BUILDING A SKILLS - Specialized Research AI at MIT

BUILDING A SKILLS
ENGINE FOR THE
HUMAN ECONOMY

MATT SIGELMAN

The future will be won by skills. Skills diﬀerentiate careers, express the dynamism
of the economy, measure the distance between people and opportunity, and open
new avenues for equity. As the engine of the economy evolves from one that re-
volves around deskilling labor to one driven by continuous development of the
skills base, our mechanisms for anticipating and identifying what skills will be
needed and for developing talent are weak. Moreover, our metrics of productivity
are rooted in a fading industrial paradigm.

The global economy is at an inflection point. We have a chance to recognize
this moment, and to leave behind a history of commoditizing labor and under-
valuing workers and to pursue national-level strategic eﬀorts to build a multi-
sector infrastructure focused on skilling workers. This represents a profound
change, and an urgent one, particularly as a new generation of artificial intelli-
gence promises to replace a far broader scope of human endeavor. It will not be
an easy transition, and employers, workers, governments, and the education sec-
tor must collaborate to build the emergent human economy.

INDUSTRIAL PRODUCTIVITY
STRATEGIES AND THEIR
CONSEQUENCES

Deskilling Labor, Replacing Labor,
Labor Arbitrage, and Labor
Flexibility

For more than a century, the economies of
the developed world have taken a reduc-
tionist approach to the people who work in
them, one that frames the relationship
within the single-minded pursuit of pro-
ductivity. In the simplest terms, productiv-

ity can be computed as output divided by
labor input. Throughout this period, the
dominant mechanism industry deployed
to increase productivity has been to ma-
nipulate labor input by altering the role,
size, location, and cost of the workforce.
From their roots in the assembly lines and
timed tasks of Frederick Taylor and the
“management science” of Alfred Sloan, the
four key levers for this approach have been
the commoditization of labor, labor re-
placement, labor arbitrage, and labor flex-
ibility.

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From a productivity perspective, the
commoditization of labor is a way to des-
kill it so that it becomes mechanizable,
substitutable, and transactional. Henry
Ford realized early on that the skilled arti-
sans he initially hired made high-quality
cars, but they took too long and produc-
tion was far too expensive. He then devel-
oped a process to employ workers who had
fewer skills but easily learned repeatable
tasks. He found that their performance
could be accelerated over time and con-
sequently was able to produce vastly more
cars at a lower cost. These core ingredients
of labor are still widely in use in the indus-
trial economy to keep down the costs of
acquiring and training new workers and
boost profitability.

Labor replacement is the process of
replacing workers with machines or with
processes and routines that reduce the
need for humans. The classic model is
automation, which frequently involves ro-
bots or other machines capable of per-
forming tasks once exclusively done by
people. This has resulted in a massive de-
cline in jobs in many industries, from the
manufacture of cars and clothing to the
widespread delivery of customer services
by software systems with a soothing voice.
Long before the recent specter of gener-
ative artificial intelligence (AI), software
and AI have been exponential drivers of

labor replacement: Microsoft Oﬃce acts as
a robot that enables many workers to per-
form tasks that once required the assis-
tance of clerical,
technical, budget,
financial, and administrative staﬀ. While
reducing the number of employees, labor-
replacement strategies also seek to increase
the productivity of those who remain.

Over the past 40 years, labor arbitrage
and oﬀshoring have played major roles in
the pursuit of productivity. Labor arbi-
trage, which has accelerated significantly
since the 1980s, in tandem with the glob-
alizing eﬀects of changes in trade policy,
has tapped into the enormous workforces
and rapidly growing economies of China,
India, Mexico, and Vietnam, among many
others. Labor arbitrage works on the sim-
ple premise that companies can reduce
costs by relocating their workplaces to
countries where workers earn less, or oﬀ-
shoring. Since wages and operating ex-
penses are so much lower in developing
countries than in the industrialized world,
oﬀshoring strategies have been broadly ap-
plied across vast swaths of the consumer
goods, manufacturing, and technology
sectors.

