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Measuring inflation expectations, part II

In the previous blog post, we looked at using survey data to measure inflation expectations. Now we consider market-based measures. The graph shows various measures of the breakeven inflation rate, which is computed as the difference in returns of constant-maturity Treasury bills: one being the traditional bill and the other being the inflation-indexed bill. If we look at 10-year Treasury bills, we can evaluate what the markets think the average yearly inflation rate will be over the next 10 years. With such a long horizon, it makes less sense to compare these expectations to realized inflation. But this graph still includes a segment to signal the Fed’s 2% inflation target announced on January 25, 2012, since the purpose of that announcement was to anchor inflation expectations.

How this graph was created: Search for “breakeven inflation” and many series will be shown. Here, we used those with a monthly frequency. For the segment, choose “Add a Series” but select “Trend line” from the pull-down menu. Once that’s added, change the initial date to “2012-01-25” and use “2” for both start and end values.

Suggested by Christian Zimmermann

View on FRED, series used in this post: T10YIEM, T20YIEM, T30YIEM, T5YIEM, T7YIEM

Measuring inflation expectations, part I

An important element of monetary policymaking, as well as financial market pricing, is the level of inflation that people expect. There is no direct measure of each individual’s inflation expectations, but we can infer expectations at the aggregate level. One way we do this is to simply ask some people what they think. The Surveys of Consumers, an initiative of Thomson Reuters and the University of Michigan, ask many questions to evaluate consumer sentiment, and one question is what the inflation rate will be over the next year. The average answer is shown in blue in the graph, with the actual inflation rate shown in red. Note that this is not an entirely fair comparison: For any particular date, the blue line shows expectations over the next 12 months and the red line shows actual inflation over the past 12 months.

We also added a short green segment: This is the Fed’s 2% inflation target, announced on January 25, 2012. We see it falls between expectations and realizations.

If you want to learn more about inflation expectations, take a look at this recent Economic Synopses essay.

How this graph was created: Search for “inflation expectations” and the Michigan Survey should be your first choice. Then add the series “CPI” to your graph, making sure to change the units to “Percent Change from Year Ago.” Finally, for the green segment, choose “Add a Series” but select “Trend line” from the pull-down menu. Once that’s added, change the initial date to “2012-01-25” and use “2” for both start and end values.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CPIAUCSL, MICH


The producer price index (PPI) is one of the oldest continuously recorded statistics in the United States. While the better-known consumer price index (CPI) computes the price of a basket of consumer goods in retail stores, the PPI looks at raw materials, intermediate goods, and goods ready to be shipped. In fact, it was previously known as the wholesale price index (WPI). FRED offers the PPI in all sorts of decompositions, over 10,000 series in total.

In the graph, we compare the CPI with the PPI. Notice that the PPI appears to be more volatile, at least in recent years, and the two indexes tracked each other much better before the 1980s than since. In particular, the PPI has increased much less than the CPI and has seen some dramatic drops.

How this graph was created: Search for PPI, and the index for all commodities will likely be your first choice. Add that series to the graph. Then add the CPI by searching for it in the “Graph” tab through the “Add Data Series” panel.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CPIAUCSL, PPIACO


FRED recently added SNAP data. SNAP stands for “Supplemental Nutrition Assistance Program”—more commonly referred to as food stamps. FRED has data at the state and county level on the number of benefit recipients for the past 30 years or so. In the graph here, we chose five states with similar population size. A few points stand out: 1) It’s remarkable how little the state rankings have changed over the years. The rise of Arizona is likely due to a relative population increase. 2) Apparent seasonal fluctuations in some states disappear after a few years. 3) Since 2000, the numbers of recipients has more than doubled; in Massachusetts, it quadrupled. This rise has occurred through both booms and recessions and cannot be explained by population increases.

How this graph was created: Search for SNAP, then restrict the results by selecting the “monthly” and “states” tags in the sidebar. Then choose your states and click “Add to Graph.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: BRAZ04M647NCEN, BRIN18M647NCEN, BRMA25M647NCEN, BRTN47M647NCEN, BRWA53M647NCEN

The demographics of the labor force participation rate

There is much lamenting about the decline in the labor force participation rate. As we recently discussed on this blog, while the rate decreased quickly during the previous recession and its recovery, the overall decline began several years before. This decline indicates there must be more than cyclical or even policy-related forces at work. One likely candidate is demographics. In the graph above, the proportion of the U.S. population 25 to 54 years of age follows a pattern similar to that of the labor force participation rate over the past 10 years. Why look at this 25-54 age range? Because this group has the highest labor force participation rate. So, if the share of this age group is declining, the aggregate labor force participation rate is likely to decline as well.

How this graph was created: For the first line, search for “population 25-54” and select “Civilian noninstitutional population—25-54 years.” To create the ratio, add the “Civilian noninstitutional population” series via the “Add Data Series” option: When you add this series, be sure to select “Modify existing series” for series 1. Then use the “Create your own data transformation” option using the formula a/b*100 so that the result is expressed in percentages. For the second line, simply add the civilian labor force participation rate as series 2.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CIVPART, CNP16OV, LNU00000060

Don’t be deceived by seasonality

In the graph above, the two series have the same label, yet they tell very different stories: The red line bounces between a few values, and the blue line shows a large increase last summer and then a decrease this winter. The difference is that the blue line reflects raw data, while the red line has been adjusted for seasonal regularities. Obviously, we need to take into account that the labor force participation rate increases every summer; only then can we correctly analyze how the economy is faring. Otherwise, one could draw false conclusions, especially by looking at a single year.

Expanding the sample period reveals the obvious seasonal variations in the path of the blue line, and the graph below shows this. (You can also use the slider bar under the graph above to achieve the same view.) Note, however, that these seasonal variations are not as strong as they used to be, presumably because the economy has become less sensitive to weather conditions.

How these graphs were created: Search for “Labor force participation,” select the two series you want, and click on “Add to Graph.” Limit the sample period to one year for the top graph and fully expand the sample period for the bottom graph.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CIVPART, LNU01300000

Who’s paid the minimum wage?

The minimum wage is back in the news, and FRED can offer some insights. FRED includes data from the Bureau of Labor Statistics—specifically, in the Current Population Survey—that tally the number of workers who are paid the federal minimum wage. Note that some states, counties, and cities mandate minimum wages above the federal level and that some workers can be paid less (e.g., service workers who receive tips). The graph tracks the number of workers paid the federal minimum wage or less, which is affected by the minimum wage itself, how wages and labor productivity have changed since the last time the minimum wage was raised, how inflation has eroded the nominal minimum wage, and finally how other polities approach their own minimum wage with respect to the federal minimum.

What does the graph show? Currently, a little over a million workers are paid the federal minimum wage, and most of them are not employed full-time. Their numbers were much higher a few decades ago, but the federal and other minimum wages were not the same back then. A little less than a million workers are paid less than the federal minimum wage, a large majority of whom are employed full-time. FRED has these latter data series only as far back as 2000, so it is difficult to judge any trends in these numbers.

How this graph was created: Go to the US Bureau of Labor Statistics source, choose “Weekly and Hourly Earnings from the Current Population Survey,” then choose the “minimum wage” tag in the sidebar. Select the series you want and add them to the graph.

Suggested by Christian Zimmerman

View on FRED, series used in this post: LEU0203127000A, LEU0203127400A, LEU0253126900A, LEU0253127000A

Median income across the United States

A recent blog post examined inflation-adjusted mean and median family income in the United States. This graph compares the real (inflation-adjusted) median family income across the four U.S. Census regions—West, Midwest, South, and Northeast. The states for each are as follows:

West: Washington, Oregon, California, Montana, Idaho, Nevada, Wyoming, Utah, Arizona, Colorado, New Mexico, Alaska, Hawaii
Midwest: North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa, Missouri, Wisconsin, Illinois, Michigan, Indiana, Ohio
South: Texas, Oklahoma, Arkansas, Louisiana, Kentucky, Tennessee, Mississippi, Alabama, West Virginia, Virginia, North Carolina, South Carolina, Georgia, Florida, Delaware, Maryland
Northeast: Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, Pennsylvania

You can see from the graph that the South has much lower median family income than the Northeast (roughly $58k vs. $71k). Of course, the cost of living in the South may be lower and, hence, compensate for the difference in median income. In the past, the South had significantly lower income than the other three regions, which bunched together very tightly. Since the 1980s, the real median family income of the regions has separated into three groups: The Northeast is clearly the highest, with the Midwest and West regions occupying the center, and the South remains the lowest.

