The FRED Blog has discussed the economic impact of war on labor markets and energy prices. Today, we discuss the impact on the price of lard. Stay with us, here…

Our FRED graph above shows data from the NBER’s Macrohistory Database on the retail and wholesale prices of lard in New York between 1911 and 1943. There were very large price swings during that time, so let’s break it down:

• Between 1911 and early 1916, lard prices were stable in the range of $0.10 – $0.15 per pound, even though World War I began in 1914 and the US didn’t enter the conflict until almost three years later.
• Between mid-1916 and mid-1919, retail lard prices (solid dark blue line) rose by 156% and wholesale prices (dashed light blue line) rose by 168%. Increased foreign demand for food items, whose production was disrupted during the Great War, helps explain those price hikes.
• After 1920, prices dropped below pre-war levels once foreign supply expanded, vegetable-based consumer alternatives to lard became available, and the Great Depression began.

World War II broke out in 1939 and the U.S. joined the conflict at the end of 1941. But the price of lard  and many other agricultural products didn’t rise nearly as much as they did two decades earlier. Changes  in consumption patterns and international finance conditions and the experience gained during World War I in managing agricultural prices all contributed to this more muted inflationary response.

At the risk of larding this post, it’s thought-provoking to discuss the ratio between the retail and wholesale prices during this time. The retail price is always higher than the wholesale price, to account for retailer overhead cost (e.g., distribution and marketing expenses) and profit margins. But the price margin between retail and wholesale prices was at its lowest between 1916 and 1919, when prices were rising the fastest. This may reflect some difficulty in passing on the full amount of the price increases to consumers.

Read more about farm product prices during the world wars in this related article by J.M. Tinley in the American Journal of Agricultural Economics. For a deeper dive into the history of lard and a gruesome reference to Upton Sinclair’s The Jungle, check this episode of the Planet Money podcast.

How this graph was created: Search FRED for and select “Retail Price of Lard for New York, NY.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Wholesale Price of Lard for New York, NY.”

Suggested by Diego Mendez-Carbajo.

Intellectual property products (IPP) such as software, patents, and original artwork have become a much larger share of the capital stock over time. Our FRED graph above shows that the IPP share of the current-cost capital stock rose from 5.6% in 1980 to 14.5% in 2023.

A unique feature of IPP is that it depreciates much faster than traditional capital. IPP isn’t usually subject to the physical depreciation from wear and tear that affects buildings, computers, and equipment. But it is subject to obsolescence from new technological innovations. A competitor’s innovation can quickly render a patent or algorithm irrelevant.

The Bureau of Economic Analysis (BEA) assumes that software, for example, depreciates at a rate of 33% per year, which is much faster than physical wear and tear.

Our second FRED graph above shows annual depreciation for equipment, structures, and IPP as a share of the current capital stock. Depreciation of structures (orange dashed line) and equipment (blue solid line) have remained stable over time, at around 3% and 13%, respectively. Depreciation of IPP (green dotted line) was 24% in 2023.

In short, this faster pace of growth and depreciation of IPP implies that more investment is necessary to maintain the current capital stock.

How these graphs were created: All these series are in millions of dollars, not seasonally adjusted. First graph: Search FRED for and select “Current-Cost Net Stock of Fixed Assets: Private: Intellectual property products.” Click “Edit Graph,” use “Customize data” to search for “Current-Cost Net Stock of Fixed Assets: Private: Nonresidential,” and click “Add.” Input the formula a/b and click “Apply.” Second graph: Search for and select “Current-Cost Depreciation of Fixed Assets: Private: Nonresidential: Equipment” and follow the same steps as above, dividing by “Current-Cost Net Stock of Fixed Assets: Private: Nonresidential: Equipment.” Use the “Add Line” tab to add the next two series and their divisors: “Current-Cost Depreciation of Fixed Assets: Private: Intellectual property products” / “Current-Cost Net Stock of Fixed Assets: Private: Intellectual property products” and “Current-Cost Depreciation of Fixed Assets: Private: Nonresidential: Structures” / “Current-Cost Net Stock of Fixed Assets: Private: Nonresidential: Structures.”

Suggested by Cassandra Marks and Hannah Rubinton.

FRED has many data series on closely related variables, such as the various measures of U.S. price levels. These include the PCE, CPI, PPI, and GDP deflator and their variants, such as core PCE. Comparing similar series can reveal subtle but important differences.

Today we look at another set of series that move together but have important distinctions: expected asset price volatility, also called implied volatility. These series are derived from the prices of options, many of which are traded on futures exchanges such as the Chicago Board of Options Exchange. Because option prices rise and fall with the expectations of market participants, they provide information about how much volatility is expected in the underlying assets.

Our two FRED graphs show four of these forward-looking measures of implied volatility over different time periods. These measures reach their highest levels during times of financial market uncertainty, such as the Great Financial Crisis (October-November 2008) and the COVID-19-pandemic shutdown (March 2020). In short, expected stock market volatility rises during crises.

Defining the measures

These four measures clearly move together, and their ranking from highest to lowest volatility is fairly consistent:

• the Russell 2000 (red)
• the 3-month-ahead S&P 500 (green)
• the 1-month-ahead S&P 500 (blue, labeled “VIX”)
• the DJIA (purple)

These measures differ because they describe expected volatility for different markets over different horizons and because the indexes themselves are weighted differently.

The Russell 2000 Index is the capitalization-weighted average of their smallest 2,000 stocks. Prices of small firms tend to be more volatile than those of large firms, other things equal.

The price indexes for the S&P 500 are also capitalization weighted, so these volatility measures can be heavily influenced by a few very large, high-tech firms whose stock prices can be sensitive to technical advances.

The DJIA index is computed from the price-weighted index of 30 well-established, large companies. Stock prices of well-established industrial firms are relatively stable compared with those of smaller or more high-tech companies.

The impact of time

The graphs show that the 3-month S&P 500 measure is usually greater than the 1-month (“VIX”) measure. This is likely because there’s greater financial risk over longer horizons and market participants must pay more to insure against losses caused by rising volatility further into the future.

But the 3-month index isn’t always greater than the 1-month index. For example, in August 2024 and April 2025, the 1-month index was often higher than the 3-month index because near-term volatility was expected to be higher than long-term volatility. In August 2024, this was likely due to a negative unemployment report stoking fears of a near-term recession. And in April 2025, the high near-term volatility was very likely due to fears of a trade war sparking recession.

Takeaways

FRED contains many sets of related series on inflation, output, asset prices, volatility, and more. Comparing similar series and considering why they may differ can help us better understand the data, why they behave as they do, and why one series may be better suited for some specific purpose.

How these graphs were created: Search FRED for “volatility” and select the “CBOE Volatility Index: VIX.” Click on “Edit Graph” in the upper right corner and select “Add Line”: Search again for “volatility” and select “CBOE S&P 500 3-Month Volatility Index,” “CBOE Russell 2000 Volatility Index,” and “CBOE DJIA Volatility Index.” Go to the “Format” tab to adjust line colors, line styles, and the order of the series. Finally, set the dates you want with the date picker above the graph.

Suggested by Anna Cole and Christopher Neely.