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Diverging forecasts Different stories from hard and soft data

Leading up to the 2017:Q1 GDP release, the two GDP tracking indicators in FRED have told starkly different stories of expected growth. These indicators, also referred to as Nowcast indicators, combine higher-frequency (e.g., monthly) economic data released before the GDP data to estimate growth in the current quarter. As shown in the graph above, in the beginning of April the GDPNow indicator from the Atlanta Fed forecasted a significant slow-down in growth, predicting 0.638 percent annualized growth in the first quarter. In contrast, the St. Louis Fed Economic News Index forecasted annualized GDP growth to be higher, at 2.89 percent in Q1.

What is driving the difference? An analysis into the data underlying the GDP trackers identifies stark differences between “hard” data and “soft” data in the first months of 2017. The Nowcasts rely on both soft data such as consumer and business surveys and hard data such as retail sales and industrial production. The GDPNow indicator uses more hard data, taking an accounting approach to building a forecast; the St. Louis Fed’s News Index is based more on soft data, which is surveyed from news reports. (For more insight on this topic, see this recent Economic Synopses essay.)

The graph below illustrates the contrast between a soft data series and a few hard data series over the beginning of 2017. The blue line is the University of Michigan Consumer Confidence Index, and the red and green lines are industrial production and retail sales, respectively. Each series is indexed to 100 at October 2016 to show the progression over the end of 2016 and beginning of 2017. Since October 2016, consumer confidence has risen dramatically while retail sales and industrial production have risen steadily but slowly. Soft data in 2017 have so far told a much more positive story of economic growth than hard data. One reasons analysts identify as a possible cause for the divergence between survey and hard economic data is consumer and business optimism after the election of Donald Trump that is not yet reflected in the hard data.

How these graphs were created: Top graph: Search for “GDPNow” in the FRED search box and graph the first series that is returned. Click the “Edit Graph” button and select the middle “Add Line” menu. First, search for “St. Louis Fed Nowcast” and add the St. Louis Economic News Index as a new line. Repeat this process for real GDP, selecting the series with units in percent change from preceding period at an annualized rate. Adjust the date range to 2015:Q3 to 2017:Q1. Bottom graph: Search for “Consumer Sentiment” in the FRED search box and graph the first series that is returned. Repeat the process above to add industrial production and retail and food services as additional lines on the graph. Once all three lines are added, select “Edit Line 1” on the “Edit Graph” menu and change the units to “Index.” Set the date to October 2016 and click “Copy to all.”

Suggested by Maximiliano Dvorkin and Hannah Shell.

View on FRED, series used in this post: A191RL1Q225SBEA, GDPNOW, INDPRO, RSAFS, STLENI, UMCSENT

Not all recessions are created equal Differences in European unemployment rates

The previous recession was a worldwide phenomenon. It originated with a financial crisis in the United States that resonated in other countries, in particular Europe. The graph above shows the unemployment rate for the U.S. and a few European countries. It is taken from the OECD’s Main Economic Indicators, which goes through the trouble of trying to harmonize the definitions across countries, thus making them comparable. What is striking is how varied the experience has been. The gray area represents the period of the U.S. recession. It is remarkable that Germany’s unemployment rate actually was going down through much of this period. In contrast, unemployment shot up in Spain and, to a lesser degree, in Italy. And the U.K., arguably with the strongest financial ties to the U.S., experienced a relatively minor increase in unemployment. How can such varied experiences be explained? For one, the financial crisis was not the only economic event happening across those countries. Second, the labor market institutions and traditions differ a lot as well. Spain in particular is a poster child of rigid labor laws, and Germany was still in the transitional phase of labor market reforms.

How this graph was created: Search for “harmonized unemployment rate total,” then use the tags in the side bar to limit choices to frequency “monthly” and “seasonally adjusted.” Check the countries you want to display and click on “Add to Graph.” Finally, let the sample period start in 2002.

Suggested by Christian Zimmermann.


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: Go to GeoFRED and select the county maps. Look for “Burdened Households” in the dropdown menu.

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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