How this graph was created: Start with a search for median family income. Then use tags to select U.S. Census regions. (Tags are on the left navigation bar: census, census region, real.) This leaves you with the real data series for all four Census regions. Click on the left side of each data series to select it, and then select “Add to Graph.”

Suggested by Katrina Stierholz.

View on FRED, series used in this post: MEFAINUSMWA672N, MEFAINUSNEA672N, MEFAINUSSOA672N, MEFAINUSWEA672N

A dashboard for a Greek tragedy

Greece is in the news again, and FRED can help you track the developments: FRED has almost 2,000 time series that pertain directly to Greece and a multitude of features to organize and automate your analysis. The first step is to open an account in FRED. Once you do that, you can enjoy some of FRED’s key benefits.

Email notifications: You can save data series in your user account, and FRED will send you an email as soon as those series are updated. Simply navigate FRED while logged into your account and click on the email notification link in the sidebar of the series you’re interested in.

Excel add-in: You can create an Excel spreadsheet of FRED data without ever touching the FRED website. Download our add-in, which allows you to search for and download data directly from Excel and refresh the data with the click of a button.

Dashboards: You can create a dashboard of graphs, tables, and data points. You can return to your dashboard anytime, and the data are always current. You can also make the dashboard public, which allows you to share it with friends, colleagues, and students. You can also add the dashboard to your bookmarks and view it without having to log in. Here is one example of a very simple with a few graphs about Greece. You can make more-complex graphs or choose to display the data in different ways, such as a table or a single number.

Web page widget: If you want your web page or blog to always display the latest FRED data for your favorite series, consider using the FRED widget. You can customize it with up to 10 series—for example, the series for Greece that’s included here in this blog post or the series in the sidebar that applies to the U.S.

API: You can create you own application that pulls data directly from FRED through the API.

How this dashboard was created: First, log in to your FRED account. Click on “FRED economic data” under the seal, then search for “Greece”; this brings up a wide range of series to choose from. Create a graph, then click on “Save Graph” in the sidebar. Repeat the operation for as many graphs as you want. Once you’re ready to assemble your dashboard, expand “My Account” on the top of the page and click on “My Dashboards.” Click the “Create” button and follow the instructions: add a title, add a description, and decide whether to make the dashboard public. Click on “Add Widget,” chose “Graph,” then choose “Saved Graph”; then select the saved graph you want to insert. Repeat with new widgets for other graphs, tables, etc.

Suggested by Christian Zimmermann

The mean vs. the median of family income

FRED has several datasets to help you investigate the distribution of income. One of them is the Income and Poverty in the United States release from the U.S. Census Bureau.

The graph above shows real family income in the United States in constant (2013) dollars. The mean is the average across all families. The median identifies the family income in the middle of the sample for every year: half of incomes are higher, half are lower. We quickly learn three things from this graph: 1. Family income has been growing much more slowly since the 1970s. 2. There are several episodes of declining income, and they become increasingly long and deep. 3. Median and mean incomes are diverging.

The last point could be an optical illusion, though, because both series have increased over time and their relative difference may have stayed constant even though the difference has increased in absolute terms. To make sure, we divide the mean by the median in the graph below; we can see that, indeed, this ratio has increased. But what does that mean? If the distribution of income is uniform (if every family has the same income), the ratio will be 1. If the distribution is unequal, the ratio will be higher than 1. For example, imagine we start with a uniform distribution of income and then the top 10% of families double their income. The median would not change, but the mean would increase by 10%. The data in the graph below clearly show that there has been an increase in inequality in family income, with a dramatic jump from 1992 to 1993.

How these graphs were created: Under “Sources,” find the Bureau of the Census and choose the Income and Poverty in the United States release. The mean and median real family income series should be among the top choices. Select them and add them to the graph. For the second graph, add the mean series as before; but, instead of adding the median series as a separate series, add it to the mean series (series 1). Finally, expand the “Create your own data transformation” panel and apply formula a/b.

Suggested by Christian Zimmermann

View on FRED, series used in this post: MAFAINUSA672N, MEFAINUSA672N

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