Federal Reserve Economic Data

The FRED® Blog

Predicting the payroll employment numbers

Most people look forward to Fridays in general, but data analysts and economists eagerly await one in particular: the Friday when the BLS’s employment situation is published. Two headline figures in this release are the unemployment rate and total nonfarm payrolls. These numbers are still subject to revision after their initial release. For example, the payroll numbers are based on about 70% of the surveyed businesses, and that number gradually increases to about 94% through revisions. Thus, relying on this first release to tell the whole story may be a bit premature, given that something could be changed by the revisions.

ADP is a company that provides payroll services to many businesses. It uses its internal data, as well as other economic indicators, to predict a few days before the BLS’s release what the final payroll number will be. The graph here compares the BLS series (in red) and the ADP series (in blue) and shows that there are some spectacular hits…and misses. Note that the misses could be on either side―too high or too low―as both are imperfect measures. Yet, if both measures agree, that’s a strong indication that they hold some truth.

How this graph was created: Search for nonfarm payrolls, select the relevant series, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: NPPTTL, PAYEMS

War: Spending spikes and new steady states

Historical data on how conflict has changed U.S. government expenditures

History books are full of wars, and economic data are even categorized by their timing before and after wars. In this post, FRED taps into the NBER Macrohistory Database to track the expenditures of the U.S. federal government from 1879 to 1947—which includes plenty of conflict.

Obviously, these expenditures have grown tremendously because the U.S. has grown tremendously since its Civil War, in both population and per capita terms. But this growth hasn’t been uniform. Indeed, the major spikes are tied to the World Wars. The U.S. began its involvement in WWI in 1917, and its expenditures shot up until they peaked in December 1918. It took until the end of 1919 to reach a new steady state, but notice that the new steady state is at a significantly higher level than before the war. This pattern happened again with WWII: The major build-up started in 1941 as the U.S. became involved, peaked in 1945, then declined to a new steady state, which again is higher than the previous one. Note that there’s a comparatively small step-up in the 1930s. That’s the New Deal associated with the Great Depression. Not a war, but still a harrowing episode.

NOTE: You probably noticed the different colors along what looks like one continuous line. That’s because the graph was built with five different series that bear the same title. The NBER Macrohistory Database compiles data from multiple sources, and they don’t always agree and there are some overlapping observations. Hence, the different series are kept separate. But if you look carefully, the numbers are pretty close.

How this graph was created: Search for federal expenditures. The set of series should be grouped together among the choices (although maybe not at the top of the list). Check each of the series, then scroll back up and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: M1505AUSM144NNBR, M1505BUSM144NNBR, M1505CUSM144NNBR, M1505DUSM144NNBR, M1505EUSM144NNBR

Which states are most invested in trade with China and Canada?

The geographic distribution of U.S. exports

If you follow this blog, chances are you’ve run across at least some standard economic theories. For example, (1) countries export what they can produce at a comparative advantage and import the other stuff and (2), with nearly unequivocal agreement, free trade is seen as beneficial overall for trading partners. You may also be following the escalating tensions between the U.S. and its trading partners (China, Canada, Mexico, Europe, etc.) over tariffs enacted by the U.S. to protect import-competing industries and the retaliatory tariffs enacted by the other countries. So let’s see if FRED data can connect a little theory with current events.

To keep it simple, we look at U.S. state exports to Canada and then to China. The first map makes it clear that northern states export more to Canada than other states. This aligns well with standard economic models: The factors that determine trade relationships include distance between countries, incomes of trading countries, common languages, and common borders. But we also see that larger states such as California and Texas are major exporters to Canada, too. In 2016, U.S. states exported goods worth around $193.7 billion to Canada. Michigan and Ohio were the largest exporters, with a combined 20.19% of total state exports to Canada.

The second map shows that many of the major exporters to Canada are also major exporters to China, including California, Texas, Michigan, and Ohio. Several of these states serve as major ports (California, Texas, and Ohio, for instance), which is one potential explanation for why these states are major exporters in both cases. U.S states exported goods worth $93.9 billion to China, with 25.75% of them originating in California and Ohio.

You might be asking yourself why state-level trade patterns matter. One reason is that states aren’t all invested in international trade to the same degree. Trade affects states differently, according to their specific industries and those industries’ exposure to foreign purchases of their products. And these trade patterns can provide insights into how tariffs and other changes in international trade could affect specific U.S. cities, states, and regions.

How these maps were created: The original post referenced interactive maps from our now discontinued GeoFRED site. The revised post provides replacement maps 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 Asha Bharadwaj and Maximiliano Dvorkin.



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