F2S and School Meal Costs

Schools are a strategic venue to promote healthier food consumption (Centers for Disease Control and Prevention 2018). SFAs attempt to keep meal costs low without compromising participation rates among students or the nutritional content of the food. Although SFAs that provide government-funded school meals are intended to operate on a cost-recovery basis to maintain a balanced budget, some programs operate at a loss in practice (Ralston and Newman Reference Ralston and Newman2015).

Standard school-food implementation costs include costs of food purchasing, preparation, and program management. School food expenditures, or food purchasing costs, account for 46% of reported meal costs, while labor for food preparation and the food service portion of management comprise 45% of reported costs (Bartlett, Glantz, and Logan Reference Bartlett, Glantz and Logan2008). Food service operations are self-operated or contracted out to a food service management company but, regardless, are nested within an SFA's administrative management structure. Bartlett, Glantz, and Logan (Reference Bartlett, Glantz and Logan2008) estimate that, on average, unreported costs account for 19% of total costs, about 61% of which is labor. School per meal costs vary depending on region; whether the school is in an urban, suburban, or rural area; and school size (Ollinger, Ralston, and Guthrie Reference Ollinger, Ralston and Guthrie2011; Ollinger and Guthrie Reference Ollinger and Guthrie2015).

Local food procurement may increase the standard costs of school food programs because of the additional effort needed to gather information, negotiate, and monitor procurement (Motta and Sharma Reference Motta and Sharma2016). The effort required to deal with F2S-specific implementation costs generally appears as labor costs and manifests as additional staff hours dedicated to F2S procurement (Fitzsimmons and Lass Reference Fitzsimmons and Lass2015). On-site food preparation challenges raise costs. These costs arise from training food service staff how to cook with local foods (labor costs) or purchasing new kitchen equipment to more effectively cook local foods (capital expenses) (Colasanti, Matts, and Hamm Reference Colasanti, Matts and Hamm2012). Distribution challenges and poor product availability can also increase costs (Gregoire, Arendt, and Strohbehn Reference Gregoire, Arendt and Strohbehn2005; Vogt and Kaiser Reference Vogt and Kaiser2008; Oberholtzer, Dimitri, and Jaenicke Reference Oberholtzer, Dimitri and Jaenicke2014). Finally, there may be a price premium associated with purchasing a local product.

The 2013–2014 F2S Census asked SFAs the kinds of problems they experienced when they procured local foods (Table 1). Product availability was the most widely reported problem (57%). High prices were a problem for 38% of respondents, and procurement coordination was problematic for 26% of respondents. Overall, 75% of SFAs reported at least one procurement problem.

Table 1.

Farm to School Implementation Procurement Problems (2013–2014)

Market Channel Structure and Costs

Researchers have investigated the impact of increasing market consolidation in the food distribution system on food prices (Fitzsimmons, Lavoie, and Lass Reference Fitzsimmons, Lavoie and Lass2018; Sexton and Xia Reference Sexton and Xia2018; Brown and Tousey Reference Brown and Tousey2019). Research within the context of local food procurement suggests that market power in intermediated local food supply chains has an ambiguous impact on equilibrium prices and quantities, depending on the extent of market power and supply chain efficiencies (Fitzsimmons, Lavoie, and Lass Reference Fitzsimmons, Lavoie and Lass2018). Intermediaries may reduce supply costs for local food products relative to farmers by eliminating redundancies and accessing efficiencies of scale (Hortascu and Syverson Reference Hortaçsu and Syverson2007; Ashenfelter, Hosken, and Weinberg Reference Ashenfelter, Hosken and Weinberg2015). For example, intermediaries may reduce F2S-specific transaction costs associated with gathering information, negotiating terms, and enforcing terms (Hobbs Reference Hobbs1997), as well as marketing costs like packaging and distribution. If intermediaries reduce these costs and pass the cost savings on to SFAs, school meal costs for local foods should be lower when procured through intermediaries rather than when procured directly from farmers.

