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The data and determinations behind dating business cycle peaks and troughs

FRED has a new recession-dating dashboard for you

The FRED graph above shows that real gross domestic product (GDP) has declined over the first two quarters of 2022, after increasing by an average of 5.3% over the previous five quarters. In the eyes of some economists and financial market participants, two consecutive quarters of negative real GDP growth is sufficient evidence to declare a recession.

In the 75-year history of quarterly estimates of real GDP growth, there has been only one episode when two consecutive quarters of negative real GDP growth was not associated with a recession episode: the second and third quarters of 1947. So, from a historical standpoint, two consecutive quarters of negative real GDP growth is a pretty consistent signal for dating recessions. But what do the arbiters of dating business cycles have to say?

The dating committee

The National Bureau of Economic Research Business Cycle Dating Committee (hereafter NBER) maintains a chronology of monthly and quarterly dates of the peaks and troughs (i.e., turning points) of the business cycle. Rather than the popular two-quarter definition, the NBER employs a more comprehensive approach to dating the beginnings and ends of recessions. Specifically, they determine both the months and the quarters when economic activity peaked and troughed. Typically, the peak month occurs in the same quarter—but not always. For example, the NBER’s monthly peak of the pandemic-spawned recession occurred in February 2020, but their quarterly peak occurred in the fourth quarter of 2019.

The indicators

To determine the months of peaks and troughs, the NBER looks at several data series, such as industrial production, nonfarm payroll employment, civilian employment, and real personal income less transfer payments. The NBER also considers two other monthly series: real personal consumption expenditures and civilian employment. Civilian employment is measured using the household survey (Current Population Survey), while nonfarm payroll employment counts the number of jobs and is measured using the establishment survey (Current Employment Statistics).

The NBER also looks at estimates of the expenditure- and income-side measures of aggregate economic activity—otherwise known, respectively, as real GDP and real gross domestic income (GDI). Theoretically, GDP and GDI should equal each other in dollar terms, but they rarely do. (This difference between the two series is known as the statistical discrepancy.) The NBER also examines average GDP and GDI.

A new resource in FRED

This is a lot of information to gather, so FRED now offers some help navigating the ebbs and flows of these key data series with a new dashboard that compiles all these series on one page.

As with any user-created FRED dashboard, it updates automatically. Now, there won’t be any commentary on the current or prospective trends in the dashboard. But FRED users can make their own determination as to the likelihood of a turning point in the business cycle. Users can also use the dashboard as a starting point for creating their own variations.

Suggested by Kevin Kliesen.

A lesson in mapping population data

A Fed-related road trip using FRED maps

First, a little lesson on the Fed. The Federal Reserve System is a centenary institution that

  • sets the nation’s monetary policy
  • supervises and regulates banking institutions
  • maintains the stability of the financial system
  • and provides financial services to depository institutions, the U.S. government, and foreign official institutions.

The Fed blends both centralized and decentralized decisionmaking, with the Board of Governors in Washington, D.C., and 12 independent Federal Reserve Banks across the nation to ensure the system works on a daily basis.

Our first FRED map (above) shows the resident population in each Federal Reserve District in 2021, each labeled with the city where the Reserve Bank is located. Hover over the map to see the thousands of persons in each District. The District boundaries were drawn in 1914, by the way, when the nation’s population was much different.

By default, the FRED map is set to show the range of data, from maximum to minimum, organized in five segments (or fractiles) calculated to contain a similar number of data points. Darker colors represent larger data values.

The population in the San Francisco District is by far the largest in the Fed System. In the map, it occupies, all by itself, the first segment. The remaining four segments include either two or three Districts each. (We tried, but dividing 12 Districts by 5 segments didn’t yield a whole number.)

The second FRED map shows the same data. But, instead of sorting the data into five segments with a similar number of data points, the data here are sorted into equal interval segments. That is, the range of data is the same size for all segments.

Once again, the San Francisco District occupies the top spot all by itself, but the second-largest data segment (slightly lighter shade of green) now includes only the Atlanta District.

As road maps help us navigate unfamiliar landscapes, FRED maps help us make sense of numbers by creating captivating color-coded comparisons between data points. Our choices in creating these data maps affect the story behind the numbers. To learn more about best practices, watch this webinar from the U.S. Census Bureau.

How these maps were created:
First map: Search FRED for “Resident Population in Federal Reserve District,” pick any District, and click the “View Map” button. Second map: Starting with the first map, click the “Edit Map” button and select “Data grouped by: Equal Interval.” To customize the interval colors, click on the colored squares to the left of each inequality sign and select your color: You have the whole rainbow to proudly choose from.

Suggested by Diego Mendez-Carbajo.

Renting or owning: Which type of housing cost has increased more?

There’s little doubt the cost of housing has been increasing for a long time, whether you own or rent. Which cost has increased more? The FRED graph above seems to indicate that there has been little difference between these two growth rates for about four decades. But let’s deconstruct this graph to understand it a little better.

First, the two series are indexed to have a value of 100 in 1982. This means that it’s useless to compare their levels. That is, we cannot tell whether renting or owning is more affordable. We can only compare how the costs have evolved since 1982, and it’s quite apparent that they track each other quite well.

Second, the CPI series for rents is collected less frequently than other CPI series, meaning that its fluctuations in the data may show a delayed reality. That doesn’t matter much here, as we’re looking at long-term trends. And this applies also to the other series depicted here.

Third, the CPI series for house ownership requires quite a few explanations. Here, the Bureau of Labor Statistics makes an attempt to calculate the “true” cost of ownership, not the cost of buying a home, which is considered an investment, as are home improvements and mortgage interest. The idea is to figure out how much an owner-occupied home would rent for, not including utilities. This measure is based on reports from owners that are adjusted based on the housing stock compositions and comparisons with similar rental properties. The details are complex and explained here. In other words, this series is only loosely related to house prices.

How this graph was created: Search FRED for and select “CPI rent.” From the “Edit Graph” panel, use the “Add Line” tab to search for “CPI rent” again and select the other series.

Suggested by Christian Zimmermann.

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