Does Gross Domestic Product (GDP), the traditional measure of the economy’s performance, still measure what matters? In today’s more services-based economy—increasingly driven by hard-to-measure assets including people, goodwill, brand reputation, and intellectual property (IP)—is there an argument to be made for discarding traditional quantitative measures, like GDP, that focus primarily on industrial processes, in favor of qualitative measures that may better fit the longer-term value creation on which stakeholder capitalism is based?
KPMG Chief Economist Diane Swonk shares her thoughts on the use(fulness) of GDP and other potentially more insightful metrics, the challenges of forecasting in a world without precedents, and the value of “leaning into the humility of a forecast, while embracing the strength of scenarios.”
Board Leadership Center (BLC): Strong GDP numbers always feel good, but how revealing is GDP of what’s actually going on in today’s economy, which is more services- and intangibles- based than the 20th century industrial economy that GDP was designed to measure?
Diane Swonk: The Commerce Department has gone to great lengths to upgrade and improve what GDP measures to better capture the world we are in, which is much more service-oriented than the world we left. They are also looking at high-frequency data and working with the large tech firms to better capture those shifts. One of the least-accredited but most useful measures the Commerce Department provided during the onset of the pandemic was the Household Pulse survey, which gave us a real-time look at how the pandemic was affecting households. Everything from child hunger to abilities to cover the basics of food and shelter to mental health were surveyed.
Those household metrics were critical, and some of the lessons learned will be incorporated into GDP going forward. Still, the world is in a revolution of information and change, while the GDP stats tend to evolve slowly over time; the government is always chasing a moving target on getting GDP measures to more accurately reflect what is actually happening.
I don’t think a lot of people are aware of the many efforts that have and will continue to improve the GDP stats. The top economists in the country on technology and innovation are working on improving those measures. GDP isn’t perfect, but it’s much more nuanced than it once was.
The hardest thing to measure is the productivity growth associated with the technology revolution and the digitization of the economy. GDP data measures what we actually spend on, versus the how of the economic activity and growth—which is about more than just the pivot from goods to services.
A better measure of overall growth within the index is GDI, or Gross Domestic Income. The divergence between that and GDP in the first half of 2022 illustrated the gaps in the GDP figures for the overall economy. That was unusual, and reflected the impact of the Russia-Ukraine war on the rest of the world compared to the U.S. We held up better, but GDI still decelerated over the course of 2022 relative to 2021. Even employment, which was still stunningly strong in 2022, slowed relative to the surge when the economy more fully reopened in 2021.
BLC: Are there other measures or frameworks that more effectively show how the economy is benefitting people’s lives? Would markets become more efficient with better information and metrics around economic well-being at a more granular level?
Diane: We have seen both the power and the shortfall of high-frequency data during the pandemic. I don’t think many people watched the daily Transportation Security Administration (TSA) throughput data or the Open Table reservations data prior to the pandemic. The frequency of the data is its strength and weakness. The same is true of daily credit card data. Bad weather can distort any given day.
Much of the high-frequency data lacks the history to accurately assess what it actually means, while cleaning the data of noise and seasonally adjusting a series that may be in response to a pandemic-induced bubble, such as the pivot to spending online, is tricky.
Consumers are likely to spend more online going forward, but not like they did at the height of quarantines. Online shopping is so ubiquitous now that it will soon be folded into the overall retail sales data and not a separate line item.
As to whether high-frequency data makes the market more efficient, I have my doubts. The data is noisy. Private-sector data can be manipulated or discontinued when the data providers do not like the story it is telling, and is not necessarily predictive. It could actually add to market volatility unnecessarily. What is useful for a company to know about its own orders and how it plans to adjust may not be the same way financial markets guess what the impact on profits may be.
It is also important to have reliable sources of data that can be revised to be more accurate but also transparent in methodology and how it is put together. Government metrics that leverage high-frequency data and more digital data, including inflation measures, must reveal all of that. Private-sector sources do not. The government is even forced to disclose any major revisions or shifts in source data and methodology.
BLC: Given the shift to remote work, the focus on energy transition, and other post-pandemic curveballs, are we in uncharted territory when it comes to certain economic metrics and forecasting? What do you see economists and corporate leaders wrestling with most today?
Diane: The metrics that we have are capturing much more than people realize. That said, we need to and we can always do better. As for forecasting, this is the toughest part. First, there is no precedent or historical norms to leverage in the data. And second, it is extremely hard to figure out seasonal adjustment of data in world that has different seasons due to climate change than it once did and is more reflective of residue of pandemic and war distortions than a change in the seasons. The latter includes hiring and spending during the holiday season.
