Several weeks ago I heard a very interesting keynote presentation by Cambridge professor Diane Coyle at the annual workshop of the Stanford Digital Economy Lab, – What Don’t We Know About Measuring the Digital Economy? Professor Coyle is also a research associate of the UK Economics Statistics Centre of Excellence. In 2017 she was a recipient of the Indigo Prize on how to measure economic activity in the digital economy for her essay Making the Future Count, co-authored with Benjamin Mitra-Kahn.

“GDP captures only market transactions at the price of exchange, and not the welfare gains, externalities, environment, distribution of wealth or innovation which occurs in an economy,” wrote the authors. “Hence almost since its creation in the 1940s it has been criticised for its inability to capture economic welfare. Now changes in the economy, being restructured by digital technology and paying the price for unsustainable growth, make the case for a new measurement framework more pressing than ever. GDP was never an ideal measure of economic welfare and its suitability has been decreasing.”

Gross Domestic Product (GDP) became the accepted international measure of economies in the1940s. While being a good measure for the 20th century industrial economy, GDP is a flawed measure for the 21st century economy. It was suitable when the economy was dominated by the production of physical goods, but GDP doesn’t adequately capture the growing share of services and other intangible assets that now characterize advanced economies. Nor does it reflect important economic activity beyond production, such as income, consumption and living standards.

In 2008, a Commission on the Measurement of Economic Performance and Social Progress led by Nobel-prize winning economists Joseph Stiglitz and Amartya Sen was convened to look at the limits of GDP as an indicator of economic performance and progress. “What we measure affects what we do; and if our measurements are flawed, decisions may be distorted.” said its report.  The Commission recommended complementing classical measures of GDP and economic production with additional measurements that captured people’s well being and longer term sustainability to help reflect the evolution of the economy into the future.

Another set of concerns has emerged in the past two decades. How do you measure the value of the increasing amounts of free information goods available over the Internet, including Wikipedia articles, Google maps, Facebook interactions, smartphone apps, and You Tube videos?

In a 2012 keynote, Why it Matters that the GDP Ignores Free Goods, Stanford professor Erik Brynjolfsson noted that despite spending more of our time consuming and developing digital goods than ever before, official government statistics don’t include their value.

“Obviously, there’s some major measurement problems in the way we keep our statistics, and that’s a real problem because, as the saying goes, you can’t manage what you don’t measure,” he said. The problem is that the marginal cost of delivering digital goods over the Internet is pretty close to zero. While in some cases their economic model is based on advertising, in many cases users contribute their time, and develop digital content for nothing. Online information may be updated every minute of the day and accessible almost anywhere in the world, but its price is usually radically lower than that of its physical counterpart, – if there even is a price.

“Digital technology is creating new goods and services directly and is leading to business model innovation across the economy (streaming music services, accommodation platforms, servitisation of manufacturing, volunteer production of digital goods such as open source software, and much more), wrote Coyle and Mitra-Kanh in their essay. “Modern democratic governments in digital economies do not need a statistic designed to measure physical productive capacity in wartime.”

The authors recommend a two stage plan for reforming economic measurements. Let me summarize their recommendations.

Straightforward Amendments to GDP

The first stage involves three relatively straightforward changes to GDP in the short term: accounting properly for intangibles; removing unproductive financial investments; and adjusting for income distribution.

Intangible assets. Tangible assets are primarily physical, – such as vehicles, land, plants, equipment, and furniture; they also include financial assets with a concrete value like stocks, bonds, account receivables, and cash. Intangible assets are neither physical nor have a concrete financial value. They include patents, copyrights, trademarks, goodwill, brand value, human capital, R&D, software, and data. Despite having no physical existence, intangibles have a monetary value since they represent potential revenue, but that value must be established based on accounting principles. And, unlike tangible assets, intangibles are difficult to value and insure.

The value of intangible assets has significantly increased over the past several decades. According to a 2019 report by the Ponemon Institute, in 1975, the overall value of the S&P 500 was $715 billion, of which 17% was intangible. In 1985, out of a total value of $1.5 trillion, 32% was intangible. By 1995, the percentages had switched, with intangibles now being 68% of $4.6 trillion. Intangible values continued to climb to 80% out of $11.6 trillion in 2005; and to 84% out of $25 trillion in 2018.

Productive and unproductive finance. Traditionally, money is saved in banks which in turn invest the money in the so-called real economy. However, while savers still put their money in financial institutions, these institutions have been increasingly creating financial assets, such as bonds, mortgages, derivatives, and interbank lending.

“This is problematic for measuring economic performance, as the profits that banks earn … are counted as part of GDP – yet these are not investments in productive capital, but in financial capital.” Financial transactions of this nature “should be reversed as it is hard to see financial institutions’ activity among themselves as productive.”

Unequal societies. Economic growth is generally reported in measures like total GDP and per capita GDP, – calculated by dividing the GDP of a country by its populations, – which obscure how the economy is doing across different segments of the population. “A focus on distribution is needed. Statistical agencies could easily make median per capita GDP the standard headline figure in regular press releases. The use of the median figure would quickly remove the biases from reporting economic growth in countries with an uneven distribution of income.”

”Median and mean incomes have diverged significantly in some developed countries, such as the US.” For example, as this graph shows, GDP per household, – a measure of average total income per household, – has increased by around 55% between 1985 and 2016, while median income per household increased by only 20% in the same timeframe, reflecting the trend toward greater inequality.

A new long-term framework

The second stage of their recommendation proposes an alternative framework for measuring the economy that replaces GDP with an assets dashboard that measure “access to the range of economic assets people need to lead a meaningful life as they conceive it.” Such a framework requires measurement of six types of economic assets:

Physical and Produced Capital.  This includes access to infrastructure, -e.g., transport, energy networks, communications networks, – as well as to other public assets and new technologies.

Human Capital. “Human capital measures look at educational qualifications – quite a crude measure of relevant skills and attributes – and use market earnings to value the human capital represented by a given level of qualification, as a starting point for accounting for people’s skills, abilities and access to training.”

Natural Capital. This includes the renewable resources provided by nature, and either demarcated by property rights or commons, as well as measurements of other components like externalities.

Intellectual Property & Data. “There is scant collection of data on data, or on the total value of intellectual property, although companies spend large sums on these investments.”

Social and Institutional Capital. This include the degree of trust affecting the transactions costs of economic exchange and the viable provision of public goods.

Net Financial Assets. This includes government’s contingent liabilities from future promises such as pensions and public financial liability payments.

“GDP has focused the post-war western economies (and beyond) on maximising output of goods and services from the current use of resources,” write the authors in conclusion. “The future has zero statistical weight. GDP has ignored individuals, and geography. Many groups in society and places have been invisible in policy debates.”

“The long-term alternative we propose, motivated by the range of assets needed to maximise individuals’ capabilities to lead the life they would like to lead, would have told a different story about the recent past. It would have painted a picture even more divided than our current one: great improvements, on balance, for people with degrees, and access to new digital services, living in areas well-served by infrastructure; deterioration, on balance, for those who left school with few qualifications, have no access to fast broadband or even fast trains, and live in areas near polluted roads with scant green space. For all the benefits of the new technologies, current and to come, there would be no complacency about the economy.”