Recipient Organization
CAL POLY CORPORATION
(N/A)
SAN LUIS OBISPO,CA 93407
Performing Department
Agribusiness
Non Technical Summary
The food retail industry has become very consolidated in recent decades. According to estimates from the USDA Economic Research Service (USDA-ERS, 2018), the four-firm concentration ratio (CR4) for grocery stores in the US increased from 16.8% in 1992 to 42.4% in 2016. This change has been driven largely through a wave of mergers and acquisitions (MA) that continues to the time of writing. A recent and notable example of this is the acquisition of Safeway by Albertsons in 2014, resulting in the third largest grocer in the United States, by revenues. Over the same time period, food wholesaling has becoming more consolidated. In 2017, two of the largest wholesalers in the U.S., Unified and Supervalu, merged. One year later, Supervalu was purchased by United Natural Foods. In the eastern U.S., C&S Wholesale Grocers, the largest wholesaler in the US, acquired Olean Wholesale in a deal that also included 270 retail locations.Our proposal seeks to build capacity for research on structural change, as defined by MA activity, in the food retailing and wholesaling sectors. In the spirit of capacity building and leveraging the Cal Poly Agribusiness Department's industry connections, our proposed scope of work focuses on California, with the intention to expand the project to study the United States beyond the timetable of the award funding. Relatively little is understood about the economic impacts of the extensive MA activity that has occurred in recent years and is ongoing today. We argue that research is motivated in three important directions. The first is to update and expand upon the research on the impacts of MA activity in the food retailing sector. The second is to explore and identify the economic impacts of MA activity in food wholesaling, by studying the firms that sell foods in bulk to retailers. The last is to investigate upstream economic impacts, broadly defined, on the agricultural production sector. We discuss each in turn.Many studies have been conducted to measure associations between retail market structure and food prices. These studies, which include Marion et al. (1979), Lamm (1981), Connor and Peterson (1992), Wright (2000), and Yu and Connor (2002), have consistently found positive and significant associations between market concentration and retail food prices. This effect is typically attributed to larger firms with decreased competition exercising market power, or raising their prices to increase their profits. However, this work needs to be revisited and updated in today's diverse food retail landscape, which features non-traditional formats competing for consumers' food dollar. A growing body of research suggests that non-traditional formats (e.g. supercenters, gourmet supermarkets, etc.) influence the nature of competition and price dynamics in food retailing (Stiegert and Sharkey, 2007; Volpe and Lavoie, 2008; Ezeala-Harrison et al., 2016; Rahkovsky and Volpe, 2017). In short, the relationship between market structure and food prices is unclear given the changes that the industry has undergone.In addition to price impacts, retail MA activity and changes in market concentration are likely to have additional economic and welfare impacts, but the research on these is thin. For example, Drs. Volpe and Cai are coauthors on a study currently under review at Food Policy that identifies linkages between retail market concentration and fruit and vegetable expenditures among US households.Argentesi et al. (2016) found that supermarket mergers resulted in decreased product variety. Davis (2010) saw that, post-merger, supermarkets offered fewer promotions, or sales. Richards and Hamilton (2006) demonstrated that supermarkets compete in quality and service in addition to price, while Richard and Allender (2013) also found evidence for competition through location. As competition, broadly defined, varies with market concentration, all of these factors and more may be impacted by MA activity.The degree of concentration and consolidation in food wholesaling is not as well understood as it is in retailing. Connor (1997) examined a high-profile merger in food wholesaling and found that it increased market concentration in at least four geographic areas to an extent that violated federal guidelines. Of course, since that time, many other large mergers and acquisitions have taken place in the industry, including the recent examples discussed above. Therefore, the number of retailers, and by extension, consumers potentially affected by wholesaling MA activity is enormous. Virtually all small and independent retailers purchase their groceries through wholesalers, but even the largest food retailers purchase as least some of their product lines from wholesalers. For example, C&S, the largest wholesaler in the U.S., sells groceries to over 4,000 supermarkets and their clients include Walmart, the largest food retailer in the world (WholesaleGrocersDirectory, 2014).A central and motivating factor behind our proposal is the fact that the great majority of research on market structure or MA activity is downstream, or consumer, focused. It is well understood that market power flows both downstream (selling power) and upstream (buying power). The latter is not well understood empirically, as the literature on vertical linkages in the food supply chain is thin. To be clear, there is a wealth of research on price transmission in the food supply chain. Perhaps the most relevant consensus finding in this respect is the timing and magnitude of price transmission from the farm to manufacturers or wholesalers, on to retailers, and finally to consumers, depends importantly on market structure (e.g. Bakucs et al. 2014). However what is not understood, and barely studied to our knowledge, are