Federal Reserve Economic Data

The FRED graph above tracks the price of eggs. Specifically, a dozen large grade A chicken eggs, in U.S. dollars, for each month since January 2021.

This graph is a histogram, otherwise known as a bar graph, so the length of each bar represents the full price. Merely glancing at the heights of the bars shows us that the price fluctuates over time, with a low of $1.47 and a high of $2.52 as of April. This runny volatility is why food prices, along with energy prices, are often excluded from monetary policy analysis.

Cracking open this first concept is fairly simple, but the FRED Blog team hatched the idea of asking another question: What would the graph look like if we purchased that same carton of eggs with bitcoins instead of U.S. dollars? The graph below shows this. Because a bitcoin is worth so much more than a carton of eggs, we multiplied the price by 100 million to express it in so-called satoshis, which is the smallest subunit of bitcoin.

The price fluctuates quite a bit, between 2829 and 6086, which is much more than it did for the U.S. dollar price. Plus, you’d need to add a bitcoin transaction fee, which has been about $2 lately, but which can spike above $50 on occasion. Hopefully, if you were making this purchase with bitcoin, you’d put many many more eggs in your basket.

How these graphs were created: First graph: Search FRED for “price of eggs”; the series we use should appear at the top of the results. Restrict the sample period to start in January 2021. From the “Edit Graph” panel, use the “Format” tab to choose “Bar” as the graph type. Second graph: Take the first and add a series to the first line by searching for and selecting “bitcoin price” and applying formula a/b*100000000.

Suggested by Christian Zimmermann.

No doubt about it, mortgage rates are up. The FRED graph above shows the rates for the most popular fixed-rate mortgages: the 15-year and 30-year. Every data point is the average rate offered at that point in time for new mortgages. Although the graph shows the recent data, at this point very few people are actually paying these increased rates.

One could say that current mortgage holders are enjoying a good deal: They’re paying a lower rate, and inflation is higher. Inflation matters because mortgage debt is nominal. So, if inflation increases all prices (and in particular wages), paying a nominal debt such as a mortgage becomes much easier. One might then consider that even new mortgages are also a good deal when there’s inflation.

The previous low-rate mortgages were not set during a time of higher inflation, and those who set the rates must not have anticipated the higher inflation to come. But the new mortgage rates now include the anticipation of higher inflation, and thus this inflation advantage is factored into the mortgage rate.

To look at the data behind this argument, we use the FRED graph below. Here, we deflate each mortgage rate by the corresponding “breakeven” rate, which takes into account the anticipated average inflation from the point of measure over the relevant number of years. (Unfortunately, there’s no 15-year breakeven rate, so we average the 10- and 20-year rates using FRED’s fancy tools.)

The result is actually not that different. The real mortgage rates are still significantly up. The reason is that inflation expectations over such long horizons (15 to 30 years) have not moved that much, likely reflecting a general expectation that inflation won’t last. Our last FRED graph documents those expectations.

How these graphs were created: First graph: Search FRED and select the 15-year mortgage rate. Once you have the graph, use the “Edit Graph” panel’s “Add Line” tab to search for and add the 30-year mortgage rate. Second graph: Start with the first, use the “Edit Line” tab for the 30-year mortgage series: Search for and add the “Breakeven 30-year” series and apply formula a-b. In a similar way, add two series to the 15-year mortgage line: “Breakeven 10-year” and “Breakeven 20-year,” and apply formula a-(b+c)/2. Third graph: Use the interest rate spreads release to select the relevant breakeven rates and click “Add to graph.”

Suggested by Christian Zimmermann.

U.S. Census data in FRED has helped us examine income inequality before, including mean and median income and the Gini ratio. Here, we examine income inequality through a different lens.

The GeoFRED map above shows the level of income inequality across U.S. counties. This particular measure is the ratio of average (mean) income for the highest earners (top 20%) divided by the average income of the lowest earners (bottom 20%) for each county. The Census data track the average income over a five-year period, in this case 2014 to 2019, to account for the fact that people’s income changes from year to year.

Measured this way, income inequality can be as high as 90 or as low as 5. That means that the most-affluent households in a particular county can earn as much as 90 times or as little as 5 times what the least-affluent households do. But those are the two extremes of income inequality. The typical (median) value is 13 times.

Because income levels vary widely across counties, two counties with similar degrees of income inequality can have very different economic profiles. For example, both Bath County, KY, and Ocean County, NJ, have a typical income inequality ratio, but the percentage of persons below the poverty line is 2.4 times higher in the Kentuckian county than in the New Jerseyan county.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Diego Mendez-Carbajo.