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.
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
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: