Brave New Math

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From the Spring 2012 Speaking in Tongues issue 

By Peter Marber

In 1968, at age 56, my grandfather had a heart attack. It surprised a lot of people. With a full head of hair, he was thin, youthful looking, and rarely sick. He received standard patient treatment for the time—prolonged bed rest and morphine. When he recovered, he continued his pre-attack lifestyle which included smoking, almost no exercise, and a diet of meat, potatoes, and my grandmother’s cream pastries. Five years later, he was dead, the result of another heart attack.

Had my grandfather been born a quarter century later, he surely would have lived longer than 61 years. Scientific advances in the last 40 years have greatly improved the prevention, diagnosis, and treatment of heart disease both in the United States and abroad. In the 1960s, the chance of dying within days of a heart attack was almost 40 percent in the U.S. By the 1970s, it dropped to 25 percent, and in the 90s, it fell to under 10 percent. Today, it’s about 6 percent. The field of medicine has advanced to provide us with early detection of heart disease, statins that reduce bad cholesterol, advanced coronary angiography to diagnose potential blockages, and reliable bypass surgery. We know that diet, exercise, and healthy lifestyle choices can also reduce heart disease risks, and new research continues to improve prevention and treatments.

In some respects, the fields of medicine and economics have much in common. Both are multidisciplinary fields that strive to improve and maintain the health of complex systems. But unlike medicine, economics hasn’t progressed much in the last 40 years. In late 2008, the United States and many other countries suffered a major economic heart attack that might have been prevented by better diagnostics. Today, more than three years later, societies on every continent appear to be recovering, though many still face the threat of relapse. The reason so much of the world is on edge, with many countries still on life-support, is that governments have simply been prescribing the equivalent of economic bed rest and morphine (low interest rates and some fiscal stimulus) without any significant lifestyle changes.

Yet there are better, more sophisticated treatments that should be prescribed—new sets of statistical indicators to help monitor economic health, as well as fresh policies based on new numbers that can help diagnose and treat these ailments to the principal organs of our fiscal well-being. Traditional measures point to an American economy that’s up even when Americans are feeling down. Across Europe and in Japan, there is also a sense of confusion over current economic directions—a universal sense that the numbers that have been our staples are increasingly meaningless to everyday people.

Newspapers, radio, and television routinely spout headlines about key statistics on GDP, inflation, and employment—astonishingly influential indicators computed in the United States by the government’s Bureau of Labor Statistics and in capitals around the world. Most seem to have little correlation with the realities on the street. Yet, governments, businesses, and individuals still use these yardsticks in their decision-making worldwide, and minor revisions in the data can have major ramifications. Inflation measurements help determine mortgage and savings rates, stock market prices, interest payments on the national debt, and cost-of-living increases for wages, pensions, and Social Security benefits. Despite dramatic shifts in the world over the last few decades, we are still using the same old gauges, nomenclature, and policies of the past. These outmoded statistics skew perceptions, leaving us with a distorted worldview and a shaky foundation as a base for policy.
If we can’t accurately diagnose the problem, we won’t cure it.


The world of 2012 is so fundamentally different from 30 to 40 years ago that traditional, commonly held economic views and perspectives seem downright quaint today. Economically, the world of the early 1970s was a patchwork of inward-focused economies, with most goods domestically made and sold, together with small quantities of cross-border trade in finished products between 20 or 30 countries. We forget that back then, much of the world operated under some communist or socialist model. Even in the United States, trade comprised less than 10 percent of the economy. The widespread abandonment of socialist and isolationist policies since the mid-1980s in favor of global trade and investment—plus new information technologies—has ushered in the first truly global era where goods, services, capital, talent, and ideas move across borders faster than ever before. Over the last generation or two, the world has been transformed into a complex system of interdependent and constantly changing relationships. Global production and distribution chains link Brazilian iron mines, Greek ships, Chinese steelmakers, German automakers, Wall Street banks, and car dealers in Peoria. Financial markets instantly entangle California pension funds, insurers in Asia, and Cayman Island hedge funds with banks everywhere.

Yet we are still using methods of a simpler past to measure, diagnose, and direct our economy today. The most widely used and closely tracked of such metrics is the Gross Domestic Product, created in the 1930s when congress asked a young University of Pennsylvania economist Simon Kuznets to develop a uniform set of national accounts. The intention was to help government officials get a grasp on Depression-era economic realities. These stats became the prototype of the GDP—the premiere measure of economic well-being the world over. GDP, defined as the total market value of all final goods and services produced in a country in a given year, has permanently changed how we look at public policy. There was some genius in Kuznets’ simple, easy-to-understand statistic. Previously, economists had rarely been consulted on public policy, but equipped with powerful new statistical tools, they have become the policy authorities of the postwar era.

