Federal Reserve Economic Data

The FRED® Blog

The burden of housing

Population density means higher living costs

The map shows, for each U.S. county, the percentage of households that are “burdened.” That seems to be a rather vague term, but in this case it has a precise definition from the U.S. Bureau of the Census: A household is considered burdened if it has to dedicate at least 30 percent of its income to rent or mortgage payments. Clearly, there are two components that can make a household burdened: low income and high housing costs. You could probably conceive of stories for the reasons that various parts of the country are more or less burdened. (On the map, the darker the color, the more burdened the county.) Consider that major population basins have higher housing costs, the South is generally poorer, and Florida has a lot of retirees on fixed income. Of course, one shouldn’t be surprised that housing costs are higher where there are more public amenities—which is where population tends to be denser. And, of course, some households may simply choose to spend more on their homes.

But why is 30 percent used as a threshold for burden? This is the maximum that is considered by rental assistance programs as well as guidance by mortgage providers. The concern is that households should have 70 percent available for other necessities. This problem tends to apply only to poorer households, although this map covers all households.

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.

Royalty payments and the incentives to conduct research and development

Countries are introducing policies to generate stronger intellectual property rights. These policies are aimed at increasing incentives for firms to conduct research and development in the country. One form of intellectual property rights is captured by patent royalty payments—that is, payments made to the owner of a patent for the right to use that asset.

The U.S. has experienced a substantial increase in patent royalty receipts and license fees since the 2000s (blue line on left Y-axis). These data are reported in the balance of payments of the country as exports of services and reflect income that firms in the U.S. receive from other countries to use their intellectual property (e.g., patents, trademarks, copyrights, and franchises).

On top of the increase of net receipts of royalty payments from the rest of the world, there has been an increase in expenditures in research and development in the U.S. during the same period (red line on right Y-axis).

As intellectual property rights become stronger, the incentive of firms to innovate strengthens as well. Part of this research and development translates into patented innovations; along with stronger intellectual property rights, this increases royalty payments by firms in other countries that want to use this knowledge.

How this graph was created: Search for “Exports of services: Royalties and license fees” and click on the series you want to create the first line. Select “Line 1” in “Edit Lines” under the “Edit Graph” tab and add the series “Gross Domestic Product: Implicit Price Deflator” to the existing line. Apply the formula a/(b/100) to deflate exports into a constant year. Use “Add Line” within “Edit Graph” to add line 2 “Real Gross Private Domestic Investment: Fixed Investment: Nonresidential: Intellectual Property Products: Research and Development” to the existing graph. Change the time line to be “2000/01/01-2015/01/01” through the two boxes next to “Edit Graph.” Finally, under the “Format” tab, select “Right” for the “Y-Axis position” for line 2.

Suggested by Ana Maria Santacreu.

View on FRED, series used in this post: B684RC1Q027SBEA, GDPDEF, Y006RX1Q020SBEA

300

This is Sparta[nburg County, South Carolina]!

This is the FRED Blog’s 300th post, a great opportunity to check out Sparta. Unfortunately, FRED’s coverage does not include classical antiquity, but it has a lot of regional U.S. data, including 153 series pertaining to Spartanburg County, one of the larger counties in South Carolina, and the Spartanburg MSA. As the graph above shows, the county seems to have gone through some rough times but is rebounding now: While the population has been steadily increasing, the labor force went through two pronounced slumps and is now on the upswing. The graph below shows some other indicators for Spartanburg County, this time related to poverty. The picture there is mixed. While the number of people in poverty and the number of those receiving food stamps seem to be increasing, the proportion of people with a credit score below 660 (considered subprime) seems to be decreasing.

How these graphs were created: Search for “Sparta” or “Spartanburg,” check the series you want displayed, and click “Add to Graph.” In cases where the units mismatch and some series aren’t visible because of a large disparity, put their units on the right axis: Click “Edit Graph,” open the “Format” tab, and switch the axes.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CBR45083SCA647NCEN, EQFXSUBPRIME045083, PEAASC45083A647NCEN, SCSPAR0LFN, SCSPAR0POP


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