A version of this article appeared in Barron's.
As a longtime consumer of economic statistics—some would say a fanatical, obsessive consumer of such stats—I have come to realize two important things about data. First, you have to look at the right numbers. And second, even the “right” numbers miss a lot when it comes to predicting future outcomes.
I’ve seen this time and again, especially at a few major economic turning points:
Having grown up in Western Pennsylvania during the 1960s, I lived in a world where 40 percent of the nation’s coal came from within 100 miles of Pittsburgh, which made it an economic powerhouse. But all the data describing the region’s growth in those halcyon days failed to anticipate the economic costs of the erosion of the steel industry and the environmental damage that would be paid over many generations.
From 1961 through 1966, average annual U.S Gross Domestic Product (GDP) grew about 5 percent, while the unemployment rate averaged 4.8 percent, and inflation was under 2 percent. Knowing just these data points, you would never get a sense of the economic and social inequities that led to 1967’s Long Hot Summer of race riots.
In 2006 and 2007 most investors and policymakers missed the signs that the engine of economic growth associated with rapid house price appreciation would prove to be the source of global financial disaster.
Today, globalization, which has brought economic prosperity to more people around the world than ever before, has also been accompanied by greater wealth and income inequality, weaker social safety nets, and rising carbon emissions with consequences that will be felt for years to come.
Many treat GDP as an all-encompassing measure of national wellbeing, but it does not measure other costs of that output nor any progress made in meeting social or environmental challenges, many of which have yet to be realized. For example, if global GDP growth of 5 percent is accompanied by the Earth’s surface temperatures rising 0.5 degrees Celsius, but surface temperatures fall when global GDP grows 3 percent, how should we compare those two growth outcomes?
GDP does not even do a great job at measuring economic output. Difficulties in measuring an increasingly digital and services-oriented economy, flawed methodologies, and significant procyclical revisions all make GDP a faulty gauge of economic security. Nor are other conventional macroeconomic indicators fully up to the task: I would be hard-pressed to say that the current 50-year low in the U.S. unemployment rate accurately conveys the wellbeing of most people.
The irony here is that data measuring progress in environmental and social challenges already exist to some extent; but business, economic, investment, and public policy communities aren’t paying the appropriate attention.
If there is any hope of reaching the United Nations’ Sustainable Development Goals (SDGs) by 2030, it is imperative that we use existing and new data sets to measure environmental and social progress in economic terms. There are several ways that this could be accomplished.
- Create a GDP adjustment factor for progress in social and environmental areas, similar to the hedonic quality adjustments that are already used to calculate real GDP. For example, an environmental adjustment factor could be developed that reduces GDP if carbon emissions increase. The end result is closer to a “net domestic product” that more fully accounts for the impact of economic activity, including the costs of remediation in the future.
- Identify a “first-tier” data series for sustainability and create market awareness around the newest releases. For example, the market should be as vested in the latest data point for global surface temperatures or wealth inequality as it is in the release of non-farm payrolls or the consumer price index.
- Make the data readily accessible in conventional news and data aggregators, such as Bloomberg. More focus by government statistical agencies should be given to improve the quality, timeliness, and visibility of these indicators.
- Link the data to market forces, such as the “cap and trade” mechanism that brings market forces to bear on carbon emissions. Another possibility is compelling companies to disclose environmental and social impact metrics along with financial results, thereby incentivizing better corporate behavior.
Peter Drucker once said that “What gets measured gets managed.” With better real-time data, we will not only be able to track environmental and social progress, but we can also be held more accountable for the results. Policymakers, who deploy conventional fiscal and monetary tools to offset rising unemployment or slowing economic growth, could develop new, data-centric, evidence-based policy to introduce potentially disruptive yet necessary measures. Policies and incentives such as a carbon tax, land use reform, and programs to raise labor force participation through the tax code could all be made more effective with the right data to measure progress.
The good news is that we no longer need to trade off economic growth for environmentally and socially sound activity or give up strong and stable returns in order to make responsible investments. In fact, in the future, I believe it will be impossible to deliver economic prosperity without a principled approach to sustainability. With the right measurement tools and advances in technology, meeting the SDGs can lift the standard of living for all at a faster pace than by clinging to older, outdated paradigms.
The biggest challenge in meeting the SDGs, however, is to induce investors and policymakers to lengthen their time horizons. While the consequences of inaction on social and environmental challenges are increasingly evident, with little immediate incentive to take action today, we ensure that the collective costs will be far greater in the future. This “tragedy of the horizon” is that once the failure to meet the SDGs becomes a defining issue, it may be too late. Until the public and private sectors are incentivized to do what is in their own economic best interests, the Sustainable Development Goals will remain elusive. By rethinking how we use data to measure progress and developing the right free-market incentives, our chances for success will be much better.
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