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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.


What is unemployment? There is more to it than not working

What is unemployment? To answer this question first requires a few definitions. A person is considered unemployed if he or she is actively seeking work and willing to take work here and now. It is therefore not sufficient to simply not be working. But this definition of unemployment does necessarily define (1) whether someone who is underemployed should be counted as well or (2) how intensely someone must search for a job to qualify as unemployed. For this reason, the Bureau of Labor Statistics provides different unemployment rates, graphed above. These are commonly called U-1 through U-6:

  • U-1 counts only those who have been unemployed for at least 15 weeks, which was traditionally a little longer than the average duration of an unemployment spell. This is considered to exclude short-term unemployment.
  • U-2 counts those who are unemployed because they have lost a job or completed a temporary job—in other words, workers in a precarious situation in the labor market, as they are more likely to find an unstable or unsatisfying job.
  • U-3 is the headline unemployment rate generally reported in the media: People who are able to work, ready to work, and have looked for work in the past four weeks. This corresponds the most closely to the definition of unemployment we started with.
  • U-4 is U-3 plus those who would like to work but have stopped looking—the so-called discouraged workers—because they believe there are no jobs for them.
  • U-5 is U-4 plus those who are marginally attached to the labor market who, for any reason, are no longer searching for work but may still work.
  • U-6 is U-5 plus those who are working part-time but would prefer to work full-time.

These various interpretations of the definition of unemployment allow us to have a better understanding of the status on the labor market. But one may still have some misgivings about them. For example, the higher-numbered definitions give equal weight to different classes of unemployed workers. For example, should a person qualifying for U-1 count as much as a person qualifying only for U-5 and U-6 when evaluating the health of the labor market? To address this question, there is the Hornstein-Kudlyak-Lange index that creates a weighted sum of the different categories. The goal is to evaluate the underutilization of labor in the economy. This index (it is available with and without the part-time workers from U-6) is plotted below along with the popular U-3.

The graph below shows the non-employment rate, which is quite different from the unemployment rate. Indeed, it counts all those who are not part of the labor force, which comprises those who are either working or unemployed. The non-employment rate, which is thus the complement to the labor force participation rate, measures those who do not want to work. Principally, these are retirees, students, people with various handicaps, people who dropped out of the labor force, and people who do not want to work. Note that military personnel are not part of any of these (civilian) calculations.

How these graphs were created: For the first graph, go to the Alternative Measures of Labor Underutilization release table from the Bureau of Labor Statistics’ Employment Situation release. Select all (seasonally adjusted) series and click “Add to Graph.” For the second graph, search for and select the monthly, seasonally adjusted unemployment rate. Then click on “Edit Graph” to add the two other lines: Search first for “non employment index” and select the base index (not the index that includes people working part-time for economic reasons). Then search for “non employment index” again and select the index that includes people working part-time for economic reasons. For the last graph, search for “labor force participation rate”, click on “Edit Graph” and apply formula 100-a.

Suggested by Christian Zimmermann.


The depth and breadth of the federal debt

Who holds the federal debt? The pie chart above shows the shares for the last available period:

  • 27.1% is held by the U.S. government, its agencies, and its trusts—such as the social security trust.
  • 42.1% is held by private individuals and entities in the U.S., which includes 14.2% held by the Federal Reserve. (This 69.2% held domestically is technically debt between Americans.)
  • 30.9% is held outside the U.S.

How have these shares evolved over time? The graph below answers this question after removing the inter-agency debt. The Fed’s share of federal debt hasn’t changed much over time. But foreign ownership of debt has: It ramped up in the 1990s and 2000s and has been declining slightly over the past decade.

The last graph shows how these shares translate to a proportion of GDP: The value of debt owed abroad is about a third of annual GDP. The value of debt owed to domestic households and businesses is about a quarter of GDP. For recent years, the lines don’t stack above 100% of GDP, as is often mentioned when talking about the federal debt. The value of debt rises above 100% of GDP only if you include inter-agency debt. And if you also exclude debt held by the Federal Reserve, U.S. federal debt currently amounts to 62% of GDP.

How these graphs were created: For the first graph: Choose the series “Federal Debt Held by Federal Reserve Banks” and “Federal Debt Held by Foreign & International Investors.” Now, to create the series that shows only private domestic holders of federal debt, select “Federal Debt Held by Private Investors” and then use “Add Line” / “Customize data” to include “Federal Debt Held by Foreign & International Investors.” Apply the data transformation a-b. Finally, add a new line after searching for “Federal Debt held by Agencies and Trusts” and divide it by 1000 because it is in different units. Then select graph type “Pie,” which will default to the last observation. For the second graph, go back to the “Edit Graph” format tab and change the graph type to “Area” and stacking to “Percent.” Remove the last series, as it has a shorter sample and makes the percentages jump. Expand the sample period to maximum. For the last graph, use the second graph, but change the stacking to “Normal” and add to each line nominal GDP (make sure not to take real GDP): Divide each line by that series and multiply it by 100 to express it in percentages.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: FDHBATN, FDHBFIN, FDHBFRBN, FDHBPIN, GDP

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