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February 11, 2021
Insights from the Updated Labor Force Participation Dynamics Tool
We recently updated our Labor Force Participation (LFP) Dynamics tool with 2020 data. The new data highlight the impact that the COVID-19 pandemic had on the U.S. labor force in 2020. For example, the fraction of the adult population aged 25–54 employed or looking for work dropped from 83.0 percent to 81.2 percent between the fourth quarters of 2019 and 2020 (see the chart).
The 1.8 percentage point drop is mostly accounted for by a 1.1 percentage point rise in the share of people saying they want a job but are not actively looking (a segment often called the shadow labor force), combined with a 0.5 percentage point rise in the share of adults saying they had family responsibilities that kept them from looking for work, and a 0.3 percentage point rise in reasons not otherwise specified (but most likely attributable to concerns about COVID-19). Only partly offsetting these increases in nonparticipation was a 0.1 percentage point drop stemming from being in school or training, which could also be a result of COVID-19.
The Labor Force Dynamics tool allows users to dig into different demographic groups to investigate COVID-19's disparate impact on labor market engagement. For instance, for Black non-Hispanics aged 25–54, the decline in LFP is almost 3 percentage points: much larger than the overall decline. Over two-thirds of the decline for prime-age Blacks is attributed to a rise in the share of Black adults who want a job but are not currently searching for work. For Hispanic women, the participation decline is just over 2 percentage points, with half of that decline attributable to increased family responsibilities, which could be due to the lack of childcare options.
I encourage you to check out the updated LFP Dynamics tool. One interesting comparison would be to contrast what has happened over the last year with what happened to LFP and the reasons for nonparticipation between the fourth quarters of 2007 and 2010 (during the Great Recession). You will find some common features, but also some important differences. Enjoy!
February 1, 2021
Ecommerce's Spillover Effects on the Local Retail Real Estate Market
Over the past decade, online shopping has become the primary shopping method for many. Consequently, concerns that ecommerce will replace traditional retail outlets and make physical stores obsolete have increased. However, the expansion of ecommerce drives online retailers to establish large fulfillment centers nationwide. To ensure that customers receive packages within two days, online retailers aggressively accelerated fulfillment center openings. A major online retailer, one that accounts for about 50 percent of total ecommerce sales, has opened more than 150 fulfillment centers, located across 88 U.S. counties as of 2018. Each fulfillment center hires its own workers, and thus a new fulfillment center's local economic impact might be the opposite of the typical negative belief about ecommerce's effects on brick-and-mortar stores. To estimate the overall effect of ecommerce on the local retail market, I use a fulfillment center of this online retailer as a proxy to an ecommerce local presence. Specifically, I focus in this post on retail real estate transaction prices, since store sales heavily influence property values. (Retail property sales price is commonly estimated using the direct capitalization method.)
Among the counties that received a fulfillment center, I select counties with a fulfillment center that opened between 2013 and 2015, giving clear three-year periods before and after the opening. Control counties are the matched counties that are strong potential fits for a fulfillment center establishment yet did not receive one. Fulfillment centers are more likely to be in counties where population density and education level are higher than average but median household income and median age are lower than average. These counties experience slightly faster retail establishment growth, but other demographic or industry factors did not influence whether a firm decided to establish a fulfillment center or not. Using those economic and demographic factors that may affect fulfillment center establishment, I select control counties that have very similar growth rates to the treatment counties with a fulfillment center establishment. Figure 1 indicates the average retail property price changes relative to fulfillment center establishment between treatment and control counties. Both treatment and control counties exhibit similar price movement prior to a fulfillment center opening, thus meeting the parallel trend assumption. However, the average retail property values move differently following the local fulfillment center establishment.
Impact of ecommerce on retail property transaction values
Using a difference-in-difference specification, I test how the expansion of ecommerce affects local retail property values. The model includes property characteristics and market controls. If ecommerce affects brick-and-mortar stores negatively, we should expect to see lower transaction values for local retail properties, but results exhibit contrary behavior. The entry of an ecommerce fulfillment center has a positive effect on local retail store values. Retail properties located in counties where a fulfillment center operates sold at a 5.2 percent premium relative to properties in similar counties without a fulfillment center. The premium persists through the fifth year, with the largest gains seen in year three. The natural environment of commercial real estate transactions explains the delayed effect on the retail property market. The average time on market for a retail property in the full sample is 357 days, which helps explain why retail properties' transaction prices experience a delay.
