Your fathers’ mistakes: Critiques of GDP and the search for an alternative

Abstract

THE GREAT INVENTION: THE STORY OF GDP AND THE MAKING AND UNMAKING OF THE MODERN WORLD Ehsan Masood New York and London: Pegasus Books, 2016 THE GROWTH DELUSION: WEALTH, POVERTY, AND THE WELL-BEING OF NATIONS David Pilling New York: Tim Duggan Books, 2018 GDP: A BRIEF BUT AFFECTIONATE HISTORY Diane Coyle Princeton and Oxford: Princeton University Press, 2014 I spent 19 years working for the Bureau of Economic Analysis—the US government agency responsible for measuring the nation's gross domestic product, or GDP. Like most medium to large organizations, the BEA used performance measures to track our agency's performance. After all, in the words of William Thompson (Lord Kelvin), “when you can measure what you are speaking about and express it in numbers you know something about it” (Crosby, 1997, p. 225). In 2008, for example, our agency of about 500 employees was using seven different performance indicators. How many indicators would we need to track the performance of a nation? Canada decided to ask its own citizens what needed to be included in an index of well-being. Their responses led to the development of the Canadian Index of Wellbeing, an index of 64 indicators centered on eight domains—community vitality, democratic engagement, education, environment, healthy populations, leisure and culture, living standards, and time use (Pilling, 2018, pp. 236–237). I think most of us would agree that all these domains are important to the well-being of a nation and its people. Yet, amazingly, when international comparisons of economic and social performance are made, they most often use just a single indicator—GDP. GDP, which measures the value of the goods and services produced by a national economy, is one of the most widely reported national numbers. In the United States, for example, each quarter private economists and Federal Reserve banks attempt to forecast the first estimate of US GDP, which is issued by the Bureau of Economic Analysis roughly 30 days after the end of the quarter. When the number is finally announced, major newspapers publicize it and analyze its implications on their front pages, and financial markets react to the news. A similar process is repeated in major countries throughout the world. And when economic data from different countries need to be compared, GDP is usually the basis for the comparisons. These three books all use the measurement of GDP as a framework for a broader discussion of economic and social policy. GDP measures economic activity, but it does not measure economic or social well-being, equality of opportunity, environmental protection, or happiness. The use of GDP as the main yardstick for judging policy success and failure says that nations value economic growth above all else. If nations wish to pursue goals other than growth, they need yardsticks other than GDP. In these books, the critique of GDP stands in for a critique of an obsession with growth, and the search for alternatives to GDP stands in for the search for other societal goals and objectives. Despite its centrality in evaluating the prosperity of nations, GDP is a surprisingly recent invention. The crisis of the Great Depression of the 1930s, followed by the need to mobilize resources for the Second World War led governments in the United Kingdom and the United States to engage the leading economists of the time in developing new systems for measuring the economy. After the war, the Marshall Plan relied on GDP to help rebuild the economies of Europe, and under the auspices of the United Nations, GDP became an international statistical standard. GDP is part of the national accounts, which is a system based on double-entry accounting principles that measures aggregates that economists are interested in. Every economic transaction involves at least two parties, a supplier and a user, and national accounts thus can measure these concepts in multiple ways. The most basic definition of GDP is the “production approach”, in which the value added of an enterprise or an industry is equal to its output minus the intermediate inputs that are used in production. For example, when I was with BEA, I worked with the territorial government of American Samoa to develop GDP estimates for the territory. The major industry of the territory (which has a population of about 60,000) is tuna canning. The output is canned tuna, and the main intermediate inputs are fish (mostly imported from non-resident fishing vessels) and metal plates for the cans. The value added, or GDP, of the tuna canning industry is the value of the output minus the value of the inputs. The GDP for the total economy is simply the sum of the value added for all industries. There are two other approaches for measuring GDP. The “expenditure approach” is based on how the products are used; it is the sum of consumption by households and by governments, investment (or capital formation), and exports, less imports. The “income approach” is based on the income earned from production; it is the sum of wage and non-wage compensation paid to employees, operating surplus and proprietors' income earned by owners of capital, and the taxes payable by producers. In principle, these three approaches to calculating GDP—production, expenditure, and