The FRED® Blog

Uncertain times in Europe

If you’ve been following international news over the past decade or so, you’ve seen the European Union’s seemingly continuous struggle to define the various facets of its economic policy. Such policy uncertainty has effects on economic activity—especially investment. And we can quantify such uncertainty, as shown in the graph above, thanks to the work of Scott Baker, Nicholas Bloom, and Steve Davis. Their work is based on the frequency of certain key words in newspapers and disagreements among economic forecasters. The graph pertains to Germany, the U.K., France, Italy, and Spain and definitely shows elevated levels of policy uncertainty since 2012, which rival and even exceed the levels during the financial crisis in 2007-08.

How this graph was created: Search for “economic policy uncertainty” and select the series for Europe (among several other uncertainty series available in FRED).

Suggested by Christian Zimmermann

View on FRED, series used in this post: EUEPUINDXM

The many flavors of unemployment

How many people are unemployed? Before answering this question, you need to define unemployment. The Bureau of Labor Statistics offers six definitions, conveniently labeled U-1 through U-6, that are increasingly inclusive. What they have in common is they measure some aspect of labor underutilization. U-1 counts only those who have been unemployed for at least 15 weeks, which is usually (but not lately) a little longer than the average duration of an unemployment spell. Hence, this excludes short-term unemployment. U-2 uses a somewhat different concept: the percentage of those who are unemployed because they have lost a job or completed a temporary job. Some of them may be included in U-1. So U-2 counts 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 traditionally reported unemployment rate, which counts people who are able to work, ready to work, and have looked for work in the past four weeks. U-4 takes U-3 and adds 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 takes U-4 and adds those who are marginally attached to the labor market: those who, for any reason, are no longer searching for work. Finally, U-6 includes all of the above plus those who are working part-time but would prefer to work full-time.

How this graph was created: Go to the Alternative measures of labor underutilization release table (A-15) from the Bureau of Labor Statistics’ Employment Situation release. Select all (seasonally adjusted) series and click “Add to Graph.”

Suggested by Christian Zimmermann

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

How likely is a recession? (And how fast is a forecast?)

Predicting a recession in real time is difficult, which is why one can make good money with a good forecast. Here, FRED offers one of many such forecasts: a recession probability index computed by Marcelle Chauvet and Jeremy Piger. This forecast is backed up by research the authors have published in the peer-reviewed journals International Economic Review and the Journal of Business and Economic Statistics, with an early St. Louis Fed working paper added here for good measure. As the graph above shows, their forecasting method’s past performance is impressive; the predicted recession dates align well with the official NBER recession dates. Of course, it is difficult to compute any forecast in a timely fashion: One has to wait for the appropriate data to be released, and only then can one compute the forecast. In this case, that translates into a delay of about three months.

How this graph was created: Search for “recession,” and the first series shown should be “Smoothed U.S. Recession Probabilities.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: RECPROUSM156N

Net migration: The Far East is the new Southwest

Recent data from the U.S. Census Bureau show that China has overtaken Mexico as the source of the largest number of immigrants to the U.S. FRED can add some insight to this topic: Although FRED doesn’t include country-by-country migration data, it does include net migration data for each country in the World Bank’s World Development Indicators release. The list of countries is long. The graph above looks at only the three countries noted here. The U.S. is a net immigration country, while China and Mexico are net emigration countries. No surprise there. What may be a little unexpected is how large the fluctuations have been from one five-year period to the next. Also, migration out of China has increased (by an order of magnitude) despite many years of impressive economic growth. Indeed, aggregate economic conditions are not likely to be the sole driver for migration choices.

Note: In 2013, the most-recent year for which complete Census data are available, Mexico actually sent the third-largest number of immigrants to the U.S. As noted above, China sent the most, but India is now in second place.

How this graph was created: Search for net migration, and the U.S. should appear first. Scroll through the list or use the “Add Data Series” tab to search for and add China and Mexico (and many other countries) to the graph.

