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The American racial patchwork Measuring racial diversity across U.S. counties

[geofred id="5qJ" esize="medium" height="900" width="1600"] The U.S. population is a patchwork of all sorts of immigrants and nationalities, which also translates into wide racial diversity. But the degree of diversity isn't uniform across the country, which we can see if we examine the "racial dissimilarity index" shown on the map." Here's the story. The Census Bureau has data from the 3,241 U.S. counties that divide the population into White and Non-White, with a national average of 78% White and 22% Non-White. They create an index that basically depicts how diverse the racial distribution of the population is within each county: They determine the proportion of each group for each county and adjust each county's score according to the share of its non-Hispanic White population that would have to move from one census tract in that county to another census tract to achieve uniformity across tracts. (Each census tract typically has a few thousand people.) This score appears to vary widely from county to county, showing that even neighboring counties can have very different racial landscapes. Also keep in mind that two counties could have essentially the same score but be mirror images of racial dissimilarity. Take Allamakee County, Iowa, and Bolivar County, Mississippi, for example. Their dissimilarity scores are very close (66.34 and 58.03), but their populations don't look the same. The United States really is a patchwork. How this map was created: Go to GeoFRED and select the county maps. Look for "White to Non-White Racial Dissimilarity" in the dropdown menu. Suggested by Christian Zimmermann.

Poverty in America An analysis of the difficulties of measuring poverty

[geofred id="5oG" esize="medium" height="900" width="1600"] This map shows, in some way, poverty across U.S. counties. We say "in some way" because poverty isn't a well-defined or stationary object. Today's poor in the U.S. could be rich in another country or in another century. So it's important to understand what is measured when people talk about poverty rates. The data shown here are based on the American Community Survey from the Census Bureau, which asks families about all their cash income (including social benefits, alimony, dividends, etc.) but before capital gains, non-cash benefits, and taxes. That income is then compared with a standard. The Census Bureau determines a threshold income that depends on the number of family members and that is adjusted for inflation. The map above is for 2015: At that point, the poverty threshold for a family of four with two children under 18 was $24,036. For a single person above 65, the threshold was $11,367. See this link for details. The map shows the proportion of families in each county who fall below these thresholds. The highest incidence is in Jefferson County, Mississippi, with 48.7%. The lowest is 1.4% in Borden County, Texas. Does this mean that there is 35 times more poverty in Jefferson County than in Borden County? Not necessarily. First, this measure says nothing about the distribution of income below the threshold. Second, the measure does not take into account living expenses. Income in, say, Manhattan, New York, is treated the same as in, say, Harlingen, Texas. Finally, Borden County has a total population of 627. Measurement of poverty with small samples is extra difficult. How to create this map: Go to GeoFRED and select the county maps. Look for the poverty data in the dropdown menu. Suggested by Christian Zimmermann.

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?

[geofred id="5am" esize="medium" height="900" width="1600"] 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

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