Labor flexibility is a strategy for ad-
justing to variations in the marketplace
and the economy by removing or adding
workers to a company. It places a premium
on reducing risk and drag so that, if the

ABOUT THE AUTHOR

Matt Sigelman is President of the Burning Glass Institute, Chairman of Lightcast, and a Visiting
Fellow at the Harvard Kennedy School. The Burning Glass Institute advances data-driven research
and practice on the future of work and the future of learning. Named by Forbes to its Future of
Work 50, Sigelman has dedicated his career to unlocking new avenues for mobility, opportunity,
and equity through skills.

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Matt Sigelman

economy or sales suddenly go soft, a com-
pany can maintain a position of relative
strength by laying people oﬀ. The rationale
is that, by only paying for workers whose
output remains high, companies stay lean
and remain competitive. Adding workers
fits into the same frame: if demand grows
or the economy booms, employers with
the flexibility to add new workers rapidly
will always have an advantage.

The Consequences of Established
Productivity Practices

Crucially, each of these approaches to labor
deemphasizes and weakens the relation-
ship between employer and worker. The
ties between them are purely transactional,
hiring and firing are interchangeable op-
tions driven by productivity considera-
tions, and worker expendability is the
norm. With commoditizing, the task de-
fines the relationship, and employers have
few incentives to invest in worker skills or
knowledge. Labor replacement, frequently
in the form of automation, not only elimi-
nates jobs and workers from a workplace,
it also restructures entire industries that
rely increasingly on AI to simulate human
social interaction in place of live workers.
Underpinning labor arbitrage is the as-
sumption that both workers and locations
are interchangeable, that the loss of
workers’ accrued experience and the dis-
ruption of local economies are limited and
controllable transactional costs. Labor flex-
ibility erodes the relationship between em-
ployer and employee, steeply reducing
incentives for either workers or employers
to build ties to one another.

The cumulative eﬀect of these ap-
proaches to productivity is a vicious cycle
of negative incentives. Employers hire and
fire at will. Without long-term prospects,
employees reciprocate by switching jobs
casually and frequently: the so-called Great
Resignation is simply the logical manifes-
tation of this trend in a seller’s market.
Given the high rates of employee turnover,

it is hard for employers to get a positive re-
turn on investment on worker training. In
this situation, few employers put much ef-
fort into developing an employee work-
force with the skills, support, and flexibility
to adapt to good times and bad. Instead,
employers tend to hire the talent they need
in the moment and to fire people when
changes occur—an approach colloquially
referred to as rip-and-replace.

While these strategies provide greater
flexibility for firms and the resulting flex-
ibility of the modern industrial economy is
widely celebrated, they also drive the des-
killing and reduced flexibility of the work-
force, which renders workers less able to
adapt. In the absence of ongoing training
or the means to identify high-demand
skills, let alone to acquire them on their
own, workers find themselves obsolete.
They have neither the personal capability
nor the employer support to reposition
themselves. In the worst case, workers are
forced to drop out of the market altogether.
Workers in their fifties in particular, after
coming of age in an economy that did not
help them acquire skills early in their ca-
reer, face steep learning challenges in get-
ting or keeping jobs and are increasingly
slipping from the labor force—a trend that
was accelerated by the COVID-19 pan-
demic.1 As advances in technology enable
automation to replicate a wider swath of
human eﬀort, such displacement could be-
come significantly more widespread.

Companies themselves are now begin-
ning to pay a heavy price for these
strategies. The success of a transactional,
commoditized labor strategy depends on
the ready availability of a large workforce
that is infinitely flexible and work that can
be fully commoditized. But negative or low
population growth in many developed and
developing countries alike, together with
low workforce participation rates in indus-
trialized economies, have created a talent
shortage that is likely to worsen over the
coming decades. Meanwhile, the quicken-

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Building a Skills Engine for the Human Economy

ing pace of skills change and a growing de-
mand for the interweaving of skills across
domains are making jobs more complex.
This in turn highlights the shortage of
workers with these essential skills and
raises critical questions about whether des-
killing labor will even be possible going
forward. Lacking the workers they need,
companies are being forced to curb their
output.

