Thursday, October 20, 2016
Monday, October 10, 2016
and the future of jobs
By Jeffrey D. Sachs
Since the early 1800s, several waves of technological change have transformed how we work and live. Each new technological marvel — the steam engine, railroad, ocean steamship, telegraph, harvester, automobile, radio, airplane, TV, computer, satellite, mobile phone, and now the Internet — has changed our home lives, communities, workplaces, schools, and leisure time. For two centuries we’ve asked whether ever-more-powerful machines would free us from drudgery or would instead enslave us.
The question is becoming urgent. IBM’s Deep Blue and other chess-playing computers now routinely beat the world’s chess champions. Google’s DeepMind defeated the European Go champion late last year. IBM’s Watson has gone from becoming the world’s “Jeopardy’’ champion to becoming an expert medical diagnostician. Self-driving cars on the streets of Pittsburgh are on the verge of displacing Uber drivers. And Baxter, the industrial robot, is carrying out an expanding range of assembly-line and warehouse operations. Will the coming generations of smart machines deliver us leisure and well-being or joblessness and falling wages?
The answer to this question is not simple. There is neither a consensus nor deep understanding of the future of jobs in an economy increasingly built on smart machines. The machines have gotten much smarter so fast that their implications for the future of work, home life, schooling, and leisure are a matter of open speculation.
We need to pursue policies so that the coming generation of smart machines works for us, and our well-being, rather than humanity working for the machines and the few who control their operating systems.
In a way, the economic effects of smarter machines are akin to the economic effects of international trade. Trade expands the nation’s economic pie but also changes how the pie is divided. Smart machines do the same. In the past, smarter machines have expanded the economic pie and shifted jobs and earnings away from low-skilled workers to high-skilled workers. In the future, robots and artificial intelligence are likely to shift national income from all types of workers toward capitalists and from the young to the old.
CONSIDER ENGLAND’S Industrial Revolution in the first part of the 19th century, when James Watt’s steam engine, the mechanization of textile production, and the railroad created the first industrial society. No doubt the economic pie expanded remarkably. England’s national income roughly doubled from 1820 to 1860. Yet traditional weavers were thrown out of their jobs; the Luddites, an early movement of English workers, tried to smash the machines that were impoverishing them; and poet William Blake wrote of the “dark Satanic mills’’ of the new industrial society. An enlarging economic pie, yes; a new prosperity shared by all, decidedly not.
Looking back at two centuries of more and more powerful machines (and the accompanying technologies and systems to operate them), we can see one overarching truth: Technological advances made the society much richer but also continually reshuffled the winners and losers. Similarly, one overarching pattern was repeatedly replayed. The march of technology has favored those with more education and training. Smart machines require well-trained specialists to operate them. An expanded economic pie favors those with managerial and professional skills who can navigate the complexities of finance, administration, management, and technological systems.
Overall, better machines caused national income to soar and the man-hours spent in hard physical labor to decline markedly. Seventy-hour workweeks in 1870 have become 35-hour workweeks today. An average of around six years of schooling has become an average of 17 years. With increasing longevity, most workers can now look forward to a decade or more of retirement years, an idea simply unimaginable in the late 19th century. It’s amazing to reflect that for Americans 15 years and over, the average time at work each day is now just 3 hours 11 minutes. Those at work average 7 hours and 34 minutes, but only 42.1 percent of Americans 15 and over are at work on an average day. The rest of the time, other than sleep and personal care, is taken up with schooling, retirement, caring for children, leisure and sports, shopping, and household activities.
Smart machines in the 19th century provided massive power (the steam engine), transport (rail, steamships, automobiles), information (telegraph), and material transformation (steel and textile mills), and also, crucially, a more and more powerful substitute for human brawn – that is, backbreaking physical labor — on the farm and in the mines. Seed drills, cotton gins, threshers, reapers, combined harvesters, and by the early 20th century, tractors, not only opened up vast new farmlands but also replaced millions of farm workers by machines. Mechanical cotton pickers in the early decades of the 20th century displaced millions of African-American sharecroppers on Southern farms and contributed to the great African-American migration to northern cities.
