Our Mis-Leading Indicators

Spring 1932: former Ford Motor Company workers protest at the company’s River Rouge Complex, where police and private security kill five and injure more than 60; unemployed World War I veterans camp out on the steps of the Capitol, demanding “adjusted compensation” for their service; more than three quarters of a million New Yorkers are relying on help from the city, and many more need assistance.

Robert La Follette Jr., a progressive United States senator from Wisconsin, tried to bring a cool head to the search for relief. Without a lucid diagnosis of the depth and breadth of the Depression, La Follette believed, an effective prescription would be elusive. In addition to the deprivation and fear afflicting so much of the population, the US faced a more abstract problem: official economic data and statistics were so rudimentary that it was impossible to grasp the totality of the nation’s crisis. La Follette called for the first official report on the country’s income levels.

The responsibility for completing the task fell to a 31-year-old Belarusian American economist named Simon Kuznets. Renowned for his statistical acumen, Kuznets had long supported moving economics away from its qualitative, philosophical past and toward becoming a more scientific and mathematical discipline. He was also deeply concerned with the country’s overall welfare and believed that reliable statistics would offer a strong empirical base for crafting socially just policies. When Kuznets submitted his final report to the Senate in 1934, he outlined two measures of national income: “National Income Produced,” the net value of goods and services produced, and “National Income Paid Out,” which calculated the income from current production received by individuals as owners of capital and as workers. The estimates were a hit. President Roosevelt used them to assess the speed of recovery during the campaign of 1936, and they provided policymakers with the means to define the scope and content of major spending initiatives thereafter. The report even became a best seller.

The unreliable statistics of the 1930s quickly gave way to a world in which aggregate numbers shaped major national policies and established the terms of political debate.

Thanks to the work of Kuznets and his colleagues, the scattered and unreliable statistics of the 1930s quickly gave way to a world in which aggregate numbers shaped major national policies and established the terms of political debate. A few years after the Senate report, Kuznets’s estimates inspired a related number, the Gross National Product (GNP), which enabled policymakers and economists to envision economic activity, and govern, in new ways. With such metrics in hand, government officials could see the entity we now know as “the national economy.” They could also devise wide-ranging policies to combat the depression, and by the onset of World War II, mobilize vast amounts of resources for the war effort. Over the next few decades, the number became the standard metric for assessing the economic health of the nation. It remained that way until the 1990s. When globalization muddied accounts of national output, the Bureau of Economic Analysis in the Department of Commerce moved away from using GNP and instead focused on a related measurement, Gross Domestic Product, or GDP. Whereas GNP includes all goods and services produced by US nationals regardless of where in the world production takes place, GDP measures only the goods and services produced within the United States’ borders and thus relates more closely to other domestic indicators such as inflation and unemployment rate.

Like GNP before it, GDP became the yardstick for measuring progress and still often serves as a proxy for overall national well-being. Policymakers think of national economic life in terms of GDP: raising GDP is a primary policy goal and people across the world look to GDP growth rates to assess how well their leaders are guiding economic policy choices. Yet today GDP is under fire from a variety of sources. Why? That question lies at the heart of three new books. Economist Diane Coyle’s GDP: A Brief but Affectionate History, financial adviser (with a PhD in history) Zachary Karabell’s Leading Indicators: A Short History of the Numbers That Rule Our World, and political scientist Lorenzo Fioramonti’s Gross Domestic Problem: The Politics Behind the World’s Most Powerful Number all explore the origins and rise to prominence of the leading economic statistics, and why the most important among them now needs reform.

The origins of GNP and GDP stretch back to 17th-century England. Scientist Sir William Petty set out to determine how much tax the Crown could levy to mobilize for the Second Anglo-Dutch War of the mid-1660s. Petty combed through sketchy tax records, census reports, and other scattered government documents to estimate the country’s economic output. Following Petty’s work, statistics about agricultural production and trade became common in many countries, but it was not until the late 19th century and early 20th century that comprehensive statistical depictions of an entire economy began to appear.

Employment and income statistics were useful for late 19th-century progressive reformers hoping to show just how unequal and unfair industrialization was making society. If they could show that a certain few (the 1% of an earlier day) held a disproportionate share of the national wealth, they could offer more persuasive arguments to reform the economic system. In the aftermath of WWI, having a clear and reliable estimate of national income was necessary to figure out just how much Germany could pay in reparations. During the Depression, La Follette and Kuznets realized, national income estimates became indispensable to government efforts to revive economic activity and generate greater employment. In the run-up to WWII, GNP was useful for US war planners determining how to mobilize domestic resources and manage military procurement to fight the Axis powers.

