Tag: consumer

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Tracking Exploding Beef Prices

Ground beef comes in a wide range of varieties, with pricing influenced by factors such as fat content, grading quality, and premium options like grass-fed beef. Every one of these characteristics affects the final retail price consumers see at the grocery store.

Historically, ground beef prices have moved in cycles, rising and falling alongside broader commodity markets, cattle supply, feed costs, transportation expenses, and consumer demand. But in recent years, something has clearly changed. The long-term trend no longer looks cyclical—it looks relentless. Prices keep climbing, and consumers are feeling it every time they walk through the meat aisle.

What was once considered one of the most affordable and dependable protein options for American families is rapidly becoming another example of persistent food inflation. And according to the data, this isn’t just perception—it’s measurable reality.

Ground Beef Price Data

Two Federal Reserve Economic Data (FRED) series publish monthly average retail pricing data for ground beef:

  • “Average Price: Ground Beef, 100% Beef” (APU0200703112)
  • “Average Price: Ground Beef, Lean and Extra Lean” (APU0000703113)

These datasets provide a long-term view into how dramatically retail beef prices have changed over time. One of the easiest ways to locate the series is by using the FRED search function or RESERVE search command:

reserve search "beef"
Search results for: "beef"

+------------------+----------------------------------------------------+------+----------------------+------------------------+
| ID               | TITLE                                              | FREQ | UNITS                | LAST UPDATED           |
+------------------+----------------------------------------------------+------+----------------------+------------------------+
| APU0000703112    | Average Price: Ground Beef, 100% Beef (Cost per... | M    | U.S. $               | 2026-04-10 07:35:48-05 |
| APU0000FC3101    | Average Price: All Uncooked Beef Steaks (Cost p... | M    | U.S. $               | 2026-04-10 07:35:55-05 |
| PBEEFUSDQ        | Global price of Beef                               | Q    | U.S. Cents per Pound | 2026-04-15 09:59:20-05 |
| PBEEFUSDM        | Global price of Beef                               | M    | U.S. Cents per Pound | 2026-04-15 09:59:19-05 |
| PBEEFUSDA        | Global price of Beef                               | A    | U.S. Cents per Pound | 2026-02-12 09:49:16-06 |
| APU0000704111    | Average Price: Bacon, Sliced (Cost per Pound/45... | M    | U.S. $               | 2026-04-10 07:35:56-05 |
| APU0000703111    | Average Price: Ground Chuck, 100% Beef (Cost pe... | M    | U.S. $               | 2026-04-10 07:35:58-05 |
| APU0000703113    | Average Price: Ground Beef, Lean and Extra Lean... | M    | U.S. $               | 2026-04-10 07:35:47-05 |
| CUSR0000SAF112   | Consumer Price Index for All Urban Consumers: M... | M    | Index 1982-1984=100  | 2026-04-10 08:08:24-05 |
| APU0200703112    | Average Price: Ground Beef, 100% Beef (Cost per... | M    | U.S. $               | 2026-04-10 07:35:50-05 |
| CUUR0000SAF112   | Consumer Price Index for All Urban Consumers: M... | M    | Index 1982-1984=100  | 2026-04-10 08:08:15-05 |
| CUUS0000SAF112   | Consumer Price Index for All Urban Consumers: M... | SA   | Index 1982-1984=100  | 2026-01-13 08:06:22-06 |
| APU0300703213    | Average Price: Chuck Roast, USDA Choice, Bonele... | M    | U.S. $               | 2026-04-10 07:35:32-05 |
| PCU311119311119H | Producer Price Index by Industry: Other Animal ... | M    | Index Dec 2011=100   | 2026-04-14 09:44:18-05 |
| WPU022101        | Producer Price Index by Commodity: Processed Fo... | M    | Index 1982=100       | 2026-04-14 09:34:54-05 |
| WPS022101        | Producer Price Index by Commodity: Processed Fo... | M    | Index 1982=100       | 2026-04-14 09:36:16-05 |
| APU0000703613    | Average Price: Steak, Sirloin, USDA Choice, Bon... | M    | U.S. $               | 2026-04-10 07:35:55-05 |
| APU0000FD4101    | Average Price: All Other Pork (Excluding Canned... | M    | U.S. $               | 2026-04-10 07:35:43-05 |
| APU0000FD3101    | Average Price: All Pork Chops (Cost per Pound/4... | M    | U.S. $               | 2026-04-10 07:35:53-05 |
| APU0100703112    | Average Price: Ground Beef, 100% Beef (Cost per... | M    | U.S. $               | 2026-04-10 07:35:53-05 |
+------------------+----------------------------------------------------+------+----------------------+------------------------+

