Federal Reserve Economic Data

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Two shades of coffee

Your coffee—be it morning, afternoon, or after dinner—can take a wide variety of forms: regular coffee, single- or double-shot espresso, and ice cold latte, to name just a few. And even a regular coffee can be prepared using many different methods and equipment, from the old-school percolator, the more-modern coffee maker, or the trendier vacuum pot and the Chemex. International variations, of course, include the use of the French press and the Italian or moka pot, as well as styles such as Turkish coffee, café cubano, and, a personal favorite, the Costa Rican café de chorreador.

A lesser known fact is that the coffee beans needed for any of the above can come from two different species of coffee: robusta (coffea canephora) and arabica (coffea arabica). The former is a sturdy tree with a high yield of beans with high acidity and bitterness and low aroma. The latter is a shrub less resistant to disease with a lower yield of beans with lower acidity and bitterness and stronger aroma.

Arabica coffee is generally considered much higher quality than robusta. This can be confirmed by looking at their market prices, which can be found in FRED. The graph above shows the global monthly bulk prices (US$ per 100 lb.) of the two types of coffee beans: the red line for arabica and the blue line for robusta.

The graph suggests some interesting patterns. First, except for 2 of the past 430 months, the price of arabica has always been above the price of robusta. Second, the gap between the two prices seems to be widening since 1986, suggesting that consumers are increasingly appreciating quality. Third, there are fluctuations in both prices but those fluctuations are more remarkable for arabica. Indeed, arabica coffee prices are quite spiky. In the past three of those spikes, arabica coffee prices reached more than double the price of robusta coffee. This blog post does not have the grounds to determine whether those spikes have been driven by supply disruptions or other factors.

How this graph was created: Use FRED’s keyword search method to find the two series: “coffee robusta” leads you to the series ID PCOFFROBUSDM and “coffee arabica” leads you to the series ID PCOFFOTMUSDM. Each series is the monthly price of the coffee in US$ per 100 lbs.

Suggested by Alexander Monge-Naranjo.

View on FRED, series used in this post: PCOFFOTMUSDM, PCOFFROBUSDM

The trouble with food and energy

There are many ways to measure inflation. One popular method used for monetary policy purposes is to look at the price index for personal consumption expenditures excluding food and energy. Why exclude food and energy? Aren’t those important items that matter a great deal to households? The reason is straightforward: These price categories are considered to be excessively volatile, and including them would make it more difficult for policymakers to pin down the inflation trend. The graph above makes this point visually by comparing the PCE inflation rates with and without food and energy.

Usually when you add items to an index, you reduce the volatility of that index. This same premise is at work when you add assets to an investment portfolio—i.e., when you diversify to reduce volatility. But this does not happen when the item you add is excessively volatile. And, again, food and energy are excessively volatile. Food is subject to large price variations due to external shocks, mostly on the supply side, such as weather. Energy is subject to shocks as well: supply shocks such as discoveries, wars, political risk, and infrastructure issues and demand shocks such as climate events. This happens with food and energy much more than it does for other items included in personal consumption expenditures.

How this graph was created: Search for “PCE.” Then go to the “Filter Series by Tags” box to the left and enter “price index.” Select the first two monthly series that appear and add them to the graph. Change the units for both series to “Percent Change From Year Ago.”

Suggested by Christian Zimmermann

View on FRED, series used in this post: PCEPI, PCEPILFE

New York City vs. suburban incomes

FRED offers plenty of U.S. county-level data, including per capita personal income. One can look closely at individual counties in FRED and create regional maps in GeoFRED. This map focuses on New York City and the surrounding counties. One peculiarity worth noting is that each city borough is also its own state county: New York (Manhattan borough), Bronx, Kings (Brooklyn borough), Queens, and Richmond (Staten Island borough). There are stark contrasts in income across all these counties, with Manhattan clearly on top. The surrounding counties, however, have incomes higher than any borough other than Manhattan. Thus, even the Big Apple obeys the rule that incomes are generally higher for suburban residents.

How this graph was created: The original post referenced an interactive map from our now discontinued GeoFRED site. The revised post provides a replacement map from FRED’s new mapping tool. To create FRED maps, go to the data series page in question and look for the green “VIEW MAP” button at the top right of the graph. See this post for instructions to edit a FRED map. Only series with a green map button can be mapped.

Suggested by Christian Zimmermann



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