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Policy Hub: Macroblog provides concise commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues for a broad audience.

Authors for Policy Hub: Macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.

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May 6, 2021

How Has GDPNow Performed during the Pandemic?

According to the U.S. Bureau of Economic Analysis (BEA), real gross domestic product (GDP) grew at an annualized rate of 6.4 percent in the first quarter of 2021, 1.5 percentage points below our final GDPNow model forecast. The size of this forecast error is large relative to GDPNow's prepandemic history but much smaller than the extremely large errors recorded last year. In short, the GDPNow model has struggled to accurately capture the unusual sources of variation in GDP caused by the COVID-19 pandemic and the various policy responses. The colored dashed lines in chart 1 show the evolution of GDPNow's forecast errors for each of the last five quarters. Unlike any of the four quarters of 2020, GDPNow's forecasts of real GDP growth last quarter was always within 5 percentage points of the BEA's first published estimate. Nevertheless, we can see that the GDPNow forecasts for GDP growth last quarter were still less accurate than before the pandemic.

Chart 1: Accuracy of GDPNow Forecasts

Despite the recent deterioration in GDPNow's accuracy, its forecasts over the past 15 months share some broad similarities with professional forecasts, as chart 2 shows. From mid-March to mid-May 2020, both GDPNow and the consensus Wall Street Journal Economic Forecasting SurveyOff-site link (WSJ) forecasts of real GDP growth declined sharply. As described here, GDPNow did not anticipate the extent of the plunge in services consumption last March and the resulting impact on GDP growth for the first quarter of last year. The nosedive and rebound in economic activity last year were evident in GDPNow's forecasts for GDP growth in the second quarter of 2020. Forecasts for third-quarter GDP growth strengthened during much of the summer and early fall, and GDPNow performed as well or better than the consensus forecast over much of that period. However, for the last two quarters, GDPNow forecasts were more erratic, and generally less accurate, than the consensus.

Chart 2: Forecasts and initial BEA estimates of quarterly real GDP growth

It's not surprising that GDPNow's accuracy has deteriorated relative to the consensus of professional forecasts. Unlike GDPNow, professional forecasters have been able to use data on personal mobility and government mitigation measures (such as hereOff-site link, hereOff-site link, hereOff-site link, and hereOff-site link), and a number of studies have shown that these measures relate to cross-country differences in GDP growth observed in 2020 (for example, hereOff-site link and hereOff-site link). Transportation services and leisure and hospitality accounted for half the decline in consumer spending in the first two quarters of 2020, and a number of high-frequency measures of activity in these sectors (such as hereOff-site link, hereOff-site link, hereOff-site link, and hereOff-site link) are available before official spending dataOff-site link are. Finally, spikes in consumer spending following the distribution of stimulus payments included in the December and March fiscal packages were evident in some high-frequency measures of consumer spending (available hereOff-site link and hereOff-site link) before they were evident in the retail salesOff-site link and personal income and outlaysOff-site link releases. In contrast, GDPNow uses a fixed methodology and source data for estimating GDP, making it much less adaptable to unusual circumstances.

Nevertheless, GDP forecasting during the pandemic has been challenging even for professional forecasters. Chart 3 shows the distributions of GDPNow and WSJ survey panelist forecast errors of real GDP growth for projections made about 20 days before the BEA's release of its initial GDP estimate. The gray cross marks represent individual WSJ panelist errors, and the blue rectangles represent interquartile ranges (IQRs), or middle halves, of the distribution of their forecast errors. The IQRs widened during the pandemic, and a number of panelists had errors in the middle two quarters of last year that were dramatically larger than usual.