On the other hand, procurement through intermediaries might increase costs to SFAs. Costs might increase if there are dis-economies of scale, if there are fixed adjustment costs that confront intermediaries in procuring local products, or if intermediaries do not pass cost savings through to SFAs. Intermediaries may not pass cost savings on to SFAs if they are able to exercise market power. Intermediary market power could limit the ability of SFAs to negotiate prices and manage procurement terms, which in turn can increase their procurement costs (Nocke and White Reference Nocke and White2007).

Although some SFAs contract with food service management companies, which hire management staff and arrange procurement, other SFAs are self-operated. Food service management companies comprise about 25% of the food service contracting industry. The top four companies in the industry accrue 66% of revenues nationwide, indicating the potential for upstream market power (Hyland Reference Hyland2018). Broadline food distribution companies that often supply self-operated SFAs are also concentrated. The top three such companies earn 60% of aggregate revenue, suggesting that even self-operated food service management may be subject to upstream market power (Technomic Inc. 2017). Despite the effort to enlist intermediaries in F2S procurement, it is unclear whether intermediaries reduce costs experienced by SFAs through increased efficiencies or increase costs through upstream market power.Footnote ¹ Both theoretical and empirical research suggests that the potential cost reductions from intermediary efficiencies must be compared to the effects of increased market concentration (Hortascu and Syverson Reference Hortaçsu and Syverson2007; Ashenfelter, Hosken, and Weinberg Reference Ashenfelter, Hosken and Weinberg2015; Belleflamme and Peitz Reference Belleflamme and Peitz2015).

Data Sources

The 2013 F2S Census surveyed public school districts that participated in the National School Lunch Program about their F2S activities during the 2011–2012 academic year. The subsequent 2015 F2S Census of the 2013–2014 academic year expanded surveyed schools to include private and charter schools and refined the survey questions. In the 2015 F2S Census, 12,585 SFAs completed the survey for a response rate of 70%.

SFA-level data are from both the 2013 and 2015 F2S Censuses. County-level data on population, per capita income, and the proportion of the population with a bachelor's degree are from the Bureau of Economic Analysis (2015). County-level food environment data on the per capita number of grocery stores and restaurants, as well as milk prices, are from the Food Environment Atlas (U.S. Department of Agriculture, Economic Research Service [USDA-ERS] 2017). Farm income is from the 2012 Census of Agriculture (USDA Reference Slusser, Cumberland, Browdy, Lange and Neumann2017). County-level rural-urban continuum codes (RUCCs) are available from the USDA-ERS (2013).

Data Set Construction

To merge the 2013 and 2015 F2S Census data sets, we first eliminate SFAs in the 2015 Census that did not have a Common Core of Data (CCD) identification number. In some instances, the CCD number was not uniquely assigned in the 2015 F2S Census. We merge respondents in the two data sets by matching on CCD number, SFA zip code, and the first eight letters of the school name.Footnote ² We restrict our data to SFAs that have an F2S program in the 2015 Census, participate in both the 2013 and 2015 Censuses, and indicate which market channel they use to source local food products. Our resulting data set of merged F2S observations thus has 2,212 observations.Footnote ³ We then merge these F2S data with the county-level data (i.e., RUCCs, socioeconomic variables, farm income, and food environment data) using Federal Information Processing Series (FIPS) county codes. There are 2,102 observations in our probit regressions. This is because we exclude observations with missing values for any of the independent variables from the regression.

