In As Time Goes By: From the Industrial Revolutions to the Information Revolution, a seminal work in cliometrics—the study of economic history—Chris Freeman and Francisco Louçã use historical data on technological advances, economic structure, salaries, and political unrest to derive a clear pattern linking innovation to the performance of the economy. These generational cycles of invention, expansion, and depression are called “Kondratiev waves” in honor of Nikolai Kondratiev, the Russian economist who first postulated their existence.

Cliometrics was founded in 1960 as a response to the simplistic models of neoclassical economics. By combining historical facts and economic theories, cliometrics seeks to create a fuller picture of economic growth than either discipline alone can provide. Combining the quantitative field of economics with the qualitative study of history leads to conclusions that may not always fit squarely under the methods of either discipline, but nonetheless the exercise tosses up some intriguing conclusions. Here are several of them.

Kondratiev waves carry transformational technologies into the market and create new industries

When we think of the industrial revolution, we think of steam engines and factories, but in fact, this was only one of many industrial revolutions. Freemand and Louçã show the correlation between repeated technological revolutions and the waves of economic growth that carry them. Each of these Kondratiev waves is driven by a “carrier-branch technology,” defined as a new way of doing things so much more efficiently than the old ways that it reshapes every aspect of the economy. The five carrier-branch technologies that Freeman and Louçã identify are:

• Water-powered machinery
• Steam power
• Electrification
• The internal combustion engine
• Computerization

Carrier-branch technologies have a core input, for example coal, or iron, or oil, or computer chips, and give rise to a whole secondary economy of supporting industries and social institutions. And each Kondratiev wave follows a similar economic pattern—the initial invention creates a period of boom, with rising material wealth, but as the technology reaches a point of saturation, the economy enters a downswing or “crisis of structural readjustment.” These upswings and downswings in the past lasted from 20 years to 30 years each, leading to a total cycle time of around 50 years.

Let’s use the familiar example of steam power. Practical steam engines were invented in 1712 by the English engineer Thomas Newcomen, but it took nearly a century for this invention to find widespread adoption. Invention is just the first step in technology lifecycle that drives a Kondratiev wave. Newcomen’s early engine was heavy and inefficient, and was used only for pumping water out of mines.

By the early 1800s, advances in metallurgy and cylinder boring allowed the creation of efficient, high-pressure steam engines. In 1829, George Stephenson demonstrated the first practical steam locomotive, kicking off a two-decade long railroad-building boom. Better mining techniques lowered the cost of iron and coal, while railroad barons made immense fortunes and businessmen everywhere benefited from the lowered cost of transport. Tourism, hotels, restaurants, and national markets all owe their origins to the low cost and high speed of rail travel. The demands of financing and administering the new railroads led to new forms of social organization such as the joint stock corporation, dedicated administrators, and new educational institutions such as the Harvard Business School.

But nothing lasts forever, and by the 1870s, all the profitable rail lines had already been created. Competitive pressures and price wars between railroad companies, along with wartime inflation from the American Civil War and Franco-Prussian War, initiated a worldwide long depression. In the United States, prices of basic commodities like grain, cotton, and iron fell by over 50 percent, devastating the earnings of farmers and industrial laborers. Unemployment reached 25 percent in some states, while businesses defaulted on over a billion dollars of loans and multiple banks collapsed. Social unrest exploded with a wave of strikes, including the Great Railroad Strike of 1877. The corrupt machine politics of the time lead to a popular disenchantment with both major parties, laying the foundation for the first progressive movement. The world economy did not recover until well into the 1890s, buoyed by new industries based around electrical power.

Similar patterns can be seen with the other Kondratiev waves, but I would like to focus on the one that we are most familiar with, having lived through it. Computing and information technology have driven unprecedented productivity gains in the U.S. economy and underpinned much of recent growth. The dawn of the computer era can’t be precisely pinned down; good arguments can be made for the creation of ENIAC in 1946 or the integrated circuit in 1959.