This search reveals these two series along with other related commodity oriented data series.

Examining the Trend

Price increases are best understood visually. While these series can certainly be exported into a spreadsheet for graphing and further analysis, RESERVE already provides both a powerful transform command set and built-in ASCII charting capabilities.

The transform commands are especially useful for converting raw data into a more macro-level view of the underlying trend. Since both ground beef datasets are published monthly, aggregating the data into quarterly or yearly intervals provides a much clearer picture of the long-term direction of prices while reducing short-term noise.

One of RESERVE’s strengths is composability. The results from the original search can be passed directly into the transform command set and then piped into the charting commands for visualization. The result is shown below:

reserve obs get APU0000703113 --start 2000-01-01 --end 2026-04-30 --format jsonl   | reserve transform resample --freq annual --method mean   | reserve chart bar
APU0000703113  2000 – 2026
2000  2.26  █
2001  2.47  ██
2002  2.58  ████
2003  2.73  █████
2004  2.93  ████████
2005  2.97  ████████
2006  2.93  ████████
2007  3.07  █████████
2008  3.26  ███████████
2009   3.4  █████████████
2010   3.5  ██████████████
2011  3.77  █████████████████
2012  4.06  ████████████████████
2013  4.86  ██████████████████████████████
2014  5.56  █████████████████████████████████████
2015   6.1  ████████████████████████████████████████████
2016  5.71  ███████████████████████████████████████
2017  5.63  ██████████████████████████████████████
2018  5.35  ███████████████████████████████████
2019  5.38  ███████████████████████████████████
2020   5.8  ████████████████████████████████████████
2021  6.03  ███████████████████████████████████████████
2022   6.5  ████████████████████████████████████████████████
2023  6.68  ██████████████████████████████████████████████████
2024  6.95  █████████████████████████████████████████████████████
2025  7.76  ███████████████████████████████████████████████████████████████
2026  8.24  ████████████████████████████████████████████████████████████████████

Source: Bureau of Labor Statistics via FRED

reserve obs get APU0200703112 --start 2000-01-01 --end 2026-04-30 --format jsonl   | reserve transform resample --freq annual --method mean   | reserve chart bar
Checking permissions for APU0200703112...
APU0200703112  2000 – 2026
2000  1.45  █
2001  1.67  ███
2002  1.68  ███
2003  1.96  ██████
2004  2.12  ████████
2005  2.31  ███████████
2006  2.15  █████████
2007  2.14  █████████
2008  2.15  █████████
2009  2.05  ████████
2010  2.11  ████████
2011  2.48  █████████████
2013  3.05  ████████████████████
2014  3.43  █████████████████████████
2015  3.73  █████████████████████████████
2016  3.29  ███████████████████████
2017  3.31  ███████████████████████
2018  3.81  ██████████████████████████████
2019  3.84  ██████████████████████████████
2020  3.88  ███████████████████████████████
2021  4.09  █████████████████████████████████
2022  4.67  ████████████████████████████████████████
2023  4.91  ███████████████████████████████████████████
2024  5.58  ████████████████████████████████████████████████████
2025  6.37  ██████████████████████████████████████████████████████████████
2026  6.85  ████████████████████████████████████████████████████████████████████

Source: Bureau of Labor Statistics via FRED

What is the Root Cause?

The grade-school explanation for rising prices usually simplifies inflation down to basic supply and demand. In reality, modern economies are tightly interconnected ecosystems where countless variables interact simultaneously—each with its own supply constraints, demand pressures, and downstream effects.