By the war’s end, a transatlantic network of economists and statisticians—led by Kuznets and British economists Colin Clark and Richard Stone—worked to standardize the “system of national accounts” and establish norms for calculating GNP. After the war, countries around the world starting calculating the number, and new international institutions, including the United Nations, grafted it onto colonies and “underdeveloped” countries. The rising generation of nationalist postcolonial leaders celebrated high GNP growth rates as a powerful symbol of modernity and evidence that they could deliver on revolutionary promises.

GNP provided an authoritative, empirical way of comparing nations and defining targets for countries to move up the ladder from underdeveloped to developed; aggregate economic statistics revealed the development process as it unfolded on accounting sheets. The numbers became tools in the Cold War as the two superpowers competed over which system, communist or capitalist, could deliver the highest numbers in the shortest amount of time. John Kennedy evoked GNP growth rates so frequently during his 1960 campaign that his opponent, Richard Nixon, dismissed it as “growthmanship.” Amid an international “stats war,” Fioramonti reports, the CIA frequently tried to persuade Third World nations of Soviet perfidy by recalculating and discrediting Soviet claims to high growth rates.

GNP provided an authoritative, empirical way of comparing nations and defining targets for countries to move up the ladder from underdeveloped to developed.

From the 1960s on, GNP began to acquire powerful momentum, dominating national policymaking and international comparisons. But, as Coyle’s, Fioramonti’s, and Karabell’s books show, the history of GNP was more contentious and compelling than a seemingly inexorable trajectory. Intense debates erupted over what, exactly, the number should count. Although many of the early comprehensive stats account for much of the nation’s economic output and all activities therein, Kuznets sought a metric to focus specifically on welfare. Writing in 1937, he argued that the measure should “subtract from the present national incomes totals all expenses on armament, most of the outlays on advertising, a great many of the expenses involved in financial and speculative activities,” as well as the “gigantic outlays in our urban civilization” such as massive infrastructure, “expensive housing,” and other such forms that amounted to an “evil necessary to make a living.” Kuznets debated this point with many influential economists, most notably Milton Gilbert of the Commerce Department. As Coyle notes, despite Kuznets’s objections, a broader calculation of government expenditure won out in the first formal estimates of “Gross National Product.” Similar disputes took place over whether to account for work outside market exchange, such as housework (neither GNP nor GDP have included it), or whether to add in government expenditures (which the metrics ultimately did).

Fundamentally, these debates were about what constituted the economy and what societies should value. Fioramonti shows that Kuznets and other leading economists harbored deep concerns about the growing reliance on GNP as a sole guide for making policies. “The welfare of a nation can scarcely be inferred from a measure of national income,” Kuznets warned in 1962, and he demanded that policymakers distinguish between “quantity” of GNP growth and the “quality” of economic change for the citizenry, in order to clarify what, precisely, growth was meant to do. These criticisms were not confined to the academy, either. On the campaign in 1968, Robert Kennedy condemned GNP for measuring everything “except that which makes life worthwhile.” By the 1960s, tensions had emerged between what GNP actually measured and how it was adopted in the broader political culture. Although GNP was not intended to gauge the welfare of a nation, postwar technocratic optimism eagerly conflated a rapidly rising GNP with growing prosperity, standard of living increases, and overall well-being.

Over time, the gulf between high GNP numbers and other indicators of well-being widened. High growth rates prevailed across the Third World in the 1960s, but both poverty and unemployment remained high as well.¹ In the late 1960s, the heterodox British economist Dudley Seers, for instance, questioned GNP’s use in Third World nations, claiming that a focus on GNP growth led national leaders to worship growth rates while failing to satisfy their populations’ basic needs. Among other reformers, Seers sought to reorient national accounts toward questions of income distribution and access to resources. Seers and fellow economists such as Mahbub ul Haq and Amartya Sen undertook pioneering research into “social” indicators like literacy rates and education levels. Their efforts gave rise to alternative aggregate metrics such as the Human Development Index (HDI), which was meant to measure social and economic development.

For Fioramonti, the reliance on GDP derives from a technocratic worldview that glorifies experts, corrodes communal values, and devalues the natural world.

Environmentalists assailed GNP for not including ecological damage, or, in economic terms, the loss of “natural capital.” Ecological economics, which began in earnest in the 1970s, aspired to rectify this. Inspired by GNP critics like Nicholas Georgescu-Roegen, Kenneth Boulding, and Herman Daly, ecological economists sought to incorporate environmental change into national statistical records, largely by putting a price on the economic value of the nonhuman world and including previous “externalities” like pollution. Such criticisms continue to this day, as does interest in creating a “Green GDP” to account for environmental degradation in economic activity.