Put these two phenomena together—
worker displacement and a shortage of
people with essential skills—and you find
an economy in which severely unfulfilled
demand exists side-by-side with an under-
leveraged and atrophied supply. The com-
mercial and human costs are intolerably
high, and rising.

THE RISE OF THE HUMAN
ECONOMY

If the historic engine of our economy has
relied on deskilling labor, an opposite force
is now emerging. The last decade has seen
the acceleration of what is sometimes re-
ferred to as the knowledge economy but is
known more comprehensively as the
human economy. In the human economy,
the driver of productivity is the level of
people’s skills. The best software developer
is not necessarily or even likely to be the
one who writes the most lines of code or
who works the fastest. Eﬀectiveness as a
worker is about a much more complex
equation that includes quality of work, cre-
ativity, willingness to learn, capacity to col-
laborate, and innovation, among other
things. Recent leaps in the capability of ar-
tificial intelligence extend the scope of
automation into the human economy, in
many cases for the first time. However, that
only further highlights the importance of
focusing on what truly distinguishes
human capability—and to defining what
will increase value in human endeavor
over time. In that context, the human
economy has a very diﬀerent framework

for boosting productivity, one that is fo-
cused more on the numerator in the pro-
ductivity equation (output) than on its
denominator (the cost of labor input). In
this calculus, the drivers of the human
economy are the development of worker
knowledge and skills, combined with the
eﬀect on employers of having workers who
are constantly developing new skills and
applying them to pressing problems, thus
forging a path to a new kind of productiv-
ity. Whereas the focus in the industrial
economy is on managing the size, location,
and cost of labor, the focus in the human
economy is on growing labor’s contrib-
ution to output through its ever-increasing
capability.