Ground beef prices are a perfect example. Retail pricing is influenced not only by consumer demand, but also by cattle inventory levels, feed costs, fuel prices, transportation expenses, labor markets, interest rates, weather conditions, and even import/export dynamics. Every layer of the supply chain contributes pressure to the final price consumers see at the grocery store.

One of the strengths of the FRED system is that this data is publicly accessible for both hobbyists and researchers to explore. For anyone interested in understanding the forces driving the long-term rise in ground beef prices, RESERVE search makes it possible to discover and analyze datasets covering everything from hay and diesel costs to cattle inventories, beef production, and international trade flows.

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What Say Kai Ryssdall?

Marketplace, the popular economics podcast, has built a reputation for making complex economic topics accessible to everyday listeners. Much of that appeal comes from host Kai Ryssdal, whose mix of sharp insight, humor, and storytelling makes even the driest data surprisingly entertaining.

On May 7, 2026, the Marketplace episode contained a segment titled “Measuring Economic Demand.” The episode focused on a lesser-known economic indicator — Final Sales to Private Domestic Purchasers. The point was to explain why economists often view it as a cleaner measure of underlying demand than headline GDP.

This post picks up where Marketplace left off. Using RESERVE, readers can explore the data themselves, track the series over time, and dig deeper into the economic story behind the numbers.

The segment also sparked an idea for a new RESERVE feature — one I’ll introduce at the end of this post.

What Did Kai Actually Say

In the Marketplace segment, Kai Ryssdal walked listeners through the basic building blocks of Gross Domestic Product (GDP): consumer spending, business investment, government spending, and net exports.

Then, in classic Kai fashion, he slowed things down and carefully unpacked a lesser-known measure: Final Sales to Private Domestic Purchasers.

At its core, the metric strips away some of GDP’s noisier components — namely net exports, government spending, and inventory swings. The intent is to focus more directly on underlying private-sector demand in the U.S. economy.

Rather than using the finalized quarterly GDP reports, the segment focused on the advance estimates, the first look economists get at quarterly economic growth data.

To find this series in RESERVE, use the following search command:

reserve search "Final Sales to Private"
Search results for: "Final Sales to Private"

+--------------------+----------------------------------------------------+------+----------------------+------------------------+
| ID                 | TITLE                                              | FREQ | UNITS                | LAST UPDATED           |
+--------------------+----------------------------------------------------+------+----------------------+------------------------+
| LB0000031Q020SBEA  | Real Final Sales to Private Domestic Purchasers    | Q    | Bil. of 2017 US $    | 2026-04-30 07:48:54-05 |
| PB0000031Q225SBEA  | Real final sales to private domestic purchasers    | Q    | % Chg. from Prece... | 2026-04-30 07:48:33-05 |
| PE0000031Q156NBEA  | Real Final sales to private domestic purchasers    | Q    | % Chg. from Qtr. ... | 2026-04-30 07:48:30-05 |
| PB0000031A225NBEA  | Real final sales to private domestic purchasers    | A    | % Chg. from Prece... | 2026-03-13 07:49:16-05 |
| LA0000031Q027SBEA  | Final Sales to Private Domestic Purchasers         | Q    | Bil. of $            | 2026-04-30 07:49:20-05 |
| LA0000031A027NBEA  | Final sales to private domestic purchasers         | A    | Bil. of $            | 2026-04-09 07:53:06-05 |
| FINSALESDOMPRIVPUR | Nowcast for Real Final Sales of Private Domesti... | Q    | % Chg. at Annual ... | 2026-05-08 11:02:58-05 |
| GOR                | Gross Output by Industry: Retail Trade             | Q    | Bil. of $            | 2026-04-09 07:34:48-05 |
| A811RC2Q027SBEA    | Ratios of private inventories to final sales of... | Q    | %                    | 2026-04-30 07:50:53-05 |
| PA0000031Q225SBEA  | Final sales to private domestic purchasers, cur... | Q    | % Chg. from Prece... | 2026-04-30 07:48:25-05 |
| PA0000031A225NBEA  | Final sales to private domestic purchasers, cur... | A    | % Chg. from Prece... | 2026-02-20 07:46:57-06 |

