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What can we claim about initial claims?

Keeping track of initial unemployment insurance claims

Initial unemployment claims is a much-watched indicator of the economy. It counts how many people have become eligible for unemployment insurance compensation in a particular week. The data are available quickly and at a high frequency (weekly), but the series has the disadvantage of being highly volatile. This is why FRED also offers a four-week moving average, shown in the graph above: Simply, it’s the average of the past four weeks. Included in the graph is also a red line that indicates the lowest value of this statistic in the course of its history—in May 1969. Currently, claims are around 230,000 per week; and, while this is low, it was lower for 126 weeks early in the sample period.

Of course, the population was much smaller in the 1960s, so the current statistics are even more impressive than they first appeared. Which is what the second graph shows, after dividing new claims by population. The red line indicates the lowest point before recent years, which occurred in April 2000. That low point has clearly been beaten—ever since May 2015, in fact. Keep in mind, though, this statistic is only part of the labor market picture. For example, average unemployment duration is still elevated (see a previous blog post on this). Also, unemployment insurance eligibility requirements may vary over time and, thus, distort the statistic.

How these graphs were created: Search for and select the 4-week moving average for “initial claims” and click “Add to Graph.” From the “Edit Graph” panel, use the “Add Line” feature to create a “user-defined line” and enter 179,000 for the start and end values. For the second graph, edit the first graph by adding a series to the first line, searching for “civilian population” and applying the formula a/(b*1000). Use the “Add Line” feature to create a “user-defined line,” and enter 0.00223 for the start and end values.

Suggested by George Fortier.

View on FRED, series used in this post: IC4WSA, LNU00000060

A mirror image of mortgages and equity

The story of the Great Recession told with two intersecting lines

Take a look at mortgage or real estate data on FRED. The main story (for a number of years, now) is all about the Great Recession, which is clear in the graph above. Let’s unpack that story.

In blue, we have the share of equity in the real estate that households own. In the 1950s, 70-80% of the value of the average house was owner equity, and 20-30% was owned by a financial institution. The share of owner equity essentially stayed within a 60-70% band until the end of the millennium. Then it quickly dropped to below 40%, before rebounding today to its previous level (from 2001 or so). What happened during the Great Recession is clearly a deviation from normal.

This being a ratio, the deviation could have come from changes on either side of that ratio: 1. Mortgages could have sharply increased without a change in owner equity. 2. Owner equity could have dramatically shrunk. To help figure this out, we can look at the red line, which tracks household mortgages normalized by GDP. It shows the opposite pattern of the blue line: Mortgages clearly become more popular in the initial years, as the financial sector develops. Then they stabilize, with a push in the 1980s before really taking off, earlier than the blue line, and then they crash; soon after, the blue line shoots up.

What does this teach us? First, there was clearly a sharp increase in mortgages before the crisis. But it was accompanied by an equivalent increase in the value of the homes, so there was no visible change in the share of owner equity until the value of homes stopped keeping up with the mortgages and even dropped. At the bottom of the crisis, owner equity is at its lowest, and it is only then that mortgages start decreasing: Virtually no new mortgages are issued, current mortgages are gradually paid off and some existing ones are foreclosed, returning to their historical levels.

How this graph was created: Search for and select “Household equity in real estate” and click “Add to Graph.” From the “Edit Graph” panel, use the “Add Line” tab to search for “home mortgages.” Select a series in levels and add it to the graph. From the “Customize data” search bar, search for and add the nominal (not real) GDP series. Finally, apply formula a/b*100.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDP, HMLBSHNO, HOEREPHRE

The importance of imports

Import tariffs, imports of production inputs, and domestic investment

U.S. trade policy continues to change, with rising tariffs on imports of capital goods and intermediate inputs from China and other countries. But how important are these types of imports for the U.S. economy, especially compared with total U.S. imports? As usual, FRED can help answer our question: The graph above plots the share of capital and intermediate inputs in aggregate U.S. imports over the period 1999-2019.

As the graph shows, the share is not small. In fact, it’s the majority of total imports, ranging from 46% to 61% over this period, with an average well above 50%. Because these imports play an important role for the domestic production of U.S. goods, one would expect that raising tariffs on these goods would have a negative impact on domestic production.

Again, FRED sheds some light on the question: The graph below shows that imported capital goods make up a substantial fraction of aggregate investment, ranging from a bit under 12% to almost 18% for 1999-2019. In particular, the share of imported capital goods in gross fixed capital formation has been growing over the past two decades: Between the 2001 recession and the Great Recession, it was in the 12% to 14% range; after the Great Recession, the values were largely above 16%.

These specific imports comprise a significant portion of both total U.S. imports and domestic investment, which suggests that the ongoing changes to U.S. trade policy might have a negative impact on firms that rely on these capital goods and inputs to conduct their productive activities. In particular, tariffs on capital goods might negatively affect aggregate U.S. investment and, thus, aggregate output.

How these graphs were created: For the first, search for and select “Imports of Goods: General Merchandise: Capital goods except automotive” and click “Add to Graph.” From the “Edit Graph” panel, under “Customize Data,” select another series to combine with the existing series. Search for and select “Imports of Goods: General Merchandise: Industrial Supplies and materials” and click “Add.” This series is now labelled as series “(b)” in the “Edit Graph” panel. Repeat this procedure to add the series: “Imports of Goods: General Merchandise.” Now use formula (a + b)/c*100.

For the second, start with the same search, then add another series by searching for “Gross Fixed Capital Formation in the United States” under “Customize Data” and clicking “Add.” The units of the two series are different, so to normalize we need to multiply the Imports of Capital Goods by one million. So, use formula (a*1000000)/b*100.

Suggested by Matthew Famiglietti and Fernando Leibovici.

View on FRED, series used in this post: IEAMGC, IEAMGI, IEAMGM, USAGFCFQDSMEI


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