Federal Reserve Economic Data

Annual gross domestic product (GDP) is a common way to compare economic standards of living between countries. A more specific measure is the inflation-adjusted value of aggregate economic activity divided by the number of persons in the population. Or real GDP per capita.

Real GDP per capita allows us to compare, for example, rich countries with very large populations with poorer countries with smaller populations.

Our first FRED graph, above, shows real GDP per capita between 1960 and 2019 in three countries: the United States (blue line), Japan (green line), and India (red line). These countries’ economic standards of living, measured in dollars valued at 2017 prices, are markedly different throughout the 59 years of available data. But a closer inspection of the data can reveal a more nuanced picture, plotted in the FRED graph below in year-over-year growth rates.

• Between 1961 and 1974, Japan’s GDP was catching up to US GDP. Economists call this process of poorer countries catching up to richer countries convergence.
• For many years between 1961 and 2000, India’s GDP wasn’t catching up to (or even keeping up with) US GDP. Economists call this process of widening gaps between poorer and richer countries divergence.

Both of these observations are revealed in a version of the top graph with a logarithmic scale, which is best suited for analyzing long time series with different growth rates.

Recent research has looked into the relationship between growth rates and levels of GDP per capita in more than 100 countries: B. Ravikumar, Dawn Chinagorom-Abiakalam, and Amy Smaldone at the St. Louis Fed show that the year 2000 marked a change in the broad trends of economic divergence and convergence. With Penn World Table 10.01 data, they show that, between 1960 and 2000, divergence was prevalent. Between 2000 and the time of this writing, living standards in low-income countries have been catching up to those in high-income countries, signaling a period of convergence.

For more about this and other research, visit the publications page of the St. Louis Fed’s website, which offers an array of economic analysis and expertise provided by our staff.

How these graphs were created: First graph: Search FRED for and select “Real GDP at Constant National Prices for United States.” From the “Edit Graph” panel, use the “Edit Line” tab to customize the data by searching for “Population United States Groningen” (Groningen is to make sure to find the same source). Don’t forget to click “Add.” Next, type the formula a/b and click “Apply.” Next, use the “Add Line” tab to repeat the process for India, and then Japan. Second graph: Take the first and, for each line, change the units to “Percent Change From Previous Year” at the bottom of the menu.

Suggested by Diego Mendez-Carbajo.

It’s common to think about cost of living when thinking about moving to a new city. One way to measure cost of living is through regional price parities (RPP), which are price levels for a region expressed as a percentage of the overall price level for the nation. An RPP of 100 is equal to the national average.

In 2022, Missouri had an RPP of 91.119, indicating a cheaper cost of living than the national average.  California’s RPP of 112.47, on the other hand, indicates a higher-than-average cost of living. Check your state’s score with this FRED map.

For a closer look, we can break down RPP into smaller components: Our first FRED map (above) shows the RPP for goods, and our second FRED map (below) shows the RPP for housing. As these two maps show, the differences in costs of living across the nation vary by component: There’s significantly less variation across states for goods than there is for housing.

One reason for this could be the differences in mobility for goods and housing. Goods are clearly easier to trade and ship around the country, which minimizes the differences in the cost of goods across states. But it’s difficult to move a house to a new location, and the location itself greatly affects the price. There are also many reasons people don’t choose to move to exploit lower housing prices elsewhere, which limits the ability for markets to equalize housing prices across states.

How these maps were created: First map: Search FRED for and select “Regional Price Parities: Goods for Missouri” (series: MORPPGOOD). In the right-hand corner, click the green “View Map” button then the red “Edit Map” button. Select 5 for the number of color groups and “User Defined Method” in the “Data grouped by:” dropdown menu. Change the bins to 69, 79, 97, 121, and 177. Click “Apply Intervals.” For Second map: Search FRED for “and select Regional Price Parities: Services: Housing for Missouri” (series: MORPPSERVERENT). Click “View Map” and “Edit Map” and again select 5 for the number of color groups. Select “Fractile Method” in the “Data grouped by:” dropdown menu.

Suggested by Maximiliano Dvorkin and Cassie Marks.

Happy Halloween!

In the past, we’ve covered the cost of candy, costumes and pumpkins. Today we celebrate this holiday by showing how any FRED user can conjure economic oddities from the dark corners of FRED’s database.

Our first FRED graph above, in the form of a 10-legged spider, tracks resident population data for 5 spooky US counties:

• Graves, Kentucky
• Erie, Pennsylvania
• Crow Wing, Minnesota
• Lac qui Parle, Minnesota
• Malheur, Oregon

The last 2 counties are extra-spooky French names: “Lake that Speaks” and “Misfortune.”

Speaking of misfortune, our second FRED graph, above, uses data from the Centers for Disease Control and Prevention to reveal the rate of premature deaths in these counties. These data adjust for the age distribution in any given county compared with a standard county, as explained in this FRED Blog post.

The CDC defines premature death as any death before the average age of death in the US population. These malheurs can be accidents, diseases, murders, and other unnatural fatal occurrences that send people to their graves.

Speaking of Graves, that county in Kentucky has the highest rate of premature deaths among the 5 counties in this haphazard list.

How this graph was created: For the first graph: Search FRED for and select “graves resident population.” From the “Edit Graph” panel, use the “Add Line” tab to search for and add the same series for Crow Wing, Lac qui Parle, Malheur, and Erie. Under “Units,” choose “Index,” with 1991-03-01 as the date, and click “Copy to All.” For the second graph: Search for and select “graves premature deaths” and select the age-adjusted series. Add the same for the rest of the counties.

Suggested by George Fortier and Christian Zimmermann.