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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.

View on FRED, series used in this post: CIVPART, NEIM156SFRBRIC, NEIPTERM156SFRBRIC, U1RATE, U2RATE, U4RATE, U5RATE, U6RATE, UNRATE

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

Demystifying the trade balance Why a trade balance deficit isn't necessarily a sign of a poor economy

The trade balance is the amount of exports minus imports. While the number generally reported in the media pertains to goods (that is, physical gizmos that are counted as they cross the border), there’s also trade in services such as software, consulting, and tourism. The graph above shows the trade deficit in red (a negative number) for the United States. The current account—that is, the change in asset holdings of the U.S. with the rest of the world—is shown in blue. Indeed, the current account comprises the trade deficit (if we import more, we owe more) plus transfers of income across boundaries. The latter can be important for some developing countries that have a lot of foreign workers sending remittances back to their families. For more-developed economies, dividend and interest incomes are more important.

In the system of National Income and Product Accounts (NIPA, a nation’s economic accounting), a trade deficit automatically implies that the country is saving less than it’s investing. Another way to understand this is that the rest of the world is investing in that country, thereby contributing to its production capacity. This accounting pertains to the capital account, which is always the counterpart to the current account: Current account plus capital account always equals zero, which is quite apparent in the graph below.

Is it bad to have a trade account deficit? If this means that your economy is booming and local production cannot keep up with demand, then no. If it implies that there is a current account deficit and, hence, foreigners are investing in your country, then also no. If this means that you can have more investment without having to save more, because the rest of the world is picking up the slack, no again. If you are worried that in the future dividends will flow abroad, then yes. But that will happen only if your economy is in good shape in the first place and will be able to afford paying such dividends.

How these graphs were created: For both graphs, look for and the first series to the graph; use the “Edit Graph” tab to add the second line; then apply a formula to adjust the units so that both lines match. Note that the trade balance needs to be multiplied by 12, as it’s expressed in monthly numbers.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: BOPGSTB, NETFI, RWLBCAQ027S

Let’s do the Twist Did the FOMC succeed in lowering long-term rates?

In September 2011, the Federal Reserve’s Open Market Committee (FOMC) announced a “Maturity Extension Program” involving the purchase of $400 billion of longer-term U.S. Treasury securities and liquidation of an equivalent amount of shorter-term securities over a 9-month period. In June 2012, the FOMC extended the program through the end of 2012, a period when the Committee purchased an additional $267 million of longer-term securities and liquidated a similar amount of short-term securities. The purpose of the program was to put downward pressure on longer-term interest rates without increasing the total size of the Fed’s securities portfolio. The program was popularly referred to as “Operation Twist,” reflecting the Committee’s intention to lower long-term interest rates relative to short-term rates and thus twist the yield curve.

The graph illustrates the program’s impact on the maturity composition of the Fed’s portfolio of U.S. Treasury securities. At its inception in September 2011, the portfolio consisted of approximately equal shares of Treasury securities with maturities of 5 years or less and securities with maturities of more than 5 years. When the program ended in December 2012, the shares of short-term and long-term securities in the Fed’s portfolio were about 22 percent and 78 percent, respectively. The Fed began to reverse the Maturity Extension Program in 2013 mainly by buying shorter-term securities as longer-term securities matured. By mid-2015, the Fed held roughly equal amounts of Treasury securities maturing in 5 years or less and longer-term securities. Since then, the Fed has continued to adjust its portfolio toward shorter-term securities, while maintaining a constant total portfolio size.

Did the Maturity Extension Program succeed in twisting the yield curve? As the graph shows, the spread between the yields on 10-year and 3-month Treasury securities fell some 75 basis points during the months when the program was in effect and then rose after the program had concluded. Longer-term yields had also declined by about 75 basis points during the 2 months before the program began, however, perhaps because market participants anticipated the program before it was formally announced. Of course, without formal analysis, it is impossible to say whether the program caused the yield curve to twist, but the behavior of the curve was consistent with the program’s intent.

How this graph was created: Search for “U.S. Treasury securities held by the Federal Reserve” and “All Maturities” should be near the top. From “Edit Graph,” add the matching series for “Maturing within 15 days,” “Maturing in 16 to 90 days,” “Maturing in 91 days to 1 year,” and “Maturing in over 1 year to 5 years” to Line 1. Apply the formula (b+c+d+e)/a. From “Edit Graph,” use the add line feature to search for the “All Maturities” series again and create Line 2. Add to that the matching series for “Maturing in over 5 years to 10 years” and “Maturing in over 10 years.” Apply the formula (b+c)/a. From “Edit Graph,” use the add line feature to add “10-Year Constant Maturity Minus 3-Month Treasury Constant Maturity” as Line 3. Modify the frequency to “Weekly, Ending Wednesday.” Under the “Format” tab, select “Right” for the “Y-Axis position” for Line 3. Finally, click on “10Y” next to the date selection pane to show the last 10 years of data.

Suggested by David Wheelock.

View on FRED, series used in this post: T10Y3M, TREAS10Y, TREAS15, TREAS1590, TREAS1T5, TREAS5T10, TREAS911Y, TREAST

Slow…labor…productivity…growth How does productivity affect our future?

Since the beginning of 2011, growth in real output in the nonfarm business sector has been slow, averaging just 2.7% percent. And most of the economic growth has been driven by increases in labor inputs and not by increases in labor productivity. The graph shows real output growth (green line) decomposed into growth in labor input (red line) and growth in labor productivity (blue line), where productivity is measured as real output per hour. Given that the output growth rates are only slightly different from—either a little above or a little below—growth in hours, the majority of growth in output has come from increases in hours instead of increases in labor productivity. Labor productivity growth averaged 0.7% over this period, accounting for just 27% percent of real GDP growth.

Labor productivity growth amounts to the average growth of how much goods and services each individual can consume and, thus, is the driving force behind increases in the standard of living. More importantly, a small difference in labor productivity growth leads to a dramatic difference in the standard of living over the long run. For example, if labor productivity growth held steady at 2%, which is the rate seen in the expansion from 2001 to 2007, the living standard would double in 35 years. If labor productivity continues to grow at 0.7%, it would take 99 years to double the standard of living.

How this graph was created: After searching for “nonfarm business sector,” select “Index 2009=100” for the three series and click on “Add to Graph.” Then go to the “Edit Graph” section and select “Percent Change from Year Ago” under “Units.” Finally, click on “Copy to all” and change the starting date to “2011-01-01.”

Suggested by Yili Chien and Paul Morris.

View on FRED, series used in this post: HOANBS, OPHNFB, OUTNFB


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