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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: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map 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 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: 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 Maximiliano Dvorkin.

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