Even its creator, however, realized the limitations of GDP. In 1934, Kuznets warned, “the welfare of a nation can scarcely be inferred from measurement of national income.” He wrote again in 1962, “distinctions must be kept in mind between quantity and quality of growth, between its costs and return, and between the short and the long run.” In other words, GDP and its components can and do give us a measure of how much we produce and consume—but reflect none of the qualitative aspects of the economy. GDP cannot answer such essential questions as whether we are consuming too much of the wrong things or saving too little. To any government statistician tallying GDP, $100 spent on textbooks is sadly no more valuable to society than $100 spent on cigarettes. Americans spend more than $80 billion on smoking each year and an estimated $160 billion on the health care costs related to smoking-induced illnesses. Together that’s about 1.5 percent of American GDP—nothing to boast about. Debt also boosts GDP in the short run by stimulating consumption but could curb future growth when both governments and households have to pay it back and spend less. Consider the over $5 trillion in new U.S. government borrowings with interest since 2000.

GDP as a statistic may have fallen victim to the phenomenon of Goodhart’s Law. Devised by an adviser to the Bank of England in the 1970s, the law states that as soon as an indicator is relied upon for policy decisions, it stops being effective. For example, the police can reduce the rate of shoplifting by diverting more resources from other crime-fighting activities. Shoplifting rates go down, but other crime rates go up. As a result, shoplifting becomes a useless indicator of overall crime trends. In this respect, when a particular yardstick like GDP is used as a performance indicator of a complex system—like a national economy—the government may choose to target the measure, improving its value but at other costs to the country. As such, GDP may improve, but it becomes less useful as a measure of the broader economy and national well-being.

While the limitations of GDP have since been echoed by many prominent economists including Nobel laureates Joseph Stiglitz and Amartya Sen (whose landmark 2010 report included dozens of important socio-economic measures drawn from the developing world), there has been little change in the obsessive overreliance on GDP as the primary economic barometer. And if GDP was an unreliable indicator in the pre-globalized world, it is woefully misleading today. Increasingly, understanding the quality of GDP and its composition, especially the weighting of its four constituent parts—consumption, government spending, investment, and net exports—is most important to our long-term national health. Yet few governments have managed to divorce themselves from the simple GDP figure, regardless of how irrelevant it has become.


GDP is not the only statistical fetish in global economics. Employment-related figures, too, are a point of obsessive attention. The unemployment rate is supposed to convey how many people are employed in the workforce, but it says nothing about the quality or security of such jobs. Without further research, the headline number used by policy-makers and lay people alike is rarely qualified. From the unemployment number alone, it is impossible to know whether true progress is being made as a society.

Simple unemployment numbers may have been informative in the past, but societies have changed dramatically since 1980. Automation and globalization have eliminated many American factory jobs and are even eliminating others in places like China, which are losing out to lower cost countries like Bangladesh and Vietnam. But the United States. has posted unemployment rates below 5 percent for the majority of the last 15 years. Nobel-winning economist Michael Spence suggests America’s employment “success” was actually the replacement of some 10 million manufacturing and export-related jobs with low-wage, low skilled service jobs like construction workers, interior decorators, or paint department managers—domestic jobs that cannot be outsourced to lower-cost labor markets. As soon as the economy took a hit in 2008, these were the first to go, because they weren’t central to consumer needs.

But stepping back from the job quality issue, a far greater failure is our inability to understand the fundamentals that enter into the employment rate—in the United States or abroad. Indeed, the real unemployment rate in the United States is likely far higher than the official figure. This confusion is rooted in an ever-changing definition of the eligible labor pool. In fact, America’s Bureau of Labor Statistics actually computes six different unemployment figures with varying definitions, with most people looking at the lowest headline number. The United States is not alone in these idiosyncrasies. While all OECD countries are supposed to use the International Labor Organization definition for “unemployment,” most create their own versions.  

On the surface, calculating the unemployment rate should be straightforward—divide the number of unemployed workers by the total labor force. However, defining an “unemployed worker” and the “total labor force” is necessarily an imprecise task. The way the BLS calculates these numbers­ – relying heavily on phone surveys of a few thousand people and self-reported data selection – is old school in the Information Age. Rather than create knowledge about labor, BLS employment numbers – and the underlying methodologies in calculating them – lead us to ask even more questions about jobs in America.