The human economy has very diﬀer-
ent financial drivers from those of the in-
dustrial economy and its four predominant
productivity strategies. Unlike the indus-
trial market and the market for local jobs
in the caring, food, and service sectors, the
human economy works with a much
higher gross margin—the diﬀerence be-
tween the direct cost of producing some-
thing and how much you made when you
sold it. For crankshafts, sounds systems, or
appliances, if you spend $400 to make it and charge $500 to sell it, your gross mar-
gin is $100, or 20 percent of your selling price. Labor and materials comprise most of the direct costs. By contrast, for soft- ware, pharmaceuticals, or movies, the con- siderable investments a company may make in initial labor are more than oﬀset by the low direct costs of future sales. Cre- ating a popular software application could cost hundreds of millions of dollars, but selling an incremental copy may have a di- rect cost of only a few cents. Meta’s average gross margin over the past five fiscal years was 83 percent, while that of General Mo- tors was 12 percent. That margin is impor- tant, because when a firm has a high gross margin it can aﬀord to worry less about the cost of labor and worry more about the value of what it produces. innovations / volume 13, number 3/4 7 Downloaded from http://direct.mit.edu/itgg/article-pdf/13/3-4/4/2113975/inov_a_00286.pdf by guest on 07 September 2023 Matt Sigelman In other words, the focus in the indus- trial economy is on making labor a scalable input, whereas the focus in the human economy is on scaling labor’s output. Saving a little on wages isn’t nearly as important in the human economy as en- suring the availability of the kind of highly skilled talent that can design and produce more valuable products. Similarly, invest- ing in skills becomes more important than investing in alternatives to labor because human capability, when well invested, can evolve even faster than the rapid advances in automation. Harvard economist David Deming’s research shows that the jobs that have been growing the fastest are those that demand both quantitative skills and social skills—a hard combination to re- place by even the emerging generation of advanced generative AI.2 Our own re- search finds that skills are blending across domains.3 For example, marketing people need to analyze and manipulate customer data, and tech and data specialists need to draw from writing, communications, and teaming skills. Intersections like these make work in the human economy harder and harder to automate, no matter how so- phisticated AI may become. What’s more, rapid change in the na- ture of work is challenging the economics of labor replacement. In my research with the Boston Consulting Group and Light- cast, we find that jobs are being redefined by new skills at an astonishing rate. In fact, the average job has seen 37 percent of its skills replaced over the past five years, and that pace accelerated further during the last two years of the pandemic.4 It’s hard to mechanize labor when the drivers of out- put are constantly changing. Even if ro- botic capabilities can match or exceed those of human economy workers, the cal- culus of automation will depend on replac- ing variable costs with fixed costs. Fixed cost investments only make sense when they can be recouped through long-term savings, but if the task that needs to be automated is itself subject to constant change, the payback window for capital in- vestment shrinks. Large language models and other recent advances in generative AI are no exception. The underlying neural network technologies on which they are based are eﬀective at mining existing knowledge but are not capable of driving fundamental innovation. As promising as this emergent set of trends may appear, the relative attractive- ness of investing in people instead of ma- chines only bears out if workers are indeed becoming increasingly skilled over time. How can modern societies rise to this im- perative? What are the most eﬀective mechanisms for investment? The scale of ongoing talent development needed for workers and employers to thrive in the human economy demands a multifaceted approach. What is needed is significant in- novation and investment by education sys- tems, employers, governments, and labor, along with new and more eﬀective collabo- rations within and across these actors. AN EDUCATION INFRASTRUCTURE FOR INVESTING IN SKILLS The transition from an industrial economy to the human economy needs to be mir- rored in fundamental change to the va- lence of our education system. An industrial economy that considers labor largely commoditized looks to education institutions simply to render additional factors of production. Colleges and univer- sities function correspondingly to bring workers into the market, but there is no ex- pectation that those institutions will con- tinue to build the capability of that talent over time. However, the human economy demands continued investment in skills over time. What will it take for institutions to shift from educating 18- to 24-year-old youths in a once-and-done degree-based model to providing agile, on-demand pro- 8 innovations / The Human Economy Downloaded from http://direct.mit.edu/itgg/article-pdf/13/3-4/4/2113975/inov_a_00286.pdf by guest on 07 September 2023 Building a Skills Engine for the Human Economy grams designed to help those already in the workforce advance? If value in the human economy is de- termined by skill, it is incumbent upon our education system to ensure two things: that students have the skills needed to launch, and that workers can acquire new skills over time. But which skills we build matters. If focused eﬀectively, education can invest students with the particular skills that drive value and unlock mobility in the human economy. In our recent re- search on how people escape the poverty trap, we find that some skills in a given oc- cupation—for example, project manage- for a customer service ment skills representative—can quadruple a worker’s probability of moving out of poverty. We need to make sure that our students are ac- quiring those skills. Given the job market’s extraordinary dynamism, and the current dearth of ana- lytical practice in the education and