OSU Applied Economics Outreach Blog

Rising food prices seem to be on everyone’s mind these days. Consumers are frustrated that prices have not come down from pandemic levels, even after the reasons for those cost increases seem to have abated. Food price inflation has slowed this year according to official CPI numbers, but consumers still perceive prices to be increasing quickly. The proposed merger between Kroger and Albertsons highlights the growing concentration in the food retail sector, and the proposal has generated concern among the public as well as federal regulators. Here in Oregon, the merger would result in the sale of 60 Kroger stores to C&S Wholesale Grocers. In the last week, the issue was further elevated when food prices were brought up by the Kamala Harris campaign and the idea of a federal ban on price gouging was floated.

Since I spend most of my time working on issues related to farm production and profitability in Oregon, for me, the discussion of food prices always leads back to the relationship between the prices that consumers face at the grocery store and the prices that Oregon farmers and ranchers receive for their products. The ‘price spread’ is the difference between what consumers pay at the store and what farmers receive. This gap covers the costs of processing, shipping, marketing, and any profits made along the way. An increase in the price spread could be caused by an increase in the cost of shipping, increased labor costs among processors, increased cost of regulatory compliance, or an increase in the profits generated by processors and retailers.

The degree to which retail prices filter down to the farm sector also depends on market power and concentration at the processing and retail levels. For example, if a competitive meat processing market became a monopoly overnight, we would expect the price spread for meat to increase because the processor would be able to charge higher prices while passing relatively little on to beef producers.

This blog post will use historical beef price data to discuss the changes in the retail-farmgate price spreads and farm share of the retail food dollar over time. Beef is a good example because cattle production is spread across many small beef producers, processing is concentrated largely in the hands of four very large firms, and recent increases in meat prices are often a source of consumer complaint. While this type of simple analysis cannot identify the specific reason for a change in the farm-retail price spread in a given quarter or year, it can reveal the degree to which food expenditures are captured by supply chain steps downstream from the farm. It can also be helpful when thinking about the impact that further consolidation might have on the food prices and farm profitability.

These figures, based on USDA-ERS meat price data, show the price spreads and farm share of the food dollar for beef. Price spreads for other commodities are available here. For the beef industry in particular, the value captured by processing, distribution, and retail has increased by a factor of 15 over the past 5 decades. The farm share of the retail beef dollar has fallen from 65% to about 45%, dipping below 35% during the pandemic. Although it is difficult to generalize, these charts can help us put the current discussion about market power and food prices into perspective.

I think there are three main conclusions to make.

1. The Covid years were very difficult for some sectors. Meat processing provides a perfect example of a Covid-related capacity constraint that temporarily changed the relationship between producers and processors and led to high retail prices (see Lusk et al., 2021 for more detail). As meat processing facilities reduced throughput because workers were sick or to comply with health and safety protocol, the volume of meat being processed fell, driving down average farmgate prices. In other agricultural sectors, shipping costs, input prices, and labor costs increased rapidly, and the processors/distributors share of the food dollar increased.
2. In the case of beef at least, the farm share of the food dollar has recovered in the last year or two as the pandemic has faded and supply chains have normalized. Cattle inventories are currently at historically low levels and many beef processors are struggling to maintain their desired production volumes. This should not be taken as a conclusion that there is not significant market power in meat processing, but the market dynamics currently give beef producers a stronger hand in the market. We may see price spreads adjust downward again as cattle inventories return to more typical levels.
3. The farm share of the food dollar will look different for commodities like beef, milk, and potatoes than it will for highly processed foods. Intuitively, the supply chain costs for highly processed foods are much higher than for “whole” foods. As diets have incorporated more highly processed foods, farm revenues have increased at a slower rate than food sales. It remains an open question whether or not the farm share of the overall food dollar will shrink further, or if we have reached a point of stabilization.

Further Reading: This is a huge topic and there is a lot of academic literature that has addressed food prices, market power, and the consolidation in the modern food system. A few interesting academic works and other resources are below:

• Ma et al., 2017: An article about market power in grocery, which reminds us that market power can also exist at the neighborhood level.
• Howard, P. H. 2016. Phil Howards book “Concentration and Power in the Food System: Who Controls What We Eat?” is not an economic approach to this topic, but he presents an interesting viewpoint and does rigorous work. There are some excellent figures.
• Raw data on retail food prices, rather than agricultural commodities are managed by the Bureau of Labor Statistics. They can be found here.

