Skip to main content

The FRED® Blog

Which measures of inflation are relevant for policy?

The Federal Reserve has set a 2% inflation target. Does it meet that target? It depends on which inflation rate you consider. FRED offers many different series for the U.S. that reveal different views of inflation because they pertain to different groups of goods and services. The graph above shows eight series that receive a lot of attention in the context of policy: Three are above and five are below that 2% target. How are they different? Some look only at consumption expenses or production costs. Some include overall economic activity. Some exclude energy and food, price categories thought to be volatile and thus capable of clouding the picture of underlying inflation. (For example, removing the currently low prices of oil and its derivatives clearly leads to higher inflation numbers.) Some focus on prices that move slowly, which are thought to be good indicators of trend inflation. And one index considers only the median in the distribution of price changes. You can consider even more series in FRED. The point is that there’s a wide spread across those inflation rates, and determining which is the most relevant isn’t easy.

How this graph was created: One of the many way to graph these series is to search for “price” and restrict the choices with tags such as “nation,” “usa,” and “sa” (seasonally adjusted). These eight are likely to be at the top of these search results. Select the series you want and click the “Add to graph” button. Some series are indexes and others are inflation rates, so modify the units to show “Percentage change from year ago” for the series in index form. Finally, to add the black horizontal line at 2%, open the “Add series” panel and select “Trend series” from the pulldown menu. Once it’s added, modify it by choosing 2 for the initial and final values and change the color to black. Oh… We also removed the axis label because it became unwieldy with eight depicted series.

Suggested by Christian Zimmermann

Time aggregation in FRED

In many instances, statistics are collected at a higher frequency than a user requires. In the example here, the unemployment rate is collected monthly, but we often have other labor market data collected annually. The question here is how to aggregate the high-frequency data into a lower-frequency statistic. In FRED, we have three options: average, sum, or end of period. In the graph, we compare annual unemployment data taking either the average over the year or the end-of-period observation. The choice of whether to use seasonal adjustment doesn’t affect the average. By definition, seasonal adjustment implies that December, the last month of the year, does not have a systematically different unemployment rate from any other month. However, averaging or summing will systematically give lower measures of variation than the end-of-period observation. The reason is simple, even without too much formal math: Suppose every month our observation is the annual number plus some monthly “noise” term. Either summing or taking the average, we essentially allow these monthly variations to cancel each other out. Taking an observation from the end of period includes all of the month-specific variation. In the graph, we can see that the red line, which takes annual unemployment as the final month’s observation, is more volatile. In fact, from 1979-2014, its coefficient of variation is 25.56%; the blue line, which takes the average, has a coefficient of variation of 24.86%.

How this graph was created: Search for “unemployment” and select the seasonally adjusted civilian unemployment rate. Using the pull-down menu, change “Frequency” to “Annual.” The default “Aggregation Method” is “Average,” and we will keep that. Then, “Add Data Series” and again search for “unemployment.” Add a new series using “unrate,” the same data as last time. Again, change it to an annual frequency. But this time, change the aggregation method to “End of Period.”

Suggested by David Wiczer

View on FRED, series used in this post: UNRATE

Federal funds rate: target vs. reality

The traditional policy tool of the Fed is to target the federal funds rate. Note the term target. Indeed, the Fed does not set this interest rate; rather, it sets the target and then conducts open market operations so that the overnight interest rate on funds deposited by banks at the Fed reaches that target. Obviously, reaching the target is sometimes harder to do, especially in times when there’s a lot of uncertainty in the markets. The graph above compares the target (or target band more recently) with the effective federal funds rate. While the two coincide quite well over most of the 10-year period, there are important deviations that correspond to various financial market events. Nevertheless, these deviations are short-lived, which shows that the open market operations do have the desired effect.

How this graph was created: Search for “federal funds rate” and these four series should be among the top choices. Select the daily rates and use the “Add to graph” button to add them to the graph.

Suggested by Christian Zimmermann

View on FRED, series used in this post: DFEDTAR, DFEDTARL, DFEDTARU, DFF

The many faces of the federal funds rate

It’s no surprise FRED has federal funds rate data. But these data aren’t as simple as you may think. They have changed form over time as the Federal Open Market Committee has changed the way it sets the funds rate: From 1982 through 2008, the target rate is a discrete number. For example, it is 9.5% on Oct. 1, 1982, 3% on Oct. 1, 1992, and 1.75% on Oct. 1, 2002. At the end of 2008 (i.e., since the financial crisis), the FOMC began setting a target range of 0.00 to 0.25%. And, to further complicate matters, the data prior to 1994 come from the working paper “A New Federal Funds Rate Target Series: September 27, 1982 – December 31, 1993,” making it an altogether different series.