income—should add up to the same value, though with real-world data there are likely to be statistical errors or discrepancies (Coyle, 2014, pp. 25–28). These three approaches refer to measuring GDP in “nominal” terms, that is measured in current prices as reflected in the value of the nation's monetary unit, such as dollars or euros. But money can be an unstable yardstick, as inflation erodes its purchasing power (or less commonly, deflation causes its purchasing power to rise). Economic analysis often requires that changes in nominal GDP be split into changes in prices and changes in the volume of goods and services produced, or “real” GDP. GDP, of course, includes thousands of goods and services, each with its own prices and volumes, so the split must be made using index numbers (Coyle, 2014, pp. 30–31). As soon as GDP was conceived, a debate commenced about whether it was the best way to assess an economy. Economist Simon Kuznets wrote: “It would be of great value to have national income estimates that would remove from the total the elements which, from the standpoint of a more enlightened social philosophy than that of an acquisitive society, represent disservice rather than service” (Pilling, 2018, pp. 29–30). Kuznets would have preferred a measure that excluded socially harmful or wasteful products, including spending on armaments and other government spending. He was looking to measure well-being rather than production. While GDP covers market production exhaustively, it is less exhaustive in its coverage of production that is not marketed. For example, services provided by volunteers are mostly not counted. Nor does GDP count most work that household members do for themselves—activities such a child care, meal preparation, and household cleaning and maintenance. If a household pays a service provider to do them, these activities are included in GDP, but not if the household members do them themselves (Coyle, 2014, pp. 37–39). The Internet and smart phones have transformed modern life, and many traditional products no longer appear in GDP. A generation ago, most people spent money on cameras, film, and developing photos; now they mostly rely on smart phones for their photography, post the photos online, and no longer need to buy or develop film. People communicate using free websites or apps, where the services are funded by advertising. These free services make people better off, but they may not be counted in GDP or the productivity statistics. More generally, official statistics have trouble taking account of the quality improvements associated with innovative technology (Pilling, 2018, pp. 73–79). Over the last three decades global economic inequality has fallen, as large, formerly poor countries like China and India have developed and raised hundreds of millions of people from poverty into the global middle class. But nations usually look at inequality within the nation, rather than globally, and several countries—especially the United States—have seen large increases in inequality. GDP is a measure of aggregate production and does not measure how equally the benefits of that production are shared. GDP can tell you that a nation's total production is growing, but it cannot tell you if incomes are rising for all segments of society (Pilling, 2018, pp. 96–99). In discussing the economic development of poor countries, Pilling (2018) writes: “Growth—and by that I mean even raw growth as measured imperfectly by GDP—has the power to transform poor people's lives” (p. 124). Growth in GDP per capita can lead to reduced child mortality, better education, and better health. But these positive outcomes are not inevitable. In Angola, for example, oil wealth flowed to a narrow elite connected to the government and failed to improve child mortality and life expectancy. Translating economic growth into improvements in welfare takes good policies and time (Pilling, 2018, pp. 132–133). Economic growth has often been accompanied by environmental degradation, which is omitted from GDP. A country that has tempered its growth to maintain clean air and water and biodiversity may rank lower in GDP than one that has not. In fact, sometimes an increase in GDP may reflect production that exists only to counteract the effects of environmental degradation, such as when patients require medical treatment for the effects of particulate emissions (Masood, 2016, p. 9). While GDP has been successful at holding the attention of economists and policy makers, it fails as a one-size-fits-all measure of national progress or of the welfare of the citizenry. To fill this gap, various alternative measures have been suggested. Nordhaus and Tobin (1972) proposed a “measure of economic welfare” that attempted to remove expenditures that do not directly contribute to welfare, such as defense expenditures and commuting costs, and to add imputations for leisure and non-market work that are omitted from GDP but do contribute to welfare. They addressed sustainability in the sense of measuring the capital requirements for maintaining per capita GDP. They also discussed natural resources but concluded that natural resource constraints would not act as a major drag on economic growth. However, while their measure of economic welfare generated academic interest, government