Skip to main content

The FRED® Blog

Are we paying more taxes than before? Reflections on the aggregate share of taxes in the nation's income

Tax season is upon us, at least for the procrastinators who haven’t filed yet. And filing a tax return may get you wondering if the tax burden just keeps increasing. While FRED can’t address your personal situation, it can look at the big picture. The graph shows aggregate U.S. federal tax receipts as a percentage of national income (GDP). Apart from the run-up during World War II, there’s no clear trend. Of course, income and population have steadily increased over this period and so have tax receipts, but they haven’t increased as a share of GDP.

Now, there are provisions in the tax code that could have led to steady increases in tax receipts. The progressivity of the tax rate is one: As income grows, one has to pay a larger share in taxes. But the thresholds for progressivity have been adjusted over time, thus negating the channel for an automatic tax revenue increase. On the other side, the thresholds for calculating the alternative minimum tax have been left largely unchanged for a long time. Introduced in 1969, when they were applicable to only 155 tax payers, the AMT now applies to several million tax payers. Since 2012, the AMT has been indexed to inflation (but not to general income growth); thus, the effect on the aggregate tax share is supposed to lessen. So what prevented the aggregate tax share from increasing over several decades? Was it more-generous deductions, less progressivity, more exclusions…? These questions are too taxing to answer in this blog post.

How this graph was created: Search for “federal receipts” and click on the series you want displayed.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: FYFRGDA188S

Moving someplace new? How will it change your commute?

Households consider many factors when deciding to move, such as the cost of housing and the quality of schools. But economic research has found that the length of your commute to work also plays a significant role in economic well-being and overall happiness. The average commute for U.S. workers in 2015 was 26 minutes (which is 52 minutes round trip). But there’s much variation in commuting times across the country. The map shows the average commute time to work for each county in 2015.

Traffic congestion along the coasts is quite evident in this picture. The county with the longest average commute is Pike County, Pennsylvania: 43.9 minutes. The second-longest commute is Bronx County, New York: 43.0 minutes. The 16 U.S. counties with the shortest commute times aren’t pictured on the map because they’re in Alaska and Hawaii. Aleutians East Borough, Alaska, has an average commute time of 4.9 minutes. Wheatland County, Montana, has the shortest average commute in the continental U.S. at 9.6 minutes.

How this map was created: Go to GeoFRED and choose “Build New Map” in the top right corner. Among the map tools, select “County” for region type. Then type “Mean Commuting Time” into the Data menu. To download the data contained in the map, choose “Download” from the tools and choose “Spreadsheet.”

Suggested by Charles Gascon.

Don’t be surprised: Employment data get revised BLS revisions to metro area employment data can be substantial but predictable

The Bureau of Labor Statistics (BLS) released the latest state and local employment data on March 13, 2017. The BLS initially estimated that St. Louis added 38,300 jobs in 2016, but the revised data show that St. Louis actually added 17,100 jobs. This isn’t the first time large revisions have occurred in St. Louis: Last year, BLS revisions to St. Louis employment added 6,800 jobs. Each March, these revisions account for data through the third quarter of the previous year. They can have a significant impact on the story of job growth in the region, so it’s important to be cautious about these preliminary estimates.

The top graph shows year-over-year growth in total nonfarm employment in the St. Louis MSA before and after the revision. Before the revision, St. Louis was estimated to have added 38,300 jobs and grown at 2.8% in 2016. St. Louis has not seen growth that strong since the 1990s. However, the BLS revised those numbers down to around 17,100 jobs added and growth of only 1.3%. While these revised numbers still show moderate growth, it is below the national rate of 1.6% and represents a significant decline from 2015’s 27,800 jobs added and 2.1% growth. Overall, the MSA has 16,600 fewer jobs than previously thought.

Data revisions occur because counting new jobs is a difficult process that relies on samples and advanced statistical techniques. As more information becomes available, data are revised. The BLS uses the monthly Current Employment Statistics (CES) survey to estimate local employment for nonagricultural industries, but the best source of local employment statistics comes from their Quarterly Census of Employment and Wages (QCEW). The QCEW includes data derived from establishments’ reports to the various unemployment insurance programs that are released with about a 6-month lag. Every March, the BLS reconciles the CES estimates with the data from the QCEW, which can result in significant revisions, as we’ve seen repeatedly here in St. Louis.

This year’s revisions underscore the importance of a cautious approach, despite the temptation to take unrevised data at face value: The BLS often revises employment data significantly, and the average absolute revision to 2016 metro area employment growth this year was 1.1 percentage points. The good news is that the revisions are generally predictable. The QCEW data had been growing at a much slower pace than the CES for much of the year for both nonfarm and leisure and hospitality employment. In addition, employment in St. Louis tends to follow the national cycle, so any large deviations in growth from the national rate should be corroborated with other sources of information. It can also be worthwhile to look at other sources such as the Fed’s Beige Book, to gain an understanding of the labor market beyond the latest BLS estimates. While the unrevised estimates were reporting strong growth in St. Louis, the Beige Book reported only modest or moderate growth for the region.

