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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: Simply go to GeoFRED and browse through the county-level data maps.

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

Demographic distribution: the young and the old around the world

Demographic change has been at the center of recent economic discussions—especially issues related to aging populations in advanced economies. In the U.S., one question is how the retirement of the Baby Boom generation may slow down the economy. In this post, we use GeoFRED to display the distributions of older (65 years or more) and younger (14 years or less) individuals in different countries around the world and then make some connections to the levels of economic development in those countries.

The first map shows the share of individuals 14 years of age or younger for each country. We can see that most of the advanced countries in the world—European nations such as Switzerland, Austria, France, and Denmark; Canada; and Asian countries such as Japan and South Korea—fall into the lower end of the spectrum. In these countries, less than 17 percent of the population is 14 years of age or younger. In the U.S., U.K., Russia, China, Australia, and Brazil, between 17 and 24 percent of the population is 14 years of age or younger. In several emerging economies, such as Peru, Brazil, India, the Philippines, and Malaysia, 24 to 30 percent of the population is 14 years of age or younger. And finally, most of sub-Saharan Africa has a relatively large percentage of young individuals: between 41 and 51 percent.

The second map shows the share of individuals 65 years of age or older for each country. We can see that the distribution is nearly the opposite of what we observed in the first map: The advanced economies with the lowest shares of younger individuals have some of the highest shares of older individuals. In most African countries, less than 5 percent of the population is 65 years of age or older. In Russia, Chile, China, and South Korea, 9 to 14 percent of population is 65 and older. In the U.S., Canada, Japan, several European countries, and Australia, 15 to 27 percent of the population is 65 and older.

There are several potential explanations behind these observations. The most obvious may be the income of these countries: Richer countries have better access to health care, which leads to longer life expectancies. Simultaneously, the birth rates in richer countries are low. These two factors have led to a decrease in the percentage of younger people and an increase in the percentage of older people in the population. For developing nations, the opposite is true. These nations often have high birth rates and low life expectancies, which increases the fraction of young people and decreases the fraction of older people in the population. Furthermore, in some extreme cases, wars and internal strife have led to declines in the proportion of middle-age and older people.

How these maps were created: Go to GeoFRED and click on “Build New Map.” From the “Tools” menu in the upper left corner, expand the option “Choose Data” and search for “Population Ages 0 to 14.” For the second graph, search for and select “Population Ages 65 and above.”

Suggested by Maximiliano Dvorkin and Asha Bharadwaj.

Crowds in the air Graphing airfares and passenger load factors

Do you feel lucky if no one sits beside you on an airplane? Lucky might be the right word for it: According to data provided by the U.S. Bureau of Transportation Statistics (BTS), the chance of having an empty seat next to you has been getting slimmer over time.

Over the past two decades, passenger load factors in the U.S. have been rising as air travel has gotten more crowded. Roughly speaking, passenger load factor is the average percentage of airplane seats occupied across all flights. A load factor of 0 percent indicates an empty flight while a load factor of 100 percent indicates a full flight. Because each flight has different capacities and travel distances, the BTS adjusts the official load factor statistics by the size of the airplanes as well as the flight distances.

The top graph plots the load factor for U.S. domestic flights. The red line and blue line indicate the load factors before and after seasonal adjustment, respectively. The load factor increased gradually from about 70 percent in 2000 to about 85 percent in 2017. This trend implies that the airline industry has taken better advantage of the capacity of airplanes over time. In addition, the significant gaps between the lines reflect the seasonality of air travel, with summers being the most popular time to fly. However, these seasonal gaps seem to shrink over time. One possible explanation could be that the airline industry has improved its use of airplane capacity in the winter when traveling is less popular.

This 15-percentage-point increase in load factor, from 70 percent to 85 percent, likely makes a passenger’s flight feel significantly more crowded. Consider a single-aisle aircraft with three seats on each side of the aisle. If the load factor were 66 percent, which is not far from the 70 percent number in 2000, then ideally no one would have to sit in the middle seat and there would be an empty seat between each passenger. As the load factor increases from 66 percent to 85 percent, the percentage of passengers sitting next to an empty seat drops from 100 percent to 30 percent. This more-confined experience could make some passengers more willing to pay extra fees for preferred seats or seats with better legroom.

However, the higher load factors produce some benefits for consumers: The average airfare has been lower because airlines have been putting their capacity to better use. The bottom graph shows that airfares have decreased over the past several years.

How these graphs were created: For the top graph, search for “domestic load factor, scheduled passenger flights,” check the seasonally adjusted and not seasonally adjusted series, and click the “Add to Graph” button near the top of the search results. For the bottom graph, search for “consumer price index for all urban consumers: airline fare,” check the seasonally adjusted series, and click the “Add to Graph” button.

Suggested by YiLi Chien.

View on FRED, series used in this post: CUSR0000SETG01, LOADFACTORD, LOADFACTORDD11

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