statistical agencies have largely ignored it. Another measure that has achieved some success in official, governmental use is the Genuine Progress Index (GPI). Based on work by Herman Daly, an ecological economist, the measure was adopted by the State of Maryland in 2010. It also starts with GDP and adjusts for income inequality, non-market benefits from the environment, and activities like volunteer work that are not counted in GDP. It is based on 26 economic, environmental, and social indicators, all expressed in dollars to produce a single number. Maryland compiled GPI back to 1960, and it showed that economic growth as measured by GDP often had offsetting effects on the environment or on congestion. State policy makers use GPI to take account of effects of economic growth on the environment or on health. The GPI has now been calculated for several other states and localities within the United States, as well as for a few countries (Pilling, 2018, pp. 223–231). Perhaps the most widely used indicator of welfare was developed by Pakistani economist Mahbub ul Haq for the UN Development Programme. In 1990, they launched the Human Development Index, the highlight indicator in the Human Development Report. The index was based on three indicators, which were available for most developing countries—life expectancy, adult literacy, and per capita income. Each indicator is measured on a scale of 0–1, and the category scores are averaged (Masood, 2016, pp. 89–97). In 2015, the countries with the highest HDI—Norway, Australia, Switzerland, Denmark, and the Netherlands—all had high incomes, but they differed from the countries with the highest GDP per capita—Qatar, Luxembourg, Singapore, Brunei, and Kuwait. This is because HDI and GDP per capita are measuring different things. While HDI reflects differences in income, it also reflects factors such as literacy and health that matter for welfare. More recently, the UN has added a measure of inequality to the index (Coyle, 2014, pp. 72–75; Pilling, 2018, pp. 232–234). During the global financial crisis and great recession of 2008, French president Nicolas Sarkozy decided that the mismeasurement of living standards was one of the problems that urgently needed to be addressed. He assembled an all-star international commission to look at alternatives to GDP, led by Stiglitz, Sen, and Fitoussi (2010). Their report, Mismeasuring Our Lives, recommends that policy makers pay less attention to GDP and pay more attention to distribution, quality of life, and sustainability. They did not think that any single indicator should replace GDP; rather, they recommended that a dashboard of indicators should describe the nation's well-being, focusing especially on the household perspective, taking account of non-market activities, and looking at indicators of health, education, governance, social connections and relationships, the environment, and insecurity. In an article in Nature, Costanza et al. (1997) attempted to measure the “value of the world's ecosystem services and natural capital”. They measured these services in dollars so that they could be compared directly with GDP. Because most of these services are not transacted in markets, they largely relied on indirect methods to value them—especially, estimates of “willingness to pay”. Their estimate of the value of ecosystem and natural capital services was huge—$33 trillion in 1997, much larger than world GDP at the time. Their estimates also proved to be controversial. Economists criticized the methodology, which required substantial extrapolation from limited data and potentially implausible assumptions. Some environmentalists, on the other hand, attacked the very idea of putting a dollar value on nature (Masood, 2016, pp. 130–140). Despite the criticism, the United Nations Environment Programme has carried out work in this area through its global initiative on “The Economics of Ecosystems and Biodiversity” or TEEB. In 2006, Nicholas Stern and a team from the UK Treasury produced a report that addressed climate change and global warming. Their review concluded that climate change reflected a massive market failure and that by spending 1% of GDP now to tackle global warming, the worlds' economies could avoid an eventual 20% loss in global consumption due to inaction. While the report had some impact, with the subsequent global recession, attention largely returned to traditional growth (Masood, 2016, pp. 150–154). If it is hard to find indicators for well-being, should we perhaps simply pursue happiness directly? In 1972, the king of Bhutan, Jigme Singye Wangchuck, called on his nation to do that when he declared that “gross national happiness”, rather than GDP, would be the prime goal of national policy. Bhutan's idea of happiness, however, was not the same thing that Western academics would measure in their surveys. It was a distinctly Buddhist version of happiness, one that, according to Bhutan's prime minister Jigme Thinley, emphasized “serving others, living in harmony with nature, and realizing our innate wisdom and the true and brilliant nature of our own minds”. Their conception of happiness did not coincide with Western consumerism; indeed, until 1999 television was banned in Bhutan (Pilling, 2018, pp. 215–217). Since 1990, the study of subjective happiness by economists has taken off. Researchers have asked: What makes people happy? What do happy people do? And they have used happiness data to try to answer various economic puzzles (Clark, 2018). Most of this work is based on survey data that asks people to assess their own happiness. The British happiness researcher, Lord Richard Layard, focuses on seven main determinants of happiness: family relationships, financial situation, work, friends, health, personal freedom, and personal values. For example, people with a stable partner, especially those who are married, report substantially higher happiness than those who are divorced, separated, widowed, or never married. Unemployment also takes a heavy toll on happiness. Religious belief seems to have a positive effect on happiness, whereas chronic pain and mental illness are sources of unhappiness. These findings may suggest some surprising possible policies for improving happiness, such as taxing status-seeking behavior or giving employees the right to opt out of after-hours emails (Pilling, 2018, pp 199–203; 206–212). Since 2012, the World Happiness Report has provided rankings of the happiest countries. Nordic countries generally dominate the list. The happy countries tend to be rich and the unhappy ones tend to be poor. But income does not explain all the differences. In the 2016 report, for example, Costa Rica ranked 14th in happiness, whereas it ranked 77th in GDP per capita. The authors of the report attribute most of the variation between happy and unhappy countries to six variables: GDP per capita, healthy life expectancy, social support (that is, having someone to count on), freedom to make life choices, generosity, and lack of corruption (Pilling, 2018, pp. 203–206). Ehsan Masood is a science writer and journalist, and his book, The Great Invention, looks at this topic through the lens of invention. These inventors in his story, however, are not scientists in lab coats or entrepreneurial engineers, but rather are academic economists, mid-level government officials, or bureaucrats at international organizations such as the United Nations. His story is largely told by a series of biographical vignettes of relatively unknown individuals who developed new ways for measuring economies and societies. We learn about Mahbub ul Haq, the Pakastani economist who created the Human Development Index, and Maurice Strong, the Canadian oil executive who became the founding executive director of the UN Environment Programme. We learn about Jigme Singye Wangchuck, the King of Bhutan who as a teenager championed the idea of Gross National Happiness, and Herman Daly, one of the creators of the subfield of ecological economics. Masood's book is well-written and engaging, and of the three books, I think this is the one from which I learned the most in terms of new information. I will also mention a couple of issues where I disagree with Masood. He concludes that because GDP is entrenched as a policy tool, the only way to conduct better policy is to radically change the definition of GDP (Masood, 2016, pp. 157–160; 169–172). But what he does not seem to recognize is that the current definition of GDP is the appropriate measure for many types of policy. For example, the central bank's monetary policy or the government's tax revenue projections would not be improved, and probably would be worsened, if the definition of GDP were changed radically to include ecosystem services or subjective happiness indicators. That does not mean that a broader indicator or set of indicators would not also be useful for policy; it just means that different measures are appropriate for different policy purposes. The other two books acknowledge that multiple indicators are needed, but Masood strongly resists the idea. The other issue where I disagree with him is about the story of the invention of gross national product, or GNP. GNP was created before GDP—probably because it is more closely tied to the statistics on national income that had already been developed—and was driven by the depression and World War II.1 The invention of GNP was a collective endeavor and not the creation of any single individual; I think all three books get that aspect right. But what I think they get wrong is in overemphasizing the importance of a few famous economists, such as Simon Kuznets (a Nobel prize winner) and John Maynard Keynes (the father of macroeconomics).2 While both men did play important roles in the story, Keynes's 1940 book, How to Pay for the War, was not the first work to calculate a measure of GNP including government expenditures. For example, Warburton (1934, 1935) at the Brookings Institution developed estimates that included government spending and were, I believe, the first to use the term “gross national product”. A full history of the invention of GNP and GDP has not yet been written, but when it is, I think it will involve more actors and be less driven by “great men” than the narrative that is popularly told. David Pilling is an editor and writer for the Financial Times and has reported from several continents. His book, The Growth Delusion, gives considerable attention to the developing world, with about a third of the book focusing on the problems of economic development. He tells stories and interviews people from many countries.3 His writing