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Posts tagged with: "CPIAUCSL"

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Eating out or staying in? FRED says bon appétit

The FRED Blog has used data from the Census Bureau’s advance retail sales release table to compare the choice of spending outlets over time and to plot the relationship between gasoline prices and sales at gasoline stations. Today we use the “advance retail sales” data available for March 2020 to show another dimension of the social distancing required to manage the spread of COVID-19.

In the FRED graph above, the data show fairly steady growth of retail sales at restaurants and bars (the black line) catching up to retail sales at food and beverage stores (the red line) in August 2018. Note that to be able to compare sales figures over time, those figures are adjusted for the cost of living. The very last observations look like vertical lines because social distancing has dramatically switched consumer demand for restaurants and bars—almost dollar for dollar—to food and beverage stores. Keep in mind that the reported sales at restaurants and bars include the food prepared there for take-out.

If you look closely, you’ll notice the decrease in retail sales at restaurants and bars during 2009, as the Great Recession peaked and economic activity started to recover. During that time, there was no uptick in retail sales at food and beverage stores, though. Finally, although this FRED Blog post describes advance retail sales, an earlier post has compared those with retail sales and found them to be identical.

How this graph was created: Search for “Advance Retail Sales: Food Services and Drinking Places.” From the “Edit Graph” panel, open the “Add Line” tab and search for “Advance Retail Sales: Food and Beverage Stores.” Next, to adjust the sales figures for the cost of living, customize each line by searching for “Consumer Price Index for All Urban Consumers: All Items in U.S. City Average (CPIAUCSL)” and clicking on “Add.” Then, further customize the lines applying the formula (a/b)*100. Edit the graph colors and salt to taste.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CPIAUCSL, RSDBS, RSFSDP

Calculating the value of women’s unpaid work

U.S. women's unpaid labor basically equals the state GDP of New York

Yesterday was International Women’s Day, so FRED is taking the opportunity to examine one economic contribution from women that’s often ignored: The value of women’s domestic labor that goes unpaid.

For this calculation, we use Oxfam’s methodology: We calculate the total amount of hours that women spend doing unpaid household work and then use the minimum wage to put a dollar value on that work: 

  1. Take the number of women above age 16 and multiply by 26.7 hours, which is, according to the Bureau of Labor Statistics, the average number of hours per week women spend on unpaid household work.
  2. Multiply this weekly value by 52, the number of weeks in a year.
  3. Multiply the result by the federal minimum wage.
  4. Divide this annual dollar amount by the consumer price index to adjust for inflation. (Note we use annual data here, aggregated at the end of each year, to make the graph easier to read.)

OK. Nice graph. But how big a number is this? To put it in context, let’s compare the value of women’s unpaid labor with all the economic activity recorded in the state of New York.

Customize | Download data

For 2018 (the most recent data available), the dollar value of women’s unpaid work in the U.S. was equal to 86% of all the economic activity recorded in the state of New York. In other years—say, the late 1990s and late 2000s—the value of women’s unpaid work even surpassed New York state GDP. And keep in mind this value is at the low end of the possible range because we use the federal minimum wage and not, for example, higher state minimum wages let alone market wages that correspond to the specific work being done.

How these graphs were created: For the first graph: Search for and select the population of women (series ID LNU00000002). From the “Edit Graph” panel, use “Edit Line”/”Customize data” to search for and add the series for the federal minimum wage (series ID FEDMINNFRWG) and CPI (series ID CPIAUCSL). Adjust frequency to annual. Apply formula ((a*26.7*52*b)/c)*100. From the “Format” tab, choose graph type “Area” and change the color to International Women’s Day purple. For the second graph: Start with the first graph. From the “Edit Graph” panel, adjust the units for CPI to 100 in 2012. Then use the “Add Line” tab to search for and select New York state GDP (series ID NYRGSP). Apply formula a*1000. Finally, adjust the sample period to a time when both series are available.

Suggested by Diego Mendez-Carbajo.

View on FRED, series used in this post: CPIAUCSL, FEDMINNFRWG, LNU00000002

Comparing the assets of the rich, poor, and middle class

Data on the asset distribution across U.S. households

The FRED Blog has covered income and wealth before: for example, distribution of wage income, net worth, and assets. This post covers household assets, but compares them across groups: the top 1%, the 90-99%, the 50-90%, and the bottom 50%. FRED has data from the Board of Governors of the Federal Reserve System’s Survey of Consumer Finances, and the graph above shows the total assets for households in these four wealth/asset groups.

It’s clear from the graph above that the bottom half of households collectively hold significantly fewer assets than any of the three other groups. Those groups hold about the same order of magnitude in assets, but with populations of very different sizes (40%, 9%, and 1% of the total number of households).

We also see that, for these three groups, total assets have grown almost continuously, except for a dip in the past recession. Of course, this could be due simply to inflation and population growth…

So, the second graph does this adjustment. It shows that total assets have increased over time for all three groups, even after this rescaling.

The third graph offers a further adjustment by dividing each line by the size of the group. This gives us an idea of the relative magnitude of the assets per capita in each group. The differences are so large that we removed the legends to make more space for the graph. The poorest group is so low, it’s not visible. So we might as well express the assets of the three top groups as a multiple of the assets of the poorest 50%, which we do in the last graph. Beyond the stark differences between the groups, it’s quite obvious that the assets of the top 1% have increased faster than those of the other two groups since the past recession. In fact, they have almost doubled relative to the poorest 50%, from 139 times to 258 times at the apex in 2017:Q1, to 235 times now.

How these graphs were created: Start with the release table for Levels of Wealth by Wealth Percentile Groups, select the four first series, click “Add to Graph.” That’s the first graph. For the second, use the first and go to the “Edit Graph” panel. For each line, in the “Edit Line…” tab, use the “customize data” tool to search for and add the CPI series and then the population series, and apply formula a/b/c. Repeat for the three other lines. For the third graph, modify the formula to divide each by 0.01, 0.09, 0.4, and 0.5, respectively. From the “Format” tab, deselect legends and axis labels to free up some space. For the last graph, for the first three lines, add series “WFRBLB50081” and add /(d/.5) to the formula. Remove the fourth line by deleting each of its constituting series.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: B230RC0Q173SBEA, CPIAUCSL, WFRBLB50081, WFRBLN09027, WFRBLN40054, WFRBLT01000

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