The recent AI boom has renewed debate about how official statistics capture changes in productivity growth and how any lags or limitations might impact monetary policy. For example, in the 1990s, some believed that low measures of productivity weren’t capturing the true benefits from new technology.

Back in 1996

Federal Reserve Chair Alan Greenspan argued in 1996 that the productivity gains associated with the information and communications technology boom were not yet visible in the official data. That judgment helped support his case for delaying preemptive interest-rate increases.

Shortly afterward, the 1999 comprehensive revision of the National Income and Product Accounts began treating software expenditures as capital investment. Together with other statistical changes, this revision raised estimates of the productivity growth that had occurred during the 1990s, bringing the official data closer in line with the acceleration in productivity that Greenspan believed had been under way.

The data, as seen in 1996, 2000, and 2026

Our ALFRED graph above compares three vintages of labor productivity growth data: The blue bars reflect early data available in September 1996. The green bars reflect the revised data available in February 2000, which incorporate the 1999 NIPA revision. The orange bars reflect the most-current data available at the time of this writing, as of June 2026.

• As of September 1996, the data indicated that labor productivity had grown by an average of just 0.89% between 1989 and 1995.
• By February 2000, average labor productivity growth for that same time period had been raised to 1.40%.
• As of  June 2026, after more revisions, it stands at 1.51%.

This comparison shows how weak measures of productivity growth appeared in real time and how subsequent revisions substantially altered the historical picture.

How this graph was created: Search ALFRED for “Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers” and select the series with ID OPHNFB. Open “Edit Graph” and add the series three times. Set the as-of dates to September 10, 1996; February 8, 2000; and June 4, 2026. For each series, change the units to “Percent Change from Year Ago,” set the frequency to “Annual,” and use “Average” as the aggregation method. Adjust the observation period to begin in 1989 and end in 1995.

Suggested by Hannah Rubinton.

The takeaway

Crude oil is used to produce jet fuel. Changes in crude oil prices, like the ones since March 2026, can affect jet fuel prices, which may or may not affect airfares.

Crude oil, jet fuel, and airfares

Fluctuations in the price of crude oil can affect the prices of oil-intensive products, such as jet fuel. In this post, we track the current increase in worldwide crude oil prices and look for any impact on airfares, given that flights are likely the consumer product that requires the most fuel.

Our FRED graph above shows the recent evolution of prices for a specific type of crude oil, kerosene-based jet fuel, and both US and European airfares.

The variety of crude oil we track is WTI: a.k.a. West Texas Intermediate light sweet crude oil. WTI is better suited for producing kerosene-based jet fuel than, say, Brent crude oil from the North Sea. So, prices for WTI and jet fuel should be aligned.

Not surprisingly, the spot price of WTI spiked once the US-Iran conflict began on February 28, 2026. Perhaps surprisingly, the spot price of kerosene-based jet fuel reacted even more strongly.

But the reaction in prices for both US and European airfares was much more muted. The reasons?

• Airfares include more than just fuel, even if it’s the largest component and 20% to 30% of airlines’ total operating expenses.
• Airlines typically buy jet fuel with futures contracts, locking-in prices months in advance.

If crude oil and kerosene prices remain elevated, the picture for airfares may change.

How this graph was created: Search FRED for “airfare” and take the CPI series for the US. Click on “Edit Graph,” open the “Add Line” tab, and search again, taking the European series. Repeat for “kerosene” and “WTI.” Change unites to “Index (100…)” with date 2026-01-01 and click on “Copy to all.” Finally, restrict the sample to the past year (at the time of this writing).

Suggested by Christian Zimmermann.

The takeaway

The unemployment rate includes only those who are actively seeking a job, and seeking a job involves different incentives for married couples versus single heads of household.

Unemployment by marriage status

Our FRED graph above shows the unemployment rate for married men (blue solid line) and married women (green dashed line) as well as women heads of household (dotted orange line). There are a few stories revealed in this graph:

• The series for married women and the series for married men move in sync across business cycles.
• In the early years, these two series were separated by about 2 percentage points until they converged in the 1990s.
• The unemployment rate is considerably higher for women who maintain families—in single-parent households or in households where they’re the main breadwinner.

To be clear: The unemployment rate is not the fraction of those not working in the general population. The unemployment rate is the fraction of persons in the total labor force who are not working but are actively available and looking for a job. The labor force is made up of these persons plus those who are actively working.

The employment dynamics of marriage

Someone who’s married may be more likely to choose not to work than a single person. Also, women, in particular married women, have progressively increased their participation in the labor force over time. Thus, both sides of the unemployment rate may be affected in complex ways by the dynamics of marriage.

If one spouse in a married couple has a job, the incentives for the other spouse to work can be lower than a single person’s incentives. So, they may voluntarily choose not to work and remove themselves completely from the labor force. A smaller labor force raises the unemployment rate. If the non-working spouse is opportunistic about finding a job, they may be included in the labor force only when they have a job. This would lower the unemployment rate.

Women who maintain families

The situation is quite different for women who maintain families: They may need a job much more than married women. Hence, they may look much more intensely and frequently for a job and are less likely to be opportunistic about it and abandon their job search. So, their unemployment rate is markedly higher.

How this graph was created: Search FRED for release table A-10 from the Current Population Survey. Select the three series shown and click “Add to Graph.”

Suggested by Christian Zimmermann.