The discrete-target funds rate for 1982-2008 is DFEDTAR in FRED. The target-range funds rate since then has a lower and upper bound—DFEDTARL and DFEDTARU, respectively.

Of course, FRED will continue to accommodate changes to the funds rate. As the U.S. economy overall and employment specifically have recovered, the FOMC has signaled a need to respond by changing the rate. And financial observers around the globe are anxious about how the FOMC will respond. If at some point in the future the FOMC moves from a target range to a discrete target, FRED will also need to respond: In this case, the FRED team plans to change the lower-bound and upper-bound series to a commensurate data point to solve this issue. This method will ensure that the history of the range remains intact, while allowing FRED users to present the data in the simplest way possible. We will not combine the series, create a new series, or update the DFEDTAR series.

How to make this graph: The FRED Team prefers to present these data by creating one graph with the three aforementioned series. First search for and add DFEDTAR to a graph. Next use the “Add Data Series” menu below the graph to search for DFEDTARL and DFEDTARU in the field that asks you to “Type keywords to search for data.” Select these series and add them to the graph with the “Add Series” button.

Suggested by Travis May.

View on FRED, series used in this post: DFEDTAR, DFEDTARL, DFEDTARU

A bit of religion in the dismal science

FRED’s main contribution to the “dismal science” of economics is its core economic and monetary data. But FRED recently added some socio-demographic indicators as well. None of these indicators covers religion, per se; but quite a few relate to religion indirectly. A recent search for “religion” in FRED yielded 185 results, and two of those series are highlighted above: (i) real private consumption expenditures dedicated to religion and other social services and (ii) real investment in religious nonresidential structures, which we presume are mostly churches. (Both series are chain-type indexes.) For comparison, we also include real GDP in the graph, with all indexes having a value of 100 in 2009. Religious consumption expenditures (which may have a variable non-religious component) have tracked GDP quite well since 1929, but church building has plummeted over the past ten years. This decline predates the construction industry’s overall decline during the previous recession. Thus, there may be non-economic factors at play here.

How this graph was created: Search for “religion” and narrow the results by clicking on the “nation” tag. You should find the first two series fairly quickly. Again: The “religious” series are chain-type indexes. Select them and click on the “Add to graph button.” Then add the “real GDP” series and change units to “Index (Scale value to 100 for chosen period)” to 2009-01-01.

Suggested by Christian Zimmermann

View on FRED, series used in this post: C309RA3A086NBEA, DSOCRA3A086NBEA, GDPCA

More about comparing oranges

Our previous post was about comparing apples and oranges. This post takes a different approach and searches FRED for just oranges. Most of the results have nothing to do with fruit, but rather are economic indicators for Orange County, California. FRED houses many regional data series, and if you look you’ll see there are seven other Orange Counties in the U.S. We compare four in the graph by looking at their unemployment rates. Indiana’s O.C. stands out, with a high and highly fluctuating rate; it is small and poor, with employment dominated by a large casino and golf resort. (Note: None of these series are seasonally adjusted.) Vermont’s O.C. is a little larger and better diversified, so it has smaller fluctuations. California’s O.C. is the largest and also has small fluctuations. North Carolina’s O.C., home of the University of North Carolina flagship campus, is of special interest, as its unemployment rate jumps up between 1999 and 2000. This could be due to a reclassification or a mass layoff. Maybe a reader knows why…

How this graph was created: Search for “Orange County unemployment,” select the counties you want to graph, and click on “Add to graph” to do so.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CAORAN7URN, INORURN, NCORAN2URN, VTORAN7URN

Comparing apples and oranges

When we talk about apples and oranges, we usually mean objects or concepts that cannot be compared. But FRED is all about comparing many kinds of economic data, and it allows you to place series that represent different concepts from different sources in the same graph. It even allows you to compare apples and oranges. Literally.

The graph above shows the producer price index (PPI) for several varieties of apples. A quick look at the graph reveals two things: First, the lines are not continuous. This very specific product doesn’t have price observations for every month. Second, the deviations across varieties of apples are sometimes large and persistent.

What about oranges? The producer price index also includes information about frozen orange juice, for which there has been a “liquid” market for decades. In the graph below, we compare the PPI for frozen orange juice with the PPI for red delicious apples (which is the apple with the most information available). What’s surprising is that the price of frozen orange juice fluctuates just as much as the price for fresh fruit. But while the price of apples is clearly influenced by seasonal factors, the price for orange juice appears more persistent, especially in recent years.