This positive pricing effect is significant in the retail and multifamily markets, but not in the office and warehousing markets. This state of affairs indicates that the local market's stronger overall economic growth drives results. A concern stems from clustering effects—that is, the pricing effect will move upward if other retailers open their warehouses in the same counties at a similar time. Using the largest big box retailer as a means to isolate this effect, I exclude the counties where this retailer also opened a warehouse within three years of the opening of the proxy online retailer's fulfillment center. The results are robust as they persist even after I account for the clustering effects involving another retailer.
Local labor market structure changes
Establishing fulfillment centers changes the composition of the local labor market of treatment counties as businesses focus on warehousing services and generate greater demand for labor. The share of employment in the warehousing-related industries (NAICS codes 48 and 49), their salaries relative to a county's average, and the number of warehouse establishments relative to total business establishments increase by 0.9 percent, 1.1 percent, and 0.5 percent, respectively, relative to control counties following a local fulfillment center opening. The positive effects may spill over into the local retail labor market as overall labor demand increases. The retail trade sector also exhibits positive spillover effects on establishment and payroll in the third year (see figure 2). However, the increase in retail employment is not statistically significant. If general economic growth of the treatment counties drives positive labor market effects, I might expect to find that the finance and insurance industry (NAICS code 52) also grows following the establishment of a fulfillment center, which is not the case.
Figure 2: Trends in Payroll, Establishment, and EmploymentTransportation and Warehousing
Finance and Insurance
Which areas are most sensitive to these effects?
Areas where warehousing employees tend to live—that is, areas with higher population densities, lower median incomes, higher renter-occupied rates, lower levels of education, younger population, and lower rates of internet access—tend to pay higher premiums for retail properties and exhibit a stronger, positive effect on local retail property values after the establishment of a fulfillment center.
Overall, the findings in this post seem to provide another silver lining to the consolidation among retailers and the increased focus on digital rather than physical footprints. Contrary to the common belief that online retailers destroy local communities, the data appear to indicate that ecommerce could positively contribute to local economies and the value of retail property. In a county with a fulfillment center, retail properties transact 5.2 percent higher compared to properties in counties that appeared to be a strong fit for a fulfillment center but did not receive one. Growth in labor demand for the warehousing sector and a potential positive spillover effect to the retail sector also support these findings. The expansion of ecommerce anchored by the establishment of a fulfillment center might help local economies and could cause local labor markets to specialize in warehousing. Finally, a heterogeneity test suggests that the positive pricing effect on local retail values is stronger in areas where fulfillment center employees are more likely to live.
While these results perhaps cast online retailers in a positive light, the persuasiveness of this post's findings are limited. Higher property values could be the result of local government subsidies to online retailers, and I don't assess the long-term impact on warehousing and retail labor markets. As this analysis concerned only the county level, its ability to capture the overall effects of ecommerce on the servicing areas is limited. To promote a mutually beneficial relationship between ecommerce firms and brick-and-mortar retailers, I suggest that local governments and policymakers need to be aware of the spillover effects resulting from fulfillment centers and implement proactive, clearly beneficial policies for both the warehousing and retail industries.
November 12, 2020
A Dashboard Approach to Monitoring Underlying Inflation
Editor's note: In December, macroblog will become part of the Atlanta Fed's Policy Hub publication.
Measuring progress toward the Federal Open Market Committee's (FOMC) dual mandates of maximum sustainable employment and price stability is often reduced to shorthand: Simply, monitoring the level of the unemployment rate relative to its longer-run trend, and tracking the level of a specific measure of underlying inflation—the so-called "core" measure of personal consumption expenditure (PCE) inflation (which excludes food and energy prices)—relative to the FOMC's 2 percent price stability goal. This ex food and energy "core" measure is often taken as a de facto proxy for the trend in overall (or "headline") inflation. While the unemployment rate and core PCE inflation are, indeed, useful in measuring the FOMC's progress toward its objectives, they are not perfect.
In an influential speech in 2013, Janet Yellen (then chair of the FOMC) argued for a broader, more inclusive approach to monitoring the health of the labor market. The unemployment rate, she pointed out, had significant shortcomings—namely, when unemployed workers became discouraged and stopped looking for work, the unemployment rate would decline. Hence, monitoring a basket or dashboard of indicators (such as payroll employment, data on gross job flows, and quits rates) could help paint a more accurate picture of the health of the labor market.
And, much like former Chair Yellen highlighted the need for a dashboard approach to monitoring the employment mandate, simply using core PCE inflation to track the underlying inflation trend is insufficient. An analogous dashboard approach is needed to monitor progress toward the FOMC's price stability mandate.
