fixed effects no yes yes yes yes yes
386 occupation fixed effects Yes yes yes yes yes yes
sample size 11187 11187 11187 11140 11140 11138
Adjusted R-squared 0.68 0.71 0.90 0.90 0.91 0.91
Figures in bold are statistically significant at the .05 level (that is, the p-value<.05). In the regressions, each observation has been weighted by its sample size. Robust standard errors underly the p-values. In the underlying calculations of inequality, observations of income were equal-weighted, that is, did not use the CPS-assigned person-weights which adjust for demographic characteristics. Year fixed effects help compensate for the changing number of occupations -- fewer occupation categories mechanically tends to mean more variation within them, even if there were no substantive change.
Table 8.
Panel B. Dependent variable is named measure of dispersion within occupation-years in CPS
Predictor
Dep var: coefficient of variation of wage-and-salary
income within occupation-years
Dep var: interquartile range (75th percentile wage income
minus 25th percentile of wage income) within occupation-
years
Dep var: standard deviation of wage-and-salary income plus
self-employment income within occupation-years
Regression 7 Regression 8 Regression 9 Regression 10 Regression 11 Regression 12
Coeff p-value coeff p-
value coeff p-value coeff p-
value coeff p-value coeff p-
valueTrend if media-amplified 0.008 0.000 0.008 0.000 0.072 0.000 0.051 0.000 0.007 0.000 0.007 0.000
Trend if high tech 0.004 0.000 0.004 0.000 0.029 0.000 0.009 0.000 0.009 0.000 0.009 0.000
Trend if care work -.0002 0.85 -0.001 0.129 0.029 0.000 0.018 0.001 0.000 0.913 -.0002 0.801
Trend if engineer -0.0001 0.905 0.011 0.496 -0.001 0.302
Trend if technician -0.001 0.214 -0.008 0.002 -0.003 0.001
Trend if doctor -0.024 0.000 -0.178 0.000 -0.002 0.427
Trend if lawyer -0.013 0.000 -0.293 0.000 0.003 0.364
Trend if scientist -0.001 0.411 -0.007 0.091 -0.001 0.615
Trend if college faculty 0.008 0.000 -0.011 0.083 0.004 0.623
Trend if manager -0.002 0.242 -0.138 0.000 -0.003 0.010
Trend if sales job 0.005 0.001 -0.013 0.063 -0.001 0.015
35 year fixed effects yes yes yes yes yes yes381 occupation fixed effects yes yes yes yes yes yes
sample size 11138 11138 11140 11140 11140 11140
Adjusted R-squared 0.81 0.81 0.67 0.73 0.75 0.75
CV stands for coefficient of variation, which is the standard deviation of the sample divided by the mean of the sample. Coefficient on regression constant is not shown.
Figures in bold are statistically significant at the .05 level. In computing the standard errors each observation has been weighted by its sample size. Robust standard errors underly the p-values. In the underlying calculations of inequality, observations of income were equal-weighted, that is, did not use the person-weights which adjust for demographic characteristics. Year fixed effects help compensate for the changing number of occupations defined by the Census -- fewer occupation categories mechanically means more variation within them, even if there is no substantive change.
The hypotheses of interest were that over this time period (a) the trend in the media-amplified occupations and the technologically uncertain occupations have been increasingly dispersed over time, and that (b) the face-to-face service occupations have not.
Table 9. Predictors of earnings dispersion in occupation-years in Census
Panel A. Dependent variable is standard deviation of log-wage-and-salary within occupation-years
Figures in bold are statistically significant at the .05 level. In computing the standard errors each observation has been weighted by its sample size. Robust standard errors underly the p-values. In the underlying calculations of inequality, observations of income were equal-weighted, that is, did not use the person-weights which adjust for demographic characteristics. Year fixed effects help compensate for the changing number of occupations defined by the Census -- fewer occupation categories mechanically means more variation within them, even if there is no substantive change.
