January | 2016 | FRED Blog | Page 2
Skip to main content
Explore Our Apps
FRED
Tools and resources to find and use economic data worldwide
FRASER
U.S. financial, economic, and banking history
ALFRED
Vintages of economic data from specific dates in history
CASSIDI
View banking market concentrations and perform HHI analysis
Release Calendar
Tools
FRED Add-in for Excel
FRED API
FRED Mobile Apps
News
Blog
About
What is FRED
Tutorials
Digital Badges
Contact Us
My Account
Explore Our Apps
Explore Our Apps
FRED
Tools and resources to find and use economic data worldwide
FRASER
U.S. financial, economic, and banking history
ALFRED
Vintages of economic data from specific dates in history
CASSIDI
View banking market concentrations and perform HHI analysis
STL Fed Home Page
Release Calendar
Tools
FRED Add-in for Excel
FRED API
FRED Mobile Apps
News
Blog
About
What is FRED
Tutorials
Digital Badges
Contact Us
Search FRED Blog
Search for:
Recent Posts
US manufacturing employment is down, but each state has its own story
Real GDP growth by state: First quarter 2026
Primary deficits: a short history
What US assets are held overseas?
Durable goods inflation and effective tariffs
Recent St. Louis Fed research
US manufacturing employment is down, but each state has its own story
Real GDP growth by state: First quarter 2026
Primary deficits: a short history
What US assets are held overseas?
Durable goods inflation and effective tariffs
Archives
July 2026
June 2026
May 2026
April 2026
March 2026
February 2026
January 2026
December 2025
November 2025
October 2025
September 2025
August 2025
July 2025
June 2025
May 2025
April 2025
March 2025
February 2025
January 2025
December 2024
November 2024
October 2024
September 2024
August 2024
July 2024
June 2024
May 2024
April 2024
March 2024
February 2024
January 2024
December 2023
November 2023
October 2023
September 2023
August 2023
July 2023
June 2023
May 2023
April 2023
March 2023
February 2023
January 2023
December 2022
November 2022
October 2022
September 2022
August 2022
July 2022
June 2022
May 2022
April 2022
March 2022
February 2022
January 2022
December 2021
November 2021
October 2021
September 2021
August 2021
July 2021
June 2021
May 2021
April 2021
March 2021
February 2021
January 2021
December 2020
November 2020
October 2020
September 2020
August 2020
July 2020
June 2020
May 2020
April 2020
March 2020
February 2020
January 2020
December 2019
November 2019
October 2019
September 2019
August 2019
July 2019
June 2019
May 2019
April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
The FRED® Blog
← Older posts
Newer posts →
The geography of income inequality
Posted on January 14, 2016
U.S. regions differ in some obvious ways: linguistics, culinary traditions, income distribution… In the two graphs, we show median family income (top) and the skewness of family income (bottom) for U.S. Census regions. Notice in the top graph that the South has remained persistently poorer than the rest of the regions, without much sign of convergence. In the beginning of the sample, in 1953, median income in the South was about $10,000 less than in all the other regions. In 2014, it still trails the Midwest and West by about as much. The Northeast’s median income, however, started its climb above all the other regions in the 1980s.
Compare this picture of between-region inequality with a picture of within-region inequality. In the bottom graph we look at the skewness of income, defined by the ratio of mean over median incomes. It is always greater than 1 because the wealthier top end of the distribution accounts for more of the variation than the poorer bottom end. The South was once the most “top heavy” region, with a more upwardly skewed distribution than any other. But it has since fallen back in line with the Northeast and West. However, the Midwest has remained consistently less upwardly skewed. This gap began to materialize significantly in the 1980s, just as the Northeast median earnings were beginning to pull away.
How these graphs were created: For the top graph, search for “real median family income in census region” and add the series for the West, Midwest, Northeast, and South to the graph. For the bottom graph, search for the same series and modify each one as follows: Once you’ve added the median series, use the “Add Data Series” / “Modify existing series” options to incorporate the corresponding mean series. Then use the “Create your own data transformation” option to apply the formula b/a. Repeat this for the three other regional series.
Suggested by David Wiczer.
