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Tumultuous tax brackets U.S. income tax brackets have varied (sometimes wildly) over time

Income tax law is complex and many of its variables change over time. One much-discussed example is its progressivity―that is, how much the tax rate increases when taxable income increases. In the United States, this progression is determined by a system of brackets: Once a taxable income threshold is reached, any additional income is taxed at a higher rate (the so-called marginal tax rate). The number of those brackets, the incomes at which they kick-in, the associated tax rates, and what constitutes taxable income are all elements in the complex formula of taxes.

The graph shows just two elements of that formula: the first and the last marginal tax rates. This highlights how much these rates have varied through history (compared, let’s say, with the past decade or two) and also how high they have been. In 1944, for example, the top-bracket tax rate was 94%! It also shows that some changes were absolutely brutal: 1917 to finance WWI and 1932 to finance the New Deal. WWII, despite the high tax rates, was actually financed mostly through debt (war bonds), which required keeping tax rates high to pay it off.

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

Suggested by Christian Zimmermann.

View on FRED, series used in this post: IITTRHB, IITTRLB

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: Go to GeoFRED and click on “Build New Map.” Under “Tools,” set Region Type to “State” and Data to “Value of Exports to Canada.” Choose data from the year 2016. Repeat the same process for the second map, with the Data option set to “Value of Exports to China.”

Suggested by Asha Bharadwaj and Maximiliano Dvorkin.

Do imports subtract from GDP? A basic explanation of GDP = C + I + G + (X-M)

The typical textbook treatment of GDP is the expenditure approach, where spending is categorized into the following buckets: personal consumption expenditures (C); gross private investment (I); government purchases (G); and net exports (X – M), composed of exports (X) and imports (M). Textbooks often capture this in one relatively simple equation:

GDP = C + I + G + (X – M).

Notice that, here, imports (M) are subtracted. On the surface, this implies that an extra dollar of spending on imports (M) will decrease GDP by one dollar. For example, let’s assume you spend $30,000 on an imported car; because imports are subtracted (e.g., “– M”), the equation seems to imply that $30,000 should be subtracted from GDP. However, this cannot be correct because GDP measures domestic production, so imports (foreign production) should have no impact on GDP.

When the Bureau of Economic Analysis (BEA; see its primer on this topic) measures economic output, it categorizes spending with the National Income and Product Accounts (NIPA). Some of this spending (which is counted as C, I, and G) is spent on imported goods. As such, the value of imports must be subtracted to ensure that only spending on domestic goods is measured in GDP. For example, $30,000 spent on an imported car is counted as a personal consumption expenditure (C), but then the $30,000 is subtracted as an import (M) to ensure that only the value of domestic production is counted. As such, the imports variable (M) functions as an accounting variable rather than an expenditure variable. To be clear, the purchase of domestic goods and services increases GDP because it increases domestic production, but the purchase of imported goods and services has no direct impact on GDP.

The misconception that imports reduce GDP also seems to be implied when the GDP components are stacked using the FRED release view. Notice that the green “net exports” area is negative. This occurs because the dollar value of imported goods and services exceeds the value of exported goods and services. While this aspect of net exports (X – M) is useful for evaluating how international trade affects economic activity, it can be misleading. Like the misleading aspects of the expenditure equation, it suggests (visually) that imports reduce overall GDP. While the graph is not incorrect, it is important to keep in mind that, when calculating GDP, the value of imports is actually subtracted from the other components of GDP (personal consumption expenditures, gross private domestic investment, government consumption expenditures, and gross investment), not exports. Again, it’s important to emphasize that the imports variable (M) is an accounting variable rather than an expenditure variable.

To learn how to create your own GDP stacking graph, see this FRED blog post. For a more complete description of GDP and the expenditures equation, read the September 2018 issue of Page One Economics.

Suggested by Scott Wolla.

View on FRED, series used in this post: GCE, GPDI, NETEXP, PCEC


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