But I prefer the mid-1960s, with the first standardized commercial computers, such as the IBM S/360 and DEC PDP-8. Like the steam engine it took a little while for society to recognize the value of a new transformational technology. The astounding growth in Silicon Valley since then has driven innovation around these machines, making them cheaper, more reliable, and more user friendly. The presence of computers, and especially networked computers, changed every aspect of business over the past 45 years, leading to whole new markets and products that could scarcely be dreamed of before, as well as socially transformative access to information and knowledge through computer networks.

The next Kondratiev wave?

Computers are rapidly approaching the point of saturation in many markets. Microprocessors are in every imaginable device, and there are over 4.6 billion cell phone users on the planet. Computer processor and memory manufacturing is a cut-throat business conducted on the slimmest of margins, and while technology keeps improving, at this point, much so-called “innovation” has become about advertising and sales, not fundamental technological breakthroughs. The dot-com bubble and recent financial crisis, which was made possible by complex computerized financial instruments, are two signs that the Kondratiev wave based off of computers may be reaching its peak, and we are now in a period of structural adjustment.

Kondratiev wave theory would posit that the Great Recession cannot be blamed only on complex derivatives, bad mortgages, or greedy bankers, or government deficits, although these are all contributing factors. Rather these are signs that we have reached the limits of our present technology. Escaping it will require a new carrier-branch technology, with all that that entails. I can’t tell you what that technology will be renewable energy, an industrial revolution founded on nanotechnology and synthetic biology, completely recyclable zero-waste products that turn trash into gold, or advances in robotics and artificial intelligence. What is certain, however, is that it will be based on a fundamental breakthrough in science and technology.

The federal government must play a crucial role in that breakthrough. Look to the historical record: The steam power revolution did not begin in England by accident; rather England held an advantage in the core inputs: iron and coal, stemming from the Crown’s casting of thousands of cannons for the Napoleonic Wars. With peace and the loss of their primary market, English ironmongers turned their ingenuity to new products and techniques. Early railroads required an Act of Parliament before they could be built, demanding the active involvement of government, and eventually changes in law that made it easy to incorporate.

In America, the Federal government played a central role in the computer revolution. The SAGE air defense network consumed approximately half of the nation’s programmers and computers in the 1950s, creating an immense base of institutional knowledge that kickstarted the computer revolution. The Internet began as a military project in the Defense Advanced Research Projects Agency, and only later found civilian applications. The Federal government has played a valuable role as the first customer for technologies too risky for industry to invest in.

Beyond the role of a customer, the Federal government can also build the foundations for the sixth Kondratiev wave, by supporting science and engineering and encouraging investment in new technologies. At the heart of America’s lead in science and technology is the human capital of its scientists and engineers. This human capital must be maintained and reinforced, through science, technology, engineering, and math, or STEM, education at the primary and secondary level, visas for skilled workers and innovators, and a world-class system of universities and research centers which can train the next generation of scientists, and attract them to interesting and useful projects. Because the next carrier branch technology is still unknown, and cannot be foreseen, all areas of science and technology should be supported robustly. Program like the Marine’s “Green Company” are a good start, but the government has to be both more creative and aggressive in finding ways to harness the power of the market.

The Federal government represents the interests of all Americans, not just for the next quarter or the next election cycle, but for the next century. Real job creation and prosperity depend on finding new carrier branch technologies to start the next Kondratiev wave sooner rather than later, and finding them in America, not overseas. But it won’t happen if we don’t invest in the building blocks of innovation here at home. With the active participation of the government in crafting forward-looking regulation and laws, funding fundamental research in our universities and national labs, helping innovative technologies navigate the commercialization “valley of death”, and supporting human capital through public science and technology education, the grand project of forging the next carrier branch technology for the 21^st century is within our reach.

Michael Burnam-Fink is a PhD student with the Consortium for Science, Policy and Outcomes at Arizona State University.

To help you understand this psychological phenomenon, I want you to imagine that you have ten years left in your career, and can choose between the following two income streams over those ten years: In the rising salary stream, you would start at salary X, and then receive a steady raise in your salary over the next ten years till you finish at salary X+Y. In the falling salary stream, you’d start right now at an salary of X+Y, and your salary would steadily decline across the ten years to end at salary X. Both choices would leave you with the exact same amount of salary over these ten years, only differing on whether your salary grows over time or declines.

