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May 18, 2020
A Couple of Insights from the April Current Population Survey
The latest reading of the Atlanta Fed’s Wage Growth Tracker indicates that wage growth is slowing. It came in at 3.3 percent for April, down from 3.5 percent in March and 3.7 percent in February. This slowing primarily reflects the relatively large decline in the employment of those who typically experience the fastest wage growth: young workers. In February, those aged 16–24 accounted for about 12 percent of employment. By April, that share had dropped to under 10 percent. This change has significant bearing on the Wage Growth Tracker because those aged 16–24 had median wage growth of around 7.8 percent on average over the last year, versus 3.6 percent for all workers. So their decreased share of employment has helped pull overall median wage growth lower (see here for more discussion).
Note that while the tracker reflects the compositional change in who is employed, it didn’t show a spike in wage growth suggested by the average hourly earnings data from the Bureau of Labor Statistics' Payroll Survey. This is because the average hourly earnings data are a snapshot of the average earnings of all workers, hence last year's average will include people who are not employed today (and vice versa). As a result, the spike in average earnings was for an awful reason: a lot of low-wage workers lost their jobs. In contrast, the tracker compares the wages of the fortunate people who were employed both today and a year earlier.
Another wage development to keep an eye on are wage freezes. During the Great Recession, there was a large and persistent increase in the fraction of workers who said their wage was unchanged from a year earlier. We will be examining the Wage Growth Tracker data for evidence of an increased incidence of wage freezes or even wage cuts. The fraction of people reporting no change in their wage has increased from 13.7 percent in February to 14.1 percent in April. In contrast, the cyclical low for this series was 12.7 percent in November of 2019.
The April data also revealed a sharp increase in the number of people who are employed but on unpaid absence from work for "other reasons." As described in this recent macroblog post, these are most likely people whose employers furloughed them. March saw an estimated 1.5 million such workers. In April, that number swelled to 6.2 million. If those people had been counted as unemployed instead of employed, the unemployment rate would have been 18.7 percent in April instead of the official number of 14.7 percent. Going forward, a gauge of the strength of the labor market recovery will be how many of these furloughed workers eventually return to work versus become unemployed—or even leave the labor force. Stay tuned.
John Robertson, a senior policy adviser in the Atlanta Fed's research department
May 15, 2020
Introducing the CFO Survey
For almost 25 years, the Duke CFO Global Business Outlook has provided policymakers, academics, and the public with an understanding of how financial executives view the economy and prospects for their business. Today, three partners—Duke University's Fuqua School of Business, the Federal Reserve Bank of Richmond, and the Federal Reserve Bank of Atlanta—announce an enhanced iteration of this survey, now called theCFO Survey. Starting with the second-quarter data release on July 8, 2020, the CFO Survey will offer the same crucial information about the economic outlook and, through some methodological updates, an enhanced look at how U.S. companies perceive and react to the current economic environment.
This partnership comes at an opportune time. As the country faces considerable economic and political uncertainty, the on-the-ground information policymakers receive from businesses has never been more important. In the longer run, the information collected through the CFO Survey will help economists and researchers understand how firms reacted amid the COVID-19 pandemic and its economic consequences. It will therefore provide a key input into our understanding of the role that sentiment and uncertainty play in corporate decision-making processes.
What will change?
Much of the Duke survey will remain the same. One change is a discontinuation of the international portion. The CFO Survey will initially survey only U.S. firms, to fully establish the U.S. panel. (After we've reached our domestic panel-composition goals, we hope to eventually engage our global partners again.) Another change is that the sampling design has moved from a repeated cross-section to a panel-data format, meaning that the same pool of business leaders will participate each quarter. Finally, the team engaged in a thorough methodological review, and the survey questions will be streamlined and the survey process made more efficient for participants. More details on changes to existing questions will be available over the course of the next few weeks, and the first set of data generated using the updated questionnaire design will be available on July 8 on the new CFO Survey website: www.cfosurvey.org.
What will stay the same?
The survey will continue to track business sentiment over time and ask questions pertaining to business leaders' most pressing concerns, their expectations for their own firms' performance, and their expectations for the performance of the U.S. economy over the year ahead. Because Duke has conducted this survey since 1996, the rich historical data will allow for contextual insights on key indicators including revenue, capital expenditures, and employment as well as illuminating trends and shifts in business sentiment. The headline CFO Optimism Index will remain unchanged—that index measures business leaders' optimism about the U.S. economy and their own firm's financial prospects.
