The Leading Indicators: A Short History of the Numbers That Rule Our Worldby Zachary Karabell
How did we come by the “leading indicators” we place such stock in? We allocate trillions of dollars and make public policy and personal decisions based upon them, but what do they really tell us?
“The leading indicators” shape our lives intimately, but few of us know where these numbers come from, what they mean, or why they rule the… See more details below
How did we come by the “leading indicators” we place such stock in? We allocate trillions of dollars and make public policy and personal decisions based upon them, but what do they really tell us?
“The leading indicators” shape our lives intimately, but few of us know where these numbers come from, what they mean, or why they rule the world. GDP, inflation, unemployment, trade, and a host of averages determine whether we feel optimistic or pessimistic about the country’s future and our own. They dictate whether businesses hire and invest, or fire and hunker down, whether governments spend trillions or try to reduce debt, whether individuals marry, buy a car, get a mortgage, or look for a job.
Zachary Karabell tackles the history and the limitations of each of our leading indicators. The solution is not to invent new indicators, but to become less dependent on a few simple figures and tap into the data revolution. We have unparalleled power to find the information we need, but only if we let go of the outdated indicators that lead and mislead us.
How did we get to the era of Big Data? Karabell, president of River Twice Research, a political and economic analysis firm, mines little known tidbits in the history of economics to explain how individuals, companies, and countries came to rely on statistics like unemployment, inflation, and gross domestic product to describe the wealth of nations—and why these traditional concepts may no longer be up to the task. Statistics about working people during Industrial Revolution fueled the labor movement, while Great Depression put terms like "unemployment" into the everyday lexicon of Americans. Yet these one-size-fits-all indicators can't really handle the intricacies the 21st century global economy. A low national unemployment rate means little to jobless people in states where higher rates prevail, nor can it predict events like the reelection of a president. Karabell proposes crafting "bespoke indicators" that harness unique data sets that users can deploy to answer questions about economic life. This slim, entertaining volume also unpacks the contributions of a host of colorful, if obscure, individuals who contributed to the field. In Karabell's hands economics is no longer "the dismal science." More storyteller than analyst here, he succeeds in livening up how "the economy" came to be for the general reader, minus the complex jargon and blizzards of numbers that can mar such books. (Feb.)
non-economists trying to make sense of the barrage of numbers with which they're pelted on a regular basis.”
nonsense nor conspiracy. Most people could read this book with enjoyment and profit.”
Our leaders regularly agonize over unemployment figures, the consumer price index, gross national product and the balance of trade. These and other leading indicators are important but also overrated, writes journalist and Reuters "Edgy Optimist" columnist Karabell (Superfusion: How China and America Became One Economy and Why the World's Prosperity Depends on It, 2009) in this lucid measurement of how the United States is faring. Censuses date from ancient times, but it was not until the mid-19th century that the industrial revolution forced a search for data to make sense of an increasingly complex "economy," a word that did not appear until that time. Governments paid little attention until the disaster of the Depression galvanized them to measure how bad things were and then place great faith in the results. Gross domestic product, the value of a nation's goods and services, became a proxy for its success. Benefits, wages, rents and raises are often pegged to the consumer price index. Everyone knows that a positive balance of trade is good and a negative balance is bad. Inevitably, these numbers became a referendum on whether people were happy and led to an index of consumer confidence and then to the human development index, which combines income, health and education to gauge a nation's genuine well-being. Karabell emphasizes that indices measure what they were designed to measure. All exclude great swatches of life (GDP omits household work, cash transactions and free Internet services such as Google). It's a mistake to use them as mirrors of reality instead of modestly helpful tools. "Our questions need to be specific," he writes, "and answers must be bounded by a sense of how to parse information, but the result should be a welcome liberation from ‘the economy' defined by our leading indicators." Readers of this intelligent introduction to iconic economic indices will agree that Karabell makes an excellent case.