Variable Description

The 2015 F2S Census asks SFAs whether school meal costs declined because of F2S program participation. This variable is equal to one if school meal costs declined and equal to zero otherwise.Footnote ⁴

School meal costs could be influenced by characteristics of the SFAs (School), the location of the SFAs (Location), characteristics of how SFAs implement F2S programs (Implementation) (Fitzsimmons and Lass Reference Fitzsimmons and Lass2015; Motta and Sharma Reference Motta and Sharma2016), procurement challenges that SFAs experience (Problem) (Hobbs Reference Hobbs1997; Gregoire, Arendt, and Strohbehn Reference Gregoire, Arendt and Strohbehn2005; Conner et al. Reference Conner, Sevoian, Heiss and Berlin2014; Fitzsimmons and Lass Reference Fitzsimmons and Lass2015; Motta and Sharma Reference Motta and Sharma2016), the food environment proximate to the SFAs (Environment) (Ralston et al. Reference Ralston, Beaulieu, Hyman, Benson and Smith2017; Botkins and Roe Reference Botkins and Roe2018; Christensen, Jablonski, and O'Hara Reference Christensen, Jablonski and O'Hara2019), and the market channel procurement strategy of SFAs (Procurement Strategy).

School variables represent the size, socioeconomic status, and demographic characteristics of SFAs. The number of students in the SFA may affect economies of scale, and the square of school size tests whether this influence is nonlinear (Botkins and Roe Reference Botkins and Roe2018). The percentage of students eligible for free or reduced-price meals is indicative of the socioeconomic status of the student body of SFAs. We include this control variable because lower-income SFAs may be more incentivized to reduce costs. Three independent variables control for the ethnic and racial composition of the student body of SFAs (Botkins and Roe Reference Botkins and Roe2018). These variables represent the percentage of the student body that is black, Hispanic, and Asian.

Location variables include the state of the SFA and the metropolitan classification of the county.Footnote ⁵ F2S policy support can vary at the state level, which could subsequently influence whether SFAs experience lower school meal costs. Other state-level factors could also influence whether meal costs changed from implementing an F2S program. As in Botkins and Roe (Reference Botkins and Roe2018), we use the county-level RUCCs of SFAs to account for differences in population density and the distances of counties from cities. We thus include eight RUCC fixed effects (the ninth is the omitted variable). Location variables also account for the socioeconomic status of the county of SFAs: per capita income, population, and the percentage of the population with a bachelor's degree. The population variable controls for population variation within RUCC classifications. Dimitri, Hanson, and Oberholtzer (Reference Dimitri, Hanson and Oberholtzer2012) found that county income levels influence local food sourcing patterns by schools, while O'Hara and Benson (Reference O'Hara and Benson2019) found the proportion of the county with a bachelor's degree to be pertinent.

Implementation variables control for differences in SFAs’ implementation of F2S programs that may affect costs. Indicator variables represent whether the local food purchases include (a) fruits or vegetables, (b) fluid milk, and (c) meat or eggs. The type of local food products that SFAs purchase may influence costs if, for example, it is less costly to procure fruits and vegetables locally than fluid milk locally. We also control for F2S activities that SFAs undertake. These variables represent whether the SFA conducted taste tests, had a school garden, organized a field trip to a farm, hosted a farmer at the school, undertook promotional efforts, and integrated F2S concepts into the curriculum. Activity variables could be positively correlated with sourcing directly from a farmer if SFAs undertake costly activities to achieve educational goals. We use responses from the 2013 Census to construct the food product and F2S activity variables to avoid simultaneity.