Chart 3: Forecast errors of real GDP growth about 20 days before GDP release

Still, it is plausible that the extreme fluctuations in the data used by the GDPNow model have embedded some inaccuracies into the GDPNow forecasts. To examine this question, let's consider GDPNow's final projections of the main subcomponent contributions to overall real GDP growth since the model was released to the public in 2014. Chart 4 shows the stacked forecast errors of these projections. By construction, the sum of the bars in any quarter is approximately equal to the black dot showing the difference between the first official estimate of real GDP growth and that quarter's final GDPNow forecast. The bars have become much larger since the pandemic, but especially for services consumption and state and local government expenditures (S&L). In this post, I've noted issues related to services consumption, but most of the huge third-quarter S&L error fell into a category referred to as "sales to other sectors Adobe PDF file formatOff-site link." GDPNow expected these sales—which are largely health, hospital, tuition, and other educational charges and fees—to continue plummeting in the third quarter instead of rebounding strongly, as they did. (The S&L contribution error is negative because the BEA subtracts these sales from the S&L subcomponent, and from GDP, to avoid double-counting). The S&L contribution errors in the last two quarters were still quite large, and mostly the result of the GDPNow model incorrectly continuing to forecast declines of these "sales to other sectors."

Chart 4: Errors of final GDPNow forecast of subcomponent contributions to real GDP growth

In the coming months, we plan to tweak the architecture behind GDPNow in hope of mitigating these sorts of errors. For instance, the day before the most recent GDP release, a modified version of the GDPNow model (one that reduces the influence of the pandemic on some of its forecasting equations) predicted first-quarter real GDP growth of 5.5 percent. More than half of the difference between this forecast and the official final GDPNow forecast of 7.9 percent growth was the result of a lower forecast for growth in the S&L subcomponent (1.3 percent), a number much closer to the BEA's S&L growth estimate (1.7 percent) than the final GDPNow forecast (14.6 percent). Such enhancements might help improve GDPNow's forecasting accuracy down the line. But GDPNow will remain susceptible to forecast inaccuracies whenever unusual events (such as the COVID-19 pandemic) hit the economy and dramatically shift spending patterns.

May 3, 2021

Is There a Global Factor in U.S. Bond Yields?

The answer to this question seems obvious simply from observing the secular comovement of global nominal yields across some advanced economies plotted in chart 1.

Chart 1: 10-Year Bond Yields, 1992-2021

This observation raises the possibility that domestic bond yields, including those in the large U.S. Treasury market, may be anchored by global economic developments (see, for example, hereOff-site link and hereOff-site link), provision of global liquidity, and international markets arbitrage. The synchronized dynamics in global yields during the last few months serve as a stark reminder of the powerful role that global bond markets play in the transmission of country-specific shocks as well as of monetary and fiscal impulses.

Yet the standard term structure models (see, for example, hereOff-site link), that policymakers and market participants use to form their expectations about the future path of the policy rate, are typically estimated only with information embedded in domestic yields. Global influences enter only via the term premia—that is, the extra returns that investors demand to hold long-term bonds—and are influenced by the flight to safety and arbitrage across international markets. But because the term premia are obtained as a residual component in the model, any misspecification of the factor structure that drives equilibrium interest rates—by omitting a common global factor, for example—may result in erroneously attributing some fundamental movements to the term premia.

Chart 2 illustrates this point, presenting a less-noticed and even overlooked empirical regularity between the term premiaOff-site link on the spread between the 10-year U.S. bond and the 10-year/2-year German bond, which is the benchmark bond for the Eurozone government bond market. This comovement has proved remarkably strong since 2014.1

Chart 2: German Bond Spread and U.S. Term Premia, 2010–21

Take, for example, the pronounced decline in the term premia and the accompanying slide in the German bond spread between 2014 and 2019. Although technical factors might be behind the downward trend in the German bond spread—for example, large Eurozone bond outflows triggered by the euro-area crisis and the introduction of negative interest rates—the slope of the yield curve could also convey important information about the fundamentals of the economy. If the term premia on the 10-year U.S. bond reflect an exogenous "distortion" in the U.S. yield curve due to a flight to safety or an elevated demand for global safe assets, yields are likely to return to normal levels when the uncertainty shock dissipates. In contrast, if investors interpret the yield curve's decline as an endogenous "risk-off" response—that is, a switch to less risky assets—to a deteriorated global environment that can spill over to the U.S. economy, the term structure model would require a "global" factor whose omission may otherwise contaminate an estimate of the term premia.

