FALL 2025
The Beef Industry Environment: An Econometric Study of Variables Affecting the Price of Beef
Maggie Burnett
Lone Star College - Montgomery
I'm Maggie Burnett and attend I attend Lone Star College. My academic goal is to get my degree in International Business and Finance. I hope that my academic path can eventually lead to travel opportunities where I can learn more about global markets and interactions.
ABSTRACT
This study determines whether economic relations, a competing industry, supply inputs, or substitute goods, have had a greater impact on the price of beef over the last 20 years. Beef was chosen as the center for this study since it is a household staple and grocery price increases continue to be a national concern for consumers. Studies published within the last 20 years, fully address the relationship between prices and supply lines; however, there is little research into product and goods price relationships. To understand the correlation of prices on a consumer level, individual products were chosen as follows: economic relations, including inflation and Covid-19; competing industries, such as milk and cheese and related products; supply inputs, such as gas and corn; and lastly, substitute goods, including pork, chicken, and fish and seafood. The price data was found using the Bureau of Labor Statistics. With the aim to include Covid as a variable, three regression analyses were run: January 2004-September 2024, January 2004- March 2020, and April 2020-September 2024. The results show that pork is the highest correlating individual good and substitute goods have the largest amount of individual goods with reliable statistics. Further backed by the Supply and Demand theories, this result showcases the importance of understanding what economic level a product’s price is most affected by and what products act in a similar manner. Future research into other grocery products could lead to greater price understanding and offer government officials and retailers insight on developing pricing interventions.
INTRODUCTION
According to a survey done in 2023 and covered by Steve Koppes in 2024, including 1,200 consumers from across the United States, 56% of consumers stated that food had the largest price increase from year to year. This belief is disproven by official data from 2023 showcasing that insurance, housing, and childcare prices have risen faster. The frequency in which food is purchased however leads to the increasing food prices being a more significant discrepancy than the statistically higher goods and services. According to Joseph Balagtas, a Professor of Agricultural Economics, 37% of Gen Z and millennials, 28% of Gen X, and 13% of Boomer-plus consumers have had to rely on savings or going into debt to secure food (Koppes, 2024).
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To understand the full extent that grocery prices change, products would all need to be considered individually, categorically, and collectively. With the level of research being conducted, only an evaluation of individual products could be investigated. Beef is a well-known household staple; its usages ranging from everyday meals to celebratory luxuries. Furthermore, “Americans aged 19-59 consuming, on average, 47.1 grams of total beef per day” showcasing just one example of statistical importance (Lau, et. al., 2023). Using this data as a foundation, this study aimed to elucidate whether economic relations, a competing industry, supply inputs, or substitute goods, had a greater impact on the price of beef over the last 20 years. This break down into individual categories spanning from large to small economic relations allows for a minimized study to be completed. Individual products were chosen to represent each of the larger categories to form threes regression analyses based on available data from the Bureau of Labor Statistics (BLS). The results concluded that substitute goods have the greatest impact on beef prices.
LITERATURE REVIEW
Kamienski’s study entitled The Factors Influencing The Price Of Beef, An Econometric Study (2006), uses a regression analysis to understand the extent that supply and demand affect the price of beef. The study lists the number of cattle slaughtered per quarter per capital, the total number of cattle available in the US, the number of firms in the country, price of substitute goods, real per capital disposable income, presence of diseases, and seasonal factors as the variables (Kamienski, 2006). Kamienski found that all variables except seasonal factors of grilling to be significant to the price of beef. With the confirmation of important variables, the supply and demand variables can be sorted for future studies as relevant or irrelevant. The price of substitute goods, total number of cattle available in the US, and the number of firms in the industry are all factors that can be re-evaluated for future studies with a reasonable belief that they will have a high impact on the price of beef. The other mentioned factors that include per capita are no longer reliable factors since prices will be denoted every month and country population cannot be determined on a monthly basis. Additionally, a new approach using up to date factors and data points is needed to further this study.
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Information about the cattle market production lines shows the integral role that each in step raising cattle has on the current supply outcome. Using pragmatics, there are two major phases of cattle raising; cow-calf operations, where “beef cows graze on forage from grasslands to maintain themselves and raise a calf with very little, if any, grain input... until the calf is weaned” and cattle feeding, where “steers and heifers for slaughter grow to market weight”(Knight, 2023). Cow-calf operations affect the supply rates due to the long gestations periods and the cost of increasing heard sizes. In the U.S. the only ending for cattle are feedlots, however other options are available such as pasture feeding or supplemental feeding as seen in Argentina and Uruguay’s’ cattle markets (Mathews & Vandeever, 2007). This step is vital to supply lines due to the balance of large and small lots. Small feedlots make up most of the U.S. operations in comparison to large feedlots that make up 80-85 percent of fed cattle (Knight, 2023).
