Federal Reserve Economic Data

Where is there a higher density of poverty in the US? The Bureau of the Census has several data sets that allow us to map the answer to this question by county.

We previously reported about the Small Area Income and Poverty Estimates (SAIPE) program that takes data from various sources (for example, SNAP benefits and IRS filings) to model and estimate poverty rates in each county. One of the sources is the American Community Survey, which has relatively small samples to work with in many counties and, thus, cannot be used in isolation to estimate poverty at a yearly frequency. But it can be more reliable when looking at five-year averages.

This five-year average is what the FRED map above shows us, with the stated year being the last of those five years. The map shows clearly that there is more poverty in the South, with several states having a majority of counties with over 50% of households living under the poverty level.

Now, to understand such a statement, one has to understand what is meant by “poverty level,” as there are many definitions. The Bureau of the Census determines how much basic needs cost in the US on average for different household configurations. Households with a total income below that cost are considered in poverty. Note that there is no geographic variation in the cost, meaning that poverty in high-cost areas is likely undercounted and poverty in low-cost areas is likely overcounted.

How this map was created: Search FRED for “population below the poverty line,” click on any county, and then click on “View map.”

Suggested by Christian Zimmermann.

The number of jobs openings is one of the main economic indicators that economists consider when trying to evaluate the health of the economy. The FRED graph above shows job openings data since 2000: There’s been an upward trend from 2010 all the way to February 2020. Job openings dropped during the pandemic but picked up and increased significantly from late 2020 through 2022.

The way these data are collected reflects how the world has evolved over time: From 1951 to 2008, the Conference Board produced a Help-Wanted Advertising Index of print advertisements that measured the change in help-wanted classified ads from 51 major US newspapers. This index was arguably one of the most important at the time, as it helped economists measure the efficiency of the job-matching process. However, as the world moves from print to digital media and the loss in newspaper readership continues, data-collection methods also must change.

Nowadays, the job openings data come from “JOLTS,” which is the Job Openings and Labor Turnover Survey conducted by the Bureau of Labor Statistics. The data are from a sample of around 21,000 US business establishments, and job openings include all positions that are open on the last business day of the reference month.

For a job to be considered “open,” it has to meet three conditions. One of them is that the employer must be actively recruiting workers from outside the establishment to fill the position. According to the BLS website, this may involve “advertising in newspapers, on television, or on radio; posting Internet notices; posting “help wanted” signs; networking with colleagues or making “word of mouth” announcements.”

How this graph was created: Search for “job openings” on FRED and the series should be among the top choices.

Suggested by Praew Grittayaphong and Paulina Restrepo-Echavarria.

The FRED Blog has discussed why measuring inflation trends is important for policy analysis. In a nutshell: The month-to-month inflation rate changes too often and by too much to reliably inform financial decisions. To address this challenge, researchers use different methods to study the difference between temporary and persistent changes in consumer prices.

The FRED graph above shows consumer price inflation (the black dashed line). It also shows two versions of the underlying inflation gauge (UIG), a recently added data series measuring the trend component—that is, the persistent component—of inflation. The series is produced by economists at the Federal Reserve Bank of New York. They use consumer price index data from the US Bureau of Labor Statistics (BLS) and a dynamic factor model, a technique that helps identify the shared features of large sets of economic data, to construct the two trend estimates:

• The full data set measure (the blue line) combines many of the BLS price data series with several macroeconomic and financial variables.
• The prices-only measure (the red line) solely employs BLS price data.

The graph shows the trend estimates are less volatile than the month-to-month inflation rates. Also, the data suggest the June 2022 peak in inflation had a large transitory component and the upward persistent trend reversed course soon afterward.

For more information about various inflation measures, check this short video of Mark Wright or read on about other series in FRED:

• The Bureau of Labor Statistics reports a consumer price index commonly known as “core CPI” that excludes the prices of two historically volatile prices: food and energy. Learn more about core CPI here.
• The Federal Reserve Bank of Cleveland reports the trimmed mean of the CPI by excluding the price components that show the most extreme monthly changes. Learn more about trimmed means here.
• The Federal Reserve Bank of Atlanta reports a sticky price CPI after sorting the components of the CPI into “flexible” or “sticky” (slow to change) categories. Learn more about the sticky CPI here.
• The Federal Reserve Bank of Dallas reports a trimmed mean personal consumption expenditures (PCE) by excluding the price components that show the most extreme monthly changes from the inflation measure targeted by the Federal Open Market Committee of the Federal Reserve. Learn more about trimmed means here.

How this graph was created: Search FRED for “Underlying Inflation Gauge: Full Data Set Measure.” Next, click the “Edit Graph” button, select the “Add Line,” and search for “Underlying Inflation Gauge: Prices-Only Measure.” Do that again to add the “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average.” Last, select the “Line 3” tab, and use the “Units” dropdown menu to select “Percent Change from Year Ago” to transform the index series into the inflation rate.

Suggested by Diego Mendez-Carbajo.