Suggested by Christian Zimmermann


Labor force participation: Is a trend or a cycle at work?

One major concern since the start of the recent recession has been the labor force participation rate. The graph above shows a clear and continuing decline. However, when you reveal the full sample, as shown in the graph below, you can see the decline started before the recession and the current level is not the lowest in postwar history. It appears, then, at least part of the current evolution of labor force participation has to do with a longer-term trend. What forces are at work here? Clearly, the rise in labor force participation had to do with many women entering the labor force. The subsequent decline has to do with the aging of the population, with a significant increase in the proportion of retirees. Also, the younger population is staying in school longer than before. Articles by Marianna Kudlyak and Maria Canon and Marianna Kudlyak provide more insight on this topic.

How these graphs were created: For the first, search for and add “Civilian Labor Force Participation” to the graph, but restrict the range to start in January 2008. (Note: The seasonally adjusted series is much easier to read.) To add the trend line, go to “Add Data Series” and select “Trend Line” from the pull-down menu: Start the line at 2008-01-01, with the value of 66.2. For the second, create the same graph but use the full sample, which starts in 1948.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CIVPART

A new trend in GDP?

Let’s look at real gross domestic product for the United States. To help us, we’ll also use a new feature of FRED graphs: custom lines. This feature can be useful to highlight a new trend that emerges as additional data points are added. The graph shows that the U.S. economy seems to be on a new trend, with a similar growth trend as before but a notch lower. It looks as if the economy lost a few years of growth during the previous recession and is now back on track. It this the whole story? Of course not. There’s much more to measuring the health of the economy than just GDP. But this particular development is quite startling.

How this graph was created: Start with a graph of quarterly real GDP. Select the starting date of 1994-10-01, either by typing that date in the box above the graph or by moving the ruler below the graph. For the first trend line, use the “Add a Series” feature and select “Trend Line” from the pull-down menu. By default, it creates a line from the first to the last data point in the visible range. Change the end date of that line to 2007-10-01 and associate with it the value of GDP at that date. (To do so, hover over the graph to find the value for that date and then add that end value to the trend line.) Add the second trend line in a similar way, but start at 2009-10-01. Finally, change the color of the second trend line to burgundy to match the first.

Suggested by Christian Zimmermann

View on FRED, series used in this post: GDPC1

Going mobile

FRED is all about economic time series. But FRED also embraces data that at first glance may not look economic. Consider the graph above: It shows the adoption of mobile devices in various countries. (The data come from the World Bank’s World Development Indicators release, which includes all sorts of exciting data.) Mobile devices have been adopted very quickly by a large portion of the world’s population, even in developing economies. That has economic ramifications. For example, use of mobile technology has allowed developing countries to overcome infrastructure problems with land lines and allowed people better access to services such as mobile banking. Mobile phones are now routinely used as payment devices in many countries.

How this graph was created: Start by searching “mobile cellular in China” and add that series to the graph. Then use the “Add Data Series” field to search for Chad, then the United States, and then Peru to add those new series to the graph. To see the full list of countries, you may search for “world bank” under “Tags” and select that collection of series; then search for “mobile” and select that collection of series. The list of available countries can be sorted in various ways, including by popularity and alphabetically by title.

Suggested by Christian Zimmermann


The GDP residual

GDP is intended to serve as a measure of all economic activity in an economy. But not every transaction is tracked, so one has to rely on estimates and models for parts of GDP. There are three ways one can measure GDP: adding up all expenditures, adding up all incomes, and adding up all value-added in the economy. All three should give you the same number. In practice, they don’t quite match up because of measurement issues. The difference between the expenditure and income approaches is called the statistical discrepancy. It is graphed above as a share of GDP, shown in red.

Another issue occurs when converting nominal values to real values: This is accomplished by applying a calculation to a basket of GDP components for a particular base year and keeping those prices constant for the other years. (The actual calculation is a bit more complex than this.) Adding up these GDP components does not exactly achieve GDP, and this difference is called the GDP residual. It is graphed above as a share of GDP, shown in blue. It is very small around the base year, but it deviates more substantially in earlier years, up to 6%.