is lively and engaging. For readers who have not studied economics, this is probably the most accessible of the three (though really, all three books are accessible and enjoyable to read). In this book we learn, for example, about the Icelandic financial crisis, the American opioid epidemic, and how GDP is measured for subsistence farmers in Africa. Unlike Masood, who would like to overhaul GDP, Pilling calls for retaining GDP as one component in a dashboard of indicators, and he provides specific examples of the indicators he would use. One criticism of Pilling's book is about his chapter on measuring wealth (Pilling, 2018, pp. 157–167). While I agree with him that wealth can be as important as income (or GDP), I found it surprising that he does not mention that some countries do produce estimates of at least some of the components of wealth. The System of National Accounts, or SNA—the international standard for national accounts—includes detailed recommendations for measuring balance sheets, and several advanced economies do produce wealth statistics. Now these statistics are not available for the less developed countries, and even for developed countries they does not cover everything Pilling would like to see. For example, he would like to see estimates of human capital, which are generally not covered in national wealth statistics (though some countries are starting to put together experimental measures). But more generally, I would like to have seen more attention given to exploring the data other than GDP that are available. Diane Coyle is an economist who has worked in business, academia, journalism, and government. Her book reflects a deep understanding of how GDP is used by economic practitioners, and the “affection” in the subtitle is genuine. (Her book is also “brief”, with the main text running to just 140 pages.) The story she tells can be summarized when she writes: “The story of GDP since 1940 is also the story of macroeconomics” (Coyle, 2014, p. 20). Quite a bit of the book is a brief economic history of Europe and North America during the post-war period. She discusses the rebuilding of Europe after World War II, the era of stagflation during the 1970s, and the financial crisis and recession of the last decade. She writes with a lively prose style that is a pleasure to read. More than the other books, her critique of GDP emphasizes the ways in which it is problematic even as a measure of production—the problems in adjusting for quality change and new goods and the failure to cover household production. My main point of disagreement with Coyle is with her criticism of the national accounts treatment of financial services. We can think about the financial services produced by a bank; it provides services to its customers, who are both depositors and borrowers. To fund these services, besides sometimes charging fees, banks also rely on the interest spread—they make money from the spread between the interest they charge borrowers and any interest they pay to depositors. The national accounts treat the spread as an indirect service charge, and they allocate part of the service to the depositors and part to the borrowers. Thus, the treatment in GDP assumes that part of the interest paid by a borrower is an indirect service charge and the remainder is “pure” interest. One of Coyle's criticisms of the treatment of financial services is that in addition to interest covering a service charge and “pure” interest, there should be a third component that covers the risk associated with defaulting on loans (Coyle, 2014, pp. 98–104, 136). The treatment recommended by the SNA does not recognize that component, which could cause GDP to overestimate the service charge. On this point, the US national accounts did recognize the criticism as valid and in 2013 changed their treatment to remove the default risk (Hood, 2013, 10–11). But Coyle wants to go further and abandon the SNA's treatment entirely to revert to “the simplest approach”. But the SNA's current approach is consistent with economic theory (as developed, for example, in William Barnett's research on monetary aggregates) and avoids some serious problems that afflicted the earlier, simpler approaches. To the extent that problems persist in measuring financial services, I think they are associated more with faulty price indexes rather than with the underlying conceptual framework. All three books provide interesting insight into the challenges of measuring the performance of the economy, the environment, and overall well-being. They also use the framework of statistics to challenge our views about what goals our society should be pursuing. One of my recommendations is that when people use statistics, they should think more carefully about the concepts that are measured and take care that the statistic is actually measuring the concept in question. GDP is almost certainly overused, and in many cases, other data such as net national income, household disposable income, or household consumption would better fit the intended use. We should become more familiar with the available statistics and learn about them so that we can make better, more informed use of them. Brent R. Moulton Economist and Consultant (retired from US Bureau of Economic Analysis) Gaithersburg, MD Email: brentmoulton@fastmail.com