What would you choose?

When I was given this hypothetical choice, I chose the increasing salary stream. I wanted to see my earnings get bigger over time, not watch them fall. Behavioral economist George Loewenstein created this hypothetical choice to illustrate an irrational side of human nature. You see, if I had been rational, I would have chosen the falling salary stream, because in theory I could have invested some of that early money and watched the interest compound over the ten years, leaving me richer at the end of the day than with the strategy I chose. Reason, in my case, lost out to instinct.

Most people, when given this hypothetical choice, are like me and choose the increasing salary. Most of us feel that our incomes are supposed to rise from year to year. The thought of watching our paychecks shrink over time goes against our instincts.

Recent battles over President Bush’s proposal to reign in Medicare costs revealed the power of this instinct. The Bush administration wanted to control costs in part by reducing physician reimbursement. On one level, the political battle was straightforward: A powerful interest group (physicians) stirred up fears to enrich itself. But on another level, the battle was more subtle. Physicians in this case weren’t after more money. They were trying not to lose money.

Daniel Kahneman won the Nobel Prize in part for elucidating people’s powerful psychological desires to avoid losses. (He conducted much of this work with Amos Tversky, who unfortunately died before the Nobel was awarded.) Kahneman and Tversky showed that people feel much worse about a modest loss than they feel good about a similarly-sized gain. In fact, people look more favorably upon a surgical intervention with a 90 percent survival rate than on one with a 10 percent mortality rate, even though there is no difference between the interventions. The mere framing of a choice as potentially involving a loss (of money, of life) triggers strong negative emotions. Human beings are hardwired with an aversion to losses.

Here’s the challenge raised by this psychological phenomenon: If we as a society want to control healthcare costs, one of the things we need to do is control physician income, which in the United States dwarfs that of most other advanced countries. (The most egregious physician incomes are generally earned by procedural specialists, with other groups, such as primary care pediatricians, receiving much more modest incomes.) But if we threaten to reduce physicians’ incomes, we will face tremendous resistance. Psychological resistance. Physicians won’t want to see their incomes go down.

The only way to control physicians’ income is to allow their income to grow over time, while controlling the rate of growth so that physicians’ incomes fall in relation to inflation. An oncologist making $400,000 last year (yes, that’s a pretty common income for such physicians) will probably fight aggressively to keep someone from reducing his income to $395,000 this year. Now, if through a new Medicare plan his income grows 1 percent this year, a rate significantly less than inflation, he will make $404,000. Psychologically speaking, he will feel like his income is growing, and he will probably be less likely to become politically active fighting the plan.

There’s another reason people fight hard to keep their incomes from declining: they often buy homes based on their income expectations. I’m not asking anyone to feel sorry for an orthopedic surgeon who, due to a loss of income, is forced to downsize from his 6,000 square foot home. But I am asking you to imagine yourself in that person’s shoes: you’ve bought your dream house and now, because of a shift in Medicare policy, you may have to move to a smaller house. How hard will you work to maintain your income? Will you donate money to a lobbying organization that will fight the Medicare plan? Will you buy a radiology practice, so you can start making money on your patients’ x-rays? Will you start operating on patients more often, including on elderly people who, in the past, you would have referred to a physical therapist?

Any politician who wants to change the healthcare system should heed the psychological power of loss aversion. Not to do so is a recipe for failure.

Peter Ubel, MD is the director of the Center for Behavioral and Decision Sciences in Medicine at the Ann Arbor VA Medical Center and the University of Michigan. His book Free Market Madness: Why Human Nature is at Odds with Economics—And Why it Matters (Harvard Business Press) is being published in January 2009.

John Marburger has an impressive title: science adviser to the president and director of the Office of Science and Technology Policy. But his predecessors had a slightly different title: assistant to the president, the highest rank of staffers within the Executive Office of the President. Much has been made of President Bush’s decision to appoint a science adviser to a diminished post, but the move resonated with the administration’s repeated maneuvers to downplay, disregard, or launch all-out assaults on science over the course of the past eight years.

