The book is about a group of scientists and engineers at NASA who spent “decades of their lives, dreaming, scheming, planning, building and flying the one and only spacecraft to Pluto“. It can be classified as a popular science book yet the book contains many a lessons for people working in corporate world. It may never make the “best business books” long list much less a short list as it is not about running a profit making enterprising led by a mercurial CEO. However, the book is chock full of case studies in career management.
This post will be the first of a new series of posts that I intend to write on lessons (mainly as applicable to corporate world) that I derive from books. Initially I intended to write a post similar to found on the blog 25iq.com but then I realized if I waited to collect my thoughts about all the lessons I learnt from the book, it will take me forever to write one post. As such, I decided to make it a series with each post dedicated to one or two lessons. This will also make me a prolific blogger 🙂 .
Alan saw Pluto as the obvious next place to explore, and he sensed that it was a good time to see if he could drum up support for that exploration among planetary scientists.
He wasn’t quite sure how to do it, but Alan thought that a good first step would be to gather Pluto scientists together in a highly visible scientific forum. At the time, there were only about one thousand active planetary scientists, and most attended the annual spring and winter meetings of the American Geophysical Union (AGU), where scientists congregate for a week to attend “sessions” of talks organized around different topics. Alan and a few colleagues decided that they would organize a technical session about new discoveries and insights into Pluto, and would propose this to the committee arranging the upcoming spring AGU meeting, in Baltimore in May of 1989.
Then they put the word out to the community of scientists doing research on Pluto to “vote with their feet” by submitting research talks and attending the session to show interest in a possible mission to explore Pluto.
It is all good and well to come up with a new idea but as in new ideas in national politics, you cannot do it all alone in a large bureaucratic corporate organization. You have to drum up support not only from your colleagues but also have to get other stake holders excited about the new initiative. You need to start a movement by getting action oriented individuals across the organizations to buy in to your idea. Once you have gathered sufficient people who have bought in the idea, use every platform available within and outside the organization to take it to stake holders and decision makers.
How could they rally the planetary scientific community to show NASA that a Pluto mission had broad support? They brainstormed ideas and formed a plan to build cred and buy-in. They scribbled action items on napkins. One was to publish a special issue of the Journal of Geophysical Research showcasing the research results from that day’s AGU Pluto session. Another was to work to excite the people they knew at NASA Headquarters, to follow up on the idea of sponsoring a mission study. They would also start to recommend Pluto mission supporters for the various committees that advise NASA about planetary mission priorities. And they would organize a letter-writing campaign to cajole colleagues to contact NASA and express support for a mission.
Nowadays companies are very conscious of their public image and follow closely trends social media. One can also push the organization to accept new ideas (well they arent exactly new but if the organization isn’t implementing them they are new of the organization) such as sustainability, diveristy, social responsibility etc by raising the issue on Twitter or Facebook pages. Needless to mention, as in NASA, one should work within the rules and parameters of working in the organization and should not run afoul of corporate policies or employment contract.
As the aforementioned quotes show, the plutophiles used every forum, platform and resource available to them and bring about a change in the bureaucratic giant that is NASA. Based on the success of the campaign, NASA gave a go ahead for carrying out a study for Pluto exploration.
In the emerging world, deindustrialization is occurring at ever earlier stages of development: an ailment that economist Dani Rodrik has labelled ‘premature deindustrialization’. When manufacturing’s share of total value added in the South Korean economy peaked in 1988, real income per person in South Korea was about $10,000, or just less than half the American level at the time. When that same peak was reached in Indonesia in 2002, its real income per person was roughly $6,000, or about 15 per cent of the American level. And when India reached that point in 2008, its real income per person was only about $3,000, or about 6 per cent of the American level of income at that time.13 Indeed, Arvind Subramanian, an economist and chief economic adviser to the Indian government, reckons that the Indian experience actually represents something like premature non-industrialization, or the fizzling out of industrialization before it ever really got going.
Countries still compete for the factories in which the vehicles are assembled: such factories still mean jobs, if fewer than in the past, and jobs are useful things to have in an economy. Yet, from a value perspective, factory assembly is a drop in the bucket. Very nearly anyone can do it. It is no surprise that state governments compete to offer incentives to car firms looking to open new production plants: firms can shop around, and capture more of the value of production, because they are in possession of the scarce know-how needed to make a car – the design and programming knowledge, the capability to manage global supply chains, and so on – while the locations competing for the plant are largely interchangeable.
The story is very much the same for something like an iPhone: Apple captures the lion’s share of the return from making them despite its outsourcing of virtually the whole of the production chain because it is the creative force behind the product design. Indeed, it is true of our consumption in general; we once devoted most of our household budgets to physical things: food and drink, clothing and furniture. Now we spend vast amounts on things like education and healthcare, or on housing, the value of which is mostly dependent on the access it provides to social capital rather than the wood in the walls and the plastic in the pipes. Subramanian describes this shift as one from ‘stuff to fluff’, and it is reflected in the trade data.