Hard physical labor declined as machines did more and more of this work; but so too did jobs and earnings for lower-skilled workers. Those lucky to get an education could obtain the higher skills needed for the new jobs. Those who could not suffered stagnant or falling wages and a further loss of social status. In the past two decades, more and more low-skilled men have simply dropped out of the labor force entirely.
The most important policy response is to ensure that students stay in school long enough to achieve the skills they need for the new and better jobs. As long as the national supply of skilled workers roughly keeps pace with the rising demand for skilled workers, while the supply of low-skilled workers declines in line with the decline in the numbers of low-skilled jobs, the gap in earnings between high- and low-skilled workers remains relatively stable. In this way, the rising school attainments of Americans during the 20th century roughly maintained a balance with the shift from low-skilled work to high-skilled work.
Yet after around 1980, the earnings of highly educated workers (notably, those with bachelor’s degrees and higher) increased sharply relative to less-educated workers (those with high-school diplomas or less). Greater international trade and offshoring probably had a role in this, and so did technology, with smarter machines replacing high-school-educated workers in a widening range of manual and repetitive tasks. The shift of the labor force toward higher-skilled workers wasn’t fast enough in recent decades. Many American lower-skilled workers have been hit hard by lost jobs and falling wages.
YET TODAY’S smart machines now are not just replacing brawn but also brains. The futurist Ray Kurzweil and others have popularized the term “singularity’’ to mean a time in the near future when machines are simply better than humans at just about everything: moving, assembling, driving, writing, calculating, war-making, teaching (yikes!), and the rest.
Several recent studies, including at Oxford University and McKinsey, have tried to estimate the share of jobs that are likely to be up for grabs by smart machines in the next 20 or so years. Each occupation is analyzed for the kinds of tasks needed. Are they highly repetitive or highly context-specific? Do they require highly specialized mechanical skills, a high degree of interaction with others, or a high measure of emotional empathy? And so on. From this categorization of job tasks, the researchers estimate the share of jobs that can be substituted by robots and artificial intelligence systems. Their answer: Roughly half of today’s jobs are susceptible to at least some kinds of replacement by smart machines.
The implications are a bit tricky. On the one hand, smarter machines mean more economic output and, in principle, a larger economic pie to share among the American people. Investing in machines, or in the companies that produce the smart systems that run them, would seem to offer high returns; capital owners would be very likely to benefit. On the other hand, smarter machines could mean a decline in the demand for workers. Young people with labor to sell but little wealth to invest could find themselves on the short end of the economic stick, with lower wages and no grand prospect of benefiting from the higher returns to capital. Older and richer Americans would tend to benefit, younger and poorer Americans would tend to fall behind.
This would not be the end of the story, however. If today’s young people find themselves without jobs, they not only will be poorer, but will also save less as a result of shrunken incomes. Yes, the smarter machines will offer a higher return to saving, but the supply of national saving will shrink. A careful theoretical analysis reveals a stark truth: Smart machines could actually set in motion a downward spiral, wherein today’s young workers can’t find decent jobs, and thereby cut back on their saving, which in turn leaves the following generation of young workers even worse off.
This is indeed a frightening vision. And yet the same analysis suggests a way out. If the rich capital owners transfer some of their windfall profits to the struggling young workers, then both the old rich and the young poor would be better off with the smart machines than without them. In effect, the rich older shareholders would compensate the poor younger workers in order to offset the fall in wages.
There are two ways this “offset’’ could happen. Within families, parents could transfer some of their increased wealth to their children; but alas, that is a solution that is likely to be relevant mainly for richer households.
For the non-rich, the real solution could and should be achieved through fiscal policy. Rich older shareholders should be taxed in order to make transfer payments to the poorer, young workers.
Such transfer payments could be carried out in many ways: a cut in payroll taxes; tuition-free higher education; an expansion of the Earned Income Tax Credit (EITC) for low-wage workers; or a “reverse’’ Social Security system with payments from the old to the young. One policy that has been suggested is a capital grant to every newborn, financed by a wealth tax. In essence, each newborn would receive a robot (or financial claim to one) at birth.
