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Assets U.S. households hold

Changes in the value of financial assets, real estate, and durable goods

Posted on June 27, 2019

FRED just expanded its coverage of the Z.1 release from the Board of Governors. Hidden behind this obscure name is a massive dataset that describes the financial situation of the nation, divided into sectors— households, businesses, government, and “the rest of the world.” Here, we look at the assets of households, which we’ve divided into three broad categories: real estate, consumer durables (cars, household appliances, furniture, etc.), and financial assets. The value of these assets has generally increased (no surprise; inflation is a factor), so we decided to divide each series by nominal GDP. This gives us a better idea of the quantities.

We can see that financial assets are the largest type of household asset, and their value relative to the other categories has continually increased; their value has also increased relative to total income (GDP). Currently, households’ financial assets are about 4 times annual GDP, rising from 2.5 times in 1987 when the data start. There hasn’t been a substantial increase in the relative value of real estate, however, which has usually been a little over one year’s worth of GDP (an exception being the run-up before the Financial Crisis). Consumer durable goods has actually decreased, from a third to a quarter of annual GDP. And financial assets comprised about 60% of all household assets in 1987, but now stand at almost 75%.

How this graph was created: From the release table for the balance sheet of households, select the series you want displayed and click “Add to Graph.” From the “Edit Graph” panel, add to each line nominal (not real) GDP. Apply formula a/b/1000 to each line, except for real estate where we don’t need to add /1000 to a/b to get the units right.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: BOGZ1FL194090005Q, BOGZ1LM155111005Q, GDP, HOOREVLMHMV

Metro pop

Growth and decline in U.S. metropolitan population data

Posted on June 24, 2019

FRED has included a lot of population data over the years, and it now offers data specifically for U.S. metropolitan statistical areas (MSAs).

First, a couple of caveats: It makes sense to study population numbers as a percent change year over year; looking at raw numbers can be misleading because size and density matter on a map. Also keep in mind that MSA definitions change, especially after a decennial census but sometimes midway between censuses; so, values at these dates may reflect changes in population, definition, or both.

The GeoFRED map above shows 2018 U.S. Census Bureau data for the 383 MSAs: 295 of them grew and 88 shrank. The largest (proportional) growth was in Midland, TX, with 4.32% in a single year, followed by 3.78% in Myrtle Beach, SC/NC, and 3.52% in St. George, UT. The largest (proportional) decline was in Charleston, WV, with -1.57%, followed by -1.55% in Pine Bluff, AR, and -1.47% in Farmington, NM. In terms of raw numbers, Dallas-Fort Worth-Arlington, TX, added over 130,000 residents in 2018 while Chicago-Joliet-Naperville, IL-WI-IN, lost about 22,000.

The map makes it easy to see exactly where population is moving in and out: Blue areas are declining, and it’s quite clear they’re almost all in the Northeast and Midwest. Red areas are growing the most, mainly in Florida, the central U.S., and the West.

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.

One rate does not rule them all

Unemployment is uneven across U.S. counties

Posted on June 17, 2019

The graph above shows the annual civilian unemployment rate from 1948 to 2018, and here are some highlights: Ten years ago, after the Great Recession, the U.S. unemployment rate peaked at 9.6%. (The only higher unemployment rate in this series was 9.7%, in 1982.) It gradually came down to 3.9% in 2018, the lowest in fifty years. (The rate in 1969 was 3.5%.)

But these national unemployment numbers mask the variation that exists across different regions in the U.S. Fortunately, we have GeoFRED to paint a clearer picture: The map below shows the unemployment rate for 2018 for 3,133 U.S. counties. The counties are split into two equally sized groups according to their unemployment rates: Those with lower unemployment are in blue, and those with higher unemployment are in red. Specifically, the blue group had a rate lower than 3.87%, and the red group had a rate between 3.87% and the maximum of 18.08%. (By the way, all counties in New Hampshire are blue and all counties in Arizona are red.) 

The map reveals that unemployment rates are unevenly distributed across the nation. Many counties in the Midwest have lower-than-average unemployment rates. In particular, Iowa and Nebraska counties, with only a few exceptions, are blue. In contrast, it’s not surprising to see that the Rust Belt region—e.g., Illinois, Michigan, and Ohio—is home to many counties with high unemployment rates. There are also many red counties in the Sun Belt and on the West Coast, which have rates higher than the national average.

With only the national average unemployment rate and without a county-level view, we wouldn’t know that lower unemployment rates concentrate in the Midwest and higher rates spread out over the rest of the nation.

How these graphs were created: For the first graph, search for and select “Civilian Unemployment Rate (UNRATE).” From the “Edit Graph” panel, select “Percent” for “Units” and modify the frequency to be “Annual.” Choose “Average” for “Aggregation Method.” 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 Sungki Hong.

View on FRED, series used in this post: UNRATE


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