For some obvious reasons, movements in the aforementioned core PCE price measure do not always reflect changes in inflation. Explaining why gets a bit academic, but embedded in every price change are, at least, two components. The first is a real component, reflecting changes in the supply and demand for a particular good or service relative to others in the consumers' market basket. The second component is a nominal component, reflecting the supply of money (or the stance of monetary policy) relative to what's needed to facilitate purchases of goods and services in the economy during a given time period. It is that second component—the inflation component—that we are attempting to uncover. Efforts to do this rely on discerning measures of underlying inflation—measures that attempt to remove transitory effects and noise from the price data.
Implicit in using an underlying inflation measure that excludes only food and energy prices is the assumption that every other price change is a reflection of a change in underlying inflation. However, that assumption is off base. Large relative price changes outside of food and energy items, having nothing to do with the FOMC's price stability mandate, often occur. In the abstract, these can be any relative price change, such as a sharp increase in excise taxes, subsidies on prices in particular markets, or temporary supply chain disruptions resulting from natural disasters, pandemics, or a disruption in global trade.
Specifically, over the past six years or so, we've seen at least three of these large and salient relative price changes that have materially affected core PCE inflation. The first example is changes in administered health care prices that lead to a sharp slowing in the price index for hospital services (and thus health care prices in general) shortly after the passage of the Affordable Care Act (often referred to as Obamacare). The second is a methodological change that the U.S. Bureau of Labor Statistics enacted in January 2017, which made cell phone service prices more sensitive to quality changes. In March 2017, a few large national carriers switched to offering largely unlimited data packages, yielding a 50 percent (annualized) decline in this component and having a striking impact on year-over-year core PCE inflation through March 2018. The third is a series of huge price swings in imputed financial services prices in late 2018 and early 2019.
These are just a few of the most salient relative price changes that have altered a "core PCE-centric" view of inflation over the prior expansion, but there have been many more. Thankfully—and in large part due to great work throughout the Fed system to understand and measure inflation—we have a variety of alternative inflation measures designed to limit the influence of these large, idiosyncratic price changes. A few of the better-known ones are the Cleveland Fed's median and 16 percent trimmed-mean CPI and the Dallas Fed's trimmed-mean PCE measure. These measures remove the influence of sharp component price swings on a monthly basis and, as a result, tend to have a lower variance than the usual "core" measures, leading to superior forecasting performance over most time horizons and in a variety of inflation forecasting models.
Another set of inflation indicators reweights, or classifies, detailed components in the consumers' market basket into different groups based on characteristics that a monetary authority (such as the Federal Reserve) should be interested in. First, let's consider indicators such as the San Francisco Fed's Cyclical Core PCE Inflation index and Stock and Watson's Cyclically Sensitive Inflation Index. These measures either exclude or deemphasize the weight of prices that do not move in tandem with the business cycle. The argument goes that certain components of the consumer's market basket (such as health care and education prices) follow strong, idiosyncratic trends, and therefore price changes among these components are more likely to reflect relative price changes rather than the influence of monetary policy working through the pricing mechanism.
A third type of inflation indicator is the Atlanta Fed's Sticky Price CPI. This measure tracks a set of CPI components that are slow to react to changing economic conditions (and hence are "stickier" than other components) and appear to incorporate expectations about future inflation to a greater degree than prices that change on a frequent basis. This measure tends to forecast inflation over longer time horizons more accurately than headline or core inflation measures.
We have pulled together these various measures into the Underlying Inflation Dashboard, which allows users to see a more complete picture of underlying inflation. A quick overview of this dashboard is in order (see chart 1). The first section of the table shows the 12-month growth rate of each underlying inflation measure. It compares the most current data to the value of a measure from one year prior. Each measure is color-coded, in 25 basis point (bp) increments, relative to its price stability target. Admittedly, the choice for each measure's price stability target is somewhat arbitrary, but given the primacy of core PCE inflation in the communications of the FOMC and the Federal Reserve, we chose to express each measure's target as 2 percent plus its average difference with core PCE inflation over the past decade. For example, the growth rate in the core CPI over the past 10 years is 30 bp higher than that of the core PCE, yielding a price stability target of 2.3 percent.
What's interesting to see is the narrative that emerges by taking the dashboard approach to monitoring underlying inflation.
Prior to the onset of COVID-19 earlier this year, the consensus view surrounding inflation was one of persistent shortfall, even after (at least) achieving maximum employment by most measures. Indeed, at least through the beginning of 2020, core PCE inflation continued to trend below target (see chart 2).
However, every other measure of underlying inflation in the dashboard had converged to a growth rate consistent with the Fed's price stability mandate by early in 2018 and stayed there up until the onset of the pandemic (see charts 1, 3, and 4). Taking a dashboard approach leads us to the conclusion that, while it took some time following the Great Recession, inflation had converged to our price stability target and remained on target until March 2020.