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Regarding the Unites States’s market, Nelson’s (2024) research shows that while current cattle inventory has been low and prices are high, producers continue to place cattle in feed lots; “Placements were 1.7 million head, up about 6% from 2023”. This decision solves the short-run economic price increases. The dilemma comes in the total number of cattle dropping and being predicted to continue to drop in the long-run. With more female cows going to feed lots, coupled with the period it takes to breed and add new cattle to the total inventory, the total cattle production is dropping. This imbalance between feedlots and cow-calf sectors of the market will solve the immediate problems but will increase long term prices when the producers have to take cattle out of feedlots to increase total supply. The current situation Nelson discusses showcases the importance of understanding the initial step in the supply process. Nelson and Knight’s research builds to produce a well rounded review of the importance of cattle placement management across both major phases of cattle raising.
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Supply lines Covid pandemic showcased a significant role in the increase of beef prices as shown in Balagtas and Cooper’s (2021) and Whitehead and Brad Kim’s (2022) research. The two research papers examine the supply and demand factors that caused price shocks across the industry. Balagtas and Cooper’s study covers basis research into the beef industry that immediately followed the pandemic. Together they found that rapid changes in consumers purchases from eating away from home, to eating at home and costly supply chain shifts contributed to price increases (Balagtas & Cooper, 2021). Whitehead and Brad Kim used Baltagtas and Cooper’s research and others to develop further statistics. The research asserts that the biggest increases in price “occurred at the beginning of the pandemic around March and April of 2020... a 39.1% increase in beef cuts” (Whitehead & Brad Kim, 2022). Both articles conclude that Covid-19 has had lasting effects on how the market operates and that beef prices had almost returned to pre-pandemic levels. Using current data, this project hopes to account for market changes that were a result of the pandemic and the shock it sent into the beef market.
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Along with identifying important factors, understanding what “price” means and how it is measured is an integral part of this study. Consumers and economists can use real price equations and consumer price indexes to understand the degree that prices increase with and without inflation as a factor. Schnell (2023) asserts that “when overall prices increase, so do price indexes”. When considered with Martin’s (2024) statistics, “from 2019 to 2023, the all-food Consumer Price Index (CPI) rose by 25.0 percent”, the price of beef is not just growing due to inflation. This means food is taking up a larger portion of household budgets. Martin’s (2024) additional research states that “U.S. consumers spent an average of 11.2 percent of their disposable personal incomes on food in 2023”. With inflation having soared to 6.5% in 2022 followed by a drop to 3.7% in 2023 (Caldwell, 2024), it is imperative to be able to understand the changes in the prices of beef with and without inflation to accurately assess long term household budget changes that surround beef.
METHODOLOGY
Selection of Independent Variables
Economic relations were the largest category of variables selected. Inflations and Covid-19 were chosen to represent this category. Inflation was considered as a variable because of its direct correlation to the increasing prices of every good across all industries. Without considering inflation for this study, the prices of beef cannot accurately be measured using the consumer price index. The next economic relations variable chosen is Covid-19, the most recent market shocking event. Market shocking events effect the economy suddenly through supply, demand, and price changes. Although many studies have reviewed the immediate effects of Covid-19, this study will aim to evaluate the continued long-term effects.
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Supply was split into competing industry supplies and input supplies because supply is one of the largest categories of information. Competing industries present the idea of opportunity cost and input supplies highlight the basic idea that companies need to make a profit greater than their expenses and together, the two categories cover a wider range of the supply line. While the dairy and beef industries both require the same primary resource, the breed of cattle and raising methods needed is different. To evaluate whether the industries are unrelated due to this difference or if they are competing industries at a production level, milk and cheese and related products were chosen as representative products. The milk stands as an initial product and the cheese and related products represent a sub-industry further in production. Furthermore, for supply inputs, gas and corn were considered. By theory, as the cost to produce a good goes up, so will the price of the good to cover the production cost. Many cattle are moved by trailer between farms, productions plants, and retailers. While it does not cover initial costs, the price of gas acts as the long-term price quantifier for transportation. The next product considered was corn. Corn serves as a quantifier for the cost of on-site production. Many goods may be used as part of the production process. In order to account for rising prices, corn was chosen since it is a recurring expense like gas.
