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The FRED® Blog

High school geography class

Education is the key to income, and high school graduation rates are often used in the economics and education literature to measure “human capital.” FRED has such data—specifically at the U.S. county level. To visualize the data geographically, you can use another convenient tool that we offer: GeoFRED. The map above is an example of what you can create with GeoFRED. You can create many, many more maps on the site.

How this graph was created: Go to GeoFRED and click on “build new graph.” In the tool menu (upper left corner), select region type “county” then search for data “high school.”

Suggested by Christian Zimmermann

How healthy is the labor market, really?

Economists, policy wonks, and the public often look at the unemployment rate to quickly assess the U.S. economy. Although the unemployment rate provides some understanding of the cyclical state of the labor market, it doesn’t account for those who have dropped out of the labor force. The labor force participation rate captures that information. Both rates (shown in the top graph) have declined since the end of the Great Recession, which may imply that there’s unmeasured slack in the labor market.

Andreas Hornstein and Marianna Kudlyak from the Federal Reserve Bank of Richmond and Fabian Lange from McGill University constructed a more comprehensive way of examining resource utilization in the U.S. labor market. Their non-employment index (NEI) counts those who are unemployed (as traditionally defined) and those who have dropped out of the labor force. The NEI weights those who have dropped out of the labor force according to their “attachment”—defined by the Bureau of Labor Statistics as the likelihood a person will transition back to employment, which is based on each group’s historical transition rate to employment relative to the highest transition rate among all groups. This weighting allows the authors to count all non-employed individuals without drawing “arbitrary distinctions on who is to be included.”

The BLS classifies the groups in the index as (1) unemployed, (2) out of the labor force but desiring a job, or (3) out of labor force but without the intention to reenter. The BLS further categorizes those who are out of the labor force but want a job as (2a) marginally attached because they’re discouraged by poor job prospects, (2b) marginally attached but haven’t looked for work during the most recent month, or (2c) temporarily out of the labor force for other reasons. Finally, the BLS classifies those who are out of the labor force but do not want a job as (3a) in school, (3b) not in school, (3c) retired, or (3d) disabled.

Hornstein, Kudlyak, and Lange recommend interpreting the NEI in comparison with the standard measure of unemployment. Generally, the two measures move in line with each other, with the exception of the period following the Great Recession, as shown in the bottom graph. This graph also includes a third series—the green line—that incorporates those who work part time in lieu of full time for economic reasons. More information on the NEI can be found here.

How these graphs were created: Top graph: Search for and select the monthly, seasonally adjusted unemployment rate. Use the “Add Data Series”/“Add new series” option to search for and select the monthly, seasonally adjusted labor force participation rate; be sure to set the y-axis position to the right. Bottom graph: Again, search for and select the monthly, seasonally adjusted unemployment rate. Then use the “Add Data Series”/“Add new series” option to add the two other series: Search for “non employment index” and select the base index (not the index that includes people working part-time for economic reasons). Then search for “non employment index” again and select the index that includes people working part-time for economic reasons.

Suggested by Travis May

View on FRED, series used in this post: CIVPART, NEIM156SFRBRIC, NEIPTERM156SFRBRIC, UNRATE

Measuring and comparing local economies: Memphis vs. Nashville

When we want to assess the national economy, we typically look at the growth rate of gross domestic product (GDP), as it accounts for all goods and services produced in the economy. Similar data are also available for local economies in a measure called gross metropolitan product (GMP).

Unfortunately, local GMP data are calculated only once per year and released with a nine-month lag. For more timely information, we use factor analysis to estimate the common trend (or factor) underlying the movement in 12 variables of regional economic activity. This common factor is used to produce an index of economic activity for 51 MSAs across the nation. Each index is calibrated to match the annual growth rate and volatility of GMP for the MSA. In other words, the value of the index can be interpreted as an annual growth rate of the local economy, which allows for ease of interpretation and comparison across metro areas. For more details, see this working paper.

Just as job growth and unemployment rates vary from one region to the next, economic growth of two areas (even in the same state) can also vary. The graph plots the economic conditions indexes of the two largest metro areas in Tennessee, and we can see how the Nashville metro area has grown at a faster pace than the Memphis metro area over the past few decades. During the 2001 recession, Nashville’s economy actually expanded most months. During the Great Recession, growth was negative in both regions, with growth resuming in Nashville in August 2009 and in Memphis in March 2010. In recent years, the Nashville economy has grown steadily at around 5 percent, while the Memphis economy has slowed from about 2.5 percent per year to around 1.5 percent.

How this graph was created:
Search for “metro area economic conditions” and select the metro areas you want to add to the graph. Or go to the Economic Conditions Index Release table, select the MSAs you want to view, and select “Add to Graph” at the bottom of the table.

Suggested by Maria Arias and Charles Gascon

View on FRED, series used in this post: MPHAGRIDX, NVLAGRIDX

The geography of income inequality

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


Changing demographics

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


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