the economic impacts of MA activity and increased supply chain market concentration on producers.Dobson et al. (2003) reviewed the literature to-date on the economic implications of retailer concentration and summarized the concerns related to oligopolistic behavior and buying power. The focus of the study was countries in the European Union, but the market structure and supply chain implications provide testable hypotheses for our project. As the prices paid for agricultural commodities fall, whether intended for use in the fresh or processed markets, only the most efficient producers are able to survive. The potential impacts on California producers may be due to increased concentration in the retailing as well as wholesaling sectors, as both are major buyers of produce. California commodity production accounts for over 13% of the nation's agricultural value (CDFA, 2019), and food retail is the single largest marketing channel for these commodities (Cook, 2014). Li and Sexton (2013) showed that retailer pricing behavior (which is necessarily a function of firm size and market power), has direct implications for the volatility of farm prices and farm income. These indicators are measured consistently by USDA, facilitating research on this topic. Given the size of both the production and retail sectors in California alone, the magnitude of these economic impacts may be quite large.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
Our proposal seeks funds to build capacity at Cal Poly for Agribusiness faculty and students to partner with industry and trade associations to study the economic impacts of MA activity in the retail and wholesaling sectors. The Agribusiness Department seeks to provide students with a holistic understanding of the food supply chain that addresses current and impactful issues and events. Therefore, we also seek to inform the curriculum with our research which will have a long-lasting impact far beyond the funding period.Goal 1. Assess and understand how food retail market concentration has changed across geographic markets.Objective 1: Measure Changes in Food Retail ConcentrationActivity 1A: Define retail markets in multiple ways (e.g. counties, legislative districts, metropolitan areas, etc.) to describe California in comprehensive and non-overlapping geographic markets.Activity 1B: Calculate market concentration, relying primarily on the HHI, by geographic market and year, for as many years of data as possible. Both the market definitions and the market concentration figures will be made available as datasets for use by other researchers.Activity 1C: Assess the trajectory of market concentration in the food retail sector in California.Goal 2. Improve the understanding of how concentrated and consolidated the food wholesaling is and how this has changed in recent years.Objective 2: Measure Market Concentration in Food WholesalingActivity 2A: Obtain annual revenue estimates for major food wholesalers selling to food retailers in California.Activity 2B: Identify the geographic scope of food wholesalers distributing to California.Activity 2C: Conduct back-of-the-envelope calculations estimating the market concentration for food wholesaling in California for as many years as possible. All data compiled and calculations conducted for objective 2 will be made available to other researchers. However, owing to the confidentiality terms governing our data access, no individual firms will be revealed by name in our findings or outputs.Goal 3. Measure the extent to which MA activity is responsible for increases in market concentration throughout California.Objective 3: Measure the Impact of Merger and Acquisition Activity on Changes in the Retail and Wholesale Market Concentration Activity 3A: Compile a list of relevant merger and acquisition activities affecting retailers and wholesalers operating in California for the years of study.Activity 3B: Identify discrete changes in retail and wholesale market concentrations across geographic markets in California occurring in the wake of mergers and acquisitions. All data compiled and calculations conducted for objective 3 will be made available to other researchers.Goal 4. Describe and measure impacts on consumers resulting from MA activity.Objective 4: Identify the Impacts of Merger and Acquisition Activity in the Food Retail and Wholesale Sectors on ConsumersActivity 4A: Quantify retail price impacts resulting from mergers and acquisitions in the retail and wholesale sectors, by market.Activity 4B: Identify changes in promotional activity resulting from MA activity.Activity 4C: Investigate changes in product category breadth and depth among food retailers, following MA activity.Goal 5. Investigate the impacts of MA activity on the upstream, i.e., production sector.Objective 5: Identify the Impacts of Merger and Acquisition Activity in the Retail and Wholesale Sectors on the Production SectorActivity 5A: Conduct interviews with grower-shippers in California to identify issues of importance to producers as they are related to MA activity in the retail and wholesale sectors.Activity 5B: Compile a dataset of farm prices and farm income to correspond with the time period including relevant retail and wholesale MA activity. This dataset will be made available to other researchers.Activity 5C: Identify changes in farm prices and farm income resulting from MA activity in the food supply chain.Activity 5D: Measure economic impacts related to factors identified in 5A, where applicable.Activity 5E: Measure economic impacts such as investments, changes in employment or wages, or any other indicators within the retail and wholesale sector and resulting from MA activity.Activity 5F: Identify inter-industry impacts on the production sector resulting from MA activity in the retail and wholesale sectors, via changes in investment, employment, etc.