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The Leading Indicators
What if I told you that many of the assumptions we make about our economic life are wrong? What if those assumptions shaped our domestic economic policies? What if they determined core aspects of our international strategy? What if they bolstered the deep and intractable funk that seized the developed world after the financial crisis of 2008–09? What if indeed.
We live in a world defined by economic numbers. We assess how we are doing personally and collectively based on what these numbers say. How fast our country is growing economically or how slow, how much prices are increasing, how much income we have, whether we are employed—these numbers rule our world. We treat our economic statistics as absolute markers of our success or failure. None of these numbers, however, existed a century ago. Most of them didn’t exist in 1950. Yet we enshrine them almost as laws of nature.
Take two recent examples: in 2012, the unemployment rate was a central factor in the US presidential election. It was widely reported that no president had ever been reelected with an unemployment rate more than 7.2%. The monthly release of the unemployment report became one of the most watched events that summer and fall, and each new number ushered in assertions that the economy was recovering and accusations that it was not. Through election day, the rate never dropped to that supposedly portentous 7.2% level, and was hovering close to 8% when Barack Obama was reelected. Obama’s victory had seemingly broken with a strong historical pattern. But did it? The answer is no, for reasons that will become clear in these pages. Our sense of probability and likely outcomes was wrong. How we came to place such stock in these numbers—and what to do now—is the subject of this book.
The other example is a widely accepted “fact” that has dramatic social and political consequences: the trade deficit between the United States and China. Few issues have weighed more heavily than this gap, and it has created substantial tension between the United States and China at least since 2001. Regardless of political party, Americans have decried unfair Chinese trade practices, the undercutting of American wages and manufacturing jobs, and the negative effects of the relationship on the global financial system. But what if the actual size of the trade deficit is significantly less, or perhaps even nonexistent? That may seem an outlandish question, but it is not. We rely on trade numbers compiled every month by the government, and those numbers tell us that there is a deficit. As we shall see, however, the world these statistics say we are living in and the one we are actually living in often diverge; the world we are living in is not the one that these statistics depict.
Every day we are showered with economic statistics such as GDP, unemployment, inflation, trade, consumer sentiment and spending, the stock market, and housing. This suite of statistics intimately shapes our perceptions of reality. We now refer to them as our “leading indicators,” and they are thought to provide key insights into the health of the economy. But they measure only what they were designed to measure at the time they were invented. The world, however, has not stayed the same.
Just how much it has changed was brought home in the middle of 2013. You may not have noticed, but one day in 2013, the US economy grew by $400 billion overnight.
That wasn’t because of normal economic growth. After all, given that the gross domestic product (GDP) of the United States is in excess of $16 trillion, even at a modest clip it will get hundreds of billions of dollars larger each year.
No, the reason for that boost was not a sudden surge of activity. One day, those billions just appeared. And not only just appeared, but apparently had been there all along. On July 31, 2013, the US Bureau of Economic Analysis (BEA), which is the government agency responsible for calculating the size of the US economy, announced that it had shifted the way it measured national output. The result was a $400 billion adjustment.
Given the language used by the agency in describing the revision, you could be forgiven for missing the import. Months before the official new number, the BEA had announced the change. But few of us sit up and take notice when greeted with this headline: “Preview of the 2013 Comprehensive Revision of the National Income and Product Accounts: Changes in Definitions and Presentations.” The subsequent official announcement in July was hardly catchier. In its bulletin describing the new methodology, the BEA stated that it would now include “creative work undertaken on a systematic basis to increase the stock of knowledge, and use of this stock of knowledge for the purpose of discovering or developing new products, including improved versions or qualities of existing products, or discovering or developing new or more efficient processes of production.”1
This inelegant prose masked a profound shift in the way that we understand the economy. Until the Great Depression, no country measured its national output. The global economic crisis of the 1930s led to efforts in both the United States and Great Britain to develop statistics that would provide some clarity about what was going on. National income and GDP were two of the most important statistics to emerge from that era. By the middle of the twentieth century, countries everywhere were using these numbers.
