Category Archives: Economics

How Technological Unemployment Can Occur

Source: Decline of Scarcity, 2014

The authors also suggest that technological unemployment—a phenomenon long thought of as impossible by mainstream economists—is in fact possible. They discuss three arguments for how technological unemployment could occur:

  1. In industries subject to inelastic demand, automation can lower the price of goods without creating any additional demand for those goods (and thus labor to make those goods). Over the long term, as human needs become relatively more satiated, this inelasticity could even apply to the economy as a whole. Such an outcome would directly undermine the luddite fallacy, which is the argument economists traditionally use to dismiss technological unemployment.
  2. If technological change is fast enough, it could outpace the speed at which workers are able to retrain and find new jobs, thereby turning short term frictional unemployment into long term structural unemployment.
  3. There is a floor on how low wages can go. If automation technology continues to drive wages down, those wages could cross a threshold below which the arrangement is not worth the employee’s time. Eventually the value of certain workers could fall so low that they are not worth hiring, even at zero price.

Paul Samuelson: Father of Modern Economics

Source: MIT, date indeterminate

Called the father of modern economics, Samuelson became the first American to win the Nobel Prize in Economics (1970) for his work to transform the fundamental nature of the discipline. He insisted that mathematics was essential, and his numerous and groundbreaking contributions provided the foundation on which modern economics is built. Samuelson’s textbook, Economics: An Introductory Analysis, is one of the most widely used in the history of American education.

Basic Income – Appease those left behind?

Source: MIT Tech Review, Jun 2016

The project is an experiment in what’s known as a “basic income”—or, when the money is given to entire populations, as a “universal basic income.” At its core, it’s a means for a government to alleviate poverty, replacing the myriad bureaucracy-bound safety-net policies in industrialized countries that struggle, with mixed results, to get money into the hands of those who most need it.


Progressives generally like such schemes, as long as they don’t leave the poor and jobless with less money than they get under existing safety-net programs. Many conservatives and libertarians are fans, too, thanks in part to the idea that a basic income would shrink government bureaucracy.

For the Silicon Valley crowd, the prime motivation appears to be a concern that automation has been displacing jobs, and that increasingly sophisticated artificial-intelligence applications could accelerate the trend.

“The idea of a basic income is a good one in a world where robots do most of the work, but we probably won’t be there for 30 to 50 years,” says Erik Brynjolfsson, who researches the digital economy at MIT’s Sloan School of Management.

at a time when the tech economy is generating huge amounts of wealth, is Silicon Valley just attempting to appease those left behind?

addressing poverty and job loss is on the minds of those in the technology crowd—when they aren’t hard at work coming up with apps that will help make it possible to automate some task or access some service that once required an employee.

Combine the concern about AI-driven job displacement with the tech community’s drive to solve difficult problems through radically new approaches, and it’s not surprising that the idea of a basic income has become Silicon Valley’s latest obsession.

Add to that a deep skepticism that government is capable of solving significant problems. And then throw in an awareness that the wealth tech workers are creating for themselves and the rest of the affluent minority is driving inequality to a point that could cause social unrest.

an annual survey by the Bureau of Labor Statistics shows that the main way the unemployed tend to use the time freed up by not working is in watching TV and sleeping, not inventing new products or mastering new skills.

Layperson Creative Contribution

Source: London School of Economics, Jul 2016

As Sir Henry Sumner Maine suggests, Britain long remained an oligarchic society that was convinced that merit was causally related to inherited social class. The United States arguably was able to assume economic leadership in part because institutions such as its educational and political systems offered inducements to all classes of society to contribute to the growth process, and allocated rewards that were commensurate with an individual’s productivity rather than his social provenance.

footnote 25:

In the period before 1820 college attendees in both countries predominantly belonged to elite families.  However, after 1820 the share of elites shrinks noticeably in the United States, and the vast majority of graduates come from nonelite backgrounds, whereas the pattern in Britain remains for the most part unchanged. The United States had set in place policies that facilitated human capital acquisition among the working class and led to social mobility through educational institutions, such as the Land Grant Act that subsidized universities with a pragmatic orientation.

there is a marked increase in the propensity to patent after 1851. This period stands out because in 1852 the British patent laws were reformed in the direction of the American system in ways that increased access to patent institutions, and strengthened the security of property rights in patents (Khan 2005). 

the kind of knowledge and ideas that produced significant technological contributions during British industrialization seem to have been rather general and available to all creative individuals, regardless of their scientific training.

More generally, the experience of the First Industrial Nation indicates that creativity that enhances economic efficiency is somewhat different from additions to the most advanced technical discoveries. The sort of creativity that led to spurts in economic and social progress comprised insights that were motivated by perceived need and by institutional incentives, and could be achieved by drawing on practical abilities or informal education and skills. Elites and allegedly “upper-tail knowledge” were neither necessary nor sufficient for technological productivity and economic progress.

