The FRED® Blog

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

Help wanted…in measuring the availability of jobs

How easily can firms find workers? How long does it take to hire them? These are crucial questions for economists who study unemployment. Unfortunately, the available data are very bad, but for very good reasons.

The main workhorse models of unemployment include, at their core, “search frictions”—forces that prevent willing workers from matching up with available jobs. The models also rely on the following premises: The more willing workers out there, the more likely an available job is filled. And the more available jobs out there, the more likely a worker finds one. But how does an economist define an available job? Is it a posted job vacancy? In the stylized world of economic models, a worker who is hired fills a vacancy that was posted; the posted vacancy is necessary for the hire. However, as we see in the graph, hires almost always outnumber posted vacancies. Clearly, then, many hires occur without an explicit posting. Elsewhere in the labor statistics world, this reality is acknowledged: Unemployment is calculated every month by asking would-be workers how they searched for a job. Responding to a vacancy is only one of a dozen other methods of searching, including asking friends and relatives. The vacancy posting measure clearly undercounts the number of available jobs.

How this graph was created: Search for and select “Hires: Total Nonfarm, Level in Thousands” (first the seasonally adjusted and then the not seasonally adjusted series) and add them to the graph. To create the ratio, we must add the job openings series. In the “Add Data Series” section, search for and select “Job Openings: Total Nonfarm, Level in Thousands, Seasonally Adjusted” and select “Modify existing series” for series 1 (the smoother blue line, which is seasonally adjusted). Then enter the formula a/b in the “Create your own data transformation” section. Now do the same for series 2 (the rockier red line) with the job openings series that is not seasonally adjusted.

Suggested by David Wiczer

View on FRED, series used in this post: JTSHIL, JTSJOL, JTUHIL, JTUJOL

The oil and gas extraction boom gives 101%

FRED recently added a large amount of data on industrial production, capacity, and capacity utilization. These series let you dig around to see how various industries are faring. Here we look at an industry that’s been in the news recently: oil and gas extraction. The graph makes it clear that a lot of capacity has been added since the boom in fracking. If you look closely, you’ll notice that capacity utilization (essentially a ratio of production to capacity) was over 100% in June 2014, an impossibility caused by the imprecision of the estimation procedure for both underlying series. Note that June 2014 is also the month when U.S. gasoline prices peaked.

How this graph was created: Search for “capacity oil gas,” and the three series you want should be among your top choices. Select the monthly, seasonally adjusted series and add them to the graph. Use the right axis for capacity utilization to make the graph easier to read.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CAPG211S, CAPUTLG211S, IPG211S

Does the market believe the change in oil prices is permanent?

Oil prices fell dramatically in the last half of 2014, from a high of $107.49 on June 13, 2014, to $54.14 on December 30, 2014, and continued to fall into early 2015. During the same period, a measure of 5-year inflation expectations declined in a similar way. The graph shows the unusual correlation between these two series from January 2014 to the present. The red line is the daily 5-year breakeven inflation rate from the beginning of 2014 to the present. (That breakeven inflation rate is computed from the difference between the 5-year Treasury inflation-protected security, or TIPS, and the 5-year Treasury and is a measure of market expectations of future inflation.) The blue line is the daily price of West Texas Intermediate crude oil.

Market expectations of the inflation rate 5 years out held steady for the most part from early 2013 to early 2014. On April 17, 2014, inflation expectations jumped up. After June 2014, oil prices fell precipitously, taking inflation expectations down with them. After January 27, 2015, oil prices stabilized and began to rise. Again, market inflation expectations rose.

While oil prices can pass through and affect other prices, the almost one-to-one movements in the two series seem to be unusual. Pass-through from oil to other prices is incomplete. If the price increase in oil was deemed to be temporary, the 5-year inflation rate would not move in unison with oil prices (little pass-through). In this case, it appears there’s at least some belief that the change in oil prices will persist, as there is substantial pass-through.

How this graph was created: Search for “crude oil prices,” select the series “Crude Oil Prices: West Texas Intermediate (WTI) – Cushing, Oklahoma,” and graph it on a daily frequency. Select the “Add Data Series” option: Search for “5-year breakeven inflation,” select the first series shown (“5-Year Breakeven Inflation Rate, Daily, Percent, NSA”), and add it as a new series. Select the “Edit Data Series 2” tab and change the y-axis position from left to right. Finally, set the start date to 2014-01-01.

Suggested by Michael Owyang and Hannah Shell.

View on FRED, series used in this post: DCOILWTICO, T5YIE

Routine European gasoline

The graph above tracks gasoline prices in Europe and the U.S. An American looking at the graph may be puzzled by the much smaller decline in Europe than in the U.S. Why the difference? One reason is that taxes on fuel are much higher in Europe, which means that a large fraction of the cost there hasn’t changed. The graph below shows another reason: The price of crude oil in euros actually hasn’t changed that much. The dollar price has declined a great deal, but the dollar has also strengthened significantly with respect to the euro, canceling out much of the decline. Indeed, the dollar price fell from about $110 to about $50. The euro price fell much less, from €80 to €50.

