When we talk about apples and oranges, we usually mean objects or concepts that cannot be compared. But FRED is all about comparing many kinds of economic data, and it allows you to place series that represent different concepts from different sources in the same graph. It even allows you to compare apples and oranges. Literally.
The graph above shows the producer price index (PPI) for several varieties of apples. A quick look at the graph reveals two things: First, the lines are not continuous. This very specific product doesn’t have price observations for every month. Second, the deviations across varieties of apples are sometimes large and persistent.
What about oranges? The producer price index also includes information about frozen orange juice, for which there has been a “liquid” market for decades. In the graph below, we compare the PPI for frozen orange juice with the PPI for red delicious apples (which is the apple with the most information available). What’s surprising is that the price of frozen orange juice fluctuates just as much as the price for fresh fruit. But while the price of apples is clearly influenced by seasonal factors, the price for orange juice appears more persistent, especially in recent years.
How these graphs were created: Searching for “apple” gives you a long list of fruit-related series sorted by popularity, but the series we want for this graph are at the bottom of that list. Apparently few people are interested in the price of apples… Searching for “PPI apples” gets you the series you want (plus a few others). Select them, click on the “Add to graph” button, and restrict the sample to the past 10 years. The price indexes have different base years. To make them uniform, we choose “Index (Scale value to 100 for chosen period)” under “Units” and enter 2008-06-01 for all series except the special index. (FRED will choose the closest date if there are missing observations.) Finally, change the color of the special index to black and make the line thicker. For the second graph, search for “PPI orange,” add that series to the graph, and then add the series for red delicious apples.
Suggested by Christian Zimmermann
The minimum wage, which has been in the news recently, seems to be part of two related but slightly different concerns. One is earnings inequality, which a higher minimum wage could potentially reduce. The other is poverty, which a higher minimum wage could also potentially reduce by helping a low-income worker afford a basic basket of goods. Putting aside the ability of the minimum wage to achieve either of these two goals (which economists actively debate), we still have these two different ways to measure the minimum wage and how it has evolved.
To quantify the purchasing power of the minimum wage, we can simply deflate the nominal value of the minimum wage. The red line shows the value of the federal minimum wage deflated by the PCE price index. We might be equally interested in whether the minimum wage pushes up the bottom of the wage distribution: How the minimum wage affects wage inequality is related to where it lands in the wage distribution. The blue line shows the fraction of hourly workers whose wages are at or below the minimum wage. This measures the value of the minimum wage by showing how many workers are directly affected by it.
The red line shows the minimum wage drifting up and down as its nominal value is eroded by inflation and as it is legislatively adjusted. The blue line shows it drifting downward consistently for the whole period as it fails to keep up with the growth in wages of most of the distribution.
How this graph was created: Search for “percent paid minimum wage” and add the annual series to the graph. Add the second series to the graph by searching for “federal minimum wage” and adding it as series 2. Then add “Personal Consumption Expenditures: Chain-type Price Index” by selecting “Modify existing series 2.” Finally, use the “Create your own data transformation” to apply the formula 100*a/b. (You need to multiply by 100 as the PCE index is normalized at 100.)
Suggested by David Wiczer.
View on FRED, series used in this post: