Gross Domestic Product is the most popular and useless quantity in economics say Erald Kolasi and Blair Fix.

For all that it purports to say, Gross Domestic Product (GDP) fails to explain anything relevant about the world. It values social services as it might domestic housework. It ignores ecological degradation by adding up all costs, regardless of what they‘re for. It says nothing about the distribution of income and wealth. It also tells us little about quality of life factors. Cuba has a much smaller GDP per capita than the United States, but Cuba‘s life expectancy is higher, or at least comparable, to that of the United States.

These issues are much discussed. But to understand why “Real” GDP, in particular, reveals so little about the state of any economy, you need to dig deeper. In this article we expose the deep underlying technical flaws of GDP calculations by explaining some of the aggregation issues that plague nearly all macroeconomic theories. We also propose other metrics for thinking about the scale of economic activity.

Growing up, you probably obsessed over your height and how fast it was changing. Every birthday, you stood tall against the wall, had a family member extend a tape measure, and listened impatiently for that special number. Three feet and seven inches – growing so fast.

Now imagine this. One day your devious brother changes the tape measure used to measure your height. In the new tape measure the tick marks for inches are slightly farther apart. Your brother measures your height and then reports that you’ve actually gotten shorter. You’re devastated. The lesson here is that for measurement to be accurate, the units must be stable.

For this reason, natural scientists have spent centuries refining their units. The kilogram, for instance, was recently redefined in terms of Planck’s constant, a fundamental constant in quantum physics. That replaced the block of platinum/iridium alloy that has been used to define the kilogram since 1889. The goal was to make the unit as immune to change as possible. This ensures that changes in the measured mass of an object reflect actual physical changes, not variations in the unit of measure.

When units are unstable, in contrast, objective measurement becomes impossible. When a unit varies over time or space, we can’t be sure that measured variation reflects actual variation, or just variation in our unit of measurement. This unstable-unit problem plagues many measures in economics, especially “real” GDP, which is often reported with exquisite precision. Unlike natural scientists, however, economists are not in the business of carefully defining units using universal physical constants. Economists instead use prices, a social construct, as their unit of analysis. To measure economic growth, they add up the market value of all new commodities produced in a year.

The problem is that prices are unstable units of measurement. Relative prices between commodities vary wildly over time. This instability means that prices fail the only requirement of a good unit – to be uniform over time. This problem is acknowledged by many experts, yet remains largely hidden from the public in reports about GDP from the government and from the media at large. Instead of reporting the severe uncertainty in “real” GDP, governments report a single official value. This value hides a myriad of subjective decisions that are used in a bid to correct for unstable prices.

“The problem is that prices are unstable units of measurement.”

When it comes to reporting “real” GDP estimates, the behaviour of governments is an interesting exercise in how not to do science. When there is uncertainty or ambiguity in a measurement, the appropriate response is to report it openly. Case in point: physicists are now engaged in an open debate about the expansion rate of the universe. Estimates based on ancient light coming from the Big Bang give an expansion rate that is different to estimates based on the movement of galaxies. This uncertainty is causing some physicists to question the standard model of cosmology. This is the right response to measurement uncertainty: reporting and debating conflicting results.

The wrong response to uncertainty is to pick a single value and declare it the truth. Yet this is exactly what governments do when reporting “real” GDP. The instability of prices means that there is a large range for the possible growth of GDP. Yet governments do not report this range of uncertainty. Instead, they report a single measure of GDP that is based on an arbitrary choice of base year, or similarly arbitrary methods for “correcting” for inflation. When GDP estimates are revised, the old estimates quietly go away.

For the most part, economists are aware that there is a problem with their holy dogma. When speaking to fellow members of the clergy, they admit that “real” GDP calculations are essentially arbitrary. In a 1995 paper, economist, Charles Steindel, had this to say about calculating “real” GDP:

“The economy consists of millions of individuals and firms producing a multitude of goods and services. This complexity virtually ensures that any method of estimating “real” GDP involves making some more or less arbitrary decision about the most appropriate way to add up data from individual sectors.”

Steindel hits the nail on the head. Methods for calculating “real” GDP are arbitrary. Worse still, the arbitrary choice of method affects the growth of “real” GDP. Again, this is common knowledge among government economists. In Chapter 4 of the NIPA Handbook, the US Bureau of Economic Analysis notes:

“The fundamental problem confronting the efforts to adjust GDP and other aggregates for inflation is that there is not a single inflation number but rather a wide spectrum of goods and services with prices that are changing relative to one another over time. The index numbers for the individual components can be combined statistically to form an aggregate index, but the method of aggregation that is used affects the movements of the resulting index.”