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The smallest subdivision used was substitute goods. This category includes goods whose demand is driven by consumers at the grocery retailer level. With many different preferences and social factors at work, fish and seafood, pork, and chicken were chosen to provide a rounded view of the substitute’s relations with beef prices. Each of the three variables cover many of the common diet restrictions known in relation to meats. Chicken represents white meats, pork represents red meats and a cultural alternative, and fish and seafood function well for pescatarian diets.
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Regression Analysis
This study used three regression analyses to determine the extent that each independent variable affects the dependent variable over the last 20 years as well as to determine the effect the Covid-19 Pandemic had on the price. First, a pre-pandemic analysis was run; it started January 2004 and went till March 2020 when the World Health Organization officially announced that Covid 19 had reached Pandemic levels (National Foundation for Infectious Diseases, 2024). The second regression analysis ran from April 2020 to September 2024 showcases how the market has changed since Covid. Lastly, a regression analysis that included all data from January 2020 to September 2024 was run to evaluate the stability of the variables’ effects on the price of beef. This method allowed for an understanding of how the post pandemic prices changed since the pandemic, as well as how the market has worked for the past 20 years.
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With the variables remaining consistent throughout each time period considered, the regression analyses were represented by the formula below:
Y= a+B1X1 + B2X2 + B3X3 + B4X4 + B5X5 + B6X6 + B7X7 + B8X8.
The Y represents the dependent variable, price of beef and veal. The X’s represent the independent variables, corn PPI, inflation, pork prices, chicken prices, fish and seafood prices, milk prices, cheese and related products prices, and all gasoline prices. The B in the equation is the coefficient solved for by the regression analysis and represents the slope of the variable. Representing the Y-intercept is the value a in the above equation. Each variable has 249 data points, each representing one month since January 2004, collected from the U.S. Bureau of Labor Statistics (BLS).
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Data Collection and Processing
Data for this study was collected and documented from BLS. The individual variables were based on categories offered by BLS that were found under the “All-Urban Consumers (Current Series)” and “Industry Data” indexes. Names of variables and the scope of the product were focused on BLS sorting. The data was first sorted by city size. For this study, “U.S. City Average” was used. This classification was chosen to ensure each variable had conclusive data and remained relevant to the whole of the beef market. Data was then categorized by large industry classification options with smaller product options available. For this study, the smaller more specific options were chosen for accuracy. When documenting the data after the categories were chosen, Excel was used to store and reformat all the collected data. Data was first retrieved from BLS utilizing the website’s generated tables. Then, after moving the data to an Excel file containing all the variables together instead of individual Excel files given by BLS, the data was reformatted to run the regression analyses. Eventually, the data was changed from a year-month table into a month-variable table, and the newly formatted data was also used to make Figure 1 and Figure 2.
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When on the BLS website, chicken was the variable with the least amount of data. Due to the necessity of chronologically matching data, chicken is the defining variable for when the data considered begins. Due to the lack of years coupled with requirement of having a minimum of forty points of data for each variable, this project considered the monthly CPI reported instead of the annual averages that BLS offers as an option.
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When Excel gave the results of the regression analysis, there were three numbers used to indicate the significance of the variable. The first important number was the T-stat; it gives the significance of the coefficient by calculating evidence disproving the null hypothesis (Kumar, 2024). Null Hypothesis in this case, is the assumption that there is no relation between the X variable and the Y variable. This study looked for a T-stat that was over 1.96. This proof gives us the greater meaning of the significance of the given coefficients. Lastly the coefficient was given. This value corresponds to the B variable in the regression analysis defined previously and is how aligned the growth rates of the X variable is to the Y variable.
RESULTS
Two Decades of Beef Relationships
Run first, the regression covering all 20 years, showcased the correlation of five variables to the price of beef and three non-correlating variable results. The five variables with correlating prices were pork, gas, inflation, fish and seafood, and corn. Chicken, cheese and related products, and milk did not give results indicating a trustable correlation due to the T-state being under 1.96. A high R-squared value of .98 was achieved, indicating that the total of the coefficients is a 98% match to the line of best fit, therefor the regression achieved reliable results.