work- force development arenas, our schools and postsecondary institutions must be em- powered with greater data capacity. They must improve the ability to track changes in the landscape of opportunity for gradu- ates and to identify the specific skills and credentials required of graduates. A major 2020 study of US career and technical edu- cation programs found that only 18 per- cent of the credentials earned are in demand by industry, while many of the needed certifications are undersupplied.5 Addressing such gaps will require edu- cators and employers to engage more eﬀec- tively with one another at all stages—from curriculum design to placement to post- hire performance reporting—so that the right feedback loops are in place. It is a mistake to focus exclusively on tech skills. Research clearly documents the essential role of foundational skills such as creativity, collaboration, research, and writing; in the human economy these skills are even more important. In fact, the jobs most likely to require skills from across do- mains—typically those most data driven and technology enabled—seek creativity skills 3.5 times more than other jobs, de- mand double the collaboration and re- search skills, and are 50 percent more likely than other jobs to require research and writing skills.6 Employers’ expectations of workers in the human economy are high indeed, and helping workers and students acquire this broad array of skills is a vast undertaking. The existing local systems that support workers and employers are under-resourced and of mixed eﬃcacy. Developing a national skills-building infrastructure for the human economy means going well beyond the bounds of primary and secondary education and even traditional postsecondary education. In the human economy, as new skills keep emerging and the mix of skills needed is constantly changing, education will con- tinue over the course of a lifetime. Creating an infrastructure that will enable workers to acquire new skills on the fly will be es- sential to ensuring that, as the economy evolves, the workforce we have can become the workforce we need—and that workers’ skills will not lag behind. The human cost of skills obsolescence is high, and in a world with a talent short- age, the cost to industry is higher still. The nations that invest in a lifelong education infrastructure will develop the agile work- force they need to become global compet- itors. They will have a mechanism for ensuring that workers are always one step ahead, invested with the skills of tomor- row, and not mired in the skills of a fading economy. INVESTING IN SKILLS IN THE WORKPLACE In the human economy, labor is the most critical production factor. Given the essen- tial importance of the workforce, placing full responsibility for its capability and readiness on students, workers, and edu- innovations / volume 13, number 3/4 9 Downloaded from http://direct.mit.edu/itgg/article-pdf/13/3-4/4/2113975/inov_a_00286.pdf by guest on 07 September 2023 Matt Sigelman cators is asking too little of industry, the primary beneficiary. The free pass many employers have come to expect within a deskilling framework has left the work- force anemically under-skilled. Workers are left to identify and pay for training on their own initiative and on their own time. Educators in many regions use guesswork and feedback from former students now in the workplace to discern the skill require- ments that employers should be sharing with them. Employers and industry bodies are urgently needed at the skills-building table. The good news is that the emergence of the human economy creates new incen- tives for employers. The rapid pace of skill change, together with the hybridization of skills across domains, makes talent less easily commoditized and therefore less re- placeable. Workers become more integral to their employers because their stronger skillsets render them scarcer and more valuable and because employers benefit as their employees grow. As such, companies have more incentive to invest directly in building the skills of their workforce. Fur- thermore, industry is best positioned to in- vest in labor not only because of the resulting enhancement of firms’ value but because their direct relationship with workers enables them to oﬀer the clearest signal of what skills are needed and of how they can best be acquired. Employers who know how to identify what new capabil- ities they will need can advise and support workers on how to increase their value most eﬃciently. The bad news is that relatively few companies have mature systems for iden- tifying what new skills are needed and for helping workers skill up rapidly. Talent management of this kind is an undevel- oped muscle for most firms, whose pro- grams are often little more than a perfunctory exercise in employee engage- ment. • To make skill development a mission- critical business process, leaders must weave together five fundamental strategies: • Know your talent. Few employers truly understand their talent base. They know their employees’ titles and tax ID numbers but not their skills and capabil- ities. Absent that awareness, companies are unable to identify high-value skills within their organization or predict crit- ical gaps relative to future talent needs. Profiling systems designed to create such an inventory are often challenged by low employee engagement, which in turn is tied to workers’ skepticism about any ac- tual benefits of participation. Companies can do better by defining the skills of each talent pool—often called role architec- ture—and by leveraging new, more inte- grated methods for cataloging each worker’s capabilities that are closely tied to opportunities for learning and ad- vancement. Know what skills will be needed. Most firms have a strategic workforce plan, but it is typically an exercise in financial plan- ning rather than a tactical program for connecting the firm’s future direction to specific talent acquisition or skill devel- opment. What skills will be needed to ex- ecute the firm’s business strategy? Future skill requirements will continuously change, even in occupations well repre- sented in the current workforce. What new skills will be needed can sometimes be predicted, but this is not always pos- sible. What matters