The world those numbers measured, however, was very much a world of nation-states making stuff. Economies were based on the output of goods, on manufacturing, farming, and production. In the decades since, however, the nature of the United States and many other economies has changed dramatically, away from manufacturing and toward services; away from making stuff in factories to inventing ideas.
For many years, the keepers of these statistics recognized that ideas and intellectual property are central to today’s economies. When the numbers were created, however, the decision was not to include activities such as research and development (R&D) as part of national output. That meant that until the BEA announced its shift in 2013, the billions spent by a pharmaceutical company to develop new drugs to save and improve lives were treated simply as an expense rather than as an investment that could yield massive future returns. When a company bought a robot for a factory, it counted as part of GDP. When Apple spent a fortune to develop the iPhone, it didn’t.
Also uncounted had been many of the creative endeavors that go into television shows, movies, and music. By adding up all of these investments—the money Lady Gaga spends writing songs, the amount Apple spends on the next iPad, the amount Pfizer invests in a new medicine—the BEA found that it had been underestimating the size of the US economy by $400 billion, an amount larger than the GDP of more than one hundred countries.
Our indicators have become so intimately woven into our lives and our sense of what is going on around us that we forget that for most of human history, there were no economic indicators, and without those numbers, there was no “economy.” Now, the “economy” is a central factor in our lives. The financial crisis of 2008–09 cemented that fact. The primary way that we relate to the economy is through numbers, through statistics that are released regularly by the government, by industry groups, and by companies. The leading indicators are a data map that we use to navigate our lives.
So when the agency responsible for maintaining key elements of that map decides to redefine one of those numbers, it alters our perception of reality. Lost in the verbiage of the Bureau of Economic Analysis bulletin announcing that $400 billion “adjustment” was the fact that these changes shape how we assess our lives, collectively and individually. While most of us pay little attention to the waves of economic numbers that come at us day after day, few of us are immune to the effects of this wave of data. We are inundated by economic statistics, and there is hardly a country in the world that does not mark its success or define its failings by what these statistics tell us.
Not only do US presidential elections now hinge on what those economic statistics say, but all of Europe has been locked in a downward spiral because of economic policies that are based on the relationship between debt and GDP. And, of course, there is China, whose ruling Communist Party sets targets of economic growth that become the party’s claim to legitimacy. Leaders everywhere trumpet strong economic statistics, and challengers use weak numbers to criticize incumbents.
The leading indicators occupy a place in our world that no one who invented them could have imagined. They were all designed with limited goals, and yet now they are used as absolute gauges of how we are doing. That is why, perhaps, the news that our economy is bigger than we thought was greeted by many with derision. Said one headline discussing the revision, “US GDP: America Is About to Look Richer—But Don’t Be Fooled.” Criticism ranged from the accusation that the Obama administration was juicing the numbers to burnish its record, to the belief that the new calculus only widens the gulf between those doing well and those struggling in today’s economy.
And indeed, just saying that we are statistically richer than we thought doesn’t actually make anyone richer. If I told you that you had $1,000 more than you thought you did five years ago, you would not suddenly have more money in your bank account, nor would you reevaluate your past experiences. In order to maintain the integrity of GDP, the BEA did not simply change its current methods of calculating; it revised all the numbers going back to 1929, so that now the money spent by Warner Bros. on blockbusters in 1955 and the R&D budgets of Hewlett-Packard and the Ford Motor Company in their mid–twentieth century heydays will be included in the GDP for those years. None of that, however, will have altered the ability of your parents or grandparents to afford a house or a new car retroactively.
