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March 23, 2020
American Firms Foresee a Huge Negative Impact of the Coronavirus
The rapid unfolding of the COVID-19 pandemic has created grave concerns for the health and welfare of the U.S. population and the economy. The economic worries are very apparent in financial markets. From the closing bell on February 21 through March 20, U.S. equities fell more than 30 percent, and stock market volatility skyrocketed.
At present, we simply don't know the extent of the overall disruption to the economy. We are still months away from confidently gauging COVID-19's impact on output growth. However, the latest wave of results from our Survey of Business Uncertainty (SBU) indicates that firms are bracing for a huge negative impact on their businesses. Our results also say the outlook deteriorated rapidly over the past two weeks.
The SBU was in the field from March 9 until last Friday, March 20. Relative to February, firms' four-quarter-ahead sales growth expectations fell sharply, from above 5 percent to below zero. This fall is, by far, the starkest one-month swing we've recorded since the inception of the SBU in October 2014. In addition, firms' uncertainty about their own sales growth rates rose 44 percent from February to March (see exhibit 1).
Interestingly, SBU respondents did not report a material softening in the outlook for employment growth during the next 12 months, or in the capital investment rate four quarters hence. However, given the latest signals from the labor market—most notably, last Thursday's initial unemployment claims report—employment is set to contract sharply for at least a few weeks. Perhaps respondent firms see the coronavirus impact as sharp but short-lived, with little impact on longer-term employment and plans for capital expenditure.
To better gauge how firms see the direct impact of COVID-19 on their sales outlook, the March SBU included the following special question: “What is your best guess for the impact of coronavirus developments on your firm's sales revenue in 2020?” Exhibit 2 summarizes the results.
Pooling responses over the survey's full two weeks in the field, firms anticipate revenue losses of more than 6 percent in 2020 as a result of coronavirus-related disruptions. Perhaps even more telling is how the responses evolved over the two-week period. As the virus spread—and as companies, households, and governments reacted—the collective judgment of responding firms about the severity of the negative hit to sales more than doubled.
In the first week, roughly two thirds of firms said coronavirus-related developments would harm their sales in 2020. Among those affected, the average expected decline was 9 percent. In the second week, nearly 85 percent said coronavirus-related developments would negatively affect their sales, with an average decline of 16 percent among affected firms—making it clear that from one week to the next, both the reach and severity of the anticipated impact rose sharply.
Exhibit 3 breaks down anticipated effects by major industry sector and shows that no sector is immune to the disruptive effects surrounding the virus and efforts to restrain its spread.
In sum, our March survey results show a very large and very negative impact on sales. Our direct question about coronavirus's anticipated impact on the sales outlook (exhibit 2) and the deterioration in the outlook from February to March (exhibit 1) suggest that recent events will lower sales by 6 percent or more. At this point, though, anticipating a sales decline of 6 percent might be too optimistic, for two reasons. First, the anticipated coronavirus impact intensified greatly from the first week to the second week of our March survey. Second, the virus continues a rapid spread domestically and abroad, prompting governments to respond with tighter travel restrictions and stricter social distancing policies.
January 16, 2020
Do Higher Wages Mean Higher Standards of Living?
Editor's note: We have updated macroblog's location on our website, although archival posts will remain at their original location. Readers who use RSS should update their feed's URL to https://www.frbatlanta.org/rss/macroblog.aspx. Also, we are implementing a new commenting system for posts. In the comments section at the end of this post, you'll be able to create an account to leave comments.
A recent macroblog post used Atlanta Fed Wage Growth Tracker data to observe that the hourly wage of the lowest-paid workers has rebounded in recent years after declining for a decade. The chart below depicts this finding, showing the median hourly wage of the lowest-paid 25 percent of workers in the Tracker sample relative to the median for all workers.
Moreover, the post showed that this recovery was not just a story about states and localities increasing their minimum wages. It also appears that there has been a significant tightening in the labor market for unskilled or low-skilled jobs.
Taken at face value, this is good news for workers employed in low-wage jobs. But here's the rub: the median wage in the first quartile is still low—$11.50 in 2019, or 55 percent of the overall median wage. Moreover, these are hourly wages before taxes and transfers (we'll get back to this shortly). They don't represent what is happening to these workers' ability to make ends meet, which depends crucially on income after taxes and transfers.