most is learning, and learning fast; the key is to become acutely aware of new and emerging skill de- mands and gaps in order to provide a prompt, clear, and reliable system for ad- dressing them. Absent this, a workforce is always playing catch-up and always at risk of obsolescence. Craft a new set of productivity metrics. Current productivity metrics are stuck in the industrial era. An industrial model asks how many calls per hour a customer • 10 innovations / The Human Economy Downloaded from http://direct.mit.edu/itgg/article-pdf/13/3-4/4/2113975/inov_a_00286.pdf by guest on 07 September 2023 Building a Skills Engine for the Human Economy service representative takes or how many door hinges per hour a worker installs on an automotive assembly line. The produc- tivity calculus of the human economy may be harder to frame, but it will be just as essential. It may be the impact an indi- vidual’s creativity has within a team, or the eﬀect an operations analysis has on product quality or timeliness. Without ef- fective metrics for output value, com- panies will be unable to evaluate the return on investment yielded by skill- building programs. That will make it hard for them to develop a scalable commer- cial logic for making and prioritizing in- vestments over time. Build an agile learning and development program. Three critical priorities in the human economy are knowing existing talent, anticipating future skills needs, and tracking the impact of skill invest- ments. To encompass all three, com- panies will need a learning and development program that can adapt to emerging imperatives, such as ensuring that the skills of workers in a certain function stay relevant or redeploying a set of workers to higher value roles. Com- panies need the ability to write “skill pre- scriptions” for specific skill-building experiences that are tailored to each tal- ent pool and each worker. At the same time, companies must have an eﬀective dispensary to fill these prescriptions, in- cluding providing a fast track that enables workers to learn and apply new skills. To do this in a way that is eﬃcient, person- alized, and agile, it will be necessary to tag learning catalogs to skills so that training programs can be compiled on demand. Engage the workforce. In the human econ- omy, employers and employees have a shared incentive to see human capital value rise. However, employers can’t as- sume that employees will know the best way to develop their abilities without clearly communicated guidance. To date, • • however, few companies, even those that spend a considerable amount on em- ployee learning programs, consistently provide eﬀective signals to their employ- ees. They often frame their learning pro- grams as a benefit rather than as an opportunity to increase earnings and earn promotions. Companies also need to provide the supports such as time, space, and reimbursement mechanisms to make it simpler for employees to take advantage of the training opportunities that are important to both parties. Data is at the heart of each of these strategies, and implementing them calls for firms to build up their analytic capacities. Whether it is understanding their own tal- ent, devising ways to identify and respond to skills gaps, reinventing productivity measures, developing more agile and sys- tematic mechanisms for skill development, or engaging workers more eﬀectively in their own careers, companies will need to be guided by data—data about their own talent, data about the broader market land- scape, and data that provide a window onto future skill requirements. But thriving in the human economy will require more than that. It will depend not only on know- ing what questions to ask but also on sys- tematizing the asking of the questions. Talent analytics—the robust understand- ing of the talent a firm has, the talent it needs, and the opportunities to connect the two—must be embedded in the gamut of commercial processes. AN ENGINE FOR SKILLS IS AN ENGINE FOR EQUITY Having an eﬀective skill development in- frastructure creates a new engine for work- force equity. The industrial era focus on hiring as a nearly exclusive talent strategy has left firms unable to fulfill the impera- tive to build a more inclusive workplace. A skills engine enables firms to identify and tap underleveraged talent pools within innovations / volume 13, number 3/4 11 Downloaded from http://direct.mit.edu/itgg/article-pdf/13/3-4/4/2113975/inov_a_00286.pdf by guest on 07 September 2023 Matt Sigelman their existing workforce, and thus to build diversity from within by developing eﬀec- tive skill pathways.7 Because the same skills engine that increases opportunity and ac- cess for historically underrepresented workers can underlie the broader, compa- nywide talent development program, pre- paring eﬀectively for the human economy will also yield a more equitable workforce and, ultimately, a more equitable society. CHANGING THE RELATIONSHIP BETWEEN INDUSTRY AND LABOR The traditional focus of organized labor has been on wages, benefits, and working conditions. Capital costs are high in the in- dustrial economy, so any work stoppage is financially crippling; loans must be repaid whether or not the machine is running. In an era in which labor was relatively undif- ferentiated by skill, the union’s sole point of leverage was withdrawal from work. As capital-intensive industries moved oﬀ- shore, unions struggled to gain equivalent leverage in the service sector; as a result, membership in many countries has de- clined. In the human economy, framing nego- tiations around wages, benefits, and work- ing conditions may be missing the bigger opportunity for both labor and employers. On the one hand, wages can only rise so much without a corresponding increase in productivity, and that productivity en- hancement can only happen through an increase in workers’ skills. On the other hand, even deftly won pay raises are un- likely to yield the same kind of boost for workers as an increase in their marketable skills and economic mobility. Traditional labor-management interactions might help a work group go from making $18 per
hour to $21, but the only way those workers will earn $35 per hour is if they
gain the skills needed to move up to alto-
gether better jobs.