The fact that knowledge work will now be integrated into our indicators does indeed make more acute the already sharp distinction between winners and losers in the contemporary economy. While GDP is a national number, it is not nationally experienced the same way. That is an often overlooked limitation of our statistics: they measure us in toto, but we then act as if they measure us individually. They do not, and they were not designed to. They were invented as tools to gauge an economy as a national system, not our own individual economic lives. The recent revisions show that those who are inventing new ideas have been benefiting even more than the numbers have shown. The fact that those efforts make us richer collectively and thereby statistically increase our “per capita income” does not mean that we each have become that much wealthier.
All of this is simply a case in point for how our numbers shape our sense of reality. For almost a century, people have been inventing statistics to measure our lives, and since the middle of the twentieth century, our understanding of the world has been integrally shaped by those numbers. Our statistical map, however, is showing signs of age. In our desire to have simple numbers to make sense of a complicated world, we forget that our indicators have a history—a reason that they were invented in the first place—and that history reveals their strengths and limitations just as our own personal histories do. Knowing how we came to live in a world defined by a few leading indicators is the first step to assessing whether we are still well served by them.
The history of these numbers is not well known, save by those academics and professional statisticians who look back for guidance on how to move forward. The impetus to invent statistics to measure what we now call “the economy” was a combination of the passion to conquer the unknown and the desire to create more social justice and equity. Our leading indicators are the offspring of progressive reform movements and the scientific drive to quantify in order to control.
The indicators were inventions meant to measure industrial nation-states of the mid-twentieth century. In their time, they did so brilliantly. The twenty-first century, however, is different. Industrial nation-states have given way to developed economies rich in services, and to emerging world industrial economies exporting goods made by multinational companies. The statistics of the twentieth century were not designed to capture that, and the assiduous efforts of statisticians notwithstanding, they cannot keep up.
The following pages tell the story of these numbers and the men (and yes, they were mostly men) who invented them. They tell as well how these statistics morphed from limited tools used by a handful of policy makers during the Great Depression and World War II into leading indicators that govern vast aspects of life in nearly every country in the world. Then, we will see how it came to pass that these statistics determine the pecking order of nations, set the parameters and shape the debate for how governments spend or refuse to spend trillions of dollars, and how all societies except for one small country measure their success.
Having traced this evolution, we will see that using these indicators to navigate today is much like using a 1950s road map to get us from point A to point B. It’s possible that you will get there, but it’s more likely that you’ll get lost. Given that, it is no surprise that our economic policies so often fail to deliver the promised or expected results. We rely on old formulas for new realities.
The temptation, then, is to find new formulas, better indicators, new statistics. The search for better numbers, like the quest for new technologies to improve our lives, is certainly worthwhile. But the belief that a few simple numbers, a few basic averages, can capture the multifaceted nature of national and global economic systems is a myth. Rather than seeking new simple numbers to replace our old simple numbers, we need to tap into both the power of our information age and our ability to construct our own maps of the world to answer the questions we need answering.
Before we get to that, however, we need to go back, far back, to the first attempts to know the world in numbers, to the dawn not of this millennium but the last, and to one of the most famous battles in the world, the outcome of which wasn’t just a shift in the political tides but one of the first attempts to measure the world.
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Enjoyable Economics for Laymen A study of the value modern economic reports would appear to be a boring topic for most laymen (including myself). Instead, I was pleased to find this book to be an entertaining mixture of economic history, philosophy, and social science. Mr. Karabell questions our reliance on most economic quantities. For example, I have normally been pleased or upset when the monthly unemployment rate shows a slight decline or increase, respectively. However, after learning why these numbers were developed, how they are calculated, and the hidden variables that contribute to final reported result, I found myself wondering if the unemployment rate has any true meaning in daily life. Likewise, statistics like rates of inflation, balance of trade, and Gross Domestic Product are analyzed and lead to similar doubts. The author’s theme is that most economic statistics which we use today were developed for a twentieth century world that no longer exists. This book is filled with examples to support his thesis. The writing flows nicely, providing an enjoyable and thought provoking experience.