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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

View on FRED, series used in this post: ITCELSETSP2CHN, ITCELSETSP2PER, ITCELSETSP2TCD, ITCELSETSP2USA

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


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