Federal Reserve Economic Data

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Where have all the workers gone?

A smaller working-age population could mean less growth

How much an economy can produce depends to a large extent on the number of persons who are old enough to work but not too old to work. One can try to make sure there are employment opportunities, but obviously you need workers. The graph shows two measures of the “working age” population for the United States, based on different age spans. The 15- to 64-year-old range covers everyone who could work up to the hypothetical retirement age of 65. The 25- to 54-year-old range excludes the youngest (likely still in some form of schooling) and the oldest (who may have already entered some form of retirement). As the overall population of the U.S. increases, these two measures ought to increase as well. But the second measure lately has not. Why?

It all boils down to the age composition of the U.S. population.

    1. The large cohort of the Baby Boomers is now almost all older than 54, so would not be included in the 25- to 54-year-old age range.
    2. Fertility has decreased, so there are fewer younger people replacing those who are retiring from the workforce.
    3. Immigration can compensate for lower fertility, as immigrants are typically of working age, but immigration doesn’t appear to be strong enough to make up the difference.

With about a 10-year delay, the number of 15- to 64-year-olds should also flatten out, with far-reaching economic implications: The U.S. economy is unlikely to be able to sustain the growth of past decades without the usual growth in its working-age population.

How this graph was created: Search for “working age population age” and the two series should be visible. If not, use the side bar options to narrow down your choices. Check the two series and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LFWA25TTUSA647N, LFWA64TTUSA647N

There’s death and then there’s death

Two GeoFRED maps of premature death rates

FRED and, by extension, GeoFRED have two sets of county-level statistics on premature deaths: the raw measure shown in the map above and the age-adjusted measure shown in the map below. What’s the difference?

First, we need to define premature death. Statistically, for our purposes here, it’s a death occurring before age 75, which is roughly the life expectancy at birth for the average U.S. resident. However, the data shown in the top map do not take into account the age of the person; it is simply the overall rate for all premature deaths. This makes it more difficult to compare counties because not all have the same age distribution for their population. A 10-year-old clearly has a different likelihood of dying than a 74-year-old. The second measure adjusts for that. The age adjustment involves calculating what the death rate would be in the long run if the age-specific death rates would prevail for the existing age distribution of the population. And after comparing the two maps, this adjustment appears to matter.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps 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 Christian Zimmermann.

Incomes determine house prices

An illustration for San Francisco

Ask someone in San Francisco what that area’s major problem is and they’ll likely complain about housing prices and how they keep getting worse. The first graph shows us this complaint is likely accurate. Indeed, house prices in the Bay Area have increased faster than the national average, with a significant run-up around the year 2000. Why has this been happening? Are people flocking there and has the increased demand for housing driven up the prices?

The second graph shows us that a large influx of residents is unlikely to be the reason behind high housing prices: The size of the working population in the area compared with the U.S. average or even the California average has in fact decreased. Thus, proportionally fewer people are living in the Bay Area, yet house prices have still gone up. What’s that all about?

The third graph traces the evolution of personal incomes in the Bay Area compared with the U.S. average. And here we see that the buying power in the Bay Area has increased significantly more than for the rest of the country. Assuming the housing stock has remained basically unchanged, there have been fewer people with much more money chasing the same houses. So house prices increase. Note how incomes increase pretty fast around the year 2000, precisely when houses got significantly more expensive. We can’t confirm this assumption because FRED doesn’t offer data for the inventory of houses in the Bay Area. Yet, the area is known for its aversion to new housing developments, so the assumption is at least likely to apply when comparing the area with the U.S. overall, which we’ve done throughout this post.

How these graphs were created: For the first graph, search for “San Francisco house price” and take the Case-Shiller series. Click on the “Edit Graph” button and add the U.S. national house price index. Apply formula a/b and choose as units the index scale, setting 100 at the end of the 1990-1991 recession. Proceed similarly for the other graphs.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: A792RC0A052NBEA, CANA, CSUSHPINSA, PAYEMS, SANF806NA, SANF806PCPI, SFXRSA


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