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The elusive 2% inflation target

The collective wisdom in monetary policy circles identifies the optimal inflation rate as somewhere between 1% and 3%, with 2% being a popular target. Ideally, in normal times, this range provides sufficient wiggle room for real prices and wages to adjust if their nominal counterparts are a bit rigid—typically in the downward direction. In other words, let’s say wages and prices don’t readily decrease, be it for technical or psychological reasons. Then it’s best to have a little bit of inflation so that, even if nominal wages and prices stay constant, they can still decrease in relative terms if that’s what’s required for markets to stay in equilibrium. More inflation is not ideal, though, because of the significant associated costs: for example, if prices need to be readjusted frequently or allocations between cash and financial assets become distorted.

So how well do central banks throughout the world achieve this goal? The graph shows the G7 countries, which all target an inflation rate of around 2%. It looks like only Japan is capable of getting inflation high enough right now, which is ironic because Japan has suffered from deflation or zero inflation for over a decade. Of course, this collective “failure” may be related to the past year’s large decrease in commodity prices, especially oil. Unless this trend continues over the next year, we should see inflation rates getting closer to where central bankers want them.

How this graph was created: The data shown in the graph are from the OECD’s Main Economic Indicators. The most direct way to find them is to search for “oecd cpi monthly growth rate from previous period.” Select the series you want and use the “Add to Graph” button to display them. Finally, restrict the sample period to the past five years.

Suggested by Christian Zimmermann

View on FRED, series used in this post: CPALTT01CAM659N, CPALTT01DEM659N, CPALTT01FRM659N, CPALTT01GBM659N, CPALTT01ITM659N, CPALTT01JPM659N, CPALTT01USM659N

Riding the macroeconomic fluctuations

At the Federal Reserve, we follow closely the aggregate fluctuations in the U.S. economy, including the behavior of labor markets in general and the unemployment rate in particular. Our key policy instrument is the federal funds rate, which is used to influence all other interest rates, especially short-term rates, and thereby influence financial, labor, and goods markets to achieve our mandate of price stability and full employment.

Not surprisingly, some markets are more sensitive than others to both the cyclical behavior of the aggregate fluctuations and to monetary policy conducted by the Fed. Among those sensitive markets is the one for durable goods. The graph above illustrates this by showing total monthly sales of cars (thick blue line) from the late 1970s to today. Notice how volatile this series is, as spikes of high sales occur fairly often during the sample period. There’s also a clear pattern related to the unemployment rate (red line): Car sales plummet during periods of increasing unemployment, most notably during recessions (shaded bars).

But unemployment is far from the whole story. As the graph shows, car sales are also driven by two of the major costs of buying a car: the cost of gasoline (orange dashed line, right axis) and interest rates. The graph shows the bank prime loan rate (green line), which is used to set the interest rate charged for most car loans. Clearly, even when unemployment is low and declining, a rise in interest rates and the cost of gasoline is associated with a decline in car sales.

How this graph was created: First search for “total vehicle sales” and select the seasonally adjusted series. To highlight this series relative to the rest, select a solid line style with width 4. Next, use the “Add Data Series” option to search for and select “U.S. civilian unemployment rate”; again, select the seasonally adjusted series to keep the graph smoother. Next, add the series “bank prime loan rate” and “consumer price index for all urban consumers: gasoline.” To compare the time behavior of these series within the graph, place the y-axis for the last series on the right side. Finally, adjust the line colors and patterns to taste.

Suggested by Alexander Monge-Naranjo

View on FRED, series used in this post: CUSR0000SETB01, MPRIME, TOTALSA, UNRATE

Christmas in Connecticut

Christmas in Connecticut is a classic romantic comedy from 1945 that depicts, as the title strongly implies, some holiday hijinks in the Nutmeg State. Although FRED does not offer much romantic intrigue, it can tell us how the retail sector is doing in individual U.S. states. The graph above shows definite seasonal regularity in Connecticut’s retail employment (the blue line) that’s associated with retail sales around Christmas. As in other U.S. markets, retail employment is always significantly higher in December—even more so for general merchandise stores that sell a larger share of gifts. So it’s a very good idea to adjust the data for this seasonal regularity. Indeed, noting that sales are higher in December than in the previous month of November doesn’t tell us much because this happens every year. So the seasonal factor must be excluded, as shown in the red line.

How this graph was created: Search for “Connecticut retail employees general merchandise” and select the two monthly series. Click on “Add to Graph” and you’re done. Consider decreasing the time period with the sliding bar below the graph: A closer look at the data shows the December peaks more clearly.

Suggested by Christian Zimmermann

View on FRED, series used in this post: SMU09000004245200001, SMU09000004245200001SA

Shopping lines: The evolution of retail in the U.S.

When it comes to shopping, Americans have many options: the corner store, the supermarket, the specialty store, the “big box,” online, and more. Above, we committed a graphing sin by displaying 12 different series in a single graph to show how retail trade has evolved across various categories.

Signs can be seen over the past two and a half decades: First, while food and beverage stores (supermarkets, convenience stores) were major destinations in the past, they have been joined by general merchandise stores, typically large suburban big box stores. A very volatile bunch are the gasoline stations, which sell mostly one commodity with a very variable price. Finally, one category seems to be steadily overtaking the others: mail-order and online shops (the line in black).

How this graph was created: Search for the Advance Monthly Sales for Retail and Food Services release and select the series you want to display. The release offers more series than the 12 we chose, but 12 is the maximum that can be shown on one FRED graph. In fact, with so many series, you need to make the graph larger by dragging the red marker in the bottom right corner of the graph. We chose the seasonally adjusted series and made the nonstore retail series black.

Suggested by Christian Zimmermann


The Canadian dollar and the price of oil

Canada’s oil sector amounts to about 10% of its GDP and 25% of its exports, almost all of which go to the U.S. It’s not too surprising, then, that the U.S./Canada exchange rate mirrors the price of oil. Of course, trade between the countries is much more than oil, but many of Canada’s other commodity exports have a price that is well correlated with the price of oil. And the financial linkages between the countries are also disproportionately tied to the mining and extractive industries.

That said, this relationship hasn’t always existed. See the graph: If you expand the time sample to more than the 10 years shown above, the correlation becomes gradually less clear. But the reason is clear: Canada has continuously expanded its oil production, and oil simply did not matter that much a few decades ago when it was not nearly the dominant revenue source it is today.

How this graph was created: Look for the Canadian/U.S. dollar foreign exchange rate and select the monthly series. Then use the Add a Series option to search for and select “WTI” (again, the monthly series). Modify this second series as follows: Switch the y-axis to the right side and create you own data transformation with formula 1/a. Finally, restrict the graph’s sample period to the past 10 years.

Suggested by Christian Zimmermann and inspired by a tweet from Paul Storer, who recently passed away

View on FRED, series used in this post: EXCAUS, MCOILWTICO

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