Organized labor could find itself shap-
ing the future of the human economy if it
focuses on the learning, training, and skill-
building needs of union members and pro-
spective members. This approach would
enable labor to take on the important new
tasks of championing the skilling of the
workforce, advocating for workers’ mobil-
ity by protecting their ability to learn and
earn, and preventing the erosion of their
skill base. The result would be a new social
contract anchored in the promise of mo-
bility rather than stability.

Employers also have a lot to gain from
forging a new kind of partnership with
labor. A return to longer term, more com-
mitted relationships with their employees
will enable employers to cultivate the
workforce they will need in the future from
within the workforce they have, making
them less vulnerable to a labor market
shortage while increasing the payback
period on their worker-training invest-
ments. Turning from an approach of
“buying what we need for now” to embrac-
ing a “building for the future” strategy
oﬀers employers new options for creating
stronger, more enduring, and more pro-
ductive ties to their workers.

MEASURING PROGRESS

Broad exhortations don’t lend themselves
to eﬀective transformation. They lack the
concreteness needed for charting a way
forward, for setting tangible goals, and for
tracking progress. The forces described
here are indeed overarching, but the emer-
gence of new skill-level data makes pos-
sible the development of
innovative
metrics that describe the strength of a
workforce in both relative and absolute
terms. In fact, these same metrics can be
applied at any unit of aggregation, whether
organizationally, regionally, sectorally, or
nationally.

For a comprehensive view, skill invest-
ment should be considered through three

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Building a Skills Engine for the Human Economy

complementary lenses: input—the value of
a talent pool as a factor of production; out-
put—the return on investment in skill de-
velopment; and impact—the value added
to the labor factor of production. Put in
diﬀerent terms, the physics of the human
economy can be measured in ways not dis-
similar from those used to measure cosmic
bodies. In the industrial economy, we have
grown used to measuring what physicists
might call the kinetic energy of labor—that
is, how much output is produced. In a
human economy that recognizes talent for
both its present value and its future poten-
tial, we also need to measure the potential
energy of a workforce—that is, the skill
value of a workforce as economic input.
What’s more, to track progress over time,
we need a measure of change in potential
energy, or what we call the human impact
of the investment made.

These new measures are relatively sim-
ple to construct and operationalize. Here
are specific opportunities to develop eﬀec-
tive metrics for input, output, and impact:
Input. Skills represent a currency for
the human economy. In any given role,
skills distinguish between high- and low-
value definitions of the work and, cor-
respondingly, between a high- and
low-value workforce. For example, a work-
force whose marketing managers have
strong product management and product
marketing skills earn a 46 percent pre-
mium over the median wage for marketing
managers overall, whereas marketing man-
agers with Adobe skills make 19 percent
less than the median. In a market economy,
wages are a proxy, albeit sometimes imper-
fectly, for expectations of productivity. As
such, a skill-based measure enables both
eﬀective relative comparison—the com-
pany whose marketing managers are in-
product management
vested with
capability is considered to have a work-
force of “potential energy” 80 percent
greater than that of peers who over-index
on the Adobe suite—and absolute value—

measured as the sum of the wages of
workers in a given role.

Output. Productivity is the most direct
measure of the output or kinetic energy of
a workforce. In our discussion above of in-
vesting in the skills of a workforce, we pro-
posed an approach
reinventing
productivity metrics for the human econ-
omy.

Impact. We can measure return on in-
vestment in skills—essentially, the change
in a workforce’s potential—by comparing
measures of skill value directly as a time-
based comparison in input metrics or,
more simply, as proxied through worker
advancement. In the direct measure, we
evaluate the addition of human capital
value through a change in skills or, more
generically, through a change in compen-
sation, adjusted for regional and sectoral
wage inflation. The advantage of the
former is that it is likely to be more accu-
rate; for example, a software engineer who
has accrued Python skills is more valuable
than one who has only Java skills, regard-
less of whether she is now paid any more,
whereas a compensation-based metric of
value-added is easier to compute. Ho-
wever, tracking worker mobility may be al-
together superior, in that such a measure
aligns with a more fundamental under-
standing of the human economy. When
people rise, they become more valuable—
not only to their employers but to them-
selves and to society. Someone who started
at a company as an accounting clerk and
now serves as a compensation and benefits
manager is doing higher value work for the
company, is presumably bringing home
higher pay, and is adding to the compet-
itiveness of their nation. Until recently,
measuring worker mobility proved sur-
prisingly elusive, but The Burning Glass
Institute’s recent American Opportunity
Index project provides a repeatable meth-
odology for tracking worker advance-
ment.8

innovations / volume 13, number 3/4

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Matt Sigelman

A NEW GLOBAL PLAYING
FIELD

Society today is at an inflection point. We
find ourselves at the confluence of a 100-
year-old economy driven by the deskilling
of labor and a new economy that can only
be fueled by skilling people up. Points of
inflection are always fraught with risk and
opportunity. The human economy may be
ascendant, but it cannot thrive in any
country without major changes in practice
across industry, education, labor, and gov-
ernment.

In the 21st century, jobs will be won or
lost based not on labor costs but on talent.
That will be a monumental shift that has
deep implications for modern education,
for national competitiveness, and for the
global society.

A new human economy defined by
skills will become a playing field on which
a wide array of nations can change their
standing and reverse their fortunes. Na-
tions that build learning workforces that
are aligned with the needs of industry and
supported by a collaborative national in-
frastructure will enjoy unprecedented
growth. This applies to all nations. Devel-
oping economies will be able to leapfrog
their more developed peers by building the
skills most likely to be sought in the future.
In an economy that is less sensitive to labor
cost advantages and more motivated by the
quality and availability of talent, developed
countries will get the chance to reset their
long-established industrial patterns and to
draw on their strengths.

But this is no time for industrialized
countries to rest on their laurels. A high-
cost, high-productivity economy can win
in this new paradigm, but many countries,
the US included, have a long way to go in
achieving this goal. The alternative—to be-
come a high-cost, low-productivity econ-
omy—is a recipe for stagnation, inequality,
and social decay. Our work is cut out for
us.

To build the workforce of the future,
we must build the skill infrastructure of the
future, one that is dynamic, prescriptive,
and endowed with the data-centricity and
capacity to build an engine for the human
economy. The recommendations oﬀered
here can be seen as a guide for employers,
labor unions, and policy-
educators,
makers; the action steps proposed oﬀer
benefits to each of them. The true promise
of this moment, however, is what these
separate entities might create in concert: an
infrastructure that can fuel the engine of
the human economy—an engine that is
uniquely valuable, truly agile, and en-
dowed with the data-centricity to empower
people, firms, and nations to reach their
full potential.

1 An 2013 AP-NORC survey found that 40
percent of older workers reported not
having the right skills for the available jobs.
Associated Press-NORC Center for Public
Affairs Research. (2013). Working longer:
Older Americans’ attitudes on work and
retirement. https://apnorc.org/wp-
content/uploads/2020/02/AP-NORC-
2013_Working-Longer-
Poll_Topline_FINAL.pdf

2 See

https://scholar.harvard.edu/files/ddeming/fi
les/deming_socialskills_aug16.pdf.

3 See https://www.burning-glass.com/wp-

content/uploads/hybrid_jobs_2019_final.p
df.

4 Sigelman, M. et al. (2022). Shifting skills,

moving targets, and remaking the
workforce. Boston Consulting Group and
Burning Glass Institute.

5 See https://www.burning-glass.com/wp-
content/uploads/2020/09/Crredentials-
Matter-Phase-2-Report.2020-Update.pdf.

6 See https://www.burning-glass.com/wp-

content/uploads/hybrid_jobs_2019_final.p
df.

7 See https://hbr.org/2021/04/to-build-a-
diverse-company-for-the-long-term-
develop-junior-talent.

8 See https://americanopportunityindex.org/.

innovations / The Human Economy