Federal Reserve Economic Data

Obviously, major wars take their toll on a country’s population. They also affect economies in distinct ways. For example, wars affect the manufacturing sector as firms ramp up production of military vehicles, munitions, and the like. The current war in Ukraine, while far away from the United States, may still be having an impact here, given that the United States has promised military equipment to Ukraine. Other countries have done the same and are also ramping up their own purchases. FRED has some related data (at least back to 1994) that may help show what’s happening on the manufacturing front.

The Manufacturers’ Shipments, Inventories, & Orders survey from the U.S. Census Bureau doesn’t detail the defense sector, but it does provide data on manufacturing with and without defense. So, we can graph the difference.

The first graph shows new orders. If the war was a complete surprise and governments are only now scrambling to acquire military equipment, we’d expect new orders to be significantly up. At the time of this writing, that does not appear to be the case. But maybe they had enough foresight and are taking deliveries now. The second graph looks at shipments. While there’s an increase, it appears to follow a trend that predates the Ukraine war quite a bit.

Another impact could be that the new demand for armaments is reducing manufacturers’ inventories. Our last graph looks at this, and inventories are actually up. Could this be in anticipation of increased demand in the near future? We can’t tell simply by looking at the graphs. So, in conclusion, we don’t see any hard evidence that this war has had any notable effect on U.S. manufacturing yet.

How these graphs were created: For each graph, start by searching FRED for the series (say, manufacturers inventories) adding the “defense” keyword to narrow the results. Once you have the graph, click on “Edit Graph,” add the other series by searching for the same keywords without “defense,” and apply formula b-a.

Suggested by Christian Zimmermann.

The FRED map above shows the proportion of disconnected youth in each U.S. county in 2020. Disconnected youth are not teenagers who’ve been grounded without internet privileges. They’re between the ages of 16 and 19, they’re not enrolled in school, and they’re unemployed or not in the labor force. These youth are typically thought to be at risk of future low income or even crime. So it’s important to try to learn something about them, such as how many there are and where they are.

Darker greens show larger proportions, but not necessarily larger concentrations. Remember that the size of a county is reflective of its land mass, not necessarily its population. In fact, large counties are typically the least-populated ones. For this reason it can be difficult to draw big conclusions from a map, but we can find some insights by comparing maps. Below, we show the same map with data from 11 years ago. (An earlier post compared similar data from 2010 and 2015.)

What’s remarkable when comparing these two maps is that they look quite alike. Darker counties in one are typically also darker in the other. Understand that the two maps do not look at the same people. The 16- to 19-year-olds in 2009 are 27- to 30-year-olds in 2020, and thus not part of the statistic. (Strictly speaking, this calculation is not that simple, as the statistics draw on 5-year averages, but the separation of cohorts still holds). This shows that there is high regional persistence for this kind of issue.

How these maps were created: Search FRED for “disconnected youth,” click on any county data, then click on the map button. Change years using the date picker.

Suggested by Christian Zimmermann.

The housing market has been a hot topic of conversation over the past two years, and the FRED Blog has discussed its cycles of sales and new construction, how fast houses sell, and state-level differences in prices and inventories. Today, we revisit the topic by exploring an evocatively named dataset: the hotness index.

Our first FRED map shows the July 2022 values of the market hotness index reported by Realtor.com. This index aims to reflect “fast moving supply and rising demand” conditions and does not necessarily represent high or rising housing prices. (See the source’s site for a description of the index.)

The data, available for selected counties, are color-coded in the map using a scale of cool blue-greens: darker equals hotter. At the time of this writing, two of the three hottest counties are less than an hour’s drive from each other in central Ohio. (Hovering over the map lets you see county names and hotness index scores.)

However, the housing market conditions measured with this index seem to be as fickle as Fall weather. The second FRED map shows the percent change in the index value between July 2021 and July 2022. The county experiencing the largest annual change in market hotness is in the northwest corner of New Mexico. The quadruple-digit change recorded there highlights another trait of the data: Even after excluding this New Mexico measure, which could arguably be labeled as an outlier, the average (mean) percent increase in market hotness was much higher than the typical (median) percent increase. That certainly signals a warming housing market.

Finally, all these county-level changes in housing market conditions are taking place while, at the national level, the number of home sales steadily declined between January 2022 and the time of this writing. You can count on the FRED Blog to continue taking the temperature of this topic for months to come.

How these maps were created: Search FRED for “Market Hotness: Hotness Score in Knox County, OH.” Select the series and click “View Map.” To change the data units to annual growth rates, use “Edit Map” and select “Units: Percent change from year ago.”

Suggested by Latham Fisher and Diego Mendez-Carbajo.