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To every thing, there is a season…

Playing with retail data

FRED recently added a lot of new data from the U.S. retail sector—just in time for the holidays. So let’s take this opportunity to play a little game. The release table for monthly retail sales shows plenty of subsectors involved in retail trade. Because these series are not seasonally adjusted, they may show some large seasonal factors at work. The game is to try to predict what the seasonal factors for each sector will look like before displaying the graph for that sector. The graph above reveals the seasonality for three sectors: Sales of office supplies peak in August with the return to school. Sales of gifts and novelties peak in December as people scramble to fill Christmas stockings. And sales of used merchandise bottom out at the start of the year for reasons that escape us. Hint: To identify the months more easily on the graph, reduce the sample period to a few years and hover over the lines to identify the months.

How this graph was created: Go to the release table for monthly retail sales (not seasonally adjusted), check the series you want, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: MRTSSM45321USN, MRTSSM45322USN, MRTSSM45330USN

What makes an economy grow?

The contributions of production factors

What makes an economy grow? At its most basic level, the production of goods and services requires people, machinery, tools, buildings, and know-how. To provide a simple context, we’ll use the growth accounting framework to track the contributions of those factors to the growth of GDP. The graph above does this for the United States, although the picture would be similar for almost any country.

  • The full area shown in the graph is the growth rate of GDP.
  • The blue area is the contribution of increased capital to the growth of output. Capital here means machinery, tools, computers, and structures in which production of goods and services takes place. It is the growth rate of capital multiplied by 0.38, because about 38% of production occurs because of capital.
  • The red area is the contribution of labor in the production process. Here, we add the growth rates of the number of people working and the average hours they work—in other words, the growth rate of the total hours worked in the economy—and multiply that by 0.62, the complement to the 0.38 from capital.
  • The green area is the “magic sauce” that’s not strictly labor or capital: It’s the know-how, the technical innovation, organization, externalities (pollution, for example), and complementarities (public goods, for example, that reinforce each other).

We can see some fluctuations, most notably with labor, but overall all three factors contribute roughly equally to the growth of GDP. It’s no secret that growing an economy requires more investment, people, and innovation and some solid means of organization.

How this graph was created: All the data in the graph are from the Penn World tables. Search for “capital” and click on the U.S. series. From the “Edit Graph” tab, choose “Percent change from previous year” as the units and apply formula a*.38. Then from the “Add Line” option, search for “persons engaged United States.” Select the numbers series. In the “Customize Data” section, search again and take the hours series, then apply formula (a+b)*.62. For the last line, search for “real GDP at constant national prices United States” (this should ensure you find the PWT series) and add the series to the graph. Then, in the “Customize Data” section, add successively the capital, number, and hours series from above. Apply equation a-b*.38-(c+d)*.62. Open the “Format” tab, select graph type “Area” with stacking. Reorder the series to make sure GDP is on top. Change the sample to start in 1952 to avoid the odd data point for capital.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: AVHWPEUSA065NRUG, EMPENGUSA148NRUG, RGDPNAUSA666NRUG, RKNANPUSA666NRUG

House hunting

State-by-state differences in house price appreciation

It’s no secret house prices differ across the U.S. There are also large differences in how these prices change over time. In the short-term, the data include a lot of noise and temporary regional peculiarities. Over the longer haul, though, clear trends can emerge. The map shows a price index for all house transactions: The index was set to a value of 100 in the first quarter of 1980, and the map shows the index values as of the third quarter of 2017. So, we can see how house prices have increased over the past 37 years. Keep in mind this is a nominal index and that price increases are expected everywhere, given general price inflation. For comparison, an index value of 316 in 2017:Q3 would reflect price increases that have exactly kept pace with the consumer price index. For example, house price increases in Missouri and New Mexico (index values of 317) are nearly even with inflation.

Unfortunately, the District of Columbia, which has the highest house price inflation, isn’t visible on this map. D.C. has an index value of 856, which reflects a 5.9% increase per year in nominal terms and a 2.8% increase per year in real, general-inflation-adjusted terms. The location with the lowest house price inflation is West Virginia, with a value of 233, which reflects a 2.3% increase in nominal terms and a –0.8% increase per year in real terms. In fact, house prices in 12 states have appreciated below the CPI, meaning that houses there have appreciated less than the average of all consumer goods.

Does this means real estate isn’t a good investment? While the numbers shown here can provide rough estimates, it’s important to understand their limitations. This index is computed by looking at transactions that involve single-family homes with conventional mortgages that satisfy the guidelines of Freddie Mac or Fannie Mae and is based on repeat sales of such properties. Thus, this map does not reveal the prices for all houses and the quality of the relevant housing stock may also change over time. For example, purchased houses may become larger over time, and the qualifications for inclusion in this index may also change.

How this map was created: From GeoFRED, choose state maps, open the cogwheel menu, and search for “house price.”

Suggested by Christian Zimmermann.



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