Still other reformers questioned whether there needed to be new aggregate statistics to account for more holistic concepts such as “well-being.” In 1972, economists James Tobin and William Nordhaus proposed a “Measure of Economic Welfare” to focus policymakers’ eyes on national welfare rather than productive capabilities (similar to what Kuznets had long desired). The most notable criticism surrounding welfare and well-being, however, became framed by notions of “happiness.” An offhand remark by the Kingdom of Bhutan’s leader in 1972 that suggested the country would strive to improve “gross national happiness” inspired a global effort to quantify national happiness levels through extensive survey. The economist Richard Easterlin added social-scientific legitimacy to the happiness trend in 1974, when he published an article that suggested growth in national income did not correspond to an increase in happiness. Like the ecological economists, the happiness reformers often brought a technocratic bent to their work, arguing that GNP and reductive economic statistics should be replaced by other numbers.

Coyle, Karabell, and Fioramonti emerge out of this long line of questioning GNP and its close relative, GDP. And while each author believes that GDP no longer suits our present needs, they differ on what should be done. Coyle dwells on the fact that the number no longer suits our current finance- and service-focused economy; nonetheless, she believes it should be kept and supplemented with environmental indicators. Karabell, by contrast, explicitly questions the usefulness of single aggregate indicators such as GDP. Instead he calls for “bespoke indicators,” numbers that are “tailored to the specific needs and specific questions of governments, businesses, communities, and individuals.” He also hopes for a wider public awakening to the limits of such numbers, requesting that leaders stop “fetishizing” numbers like GDP, though he offers little guidance for achieving such a transformation.

Fioramonti presents the most scathing critique. He criticizes the deeply held faith that raising GDP can solve all political and social problems—what he calls the “dogma of infinite GDP growth.” For him, the reliance on GDP derives from a technocratic worldview that glorifies experts, corrodes communal values, and devalues the natural world. In addition to inveighing against this worldview throughout his book, he highlights contemporary social movements that are challenging both the use of GDP and mainstream society more broadly. He explains the “transition” and “de-growth” movements, which seek to downscale production and consumption, encourage participatory decision-making, decentralize power towards the community level, redistribute resources along more equitable lines, and lower humankind’s ecological footprint. Similarly, he recounts the efforts of communities that use their own currencies and banking systems to break free of the larger financial power structures (states, multinational corporations) that dominate economic transactions worldwide. In general, he sees technocracy and its GDP “dogma” as powerful centralizing and anti-democratic forces, and he celebrates grassroots, local movements that show “alternative ways of life are not just possible but also desirable.” His prescriptions are thus cultural and political; no merely technical fixes will suffice.

A demonstrator waves a red and black flag during an intersection occupation outside the World Bank in Washington DC (October 20, 2007). Wikimedia Commons

The books also enable us to reflect on how official statistics expose underlying values and assumptions about ends and means. The continued use of reductive statistics, Karabell and Fioramonti suggest, necessarily limits policy options because leaders often favor social projects and programs whose results can be easily quantified. “If you can’t measure it, you can’t manage it,” opined former New York City Mayor Michael Bloomberg. And as Fioramonti rightly emphasizes, abstracting the social world into numbers invites simplistic reasoning. Thinking in terms of GDP has also blinded leaders to environmental destruction, encouraged many observers to confuse measurement with assessment, and reinforced a mindset in which market principles and economic terminology suffuse every aspect of life.

These books also provoke a deeper historical question: why have economic statistics and economists gained such policy influence and cultural authority in the 20th century? The power of economic expertise rose on the back of probability mathematics, scientific management techniques, and a growing appetite for predictions in national governance. Postwar faith in technocrats benefitted too from the “golden years” boom. As the United States, Western Europe, and Japan began to move from scarcity economies toward material abundance, economists and statisticians won favor for apparently solving the long-standing problem of how to ensure citizens both basic security and ever-greater access to consumer goods.

The criticisms of the 1960s and 1970s reflected more than frustration with GNP; they gave voice to anxieties about the mindset and expectations that had elevated the metric’s popularity in the first place. For instance, the phrase “economic growth” became popular only after World War II, as scholars such as Robert Collins have shown.² Measuring growth rates in terms of GNP generated a way of thinking that prized ever-greater production and consumption of goods; government technocrats and politicians alike began to look to growth to solve the problems of income distribution that had afflicted nations since the industrial revolution. Instead, growth failed to eliminate poverty, contributed greatly towards ecological instability, exacerbated inequality, and often contributed to social dislocation and anomie.

The financial crisis of 2007–08 provided ample inspiration for a renewed round of criticism. The growth rates of the 1990s and early 2000s masked an increasingly volatile financial system, and few observers predicted the fallout. The long history of critics assailing GNP and GDP and its underlying assumptions suggest that the latest economic turmoil has only rekindled long-simmering uneasiness with the path to modern material abundance. Our leading—or misleading—indicators have wrought the world we live in. The question remains of how to assess what we will and should count as progress.