Category Archives: Economics

US Productivity has Declined

Source: Zero Hedge, Aug 2017

Aside from short-lived booms in the 1990s and 2000s, US productivity growth has averaged just 1.2% from 1975 up to today after peaking above 3% in 1972.

As we detailed previously, adjusting for the WWII anomaly (which tells us that GDP is not a good measure of a country’s prosperity) US productivity growth peaked in 1972 – incidentally the year after Nixon took the US off gold.

The productivity decline witnessed ever since is unprecedented. Despite the short lived boom of the 1990s US productivity growth only average 1.2 per cent from 1975 up to today. If we isolate the last 15 years US productivity growth is on par with what an agrarian slave economy was able to achieve 200 years ago.

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Productivity Growth

Source: Obama White House archives, Jul 2015

The third level of mystery is explaining the conceptual drivers of productivity growth. Even if we agreed on the facts of historical productivity growth, explaining those facts is more difficult still. Moses Abramovitz famously called TFP a “measure of our ignorance,” the unexplained gap between input and output.1 And a rigorous conceptual understanding of that gap continues to elude economists

Figure 2—and all subsequent references to annual U.S. labor productivity in these remarks—references real output per hour worked in the private nonfarm business sector (excluding government enterprises) as reported by the Bureau of Labor Statistics (BLS). In other contexts, I have referenced the BLS’ labor productivity series for the nonfarm business sector (including government enterprises). The two series are closely correlated and exhibit the same trends, but excluding government enterprises permits the analysis of total factor productivity (TFP) that follows.

a simple thought experiment provides a sense of how important productivity is to incomes: what if productivity growth from 1973 to 2013 had continued at its pace from the previous 25 years? In this scenario, incomes would have been 58 percent higher in 2013. If these gains were distributed proportionately in 2013, the median household would have had an additional $30,000 in income. Had income inequality and labor force participation not worsened markedly, middle-class incomes would be nearly twice as high.

Virtually all the variation in labor productivity growth is accounted for by variation in TFP.

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.