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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

Household debt meets corporate debt

Households take on debt for a variety of reasons, such as financing education and purchasing a house. Household debt in the U.S. increased from 59% of GDP in 1990 to 98% of GDP in 2009, and many economists argue that the Great Recession was “Great” because household leverage was so high at the time. It has since declined steadily. In fact, in 2019, household debt and corporate debt were the closest they have been in nearly 30 years.

The FRED graph above shows both series as a percentage of GDP: household debt and corporate debt. Household debt has exceeded corporate debt since the early 1990s, and this difference was particularly large in the years leading up to the Financial Crisis of 2008. For instance, in the third quarter of 2006, household debt was greater than corporate debt by as much as 31% of GDP. In the years since the Great Recession, however, U.S. household debt has steadily decreased. This decline, accompanied by an increase in corporate debt since 2012, has reduced the gap between household and business debt. In fact, in the last quarter of 2019, household debt and corporate debt were both around 74% of GDP.

What has driven this decrease in household debt? There are many types of household debt: mortgages, student loans, auto loans, credit card loans, etc. The second FRED graph decomposes household debt into some of these categories and shows that the decrease in household debt is driven primarily by the decline in mortgages over the recent decade. Auto loans have remained stable as a percentage of GDP; student debt has increased slightly, but not nearly enough to offset the large decrease in mortgage debt.

How these graphs were created: First graph: Search for and select “Nonfinancial Business; Debt Securities and Loans; Liability; Level.” From the “Edit Graph” menu, add the series “Households and Nonprofit Organizations, Debt Securities; Liability, Level.” For both lines, add the second series “Gross Domestic Product, Billions of Dollars, Seasonally Adjusted Annual Rate.” To rescale the series as a percentage of GDP, change the formula to (a*100/b) in the formula bar. Second graph: Search for and select “Households and Nonprofit Organizations, Debt Securities; Liability, Level.” From the “Edit Graph” tab, search for and add each of the following FRED series IDs: HHMSDODNS, MVLOAS, SLOAS. For each line, also add the series for GDP and then change the formula to (a*100/b).

Suggested by Asha Bharadwaj and Miguel Faria-e-Castro.

View on FRED, series used in this post: CMDEBT, GDP, HHMSDODNS, MVLOAS, SLOAS, TBSDODNS


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