Economists are clearly aware of the problems we raise here. Arbitrary decisions about how to correct for changing prices affect the resulting growth of “real” GDP. Again, this stems from the fact that prices are an unstable unit of analysis.

Our response to this problem is to conclude that the growth of “real” GDP is fundamentally uncertain. Or perhaps a better word is ambiguous. When scientists speak of uncertainty, they tacitly assume that there is a true value on which to converge. Two centuries ago, there was large uncertainty about measurements of the speed of light. But over time, better instruments allowed measurements to converge on a single value.

But with “real” GDP, there is no truth on which to converge. Because “real” GDP is based on an unstable unit, its value is fundamentally ambiguous. Different methods of correcting for inflation yield different values for the growth of “real” GDP. And as Steindel admits, the choice of inflation-adjusting method is fundamentally arbitrary. The proper conclusion is that the growth of “real” GDP is inherently ambiguous.

“For the most part, economists are aware that there is a problem with their holy dogma.”

Faced with the same evidence, however, government economists reach a very different conclusion. Their response is to simultaneously admit that calculating “real” GDP requires arbitrary choices, but then to report a single value as though it was the truth. The US government currently calculates “real” GDP by adjusting nominal GDP with an aggregate index formed through the multiplication of successive Fisher1 indexes in adjacent time periods. In popular parlance, this method is called chain-weighting. Rather than choose a single base year in which to fix prices, chain-weighting uses a technique that resembles a rolling base year. The method is meant to simulate the effect of changing prices and spending patterns over time.

Chain-weighted GDP was adopted back in the 1990s. The official justification was that structural changes in the US economy, especially rapidly falling computer prices, compelled the government to end the fixed-base-year method. But contrary to the assertions of the Bureau of Economic Analysis, this change did not produce a more “accurate” estimate. Such a statement implies that a true value of “real” GDP exists independently of the techniques used to measure it, which is simply false, as we have shown above. Whether using a fixed or a rolling base year, “real” GDP is still an arbitrary statistical construct defined by subjective choices.

“Real” GDP belongs in the dustbin of history. In its place we should adopt a plurality of measures.

We believe it is important to distinguish between economic distribution and economic scale. “Real” GDP assumes that prices can be used to measure economic scale. In contrast, we assume that prices do nothing of the sort. Prices are a tool for distributing resources. The proper place for prices, then, is for understanding economic distribution.

The game of distribution is all about the command of market value. If a lawyer can charge more for his services than a janitor, the lawyer wins the game of distribution. This is not about productivity. It is largely about power. For example, individual incomes within firms correlate strongly with their hierarchical power, which is their control
over subordinates. Political economists Jonathan Nitzan and Shimshon Bichler convincingly argue that the capitalisation of firms indicates their “differential power”, which is their ability to use property rights to expand their own economic advantage over workers and other peer competitors. Dominant classes and corporations exploit this differential power to adjust wages, profits, and prices as they see fit, gaining more power over labour along the way.

When we treat prices as a tool for distribution, the proper thing to do is to compare prices. Nitzan and Bichler call this differential analysis, and it allows us to compare the nominal market value of different firms, or different groups of firms. Likewise, we can compare the income of individuals or groups of individuals. The meaning is in the comparison, not the aggregate value itself. The focus here is on how relative prices change over time, not what they reveal about the “real” sphere of production.

But if prices are used solely for studying distribution, we must find an alternative dimension for studying economic scale. There are many possibilities. The choice of dimension should depend on our goals.

To measure the scale of the economy, we think it is appropriate to focus on energy. Physicist Eric Chaisson argues that energy is the universal currency of science. By measuring economic scale using energy, we put economics in line with the rest of science. And if we are concerned with sustainability, there is no better starting point than to focus on energy use. After all, the profligate use of fossil fuels under a capitalist economy is the primary driver of climate change.

Energy has many forms as it is flows through society. One possibility is to focus on primary energy consumption, and see how this relates to changes in social structure. Another possibility is to measure useful work – the consumption of end-use energy. Still another possibility is to measure the aggregate flow rate, which is a measure of all annual energy conversions in an economic system.

The key is to separate the study of economic distribution from the study of economic scale. The former is the appropriate domain of prices. The latter is best measured using biophysical units. Of course, scale and distribution are causally related, but any connection between the two is vastly more complex than what has been traditionally recognised in economics.

1. The Fisher Price Index, also called the Fisher’s Ideal Price Index, is a consumer price index (CPI) used to measure the price level of goods and services over a given period.

Blair Fix

Blair is a political economist based in Toronto. He researches how energy use and income inequality relate to social hierarchy. His first book, ‘Rethinking Economic Growth Theory From a Biophysical Perspective‘ …

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Erald Kolasi

Erald received his PhD in physics from George Mason University in 2016.

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