SFAs that experience procurement challenges in local sourcing may be less likely to report lower school meal costs from F2S programming. We develop five Problem variables from the 20 questions regarding F2S challenges because they are highly correlated with each other. To do so, we consolidate the questions according to literature on F2S barriers and value-chain market structure (Table 1). One commonly cited barrier to F2S implementation addresses internal barriers within schools (Vogt and Kaiser Reference Vogt and Kaiser2008). We create an “on-site problem” variable that is one if schools indicate that they lack capacity to process/prepare local foods or comply with institutional regulations and policies, and zero otherwise. Another barrier is the premium price of local foods (Vo and Holcomb Reference Vo and Holcomb2011; Boys and Fraser Reference Boys and Fraser2019). We create a binary “price problem” variable that takes the value of one if the SFAs indicate that they experience higher or unstable prices. Transaction costs such as gathering information, negotiating, and enforcing procurement terms are frequently cited as a barrier to F2S procurement and challenge in values-based supply chains (Gregoire, Arendt, and Strohbehn Reference Gregoire, Arendt and Strohbehn2005; Conner et al. Reference Conner, Sevoian, Heiss and Berlin2014). The “transaction problem” variable takes the value of one if the SFA struggles to obtain bids, coordinate procurement of local products with regular procurement, find new farmers or distributors, get information, place orders with vendors, resolve problem deliveries, or pay farmers according to farmers' needs; and zero otherwise. “Product problems” include seasonal availability, a lack of processed products, product shelf life/appearance, and a lack of local supply/producers (Vogt and Kaiser Reference Vogt and Kaiser2008; Oberholtzer, Dimitri, and Jaenicke Reference Oberholtzer, Dimitri and Jaenicke2014). The “product problem” variable takes the value of one if the SFA experiences challenges with year-round availability of key items or processed products, vendors not having items, or getting products of low quality; and zero otherwise. Finally, “distribution problems” associated with supply chain infrastructure are cited as barriers to F2S procurement and values-based supply chains (Gregoire, Arendt, and Strohbehn Reference Gregoire, Arendt and Strohbehn2005; Oberholtzer, Dimitri, and Jaenicke Reference Oberholtzer, Dimitri and Jaenicke2014). The “distribution problem” variable takes the value of one if the SFA indicates that deliveries are not reliable, are not on time, or have incorrect quantities.

Four county-level variables control for the SFAs’ external Food Environment. We control for the density of food outlets using the number of grocery stores per capita and the number of restaurants per capita (USDA-ERS 2017). We also include two variables used by Botkins and Roe (Reference Botkins and Roe2018) as controls: the ratio of local milk prices to national milk prices and farm income (USDA 2017; USDA-ERS 2017). In the former case, this variable is a proxy for the relative costs of local products because fluid milk is a prominent locally sourced food product. Variation in the prices of local products could influence whether school meal costs declined from F2S programing. In the latter instance, higher levels of farm income could reflect conducive conditions for conventional commodity production. This, in turn, could be a disincentive for farmers to sell products directly to schools, thus increasing the effort SFAs must expend to procure local foods.

Procurement Strategy variables indicate whether SFAs procure local foods by purchasing local foods directly from farmers exclusively, from intermediaries exclusively, and from both farmers and intermediaries.Footnote ⁶ The three variables are mutually exclusive. The “direct exclusive” variable includes procurement sources in which farmers can negotiate prices and terms with the SFA. The “direct exclusive” variable is binary. It equals one if SFAs procure local products directly from a farmer, rancher, or fisher; from a producer cooperative; at a farmers’ market; and/or through a community-supported agriculture model. (We refer to such direct sourcing as purchasing directly from a “farmer” for brevity.) The “intermediated exclusive” variable includes procurement sources in which intermediaries may set prices and terms for the SFA. This is a binary variable that equals one if the SFA procures local foods exclusively through a food processor or manufacturer, distributor, food buying cooperative, food service management company, Department of Defense Fresh Program vendor, USDA Foods, food hub, and/or state F2S program office. The “both direct and intermediated” variable is a binary variable that equals one if the SFA procures local products through both direct and intermediated market channels and is equal to zero otherwise.

Summary Statistics

Twenty-two percent of SFAs in our sample reported lower school meal costs from participating in F2S programs (Table 2). Ten percent of SFAs purchased local food exclusively from a farmer, whereas 47% made such purchases directly from farmers and from intermediaries. Fruits and vegetables were the most frequently purchased local food products. The average student size of an SFA was 5,300, and on average, 47% of students were eligible for free or reduced-price lunch. The SFAs have a balanced distribution among the geographic regions established by FNS, and 61% of the SFAs were in a metropolitan county. Nearly three-quarters of SFAs (74%) reported problems with product quality or availability, and nearly half reported problems with product pricing.

Table 2.

Descriptive Statistics

Notes: We calculated the statistics from the 2,102 observations used in the probit regressions. RUCC, rural-urban continuum code; SD, standard deviation; SFA, school food authority.