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All that glitters is not necessarily a good store of value The evolution of precious and not-so-precious metal prices

The FRED team is busy adding new data almost every day, as new data are released almost every day. That includes the week between Christmas and the new year. Still, we found some time to create this FRED graph, which shows the prices of gold, copper, and nickel. You may have noticed the colors of the lines match the colors of their metals, thanks to FRED’s flexible graph formatting tool. Note also that we displayed the price of gold on a different scale, as it’s an order of magnitude or two higher than the others.

The prices of these metals, as is often the case with commodities, are quite volatile. There seems to be a connection between the price of copper and the price of nickel: Both, for example, are used as an alloy in the manufacture of coins. But the price of gold seems to follow its own laws. At any rate, none of these metals instills confidence that its price is certain to appreciate, despite what some advertisements claim. This lack of certainty becomes even more apparent when you adjust for inflation, as shown in the graph below.

How this graph was created: Search for “copper price” and open the monthly graph. From the “Edit Graph” section, open the “Add line” tab and search for “nickel price” and add it. Add another line by searching for and selecting the gold price. Finally, in the “Format” tab, set the axis for the gold price to the right and play with the color settings for each line. For the second graph, take the first graph and do this for each line: Add the non-seasonally adjusted CPI series (to match the non-seasonally adjusted metal price series) and apply formula a/b.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CPIAUCNS, GOLDAMGBD228NLBM, PCOPPUSDM, PNICKUSDM

Busting a Santa myth How hard are the elves working?

We’ve all heard that Santa and his elves are wildly busy, especially through December, making toys and other gifts for the Christmas season. Can FRED tell us anything about how busy they are? As it turns out, FRED does have quite a bit of employment data on Santa’s neighborhood: Alaska! (Which includes the town of North Pole!)

Given the quantity of gifts distributed on Christmas eve and the size of Alaska’s economy, we reckon that Santa’s enterprise is a major player and that Alaska’s economy is a good proxy for what’s happening in Santa’s shop.

We’re sorry to say that Alaska’s employment data do not corroborate the story that Santa and his elves keep busy in December. The graph above shows the total number of employees in Alaska’s private businesses. This measure excludes government employees, but it’s reasonable to assume Santa isn’t part of the government.

What’s really striking about this graph is the strong seasonal pattern. Significantly more people work in some months than in others, and the differences aren’t small: There’s a 20% difference between the top and bottom in each year. If you look closely (either by shortening the sample size or by hovering over the graph), you see that January has the least employees, which is expected, since Santa has just finished the deliveries and is likely on vacation with the elves. But the top months are all in the summer. This means the elves aren’t scrambling right before Christmas, but instead have planned their production well ahead of time. The graph below tells a similar story, in that weekly hours worked follows the same pattern as the employment measure: They actually bottom out every December.

In conclusion, the story that elves are overworked making toys right up to Christmas is simply a myth.

How these graphs were created: Search for “Alaska private employment,” and both series will be among the choices. Choose on the monthly, not seasonally adjusted series.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: SMU02000000500000001, SMU02000000500000002

Houses, up and down An international comparison of house price movement

We recently highlighted state-by-state comparisons of house price appreciation. Today, we’re going international. Thanks to the Bank for International Settlements, we have residential property prices for a selection of countries, in both nominal and real terms. Here we focus on the latter, which show how house prices evolve compared with other prices. We also focus on countries with relatively long sample periods so we can document long-term trends.

The graph above shows data for a set of countries where houses have significantly appreciated over the long haul. It’s not a steady trend (e.g., Hong Kong) and doesn’t last through the whole period (e.g., the U.K.’s “weak” property market over the past 10 years); these patterns highlight the adage that past behavior isn’t necessarily a good predictor of future behavior. The graph below shows a different set of countries where the long-term trend is more mixed, even downward facing. The U.S. is part of this group with its distinct “bubble” that the housing market is still recovering from. Switzerland is surprisingly stagnant despite strong population growth, and Korea is even trending down.

How these graphs were created: Search for “BIS house price,” then click the “real” tag in the side bar. Check the series you want shown, and click “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: QCAR628BIS, QCHR628BIS, QGBR628BIS, QHKR628BIS, QKRR628BIS, QNZR628BIS, QUSR628BIS, QZAR628BIS

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.


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