Predictors of std dev of ln(wage) in Census
Regression 0 Regression 1 Regression 2
coeffp-
value coeffp-
value coeffp-
valueTrend if media-amplified 0.026 0.000 0.025 0.000 0.023 0.000
Trend if high tech 0.025 0.001 0.014 0.000 0.013 0.000Trend if care work -0.005 0.474 -0.005 0.221 -0.009 0.077Trend if engineer -.0003 0.940
Trend if technician -0.005 0.361Trend if doctor 0.012 0.434Trend if lawyer 0.015 0.278
Trend if scientist -0.004 0.445Trend if college faculty 0.004 0.370
Trend if manager 0.017 0.005Trend if sales job 0.007 0.240
Annual trend overall -.010 0.0055 year fixed effects no yes yes
387 occupation fixed effects yes yes yessample size 1635 1635 1635
Adjusted R-squared 0.77 0.88 0.88
Table 9.
Panel B. Dependent variable is named measure of dispersion within occupation-years in Census
Predictors
Dep var: coefficient of variation of wage-and-salary
income within occupation-years
Dep var: interquartile range (75%ile to 25 %ile) of wage-
and-salary income within occupation-years
Dep var: coefficient of variation of wage-and-salary income plus self-employment
income within occupation-yearsRegression 1 Regression 2 Regression 3 Regression 4 Regression 5 Regression 6
coeffp-
value coeffp-
value coeffp-
value coeffp-
value coeffp-
value coeffp-
valueTrend if media-amplified 0.006 0.009 0.01 0.03 0.07 0.00 0.06 0.001 0.01 0.001 0.01 0.001
Trend if high tech 0.001 0.593 0.00 0.41 0.03 0.13 0.01 0.712 0.012 0.006 0.008 0.031
Trend if care work -0.004 0.011 -0.01 0.00 -0.02 0.23 -0.02 0.142 -0.004 0.026 -0.006 0.007
Trend if engineer 0.00 0.00 0.03 0.025 0.009 0.004
Trend if technician 0.01 0.03 -0.02 0.459 -0.002 0.547
Trend if doctor -0.02 0.00 -0.03 0.714 0.016 0.001
Trend if lawyer -0.01 0.00 -0.13 0.053 0.012 0.145
Trend if scientist 0.00 0.00 0.03 0.132 0.001 0.702
Trend if college faculty 0.00 0.15 0.03 0.197 0.006 0.007
Trend if manager -0.01 0.00 -0.08 0.013 0.002 0.264
Trend if sales job 0.00 0.22 -0.02 0.558 0.003 0.199
Constant yes yes yes yes yes yes
35 year fixed effects yes yes yes yes yes yes387 occupation fixed effects yes yes yes yes yes yes
sample size 1635 1635 1635 1635 1635 1635
Adjusted R-squared 0.91 0.91 0.69 0.71 0.91 0.91
CV stands for coefficient of variation, which is the standard deviation of the sample divided by the mean of the sample. Coefficient on regression constant is not shown.
Figures in bold are statistically significant at the .05 level. In computing the standard errors each observation has been weighted by its sample size. Robust standard errors underly the p-values. In the underlying calculations of inequality, observations of income were equal-weighted, that is, did not use the person-weights which adjust for demographic characteristics. Year fixed effects help compensate for the changing number of occupations defined by the Census -- fewer occupation categories mechanically means more variation within them, even if there is no substantive change.
Table 10. Earnings regression from 1968-2003 CPS(last updated aug 18, 2005)
The dependent variable is the log of the weekly earnings (defined as wage and salary plus self-employment income) for individuals with over $40 in weekly earnings.