For example, let’s say there are 100 eligible workers, and five can’t find jobs—that’s simply 5 percent unemployment. One year later, the economy hits a rough patch and five more people lose jobs. Now we have 10 percent official unemployment. But let’s assume that of the original five unemployed people, three stop looking for work – they're discouraged. The way government statisticians adjust for this is to reduce the total labor force by three to 97. Official stats now calculate a labor force of 97, with seven more unemployed, dropping the “unemployment rate” to 7.2 percent. 

According to government statistics, if the same number of Americans were job-hunting today as in 2007, the official unemployment rate would be more than 11 percent, not the official rate of 8.3 percent released in early 2012. The labor pool has been reduced by the so-called “discouraged” workers who permanently drop out of the official numbers. Logic tells us more “discouraged workers” are a bad sign for any economy. Yet such a practice actually makes the official unemployment rate look better. In Japan, the historic practice of companies keeping idle employees on the books versus outright firing them is believed to depress unemployment rates substantially. Some economists believe the real rates may be as high 12.2 percent compared to the current “official” rate of 4.6 percent.

Even with headline 8.3 percent unemployment (or higher, unofficially), most Americans would be surprised to learn that the United States has labor shortages today. A 2011 Manpower Group talent survey found that that 52 percent of 1,200 key employers are experiencing difficulty filling mission-critical positions, up from 14 percent in 2010. The number of American employers struggling to fill positions is at an all-time survey high despite high official and unofficial unemployment rates. This tells us something very unflattering about our workforce,  and the United States is not alone. In the Manpower survey, Japan—with its low birth rates and near-zero immigration—reported a survey-high 80 percent of their companies couldn’t fill needed jobs followed by India and Brazil. The United States ranked seventh in the survey.


It’s difficult to quantify national competitiveness using traditional government calculations of productivity, created by statisticians who simply divided GDP by hours of employment. Headline productivity has been growing for the last couple of decades, but there is mounting evidence that something is off here, too. Many critics suggest that shifts in global companies’ worldwide sourcing formulas designed to take advantage of lower costs—the very essence of globalization—are incorrectly captured in official statistics of any single developed country, especially the United States. Unfortunately, government statistical groups are ill equipped to deal with the offshoring or global supply chains that characterize the 21st century economy.

Michael Mandel of the Progressive Policy Institute and Susan Houseman of McKinsey & Co. suggest that increased productivity figures may be attributed to sources other than a more efficient national workforce. Let’s say Ford Motor Company normally sources one million car parts from an American supplier at $10 per part, or $10 million. In scenario one, Ford reengineers its production process, reducing the parts it needs by half, dropping its cost of goods to $5 million—effectively a productivity gain. Alternatively in scenario two, if Ford can simply source the $10 part for $5 from China, the costs of goods also drops to $5 million. In either case, Ford’s productivity goes up (sales minus the cost of materials), as does its profitability (sales minus cost of labor and materials) and measured productivity (value-added per worker). These two scenarios are virtually indistinguishable in productivity statistics. But while neither scenario helps the employment picture, one shows a country getting better, and one doesn’t. In a globalized economy, statisticians cannot realistically track all these underlying data streams to truly give us an accurate view on labor productivity or competitiveness. Such challenges bedevil most advanced economies.


Another touchstone of economic health is international trade, but again, global supply chains heavily distort trade statistics and our notion of whether our tradable economy is doing better or worse. According to the World Bank, some two-thirds of international trade is now in so-called intermediate goods—a component that goes into another product like a hard drive that goes into a computer. Global supply chains lead to segmented production processes across borders and create distinct challenges for measuring and understanding our economy and international interdependence.

Distortions can emerge at each step of the manufacturing or distributing process. First, conventional trade statistics count the gross dollar value of goods crossing each border, rather than the net value added. This is a common double-counting problem whereby the full values of the import and the export overstate the domestic value-added content of exports. For example, when China imports $143 worth of intermediate parts for an iPod, assembles them, then exports the finished iPod to the United States for $150, China officially registers $150 in exports. But the value-added component of the Chinese export is only $7. Some economists estimate that the import content of exports is 15 percent to 20 percent in countries like the United States and up to 50 percent in heavy manufacturing countries like China. So the more expensive the imported content, the more distorted trade may become. In this respect, many lower-end emerging markets may actually be less of a trade threat than more advanced countries. We might think we run a large trade imbalance with China, for example, because we run a huge headline deficit with that country. But our true value-added trade volume with China is probably lower than our politicians would have us believe. In this case, the reality is better than statistics suggest, but this deceptive data feeds the overheated anti-China rhetoric of this election year.


