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Assets U.S. households hold

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

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

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: From GeoFRED, look for “Metropolitan Statistical Area,” then choose “Resident Population,” and select units “Percent Change from Year Ago.”

Suggested by Christian Zimmermann.

Something changed in Black unemployment

Unemployment data reveal several differences for race and gender

This post is a bit long, with a puzzling observation and, even after five FRED graphs, no definitive explanation. But sometimes the journey must be the destination…

The (first) graph above shows unemployment rates by race and gender since the start of the Great Recession. It’s clear men’s rates overall are higher than women’s, possibly due to factors such as women’s less-harmonious attachment to the labor market and different gender composition across industries and occupations. Also, Whites overall enjoy a lower unemployment rate than Blacks, which is at least partly due to the differences in the industries and occupations Blacks and Whites tend to work in.

The movements in the unemployment rates also differ, and this is the puzzle we focus on here. Look closely and you’ll see that unemployment rates didn’t start to decline until late 2011 and early 2012 for Black men and mid-2013 for Black women. The decline for Whites occurs much earlier: early 2010 for men and gradually from 2010 to 2012 for women. Has this difference always existed? We’ll need to look back in time to investigate…

…and fortunately we can use FRED’s time slider at the bottom of the graph to do that. The (second) graph above shows the previous recession in 2001; again, we see a longer lag for Blacks than for Whites for unemployment rates to decline.

Stepping back a bit farther… The (third) graph above shows the 1990-91 recession; it looks like all unemployment rates peaked simultaneously and declined over the same time frame.

The (fourth) graph above shows the 1981-82 recession, where the unemployment rates again peaked simultaneously and declined over the same time frame. We can skip the 1980 recession, as it’s so close to this one.

The (fifth) graph above shows the 1974-75 recession, where the unemployment rates again peaked simultaneously and declined over the same time frame, except for Black women, whose unemployment rates don’t seem to have recovered at all.

Let’s summarize: For the 1974-75, 1981-82, and 1990-91 recessions, Black and White unemployment rates essentially peaked and declined over the same time frame. For the 2001 recession and Great Recession, Black rates took longer to decline than White rates.

What changed?

Even if we can’t provide an answer here, we can suggest where you might do some additional research on the topic. Look to FRASER, FRED’s sibling site, for a deeper examination of historical demographics related to employment: The statistical publications “Employment and Earnings” (1954-2007) and “Women in the Labor Force: A Databook” (2004-2010) are good examples. The latter focuses mainly on differences between the sexes, but also provides statistical tables that relate to race, including one on multiple jobholders.

How these graphs were created: From the employment situation release table, select the series you want according to race, gender, and age and click “Add to Graph.” For all FRED graphs, you have three ways to select the dates you want to display: (1) the date picker above the graph, (2) the time slider below the graph, or (3) selecting the range to highlight within the graph (click and drag).

Suggested by Christian Zimmermann.

View on FRED, series used in this post: LNS14000028, LNS14000029, LNS14000031, LNS14000032


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