Project Methods
The research team is partnering with Dr. Andrew Lumpe, who has developed an evaluation plan to assess progress on the multiple project activities. We discuss the approach and methodology for each proposed activity in turn.Activity 1A: The research team will rely on Nielsen TDLinx to define retail markets. TDLinx is a proprietary store-level dataset that includes banner names, addresses, categorical annual food sales, and ownership details. We have legal access to this data via a third-party agreement with USDA ERS and we are budgeting for annual physical access via NORC.Activity 1B: Market concentration will be calculated primarily using the HHI, in order to generate findings of direct policy relevance to the US DOJ, among other institutions. With the TDLinx data, we will use the midpoint of each firm's revenue category, by market, to estimate market shares, and calculate the HHI. It will be feasible to calculate alternative concentration measures, e.g. the CR4, to assess statistical robustness.Activity 1C: Once the concentration measures are calculated by market, standard statistical techniques will be utilized to assess the respective changes in concentration. Trends for areas larger than individual markets, e.g. the San Francisco Bay Area, can be measured using population-weighted averages across markets over time.Activity 2A: Annual revenue estimates for wholesalers are available from Supermarket News by year. We intend to engage with stakeholders in order to aggregate and utilize revenue estimates without disclosing sensitive information. These can be combined with estimates of nationwide wholesale food sales in order to calculate market shares and, in turn, market concentration.Activity 2B: Some information on retail-wholesale relationships is publicly available. A more comprehensive understanding of the wholesalers with retail clients in California can be gleaned with cooperation from the CGA member staekholders. We will not publish or even identify individual relationships between retailers and wholesalers, but only identify the number and size of wholesalers with food revenues generated in California.Activity 2C: Combining the findings of 2A and 2B, we will estimate market shares for wholesalers operating in California and estimate the HHI to measure market concentration. Owing to the uncertainty in the calculation of market shares, we will conduct a sensitivity analysis to provide a range of concentration estimates, by year, for food wholesaling.Activity 3A: Industry publications including Progressive Grocer and Supermarket News track MA activity over time for the food supply chain. We will combine information from these sources and, in conjunction with our findings from 2B, compile comprehensive lists of MA activities in the retail and wholesale sectors directly affecting California. We will discuss our findings with stakeholders to assess the comprehensiveness of our database.Activity 3B: To disentangle the effects of MA activity on concentration with the changes in firms' respective market shares over time, we will rely on a difference-in-difference (DID) approach. This powerful statistical technique, when used in a time-series setting, controls for unobservable factors. The concept requires control and treatment samples. In this case, treatment samples are geographic markets where MA activity occurs.Activity 4A: The IRI store scanner data is vital to estimating MA impacts on consumer prices. This is another dataset to which the research team has access through our collaboration with USDA ERS. We will follow the literature in estimating potential price impacts of MA activity. We will conduct a multivariate regression analysis with food prices, as measured using IRI store scanner data, as the dependent variable. Food prices can be measured in a number of different ways, and as a starting point, we will likely use the aggregate price of a basket of frequently purchased groceries. The key independent variable is MA activity, which can be measured using dummy variables or continuously based on their effects on market concentration. To identify our key relationship of interest, the regression model will control for potentially confounding factors such as demographics, key cost measures for retailers such as electricity and transportation, geographic fixed effects, and state GDP.Activity 4B: Promotional activity can be measured using the IRI store scanner data, primarily through identifying temporary reductions in the prices paid by consumers. We intend to rely on established techniques in this respect, as used in the extant literature (Volpe and Li, 2012). Once identified, we can calculate promotional frequency (the percentage of time products are on sale) and promotional depth (the percentage by which prices are reduced during promotions) at the product category, department, and store level.Activity 4C: This activity is exploratory, in the spirit of capacity building. We plan to develop metrics for quantifying product breadth (the variety of brands) and depth (the variety of sizes and variations within brands) by category. We will then conduct a statistical analysis to investigate changes in these metrics following MA activity.Activity 5A: We are requesting travel funds to visit and interview agricultural producers in California, who are also stakeholders for this project. This is investigative work, in the spirit of building capacity, and we anticipate our geographic range will extend from Ventura, CA to Salinas, CA, facilitating day trips from San Luis Obispo. To identify the best growers to interview and to help coordinate the interviews we will rely on support and assistant from Western Growers Association (WGA), who are project stakeholders.Activity 5B: Data on farm prices and farm income are available from USDA National Agricultural Statistics Service (NASS) and USDA ERS. In many cases the data are disaggregated and we intend to compile the data into Excel workbooks that will then be readily converted into SAS or Stata datasets for advanced statistical analysis.Activity 5C: To measure MA impacts on farm prices and farm income, we will employ a regression framework. The methodology is comparable to the one used to measure retail price impacts, although the model controls will depending on a review of the literature.Activity 5D: This activity, much like 4C, is exploratory. It is not yet known what factors may arise as important to producers, as identified in 5A. Moreover, it is not clear to what extent they can be quantified using secondary datasets. To the extent that the producers interviewed yield a consensus on economic factors to study, we intent to investigate them using a battery of statistical techniques.Activity 5E: For many mergers and acquisitions, relevant economic details are publicly available via outlets such as Progressive Grocer or Supermarket News. In most cases these involve investments on the part of retailers, however they may include the valuations of new construction or developments, changes in labor forces, and changes in wages.Activity 5F: The economic outcomes discussed in 5E likely have positive spillovers into other industries. This includes agricultural production, the industry that provides the basic sources for nearly all retail foods and beverages. Implan is an economic impact modeling software that allows researchers to estimate widespread and highly specific impacts owing to events, and also facilitates the calculation of estimated impacts for MA activity across geographic markets in California. To identify industries to investigate, we intend to conduct informal discussions with industry professionals throughout California agribusiness, many of whom are stakeholders for this project.