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Where do states get their tax revenue?

Income, sales, fuel, corporate, property, license, tobacco, alcohol...

State governments run on tax revenue in much the same way the federal government does. The FRED graph above shows the specific shares of state tax revenue from many sources. The two major sources are sales tax and individual income tax. While there’s a clear seasonal pattern (mostly from income taxes), there are no strong trends: The shares seem rather stable. If we look a little more closely, though, we can see a shift from corporate income tax to individual income tax and a decrease in motor fuel tax revenue. Granted, it’s not perfectly clear unless you look at the numbers directly. So, if you’re using a mouse, hover over the graph to reveal the values for each series for a particular date, including the percentages. Given the seasonal pattern, it’s best to compare the same quarter over several years—say, the yearly peaks in the second quarter.

How this graph was created: From the release table on State and Local Tax Revenue, click on “national totals of state government tax revenue,” select the quarterly taxes, and click on “Add to Graph.” From the “Edit Graph” panel, open the “Format” tab, select graph type “Area” and stacking “Percent.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: QTAXT01QTAXCAT2USNO, QTAXT09QTAXCAT2USNO, QTAXT10QTAXCAT2USNO, QTAXT13QTAXCAT2USNO, QTAXT16QTAXCAT2USNO, QTAXT24T25QTAXCAT2USNO, QTAXT40QTAXCAT2USNO, QTAXT41QTAXCAT2USNO

Zoom in on unemployment

GeoFRED maps and BLS data help us view unemployment up close

The U.S. is a large, diverse country with many differences in sectoral composition, demographics, geography, labor mobility, climate, and much more. FRED recently added a bunch of data on U.S. unemployment rates, which is a dataset with its own diverse set of differences and facets. This post explores one of those facets: geographies. We often hear about the national unemployment rate, but the Bureau of Labor Statistics provides a decomposition of unemployment at many geographic levels. Zooming in to these specific areas can help us better understand the country’s economic challenges.

The first two maps look at different ways the Census Bureau has divided up the country: The first has four U.S. Census regions, and the second has nine Census divisions.

The next map shows the 48 continental U.S. states, which obviously vary in size and population density; so, one has to be careful when interpreting the visuals. But clearly, interstate differences can be stark, even for neighboring states. Now let’s go a step further.

The next map show county-level data. In a few cases, the boundaries of a state can be recognized. But, for the most part, counties have their own unemployment experiences beyond any state averages. There are also streaks of color that span over several parts of states, like the ones that run from the Southwest to the Northwest and from the Gulf of Mexico to Lake Erie.

The last graph includes metropolitan statistical areas, which usually span several counties and sometimes multiple states. Not all U.S. territory is encompassed by MSAs, so you’ll notice that many areas don’t have an unemployment rate in this map.

How these maps were created: For each of them, go to GeoFRED, select the geographic unit, and in the cogwheel menu in the upper left select “unemployment rate” as the data.

Suggested by Christian Zimmermann.

What’s a countercyclical capital buffer?

A new monetary policy tool, that's what

The Federal Reserve has tools to achieve its monetary policy goals: the discount rate, reserve requirements, open market operations, the interest rate on reserves… and now also the countercyclical capital buffer (CCyB). The CCyB is intended to avoid the banking failures of the Great Recession by ensuring individual banks and the banking sector as a whole have enough capital on hand to provide a flow of credit to the economy without jeopardizing the solvency of this sector. To achieve this goal, a monetary authority (such as the Fed) would require banks to hold a percentage of their capital in a reserve or buffer account. The exact percentage can be changed depending on the state of the economy. If the authority believes too much credit exists, then the percentage of banks’ capital held in reserve must be increased to reduce banks’ ability to lend or provide credit. On the other hand, if the authority believes the economy needs to be stimulated, the percentage of banks’ capital held in reserves can be reduced to encourage more lending.

