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How COVID shocked state and local revenue

BEA data track the ups and downs of federal grants-in-aid and local tax revenue

State and local governments receive two major sources of revenue: transfers from the federal government and their own tax receipts. Each of these series (since 1960) is plotted in the FRED graph above in billions of dollars at a seasonally adjusted annual rate. Both series trend upward over the past 70 years, as each has grown with the U.S. economy overall.

The graph shows the pandemic’s effect on the economy. First, the CARES Act, signed into law in March 2020, allocated hundreds of billions of dollars to state and local governments to fight the pandemic. The blue line spikes in the second quarter, with the surge in federal grants-in-aid to state and local governments, such as $150 billion through the Coronavirus Relief Fund. In the next two quarters, grants-in-aid remained above their long-run trend but fell from their very high level in April through June.

Second, there was concern that state and local tax revenues might be diminished by the pandemic. The graph shows an initial dip in tax revenue during the second quarter of 2020, but tax revenue largely recovered and ended 2020 at or slightly above its long-run trend.

Note that the most recent reported data end in December 2020. So, we don’t yet see the $350 billion in grants from the American Rescue Plan Act, which was passed in March of this year.

How this graph was created: Search FRED for “state and local government grants” and click on the relevant result. From the “Edit Graph” panel, use the “Add Line” tab to search for and select “state and local government current tax receipts.”

Suggested by Bill Dupor.

A new measure of economic health

New FRED data decomposes the evolution of monthly GDP

FRED just added a new family of data that can help us get a read on the U.S. economy.

The BBKI (Brave-Butters-Kelley Indexes) draw on about 500 indicators and search for some commonality among them, thanks to a technique called dynamic factor analysis. This analysis allows for an estimate of monthly GDP and decomposes it into different components. (GDP measures are typically quarterly, and this innovation is meant to be more timely.)

The graph above shows the monthly GDP estimate along with the coincident and leading indicators for a period spanning the past two recessions. Clearly, the leading indicator was able to accurately determine the direction of the changes in this current and strange recession. Anticipating the turning points, of course, is very difficult in forecasting.

The graph below shows a decomposition of the monthly GDP indicator into various components:

  1. a trend, which varies very little through time
  2. a leading component—that is, which current data will influence future GDP
  3. a lagging component that was largely determined from the previous period
  4. a cycle component—that is, a deviation from the trend that has some persistence
  5. and an irregular component of random events and one-offs with no persistence.

As with many graphs lately, things are a little bit difficult to distinguish because of the scale of the data in our current environment. So let’s concentrate on the past year. The graph below shows that the large swings in 2020 were due to different components. The large downturn was due to the cyclical and irregular components, but the large upswing was mostly irregular, which then swings back down. This back and forth isn’t cyclical, at least not at the frequency that economists typically think a business cycle should last (2 to 8 years). And indeed, these wide swings didn’t have any economic fundamentals; they were tied to the evolution of health-related concerns.

How these graphs were created: Start from the BBKI release table, check the series you want displayed, and click “Add to Graph.” Adjust the time period to taste.

Suggested by Christian Zimmermann.

Who’s online? Mapping Internet use around the world

World Bank data on national income and Internet use

The FRED Blog has looked at the speed of Internet adoption in a few countries: the U.S., China, Korea, Germany, and India. Today, we use World Bank data to widen our view and map Internet use rates around the world. Then we connect those rates to countries’ per capita GDPs.

Our first GeoFRED map identifies the number of Internet users per 100 people in each country. In countries colored blue, over 80% of the population uses the Internet: Liechtenstein is at the top, with a ratio of 99.55%. In countries colored red, under 20% of the population uses the Internet: Eritrea is at the bottom, with a ratio of 1.31%.

Our second map shows inflation-adjusted gross domestic product (GDP) per person. In countries colored blue, GDP per person (at 2010 prices) is more than $28,000 per year: Monaco, where the figure is above $186,000, is at the top. In countries colored red, GDP per person is less than $1,600: Burundi, where the figure is $214, is at the bottom.

Consider the examples of Austria, Belgium, Finland, and Germany, which have Internet use rates of 84% to 88% and per capita GDPs of $48,000 to $50,000. Now consider that Cambodia, Honduras, India, and Sudan have Internet use rates of 31% to 32% and per capita GDPs of $1,000 to $2,000.

Comparing the maps reveals the close correspondence between Internet use rates in the population and GDP per capita. Access to the Internet requires investment in physical capital, so its positive correlation with prosperity is pretty much expected. The scatterplot below also illustrates this overall relationship (read from left to right and from bottom to top) between the two variables for the 184 nations and territories during 2017:

To account for their large range of variation, the values of constant GDP per capita are plotted in logarithms. Each dot represents a country and the general shape of the dot cloud indicates a positive relationship between income levels and Internet use: On average, the richer the country, the larger the fraction of its population that is online.

By the way, more countries and territories report their Internet use than their GDP per capita. The production of economic information, much like the provision of internet services, requires investment.

How these maps were created: For the first map, go to GeoFRED and click the green “Build New Map” button. In the cog-wheel tool menu, use the drop-down menu under “Data” to find “Internet users by Nation” and select “2017” for the date. Next, click on “Edit Legend” tab and choose “Interval Method: Equal Interval.” Last, click on the “Choose Colors” tab and take your pick. For the second map, find “Constant GDP per capita” and select “2017” for the date. Next, click on “Edit Legend” tab and choose “Interval Method: Fractile.” Last, click on the “Choose Colors” tab and take your pick.

Suggested by Diego Mendez-Carbajo.

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