How these graphs were created: For the first graph, search for “europe transportation fuel price” and “gasoline CPI” (monthly, not seasonally adjusted) and add the series. To align both series at the recent peak price for the U.S., choose “Index (Scale value to 100 for chosen period)” for the units and 2014-06-01 for the date. For the second graph, search for “oil Brent” and select the monthly series. Then add the same series again as series 2. Finally, add the dollar/euro exchange rate to series 1 and apply transformation a/(b). (The parentheses in the formula simply make the label in the graph easier to read.)

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CP0722EZCCM086NEST, CUUR0000SETB01, EXUSEU, MCOILBRENTEU

The price of fun

If we define fun as games, toys, and hobbies, then the European Union as a whole has kept the price of fun pretty steady over the past decade or so. Ireland has even managed to reduce the price of fun over the same period—perhaps more than any other European country. The price of fun in Turkey, on the other hand, has skyrocketed.

How this graph was created: Search for “games,” scroll through the available countries, and select the series you’d like to graph.

Suggested by George Fortier

View on FRED, series used in this post: CP0931EU27M086NEST, CP0931IEM086NEST, CP0931TRM086NEST

More on churning in the labor market

The U.S. labor market churns with hirings and firings. The graph above represents this dynamic situation: In red (in negative territory) are all the job separations and in blue (in positive territory) are all the new hires. The end result is the net creation of jobs, shown in white. The series used here are not seasonally adjusted, so one can easily see strong patterns—both throughout individual years and during recessions and booms.

How this graph was created: Search for “Hires: Total Nonfarm” (level in thousands, not seasonally adjusted) and graph that series. Add the series “Total Separations: Total Nonfarm” (level in thousands, not seasonally adjusted). Transform the latter series by applying the formula -a. Create a third series, again using “Hires: Total Nonfarm” (level in thousands, not seasonally adjusted). Choose white for the third series. Add “Total Separations: Total Nonfarm” (level in thousands, not seasonally adjusted) to the third series. Transform the third series using the formula a-b. Then choose graph type “Area.”

A previous post also covered this topic.

Suggested by John Chilton

View on FRED, series used in this post: JTUHIL, JTUTSL

The many flavors of inflation

Inflation is the rate of growth of prices. But which prices? It all depends. Above, we have four popular measures of inflation for different slices of the economy. The consumer price index (CPI) looks at a typical U.S. consumer’s basket of goods and evaluates its price over time. The producer price index (PPI) looks at the cost of inputs into the production process. The GDP deflator considers all goods that are part of GDP, which excludes imports and includes exports (the opposite of CPI and PPI). Finally, the personal consumption expenditures (PCE) price index uses a continuously changing basket of goods that is the basis for the private consumption component of GDP. The graph shows similar trends for these series over the past 10 years, except that the PPI is much more volatile. Use the slider to look at other years, where the pattern holds.

And there’s more. Each of these inflation indicators can be broken down into more-specific versions. In FRED, you can find many subsets of data in our new release tables for CPI, PPI, GDP deflator, and PCE price index. A popular version of the CPI is the one that excludes food and energy, two highly volatile components with strong seasonal fluctuations. Some people use this version of CPI when they want to track “core inflation.” FRED recently added two new subsets of price information as well: One is an experimental dataset that calculates the CPI for those over 62 years of age, and the other is compiled by State Street and computes an index from prices posted on websites. The graph below contains these three price indexes, plus the CPI from the above graph. As expected, the CPI excluding food and energy is more stable. It is perhaps a surprise that inflation for website prices (the State Street index) is fluctuating so much, which could mean that goods offered online have special characteristics.

For more on inflation, take a look at these educational resources from the St. Louis Fed:

How these graphs were created: Start from a series page, modify the graph to show the units “Percent Change From Year Ago,” and then add the other series through the search feature within the form. Note that the units of these series will be automatically converted to percent change as you add them. For the bottom graph, you need to be sure to undo this conversion for the State Street index, as it is already expressed in percent change, and then apply the data transformation a*12 to this last series, as the original is a monthly inflation rate.

Suggested by Christian Zimmermann


Some economies get stuck

If you want to compare economies, a good source is the World Development Indicators from the World Bank. Economic definitions differ and data exist in different currencies, but the World Bank makes the relevant reconciliations. For example, their data are in 2005 U.S. dollars (and thus in real, not nominal, terms). These graphs depict two countries currently in the news whose economies have stagnated. The first is Venezuela, which had been much richer than its neighbor Colombia but has had essentially no growth over the sample period. The second is the Ukraine, which suffered a deep recession in the early 1990s, along with other former Soviet bloc countries. The Ukraine never recovered, while its neighbor to the north, Belarus, did. In fact, the Ukraine’s situation is even grimmer: The data show GDP per capita, but do not show that the population in the Ukraine has actually been falling for several years, which means total GDP has been on a sharp decline.

How these graphs were created: For both, you can either start from the World Development Indicators release and narrow down the choices using the tags or simply search for “constant GDP per capita” for the countries of your choice.

Suggested by Christian Zimmermann


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