But on the eve of a new administration, it’s time to look forward and think about the scientists who will advise the next president. The National Academies have just weighed in with their take on the issue, offering a report detailing the most critical presidential science appointments in the executive branch and ways to streamline the process of getting new hires into their posts. Their first recommendation, however, is to hire the top science adviser at the level of assistant to the president:

White House leadership in science and technology requires three steps. Immediately after the election, the president-elect should identify his candidate for the position of Assistant to the President for Science and Technology (APST). This individual will provide advice, identify and recruit other science and technology presidential appointees. After inauguration, the President should promptly both appoint this person as APST and nominate him or her as the director of the White House Office of Science and Technology Policy (OSTP). The director position should be cabinet-level, with an office in the Old Executive Office Building.

Many of the most pressing matters of the new administration will require forthright scientific advice, and only through an assistant to the president who can participate in cabinet-level discussions and coordinate with other senior staffers in economic, domestic, national security, and energy policy will the next commander in chief get the advice that he needs. NAS is not the first group to argue that the science adviser post must be elevated back to the assistant level.

Moreover, NAS recommends that the president not dawdle on the matter of the thousands of other appointments across the administration. That means getting to work well in advance. Like today.

Who do readers think the next president should appoint as the top science adviser?

AP

Don’t forget the trucking: One of the major blind spots in calculating a company’s total greenhouse gas emissions is not counting supply chain inputs.

Corporations typically underestimate their carbon footprints by an average of 75 percent, according to a new study from Carnegie Mellon researchers. One of the major blind spots is in calculating the total greenhouse gas emissions from myriad supply chain inputs, as opposed to the direct emissions involved in primary operations. The study authors told Christa Marshall at ClimateWire (subscription) that “most companies focus solely on direct emissions from their operations, such as those spewing from headquarters and accompanying facilities, and from purchased energy.”

As a result, study author H. Scott Matthews suggested that “most companies were calculating ‘toe prints’ rather than carbon footprints.”

The study indicates that another problem is that the term “carbon footprint” itself does not have a fixed meaning:

The definition of “carbon footprint” is surprisingly vague given the growth in the term’s use over the past decade. The term itself is rooted in the literature of “ecological footprinting” (3): attempting to describe the total area of land needed to produce some level of human consumption. Because the land use to make most consumer products is fairly distant in time and space from the final consumer, the ecological footprint is inherently a full life-cycle calculation. However, this does not seem to be true for the term’s new successor, the carbon footprint; Wiedmann and Minx (4) found a large variety of definitions that differ in which gases are accounted for, where boundaries of analysis are drawn, and several other criteria.

The researchers have developed an “Economic Input-Output Life Cycle Assessment” that utilizes publicly available data on various economic sectors. But they are careful to note that any lifecycle GHG calculation will be imperfect, regardless of model or input data. Moreover, the accuracy of a company’s calculations hinges on the firm’s own accountability:

We argue that the footprints should be useful in pursuing more effective climate change policies. However, the information contained in a carbon footprint varies depending on how it was calculated and how much responsibility the entity being “footprinted” is willing to take on. There is an inherent trade-off between comprehensiveness (i.e., the percentage of total world GHG emissions included in a system) and participation (i.e., the percentage of businesses or consumers taking part in the system). Because consumers can influence the carbon footprints of goods and services through their purchase decisions, a broad estimation of carbon footprints including supply chain effects is appropriate. Similarly, as a corporation can influence its suppliers, a broader estimation can similarly motivate more effective corporate climate change policies

Modeling lifecycle carbon emissions is not a trivial matter. The Environmental Protection Agency is currently working with a variety of complex models as they move forward with the rulemaking process for the Renewable Fuels Standard. GreenWire reports that the four major consulting firms are all moving to help corporations with their emissions calculations. Having accurate, well-understood models will be critical for many industries when Congress and the next administration move ahead with comprehensive climate change legislation.