Developing economies are discovering that this evolution presents them with serious difficulties. The growing importance of knowledge (and the growing irrelevance of other cost sources) means that the advantage to rich-world firms of moving anything abroad is decreasing. ‘Reshoring’ in manufacturing, or the relocation of industrial production back to the rich economies that were priced out of such businesses decades ago, is often framed as a labour-cost phenomenon and a potential boon for middle-skill workers in advanced economies: with Chinese wages rising, some believe, it is increasingly attractive for firms to keep assembly in America, and to employ thousands of manufacturing workers in the process. But that is not, for the most part, what is occurring. Reshoring is predominantly a function of the rising knowledge-intensity of production, which means that variations in the cost of unskilled labour no longer matter all that much. Better for Tesla to keep production close at hand (in Fremont, California, on the eastern shore of San Francisco Bay) where its skilled engineers can keep a watchful eye on the code operating the plants, than to move assembly abroad in search of modest savings on the wage bill. And sure enough, the reshoring phenomenon, where it has occurred, has not brought back mass employment of less-skilled workers.
That means that economies which were hoping to establish an industrial foothold for themselves by using their low labour costs to wiggle onto a supply chain are increasingly out of luck. There are exceptions, but they are of a particular and unhelpful sort: where labour is so incredibly cheap that it remains economical to use people in place of available technologies. But in these cases the advantage to firms of locating in poor economies is precisely that the use of more sophisticated technologies is not necessary, which means that any transfer of technological knowledge to the local workers will be extremely limited, and the rungs which might otherwise have led to a more productive, sophisticated state of economic activity have been removed.
Canada To Lose Up To 7.5 Million Jobs in 10–15 years
I rarely watch TV but read a lot of articles, blogs, magazines etc on the topics I am interested in. However, most of the times when someone asks me to tell me what my thoughts are or what my research says about a particular topic, my memory fails me and I can’t even remember where and what I read about that topic. But on rare occasions, I read a passage or an article and it is as if someone has opened the floodgates of my minds and all the writings I have read start falling into place like the blocks in the game of Tetris.
Canada should consider radical changes to its social safety net as the country faces the loss of up to 7.5 million jobs to automation in the next 10 to 15 years, says a new study from a think tank at the University of Toronto.
There are academics mainly in economics who think that as industrial revolution eventually created more jobs than it destroyed, eventually this new phase will too. But these are feeble attempts at trying to fit the earlier experience on the tech future. However, if one has read the published work that is coming out, predicting the future of jobs isn’t as simple as that. I will cite two works that have received a lot of coverage.
One, the acclaimed 2014 book The Second Machine Age by two thinkers on the forefront of automation ie MIT professors Andrew McAfee and Erik Brynjofsson.
Most important, humanity has recently become much better at building machines that can figure things out on their own. By studying lots of examples, identifying relevant patterns, and applying them to new examples, computers have been able to achieve human and superhuman levels of performance in a range of tasks: recognizing street signs, parsing human speech, identifying credit fraud, modeling how materials will behave under different conditions, and more.
Building machines that can learn on their own is critical, because when it comes to accomplishing many tasks, we humans “know more than we can tell,” as the scientist and philosopher Michael Polanyi put it. Historically, this served as a hard barrier to digitizing much work: after all, if no human could explain all the steps followed when completing a task, then no programmer could embed those rules in software. Recent advances mean that “Polanyi’s paradox” is not the barrier it once was; machines can learn even when humans can’t teach them.
As a result, jobs that involve matching patterns, in particular, from customer service to medical diagnosis, will increasingly be performed by machines. Because U.S. companies are both the world’s most prolific producers and the world’s most enthusiastic consumers of technology, many of the effects of the digital revolution will likely be seen first in the United States. Low-wage jobs are especially at risk: in its 2016 report to the president, the U.S. Council of Economic Advisers estimated that 83 percent of jobs paying less than $20 per hour could be automated.
Most of the economist think that humans will follow the same path as they did during industrial revolution ie as they shifted from farm labor to industrial labor similarly they will shift from industrial labor to tech workers. One has to only look at Detroit (car manufacturing capital of the world) to realize that jobs once exported or automated are gone to never come back. The path of transformation of work will not be from the farm worker to industrial worker but rather of horse to car. Humans will go the way of horses and may no longer be required for work.
I think the digital revolution is probably going to be as important and transformative as the industrial revolution. The main reason is machine intelligence, a general-purpose technology that can be used anywhere, from driving cars to customer service, and it’s getting better very, very quickly. There’s no reason to think that improvement will slow down, whether or not Moore’s Law continues.
I think this transformative revolution will create an abundance of labor. It will create enormous growth in [the supply of workers and machines], automating a lot of industries and boosting productivity. When you have this glut of workers, it plays havoc with existing institutions.
To a different question about increasing a minimum wage (the bottom rung of the workers) he says
It will be interesting to see how increases in the minimum wage affect this. You see stories of robotic burger-flippers and people ordering food through iPads. It’s possible you see more of that as minimum wages rise. It seems like there is technology waiting on the shelf to displace that work.