View on FRED, series used in this post:
MAFAINUSMWA672N, MAFAINUSNEA672N, MAFAINUSSOA672N, MAFAINUSWEA672N, MEFAINUSMWA672N, MEFAINUSNEA672N, MEFAINUSSOA672N, MEFAINUSWEA672N
Tagged MAFAINUSMWA672N, MAFAINUSNEA672N, MAFAINUSSOA672N, MAFAINUSWEA672N, MEFAINUSMWA672N, MEFAINUSNEA672N, MEFAINUSSOA672N, MEFAINUSWEA672N
--
Changing demographics
Posted on January 11, 2016
The overall U.S. population is aging. As the top graph shows, the percent of the population between 16 and 64 years of age (generally considered working age) has been declining since about 2007. At the same time, the percent of the population 65 years and older has been increasing. From 2007 to 2014, the working age population as a percent of the total population fell from 64.9% to 63.6%, while the 65+ population rose from 12.5% to 14.4%. As the working age population shrinks relative to the total, the dependency burden (the ratio of dependent young and old to those of working age) increases.
The aging U.S. population is explained mostly by differences in fertility rates before and after 1970. Although FRED data begin only in 1960, research estimates that the U.S. fertility rate increased after World War II and peaked around 1960. This period of high fertility is the “Baby Boom.” As the bottom graph shows, starting in 1960 the rate fell dramatically—from 3.6 births per woman to below 2—and has lingered around 2 since. With a fertility rate below 2 births per woman, the flow into the working age population is lower than the outflow from the aging and retirement of the Baby Boomers, which contributes to the fall in the working age population ratio.
Note: The top graph includes OECD data, which use 15-64 years as the working age population; U.S. data typically use 16-64 years.
How these graphs were created: For the top graph, search for “working age population” and choose the series with an annual frequency. In the “Add Data Series” field, search for and select “Population, Total for United States.” Use this series to modify data series 1: In the “Edit Data Series 1” / “Create your own data transformation” section, insert the formula (a/b)*100. Then add the “population ages 65 and up” series with an annual frequency, with units set as a percent of total. Move the y-axis position to the right and adjust the date range to be between January 2000 and January 2014. For the bottom graph, simply search for and select “US fertility rate.”
Suggested by Maximiliano Dvorkin and Hannah Shell
View on FRED, series used in this post:
LFWA64TTUSA647N, POPTOTUSA647NWDB, SPDYNTFRTINUSA, SPPOP65UPTOZSUSA
Tagged LFWA64TTUSA647N, POPTOTUSA647NWDB, SPDYNTFRTINUSA, SPPOP65UPTOZSUSA
--
Business lending in recessions
Posted on January 7, 2016
Commercial business lending, especially to small businesses, took a long time to recover after the 2008 financial crisis. The graph above shows annual commercial and industrial loan growth over the past three recessions, with each series indexed to 100 at the peak before the recession. The green line represents the most recent recession: Compared with the other series, recent C&I loan growth is much flatter in the 15th to 20th post-peak quarters.
Slow loan growth could be due to demand factors, supply factors, or a combination. One way to look at the supply side of business lending is the Senior Loan Officer Opinion Survey. This quarterly survey from the Federal Reserve Board of Governors asks loan officers whether lending standards and loan officer perceptions of demand have changed over the past three months. FRED has the data, which are compiled into diffusion indexes. The graph below shows the net percent of loan officers tightening standards on C&I loans to small and large firms. Each series is indexed to 100 at the peak of the business cycle before the 1990, 2001, and 2007 recessions. The green and purple lines show that lending standards tightened much more dramatically in the most recent downturn compared with the others. Moreover, standards for small business loans tightened almost twice as much as standards for large businesses. Tightening of standards may generate a sharp reduction in loan supply, which can explain part of the tepid loan growth coming out of the 2007 recession.
How these graphs were created: Search for “commercial and industrial loans,” then add the quarterly seasonally adjusted annual rate data to a graph. Change the units to “Index (Scale value to 100 for chosen period)” and select the 1981 U.S. recession peak for the value. Then select the option to display integer periods instead of dates and make the range from 0 to 20 (five years). Add the same data series for different periods with the “Add Data Series” option, choosing the same units but selecting the other recession peaks. For the second graph, follow the same steps but search for “net percentage of domestic banks tightening,” and select the series for large and middle-market firms and then for small firms.
Suggested by Maximiliano Dvorkin and Hannah Shell
View on FRED, series used in this post:
CILACBQ158SBOG, DRTSCILM, DRTSCIS
Tagged CILACBQ158SBOG, DRTSCILM, DRTSCIS
--
← Older posts
Newer posts →
Back to Top
Filter
0
Subscribe to the FRED newsletter
Subscribe
Follow us
Saint Louis Fed linkedin page
Saint Louis Fed facebook page
Saint Louis Fed X page
Saint Louis Fed YouTube page
 
Need Help?
Questions or Comments
FRED Help
Legal
Privacy Notice & Policy