Federal Reserve Economic Data

The title of this post may have reminded you that you need to buy some chocolate for some event in a couple of days. If you do, you may also notice that chocolate has become quite expensive. If you already made the trip, you may have bought less than usual or switched to some other sweet product. Either way, let’s look at the price of chocolate.

First, let’s be clear that chocolate has indeed become more expensive. Our FRED graph above shows the evolution of two types of candy: those with cacao-based chocolate and those without. The prices of both types have increased lately, but it’s very clear that chocolate and its derivatives have become significantly more expensive.

Why? The main ingredient of chocolate is cacao. (Cocoa is the term for its roasted form.) Its cultivation is concentrated in a few countries for climatic reasons, and it’s not produced domestically in the US. Cacao crops have been particularly bad in the past couple of years.

• Because of climate changes, current cacao trees aren’t optimal for their location.
• New trees take a while to grow and take 3 to 4 years to bear fruit.
• A virus is afflicting current plantations.

This lack of cacao supply has led to a marked increase in the world price for this commodity, as seen in our FRED graph below.

From a US perspective, do tariffs enter into the picture? The US imposed “reciprocal” tariffs on cacao-producing countries in February 2025, typically 15%. But these tariffs and some for other commodities that cannot be grown in the US were removed in November 2025. Thus, tariffs shouldn’t be a factor for this year’s Valentine purchase unless your purchase isn’t that fresh.

How these graphs were created: Search FRED for “Chocolate products” and select the right series. Click on “Edit Graph” then on the “Add Line” tab. Search for non-chocolate and select the right series. Click on the “Edit Lines” tab and select units “Index (Scale value to 100…)” with date 2011-12-01 and click on “Copy to all.” Open the “Format” tab, change the color of the first line to brown and the frame to pink. Finally start the graph on 2011-12-01. For the second graph, just search for “cocoa world price.”

Suggested by Christian Zimmermann.

FRED recently added 7 data series about the business conditions and outlook of upstream oil and gas energy firms headquartered in the 11th Federal Reserve District.

In industry lingo, upstream refers to oil and gas exploration and production and the related services that support those activities. Geographically, the 11th District of the Federal Reserve consists of Texas, northern Louisiana, and southern New Mexico. The regional Reserve Bank in the 11th District is in Dallas, Texas, so the dataset itself is called the Dallas Fed Energy Survey.

The FRED graph above shows the three broadest indicators of business conditions captured by the survey:

• level of business activity (solid blue line)
• company outlook (dashed green line)
• uncertainty (dashed orange line)

The data are reported as diffusion indexes. You can read about another example of this type of index here. In short: The direction of change in the value of the index indicates rising or falling values of the underlying concept being assessed.

What do the indexes show?

The indexes of company outlook and level of business activity generally move in the same direction and at the same time. Between Q3 2024 and Q3 2025 (the last four observations available at the time of this writing), those indexes ranged between 7.1 and -17.6. That suggests relatively stable outlook and activity conditions. However, the index measuring uncertainty was above 40 during most of that time. This is noteworthy because, since 2016, when data are first available, the uncertainty index generally moved in the opposite direction of the indexes of business activity and company outlook. Perhaps that could be expected because oil and gas exploration and production activities are large scale and expensive operations that take many years to plan and execute. In short: Uncertainty undermines this industry.

How this graph was created: Search FRED for and select “Dallas Fed Energy Survey – Level of Business Activity.” Click on the “Edit Graph” button and select the “Add Line” tab to search for “Dallas Fed Energy Survey – Company Outlook.” Don’t forget to click on “Add data series.” Repeat the last two steps to search for and add “Dallas Fed Energy Survey – Uncertainty.”

Suggested by Diego Mendez-Carbajo.

In a recent FRED Blog post, we discussed how confidence intervals show the level of certainty about the accuracy of an estimate. In short: Wider confidence intervals signal more uncertainty.

Also, larger survey sample sizes increase the statistical accuracy of the data collected and allow data users to confidently compare apples to apples. Today’s post offers a bite-size example.

Our FRED graph above shows US Census estimates for median household income in three US counties:

• Pitkin, CO (solid blue line), home to the town of Aspen.
• New York, NY (dashed red line), the borough of Manhattan in New York City.
• Teton, WY (solid green line), home to the town of Jackson in the Jackson Hole valley.

Between 1989 and 2023, estimated median household income was frequently very similar for all three locations listed above: the coastal urban center and the two mountainous rural areas. But the number of residents was vastly different.

Population size influences the number of households sampled to collect income data: The more populous the county, the more households are sampled. So the estimated data are relatively more precise in more populous counties (e.g., New York County) than in less populous counties (e.g., Pitkin and Teton counties).

FRED also has data that capture the confidence intervals reported along with the estimated income data. The relative confidence interval for New York, NY, data can be as much as five times smaller than those for the Pitkin, CO, and Teton, WY, data, as seen in the graph below. That means far less uncertainty about the accuracy of the reported figures.

So, clearly, it’s best to be cautious when you try to make fair apples-to-apples data comparisons, including data from the Big Apple itself.

How these graphs were created: Search FRED for and select “Estimate of Median Household Income for Pitkin County, CO.” Click on the “Edit Graph” button and select the “Add Line” tab to search for “Estimate of Median Household Income for New York County, NY.” Don’t forget to click on “Add data series.” Repeat the last two steps to add the third series: “Estimate of Median Household Income for Teton County, WY.” Lastly, use the “Format” tab to customize the line styles. For the second graph, follow the same general procedure, except that each line is now composed of three series: Search first for “90% Confidence Interval Upper Bound…,” then “90% Confidence Interval Lower Bound…,” and finally the above mentioned estimate. Then apply the formula (a-b)/c on each line.

Suggested by Diego Mendez-Carbajo.