As we enter 2022, and as we are about to start the “earnings season” with reports of the Q4 and full year of 2021, we also continue to examine the “statistical discrepancy” between the estimates of GDP (gross domestic product) and GDI (gross domestic income). Here are our three previous pieces on this subject:
“Statistical Discrepancy: A Mystery About Profits?”
https://www.cumber.com/market-commentary/statistical-discrepancy-mystery-about-profits-michael-drury
“David Blond Helps Us Understand Statistical Discrepancy,”
https://www.cumber.com/market-commentary/david-blond-helps-understand-statistical-discrepancy
“More About the Statistical Discrepancy,”
https://www.cumber.com/market-commentary/more-about-statistical-discrepancy
As is customary with data releases from government sources, there has been a recent set of updates; and Philippa Dunne and Doug Henwood, who contributed our most recent piece on the statistical discrepancy, have published a follow-up. With their kind permission, we are sharing their entire research missive below. Their commentary comes from Philippa and Doug’s excellent daily research note, TLRwire, which I read daily upon receipt. Here is how to subscribe: https://www.tlranalytics.com/category/tlr-wire/. It is low-cost and well worth the price.
Some readers have asked why we are so obsessed with this technical calculation. The question is fair.
Here’s why.
First, the discrepancy is at a remarkably wide level in the history of this data series. So we focus on why that is so and what it means for our estimates and our responsibility to our clients. Serious investors may want to take the time to carefully peruse our discussion of the issue in the prior pieces cited above.
Secondly, it is from the GDP side of national income and products accounting that profits estimates are derived, using calculations reported in a manner designed to deliver a very consistent series. The series has decades of history. Those profits (before and after taxes) are estimates made and subsequently revised as time passes and data clarity is achieved. The estimates include the profits of both privately owned and publicly traded businesses. Example: Cumberland Advisors is a privately owned (17-shareholder) company. The majority of the shareholders are also employees. The profits of the company (fortunately, we have them) are part of the national GDP profit series but not part of the reported earnings of those publicly owned companies that trade on the stock exchanges.
Note that about half the profits estimated in GDP accounts are those estimated to be made in privately owned businesses. It is the half that contribute to the earnings of the companies that trade on the stock exchanges that are of concern to our investment advisory clients.
Cumberland Advisors is a money management firm (a Registered Investment Advisor) and is part of the broadly defined financial services sector. Some of what we do examines investments in state and local government debt. We assess creditworthiness, and we manage portfolios using that debt.
But we also invest in the stock market.
Thus the profits of the publicly traded companies are very important part of our decision-making about whether or not to buy, sell, or hold an equity security (like Microsoft) or a specialized sector (like Medical Devices) or an industry (like Defense and Aerospace) or a broad stock market index (like the S&P 500). Disclosure: As this is written, all four of those positions appear in one or more of the several different investment styles that Cumberland uses on behalf of its clients. Yes, we have several different investment styles. And, yes, we apply them differently depending on the needs and desires of the client.
Thus the earnings of all of those companies and hundreds more are important to us. In addition, we also examine the creditworthiness of public companies as part of our bond management for clients. Thus the profits of a company, which secure payments to bondholders and which support the company’s credit analysis and rating, are very important.
Bottom line: Profits are critical, and the earnings of public entities come from these macro GDP profits. Hence, any discrepancy is important; and a wide, historically record-setting statistical discrepancy is very important to understand.
Before we get to the guest piece by TLR on the latest issues, we want to offer our outlook. We expect the stellar profit recovery of the last five calendar quarters to continue. We expect the January and February earnings reports to contain many positive surprises. And we expect the GDP estimates and revisions to demonstrate the macro argument in favor of those profits and, hence, earnings. We expect the statistical discrepancy to eventually be narrowed, either by computational revisions and explanations or by clarity as post-pandemic data emerges from the fog of the two-years-long COVID shock.
In our US Equity ETF portfolio we start 2022 at a fully invested position. In our opinion, earnings momentum will be upward in 2022.
Now let’s get to Philippa and Doug and their update on the statistical discrepancy.
David R. Kotok
Chairman and Chief Investment Officer
Email | Bio
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About Those Revisions
TLR Analytics
The statistical discrepancy we wrote about earlier this week took a small step back in the third release of 21Q3 GDP. As a share of GDP, GDP less GDI narrowed from -2.7% in the second release to -2.6% in the third. Two more such moves and it will be back to the then-record low of -2.3% it hit in both 20Q4 and 21Q1.
We had suggested in our note that with nominal GDP 19% above its 2020 trough, and GDI 15% above, a jumpy discrepancy seemed almost to be expected, so let’s move on to another example of magnitudes run wild, monthly payroll revisions in the Covid era.
Monthly revisions worthy of benchmarks
We used to joke that if the monthly nonfarm payroll estimates were subject to revisions of similar magnitudes to those in the retail sales series we’d be facing monthly payroll revisions, as distinct from the annual benchmarking process that employs the UI data compiled in the Quarterly Census of Employment and Wages, of something like a million jobs. The Bureau of Labor Statistics’ official lingo for the three releases are first and second preliminary, and final sample-based estimate, but we’re calling them advanced, preliminary and final for clarity. The final estimate is, of course, subject to the benchmark and additional adjustments of the years.
A million jobs seemed preposterous two years ago, but we got close in the early pandemic months. It’s absolutely no surprise, and no one’s fault, that between the advance and final estimates for March 2020 the level fell by 949,000, and in April by 769,000, and the surprisingly small official 2020 benchmark and preliminary 2021 benchmark both suggest the final estimate did indeed have more accurate information.
Revisions turned positive in the summer of 2020, with an absolute average of 83K through the end of the year. Except for March they have been positive in 2021, and have really picked up in the last few months, months not included in the preliminary 2021 benchmark. Between the advance and final estimates, levels rose 143,000 in June; 172,000 in July; 286,000 in August; and 302,000 in September, an absolute average to 226K, following 91K for the preceding five months.
The negative revisions to the early pandemic months, -0.6%, would be considered large if they were annual benchmarks, which have averaged +/-0.1% over the last ten years, and recent months are worthy of or exceed average annual benchmarks. This is a consequence of the long downtrend in response rates, but worth a thought when we draw inferences from these monthly numbers.
We’ll see what the 2022 benchmark, which will run through March 2022, has to say about all of this when the preliminary percentage changes are released next August. Until we have that information, both in extrapolating from the estimates and from the revisions, we “urge caution.”
Philippa Dunne & Doug Henwood
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