The previous discussion made the case for using a dashboard approach when evaluating underlying inflation prior to the onset of the COVID-19 pandemic. Since then, a series of dramatic relative price swings, along with direct complications in the physical measurement of prices, have further complicated the measurement of underlying inflation.
One particularly salient example comes from used auto prices. Auto prices have surged, rising at a record annualized rate of 75 percent from July to September. This spike is likely the result of a combination of increased demand stemming from commuters attempting to avoid mass-transit options, less confidence over future incomes, and a temporarily reduced supply of new vehicles. This relative price change alone has pushed the 12-month growth rate in core PCE goods prices up by nearly a full percentage point and added nearly three tenths of a percent to the 12-month trend in core PCE inflation. In contrast, trimmed-mean estimators (such as the Cleveland Fed's 16 percent trimmed-mean CPI and the Dallas Fed's trimmed-mean PCE) have largely ignored (hence the "trimmed") the influence of this rather dramatic price swing over the prior three months—leading to much more stable month-to-month estimates of underlying inflation.
It is precisely these types of dramatic relative price swings that argue for the broader approach we've sought to provide with the Underlying Inflation Dashboard. We hope you'll give it a whirl and let us know what you think.
October 22, 2020
COVID, Election Uncertainty Weigh Heavily on Firms' Outlook
Editor's note: In December, macroblog will become part of the Atlanta Fed's Policy Hub publication.
The book A Mathematician Plays the Market, written by the mathematician and writer John Allen Paulos, includes this line, which is a fitting description of the current economic outlook: "Uncertainty is the only certainty there is, and knowing how to live with insecurity is the only security." At the moment, two sources of uncertainty in particular—COVID-19 and the 2020 election—appear to be weighing very heavily on firms' decision-making. And firms in our Survey of Business Uncertainty (SBU) also appear to know how to "live with insecurity," as they are slashing their capital expenditures budget over the next two years by 20 percent, on average.
During the past two months, we've asked firms in our SBU to rank the top three sources of uncertainty influencing their business decisions at the moment. The results, seen in chart 1, show that firms are most concerned about uncertainty surrounding the coronavirus and the upcoming 2020 election. Together, these were firms' top sources of uncertainty and account for close to half of all responses, regardless of whether they are ranked as the first, second, or third source of uncertainty.
While it likely comes as no surprise that COVID-19 and the pending election are front and center as sources of uncertainty affecting business decisions at the moment, what is interesting is how firms are choosing to deal with these uncertainties. In October, we asked a follow-up question: "By what percentage has the net budgeted dollar amount of your capital expenditures for calendar years 2021 and 2022 changed due to the uncertainties you identified [in the previous question]?" The responses, shown in chart 2, suggest that uncertainty is weighing quite heavily on firms' collective outlook for capital investment.
Of the 407 responses we've collected so far, more than half of firms responded that the uncertainties they identified caused them to decrease their net budgeted amount of capital expenditures over the next two years. Furthermore, just 5 percent (20 firms) have increased their capex budget as a result of uncertainties they identified (with the remaining 44 percent leaving their current budgets intact). On average, firms are slashing their capital spending budgets over the next two years by nearly 20 percent. Across major industry sectors, the impact of uncertainty on capital budgets is uniformly negative. However, it appears more severe for capital-intensive industries such as manufacturing, construction, and mining and utilities.
To put these results in context, we previously posed a battery of special questions to this panel in 2018 and 2019 concerning the impact of tariff hikes and trade policy uncertainty on capital expenditures and found only modest (low single-digit) impacts for the overall panel and across most broad industries.
Turning to comprehensive gross domestic product (GDP) data for the U.S. economy, business fixed investment posted its second steepest decline on record (decreasing at an annualized pace of 27.2 percent) in the second quarter of 2020. Our current results imply a sluggish trajectory for business fixed investment for continuing firms (those that are not just starting up), which may contribute to a tepid recovery for overall GDP growth. One caveat worth mentioning is that recent business formation statistics from the U.S. Census Bureau suggest an increase in high-quality business startups, potentially offsetting some anticipated weakness in business fixed investment.
In sum, it's fairly certain (to us at least) that uncertainty—particularly surrounding the COVID-19 pandemic and the outcome of the 2020 election—is weighing heavily on firms at the moment. Consequently, many firms have chosen to slash their capex budgets over the next two years.
As current uncertainties become eventualities, it will be interesting to see if, and to what extent, firms reassess their plans. We'll continue to put the SBU to good use for just that purpose, so watch this space.
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