Inflation is another central measure of our economic health—helping adjust prices to suggest whether true purchasing power, hence wealth, is rising or falling. Calculating a Consumer Price Index (CPI) helps us deflate nominal GDP increases caused by rising prices. This is our first problem with inflation—if not correctly calculated, then real rates of GDP growth are probably miscalculated too. If CPI is rising, GDP would have to be lowered. So there is a perverse incentive to keep inflation low to help growth look better because so many elements of advanced economies hinge on GDP expansion or contraction—interest rates, stock market multiples, inflation-linked benefits.

Most countries price a basket of goods each month to track CPI. However, this basket is composed differently from country to country. In emerging markets, food often makes up 50 percent of the basket, while in most wealthy countries, it’s less than 15 percent. This means inflation rates in one country really can’t be compared to another’s without some serious analysis. Moreover, Washington statisticians make what are called hedonic (from hedonism or pleasure-related) adjustments to the CPI to reflect improvements that go into certain goods. If a 27-inch flat screen costs $500 in year one, but the following year a 30-inch model comes out at the same price, hedonically the price of TVs is declining, because you are getting more for the same money. This would register as deflation in terms of official inflation statistics. But statisticians are doing this on a myriad of goods including complex items like housing and medical care, where quality may be more challenging to quantify than the size of a TV screen. All told, a large amount of subjectivity goes into determining the official inflation rate. Considering the enormous impact small CPI changes can have across the world, it seems odd that there is so little transparency in these calculations. But for anyone living in the United States, college education, housing, and health care costs have been exceeding inflation rates for years. Since 1986, official inflation has risen overall by over 105 percent, but average American college tuitions have risen nearly 500 percent in the same period. Since two years of college is now essentially a prerequisite to earn a median U.S. income, a case could be made for having raised its slim 3.2 percent basket weighting—a legacy from decades ago. But the result would probably be a sharper increase in inflation, triggering higher interest rates—not a result much sought after by the government or Wall Street.


An obsessive focus on GDP expansion, coupled with misleading numbers on employment, trade, productivity, and inflation, may suggest a revisionist narrative.

The United States grew rapidly after World War II while Europe and Japan were reindustrializing. Americans bought and built houses in suburbs while domestic factories were manufacturing most of the objects that filled their homes. New roads connected these suburbs, and the cars that filled them were made in Detroit. But by the 1960s, Japanese imports arrived—bringing cheaper goods, cars, and foreign oil. We bought more, and official GDP hummed along. Employment was slowly shifting from manufacturing—a decline that began in the 1950s and accelerated into the 1980s—to retail and service. Jobs in the booming housing sector—from contractors to decorators to mortgage bankers to Wall Street securitizers—replaced old factory work. America’s focus was increasingly inward at the very moment the world was globalizing. A steady decline in American interest rates and a belief that monetary policy could control the economy’s heat like a perfect thermostat kept asset prices high and the American dream within reach. Globalization and complex supply chains concealed shaky statistics regarding the quality of American life. Government and households borrowed and spent more, fueling GDP growth, but left us with huge debts to repay. Similar traits were exhibiting themselves in Western Europe, flowing outward to the continent’s fringe as eastern and central European nations exited the communist economic world and entered the capitalist community. All of our economic doctors told us we were fine and to keep doing what we were doing, but the heart attack was looming. The crash in late 2008 woke America and the world to the vulnerabilities of our overly domestic-focused economy in a world growing intensely competitive. Economists tell us we’re regaining our health, but many in America and abroad, are still feeling a lot of pain.

If economists begin to consider what progress really means in the modern world, then new ways of measuring, analyzing, and gathering data may more accurately reflect our holistic well-being. With a clearer and more realistic picture, better policies could be crafted to prevent future economic heart attacks.

There are already important strides in this direction, particularly in understanding progress beyond GDP growth. The UN’s Human Development Index is a single statistic that measures health, education, and living standards, with yearly country rankings. Similarly, the Organization for Economic Cooperation and Development developed the Better Life Index—a composite of 11 broad topics that include housing, income, and jobs as well as quality of life (community, education, environment, governance, health, life satisfaction, safety, and work-life balance). The Index already covers the 34 OECD member countries with plans to expand to its partner countries including China, India, Indonesia, Russia, and South Africa.

