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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

Extra, extra, read all about it! There’s more to the story than headline unemployment

Earlier this month, the Bureau of Labor Statistics reported September’s unemployment rate was 4.2% and the number of unemployed persons decreased by over 300,000. Does that mean every one of those 300,000 individuals found a job? Many Americans view decreases in unemployment positively, but there’s more to the unemployment story than just the headline. The reported unemployment rate is a proportion of members of the civilian labor force who have actively but unsuccessfully searched for a job in the past four weeks. This measure is frequently criticized for ignoring two segments of the population: those who have given up searching for work (i.e., discouraged workers) and those who would accept a job if offered one but aren’t actively searching (i.e., marginally attached workers). Moreover, those working part-time are counted equally in employment data as those working full time, regardless of what they’d prefer.

Because the BLS understands the shortcomings of the headline rate, they record unemployment data for all these groups and release six different measures of unemployment. The graph shows some of these measures for the state of Missouri. The teal line on the bottom shows the headline unemployment rate, and the green line adds the percentage of discouraged workers. Notice that the number of discouraged workers increases after a spike in the headline rate. While headline unemployment reached a peak in the first quarter of 2010, the number of discouraged workers was highest throughout 2011 and 2012, reaching 1.3%. The increase in individuals not actively searching for a job indicates some of the social costs of unemployment: pessimism toward the labor market and a pervasive belief that jobs are increasingly difficult to find, causing workers to stop searching for jobs in the first place. This collective shift may take years to improve, explaining the delayed yet sustained increase.

The measure shown by the dark line includes the percentage of marginally attached workers but does not include discouraged workers. This category includes individuals who have searched for a job in the past 12 months, but not in the past four weeks. This measure of marginally attached workers as a percentage of the whole remains fairly consistent over time. The final segment of the unemployed population, shown by the blue line, adds the percentage of individuals employed part-time for economic reasons: They desire a full-time job, but work fewer than 35 hours per week. We expect the proportion of these individuals to increase during times of economic downturn as employers may cut hours before firing employees. However, the percentage is highest in the second quarter of 2010, at 5.4%, and not earlier in the Great Recession. Reasons for this delay may be economic uncertainty and the possibility of workers on contracts that delay employers’ responses. Furthermore, many workers may have been satisfied with a part-time job during the Great Recession, but then began reporting their part-time work as involuntary as the recovery started and the outlook improved.

How this graph was created: Search for “unemployment in Missouri” and select the seasonally adjusted series. From the “Edit Graph” tab, click “Add line” and search for “unemployed plus discouraged Missouri” and select the relevant series. Add a third line and search for “unemployed plus marginally attached Missouri” and select the series. Finally, add a fourth line and search for “unemployed plus part-time Missouri.” Change the start date to 01-01-2004.

Suggested by Maria Hyrc and Christian Zimmermann.

View on FRED, series used in this post: MOUR, U4UNEM4MO, U5UNEM5MO, U6UNEM6MO

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