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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

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

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