I spent most of last week traveling in Eastern Oregon to speak with livestock producers, and the subject of Pasture, Rangeland, Forage (PRF) insurance came up in conversation. It is clear that PRF has become increasingly popular, and may even be impacting land prices and lease arrangements for grazing land. In this blog post I will discuss some basic information about the PRF insurance product and share participation and loss ratio outcomes for Oregon. Later in the post, I link to a spreadsheet that contains more detailed county level data on PRF participation, premiums, and payments.

The PRF insurance program is designed to help livestock producers manage the risk of poor forage production. It is essentially a rainfall “index insurance”, meaning that neither the insurance premiums nor the insurance payouts (known as “indemnities”) depend on the management or production outcomes of the individual producer. Rather, the premiums and payouts of the PRF program are based on monthly rainfall totals in a specified grid location. This type of index insurance has the advantage of low administration costs, as farm-level yields and losses do not need to be verified before payouts are made. The drawback is that the forage production on individual pastures may not be highly correlated with the rainfall index for the larger grid, which weakens the effectiveness of PRF as a risk management tool for individual ranches.

Livestock producers are eligible to insure at coverage levels up to 90% of the expected precipitation amount and must choose how to distribute their coverage over the calendar year. Like other USDA crop insurance programs, PRF premiums are subsidized, with the subsidy percentage based on the coverage level chosen by the producer. For more detailed explanation of the purchase process, and for information on national PRF outcomes, please see this excellent article by Jay Parsons, John Hewlett, and Jeff Tranel.

PRF Insurance has become increasingly popular in Oregon. Figure 1 shows the number of acres insured under PRF from 2016 to 2024. Insured acreage has grown rapidly in the state, with roughly 17 million acres insured. There has been similar participation growth at the national level, with nearly 300 million acres insured in 2023 (USDA-RMA, 2024).

Producers that buy coverage under the PRF program will receive payments if the precipitation index is below the trigger level for the specified months. Given that recent years have been drier than average in much of the state, a large percentage of purchased PRF policies have resulted in indemnity payments. Figure 2 shows the average “producer loss ratio” for PRF coverage in each county in 2022. The producer loss ratio is defined as the insurance indemnity divided by the net premium paid by the producer (i.e. premium less the premium subsidy). For example, in Union County, the producer loss ratio is 2.26, meaning that for each dollar that the producer paid in PRF premiums, they received an average of $2.26 back in indemnity payments. This is close to the Oregon average producer loss ratio since 2016. A spreadsheet with county level PRF outcomes from 2016 to 2023 can be found here.

Because the PRF insurance is based on a rainfall index, the payouts are correlated with drought conditions. Figure 3 shows the drought monitor map for October 11, 2022. While not a perfect match, we can see that the areas in drought are more likely to receive higher payouts from PRF coverage.

Crop insurance is often viewed as a gamble, with farmers and ranchers wondering if the decision to insure will pay off. Looking at the map of producer loss ratios alongside the drought monitor map is a good reminder that this program is designed to reduce the financial risk posed by low rainfall and reduced forage production. A “good year” for forage is more likely to be a “bad year” for PRF insurance, and vice versa. Livestock producers should carefully consider how a year of poor pasture conditions will affect their revenues, and whether PRF insurance could help protect them from these outcomes. Also worth considering is that in Oregon and in the U.S. as a whole, in each year since 2016 producers have received more than they’ve paid in premiums.

Tim Delbridge

[updated 3/19/24]

Along with colleagues at Oregon State (NMPAN, OSU Clark Meat Science Center) and Blue Mountain Community College, I am a part of a USDA-NIFA funded project that aims to strengthen the development of the meat processing workforce in the Pacific Northwest. A lot of attention has been focused lately on the organization of the meat supply chain, and how regulatory structure and market concentration at the processing level affects market access for small scale beef producers and the resiliency of this part of the food system. This is an issue that impacts consumers, producers, and ultimately, the economies of rural communities. 