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Pork had a T- stat of 12.96 and a coefficient value of 1.41 achieving the position of the top correlating variable. The positive value of the coefficient reflects the direct relationship between pork and beef. Pork likely has the highest causal relationship with beef because both products meet similar cultural and dietary categories making them near perfect substitutes. This means price changes will inversely correlate by the law of demand in the short run but directly correlate in the long run.
Unsurprisingly, inflation was the second highest correlating variable. As inflations is often described as a rise in all items’ general prices and the current economic situation, a coefficient of .58 and T-stat of 5.23 can be considered a reasonable correlation. Inflation as a cause to the price of beef increasing can be attributed to the cost of input resources increasing. This therefore makes production costs, and the price needed to make a profit, increase in direct relation.
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Fish and seafood had the third highest correlation with a coefficient of .35 and T-stat of 3.25. The correlation between prices likely follows similarly to pork. While the products are both substitutes in the way of being meat, differences arise in the level of similarity. Fish and seafood and beef are in many ways different types of meat. Large discrimination between the meats by things like diets, cultural practices, and availability is likely the reason for the mediocre coefficient.
Following next were gas prices. Gas’s T-stat was -6.08 and its coefficient was -0.14. The absolute value of the coefficient could prove the logical reasoning hypothesis that gas prices affect the price of beef by being a supply input. The results however differ from the hypothesis due to the inverse correlation given by the negative coefficient; this section of the result would need further textual research to explain.
Corn had the lowest relation to beef prices with a Coefficient of -.05. With such a low coefficient, corn disproves the original hypothesis of corn having a high effect on the price of beef. However, the T- stat was -3.2 which is over the 1.96 requirement, showcasing that there was still a relation, albeit small. This relations explanation follows similarly to gas. Even the negative coefficient wagers the same question as gas: What implications does the negative value holds on the relationship the variable has to the price of beef?
Covid 19 Disruptions
Pre-Covid results came back almost identical to the 20-year analysis. This may be due to the data evaluated in the pre-Covid test making up 78.31% of the 20-year analysis. Chicken jumps to third place pushing both fish and seafood and gas down. No other changes occurred for the pre-Covid results; pork and inflation remained as the first and second highest correlating variable respectively. Expectedly, the results for the post-Covid test were significantly different from either of the two other tests.
Post-Covid results expectedly had the greatest variation. Since the amount of data analyzed only made up 21.69% of the 20-year analysis’s data, the results are not as stable over time. Moreover, the T-stats across all correlating variables were significantly lower, furthering the possibility of variables changing rankings over time. In this test milk was the greatest correlating variable with pork following second, chicken in third, inflations fourth and corn last.The remaining three variables, Fish and seafood, cheese and related products, and gas, all resulted in inconclusive coefficients.
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Milk became the number one affecting variable after covid, pushing pork into second place. This was the greatest and most shocking difference. While it has a higher coefficient of -1.85, the T state is lower than pork and inflation by 1.6 and 1.94 respectively. Using the given numbers, it can be inferred that milk might currently be the highest correlating variable but may not remain that way due to the low T-stat. This also makes sense when analyzed next to the pre-covid results and 20 years, as both previous tests resulted in unusable results. Milk’s coefficient is greater then either of the previous test’s results largest coefficient, further supporting the hypothesis that milk’s correlation is not causal to the price of beef. The cause of this spike in the correlation between milk and beef prices likely lies in the effect Covid 19 had on production lines. As mentioned in the studied research, beef production lines have many steps. Even after the farm and feed lots, the cattle move through butchers, restaurants, and grocery stores, leading to a very long supply process. Milk has a similarly long supply line with cattle being raised and milked on farms before the milk is moved to a processing plant. The final step is to be transported to grocery stores. Covid 19’s most famous disruption was in industry supply lines. With both supply processes being long and including processing factories, known for having many workers in enclosed spaces, the correlation is likely do to both industries having similar health restrictions on production workers.​
The Key Driver of Price Fluctuation
Accounting for all coefficients and T-stats, substitute goods appear to be the largest driver of beef price fluctuations as seen in Figure 2. The classification appears seven times across all three regression analyses and the individual variables have greater coefficients on average. On top of the statistical results, economic theories such as the dollar vote principle, the Law of Demand, and the Law of Supply showcase the theoretical strength of the results.
