Federal Reserve Economic Data

Does it always feel like your flight is full?

It makes sense that more people experience full flights because there are, by definition, fewer people on less-crowded flights. The other reason is that, yes, flights are indeed mostly full.

Our FRED graph above shows “load factors” for US airlines: that is, the percentage of seats sold.

The red line shows clear seasonal patterns: If you don’t like crowds, avoid flying in June and July and instead fly in January and February.

The blue line shows the same series, but removes the regular seasonal patterns. Here we can see how the load is trending within a year without having to compare with the same month in the previous years. And we see that the de-seasonalized load is fairly constant over time.

Our second FRED graph, below, shows seasonal data for domestic flights (blue line) and international flights (red line). There’s little difference between the two series, except for the period right after the pandemic. In particular, it doesn’t look like there’s much room for airlines to arbitrage between domestic and international flights during the year if the same planes could be used for both.

How these graphs were created: Search FRED for and select one of the load factor series. Click “Edit Graph” and use the “Add Line” tab to search again for the other series. Use the “Format” tab to change the settings of the second line. Proceed similarly for the second graph.

Suggested by Christian Zimmermann.

Every month, the Bureau of Labor Statistics (BLS) releases data on total nonfarm employment in two forms: seasonally adjusted and not seasonally adjusted.

• Seasonally adjusted (SA) employment data have had the effects of seasonal changes removed, such as the typical increase in hiring during the holiday season. This allows us to more clearly see the business cycle trends in employment.
• Not seasonally adjusted (NSA) employment data are the raw employment levels at a given time.

Our FRED graph above tracks the past two years of SA and NSA employment levels, showing how the number of workers rises and falls throughout the year. Looking at the latest initial data for July 2025, we can see the NSA number of jobs declined by over 1 million, while the SA number increased by 73,000.

With almost 160 million workers, the BLS cannot count each job every month. They use a sample of data from the Current Employment Statistics (CES) survey and a model to estimate hiring and layoffs by new firms that arose and former firms that went out of business since the previous survey. The BLS website provides more information on this process.

Because of this estimation, revisions to employment data are common. The BLS receives additional responses after their initial release of monthly data and adjusts as seasonal factors are more accurately calculated for the year. Check this FRED Blog post for more insight into BLS revisions.

Our next two graphs come from ALFRED: They show the revisions to the May and June SA and NSA employment numbers between the release of the data on July 3, 2025, and the next release of the data on August 1, 2025. The changes in SA employment were revised down by 258,000. This feeds into the total employment for June, which decreased from 159,724,000 to 159,466,000, or a –0.16% change in total employment.

To see which portion of the revision was driven by late-survey responses and which portion came from revisions to the seasonal adjustment, we can use the NSA revisions to decompose this number. Looking at the NSA revisions in the change in employment below, May and June were revised down by a combined 182,000. Taking this number to the seasonally adjusted number and subtracting it, we can see a downward revision of 76,000 came from the new calculations of seasonal adjustment. Both of these revisions were in the same direction, but sometimes they offset one another, which is why we subtract the NSA from the SA numbers.

A second and final revision for the June numbers will occur on September 5, when the first revisions for the July numbers will also be available. The May numbers were finalized in the August release.

How these graphs were created: First graph: Search FRED for “All Employees, Total Nonfarm” and click the first link to get the seasonally adjusted numbers. Then click the blue “Edit Graph” and open the “Add Line” tab. Search for PAYNSA and click the first result for the not seasonally adjusted numbers and then go to the format tab and click customize to change the line style to dash and the color to red. Then change date range to the last two years. Next two graphs: Go to ALFRED and search for All Employees, Total Nonfarm and click the first series labeled Monthly, Seasonally Adjusted. Use the “Edit Graph” panel to change the units to Change, Thousands of Persons. Select Bar 2 and do the same. Repeated this process for the Monthly, Not Seasonally Adjusted series.

Suggested by John Fuller and Charles Gascon.

The FRED Blog has discussed the dwindling supply of multifamily dwellings with 2 to 4 units. This trend even has its own name: the “missing middle.” Today, we tap into newly added data from the Federal Reserve Bank of Philadelphia to offer additional insights into this segment of the housing market.

Our FRED graph above shows the share of mortgage balances that are 60 or more days past due, broken down by property type:

• single family (solid blue line)
• condo/co-op (dotted light blue line)
• 2 to 4 units (dashed orange line)
• townhouse/planned (dashed-dotted purple line).

Quarterly data are available since 2013. Between then and the time of this writing, overdue mortgage payments for multi-family housing with 2 to 4 units have consistently been more frequent than for any other type of residential property.

What could explain this pattern? Research coauthored by Raphael Bostic from the Atlanta Fed notes that small and medium multifamily properties are the majority of rental units across the country and also house the largest percentage of the lowest-income households. Disruptions to the flow of rental income—whether from vacancy, nonpayment, or broad economic downturns—are likely to increase the risk of mortgage delinquency.

To learn more about the role of housing on the economic well-being of US households, read this report from the Board of Governors of the Federal Reserve System.

How this graph was created: Search FRED for and select “Large Bank Consumer Mortgage Balances: 60 or More Days Past Due by Property Type: Accounts Based: Single Family.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Large Bank Consumer Mortgage Balances: 60 or More Days Past Due by Property Type: Accounts Based: Condo / Co-op.” Be sure to click “Add data series.” Repeat the last two steps to add the other two data series: “Large Bank Consumer Mortgage Balances: 60 or More Days Past Due by Property Type: Accounts Based: 2-4 Units” and “Large Bank Consumer Mortgage Balances: 60 or More Days Past Due by Property Type: Accounts Based: Townhouse / Planned.”

Suggested by Sarah Lepkowitz and Diego Mendez-Carbajo.