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The FRED® Blog

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: Go to GeoFRED and click on “Build New Map.” Open the tool bar in the top left corner: Under “Region Type,” select “County.” Under “Data,” search for and select “Per Capita Personal Income.”

Suggested by Christian Zimmermann

The state of median household income

Fortunes can vary widely from one neighborhood to the next and also from one state to the next. The map above, which looks at U.S. states, shows median household income. This measure of income is basically a line through the middle: Half the households within that state receive more income and half receive less income. The map shows some regional correlations, but it is quite interesting to see how median income in one state can be almost twice as high (or low) as it is in a neighboring state. Use the “View on GeoFRED” link above to visit the site. There you can interact with each state on the map and gather more details about it. You can also change the applicable date to see how the distribution of the median income has changed over the years.

How this map was created: Go to GeoFRED and click on “Build New Map.” Open the tool bar in the top left corner: Under “Region Type,” select “State.” Under “Data,” search for and select “Estimate of Median Household Income.”

Suggested by Christian Zimmermann

The trend in teenage fertility

The World Bank collects all sorts of socio-economic indicators for many countries, and FRED is proud to feature them. The series we discuss here is the adolescent fertility rate, defined as the number of births per 1,000 women aged 15 to 19. The graph shows the rate for one country in each of the five most-populous continents. It is remarkable that the rate has been decreasing in all countries (although the trend isn’t nearly as pronounced in the Republic of Congo). One can think of many reasons for this. Among the most salient are the increase in schooling and educational opportunities among girls, adoption of new forms of birth control, and more generally the emancipation of women worldwide.

How this graph was created: Search for fertility and select the “15 to 19 years” tag at the left. Select the countries you want to display and click “Add to Graph.” Use the “move up / move down” option at the bottom of the graph tab for each series to match the order of the series in the legend with the order in the graph.

Suggested by Christian Zimmermann


Mapping the young and the old

FRED is gathering more and more international data, including socio-demographic data. The map above was built in GeoFRED and shows the World Bank’s “age dependency ratio.” This particular measure is the ratio of older “dependents” to “workers.” A higher number indicates more potential retirees (those 65 and older) for every 100 persons considered to be in their most-productive working years (15 to 64). The concept behind this terminology is that retirees are in some ways economically “dependent” on those who still work. Of course, there are qualifications: Many younger persons are in school or other training, and many older persons work effectively after age 65.

The map shows stark differences in this ratio across the world. Look at the legend: The ratios span an almost tenfold range from the bottom to the top category. There are two main reasons. Less-developed economies have shorter expected lifespans, reducing their proportion of potential retirees. Developed economies have lower birth rates, reducing their proportion of younger workers.

How this map was created: Go to GeoFRED and click on “Build New Map.” Open the tool bar in the top left corner: Under the “Data” section, search for and select the age dependency ratio data.

Suggested by Christian Zimmermann

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