The BLS revised employment numbers in many of the MSAs in the Eighth District. In Memphis, the unrevised data reported employment gains of 2,700 and growth of 0.4% in 2016, but the revision brought those numbers up to 7,900 and 1.2%. In Louisville, jobs for 2016 increased from 4,400 to 5,400 and growth increased from 1.7% to 2.7%. In Little Rock, numbers were revised down only slightly, beginning in July 2015, with minimal effects on jobs added and growth in 2016.

How these graphs were created: The St. Louis Fed maintains records of all data revisions in its ALFRED® database, which allows you to retrieve vintage versions of data that were available on specific dates in history. On the “All Employees: Total Nonfarm in St. Louis, MO-IL (MSA)” page on FRED, click on “ALFRED Vintage Series” in the “Related Content” section underneath the chart to retrieve the two most recent releases, which currently include the revision. Under the “Edit Graph” button, click on “Format” and change the graph type from bar to line. Click on “Edit Lines” and select “Percent Change from Year Ago” for the units and copy to all. The three other graphs are built in a similar fashion.

Suggested by Charles Gascon and Paul Morris.

View on FRED, series used in this post: LOINA, LRSNA, MPHNA, STLNA

The distribution of subprime borrowers

Most economists agree the financial sector and high levels of household debt played an important role in the previous recession. But since 2008, the levels of both household debt relative to income and debt service payments relative to income have fallen. The reasons for the fall are hard to pin down and could be driven by a lower demand for credit by borrowers or stricter lending requirements by lenders. Nonetheless, an important implication of lower levels of debt and lower debt payments is an improvement in borrowers’ credit scores, as these factors would translate into less debt and fewer missed payments, which have an important weight in how these scores are computed.

The two graphs show the percentage of the population with a credit score below 660 in each U.S. county in 2009 and 2016. A person with a score below 660 will have a harder time securing credit from a lender and may have to pay a higher interest rate if a loan is secured. Comparing 2009 and 2016, we see that the percentage of the population with a subprime credit score has decreased substantially, consistent with the recent changes described above. In addition, the graphs show that counties in the south and southeast have a larger-than-average concentration of subprime population.

How these maps were created: Select GeoFRED’s “Build New Map” option at the top right of the home page. Use the “Tools” menu on the top left to set “Region Type” to “County.” Type “Subprime Credit Population” in the search box. Select the desired date from the drop-down menu. Finally, under “Edit Legend,” change the number of color classes to 9 and set the interval values to 10, 17, 24, 31, 38, 45, 52, 59, and 66.

Suggested by Maximiliano Dvorkin.

The recent evolution of population in U.S. regions

In most advanced economies, including the U.S., population grows at a modest pace. Since 2002, the U.S. population growth rate has been close to or slightly below 1 percent per year. But this national rate masks some important regional differences: The graph shows the evolution of U.S. population by region since 2002, where the population levels of all regions are normalized to 100. The populations in the Southwest and Rocky Mountains regions have grown at a much faster pace than the national average, while in other regions, such as the Great Lakes, total population has barely changed in the past 15 years. These differences are by and large the results of interstate and international migration and reflect, in part, differences in economic conditions and cost of living. Areas in the northeast and the Rust Belt (New England, Mideast, and Great Lakes regions) with high cost of living and a shrinking manufacturing sector aren’t the desirable places to live they once were and thus exhibit very low rates of population growth.

Bureau of Economic Analysis regions and their states:
New England: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont.
Mideast: Delaware, District of Columbia, Maryland, New Jersey, New York, and Pennsylvania.
Great Lakes: Illinois, Indiana, Michigan, Ohio, and Wisconsin.
Plains: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and South Dakota.
Southeast: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia.
Southwest: Arizona, New Mexico, Oklahoma, and Texas.
Rocky Mountains: Colorado, Idaho, Montana, Utah, and Wyoming.
Far West: Alaska, California, Hawaii, Nevada, Oregon, and Washington.

How this graph was created: First search for “Resident Population.” To narrow down the search, use the tags (in the left sidebar): Under “Geography Types,” select “U.S. Department of Commerce: Bureau of Economic Analysis Region.” Select all regions and then click “Add to Graph.” Once the series are graphed, click the orange “Edit Graph” button and select “Edit Line 1” under the “Edit Lines” drop down. Select “Index” as the units and chose the date 2002-01-01. You can then click on “Copy to All” to repeat the same procedure for all series in the graph.

Suggested by Maximiliano Dvorkin.


Subscribe to our newsletter

Follow us

Twitter logo Google Plus logo Facebook logo YouTube logo LinkedIn logo
Back to Top