The objectives of the survey will not change, nor will the target participants. In addition to chief financial officers, the CFO Survey panel includes treasurers, vice presidents, and directors of finance, owner-operators, accountants, controllers, and others with financial decision-making roles in their organizations. To get the broadest view possible, the CFO Survey panel includes representatives from firms that range in size from owner-operators to Fortune 500 companies and covers all major industries. Finally, the survey will remain quarterly, and aggregated survey results and analysis will be publicly available via the new CFO Survey website.
We are excited to continue to provide this valuable complement to the array of existing data available to policymakers, business decision-makers, academic researchers, and the public. For more information and for the new quarterly results, check www.cfosurvey.org on July 8, 2020. Of course, we'll also alert you here when the time comes!
John Graham, the D. Richard Mead Jr. Family Professor of Finance at Duke University's Fuqua School of Business,
Brent Meyer, a policy adviser and economist in the Atlanta Fed's Research Department,
Nicholas Parker, the Atlanta Fed's director of surveys, and
Sonya Ravindranath Waddell, a vice president and economist at the Federal Reserve Bank of Richmond
May 12, 2020
Challenges in Nowcasting GDP Growth
Real gross domestic product (GDP) declined at an annualized rate of 4.8 percent in the first quarter, according to the first estimate from the U.S. Bureau of Economic Analysis (BEA), 3.8 percentage points more than the decline anticipated by the Atlanta Fed's final GDPNow model projection. Why was the error, which was easily the model's largest on record for final GDPNow forecasts, so big? Chart 1 looks at GDPNow's forecast errors since the model went live in mid-2014 and breaks them down into forecast errors for the various subcomponents' contributions to GDP growth.
The clear culprit is the fact that the GDPNow model did not anticipate the record 9.5 percent monthly decline (not annualized) in real services consumption in March. At that time, GDPNow had March services data available only for electricity and natural gas use and purchased meals and beverages, as well as revised February data for net foreign travel. If the model would have correctly forecasted the March growth rate in services consumption for the subcomponents besides these, it would have actually slightly overstated the first-quarter decline in real consumer spending. (I should note that because of the timing and impact of last quarter's social distancing efforts stemming from COVID-19, the BEA used data outside of the scope of its routine procedures to estimate part of services spending in March—in particular, data about private credit card transactions for health care and recreation services.)
By design, GDPNow is a purely model-based prediction method as opposed to the models of some private forecasters, who were able to incorporate developments related to COVID-19 into their April forecasts for first-quarter GDP growth in a way that GDPNow did not and could not. As a result, their GDP predictions turned out to be relatively more accurate. For example, the consensus forecast of first-quarter GDP from the Wall Street Journal Economic Forecasting Survey in the first full week of April was a decline of 3.3 percent, and the CNBC Rapid Update survey late in the week prior to the GDP release anticipated a decline in GDP of 5.3 percent. Private forecasts will continue to be able to use news developments and high-frequency or nonstandard data sources (such as initial unemployment claims and OpenTable restaurant dining data) in a way that GDPNow and similar nowcasting models do not. The New York Fed's recently introduced Weekly Economic Index combines a set of weekly indicators into a single index with units comparable to four-quarter GDP growth, but it does not actually nowcast quarterly GDP growth.
Around the time of recessions, macroeconomic projections from professional forecasters tend to be less accurate and show more dispersion than during nonrecessionary periods. And though the National Bureau of Economic Research has not identified a 2020 business cycle peak, recent GDP forecasts show much more dispersion than they did during, or close to, past recessions. Chart 2 shows the difference between the top 10 and bottom 10 average forecasts of real GDP growth (for both the current quarter and one quarter ahead) in the Blue Chip Economics Indicators survey administered in the middle month of each quarter since 1991.
The top 10/bottom 10 difference for current-quarter GDP forecasts in the May 2020 survey is clearly much larger than around past recessions. In fact, it's larger than the difference between the highest and lowest quarterly growth rate of GDP after 1983, around the time economists date the onset of the Great Moderation—in reference to the decline in macroeconomic volatility—in the mid-1980s. Prior to the May 8 employment release, GDPNow was more optimistic about second-quarter GDP growth than most private forecasters were, but after the model forecast was revised down from a decline of 17.6 percent to a decline of 34.9 percent on the heels of that report, it fell more in line with the others.