Predictor coefficient p-valueyear trend 0.040 0.000age .242 0.000age squared -.007 0.000age cubed .00008 0.000age to the fourth -.0000004 0.000years of education -.060 0.000Year of educ squared .004 0.000age * educ .0007 0.000
occupation fixed effects (387 categories) included
sample size 2,508,091Adjusted R-squared 0.57
10b from census
The dependent variable is the log of the weekly earnings (defined as wage and salary plus self-employment income) for individuals with over $40 in weekly earnings.Sample size 9,739,077R-squared is .51
Predictor coefficient p-value
Age .075 0.000age squared -.0007 0.000years of education -.024 0.000Year of educ squared .003 0.000Year 2000 1.772 0.000Year 1990 1.456 0.000Year 1980 .978 0.000Year 1970 .401 0.000occupation fixed effects (387 categories) included
Table 11. Residuals from earnings regression from 1968-2003 CPS
The dependent variable is the square of the residual from the wage regression in Table 10. Very little of the change in residual magnitudes is explained by the regression – a third of one percent, using the R-squared measure. The purpose of this regression is to see if it possible to detect technological uncertainty or media-amplification from the data, or to distinguish between them statistically. It does seem that the media-amplification attribute raises the magnitude of the residuals over this period, but not for the high tech turbulent professions.
Predictor
Regression 1 Regression 2
coefficient p-value coefficient p-value
trend if media-amplified 0.449 0.000 0.473 0.000trend if high tech turbulent -0.026 0.001 0.022 0.004trend if care work 0.139 0.000 0.114 0.000trend if engineer 0.024 0.006trend if technician -0.219 0.000trend if doctor 0.682 0.000trend if lawyer 0.468 0.000trend if scientist -0.149 0.000trend if manager 0.016 0.344
trend if college faculty 0.128 0.000trend if sales job 0.116 0.000Year effects yes yes
sample size 2,508,091R-squared 0.02
Table 12. Computer use by occupation in 1984
An October 1984 CPS survey supplement recorded answers to the question, “Does [respondent] directly use a computer at work?” This illustrates that the use of a computer is not very closely linked to technological uncertainty as discussed in the text. Some closer involvement with technology change is needed.
Occupation group % of work force % of these who use computer at work
Public officials and administrators 0.46% 33%Other managerial and administrative 6.98% 34%Management-related 2.76% 51%Engineers 1.35% 58%Mathematical and computer scientists 0.39% 83%Natural scientists 0.35% 54%Health diagnosing 0.61% 21%Health management and treatment 1.72% 24%Teachers, college and university 0.64% 39%Teachers outside college and university 3.34% 27%Lawyers and judges 0.58% 27%Other professional specialty 3.09% 23%Health technologists and technicians 1.00% 26%Engineering and science technicians 0.94% 41%Other technicians 0.86% 70%Supervisors and proprietors 2.90% 23%Sales representatives, finance and business 1.69% 40%Sales representatives, commodities 1.24% 25%Sales workers, retail and personal 5.94% 9%Sales related 0.05% 5%Administrative support supervisors 0.55% 59%Computer equipment operators 0.58% 90%
Secretaries, stenographers, and typists 4.55% 40%Financial records processing 2.29% 38%Mail and message distributor 0.73% 8%Other administrative, including clerical 6.62% 40%Private household service 1.34% 1%Protective service 1.53% 17%Food service 5.42% 2%Health service 1.69% 6%Cleaning and building 2.91% 2%Personal service 2.03% 3%Mechanics and repair workers 3.84% 12%Construction trades 4.48% 3%Other precision production, craft, and repair 3.65% 12%Machine operators and tenders, except precision 5.07% 5%Fabricators, assemblers, inspectors, samplers 2.67% 7%Motor vehicle operators 3.11% 2%Other transportation and material-moving 1.33% 4%Construction laborers 0.79% 0%Freight, stock, and materials handlers 1.45% 3%Other handlers, equipment helpers, laborers 2.24% 3%Farm manager and operators 1.60% 4%Farm workers and related 2.37% 1%Forestry and fishing 0.22% 4%Armed forces 0.06% 0%Overall 100%, 77452 observations 20%
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