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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.

BRICS and blocs Beware of categorizations

Understanding the global economy has become more important for policymakers, given the increased interdependence in trade and capital flows. For the same reason, though, tracking the different economies has also become more complex. So it’s not surprising analysts find it convenient to group countries in blocs according to a characteristic or commonality. Examples include the G-7 (Canada, France, Germany, Italy, Japan, U.K., and U.S.), meant to designate the largest economies in the world, and the BRICS (Brazil, Russia, India, China, and South Africa), meant to designate the most significant emerging economies for their size and fast growth. And then there are the so-called PIIGS (Portugal, Ireland, Italy, Greece, and Spain), meant to designate European countries that were struggling to service their external debt a few years ago.

These classifications can be useful at certain points in time, but analysts and policymakers should keep in mind at least two important limitations. Countries in these blocs can behave very differently. And the classifications can very quickly become outdated, even if they continue to remain in use. I’ll illustrate these two limitations with FRED data for the BRICS.

Let’s look at real total GDP for these five countries, normalized so they all equal 100 in 1990. Using an index allows us to abstract from the large size differences of these countries and also gives a more transparent picture of how quickly each of these countries has grown between 1990 and 2014. The graph shows some large differences, indeed. China has grown dramatically since 1990—by a factor of 6. India is a distant second, growing by a factor of 4. Both Brazil and South Africa, in the middle, have doubled the size of their economies. Russia, which is last, has grown by barely 18% during the sample period; in fact, if anything, Russia’s economy was below its initial 1990 size for much of the 1990s and early 2000s.

So, should the BRICS be grouped together as a bloc? Only Brazil and South Africa behave in a reasonably similar way. Otherwise, these countries have big differences in economic behavior and their resulting relative importance.

A similar exercise could be done for the G7, where the relative importance of countries has also changed considerably. You may have known this already, but the G-7 no longer represents the seven largest economies: Canada and Italy have been replaced by China and India. And the internal rankings have also changed for the European countries, with Germany in 4th place, the U.K. in 5th, and France in 6th.

How this graph was created: Search for “real GDP at constant national prices for [country]” where [country] can be replaced by the actual name of the country you want. Select the units so that all variables are scaled by an index that sets the value of 100 for 1990. Choose 3 for the width for all lines.

Suggested by Alexander Monge-Naranjo.

View on FRED, series used in this post: RGDPNABRA666NRUG, RGDPNACNA666NRUG, RGDPNAINA666NRUG, RGDPNARUA666NRUG, RGDPNAZAA666NRUG

How a year of NAFTA news affected exchange rates Markets overshoot in the short term

Exchange rates are among the most volatile macro series. When allowed to float, exchange rates are much more responsive to news and shocks than interest rates or the prices of goods and services. They are volatile at lower frequencies (e.g., monthly and quarterly) but especially volatile at higher frequencies (e.g., hourly, daily, or weekly). Moreover, exchange rates tend to “overshoot,” with much stronger responses to news and shocks in the short term than in the medium and long terms. This behavior, which is the opposite of Paul Samuelson’s Le Chatelier’s principle, was first formalized by Rudiger Dornbusch more than forty years ago.

A relevant example is the recent exchange rate behavior for Mexico and Canada, two of the U.S.’s main trading partners. The graph shows the daily exchange rate of the Mexican peso (left axis) and the Canadian dollar (right axis) in terms of U.S. dollars. And the three vertical lines identify three recent U.S. events: election day (Nov. 8, 2016) in green, inauguration day (Jan. 20, 2017) in purple, and the day the president announced his intention to renegotiate NAFTA (April 27, 2017) in blue.

During the recent presidential campaign, the Republican candidate clearly and ardently advocated restraining some international trade in general and terminating NAFTA specifically. However, the dominant view (both within the U.S. and worldwide) was that the Democratic candidate would win the election. The 2016 election result was a surprise for most observers and, as argued here, for investors as well. Between Tuesday, Nov. 8, and Thursday, Nov. 10, there’s a clear and significant jump in the peso-to-dollar exchange rate: from 18.435 pesos per U.S. dollar to 20.493. That’s more than 10% in just a few hours. The Canadian dollar also depreciated, but only about 1.3%, from 1.333 to 1.347. The responses of these exchange rates seem to suggest that, on Nov. 8, investors took seriously the prospect that NAFTA would be terminated or renegotiated and that such a change could reduce the value of investments in Mexico.

After the election, both exchange rates stabilized and even moved downward. However, the Mexican peso began a bumpy ride: It depreciated significantly, peaking on Jan. 20, 2017, the date the new administration took office. The peso then appreciated continuously but only until April, when the president used Twitter to announce his inclinations to terminate NAFTA. This position was reinforced by the fact that the U.S. also imposed preliminary countervailing duties on Canadian imports of softwood. Both the peso and the Canadian dollar depreciated with respect to the U.S. dollar. On April 27, 2017, the president announced his intention to renegotiate NAFTA. Eventually, both currencies appreciated.

Since September, though, both currencies have depreciated: 5% for the Canadian dollar and 9% for the Mexican peso. Two factors may be at work: a potential increase in U.S. interest rates and the news that renegotiating NAFTA has proven to be very difficult for all three countries.

How to make this graph: Search for the daily series of the Mexican peso exchange rate: DEXMXUS. From the “Edit Graph” menu, choose “Add Series” and search for the similar Canadian dollar exchange rate: DEXCAUS. From the “Format” menu, select the right axis for the Canadian exchange rate. In both series, select 3 for the line width. To create the vertical lines, open the “Add Line” tab and select “user-defined line”: Enter the date you want for both the start and end dates, and use values to fill most of the vertical space. (By the way, the exchange rates aren’t tracked on holidays or weekends, so there are some blank segments in the series.)

Suggested by Alexander Monge-Naranjo.

View on FRED, series used in this post: DEXCAUS, DEXMXUS


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