In practice, this is accomplished by setting a minimum value for the ratio of banks’ Tier 1 capital relative to its risk-weighted assets. Tier 1 capital can be thought of as stockholder equity and retained earnings, while risk-weighted assets are mostly the risk-weighted value of outstanding loans. The more risky the outstanding loans relative to held capital, the lower this ratio will go. To raise this ratio, banks can hold more capital and/or issue fewer risker loans.

The first graph shows the ratio of Tier 1 capital to risk-weighted assets since 2010 (blue-line) for the banking sector. Even without the CCyB, the minimum capital requirement and capital conservation buffer ensures that this ratio should not decline below 8.5% for any bank (the lower green line). The Fed could use the CCyB to raise this minimum value by up to 2.5 percentage points, to 11% (the higher green line). Currently, however, the Federal Reserve hasn’t used its authority to set a CCyB and so the lower bound for this ratio remains at 8.5%. As the graph shows, the aggregate banking sector ratio (blue line) hasn’t fallen below the 11% maximum ratio that the Federal Reserve can put in place.

In some sense, the aggregate ratio of Tier 1 capital to risk-weighted assets could be misleading: Even though the overall average is relatively high, the individual ratio of any given bank may very well be below the 11% or even the 8.5% cutoff. By looking at public call report data, we can get some idea of the position of individual banks. From our calculations, assuming the data are normally distributed, about 16% of banks will have ratios below the dotted red line. The line for this subset of banks is above the 8% line and crossed the 11% line beginning in the first quarter of 2018. This suggests that raising the minimum ratio level would have a significant effect on many banks. (Of course, this is only an approximation: The data aren’t exactly normally distributed. Instead, there are a substantial amount of firms clumped around the 8.5% lower limit, because dipping below that limit will introduce substantial regulatory burdens. In either case, however, the conclusion that a significant portion of banks would be effected by a rising CCyB holds true.)

So how do the monetary authorities decide when to use a CCyB in this way to limit the amount of credit in the economy? In other words, how much credit in the economy is “too much?” It’s not enough to simply look at the total amount of credit because any given amount of credit may be too much or too little depending upon the size of the economy. To resolve this quandary, the Federal Reserve monitors the ratio of total credit to GDP and detrends the series to ignore any long-run movements in the ratio unlikely to be associated with any given financial crisis. Perhaps the simplest method of detrending is to create a line of best fit for the data, as we have done in the second graph. Whenever the credit-to-GDP ratio gets high enough above the trend line, say, by 2.5% of GDP, a monetary authority may want to consider increasing the capital buffers to slow the growth of credit.

How these graphs were created: First graph: Search for and select “Financial Soundness Indicator; Regulatory Tier 1 Capital as a Percent of Risk-weighted Assets, Level” and click “Add to Graph. From the “Edit Graph” panel, use the “Add line” feature to add the other data series (red) and the two (green) user-defined lines: For the red line, add the original series again and use the “Formula” bar to specify either plus three or minus three depending on what you’d like to show. For the green lines, use the “Create user-defined line” option and click on “Create line”: Change the “Value start/end” fields to reflect the constant value you want to display (we graphed 8.5 and 11). All colors and line styles can be changes in the “Format” panel.

Second graph: Search for and select “Total Credit to Private Non-Financial Sector, Adjusted for Breaks, for United States.” Then, below the series, select “Percentage of GDP” option to see the ratio of interest. Detrending this data series is the tricky part: FRED doesn’t yet have a built-in function for detrending; but you can add a line to the graph as we did for the previous graph and set it so that seems to come as close to as much of the graph as possible. We downloaded the data as a CSV file using the download button and then performed a quick regression. Next we used the fitted value for 01/01/1952 (60.763) and the fitted value for 10/1/2018 (163.7093) to draw the computer-generated line of best fit in the FRED graph. If you’re more experienced in statistics, read on: The Basel Committee, an international body that set initial international guidelines on the use of CCyBs, recommends detrending with a Hodrick-Prescott filter with a lambda value of 400,000. We did this, too, and our results aren’t substantially different.

Suggested by Ryan Mather and Don Schlagenhauf.

View on FRED, series used in this post: BOGZ1FL010000016Q, QUSPAM770A


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