The FRED Blog has discussed how stock market fluctuations don’t accurately reflect overall economic conditions in the U.S. Today, we throw real estate prices into the mix and see what patterns we can find.
The FRED graph above tracks total stock shares in blue and Case-Shiller national home prices in red during the most recent economic downturn. We use an index equal to 100 in the first quarter of 2020, the start of the COVID-19-induced recession, to help us easily compare these growth rates over time.
Real estate prices took off during the second half of 2020. Stock prices slumped during the first half of the year and did not quite catch up by the second half. (The same pattern is visible when comparing the Case-Shiller home price index to the Dow Jones Industrial Average.) But during the first quarter of 2021, stock prices grew steadily and ended up topping the year-to-year growth in home prices. If this were a game of rock-paper-scissors, paper (stocks) would have beaten rock (real estate).
Now let’s look at these same asset prices during the previous economic contraction—the Great Recession. In this graph, we see the slump in stock prices was deeper and lasted longer. Although home prices also declined, they did so much more gradually. In that round, the house of bricks (real estate) beat the house of cards (stocks).
How these graphs were created: Search for and select “Total Share Prices for All Shares for the United States.” From the “Edit Graph” menu, use the “Add Line” tab to search for “S&P/Case-Shiller U.S. National Home Price Index.” Again from the “Edit Graph” panel, select the “Edit Line 1” tab. In the “Units” drop-down menu, select “Index (Scale value to 100 for chosen date)” and choose “2020-02-01” for the first graph and “2007-12-01” for the second graph. Adjust the date range to mirror the dates shown in the blog post.
Suggested by Diego Mendez-Carbajo.
World Bank data on life expectancy and GDP in low-income vs. high-income countries
The World Bank has many data series that allow comparisons among countries over time, and today’s FRED graph reveals some trends in life expectancy and national income.
Lower life expectancy in low-income countries has been catching up. In 1982, life expectancy at birth in low-income countries was about 66% of what it was in high-income countries. Then life expectancy increased at a faster pace in low-income countries, and the value rose to 78% by 2018. This rising longevity, especially in relation to longevity in high-income countries, is remarkable because it doesn’t coincide with an improvement in relative economic performance.
In 1982, real GDP per capita in poor countries was 2.8% of what it was in rich countries. In 2018, it was 1.8%. Despite poor countries losing ground to rich countries on the economic front (GDP per capita), they gained ground on the health front (life expectancy at birth).
For more information, read on… Countries in this analysis are classified as low income or high income depending on their 2019 gross national product per capita. And countries aren’t always in the same group from one year to the next, of course.
- This variability doesn’t affect the conclusion here that there’s a disconnect between economic performances and life expectancy.
- This general conclusion from the FRED graph also holds for individual countries. For example, life expectancy in Benin grew from 63% of life expectancy in the U.S. in 1980 to 70% in 2018. But Benin’s GDP per capita remained below 1% of U.S. GDP per capita for that period.
- Finally, the statistical correlation of life expectancy and GDP per capita across individual countries has been steadily declining since the 1960s, shown in the graph below. The FRED graph above is a manifestation of this decline.
How this graph was made: Search for and select “Life Expectancy at Birth, Total for Low income Countries.” From the “Edit Graph” panel, use the “Customize data” search field to search for and add the series “Life Expectancy at Birth, Total for High Income Countries” to the same line. In the formula bar, type a/b*100. Next, under the “Add Line” tab, search for and add “Constant GDP per capita for Low Income Countries” and “Constant GDP per capita for High Income Countries.” In the formula bar, type a/b*100.
Suggested by Guillaume Vandenbroucke.
A closer look at disability in the U.S. civilian labor force
The Americans with Disabilities Act (ADA) was signed into law 31 years ago. The FRED Blog has used data from the U.S. Bureau of Labor Statistics (BLS) to show that the fraction of people outside of the labor force because of disability is approximately constant. Today we revisit the general topic by looking at the percentage of people with a disability inside the labor force.
As a reminder, the civilian labor force is made up of workers who either (i) have a job or (ii) don’t have a job but are actively looking for one. And like it sounds, the civilian labor force doesn’t count those in the armed forces.
Our FRED graph above shows the percentage of workers with a disability who are in the labor force: Men are in green and women are in purple. The shares of these men and women are almost identical: 3.1%, or slightly more than 1 out of every 30 workers, on average. These shares declined slightly between 2009 (the first available data, at the end of the Great Recession) and 2014-2015. The shares increased modestly and unevenly up to 2019, the last year before the COVID-19-induced recession.
The available data cover only the period between two recessions, so we can’t separate the cyclical patterns from the long-term trend patterns in the data. But the BLS provides more detail about the distribution of employed persons with a disability across different types of jobs in this issue of The Economics Daily. And this 2018 working paper by current and former St. Louis Fed economists illuminates the roles of economic activity and the evolution of the labor force.
How this graph was created: Search for and select “Civilian Labor Force – With a Disability, 16 to 64 Years, Women.” From the “Edit Graph” panel, use the “Edit Line 1” tab to customize the data by searching for and selecting “Civilian Labor Force Level – Women.” Next, create a custom formula to combine the series by typing in a/b*100 and clicking “Apply.” Last, click on “Add Line” and repeat the same steps for men in the civilian labor force.
Suggested by Diego Mendez-Carbajo.