I think I would say: If in 10 years time, workers in those jobs have received a substantial raise relative to current levels, and employment has not fallen, and productivity has not gone up, that would be evidence that I’m wrong. But my expectation is that as these workers become more expensive, you’ll see more interest in the technology that could displace them.
Joseph Stiglitz Nobel Laureate
Nobel laureate Joseph Stiglitz in his latest working paper for NBER which was published last week stated that future is about jobless recoveries. Though he was talking about monetary policy and low interest rates, but this excerpt fits nicely into our theme.
(c) Choice of technique/creating a jobless recovery
Here, we discuss one piece of evidence that reliance on changing intertemporal prices for equilibrating the economy may not be optimal. There are many alternative theories attempting to explain why the economy fails to attain full employment, including those related to wage and price rigidities (with those rigidities in fact being endogenous in some variants of these theories.) Monetary policy attempts to correct for these distortions by controlling the interest rate (usually the short term interest rate), setting it at a level different from what it otherwise would be. But intuitively, if the source of the distortion is in the labor or product market, it might make far more sense to attempt to correct at least some of the distortion more directly.
The standard argument for monetary policy is that it increases investment (and possibly consumption) leading to higher GDP and thus employment today. But there is another effect: lower interest rates induce firms to invest in more capital intensive technologies, lowering future demand for labor. It affects the choice of technique. Even if real wages go down in a recession, the decline in the cost of capital is every larger. The original distortion is an excessively high price of labor relative to capital because of wage rigidities; the interest rate policy exacerbates that distortion. We see the consequences: firms replacing unskilled checkout clerks and tellers with machines. Thus, as the economy recovers, there will be a lower demand for labor than there would otherwise have been — it will take a higher level of GDP to achieve a restoration of full employment.
Though a lot of people talk about “gig economy” and how Uber and Task Rabbit are creating jobs that provide “flexibility” and “additional source of income”. Brookings Institution came out with this
To be sure, a first reading of the data lends credence to the view held by enthusiasts of the “sharing” economy that Uber and Airbnb are serving unmet consumer demand or stimulating new demand — and so are creating new work opportunities that complement those in existing taxi or hotel companies. You can see that in our data, which show that both nationally and in most of the 50 largest U.S. cities, payroll employment has actually increased somewhat in those industries during the years 2010–2014, even despite the influx of nonemployer contractors working for Uber and Airbnb.
However, payroll growth in the two industries looks surprisingly weak by our count. And I believe that a closer look at the metro-by-metro data suggests that platform freelancing was already substituting for some payroll employment and cannibalizing it in the 2012–2014 period.
New Social Contract
All of the authors cited above, including the researchers at University of Toronto that started this conversation, go on to say that this jobless future calls for a new institutions to provide a safety net. I quote from Brookings Institute piece below and though what they are saying is about gig economy workers, yet it also holds true for those made redundant by automation.
the shift to alternative work arrangements matters for policy makers because it represents a fundamental reorientation of the social contract within which millions of Americans work. Most notably, the rise of online temping, freelancing and independent contracting means that millions of workers increasingly lack access to the once-ubiquitous labor standards that defined the “good jobs” economy that came out of the New Deal era. Gig workers, for example, retain limited access to income security protections, such as unemployment insurance, workers’ compensation and disability payments. Minimum-wage and antidiscrimination laws may not apply to such contractors, nor do they often receive retirement benefits such as Social Security. And for that matter access to credit, training and credentialing becomes even more tenuous than elsewhere in the economy.
In short, the expansion of the gig economy — left to itself — is likely going to contribute to larger trends that are reducing the share of American workers that can achieve basic economic security through their work.
Given that, the question becomes not just whether the gig economy will supplant large portions of the payroll economy, but how can the technological and business-model innovation of the gig economy be managed and enhanced to ensure it helps deliver a measure of economic security for the millions of Americans who are beginning to depend on it? Whether it is about benefits contributions by gig-economy companies, portable benefits accounts, or new kinds of safety nets, the conversation needs to widen as fast as the gig economy is growing.
How unsafe you are in the so called gig economy? Very unsafe.
Many in tenured track positions in academia or press may not be able to see the future, but Canadians are worried about their employment prospects.
Low Quality of Jobs created in Canada
Below is the same data I presented yesterday regarding housing affordability but it is also equally applicable to today’s post. From CIBC research report
Is the quality of employment in Canada in decline? We think so. By looking at the distribution of part-time vs. full-time jobs; self-employment vs. paid-employment; and the compensation of full-time paid employment jobs in more than 100 industry groups, we observe a slow but steady deterioration.
As illustrated in Chart 5, the declining share of young Canadians in the labour market can bias our direct measure upward. At the same time, the rising share of older Canadians that are less engaged in the labour market can bias the measure downward. Chart 9 overcomes that problem by focusing on the age group between 25 to 54. The story is the same: The share of lower-paying jobs has been on the rise.