So how sensitive is the estimate of the future path of policy rate to model specification? I next illustrate this sensitivity by augmenting the factor space in a standard (five-factor) term structure model with incremental information from an additional global factor, not contained in the other factors. Given the reasonably tight correlation between the term premia for the 10-year U.S. bond and the 10-year/2-year German bond spread, it seems natural to use the latter as an observed proxy for a global factor, although other statistical approaches for extracting one or more common global factors are certainly possible.

To quantify the potential effect of the global factor, I focus on yield curve dynamics seen in 2019, a period characterized by elevated economic, trade, and geopolitical uncertainty that led to a material decline in observed yields. But did a fundamental shift in the expected path of policy rate, or lower term premia, drive this decline? In the left panel of chart 3, I plot the expected policy rate paths for the second quarter (or midpoint) of 2019, obtained from models with and without a global factor. (Recall that in the second quarter of 2019, the target range for the federal funds rate was 2.25 percent to 2.50 percent.)

Chart 3: Model-Implied Paths of Policy Rate

The difference in the shape of the expected policy rate paths implied by the two models is striking. (The models' estimates use unsmoothed yield data at quarterly frequency, with continuous bond maturities from one to 80 quarters.) Although the expected policy rate path for the standard model is fairly flat, the rate path for the model with a global factor is deeply inverted up to five-year maturities, suggesting that over this horizon one could have expected rate cuts of almost 100 basis points. These expectations occurred against the backdrop of stable growth and inflation outlook in the United States but deteriorating global economic and trade conditions. The right panel of chart 3 displays the evolution of the expected rate path, estimated from the global factor model, for the two quarters before and the two quarters after the second quarter of 2019, as the Federal Reserve started to adjust its policy rate lower. It is worth noting that the strong effect of the German 10-year/2-year spread in the term structure model with global factor is a relatively recent phenomenon. (Additional results suggest that this factor has only a muted impact on the model estimates prior to 2014.)

The policy implications of these findings warrant several remarks. One direct implication is that the common global determinants of the neutral rate of interest, as well as inflationary dynamics, could constrain the potency of domestic monetary policy. A prime example of these constraints was the policy rate normalization phase undertaken by the Fed during the 2016–18 period, which was characterized by global disinflationary pressures, underwhelming economic performance in Europe and Japan, slowing economic growth in China, and escalating trade tensions. These forces were potentially counteracting the Fed's policy efforts and exerting downward pressure on the global neutral rate of interest. The recent economic and financial developments resulting from the COVID-19 pandemic (such as the global nature of the shock, synchronized monetary and fiscal response across countries, and international financial market comovements) and the ongoing recovery appear to only strengthen the case for the importance of incorporating global information in bond-pricing models.

1 [go back] I should note that the correlation between the two series increased from 52.9 percent before 2014 to 76.2 percent after 2014. Interestingly, the beginning of 2014 marks another important shift in financial markets: a sharp and persistent compression in the breakeven inflation forward curve, as a Liberty Street Economics blog postOff-site link recently discussed. A similar flattening is present in the forward term premia of nominal bonds. This is consistent with the interpretation that such flattening—starting in 2014—is likely the result of a new regime, characterized by the compression of inflation risk across maturities.

April 7, 2021

CFOs Growing More Optimistic, See Only Modest Boost from Stimulus Plan

During the past few months, alongside an increase in COVID-19 vaccinations and amid a fresh round of fiscal support, optimism about the economic recovery from the COVID-19 pandemic has grown. Although reasons for concern over the potential unevenness of the recovery still exist, many economistsOff-site link, policymakers Adobe PDF file formatOff-site link, and market participantsOff-site link have ratcheted up their growth expectations for 2021.

