Federal Reserve Economic Data

The FRED Blog frequently provides context to help tell the story behind the data. Today we need a bit of history as well to help clarify the names of the data series themselves.

In 1876, the city of St. Louis, Missouri, became its own county, separating its local government affairs from the rest of St. Louis County, Missouri. Since then, the U.S. Census has tallied population statistics across subdivisions, or tracts, of these two separate counties with very similar names.

The FRED graph above shows the racial dissimilarity index for St. Louis City (in blue) and St. Louis County (in red). The Census reports the index as a percent of the non-Hispanic White population that would have to move from one census tract in a county to another census tract in the same county to achieve an even distribution of racial groups across that county.

Consider 2009, when the first data in the series are available: At that time, about two out of every three non-Hispanic White residents in the city of St. Louis would have had to change where they lived for this specific type of racial dissimilarity to disappear within St. Louis City. Slightly more than half of the residents in St. Louis County would have had to do the same to eliminate this racial dissimilarity in that county.

Twelve years later, the population landscape has changed. Since 2020, in terms of their racial makeup, the tracts in St. Louis City are noticeably more like one another than the tracts in St. Louis County.

However, the overall racial makeup of these two similarly named neighboring counties is very different and should be taken into consideration when interpreting the data. To begin with, almost two out of every three County residents are non-Hispanic White. In the City, the ratio is almost one-to-one. The population trends are also different: Between 2010 and 2020, the city lost almost 6% of its residents, while the county added 5%.

So, at least two different population trends could be at play here. Perhaps, on average, neighborhoods in the City are becoming more racially integrated while neighborhoods in the County remain steadfastly less integrated. Or it could be that population loss of racial minorities in some City neighborhoods is making the overall racial distribution there more even. Of course, both trends can be at play here. An in-depth analysis of tract-level Census data is needed to come to a definite conclusion.

How this graph was created: In FRED, search for “White to Non-White Racial Dissimilarity (5-year estimate) Index for St. Louis city, MO.” Next, click “Edit Graph” at the top right corner and navigate to the “Add Line” tab. Search for “White to Non-White Racial Dissimilarity (5-year estimate) Index for St. Louis County, MO” and click on “Add data series.”

Suggested by Diego Mendez-Carbajo.

To help FRED users navigate the rapidly changing economic and financial environment, the Federal Reserve Bank of St. Louis has assembled two dashboards of FRED graphs. The first dashboard collects higher-frequency financial market variables. The second dashboard collects mostly monthly indicators that track expenditures, employment and unemployment, and key business and consumer surveys.

For some background on why and how economists and other analysts track economic and financial variables during stressful times, read on:

The World Health Organization declared the novel coronavirus—known as COVID-19—a pandemic. Johns Hopkins University is monitoring the spread of the virus and mapping the number of confirmed COVID-19 cases and fatalities worldwide.

The number of confirmed cases in the United States is rising, and U.S. financial markets have been tumultuous. For example, since hitting an all-time high on February 12, the Dow Jones Industrial Average has fallen by about 33 percent as of the writing of this post. Yields on 10-year Treasury securities plunged to an all-time low of 0.54 percent on March 9, though they have since rebounded modestly. Other key financial market indicators, such as commercial paper yields and yields on corporate bonds, have also exhibited stress. Financial market–based measures of inflation expectations have fallen sharply.

These financial market stresses have triggered numerous policy responses by the Federal Open Market Committee (FOMC), including two reductions in the FOMC’s federal funds target rate.

Clearly, the COVID-19 outbreak is a significant and rare event in U.S. history. It has led to widespread disruptions in economic activity, with an unknown duration and magnitude. But it can be characterized and monitored as an economic shock. So, economists and policymakers are monitoring key cyclically sensitive indicators such as initial claims for unemployment insurance, changes in employment, retail sales, and sales of light motor vehicles and new and previously sold (existing) homes.

During times of high and rising uncertainty, financial market variables often serve as reliable forward-looking signals of future economic conditions in the broader economy. A key example is the Treasury yield curve, which usually inverts prior to recessions. This forward-looking perspective is important because most of the important “real” data that economists and policymakers monitor—such as the unemployment rate or industrial production—are backward-looking. For example, the payroll employment numbers for March 2020 will be released on Friday, April 3. However, they will capture only payrolls for the survey week ending March 12. Labor market conditions could have changed dramatically since then, given the fast-moving nature of the COVID-19 outbreak and the responses by firms and the government. To get a more timely measure of labor market conditions, an analyst might instead look at the weekly initial claims data.

Our two new FRED dashboards collect these useful variables to help you monitor and better understand the trajectory of the economy and the state of financial markets. FRED account holders can create their own dashboards, either from scratch or by taking these two as starting points.

Suggested by Kevin Kliesen.