While our larger workforce development project involves some teaching infrastructure and curriculum design, I’ve been exploring the challenges that small-scale processors face in hiring and retaining staff, and how the experience of meat processing firms relates to broader economic trends. In this blog post, I’m going to provide some background on labor within the meat processing sector in Oregon, Washington, and Idaho that I shared at the Northwest Meat Processing Association annual meeting last Friday, and discuss in more detail how the available employment data provide insights into the challenges that our project seeks to address.

The exploratory analysis presented in this post is based on a combination of data, including the Oregon Quarterly Census of Employment and Wages (QCEW) and unemployment insurance records managed by the great team at the Oregon Employment Department (OED), and individual wage record data managed by the US Bureau of Labor Statistics (BLS). These data and results are not official OED or BLS releases and are intended for exploratory research only. There are five key conclusions I’d like to share.

1. The number of meat processors and meat processing employees has been rising in recent years, and this growth has largely come from small firms. Figure 1 shows the number of meat processing firms in Oregon by year, with firms with 10 or fewer employees represented by the darker color in the stacked bar chart. The yellow line shows the number of total meat processing employees in the state and shows significant growth in the last 15 years.

Figure 2 shows the upward trend in meat processing employment at the regional level. The increase in Oregon is matched by similar increases in Idaho and Washington, and closely mirrors the rate of growth in the overall labor force in the Pacific Northwest.

2. Meat processing wages have risen significantly in recent years. Figure 3 shows the average hourly wage rate paid by Oregon meat processing firms, along with hourly wages by firms in commercial and residential construction. This figure highlights the fact that not only have wages been increasing rapidly in many industries in recent years, but meat processing wages have “caught up” to those in industries that often compete for workers. The fastest wage increase in meat processing occurred in 2020 during the height of the Covid pandemic, and did not come back down after serious infections declined in number and supply chains recovered.

3. Retention seems to have gotten harder. Despite the higher pay, the meat processing industry in the Pacific Northwest has been able to retain fewer of the employees that leave their jobs. Based on individual employment histories contained in the BLS wage records, figure 4 shows the industry retention rates for two cohorts, those leaving their jobs in 2013 and those leaving in 2018. Among employees separating from their employers in 2013 (represented by the dashed lines), between 54% and 60% were working in meat processing in the PNW two years later. Among those that left their jobs in 2018 (solid lines), between 45% and 50% were still working in meat processing after two years. Again, while some of this may have been caused by the Covid pandemic, these workers did not return to the industry.

4. When employees leave their meat processing employers, they appear to do so for higher hourly pay, though they might not earn more in total wages. Figure 5, also based on the individual wage records managed by the BLS, shows that workers that left their meat processing employers generally tended to earn higher hourly wages in the same quarter in the subsequent year. This shouldn’t be surprising, as higher pay is a common reason to take a new job. However, figure 6 shows that overall wages go down, on average, after leaving meat processing. This seems to suggest that many employees are leaving the industry for higher paying jobs in which they work fewer hours.

[Note: From what I’ve heard from my colleagues in NMPAN and the NWMPA, this conclusion runs counter to the experience of many small scale processors. It is important to point out that a close look shows that the average hours worked before leaving a meat processing employer are high (close to full time) and well above the average hours worked by employees at small processors (see figure 7). Moreover, the BLS queried these data for the 4th quarter of each year, which is a particularly busy time for the industry. Further analysis is needed to determine if this trend holds for firms of all size and if results would look similar in all seasons, or if these averages are dominated by employees leaving large meat processors and numbers are skewed by big fourth quarter pay checks.]

5. All size classes show seasonality in terms of hours per worker per week, though workers for larger firms work more hours on average. The third and fourth quarters of the year (July through December) are traditionally the busiest times for meat processors. Figure 7 shows that in these quarters, the hours worked per employee are higher than in quarter 1 and 2, and that this holds for small, medium, and large meat processing firms. Interestingly, workers for larger firms tend to work more hours in all quarters than those at smaller firms. This likely indicates that small-scale processors rely more on part-time workers, and/or that they struggle to keep volume high enough in Q1 and Q2 to fully employ their staff, thereby contributing to retention challenges.