Some countries are designing their own national indexes to measure well-being. The UK is developing an index that not only measures economic performance of the country but also takes into account environmental and sustainability issues. Similarly, Canada has adopted something called the Genuine Progress Indicator, which starts with GDP but adjusts for negatives and economic regrettables like health care and law enforcement. In 2005, the tiny Himalayan nation of Bhutan developed the Gross National Happiness index, which takes into account health, culture, education, ecology, good governance, community vitality, and living standards—a broad way of assessing progress beyond pure GDP growth. Ron Inglehart, a pioneering social scientist at the University of Michigan, has produced his World Values Survey for almost 30 years, covering more than 40 countries, with dozens of questions that help construct an index of subjective well-being that reflects happiness and general life satisfaction.

Unlike other areas that have become multi-disciplinary, economics has been cloistered and slow to evolve. But increasingly, academics from a diverse range of social and hard sciences are seeking to understand and explain economic phenomena in new ways. Urban planner Richard Florida, for example, argues that governments should cultivate concentrated economic activity in cities or larger “megaregions” versus watered-down national efforts. Florida has identified some 40 global megaregions in advanced and emerging markets that comprise less than 18 percent of the world’s population but account for two-thirds of global economic activity and more than 83 percent of scientific research and patent innovations. Florida believes that these megaregions have been successful at attracting and cultivating his “3Ts”—talent, technology, tolerance—which appear to foster innovative, sustainable economic activity. He’s created several new indexes trying to capture data ranging from urban light emissions to scholarly scientific publications, patents, gay and artisan populations, and education levels that correspond to creativity, economic activity, and increased productivity.

Physicist Geoff West of the Santa Fe Institute is doing similar studies of urban economies, looking into what he calls “economic metabolism” and its potentially accelerating factors. Like Florida, West finds human creativity, innovation, and problem solving abilities at the core of such growth. In this vein, Parag Khanna has suggested indexes to rate city or megaregion competitiveness on a statistical basis by quantifying their infrastructure, how many multinational corporations and start-ups they host, and educational density. Khanna believes such rankings would help cities or megaregions know where they stand comparatively and help shape policies for improvement.

Private sector initiatives are also working to improve existing economic indicators. For years, the payroll processing company ADP has generated monthly payroll data on 23 million working Americans that helps illuminate labor trends. produces the Monster Employment Index, a multi-country monthly compilation of recruitment data. Because recruitment typically precedes actual hiring by a month or two, the index is considered an interesting forward-looking barometer of the labor market and overall economy. Google has created the Google Price Index, an alternative to official inflation statistics. It uses a database of real-time Internet shopping figures, measured on a daily basis as opposed to official figures, published at least a month after the period they cover. Economists are also using digital data from sites such as Twitter, Google, and Craigslist to gauge economic performance by measuring unemployment and home sales. These sources—as opposed to the official sample surveys taken once a month—may prove more accurate. As the world completely digitizes, one would hope valuable real-time data could be mined in ways statisticians couldn’t have imagined even 30 years ago.

Finally, a number of neurological studies suggest how humans are hardwired to respond to relative progress—becoming upset even while gaining if they see others gain more. This may in part explain the Occupy Wall Street movement, which has been adopted far beyond its origins in New York’s Zuccotti Park, spreading across the United States and much of Western Europe. People get upset when they see huge income disparities. Governments in a broad range of countries need to look increasingly at broadening and diffusing income creation, in contrast to simple, gross creation of wealth, to counter the more pernicious trends of the last generation.

By going beyond simple GDP and looking at a diversity of timely data we can better diagnose our economic health. In the global age, new economic thinking needs to be oriented around developing human capital, not blindly stoking GDP through low interest rates that encourage buying bigger houses filled with more foreign-made goods. By digging deeper into trade data and devising a truer statistical picture of labor, productivity, and employment, one can determine what human capital should be cultivated to remain competitive, returning an economy to a healthier state. And as Florida notes, maybe the fixation on a “national” economy is too obtuse. Perhaps one should focus policies on cultivating smaller megaregion engines that tow the broader country. The field of economics needs to look no further than one of its greatest patron saints, Joseph Schumpeter, whose life work underscored that capitalism can only be understood as an evolutionary process of continuous innovation and “creative destruction.” For everyone’s sake, let’s hope economists, too, can rid themselves of their dependence on old data and develop new, more sophisticated metrics to keep the world economy healthy.



Peter Marber is a professional money manager and faculty member at Columbia University. He is the author of Seeing the Elephant: Understanding Globalization from Trunk to Tail (2009) and the forthcoming Brave New Math: Why We Need New Economic Thinking in the Global Age (2012).

[Photo: Andres Musta]

(PDFs of World Policy Journal articles can be purchased through SAGESubscribe to WPJ here)


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