The idea that consumers influence the market by what product they choose to purchase based on preferences, needs, and wants is called the dollar vote principle. This principle is the reason the substitute goods have causal relationship instead of a correlating relationship like the milk has in the post-pandemic test. In relation to this study, the dollar vote principle implies that consumers are consistently comparing beef to the three substitute goods used and finding the best option available. Due to limitations the comparison examined for this study is only prices. Further reflecting the idea of consumers driving the market, by the Law of Demand, the consistent hunt of the cheapest price drives the demand for an item up and therefore its price. The individual in the category becomes less popular and another item takes its place as the best option available. This ultimately leads to all meat prices increasing gradually because of the Law of Supply. With more people willing to pay gradually higher price, the price of all meats gradually increases.
CONCLUSIONS
While the results analyzed in this paper conclude substitute goods are the largest affecting factor of beef, this is only one product in the large grocery industry. The meat industry may vary greatly from other industries that produce goods sold in grocery stores. There are four different market types that products can fall into. Each market type utilizes supply and demand differently to set prices, meaning that the company circumstance that a product category is sold in can contribute greatly to prices and consumer influence. With meat being the category used in this study, the substitute goods’ characteristics varied greatly compared to beef. This means that consumers have a broad scope of products from which to choose. This may be different to products such as salt that does not have any close substitutes. This difference highlights the need to look at products individually, categorically, and collectively. Moreover, the results showcase the importance of microeconomic studies in the prices of groceries by demonstrating that macroeconomic studies may not get to the root cause of price increases across all products equally. A shift to studying theses individual items and what level of economics effects their prices could lead to further understanding of retail prices and ultimately aid in price control in the grocery industry.
Balagtas, J. V., & Cooper, J. (2021). The impact of covid-19 on united states meat and livestock markets. Choices , 36(3).
Caldwell, P. (2024, September 18). Why We Expect inflation to Fall in 2024. Retrieved from Morningstar: https://www.morningstar.com/economy/why-we-expect-inflation-fall-2024
Kamienski, J. D. (2006). The factors influencing the price of beef, an econometric study. University of Northern Iowa.
Knight, R. (2023, August 30). Economic Research Service. Retrieved from U.S. DEPARTMENT OF AGRICULTURE: https://www.ers.usda.gov/topics/animal-products/cattle-beef/sector-at-a-glance/
Koppes, S. (2024). Consumers see food prices as rising more than other goods and services, findways to adapt. Purdue University.
Kumar, A. (2024, May 7). Analytics Yogi: Reimagining Data-driven Society with Data Science & A. (Vital Flux) Retrieved from Linear Regression T-test: Formula, Example: https://vitalflux.com/linear-regression-t-test-formula-example/#:~:text=Magnitude%20and%20Sign%3A%20The%20magnitude,the%20direction%20of%20the%20relationship.
Lau, C. S., Fulgoni III, V. L., Van Elswyk, E. M., & McNeill, S. H. (2023). Trends in Beef Intake in the United States: Analysis of the National Health and Nutrition Examination Survey, 2001–2018. National Library of Medicine: National Center for Biotechnology Information.
Martin, A. (2024, June 27). Economic Research Service U.S. DEPARTMENT OF AGRICULTURE. Retrieved from Food Prices and Spending : https://www.ers.usda.gov/data-products/ag-and-food-statistics-charting-the-essentials/food-prices-and-spending/
Mathews, K. H., & Vandeever, M. (2007). Beef Production, Markets, and Trade in Argentina and Uruguay An Overview. United States Department of Agriculture. Outlook.
Mead, D., Ransom, K., Reed, S. B., & Sager, S. (2020). The impact of the COVID-19 pandemic on food price indexes and data collection. Monthly Labor Review.
National Foundation for Infectious Diseases. (2024, June). COVID-19. Retrieved from https://www.nfid.org/infectious-diseases/covid-19/
Nelson, B. (2024, August 30). Farm Bureau . Retrieved from Beef Prices Soar to Record Highs, Yet Farmers Struggle to Reap the Benefits: https://www.fb.org/market-intel/beef-prices-soar-to-record-highs-yet-farmers-struggle-to-reap-the-benefits
Schnell, C. (2023, July 12). Federal Reserve Bank of St. Louis . Retrieved from What Are RealValues, and How Are They Used?: https://www.stlouisfed.org/open-vault/2023/july/real-values-how-they-are-used
U.S. Bureau Of Labor Statistics. (2004-2024). Inflation and Prices . All Urban Consumers CPI and Industry Data PPI.
Whitehead , D., & Brad Kim, Y. H. (2022). The Impact of COVID 19 on the Meat Supply Chain in the USA: A Review. Food Science of Animal Resources Vols. 42, 762–774.
REFERENCES
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