This growing optimism extends to decision makers who participate in The CFO SurveyOff-site link—a collaborative effort among the Atlanta Fed, Duke University's Fuqua School, and the Richmond Fed. CFOs and other financial decision makers in our survey grew more optimistic about the U.S. economy and their own firms' financial prospects, according to the first quarter's data released on April 7. Moreover, these firms see stronger prospects for sales revenue and employment growth in 2021 (similar to results from other business surveys, including the Atlanta Fed's Survey of Business Uncertainty).

Many people think the recently passed $1.9 trillion American Rescue Plan ActOff-site link (ARPA) is behind these brighter expectations. However, the results of our CFO Survey suggest that many firms anticipate that the fiscal stimulus will have only a modest impact on their own future business activity.

In the first-quarter CFO Survey (fielded March 15–26, 2021), we posed a question asking respondents about the impact that ARPA might have their own firm's revenue growth, number of employees, representative price (the price of the product, product line, or service that accounts for the majority of their revenue), and total wage and salary costs (see chart 1). Firms had five response options, ranging from "decrease significantly" to "increase significantly." A majority of firms expect the recent fiscal measure to have "little to no impact" across all areas of their business activity. The results are perhaps most striking for employment, as nearly 80 percent of firms anticipate ARPA to bring little to no change in that area.

Chart 1 of 1: Anticipated Impact of Recent Fiscal Stimulus

Considering the tepid impact of the stimulus on employment expectations, the survey results for total wage and salary costs are also interesting. Here, nearly 30 percent of the panel anticipates modest to moderate upward pressure on wage and salary costs, with another 5 percent or so expecting "significant" impact on their wage bill. The reasons for the expected effect on firms' total wage and salary costs are unclear, but we should note that labor quality and availability remain very high on CFOs' list of most pressing concerns.

Expectations around ARPA's impact on revenue growth appear a bit more diffuse. Though the survey's typical (or median) firm still anticipates that the bill will bring little to no change in sales revenue growth, nearly 40 percent of respondents expect the legislation to have a positive impact on sales, and a very small share of firms anticipate a negative impact on revenue.

Given the nature of these responses, we were curious whether CFOs who anticipated a positive impact from ARPA also held higher quantitative expectations for firm-level growth than firms who saw little-to-no impact. t. The CFO Survey elicits firms' quantitative expectations for sales revenue, employment, price, and wage growth early in the questionnaire, providing a useful way to check for consistency. Table 1 reports these results.

Table 1 of 1: Average Expectations for 2021 by Anticipated Stimulus Impact

Apart from firms' anticipated growth in wage and salary costs, it does appear that firms that foresee a boost from the fiscal stimulus also hold higher growth expectations. The increase in expectations is particularly stark for employment growth and prices.

If we dig a little deeper into the small share of firms anticipating increased employment due to the stimulus—45 total—we find that 40 of them are in service-providing industries and employ fewer than 500 workers. We know from academic researchOff-site link, government statisticsOff-site link, and anecdotal reportsOff-site link that the COVID-19 pandemic has hit smaller, service-providing firms particularly hard, so it's perhaps not surprising to see these types of firms expecting the stimulus to aid in a rebound. These firms are also anticipating a stimulus-induced boost to the prices they can charge. The price growth for services has slowed markedly since the onset of the pandemic. As the economy begins to open up more fullyOff-site link, these firms might believe that measures to bolster household income (among other aspects of ARPAOff-site link) will lead to a bit more pricing power.

Overall, however, our results suggest that the majority of firms anticipate ARPA to have little to no impact on their sales revenue, employment, prices, and wages. The smaller share of firms that do anticipate increased activity resulting from the stimulus largely expect the increase to be modest to moderate.