The dispersion in the forecasts for GDP in the third quarter of 2020 is even starker. The optimistic forecasters project 2020:Q3 growth to be well above the highest rate on record (15.7 percent in 1950:Q1), and the pessimistic forecasters project contracting GDP. Of course, we will not be able to determine how accurate forecasts of second- and third-quarter GDP growth are until later in the year. Nevertheless, the wide range of forecasts implies that at least some forecasters' GDP projections will be wildly off by historical standards. As St. Louis Fed economist Michael McCracken recently noted, what the late Yankees catcher Yogi Berra said is more true than ever: "It's tough to make predictions, especially about the future." Berra's wisdom also will also apply to producing accurate and reliable economic forecasts for some time to come.
January 8, 2020
Is There a Taylor Rule for All Seasons?
In September 2016 we introduced the Taylor Rule Utility, a tool that allows a user to plot the federal funds rate against the prescription from an equation called the Taylor rule, shown below:
Broadly speaking, the Taylor rule translates readings of inflation (πt) and resource slack (gapt)—often measured by comparing real gross domestic product (GDP) or the unemployment rate to some measure of its "potential" or "natural" level—into a recommended setting for the fed funds rate. The default settings of the rule as of September 2016 (incorporated in the blue dashed line in the chart below) were, apart from some minor differences in variable choices, consistent with the settings used in John Taylor’s landmark 1993 paper that introduced the Taylor rule.
As the chart shows, for most of this decade, the funds rate prescription from this original Taylor rule consistently exceeded the actual rate by 1 to 3 percentage points, and as Wall Street Journal columnist Michael Derby noted last August, the prescription was well above the actual funds rate in the third quarter of 2019. Much of this difference can be explained by the setting of the natural (real) interest rate, or r*, in the above equation. Taylor set r* at 2 percent in his original rule based on average real GDP growth since 1984 and, according to estimates from the Laubach-Williams (LW) model, 2 percent continued to be a reasonable, if slightly low, estimate of r* up until the 2007–09 recession. Since 2009, estimates of r* from the LW model have generally hovered between 0 and 1 percentage point. Since July 2017, the semiannual Monetary Policy Report from the Board of Governors to Congress has included a section on monetary policy rules. And in these sections, r* has been estimated with the consensus long-run projection of a short-term interest rate from Blue Chip Economic Indicators. Since 2015, these Blue Chip interest rate projections have also been consistent with estimates of r* between 0 and 1 percent.
Setting r* to the LW model estimate (instead of 2 percent) in the Taylor rule results in a prescription corresponding to the solid blue line in the above chart. We can see this line is much closer to the actual fed funds rate for most of this decade. Nevertheless, it’s not clear that rules using LW-model estimates of r* and Congressional Budget Office (CBO) estimates of potential GDP or the natural unemployment rate are the most relevant for monetary policymakers. For example, in the December 2019 Summary of Economic Projections (SEP), the central tendency of Federal Open Market Committee (FOMC) participants’ longer-run projections of the unemployment rate was 3.9 to 4.3 percent. Conversely, the CBO’s latest estimate of the natural unemployment rate in the fourth quarter of 2019 rounds up to 4.6 percent, while its latest estimate of the natural rate in 2025 rounds up to 4.5 percent. The orange line in the chart above uses the FOMC/SEP longer-run projections of the fed funds rate and the unemployment rate.
Both the LW/CBO and FOMC/SEP variants of the Taylor 1993 rule prescribed an earlier "liftoff" of the fed funds rate than actually occurred. Former Fed chairs Ben Bernanke and Janet Yellen have sometimes referred to an alternative rule known as Taylor 1999. The FOMC/SEP Taylor 1999 rule, which puts twice as much weight on the resource gap as the FOMC/SEP Taylor 1993 rule, is the green line in the above chart that is identical to the orange line apart from a doubling of the resource gap coefficient in the above equation. This rule prescribed a later liftoff date than the other rules depicted in the chart. Because of the low unemployment rate, its current funds rate prescription is now above the rate that the FOMC/SEP 1993 rule prescribes.
By now, it’s probably clear that the answer to the question I posed in this blog post’s title is no, there is not a Taylor rule for all seasons—or at least not one that would satisfy everybody. For this reason, we have modified the interactive chart in our Taylor Rule Utility to show prescriptions from up to three versions of the Taylor rule. The default settings of these three rules in the interactive chart coincide exactly with the solid blue, orange, and green lines in the above figure. But you can modify all of the rules to generate, for example, the dashed blue Taylor 1993 line shown above. We hope that users find this a useful enhancement to the tool.
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