The series ID for the quarterly updated version of Real Final Sales to Private Domestic Purchasers is PB0000031Q225SBEA . Note, that if this series is examined with the META SERIES command and formatted in JSON, RESERVE will give the user almost verbatim Kai’s explanation (minus the humor). See the NOTES field for the definition.

reserve meta series PB0000031Q225SBEA --format json
{
  "kind": "series_meta",
  "generated_at": "2026-05-09T15:54:44.926228-05:00",
  "command": "meta series PB0000031Q225SBEA",
  "data": [
    {
      "id": "PB0000031Q225SBEA",
      "title": "Real final sales to private domestic purchasers",
      "observation_start": "1947-04-01",
      "observation_end": "2026-01-01",
      "frequency": "Quarterly",
      "frequency_short": "Q",
      "units": "Percent Change from Preceding Period",
      "units_short": "% Chg. from Preceding Period",
      "seasonal_adjustment": "Seasonally Adjusted Annual Rate",
      "seasonal_adjustment_short": "SAAR",
      "last_updated": "2026-04-30 07:48:33-05",
      "popularity": 38,
      "notes": "BEA Account Code: PB000003\n\nFinal sales to domestic purchasers less government consumption expenditures and gross investment. \nA Guide to the National Income and Product Accounts of the United States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)",
      "source_name": "Bureau of Economic Analysis",
      "copyright_status": "public_domain_citation_requested",
      "citation_text": "Source: Bureau of Economic Analysis via FRED",
      "usage_allowed_commercial": true,
      "usage_allowed_educational": true,
      "usage_allowed_personal": true,
      "raw_rights_tags": [
        "public domain: citation requested"
      ],
      "last_rights_check_at": "2026-05-09T20:53:26.738003Z",
      "fetched_at": "2026-05-09T20:53:26.738011Z"
    }
  ],
  "stats": {
    "cache_hit": false,
    "duration_ms": 0,
    "items": 1
  }
}

Also note in the original search that pulling data from the affiliated series PB0000031Q225SBEA will provide the percentage change since last published series which is that 2.5% number Kai referenced.

reserve obs latest PB0000031Q225SBEA
+-------------------+------------+--------------+
| SERIES            | DATE       | LATEST VALUE |
+-------------------+------------+--------------+
| PB0000031Q225SBEA | 2026-01-01 | 2.5          |
+-------------------+------------+--------------+

Source: Bureau of Economic Analysis via FRED

The point of this exercise is that podcast like Marketplace offer incredible insight and perspective on economics data. If a listener finds this interesting, they can use RESERVE and their free API key from FRED® to explore the topic even further. That is the point of RESERVE–lowering the barrier of entry to the over 800 data series made accessible by the Federal Reserve Bank of St. Louis.

What Upcoming Feature Did Kai Inspire?

In Kai fashion, he spoke the words “Final Sales to Private Domestic Purchasers” very slowly. Why? It’s a mouthful that’s why. However, the corresponding series data he referenced is not only a mouth full but it is cryptic! “PB0000031Q225SBEA”

Coming in RESERVE 1.1.5 the intent is to introduce an ALIAS command that would support the following:

reserve alias set FSPDP PB0000031Q225SBEA --note "Real Final Sales Dom. Producers "
reserve alias list
reserve alias get FSPDP
reserve alias delete FSPDP

The output would be something like:

reserve alias list
+-------+-------------------+------------------------------------------+
| ALIAS | SERIES            | NOTE                                     |
+-------+-------------------+------------------------------------------+
| 10yr  | DGS10             | 10-Year Treasury Constant Maturity Rate  |
| cpi   | CPIAUCSL          | Consumer Price Index for Urban Consumers |
| rfsdp | PB0000031Q225SBEA | Real Final Sales Dom. Producers          |
+-------+-------------------+------------------------------------------+

Thanks Kai!