THE NEW AGE of smart machines has already seen a shift in national income away from wages and toward profits. In automobile manufacturing, for example, where robots have already displaced many assembly-line workers, the share of wage compensation in the industry’s value-added has tumbled from 57 percent in 1997 to 47 percent in 2014. For the economy as a whole, a recent study reports a decline in the labor share of national income from around 68 percent in 1947 to 60 percent in 2013. The shift toward capital income seems to be well underway, and would seem to be a key factor in America’s sharply higher inequality of income. As machines become even smarter in future years, the economy-wide shift from wage income to profit income is likely to continue.
In addition to income redistribution from capital owners to workers (and from old to young) there are three other steps we should plan to take.
First, as old jobs disappear and new ones are created, we should emulate Germany’s successful apprenticeship programs, which train young workers in the skills needed in the economy. The President’s Council of Economic Advisers has rightly emphasized the need for scaling up this kind of active training.
Second, we should prepare for a workforce in which workers will change jobs with much greater frequency than in the past. In an age of disruptive technology, we should plan for disruption. Changing jobs should be regarded as normal; training and skill upgrading should be life long, and health care and other benefits should follow workers, not jobs.
Third, and finally, let us remember that ever-smarter machines could enable us to enjoy much more leisure time, and more hours of the day at valuable but nonremunerated activities and volunteer work.
Suppose that singularity indeed arrives, so that robots and expert systems really do perform all the unpleasant and humdrum work of the economy. As long as fiscal policies ensure that everybody, young and old, can share in the bounty, the results could be a 21st-century society in which we have much more time — and take more time — to learn, study, create, innovate, and enjoy and protect nature and each other.
Jeffrey D. Sachs is University Professor and Director of the Center for Sustainable Development at Columbia University, and author of “The Age of Sustainable Development.’’
Tuesday, October 4, 2016
In his new book, Against Luck: The Story of Innovation and Customer Choice, Clay Chistensen argues: "Successful innovation can seem like a matter of luck—but it need not be so. Every day, “jobs” arise in people’s lives that they need to resolve. Needing to get one of these jobs done is the mechanism that causes people to “hire” an offering—whether it be a product or a service.."
So what are the jobs in education that school districts want to "hire" services to help resolve:
So what are the jobs in education that school districts want to "hire" services to help resolve:
- Manage Competencies. An individual educator or an education entity can create a competency framework that aligns to other source standards documents like the Common Core, TEKS, and Next Generation Science Standards to be used by assessment, content and curriculum management, and student learning record tools.
- Manage Digital Content and Curriculum. An individual educator or an education entity can curate a collection of content organized in a logical progression and tagged to learning standards that can be used (via LTI or TCC) by one or more learning management system to support standards-aligned instruction.
- Deliver Classroom Competency Assessments. An educator can create and administer a classroom assessment that produces standards-aligned outcome and proficiency level that can be integrated with other assessment results (though xAPI/Caliper).
- Deliver Interim Benchmark Assessment. A district assessment office can curate and support classroom administration of common interim benchmark assessments that can be integrated with other assessment results (though xAPI/Caliper).
- Integrate Embedded Assessment Learning Tools. An educator, parent, or learner can use an assessment embedded learning tools (e.g. Kahn, IXL, 10 Marks, Accelerated Reader) to supplement and support learning and produce diagnostic assessment data that can be integrated with other assessment results (though xAPI/Caliper).
- Sustain Access to an Integrated Student Learning Record. A user can access a view of a student’s learning profile that integrates (though xAPI/Caliper) standards-aligned assessment results (outcomes) from multiple sources including assessment embedded learning tools, interim benchmark assessments, and classroom assessments scored to standard rubrics.
- Support Personalized Learning. An educator, learner, parent, or extended learning agent authorized by the parent can access an integrated learning profile that summarizes from multiple assessments instruments the competencies a learner has demonstrated a level of performance and provides access to instructional materials and interventions that are customized to that learner’s profile.