How these graphs were created: Searching for “apple” gives you a long list of fruit-related series sorted by popularity, but the series we want for this graph are at the bottom of that list. Apparently few people are interested in the price of apples… Searching for “PPI apples” gets you the series you want (plus a few others). Select them, click on the “Add to graph” button, and restrict the sample to the past 10 years. The price indexes have different base years. To make them uniform, we choose “Index (Scale value to 100 for chosen period)” under “Units” and enter 2008-06-01 for all series except the special index. (FRED will choose the closest date if there are missing observations.) Finally, change the color of the special index to black and make the line thicker. For the second graph, search for “PPI orange,” add that series to the graph, and then add the series for red delicious apples.

Suggested by Christian Zimmermann

View on FRED, series used in this post: PCU31141131141117, WPU01110208, WPU01110209, WPU01110211, WPU01110215, WPU01110216, WPUSI01102A

The value(s) of the minimum wage

The minimum wage, which has been in the news recently, seems to be part of two related but slightly different concerns. One is earnings inequality, which a higher minimum wage could potentially reduce. The other is poverty, which a higher minimum wage could also potentially reduce by helping a low-income worker afford a basic basket of goods. Putting aside the ability of the minimum wage to achieve either of these two goals (which economists actively debate), we still have these two different ways to measure the minimum wage and how it has evolved.

To quantify the purchasing power of the minimum wage, we can simply deflate the nominal value of the minimum wage. The red line shows the value of the federal minimum wage deflated by the PCE price index. We might be equally interested in whether the minimum wage pushes up the bottom of the wage distribution: How the minimum wage affects wage inequality is related to where it lands in the wage distribution. The blue line shows the fraction of hourly workers whose wages are at or below the minimum wage. This measures the value of the minimum wage by showing how many workers are directly affected by it.

The red line shows the minimum wage drifting up and down as its nominal value is eroded by inflation and as it is legislatively adjusted. The blue line shows it drifting downward consistently for the whole period as it fails to keep up with the growth in wages of most of the distribution.

How this graph was created: Search for “percent paid minimum wage” and add the annual series to the graph. Add the second series to the graph by searching for “federal minimum wage” and adding it as series 2. Then add “Personal Consumption Expenditures: Chain-type Price Index” by selecting “Modify existing series 2.” Finally, use the “Create your own data transformation” to apply the formula 100*a/b. (You need to multiply by 100 as the PCE index is normalized at 100.)

Suggested by David Wiczer

View on FRED, series used in this post: FEDMINNFRWG, LEU0203127200A, PCECTPI

Labor market tightness

Unemployment is high during a recession, and job vacancies are numerous during an economic boom. That should surprise no one. This is why these two measures are useful in determining the state of an economy throughout its business cycle. One way to do this is to look at labor market tightness, defined as the ratio of vacancies to unemployment, which we show above. One should realize, though, that while the number of unemployed is reasonably well estimated from surveys, the number of vacancies is estimated with much less confidence. Indeed, at least in the U.S., it is not mandatory to post openings at an employment agency. In fact, some statistical agencies used to measure the square footage of job ads in newspapers, which obviously isn’t possible now that jobs are advertised in many different media and likely multiple times. In the U.S., a survey across businesses about their openings has been conducted only since 2000.

How this graph was created: Search for “job vacancies” and select the monthly seasonally adjusted series for the U.S. Then add the series “unemployment level,” making sure to check “Modify existing series 1.” Finally, create your own data transformation with the formula a/b/1000.

Suggested by Christian Zimmermann

View on FRED, series used in this post: LMJVTTUVUSM647S, UNEMPLOY

A good use of moving averages

Some data series are very volatile. That is, they don’t follow a smooth or step-by-step pattern. And it’s difficult to draw conclusions when new data are added to a volatile series. The weekly release of initial claims for unemployment insurance is a great example. In this and similar cases, it is useful to adopt some kind of smoothing mechanism: Here we provide a four-week moving average. Traditionally, a moving average is centered—say, the average of two periods before and two periods after. This moving average takes the last four observations, which allows you to better read trends, especially if you’re focusing on the most recent data. Of course, trends become more obvious if you look at longer spans of time. This graph shows a span of five years. Narrow or expand the sample with the slide bar to see how a moving average can help you interpret the data and avoid the pitfalls of volatility.

How this graph was created: Search for “initial claims,” select the two (seasonally adjusted) series, and add them to the graph. Finally, restrict the sample to the last 5 years, which is done by using the settings above the graph on the right.

Suggested by Christian Zimmermann

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

Subscribe to our newsletter

Follow us

Twitter logo Google Plus logo Facebook logo YouTube logo LinkedIn logo
Back to Top