Federal Reserve Economic Data

The FRED® Blog

Data on families

Maps and charts for Mother’s Day

Did you remember to call your mother yesterday? Did you send flowers? Why not also send her a (belated) FRED dataset? Above is one example—a colorful GeoFRED map showing county-level data on single-parent households with children. Below is another—a pie chart showing the percentages of family types with their own children: married couples, single mothers, and single fathers.

Once you’ve selected some FRED data, explore more graph formats by clicking on “Edit Graph” from the series page. You can get to the series page for the FRED data shown here by clicking on “View on GeoFRED” and “Customize” at the bottom of the image. From the “Format” tab, navigate through the options, including colors and patterns.

And don’t forget to say “Thank you” to Mom!

How the 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.
How the pie graph was created: In FRED, search for “Total Families with Children under 18 Years Old with Married Couple.” From the “Edit Graph” panel, use the “Add Line” feature to search for and select the “Total One Parent Families with Children under 18 Years Old with Mother.” Do the same to add the series “Total One Parent Families with Children under 18 Years Old with Father.” From the “Format” tab select “Graph type: Pie” and pick segment colors to taste.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: FMLWCUMC, OPFWCUFO, OPFWCUMO

Reckoning with premature deaths

CDC data on premature deaths for the St. Louis area

The COVID-19 pandemic has affected everyone in some way. The mildest cases involve inconveniences such as being confined at home to avoid spreading the virus. Other cases involve unemployment, lost businesses, and accumulating debt. The worst cases involve coping with the premature deaths of loved ones.

Each death—and its associated life—has unique and powerful elements. Yet, some deaths can be considered more “normal” than others, especially if they’re associated with very old age. The FRED graph above explores CDC data on premature deaths for FRED’s hometown, St. Louis city, as well as neighboring St. Louis County (separate from the city). Solid lines show the crude rates while dashed lines show the age-adjusted rates from 1999 to 2017.

According to the CDC, the premature death rate includes all deaths of those younger than 80 years of age. The crude death rate is simply the number of deaths reported each calendar year per 100,000 people. The age-adjusted death rate is a weighted average of the age-specific death rates, where the weights are associated with a fixed population by age. This is an important adjustment because differences in the composition of the population over time or across counties make comparisons difficult.

First, there’s a dramatic difference between St. Louis’s city and county. This can be expected from the economic asymmetries between the two locations: On average, St. Louis County residents are economically much better off than city residents. In 2017, the rate for the city was 675, which is more than 50% higher than the rate for the county, 446. The age-corrected rates have an even wider (70%) gap, with 594 for the city and 348 for the county.

Compounding factors make a difference: demographic (e.g., age, education), economic (e.g., occupation, nutrition, access to care), social (e.g., exposure to crime, access to care), and environmental (e.g., pollution, access to parks). (A previous FRED Blog post discusses the large variation observed in U.S. premature death rates.) Since these factors move with the economy, a natural hypothesis is that, as the economy grows, its rate of premature deaths should decline.

But premature death rates are on the rise in many locations. During the almost 20 years covered in the graph, both locations have made very little progress. Before the Great Recession of 2007-2009, both locations were either in a stagnant state (a stable rate for the county) or on a favorable trend (a declining rate for the city). After that, both locations entered an adverse trend, almost reversing the gains of the previous years. This result is eliminated once we look at the age-corrected series. Yet, the age-corrected rates still show a troublesome upward trend for premature deaths.

How to create this graph: Search FRED for “premature death” and choose the series for St Louis city. From the “Edit Graph” panel, use the “Add a Line” feature and add the same series for St. Louis County. Likewise, add the series for age-corrected rates. Select the colors and line thicknesses to make the graph easy to read.

Suggested by Alexander Monge-Naranjo.

View on FRED, series used in this post: CDC20N2U029189, CDC20N2U029510, CDC20N2UAA029189, CDC20N2UAA029510

Labor force participation rates of armed forces veterans

May the force be with you

The FRED Team has been reporting on a lot of dire economic data lately. Today, May the 4th, offers the chance for some light(saber)ness—using a multilayered Star Wars pun to salute our armed forces.

The FRED Blog has shown the labor force participation rates of men and women worldwide—in the U.S. and across the OECD. Today, we look at the labor force participation rate of men and women veterans of the U.S. armed forces.

The men’s rate is the solid orange line, the women’s rate is the solid magenta line, and the average across both genders is the dashed red line. As with the labor force participation rate of the overall civilian population, the rates in this graph are decreasing.

Notice how different the labor force participation rates of veteran men and women are, particularly relative to the average across genders. This is because the proportion of veteran men to veteran women is very high. This is called the composition effect. These past blog posts have additional examples of the composition effect on labor markets and on housing prices.

And yes: May the fourth be with you!

How this graph was created: Search for “Labor Force Participation Rate – Women, Total Veterans, 18 Years and Over.” From the “Edit Graph” panel, use the “Add Line” feature to search for and select the “Labor Force Participation Rate – Total Veterans, 18 Years and Over.” Do the same to add the series “Labor Force Participation Rate – Men, Total Veterans, 18 Years and Over.” From the “Format” tab, select line colors and styles to taste.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: LNU01349526, LNU01349527, LNU01349528


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