For households at the bottom of the income distribution, means-tested transfers can play an especially important role. Means-tested transfers—cash payments and in-kind benefits from federal, state, and local governments designed to assist individuals and families with low incomes and few assets to meet their basic living needs—represent about 70 percent of income before taxes and transfers for households in the bottom quintile of the income distribution, according to a recent report by the Congressional Budget Office. However, the size of the transfers tends to decrease as earnings increase, and they stop altogether when a worker exceeds income- and asset-eligibility thresholds.
The interaction between changes in earnings and various means-tested public assistance programs is an important public policy issue, and it is one that staff at the Atlanta Fed are studying. In a March 2019 macroblog post, David Altig and Laurence Kotlikoff reported that this interaction results in low-income households facing a higher median effective marginal tax rate than high-income households. For low-income households with children, this effect can be especially severe because the presence of children increases the value of programs such as the Supplemental Nutrition Assistance Program (or SNAP, formerly known as the food stamp program) and the likelihood of enrollment in additional programs such as federally subsidized child care. (You can read further research on the effective or implicit marginal tax rates of low-income households at Congressional Budget Office (2016), Romich and Hill (2018), and Chien and Macartney (2019).)
To illustrate the point, the Atlanta Fed team studied the case of a hypothetical single mother with two young children who works in a near-minimum-wage, full-time job and whose basic living expenses are helped by various transfer programs. One avenue to improving her family's standard of living is if she were to return to school and pursue a higher-paying career as a nurse. Over the long term, the net gains from education and career advancement are unambiguous. However, the Atlanta Fed's analysis shows that as long as her children still require care, the reduction in payments from various benefit programs could partially or even completely offset the gains. Look for an Atlanta Fed paper discussing this very real dilemma coming soon on the Bank's Economic Mobility and Resilience webpage.
What do findings like this mean for interpreting the Wage Growth Tracker's evidence that people in the bottom part of the wage distribution are experiencing relatively larger wage gains? Perhaps there is a bit less to celebrate than meets the eye. Around 46 percent of these individuals are in households with children. To the extent that they also participate in means-tested public assistance programs, the relative increase in their family's standard of living could be much less than the size of their pay raise would suggest.
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.
December 16, 2019
Faster Wage Growth for the Lowest-Paid Workers
On November 25, Fed chair Jay Powell gave a speech titled "Building on the Gains from the Long Expansion," in which he observed that
Recent years' data paint a hopeful picture of more people in their prime years in the workforce and wages rising for low- and middle-income workers.
In making this point, Chair Powell used a cut of the Atlanta Fed's Wage Growth Tracker that looks at the median annual wage growth of workers in the lowest 25 percent of the wage distribution. As the following chart shows, the lowest-paid workers have been experiencing higher median wage growth (the blue line) in the last few years than workers overall (the green line). This reverses the pattern seen in the wake of the Great Recession, when median wage growth for lower-paid workers slowed by more than for workers overall.
The faster median wage growth for lower-wage workers shown in chart above has also translated into an increase in the relative median wage level of these workers. To see this, the following chart shows the median wage level for those in the lowest wage quartile relative to the median for all workers in the Wage Growth Tracker dataset.
The chart shows that for workers in lower-wage jobs, their relative median wage over the 2000s has deteriorated, and that erosion has reversed course only in the last few years. This reversal may reflect increasing tightness of the labor market for lower-wage jobs relative to other jobs over the last few years. The challenge of filling jobs requiring few skills is something we have been hearing about a lot recently from the businesses we talk to (for example, see here), and this sort of challenge could be behind higher wages for those workers. However, several state and local governments have increased the minimum wage in recent years, which would also push up the relative pay for those in the lowest-paid jobs.
Are the observations in the previous chart solely attributable to minimum wage increases? To get some idea, the next chart contrasts the relative median wage in states that increased their minimum wage at some point between 2014 and 2019 to those that did not. The blue line is the relative median wage of the lowest quartile in the 28 states that increased their minimum wage (23 states introduced new minimum wage levels, and five implemented increases legislated before 2014), and the green line is relative median wage for the states that did not increase their minimum wage.
We would expect to see a rise in the relative median wage in the states that raised their minimum wage, and indeed we do. For the group of states that increased minimum wages (the blue line), the relative median wage is now closer to that of states that did not increase their minimum wage (the green line). Interestingly, though, even in the "no increase" states, the relative median wage has improved, suggesting that the increased tightness of labor markets, or some other factor than hikes in state minimum wages, is playing a role in pushing up the pay for those in lower-wage jobs. Consistent with the message of Chair Powell's speech, the good news is that there is scope to continue to build on the gains from the long and ongoing expansion for workers at the bottom end of the wage distribution.
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