How this graph was created: Search for GDP residual and add “Real Gross Domestic Product: Residual” (billions of chained 2009 dollars, quarterly, seasonally adjusted) to the graph. Then add real GDP (billions of chained 2009 dollars, quarterly, seasonally adjusted) to series 1 and apply transformation a/b. For the second line, search for GDP discrepancy and add “Gross Domestic Product (GDP); statistical discrepancy…” (quarterly, millions of dollars, seasonally adjusted) to the graph. Then add nominal GDP (quarterly, billions of dollars, not seasonally adjusted; not real GDP, as the discrepancy is nominal) to series 2 and apply transformation a/b/1000 . The division by 1000 here is because one series is in billions of dollars and the other is in millions of dollars.

Suggested by Christian Zimmermann

View on FRED, series used in this post: A959RX1Q020SBEA, GDP, GDPC1, GDPSDCQ027S

To rent or not to rent

When it’s time to decide to rent or own a home, many factors have to be weighed. The first hurdle of owning is, of course, the down payment. Another consideration is the overall cost of owning compared with the cost of renting. How has this cost ratio changed over time? The consumer price index can help answer this question, as it has one sub-index that measures the cost of renting and another that measures the cost-equivalent of renting for a homeowner. The graph above shows that the changes in rent and changes in the cost of owning track each other quite closely. Until the mid-1990s, ownership inflation was slightly higher; rental inflation has been slightly higher since then. And do these differences accumulate? The graph below shows the levels of these CPI sub-indexes: By definition, they start at the same level in 1982. Ownership became 10% more expensive than renting (compared with their relative costs in 1982), but now they are even again.

How these graphs were created: Search for “CPI rent,” select the two monthly, seasonally adjusted indexes for primary residence, and click the “Add to graph” button. That’s the bottom graph. For the top graph, select as the units “Percent Change from Year Ago” for both series.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CUSR0000SEHA, CUSR0000SEHC01

Employment’s ebb and flow

When the unemployment rate rises, it’s partly that more employed persons are losing their jobs and partly that fewer unemployed persons are finding jobs. Although there’s no consensus on which is more important, some research finds that the flow of persons from unemployment into employment accounts for the lion’s share of changes in the unemployment rate. These unemployment-to-employment flows are cyclical and fell starkly in the Great Recession, as the graph above shows. Persons may also flow from outside the labor force (neither employed nor unemployed) directly into employment; this is also an indicator of the ease or difficulty of getting a job. This flow from “nonparticipation” to employment is expected: Some persons who’d like a job aren’t formally searching and so aren’t counted in the BLS measurement of unemployment. There are also new entrants into the workforce, such as recent graduates and parents returning after a hiatus for child care. The flow from nonparticipation into employment (that is, the proportion of nonparticipants taking a job) is much lower than the flow from unemployment to employment (graph above), but the two series track each other nearly perfectly in their cyclical fluctuations (graph below).

How these graphs were created: Search for Labor Force Flows and select the (seasonally adjusted) “Unemployed to Employed” and “Not in Labor Force to Employed” series and add them to the graph. Then in the “Add a Data Series” section, search for “Unemployed” (monthly, thousands of persons, seasonally adjusted) and select “Modify existing series” for series 1. Repeat these steps with “Not in Labor Force” for series 2. For both series, in the “Create your own data transformation” section, apply the formula a/b. Start with the first graph to create the second, but change the y-axis of series 2 from left to right. Note: These measures of the rates of flow aren’t precise because of “time aggregation bias”: That is, these measures compare employment status at two points in time (the beginning and the end of the period), but they don’t take into account any changes in employment that may have occurred between those two points.

Suggested by David Wiczer

View on FRED, series used in this post: LNS15000000, LNS17100000, LNS17200000, UNEMPLOY

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