Importantly, these results do not rule out a surge in growth as the pandemic recedes and the vaccination rollout continues. As we've noted, most CFOs expect growth to occur regardless of ARPA's role in that growth. But the survey shows that firms, in general, do not pin any surge in demand on the legislation.

March 23, 2021

Hourly and Weekly Perspectives on Wage Growth during the Pandemic

Despite record-setting job losses during the COVID-19 pandemic, median growth in the hourly rate of pay for those who stayed employed has held up remarkably well, which we can see in the Atlanta Fed's Wage Growth Tracker (see chart 1).

Chart 1 of 4: Wage Growth Tracker

The Wage Growth Tracker compares individual hourly wages in the current month with what the same individual's hourly wage was 12 months earlier and calculates the change. The fact that the median wage growth has not slowed, despite the increase in unemployment, suggests that the pandemic's impact on the labor market has been quite unusual.

During the Great Recession, the slowing in median hourly wage growth coincided with a large increase in the share of workers reporting that their hourly rate of pay was unchanged from a year earlier. As chart 2 shows, the share of workers reporting zero change in their hourly rate of pay has ticked up a bit during the COVID-19 pandemic, but so far, what we see differs from observations we made during the Great Recession.

Chart 2 of 4: Share of Workers Reporting No Change in Hourly Rate of Pay

Why did the COVID-19 pandemic have a relatively smaller impact on median hourly wage growth compared to the Great Recession? One explanation is that the supply of unemployed job seekers far exceeded job vacancies in the earlier recession. That is, employers typically received many more applicants for each available position. As chart 3 shows, at the Great Recession's peak, there were 6.5 unemployed workers for each job posting and 5.7 unemployed not on temporary layoff for each job posting. I think unemployed workers not on temporary layoff is a more useful measure of unemployed job seekers because those on temporary layoff expect to be recalled by their employer and hence are not necessarily looking for another job. Contrast that with January 2021, when there were 1.5 unemployed workers for each opening and 1.1 unemployed workers not on temporary layoff for each job vacancy. In this sense, the labor demand and supply during the COVID-19 pandemic has been more in balance than during the Great Recession. Compared with the Great Recession, apart from the period during the initial lockdown, total vacancies by firms has scaled back relatively modestly during the pandemic while the number of workers looking for a job has increased by less.

Chart 3 of 4: Number of Unemployed per Job Vacancy

Nonetheless, during both the Great Recession and the COVID-19 pandemic, many workers who remained employed have experienced an involuntary reduction in their work hours, which has dragged down workers' weekly paychecks even when their hourly rate of pay hasn't fallen. In February 2021, about 6.5 million workers were classified by the U.S. Bureau of Labor Statistics (BLS) as working part-time for economic reasons—almost 2 million more than in February 2020, just before the pandemic hit the U.S. economy. For this reason, I've constructed an alternate version of the Wage Growth Tracker, which shows the median growth of individual weekly earnings. This new measure uses the same data (from the Current Population SurveyOff-site link, jointly administered by the BLS and the U.S. Census Bureau) as the hourly earnings measure, and I show both series in chart 4 for comparison.

Chart 4 of 4: Wage Growth Tracker

Generally, the two series move in tandem, with the weekly series slightly outpacing the hourly series during economic expansions as hours worked tend to rise. However, as we see here, during both the Great Recession and the COVID-19 pandemic, reduced hours worked each week lowered many workers' median growth in weekly earnings relative to hourly earnings.

As the economy recovers from the COVID-19 pandemic, watching both the hourly and weekly versions of the Wage Growth Tracker will be useful. As fewer worker face reduced hours, I expect to see median weekly wage growth recover and at least match the pace of hourly wage growth. A tighter labor market should result in higher wage growth on both an hourly and weekly basis. I'll write about the developments using new Wage Growth Tracker data we'll post soon, so check back.

Note: If you are interested in tracking the hourly and weekly versions of the Wage Growth Tracker you can do that here, or via the EconomyNow app, which also features several other Atlanta Fed data tools.