Most people do not realize that the National Oceanic and Atmospheric Administration (NOAA) is housed within the Department of Commerce to better connect the thinking around climate and the economy. That is important.
The hardest issue that business leaders and economists wrestle with is forecasting in a world with no precedent and so much uncertainty and volatility. I have found it much more useful to merge more intel on geopolitics, climate, demographics, sociologists, and related professionals and all kinds of medical research.
People lose sight of the obvious. The health of any economy is inherently dependent on the health of its people. Moreover, physical and mental health are intimately intertwined and can supplement or undermine productivity growth.
Wages and benefits are only the table stakes. In one study that I analyzed with a former colleague, we that found employee retention rates in a hospital system could be boosted dramatically by providing the employees’ families with access to the experts they worked for. Engagement boosts productivity growth, but we can’t guess what workers want. The studies I have seen by managers and HR professionals suggest they are poor at guessing.
Too often, we impose our own ideological bias versus actually gathering the information on what will improve engagement and retention. The data is telling, but only if you ask the correct questions of it.
BLC: Do you see AI or other technologies playing a role in getting a better, more real-time view of what’s going on in the economy—e.g., enabling more focus on leading versus lagging indicators? Generally speaking, is economic forecasting becoming more precise?
Diane: AI is improving and has extraordinary potential to bridge skills gaps and boost both productivity and wages in ways we once only imagined.
Thus far, it has not been able to predict and has led to some very wrong predictions as it is subject to the same biases or even more, given how narrow the demographics of those who write AI programs can be. There have been gross errors in healthcare and criminal justice that have exacerbated inequality and made it harder for the economy to perform well.
We need to be aware of this. We got lulled into a similar false narrative on economic modeling more than half a century ago as economists shifted from modeling out the economy and behavioral responses on paper and in theory to modeling with statistics that do not fully capture behavioral shifts. Again, nuance is key.
The hardest part of my job is leaning into the humility of a forecast, while embracing the strength of scenarios. A mentor taught me many years ago that economists are asked the wrong questions. It is our job to reframe the debate on longer term issues—which economics, when paired with the work of other fields, is much better at.
BLC: Similar to the GDP question, is there a case to be made at a company-level for thinking differently about quarterly earnings versus other measures of corporate performance and value creation? Are quarterly earnings becoming a relatively “blunt indicator” of value creation and a company’s longer-term well-being?
Diane: Trust in institutions has been eroding for decades. It is at a really low level and that includes large, publicly traded companies and the gap between owners of capital and workers.
Companies report, and the economic research validates, that they do better profit-wise when they are more focused on what were once considered social or government problems, such as ESG. There is a huge need to solidify targets so that companies can report across a broader spectrum of results and show the correlation with profits. It has also proven key to recruiting and retaining the best talent.
BLC: Is there a specific metric that you have found to be surprisingly illuminating—and that tends to generate a particularly rich discussion—when it’s raised in a boardroom conversation?
Diane: I am always surprised by how little is understood about the data that companies and boards are looking at—what is shaping it and how it is compiled. And that includes government and firm data. It’s one reason tech companies are now the largest employer of economists. Trying to better understand AI beyond correlation to causality, for example. Asking the right questions is the key—and it’s not easy.
BLC: At a macro level, when you think about economic progress and prosperity, are there two or three trends that you’re particularly optimistic or concerned about?
Diane: Inequality is destabilizing and non-productive. How we equalize the economy across a broad spectrum of metrics is critical. We need to invest and enhance the earning power of workers, which spans shifts in education, access to transit, healthcare, and childcare.
I am stunned by how cavalier organizations can be about retaining talent. I, myself, fit into many boxes and faced significant hurdles that often hold people back, including having dyslexia. I also know that it is one of many things that made me resilient, tenacious, and someone who thinks outside of the box.
We are all more comfortable being around those who are more similar than different, but the longer we deliberate the facts and think through scenarios with a much more diverse set of leaders, the better the outcome. It is in this discomfort that we get to deeper insights and better decisions.
BLC: “Sustainable growth” and even “de-growth” have joined the macroeconomic debate alongside the “all growth is good” point of view. Does corporate America have a role to play in influencing how we think about and measure growth and economic well-being going forward?
Diane: Corporate America has a huge role to play in understanding the economy and how we measure growth. Advocating for quality data and data lending—which many firms do—to enhance the quality of the data that the government is provided with is critical.
Well-being is another measure that goes well beyond GDP statistics. That large firms are participating in economic research on the role that mental and physical health, and empathetic managers, play in promoting retention and productivity at their companies has also contributed to huge leaps in our understanding of the economy.
Read more from Diane Swonk and KPMG Economics.
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