In the twenty-first century, specialized human capital and scientific knowledge undoubtedly enhance and precipitate economic growth in the developed economies.

However, for developing countries with scarce human capital resources, such inputs at the frontier of “high technology” might be less relevant than the ability to make incremental adjustments that can transform existing technologies into inventions that are appropriate for general domestic conditions.

As Thomas Jefferson pointed out, a small innovation that can improve the lives of the mass of the population might be more economically important than a technically-advanced discovery that benefits only the few.



WH Council of Economic Advisors on AI

Source: White House website, Jul 2016

… before turning to concerns about some of the possible side effects from AI, I want to start with the biggest worry I have about it: that we do not have enough of AI.

Measured productivity growth has slowed in 30 of the 31 advanced economies, slowing from a 2 percent average annual growth rate from 1994 to 2004 to a 1 percent average annual growth rate from 2004 to 2014. Notably, the United States still has the fastest productivity growth of any G-7 country, with annual productivity growth of 1.1 percent from 2004 to 2014 as compared to 2.3 percent from 1994 to 2004, as shown in Figure 1.

the recent advances in deep learning built on research on neural nets by university labs which was largely funded by the Defense Advanced Research Projects Agency (DARPA) and other government agencies in the 1980s and 1990s.

There is no reason the economy cannot generate substantial levels of employment at much higher levels of technology and productivity than we have today.

What matters, however, is how our labor market institutions cope with these changes, help support the creation of new jobs, and successfully match workers to them. Some of the policies along these lines proposed by the President were discussed extensively in the same recent CEA report, and include expanding aggregate demand, increasing connective tissue in labor markets, reforming taxes to encourage work, and creating more flexibility for workers (CEA 2016b).

Other policy responses include expanding education and training so more people have skills that complement and benefit from innovations, increasing the progressivity of the tax system to make sure that everyone shares in the overall benefits of the economy, and expanding institutional support for higher wages, including a higher minimum wage and stronger collective bargaining and other forms of worker voice (Furman 2016a).

… the socially optimal level of R&D investment—the amount that would produce the greatest rate of economic growth—is two to four times greater than actual spending (Jones and Williams 1998; Bloom, Schankerman, and Van Reenen 2013; Akcigit, Hanley, and Serrano-Velarde 2013). This gap is particularly large for basic research, since its role as the “seed corn” of future innovations means that it generates the largest spillovers.

The biggest worry I have about AI is that we will not have enough of it, and that we need to do more to make sure we can continue to make groundbreaking discoveries that will raise productivity growth, improving the lives of Americans and people throughout the world.

Open Innovation Enables Service Platforms

Source: MIT Technology Review, May 2016

how innovation plays out in service businesses. Most of the top 40 economies in the Organization for Economic Cooperation and Development (OECD) get half or more of their gross domestic product (GDP) from that sector, and many companies are witnessing a shift to services as well.

More generally for services, innovation must negotiate a tension between standardization and customization. The former allows activities to be repeated many times with great efficiency, spreading the fixed costs of those activities over many transactions. The latter allows each customer to get what he or she wants for high personal satisfaction. The problem is that standardization denies customers much of what they prefer, while customization undermines the efficiencies available from standardization.

The resolution to this dichotomy is to construct service platforms, which invite others to build on top of your own platform offering. This allows economies to emerge from the standardization of the platform, and it creates customization through the addition of many others to the platform.

A fundamental premise of open innovation is that “not all the smart people work for you.” That means that there’s more value in creating the architecture that connects technologies in useful ways to solve real problems than there is in creating yet another technological building block. System architecture, the system integration skill to combine pieces in useful ways, becomes even more valuable in a world where there are so many building blocks that can be brought together for any particular purpose.

Paul Romer: Chief Economist @ the World Bank

Source: FT, Jul 2016

The World Bank is set to appoint Paul Romer, a longtime advocate of the economic power of human capital and student of urbanisation.

Mr Romer is an ardent supporter of the power of economic growth to reduce poverty and will be joining the World Bank at a time when slowing emerging economies are presenting it with a host of new challenges. Economists at the bank last month warned that slowing developing economies had set back their efforts to catch up with rich economies like the US by decades. 

“We often lose sight of how important even small changes in the average rate of growth can be,” Mr Romer wrote in a blog post published on Saturday. 

Too often, he argued, the need for data to prove a theory led economists in the path of small ideas and projects rather than bigger bolder ones whose eventual impact on poverty were exponentially larger. 

“Our goal should be to recommend treatments and policies that maximise the expected return, not to make the safest possible treatment and policy recommendations,” he wrote. 

“We have to weigh the trade-offs we face between getting precise answers about such policies as setting up women’s self-help groups [against] other policies [like] facilitating urbanisation or migration that offer returns that are uncertain but have an expected value that is larger by many orders of magnitude.”