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The Domino’s Problem: Are Consumers Starting to Tap Out?

As of May 2026, the high-level narrative still points to strong consumer spending. Visa’s latest earnings report highlighted 17% revenue growth, partly driven by continued consumer strength.

But this is a K-shaped economy—and not all consumers are participating equally.

On April 27th, Domino’s Pizza told a very different story:

Domino’s Pizza (DPZ) Q1 2026 earnings, reported April 27, 2026, missed analyst expectations for both earnings per share (EPS) and revenue.

More notably:

Same-store sales increased just 0.9%, well below the expected 2.3%.

So which is it? Strong consumer… or weakening demand?

Instead of relying on earnings commentary, we can go straight to the data.

The Signals

Using RESERVE, we’ll look at a few core indicators:

  • Real income
  • Wages (inflation-adjusted)
  • Inflation (overall + food away from home)
  • Consumer sentiment
  • Credit usage

Here’s the command:

reserve obs get AHETPI DSPIC96 CPIAUCSL CUSR0000SEFV UMCSENT REVOLSL \
  --start 2026-01-01 --end 2026-03-31 --format jsonl \
| reserve analyze summary --by-series

The Data

+--------------+-------+----------+--------------+----------+--------------+--------------+--------------+------------+
| SERIES       | COUNT | MISSING  | MEAN         | STD      | MIN          | MEDIAN       | MAX          | CHANGE PCT |
+--------------+-------+----------+--------------+----------+--------------+--------------+--------------+------------+
| AHETPI       | 3     | 0 (0.0%) | 32.0100      | 0.0656   | 31.9400      | 32.0200      | 32.0700      | 0.41%      |
| CPIAUCSL     | 3     | 0 (0.0%) | 328.1137     | 1.9371   | 326.5880     | 327.4600     | 330.2930     | 1.13%      |
| CUSR0000SEFV | 3     | 0 (0.0%) | 391.6097     | 1.0937   | 390.4710     | 391.7060     | 392.6520     | 0.56%      |
| DSPIC96      | 3     | 0 (0.0%) | 18138.2000   | 42.5687  | 18108.7000   | 18118.9000   | 18187.0000   | -0.43%     |
| REVOLSL      | 2     | 0 (0.0%) | 1327241.8450 | 501.4731 | 1326887.2500 | 1327241.8450 | 1327596.4400 | 0.05%      |
| UMCSENT      | 3     | 0 (0.0%) | 55.4333      | 1.8502   | 53.3000      | 56.4000      | 56.6000      | -5.50%     |
+--------------+-------+----------+--------------+----------+--------------+--------------+--------------+------------+

What does the data say?

First, real disposable income (DSPIC96) is falling while prices (CPIAUCSL) are rising. Yet, food away from home (CUSR0000SEFV) is still increasing by 0.56%. Despite the income-to-price gap, there is still a baseline level of consumer activity—but it is happening under pressure.

Real wage growth (AHETPI) is expanding at 0.41%, but it is being outpaced by inflation (CPIAUCSL) at 1.13%. In the context of Domino’s same-store sales, the interpretation is not that consumer spending has collapsed, but that it is quietly falling behind. Credit (REVOLSL) is only marginally expanding, suggesting it is not meaningfully offsetting the wage-to-inflation gap. At the same time, consumer sentiment (UMCSENT) stands out as the clearest signal, dropping sharply by 5.50%.

How does this explain Domino’s Pizza results?

Domino’s core customer skews middle to lower-middle income. In a K-shaped economy—where wage growth lags rising costs for this group—that pressure shows up in softer same-store sales.

By contrast, Taco Bell (Q1 comps +8%) sits further down the curve and is capturing trade-down behavior through aggressive value pricing and promotions.

Bottom line: this isn’t a demand collapse—it’s a margin-sensitive consumer pulling back at the edges.

No affiliation with the Federal Reserve Bank of St. Louis

Not endorsed nor supported by the FRED® API technical team

© 2026 Derick Schaefer