Federal Reserve Economic Data

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Do government dollars drive recovery?

The conventional wisdom and data behind government spending during recessions

Conventional wisdom suggests that, once you determine the appropriate level of government spending on goods and services, this level should grow more or less in line with the growth of the broader economy. Keeping the growth rate of government spending stable over the business cycle helps stabilize the business cycle.

But let’s see what the data show: The FRED graph above plots (1) the percentage change year-to-year of total government spending on goods and services and (2) the employment-to-population ratio. The three shaded regions in the graph represent periods of recession, each characterized by a rapid decline in employment followed by a gradual recovery. But the growth in nominal government spending wasn’t the same across these three recessions: It decelerates in the early 1990s recession, remains relatively stable in the early 2000s recession, and declines precipitously into negative territory in the most recent recession.

The recessions themselves were also different: The recession in the early 2000s was noticeably mild. Was this in part due to the stable pace of government spending? In contrast, the 2007-09 recession was very deep and had a very slow recovery. Was this in part due to the unprecedented cuts in government spending? At the state and local level, these cuts were made largely in response to diminished state tax revenue and the inability to issue debt. At the federal level, they were motivated more by unwillingness to expand the federal debt even further.

Austerity during a downturn may have its merits. But the fiscal retrenchment during the most recent recession almost surely contributed to the recession’s severity and very slow recovery. Given that interest rates and inflation remained unusually low, it seems difficult to justify the sharp cyclical cuts in government spending that took place at that time. If a smaller government spending program is a long-term policy goal, the textbook recommendation is that this policy should be implemented only after the economy has fully recovered from recession.

How this graph was created: Search for “Government Consumption Expenditures and Gross Investment (GCE).” From the “Edit Graph” panel, change “Units” to “Percent Change from Year Ago.” Select “Add Line” in the same editing panel and search for the “Employment Population Ratio: 25 – 54 years.” Select the first result. Under “Edit Line 2” (still in the same editing panel), change “Units” to “Percent.” Finally, change the start and end dates on the graph to “1990-01-01” and “2019-12-01.”

Suggested by David Andolfatto and Mahdi Ebsim.

View on FRED, series used in this post: GCE, LNS12300060

Working 9 to 5: Women make up more of the workforce

A look at women in the workforce by sector

“Tumble out of bed and I stumble to the kitchen. Pour myself a cup of ambition…”  —Dolly Parton

The song and the movie 9 to 5 were released in 1980, back when women made up only 41% of employed workers in the U.S.

The Bureau of Labor Statistics has continued to collect the data and recently announced that women have broken through the 50/50 threshold in the U.S. workforce: That is, more women are employed than men. As the FRED graph above shows, this is the second time in U.S. history this threshold has been crossed. (Note that the December 2019 number is 50.0% in this graph, but a more exact value is 50.04%.)

FRED has more-specific data to help illuminate this event. To begin with, there have been more employed women than employed men in the private service-providing industry since 1985; and this share has grown over time. In the goods-producing industry, the percentage of women peaked at the end of 1991, at 27.8%.

The trends within the service-providing industry show a shifting landscape:

  • The ratio of women employees to all employees has recently increased in professional & business services and in leisure & hospitality.
  • It has remained effectively constant in education & health services and in trade, transportation & utilities.
  • And it has decreased in financial activities and information over the past 20 years or so.

Thus, it looks like the employment share of women has increased or at least persisted in sectors where women have achieved a strong presence. But it doesn’t seem like women are increasing their share in every given sector. Economists call this the composition effect. In this case, one single figure—or ratio—doesn’t tell the whole story: For example, Dolly Parton’s movie character, when heading to her job in professional & business services, is still entering a workplace where less than half of her coworkers are women.

How these graphs were created: All the data series are part of Table B-5. Employment of women on nonfarm payrolls by industry sector, Seasonally adjusted. For the first graph: Select “Total nonfarm,” click “Add to Graph,” open the “Format” tab, select “Add Line” and “Create user-defined line,” then enter 50 as the “value start/end.” The two other graphs are similar, but select “Goods producing” and “Private service-producing” and then the six different industries within the private service-providing sector. Adjust line colors and shapes in the “Format” tab so that the trends across individual industries are easier to see.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CES0000000039, CES0600000039, CES0800000039, CES4000000039, CES5000000039, CES5500000039, CES6000000039, CES6500000039, CES7000000039

Native Americans in the U.S.

A look at county-level Native American population data

FRED has added a great deal of county-level data recently, including population estimates, which include various race and ethnicity categories. These new data are especially well-suited for viewing on a map. So, of course, we look to GeoFRED: The map above shows non-Hispanic Native Americans. Note that the color scheme relates to the total number of Native Americans in each county, not in proportion to the county’s overall population.

Clearly, large numbers of Native Americans live in the West, especially the Southwest, and in Oklahoma—for obvious historical reasons that involve displacement of Native peoples. There are also quite a few counties where hardly any Native Americans live: For example, there are none in the Kansas counties of Comanche and Cheyenne. On the other side of the range, the Arizona counties of Navajo and Apache are in the top five most-populated. Unfortunately, we have no data for Oglala Lakota County in South Dakota.

How this map was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann.



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